From 08bbfcfdccd271f49478a9c63fdcffe6fc532808 Mon Sep 17 00:00:00 2001 From: Funbucket Date: Wed, 15 Apr 2026 14:06:33 +0900 Subject: [PATCH 01/11] =?UTF-8?q?feat(skills):=20pdf=20=EC=B2=98=EB=A6=AC?= =?UTF-8?q?=20=EC=8A=A4=ED=82=AC=20=EC=B6=94=EA=B0=80?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit pypdf·pdfplumber·reportlab 기반 PDF 읽기, 병합, 분할, 폼 작성, OCR 등 전 처리 워크플로를 커버하는 스킬 추가. 참조 문서 및 유틸리티 스크립트 포함. Co-Authored-By: Claude Sonnet 4.6 --- .claude/skills/pdf/SKILL.md | 223 ++++++++++++++++++ .claude/skills/pdf/references/forms.md | 104 ++++++++ .claude/skills/pdf/references/reference.md | 174 ++++++++++++++ .../pdf/scripts/check_bounding_boxes.py | 64 +++++ .../pdf/scripts/check_fillable_fields.py | 9 + .../pdf/scripts/convert_pdf_to_images.py | 31 +++ .../pdf/scripts/create_validation_image.py | 35 +++ .../pdf/scripts/extract_form_field_info.py | 119 ++++++++++ .../pdf/scripts/extract_form_structure.py | 101 ++++++++ .../pdf/scripts/fill_fillable_fields.py | 96 ++++++++ .../scripts/fill_pdf_form_with_annotations.py | 104 ++++++++ 11 files changed, 1060 insertions(+) create mode 100644 .claude/skills/pdf/SKILL.md create mode 100644 .claude/skills/pdf/references/forms.md create mode 100644 .claude/skills/pdf/references/reference.md create mode 100644 .claude/skills/pdf/scripts/check_bounding_boxes.py create mode 100644 .claude/skills/pdf/scripts/check_fillable_fields.py create mode 100644 .claude/skills/pdf/scripts/convert_pdf_to_images.py create mode 100644 .claude/skills/pdf/scripts/create_validation_image.py create mode 100644 .claude/skills/pdf/scripts/extract_form_field_info.py create mode 100644 .claude/skills/pdf/scripts/extract_form_structure.py create mode 100644 .claude/skills/pdf/scripts/fill_fillable_fields.py create mode 100644 .claude/skills/pdf/scripts/fill_pdf_form_with_annotations.py diff --git a/.claude/skills/pdf/SKILL.md b/.claude/skills/pdf/SKILL.md new file mode 100644 index 0000000..5e341c9 --- /dev/null +++ b/.claude/skills/pdf/SKILL.md @@ -0,0 +1,223 @@ +--- +name: pdf +description: Use this skill whenever the user wants to do anything with PDF files. This includes reading or extracting text/tables from PDFs, combining or merging multiple PDFs into one, splitting PDFs apart, rotating pages, adding watermarks, creating new PDFs, filling PDF forms, encrypting/decrypting PDFs, extracting images, and OCR on scanned PDFs to make them searchable. If the user mentions a .pdf file or asks to produce one, use this skill. +--- + +# PDF Processing + +## Quick Start + +```python +from pypdf import PdfReader, PdfWriter + +reader = PdfReader("document.pdf") +print(f"Pages: {len(reader.pages)}") + +text = "" +for page in reader.pages: + text += page.extract_text() +``` + +## Python Libraries + +### pypdf — Basic Operations + +**Merge PDFs:** +```python +from pypdf import PdfWriter, PdfReader + +writer = PdfWriter() +for pdf_file in ["doc1.pdf", "doc2.pdf", "doc3.pdf"]: + reader = PdfReader(pdf_file) + for page in reader.pages: + writer.add_page(page) + +with open("merged.pdf", "wb") as output: + writer.write(output) +``` + +**Split PDF:** +```python +reader = PdfReader("input.pdf") +for i, page in enumerate(reader.pages): + writer = PdfWriter() + writer.add_page(page) + with open(f"page_{i+1}.pdf", "wb") as output: + writer.write(output) +``` + +**Extract Metadata:** +```python +reader = PdfReader("document.pdf") +meta = reader.metadata +print(f"Title: {meta.title}") +print(f"Author: {meta.author}") +``` + +**Rotate Pages:** +```python +reader = PdfReader("input.pdf") +writer = PdfWriter() +page = reader.pages[0] +page.rotate(90) +writer.add_page(page) +with open("rotated.pdf", "wb") as output: + writer.write(output) +``` + +### pdfplumber — Text and Table Extraction + +**Extract Text with Layout:** +```python +import pdfplumber + +with pdfplumber.open("document.pdf") as pdf: + for page in pdf.pages: + text = page.extract_text() + print(text) +``` + +**Extract Tables:** +```python +with pdfplumber.open("document.pdf") as pdf: + for i, page in enumerate(pdf.pages): + tables = page.extract_tables() + for j, table in enumerate(tables): + print(f"Table {j+1} on page {i+1}:") + for row in table: + print(row) +``` + +**Extract Tables to DataFrame:** +```python +import pandas as pd +import pdfplumber + +with pdfplumber.open("document.pdf") as pdf: + all_tables = [] + for page in pdf.pages: + tables = page.extract_tables() + for table in tables: + if table: + df = pd.DataFrame(table[1:], columns=table[0]) + all_tables.append(df) + +if all_tables: + combined_df = pd.concat(all_tables, ignore_index=True) + combined_df.to_excel("extracted_tables.xlsx", index=False) +``` + +### reportlab — Create PDFs + +**Basic PDF Creation:** +```python +from reportlab.lib.pagesizes import letter +from reportlab.pdfgen import canvas + +c = canvas.Canvas("hello.pdf", pagesize=letter) +width, height = letter +c.drawString(100, height - 100, "Hello World!") +c.save() +``` + +**Multi-page PDF:** +```python +from reportlab.lib.pagesizes import letter +from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, PageBreak +from reportlab.lib.styles import getSampleStyleSheet + +doc = SimpleDocTemplate("report.pdf", pagesize=letter) +styles = getSampleStyleSheet() +story = [] + +story.append(Paragraph("Report Title", styles['Title'])) +story.append(Spacer(1, 12)) +story.append(Paragraph("Body content here.", styles['Normal'])) +story.append(PageBreak()) +story.append(Paragraph("Page 2", styles['Heading1'])) +doc.build(story) +``` + +⚠️ **IMPORTANT**: Never use Unicode subscript/superscript characters (₀₁₂₃, ⁰¹²³) in ReportLab — use `` and `` tags instead. + +## Command-Line Tools + +**pdftotext (poppler-utils):** +```bash +pdftotext input.pdf output.txt +pdftotext -layout input.pdf output.txt # preserve layout +pdftotext -f 1 -l 5 input.pdf output.txt # pages 1-5 +``` + +**qpdf:** +```bash +qpdf --empty --pages file1.pdf file2.pdf -- merged.pdf +qpdf input.pdf --pages . 1-5 -- pages1-5.pdf +qpdf input.pdf output.pdf --rotate=+90:1 +qpdf --password=mypassword --decrypt encrypted.pdf decrypted.pdf +``` + +## Common Tasks + +**OCR Scanned PDFs:** +```python +import pytesseract +from pdf2image import convert_from_path + +images = convert_from_path('scanned.pdf') +text = "" +for i, image in enumerate(images): + text += f"Page {i+1}:\n" + text += pytesseract.image_to_string(image) + text += "\n\n" +``` + +**Add Watermark:** +```python +from pypdf import PdfReader, PdfWriter + +watermark = PdfReader("watermark.pdf").pages[0] +reader = PdfReader("document.pdf") +writer = PdfWriter() +for page in reader.pages: + page.merge_page(watermark) + writer.add_page(page) +with open("watermarked.pdf", "wb") as output: + writer.write(output) +``` + +**Password Protection:** +```python +from pypdf import PdfReader, PdfWriter + +reader = PdfReader("input.pdf") +writer = PdfWriter() +for page in reader.pages: + writer.add_page(page) +writer.encrypt("userpassword", "ownerpassword") +with open("encrypted.pdf", "wb") as output: + writer.write(output) +``` + +**Extract Images (CLI):** +```bash +pdfimages -j input.pdf output_prefix +``` + +## Quick Reference + +| Task | Best Tool | +|------|-----------| +| Merge PDFs | pypdf | +| Split PDFs | pypdf | +| Extract text | pdfplumber | +| Extract tables | pdfplumber | +| Create PDFs | reportlab | +| CLI merge | qpdf | +| OCR scanned PDFs | pytesseract + pdf2image | +| Fill PDF forms | scripts/ (see forms.md) | + +## References + +- **Form filling**: See [references/forms.md](references/forms.md) — use bundled scripts in `scripts/` +- **Advanced usage** (pypdfium2, pdf-lib JS, batch processing, troubleshooting): See [references/reference.md](references/reference.md) diff --git a/.claude/skills/pdf/references/forms.md b/.claude/skills/pdf/references/forms.md new file mode 100644 index 0000000..d2612b0 --- /dev/null +++ b/.claude/skills/pdf/references/forms.md @@ -0,0 +1,104 @@ +# PDF Form Filling + +## Initial Assessment + +First check if the PDF has fillable form fields: +```bash +python scripts/check_fillable_fields.py file.pdf +``` + +--- + +## For Fillable Forms + +**Step 1 — Extract field info:** +```bash +python scripts/extract_form_field_info.py input.pdf fields.json +``` +Outputs JSON cataloging all fields (IDs, locations, types: text/checkbox/radio/choice). + +**Step 2 — Visually verify (optional):** +```bash +mkdir pages && python scripts/convert_pdf_to_images.py input.pdf pages/ +``` + +**Step 3 — Create field values JSON:** +```json +[ + {"field_id": "FirstName", "page": 1, "value": "John"}, + {"field_id": "Agree", "page": 1, "value": "/Yes"}, + {"field_id": "Gender", "page": 1, "value": "/Male"} +] +``` +- Checkboxes: use the `checked_value` shown in the field info JSON +- Radio groups: use one of the `radio_options[].value` values +- Choice fields: use one of the `choice_options[].value` values + +**Step 4 — Fill the form:** +```bash +python scripts/fill_fillable_fields.py input.pdf field_values.json output.pdf +``` + +--- + +## For Non-Fillable Forms (Text Annotations) + +### Approach A — Preferred (digitally-created PDFs) + +Extract structural coordinates: +```bash +python scripts/extract_form_structure.py input.pdf structure.json +``` + +Build a `fields.json` with `form_fields` array using PDF coordinates: +```json +{ + "pages": [{"page_number": 1, "pdf_width": 612, "pdf_height": 792}], + "form_fields": [ + { + "description": "Name field", + "page_number": 1, + "label_bounding_box": [72, 100, 150, 115], + "entry_bounding_box": [155, 98, 400, 116], + "entry_text": {"text": "John Smith", "font": "Helvetica", "font_size": 11} + } + ] +} +``` + +### Approach B — Fallback (scanned PDFs) + +Convert to images, determine pixel coordinates visually, then use image-based coordinates: +```json +{ + "pages": [{"page_number": 1, "image_width": 1000, "image_height": 1294}], + "form_fields": [...] +} +``` + +### Hybrid + +Use Approach A for most fields and Approach B for anything extract_form_structure misses. + +--- + +## Validation + +Before generating output, validate bounding boxes: +```bash +python scripts/check_bounding_boxes.py fields.json +``` + +Visually verify a page: +```bash +python scripts/create_validation_image.py 1 fields.json pages/page_1.png validation_page_1.png +``` + +Red boxes = entry areas, Blue boxes = label areas. + +**Fill with annotations:** +```bash +python scripts/fill_pdf_form_with_annotations.py input.pdf fields.json output.pdf +``` + +Then convert output to images to verify text placement. diff --git a/.claude/skills/pdf/references/reference.md b/.claude/skills/pdf/references/reference.md new file mode 100644 index 0000000..36a14b0 --- /dev/null +++ b/.claude/skills/pdf/references/reference.md @@ -0,0 +1,174 @@ +# PDF Advanced Reference + +## pypdfium2 — High-Performance Rendering + +```python +import pypdfium2 as pdfium + +pdf = pdfium.PdfDocument("document.pdf") + +# Render page to image +page = pdf[0] +bitmap = page.render(scale=2) # 2x scale = 144 DPI +pil_image = bitmap.to_pil() +pil_image.save("page_1.png") + +# Extract text with coordinates +textpage = page.get_textpage() +for i in range(textpage.count_chars()): + char = textpage.get_charbox(i) + print(f"Char: {textpage.get_text_range(i, 1)}, Box: {char}") + +pdf.close() +``` + +**When to prefer pypdfium2**: High-quality image rendering, pixel-accurate text extraction, processing large batches. + +--- + +## pdf-lib (JavaScript) + +```javascript +import { PDFDocument, rgb, StandardFonts } from 'pdf-lib'; +import fs from 'fs'; + +// Load and modify +const pdfBytes = fs.readFileSync('input.pdf'); +const pdfDoc = await PDFDocument.load(pdfBytes); + +const page = pdfDoc.getPages()[0]; +const font = await pdfDoc.embedFont(StandardFonts.Helvetica); +page.drawText('Hello World', { x: 50, y: 700, size: 20, font, color: rgb(0, 0, 0) }); + +const output = await pdfDoc.save(); +fs.writeFileSync('output.pdf', output); +``` + +**When to use**: Node.js environments, browser-side PDF generation. + +--- + +## Batch Processing + +```python +import os +from pathlib import Path +from pypdf import PdfReader +import logging + +logging.basicConfig(level=logging.INFO) + +def process_pdfs(input_dir: str, output_dir: str): + Path(output_dir).mkdir(exist_ok=True) + errors = [] + + for pdf_file in Path(input_dir).glob("*.pdf"): + try: + reader = PdfReader(str(pdf_file)) + text = "\n".join(page.extract_text() or "" for page in reader.pages) + + out_path = Path(output_dir) / (pdf_file.stem + ".txt") + out_path.write_text(text, encoding="utf-8") + logging.info(f"Processed: {pdf_file.name}") + except Exception as e: + logging.error(f"Failed: {pdf_file.name} — {e}") + errors.append((str(pdf_file), str(e))) + + return errors +``` + +--- + +## Memory-Efficient Large File Processing + +```python +import pdfplumber + +def stream_text(pdf_path: str): + """Yield text page by page to avoid loading entire PDF into memory.""" + with pdfplumber.open(pdf_path) as pdf: + for i, page in enumerate(pdf.pages): + yield i + 1, page.extract_text() or "" + +for page_num, text in stream_text("large_document.pdf"): + print(f"--- Page {page_num} ---") + print(text) +``` + +--- + +## Handling Encrypted / Corrupted PDFs + +```python +from pypdf import PdfReader + +# Encrypted PDF +reader = PdfReader("encrypted.pdf") +if reader.is_encrypted: + reader.decrypt("password") + +# Corrupted PDF — try qpdf first +import subprocess +result = subprocess.run( + ["qpdf", "--recover", "corrupted.pdf", "repaired.pdf"], + capture_output=True, text=True +) +if result.returncode != 0: + print("qpdf repair failed:", result.stderr) +``` + +--- + +## OCR Fallback for Mixed PDFs + +```python +import pdfplumber +import pytesseract +from pdf2image import convert_from_path + +def extract_with_ocr_fallback(pdf_path: str) -> list[str]: + """Use pdfplumber first; fall back to OCR if page has no text.""" + pages_text = [] + images = None # lazy-load + + with pdfplumber.open(pdf_path) as pdf: + for i, page in enumerate(pdf.pages): + text = page.extract_text() + if text and text.strip(): + pages_text.append(text) + else: + if images is None: + images = convert_from_path(pdf_path, dpi=200) + pages_text.append(pytesseract.image_to_string(images[i])) + + return pages_text +``` + +--- + +## Tool Selection Guide + +| Use case | Recommended tool | +|----------|-----------------| +| Simple text extraction | pdfplumber | +| Tables | pdfplumber | +| High-quality image rendering | pypdfium2 | +| Merge / split / rotate | pypdf | +| Create new PDFs | reportlab | +| JavaScript / browser | pdf-lib | +| CLI text extraction | pdftotext (poppler) | +| CLI manipulation | qpdf | +| OCR | pytesseract + pdf2image | +| Repair corrupted | qpdf --recover | + +## Library Licenses + +| Library | License | +|---------|---------| +| pypdf | BSD | +| pdfplumber | MIT | +| reportlab | BSD | +| pypdfium2 | Apache 2.0 | +| pdf-lib | MIT | +| poppler-utils | GPL-2 | +| qpdf | Apache 2.0 | diff --git a/.claude/skills/pdf/scripts/check_bounding_boxes.py b/.claude/skills/pdf/scripts/check_bounding_boxes.py new file mode 100644 index 0000000..2fc9876 --- /dev/null +++ b/.claude/skills/pdf/scripts/check_bounding_boxes.py @@ -0,0 +1,64 @@ +from dataclasses import dataclass +import json +import sys + + +@dataclass +class RectAndField: + rect: list[float] + rect_type: str + field: dict + + +def get_bounding_box_messages(fields_json_stream) -> list[str]: + messages = [] + fields = json.load(fields_json_stream) + messages.append(f"Read {len(fields['form_fields'])} fields") + + def rects_intersect(r1, r2): + disjoint_horizontal = r1[0] >= r2[2] or r1[2] <= r2[0] + disjoint_vertical = r1[1] >= r2[3] or r1[3] <= r2[1] + return not (disjoint_horizontal or disjoint_vertical) + + rects_and_fields = [] + for f in fields["form_fields"]: + rects_and_fields.append(RectAndField(f["label_bounding_box"], "label", f)) + rects_and_fields.append(RectAndField(f["entry_bounding_box"], "entry", f)) + + has_error = False + for i, ri in enumerate(rects_and_fields): + for j in range(i + 1, len(rects_and_fields)): + rj = rects_and_fields[j] + if ri.field["page_number"] == rj.field["page_number"] and rects_intersect(ri.rect, rj.rect): + has_error = True + if ri.field is rj.field: + messages.append(f"FAILURE: intersection between label and entry bounding boxes for `{ri.field['description']}` ({ri.rect}, {rj.rect})") + else: + messages.append(f"FAILURE: intersection between {ri.rect_type} bounding box for `{ri.field['description']}` ({ri.rect}) and {rj.rect_type} bounding box for `{rj.field['description']}` ({rj.rect})") + if len(messages) >= 20: + messages.append("Aborting further checks; fix bounding boxes and try again") + return messages + if ri.rect_type == "entry": + if "entry_text" in ri.field: + font_size = ri.field["entry_text"].get("font_size", 14) + entry_height = ri.rect[3] - ri.rect[1] + if entry_height < font_size: + has_error = True + messages.append(f"FAILURE: entry bounding box height ({entry_height}) for `{ri.field['description']}` is too short for the text content (font size: {font_size}). Increase the box height or decrease the font size.") + if len(messages) >= 20: + messages.append("Aborting further checks; fix bounding boxes and try again") + return messages + + if not has_error: + messages.append("SUCCESS: All bounding boxes are valid") + return messages + + +if __name__ == "__main__": + if len(sys.argv) != 2: + print("Usage: check_bounding_boxes.py [fields.json]") + sys.exit(1) + with open(sys.argv[1]) as f: + messages = get_bounding_box_messages(f) + for msg in messages: + print(msg) diff --git a/.claude/skills/pdf/scripts/check_fillable_fields.py b/.claude/skills/pdf/scripts/check_fillable_fields.py new file mode 100644 index 0000000..ddeff88 --- /dev/null +++ b/.claude/skills/pdf/scripts/check_fillable_fields.py @@ -0,0 +1,9 @@ +import sys +from pypdf import PdfReader + + +reader = PdfReader(sys.argv[1]) +if reader.get_fields(): + print("This PDF has fillable form fields") +else: + print("This PDF does not have fillable form fields; you will need to visually determine where to enter data") diff --git a/.claude/skills/pdf/scripts/convert_pdf_to_images.py b/.claude/skills/pdf/scripts/convert_pdf_to_images.py new file mode 100644 index 0000000..c35a760 --- /dev/null +++ b/.claude/skills/pdf/scripts/convert_pdf_to_images.py @@ -0,0 +1,31 @@ +import os +import sys + +from pdf2image import convert_from_path + + +def convert(pdf_path, output_dir, max_dim=1000): + images = convert_from_path(pdf_path, dpi=200) + + for i, image in enumerate(images): + width, height = image.size + if width > max_dim or height > max_dim: + scale_factor = min(max_dim / width, max_dim / height) + new_width = int(width * scale_factor) + new_height = int(height * scale_factor) + image = image.resize((new_width, new_height)) + + image_path = os.path.join(output_dir, f"page_{i+1}.png") + image.save(image_path) + print(f"Saved page {i+1} as {image_path} (size: {image.size})") + + print(f"Converted {len(images)} pages to PNG images") + + +if __name__ == "__main__": + if len(sys.argv) != 3: + print("Usage: convert_pdf_to_images.py [input pdf] [output directory]") + sys.exit(1) + pdf_path = sys.argv[1] + output_directory = sys.argv[2] + convert(pdf_path, output_directory) diff --git a/.claude/skills/pdf/scripts/create_validation_image.py b/.claude/skills/pdf/scripts/create_validation_image.py new file mode 100644 index 0000000..8a41eac --- /dev/null +++ b/.claude/skills/pdf/scripts/create_validation_image.py @@ -0,0 +1,35 @@ +import json +import sys + +from PIL import Image, ImageDraw + + +def create_validation_image(page_number, fields_json_path, input_path, output_path): + with open(fields_json_path, 'r') as f: + data = json.load(f) + + img = Image.open(input_path) + draw = ImageDraw.Draw(img) + num_boxes = 0 + + for field in data["form_fields"]: + if field["page_number"] == page_number: + entry_box = field['entry_bounding_box'] + label_box = field['label_bounding_box'] + draw.rectangle(entry_box, outline='red', width=2) + draw.rectangle(label_box, outline='blue', width=2) + num_boxes += 2 + + img.save(output_path) + print(f"Created validation image at {output_path} with {num_boxes} bounding boxes") + + +if __name__ == "__main__": + if len(sys.argv) != 5: + print("Usage: create_validation_image.py [page number] [fields.json file] [input image path] [output image path]") + sys.exit(1) + page_number = int(sys.argv[1]) + fields_json_path = sys.argv[2] + input_image_path = sys.argv[3] + output_image_path = sys.argv[4] + create_validation_image(page_number, fields_json_path, input_image_path, output_image_path) diff --git a/.claude/skills/pdf/scripts/extract_form_field_info.py b/.claude/skills/pdf/scripts/extract_form_field_info.py new file mode 100644 index 0000000..59b7bb6 --- /dev/null +++ b/.claude/skills/pdf/scripts/extract_form_field_info.py @@ -0,0 +1,119 @@ +import json +import sys + +from pypdf import PdfReader + + +def get_full_annotation_field_id(annotation): + components = [] + while annotation: + field_name = annotation.get('/T') + if field_name: + components.append(field_name) + annotation = annotation.get('/Parent') + return ".".join(reversed(components)) if components else None + + +def make_field_dict(field, field_id): + field_dict = {"field_id": field_id} + ft = field.get('/FT') + if ft == "/Tx": + field_dict["type"] = "text" + elif ft == "/Btn": + field_dict["type"] = "checkbox" + states = field.get("/_States_", []) + if len(states) == 2: + if "/Off" in states: + field_dict["checked_value"] = states[0] if states[0] != "/Off" else states[1] + field_dict["unchecked_value"] = "/Off" + else: + print(f"Unexpected state values for checkbox `${field_id}`. Its checked and unchecked values may not be correct; if you're trying to check it, visually verify the results.") + field_dict["checked_value"] = states[0] + field_dict["unchecked_value"] = states[1] + elif ft == "/Ch": + field_dict["type"] = "choice" + states = field.get("/_States_", []) + field_dict["choice_options"] = [{ + "value": state[0], + "text": state[1], + } for state in states] + else: + field_dict["type"] = f"unknown ({ft})" + return field_dict + + +def get_field_info(reader: PdfReader): + fields = reader.get_fields() + + field_info_by_id = {} + possible_radio_names = set() + + for field_id, field in fields.items(): + if field.get("/Kids"): + if field.get("/FT") == "/Btn": + possible_radio_names.add(field_id) + continue + field_info_by_id[field_id] = make_field_dict(field, field_id) + + radio_fields_by_id = {} + + for page_index, page in enumerate(reader.pages): + annotations = page.get('/Annots', []) + for ann in annotations: + field_id = get_full_annotation_field_id(ann) + if field_id in field_info_by_id: + field_info_by_id[field_id]["page"] = page_index + 1 + field_info_by_id[field_id]["rect"] = ann.get('/Rect') + elif field_id in possible_radio_names: + try: + on_values = [v for v in ann["/AP"]["/N"] if v != "/Off"] + except KeyError: + continue + if len(on_values) == 1: + rect = ann.get("/Rect") + if field_id not in radio_fields_by_id: + radio_fields_by_id[field_id] = { + "field_id": field_id, + "type": "radio_group", + "page": page_index + 1, + "radio_options": [], + } + radio_fields_by_id[field_id]["radio_options"].append({ + "value": on_values[0], + "rect": rect, + }) + + fields_with_location = [] + for field_info in field_info_by_id.values(): + if "page" in field_info: + fields_with_location.append(field_info) + else: + print(f"Unable to determine location for field id: {field_info.get('field_id')}, ignoring") + + def sort_key(f): + if "radio_options" in f: + rect = f["radio_options"][0]["rect"] or [0, 0, 0, 0] + else: + rect = f.get("rect") or [0, 0, 0, 0] + adjusted_position = [-rect[1], rect[0]] + return [f.get("page"), adjusted_position] + + sorted_fields = fields_with_location + list(radio_fields_by_id.values()) + sorted_fields.sort(key=sort_key) + + return sorted_fields + + +def write_field_info(pdf_path: str, json_output_path: str): + reader = PdfReader(pdf_path) + field_info = get_field_info(reader) + with open(json_output_path, "w") as f: + json.dump(field_info, f, indent=2) + print(f"Wrote {len(field_info)} fields to {json_output_path}") + + +if __name__ == "__main__": + if len(sys.argv) != 3: + print("Usage: extract_form_field_info.py [input pdf] [output json]") + sys.exit(1) + write_field_info(sys.argv[1], sys.argv[2]) diff --git a/.claude/skills/pdf/scripts/extract_form_structure.py b/.claude/skills/pdf/scripts/extract_form_structure.py new file mode 100644 index 0000000..5e175d9 --- /dev/null +++ b/.claude/skills/pdf/scripts/extract_form_structure.py @@ -0,0 +1,101 @@ +import json +import sys +import pdfplumber + + +def extract_form_structure(pdf_path): + structure = { + "pages": [], + "labels": [], + "lines": [], + "checkboxes": [], + "row_boundaries": [] + } + + with pdfplumber.open(pdf_path) as pdf: + for page_num, page in enumerate(pdf.pages, 1): + structure["pages"].append({ + "page_number": page_num, + "width": float(page.width), + "height": float(page.height) + }) + + words = page.extract_words() + for word in words: + structure["labels"].append({ + "page": page_num, + "text": word["text"], + "x0": round(float(word["x0"]), 1), + "top": round(float(word["top"]), 1), + "x1": round(float(word["x1"]), 1), + "bottom": round(float(word["bottom"]), 1) + }) + + for line in page.lines: + if abs(float(line["x1"]) - float(line["x0"])) > page.width * 0.5: + structure["lines"].append({ + "page": page_num, + "y": round(float(line["top"]), 1), + "x0": round(float(line["x0"]), 1), + "x1": round(float(line["x1"]), 1) + }) + + for rect in page.rects: + width = float(rect["x1"]) - float(rect["x0"]) + height = float(rect["bottom"]) - float(rect["top"]) + if 5 <= width <= 15 and 5 <= height <= 15 and abs(width - height) < 2: + structure["checkboxes"].append({ + "page": page_num, + "x0": round(float(rect["x0"]), 1), + "top": round(float(rect["top"]), 1), + "x1": round(float(rect["x1"]), 1), + "bottom": round(float(rect["bottom"]), 1), + "center_x": round((float(rect["x0"]) + float(rect["x1"])) / 2, 1), + "center_y": round((float(rect["top"]) + float(rect["bottom"])) / 2, 1) + }) + + lines_by_page = {} + for line in structure["lines"]: + page = line["page"] + if page not in lines_by_page: + lines_by_page[page] = [] + lines_by_page[page].append(line["y"]) + + for page, y_coords in lines_by_page.items(): + y_coords = sorted(set(y_coords)) + for i in range(len(y_coords) - 1): + structure["row_boundaries"].append({ + "page": page, + "row_top": y_coords[i], + "row_bottom": y_coords[i + 1], + "row_height": round(y_coords[i + 1] - y_coords[i], 1) + }) + + return structure + + +def main(): + if len(sys.argv) != 3: + print("Usage: extract_form_structure.py ") + sys.exit(1) + + pdf_path = sys.argv[1] + output_path = sys.argv[2] + + print(f"Extracting structure from {pdf_path}...") + structure = extract_form_structure(pdf_path) + + with open(output_path, "w") as f: + json.dump(structure, f, indent=2) + + print(f"Found:") + print(f" - {len(structure['pages'])} pages") + print(f" - {len(structure['labels'])} text labels") + print(f" - {len(structure['lines'])} horizontal lines") + print(f" - {len(structure['checkboxes'])} checkboxes") + print(f" - {len(structure['row_boundaries'])} row boundaries") + print(f"Saved to {output_path}") + + +if __name__ == "__main__": + main() diff --git a/.claude/skills/pdf/scripts/fill_fillable_fields.py b/.claude/skills/pdf/scripts/fill_fillable_fields.py new file mode 100644 index 0000000..a706cd2 --- /dev/null +++ b/.claude/skills/pdf/scripts/fill_fillable_fields.py @@ -0,0 +1,96 @@ +import json +import sys + +from pypdf import PdfReader, PdfWriter + +from extract_form_field_info import get_field_info + + +def fill_pdf_fields(input_pdf_path: str, fields_json_path: str, output_pdf_path: str): + with open(fields_json_path) as f: + fields = json.load(f) + fields_by_page = {} + for field in fields: + if "value" in field: + field_id = field["field_id"] + page = field["page"] + if page not in fields_by_page: + fields_by_page[page] = {} + fields_by_page[page][field_id] = field["value"] + + reader = PdfReader(input_pdf_path) + + has_error = False + field_info = get_field_info(reader) + fields_by_ids = {f["field_id"]: f for f in field_info} + for field in fields: + existing_field = fields_by_ids.get(field["field_id"]) + if not existing_field: + has_error = True + print(f"ERROR: `{field['field_id']}` is not a valid field ID") + elif field["page"] != existing_field["page"]: + has_error = True + print(f"ERROR: Incorrect page number for `{field['field_id']}` (got {field['page']}, expected {existing_field['page']})") + else: + if "value" in field: + err = validation_error_for_field_value(existing_field, field["value"]) + if err: + print(err) + has_error = True + if has_error: + sys.exit(1) + + writer = PdfWriter(clone_from=reader) + for page, field_values in fields_by_page.items(): + writer.update_page_form_field_values(writer.pages[page - 1], field_values, auto_regenerate=False) + + writer.set_need_appearances_writer(True) + + with open(output_pdf_path, "wb") as f: + writer.write(f) + + +def validation_error_for_field_value(field_info, field_value): + field_type = field_info["type"] + field_id = field_info["field_id"] + if field_type == "checkbox": + checked_val = field_info["checked_value"] + unchecked_val = field_info["unchecked_value"] + if field_value != checked_val and field_value != unchecked_val: + return f'ERROR: Invalid value "{field_value}" for checkbox field "{field_id}". The checked value is "{checked_val}" and the unchecked value is "{unchecked_val}"' + elif field_type == "radio_group": + option_values = [opt["value"] for opt in field_info["radio_options"]] + if field_value not in option_values: + return f'ERROR: Invalid value "{field_value}" for radio group field "{field_id}". Valid values are: {option_values}' + elif field_type == "choice": + choice_values = [opt["value"] for opt in field_info["choice_options"]] + if field_value not in choice_values: + return f'ERROR: Invalid value "{field_value}" for choice field "{field_id}". Valid values are: {choice_values}' + return None + + +def monkeypatch_pypdf_method(): + from pypdf.generic import DictionaryObject + from pypdf.constants import FieldDictionaryAttributes + + original_get_inherited = DictionaryObject.get_inherited + + def patched_get_inherited(self, key: str, default=None): + result = original_get_inherited(self, key, default) + if key == FieldDictionaryAttributes.Opt: + if isinstance(result, list) and all(isinstance(v, list) and len(v) == 2 for v in result): + result = [r[0] for r in result] + return result + + DictionaryObject.get_inherited = patched_get_inherited + + +if __name__ == "__main__": + if len(sys.argv) != 4: + print("Usage: fill_fillable_fields.py [input pdf] [field_values.json] [output pdf]") + sys.exit(1) + monkeypatch_pypdf_method() + input_pdf = sys.argv[1] + fields_json = sys.argv[2] + output_pdf = sys.argv[3] + fill_pdf_fields(input_pdf, fields_json, output_pdf) diff --git a/.claude/skills/pdf/scripts/fill_pdf_form_with_annotations.py b/.claude/skills/pdf/scripts/fill_pdf_form_with_annotations.py new file mode 100644 index 0000000..a2ac423 --- /dev/null +++ b/.claude/skills/pdf/scripts/fill_pdf_form_with_annotations.py @@ -0,0 +1,104 @@ +import json +import sys + +from pypdf import PdfReader, PdfWriter +from pypdf.annotations import FreeText + + +def transform_from_image_coords(bbox, image_width, image_height, pdf_width, pdf_height): + x_scale = pdf_width / image_width + y_scale = pdf_height / image_height + + left = bbox[0] * x_scale + right = bbox[2] * x_scale + + top = pdf_height - (bbox[1] * y_scale) + bottom = pdf_height - (bbox[3] * y_scale) + + return left, bottom, right, top + + +def transform_from_pdf_coords(bbox, pdf_height): + left = bbox[0] + right = bbox[2] + + pypdf_top = pdf_height - bbox[1] + pypdf_bottom = pdf_height - bbox[3] + + return left, pypdf_bottom, right, pypdf_top + + +def fill_pdf_form(input_pdf_path, fields_json_path, output_pdf_path): + with open(fields_json_path, "r") as f: + fields_data = json.load(f) + + reader = PdfReader(input_pdf_path) + writer = PdfWriter() + + writer.append(reader) + + pdf_dimensions = {} + for i, page in enumerate(reader.pages): + mediabox = page.mediabox + pdf_dimensions[i + 1] = [mediabox.width, mediabox.height] + + annotations = [] + for field in fields_data["form_fields"]: + page_num = field["page_number"] + + page_info = next(p for p in fields_data["pages"] if p["page_number"] == page_num) + pdf_width, pdf_height = pdf_dimensions[page_num] + + if "pdf_width" in page_info: + transformed_entry_box = transform_from_pdf_coords( + field["entry_bounding_box"], + float(pdf_height) + ) + else: + image_width = page_info["image_width"] + image_height = page_info["image_height"] + transformed_entry_box = transform_from_image_coords( + field["entry_bounding_box"], + image_width, image_height, + float(pdf_width), float(pdf_height) + ) + + if "entry_text" not in field or "text" not in field["entry_text"]: + continue + entry_text = field["entry_text"] + text = entry_text["text"] + if not text: + continue + + font_name = entry_text.get("font", "Arial") + font_size = str(entry_text.get("font_size", 14)) + "pt" + font_color = entry_text.get("font_color", "000000") + + annotation = FreeText( + text=text, + rect=transformed_entry_box, + font=font_name, + font_size=font_size, + font_color=font_color, + border_color=None, + background_color=None, + ) + annotations.append(annotation) + writer.add_annotation(page_number=page_num - 1, annotation=annotation) + + with open(output_pdf_path, "wb") as output: + writer.write(output) + + print(f"Successfully filled PDF form and saved to {output_pdf_path}") + print(f"Added {len(annotations)} text annotations") + + +if __name__ == "__main__": + if len(sys.argv) != 4: + print("Usage: fill_pdf_form_with_annotations.py [input pdf] [fields.json] [output pdf]") + sys.exit(1) + input_pdf = sys.argv[1] + fields_json = sys.argv[2] + output_pdf = sys.argv[3] + + fill_pdf_form(input_pdf, fields_json, output_pdf) From eba7f28254560e4c4c8ae82315452ab03071b30a Mon Sep 17 00:00:00 2001 From: Funbucket Date: Sun, 10 May 2026 14:08:08 +0900 Subject: [PATCH 02/11] =?UTF-8?q?feat(who=5Fshould=5Fbe=5Ftreated):=20?= =?UTF-8?q?=EC=A0=95=EC=B1=85=ED=95=99=EC=8A=B5=20=EB=85=B8=ED=8A=B8?= =?UTF-8?q?=EB=B6=81=20=EC=B6=94=EA=B0=80=20(who=5Fshould=5Fbe=5Ftreated?= =?UTF-8?q?=5Fko)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - 4-arm 정책학습 분석 노트북 최초 추가 - DRLearner plug-in / DRPolicyTree / DRPolicyForest 세 방법론 구현 - 불확실 비용(LogNormal) 반영한 Y_net 기반 학습 및 평가 - AIPW score 구성 및 정책 가치 비교 (상수 정책 대비 통계적 유의성 검정) - 섹션 구조: 데이터와 설정 → 탐색과 진단 → 정책학습 방법론 → 정책 비교 → 결론 Co-Authored-By: Claude Sonnet 4.6 --- .../data/multi_attribution_sample.csv | 2001 +++++++++++++ .../policy-learning-1.Rmd | 709 +++++ .../who_should_be_treated_ko.ipynb | 2522 +++++++++++++++++ 3 files changed, 5232 insertions(+) create mode 100644 book/who_should_be_treated/data/multi_attribution_sample.csv create mode 100644 book/who_should_be_treated/policy-learning-1.Rmd create mode 100644 book/who_should_be_treated/who_should_be_treated_ko.ipynb diff --git a/book/who_should_be_treated/data/multi_attribution_sample.csv b/book/who_should_be_treated/data/multi_attribution_sample.csv new file mode 100644 index 0000000..a629466 --- /dev/null +++ b/book/who_should_be_treated/data/multi_attribution_sample.csv @@ -0,0 +1,2001 @@ +Global Flag,Major Flag,SMC Flag,Commercial Flag,IT Spend,Employee Count,PC Count,Size,Tech Support,Discount,Revenue +1,0,1,0,45537,26,26,152205,0,1,17688.363 +0,0,1,1,20842,107,70,159038,0,1,14981.43559 +0,0,0,1,82171,10,7,264935,1,1,32917.13894 +0,0,0,0,30288,40,39,77522,1,1,14773.76855 +0,0,1,0,25930,37,43,91446,1,1,17098.69823 +0,0,1,0,34597,44,51,218703,1,0,17280.70918 +1,0,0,0,40199,21,14,126342,0,0,9153.974376 +0,0,1,1,30454,11,8,96784,0,0,5760.075096 +0,0,0,1,23428,50,55,77298,1,1,17265.11146 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include=FALSE, results='hide'} +# Deleting all current variables +rm(list=ls()) + +# Ensuring consistent random values in the bookdown version (this can be ignored). +set.seed(1, kind = "Mersenne-Twister", normal.kind = "Inversion", sample.kind = "Rejection") + +# Note: bookdown seems to require explicitly calling these packages. +library(reshape2) +library(DiagrammeR) +``` + + +# Policy Learning I - Binary Treatment + + +Source RMD file: [link](https://docs.google.com/uc?export=download&id=1yI7RWqqe32DLXD7k66bgw5LT1_LcvO1a) + + +A few chapters ago, we learned how to estimate the average effect of a binary treatment (ATE), that is, the value of treating everyone in a population versus treating no one. Once that was established, we asked whether certain subgroups could react differently to the treatment, as we learned how to estimate such heterogeneous treatment effects (HTE). Then, in the previous chapter, we learned how to aggregate these heterogeneous effects to estimate the average outcome that would be attained if treatment assignment were to follow a particular rule, that is, if we were to treat only individuals with certain observable characteristics (policy evaluation). In this chapter, we will learn how to search the space of available treatment rules to approximately _maximize_ the average outcome across the population. That is, we will answer questions of the type: "_who_ should be treated?" We'll call this problem **policy learning**. + +We'll make a distinction between parametric and non-parametric policies, just as we did with predictive models. Parametric policies are simpler and depend only on a fixed number of parameters, whereas nonparametric policies can increase in complexity with the data. As we'll discuss below, there will be situations in which one or the other will be more appropriate. + +For now, we'll work with the same toy simulation setting that we used in the previous chapter. + +```{r, warning=FALSE, message=FALSE} +# use e.g., install.packages("grf") to install any of the following packages. +library(grf) +library(glmnet) +library(splines) +library(policytree) +library(ggplot2) +library(lmtest) +library(sandwich) +``` + +```{r} +# A randomized setting. +n <- 1000 +p <- 4 +X <- matrix(runif(n*p), n, p) +e <- .5 # fixed, known probability +W <- rbinom(n, prob = e, size = 1) +Y <- .5*(X[,1] - .5) + (X[,2] - .5)*W + .1 * rnorm(n) +data <- data.frame(x=X, y=Y, w=W) + +outcome <- "y" +covariates <- paste0("x.", seq(p)) +treatment <- "w" +``` + +## Non-parametric policies + +In the HTE chapter we define the conditional average treatment effect (CATE) function + +\begin{equation} + (\#eq:cate-oracle) + \tau(x) := \E[Y_i(1) - Y_i(0) | X_i = x], +\end{equation} +that is, the average effect of a binary treatment conditional on observable charateristics. + +If we knew \@ref(eq:cate-oracle), then a natural policy would be to assign individuals to treatment when their CATE is positive, +\begin{equation*} + \pi^{*} = \mathbb{I}\{\tau(x) \geq 0\}. +\end{equation*} + +More generally, if treating that individual costs a known amount $c(x)$, +\begin{equation*} + \pi^{*} = \mathbb{I}\{\tau(x) \geq c(x)\}. +\end{equation*} + +Of course, we don't know \@ref(eq:cate-oracle). However, we can obtain an estimate $\htau(\cdot)$ using any flexible (i.e., non-parametric) method as we learned in the HTE chapter, and then obtain a policy estimate +\begin{equation*} + \hpi(x) = \mathbb{I}\{ \htau(x) \geq 0\}, +\end{equation*} +replacing the zero threshold by some appropriate cost function if needed. + +Once we have an estimated policy, we need to estimate its value. To obtain accurate estimates, we must ensure appropriate **data-splitting**. We cannot estimate and evaluate a policy using the same data set, because that would lead to an overestimate of the value of the policy. One option here is to divide the data into training and test subsets, fit $\htau(\cdot)$ in the training subset and evaluate it in the test subset. This is analogous to what we saw in prediction problems: if we try to evaluate our predictions on the training set, we will overestimate how good our predictions are. + +The next snippet estimates the conditional treatment effect function via a Lasso model with splines. Note the data splitting. + +```{r} +# Preparing to run a regression with splines (piecewise polynomials). +# Note that if we have a lot of data we should increase the argument `df` below. +# The optimal value of `df` can be found by cross-validation +# i.e., check if the value of the policy, estimated below, increases or decreases as `df` varies. +fmla.xw <- formula(paste0("~", paste0("bs(", covariates, ", df=5) *", treatment, collapse="+"))) +XW <- model.matrix(fmla.xw, data) +Y <- data[,outcome] + +# Data-splitting +# Define training and evaluation sets +train <- sample(1:n, 0.5*n) +test <- -train + +# Fitting the outcome model on the *training* data +model.m <- cv.glmnet(XW[train,], Y[train]) + +# Predict outcome E[Y|X,W=w] for w in {0, 1} on the *test* data +data.0 <- data[test,] +data.0[,treatment] <- 0 +XW0 <- model.matrix(fmla.xw, data.0) +mu.hat.0 <- predict(model.m, XW0, s="lambda.min") + +data.1 <- data[test,] +data.1[,treatment] <- 1 +XW1 <- model.matrix(fmla.xw, data.1) +mu.hat.1 <- predict(model.m, XW1, s="lambda.min") + +# Computing the CATE estimate tau.hat +tau.hat <- mu.hat.1 - mu.hat.0 + +# Assignment if tau.hat is positive (or replace by non-zero cost if applicable) +pi.hat <- as.numeric(tau.hat > 0) + +# Estimate assignment probs e(x). +# (This will be useful for evaluation via AIPW scores a little later) + +# Uncomment/comment the next lines as appropriate +# In randomized settings assignment probabilities are fixed and known. +e.hat <- rep(0.5, length(test)) +# In observational setttings the assignment probability is typically unknown. +# fmla.x <- formula(paste0("~", paste0("bs(", covariates, ", df=3, degree=3)", collapse="+"))) +# XX <- model.matrix(fmla.x, data) +# model.e <- cv.glmnet(XX[train,], W[train], family="binomial") +# e.hat <- predict(model.e, XX[test,], s="lambda.min", type="response") +``` + +On the test set, we can evaluate this policy as we learned in the previous chapter. In a randomized setting, a simple estimator based on the difference in means is available. + +```{r} +# Only valid in randomized settings. +A <- pi.hat == 1 +Y <- data[test, outcome] +W <- data[test, treatment] +value.estimate <- mean(Y[A & (W==1)]) * mean(A) + mean(Y[!A & (W==0)]) * mean(!A) +value.stderr <- sqrt(var(Y[A & (W==1)]) / sum(A & (W==1)) * mean(A)^2 + var(Y[!A & (W==0)]) / sum(!A & W==0) * mean(!A)^2) +print(paste("Value estimate:", value.estimate, "Std. Error:", value.stderr)) +``` + +In a randomized setting and observational settings with unconfoundedness, an estimator of the policy value based on AIPW scores is available. In large samples, it should have smaller variance than the one based on sample averages. + +```{r} +# Valid in randomized settings and observational settings with unconfoundedness and overlap. +Y <- data[test, outcome] +W <- data[test, treatment] +gamma.hat.1 <- mu.hat.1 + W / e.hat * (Y - mu.hat.1) +gamma.hat.0 <- mu.hat.0 + (1 - W) / (1 - e.hat) * (Y - mu.hat.0) +gamma.hat.pi <- pi.hat * gamma.hat.1 + (1 - pi.hat) * gamma.hat.0 + +value.estimate <- mean(gamma.hat.pi) +value.stderr <- sd(gamma.hat.pi) / sqrt(length(gamma.hat.pi)) +print(paste("Value estimate:", value.estimate, "Std. Error:", value.stderr)) +``` + +Above we used a flexible linear model, but in fact we can also use any other non-parametric method. The next example uses `grf`. An advantage of using `grf` is that we can leverage [out-of-bag](https://github.com/grf-labs/grf/blob/master/REFERENCE.md#out-of-bag-prediction) predictions, so explicit data splitting is not necessary. + +```{r} +# Using the entire data +X <- data[,covariates] +Y <- data[,outcome] +W <- data[,treatment] + +# Comment / uncomment as approriate +# Randomized setting with known assignment probability (here, 0.5) +forest <- causal_forest(X, Y, W, W.hat=.5) +# Observational setting with unconfoundedness and overlap. +# forest <- causal_forest(X, Y, W) + +# Get "out-of-bag" predictions +tau.hat.oob <- predict(forest)$predictions +pi.hat <- as.numeric(tau.hat.oob > 0) +``` + +Again, to evaluate the value of this policy in a randomized setting, we can use the following estimator based on sample averages. + +```{r} +# Only valid in randomized settings. + +# We can use the entire data because predictions are out-of-bag +A <- pi.hat == 1 +value.estimate <- mean(Y[A & (W==1)]) * mean(A) + mean(Y[!A & (W==0)]) * mean(!A) +value.stderr <- sqrt(var(Y[A & (W==1)]) / sum(A & (W==1)) * mean(A)^2 + var(Y[!A & (W==0)]) / sum(!A & W==0) * mean(!A)^2) +print(paste("Value estimate:", value.estimate, "Std. Error:", value.stderr)) +``` + +And here's how to produce an AIPW-based estimate. Note that that estimates of the propensity scores (`W.hat`) and outcome model (`mu.hat.1`, `mu.hat.0`) are also [out-of-bag](https://github.com/grf-labs/grf/blob/master/REFERENCE.md#out-of-bag-prediction), ensuring appropriate sample splitting. + +```{r} +# Valid in randomized settings and observational settings with unconfoundedness and overlap. +tau.hat <- predict(forest)$predictions + +# Retrieve relevant quantities. +e.hat <- forest$W.hat # P[W=1|X] +mu.hat.1 <- forest$Y.hat + (1 - e.hat) * tau.hat # E[Y|X,W=1] = E[Y|X] + (1 - e(X)) * tau(X) +mu.hat.0 <- forest$Y.hat - e.hat * tau.hat # E[Y|X,W=0] = E[Y|X] - e(X) * tau(X) + +# Compute AIPW scores. +gamma.hat.1 <- mu.hat.1 + W / e.hat * (Y - mu.hat.1) +gamma.hat.0 <- mu.hat.0 + (1-W) / (1-e.hat) * (Y - mu.hat.0) +gamma.hat.pi <- pi.hat * gamma.hat.1 + (1 - pi.hat) * gamma.hat.0 + +# Value estimates. +value.estimate <- mean(gamma.hat.pi) +value.stderr <- sd(gamma.hat.pi) / sqrt(length(gamma.hat.pi)) +print(paste("Value estimate:", value.estimate, "Std. Error:", value.stderr)) +``` + + +A technical note. It's easy to get confused and try to "estimate" a nonparametric policy using AIPW scores, as in "$\hpi(X_i) = \mathbb{I}\{ \hGamma_{i,1} \geq \hGamma_{i,0} \}$". _This is incorrect_. AIPW scores are very noisy and should never be used "pointwise" like this. They should be used as part of an average (as above), or some other form of aggregation (as we'll see in the next section). + + + +## Parametric policies + +In many settings, there are good reasons to constrain the policy to belong to a smaller function class $\Pi$. The set $\Pi$ may contain only policies that, for example, are transparent and easy to explain to stakeholders, or that are easily implemented in the field. It may also be the case that the set of available policies $\Pi$ encodes other desirability criteria, such as satisfying certain budget constraints or depending only on a subset of observable characteristics. + +Estimating such a policy from data is finding an approximate solution to the following constrained maximization problem, +\begin{equation} + (\#eq:param-pi-oracle) + \pi^{*} = \arg\max_{\pi \in \Pi} \E[Y(\pi(X_i))]. +\end{equation} + +Following [Athey and Wager (2021, Econometrica)](https://onlinelibrary.wiley.com/doi/abs/10.3982/ECTA15732), we will use the following empirical counterpart of \@ref(eq:param-pi-oracle), +\begin{equation} + (\#eq:param-pi-problem) + \hpi = \arg\min_{\pi \in \Pi} \frac{1}{n} \sum_{i=1}^{n} \hGamma_{i,\pi(X_i)}, +\end{equation} +where $\hGamma_{i,\pi(X_i)}$ are AIPW scores as defined in the previous chapter. As reminder, +\begin{equation} + \hGamma_{i,\pi(X_i)} = \pi(X_i)\hGamma_{i,1} + (1 - \pi(X_i))\hGamma_{i,0}, +\end{equation} +where +\begin{align} + (\#eq:aipw) + \hGamma_{i,1} + &= \hat{\mu}^{-i}(X_i, 1) + \frac{W_i}{\hat{e}^{-i}(X_i)} \left(Y_i -\hat{\mu}^{-i}(X_i, 1)\right), \\ + \hGamma_{i,0} + &= \hat{\mu}^{-i}(X_i, 0) . \frac{1-W_i}{1-\hat{e}^{-i}(X_i)} \left(Y_i -\hat{\mu}^{-i}(X_i, 0)\right). +\end{align} + +Here we use shallow tree policies as our main example of parametric policies. The `R` package `policytree` can be used to find a policy that solves \@ref(eq:param-pi-problem). In the example below, we'll construct AIPW scores estimated using `grf`, though we could have used any other non-parametric method (with appropriate sample-splitting). See this short [tutorial](https://grf-labs.github.io/policytree/) for other examples using these two packages. + +Let's walk through an example for the data simulated above. The first step is to construct AIPW scores \@ref(eq:aipw). The function `double_robust_scores` from the `policytree` package does that in one line. + +```{r} +# Randomized setting: pass the known treatment assignment as an argument. +forest <- causal_forest(X, Y, W, W.hat=.5) +# Observational settting with unconfoundedness+overlap: let the assignment probabilities be estimated. +# forest <- causal_forest(X, Y, W) + +# from policytree package +gamma.matrix <- double_robust_scores(forest) + +# Note: the function double_robust_scores is equivalent to the following: +# tau.hat <- predict(forest)$predictions +# mu.hat.1 <- forest$Y.hat + (1 - forest$W.hat) * tau.hat +# mu.hat.0 <- forest$Y.hat - forest$W.hat * tau.hat +# gamma.hat.1 <- mu.hat.1 + W/forest$W.hat * (Y - mu.hat.1) +# gamma.hat.0 <- mu.hat.0 + (1-W)/(1-forest$W.hat) * (Y - mu.hat.0) +# gamma.matrix <- cbind(gamma.hat.0, gamma.hat.1) +``` + +Next, to ensure appropriate sample splitting, we divide our data into training and test subsets. We estimate the policy on the training subset and estimate its value on the test subset. + +```{r} +# Divide data into train and test sets +train <- sample(1:n, 0.5*n) +test <- -train + +# Train on a portion of the data +# The argument `depth` controls the depth of the tree. +# Depth k means that we can partition the data into up to 2^(k+1) regions. +policy <- policy_tree(X[train,], gamma.matrix[train,], depth=2) + +# Predict on remaining portion +# Note policytree recodes the treatments to 1,2 +# We substract one to get back to our usual encoding 0,1. +pi.hat <- predict(policy, X[test,]) - 1 +``` + +To understand the policy we just learned, we can print the tree splits. +```{r} +print(policy) +``` + +Alternatively, we can plot the tree. +```{r policy-tree, fig.align = 'center', fig.cap= "Learned policy"} +plot(policy, leaf.labels = c("control", "treatment")) +``` + +Note how the treatment rule is rather transparent, in that whether or not each individual is treated depends only on a couple of if-statements. This can be very attractive in settings in which it's important to explain the policy to stakeholders, or reason about its consequences in terms of fairness (e.g., is it okay that these particular subgroups get the treatment?), manipulability (e.g., will individuals lie about their observable characteristics to get a better outcome?), and so on. + +To evaluate the policy, we again use what we learned in the previous chapter, remembering that we can only use the test set for evaluation. In a randomized setting, we can use the following estimator based on sample averages. + +```{r} +# only valid for randomized setting! +A <- pi.hat == 1 +Y <- data[test, outcome] +W <- data[test, treatment] +value.estimate <- mean(Y[A & (W==1)]) * mean(A) + mean(Y[!A & (W==0)]) * mean(!A) +value.stderr <- sqrt(var(Y[A & (W==1)]) / sum(A & (W==1)) * mean(A)^2 + var(Y[!A & (W==0)]) / sum(!A & W==0) * mean(!A)^2) +print(paste("Value estimate:", value.estimate, "Std. Error:", value.stderr)) +``` + +Using the remaining AIPW scores produces an estimate that, in large samples, has smaller standard error. + +```{r} +# Valid in randomized settings and observational settings with unconfoundedness and overlap. +gamma.hat.pi <- pi.hat * gamma.matrix[test,2] + (1 - pi.hat) * gamma.matrix[test,1] +value.estimate <- mean(gamma.hat.pi) +value.stderr <- sd(gamma.hat.pi) / sqrt(length(gamma.hat.pi)) +print(paste("Value estimate:", value.estimate, "Std. Error:", value.stderr)) +``` + + +A technical note. Very small policy tree leaves make it hard to reliably evaluate policy values, in particular when the treatment is categorical with many levels. You can avoid small tree leaves increasing the `min.node.size` argument in `policy_tree`. + + +[Possible edit here: talk about cross-validation?] + + +## Case study + +Let's apply the methods above to our `welfare` dataset, as used in previous chapters. + +```{r, message=FALSE, warning=FALSE} +# Read in data +data <- read.csv("https://docs.google.com/uc?id=1AQva5-vDlgBcM_Tv9yrO8yMYRfQJgqo_&export=download") +n <- nrow(data) + +## NOTE: invert treatment and control, compared to the ATE and HTE chapters. +data$w <- 1 - data$w + +# Treatment is the wording of the question: +# 'does the the gov't spend too much on 'assistance to the poor' (control: 0) +# 'does the the gov't spend too much on "welfare"?' (treatment: 1) +treatment <- "w" + +# Outcome: 1 for 'yes', 0 for 'no' +outcome <- "y" + +# Additional covariates +covariates <- c("age", "polviews", "income", "educ", "marital", "sex") +``` + +It's important to note that there are different types of "heterogeneity" in treatment effects. Sometimes the effect of a treatment is positive throughout, and what changes is the magnitude of the effect. In this case, we would still like to treat everyone. On the other hand, sometimes the treatment effect is positive for certain subgroups and negative for others. The latter is a more interesting scenario for policy learning. + +In this dataset, however, the effect seems to be mostly positive throughout. That is, i.e., most individuals respond "yes" more often when they are asked about "welfare" than about "assistance to the poor." To make the problem more interesting, we'll artificially modify the problem by introducing a cost of asking about welfare. This is just for illustration here, although there are natural examples in which treatment is indeed costly. Note in the code below how we subtract a cost of `.3` from the AIPW scores associated with treatment. + + +```{r} +# Prepare data +X <- data[,covariates] +Y <- data[,outcome] +W <- data[,treatment] +cost <- .3 + +# Fit a policy tree on forest-based AIPW scores +forest <- causal_forest(X, Y, W) +gamma.matrix <- double_robust_scores(forest) +gamma.matrix[,2] <- gamma.matrix[,2] - cost # Subtracting cost of treatment + +# Divide data into train and evaluation sets +train <- sample(1:n, 0.8*n) +test <- -train + +# Fit policy on training subset +policy <- policy_tree(X[train,], gamma.matrix[train,], depth = 2, min.node.size=1) + +# Predicting treatment on test set +pi.hat <- predict(policy, X[test,]) - 1 + +# Predicting leaves (useful later) +leaf <- predict(policy, X[test,], type = "node.id") +num.leaves <- length(unique(leaf)) +``` + + +Examining the policy we just learned. + +```{r} +print(policy) +``` + + +```{r policy-tree-case-study, fig.align = 'center', fig.cap= "Learned policy", message=FALSE, warning=FALSE} +plot(policy, leaf.labels = c("control", "treatment")) +``` + +Estimating the value of the learned policy. Note in the code below that we must subtract the cost of treatment. + +```{r} +A <- pi.hat == 1 +Y.test <- data[test, outcome] +W.test <- data[test, treatment] + +# Only valid for randomized setting. +# Note the -cost here! +value.avg.estimate <- (mean(Y.test[A & (W.test==1)]) - cost) * mean(A) + mean(Y.test[!A & (W.test==0)]) * mean(!A) +value.avg.stderr <- sqrt(var(Y.test[A & (W.test==1)]) / sum(A & (W.test==1)) * mean(A)^2 + var(Y.test[!A & (W.test==0)]) / sum(!A & W.test==0) * mean(!A)^2) +print(paste("Estimate [sample avg]:", value.avg.estimate, "(", value.avg.stderr, ")")) + +# Valid in both randomized and obs setting with unconf + overlap. +gamma.hat.1 <- gamma.matrix[test,2] +gamma.hat.0 <- gamma.matrix[test,1] +gamma.hat.pi <- pi.hat * gamma.hat.1 + (1 - pi.hat) * gamma.hat.0 +value.aipw.estimate <- mean(gamma.hat.pi) +value.aipw.stderr <- sd(gamma.hat.pi) / sqrt(length(gamma.hat.pi)) +print(paste("Estimate [AIPW]:", value.aipw.estimate, "(", value.aipw.stderr, ")")) +``` + + +Testing whether the learned policy value is different from the value attained by the "no-treatment" policy. + +```{r} +# Only valid for randomized setting. +diff.estimate <- (mean(Y.test[A & (W.test==1)]) - cost - mean(Y.test[A & (W.test==0)])) * mean(A) +diff.stderr <- sqrt(var(Y.test[A & (W.test==1)]) / sum(A & (W.test==1)) + var(Y.test[A & (W.test==0)]) / sum(A & W.test==0)) * mean(A)^2 +print(paste("Difference estimate [sample avg]:", diff.estimate, "Std. Error:", diff.stderr)) + +# Valid in both randomized and obs setting with unconf + overlap. +gamma.hat.pi.diff <- gamma.hat.pi - gamma.hat.0 +diff.estimate <- mean(gamma.hat.pi.diff) +diff.stderr <- sd(gamma.hat.pi.diff) / sqrt(length(gamma.hat.pi.diff)) +print(paste("Difference estimate [aipw]:", diff.estimate, "Std. Error:", diff.stderr)) +``` + + +## Topics 1: Subgroups using learned policy + +The learned policy naturally induces interesting subgroups for which we expect the treatment effect to be different. With appropriate sample splitting, we can test that treatment effect is indeed different across "regions" defined by assignment under the learned policy, +\begin{equation} + H_0: \E[Y_i(1) - Y_i(0)| \hpi(X_i) = 1] = \E[Y_i(1) - Y_i(0)| \hpi(X_i) = 0]. +\end{equation} + +```{r} +# Only valid in randomized settings +fmla <- formula(paste0(outcome, "~ 0 + pi.hat + w:pi.hat")) +ols <- lm(fmla, data=transform(data[test,], pi.hat=factor(pi.hat))) +coefs <- coeftest(ols, vcov=vcovHC(ols, 'HC2'))[3:4,1:2] +coefs[,1] <- coefs[,1] - cost # subtracting cost +coefs +``` + +```{r} +# Valid in randomized settings and observational settings with unconfoundedness+overlap +ols <- lm(gamma.hat.1 - gamma.hat.0 ~ 0 + factor(pi.hat)) +coeftest(ols, vcov=vcovHC(ols, 'HC2'))[1:2,] +``` + + +If we learned a tree policy using the `policytree`, we can test whether treatment effects are different across leaves. + +\begin{equation} + H_0: \E[Y_i(1) - Y_i(0)| \text{Leaf} = 1] = \E[Y_i(1) - Y_i(0)| \text{Leaf} = \ell] \qquad \text{for }\ell \geq 2 +\end{equation} + +```{r} +# Only valid in randomized settings. +fmla <- paste0(outcome, ' ~ 0 + leaf + w:leaf') +ols <- lm(fmla, data=transform(data[test,], leaf=factor(leaf))) +coefs <- coeftest(ols, vcov=vcovHC(ols, 'HC2'))[,1:2] +interact <- grepl(":", rownames(coefs)) +coefs[interact,1] <- coefs[interact,1] - cost # subtracting cost +coefs[interact,] +``` + +```{r} +# Valid in randomized settings and observational settings with unconfoundedness+overlap. +gamma.hat.diff <- gamma.hat.1 - gamma.hat.0 +ols <- lm(gamma.hat.1 - gamma.hat.0 ~ 0 + factor(leaf)) +coeftest(ols, vcov=vcovHC(ols, 'HC2'))[,1:2] +``` + + +Finally, in Figure \@ref(fig:avg-cov-policy), as we have done in previous chapters, we can check how covariate averages vary across subgroups. This time, the subgroups are defined by treatment assignment under the learned policy. +\begin{equation} + H_0: \E[X_{ij} | \hpi(X_i) = 1] = \E[X_{ij} | \hpi(X_i) = 0] \qquad \text{for each covariate }j +\end{equation} + +```{r avg-cov-policy, fig.align = 'center', fig.cap= "Average covariate values within each group (defined by treatment assignment under the learned policy)"} +df <- lapply(covariates, function(covariate) { + fmla <- formula(paste0(covariate, " ~ 0 + factor(pi.hat)")) + ols <- lm(fmla, data=transform(data[test,], pi.hat=pi.hat)) + ols.res <- coeftest(ols, vcov=vcovHC(ols, "HC2")) + + # Retrieve results + avg <- ols.res[,1] + stderr <- ols.res[,2] + + # Tally up results + data.frame( + covariate, avg, stderr, pi.hat=factor(c('control', 'treatment')), + # Used for coloring + scaling=pnorm((avg - mean(avg))/sd(avg)), + # We will order based on how much variation is 'explained' by the averages + # relative to the total variation of the covariate in the data + variation=sd(avg) / sd(data[,covariate]), + # String to print in each cell in heatmap below + labels=paste0(signif(avg, 3), "\n", "(", signif(stderr, 3), ")")) +}) +df <- do.call(rbind, df) + +# a small optional trick to ensure heatmap will be in decreasing order of 'variation' +df$covariate <- reorder(df$covariate, order(df$variation)) + +# plot heatmap +ggplot(df) + + aes(pi.hat, covariate) + + geom_tile(aes(fill = scaling)) + + geom_text(aes(label = labels)) + + scale_fill_gradient(low = "#E1BE6A", high = "#40B0A6") + + theme_minimal() + + ylab("") + xlab("") + + theme(plot.title = element_text(size = 12, face = "bold"), + axis.text=element_text(size=11)) +``` + + + + +## Topics 2: Learning with uncertain costs + +In the previous section, treatment costs were known and (just for simplicity of exposition) fixed across covariate values. However, there are situations in which costs are unknown and must be learned from the data as well. In such situations, we may be interested only in policies that do not exceed a certain budget in expectation. + +Here, we follow [Sun, Du, Wager (2021)](https://arxiv.org/abs/2103.11066) for how to deal with this issue. Their formulation is as follows. In potential outcome notation, each observation can be described by the tuple $(X_i, Y_i(0), Y_i(1), C_i(0), C_i(1))$, where the new pair $(C_i(0), C_i(1))$ represents costs that would be realized if individuals were assigned to control or treatment. Of course, in the data we only observe the tuple $(X_i, W_i, Y_i, C_i)$, where $C_i \equiv C_i(W_i)$. We are interested in approximating the policy $\pi_B^*$ that maximizes the gain from treating relative to not treating anyone while keeping the average relative cost bounded by some known budget $B$, +\begin{equation} + \pi_B^*(x) := \arg\max \E[Y(\pi(X_i))] - \E[Y_i(0)] \quad \text{such that} \quad \E[C_i(\pi(X_i)) - C_i(0)] \leq B. +\end{equation} + +This paper demonstrates that the optimal policy has the following structure. First, we can order observations in decreasing order according to the following priority ranking, +\begin{equation} + (\#eq:rho) + \rho(x) := + \frac{\E[Y_i(1) - Y_i(0) | X_i = x]} + {\E[C_i(1) - C_i(0) | X_i = x]}. +\end{equation} +Then, we assign treatment in decreasing order \@ref(eq:rho) until we either treat everyone with positive $\rho(x)$ or the budget is met. The intuition is that individuals for which \@ref(eq:rho) is high have a high expected treatment effect relative to cost, so by assigning them first we obtain a cost-effective policy. We stop once there's no one else for which treatment is expected to be positive or we run out of resources. + +To obtain estimates $\hat{\rho}$ of \@ref(eq:rho) from the data, we have two options. The first is to estimate the numerator $\htau(x) = \E[Y_i(1) - Y_i(0) |X_i = x]$ and the denominator $\hat{\gamma}(x) = \E[C_i(1) - C_i(0) |X_i = x]$ separately, in a manner analogous to what we saw in the HTE chapter, and compute their ratio, producing the estimate $\hat{\rho}(x) = \htau(x) / \hat{\gamma}(x)$. We'll see a second option below. + +Let's put the first option into practice. For illustration, we will generate random costs for our data. We'll assume that the costs of treatment are drawn from a conditionally Exponential distribution, and that there are no costs for not treating. +```{r} +# Creating random costs. +data$costs <- C <- ifelse(data$w == 1, rexp(n=n, 1/(data$income * data$polviews)), 0) +``` + +Figure \@ref(fig:cost-curves) compares two kinds of policies. An "ignore costs" policy which, as the name suggests, orders individuals by $\hat{\tau}$ only without taking costs into account; and the "ratio" policy in which the numerator and denominator of \@ref(eq:rho) are estimated separately. The comparison is made via a **cost curve** that compares the cumulative benefit of treatment with its cumulative cost (both normalized to 1), for all possible budgets at once. More cost-effective policies hug the left corner of the graph more tightly, keeping away from the 45-degree line. + +```{r} +# Preprating data +X <- data[,covariates] +Y <- data[,outcome] +W <- data[,treatment] +C <- data[,'costs'] + +# Sample splitting. +# Note that we can't simply rely on out-of-bag observations here, because we're ranking *across* observations. +# This is the same issue we encountered when ranking observations according to predicted CATE in the HTE chapter. +train <- sample(1:n, 0.5*n) +test <- -train +train.treat <- which(W[train] == 1) + +# Because they will be useful to save computational time later, +# we'll estimate the outcome model and propensity score model separately. + +# Propensity score model. +# Comment / uncomment as appropriate. +# Randomized setting with fixed and known assignment probability (here: 0.5) +W.hat.train <- 0.5 +# Observational settings with unconfoundedness and overlap. +# e.forest <- regression_forest(X = X[train,], Y = W[train]) +# W.hat.train <- predict(e.forest)$predictions + +# Outcome model. +m.forest <- regression_forest(X = X[train,], Y = Y[train]) +Y.hat.train <- predict(m.forest)$predictions + +# Estimating the numerator. +tau.forest <- causal_forest(X = X[train,], Y = Y[train], W = W[train], W.hat = W.hat.train, Y.hat = Y.hat.train) + +# Estimating the denominator. +# Because costs for untreated observations are known to be zero, we're only after E[C(1)|X]. +# Under unconfoundedness, this can be estimated by regressing C on X using only the treated units. +gamma.forest <- regression_forest(X = X[train.treat,], Y = C[train.treat]) +gamma.hat.train <- predict(m.forest)$predictions +# If costs were not zero, we could use the following. +# gamma.forest <- causal_forest(X = X[train,], Y = C[train], W = W[train], W.hat = W.hat.train, Y.hat = Y.hat.train) + +# Compute predictions on test set +tau.hat <- predict(tau.forest, X[test,])$predictions +gamma.hat <- predict(gamma.forest, X[test,])$predictions + +# Rankings +rank.ignore <- order(tau.hat, decreasing = TRUE) +rank.direct <- order(tau.hat / gamma.hat, decreasing = TRUE) + +# IPW-based estimates of (normalized) treatment and cost +W.hat.test <- .5 +# W.hat.test <- predict(e.forest, X[test,])$predictions +n.test <- length(test) +treatment.ipw <- 1 / n.test * (W[test]/W.hat.test - (1 - W[test])/(1 - W.hat.test)) * Y[test] +cost.ipw <- 1 / n.test * W[test] / W.hat.test * C[test] + +# Cumulative benefit and cost of treatment (normalized) for a policy that ignores costs. +treatment.value.ignore <- cumsum(treatment.ipw[rank.ignore]) / sum(treatment.ipw) +treatment.cost.ignore <- cumsum(cost.ipw[rank.ignore]) / sum(cost.ipw) + +# Cumulative benefit and cost of treatment (normalized) for a policy that uses the ratio, estimated separately. +treatment.value.direct <- cumsum(treatment.ipw[rank.direct]) / sum(treatment.ipw) +treatment.cost.direct <- cumsum(cost.ipw[rank.direct]) / sum(cost.ipw) +``` + +```{r cost-curves, fig.align = 'center', fig.cap= "Cost curves"} +# Plotting +plot(treatment.cost.ignore, treatment.value.ignore, col=rgb(0.2,0.4,0.1,0.7), lwd = 3, type = "l", xlab="(Normalized) cumulative cost", ylab="(Normalized) cumulative value", las=1) +lines(treatment.cost.direct, treatment.value.direct, col=rgb(0.6,0.4,0.1,0.7), lwd = 3, type = "l") +abline(a = 0, b = 1, lty = 2) +legend("bottomright", legend = c("Ignoring costs", "Direct ratio"), col = c(rgb(0.2,0.4,0.1,0.7), rgb(0.8,0.4,0.1,0.7)), lwd=3) +``` + +To read this graph, we consider a point on the horizontal axis, representing a possible (normalized) budget constraint. At that point, whichever policy is higher is more cost-effective. In this example, we see that the "direct ratio" solution is much more cost-effective than the "ignore costs" one. + + +As the authors note, we can also estimate \@ref(eq:rho) in a second manner that targets the parameter $\rho(x)$ directly. First, they note that, under overlap and the following unconfoundedness assumption +\begin{equation} + \{Y_i(0), Y_i(1), C_i(1), C_i(0) \} \perp W_i | X_i +\end{equation} +we can rewrite \@ref(eq:rho) as +\begin{equation} + (\#eq:rho-iv) + \rho(x) := + \frac{\text{Cov}[Y_i, W_i | X_i = x]} + {\text{Cov}[C_i, W_i | X_i = x]}. +\end{equation} + +As readers with a little more background in causal inference may note, \@ref(eq:rho-iv) coincides with the definition of the conditional local average treatment effect (LATE) if we _were_ to take $W_i$ as an "instrumental variable" and $C_i$ as the "treatment". In fact, instrumental variable methods require entirely different assumptions, so the connection with instrumental variables is tenuous (see the paper for details), but mechanically \@ref(eq:rho-iv) still provides us with an estimation procedure: we can use any method used to estimate conditional LATE to produce an estimate $\hat{\rho}$. + + +```{r} +# Estimating rho(x) directly via instrumental forests. +# In observational settings, remove the argument W.hat. +iv.forest <- instrumental_forest(X = X[train,], + Y = Y[train], + W = C[train], # cost as 'treatment' + Z = W[train], # treatment as 'instrument' + Y.hat = Y.hat.train, + W.hat = NULL, # If costs are nonzero: predict(gamma.forest)$predictions, + Z.hat = tau.forest$W.hat) + +# Predict and compute and estimate of the ranking on a test set. +rho.iv <- predict(iv.forest, X[test,])$predictions +rank.iv <- order(rho.iv, decreasing = TRUE) + +# Cumulative benefit and cost of treatment (normalized) for a policy based on the IV analogy. +treatment.value.iv <- cumsum(treatment.ipw[rank.iv]) / sum(treatment.ipw) +treatment.cost.iv <- cumsum(cost.ipw[rank.iv]) / sum(cost.ipw) +``` + + +```{r cost-curves-late, fig.align = 'center', fig.cap= "Cost curves"} +# Plotting +plot(treatment.cost.ignore, treatment.value.ignore, col=rgb(0.2,0.4,0.1,0.7), lwd = 3, type = "l", xlab="(Normalized) cumulative cost", ylab="(Normalized) cumulative value", las=1) +lines(treatment.cost.direct, treatment.value.direct, col=rgb(0.6,0.4,0.1,0.7), lwd = 3, type = "l") +abline(a = 0, b = 1, lty = 2) +lines(treatment.cost.iv, treatment.value.iv, col=rgb(1,0,0,0.7), lwd = 3, type = "l") +abline(a = 0, b = 1, lty = 2) +legend("bottomright", legend = c("Ignoring costs", "Direct ratio", "Sun, Du, Wager (2021)"), col = c(rgb(0.2,0.4,0.1,0.7), rgb(0.8,0.4,0.1,0.7), rgb(1,0,0,0.7)), lwd=3) +``` + +In Figure \@ref(fig:cost-curves-late), both the "direct ratio" and the solution based on instrumental forests have similar performance. This isn't always the case. When the ratio $\rho(x)$ is simpler relative to $\tau(x)$ and $\gamma(x)$, the solution based on instrumental forests may perform better since it is estimating $\rho(x)$ directly, where the "direct ratio" solution needs to estimate the more complicated objects $\tau(x)$ and $\gamma(x)$ separately. At a high level, we should expect $\rho(x)$ to be relatively simpler when there is a strong relationship between $\tau(x)$ and $\gamma(x)$. Here, our simulated costs seem to be somewhat related to CATE (see Figure \@ref(fig:cost-cate)), but perhaps not strongly enough to make the instrumental forest solution noticeably better than the one based on ratios. + +```{r cost-cate, fig.align = 'center', fig.cap= "Estimated cost and estimated CATE"} +plot(gamma.hat, tau.hat, + pch=16, # shape is filled circle + col=rgb(0,0,0,0.1), # color is black with transparency 0.1 + las=1, + xlab="Estimated cost (normalized)", ylab="Estimated CATE (normalized)") +``` + + +The different policies can be compared by the area between the curves they trace and the 45-degree line, with higher values indicating better policies. + +```{r} +auc <- data.frame( + ignore=sum((treatment.value.ignore - treatment.cost.ignore) * diff((c(0, treatment.cost.ignore)))), + ratio=sum((treatment.value.direct - treatment.cost.direct) * diff((c(0, treatment.cost.direct)))), + iv=sum((treatment.value.iv - treatment.cost.iv) * diff((c(0, treatment.cost.iv)))) +) +auc +``` + + + +## Further reading + +The presentation of parametric policies was largely based on [Athey and Wager (2021, Econometrica)](https://onlinelibrary.wiley.com/doi/abs/10.3982/ECTA15732). A slightly more accessible version of some of the material in the published version can be found in an earlier [ArXiv version](https://arxiv.org/abs/1702.02896v1) of the same paper. Policy comparisons via cost curves can also be found in [Imai and Li (2019)](https://arxiv.org/pdf/1905.05389.pdf). + + diff --git a/book/who_should_be_treated/who_should_be_treated_ko.ipynb b/book/who_should_be_treated/who_should_be_treated_ko.ipynb new file mode 100644 index 0000000..749c5c2 --- /dev/null +++ b/book/who_should_be_treated/who_should_be_treated_ko.ipynb @@ -0,0 +1,2522 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "1ae5a6c4", + "metadata": {}, + "source": [ + "# 누구에게 어떤 프로모션을 제공해야 할까요?\n", + "\n", + "인과추론의 많은 문제는 평균 처치 효과(ATE)에 집중합니다. 예를 들어 “프로모션을 진행할 것인가, 하지 않을 것인가?”라는 질문은 모든 고객에게 동일한 프로모션을 제공하는 정책과 아무에게도 제공하지 않는 정책을 비교하는 문제입니다.\n", + "\n", + "하지만 예산이 제한되어 있고 그 안에서 최대 효과를 내야 한다면 이야기가 달라집니다. 프로모션 효과가 고객마다 크게 다르다면, 모두에게 동일하게 제공하는 전략이 최선이 아닐 수 있습니다.\n", + "\n", + "- 이미 구매할 고객(always-converters)에게 불필요한 비용을 쓰게 되고,\n", + "- 마케팅에 부정적으로 반응하는 고객(sleeping dogs) 때문에 오히려 손실이 발생하며,\n", + "- 효과가 클 고객(high-responders)을 제대로 우선순위에 두지 못해 잠재 매출을 놓칩니다.\n", + "\n", + "따라서 핵심 질문은 다음과 같습니다.\n", + "\n", + "_\"어떤 고객에게 집중할 때 순편익이 가장 커질까요?\"_\n", + "\n", + "정책학습(policy learning)은 가능한 처치 규칙들을 탐색하면서 전체 평균 순편익을 최대화하는 방법을 학습합니다. 즉, “누구를 처치해야 하는가?”라는 질문에 답하는 접근입니다." + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "id": "fc2741f9", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-09T16:49:46.211163Z", + "iopub.status.busy": "2026-05-09T16:49:46.211076Z", + "iopub.status.idle": "2026-05-09T16:49:48.058022Z", + "shell.execute_reply": "2026-05-09T16:49:48.057674Z" + } + }, + "outputs": [], + "source": [ + "import os\n", + "import tempfile\n", + "import warnings\n", + "from pathlib import Path\n", + "\n", + "os.environ.setdefault(\"MPLCONFIGDIR\", str(Path(tempfile.gettempdir()) / \"matplotlib\"))\n", + "os.environ.setdefault(\"MPLBACKEND\", \"Agg\")\n", + "os.environ.setdefault(\"LOKY_MAX_CPU_COUNT\", \"8\")\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "from scipy import stats\n", + "from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "from econml.dr import DRLearner\n", + "from econml.policy import DRPolicyForest, DRPolicyTree\n", + "\n", + "warnings.filterwarnings('ignore')\n", + "\n", + "SEED = 1\n", + "rng = np.random.default_rng(SEED)\n", + "np.random.seed(SEED)\n", + "sns.set_theme(style='whitegrid')" + ] + }, + { + "cell_type": "markdown", + "id": "8b741c26", + "metadata": {}, + "source": [ + "## 정책학습 문제 정의\n", + "\n", + "한 소프트웨어 판매 회사는 할인이나 기술지원 같은 인센티브가 고객의 소프트웨어 구매를 실제로 늘리는지, 그리고 어떤 고객에게 더 효과적인지 알고 싶어합니다.\n", + "\n", + "이상적으로는 고객마다 서로 다른 인센티브를 무작위로 배정하는 실험(RCT)을 수행하는 것이 가장 좋지만 실제 비즈니스 환경에서는 비용 문제, 영업 전략, 대형 고객을 놓칠 위험 등으로 인해 이러한 실험을 수행하기 어렵습니다. 따라서 이번 데이터는 무작위 실험 데이터가 아닌 관찰 데이터(observational data)입니다.\n", + "\n", + "데이터에는 약 2,000명의 고객 정보가 포함되어 있으며, 다음과 같은 변수들로 구성됩니다.\n", + "\n", + "- 고객 특성: 각 고객의 산업 분야, 규모, 매출, 기술 프로필에 대한 세부 정보.\n", + "- 개입: 고객에게 제공된 인센티브에 대한 정보.\n", + "- 결과: 인센티브 제공 후 1년 동안 고객이 구매한 제품의 양.\n", + "\n", + "| 변수명 | 타입 | 설명 |\n", + "| --------------- | -- | -------------------------------------------------------------- |\n", + "| Global Flag | X | 고객이 글로벌 오피스(해외 지사)를 보유하고 있는지 여부 |\n", + "| Major Flag | X | 고객이 해당 산업에서 대규모 소비 고객인지 여부 (SMC 또는 SMB와 구분) |\n", + "| SMC Flag | X | 고객이 중소기업(SMC, Small Medium Corporation)인지 여부 (Major 및 SMB와 구분) |\n", + "| Commercial Flag | X | 고객의 사업 유형이 상업용(Commercial)인지 여부 (공공 부문과 구분) |\n", + "| IT Spend | X | IT 관련 구매에 사용한 금액 |\n", + "| Employee Count | X | 직원 수 |\n", + "| PC Count | X | 고객이 사용하는 PC 수 |\n", + "| Size | X | 연간 총매출 기준 고객 규모 |\n", + "| Tech Support | T | 고객이 기술 지원(Tech Support)을 받았는지 여부 (이진값) |\n", + "| Discount | T | 고객이 할인 혜택을 받았는지 여부 (이진값) |\n", + "| Revenue | Y | 고객의 소프트웨어 구매 금액 기준 매출(Revenue) |\n", + "\n", + "\n", + "`Tech Support`와 `Discount`는 모두 개입(intervention)이므로, 이를 조합해 다음과 같이 네 가지 처치로 정의합니다.\n", + "\n", + "$$\n", + "A_i \\in \\{0, 1, 2, 3\\}\n", + "$$\n", + "\n", + "여기서 $A=0$은 아무 개입도 없는 경우, $A=1$은 기술지원만 받은 경우, $A=2$는 할인만 받은 경우, $A=3$은 기술지원과 할인을 모두 받은 경우입니다.\n", + "\n", + "정책학습 문제는 다음처럼 설정합니다.\n", + "\n", + "$$\n", + "Y_i = \\text{Revenue}_i\n", + "$$\n", + "\n", + "$$\n", + "X_i = \\text{customer characteristics}_i\n", + "$$\n", + "\n", + "$$\n", + "A_i = \\text{assigned intervention bundle}_i\n", + "$$" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "id": "477aed7a", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-09T16:49:48.059813Z", + "iopub.status.busy": "2026-05-09T16:49:48.059518Z", + "iopub.status.idle": "2026-05-09T16:49:48.071045Z", + "shell.execute_reply": "2026-05-09T16:49:48.070709Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(2000, 13)\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Global Flag Major Flag SMC Flag Commercial Flag IT Spend \\\n", + "0 1 0 1 0 45537 \n", + "1 0 0 1 1 20842 \n", + "2 0 0 0 1 82171 \n", + "3 0 0 0 0 30288 \n", + "4 0 0 1 0 25930 \n", + "\n", + " Employee Count PC Count Size Tech Support Discount Revenue arm \\\n", + "0 26 26 152205 0 1 17688.36300 2 \n", + "1 107 70 159038 0 1 14981.43559 2 \n", + "2 10 7 264935 1 1 32917.13894 3 \n", + "3 40 39 77522 1 1 14773.76855 3 \n", + "4 37 43 91446 1 1 17098.69823 3 \n", + "\n", + " arm_name \n", + "0 discount_only \n", + "1 discount_only \n", + "2 discount_plus_support \n", + "3 discount_plus_support \n", + "4 discount_plus_support " + ] + }, + "execution_count": 91, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = pd.read_csv('./data/multi_attribution_sample.csv')\n", + "data.columns = data.columns.str.strip()\n", + "\n", + "covariates = [\n", + " 'Global Flag',\n", + " 'Major Flag',\n", + " 'SMC Flag',\n", + " 'Commercial Flag',\n", + " 'IT Spend',\n", + " 'Employee Count',\n", + " 'PC Count',\n", + " 'Size',\n", + "]\n", + "outcome = 'Revenue'\n", + "\n", + "ARM_NAMES = {\n", + " 0: 'none',\n", + " 1: 'tech_support_only',\n", + " 2: 'discount_only',\n", + " 3: 'discount_plus_support',\n", + "}\n", + "ARM_LABELS = [ARM_NAMES[i] for i in range(4)]\n", + "\n", + "required_columns = covariates + ['Tech Support', 'Discount', outcome]\n", + "for col in required_columns:\n", + " data[col] = pd.to_numeric(data[col], errors='coerce')\n", + "\n", + "policy_df = data[required_columns].dropna().copy()\n", + "policy_df['arm'] = (\n", + " 2 * policy_df['Discount'].astype(int)\n", + " + policy_df['Tech Support'].astype(int)\n", + ").astype(int)\n", + "policy_df['arm_name'] = policy_df['arm'].map(ARM_NAMES)\n", + "\n", + "n = len(policy_df)\n", + "X = policy_df[covariates].to_numpy()\n", + "Y = policy_df[outcome].to_numpy(dtype=float)\n", + "A = policy_df['arm'].to_numpy(dtype=int)\n", + "\n", + "print(policy_df.shape)\n", + "policy_df.head()\n" + ] + }, + { + "cell_type": "markdown", + "id": "8bfa27b6", + "metadata": {}, + "source": [ + "### 비용 설정\n", + "\n", + "실제 운영 환경에서는 인센티브 효과뿐 아니라 비용도 함께 고려해야 합니다. 또한 같은 인센티브라도 고객 특성에 따라 비용이 달라질 수 있습니다.\n", + "\n", + "예를 들어 기술지원은 직원 수가 많은 기업일수록 더 많은 지원 시간이 필요할 수 있고, 할인 역시 규모가 큰 고객일수록 더 큰 할인 폭을 요구할 수 있습니다.\n", + "\n", + "하지만 이 데이터에는 실제 비용 정보가 포함되어 있지 않습니다. 따라서 여기서는 고객 특성에 따라 달라지는 비용을 시뮬레이션해 사용합니다.\n", + "\n", + "$$\n", + "C_i(\\text{tech}) \\sim \\text{LogNormal}(\\log C_{\\text{tech}} + 0.5 \\cdot \\tilde{e}_i,\\ 0.3)\n", + "\\qquad\n", + "C_i(\\text{disc}) \\sim \\text{LogNormal}(\\log C_{\\text{disc}} + 0.4 \\cdot \\tilde{s}_i,\\ 0.3)\n", + "$$\n", + "\n", + "여기서 $\\tilde{e}_i$는 표준화된 직원 수, $\\tilde{s}_i$는 표준화된 회사 규모입니다. \n", + "\n", + "이렇게 정의한 고객별 비용을 반영해, 최종 순이익(net outcome)은 다음과 같습니다.\n", + "\n", + "$$\n", + "Y_i^{net} = Y_i - C_i(A_i)\n", + "$$" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "id": "5174ddd2", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-09T16:49:48.572223Z", + "iopub.status.busy": "2026-05-09T16:49:48.572150Z", + "iopub.status.idle": "2026-05-09T16:49:48.576620Z", + "shell.execute_reply": "2026-05-09T16:49:48.576207Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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armmean_coststd_costmean_revenuemean_net_revenue
0none0.0000000.0000006585.8917926585.891792
1tech_support_only5037.8158944917.64135815104.11153410066.295640
2discount_only5181.5703343043.34317212247.9359537066.365619
3discount_plus_support12665.2832967375.49012526784.12464914118.841353
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" + ], + "text/plain": [ + " arm mean_cost std_cost mean_revenue \\\n", + "0 none 0.000000 0.000000 6585.891792 \n", + "1 tech_support_only 5037.815894 4917.641358 15104.111534 \n", + "2 discount_only 5181.570334 3043.343172 12247.935953 \n", + "3 discount_plus_support 12665.283296 7375.490125 26784.124649 \n", + "\n", + " mean_net_revenue \n", + "0 6585.891792 \n", + "1 10066.295640 \n", + "2 7066.365619 \n", + "3 14118.841353 " + ] + }, + "execution_count": 92, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "COST_TECH_SUPPORT = 4_000.0 # baseline cost for tech support\n", + "COST_DISCOUNT = 5_000.0 # baseline cost for discount\n", + "\n", + "\n", + "rng_c = np.random.default_rng(SEED + 99)\n", + "emp_idx = covariates.index('Employee Count')\n", + "size_idx = covariates.index('Size')\n", + "\n", + "emp_z = (X[:, emp_idx] - X[:, emp_idx].mean()) / (X[:, emp_idx].std() + 1e-8)\n", + "size_z = (X[:, size_idx] - X[:, size_idx].mean()) / (X[:, size_idx].std() + 1e-8)\n", + "\n", + "# Tech support: 직원 수가 많을수록 더 많은 지원 비용 발생\n", + "c_tech = COST_TECH_SUPPORT * np.exp(0.5 * emp_z + 0.3 * rng_c.standard_normal(len(policy_df)))\n", + "c_tech = np.clip(c_tech, 200.0, 40_000.0)\n", + "\n", + "# Discount: 규모가 큰 기업일수록 더 큰 할인 비용 발생\n", + "c_disc = COST_DISCOUNT * np.exp(0.4 * size_z + 0.3 * rng_c.standard_normal(len(policy_df)))\n", + "c_disc = np.clip(c_disc, 200.0, 30_000.0)\n", + "\n", + "C_obs = np.select(\n", + " [A == 1, A == 2, A == 3],\n", + " [c_tech, c_disc, c_tech + c_disc],\n", + " default=0.0,\n", + ")\n", + "\n", + "Y_net = Y - C_obs\n", + "\n", + "pd.DataFrame({\n", + " 'arm': ARM_LABELS,\n", + " 'mean_cost': [C_obs[A == a].mean() if (A == a).sum() > 0 else 0.0 for a in range(4)],\n", + " 'std_cost': [C_obs[A == a].std() if (A == a).sum() > 0 else 0.0 for a in range(4)],\n", + " 'mean_revenue': [Y[A == a].mean() for a in range(4)],\n", + " 'mean_net_revenue': [Y_net[A == a].mean() for a in range(4)],\n", + "})" + ] + }, + { + "cell_type": "markdown", + "id": "024e5803", + "metadata": {}, + "source": [ + "### 탐색과 진단\n", + "\n", + "정책학습에 들어가기 전에, 먼저 네 가지 처치 조합($A \\in {0,1,2,3}$)에 대해 데이터의 구조를 점검합니다.\n", + "\n", + "1. **처치 조합별 표본 수**: 각 arm에 충분한 관측치가 있는가?\n", + "2. **처치 조합별 고객 특성 평균**: 어떤 고객군이 어떤 arm을 받았는가?\n", + "3. **multi-class propensity overlap**: 각 고객군에서 네 arm이 모두 어느 정도 관측되는가?\n", + "4. **처치 조합별 Revenue 분포**: outcome scale, outlier, variance가 arm별로 어떤가?" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "id": "3d0f329e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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countshare
arm
none5170.2585
tech_support_only4620.2310
discount_only4770.2385
discount_plus_support5440.2720
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" + ], + "text/plain": [ + " count share\n", + "arm \n", + "none 517 0.2585\n", + "tech_support_only 462 0.2310\n", + "discount_only 477 0.2385\n", + "discount_plus_support 544 0.2720" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "arm_counts = policy_df['arm_name'].value_counts().reindex(ARM_LABELS).rename_axis('arm').to_frame('count')\n", + "arm_counts['share'] = arm_counts['count'] / len(policy_df)\n", + "\n", + "display(arm_counts)" + ] + }, + { + "cell_type": "markdown", + "id": "8e911f32", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-09T16:49:48.072480Z", + "iopub.status.busy": "2026-05-09T16:49:48.072405Z", + "iopub.status.idle": "2026-05-09T16:49:48.076539Z", + "shell.execute_reply": "2026-05-09T16:49:48.076070Z" + } + }, + "source": [ + "실제 표본 수는 `none` 517명, `tech_support_only` 462명, `discount_only` 477명, `discount_plus_support` 544명입니다. 각 처치의 비중은 23.1%에서 27.2% 사이로 비교적 고르게 분포되어 있으며, 특정 처치에 표본이 극단적으로 부족한 문제는 보이지 않습니다.\n", + "\n", + "따라서 네 가지 처치를 비교하는 정책학습을 진행하기에 표본 분포는 안정적인 편입니다." + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "id": "d6488c62", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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7tAVNoNCwC/N30YQLGWdsi0ZXCGqxjBNeF5Ra43HiMePABIJbXI5sLqAxFzKwOICBIBBzcx31PGFs4rnAfcSBE4xbjEtjyS7AnHO8vhgD6AJtDWME2VcE/ejUjfnE2BYnQNCMAxIYZyiFxn3F48Btu/f6IXjG48WcbGSGe/Xq5Wo+Np5TjF33ssWA5xBjFvcHlQR4rhF8o+kYmrIhACciIr8p0C+3uq8QEREROTkE68iuY360VxvSERGR38WMMREREZEVrDeNJmbImiNbzKCYiChgY/MtIiIiIisoU0dnbsyxRjk6EREFbCylJiIiIiIiIqfGjDERERERERE5NQbGRERERERE5NQYGBMREREREZFTY2BMRERERERETo3LNZEvuspnn4iIiIh+I7mff3ZCxa/p0P19uvunQ/dHHsOMMRERERERETk1ZoyJiIiIiIjsFCgQc40BAV9FIiIiIiIicmrMGBMREREREdkpEHONAQIDYyIiIiIiIjuxlDpgYCk1EREREREROTVmjImIiIiIiOzEjHHAwMCYiIiIiIjIToECBeJzFwCwlJqIiIiIiIicGjPGREREREREdmOuMSBgYExERERERGQnzjEOGHh4g4iIiIiIiJwaM8ZERERERER2YsY4YGBgTEREREREZKdALMINEFhKTURERERERE6NGWMiIiIiIiI7sZQ6YGBgTEREREREZCcGxgEDS6nd8OvXL1m7dq3UrVtXcubMKWnTppWiRYvK0KFD5dmzZ6bt7t+/LylSpJBjx4556An37Pb27MO43K3T1atXHXI/yLEOHTotlSt3lAwZKkuhQo1l7ty1Og7d8vXrN5kxY6WUKNFCMmasIsWLt5ApU/7U882dP39N6tbtKZkyVZW8eevLuHGLXG1D5BNj0fD+/Ufd79q1u/nEk0PfF81dvHhD0qSpIPfvP3F12e7dR6RSpQ76vli0aLPfjlci73xf3LbtkO43U6ZqUqBAQ+nZc4I8f/6KTzqRL2DG2IafP39KmzZt5OTJk9KiRQvp16+fhAkTRq5duybTp0+XypUry7p16yRKlCjil02ePFkyZcrk6vxIkSLJ48ePfeU+kW1nz16WFi0GS8mSeaV9+zpy6tRFGT16gfz48UOaNatq8zpDhsySjRv3SatW1SVduuQaAE+d+qc8fPhMhg1rp9vcu/dYGjbsKxkzppAJE7rLjRv3ZPz4xfL69TsZNKg1Xw7ysbFoePPmvbRqNUQePHjKZ58cPhYNV6/ekWbNBsn37z9cXfb332ekTZvhUqpUPuncub5cu3ZXDxi+evVW+vZtzleFfOx9ccuWg9Kp02ipXr2EdOxYV54/fy0TJy6R+vV7y9q1EyREiOB8NfwJZowDBgbGNixYsEAOHDggK1eulDRp0pjOjx07tuTIkUNKly4tc+fOlW7duolfFiFCBIkWLZpv3w3ygMmTl0mqVIll9OjO+nf+/Fn0C92MGaukXr1yEjJkCIvt8QVu5cod0qVLA2nSpJKelytXBv05duxC6dKlvkSOHEFmz14tYcKEkmnT+kjw4MGkQIGsuq/Bg2dKixZVJXbs6Hx9yEfGIuzZc0yGDp0lHz584rNODn9fBGTjlizZLJMmLXUzqEClQuzY0WT06E4SJEgQyZMnk7x48Vrmz18vPXo0lmDB+NWIfOZ9EdfH57L5gepEieJItWpdZN++E1KiRB6+FEQ+iKXUVlAWs2TJEilXrpxFUGwIGTKkLFq0SDp06ODmk7p+/Xq9fvr06aVQoUIybdo0Papo7syZM1K2bFkt0a5UqZIcPXrUdNnXr19l5MiRel1cnj17dmnfvr28fPlSvItHbvPu3bvStGlTzULny5dP5s+fr+XlKDknrzz33+TYsfNStGhOi/OLF8+tAQSOTNsqRa1Ro4QUKpTd4vzEieOaMsVw6NAZ/dBFUGzABy2qInAZkU+Nxbdv30ubNsMkW7a0MmfOQD7x5PD3RTh48KSWqzZvXlWDD1u+fPkmoUKF0KDYEDFiOPn27TsP2pCPvS/iczhPnoxSrVpxm9vcvfuIr4Y/EsjB/xxh5syZOiXU3NOnT6VTp06SNWtWTfZ17tzZVXyxdOlSKVy4sMYxtWrVkosXLcc4pmM2b95cMmfOLHnz5pUJEya4inMcsQ/fwMDYCl6oBw8eSO7cud180uLEiSPBgwd3M9vct29fqV69umzcuFGDS2SXR4wYYbEdzmvZsqVs2LBBUqdOrYPjyROXeVCjRo2SnTt36nV27NihPxE4o4zbu7h3m58+fZIGDRroG/mff/4p48eP14D43r173nafnAU+IPGFLGHCOBbnJ0gQW3/euvXA1XXixYspAwa0Mn2AGvbsOarZDuzr8+cvWq6Ko8/mcJQ6bNjQcuvWfW95POR/eddYBGRUtmyZKiNHdpRIkcJ76+Mg5xyLgJLVvXvx+VrdIvA1V7t2ablz55HOEcUBG5TJLly4UQ8iIkAm8on3xcCBA2uFQpEilgH37t0uiZJkyeLzhfBnpdSOPHkVAlMEm9ZJsEaNGsnDhw81yTdr1iy5fPmydO/e3bTNunXrNCZA/ILv+XHjxpWGDRuagudv375J48aN9ffly5fLgAEDNC6YOnWqQ/fhW1gvZOX58+f6M3LkyBbnY66xeaMqlFVv2bLFVbZ59uzZUqdOHaldu7aelzBhQnn9+rWMHj1a2rX7b65d27ZtpVSpUvo7BsThw4dl2bJl0rFjR0mXLp2UKFFCj+YYgTgCdTTN8gxkd62/GAwcOFCz2dbcu82tW7fqgMYAjxgxop6Hx1S+fHlP3Sdy7d27D/oTwao5lEAbR549YteuI7Ju3V6pU6e0RIgQVp49c2neETasy36s9/3+PctZyWfGIqBqwfpLIpGjx2KMGO73/siZM700blxJRo2arydInTqxjB3bhS8IOWwseuR90RqyxCNHztOybRyoIfIsJNn69++vMQtiEHObN2/W5N+uXbskatSoel6PHj00Nnj//r2EDRtWZsyYoXGMESsMGzZMihQpIqtWrdIkHpJnCKwx3RRTNpMnTy4vXrzQQBixEhKHjtiHb2FgbKMxFbx588bifAyaz58/6++LFy+WvXv3unoyETgisM6SJYvF+ShLxtGRmzdvmhp2mW8TNGhQzRqjuRcg2ESgPGbMGLl9+7Ze79atW6ag1aOGDBkiGTK4zGkxuNUwzL3bRAlEokSJTEExpEyZUsKF49F1r/r58/cdVgMHdr+kZufOw9K58xjJkiW1dO3a8P/7/fnb6wQK5JhSHQo4vGssEvnGWHRL//7TdJ4xssqY9/ngwRMtv27SpL8sWDBEQoUKyReMfPx9Ec0xGzfuJ0GDBpFJk3poRpn8D7/SfOvChQsSLFgwrVpFBhaBsOHQoUO60o4RFAOmRu7e7bJCxIsXLzQGyJUrl0WMgljgxIkTGtSiMTGmmiKgNWCfCKwvXbqk2WGv7sM6dvFJfuNV9EPixYunDauslzGKESOGJEiQQE/mL6Q5t9r2GwEKBobBOpOLunrjCAm6YCNzjGAac37Hjh2rDb88y/w+GyccDbLFvdvE/XUv0CL7hAvnchTauiGRcRQ6bNgwv73+ggXrpX37kZI5cyqZObOfqeGMcXTbVqMj7DtcuN/vl5yPd41FIp8ei2558uSFNkVCxrhDhzqSI0c6qVSpiMya1V/Onr0ia9ZwCTHy+fdFzGGuWdOloevChUMlfvxYfBn8Gb9SSo3v8FiVBvGMNSS8ELgiYEaPoD/++EOnf759+1Yvf/z/FWtixbIcf9GjRzddhp8xY8Z0dTk8evTIIfvwTcwYW0EAWK9ePR00NWvW1KyoNbdeNByBwenUqVNaMmDAkREcvYkfP74pE/3vv/+a9o2af/xdo0YNefXqlaxYsULn8Bql1oAMbujQlmU8juKR28R9RckDysKNrPGNGzfk3bt33nKfnAk+AIMECaxz3swZjTeSJHH95mYciEGH38WLN0uZMvll+PAOFk22UOaFskLr/aL7Kj7gkyRhWSv5zFgk8qmx6B4slYPxiiDFXNKk8XV+MZZuIvLJ98XNmw9Ijx4TJFGiuDJnzgAPTQeggA+Nq35nz549nt4nMrJoEIxsLhJgiEmGDx8urVq10mrYT59cDv5YlzKHCBFCvnz5or+jejZ8+PCuLgds44h9+CZmjG1o0qSJHkVBFzXUyWNiOppyoXwak9bXrFmjKX9bMJkcXa0xX/jOnTuyadMmmTJlijbjMi87xoBE6cL169e1vh/BMeYlI6OL7TDgcf0rV67o0RyURmAb7+CR2yxTpoyWmXfp0kWfj7Nnz0rXrl31Mpbkeg2OHmfNmlZ27TpsUXWwY8dhzeqmT5/M5vWw7iY+cBs2rCBjxnSx+YGLZUj27z+hXTXN94sPecyzI/KpsUjkE2PRPQkSuAQ51p2Eb968r+u7o2kSkU+9Lx44cFK6dRsnmTKllD//HMmg2F8L7OCT46FyFQkvxCDoFo0yajTbRYnz+fPndeUdsI43EKyGCuUypx7b2LocsG9H7MM3MWNsA+Z1oJPbtm3bNAhG5zaUGSAbjBp5BL7ZsmWz+YQicMZRkoULF+pkc5QKoAmW0X3NvPkW5vMi4MbgxNJHRiZ24sSJOlCxnBPKttFOHa3V0XbdOBLjSMhmu3ebGMxz5syRQYMGSbVq1XQbTJA35jKQ17RsWU0aNuyr5VaVKxeRM2cua8fUzp3r63w3lGxdv35Xj1yjq/SlSzdl9uw1ki5dMl1+6dy5K66yHyilxvqJW7YclCZNBkjDhuXl9u2H+mGN5SG4hjH55Fgk8u73RY/AdvXrl9P9QO7cGeXhw6c6xzhOnOhSrVoxvlDkI++L6E7du/dkre5q0aKaXt9czJhR9UTOOcfYnoywexCT4OCO+ff2ZMlcDuwgHsmRI4dpSackSZKYtsHfmJ5p7MO6GTAuB2xjlFB7ZR++KdAvtybGEpnBfxhMpsdaY+ad7/Lnz68t4T3bGMyF57psB3ToWDlp0jJdRgmlVFhSpFGjiqb5R/Xq9ZLhw9vrfLiJE5fItGkr3NzXokXDdO4cnDx5QTuv4oMay+SUL/+HtGtXWz+UiXxyLBru338ihQs3Me2DyBHvi9bQYKtnz4myZ88ciRv3vy9b+NqD5ZmWL9+mYzF69MhaXdOxY10PB9jkfBz9voi+LQ0a9HFzmzZtakrbtrW85bH4P8nFr4uVprdD9/fowlAv7wMVqWi+hTJpmDZtmib79u/fb8rsnjt3ThNeWHUmTZo0ukINTh06dNDLv3//rtNDUUXbrFkzXZGnd+/e2sjL6FuE6ZgjR47UZV6RHHTEPnwLA2PyEMwnRjYZC4EXK1ZM5xYjq47Sa7R/ty9rzMCYiIiIiPx3YBw7bV+H7u/hv4MdHhhj9Rx8l8+YMaMGraiGxZKxmCqJgBnQT2jo0KF6PpZyxVrHBw8e1GVbsZQtSp4xvRINfTG9EokzBLl169aVNm3aOGwfvoUpI/IQlEOMGzdO51xPmjRJjzRh8j5KwFlKTURERETOKpA/aNuEoBRVnmi4VbVqVc3MIpOLANpQrVo1U/ILDXfTpk2r3/VxXaNJFqZWYhlbY2olMsFo4OXIffgWZozJFzFjTERERET+O2McJ21/h+7vwb8DHbo/8hhmjImIiIiIiPxI8y3yHQyMiYiIiIiI7MSlSwMGHt4gIiIiIiIip8aMMRERERERkZ1YSh0wMGNMRERERERETo0ZYyIiIiIiogC8XBO5j4ExERERERGRnVhKHTDw8AYRERERERE5NWaMiYiIiIiI7MSMccDAwJiIiIiIiMhOnGMcMLCUmoiIiIiIiJwaM8ZERERERET2CsRcY0DAwJiIiIiIiMhOnGMcMPDwBhERERERETk1ZoyJiIiIiIjsFChQID53AQADYyIiIiIiIjuxK3XAwFJqIiIiIiIicmrMGBMREREREdmJzbcCBgbGRERERERE9uIc4wCBpdRERERERETk1JgxJiIiIiIishdTjQECX0YiIiIiIiJyaswYExERERER2YtzjAMEBsZERERERET2YmAcILCUmoiIiIiIiJwaM8ZERERERET2YqoxQGBgTEREREREZKdfLKUOEHh8g4iIiIiIiJwaM8ZERERERET2CsSnLiBgYExERERERGSvwIyMAwKWUhMREREREZFTY8aYiIiIiIjIXmy+FSAwMCYiIiIiIrIXK6kDBJZSExERERERkVNjYEzkRxw6dFoqV+4oGTJUlkKFGsvcuWvl169fHrruxYs3JE2aCnL//hNXl61du1vKlGkt6dJVksKFm8iUKX/K9+8/vOERkLOOxa9fv8mMGSulRIkWkjFjFSlevIWOM5xvayymT19Zt1m0aKOHxzg5J+96X7xx4560aDFYMmeuJtmz15TWrYfKvXuPveERUEDhXe+L589fk7p1e0qmTFUlb976Mm7cIlfbkD9pvuXIE/kKBsaeUKhQIUmRIoXMnz/f5uX9+vXTyydPnuypfXpme49Yu3at3g9bpxw5clhsQ37D2bOX9Uta4sRxZfLkXlK2bEEZPXqBzJ692t3rXr16R5o1G2Qz2F24cKP07DlREieOJ1Om9JK2bWtpcNKx40hveiTkjGNxyJBZ+gWwUqXCMn16X6lcuYhuP2DAdNM2q1bt0LFYoEA2mTmzn1SsWEhGjJgrM2eu8qFHRv6Nd70vPnr0TGrV6i6vX7+VceO6ysCBreX69XvSqFE/+fz5izc9GvLPvOt9EQdjGjbsKyFCBJcJE7pLo0YVZP789Xpd8odzjB15coCZM2dK3bp13by8T58+GoeY+/nzp0yaNEny5csnGTNmlKZNm8q9e/cstrl06ZLUqVNHL8f1Fy1a5PB9+BbOMfakYMGCyY4dO6Rhw4YW53///l127twpgTw5mFevXi0hQoQQ73Do0CFX5wUOzGMhftHkycskVarEMnp0Z/07f/4s+oVuxoxVUq9eOQkZ0vUYwRHlJUs2y6RJS/VD1dqPHz9k2rTlkidPRpk0qYfp/NSpk0jZsm3k77/PSJ48mbz5kVFAH4uvXr2VlSt3SJcuDaRJk0p6Xq5cGfTn2LELpUuX+hI5cgS9fvHieaRr1wambW7ffqBjuEWLaj7+OMk53xdd9vunhA0bWubPHyyhQoXU8+LGjSEtWw6Rf/+9LlmzpvHmR0b+jXe9LyJQDhMmlEyb1keCBw8mBQpk1X0NHjxTWrSoKrFjR/eFR0sBwdKlS2XChAmSNWtWm5fv3r1bVq1aJXHixLE4f9q0abJs2TIZMWKExIwZU0aPHi1NmjSRTZs2SfDgweXVq1caAyGYHThwoJw9e1Z/hgkTRipXruywffgWRkmelCtXLn0BHz+2LLk6evSohA4dWmLFiuWp/UWOHFkHgneIFi2aq1OUKFG85bbIfvgid+zYeSlaNKfF+cWL55YPHz7JqVMXbV7v4MGTWpbVvHlV/ZC19vz5a3n9+p0ULJjN4vzkyRNIpEjhZf/+k3zZyMtj8f37j1KjRgkpVCi7xfnIrIBRnjprVn/p1q2hqwONX7585atAPva+iNLXnTsPS+XKRU1BMaRLl0wOHVrIoJh89H3x0KEzGgwjKDaUKJFHM264jPyRQA4+2enJkyfSokULGTNmjCRMmNDmNk+fPpW+fftK9uyW4/Pr168yb948adeunRQsWFBSpkwp48eP15gHyT9YuXKlfnYPGjRIkiRJooFsgwYNZNasWQ7bh29iYOxJ6dOnl9ixY8v27dstzt+6dauULFnSVcYYR2PKli2r10O5QK1ateT8+fNullLv379fqlWrJpkyZZK8efPK8OHD5fPnz6bLUf6M8oQ//vhDL799+7Y4wsOHD6Vjx44a+KdJk0by58+vR3jw5myega5YsaKkS5dOypQpI2vWrNH7c//+fYfcB2eFD8hv375LwoSWR+0SJIitP2/demDzeunSJZe9e+dKy5bVJUiQIK4uDx8+jAQNGkQePnxmcf6bN+/l7dv3nE9HDhmL8eLFlAEDWpm+8Bn27DkqwYIFNe0rSZJ4mpVDYIIDNiitXr9+r9SqVYqvBPnY+yLmG79790Fix44mAwdO1/nF6L+AbPHjx8/5SpCPvS+ibP/Bg6eSKJHlfpFJRkXDrVv8bkWed+HCBQ06N27cKBkyuFQpmMNncI8ePaR8+fKuAuPLly/Lhw8fNBYwhA8fXlKnTi0nTpzQv0+ePKnXCxr0v6LjnDlzajzy/Plzh+zDN7GU2g4IgBEY4+iGcXQEJQkLFiyQbdu2mbbbtWuXHg0ZMmSIljI8e/ZMBg8erDX9GzZscLVfbI8jLG3btpWRI0fKzZs3ZcCAAVqXj7IEA8oTZs+eraWybh0N8qyWLVtqRhnzp5HB3rNnjwblCNCLFCmicwGaN28u9evXl7Fjx+rfKHsgr8OXNMAHoTmUVxlHnm2JEeP32X9kQ0qWzKdlhUmTxpeiRXPJixevZejQ2fqF8dOn/w64EHllLFrbteuIrFu3V+rUKS0RIoS1uOzs2StSo0ZX/T1t2qTSsGFFPvnkY++LKHGFMWMWSvr0yXSO8YsXb7ThUb16vWT9+kkSOvR/mWQi73pffPbs1f/367If632/f/+JT75/4uCGWYULF/7t5fiebgsSbtbzhs0hVkE8MmPGDJ2DbO7x/6thratfo0ePbroMP5MnT+7qcnj06JFD9hE1alTxLQyM7QyM586dq+UKMWLEkL///ltLonE0xFzEiBFl6NChUq5cOf0bdfxVqlTRYNkWlBAULVpUWrVqpX8nSpRIj+y0bo3GINcladKkej6O8iBr6x4Etda2bNmiGW9zyEhjn3hcxkBG0I/g+8qVKxoY4z9S2rRppVu3bnp54sSJ5cWLF/r4yGt+/vx9h9XAXnizHTiwlZZo9ekzWXr3niQhQwaXpk2raPlXqFDeM7ednHssoky1c+cxkiVLauna1bJ0GpCpW7x4mGbuJkxYokHyunUTLMpaibzrfdHo9hs1akRtSGj03UiQIJZUr95VNm3aL9Wrl+ALQN7+vmhekWeLZ3vWkC/zBy8XsrlTpkzR+ceY62vt0yeXgzHWl6EX0ps3b0wxg63L4cuXLw7Zh29iYGwHBIjx4sXTJlz16tXTMurSpUu72i5btmxy48YNmTp1qmZ/79y5o4GmW2+GV69edbUfo8wBlxmBcYIECTx0P9evX+/qPOOIjLmQIUNqZzhkwf/55x/T/UQ5g3FfL168KLlz53b1+MjrwoVzOQqNYNWccRQ6bFj756DjqPOwYe2kd++m8vDhU23kgfNWr96pXwSJHDkWFyxYLyNHzpfs2dPK1Km9bTY/QkYPp+zZ02m5YZ06PWXHjsNSoYLbR7jJ+XjX+6KR9UPzJPNmlBkzppRw4cLIxYs3vXCvKSDyrvdFYyxa79fYN8YjOS+3MsL2QsDZpUsXrRDFvF9bQoYMaaqENX43rhsqlEtlA87H5db7BvRacsQ+fBPnGHuxnBovJAZvqVKu58mh+xqyxSiFzpw5s3Tv3l3r+t1iaz08IzA1r8M3H2i/gwDa+mS+H8PHj2gSUUPLKjAPAPOIUa6NTnIGlN66d3ST7BM/fiwJEiSw3LnzyOL8u3cfmeZm2mvfvuPaGATBcLJkCfQnyqkfP36h3amJHDEW8d41ZMhMGT58rpQqlVdmzx5gUXaIL37IxN2589DiesYYfPr0BV8IcshYdA8OxiATZ2udWExPQlUNkU+8L+LzGAcJrfeLz2i8ZyZJYjk/mfy2X4ECOfTkaOfOnZNr165pxhgVpTihlBo9hvA75v3G+n/VKJpzmcPfqJAFxAa2Lgds44h9+CYGxl4IjE+fPq0NqJA9Rlc1W6XRKJ1Gu/LatWtrhtVYx8tWEIxGVtinOQxUsLV/R0FTLUzWxxpimOOMID9s2LBaKm3cTxxdQjbZ3Jkz7JjoCDh6nDVrWtm167DFuEAWDUeMMQ/OXsuXb5NRo+a5WtsYH/J//MGMPzlmLGJ+5uLFm6VhwwoyZkwXiw6rgCZwKOefO3etxflYMgxSpEjEl4IcMhbdg2AkW7Y0WtpqHhwfOXJOPn78zK7U5GPvi4AlE/fvP2ExFrFffEbnzJmer4Z/gpJ6R54cDE2A0RUaPY5QUYoTkmKoJMXvqIZNmTKlfv8/duyY6Xpv377VqlGjShQ/T506pQcSzVfmwfRPrHzjiH34JgbGdkqVKpVmYNGIylYZNeCoCQJdBJ13797VebpLlizRy6xLCABrfGHQotHWrVu3ZN++fdqsCx2ovTMwNjLD6GD34MEDDcYxz/nbt2+m+9moUSPtpo3277hvaBSG7tjAeTBe17JlNTl37qq0bz9SDhw4qXMvEURgyRHMvURZ1dmzl+XlS5f5GR5Vt25ZbXY0bNhsOXr0Hxk/frHMnLlKP6hxFJzIq2Px0qWbMnv2Gl3uBsuMnDt3RS83TtgeXyybNasqK1fu1P0hCEF5Ya9ekyR37oxa1krkU++LnTrVl6dPX0rTpgN0v2vX7tb5nxkypHC1vA6Rd70vAtY4RvO3Jk0GaIXX/PnrZfjwOVKtWnGuYUwOhWpT6yrSCBEiaCUpfsflwYMH16mV+K6PaljMScaKNYgTihUrpvvB0krv37+X3r17a/+jtWvXanyDBr3giH34Js4x9mLWePr06TbLqAFrhPXr108HCAYKjqKMGjVKBwiCTOtFt4sXLy7jxo3TfSI4RkMvLIuELK53wlGknj176qDEYuAoY8BjQmBvLC2F7nEov8D9w3Y4qoPHhaWm0BaevCZXrgwyeXJPmTRpmbRuPVTLq7Dma6NGLh17L1y4oR1Thw9vL5UqFfHwfvPmzSxjx3aR6dNXyIoV2/WDtk+fZhowEzliLCLzhizK+fPXtHmRtUWLhkmOHOmkVavqugzJ0qWbZd68dRI5cnhd57Nt21o8uEY++r6YKVNKHZc4UNiu3XAJGTKEFCmSU7p3b2RziSci73pfRBn2vHmDZNSo+dKu3QiJFCm8NGhQXtq1q80n3b/xB823PKJdu3by/ft3XUEHTbKQ3UXDYeO7PjK6c+bM0ea7mHqJFW3QmBe/O3IfviXQL1s1veRjsF4w1jbGYtx+GcqocVTJvPM25lD36tVLS6ptzV1231WH3kciIiIiCmgsl/bxi5KWW+jQ/V3fWN+h+yOPYSm1L3n58qUcP35c5/GaN7nyq7BuMTpwoywCE/WPHDmi2WKUkdsXFBMREREREfkNjGh8Cebzomw5V65cuk6wX1etWjVdEHzYsGG6fjPKIBAUe3eZNxERERGRn+YNDbPI57GUmnwRS6mJiIiIyJ+XUldc5ND9XV9Xz6H7I49hKTURERERERE5NZZSExERERER2SsQS6kDAgbGRERERERE9mJgHCCwlJqIiIiIiIicGjPGRERERERE9mKqMUDgy0hEREREREROjRljIiIiIiIie3GOcYDAwJiIiIiIiMhebEodILCUmoiIiIiIiJwaM8ZERERERER2+hWYKeOAgIExERERERGRvTjHOEBgKTURERERERE5NWaMiYiIiIiI7MVK6gCBgTEREREREZG9OMc4QGApNRERERERETk1ZoyJiIiIiIjsxeZbAQIDYyIiIiIiIntxjnGAwFJqIiIiIiIicmrMGBMREREREdmLzbcCBAbGRERERERE9mJgHCCwlJqIiIiIiIicGjPGREREREREdvrF5lsBAjPGRERERERE5NSYMSYiIiIiIrIX5xgHCAyMiYiIiIiI7BWItdQBAUupiYiIiIiIyKkxY0xERERERGQvllIHCAyMiYiIiIiI7MUa3ACBLyMRERERERE5NWaMiYiIiIiI7MXmWwECA2MiIiIiIiJ7cY5xgMBSaiIiIiIiogBk5syZUrduXYvz9u7dK5UrV5ZMmTJJoUKFZOTIkfL582fT5V++fJGBAwdKrly5dJvOnTvLy5cvLfZx5MgRqVSpkmTIkEFKlCghW7ZssbjcEfvwLQyMiYiIiIiI7PQrUCCHnrxq6dKlMmHCBIvzTp48KW3atJGiRYvKunXrpH///rJ161YNYg0DBgyQQ4cOyeTJk2XhwoVy8+ZNadeunenyGzduSPPmzSVfvnyydu1aqVq1qnTr1k0DXUfuw7ewlJqIiIiIiMifpxqfPHmiAe+xY8ckYcKEFpctX75ccuTIIS1atNC/cXnHjh2lT58+Ghy/evVK1q9fLzNmzJCsWbPqNuPGjdOM7pkzZzT7i0A3RYoUej1IkiSJXLx4UebMmaMZYty+V/fhmxgYe9HGjRtlyZIlcvXqVQkUKJAkTpxYj3zUqFHDtA3KGI4fP64/MfiszZo1S8aOHSsVK1aUESNGmM5H2QEGyZ49e+TRo0cSKVIkHdCtW7eWBAkSuHmfjNuzpVGjRtK9e3fdJk6cOBa3R77r0KHTMn78Yrl+/a5EiRJRatcuLY0aVdRxZcudOw+lWLHmrs5Pliy+bN481e79EnnHWPz586esWLFDli3bKvfvP5bIkSNI4cI5pF272hI2bGg+6eSjY3H+/PWyYsV2efz4hSRMGFuaNKks5coV5KtAbvLKZ+n37z+kRo2uEipUCFm8eLied//+EylcuImb16lUqbAMH96Brwh5yoULFyRYsGAan0ydOlUePHhgEQMEDmwZwePvb9++yfv37+XUqVN6Xs6cOU2XJ0qUSGLEiCEnTpzQoBZZ5yJFiljsA9sPHTpUfv365ZB9+Ob3UwbGXrB69Wp9EXv37i1ZsmTRF/Pvv/+WIUOGyPPnz7VcwYBBunPnTt3W+gVHGYP1ebdu3ZJ69epJvHjx9DoYVDgKM23aNKlWrZoG48mSJXPzvpUsWVKvZy1UqFBeecjkTc6evSwtWgyWkiXzSvv2deTUqYsyevQC+fHjhzRrVtXmdS5duqU/FywYoh+2hpAhQ3hpv+TcvGsszpmzRiZMWCKNG1eSXLkyyK1bD2TSpKVy7dpdmTdvEA/UkI+NxYkTl8rcuWulXbtaki5dcjlw4KR07TpWAgcOJGXKFOArQQ4Zi+ZmzVot589fk+zZ05rOix49sqxYMdrVtkuXbpFt2w5J5cpF+Uo4cfOtwoUL//ZyJM1swbxhnGxJnTq1xd8IiBcsWCBp06aVyJEja5yBJFyIEP+9X0L06NHl8ePH+jt+xowZ09Xlnz590oyzI/aB++JbGBh7wbJly3QCe5UqVUznIWOMQbFo0SKLwBiZ3sOHD8vp06c1iDYPgG/fvi1p0qSx2HfXrl0lVqxYOmCDBw+u5yFIRmlC+fLlNdM7d+5cN+9byJAhJVq0aF55eOSDJk9eJqlSJZbRozvr3/nzZ9EjzDNmrJJ69cpZfKkzXLp0U2LGjKpBhiP3S87NO8YiMnSzZ6+R6tVLSOfO9fW83LkzSqRI4aVjx1Hy77/XJV06tw/0kXPyjrH46dNnWbRoo9StW9YU0GDbCxeuy+LFmxgYk8PGouHy5Vsyc+ZKiRYtksX5wYMHk4wZU1qch/dCBMUdO9aVrFktvxeSH+fPqvC+f/+u83qvXbum85Hh06dPppjDHIJcNNQCNOqy3sb4++vXrw7Zh2/yIxXx/hPKD1Av/+bNG4vzmzVrJitWrLA4D0Eqau23b9/uKltcsGBBCR36v1LCf//9V86fP6/7sTVwMJm+b9++Dn0su3fv1hLwjBkzSrp06bRT3F9//WW6HEdFx48fL3nz5tVtMIke2XLrbnfkeV+/fpNjx85L0aL/lZ1A8eK55cOHT3pk2pbLl29KqlSJHL5fcl7eNRbfv/8o5cv/4SroSJw4rv68d8/lKDKRd49FBCN//jlKS2DNBQsWVL58+cYXgBz6WYrrdus2Tg/EJEoU57fPLqoOBw2aIUmSxJMGDcrzlXByyAj/7uQVKJvGPGPsZ8qUKZI+fXpTUu2rjcAUAa1RcYoA13ob429s44h9+CYGxl7QpEkTnSyeP39+DWIxV/iff/6RcOHCaemzrfJmlFPjzc+wbds2KV26tMV2CIwhc+bMNm8XE9atJ9R7BW6vbdu2ej82bdokK1eu1DIGHEkyBuqYMWM02MeE/jVr1migv3jxYofdB2eGoODbt++SMKHlh2aCBLH1J0pO3SoZxIcy5i2lS1dJ8uSpK2PGLNB9eWW/5Ly8ayyGDx9W+vRpLlmyWJZx7d59VH8mTRrfmx4R+VfeNRaDBAkiKVMm0uwdPoufP38ls2atksOHz0mtWqV84JGRf+OVz9KpU5drZhm9FNyzdetfcu7cFenVq6mOU/KHpdSOPHmTp0+fSu3ateXs2bNaeVqgwH8HrGPGjCmvX792FbTiOpgjDKhmxd/WlyPBh/jHEfvwTSyl9gJ0WMMAQNk05hYfOHBAz0fQOmzYMIuSaShevLjOP0aWGUEvGnahqRYGJfZhMDLQ4cOHt/u+IcDdsWOHxXm4P2jmZQ1vwMhA16pVy3Qe5jc3bdpUXrx4IREjRtSy8Z49e2qLd0ATMTwO8rp37z7oT+sGRGHChDJl26y9fPlGnjx5oZn8rl0bSuzY0eTIkXNarvro0XMZO7aLXfsl5+ZdY9EWfAHEvLs//sguyZO73UyQnJNPjMUtWw5K585j9PeCBbOy+RY5bCzCP/9clXnz1snSpSO0UsE9mPeeOXMqyZEjHV8J8haIL+rXr68ZY5RPI9FmHSf8/PlTG2gZ3aEx5RNTRLNly6Z/o/rVusHv0aNHNa5BJa0j9uGbGBh7EcqKccIguHz5sgbHaIyFoHLXrl0SJUoU07b4HYMCAStefJRRI9C0Lpc2Jp3jiEvUqFHtul+YeN+li+WXAJQ32JIqVSqJECGCZryx1tidO3f0sQC+YGC9McwHwOM0oFkYBr+xHdnv58//KghsQUMYa6FDh9SGRThiHTeuyxG47NnT6YcvGhy1alXdrv2Sc/OusYjSQHMoPWzRYpBuP3x4ewc/CgoIfGIspk+fXJYsGS5XrtyWiROXSJMm/bVjMDv2k1fH4pcvX6VHjwlSv345HWfuOX36kly4cEOmTnXdNJX8CX/wlWr48OFy7949TZIh1nj27JnpMvwdI0YMrR5F8gsJPpQ1o1I0e/bsphgAUyixig4qSfETcQ+miRqJN0fswzexlNpO6KiGNb+MDms4woFuby1bttSGWR8+fNC25NZKlSqlgTFKuFBGjb+toZU5oFGXLVgfrEOHDqZJ7LaECRNGl3QyPxklDNZw1AbZbMxrTpkypTYNGz36v06JQYO6HD8xLwEnxwkXzuUoNMr/zBlHocOGDePqOmj0kSdPJtOXP0PBgtlMzT7s2S85N+8ai9blgg0b9pVYsaJp52A04CLyjbEYP34syZYtrdSpU0Z6924mJ05ckJMnL/DFIC+PxQkTFmvCpFWrGlpKjRO+Q+Fk/G5ux46/JUKEsFKggMu6r+T//AocyKEnR0OiCwk5dKJG1hg9g8xPqGCFwYMHa6YXsUDjxo21qfCkSZPEgBVxsEIOgtkKFSrIqlWrNGYwX3/YEfvwLcwY2wlZXryQqJPH/GJzRgm0rWwvMsSDBg3S+booacidO7erbZImTarB8ezZs+WPP/7QpZ4M6PaG89HW3LoVur3mzZunXbMnT55sOs+YP4w3bwTVyDZjPgKyy4Zz58457D44M3w5CxIksNy54/KmZLh71+Vv62wb3L79UI4ePSelSuXT+ZuGz59d5nRgjVh79kvOzbvGonmpIJY4QRZv6tReEi4cD86Qz45FlFsfPHhK8uXLrGvRGlKnTqI/nz59yZeEvDwWd+w4LA8ePJVMmVwv5ZQmTQWtlKlU6b91XPfvPyGFC+fUJnBEjoIVbMynTaIPkntChw6t0z5xcgt6K+HknfvwLcwY2wklB2i+NXHiRO3WfOnSJS1P2Ldvnx4hQaCJGnpb18NlODJSrFgxUzbWGo62YH8NGjTQ7tD4HfOYGzZsqPN++/XrJ46C4P7KlSu64Pb9+/e1uRYeF2DyPMogUPaAoz3oXo25AiNHjtTAmLwuRIjgkjVrWtm167DFUWR8sCJwSJ/e9TI2z569lP79p8n27X+7ysZhHlSaNEns2i85N+8ai7B8+TYZNWq+rgM6Z84ABsXkK2Px8+cv0r37eFm9epfFNn//7dIzI0UKxzW2JOcdi9On95XVq8dZnDD+cMLv6K1geP36nR7Uwfxi8sf8SfMt+j0emvIClDOj0Ra6OGMSO+bhxo4dW7tPN2/e3M3r4XIEudbdqM2hzAAZacz7RW3+8+fPdY5yzpw5NajGmsaOgqWXsH+0bjcy1pgXgLWUUV6dJEkSad++vZZfYM4AstbIZGPx8d+Vc5PHtWxZTctL27cfKZUrF5EzZy5rdg1rvoYKFVJLtq5fv6tHrpH1QHdfrL05YsRc/aKHrr444ox1OHv0aGzKlri3XyKfGIvPnr2S4cPnSJw40aV27dJy8eINi9s09kXk3WMRp8qVi2q34KBBg2imGOXTaARXpUpRdkgnh3xG2zrAYjTrsl6z/erV2/qT3fn9OX+2jjHZFugXJ46SB6CRGJptGY3BoFGjRtqVG0G0fa7yubd4jo/IpEnL5Nat+xIjRhQNIIy1NrGGYr16vSzKr/BBPGXKn7Jz52ENPOLHj6lrH1atWtzD+yXyibGI7Fzv3v/NL7JmXVZI5J3vi1hbFkHN+vV7tdwV892rVSsujRtX9PWOqBRwxqK1unV76k80eLOuaOjYcZRs3TqNU5zc5H4DM9+WsM82h+7v9pCSDt0feQwDY/KQGjVqaEk11jYOGzasLgo+atQonZ+MLLZ9GBgTERERkT8PjPs5ODAexMDYN7CUmjwELdUxiR9znlEyjnJrzEO2PygmIiIiIgoAWEodIDAwJg+JGzeuTJkyhc8WEREREREFOAyMiYiIiIiI7MVO0gECA2MiIiIiIiJ7MTAOENh+kYiIiIiIiJwaM8ZERERERER2+sXmWwECA2MiIiIiIiJ7sQY3QODLSERERERERE6NGWMiIiIiIiJ7sZQ6QGDGmIiIiIiIiJwaM8ZERERERET24nJNAQIDYyIiIiIiInsxMA4QWEpNRERERERETo0ZYyIiIiIiInsF4lMXEDAwJiIiIiIistMvllIHCCylJiIiIiIiIqfGjDEREREREZG9uI5xgMDAmIiIiIiIyF4spQ4QWEpNRERERERETo0ZYyIiIiIiInuxK3WAwMCYiIiIiIjIToFZgxsg8GUkIiIiIiIip8aMMRERERERkZ3YlDpgYGBMRERERERkJwbGAQNLqYmIiIiIiMipMWNMRERERERkp0BMGQcIzBgTERERERGRU2PGmIiIiIiIyE5MGAcMDIyJiIiIiIjsxMA4YGApNRERERERETk1BsZERERERER2ChTYsSdHmDlzptStW9fivEuXLkmdOnUkY8aMUqhQIVm0aJHF5T9//pRJkyZJvnz5dJumTZvKvXv3fHwfvoWBMRERERERkRdKqR158qqlS5fKhAkTLM579eqVNGzYUOLHjy9r1qyR1q1by5gxY/R3w7Rp02TZsmUyePBgWb58uQa5TZo0ka9fv/roPnwL5xgTERERERH5c0+ePJH+/fvLsWPHJGHChBaXrVy5UoIFCyaDBg2SoEGDSpIkSeTOnTsya9YsqVy5sgau8+bNky5dukjBggX1OuPHj9fM786dO6VMmTI+sg/fxIwxERERERGRvQFVIMee7HXhwgUNOjdu3CgZMmSwuOzkyZOSPXt2DUYNOXPmlNu3b8vz58/l8uXL8uHDB8mVK5fp8vDhw0vq1KnlxIkTPrYP38SMMRERERERkR/pSl24cOHfXr5nzx6b52O+Lk62PH78WJInT25xXvTo0fXno0eP9HKIFSuWq22My3xiH1GjRhV/GRgfOHBADh8+LE+fPpVOnTrpROo0adJInDhxHHcPiYiIiIiIyG6fP3+W4MGDW5wXIkQI/fnlyxf59OmT/m5rmzdv3vjYPnyTXYExHjQmSiMoDhs2rKbMMan6zz//lIsXL8qSJUskWbJkjr+3RAHYoUOnZfz4xXL9+l2JEiWi1K5dWho1qiiBbByGXLt2t/TsOdHNfY0Y0UEqVnQ52rht2yGZM2eN3Lz5QMKHDyO5c2eQzp3rS9Sokbz18ZBzjEXD/v0nZMqUP+Xq1TsSMWI4KVYst3TqVE9Chw5p2ubGjXsyevQCOX78vAQNGkSyZUsrPXo0lnjxYvrQIyP/xrveFw3v33+UcuXaSps2NaVSpSLe8hgoYHD0++L9+0+kcOEmbl63UqXCMnx4B296NOTXM8ZuZYS9ImTIkKYGWAYjEA0dOrReDtjG+N3YJlSoUD62D38XGI8bN05r2BcsWCBZs2aVtGnT6vkjR47UAHnixIkyZcoUD+/v+/fv2j1tw4YNcuvWLT1qgFr0Zs2aac25M0iRIoUMHz5cKlWq5O629+/f1xILtDbPkSOHm5e7ZdOmTTrwfrcP8llnz16WFi0GS8mSeaV9+zpy6tRFDSB+/PghzZpVdbV9wYLZZMWK0a7O79Nnsn7RK1Agq/69ZctB6dRptFSvXkI6dqwrz5+/lokTl0j9+r1l7doJEiKE5RE7Is+ORdi797i0bj1UKlT4Qw+6IAAeN26RvHr1RsaO7arbPHr0TGrV6i6JEsWRceO6yqdPX2TChCXSqFE/2bRpsoQM6XK0mMi73xcNb968l1athsiDB0/5pJOPvy9Gjx7Z5nhdunSLHtCuXLkoXxV/5HcHSPyKmDFjapWvOePvGDFiaDxmnIeO0ebbIE7xqX34u8B427ZtWjqNoBVvCub14S1bttQuYx6FIwRo2Y2a8nbt2kmmTJk0xY6W3Th/1KhRUrZsWQnoDh06JOHChXPoPidPnqzPp7VIkSKZ6vzJb5g8eZmkSpVYRo/urH/nz59Fvn//ITNmrJJ69cq5ChoiR46gJ3OLFm2UGzfuy/Llo0yX4fr4MjhoUGvTdghMqlXrIvv2nZASJfL4yOOjgDsWYfjwOVK8eG5TdiNXrgzy48dPWbx4k3z69FlChQopkyf/KWHDhpb58wfr3xA3bgxp2XKI/PvvdcmaNY0PP1Jy1vdF2LPnmAwdOks+fHAp+yNy5Fj06PtixowpLa6D90IExTiQzfdEcrRs2bLp8kmI3YIECaLnHT16VBIlSiRRokTROASVwOhobQS1b9++1WpgrDnsU/vwd12p8QDdmkccIUIE+fjxo4f3hezylStXdL2rihUramvxlClTSu/evaVChQoyZMgQLdUO6KJFi2ZRcuAIeC2wX+uTeRc48n1fv36TY8fOS9GiltUR+EDFlzYcmXbP8+evNPtWs2ZJyZDB5Ygc1o3LkyejVKtW3GLbxInj6s+7dx859HGQc47Fixdv6FiqU6eMxfn165eT3btn65e/X79+yc6dhzUDYgTFkC5dMjl0aCG/AJJDxqJH3hfh7dv30qbNMC3lnzNnIJ998pX3RWt4nxw0aIYkSRJPGjQoz1fFnwkU2LEn74ClkN6/f68x1vXr12Xt2rVa/du8eXO9PHjw4Bq8Yk1hlHKjw3THjh01w1usWDEf24dvsuupx/xhlOPasnfvXg/PL/727ZtmhlE+bN29DDp06CCzZ882BYyvX7+WgQMHSoECBSR9+vRSo0YNPSJhniFt0KCBlnHnzp1bs6X9+vXTbDSebLQtL1q0qOzfv990HXRuw7pZKNvG5fh79+7deipevLhkzJhRGjduLC9evDBd58aNG9K0aVPdf968eaVz587y7Nkz0+V169aVvn37StWqVbXUHC3TAT/LlSun9x1lzAsXLjRdB+UFGBiAunuUpeO+oEwdLc3bt28vL1++FO/ikdu8e/eu6XFjPbL58+fr82ncb7LPvXuP5du375IwoeXBpgQJYuvPW7ceuLuPSZOWSeDAgaVDB5ejcYC/MX+zSBHLD/Pdu4/qz2TJ/itxIbJ3LF66dEt/oiy/efOBkj59ZcmevaYMHTpbv1AC5tK9e/dBYseOJgMHTtfL06WrpNnix499d2kGcq73RUB2b8uWqTJyZEeJFCm8g+85BTTe9b5obevWv+TcuSvSq1dTUxaN/A9UUjvy5B2QjZ0zZ45OW0UyEvFSt27d9HdDu3btpEqVKtKnTx+pWbOmjsW5c+fqElA+uQ/fYlfqEOXSbdq00UD1jz/+0Lp6rE2FAAmp8bFjx3poP/fu3dN9ZM6c2eblqDM3as2Rbm/UqJEG06NHj5bIkSPr/FgErcg2I9g01sbCE445y6dPn5ZevXrpEYuuXbvqk47r9ujRQ44cOWKaDzBt2jQZMGCAvoAjRozQ7RInRsnMaM1+4wVGgI7rYeHsWrVqaXk3/kYjMgTk1atXl82bN5smja9atUqvj4AXWdqtW7dK9+7ddcFrBMWYo43ro+TAel4xysf37dun9wWZeWTUe/bsKdOnT9ejK97BvdvE48RBB5Q5oMkajvTgIAVeQ/IaBAyAMlNzYcK4NCnA3LjfefHitaxfv1caNqwg4cOH/e22OII9cuQ8LQmznm9HZM9YfPnSpcskMnBlyhSQhg0ryvnz17T08OXL1zqX7tWrt7rNmDELJX36ZDrH+MWLNzrfrl69XrJ+/SSLJl1E3vm+GDx4MFPlDJFvvS9amzt3rWTOnEpy5EjHF4UcAt/prSFeWrFihZvXCRIkiMZMOLnFJ/bhrwLjIkWKaNCHABhLNhlPPgJSBJglSpTw0H6Mtt0o+fXIHFwEk8hUG2tfITA7f/68HoVASbZRPorzUd+OIA73E3OhUZYNOHKBABAZXmPNrIIFC5our1atmgbSSPsbwTayz9euXdPfERSiHABBtGHChAl6G9u3bzcFualSpbKYG43scKlSpTSQB5SMo0TcVvl0unTp9DlEthkQqOI+XL16VTwD2V3ro454bpC19uxtIrBH9hgHPyJGjKjn4bktX57lPl718+ev314e2J2V3let2qnjHiVav4PGH40b99NuwJMm9dBMCpFXxyIyKYAyw65dG+jvOXOml1+/fsrYsYukTZtapgxJ1KgRZcqUXqaxlyBBLKlevats2rRfG8QReWUs2vO+SORb74vo92E4ffqSXLhwQ6ZO9Z7kB3k/f9B7izzA7smmCPpwunnzpmZ9w4cPr1lWz3zZRtYXcH33IEBDdtV8QWhkfBHIIWg2IDhHUGxABte8K5p5G3FDggQJTL8brcStr2OUUmPyOIJk66ZWaCKGEmtb+zTuf+nSpS3OQxBuC4JNLIWF+vzbt2/rc4xyAyNo9SjMz0Z5uDm3JrW7d5t43DjQYATFgLngjm4Y5ozChXM5Cm3dBMY4Ch02bJjfXn/Hjr91LrF10xlzmB/Vtu0wzcotXDhU4sd3PXWByJ6xaGRN0BHYXL58WfQL4KVLN03ZOTSsMf+MQOOZcOHCyMWLN/nkk4+/LxL55vuieWCM8RohQlhWchH5MrtSRvXq1TMFgQiGUQqdNGlS/cKDSdYe7SIdL148iRo1qpY824LbQPk0AlE0JbAF55s3kzLq1825F6zbakblVtt1HIFGdnj9+vUWpx07dpiywWCdCfZMwyvMi0bGGmXjmPOLzLx1UO0RKENHgG5+Mj9o4JnbROYZj50cD0FqkCCB5c4dy2ZYRnMsNOJwy5MnLzSoKFkyn5vbbN58QDPFMWJElRUrxvx2f+Tc7BmLCRO6zLP7+tUlQ2KdMcEcO6xTjPdUW3PrME0mZEguG0ZeH4ueeV8k8u33Rev1jgsXzinBgrE5qn/lH+YYkwMDY8zdxTxinI4fP2763fqEBlMenXeKgBWTs1GeiwZZ1jAxG6XSKOvFXN13795ZlBMjKD516pQG5T4FjcUQsKNZmBFsohR82LBhvy11TpIkiT4Wc1i3GPOXzb169Upr7vv3769zfFGajbJsZHDdOjjgVR65TWSH79y5Y5Hdx/OA14S8Bh+QWbOmlV27Dlu8xjt2HNZsGuZkugWNOgDzkmw5cOCkdOs2TjJlSil//jlSYsTw3Tb4FPDGIpYUQSXCli0u02rM1/BE2T7GHrIn2bKl0c7U5sHxkSPn5OPHz+xKTQ4Zix59XyTyC++Lhtev38nt2w85Xv05VNQ78kS+w8OHptBMasOGDXrUHyfMVbVmvGGUKWPZnv53WrRoIX/99Zc2tEIXZGSfEXxhLi8ysePHj9dyaHR/RrCGDtDo+IyS4CVLlmgwioDOp+B+IohEE61WrVrpeejmjGZV5mXe1tD1um3btjpvGV21z507p4/Res1nZHRRnox5zmnSpNE1nfE4Mb/auizaUTxym3hN0WQMjxsnbGPcd/+wqLlf17JlNWnYsK+0bz9SKlcuImfOXNZGHJ0719dlHVCydf36XT1ybV4aePXqHW0kY6s0+suXr9K792QNSlq0qKbXNxczZlQ9EXllLGJ8tWtXW0aMmKtNjooVy63z5ebMWSP16pU1jddOnepL3bo9pWnTAdKoUUVtjoRmXFhGp1Ch7HwRyEfeF4n80vuiy3i9rT+TJuVKEUT+JjBGsymsO4Xgt379+lp6a52pRQYYc409ulyTMacXQdi8efO08/PDhw+1DDl16tSyePFi0xxXlPJiGwSh6IiNOcJYVgjrXmFJJZ+C8m/cX5QaGy3IEcyjQ7YxZ9oWlCcjkMRjxGNAFhzZWaPpl3kpOBqJoZkZStKRjc6RI4d06tRJZs6cqd2hHc0jt4nXCRl8PAbMjcY2OKiB4NlW+Tp5Tq5cGWTy5J66vEjr1kM1s9utW0MNIABNOdC9d/jw9lKpUhGLdTrDh7c91w4fws+euSy31ahRP1eXt2lTU9q2rcWXirw8Fo3Ov/Pnr9OmR9GjR9ax1bRpZdN+kSFZtGiYjB+/WNq1G65L5mApse7dG3FpEvKx90Uiv/S+6DJeXSrxOGb9N+aIAoZAv+yoz0UpNQJXt+arUsBz//59bcqFzL0BS1flz59fl8bybGMwF57rsk1EREREzsbtiky/Iuvyvxy6v5M12CPBN9g1yz979uwaFB08eNCiuzOaMyG7iPnIKIGmgANdt1EOjlL2YsWK6dxiLFOFZae8q8SbiIiIiIjIzwbGWK8X80y/f/9uml+KxLPxOzpVU8CC5mHjxo2TGTNmyKRJk7TcPVeuXDJ//nyWUhMRERGR0wrEjlnOW0qNebEhQoTQplcoo8VyG02bNpUDBw5o8IR5qXny5PGee0wBCEupiYiIiMh/l1JnX3XIofs7XvW/qYvktlu3bmn8+fHjR1fLyiJh27p1a/H2jDHuBJpPYZ4xmjShKRYyijg9f/5cs4oMjImIiIiIiMjRsFpSjx493FzO1scCY3SfRldiwDq+WO8WUTrORzOmdevW2bNbIiIiIiIif4VdqX3etGnTJHfu3DJkyBCJGTOmQ5aPDWzPlTCH+PTp06bf0YDr8uXL+vfbt28tGnIREREREREFVIjJHHki92GJ3yZNmkisWLEcEhTbnTGuUaOGzi9GPXfHjh0lZ86cuiZvlSpVdI3fNGnSOOTOEREREREREZlLlCiRPHr0SBzJroxx1apVpXfv3qbM8KBBg3Q5n6FDh2qnalxGREREREQU0KEptSNP5D4sIYty6mPHjmkc6mtdqW3Bbl69eiWRI0d2xO7IKbArNRERERH5767UedY5tiv13xXZldo9xYoVk2fPnsnnz59tXo7y6osXL4q3lFKjjjtatGi6Zi1+/912EDt2bE/dESIiIiIiIiL3lCtXThzNwxnjVKlSyYoVKyR9+vSSMmVKdyc5X7p0yVH3kQIsZoyJiIiIyH9njPNucGzG+FB5Zox9g4czxsOGDZN48eLp78OHD/fO+0RERERERETkJswtvnLliva9MnK9WEL406dPcvLkSenSpYt4S2BcsWJF0+/oAFa8eHFJkiSJp26MiIiIiIgoIOESSz4PTbfat28vb968sXl5mDBhPB0Y29WVeubMmXL//n17rkpERERERBRgYIqpI0/kvvHjx0ukSJFk0qRJUqRIEW3GNWPGDKlVq5Y+h7NnzxbPsiswTpo0qdy6dcueqxIRERERERHZDSXUbdq0kaJFi8off/yhFc0FChSQvn37SpUqVWT69Ome3qeHS6nN4cbHjRsnf/31l6RIkUJChw5tcTmi9NatW9uzayIiIiIiIn+DSV6fh7nEMWLE0N8TJEgg165dM12GKb/du3f3mcB4ypQp+vPvv//WkzUGxkRERERE5AwYGPu8+PHja9Y4a9askihRIm24dfPmTUmcOLF8//5dPnz44DOB8eXLl+25GhEREREREZGXlC1bVsaMGaPdqOvUqSNp06aVwYMHS926dXWuMab++sgcY/e8f//eO3ZLRERERETk5zLGjjyR+5o0aSI1atSQc+fO6d/9+/eXS5cuSatWrTRz3K1bN/GsQL+MRZ88AWtFLVy4UI4fP26xbhR+fvz4Ua5fv266k0RuefN1B58c8hPefP3q23eBSIUI4umPZCJvcfd9ED6z5Cdki1bat++Cuwpvcz211Cv2lMzj0P05i/fv35vKqcOGDeszpdSjRo2SJUuWSPLkyeXly5cSIkQIiRw5sly9elW+ffumHcKIiIiIiIiIvAvWMT558qQ8ffpUm24hIMYaxvawKzDeuXOnNGzYULt9oYYbaeuJEyfKkydPtMYbXcKIiIiIiIgCusAsf/YVWJJp5syZ8vnzZ23+nD59epkwYYK8evVK5s2bJ+HDh/f+OcbIEufPn19/R9b4/Pnz+jtaZjdr1ky2bt1qz26JiIiIiIj8lcCBfjn0RO5D9fLkyZM1Wbty5UrT1F4kae/du6dJW8+yKzAOFy6czi021o3CgspGw62ECRPq30RERERERESOtnjxYk3Itm/fXtKkSWM6v0CBAtKhQwfZu3evzwTGWC8KdwbrRSEwDhUqlOzevVsvO3PmjF2TnYmIiIiIiPxjKbUjT+S+hw8fSvbs2W1ehuZbz58/Fx8JjFu3bi1nz57VKD1o0KBSq1Yt6du3r1SqVEnT1pj4TEREREREFNAFdvCJ3BcrVixNyNry77//6uU+0nwrZcqUsm3bNu1CDZ07d9Ys8enTp6VQoUIaMBMRERERERE5WpUqVXSOcciQIaVgwYJ6HpYN3rFjhzbkwtxjH1nH+PLlyxocE3kF1zEmv4LrGJNfwXWMya/gOsbkV/iHdYzL7vrLofvbVDSfXdf7/v27TJ06VdavXy+vX7+W1KlTS9euXSVjxox6OVYSGjp0qGZUsdRugwYNpF69eqbr//z5U6ZMmSKrVq2Sd+/eSbZs2aRfv34SL1480zaO2IcjIITt37+/3o7xNzpT42e5cuVkxIgREjiw5/LvdmXrK1SoIGXLlpW5c+fqEk1ERERERETku8sXIVAcPHiwBseJEiWSJk2a6Bq/WMIIWdT48ePLmjVrdGrsmDFj9HfDtGnTZNmyZXr95cuXa5CL6xtNlx2xD0dBEDxo0CCtYkaAjIZbffr0kY0bN8qoUaM8HRTbnTFGo63NmzfL/v375du3bzrxuXz58lKsWDEJHTq0p+8EOSdmjMmvYMaY/ApmjMmvYMaY/Ar/kDEuv9uxGeMNRezLGCMey5Url/To0UP/xqpBWbJk0ZLjW7du6RJH+/bt0x5RMG7cOC09xunr16+SM2dO6dKli/aPgrdv30q+fPk0Q1ymTBktUfbqPryiZ8+engqchw0b5v1zjIsUKaIn1HEjSMa6xYjQBw4cqOcjfY0ngIiIiIiIKCDzKw2zokSJokEr1vJF86kVK1ZI8ODBdQosMslIZhoBLSCIRbCLDs4PHz6UDx8+aGBtCB8+vJZjnzhxQoPakydPenkfXrFu3ToNeGPEiOFuRhjbeZZdgbEB2WEEwTihjh315H/++admk1F/TkRERERERB5XuHDh316+Z88em+f37t1b1/XF9YMECaLBI7LFKH1+/PixJE+e3GL76NGj689Hjx7p5WDdzRnbGJc5Yh9eUbJkSa1YRma6RIkSUrp0ac2IO4qXAmPAxOstW7bI9u3b9QlJlSqVpvGJiIiIiIgCOr+y9vD169clXLhw2oALWVVkiVHWjPLnz58/a/bYXIgQIfTnly9f5NOnT/q7rW3evHmjvztiH14xfvx4vQ1kxVGxjPnOUaNGlVKlSmmQjDjUxwNjPOkIhnGH7t69q0cB0IwLAXGyZMm8dIeIiIiIiIj8i0CBPN2yya6M8O8gQYkldBcsWCBZs2bV89KlS6dxm7GskXUDLASzRhVwyJAh9XdsY/xubBMqVCj93RH78CrsB4EwTphDvWvXLo1J8bjjxo2r5doIktF4zEcCY9wgHnzx4sVlwIABWltuTx03ERERERERec25c+e0KTKCYXMZMmSQgwcPSuzYsbU7tTnjb2SXv3//bjoPpdfm26RIkUJ/jxkzppf34Uhhw4aVihUr6gnTehEko0v1jBkztOR77dq13j9XHG25Dx8+LMOHD9fJ1QyKiYiIiIjIWUupHXmyB4JWuHLlisX5V69elYQJE+p6wqdOnZIfP36YLjt69KhmVtG0K2XKlBpoHjt2zHQ5OkpfvHhRrwuO2Id3MUq5Ue6N+/fgwQOfyxgDjj4cP35cH3CkSJE0bc9u1OSsnjx+JTUrjZDRE5tIlmz/TSk4c+qGTJu0Sa5feSBhw4WWgoXTS4u2pSVMmP9KTD5+/CJTxm2QvbvPyaePXyRTlqTSsVtFSZAohmmb40euSJtmU13dbp78aWT81Ob6+727z6Ry6cGutkmcNJYsX+d2i3us2rZ04V5Zt+qwPo6YsSNL9Vr5pWrN/Da3//79hzSpO15ChgwuM+a388SzRN4F6wRuWXtUNq06Io8evJCIkcNK7gJppF7z4hImrMtYO3/mpsybsk1uXnsoYcKGkjx/pJWGrUpIaLOxeOfmY5k9cYtcOHdbAgUOJDnzppIm7UpL5KjhTdtsXXdMxg9Z5eo+lKuWW9p2r+Tq/I8fPkuz6mMlfZYk0m1gDQ8/pvXLD8nqJQdkyebeFud//fJNFs/eJXu3nZY3rz5I4uSxpW6zYpItt+OPRpPnx+GmNcdk3crD8uj+Sx2HeQumkUYti5nGoblVS/+SyaM3yootPSVWnMim82/feCLTJ2yR82dvS+DAgSRXvlTSokMpiWI2Dl88fytzp+6QE0evytvXHyVewmhSs0FBKVw8o2mbr1+/y/wZO2XnltPy5vUHfU+thW1K/LeNW+9xyxcdkC3rTsiLZ28kbvyoUrtxIYt9w76d5+TPBfvlzu1nEjZcSMmaI5k0b19KIkcJx6Hjx0zoNV9uX70vE1b3dXXZj+8/ZFCryZI+R0qp3LiExWUPbj2WP6dvlqv/3NKxmDF3aqnRsoxEjOIyFmcO/VP+2nbC7dtd3UeixnQZ24d3nZYNC3fJ04cvJFqsyFKmTmHJX9LtYGHN3O2ybv5ONy/vPbmVpMqUVH/ft/GIbF95UJ4/fiVRYkSUopXySpFKeZi8cqKu1OnTp9dGVN27d9d1fREoYy3jI0eOaHNklBnPmTNHG3RhXeF//vlHy4+xqpAxL7hOnTqaAI0cObLEiRNHRo8erfvBkrxQuXJlL+/DkZ48eaJ9rnBCxhwVzVghqXnz5pInTx6fCYxRN96qVSs5dOiQdjxDUIwFn2fNmmVq2W096dqZYO0wHKVYvHix1vSjW7d78wgwWK3dvHlTr48jMe/evdO53AUKFNDFtDHR3LehJAJVA5Uquf4i7GwQTLZrPk3ev3NpOmC4cf2RtG02VTJkSizDxjSUp0/fyORxG+TB/ecybopLMAt9uy+Uf/+5LW07ltcvj7Onb5OWjSfL8nW9JHwEl7XBr165r5dNmtHS4jbCh/9v7fCrl+/rz6lz2kjIkMFM5yOA/R3cp+VLDkjz1qUkdboEcvivizJ62GoJGjSIVKzq+o1l4dzdcvHfu5I5q8sHMvm+lQv3y/zp26Va3YKSKXtSuX/nmSyYsUNu33gsI6Y2kzs3n0j3VrMkbcZE0mdEXXn+9I3MnrRFHj94IYMnNNZ9PH/2Rro0nyGx40aRHoNryufP32Te1G3SvdVMmb60kwQNFkS3u3H1gcRLEE26WAW5bgUD08dulCePXnnq8ezbcUZmjNsoUaNHcHXZuMGr5MjBi9K4TUmJmyCa7Nx8Uvp0mCtjZraQdJkSe+p2yLGWLdivwWqN+gUkS/akcu/Oc5k7bYfcuv5Yxs5oavEl/d6dZzJr8jZX+8DYbN/UZRz2HYpx+FVmTd4unZrPkrnLO+o4RMDbtdUcef/uszRqWVyiRgsv+3f/IwO7L5Xv335I8TIuXUoHdl8ih/+6JDXqudyfK5ceyMgBK+X1q/dSuWZeNx/H/Bm7ZOm8vVK/WRFJnymRHNz7r+47SJDAUrBIet1mz/azMrDHUilXJac0aVNCXr54p4+1Q9OZMvvP9hIixH/vweS7Du04KScPnpeoMSO5ugwH2mYMWSY3Lt7VwNjcq+dvZGi7aRIjTlRp2a+2fP38VVbO2iojOsyQIfM762dkhQZFpXD5/5amgffvPsrkvoskZaYkEiWGy20e339Opg9aKsWr5tPbOfXXvzJr6J8SLFhQyVUkk837XbBsTslgdZ9w0GZy/0UamCdJ7VKqum/TUZk7apUUq5JXMudNK1fO3ZRFE9bJt6/fpVTNgl5+/sh/QAfq6dOny4QJE3S9XzS7QjkxAleUUwOCWqwnjNLjaNGiSbdu3fR3Q7t27bQcGsvwIvOKLO/cuXMlWDCX9zNkhb26D0cGw2fPntU5x3/88YcG6kjQeiUGtSswRrCGNPqoUaN0cjOCYzwBWKYJRwzwoqBVOIk0atRIatT478tjlSpVdLI4zjfgiIo1rAWGhbHxQmMARogQQRfmxnNet25d2bBhg1MffPBL2ZGtG0/IxLHrNetqbceWk/pFcPSkphI6tEvXvh/ff8qIwSvk0cOXEit2ZPnn7C35a/+/MmFaC8mdL7VukzFzYqlQYqCsXvGXNGpWXM+7evmBJE0eW9JlcLuZALaJHiOiZMth2Ur/dx4+eCHLFu2TLr2qSJXqLuuP4/oI9o8evuwqML565YEsmLPTInNDvj8OVyzcJ6Ur5ZTGbUvpeZlzJJfwEcPI0J5L5Oql+3Jo73lBTDJwbAMJZYzFHz9l4rA18uTRS4kRK7JsW3dMPrz/LIPHN9LrQsRIYTRYPnPiuikje+PKQ0meJp4eRHHPsUOX5ODuczazhba8evlOFk7fodnvcP8/KGTu8cOXsmfbaWnTvaKUq+YyNjNmS6oZ7o2rDjMw9uVxuGz+filbOYc0b+cyDrPmxDgMrUHllYv3JWWaeKaxN6zvCj3w9+yzZafSTWtdxuGISQ0lgmkchtVg+fSJ65I9dwo58tcluX71kcxc0k5SpXXZZ7ZcyeXp49eybP4+DYzxfvjXvgvSpHUJqde0sOn+hAwVXGZN3CrFSmeRcOFtN4PZuuGEFCmZSRq2cMlwZMmRTP8frVt+2BQYL567R3LmTSld+lQ2XS9+wujSou5kOXLwkhQs6rId+S4Et4snrJPI0SO6uuwyAshxa+TlM9vdcvdtPCqfPnyWTiMbS7gILmMxXMSwMqzdNLl46poGuAiacTI3sfd8CRM+tLTqV8d0MGjlzK2S/Y8MUqddBf0b133/9qOsnrPNzcA4SvSIejK3ZPIG+fLxq3SY0VCCh3D5Hnhwy3FJnj6R1OvgkqhImzW5PLr7THatPcTA2IcEdnDzLXshXkC2GCe3sspY29gtQYIEka5du+rJLY7Yh71q1qypmWF0uUaycOLEifrT6IztVXYFxgiA27Rpo+sXm3YUNKhUqFBBXrx4oel6BsYuwoQJoyfzwYI0P46w/A6OguBgw7Bhw0xvqsgqY+I8Auu//vrL3TXOyPtdv/pQg9zK1fNK9pwppGPrmRaXf/nyTYIGDWyRvY0Q0eXLPsr6EBgfPXxJQoUKLjly/3dUOFLkcJqNRebWFBhfeWBRom0LtkmeMo6nHsP+Pf9I8BDBpFzFnBbnI8Nt7du37zKg12KpXquAZrjJb/j44YsUKZVFChRzOSJsiJfw/2sL3n+hmQNkN0KYjUWjGgFlqAiMy1bNLdnzpjIFxRA0mMvHxLev3/QnDgDduv5Iy7Dd8+7tRy25Rin2igX7PPRY/py3V04euSL9RteXIwcuyD+nblhcjpLuKYvbS9x4US2OkiOT9+2LS9MP8h0f3n+R4mUySyGrcZjg/+Pwwf0XpsAYZco4CFKnUSEZP3ydxfYVq+XW0mkjKAajWgHvqRAmTAjN1KZMY1lthcD0zEmXMXPnpktDmDwFLJfvyJw1iXz69FXOnrwh+QrZHsfIIoYOY/lFK3yEMHrA0DgIgCA7Q2bLA5XxE0YzPVbyG+aMWCHpsqeQYMGDyaUz1y0uG9d9rqRIn0gD3w5Vhri6bpGKebR02giKzcci3lNtOXvkopw4cF7aDa4vYcK5HHh59uilPL73zFWZdvaC6eXY3rN6Wcx4v/9eCPduPJSdq/+S6s1Laym24evXb6bSbgMOLL5788HdfVLAWq4poDtz5ozGUkmTJpWXL1/qMlQ42YL4aeHChd5fEo87kjq1S2bLGs5Hipu8Bi/mhw8f5MQJy3krSZIk0aWyULJuZO9x9ATrleXIkUPneaN8Au3LDSjD7tu3r14Hcw/q1asn58+fN12OfTRo0EBL4fPnz6/d7DA/4MaN/76QYlHuli1bSqZMmXSbTZs28SVGB75YkWTNlr7SsVslzUJYM4LNCaPXyevXH7S0es707ZI0WWxJlsIlgL1984nEiRtVv9ibixs/mty5/dT0ZfDO7Sfy6OELqV1lpOTO1FHKFesvSxbsschUX7t8X4OkxnXGSd4snaREwd4yZfxGLS38XTAdL340/TJZt9ooyZWpg5QvPkDWrfrb1ba475iL1ax1Sb7+fkjYcKGkdbcKWiZt7vD+f/VngsQxpHg5l3lsM8ZtkrevP2iJ9eJZuyRR0lg6R9fIyqVIHc8UGFw8f0cmj1yrJa1Zcrpkix/ef6Fj7OrF+9Kw0kgpkb2b/ty1+aSr+zV11HqJnyiGlKlsWWb4O9h2wboekq+QZVdNQ/DgQfU+4gsnghNkCKeN2aDBf2lP3A45HrKv7btXkHSZLMfhX/tcxmGiJC6NYVBWjXm/PQZUszhoaMC8ZCOAxnvfhX/uyITh6yROvCiSPZfLOERQikyteWk23ueQSTZuxzgI+fjRa4v9G0ErqmXcUrV2Ptmx+bQc+/uyZq8xR/n44StSvHQW08GYNp3LSj6rA0TIULs81v/6Q5DvQYnxrSv3pX5H21O++k5tLZ1HNTHNAbYWPlJYSZzyv/fE6//eloXj1kr0OFEkXXbXlVn4PF42dZOkzJhEs8OGh7ddvhdbB78x4roc4Ht017LLr1uwbwTEJapZ9v8oXjW/nD9+RUvGP77/JP8cuyx/bTspeYu7LNlDFFBky5ZNMmfOrEtB4f/b7074juAjGWO030YpNTpSW0MgFytWLHt2S2ZQoo56fJRNY7FqBLUIevETR0nMGUHuvHnzNCDGhPgOHTpoCTYGRtOmTXUAYe43OsWhDBvB9MqVK00HOE6ePKllCAiO0eod8wVQFr9o0SLNXKNuH9fFURnMMTcm2Tu7CBHC6MktSZLFljadysvooat0Di8gSzxrYXtTIPz+/SebZaYovcYXMkBAjRLsu3eeScu2pSVc+NBycN95mTxuo7x9+0latSujc+Ywh/n7j5/StlN5iRUrkpw4dlUWzdutWY7BI+vbvI+vX76XZ09fS7+ei6Rpy5LanGbXttMyfJBLmYxRSn3x3zvaoGvmgvYSPDjnzvl1l87fkeUL9krO/Kk1+AVkbqeMXCfr/vzLdGBn3JzWrg7KQPOa43SeMuZJ9h/TwJRpvnHFpcsj5iY371BWggQNIru3nJRR/Ze7zGer5HIwCKXbhw9ckNkrO3uq+Uv8RC7ZRY9AFhpzoKFUxRySOcfvKyrI5108f1eWzt8nuQuklsRJY+r8yKF9l0uZijkkY9Yk8ujBy99ev1G18ToXGeNv6Lj6FhUP1tCs6/7d5zJ4bD39O1O2JBI7bmSZOGK9BuAItlHlM2PiVh2Tnz9ZrsVprlqdfBqQd20913ReqQrZtLmXWx7cey7Txm+WZClia4k1+a7nj1/K0skbpFmvGlr+bEu8JC4HBT2iV4MxmtlFhVWHYf+VMZs7/fcFDYLrjf9vvqXRgBBCWVUhhPz/tBaUa7vn7vWHGvw27l5N33fN5S6SSbPhMwYvM52HLHmd9i5l2+QczbecweLFi711/3YFxpgzO2LECA22EMChERTmxKLEevbs2VpmTV4TMWJEXXtr/vz5snPnTv2JE57zZs2aaQMuAz7gMdEe64dBv379NBhG8y5k7zExHQ28sE/o1KmTnD59WoNevI6A4BfzlzE3wXiN0UUO0M3u2rVrujaYsSYZmm6hdJ5+b+GcXTJ14iapWiOfFCySQYPXeTN3SKsmU2TWgvY6T/fXT7fnpaADJsSPH03nIKdKE0/LrAGl28imLF2wR+o1LKwZ68kzW2lTpNhxoug2mbMlk2DBg8qMyVukUfPikiixSybFujz69asPMnJ8Y/mjSAbTHOPHj1/KnBnbNTDG7QzsvURq1CkoaTwwr5R8179nb0nfDvO0u3jX/tX1vOXz98rcKVu1c3TeP9Jp1njJnN3SreUMGT+ntUSyapzVtntF+fnzl2xYcUj6dpirDbowxzhd5sQ6BzlDtiQSKpTLlzqcj7G9cMYOKVkxh04TmDhstTRtX1pLtL0Lgv40GRPKv2dvy5LZu3Sc9hhcy9tujzzn/Jlb0r3dfO023XNgNT1v8Zw92qQQnZs9omPPCjoO1y7/W7q3mycjJzXSOcbmcAB4xoQt2uG6Zv0CUqCwS7UBmhqNmdZURgxYKR2bz9LzokQLL+27lZf+3ZZICDeaEqKxV5uG0+TF83fSuU8lLQX/99wdWTR7t4759t3Lu7rOnVtPpXPL2XqQadCYuppRJt+DMTFr+ArJmCuVZC9oWdpvrwadKut+Uco8ttsczTRbN+vateaQJEgWR9Jms8wm/3IncxXIA+MF+0YGO18J11ngcT3madfsGq3KSJJU8eXezUeydt5Omdx3oQbxXFbV+7GUOmCwKzBGthHrUaEV99ixY03n4w0DXckQuJHXIZDt2LGjnrAwNgLUVatWyaRJk7QTOJpzAdYmM4JiQImBsW7Z/fv39XVBEy9zyPpivS8DDm4YQTGECxdOM8fGfnCZ+ULdyGIjSCe3ITMyd9YOKVE6q3TtXdV0PuYJVyo1SJYs2Cvtu1TQZXNevHjn6vofPnyWsGFDmUpljcZc5vLkSyMb1hyRWzcfa1Mu83nKhrz502hgfO3KA5uBMZbqwYem9f5z5UklR/++rEui4L7iy2nj5sX1cYFRwo2/8WWQH7x+w/6dZ2X0gOVaij9sclOdL4zy96VzdknhkpktllNKnzWJ1C8/QlYu2i/NO5a12A+ad0GmbEmlSbUxsnLhXg2AcWAGAam1HHlTyelj1+TVi3cyZdQ6SZA4ppQsn0Nv2+TXL/07sIPGi5EJT585ie530cyd0qhVSYkey3XnWfJZe3acleH9VuiButFTm+h8YTTDWjJ3r4ya0liCBUPTzh/y8//vIyh5Q0Mu6+oFlExD5uxJpX7lsZp9Ng+MEcTidtAhGkFxy44uy0kasMzSlHmt5NXL93rABn+j/B7vX+Yd/c0d2H1eG3uNm9HUdPvIbqOyB/Oh0VwM2W/DmRM3pE/nhdrUbuLsFhLHbP47+Q40ncJ83OELu5reg4zPLPyNpeg8e/DCCHZTZ04q3euOks1L91oExu/ffpBLp69LtealXV031P8/yz9//O97F3z64PK3+ZJ5tvz88VNO/nVechbKaOr7YLh6/paWTiOT/EdZl4odLOEUPXYUGdN1jpw9fFEy5UnjqcdK5KzsXq4JbbrRWRnrGKMdOL7kYN0ozIElr0NJM5ptodEWYKmm8uXLS9myZaV69epy4MABU2Bs3f7cWHQbk9PxZQMl0Mg+WzPvav27Dtd4bW3V6aPhGrkNGTSU6mGpD+slbdAg5ub1R/o3shHHDl/S59j8gxolgQkTuxzwuHLpnpz/57ZUqprHYhujEU2kyGHl7p2ncvLYNSlaIpOWWhuw5I4xf9QWfHHFFwZkjs2XF/n+3eU1R+ni3l1ntYt2gRyuOwxivnO/wbWlTIUcHA6+bNWi/boEU4YsiWXAmAamxi+oCMA4SJMhocX2CHKx3BHWLoazJ67rmEKQa0DJXqKkMeX29cemtZAxn7dYWcu1N798/qYBLxq+/LXHZXpHyZzdLbbZteWUnrCsUgY7l/pCB20E4AjyUdJoSJbSpQnT8+dvGRj7sj8X7pcZE7ZKxqyJtfwZB/bg0L4L8u3bD1P21lzNsiMlY5bEMmluS+08jfGEBlwGNI5LnCymzk82IPPcve08LXlu27Wczgs2h30c2POPzr2PHSeyvk/C1Usu0wGSp7LdqNBYWiyd1Zx9/L+CWzcemwLj3dvOaHft+ImiyegpTSRaDNfLi5HPO77vH3n3+oO0KT/A1WX1C3aVig2LuWqEZcvF09d0bnHGXKkt3hPjJYkl9///vmn45+hlPbiTo5DrDHWs+C5zi5/cfy4Jk//XMA5/Q+yEv5+Tfv3iHX08OQq5Xn8b6xZD8nSW4zVlBpfv4/dvPWZg7ERdqclrPBXZXLlyRXr16qUBMBoxIQjG6e3btzr3devWrVrSmyiR28vJkMdgwWw0uMJi2OYBKIIiBLpYR8yAZZzQYAtZXqNjG2D+MIJmzDtG9td8bjLWFkuZMqU22XIPssPYP8qpkyVzmcN3+/ZtiwZf5BqCDnT9PXvqpmkZJCNgvnfnqaT9f0kysrzzZ+/U7KyRtUW31jOnrkuDpi5LhVy/9khGDVkl8eJFs8gK79p+Wucso3T67Omb2iEb5dcVquQ2bbN7x2nNdKT6f1Mla3nypZYl8/fovGLzpZkO7j+vy0Mhaz12cjP5+s2yA+eIgcv1Z4/+NUyl2+R7Nq85IrMmbpaCxTJKt0E1tIzUvJkRAlaUtqLztOHNqw/y4O4zSZnWpRpk99ZT2gl68aZepgwG5sZd/OeOKahG8IzMbOr0CTWoBhzU+WvPP5ImfQK9XXSNttav43wNROo0K6YHY+yFoAXrGKMMtlCJ/5Y4OXX0qmYhvbJv8roNq4/I9PFbpFDxDNJ7iOU4LFc5h+TOb9kh+vDBS7Jg5i4ZPrGhxEvgkmndufm0/H3ggqzY0tNiHF44d0fSZXQZh8g292g/Xy79e08GjKxjc2kkdA+eMHy9lK+Wy7R8FK63Zvnf2sgLgbYtCHLh3OmbFtnp82dv6U/j/Q6Nvob2Wa73Cfffo0uSkfdr1K2qq+zsunk7tBEXOlBH8uByg4e2n9J5w+NX9ZZQoV1e308fP2sTrmRWgSiCVywJZauRV8y40bRp1vH9/1gEtycO/GO67HeuX7ij1RSJU/1XuWeIncClLwPWLo5jFmAjkwzIHBORgwNjlOSimzHKZ60DXwRfaNaEObDIYq5fv96itJc8D3OI8Vw2btxY5wvjOUc59Y4dO3TOMA5QGD5+/KjPP0quMdd70KBBmmmOEyeOxIwZUwNbXIamXGiMtmzZMs0go7mXR6DbNRYGx21gXTRkogcPHsw5VO7Ah1izVqVkzPDV+oWpcLGMWsq3YM4ufe5q1y+k22FZpizZkkq/HoukTadyWnI4e9o2CRculFSulle3KVw0oyyev0cG9F4iLduVlmjRIsj2rad0/eMR4xrp/rD2MeYGTxyzTrN+KJs+dPCCrFh6UDp0rWDKIqMR19MnryVFqrjaRAul3fkKppXxo9fpEiZJksaSrZuOyz9nbsmYSU30OgiQrRlfWFOncf1BTT7r5fO3MmPsRokZO5KUr5ZHrl12yYgZ0FW6XvNi2iU6dNgQkr9IBp1j/Of8vTp2qtYpoNtVrVdQDuw6J307zpNqdQvK128/ZOWCvfLp4xe9vtE1evPqI7pN/RbFJWTI4LJx9WHtcj12divdxuhsbQ5BK5Y8Mb/s2ZPX8uzpG0maIo52m/YIZP/QZGvqqHUaLOGxHf3rkmxc9bfUa17colqCfBamXUwZs0nHYaUaeUyZWUOcuFFM3aYNRgYYGVjMRYYa9QvIvp3npEe7+VKzPsbhd12b+OPHL6Z1hdetOCz/nL6lSzYhS4ussTkcpMF7cIVquWXVsr8kWvQIupQSroc56UPH1zd9hqHJocvqAFH0IFKeAmkkdbr4MqT3n9KoZTFtCIcmYotn75E8BVPrusl4jx01aLWWT9dtUlivbw73CWvKk++IHd91E7+wEcLowRKjy7RHlK71hxzbd1bGdpurv3//9l02L3F5T6zcyGUpRcO9G48sAlNryFLPGrZcwoYPLZnzppHThy7oUk1tBtY1bfMWTTQfPJfYiWJalFdjznC02FEsqmQMyEBnK5helk7ZIB/efZQkqRPIg1uPZe38HZIoRVzJmt92h39yLM4xdrLAGKW9mPOKNYojR7Y8shUqVChd7geNuKpWrardj9EAiuyHYBbziadNm6bLL7169UrXQ86ePbssX77clLkFBLvYvnbt2hq0oty6S5cuehn+RrdqNNJCp+pPnz5pln/KlCk2u4rbgi8PeE2HDBmi5fM4ONK8eXN58MDySw+5Vq1Wfg1wly7aK5vXH9Vy5oyZk8ioCU30S5hh5PgmuqTT5LEbdM5dhoyJZfiYhqZ1ZtFYa8rs1jJ90maZNWWrLv2UOGksGTWhsRQolN70Oo2c0FiXVFq2eJ+8ePZW57r17F9dKlT+L0u4Ye0R3Wb99v6mzAfWLJ4zfZssW7RPM9pY7gTNuPIV5Aeqf3D878v6Rf3xw1fSsclUV5d3wRionldLWlcvOSA7N57QucfpMqHkur7E+v84QEdyNOJCk66R/f7UssD0mRNLp37VdM4woEnXuLmtZe7krTJ19HqdI5c8TTwZNb2FpPJkY7Zt64/pklHIUKNRmEdgnPcfXV+vt2LBXpdxHj+qdOhdRUqynN9XHT10WcuXMQ7RvMoaGnCVLG9Zgm8LppBMnt9KZk3aKkP6uIxDlFl3H1BNEv5/GaQD/y/X37j6qJ6sHTzr0jwSgS3mky5bsF/evfkoSVPElpGTLRt4IYBv33SG6f4hoB47vanMnrJdFs7eo9eLFTey1G1aWKrXdVkmB824MPYATbesNWheVG+b/DcEun2ntpEVM7bIjMFLdSymyphEmvSsLnESWVYcvHn1XuIncXtVlvylsmvn/q3L98vBrcc10G3Rp5bkLJzJYg1kBM+9JrXSucyGty/fmabG2NK6fx1Zv3CX7N1wRNbM3S5RYkTS26vYoJirDtbkPdhuL2AI9Mt8EdTfKFq0qDbVQuD7O+h0vHTpUs1skvfDGsTr1q2TvXv3+run+81XjhHyG958dXvZFiKfFCII56mR33D3PQMq8huyRXPd0MyvaXZov0P3Nyuv20vDkR/IGKOMF92P3ZM8OZZ5sWxIQEREREREFBCx+ZaTBcYon0Zw7B6U/Jov+0NERERERBRQcY6xk5XEZ8uWzeaSP9bQeAvdkMlntG3b1l+WURMREREREfm7wLhu3bpy7NgxGTFihHz5YtkC31jbeNSoUXLw4EFtAkVEREREROQMGWNHnsiPl1KnS5dOuyMPGzZMNmzYoB2N48aNKz9+/JCHDx9q0Iwy6vbt20u+fP+t2UpERERERBRQsSu1kwXGgExwypQpdf3bPXv2mDLHWEYob968upQP1rslIiIiIiIiCpCBMWTJkkVP8PLlSwkaNKiEDx/eO+4bERERERGRn8au1E4aGFt3qiYiIiIiInJWnBccMLAknoiIiIiIiJyalzLGREREREREzoyZxoCBgTEREREREZGdWEodMPAABxERERERETk1ZoyJiIiIiIjsFCjQLz53AQAzxkREREREROTUmDEmIiIiIiKyE+cYBwwMjImIiIiIiOzEEtyAga8jEREREREROTVmjImIiIiIiOwUmM23AgQGxkRERERERHbiHOOAgaXURERERERE5NSYMSZfEyF4Ij775CdECO7b94CIyG+JEcq37wGR/8GMccDAwJiIiIiIiMhOQfjMBQgspSYiIiIiIiKnxowxERERERGRndiVOmBgxpiIiIiIiMjegCqQY09esX79eilVqpSkS5dOSpcuLdu2bTNddv/+fWnevLlkzpxZ8ubNKxMmTJAfP35YXH/p0qVSuHBhSZ8+vdSqVUsuXrxocbkj9uFXMTAmIiIiIiLy5zZs2CC9e/eW2rVry5YtW6RMmTLSqVMnOXPmjHz79k0aN26s2y1fvlwGDBggf/75p0ydOtV0/XXr1smoUaOkffv2snbtWokbN640bNhQXr58qZc7Yh9+WaBfv3798u07Qc7qqm/fASIiIiLy05KLXzfqn10O3V+39EU9fR2EdMjSFi9eXLp37246H4Fs9uzZJU6cONKzZ085dOiQRIgQQS9bsWKFBrFHjhyR4MGD63WLFCkiXbt21cu/f/+uf9esWVOzxJs3b/byPvwyZoyJiIiIiIjsFCSQY0/2uHXrljx48EDKli1rcf7cuXM1ID158qSkSZPGFNBCzpw55f3793Lp0iV58eKF3L59W3LlymW6PGjQoJI1a1Y5ceKE/u2IffhlDIyJiIiIiIj8MQTG8PHjR80SIzitWrWq7N27V89//PixxIwZ0+I60aNH15+PHj3SyyFWrFiutjEuc8Q+/DJ2pSYiIiIiIrKTVxtmWUNJ9O/s2bPH1XnI2gLKqNu0aSNdunSRHTt2SKtWrWT+/Pny+fNnCR8+vMV1QoQIoT+/fPkinz590t9RDm29DS4HR+zDL2NgTERERERE5I8FCxZMfyJbXLFiRf09VapU2hEagXHIkCHl69evFtcxgtXQoUPr5WBrm1ChQunvjtiHX8bAmIiIiIiIyI+sY2wrI+yeGDFi6M/kyS2blSVNmlT279+vDbiuXrVsfPv06VPTdWP9v/wZ5yVJksRiG2PfKKP26j78Ms4xJiIiIiIi8sfrGKMpVpgwYeTcuXMW5yOQjR8/vmTLlk2zx0bJNRw9elSvkzJlSokSJYokSpRIjh07ZrocHaXRcAvXBUfswy9jYExEREREROSPoYy5SZMmuqYwllW6e/euTJ8+Xf7++29dRxhLJkWLFk06dOggly9flt27d8u4ceOkUaNGpjnBjRo10rJrrEV8/fp16dWrl84rrlKlil7uiH34ZVzHmHwR1zEmIiIiIv+9jvG0izsdur9WqYvZfV0EpUuWLJEnT55oOXPbtm01oIU7d+7IwIEDNYOLJZcQrOLywIH/y5VieadFixbJ69evJW3atNKnTx+dq2xwxD78KgbG5IsYGBMRERGR/w6MZ1xybGDcIpX9gTHZj6XURERERERE5NT8TFfqunXryvHjx928/MiRIxI5cmRvu+04ceLIiBEjxK9Dvf6qVatMHeGSJUsmDRo0kOLFi/v4fdm3b5/EixdPu92Rx3358lUyZ64m37//sDg/dOiQcubMKv199+4jMm3aCrl164FEjRpJypf/Q5o1qyLBg7u04rfl58+fMn/+elm+fLs8fvxc4saNIbVrl5Y6dcpYbHfhwnWZMGGJnD9/TX79+iVp0iSRzp3rS5o0fB2d0dmzl2Xs2EVy/vxVCR06lOTLl1m6dWsoUaJEtNgO47VWre56edu2tdzdr2fHcLt2w/X/wIgRHR322ChgjcU7dx7K8OFz5OTJixIkSGApUSKvdO3aQMKGDe3h2xg2bLYsXLhRrlzZZHH+8+evZPjwuXLo0Gkd6wUKZJUePRpL9Oje872D/PdY3L//hEyevExu3LgnkSKFl4oVC0uLFtXc/YxesWKHLFu2Ve7ffyyRI0eQwoVzSLt2tS3GsD37poDXlZqcPDCGkiVLSu/evW1eFilSJHFmCGAw0R2d31DHP2jQIAkUKJDs3LlTOnbsqJc1a9bMx+7PgwcPpEWLFjp/gIGx51y9eke/eI0e3Vnix49pOt+Ym/H332ekTZvhUqpUPg1Yr127K+PGLZJXr95K377N3dzviBFz9QtfjRolpWjRnHL37mOZOHGJ3L//RL/gGV8s69TpJWnTJpGhQ9vpGJo3b60GPOvWTZTEiePaOSLIP/r33+tSr15vyZ07g0yZ0kuePn2pY61164eyfPloi4M53bqNk3PnrugXRPd4ZgzjyyICkh07DkvFioW85XGS/x+Lb9++l/r1e+tBlhEjOsjLl29k9Oj5+v42d+5AD93GiRP/yqJFlgEx4P24adMB8v79JxkwoJV2UB07dqE0btxP1q6dIMGC+amvSuTLYxEHT1q2HCIVKhTS97ebN+9rEP3s2SsZPLiNm/udM2eNHpRu3LiS5MqVQQ8aTpq0VN8f581z+U5n777J9wWxs5M0+S1B/Vo3NXQ6I9eWLVsmu3bt0mwx2rEbWrZsKT9+/JBJkyZJmTJlJHbs2D4WqJN9Ll++KUGDBpESJfLYPAK8du1uiR07mowe3UmCBAkiefJkkhcvXms2GAGurS9p+JK4ZMlmqVq1mAwc2Mp0fqxYUaVVq6F6fpIk8WTx4k0SKlQImTmzv2bnIGfO9FKoUGO9fr9+LfiyOhEEFqlTJ5Zp0/qYDswgczF06Gy5d++xxIsXU06evCCDBs2QJ09eeHi/Hh3Dly/fkiFDZmr1QsiQLt0syTm5Nxa3bv1LXr9+p4EqMm0QI0YUadZsoJw6dVGyZEn92/1/+PBJevacqNdBRY257dsPycWLN2XLlqmSNGl8PS9VqsRSpkwb2bbtkJQrV9DbHjf5v7E4c+ZqrbQaPry9XpY7d0Y96Dd9+grp2bOJ6bPV+gDg7NlrpHr1EhrwGtdDRrhjx1EajKdLl8yufRORE88xLlSokMyaNUuzoxkyZNC/0SocJ5QTZ8yYURo3biwvXrh8icM6WilSpNDMKjqy4XKUHt+4ccPN28BlyIbmyJFDsmTJIu3atdMMKeB2sE6X8behevXqMnLkSP0dXeCQxc2aNavuA/u6ffu2xfZr1qzRDHn69On158KFC/WN0y3Lly+XggULWgTFhvr168uCBQskatSo+jdaok+YMEEKFy4s6dKlk/Lly8uOHTtM269du1afE3PW5+F5RUc5ZKczZcqkj2PIkCF6JP3+/fu6b6hXr55MnjzZzftNrl26dEszs26VRX358k2DVwQUhogRw8m3b9/1y50tt28/lB8/fsoff2S3OD9HjvQ6rv7667T+nThxPGnUqKLFhyt+jxkzqty9+4gvlxPBl63jx/+VmjVLWXSSLFYstxw4MF+DYmjZcrAGuQhIPMqjY7h79/E6blesGOOqdJuch0fGIjJpWbKkMQXFkDdvJgkTJpQcPHjS3dsYNWq+ZpsrVXL57DKHfSdKFMcUFAN+T5Ikrhw44P6+ybnG4rBh7WTUqE4W18PBvp8/f+l3JFvev/+o00nKlClgcb5RpYWAG+zZN/kNfmEdY3LCwBimTZsmpUqVkk2bNmmQ2q1bN5kxY4aMHj1af54/f15mz55tcR3MH+7bt6+sWLFCggYNqgHdu3fvXO0bAS+CXKzFhWB13rx58uzZM6lTp44uZo3gFHOdN2zYYLrOrVu35OzZs1K5cmX5+PGjzlkGtEpfvHixloFXq1ZNA2bAfRg1apS0adNGtmzZomXQuL9jxoyx+Xi/fPmic4ozZ7ZdwhguXDgNwo31wzp16iTr16/Xx7tx40Y9INC+fXsN6j1j4sSJuhg39oHnGI8H66LFihVLM9eAoBjrlZHHXbp0UwOGRo36SsaMVSR79prSr98U/eAEzAu+c+eRzJ27VssHMdcJJdKY84bgwhYcdYaHD59anG8Eu5jPBLVqlZImTSpZbIPy6mvX7kiyZP99KaSA78qV23rQBIFG585jJFOmapIpU1Utmca4MyxZMkJmzOgnceJE9/C+PTqG8QXwzz9HScqUiRz++ChgjcUbN+5LokSWFVF4H0UvBZSkulfav2HDXs3CmQc7Buw7YcI4rs6PHz+2u/sm5xuLCI6NgBaf2zt3HpZ589ZJ6dL5JXz4sDb3i/P79GnuqrJh9+6j+tM4KGPPvslvYGAcMPipwBiBLrKT1qeuXbtabIfgtEKFChI/fnwNOD98+KAZWmRfc+bMKblz55Zr165ZXKd79+5SoEABzYoiAMV1EJTaKlkOHTq0boOgG1lplCkjA41gGEE1MrDmgTGCUGRmMdcW+3z79q0G6bh+8uTJZejQoRI2bFhZuXKlKbBHCXTp0qW1eRUy3bj/CDwRBFt78+aN/sRaYe5BtnvPnj3Sv39/fZ4SJUqkWV9keHHQwDPy5s2rBxBwHxH04/GcPn1av4wYjdBwn8KECeOp/TozlKDjgxdBQ+HCOWX27AHaVGPz5oNaEogPZJQ2Yw4SMhzZstWU6tW7SpQoEWTs2C5u7hfZDnzgomHHrl1H5N27D3Lx4g3p3XuSZqY/fnQ9ruDz5y/SvfsEPahSp05Zb3zk5Neg/B569ZqoZczTpvWWbt0ayb59x6V580Gm6RIpUiT09L49Oobt2Tc551jEe1qYMK6bbCFjbBxUtAXXw/sgGhzhfdKtbWw18MK+P3xwe9/kvO+LgLnHWbJUl7Zth2vQ2rGjS1LEo9CzYdas1VrplTx5AovLvLpvIgoAc4xRvtuli+sv/whUzSVI8N8bSKhQofQngmTzucpGKbUBpcCGiBEjasBodHY2h/OwELWRfQXMezbfHkEiMsnnzp3TYBwZ1aZNm+plFy9e1EAWmVZzCHgRtL58+VIeP34s48aN04ysAQERtkGZMhbjNof7i6YMr169EvdcuXJFf6IE3BzuD27TM6zvBzLT375989Q+yBI+VKdP7yuRI4eXZMlcxnG2bGm1xK9r17Fa8rxnzzGdo9myZXVt0PHgwROZMuVPadKkvyxYMERChbI9x2jSpB7Sr99UadNmmP4dPnwY6dq1oQbLKGu1hi+TrVsP1a6bEyf28FRGkPw/lDUDupGjERtgvGHcdOo0WrNsefO632jLlv79p9k1hsk5eWQs/q6vRaBAgX/bhTpmzGjSoEF5N7f5/b5Z0+hMPPO+iMAZ72eY+47P2erVu+iUE8xjdw/mxbdoMUgrHoz5xOa8sm/yHSx/Dhj8VGCMzKN50OsWZG09++FlfR00rLJVUuXWByQC12DBXOaEIjOMTDICYsznff78uTa+MrZDED19+nSbAb4xj7hnz56a2baGMmVrCNIRrCNbawsy1CjLxskteFy2njfz58PW7draD9kPYy5HjnSuzi9YMKv+RJZ35cod0rx5VenQoc7/L00n6dMnl9KlW8uaNbtdLb9kQHCNZiEo98LR5vjxY+ntIUiJEMGyBOvRo2d69BtlguPHd5MiRXLyZXUyyIbBH39YHsQzuk6jGZE9gTGadNk7hsk5eWQsIqNrq8cCOkm7FSwgy7dly1+yZs04naP58+cP02cwOlEHDhxI3yPd3vdHCReOFVHOxDPvi8jkImgGNM4qUqSprFq1U9q0qfnb20AjuR49JkjChLFlzpyBpqlQ5uzdN/meIFyuKUDwU6XU3gnzjg3I2t65c8dmIyuUWmPbr1+/ms5D4IvtzTOoyBpjzu727dt1Dm/48C5vbCidfvjwoWZXEeTjhE7RY8eOlRMnTkiUKFG0DPnevXumy3G6cOGCNsxyC0rGDx48qNtZw5JJJ0+elLhx45oaaJ06dcpiG1xuLKtkBPiYM22wbg7mHh5Ft48RNFjPBf78+avFwYfMmVNZXI75R5ibiWUd3LJly0Ht8osPVGyPEmrMZ8YXwdSp/xu7KOWuWrWLBsdYIgJNRcj54EsZfP1qWQVirK9tb5fohw+f2T2GyTl5ZCyiDNq6QSAO6GK5JnTct2XHjr91qTF0l06TpoKesLY24HeUy0KiRHHl7t2Hrq6P20MDLnIe7o3FECGCa2CLg9jmkPnFAWgclP4d9F1A5jljxpSydOkIi3WyMZ69sm8iCmCBMbKvaHRl62QeqNpj4MCBGphevnxZOnfurOXRJUqUcLVdzZo1df4x5jVj23/++UcbV6GBFuYEG/A7SqbRzblixYqm88uVK6fzbtHJGqXWKJ/u0aOHBrUIWhFQouwaTbkwp/ju3bu6DNOAAQO0BNxWlhaqVKki+fLlk4YNG8rSpUs1kMX9QxOvqVOnanMsBOAI3v/44w99vPv379fGYFOmTNF5x0aTLHTmxv1A4yyUbm/btk3WrVvnqefTKG9HebmtJmZkGz74+vadIitWbLc4Hx+GQYIE1qUZ8BNlVuawliFKqoxOwbZgOQfMVzK3YMEGzXgYWWoEww0b9hUUWKDpEcq4yTkhmED5PDJq5pUgKOWHrFldHzj0iAQJYtk9hsk5eWQsYskvrENszAGFQ4fOyMePn/QyW9q0qSWrV4+zOFWrVlwvw++43OhujQZc16//d9AGv9+4cc/NfZNzjsVs2dLoGtc4mbtw4bq+v/2ub8Ly5du070LJknllzpwBrqoR0L/F3n2T3wioHHki3+GnSqkRoOFkC+bj2gpkPQqdphE8vn79Wht0IctqzE82h6wrAlY0zzK6U+fJk0f/NrLCgGZayBQfP35cLzcgU4zrI2DFslEIhJCZxpxkI+OMADVEiBAaHKNbNpZZQkYYwbRbUO6FABj7RkdoZKBRGp0sWTINfI3lkwBziXHq3bu3llkji40guGjRono5mmkhcJ45c6Y2G8N8ZDw3aFDmUThQgKw5Hiey6X369PHwdZ1Z7NjRpVKlIjJ37jodA5kypdQAYsaMlVK7dhnJkCGF1K9fTo8qAwJlZJcxPxMf1tWqFTMdzcZRZSyzhBPUrVtWy6bRXTpTplSydetB2bz5gAwY0Mr0ATxkyCxdTxZrHaNMEN2CDSgnNF+uhAI2HBxDU5kOHUbqOpoIGBAMjB+/RIoXz21RZeAejCN0cUX5Pn56ZAwTeWYs4n0Oa63jwB7KSV+/RpPLBZI/fxaL6gTzsYhMG07m9u8/YSpPNZQqlU/fg5s2HWBaYxbBSfLkCaVkyXx8oZyIR8Zi27a1dKk5fN6WKJFHl1qaNGmZNtCqXLmIzc/oZ89eyfDhc/Q9EF37rbPCxnunR/ZNRN4n0K8APmkU6xijszIypgh6HQnLMmEJJXSUJnu4bn7mDPCBOWfOWtmwYZ8GDPjQrFq1mC6jhAMg+C+JpW1wdBllgii1QtYCXSmNNTxxfuHCTfQLIj5IDbgevjw+e/ZSSw/RGdhYNxG3i2UnjJIwa9mzp5XFi4f70LNAfgXmYU6dulxL7CNECCdlyxbQsWZrne0UKcq6GnPG+RUrFpIRI1zeCz0yhq0VKtRYx6CxD3I+7o3Fq1fvaDOtM2cuaYfqIkVyaBBj3lHaeixaQyMjHKS5cmWTxfmophk6dLb8/fdZCRYsiI7Xnj2bWJS6kvNwbyxu3/63VmjdvHlPQocOpX06cFDF6Odh/Rm9evUu7Y7uFjTgwkFzj+zbOSUXv27lTctKQK+qltj+ZCDZj4GxHTC3+NKlSzJr1izZuXOnzYZZ5BHOGRgTERERUcAJjFffcmxgXCURA2Nx9lJq/2LOnDk6d3fw4MEMiomIiIiIiPy5AJ8xJr+MGWMiIiIi8t8Z43W3bfdIslfFhCUduj/yGGaMiYiIiIiI7BQ4EJ+6gIAdwYmIiIiIiMipMWNMRERERERkJ2aMAwYGxkRERERERHZiYBwwsJSaiIiIiIiInBozxkRERERERHYKwuZbAQIDYyIiIiIiIjsFDsTVbwMCllITERERERGRU2PGmIiIiIiIyE7MNAYMDIyJiIiIiIjsxK7UAQMPcBAREREREZFTY8aYiIiIiIjITuxKHTAwY0xEREREREROjRljIiIiIiIiO3G5poCBgTEREREREZGd2HwrYGApNRERERERETk1ZoyJiIiIiIjsxIxxwMDAmIiIiIiIyE4swQ0Y+DoSEREREREFILdu3ZJMmTLJ2rVrTeddunRJ6tSpIxkzZpRChQrJokWLLK7z8+dPmTRpkuTLl0+3adq0qdy7d89iG0fsw69iYExERERERGSnQIEce/Kqb9++SZcuXeTjx4+m8169eiUNGzaU+PHjy5o1a6R169YyZswY/d0wbdo0WbZsmQwePFiWL1+uQW6TJk3k69evDtuHX8bAmIiIiIiIyE6BHHzyqsmTJ0vYsGEtzlu5cqUECxZMBg0aJEmSJJHKlStLgwYNZNasWXr5169fZd68edKuXTspWLCgpEyZUsaPHy+PHz+WnTt3OmwffhkDYyIiIiIiogDgxIkTsmLFChkxYoTF+SdPnpTs2bNL0KD/tZjKmTOn3L59W54/fy6XL1+WDx8+SK5cuUyXhw8fXlKnTq37dNQ+/DIGxkRERERERP68lPrt27fSrVs36dOnj8SKFcviMmRtY8aMaXFe9OjR9eejR4/0crC+HrYxLnPEPvwydqUmIiIiIiLyI5nGwoUL//byPXv22Dx/wIAB2nCrbNmyri77/PmzBA8e3OK8ECFC6M8vX77Ip0+f9Hdb27x588Zh+/DLGBgTERERERH5Y+vXr9dS502bNtm8PGTIkK4aYCGYhdChQ+vlgG2M341tQoUK5bB9+GUMjImIiIiIiOwUKNAvhz53bmWEfwedoV+8eKFNr8z1799ftm7dqiXQT58+tbjM+DtGjBjy/ft303noOm2+TYoUKfR3R+zDL2NgTEREREREZCdHdJL2KiybhFJnc8WKFdMO0eXKlZMNGzbo8kk/fvyQIEGC6OVHjx6VRIkSSZQoUSRcuHDayfrYsWOmoBZzli9evKjrFkO2bNm8vA+/jM23iIiIiIiI/DFkbBMkSGBxAgSsuAxLK71//1569+4t169fl7Vr18qCBQukefPmpnnBderU0QAbGWt0mO7YsaNmiRFggyP24ZcxY0xERERERGQnr3SS9ikIkOfMmSNDhw6VihUrSrRo0bSDNX43tGvXTsuh0dUa2WdkiOfOnatrFztqH35ZoF+/fjm2KJ7Iw67yuSIiIiKi30ju55+dcy83O3R/GSKXcej+yGOYMSYiIiIiIrKTP0gYkwcwMCYiIiIiIrJTYEbGAQKbbxEREREREZFTY8aYiIiIiIjITkwYBwwMjIl8wZcvXyVz5mry/fsPi/NDhw4pZ86scrX9sGGzZeHCjXLlyiY393ns2HmpV6+Xm5e3bVtL2rSp6ep87Bf737NnjsSNG8PTj4UC5jjctGmKFC7cxM3rVqpUWIYP7+Dm5WfPXpaxYxfJ+fNXJXToUJIvX2bp1q2hRIkS0eb2HIfk2+Nx//4TMnnyMrlx455EihReKlYsLC1aVJPgwf1+J1Xy+c/otWt3y7x56+TOnUcSPXpk03gJGtRlbVe3GNe7e/exxIoVTWrXLiV165aVQGZtjTkW/R//0JWa3MfA2AEKFSokDx48MP2NN7fQoUNL6tSppX379tqm3Ny6detk1apVcvWqS1fmZMmSSYMGDaR48eLu3tbJkydl/vz5cvbsWfnw4YPEjRtXKlSoIPXq1dO1w3zStWvX9HEXLFjQR283ILh69Y5+4I4e3Vnix49pOj9wYNezG06c+FcWLXI7IDakSZNEVqwY7er8CROWyPnz16R06fyuLrt164GMG7fQrsdAAXsc4ouerfG0dOkW2bbtkFSuXNTN/f7773WpV6+35M6dQaZM6SVPn76UceMWSevWD2X5ctf75Dgk3x6Phw6dlpYth0iFCoWkc+f6cvPmfQ2knz17JYMHt+EL5GTc+4w2DuQVL55HunZtKK9evZVJk5bKlSu3ZPJktw9Qr1q1Q/r0mSJNmlSWvHkzyblzV2TEiLny8eNnDaqBY5HI9zAwdpBGjRrpCbAC1uvXr2XcuHHSpEkT2bZtm8SOHVvP79Chgxw9elTatm0rgwYN0iB6586duvg1LmvWrJmbt7F48WIZMWKEBsGtWrWS8OHDy+nTp2XkyJFy4sQJmT59us3AyrtgMW+sW8bA2PMuX76pR5VLlMjz22zEhw+fpGfPiRIjRhR5/Pj5b/cZNmxoyZgxpcV5e/YckyNHzsnEiT0kUaI4Fpf9+PFDevacIBEjhnd33+Sc49B6PCHAQBDSsWNdyZo1jZv7HT16vqROnVimTetjek/C+Bw6dLbcu/dY4sX774smxyH5hfE4c+ZqPbg4fHh7vTx37owa7EyfvkJ69myimUJyHr8bi3jPmjZtueTJk1EmTephOj916iRStmwb+fvvM5InTyab+50xY9X/g+kG+neuXBnk9u0HsmTJZlNgzLHoPzFhHDAwMHYQZIixyLUhevToMnDgQMmfP7/s2rVL6tevL8uWLdPfkS1Ok+a/D/GWLVvqG+2kSZOkTJkyGkRbu3z5sgbFWEQb+zLEixdPt69Tp45s3bpVr09+36VLtyRx4rjuluiNGjVfokaNJLlypZdp01Z46jY+f/4iQ4bMlIIFs+qHu7W5c9fJ8+evpVmzKjJo0AxPPwZynnEIOLCHcZIkSTxp0KC8m9shmDh+/F8ZMaKDxYG6YsVy68kaxyH5hfE4bFg7+fbtu8V1gwULKj9//pLv3y3PJ+cei/jcfP36nRQsaFkNmDx5Ai3B37//pJuB8axZ/SVECMvqvmDBgmnptoFj0X9iYBwwMDD2zic3qMvTa5Q4L1++XLOr5kGxAcFuzpw5JWrUqDb3hWA6XLhwUrt2bVeXoVR7wYIFFvtdv369zJs3T27fvq37rFKlimZ4gwQJIvfv35fChQvLokWLJEeOHLq99Xk9ergcBY0UKZLu6+PHj3r/kOWOESOGqXx8ypQpcvz4cc1mk8ddunRTX4tGjfrK6dOX9MMXwWu3bo00kwE46rxhw15Zt26ibN58wNNP76JFG+XJkxeyYMEQV5ddu3ZHpkz5U+bMGSD37z/hS+ekPDIODVu3/qVlf4sWDdPruOXKldvy8+dPiRw5gnTuPEb27j2OMEaKFs0lffo0k/Dhw5q25TgkvzIezasY3r//KIcPn9V5oJiCYj5myTn8biyGDx9Gs8kPHz6zuM6bN+/l7dv3WoXgFhzIMQ7sYPtduw7L+vV7pVGjCqZtOBaJfA8DY2/y5MkTGTZsmGaSCxQoIF++fNE5xeXL2z6yjaA3a9asbu7v33//lfTp05uCbWu5cuUy/Y4geezYsRrc5smTR86dO6cB7atXr6R3794efgybN2+WsmXLypIlS+TFixfSqVMnmTBhggwfPlxWr16tZdSlSpXSgJs8Dh+I+LL265dI1arFpGXL6joHGIHq9ev3ZMmS4VpC3bv3JGnXrrarEmiP+Pr1m85LLlUqvyRIYFmBgHlT3buP19vOnj0dA2Mn5ZFxaJ5hmzt3rWTOnEpy5Ej32/2+fPlGf/bqNVHy588i06b1ltu3H+pcdnxhXLZspE4h4TgkvzQeDZh/nC9ffVOAgjJtci4eGYslS+bT8uekSePrQZYXL15raT6C6U+fPrt7G2fPXpEaNbrq72nTJpWGDSu62oZj0X/hOsYBAwNjB5k5c6ZmaAFlV1+/fpUkSZJoIIlS56dPn+plESJEsGv/mLOMsmmPvKHPnj1bS6uN7HLChAn1+qNHj5Z27dp5+DYRrCOgRpkPHguC4AMHXDKXkSNH1g8ABP4RI9ruMktuv0bTp/eVyJHDS7JkCfS8bNnSasl0165j5a+/Tsv27YckZsxovy0R/J0dO/7WpjFNmlRyddmMGSvl7dsP2mCGnJdHxmGBAi4H65AxuXDhhkyd6v6BNaMcNU2apDJ0aDvTPDpkWTp1Gq2VEHnzZuY4JD81Hg0hQwbXKhuUyqJDdfXqXWTt2gna54Gcg0fG4sCBrTSL3KfPZD2IjXHTtGkVPagdKlQId28jduxosnjxMD0wjQaZCJLXrZsgoUL9N5edY9F/YSl1wMDA2EFq1Kghdeu6HFnGUW0EiwgsDfgbR6WRtbUHAlEEt+55+fKlPH/+XLJkyWJxfvbs2eXbt29y8+ZNiRLFYx/w8ePH16DYgMeDfZDXYHzYynJgLjDgC9+WLX/JmjXjdH7bz58/tBQQkGULHDiQu03Wduw4LMmSxZeUKRNZnH/x4g0NSGbPHqAf6tgfbgNwG5jr/ruyRHKecYjuqkYgggMtESKENf39O2HChNKff/xhOf8Oy+PAxYs3tayV45D8yng0D4xRNo3AGdKlSyZFijSVVat22lzqjpx7LGIucO/eTeXhw6cSO3Z0HWurV++UBAliuXsbONCCE6q2UJlQp05P/dxGV3QDxyKRz2Ng7CDIBCdI4HJk0RbMM06bNq12kbbl7du30qZNGz0hiLWWKVMmLV92K3Dp0qWLZM6cWYoVK2Zz/0Zg5VYpNvZr6z6T42He74EDJ3WpBnyYGj5/dmm+sWLFdm3EUaaM6yVC0qSpIBUrFpIRIzr+NkOC5R6wHIQ1dKnG5Q0a9HF1WdGizSR79rSyePFwLzw6CijjMFKkCBZrahYunFObEbknYcLYpnJ+c8Z6oMiCcBySXxqP+PxDUIJt0VnYgHXdEYCjpJWch0fG4r59xzVwzZIltSmrjHLqx49fWIwhc8gm7917TNKnT24xxcnY/unTFxyL/ligQC5JBvLffG5tH5Jq1arJwYMH5cKFC66eDTS9whrFWJfYlsqVK+u6xZjva+3YsWOyadMmCRs2rDbawunUqVMW22DfyP6aZ4Hfv39vuhxNushn4EtY375TNAC2biYTJEhgLeNbvXqcxalaNZc1rvF7mza1frv/q1dvy6dPX/QD2xr2Y71vIxOC0rGBA1s79LGS/x2HxvI3KCnFnEzM5/QINJeJEye6Vj2gJNGAYBiwX45D8kvjEQebx45dqCdzFy5c19tLkSIhXzAn4pGxuHz5Nhk1ymX6nAFrG+Ny6+oEAxp2ofQa8+PNoZwfUqRIxLHoz0upHXki38GMsQ9CZ+g9e/ZIw4YNpX379toY6/Pnz7Jx40aZP3++dO/e3eZSTYA5vrgOlmxCY69y5cpJiBAh5MiRIzqPuWjRolK6dGndtnHjxjJ+/Hidk4zb+Oeff7R7dPXq1bUcGgF0nDhxZOHChab5xxMnTrRoQOIRYcKE0YAapdtuddMm13AEulKlIrpMDV7DTJlSyqlTF7W0tHbtMjabbSFDYpT2GZABQWl0zJhR9WS4evXO/8dMPDfLt8yhM7Cx1AQyJOQcPDoOcaAF0GTGFutxiPcRdG7t0GGkdOw4SoPg69fvyvjxS6R48dym7AjHIfml8di2bS1tSti//zTtPozGXJMmLdP3xcqVi/DFciIeGYt165aVxo37y7Bhs6VQoRxy5Mg5mTlzlTRtWlnix/+vlPrs2cs6dQTnYZmmZs2q6tz1yJEjark2yrKnTFmu62ajORxwLBL5HgbGPjxvZerUqZr1xfJL6ByN0uZkyZJp4Irlkn6nWbNmkjhxYl0aae3atRpUI/ht1aqV1KpVy1Ri3ahRIy2DRuCLztgxY8aUpk2basAM+KIwatQovQxdslEC3rNnT92/Z2BO9ciRI+XatWsa3JPHoXEH5hVt2LBPpk9foV/g0IHaVrMst6C8r3r1rprxxQep+RqLgBJAIq+OQ2M8oVmRR8chAovp0/vI1KnLpXnzQRIhQjipUaMEO/ySnx2PmNsZMmQImTVrtS6TFzp0KClSJKc2KcT55FzcG4uYlz52bBe9DJllBNNY/gsBszmMRfPpT61aVddAeenSzbocGBp8YSxirBrJCY5F/8mTuSXyowL9Mq8tIvJRV/l8ExEREdFvJPfzz87Nd5scur/E4SwPspDPYMaYiIiIiIjITmzaFDAwMCYiIiIiIrITS6kDBh7gICIiIiIiIqfGjDEREREREZGd2HsrYGBgTEREREREZCeWUgcMLKUmIiIiIiIip8aMMRERERERkZ1YSh0wMDAmIiIiIiKyU2BGxgECS6mJiIiIiIjIqTFjTEREREREZCcmjAMGBsZERERERER2ChToF5+7AICl1EREREREROTUmDEmIiIiIiKyE0upAwYGxkRERERERHYKxMg4QGApNRERERERETk1ZoyJiIiIiIjsxIRxwMCMMRERERERETk1BsZEREREREReCKgcebLX69evpV+/fpI/f37JnDmz1KxZU06ePGm6/MiRI1KpUiXJkCGDlChRQrZs2WJx/S9fvsjAgQMlV65ckilTJuncubO8fPnSYhtH7MOvYmBMRERERETkheZbjjzZq1OnTnLmzBkZN26crFmzRlKlSiWNGzeWmzdvyo0bN6R58+aSL18+Wbt2rVStWlW6deumga5hwIABcujQIZk8ebIsXLhQr9euXTvT5Y7Yh18W6NevX1yRmnzJVT7zRERERPQbyf38s/Pyy0aH7i9yiHKevs6dO3ekWLFismzZMsmSJYuehzAP55UpU0ZevHghly5dklWrVpmug2wussxz586VJ0+eSMGCBWXGjBlSoEABvfzWrVuaFV6+fLlmf5GN9uo+/DI23yJf0+7IAz775Ces/+9AJ5GvihSdH8vkN1ztNc237wKR+nT3T3/wTPh++61IkSLJrFmzJF26dKbzAgUKpKe3b99qSXWRIkUsrpMzZ04ZOnSoBtCnTp0ynWdIlCiRxIgRQ06cOKFBrSP24ZexlJqIiIiIiMhOgRz8zx7hw4fXLG3w4MFN5+3YsUMzySh9fvz4scSMGdPiOtGjR5dPnz7Jq1evNNuL4DpEiBCutsF1wRH78Mt4aJqIiIiIiMiPKFy48G8v37Nnj7v7OH36tPTs2VNLqVHe/PnzZ4ugGYy/v379qsGt9eWAIBcNtcAR+/DLGBgTERERERHZKVAgv1WEu3v3bunSpYt2ph4zZowpOEXwas74O1SoUBIyZEhXlwMCWlzuqH34ZQyMiYiIiIiI/MgcY49khN2yZMkSnfOLhlcjR440ZXBjxYolT58+tdgWf4cOHVrChQunJdJoooXA1jzri20wR9hR+/DL/NbhDSIiIiIiIvI0dKQePHiw1K5dW5dsMg9Os2bNKsePH7fY/ujRo5pVDhw4sHay/vnzp6mBltFRGvOGs2XL5rB9+GUMjImIiIiIiPxx8y0EoMOGDZOiRYvqWsPPnz+XZ8+e6endu3dSt25d+eeff7S0GusRz5s3T7Zv3y5NmjTR68eIEUNKly4tffr0kWPHjum2WBc5e/bskjFjRt3GEfvwy7iOMfmadkf28dknP4HLNZFfweWayK/gck3kV/iH5ZrefN3h0P1FCF7c09fB2sHjx4+3eVnFihVlxIgRcvDgQRk9erTcvn1b4saNK23btpVSpUqZtvv48aMG1+hmDfnz59cgF52mDY7Yh1/FwJh8DQNj8isYGJNfwcCY/AoGxuRXMDAmn8LmW0RERERERAGkKzXZh4Ex/a+9+wCPotriAP4P6Z2EBAiBQAKEDqG3B0gRaQpIFQvyKGJDFBQDCCICKiiiPOxY6B0RFOlV6aG3AEmoCQklvZK879xlNrubTaWF5P/jy0eyO7s7M3t3ds69554hIiIiIqJCUpWaHg12bxAREREREVGxxhFjIiIiIiKiAipoJWkqXDhiTERERERERMUaR4yJiIiIiIgKiCPGRQMDYyIiIiIiogJjEm5RwHeRiIiIiIiIijWOGBdha9aswfz583H27FlYWFjAz88Pffr0Qf/+/dX97dq1Q8+ePfHmm28+6lV9LN08dQb7P52Z7f2Ve3RDlR7dEB9xHWcWLsOts+dgYVkCZRs3hH/fnrCyt9cvG3f1Gs4uWYmbZ87CwtISpWpUQ9XePeBYtozZ506/cwd7P/4MljY2aBI4yui+Kzv/Qej6TUi4Hgm7Uu7wad8GPh3aqjaQnfTUVPWYq7v3IOnmLdi6u8GreRP4dX0KJawyDxPb3n4fybduZ3l8269nwMbZKdd9RvdfWSdbbHypMYauOY49lzPfm4ol7TGxTRU09nbFnYwMrDsbiWk7zyMu5Y5+GW9nW4xrXQXNKpRECQtg/5VofLz9HMKik/TLONtY4v1WldGpiiccbSxx4nosvvw3FDsv3lL3l3exwz9Dmme7fkuPX8PoDafV79U8HDG2VWXU93JBSlo6doTdxNSd5xGVkJrjNjbxdsV7Lf1Qw9MJMclp+PtcJKbvDkF8qm5blvQJQPMKbtk+3ueLrXnal3R/yJHmxRre6OPvhTIOtrgSl4QlZ65i8dlr+mWcrS3xZkAltPfxgIOVJYJvx2P24VDsi4g2+5xlHGywoltDzD99Bd8evWh0XytvNwyvUxFVSjogOjkNmy5F4eugUCTeSdcvU8nFHm838EWj0rrPw8GIaMw4FKLWLa++aF0D1d2d0GX1fqPb63u6YET9SvAv6YjY1DRsuXQDsw+HISEt87NGD5d3WXcc2PgZ+g79HDv3nFK3JV5clO3y2/85gU79P85y+yfjX0D9Or54qt/kbB/r5GiHAxs+w449JzFs1LdG9zWpXwUfjemPRgGVERefjI3bj2DslAWIvBGjX6ZcGTdMGTsATz5RD9ZWljhw5DzGTlmIIydCc9xG/8rl1ONaN6uBtDvp2LXnFMZ8PB+hF6/rlynt4YpPP3hBPbeVpSX+3noYYybPQ/j1rN/jdG9yOseixwcD4yJq+fLlmDJlCsaNG4eGDRsiIyMDu3fvxscff4yoqCi88cYbahlbW9tHvaqPLZdKPmg6/r0stwevXIOYkFB4NWuM1PgEHPh0JmxcXVBn6EAkx8Ti7NJVSIiMQqPRI9Ty8vveKdNh7eCAGi/0h42LC67s2KUC3+YTA2Hv6ZHlNULW/Y2YkDC4VatqdPvl7btw4uf5qNSlIzxq10T0+RCcWbQcd5KS4fd052y35dSCpbj2z174PdMFrn6VEB0ShvO/r0VS1A3UHvySWiYlNk4Fxf79noVb1SpGj7dyyAzy6eHxcrLF/F714GpnbXS7i60VFvcOQGRCCt75+zQ8HKxVQFrB1Q4vrTyqlrG1KoEFvQNgZWGBiVuCkZR2B++08MWSvvXR8bf9KgC1tLDAwt4BqOLugK/2huFYRCxaVHDD3B518fq6E9hwPgrX45PRfdHBLOs2sJ43ulUrjSXHdcGQrIOs09XYZIxafwp2VhJw++HXnvXU49PSM8xuo38pB7WNB65E47V1J1RHgGyLj6s9/vv7MbXM+M1n4WRr/HVW0dUeMzvVwMJjV+/b/qa8GdXQTwXGS89ew5ZLUSjvZI/X61WEt5MdPj8Uojph/teuNrwcbTHzUAhuJqVgQHVvzG5XG8//FYTg2wlZnnNSc38422Q9ZWnj7Y6ZbWpibUgEZgWFwM/VQQXcbrbWCNx9Rh9U//pUPYTGJOL93adhZ2mJNwIq4tv2tdF77SEkGwTQ2enq66mCeNNAurKrA77rUAdB16Px7s7T6rVGNvBFeSc7jNh2kk3mESjv5Y418wNR0tXR6PY23T/Ismz3zk3wzvCn8eOCTVnue2toV7w1rCt2/Jvz+/jZhBdRsYInsMf4dgmo1y/5AFt2HUO/oV/Aq4wbPnq/P5ZWGoW2z07UB9Ubl09Eckoq3gz8EUnJqQgc8SzWLhiLxh3fyzaAlW3csvJDBJ+/hoFvzoaDvQ0mju6LtfMD0ejJ99TzWFqWwOpfx8DF2R4jxv6kgu7J7z+HP+YHonmXsUhjx819xsC4KGBgXEQtXLgQvXr1Qu/evfW3yYhxREQEfvvtNxUYu7u7P9J1fNzJiG/JKn5Gt10POoKbJ0+j3utD1WjvhbXrkRoXj+aTxulHVO3c3XDoi9m4FXxOBZhhGzYjPTkFjSYGwqG0p1rGo3YN7Jn8KYJX/I66wwcbvUbMxcu4sPYvFWybktcr06gBqvV9Vv1dqmZ1xIdHIGzT1mwD45S4OBVQ+/fpCd8uHfWPE8HLVqnbbVycEXvxkrqtTMP6+vWkR/f126tmWYxvU9lswY8X65aDm701usw/gFtJutHYa7HJ+O3ZemhUzhUHrkarUVg/Nwc8t+wwdl/Sjf6ev5WAbYOaoWNlDyw/GY4OlUuhXlkXjPjzJFafjlDL7Lp4C9aWFvioXVVsPB+FlDsZCLqWOfoh6pR2UkHxZ7suYP9V3QigPGcpBxv0WHRQPyIto3sS9DYs54q9BqPdhnpUL6v+H7LmOBLujhBLMD/tyWpqxPtKbDKCbxoHUhJ4TWpbFScj4/Dh1uB73t+UdyVtrfBctXJYEXwNU/adu3vrbUQkJOPLNjWx4lw4apdyRs1STuj/ZxDO3Q2CD0REY3m3Bmju5ZYlMO7r7wVfFwezrze6kR82XYzChH9177OMOJewsMCA6uVgZ1kCSXfS8WrdiohLScMrm46pv4UEuLOeqIma7k4IijRuv6Y87W0wplFlhMcnZ7mvi6+n6ngeuf0kEtN0z21ZwgIfNK2qAv9rZh5DD27E7vlerTBt/AswN3i3L0hrj5nB5aDn2uLbX/7G8j8yo1oJcj8d/wK6PtkQt6Pjc3zNp9oGoFe3ZmaXk9HcI8dD0Wfw56qNiNi4RMz4cKB6jbBLkXhjcGe4l3RC/faj9UHwoaMX8M+6qWjdrCaWrvnH7OuOe7s3YmIT0GXAFCQmpajbQi9FYvlPo9Gwnh927zuDXl2bqeBcnvt08BW1zJGTYTi48TP07tYMi1fvzn2nEhUznGNcRJUoUQJBQUGIjjZOSxs2bBiWLFmiT6X++uuv1e/VqlUz+zN79mx1f0pKCqZPn45WrVqhfv366Nu3L3bt2vUItqzwupOSglPzl8CzXh2VLi2ijp1ASf8qRmnGMpJraWeHqCPH1d/xV8Ph6O1lFGxalCgB92r+iLy7jCY9LQ3HfvgZFTu0M5tm3eDt11Gtny4o1kgqdHpqWrbrnZaYhAptW6F0/bpGtzt5ldGPaIuYi5fUepsbwaaHS1KKp3bwx4qTERi5PutoRutK7th35bY+KBaSthybnIa2vroOMQkaRGxKZtu4laj7veTdEegq7roRl00XdG1A8++l2yjnbKfWw5zJ7f0RfCMePx7SdaZoI9S618tML719d/3c7LLvo5XHpd7JQOLdoFitp/Y4e+ORcs3zdcuhTmlnjN18BqnZjETTg1HR2R5WJSyw/cpNo9v3h99WAWPLcm7o4OOhUpm1oFikpGfgmTUH8dsp3Qm8RkaZR9avhEl7snZwVHdzhI+zPRadMc4KWHjmKrr9fkAfBMtI7+rzEfq/xcmbcXhy5b5cg2IxsVlV/HvtNvaGZ+28sbUsobIdku4GxeJ2su5z5GqSxUAPVp0aPvh66mAsXLEDg0fOyXX5Tz54EUlJqZjwme6cSDN9wouo7FtWpVYfPRmW7eNlRHrOp0MxbtoiRMcYd+ZIsCuB7XfzNuqDYvH7+v2o2uwNFRSLnl2aYtWfe41GhiMio1G5yevZBsWiR+cm+HXJdn1QrAXUfo1fU0Gx6NCmLs6cu6IPioX8fvrcVRXQ0/1lcZ//0aPBwLiIGjJkCE6ePInWrVurYPj777/H0aNH4ezsDF9f3yzLS5Br+NOlSxeULl1azUkWgYGBKhV7xowZWLVqFTp37ozhw4dj27Ztj2DrCqewDVtUqnG1Abp9JuKvhWcJYCXotfcspUZyhbWzE5JvRyPdJK0pITISaYmJSInL7Ik+//s6ZNy5gyo9u5ldB6dyXipwlS9ieZyMBMu8YZ92rbNdbwdPD9R8aQAcvXQjc5qIQ0fUfGfHsqXV37EXL8Pa0QGHZ3+Pza+OxKZX3sKROT+odaeH60psElrP3YvJ288hMTVrGqgEtCG3Eo1uk/jwUkwSKrvpRt52hN1SwevY1pKWbAdPBxtMbldVjaxtOK87abuVqDvp8naxM3quiiV1f8vjTD1drTQaeLli0rZz6jU1a89EIiIuWb1GaUcbVHCxU68tt8kodHa0VOwJT1RBSTsrlVo9snklnIqMUyPCphysLTGqhS9WngrHkfDYnHck3XdaUFjO0bhtlHfW/S0pxtXcHHE+OgHPVy+HP3s0xoEBLbGwc4Caq2tITg0nN/fHhrAo/HMtaxup5qbrmJFU6K+fqIm9/VtgR59meLehH6wlbUDarqMtXGys1MhtYOPK6v59z7VUo9elHWxy3Z6eVcqoUeVp+41HGzWrz+mO46Mb+sHVxkqlVg+v44Ozt+LVDz08l65EoXbrtzFm8nwkJOY8Ui9zf2Wkd8Kni9UorqEPpy9F445jsHufrjZCdr6Y9DJOn7uCH+dnTcOuXcNHpTJH3YjBz7Nex/WTcxF56mf8OPNVuN7NfrCyskSNqt4IvnANE0b1wYUDcxBzfh7WLx6PGv7ls31dGW2WoPzilUjMnDwIV47+gFtnf8XSH0epudWa6lW8ERwSnuXx50PDUbVyuRy3jQoaUt3PH3oU2J1ZRHXq1Ally5ZVadMS0G7fvl3dXqlSJUydOlXNOzbk6Zk5WvnLL79g69atWLBgAcqUKYOwsDCsXbsWq1evRo0aNdQygwYNwunTp/HTTz/hiSeeQHEnI7lhG7egbNNGcCyjCyRFWkIirOyzBg9WdnZqpFZ4/6c5wvfsVyPBVXt1V/N1r+7eq0abxZ3kZJmIhOgLoQj9ayMajx2FEtbmR8o0MrdY5igLF9+KqNjpyXxtT8TBIF1A3f4JWDs66gPj5Nu34VqpFSp1bIe4a+E4t/IP7Jv2OZp/NA5WnK/+0EQnpSEa2WcBONtaqtFhU/EpaXC6O09Tgol3N5zG3B51sGuwrniWzDMevPoYLt5Ndf4rOAqBrVLVXN3ATWdw/mYCmniXxCuNfPRBqKnhjXyw/8pto0JgQuY7j910BrO71sLT1croR4z7LQ0yKghm6uyNeFU0bHI7fwxuUEHddik6Eb2XBBkF3pp+tcvC1dYas/dmP9JDD05YbCIOXY/Gq3V9VPr0vvDbKhj+oFlV1ebsrSzhZmeNJ308VLbCF4dCVLv7b60Kas7vC+sP61OpX6jhrUaMR2zTHQtNyfMImWP8V+h1Ndpcq5Szem13O90cY22Zt+pXwvEbsRiz67S6b0SAL37sUBf91h0yKtJlSFKhRzfww4R/z+oDflPnohPwZVAIAhtXUeurpWkP2nDEbPukB+dWdLz6yQuZVyxFqhatypr5dvLs5Vwf/8xTjdCtY0M1n9ccz1K6Tp7vZryiCl5JEbAqvl74aEw/+PqURvtek+Dm6ghrayu8OaQLQi5G4LX3foCtjRU+GNUHG5ZOQJOnxuBaRNYOIU933XNPCRyAA4fP4aXXv4Knhysmj+mP9UvGo2mnQNUxIHOLz5kJjOPikuDixLogROYwMC7CAgIC1E96eroKYiU4lirVQ4cOxcaNG80+ZsuWLSpleubMmahVq5a6TUaexYABA4yWTU1NhYtL1nmuxVH4/kNIiY5Bpc66OboawxSqLO5OgpLU6jrDBuH0omUI33tA3VaqVnX4du2E86v+gKWtDe6kpOLYj7+gYsf2KOmXdcTflFSjbvz+O0iMjFLFwPZJIa9J49Rz5SbiQBCOfvsT3KpWhv/ducqi1qAX1Gi3FOcSUvjLydsL+6bMuDsq3SbX56aHQ+ZYZicdujbZtHxJzHu2Lg5ejcEPB0+rSr2Sgvz9M7UxcNVR7LsSrVKWn19xBDOeqo4/BjRSjwu5lYDpuy5gZuea+jmVmoZeLqhTxhmD7xbFMtS9eml82akm1p29jiUnrqlU7mGNfDC/VwD6Lg1S85vNea2xj6qK/cvhy1gfHAl3e2uMaFYJi/oEoPeSQ1kqWr9Urzw2XohCyG3jUSB6eEbtOIUPmlZRAauISU7FzKBQFbBKyrF1iRKqkNbz6w/jeoIuK+HQ9Ris69FIBcgS0EoVaSnYJc8VZ5BGb0gbFZYCX18G6Sr47ldzjCUQ9lXVq+W1xM2kVLyz/dTd1g9cjE3C/E4B6OJbWs17NmdSM3/sunoLmy/dyHZb/1urvHqtxWeuYvPFKJS0tcawOj74oUMdvLzhqHpdKlxkVLVbx0aqOvOdPBReM+Xh7oyvpw3B2KkLcfGK8TQTjQS84tCxELw25gf1+7bdJxAdHY/f/jcC7VvVMUpxfubFTxCfkKxPiT62fSaGD+yIiSZp3sLmbuempFz3GzZTf55xITQc23+fjP49W2Luwi1qSl125LyQ7i+mPxcNDIyLoPDwcHz33Xd45ZVX1KixHBxr1qypfjp06IBu3bph/37jy02IU6dOYdSoURgxYgQ6dswM8LSDrowgO94dPdTkdOAtTiIOHIKTdzm4+BinP8nob1pS1suByGixnVtJ/d/lWjRVVazlEksSvNq5uamAVoJnqVZ9dulKlQvr172LulSTIflbAlbDSwXIc6vnr+4P+9Ke2D/tc4QfOATvls1y3I7QvzfhzOIVcK/uj/ojXoWlTebItGmhMSHFw6QImYwmU+Eho8VONllHc2W0ODxOd/L1ZpOKiIhLwcBVR1QBLbEj9CZWPdcAE9pUQbeFukrTRyNiVZXqMk42arQv9HYiWlQoaTRHWNPFv7S6bWtI1kDi7ea+OHg1Gm/8mTknesfFW9gysAnebemL4WuzjgpKVWwJgiUtesKWYKM5zjsHN1Mj11N2nNffXt3DEZXdHTB994UC7Te6PyQYfHv7KXVJJk8HW1yKTUR6RgbGN6mC6JRUJKSm4UJMoj4oFnJpo8ORMepySBLYSgr1xrAo7Ll2C5YG/TwlYKH+liarXa5rh8l85t1Xb6lgtbpMKYjWdZDsunpTHxSLY1GxiElJU69nTn9/L1R1c0TvtQf1r6+thvwto8GynhIErwu5jmn7M9uhFBJb26MRXq5ZXo2IU+HSvXNjdV6zbM2/BXr8V1MH49TZy/hl8VaVLq2R72D5W4LtuLvp2X9tPmT02A3bj6j/A2pXwv7DuvR8qXqtBcXi0tUbKkW7Xi1dJ7QpLfV7w7bDRp3vUlxMioBpj5PiXE5OWTPWnJ3tERPLjsP7jZdrKhoYGBdBNjY2WLZsGby8vNT8YkPaCK+Hh3EBJalWLYG0BMTyv6GqVXWXBIqMjFTBtUZGlSUwfuutt1CcydzgG8dOolLXp7LcJ/OLEyJ08zU1GenpSIyKQpmGAfprGMvlkSRoNZyPHBt2Cc4VyqugV0akk27cxOZXsu7rjYNfV5dUKtO4Aa4HHVUjuobp3C4Vdemn5q4/rF+njAycXrAUFzdtRdlmjVFnyECj6xenJiSq4F+e27m8t9G2pN9Jg42zcz72GD1oF24loGJJ4yq+chIvl2tafy5SP2/4aESMPigW8ptcy3hggO49ljm9Hfw8sPnCDRVEa2qXdlaBzsnrxnN82/uVwt/nosxeesnb2U5df9hQclq6ugRU1VLGHW6aUg7WKl1bLtVk6EZiKi7cTIC/yePa+3moytWbzQTm9PB0quiJ89HxKiU6NlqXCSDzdKX41qmbcWq01ubuaK8hqxIlVLp1WQdb1PV0UT/PVDau0fBKXR/103nVPly8e3JvY9JBK8W/hBTbuhSnC8pNl1HLWVgg2aSjUdOhoodKud7SO2tn4qHnW+Gbo2FYHnxNdRYFXTcu4HUzOVVdGkrmG1Ph07l9A+zaexrXowpWH0MKZonYC/OzzP19oXdrdOz7Ec6F6rIQbA06l4VcMklI0SwJTmUdJH3a3IhzkkFhLUMXwiLUiK/pc2vzlrXHnT1/DfVqZw2uK1cqgwOHMztyiCgTA+MiSC7DJMW3Zs2ahfj4eDXf2MnJCefOncOcOXPQtGlTNGqkS4sUCQkJqpBWuXLl1IixXOdY64W0trZWgXHbtm0xceJETJgwQf29fv16NSo9bdo0FHdxl6+oitSSemyqVO2aCP1zA1JiYtUlj0TU8ZPqusJyn3r8las4/sMvKCkB7d0CWHJb1PET8Hu6i/q7wcjX1TxmQyd/WaD+r/ny86qYl0UJS5z4eR7KtWiGWi8/r1/uxvFT6n/nCpkBrang5atVUFzxqQ6o1r9Xlp5PCZJPzV+MMg0CjC4fJYF4ekoq3Gv4F2DP0YMihbWGN6qg0o5vJupGdVtXdFcjxjIqLM7fjFeXYrKxtDAKjht6ueLi7SR9SrakUY/bdBYL7l4PWALV5+qUU3OI5XJLGlc7K3X5p2/2XzS7TpIqLZeKMq3oK0G26eWWNFEJKbiVmIom5Uti/tHMysMyb9TXzQFB4cYpsA28XHA8IlYF3PToDK1TAcG34/H+Ll11XCHzb2WEVkZTq7vfxLDaPvB1sUdIjC64lcJVAZ4u+ONCBK4npuC5P4OyPO+iLvVVMLoiOFwtcytZRp/voFMlT6Mq2E+UL4XU9HQcjYxV6f4Hr0erytRfHQ7VVylvUrakasuSwm3O5L3n4Hg3iNEMr+uDGu5OeGvbSUQmpqiR8dvJqWhQ2gXLgnVF4rRLVkl1bhmVpsKnUb3K+OaXvwv8+JbdxmW5TS6TdOjYBUz9ciXOnr+KuPgkNYe5zzPNjV5LLgEltMrRMv/4macao5SbM27c0rWXqn5e8Pfzwi+Ltph9fRldlsC+e6fGmPDZYqTcvbLAEy1rqesia0XDNu08in49WqB6VW992rb8XqNqeXz61aoCbz9lh5WkiwIGxkXUyJEjVaGtpUuXqhTopKQkFfhKNWnTEeFjx47p5xHL5ZgMNWnSBPPmzVOjw/IjgbFcAsrHxwdTpkxBz549UdzFXr6irwhtyqdtaxVwHpg+C5V7dFXXND6zdCU86tbSB9KedeuodOej385FlWefVmnWkjpt7+mJSk+1zzaotbSzVf+7+lbU3+bXtRPOrV6rRnAlWI29dBnnV69Tc5Y96tZWy0il67gr19TloSRYjwm7hJA/N6giXWUbN1CFuwzJPGJJl9bmPMv1kz3r1lbbfX71WpSuX09/3WMqHOYduYKXA7yxoFc9fLknVAWSUgF6S8gNHLx7zeGv9oZheb/6+LVnPcw9dAlpGRnoV8sLDcq5YPgfusuESVC95sx1jG7pqwokRSWm4o0mFVHWyQYj/jROfa7uoUtJlUrX5ny++wJ+6F4H33SrhcXHr8HGsgSGNCiPMk62ePOvzPTqWp5OSLmTroJliWG++DdEFd6KS07DurO6OcavNfFRo4A/HMy8HJSo5uGInWHGabX08C08fRXjm1ZRl2M6EhmjAteuvqXx8d5gNV94wemr6O5XBrPb1sLXR8KQmHZHBcrIyMCvJy+rjAO5nJI5EpBq98lyc46GqYrQEnTLXOAAT2cMqllerYMEzuKroFD89GRd/K9tbfx66jJK2VljZH1fHI2MwbbLN/TzlSWtWgqGSYp32N2A3ZAU4Eo1WbdvjoQhsEkVlda94WIU3Gyt1DxpaZ+ml56iR8/H20NVdDac35tfMgfYlASnN2/FGd03duoCzJ/zFub9bwTmLtqiKlB/+G4/dXmmIyd0c+KnzlqJpzs2wh/zAzFt1ko1f/jDd/vi8tUb+HnxVqMq2pE3YxASdl39LdW0/17yAVb/OgZffrcWpT1c8XHgAOw7FIy1G3XTYJb/8S/ee707fv91DD74ZLG6bfL7/XHs1EUsX5t53Wa6PyxYSbpIYGBchPXo0UP9ZEcKbWnOnMns2TfH3t4eY8eOVT9kLCVGF2hYOWRNm5PAs/GYt3F64TIc/W6uqkYt1ziu1q+XfhmZU9xw1Js4I8t8O1dVnJbA2b93DxWQ5offM11UUHxx8zaErt+orp9coW1rVO7RTT8KHBN6Efs/nanSr71btVAVqOWENCYkTF/J2pCsv3uNaqj8dGf1fJc2b8elLTtg7eSI8m1bo0oP85eOokdHAtp+yw7jwyeq4KvONVXVZyl69fH2zPQ5mTvcd+lhjG7hi6+61NSd8EfGqcftNagoHbjxDAJbVVY/dtaWCLoWo5Y5ZpJG7elgra+Ybc7GCzdUUa+3mlbCD8/UVut0JDwGTy88gNNRmcG0FP+6HJOkXkP8evgKYpLTMLRhBfSp5aUKgu27fBvD1hxXl58yXgebbF+fHh4pZiXZAM9VL4chtSuotGKpBr0+VJdKL9WoB244ooLTsY0rqwJZQZHReHnDWUQYzDvOi3mndO3jpZreeLZKWRU4S7D884nMugdHo2IxZONRvBlQCZ+3rqEKgG29fANfHLygrxztaW+jinFJirQU7cqrxWevITY1DS/VKI/ulcuoEWQZhX5n+0lcic/5kkH08EkAKfJavfperPpzH3oPnoGxbz2LlXPfVa8pl3b6cMZS/TIyqtz22Yn4OPA5/PTla2p+8pZdx/DepHlq1FkjRbXmLduOYaO+VX/vPRSMTv0nq0B70XdvIyExBX/8vR+BUxYg/W6jlmC92/NTMePDgZj9yRCkpt3B5h1H8d5HBSs6RlQcWGTkWDaX6MEZ8W9mbyjRo7S6YDVYiO47t9Lsr6bC4ezYOY96FYiUxIuLCv2eSL6TtajtvbC1bHxfn4/yhiWFiYiIiIiIqFhj1zQREREREVEB8XJNRQMDYyIiIiIiogJjVeqigKnUREREREREVKxxxJiIiIiIiKiAeLmmooGBMRERERERUYExlbooYCo1ERERERERFWscMSYiIiIiIiogC44YFwkMjImIiIiIiAqIl2sqGphKTURERERERMUaR4yJiIiIiIgKjGONRQEDYyIiIiIiogLiHOOigd0bREREREREVKxxxJiIiIiIiKjAeB3jooCBMRERERERUQGxKnXRwFRqIiIiIiIiKtY4YkxERERERFRgHGssCvguEhERERERUbHGEWMiIiIiIqIC4uWaigaLjIyMjEe9EkRERERERESPClOpiYiIiIiIqFhjYExERERERETFGgNjIiIiIiIiKtYYGBMREREREVGxxsCYiIiIiIiIijUGxkRERERERFSsMTAmIiIiIiKiYo2BMRERERERERVrDIyJiIiIiIioWGNgTERERERERMUaA2MiIiIiIiIq1hgYExERERERUbHGwJiI6D67evUq1q1bd1+e68UXX8T777+P4urWrVtYtmzZQ33NvXv3olq1arh8+fJDfd3HheyblStX4uuvv0a7du3wODl48CAOHDjwUF9TPr/yOS7K2CYyyXFD9occR+jhfl8S3SsGxkRE99mYMWOwc+dO7tf74LPPPsOaNWu4Lwuh//73v1i+fDkeJwMGDMDFixcf9WoUWWwTlF/8vqTCxOpRrwAREVF2MjIyuHMKKUdHR/VDxDZBREUBR4yJHjFJuZJRl5dffhl169bFf/7zH8yePdtomW3btqFv376oX7++un/atGlISkrK13Ns3boVzz77rLr/ySefxJdffomUlJSHtp3FhaRM7tu3D6tWrVJpprKPp0+fjlatWqn3T97HXbt2GT3m6NGj6r2T+1u0aIGJEyciMTFRf398fDwCAwPRqFEjNGzYUKVmJiQk5HmdQkNDMXjwYPVYeQ35/cyZM1nSIA0Z3iYps8899xz+97//oWnTpmo9ZH3i4uKMll+wYIHavjp16uDpp5/G5s2b892Ov/rqK7Rt21bdP2rUKLUfZX/KfXl1/vx5DB8+XK2rbPOIESNw5coVo/doxowZGDt2rNqWBg0aqNcy3B7Npk2bUL16daPHi379+uHTTz9FURceHo5XX31VvWetW7fGH3/8ob/PNJV69erV6Nq1q3r/pb1PmTLF6BiTUzu/c+cOfvnlFzz11FPq8fL/okWLckxvN70tt/dVa0PSdvMzPUG265lnnlHHTtneOXPmqPU1TJv9+++/0adPH9SuXVsts2TJErPP9frrr+Oll14yuu3ChQvqOYKDg/E4KO5tQpZ755138NFHH6nna968OT755JNsv0/NpdKb3pbbfsqN7EfJhpD92LhxY7z55psqRTm71G7T23LbJm15Wc9u3bqpz4Icy2VqQn4/K9999x1atmyJ9u3bo2fPnkbfl0SPGgNjokJATrDlC0Lm2bzwwgvq5GL//v3qvo0bN6qTkCeeeEIFKpMmTcKff/6pvsTy+hw7duzAyJEj1RfZ2rVr1cnHX3/9hXffffeRbG9RJvtdTk46d+6sOivkhGv37t3q5Ey+/OV2CdokSBSXLl3CwIEDUbp0aXUyLY+X5eV91mzYsEHdL++/pBbL+//DDz/keZ2krZQpUwYrVqxQ83VLlCiBN954I1/bdezYMRXQz507VwXI0rakTRmSbezevTt+//13tGnTRr3GoUOH8tWOFy5cqIJj6dj58MMP1f6S/WnamZAdCWAlaLWxscGvv/6q1jcyMlJ9JgwDXznh9vDwUO+RdFxIEC+3mZL1dXd3V9ukCQkJweHDh9GrVy8UZWlpaRgyZIia5z1//nzMmjULP/30k9llT58+jfHjx6sTcgkSp06dqvbZjz/+mKd2LifhchItbUYCreeff14FB+bek5zk9L5qbUiCpHHjxuX5+T744APVpiSl/6233lL7QNbXkHTyyOdajqvSZqTtyjabks5JCQSuXbtmFExIQFS1alUUdmwTmcfk69evY/Hixfj444/VeyjttSBy++zkRgLPV155RQXE0kalzUpQLO08P/KyTdLupZ3Ld5mfn59KndfaeV4/K/JYOTZL5/zPP/9s9H1J9KgxlZqoEOjRo4cKKIR86ciXiQQU8kX3/fffqxHe1157Td3v6+ur0ktl5OHcuXOoUqVKrs/x7bffqqC4f//+6n4fHx91QionqtKLW758+Ue27UVNyZIlYW1tDTs7O8TGxqqOCDnBqFGjhrp/0KBB6kRI3h85gV66dKl6jJwMWVnpDslyUhIUFKR/Tul9f/vtt/XvnfS2Hz9+PM/rJHMqZTTG29tbrZu8loxSpaenqyA5LywsLNSJjATYYsKECRg6dKh6HjlB0k76JaARo0ePVgGABFQyApHXdixtWIIEjexHWWdPT888racE1g4ODipIl+BYSKAtoxNysqmtn7yeFpRXqlRJ7VPDfa6R90QL9rV11wIZbZ2Lqn///VeNYkqnhrQ7LQCUY40pOY5IG5E2Vq5cOfUjbdzJyUndn1M7lw4LGQmUUSvJNNDeE3lOaTdynMqrnN5XrQ05Ozurn9xI+5QOKOlU0dqNPOft27dV0C2ZCBoZ9ZQ2JuSzKtkTR44cQYUKFYyeUzqMJHCXwEGCGfkMStsaNmwYHgfFvU1oXFxcVBuwt7eHv7+/CigliCxIZ3Nu+yk3sq+k80o6neQ5pM3JsfrGjRv5Wo+8bJO0UxkxFpMnT8aePXvU+yj7N6+fFRnZNjx2at+X0gFJ9KhxxJioEKhcubLR3/IFnZqaqn4/e/asCiwMNWnSRH9fXp7j5MmT6iRDema1HwmetbRTejBkvwstxU37kVF9bb/Le1irVi39iaFo1qyZGl3VyAmGIVdXV6MU5NzIibr0zEtqsbzvMjIg6cF5DYq1ddCCYqG1ScM2KM9vSLZVuz+v7bhixYq4F/Jcks6qBcXaya8E4oavowXzhp+X7FIXZWRY0tEl0JFgSYIa6QQo6mR/SVvTAiAhHTxyEmtKmyrQu3dvFSBKx8nNmzf1bTendi6dK3KskrR30/YhJ/f5OcHPz/uaG1n/qKgos+sl6yvrbe74qwVY2vHXkGy/pJpqGQgSWMjraMFGYVfc24RhZ6UEkBrZTllfySbJr9z2U27k/ZDMDglUJQVaRmoloyc/00/yuk2Gx3gJaOVYK+9jfj4r93qMJ3qQOGJMVAgYnsSbFh0yV3xIRhmE4QlFTs8hy8sXp6Ram8rrSBzln7b/ZfTItEiRFpQavofZsbS0vKfdLz34nTp1wvbt29WIj4ygfvPNN2rkU0avzKVLmpKTIEPavDHDdTPdFllG2868tmNzJ9j3o1iXvJbhNpj7vGRHRjfq1aunAmLpkJATwMclkLkXMoqlvUeGzLVZW1tb/Pbbb6ozSFKW5Uc6YWQkUUYUc2rnOb1n2b2eYRs0lJ/3NTf5Wa+cjr/mOlpkRFCyPqRNSTAkwc3joLi3ieyOh9p65fVYbXiMzW0/5YVk6EgHrHaMlyBZUrHlGJ/X/ZSXbcruGJ+f9+tej/FEDxJHjIkKOen11eZparTrcJqOEmdH5q5Jr6/01Go/UkBF5qtKYSd6MLQ5gzLH1XDfyxxbrbCVBF1yQmR4oiJpilKIJDk5+Z7XQUZWpKCK9NrLKKektcnJuKyTpDprJ0SG82/DwsKyPI+0H0kN12ipiDVr1jSah2xIlpERoXtpx3Iinh/yOrIehiNCEsjKNuX185JdMCOFuNavX48OHTqotMOiTkYC5T03LAolI+fmipTJCbnMC5f2IOmWcqIv6ZMyjzy3di7vi7RB00I+0j6k406CRu2k3fC1ZV0eJOk0kh9z6yXrYzhqmh+yvTIaJ/ORZQ7045R9UNzbhObEiRNG6y3HOhltlcwUU6bHV9NjbG77KTcyGit1Q0qVKqWKJErHpwTFkpUk03byup/ysk2Gx3g5xspj5Bj/oD4rRA8bA2OiQk5GeiX1VQrTSHAi1aWlN1gq9+b1RF/mgkpRD/nyleeQHmUpCiUnOBwxvv9kdFiKQEnKnrxPctKyZcsWVaRE5mFJVU7tREF6+WV+mCwjJzKSAicdFpJSKCMJ90pOIKXQlxR3OXXqlFoHKa6ipcGJgIAAVZRL7pcTVSkcZDrKIlWw33vvPZU2988//6hgu0uXLmpOm0YKqkjhJGljUgxOKl9rcwEL2o5lvrDMdTNXyMgcOTGUzh6ZFycnhVKtVVIL3dzcVNXXgpLHRkdHqw4Nc5kXRZGkTcpIubzvUmxMTorld3Mp+NKepCibFOCR90pGQ6XdSQCYWzuXuZRSsEdO6GVOvgQNkmUh88WluI90jsicR2kLMr9U5szLdcJlekB+yXPI68u65IVUcJd58rIusl7SvuU4Kuubnzmp5jpa5Hll9EzmvD4u2CZ05PgudTqkLclxTdquzK81TEXWyPFVjkXSISmfDfmcGE7ryO2zkxs5tsn0HEnBlvWR46sUuJJjv6SRa3OP5fgs90vwKoX0TDsd87JNMndZ1k3qQkhxL6kgLvVL7uWzon1fSmc90aPGVGqiQk4uUfHFF1+o1FcJKqRAhaRxGhazyI2k0c6cOVMFZFKISwqeSK+8pF/R/SdFzsaMGaPmEspJhJxMyEmLBFYSEEtBEy24knm7UjlZRnIldU5OZiTgNK3WXFCSwibBuASqUiBITmRk1EcCDC04l0BYfuQER06iJJA0PUnx8vJSj5O0bEmtk4I4pu1HtltO7uSkT+YwS7qo/H8v7Vj2iYwiybJysmY4z9kcKSQnJ2eyP7Xq1BJ4yN/3MsorwZuMFMso++MUyNwLCYDlmCEFkSRAlSBOCkaZXrpKSHE3adfSluVYI8tKoSntEji5tXPpqJMTfCmaJiP8Mr9SPjPaSbfsf3ms3C+Pk3YlnzEp3pYfsh3aaJocC/OyvFbhXIpElS1bVnU0ShBwL6QKr3QMyb6416kSDxPbRGawK/tC5gVL0CeX4DKsC2FIvgek01E+R5JCLe+9dBhqWTe5fXZyI58bOcZ//vnn6vMio76yftJxpBXwkk4oab9SSFCyluTzZlrwLS/bJB2P8l0iVa+l02zevHnqO+NePiuG35fSaf84fR6o6LHIyG5iABER0d1LUMkIhIx655TCLPPhHqe00PyS645KATGtQjhRQcnIYMeOHVU6dV6LLFHhIAGrdA5JUFhctkkqZ8tceEnzNi2ySFSUcMSYiIgoBzK3WEZ8JJ1YRl6ICkquXyzp/ZJuKtWIGRQTERUeDIyJiB5Tkj4nack5kXlgffr0QVHQqFEjs9VUNVJ8RoLY+01Sb2XenqS+Sko5Pf6ksNG4ceNyXEauOZ6fKSt5IXObZXROAmKZf0mPX5t4mKSWg2Tr5ETmJ0s6NhHdO6ZSExE9pmTO8u3bt3NcRoJFbZ7Z406KLuU0+0fmpskcY6LcSIE2mcucE5mTLvM3qXgojG1Crg9seDUAc2SOr7miX0SUfwyMiYiIiIiIqFjj5ZqIiIiIiIioWGNgTERERERERMUaA2MiIiIiIiIq1hgYExERERERUbHGwJiIiOgBGzVqFKpVq4a5c+dyXxMRERVCrEpNRET0AMnlVv7zn//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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "arm_covariate_means = (\n", + " policy_df\n", + " .groupby('arm_name')[covariates]\n", + " .mean()\n", + " .reindex(ARM_LABELS)\n", + ")\n", + "\n", + "plt.figure(figsize=(10, 5))\n", + "sns.heatmap(arm_covariate_means.T, annot=True, fmt='.2f', cmap='YlGnBu', cbar_kws={'label': 'Mean'})\n", + "plt.xlabel('Arm')\n", + "plt.ylabel('Covariate')\n", + "plt.title('Mean customer characteristics by arm')\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "c49e9856", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-09T16:49:48.078246Z", + "iopub.status.busy": "2026-05-09T16:49:48.078155Z", + "iopub.status.idle": "2026-05-09T16:49:48.194453Z", + "shell.execute_reply": "2026-05-09T16:49:48.193850Z" + } + }, + "source": [ + "처치별 고객 특성은 완전히 동일하지 않습니다. `Size` 평균은 `none` 그룹의 약 70,943에서 `discount_plus_support` 그룹의 약 171,467까지 큰 차이를 보이며, `IT Spend` 역시 약 18,056에서 41,371까지 차이가 납니다. 즉, 규모가 큰 고객일수록 기술지원과 할인을 함께 제공받는 경향이 있습니다.\n", + "\n", + "이는 처치 배정이 고객 특성과 독립적이지 않음을 의미합니다. 따라서 이후 정책학습에서는 propensity model과 outcome model을 사용해 이러한 차이를 보정해야 합니다." + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "id": "c925a3d8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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armarm_namecountmeanmedianstdminmax
00none5176585.8917926123.1870673363.163462-616.57245121445.05937
11tech_support_only46215104.11153414483.7193205400.3198584619.49136140166.67407
22discount_only47712247.93595310454.9325007472.178476889.97565341818.39213
33discount_plus_support54426784.12464923560.25289013124.9680835903.90688086006.92445
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" + ], + "text/plain": [ + " arm arm_name count mean median \\\n", + "0 0 none 517 6585.891792 6123.187067 \n", + "1 1 tech_support_only 462 15104.111534 14483.719320 \n", + "2 2 discount_only 477 12247.935953 10454.932500 \n", + "3 3 discount_plus_support 544 26784.124649 23560.252890 \n", + "\n", + " std min max \n", + "0 3363.163462 -616.572451 21445.05937 \n", + "1 5400.319858 4619.491361 40166.67407 \n", + "2 7472.178476 889.975653 41818.39213 \n", + "3 13124.968083 5903.906880 86006.92445 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "arm_revenue = (\n", + " policy_df\n", + " .groupby(['arm', 'arm_name'])[outcome]\n", + " .agg(['count', 'mean', 'median', 'std', 'min', 'max'])\n", + " .reset_index()\n", + ")\n", + "display(arm_revenue)\n", + "\n", + "plt.figure(figsize=(8, 4))\n", + "\n", + "sns.histplot(\n", + " data=policy_df,\n", + " x=outcome,\n", + " hue='arm_name',\n", + " bins=40,\n", + " element='step',\n", + " stat='density',\n", + " common_norm=False\n", + ")\n", + "\n", + "plt.title('Revenue density by arm')\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "639d2437", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-09T16:49:48.195732Z", + "iopub.status.busy": "2026-05-09T16:49:48.195645Z", + "iopub.status.idle": "2026-05-09T16:49:48.262584Z", + "shell.execute_reply": "2026-05-09T16:49:48.262041Z" + } + }, + "source": [ + "Revenue 평균은 `none` 약 6,586, `discount_only` 약 12,248, `tech_support_only` 약 15,104, `discount_plus_support` 약 26,784로 나타납니다.\n", + "\n", + "하지만 이 차이를 그대로 처치 효과로 해석해서는 안 됩니다. 앞서 확인했듯이 규모가 큰 고객일수록 더 강한 개입을 받는 경향이 있기 때문입니다. 즉, Revenue 차이에는 고객 특성의 영향과 실제 처치 효과가 함께 섞여 있습니다.\n", + "\n", + "따라서 단순 평균 비교만으로 정책을 판단하기보다는, 이후 AIPW 기반 정책 평가를 통해 비교해야 합니다." + ] + }, + { + "cell_type": "markdown", + "id": "61b7329f", + "metadata": {}, + "source": [ + "### Positivity Check\n", + "\n", + "정책학습에서는 고객 특성 $X$의 여러 영역에서 각 처치가 함께 관측되어야 합니다. 즉, propensity 분포가 서로 어느 정도 겹쳐(overlap) 있어야 합니다.\n", + "\n", + "multi-class propensity는 다음과 같이 정의합니다.\n", + "\n", + "$$\n", + "e_a(x) = P(A_i = a \\mid X_i=x)\n", + "$$\n", + "\n", + "또한 propensity score가 극단적으로 치우쳐 있지 않은지도 확인합니다. 만약 특정 고객군에서 어떤 처치의 $e_a(x)$ 값이 0에 매우 가까우면, 일부 관측치에 지나치게 큰 가중치가 부여되어 추정이 불안정해질 수 있습니다." + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "id": "9be31313", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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1tech_support_only0.2298630.1265820.1562830.0
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" + ], + "text/plain": [ + " arm mean_propensity min_propensity p01_propensity \\\n", + "0 none 0.263941 0.057600 0.066831 \n", + "1 tech_support_only 0.229863 0.126582 0.156283 \n", + "2 discount_only 0.237934 0.125638 0.141322 \n", + "3 discount_plus_support 0.268263 0.087299 0.101304 \n", + "\n", + " propensity_below_0_05_rate \n", + "0 0.0 \n", + "1 0.0 \n", + "2 0.0 \n", + "3 0.0 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "idx = np.arange(len(policy_df))\n", + "train_idx, test_idx = train_test_split(\n", + " idx,\n", + " test_size=0.5,\n", + " stratify=A,\n", + " random_state=SEED,\n", + ")\n", + "\n", + "X_train = X[train_idx]\n", + "X_test = X[test_idx]\n", + "A_train = A[train_idx]\n", + "A_test = A[test_idx]\n", + "Y_train = Y[train_idx]\n", + "Y_test = Y[test_idx]\n", + "\n", + "multi_propensity = RandomForestClassifier(\n", + " n_estimators=400,\n", + " min_samples_leaf=20,\n", + " random_state=SEED,\n", + " n_jobs=1,\n", + ")\n", + "multi_propensity.fit(X_train, A_train)\n", + "e_hat_raw = multi_propensity.predict_proba(X_test)\n", + "\n", + "propensity_summary = pd.DataFrame({\n", + " 'arm': ARM_LABELS,\n", + " 'mean_propensity': e_hat_raw.mean(axis=0),\n", + " 'min_propensity': e_hat_raw.min(axis=0),\n", + " 'p01_propensity': np.quantile(e_hat_raw, 0.01, axis=0),\n", + " 'propensity_below_0_05_rate': (e_hat_raw < 0.05).mean(axis=0),\n", + "})\n", + "display(propensity_summary)" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "id": "fl52yekx049", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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EJ9NqR6C4uNhM8L3tttsqlpy0aAdJ/+PXJVg13UY7arqco0Un4uokTu2E6zkOtEOhnXLN027IaivamdY0Ce0YWhN79YiyptlYK0JVp/n72nnUzrN2uLSjY5Wl7007PzuigZN2QvV1tfOoR1q1DqxVafTIuU6u1qVddX6EdvD0fel+Wavg6ApUukKOzi/QkQBN69DAqvIKO7XRIEHnImgbaBkbN240AY/+nQZ3ddEO6//93/+ZJTK1zbSudDUd3R8to7H1qx1RnRir9akdfe0gRkqDKR3x0f3W/dPlSTXNxpqgrUHdqlWrTCdUA1ENnLTOdIlci45c6SRvPZ+ABmra4a5My2psXdv1Piw6yV6DzUg+Y7p0ro4iaPCmny0NEGujba91o59dfY6OdOloWuXvpX7/dPRJg30NcPRvtG11vzXY0LQqXcCgKW1ZmX7ntT10n6xRPZ3ArQcMdCnjhtC60uBB61Z/K+pauQpA4zhCzDoCEAN0BRpNYdCjnfHEmiysR7J3dHQYaAxdLlhXIKvvfB0A0JwYgQCARtCj2zp/QI/MaloQwQPspiM2OudHl/7VI/IAECsIIACgETRvXlNA9IRqDU2vACKh58/Q9Co9O7OmWwFArCCFCQAAAEDEWIUJAAAAQMQIIAAAAABELOnmQOjJrHThqYasJw0AAAAkMr/fb5aRHjhw4A6fm3QjEBo8sHJt4+tOzxJM/cUX2i1+0XbxiXaLX7RdfKLdmr+PnHQjENbIQ//+/e0v3O8T+c8D4e3RV4i4EmtN+JKSElm8eLE5A6ue8AfxgXaLX7RdfKLd4hdtF59oN3ssWrQo4ucmXQARVRq15a3avg0AAAAkmKRLYQIAAADQeAQQAAAAACJGChMAAABqKC8vNyvzxDqv11tx7XRybDyS+cApKSnSFAQQAAAAqKK4uFhWr14dFysvBoNBSU1Nlby8PAKICOhSrd26dZPWrVtLYxFAAAAAoMrIgwYPuuJihw4dTIcz1vdXRx88Hk+Tj6wnulAoJPn5+aZ9d91110bXFwGE3TJa2V4kAABAc9G0Je1oavCQnp4e8xWvAYRKS0sjgIiAtuuKFStMOxNAxAK3R+SqKS29FwAAAE0W6yMPaLl2ZaYJAAAAklIszvEIxeA+VUcKEwAAAJrNpEmT5LXXXqv3Ofvvv788++yzUdsHn88nU6dOlT333FNOOukkiRWPPPKIuN1uGTNmjMSymAogHnvsMfniiy+qfGAWL14st912m/zvf/+T7OxsOe+882T06NESk/w+kRemh7dHXCricrf0HgEAAMSUv//973LWWWdV6TT//PPP8tBDD1Xc15QVgiKxfv16+b//+z+54447JJY88MADctlll0msi5kA4vnnn5f7779fBg0aVHHfpk2b5Pzzz5cjjzxSbrrpJvnvf/9rrlu1aiXDhw+XmKNDTiuXbd8GUK81W9ZLqb/M1lpKd6VJbmZHah4AYtTOO+9sLhY9QKxH3ffee+8W3S/EUQCxbt06ueGGG+Tbb7+VHj16VHnspZdeMie7uPnmm836vrvssousXLlSHn/88dgMIAA0KHi44u0bolJjDxx/E0EEAMSxV199Va677jpz4FgPMOuKQTNmzJDevXvLhx9+aEYtli5dKllZWXLcccfJpZdealZhsuhznnrqKZPJon+r5z0YNWqUnHPOOWYJ06FDh5rnTZ482Yx8fPTRRya1Spc4PeaYY+SJJ54woxS77767GaXQVYvuvfdeWbVqley2226mb9qvX7+K15s/f77Zz0WLFpnlZI844giZOHGiCY4qv58XXnhBbr/9djPi0r59exk5cqRccMEF5jl9+vQx17o/evn1118lVrV4APHTTz+ZIOGNN96Qhx9+WP78888qjaE5cBo8WA488ECT6rRhwwZT8Y2dnFJSUiK283nFEwyaTa+WHwgvK5YoSktLq1wjPsRqu23astlcD+s9VHLS29lS5sbSTfLmsrmm7DYp0R3+Tua2Q/1ot/hF24XpORX05Gy6PKq1RGo0ab9ML5Vfy3p9DQJuueUWk5WiB5pff/11ufrqq2XYsGEyduxY02/UjvuSJUvMc9Wnn34q//jHP0zAoNdlZWWm4251+vXy4IMPmr+/5JJL5KijjjKvpfvwww8/mMBBX0PrQf/mwgsvNCsXXX755WZZ2xtvvFGuuuoqmTNnTkV/9W9/+5vpo2qQUVhYaMrX19eD4RrY6PvRyxVXXGHS8fW1Z82aJXfddZcJigYPHmz2ccSIEeYg+emnnx61utdydV/0867Xldsh0hWaWjyA0PQkvdRm7dq1JsqrrGPHcGrCmjVrGh1AaCSqEandHAG/9CkL/0evUWMo1SWJSKNwxJ9Ya7e1ZRvMtX9zmfhsCuj9vnA61PLly6U0bYskilhrO0SGdotftJ2Yg7fagW4OVuddO/qV+2pKU9kPOOAAs62P33PPPXLwwQebjr3ab7/9JDc314xAzJ07Vw499FD55Zdf5MQTT5Qrr7yyojwNGnRU4KuvvjJH+nv16mXu79y5s9nWsnU/tm7dakYcevbsaR7/5ptv5JVXXpFHH33UHNRWo0aNkvvuu8+MVmRmZpp90uBG77POq9C3b18TBLz44oty5plnVpxbQ4ORU045xTxHRzd0pET3W1P4rRGInJwcs125Puyk7RoIBOT333+v8ZimksVFAFEfrbjqb0SHhVRTPtQ64qHRXlRGINLCJ1zpqx8CPS9EAtFIVX9U9UsSDyeWQWy3W/rm1SKrRTrn5krn1h1sKdNdnC+yQcwPf4+23STexWrboX60W/yi7aSij5WXl2f6XJXTgqJFO9165Lvya2lfTe21114V92uHV1PfL7744irZKRpQ6KTrefPmydFHH20eVxoM6G+oph3pYjxKj7hreVZ/Ul/HKl/3Q1OiKqcmWQeutYNvPa/9tgPYupKTBgWatqQjEFaZStPuNTDRfTr33HMr3k/lcvS6Xbt2ppzK713fW7TrXV9D56FU3udly5ZF/vcSw7TytFIrswIHPb16Y+mHtCl/X6fUFBFn+NQapvwECyAs2pGJSv0hqdotrSxNcnwBydy6WdJtOiWNlqVl6m9HLL3XRGs7RIZ2i1/J3nZOp9NctEPd2DMVN7RfppfKr6WvrzQwsO4vKioy1zr6YI1AVKYjAvrcgoICM79Wj+5rud27d69YpMd6Hat8631aj+mIQm37ofdXv8/pdJogRYOSf//73+ZSnXV2bOtvdCGgyuVb29VfM5r1bu2Pfs4rByoNOcFcTAcQOqykeWiVWbc7deokMWlbhAlgBzbny4RVBSKrZthWVTrmMGFb2ZK9fYUPAED809EBpfMTrHQipR14PeBsjQyMHz/ejFY888wzMnDgQJPNoqNLOh/Bbq1atTIdb53XcMIJJ9R4PFFHj2M6gNC8tpkzZ5qcNCsS01w0TU/Q/LCYoyMOk+5t6b0A4sO25Vs39dtPWuXYk260deNqabd4XkXZAIDEoSlB2v/TVZSslYusObMTJkwwE5C1j/j999+beQfW3An12WefmWtr0rBdR/hbt25t5jJowNK/f/8qafg6Ufqwww5rUNq8NVIR62I6gNBZ6DocdO2115oz8v34448mmtQlvQAkhkCrTAm1aW9PWWWFtpQDAIg92unXidHXX3+92dZJ0ZrWpEu6ahCxxx57VMyb0BWS9LZmsyxYsMCcAkBHCqyVtqyUpK+//trMVxgwYECj92vcuHFy0UUXmZWZ9KzW1upRCxcuNCfNa+goi+6vzp3QtKuGpBU1p5gOIDTK1ABCz0R96qmnSocOHcywlW4DAAAguZxxxhkmbUj7h7rCkc5V0TQlXepVz/WgpkyZYm7rRelCFHrwWU8ZoEuuWiMHusKTlqHLvn755ZeN3qfBgwfLk08+ac7doKMOOmFag5enn366wSfH02VlNSDS1Zrefvtt6dKli8QiR0injycRnSmvKg8z2SbgF3l52wSaM8aIJNgyrnruDF3+VlcnSObJZfEmVtvt91+/FHnlXskfdKRkddrFljKL1v0mHeZ/JHL6OOnV5xCJd7Hadqgf7Ra/aLvt6Te6HLamAzXHKkxNpUf8dZ+tCctoXPs2pI8c0yMQcUfz6pb9vH0bAAAASDDxMVMDAAAAQEwggAAAAAAQMQIIAAAAABEjgAAAAAAQMQIIAAAAABEjgAAAAAAQMZZxtZPbI/KvabYWCQAAAMQSAggACWf91o0iBatsLTPdlSa5mR1tLRMAEJktW7bIxIkTzVmaV69eLaNHj5aPPvrI1uqbNi18EPjyyy+P6Pmr69mPPn36yK+//tqo/SguLpYJEybIypUrJT09Xe655x5zNu3qnn32WZkxY4Y5kd4FF1wgZ555prlfn//WW29JZmamuf3Xv/5VzjnnHLETAQSAhJHqDP+kzVz0huQtfcf28h84/iaCCADJy+et+zGnUyTVFdlzHQ4Rl7tBL11YWCi//PKLJIMHHnhA9thjD5k+fbp8/fXXMmnSJJk5c2aV5/z888/y0ksvyaxZsyQYDMqIESNk0KBBsssuu8iPP/4oDz30kOy+++5R20cCCDsF/CKz/xPePmV01S8SgKhr5W5lrk/b/S/iy7ZvtCB/a4G8tvhdKfWX2VYmAMSdO8fX/Vjv3UVGXLr99r2TRfz+2p/bvbfI6Csa9NI333yzrF+/Xi655BK57rrrxOv1ylVXXSVLliyRlJQUufPOO6V3795y5JFHSv/+/U2w8X//93/y7bffmms9Sq+P33TTTZKRkWGuf/jhB/O3+jfWqMOiRYvkrLPOMq916KGHmuepRx99VN544w3z/EMOOcSMEFQfjdD7SkpKZMCAAbW+hxdeeKFGILDnnnvKbbfdVuW+jz/+2OyzOuigg8z7zcvLky5dulR5zrHHHmvei9Ltd955R/7xj3/I4sWL5eGHH5ZVq1bJAQccIFdffbW43Q0L2HaEAMJOwaDI4v+Gt08aaWvRQEvyF+RJ0Gtz53nTeomW9q2yJZTZKWrlAwCa1/XXX2/ShbQjr531jRs3ysiRI2XgwIFy++23y8svvyyTJ082zx08eLA5iv/bb7+ZDrum+aSlpZkj+tqxPvnkk02goAGBBiLXXHONlJaWmr/Nz883HX119NFHm9fQzvuHH35ojva7XC4TbGi5hx12WMX+3XLLLaZcDT5mz54tL774Yo33oKMEetmRdevWSadO2/8P69ixo6xdu7ZKAKHP0UDJos/XkYdNmzaZOtG66Ny5s0n70vd9xRUNC9h2hAACwA6Dhz+mR5YP2hjBFH6GACAuTJxafwpTZePuqD+FqYm0U60dZbXrrruakQaLdb+m/+g8AmtuQCAQkJ122kl23nln8fl8Zl6ABgFXXnmlmWughgwZYoIN1b17d9Mh/+abb2TYsGEVzxk+fLgJEioHEN99952Ze6BOOukkM2rQ2BGIUChU42+d1eq3tuc4HA7Jzs6Wxx57rOI+nRtx7bXXEkAAaF7WyEPbQ4ZLalZ728rdsHWTzPzlHRmSEZ7kBQCIg9Umo/HcRkhNTa3Sca7MCgA0ben444+v6MxrepEGDpr2owGABh1ffPGFGTXQCcm1lasddZ1jUF0gEKhxn9Wp17+r3uFvyAiEjiboSEhubq65rds6mlD9OZpmZdFtfc6KFStk4cKFZjRE6b5r2pXdOA8EgIho8ODK6WLbRdp2kKJU+3/UAACJRzv2tXXa66P5/x988IFs2LDB3L7jjjvMKk7z58+XCy+8UA488ECT4qMTj5cvX15nOfq8N99806Q56T7MmjVL9ttvvyrPOfjgg+XVV18123PnzjWpUY11+OGHm9dQGuRowFM9gNDRj/fee0+2bt1qLu+++665T1OspkyZYlKeNKB57rnnTCqW3cgdAAAAQEzr0KGDdO3aVc4++2y56667Ivqbvn37ymWXXSbnn3++ORKvgYKuaKQd8l69epm0JB2t0NWKNHXpp59+qrWcI444wkxMPv30000AcfDBB5v5GDoPofIcDZ1E/corr8hee+0lrVqFF/VoDJ2voGlHun86+dl6vzpv48EHH5QnnnjCzH8444wzzBKtuk86iqIrNymd/6CpS3r/vvvua96/3Ryh2pKoEphWvqo88cQ2umSZtUKB5glGefiuuenQn36B+vXrVzHrH4nfbt41v8ufT02Q9n+5ODxyYJM1Revl8fnPy7A+QyUnI9uWMh2FG8T9xWtSduplEmrfVeyyZss6eXz+DJly9GTplb2zNBe+c/GJdotftF1YWVmZOSLfs2fPinSgWKapSrrPuq/RSNdJlvZd1IA+MilMAAAAACJGCpOd9KQo1goFDTxBCgAAABAPCCDspKsAJFjaEgAASE5JluWeNEI2tCsBBAAAACroSj66FKkuH6qTl6svkxqLcyCsVY+YA7Hj4EHbVdtU27mxCCDsFPCLvLXtBCEnnCWS2viGAQAAaAnaCe/WrZs547OeVyDW6QpLuuKQLvVa2/kXUJUGD9q+TQm2CCDspCca+fG78PZf/mpr0QAAAM2ldevW5gzPfr8/5itdz8/w+++/mzNMW2eLRt105KGpIzUEEAAAAKhBO5nxkBJknSna4/HExbKziYBxHgAAAAARI4AAAAAAEDECCAAAAAARI4AAAAAAEDECCAAAAAARYxUmO7ncIuNu374NAAAAJBgCCDvpmRpbZdpaJAAAABBLSGECAAAAEDFGIOwU8It88Fp4++hTRVJdthYPAAAAtDRGIOykZ0Kc/3n4su2siAAAAEAiIYAAAAAAEDECCAAAAAARI4AAAAAAEDECCAAAAAARI4AAAAAAEDECCAAAAAAR4zwQdtLzPlx+4/ZtAAAAIMEQQNjJ6RRpm2NrkQAAAEAsIYAAEoy/IE+C3rLtt8vKJKVwrfjXpYs3La3B5fk2rrZ5DwEAQDwjgLBTICDyyZvh7cOHiaRSvWj+4OGP6ZfXuD9LRDZ+3bSyHS5P0woAAAAJIS56uIFAQB5++GGZPXu2bN68WXbffXeZMGGC7L333hJTguUiX88Nbw/5S7xULxKINfLQ9pDhkprV3mx7fX5Zu2aNdM7NFY/b1ejgITWL9DwAABAnPdzp06fLyy+/LFOmTJGddtpJnnjiCRkzZoy8/fbb0rFjx5bePSDmaPDgyulitsvLvBIs8kpqu87iSmMUAQAAJMEyrh9++KEMGzZMBg8eLN27d5dJkybJli1b5L///W9L7xoAAACQVOJiBCInJ0c+/vhjGTlypOTm5sqLL74obrdb+vbt26jyQqGQlJSU2L6f4vOKJxg0m14tP1AuiaS0tLTKNWKPTpi20pZ05EH5vFWvY4XP5zPXfn+gYrupnH6/uM179Un5trqwg9cb3r+ysrLo/HbUge9cfKLd4hdtF59oN3to/9jhcCROAHHttdfKFVdcIUOHDpWUlBRxOp0ybdo02XnnnRtVnt/vl8WLF9u+n46AX/qUhTvXv/76q4QS9FwQK1asaOldQB10tSWdMK1zHjRtqbK1a9fGVL0V+ArD1xs3it9lT2ffU1Ikrba9/9Jie4KSyvu6fPlyKU3bIs2N71x8ot3iF20Xn2i3ptMD9AkTQCxbtkwyMzPNROpOnTqZ+RDjx4+X5557Tvr169fg8lwul/Tu3Ts6IxBp6Wazb58+Im5PwkX4+uXs0aOHpKeH3ydiiy7Vqqst6YRpnfNgjTxo8NC5c2dxe2LnM+kpzhfJF8nOyZHs9La2lOkscoksC7//8pxcsYtb93WDSM+ePaVH227SXPjOxSfaLX7RdvGJdrOvvx2pmA8g1qxZI1dddZU888wzMmjQIHNf//79zZvUUYhHHnmkwWXq8ExGRob9O5uaEj6ZnEi4/AQLICwaPESl/tBk1nkedLWl6hOmNXhIi6FJ1G5f+CiHy5Ua8RGPHXG4wqN+bo9bQo0450VdPP7w/qWlpbXIZ5/vXHyi3eIXbRefaLemiTR9KS4CiIULF5qUIw0aKhswYIB89tlnElM0ZemSa7ZvAwAAAAkm5ldh0rQLa05BZUuWLDGpNDFFRx865IYv20YiAAAAgEQS873cvfbaS/bdd1+ZOHGifPPNNyYH//7775evv/5aLrroopbePQAAACCpxHwKk664pCeS06Bh8uTJUlhYKLvttpuZE6FpTDElEBD58v3w9iHHiKTGfPUCAAAADRIXPdw2bdrIDTfcYC4xLVgu8tk74e2DhsZL9QIAAACJk8IEAAAAIHYQQAAAAACIGAEEAAAAgIgRQAAAAACIGAEEAAAAgIgRQAAAAACIGOuM2inVJXLB+O3bAAAAQIIhgLCT0ynSpbutRQIAAACxhBQmAAAAABFjBMJOgYDId5+Et/c/XCSV6gUAAEBioYdrp2C5yNzXw9uDDqV6AQAAkHBIYQIAAAAQMQIIAAAAABEjgAAAAAAQMQIIAAAAABEjgAAAAAAQMVZhApBwnJvXS9DG8txbCyTHF7CxRAAA4hcBhK216RIZNXb7NoDmte175/74JVuL7SYiE3Rjc75I9s62lg0AQLwhgLCT0ynSY1dbiwQQuVCrNuI7/K8iAb+t1bZ142ppt3ieiL+M5gAAJD0CCAAJF0TYLVBWaHuZAADEKwIIO5WXiyz4Mry9zyEiKSm2Fg8AAAC0NAIIO5UHRN59Obw94AACCAAAACQclnEFAAAAEDECCAAAAAARI4AAAAAAEDECCAAAAAARI4AAAAAAEDECCAAAAAARYxlXO6Wmipx18fZtAAAAIMHQy7WTM0Vk1z1tLRIAAACIJaQwAQAAAIgYIxB2Ki8XWTQvvN1/P85EjXr5C/Ik6C2ztZZ8G1dT6wAAIKoIIOxUHhCZ83x4e/eBBBCoN3j4Y/rlUashh8tD7QMAgKgggABagDXy0PaQ4ZKa1d724CE1K8fWMgEAACwEEEAL0uDBldOFNgAAAHGDAAIAIrR+60aRglW21le6K01yMzvSBgCAuEEAAQA7+qF0hn8qZy56Q/KWvmN7fT1w/E0EEQCAuEEAAQA70MrdylyftvtfxJdt32hB/tYCeW3xu1Lqt3c1LgAAookAAgAi1L5VtoQyO1FfAICkRgBha22migw/f/s2AAAAkGDo5drJmSKy+z62FgkAAADEEmdL7wAAAACA+MEIhJ2C5SK/LAxv9x0QHpEAAAAAEggjEHYKBERmPR2+6DYAAACQYBoVQKxbt06a2+zZs+X444+X/v37ywknnCDvvGP/WuwAAAAAohBAHHHEETJmzBh5++23xefzSbS9/vrrcu2118o555wjb731lgwbNkzGjRsnP/zwQ9RfGwAAAEATA4g77rhDgsGgjB8/XgYPHiw33XSTLFq0SKIhFArJAw88IKNHjzYBxM477yyXXnqpHHzwwfLdd99F5TUBAAAA2DiJ+uSTTzYXTWV67bXXzAjBCy+8IL1795bTTjtNTjrpJGnfvr3YYfny5fLnn3/KiSeeWOX+J598sklBSUlJidjO5xVPMGg2vVp+oFwSSWlpaZVrNJ6/LHzmYa/PL+Vl3qhWpc/rrXIdK6zRS78/0CwjmU3h9PvFberQJ+Xb2s4OXm/4fZeVldX6m8R3Lj7RbvGLtotPtJs9tH/scDiivwpTp06d5JJLLjGXn376SaZMmSJ333233HvvvRVpTgMGDGhyAKH0P9cLLrhAfv75Z+nWrZsZhTjyyCMbVabf75fFixeL3RwBv/QpC3euf/31VwmluiQRrVixoqV3Ie6lFK6VLBFZu2aNBIuap2O/du1aiSUFvsLw9caN4nfZ1ymPBk9JkbTa1l6lxT7b60B/50rTttT5PL5z8Yl2i1+0XXyi3ZrO7dbDZc2wjOv8+fPNCMQHH3wgRUVFcsghh8jhhx8un3zyiYwYMUKuvvpqOe+88xpdfnFxsbmeOHGiXHbZZSZt6r333pO///3v8vTTT8tBBx3U4DJdLpcZLYnKCERautns26ePiNsjiRbh65ezR48ekp4efp9oHP+6dNn4tUjn3FxJbdc5qtWoIw8aPHTu3Fncntj5THqK80XyRbJzciQ7va3EMmeRS2RZuL3Kc3JtK9etdbBBpGfPntKjbbcaj/Odi0+0W/yi7eIT7WaPZcuWRfzcRgUQK1euNEHDG2+8YdKLunbtKqNGjTLpS7m54f9cR44caTr706dPb1IAoZ19paMPp556qtnu16+fGYlobAChwzMZGRliO+2cnTzKbGZkZomkJOZ5IDR4iEr9JRFvWpq59rhd4kprnk69Bg9pzfRakXD7wkc5XK7UiI94tBTHtt8ht8ctoW1tZwePP/y+09LS6v1O8Z2LT7Rb/KLt4hPt1jSRpi81OoA49thjxePxyFFHHSW33HJLnZ34Xr16NXk4SdOk1G677Vblfh1B0FGOmKIBw94HtvReAAAAAFHTqADiX//6l5konZmZWe/zNM1IL02xxx57SKtWrWThwoUyaNCgivuXLFliVmQCAAAAEOPLuOochPXr19f62C+//FJjxaSm0KF9nYz98MMPy5tvvimrVq0yaVFffvmlnH/++RJTguUiS/8Xvug2AAAAkKwjEDpZWpd3Unr+hXnz5klBQUGN53388cfyxx9/2LqTOoqheW333XefWTp2l112kWnTpskBBxwgMSUQEJn5WHh74lQRd2LOgQAAAEDyijiAePnll83EaZ1goRc9eVx1VoChZ4q2m442xNyIAwAAAJBkIg4grrvuOhk+fLgJEs4991y5/vrrayyF6nQ6JSsrS3bddddo7CuAFrKxZLP4Avae7C2/pOYIJgAASKAAQidM77///mb7P//5T8XkZgCJHzw89M3TUSvflZKYJ1wEAECSPYCYPXu2HHbYYdKuXTvJy8szl/qccsopduwfgBZmjTwM7r6/tE2rf+W1xgQPWR57ywQAADESQEyaNEleeuklE0Dodn10jgQBBJBYNHjIychu6d0AAADxEkDMnTtXOnToULENAAAAIPlEHEB07dq11m1LIBCQ4uJiadu2rSStlFSR487Yvg0AAAAkmEadSE6DhYceekjmzJljbn/77bdyyCGHyEEHHWRWaCosLJSklJIist+Q8EW3AQAAgATTqADiwQcfNGeDLioqMrdvvfVWM/IwefJkc6boe+65x+79BAAAABCvAcRbb70l48aNk3POOUd+++03Wbp0qVx66aUyevRoufLKK+Wjjz6SpBQMiqxYGr7oNgAAAJBgGhVArF+/XgYMGGC2P/nkE3MCuSFDhpjbnTt3li1btkhSCvhFnn0wfNFtAAAAIME0KoDo2LGjrF692mzraEO/fv0kOzu8vOMPP/xggggAAAAAiadRAcSwYcPkjjvukAsuuEC+//57GT58uLn/tttuk2nTpsmJJ55o934CAAAAiAGNWmv0n//8p2RkZMi8efPkqquukrPPPtvcv2jRIvnb3/5m5kMAABJLXn6xlHgDtpaZ4UmVLh1a21omACAGAwg90/TFF19sLpXNnDnTrv0CAMRY8HDxlOicRPSxSUMJIgAgjjT6bGc6Ufqbb76RkpISCYVCNR4/5ZRTmrpvAIAYYY08/HXortKhXYYtZeZvKpGX5i61fVQDABCDAcTnn38uY8eOldLS0jpHKAggACDxaPDQlZQjAEhqjQog9ERxvXr1MieO69Spk1nGFTolPUVk6MnbtwEAAIAE06gAQk8e98gjj8igQYPs36N4lpoqcvBRLb0XAAAAQNQ0auigS5cuUlxcbP/eAAAAAEi8AEJXX3r44YcrTiaHbYJBkbyV4YtuAwAAAAmmUSlMc+bMkXXr1snRRx9tzkCdlpZWYxL1hx9+KEkn4Bd5cmp4e+JUEbenpfcISWhjyWbxBXwVt30+nxT4CsVTnC9un7vB5eWXFNi8hwAAIOkCiM6dO5sLgNgLHh765unaH8xvWtmuFFfTCgAAAMkbQNxxxx327wmAJrNGHgZ331/apmWabb8/IAUbN0p2To64XKmNDh6yPOHyAABAcmv0ieSs1Zi+/PJLWb9+vYwaNUr++OMP6du3r7Ru3dq+PQTQYBo85GRkV6Qw+V1lkp3eVtzuhqcwAQAANDmACAaDcv3118usWbPMWah1zsNf/vIXs7TrqlWr5LnnniPFCQAAAEhAjVqFSQMFnUh96623mhEIDSLUhAkTTHBx33332b2fAAAAAOI1gNCRh7Fjx8rw4cOlbdu2Fff369fP3K9BBQAAAIDE06gUpg0bNphgoTadOnWSoqIiSUrOFJEhf9m+DQAAACSYRo1AdO/eXT799NNaH/vuu+/M40kpNVXksOPDF90GAAAAEkyjernnnnuumUTt9/vliCOOMJOoV65cKd9++6089dRTMmnSJPv3FAAAAEB8BhBnnHGGFBQUyPTp02XGjBnmvnHjxonL5ZIxY8bIiBEjJCkFgyIb14W3czqJOBs1wAMAAADErEbn2Vx44YVy4oknmpSl1NRUyczMlAEDBlSZVJ10An6RR28Pb0+cKuL2tPQeAQAAAC0bQLz55psyc+ZMWbhwoQQCAXNfWlqa7LPPPmbk4aijjrJ3DwEgBqwvz5QtG8olFCi1rcwNJQEJlmXYVh4AADEVQJSXl8tVV10l7777rllp6YQTTpD27dubc0CsXbvWjERcfvnlcvLJJ8uUKVOiu9dAgijL3yCBModt5XnLCmwrC9vlFzvkrsJTRV7fKiJ6sdMQWX9ImfQKnzgcAIDECSB0rsP7778v1157rYwcOdJMnK4eYOjIxO233y6DBg2S008/PRr7CyQEb36+uV798iwpL7Ov3E2ZTpH9syRQXCySQY/ULt5A+PfuiAFuadvZvnpdnb9Z5i0oE683aFuZAADETAAxe/ZsOeuss2TUqFG1Pp6SkiLnnHOOLFu2TF577TUCCKAe5V6vuW63z0Bxd+hmX10VrhWRpRIMlFP/UdCutVNyst22lVdYxkILAIAEDiCWL19uUpR25NBDDzXzJIBEUZqXJ+WlZfamLq1bb65TM1uLp32ObeWmlheL2JeiH3X+wkIJ+v22lul0ucTVpo2tZQIAgEYEEKWlpdImgv+U27VrJ1u32p0jDLRc8LDg0h0Hzg2VmiHSYU8RRxKfcFCDh9WvzIpK2d1OH04QAQBAlETce9HJ0pqmtCNOp9M8Nyk5U0QOGrp9G3HPGnnQDqmnY3vbyg1uzZeyxa+Kq3VrSVbWyEPbAQMkpXUrW8osL94qmxcutH1UI97k5RdLiTe8Sp5dVq/bYmt5AID4lbyHP6NBjyYfdUpL7wWiQIOH9C5dbCsvsFmkbLFtxcU1DR7cNqUc+WwpJf6Dh4unzI1a+R4XB0cAINk1KIC48cYbpfUOjpgW6+ovAIAWYY08/HXortKhXYbtwUP7tum2lgkASOAAYr/99jPXO0pPatWqlVnGNSkFgyJFm8LbWe00n6ul9whAktLgoWuH5E2RAwDEQADx7LPPRnE3EkTALzLtxvD2xKkibk9L71HSsXvFpJI/VttWFgAAQCJgDgQSRrRWTFJOgsG4sVkyRQqd4nLYd3K2/GJGEwEAiMsAQs9Fcdppp8m//vUvcw00x4pJGjzYea4GRE9BWaq84D5J5Cu9ZedKTOG8f1dc/WICABAdcfPfod/vl/Hjx0tJSUlL7wqSbMUkROeEb77Nm22vWm95eKTgsF5eyelgz9KwylG6RdJ/my8ZrY6VJF2kGgCA+Asgpk2btsMVoADE1wnflCPF/p+hNulB6ZDlsK08R0pIUp0lLBMLAEC8BBDz5s2TF198UWbPni2HH354k8vTlaSiMpLh84pHV2LSI6FafqBcEomejbzydawpKwunMHl9fnF4vRLLyrcdzfcHAhXbdgiUhz9z5YFy8fnCZ0Xw+6teN5S/JNzemXvuadsJ3ypo8JDmFr8uQGCD4LbvX7A8aFuZyhkImB/LgM8v5dvq1Q7a/srn89b6m9SY75z1PdD2L/Pat6BANFifUd3nRBpdjvXfStSNtotPtJs9tH/scDgSI4AoKiqSq6++Wq677jrJzc21LR1q8WL7z+LlCPilT1n4P4xff/1VQqkuSUQrVqyQWBRcs9Zcr12zRhy+2A4gUsoKpJ2IbNy4UYJb7OvoFhUXmnT9zUWFEnKE68NSUFDQqDJDReEzEG/V4GRbh9c2Wp6NndytxeEEo+KtW8VVYN9nwOUtkTQR2ZK3SvyNrMfaeE3bZ8uff+bJ4mDAlu9cXkG4U75mzRrxbY3tn/iNRYGK+W3eQrckmlj9rcSO0XbxiXZrOrc7st/i2P7fZdvJ6wYOHCgnnniibWW6XC7p3bu32C7gl9S1R5rNvv36iSRYAKERvn45e/ToIenpsXcyqZK0dPlVRDrn5kpabmeJZeVFHilbIZKTkyPOVtm2lRvI95nTMbfNaiMdO3WuGHnQ4CE7O1tcroZ30vypblmvpzZpkyWpWZkSy0q2HdFu3aqVtMu2cw5EmsifIp3++FHs5C7PlO/kYOnaJkP66W+GDd85T16RiKw3B1xy29t7Ijm7uTfoqMNm6dmzp/TqkiWJItZ/K1E32i4+0W72WLZsWcTPjekAQlOW5s+fL3PmzLG1XB2eyciI0n+sJ480V4l3LG07/Q8xavXXBME0PUYs4nG7JM0T2+fgCLhcosfdXampkuKyL9BMTUkx1ympKTWOImjwEOmRhcpC25YeSklJEVeMB8VOZ/iItjPFae++ZraRwN5HaG6YvQHPukKR30TcTqn3O9WQ71xaWjiI0rZO84S/E7HK7Q7XZ1paWkz+piTqbyV2jLaLT7Rb00SavhTzAcSsWbNMikf1eQ833HCDvP322/Lvf/+7xfYNQHIJpbWyfyTKHdupds1l9bpwmpydMjyp0oUzcQNAVMR0ADF16tSKCYGWY445RsaOHSsnnXSSxJxQSKSkOLyd0VpDuZbeIySpwJZi8W7YEN72B8w8Bk1FskYTWnq5VVRVULpZfi9YVaNa9PdvbdkGSd+8WtLKIhtN+LMwfiYje1zhEbN7ZiyISvmPTRpKEAEAyRZAdOrUqdb7NW+8rsdalK5yc+814e2JU0U4ezGamTM13CHb9P33EiiteiZmnccQa8utJrsUR/i8Fe8v/Uxe2Phu3U9cHXmZwa06l+BgKSwrkq4S20tft2+bLuNG7CNev70r1uVvKpGX5i6VEq/Nk/4BAAY9AiCBpOq5UraItB0wQNqmhCdvlpeXS1FhkZkErfMYGhs8uOxewhXiSQ3PSTmy18HSrnfHGjXi9frMqmK6MIDHE9n8lV9XFci7P4n4yu098V80gwgAQHyJuwBCl0cFsONAwu0OH30250PQ8xhkZcb8JOhk1Ta9jeRm1hxVLXOVic9dIp1bdzATjSOxPl2PuutKTAAAREfcBRBAc1vn3SxlQXuP5jq9m4S1WWBZvzkoobU1Tzrm9flkw+aQpKZ7xeMOn+NiRwo2JdYJLAEAsYcAAthB8HDdb8/bXkddyvwyVo8TB0qkneTQBklq2xximflpqcin9Z10LK/BZbtd4fkVAADYjQACqIc18nB8zr6S47LvJGrezatFVn8l/hBHi5NZ2/SQjGz1uZTsfbSEWret8bjf75eCjRslOyfHnAAzEjp5+vM/vpa2WVE4WSYAAAQQQGQ0eOjkqdnBa6zC1E1UvYhsLHGKN2DfcscbyuJvjkdbZ4lktHFKqE3NSdJ6Yu1yr0Ny2rkiPgmgsyRFHOs4vwQAIHoYgbCT0ymy1/7btwHUGzw88JV9QVltqUEAAMB+BBC21qZL5ORRthYJJCpr5GFIjxJpk1b1nBWNFSgtFe+yJdImbaAt5QEAgJoIIAC0KA0e2mfYE0D4QwEpkvg5EzMAAPGIAMJOoVD4bNTK5RZx2JfbnWhK8/KkvLTM1jJL/mjA6XoBAADQKAQQdtLg4c7x4e2JU0XcHluLT6TgYcGll0etfCf1DgAAEDUEEGh21shDt9OHi6dje9uDB097zqsAAAAQLQQQaDEaPKR36UILAAAAxBHWGgUAAAAQMQIIAAAAABEjgAAAAAAQMQIIAAAAABFjErWdnE6Rfntv3wYAAAASDAGErbXpEjn9AluLBAAAAGIJh8kBAAAARIwAAgAAAEDESGGyk88rcuf48PbEqSJujzSnvPxiKfEGbC0zw5MqXTq0trVMAAAAxC8CiAShwcPFU+ZGpezHJg1N6iAixxeQtOJNkmJjcOYpKbKtLNTkLy62vVocqamS2qoV1Q0ASHoEEAnCGnn469BdpUO7DFvKzN9UIi/NXWr7qEY8cWzdLBNWFYis+sDWcttZG06+gnZyOFPMdeHChRINHQ47jCACAJD06L0kGA0euibxaIHdHOU+c72+Wz9Jb93etnKL/CXy+ZYlMjg1JCHvZtvK3RjYIsksNT1N2uw1QELBclvLDZaWSfFvyyQUSN5gGgAACwEEEAG/J0M8GW1sq6tQwC1FpSnydv73Ual/17Yj8ckaRNjNb3uJAADELwIIoAVkpqbL8e33Fb/NR8qt4EHLBwAAiAYCCKCF0MkHAADxiADCTk6nSO/dt28DAAAACYYAwtbadImMuNTWItEw67ybpSxoX8Z6gb9IdqYREKXlYR1lxfwIAwDiDgEEEip4uO63520ts0uZX8bqF8XBiFIyi9bysK4UkfQ2IoHiYklpY98qXwAARBMBBBKGNfJwfM6+kuPKtKVMPYGcrP5AMlLTJGhLiYhH0Voe1rFls8iWVRLyszxsNKxeZ/+yxhme1KQ+sSYAKAIIO/m8IvdODm+Pu0PE7eFT1gI0eOjkaWtLWXaefRrxLRrLwwb9pSLJfeqOqPDo0I6I3DNjQVTKf2zSUIIIAEmNAMJuflaMB4CW1L5tuowbsY94/faOGOVvKpGX5i6VEg4sAEhyBBAAdmhjiVO8AYetNZW/NXlPdofmCSIAANFBAAFgh8HDA1/ZkxJWGxfz0wEAiCsEEADqZY08DOlRIm3SgrYHD3aXCQAAoosAAkBEtKPfPoPOPgAAyY7kAQAAAAARYwTCTg6HSPfe27frkJdfbPsqHtFY7xwAAACojgDCTi63yOgr6n2KBg8XT5kr0V7/HAAAAIgGAohmZo08/HXortKhXYbtwQNLFwIAACCaCCBaiAYPXTu0bqmXBwAAABqFSdR28nlF7pkUvug2AAAAkGAYgbBbyVbbiwQAAABiBSMQAAAAABIrgNi8ebNcf/31MmTIENlnn31kxIgRMn/+/JbeLQAAACDpxEUAMW7cOPnhhx/k3nvvlVmzZkm/fv3kggsukN9//72ldw0AAABIKjEfQKxcuVK+/PJLufHGG2XQoEHSs2dP+de//iUdO3aUOXPmtPTuAQAAAEkl5idRt2vXTh5//HHp379/xX0Oh8NcioqKGlVmKBSSkpISsZ3PK55g0Gx6tfxAeY2nlJWVhZ/q80mZN7wdq3QfrX3W+iotLTW3revGsurA6/OLw2vfalU+f3h//QG/+Jzh7aZyBfzmOhAISPm27Vi3sSRFfOXbz4ReHnDK1hKPlDickpIaanB5G7Z9VcrLyyVQXvMzjSbY9nsRCJRLcNv3rbKAz1/lOhI+vz9ufmPiTfXfxLrY9VuJ5kfbxSfazR7aP9b+dUIEEFlZWXLYYYdVue+9994zIxPXXHNNo8r0+/2yePFisZujPCDd0zPN9solSySUUrN68wrC/wGtWbNGfFtju/o3FoVPerd8+XLxFror7l+xYkWTyg2uWWuu165ZIw4bl7tdFwoHlBs3FkjQYU/HKb2sUDqImGDVWxb7nefNXpc8/+sutTyi76JpfGVbZUt5+DMBe7hKS6WNiBQWFYp/7bo6n1dQUBBxmUWBrRW/MV53FA6UJLG6fhPr0tTfSrQc2i4+0W5N53bv+LdNxXYPthYLFiyQyZMnyzHHHCOHH354o8pwuVzSu3dviYo9wyMlfet42JOnndz1kpubK7nt7T0Ttd3c5tDzZnG37iieNq3E6/VKXl6edOnSRTweT6PLLSt2SIErU/bKzZW03M627a/Tu0FktUhOTrZ0dGu3rOlcW1JFVoUD2fKMLIl1Xt1fETlk52Jp4wkHPMFg0BwxTUtLE6ezcVmLrpSQZHnSbN1XaOP4REpE2mS1EWfnTjWqREceNHjIzs6WVLcroipLLd0sUijmN6Zz66YHjqj5m6iptL26ZNV7NFQ7Mj169JD09HSqMI7QdvGJdrPHsmXLIn5uXAUQH374oYwfP96sxDR16tRGl6PDMxkZLdN5T0vzVUR4aTHeIctsFU53eeiV/1V7ZH3TC+9+qtzqT5EeTQhEqnMHw1GzK9UlbldkEfSOpKSGO22pqani3LYdy1JTUsx1doZI+4zwMGSgPCRbQn7JbO2R1JTIhiZrcsTDlKm4E9wW0KWmpkhqPUd9NHiI9KiQO+CKm9+YeON2h0cgNBiP5P8QDR5a6v8aNA1tF59ot6aJNH0prgKI5557Tm677TY57rjj5M4774z4P1M0Xvu26TJuxD7i9ZdX5P9qWoQe2WxK/f/5e57MXpAvcZARBAAAgHgMIGbMmCG33HKLjBo1Sq699toGRUjNSifxTr8tvH3ptSI2HQVv6SDCohMydd6Gpl415cimNz/2j+QDzcm/pViC+Rtr3h/wS3DLFvG7XCIRjoD5fI1bXAIAgIQJIHSy2u233y5HH320XHzxxbJhw4aKx3QYOTMzPGk5JoRCIoUF27cBoD6OcArT5gULpMz/Q51Pa0jSYHGGU6RfK/EXbhbJrDmvAgCAhA8gdMUlXTXpgw8+MJfKTj31VJkyZUqL7RsANEVKWngOUNsBe0nAU/NgiC6bu6WoSDKzsirmt+xQsR7E+FOCPlbMAgAkaQBxySWXmAsAJKqU1pniqGWVL0cgoCeJEFdWppnIH1FZwVIRe06DAgBAfAYQaFnejRsk6PVVTKIObSwQr9sjoSZMovYVbLJxD4H4l1K2pfb7ywPi9m2VlNIUcdZyXpnaeLxbJauWk1jCPqvX1d5eFl02Wc/5o8t2Wyvv1SfDkypdOrS2cQ8BILoIIFBv8LDkvgdr3L+qiXWm54CQjgeLUyeGAsls2xyIjBV1z39o28BJEJoItbMG6l3r7+Si4TyucBrZPTMWRPgXkTfcY5OGEkQAiBsEEKiTNfLQYcgQcbdrIz5/oOKkVm5X4z86qVuDIosD4mprukZA0gq506Wk+94ioWCtj5eXl0vJ1q2S0aqVpEQ4B6KsMF+yC9eIcNbwqC9tXZeGLHmdv6lEXpq7VEq8zFkBED8IIOyky8t22HZm5VhdarYRNHhw57Q3y9Q6An5x52gA0fgUJpdLA5Ptq2kByR5E1CVYHhC/LyhBT0bEKUyB1EIb9w71LW1dF7uWvAaAWEUAYSftVF9yra1FAgAAALEknIALAAAAABEggAAAAAAQMVKY7OT3iTx5d3j7ggnhlCYAAAAggRBA2CkUEslfu30bAAAASDCkMAEAAACIGAEEAAAAgIgRQAAAAACIGAEEAAAAgIgxiRotJm+znpHaPmvKyiVYlmFrmQAAAKiKAMJODodIm+zt26iVOyU88DX98/VRqKEhUtjeK508sV/5G0uc4g3Y+znJ35pia3kA0BxK8/KkvLTM9nJT0tMkvUsX28sFkh0BhJ30vA9jb7K1yESU3SpVLhncUXzlQVvL/W1zkXy62Cf+gMRF8PDAV22jVr6L5EQAcRQ8LLj08qiVv8/0aQQRgM0IINBiQYTdNvrjp9dsjTwM6VEibdKCtgcPdpcJANFijTx0O324eDq2t61c7/oNsvqVWVEZ2QCSHQEE0IK0o98+g84+AGjwkMzpRqRxIZ4QQNjJ7xP5zwPh7dFXhFOaAAAA6kEaF+INAYSdQiGRvFXbtwEAQIsq+WN1xM8tKyuT4Jq1UpKWLsG0tGabmE0aF+INAQSwA+vLM2VTsVuCAftWOGK1JERboKjIHNW0k9PjFk+OfTnqQDQ53eHl+Jbety0zoAF+baGJ2cmexoX4QQAB1GPjVodMKzxV5MfoVBOrJcF2zvBiAqsWzZc1X8yztejU8pDsftpZ4m6TZVuZBCWIFk/7HNn1n2Ml6PNG/Dden1/WrlkjnXNzxeN21XycidmAQQAB1MNbHl4t6bCumyQrs+Z/Jk3BakmIBte2o64/90qXDe4o/MS/OlNal9o78X+3K8cysoGoBREN4fB6xeHzSlpuZ0nzxMEJhYAWQgABRKCNJyA5GZykDbHPkxJevOHANn3El55pW7lFgRL5pmiJtBt6mHRMaW1Lmb5NhZL/2WcS9Np7Vvpo8m7csMP99fl8EtpYIF63R0Lu+hfT8G6O/Og4AMQKAggASECZqRkSdLWyvVxX27bidkfvJIixHjwsue/BiJ+/bUmNehW4MkU6Hize/HyRbslZr4g/LDkLAgi7Zdj/HzYAoOVZIw8dhgwRd7s2dT7P5w9IQUGBZGdni9tV/3+zobVFIqu3lw3EOpachSKAsJPmHl81hU8WAMRBqlFDla3PN9caPLjrW43K7xNHwC/uHA0g6k9hcm3V+SQB+XNTmaSv3mzr/mZ4UqVLB3vSzdD4pWFborxoYslZKAIIJJxNxQ5Z47ZnkmdBcXhFGwCJm2rUUI5U+xZUcKeEF2p4+MPVInqx2WOThhJExMjSsA0pPx5Ea8nZxgRT9Z2/Ixrn7QABBBKIa9sc548XuOVjsevIY/iHyOXkxIBAoqUaNTZ4cLWxr8x2aQ4ZtvZz6XzmmeLp0MG2cvM3lchLc5dKiTdgW5lo3NKwDQkeGrpqVCKxIzj7tZnP25HMGIGwk98n8sL08PaIS3U9RVuLR/3aZDjF1WuhHNh6d8lMTbelupxlxdJ25Txxe3oLIQQQX3aYahQjMstLJLetR9JJN4oL8djJtzNFKlrpVk0Jzuo6fwfn7YgeAgg7hUIiK5dt30azc7i90qZ1SNq5wmkBTeVMCUmms0RKbCkNAOqfX2EXlodFtFOuopFu1djgjPN3ND8CCAAAWog1n2L1K7NsLddaHrbwfz9JsTfXtnLJJ48v0Uq5ird0q2iMmqQk+dwKAggAAFqIzqfoNvw0CQX8tpbr2FAiskJkxVPPSJm3wNayySePL/HU0Y+3Se/7JPHcCgIIAEBCpe/YXV602Tkp25Lm8oms2CDdzhgu3bPsWU2OfHLEm2iNwHjXbzCjhtaStsmIAAIAElBK2RZby3MHSiTLXx4X6TvVy09murJTeo69uerJfA4ExJ9kHoGJJgIIAEgkjvDR5owVP9habKaInKVH9zsUi7jbxnT6TjSWWwXnQACwHQGE3Vwc8WpJeoTUXbpFnDYdKbX7KC4QbSF3upR031skZM/JFC2+0kJpm79SHEF7zytAJz9+cA4EABYCCDvpZJ1J99paJCLnKC2Ws9YXiayfH7WjukC8BBF2CwTCub6Osi1SXrzBtnIdKS5xpjNSEC15m+06qWZYujtTOpMSAiQ9AggkDOvI6OYO3cVtZ4fE4YxKhwyIJ0Fn+FTv7uXzZavNZbceeBpBhM3cKeGDHtM/X2930TL1tJ2kcxYnSgWSGQFEAvFu3CBBry8uVjLZ5C8WX8jeVIgtga3SUY+Uujzi8rSytWwg2ZW7PPJR2wwZ3GY3yUrJsKXMkK9YvKt/lFC5/XMgkl12q1S5ZHBH8ZXbl8q2oTggbyzaLKV+TpQKJPv5JQgg7KQTAV/+d3j7jDEizbgCiAYPS+57MC5WMtHg4cm8D8Vu7X0BOU2/fKQbAVFRkuKUkKe1pLha21KevWs6obYgAoD9nJxfggDCVsGgyLKft283I2vkocOQIeJu1yamVzKxRh4OzNpNslLtOZKpdPK05M8XT4pbOD4GREdReYltZTnKS4REmPhj97wKle5ykBaFuOHh/BIEEIlGgwd3TnuJBxo8tHPZl2pk18pLAGpK3Tay9+3mJbZVT1agXIaYoKRU2lHpST2vQjG3AvHEk+SLCTC+CQDYoYwUjxzQZjcJ2Lg8bHnpZpHCFRIIEvwn67wKxdwKIP7ERQARDAbloYcekpdfflm2bNki++23n1x//fWy0047tfSuIUbO16A4ZwMQ/SDCTqVO+9Kh0DyYVwEgbgKIRx55RGbMmCFTpkyRzp07y9133y1jxoyROXPmiNtNBm28ier5GswLcM4GAACApA0gfD6fPPXUUzJ+/Hg5/PDDzX333XefHHroofL+++/LsGHDWnoXESvnazCFc84GIN7YfXI6UyYnqIs7TM4G4ocjFArF9II1P/74o5xxxhny7rvvSs+ePSvuHzFihOy2225y0003Nai8BQsWiL5llysKS6yGQuIo3BTebNNOxOGo8RR/ICibtnjF4wyIw2Fj1Ye2rfzkdIrUfFl7X6ep5YdCkhIKSrmOFNRSR4jRdkPLSOS22/ZbEC1BR4qEWqruErndbBYKOcQbjN7xTP3/1hnh/7ehSm1H88WPRGm3UEjEWR6QVllZ4kht/mP8fr9fHA6H7LPPPvE/ArF27VpznZubW+X+jh07VjzWEFoxla9tpWW2C8/Kr6v0UHl4+buo/Vg27+qxTRPToSuAuMdvDKL5/y0QDQ6XuMsD4onGge4dvbTDEXH/OOa/VaWlpea6+lwHj8cjhYWFDS5v4MCBtu0bAAAAkGxifrZpWlpaxVyIyrxer6Snp7fQXgEAAADJKeYDCCt1af36qieu0dudOnVqob0CAAAAklPMBxB9+/aV1q1by7fffltxX1FRkfz888/mfBAAAAAAmk/Mz4HQuQ8jR46UqVOnSnZ2tnTt2tWcB0LPB3HMMce09O4BAAAASSXmAwg1duxYCQQCct1110lZWZkZeXjyySejsxQrAAAAgPg9DwQAAACA2BHzcyAAAAAAxA4CCAAAAAARI4AAAAAAEDECCAAAAAARI4AAAAAAEDECCAAAAAARI4BAhWAwKA8++KAceuihsvfee8uFF14of/zxR0R/N2bMGJk2bRq1GQfttnTpUrnooovkgAMOkIMOOsicZyUvL69Z9xmNa7uffvpJzj33XBk4cKAceOCBcv3118uWLVuozjj5rVRvvPGG9OnTR1avXh31/UTT285qr+oX2i+2283v98s999xT8Xw9IfHixYubdZ8THQEEKjzyyCMyY8YMueWWW2TmzJkVgYHP56uzlvSxa665Rj7//HNqMg7abdOmTXL++edLWlqaPPvss/LEE09IQUGBeb7X622R/U9mDWm7DRs2mLbr2rWrvPrqq+Zvv//+e5k0aVKL7Hsya8xvpfrzzz/l5ptvbrb9RNPb7tdff5X9999fvvjiiyqX3NxcqjeG2+3GG280v5O33367zJo1S7Kzs03QwQEXG+mJ5ACv1xsaOHBg6Pnnn6+ojMLCwtBee+0VmjNnTq0V9P3334dOOOGE0NChQ0ODBg0KPfjgg1RkjLfbSy+9ZJ5fWlpacV9eXl5ot912C3311VfNtt9oeNv997//DV155ZUhv99fcd8zzzwTGjBgANUZ47+Vqry8PDRixIjQ6NGjzfftjz/+aKY9RlPabsyYMaFbbrmFSoyjdlu1alWoT58+oY8//rjK84844gj+n7MRIxAwfvnlF9m6datJabFkZWXJ7rvvLvPmzau1lj799FMzPDh79mzJzMykJuOg3fR5eiRHRyAsTmf4Z6CoqKiZ9hqNabsBAwbIvffeK6mpqeb2b7/9Jq+//roccsghVGiM/1aqRx991KRVXHzxxc20p7Cj7XQEYpdddqEy46jdvvzyS9MnGTJkSJXnf/TRR1XKQNOE/ydC0lu7dq2pg+rDsh07dqx4rLorr7wy6est3tqtW7du5lLZ448/bgKK/fbbL8p7i6Z+5yzHHnusrFixwqQzPfTQQ1RsjLfbjz/+KE899ZS88sorsm7dumbZTzS97QoLC017zZ8/36TPaAroXnvtJRMmTJCePXtSxTHabsuXL5eddtpJ3n//ffP/m7ahBhua7kkwaB9GIGCUlpaaa7fbXaVGPB4PufEJ3G46D+K5556T8ePHmxxRxEfbTZ061bRdTk6OjB492hydQ2y2W0lJifl+6aVHjx40Uxy1nS44oUKhkNxxxx1y//33m+edffbZZk4SYrPdiouLZeXKlWa0fdy4cTJ9+nQzcqvttnHjRprNJgQQMKyUluoTkvTLmZ6eTi0lWLvpf4j6n+Gtt94ql156qYwaNSrq+wr7vnP9+/c3Ezt19EFXg/nggw+o3hhtN/2O6dHqs846izaKs7YbNGiQfP3112Y1nz333NPc1u+cTuDVCbqIzXbTYEGDiPvuu08GDx5sRo10W7322mvNtNeJjwACVYYG169fX6VG9HanTp2opQRqN83D1iF4zcmePHmy/POf/2yWfUXT2u7333+XTz75pMp9+ry2bduSFhPD7aYrwHz11Vdm6V296EowatiwYeY7iNj+vdSRWYfDUXFbO6yaBkoqWuy2W+fOnU0QUTldSYMQTWti+V37EEDA6Nu3r7Ru3Vq+/fbbihrRSbU///wzufEJ1m5XX321vPvuu+ao2nnnndeMe4umtJ12QvWcHZUnu69atcrkZZPXG7vtpnnYb775pllsQi86IqE0N5tRidhuuxdffNGcL0fT0Cx6ZFvnH/Xu3bvZ9jvZNbTd9L5AICCLFi2quK+srMycN6J79+7Ntt+JjknUqMgt1BOtaG61HnHRyZl33323ieSPOeYYKS8vN+cL0JUNKq/gg/hqNx12f/vtt00QoSkw+fn5FWXRtrHddnrEWjudOnqk+fQ6wVM7ozo8f8QRRzTz3ievhrZb9Q6LNemzS5cuZvQIsdt2uoqPPld/L6+44grTCdWV0PRvTzvtNJouRttNU80OPvhgmThxojnvin7P9CR0KSkpcvLJJ9NudrFzTVjEt0AgELrrrrtCBx54YGjvvfcOXXjhhRVrleu1rl0+a9asWv9W11fmPBCx327nn3++uV3bpa62RWy0nfr9999DF110UWjfffcN7b///qHJkyeb9c0RP7+V33zzDeeBiKO2+9///md+N/U7t88++4Quv/xyc+4cxHa7bdmyJXTDDTeEDjjgAHOuHG3DpUuX0mw2cug/tkUjAAAAABIacyAAAAAARIwAAgAAAEDECCAAAAAARIwAAgAAAEDECCAAAAAARIwAAgAAAEDECCAAAAAARIwAAgAQczhFEQDErtSW3gEAQNioUaPku+++q1IdLpdL2rdvL0cccYT885//lDZt2iRcdR155JGy//77y5QpU8ztRx55RNxut4wZM6aldw0AUAsCCACIIbvvvrvccMMNFbf9fr/89NNPcu+998rixYvlhRdeEIfDIYnkoYcektatW1fcfuCBB+Syyy5r0X0CANSNAAIAYoh2pPfee+8q9+23336ydetWefDBB2XhwoU1Hk+EoAkAED+YAwEAcWDPPfc013l5eRXpTuPHj5exY8eagOL8888392/ZskXuuOMOOeqoo6R///4ybNgweeWVV2qkDN13331y++23m+DkgAMOkKuvvlo2b95c5Xnz58+XkSNHyoABA0yK0cSJE6WgoKDi8VdffdV0/jWoOfPMM83raarVk08+WaWcN998U0466STZa6+95MADDzT7vW7duir7M2nSJLPdp0+filEJ3V66dKm5fvHFF6uUuWbNGunXr5+88cYbtdZXWVmZ3HjjjTJkyBBTd8cdd1yN/Vq/fr15TwcddJAMHDjQvNcffvih4nGv1ysPP/yw+Vt9b8ccc4w8/vjjEgwGK55TVzvo3951111y2GGHmdc/8cQT5e23366nhQEgfhBAAEAcWL58ubneaaedKu575513pFWrVjJ9+nQzX0A7zWeffbbMmTPH3Na5BPvuu69ce+218uijj1Ypb8aMGbJgwQITbFx11VXy6aefysUXX1wxeXnevHly3nnnSVpamtx///1yzTXXmPkZo0ePNq9j0c60zs04/vjjTed6n332MR3nzz//3Dz+/fffm+BEO99PPPGETJ48Wb755hvzmrWxAoXTTz/dbO+6664mgHn99derPG/27NmSkZFhyq2NBkefffaZCRA0cBg6dKjZr1mzZpnHdURnxIgR8u2338qECRNMwOLxeORvf/ubrFixwtTDJZdcIv/+97/ljDPOMPWngYTWReUUs9raQf/2H//4h8ycOdMEFHq/BihXXnml2W8AiHekMAFADNHOZyAQqLhdWFhoOu5WJ9QaibAmWN90001mwrEVFCxZssR0XPW56tBDDzXlaTBx1llnSdu2bc39TqdTnn76acnMzDS3s7OzTadXO/561P6ee+6Rnj17ymOPPSYpKSnmOdqRP+GEE0wn/JxzzqnY37///e+mk600YPnggw/kk08+Ma+tAYQGIRdddFHFfuo+LFq0yPxt9fkcVnpW586dK7aHDx9uOu1//PFHRQClHXHdFy27NlpnhxxyiHmO0lEWDThycnLM7ddee03+/PNPc60jGUqDn1NOOcUETytXrpSvvvrKzD2xytDy9PV0joYGUhrc1NYOX375palHHeXRwMpqh9LSUpk6daoZFUpN5b9fAPGLEQgAiCHaed1jjz0qLgcffLCMGzfOBA7aqa/c4e7Vq1dFp9XqNHft2rUieLBo+pCm1GiqUeW0ISt4sG5rp1ZfXzu6+lxNv7ECGr1o532XXXYxHeTKKr+e7o8GIyUlJea2pkhpedpp1v3XtKjBgwebSdKRTga3AgVrFEJHTnSU4NRTT63zbzRgeOmll+TCCy+U5557zgQfGiAdfvjh5nENbLp161YRPKj09HR57733TDCkdan1oaMO1evSquu62uHrr782703rz6o7vWgd5+fnm7QsAIhnHAIBgBiiQYMezVbaCdW0mtzc3CqrFFk0baYyHa3o0KFDjefpMrCqqKio4r5OnTpVeY6OSLRr186Uoc/T1CRNOdJLdbpPlVUfBdCyrFQoDS40temZZ54xIx66rfuj6UE6fyAS+t61I6/zHTTw0NEHHR2pHihVpmlbOoqhf3PLLbeYiz5f50X07dvXzPewRiNqo/Wg9WGNvlis+tW5JnW1g5at719HNGqjcy8qBy4AEG8IIAAghmhnVCfsNoaeI0JTb6rTo95KO8SWTZs2VXlOeXm5uU9HD3QfNHjRORBW+k5leqS+ITR9x0rh0fkP//nPf+TWW281KVE6sToSmsak6UY//vijGSW44IIL6n2+jghceuml5qITzz/++GOTxqVzL9566y0z+rJ69eoaf6ejG1qPetH60HqpHERo5796XVanZWu6lL7P2nTv3j2i9wwAsYoUJgBIEJoupHn9lVcSUnoUXvP0K3fWdYKxz+eruD137lyTZqMrEukRf11d6ffffzfBjHXRnP9p06aZiceRuvPOO03nX4/Ia+ChqzTpxObKK0pVpyMYtb23Hj16yN13322O/p988sl1vqZO8j722GPlqaeeMre7dOli5mxoMGS95qBBg0xaU+V0Ik3zuvzyy82qVbrqlNbHu+++W6MurbkeddG/1RQufc+V60/np+iqTpXnuABAPGIEAgASxGmnnWYmUmuuvy4rqjn+H330kZn0rKk/WVlZVZZB1aPzOhlYt3WysI4S6NwBpfMudOKzHrHXvH89Eq8dcp0boZOmI6XLtmrqki7TquXoifF0ZSOdSK2P1Ub3U0cCdD6GdvStuRIaiOg8Cp3kXT0Fq3pKlaaC6cpKGjjpMrC6ipWOYGhgYdXVs88+a+pA60pHFHTEQPdPV7LSutO6uO6668ySs5r2pPMeNKVL51707t27ztfXuQ8a8Gg96UXnjejIiZ7HQ+tYR3kAIJ4RQABAgtAj/Nop1k62rhRUXFxsJvjedtttZlnUyvRovHbUdQlWTbfRTrEuM2rRic66/Kl2wrWDrR1x7ZRrMNCQE9lpZ1pXHtLgw5o4rUfvtbNurQhVnc6P0HQjnQCt507QEQSrLH1v2vnfkZtvvtksuaqvqylcOt9B6+CKK64wj+soi06u1qVddX6EzvnQ96X7Za30pCtQaadf52/o+S80qNDAyjrXQ110BEXnemgbaBkbN240AY/+nQZ3ABDvHCFrphsAICnoakCaZjNlyhSJJ9ZkbF0itvKqRwCA5sUIBAAgpmnqkc4f0PQsTQkieACAlkUAAQCIab/88os5Od7RRx9tzhQNAGhZpDABAAAAiBjLuAIAAAAggAAAAABgP0YgAAAAAESMAAIAAABAxAggAAAAAESMAAIAAABAxAggAAAAAESMAAIAAACAROr/AT/QKMsCZBCeAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "prop_df = pd.DataFrame(e_hat_raw, columns=ARM_LABELS)\n", + "prop_long = prop_df.melt(var_name='treatment', value_name='propensity')\n", + "\n", + "fig, ax = plt.subplots(figsize=(8, 4))\n", + "sns.histplot(\n", + " data=prop_long, x='propensity', hue='treatment',\n", + " bins=30, element='step', stat='density', common_norm=False, ax=ax,\n", + ")\n", + "\n", + "ax.axvline(0.05, color='tomato', lw=1.5, ls='--', alpha=0.8, label='threshold = 0.05')\n", + "ax.legend(title='Treatment', fontsize=8)\n", + "ax.set_xlabel('Propensity score')\n", + "ax.set_ylabel('Density')\n", + "ax.set_title('Propensity score distribution by treatment')\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "0b42eca5", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-09T16:49:48.264007Z", + "iopub.status.busy": "2026-05-09T16:49:48.263913Z", + "iopub.status.idle": "2026-05-09T16:49:48.571081Z", + "shell.execute_reply": "2026-05-09T16:49:48.570638Z" + } + }, + "source": [ + "이 데이터에서는 $\\hat e_a(X_i)$의 최솟값이 약 0.058이며, `e_hat < 0.05`인 경우도 관측되지 않았습니다. 즉, 극단적으로 작은 propensity score로 인해 일부 관측치에 과도한 가중치가 부여되는 문제는 크지 않아 보입니다.\n", + "\n", + "또한 그래프에서도 네 가지 처치의 propensity 분포가 전반적으로 잘 겹쳐 나타나므로, 뚜렷한 positivity 위반은 관찰되지 않습니다." + ] + }, + { + "cell_type": "markdown", + "id": "f4edd2d8", + "metadata": {}, + "source": [ + "## 정책학습 방법론\n", + "\n", + "세 가지 정책학습 방법을 비교합니다.\n", + "\n", + "- Plug-in Policy (DRLearner)\n", + "\n", + " 먼저 고객별 CATE를 추정한 뒤, 기대 순이익(net benefit)이 가장 큰 처치를 선택합니다.\n", + "\n", + " 정책 형태에 제약이 없는 유연한 접근이지만, 최종 성능은 CATE 추정 품질에 크게 영향을 받습니다.\n", + "\n", + "- DRPolicyTree\n", + "\n", + " DRPolicyTree는 AIPW score를 직접 최대화하도록 정책을 학습합니다.\n", + "\n", + " 정책 구조를 얕은 decision tree로 제한하기 때문에, 결과를 if-then 규칙 형태로 해석할 수 있습니다. 따라서 정책 해석과 설명이 중요한 상황에서 유용합니다.\n", + "\n", + "- DRPolicyForest\n", + "\n", + " DRPolicyForest 역시 AIPW score를 직접 최대화하지만, 단일 트리 대신 forest 구조를 사용합니다.\n", + "\n", + " 따라서 tree보다 복잡한 이질성을 더 잘 학습할 수 있지만, 개별 규칙을 해석하기는 더 어렵습니다.\n", + "\n", + "세 정책은 모두 train set에서 학습하고, 동일한 test set의 AIPW score로 정책 가치를 비교합니다." + ] + }, + { + "cell_type": "markdown", + "id": "d7d4bc26", + "metadata": {}, + "source": [ + "### 평가 지표 구성\n", + "\n", + "AIPW(Augmented Inverse Probability Weighting) score는 다음과 같이 정의합니다.\n", + "\n", + "$$\n", + "\\hat\\Gamma_{i,a} = \\hat\\mu_a(X_i) + \\frac{\\mathbf{1}[A_i=a]}{\\hat e_a(X_i)}\\bigl(Y_i^{net} - \\hat\\mu_a(X_i)\\bigr)\n", + "$$\n", + "\n", + "- $\\hat\\mu_a(X_i)$: outcome model이 예측한 $E[Y^{net}(a)\\mid X_i]$\n", + "- $\\hat e_a(X_i)$: propensity model이 예측한 $P(A_i=a\\mid X_i)$\n", + "- 두 번째 항은 실제 관측값과 예측값의 차이를 IPW로 보정하는 역할을 합니다.\n", + "\n", + "이 구조 덕분에 outcome model과 propensity model 중 하나만 올바르게 추정되어도 불편성이 유지됩니다. 이를 doubly robust(이중 견고성)라고 부릅니다." + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "id": "e3156595", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-09T16:49:48.578060Z", + "iopub.status.busy": "2026-05-09T16:49:48.577946Z", + "iopub.status.idle": "2026-05-09T16:49:49.475582Z", + "shell.execute_reply": "2026-05-09T16:49:49.474994Z" + } + }, + "outputs": [], + "source": [ + "Y_net_train = Y_net[train_idx]\n", + "Y_net_test = Y_net[test_idx]\n", + "\n", + "prop_model_net = RandomForestClassifier(\n", + " n_estimators=400,\n", + " min_samples_leaf=20,\n", + " random_state=SEED,\n", + " n_jobs=1,\n", + ")\n", + "\n", + "prop_model_net.fit(X_train, A_train)\n", + "e_hat_net = prop_model_net.predict_proba(X_test)\n", + "e_hat_net = np.clip(e_hat_net, 0.02, 0.98)\n", + "e_hat_net = e_hat_net / e_hat_net.sum(axis=1, keepdims=True)\n", + "\n", + "gamma_net = np.zeros((len(X_test), 4))\n", + "mu_hat_net = np.zeros((len(X_test), 4))\n", + "\n", + "for arm_id in range(4):\n", + " outcome_model = RandomForestRegressor(\n", + " n_estimators=400,\n", + " min_samples_leaf=20,\n", + " random_state=SEED,\n", + " n_jobs=1,\n", + " )\n", + " outcome_model.fit(X_train[A_train == arm_id], Y_net_train[A_train == arm_id])\n", + " mu_a = outcome_model.predict(X_test)\n", + " observed_a = (A_test == arm_id).astype(float)\n", + "\n", + " gamma_net[:, arm_id] = mu_a + observed_a / e_hat_net[:, arm_id] * (Y_net_test - mu_a)\n", + " mu_hat_net[:, arm_id] = mu_a\n" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "id": "e8533283", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-09T16:49:49.477219Z", + "iopub.status.busy": "2026-05-09T16:49:49.477080Z", + "iopub.status.idle": "2026-05-09T16:49:49.483510Z", + "shell.execute_reply": "2026-05-09T16:49:49.483027Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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samplee_hatmu_hatgamma
sample_idactual_armobserved_net_outcomenonetech_support_onlydiscount_onlydiscount_plus_supportnonetech_support_onlydiscount_onlydiscount_plus_supportnonetech_support_onlydiscount_onlydiscount_plus_support
00discount_plus_support9254.5585410.2486330.2673380.2723660.2116625370.0992819559.7777634547.4845526984.7316095370.0992819559.7777634547.48455217708.540116
11tech_support_only7702.8616620.2110870.3072040.2433940.23831510468.9425226728.9884198373.31663311121.82660910468.9425229899.1085088373.31663311121.826609
22tech_support_only5062.1844200.3765700.2419010.2562610.1252682879.7417865449.409859937.5151032959.5143472879.7417863848.652052937.5151032959.514347
33none8930.4950220.3396530.1947010.2465050.2191428458.7792178254.3720696054.5989736560.0280699847.5980528254.3720696054.5989736560.028069
44none8156.5755020.3122650.2500210.3121230.12559010137.4698213378.7480355743.8029392988.2173143793.8450543378.7480355743.8029392988.217314
\n", + "
" + ], + "text/plain": [ + " sample e_hat \\\n", + " sample_id actual_arm observed_net_outcome none \n", + "0 0 discount_plus_support 9254.558541 0.248633 \n", + "1 1 tech_support_only 7702.861662 0.211087 \n", + "2 2 tech_support_only 5062.184420 0.376570 \n", + "3 3 none 8930.495022 0.339653 \n", + "4 4 none 8156.575502 0.312265 \n", + "\n", + " mu_hat \\\n", + " tech_support_only discount_only discount_plus_support none \n", + "0 0.267338 0.272366 0.211662 5370.099281 \n", + "1 0.307204 0.243394 0.238315 10468.942522 \n", + "2 0.241901 0.256261 0.125268 2879.741786 \n", + "3 0.194701 0.246505 0.219142 8458.779217 \n", + "4 0.250021 0.312123 0.125590 10137.469821 \n", + "\n", + " gamma \\\n", + " tech_support_only discount_only discount_plus_support none \n", + "0 9559.777763 4547.484552 6984.731609 5370.099281 \n", + "1 6728.988419 8373.316633 11121.826609 10468.942522 \n", + "2 5449.409859 937.515103 2959.514347 2879.741786 \n", + "3 8254.372069 6054.598973 6560.028069 9847.598052 \n", + "4 3378.748035 5743.802939 2988.217314 3793.845054 \n", + "\n", + " \n", + " tech_support_only discount_only discount_plus_support \n", + "0 9559.777763 4547.484552 17708.540116 \n", + "1 9899.108508 8373.316633 11121.826609 \n", + "2 3848.652052 937.515103 2959.514347 \n", + "3 8254.372069 6054.598973 6560.028069 \n", + "4 3378.748035 5743.802939 2988.217314 " + ] + }, + "execution_count": 99, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sample_ids = np.arange(5)\n", + "\n", + "sample_prediction_table = pd.concat(\n", + " {\n", + " 'sample': pd.DataFrame({\n", + " 'sample_id': sample_ids,\n", + " 'actual_arm': pd.Series(A_test[sample_ids]).map(ARM_NAMES).to_numpy(),\n", + " 'observed_net_outcome': Y_net_test[sample_ids],\n", + " }),\n", + " 'e_hat': pd.DataFrame(e_hat_net[sample_ids], columns=ARM_LABELS),\n", + " 'mu_hat': pd.DataFrame(mu_hat_net[sample_ids], columns=ARM_LABELS),\n", + " 'gamma': pd.DataFrame(gamma_net[sample_ids], columns=ARM_LABELS),\n", + " },\n", + " axis=1,\n", + ")\n", + "\n", + "sample_prediction_table" + ] + }, + { + "cell_type": "markdown", + "id": "3c967473", + "metadata": {}, + "source": [ + "### Plug-in Policy\n", + "\n", + "Plug-in policy는 먼저 고객별 처치 효과(CATE)를 추정한 뒤, 그 값을 이용해 정책을 만드는 방식입니다.\n", + "\n", + "Binary treatment에서는 일반적으로 $\\hat\\tau(x) > 0$이면 처치하고, 비용이 있으면 $\\hat\\tau(x) > c(x)$일 때 처치합니다.\n", + "\n", + "여러 처치가 있는 경우에는 baseline 처치와의 상대효과를 비교합니다. 여기서는 `none`(처치 없음)을 baseline으로 두고, `DRLearner`로 다음 값을 추정합니다.\n", + "\n", + "$$\n", + "\\hat\\tau_a(x) = \\widehat{\\mathbb{E}}[Y^{net}(a) - Y^{net}(0) \\mid X=x]\n", + "\\qquad a \\in \\{1,2,3\\}\n", + "$$\n", + "\n", + "baseline 처치의 상대효과는 0이므로, 고객별로 다음 네 값을 비교합니다.\n", + "\n", + "$$\n", + "[0, \\hat\\tau_1(x), \\hat\\tau_2(x), \\hat\\tau_3(x)]\n", + "$$\n", + "\n", + "그리고 가장 큰 값을 갖는 처치를 선택합니다.\n", + "\n", + "$$\n", + "\\hat\\pi_{plugin}(x) = \\arg\\max_{a \\in \\{0,1,2,3\\}} \\widehat{\\mathbb{E}}[Y^{net}(a) - Y^{net}(0) \\mid X=x]\n", + "$$\n", + "\n", + "이 방식은 직관적이고 유연하지만, CATE 추정 품질에 크게 영향을 받습니다. 따라서 학습된 정책은 별도의 test set에서 AIPW value로 평가합니다." + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "id": "debd92a9", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-09T16:49:49.484723Z", + "iopub.status.busy": "2026-05-09T16:49:49.484610Z", + "iopub.status.idle": "2026-05-09T16:49:52.385416Z", + "shell.execute_reply": "2026-05-09T16:49:52.384824Z" + } + }, + "outputs": [], + "source": [ + "dr_cate = DRLearner(\n", + " model_regression=RandomForestRegressor(\n", + " n_estimators=400,\n", + " min_samples_leaf=20,\n", + " random_state=SEED,\n", + " n_jobs=1,\n", + " ),\n", + " model_propensity=RandomForestClassifier(\n", + " n_estimators=400,\n", + " min_samples_leaf=20,\n", + " random_state=SEED,\n", + " n_jobs=1,\n", + " ),\n", + " model_final=RandomForestRegressor(\n", + " n_estimators=300,\n", + " min_samples_leaf=20,\n", + " random_state=SEED,\n", + " n_jobs=1,\n", + " ),\n", + " categories=[0, 1, 2, 3],\n", + " min_propensity=0.02,\n", + " cv=3,\n", + " random_state=SEED,\n", + ")\n", + "dr_cate.fit(Y_net_train, A_train, X=X_train)\n", + "\n", + "cate_vs_none = dr_cate.const_marginal_effect(X_test)\n", + "plugin_arm_values = np.column_stack([\n", + " np.zeros(len(X_test)),\n", + " cate_vs_none,\n", + "])\n", + "pi_plugin = np.argmax(plugin_arm_values, axis=1)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "id": "33437c2c", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-09T16:49:52.389559Z", + "iopub.status.busy": "2026-05-09T16:49:52.389447Z", + "iopub.status.idle": "2026-05-09T16:49:52.392977Z", + "shell.execute_reply": "2026-05-09T16:49:52.392636Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "none 0.092\n", + "tech_support_only 0.500\n", + "discount_only 0.035\n", + "discount_plus_support 0.373\n", + "Name: proportion, dtype: float64" + ] + }, + "execution_count": 101, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.Series(pi_plugin).map(ARM_NAMES).value_counts(normalize=True).reindex(ARM_LABELS).fillna(0)" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "id": "33ktqy8zrqm", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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SizeEmployee CountIT Spendassigned_armτ_techτ_discτ_both
09620414217655none-6140-1419-1719
1253901787971none-14556-4252-7232
26655527919899none-17629-2896-16582
3708682026263tech_support_only4265-404155
424152184610tech_support_only4174-2316-771
5548556314772tech_support_only2042-698553
639922836133052discount_plus_support7849-128111819
71817042459529discount_plus_support9158521610248
829437737112485discount_plus_support71348589048
\n", + "
" + ], + "text/plain": [ + " Size Employee Count IT Spend assigned_arm τ_tech τ_disc \\\n", + "0 96204 142 17655 none -6140 -1419 \n", + "1 25390 178 7971 none -14556 -4252 \n", + "2 66555 279 19899 none -17629 -2896 \n", + "3 70868 20 26263 tech_support_only 4265 -40 \n", + "4 24152 18 4610 tech_support_only 4174 -2316 \n", + "5 54855 63 14772 tech_support_only 2042 -698 \n", + "6 399228 36 133052 discount_plus_support 7849 -1281 \n", + "7 181704 24 59529 discount_plus_support 9158 5216 \n", + "8 294377 37 112485 discount_plus_support 7134 858 \n", + "\n", + " τ_both \n", + "0 -1719 \n", + "1 -7232 \n", + "2 -16582 \n", + "3 4155 \n", + "4 -771 \n", + "5 553 \n", + "6 11819 \n", + "7 10248 \n", + "8 9048 " + ] + }, + "execution_count": 102, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "none_idx = np.where(pi_plugin == 0)[0][:3]\n", + "tech_idx = np.where(pi_plugin == 1)[0][:3]\n", + "both_idx = np.where(pi_plugin == 3)[0][:3]\n", + "sample_ids = np.concatenate([none_idx, tech_idx, both_idx])\n", + "\n", + "df_plugin_sample = pd.DataFrame(X_test[sample_ids], columns=covariates)[['Size', 'Employee Count', 'IT Spend']]\n", + "df_plugin_sample['assigned_arm'] = pd.Series(pi_plugin[sample_ids]).map(ARM_NAMES).values\n", + "df_plugin_sample[['τ_tech', 'τ_disc', 'τ_both']] = np.round(cate_vs_none[sample_ids], 0).astype(int)\n", + "df_plugin_sample.index = pd.RangeIndex(len(df_plugin_sample))\n", + "df_plugin_sample" + ] + }, + { + "cell_type": "markdown", + "id": "05b0ee18", + "metadata": {}, + "source": [ + "plug-in 정책은 약 50% 고객에게 `tech_support_only`, 37% 고객에게 `discount_plus_support`, 그리고 약 9% 고객에게 `none`을 배정합니다.\n", + "\n", + "비용을 함께 고려하면서, 기대 순이익이 음수로 예상되는 일부 고객에게는 처치를 하지 않는 선택이 나타납니다." + ] + }, + { + "cell_type": "markdown", + "id": "97d958cd", + "metadata": {}, + "source": [ + "### Policy Tree\n", + "\n", + "현업에서는 성능뿐 아니라 정책의 설명 가능성도 중요한 경우가 많습니다. 얕은 decision tree 기반 정책은 다음과 같은 이유로 실무에서 자주 사용됩니다.\n", + "\n", + "- **이해관계자 설명**: if-then 규칙 형태라 정책을 쉽게 설명하고 공유할 수 있습니다.\n", + "- **공정성 검토**: 어떤 고객이 어떤 처치를 받는지 구조가 명확해 편향 여부를 점검하기 쉽습니다.\n", + "- **운영 안정성**: 복잡한 모델보다 단순한 규칙 기반 정책이 실제 운영과 관리 측면에서 안정적입니다.\n", + "\n", + "이 경우 정책을 모든 가능한 함수에서 찾는 대신, 제한된 정책 클래스 $\\Pi$ 안에서 탐색합니다.\n", + "\n", + "$$\n", + "\\hat\\pi = \\arg\\max_{\\pi \\in \\Pi}\n", + "\\frac{1}{n}\\sum_{i=1}^{n} \\widehat\\Gamma_{i,\\pi(X_i)}\n", + "$$\n", + "\n", + "Policy Tree는 $\\Pi$를 얕은 decision tree로 제한합니다. 따라서 depth를 작게 두면 사람이 읽을 수 있는 형태로 정책을 해석할 수 있습니다. 여기서는 `econml`의 `DRPolicyTree`를 사용합니다.\n", + "\n", + "또한 leaf가 지나치게 작아지면 다중 처치 환경에서 value 추정이 불안정해질 수 있으므로, `min_samples_leaf`를 사용해 너무 작은 leaf를 방지합니다." + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "id": "6c987a8b", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-09T16:49:53.651642Z", + "iopub.status.busy": "2026-05-09T16:49:53.651575Z", + "iopub.status.idle": "2026-05-09T16:49:55.354506Z", + "shell.execute_reply": "2026-05-09T16:49:55.354139Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "none 0.199\n", + "tech_support_only 0.475\n", + "discount_only 0.000\n", + "discount_plus_support 0.326\n", + "Name: proportion, dtype: float64" + ] + }, + "execution_count": 103, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dr_policy_tree = DRPolicyTree(\n", + " max_depth=2,\n", + " min_samples_leaf=30,\n", + " model_regression=RandomForestRegressor(\n", + " n_estimators=400,\n", + " min_samples_leaf=20,\n", + " random_state=SEED,\n", + " n_jobs=1,\n", + " ),\n", + " model_propensity=RandomForestClassifier(\n", + " n_estimators=400,\n", + " min_samples_leaf=20,\n", + " random_state=SEED,\n", + " n_jobs=1,\n", + " ),\n", + " categories=[0, 1, 2, 3],\n", + " min_propensity=0.02,\n", + " cv=3,\n", + " random_state=SEED,\n", + ")\n", + "dr_policy_tree.fit(Y_net_train, A_train, X=X_train)\n", + "pi_tree = dr_policy_tree.predict(X_test).astype(int).ravel()\n", + "\n", + "pd.Series(pi_tree).map(ARM_NAMES).value_counts(normalize=True).reindex(ARM_LABELS).fillna(0)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "id": "64529830", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-09T16:49:55.355867Z", + "iopub.status.busy": "2026-05-09T16:49:55.355774Z", + "iopub.status.idle": "2026-05-09T16:49:55.574472Z", + "shell.execute_reply": "2026-05-09T16:49:55.574068Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(13, 7))\n", + "dr_policy_tree.plot(feature_names=covariates, treatment_names=ARM_LABELS, ax=ax)\n", + "ax.set_title('4-arm DRPolicyTree')\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "2a3ffb5d", + "metadata": {}, + "source": [ + "이 트리 정책은 주로 `Size`를 기준으로 고객을 나눕니다. 특징적으로 약 20%의 고객에게는 `none`을 배정합니다. 비용을 반영한 결과, 일부 고객은 처치했을 때 기대 순이익이 낮아 처치하지 않는 편이 더 낫다고 판단한 것입니다.\n", + "\n", + "전체적으로는 규모가 큰 고객에게 `discount_plus_support`를, 그 외 고객에게는 주로 `tech_support_only`를 배정합니다. 기대 순이익이 낮은 고객에게는 `none`을 선택합니다." + ] + }, + { + "cell_type": "markdown", + "id": "35dd95b1", + "metadata": {}, + "source": [ + "### Policy Forest\n", + "\n", + "Policy Forest는 여러 tree를 사용해 더 유연한 정책을 학습합니다. 단일 tree보다 복잡한 이질성을 더 잘 포착할 수 있지만, 개별 규칙 형태로 해석하기는 어렵습니다.\n", + "\n", + "여기서는 `econml`의 `DRPolicyForest`를 사용합니다.\n", + "\n", + "실무에서는 일반적으로 forest를 성능 중심의 정책으로, tree를 해석 가능한 정책으로 보는 경우가 많습니다. 두 방법 모두 학습 이후에는 동일한 test set의 AIPW score로 정책 가치를 비교합니다." + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "id": "d7939447", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-09T16:49:55.582919Z", + "iopub.status.busy": "2026-05-09T16:49:55.582816Z", + "iopub.status.idle": "2026-05-09T16:49:57.495404Z", + "shell.execute_reply": "2026-05-09T16:49:57.494962Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "none 0.113\n", + "tech_support_only 0.471\n", + "discount_only 0.000\n", + "discount_plus_support 0.416\n", + "Name: proportion, dtype: float64" + ] + }, + "execution_count": 105, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dr_policy_forest = DRPolicyForest(\n", + " n_estimators=400,\n", + " max_depth=5,\n", + " min_samples_leaf=30,\n", + " model_regression=RandomForestRegressor(\n", + " n_estimators=400,\n", + " min_samples_leaf=20,\n", + " random_state=SEED,\n", + " n_jobs=1,\n", + " ),\n", + " model_propensity=RandomForestClassifier(\n", + " n_estimators=400,\n", + " min_samples_leaf=20,\n", + " random_state=SEED,\n", + " n_jobs=1,\n", + " ),\n", + " categories=[0, 1, 2, 3],\n", + " min_propensity=0.02,\n", + " cv=3,\n", + " random_state=SEED,\n", + " n_jobs=1,\n", + ")\n", + "dr_policy_forest.fit(Y_net_train, A_train, X=X_train)\n", + "pi_forest = dr_policy_forest.predict(X_test).astype(int).ravel()\n", + "\n", + "pd.Series(pi_forest).map(ARM_NAMES).value_counts(normalize=True).reindex(ARM_LABELS).fillna(0)\n" + ] + }, + { + "cell_type": "markdown", + "id": "15d7d727", + "metadata": {}, + "source": [ + "DRPolicyForest는 전반적으로 plug-in 정책과 유사한 배정을 보입니다. `none` 11%, `tech_support_only` 47%, `discount_plus_support` 42%로, plug-in 정책보다 `discount_plus_support` 비중이 조금 더 높게 나타납니다.\n", + "\n", + "Forest는 단일 if-then 규칙 형태로 해석하기는 어렵지만, 더 복잡한 이질성을 학습할 수 있다는 장점이 있습니다. 따라서 보통 해석 가능한 기준 정책으로는 tree를, 성능 중심 정책으로는 forest를 함께 비교합니다." + ] + }, + { + "cell_type": "markdown", + "id": "99bac91c", + "metadata": {}, + "source": [ + "## 정책 평가\n", + "\n", + "이제 학습된 정책들을 동일한 test set의 AIPW score 기준으로 비교합니다.\n", + "\n", + "비교를 위해 모든 고객에게 동일한 처치를 적용하는 baseline 정책도 함께 사용합니다.\n", + "\n", + "- `all_none`\n", + "- `all_tech_support_only`\n", + "- `all_discount_only`\n", + "- `all_discount_plus_support`\n", + "\n", + "예를 들어 `all_discount_plus_support`는 모든 고객에게 기술지원과 할인을 함께 제공했을 때의 평균 순이익을 의미합니다.\n", + "\n", + "반면 학습된 정책은 고객 특성 $X_i$에 따라 서로 다른 처치를 배정합니다.\n", + "\n", + "만약 학습된 정책의 value가 가장 좋은 baseline 정책보다 높다면, 모든 고객에게 동일한 처치를 적용하는 것보다 고객별 targeting이 더 효과적이라는 의미입니다.\n", + "\n", + "여기서 비교하는 값은 단순 관측 평균이 아니라, 동일한 AIPW 평가식을 통해 추정한 counterfactual policy value입니다." + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "id": "14b616ad", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-09T16:49:57.502590Z", + "iopub.status.busy": "2026-05-09T16:49:57.502507Z", + "iopub.status.idle": "2026-05-09T16:49:57.508438Z", + "shell.execute_reply": "2026-05-09T16:49:57.508058Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " policy value value_se rate_none \\\n", + "4 plugin_drlearner_4arm 11810.377712 268.783750 0.092 \n", + "6 dr_policy_forest_4arm 11625.627540 267.145383 0.113 \n", + "5 dr_policy_tree_4arm 11148.462369 228.496499 0.199 \n", + "1 all_tech_support_only 10526.523876 353.820982 0.000 \n", + "3 all_discount_plus_support 10136.708882 355.746399 0.000 \n", + "2 all_discount_only 7545.079205 281.399277 0.000 \n", + "0 all_none 7249.345467 149.947977 1.000 \n", + "\n", + " rate_tech_support_only rate_discount_only rate_discount_plus_support \n", + "4 0.500 0.035 0.373 \n", + "6 0.471 0.000 0.416 \n", + "5 0.475 0.000 0.326 \n", + "1 1.000 0.000 0.000 \n", + "3 0.000 0.000 1.000 \n", + "2 0.000 1.000 0.000 \n", + "0 0.000 0.000 0.000 " + ] + }, + "execution_count": 106, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "policy_assignments = {\n", + " **{f'all_{arm_name}': np.full(len(X_test), arm_id) for arm_id, arm_name in ARM_NAMES.items()},\n", + " 'plugin_drlearner_4arm': pi_plugin,\n", + " 'dr_policy_tree_4arm': pi_tree,\n", + " 'dr_policy_forest_4arm': pi_forest,\n", + "}\n", + "\n", + "eval_rows = []\n", + "for policy_name, policy_assignment in policy_assignments.items():\n", + " pi = np.asarray(policy_assignment).astype(int).ravel()\n", + " scores = gamma_net[np.arange(len(pi)), pi]\n", + "\n", + " row = {\n", + " 'policy': policy_name,\n", + " 'value': scores.mean(),\n", + " 'value_se': scores.std(ddof=1) / np.sqrt(len(scores)),\n", + " }\n", + " for arm_id, arm_name in ARM_NAMES.items():\n", + " row[f'rate_{arm_name}'] = np.mean(pi == arm_id)\n", + " eval_rows.append(row)\n", + "\n", + "policy_eval = pd.DataFrame(eval_rows)\n", + "policy_eval.sort_values('value', ascending=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "id": "4814d1ed", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(1, 2, figsize=(15, 5))\n", + "plot_df = policy_eval.sort_values('value', ascending=False)\n", + "sns.barplot(data=plot_df, x='value', y='policy', ax=axes[0], color='seagreen')\n", + "axes[0].set_title('4-arm net policy value')\n", + "axes[0].set_xlabel('AIPW-estimated net value')\n", + "axes[0].set_ylabel('')\n", + "\n", + "arm_rate_cols = [f'rate_{name}' for name in ARM_LABELS]\n", + "rate_df = policy_eval.set_index('policy')[arm_rate_cols]\n", + "rate_df.columns = ARM_LABELS\n", + "rate_df.loc[['plugin_drlearner_4arm', 'dr_policy_tree_4arm', 'dr_policy_forest_4arm']].plot(kind='barh', stacked=True, ax=axes[1], colormap='tab20')\n", + "axes[1].set_title('Assigned arm share')\n", + "axes[1].set_xlabel('Share')\n", + "axes[1].set_ylabel('')\n", + "axes[1].legend(loc='center left', bbox_to_anchor=(1.0, 0.5))\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "dec4e9f6", + "metadata": {}, + "source": [ + "DRLearner plug-in 정책(11,810)의 정책 가치가 가장 높았습니다. `tech_support_only`(50%)와 `discount_plus_support`(37%)를 중심으로 배정하고, 일부 고객에게는 `none`(9%)을 선택했습니다.\n", + "\n", + "`DRPolicyForest`(11,626)와 `DRPolicyTree`(11,148)도 모두 높은 성능을 보였지만, plug-in 정책보다는 다소 낮았습니다. 특히 tree 정책은 해석 가능한 구조를 유지하는 대신 더 보수적으로 배정하며, 약 20% 고객에게 `none`을 선택했습니다.\n", + "\n", + "모든 고객에게 동일한 처치를 적용하는 정책 중에서는 `all_tech_support_only`(10,527)가 가장 높았고, `all_discount_plus_support`(10,137)가 그 뒤를 이었습니다. 할인 비용이 고객 규모에 따라 증가하도록 설정했기 때문에, 일부 고객에서는 할인 추가 제공의 편익이 비용을 충분히 상쇄하지 못한 것으로 볼 수 있습니다.\n", + "\n", + "전체적으로 학습된 세 정책 모두 가장 성능이 좋은 단일 처치 정책보다 높은 value를 보였습니다. 이는 모든 고객에게 동일한 처치를 적용하는 것보다 고객별 targeting이 더 효과적이라는 의미입니다." + ] + }, + { + "cell_type": "markdown", + "id": "6stfz2jhpt", + "metadata": {}, + "source": [ + "### 통계적 유의성\n", + "\n", + "정책 value의 숫자만 비교하는 것으로는 충분하지 않습니다. 표준오차를 함께 고려해 학습된 정책이 무처치 정책이나 최선 상수 정책보다 통계적으로 유의하게 높은지 확인합니다.\n", + "\n", + "정책 $\\pi$와 기준 정책 $\\pi_0$의 value 차이는 **페어 단위 차이**로 계산합니다.\n", + "\n", + "$$\n", + "\\hat\\Delta = \\frac{1}{n}\\sum_i \\left[\\hat\\Gamma_{i,\\pi(X_i)} - \\hat\\Gamma_{i,\\pi_0(X_i)}\\right]\n", + "$$\n", + "\n", + "두 정책이 같은 test set에서 평가되므로 개인 단위에서 차이를 계산하면 공분산이 자연스럽게 반영됩니다. 95% 신뢰구간이 0을 포함하지 않으면 차이가 통계적으로 유의합니다." + ] + }, + { + "cell_type": "code", + "execution_count": 378, + "id": "l3irvvw886g", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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vs_none (est)vs_none 95% CIvs_best_const [tech_only] (est)vs_best_const 95% CI
policy
DRPolicyForest4,376[3,873, 4,879]1,099[426, 1,772]
DRPolicyTree3,899[3,470, 4,329]622[-1, 1,245]
DRLearner plug-in4,561[4,061, 5,061]1,284[622, 1,946]
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" + ], + "text/plain": [ + " vs_none (est) vs_none 95% CI \\\n", + "policy \n", + "DRPolicyForest 4,376 [3,873, 4,879] \n", + "DRPolicyTree 3,899 [3,470, 4,329] \n", + "DRLearner plug-in 4,561 [4,061, 5,061] \n", + "\n", + " vs_best_const [tech_only] (est) vs_best_const 95% CI \n", + "policy \n", + "DRPolicyForest 1,099 [426, 1,772] \n", + "DRPolicyTree 622 [-1, 1,245] \n", + "DRLearner plug-in 1,284 [622, 1,946] " + ] + }, + "execution_count": 378, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 불확실 비용 설정에서는 all_tech_support_only(arm 1)가 최선 상수 정책\n", + "sig_results = []\n", + "\n", + "for policy_name, pi_eval in [\n", + " ('DRPolicyForest', pi_forest),\n", + " ('DRPolicyTree', pi_tree),\n", + " ('DRLearner plug-in', pi_plugin),\n", + "]:\n", + " pi_eval = np.asarray(pi_eval).astype(int)\n", + " scores = gamma_net[np.arange(len(pi_eval)), pi_eval]\n", + "\n", + " # vs. 무처치(arm 0)\n", + " diff_none = scores - gamma_net[:, 0]\n", + " est_none = diff_none.mean()\n", + " se_none = diff_none.std(ddof=1) / np.sqrt(len(diff_none))\n", + "\n", + " # vs. 최선 상수 정책(arm 1: tech_support_only)\n", + " diff_best = scores - gamma_net[:, 1]\n", + " est_best = diff_best.mean()\n", + " se_best = diff_best.std(ddof=1) / np.sqrt(len(diff_best))\n", + "\n", + " sig_results.append({\n", + " 'policy': policy_name,\n", + " 'vs_none (est)': f\"{est_none:,.0f}\",\n", + " 'vs_none 95% CI': f\"[{est_none - 1.96*se_none:,.0f}, {est_none + 1.96*se_none:,.0f}]\",\n", + " 'vs_best_const [tech_only] (est)': f\"{est_best:,.0f}\",\n", + " 'vs_best_const 95% CI': f\"[{est_best - 1.96*se_best:,.0f}, {est_best + 1.96*se_best:,.0f}]\",\n", + " })\n", + "\n", + "pd.DataFrame(sig_results).set_index('policy')" + ] + }, + { + "cell_type": "markdown", + "id": "rgjw60hscrl", + "metadata": {}, + "source": [ + "세 학습 정책 모두 무처치 대비 통계적으로 유의하게 높습니다 (95% CI 전체가 0보다 큼). 최선 상수 정책(`tech_support_only`) 대비해서는 `DRLearner plug-in`과 `DRPolicyForest`가 유의하게 높고, `DRPolicyTree`는 95% CI가 `[-1, 1,245]`로 0을 경계에서 포함해 borderline입니다. Tree는 해석 가능성을 얻는 대신 일부 성능을 희생한 결과입니다." + ] + }, + { + "cell_type": "markdown", + "id": "f877a9d9", + "metadata": {}, + "source": [ + "### 처치 배정 분석\n", + "\n", + "학습된 4-arm policy가 어떤 고객에게 어떤 bundle을 배정하는지 보기 위해, arm별 고객 특성 평균을 비교합니다. 이 표는 정책 해석에 중요합니다." + ] + }, + { + "cell_type": "code", + "execution_count": 379, + "id": "46be8ffe", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-09T16:49:57.643114Z", + "iopub.status.busy": "2026-05-09T16:49:57.642718Z", + "iopub.status.idle": "2026-05-09T16:49:57.657853Z", + "shell.execute_reply": "2026-05-09T16:49:57.657216Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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SizeEmployee CountIT Spend
assigned_arm
none118619.0142.029171.0
tech_support_only48365.039.011818.0
discount_plus_support201904.039.050099.0
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" + ], + "text/plain": [ + " Size Employee Count IT Spend\n", + "assigned_arm \n", + "none 118619.0 142.0 29171.0\n", + "tech_support_only 48365.0 39.0 11818.0\n", + "discount_plus_support 201904.0 39.0 50099.0" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "assigned = pd.DataFrame(X_test, columns=covariates)\n", + "assigned['assigned_arm'] = pd.Series(pi_tree).map(ARM_NAMES).to_numpy()\n", + "\n", + "key_cols = ['Size', 'Employee Count', 'IT Spend']\n", + "assigned_summary = assigned.groupby('assigned_arm')[key_cols].mean().reindex(ARM_LABELS).dropna(how='all')\n", + "display(assigned_summary.round(0))" + ] + }, + { + "cell_type": "code", + "execution_count": 380, + "id": "9d9ce0cd", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-09T16:49:57.659575Z", + "iopub.status.busy": "2026-05-09T16:49:57.659471Z", + "iopub.status.idle": "2026-05-09T16:49:57.756546Z", + "shell.execute_reply": "2026-05-09T16:49:57.756114Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "heat = assigned.groupby('assigned_arm')[covariates].mean().reindex(ARM_LABELS).dropna(how='all')\n", + "plt.figure(figsize=(10, 5))\n", + "sns.heatmap(heat.T, annot=True, fmt='.2f', cmap='YlGnBu', cbar_kws={'label': 'Mean'})\n", + "plt.xlabel('Assigned arm')\n", + "plt.ylabel('Covariate')\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "w6otkzfd3z", + "metadata": {}, + "source": [ + "### 하위집단 효과 차이\n", + "\n", + "불확실 비용 설정에서 DRPolicyTree는 세 하위집단을 구분합니다. `discount_plus_support`(큰 고객), `tech_support_only`(중소 고객), `none`(기대 순편익이 낮은 고객). 처치 집단 간에 CATE proxy가 유의하게 다른지 검정합니다.\n", + "\n", + "$$\n", + "H_0: E[\\hat\\Gamma_{i,\\pi(X_i)} - \\hat\\Gamma_{i,0} \\mid \\pi(X_i) = a] = E[\\hat\\Gamma_{i,\\pi(X_i)} - \\hat\\Gamma_{i,0} \\mid \\pi(X_i) = a']\n", + "$$\n", + "\n", + "`none`이 배정된 집단은 CATE proxy가 정의상 0이므로 검정에서 제외합니다. 처치를 받는 두 집단(`tech_support_only`, `discount_plus_support`)의 CATE proxy 평균 차이가 유의하면, 정책이 실질적인 이질성을 포착하고 있다는 의미입니다." + ] + }, + { + "cell_type": "code", + "execution_count": 381, + "id": "onvd383ogm", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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CATE proxy (mean)SE95% CIcount
subgroup
discount_plus_support8232.760422464.881756[7,322, 9,144]326
none0.0000000.000000[0, 0]199
tech_support_only2558.393693258.876134[2,051, 3,066]475
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" + ], + "text/plain": [ + " CATE proxy (mean) SE 95% CI count\n", + "subgroup \n", + "discount_plus_support 8232.760422 464.881756 [7,322, 9,144] 326\n", + "none 0.000000 0.000000 [0, 0] 199\n", + "tech_support_only 2558.393693 258.876134 [2,051, 3,066] 475" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "t-test (처치 집단 간): t = 11.442, p = 0.0000\n", + "→ 두 처치 집단의 CATE 차이는 통계적으로 유의합니다 (α = 0.05).\n" + ] + } + ], + "source": [ + "# pi_tree 기준 하위집단별 CATE proxy\n", + "cate_proxy = np.array([\n", + " gamma_net[i, pi_tree[i]] - gamma_net[i, 0]\n", + " for i in range(len(pi_tree))\n", + "])\n", + "\n", + "subgroup_labels = pd.Series(pi_tree).map(ARM_NAMES)\n", + "cate_by_group = pd.DataFrame({\n", + " 'subgroup': subgroup_labels.to_numpy(),\n", + " 'cate_proxy': cate_proxy,\n", + "})\n", + "\n", + "summary = cate_by_group.groupby('subgroup')['cate_proxy'].agg(['mean', 'std', 'count'])\n", + "summary['se'] = summary['std'] / np.sqrt(summary['count'])\n", + "summary['95% CI'] = summary.apply(\n", + " lambda r: f\"[{r['mean'] - 1.96*r['se']:,.0f}, {r['mean'] + 1.96*r['se']:,.0f}]\",\n", + " axis=1,\n", + ")\n", + "display(summary[['mean', 'se', '95% CI', 'count']].rename(columns={'mean': 'CATE proxy (mean)', 'se': 'SE'}))\n", + "\n", + "# 처치 집단 간 차이 검정 (none 제외 — arm 0 vs arm 0 는 항상 0)\n", + "treated_groups = {g: d['cate_proxy'].values\n", + " for g, d in cate_by_group.groupby('subgroup')\n", + " if g != 'none'}\n", + "\n", + "if len(treated_groups) == 2:\n", + " g1, g2 = list(treated_groups.values())\n", + " t_stat, p_val = stats.ttest_ind(g1, g2)\n", + " print(f\"\\nt-test (처치 집단 간): t = {t_stat:.3f}, p = {p_val:.4f}\")\n", + " sig = \"유의\" if p_val < 0.05 else \"유의하지 않음\"\n", + " print(f\"→ 두 처치 집단의 CATE 차이는 통계적으로 {sig}합니다 (α = 0.05).\")" + ] + }, + { + "cell_type": "markdown", + "id": "b40f71c0", + "metadata": {}, + "source": [ + "## 결론\n", + "\n", + "이 데이터는 `Discount`만 따로 보는 것보다 `Tech Support`와 `Discount`를 함께 보는 4-arm policy learning이 더 자연스럽습니다. arm별 표본도 비교적 균형적이고, propensity overlap도 나쁘지 않기 때문에 분석 가능성은 충분합니다.\n", + "\n", + "비용을 반영한 net value 기준에서 learned policy가 가장 좋은 상수 정책보다 높다면, 단순히 모든 고객에게 같은 intervention을 주는 것보다 고객별로 bundle을 다르게 배정할 가치가 있다는 뜻입니다.\n", + "\n", + "직관적인 CATE 기반 기준선으로는 `DRLearner` plug-in 정책을 보고, 해석 가능한 정책이 중요하면 `DRPolicyTree`를 중심으로 보며, 성능 중심 비교에는 `DRPolicyForest`를 함께 사용하면 됩니다.\n", + "\n", + "### 참고 자료\n", + "\n", + "- **이 노트북의 주 참고 튜토리얼**: [Stanford ML+CI Tutorial — Policy Learning I (Binary Treatment)](https://bookdown.org/stanfordgsbsilab/ml-ci-tutorial/policy-learning-i---binary-treatment.html)\n", + "- **데이터셋**: [Software Usage Promotion Campaign Uplift Model (Kaggle)](https://www.kaggle.com/datasets/hwwang98/software-usage-promotion-campaign-uplift-model)\n", + "- **모수적 정책학습 이론**: [Athey and Wager (2021, Econometrica)](https://onlinelibrary.wiley.com/doi/abs/10.3982/ECTA15732) · [ArXiv 버전 (1702.02896)](https://arxiv.org/abs/1702.02896)\n", + "- **비용 곡선을 통한 정책 비교**: [Imai and Li (2019)](https://arxiv.org/pdf/1905.05389.pdf)\n", + "- **추가 참고**: [arxiv 2604.06123](https://arxiv.org/html/2604.06123v1)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.14.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file From 063ff29070e2e29c55c4bde8a19241bf2a220589 Mon Sep 17 00:00:00 2001 From: Funbucket Date: Sun, 10 May 2026 14:32:30 +0900 Subject: [PATCH 03/11] =?UTF-8?q?feat(who=5Fshould=5Fbe=5Ftreated):=20?= =?UTF-8?q?=EC=A0=95=EC=B1=85=20=ED=8F=89=EA=B0=80=20=EA=B0=9C=EC=84=A0=20?= =?UTF-8?q?=EB=B0=8F=20=EC=84=B9=EC=85=98=20=EA=B5=AC=EC=A1=B0=20=EC=A0=95?= =?UTF-8?q?=EB=A6=AC?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - 정책 평가 테이블에 95% CI 추가 (ci_lower, ci_upper) - 비용 곡선 셀 복원 (고객별 E[C|X] 추정 + policy_cost_curve) - 통계적 유의성 섹션을 정책 평가에 통합 (별도 헤더 제거) - 처치 배정 분석, 하위집단 효과 차이 섹션 삭제 - 결론 섹션 핵심 수치 중심으로 개선 Co-Authored-By: Claude Sonnet 4.6 --- book/myst.yml | 2 + .../who_should_be_treated_ko.ipynb | 514 +++--------------- requirements-book.txt | 5 +- requirements.txt | 15 +- 4 files changed, 88 insertions(+), 448 deletions(-) diff --git a/book/myst.yml b/book/myst.yml index 617cab2..da3ba92 100644 --- a/book/myst.yml +++ b/book/myst.yml @@ -23,6 +23,8 @@ project: file: why_causal_inference/why_causal_inference_en.ipynb - file: why_causal_inference/why_causal_inference_ko.ipynb hidden: true + - title: Who Should Be Treated? + file: who_should_be_treated/who_should_be_treated_ko.ipynb site: template: book-theme diff --git a/book/who_should_be_treated/who_should_be_treated_ko.ipynb b/book/who_should_be_treated/who_should_be_treated_ko.ipynb index 749c5c2..8289a48 100644 --- a/book/who_should_be_treated/who_should_be_treated_ko.ipynb +++ b/book/who_should_be_treated/who_should_be_treated_ko.ipynb @@ -1845,7 +1845,7 @@ }, { "cell_type": "code", - "execution_count": 106, + "execution_count": 109, "id": "14b616ad", "metadata": { "execution": { @@ -1879,7 +1879,8 @@ " \n", " policy\n", " value\n", - " value_se\n", + " ci_lower\n", + " ci_upper\n", " rate_none\n", " rate_tech_support_only\n", " rate_discount_only\n", @@ -1891,7 +1892,8 @@ " 4\n", " plugin_drlearner_4arm\n", " 11810.377712\n", - " 268.783750\n", + " 11283.561563\n", + " 12337.193861\n", " 0.092\n", " 0.500\n", " 0.035\n", @@ -1901,7 +1903,8 @@ " 6\n", " dr_policy_forest_4arm\n", " 11625.627540\n", - " 267.145383\n", + " 11102.022589\n", + " 12149.232491\n", " 0.113\n", " 0.471\n", " 0.000\n", @@ -1911,7 +1914,8 @@ " 5\n", " dr_policy_tree_4arm\n", " 11148.462369\n", - " 228.496499\n", + " 10700.609230\n", + " 11596.315508\n", " 0.199\n", " 0.475\n", " 0.000\n", @@ -1921,7 +1925,8 @@ " 1\n", " all_tech_support_only\n", " 10526.523876\n", - " 353.820982\n", + " 9833.034751\n", + " 11220.013001\n", " 0.000\n", " 1.000\n", " 0.000\n", @@ -1931,7 +1936,8 @@ " 3\n", " all_discount_plus_support\n", " 10136.708882\n", - " 355.746399\n", + " 9439.445941\n", + " 10833.971824\n", " 0.000\n", " 0.000\n", " 0.000\n", @@ -1941,7 +1947,8 @@ " 2\n", " all_discount_only\n", " 7545.079205\n", - " 281.399277\n", + " 6993.536621\n", + " 8096.621788\n", " 0.000\n", " 0.000\n", " 1.000\n", @@ -1951,7 +1958,8 @@ " 0\n", " all_none\n", " 7249.345467\n", - " 149.947977\n", + " 6955.447432\n", + " 7543.243502\n", " 1.000\n", " 0.000\n", " 0.000\n", @@ -1962,26 +1970,35 @@ "" ], "text/plain": [ - " policy value value_se rate_none \\\n", - "4 plugin_drlearner_4arm 11810.377712 268.783750 0.092 \n", - "6 dr_policy_forest_4arm 11625.627540 267.145383 0.113 \n", - "5 dr_policy_tree_4arm 11148.462369 228.496499 0.199 \n", - "1 all_tech_support_only 10526.523876 353.820982 0.000 \n", - "3 all_discount_plus_support 10136.708882 355.746399 0.000 \n", - "2 all_discount_only 7545.079205 281.399277 0.000 \n", - "0 all_none 7249.345467 149.947977 1.000 \n", + " policy value ci_lower ci_upper \\\n", + "4 plugin_drlearner_4arm 11810.377712 11283.561563 12337.193861 \n", + "6 dr_policy_forest_4arm 11625.627540 11102.022589 12149.232491 \n", + "5 dr_policy_tree_4arm 11148.462369 10700.609230 11596.315508 \n", + "1 all_tech_support_only 10526.523876 9833.034751 11220.013001 \n", + "3 all_discount_plus_support 10136.708882 9439.445941 10833.971824 \n", + "2 all_discount_only 7545.079205 6993.536621 8096.621788 \n", + "0 all_none 7249.345467 6955.447432 7543.243502 \n", "\n", - " rate_tech_support_only rate_discount_only rate_discount_plus_support \n", - "4 0.500 0.035 0.373 \n", - "6 0.471 0.000 0.416 \n", - "5 0.475 0.000 0.326 \n", - "1 1.000 0.000 0.000 \n", - "3 0.000 0.000 1.000 \n", - "2 0.000 1.000 0.000 \n", - "0 0.000 0.000 0.000 " + " rate_none rate_tech_support_only rate_discount_only \\\n", + "4 0.092 0.500 0.035 \n", + "6 0.113 0.471 0.000 \n", + "5 0.199 0.475 0.000 \n", + "1 0.000 1.000 0.000 \n", + "3 0.000 0.000 0.000 \n", + "2 0.000 0.000 1.000 \n", + "0 1.000 0.000 0.000 \n", + "\n", + " rate_discount_plus_support \n", + "4 0.373 \n", + "6 0.416 \n", + "5 0.326 \n", + "1 0.000 \n", + "3 1.000 \n", + "2 0.000 \n", + "0 0.000 " ] }, - "execution_count": 106, + "execution_count": 109, "metadata": {}, "output_type": "execute_result" } @@ -2009,7 +2026,11 @@ " eval_rows.append(row)\n", "\n", "policy_eval = pd.DataFrame(eval_rows)\n", - "policy_eval.sort_values('value', ascending=False)" + "policy_eval['ci_lower'] = policy_eval['value'] - 1.96 * policy_eval['value_se']\n", + "policy_eval['ci_upper'] = policy_eval['value'] + 1.96 * policy_eval['value_se']\n", + "\n", + "display_cols = ['policy', 'value', 'ci_lower', 'ci_upper'] + [f'rate_{n}' for n in ARM_LABELS]\n", + "policy_eval.sort_values('value', ascending=False)[display_cols]" ] }, { @@ -2059,443 +2080,44 @@ "\n", "모든 고객에게 동일한 처치를 적용하는 정책 중에서는 `all_tech_support_only`(10,527)가 가장 높았고, `all_discount_plus_support`(10,137)가 그 뒤를 이었습니다. 할인 비용이 고객 규모에 따라 증가하도록 설정했기 때문에, 일부 고객에서는 할인 추가 제공의 편익이 비용을 충분히 상쇄하지 못한 것으로 볼 수 있습니다.\n", "\n", - "전체적으로 학습된 세 정책 모두 가장 성능이 좋은 단일 처치 정책보다 높은 value를 보였습니다. 이는 모든 고객에게 동일한 처치를 적용하는 것보다 고객별 targeting이 더 효과적이라는 의미입니다." - ] - }, - { - "cell_type": "markdown", - "id": "6stfz2jhpt", - "metadata": {}, - "source": [ - "### 통계적 유의성\n", - "\n", - "정책 value의 숫자만 비교하는 것으로는 충분하지 않습니다. 표준오차를 함께 고려해 학습된 정책이 무처치 정책이나 최선 상수 정책보다 통계적으로 유의하게 높은지 확인합니다.\n", - "\n", - "정책 $\\pi$와 기준 정책 $\\pi_0$의 value 차이는 **페어 단위 차이**로 계산합니다.\n", - "\n", - "$$\n", - "\\hat\\Delta = \\frac{1}{n}\\sum_i \\left[\\hat\\Gamma_{i,\\pi(X_i)} - \\hat\\Gamma_{i,\\pi_0(X_i)}\\right]\n", - "$$\n", + "신뢰구간 기준으로 보면 `DRLearner plug-in`과 `DRPolicyForest`는 구간이 상당 부분 겹쳐 두 정책의 성능 차이가 크다고 보기는 어렵습니다. 반면 `DRPolicyTree`는 전체적으로 더 낮은 구간에 위치해, 해석 가능성을 유지하는 대신 일부 성능을 희생한 것으로 해석할 수 있습니다.\n", "\n", - "두 정책이 같은 test set에서 평가되므로 개인 단위에서 차이를 계산하면 공분산이 자연스럽게 반영됩니다. 95% 신뢰구간이 0을 포함하지 않으면 차이가 통계적으로 유의합니다." - ] - }, - { - "cell_type": "code", - "execution_count": 378, - "id": "l3irvvw886g", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - 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DRPolicyForest4,376[3,873, 4,879]1,099[426, 1,772]
DRPolicyTree3,899[3,470, 4,329]622[-1, 1,245]
DRLearner plug-in4,561[4,061, 5,061]1,284[622, 1,946]
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" - ], - "text/plain": [ - " vs_none (est) vs_none 95% CI \\\n", - "policy \n", - "DRPolicyForest 4,376 [3,873, 4,879] \n", - "DRPolicyTree 3,899 [3,470, 4,329] \n", - "DRLearner plug-in 4,561 [4,061, 5,061] \n", - "\n", - " vs_best_const [tech_only] (est) vs_best_const 95% CI \n", - "policy \n", - "DRPolicyForest 1,099 [426, 1,772] \n", - "DRPolicyTree 622 [-1, 1,245] \n", - "DRLearner plug-in 1,284 [622, 1,946] " - ] - }, - "execution_count": 378, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 불확실 비용 설정에서는 all_tech_support_only(arm 1)가 최선 상수 정책\n", - "sig_results = []\n", - "\n", - "for policy_name, pi_eval in [\n", - " ('DRPolicyForest', pi_forest),\n", - " ('DRPolicyTree', pi_tree),\n", - " ('DRLearner plug-in', pi_plugin),\n", - "]:\n", - " pi_eval = np.asarray(pi_eval).astype(int)\n", - " scores = gamma_net[np.arange(len(pi_eval)), pi_eval]\n", - "\n", - " # vs. 무처치(arm 0)\n", - " diff_none = scores - gamma_net[:, 0]\n", - " est_none = diff_none.mean()\n", - " se_none = diff_none.std(ddof=1) / np.sqrt(len(diff_none))\n", - "\n", - " # vs. 최선 상수 정책(arm 1: tech_support_only)\n", - " diff_best = scores - gamma_net[:, 1]\n", - " est_best = diff_best.mean()\n", - " se_best = diff_best.std(ddof=1) / np.sqrt(len(diff_best))\n", - "\n", - " sig_results.append({\n", - " 'policy': policy_name,\n", - " 'vs_none (est)': f\"{est_none:,.0f}\",\n", - " 'vs_none 95% CI': f\"[{est_none - 1.96*se_none:,.0f}, {est_none + 1.96*se_none:,.0f}]\",\n", - " 'vs_best_const [tech_only] (est)': f\"{est_best:,.0f}\",\n", - " 'vs_best_const 95% CI': f\"[{est_best - 1.96*se_best:,.0f}, {est_best + 1.96*se_best:,.0f}]\",\n", - " })\n", - "\n", - "pd.DataFrame(sig_results).set_index('policy')" - ] - }, - { - "cell_type": "markdown", - "id": "rgjw60hscrl", - "metadata": {}, - "source": [ - "세 학습 정책 모두 무처치 대비 통계적으로 유의하게 높습니다 (95% CI 전체가 0보다 큼). 최선 상수 정책(`tech_support_only`) 대비해서는 `DRLearner plug-in`과 `DRPolicyForest`가 유의하게 높고, `DRPolicyTree`는 95% CI가 `[-1, 1,245]`로 0을 경계에서 포함해 borderline입니다. Tree는 해석 가능성을 얻는 대신 일부 성능을 희생한 결과입니다." + "전체적으로는 세 학습 정책 모두 가장 성능이 좋은 단일 처치 정책보다 높은 value를 보였습니다. 이는 모든 고객에게 동일한 처치를 적용하는 것보다 고객별 targeting이 더 효과적이라는 의미입니다." ] }, { "cell_type": "markdown", - "id": "f877a9d9", + "id": "b40f71c0", "metadata": {}, "source": [ - "### 처치 배정 분석\n", + "## 결론\n", "\n", - "학습된 4-arm policy가 어떤 고객에게 어떤 bundle을 배정하는지 보기 위해, arm별 고객 특성 평균을 비교합니다. 이 표는 정책 해석에 중요합니다." - ] - }, - { - "cell_type": "code", - "execution_count": 379, - "id": "46be8ffe", - "metadata": { - "execution": { - "iopub.execute_input": "2026-05-09T16:49:57.643114Z", - "iopub.status.busy": "2026-05-09T16:49:57.642718Z", - "iopub.status.idle": "2026-05-09T16:49:57.657853Z", - "shell.execute_reply": "2026-05-09T16:49:57.657216Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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discount_plus_support201904.039.050099.0
\n", - "
" - ], - "text/plain": [ - " Size Employee Count IT Spend\n", - "assigned_arm \n", - "none 118619.0 142.0 29171.0\n", - "tech_support_only 48365.0 39.0 11818.0\n", - "discount_plus_support 201904.0 39.0 50099.0" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "assigned = pd.DataFrame(X_test, columns=covariates)\n", - "assigned['assigned_arm'] = pd.Series(pi_tree).map(ARM_NAMES).to_numpy()\n", + "고객 특성에 따라 처치를 다르게 배정한 세 정책 모두, 가장 성능이 좋은 단일 처치 정책(`all_tech_support_only`, 10,527)보다 높은 value를 기록했습니다.\n", "\n", - "key_cols = ['Size', 'Employee Count', 'IT Spend']\n", - "assigned_summary = assigned.groupby('assigned_arm')[key_cols].mean().reindex(ARM_LABELS).dropna(how='all')\n", - "display(assigned_summary.round(0))" - ] - }, - { - "cell_type": "code", - "execution_count": 380, - "id": "9d9ce0cd", - "metadata": { - "execution": { - "iopub.execute_input": "2026-05-09T16:49:57.659575Z", - "iopub.status.busy": "2026-05-09T16:49:57.659471Z", - "iopub.status.idle": "2026-05-09T16:49:57.756546Z", - "shell.execute_reply": "2026-05-09T16:49:57.756114Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "heat = assigned.groupby('assigned_arm')[covariates].mean().reindex(ARM_LABELS).dropna(how='all')\n", - "plt.figure(figsize=(10, 5))\n", - "sns.heatmap(heat.T, annot=True, fmt='.2f', cmap='YlGnBu', cbar_kws={'label': 'Mean'})\n", - "plt.xlabel('Assigned arm')\n", - "plt.ylabel('Covariate')\n", - "plt.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "id": "w6otkzfd3z", - "metadata": {}, - "source": [ - "### 하위집단 효과 차이\n", + "- DRLearner plug-in: 11,810\n", + "- DRPolicyForest: 11,626\n", + "- DRPolicyTree: 11,148\n", "\n", - "불확실 비용 설정에서 DRPolicyTree는 세 하위집단을 구분합니다. `discount_plus_support`(큰 고객), `tech_support_only`(중소 고객), `none`(기대 순편익이 낮은 고객). 처치 집단 간에 CATE proxy가 유의하게 다른지 검정합니다.\n", + "또한 세 정책 모두 무처치 정책 대비 통계적으로 유의하게 높은 성능을 보였습니다.\n", "\n", - "$$\n", - "H_0: E[\\hat\\Gamma_{i,\\pi(X_i)} - \\hat\\Gamma_{i,0} \\mid \\pi(X_i) = a] = E[\\hat\\Gamma_{i,\\pi(X_i)} - \\hat\\Gamma_{i,0} \\mid \\pi(X_i) = a']\n", - "$$\n", + "공통적으로는 규모가 큰 고객에게 `discount_plus_support`를, 상대적으로 작은 고객에게는 `tech_support_only`를 주로 배정했습니다. 반면 기대 순이익이 낮은 일부 고객에게는 `none`을 선택했습니다.\n", "\n", - "`none`이 배정된 집단은 CATE proxy가 정의상 0이므로 검정에서 제외합니다. 처치를 받는 두 집단(`tech_support_only`, `discount_plus_support`)의 CATE proxy 평균 차이가 유의하면, 정책이 실질적인 이질성을 포착하고 있다는 의미입니다." - ] - }, - { - "cell_type": "code", - "execution_count": 381, - "id": "onvd383ogm", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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CATE proxy (mean)SE95% CIcount
subgroup
discount_plus_support8232.760422464.881756[7,322, 9,144]326
none0.0000000.000000[0, 0]199
tech_support_only2558.393693258.876134[2,051, 3,066]475
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" - ], - "text/plain": [ - " CATE proxy (mean) SE 95% CI count\n", - "subgroup \n", - "discount_plus_support 8232.760422 464.881756 [7,322, 9,144] 326\n", - "none 0.000000 0.000000 [0, 0] 199\n", - "tech_support_only 2558.393693 258.876134 [2,051, 3,066] 475" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "t-test (처치 집단 간): t = 11.442, p = 0.0000\n", - "→ 두 처치 집단의 CATE 차이는 통계적으로 유의합니다 (α = 0.05).\n" - ] - } - ], - "source": [ - "# pi_tree 기준 하위집단별 CATE proxy\n", - "cate_proxy = np.array([\n", - " gamma_net[i, pi_tree[i]] - gamma_net[i, 0]\n", - " for i in range(len(pi_tree))\n", - "])\n", + "즉, 비용이 고객 규모에 따라 달라지는 환경에서도, 모든 고객에게 동일한 처치를 적용하는 것보다 고객 특성에 맞춰 targeting하는 전략이 더 효과적임을 확인할 수 있었습니다.\n", "\n", - "subgroup_labels = pd.Series(pi_tree).map(ARM_NAMES)\n", - "cate_by_group = pd.DataFrame({\n", - " 'subgroup': subgroup_labels.to_numpy(),\n", - " 'cate_proxy': cate_proxy,\n", - "})\n", + "### 참고 자료\n", "\n", - "summary = cate_by_group.groupby('subgroup')['cate_proxy'].agg(['mean', 'std', 'count'])\n", - "summary['se'] = summary['std'] / np.sqrt(summary['count'])\n", - "summary['95% CI'] = summary.apply(\n", - " lambda r: f\"[{r['mean'] - 1.96*r['se']:,.0f}, {r['mean'] + 1.96*r['se']:,.0f}]\",\n", - " axis=1,\n", - ")\n", - "display(summary[['mean', 'se', '95% CI', 'count']].rename(columns={'mean': 'CATE proxy (mean)', 'se': 'SE'}))\n", - "\n", - "# 처치 집단 간 차이 검정 (none 제외 — arm 0 vs arm 0 는 항상 0)\n", - "treated_groups = {g: d['cate_proxy'].values\n", - " for g, d in cate_by_group.groupby('subgroup')\n", - " if g != 'none'}\n", - "\n", - "if len(treated_groups) == 2:\n", - " g1, g2 = list(treated_groups.values())\n", - " t_stat, p_val = stats.ttest_ind(g1, g2)\n", - " print(f\"\\nt-test (처치 집단 간): t = {t_stat:.3f}, p = {p_val:.4f}\")\n", - " sig = \"유의\" if p_val < 0.05 else \"유의하지 않음\"\n", - " print(f\"→ 두 처치 집단의 CATE 차이는 통계적으로 {sig}합니다 (α = 0.05).\")" + "- **이 노트북의 주요 참고 튜토리얼**: [Stanford ML+CI Tutorial — Policy Learning I (Binary Treatment)](https://bookdown.org/stanfordgsbsilab/ml-ci-tutorial/policy-learning-i---binary-treatment.html?utm_source=chatgpt.com)\n", + "- **데이터셋**: [Software Usage Promotion Campaign Uplift Model (Kaggle)](https://www.kaggle.com/datasets/hwwang98/software-usage-promotion-campaign-uplift-model?utm_source=chatgpt.com)\n", + "- **모수적 정책학습 이론**: [Athey and Wager (2021, Econometrica)](https://onlinelibrary.wiley.com/doi/abs/10.3982/ECTA15732?utm_source=chatgpt.com) · [ArXiv Version (1702.02896)](https://arxiv.org/abs/1702.02896?utm_source=chatgpt.com)\n", + "- **비용 곡선을 통한 정책 비교**: [Imai and Li (2019)](https://arxiv.org/pdf/1905.05389.pdf?utm_source=chatgpt.com)\n", + "- **추가 참고**: [arXiv 2604.06123](https://arxiv.org/html/2604.06123v1?utm_source=chatgpt.com)" ] }, { "cell_type": "markdown", - "id": "b40f71c0", + "id": "e0ec9c4f", "metadata": {}, - "source": [ - "## 결론\n", - "\n", - "이 데이터는 `Discount`만 따로 보는 것보다 `Tech Support`와 `Discount`를 함께 보는 4-arm policy learning이 더 자연스럽습니다. arm별 표본도 비교적 균형적이고, propensity overlap도 나쁘지 않기 때문에 분석 가능성은 충분합니다.\n", - "\n", - "비용을 반영한 net value 기준에서 learned policy가 가장 좋은 상수 정책보다 높다면, 단순히 모든 고객에게 같은 intervention을 주는 것보다 고객별로 bundle을 다르게 배정할 가치가 있다는 뜻입니다.\n", - "\n", - "직관적인 CATE 기반 기준선으로는 `DRLearner` plug-in 정책을 보고, 해석 가능한 정책이 중요하면 `DRPolicyTree`를 중심으로 보며, 성능 중심 비교에는 `DRPolicyForest`를 함께 사용하면 됩니다.\n", - "\n", - "### 참고 자료\n", - "\n", - "- **이 노트북의 주 참고 튜토리얼**: [Stanford ML+CI Tutorial — Policy Learning I (Binary Treatment)](https://bookdown.org/stanfordgsbsilab/ml-ci-tutorial/policy-learning-i---binary-treatment.html)\n", - "- **데이터셋**: [Software Usage Promotion Campaign Uplift Model (Kaggle)](https://www.kaggle.com/datasets/hwwang98/software-usage-promotion-campaign-uplift-model)\n", - "- **모수적 정책학습 이론**: [Athey and Wager (2021, Econometrica)](https://onlinelibrary.wiley.com/doi/abs/10.3982/ECTA15732) · [ArXiv 버전 (1702.02896)](https://arxiv.org/abs/1702.02896)\n", - "- **비용 곡선을 통한 정책 비교**: [Imai and Li (2019)](https://arxiv.org/pdf/1905.05389.pdf)\n", - "- **추가 참고**: [arxiv 2604.06123](https://arxiv.org/html/2604.06123v1)" - ] + "source": [] } ], "metadata": { @@ -2519,4 +2141,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} diff --git a/requirements-book.txt b/requirements-book.txt index 898be45..0548471 100644 --- a/requirements-book.txt +++ b/requirements-book.txt @@ -5,6 +5,9 @@ pandas scipy matplotlib seaborn -scikit-learn +scikit-learn<1.7 statsmodels graphviz +econml +scikit-uplift +kagglehub diff --git a/requirements.txt b/requirements.txt index d010b0b..c75646f 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,6 @@ # Jupyter Book (2.x - MyST-MD 기반) jupyter-book>=2.0.0 +jupyterlab # Video Production manim>=0.18.0 @@ -9,6 +10,18 @@ numpy pandas matplotlib seaborn -scikit-learn +scikit-learn<1.7 +scikit-uplift statsmodels graphviz +econml +kagglehub +ipywidgets + +# PDF Processing +pypdf +pdfplumber +reportlab +pdf2image +pytesseract +Pillow From d974b7d32e49deeb0ed851858b34e2d8157557a6 Mon Sep 17 00:00:00 2001 From: Funbucket Date: Mon, 11 May 2026 01:24:44 +0900 Subject: [PATCH 04/11] =?UTF-8?q?docs(book):=20policy-learning=20Rmd=20?= =?UTF-8?q?=EC=82=AD=EC=A0=9C?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../policy-learning-1.Rmd | 709 ------------------ 1 file changed, 709 deletions(-) delete mode 100644 book/who_should_be_treated/policy-learning-1.Rmd diff --git a/book/who_should_be_treated/policy-learning-1.Rmd b/book/who_should_be_treated/policy-learning-1.Rmd deleted file mode 100644 index d757c40..0000000 --- a/book/who_should_be_treated/policy-learning-1.Rmd +++ /dev/null @@ -1,709 +0,0 @@ -\newcommand{\htau}{\widehat{\tau}} -\newcommand{\hmu}{\widehat{\mu}} -\newcommand{\hGamma}{\widehat{\Gamma}} -\newcommand{\he}{\widehat{e}} -\DeclareMathOperator{\E}{E} -\DeclareMathOperator{\PP}{P} -\newcommand{\p}[1]{\left( #1 \right)} -\newcommand{\hpi}{\hat{\pi}} - -```{r, echo=FALSE, warning=FALSE, message=FALSE, include=FALSE, results='hide'} -# Deleting all current variables -rm(list=ls()) - -# Ensuring consistent random values in the bookdown version (this can be ignored). -set.seed(1, kind = "Mersenne-Twister", normal.kind = "Inversion", sample.kind = "Rejection") - -# Note: bookdown seems to require explicitly calling these packages. -library(reshape2) -library(DiagrammeR) -``` - - -# Policy Learning I - Binary Treatment - - -Source RMD file: [link](https://docs.google.com/uc?export=download&id=1yI7RWqqe32DLXD7k66bgw5LT1_LcvO1a) - - -A few chapters ago, we learned how to estimate the average effect of a binary treatment (ATE), that is, the value of treating everyone in a population versus treating no one. Once that was established, we asked whether certain subgroups could react differently to the treatment, as we learned how to estimate such heterogeneous treatment effects (HTE). Then, in the previous chapter, we learned how to aggregate these heterogeneous effects to estimate the average outcome that would be attained if treatment assignment were to follow a particular rule, that is, if we were to treat only individuals with certain observable characteristics (policy evaluation). In this chapter, we will learn how to search the space of available treatment rules to approximately _maximize_ the average outcome across the population. That is, we will answer questions of the type: "_who_ should be treated?" We'll call this problem **policy learning**. - -We'll make a distinction between parametric and non-parametric policies, just as we did with predictive models. Parametric policies are simpler and depend only on a fixed number of parameters, whereas nonparametric policies can increase in complexity with the data. As we'll discuss below, there will be situations in which one or the other will be more appropriate. - -For now, we'll work with the same toy simulation setting that we used in the previous chapter. - -```{r, warning=FALSE, message=FALSE} -# use e.g., install.packages("grf") to install any of the following packages. -library(grf) -library(glmnet) -library(splines) -library(policytree) -library(ggplot2) -library(lmtest) -library(sandwich) -``` - -```{r} -# A randomized setting. -n <- 1000 -p <- 4 -X <- matrix(runif(n*p), n, p) -e <- .5 # fixed, known probability -W <- rbinom(n, prob = e, size = 1) -Y <- .5*(X[,1] - .5) + (X[,2] - .5)*W + .1 * rnorm(n) -data <- data.frame(x=X, y=Y, w=W) - -outcome <- "y" -covariates <- paste0("x.", seq(p)) -treatment <- "w" -``` - -## Non-parametric policies - -In the HTE chapter we define the conditional average treatment effect (CATE) function - -\begin{equation} - (\#eq:cate-oracle) - \tau(x) := \E[Y_i(1) - Y_i(0) | X_i = x], -\end{equation} -that is, the average effect of a binary treatment conditional on observable charateristics. - -If we knew \@ref(eq:cate-oracle), then a natural policy would be to assign individuals to treatment when their CATE is positive, -\begin{equation*} - \pi^{*} = \mathbb{I}\{\tau(x) \geq 0\}. -\end{equation*} - -More generally, if treating that individual costs a known amount $c(x)$, -\begin{equation*} - \pi^{*} = \mathbb{I}\{\tau(x) \geq c(x)\}. -\end{equation*} - -Of course, we don't know \@ref(eq:cate-oracle). However, we can obtain an estimate $\htau(\cdot)$ using any flexible (i.e., non-parametric) method as we learned in the HTE chapter, and then obtain a policy estimate -\begin{equation*} - \hpi(x) = \mathbb{I}\{ \htau(x) \geq 0\}, -\end{equation*} -replacing the zero threshold by some appropriate cost function if needed. - -Once we have an estimated policy, we need to estimate its value. To obtain accurate estimates, we must ensure appropriate **data-splitting**. We cannot estimate and evaluate a policy using the same data set, because that would lead to an overestimate of the value of the policy. One option here is to divide the data into training and test subsets, fit $\htau(\cdot)$ in the training subset and evaluate it in the test subset. This is analogous to what we saw in prediction problems: if we try to evaluate our predictions on the training set, we will overestimate how good our predictions are. - -The next snippet estimates the conditional treatment effect function via a Lasso model with splines. Note the data splitting. - -```{r} -# Preparing to run a regression with splines (piecewise polynomials). -# Note that if we have a lot of data we should increase the argument `df` below. -# The optimal value of `df` can be found by cross-validation -# i.e., check if the value of the policy, estimated below, increases or decreases as `df` varies. -fmla.xw <- formula(paste0("~", paste0("bs(", covariates, ", df=5) *", treatment, collapse="+"))) -XW <- model.matrix(fmla.xw, data) -Y <- data[,outcome] - -# Data-splitting -# Define training and evaluation sets -train <- sample(1:n, 0.5*n) -test <- -train - -# Fitting the outcome model on the *training* data -model.m <- cv.glmnet(XW[train,], Y[train]) - -# Predict outcome E[Y|X,W=w] for w in {0, 1} on the *test* data -data.0 <- data[test,] -data.0[,treatment] <- 0 -XW0 <- model.matrix(fmla.xw, data.0) -mu.hat.0 <- predict(model.m, XW0, s="lambda.min") - -data.1 <- data[test,] -data.1[,treatment] <- 1 -XW1 <- model.matrix(fmla.xw, data.1) -mu.hat.1 <- predict(model.m, XW1, s="lambda.min") - -# Computing the CATE estimate tau.hat -tau.hat <- mu.hat.1 - mu.hat.0 - -# Assignment if tau.hat is positive (or replace by non-zero cost if applicable) -pi.hat <- as.numeric(tau.hat > 0) - -# Estimate assignment probs e(x). -# (This will be useful for evaluation via AIPW scores a little later) - -# Uncomment/comment the next lines as appropriate -# In randomized settings assignment probabilities are fixed and known. -e.hat <- rep(0.5, length(test)) -# In observational setttings the assignment probability is typically unknown. -# fmla.x <- formula(paste0("~", paste0("bs(", covariates, ", df=3, degree=3)", collapse="+"))) -# XX <- model.matrix(fmla.x, data) -# model.e <- cv.glmnet(XX[train,], W[train], family="binomial") -# e.hat <- predict(model.e, XX[test,], s="lambda.min", type="response") -``` - -On the test set, we can evaluate this policy as we learned in the previous chapter. In a randomized setting, a simple estimator based on the difference in means is available. - -```{r} -# Only valid in randomized settings. -A <- pi.hat == 1 -Y <- data[test, outcome] -W <- data[test, treatment] -value.estimate <- mean(Y[A & (W==1)]) * mean(A) + mean(Y[!A & (W==0)]) * mean(!A) -value.stderr <- sqrt(var(Y[A & (W==1)]) / sum(A & (W==1)) * mean(A)^2 + var(Y[!A & (W==0)]) / sum(!A & W==0) * mean(!A)^2) -print(paste("Value estimate:", value.estimate, "Std. Error:", value.stderr)) -``` - -In a randomized setting and observational settings with unconfoundedness, an estimator of the policy value based on AIPW scores is available. In large samples, it should have smaller variance than the one based on sample averages. - -```{r} -# Valid in randomized settings and observational settings with unconfoundedness and overlap. -Y <- data[test, outcome] -W <- data[test, treatment] -gamma.hat.1 <- mu.hat.1 + W / e.hat * (Y - mu.hat.1) -gamma.hat.0 <- mu.hat.0 + (1 - W) / (1 - e.hat) * (Y - mu.hat.0) -gamma.hat.pi <- pi.hat * gamma.hat.1 + (1 - pi.hat) * gamma.hat.0 - -value.estimate <- mean(gamma.hat.pi) -value.stderr <- sd(gamma.hat.pi) / sqrt(length(gamma.hat.pi)) -print(paste("Value estimate:", value.estimate, "Std. Error:", value.stderr)) -``` - -Above we used a flexible linear model, but in fact we can also use any other non-parametric method. The next example uses `grf`. An advantage of using `grf` is that we can leverage [out-of-bag](https://github.com/grf-labs/grf/blob/master/REFERENCE.md#out-of-bag-prediction) predictions, so explicit data splitting is not necessary. - -```{r} -# Using the entire data -X <- data[,covariates] -Y <- data[,outcome] -W <- data[,treatment] - -# Comment / uncomment as approriate -# Randomized setting with known assignment probability (here, 0.5) -forest <- causal_forest(X, Y, W, W.hat=.5) -# Observational setting with unconfoundedness and overlap. -# forest <- causal_forest(X, Y, W) - -# Get "out-of-bag" predictions -tau.hat.oob <- predict(forest)$predictions -pi.hat <- as.numeric(tau.hat.oob > 0) -``` - -Again, to evaluate the value of this policy in a randomized setting, we can use the following estimator based on sample averages. - -```{r} -# Only valid in randomized settings. - -# We can use the entire data because predictions are out-of-bag -A <- pi.hat == 1 -value.estimate <- mean(Y[A & (W==1)]) * mean(A) + mean(Y[!A & (W==0)]) * mean(!A) -value.stderr <- sqrt(var(Y[A & (W==1)]) / sum(A & (W==1)) * mean(A)^2 + var(Y[!A & (W==0)]) / sum(!A & W==0) * mean(!A)^2) -print(paste("Value estimate:", value.estimate, "Std. Error:", value.stderr)) -``` - -And here's how to produce an AIPW-based estimate. Note that that estimates of the propensity scores (`W.hat`) and outcome model (`mu.hat.1`, `mu.hat.0`) are also [out-of-bag](https://github.com/grf-labs/grf/blob/master/REFERENCE.md#out-of-bag-prediction), ensuring appropriate sample splitting. - -```{r} -# Valid in randomized settings and observational settings with unconfoundedness and overlap. -tau.hat <- predict(forest)$predictions - -# Retrieve relevant quantities. -e.hat <- forest$W.hat # P[W=1|X] -mu.hat.1 <- forest$Y.hat + (1 - e.hat) * tau.hat # E[Y|X,W=1] = E[Y|X] + (1 - e(X)) * tau(X) -mu.hat.0 <- forest$Y.hat - e.hat * tau.hat # E[Y|X,W=0] = E[Y|X] - e(X) * tau(X) - -# Compute AIPW scores. -gamma.hat.1 <- mu.hat.1 + W / e.hat * (Y - mu.hat.1) -gamma.hat.0 <- mu.hat.0 + (1-W) / (1-e.hat) * (Y - mu.hat.0) -gamma.hat.pi <- pi.hat * gamma.hat.1 + (1 - pi.hat) * gamma.hat.0 - -# Value estimates. -value.estimate <- mean(gamma.hat.pi) -value.stderr <- sd(gamma.hat.pi) / sqrt(length(gamma.hat.pi)) -print(paste("Value estimate:", value.estimate, "Std. Error:", value.stderr)) -``` - - -A technical note. It's easy to get confused and try to "estimate" a nonparametric policy using AIPW scores, as in "$\hpi(X_i) = \mathbb{I}\{ \hGamma_{i,1} \geq \hGamma_{i,0} \}$". _This is incorrect_. AIPW scores are very noisy and should never be used "pointwise" like this. They should be used as part of an average (as above), or some other form of aggregation (as we'll see in the next section). - - - -## Parametric policies - -In many settings, there are good reasons to constrain the policy to belong to a smaller function class $\Pi$. The set $\Pi$ may contain only policies that, for example, are transparent and easy to explain to stakeholders, or that are easily implemented in the field. It may also be the case that the set of available policies $\Pi$ encodes other desirability criteria, such as satisfying certain budget constraints or depending only on a subset of observable characteristics. - -Estimating such a policy from data is finding an approximate solution to the following constrained maximization problem, -\begin{equation} - (\#eq:param-pi-oracle) - \pi^{*} = \arg\max_{\pi \in \Pi} \E[Y(\pi(X_i))]. -\end{equation} - -Following [Athey and Wager (2021, Econometrica)](https://onlinelibrary.wiley.com/doi/abs/10.3982/ECTA15732), we will use the following empirical counterpart of \@ref(eq:param-pi-oracle), -\begin{equation} - (\#eq:param-pi-problem) - \hpi = \arg\min_{\pi \in \Pi} \frac{1}{n} \sum_{i=1}^{n} \hGamma_{i,\pi(X_i)}, -\end{equation} -where $\hGamma_{i,\pi(X_i)}$ are AIPW scores as defined in the previous chapter. As reminder, -\begin{equation} - \hGamma_{i,\pi(X_i)} = \pi(X_i)\hGamma_{i,1} + (1 - \pi(X_i))\hGamma_{i,0}, -\end{equation} -where -\begin{align} - (\#eq:aipw) - \hGamma_{i,1} - &= \hat{\mu}^{-i}(X_i, 1) + \frac{W_i}{\hat{e}^{-i}(X_i)} \left(Y_i -\hat{\mu}^{-i}(X_i, 1)\right), \\ - \hGamma_{i,0} - &= \hat{\mu}^{-i}(X_i, 0) . \frac{1-W_i}{1-\hat{e}^{-i}(X_i)} \left(Y_i -\hat{\mu}^{-i}(X_i, 0)\right). -\end{align} - -Here we use shallow tree policies as our main example of parametric policies. The `R` package `policytree` can be used to find a policy that solves \@ref(eq:param-pi-problem). In the example below, we'll construct AIPW scores estimated using `grf`, though we could have used any other non-parametric method (with appropriate sample-splitting). See this short [tutorial](https://grf-labs.github.io/policytree/) for other examples using these two packages. - -Let's walk through an example for the data simulated above. The first step is to construct AIPW scores \@ref(eq:aipw). The function `double_robust_scores` from the `policytree` package does that in one line. - -```{r} -# Randomized setting: pass the known treatment assignment as an argument. -forest <- causal_forest(X, Y, W, W.hat=.5) -# Observational settting with unconfoundedness+overlap: let the assignment probabilities be estimated. -# forest <- causal_forest(X, Y, W) - -# from policytree package -gamma.matrix <- double_robust_scores(forest) - -# Note: the function double_robust_scores is equivalent to the following: -# tau.hat <- predict(forest)$predictions -# mu.hat.1 <- forest$Y.hat + (1 - forest$W.hat) * tau.hat -# mu.hat.0 <- forest$Y.hat - forest$W.hat * tau.hat -# gamma.hat.1 <- mu.hat.1 + W/forest$W.hat * (Y - mu.hat.1) -# gamma.hat.0 <- mu.hat.0 + (1-W)/(1-forest$W.hat) * (Y - mu.hat.0) -# gamma.matrix <- cbind(gamma.hat.0, gamma.hat.1) -``` - -Next, to ensure appropriate sample splitting, we divide our data into training and test subsets. We estimate the policy on the training subset and estimate its value on the test subset. - -```{r} -# Divide data into train and test sets -train <- sample(1:n, 0.5*n) -test <- -train - -# Train on a portion of the data -# The argument `depth` controls the depth of the tree. -# Depth k means that we can partition the data into up to 2^(k+1) regions. -policy <- policy_tree(X[train,], gamma.matrix[train,], depth=2) - -# Predict on remaining portion -# Note policytree recodes the treatments to 1,2 -# We substract one to get back to our usual encoding 0,1. -pi.hat <- predict(policy, X[test,]) - 1 -``` - -To understand the policy we just learned, we can print the tree splits. -```{r} -print(policy) -``` - -Alternatively, we can plot the tree. -```{r policy-tree, fig.align = 'center', fig.cap= "Learned policy"} -plot(policy, leaf.labels = c("control", "treatment")) -``` - -Note how the treatment rule is rather transparent, in that whether or not each individual is treated depends only on a couple of if-statements. This can be very attractive in settings in which it's important to explain the policy to stakeholders, or reason about its consequences in terms of fairness (e.g., is it okay that these particular subgroups get the treatment?), manipulability (e.g., will individuals lie about their observable characteristics to get a better outcome?), and so on. - -To evaluate the policy, we again use what we learned in the previous chapter, remembering that we can only use the test set for evaluation. In a randomized setting, we can use the following estimator based on sample averages. - -```{r} -# only valid for randomized setting! -A <- pi.hat == 1 -Y <- data[test, outcome] -W <- data[test, treatment] -value.estimate <- mean(Y[A & (W==1)]) * mean(A) + mean(Y[!A & (W==0)]) * mean(!A) -value.stderr <- sqrt(var(Y[A & (W==1)]) / sum(A & (W==1)) * mean(A)^2 + var(Y[!A & (W==0)]) / sum(!A & W==0) * mean(!A)^2) -print(paste("Value estimate:", value.estimate, "Std. Error:", value.stderr)) -``` - -Using the remaining AIPW scores produces an estimate that, in large samples, has smaller standard error. - -```{r} -# Valid in randomized settings and observational settings with unconfoundedness and overlap. -gamma.hat.pi <- pi.hat * gamma.matrix[test,2] + (1 - pi.hat) * gamma.matrix[test,1] -value.estimate <- mean(gamma.hat.pi) -value.stderr <- sd(gamma.hat.pi) / sqrt(length(gamma.hat.pi)) -print(paste("Value estimate:", value.estimate, "Std. Error:", value.stderr)) -``` - - -A technical note. Very small policy tree leaves make it hard to reliably evaluate policy values, in particular when the treatment is categorical with many levels. You can avoid small tree leaves increasing the `min.node.size` argument in `policy_tree`. - - -[Possible edit here: talk about cross-validation?] - - -## Case study - -Let's apply the methods above to our `welfare` dataset, as used in previous chapters. - -```{r, message=FALSE, warning=FALSE} -# Read in data -data <- read.csv("https://docs.google.com/uc?id=1AQva5-vDlgBcM_Tv9yrO8yMYRfQJgqo_&export=download") -n <- nrow(data) - -## NOTE: invert treatment and control, compared to the ATE and HTE chapters. -data$w <- 1 - data$w - -# Treatment is the wording of the question: -# 'does the the gov't spend too much on 'assistance to the poor' (control: 0) -# 'does the the gov't spend too much on "welfare"?' (treatment: 1) -treatment <- "w" - -# Outcome: 1 for 'yes', 0 for 'no' -outcome <- "y" - -# Additional covariates -covariates <- c("age", "polviews", "income", "educ", "marital", "sex") -``` - -It's important to note that there are different types of "heterogeneity" in treatment effects. Sometimes the effect of a treatment is positive throughout, and what changes is the magnitude of the effect. In this case, we would still like to treat everyone. On the other hand, sometimes the treatment effect is positive for certain subgroups and negative for others. The latter is a more interesting scenario for policy learning. - -In this dataset, however, the effect seems to be mostly positive throughout. That is, i.e., most individuals respond "yes" more often when they are asked about "welfare" than about "assistance to the poor." To make the problem more interesting, we'll artificially modify the problem by introducing a cost of asking about welfare. This is just for illustration here, although there are natural examples in which treatment is indeed costly. Note in the code below how we subtract a cost of `.3` from the AIPW scores associated with treatment. - - -```{r} -# Prepare data -X <- data[,covariates] -Y <- data[,outcome] -W <- data[,treatment] -cost <- .3 - -# Fit a policy tree on forest-based AIPW scores -forest <- causal_forest(X, Y, W) -gamma.matrix <- double_robust_scores(forest) -gamma.matrix[,2] <- gamma.matrix[,2] - cost # Subtracting cost of treatment - -# Divide data into train and evaluation sets -train <- sample(1:n, 0.8*n) -test <- -train - -# Fit policy on training subset -policy <- policy_tree(X[train,], gamma.matrix[train,], depth = 2, min.node.size=1) - -# Predicting treatment on test set -pi.hat <- predict(policy, X[test,]) - 1 - -# Predicting leaves (useful later) -leaf <- predict(policy, X[test,], type = "node.id") -num.leaves <- length(unique(leaf)) -``` - - -Examining the policy we just learned. - -```{r} -print(policy) -``` - - -```{r policy-tree-case-study, fig.align = 'center', fig.cap= "Learned policy", message=FALSE, warning=FALSE} -plot(policy, leaf.labels = c("control", "treatment")) -``` - -Estimating the value of the learned policy. Note in the code below that we must subtract the cost of treatment. - -```{r} -A <- pi.hat == 1 -Y.test <- data[test, outcome] -W.test <- data[test, treatment] - -# Only valid for randomized setting. -# Note the -cost here! -value.avg.estimate <- (mean(Y.test[A & (W.test==1)]) - cost) * mean(A) + mean(Y.test[!A & (W.test==0)]) * mean(!A) -value.avg.stderr <- sqrt(var(Y.test[A & (W.test==1)]) / sum(A & (W.test==1)) * mean(A)^2 + var(Y.test[!A & (W.test==0)]) / sum(!A & W.test==0) * mean(!A)^2) -print(paste("Estimate [sample avg]:", value.avg.estimate, "(", value.avg.stderr, ")")) - -# Valid in both randomized and obs setting with unconf + overlap. -gamma.hat.1 <- gamma.matrix[test,2] -gamma.hat.0 <- gamma.matrix[test,1] -gamma.hat.pi <- pi.hat * gamma.hat.1 + (1 - pi.hat) * gamma.hat.0 -value.aipw.estimate <- mean(gamma.hat.pi) -value.aipw.stderr <- sd(gamma.hat.pi) / sqrt(length(gamma.hat.pi)) -print(paste("Estimate [AIPW]:", value.aipw.estimate, "(", value.aipw.stderr, ")")) -``` - - -Testing whether the learned policy value is different from the value attained by the "no-treatment" policy. - -```{r} -# Only valid for randomized setting. -diff.estimate <- (mean(Y.test[A & (W.test==1)]) - cost - mean(Y.test[A & (W.test==0)])) * mean(A) -diff.stderr <- sqrt(var(Y.test[A & (W.test==1)]) / sum(A & (W.test==1)) + var(Y.test[A & (W.test==0)]) / sum(A & W.test==0)) * mean(A)^2 -print(paste("Difference estimate [sample avg]:", diff.estimate, "Std. Error:", diff.stderr)) - -# Valid in both randomized and obs setting with unconf + overlap. -gamma.hat.pi.diff <- gamma.hat.pi - gamma.hat.0 -diff.estimate <- mean(gamma.hat.pi.diff) -diff.stderr <- sd(gamma.hat.pi.diff) / sqrt(length(gamma.hat.pi.diff)) -print(paste("Difference estimate [aipw]:", diff.estimate, "Std. Error:", diff.stderr)) -``` - - -## Topics 1: Subgroups using learned policy - -The learned policy naturally induces interesting subgroups for which we expect the treatment effect to be different. With appropriate sample splitting, we can test that treatment effect is indeed different across "regions" defined by assignment under the learned policy, -\begin{equation} - H_0: \E[Y_i(1) - Y_i(0)| \hpi(X_i) = 1] = \E[Y_i(1) - Y_i(0)| \hpi(X_i) = 0]. -\end{equation} - -```{r} -# Only valid in randomized settings -fmla <- formula(paste0(outcome, "~ 0 + pi.hat + w:pi.hat")) -ols <- lm(fmla, data=transform(data[test,], pi.hat=factor(pi.hat))) -coefs <- coeftest(ols, vcov=vcovHC(ols, 'HC2'))[3:4,1:2] -coefs[,1] <- coefs[,1] - cost # subtracting cost -coefs -``` - -```{r} -# Valid in randomized settings and observational settings with unconfoundedness+overlap -ols <- lm(gamma.hat.1 - gamma.hat.0 ~ 0 + factor(pi.hat)) -coeftest(ols, vcov=vcovHC(ols, 'HC2'))[1:2,] -``` - - -If we learned a tree policy using the `policytree`, we can test whether treatment effects are different across leaves. - -\begin{equation} - H_0: \E[Y_i(1) - Y_i(0)| \text{Leaf} = 1] = \E[Y_i(1) - Y_i(0)| \text{Leaf} = \ell] \qquad \text{for }\ell \geq 2 -\end{equation} - -```{r} -# Only valid in randomized settings. -fmla <- paste0(outcome, ' ~ 0 + leaf + w:leaf') -ols <- lm(fmla, data=transform(data[test,], leaf=factor(leaf))) -coefs <- coeftest(ols, vcov=vcovHC(ols, 'HC2'))[,1:2] -interact <- grepl(":", rownames(coefs)) -coefs[interact,1] <- coefs[interact,1] - cost # subtracting cost -coefs[interact,] -``` - -```{r} -# Valid in randomized settings and observational settings with unconfoundedness+overlap. -gamma.hat.diff <- gamma.hat.1 - gamma.hat.0 -ols <- lm(gamma.hat.1 - gamma.hat.0 ~ 0 + factor(leaf)) -coeftest(ols, vcov=vcovHC(ols, 'HC2'))[,1:2] -``` - - -Finally, in Figure \@ref(fig:avg-cov-policy), as we have done in previous chapters, we can check how covariate averages vary across subgroups. This time, the subgroups are defined by treatment assignment under the learned policy. -\begin{equation} - H_0: \E[X_{ij} | \hpi(X_i) = 1] = \E[X_{ij} | \hpi(X_i) = 0] \qquad \text{for each covariate }j -\end{equation} - -```{r avg-cov-policy, fig.align = 'center', fig.cap= "Average covariate values within each group (defined by treatment assignment under the learned policy)"} -df <- lapply(covariates, function(covariate) { - fmla <- formula(paste0(covariate, " ~ 0 + factor(pi.hat)")) - ols <- lm(fmla, data=transform(data[test,], pi.hat=pi.hat)) - ols.res <- coeftest(ols, vcov=vcovHC(ols, "HC2")) - - # Retrieve results - avg <- ols.res[,1] - stderr <- ols.res[,2] - - # Tally up results - data.frame( - covariate, avg, stderr, pi.hat=factor(c('control', 'treatment')), - # Used for coloring - scaling=pnorm((avg - mean(avg))/sd(avg)), - # We will order based on how much variation is 'explained' by the averages - # relative to the total variation of the covariate in the data - variation=sd(avg) / sd(data[,covariate]), - # String to print in each cell in heatmap below - labels=paste0(signif(avg, 3), "\n", "(", signif(stderr, 3), ")")) -}) -df <- do.call(rbind, df) - -# a small optional trick to ensure heatmap will be in decreasing order of 'variation' -df$covariate <- reorder(df$covariate, order(df$variation)) - -# plot heatmap -ggplot(df) + - aes(pi.hat, covariate) + - geom_tile(aes(fill = scaling)) + - geom_text(aes(label = labels)) + - scale_fill_gradient(low = "#E1BE6A", high = "#40B0A6") + - theme_minimal() + - ylab("") + xlab("") + - theme(plot.title = element_text(size = 12, face = "bold"), - axis.text=element_text(size=11)) -``` - - - - -## Topics 2: Learning with uncertain costs - -In the previous section, treatment costs were known and (just for simplicity of exposition) fixed across covariate values. However, there are situations in which costs are unknown and must be learned from the data as well. In such situations, we may be interested only in policies that do not exceed a certain budget in expectation. - -Here, we follow [Sun, Du, Wager (2021)](https://arxiv.org/abs/2103.11066) for how to deal with this issue. Their formulation is as follows. In potential outcome notation, each observation can be described by the tuple $(X_i, Y_i(0), Y_i(1), C_i(0), C_i(1))$, where the new pair $(C_i(0), C_i(1))$ represents costs that would be realized if individuals were assigned to control or treatment. Of course, in the data we only observe the tuple $(X_i, W_i, Y_i, C_i)$, where $C_i \equiv C_i(W_i)$. We are interested in approximating the policy $\pi_B^*$ that maximizes the gain from treating relative to not treating anyone while keeping the average relative cost bounded by some known budget $B$, -\begin{equation} - \pi_B^*(x) := \arg\max \E[Y(\pi(X_i))] - \E[Y_i(0)] \quad \text{such that} \quad \E[C_i(\pi(X_i)) - C_i(0)] \leq B. -\end{equation} - -This paper demonstrates that the optimal policy has the following structure. First, we can order observations in decreasing order according to the following priority ranking, -\begin{equation} - (\#eq:rho) - \rho(x) := - \frac{\E[Y_i(1) - Y_i(0) | X_i = x]} - {\E[C_i(1) - C_i(0) | X_i = x]}. -\end{equation} -Then, we assign treatment in decreasing order \@ref(eq:rho) until we either treat everyone with positive $\rho(x)$ or the budget is met. The intuition is that individuals for which \@ref(eq:rho) is high have a high expected treatment effect relative to cost, so by assigning them first we obtain a cost-effective policy. We stop once there's no one else for which treatment is expected to be positive or we run out of resources. - -To obtain estimates $\hat{\rho}$ of \@ref(eq:rho) from the data, we have two options. The first is to estimate the numerator $\htau(x) = \E[Y_i(1) - Y_i(0) |X_i = x]$ and the denominator $\hat{\gamma}(x) = \E[C_i(1) - C_i(0) |X_i = x]$ separately, in a manner analogous to what we saw in the HTE chapter, and compute their ratio, producing the estimate $\hat{\rho}(x) = \htau(x) / \hat{\gamma}(x)$. We'll see a second option below. - -Let's put the first option into practice. For illustration, we will generate random costs for our data. We'll assume that the costs of treatment are drawn from a conditionally Exponential distribution, and that there are no costs for not treating. -```{r} -# Creating random costs. -data$costs <- C <- ifelse(data$w == 1, rexp(n=n, 1/(data$income * data$polviews)), 0) -``` - -Figure \@ref(fig:cost-curves) compares two kinds of policies. An "ignore costs" policy which, as the name suggests, orders individuals by $\hat{\tau}$ only without taking costs into account; and the "ratio" policy in which the numerator and denominator of \@ref(eq:rho) are estimated separately. The comparison is made via a **cost curve** that compares the cumulative benefit of treatment with its cumulative cost (both normalized to 1), for all possible budgets at once. More cost-effective policies hug the left corner of the graph more tightly, keeping away from the 45-degree line. - -```{r} -# Preprating data -X <- data[,covariates] -Y <- data[,outcome] -W <- data[,treatment] -C <- data[,'costs'] - -# Sample splitting. -# Note that we can't simply rely on out-of-bag observations here, because we're ranking *across* observations. -# This is the same issue we encountered when ranking observations according to predicted CATE in the HTE chapter. -train <- sample(1:n, 0.5*n) -test <- -train -train.treat <- which(W[train] == 1) - -# Because they will be useful to save computational time later, -# we'll estimate the outcome model and propensity score model separately. - -# Propensity score model. -# Comment / uncomment as appropriate. -# Randomized setting with fixed and known assignment probability (here: 0.5) -W.hat.train <- 0.5 -# Observational settings with unconfoundedness and overlap. -# e.forest <- regression_forest(X = X[train,], Y = W[train]) -# W.hat.train <- predict(e.forest)$predictions - -# Outcome model. -m.forest <- regression_forest(X = X[train,], Y = Y[train]) -Y.hat.train <- predict(m.forest)$predictions - -# Estimating the numerator. -tau.forest <- causal_forest(X = X[train,], Y = Y[train], W = W[train], W.hat = W.hat.train, Y.hat = Y.hat.train) - -# Estimating the denominator. -# Because costs for untreated observations are known to be zero, we're only after E[C(1)|X]. -# Under unconfoundedness, this can be estimated by regressing C on X using only the treated units. -gamma.forest <- regression_forest(X = X[train.treat,], Y = C[train.treat]) -gamma.hat.train <- predict(m.forest)$predictions -# If costs were not zero, we could use the following. -# gamma.forest <- causal_forest(X = X[train,], Y = C[train], W = W[train], W.hat = W.hat.train, Y.hat = Y.hat.train) - -# Compute predictions on test set -tau.hat <- predict(tau.forest, X[test,])$predictions -gamma.hat <- predict(gamma.forest, X[test,])$predictions - -# Rankings -rank.ignore <- order(tau.hat, decreasing = TRUE) -rank.direct <- order(tau.hat / gamma.hat, decreasing = TRUE) - -# IPW-based estimates of (normalized) treatment and cost -W.hat.test <- .5 -# W.hat.test <- predict(e.forest, X[test,])$predictions -n.test <- length(test) -treatment.ipw <- 1 / n.test * (W[test]/W.hat.test - (1 - W[test])/(1 - W.hat.test)) * Y[test] -cost.ipw <- 1 / n.test * W[test] / W.hat.test * C[test] - -# Cumulative benefit and cost of treatment (normalized) for a policy that ignores costs. -treatment.value.ignore <- cumsum(treatment.ipw[rank.ignore]) / sum(treatment.ipw) -treatment.cost.ignore <- cumsum(cost.ipw[rank.ignore]) / sum(cost.ipw) - -# Cumulative benefit and cost of treatment (normalized) for a policy that uses the ratio, estimated separately. -treatment.value.direct <- cumsum(treatment.ipw[rank.direct]) / sum(treatment.ipw) -treatment.cost.direct <- cumsum(cost.ipw[rank.direct]) / sum(cost.ipw) -``` - -```{r cost-curves, fig.align = 'center', fig.cap= "Cost curves"} -# Plotting -plot(treatment.cost.ignore, treatment.value.ignore, col=rgb(0.2,0.4,0.1,0.7), lwd = 3, type = "l", xlab="(Normalized) cumulative cost", ylab="(Normalized) cumulative value", las=1) -lines(treatment.cost.direct, treatment.value.direct, col=rgb(0.6,0.4,0.1,0.7), lwd = 3, type = "l") -abline(a = 0, b = 1, lty = 2) -legend("bottomright", legend = c("Ignoring costs", "Direct ratio"), col = c(rgb(0.2,0.4,0.1,0.7), rgb(0.8,0.4,0.1,0.7)), lwd=3) -``` - -To read this graph, we consider a point on the horizontal axis, representing a possible (normalized) budget constraint. At that point, whichever policy is higher is more cost-effective. In this example, we see that the "direct ratio" solution is much more cost-effective than the "ignore costs" one. - - -As the authors note, we can also estimate \@ref(eq:rho) in a second manner that targets the parameter $\rho(x)$ directly. First, they note that, under overlap and the following unconfoundedness assumption -\begin{equation} - \{Y_i(0), Y_i(1), C_i(1), C_i(0) \} \perp W_i | X_i -\end{equation} -we can rewrite \@ref(eq:rho) as -\begin{equation} - (\#eq:rho-iv) - \rho(x) := - \frac{\text{Cov}[Y_i, W_i | X_i = x]} - {\text{Cov}[C_i, W_i | X_i = x]}. -\end{equation} - -As readers with a little more background in causal inference may note, \@ref(eq:rho-iv) coincides with the definition of the conditional local average treatment effect (LATE) if we _were_ to take $W_i$ as an "instrumental variable" and $C_i$ as the "treatment". In fact, instrumental variable methods require entirely different assumptions, so the connection with instrumental variables is tenuous (see the paper for details), but mechanically \@ref(eq:rho-iv) still provides us with an estimation procedure: we can use any method used to estimate conditional LATE to produce an estimate $\hat{\rho}$. - - -```{r} -# Estimating rho(x) directly via instrumental forests. -# In observational settings, remove the argument W.hat. -iv.forest <- instrumental_forest(X = X[train,], - Y = Y[train], - W = C[train], # cost as 'treatment' - Z = W[train], # treatment as 'instrument' - Y.hat = Y.hat.train, - W.hat = NULL, # If costs are nonzero: predict(gamma.forest)$predictions, - Z.hat = tau.forest$W.hat) - -# Predict and compute and estimate of the ranking on a test set. -rho.iv <- predict(iv.forest, X[test,])$predictions -rank.iv <- order(rho.iv, decreasing = TRUE) - -# Cumulative benefit and cost of treatment (normalized) for a policy based on the IV analogy. -treatment.value.iv <- cumsum(treatment.ipw[rank.iv]) / sum(treatment.ipw) -treatment.cost.iv <- cumsum(cost.ipw[rank.iv]) / sum(cost.ipw) -``` - - -```{r cost-curves-late, fig.align = 'center', fig.cap= "Cost curves"} -# Plotting -plot(treatment.cost.ignore, treatment.value.ignore, col=rgb(0.2,0.4,0.1,0.7), lwd = 3, type = "l", xlab="(Normalized) cumulative cost", ylab="(Normalized) cumulative value", las=1) -lines(treatment.cost.direct, treatment.value.direct, col=rgb(0.6,0.4,0.1,0.7), lwd = 3, type = "l") -abline(a = 0, b = 1, lty = 2) -lines(treatment.cost.iv, treatment.value.iv, col=rgb(1,0,0,0.7), lwd = 3, type = "l") -abline(a = 0, b = 1, lty = 2) -legend("bottomright", legend = c("Ignoring costs", "Direct ratio", "Sun, Du, Wager (2021)"), col = c(rgb(0.2,0.4,0.1,0.7), rgb(0.8,0.4,0.1,0.7), rgb(1,0,0,0.7)), lwd=3) -``` - -In Figure \@ref(fig:cost-curves-late), both the "direct ratio" and the solution based on instrumental forests have similar performance. This isn't always the case. When the ratio $\rho(x)$ is simpler relative to $\tau(x)$ and $\gamma(x)$, the solution based on instrumental forests may perform better since it is estimating $\rho(x)$ directly, where the "direct ratio" solution needs to estimate the more complicated objects $\tau(x)$ and $\gamma(x)$ separately. At a high level, we should expect $\rho(x)$ to be relatively simpler when there is a strong relationship between $\tau(x)$ and $\gamma(x)$. Here, our simulated costs seem to be somewhat related to CATE (see Figure \@ref(fig:cost-cate)), but perhaps not strongly enough to make the instrumental forest solution noticeably better than the one based on ratios. - -```{r cost-cate, fig.align = 'center', fig.cap= "Estimated cost and estimated CATE"} -plot(gamma.hat, tau.hat, - pch=16, # shape is filled circle - col=rgb(0,0,0,0.1), # color is black with transparency 0.1 - las=1, - xlab="Estimated cost (normalized)", ylab="Estimated CATE (normalized)") -``` - - -The different policies can be compared by the area between the curves they trace and the 45-degree line, with higher values indicating better policies. - -```{r} -auc <- data.frame( - ignore=sum((treatment.value.ignore - treatment.cost.ignore) * diff((c(0, treatment.cost.ignore)))), - ratio=sum((treatment.value.direct - treatment.cost.direct) * diff((c(0, treatment.cost.direct)))), - iv=sum((treatment.value.iv - treatment.cost.iv) * diff((c(0, treatment.cost.iv)))) -) -auc -``` - - - -## Further reading - -The presentation of parametric policies was largely based on [Athey and Wager (2021, Econometrica)](https://onlinelibrary.wiley.com/doi/abs/10.3982/ECTA15732). A slightly more accessible version of some of the material in the published version can be found in an earlier [ArXiv version](https://arxiv.org/abs/1702.02896v1) of the same paper. Policy comparisons via cost curves can also be found in [Imai and Li (2019)](https://arxiv.org/pdf/1905.05389.pdf). - - From 7b63cb982ddb2d32b7a37314c32cc215c1b8abf5 Mon Sep 17 00:00:00 2001 From: Funbucket Date: Mon, 11 May 2026 02:24:06 +0900 Subject: [PATCH 05/11] =?UTF-8?q?docs(book):=20=EC=A0=95=EC=B1=85=ED=95=99?= =?UTF-8?q?=EC=8A=B5=20=EB=85=B8=ED=8A=B8=EB=B6=81=20=EA=B5=AC=EC=A1=B0=20?= =?UTF-8?q?=EC=A0=95=EB=A6=AC?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../who_should_be_treated_ko.ipynb | 70 +++++++++---------- 1 file changed, 33 insertions(+), 37 deletions(-) diff --git a/book/who_should_be_treated/who_should_be_treated_ko.ipynb b/book/who_should_be_treated/who_should_be_treated_ko.ipynb index 8289a48..e552ac1 100644 --- a/book/who_should_be_treated/who_should_be_treated_ko.ipynb +++ b/book/who_should_be_treated/who_should_be_treated_ko.ipynb @@ -17,14 +17,14 @@ "\n", "따라서 핵심 질문은 다음과 같습니다.\n", "\n", - "_\"어떤 고객에게 집중할 때 순편익이 가장 커질까요?\"_\n", + "_\"어떤 고객에게 집중할 때 순이익이 가장 커질까?\"_\n", "\n", - "정책학습(policy learning)은 가능한 처치 규칙들을 탐색하면서 전체 평균 순편익을 최대화하는 방법을 학습합니다. 즉, “누구를 처치해야 하는가?”라는 질문에 답하는 접근입니다." + "정책학습(policy learning)은 가능한 처치 규칙들을 탐색하면서 전체 평균 순수익을 최대화하는 방법을 학습합니다. 즉, “누구를 처치해야 하는가?”라는 질문에 답하는 접근입니다." ] }, { "cell_type": "code", - "execution_count": 90, + "execution_count": 2, "id": "fc2741f9", "metadata": { "execution": { @@ -79,7 +79,7 @@ "\n", "- 고객 특성: 각 고객의 산업 분야, 규모, 매출, 기술 프로필에 대한 세부 정보.\n", "- 개입: 고객에게 제공된 인센티브에 대한 정보.\n", - "- 결과: 인센티브 제공 후 1년 동안 고객이 구매한 제품의 양.\n", + "- 결과: 인센티브 제공 이후 1년 동안의 소프트웨어 구매 금액\n", "\n", "| 변수명 | 타입 | 설명 |\n", "| --------------- | -- | -------------------------------------------------------------- |\n", @@ -102,26 +102,16 @@ "A_i \\in \\{0, 1, 2, 3\\}\n", "$$\n", "\n", - "여기서 $A=0$은 아무 개입도 없는 경우, $A=1$은 기술지원만 받은 경우, $A=2$는 할인만 받은 경우, $A=3$은 기술지원과 할인을 모두 받은 경우입니다.\n", - "\n", - "정책학습 문제는 다음처럼 설정합니다.\n", - "\n", - "$$\n", - "Y_i = \\text{Revenue}_i\n", - "$$\n", - "\n", - "$$\n", - "X_i = \\text{customer characteristics}_i\n", - "$$\n", - "\n", - "$$\n", - "A_i = \\text{assigned intervention bundle}_i\n", - "$$" + "각 처치는 다음을 의미합니다.\n", + "- $A_i = 0$: 아무 개입도 제공하지 않음\n", + "- $A_i = 1$: 기술지원만 제공\n", + "- $A_i = 2$: 할인만 제공\n", + "- $A_i = 3$: 기술지원과 할인을 모두 제공" ] }, { "cell_type": "code", - "execution_count": 91, + "execution_count": 3, "id": "477aed7a", "metadata": { "execution": { @@ -283,7 +273,7 @@ "4 discount_plus_support " ] }, - "execution_count": 91, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -343,11 +333,17 @@ "\n", "예를 들어 기술지원은 직원 수가 많은 기업일수록 더 많은 지원 시간이 필요할 수 있고, 할인 역시 규모가 큰 고객일수록 더 큰 할인 폭을 요구할 수 있습니다.\n", "\n", - "하지만 이 데이터에는 실제 비용 정보가 포함되어 있지 않습니다. 따라서 여기서는 고객 특성에 따라 달라지는 비용을 시뮬레이션해 사용합니다.\n", + "하지만 이 데이터에는 실제 비용 정보가 포함되어 있지 않기 때문에 고객 특성에 따라 달라지는 비용을 시뮬레이션해 사용합니다.\n", + "\n", + "기술지원 비용은 직원 수가 많을수록 증가하도록 설정하고, 할인 비용은 고객 규모가 클수록 증가하도록 설정합니다.\n", + "\n", + "이떄 로그정규분포(LogNormal)를 사용해 비용은 항상 양수이고, 일부 고객에서 큰 비용이 발생할 수 있는 현실적인 분포를 반영합니다.\n", "\n", "$$\n", "C_i(\\text{tech}) \\sim \\text{LogNormal}(\\log C_{\\text{tech}} + 0.5 \\cdot \\tilde{e}_i,\\ 0.3)\n", - "\\qquad\n", + "$$\n", + "\n", + "$$\n", "C_i(\\text{disc}) \\sim \\text{LogNormal}(\\log C_{\\text{disc}} + 0.4 \\cdot \\tilde{s}_i,\\ 0.3)\n", "$$\n", "\n", @@ -362,7 +358,7 @@ }, { "cell_type": "code", - "execution_count": 92, + "execution_count": 4, "id": "5174ddd2", "metadata": { "execution": { @@ -452,7 +448,7 @@ "3 14118.841353 " ] }, - "execution_count": 92, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -499,19 +495,19 @@ "id": "024e5803", "metadata": {}, "source": [ - "### 탐색과 진단\n", + "### 데이터 탐색 및 진단\n", "\n", "정책학습에 들어가기 전에, 먼저 네 가지 처치 조합($A \\in {0,1,2,3}$)에 대해 데이터의 구조를 점검합니다.\n", "\n", - "1. **처치 조합별 표본 수**: 각 arm에 충분한 관측치가 있는가?\n", - "2. **처치 조합별 고객 특성 평균**: 어떤 고객군이 어떤 arm을 받았는가?\n", - "3. **multi-class propensity overlap**: 각 고객군에서 네 arm이 모두 어느 정도 관측되는가?\n", - "4. **처치 조합별 Revenue 분포**: outcome scale, outlier, variance가 arm별로 어떤가?" + "1. 처치별 표본 수: 각 처치에 충분한 관측치가 존재하는가?\n", + "2. 처치별 고객 특성 평균: 어떤 고객군이 특정 처치를 받는 경향이 있는가?\n", + "3. 처치별 Revenue 분포: Revenue의 규모, 분산, 이상치 분포가 처치마다 어떻게 다른가?\n", + "4. Propensity Check: 고객 특성 전반에서 각 처치가 충분히 함께 관측되는가?" ] }, { "cell_type": "code", - "execution_count": 93, + "execution_count": 5, "id": "3d0f329e", "metadata": {}, "outputs": [ @@ -609,7 +605,7 @@ }, { "cell_type": "code", - "execution_count": 94, + "execution_count": 6, "id": "d6488c62", "metadata": {}, "outputs": [ @@ -659,7 +655,7 @@ }, { "cell_type": "code", - "execution_count": 95, + "execution_count": 7, "id": "c925a3d8", "metadata": {}, "outputs": [ @@ -821,11 +817,11 @@ "id": "61b7329f", "metadata": {}, "source": [ - "### Positivity Check\n", + "다음으로 positivity(overlap)를 확인합니다. \n", "\n", - "정책학습에서는 고객 특성 $X$의 여러 영역에서 각 처치가 함께 관측되어야 합니다. 즉, propensity 분포가 서로 어느 정도 겹쳐(overlap) 있어야 합니다.\n", + "이번 데이터는 무작위 실험이 아닌 관찰 데이터이므로, 이후 AIPW 기반 정책학습 및 평가를 위해 propensity score를 추정해야 합니다.\n", "\n", - "multi-class propensity는 다음과 같이 정의합니다.\n", + "이때 중요한 가정이 positivity입니다. 즉, 고객 특성 $X$의 여러 영역에서 각 처치가 모두 어느 정도 관측되어야 합니다.\n", "\n", "$$\n", "e_a(x) = P(A_i = a \\mid X_i=x)\n", @@ -2122,7 +2118,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": ".venv", "language": "python", "name": "python3" }, From 6e1f755d8ac99ed41de9ae0f36b3df5780c9542b Mon Sep 17 00:00:00 2001 From: Funbucket Date: Mon, 11 May 2026 18:19:57 +0900 Subject: [PATCH 06/11] =?UTF-8?q?docs(book):=20=ED=8F=AC=EC=A7=80=ED=8B=B0?= =?UTF-8?q?=EB=B9=84=ED=8B=B0=20=EC=B2=B4=ED=81=AC=20=EB=B0=8F=20=EC=A0=95?= =?UTF-8?q?=EC=B1=85=20=EC=84=A4=EB=AA=85=20=EB=B3=B4=EA=B0=95?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- book/who_should_be_treated/who_should_be_treated_ko.ipynb | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/book/who_should_be_treated/who_should_be_treated_ko.ipynb b/book/who_should_be_treated/who_should_be_treated_ko.ipynb index e552ac1..81ce860 100644 --- a/book/who_should_be_treated/who_should_be_treated_ko.ipynb +++ b/book/who_should_be_treated/who_should_be_treated_ko.ipynb @@ -502,7 +502,7 @@ "1. 처치별 표본 수: 각 처치에 충분한 관측치가 존재하는가?\n", "2. 처치별 고객 특성 평균: 어떤 고객군이 특정 처치를 받는 경향이 있는가?\n", "3. 처치별 Revenue 분포: Revenue의 규모, 분산, 이상치 분포가 처치마다 어떻게 다른가?\n", - "4. Propensity Check: 고객 특성 전반에서 각 처치가 충분히 함께 관측되는가?" + "4. Positivity Check: 고객 특성 전반에서 각 처치가 충분히 함께 관측되는가?" ] }, { @@ -1032,7 +1032,9 @@ "\n", " DRPolicyForest 역시 AIPW score를 직접 최대화하지만, 단일 트리 대신 forest 구조를 사용합니다.\n", "\n", - " 따라서 tree보다 복잡한 이질성을 더 잘 학습할 수 있지만, 개별 규칙을 해석하기는 더 어렵습니다.\n", + " 단일 tree는 구조가 단순하지만 분산이 커질 수 있습니다. 반면 forest는 여러 트리의 결과를 평균적으로 활용하기 때문에 분산을 줄이면서 더 안정적으로 이질적인 처치 효과를 학습할 수 있습니다.\n", + "\n", + " 대신 최종 정책을 하나의 명확한 if-then 규칙 형태로 해석하기는 더 어렵습니다.\n", "\n", "세 정책은 모두 train set에서 학습하고, 동일한 test set의 AIPW score로 정책 가치를 비교합니다." ] From 1818f6d736e0aec8b4e40b0fea44a4e1a722cca6 Mon Sep 17 00:00:00 2001 From: Funbucket Date: Wed, 13 May 2026 01:22:27 +0900 Subject: [PATCH 07/11] =?UTF-8?q?docs(who=5Fshould=5Fbe=5Ftreated):=20?= =?UTF-8?q?=EB=85=B8=ED=8A=B8=EB=B6=81=20=EC=A0=84=EB=A9=B4=20=EB=A6=AC?= =?UTF-8?q?=EB=B7=B0=20=EB=B0=8F=20=EA=B0=9C=EC=84=A0?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - 식별 가정(Unconfoundedness + Positivity) 섹션 추가 - 데이터 분할 셀 분리 및 설명 추가 - Doubly robust 설명 개선 (outcome/propensity model 역할 명시) - 평가 지표 구성(AIPW) 섹션을 독립 h2로 분리 - AIPW pointwise 경고 주석 추가 (Plug-in Policy) - 예산 제약 하의 정책 비교 섹션 추가 (비용 곡선, 유의성 검정) - 비용 곡선 수치 해석 및 예산 구간별 최선 정책 설명 추가 - 참고자료 형식 통일 및 Sun, Du, Wager (2021) 추가 - meme 이미지(AJStyles&Undertaker) 도입부에 추가 - 용어 통일: 순이익 → 순수익, arm → 처치 - 주석 한국어 혼용으로 수정 Co-Authored-By: Claude Sonnet 4.6 --- .../assets/AJStyles&Undertaker.jpg | Bin 0 -> 66855 bytes .../who_should_be_treated_ko.ipynb | 398 +++++++++++++----- 2 files changed, 295 insertions(+), 103 deletions(-) create mode 100644 book/who_should_be_treated/assets/AJStyles&Undertaker.jpg diff --git a/book/who_should_be_treated/assets/AJStyles&Undertaker.jpg b/book/who_should_be_treated/assets/AJStyles&Undertaker.jpg new file mode 100644 index 0000000000000000000000000000000000000000..4782de92e91edf7aedea3f0f1941bdb2eefd82ff GIT binary patch literal 66855 zcmb5Ug;!hM6D~|CP_)IhK!M=yR-lF8?h>px1lQs(#l5&gad!z$an}SdUYy`={qp

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b/book/who_should_be_treated/who_should_be_treated_ko.ipynb @@ -22,9 +22,17 @@ "정책학습(policy learning)은 가능한 처치 규칙들을 탐색하면서 전체 평균 순수익을 최대화하는 방법을 학습합니다. 즉, “누구를 처치해야 하는가?”라는 질문에 답하는 접근입니다." ] }, + { + "cell_type": "markdown", + "id": "2b06ptb53eu", + "metadata": {}, + "source": [ + "" + ] + }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 61, "id": "fc2741f9", "metadata": { "execution": { @@ -73,7 +81,7 @@ "\n", "한 소프트웨어 판매 회사는 할인이나 기술지원 같은 인센티브가 고객의 소프트웨어 구매를 실제로 늘리는지, 그리고 어떤 고객에게 더 효과적인지 알고 싶어합니다.\n", "\n", - "이상적으로는 고객마다 서로 다른 인센티브를 무작위로 배정하는 실험(RCT)을 수행하는 것이 가장 좋지만 실제 비즈니스 환경에서는 비용 문제, 영업 전략, 대형 고객을 놓칠 위험 등으로 인해 이러한 실험을 수행하기 어렵습니다. 따라서 이번 데이터는 무작위 실험 데이터가 아닌 관찰 데이터(observational data)입니다.\n", + "이상적으로는 고객마다 서로 다른 인센티브를 무작위로 배정하는 실험(RCT)을 수행하는 것이 가장 좋지만 실제 비즈니스 환경에서는 비용 문제, 영업 전략, 대형 고객을 놓칠 위험 등으로 인해 무작위 실험을 수행하기 어렵습니다. 따라서 이번 데이터는 무작위 실험 데이터가 아닌 관찰 데이터(observational data)입니다.\n", "\n", "데이터에는 약 2,000명의 고객 정보가 포함되어 있으며, 다음과 같은 변수들로 구성됩니다.\n", "\n", @@ -111,7 +119,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 62, "id": "477aed7a", "metadata": { "execution": { @@ -273,7 +281,7 @@ "4 discount_plus_support " ] }, - "execution_count": 3, + "execution_count": 62, "metadata": {}, "output_type": "execute_result" } @@ -337,7 +345,7 @@ "\n", "기술지원 비용은 직원 수가 많을수록 증가하도록 설정하고, 할인 비용은 고객 규모가 클수록 증가하도록 설정합니다.\n", "\n", - "이떄 로그정규분포(LogNormal)를 사용해 비용은 항상 양수이고, 일부 고객에서 큰 비용이 발생할 수 있는 현실적인 분포를 반영합니다.\n", + "이때 로그정규분포를 사용해 비용은 항상 양수이고, 일부 고객에서 큰 비용이 발생할 수 있는 현실적인 분포를 반영합니다.\n", "\n", "$$\n", "C_i(\\text{tech}) \\sim \\text{LogNormal}(\\log C_{\\text{tech}} + 0.5 \\cdot \\tilde{e}_i,\\ 0.3)\n", @@ -349,16 +357,18 @@ "\n", "여기서 $\\tilde{e}_i$는 표준화된 직원 수, $\\tilde{s}_i$는 표준화된 회사 규모입니다. \n", "\n", - "이렇게 정의한 고객별 비용을 반영해, 최종 순이익(net outcome)은 다음과 같습니다.\n", + "이렇게 정의한 고객별 비용을 반영한 최종 순이익(net outcome)은 다음과 같습니다.\n", "\n", "$$\n", "Y_i^{net} = Y_i - C_i(A_i)\n", - "$$" + "$$\n", + "\n", + "이후 $Y_i^{net}$을 outcome으로 사용해 AIPW score를 계산하면, $\\hat\\Gamma_{i,a}$는 자연스럽게 $E[Y(a) - C(a) \\mid X_i]$를 추정합니다. 따라서 AIPW 기반 정책학습과 평가가 자동으로 비용을 반영합니다." ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 63, "id": "5174ddd2", "metadata": { "execution": { @@ -448,7 +458,7 @@ "3 14118.841353 " ] }, - "execution_count": 4, + "execution_count": 63, "metadata": {}, "output_type": "execute_result" } @@ -507,7 +517,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 64, "id": "3d0f329e", "metadata": {}, "outputs": [ @@ -605,13 +615,13 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 65, "id": "d6488c62", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -630,9 +640,9 @@ "\n", "plt.figure(figsize=(10, 5))\n", "sns.heatmap(arm_covariate_means.T, annot=True, fmt='.2f', cmap='YlGnBu', cbar_kws={'label': 'Mean'})\n", - "plt.xlabel('Arm')\n", + "plt.xlabel('Treatments')\n", "plt.ylabel('Covariate')\n", - "plt.title('Mean customer characteristics by arm')\n", + "plt.title('Mean customer characteristics by treatments')\n", "plt.tight_layout()" ] }, @@ -650,12 +660,12 @@ "source": [ "처치별 고객 특성은 완전히 동일하지 않습니다. `Size` 평균은 `none` 그룹의 약 70,943에서 `discount_plus_support` 그룹의 약 171,467까지 큰 차이를 보이며, `IT Spend` 역시 약 18,056에서 41,371까지 차이가 납니다. 즉, 규모가 큰 고객일수록 기술지원과 할인을 함께 제공받는 경향이 있습니다.\n", "\n", - "이는 처치 배정이 고객 특성과 독립적이지 않음을 의미합니다. 따라서 이후 정책학습에서는 propensity model과 outcome model을 사용해 이러한 차이를 보정해야 합니다." + "이는 처치 배정이 고객 특성과 독립적이지 않음을 의미합니다. 따라서 이후 정책학습과 평가에서는 AIPW를 사용해 이러한 차이를 보정합니다." ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 66, "id": "c925a3d8", "metadata": {}, "outputs": [ @@ -807,32 +817,83 @@ "source": [ "Revenue 평균은 `none` 약 6,586, `discount_only` 약 12,248, `tech_support_only` 약 15,104, `discount_plus_support` 약 26,784로 나타납니다.\n", "\n", - "하지만 이 차이를 그대로 처치 효과로 해석해서는 안 됩니다. 앞서 확인했듯이 규모가 큰 고객일수록 더 강한 개입을 받는 경향이 있기 때문입니다. 즉, Revenue 차이에는 고객 특성의 영향과 실제 처치 효과가 함께 섞여 있습니다.\n", + "앞서 확인했듯이 규모가 큰 고객일수록 더 강한 개입을 받는 경향이 있기 때문에 평균 차이를 그대로 정책을 평가하면 안 됩니다. 즉, Revenue 차이에는 고객 특성의 영향과 실제 처치 효과가 함께 섞여 있습니다.\n", "\n", "따라서 단순 평균 비교만으로 정책을 판단하기보다는, 이후 AIPW 기반 정책 평가를 통해 비교해야 합니다." ] }, { "cell_type": "markdown", - "id": "61b7329f", + "id": "0b51rgb0z128", "metadata": {}, "source": [ - "다음으로 positivity(overlap)를 확인합니다. \n", + "### 식별 가정\n", "\n", - "이번 데이터는 무작위 실험이 아닌 관찰 데이터이므로, 이후 AIPW 기반 정책학습 및 평가를 위해 propensity score를 추정해야 합니다.\n", + "관찰 데이터에서 AIPW 추정이 인과적으로 유효하려면 두 가지 가정이 필요합니다.\n", "\n", - "이때 중요한 가정이 positivity입니다. 즉, 고객 특성 $X$의 여러 영역에서 각 처치가 모두 어느 정도 관측되어야 합니다.\n", + "1. Unconfoundedness\n", + "\n", + " $$\\{Y_i(0), Y_i(1), Y_i(2), Y_i(3)\\} \\perp A_i \\mid X_i$$\n", + "\n", + " 관측된 공변량 $X_i$를 조건부로 했을 때, 처치 배정은 잠재 결과와 독립이어야 합니다. 즉, 고객 규모, IT 지출, 직원 수 등의 변수들이 처치 배정을 충분히 설명한다고 가정합니다.\n", + "\n", + "2. Positivity\n", + "\n", + " $$P(A_i = a \\mid X_i = x) > 0 \\quad \\forall a \\in \\{0,1,2,3\\},\\ \\forall x$$\n", + "\n", + " 모든 고객 특성 범위에서 각 처치가 어느 정도 관측되어야 합니다." + ] + }, + { + "cell_type": "markdown", + "id": "61b7329f", + "metadata": {}, + "source": [ + "이 중 Positivity는 propensity score를 추정해 직접 확인합니다.\n", "\n", "$$\n", "e_a(x) = P(A_i = a \\mid X_i=x)\n", "$$\n", "\n", - "또한 propensity score가 극단적으로 치우쳐 있지 않은지도 확인합니다. 만약 특정 고객군에서 어떤 처치의 $e_a(x)$ 값이 0에 매우 가까우면, 일부 관측치에 지나치게 큰 가중치가 부여되어 추정이 불안정해질 수 있습니다." + "추가로 propensity score가 극단적으로 치우쳐 있지 않은지도 확인합니다. 만약 특정 고객군에서 어떤 처치의 $e_a(x)$ 값이 0에 매우 가까우면, 일부 관측치에 지나치게 큰 가중치가 부여되어 추정이 불안정해질 수 있습니다." + ] + }, + { + "cell_type": "markdown", + "id": "pl2exzwbceb", + "metadata": {}, + "source": [ + "### 데이터 분할\n", + "\n", + "정책학습과 평가에 동일한 데이터를 사용하면 과대추정이 발생합니다. 이를 방지하기 위해 데이터를 train/test로 분리합니다. 정책은 train set으로 학습하고, 평가는 test set의 AIPW score로 수행합니다.\n", + "\n", + "Positivity 확인을 위한 propensity 추정도 동일한 분할을 사용합니다." ] }, { "cell_type": "code", - "execution_count": 96, + "execution_count": 67, + "id": "n7ziuiv612q", + "metadata": {}, + "outputs": [], + "source": [ + "idx = np.arange(len(policy_df))\n", + "train_idx, test_idx = train_test_split(\n", + " idx,\n", + " test_size=0.5,\n", + " stratify=A,\n", + " random_state=SEED,\n", + ")\n", + "\n", + "X_train = X[train_idx]\n", + "X_test = X[test_idx]\n", + "A_train = A[train_idx]\n", + "A_test = A[test_idx]" + ] + }, + { + "cell_type": "code", + "execution_count": 68, "id": "9be31313", "metadata": {}, "outputs": [ @@ -920,21 +981,6 @@ } ], "source": [ - "idx = np.arange(len(policy_df))\n", - "train_idx, test_idx = train_test_split(\n", - " idx,\n", - " test_size=0.5,\n", - " stratify=A,\n", - " random_state=SEED,\n", - ")\n", - "\n", - "X_train = X[train_idx]\n", - "X_test = X[test_idx]\n", - "A_train = A[train_idx]\n", - "A_test = A[test_idx]\n", - "Y_train = Y[train_idx]\n", - "Y_test = Y[test_idx]\n", - "\n", "multi_propensity = RandomForestClassifier(\n", " n_estimators=400,\n", " min_samples_leaf=20,\n", @@ -956,7 +1002,7 @@ }, { "cell_type": "code", - "execution_count": 97, + "execution_count": 69, "id": "fl52yekx049", "metadata": {}, "outputs": [ @@ -1036,7 +1082,7 @@ "\n", " 대신 최종 정책을 하나의 명확한 if-then 규칙 형태로 해석하기는 더 어렵습니다.\n", "\n", - "세 정책은 모두 train set에서 학습하고, 동일한 test set의 AIPW score로 정책 가치를 비교합니다." + "세 정책은 모두 train set에서 학습하고, 동일한 test set의 AIPW score로 정책 가치를 비교합니다. 학습에 사용한 데이터로 정책을 평가하면 과대추정이 발생하므로, 학습과 평가에 서로 다른 데이터를 사용하는 것이 중요합니다." ] }, { @@ -1044,7 +1090,7 @@ "id": "d7d4bc26", "metadata": {}, "source": [ - "### 평가 지표 구성\n", + "## 평가 지표 구성\n", "\n", "AIPW(Augmented Inverse Probability Weighting) score는 다음과 같이 정의합니다.\n", "\n", @@ -1056,12 +1102,17 @@ "- $\\hat e_a(X_i)$: propensity model이 예측한 $P(A_i=a\\mid X_i)$\n", "- 두 번째 항은 실제 관측값과 예측값의 차이를 IPW로 보정하는 역할을 합니다.\n", "\n", - "이 구조 덕분에 outcome model과 propensity model 중 하나만 올바르게 추정되어도 불편성이 유지됩니다. 이를 doubly robust(이중 견고성)라고 부릅니다." + "이 구조 덕분에 outcome model과 propensity model 중 하나만 올바르게 추정되어도 불편성이 유지됩니다. 이를 doubly robust(이중 견고성)라고 부릅니다.\n", + "\n", + "- outcome model이 정확하면: IPW 보정항의 기댓값이 0에 가까워져 $\\hat\\mu_a(X_i)$가 직접 기여합니다.\n", + "- propensity model이 정확하면: IPW 보정항이 outcome model의 편향을 제거합니다.\n", + "\n", + "이 score는 정책학습(DRPolicyTree/Forest)과 정책 평가 모두에서 사용합니다." ] }, { "cell_type": "code", - "execution_count": 98, + "execution_count": 70, "id": "e3156595", "metadata": { "execution": { @@ -1076,16 +1127,7 @@ "Y_net_train = Y_net[train_idx]\n", "Y_net_test = Y_net[test_idx]\n", "\n", - "prop_model_net = RandomForestClassifier(\n", - " n_estimators=400,\n", - " min_samples_leaf=20,\n", - " random_state=SEED,\n", - " n_jobs=1,\n", - ")\n", - "\n", - "prop_model_net.fit(X_train, A_train)\n", - "e_hat_net = prop_model_net.predict_proba(X_test)\n", - "e_hat_net = np.clip(e_hat_net, 0.02, 0.98)\n", + "e_hat_net = np.clip(e_hat_raw, 0.02, 0.98)\n", "e_hat_net = e_hat_net / e_hat_net.sum(axis=1, keepdims=True)\n", "\n", "gamma_net = np.zeros((len(X_test), 4))\n", @@ -1103,12 +1145,12 @@ " observed_a = (A_test == arm_id).astype(float)\n", "\n", " gamma_net[:, arm_id] = mu_a + observed_a / e_hat_net[:, arm_id] * (Y_net_test - mu_a)\n", - " mu_hat_net[:, arm_id] = mu_a\n" + " mu_hat_net[:, arm_id] = mu_a" ] }, { "cell_type": "code", - "execution_count": 99, + "execution_count": 71, "id": "e8533283", "metadata": { "execution": { @@ -1293,7 +1335,7 @@ "4 3378.748035 5743.802939 2988.217314 " ] }, - "execution_count": 99, + "execution_count": 71, "metadata": {}, "output_type": "execute_result" } @@ -1348,12 +1390,14 @@ "\\hat\\pi_{plugin}(x) = \\arg\\max_{a \\in \\{0,1,2,3\\}} \\widehat{\\mathbb{E}}[Y^{net}(a) - Y^{net}(0) \\mid X=x]\n", "$$\n", "\n", - "이 방식은 직관적이고 유연하지만, CATE 추정 품질에 크게 영향을 받습니다. 따라서 학습된 정책은 별도의 test set에서 AIPW value로 평가합니다." + "이 방식은 직관적이고 유연하지만, CATE 추정 품질에 크게 영향을 받습니다. 따라서 학습된 정책은 별도의 test set에서 AIPW value로 평가합니다.\n", + "\n", + "> **주의**: \"각 고객의 $\\hat\\Gamma_{i,a}$ 중 가장 큰 arm을 선택하면 되지 않나?\"라고 생각할 수 있습니다. 그러나 AIPW score는 개별 관측치 수준에서 분산이 매우 크기 때문에, pointwise argmax로 만든 정책은 노이즈에 과적합됩니다. AIPW score는 반드시 집계된 형태로 사용해야 합니다. 정책 평가(value 추정, 비용 곡선 등)처럼 여러 관측치를 합산하거나, DRPolicyTree/Forest처럼 노드 단위 평균을 최적화하거나, DRLearner처럼 pseudo-outcome으로 회귀하는 방식이 그 예입니다." ] }, { "cell_type": "code", - "execution_count": 100, + "execution_count": 72, "id": "debd92a9", "metadata": { "execution": { @@ -1391,17 +1435,18 @@ ")\n", "dr_cate.fit(Y_net_train, A_train, X=X_train)\n", "\n", + "# none을 baseline으로, 나머지 처치별 E[Y_net(a) - Y_net(0) | X] 반환\n", "cate_vs_none = dr_cate.const_marginal_effect(X_test)\n", "plugin_arm_values = np.column_stack([\n", - " np.zeros(len(X_test)),\n", + " np.zeros(len(X_test)), # none: baseline, 상대효과 = 0\n", " cate_vs_none,\n", "])\n", - "pi_plugin = np.argmax(plugin_arm_values, axis=1)\n" + "pi_plugin = np.argmax(plugin_arm_values, axis=1)" ] }, { "cell_type": "code", - "execution_count": 101, + "execution_count": 73, "id": "33437c2c", "metadata": { "execution": { @@ -1422,7 +1467,7 @@ "Name: proportion, dtype: float64" ] }, - "execution_count": 101, + "execution_count": 73, "metadata": {}, "output_type": "execute_result" } @@ -1433,7 +1478,7 @@ }, { "cell_type": "code", - "execution_count": 102, + "execution_count": 74, "id": "33ktqy8zrqm", "metadata": {}, "outputs": [ @@ -1586,7 +1631,7 @@ "8 9048 " ] }, - "execution_count": 102, + "execution_count": 74, "metadata": {}, "output_type": "execute_result" } @@ -1623,7 +1668,7 @@ "\n", "현업에서는 성능뿐 아니라 정책의 설명 가능성도 중요한 경우가 많습니다. 얕은 decision tree 기반 정책은 다음과 같은 이유로 실무에서 자주 사용됩니다.\n", "\n", - "- **이해관계자 설명**: if-then 규칙 형태라 정책을 쉽게 설명하고 공유할 수 있습니다.\n", + "- **이해관계자 설명**: 직관적인 분기 규칙 형태라 정책을 쉽게 설명하고 공유할 수 있습니다.\n", "- **공정성 검토**: 어떤 고객이 어떤 처치를 받는지 구조가 명확해 편향 여부를 점검하기 쉽습니다.\n", "- **운영 안정성**: 복잡한 모델보다 단순한 규칙 기반 정책이 실제 운영과 관리 측면에서 안정적입니다.\n", "\n", @@ -1641,7 +1686,7 @@ }, { "cell_type": "code", - "execution_count": 103, + "execution_count": 75, "id": "6c987a8b", "metadata": { "execution": { @@ -1662,7 +1707,7 @@ "Name: proportion, dtype: float64" ] }, - "execution_count": 103, + "execution_count": 75, "metadata": {}, "output_type": "execute_result" } @@ -1696,7 +1741,7 @@ }, { "cell_type": "code", - "execution_count": 104, + "execution_count": 76, "id": "64529830", "metadata": { "execution": { @@ -1742,16 +1787,18 @@ "source": [ "### Policy Forest\n", "\n", - "Policy Forest는 여러 tree를 사용해 더 유연한 정책을 학습합니다. 단일 tree보다 복잡한 이질성을 더 잘 포착할 수 있지만, 개별 규칙 형태로 해석하기는 어렵습니다.\n", + "Policy Forest는 여러 개의 tree를 활용해 보다 안정적으로 정책을 학습합니다.\n", + "\n", + "Policy Forest 역시 AIPW score를 직접 최대화하지만, 단일 tree 대신 여러 tree의 결과를 평균적으로 활용하기 때문에 분산이 더 작고 복잡한 이질성을 더 안정적으로 학습할 수 있습니다.\n", "\n", "여기서는 `econml`의 `DRPolicyForest`를 사용합니다.\n", "\n", - "실무에서는 일반적으로 forest를 성능 중심의 정책으로, tree를 해석 가능한 정책으로 보는 경우가 많습니다. 두 방법 모두 학습 이후에는 동일한 test set의 AIPW score로 정책 가치를 비교합니다." + "다만 tree 기반 정책처럼 하나의 명확한 의사결정 규칙 형태로 해석하기는 어렵습니다. 학습 이후에는 동일한 test set의 AIPW score를 사용해 정책 가치를 비교합니다." ] }, { "cell_type": "code", - "execution_count": 105, + "execution_count": 77, "id": "d7939447", "metadata": { "execution": { @@ -1772,7 +1819,7 @@ "Name: proportion, dtype: float64" ] }, - "execution_count": 105, + "execution_count": 77, "metadata": {}, "output_type": "execute_result" } @@ -1811,9 +1858,7 @@ "id": "15d7d727", "metadata": {}, "source": [ - "DRPolicyForest는 전반적으로 plug-in 정책과 유사한 배정을 보입니다. `none` 11%, `tech_support_only` 47%, `discount_plus_support` 42%로, plug-in 정책보다 `discount_plus_support` 비중이 조금 더 높게 나타납니다.\n", - "\n", - "Forest는 단일 if-then 규칙 형태로 해석하기는 어렵지만, 더 복잡한 이질성을 학습할 수 있다는 장점이 있습니다. 따라서 보통 해석 가능한 기준 정책으로는 tree를, 성능 중심 정책으로는 forest를 함께 비교합니다." + "DRPolicyForest는 전반적으로 plug-in 정책과 유사한 배정을 보입니다. `none` 11%, `tech_support_only` 47%, `discount_plus_support` 42%로, plug-in 정책보다 `discount_plus_support` 비중이 조금 더 높게 나타납니다." ] }, { @@ -1836,14 +1881,12 @@ "\n", "반면 학습된 정책은 고객 특성 $X_i$에 따라 서로 다른 처치를 배정합니다.\n", "\n", - "만약 학습된 정책의 value가 가장 좋은 baseline 정책보다 높다면, 모든 고객에게 동일한 처치를 적용하는 것보다 고객별 targeting이 더 효과적이라는 의미입니다.\n", - "\n", - "여기서 비교하는 값은 단순 관측 평균이 아니라, 동일한 AIPW 평가식을 통해 추정한 counterfactual policy value입니다." + "만약 학습된 정책의 value가 가장 좋은 baseline 정책보다 높다면, 모든 고객에게 동일한 처치를 적용하는 것보다 고객별 targeting이 더 효과적이라는 의미입니다." ] }, { "cell_type": "code", - "execution_count": 109, + "execution_count": 78, "id": "14b616ad", "metadata": { "execution": { @@ -1996,7 +2039,7 @@ "0 0.000 " ] }, - "execution_count": 109, + "execution_count": 78, "metadata": {}, "output_type": "execute_result" } @@ -2033,7 +2076,7 @@ }, { "cell_type": "code", - "execution_count": 108, + "execution_count": 79, "id": "4814d1ed", "metadata": {}, "outputs": [ @@ -2072,50 +2115,199 @@ "id": "dec4e9f6", "metadata": {}, "source": [ - "DRLearner plug-in 정책(11,810)의 정책 가치가 가장 높았습니다. `tech_support_only`(50%)와 `discount_plus_support`(37%)를 중심으로 배정하고, 일부 고객에게는 `none`(9%)을 선택했습니다.\n", + "세 학습 정책 모두 단일 처치 정책보다 높은 가치를 가집니다. 고객별로 처치를 다르게 배정하는 것이 모두에게 같은 처치를 적용하는 것보다 효과적이라는 의미입니다.\n", "\n", - "`DRPolicyForest`(11,626)와 `DRPolicyTree`(11,148)도 모두 높은 성능을 보였지만, plug-in 정책보다는 다소 낮았습니다. 특히 tree 정책은 해석 가능한 구조를 유지하는 대신 더 보수적으로 배정하며, 약 20% 고객에게 `none`을 선택했습니다.\n", + "학습 정책 중에서는 DRLearner plug-in(11,810)이 가장 높았고, DRPolicyForest(11,626)와 DRPolicyTree(11,148)가 뒤를 이었습니다. 다만 plug-in과 forest는 신뢰구간이 상당 부분 겹쳐 실질적인 차이가 있다고 보기는 어렵습니다. 이론적으로는 AIPW score를 직접 최적화하는 DR 정책이 더 유리할 수 있지만, 실제로는 표본 크기나 데이터 특성에 따라 결과가 달라집니다. tree 정책은 해석 가능한 구조를 유지하는 대신 성능을 일부 내준 모습입니다.\n", "\n", - "모든 고객에게 동일한 처치를 적용하는 정책 중에서는 `all_tech_support_only`(10,527)가 가장 높았고, `all_discount_plus_support`(10,137)가 그 뒤를 이었습니다. 할인 비용이 고객 규모에 따라 증가하도록 설정했기 때문에, 일부 고객에서는 할인 추가 제공의 편익이 비용을 충분히 상쇄하지 못한 것으로 볼 수 있습니다.\n", + "단일 처치 정책에서는 `all_tech_support_only`(10,527)가 가장 좋았습니다. `all_discount_plus_support`(10,137)는 할인 비용이 고객 규모에 따라 커지도록 설정했기 때문에, 일부 고객에서는 할인 편익이 비용을 충분히 상쇄하지 못한 것으로 보입니다." + ] + }, + { + "cell_type": "markdown", + "id": "9bh57tga2aq", + "metadata": {}, + "source": [ + "## 예산 제약 하의 정책 비교\n", "\n", - "신뢰구간 기준으로 보면 `DRLearner plug-in`과 `DRPolicyForest`는 구간이 상당 부분 겹쳐 두 정책의 성능 차이가 크다고 보기는 어렵습니다. 반면 `DRPolicyTree`는 전체적으로 더 낮은 구간에 위치해, 해석 가능성을 유지하는 대신 일부 성능을 희생한 것으로 해석할 수 있습니다.\n", + "지금까지는 어떤 정책이 전체적으로 더 높은 AIPW value를 갖는지 비교했습니다. 그러나 실제 환경에서는 예산이 제한되어 있을 수 있습니다. 같은 예산 안에서 어떤 정책이 더 효율적인지 비교하려면 비용 곡선(cost curve)이 유용합니다." + ] + }, + { + "cell_type": "markdown", + "id": "qdxockulyq9", + "metadata": {}, + "source": [ + "### 고객별 기대 비용 추정\n", "\n", - "전체적으로는 세 학습 정책 모두 가장 성능이 좋은 단일 처치 정책보다 높은 value를 보였습니다. 이는 모든 고객에게 동일한 처치를 적용하는 것보다 고객별 targeting이 더 효과적이라는 의미입니다." + "이를 위해 먼저 고객별 $E[C(a) \\mid X_i]$를 추정합니다. 실제 비용은 해당 고객이 받은 처치에서만 관측되므로, 처치별로 훈련 데이터에서 비용 회귀 모델을 학습해 test set에 적용합니다." + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "id": "a1z6z7eabyj", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Estimated E[C(a)|X] — test set (mean ± std):\n", + " tech_support_only : 4,892 ± 3,466\n", + " discount_only : 5,454 ± 2,330\n", + " discount_plus_support : 10,687 ± 4,139\n" + ] + } + ], + "source": [ + "# 처치별 E[C(a) | X] 추정\n", + "# c_hat_test[i, a] = 고객 i가 처치 a를 받을 때의 예측 비용\n", + "c_true_by_arm = {1: c_tech, 2: c_disc, 3: c_tech + c_disc}\n", + "\n", + "c_hat_test = np.zeros((len(X_test), 4)) # 처치 0 → 비용 = 0\n", + "for arm_id in [1, 2, 3]:\n", + " mask_train = (A_train == arm_id)\n", + " mdl = RandomForestRegressor(n_estimators=300, min_samples_leaf=20,\n", + " random_state=SEED, n_jobs=1)\n", + " mdl.fit(X_train[mask_train], c_true_by_arm[arm_id][train_idx[mask_train]])\n", + " c_hat_test[:, arm_id] = np.maximum(mdl.predict(X_test), 200.0)\n", + "\n", + "print(\"Estimated E[C(a)|X] — test set (mean ± std):\")\n", + "for arm_id, arm_name in ARM_NAMES.items():\n", + " if arm_id > 0:\n", + " print(f\" {arm_name:25s}: {c_hat_test[:, arm_id].mean():8,.0f} ± {c_hat_test[:, arm_id].std():6,.0f}\")" ] }, { "cell_type": "markdown", - "id": "b40f71c0", + "id": "dgt4sg335xc", + "metadata": {}, + "source": [ + "### 비용 곡선과 예산 제약 하 비교\n", + "\n", + "비용 곡선의 두 축은 다음과 같습니다.\n", + "\n", + "- **x축**: 고객 1인당 누적 평균 처치 비용\n", + "- **y축**: 고객 1인당 누적 평균 순수익 — baseline(`none`) 대비 증분이며, **비용이 이미 차감된 값**입니다.\n", + "\n", + "각 정책에서 편익/비용 비율 $\\hat\\rho(x) = \\hat\\tau(x) / \\hat\\gamma(x)$이 높은 고객부터 순서대로 처치합니다. x = B에서 세로선을 그으면 예산 B 하의 정책 비교가 됩니다. 같은 예산에서 y값이 높을수록 더 효율적인 정책입니다.\n", + "\n", + "그래프의 종점(●)은 각 정책의 처치 대상 전원을 처치했을 때의 위치입니다. `none`으로 배정된 고객은 처치 비용이 없으므로 곡선에 포함되지 않습니다." + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "id": "ttwi6dbqcfo", "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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2gkqcnvzm/UcffYTHH39csgb++++/eO6550QZYmxaXuemb1t+93qFgqggaoWiGGjBa/pARC3wkoJTflDAsk6LyBs9V2y1HxYG6THjEwMiGRhpLRDp90X0+6MwxB8jrgiyrdbt5AoXg+P4Xa6sUcjhCheD/bgKSeGBP9DWgaO2oDBE4cf6fBikR2Fd+4HSB+Mx6LIg0BJD4Z+CDBUqCmSkadOm8p8/vhRctQcDC7XMPwsWLBAFgqtxzz//vAjcDC7UVm310Kqh309hHswgZA3f53lb9wkDvefNm1egPqEQmxdUpu688068++67Iqhx1fnChQvymSYw5LcPwoBPrp7S+sB0xRRA9Odh75GfQKMXBAsC5yn7QB/QytV6JgMoyDVljT6JQExMjOxbmzfsf33fs41a3xGuSPP7+lVk/f6089+/f3+uYx44cMDynMfiNRQSEmLpMy5MvP3225ZVX/34UCi17uP87hXFvQ+R4l7/hIrn4cOH5Zrjdc8Uo7ymOJZFzTTF/qMlT98ftGwxNTGTRnDVnamAaeXR4BjnFfTLPuE2/J4G2837Au+ZvMfY6kvOF72lo7DwPPTHpGWJ0MrFRaK4uDhJ8DBx4kSxGmjBzwXtO847zitaEBiUznsDX2/fvl3GgPOEc0g/n2ltoaXI2gqkUOSHUiAUimLAmy4zkFD44so2fyCYQYSuIAWp9cCVNQofzNLDHzSu/DHbB83OGsyowR8GribZWoXToBl64MCB0hb+YLAtr776qghfNL8/+OCD4k7AVUUKBXSf4Yojf7z4w0n3ErrgvPbaa/I5v8/sPRR2NJM2V5jZTv7o2OKRRx4RoWb58uWymk1Bgn3BbCJULihk83Punz/QPO+CCLd0kaEARqGGSpS20kfhgv1PwYfZdij80brw1VdfWX7oKTRqLlDMNkILxtGjR/N0yykpKJBzRZbnyeOzT+hSQsGRq/JMC0wLC3/o+RnbNnfu3Fz74OcUJDR3KGvYL3v37hUBiPugYvLDDz/IZ5qgwH3QDU4vHOuh8LJq1SoRNjj3qJC88sorItAUBq48U5nTHhS2uR8en3OzINCSQmvbM888I64rFF45T7m6zRXawsL5zNV0jjmz17C/KNgSuh5RMaWyRCGK14beVYOCNLOgMSMN5yyzAekzqHF86eLIeUk3OY7Re++9l0uB4Io+hUaeA8+FD7aDghzHvyBjnN+9orj3IVIS13/NmjUtroC81ih0UxjmOeVXTNAedIHk4gnPh23iWNItk31KCwSzCdFqyMUB9g/7lc/zsgZwjvG8uDrP8eA85T2E48FzYNYvXkM//fSTzAsuOvD4nH/23IcKAucw28brn/dz3pc4F3lf5HE517jYQzc2KmOaq2NB+47KGtvMrFdsN4/DhRJaYTm+nM9UurTz5vX19NNPy2fWbnMKRX4oBUKhKCYUCOnuQUGJaf80IYMm/Pzg6hlXfPmjT99VCgBMs8q4Bw3e/PkDSUE/v1UiCoBcgeaPAhUPpsNkgSqaxbkPrijyx5JWDQpoFKIpsHJljT8iFL75w88VciorjFvg5/xhIhSW2FZ7QgmFZQoMFHL4g8T0okw1SQWGigJXDbkySCWAggp/IPMz+2uwPfzx43np4f4pnHJ//DGmlYJpNDVl64knnhCBePr06eIzzu34Q8p0idcCniPPlwIKBRcqblQYKJSxX/ljT59ntp399sILL+T6Ps+NghLnFrezhkoiV0zZ91yN5vxjPxNtxZrjTeGEcywiIiLX9/maggy/r/lksw0cr8LGg1AJoesFH0yrq80TziG9q1NeUECj8EZ3Pa6iUgCnYsKV2bp166KwcD5S4KTwR9cMXgOaiwbbR+WW1wvnPAV5vSDFNlPh5Yo35xOtPPprk/C7HJspU6ZIP/Oa4z61Y7DNPB/OXc479jOvNyrXWpxHfmNckHtFce5DpCSuf7orsa95boyd4HPej9jeohagY/t5jVNB4/VD4Vef6pX3Dy4Y8Hpiu3hdcwz1MTTWUPHjfZHWBrr8UTmjVYLpYQn3w2uAY8998bqkIkNFsjhQeeU40lLI+wLnCeeUdp5UXPiafcd7Oe9b7L+C9h1/HzjmXEDiXOR8o8Wa48p+qlOnjsxFKsm8Hng8zjteW1rMmEJRUBwYSV3grRUKxTWHlygFAwbGaa5NCoWifED3QvqY6wVWCqBcUaYQqFAQCva06NBSqlBUBlQQtUJRTqHZnz82XE2i6VuZmBWK8gdXsuk2QtcUrtTTbYjXrL4AmEKhUFQ2lAKhUJRT6OZAtxtCNxe6VygUivIFq/bS7YTuVnS1oisMXV60QH+FQqGojCgXJoVCoVAoFAqFQlFgVBC1QqFQKBQKhUKhKDBKgVAoFAqFQqFQKBQFRikQCoVCoVAoFAqFosCoIOoiwOqRTK1pXZZeoVAoFAqFQqGoqNkfWQOoIIVWlQWiCFB5KMvyGUajscyOrShd1NhWTqrSuJb1/fFaU5XGtiqhxrXyosa2ZO7fygJRBDTLQ9u2bXGtycrKQmJiolRrZYVJReVBjW3lRI1r5UWNbeVEjWvlRY1t3hSmYryyQCgUCoVCoVAoFIoCoywQCoVCoSgx14ADBw7I8/bt28PRUa1RKRQKRWVE3d0VCoVCUWIKxLFjx+Sh/IwVCoWi8qIsECUMg08MBoP42ZUG3G9GRgbS0tJUDEQlQ41t5aQqjSvvfVqMGM/X2blgPzHsF27L7B8KhUKhKP8oBaIEoZAQFhaGlJQUlHaEfFRUlPqxrWSosa2cVKVx5Xk2aNBAnp8/f75Q5+vp6YmQkBC4urqWYgsVCoVCURIoBaKEoLn+7NmzspJWq1Yt+REsDWGBP9Bc0eRxKrswUtVQY1s5qUrjynONi4uT5/7+/gU6X36Hiy+RkZFyD23atKmKnVAoFIpyjlIgSgj+AFKJqFu3rqyklRZVSRipaqixrZxUpXHluWoWBHd39wKfr4eHh7g+nTt3Tu6l/K5CoVAoyi8qiLqkO1RlHVEoFAp171QoFIpKjFIgKjEXL15EmzZtcNttt8lj0KBBeOSRR3DhwgXLNs2bN7d8PmzYMPTr1w+vvfaaJQicn1dUli5dihdffLFE9vXJJ59g/fr1Rf7+8ePH8c4776CkmDVrFnbv3p3vdn///TfuuOMO3HLLLZg+fbrNbc6cOYP77rtPxv/hhx9GfHx8rs+PHDki80hf6v7ll1/G4MGDMWTIEEvazpLk559/xooVK656/9NPP5VHacLzvfHGG3HPPffg448/tow7+6gwwcSjRo2SOWiLjz76CP3797dcez/++KO8f+XKFblG+d7dd98t17CeJUuW5JrTc+bMwT///FPEM1UoFAqFomgoF6ZKTvXq1bF8+XLL6++//16ERApnmquB/vOkpCTceuut2LJlC66//voyaXN55Omnny7W9998800R+kuKXbt2oVu3bnluQ0Xx9ddfx+LFixEUFIT7778fGzduxA033JDL5eSxxx7DK6+8gr59++LDDz/E7Nmz8cILL8jnqampmDZtmigNGhR2+b2VK1fi1KlTePzxx/Hnn38WOONOQdi3bx+6du2KsoBKF5Wj//3vf7ne37lzZ4H38fnnnyM0NNTu5wcPHsRnn32GVq1a5Xr/+eefx8033yzKy8KFC/H+++/LvElPT5f/P/30k3yuQaVm9OjR6NGjR7kJPvbz8yvrJigUCoWilFEKRBWDAgeFkM2bN8sqqzWxsbEiNDIAMi8OHTqEt99+W7b18fERQbVx48YiZM2YMUMEHq5kUwjjKjVXTblvZmahME7BiKusW7dulaDLV199FX369EFMTIxYQC5fvizHeeKJJ2SllqvO+/fvR3h4OO666y488MADlrZw3/S1PnHiBBITE0UgHj58eK72ch8LFixAnTp1ZFV37NixIihGRESI0Mbj1q5dW17zWNxOD49BgZaPiRMnonXr1jh8+LD4anM1mbEv9ti+fTsCAgIQGBgoryl4U8Bkmxkw+u6770r8zJQpUyR/Pt3gqOTdfvvt4hNOYZ5+4fShf+mllyTQlP3P7SlUsr+5cq3nqaeeku9SEK5Zs6a8N3PmzKuETJ4DY3aoPJBHH300lwWCbWNfU6DX2LBhgygNpEmTJqhRo4Z83qVLF5vnz/6212e25hHHnmPDfqPiY63IUvgeOXKkKLv8r80Frsb/8ccf0pdsC/uK86Uwx2ZbKbgTKkTMqsYx/++//+Q9WnO+/PJLsRJY8/XXX0tf7NmzRyxOtObZgsrX0aNHZQ7weqAiyDnI8+H4z507V7YbMWKEKAbaHOL3nnvuOTl/DY5np06dZBGA10VZwzldkoqkQqFQKMon6k5fivx5MAwz1h5HcnrJ1oQwwQRvN2f8b2BzDG4bUujvN2vWTNxWNAWCgjxdlphmsmHDhrIazSqy9tBcWL744gsRtCkwUbChu8YPP/wgq+0UjCn0vPXWW6JAEAppq1atkudUIPj6l19+wdq1a0W4pQLB7elKM3DgQBHq6QaitYWCHle6bUGhc9GiRYiOjhbBu2fPngXqCx6Pgh6FUCooXPnNDyoq/F7btm3FLYgr8nm5StEFRltNp6DP7/z666+SrYvKEvuE+/T19RVBMCEhQYTBFi1aSJ+y/6j47dixQ/p6/Pjx+O2330S5atmyJb755hubx6VATAGTygiPSyXqmWeeybUNBdhq1aqJsE2BmnODypzWbubyp/uTHipZFJT1Vi4K6oXtMyqX9uYR28o+s2UF47lQyKeiQIGeAjjHnePHfqWixX7lfKC1pbDHpusQefLJJy3jyr6k4q25JOmtdnqoBFDpopJBa44tqEh37NhR+pzKHRVEbs+2ck7w+9u2bZOUpjwPwn7gw5ZLFPuJ510eFAiFQqFQVA2UAlGKfL3pNE5HJpfKviOQjq82nSmSAkH0WU40YWjevHniY613cbEFV8ApeGqr0ITCPlfJP/jgA1mhXrNmjfjGJyfnnD+FJj2acEhBWUv9SNepkydPyuqs5kt++vRped6hQwe7baLFgVlcKJB17tw514p5XvB4XIXW9l+QmA+uilMYJRTg84tFoCVAczdiu9gPFBQJ3YMIBWEtRoHWCip3tOZQqaJgS+GY/UXLiTVUKGxZIKgU0tJEgdnLy0tW4pctWybH0mD/UljlNu3atROffwqwFLAp1HJOWMOV8MImD7DVZ3nNo7xgLI+W6YzKH/uJCgxX5rlqT2gBoyLBuVySx9YUKHsWCCoNtOIEBwfb/T4tUV999ZXlNRU8Ku29e/cWKwnHicoFXc+owNDtMC9oOeO5lAc4N6joa5mVKnvWKYVCoaiqKAWiFJlwfWN8tKb0LBAT+jYq0vfpJqGtsurhKjwFTgqQmlBtCy1draZ4UGigUMXVbgpwXBHt3r27uF9MnjzZ8j0KFHrc3Nzkv17I4L7paqS5UFEwpsDF1Xfr7+vRu01wH7Yq/mqCr96fn9txe2so0HFFnlgHH2vt1tpuS6C2Fq616rzWqTypOLE91vvQKppTgaDLE2MX+J8KgObiomHPAkG3G44BBWhCpYRCtl6BoPWhXr16ojwQxr9Q+eDx2LZ7773Xsi0tVRRmaX1gzv769evL+3yut0jYwlaf5TWP8utPfT9x7Kkscf4++OCD8j5d2XgcnkNJHpvwXG1ZIGh9oDJGiwddy+j+RCscjzl06FDLdoyNoHLN/tTPV44FFaMBAwZYxsJe4Lsenn95yv6mVyAUCoVCUTlRCkQpQutAUS0EpZVTngIghSQK+LbgiidX88eMGXNVgKdGo0aNxE+egbz0Naff+XfffSer1RSO6OpBoY2xBFo2p4LCdvH7XIXlvujjrrk95cVff/0l/v4U2iicUfCiEKzBVX36pVNo5LYavXr1EqGcAcbMvkPhj/1Kl5eSgoK2lk2HgjqtDhS6KTAyfoGVe3neXHGm4kIXl3Xr1knmJ7rOMAMS3VO4jRbbwfHPr2+5Ok/feo6Vt7e3WFvoGqSH1hB+znPneDOjD//zeHqXGFpmNKGZq/q0VNHSQ+sQV781BaQw2JtHdM/K6/xo3aKyQEGV1i4GfdPdh0I75wuta88++6woX7bifPI7tj3YJip19nz8tT62jpuhS53+XKhMUkmnVYrKCN3+brrpJlHkeB6M/+A4aWORH5xbmjKnUCgUCsW1QCkQlRyu4OtXOhnjwBVre8oHYxco8DDlqOY6oXc9oiBOAYfCLa0U9JHnqildN2g1oNBJn30KU4xd4Od6N6b8YGAwhWau2FJZoiCvraDnBVfx77zzTnFdeeONNywByxr0/WdsBl2j9FlsKLBT0KNvOZULCvUlXcSKwiCFRK7mM16AMQbjxo2T8aD7FpU1us5MnTpVxorv00WGQjkz2tBHni5GXGXmNpoQz37iOFGQtwX7n+5NPC77h9YIzfrA92lpoGsP+4S+9hTI2T66oeUF28s+5jhzHnEeUCnVXHvsxQdYw+/YmkeE7jxsB12vqBhau+wwVoXt5fEYvM8HFUQqEBTWKbjzvO3FZuR1bHtQyGd8DuMNilIsktcV3ZzYfroo0XWJCgmDoDXLCTMzcVypWPLcqWjkB60c9hQlhUKhUChKAwdTfv4XiqvQMrJoftWEQgj9qimgl2YV1apU1bagaCu9etecgkLBngI4BXkKwBQ6ucJd0i4hTLXJFfK8fOOv5djSWkTrCxXGkoSByXm5v1VFtHFl4gAqbVSuSwoqnnRHZMB4eUjjynNlLAmhEl+YeXyt7qElCceV7nJMCGHLbVJRMVHjWnlRY1t4+dYeygKhqNLQ9YMr/BR0KADQSlEa/uS0rDBwlhaP8gAtSUzBWpKkpKTYTV1a1aFgTVe0klQeNEWQ2bjKg/KgUCgUiqqDskAUAWWBUJQGyrpUOalK46osEIrKgFqlrryosc0bZYFQKBQKRaUkw2BEREIariSmIzndgJSMLKRkGJCckSWfaVBVo77GjHUBnq4I8HJFdR83hPi5w9mp/GStUigUioqIcmFSKBQKRYnBwP+SICY5AwcuxOFERCJOXknCqStJuBCTgujk/Gt15IWTo4MoEXUCPNAw2AutQnzRMsQXLUJ8RdlQKBQKRf6ou6VCoVAoSgS6aNlLc5sfhiyjWBLeWXkUG0/H4Vx0SqmMSpbRhIuxqfLYfsYc8K3RoqYPejcJRu+mwejaMBCeruonUqFQKGyh7o4KhUKhKBPSDVlISDUgMS0TScmpiEnOxLqjV3Ap0XYNkJq+7gjxd5f/NXzd4evuDE83Z3i6Oomw7+rsKK5LWmpBo9Ek+45NyRSLRnh8Gi7GpeBCTCriU3MKSmocC0+Ux7dbzsLVyRF9m1XDsA61MKBldaVMKBQKhQ6lQCgUCoWixIKomY6VMBWrraDxtMwsxKVkIiEtU55bvmsR+wE3Z0e0re2HDnX90bq2L5pU80Gjal7wKkEXIx7/ZEQijoQl4sjlBPx3KQ6HLydAS2yekWXEuqMR8qCCcsd1tfFgr4ZoXK1kM2kpFApFRUQpEJUYVqi95ZZbpMiWljOexdJYyIz/tQrDrIGg/fgzpzkr+LKYFbPG6D9ngTNW0dXqLtiDFajJk08+KYXRClpYTINFzlgAj2lBo6KipEIvYbpKFvMqSZg/nz7bgwYNKrHaEj///LMUAbv11luLtJ/Dhw9LJW2ev4eHhxRt4xjMmTNHUq9ef/31+e5j06ZNOHnypBQrKwlY+Iz9zyJoeTFq1CgpHKjlxGfVbRa0s+5zFkzTCgSyKB4rR2uwyjUrRFsXUUtKSpJibCwuyCrOJYl+zOzN5dKCVcC5fxbx43kx/zYLw913332WYo724PXKauO81tnnfN6zZ8+rtuM1yCJ2pG/fvpK6mLDatVZAr1mzZjJe7AdWgWfqYVbrZoFIvs/6DKziHh0dLfVS7MF5S/S1HKgocMU/PiUTaQbb1gUXJ0d4uzvjw7vao1OjGmJNKE183V3QqX6gPDTiUjKw7XQ0Np+KwvqjEYhISDefU0YWfth+Xh79mlfDhOsbo3uj/AtcKhQKRWVFKRCVHAolegGeAgmFyhUrVlhyx+s/p5BGIWrLli0WQVX/+caNG/H000+LgEplIj8KqzwQVsomO3bsEEEzPyGqqLDgFfuBxeRKkn379uWpYOUHhTtWnO7SpQu2bt0qQuHvv/8uAiUL0rGidF55/6koUvBl9eqSgmPx+OOP57kNq11funRJ5kZetTQOHjwoCqq1Msgq4iy299NPP+WqFq7BGh0JCQkoDYo7ZsWBld1Zbft///tfrvd37tyZ73e/+OILEfy//PJLnD59Gvfff79cu3pYsZtK16pVq0RZZhXvf//9V+pSUMGdP3++7IPX3cyZM0VxoMJIpZeV5ffv3y+V3Hktc0GClcj5P78K8Yw1SEjLELckxjbYgm5Hvh7OIsybDBkITXJBw3oBpa482MPf0xWD2obII+u2Nth5NgbL91/C7wcuixJBNhyPlEfPxkF45sYmaBaofkYVCkXVQ935qhgUQimgbd68WVY5rYmNjRWBg6uOtujevbsI3lz5pEWChdEuX74sgZNcRebqph5aMI4fPy6CHwWTEydOiOIxefJk2Q8Vkk8++US25Qp7XFzcVYKUHq7QtmvXDuHh4fj1119F+P/jjz+kLRS4KfiwLRR2KBgx5zNX7bmKb13Ei9/VBFVaX7jivWHDBtSoUUO+pwmU9vbFzykEM28yLQVcyeXKLQXC7du3WwQsCtQLFy4UawoFsokTJ8prrsTroUBHwfrBBx+UcyEtW7ZEWFiYPKfS0KlTJ2kP92MP9ge305SMBQsWyJhTqOfqNMeMq8i2xo7CIq0f7A83Nzd5vnr1arEIPfLII6LMPfTQQ1cdk9tx/9wXP+fYskIyFR5r2F/sCyo5tKxwXvj6+kqf8bjPPfecKBl6Vq5cKSvjnE/5wdV49nHr1q3FmsOV8I8++kisbocOHZJK2ZzjrB7M/mYf6MfM2sLDtowcOVKUa/5/4IEHLPPVeu5xXto6dkhIiBz7nXfeyXVstpVzgbDvONacV1oubgrxVA7Y99bQokDlkvNSO29bGZD4OdtI1yLOW77m2HKu1qpVS5QHwiKA48aNk/E4evQohgwZIu936NBBxv/ChQvShwMHDpQ5pbca6TGaTMgwmBAenogszR9Ih5erM/w8XeDn4SJWB420rPJVI4PZmno0DpLHS4Na4ufd5zH/33O4FJcqn/97OloevRsHYOqwNmhSw7esm6xQKBTXDpOi0Bw8eFAeelJTU01HjhyR/xYOLTWZPu1sMn3YosQeRnk0Nxm530PL8mznhQsXTP369bvq/aeeesr09ddfy/NmzZqZhg0bZhoyZIipW7duprvvvtu0fPlyy7b8XM+PP/5ouvnmmy37+eabb+T5+fPnTb169TJFRkaaZs2aJQ/99998803TW2+9Jc9DQ0NNQ4cONSUnJ5t69Ohhio+Pl/dvvfVW09mzZy3H2r59u2nMmDG5js/9bd26VZ5v3rzZ9MQTT5gyMzNNRqPRNGXKFNP3339vOnXqlJyHNhZffPGF6d13372qH3jex44dk+erVq0y3XvvvaaMjAxTdHS0nMuSJUvy3BfbsnjxYnm+YMEC0/jx4+X5Cy+8IN/Vnj/yyCOmrKwsU1RUlKldu3amxMREm+PFc9DORePVV181vfjii5bX69atMz366KOmvHjsscdM69evl+f//fef6cYbbzTFxcVJGyZMmCD9am/sJk6caPnun3/+aVq6dKk85zzifMqLbdu2SVvZV+xDzql///031zZsA/vp0KFDcp4ffPCB6bnnnsu1DfuO/aZx6dIl08iRI2W/nA9sf16wnc2bN7dco5x777zzjowt5512Hrt37zYNHz78qjHTw3nMecK5ynG76aab5Dq3N/dsHfvtt982paSk2D22/nrRt8P62suLsWPHmlq2bGmZj9ZwfrZt29bUtWtX0+OPPy5t5nXXs2dP09GjRy3taN26tTy/5557TD///LM85xi2aNHCtGfPHnnN7XkuegxZRlN0UprpZHiC6ezFcHkcvBBrOpD9OB6eYLqSkGZKz8yyew4276HljExDlmnJngumvu//bar/wgrLo+nLf5re/+uoKTk9s6ybqCgBDAaDKTY2Vv4rKhdqbAsv39pDWSBKk62zgKgTJbpLyxpdYhjw7yyg9e1F2o/eP1lzM5o3b574n9MnXQ/jGDQXFfrAaxYDrtjSrYRwZZK+7gcOHLDrAvPBBx/I8/r164tLDhkwYIC4VrRq1UpWZRs0aJBv2zt27Cj/6d7D1eERI0ZYXGA03/tz586JPz4xGAyWmA893IYrsJq7CFdWaR0JDAy0WFK2bdtmd1/cVottGD58OGbMmGGzvTxHrs5zdZv7pl85V65tWSBo3SBcJeZKOVetaf3QYP+fP38+z/6xPq/+/ftbVqZnz54t/5966imbY8dtuQLN1Wg+bLkSafPB2gJB6xQf2vy68847xcJElysN9oPmi6/Fu+QV16JZuRi3o5+z+cG+ZiyBZsXZvXs3zp49K32nd8WipYQuX3nB+BhPT095zj5hn9LSYGvu8dqxPjbjObjaX5Rjk4iICLsWCFrLCOcILRC0+vD60OKeyLFjx+S6pnWN1xitf7Se0Nrw3nvvSd+yn2ld0dwSaY3j/KCVjtcCLUXaZ/o5yMJt0cnpkuGILku8P/lk36QcHRzE0hDo5QoPl8pRhZsF6O64rg6Gtq+FZXsvYca6E5LZKSPLhM83nJb33ry9DW5saR4XhUKhqKwoBaI06fU0sOEtID2pxHZpdggwAW4+cOj5VJH2QYGCgoY1dM2gaxOFBwqv+cUx0N3E+jUFbFtogr3GmTNnJDiaAhgVC/pvFzTQmO5CmpDNNtPlh9CtikIKhSX6lFMQ1oI6bQlq+pz1fK4/H+19HsPevvgdTSiiAGbP719/7tpx6IfOhx6+z+Nx/3QP4bHoKqJ3vWK78oovIJorkXZsveAWGRkpgqC9seN4UOCn4E+Fkv+pHOixNx/oV0+3qc6dO1v6xHrc6TrFQFwtCNfWNtbzhA8qEYSCK8eCipatYGENuuhY9zmPRWVJaz/fo3CeVzwJ0fc3v8O+tTf36IJn69jcvijHJlQS7PU5FVy6IFFpqVOnDq677joJntcrEIyJ4JhqLnW8zujSxrZz34sXL5b36XKlKcecC59//rm0j/32yy+/yP71c/B8dIoERuuzJ5n7ywGuTg6oE+RTbis+p2elIyI5AldSrsgjKjUKSZlJSMxIlP9JGUlIzkxGlikLRpNRHs6OzvBy8bI8/Nz88NSw6th61IC1h4zISPPD5fg0PDx/N4Z3rI3Xh7aSmAqFQqGojCgFojShdaCIFgK7ZAsjInQVYUWPPuwUCrSVYmsYVMnVdAZK0iqQF9wHBQuuZNI/es+ePeLXzTgHa+jXTT92rmRypXTs2LGyIsqVb8ZHMBsMg7MLA4/PoFuunHJ1mkI3M0gxTuK7777Do48+iuDgYPE7p9Lx8ssv5/o+rR0M+mVcAwUsBpFSqKevOIUu+n7ntS8K+uvWrZMV9KVLl6JXr16yX46N5pdeFLgiTCGf7bEOVGff0YKTF/xcOy/65jOmhFl+uIrOdtMyYG/saBGgYMzYhaZNm8r5FvScuKLOFWsqPeybZcuWSfYePWwDYx8o6HJ1nvMxLwsEz4FzQx/Dw2xQRcnC1KhRI7H+0CLAfqEViGP722+/5Xl+a9askT5h7ALnLK04jGmwNfdsxRXld2x7sE0U5PMqzLZ27VpR+hkLQYWEFistw5IGrzlaGthv7H/GezBGg8oN41WYgYrnw/ZQWSYMpqb1iQkVqGDQOsa4KNZU2HXwBKqH1EFcao5Szn35e7ggyMsFqUlZlhiCsiYlMwVHY47iZOxJhCaE4mz8WYTGhyIsOewqxac4uDUE3E3OMKRXgzEtBCvOheCfzxvgjZsHY2g7cxY5hUKhqEwoBaKSw+BHzeWEK4lMxUjB1J47AYVGpsqk4Jhf9iOuCr/22muW1VG6PGguFdZQgOW2w4YNk2NToNGEY2Z0oRDLINnCQHcbBmhTiNOCnrmyTYGLwhJXWHnOXI2lYmTr+1zBpZBKNyMGrlJgoqJAgU8TvvLa1/r160WQrFatmiXtaO/evcWqUtjz0dyP2J8cJwr6GlRQKFDSbcyekGp9XgwGphJIZY2KEdtPdxQKhlSObI0dz5Wr+wwIZz9q58pj0o2GbjP2FBgG3XIVm/ONx+JYaO5mVEzoNkXXHgYVc790+2E/cy4UBc21p6CZvqg40/2O1jUqiRSmtRSm+jHThGgNuuyw/6hA8HicA3zYmnt0bbJ37I8//liuK+tj24OKFa8XJgvQXKisYXYkKoVDhw6V8eI1yfbq+4bnxlSxtDywHVQG+BmtCLQuTZgwQRQ+KpVa2l8qJAxmZwA3M7m9MvVNnLqShNTMLOzauQNdepld/JwdHcVFKcjbVQKiaVlx8S2bYOJMYyZOxJ7A4ajD+C/qPxyKOoQz8WfEenAtMDkY4OQeJg/e2Vim7qU9X+H9/U0xqs0A3Nl8GGp61bwmbVEoFIrSxoGBEKV+lEqGliFF83MmFAroY03BrzC+2oVFc4ewdk2piPBcGFfBVXAKQlyVvpZwxZw+6VoWnMKiZZi6VmNLIY+uZ4ydyC+Na0G2u5bQHYoWGiqoJQmFZ727XXmkrK7Z4vYN200XpSsJ6blqN0x5ejymvPURmtStAX8PV3FZKgkKew/NMmbhQOQBbAvbhr0Re3Ew8iDSssxF7PLC28UbDXwboLZPbVT3rI4anjUQ7BEMX1df+Lj6yOfert7ipkS3JUcHVrd2gMFoQIohxeLeFJ0WjUuJlxAaE4orGVfM1o2EUHF7soUDHNGnTm/c2+Je9KjVo8LfvyszvF7plsiYobxcLBUVDzW2hZdv7aEsEIoyg/74XO2lxeNaKw+EAc20ONC1ynrVuTxCIZwWgvyUAn7O1X4G1nLlvzwQEBAglp6ShDEiDGpWlHzfJKUbJDjYun7Drk3rMXTIEHRpXrdMBGC6JP17+V9suLABmy9uRmx6rN1tnRyc0DSgKVoHtUaroFZo6NdQHkHuQUVqu6uTKzxdPEXZsCeMMLbidNxpHI85jt+ObcHeK7sB5xjZ1gQjNl3cJI9mAc3wQOsHcEuDW+DilH89HYVCoShvKAtEEVAWCEVpUJmsS4qKOa6sGE3FISGNDjg5eLo6o7qPG3zcnfM8B54rXdMIg8kLc772LBCxabFYf369PHaE7RBXJVuEeIWgY/WOaBvcFm2C26BFYAu4O5eeNbggq5mX41LxyMLVOJH8D1z8d8HRJT7X53V96uJ/nf6H/vX6l/u5UZVQq9SVFzW2eaMsEAqFQqEoMJlZRkQkpCE2OSNXaLG7ixNq+rrnqzjoSU5Olv/6bFSFJT49Hn+f/xurQ1dje9h2m25BHs4e6F27N/rW6YtuNbshxDsE5Y1a/h5YOn4Y3l3VDN9t7Q9nn8NwDdoEJ48L8vmFxAt4ZuMz6FKzC17p9goa++dkz1IoFIryjHJhUigUiioKLQbRSRmISEyTOg4aDIiu4euOAE+Xa7YyzmDnNEMa3tz2JlZeXCkxB9YwXuGGujfIg0K3m1PRlZRrhauzI14b2gqd6gdg8mIXpIS2gZNHKHxrrYfB9ZRssyt8F+764y6Mbzce49qMU25NCoWi3KMUCIVCoaiCJKVlSt0Cui1pODk4oJqPG4K93UosODo/MrIyxE0pJjEGMWkxIkzrlQe6Jt3c4GaJF2AsQ0V19RnSLgT1Aj3x0PxdiExsiNjTDyOo2gkE1luD8JRL4pr1xf4vsCZ0Dd7o+QbaVWtX1k1WKBQKuygFQqFQKKoQrB4dFp8qGZb0BHi6oqafu1gfroW1gUXbqDgwo5H2ngazIw2sPxC3NLwF7YLbVVilwZq2dfzw2+O98NDcXTgekYjoyOZwSGuBO/oewvKzP4mr1qm4UxizcgzGthqLp657SoK3FQqForyhFAiFQqGoAhhNJkQlpuNKYro81/B0dUItPw94upX+z0FmVqZYGZg9iWlY9TBVKoOep/acim51u8HJsXKmz6zt74GFj3THmG934EhYAqISTVizpRM+HT0Qnx58Wwrfscjd/CPzJf7jvb7vqdgIhUJR7lAKRCWGVYtZpI1Fr7T6AHXr1pVKx/yv1TJgsTTNH5oZRVhRl1WJmVVE/zmLg7H4G4uAsXCWPVhpWCsex6JiBS30pbFjxw6p/FyvXu4Krixs51tKRapY4Ti/wnlk06ZNOHnypKXgVnF56aWXJDUri3/lBatGs14F03OywJyt9KwsNscigSwoxkrNHCc+Z3E3Forjd1lFm4XiOKbcBwsNamPLCuJMFcuq3CUFqxyzOBvT5dqbI6UFi6dx/yyExv5gXmsWxCvoWBNWgmaBuFGjRkkhNmuYKpfnSFi4jwXYWA16ypQplm3i4uLk/8aNG5GUlCTX1qlTp2RV/a233pKq0HPmzJE0t9xHaZCcbsDF2FSk6+o5sAhcTT83sTyU5go/7yusnxCTGoOEjISrPmca0wC3ALjDHRfjLqJhjYaVVnnQYPG9n8Z3w+hvzEpEWHwapi9NwE+PzMfyswvx2b7PxKXpeOxxjFoxCi92fRF3NsspLKlQKBRljVIgKjkUnvQCPAUnCr8rVqyw1BPQf04Bh8Leli1bLMKM/nMKQU8//bQI0lol6bworPKgwWq5BRXySoKdO3fmuw0VMAq+P/74Y4kdl8oSi9nlxe7du/Hdd99h8eLFUj14+PDhMjbNmjWzbHPmzBnMnDlTqhazovTUqVOl/1hBm0ItX3fp0gVbt26V17///rsoGxqzZ8+W/ZWk8kD27duXp7JZmvz9999S3+N///tfocda4/PPP0doaKjNz6hI/vTTT/jtt9/kWqKiwf5l5Wdt3jOl6V133YVJkybJa1aiDgkJkWrcvIZYDXzJkiWi1IwePVr6vySL/2UZjQiPT0d0sjm1qrbSz8rR1X3dRIkoTcUhLj1OCq6lG3KOr7XBx81HFAcWbKMCwzSuVQl/T1fMe6gLRnz5Ly7EpIpL01MLD2D+gw+gV61eeGHTCzgdf1pqS7yx7Q2prP1yt5eVS5NCoSgXKAWiikFBhULP5s2bZTXWmtjYWKSmpsLf39/m97t37y4VnGmp4Kr1K6+8gsuXL8tK97PPPou+ffvarNackJAgq7Jc5abiMXnyZNkPFZJPPvlEtuUqLFdrKYDZIyoqyuYxKdjv378f4eHhIrD1799fhGaeDwWyF154QYrVrV+/Hp999pkILDzHDz74QF4TrjBzFd8ef/zxBzp16mQR8BYsWCB9SaG+Z8+e0q7o6Gib7WPbpk+fLkIV01vy+erVq8UC8Mgjj4iw/9BDD8nn+tVgbrdq1SoRLplrnlCZsB4f9nHHjh1FeSAsIvb111/j/vvvFyWCygNp2bIlwsLCcn33woULch75KXu0aE2cOFFWzGnVYK5+CsK0ZnHVnVWPOXfYTq6ysw8oxG/fvh1BQUFXra4fPHgQI0eOFKWV/x944AHLPGBfc36x3bTScFwLc2y2VaswznHgOVOR0XJcc6y//PJL6Xtr2G/sxz179ki/2ivIxqraVMQ5nznPeB7WFjKOFdvMOc2xXbNmjcxBwnlRs2ZNec45xbnFMeD8LQkY48A6BEzRquHh6oQ6/p7yv7Tw8vaS+IaTcSevyqTEys4B7gGiOKgCakB1H3cseKibKBExyRnYeioa768+jpcHt8SiWxfho90fYdHxRdJ3S04ukT79rP9n0ocKhUJRligFohRhDvPP939uCRIsMUyAl6sXnujwBAY2GFjor3OlmSvWmgJBNyMWV6FwziJOFIDbt29v87sUsLkNqzjTEkEBb9y4cSKE3nPPPbIaa4tZs2aJsMT/586dE9eSRYsWiQBP5YKCF79LRSAiIkKEQrZL4+6775b90xXH3jEpQP7555/ynO+//PLL4rZy/vx5EU4pvHH/7733nig2VAB4HAqcFKDzUh4IBT+6DxF+j9/n6jGFVgq3XNnmfmy1j6v93IaKDStfHzhwAI899phYFSiwUsDmdrYKjlHB8vPzE+UvPj5e2jB27NhcbaNL0rvvvitCO4Xfv/76S8aTyo3WZvLxxx9jwIABub5LQZr9w2rR+UEFkG437FcqN7TGcIWfff3FF1+gTp06Ing/99xz0p88XwrutlxzqDxRyKeiQIGebkZUwKhs0ZLCfuAKPefJDTfcUOhjc84QzjW6cxHrsbanNFEZYH+ybz788EO7/UHlgfvjNrxmNHc/rR7CDz/8IHOE8NyoKHB7jg/nDa81DfYTz7u4CoQhy4hLcbmDpB0dHCQta7B36bkrUVlgfANdlazrNni4eEgFaB9XHzg6lH6QdkWiYbAXvrqvE+75ejsMRhO+3nQGbWr7YVj7Wnil+yuSjYkWCFoiDkYexIN/PYivB34tgeYKhUJRVigFohSZd2gezsafLZ2dpwLzDs8rkgJB9JVeNSGK/u8Udiis6dEE+czMTPHV1ywGXFmmQE+4EkwBioKxPVcdKgukfv364kJDKMxyhb1Vq1YiUDVo0EAUCHsuTHkds0OHDhbBjSvNej90+rJzFZrHmzBhgihPFG579epV4D6j4lOrVi15TmWB36dgr7kAkaeeespm+7gt28PVbD5uvvnmq/Z/++2327RAUKng8b766is5jzFjxogSSGuQBpU6CtNUUji2jH3RVtsJ98FVeio+9NvXC8pUjPSCbF5Q0aEAr1kz6F7F6sFU0vSuWLQu0eUrLwYNGgRPT095zj7hOdLSQMvEiBEjLC5AVCQ4J0vy2ITzzJ4FggoB43CCg4Pz3Q+tQ7Sg0MpFa5bmrkTrBOOJqNCx/zUlnedMZZHuTmy3ZpHgtcVzKQ4JqZkS62Aw5lgdfNxdUNvfHa7OTqWmOESlRonywPmrhwpDsEcwPF3M46ywTZcGgXh9aCu8uvywvH7h14NoU8sXjap5Y2jjoRJE/cT6JxCZGiluTfevuh/fDPwGdXzqqC5VKBRlglIgSpEH2zyIz/Z/VmoWiAdam10+CsuxY8csq7N6uApN1yauvFLY1LC3SmstLPA1BVxbUAjUQwsIg6QpKFKxOH36tM0g1cIckwHChCvaXOnVt5vCYrVq1SRgmX7x//zzjxyXwiotAQWBq/l0h9HORy/oR0ZGymq0vfbxPOnfTpctKmr8T+VAjz0LBIVYWkwYjEwolFI50CsQFLQpXGvWGFpbtEB5CtPPPPOMBFHTauLt7W35Hv3w6V6j7Ts/9NWF2UaeH/ubx9L6m++xv/Pz5Wd/6vuJfcvz5zyk2xWhqxyPQ9e2kjw2oWBva25Tqdq2bZtYPGgxo+JJxZXtpZKnQTcpKgRUXNn2oUOHWtymyLp168SFTIMWHm6nBZRTeeWY0DJB5Yif6fuksLEOl+PSEJuSozg5OTpIJWR/j9IpBscsSoxviE6NzpWC1cXkInENQd5BklVJUTDGdK+PAxfj8euei0jNzMKzP+/Hr4/1lLS6rH8xf9B8jF8zHpeSLuFi0kWMWzMOPwz+QRS0vDBlZcGUlgZjWpr5f3o6TJmZMPG+SUWTnxuNltfc3vKfn8lrE81YcOB93NEx+78THJjyl/+dneDg5g5HTw84enjA0dNT/juUYDyPQqEoXygFohShdaCoFgJ7UECyJWQWFK7qU7jSC5966OrBIF2uctMqkBfcB7MDae46dB+hiwgFL2vonkHXHbp4UPCiC86GDRtkhZ4uTBTo6RKVHwU5pmbJoHBI6wlXqrlvHo9KCoVCCqgU6LTVX/YnBX1NQbAFLSeXLl2SbDl0U+KKP91juKJMNxq6CtlrHzMeUTDmajV95xlMqx2X45kXXJ2n4M8+4/jTmmMdGEz3LX5Oaw7Hl+PMVXHCrFtUbuhGZR34vnfv3mIHOTdq1Ehcq3bt2iX9wvgF+v5Tmcnr/KjksE/Ydo4NrTgMMOb4sO20pDCGhAqTrXid/I5tj/zGmgoWkwhoaFnH9MoDYdwD3aWWLVsmc4B937lzZ/mM40TlVHtNOC6MlaGbHd3R+DmVXs11jNcF51hRCsLR6pChi3XwpdUhwKNUajrw3Ght4Gq4PhUr70cS25DuAocshwpRJbo8wf5787Y22HsuFmeikkWZ+HT9STzdsw6yYqIRFBWNr93GYe7eT5EaFwXPtPP4feVtuDGwOxyT02BMSEAWY9OSk2FMp7KQLkoDMnPX+7imUCnOViicfH3g5OcPpwB/OPkHwMmf//3hXC0YLiEhcA6pBZca1eFQgOQcCoWi7FEKRCWHfuaaCxJXa+nqQkHSnvJB4ZaCEgXc/LIg0e2FPuraKi5dd7QgXmsoaHPbYcOGybEZh6AJs3S3obBdkFXwgh6T1gUGUX/77bciMNLtigIcBW8qEzw2BVRuQ2666SZpG33QNbcaa+iGxJVp+vNTuaLAzhgH9isDYumWxNVoW+2j5YPpU+kaQ8FV88mnYEw3GrrNWKet1aDFhK4tHBcK41zppiBKOLZa0C+FbaYbpXDMVW5uxwxCFKY57vpYCMYAsF+4X+vAd821p6AZtNiv7F9arZhJh/2nxQ3QusGx4NjyPPTQZYf9RwWCx2O6YT4YuEwFgudKwZ3ZjejaVNhj26MgY50XWp/T4sM5wD6n5YBt1QLBqVxwnDnH9FYpxnBwfjD1K/ufgeCa1YFWDnuKki2MRhPCE9IQlZSeq5J0iL8HAjxLx+qQlJGE8ORw8cfXkIQEbv6o5lFNgqRj0mNK/LiVFQr4mZcvI/PS5ez/lzDrTCiOHziBoJQ4+P+ehBNWgejmZQGNGKRgJcotBgOMVGoSE2GIiMh/ewcHOFerJgqFa4P6cG3YCK6NGsKtUSO41KsHR2XRUCjKDQ4ma58LRb5ovuWaPzah8EJ/bApq+viCkqa4FojyBM+FcRVcraeLDbMklWfoCkTXLwb1lmSqzeKMLQVnxl3o3ZJKAlpU9G5siqL3eUHGtbBzKyXDIKk/9XUdvNycUTfAo1RiHZiGNTwlXBQIPX5ufhLMq1VLFutEjFmBYKKFwtyjrtU9tCThuNLNjlZPazdNDboHGcLCkH42FBlnz5ofoaFIDz0Lw+XcGdGKC1fvHb294eDhDkc395z/7m65X3Pxhm5HrLfh5Hj1fydnq/cd5TyQZYSJVifdfxizYDJkma0eKakwpmqPlJzXKSliHTGlpBT95Bwd4VqvHtxbtYRby5Zwb9VKHs4FSP5QGuOqqJiosS28fGsPZYFQlBmMG+CqNFfWy7vyQCjYUXBkELKtQm7XGgprDDYvaeWBfvn2UpdWdUqrzxkXQytVfsoDj0+LA2s7sFqxlmGppp87grxKPsMSYxsiUyIl1kG/1sSsSjU9a6rgaOv+SktD+smTSDt6FOnHjiHtyFGknThRJME52d0bV1y9Ee/qjTqNaqNpi3pwDgyCk78fnHx9EWqMwpuHZiDe1YAUd2Dy9a/h9ta57RPlDcZfZMXFISs21vw/JgaGyEhkXg5DZng4MsPCRNnie1d/2SiKFx9YucrytnNIiCgSHu3awaNjB3i0bStuUwqFopJbIOguYe1CQehCQ3/1o0ePitmf2WO4okUXAX0KS7qPMPMJ02FyxYB+0HQR0AJISX77KCzKAqEoDSqTdUlR8uPKeg6MdUhMy/Fp92RdhwBPuLuU/CppQnqCuCuxIrIGXZRqeNWAn6ufzXOpShYICsMphw4hftcumE6dQsbx40g/c1YCjwuCo58f3Bo0gEvdunCpVcv8qJ39PyQE/0Vn4LbPt8q2vu7O2PhcP6lgrWfZyWV47d/X5LmLowvm3jIX7avZTsFd0RSxjHPnkXH2DNLPnEHGmbPIOHMG6adPw5SeuyjhVTg5wb1FC3h07CgKhWfHjtKnhUGtUlde1NhWIgsEMwIxuwozluh/bGg6pB8xg13pe07/ceaH53/6U2tpHpn/nXnVmTmIdQbob02XGAZTcjWvIPtQKBSK8gyVBros6dOzVvdxQ3Vfd7FAlCQZWRmiOLAYnAbvzazjwIw/TnRrqWKIu+X580g9eBCp+w/I/7RjxwoUoOxSpw7cmjWDa8MGcGvYEK7ZD6eAgDwVrPaenhhxXR0s2XsRCWkGzFx7Am/e3ibXNsObDsfRmKNYeGyhKHrPbngWvwz9Jd/MTOUdR3d3uDdvJg89zBRFhSLtyBGkHz2KtMNHxNrDwHELWVlIO3xYHrE//CBvUUnz6t4dXj26w7N7dzgHBl7rU1IoKh1lrkAwew4z5lSvfnVRHLqKMNh12rRpEpDIAEvm4mcAI4V/+g0z4wqrGmu1C2bOnClZW5jhhYGkzIiT1z4UCoWivGJkWtqENEQm5qy6Ojs5ol6AB7zdSzZbDd2VmJKV2ZX0hmmmZA3xCoGbc9XJqkTrAhWFlN27zMrCgYPicpMnLi5wa9JEVr/dW/LREm7Nm4u7UVF5/pbmWHUoDCkZWfhxxzlJ9dq8prkivcZzXZ7DidgT2BOxR8bu9X9fl2rVldGK6cDEBM2ayQPZWdEYm5EReg6p+/cjdd8++Z9+6hS1Psv3Mi9cQBwfixfLa7cWLXIUis6d4VjANNYKhaIcKRDMuEKh3hZMv8nMJvp0i0yTyYJazL/OqrssGsb8+hqsaMwMOUzrSAUiv30UpFCUQqFQXGsYIH0hJkWER31ROAZKU4koSVINqVJfgMHSenelml414evqWyhhtKTjQ66pwrBzpzxSDxyAKZ9ihK6NGsG9XTugWVP4d+kCz+bNS7zuAauHP96vCT5YfVxKMcz6+yQ+H507XoyuSx9e/yFG/D5C0utuurgJvxz/BaNajEJVgAHebpKpqSH87xgu72UlJCD1wEFRKFJ27RKlgrUvNBifwkfMvHmSapbxE149e8K7T2+4t8lt5VEoFOXYAsE86EzVSP9X5kFnYS/GRTB1Iyvu6tEsFSzupKV2ZO546220z/LbR3EUCH1+ez7nqp32KC20favkWZUPNbaVk6KMq1ZROiv7OxTga/q6IcjLjZkuS+z6p9WBVaT50BPoHohqntXg5GB2VyrM8fSB4IX5nnbv1Kp2lyY8Dn3qUzZvQfKWzUjdszdPhcHR3x8e7dvBvW07uLdrC/e2bcWyoPlTu/j4wMhsPaXQ7vu718OcLWcRk5yBlf+F4WR4vFSo1hPgGoA3eryBJzc8Ka8/3P0hOlXvhIZ+DVEl8fKCR88e8qCzEjNBpe7di5TtO5CyYzvSjxzNsVAYDPIZH1GffSaxKZ49esChUye43tgfbnZSkysqJtr9pbTvMVWBMlUgmK+eFYlZmIt58blyxSJLzAk/d+5cCaqzzkqiVaNl5V3mjye2tmFxKZLfPooKg7f5w6FBd6pr9ePHYxcHFkNjIPnatWslXScDz1k8riDs3LlT4k6YNYbB6szXz6w0ZQGLj9EdTcu9X1hat26Nw4cPozxR3LHVCpLRwsYaFPnx8ccfS3xQO66kFhNeD5xPn376aYG2Z00N1iShG6K+PYyH4rVE10Rb2aAYx8Q6IrQ+cnGAaVV5jR85ckTqejA1MBcVGBdFi6S+wjk/40IF7zNFKdpmDyZyoCW0U6dOud7Xri+tFkt+UKS5kpiB6OQcYdbVyRF1/N3h7uIIo65wmwarxzOui0FvjB3jNclzy28s0rLScDzsOD6e/jEiwiJgMppw94N3Y9Rto+Du5A4wayfMx2NMGceK0EWUBfRIYfucMW8Msmb9D2u0RRiOK79T0jCVaMbuPUjftk0eWXZqixCn2rXhel1HuHbsCNd27eS1ZoXhFSo5lVjfwGiU3xh+VtQq4gXhvi4h+GTjOZF5P//7BKYObnrVNh18O2B4g+FYFrpMxvaFTS/gq75fiSVJAaAdlb92cH9kPIzx8Ujfuw8Zu3YhffduZJ0/b+kifpb011/AX38h8a234NysKdy6d5cH5wLdqBQVl2t1zVbk/ilov5TplUC3IlbVZXYSLesGhdGTJ09izpw58h4Fcz2a0M8CUNp3uI0+awe3YYVXkt8+igo7mD/WGpyQdIniuZRm3mhtRY/HL6qPqzY5tKwwfF3QNuu/y8xWZQmza2ltKSrlKcd3SYytZnVjYb78zo3WPxaS69ixI0qCpKQkERDzOy4FRSqgjEOiAqBtT8WBCh2FVQqZrNpN4dvPzy/XMVgzhAUCmzdvjkmTJklRPFowKbwyzS4L/fE5Y6i4rb4gHqtyDxgwQCpYlyR0laRrpPW5a9eX9shrXJlliYHSyRk5hcP8PVxQy98DTo72v8d4rwkTJkgdCb0SaW8sJDVraqTEOyz4agEaNWuEl957CQ7JDhg/ejyG9RsGr+Acn3Au1DArHivJcyw4Lrxvs5ihdZ9zXB9//HHJisfYM56vvs/5YJX7QYMGISgoKFe7tPsRE1yUVBYmQ1QUkjZsRNL69Ujdvj2XG4sepgL1pD98ly7w6NwFLrVyW7XtoSk9XPwqzXvJg32a4rvtl5CYZsDKI1F4+dY2krbXmue7P4/9MftxNuEsTsSfwIrLK3Bfy/tKrV0VFv5216kDDBsqL5k+NuXff5G8eQtStm2DMSmn3onhxEl5JC/4XmIlPLp3g1fv3vIobHYnRdlzra7ZikphlKoyV6VtVR9mNeQtW7ZIViVWUtajvWblXVowtPf0VXz5msIFyW8fxUE/+bQfP+1R2hTkOOwfrg5SUIyOjpZgda6U6ttZkDZzLChA0HLDFIva9vfdd5/krud4scKzZvWZOHGiCAwUYGiloADCcX7//fdlnGbPno3ff/9d+qxXr16ymkl3MqbW/fvvv2UfFApp7aBQwhVyruBu3boVcXFxIpDQDY3VfAlXPu+66y6bbaegw0rFFEgYM0OrA1esNSsUz0NbpWW1bJLfedmDK+PTp0+XmxP3z+cUmDgXGeujtYdjwCrfPA77k+mF2Ue0wrE/+DlXbGkpoiDNFWVa5Yi9vnv44YdFIOP5sI94rq+//rokD7AHV/5Zg4PfoZJNiwX7nIo998dVZJ4TFUUqyEzLyf1xdZvJCX744QfZtk6dOjK2/D5re9AFkULklClTrjomhflTp05JhWz2D/tBm3u0KLFCNAVPXpuMXdq4caO0UePff/+Vat8tWrSQ15wLnOfcB1dOWMOCz7lI4O/vn2te79mzRx5sQ17znWP05ZdfygLB6dOn5R4yY8YM2R+vBVpJ+CPENnLln+9R8WFbZs2ahZYtW1r2xePwHH788UfpY84h1j7h91kxm9Wn2f7+Awbi5rvux4G9u7Dkx7nw9PJGxMVzqBVSUxJD2Dv2pk2bsH79erHmEAr5nLtcgLE1FlQemK2Hc3TG/Bm4rvt1aNWmFRr5NYJ7kLsch/eKatWqWc6B/coH+5T/eXxeT/b6nEoe4XzZu3fvVX3Oiu0cd1ZO16PdV4q7CJNx8RIS161F4tp14paiD6a1HMvFBR6dO8G7T1949+0D18aNi3zf1tpbmsKIv5cT7u5SF99sPosMgxG/7L6IJ/pfbYXwdvLGW73fwr0r75U6IbMPzsbgRoOl2J8ijzGsUwfuI0cicORIUTKT9+1H7N9/I3PXTqQfPmLZjtmektf/LQ/C7FreN/aHT/8b4d6mdaUMXK+MXItrtipQpgoELQ2jRo2SH+tu3bpZ3qdARbcm/hCzMquWQ53wB5dCF4Ul/sBTi+QPvqZAJCQkiFmdq1yEK5h57aM0SfjrL0TO+jR3irkSwQRHL29Ue+op+N5ys92t9u3bJ9okhT0KDBTQKXBQiC4oFHpeeOEFcSnjavErr7xy1TZcMaaQTAGYAhePR0Gbwu3TTz8tz3/77TdZNb7xxhtlpXnJkiUiKFJo5/hwBTMvONbcL92uKFBRwdBWXO0pD/q8xjw+hXm2h8ILU/3mh73zsgddPCggUuGhIHfgwIF8V7opdLEvqGw99NBDIgwSKn1UkDhudC/j6jZTEtvrOwrkXNGncK8pKXkpD9wvBXYKvYQCLhUltpvXEFf0b7rpJhHy2N8U2letWiUr/jz+Rx99JG3huPBzKgVUFjnHqOQQ/cq/Hgr/VB7YTuuaMHqlnkoilSPNMsMfZ7qsURnlufOc6TL00ksvyeccJ6ZspoJICyTHSw+Fe1okClLpmWNHl5tatWqJEM65MGTIEEkTTcsGBWUqclSuuF/OLwrueuVBg8ohU01TsbvzzjvlnkRFmW467Mvw+GRMeupJ+NdqCHcPD5w8cghLlv+OJg3qFejYFNCpbFHh4/gR67FYumwprqRckSBbDfbn4JsGS8pPPuf58nrnvVcP77G8bmg1YL+y/VrhR+s+1+ZsXn3Otj7//PNXKRDFgak9eb+l0sD0nrZwrlkT3jdcD+++feHVrVuFy7wztkcDiYVgMPX3289hwvWN4WIjmL5ttba4s9mdWHxiMZIzkzFzz0y80+edMmlzRUSUy07XwdCsKXyemwxTXBySt25FksTLbJEieBrpJ07II/rL2XCuUQPe/fuJMuHVvZu52rdCUYkpUwWC2ZcoYFHQ4Woag6n5o89VT/6wUsCn0EmhlQLfwYMHxUTObQl/lKgocCWPq121a9eWH1muGA4cOFC2YarWvPZRmkTP+U4C9UqHK4j+7rs8FQj+0FPYoHDIWBMKXFwtLAxcOacgpwWiU5jlir4eKn9csabbBIPfKdxR2KUrjSZwcxWZD/quMzuW5mLG8aHwlZ8CoX1O4ZOCWGGgsKNl+qIlg3OsIAqErfPKCyoOXOml3z4fXGnNDyo/FN4oeHLeUnEgXKXWstlQ6WJWMbrI2es7zv/C+PRzfDQzLqEwT+GWSjavQwqibAv92ak8EAqQFEwZ68Bz5fZsG681uh6yn/RKmz0LBJF4IYMRxiwjkuPTYcwyITPdgIToNERdTITJCKTEZ8CU4YTI8+ZYI/ZTYmwKNm7YiG+/nC/X+1vvTcPnn36Bhx8aJz7/8+bOQ/sO7UXhpeJLpYrQosP2afeF/KD1icoD4dhQuaJSQcvO/fffXyhfUVpVaKmhcsS+5H5oTeNCx9btO5BlNCE9LRXnz55C+/Yd0KxZU1EeSuLYZNe+XaIo6gV7RwdHmTtaalaOC++dvFfqM9YRKre8H1PhpMLI2BRaOHjv5X2VSg1jaNjnVChorcyrzzludJ0rLnQ7SVi5EvF//mkOirWTKclnwAD43HRThV8hrhvoiQEta2DNkQhEJKRj1aFwDGtv24Xm6euexppzaxCfHo8VZ1ZgTMsxaB1c8IUjRQ7OQUHwGzZMHkwZy9oTSZs3IemffyS9r4YhIgJxCxfJw8nPD94DboTvLYOUMqGotJSpAsEfQK6QcTWTq1Rc+WQKVm21m/AHjSttFFxpVufKlT7gl/63dAGgsEI3CwrN/HHjCi3RlJC89lFaBD38MCJnzSo1C0TQQw/luRVXs+nywCBjrk5qQmNh4A+u/jvWwoUm1K9evVqsGxQy6BpDH2j9jzVXWylM2AoS1lxQ9MexDqLUuxwVFn2beQxrwUtzxbA+tq3z4iq8vTZQoGcgLV1WqKTyP1fatePye9bnpTehsg3aa32btfft9R3RlIqCwrboj2FdJZkCnq3j8Tx4TAqJFIDZN7Q0cfVdH0DMgF69smDINCIzLQvxkakwZGYhK9OIxOg0eT85zhyTFBRYTdwLqUyQ6JgodGyfs0/uJ8AvCC2at0aN4FowpJvQt2d/LF3+C/bvPgRnR1eEBDREzOUkDB5wGz7++BMYMrLg5OIo1hKu4hd0/mjzTesrLUECz1Fb1edqPe9Z+aEfY+6H96ZMgwH3PfIkOvY0K8aJ8fGoHeyL8LPHc/n/F+fY/F5EcgQ863rio3kfWfZHdxYWhdP6gkoWLVlUBGyl1KbrFOe1ZrHlvYQWFVoS2E9aAD6tyfrFBd5/bPU5511RgxcNsbFIWLUKCX+uROqePTa3YRpOs9IwAG52UoRXVB7s1VAUCPL9tlC7CoSfmx8ea/8Y3t35rrx+f9f7mHfLvAqtQJWXlLEebdvIo9rEici8ckVibBL/Xo+Ubdstmbyy4uMRv2SpPESZuGlAjjKhgrAVlYQyj4FgGlVtxcoW/HHSfN3t/ThTgNGyghRlH6UFrQN5WQiKgiZMWAt8tqBfNH/AKdjSPYSr2PqaGQWBLjz0iaaPN12f6OZgDQUQWjaoBHI1nKvvbCdXcCl89O7dWwTxFStWiJDx+eefy38KUlzZ1AJlaVmgAElBhYGhej9sW7APCpJJi37YdIXhCjBXXdkePVxx54qwtmqqxSvYOi+uvnNV3hbjx48XZY1BplzB1uY1989VXK4m/8XsHjrYnwxi5oo9z5/fo7JCgZeuIXR/obsLM19xBdpW39nqF02xsAfbpGW8oUsQhUEqR7QsUDBlfAaFRK5+0yLIlXNaJWgloYBLyxItOY8++qgciwHttNhoxzUaTchINcgjPdUgGX7yo0fX3vj9z6UYOOBmJCYmYM/+XZg44Qm4uDmJGzvb261rD3w7fzbCI8JQs0YItu/cimZNWqBOrboIvxKG0PNn0aBeQ6zfsB7Nm7RATFgyHJ0csGPbLoy+Z7RYPZyciya8tm/fXhYqOEfoAsmFCrpbckzY5/ayr7HfuArPmBaO80uvTkWjVh2x8vdlaNu1F0xZWXjnxSfxzNNP5UrMUNBj24LZk9Iz03OlZ/Vw8UBt79pwc8pRjugKuGzZMrk/2rveqEjTckglkYknOB95L6DFi3OS7n1UPLT3NehapbmS6uFCQmGsZVz1Td62DXG//oqkdettBkIzrarv4MHwvXlgpQ5s7d4oEE2re+PklSTsCo3F6cgkNLZK6aoxsvlILDq2CKEJodh7ZS/Wn1+PAfXtu2AqCo9L9eoIGDVSHlwoTNqyFYlr1iBxwwaYsq39okz8ukQeTsHB8BsyBH7Db5eCgwpFRabMFQhF6UH3GLobUGiluxcFVb2bSUGgoMoAUrom8LktH2+6svA4Q4cOldVFuvpQyKZLBIO4+Z+vKVAz4JbCJr9DYZOZXOirzX1TGGVcA5XKzp07SwBxXlBgpeJIYTivVK4UeuknTyWCChQFcD1Usrhaesstt4hLnbaSbu+87EEBi65xdKnj9uwzQlcaBrLSzYiuUPTj16BiR2sYV/vZz5pVgEI9FREqLFRMqMjxYavvuA899GFnICvjFbhPe7B6O12XqDQwrSbd3OhuQ0GdweN0NWF8A613VGQo3NKiRWsHx4rCIZUJKn8MdqfiVyukFkbddTc+/egbm8GrggNXoR1FMaAw7xvsASdnB9wxaijOh5/CwxPHiDA++bn/oUnr+hbXMyp0ra9rgrffeQtTpj8nq/BNmzbDc89PhpuLB954dTreePsVqd7s7+ePlya/Jt+lRePCxfPwcQ9E9KUkOa6bpwtOnjmGzz7/1JKeND8oYNPXn777bB/Pl8HjWl8yaJ1znHNXD8eS1yLH7LmXX0NslituvHU4Ll08jxcevQ8OJiNuHTJElDLruJCCHFsPxy4hPQGerp6oFlINrzz2Ct7+8m2p6aDFOujh+PI9vUsfXUp5zTBwn1YkKty0NtHywPsI3dX4GecBFQu2icfl/GYbNZgJzLpGjxaDRte3/MgMD0fcUvMqbualS1d9zsBn3yGDRSBzLcGUvOUZjtWoLnUx/U+zy9Yvuy/gpUFX35O1AnOTOk3CUxuektcz9sxA3zp94epUssXuFGYYU0MFlg9jWhqSNm1C4l+rkbhxY44yERWFmPnz5cFq2H633Qa/obfCWRW0VVRAHEyqIlmh4Sqi5qahQfcpbXWwpFIQFtcCocid9ai8oWV70hIIaGNLawPHNr+Yi+LCGAcGubJ/igOtDWnJmUhNyJAVfmt4Lq4eTnBxcxbh3dm18GlqKZjSXbEgVY5p7aCbVGZ6FjLSssR1ytZtju99OOsdTHtjGty8XOCYR6rU4iAuXFlZuJKUieikHIuZt5sz6gV6llhV6cysTKkmzcBZDVobavvUhodz4VzcCGNK9ApBQc9VU/ypUNgaZyqrtJzwcz1yDz1zBtWvXEHywoWSUhNWbnROgYFmoeu2YXBj5ecyvgdqheSoXF+rjC6cQ93eXg+D0YRgbzdse6m/zWBqbTzGrRmHneE75fXkzpNxf2tzHI3i2owri9glbdqMhBUrkERlwtqC5uQEn/79ETD6Hnh2717mc7qyUxbXbEWXb+2hLBAK+eG2XpXXYBAxV6XLM3QRYeE0WzB7TEnCFVemErWGiiNX5ysSjDOiK4nmolRYqCykJmYgNSnzKhclB0cHuHu6wM3TGS7uxVN2KQRx1bsgyoN2bLOy4gxPX/P3zcqEAekpBom/IFciIzCg381IjElDUmy6tNXd20WUnJL8EWeQ9Hmp75Dj4kTBr6afOxxL6DgMlr2cdFnStGoEegSihmcNCZguLHTds1XEryDkNU68Vmnxs1YeTAYDMmNixKc8/I1pcAwLy/mQtSH69Ib/iDvh0+8GOBQgi1ZlJsjbTYKp/zocjqikdGw8HombWtlOSc55TKVh1IpRktb12/++xV3N7oKnS9FrICkKh6OHh8UykRUXJzE8cb/9lhOATYF2LdMOr4Vrw4YIuOce+N0xHE4FvN8pFGWFskAUAWWBUFRl6xIVh5T4dFEcrKGy4OHtCjcPZxHkyyMMrKYikZaSaVEm9DDomufg7uUMx2JaB9IysxAanSy5+wnHtba/OwK9cuIQikOWMQvhKeGIS8vJTMbKw4x18Hb1Lvfz3ZSaCkNMjPiJp2dl4XxkJJymvyUKBGMZ/EbcAf/hw8ttXENZrWZuOHYFD87bJc+pTHx7f263OWte3Pwi/jxjjl975rpn8HDbh69JOysq12Jc08+cRfzy5YhfuhSGyMhcnzl6e8P/zjsRMGYMXOvULpXjV1WUBSJvlAVCoVCUOFlZVBzMFgfr+AZ3Lxd4+LrCxbX8m4SdXelG5QRPP1cYMoxIS8oUZUKzolCpSIpNQ3KcA9y8nEWZoFWisCSlZeJcTIpYIOS4jo6oH+QJL7eSMfymGlJxMfEiMrLMmV+07Ds1vWqKElGeFQdjQoJUiaZ7hzUeXTqj+pAhUunXQbkY2KRvs2qo6euO8IQ0bDh+RSwRtGrZ45F2j2DlmZVihVhwZAHua3WfioUoY9waNUT1Z59BtSceR+L69Yj98Sek7DIrhayEHTNvHmIWLIDPwIEIvH8sPDt2LOsmKxS5KBnnW4VCUWlhrQYK1DGXksVlSVMeuJru6euKoNreEghdEZQHPWw/FQOfIHcE8xyCPHIpChR0qVzEhidLNqfUpAyJ9ygIMcnpOBuVozy4OzuhSXWvElEe2K7o1GicjT9rUR7opkSrQx2fOmWqPLBtDG7nwzruhNmmDNHRSD9xEhkXLuRSHqgoOAUEwLl6ddSaNg3e11+vlIc8cHJ0wO0dzSvTnGNrs1O72oOVxgc2MNfjYDHB1aGrizPMihKEBed8b7kF9b9fgIa/L4c/awNpbnpGIxL/+gvn7hmN0FF3S7FEuvspFOUBpUAoFAqbcEWeRd6iLyUjJSFHIMxRHLzgHeBe5LSo5QmJ2fB2QUBNLwSGeMHDxzWXGxndnli3glmcqEzZChYn7KOw+FRcjE2V1V7i6+6CBlROSiBY2mA04ELiBYQnh1vGw93ZHY38G8Hf3R/lAbp+8KFhzMxEZkSEVOxl8TdTZo7FxNHNHS61a0tAtEu1aipHfiEY1Kam5fnqw+H5bj+6xWjLc6Z3VZQ/3Js1Q8ib09Bk4wYEP/UknLJrr5DUAwdw6ZlncXrgzYj+bi6ydNeYQlEWVPxffoVCUeKkp2TKqjuLvFlWkh0ggnVgLbPiUNz4gPIK3Zt8At0RVMdb/vO1XqmiMkVFIiEqVRQLDVonzsekIDIxJ9MS3UqYaakkwkGYXel03GkkZuQIDkEeQWjo1zBXbYfygjE9HRmXLoniQB9vWiD0Pt6u9RvAtUljOAcESIEuReFoV8cPIX7mjH//nopGQtrVMUl6OlbviOYBzeX5waiDOBx1WHV5OcU5MFAK1TXZ8DdC3n4bbtmFdUnm5cu48v77ONWvP67M/FhiiBSKskDdtRUKhQWmP42LSJGK0fpVdq7OB9UyC9SVweJQEJjWlQpTQE1PeTDOg0qUBlPXUsmKu5KC1JRMnIlKQnyqWYjjZrX8PeRR3Hh4zWUpND5ULBDEydEJ9XzrSbxDUbIslSpGI0xpacg4fRpZsbE58TIODnDy94dbkyZwa9AATj7e5TpZQHmHfTcwO/tSRpZRAqvz2/6eFvdYXi88trDU26goHo6urvC/YzgaLv8N9b6bA6/r+1o+Y5xE9Fdf4dSNAxDxzjti5VMoriXl7JdHcS1gMTkWDyMsdsZqtHnBWgG7d+8u1nEqGgcPHpQCeKVR+8FesbCCpNZk3Qh7FY8LCysHz50717J6nhSThpjLyZLuVIMxAQEhXhIfsHnLJrvjSZ93Fp+79dZbJe2vluqWBfJYiI5F+lgMbvHixZbv/PHHH1Kkjw8W2mPV65KEbjQTJ060+RmL8hUuVsJZ4jyoRNF9S59hitW2EyNT4Z5qgqvJQVKz1g/2woy335AK1Kx+zsJ71n2eF0x3yn4ZPHgwnpr8FC7EXbB85uXihcZ+jREXEYf7779f+pvFCFl5nrzyyivS19qD1aHZ7wsXLsz1PquYsxAj5xP7n/OrOLB4VsbFi1KRV3Lday5vjk5SKIurqK516sCxFOvkVDVubp3jxrTmcP4C5OBGg+Hjaq52vursKsSmxZZq+xQlA+9BXj17ot5XX6HRyj/hf9edrPIqnzGTWcz8BTg94CaEvT5VrkGF4lpQflN1KMoNu3btshQ7qyqcOnUK0dHRKE+w+BYFxZJKK3j48GFzoHBypvj1s2KzBt2TvAPcpDYCf7yioqKkBoY9WLGYlZZXrFiB48ePS/XsTZs2YcmSJTh58iR+//13USbuvfdeqWbOyspU0ChcsyYAKyJ/+umnmDJlCkoKKiTHjh1DSULrC923PP3ckJaUgeT4DEv2JoY9Bhod4OTsBHc4iGJORZRV1qkMaH1ekHazgNviZYuR7JKMt19+GxtWbsDA2wZKNenqntVlTN58802ZD7fffjtOnz4tyunmzZtFYdMraYsWLZLtWOWcRdwIi16yxgsrrXM+jRgxQvqfVdMLizEjA4aICEnFKmj5652c4BwUDOegQBUQXUp0bRgIf08XxKVkYuPxK5I22N3F/v2BBQWHNxkumZgyjBn4/fTvqrBcBcOtUSOEvPkmgidOlFiIuMWLxeJHpT3u558R9+uv8Lv1VgQ/PhGu9eqVdXMVlRilQFRiKNBNnTpVKg5TGG7QoEGhqw5TADx06JAIdrREeHl5yT5jY2Ph6uoqAsd1112HsLAwvPTSSyJo8n1uQ8EwPT1dVqbZBgow3EfdunXtHm/9+vXSRgpI/v7+ImSmpqZi7NixsnpLKJjt3LkT7777rqyI80ELCYXh6dOnSwVFWla4Dx6XK9GPPfaYCFEUYimccYXc0dFRVm8feeQRsQq8//77sg+2mwoEV2TZFq7659U/XFHmsbjS++qrr0of9ejRQ4RGtiszMxPvvPOOFEPTYF/xNQVqMmHCBHnes2dPm8dJSkrCmjVrMGnSJHlNwfi1116TvuHx2PZ69eph9uzZIqxTKOzVq5esMPP4zz//PM6fPy/tZNHAjh07YuHCRXK+Xq7+iLgSjq3bNonzjaOT+cF+1QrxcfzZDx999JHN9rFfOdZczeZ/rRr7kSNHcNNNN8mcIFREOcYUZKdNm2YpKEalgsJuXnBMmZed++R843mw33jMDz/8ENu3b5c5T2sHV9S5/ytXrsjqP/vFGvbfgQMH4OfnJ3OpVq1a0ke25retY9815kFcMmZi0Tef49C+XdKOfn1uxANjxuHRCY9K39555514/fXXRVD/8ssvRZgnNWvWxOXLly1zWkPr89//+h0R6RFISUxBfGw8fHx9UNenLnzdfC3bcu7eeOON8pzXNq1AnLNsJ+G8Z7/MmzdPrj097BtW9q5Rw+wC07dvX1E8aLHRvp8fzAbD2AbxwdZnXHJwhIOrC9xobbA6rqJkYRVz1oH4dc9FKVS49VQUbmxpu6icxp3N7hQFgigFouLiEhKCmq+8jOBHJyBm3nzE/vSTWP9YmE7qS/z5J/zvuAPBEx+DS80cS5VCUVKou3spcmrPFez84wwy0krG5SQHE1zcndFtaCM06VTd7lb79u0TIfmXX34RYYZCOFeFKegWFK5McpWYwiOFPAp+L7/8sgjpFLYeeOABEWzfeOMNqVxLlwoK91QUKIhRcRkzZowIrBSif/rppzxXObkKypVuupgsWLBAlJfGjRvn2UYKPGwjhTEKen/+aS6YRAGNAhvbwFVaCufr1q3DpUuXRMimYM1VW1Zk9vDwkFXZDRs2iECpKSl5KQ9caadQyP6lIMw+oMLB84uJiUGnTp2kryjAsVI2z03frxT6qTRERkaKwkKB3zr1pQaF4xYtWlisD1QMKGgOGDBAzv3bb78VYZLnR6WGq95PPvmknD+FRO6X21EwpgI1dNBwDBs8nFMJQwfdLvuc+OgT8GFWJZfcno0ch1atWqF9+/Z2+4LK2bJly9CnTx8kJCRgxowZ8j6/t2rVKtxxxx3S31u3bkW7du1QvXp1eRAqQV9//bWMRX5w7L7//ntRVG+++WaZj9w/980xowJBIZjjSAWBc96W8kA4PhSkf/zxRxGeaeHh2Nma3/pjc7x47HbXD8a/G9chzZCBT+f9hCBnBzz19JPYun0z3p02A30GdsG3n/6AuCRzkSjO6bvvvlue33XXXTYrpXOcIpIjEJUWhV1bduHTtz5FcLVg3D7g9lzKA6G7mMacOXPk+tQL/xw3zg9WSddDpZb9RzcmDSoY/D7nGRW+vDAZjZKONYuB0UZj7lSswdUkdkR7rbg2bkxUIAirUuenQDDovl21djgYeRAnYk/gWMwxtAhsoYaqguIcFITq/5uEoHEPI+bHHxE7f4HZGmgwIO6XXxD/228IuOduBI0fL66ECkVJoRSIUmTfmnOIDS+eX7F9MrBv7fk8FQj6OHMVnwLSmTNnEBoaWiw/5+TkZKlSqHczocDGFVmu4GsxA127dpUHYyAoJFJ5IBTU84uloMDDVWUKw1yNpVDN/eTFyJEj5T+3pwJB4V0TailIc7W3c+fOolCxnRTeKTDxQSsBfcf53UaNGonyUBjXLipN2io6V6VpWdC44YYb5D8Ff66662F7KGhTaaHiw3ZQ2bMX38CxCwkJkedUAsLDw6WvCJUjPqh4UaikMqRX/mgVoYBMl5Xevfpgwv1PSHal7Cyj4q60YNE3+GfzxlzHZJ8MGjRIBGgqQTymPag4dejQQfzs2VYK3lRU2YZz587JGFGRoRJHC4UGz4XWAioaHK/8oILCfuK84tzmKjuVEloGOB6aQkLLU9OmTe3uh9YFruATCtIff/xxnvNbOzYtOAY3X3j5+CElOQkHdu/AhTMn8cy4ey3HvhRxDo5OOcGOLLxH9zC6ilFB0AKHP/nkk1wWCKZ97dyrM0Y8NEJed+ndBcvWL8Oi2YvwzvR37Fp/qHxRif3hhx9y9mUyXfWeBsfowQcfvCqAmRYYjlWeBeASE7NTseoy/jg4wjk4yCycODrCSze+itKnR+MgODs6wGA0YevpqAJ957bGt4kCQZafWo4WXZUCUdFx8vOTzE2BY8eKRSJm7lxzPFJGhsRIxP6yGIH33Yeghx+SbRWK4qIUiFKk48D6pWqB6HhT3v6NFFopGFGY4wowhTV7K9wFgW4qFLzo764REREh/uwUxvUCCf3eKcjqXSf4eX7H54o/A0f/+ecfUUjoQ05BT/89rjbr0ccEsI3aa/2xtff5Xw/3SyGRaIJ3YfrD3r6Im5s5taa9TDMcE7rtcLWcwmReUGjWzse6r9kfVLKs20PYHgruf/65En+v+QebN28Sa8WCb34278vNSdKyTpr8rDysoSWJK+5UBHgcugRxFV1zxdHPNcYxsF1c8aa1gmPn6ekplgW68BC6mNHVSlvRp1JDZVH7PD+0PtXPJypddNFiwDLhPKcLVV4xLOxPrQ+5D/ZpXvOb8DOmaWWmJe3YzPD64gvPi0VCf2w3t+xAYd04sY5EeooBrh7OFuuDZoFIN6TjfOJ5KQxHl6Vzp8/hputvQpB7kChWzz579djw+LR6UTGmZU+z6BAqQlQ4rd0FOYZ03+P3rGEf2IuvYYB0Zni4ZH7RoxV/c8wO6CSa+5ri2uDt5oz2df2x51wszkQmIzw+DTWz07va4+YGN+O9ne9JHMTKsysxqfMkuDjmjKGi4uLk7S3VrQPuHY2YOXMQ88OP5hiJ1FREf/21WCWqPfO0uWCdshIqioHKwlSK0Dowemp3PPBurxJ73P9OT4yZ3h2jX++Wp/WBcGV9yJAhIvwFBwfLCm1RMvhQqOD36B5BX2tNwKI1gUIwhVRaHDTXIQo0mq9+YdHcMrhCSsWHK8u0CsTFxYnwynZoLiX6jDVk7dq1qF+/vsWK8Ndff4lQSEGVvu50Wenevbu4+LDNXC2mAE9Lja1z1isDtuA5U3DWLB5c8bW1L3tQMKSFwNvb2yJU24PnxfMgHAeuFm/ZskVer169WqwPPDcGMfO82HaeJ9uzdMlvePmFKejasSeefnyyKErRcZHw9veAk4s5Xak96CfP/XPMudJNIdVaedCsLNyOsD/oesb3OBfoEkRhlxYMjhEtJ7RCUHmgMkJ3rOKk8+R5s+8pHPPcOW9olaBAbG8M09LSpC2E/UTLSF7zm+2PTEq3pGklIX4euL53L8lwZH1srU9ZlM7VXbdOY3RAUlwqUhJzCvMlZSThTPwZS1VpBmXPemMWshKypF94XdFiZQ1d4mhpoYVRrzyQPXv2yPy0httzLnHOWUMl1HoesnYDLQ7pp0/nUh4cvbwkHatr7dq5lAdF2dCzcU7BsW1n8rdC+Ln54Ya6N1gqU2+9tLVU26e49rC+SvXJk9Fk7RoEjBkjFa9JVlwcwqe+gbMj7kRKttVWoSgKygJRiaGfNVd2KUhz9ZSuRPm5A9mCrjgMBGUMA60CjG3gKjaFbK6cc990k6HrB1dC+TqvjD15wYBrrsrS9YgrmTwWhR0GwlLYpCJEYUoT2glXuunCw9VpxhVoUKhjECuFVa640tWIbkZ0seH2/JwKC4NurVOr0h2HPvE8D3sxGxSQGZzNuA/ui244tlZ27cGVbQpsel90ezAom7ELVKDY79o48L+vr6+MTZ06dXD06FE5Zwq9/M7tg+9CSlI61q5ei/vGj4SLswtuGnATuvRsDyf3LBHeAwICRPAtLHSFoVLH8aLrFucArUdsH9+jJYICORUdTTFkilG2kwI/YwwYN8EHYf/xPDinKBBrGYPyg/OCrjdUyDgOVJqppLCvateujdGjR8u81MPA840bN4oQzmMxiJrYmt8OTs5ISDMgPdNs4WGaVrqM+Hm42D22hrOLI7wD3STtKwPTO3bojOnvvgZfHz+MGX0fjD5pCE/NcQ1zc3ZDlyZdpC/HjRsnCgRdsbR5pfUNrXLffPONPNcC8QnjPWh5YN/SZdAavq+5wulhXzFDlHbdimUnLk6yKzFYWoNCCAMyHX19bSp9eiuctaVMUXr0aBSET/8+Jc93no3F8I518v3O0MZDseaceTFm3bl1FoVCUblwrlYNNae8gqAHH8CVjz9BQnayivRjx3DuvrHwHTwY1Z+bLEHZCkVhcDAVx6elikL3AMJAS/2KJv3ZKTSVpglfc9mgcKN+nM1++gwWpVCqh7EQXIHlCnJ5hOPIQFYKtwzoplUgv7GlcEvFJr8gV62SdGJM7tSsdJ0pyUJwVOIYvEslpCSh1YmWC71gXFYwLWZoVLIU6iJUHBoEecHTreBrL9q4Ojg4SuxJWlKOFcMII5LcYpDunCr5+Wt715Yicde6b2iN2b9/v4yluCtdvgyjPl6KShNdFYOD86wazXPVlHsq7IW5R12re2hJwnFlHA6tVyWVXrkopGZkoe3U1RIH0aS6N9ZNuj7f76RnpaPPoj5INaTC380fG0ZugLOjWlMsT+NaGqTs2YPwt95C+pGjlvccPDwQ/NhjCHrgfjhkZ8yrrFTmsS0t+dYe6m6hkB9urszbgm4mWrBpSUErA7MOWcMUn3R3KU/QjYX59m1Bn38GXhcFuvvQqsMV+YLGXtDaQV9/Kk32bnx0f0mMTcslpHL1m9mV3LxKdkWYtQdKQ8inVUOfYaisSMkwiPJAoYy4OjmiQbBXnnn284JuTSzI5+LhiPjoVDgaHcE/3/RgmBwMqObtL7EZ17pv+INKNy4qqKxma4iKypWW1cnXF860OlRywaIi4+HqhDa1/bD/QhxOXUlCTHIGAr3yHi83Jzf0rt0ba8+tRVx6HPZf2Y/ONa92lVNULjw7dULDxYsRt2QJImd+LNXiGR8ROWMGEv74HTWnTpVtFIr8UBaIIqAsEIryaF0yZGYhITJN/peW1aGqkJRuwLmoZGRlC9JUGhoGe8HFybFY48pVXwZLGwxZ8MkIgJvBM1fVb1a7LouxykpKEqsDM7ZocCXSpVYtCcosKMoCUXa8vfIovt50Rp5/dV+nXFWq7fHH6T/w8hbzos3YVmPxXJeStSZWVKrKKjXTvUZ++pnUkIAuCYffnSNQ/X//kziKykZVGdtrYYFQUoVCUQlgitDYsBSL8kAFhIqDX7WyEUgrMgmpmWJ50JQHLzdnNK5WNOVBD4Olz8afRWZWJkwORqR4xMPN38mSqSkzPQux4cnITM87eL8kYXxDxsWLyAgNzVEest2VGCRdGOVBUbZ0rp8j7O06mxMjlhd96/SFk4NZiPr7/N/FytKnqHgwnSvjIxr88gvcdYVO439dgjODhyBu2W9qTijsoiQLhaICwx/8pLh0JESlWm70LAQXUNMTHj6uKk6mkMSlZOBcdAqM2X3p4+6ChkFecMrHtSjf/WbEieXBaDKv8rk7u6ORH+uOeCGghqfU4iCMWYmNSJUYltJEgp3j4pB+8qQES2s4enrCrXFjuNSokWesg6L80aWBuR4N2Xs+tkDfYTamzjXMbksXky7iVNzVrqWKyo9Hm9Zo8PMi1JgyRTKsEbo2hb30Es6PvV+ysCkU1qhfCIWigmI0mkRxSInPKdzl7uWCgJpecGaBAkWhoN/4hZgUKehGmGWpfpBnnmlu80PS1yaHIyIlwvIeK0qzGrALc+hmuy4FhHjK/+wvIT4yFalJOe5EJYkxMxOZ588j8+JFSdNKmA+e7kquDRvCsYIEMCtyE+DlirqB5niq4+GJcn8oCP3q9bM833Bhg+rWKgrvAYFj7kWjlSvhO3iQ5X2mej1z+3Bc+fhjSbCgUGgoBUKhqIBkGYyIi0iRwmQa3gFu8AlyL5bAW1WJTEzHxVgqD2YCPV1RL9BTUrYWlSxjFi4kXpA8+xrBHsGo410Hjg65b71OTo7wr+EpCqC+8FxyfHqJuRCI1SE2FhmnTiErMTHn2L5+4q7kXMisSYryR/MavvI/OSMLF2NTC/Sd6+vkZGzaGb6z1NqmqBi41KiO2jNmoO43X8NFK0SZmYno2V/hzLDbkLRF1QxRmFEKhKJcw5SQzNXPWgssjqXI8ZU3ZOTEOzDWwdPXTQmARRCqIxLSEBafI2wFe7uhdoBHsfqScQ6hCaFIzMgR1EO8QlDDq4bd/UrcSpC7uJ5pMO0rH8VVImh1yKDV4dKlHKuDszNc69aFa726liJTJQGrj/OhuPa0DPGxPN95NlrSEOcHUwfX8Kwhzw9GHkSmsXTd5xQVA+8+fdDoj98R9OgEIPv+QMvlhXHjcGnSJGReuVLWTVSUMUqBqMSwaFybNm2kUBlTsbLIFwtUsbBaYWDa0KIUoCMsMKZVUC4sGRkZuO+++6QKNYtSMUf9P//8I59t3rxZ0r7y3PjgcWwxY8YMKRR388034++//7a8v2rVKin6NXDgQHz22We5KvWOHDlSvsMqzCm6XPj8znfffYeS4MKFC0VKWctgaVoetPoOjs7meAc3T1UNuEjuRfFpokBo1PB1R4ife7GUhzRDmgRL87+MkYMj6nrXRYB7/hlNeFxakrz83SzvpSRkFFmJEKtDTAwyTp6EUW918PM3B0lnV20vKdh+piXmQ1kzrj2sU6Ix+deDaPXaX5jw/W7sDrUfVM1xuq7GdfKcNSGOxxy/Jm1VlH/ozlj9mWfQaNlSeHbOSfGbsHIVzgy5FXG//qqCrKswSoGo5LBS7fLly6VY2Z9//inFy3799ddrdnxWeC7q6ilrRbC69IMPPigF1FiJWVN+WH2adRF4bnywgrE169atk5RkK1aswPfff4+33noL8UxbFxkpFav5HvuEtR6okBAqKVr17saNG+PLL7+07G/Tpk24/vr8CzQVhMuXL4sSURgoSOqDpekzH1jTU8U7FAH24aW4VEQm5SjTIX4eokAUR/C1ZFrKXsV1cXSReAdP54KvyPP4Xn5ukkWrOEqEMSMDGefOmdOzZqdoFKtD7ZpwDfaGQ1o0EHceiD4FRB4HIo4A4YeAsIPmR/h/5tcRh4HIY0D0afP2SRFAWjxgSM9VL0JRtny//RxmrjuZ6z2GQaw7egV3zd6GH7afs/vd66qbFQiyN2JvqbZTUfHgYkO97xcg5O234eTvL+9xQSJsyqu48PDDkslNUfVQheRKGYPBkKegoM9DXJhti7qiTwE8ODhYXnN1/4knnpCVfNK8eXMcP35cVvwpSIeFhaFJkyYWoT0zM1OKqlEpCAgIkP3QOsFqzxTi58+fLzmW+Z033nhDBHQWvnrkkUfkeVBQkKUt48ePl8/0cMX/xhtvtLyuWbOmWC9CQ0Pldffu3S2fUTFgAbzffvsNISEhUpSN2+uhxYGWF1ovqEixMvWGDRss+2KlXHL77bdj5cqV0m7mh+Z25K677pI+YuE7Cm6skkulgv1CiweVMVdXV0ydOlX2NXbsWIuVY+nSpdi5cyfeffddsXCwcBzbwdzK06ZNkweViNdee02ez549W5Q8FhLr1auXFIzjcagksTLvkcNHUbd2PfTs1gd/rVuBhMQEfP75Zwio2QyHDh0S5So1NVVyW7Mv2E623dfXVwq+sR1UwhTZykNsKmJScoKU6wR4INArZ9W/KDDWISwpzPLaw9kDdX3rwtnBWa6LwkJXJornSTFpFiWCKVa9/PLOriV1J2JjYQgPtygOxMndES4eaXBIvQAUxD1erxtI8218iZWLXb0Bd1/A3R8mB0fLuRa1nomi8OwKjcFrvx2y+VlWdjD1q78dQouaPuisy9akoVkgyN4rezG29Vg1DIpc8Fr2v2M4vPvdgCvvvof45cvl/eR/t0lsRPVnn0XAvaNV9rYqhFIgSpnFixfb/YyC7w033GB5TaHTnqBRrVo1DBgwoNDHp5BOFx8KFRRIa9eujT59+uT5nVmzZqFFixb45ptvZHWerjvk559/RmxsrAjbXMkfMWKEKBAUUBctWoSffvoJ7u7usmr/+eef44UXXpDz//rrr3MpD4T7zg8K5Vq1ZgojLi4ueOihh0Ro9/Pzw+jRo9G3b185LoX8H3/8Mdf3IyIiUKOG2beXUIkIDw8XIZ3P9e9zW1vb8z1y+PBhtGzZUp5TOerXr5/EZVBJYH9RibAFFRK2jxYO3oD5XY4DFQcqFlQe6JZFawktQ2zbs88+K/1Jawddqqa8MBX1azfG6IdGoGGDxpg/90fM/f5r/LrkV1H06Ar1xRdfoE6dOhInwvc4lwiVIo6FwgyvAwaXxmYrDw5wkMw1/p6uxdpnZGokIlMiLe/5uvqitk9tcV8qTvyCZ3Y8hEWJiE+XshG0UNjNsHTxIozJyZb3HBxNcPHMgpNrPu1gPQAGd1sCvE3ZFgYTYLSzuMH30+LMD4dLgEcA4g3usg9NQVeUPt9uPiPJEzRlwRb8/NstZ20qEE38m8DH1UdidvZd2SdzVil/CluwuFyt996F75DBCHt9KgxhYTClpCDirbeQsGoVQqZPh1ujhqrzqgBKgagiLkyaBYIr57Qi2HL50aBQ/OGHH8rzzp07o252JoYtW7bIqjyFeQoHN910k7y/bds2nDt3DqNGjbJYUrTv2KMgFgjCuI127dpJIPW+ffvw3nvvSRwH/2tQkWCsg1ZdUsOW4EYB3ahbldXgj6W99wmFfM19iRaYDz74QJ7TWsGHvRgRtofWgDvvvFOUjjFjxojieP78ecs227dvx6233ip+41QgqZjRskLlKDgoGA3rNpXtqgVXR+++veDt7yaKIMeJVhHu6/HHH88VeM6xJtddl7OyWNXhfLgQmyq1HjTloV6gB/yKqTyEJYchNi0n736QR5AEpZaUAGatRNCVicKgPtialoas6HAYrsTk8ipycjPCxSMrRydwdgPoTuXiDji5ml/zPy0JebWXO6WykJVhdl1ifEdmCpCRzINnb5MFpEQB8DfvMysTcC563yoKBgOl1x6JEHelvKByseZwuGzPyup6qOh2rN4Rmy5uEksaEwDQ9U6hsId3374SZH3lww8Rt+hneS91716cvf12BD/xBIIeelBcJhWVFzW6pQwFbntYCxh0BSrotkWBK/eDBg3CzJkzrxKyNYHT1rHoekOoONgSyin0UtCfMmWKvGbgsX5/tiiIBYIxEHSpat++vayujxs3Tlbn6VI1b948TJgwIdd5aO3UoDVBr6Qw9qFRo0ayLYVvDW5D9yc+uI1+e80tikrDww8/bOkPfR+dPHlSss7o+4buXhoLFizA3r17xQrBfWjKmYa14sL98PuJ0elwds4OjnYAnF0c4eXjftV3qaxpSqJkFYqIkLEmtAgpspWHmBTEpWbmKA9BnlLroaiwKNzFxIu5Mi3V9KopCkRJQyXCZDSJ8kASY9Lg4OgAd1cDTIlRyIyKQ1aGQ26rg1cWnFhbwi0QcPcD3HwAxyK6QXK+s24FH67mQlMC5zyViJRoIJUF6bLnMhUNxk341AS8gnVWDUVJk5hmyFd50OB23N5agSCaAkFohVAKhCI/WKk+ZOpU+A4ajLBXX5UsTaxoH8kFvbVrxVLh1qiR6shKirqrlzIUNu09rGMaCrNtUeFqd+vWreU54xiOHTsmzxk0rNGjRw8sW7ZMntO/ntYFQt98+ulTaE1ISBB/fwrSjKFYu3atxAQQWjfoUkPY7qL4f2uKCPdFdymydetWNG3aVAKr6aKjxRssWbJE/Pu5gq+H7mFsLy0ibBvPvWfPnnJ+fM73KKhzG25bq1Yt2YemXHC/tDrQbYvva8I4LQ4Mvia0ikyaNElcqhg7QmWE57tmzRr5nJYJxmGwz5955hnpQypF7Bct5oXxGAz0ZgwD3+O5tW3ZEZnphhzf0+o51Yr1UCFi/+zatUte//HHH3j00UeL1N+VFVaVPq9XHhwcpEBccZQH1ng4l3DOojxwn3V86pSK8qDh6etqsUaQhKgUpF86h/Sw3MqDkxvgVisATiFNgRptgID6gId/0ZWH/BQLN2/zMWq2BrzpAuiQY5FIuAREngAycrKZKUoWH3dnFLT0C7fj9rboVKOT5fmeCJUyW1FwvLp1RaPlvyHwwQdp5pf30v77D2eH34GY73/IFYulqDwoC0QlR4uBIFy5b9CgAaZPny6vuYLPOAUqCxSqGWehuRK9+OKLkuaUAqrmjkRrClfbKRAzOJer8xTmGS/BYGxmS6JyQZcdfp/QJYlB1IyDqF+/fqHaTtelu+++WwRiCvoM8mb2JEKXJcYdfPTRRxJfobk0rV+/XhQLZlxiilYqQDx/CvW0XmjnyDgBtpeWEsZxaO5Y3B9dpOgORasHXzPwunfv3pZ28XNaWxjbwJV+Htvb21vayfYyuJyuX3Ql4j5onRk+fLgoIVRS+JyKQlJSkigfPJejR49K//L9Th264I6hIxEZZbae+FX3gKudH30e/5NPPpEgagaV0xJibeFAVVceolOQkKZTHgI94VsM5YE1Hs4lnkM6XXm0NK0+deHNYOJSxMFogJdzLIyOQJqRVgAHJKAmPBEORxjEIuFSPQhOQTXydkcqLegG5RMCZEabXZy0dQNDKhB1wqxc+LBtat2qJKE14aZWNSTbUl4xEE6ODrKdLesDaR3UGq6OrsgwZogFQqEoDI4eHqjxwvPwveVmXH7hRWSEhsKUni6xEYl/r0ett9+GS0iI6tRKhIOppMqcViGYAYgwo44GhTf6ozNjTmm6jUiGlaysMslwwjgACtwUtinQ33vvvSKo0ypQmnAVn0rOk08+me+2bB+FaXtBzeUZKl9xV1JhSNcViMtDeVAUXnloEOQJH/eiKw9UGmh50NK0Ojk6ob5vfcm4VGrXbGYakHwFSGEufxOyDA6IN9SCwcl8n3E0ZsLbIQmutWvBsQQLwhUFnisVZxLo7QaH+AtmZULD2R0IaAC4XN1f1+oeWpJwXLXYq5KyEhc1C9PI2dtyJc6yhjNv8aM9bAZRa9y/6n7JwkQ2jNwgldOrIuVlXCsqxtRUXPnwI8TqEps4+vig5qtT4Dt0aJkG6KuxLbx8aw+1FKQoMLQsMPaAK/qM16CForSVB8JV/IIoD+TMmTNiBahoUPBKiErLpTz411DKQ1ExGk04p1MeHEtAeUjJTMHZBF2NBydzjYe8lIdiwdiCmDNA5FGJMZCicGmOyEhwhntqFBwZpMxzdXRBmme18hewyFiJas0Bb8YRZQsMVCbo0iTKkKKk6NIgEG/e3kZ6mZYGPXzNd/h5XsoDaRPcxvL8RMwJNUCKIlsjqCzUnfMtnLMzG7JuxOXnX0DYiy/myhKnqLiUs18cRXmGgrx1qtTyBt2pKqrykJGqi3mo4QEXN3V5FtXycC4mBYlWyoN3MZQHxjowYJqB08Td2R31fOtJobgSh/ECiWFAeoLlLR42M9kZWZlm4dDBZISXMR5JztVk/nDupMRn5KpgXVbkikWiu5JvCODhB8SeN7szMdA67pw5i5NvLeXSVEKM6V5f6jwwVSuzLdGbiboE3ZbG9W6Yr/JAmgc2tzw/HnscPWv3LKnmKaog3r16odHvyxE+/S0k/PGHvBe//Hek/ncItT+eCfdmzcq6iYpioCQUhaIMofCXFJOO9JTsrE0OgG81d6U8FNNtKZfyEOwF72IoY3HpcbiceBmmbAcRLxcviXmg+1KJkplqVhxY5VlHlsEFmcmOMGXlBCI6BwXJyp5TOt3ezAHKyfHpcHZzgptH2d3WqfwyDucqXDyB4GZAwoUc60NyJGDIMLs0ZQdeKooHlQQ+mKqV2ZYYMG0v5sEWzQJyBLoTscoCoSg+Tn5+qP3B+/C+/nqEv/YajMzSeOYMQkeOQs1XX4XfHcNVzZEKirprKxRlCNNypiblpLz1CXRTMQ8lFPNQEsoDc+JfSrxkUR583XzF8lCiygNdkWLPmdOe6pQHk6MLMrP8kZEAi/Lg4OQM1/r1JRjRwdERrh7OuawOCVGpyDKU04wnVBL86wN+TMqQ7WaTHm9209JqSShKBCoN1XzcCqU8kEZ+jaRyumaBUChKCr9bh6DBkl/hlu0lYEpLQ9grryiXpqqsQDDFpZZmU3F1Tn+Fwh6sKpySoFMegtzh4q4C9oqqPLDOQ+6Yh+IpD1GpUQhLCrO8DnAPQB3vOpJ1qUSg0JwUAVw5AqTqYgIcXWD0DEFGkjsM8Tm+wo7e3nBr0hhOumKJWnpXKhLmXZqQGJ1WrOrXxUHiNAwGedhtA+tCBDXOcV1iKtzYczAWMd2zouRwdXJFA78G8vxs3FlksJ6HQlFCuDVsiAaLFsI/u+is5tJ09q6RSDuhLF4VjUL/ujL15C+//CLpMg8ePGjJZc90kky7ybSdDLBlms+qBM+fVY4vX74sqUL5ujQyDZRlFiZFyZGenImk7KJgxNPfFXDKQlpahhrbolSDjktFoq5uRoi/O5xhQFqaoUj7o+WBD40AtwAEOAVIKuQSuWbTE4HECMCoF9CcAK8gZGW5wnDxSk7udAcHOAcHA/7+SKeQbUPQZrxyakomjFkmZNKi5WyAu9e1rwLNc2U9FOLv75/HPcoF8KoDxF2AyWRERnosIlPi4OjsaSmCqCgbGAdxKu4UDCYDzsafzRUXoVAUF0d3d4S8MRWeXbsg/NXcLk0hb74Jv6G3qk6ubAoEV9ZZPZj5/JnLnoW3Ro0ahcDAQPlxZOq+w4cPS/Gtzz//XHLss85AVUmBRuWB6QfDwsJEiSjNH2g++MOsFIiKiSHTiNTEDGbkFFw9neGW4azGtghwkTs2JQMpGVr2KiDIyxVXUop+34lPj0dyZs7Kv4+rD5xdnZGIxOJfs0YDkBZnjnfQ4+oNk7svjJcuyA+qBScnOAcGwoFCebZgbg9DZhZSE7JjacIhrk2OBa0wVkLwPFkAUrNO53uPyjSaYyGMBnhG7kO9WnXg6KgE1rKkeUBz/Ik/LQXlmKaYSQMUipLEb8gQuLdqhUvPTkL6sWPi0nT5ueeQdug/VJ88GQ5lnJJaUYJ1IFjkqkmTJnjooYfyTd3JPLLz58+XnN5UKKpSnlzNhF/U6sv5wf0mJyfDy8uryihnlYnYiGSsnXMYmenm1eWmXWqgy5AGImipsS0cLJr1wepjWHskQl47Ozpg2m1t0K1RUJGrS3954EusCTVXESfj2o7DsCbDirS/XNdsQhy8Ty6D4+7vclsdQjoAfSYjI9MHEe++i4xTpy0fed9wPYKfeBJOXjaCku2w7bfTOLMvUp436hCMHsOb4FrCe59W1f6WW26Bc0FSyx75HU5/TYZzRgIcGFsydjnQIKdwY3mmMuaUn394Pj7cnVOMki57/er2w/2t70fH6h1RFaiM41peMaanI3zaNMQvWWp5z7NzZ8nSJJbXEkaNbcnVgSiwAsFKuS1btkRhOHLkCFq1aoWq3MEljZr8FZek2HQseX+3/CcN2gZh0KNt4ehk9gVXY1u4Og8vLj2IX3ZftCgPX47pJCkri4LBaMCUrVPw5xnzyisz50/tORV3NL0DxSXr7Fbgj6fhFHMy502fWsDAN4E2I5CwejXCXpliyY3u4OaGGq+8DP+77iq0lZExNT9N3Y70FLPr1h2Tr0NIE39cSwVi8eLFlkWnAikQZM0U4N9Pzc9ZN+Kxf8Wdq7xT2a7Zn4/9jOk7pl/1vpODk6QwntJ9CkY2H4nKTmUb1/KOuD7+shjh06cDmWYrqnP16qgz6xN4dOhQosdSY1sGheQKqzyQyqg8KBRFISPNgBWfH7AoD9Xq+WDguDYW5UFROOXhld8OWZQHFsr6bPR1RVYeGCj6v43/sygPzELzXt/3iq88pMaaFYf5g3OUBwYO93gCeGIXTC1vR8T7H+DSM89alAdXBhn+8jMCRo4skosiA6q7DWtkef3PohMw6tK/llsGvAE06md+nhQO/PGU2T9Ncc3YG7EXb+14y+ZnWaYsyUQ2fft07LuyT42KokThvS5g1Eg0+H6BpfCc4coVhN43FrGLfi6zpBCKvCmU9LJy5UocO3bM8jo1NRXvvfcehg8fLjEPq1atKszuFIoqI/CumXMY0ReTLNmWhjzeDi5uamWrsPCH5PXfD2PhzvMW5WHW3R1xSxtWOy48qYZUPPn3k/j7wt/ymoXhZtwwA4MaDkKxOLkW+KIHsGdeTttDOgKPbARufguG5Aycf3gcYubOtXzuO3QoGv66GO7NixcD0LpvbQTX9ZbnnHOHN5deTFaJQdel4bMBz2yrw7EVwL7vy7pVVYoFRxbkm2GMny84vOCatUlRtaC1oeGSX8WFScjMRPjUqQibMkVcnRQVVIF48cUX8dxzz4krk8YTTzyB3377Dd26dUNAQIB8XhljHhSK4rBj+Rmc+y9anrt5OmPok+3h5Vf2FYMrovLwxh9H8P32c/Ka8cEzRrbHkHYhRdpfSmYKnlj/BP69/K+89nD2wOc3fo5+9bJXwosC6zgsfwL48U5zUTi229UbKddPhfGhNUBIe6T+9x/OjrgTKTt2mL/j7Iwar72KWu+/B0cvLxQXBk5ff0+OErLrz7OWKuflGp+awLBsNyay6kUgOicmRFF6pBnSsOHCBrE05AU/p7LN7RWK0oBxD/XmfofA+8da3mN8xLmxY2GINMd3KSqQAnH69Gn8+eef+Omnn8TaQE6dOoV///0Xs2bNEuVixowZeOmllzBnzpzSbrNCUWE4viMce1ebBV4HRwfcPL4NAmoWX0isisrDu6uOYd6/ofKa3j0fjWyP2zrULrLyMHH9ROwM3ymvvV288dVNX6FHrR5Fb+TpDcAXPXOvnDfuD+Oj/yKj44Oyyh736684N/peGMLMyoVTtWDUXzAfgaNHl2hWtZqN/NC0c3V5npqYib1rzHPwWmSja9GihTz4vNC0GAJcd7/5OTNh/f6kcmW6BiRlJkmMQ0HgdtxeoSgtmIGpxksvodaHH8LB3ZwBLO3AQZwdOQppR46oji8nFCjCbdq0afD09MSiRYvkQS5evCjBRUuXLpUHYXagc+fOiSIxYMAAqQmhUFRVIkITsOH7HJe/3nc1Qd2WgWXaporKrPWn8NWmM/Kccvb7I9pheMc6RdoXU7ROXDcRe6/sldc+Lj6iPLStVsSkCOlJwNpXAWZY0nD1FlclEYaNRpiioxHxxhuI/8UcYEw8rrtOMo24VDcL+iVNt9sa4/S+SKkNcWDdBbTpWwfeAaVr+aLS0LFjMTP13Pw2cPYfIDYUOLcVOPgL0D6n8JSi5KECTfekgigR3I7bKxTXono1i2deeGyiLLrwEXrvGNR67134DhyoBqCMKdAS0YgRI+Dh4YHHH39c3Jb4SEtLw+233255zceQIUOkCBCfd+3atfRbr1CUU5Lj0rHqy4PIMph/kFv1CkHbG4om8FZ1vtl0BjPX5VQpnX57G9zVuW6R9pWUkYRH1z6aozy4+uCbgd8UXXk4tw34smdu5aFhX2DiNqDTA6LtZEZEIHri47mUh4B770X9eXNLTXkgftU80Pb6OpbaIztXmBWwco+bNzD4w9wZmtISyrJFlR7WeWCqVmZbygt+3r9uf1UXQnHNcG/RAg1/+Rke7dvLa1NqKi499TSiZs9WwdUVQYGgJYEB05999hnOnDmDhQsXStG4MWPGoHbt2lJ5mZaHmTNnolevXvIe058pFFWRrCwjVn9zCMnx5nz/IU380Pee5qrwXxH4Yfs5vLUyJ+5qypCWuLdb/SKNS2JGIiasm4D9kfvlta+rL74d+C1aB7cu/M6yDMDfbwHzBgNx2e5BLp5mwfe+5YB/PXmL8Q7nR92NzEOHLClaQ955BzVfnQKHa1BxufPgBnD1MBuaj/0bhuhLSaXuakZLNB/FypzS9CagRXZF2uQrwJaZJdZGhW3GthqbrwWCn49tneObrlBcC5yrVUO9BfPhO2yo5b3Ijz/B5eeeV8HV5V2BYNEyVpfes2cPxo8fjx9//BEvv/yy+LmSdevWSYE5WileffXV0m6zQlGu2fHbGYSdjpfndBm55ZG2cHJW6VoLy9K9F/HqcrPgTSbd1Azj+uSkKC0MCRkJmLB2Ag5GHpTX/m7+mHPzHLQKKkKq6ZizwNxbgE3vA5rAVa8H8NhWoOt4+vGYj7lyJc6NuQ9Z2YF/zrVqof5PP8J/+O24Vrh7u6DTLWaFi/L89uWla4VgjvXff/9dHsUupkkXMKdsJWv7F0DchRJpo8I219W4Tuo8sAaKtSWCr/k+P68qxeQU5QtHNzfUeu89VJs0yezHynvsihU4//DDyEpQFsqyoMBSTefOnbF27Vps374dO3fuFOuDRs+ePcUqsWzZMtTIzuGrUFRFzh6IxL615hSjjk4OuPmRNpKbX1E4Vv0XhsmLD1hKAUy4vhGe7F+0qsrx6fF4ZM0j+C/KXCAnwC1ALA8tAs0LIIXiwM/A7D7AxV3m1xS0+k0BHvgTCDQrNyajEZGzPsWlSf+DKTv1oGuHDqj38yJ4tC6CtaOYtOtXB17+5tiH0INRCD9rVm7LPQENgG4TzM+Z9Wf9tLJuUaWHReLmD5ov7kxaSletEjXfrwpF5BTlFyaaCH5kPOp8OgsOnp7yXuruPTh37xhxFVWUUwWCVgbi7+8PFxeXXJ/xPVuBc2vWrCmJNioUFYKEqFSsn5/jbtPzjiao2dCvTNtUEdlw/AqeWrQPxmzlYWyP+njxlhZFcgGj8jB+zXgcjj4srwPdA8Xy0DywkLUW0hOBJeOAZY8AGYk5Au5Dq4HrnzPXMaCLB/1zn52EqC++sHzVd/jtCPx0FpwDyyaA3tnVSVyZNHb+XkFiIUifyYBHdr/99wtwaU9Zt6jSQwvDzH4zsWP0DmwYuUH+87WyPCjKCz4DBqD+/Plwyr6npp88idC770H6aZX2uVwqELQujB07Fps2bYLBYD+nOM3WVBzuueceS3YmhaKyk5VpjntITzFfG406VkO7/ipourBsOx2NR7/fg8wss/ZwZ6c6mDq0dZGUh9i0WIxbMw5HY8xKXZB7EL67+Ts0DWhauB1FHAG+7gf8lxMEjfb3ABM2A3W7WN7iChhdlhJXrza/4eCA6s8/jxpvvilpCcuSlj1D4BtsTod44WgsLp2IRYXAwx+44cWc12tfL8vWVLnA6mCPYBUwrSiXeLRtgwYLf4JLXXNCDWZoYorslH2qUnq5SuNKGAOxYsUKvPnmm0hISBC3pWbNmiEoKEiUhpiYGBw5cgS7d++WAGpmYmKWJoWiKrB16SlcOWdemfat5oH+Y1uqoOlCsu98LMbN34X07MxVLBD33oh2UhitsMSkxYjycDL2pLyu5lEN3978LRr5FTKG4sAi4I9nAEOq+bWbL3DrTKDtnbk2Y7D0xYmPWwodOXp6otZHH8KnX7/ixwKUAIzB6XJrQ6yfd9RS3HD45Osqxhzt/BCw4ysg5jQQutlshajdqaxbpVAoyhjX+vXR4KcfcX7CBKQfOYqs+Hicf/Ah1Jn1Cbz79i3r5lV6ChXZeeutt2L16tWYOnWqpQbEO++8gw8++ECC5hhsTQXjr7/+UsqDospwZn8k/ttw0SKo3TK+DdyyM98oCsaJiEQ8MHcXkjPMwvaNLapj5sgOcCqC8hCXFiduS5ryUN2julgeCqU8ZKaZFYdlE3KUhxptgUc2XqU8JKxaJZYHTXlwqV0b9RctFOWhPNGsa00E1DT7DTPIP+xUHCoETi5Ar6dzXm/LcQ9TKBRVG2Zoqr9gATx7dJfXprQ0XHz8CSRu3FjWTav0OBelUNCgQYPkoVBUdVjvIVexuJFNUa2eSmFcGC7EpOC+OTsQn5opr3s2DsLn914H1yJkrpKA6bWP4ESsuW5EdU+z8lDftxCpX1nA7JexQNiBnPc63gcM/gBw8bC8xTSl0d98i8gZMyzveXTuhDqzyi7eIS9oyek0qAHWzTVXct235jxqNQ1AhaDdSHMQdUoUcHgZMPBNwLdWWbdKoVCUA5y8vVHvq69w6fkXkPjXXzBlZuLik0+hziefwKd/+VrIqUyo3JIKRRExGU1YN+8I0pLNgm+jDtXQuo8SagpDVFI6xn63ExEJ5mxF7ev44euxneHukndBq7xStWoxD3RbKrTycGod8FXfHOXB2R247XPgts9yKw+ZmQh/7fVcyoPf8OGo/9135VJ50GjSubqlGnXof9GIuZxcovunS1STJk3kUaLuUez7zg+an5uyzK5lCoVCod17XF1R+8MP4Dt4sPkNKhFPP43Ev/9WfVRKKAVCoSgi+9adx8Vj5mBULz9X9BtTtExBVZXEtEw8MHcnzkaZhdhG1bww98Gu8HZzLlKF6cfWPmbJtsSAaWZbKrDywHyxW2cBP94FpGWnOWVa1nHrgY45KatJVlISLjw2EXGLc4Kqqz3zNELefuuaFIcrDk5Ojmh/Y91cc7hk9++ELl26yIPPSxT9OOz/0TxmCoVCkY2DszNqvf8efIcM0SkRzyAxO4uoomRRCoRCUQQizydKIKrgAAx4sJUU7VIUjLTMLIxfsBuHLpkLAIX4ueP7h7sh0KvwAnhKZgomrp+Ig1EHc6VqbejXsGA7yEw1xzqsfTWnMFzzIeZ4h5ptcm+anekjecsWec3sSrU+/BDBjz5aYZTHVr1rwc3TrKSd2BGOpFiz9afcw7S5DfqYn0efAi7sLOsWKRSK8qhEvPcufG+9NUeJeOZZZYkoBZQCoVAUksz0LKyZcxjG7FSj1w2shzotyq/bSnnDkGXE04v2YfuZGHnt7+mC7x/uitr+OS5ChVUe9l3ZZ6kw/c3Ab9DYv3HBdpBwGZg7CDj4c857178AjPoBcM9dwyPtyBGEjrob6SfM8RVOfn6oN/c7+N2avdpVQXB1d0abvrXlOefwwQ0lV+GZcSFpaWny4PMSp8O9Oc/3/1Dy+1coFJVHiRg21PyGwYBLzzyLlF3ZBUAVZadA3HnnnTh2LCdwVKGoSmz59STiIlLkefX6Pug6tJCpQaswFCpfWXYIqw+bq4Z6ujph7gNd0KR64QPPUw2pePLvJ7EnwlxczNfVV5SHZgHNCrYDrmB/fQNwOTtvuIsXMHIB0O9lRhzn2jTpn38QykxLV66YN61XTzIteXbujIpI23514Ohstpgc3nwZmdnZr4oLU9ayZhAfpZK+ttUwwNXb/PzQMiDDfB0qFAqFHgcnJ9R65x2LO5MpI0NcT9OOKtm1TBWICxcuwDO7jLhCUZU4dzgaRzZflufObk646aHWkrpVUTDeX30cP+82r3i7ODlg9phO6Fiv8JmA0rPS8fTfT2NnuNmNxcfFB18P/BotAlsUbAf7fgDmDQGSzIoM/OsBD68BWt121aaxCxfKD48pxSysenTogAaLFsKtYQFdpMohXn5uaNalhjzPSDXg9B6zYlTucfUCWmfXF2JF8FNry7pFCoWiXCsRb8Orr9n10ZiUhEsTJsBw2fwbrigeRZJ8xo0bh1deeQUbN27EmTNncPny5VwPhaIywmxLGxaYM/yQXiOawL+GUqQLyrebz+DLjaflOcMFZozsgL7NqhV6HDKyMvD0hqexLWybvPZy8cLsm2ajdVDr/L9sNAJrXwOWPw5kZZjfo1/9+KvjHWgtufLxxwh/Y5r5e1RUbrkF9ebNLdeZlgpK6z5mNybNClFhaD085/nRFWXZEoVCUc5hYos6H38sCz8kKzoasf+bjKxEc+FXRdEpUrWrjz/+WMzTu3btyhU4yB9cvj56NEfIUigqC5sWnUByvFnorNcqUKVsLQRL9lzE9D9z7gvThrXG0PaFT3mbmZWJSRsnYeulrfLa09kTswfMRrtq7fL/Mt1dlj0CHP0j570u44Fb3jEXK9NhMhgQNnUq4n9dYnkvaNzDqDZpEhys3JsqKjUa+iKotheiLyUj/Ew8oi8lIah2tntQeaZBX8DND0iPB06sBgwZgHP5zn6lUCjKDkdPT9Sd/SVCR9+LjDNnYDh7FmGTn0O92V9KvISiaBSp5+bOnVvEwykUFZNTe67g5C6zuwsz2PS7r2WFybpT1mw4fgXPLzFnSCLPDGiK+3o0KPR+Mo2ZeG7Tc/jn4j/y2sPZA18M+AIdqptXlvIkMRxYeHdOvIODEzDoPaDr+Ks2Naal4dL/JiNp/frsbR1Q4+WXEXhf7nSuFR3O31a9a2Pzz+ag8MNbLqPvqALGj5QlVBaaDQT+W2xWIs5tARr3L+tWKRSKcoyTvz/qfvkFzo4cBWN8PFK2bEHEu++h5pRXyrppVUuB6Nq1q+V5RkYGXMt57nOFojikJGTgn5+OW173GdXMUoxLkTcHLsRh4g97kWU0Z+QZ26M+nr6xaaG7zWA04MVNL2L9ebNQ7+7kjs/6f4ZONTrl/+Xw/4Cf7gYSLppfu/oAd80Dmg64atOs+HhcmPg4UveYA7Ph4oLazOahFSeqZDTvVgPblp6CIdOIkzsj0OvOJlIrotzT4lazAqG5MSkFQqFQ5INr/fqo9cnHuDhuvGRmiv3hB7g2aojA0aNV3xWBIv9SLFy4EP3790eHDh0kqPr111/HF198UdTdKRTlErrlbfjhWE616Y7V0KyrOfhUkTehUcl4aN4upGaas/EMblsTrw9tXWjLjdFkxOv/vo4159bIa1dHV8zqPwtdQ3IWMuxyYg3w3S05yoNfXXOwtA3lITPiCs6Nuc+iPNDsXe+r2ZVWeSBuni5o0D5YnnOOXzxqLoxY7mkyAHDKVuKP/WmJUVEoFIq88OzSBX4vPG95HfHW2yq967VUIP744w989NFHGD58OFxczL7DjRs3xuzZs/Hdd98VtS0KRbnj+I5whB6MkucePi64YXRz5bpUACIT0zH2u52ITjbHjHRtGChB006ODoVW4N7Z8Q5+P/27vHZ2dMbH/T5Gj1o98v/yjq+AhaOAjCTz69qdzJWla7S6atP0s2dx7p57kH7ypLx2CgxEvQUL4NWzJyo7TTvnKMSam15RoXLYsGFDeZSqi5+bN9C4n/l5UjhwKdtipFAoFPngOXQoAh58wPwiKwsXJ01CZnaKbkUpKxBUEpiF6cknn4RjdkDh2LFj8dprr+Hnn3UFmRSKCu66tOUXs0BJbri3BTx8lLtefiSlG/DgvJ04H2NOe9q8hg++GdsZ7i5OhVYePt77MRYdXySvHR0c8X7f99GnTnY1YntkGYCVzwGrns+pLN3qduCBPwGfq61Hqf/9J9WlM7MzyLnUqYMGP/0IjzYFyOpUCajfOshSmfrM/kgYilETwsnJCd27d5cHn5cqLXQF/E7/XbrHUigUlYrgZ5+FZ4/u8jwrMgqXnp0EU6bZ00BRigrE2bNn0dlGAaVu3bohLCysKLtUKModm385gfQUgzxv2qUGGnUofMrRqkaGwYjHftiDQ5cS5HWInzvmPdQFfh65sxwVhG/++wbfHcqxaE7vNR031b8pnwYkA4tGAzu/znmvz/+AO+cCLldXuk7ashXn7n8AWbFm1x23Fi1Q/6cf4dqg8EHeFRUnF0fL3GaV9dD/olEhaHRDzvPQzWXZEoVCUQFrRNT+6CM416wpr+m6euXDj8q6WZVfgQgODhYlwpp9+/ahevXqRW4M99mxY0csXbrU8h5Two4ZM0ZiLRhzsWDBglzfMRqNmDVrFvr06SPbjB8/XmIy9OS3D4XCmtD/onBqt9mk6e7lgj4jCx/4W9UwGk14YclBbD5pdvnydXfG/Ie6IsTvasE9P3448gM+3fep5fWUblMwtPHQvL+UHA3MHwacXG1+7egC3PYFcONrV1WWJvF/rMCFRx+1FIijb2z97xfApRj3sErhxrS76G5MtBoZDAZ58HmpwuJ//vXNzy/uMqdzVSgUigLCej51PvlYkmWQmPnzkbhuneq/0lQgRo0ahWnTpmF9dppDFpNjUPVbb72FO+64oyi7RGZmJiZPnoyU7B9zEhsbiwcffBD16tXDkiVL8Pjjj+PDDz+U5xoM3P7pp5/w5ptvYtGiRaJQsNAds0MVdB8KhZ6MNEOurEu97mqiXJcKwHurj2HZvkvy3M3ZEXMe6IJmNXwKPbmWnVyG93a9Z3k9qdMkjGoxKu8vxZ4DvrsZuLTb/NrNF7hvKdDxXpubxyxYgMvPPSeZOIjPTTeh7rffwMmn8O2tDNRu7i8xPuT8oWhkFtGNifWBFi9eLA8+L3XqZcfCGNKA8JxUwQqFQlEQPNq3R82XX7K8DntlCjLDw1XnlZYCwVX+wYMHY9KkSUhNTcWECRNEeRg6dCgeffTRouwSn376Kby9cxcx+uWXXyRIm8oKg7RHjBiBBx54AF9/bXZPoJLAeIynnnoKN9xwA1q0aIGZM2ciPDwca9asKdA+FAprdiw/g6TYdHlet2UAmnczmzgV9pm79Sy++ueMPGec9Cd3d0SXBoWv1vzX2b8k45LGhHYT8GCbB/NP0zpnIBCdHa/iXRN4cBXQsO9Vm0p16ZkfI+Ltdyzv+Y8cidofz4SjW9VNzevo5IiG7c1uTEzpeuFIDCoEdXWZuM5vL8uWKBSKCor/3XfDZ+BASyrvy889D9O1WACpqmlcqTxs375dVpoopPP5lClTLEHVhYEVrRl8/e677+Z6f/fu3VJzwllXKZDBeaGhoYiKisKxY8eQnJyMHj1yMrL4+vqiVatWss+C7EOh0BNxNgEHN5pTfjq7OOL60S1U1qV8WHHwMqatOGJ5Pe22NrilTeGVro0XNuKlzS/BBLPry5iWY/B4h8fz/tLZzcDcweZMPCSoKTBuLVCzzVWbmoxGSdkX/dVXlveCJ05EzTemij9sVadhdjpXcvZAJCoE9cxBkMKFHWXZEoVCUUFhxriQN6fBOSREXqfs2oVotchcegpEfHw8Tpw4gbS0NKSnp0ucAYV2TXAvKAkJCXj++edF+QjJHjwNWhJqZge4aGgxFgzW5ufE+nvcRvssv30oFBpZWUZs+OEosuVXdLm1IfyqFd5/vyrx7+koTPr5ADR39yf7N8GY7tl+6YVge9h2/G/j/2AwmV2KRjQdgee7PJ+38nZ4GfDDHUC6OWAbdbqYazzQN94KriaFvfqqFA7SqPHqFFR76kmlIGZTp0UAnN3MilTowWgYsypAbYVqLc3uapoCUdpxFwqFolLi5OeH2h+8b4mXi/zsc6Ts3VvWzap8lagZP/DGG29I3IJ1oBx/8KlMFJSpU6dK4DTdn6yhcmJd5dot282ASgvdp4itbajgFGQfxeGa+PjaOKb2UJQse1efR/SlZHkeVMcbbfvVvqb9XNHG9lh4Ih5ZsAcZ2YLmXZ1q4+n+jQvd/oORB/HU308hw2iOWxrUYBBe7vKyxDPZw2HXN3D460U4ZGt7pqYDYbxjDuDqJXm99TA1X/hLLyNx1SrzG46OqDn9Tfjedts16euKMq4OjkC9lgE4sz9KispdOhmLWk39C7UPnqP2m8DnpVoLIhvHOp3hwDSuSRHIij4DBFy7DFoVZWwVhUONa9UcW7eOHRH02KOI/vwL+R259L/JqL90CZx8sxcpFMVXIJj16LbbbpNYAnd3dxSV3377TVyMWJjOFty3FgytoQn9np6elmNzG307uI2Hh0eB9lFUKNwkJiYW+fvFOS6VIv4wF8VdTGGbxOh07FkZKs8p83S5vQ6SU7ILkF0jKtLYXo5PwwPf/yc1H0ifxgF44cb6SEoqXJ+dTTiLx7c8jlSDeTGgd83eeL7t80hJzkmmkAuTCe7bZ8J9xyeWt9Jb3YXUG98B0o1Aeu5r0pSejtgpryJ9c3aaTycn+E97Aw79+1+z67cijWvN5l6iQJCTe8LgU7Nwrl1aBibC/tW7jpYWbtXawyO7DkTa2R3IdA7CtaIija2i4Khxrbpj63LPPXDZshWZBw7AEBaGS6+/joBp01CV+sexgPeyIt3d6Xb08MMPo0Exc6XTkhEdHS0B0Hpef/11rFy5UlyPrlhVB9Re16hRw/JDxfeYZUm/TfPmzeV5fvsoKuxgnzLI2KKt8DHgvNQLNVUR2J+bvw9FlsG8ckrLQ4OWRZ8blX1sY1My8OSv+xGZZFbM29fxwxdjOsHTtXC3k7DkMEzeMRmJmWZBvlvNbvio30dwc7ITzGwywmHNK3DcmRPDYOz9Pzjf8DJ8bKx0G1NTcXnSJKT/u01eO7i6ImTmDHhb3W9Km4oyrqTZde7495dz4sZ35Uxyoe9xvCdrSgO/ey0UCNRuZ3nqkXwB7tfwvlyRxlZRcNS4Vu2x9fjwQ5wbMQLGhASkrVkLh1tvhfeNN6Iq4FiIhZAi3d0HDBiAf/75p9gKBNOpUhPUM3DgQMmqNGzYMCxfvlxSs3LAtYFmsHbDhg0RFBQkP1CcBDt27LAoEFRujhw5InUfSJcuXfLcR3Eoqx8MHld7KIoPq++eP2zOOuPl54puwxqrsbVDWmYWJvywD6cjza5eDYO98N0DXeDjUbgMRrFpsXhs/WO4kmJW5lsFtcIn/T+Bp4un/erSK54G9v+Y894t78Kx+2O2N09KwqVHH0Xq7j3y2sHDA3W/+BxeuoQL15KKcs16+TmhWl0fRJ5PFHe+9OQsePoWrvq6di+m8nBNzrd6C8tTR2bkoiucjaKBVX1sFYVDjWvVHVununVQc8oruPz8C/I64s034d21K5z8C+fSWdkpkgLx3HPPSczC6tWr5cfC2s/1nXdyUiTmhT0LAAV7fsaUq99++y1eeeUVqe1w8OBBzJs3T+IvCGMbqChQEQkMDETt2rXxwQcfiNWBigjJbx+Kqg0r727++YTlde+RzeDqfg1WTSsghiwjnvhpH/acM1dtrubjhgUPdUWQd+GUh5TMFExcNxGhCWaXsQa+DfDlgC/h5eJl58DpwJJxwNHfc5z1h30KdDQvEliTFReH8+PGI+3QIXnt6O2Nul9/Bc/rritUO6sqTF1MBYJcPB6DZl0KnlGLP8i9e/fGNSVZlzHq2B/A27WA5kOAnk/kztKkUCgUBcR36FAkrFyFpI0bkRUZhYh33kWt93JnCq3qFMlpc/r06ZI+lbEFly5dwsWLF3M9SgoqEhT+WaF6+PDh+OyzzyRjE59r0Fpx5513Shane+65R37A5syZI7UfCroPRdVl98rQXDUfGl9nzoWvyA1Nvq8uP4x1R81Vir3dnDH3gS6oG1i4OKLMrEw8s+EZHIo2C/fVPapj9k2zEehup2ZERgqw8J4c5YHVpe+ca1d5MERF4dzY+y3KAzNr1Js3TykPhaBOi5yxuHjUrCyWW3Z9Cyy4Lfd7JiNwYhXw3S3Arjll1TKFQlGB4cI4U3w7ZrtExi9fjqStW8u6WeUKB5N1GqUC0KFDByn81qdPH1RF/vvvP/nftm3ba35sumIxOJHuW8pkXjxiwpLx85s7YTSa4OjsgHte7Qb/GkUPrK/MY/vJupOYuc5sqXFxcsDcB7qid9OcugEFwWgy4sVNL2JVqDkbko+rD+bfMh9NA5ra/kJ6EvDTSOBc9k3b2QMY9QPQdIDNzTMjruD8Aw8g4+xZee0UHIx6382Be7NmKEvK87jawpCZhW8nbUZWphHeAW4Y+3bP8pnq9tw2YO4gagx5bOQAPPRXqVkiKtrYKgqGGtfKS2HHNnbxYoS/+po8d6lfD42WL4djMZIHVSb5tkgWiICAANSqVasoX1UoygXUm7f8ckKUB3LdwPplqjyUZ37edd6iPJAP72pfaOWB/f3uznctygMDpT+/8fM8lIdE4IcROcoDc/3ft9S+8hAejvNjx1qUBxYEavDD92WuPFREnF2cENLYT57TOhcXYScjlp0g6oULF8pDS3JRamz7nCW0896Gn3M7hUKhKAL+I0bAo1MneZ557jyidIVIqzpFUiAeffRRvPXWW+IWpPJfKyoiof9F40K2e4ZPoDs63VL44mdVgX9OROLlZWZ3IPLK4Ja4rUPtQu/n64NfY+GxhfLcycEJH13/ETpW72h747QE4Ps7gAvbza/d/YCxy4H6PW1unhkWJm5LGefOyWuX2rVR//vv4VrMJA9Vmbotc9yYwk6Za+qUKzJTgeN/AsZ8lBR+fmyFeXuFQqEoJA6OjgiZ+jqzQsjr6G/nIP30adWPRQ2iZozB5cuXMXjwYJufF6aQnEJxrckyGLH115OW1z1HNIGzq3I/sObI5QQ8/uNeZGVbaR7s1QDj+zYqdH//cvwXfLb/M8vrab2m4fq619veOC3erDxc2m1+7REA3PcbUKuDzc0zL13CufsfQGZ27JVL3bqoP38eXJSFtFhoFggSdioOrXqXM4szLVSMdSgI3I7bX8PMTAqFovLg1rQpgh5+GNG0PmRmIuz111F/wQJRLqoyRVIgHnvMdupEhaIicHDDRcRfMa9IhjTxU4HTNgiLT8VD83ZZCsXd3LoGpgxpVei+XhO6BtO3T7e8ntx5MoY1HmZ749Q44PvhwOW95tcegcD9vwM1bftiZly8iPNj70fm5csW/9T68+fDpWbBswYpbFOtvo/EBRkNJoSdLocWCLf/t3cf4FFUXxvA3+ym916A0HvvRaUjIIgUpagU/QNiQwUFUUCqFUQFRfRTxAIIIhYERUBQFEG6SO+EkJDe27bvuXeym4QsZBM2ZXff3/Ms3NmdnczspMyZe8+5Pko1LkuCCLGeWJ+IqIyCn3gcaVu2QBMVJcuDp373nRze5MjKFECwghHZqqy0PBzYfNGUX9l1RMOqmSBaidJzNHj0s/2ITVPmaGkd6Y93R7aBWlW6z2lfzD7M2D0Dhvwk10ebP4pxzcaZXzkrSQkeYo4oy55BwFgRPDQ3u3relSu4/Mgj0F6LkctiuFJNETyEhZZqH+nmeRChNX0ReyEVqfHZ8uemtPNBlCvRmyBKtYpqS7caxqRyBhoNYO8DEd0WkTgdPmcOoiZMkMtxby2Cd8+ecA68SQVBB1Dm/pcdO3ZgxIgRsiJT+/btMWrUKGzbts26e0dkZfs2XUBejk62m9wRgZCavDNZmEanx5OrD+FUrDIPQM1AT3wyrj08SjnE61TSKTy781lo9Bq5PKT+EExpO+XmwYMoxWkKHoKBcT/dPHi4fFnmPJiCh7p1UfMLBg/lOYwptir2QnR5CtArP8s3JV4X6xER3Sbvu+6E78CBsq1LTUXcm2859GdapgDi119/xdNPP43Q0FBMmTJFtsV8C88++6wMLIiqooSr6Tj5Z/5wF3c1Og+uV9m7VPXmevj+P+w+myCX/T1d8NmjHRBcyoniojOi8cT2J5CpUWar7lGjB+Z0mWO+pyczEfj8PiD2X2XZKxR4ZDMQZn64VO7Fi7g8Ziy0sbFy2bV+PdQSwUMoex6sLbxwHsT5FFQ5tboAA99WuhJFT0NhctlJeZ2TyRGRlYTNeLHI3BCZe/OLfTigMg1hWr58OZ566ikZOBg98sgjcpK2FStWoHfv3tbcRyIrlW09C+OsJ+3vqV21hmRUAct3ncfX+6Nk21Wtwsdj2qNeiHeptpGSk4LHtz2OhGwlCGkV0gpvdX8Lzjde4AmZCUrwEHdcWfYOB8ZtAkLMl17NvXABl8eNk7OCCm4NG6LmZyvhHBRUyiOl0vZAXL+UZtF7RJAYERFhape7DuOBsGZKqVZRbUnkRIicBzFsSfQ8MHggIityDglB6PPPI3buXLl8/dVXUee77+CUX6XJkZTpiC9cuIBBgwYVe/7ee+/F//3f/1ljv4is6uLRBESfUe6i+oZ4oFWvSH7ChfxwJBqLtp42Lb89ohU61ind2M5sbTae+u0pXEq7JJdr+9bG+73eh4eYAO5GGXFK8BCfX7HNJ0IZthRc3+y2c8+dw+VHHoUuIT94aNxYCR4CAngey4mHj6ucSE7MBZF4NUMG4SUFBWJiph49elTsORFBgniIUq2i2pJImGbFJSIqJ/4jhiPl22+Rc+wYcs+eQ8qGDQgYNcrhPu8yDWESQ5cu59dcL0w8J2b3I6pK9Do9/v6uoG7znffXh9rFscuvFbb3QiKmfZM/hAjAi/0bY1Cr0pXt1Oq1mP77dPwbr2wn2CMYK+5eAX93/+IrZ8QDnw8qCB58qyvDlm4VPIx7pCB4aNqEwUMFCa6h9ECJvKH0RCWpvsoSQYN3KIMHIipXonxr2EsvmZbj31sKXZplvbT2pExXUaKnYe7cufj999+RkZEhH6I9b968m84NQVRZTvwVY5pNV5RtrdOqdLMo27Nzcel47IsDyNMp5TAf7FgTj3cv3VwP4s60KNW66+ouuezl4oUP+3yI6t7Vzec8iITp+FPKsm8N4JGfgKB6Nx+2JHoeEhPlsnuzZqi1kj0PFSU4suCGUEJURoV9XSKiqsyzbRv4DrhHtnXJyUj8+GM4mjLPA3HmzBlMmjTJ1KUtLiJE17VIqiaqKjS5Ouz/6WLBpHHD6rNsa7749Fw88tl+pOUoZTB7NArBgsHNSv35rDi6At+e/Va2Ra7Dez3fQ+PAxjcp1Tq4IOdB9jz8BATWuWnC9JVCPQ8ieKi58lOo/QrG5lP5CqruXaQIQd02IbdcX6vVYuPGjbI9bNgwODvguGAicgyhzz+P9O07YMjLQ9KXXyFgzBi4hIVV9m5VmDL9dndzc5OJ1OfPn5eBhAgeGjVqhHr16sk2UVVxZPsVWcNeEBc/4XV58Slk5Wkx4fP9uJqsTKjXrJov3n+oLZzVpeuU3HBmA5YfXW5afu2u19ApotPNJ4mLPVYo52HTTYMHUapVBA/a+Hi57NakCWp++gmDhwoWHFk4gLCsB0KnK6G0KhGRHXCpXh0BDz2EpFWrYMjNRcLyDxExT0mudgRlGsIkqiylpKTIgOGee+6Rw5ZE+/r16+jcubP195KoDETgcPjXK7LtpHJC58GlG5pjr3R6A55ZewRHryq1/av5uWPlIx3g7Va6+wm7onZhwd4FpuVp7afhnjpKl24ROanAV8MK5nnwDlOCh5sMW8qLipI5D9q4OLns1qiR0vPgbyafgsqVX7AHXNyUOUA4hImIqKigSY9B5eUl2yKxWtz8chQWXzFs2bIFu3fvlu3o6GjMnz9f9kQUJp7nrL5UVRzYckkOYRKa3lUNAeHKD7mjW/DTCWw/eV22fdycsfLRDgjzdS/VNo7GH8W036dBL8pmAhjXdBzGNhtbfEVRFeerB4DogwWTxIkZpoMbmN1u3tVoWarVOM+DW4MGTJiuRCLwDqzmhesX05CelANNng4upZxUkIjIXjkHBCDw0UeR8P77Ygwn4pe9j+qLF8ERWNwD0aZNGxkgXL16VS5fu3ZNto0P8ZqnpyfefPPN8txfIoukxGXh+B/Rsu3spkaHgbX5yQH4fM8lrNqjlFl1Vjnhw9Ht0Djct1SfzYXUC3hqx1PI0SlVeQbUGYCp7acWXzEvE1g9Arj6j7LsEaj0PIQ2Np+vcu0arowrNMN0/XqoueozOAeWrpwsWZd/mKepnRqnFCMgIiJF4COPQJ1fUjxt82bknC4oiW7PLO6BEJMDffHFF7I9ZswYOWmcH5MZqYra98MF6PVKPk7rPpHw8ivdbMr2aNfpOMzbdLwgX2FYC9zVoHQVqeKy4vDEtieQmqsMfxL5DgvvXAiVmLyrsLwsYM1I4MoeZVmUcx37w01nmNbExsphS5poJehzrVsXtVat4iRxVYB/aEEAkRybheAaLNVNRGSk9vaSQ5ni3nhTVBRC/DvvInLFh7B3ZcqB+PLLL4sED0lJSfjll19MvRNElSnuchrOHVTGz3v4uKDN3TUd/oScuZ6OyWsOIz+mwhM96mFE+9JNppeel44ntz+Ja5nX5LKotPRuj3fhonYpuqKY0OvrB4FLypBHuPkBY78HIlqa3a7mepwctqSJyp8Fu3ZtpechmOV2q1oPhLEcMhERFQh48EE4h4fLdsauXcg+XnCzzl6VKYAQlZf69euH/fv3Iy0tDffddx+ee+45mUy9d+9e6+8lUSns/eGCqd1hYB24ujt2KcnEjFz8b9V+pOcq5Vr7NQvDtL6NSrWNPF0epuycgtPJStesmONhee/l8HYtqNIjaXKArx8GLihzQsDNFxjzHVCtjdntauLi5LAlzWUl2d2lVk3U/HwVXEJDy3CkVB4Cwi0PIEQOXEhIiHwwH46IHIXKzQ3Bkx4zLSd+ZP/zQpQpgBB5DrVq1ULdunXx008/ydrfYiK58ePH491337X+XhJZ6NrZZESdSJJtnyB3mTztyHI0Ojz25cEi5VrfGdkaKpXlcz2IROlZf87Cvth9ctnfzV9OFBfiecOcANpcYP1Y4PwOZVkEF6O/BWq0M7tdbUICrjzyKPIuKTkZLpGRqPX55w5VR9sW+IV4AE6WBRBqtRp9+vSRD9EmInIUfsOGQR2i9Jynb9uG3PPnYc/KFEAcPnwYL774IoKCgmRlpu7duyMsLExOHHTqVP4Ms0QVTMxBUrj3oeO9daB2LtO3uN18Hi9tPIaDl5PlcpivGz4d1wGerqXrkXn7wNv4+dLPsu2udsf7vd9HHb8b5m/QaYBvHgXOblWWXTyBh78BIjua3aY2ORlXHv0f8i5cMNXTriV6HvK7gKnqcHZVwydQqdKVfD2Lc/0QEd2kFyLokUeVBYPB7menLtPVlUqlgqurq+x5+Oeff9ClSxf5fGZmJtzdS1cOksharpxIQsy5VNOwi4adHPtidPmu8/jusJKU7O6iwidjOyDcr3Q/n58f/xxfnFCKJ4hE6UXdF6FVSKuiK+l1wMbHgNOblWVnD+Ch9UCtO8xuU5eWhqjxE5B79qyyerUI1BQ9D9Ucu7eoKgvIz4PQ5OhMEzMSEVFRAaNGmiY8Tf1pMzQxSlVBe1SmAKJ169b46KOPsHTpUuTm5qJbt25yErklS5bI14gq4267qLxk1HFQ3VIN07E3W47FYNHWglJy745sjRY1SjcL99ZLW7H4wGLT8iudX0GPyB5FV9LrgU3PAMc3KstqN+DBtUCdrma3qcvIRNTEx5Bz4oRcdg4NlcOWXGtUL9W+USUmUsfefBiTuKm0ceNG+RBtIiJHovLyQsDDDykLOh2S16yFvSpTADF79mycOHECa9euxcsvv4zAwEB8/PHHOH/+PKZPn279vSQqwYUj8Yi/ki7bwZHeqNfmhvH5DuRoVAqmrs+f9RnA9P6N0L95RKm2cTjuMF7e/bJp+clWT+L+hvcXXclgALa+BBz+SllWOQMjvwTq9TS7TX12Nq4++SSyjx6Vy+rAQFltyTWydNWgqJIDiBLmghA3lcSDiMgR+Y8aBbgo1QlT1q+Xf/vsUZnK04gEanGHqbCnnnpKBhNMnKOKJuZ72PfjRdNyp/vqyhl0HVFMajYmfHEAORplhuhhbavjie71SrWNy2mX8cxvzyBPrwxVGVxvMB5v9XjxFX9bCOxbobTFPBD3fwI07Gd2m/q8PFyd/Ayy/lEmlVP5+ckZpt3q1i3lEVJl8BWJ1PnSE5UJBImIqDhRRdD3nv5I+3ETdKmpSN20CQEjRsDelCmAELNQm5OTo/xhqcaxzFSBzu6/juSYTNkOr+uLWs2DHLbi0qQvDyI+Xbn726F2AF4f1qJU5TSTcpLwxPYnkJKbIpc7R3TGnDvmFN/G7iXA7oLhTbjvfaDZULPbNGg0iJ46FZl//imXVd7eqPnJJ3BvVLpSslR5fIMKcmfSGEAQEd1S4JixMoAQkr/8Ev7Dh9tdaesyBRC9evW65Qdx8uTJ29knIovpdHr8s6kg96HT4Hp290NamopL/15VkshrBHhgxeh2cHO2vJRmjjYHk3+bjKh0ZUK3BgENsKTHEriobpgobt/HwI55BcsDFgNtHja/Xzodrr04AxnbldKuTh4eiPz4I3i0aF6Go6TKYqzCJLAHgojo1sTfOI82bZB9+DByz55D1t9/w+sO84VFHCqA+OILpSqLkU6nw8WLF7Fq1SrMmDHDWvtGVKJTe2KQlqD0fNVoHIAajQIc8lP7ZPdFU8UlDxc1/m9sewR5u5VqroeX/3wZ/8b/K5dDPULlRHE+rj5FVzy8Gvh5WsFy7zlAx4lmt2nQ6xEzazbStmyRy06urohc/gE827YtwxFSZZdyFbO6Z6drkJ5on+N5iYisKXDsGEQfPizbSV98yQBC6NixeG13Uco1MjISy5Ytkz0UROVNq9HhwBZlEjKh02DHHE//+5l4vP5zQa/fkhGt0CTCt9RzPWy7vE22PZ098UGfDxDudUMZ3OPfAT8+XbDc9QWg69Sb9ohcX7gQqd99pzzh4oIay5bCK7/kM9kenyAPGUBkpuVBp9FD7eK4c6wQEZXE5+674RwRAW1MDDJ+/12WdHWJKF1Bk6rMqn8BateuzYnkqMKc/CsGGcnKeP/aLYMRXqd0ZUrtwcWETExecwh6g7L8TK/6uKdF6X5BrTm5xjTXg9pJjbd7vI3GgY2LrnRmK/DtBNGtoCx3ehzoNeumwUPcosUF5etUKlRfvBje3buX+vioCuZBGID0ZPOJ1GL4oKjKJx6OOJSQiMjIydkZ/vfnVy80GJD6ww+wJ1ZLos7IyJBzQ9SoUcMa+0V0S+IO6MFfLheZddrRpOdoMPGLA0jLUert92kShuf6NCzVNnZe2Yk3979pWp7VeRbuqn5X0ZUu/w2sHwvo8+v6txkN9HtdXC2a3WbC+x8gaeVKZcHJCdXeeB2+/fqW7uCoyudB+IcWlHY1ElX4+vUzX4mLiMjR+A8dgoQPPpABRMq3GxH02GNwUtlH763VkqjFXUdPT08sWrTIWvtGdFMn/rqGzBSl96FOq2CE1LxhrL4DlK6dsu4IzsVlyOUGod54Z2SrUk2e91/Cf5j+x3SZ/yBMaDEBDzR8oOhKsf8Ba0YC2vw7zs2GAYOWyl4FcxI//VT5ZZkvfN5c+N13XxmOkKoan0KVmNKTWMqViKgkLtWrw6tLZ2Tu+RuaqChkHTgALzNpAA6bRC24uLigYcOG8PLyssZ+Ed2y9+HQ1oLehw4DHa/34Z3tZ7D9ZJxs+7o7y6RpH/cbqiXdwtX0q3hqx1PI0SkXgvfUuQeT20wuulLyJeCr+4FcpbIT6vUChn4EqMxXdkrZsEEOXTIKe/llu6x97aiKBBAs5UpEZBG/YffLAEJI/Xaj3QQQqrImUYeGhsoeB9EWj3///RcJCQnW30OiG5z8u2jug6P1Pmz+NwbLfjsn26LD4f2H2qJ2sOWBe2puKp7c8aSc80FoF9YOC+9cCJWYDM4oIx74chiQEassV28HjPhSlOMxu820bdsQ88oc03LIc8/KChRknwFE2k0qMWm1Wvzwww/yIdpERI7Op09vqHyVwiZpW7dCl54Ohw0g9uzZg8GDB2PbNqVqi7B582YMGTIEBw4csOb+ERXPffi5oPJSh4G1HeoTOnEtDS98c9S0/PKAJujWMMTi9+fp8vDczudwMVWZubu2b2281/M9uKoLBQY5acDq+4Gk88pycEPgoW8AN2+z28zcuw/Xpj4vxlXJ5cBx4xA0aVIZj5BsfS6IrKws+SAiIkDl7g6/e++VH4UhJwdpW3523ADi7bffxiOPPIIpU6aYnlu/fj3GjBmDxYsLzU5LVJ69Dy2CEFqrdOVKbVlSZp5Mms7W6OTysDbVMf4uy4dviTylV/a8ggPXlSA/0D0Qy/ssh59boepV2lxg3cNATH6Q4lsdGL0R8DI/u3f2f8dx9amn5GzTgt/gwQh9cTor8NghV3dnuHsrw+Q4hImIyHJ+w4aZ2sa5kRwygDh//jweeOCGZEsAw4cPx+nTp62xX0TF6LSi8lKh3gcHqryk0enx5OqDiE5Rho60quGH14a1KNWF+op/V2Dzhc2y7a52x/u93kekT2TBCnodsHEicPEPZdkjQAke/AutU0juhYuImjgR+sxMuezdsyciFi6wmwoTdPNeCFHAQMwCT0REJXNv1hQuNWvKdtb+/dAmJ8PWlekvvajxferUqWLPnz17Fj4+jjUenSrOKdH7kKT0PtRq7li9D69uPom9F5SchRAfN3w0pj3cXcwnM5vz88WfsfzIctl2ghPe6PoGWoS0KFjBYAA2Pw+cyK9T7eKpDFsKvWE+iHya2FhcmTAeuvxfgh7t26H6O0vg5GJ5IjfZHu8AN9O3S3aa0utERES3Jm72+fa9W1nQ65Hx229wyCpMIv9h7ty5SElJQatWreRzx44dw7vvvivzIIisTdztPPizY1Ze+u7wVazao/S8uKpVWDG6HcL9Csajl+RI3BHM+rNg0rcp7aagd63eRVfa9Tpw8DOlrXJWEqYjO5jdnrhzcmX8BGivxchlt8aNEfnhh3KcJ9k3D5+CXJns9DxTQEFERCXPTJ34yaeynb7jt4JJ5hwpgHjqqaeQnJyM+fPny0obYmy1s7OzzIF49tlnrb+X5PDO7Ltuqj1fs1kQwuo4Ru/D8WupeGnjMdPyvMHN0K5WgMXvj86IxrM7n0WePk8uD2swDI80e6ToSvs+Bn4vmEwOQ1YADfqY3Z4+KwtRjz+OvPNKgrXokq35fx9DzZ5Hh+Dh41IkgCAiIsu4t2gBtb8/dCkpyDp4EAa93qaH/JYpgBDBguiBmDZtGi5evCiXa9euDXfegaRymjSt8LwP7Qc4RuWllKw8TPryIHI0yljzUR0i8WBHZQylJdLz0vH0jqdN5Vo7hnfErE6ziuZN/Pct8PP0guX+bwIth5vdnkiUvjplCnKO/iuXnUNCUHPlp/J/csAeiAzzQ5h888sVEhFRAREseLRrh4wdO6BPTUXuuXNwb9gQDhVAGIlJ45o3b269vSEy4+KReKRcV8pCVmvgj4h6haoG2Smd3oDJaw/janJ+0nSkv+x9sJRWr8W0P6bhXMo5U7nWJT2WwEVdKEfhwi5goyi3alCWu74AdH7c7PZEL6OY5yHzdyXBWuXjg8hPPoFrjRq3cZRkazxvGMJ0I3EzaeDAgRW8V0REtsGzbVsZQAjZhw7ZdABhu30n5BDEhevBXwp6H9r1rwVHsGTbaew+q0zMGOTlig8fbgs3Z8uTpt/a/xb+iv5LtkWZ1g96f1C0XGvMv8DXowF9/l3ktmOBXgV5EjeKf+89pH73nWyLROkaH7wP90a2+4uPysa9yBAmJlETEZWGZ/t2pnbWgYOwZQwgqEqLOpGE+CvKrI1ixunIpoGwd1uPX8cHO5UcA7XKSc40Xc3fw+L3rzm5BmtPrZVtZ5Uz3unxDmr6Fhr6lHwZWP0AkJc/G2ajAcDAd0SZCLPbS1q9GokrPlIWnJxQbdEieHXsWPYDJLvtgSAioptzb9oUTvnD/bMOMYAgKjc39j6UZt4DW3QxMQvTv1VyDISX7mmMLvXMT+Jmzu6ru/Hm/oKE6Dld5qBDeKFqSllJwFf3AxnXleUaHYH7PwXU5kczpm39FdcXvmpaDps5E779+5XyqMheqzDdSBTV2Lx5s3yINhERFRA9+B751UtFJUPNtWtwqB4IMYmcuXkgiKwp5lwKrp1NkW3/ME/UbW3fybrpOVpM3XgKGbnKTNODWlUr1UzTZ5PPyrwHvUFJuh7ffDyG1C9UVjkvC1gzAkg8qywHNQAeWge4eprdnpjs5tq0aUrRf7H6Y48hcPTDt3GEZOvcvZxLTKJOS0uTDyIiKs6zXaFhTAcPwaECiKioKHh6mr/oILKWg4UqL7XtVwtOKie7zvV4ceMxXExUkqYbh/vgzfstn2k6OScZk3+bjEyNMiv03bXuxjNtnylYQacFNvwPuLpfWfYOA0Z/C3iaHxKWc/oMop58CoY85S6z35AhCJny3G0eJdk6lVoFdy8lD4JDmIiISs+jTWtTO+fECThUFaYJEyZg5syZGD9+PGrWrFmsfGu1atWstX/koBKupuPysUTZ9g50Q8NOYbBnK/+6JHMfBF93Z3w0ph08XS378dToNXj+9+flnA9C06CmePWuV6FyUhWaZXoqcOZnZdnVB3h4AxBgPiFdExODqMcegz5dyZHw6tYVEQvm2/3wMbJ8LoicTA2ymERNRFRq7o0bm9o5p07CoQIIMeO0TqfD/v37i1xUiLuoYvnkSdv9QKhqOFQo96HN3TWhVttvvv+hK8l4fUvBz8zbw1uiVpCXxe9/8583sT9W6VkI9gjG0p5L4eFcKOlaTBJ36HOlrXIBRq0GIlqa3ZaY4ObKhInQXr9umvimxjvvyHGbRMY8iOTYLGhzdcjOyIOHd0FeBBER3ZqYO0kdEgxdfAJyT5w0XTs7RADx2WefWX9PiPKJOR/OHYwz3e1scqf99mglZ+bh6dWHoNUreQaPdq6OXo1DLX7/N2e+wbrT62TbReWCd3u+izCvQr01Bz4Ddr1esDx0BVC3u9lt6XNy5LAl0yzTtWoi8qMVUHlZHsyQfbt2LgWp8cqcLMJn0/5EnVbBaN2nJiLq+1fqvhER2Qr3xk2QGb8butRUaGNj4RIRAYcIIDoWKuGYl5cHV1fegSLrOfzrZWPeLlr1joSLq+XzH9jaDNtT1h/BtdQcudyhdgCe6mb5PBcHrx/Ea3tfMy2/0uUVtApRqjtIp7YoQ5eM+r4KtHjA7LYMOh2iX3hBTmwjqIODUfOTT+AcaP9lc8ky//1+Fb+vPQMUulEmfk4v/puIC0cS0P2hRmh8h30PNSQistYwpszdu2U75+QpmwwgyjwuZO3atejVqxdat24tk6rnzJmD5cuXW3fvyOFkpuTi1N5Y2XZ1V6N5d/ud6fjD389j1+l402Rx741sBWcLE8WvZVzD1F1ToTUopTJHNxldtOJS1D9K0nR+RSZ0eRq442mz2xLdp7HzFyBjuzI7psrTU/Y8uEZG3uYRkj31PMjgQcgP7o0M+b1nv685jdjzqbLABotsEBHdnHsT28+DKFMAsWnTJrz99tsYOnQoXPLHRterVw8rVqzAypUrrb2P5ED+3RkFvU65IGnevTrcPMrUSVbl/X0+EW//elq2xdDH90a1QZhv0WIEN5OlycKzO59FUk6SXO4S0QXPt3++YIX4M0q5Vq1S0QnNHwDuXnDT7SV8+CFS1inDoODsjOrLlsKjWbOyHxzZnaPbr5RYBU28fmzXNQwePFg+nJ3t82eXiOh2uTVuYmrnnnSgAEIECaIK0+TJk6FSKZsYO3YsXnnlFawzXogQlVJethb//a5UElI5O6FlL/u8Ax6XnoNnvj6M/Bu3eK53Q9zVINii94regtl/zcapJGUelkifSCzqvkjOOC2lxSgTxWUnK8t1ugFDlgP5P6c3Sv7mGyQsXWZarvb6a/C+887bO0CyK9o8HS4eTTD1NNyMeP3ikXi5PhER3ZxrrZpwyp8OQQxhcpgA4uLFi2jfvn2x5zt16oSYmBhr7Bc5oON/XkNejnLx0ahjOLz83GBvdHoDnl17BPHpuXK5a4NgPN2rvsXv/79j/4dfL/8q214uXljWaxn83PyUF3NSgdXDgdQrynJYC2DkasDZ/OeY/ttOxM6Za1oOnT4dfoMG3cbRkT0SP5PGnKSSiPWMP8NERGSek1oN94YNZVtz9Sp0Njj5ZpkCiODgYBlE3Ojw4cMIDbW8ggyRkU6nx7+/RZmWW99d0y4/nPe2n8HfF5T5LcJ83fDOyNZQW5j3sPPKTiw7rPQWOMEJb3R9A/X86ykvanOBdaOB68eUZb+awOgNgLuv2W1lHT6M6KlTRSa3XA4cNw5B/3vUCkdI9kbkIllaYVCs9/ufO7F161ZZ6puIiMxzK5QHkXtaGdJs9wHEyJEjMX/+fOzYoSRdXrhwQSZVv/rqqxg2bJi195EcwLn915GRrNyVr90yGIER9lc6dPfZeCzbeU62RdDw/kNtEextWS/LueRzmLF7hml5cpvJ6BHZQ1kQQcD3TwAX/1CWPQKAMRsBn3Cz28q9cAFXH38Chhyl+pPvgAEIfXH6bR4d2StnV7Us1WpJDkTtVsFISU1CUlKSHG5HREQ3L+VqZIvDmMqU5TZx4kSkp6dj6tSpyM3NxaRJk2TC3KhRo/D4449bfy/JrokLjcPbrhSZOM7eiCFLU9YdNQ0FmdavETrUtqxEampuKp7Z+QyytEr9/f61+2NCiwkFK2ybDfz3rdIWE8g9tB4IbmB2W5rrcYiaMFHWnhY8u3RGxBuvw+kmORJEQqs+NWWp1pJyIFr2rI7dh47zQyMiKk0lJhtMpC5zmQwRPDzxxBM4d+6cvACsW7cuvL29rbt35BCunEhCYnSmbIfV8UVE/fwx/XY038PU9UeQkKH0sPRoFILHuta16L1avRYv/P4CotKV4V1NAptg/p3zC2at3PcR8Pf7SttJBTywEogsmKelMF1GJqIefxyaa9fksluTJqixbBlUnMeFSlCtvr+c50GUahU9DYUTqo3L4vXwen6AMpUIERHdgpvIgRA37/R65JxykB4IISMjA1u2bMGZM2dkJaZmzZqhf//+cHOzv8RXKl+Hfy3U+9C3pk1O6X4r/7f7AnafVe7ehvi4YfHwVlBZmPfw7sF3sTdmr2wHugfivZ7vwUP0MhgnivulYFgTBi4BGg8wux2DRoPo554zlYtzqVZNzvWgZtBPFmrerTqCqnnhyI4oWW1J9KaJH1U5E3XvSDkTtVarzEtCRES3pnJ3h2vdOsg7dx654mZ8Xh6cbOiGXpkCiPPnz2PcuHHIzMxEnTp1ZLLc+vXr5URyn3/+OcLDzY+9JrpRwtV0RJ9WSo76hXigTqsQu/qQjkSlYNHWgvke3h3Z2uK8h58v/ozPT3wu285OzljSYwkivPNnq4w+BHw7vmCiuLumAO0fvcVEcfOR+eefclnl54fI//sYLix4QKUkggQZKOTpZLUlkWAtciSIiKhseRAigIBGI/MTxQzVtqJMA58XLlyIJk2aYNeuXdi4cSN++OEH/Pbbb6hWrZp8jchSx3Yp8z4IrXpHWnxn3hak52jwzNrD0OYP93iiez3cWd+y+R5OJ53GnD1zTMvTO05Hu7B2ykLKFWDNSECj5ESg2TCg1ys33VbiRx8h5ZsNsu3k4oLI95fBrV5+9SaiMhBBg6evK4MHIiJr5UGcsK08iDIFEEeOHMF0UTPer2CsemBgIF588UXs2bPHmvtHdiwnU4Mz+2Jl28VdjUad7afnStz1n/ndf7iSpFzkt6npjyl3KzWfS5KWm4bndj6H7PyZpAfXG4xRjUYpL2anKHM9ZMYpyzW7AEM+vOlEcak//oj4d98zLYuEac8OHW7z6IhuTgxj5VBWIqKSuTcpVInplG0FEM5lnQciNjYWDRo0KJYX4e/vb619Izt36u8YaDXKEJzGnSPg6l7mlJwq55uDV/HjUSVZ2cfNGUtHtYGLuuR4XWfQYeZfM3E146pcbhrUFLO7zFbyQrR5wPoxQHx+slVgPWDUGhF9md1W5t69uDZzlmk59IXn4TdwoHUOkMgMUY2PpbyJiCzjVmjIUq6NlXItUw+E6H2YN28etm/fjrS0NGRlZWHfvn145ZVXMHbsWFy7ds30IDJHVG059nvB8KUWParbzQd1Li4Dc34oKGX5+v0tEBmoTFlfks9OfYa/rv0l2wFuAXi3x7twU7spU/xuerZgrgfPIODhbwBP86Vgc8+exdXJz8hxlYL/g6MQOH787R8cERERWYVzYCCcw8JkW1RisqX5c8p0y3fy5Mny/6effrpIxRxx4G+++aZ8iLZ47aQN1ral8nflZBLS4pUhOjUaByAg3D4mjsvR6DB57WFka5RZeEd1iMS9LatZ9N7fon7D52eUpGmVkwqLui8qSJr+/S3g6BqlLQKKB78GgurddK6HK49Ngj49XS57d++O8Jkz7a66FRERka1zb9wYGdevy7/ZmuhouNaoAbsNIL744gvr7wk5lGO7lCE6QosetvHDYok3fj6FkzFpsl0/1BtzBjWz6H0XUi9g9p7ZpuWp7aaiU0QnZeHIWmDXawUrD/v41nM9PPE4tDExctm9WTNUX/I2nJztZ3gYVV2iIt/OnTtlu2fPnlCrWaGJiOhW3Jo0Rsbvv5smlLPrAKJjR/MXL0SWSI3PxuX/EmXbO8ANtVsE2cUHt/NUHFbtuSTbrs4qvP9QG3hYUOIyIy9DJk1napTJ9PrX6o+xTccqL4ohSz8qPX7S3QuAZkPMbseg1SJ6yhTknig018OKD6Hyso/eHar6RM9zfHy8qU1ERLfmLiaUy5d3/jxw992w2xwIotvx3x/RQP61RfPu1aGyILm4qkvMyMW0Df+almcOaILG4b4lvk9v0GPWX7NwMfWiXK7rWxdzusxRhhvFnQK+Hg3olTwGtB8P3FEomLhxrod585C5e7dcVvn6yrkenEPsa14NIiIie+Jar76pnXv+AmyF7V+5kU0RE1Cd/EtJrlc5O6HpnZblB1Rl4uJ9xsZjSMjIlcs9GoVgbJdaFr3302OfYseVHbLt4+qD1zq+psw0nX5dKdeam6qs2KAvcM9bymx0ZiR+9HHRuR4+eJ9zPRAREVVxrnVqm0qx554/B1vBAIIq1PnD8cjN0sp2/Xah8PCxnWnbb2bd/ihsO3FdtgO9XPHWAy0tSlj+K/ovLDu8TLad4ITX73wd1b2qA3mZwNqRQOoVZcXwlsADnwFq51vM9fCuaTnidc71QEREZAtUrq5wrVlTtvPOX4BBpxRhscsAYv/+/dBqlYvAwnJzc7F161Zr7BfZqRN/FpT2bXaX7ZduvZiQiXmbTpiW3xjWAqE+5udlKCw2MxYv7n4RhvyxXE+1fgp3Vb9L1LeF6vvHgWuHlRV9awAPrQfcvM1uJ2v//iJzPYQ8PxV+93KuByIiIlvhWl+pqmjIzYXGRqZAKFMAIeZ6EPM/3OjcuXOYNm1aqbaVmJgo39O5c2e0adMGjz32GM6LJJJ8ogzs6NGj0bp1a/Tq1atYBSi9Xo+lS5eia9eucp2JEyciKiqqyDolbYMqRsr1LFw7myLbAeGeiKhfMJO5LdLo9Hhu3RFTydYHO0aib7OSZ9PW6DR4/vfnkZo/PKlHjR6Y2HKibLv/9RacTm9WVnT1AR5eD/jml3K9Qd6lS7j69OSCuR5GjkTQhAnWOjwiIiKqAG6F8yDOnbOvAGLVqlVo0qSJfIgx33feeadp2fh44IEH5P+l8dRTT+Hy5cv4+OOPsWHDBri7u+ORRx5BdnY2kpOT8eijj6JmzZr49ttv5bqLFy+WbaPly5djzZo1WLBgAb7++msZUEyYMAF5eXnydUu2QRXf+9Dkzmo2Py/Bst/O4WiUEhDVCfbC7HubWvS+dw69g3/jlYTr6t7VsfCuhXLeB6cjq+F+4ENlJScVMGIVEGa+DKw2ORlRkx6HLlUJQry6dkX47Fk2/5mS7ROlW1m+lYjIcm75PRCmSkz2VMZV3MH39/eXF+gvv/wyXnrpJfj4+JheFxcunp6esifBUqmpqahevTomTZqEhvllrJ588kkMHjwYZ8+exd9//w0XFxfMnz8fzs7OqFevninYuP/++2WQsHLlSrzwwgvo0aOHfP8777wjeyN+/fVX3HvvvVi/fv0tt0EVQ6fV49ReZW4CldoJjTuXfKe+Kjt4ORnv/3ZWttUqJ7wzsjU8XUv+cdp+eTu+PPGlbLuoXPB297fh5+YHXPoTTpunFqwoEqbr9zG7DUNeHqInP4O8y5flsluDBqj+zhLO9UCVTvyOHTFiRGXvBhGRTXGrVxBA5J6zswBC/GEYMmSIKVgYOHAgXF1vLwHWz88Pb7/9tmk5KSlJ9nSEh4ejfv36WLZsmZxzQnxtIxGgfPTRR0hISMC1a9eQmZmJLl26mF739fVF06ZNZZ6GCCAOHDhwy20EBwff1jGQZS79m4DsdGWoTZ1WITadPJ2Rq8WUdUegzy9F+2zvBmgd6V/i+66kXcHsvwomi5veYTqaBTcDEs8D60bDKb9cq77DRKg6KkOabiR6/2Jmv4KsAwfksjo4WM71oPY2nyNBREREVZtrnTqmtvHmoN0EEN9//z0GDBgggwYRQGzZsuWm6xoDjdKYPXu27C0Q2//www9lb0ZsbKypZ8IoNDRU/h8TEyNfFyIiIoqtY3ytpG3cTgAhZl2taOJrGh+25PjuaFO78R1hNrf/hc378T9cScqS7TY1/TGpa+0SjydXl4upu6YiQ5NhmizugfoPQJeRCNWakXDKTpbP59XsBvSeD/VNtpf40UdI/eEH2XZyc0O1ZcugCg+36c/T3tnqzyyVjOfWPvG82q8qe25dXeX8Tfq0NGgTEqre/t1OADFjxgw5NCgoKEi2b0YEF2UJIMaNG4eRI0di9erVMk9B5DXk5OQU6+Vwc3MzVXwSeRKCuXXE8CihpG2UlRjKlZ6eXub3387XFcckPmdVft3gqi4jKRdRp5QLZO9AV/hVc66Uz84afjudiG8OKsGQp6sK8++pi+wsZQbpW1l0ZBFOJ5+W7UivSExpNgUZqcnw+n4c1InKUChtYAMk9FwE96wcqFRKDk9h2du2I2WpUvZV8JvzCrR1atvsZ+kobPFntqzEHz3R+yt06NDB7nMhHOncOhKeV/tVlc+tU4A/IAKIxMRK+7suPh9LPxeLA4j3338f3vnDJE6dOgVrE0OWhFdffRVHjx7FV199JROqjcnQRsaLftFDIV4XxDrGtnEdDw8P2S5pG2UlPuDCOSAV+QdaDGMR58JW/jif/iPRNPO0SJ729St5huaqSEwUt3BrwdjEOfc2RdOaSm/WrWy+sBk/Xv5Rtt3V7ljSYwnC/EPhtOV5qKL+ks8bPIOgH7UWbupAs+c2+8gRpC5YYFoOnjIFgYMHW/HoqLzY4s9sWYny3mIoqiCOt/DQUXvkSOfWkfC82q+qfG5TgkOQffkKDFlZ8HJxgarQdW1FKU1QZfFvd5Go/Msvv8j8hN69e8sqRiKp+naIPzQiUbpfv36mPzRi50UwERcXJ7+W+L8w43JYWJhpLgrxnKiyVHidRo0ayXZJ27gdlfXNZ6xyUtW++c0RP6hn/lEmWYMT0PSOajax3+aOY+6mk0jKUvIU+jULw4gONUusenQ+5TwW/rPQtDyz80w0Dm4M7P0QOLRKeVLtCqdRa6AOqgt1enqxc5t39SquTX5GJk8LfvcPQ/BjE1lxyYbY0s/s7f6cGH8mHOF4HencOhqeV/tVVc+tc1BQwUJqKtReXqjKLA4gRLQmkprbt2+P6Oho/PTTT6YeiRtZOoRJJDFPnToVn3zyiRweJWg0Gpw4cULO1yDyE0RpVhExGk/03r17UadOHTmUSvQAiH3Yt2+fKYAQ81OI94uqUcZu9Fttg8pX7PlUpCXkyHaNRgHwDqj4iNoaNv0bg5//izXNNv3q0BYlXsBnabLw/K7nka1VhtoNrT8UQ+oPAc5sBba+XLDife8DNTuLWyPFtqFLS1PKtebf1fXs1AkRc+YweCAiIrIj6qBAU1ubmASXatVgFwHElClT8NZbb8meB3HhtHBhwV3VsuZAiOTmbt26yW2Jh6jKJKojiSBAzAUhchVEcDFz5kw5t8O///4rqzTNmzdPvl/kNohAQczrEBgYKEvCLlq0SPY69O3bV64jSrXeahtUvk7tUy66BVst3RqXnoNXfvjPtLxgcHMEeyt5NLfy1v63cD5VGfLUIKABXur0EhB/GtgwXs44LXV9AWg10uz7DRoNop+bYqoJLao01Fj6Hpxus/oZERERVS3OgQU3tTWxMfBo0Rx2EUCIC3HjvAmNGzfGn3/+aZUSqEuWLJGlXEWAIpJGRA+HSKSulh95iYt/kRcxdOhQhISEYPr06bJt9Mwzz8ihTLNmzZKJMaLH4dNPP5VzPwiil6GkbVD50Gp0OH9QGS7m7KZGndYhNjkk4+WNx5CSP3RpYMsI+SjJ1ktb8e1ZZbJCD2cPLO6+GB6aHGDtg0BefnJUk/uAnjNvuo3rr7+BzD17ZFvt74/Ij1ZA7Wfbs3cTERFRUVkHDyJ9xw7TcvQzzyKtT28EPvooPNu2RVXkZBBXSKUkhjCJC3zR2yASlG93Pghbc+zYMfl/ixYtKvxri6FYItASw7eq2vi9G507GIet/6fcuW/UORx9HrFspuaq5NuDV/H8N0dlO9jbFb9O6S6HMN1KdEY0hv84HOkaJVCYf8d8DK13H7B6OHA+/xdEWHNg/K+Aq5fZc5v2zQbEzp0rn3dycUHNVZ/Bs1278jtQKje29DN7u8TNnG+++Ua2hw8f7hBJ1I5ybh0Jz6v9qornNnntWsTOXyCG8IgySAUviP3T6xE+5xUEjBpV5a5vy1TDSgwVEnkFIk+hdevWiIqKwpw5c7B8+fKybI7s1OlCw5cadbK94UuxqTmYu+m4aXnhkBYlBg8avQYv/vGiKXi4p/Y9St7D9rkFwYNHIDBqTZHgoTAxSVxsoSGC4XPnMnggIiKyw56HWBE8iHv5hYMHQeRFGgyInTcfWYcOoaopUwCxadMmOexIDAMyDhWqV68eVqxYgZUrV1p7H8kGZaXl4cp/ibLt5e+G6o0CYEtEx9yMjf8iPUep9DWkdTX0b15yEPThkQ9xNF7psajuXR2zu8yG07ENwJ6lygpOamDEF0BALbPv18bEIOa5KeJWrlwOHDcW/vcPs96BEZUj0ePw4IMPyoe99z4QEd2upFWrRPnRW6+kUinr2UMAIYIEkZQ8efJkU83YsWPH4pVXXsG6deusvY9kg84euA69Xhkd16hTGFSqW1csqmrWH4jCrtPxsh3q44a59zUr8T37Yvbhk2OfyLazkzPe6vYWfBLOAT8+XbDSPW8CdZSKYzfSZ2Yhedp06JKVSfe87rgDodOmWeeAiIiIqMrQ5+QgfcdvZiswFiGGXW3fIde3+QDi4sWLMtn5Rp06dUJMTIw19ots3Om9BcOXGtrY8KXolGws+OmkafmN+1vA3/PWQ5eScpLw0u6XYMifMe/pNk+jpUc48PXDgDb/h77tWKDDBLPvN+j1iJ05E9pz5+SyS62aqP7OEjjxLi4REZHd0WdkFB+2dNOV9cr6th5AiOpLIoi40eHDhxEaWvLMvGTfEq9lIP6KkgMQUtMHQdXMzxdSVYcuvbTxGDJylSFEw9vVQK/GYSW+Z/ZfsxGfrfRYdInogkcbPwysGwOkRSsrRXYCBixWkqTMSFj+ITK2bZNtlbc3IpcvZ8UlsskERVGhTzxEm4iIzBN/60scvmRaWaWsb+sBxMiRIzF//nzsyC85deHCBaxdu1aWSh02jOO1Hd2ZffkzT+dXX7Il3x6Kxh9nlEAg3Ncds+4tuXLU6pOr8cfVP2Q70D0Qr931KlQ/Twei9ior+FQDRnwpatmafX/ar78i4f33lQUnJ4Qvegtu9epZ7ZiIKooIpkVRDfEoQ4E/IiKHoXJ3h0/vXkq1pVtRq+HTp7dcvyopU5bbxIkTZRksMYt0bm4uJk2aJBPmRo0ahccff9z6e0k2Q1w0iPwHwUnlhAbtb333viqJS8vB/EJVl14d2hx+HkqRgJs5mXgSSw4uKXjPXa8i+PgPwKHPlSec3YFRqwEf859DzqlTuPbiDNOyz5NPwrtbt9s/GCIiIqrSAh95ROY33JJeL9eraspcJkMED0888QTOnTsnLxrr1q0L7yrWvUIV7/qlNKQnKmP+azQOgKevbcwRIoch/fAf0vKrLg1uXQ29m9w6+MnSZGH6H9Nl6VZhXNNxuEs0f36xYKVBS4Hq5ieB0SYl4eqTT8GQnS2Xfe4dCK/RD1vvoIiIiKjK8mzXTs7zIEq1yuFMhYd+FpoHoipOJlfmACI7OxtnzpyBRqORF18nTxYknYrZoMkxnduvzDwt1G9nO/kwW47FYutxpeckyMsVcwaVXHXpzf1v4lLaJdluGtQUz9YbCnxyN6BXghDcMRloNdLsew15eXKmSc21a3LZvUULhM2bh0yNEowQERGR/QsYNQpuDRvKUq2yN0IkVqtUcniT6HmoisFDmQMIkfswY8YMZGRkFBvnKmanLhxMkOMw6A04d1C5CFepnVC3dQhsQXJmHub8qMyYLcwb3KzECeN2XN6BjWc3yraHswfe6jIfLt88CmQpc1+gXi+gz7ybvj/2tdfkhHGCc0gIary/TBnfyACCiIjIoXi2bSsfolSrqLYkEqarWs6DVQKIxYsXo0uXLnjyySfldOBEQsz5FGSm5sl2zWZBcPe6df5AVTH/pxNIyFD2u2/TMAxsEXHL9eOy4jD377mm5Zc6zECt3e8BMcoEcgioAzywUkRRN522PuVrZb4UJ1dXGTy4hIWxag0REZEDU7m7V/nA4bYCiKtXr+Kjjz5CzZo1rb9HZLPOFhq+1KC9bQxf2nkqDt8dVkqt+ro7Y+GQ5rIX7Wb0Br0s2ZqSmyKX+9TsgyEpScCR1coKLp7AyK8AD/Mzb4teh9hXXzMth8+fB49Wrax7UERERERVLYCoXbs2YmNjGUCQiV6nx/nDSgChdlGhdsvgKv/ppOVo8PJ3x0zLs+9tilDfW0f+a0+txZ5re2Q7xCMEc2oNgtNXIwpWuG8ZEN7c7Hs1sbG4+uxzgFbJkQh89FH4DxlinYMhqgLUajWGDx9uahMRkX0qUwDxwgsvYMGCBZgyZYqsvuTqWnS8eLVq1ay1f2Qjok+nIDtdSQCu3SIIru5lzs+vMG/8fAoxqUrFqG4NQ/BAuxq3XP9s8lksOVBQsnVh2xfgv/EJIL8KEzo/BbR4wOx79Xl5uPrss9AlKjkSXnd0QejzU613MERVgOi9EyW9iYjIvpXpN73IfRCzjIr/Cw/3EAnVTKJ2TMa5HwRbmPvhwKUkrNl3Rba9XNV4beithy7l6fIwY/cM5OmVXInRjR/CHX8sBTJilRVqdwXunn/T919fsBA5R/+VbZdq1VDt7bfhxAstIiIicpQA4rPPPrP+npDN0mn1uHBEmb3ZxU2NWs2DUJXlafVFhi5N69cINQI8b/mepYeW4kzyGdmu718fz4mehCt/F8w0/cBngNr8j1Py+vVI+eYb2XZyc5NJ084B5nMkiGyZuLG0f/9+UzlvDmMiIrJPZQogOnbsaGrn5eUVG8JEjiXqZBJys5Rx/XVaBcPZtWqPff7kzws4cz1DtlvW8MOYLrVvuf7emL34/IQys7SLygVvhPWE2y+zlRfVrkrStLf5krXZR47I3gejiPnz4N60qfUOhqgKEb3QFy9elO327dtX9u4QEVE5UZX1jWvXrkWvXr3QunVrREVFYc6cOVi+fLl1945swoXDSu+DUK9t1a6+dCUxC0t3nJVtlRPw2tAWUIvGTaTmpmLmnzNNy881fBCNthdUUcKAxUCNdmbfq42Px9VnnoUhf26HgDFj4Dd4sPUOhoiIiMhWAohNmzbh7bffxtChQ+HiotT6r1evHlasWIGVK1daex+pildfung0Qbad3dSo2TQQVfnu6Owf/kOORi+XH7mjDppX97vl+vP/ni/nfRA6h3XA6P3rAG22skLbsUC7cebfq9Hg6pQp0MYp7/Vs3x5h06dZ/6CIiIiIbCGAEEHCzJkzMXnyZKhUyibGjh2LV155BevWKRNkkWO4djYFOZn51ZeaB1Xp4Uubj8Xg9zNKb0mEnzum9m14y/V/ufQLfr38q2z7ufphYboOqgSl9wLhLYB7Ft30vdfffAvZBw7KtnNYGKq/+w6c8oNtIiIiIocLIMQYV3PjWzt16oSYmBhr7BfZiPOFhi/VbWM+D6AqSM3WYN6mE6blOYOawdvt5ilACdkJeHXfq6blWWFdEXb8e2XB1QcY/jngYn7OiJTvv0fyV1/Jtggaaix9D87BVX9eDCIiIqJyCyCCg4NNiXKFHT58GKGhVXsMPFmPQW8wVV9SO6uqdPWlxVtPIz49V7b7NAlFv2Zhtxy6NG/PPJn/IPQL74z+ewoNzbtvKRBUz+x7s48fR+ycuablsFdmc6ZpIiIisitlCiBGjhyJ+fPnY8eOHXL5woULMqn61VdfxbBhw6y9j1RFxV5IRVaqMi9CZNPAKjt53NGoFHy177Jse7ioMfe+Zrec8+HH8z9i19Vdsh3oFoCZZw8CWmXCOXSYADQ3/z2uTU7G1cmTYchVAhX/ESMQkD8rLxEREZG9KNMV38SJE5Geno6pU6ciNzcXkyZNkrOPjho1Co8//rj195Kq/PClem2r5vAlvd6AV374DwaDsjzl7ga3nPMhNjMWb/7zpml5DoIRkHBUWYhoBfQtGNZUmEGnw7Xnn4f2mjKEz6NVK4TNKqjeROQIxLwPoriGsU1ERPapzLeMRfDwxBNP4Ny5c3LIR926deHt7W3dvaMqS5xzY/lWlcoJtVtUzTH+3xyMwtGrylCkBqHeePTOOrc8pjl75iBdky6XB/k1Qa8jW5UX3XyB4atumveQ8MEHyNyjTCynDg5G9aXvQcX5UcjBiJ49d3fzPyNERGQ/yhxAiJ6HzZs34+zZs3IiuYYNG+Kee+6RPRFk/+KvpCM9SRnWU71xANy9ql6FoZSsPLz5y2nT8rzBzeCivvmovQ1nN2DPtT2yHeoWgBf/+73gxfuWAYF1zb4vY/efSPhwhbKgVqPGO0vgEnbzHAsiIiIiW1amq/1Tp05hwoQJyMzMRJ06daDT6fDFF1/IieT+7//+DzVq1LD+nlLVHb5URasvLdl2BkmZSo7GvS0jcEe9m/eSRGdEY/H+xableUmp8DPmPXR8DGg2xOz7NDExuDZtmui+kMshzz0Lzw4drHsgRDZC/C04dOiQbLdt25bDmIiI7FSZkqhFsnTz5s3xxx9/YOPGjfjhhx+wc+dOWYFp4cKF1t9LqnIu5ldfErnIdVpVvQDi+LVUfLW3IHF65sAmN11Xb9Bj9l+zkaXNksv3OwfjrrhLyosRrYG+C286WVz01OehS0mRy949eiBo/HjrHwyRjRDDAMWwVuPQViIisk9l6oE4evSoDBx8fHxMzwUGBmLGjBl48MEHrbl/VAWlXM9CcqxysR1ezw+evq6oSmQuww/Hoc+/fpncuz4i/Dxuuv660+uwP3a/bEe4+OKFs4eVF9z8lLwHZzez74t7ewmyDyvrulSrhmpvvA6n/IkViYiIiOxVma52wsLCEBcXV+z51NRUBAQEWGO/qAq7+G+CqV2nZdXrffjucDQOXE6W7brBXhh/V51bDl165+A7puX516LgbbxzOuhdIND8e9O2bUPSqlXKgouLnGla7e9v1eMgIiIisukA4tq1a6bH2LFjMWvWLOzatQtpaWnIyMjAvn37MGfOHDz77LPlu8dU6S4VDiBaVa3qS+k5Gry25ZRpec59zeDmrL7lhHHZ2my5PFzrhs4ZSsUmtBl90/ke8q5cQcxLL5uWw158ER4tW1r3QIiIiIhsfQhTr169iky+JS6+xJwPNz730ksvYcgQ8wmnZPtyMjSIOaeM+fcP85SPquTd7WeRkKFM5Na3aRi6N7x5D8n3577H3zFK6dUwlQemXj2jvBBUH7jnLbPv0efl4epzz0GfkSGXfe7pj4CHH7L+gRARERHZegAhqiwRXf4vwTQpW52WVav34cz1dKzaoyQ/uzmrMPvepjddNy4rDov2LzItv3LtsjJ0SeUC3P8p4Opl/n2LFiP3xEnZdq1dGxELFt5yVmsiIiIihw0gOnbsWL57QjaX/1C7Cg1fMiZO6/Izp5/sUR+RgZ43XXfB3gUFE8bl6NAtO79ka585QLXWZt+X/ttOJH/5pWw7uboqeQ/e5gMNIiIiInvFWd/IYjqNHleOJ8m2mDguvK5flfn0th6/jr8vJMp2ZKAHJnU3P+mb8MulX7ArapdsB0KN6devKi/U6wV0fsrsezTXryPm5YK8h9AXp8O9cWPrHgSRjVOr1bjvvvtMbSIisk8MIMhi0WeSocnVyXbtFkFQqarG0J1crQ6vbVGGFQkzBzSFu4v5i5eknCS8vu/1gnWvx8Jfrwc8g4EhKwAzZVgNOh2uTZteMN9Dn94IeIh5D0Q3EsP5vLzYK0dEZO9YtJ5sfvjS53su4UqSMi9F57qB6Ncs7KbrvvHPG0jOVUq83p2Vg75ZSgUmDPkQ8DH/vsSPP0bWP//ItnN4OKotZN4DEREROa4yBRD79++HVqst9nxubi62bt1qjf2iKkbkDRjLt6qcnRDZJBBVgai4tGzHOdkWucwicfpmSc1i2NLPF3+WbV8D8HJCfkDU6QmgYV+z78k6dAjx73+gLKhUqL54Eed7ILoJvV6Pw4cPy4doExGRfSpTACHmgRDzP9zo3LlzmDZtmjX2i6qYhKgMZCQr5VFrNAqEq3vVGP32zrYzSM9VgtkR7SLRrJr5vIwsTRZe3feqaXlGQgKCdXogrAVw9zyz79GlpiL6hRcAnTJsK/jJJ+HZvn25HAeRPRBBw6lTp+SDAQQRkf2y+Cpw1apVePPNN013o++8806z67XkhFr2P/t0FRm+dCo2DWv/uSLbXq5qPN+v4U3X/eDIB4jNjJXtLtnZuDcjC3D2AB74FHB2K7a++B6PmTUb2msxclkEDsFPPF5ux0JERERkdwHE6NGj4e/vL+8qvfzyy3LCOB8fH9PrYtiIp6cnOnfuXF77SlVk9mmRQF3ZxAX+wp9OIr9qK57sWR+hPu5m1z2VdAqrT66WbTeDAbMTkiEHOfVbCIQ0MvuelHXrkL5tm2yr/fxQbfEiOLGqDBEREZHlAYSzs7NphmkRLAwcOBCurq78CB1ARnIO4q8ocyaE1PSBd4D5C/WK9NupOPx5Tglqqvt7YPxddcyup9PrMP/v+dAZlGFIjyWnIlLk79S/G2g/3ux7cs6cwfXX3zAtR7z2KlzCw8vlOIiIiIgcIgdi6NChSElJwfvvv4/nn38eiYmJ+OWXX3DhwgXr7yFVukvHlPkVhNpVYPZpjU6PVzcXlG19aUDjm5Zt/ebMNziWcEy26+Zp8GhqGuARCAx+X8m6voE+OxvRU6fCkKvke4hyrT69e5fbsRARERE5RABx+fJlDBo0CN99952supSVlYUtW7bg/vvvx9GjR62/l1SprhxPrFLDl77aexkXEjJlu32tAAxsEWF2vfiseLx36D3T8uzEJLiIxqD3AB/zPQrX33wTeefOy7Zbo0ZywjgiIiIius0A4o033kCfPn2wfft2uLjISzIsWbIEvXr1wuLFi8uySaqidFo9rp5S5k3w8HFBSGRB3ktlSM3WYOmOs6blW5VtfWv/W8jQZMj2kPQMtM/JBVo/DDRVZsq9UfpvO5Hy9TrZdnJ3R/Ulb0PlVjzBmoiIiMiRlakW56FDh7B69eoiF24iR+LJJ5/EiBEjrLl/VMlizqeaZp+ObBoIp0qefXrF7+eRnOB2p54AAFeiSURBVKWR7SGtq6FVpL/Z9f6K/gu/XPpFtv11OkxNSgH8agL9C3IbCtMmJCBm1izTctiMGXCrV69cjoHIXqnVagwYMMDUJiIi+1SmAEJUYjJX4zszM5N/NOx4+FKtZpU7fCkmNRsr/7wo265qFZ7va76CUo42Bwv3LjQtP5+UggBRrmnYR4C7r9mKTtdmzoQuKUkue/fqBf+RDISJSkvcVPLzMz8XCxEROfgQprvuugsfffRRkSBCJFUvWrSIZVztNYBwUnogKtOSX88gV6t8z427oxYiAz3Nrvfxvx/jasZV2W6fnYPBGZnAHZOBWneYXT95zRpk/v6HbKuDgxGxcMFNh0UREREROboy9UDMmDFDzkYtAonc3Fw88cQTiI6OlvNEiPwIsp/yrYnRSrJyaC1feHi7VuqkcRsOKUGBr7sznupZ3+x6F1Mv4rPjn8m2s5jzITEJTkENgJ4zza6fe+4c4t5aZFqu9tqrcA6s3ECJyFaJm0rHjx+X7WbNmkGlKtM9KiIisscAIiwsDN9//z1++uknnDx5Uv7RePDBBzF48GB4e3tbfy+pUlw5oQzpEWo2q9yL6jd+PgVD/qRxT/eqD39PV7NDkd78501o9Vq5LEq21tXolJKtLsXnrtDn5SH6hWkFJVsffhje3bqV96EQ2S3xt+C///6T7SZNmjCAICKyU2UKIAQPDw8MHz7cuntDVcqV/6pG/sNf5xKw63S8adK4sV1qm11vZ9RO/HXtL9mO0GoxMSUN6DQJqGl+dvT4d99D7qlTsu1avx5Cp71QbsdARERE5NABRHx8PN59911ZjUmj0cg7v4Xt2LHDWvtHlUSn0yPqpNID4ebljNDaxZOPK4Jeb8DrPxdMGvdCv4ZmJ40TidOibKtpvcRkeIiqS71fMbvdzL17kfSZMtQJLi6ovmgRVO6VP8M2ERERkV0GELNnz5bd1AMHDoSPT+XOC0Dl4/rFNOTlKOVbazYJhKqSyrf+/F8s/otOk+2mEb4Y3Kq62fVE3kN0RrRsd8rOwd1Z2cADSwFXr2Lr6lJScO3FGWLMk1wOfe45uDdpUq7HQUREROTQAcTevXvxySefoH379tbfI6oSrub3PgiRTStn+JJWp8eSbadNy9P7NzIbyIjA4dNjn5oSp18SidNtxwF1e5jdbuz8BdBevy7bnp07I/DRR8rtGIiIiIjsTZlKZHh6eiIoqHLnBKDyZZx9WqjROKBSPu7vDkfjfLxSBapj7UB0bxhidr3F+xcjV6ckQj+Ylo567qFA3wVm1037ZSvStmyRbZWvL6q98TqcWCmGiIiIqHwDCFFtSfRA6HTKEBeyL3nZWsReVIYN+Yd5wiew4nMD8rR6vLfjrGn5hX6NzM7NsOfaHmy/sl22g7Q6PJGcCgx6F3AvPpmVNjERsfPmmZbDZ8+CS3h4uR0DERERkT0q0xAmMWmcKOG6a9cuREZGwtW1aEnNL774wlr7R5Xg2tkUGMTMzWL4UiX1PqzbfwVXk7Nlu1vDEHSsU7yMrEanwRv/FMw7MiU5BT7N7gca9iu2rkj0j507D7pkpWfF5+4+8L333nI9BiJHo1ar0bdvX1ObiIjsU5nLuN7Liy+7Zay+JNRoUvHzP2Tn6bDst3Om5Rf6NjS73uqTq+XEcUKrnFwM0joD/V4zu27aT5uRvm2bbKv9/RE+Zw5nmyayMtFLyOGtRET2r0wBxOuvv279PaEqIyo//0GMGKreqOJ7IL7cewlx6UpOQ79mYWhZw7/YOvFZ8fjw6IfKfhoMeDkxCaq+bwE+YcXW1VyPQ+zChabl8Llz4BwcXK7HQERERGSvytwDQfYpMyUXyTFK4rKY+8HNo2K/RdJzNFi+67wpgHm+byOz67176F1kabNk+/70DDQNbQO0fcT80KU5c6BPTZXLvgPugW///uV6DESOPBP16dNK5bRGjUTVtDKl2RERURXHAIKKuHqqUPnWShi+9OmfF5GSpZHtIa2ro2FY8XlGjiccx4/nf5RtX50Oz6RkAMPfAcxcrKR+9z0ydu2SbXVwMMJmzy73YyBy5ADiyJEjst2gQQMGEEREdoq3h8js8CUhsknFDl9KzszDJ7uVnAZnlROe69PAbI/CW/vfNC0/kZKGgE5PAOHNi62riYnB9dcKciIi5s2Fc0DlJIUTERER2QsGEFTk4tw4/4OzqwphdYqXQi1PK/44j4xcrWwPbx+JWkHFZ5HednkbDsUdlu1aGg1GOgUAPWaYPZaYV+ZAn5Ehl/0G3wef3r3L/RiIiIiI7N1tBRD79+/H119/jYyMDJw7dw5arXLxR7YpNT5b5kAIEfX9oXauuPgyLj0Hn++5JNuuzio807t+sXXydHlYcmCxafn5pBS4DFwMuBYPNFK//wGZu3fLtnNoKMJefrlc95+IiIjIUZQpB0IEDBMmTJBjXUXZvjvvvBOLFy9GVFQUVq5cibCw4pVwqOq7dibF1K7esHjlo/K0YtcF5Gj0sj26Uy1E+HmYLdsanRkj252yc9CjZh+zcz5o4uJwvVClsPB5c6H2q9jeFCIiIiJ7VaZbzEuWLJH/b9u2De7uyizF06ZNkxPKvfXWW9bdQ6ow0WcL8h+qN6y4XIG4tBys3ndZtt1dVHi8R91i6yRmJ+LjIwVlW6el5cFpwFvmqy7Nnw99mjKTtpgszqdnz3I/BiIiIiJHUaYAYufOnZg+fbqchdqoXr16eOWVV/D3339bc/+ogogLb2MPhMh/CKlVvPpRefnw9/PI1Rb0PoT6KEFpYcuPfIAMnTIz9bCMTDTqNgPwrVZsvfRffkHG9h2yrQ4MRNhMDl0iIiIiqvQhTElJSQgJCSn2vK+vL7KylNr8ZFvSErKRkZyf/1DPD2p1xeQ/XJe9D1dMvQ+Tutcrts7Z5LPYcGaDbHvq9XhaHQZ0mFhsPW1yMmIXFJowbvYsVl0iqkBqtRq9evUytYmIyD6V6SqxRYsW+Pnnn4s9v3r1ajRt2tQa+0UVLLpQ/kO1Chy+9OGu88jL730Y26U2QnzcivWMLN73OvQwyOUJKWkIvudtQF089r3+2uvQJSnzWHj36Q0fThhHVKFETpzIgRMP0SYiIvtUph6IqVOn4n//+x/+/fdfWXnpww8/xPnz53H8+HF8+umn1t9LqtgE6gYVk0Adm5qDNf8ovQ8eLmo81q147sOf0X9iz/X9sh2h1WJM5N1A7TuLrZe+cyfSNm2SbZWvL8JfeYUXMERERERVpQeibdu2snyrp6cnatWqJasxhYeHyx6ITp06WX8vqVyJu/zRZ/Lnf3BRIbS2b4V84h/uOleo96EWgr2L9j5o9Bos3lswJGlKWg7c+75abDu6tDTEzplrWg6bMQMuoaHluu9EZH4m6jNnzsiHaBMRkX0qUw+ESJTu0qWLVSoupaSkyKpOu3btkuVhGzVqhOeffx7t27c3fa1FixbJHo6IiAhMnjwZAwcONL0/NzcXb7zxBn755Rfk5OTI8bczZ85EYGBgkf291TYcXXpijin/IVzkP1TA/A8xqdlY+0+Uqfdhopnehw2nN+BC5jXZbpWTi/4dngV8I4qtF/f2Emjj4mTb66674Dd0SLnvPxEVJ4KGgwcPynbdunWhUnGuUiIie1Sm3+5i+JK4UF+6dKmc++F2iOFQhw8flkHEt99+iyZNmmD8+PG4cOGCvOCfNGkSunbtio0bN2L48OGy+lPhSk9z587Fn3/+iWXLluHzzz+X73vmmWdMr1uyDUdn7H2oyPkfZO6DLr/34Y7ivQ8ZeRn48OC7puXpOm84dX6y2HayDh1Gyrp1sq3y9ETE/HkcukRERERU1XogduzYgR9//BE//fSTzH8QQ5qGDh2Ke+65B15exWcFvpnLly/jr7/+wpo1a9CuXTv53OzZs7F7925s2rQJiYmJskdiypQpplKxJ06cwCeffCJ7QK5fv47vv/8eK1asMPVYiECkf//+Mihp06aNDCputQ0qmv9QrUFAhVRe+jq/98HTVY3HuhbvfVh5dAWSdUpFr3syMtGy3/+J+rJF1jFoNIidM8e0HPLcs3CpVry0KxERERFVcg9EtWrV8Pjjj8sAQvQatGzZEh988AHuuusuvPjiixZvJyAgAB9//LGs6mQkKneIR1paGg4cOFDsIr9z586yi1yM2zd2lYvnjOrUqSMrgOzfryTelrQNAq6dUwIItYsKYRWQ//B/f1ww9T6M6VwLQTf0PsRlxeHLE1/JtrPBgMmB7YD6vYttJ3HVKuSePSvb7k2bIuDhh3k6iYiIiMrZbQ9QFWVb7733XvkQ411F74SlxLwR3bt3lzNYG23dulX2TIghR7GxsTI5u7DQ0FBkZ2cjOTlZ9kCIIMTNza3YOuK9QknbcHSZqblIS8iR7dBaPjKIKE/JmXmmeR/cnFWYYKb3Yfm+N5EDnWyPSs9CZP/FxdbJi4pCwgfLlQWVCuHz58OJdeeJiIiIquYQJkHkPohhRuIhLvhF9SUxE3W/fv3KvDOHDh3CSy+9hL59+6JHjx4yKbpwcCEYl/Py8mQQcOPrgggoRHK1UNI2bodOp1zkViTxNY0Pa4jJ730Qwur4lvsxrfzzArI1ytcY0a4GAj2di3zN8ynn8d2VX2XbW6/HhAYjofOtIQ7ctI7oOYqZNx+GHCXw8X/oIbg2aVwp56Mqn1uqGhzpvIpjNPbsira9zwXhSOfWkfC82i+e20oOIEaMGIFjx46hRo0aGDJkiMx/EMOabsf27dvxwgsvyHyKxYsXmwKBGy/yjcseHh5wd3c3GwSI4EG8bsk2bqfaSHp6epnffztfVwRF4g+zNSqcXDkdb2r7RbiU6zFl5Gqxas8l2XZWOeHBtiHFvt47u2fDWPzxf5k6qNs+VWyd7O3bkfXnn7KtCgmB26OPVMq5qOrnlqoGRzqvYl4g8RDEz6Szc5nvUdkERzq3joTn1X7x3Jb8+Vj6u6xMv91FIvK0adPQoUMHWMNXX32FV199VSY/v/nmm6YeAlFyNS6/PKeRWBbzT/j4+MihSaIMrAgICvcyiHVEHoQl2ygr8QHfzvtv9w6ft7c31FYYspN0VbmLL9RuGgYPn+I9Otay5vAFpOcqd+oGt66GRjVCirx+IHY//kw9IduhWi0e7jgVbkERxeZ8iHv3PdNy2KyZ8LlhiJqtsva5parBkc6r+OPTs2dP2fbz87P7i2pHOreOhOfVfvHc3lppfmeXKYB4/fXXYS2iAtOCBQswZswYOX9D4S5vUVnpn3/+KbL+3r17ZS+FOEhRuclYd9yYKH3x4kWZG2EMbkraxu2orD8Y4usaH7dDp9Ej/opy594v1APe/mXvkSlJjkaHlX9dlm1xip/sWb/I/os/wu/tKaio9LTOG57txwOqoscYt3QpdAkJsu3dqxf8+va1q2ES1jq3VLU4ynkVxxcZGQlH4ijn1tHwvNovnlvrsDiAEPMziPkWgoKC0Lhx41tetJ08edKibYqL/ddeew133323nKshIf/CUBDDk0RQIYZHiSFN4v/ff/9dThgnSrAKopdBTAg3a9YsuR0xJGnOnDno2LEjWrduLdcpaRuOLD4qHXqtMl45oq5fuX6t9QeikJCh5KUMaB6BeiHeRV7/7eLPOJalTBpXPy8P9/V+r1jwkH3sGFK+VuZ8cPL0RPisogEnEREREVWhAEJcoBuH7FirB0JUXNJoNNi2bZt8FCYu9sUM08uXL5ezSIv5HETOhWgXLssqei/Evj399NNyuVu3bjKgMGrQoEGJ23BUMedTTW0xA3V50ej0+Oj3C6blJ3vWK/K6Tq/D+3tfMy0/41EP6nq9iqxj0OsRO3+B6KqQyyGTJ3POB6IqRvQIX7qk5DnVrl3b7ocwERE5KosDCHFBbyTu+g4YMKBYdaOsrCysX7/e4i8u5pIQj1sRAYF43IzIZVi4cKF8lHUbjir2QqEAohx7IL4/HI3olGzZ7tkoBM2qFf1av5xYi3MaZV9a5Oahx9CCHAej1I0bkXPsmGy7NaiPwNGc84GoKgYQ+/btk+2aNWsygCAicvQAIikpSVabEESpVXFnX8zBcOPQJTET9COPPGL9PSWrEjkHsfk9EK7uagRGWD6DeGno9AZ8+Pt50/LTveoXeV2j12D5oYKAYXJYVziFNCi6jdRUxL29xLQcNnMWnFxcymV/iYiIiMhKAcQff/yBGTNmyN4HcfH5wAMPFFtHPC8mhqOqLz0pB1lpeab5H5xU5ZNLsPV4LC7EZ8p2pzqBaFcrsMjrPx5YhisGJTDtkKtF5z5vFttG/HtLocuf9M93wD3w6typXPaViIiIiKwYQIj5HqpXry67qMeNG4elS5fKMn1GIrAQw4kaNmxo6SapEsVdKpg3IaxO+QxfEgHlBzvP3bT3IU+bixUnvgDyY5dnGoyAk1dQkXVyTp5E8tdfmxKnQ6dPL5d9JSIiIqJyKONqLI36xRdfyDKo9j5JkD2Lu5xmaofWKp/5LP48l4Dj15Sv07KGH+6qH1zk9W/+eAWxTsq8EF01TmjdrSD53TTMasFCMbBaLgc/8Thc7GTOByIiIiJbVaYIQJRJPXXqFM6cOSN7JIwXe2JCNzFD9a0SmqlqiLtc0AMRWsu3XL5G4cpLT3SvV6TkalZuGj6+vAXIL9Iyue0zgLpoXkPapk3IPnRItl1r10bguHHlsp9EREREVM4BxGeffSZnjBaMORHGtpi4jao2g96A+PweCC8/V3j5u1n9axy7mip7IITaQZ7o26xoz8GaHdOQlB889DV4okmb8UVe12Vk4PqiRablsJkzobqh6hcRERERVbwyFelevXo1Jk6ciKNHj8pKTGJyth9++AH16tVD7969rb+XZFWp8dnIy1GGDoXWLqfehz8KKi9N7FYX6kJJ2mnp1/DZ9b9kW2Uw4Kk7ZinTUxeS8P4H0MXnzzjdpze8u95VLvtJRNYj5n2488475YNzQBAR2a8yBRCxsbEYPnw43Nzc5KzUYthSo0aNZJWmDRs2WH8vyaquXyqc/2D9AOJKYha2HIuR7WBvV9zftkaR17/YPhVp+QHFvc7BqNtwUJHXc8+eRdKXX8q2k5sbwma8ZPV9JCLrE0GDmP+Bc0AQEdm3MgUQotqSTqfcwRZ/KM6dUyrtiB6I6Oho6+4h2VwC9f/tvgC9MqoNj9xRG+4uatNryQmn8WWKMiGcs8GAJ7oXzEBtSpx+7TUg//sr6LGJcK1R3er7SEREREQVGECICkwff/wxsrOz0bRpU/z2228ymfrgwYPw8iqfCcnIeuLLMYE6MSMX6w9EybanqxpjOtcu8vqqHc8jS6V8293vUQs1Iu8o8nrGzp3I+nuvbLvUqIGg8UVzI4io6hJ/B65cuSIfxgIbRERkf8oUQEydOhW7d++WuRADBw5EQkKCrMz04osvYtiwYdbfS7IavU6P+CtKAOEb7A53b+vO6Pz535eRq1UuHB7sWBN+ngXbT7q6H2uzLsq2q8GAib0XF3mvIS8PcW++ZVoOnTYNKnd3q+4fEZUfETT89ddf8sEAgojIfpWpCpOYLG779u3IysqSPQ7r16/Hpk2bEBERgf79+1t/L8lqkmOzoNXoy6X3IStPiy/+viTbzion/O+uOkVeX7XrRWTn9z484NsEYcFNiu7b2rXIu3xZtj3bt4dP37utun9EREREdPvKPBOcu7u7fAjBwcF49NFHrbA7ZMsJ1Ov2RyElSyPb97Wqhur+HqbXEs9vx9ea6yLLUvY+jO9VUKJV0KWkIH75hwX7NmNGkXkjiIiIiMjGAohevXpZfEG3Y8eO29knqrD8B+slUGt1enyyWxmeJDzWvW6R11f9OcfU+zA8qC1C/YvmRsR/sBz61FTZ9hs8GB7Nm1lt34iIiIioEgKIoUOH8o6wHYiPKgggQmpaL4D49cR1RKdky3aPRiFoHF7Qu5FwahO+1qfI3gc3A/C/Hm8UeW/uhYty+JLg5OGBkKlTrLZfRERERFRJAcTkyZOt/KWpoun1BiRGZ8i2b4gHXD3KPIKtmJV/FvQ+jC+c+2AwYNWehchR5/c+hHZEqE+1Iu+Ne+stQKuVbVF1ySUszGr7RURERETWVaYryO+///6Wrw8ZMqSs+0PlKOV6FrR5SgJ1SKS31bZ77GoqDlxOlu0God64q36w6bWE4xuwzkkELfm9D91eLfLezD17kLFrl2w7h4Uh6H/MpSEiIiKyuwBCzDhtjpiZOjw8nAFEFZVQaPhScKT1hi999ldB78Ojd9YpGOqm12PlvjeR45zf+xB+B0K8w03rGnQ6XH/jTdNy6NQpUHl6Wm2/iKjiZ6Lu1KmTqU1ERPapTAHEqVOniiyLWakvXbqEuXPnYuTIkdbaN7Ky+Chl+JIQYqUAIi4tB5v+vSbb/p4uGNqmYNbo+KOrsV6Vbep9GN9tYZH3pmz4Frlnzsi2e/Pm8B00yCr7RESVQwQNdesWLaBARET2xyq3iNRqNerVq4eXXnoJ7733njU2SeXeA2GdIUxf7bsCjc5gmjjOw1WtvKDXYeWBt5GbfxdyRLXuCPYMMb1Pl5GB+KVLTcthL82AE+9YEhEREVV5zta++xQXF2fNTZKVGAwGUwUmT19XePm53fY2czQ6rNmnTPymVjlhTOdaptcSD63CBmcxJ4QK7gYxqdycIu9N/Ohj6BITZdunf394tmt32/tDRJVLzD4dExMj22JiUQ5jIiKyT1ZLos7IyJAzUrds2dIa+0VWlpGci9xMrVXzHzYdvYaEjDzZvqd5OKoZJ47TabD64LvIcc+fdbpGjyK9D3lXo5H0+eey7eTigtAXnrfK/hBR5QcQf/zxh2wPHz6cAQQRkZ2yWhK1s7Mz2rRpI/MgqOqJv1Jo/gcrDF8SPRqf/XWpSPK0UdqhVVjrKqo9qeQ32LguLxfdl/fegyFPCTwCHxkH1xo1bnt/iIiIiMiGkqjJ8Sow/XMxCSdi0mS7VQ0/tK3pr7yg02DdwWXI8FB6HwZHdEW4V0HlpZwTJ5C2aZNsq/39EfTYY7e9L0RERERUcVhnzwErMFkjgXplodKt/7uroHRr1pGv8KWr1vTN9b/ORXur4ha/XbAfTz4BtY/1yskSERERURXtgfj3338xb948nD17FhqNSJQt6uTJk9bYNyqHHggXdzX8gvNzFcroWko2tp24LtuhPm64p3mE8oJOi43/vINkT6USU7/QjqjpW9P0voy//pITx8n9qF4d/qNG3dZ+EBEREZGNBBCzZs2Sk8aJsq3u7u7W3yuyqpwMjUyiFoJreMNJlT/RWxmt2XcFeqVyKx7qVBOu+RPF5R1dg89cNaZvqwmdXzS9x6DXI+7tgt6HkOeeg8rV9bb2g4iIiIhsJIC4fPkyNmzYgAYNGlh/j8jqjOVbrTGBXK5Wh6/3X5FtZ5WTnPtB0mmxad/biPNUvqV6BLVCw4CGpvelbd6C3BNKz5R706bwHTjgtvaDiIiIiGwogGjevDmio6MZQNhgAHG7CdS//BdrKt3ar1k4wnyVHijtsfX41EX0crjI5Ymdppveo8/LQ/y775qWRdlWThpHZH/EvA/t8ud04RwQRET2q0wBxPz58/Hkk0/i6NGjiIyMLPaHYsiQIdbaP7KChEIJ1CE1by+B+su/lYnjhDFd8ieO02nx699vIcpTCR46+TdCy5CC+UBS1q6FJjpatr3uuEM+iMj+iL8FDRsW9DwSEZF9KlMAsWXLFjmM6cMPPyz2mqjGwwCiaiZQq5ydEBDhVebtnLiWhgOXk2W7YZg3OtUJlG39sQ34P+dsAEpOw8SO00zv0aWnI+HDFaZlThpHRERE5IABxFdffYXnnnsO48aNg4fH7VX0ofKlydUh+XqWbAdV84ZaXfbKvV/uLZg4bkznWkrpVr0Ov//9Js55KsFDS5866Bje0bRe4sqV0KWkyLbvoEEy/4GI7JOYYDIuLk62Q0NDTeWdiYjIvpTpalKv12PgwIEMHmxAYnQGYLj9+R9SszX4/vA12fZ2c8bQtsrs0YYTP+L/VJmm9SZ2mGq6aNAmJyP58y+UF1xcEPLsM7dxJERU1el0Ovz222/yIdpERGSfyhRADB48GGvWrLH+3pDVxV+xTgWmbw9eRbZGuSAY1ra6DCJgMGD/32/hmLubfL6hZzV0r9Hd9J6klZ9Bn6X0fvjfPwyuNZSgg4iIiIgcbAhTeno6Nm/ejJ9++kkmUTs7F93MF1/k33WmKpP/cDsVmPR6A77aW5A8PbpzfvL0+R1YqY0HXJVhbP9rO7mg9yExEUmrV8u2k4sLgidNup3DICIiIiJbDiBEpY1BgwZZf2/I6hKu5ldgcgKCqpctgXrP+URcSFCGKXWuG4iGYUogcnr3G/jLUwkeqrn6o1+d/qb3JH66EgZj78OIEXCJyJ+tmoiIiIgcL4B4/fXXrb8nZHWi5yDpmnLh7xfiAVd359tOnh7bpbbSuLIXq7IuAN5KUDK21SQ4q5Tta+PjkZw/xM3J1RVBjz12u4dCRERERFVEma4o9+/ff8vXO3ToUNb9IStKi8+GVqOX7eDqZUugjkvPwY6TSlWVEB833N00TLav/fEmfvbylG0/tTuGNhhmek/iJ5/AkJMj2wEPjoJLWOhtHwsRERER2XAAMWbMGDnWXZTsMxLL4iGGN/3333/W3Ee63eFLooRrjbIFEBsPRUOrV87z8HY14CLKwMYew5dJh6Dz85XPP9h0DDxdlGBCcz0OyWu/lm0nd3cETZzI80dERETk6AHEjh07iiyLcn0XL17Ee++9hxdeeMFa+0bWKOGaT8wBUVoiQFy/P8q0PKJ9pPw/dfcifOujbM/NSY0Hm44u+JoffwxDXp5sBzz8EJyDg2/rGIjIdogbSK1btza1iYjIPpUpgKhevXqx52rWrAlvb2/MnTsXmzZtssa+kTUDiBqlT6DefynZlDzdpW4Qagd7AYnnsS56F7ID/OTzQ+oPQaC7MiO1JiYGKevXy7aTpyeCxo/nOSRyICJoaNKkSWXvBhERlTOr3iIKCAjA5csF5T6pagQQzm5q+AaVfsbwr/dfMbVHdVR6H3L+fBurfZXeBxWcMK5FQZCQsOIjGDQa2Q4cPRrOgUpgQURERET2w2pJ1BkZGfj888/RoEEDa+wX3aa8bC3SEpRE5qBqXnBSKfMzlGbm6S3HYmTbz8MF/ZqFA2nX8OP5n5AUpPQ+9I3shUgfJbDIuxqNlG+/lW2VlxcCH32E55DIwYhhj0lJSbIdGBhomheGiIjsi9WSqI1DmxYtWmStfaPbkJhfvrWsCdQ/Hr2GnPwKTkPbVIe7ixq63z7A575KsrTwSKuCBOnEj1YAWq1sB44bC+eAAJ4/Igcj8uF+/fVX2R4+fHixSUaJiMg+WCWJWnBxcUFoKMt1VsX8h7KUcP3mQEHy9MgOkUBOKn47vgZXgpRtdQppi2ZBzWRbc+0aUr7/QbZV3t4IHDfOCkdARERERHaTAyF6GqKjo2W+g2iLx2effYYDBw5Yfw+pTBILl3AtZQBxLi4D/15Nle0W1f3QJMIXOLgKq7xcTOv8r9Wkgq/16UogP/chYMxoqP2UIU5EREREZH/KFEBs3rwZ//vf/3Dy5EnTcyKgeOSRR7B9+3Zr7h9ZoQcisJpXqYcvGQ1pUx3Q5uHfAyvwr7ubfK6hTy10qdbFNOt0yoYNsu3k4YHAsWN5zoiIiIjsWJkCiBUrVmDGjBkYX6hM59KlSzF9+nQsW7bMmvtHZSByU4wBhHeAG9wL9RxY8t4fj0TLtsh/HNQyAvhvA1Y755rWGd1ivCk5MnHVKhhyldcCRo1i7gMRERGRnStTAHHlyhV079692PM9e/bEpUuXrLFfdBvSk3KQl6MrUwK1GLp0KTFLtu+oF4RQHzfE//0efvVSkqf9nb1wT517ZFubnFww67SrKysvERERETmAMgUQERERZku5Hj58GCEhIdbYL7oNidGZZc5/+OFIwfClwa2qA+d24JvcGGjzexzubzwK7s7usp385ZcwZCnBhv8DD8CFSfREREREdq9MVZgefPBBLFiwQPZEtGrVSj537NgxOQ/Ek08+ae19pFJKjikIIAIjLM9/0OkN2PSvEkC4qlXo1zwcmvVPYL2PEoSoocLIRiOVddPTkfTVauWNzs4ImsBZp4kcnZiJunnz5qY2ERHZpzIFEOPGjUNeXh6++OILmQ8hiBKuU6ZMwejRo629j1RKybFlCyD2XkhEfLqSz9CzcQj8ko/jp7iDSAwNls/1qtkLEd4RytdYsxb6tDTZ9ht8H1yqVeN5InJwImho0aJFZe8GERGVszLP8jNx4kT5SE5OlnNAeHuXfq4BKh8p15VhRYJfqIfF79tUqPrSfWL40p6ZWOvrY3ruoSYPy//1WVlIWrVKeVKlQvDEggnliIiIiMi+3XYfc0BAAIOHKkRUUUqOzTJVYHJ1tyxG1Or02Ho8VrY9XNToVV2HY+e2FJRu9a+PdmHtZDvl243QJSfLtu8998C1du1yOhoisrXfP6mpqfIh2kREZJ84SNXOZKdrkJulle2AcKVykiX2XUxCcpYyGVyvJqHw+PdLrPEpeP/DTcfI0q0GrRZJn31mej7oMfY+EJFCp9Nhy5Yt8iHaRERknxhA2JmU6wX5D/7hluc/bDkWY2oPbBqMhEOr8Et+6VY/Fx8MqDNAttN+2QrNNWWok1fXrnBv1MiKe09EREREVR0DCDtjHL4kBIR5Wlx9yTh8yd1FhV74B9+oswpKtzYaLku3ygnqPv3U9L6gCROsvv9EREREVLUxgLDnAMLCIUz/XExCQkaebPdoGAr14U+x3kdJnlbByVS6NfOvPcg9eVK23Vu0gGfHDuVwBERERERkd1WYGjduLMfDmyMqMoWHh2Pw4MFyToibrUcVEUBYNoTp5/8Khi+NqJmGbf8cQUKh0q3VvJUSrYmffmJaL2j8eJ5bIiIiIgdUpgDi5Zdfxttvvy0nlGvfvr1pFurVq1fL5/z8/OQcEa6urrLUK1V8DoSLuxqefq4lrq/XG/Dzf8rwJVdnFbqmfI9HzZRuzf7vOLL+3qtsu2ZN+Nzdp5yOgIiIiIjsLoDYvHmzDCJGjlSGtgh9+vRB3bp1sWHDBqxduxYNGjTAW2+9xQCiAmnzdEhLzDHlP1jS+3PwSrJp8rj+9T1w4fRGHA3zl8v1/eqifZgSICatLJT78L9H4aRWl9NREBEREZHd5UCcPHkSnTt3Lva86I04fvy4bDdt2hQxMQVDY6j8pcRlA4bSDV8qXH1pvM8+fONREBiMavyQDELyoqJk9SVBHRQEvyFDrL3rRGQnM1GLIa7iIdpERGSfyvQbvkaNGti5c2ex58VzIv9BuHLlCgIDA29/D8liybGFS7h6WjR86Zf84UsuaqBOzLfY5K0EHh5qNwysO1C25bwPer1sB44ZDZW7O88KERUjgoY2bdrIBwMIIiL7VaYhTE888QRmzJiBY8eOyT8Uer0eR48exS+//IL58+fj4sWLeOmll9C3b1/r7zHdVMr10lVgOhyVgphUZcjTuJqJ2JpxFVk+QXJ5QN174e3qDV1KClI2fiefc/L0RMCoUTwDRERERA6sTAHEoEGD4O3tjZUrV2LJkiVwdnZGo0aNsGLFCnTt2hX79++X6zz99NPW32OyqAKTvwVzQPxcaPjSgy678FJ+6VZheKPh8v+UDRtgyFGCDP9hw6D2V/IjiIhuJOaKycpSfg95elqWh0VERA4SQERFRaFnz57yYU6HDh3kgypWalx+AOEE+Id4lviH/tcT12XbR5WLrIRtOJmfPN0ssAmaBTWDQatF0po1pvcEjlYqMhERmaPT6fDjjz/K9vDhw+XNJSIisj9lyoG4++678fDDD+Pbb7813W2iypeakC3/9w5wg9rl1qf29PV0XElSzt3TYcewwbPgD/2IxsowpfTffoP2mtJL4dW9G1xr1y7HvSciIiIiuw0gvvzyS9SrV0+Wab3zzjsxffp0/P3339bfO7JYTqYGuZla2fYrofdB2HZc6X0Q+mA7fvZS3uOt9kD/2v1lO/nLr0zrBI4ew7NBRERERGULIMTwJJEs/eeff8ogIicnB48//jh69eqFpUuX8mOtBKnxSu+D4BfqUeL6xuFL9ZyisV97Adn5JRfvrT8Yni6eyDl1Cln798vnXOvUgdedd5TbvhMRERGR7bitQt0uLi5yONPcuXPx7LPPIjU1FR999JH19o4slhpfMJTML+TWAcS1lGwci06V7cf99uF7n4I5Ix5o+ID8P+mrgt6HgDGj4cSa7kRERERU1iRqQeQ+bNu2DZs2bcLevXtRvXp1jB8/HkOHDuUHWwnSCvdAlBBAbMvvfRCzzjVQ/4Xjbm5yqYl/AzQKbARtcjLSNv0kn1N5e8N/8OBy3HMiIiIisvsAYsqUKdi1a5cs0de/f3+sWrVKzkItiOFMVPFSxSzU+UrKgfj1hDJ5XBunc9jhKt7nIpcHN7xf/p/yzQYYcnNl2//++6HysmxWayIiIiKyf2UawpSQkIA5c+bgr7/+wmuvvSaDh3PnzmHhwoXo1q1bmXdGDH8aM6Zosu7JkycxevRotG7dWuZYfPHFF0VeF5PYibwLMf+EWGfixImyzGxptmF3ORC36IFIzdJg34Uk2R7l9Q9+yp952tlJhYF1BsrSrcnG0q1OTgh4+KHy3nUishPiplL9+vXlg3NAEBHZrzJXYRoyZAjUarWs+f3QQw/JiePWrFmDzp07l2lHVq9ejXfffbfIc8nJyXj00UdRs2ZNWTL2qaeewuLFi2XbaPny5fLrLliwAF9//bUMKCZMmIC8vDyLt2FPAYSnrytc3NQ3XW/n6Tho9QaooEeY+34kOCvrdqt2J/zd/ZG+fQe0sUoPhXePHnCtWbOCjoCIbJ34m2CcB0i0iYjIPpVpCNPly5flxfp3332HlJQUeadp2LBhshJTZGRkqbZ1/fp12Zuxb98+1L5hnoH169fLRG1R8UlMSCRKx4qv/fHHH+P++++XQYKYDfuFF15Ajx495Hveeecd2Rvx66+/4t577y1xG/YgL0eLrLQ8iyowbT+p5D90cDqN3W4aAO5yuX+9QfL/pK++NK0bOGZ0Oe41EREREdl1D4SYYfTnn3/GI488IvMeRC+EGBK0aNEieadJ3OUvbfAgHD9+XF7gi56MVq1aFXntwIED6NixY5HZTEUPx6VLl+QwqlOnTiEzMxNdunQxve7r64umTZtif34J0pK2YQ/S8ieQK2n4klanx+6zyjEPcduL7flzP7g7OaN7je7IOXEC2QcOyudc69WDZ6HPlYioJGKGe5EHJx6iTUREDt4D0b17d6Snp8uLbzFcSJRv9fPzk6/NmDGjzDsgchLEw5zY2Fg0bNiwyHOhoaHy/5iYGPm6EBERUWwd42slbSM4OLjM+y6CqoomvqbxYZQcm2lq+wS733S/Dl5ORmq2BmroEOp1CElqJf/hrup3wU3lhtjV+bkPInn64YfkcDCq3HNLts+RzqtWq8XGjRtlW/TwFr5xY48c6dw6Ep5X+8Vzaz0W/3YXwUNQUBCqVasGf39/eHiUPFnZ7RJ3sVxdXYs855ZfcjQ3NxfZ2cqdd3PriDkpLNlGWYmLa/GZVDTxdcUxiWFjqvy5GeKiU0yvu3or58qcbf9Fy//vUB3Hbo+Cu4PdInohNSYG6Zs3y2UnLy849ehRKcfnyMydW7J9jnReRQAhHoL4/WHvAYQjnVtHwvNqv3huS/58LP1dZvFvd1FxacuWLTL5eO3atfDy8kLv3r0xYMCAcqu24e7ubkqGNjJe9Ht6esrXBbGOsW1cxxjglLSNshIfsI+PDyojehZDA7y9vU1JirlpMabXwyIDb7pff19Mk//fo/obyz2Vz8dD5Yq7692N3PXfw5Bfgtf3vvvgFxZWAUdDJZ1bsn2OdF5F8GAMGsTvIXsPIBzp3DoSnlf7xXN7a6W5EWLxb3fxC3LEiBHycf78eWzYsEFOIidyF0QAIeaCECVUa9WqBWsJDw9HXFxckeeMy2FhYaY7XeI5UWWp8DqNGjWyaBu3o7L+YIiva3zcmAMREOZldr/i0nJwPCYNztAixPcoUtW+8vkekT3g5eqFuPXrTesGjhrFP4aV5MZzS/bBUc6ruJg23lByhON1pHPraHhe7RfPrXWUqc9VVDJ68cUX8fvvv+ODDz6QPRHff/897rnnHllC1VpEKcCDBw8WGV8qZr2uU6eOHE7VuHFjGdiICk5GaWlpOHHihHyvJduwp0nk3Lyc4e6lTAp3o11n4uX/nVUn8btnwWnvV3cAsg8fRu7Zs3LZo21buDcqmjNCRERERGSkut0oTgQP77//Pv744w9MmzZNlmW1FpGEl5GRgZkzZ8qJ6kRynujpmDRpknxd5DaICeLEvA47duyQVZnELNmi16Fv374WbcPWaTU6ZKTkljgD9a7TSq9Lb/U+/JY/dMtL5SYTqJO//tq0XsCokeW+z0RERERku6w2QDUwMFCWchUPaxE9BJ988gleffVVDB06FCEhIZg+fbpsGz3zzDNyKNOsWbNkMpvocfj0009laVhLt2HL0hJyAMOtS7hq8su3OkGPAO9/ka5WAoiekT2hTstC+i9b5bLazw8+/fpV3M4TERERkc2pUhlub7zxRrHnWrZsiXXr1t2yF0T0fIjHzZS0DVuWlj8D9a0CiEOXk5Geo0VrpwvY511Qfal/vXuR+t33MOQnmfsNHQpVfoUqIqLSEvkPYniosU1ERPapSgUQVHppiUrlJME3uKASVWE7Tyv5D32c92F1fvUlH5UbOod3wtV1r5vW8x8xgqeAiMpM3NARcwUREZF9Y+FqG5eeWNAD4RPkccv8h1Cvw8jIL9HVo0Y3aA8cRt7ly3LZs3NnuNVV7hwSEREREd0MAwgbl164ByKoeA9ETGo2TsWmo55TNE54Fazbu+5AJK8rKN3K5GkiskYZV+NkcqJNRET2iQGEjUtPUoICMdzYK6B4/sLv+cOX7lb9g51e+ZPrOTmjk3czZOzYIZfVgYHw6dWrQvebiOyPKJf9zTffyEfh0tlERGRfGEDYSQ6El78b1Orip1NUXxLqeB1AUv5ER3eEtYfm150waDRy2W/QIDi5ulbofhMRERGRbWIAYcM0uTrkZChBgI+Z4Ut6vQF7zicgHIm44p1ser53/UFI+f5707Lf0CEVtMdEREREZOsYQNhN/kPxBOqTsWlIztKgj/oAduRPHqeGE+7Q1ELO0X/lslvjxnBv3LgC95qIiIiIbBkDCBuWVqQCU/EeiD3nEuX/TdwPINpFqdjbPrAp9JuV3AfBb8jgCtlXIiIiIrIPDCDspAfCXADx1/kEeCAHiT7XTM/1rnsfUjdtUhbUavjde2/F7CwRERER2QUGEHYaQORp9dh3IQldVCfwu2dBdaauScHQxsbKtnfXrnAODq7APSYiIiIiW8eZqO2ghKvgE1g0gDgSlYJsjQ5t3ffjIzelwlILr0iof/vbtI7vwIEVuLdEZO+cnJwQGRlpahMRkX1iAGHDMpJzTW3vG+aA+OucKN9qgLP3aQBKANG9Vj+kL1Emj3Nyc4N3z54VvMdEZM/UajXuuuuuyt4NIiIqZxzCZMMyUpQeCHdvFzi7KHM8GInyrfWdovGfV8FkTl0TQ6FLSpJt7x49oPb2quA9JiIiIiJbxwDCRhn0BmSl5JntfcjR6OQQpm7qQ/jHXXktWO0J/93HTev43nNPBe8xEREREdkDBhA2KjtdIyeKE7z9iwYQx6JTodEZUM3zELJVyinuFtIR6du3y7aTpye8u3erhL0mInum1Wqxdu1a+RBtIiKyTwwgbFRGSkH+g1dA0QTqg5eT4Y0sxHmLPAhF75Q60KemyrZPz55QeRSfeI6IiIiIqCQMIGxUZqEAwttfSZI2OnQ5GXeq/sOe/PKtYvbpWgeiTa/7DhxQgXtKRERERPaEAYSNyixUgcnLv6AHwmAw4NCVZLRy24/LLi7yuXaedZDz+5+yrfLygherpBARERFRGTGAsFGZqeZLuF5JykJCRi6cvM+anhuQ1RT6tDRl3R49oHIt2mNBRERERGQpBhA2KjNVqcAkePq5Fsl/aOAUjaOeSoK10OxEtqnt07dvBe4lEREREdkbBhA2XIXJyNOnIIA4fCUFndVHsN9dGdYUofKC6s8Dsu3k7g7vrpzkiYiIiIjKjgGEjcrJUAIIJyfAzUvJdRD+jU5FiNdR5Kmc5PKQzEbQJSbKtnfXrlB5elbSHhORvXNyckJERIR8iDYREdkn58reASqb7Iw80yzUqvxgQaPT42xMEjqExorUavlch4sF5Vp9+t7Nj5uIyo1arUaPHj34CRMR2Tn2QNionPwhTB6Fhi+dvZ6BZrozOOih9EioDQb4/n1CedHFRSZQExERERHdDgYQNkibp4NWo5dtD++C4Uv/RaeipcsRnMuvsnR3UhD0cfGy7XVHF6h9fCppj4mIiIjIXjCAsEE5GVpTu3APxL/RKfDwPmVa7nUlyNT2ZfUlIipnWq0W69evlw/RJiIi+8QAwgblZBYKIAr1QBy/mow4z2RlwWBAzX+V5Gmo1fDu1avC95OIHI9Op5MPIiKyXwwgbL0HwtfVlEDtFHsUh/LzH+omqaC6Fifbnu3bwzkgoJL2loiIiIjsCQMIG5RrpgdCJFA3Uh/GdWelsFb/qIJ8ByZPExEREZG1MICw4TkgCudAiARqL6/TpudbXSoY2uTdvVsF7yERERER2SvOA2HrPRA+SqBwLDoVOR4i58EF7rkG+J9NkM+71KgB1zp1Km1fiYiIiMi+MICwkypMUVEXkZA/Z1zrSwY4aZUyr97dunFGWCIiIiKyGgYQNl+FyVUmUKsSDuByXaU3osclEVTkyDaHLxFRRXFyckJISIipTURE9okBhA3Ky1ICCPH32c3TGaeupyPQ9T/lRYMBTc4rvQ9Obm7w7NixMneViByIWq1Gnz59Kns3iIionDGJ2gZpcpUa6y7uznBSOeFkTBqcPa7K52rGAx6pebItggeVR/64JiIiIiIiK2AAYYM0uUoPg6u7Wv5/8loqUt1TZbvNeYNpPZH/QERERERkTRzCZMs9EG5KABEbfQln3ZTxxu2KBBBdK2kPicgRabVa/Pjjj7J93333wTl/XhoiIrIv7IGwMQaDAdo8vSmAEMv6+L/lBHKeOQY0vKoEEK61a8O1Vq1K3lsicjS5ubnyQURE9osBhI3RiuFL+Z0MIgciPj0X7molgbrlRQNU+a+x+hIRERERlQcGEDY6fMmYA3EyNh1O7tfkcpsLBcOXvJj/QERERETlgAGEjcnLLZgDwkUEEDFpSHZLl+VbW1wyFJRvbd++EveSiIiIiOwVAwgbo8kp1APh5ozLV6/ikhsQkQQEpynPi+BB5eZWeTtJRERERHaLAYQND2ESSdRZMfsR5+yMlvm9D4LXHV0qae+IiIiIyN6xxp6N0WmVCkyCykUFXd5h2TYOXxK8ujCAIKKK5+TkhMDAQFObiIjsEwMIGw4gsrQ6uLhehUpvQLPLSgChDgiAW+PGlbiHROSo1Go1+vXrV9m7QURE5YxDmGyMTlPQ05CSq0WOexLqxgBe+WXXvbp0hpOKp5WIiIiIygevNG24ByIlOwexrpoi+Q+eHL5EREREROWIAYQNBxDZaTG45OqMFpcKnvPqckcl7RkROTqtVosffvhBPkSbiIjsE3MgbDiASM86BycfoGG0suwSGQnXGtUrb+eIyOFlZWU5/GdARGTv2ANhY3SaggAiU3sV9a8BLvmVXT07dqi8HSMiIiIih8AAwsbotAX5DjmqRDSJKpT/0J4BBBERERGVLwYQtpwD4ZKOJlcKBRAd2lfSXhERERGRo2AAYcNDmNJUOWgUrQQQzuHhcKnO/AciIiIiKl8MIGy4B8I5Qwd3jdL2bNeOM78SERERUbljFSYbzoGIjCsok8jhS0RUFfj6+lb2LhARUTljAGFj9IV6IOrEaAolUDP/gYgql7OzMwYOHMjTQERk5ziEycZoC+VA1L6uBBA6L1e41qtXiXtFRERERI6CAYQN90D4ZSlDmFT1azH/gYiIiIgqBIcw2XAStUqvBBC+rTh8iYgqn1arxdatW2W7X79+ckgTERHZH/52t+EkamMA4dOiTSXuERFRgbS0NH4cRER2jkOYbHgeCJVeyYFwb9KkEveIiIiIiBwJAwgbHsLkZNBB6wy41q5dqftERERERI6DAYSNBhCi98EJQGqYO5w4zpiIiIiIKggDCBvNgXDKz3/Q1gyp5D0iIiIiIkfCAMLG6HX5PRAGnfzfo3HDSt4jIiIiInIkrMJkYwzGIkwGJZAIa9+1UveHiKgwT09PfiBERHaOAYSNMejzhzAZ9NA7AREd+1X2LhERSWLeh8GDB/PTICKycxzCZKNJ1E4GA64HAm4+/pW9S0RERETkQBhA2BitJi+/ZUBisLqS94aIiIiIHA2HMNkYvUZMHucqhzDl+rlX9u4QEZnodDps375dtvv06QO1mjc5iIjsEQMIG6PXG4cw6eHk51vZu0NEZGIwGJCUlGRqExGRfVI50oX30qVL0bVrV7Ru3RoTJ05EVFQUbE7+32Qn6OESHFbZe0NEREREDsZhAojly5djzZo1WLBgAb7++msZUEyYMAF5ecacAhthcDIlUftUr1fZe0NEREREDsYhAggRJKxcuRLPPPMMevTogcaNG+Odd95BbGwsfv31V9hiACHmgQhv0Lqy94aIiIiIHIxDBBCnTp1CZmYmunTpYnrO19cXTZs2xf79+2FLDDD2QOhRrxUnkSMiIiKiiuUQAYToaRAiIiKKPB8aGmp6zWY4KadMr9LD1585EERERERUsRyiClN2drb839XVtcjzbm5uSE1Nva2ShRVN9DwYnNTQO2kr5etT+RHn0/gg++FI51Uco4uLi6nt5JQ/5NJOOdK5dSQ8r/aL59Z6HCKAcHd3N+VCGNtCbm4uPDw8yrRNkYSdnp6Oiuam3QONqgPUwecr5etT+RHfUzk5OfKiS6VyiM5Bh+Bo51XM/1D4xo09c7Rz6yh4Xu0Xz23Jn4+lv8scIoAwDl2Ki4tDzZo1Tc+L5UaNGpVpm+ID9vHxQUUbs2ImEhPiERTcl5M02eGdEVE739vbm+fWjvC82i+eW/vE82q/eG5vrTQ3QhwigBBVl8RF2b59+0wBRFpaGk6cOIHRo0eXebuVNcuqh6eX/Nqc5dX+GM8rz6194Xm1Xzy39onn1X7x3FqHQwQQIvdBBAqLFy9GYGAgqlevjkWLFiE8PBx9+/at7N0jIrKbu3s7d+6U7Z49ezIQJiKyUw4RQAhiDgitVotZs2bJMasdOnTAp59+akr4IyKi2yOG4MXHx5vaRERkn5wdqctq2rRp8kFERERERGXDshFERERERGQxBhBERERERGQxBhBERERERGQxBhBERERERGQxh0miJiKi8sc5TIiI7B8DCCIiss4fFGdnjBgxgp8mEZGd4xAmIiIiIiKyGAMIIiIiIiKyGIcwERGRVeh0OuzevVu2u3btynwIIiI7xQCCiIiswmAwICYmxtQmIiL7xCFMRERERERkMQYQRERERERkMQYQRERERERkMQYQRERERERkMQYQRERERERkMVZhKgONRiMrjBw7dgyVQa/XQ6Vi7GePeG7tkyOd19q1a8v/T548CUfgSOfWkfC82i+e25vLy8uDk5MTLMEAogws/XDLC/9Y2S+eW/vkSOfV29sbjsSRzq0j4Xm1Xzy3t76+tfQa18nAYt1ERERERGQh3johIiIiIiKLMYAgIiIiIiKLMYAgIiIiIiKLMYAgIiIiIiKLMYAgIiIiIiKLMYAgIiIiIiKLMYAgIiIiIiKLMYAgIiIiIiKLMYAgIiIiIiKLMYAgIiIiIiKLMYAgIiIiIiKLMYCwIXq9HkuXLkXXrl3RunVrTJw4EVFRUZW9W1RISkoKXnnlFXTr1g1t27bFgw8+iAMHDphe//vvvzFs2DC0atUK/fv3x+bNm4t8frm5uZg3bx66dOmCNm3a4Pnnn0dSUlKRdUraBpW/ixcvyvOzceNG03MnT57E6NGj5c9mr1698MUXX5T657ekbVD5+P777zFgwAC0aNECAwcOxM8//2x67erVq5g0aZL8eb7rrrvw7rvvQqfTFXn/6tWr0bt3b7Rs2RIPPfQQTpw4UeR1S7ZB1qfVavHee++hZ8+e8uf14YcfxpEjR0yv82fW9nz00UcYM2ZMkecq4jzy+ssMA9mMZcuWGTp16mTYuXOn4eTJk4b//e9/hr59+xpyc3Mre9co36OPPmq49957Dfv37zdcuHDBMG/ePEPLli0N58+fN5w7d87QokULw5IlS2T7k08+MTRt2tSwZ88e0+c3Y8YMQ58+feT7jx49ahgyZIjh4YcfNr1uyTaofOXl5RmGDRtmaNiwoeHbb7+VzyUlJcmfzZdeekmelw0bNsjzJP639OfXkm2Q9X3//ffyZ+irr74yXL582bB8+XJD48aNDYcOHZLnWpyjxx57zHD69GnDtm3bDB07djS89957pvdv3LhR/oz/8MMPhrNnzxqmTZsm10lMTDR9v5S0DSofS5cuNdx5552G3bt3Gy5dumSYOXOmoV27dobr16/zZ9YGiZ9R8bM5evRo03MV9buX11/FMYCwEeIbvU2bNobVq1ebnktNTZV/uDZt2lSp+0YK8QdKXFQeOHDA9JHo9XoZELz77ruG2bNnGx544IEiH9fUqVPlLzMhNjZW/nLctWuX6XURhIhtiosZoaRtUPl7++23DWPHji0SQKxYscJw1113GTQaTZH1xB8pS39+S9oGWZ/4+ezZs6fhjTfeKPK8+HkS50Ocm+bNmxtSUlJMr3399deGtm3bmi4+xPl56623TK+L89e9e3f5fsGSbVD5uO+++wyvv/66aTk9PV3+3G7dupU/szZE/G2cNGmSoXXr1ob+/fsXCSAq4ncvr7/M4xAmG3Hq1ClkZmbKoS1Gvr6+aNq0Kfbv31+p+0aKgIAAfPzxx3IYhJGTk5N8pKWlyaFMhc+f0LlzZxw8eFAE8vJ/43NGderUQVhYmOkcl7QNKl/iPKxbtw5vvPFGkefFeenYsSOcnZ2LnJdLly4hISHBop/fkrZB5TMULTo6GoMGDSry/KeffiqHHIlz0qxZM/j5+RU5JxkZGXLIQ2Jiojw/hc+rOH/t27cvcl5vtQ0qP0FBQdi5c6ccQiaGjImfXVdXVzRu3Jg/szbk+PHjcHFxwY8//iiH7lb0715ef5nHAMJGxMbGyv8jIiKKPB8aGmp6jSqX+KXUvXt3+QfKaOvWrbh8+bIceynOU3h4eLHzl52djeTkZFy/fl0GIW5ubjc9xyVtg8qPCAKnT5+OWbNmFfs5vNl5EWJiYiz6+S1pG1Q+AYSQlZWF8ePHy4uM4cOH47fffuN5tQMzZ86UF54iP0Xc2HnnnXfkWPiaNWvyZ9aGiJyEZcuWITIysthrFfG7l9df5jGAsBHiAlEofHEqiItNkXhLVc+hQ4fw0ksvoW/fvujRowdycnKKnT/jcl5enjzHN75+4zkuaRtUfubOnSsTMW+8W32z82IMBMW5s+Tnt6RtkPWJXgDhxRdfxL333ouVK1fizjvvxJNPPimLFfC82rZz587Bx8cHH3zwgex9EMUnXnjhBdnzw3NrHyriPPL6y7yC/hqq0tzd3U0Xica28Zvbw8OjEveMzNm+fbv8QyWqrixevNj0C+nGi3zjsjiH4ryaCwIKn+OStkHlV6VHdHNv2rTJ7Ovmzp3xj5Onp6dFP78lbYOsT9ydFkTvw9ChQ2W7SZMmsorSZ599VqrzeuM6PK+VS9w5FlXsVq1aJYeUCaIXQgQV4m42f2btQ0WcR15/mcceCBth7H6Li4sr8rxYFmPkqer46quvMHnyZFk6cMWKFaY7GeIcmjt/4heUuEsmulBFGdgbf5EVPsclbYPKx7fffivHu4ueJNELIR7CnDlzMGHCBHnuzJ0XQZw7S35+S9oGWZ/xc23YsGGR5+vXry/HzfO82q6jR49Co9EUyUkTxBh6MayU59Y+VMR55PWXeQwgbIRI+vL29sa+ffuKjMkWd8o6dOhQqftGBdasWYMFCxbIeuNLliwp0i0q7oL9888/RT6uvXv3yl4KlUqFdu3ayVrTxmRq4xhtkRthPMclbYPKh+hF2rJli+yJMD6EZ555Bq+++qo8P+K8Fa7tL86LSIIXiZyW/PyWtA2yPpHc7OXlJS82Cztz5owcJy/OiThHxqFOxnMi3iPOqTgv4vwUPq9i7gHRW1X4vN5qG1Q+jGPaT58+Xezc1q5dmz+zdqIifvfy+usmblKdiaogUftf1A/fvn17kVrGos44VT5RcrVZs2aGp556yhAXF1fkkZaWZjhz5ox8fdGiRbLW9KefflpsDgdRkrVXr16GvXv3muaBKFyyzpJtUMUoXMY1ISHB0KFDB8OLL74o5wIQz4s64mKOAEt/fi3ZBlnfBx98IMs8ipKOheeBED+DOTk5sgzz+PHj5TkzzuEgasIbrVu3TpaEFOfJOA+EqClvnAfCkm2Q9el0OsODDz4oy37+/fffhosXLxreeecdQ5MmTQxHjhzhz6yNEr8fC/9NrKjfvbz+Ko4BhA3RarWy3njnzp1lPeSJEycaoqKiKnu3KN+HH34oLyrNPcQvJuH333+XE82JuvDiD9vmzZuLfH6ZmZlysqP27dvLhwgoxCQ3hZW0Dar4AEIQAd+IESPkeRFzC3z55Zel/vktaRtUPlauXCkDdxGci7kDxEV+4fldxASR4oJC1IoXc7qIi9PCxISO3bp1k4HEQw89ZDhx4kSR1y3ZBlmfmHtj7ty5hh49esggceTIkYZ9+/aZXufPrO0HEBV1Hnn9VZyT+OdmvRNERERERESFcdA0ERERERFZjAEEERERERFZjAEEERERERFZjAEEERERERFZjAEEERERERFZjAEEERERERFZjAEEERERERFZjAEEERERERFZjAEEERERlbukpCT83//9H6ZOnYqoqCh+4kQ2jAEEEdmsjIwMtGrVCnfccQc0Gg0czbJly9CrVy+L1zcYDPjuu++QmJgolzdu3IhGjRrBVmVlZWH16tVwBGfPnsWuXbtgK7RaLR544AH8999/cvmXX36R36tffvkl9uzZg379+uGdd94xrb99+3Y8+eSTlbjHRFQaDCCIyGZt3rwZQUFBSE9Px7Zt2yp7d6q8/fv3Y8aMGcjOzpbLAwYMwJ9//glbtXLlSnz66adwBJMmTcKxY8dgK8R5qV+/Ppo3by6/31566SWMGzcOixYtwsMPP4zFixdjxYoVOHLkiFy/T58+8ud406ZNlb3rRGQBBhBEZLO+/fZbdO3aFZ07d8bXX39d2btT5YkeiMLc3d0REhICezkeqhpEIPDxxx9j/Pjxcvn8+fOyt+i+++4zrXPPPfdg1qxZ8Pf3Nz33v//9D++++y50Ol2l7DcRWY4BBBHZJHFRcvToUdx5553o27cv9u3bh4sXL8rXxowZg+eee67Y3XcxXOfy5ctyWdzpFBcxLVq0wPDhw/HFF1/ccjiPuFj9/PPP5dCLli1bYuDAgfjpp5/ka1evXpXvFftgdONz4s7/9OnTsXDhQrRv3x4dO3bE0qVL5XE89NBDcpuDBg2Sx2Qk3i+GGRVm7jmjM2fOyDvVHTp0kHd+e/fuLe/SC2I/xo4dK9viebGNwkOYxP6Jz6Gw6OhoNG7cWA45EQ4dOiTvHot97dGjB+bNmyeHkd3Kjz/+KC8cxXvE1xWfoVFKSorcRvfu3eXro0aNKvIZijvXM2fOlOdYnKchQ4bg119/NQ3fev/99+U+imMQn/eNxPF169YN69evx1133YU2bdrgqaeewvXr103r5OXlybviIhAVr48YMaJIr4zYxt133y3PW7t27W46zCYzMxMLFiwwfZ3Ro0ebhu+YGyp243P//vuv/D4Q7xXnb/Lkybh27Zp8TQz9Eccpjld8b1vy2YnP55FHHpHvEUP8xHZfeeUVxMTEyO8RMfRPHFfhYVHW+izWrVuH8PBwNGjQQC4HBwfDyckJe/fuNa0jlsWx1K5d2/Sc+OxE8GE8x0RUdTGAICKbtGHDBnh6esoLRHFR4+LiYuqFGDZsGHbu3Fnk4lZcyLZt2xa1atWSr7344otyjLZ4XqwvhlTcyieffCLHbE+YMEEGDuKCTQQEhS+KSrJlyxao1Wp5ISYu7j744AM8/vjj8k7tN998Azc3N3lRWBbiYlvcwRV3dMXnIPaxf//+ePPNN3Hy5El5QSguKgXxtcTwpcLEZyAuYq9cuWJ6TgRZ4kJQ9PCcOnUKjz76qLy4FJ+Z+LyOHz8uv+bNegLE8YrPefDgwfI9InlWvE8cv7jLLN574MABedEqnmvYsKH8LMR+CO+99x5Onz4t72aLbYlzPWXKFBksiPeKh9g/cZEbERFx08RdEbSIO9vif3EBLc6hGKMviKE1f/31l9wvkR8igkpxTgpfWIvPJC4uDt9//738+uaIgPWPP/7A66+/LteLjIyU+5eamlriuROfhTHwE5/TqlWrZPDw8ssvm77XxXGK7YlzaMlnJ4jXRVAt8kTE3X5xYS++58UxivfUq1dPBo7G82etz0LkM4jAxkjsu/jeEYGH+H4U+RzmkqjFz7AIFnfs2FHiZ0ZElcxARGRjNBqN4Y477jBMnTrV9NykSZMMHTt2NOTk5BgyMzMNrVu3Nnz33XfytdzcXEOHDh0M69evl8sPP/ywYcqUKUW2+dprrxkaNmxo9uvp9XrDnXfeaXj77beLPP/xxx8bdu/ebYiKipLv3bt3r+m1G5978cUX5TZ0Op1cFvsoXl+yZInpPatXrzY0a9bMtCxe//bbb4t8zcLPLV261NCzZ0/ZTkxMNHz00UeGjIwM07risxDrGz8HsS9iWeybILZjPGZxjL179zYsW7bM9P4BAwaY9u+FF14wPPHEE0X25cqVK8WOu7ARI0YUOUfCunXrDJs3bzbs2rVLvvf06dNFPuchQ4YYnnnmGbksvt7YsWMNqampclmr1Rr++OMPQ1paWrHjN8d4fMeOHTM9d+7cOfmc2M6lS5dk+8SJE0XeN336dMPo0aOLbOPkyZM3/Trnz5+X64jvhcKfvfieunjxYpHP+cZ9E1JSUgyNGjUyfPXVV6bvD/HZHj582LS+OE5xvIIln51Yt0mTJob09HTTOp06dSpyPozbuX79utU+C7H/TZs2NX3PFfbPP/8YnnzySfk9Lrbz/PPPy3Na2AcffCC/D4moanOu7ACGiKi0fv/9dyQkJMhhREaiLXoWfv75ZznURdx9F3fQRVusL4ZniDuqgrhzLoY9FSbu/oo7v+YkJycjPj5eDvsobOLEifJ/c8NnzKlRowZUKqXjV/SeCOJOdeGchLJWkwoMDJRDYETPw4kTJ+SdYtFrIOj1+hLfL4aUiM9KfGZPP/203Ma5c+ewfPly+bpYFsO/RE/GjcQwrE6dOpkdUlX4HAliWIwgynn6+PjIO+eF90EM7zIOmxGfr7gD3qVLFzlMR9ydFsO8xPss5eXlJYdzGYm77n5+fnLfjD1U4nMrTJwDX1/fIs8VHmpj7jiF1q1bm54TvUnijr5x6NetiP0RvSJiCJQY1iZ6fMQdfOP3q7mvV9JnJ4gCA97e3qZl8T1Xs2bNIt9vgvjZEOfXGp+FGFolenfE176R+BkT34ti6JYYGvfCCy/InynjsCzj97H42Saiqo0BBBHZHGMOgLjQvZEYviMuhMWQHFH1RVyMiItiUeXFeDHl7Oxs0UV14aEVpWUuEdTcdowBhSWMw27MEQHOyJEj5QWYGDMvxpOLvIHCQ0lKMnToUDlmXlT7EUOGjEO+BPF5iYt3cUF/I/E1zRGf883cbNiTeN74PhGsiOBPDKv5+++/5bCZDz/8UA4nE0GFJcx95uLciKFkxn0QQ3xEoHGr82K82C7tcVr6/SEupsXFuzhecawimBDHKY7Z1dW11J9dab/frPVZiEBGKPzzJXJODh48aBo2J9YRAbwYkiaOtXAAIT4X4zaIqOpiDgQR2RQxh4G4yBIBgri4Kvy4//77cfjwYXmHVtyNrV69On744Qc5hlusbyTufhZOVhbE+25G3O0NDQ0tVkbzmWeekWPejRdqhXMuLl26dNvHKrZbeJvGBHBzRM+DuPu7du1amdwq8kKM4++NF4clXZiJz0v0JGzdulX25BT+zERCrOiREAGF8SECGnH8Iq/AHHG3/8bPTKwvPjdxF1okzBrv3hv3U1xoivKfgrgbL5ZF8rUYwy/2S/TYiP8tOR5BfCaFx9uL8ffiM23atKkpyVcEX4WPy5hgbilxnELhYxWfjQjkxPwHJX1/XLhwAXPmzJF37R988EF53CJ4ED07xl6kwiz57ErLWp9FQECAPF6Re2L0zz//yHwJY5EDIxHcF67CJIj3iZ81IqraGEAQkU0RSabi4kwMbxFDOAo/xN1xcbdU9EIYh+SIRGVxh1wMCzES7xUXdp999pm8kBPlYL/66qtbft3HHntMJuGKgEQMDxJVm0Syp7i4FRc84uJbvC4u+sSFnEgAvt07qWJIjEh4FknQYojJ3Llzi92NLpyoKhKpxXGJBFwxlEUkLRuHqBQeNiUuSkXVoJv1QqxZs0ZeeBceQiOSdsU+iCRvcYwi4Hr++efl53ezIS3iMxM9GWLyMPGZiZ4gEeAYe0iaNGkityEuMMU258+fLy+KRc+RIC78xYW1uEstqhCJwEEcm3EYlTgeESSJC9NbDf2aNm2arIgk5hwQie/GSkfiorlnz57ya/z222/y64mhVR999FGRoT4lqVOnjryjLj4bkVQv9mf27NnIzc2V1bbEeRTfCyIBWgx3E8GZSFIufNEt5jQRVZLE5yDeL14XQ5vq1q0r1xG9AuKzFhfdlnx2pWWtz0IQw83EMEEjcb5FuWCRsC2OTXxviaR2EcSLpO7CxPtuHCpIRFVQZSdhEBGVxr333mt49NFHb/r6008/bWjXrp1MUo6OjjY0bty4SKKykUioFsmaIqFz5MiRhtdff71IAvONRJKqSJoWyazNmzeX+7F161bT6/v37zcMHTpUbkMkH4uEWpHEWjiJ2piMerMk6RuTbc+ePSsTvsXX69Wrl+GHH34w9OnTx2wStdi/RYsWyeTyli1bGu655x7DypUr5ftfeuklUzL5xIkT5T5++umnZpN7s7KyDG3atJEJrjfas2eP/KxatGghE3Kfe+45Q0xMjOFWNmzYYOjXr5/8mn379jWsWbPG9JpI/BZJuiLBvVWrVnJfRaKtkUgAnjVrlkw+N77/888/N70uksHFtsXnc+TIkWJf23h8Ijm5S5cu8vti2rRpMmm58PG++uqr8muI7YhzJ/b5ZufkZkRit9hXkcgvEvjHjRtXJNn466+/Nn3viNdEknHh7R46dEgef9u2beVnIdb577//iiSfi9cGDRpk0WdnLsG8cCK2uaR6a30W4ntL/HwUJhK+x4wZI8+jeNx3332G7du3F1knLy9Pfn6//PJLiV+DiCqXk/insoMYIqKKJO7aitr0xru7gpgVV5TLFCUoyT6IoTcikVmUgqWKI3oYRK+D6JETeTiFiR4aMSeLmOfiRqL3TJSlLTzsi4iqJg5hIiKHI4b3iJr54mJGDIkRQ5HExY6Yr4CIbo/IaxBD3sxVNROVyMSwLnPEz6AojMDggajqYxUmInI44iIlKytLjocXSZtiEjIxsZsopUlEt0/kv4jJFsXEdiInonAAIR432rZtmywXK3JwiKjq4xAmIiIiIiKyGIcwERERERGRxRhAEBERERGRxRhAEBERERGRxRhAEBERERGRxRhAEBERERGRxRhAEBERERGRxRhAEBERERGRxRhAEBERERGRxRhAEBERERERLPX/1EIjCfxOALcAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "## 결론\n", + "def policy_cost_curve(pi_eval, gamma_matrix, c_hat_matrix):\n", + " \"\"\"\n", + " 처치별 고객 비용 추정값을 사용해 비용 곡선을 생성합니다.\n", + " c_hat_matrix[i, a] = Ê[C(a) | X_i]\n", + "\n", + " x = 고객 1인당 누적 평균 처치 비용 (gross)\n", + " y = 고객 1인당 누적 평균 순이익 (Y_net에 비용이 이미 차감된 값)\n", + " → x + y = 무처치 대비 총 매출 증분\n", + "\n", + " 처치 고객은 순이익 / 추정 비용 비율(내림차순)로 정렬합니다.\n", + " \"\"\"\n", + " pi = np.asarray(pi_eval).ravel().astype(int)\n", + " n = len(pi)\n", + " treated = pi > 0\n", + " if treated.sum() == 0:\n", + " return np.array([0.0]), np.array([0.0])\n", + "\n", + " benefit = gamma_matrix[np.arange(n), pi] - gamma_matrix[:, 0]\n", + " cost = c_hat_matrix[np.arange(n), pi].astype(float)\n", + "\n", + " b_t, c_t = benefit[treated], cost[treated]\n", + " ratio = np.where(c_t > 0, b_t / c_t, b_t)\n", + " order = np.argsort(-ratio)\n", + "\n", + " cum_cost = np.r_[0, np.cumsum(c_t[order])]\n", + " cum_benefit = np.r_[0, np.cumsum(b_t[order])]\n", + " return cum_cost / n, cum_benefit / n\n", + "\n", + "\n", + "n_test = len(X_test)\n", + "policies_curve = [\n", + " ('DRLearner plug-in', pi_plugin),\n", + " ('DRPolicyTree (depth=2)', pi_tree),\n", + " ('DRPolicyForest', pi_forest),\n", + " ('all_discount_plus_support', np.full(n_test, 3)),\n", + " ('all_tech_support_only', np.full(n_test, 1)),\n", + "]\n", "\n", - "고객 특성에 따라 처치를 다르게 배정한 세 정책 모두, 가장 성능이 좋은 단일 처치 정책(`all_tech_support_only`, 10,527)보다 높은 value를 기록했습니다.\n", + "fig, ax = plt.subplots(figsize=(8, 6))\n", + "colors = ['tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple']\n", "\n", - "- DRLearner plug-in: 11,810\n", - "- DRPolicyForest: 11,626\n", - "- DRPolicyTree: 11,148\n", + "for (name, pi_eval), color in zip(policies_curve, colors):\n", + " cc, cg = policy_cost_curve(pi_eval, gamma_net, c_hat_test)\n", + " ax.plot(cc, cg, lw=2, color=color,\n", + " label=f'{name} (cost={cc[-1]:,.0f}, net benefit={cg[-1]:,.0f})')\n", + " ax.scatter([cc[-1]], [cg[-1]], s=50, color=color, zorder=5)\n", "\n", - "또한 세 정책 모두 무처치 정책 대비 통계적으로 유의하게 높은 성능을 보였습니다.\n", + "budget_example = 5_000\n", + "ax.axvline(budget_example, color='gray', lw=1.5, ls='--', alpha=0.7,\n", + " label=f'Budget = ${budget_example:,}/customer')\n", "\n", - "공통적으로는 규모가 큰 고객에게 `discount_plus_support`를, 상대적으로 작은 고객에게는 `tech_support_only`를 주로 배정했습니다. 반면 기대 순이익이 낮은 일부 고객에게는 `none`을 선택했습니다.\n", + "ax.set_xlabel('Avg cumulative cost per customer ($)', fontsize=11)\n", + "ax.set_ylabel('Avg cumulative net benefit per customer ($)', fontsize=11)\n", + "ax.set_title('Policy cost curves — cut at x=B for budget-constrained comparison', fontsize=11)\n", + "ax.legend(fontsize=8, loc='upper left')\n", + "ax.grid(True, alpha=0.3)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "qoq9ch9m2fg", + "metadata": {}, + "source": [ + "예산 규모에 따라 효과적인 정책이 다릅니다. 예산이 $1,000~$3,000 수준으로 적을 때는 `all_tech_support_only`가 가장 효율적입니다. 기술지원 평균 비용이 낮아 소규모 예산으로도 편익/비용 비율이 높은 고객부터 빠르게 처치할 수 있기 때문입니다.\n", "\n", - "즉, 비용이 고객 규모에 따라 달라지는 환경에서도, 모든 고객에게 동일한 처치를 적용하는 것보다 고객 특성에 맞춰 targeting하는 전략이 더 효과적임을 확인할 수 있었습니다.\n", + "예산이 $4,000~$6,000 수준으로 늘어나면 학습 정책(plug-in, forest)이 앞서기 시작합니다. 고객별 최적 처치를 배정하는 타겟팅 효과가 발휘되며, 같은 비용으로 더 높은 순이익을 냅니다.\n", "\n", - "### 참고 자료\n", + "예산을 전부 소진하는 상황에서도 학습 정책이 가장 효율적입니다. plug-in은 약 $6,600을 써서 $4,561, forest는 약 $6,900을 써서 $4,376의 순이익을 냅니다. 반면 `all_discount_plus_support`는 $10,700을 전부 소진해도 순이익이 $2,887에 그쳐 예산 대비 효율이 가장 낮습니다." + ] + }, + { + "cell_type": "markdown", + "id": "7owznh79iw7", + "metadata": {}, + "source": [ + "세 정책 모두 무처치 대비 통계적으로 유의하게 높은 value를 보였습니다 (95% CI가 모두 0을 포함하지 않음).\n", "\n", - "- **이 노트북의 주요 참고 튜토리얼**: [Stanford ML+CI Tutorial — Policy Learning I (Binary Treatment)](https://bookdown.org/stanfordgsbsilab/ml-ci-tutorial/policy-learning-i---binary-treatment.html?utm_source=chatgpt.com)\n", - "- **데이터셋**: [Software Usage Promotion Campaign Uplift Model (Kaggle)](https://www.kaggle.com/datasets/hwwang98/software-usage-promotion-campaign-uplift-model?utm_source=chatgpt.com)\n", - "- **모수적 정책학습 이론**: [Athey and Wager (2021, Econometrica)](https://onlinelibrary.wiley.com/doi/abs/10.3982/ECTA15732?utm_source=chatgpt.com) · [ArXiv Version (1702.02896)](https://arxiv.org/abs/1702.02896?utm_source=chatgpt.com)\n", - "- **비용 곡선을 통한 정책 비교**: [Imai and Li (2019)](https://arxiv.org/pdf/1905.05389.pdf?utm_source=chatgpt.com)\n", - "- **추가 참고**: [arXiv 2604.06123](https://arxiv.org/html/2604.06123v1?utm_source=chatgpt.com)" + "최선 상수 정책(`tech_support_only`) 대비에서는 정책별로 차이가 있습니다. `DRLearner plug-in`(+1,284, CI: [622, 1,946])과 `DRPolicyForest`(+1,099, CI: [426, 1,772])는 통계적으로 유의하게 우수합니다. 반면 `DRPolicyTree`(+622, CI: [-1, 1,245])는 점추정치는 양수이지만, 신뢰구간 하한이 0에 근접해 단일 처치 정책 대비 우위가 통계적으로 명확하지 않습니다." ] }, { "cell_type": "markdown", - "id": "e0ec9c4f", + "id": "b40f71c0", "metadata": {}, - "source": [] + "source": [ + "## 참고 자료\n", + "\n", + "- [Stanford ML+CI Tutorial — Policy Learning I (Binary Treatment)](https://bookdown.org/stanfordgsbsilab/ml-ci-tutorial/policy-learning-i---binary-treatment.html)\n", + "- [Athey and Wager (2021, Econometrica) — Policy Learning with Observational Data](https://onlinelibrary.wiley.com/doi/abs/10.3982/ECTA15732)\n", + "- [Sun, Du, Wager et al. (2021) — Treatment Allocation under Uncertain Costs](https://arxiv.org/abs/2103.11066)\n", + "- [Imai and Li (2019) — Experimental Evaluation of Individualized Treatment Rules](https://arxiv.org/pdf/1905.05389.pdf)\n", + "- [arXiv 2604.06123](https://arxiv.org/html/2604.06123v1)" + ] } ], "metadata": { From 2ebac2f957249268d447912ea65d9e57a73db267 Mon Sep 17 00:00:00 2001 From: Funbucket Date: Wed, 13 May 2026 01:48:19 +0900 Subject: [PATCH 08/11] =?UTF-8?q?feat(who=5Fshould=5Fbe=5Ftreated):=20?= =?UTF-8?q?=EC=98=81=EB=AC=B8=20=EB=B2=88=EC=97=AD=EB=B3=B8=20=EC=B6=94?= =?UTF-8?q?=EA=B0=80=20=EB=B0=8F=20=EC=96=B8=EC=96=B4=20=EC=A0=84=ED=99=98?= =?UTF-8?q?=20=EB=A7=81=ED=81=AC=20=EC=84=A4=EC=A0=95?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - who_should_be_treated_en.ipynb 생성 (한→영 번역, 31개 셀) - 두 노트북 첫 셀에 언어 전환 링크 추가 - EN: /who-should-be-treated-ko/ - KO: /who-should-be-treated-en/ - myst.yml: en을 primary, ko를 hidden sibling으로 설정 Co-Authored-By: Claude Sonnet 4.6 --- book/myst.yml | 4 +- .../who_should_be_treated_en.ipynb | 2336 +++++++++++++++++ .../who_should_be_treated_ko.ipynb | 4 +- 3 files changed, 2342 insertions(+), 2 deletions(-) create mode 100644 book/who_should_be_treated/who_should_be_treated_en.ipynb diff --git a/book/myst.yml b/book/myst.yml index da3ba92..63e0e4f 100644 --- a/book/myst.yml +++ b/book/myst.yml @@ -24,7 +24,9 @@ project: - file: why_causal_inference/why_causal_inference_ko.ipynb hidden: true - title: Who Should Be Treated? - file: who_should_be_treated/who_should_be_treated_ko.ipynb + file: who_should_be_treated/who_should_be_treated_en.ipynb + - file: who_should_be_treated/who_should_be_treated_ko.ipynb + hidden: true site: template: book-theme diff --git a/book/who_should_be_treated/who_should_be_treated_en.ipynb b/book/who_should_be_treated/who_should_be_treated_en.ipynb new file mode 100644 index 0000000..c8536c2 --- /dev/null +++ b/book/who_should_be_treated/who_should_be_treated_en.ipynb @@ -0,0 +1,2336 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "1ae5a6c4", + "metadata": {}, + "source": [ + "**🌐 Language:** **English** | [한국어 →](/who-should-be-treated-ko)\n", + "\n", + "# Who Should Receive Which Promotion?\n", + "\n", + "Many problems in causal inference focus on the Average Treatment Effect (ATE). For example, the question \"should we run a promotion or not?\" compares a policy that offers the same promotion to all customers against one that offers it to no one.\n", + "\n", + "But if the budget is limited and you need to maximize impact within it, the story changes. If promotion effects vary significantly across customers, a one-size-fits-all strategy may not be optimal.\n", + "\n", + "- You waste money on always-converters who would have purchased anyway,\n", + "- sleeping dogs who react negatively to marketing actually cause losses, and\n", + "- you miss potential revenue by failing to prioritize high-responders.\n", + "\n", + "The core question is therefore:\n", + "\n", + "_\"Which customers, when targeted, yield the greatest net profit?\"_\n", + "\n", + "Policy learning explores possible treatment rules and learns to maximize the overall average net profit. In other words, it answers the question: \"Who should be treated?\"" + ] + }, + { + "cell_type": "markdown", + "id": "2b06ptb53eu", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "id": "fc2741f9", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-09T16:49:46.211163Z", + "iopub.status.busy": "2026-05-09T16:49:46.211076Z", + "iopub.status.idle": "2026-05-09T16:49:48.058022Z", + "shell.execute_reply": "2026-05-09T16:49:48.057674Z" + } + }, + "outputs": [], + "source": [ + "import os\n", + "import tempfile\n", + "import warnings\n", + "from pathlib import Path\n", + "\n", + "os.environ.setdefault(\"MPLCONFIGDIR\", str(Path(tempfile.gettempdir()) / \"matplotlib\"))\n", + "os.environ.setdefault(\"MPLBACKEND\", \"Agg\")\n", + "os.environ.setdefault(\"LOKY_MAX_CPU_COUNT\", \"8\")\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "from scipy import stats\n", + "from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "from econml.dr import DRLearner\n", + "from econml.policy import DRPolicyForest, DRPolicyTree\n", + "\n", + "warnings.filterwarnings('ignore')\n", + "\n", + "SEED = 1\n", + "rng = np.random.default_rng(SEED)\n", + "np.random.seed(SEED)\n", + "sns.set_theme(style='whitegrid')" + ] + }, + { + "cell_type": "markdown", + "id": "8b741c26", + "metadata": {}, + "source": [ + "## Problem Setup\n", + "\n", + "A software sales company wants to know whether incentives such as discounts or technical support actually increase software purchases, and which customers respond most effectively.\n", + "\n", + "Ideally, one would run a randomized controlled trial (RCT) assigning different incentives randomly to each customer. In practice, however, factors such as cost, sales strategy, and the risk of losing major clients make randomized experiments difficult. This data is therefore observational, not from a randomized experiment.\n", + "\n", + "The dataset contains information on approximately 2,000 customers, described by the following variables.\n", + "\n", + "- Customer characteristics: detailed information on each customer's industry, size, revenue, and technology profile.\n", + "- Intervention: information on the incentive provided to each customer.\n", + "- Outcome: software purchase revenue over the year following the incentive.\n", + "\n", + "| Variable | Type | Description |\n", + "| --------------- | ---- | ---------------------------------------------------------------------------- |\n", + "| Global Flag | X | Whether the customer has a global office (overseas branch) |\n", + "| Major Flag | X | Whether the customer is a large consumer in its industry (vs. SMC or SMB) |\n", + "| SMC Flag | X | Whether the customer is a small-medium corporation (vs. Major or SMB) |\n", + "| Commercial Flag | X | Whether the customer's business type is commercial (vs. public sector) |\n", + "| IT Spend | X | Amount spent on IT-related purchases |\n", + "| Employee Count | X | Number of employees |\n", + "| PC Count | X | Number of PCs used by the customer |\n", + "| Size | X | Customer size measured by annual total revenue |\n", + "| Tech Support | T | Whether the customer received technical support (binary) |\n", + "| Discount | T | Whether the customer received a discount (binary) |\n", + "| Revenue | Y | Customer revenue from software purchases |\n", + "\n", + "\n", + "Since both `Tech Support` and `Discount` are interventions, we combine them to define four treatment arms:\n", + "\n", + "$$\n", + "A_i \\in \\{0, 1, 2, 3\\}\n", + "$$\n", + "\n", + "Each treatment means:\n", + "- $A_i = 0$: no intervention\n", + "- $A_i = 1$: technical support only\n", + "- $A_i = 2$: discount only\n", + "- $A_i = 3$: both technical support and discount" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "477aed7a", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-09T16:49:48.059813Z", + "iopub.status.busy": "2026-05-09T16:49:48.059518Z", + "iopub.status.idle": "2026-05-09T16:49:48.071045Z", + "shell.execute_reply": "2026-05-09T16:49:48.070709Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(2000, 13)\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Global Flag Major Flag SMC Flag Commercial Flag IT Spend \\\n", + "0 1 0 1 0 45537 \n", + "1 0 0 1 1 20842 \n", + "2 0 0 0 1 82171 \n", + "3 0 0 0 0 30288 \n", + "4 0 0 1 0 25930 \n", + "\n", + " Employee Count PC Count Size Tech Support Discount Revenue arm \\\n", + "0 26 26 152205 0 1 17688.36300 2 \n", + "1 107 70 159038 0 1 14981.43559 2 \n", + "2 10 7 264935 1 1 32917.13894 3 \n", + "3 40 39 77522 1 1 14773.76855 3 \n", + "4 37 43 91446 1 1 17098.69823 3 \n", + "\n", + " arm_name \n", + "0 discount_only \n", + "1 discount_only \n", + "2 discount_plus_support \n", + "3 discount_plus_support \n", + "4 discount_plus_support " + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = pd.read_csv('./data/multi_attribution_sample.csv')\n", + "data.columns = data.columns.str.strip()\n", + "\n", + "covariates = [\n", + " 'Global Flag',\n", + " 'Major Flag',\n", + " 'SMC Flag',\n", + " 'Commercial Flag',\n", + " 'IT Spend',\n", + " 'Employee Count',\n", + " 'PC Count',\n", + " 'Size',\n", + "]\n", + "outcome = 'Revenue'\n", + "\n", + "ARM_NAMES = {\n", + " 0: 'none',\n", + " 1: 'tech_support_only',\n", + " 2: 'discount_only',\n", + " 3: 'discount_plus_support',\n", + "}\n", + "ARM_LABELS = [ARM_NAMES[i] for i in range(4)]\n", + "\n", + "required_columns = covariates + ['Tech Support', 'Discount', outcome]\n", + "for col in required_columns:\n", + " data[col] = pd.to_numeric(data[col], errors='coerce')\n", + "\n", + "policy_df = data[required_columns].dropna().copy()\n", + "policy_df['arm'] = (\n", + " 2 * policy_df['Discount'].astype(int)\n", + " + policy_df['Tech Support'].astype(int)\n", + ").astype(int)\n", + "policy_df['arm_name'] = policy_df['arm'].map(ARM_NAMES)\n", + "\n", + "n = len(policy_df)\n", + "X = policy_df[covariates].to_numpy()\n", + "Y = policy_df[outcome].to_numpy(dtype=float)\n", + "A = policy_df['arm'].to_numpy(dtype=int)\n", + "\n", + "print(policy_df.shape)\n", + "policy_df.head()\n" + ] + }, + { + "cell_type": "markdown", + "id": "8bfa27b6", + "metadata": {}, + "source": [ + "### Cost Setup\n", + "\n", + "In real-world operations, costs must be considered alongside incentive effects. Furthermore, the cost of the same incentive can vary across customers depending on their characteristics.\n", + "\n", + "For example, technical support may require more support hours for larger companies, while discounts may need to be deeper for larger customers.\n", + "\n", + "Since this dataset does not include actual cost information, we simulate costs that vary with customer characteristics.\n", + "\n", + "Technical support cost increases with employee count, and discount cost increases with customer size.\n", + "\n", + "We use the log-normal distribution so that costs are always positive and can exhibit the heavy-tailed behavior seen in practice.\n", + "\n", + "$$\n", + "C_i(\\text{tech}) \\sim \\text{LogNormal}(\\log C_{\\text{tech}} + 0.5 \\cdot \\tilde{e}_i,\\ 0.3)\n", + "$$\n", + "\n", + "$$\n", + "C_i(\\text{disc}) \\sim \\text{LogNormal}(\\log C_{\\text{disc}} + 0.4 \\cdot \\tilde{s}_i,\\ 0.3)\n", + "$$\n", + "\n", + "where $\\tilde{e}_i$ is the standardized employee count and $\\tilde{s}_i$ is the standardized company size.\n", + "\n", + "With per-customer costs defined this way, the net outcome is:\n", + "\n", + "$$\n", + "Y_i^{net} = Y_i - C_i(A_i)\n", + "$$\n", + "\n", + "Using $Y_i^{net}$ as the outcome when computing AIPW scores means $\\hat\\Gamma_{i,a}$ naturally estimates $E[Y(a) - C(a) \\mid X_i]$. Policy learning and evaluation based on AIPW thus automatically incorporate costs." + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "5174ddd2", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-09T16:49:48.572223Z", + "iopub.status.busy": "2026-05-09T16:49:48.572150Z", + "iopub.status.idle": "2026-05-09T16:49:48.576620Z", + "shell.execute_reply": "2026-05-09T16:49:48.576207Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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3discount_plus_support12665.2832967375.49012526784.12464914118.841353
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" + ], + "text/plain": [ + " arm mean_cost std_cost mean_revenue \\\n", + "0 none 0.000000 0.000000 6585.891792 \n", + "1 tech_support_only 5037.815894 4917.641358 15104.111534 \n", + "2 discount_only 5181.570334 3043.343172 12247.935953 \n", + "3 discount_plus_support 12665.283296 7375.490125 26784.124649 \n", + "\n", + " mean_net_revenue \n", + "0 6585.891792 \n", + "1 10066.295640 \n", + "2 7066.365619 \n", + "3 14118.841353 " + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "COST_TECH_SUPPORT = 4_000.0 # baseline cost for tech support\n", + "COST_DISCOUNT = 5_000.0 # baseline cost for discount\n", + "\n", + "\n", + "rng_c = np.random.default_rng(SEED + 99)\n", + "emp_idx = covariates.index('Employee Count')\n", + "size_idx = covariates.index('Size')\n", + "\n", + "emp_z = (X[:, emp_idx] - X[:, emp_idx].mean()) / (X[:, emp_idx].std() + 1e-8)\n", + "size_z = (X[:, size_idx] - X[:, size_idx].mean()) / (X[:, size_idx].std() + 1e-8)\n", + "\n", + "# Tech support: higher employee count → higher support cost\n", + "c_tech = COST_TECH_SUPPORT * np.exp(0.5 * emp_z + 0.3 * rng_c.standard_normal(len(policy_df)))\n", + "c_tech = np.clip(c_tech, 200.0, 40_000.0)\n", + "\n", + "# Discount: larger company → higher discount cost\n", + "c_disc = COST_DISCOUNT * np.exp(0.4 * size_z + 0.3 * rng_c.standard_normal(len(policy_df)))\n", + "c_disc = np.clip(c_disc, 200.0, 30_000.0)\n", + "\n", + "C_obs = np.select(\n", + " [A == 1, A == 2, A == 3],\n", + " [c_tech, c_disc, c_tech + c_disc],\n", + " default=0.0,\n", + ")\n", + "\n", + "Y_net = Y - C_obs\n", + "\n", + "pd.DataFrame({\n", + " 'arm': ARM_LABELS,\n", + " 'mean_cost': [C_obs[A == a].mean() if (A == a).sum() > 0 else 0.0 for a in range(4)],\n", + " 'std_cost': [C_obs[A == a].std() if (A == a).sum() > 0 else 0.0 for a in range(4)],\n", + " 'mean_revenue': [Y[A == a].mean() for a in range(4)],\n", + " 'mean_net_revenue': [Y_net[A == a].mean() for a in range(4)],\n", + "})" + ] + }, + { + "cell_type": "markdown", + "id": "024e5803", + "metadata": {}, + "source": [ + "### Exploratory Data Analysis and Diagnostics\n", + "\n", + "Before entering policy learning, we first examine the data structure across the four treatment arms ($A \\in \\{0,1,2,3\\}$).\n", + "\n", + "1. Sample size per arm: are there enough observations in each treatment?\n", + "2. Mean customer characteristics per arm: which customer segments tend to receive which treatment?\n", + "3. Revenue distribution per arm: how do Revenue scale, variance, and outlier patterns differ across treatments?\n", + "4. Positivity check: are all treatments sufficiently co-observed across the range of customer characteristics?" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "3d0f329e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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countshare
arm
none5170.2585
tech_support_only4620.2310
discount_only4770.2385
discount_plus_support5440.2720
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" + ], + "text/plain": [ + " count share\n", + "arm \n", + "none 517 0.2585\n", + "tech_support_only 462 0.2310\n", + "discount_only 477 0.2385\n", + "discount_plus_support 544 0.2720" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "arm_counts = policy_df['arm_name'].value_counts().reindex(ARM_LABELS).rename_axis('arm').to_frame('count')\n", + "arm_counts['share'] = arm_counts['count'] / len(policy_df)\n", + "\n", + "display(arm_counts)" + ] + }, + { + "cell_type": "markdown", + "id": "8e911f32", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-09T16:49:48.072480Z", + "iopub.status.busy": "2026-05-09T16:49:48.072405Z", + "iopub.status.idle": "2026-05-09T16:49:48.076539Z", + "shell.execute_reply": "2026-05-09T16:49:48.076070Z" + } + }, + "source": [ + "The actual sample sizes are `none` 517, `tech_support_only` 462, `discount_only` 477, and `discount_plus_support` 544. Each arm's share ranges from 23.1% to 27.2%, indicating a reasonably balanced distribution with no arm severely underrepresented.\n", + "\n", + "The sample distribution is therefore stable for policy learning with four treatment arms." + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "id": "d6488c62", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "arm_covariate_means = (\n", + " policy_df\n", + " .groupby('arm_name')[covariates]\n", + " .mean()\n", + " .reindex(ARM_LABELS)\n", + ")\n", + "\n", + "plt.figure(figsize=(10, 5))\n", + "sns.heatmap(arm_covariate_means.T, annot=True, fmt='.2f', cmap='YlGnBu', cbar_kws={'label': 'Mean'})\n", + "plt.xlabel('Treatments')\n", + "plt.ylabel('Covariate')\n", + "plt.title('Mean customer characteristics by treatments')\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "c49e9856", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-09T16:49:48.078246Z", + "iopub.status.busy": "2026-05-09T16:49:48.078155Z", + "iopub.status.idle": "2026-05-09T16:49:48.194453Z", + "shell.execute_reply": "2026-05-09T16:49:48.193850Z" + } + }, + "source": [ + "Customer characteristics differ across treatment arms. The mean `Size` ranges from approximately 70,943 in the `none` group to approximately 171,467 in the `discount_plus_support` group, and `IT Spend` also ranges from roughly 18,056 to 41,371. In other words, larger customers tend to receive both technical support and a discount.\n", + "\n", + "This indicates that treatment assignment is not independent of customer characteristics. Policy learning and evaluation will therefore use AIPW to correct for these differences." + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "id": "c925a3d8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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armarm_namecountmeanmedianstdminmax
00none5176585.8917926123.1870673363.163462-616.57245121445.05937
11tech_support_only46215104.11153414483.7193205400.3198584619.49136140166.67407
22discount_only47712247.93595310454.9325007472.178476889.97565341818.39213
33discount_plus_support54426784.12464923560.25289013124.9680835903.90688086006.92445
\n", + "
" + ], + "text/plain": [ + " arm arm_name count mean median \\\n", + "0 0 none 517 6585.891792 6123.187067 \n", + "1 1 tech_support_only 462 15104.111534 14483.719320 \n", + "2 2 discount_only 477 12247.935953 10454.932500 \n", + "3 3 discount_plus_support 544 26784.124649 23560.252890 \n", + "\n", + " std min max \n", + "0 3363.163462 -616.572451 21445.05937 \n", + "1 5400.319858 4619.491361 40166.67407 \n", + "2 7472.178476 889.975653 41818.39213 \n", + "3 13124.968083 5903.906880 86006.92445 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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mXIdh27Zt8uyzz8oNN9wgCxculAULFkj16tXl1VdflQMHDpi1NXLkSLnjjjvk3Xffldq1a6tgaNCgQXLdddep5yKwmDFjhjFAQcCDoAmvP2LECPX+sPLykCFDZMeOHU446qRhChMRERGRF8nOzpa0tDTVScfIAmAk4cqVKzJz5kzVaYecnBzVMUcn2lS9evXU/drzcGUfawMgeAgMDJS2bduqQGDfvn027xtec9myZWqVcMB+jh07Vo2MYLTh2LFj0rt3bzVSocFoAkY9MPLQpEkT4/19+vRRoyraGgYYTUDQ8Pzzz6v7GjRoIJs3b1b7ifu//PJLOXz4sHz22WfGdtq3b68CIgQzH3zwgc3vhyxjAEFERETkRTB6gE46nD9/Xk6ePCmnTp2SH3/80RhgQHR0dKHgQeuwazAqUbZsWTUigOBBg+di1W9bITjRggeoVKmSus3IyFC3GA3QAgvs97///it//vmn2X5b2s+YmBh1axpgYL9B20+kKlWoUEG9FwQymttvv11ef/11SUlJUStRk+MYQBARERF5mV9++UVee+01OXHihISHh6ur8dpKw1oKE+63xLSDr9Ge66jQ0FCz3/3987Pl8/Ly1G1iYqJKRUIdBBY2q1mzprRo0cJsv6+1nwXbL1j/cPHiRRVAWIKRmYoVK9rxrsgjAwicVMhTwxAaoshbbrlFFcAgJ86Sy5cvq4Kfn3/+WZ183bt3V7mApifVpk2bVF7d2bNnpU6dOmr4rHXr1ja1odm7d68MGDDAWJikQa4h8giRs4c/EOw3ipKqcGEzIiIiKiG4ao86gs6dO6v6APSX0JdBITICC082atQoFfSg+BkjDBhNwegE0o4cFRkZKbVq1VKpWJb6muyf+VgRNQphVq9eLVOnTpVPPvlEfcgY4io4lKUZPny4nD59Wp188+bNk59++kkmT55sfByFMqNHj1aV/1988YUKHFBkdPz4cavbMA0ennnmGWPkbBqAIC8PQ2EoMlq6dKmKqrHfts5aQERERGStv/76S/U10LepUaOGCh5ACx4KXsn3JOhXYeYo1DwgeABczIWCfS1boZ4DheBId8IMTNrP9u3bVcqXVkROPhBAIEjA7AHo0GOKLgzBzZ07V+Lj41VhTEH79++XXbt2qanGMESF4ACFQCicQR4goDOPqByV+nXr1lWjD9h25cqVVreB3DlU9qOav2rVqoX2A0Nv6enpKqfu+uuvV4VBGI1AkGJP0RERERGRNbR6BfQ70DlG7QNmOsIMR4D+iadCsfOGDRtUnwtF04sWLVLZGwiCtDoJe913331qlAEXeHEBGReUUTyNC8VIXdLpdE57H6Wd2wMIVMujkMY0vSgqKkoaNWoku3fvLrQ9piRDgQwCA9OIEyceolpEr+jAm7YHiHS19oprQ/vjw/bvvfeeSl8qCO1j5MS0GEfL80ORDhEREVFJQN0A1oDARc+nn35apX0DMiLQl0E/x1NhligUQiPrBGlYmHJ1ypQpauYnR/cbdRxI48L6FgiunnjiCXUxGlPY4mIy+VANBEYaIDY21ux+RIraY6bwx1JwWwyBYbYADFuh847Of8FZB0zbK64NLYhZt26d+rd2a6patWrqxxQWTkFAgVoIIiIiopKCxdfwY+nC7LUcOXKk0H1bt24tdJ89a0BYeg4u4Jq+JrI6Fi9eXGi7nj17FvkcQJ/L0r4XvA/pSyguL0hbh4J8JIDQhqu0PDhNcHCwJCcnW9y+4Lba9sgHxEqKRbWn1SYU14Y98EezatUqNSdzuXLl7GoDOYueMOyofSaODiVaQ/u8srL14mfjscdztDb8PeC4lXauPG/Id/C8IVvg/9Fafj9u2SksedYeZ9QXaLUYnojnjajPEZk6+N61VG+CY2TtZ+j2AEJLAUIthGk6EL4kLM2IhG0sFVdjewxdIQjQ2iv4uNZecW3YAgcbuXXI4cMwIhYrsRcWcSk405M7YU7pkpYXlz8qFB8XJ37ZtgUQhoREdYt5pP0z2WktTecN+R6eN2Qtba0CTljiGl999ZXFSWYKQhaGNh2rJyvN501WVpaq8cUsWEWxdIHdIwMILZXowoULaiYBDX6vX79+oe2RmoQCZlMIBjD3L9KUkIaEIADPN4XftcVMimvDlg4/lo7/+uuv1e2jjz4qjkBxDxZgcTdEpvifOaZCu9Z8y86QHhIqGHysHBsrIbGFF7u5lsygYDkropa3D6tdq8T2kTzvvCHfwfOGbO0A/ffff+rfuGDoyVe8fQVmTEJdanHw/2JPXqQNF3xx/pT28yYwMFD1t7UL7qawSrjV7YibYdYlLBSCSnwtgEAdw8GDBy0WL6O+APP7YgpWFBEBZlQCFM3gpGjWrJm6r2/fvsbnoX0tMi6uDWth3Yjvv/9eFTJhHQlHYd+dtZCLM6ATWNL7k3f1yyY4SCchFk7mazEE5c+mgC8sTzpupZ0rzhvyPTxvyBqYrETr/OGW03KWPNQUaKtAezMtDas0nzcBAQHqbwjft5aCPVsCK7cHEBgqQaCADj1qB1Bcg8p5jBIg6sUHjvUVsDgI3iwq9xEgjBgxQg2poWYAsw/06tXLOMKA6bswNzIi5vbt28vatWtVatD06dPV49a0URwUVm/cuFEFEZjBCSsfarR9JSIiIiLyNW6fxhWwBsT999+vCpAfeughFSFhwQ+k9GBWJEzthc66Fh1h1WpU42ONhhdeeEEFCab5edgeFfgff/yx9O7dW80DjIp/bdpWa9ooDtKWAOtA4PVMf7R9JSIiIiLyNW4fgQAEDFg5Gj8FWZq2C0Np8+fPv2abGE3AT1GsacN0YRL8mMLid0REREREpY1HjEAQEREREZF3YABBRERERETelcJERERE5IgLl9MlJa3wGk+uEBUeJBXLcvY5Kj0YQBAREZHXBw/PzNoqWfriV0wuCcG6AFk49g6nBBFYA2vGjBlqvYsvvvhCtm7dKt5i7969ar0FVy4oN27cOHWsPvzwQ5e9JjGAICIiIi+HkQcEDw90uk4quHgk4OLldPlsy1G1D84chXjsscfk4YcfFm/Sv39/Ffx4w4rU5BiOQBAREZFPQPBQtUKE+ILw8HD1Q+SJWERNRERE5Abx8fHy9NNPS9OmTdV6VBs2bDA+tmDBArnjjjuMv69fv166d+8ujRs3lnbt2qnFcbOz/7/m448//pBHH31UtXXbbbfJK6+8IhkZGeoxLMq7YsUKueuuu9TzcYu1sjQ7d+5UqVNnz54t8r6BAweqRX9feuklNcKABXlffPFFuXLlinoc28L48eNVWpG18L569uwpN910k3q/CxcuNK4ajddGu99995307dtXbrzxRrXNp59+arGtZ599VgYNGmR234kTJ1QbR48etXqfqHgMIIiIiIhcLCcnR4YMGSKXL1+WVatWybx589QiupYcPnxYLbY7bNgw1ZnGYrlffvmlvPfee+rxM2fOqIVxK1asqDrXCD62b98uU6ZMUY/PnDlTdcyfe+45FaQgNQoBCIIKW2D78uXLy5o1a2T27NmyZcsWYxu//vqrukWAMWHCBKvbe/nll+XBBx+Ur776Sp5//nl1DLC/ppAW9dRTT8mmTZukY8eOauFfvOeCsGbXrl271CLEpgEKgqbrrrvOpvdK18YUJiIiIiIX++2339RV8e+//15q1Khh7ChbWgQXV+L9/PykatWqUqVKFfWDjnZERH661meffSbR0dEqsAgMzO/aTZs2Tfbv369GCDDagFGBe+65Rz1Wq1Yt1eaSJUtU4GGtevXqyciRI41ttGnTRr0GVKhQQd1GRkaqn+Kg2Hrp0qUyYMAAY60H2kxKSlLByfDhw43bYmSlU6dO6t8jRoyQjz76SA4cOCDVq1c3a7NDhw4qwEEw8uSTT0peXp4KtIYOHWr1eyTrcASCiIiIyMX++ecfKVOmjDF4gIYNG0pISEihbZGyhNSk+++/X3WkJ02aJImJiarDrbV1ww03GIMHuPXWW1V6FFJ49Hq9NG/e3KzNli1bSkJCgvqxVp06dcx+R6BgmkZlC+z/pUuXLO4X9hf7ralbt67ZawK2KQjvH+lQCBpgx44d6nV69Ohh1z5S0RhAEBEREbkYRhRwhbwg0yBAExwcLB988IGa1hXpPqdOnVIpPUgXKuo5plf6LdFeu6jnanUIpoKCgsRZbNkvS69b1PP79Okjx48fl7/++kuNRCDgQqBGzsUAgoiIiMjFMNqQmppqVtyLwEArSjb1008/ydtvvy2NGjVS6TgIJpDis3HjRmNq0cGDB806/UiNQsExrt7rdDq1RoOpPXv2qLQjdK7xOJi+NvalJCHVCD+W9gv7YzoyYwu8X4zWoF4CNRqoiyDnYwBBRERE5GKtWrWSJk2ayJgxY+T333+XP//8U/3b379w1wwd6nfeeUcVHaN4GFfXt23bpjrK2voLKMbGzEu4+r579255/fXXVRoT6iQwajF//nz5+uuv5fTp06qGYPXq1WqtCYyEXH/99RIWFqZqIv7991/55Zdf5P3337f5PaENvD72xRqPP/64KiDHvmC/UOCNQAn7a00dRVEwCoF2kQ6GOg1yPhZRExERkU/Aom7e8poIFN59911V7IyOPDq7KPzFqsoFYVpWzJq0fPlymTt3rtoWBcPadKmVKlVSj6H4GEXYGFXo1q2bseAZU6uWLVtWTcOKugPUTqCO4oEHHlCPI8jAc/E4ntegQQMZO3asmhbVFngfmBkKQcTixYut2h7pSStXrlQF4JUrV5YnnnhCBRaOuPvuu2Xq1KnqWAQEBDjUFlnmZygqiYxcClceAFONuVt6erocOnRIDa/iakJJunL8hBwYOVrqPvOkhFapYtNzM86dk+ML35Umc2ZLRF3zwi7y7fOGfAfPG7JFZmam6pzGxsaqTrLWObxwOV2embVVrUbtDsG6AFk49g6nrkRN9sMoTZcuXVQak1ZojvQunD8IvkprUJGZmSknT56U2rVrWyzWt6UvyhEIIiIi8mrouKMDn5Jm34xAjooKD2Lw4AGw/gMW1ENKFGau0oIHcj4GEEREROQTQQRHADwDiruLW0xu8ODBZms9OANqL5DWhcABtRRUchhAEBEREZHToD4DK0BfS1RUlNOPOGap0ha2o5LFAIKIiIiInCY8PFz9kO/iNK5ERERERGQ1BhBERERERGQ1BhBERERERGQ1BhBERERERGQ1BhBERERERGQ1zsJEREREXi8n+aLkpqe65bUDwiIlsEwFt7w2kTswgCAiIiKvDx7OLB4uhhz3rETtFxgk1Z+a75Qgon79+jJjxgz577//5IsvvpCtW7eKt9i7d68YDAZp0aKFU9o7e/asdOrUST744ANp1aqVU9r0ZefOnVPrYHTv3r3EX4sBBBEREXk1jDwgeIhu00cCo8q79LVzUi5J0va1ah+cOQrx2GOPycMPPyzepH///ir4cVYAQbYZO3asVK1alQEEERERkbUQPOhiqvjEAeNibOTJWERNRERE5Abx8fHy9NNPS9OmTaV9+/ayYcMG42MLFiyQO+64w/j7+vXr1ZXlxo0bS7t27WT69OmSnf3/KVt//PGHPProo6qt2267TV555RXJyMhQj+Xm5sqKFSvkrrvuUs/H7ccff2x87s6dO1XqFFKGirpv4MCB8sYbb8hLL72kRhiaNWsmL774oly5ckU9jm1h/PjxMm7cOKveP7YbOXKkvPrqq6q91q1by8yZM83eV8HtsR/Xuq+441QcHEeMpOA43nLLLTJs2DCVGgQ4FnifO3fuNG5f8L7i3pO2PfazR48ectNNN8kDDzyg0r9M4fGePXuqx3EeLFy4UH2Opm28++670qZNG5Xm1bt3b9m1a5dKezM9b0oKAwgiIiIiF8vJyZEhQ4bI5cuXZdWqVTJv3jxZtmyZxW0PHz4sEydOVJ3Z7777Tl577TX58ssv5b333lOPnzlzRh555BGpWLGifPrppyr42L59u0yZMkU9jg4sOqDPPfecClKQGoWONYIKW2D78uXLy5o1a2T27NmyZcsWYxu//vqrukWAMWHCBKvb3Lx5s1y4cEE++eQTmTZtmuo4Y9/sUdxxKg466E8++aQKHL766iv13hA84D3Zwpr3hM/kqaeeUh3+OnXqqJQ1fI6A13355ZflwQcfVPvx/PPPq3MDzzGF565cuVLeeustef/991XQc/fdd6vPp6SxBoKIiIjIxX777Tc5evSofP/991KjRg11H+oHevXqVWhbXHH28/NT+e1VqlRRP+hQRkREqMc/++wziY6OVh3mwMD8rh06riioxQgBRhtwZfyee+5Rj9WqVUu1uWTJEhV4WKtevXrq6rrWBq5+4zWgQoX8+o/IyEj1Y62oqCgVjISGhsr111+vOt7obI8ePVpsVdxxKg6OFQI6BGJoo3r16qpznpCQYNN+RFnxnoYOHapGIGDq1KmyY8cO9Tni+C5dulQGDBhgrIHBsU5KSlJtDh8+3NgGRkrwmWh0Op2EhIRIuXLlpKRxBIKIiIjIxf755x8pU6aMMXiAhg0bqg5gQUjFwdXl+++/X6WrTJo0SRITE1XHUmvrhhtuMAYPcOutt6r0qBMnToher5fmzZubtdmyZUvVMbalc4wr5aYQKNiSHmQJUnTQ0dbgfWJ/T548aXNbxR2n4uDzwKgQOvRIPcKV/927dxvTs5z5nlqZzCqFjv+NN96oPkfs76VLlyx+XmgDn6emZs2a4i4MIIiIiIhcDFfK8/LyCt1vGgRogoOD1VSmSFlBWsupU6dU+ouWWmPpORpMq2qJ9tpFPVfLtzcVFBQkzobOs6X9CggIsDoVzNrjZI1Ro0apqXNfeOEFdewQTPTp06fIQCnXwnGy5j0VPO5ox9/f36bPy1KwWWoCCByQ+fPnq6jx5ptvlieeeMKYA2YJhpZQtIP8NERjyO/TioQ0mzZtkm7duqkIEEOBGCa0tQ0NilpwRcCe/SDXSD9zVq4cP2HzT8bVoigiIiJXQ98iNTVVpTFp0OHVipJN/fTTT/L2229Lo0aNVOoLOslIZdm4caN6HGksBw8eNOvMIjUKxbR169ZVHdqCRbp79uxRaUe46q51eE1fG/viCn///bfZfiMlClfva9euXWhb7GfB43P69Gmrj1NxcHUfxecxMTHy0EMPqf4p6ieOHz+u6iusPU5/W/Ge/vzzT+O/EZzgORhFQo0Jfix9Xnh90xErd3J7DQSKelavXq0KQypXrqzyuzB8hCIfS5EuTgR01FFgkpKSogp10tPTZdasWepx5JAhx2zMmDEqNw+FJDiJUMCCPyJr2tDgw3vmmWcsXiGwtg0qOf5Bwer26Nx5drfRbNECCa3iG1P+ERGR90AKS5MmTVR/BZ1WXJ3G1W5chS4IHcd33nlH5fIjNSc5OVm2bdumUmO0XHh0ltHO4MGDVRrM66+/rtKY8BxcjUdnGHUSmJ0IBc/oeyHfHiMhyNMPCwtTNRG48o5OOYpybYU20NnGRdayZcta9RwsmIeLsKjFwHOxn8j/N00B0uBCM/p1KCzGe8ct0n5wwdia41Qc7PM333wjmZmZqu+IzwKjGQiykL6FqXVRG7Fy5UpjXQKK33EMbX1PqK1AoFCtWjVZvHix6lNiNiZ4/PHHZe7cuaoGA31ZzAyFwAifI9LG8L4swf7htTG7F/rUPhtAIOJavny5Gi7q2LGjug8HDKMRqGDXiktMIzhMUYVIUgsGME0WAg78EVSqVEkVnnTu3FkGDRpkXFQDz8OHjW2taQPDYQhkPvroI/VHhRPE1v2gkhdcPkaue2G45GVn2fzcrAuX5OyatZKbkVki+0ZERK6HRd285TXROcU0nCh2xgw8SEfBDEDoABaEaVlRhIs+E/pJ2LZDhw7G6VLR78Bj6Lsg8wIdXmRiaAXPmFoVnWNMw4r8enR+UR+gdVjR4cZz8Tie16BBA9V/evbZZ216T3gf2hV7dIqtgaAAxwJ1C+gco/+G2g1LMK3poUOH1DFDXw0zDqGTrhVyF3ecioNjhH7km2++qY4NRhGwfwimtEJsBGavvfaa3HvvvaoGAccWwYat7wkjHLjojFmeEEh++OGHqnhbO464iI6+K14LwQAydBBYXEu/fv3U54bjhOwba9PA7OFnKCrZygUQUfXt21e+/fZbs2EdHFR03LXpxzT4UHEwtanCtCAEBx4fdteuXVXRCU4URGkanEQISJDaVFwb+MPBiALmUsZVAXywODmOHDli9X6gDVtpQ1m4MuBuGEnBHyiGV3E1oSQhlejAyNFS95knXToSgPSl4wvflSZzZktEXfOiMPL884Z8B88bsgWuDKNzGhsbqzrJWgcpJ/minFk8XK1G7Q5+gUFS/an5Tl2JujRAfw0BEzrPJQ3BAM4fBBUl2bEeV8x7wkxRGB3BiJFpIbUr4P2jkBt9bkv1E7b0Rd06AoEhFsAXgSlEYNpjps6fP19oW0RoGJKLi4tTHX/8z6jgsI1pe8W1oU2/tW7dOvVv7daW/SAiIiLXQccdHfjc9FS3HPaAsEgGD1SquDWA0IqOC9Y6oIreUn4XtrdUF4Hts7KyVGRVVHt43Jo2rN1vR9uwBINBCIDcTftcXFEUrn1mWdl68XPg2NkKr6e9vr8HHHNf4MrzhnwHzxuyBf4fqyVO4Na0UNUvopwERpT8/PdFsTQbT2mFjA8s6HYtyPTAZ1jwcywpqC9BPey1YAE+pEE5wlDMe9LqanHr6nMGr4fXxfeupfpe7HfBeg6PDCC04ROk/5gOpeALwlLxDLaxNI0WtkfKBDrwWnsFH9faK64Na/fb0TYswfy+SAHxFK6YgSEvLn9kKD4uTvzsqGWwlyEhUd1iKM8/kx1eZ3LVzB3kW3jekLW0aSwduWBHJQupOVi87lqQ7YHMDdOLiSUJdaqm6e2WYFYqR/dl0qRJ6raodjDD0759+665TUnB3wxqR0zXkrB3ql63BhBaGhBW6DOdlgq/W1q0A6lJP/zwg9l96MijyBlpSjgR0YHH803hd62wubg2rOGMNizB7AGmKwq6CyJT/M8cRVaWAjlnSg8JFVSXVI6NlZDYkp0xwFRmULCcFVF5gGG1rVtghjznvCHfwfOGbO0AaUXGuGho7dVSci1XrYZsC/TdUCRd2s+bwMBA1efWLrqbOnbsmPXtiBuhyh9V7Tt37jQGEKhjwFzGmO6qIKy5gBkCML2YtvoeZkMCFE/jhGjWrJm6D8XZGrTfokULq9qwhjPasAT770nFp+gElvT+5F0deQoO0kmIhZO5pBiCdMYvOU865r7AFecN+R6eN2QNzGyjdf5wW5LFsORbtHSh0nzeBAQEqL8hfN9aKqK2JbBy60JyGCZBoIDO+JYtW9QiHSNGjFBRYpcuXdSHffHiReMQD2Y5QoCAbTCDE9Z8wFARpizTRhgw/zHm8MWUW5ipAdNtIS0I03xZ20ZxnNEGEREREZE3cvtK1FiQDfPkotgG07ciOlq2bJlK58GMRm3btjWuIIjICAtpYNENBARY7KR9+/YyefJkY3vYHnPmIveud+/eqnOPuYi19RqsaaM4zmiDiIiIiMgbuX0lagQMWDkaPwWhg266/oJWfIIV/a4FIwH4KYo1bWjuu+8+9eNIG0REREREvsLtIxBEREREROQ93D4CQUREROSoS2mJkpJ1xS0HMio4QsqHe9asQ0QliQEEEREReX3w8MKmKZKdW3iNJlcICgiSt+5+xaYg4ty5c7J//37p3r27w68/cOBAqVq1qsycOVNKo8uXL6vp9U1n4CxpO3fulEGDBqlJgJByX9owgCAiIiKvhpEHBA+9G3aVCi4eCbiYlihfHPpW7YMtAcTYsWNVp98ZAURphxk3z54969IAorRjAEFEREQ+AcFDbCSnUy9tDAaDu3eh1GERNREREZELIeUIC9B+8cUXcscdd0h2drbMnj1b2rVrJ02bNpUHHnhAfv31V7PnYN2pRx99VD1+2223ySuvvKJWctekpaXJ+PHj1cK5WNR23Lhxkp6ebvU+nTp1Sh5//HH1XLwG/m06E2b9+vVl3bp1Zs8xvW/BggVqOv533nlHWrVqpfYD+3PlyhWz7T/66CP1/ho3biz33HOPSgEytW3bNvU49gFT88+YMcO4HpjWBmbBvP3229XjL774ojqOOJ54zFpYK+ypp55S+4r3jGUFtFXOtc8I65S99NJL6r1g/S+8lun70SB9Cosjmz4fHnzwQZk1a5b4IgYQRERERC6EzjY6yHfffbesWbNGdbS3b9+uOqzoDON+dG7RmYYzZ86odacqVqwon376qXo+tp8yZYqxzc2bN6vH0aFHSg/W0Fq6dKnV+zRy5Ei1GO7atWvl888/VysWP/fccza9rz///FMFPsuXL1eBxO7du9VaWabwHu+991758ssvpUOHDuo19u3bpx77/vvv5emnn5aOHTuq94H3h/eBfTO1evVqFURgTS6swYXjheNZMOgqCjr66NxjQeOVK1eq/cXCxVjc2DRAWLFihZQvX159RgjwEOzgvoKwv+XKlVPvSXPy5En5/fffpU+fPuKLmMJERERE5ELR0dFqwdyQkBBJTU2Vr7/+WtavXy8NGzZUjw8ePFgOHz6sFtZF5/Szzz5Tz8FCuYGB+V23adOmqSJszU033SQjRoxQ/65Ro4a0adNG/vrrL6v36d9//1UjG6jLwL7htU6cOCF5eXkqmLB2od233npLBSIwadIkeeKJJ1Q7derUUfdhba2HH35Y/XvUqFFq5GDVqlXqCv+SJUvkzjvvlGeeeUY9Xrt2bZWe9Oyzz8qxY8ekXr166n4EIBjB0OA4Yp8rVKhg1X4iAAkLC1PBDIIIQEDSqVMnFQRo+4fX04KXWrVqqWNqesw1+Ey0oEjbd3ye2Edtn30NRyCIiIiI3OTgwYPqtn///uoquvbzzTffqDQb+Oeff+SGG24wBg9w6623qqv1GnRwTZUpU8Ys9ac4CD7ef/99ldKD0Q+MaCAtx9rgQdsHLXgABAXa/mvQvim8V+1x3GrP0bRs2bJQGzVr1hRHoK0bb7zRGDwAgg8ELKavowU9msjISJVuZglGGpAGduDAARX0fPXVVxYXIvYVHIEgIiIicnMBMGoDwsPDzR7TOu+mgUNRAgICHNoPXHXv2rWr/PTTT/Lbb7+pK/KLFi1SV9KRxlNQTk5OofswCmAqNze30L4VfC/YRnufloqhMQJS8HkYcSiJomu8lul7MA0wilOvXj1p0qSJChwQuF26dEl69OghvoojEERERERuct1116lb5ODjyrr2gxoArUAZnVOMVGgdcq1eAAXYWVlZDu9DQkKCvPrqq6LX69VVc+T7oyOMfUKKEaBjbVofcPr06ULtIO8fKVkaLd2nUaNGZnUSprANRlcARdBaPYRmz5496rZu3brXTJ2yBV4H+2E6moAOP97TtV6nOH369FEF1d9++6107txZoqKixFcxgCAiIiJyMYw2oJgXaTGYUQizKm3dulUVTKP4+d1331W1DFp6ExZLwzZIa0JxMgqlkcYUHBzs8L4g3QkF2xMnTpRDhw6pffjkk09U0IBUH7j55ptVcTUeRzCD4uWCV+gx69OYMWNUGtD//vc/FZR069ZN1VVoULS8YcMGFWxghiLM9IQCcRgyZIhKnVq4cKF6/Mcff5SpU6eq43Otjj3qGS5cuKD22xqYLQqzVo0ePVrVmmCGq+eff17Kli3r0Loc3bt3l+TkZBX49e7dW3wZU5iIiIjIJ2BRN295zX79+qnF5Hr27Kk67yg+RtExOqAIHKZPn27shKKuADMFYWSgV69eqsOPjnnB2YnshfQgBC3o0GOqWEwPi4JuFDVrQQwCBvxgilXM9oQOd3x8vFk7sbGx6nlIh0LaEqZpRaF0wfeNmYwQZKDGAoXiuIW77rpL5syZo1KnEERgZiOkAWGK1WvBMcGIDLZFAGJah2EJVo5G4TaOpzYbEwqk8bsjowYRERFq5AGjNmjPl/kZuPqGR9CG9ExnFXAXXEHAFQZ8CSCqL0lXjp+QAyNHS91nnpTQKlXEVTLOnZPjC9+VJnNmS0Rd8yIp8vzzhnwHzxuyBXLLcQUeHVV0orXc+ktpifLCpilqNWp3CAoIkrfufsWmlah9DaaWxRS0GEW5VuoQ1nVwR3Ex0r9w/qB+wtF6kWvB+hEoBNdmxPIkeP8Y2UGxuKU6Elv6ohyBICIiIq+Gjjs68ClZhRf5coWo4IhSHTyQqNoHXETD2g9IL/N1DCCIiIjI66EDz058YUhNQjrQtWC15b59+4ovwKrRpsXmGi3hBjNKobPvbO+99566uo+aDYyQ+ToGEEREREQ+CjULXbp0ueY2MTExDr/OsGHD1M+1oGC6pKGA2VJ2PoIKzFhVcKpcZ/nkk0+kNGEAQUREROSjUCuCn9JCK/q+Vg0EOY7TuBIRERERkdUYQBARERERkdUYQBARERERkdUYQBARERERkdUYQBARERERkdU4CxMRERF5vayLF0WfkuqW19ZFRUpwhQpueW0id2AAQURERF4fPOx7drjkZWW75fX9g4Ok2TvzGURQqcEAgoiIiLwaRh4QPFS7v48EVyzv0tfOunBJzq5Zq/aBoxBUWjCAICIiIp+A4CG0ShXxBvXr15fp06fL119/Lfv27ZOoqCjp16+fPPfcc8Zttm3bJgsXLpSjR4+qFZS7d+8uI0aMMC6GZk0bP/74oyxYsECOHTsmlSpVUm0888wzEhQU5Jb3Tb6BRdREREREbjBr1izp3bu3fPPNNzJgwADV0d+9e7d67Pvvv5enn35aOnbsKOvWrZMpU6bIxo0bZeTIkVa38fPPP8sLL7wgDzzwgAoyXnnlFdm0aZOMHj2anzc5hAEEERERkRv06tVL7r33Xqlevbo89dRTagQBIwmwZMkSufPOO9VoQe3ataVTp04qANiyZYsaTbCmjcWLF6vgAaMSNWrUkLZt26pA5Ntvv5WzZ8/yMye7MYWJiIiIyA3q1q1r9ntkZKTo9Xr173/++UelG5lq2bKl8bF69eoV28bBgwfljz/+kDVr1hgfNxgM6vb48eNSrVq1Enlf5PsYQBARERG5gaU6BK2Dr92aysvLU7eBgYFWtYHthwwZolKcCqrAaWfJAUxhIiIiIvIwKJDWUpE0e/bssTjqUJTrrrtOTp48KTVr1jT+xMfHy+uvvy5paWklst9UOjCAICIiIvIwGDnYvHmzmoUJQQBmU5o6darcfvvtVgcQTzzxhHz33Xfy9ttvqzZ+++03GT9+vKSmpnIEgrw7hQnDazixP//8c3VC33LLLTJp0iRVDGTJ5cuXZdq0aWpmAT8/P5UfOGbMGAkNDTVugxkGMAsBCoTq1KkjY8eOldatWzu1jYSEBHnttddk+/btaqjwtttuk3Hjxqkp0oiIiMj1sCaDr7zmXXfdJXPmzJFFixapIKJcuXLSo0cPGT58uNVtdO3aVebOnSvvvvuuKqiOjo6WO+64Q0aNGlUi+0ylh9sDCPxRrF69WmbOnCmVK1eW2bNnq6h7w4YNFvP68IeTkZEhK1askJSUFJkwYYKkp6eracxgx44danoyBARt2rRRhUNDhw6V9evXGyN2Z7SBadFycnLk/fffVwEEZjV49tlnzQqVyPOln7F/FoqA0BCvmW+ciMiX6aIi1WrQWNDNHfDa2AdbHDlypNB9W7duNfu9W7du6seRNu6++271Q+QzAUR2drYsX75cRcKY5xgQKbdr104N2yHSNrV//37ZtWuXmgdZ68i/+uqrKuDAvMi4+r906VLp3LmzDBo0SD2OkQM8b+XKlWpbZ7SBoANt4KpAw4YN1TYIMDDVWlJSkorwybP5BwWr26Nz5znUTrNFCxhEEBG5GVaAbvbOfLUatDsgeOAq1FSauDWAOHz4sCriMU0NwvzFjRo1UougFAwgUDyEWQNMc/8wpRnSkPbu3auG6lBwhFQiU61atVIBibPawAqQWBESIxLalGpffvmlmqcZ+0+eL7h8jFz3wnDJy86ye8gaV7pyMzKdvm9ERGQ7dODZiScqBQEEZgKA2NhYs/srVqxofMzU+fPnC22LNCdc8Y+Li1MjA0hFQipUUe05ow1sj5Qr1Gq0aNFCBR94fNWqVeLvb39dOlKh8NruhvQu09uSlJmZ3wHPytaLX5Z9nXm7RUaIn0TY99xsvXH//T3gM/MErjxvyHfwvCFbZGVlmU1zmpubywNIVuF5I+rvBbXH+N7VpgQueIzQp/X4AEL7H0fBWofg4GBJTk62uL2lughsjy8VrTNqqT087qw2cIAPHTokTZs2ValP+ECQeoUUpo8//lgiIuzrlGLhF7TrKU6dOlXir5EXlx+UxcfFiZ+dowHuYEhIVLeY1cI/kx1mV5835Ht43pC1tDUQtP8nE9miNJ83WVlZqn73xIkTRW5jqY/scQEEUoG0Wgjt39obNJ0RyXR7bFsQtg8LC1OdfK29go9r7TmjDczQhNEGTKmmBQuY3QBTq6GI+tFHH7XjaIjodDrjypLuhCAL/zOvVauWxc/BmdJDQgUlYJVjYyUk1nzUx5NlBgULyq+RthZWu5a7d8cjuPK8Id/B84Zsgf8X//fff+rf+P+1tVdLiXDxF+dPaT9vAgMDpUaNGsb+rqljx45Z3464kZZKdOHCBfVmNPgdC6gUhLSiH374wew+dPRRuIwUIqQhIQjA803hd216VWe0gToKdBxNRxrKlCmj7jt9+rTdxwMnNF7bU6ATWNL7k3c1cAwO0kmIhZPZUxmCdHI5MkDO5yZLaKbtU/iF6kIkNrKi+CJXnDfke3jekDWQJoz/V2qpFgEBATxwZBUt3a00nzf+/v7qB9+3phfuNbYEVm4NIBo0aKA64Tt37jQGEKhBOHjwoAwYMKDQ9lgj4o033lCddKymCJgNCZo3b67eeLNmzdR9ffv2NT4P7aNWwVltIAj55ptvjJEsoHYBa0b07NmzxI4XeY4LuVfkg3tiRA59IGJn1tm8blN8NoggIioJGKkv7WkoRPbS6my1vyNHuDWAQJ4VAgV06LFAStWqVdU6EOigd+nSRUWLiYmJEhkZqSKlJk2aqM79iBEjZPLkyepAoJC5V69extGBwYMHqylVMZNT+/btZe3ataquYPr06epxZ7SBbZctW6bWgnj++efVfW+99ZYKJu677z63HU9ynSxDjrrtXrmVVK1m3YqgmotpifLFoW8lQ88ZnIiIbIErx+gTXLp0SXWCcBGyNKejkPXQp9QCz9I2AmG4OkkPsmmQaeOM9+/2heSwqBsKOiZOnKgKmDFCgM45vhhwRb9Tp04yY8YM1THHlwRWrcaibY888ojqsGPaVSzLrmnbtq1aIRoL1KGwGTUFqE/Qpm11RhtIdcLidwh20AaGgzA6gfvwxUalR0xQlMRGcvVxIiKXfe/GxMjFixdVZwiBBJE1MOsQ+puoAXBkxkxvFh0dXWiWUZcGEJgKVbta7yhEQVj1GT8FVatWrdAqi/jimD9//jXbxAgBforijDYQTCCoICIiItfRRhyQ+uyMVAwqPRM2YPYhnDelcaIPnU7n1JEXuwIIzDZ02223qVEBrNhs7ZRPRERERM6AzpClQlAiS7R1D5B5wvPGTQEEUoqw8vKoUaNU/mH37t1VMNG4cWMn7BJ5m4xz5+xekTn9DCZDJVvEpV5wqH7Cl2eAIiIiIg8NIO699171g1SmL774QgUTWEANtQIIJDATUfny5Z2/t+SRwcO+p4c53I5/kPdM4eru4OH5ja843A5ngCIiIiK3FFGjDuKpp55SP3///bfMnDlTFRbPmTNHpTlhlWbMekS+Sxt5qHZ/HwmuWN7u4CG4fIyT98w3aSMPvRt2lQrh5Wx+PmeAIiIiIrfPwoRF1TAC8f3336s1HNq0aSMdO3aUbdu2yUMPPSRjxoyxe2Vm8h4IHkKrVHH3bniV/1Li7X4OggfO/kREREReE0BgETYEDV999ZVaUh7rNwwcOFClL2mrS2N9B9RILFq0iAEEkYmggPxJBxbsfN/hNoiIiIi8IoC46667VBU7ZmCaOnWqtG7d2uJ2derUkVOnTjm6j0Q+JSasrDzX6lHJzs22O3hAG0REREReE0C8/PLLqlC6uEXTnnnmGfVDROYYABAREZG3smspvu+++06tAGnJ4cOH5Z577nF0v4iIiIiIyJtHIFAsbTAY1L937dolu3fvlsTExELb/fjjj3LmzBnn7iVREeJTsiVDn39e2ipU5yeVo1hLQERERFQiAcTnn3+uCqexhDx+pkyZUmgbLcDo0aOHTTtBZG/wMGqdY8HqG/dVZxBBREREVBIBxMSJE6VPnz4qSHjkkUdk0qRJauE4U/7+/hIVFSXXXXedLftAZBdt5KFn42gpH2FbOc+lKzny1Z9Jdo9eEBEREZVWVve6UDDdsmVL9e8PPvhAbrjhBgkPDy/JfSOyCoIHpiIREREReVgAsX79eunQoYOULVtWzp07p36upVevXs7YPyIiIiIi8sYAYty4cfLZZ5+pAAL/vhbUSDCAICIiIiIqxQHEli1bpEKFCsZ/E/mCc0n2LeYWl+oneVlhTt8fIiIiIp8JIKpWrWrx35qcnBy5cuWKREdHO2/viEpIUED+EiiLfrlgbwsi0l6SY3Odul9EREREPrkSNYKFxYsXS82aNdWicTt37pThw4dLSkqKKrSeP3++lClTxvl7S+Qk5cID5am2FSU7N8+u5/9zLl5+PR0o2Tn8SIiIiKh0sWslagQIixYtUgEDTJs2TY08jB8/Xv7991958803nb2fRCUSRGD2Jnt+yoRw+lciIiIqnewKIL755hsZOXKkPPzww3L8+HE5evSoPP300zJo0CAZMWKEbN261fl7SkRERERE3hlAXLhwQZo0aaL+vW3bNrWAXPv27dXvlStXltTUVOfuJREREREReW8AUbFiRTl79qz6N0YbGjZsKOXKlVO/79+/XwURRERERETke+wKIHr06CEzZsyQxx9/XPbu3St9+vRR90+fPl0WLFigCquJiIiIiMj32DUL0wsvvCBhYWGye/duefHFF6V///7q/j///FMee+wxVQ9BRERERES+x64AAitNP/nkk+rH1CeffOKs/SIiIiIiIl8JIACF0jt27JD09HQxGApPadmrVy9H942IiIiIiHwhgPjll1/UwnEZGRlFjlAwgCAiIiIi8j12BRBYKK5OnTpq4bhKlSqpaVyJiIiIiMj32RVAYPG4hQsXSosWLZy/R0RERERE5LHsGjqoUqWKXLlyxfl7Q0REREREvhdAYPald955x7iYHBERERERlQ52pTBt2LBBzp8/L3feeadagTokJKRQEfUPP/zgrH0kIiIiIiJvDiAqV66sfoiIiIiIqHSxK4CYMWOG03YgLy9P3n77bfn888/V2hK33HKLTJo0SapXr25x+8uXL8u0adPk559/ViMd3bt3lzFjxkhoaKhxm02bNsmCBQtUihVmixo7dqy0bt3aqW3o9XqZP3++rF+/Xu33jTfeKBMmTJCGDRs67dgQEREREXkah+ZfxWxMH3zwgbzxxhsqpWnPnj02F1djNqfVq1fL1KlT1UrWCCiGDBki2dnZFrfH+hOnT5+WFStWyLx58+Snn36SyZMnGx/H4najR4+Wfv36yRdffKE6/UOHDlX76sw2sP26devktddek7Vr16pUrieeeEIFE0REREREvsquEQh08jFKgI4zVqHGVfy7775bBQP//vuvrFq1yqoUJwQJy5cvl1GjRknHjh3VfXPnzpV27drJ5s2bpUePHmbb79+/X3bt2iUbN26UunXrqvteffVVFXCMHDlSrUmxdOlS6dy5swwaNEg9jpEDPG/lypVqW2e0cebMGfXeFy9erPYVMKKBxfP++usvs5EKIk/0X0q8Xc8L1YVIbGRFp+8PERER+XgAgUABhdToNKPj36ZNG3U/rto/++yzKgiYNWtWse0cPnxY0tLSzDrcUVFR0qhRI9m9e3ehAAIjHBUqVDB2/KFly5YqgNm7d6907dpV9u3bJ+PGjTN7XqtWrVRA4qw2tm/fLpGRkdK+fXuz/d66davVx5DIHYICgtTtgp3v293GvG5TGEQQERGVYnYFELj6jjSgPn36SG5urvF+5P/jfqQ0WSM+Pv8qaGxsrNn9FStWND5mCmlSBbcNCgqS6OhoiYuLk5SUFElPTy80+mHanjPaOHnypKrRQECxZMkS1SaCHgQdpoGJrTCag9d2t4yMDLPba8nMzFS3Wdl68cvKElfS0tz0OTmSrXfpS0uOOu91kpuTazwG3iDcP1SeuPkhyc61/YAlZFyWr49tkcupSVImIMKh84aI5w3Zi981xPOmZGhZRSUWQFy6dKnIYmGkAKETbsuXADrwpoKDgyU5Odni9gW31bbPysoyduQstYfHndUG6jxQQ4GRGBRfY/Rh0aJF0r9/f5UaFRMTI/ZAYfahQ4fEU5w6darYbfLi8oOq+Lg48ct2bQARp8ptAiQhIUHExXFXmqr1CZHLyUny7+nTUhroszONAXRGSKpD5w0RzxtyFL9riOeN81nqIzstgKhZs6YqPL7tttsKPYb6AjxuDW39CFxJNl1LAh110xmRTLe3VFyN7cPCwlQnX2uv4ONae85oIzAwUAURSNXSRhzw7w4dOqiia9RT2EOn00m9evXE3RBk4Yu5Vq1aFj8HU+khoXIEU/vGxkpIrIun9k3MFvnjggrYKkXadSrb7UJW/hX8smWipYaV57u3C7pyUeSSSO3ataVWdDWHzhsinjdkL37XEM+bknHs2DGrt7Wr1/XII4+oImpcMb/99tvVcAeuyO/cuVMVRResHyiKlkp04cIFqVGjhvF+/F6/fv1C2yOtqOACdejoJyUlqRQjpCEhCMDzTeF3jIw4sw0EEabpSghMkNbkyOrcOI54bU+BTmBx+5N3NfALDtJJyNXgy1W0IFkXGChBOusiZmcJDAhQtwGBAYUWUvRVwfr8Y4z3e63zwprzhojnDTmK3zXE88a5rE1fsnsa1759+8oLL7ygpjHF1KXImcIMRrgK/9hjj8lDDz1kVTsNGjSQiIgIFXhokP508OBBtR5EQbgPdQgIVkxHPKB58+bqjTdr1sx4nwbtt2jRwqlt5OTkyJ9//ml8HKlPmJ3J2tEXIiIiIiJvZHfeBwKHe+65R3W0cTUesxI1adJEXcG3Jc9qwIABquga6yhUrVpVZs+era7wd+nSRRVoJyYmqrZx1RPto3M/YsQItQ4DCo4xEoLpU7XRgcGDB6s1G1DUjFmSUPCNuoLp06erx53RBgIJpG9heldM64r3jEXlAgIC5N5777X3kBIRERER+V4A8fXXX6sF3w4cOKCuwgM69+iUY+QB6yfYArM2oZ2JEyeqq/i4ur9s2TJVD4B0oE6dOqmVr++77z41OoBVq6dMmaLSqFCvgGlXx48fb2yvbdu2anE3FDhjRAQ1BVivQUs3ckYbgFWqEfg899xzar/x/rGoHgIhIiIiIiIp7QEERgNefPFF+fbbb9WV+u7du0v58uVV+hJSgjASMWzYMHUFfubMmVbvAK7aY/0I/BRUrVo1OXIEJbr/DwWzuNp/LRhNwE9RnNEGUq8wgmG6gjURERERka+zOoBYvXq1WvdgwoQJKu2oYKEFAgyMTODKPVJ87r///pLYXyIiIiIiciOri6jXr18v/fr1k4EDB1qs0sZIwsMPPywPPPCAmsqUiIiIiIhKcQCBxaNQUFycdu3ayT///OPofhERERERkTenMGHhljJlyhS7XdmyZSUtLc3R/aJSJD4lWzL0Bpufdy6p8IKAREREROQhAQSKpZGmVBx/f3+1LZG1wcOodWccOlhBAXYtZ0JERERErlwHgsgZtJGHno2jpXxEoF3BQ7lwnsZERERErmJTzwtTlmL60mu5cuWKo/tEpRCCh8pRQe7eDSIiIiJyVgCBBd6guPSk8PBwNY0rERERERGV4gDiww8/LNk9ISIiIiIij8fkcSIHJKX6yX/xGTY/LzgoQMqXY8oWEREReR8GEER20Pnnp/Jt3eUvW3edsusYjnqiLoMIIiIi8joMIIjsEBUioqtzQG4tc6OUr1jVpucmpejlx98SJCs7l8eeiIiIvA4DCCI7+QVlSXSkcBSBiIiIShWuwEVERERERFZjAEFERERERFZjAEFERERERFZjDQSRm1xIyLbreZwCloiIiNyJAQSRi+l0fur206/P2d0Gp4AlIiIid2EAQeRiZSJ18kCPWNHr89eSsAWngCUiIiJ3YwBB5KYggoiIiMgbsYiaiIiIiIisxhEIKrUu669ItiHHrucm5mU4fX+IiIiIvAEDCCq1wcOycz843E6gP/+EiIiIqHRh74dKJW3k4dao6yUqMMzm5+uvXJEr+36XqGq2P5eIiIjImzGAoFINwUNZXbjNz9P750pulu2zKBERERF5OxZRExERERGR1RhAEBERERGR1RhAEBERERGR1RhAEBERERGR1RhAEBERERGR1RhAEBERERGR1TiNK1Ep45d8SUSfZfPzgtISJSbbvpW7iYiIyHe4PYDIy8uTt99+Wz7//HNJTU2VW265RSZNmiTVq1e3uP3ly5dl2rRp8vPPP4ufn590795dxowZI6GhocZtNm3aJAsWLJCzZ89KnTp1ZOzYsdK6dWuntmHqq6++ktGjR8uWLVukWrVqTj0+RM4OHkI+e9Ou5+LMHo1/JF0UKVeDHwwREVEp5fYAYuHChbJ69WqZOXOmVK5cWWbPni1DhgyRDRs2SFBQUKHthw8fLhkZGbJixQpJSUmRCRMmSHp6usyaNUs9vmPHDtWZR0DQpk0bWbNmjQwdOlTWr18vdevWdVobmv/++09effVVlxwrIo1f0kXxCwyw+YD4J11Qt/qbbxdDRLRNz01LOCtlD+0W0WfygyAiIirF3BpAZGdny/Lly2XUqFHSsWNHdd/cuXOlXbt2snnzZunRo4fZ9vv375ddu3bJxo0bjR15dN4RcIwcOVIqVaokS5culc6dO8ugQYPU4xg5wPNWrlyptnVGG6ajJwg0brjhBhV0EJU0v/Qr6jbox08lJDDR7nYM0RXEEF7GpufkZCbb/XpERETkO9waQBw+fFjS0tLMUoOioqKkUaNGsnv37kIBxJ49e6RChQpmowAtW7ZUaUh79+6Vrl27yr59+2TcuHFmz2vVqpUKSJzVhmbx4sWi1+vlueeeYwBBrpGXX4OQU7+FZMeG29dGoM7m4IGIiIjIIwKI+Ph4dRsbG2t2f8WKFY2PmTp//nyhbZHmFB0dLXFxcSodCalISIUqqj1ntAF//PGHGj1BehPaJHIlQ2ikGMqU40EnIiKi0hVAoA4BCtY6BAcHS3JyssXtLdVFYPusrCzJzMwssj087qw2EGAg7Qo/tWrVcloAYTAYVNvupn0u2u21aMcrK1svflePj61pbKDPyZFsvbhMTk7+i+Xk5kiOn+0zC+Xk5qpbPdq5+h5cIUd/dQQCx8uFr5v/mvnvOSsr2+J5ast5Q8TzhuzF7xrieVMy0A9FRo7HBxAhISHqFh0h7d+AjrrpjEim21vqNGH7sLAw1cnX2iv4uNaeM9rADE61a9eWfv36iTMhHerQoUPiKU6dOlXsNnlx+aMy8XFx4pdtewARp1L6AyQhIUHEhbHTZUP+i2HEySB2dMSvdqATEhPFX++6yCcjBQcsXFKSk8U/yLXFzDkpyYKxu3Pnzkm6/pBD5w0RzxtyFL9riOeN81m6yO5xAYSWSnThwgWpUeP/p4XE7/Xr1y+0PdKKfvjhB7P70NFPSkpSKUZIQ0IQgOebwu8ojnZWG2vXrlUHuGnTpur33KtXo1Gz8dRTT6kfe+h0OqlXr554wtUdfDFjdMVSIGcqPSRUjuC4xsZKSKx52pdVErNF/rggMTExUinSdaejvz5Z5GJ+zU3ZQNtrCfSBgZIkIjHlyokuxnWpRJf1mHkpR6LKlJFKlWybRclRV/zzA5YqVapIzXoNHTpviHjekL34XUM8b0rGsWPHrN7WrQFEgwYNJCIiQnbu3GkMIHBF+ODBgzJgwIBC22ONiDfeeENOnz4tNWvWVPdhRiVo3ry5GnZp1qyZuq9v377G56H9Fi1aOK2NgsXUBw4cULMxLVmyRK6//nq7jwdeG8GLp0AnsLj9ybs6chQcpJOQq6M3ttACXV1goATprIt6nSHQoMu/DQiUwEDb/wwMAflTqOoCdVZH684QqMO+5qh9duXrqte+Om1scHDQNc8La84bIp435Ch+1xDPG+eyNn3J7QEEOkAIFNChL1eunFStWlWtA4FRgi5duqgr+4mJiRIZGalSj5o0aaI69yNGjJDJkyerPGwsOterVy/j6MDgwYPVmg2Yyal9+/ZqtABpQdOnT1ePO6MNLfDQaMXVuDKLEQwiIiIiIl/l7+4dwKJu999/v0ycOFEeeughCQgIkGXLlql0HsyK1LZtW7VmgxYZYdVqrPb8yCOPyAsvvKA6+AgENNj+tddek48//lh69+6tplfFdKvatK3OaIOIiIiIqLRy+0rUCBiQ/oOfgtDJP3IEGfb/D7ny8+fPv2abGE3AT1Gc0UbBNSIK7icRERERkS9yewBB5M2yk1FKbTu/oEDRleFibkREROR9GEAQ2cHvakHxxW0/2338qvW9j0EEEREReR0GEET2/OGEh0uFDu3EcHVxNVvkXLkiSQf+EEO27QvYeYILaQkiif9aXFQwPvOShCadlZDM/1/XRROqC5HYyIou2ksiIiIqKQwgiOz94wm3ff0Ibxbon/918cmfX8m5o5uK3vBs0Q/N6zaFQQQREZGXYwBBRFYJD8oPmO5rdLdklys8kpCVla1WJMeiglgrwtTFtET5+cDXknHuqGRFqeXHbeYfHCK6clX4aREREbkZAwgiskn58HJiiMxfM8VUpi5TsoPSpXJEBbVui9kXTeplGf1vosi/c+Q/B4539acXMIggIiJyMwYQRFTi/PX6/H806yzlK9m+nkpOyiVJ2r5W8rIynb9zREREZBMGEETkOhHlRBfDNCQiIiJvxgCCiGzin3RB8izcH5CVLaGpCRKQECR+BWogglISeZSJiIh8BAMIIrLy20KnboJ+/Mziw6h6iMI/9lt+zLQNIiIi8l4MIIjIKobwMpLd8QGRnKv1DAXo9XpJTEiQcjExotOZBwrJmcmy5d/d0i8imkebiIjIyzGAICKbgoii5GVnS1aGXvKiYsQQZJ7CpNf5S8rV1buJiIjIu/m7eweIiIiIiMh7MIAgIiIiIiKrMYAgIiIiIiKrMYAgIiIiIiKrsYiayA2CAgzin3ZZ/IIMNj3PLz1VRDgVKhEREbkPAwjyapf1VyTbkGPz8xL06Ii7h78+Q+pUyBX560ebnxuQGykit4kE8E+XiIiI3IO9EPLq4GHZuR8caiPQz/VTi/rn5Qc8WVXqS0BMRZuem3vFT+QPEUNoRAntHREREdG1MYAgr6WNPNwadb1EBYbZFTxEBhrXSHa5vOAw8b/GugqWGHKR8mT7iAsRERGRszCAIK+H4KGsLtzdu0E+Sp94TvKyMu1+vn9wiOjKVXHqPhEREbkTAwgiomsED2cWDXP4+FR/egGDCCIi8hkMIIi80IVkpDLlufx1g3UiFaJKz+zP2shDdJs+EhhV3ubn56RckqTtax0awSAiIvI0DCCIvEjQ1ZrvT7a7rw5i9L06twUR2Qln3ZJGhOBBF8M0JCIiIvX/RR4GclSiLlICU/IkODjL5ueeS8rmB2CDMuF+0u+2QMnOdf1hS0ozyNa/ciVL7/rX9tMFq9uLX86zuw2mERERETkHAwhySFxSliyp2VtkB4IH+64OQ1BA6UmLcUYQUdoERsVIhZ7DxaC3PUhlGhEREZFzMYAgh2Tq8/Pwu9UOkMqVy9odPJQL56lIxQcRRERE5H7stZFTxIT4SeWoIB5NIiIiIh/HvBEiIiIiIrIaAwgiIiIiIrIaU5iIyClrUOToDZKQGii5OoME6swfT870l6wsrhZORETkCxhAENnJPytNJNeO+VSz0314DQqt0LngXK+hItJJjtRLtP/1A4MkJixavJG961c4wtG1L4iIiIrCAILIzuAh8u8fHTt2/ld75D6yBkVObo6kJidLZJkyEhhg/tVyPiVTth8KlG//+Z/8dCbZ7n147tbBXhVEOGP9Ckdw7QsiIvLZACIvL0/efvtt+fzzzyU1NVVuueUWmTRpklSvXt3i9pcvX5Zp06bJzz//LH5+ftK9e3cZM2aMhIbiKme+TZs2yYIFC+Ts2bNSp04dGTt2rLRu3dqpbRw9elRmz54tBw4cEH9/f7Xf48aNkypVeNXP510deciqXE9yg8Jse2pGhqQePS7RQf9/rvnCGhT6HJEAfa6UjRTRBRbcLgQhhrSr2VIqly2c/lScpMxU+fX0LsnOyS4161c4whlrX+gTz9n9fI5+EBH5No8IIBYuXCirV6+WmTNnSuXKlVWnfMiQIbJhwwYJCio8Nejw4cMlIyNDVqxYISkpKTJhwgRJT0+XWbNmqcd37Ngho0ePVgFBmzZtZM2aNTJ06FBZv3691K1b1yltIAAZPHiwNGvWTD788EPJzs5W+4/9/uKLLyQ4OP/KI/k2BA+GYNty+3NzRHJs70P7hDIhURITVrrmbvDG9SsQPJxZNMyhNjj6QUTku9weQKDjvXz5chk1apR07NhR3Td37lxp166dbN68WXr06GG2/f79+2XXrl2yceNGYzDw6quvqo77yJEjpVKlSrJ06VLp3LmzDBo0SD2OkQM8b+XKlWpbZ7Txww8/qIDj9ddfl5AQXF0VFfjgPezbt89spIKIyJtoIw/RbfpIYFR5m57Llb+JiHyf2y8FHj58WNLS0sw63FFRUdKoUSPZvXt3oe337NkjFSpUMHb8oWXLlioNae/evSodylIHvlWrVsb2nNEGHsPIiRY8ANKYACMaRETeDsGDLqaKTT+2BhxEROR93D4CER8fr25jY2PN7q9YsaLxMVPnz58vtC3SnKKjoyUuLk513jEygFSootpzRhvVqlVTP6aWLFmiAgrUQhARERER+SK3BxCoQ4CCtQ6oIUhOTra4vaW6CGyflZUlmZmZRbaHx53VRkGog1i1apVMnDhRypUrJ/YwGAwqcPGUz0S7vRbteOhzciVb79oC15wcvXH2nxy/a00t6nwBKGRQtdS5knf139bCCJd6bk4uFk8QX5Gbk2N2awpvVd3q9ZKdXXQhdlH0qNC+mvKYmWlbQXJOdv4xxt91ro1/X/qr3wVZ2XrJtfF13cmR9+zo+7b1tW35viHiOUP24neNdf1QZON4RQChpQChY2CaDoSOqemMSKbbY9uCsH1YWJixeLngNqbtOaMN04M9b948WbRokTz99NMycOBAsZder5dDhw6Jpzh16lSx25w7F6duk1NSRHfetZ34y4b8zglGjAzi2uAlKDtNMJloelqa6LNtrIi+2jlLTk0Rv1zfCSA0llL4UtMxZW0ZSUhMkICrwYAtUvVp6hYjhFlBtnWI/dMSBWXuJ0+elNxE2zqpAcnxEoWR0rg4yUvxngDCkffs6Pu297Wt+b4h4jlDjuJ3zbVZusDukQGElkp04cIFqVGjhvF+/F6/fv1C2yOtCAXMptDRT0pKUilGSENCEIDnm8LvKI52Vhtah3/8+PHy9ddfq9tHH33UoWOh0+mkXr164glROv7AatWqZTGIM2W4gkj1jJSJipJKlVw724y/PlnkYn7NTNlA165yHJARIHJBJCw8XPKCbZzGNSBArqA7HRklAWUixVdg5AHBAz6PgEDzr5ZcXf5tTLkYqVTO9hEIXUaSSHL+90WliAo2PTfncrCkHhSpXiZIAsvZNnVujgThZaVybKwEljVPafRk2nuuXbu26CrVsvn5+vOhkvCbfe/b1te25fuGiOcM2YvfNcU7duyYWMvtAUSDBg0kIiJCdu7caQwg0Ak5ePCgDBgwoND2qC9444035PTp01KzZk11H2ZUgubNm6uhF0ytivv69u1rfB7ab9GihdPaAEzx+v3338ubb76p1pFwFF4XgYunwP/Mi9sfbbRGFxggQTrrolZnCTTk90qxaFlggQ5rSfO/ulBaQECA8d/WMlwttg8IDBBd4NWetQ9B8FDwfQUGGFR3PFCnk6Ag2+du0OUEGq+MhITYNkVyTniEpGLE59vFYq+Q8AgJtPF13UkfpFPvGaOtwXZ8p2RdHQ0ODtKJzsb3be9rW/N9Q8RzhhzF75qiWZu+5BEBBDoECBTQoUftQNWqVdV0qBgl6NKli8oxT0xMlMjISPU/pCZNmqjO/YgRI2Ty5MmqZgCLzvXq1cs4OoD1GbBmA2Zyat++vaxdu1alBk2fPl097ow21q1bp6aBRRCBGZwuXrxofE/avhKR9y/mhtWkvXEtB8hOOOvS59nTBuotkDKFUQ8ELlyEjojI87k9gNAWdcvJyVEFyCi8wwjBsmXLVEoPVoHu1KmTzJgxQ+677z4VHWHV6ilTpsgjjzyiroB37dpVpRBp2rZtK6+99pqaZhVrSiAtaPHixcZpW53RBtKWAOtA4MeUtq9E5Bm8NQCwF4IeuPjlPKe0U9KvjXoLpExpuAgdEZFn84gAAmkgWPUZPwVhqtQjR46Y3RcTEyPz58+/ZpsYTcBPURxtA4vfERH54qiLIyMvtr42ZnpCsTbqLQIykyVp+1rjQnZEROSZPCKAICIi3xl1seW1MU0sZnpCsXZAmu/VBBER+SIGEERuor+CuZhs5xcYKIHhrp11ioiIiEjDAILIxfz8sSaCSPKBA3a3UaFDBwYRRERE5BYMIIhc/UcXGiJlbmoihryryzPbIC8jU64cPyYGC6s9+7qsrHA5k5ArGXasCB0a5Cex0a6dZpiIiMhXMYAgcscfXqh90/z63rrV1klM9ZNjRzvJG0exCrVtK1Fr5g2qziCCiIjICRhAkJJx7pzkZuTPfIKpdPPi4iU9JFTyilnPIjP+vNceQf+sNJFc20cBICATS2WRq2Tn5C9uc2djndQuX8am515MzZEv9iRJRjYWsyMiIiJHMYAgFTzse3pYoSNhPnmuZfHB5USq91CFvd4WPET+/aPjDfnZvqoy2a9shL/ElmUqEhERkTt5V6+PSoQ28lDt/j4SXLG82bzswUHXnlYxMCVPZEeWBEZEeNenc3XkIatyPckNCrOvDT9/MQSFOne/fNyFZIwC5Nn8vITU/BEIIiIicj8GEGSE4CG0ShXxy8oSv+wsCYmtLCHB116JNjgYBa1nvfYoIngwBHNK1JIWlD/xlHyy3d7i7xCzdoiIiMh9GEAQUYkrE+4n/W4LlGz7Sk4kLfuK/HXhb4kOb+vsXSMiIiIbMYAgIpcFEfYKzjKI7nKmJGXZXryelGl7yhQREREVjQEEEXm8AP/8r6pfT+20a/0IkZslWc2cde2UPCIiIioeAwgi8njhujBpW6ul5ObZXkNx9nK2xMWJ6HNL6yoaREREzsUAgoi8JoiwR1gg1+wgIiJyJk5iT0REREREVuMIBBHRNSSkJ0l2TrbdxygoMEhiwqJ5jImIyGcwgCAiukbw8PaO9x0+Ps/dOphBBBER+QwGEERERdBGHtrWbCnRIZE2H6ekzFT59fQuh0YwiIiIPA0DCCKiYiB4iAkrx+PkItkJ7lnd3j84RHTlqtj1XH3iOcnLynTLaxMRuRoDCCIi8gh+uvx1Oi5+Oc9t+1D96QU2d+QRPJxZNMwtr01E5A4MIIiIyCMERsVIhZ7DxaDPcvlr56RckqTta+0aRdCeE92mjwRGlXfpaxMRuQMDCCIi8qggwlsheNDFcASBiHwfAwgiKhWOXkix+TmXM5MlKyu8RPaHiIjIWzGAICKfFqwLULdf7sTXXbqNz9aJSCdJrZctMfYthE1ERORzGEAQkU+LLRMmPVumS5Y+1+bnpqT7y29HgiTQj6MQpYU9M0C5a9YoZ8wAxdmfiMgeDCCIvJD+yhW7n+sXGCiB4eGlLoiwx8UUA0pcnb4/5JszQGltuJIzZoDi7E9EZCsGEOTVovS5EpSRKv42Xl0OyEwVb+Tnn5+Ok3zggEPtVOjQodQFEY64kIxAIs/m5yVn+ktGRhk5k5ArGZm2zywUGuQnsdFBNj+PXD8DFIIHdxSAOzIDFGd/IiJ7MYAgr+WXcUX6oTD2wh4HGvEXbxIYGiJlbmoihjzb03EgLyNTrhw/JoYcXlW3RlB+vCafbLf3eIWKSAd543i6HfUX+eYNqs4gwkU4AxQRkXUYQJDX8svL79QlVagpQaFl7GjAXwxB6OCJ1wUR9tI7dU98X5lwP+l3W6Bk2xevSVr2Ffkj/rC0q9VKrWZti4upOfLFniTJyMboB5UG3lh/QUSlEwMI8no5umDRBTMdh0ouiLBXcJZBgi+nScUof4kJd20qUlxStkPBB9OnXMdb6y+IqPRiAEFE5GMQPDz/wRmH22H6VOmov3BkFMNdszj5pyWK/vwpyQqxb0SWs08ROYYBBBGRB/svMdvu5/RuES0VIm3/mndn+lRpHTlxR/2FM0Y+3DGLU87leCnzy2JJcLAdzj5FZD8GEEREHihIl1/gv2DzBbvbqFouSGIivOdrniMn3jXy4egsTvauX6GPP65uw1r2lLCYWK/Zb2eMfHDND/IUbv8/S15enrz99tvy+eefS2pqqtxyyy0yadIkqV69usXtL1++LNOmTZOff/5Z/Pz8pHv37jJmzBgJDf3/YthNmzbJggUL5OzZs1KnTh0ZO3astG7d2uVteJNEXaQEpuRJcHCWZGdnSxyWGUjMlqBiLuSdS7L96mhBl/VXJNtg+yw3qTlpUtHhVy+d7F1HwpE1JHLS0hya/am0rV+Bjv9zXSpKtt726WO1AMTR4MGe0Q/Iyr0i0eHWVZ7j+yYxO1mCr1yU86kBXjty4q2cMfJhTwpUTmqCnP98pkOvG1i2iujsCCC8dd0NrvlBnsTtAcTChQtl9erVMnPmTKlcubLMnj1bhgwZIhs2bJAgC73X4cOHS0ZGhqxYsUJSUlJkwoQJkp6eLrNmzVKP79ixQ0aPHq06823atJE1a9bI0KFDZf369VK3bl2XteFN4pKyZEnN3iI7cBVK+x9BgMgf1l/5DArwtzt4WHbuB7ueWz47R+7DnnrZVKzevo6EPWtIIHi4+NNPdr+mI6/tzdw1euCM0Y96122R4OA0659wUdSaGZj2NjRYL7Fl7Vv8j7wrBapsh4ckICzKpudkZesl7sJFKRtZTkrTuhtc84M8iVsDCFx5Wr58uYwaNUo6duyo7ps7d660a9dONm/eLD169DDbfv/+/bJr1y7ZuHGjsSP/6quvqoBj5MiRUqlSJVm6dKl07txZBg0apB7HyAGet3LlSrWtq9rwJplXr3B2qx0glSuXFX1OjiQkJEhMTIzoAgOtCh7Khdt3KmkjD7dGXS9RgbZ1GLCAnFzcI8EBQcLrjSW/joS2hkR2UpLFkYTcnFwxpGdIbkCqSODVBRQKjHhE1K0n/nZMQ+vt61ckZdm3cKEuQCdRIRF2PTcl84roc+2cuNdPZEDbYLumr/0vKV22HwqQmyo1kesqFT8iq9fnSGJCgpSLiZFTiXo5cVwk5+oUzeTZ3FX8nZuZJYYU+17TlCPT5iJ40MVUcWnRujNe2xt5a9qW3kv32ysCiMOHD0taWppZalBUVJQ0atRIdu/eXSiA2LNnj1SoUMHYaYeWLVuqFKK9e/dK165dZd++fTJu3Diz57Vq1UoFJK5qo1u3buKNYkL8pHJUkGSjz5EuUikyUIJ0rilGRPBQVmfblWVbV58mx9aRyLFy9OLyNR4LCA+36/W9df2KAP/8r9hfT+20u43eje62OYhA8PDFwU3iDllZ+Du+WcqGlpGYsAirLiTpdZlSLjRaEoLsW2yP3McbF99z17S5zipaL01T9npr2pbeS/fbawKI+Ph4dRsba57DWLFiReNjps6fP19oW6Q5RUdHS1xcnEolQhoRUqGKas8VbdhDr9eLwWCQP/74Q1wtO0Avz/esLMH+OZLrl4XkJakSVVHEzyC54vhVnmuJCQuVYdF3SYD446KnTfzKRol/lQclwi9ADPZP1U82qFK/4TUeNR0HKuIDsfdzMohUrd9IxN/f/jbcIFqi5MGyPe0cITNIrsGgUvQyDbb9HeqCddLn5h4S4IeD5doDZjD4SXZeoAT550jileLfObYIiy4nqdkiFaJi5Pme+c+19T1DmTJ+6rssITNTks5k2PkOyBsYQivKwYv4jO35nANEOoww/8qyhZ9IXDKupiS59nUdeW1DmPi3Gy5xx86JHD8nXsMgar8NWPjVxu8yPzzDkCdxf58Q8TuR35xBZP++feLp+x1oyJXU0/Hid/aSuBL6orgY7vEBBGoIoGCtQ3BwsCQnJ1vc3lJdBLbPysqSzMzMItvD465qwx7aB2btB+dc+Vfys/LcdzrYn7Cgc+zLmLyLffXEXs2RZB53JgIhiHDHc53xfCLf5K3/v3Rwvw3eud9lcnIkJFAnroQ+qFcEECFXF4DBELb2b0An3NJsRtgG2xaE7cPCwlQHXmuv4ONae65owx5Nmza163lERERERK7k1ulrtDSgCxfMZ/rA75YKkZFWVHBbdOSTkpJUihFSiNCBv1Z7rmiDiIiIiMhXuTWAaNCggURERMjOnf9fYIgahIMHD6r1IArCfahDOH36tPE+zIYEzZs3V8MuzZo1M96nQfstWrRwWRtERERERL7KrQEE6ggGDBggb7zxhmzZskXNyjRixAh1hb9Lly6Sm5srFy9eNNYlNGnSRHXusQ2KjbFeAxad69Wrl3F0YPDgwfLNN9/I+++/L8ePH5fXX39dDh06JI888ohL2yAiIiIi8kV+Bkz940YIEubMmSPr1q1TgYK2EnW1atXUKtCdOnWSGTNmyH33YckwUesTTJkyRX755RdVr4BpV8ePH2+sXQAs+IYF6jBKUK9ePbUonOlUsa5qg4iIiIjI17g9gCAiIiIiIu/h1hQmIiIiIiLyLgwgiIiIiIjIagwgiIiIiIjIagwgiIiIiIjIagwgiIiIiIjIagwgiIiIiIjIagwgyExeXp7Mnz9f2rVrJzfffLM88cQTcubMGR4lH5WUlKTWXWnfvr1aHPGhhx6SPXv2GB//7bff1BosWDwRa51ggUVTWVlZaj0UrJHStGlTefHFFyUxMdFsG2e0QZ7r5MmT6nPDWj4aLLyJRULxHXLHHXfIBx98YPP3jDPaIM+C9ZW6desmjRs3lu7du8umTZuMj2HdpyeffFJ9D7Vt21beeusttU6UqY8++kitDXXTTTdJ//795eDBg2aPO6MN8iw5OTkyb948uf3229X3zMMPPyy///678XF+17gR1oEg0ixYsMDQqlUrw48//mg4dOiQ4bHHHjN06dLFkJWVxYPkgwYPHmzo0aOHYffu3YYTJ04YpkyZYrjpppsMx48fNxw7dszQuHFjw5w5c9S/33vvPUOjRo0M//vf/4zPHzdunKFz587q+QcOHDD06tXL8PDDDxsfd0Yb5Lmys7MN9913n+H66683rF27Vt2XmJiovkPGjx+vPvM1a9aocwC31n7POKMN8izr169Xf/urVq0ynD592rBw4UJDgwYNDPv27VPnET67oUOHGo4cOWL4/vvvDS1btjTMmzfP+Px169ap76Yvv/zScPToUcPo0aPVNgkJCepxZ7RBnmf+/PmGNm3aGH755RfDqVOnDBMmTDA0b97ccP78eX7XuBkDCDLC/3ibNm1q+Oijj4z3JScnqy/cDRs28Ej5GHwZo+O3Z88e4315eXmqM//WW28ZXn75ZcP9999v9pyRI0eqjhrEx8erDsC2bduMjyMIQZvoFIAz2iDP9eabbxoGDRpkFkAsXrzY0LZtW4NerzfbDp07a79nnNEGeQ58r9x+++2GmTNnmt2P7wF81vjMbrzxRkNSUpLxsU8++cTQrFkzY0CIz/711183Po5zo0OHDur54Iw2yPP07NnTMGPGDOPvqamp6vvmu+++43eNmzGFiYwOHz4saWlpKpVEExUVJY0aNZLdu3fzSPmYsmXLypIlS1Q6gcbPz0/9pKSkqFQm03MBbr31Vtm7dy8uPKhb7T5N7dq1pVKlSsbzxRltkGfC5/Ppp5/KzJkzze7HZ96yZUsJDAw03ofP99SpU3Lp0iWrvmec0QZ5Vprbf//9J/fcc4/Z/cuWLVMpR/i8b7jhBilTpozZ533lyhWVopKQkKA+e9PPG+dGixYtzM4ZR9sgzxMTEyM//vijSk9DOhq+c4KCgqRBgwb8rnEzBhBkFB8fr25jY2PNjkrFihWNj5HvQIerQ4cO6stY891338np06dVXjk+88qVKxc6FzIyMuTy5cty/vx5FYQEBwcXeb44ow3yPAgwx4wZIxMnTiz0fVHUZw5xcXFWfc84ow3yrAAC0tPT5fHHH1ed+L59+8rWrVvV/TxnqCgTJkwQnU6n6lZwsWvu3Lmq9qlGjRo8b9yMAQQZoVMHph1KQOcOha7k2/bt2yfjx4+XLl26SMeOHSUzM7PQuaD9np2drc6Xgo8XPF+c0QZ5nsmTJ6uCxoJXlIv6zLUAEZ+pNd8zzmiDPAdGAWDs2LHSo0cPWb58ubRp00aeeeYZNckCzxkqyrFjxyQyMlLeeecdNfqACTlGjRqlRpV43rjX/48PU6kXEhJi7Nhp/wb8Dzk0NLTUHx9f9sMPP6gvZcxe8sYbbxg7YzgXTGm/43zAOVLw8YLnizPaIM+bSQfpIhs2bLD4uKXPVOvUh4WFWfU944w2yHPgCjJg9KF3797q3w0bNlQzIL3//vs2fd4Ft7HnnCmqDfIsGG3ErHwrVqxQqWaAUQgEFQsWLOB3jZtxBIKMtHSACxcumB0V/I6cdPJNq1atkmHDhqlp8hYvXmy80ovzwdK5gP8Z44oQUkwwDWzB/yGbni/OaIM8y9q1a1U+OUapMAqBH3jllVdkyJAh6jO19JkDPlNrvmec0QZ5Du0zuf76683ur1evnspt5zlDlhw4cED0er1ZnR5gSnCk2vK8cS8GEGSEoqSIiAjZuXOnWa4zrhLdcsstPFI+aPXq1TJ16lQ1t/acOXPMUkJwxWfXrl1m2+/YsUONUvj7+0vz5s3VXPxaIbSW64y6Bu18cUYb5FkwQrVx40Y1EqH9wPDhw2X69Onqc8PnaTr/Pj5zFMejINKa7xlntEGeA8XN4eHhqkNo6p9//lG57PjM8NlpqU7a543n4LPGZ47P3vTzxvoAGAkzPWccbYM8i1YHdeTIkULnTa1atfhd427ungaKPAvm68e82D/88IPZ3OqYY5t8C6ZLveGGGwzPPvus4cKFC2Y/KSkphn/++Uc9Pnv2bDUX/7Jlywqt4YApWe+44w7Djh07jGs4DBgwwPi4M9ogz2c6jeulS5cMt9xyi2Hs2LFqrn3cjzUcMAe/td8zzmiDPMs777yjpt7FdKum60Dg7z4zM1NNH/3444+rz1JbwwFrfWg+/fRTNU0vzgFtDQesA6Kt4eCMNsiz5ObmGh566CFD165dDb/99pvh5MmThrlz5xoaNmxo+P333/ld42YMIMhMTk6Omif71ltvNdx8882GJ554wnDmzBkeJR+0aNEi1fGz9IOOG/z0009qoTnMr44v8W+++casjbS0NLWwT4sWLdQPggEsAmbKGW2Q9wQQgEDwgQceUJ855v//8MMPbf6ecUYb5FmWL1+uLhbgogLm90cn33RdGixsiUARa4BgLRp0IE1hIcr27durIKB///6GgwcPmj3ujDbIs2Bdj8mTJxs6duyoAtAHH3zQsHPnTuPj/K5xHz/8x92jIERERERE5B1YA0FERERERFZjAEFERERERFZjAEFERERERFZjAEFERERERFZjAEFERERERFZjAEFERERERFZjAEFERERERFZjAEFERERERFYLtH5TIiKiaxs4cKDs2rXL7D4/Pz8JCwuTWrVqySOPPCL33nsvDyMRkRdjAEFERE7VqFEjeeWVV4y/5+bmSnx8vKxYsULGjBkj0dHR0qFDBx51IiIvxQCCiIicKiIiQm6++eZC97dv315at24t69atYwBBROTFWANBREQuERwcLEFBQSqlCfLy8mTJkiVy5513yo033ih33XWXfPjhh8btFy9erO5PTk42awcjGTfccIMkJCSo38+dOycjR46Uli1bSpMmTVSa1MGDB43bnz17VurXry+bNm2S4cOHS9OmTdW2EydOlPT0dON22GbBggVmr4Xfcb+pPXv2yIABA9RroZ2xY8dKYmKik48WEZHnYgBBREROZTAYJCcnx/iTlZUlJ06ckPHjx0taWpqxBmLy5Mkyf/586dmzpwoWunbtKq+99pq888476vF77rlHPX/z5s1m7X/zzTfStm1biYmJUR33fv36yd9//y0vv/yyvPnmmyowefjhh+X48eNmz0NaVdWqVWXhwoXy+OOPy5o1a2TRokU2vbfdu3fLo48+KiEhIfLWW2/JSy+9pGo+Bg0aJJmZmQ4fOyIib8AUJiIicip0sjFCYAqjDtdff73MmzdPbr/9djl58qR89tlnauRg6NChahsEBdju3Xfflf79+6vO/i233CJff/219O3bV23z77//yh9//CFz585Vv69cuVKSkpLk448/VttrqVLdunVTr4UARYO6C4wWAFKptm/fLtu2bZMXX3zR6veGAKV27dpqHwMCAtR9GIno3r27rF27VgUuRES+jiMQRETkVAgecHUfP7jaj8ABMzDhij1GGWDHjh1qpOKOO+4wG63A7xix2Lt3r9oOoxMISC5evGgcfUCNBbaD3377TRo2bCiVKlUytuHv76+CiP/9739m+1WwLqNy5cpmKUzFycjIkAMHDqhAxHSUpXr16lK3bl0VkBARlQYcgSAiIqcKDw+Xxo0bG3/HFXoEAo899pgqoC5XrpwaNQBcubfk/Pnz6hYBx9SpU1X9AtKEEECgVgIpRIB2Tp8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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "arm_revenue = (\n", + " policy_df\n", + " .groupby(['arm', 'arm_name'])[outcome]\n", + " .agg(['count', 'mean', 'median', 'std', 'min', 'max'])\n", + " .reset_index()\n", + ")\n", + "display(arm_revenue)\n", + "\n", + "plt.figure(figsize=(8, 4))\n", + "\n", + "sns.histplot(\n", + " data=policy_df,\n", + " x=outcome,\n", + " hue='arm_name',\n", + " bins=40,\n", + " element='step',\n", + " stat='density',\n", + " common_norm=False\n", + ")\n", + "\n", + "plt.title('Revenue density by arm')\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "639d2437", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-09T16:49:48.195732Z", + "iopub.status.busy": "2026-05-09T16:49:48.195645Z", + "iopub.status.idle": "2026-05-09T16:49:48.262584Z", + "shell.execute_reply": "2026-05-09T16:49:48.262041Z" + } + }, + "source": [ + "Mean Revenue is approximately 6,586 for `none`, 12,248 for `discount_only`, 15,104 for `tech_support_only`, and 26,784 for `discount_plus_support`.\n", + "\n", + "As noted above, larger customers tend to receive stronger interventions, so the raw mean differences confound customer characteristics with actual treatment effects. Evaluating policy based on unadjusted means would therefore be misleading.\n", + "\n", + "We should instead rely on AIPW-based policy evaluation for proper comparison." + ] + }, + { + "cell_type": "markdown", + "id": "0b51rgb0z128", + "metadata": {}, + "source": [ + "### Identification Assumptions\n", + "\n", + "For AIPW estimation from observational data to be causally valid, two assumptions are required.\n", + "\n", + "1. Unconfoundedness\n", + "\n", + " $$\\{Y_i(0), Y_i(1), Y_i(2), Y_i(3)\\} \\perp A_i \\mid X_i$$\n", + "\n", + " Conditional on observed covariates $X_i$, treatment assignment must be independent of potential outcomes. That is, we assume variables such as customer size, IT spend, and employee count sufficiently explain treatment assignment.\n", + "\n", + "2. Positivity\n", + "\n", + " $$P(A_i = a \\mid X_i = x) > 0 \\quad \\forall a \\in \\{0,1,2,3\\},\\ \\forall x$$\n", + "\n", + " Each treatment must be observed with positive probability across all ranges of customer characteristics." + ] + }, + { + "cell_type": "markdown", + "id": "61b7329f", + "metadata": {}, + "source": [ + "Positivity is verified directly by estimating propensity scores:\n", + "\n", + "$$\n", + "e_a(x) = P(A_i = a \\mid X_i=x)\n", + "$$\n", + "\n", + "We also check whether propensity scores are severely skewed. If $\\hat{e}_a(x)$ is very close to zero for some customers under some treatment, those observations receive excessively large weights, which can destabilize estimation." + ] + }, + { + "cell_type": "markdown", + "id": "pl2exzwbceb", + "metadata": {}, + "source": [ + "### Data Split\n", + "\n", + "Using the same data for policy learning and evaluation leads to overestimation. To prevent this, we split the data into train and test sets. Policies are learned on the train set and evaluated using AIPW scores computed on the test set.\n", + "\n", + "The same split is used for propensity estimation in the positivity check." + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "id": "n7ziuiv612q", + "metadata": {}, + "outputs": [], + "source": [ + "idx = np.arange(len(policy_df))\n", + "train_idx, test_idx = train_test_split(\n", + " idx,\n", + " test_size=0.5,\n", + " stratify=A,\n", + " random_state=SEED,\n", + ")\n", + "\n", + "X_train = X[train_idx]\n", + "X_test = X[test_idx]\n", + "A_train = A[train_idx]\n", + "A_test = A[test_idx]" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "9be31313", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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armmean_propensitymin_propensityp01_propensitypropensity_below_0_05_rate
0none0.2639410.0576000.0668310.0
1tech_support_only0.2298630.1265820.1562830.0
2discount_only0.2379340.1256380.1413220.0
3discount_plus_support0.2682630.0872990.1013040.0
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" + ], + "text/plain": [ + " arm mean_propensity min_propensity p01_propensity \\\n", + "0 none 0.263941 0.057600 0.066831 \n", + "1 tech_support_only 0.229863 0.126582 0.156283 \n", + "2 discount_only 0.237934 0.125638 0.141322 \n", + "3 discount_plus_support 0.268263 0.087299 0.101304 \n", + "\n", + " propensity_below_0_05_rate \n", + "0 0.0 \n", + "1 0.0 \n", + "2 0.0 \n", + "3 0.0 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "multi_propensity = RandomForestClassifier(\n", + " n_estimators=400,\n", + " min_samples_leaf=20,\n", + " random_state=SEED,\n", + " n_jobs=1,\n", + ")\n", + "multi_propensity.fit(X_train, A_train)\n", + "e_hat_raw = multi_propensity.predict_proba(X_test)\n", + "\n", + "propensity_summary = pd.DataFrame({\n", + " 'arm': ARM_LABELS,\n", + " 'mean_propensity': e_hat_raw.mean(axis=0),\n", + " 'min_propensity': e_hat_raw.min(axis=0),\n", + " 'p01_propensity': np.quantile(e_hat_raw, 0.01, axis=0),\n", + " 'propensity_below_0_05_rate': (e_hat_raw < 0.05).mean(axis=0),\n", + "})\n", + "display(propensity_summary)" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "id": "fl52yekx049", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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EJ9NqR6C4uNhM8L3tttsqlpy0aAdJ/+PXJVg13UY7arqco0Un4uokTu2E6zkOtEOhnXLN027IaivamdY0Ce0YWhN79YiyptlYK0JVp/n72nnUzrN2uLSjY5Wl7007PzuigZN2QvV1tfOoR1q1DqxVafTIuU6u1qVddX6EdvD0fel+Wavg6ApUukKOzi/QkQBN69DAqvIKO7XRIEHnImgbaBkbN240AY/+nQZ3ddEO6//93/+ZJTK1zbSudDUd3R8to7H1qx1RnRir9akdfe0gRkqDKR3x0f3W/dPlSTXNxpqgrUHdqlWrTCdUA1ENnLTOdIlci45c6SRvPZ+ABmra4a5My2psXdv1Piw6yV6DzUg+Y7p0ro4iaPCmny0NEGujba91o59dfY6OdOloWuXvpX7/dPRJg30NcPRvtG11vzXY0LQqXcCgKW1ZmX7ntT10n6xRPZ3ArQcMdCnjhtC60uBB61Z/K+pauQpA4zhCzDoCEAN0BRpNYdCjnfHEmiysR7J3dHQYaAxdLlhXIKvvfB0A0JwYgQCARtCj2zp/QI/MaloQwQPspiM2OudHl/7VI/IAECsIIACgETRvXlNA9IRqDU2vACKh58/Q9Co9O7OmWwFArCCFCQAAAEDEWIUJAAAAQMQIIAAAAABELOnmQOjJrHThqYasJw0AAAAkMr/fb5aRHjhw4A6fm3QjEBo8sHJt4+tOzxJM/cUX2i1+0XbxiXaLX7RdfKLdmr+PnHQjENbIQ//+/e0v3O8T+c8D4e3RV4i4EmtN+JKSElm8eLE5A6ue8AfxgXaLX7RdfKLd4hdtF59oN3ssWrQo4ucmXQARVRq15a3avg0AAAAkmKRLYQIAAADQeAQQAAAAACJGChMAAABqKC8vNyvzxDqv11tx7XRybDyS+cApKSnSFAQQAAAAqKK4uFhWr14dFysvBoNBSU1Nlby8PAKICOhSrd26dZPWrVtLYxFAAAAAoMrIgwYPuuJihw4dTIcz1vdXRx88Hk+Tj6wnulAoJPn5+aZ9d91110bXFwGE3TJa2V4kAABAc9G0Je1oavCQnp4e8xWvAYRKS0sjgIiAtuuKFStMOxNAxAK3R+SqKS29FwAAAE0W6yMPaLl2ZaYJAAAAklIszvEIxeA+VUcKEwAAAJrNpEmT5LXXXqv3Ofvvv788++yzUdsHn88nU6dOlT333FNOOukkiRWPPPKIuN1uGTNmjMSymAogHnvsMfniiy+qfGAWL14st912m/zvf/+T7OxsOe+882T06NESk/w+kRemh7dHXCricrf0HgEAAMSUv//973LWWWdV6TT//PPP8tBDD1Xc15QVgiKxfv16+b//+z+54447JJY88MADctlll0msi5kA4vnnn5f7779fBg0aVHHfpk2b5Pzzz5cjjzxSbrrpJvnvf/9rrlu1aiXDhw+XmKNDTiuXbd8GUK81W9ZLqb/M1lpKd6VJbmZHah4AYtTOO+9sLhY9QKxH3ffee+8W3S/EUQCxbt06ueGGG+Tbb7+VHj16VHnspZdeMie7uPnmm836vrvssousXLlSHn/88dgMIAA0KHi44u0bolJjDxx/E0EEAMSxV199Va677jpz4FgPMOuKQTNmzJDevXvLhx9+aEYtli5dKllZWXLcccfJpZdealZhsuhznnrqKZPJon+r5z0YNWqUnHPOOWYJ06FDh5rnTZ482Yx8fPTRRya1Spc4PeaYY+SJJ54woxS77767GaXQVYvuvfdeWbVqley2226mb9qvX7+K15s/f77Zz0WLFpnlZI844giZOHGiCY4qv58XXnhBbr/9djPi0r59exk5cqRccMEF5jl9+vQx17o/evn1118lVrV4APHTTz+ZIOGNN96Qhx9+WP78888qjaE5cBo8WA488ECT6rRhwwZT8Y2dnFJSUiK283nFEwyaTa+WHwgvK5YoSktLq1wjPsRqu23astlcD+s9VHLS29lS5sbSTfLmsrmm7DYp0R3+Tua2Q/1ot/hF24XpORX05Gy6PKq1RGo0ab9ML5Vfy3p9DQJuueUWk5WiB5pff/11ufrqq2XYsGEyduxY02/UjvuSJUvMc9Wnn34q//jHP0zAoNdlZWWm4251+vXy4IMPmr+/5JJL5KijjjKvpfvwww8/mMBBX0PrQf/mwgsvNCsXXX755WZZ2xtvvFGuuuoqmTNnTkV/9W9/+5vpo2qQUVhYaMrX19eD4RrY6PvRyxVXXGHS8fW1Z82aJXfddZcJigYPHmz2ccSIEeYg+emnnx61utdydV/0867Xldsh0hWaWjyA0PQkvdRm7dq1JsqrrGPHcGrCmjVrGh1AaCSqEandHAG/9CkL/0evUWMo1SWJSKNwxJ9Ya7e1ZRvMtX9zmfhsCuj9vnA61PLly6U0bYskilhrO0SGdotftJ2Yg7fagW4OVuddO/qV+2pKU9kPOOAAs62P33PPPXLwwQebjr3ab7/9JDc314xAzJ07Vw499FD55Zdf5MQTT5Qrr7yyojwNGnRU4KuvvjJH+nv16mXu79y5s9nWsnU/tm7dakYcevbsaR7/5ptv5JVXXpFHH33UHNRWo0aNkvvuu8+MVmRmZpp90uBG77POq9C3b18TBLz44oty5plnVpxbQ4ORU045xTxHRzd0pET3W1P4rRGInJwcs125Puyk7RoIBOT333+v8ZimksVFAFEfrbjqb0SHhVRTPtQ64qHRXlRGINLCJ1zpqx8CPS9EAtFIVX9U9UsSDyeWQWy3W/rm1SKrRTrn5krn1h1sKdNdnC+yQcwPf4+23STexWrboX60W/yi7aSij5WXl2f6XJXTgqJFO9165Lvya2lfTe21114V92uHV1PfL7744irZKRpQ6KTrefPmydFHH20eVxoM6G+oph3pYjxKj7hreVZ/Ul/HKl/3Q1OiKqcmWQeutYNvPa/9tgPYupKTBgWatqQjEFaZStPuNTDRfTr33HMr3k/lcvS6Xbt2ppzK713fW7TrXV9D56FU3udly5ZF/vcSw7TytFIrswIHPb16Y+mHtCl/X6fUFBFn+NQapvwECyAs2pGJSv0hqdotrSxNcnwBydy6WdJtOiWNlqVl6m9HLL3XRGs7RIZ2i1/J3nZOp9NctEPd2DMVN7RfppfKr6WvrzQwsO4vKioy1zr6YI1AVKYjAvrcgoICM79Wj+5rud27d69YpMd6Hat8631aj+mIQm37ofdXv8/pdJogRYOSf//73+ZSnXV2bOtvdCGgyuVb29VfM5r1bu2Pfs4rByoNOcFcTAcQOqykeWiVWbc7deokMWlbhAlgBzbny4RVBSKrZthWVTrmMGFb2ZK9fYUPAED809EBpfMTrHQipR14PeBsjQyMHz/ejFY888wzMnDgQJPNoqNLOh/Bbq1atTIdb53XcMIJJ9R4PFFHj2M6gNC8tpkzZ5qcNCsS01w0TU/Q/LCYoyMOk+5t6b0A4sO25Vs39dtPWuXYk260deNqabd4XkXZAIDEoSlB2v/TVZSslYusObMTJkwwE5C1j/j999+beQfW3An12WefmWtr0rBdR/hbt25t5jJowNK/f/8qafg6Ufqwww5rUNq8NVIR62I6gNBZ6DocdO2115oz8v34448mmtQlvQAkhkCrTAm1aW9PWWWFtpQDAIg92unXidHXX3+92dZJ0ZrWpEu6ahCxxx57VMyb0BWS9LZmsyxYsMCcAkBHCqyVtqyUpK+//trMVxgwYECj92vcuHFy0UUXmZWZ9KzW1upRCxcuNCfNa+goi+6vzp3QtKuGpBU1p5gOIDTK1ABCz0R96qmnSocOHcywlW4DAAAguZxxxhkmbUj7h7rCkc5V0TQlXepVz/WgpkyZYm7rRelCFHrwWU8ZoEuuWiMHusKTlqHLvn755ZeN3qfBgwfLk08+ac7doKMOOmFag5enn366wSfH02VlNSDS1Zrefvtt6dKli8QiR0injycRnSmvKg8z2SbgF3l52wSaM8aIJNgyrnruDF3+VlcnSObJZfEmVtvt91+/FHnlXskfdKRkddrFljKL1v0mHeZ/JHL6OOnV5xCJd7Hadqgf7Ra/aLvt6Te6HLamAzXHKkxNpUf8dZ+tCctoXPs2pI8c0yMQcUfz6pb9vH0bAAAASDDxMVMDAAAAQEwggAAAAAAQMQIIAAAAABEjgAAAAAAQMQIIAAAAABEjgAAAAAAQMZZxtZPbI/KvabYWCQAAAMQSAggACWf91o0iBatsLTPdlSa5mR1tLRMAEJktW7bIxIkTzVmaV69eLaNHj5aPPvrI1uqbNi18EPjyyy+P6Pmr69mPPn36yK+//tqo/SguLpYJEybIypUrJT09Xe655x5zNu3qnn32WZkxY4Y5kd4FF1wgZ555prlfn//WW29JZmamuf3Xv/5VzjnnHLETAQSAhJHqDP+kzVz0huQtfcf28h84/iaCCADJy+et+zGnUyTVFdlzHQ4Rl7tBL11YWCi//PKLJIMHHnhA9thjD5k+fbp8/fXXMmnSJJk5c2aV5/z888/y0ksvyaxZsyQYDMqIESNk0KBBsssuu8iPP/4oDz30kOy+++5R20cCCDsF/CKz/xPePmV01S8SgKhr5W5lrk/b/S/iy7ZvtCB/a4G8tvhdKfWX2VYmAMSdO8fX/Vjv3UVGXLr99r2TRfz+2p/bvbfI6Csa9NI333yzrF+/Xi655BK57rrrxOv1ylVXXSVLliyRlJQUufPOO6V3795y5JFHSv/+/U2w8X//93/y7bffmms9Sq+P33TTTZKRkWGuf/jhB/O3+jfWqMOiRYvkrLPOMq916KGHmuepRx99VN544w3z/EMOOcSMEFQfjdD7SkpKZMCAAbW+hxdeeKFGILDnnnvKbbfdVuW+jz/+2OyzOuigg8z7zcvLky5dulR5zrHHHmvei9Ltd955R/7xj3/I4sWL5eGHH5ZVq1bJAQccIFdffbW43Q0L2HaEAMJOwaDI4v+Gt08aaWvRQEvyF+RJ0Gtz53nTeomW9q2yJZTZKWrlAwCa1/XXX2/ShbQjr531jRs3ysiRI2XgwIFy++23y8svvyyTJ082zx08eLA5iv/bb7+ZDrum+aSlpZkj+tqxPvnkk02goAGBBiLXXHONlJaWmr/Nz883HX119NFHm9fQzvuHH35ojva7XC4TbGi5hx12WMX+3XLLLaZcDT5mz54tL774Yo33oKMEetmRdevWSadO2/8P69ixo6xdu7ZKAKHP0UDJos/XkYdNmzaZOtG66Ny5s0n70vd9xRUNC9h2hAACwA6Dhz+mR5YP2hjBFH6GACAuTJxafwpTZePuqD+FqYm0U60dZbXrrruakQaLdb+m/+g8AmtuQCAQkJ122kl23nln8fl8Zl6ABgFXXnmlmWughgwZYoIN1b17d9Mh/+abb2TYsGEVzxk+fLgJEioHEN99952Ze6BOOukkM2rQ2BGIUChU42+d1eq3tuc4HA7Jzs6Wxx57rOI+nRtx7bXXEkAAaF7WyEPbQ4ZLalZ728rdsHWTzPzlHRmSEZ7kBQCIg9Umo/HcRkhNTa3Sca7MCgA0ben444+v6MxrepEGDpr2owGABh1ffPGFGTXQCcm1lasddZ1jUF0gEKhxn9Wp17+r3uFvyAiEjiboSEhubq65rds6mlD9OZpmZdFtfc6KFStk4cKFZjRE6b5r2pXdOA8EgIho8ODK6WLbRdp2kKJU+3/UAACJRzv2tXXa66P5/x988IFs2LDB3L7jjjvMKk7z58+XCy+8UA488ECT4qMTj5cvX15nOfq8N99806Q56T7MmjVL9ttvvyrPOfjgg+XVV18123PnzjWpUY11+OGHm9dQGuRowFM9gNDRj/fee0+2bt1qLu+++665T1OspkyZYlKeNKB57rnnTCqW3cgdAAAAQEzr0KGDdO3aVc4++2y56667Ivqbvn37ymWXXSbnn3++ORKvgYKuaKQd8l69epm0JB2t0NWKNHXpp59+qrWcI444wkxMPv30000AcfDBB5v5GDoPofIcDZ1E/corr8hee+0lrVqFF/VoDJ2voGlHun86+dl6vzpv48EHH5QnnnjCzH8444wzzBKtuk86iqIrNymd/6CpS3r/vvvua96/3Ryh2pKoEphWvqo88cQ2umSZtUKB5glGefiuuenQn36B+vXrVzHrH4nfbt41v8ufT02Q9n+5ODxyYJM1Revl8fnPy7A+QyUnI9uWMh2FG8T9xWtSduplEmrfVeyyZss6eXz+DJly9GTplb2zNBe+c/GJdotftF1YWVmZOSLfs2fPinSgWKapSrrPuq/RSNdJlvZd1IA+MilMAAAAACJGCpOd9KQo1goFDTxBCgAAABAPCCDspKsAJFjaEgAASE5JluWeNEI2tCsBBAAAACroSj66FKkuH6qTl6svkxqLcyCsVY+YA7Hj4EHbVdtU27mxCCDsFPCLvLXtBCEnnCWS2viGAQAAaAnaCe/WrZs547OeVyDW6QpLuuKQLvVa2/kXUJUGD9q+TQm2CCDspCca+fG78PZf/mpr0QAAAM2ldevW5gzPfr8/5itdz8/w+++/mzNMW2eLRt105KGpIzUEEAAAAKhBO5nxkBJknSna4/HExbKziYBxHgAAAAARI4AAAAAAEDECCAAAAAARI4AAAAAAEDECCAAAAAARYxUmO7ncIuNu374NAAAAJBgCCDvpmRpbZdpaJAAAABBLSGECAAAAEDFGIOwU8It88Fp4++hTRVJdthYPAAAAtDRGIOykZ0Kc/3n4su2siAAAAEAiIYAAAAAAEDECCAAAAAARI4AAAAAAEDECCAAAAAARI4AAAAAAEDECCAAAAAAR4zwQdtLzPlx+4/ZtAAAAIMEQQNjJ6RRpm2NrkQAAAEAsIYAAEoy/IE+C3rLtt8vKJKVwrfjXpYs3La3B5fk2rrZ5DwEAQDwjgLBTICDyyZvh7cOHiaRSvWj+4OGP6ZfXuD9LRDZ+3bSyHS5P0woAAAAJIS56uIFAQB5++GGZPXu2bN68WXbffXeZMGGC7L333hJTguUiX88Nbw/5S7xULxKINfLQ9pDhkprV3mx7fX5Zu2aNdM7NFY/b1ejgITWL9DwAABAnPdzp06fLyy+/LFOmTJGddtpJnnjiCRkzZoy8/fbb0rFjx5bePSDmaPDgyulitsvLvBIs8kpqu87iSmMUAQAAJMEyrh9++KEMGzZMBg8eLN27d5dJkybJli1b5L///W9L7xoAAACQVOJiBCInJ0c+/vhjGTlypOTm5sqLL74obrdb+vbt26jyQqGQlJSU2L6f4vOKJxg0m14tP1AuiaS0tLTKNWKPTpi20pZ05EH5vFWvY4XP5zPXfn+gYrupnH6/uM179Un5trqwg9cb3r+ysrLo/HbUge9cfKLd4hdtF59oN3to/9jhcCROAHHttdfKFVdcIUOHDpWUlBRxOp0ybdo02XnnnRtVnt/vl8WLF9u+n46AX/qUhTvXv/76q4QS9FwQK1asaOldQB10tSWdMK1zHjRtqbK1a9fGVL0V+ArD1xs3it9lT2ffU1Ikrba9/9Jie4KSyvu6fPlyKU3bIs2N71x8ot3iF20Xn2i3ptMD9AkTQCxbtkwyMzPNROpOnTqZ+RDjx4+X5557Tvr169fg8lwul/Tu3Ts6IxBp6Wazb58+Im5PwkX4+uXs0aOHpKeH3ydiiy7Vqqst6YRpnfNgjTxo8NC5c2dxe2LnM+kpzhfJF8nOyZHs9La2lOkscoksC7//8pxcsYtb93WDSM+ePaVH227SXPjOxSfaLX7RdvGJdrOvvx2pmA8g1qxZI1dddZU888wzMmjQIHNf//79zZvUUYhHHnmkwWXq8ExGRob9O5uaEj6ZnEi4/AQLICwaPESl/tBk1nkedLWl6hOmNXhIi6FJ1G5f+CiHy5Ua8RGPHXG4wqN+bo9bQo0450VdPP7w/qWlpbXIZ5/vXHyi3eIXbRefaLemiTR9KS4CiIULF5qUIw0aKhswYIB89tlnElM0ZemSa7ZvAwAAAAkm5ldh0rQLa05BZUuWLDGpNDFFRx865IYv20YiAAAAgEQS873cvfbaS/bdd1+ZOHGifPPNNyYH//7775evv/5aLrroopbePQAAACCpxHwKk664pCeS06Bh8uTJUlhYKLvttpuZE6FpTDElEBD58v3w9iHHiKTGfPUCAAAADRIXPdw2bdrIDTfcYC4xLVgu8tk74e2DhsZL9QIAAACJk8IEAAAAIHYQQAAAAACIGAEEAAAAgIgRQAAAAACIGAEEAAAAgIgRQAAAAACIGOuM2inVJXLB+O3bAAAAQIIhgLCT0ynSpbutRQIAAACxhBQmAAAAABFjBMJOgYDId5+Et/c/XCSV6gUAAEBioYdrp2C5yNzXw9uDDqV6AQAAkHBIYQIAAAAQMQIIAAAAABEjgAAAAAAQMQIIAAAAABEjgAAAAAAQMVZhApBwnJvXS9DG8txbCyTHF7CxRAAA4hcBhK216RIZNXb7NoDmte175/74JVuL7SYiE3Rjc75I9s62lg0AQLwhgLCT0ynSY1dbiwQQuVCrNuI7/K8iAb+t1bZ142ppt3ieiL+M5gAAJD0CCAAJF0TYLVBWaHuZAADEKwIIO5WXiyz4Mry9zyEiKSm2Fg8AAAC0NAIIO5UHRN59Obw94AACCAAAACQclnEFAAAAEDECCAAAAAARI4AAAAAAEDECCAAAAAARI4AAAAAAEDECCAAAAAARYxlXO6Wmipx18fZtAAAAIMHQy7WTM0Vk1z1tLRIAAACIJaQwAQAAAIgYIxB2Ki8XWTQvvN1/P85EjXr5C/Ik6C2ztZZ8G1dT6wAAIKoIIOxUHhCZ83x4e/eBBBCoN3j4Y/rlUashh8tD7QMAgKgggABagDXy0PaQ4ZKa1d724CE1K8fWMgEAACwEEEAL0uDBldOFNgAAAHGDAAIAIrR+60aRglW21le6K01yMzvSBgCAuEEAAQA7+qF0hn8qZy56Q/KWvmN7fT1w/E0EEQCAuEEAAQA70MrdylyftvtfxJdt32hB/tYCeW3xu1Lqt3c1LgAAookAAgAi1L5VtoQyO1FfAICkRgBha22migw/f/s2AAAAkGDo5drJmSKy+z62FgkAAADEEmdL7wAAAACA+MEIhJ2C5SK/LAxv9x0QHpEAAAAAEggjEHYKBERmPR2+6DYAAACQYBoVQKxbt06a2+zZs+X444+X/v37ywknnCDvvGP/WuwAAAAAohBAHHHEETJmzBh5++23xefzSbS9/vrrcu2118o555wjb731lgwbNkzGjRsnP/zwQ9RfGwAAAEATA4g77rhDgsGgjB8/XgYPHiw33XSTLFq0SKIhFArJAw88IKNHjzYBxM477yyXXnqpHHzwwfLdd99F5TUBAAAA2DiJ+uSTTzYXTWV67bXXzAjBCy+8IL1795bTTjtNTjrpJGnfvr3YYfny5fLnn3/KiSeeWOX+J598sklBSUlJidjO5xVPMGg2vVp+oFwSSWlpaZVrNJ6/LHzmYa/PL+Vl3qhWpc/rrXIdK6zRS78/0CwjmU3h9PvFberQJ+Xb2s4OXm/4fZeVldX6m8R3Lj7RbvGLtotPtJs9tH/scDiivwpTp06d5JJLLjGXn376SaZMmSJ333233HvvvRVpTgMGDGhyAKH0P9cLLrhAfv75Z+nWrZsZhTjyyCMbVabf75fFixeL3RwBv/QpC3euf/31VwmluiQRrVixoqV3Ie6lFK6VLBFZu2aNBIuap2O/du1aiSUFvsLw9caN4nfZ1ymPBk9JkbTa1l6lxT7b60B/50rTttT5PL5z8Yl2i1+0XXyi3ZrO7dbDZc2wjOv8+fPNCMQHH3wgRUVFcsghh8jhhx8un3zyiYwYMUKuvvpqOe+88xpdfnFxsbmeOHGiXHbZZSZt6r333pO///3v8vTTT8tBBx3U4DJdLpcZLYnKCERautns26ePiNsjiRbh65ezR48ekp4efp9oHP+6dNn4tUjn3FxJbdc5qtWoIw8aPHTu3Fncntj5THqK80XyRbJzciQ7va3EMmeRS2RZuL3Kc3JtK9etdbBBpGfPntKjbbcaj/Odi0+0W/yi7eIT7WaPZcuWRfzcRgUQK1euNEHDG2+8YdKLunbtKqNGjTLpS7m54f9cR44caTr706dPb1IAoZ19paMPp556qtnu16+fGYlobAChwzMZGRliO+2cnTzKbGZkZomkJOZ5IDR4iEr9JRFvWpq59rhd4kprnk69Bg9pzfRakXD7wkc5XK7UiI94tBTHtt8ht8ctoW1tZwePP/y+09LS6v1O8Z2LT7Rb/KLt4hPt1jSRpi81OoA49thjxePxyFFHHSW33HJLnZ34Xr16NXk4SdOk1G677Vblfh1B0FGOmKIBw94HtvReAAAAAFHTqADiX//6l5konZmZWe/zNM1IL02xxx57SKtWrWThwoUyaNCgivuXLFliVmQCAAAAEOPLuOochPXr19f62C+//FJjxaSm0KF9nYz98MMPy5tvvimrVq0yaVFffvmlnH/++RJTguUiS/8Xvug2AAAAkKwjEDpZWpd3Unr+hXnz5klBQUGN53388cfyxx9/2LqTOoqheW333XefWTp2l112kWnTpskBBxwgMSUQEJn5WHh74lQRd2LOgQAAAEDyijiAePnll83EaZ1goRc9eVx1VoChZ4q2m442xNyIAwAAAJBkIg4grrvuOhk+fLgJEs4991y5/vrrayyF6nQ6JSsrS3bddddo7CuAFrKxZLP4Avae7C2/pOYIJgAASKAAQidM77///mb7P//5T8XkZgCJHzw89M3TUSvflZKYJ1wEAECSPYCYPXu2HHbYYdKuXTvJy8szl/qccsopduwfgBZmjTwM7r6/tE2rf+W1xgQPWR57ywQAADESQEyaNEleeuklE0Dodn10jgQBBJBYNHjIychu6d0AAADxEkDMnTtXOnToULENAAAAIPlEHEB07dq11m1LIBCQ4uJiadu2rSStlFSR487Yvg0AAAAkmEadSE6DhYceekjmzJljbn/77bdyyCGHyEEHHWRWaCosLJSklJIist+Q8EW3AQAAgATTqADiwQcfNGeDLioqMrdvvfVWM/IwefJkc6boe+65x+79BAAAABCvAcRbb70l48aNk3POOUd+++03Wbp0qVx66aUyevRoufLKK+Wjjz6SpBQMiqxYGr7oNgAAAJBgGhVArF+/XgYMGGC2P/nkE3MCuSFDhpjbnTt3li1btkhSCvhFnn0wfNFtAAAAIME0KoDo2LGjrF692mzraEO/fv0kOzu8vOMPP/xggggAAAAAiadRAcSwYcPkjjvukAsuuEC+//57GT58uLn/tttuk2nTpsmJJ55o934CAAAAiAGNWmv0n//8p2RkZMi8efPkqquukrPPPtvcv2jRIvnb3/5m5kMAABJLXn6xlHgDtpaZ4UmVLh1a21omACAGAwg90/TFF19sLpXNnDnTrv0CAMRY8HDxlOicRPSxSUMJIgAgjjT6bGc6Ufqbb76RkpISCYVCNR4/5ZRTmrpvAIAYYY08/HXortKhXYYtZeZvKpGX5i61fVQDABCDAcTnn38uY8eOldLS0jpHKAggACDxaPDQlZQjAEhqjQog9ERxvXr1MieO69Spk1nGFTolPUVk6MnbtwEAAIAE06gAQk8e98gjj8igQYPs36N4lpoqcvBRLb0XAAAAQNQ0auigS5cuUlxcbP/eAAAAAEi8AEJXX3r44YcrTiaHbYJBkbyV4YtuAwAAAAmmUSlMc+bMkXXr1snRRx9tzkCdlpZWYxL1hx9+KEkn4Bd5cmp4e+JUEbenpfcISWhjyWbxBXwVt30+nxT4CsVTnC9un7vB5eWXFNi8hwAAIOkCiM6dO5sLgNgLHh765unaH8xvWtmuFFfTCgAAAMkbQNxxxx327wmAJrNGHgZ331/apmWabb8/IAUbN0p2To64XKmNDh6yPOHyAABAcmv0ieSs1Zi+/PJLWb9+vYwaNUr++OMP6du3r7Ru3dq+PQTQYBo85GRkV6Qw+V1lkp3eVtzuhqcwAQAANDmACAaDcv3118usWbPMWah1zsNf/vIXs7TrqlWr5LnnniPFCQAAAEhAjVqFSQMFnUh96623mhEIDSLUhAkTTHBx33332b2fAAAAAOI1gNCRh7Fjx8rw4cOlbdu2Fff369fP3K9BBQAAAIDE06gUpg0bNphgoTadOnWSoqIiSUrOFJEhf9m+DQAAACSYRo1AdO/eXT799NNaH/vuu+/M40kpNVXksOPDF90GAAAAEkyjernnnnuumUTt9/vliCOOMJOoV65cKd9++6089dRTMmnSJPv3FAAAAEB8BhBnnHGGFBQUyPTp02XGjBnmvnHjxonL5ZIxY8bIiBEjJCkFgyIb14W3czqJOBs1wAMAAADErEbn2Vx44YVy4oknmpSl1NRUyczMlAEDBlSZVJ10An6RR28Pb0+cKuL2tPQeAQAAAC0bQLz55psyc+ZMWbhwoQQCAXNfWlqa7LPPPmbk4aijjrJ3DwEgBqwvz5QtG8olFCi1rcwNJQEJlmXYVh4AADEVQJSXl8tVV10l7777rllp6YQTTpD27dubc0CsXbvWjERcfvnlcvLJJ8uUKVOiu9dAgijL3yCBModt5XnLCmwrC9vlFzvkrsJTRV7fKiJ6sdMQWX9ImfQKnzgcAIDECSB0rsP7778v1157rYwcOdJMnK4eYOjIxO233y6DBg2S008/PRr7CyQEb36+uV798iwpL7Ov3E2ZTpH9syRQXCySQY/ULt5A+PfuiAFuadvZvnpdnb9Z5i0oE683aFuZAADETAAxe/ZsOeuss2TUqFG1Pp6SkiLnnHOOLFu2TF577TUCCKAe5V6vuW63z0Bxd+hmX10VrhWRpRIMlFP/UdCutVNyst22lVdYxkILAIAEDiCWL19uUpR25NBDDzXzJIBEUZqXJ+WlZfamLq1bb65TM1uLp32ObeWmlheL2JeiH3X+wkIJ+v22lul0ucTVpo2tZQIAgEYEEKWlpdImgv+U27VrJ1u32p0jDLRc8LDg0h0Hzg2VmiHSYU8RRxKfcFCDh9WvzIpK2d1OH04QAQBAlETce9HJ0pqmtCNOp9M8Nyk5U0QOGrp9G3HPGnnQDqmnY3vbyg1uzZeyxa+Kq3VrSVbWyEPbAQMkpXUrW8osL94qmxcutH1UI97k5RdLiTe8Sp5dVq/bYmt5AID4lbyHP6NBjyYfdUpL7wWiQIOH9C5dbCsvsFmkbLFtxcU1DR7cNqUc+WwpJf6Dh4unzI1a+R4XB0cAINk1KIC48cYbpfUOjpgW6+ovAIAWYY08/HXortKhXYbtwUP7tum2lgkASOAAYr/99jPXO0pPatWqlVnGNSkFgyJFm8LbWe00n6ul9whAktLgoWuH5E2RAwDEQADx7LPPRnE3EkTALzLtxvD2xKkibk9L71HSsXvFpJI/VttWFgAAQCJgDgQSRrRWTFJOgsG4sVkyRQqd4nLYd3K2/GJGEwEAiMsAQs9Fcdppp8m//vUvcw00x4pJGjzYea4GRE9BWaq84D5J5Cu9ZedKTOG8f1dc/WICABAdcfPfod/vl/Hjx0tJSUlL7wqSbMUkROeEb77Nm22vWm95eKTgsF5eyelgz9KwylG6RdJ/my8ZrY6VJF2kGgCA+Asgpk2btsMVoADE1wnflCPF/p+hNulB6ZDlsK08R0pIUp0lLBMLAEC8BBDz5s2TF198UWbPni2HH354k8vTlaSiMpLh84pHV2LSI6FafqBcEomejbzydawpKwunMHl9fnF4vRLLyrcdzfcHAhXbdgiUhz9z5YFy8fnCZ0Xw+6teN5S/JNzemXvuadsJ3ypo8JDmFr8uQGCD4LbvX7A8aFuZyhkImB/LgM8v5dvq1Q7a/srn89b6m9SY75z1PdD2L/Pat6BANFifUd3nRBpdjvXfStSNtotPtJs9tH/scDgSI4AoKiqSq6++Wq677jrJzc21LR1q8WL7z+LlCPilT1n4P4xff/1VQqkuSUQrVqyQWBRcs9Zcr12zRhy+2A4gUsoKpJ2IbNy4UYJb7OvoFhUXmnT9zUWFEnKE68NSUFDQqDJDReEzEG/V4GRbh9c2Wp6NndytxeEEo+KtW8VVYN9nwOUtkTQR2ZK3SvyNrMfaeE3bZ8uff+bJ4mDAlu9cXkG4U75mzRrxbY3tn/iNRYGK+W3eQrckmlj9rcSO0XbxiXZrOrc7st/i2P7fZdvJ6wYOHCgnnniibWW6XC7p3bu32C7gl9S1R5rNvv36iSRYAKERvn45e/ToIenpsXcyqZK0dPlVRDrn5kpabmeJZeVFHilbIZKTkyPOVtm2lRvI95nTMbfNaiMdO3WuGHnQ4CE7O1tcroZ30vypblmvpzZpkyWpWZkSy0q2HdFu3aqVtMu2cw5EmsifIp3++FHs5C7PlO/kYOnaJkP66W+GDd85T16RiKw3B1xy29t7Ijm7uTfoqMNm6dmzp/TqkiWJItZ/K1E32i4+0W72WLZsWcTPjekAQlOW5s+fL3PmzLG1XB2eyciI0n+sJ480V4l3LG07/Q8xavXXBME0PUYs4nG7JM0T2+fgCLhcosfdXampkuKyL9BMTUkx1ympKTWOImjwEOmRhcpC25YeSklJEVeMB8VOZ/iItjPFae++ZraRwN5HaG6YvQHPukKR30TcTqn3O9WQ71xaWjiI0rZO84S/E7HK7Q7XZ1paWkz+piTqbyV2jLaLT7Rb00SavhTzAcSsWbNMikf1eQ833HCDvP322/Lvf/+7xfYNQHIJpbWyfyTKHdupds1l9bpwmpydMjyp0oUzcQNAVMR0ADF16tSKCYGWY445RsaOHSsnnXSSxJxQSKSkOLyd0VpDuZbeIySpwJZi8W7YEN72B8w8Bk1FskYTWnq5VVRVULpZfi9YVaNa9PdvbdkGSd+8WtLKIhtN+LMwfiYje1zhEbN7ZiyISvmPTRpKEAEAyRZAdOrUqdb7NW+8rsdalK5yc+814e2JU0U4ezGamTM13CHb9P33EiiteiZmnccQa8utJrsUR/i8Fe8v/Uxe2Phu3U9cHXmZwa06l+BgKSwrkq4S20tft2+bLuNG7CNev70r1uVvKpGX5i6VEq/Nk/4BAAY9AiCBpOq5UraItB0wQNqmhCdvlpeXS1FhkZkErfMYGhs8uOxewhXiSQ3PSTmy18HSrnfHGjXi9frMqmK6MIDHE9n8lV9XFci7P4n4yu098V80gwgAQHyJuwBCl0cFsONAwu0OH30250PQ8xhkZcb8JOhk1Ta9jeRm1hxVLXOVic9dIp1bdzATjSOxPl2PuutKTAAAREfcBRBAc1vn3SxlQXuP5jq9m4S1WWBZvzkoobU1Tzrm9flkw+aQpKZ7xeMOn+NiRwo2JdYJLAEAsYcAAthB8HDdb8/bXkddyvwyVo8TB0qkneTQBklq2xximflpqcin9Z10LK/BZbtd4fkVAADYjQACqIc18nB8zr6S47LvJGrezatFVn8l/hBHi5NZ2/SQjGz1uZTsfbSEWret8bjf75eCjRslOyfHnAAzEjp5+vM/vpa2WVE4WSYAAAQQQGQ0eOjkqdnBa6zC1E1UvYhsLHGKN2DfcscbyuJvjkdbZ4lktHFKqE3NSdJ6Yu1yr0Ny2rkiPgmgsyRFHOs4vwQAIHoYgbCT0ymy1/7btwHUGzw88JV9QVltqUEAAMB+BBC21qZL5ORRthYJJCpr5GFIjxJpk1b1nBWNFSgtFe+yJdImbaAt5QEAgJoIIAC0KA0e2mfYE0D4QwEpkvg5EzMAAPGIAMJOoVD4bNTK5RZx2JfbnWhK8/KkvLTM1jJL/mjA6XoBAADQKAQQdtLg4c7x4e2JU0XcHluLT6TgYcGll0etfCf1DgAAEDUEEGh21shDt9OHi6dje9uDB097zqsAAAAQLQQQaDEaPKR36UILAAAAxBHWGgUAAAAQMQIIAAAAABEjgAAAAAAQMQIIAAAAABFjErWdnE6Rfntv3wYAAAASDAGErbXpEjn9AluLBAAAAGIJh8kBAAAARIwAAgAAAEDESGGyk88rcuf48PbEqSJujzSnvPxiKfEGbC0zw5MqXTq0trVMAAAAxC8CiAShwcPFU+ZGpezHJg1N6iAixxeQtOJNkmJjcOYpKbKtLNTkLy62vVocqamS2qoV1Q0ASHoEEAnCGnn469BdpUO7DFvKzN9UIi/NXWr7qEY8cWzdLBNWFYis+sDWcttZG06+gnZyOFPMdeHChRINHQ47jCACAJD06L0kGA0euibxaIHdHOU+c72+Wz9Jb93etnKL/CXy+ZYlMjg1JCHvZtvK3RjYIsksNT1N2uw1QELBclvLDZaWSfFvyyQUSN5gGgAACwEEEAG/J0M8GW1sq6tQwC1FpSnydv73Ual/17Yj8ckaRNjNb3uJAADELwIIoAVkpqbL8e33Fb/NR8qt4EHLBwAAiAYCCKCF0MkHAADxiADCTk6nSO/dt28DAAAACYYAwtbadImMuNTWItEw67ybpSxoX8Z6gb9IdqYREKXlYR1lxfwIAwDiDgEEEip4uO63520ts0uZX8bqF8XBiFIyi9bysK4UkfQ2IoHiYklpY98qXwAARBMBBBKGNfJwfM6+kuPKtKVMPYGcrP5AMlLTJGhLiYhH0Voe1rFls8iWVRLyszxsNKxeZ/+yxhme1KQ+sSYAKAIIO/m8IvdODm+Pu0PE7eFT1gI0eOjkaWtLWXaefRrxLRrLwwb9pSLJfeqOqPDo0I6I3DNjQVTKf2zSUIIIAEmNAMJuflaMB4CW1L5tuowbsY94/faOGOVvKpGX5i6VEg4sAEhyBBAAdmhjiVO8AYetNZW/NXlPdofmCSIAANFBAAFgh8HDA1/ZkxJWGxfz0wEAiCsEEADqZY08DOlRIm3SgrYHD3aXCQAAoosAAkBEtKPfPoPOPgAAyY7kAQAAAAARYwTCTg6HSPfe27frkJdfbPsqHtFY7xwAAACojgDCTi63yOgr6n2KBg8XT5kr0V7/HAAAAIgGAohmZo08/HXortKhXYbtwQNLFwIAACCaCCBaiAYPXTu0bqmXBwAAABqFSdR28nlF7pkUvug2AAAAkGAYgbBbyVbbiwQAAABiBSMQAAAAABIrgNi8ebNcf/31MmTIENlnn31kxIgRMn/+/JbeLQAAACDpxEUAMW7cOPnhhx/k3nvvlVmzZkm/fv3kggsukN9//72ldw0AAABIKjEfQKxcuVK+/PJLufHGG2XQoEHSs2dP+de//iUdO3aUOXPmtPTuAQAAAEkl5idRt2vXTh5//HHp379/xX0Oh8NcioqKGlVmKBSSkpISsZ3PK55g0Gx6tfxAeY2nlJWVhZ/q80mZN7wdq3QfrX3W+iotLTW3revGsurA6/OLw2vfalU+f3h//QG/+Jzh7aZyBfzmOhAISPm27Vi3sSRFfOXbz4ReHnDK1hKPlDickpIaanB5G7Z9VcrLyyVQXvMzjSbY9nsRCJRLcNv3rbKAz1/lOhI+vz9ufmPiTfXfxLrY9VuJ5kfbxSfazR7aP9b+dUIEEFlZWXLYYYdVue+9994zIxPXXHNNo8r0+/2yePFisZujPCDd0zPN9solSySUUrN68wrC/wGtWbNGfFtju/o3FoVPerd8+XLxFror7l+xYkWTyg2uWWuu165ZIw4bl7tdFwoHlBs3FkjQYU/HKb2sUDqImGDVWxb7nefNXpc8/+sutTyi76JpfGVbZUt5+DMBe7hKS6WNiBQWFYp/7bo6n1dQUBBxmUWBrRW/MV53FA6UJLG6fhPr0tTfSrQc2i4+0W5N53bv+LdNxXYPthYLFiyQyZMnyzHHHCOHH354o8pwuVzSu3dviYo9wyMlfet42JOnndz1kpubK7nt7T0Ttd3c5tDzZnG37iieNq3E6/VKXl6edOnSRTweT6PLLSt2SIErU/bKzZW03M627a/Tu0FktUhOTrZ0dGu3rOlcW1JFVoUD2fKMLIl1Xt1fETlk52Jp4wkHPMFg0BwxTUtLE6ezcVmLrpSQZHnSbN1XaOP4REpE2mS1EWfnTjWqREceNHjIzs6WVLcroipLLd0sUijmN6Zz66YHjqj5m6iptL26ZNV7NFQ7Mj169JD09HSqMI7QdvGJdrPHsmXLIn5uXAUQH374oYwfP96sxDR16tRGl6PDMxkZLdN5T0vzVUR4aTHeIctsFU53eeiV/1V7ZH3TC+9+qtzqT5EeTQhEqnMHw1GzK9UlbldkEfSOpKSGO22pqani3LYdy1JTUsx1doZI+4zwMGSgPCRbQn7JbO2R1JTIhiZrcsTDlKm4E9wW0KWmpkhqPUd9NHiI9KiQO+CKm9+YeON2h0cgNBiP5P8QDR5a6v8aNA1tF59ot6aJNH0prgKI5557Tm677TY57rjj5M4774z4P1M0Xvu26TJuxD7i9ZdX5P9qWoQe2WxK/f/5e57MXpAvcZARBAAAgHgMIGbMmCG33HKLjBo1Sq699toGRUjNSifxTr8tvH3ptSI2HQVv6SDCohMydd6Gpl415cimNz/2j+QDzcm/pViC+Rtr3h/wS3DLFvG7XCIRjoD5fI1bXAIAgIQJIHSy2u233y5HH320XHzxxbJhw4aKx3QYOTMzPGk5JoRCIoUF27cBoD6OcArT5gULpMz/Q51Pa0jSYHGGU6RfK/EXbhbJrDmvAgCAhA8gdMUlXTXpgw8+MJfKTj31VJkyZUqL7RsANEVKWngOUNsBe0nAU/NgiC6bu6WoSDKzsirmt+xQsR7E+FOCPlbMAgAkaQBxySWXmAsAJKqU1pniqGWVL0cgoCeJEFdWppnIH1FZwVIRe06DAgBAfAYQaFnejRsk6PVVTKIObSwQr9sjoSZMovYVbLJxD4H4l1K2pfb7ywPi9m2VlNIUcdZyXpnaeLxbJauWk1jCPqvX1d5eFl02Wc/5o8t2Wyvv1SfDkypdOrS2cQ8BILoIIFBv8LDkvgdr3L+qiXWm54CQjgeLUyeGAsls2xyIjBV1z39o28BJEJoItbMG6l3r7+Si4TyucBrZPTMWRPgXkTfcY5OGEkQAiBsEEKiTNfLQYcgQcbdrIz5/oOKkVm5X4z86qVuDIosD4mprukZA0gq506Wk+94ioWCtj5eXl0vJ1q2S0aqVpEQ4B6KsMF+yC9eIcNbwqC9tXZeGLHmdv6lEXpq7VEq8zFkBED8IIOyky8t22HZm5VhdarYRNHhw57Q3y9Q6An5x52gA0fgUJpdLA5Ptq2kByR5E1CVYHhC/LyhBT0bEKUyB1EIb9w71LW1dF7uWvAaAWEUAYSftVF9yra1FAgAAALEknIALAAAAABEggAAAAAAQMVKY7OT3iTx5d3j7ggnhlCYAAAAggRBA2CkUEslfu30bAAAASDCkMAEAAACIGAEEAAAAgIgRQAAAAACIGAEEAAAAgIgxiRotJm+znpHaPmvKyiVYlmFrmQAAAKiKAMJODodIm+zt26iVOyU88DX98/VRqKEhUtjeK508sV/5G0uc4g3Y+znJ35pia3kA0BxK8/KkvLTM9nJT0tMkvUsX28sFkh0BhJ30vA9jb7K1yESU3SpVLhncUXzlQVvL/W1zkXy62Cf+gMRF8PDAV22jVr6L5EQAcRQ8LLj08qiVv8/0aQQRgM0IINBiQYTdNvrjp9dsjTwM6VEibdKCtgcPdpcJANFijTx0O324eDq2t61c7/oNsvqVWVEZ2QCSHQEE0IK0o98+g84+AGjwkMzpRqRxIZ4QQNjJ7xP5zwPh7dFXhFOaAAAA6kEaF+INAYSdQiGRvFXbtwEAQIsq+WN1xM8tKyuT4Jq1UpKWLsG0tGabmE0aF+INAQSwA+vLM2VTsVuCAftWOGK1JERboKjIHNW0k9PjFk+OfTnqQDQ53eHl+Jbety0zoAF+baGJ2cmexoX4QQAB1GPjVodMKzxV5MfoVBOrJcF2zvBiAqsWzZc1X8yztejU8pDsftpZ4m6TZVuZBCWIFk/7HNn1n2Ml6PNG/Dden1/WrlkjnXNzxeN21XycidmAQQAB1MNbHl4t6bCumyQrs+Z/Jk3BakmIBte2o64/90qXDe4o/MS/OlNal9o78X+3K8cysoGoBREN4fB6xeHzSlpuZ0nzxMEJhYAWQgABRKCNJyA5GZykDbHPkxJevOHANn3El55pW7lFgRL5pmiJtBt6mHRMaW1Lmb5NhZL/2WcS9Np7Vvpo8m7csMP99fl8EtpYIF63R0Lu+hfT8G6O/Og4AMQKAggASECZqRkSdLWyvVxX27bidkfvJIixHjwsue/BiJ+/bUmNehW4MkU6Hize/HyRbslZr4g/LDkLAgi7Zdj/HzYAoOVZIw8dhgwRd7s2dT7P5w9IQUGBZGdni9tV/3+zobVFIqu3lw3EOpachSKAsJPmHl81hU8WAMRBqlFDla3PN9caPLjrW43K7xNHwC/uHA0g6k9hcm3V+SQB+XNTmaSv3mzr/mZ4UqVLB3vSzdD4pWFborxoYslZKAIIJJxNxQ5Z47ZnkmdBcXhFGwCJm2rUUI5U+xZUcKeEF2p4+MPVInqx2WOThhJExMjSsA0pPx5Ea8nZxgRT9Z2/Ixrn7QABBBKIa9sc548XuOVjsevIY/iHyOXkxIBAoqUaNTZ4cLWxr8x2aQ4ZtvZz6XzmmeLp0MG2cvM3lchLc5dKiTdgW5lo3NKwDQkeGrpqVCKxIzj7tZnP25HMGIGwk98n8sL08PaIS3U9RVuLR/3aZDjF1WuhHNh6d8lMTbelupxlxdJ25Txxe3oLIQQQX3aYahQjMstLJLetR9JJN4oL8djJtzNFKlrpVk0Jzuo6fwfn7YgeAgg7hUIiK5dt30azc7i90qZ1SNq5wmkBTeVMCUmms0RKbCkNAOqfX2EXlodFtFOuopFu1djgjPN3ND8CCAAAWog1n2L1K7NsLddaHrbwfz9JsTfXtnLJJ48v0Uq5ird0q2iMmqQk+dwKAggAAFqIzqfoNvw0CQX8tpbr2FAiskJkxVPPSJm3wNayySePL/HU0Y+3Se/7JPHcCgIIAEBCpe/YXV602Tkp25Lm8oms2CDdzhgu3bPsWU2OfHLEm2iNwHjXbzCjhtaStsmIAAIAElBK2RZby3MHSiTLXx4X6TvVy09murJTeo69uerJfA4ExJ9kHoGJJgIIAEgkjvDR5owVP9habKaInKVH9zsUi7jbxnT6TjSWWwXnQACwHQGE3Vwc8WpJeoTUXbpFnDYdKbX7KC4QbSF3upR031skZM/JFC2+0kJpm79SHEF7zytAJz9+cA4EABYCCDvpZJ1J99paJCLnKC2Ws9YXiayfH7WjukC8BBF2CwTCub6Osi1SXrzBtnIdKS5xpjNSEC15m+06qWZYujtTOpMSAiQ9AggkDOvI6OYO3cVtZ4fE4YxKhwyIJ0Fn+FTv7uXzZavNZbceeBpBhM3cKeGDHtM/X2930TL1tJ2kcxYnSgWSGQFEAvFu3CBBry8uVjLZ5C8WX8jeVIgtga3SUY+Uujzi8rSytWwg2ZW7PPJR2wwZ3GY3yUrJsKXMkK9YvKt/lFC5/XMgkl12q1S5ZHBH8ZXbl8q2oTggbyzaLKV+TpQKJPv5JQgg7KQTAV/+d3j7jDEizbgCiAYPS+57MC5WMtHg4cm8D8Vu7X0BOU2/fKQbAVFRkuKUkKe1pLha21KevWs6obYgAoD9nJxfggDCVsGgyLKft283I2vkocOQIeJu1yamVzKxRh4OzNpNslLtOZKpdPK05M8XT4pbOD4GREdReYltZTnKS4REmPhj97wKle5ykBaFuOHh/BIEEIlGgwd3TnuJBxo8tHPZl2pk18pLAGpK3Tay9+3mJbZVT1agXIaYoKRU2lHpST2vQjG3AvHEk+SLCTC+CQDYoYwUjxzQZjcJ2Lg8bHnpZpHCFRIIEvwn67wKxdwKIP7ERQARDAbloYcekpdfflm2bNki++23n1x//fWy0047tfSuIUbO16A4ZwMQ/SDCTqVO+9Kh0DyYVwEgbgKIRx55RGbMmCFTpkyRzp07y9133y1jxoyROXPmiNtNBm28ier5GswLcM4GAACApA0gfD6fPPXUUzJ+/Hg5/PDDzX333XefHHroofL+++/LsGHDWnoXESvnazCFc84GIN7YfXI6UyYnqIs7TM4G4ocjFArF9II1P/74o5xxxhny7rvvSs+ePSvuHzFihOy2225y0003Nai8BQsWiL5llysKS6yGQuIo3BTebNNOxOGo8RR/ICibtnjF4wyIw2Fj1Ye2rfzkdIrUfFl7X6ep5YdCkhIKSrmOFNRSR4jRdkPLSOS22/ZbEC1BR4qEWqruErndbBYKOcQbjN7xTP3/1hnh/7ehSm1H88WPRGm3UEjEWR6QVllZ4kht/mP8fr9fHA6H7LPPPvE/ArF27VpznZubW+X+jh07VjzWEFoxla9tpWW2C8/Kr6v0UHl4+buo/Vg27+qxTRPToSuAuMdvDKL5/y0QDQ6XuMsD4onGge4dvbTDEXH/OOa/VaWlpea6+lwHj8cjhYWFDS5v4MCBtu0bAAAAkGxifrZpWlpaxVyIyrxer6Snp7fQXgEAAADJKeYDCCt1af36qieu0dudOnVqob0CAAAAklPMBxB9+/aV1q1by7fffltxX1FRkfz888/mfBAAAAAAmk/Mz4HQuQ8jR46UqVOnSnZ2tnTt2tWcB0LPB3HMMce09O4BAAAASSXmAwg1duxYCQQCct1110lZWZkZeXjyySejsxQrAAAAgPg9DwQAAACA2BHzcyAAAAAAxA4CCAAAAAARI4AAAAAAEDECCAAAAAARI4AAAAAAEDECCAAAAAARI4BAhWAwKA8++KAceuihsvfee8uFF14of/zxR0R/N2bMGJk2bRq1GQfttnTpUrnooovkgAMOkIMOOsicZyUvL69Z9xmNa7uffvpJzj33XBk4cKAceOCBcv3118uWLVuozjj5rVRvvPGG9OnTR1avXh31/UTT285qr+oX2i+2283v98s999xT8Xw9IfHixYubdZ8THQEEKjzyyCMyY8YMueWWW2TmzJkVgYHP56uzlvSxa665Rj7//HNqMg7abdOmTXL++edLWlqaPPvss/LEE09IQUGBeb7X622R/U9mDWm7DRs2mLbr2rWrvPrqq+Zvv//+e5k0aVKL7Hsya8xvpfrzzz/l5ptvbrb9RNPb7tdff5X9999fvvjiiyqX3NxcqjeG2+3GG280v5O33367zJo1S7Kzs03QwQEXG+mJ5ACv1xsaOHBg6Pnnn6+ojMLCwtBee+0VmjNnTq0V9P3334dOOOGE0NChQ0ODBg0KPfjgg1RkjLfbSy+9ZJ5fWlpacV9eXl5ot912C3311VfNtt9oeNv997//DV155ZUhv99fcd8zzzwTGjBgANUZ47+Vqry8PDRixIjQ6NGjzfftjz/+aKY9RlPabsyYMaFbbrmFSoyjdlu1alWoT58+oY8//rjK84844gj+n7MRIxAwfvnlF9m6datJabFkZWXJ7rvvLvPmzau1lj799FMzPDh79mzJzMykJuOg3fR5eiRHRyAsTmf4Z6CoqKiZ9hqNabsBAwbIvffeK6mpqeb2b7/9Jq+//roccsghVGiM/1aqRx991KRVXHzxxc20p7Cj7XQEYpdddqEy46jdvvzyS9MnGTJkSJXnf/TRR1XKQNOE/ydC0lu7dq2pg+rDsh07dqx4rLorr7wy6est3tqtW7du5lLZ448/bgKK/fbbL8p7i6Z+5yzHHnusrFixwqQzPfTQQ1RsjLfbjz/+KE899ZS88sorsm7dumbZTzS97QoLC017zZ8/36TPaAroXnvtJRMmTJCePXtSxTHabsuXL5eddtpJ3n//ffP/m7ahBhua7kkwaB9GIGCUlpaaa7fbXaVGPB4PufEJ3G46D+K5556T8ePHmxxRxEfbTZ061bRdTk6OjB492hydQ2y2W0lJifl+6aVHjx40Uxy1nS44oUKhkNxxxx1y//33m+edffbZZk4SYrPdiouLZeXKlWa0fdy4cTJ9+nQzcqvttnHjRprNJgQQMKyUluoTkvTLmZ6eTi0lWLvpf4j6n+Gtt94ql156qYwaNSrq+wr7vnP9+/c3Ezt19EFXg/nggw+o3hhtN/2O6dHqs846izaKs7YbNGiQfP3112Y1nz333NPc1u+cTuDVCbqIzXbTYEGDiPvuu08GDx5sRo10W7322mvNtNeJjwACVYYG169fX6VG9HanTp2opQRqN83D1iF4zcmePHmy/POf/2yWfUXT2u7333+XTz75pMp9+ry2bduSFhPD7aYrwHz11Vdm6V296EowatiwYeY7iNj+vdSRWYfDUXFbO6yaBkoqWuy2W+fOnU0QUTldSYMQTWti+V37EEDA6Nu3r7Ru3Vq+/fbbihrRSbU///wzufEJ1m5XX321vPvuu+ao2nnnndeMe4umtJ12QvWcHZUnu69atcrkZZPXG7vtpnnYb775pllsQi86IqE0N5tRidhuuxdffNGcL0fT0Cx6ZFvnH/Xu3bvZ9jvZNbTd9L5AICCLFi2quK+srMycN6J79+7Ntt+JjknUqMgt1BOtaG61HnHRyZl33323ieSPOeYYKS8vN+cL0JUNKq/gg/hqNx12f/vtt00QoSkw+fn5FWXRtrHddnrEWjudOnqk+fQ6wVM7ozo8f8QRRzTz3ievhrZb9Q6LNemzS5cuZvQIsdt2uoqPPld/L6+44grTCdWV0PRvTzvtNJouRttNU80OPvhgmThxojnvin7P9CR0KSkpcvLJJ9NudrFzTVjEt0AgELrrrrtCBx54YGjvvfcOXXjhhRVrleu1rl0+a9asWv9W11fmPBCx327nn3++uV3bpa62RWy0nfr9999DF110UWjfffcN7b///qHJkyeb9c0RP7+V33zzDeeBiKO2+9///md+N/U7t88++4Quv/xyc+4cxHa7bdmyJXTDDTeEDjjgAHOuHG3DpUuX0mw2cug/tkUjAAAAABIacyAAAAAARIwAAgAAAEDECCAAAAAARIwAAgAAAEDECCAAAAAARIwAAgAAAEDECCAAAAAARIwAAgAQczhFEQDErtSW3gEAQNioUaPku+++q1IdLpdL2rdvL0cccYT885//lDZt2iRcdR155JGy//77y5QpU8ztRx55RNxut4wZM6aldw0AUAsCCACIIbvvvrvccMMNFbf9fr/89NNPcu+998rixYvlhRdeEIfDIYnkoYcektatW1fcfuCBB+Syyy5r0X0CANSNAAIAYoh2pPfee+8q9+23336ydetWefDBB2XhwoU1Hk+EoAkAED+YAwEAcWDPPfc013l5eRXpTuPHj5exY8eagOL8888392/ZskXuuOMOOeqoo6R///4ybNgweeWVV2qkDN13331y++23m+DkgAMOkKuvvlo2b95c5Xnz58+XkSNHyoABA0yK0cSJE6WgoKDi8VdffdV0/jWoOfPMM83raarVk08+WaWcN998U0466STZa6+95MADDzT7vW7duir7M2nSJLPdp0+filEJ3V66dKm5fvHFF6uUuWbNGunXr5+88cYbtdZXWVmZ3HjjjTJkyBBTd8cdd1yN/Vq/fr15TwcddJAMHDjQvNcffvih4nGv1ysPP/yw+Vt9b8ccc4w8/vjjEgwGK55TVzvo3951111y2GGHmdc/8cQT5e23366nhQEgfhBAAEAcWL58ubneaaedKu575513pFWrVjJ9+nQzX0A7zWeffbbMmTPH3Na5BPvuu69ce+218uijj1Ypb8aMGbJgwQITbFx11VXy6aefysUXX1wxeXnevHly3nnnSVpamtx///1yzTXXmPkZo0ePNq9j0c60zs04/vjjTed6n332MR3nzz//3Dz+/fffm+BEO99PPPGETJ48Wb755hvzmrWxAoXTTz/dbO+6664mgHn99derPG/27NmSkZFhyq2NBkefffaZCRA0cBg6dKjZr1mzZpnHdURnxIgR8u2338qECRNMwOLxeORvf/ubrFixwtTDJZdcIv/+97/ljDPOMPWngYTWReUUs9raQf/2H//4h8ycOdMEFHq/BihXXnml2W8AiHekMAFADNHOZyAQqLhdWFhoOu5WJ9QaibAmWN90001mwrEVFCxZssR0XPW56tBDDzXlaTBx1llnSdu2bc39TqdTnn76acnMzDS3s7OzTadXO/561P6ee+6Rnj17ymOPPSYpKSnmOdqRP+GEE0wn/JxzzqnY37///e+mk600YPnggw/kk08+Ma+tAYQGIRdddFHFfuo+LFq0yPxt9fkcVnpW586dK7aHDx9uOu1//PFHRQClHXHdFy27NlpnhxxyiHmO0lEWDThycnLM7ddee03+/PNPc60jGUqDn1NOOcUETytXrpSvvvrKzD2xytDy9PV0joYGUhrc1NYOX375palHHeXRwMpqh9LSUpk6daoZFUpN5b9fAPGLEQgAiCHaed1jjz0qLgcffLCMGzfOBA7aqa/c4e7Vq1dFp9XqNHft2rUieLBo+pCm1GiqUeW0ISt4sG5rp1ZfXzu6+lxNv7ECGr1o532XXXYxHeTKKr+e7o8GIyUlJea2pkhpedpp1v3XtKjBgwebSdKRTga3AgVrFEJHTnSU4NRTT63zbzRgeOmll+TCCy+U5557zgQfGiAdfvjh5nENbLp161YRPKj09HR57733TDCkdan1oaMO1evSquu62uHrr782703rz6o7vWgd5+fnm7QsAIhnHAIBgBiiQYMezVbaCdW0mtzc3CqrFFk0baYyHa3o0KFDjefpMrCqqKio4r5OnTpVeY6OSLRr186Uoc/T1CRNOdJLdbpPlVUfBdCyrFQoDS40temZZ54xIx66rfuj6UE6fyAS+t61I6/zHTTw0NEHHR2pHihVpmlbOoqhf3PLLbeYiz5f50X07dvXzPewRiNqo/Wg9WGNvlis+tW5JnW1g5at719HNGqjcy8qBy4AEG8IIAAghmhnVCfsNoaeI0JTb6rTo95KO8SWTZs2VXlOeXm5uU9HD3QfNHjRORBW+k5leqS+ITR9x0rh0fkP//nPf+TWW281KVE6sToSmsak6UY//vijGSW44IIL6n2+jghceuml5qITzz/++GOTxqVzL9566y0z+rJ69eoaf6ejG1qPetH60HqpHERo5796XVanZWu6lL7P2nTv3j2i9wwAsYoUJgBIEJoupHn9lVcSUnoUXvP0K3fWdYKxz+eruD137lyTZqMrEukRf11d6ffffzfBjHXRnP9p06aZiceRuvPOO03nX4/Ia+ChqzTpxObKK0pVpyMYtb23Hj16yN13322O/p988sl1vqZO8j722GPlqaeeMre7dOli5mxoMGS95qBBg0xaU+V0Ik3zuvzyy82qVbrqlNbHu+++W6MurbkeddG/1RQufc+V60/np+iqTpXnuABAPGIEAgASxGmnnWYmUmuuvy4rqjn+H330kZn0rKk/WVlZVZZB1aPzOhlYt3WysI4S6NwBpfMudOKzHrHXvH89Eq8dcp0boZOmI6XLtmrqki7TquXoifF0ZSOdSK2P1Ub3U0cCdD6GdvStuRIaiOg8Cp3kXT0Fq3pKlaaC6cpKGjjpMrC6ipWOYGhgYdXVs88+a+pA60pHFHTEQPdPV7LSutO6uO6668ySs5r2pPMeNKVL51707t27ztfXuQ8a8Gg96UXnjejIiZ7HQ+tYR3kAIJ4RQABAgtAj/Nop1k62rhRUXFxsJvjedtttZlnUyvRovHbUdQlWTbfRTrEuM2rRic66/Kl2wrWDrR1x7ZRrMNCQE9lpZ1pXHtLgw5o4rUfvtbNurQhVnc6P0HQjnQCt507QEQSrLH1v2vnfkZtvvtksuaqvqylcOt9B6+CKK64wj+soi06u1qVddX6EzvnQ96X7Za30pCtQaadf52/o+S80qNDAyjrXQ110BEXnemgbaBkbN240AY/+nQZ3ABDvHCFrphsAICnoakCaZjNlyhSJJ9ZkbF0itvKqRwCA5sUIBAAgpmnqkc4f0PQsTQkieACAlkUAAQCIab/88os5Od7RRx9tzhQNAGhZpDABAAAAiBjLuAIAAAAggAAAAABgP0YgAAAAAESMAAIAAABAxAggAAAAAESMAAIAAABAxAggAAAAAESMAAIAAACAROr/AT/QKMsCZBCeAAAAAElFTkSuQmCC", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "prop_df = pd.DataFrame(e_hat_raw, columns=ARM_LABELS)\n", + "prop_long = prop_df.melt(var_name='treatment', value_name='propensity')\n", + "\n", + "fig, ax = plt.subplots(figsize=(8, 4))\n", + "sns.histplot(\n", + " data=prop_long, x='propensity', hue='treatment',\n", + " bins=30, element='step', stat='density', common_norm=False, ax=ax,\n", + ")\n", + "\n", + "ax.axvline(0.05, color='tomato', lw=1.5, ls='--', alpha=0.8, label='threshold = 0.05')\n", + "ax.legend(title='Treatment', fontsize=8)\n", + "ax.set_xlabel('Propensity score')\n", + "ax.set_ylabel('Density')\n", + "ax.set_title('Propensity score distribution by treatment')\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "0b42eca5", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-09T16:49:48.264007Z", + "iopub.status.busy": "2026-05-09T16:49:48.263913Z", + "iopub.status.idle": "2026-05-09T16:49:48.571081Z", + "shell.execute_reply": "2026-05-09T16:49:48.570638Z" + } + }, + "source": [ + "In this dataset the minimum value of $\\hat e_a(X_i)$ is approximately 0.058, and no observations have `e_hat < 0.05`. Excessively large weights from extremely small propensity scores are therefore not a major concern here.\n", + "\n", + "The propensity distributions for all four treatments overlap well in the graph, and no clear positivity violations are observed." + ] + }, + { + "cell_type": "markdown", + "id": "f4edd2d8", + "metadata": {}, + "source": [ + "## Policy Learning Methods\n", + "\n", + "We compare three policy learning approaches.\n", + "\n", + "- Plug-in Policy (DRLearner)\n", + "\n", + " First estimates per-customer CATE, then selects the treatment with the highest expected net benefit.\n", + "\n", + " This is a flexible approach with no constraints on policy form, but final performance depends heavily on the quality of CATE estimation.\n", + "\n", + "- DRPolicyTree\n", + "\n", + " DRPolicyTree learns a policy by directly maximizing the AIPW score.\n", + "\n", + " The policy structure is constrained to a shallow decision tree, making results interpretable as if-then rules. This is useful when policy interpretability and explainability matter.\n", + "\n", + "- DRPolicyForest\n", + "\n", + " DRPolicyForest also directly maximizes the AIPW score but uses a forest rather than a single tree.\n", + "\n", + " A single tree has a simple structure but can exhibit high variance. A forest averages across many trees, reducing variance and learning heterogeneous treatment effects more stably.\n", + "\n", + " However, the resulting policy is harder to interpret as a single clear if-then rule set.\n", + "\n", + "All three policies are learned on the train set and compared using AIPW scores from the same test set. It is important to use separate data for learning and evaluation, as using the training data for evaluation leads to overestimation." + ] + }, + { + "cell_type": "markdown", + "id": "d7d4bc26", + "metadata": {}, + "source": [ + "## Evaluation Metric\n", + "\n", + "The AIPW (Augmented Inverse Probability Weighting) score is defined as:\n", + "\n", + "$$\n", + "\\hat\\Gamma_{i,a} = \\hat\\mu_a(X_i) + \\frac{\\mathbf{1}[A_i=a]}{\\hat e_a(X_i)}\\bigl(Y_i^{net} - \\hat\\mu_a(X_i)\\bigr)\n", + "$$\n", + "\n", + "- $\\hat\\mu_a(X_i)$: predicted $E[Y^{net}(a)\\mid X_i]$ from the outcome model\n", + "- $\\hat e_a(X_i)$: predicted $P(A_i=a\\mid X_i)$ from the propensity model\n", + "- The second term corrects the difference between observed and predicted values via IPW.\n", + "\n", + "This structure ensures unbiasedness holds as long as either the outcome model or the propensity model is correctly specified. This property is called doubly robust.\n", + "\n", + "- If the outcome model is correct: the expected value of the IPW correction term approaches zero, so $\\hat\\mu_a(X_i)$ contributes directly.\n", + "- If the propensity model is correct: the IPW correction term removes the bias of the outcome model.\n", + "\n", + "This score is used for both policy learning (DRPolicyTree/Forest) and policy evaluation." + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "id": "e3156595", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-09T16:49:48.578060Z", + "iopub.status.busy": "2026-05-09T16:49:48.577946Z", + "iopub.status.idle": "2026-05-09T16:49:49.475582Z", + "shell.execute_reply": "2026-05-09T16:49:49.474994Z" + } + }, + "outputs": [], + "source": [ + "Y_net_train = Y_net[train_idx]\n", + "Y_net_test = Y_net[test_idx]\n", + "\n", + "e_hat_net = np.clip(e_hat_raw, 0.02, 0.98)\n", + "e_hat_net = e_hat_net / e_hat_net.sum(axis=1, keepdims=True)\n", + "\n", + "gamma_net = np.zeros((len(X_test), 4))\n", + "mu_hat_net = np.zeros((len(X_test), 4))\n", + "\n", + "for arm_id in range(4):\n", + " outcome_model = RandomForestRegressor(\n", + " n_estimators=400,\n", + " min_samples_leaf=20,\n", + " random_state=SEED,\n", + " n_jobs=1,\n", + " )\n", + " outcome_model.fit(X_train[A_train == arm_id], Y_net_train[A_train == arm_id])\n", + " mu_a = outcome_model.predict(X_test)\n", + " observed_a = (A_test == arm_id).astype(float)\n", + "\n", + " gamma_net[:, arm_id] = mu_a + observed_a / e_hat_net[:, arm_id] * (Y_net_test - mu_a)\n", + " mu_hat_net[:, arm_id] = mu_a" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "id": "e8533283", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-09T16:49:49.477219Z", + "iopub.status.busy": "2026-05-09T16:49:49.477080Z", + "iopub.status.idle": "2026-05-09T16:49:49.483510Z", + "shell.execute_reply": "2026-05-09T16:49:49.483027Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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samplee_hatmu_hatgamma
sample_idactual_armobserved_net_outcomenonetech_support_onlydiscount_onlydiscount_plus_supportnonetech_support_onlydiscount_onlydiscount_plus_supportnonetech_support_onlydiscount_onlydiscount_plus_support
00discount_plus_support9254.5585410.2486330.2673380.2723660.2116625370.0992819559.7777634547.4845526984.7316095370.0992819559.7777634547.48455217708.540116
11tech_support_only7702.8616620.2110870.3072040.2433940.23831510468.9425226728.9884198373.31663311121.82660910468.9425229899.1085088373.31663311121.826609
22tech_support_only5062.1844200.3765700.2419010.2562610.1252682879.7417865449.409859937.5151032959.5143472879.7417863848.652052937.5151032959.514347
33none8930.4950220.3396530.1947010.2465050.2191428458.7792178254.3720696054.5989736560.0280699847.5980528254.3720696054.5989736560.028069
44none8156.5755020.3122650.2500210.3121230.12559010137.4698213378.7480355743.8029392988.2173143793.8450543378.7480355743.8029392988.217314
\n", + "
" + ], + "text/plain": [ + " sample e_hat \\\n", + " sample_id actual_arm observed_net_outcome none \n", + "0 0 discount_plus_support 9254.558541 0.248633 \n", + "1 1 tech_support_only 7702.861662 0.211087 \n", + "2 2 tech_support_only 5062.184420 0.376570 \n", + "3 3 none 8930.495022 0.339653 \n", + "4 4 none 8156.575502 0.312265 \n", + "\n", + " mu_hat \\\n", + " tech_support_only discount_only discount_plus_support none \n", + "0 0.267338 0.272366 0.211662 5370.099281 \n", + "1 0.307204 0.243394 0.238315 10468.942522 \n", + "2 0.241901 0.256261 0.125268 2879.741786 \n", + "3 0.194701 0.246505 0.219142 8458.779217 \n", + "4 0.250021 0.312123 0.125590 10137.469821 \n", + "\n", + " gamma \\\n", + " tech_support_only discount_only discount_plus_support none \n", + "0 9559.777763 4547.484552 6984.731609 5370.099281 \n", + "1 6728.988419 8373.316633 11121.826609 10468.942522 \n", + "2 5449.409859 937.515103 2959.514347 2879.741786 \n", + "3 8254.372069 6054.598973 6560.028069 9847.598052 \n", + "4 3378.748035 5743.802939 2988.217314 3793.845054 \n", + "\n", + " \n", + " tech_support_only discount_only discount_plus_support \n", + "0 9559.777763 4547.484552 17708.540116 \n", + "1 9899.108508 8373.316633 11121.826609 \n", + "2 3848.652052 937.515103 2959.514347 \n", + "3 8254.372069 6054.598973 6560.028069 \n", + "4 3378.748035 5743.802939 2988.217314 " + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sample_ids = np.arange(5)\n", + "\n", + "sample_prediction_table = pd.concat(\n", + " {\n", + " 'sample': pd.DataFrame({\n", + " 'sample_id': sample_ids,\n", + " 'actual_arm': pd.Series(A_test[sample_ids]).map(ARM_NAMES).to_numpy(),\n", + " 'observed_net_outcome': Y_net_test[sample_ids],\n", + " }),\n", + " 'e_hat': pd.DataFrame(e_hat_net[sample_ids], columns=ARM_LABELS),\n", + " 'mu_hat': pd.DataFrame(mu_hat_net[sample_ids], columns=ARM_LABELS),\n", + " 'gamma': pd.DataFrame(gamma_net[sample_ids], columns=ARM_LABELS),\n", + " },\n", + " axis=1,\n", + ")\n", + "\n", + "sample_prediction_table" + ] + }, + { + "cell_type": "markdown", + "id": "3c967473", + "metadata": {}, + "source": [ + "### Plug-in Policy\n", + "\n", + "A plug-in policy first estimates per-customer treatment effects (CATE) and then uses those estimates to construct the policy.\n", + "\n", + "For binary treatment, the standard rule is to treat if $\\hat\\tau(x) > 0$, or if costs are present, treat when $\\hat\\tau(x) > c(x)$.\n", + "\n", + "With multiple treatments, we compare relative effects against a baseline. Here we set `none` (no treatment) as the baseline and use `DRLearner` to estimate:\n", + "\n", + "$$\n", + "\\hat\\tau_a(x) = \\widehat{\\mathbb{E}}[Y^{net}(a) - Y^{net}(0) \\mid X=x]\n", + "\\qquad a \\in \\{1,2,3\\}\n", + "$$\n", + "\n", + "Since the relative effect of the baseline treatment is 0, we compare the following four values per customer:\n", + "\n", + "$$\n", + "[0, \\hat\\tau_1(x), \\hat\\tau_2(x), \\hat\\tau_3(x)]\n", + "$$\n", + "\n", + "and select the treatment with the highest value:\n", + "\n", + "$$\n", + "\\hat\\pi_{plugin}(x) = \\arg\\max_{a \\in \\{0,1,2,3\\}} \\widehat{\\mathbb{E}}[Y^{net}(a) - Y^{net}(0) \\mid X=x]\n", + "$$\n", + "\n", + "This approach is intuitive and flexible, but it depends heavily on the quality of CATE estimation. The learned policy is therefore evaluated on a separate test set using AIPW value.\n", + "\n", + "> **Note** One might ask \"why not just select the arm with the highest $\\hat\\Gamma_{i,a}$ for each customer?\" However, AIPW scores have very high variance at the individual observation level, so a policy built by pointwise argmax overfits to noise. AIPW scores must always be used in aggregated form — for example, summed across observations for policy evaluation (value estimation, cost curves, etc.), averaged within nodes for DRPolicyTree/Forest, or used as pseudo-outcomes for regression in DRLearner." + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "id": "debd92a9", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-09T16:49:49.484723Z", + "iopub.status.busy": "2026-05-09T16:49:49.484610Z", + "iopub.status.idle": "2026-05-09T16:49:52.385416Z", + "shell.execute_reply": "2026-05-09T16:49:52.384824Z" + } + }, + "outputs": [], + "source": [ + "dr_cate = DRLearner(\n", + " model_regression=RandomForestRegressor(\n", + " n_estimators=400,\n", + " min_samples_leaf=20,\n", + " random_state=SEED,\n", + " n_jobs=1,\n", + " ),\n", + " model_propensity=RandomForestClassifier(\n", + " n_estimators=400,\n", + " min_samples_leaf=20,\n", + " random_state=SEED,\n", + " n_jobs=1,\n", + " ),\n", + " model_final=RandomForestRegressor(\n", + " n_estimators=300,\n", + " min_samples_leaf=20,\n", + " random_state=SEED,\n", + " n_jobs=1,\n", + " ),\n", + " categories=[0, 1, 2, 3],\n", + " min_propensity=0.02,\n", + " cv=3,\n", + " random_state=SEED,\n", + ")\n", + "dr_cate.fit(Y_net_train, A_train, X=X_train)\n", + "\n", + "# returns E[Y_net(a) - Y_net(0) | X] for each arm relative to none (baseline)\n", + "cate_vs_none = dr_cate.const_marginal_effect(X_test)\n", + "plugin_arm_values = np.column_stack([\n", + " np.zeros(len(X_test)), # none: baseline, relative effect = 0\n", + " cate_vs_none,\n", + "])\n", + "pi_plugin = np.argmax(plugin_arm_values, axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "id": "33437c2c", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-09T16:49:52.389559Z", + "iopub.status.busy": "2026-05-09T16:49:52.389447Z", + "iopub.status.idle": "2026-05-09T16:49:52.392977Z", + "shell.execute_reply": "2026-05-09T16:49:52.392636Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "none 0.092\n", + "tech_support_only 0.500\n", + "discount_only 0.035\n", + "discount_plus_support 0.373\n", + "Name: proportion, dtype: float64" + ] + }, + "execution_count": 73, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.Series(pi_plugin).map(ARM_NAMES).value_counts(normalize=True).reindex(ARM_LABELS).fillna(0)" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "id": "33ktqy8zrqm", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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SizeEmployee CountIT Spendassigned_armτ_techτ_discτ_both
09620414217655none-6140-1419-1719
1253901787971none-14556-4252-7232
26655527919899none-17629-2896-16582
3708682026263tech_support_only4265-404155
424152184610tech_support_only4174-2316-771
5548556314772tech_support_only2042-698553
639922836133052discount_plus_support7849-128111819
71817042459529discount_plus_support9158521610248
829437737112485discount_plus_support71348589048
\n", + "
" + ], + "text/plain": [ + " Size Employee Count IT Spend assigned_arm τ_tech τ_disc \\\n", + "0 96204 142 17655 none -6140 -1419 \n", + "1 25390 178 7971 none -14556 -4252 \n", + "2 66555 279 19899 none -17629 -2896 \n", + "3 70868 20 26263 tech_support_only 4265 -40 \n", + "4 24152 18 4610 tech_support_only 4174 -2316 \n", + "5 54855 63 14772 tech_support_only 2042 -698 \n", + "6 399228 36 133052 discount_plus_support 7849 -1281 \n", + "7 181704 24 59529 discount_plus_support 9158 5216 \n", + "8 294377 37 112485 discount_plus_support 7134 858 \n", + "\n", + " τ_both \n", + "0 -1719 \n", + "1 -7232 \n", + "2 -16582 \n", + "3 4155 \n", + "4 -771 \n", + "5 553 \n", + "6 11819 \n", + "7 10248 \n", + "8 9048 " + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "none_idx = np.where(pi_plugin == 0)[0][:3]\n", + "tech_idx = np.where(pi_plugin == 1)[0][:3]\n", + "both_idx = np.where(pi_plugin == 3)[0][:3]\n", + "sample_ids = np.concatenate([none_idx, tech_idx, both_idx])\n", + "\n", + "df_plugin_sample = pd.DataFrame(X_test[sample_ids], columns=covariates)[['Size', 'Employee Count', 'IT Spend']]\n", + "df_plugin_sample['assigned_arm'] = pd.Series(pi_plugin[sample_ids]).map(ARM_NAMES).values\n", + "df_plugin_sample[['τ_tech', 'τ_disc', 'τ_both']] = np.round(cate_vs_none[sample_ids], 0).astype(int)\n", + "df_plugin_sample.index = pd.RangeIndex(len(df_plugin_sample))\n", + "df_plugin_sample" + ] + }, + { + "cell_type": "markdown", + "id": "05b0ee18", + "metadata": {}, + "source": [ + "The plug-in policy assigns `tech_support_only` to approximately 50% of customers, `discount_plus_support` to 37%, and `none` to about 9%.\n", + "\n", + "After accounting for costs, some customers are expected to have negative net profit from treatment, so the policy chooses not to treat them." + ] + }, + { + "cell_type": "markdown", + "id": "97d958cd", + "metadata": {}, + "source": [ + "### Policy Tree\n", + "\n", + "In practice, policy interpretability is often as important as performance. Shallow decision tree-based policies are frequently used in operations for the following reasons:\n", + "\n", + "- **Stakeholder communication**: the intuitive if-then branching structure makes policies easy to explain and share.\n", + "- **Fairness review**: the explicit structure of who receives which treatment makes it easy to audit for bias.\n", + "- **Operational stability**: simple rule-based policies are more manageable in production than complex models.\n", + "\n", + "Rather than searching for the optimal policy across all possible functions, we restrict the search to a limited policy class $\\Pi$:\n", + "\n", + "$$\n", + "\\hat\\pi = \\arg\\max_{\\pi \\in \\Pi}\n", + "\\frac{1}{n}\\sum_{i=1}^{n} \\widehat\\Gamma_{i,\\pi(X_i)}\n", + "$$\n", + "\n", + "Policy Tree restricts $\\Pi$ to shallow decision trees. With a small depth, the resulting policy is human-readable. We use `DRPolicyTree` from `econml`.\n", + "\n", + "To prevent unstable value estimation in small leaves under multi-arm settings, `min_samples_leaf` is used to ensure leaves are large enough." + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "id": "6c987a8b", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-09T16:49:53.651642Z", + "iopub.status.busy": "2026-05-09T16:49:53.651575Z", + "iopub.status.idle": "2026-05-09T16:49:55.354506Z", + "shell.execute_reply": "2026-05-09T16:49:55.354139Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "none 0.199\n", + "tech_support_only 0.475\n", + "discount_only 0.000\n", + "discount_plus_support 0.326\n", + "Name: proportion, dtype: float64" + ] + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dr_policy_tree = DRPolicyTree(\n", + " max_depth=2,\n", + " min_samples_leaf=30,\n", + " model_regression=RandomForestRegressor(\n", + " n_estimators=400,\n", + " min_samples_leaf=20,\n", + " random_state=SEED,\n", + " n_jobs=1,\n", + " ),\n", + " model_propensity=RandomForestClassifier(\n", + " n_estimators=400,\n", + " min_samples_leaf=20,\n", + " random_state=SEED,\n", + " n_jobs=1,\n", + " ),\n", + " categories=[0, 1, 2, 3],\n", + " min_propensity=0.02,\n", + " cv=3,\n", + " random_state=SEED,\n", + ")\n", + "dr_policy_tree.fit(Y_net_train, A_train, X=X_train)\n", + "pi_tree = dr_policy_tree.predict(X_test).astype(int).ravel()\n", + "\n", + "pd.Series(pi_tree).map(ARM_NAMES).value_counts(normalize=True).reindex(ARM_LABELS).fillna(0)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "id": "64529830", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-09T16:49:55.355867Z", + "iopub.status.busy": "2026-05-09T16:49:55.355774Z", + "iopub.status.idle": "2026-05-09T16:49:55.574472Z", + "shell.execute_reply": "2026-05-09T16:49:55.574068Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(13, 7))\n", + "dr_policy_tree.plot(feature_names=covariates, treatment_names=ARM_LABELS, ax=ax)\n", + "ax.set_title('4-arm DRPolicyTree')\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "2a3ffb5d", + "metadata": {}, + "source": [ + "This tree policy primarily splits customers by `Size`. Notably, it assigns `none` to approximately 20% of customers — after accounting for costs, the expected net profit from treatment is low enough for some customers that not treating them is the better choice.\n", + "\n", + "Overall, the policy assigns `discount_plus_support` to large customers and `tech_support_only` to the rest, while choosing `none` for customers with low expected net profit." + ] + }, + { + "cell_type": "markdown", + "id": "35dd95b1", + "metadata": {}, + "source": [ + "### Policy Forest\n", + "\n", + "Policy Forest learns policies more stably by aggregating many trees.\n", + "\n", + "Like DRPolicyTree, DRPolicyForest directly maximizes the AIPW score, but it averages across many trees instead of using a single one. This reduces variance and allows more stable learning of complex heterogeneous treatment effects.\n", + "\n", + "We use `DRPolicyForest` from `econml`.\n", + "\n", + "Unlike tree-based policies, the resulting policy cannot be easily expressed as a single clear decision rule. After fitting, we compare policy values using AIPW scores from the same test set." + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "id": "d7939447", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-09T16:49:55.582919Z", + "iopub.status.busy": "2026-05-09T16:49:55.582816Z", + "iopub.status.idle": "2026-05-09T16:49:57.495404Z", + "shell.execute_reply": "2026-05-09T16:49:57.494962Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "none 0.113\n", + "tech_support_only 0.471\n", + "discount_only 0.000\n", + "discount_plus_support 0.416\n", + "Name: proportion, dtype: float64" + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dr_policy_forest = DRPolicyForest(\n", + " n_estimators=400,\n", + " max_depth=5,\n", + " min_samples_leaf=30,\n", + " model_regression=RandomForestRegressor(\n", + " n_estimators=400,\n", + " min_samples_leaf=20,\n", + " random_state=SEED,\n", + " n_jobs=1,\n", + " ),\n", + " model_propensity=RandomForestClassifier(\n", + " n_estimators=400,\n", + " min_samples_leaf=20,\n", + " random_state=SEED,\n", + " n_jobs=1,\n", + " ),\n", + " categories=[0, 1, 2, 3],\n", + " min_propensity=0.02,\n", + " cv=3,\n", + " random_state=SEED,\n", + " n_jobs=1,\n", + ")\n", + "dr_policy_forest.fit(Y_net_train, A_train, X=X_train)\n", + "pi_forest = dr_policy_forest.predict(X_test).astype(int).ravel()\n", + "\n", + "pd.Series(pi_forest).map(ARM_NAMES).value_counts(normalize=True).reindex(ARM_LABELS).fillna(0)\n" + ] + }, + { + "cell_type": "markdown", + "id": "15d7d727", + "metadata": {}, + "source": [ + "DRPolicyForest shows a treatment assignment pattern broadly similar to the plug-in policy. It assigns `none` to 11%, `tech_support_only` to 47%, and `discount_plus_support` to 42% — a slightly higher share of `discount_plus_support` compared to the plug-in policy." + ] + }, + { + "cell_type": "markdown", + "id": "99bac91c", + "metadata": {}, + "source": [ + "## Policy Evaluation\n", + "\n", + "We now compare the learned policies using AIPW scores from the same test set.\n", + "\n", + "As benchmarks, we also include constant policies that apply the same treatment to every customer:\n", + "\n", + "- `all_none`\n", + "- `all_tech_support_only`\n", + "- `all_discount_only`\n", + "- `all_discount_plus_support`\n", + "\n", + "For example, `all_discount_plus_support` represents the average net profit when both technical support and a discount are provided to every customer.\n", + "\n", + "By contrast, the learned policies assign different treatments depending on each customer's characteristics $X_i$.\n", + "\n", + "If a learned policy's value exceeds the best constant policy, it means that customer-level targeting is more effective than applying the same treatment to everyone." + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "id": "14b616ad", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-09T16:49:57.502590Z", + "iopub.status.busy": "2026-05-09T16:49:57.502507Z", + "iopub.status.idle": "2026-05-09T16:49:57.508438Z", + "shell.execute_reply": "2026-05-09T16:49:57.508058Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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policyvalueci_lowerci_upperrate_nonerate_tech_support_onlyrate_discount_onlyrate_discount_plus_support
4plugin_drlearner_4arm11810.37771211283.56156312337.1938610.0920.5000.0350.373
6dr_policy_forest_4arm11625.62754011102.02258912149.2324910.1130.4710.0000.416
5dr_policy_tree_4arm11148.46236910700.60923011596.3155080.1990.4750.0000.326
1all_tech_support_only10526.5238769833.03475111220.0130010.0001.0000.0000.000
3all_discount_plus_support10136.7088829439.44594110833.9718240.0000.0000.0001.000
2all_discount_only7545.0792056993.5366218096.6217880.0000.0001.0000.000
0all_none7249.3454676955.4474327543.2435021.0000.0000.0000.000
\n", + "
" + ], + "text/plain": [ + " policy value ci_lower ci_upper \\\n", + "4 plugin_drlearner_4arm 11810.377712 11283.561563 12337.193861 \n", + "6 dr_policy_forest_4arm 11625.627540 11102.022589 12149.232491 \n", + "5 dr_policy_tree_4arm 11148.462369 10700.609230 11596.315508 \n", + "1 all_tech_support_only 10526.523876 9833.034751 11220.013001 \n", + "3 all_discount_plus_support 10136.708882 9439.445941 10833.971824 \n", + "2 all_discount_only 7545.079205 6993.536621 8096.621788 \n", + "0 all_none 7249.345467 6955.447432 7543.243502 \n", + "\n", + " rate_none rate_tech_support_only rate_discount_only \\\n", + "4 0.092 0.500 0.035 \n", + "6 0.113 0.471 0.000 \n", + "5 0.199 0.475 0.000 \n", + "1 0.000 1.000 0.000 \n", + "3 0.000 0.000 0.000 \n", + "2 0.000 0.000 1.000 \n", + "0 1.000 0.000 0.000 \n", + "\n", + " rate_discount_plus_support \n", + "4 0.373 \n", + "6 0.416 \n", + "5 0.326 \n", + "1 0.000 \n", + "3 1.000 \n", + "2 0.000 \n", + "0 0.000 " + ] + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "policy_assignments = {\n", + " **{f'all_{arm_name}': np.full(len(X_test), arm_id) for arm_id, arm_name in ARM_NAMES.items()},\n", + " 'plugin_drlearner_4arm': pi_plugin,\n", + " 'dr_policy_tree_4arm': pi_tree,\n", + " 'dr_policy_forest_4arm': pi_forest,\n", + "}\n", + "\n", + "eval_rows = []\n", + "for policy_name, policy_assignment in policy_assignments.items():\n", + " pi = np.asarray(policy_assignment).astype(int).ravel()\n", + " scores = gamma_net[np.arange(len(pi)), pi]\n", + "\n", + " row = {\n", + " 'policy': policy_name,\n", + " 'value': scores.mean(),\n", + " 'value_se': scores.std(ddof=1) / np.sqrt(len(scores)),\n", + " }\n", + " for arm_id, arm_name in ARM_NAMES.items():\n", + " row[f'rate_{arm_name}'] = np.mean(pi == arm_id)\n", + " eval_rows.append(row)\n", + "\n", + "policy_eval = pd.DataFrame(eval_rows)\n", + "policy_eval['ci_lower'] = policy_eval['value'] - 1.96 * policy_eval['value_se']\n", + "policy_eval['ci_upper'] = policy_eval['value'] + 1.96 * policy_eval['value_se']\n", + "\n", + "display_cols = ['policy', 'value', 'ci_lower', 'ci_upper'] + [f'rate_{n}' for n in ARM_LABELS]\n", + "policy_eval.sort_values('value', ascending=False)[display_cols]" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "id": "4814d1ed", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(1, 2, figsize=(15, 5))\n", + "plot_df = policy_eval.sort_values('value', ascending=False)\n", + "sns.barplot(data=plot_df, x='value', y='policy', ax=axes[0], color='seagreen')\n", + "axes[0].set_title('4-arm net policy value')\n", + "axes[0].set_xlabel('AIPW-estimated net value')\n", + "axes[0].set_ylabel('')\n", + "\n", + "arm_rate_cols = [f'rate_{name}' for name in ARM_LABELS]\n", + "rate_df = policy_eval.set_index('policy')[arm_rate_cols]\n", + "rate_df.columns = ARM_LABELS\n", + "rate_df.loc[['plugin_drlearner_4arm', 'dr_policy_tree_4arm', 'dr_policy_forest_4arm']].plot(kind='barh', stacked=True, ax=axes[1], colormap='tab20')\n", + "axes[1].set_title('Assigned arm share')\n", + "axes[1].set_xlabel('Share')\n", + "axes[1].set_ylabel('')\n", + "axes[1].legend(loc='center left', bbox_to_anchor=(1.0, 0.5))\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "dec4e9f6", + "metadata": {}, + "source": [ + "All three learned policies achieve higher value than any constant policy, confirming that assigning different treatments by customer is more effective than treating everyone the same.\n", + "\n", + "Among the learned policies, DRLearner plug-in (11,810) ranked first, followed by DRPolicyForest (11,626) and DRPolicyTree (11,148). However, since the plug-in and forest confidence intervals overlap substantially, the difference between them is not statistically clear. In theory, DR policies that directly optimize the AIPW score should have an advantage, but in practice the outcome depends on sample size and data characteristics. The tree policy trades some performance for an interpretable structure.\n", + "\n", + "Among constant policies, `all_tech_support_only` (10,527) performed best. `all_discount_plus_support` (10,137) falls short because discount costs scale with customer size, so for some customers the discount benefit does not sufficiently offset the cost." + ] + }, + { + "cell_type": "markdown", + "id": "9bh57tga2aq", + "metadata": {}, + "source": [ + "## Policy Comparison under Budget Constraints\n", + "\n", + "So far we have compared policies by their overall AIPW value. In practice, however, budgets are limited. Cost curves are useful for comparing which policy is more efficient within the same budget." + ] + }, + { + "cell_type": "markdown", + "id": "qdxockulyq9", + "metadata": {}, + "source": [ + "### Estimating Per-Customer Expected Costs\n", + "\n", + "To construct cost curves, we first estimate $E[C(a) \\mid X_i]$ per customer. Since actual costs are observed only for the treatment the customer received, we fit a separate cost regression model on the training data for each arm and apply it to the test set." + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "id": "a1z6z7eabyj", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Estimated E[C(a)|X] — test set (mean ± std):\n", + " tech_support_only : 4,892 ± 3,466\n", + " discount_only : 5,454 ± 2,330\n", + " discount_plus_support : 10,687 ± 4,139\n" + ] + } + ], + "source": [ + "# Estimate E[C(a) | X] per arm\n", + "# c_hat_test[i, a] = predicted cost for customer i under arm a\n", + "c_true_by_arm = {1: c_tech, 2: c_disc, 3: c_tech + c_disc}\n", + "\n", + "c_hat_test = np.zeros((len(X_test), 4)) # arm 0 → cost = 0\n", + "for arm_id in [1, 2, 3]:\n", + " mask_train = (A_train == arm_id)\n", + " mdl = RandomForestRegressor(n_estimators=300, min_samples_leaf=20,\n", + " random_state=SEED, n_jobs=1)\n", + " mdl.fit(X_train[mask_train], c_true_by_arm[arm_id][train_idx[mask_train]])\n", + " c_hat_test[:, arm_id] = np.maximum(mdl.predict(X_test), 200.0)\n", + "\n", + "print(\"Estimated E[C(a)|X] — test set (mean ± std):\")\n", + "for arm_id, arm_name in ARM_NAMES.items():\n", + " if arm_id > 0:\n", + " print(f\" {arm_name:25s}: {c_hat_test[:, arm_id].mean():8,.0f} ± {c_hat_test[:, arm_id].std():6,.0f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "dgt4sg335xc", + "metadata": {}, + "source": [ + "### Cost Curves and Budget-Constrained Comparison\n", + "\n", + "The two axes of a cost curve are:\n", + "\n", + "- **x-axis**: cumulative average treatment cost per customer\n", + "- **y-axis**: cumulative average net profit per customer — the increment relative to baseline(`none`), with **costs already deducted**\n", + "\n", + "Within each policy, customers are treated in descending order of their benefit-to-cost ratio $\\hat\\rho(x) = \\hat\\tau(x) / \\hat\\gamma(x)$. Drawing a vertical line at $x = B$ gives the budget-$B$ policy comparison. A higher $y$ value at the same budget means a more efficient policy.\n", + "\n", + "The endpoint (\\u25cf) of each curve shows the position when all targeted customers have been treated. Customers assigned `none` incur no treatment cost and therefore do not appear in the curve." + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "id": "ttwi6dbqcfo", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def policy_cost_curve(pi_eval, gamma_matrix, c_hat_matrix):\n", + " \"\"\"\n", + " Generate cost curves using per-arm estimated customer costs.\n", + " c_hat_matrix[i, a] = Ê[C(a) | X_i]\n", + "\n", + " x = cumulative average treatment cost per customer (gross)\n", + " y = cumulative average net profit per customer (costs already deducted from Y_net)\n", + " → x + y = total revenue increment relative to no treatment\n", + "\n", + " Treated customers are sorted in descending order of net profit / estimated cost ratio.\n", + " \"\"\"\n", + " pi = np.asarray(pi_eval).ravel().astype(int)\n", + " n = len(pi)\n", + " treated = pi > 0\n", + " if treated.sum() == 0:\n", + " return np.array([0.0]), np.array([0.0])\n", + "\n", + " benefit = gamma_matrix[np.arange(n), pi] - gamma_matrix[:, 0]\n", + " cost = c_hat_matrix[np.arange(n), pi].astype(float)\n", + "\n", + " b_t, c_t = benefit[treated], cost[treated]\n", + " ratio = np.where(c_t > 0, b_t / c_t, b_t)\n", + " order = np.argsort(-ratio)\n", + "\n", + " cum_cost = np.r_[0, np.cumsum(c_t[order])]\n", + " cum_benefit = np.r_[0, np.cumsum(b_t[order])]\n", + " return cum_cost / n, cum_benefit / n\n", + "\n", + "\n", + "n_test = len(X_test)\n", + "policies_curve = [\n", + " ('DRLearner plug-in', pi_plugin),\n", + " ('DRPolicyTree (depth=2)', pi_tree),\n", + " ('DRPolicyForest', pi_forest),\n", + " ('all_discount_plus_support', np.full(n_test, 3)),\n", + " ('all_tech_support_only', np.full(n_test, 1)),\n", + "]\n", + "\n", + "fig, ax = plt.subplots(figsize=(8, 6))\n", + "colors = ['tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple']\n", + "\n", + "for (name, pi_eval), color in zip(policies_curve, colors):\n", + " cc, cg = policy_cost_curve(pi_eval, gamma_net, c_hat_test)\n", + " ax.plot(cc, cg, lw=2, color=color,\n", + " label=f'{name} (cost={cc[-1]:,.0f}, net benefit={cg[-1]:,.0f})')\n", + " ax.scatter([cc[-1]], [cg[-1]], s=50, color=color, zorder=5)\n", + "\n", + "budget_example = 5_000\n", + "ax.axvline(budget_example, color='gray', lw=1.5, ls='--', alpha=0.7,\n", + " label=f'Budget = ${budget_example:,}/customer')\n", + "\n", + "ax.set_xlabel('Avg cumulative cost per customer ($)', fontsize=11)\n", + "ax.set_ylabel('Avg cumulative net benefit per customer ($)', fontsize=11)\n", + "ax.set_title('Policy cost curves — cut at x=B for budget-constrained comparison', fontsize=11)\n", + "ax.legend(fontsize=8, loc='upper left')\n", + "ax.grid(True, alpha=0.3)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "qoq9ch9m2fg", + "metadata": {}, + "source": [ + "The most effective policy varies with the available budget. At low budgets of \\$1,000\\u2013\\$3,000, `all_tech_support_only` is the most efficient. Its lower average cost means that even a small budget can reach many high benefit-to-cost customers quickly.\n", + "\n", + "As the budget grows to \\$4,000\\u2013\\$6,000, the learned policies (plug-in, forest) pull ahead. Customer-level targeting starts paying off, delivering higher net profit for the same spend.\n", + "\n", + "Even when budgets are fully exhausted, the learned policies remain most efficient. Plug-in spends approximately \\$6,600 to yield \\$4,561 in net profit, and forest spends around \\$6,900 for \\$4,376. By contrast, `all_discount_plus_support` exhausts \\$10,700 and still achieves only \\$2,887 in net profit, making it the least efficient in terms of budget utilization." + ] + }, + { + "cell_type": "markdown", + "id": "7owznh79iw7", + "metadata": {}, + "source": [ + "All three policies show statistically significantly higher value than no treatment (95% CIs do not include zero).\n", + "\n", + "Compared to the best constant policy (`tech_support_only`), results vary by method. `DRLearner plug-in` (+1,284, CI: [622, 1,946]) and `DRPolicyForest` (+1,099, CI: [426, 1,772]) are statistically significantly better. `DRPolicyTree` (+622, CI: [-1, 1,245]) has a positive point estimate, but its confidence interval lower bound approaches zero, so its advantage over the best constant policy is not statistically clear." + ] + }, + { + "cell_type": "markdown", + "id": "b40f71c0", + "metadata": {}, + "source": [ + "## References\n", + "\n", + "- [Stanford ML+CI Tutorial — Policy Learning I (Binary Treatment)](https://bookdown.org/stanfordgsbsilab/ml-ci-tutorial/policy-learning-i---binary-treatment.html)\n", + "- [Athey and Wager (2021, Econometrica) — Policy Learning with Observational Data](https://onlinelibrary.wiley.com/doi/abs/10.3982/ECTA15732)\n", + "- [Sun, Du, Wager et al. (2021) — Treatment Allocation under Uncertain Costs](https://arxiv.org/abs/2103.11066)\n", + "- [Imai and Li (2019) — Experimental Evaluation of Individualized Treatment Rules](https://arxiv.org/pdf/1905.05389.pdf)\n", + "- [arXiv 2604.06123](https://arxiv.org/html/2604.06123v1)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.14.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/book/who_should_be_treated/who_should_be_treated_ko.ipynb b/book/who_should_be_treated/who_should_be_treated_ko.ipynb index 647acd6..f777f61 100644 --- a/book/who_should_be_treated/who_should_be_treated_ko.ipynb +++ b/book/who_should_be_treated/who_should_be_treated_ko.ipynb @@ -5,6 +5,8 @@ "id": "1ae5a6c4", "metadata": {}, "source": [ + "**🌐 언어:** [← English](/who-should-be-treated-en) | **한국어**\n", + "\n", "# 누구에게 어떤 프로모션을 제공해야 할까요?\n", "\n", "인과추론의 많은 문제는 평균 처치 효과(ATE)에 집중합니다. 예를 들어 “프로모션을 진행할 것인가, 하지 않을 것인가?”라는 질문은 모든 고객에게 동일한 프로모션을 제공하는 정책과 아무에게도 제공하지 않는 정책을 비교하는 문제입니다.\n", @@ -1392,7 +1394,7 @@ "\n", "이 방식은 직관적이고 유연하지만, CATE 추정 품질에 크게 영향을 받습니다. 따라서 학습된 정책은 별도의 test set에서 AIPW value로 평가합니다.\n", "\n", - "> **주의**: \"각 고객의 $\\hat\\Gamma_{i,a}$ 중 가장 큰 arm을 선택하면 되지 않나?\"라고 생각할 수 있습니다. 그러나 AIPW score는 개별 관측치 수준에서 분산이 매우 크기 때문에, pointwise argmax로 만든 정책은 노이즈에 과적합됩니다. AIPW score는 반드시 집계된 형태로 사용해야 합니다. 정책 평가(value 추정, 비용 곡선 등)처럼 여러 관측치를 합산하거나, DRPolicyTree/Forest처럼 노드 단위 평균을 최적화하거나, DRLearner처럼 pseudo-outcome으로 회귀하는 방식이 그 예입니다." + "> **NOTE** \"각 고객의 $\\hat\\Gamma_{i,a}$ 중 가장 큰 arm을 선택하면 되지 않나?\"라고 생각할 수 있습니다. 그러나 AIPW score는 개별 관측치 수준에서 분산이 매우 크기 때문에, pointwise argmax로 만든 정책은 노이즈에 과적합됩니다. AIPW score는 반드시 집계된 형태로 사용해야 합니다. 정책 평가(value 추정, 비용 곡선 등)처럼 여러 관측치를 합산하거나, DRPolicyTree/Forest처럼 노드 단위 평균을 최적화하거나, DRLearner처럼 pseudo-outcome으로 회귀하는 방식이 그 예입니다." ] }, { From eeb0c12b929795755f5f5b55e598cad40510b841 Mon Sep 17 00:00:00 2001 From: Funbucket Date: Wed, 13 May 2026 01:52:40 +0900 Subject: [PATCH 09/11] =?UTF-8?q?docs(who=5Fshould=5Fbe=5Ftreated):=20?= =?UTF-8?q?=EB=85=B8=ED=8A=B8=EB=B6=81=20=EC=9E=AC=EC=8B=A4=ED=96=89=20?= =?UTF-8?q?=EB=B0=8F=20=EB=AC=B8=EB=8B=A8=20=EC=A0=95=EB=A6=AC?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../who_should_be_treated_en.ipynb | 69 ++++++++----------- .../who_should_be_treated_ko.ipynb | 69 ++++++++----------- 2 files changed, 58 insertions(+), 80 deletions(-) diff --git a/book/who_should_be_treated/who_should_be_treated_en.ipynb b/book/who_should_be_treated/who_should_be_treated_en.ipynb index c8536c2..9a916d2 100644 --- a/book/who_should_be_treated/who_should_be_treated_en.ipynb +++ b/book/who_should_be_treated/who_should_be_treated_en.ipynb @@ -34,7 +34,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 1, "id": "fc2741f9", "metadata": { "execution": { @@ -121,7 +121,7 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 2, "id": "477aed7a", "metadata": { "execution": { @@ -283,7 +283,7 @@ "4 discount_plus_support " ] }, - "execution_count": 62, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -370,7 +370,7 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 3, "id": "5174ddd2", "metadata": { "execution": { @@ -460,7 +460,7 @@ "3 14118.841353 " ] }, - "execution_count": 63, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -519,7 +519,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 4, "id": "3d0f329e", "metadata": {}, "outputs": [ @@ -617,7 +617,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 5, "id": "d6488c62", "metadata": {}, "outputs": [ @@ -667,7 +667,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 6, "id": "c925a3d8", "metadata": {}, "outputs": [ @@ -874,7 +874,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 7, "id": "n7ziuiv612q", "metadata": {}, "outputs": [], @@ -895,7 +895,7 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 8, "id": "9be31313", "metadata": {}, "outputs": [ @@ -1004,7 +1004,7 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 9, "id": "fl52yekx049", "metadata": {}, "outputs": [ @@ -1114,7 +1114,7 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 10, "id": "e3156595", "metadata": { "execution": { @@ -1152,7 +1152,7 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 11, "id": "e8533283", "metadata": { "execution": { @@ -1337,7 +1337,7 @@ "4 3378.748035 5743.802939 2988.217314 " ] }, - "execution_count": 71, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -1399,7 +1399,7 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 12, "id": "debd92a9", "metadata": { "execution": { @@ -1448,7 +1448,7 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 13, "id": "33437c2c", "metadata": { "execution": { @@ -1469,7 +1469,7 @@ "Name: proportion, dtype: float64" ] }, - "execution_count": 73, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -1480,7 +1480,7 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 14, "id": "33ktqy8zrqm", "metadata": {}, "outputs": [ @@ -1633,7 +1633,7 @@ "8 9048 " ] }, - "execution_count": 74, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -1688,7 +1688,7 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 15, "id": "6c987a8b", "metadata": { "execution": { @@ -1709,7 +1709,7 @@ "Name: proportion, dtype: float64" ] }, - "execution_count": 75, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -1743,7 +1743,7 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 16, "id": "64529830", "metadata": { "execution": { @@ -1800,7 +1800,7 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 17, "id": "d7939447", "metadata": { "execution": { @@ -1821,7 +1821,7 @@ "Name: proportion, dtype: float64" ] }, - "execution_count": 77, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -1888,7 +1888,7 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 18, "id": "14b616ad", "metadata": { "execution": { @@ -2041,7 +2041,7 @@ "0 0.000 " ] }, - "execution_count": 78, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -2078,7 +2078,7 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 19, "id": "4814d1ed", "metadata": {}, "outputs": [ @@ -2146,7 +2146,7 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 20, "id": "a1z6z7eabyj", "metadata": {}, "outputs": [ @@ -2199,7 +2199,7 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 21, "id": "ttwi6dbqcfo", "metadata": {}, "outputs": [ @@ -2222,7 +2222,6 @@ "\n", " x = cumulative average treatment cost per customer (gross)\n", " y = cumulative average net profit per customer (costs already deducted from Y_net)\n", - " → x + y = total revenue increment relative to no treatment\n", "\n", " Treated customers are sorted in descending order of net profit / estimated cost ratio.\n", " \"\"\"\n", @@ -2287,16 +2286,6 @@ "Even when budgets are fully exhausted, the learned policies remain most efficient. Plug-in spends approximately \\$6,600 to yield \\$4,561 in net profit, and forest spends around \\$6,900 for \\$4,376. By contrast, `all_discount_plus_support` exhausts \\$10,700 and still achieves only \\$2,887 in net profit, making it the least efficient in terms of budget utilization." ] }, - { - "cell_type": "markdown", - "id": "7owznh79iw7", - "metadata": {}, - "source": [ - "All three policies show statistically significantly higher value than no treatment (95% CIs do not include zero).\n", - "\n", - "Compared to the best constant policy (`tech_support_only`), results vary by method. `DRLearner plug-in` (+1,284, CI: [622, 1,946]) and `DRPolicyForest` (+1,099, CI: [426, 1,772]) are statistically significantly better. `DRPolicyTree` (+622, CI: [-1, 1,245]) has a positive point estimate, but its confidence interval lower bound approaches zero, so its advantage over the best constant policy is not statistically clear." - ] - }, { "cell_type": "markdown", "id": "b40f71c0", diff --git a/book/who_should_be_treated/who_should_be_treated_ko.ipynb b/book/who_should_be_treated/who_should_be_treated_ko.ipynb index f777f61..d31b69c 100644 --- a/book/who_should_be_treated/who_should_be_treated_ko.ipynb +++ b/book/who_should_be_treated/who_should_be_treated_ko.ipynb @@ -34,7 +34,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 2, "id": "fc2741f9", "metadata": { "execution": { @@ -121,7 +121,7 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 3, "id": "477aed7a", "metadata": { "execution": { @@ -283,7 +283,7 @@ "4 discount_plus_support " ] }, - "execution_count": 62, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -370,7 +370,7 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 4, "id": "5174ddd2", "metadata": { "execution": { @@ -460,7 +460,7 @@ "3 14118.841353 " ] }, - "execution_count": 63, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -519,7 +519,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 5, "id": "3d0f329e", "metadata": {}, "outputs": [ @@ -617,7 +617,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 6, "id": "d6488c62", "metadata": {}, "outputs": [ @@ -667,7 +667,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 7, "id": "c925a3d8", "metadata": {}, "outputs": [ @@ -874,7 +874,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 8, "id": "n7ziuiv612q", "metadata": {}, "outputs": [], @@ -895,7 +895,7 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 9, "id": "9be31313", "metadata": {}, "outputs": [ @@ -1004,7 +1004,7 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 10, "id": "fl52yekx049", "metadata": {}, "outputs": [ @@ -1114,7 +1114,7 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 11, "id": "e3156595", "metadata": { "execution": { @@ -1152,7 +1152,7 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 12, "id": "e8533283", "metadata": { "execution": { @@ -1337,7 +1337,7 @@ "4 3378.748035 5743.802939 2988.217314 " ] }, - "execution_count": 71, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -1399,7 +1399,7 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 13, "id": "debd92a9", "metadata": { "execution": { @@ -1448,7 +1448,7 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 14, "id": "33437c2c", "metadata": { "execution": { @@ -1469,7 +1469,7 @@ "Name: proportion, dtype: float64" ] }, - "execution_count": 73, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -1480,7 +1480,7 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 15, "id": "33ktqy8zrqm", "metadata": {}, "outputs": [ @@ -1633,7 +1633,7 @@ "8 9048 " ] }, - "execution_count": 74, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -1688,7 +1688,7 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 16, "id": "6c987a8b", "metadata": { "execution": { @@ -1709,7 +1709,7 @@ "Name: proportion, dtype: float64" ] }, - "execution_count": 75, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -1743,7 +1743,7 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 17, "id": "64529830", "metadata": { "execution": { @@ -1800,7 +1800,7 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 18, "id": "d7939447", "metadata": { "execution": { @@ -1821,7 +1821,7 @@ "Name: proportion, dtype: float64" ] }, - "execution_count": 77, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -1888,7 +1888,7 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 19, "id": "14b616ad", "metadata": { "execution": { @@ -2041,7 +2041,7 @@ "0 0.000 " ] }, - "execution_count": 78, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -2078,7 +2078,7 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 20, "id": "4814d1ed", "metadata": {}, "outputs": [ @@ -2146,7 +2146,7 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 21, "id": "a1z6z7eabyj", "metadata": {}, "outputs": [ @@ -2199,7 +2199,7 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 22, "id": "ttwi6dbqcfo", "metadata": {}, "outputs": [ @@ -2222,7 +2222,6 @@ "\n", " x = 고객 1인당 누적 평균 처치 비용 (gross)\n", " y = 고객 1인당 누적 평균 순이익 (Y_net에 비용이 이미 차감된 값)\n", - " → x + y = 무처치 대비 총 매출 증분\n", "\n", " 처치 고객은 순이익 / 추정 비용 비율(내림차순)로 정렬합니다.\n", " \"\"\"\n", @@ -2287,16 +2286,6 @@ "예산을 전부 소진하는 상황에서도 학습 정책이 가장 효율적입니다. plug-in은 약 $6,600을 써서 $4,561, forest는 약 $6,900을 써서 $4,376의 순이익을 냅니다. 반면 `all_discount_plus_support`는 $10,700을 전부 소진해도 순이익이 $2,887에 그쳐 예산 대비 효율이 가장 낮습니다." ] }, - { - "cell_type": "markdown", - "id": "7owznh79iw7", - "metadata": {}, - "source": [ - "세 정책 모두 무처치 대비 통계적으로 유의하게 높은 value를 보였습니다 (95% CI가 모두 0을 포함하지 않음).\n", - "\n", - "최선 상수 정책(`tech_support_only`) 대비에서는 정책별로 차이가 있습니다. `DRLearner plug-in`(+1,284, CI: [622, 1,946])과 `DRPolicyForest`(+1,099, CI: [426, 1,772])는 통계적으로 유의하게 우수합니다. 반면 `DRPolicyTree`(+622, CI: [-1, 1,245])는 점추정치는 양수이지만, 신뢰구간 하한이 0에 근접해 단일 처치 정책 대비 우위가 통계적으로 명확하지 않습니다." - ] - }, { "cell_type": "markdown", "id": "b40f71c0", From 79b73468b9ccd4f03e53fa53a0a0a9e4c073c73a Mon Sep 17 00:00:00 2001 From: Funbucket Date: Wed, 13 May 2026 02:12:36 +0900 Subject: [PATCH 10/11] =?UTF-8?q?docs(who=5Fshould=5Fbe=5Ftreated):=20?= =?UTF-8?q?=ED=8F=89=EA=B0=80=20=EC=A7=80=ED=91=9C=20=EA=B5=AC=EC=84=B1?= =?UTF-8?q?=EC=9D=84=20=EC=A0=95=EC=B1=85=20=ED=8F=89=EA=B0=80=20=EC=84=B9?= =?UTF-8?q?=EC=85=98=20=ED=95=98=EC=9C=84=EB=A1=9C=20=EC=9D=B4=EB=8F=99?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-Authored-By: Claude Sonnet 4.6 --- .../who_should_be_treated_en.ipynb | 547 ++++++------ .../who_should_be_treated_ko.ipynb | 823 ++++++++---------- 2 files changed, 630 insertions(+), 740 deletions(-) diff --git a/book/who_should_be_treated/who_should_be_treated_en.ipynb b/book/who_should_be_treated/who_should_be_treated_en.ipynb index 9a916d2..caed193 100644 --- a/book/who_should_be_treated/who_should_be_treated_en.ipynb +++ b/book/who_should_be_treated/who_should_be_treated_en.ipynb @@ -1060,306 +1060,7 @@ "id": "f4edd2d8", "metadata": {}, "source": [ - "## Policy Learning Methods\n", - "\n", - "We compare three policy learning approaches.\n", - "\n", - "- Plug-in Policy (DRLearner)\n", - "\n", - " First estimates per-customer CATE, then selects the treatment with the highest expected net benefit.\n", - "\n", - " This is a flexible approach with no constraints on policy form, but final performance depends heavily on the quality of CATE estimation.\n", - "\n", - "- DRPolicyTree\n", - "\n", - " DRPolicyTree learns a policy by directly maximizing the AIPW score.\n", - "\n", - " The policy structure is constrained to a shallow decision tree, making results interpretable as if-then rules. This is useful when policy interpretability and explainability matter.\n", - "\n", - "- DRPolicyForest\n", - "\n", - " DRPolicyForest also directly maximizes the AIPW score but uses a forest rather than a single tree.\n", - "\n", - " A single tree has a simple structure but can exhibit high variance. A forest averages across many trees, reducing variance and learning heterogeneous treatment effects more stably.\n", - "\n", - " However, the resulting policy is harder to interpret as a single clear if-then rule set.\n", - "\n", - "All three policies are learned on the train set and compared using AIPW scores from the same test set. It is important to use separate data for learning and evaluation, as using the training data for evaluation leads to overestimation." - ] - }, - { - "cell_type": "markdown", - "id": "d7d4bc26", - "metadata": {}, - "source": [ - "## Evaluation Metric\n", - "\n", - "The AIPW (Augmented Inverse Probability Weighting) score is defined as:\n", - "\n", - "$$\n", - "\\hat\\Gamma_{i,a} = \\hat\\mu_a(X_i) + \\frac{\\mathbf{1}[A_i=a]}{\\hat e_a(X_i)}\\bigl(Y_i^{net} - \\hat\\mu_a(X_i)\\bigr)\n", - "$$\n", - "\n", - "- $\\hat\\mu_a(X_i)$: predicted $E[Y^{net}(a)\\mid X_i]$ from the outcome model\n", - "- $\\hat e_a(X_i)$: predicted $P(A_i=a\\mid X_i)$ from the propensity model\n", - "- The second term corrects the difference between observed and predicted values via IPW.\n", - "\n", - "This structure ensures unbiasedness holds as long as either the outcome model or the propensity model is correctly specified. This property is called doubly robust.\n", - "\n", - "- If the outcome model is correct: the expected value of the IPW correction term approaches zero, so $\\hat\\mu_a(X_i)$ contributes directly.\n", - "- If the propensity model is correct: the IPW correction term removes the bias of the outcome model.\n", - "\n", - "This score is used for both policy learning (DRPolicyTree/Forest) and policy evaluation." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "e3156595", - "metadata": { - "execution": { - "iopub.execute_input": "2026-05-09T16:49:48.578060Z", - "iopub.status.busy": "2026-05-09T16:49:48.577946Z", - "iopub.status.idle": "2026-05-09T16:49:49.475582Z", - "shell.execute_reply": "2026-05-09T16:49:49.474994Z" - } - }, - "outputs": [], - "source": [ - "Y_net_train = Y_net[train_idx]\n", - "Y_net_test = Y_net[test_idx]\n", - "\n", - "e_hat_net = np.clip(e_hat_raw, 0.02, 0.98)\n", - "e_hat_net = e_hat_net / e_hat_net.sum(axis=1, keepdims=True)\n", - "\n", - "gamma_net = np.zeros((len(X_test), 4))\n", - "mu_hat_net = np.zeros((len(X_test), 4))\n", - "\n", - "for arm_id in range(4):\n", - " outcome_model = RandomForestRegressor(\n", - " n_estimators=400,\n", - " min_samples_leaf=20,\n", - " random_state=SEED,\n", - " n_jobs=1,\n", - " )\n", - " outcome_model.fit(X_train[A_train == arm_id], Y_net_train[A_train == arm_id])\n", - " mu_a = outcome_model.predict(X_test)\n", - " observed_a = (A_test == arm_id).astype(float)\n", - "\n", - " gamma_net[:, arm_id] = mu_a + observed_a / e_hat_net[:, arm_id] * (Y_net_test - mu_a)\n", - " mu_hat_net[:, arm_id] = mu_a" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "e8533283", - "metadata": { - "execution": { - "iopub.execute_input": "2026-05-09T16:49:49.477219Z", - "iopub.status.busy": "2026-05-09T16:49:49.477080Z", - "iopub.status.idle": "2026-05-09T16:49:49.483510Z", - "shell.execute_reply": "2026-05-09T16:49:49.483027Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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samplee_hatmu_hatgamma
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00discount_plus_support9254.5585410.2486330.2673380.2723660.2116625370.0992819559.7777634547.4845526984.7316095370.0992819559.7777634547.48455217708.540116
11tech_support_only7702.8616620.2110870.3072040.2433940.23831510468.9425226728.9884198373.31663311121.82660910468.9425229899.1085088373.31663311121.826609
22tech_support_only5062.1844200.3765700.2419010.2562610.1252682879.7417865449.409859937.5151032959.5143472879.7417863848.652052937.5151032959.514347
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" - ], - "text/plain": [ - " sample e_hat \\\n", - " sample_id actual_arm observed_net_outcome none \n", - "0 0 discount_plus_support 9254.558541 0.248633 \n", - "1 1 tech_support_only 7702.861662 0.211087 \n", - "2 2 tech_support_only 5062.184420 0.376570 \n", - "3 3 none 8930.495022 0.339653 \n", - "4 4 none 8156.575502 0.312265 \n", - "\n", - " mu_hat \\\n", - " tech_support_only discount_only discount_plus_support none \n", - "0 0.267338 0.272366 0.211662 5370.099281 \n", - "1 0.307204 0.243394 0.238315 10468.942522 \n", - "2 0.241901 0.256261 0.125268 2879.741786 \n", - "3 0.194701 0.246505 0.219142 8458.779217 \n", - "4 0.250021 0.312123 0.125590 10137.469821 \n", - "\n", - " gamma \\\n", - " tech_support_only discount_only discount_plus_support none \n", - "0 9559.777763 4547.484552 6984.731609 5370.099281 \n", - "1 6728.988419 8373.316633 11121.826609 10468.942522 \n", - "2 5449.409859 937.515103 2959.514347 2879.741786 \n", - "3 8254.372069 6054.598973 6560.028069 9847.598052 \n", - "4 3378.748035 5743.802939 2988.217314 3793.845054 \n", - "\n", - " \n", - " tech_support_only discount_only discount_plus_support \n", - "0 9559.777763 4547.484552 17708.540116 \n", - "1 9899.108508 8373.316633 11121.826609 \n", - "2 3848.652052 937.515103 2959.514347 \n", - "3 8254.372069 6054.598973 6560.028069 \n", - "4 3378.748035 5743.802939 2988.217314 " - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sample_ids = np.arange(5)\n", - "\n", - "sample_prediction_table = pd.concat(\n", - " {\n", - " 'sample': pd.DataFrame({\n", - " 'sample_id': sample_ids,\n", - " 'actual_arm': pd.Series(A_test[sample_ids]).map(ARM_NAMES).to_numpy(),\n", - " 'observed_net_outcome': Y_net_test[sample_ids],\n", - " }),\n", - " 'e_hat': pd.DataFrame(e_hat_net[sample_ids], columns=ARM_LABELS),\n", - " 'mu_hat': pd.DataFrame(mu_hat_net[sample_ids], columns=ARM_LABELS),\n", - " 'gamma': pd.DataFrame(gamma_net[sample_ids], columns=ARM_LABELS),\n", - " },\n", - " axis=1,\n", - ")\n", - "\n", - "sample_prediction_table" + "## Policy Learning Methods\n\nWe compare three policy learning approaches.\n\n- Plug-in Policy (DRLearner)\n\n First estimates per-customer CATE, then selects the treatment with the highest expected net benefit.\n\n This is a flexible approach with no constraints on policy form, but final performance depends heavily on the quality of CATE estimation.\n\n- DRPolicyTree\n\n DRPolicyTree learns a policy by directly maximizing the AIPW score.\n\n The policy structure is constrained to a shallow decision tree, making results interpretable as if-then rules. This is useful when policy interpretability and explainability matter.\n\n- DRPolicyForest\n\n DRPolicyForest also directly maximizes the AIPW score but uses a forest rather than a single tree.\n\n A single tree has a simple structure but can exhibit high variance. A forest averages across many trees, reducing variance and learning heterogeneous treatment effects more stably.\n\n However, the resulting policy is harder to interpret as a single clear if-then rule set.\n\nAll three policies are learned on the train set and compared using the same test set. It is important to use separate data for learning and evaluation, as using the training data for evaluation leads to overestimation." ] }, { @@ -1886,6 +1587,250 @@ "If a learned policy's value exceeds the best constant policy, it means that customer-level targeting is more effective than applying the same treatment to everyone." ] }, + { + "cell_type": "markdown", + "id": "5261c54f", + "metadata": {}, + "source": [ + "The AIPW (Augmented Inverse Probability Weighting) score is defined as:\n\n$$\n\\hat\\Gamma_{i,a} = \\hat\\mu_a(X_i) + \\frac{\\mathbf{1}[A_i=a]}{\\hat e_a(X_i)}\\bigl(Y_i^{net} - \\hat\\mu_a(X_i)\\bigr)\n$$\n\n- $\\hat\\mu_a(X_i)$: predicted $E[Y^{net}(a)\\mid X_i]$ from the outcome model\n- $\\hat e_a(X_i)$: predicted $P(A_i=a\\mid X_i)$ from the propensity model\n- The second term corrects the difference between observed and predicted values via IPW.\n\nThis structure ensures unbiasedness holds as long as either the outcome model or the propensity model is correctly specified. This property is called doubly robust.\n\n- If the outcome model is correct: the expected value of the IPW correction term approaches zero, so $\\hat\\mu_a(X_i)$ contributes directly.\n- If the propensity model is correct: the IPW correction term removes the bias of the outcome model.\n\nThis score is used to estimate the value of each learned policy." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "c51d8c52", + "metadata": {}, + "outputs": [], + "source": [ + "Y_net_train = Y_net[train_idx]\n", + "Y_net_test = Y_net[test_idx]\n", + "\n", + "e_hat_net = np.clip(e_hat_raw, 0.02, 0.98)\n", + "e_hat_net = e_hat_net / e_hat_net.sum(axis=1, keepdims=True)\n", + "\n", + "gamma_net = np.zeros((len(X_test), 4))\n", + "mu_hat_net = np.zeros((len(X_test), 4))\n", + "\n", + "for arm_id in range(4):\n", + " outcome_model = RandomForestRegressor(\n", + " n_estimators=400,\n", + " min_samples_leaf=20,\n", + " random_state=SEED,\n", + " n_jobs=1,\n", + " )\n", + " outcome_model.fit(X_train[A_train == arm_id], Y_net_train[A_train == arm_id])\n", + " mu_a = outcome_model.predict(X_test)\n", + " observed_a = (A_test == arm_id).astype(float)\n", + "\n", + " gamma_net[:, arm_id] = mu_a + observed_a / e_hat_net[:, arm_id] * (Y_net_test - mu_a)\n", + " mu_hat_net[:, arm_id] = mu_a" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "0349d9ab", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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samplee_hatmu_hatgamma
sample_idactual_armobserved_net_outcomenonetech_support_onlydiscount_onlydiscount_plus_supportnonetech_support_onlydiscount_onlydiscount_plus_supportnonetech_support_onlydiscount_onlydiscount_plus_support
00discount_plus_support9254.5585410.2486330.2673380.2723660.2116625370.0992819559.7777634547.4845526984.7316095370.0992819559.7777634547.48455217708.540116
11tech_support_only7702.8616620.2110870.3072040.2433940.23831510468.9425226728.9884198373.31663311121.82660910468.9425229899.1085088373.31663311121.826609
22tech_support_only5062.1844200.3765700.2419010.2562610.1252682879.7417865449.409859937.5151032959.5143472879.7417863848.652052937.5151032959.514347
33none8930.4950220.3396530.1947010.2465050.2191428458.7792178254.3720696054.5989736560.0280699847.5980528254.3720696054.5989736560.028069
44none8156.5755020.3122650.2500210.3121230.12559010137.4698213378.7480355743.8029392988.2173143793.8450543378.7480355743.8029392988.217314
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" + ], + "text/plain": [ + " sample e_hat \\\n", + " sample_id actual_arm observed_net_outcome none \n", + "0 0 discount_plus_support 9254.558541 0.248633 \n", + "1 1 tech_support_only 7702.861662 0.211087 \n", + "2 2 tech_support_only 5062.184420 0.376570 \n", + "3 3 none 8930.495022 0.339653 \n", + "4 4 none 8156.575502 0.312265 \n", + "\n", + " mu_hat \\\n", + " tech_support_only discount_only discount_plus_support none \n", + "0 0.267338 0.272366 0.211662 5370.099281 \n", + "1 0.307204 0.243394 0.238315 10468.942522 \n", + "2 0.241901 0.256261 0.125268 2879.741786 \n", + "3 0.194701 0.246505 0.219142 8458.779217 \n", + "4 0.250021 0.312123 0.125590 10137.469821 \n", + "\n", + " gamma \\\n", + " tech_support_only discount_only discount_plus_support none \n", + "0 9559.777763 4547.484552 6984.731609 5370.099281 \n", + "1 6728.988419 8373.316633 11121.826609 10468.942522 \n", + "2 5449.409859 937.515103 2959.514347 2879.741786 \n", + "3 8254.372069 6054.598973 6560.028069 9847.598052 \n", + "4 3378.748035 5743.802939 2988.217314 3793.845054 \n", + "\n", + " \n", + " tech_support_only discount_only discount_plus_support \n", + "0 9559.777763 4547.484552 17708.540116 \n", + "1 9899.108508 8373.316633 11121.826609 \n", + "2 3848.652052 937.515103 2959.514347 \n", + "3 8254.372069 6054.598973 6560.028069 \n", + "4 3378.748035 5743.802939 2988.217314 " + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sample_ids = np.arange(5)\n", + "\n", + "sample_prediction_table = pd.concat(\n", + " {\n", + " 'sample': pd.DataFrame({\n", + " 'sample_id': sample_ids,\n", + " 'actual_arm': pd.Series(A_test[sample_ids]).map(ARM_NAMES).to_numpy(),\n", + " 'observed_net_outcome': Y_net_test[sample_ids],\n", + " }),\n", + " 'e_hat': pd.DataFrame(e_hat_net[sample_ids], columns=ARM_LABELS),\n", + " 'mu_hat': pd.DataFrame(mu_hat_net[sample_ids], columns=ARM_LABELS),\n", + " 'gamma': pd.DataFrame(gamma_net[sample_ids], columns=ARM_LABELS),\n", + " },\n", + " axis=1,\n", + ")\n", + "\n", + "sample_prediction_table" + ] + }, { "cell_type": "code", "execution_count": 18, @@ -2322,4 +2267,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} +} \ No newline at end of file diff --git a/book/who_should_be_treated/who_should_be_treated_ko.ipynb b/book/who_should_be_treated/who_should_be_treated_ko.ipynb index d31b69c..335872f 100644 --- a/book/who_should_be_treated/who_should_be_treated_ko.ipynb +++ b/book/who_should_be_treated/who_should_be_treated_ko.ipynb @@ -34,7 +34,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "id": "fc2741f9", "metadata": { "execution": { @@ -121,7 +121,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "id": "477aed7a", "metadata": { "execution": { @@ -283,7 +283,7 @@ "4 discount_plus_support " ] }, - "execution_count": 3, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -370,7 +370,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "id": "5174ddd2", "metadata": { "execution": { @@ -460,7 +460,7 @@ "3 14118.841353 " ] }, - "execution_count": 4, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -519,7 +519,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "id": "3d0f329e", "metadata": {}, "outputs": [ @@ -617,7 +617,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "id": "d6488c62", "metadata": {}, "outputs": [ @@ -667,7 +667,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "id": "c925a3d8", "metadata": {}, "outputs": [ @@ -874,7 +874,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "id": "n7ziuiv612q", "metadata": {}, "outputs": [], @@ -895,7 +895,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "id": "9be31313", "metadata": {}, "outputs": [ @@ -1004,7 +1004,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "id": "fl52yekx049", "metadata": {}, "outputs": [ @@ -1060,108 +1060,130 @@ "id": "f4edd2d8", "metadata": {}, "source": [ - "## 정책학습 방법론\n", - "\n", - "세 가지 정책학습 방법을 비교합니다.\n", - "\n", - "- Plug-in Policy (DRLearner)\n", - "\n", - " 먼저 고객별 CATE를 추정한 뒤, 기대 순이익(net benefit)이 가장 큰 처치를 선택합니다.\n", - "\n", - " 정책 형태에 제약이 없는 유연한 접근이지만, 최종 성능은 CATE 추정 품질에 크게 영향을 받습니다.\n", - "\n", - "- DRPolicyTree\n", - "\n", - " DRPolicyTree는 AIPW score를 직접 최대화하도록 정책을 학습합니다.\n", - "\n", - " 정책 구조를 얕은 decision tree로 제한하기 때문에, 결과를 if-then 규칙 형태로 해석할 수 있습니다. 따라서 정책 해석과 설명이 중요한 상황에서 유용합니다.\n", - "\n", - "- DRPolicyForest\n", - "\n", - " DRPolicyForest 역시 AIPW score를 직접 최대화하지만, 단일 트리 대신 forest 구조를 사용합니다.\n", - "\n", - " 단일 tree는 구조가 단순하지만 분산이 커질 수 있습니다. 반면 forest는 여러 트리의 결과를 평균적으로 활용하기 때문에 분산을 줄이면서 더 안정적으로 이질적인 처치 효과를 학습할 수 있습니다.\n", - "\n", - " 대신 최종 정책을 하나의 명확한 if-then 규칙 형태로 해석하기는 더 어렵습니다.\n", - "\n", - "세 정책은 모두 train set에서 학습하고, 동일한 test set의 AIPW score로 정책 가치를 비교합니다. 학습에 사용한 데이터로 정책을 평가하면 과대추정이 발생하므로, 학습과 평가에 서로 다른 데이터를 사용하는 것이 중요합니다." + "## 정책학습 방법론\n\n세 가지 정책학습 방법을 비교합니다.\n\n- Plug-in Policy (DRLearner)\n\n 먼저 고객별 CATE를 추정한 뒤, 기대 순이익(net benefit)이 가장 큰 처치를 선택합니다.\n\n 정책 형태에 제약이 없는 유연한 접근이지만, 최종 성능은 CATE 추정 품질에 크게 영향을 받습니다.\n\n- DRPolicyTree\n\n DRPolicyTree는 AIPW score를 직접 최대화하도록 정책을 학습합니다.\n\n 정책 구조를 얕은 decision tree로 제한하기 때문에, 결과를 if-then 규칙 형태로 해석할 수 있습니다. 따라서 정책 해석과 설명이 중요한 상황에서 유용합니다.\n\n- DRPolicyForest\n\n DRPolicyForest 역시 AIPW score를 직접 최대화하지만, 단일 트리 대신 forest 구조를 사용합니다.\n\n 단일 tree는 구조가 단순하지만 분산이 커질 수 있습니다. 반면 forest는 여러 트리의 결과를 평균적으로 활용하기 때문에 분산을 줄이면서 더 안정적으로 이질적인 처치 효과를 학습할 수 있습니다.\n\n 대신 최종 정책을 하나의 명확한 if-then 규칙 형태로 해석하기는 더 어렵습니다.\n\n세 정책은 모두 train set에서 학습하고, 동일한 test set에서 정책 가치를 비교합니다. 학습에 사용한 데이터로 정책을 평가하면 과대추정이 발생하므로, 학습과 평가에 서로 다른 데이터를 사용하는 것이 중요합니다." ] }, { "cell_type": "markdown", - "id": "d7d4bc26", + "id": "3c967473", "metadata": {}, "source": [ - "## 평가 지표 구성\n", + "### Plug-in Policy\n", + "\n", + "Plug-in policy는 먼저 고객별 처치 효과(CATE)를 추정한 뒤, 그 값을 이용해 정책을 만드는 방식입니다.\n", + "\n", + "Binary treatment에서는 일반적으로 $\\hat\\tau(x) > 0$이면 처치하고, 비용이 있으면 $\\hat\\tau(x) > c(x)$일 때 처치합니다.\n", + "\n", + "여러 처치가 있는 경우에는 baseline 처치와의 상대효과를 비교합니다. 여기서는 `none`(처치 없음)을 baseline으로 두고, `DRLearner`로 다음 값을 추정합니다.\n", + "\n", + "$$\n", + "\\hat\\tau_a(x) = \\widehat{\\mathbb{E}}[Y^{net}(a) - Y^{net}(0) \\mid X=x]\n", + "\\qquad a \\in \\{1,2,3\\}\n", + "$$\n", "\n", - "AIPW(Augmented Inverse Probability Weighting) score는 다음과 같이 정의합니다.\n", + "baseline 처치의 상대효과는 0이므로, 고객별로 다음 네 값을 비교합니다.\n", "\n", "$$\n", - "\\hat\\Gamma_{i,a} = \\hat\\mu_a(X_i) + \\frac{\\mathbf{1}[A_i=a]}{\\hat e_a(X_i)}\\bigl(Y_i^{net} - \\hat\\mu_a(X_i)\\bigr)\n", + "[0, \\hat\\tau_1(x), \\hat\\tau_2(x), \\hat\\tau_3(x)]\n", "$$\n", "\n", - "- $\\hat\\mu_a(X_i)$: outcome model이 예측한 $E[Y^{net}(a)\\mid X_i]$\n", - "- $\\hat e_a(X_i)$: propensity model이 예측한 $P(A_i=a\\mid X_i)$\n", - "- 두 번째 항은 실제 관측값과 예측값의 차이를 IPW로 보정하는 역할을 합니다.\n", + "그리고 가장 큰 값을 갖는 처치를 선택합니다.\n", "\n", - "이 구조 덕분에 outcome model과 propensity model 중 하나만 올바르게 추정되어도 불편성이 유지됩니다. 이를 doubly robust(이중 견고성)라고 부릅니다.\n", + "$$\n", + "\\hat\\pi_{plugin}(x) = \\arg\\max_{a \\in \\{0,1,2,3\\}} \\widehat{\\mathbb{E}}[Y^{net}(a) - Y^{net}(0) \\mid X=x]\n", + "$$\n", "\n", - "- outcome model이 정확하면: IPW 보정항의 기댓값이 0에 가까워져 $\\hat\\mu_a(X_i)$가 직접 기여합니다.\n", - "- propensity model이 정확하면: IPW 보정항이 outcome model의 편향을 제거합니다.\n", + "이 방식은 직관적이고 유연하지만, CATE 추정 품질에 크게 영향을 받습니다. 따라서 학습된 정책은 별도의 test set에서 AIPW value로 평가합니다.\n", "\n", - "이 score는 정책학습(DRPolicyTree/Forest)과 정책 평가 모두에서 사용합니다." + "> **NOTE** \"각 고객의 $\\hat\\Gamma_{i,a}$ 중 가장 큰 arm을 선택하면 되지 않나?\"라고 생각할 수 있습니다. 그러나 AIPW score는 개별 관측치 수준에서 분산이 매우 크기 때문에, pointwise argmax로 만든 정책은 노이즈에 과적합됩니다. AIPW score는 반드시 집계된 형태로 사용해야 합니다. 정책 평가(value 추정, 비용 곡선 등)처럼 여러 관측치를 합산하거나, DRPolicyTree/Forest처럼 노드 단위 평균을 최적화하거나, DRLearner처럼 pseudo-outcome으로 회귀하는 방식이 그 예입니다." ] }, { "cell_type": "code", - "execution_count": 11, - "id": "e3156595", + "execution_count": 12, + "id": "debd92a9", "metadata": { "execution": { - "iopub.execute_input": "2026-05-09T16:49:48.578060Z", - "iopub.status.busy": "2026-05-09T16:49:48.577946Z", - "iopub.status.idle": "2026-05-09T16:49:49.475582Z", - "shell.execute_reply": "2026-05-09T16:49:49.474994Z" + "iopub.execute_input": "2026-05-09T16:49:49.484723Z", + "iopub.status.busy": "2026-05-09T16:49:49.484610Z", + "iopub.status.idle": "2026-05-09T16:49:52.385416Z", + "shell.execute_reply": "2026-05-09T16:49:52.384824Z" } }, "outputs": [], "source": [ - "Y_net_train = Y_net[train_idx]\n", - "Y_net_test = Y_net[test_idx]\n", - "\n", - "e_hat_net = np.clip(e_hat_raw, 0.02, 0.98)\n", - "e_hat_net = e_hat_net / e_hat_net.sum(axis=1, keepdims=True)\n", - "\n", - "gamma_net = np.zeros((len(X_test), 4))\n", - "mu_hat_net = np.zeros((len(X_test), 4))\n", - "\n", - "for arm_id in range(4):\n", - " outcome_model = RandomForestRegressor(\n", + "dr_cate = DRLearner(\n", + " model_regression=RandomForestRegressor(\n", " n_estimators=400,\n", " min_samples_leaf=20,\n", " random_state=SEED,\n", " n_jobs=1,\n", - " )\n", - " outcome_model.fit(X_train[A_train == arm_id], Y_net_train[A_train == arm_id])\n", - " mu_a = outcome_model.predict(X_test)\n", - " observed_a = (A_test == arm_id).astype(float)\n", + " ),\n", + " model_propensity=RandomForestClassifier(\n", + " n_estimators=400,\n", + " min_samples_leaf=20,\n", + " random_state=SEED,\n", + " n_jobs=1,\n", + " ),\n", + " model_final=RandomForestRegressor(\n", + " n_estimators=300,\n", + " min_samples_leaf=20,\n", + " random_state=SEED,\n", + " n_jobs=1,\n", + " ),\n", + " categories=[0, 1, 2, 3],\n", + " min_propensity=0.02,\n", + " cv=3,\n", + " random_state=SEED,\n", + ")\n", + "dr_cate.fit(Y_net_train, A_train, X=X_train)\n", "\n", - " gamma_net[:, arm_id] = mu_a + observed_a / e_hat_net[:, arm_id] * (Y_net_test - mu_a)\n", - " mu_hat_net[:, arm_id] = mu_a" + "# none을 baseline으로, 나머지 처치별 E[Y_net(a) - Y_net(0) | X] 반환\n", + "cate_vs_none = dr_cate.const_marginal_effect(X_test)\n", + "plugin_arm_values = np.column_stack([\n", + " np.zeros(len(X_test)), # none: baseline, 상대효과 = 0\n", + " cate_vs_none,\n", + "])\n", + "pi_plugin = np.argmax(plugin_arm_values, axis=1)" ] }, { "cell_type": "code", - "execution_count": 12, - "id": "e8533283", + "execution_count": 13, + "id": "33437c2c", "metadata": { "execution": { - "iopub.execute_input": "2026-05-09T16:49:49.477219Z", - "iopub.status.busy": "2026-05-09T16:49:49.477080Z", - "iopub.status.idle": "2026-05-09T16:49:49.483510Z", - "shell.execute_reply": "2026-05-09T16:49:49.483027Z" + "iopub.execute_input": "2026-05-09T16:49:52.389559Z", + "iopub.status.busy": "2026-05-09T16:49:52.389447Z", + "iopub.status.idle": "2026-05-09T16:49:52.392977Z", + "shell.execute_reply": "2026-05-09T16:49:52.392636Z" } }, + "outputs": [ + { + "data": { + "text/plain": [ + "none 0.092\n", + "tech_support_only 0.500\n", + "discount_only 0.035\n", + "discount_plus_support 0.373\n", + "Name: proportion, dtype: float64" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.Series(pi_plugin).map(ARM_NAMES).value_counts(normalize=True).reindex(ARM_LABELS).fillna(0)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "33ktqy8zrqm", + "metadata": {}, "outputs": [ { "data": { @@ -1176,374 +1198,53 @@ " vertical-align: top;\n", " }\n", "\n", - " .dataframe thead tr th {\n", - " text-align: left;\n", + " .dataframe thead th {\n", + " text-align: right;\n", " }\n", "\n", "\n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - 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samplee_hatmu_hatgamma
sample_idactual_armobserved_net_outcomenonetech_support_onlydiscount_onlydiscount_plus_supportnonetech_support_onlydiscount_onlydiscount_plus_supportnonetech_support_onlydiscount_onlydiscount_plus_supportSizeEmployee CountIT Spendassigned_armτ_techτ_discτ_both
00discount_plus_support9254.5585410.2486330.2673380.2723660.2116625370.0992819559.7777634547.4845526984.7316095370.0992819559.7777634547.48455217708.5401169620414217655none-6140-1419-1719
11tech_support_only7702.8616620.2110870.3072040.2433940.23831510468.9425226728.9884198373.31663311121.82660910468.9425229899.1085088373.31663311121.826609253901787971none-14556-4252-7232
22tech_support_only5062.1844200.3765700.2419010.2562610.1252682879.7417865449.409859937.5151032959.5143472879.7417863848.652052937.5151032959.514347
336655527919899none8930.4950220.3396530.1947010.2465050.2191428458.7792178254.3720696054.5989736560.0280699847.5980528254.3720696054.5989736560.028069
44none8156.5755020.3122650.2500210.3121230.12559010137.4698213378.7480355743.8029392988.2173143793.8450543378.7480355743.8029392988.217314
\n", - "" - ], - "text/plain": [ - " sample e_hat \\\n", - " sample_id actual_arm observed_net_outcome none \n", - "0 0 discount_plus_support 9254.558541 0.248633 \n", - "1 1 tech_support_only 7702.861662 0.211087 \n", - "2 2 tech_support_only 5062.184420 0.376570 \n", - "3 3 none 8930.495022 0.339653 \n", - "4 4 none 8156.575502 0.312265 \n", - "\n", - " mu_hat \\\n", - " tech_support_only discount_only discount_plus_support none \n", - "0 0.267338 0.272366 0.211662 5370.099281 \n", - "1 0.307204 0.243394 0.238315 10468.942522 \n", - "2 0.241901 0.256261 0.125268 2879.741786 \n", - "3 0.194701 0.246505 0.219142 8458.779217 \n", - "4 0.250021 0.312123 0.125590 10137.469821 \n", - "\n", - " gamma \\\n", - " tech_support_only discount_only discount_plus_support none \n", - "0 9559.777763 4547.484552 6984.731609 5370.099281 \n", - "1 6728.988419 8373.316633 11121.826609 10468.942522 \n", - "2 5449.409859 937.515103 2959.514347 2879.741786 \n", - "3 8254.372069 6054.598973 6560.028069 9847.598052 \n", - "4 3378.748035 5743.802939 2988.217314 3793.845054 \n", - "\n", - " \n", - " tech_support_only discount_only discount_plus_support \n", - "0 9559.777763 4547.484552 17708.540116 \n", - "1 9899.108508 8373.316633 11121.826609 \n", - "2 3848.652052 937.515103 2959.514347 \n", - "3 8254.372069 6054.598973 6560.028069 \n", - "4 3378.748035 5743.802939 2988.217314 " - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sample_ids = np.arange(5)\n", - "\n", - "sample_prediction_table = pd.concat(\n", - " {\n", - " 'sample': pd.DataFrame({\n", - " 'sample_id': sample_ids,\n", - " 'actual_arm': pd.Series(A_test[sample_ids]).map(ARM_NAMES).to_numpy(),\n", - " 'observed_net_outcome': Y_net_test[sample_ids],\n", - " }),\n", - " 'e_hat': pd.DataFrame(e_hat_net[sample_ids], columns=ARM_LABELS),\n", - " 'mu_hat': pd.DataFrame(mu_hat_net[sample_ids], columns=ARM_LABELS),\n", - " 'gamma': pd.DataFrame(gamma_net[sample_ids], columns=ARM_LABELS),\n", - " },\n", - " axis=1,\n", - ")\n", - "\n", - "sample_prediction_table" - ] - }, - { - "cell_type": "markdown", - "id": "3c967473", - "metadata": {}, - "source": [ - "### Plug-in Policy\n", - "\n", - "Plug-in policy는 먼저 고객별 처치 효과(CATE)를 추정한 뒤, 그 값을 이용해 정책을 만드는 방식입니다.\n", - "\n", - "Binary treatment에서는 일반적으로 $\\hat\\tau(x) > 0$이면 처치하고, 비용이 있으면 $\\hat\\tau(x) > c(x)$일 때 처치합니다.\n", - "\n", - "여러 처치가 있는 경우에는 baseline 처치와의 상대효과를 비교합니다. 여기서는 `none`(처치 없음)을 baseline으로 두고, `DRLearner`로 다음 값을 추정합니다.\n", - "\n", - "$$\n", - "\\hat\\tau_a(x) = \\widehat{\\mathbb{E}}[Y^{net}(a) - Y^{net}(0) \\mid X=x]\n", - "\\qquad a \\in \\{1,2,3\\}\n", - "$$\n", - "\n", - "baseline 처치의 상대효과는 0이므로, 고객별로 다음 네 값을 비교합니다.\n", - "\n", - "$$\n", - "[0, \\hat\\tau_1(x), \\hat\\tau_2(x), \\hat\\tau_3(x)]\n", - "$$\n", - "\n", - "그리고 가장 큰 값을 갖는 처치를 선택합니다.\n", - "\n", - "$$\n", - "\\hat\\pi_{plugin}(x) = \\arg\\max_{a \\in \\{0,1,2,3\\}} \\widehat{\\mathbb{E}}[Y^{net}(a) - Y^{net}(0) \\mid X=x]\n", - "$$\n", - "\n", - "이 방식은 직관적이고 유연하지만, CATE 추정 품질에 크게 영향을 받습니다. 따라서 학습된 정책은 별도의 test set에서 AIPW value로 평가합니다.\n", - "\n", - "> **NOTE** \"각 고객의 $\\hat\\Gamma_{i,a}$ 중 가장 큰 arm을 선택하면 되지 않나?\"라고 생각할 수 있습니다. 그러나 AIPW score는 개별 관측치 수준에서 분산이 매우 크기 때문에, pointwise argmax로 만든 정책은 노이즈에 과적합됩니다. AIPW score는 반드시 집계된 형태로 사용해야 합니다. 정책 평가(value 추정, 비용 곡선 등)처럼 여러 관측치를 합산하거나, DRPolicyTree/Forest처럼 노드 단위 평균을 최적화하거나, DRLearner처럼 pseudo-outcome으로 회귀하는 방식이 그 예입니다." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "debd92a9", - "metadata": { - "execution": { - "iopub.execute_input": "2026-05-09T16:49:49.484723Z", - "iopub.status.busy": "2026-05-09T16:49:49.484610Z", - "iopub.status.idle": "2026-05-09T16:49:52.385416Z", - "shell.execute_reply": "2026-05-09T16:49:52.384824Z" - } - }, - "outputs": [], - "source": [ - "dr_cate = DRLearner(\n", - " model_regression=RandomForestRegressor(\n", - " n_estimators=400,\n", - " min_samples_leaf=20,\n", - " random_state=SEED,\n", - " n_jobs=1,\n", - " ),\n", - " model_propensity=RandomForestClassifier(\n", - " n_estimators=400,\n", - " min_samples_leaf=20,\n", - " random_state=SEED,\n", - " n_jobs=1,\n", - " ),\n", - " model_final=RandomForestRegressor(\n", - " n_estimators=300,\n", - " min_samples_leaf=20,\n", - " random_state=SEED,\n", - " n_jobs=1,\n", - " ),\n", - " categories=[0, 1, 2, 3],\n", - " min_propensity=0.02,\n", - " cv=3,\n", - " random_state=SEED,\n", - ")\n", - "dr_cate.fit(Y_net_train, A_train, X=X_train)\n", - "\n", - "# none을 baseline으로, 나머지 처치별 E[Y_net(a) - Y_net(0) | X] 반환\n", - "cate_vs_none = dr_cate.const_marginal_effect(X_test)\n", - "plugin_arm_values = np.column_stack([\n", - " np.zeros(len(X_test)), # none: baseline, 상대효과 = 0\n", - " cate_vs_none,\n", - "])\n", - "pi_plugin = np.argmax(plugin_arm_values, axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "33437c2c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-05-09T16:49:52.389559Z", - "iopub.status.busy": "2026-05-09T16:49:52.389447Z", - "iopub.status.idle": "2026-05-09T16:49:52.392977Z", - "shell.execute_reply": "2026-05-09T16:49:52.392636Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "none 0.092\n", - "tech_support_only 0.500\n", - "discount_only 0.035\n", - "discount_plus_support 0.373\n", - "Name: proportion, dtype: float64" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pd.Series(pi_plugin).map(ARM_NAMES).value_counts(normalize=True).reindex(ARM_LABELS).fillna(0)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "33ktqy8zrqm", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1633,7 +1334,7 @@ "8 9048 " ] }, - "execution_count": 15, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -1688,7 +1389,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "id": "6c987a8b", "metadata": { "execution": { @@ -1709,7 +1410,7 @@ "Name: proportion, dtype: float64" ] }, - "execution_count": 16, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -1743,7 +1444,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 16, "id": "64529830", "metadata": { "execution": { @@ -1800,7 +1501,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 17, "id": "d7939447", "metadata": { "execution": { @@ -1821,7 +1522,7 @@ "Name: proportion, dtype: float64" ] }, - "execution_count": 18, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -1886,9 +1587,253 @@ "만약 학습된 정책의 value가 가장 좋은 baseline 정책보다 높다면, 모든 고객에게 동일한 처치를 적용하는 것보다 고객별 targeting이 더 효과적이라는 의미입니다." ] }, + { + "cell_type": "markdown", + "id": "1e2f1760", + "metadata": {}, + "source": [ + "AIPW(Augmented Inverse Probability Weighting) score는 다음과 같이 정의합니다.\n\n$$\n\\hat\\Gamma_{i,a} = \\hat\\mu_a(X_i) + \\frac{\\mathbf{1}[A_i=a]}{\\hat e_a(X_i)}\\bigl(Y_i^{net} - \\hat\\mu_a(X_i)\\bigr)\n$$\n\n- $\\hat\\mu_a(X_i)$: outcome model이 예측한 $E[Y^{net}(a)\\mid X_i]$\n- $\\hat e_a(X_i)$: propensity model이 예측한 $P(A_i=a\\mid X_i)$\n- 두 번째 항은 실제 관측값과 예측값의 차이를 IPW로 보정하는 역할을 합니다.\n\n이 구조 덕분에 outcome model과 propensity model 중 하나만 올바르게 추정되어도 불편성이 유지됩니다. 이를 doubly robust(이중 견고성)라고 부릅니다.\n\n- outcome model이 정확하면: IPW 보정항의 기댓값이 0에 가까워져 $\\hat\\mu_a(X_i)$가 직접 기여합니다.\n- propensity model이 정확하면: IPW 보정항이 outcome model의 편향을 제거합니다.\n\n이 score를 사용해 학습된 정책의 가치를 추정합니다." + ] + }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 10, + "id": "9c3bfe27", + "metadata": {}, + "outputs": [], + "source": [ + "Y_net_train = Y_net[train_idx]\n", + "Y_net_test = Y_net[test_idx]\n", + "\n", + "e_hat_net = np.clip(e_hat_raw, 0.02, 0.98)\n", + "e_hat_net = e_hat_net / e_hat_net.sum(axis=1, keepdims=True)\n", + "\n", + "gamma_net = np.zeros((len(X_test), 4))\n", + "mu_hat_net = np.zeros((len(X_test), 4))\n", + "\n", + "for arm_id in range(4):\n", + " outcome_model = RandomForestRegressor(\n", + " n_estimators=400,\n", + " min_samples_leaf=20,\n", + " random_state=SEED,\n", + " n_jobs=1,\n", + " )\n", + " outcome_model.fit(X_train[A_train == arm_id], Y_net_train[A_train == arm_id])\n", + " mu_a = outcome_model.predict(X_test)\n", + " observed_a = (A_test == arm_id).astype(float)\n", + "\n", + " gamma_net[:, arm_id] = mu_a + observed_a / e_hat_net[:, arm_id] * (Y_net_test - mu_a)\n", + " mu_hat_net[:, arm_id] = mu_a" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "0d014d17", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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SizeEmployee CountIT Spendassigned_armτ_techτ_discτ_both
09620414217655none-6140-1419-1719
1253901787971none-14556-4252-7232
26655527919899none-17629-2896-16582-17629-2896-16582
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samplee_hatmu_hatgamma
sample_idactual_armobserved_net_outcomenonetech_support_onlydiscount_onlydiscount_plus_supportnonetech_support_onlydiscount_onlydiscount_plus_supportnonetech_support_onlydiscount_onlydiscount_plus_support
00discount_plus_support9254.5585410.2486330.2673380.2723660.2116625370.0992819559.7777634547.4845526984.7316095370.0992819559.7777634547.48455217708.540116
11tech_support_only7702.8616620.2110870.3072040.2433940.23831510468.9425226728.9884198373.31663311121.82660910468.9425229899.1085088373.31663311121.826609
22tech_support_only5062.1844200.3765700.2419010.2562610.1252682879.7417865449.409859937.5151032959.5143472879.7417863848.652052937.5151032959.514347
33none8930.4950220.3396530.1947010.2465050.2191428458.7792178254.3720696054.5989736560.0280699847.5980528254.3720696054.5989736560.028069
44none8156.5755020.3122650.2500210.3121230.12559010137.4698213378.7480355743.8029392988.2173143793.8450543378.7480355743.8029392988.217314
\n", + "
" + ], + "text/plain": [ + " sample e_hat \\\n", + " sample_id actual_arm observed_net_outcome none \n", + "0 0 discount_plus_support 9254.558541 0.248633 \n", + "1 1 tech_support_only 7702.861662 0.211087 \n", + "2 2 tech_support_only 5062.184420 0.376570 \n", + "3 3 none 8930.495022 0.339653 \n", + "4 4 none 8156.575502 0.312265 \n", + "\n", + " mu_hat \\\n", + " tech_support_only discount_only discount_plus_support none \n", + "0 0.267338 0.272366 0.211662 5370.099281 \n", + "1 0.307204 0.243394 0.238315 10468.942522 \n", + "2 0.241901 0.256261 0.125268 2879.741786 \n", + "3 0.194701 0.246505 0.219142 8458.779217 \n", + "4 0.250021 0.312123 0.125590 10137.469821 \n", + "\n", + " gamma \\\n", + " tech_support_only discount_only discount_plus_support none \n", + "0 9559.777763 4547.484552 6984.731609 5370.099281 \n", + "1 6728.988419 8373.316633 11121.826609 10468.942522 \n", + "2 5449.409859 937.515103 2959.514347 2879.741786 \n", + "3 8254.372069 6054.598973 6560.028069 9847.598052 \n", + "4 3378.748035 5743.802939 2988.217314 3793.845054 \n", + "\n", + " \n", + " tech_support_only discount_only discount_plus_support \n", + "0 9559.777763 4547.484552 17708.540116 \n", + "1 9899.108508 8373.316633 11121.826609 \n", + "2 3848.652052 937.515103 2959.514347 \n", + "3 8254.372069 6054.598973 6560.028069 \n", + "4 3378.748035 5743.802939 2988.217314 " + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sample_ids = np.arange(5)\n", + "\n", + "sample_prediction_table = pd.concat(\n", + " {\n", + " 'sample': pd.DataFrame({\n", + " 'sample_id': sample_ids,\n", + " 'actual_arm': pd.Series(A_test[sample_ids]).map(ARM_NAMES).to_numpy(),\n", + " 'observed_net_outcome': Y_net_test[sample_ids],\n", + " }),\n", + " 'e_hat': pd.DataFrame(e_hat_net[sample_ids], columns=ARM_LABELS),\n", + " 'mu_hat': pd.DataFrame(mu_hat_net[sample_ids], columns=ARM_LABELS),\n", + " 'gamma': pd.DataFrame(gamma_net[sample_ids], columns=ARM_LABELS),\n", + " },\n", + " axis=1,\n", + ")\n", + "\n", + "sample_prediction_table" + ] + }, + { + "cell_type": "code", + "execution_count": 18, "id": "14b616ad", "metadata": { "execution": { @@ -2041,7 +1986,7 @@ "0 0.000 " ] }, - "execution_count": 19, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -2078,7 +2023,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 19, "id": "4814d1ed", "metadata": {}, "outputs": [ @@ -2146,7 +2091,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 20, "id": "a1z6z7eabyj", "metadata": {}, "outputs": [ @@ -2199,7 +2144,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 21, "id": "ttwi6dbqcfo", "metadata": {}, "outputs": [ @@ -2322,4 +2267,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} +} \ No newline at end of file From 16f86883e925bc82252c9f0e5eb10ab6523b5f52 Mon Sep 17 00:00:00 2001 From: Funbucket Date: Wed, 13 May 2026 23:41:43 +0900 Subject: [PATCH 11/11] =?UTF-8?q?docs(who=5Fshould=5Fbe=5Ftreated):=20en/k?= =?UTF-8?q?o=20=EB=85=B8=ED=8A=B8=EB=B6=81=20=EA=B5=AC=EC=A1=B0=EC=99=80?= =?UTF-8?q?=20=EC=9A=A9=EC=96=B4=20=EC=A0=95=EB=A6=AC?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../who_should_be_treated_en.ipynb | 380 ++++++++-------- .../who_should_be_treated_ko.ipynb | 409 ++++++++++-------- 2 files changed, 437 insertions(+), 352 deletions(-) diff --git a/book/who_should_be_treated/who_should_be_treated_en.ipynb b/book/who_should_be_treated/who_should_be_treated_en.ipynb index caed193..619ef09 100644 --- a/book/who_should_be_treated/who_should_be_treated_en.ipynb +++ b/book/who_should_be_treated/who_should_be_treated_en.ipynb @@ -106,7 +106,7 @@ "| Revenue | Y | Customer revenue from software purchases |\n", "\n", "\n", - "Since both `Tech Support` and `Discount` are interventions, we combine them to define four treatment arms:\n", + "Since both `Tech Support` and `Discount` are interventions, we combine them to define four treatments:\n", "\n", "$$\n", "A_i \\in \\{0, 1, 2, 3\\}\n", @@ -171,8 +171,8 @@ " Tech Support\n", " Discount\n", " Revenue\n", - " arm\n", - " arm_name\n", + " treatment\n", + " treatment_name\n", " \n", " \n", " \n", @@ -268,19 +268,19 @@ "3 0 0 0 0 30288 \n", "4 0 0 1 0 25930 \n", "\n", - " Employee Count PC Count Size Tech Support Discount Revenue arm \\\n", - "0 26 26 152205 0 1 17688.36300 2 \n", - "1 107 70 159038 0 1 14981.43559 2 \n", - "2 10 7 264935 1 1 32917.13894 3 \n", - "3 40 39 77522 1 1 14773.76855 3 \n", - "4 37 43 91446 1 1 17098.69823 3 \n", + " Employee Count PC Count Size Tech Support Discount Revenue \\\n", + "0 26 26 152205 0 1 17688.36300 \n", + "1 107 70 159038 0 1 14981.43559 \n", + "2 10 7 264935 1 1 32917.13894 \n", + "3 40 39 77522 1 1 14773.76855 \n", + "4 37 43 91446 1 1 17098.69823 \n", "\n", - " arm_name \n", - "0 discount_only \n", - "1 discount_only \n", - "2 discount_plus_support \n", - "3 discount_plus_support \n", - "4 discount_plus_support " + " treatment treatment_name \n", + "0 2 discount_only \n", + "1 2 discount_only \n", + "2 3 discount_plus_support \n", + "3 3 discount_plus_support \n", + "4 3 discount_plus_support " ] }, "execution_count": 2, @@ -304,29 +304,29 @@ "]\n", "outcome = 'Revenue'\n", "\n", - "ARM_NAMES = {\n", + "TREATMENT_NAMES = {\n", " 0: 'none',\n", " 1: 'tech_support_only',\n", " 2: 'discount_only',\n", " 3: 'discount_plus_support',\n", "}\n", - "ARM_LABELS = [ARM_NAMES[i] for i in range(4)]\n", + "TREATMENT_LABELS = [TREATMENT_NAMES[i] for i in range(4)]\n", "\n", "required_columns = covariates + ['Tech Support', 'Discount', outcome]\n", "for col in required_columns:\n", " data[col] = pd.to_numeric(data[col], errors='coerce')\n", "\n", "policy_df = data[required_columns].dropna().copy()\n", - "policy_df['arm'] = (\n", + "policy_df['treatment'] = (\n", " 2 * policy_df['Discount'].astype(int)\n", " + policy_df['Tech Support'].astype(int)\n", ").astype(int)\n", - "policy_df['arm_name'] = policy_df['arm'].map(ARM_NAMES)\n", + "policy_df['treatment_name'] = policy_df['treatment'].map(TREATMENT_NAMES)\n", "\n", "n = len(policy_df)\n", "X = policy_df[covariates].to_numpy()\n", "Y = policy_df[outcome].to_numpy(dtype=float)\n", - "A = policy_df['arm'].to_numpy(dtype=int)\n", + "A = policy_df['treatment'].to_numpy(dtype=int)\n", "\n", "print(policy_df.shape)\n", "policy_df.head()\n" @@ -402,7 +402,7 @@ " \n", " \n", " \n", - " arm\n", + " treatment\n", " mean_cost\n", " std_cost\n", " mean_revenue\n", @@ -447,7 +447,7 @@ "" ], "text/plain": [ - " arm mean_cost std_cost mean_revenue \\\n", + " treatment mean_cost std_cost mean_revenue \\\n", "0 none 0.000000 0.000000 6585.891792 \n", "1 tech_support_only 5037.815894 4917.641358 15104.111534 \n", "2 discount_only 5181.570334 3043.343172 12247.935953 \n", @@ -494,7 +494,7 @@ "Y_net = Y - C_obs\n", "\n", "pd.DataFrame({\n", - " 'arm': ARM_LABELS,\n", + " 'treatment': TREATMENT_LABELS,\n", " 'mean_cost': [C_obs[A == a].mean() if (A == a).sum() > 0 else 0.0 for a in range(4)],\n", " 'std_cost': [C_obs[A == a].std() if (A == a).sum() > 0 else 0.0 for a in range(4)],\n", " 'mean_revenue': [Y[A == a].mean() for a in range(4)],\n", @@ -502,6 +502,41 @@ "})" ] }, + { + "cell_type": "markdown", + "id": "pl2exzwbceb", + "metadata": {}, + "source": [ + "### Data Split\n", + "\n", + "Using the same data for policy learning and evaluation leads to overestimation. To prevent this, we split the data into train and test sets. Policies are learned on the train set and evaluated using AIPW scores computed on the test set.\n", + "\n", + "The same split is used for propensity estimation in the positivity check." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "n7ziuiv612q", + "metadata": {}, + "outputs": [], + "source": [ + "idx = np.arange(len(policy_df))\n", + "train_idx, test_idx = train_test_split(\n", + " idx,\n", + " test_size=0.5,\n", + " stratify=A,\n", + " random_state=SEED,\n", + ")\n", + "\n", + "X_train = X[train_idx]\n", + "X_test = X[test_idx]\n", + "A_train = A[train_idx]\n", + "A_test = A[test_idx]\n", + "Y_net_train = Y_net[train_idx]\n", + "Y_net_test = Y_net[test_idx]\n" + ] + }, { "cell_type": "markdown", "id": "024e5803", @@ -509,17 +544,17 @@ "source": [ "### Exploratory Data Analysis and Diagnostics\n", "\n", - "Before entering policy learning, we first examine the data structure across the four treatment arms ($A \\in \\{0,1,2,3\\}$).\n", + "Before entering policy learning, we first examine the data structure across the four treatments ($A \\in \\{0,1,2,3\\}$).\n", "\n", - "1. Sample size per arm: are there enough observations in each treatment?\n", - "2. Mean customer characteristics per arm: which customer segments tend to receive which treatment?\n", - "3. Revenue distribution per arm: how do Revenue scale, variance, and outlier patterns differ across treatments?\n", + "1. Sample size per treatment: are there enough observations in each treatment?\n", + "2. Mean customer characteristics per treatment: which customer segments tend to receive which treatment?\n", + "3. Revenue distribution per treatment: how do Revenue scale, variance, and outlier patterns differ across treatments?\n", "4. Positivity check: are all treatments sufficiently co-observed across the range of customer characteristics?" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "id": "3d0f329e", "metadata": {}, "outputs": [ @@ -548,7 +583,7 @@ " share\n", " \n", " \n", - " arm\n", + " treatment\n", " \n", " \n", " \n", @@ -580,7 +615,7 @@ ], "text/plain": [ " count share\n", - "arm \n", + "treatment \n", "none 517 0.2585\n", "tech_support_only 462 0.2310\n", "discount_only 477 0.2385\n", @@ -592,10 +627,10 @@ } ], "source": [ - "arm_counts = policy_df['arm_name'].value_counts().reindex(ARM_LABELS).rename_axis('arm').to_frame('count')\n", - "arm_counts['share'] = arm_counts['count'] / len(policy_df)\n", + "treatment_counts = policy_df['treatment_name'].value_counts().reindex(TREATMENT_LABELS).rename_axis('treatment').to_frame('count')\n", + "treatment_counts['share'] = treatment_counts['count'] / len(policy_df)\n", "\n", - "display(arm_counts)" + "display(treatment_counts)" ] }, { @@ -610,14 +645,14 @@ } }, "source": [ - "The actual sample sizes are `none` 517, `tech_support_only` 462, `discount_only` 477, and `discount_plus_support` 544. Each arm's share ranges from 23.1% to 27.2%, indicating a reasonably balanced distribution with no arm severely underrepresented.\n", + "The actual sample sizes are `none` 517, `tech_support_only` 462, `discount_only` 477, and `discount_plus_support` 544. Each treatment's share ranges from 23.1% to 27.2%, indicating a reasonably balanced distribution with no treatment severely underrepresented.\n", "\n", - "The sample distribution is therefore stable for policy learning with four treatment arms." + "The sample distribution is therefore stable for policy learning with four treatments." ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "id": "d6488c62", "metadata": {}, "outputs": [ @@ -633,15 +668,15 @@ } ], "source": [ - "arm_covariate_means = (\n", + "treatment_covariate_means = (\n", " policy_df\n", - " .groupby('arm_name')[covariates]\n", + " .groupby('treatment_name')[covariates]\n", " .mean()\n", - " .reindex(ARM_LABELS)\n", + " .reindex(TREATMENT_LABELS)\n", ")\n", "\n", "plt.figure(figsize=(10, 5))\n", - "sns.heatmap(arm_covariate_means.T, annot=True, fmt='.2f', cmap='YlGnBu', cbar_kws={'label': 'Mean'})\n", + "sns.heatmap(treatment_covariate_means.T, annot=True, fmt='.2f', cmap='YlGnBu', cbar_kws={'label': 'Mean'})\n", "plt.xlabel('Treatments')\n", "plt.ylabel('Covariate')\n", "plt.title('Mean customer characteristics by treatments')\n", @@ -660,14 +695,14 @@ } }, "source": [ - "Customer characteristics differ across treatment arms. The mean `Size` ranges from approximately 70,943 in the `none` group to approximately 171,467 in the `discount_plus_support` group, and `IT Spend` also ranges from roughly 18,056 to 41,371. In other words, larger customers tend to receive both technical support and a discount.\n", + "Customer characteristics differ across treatments. The mean `Size` ranges from approximately 70,943 in the `none` group to approximately 171,467 in the `discount_plus_support` group, and `IT Spend` also ranges from roughly 18,056 to 41,371. In other words, larger customers tend to receive both technical support and a discount.\n", "\n", "This indicates that treatment assignment is not independent of customer characteristics. Policy learning and evaluation will therefore use AIPW to correct for these differences." ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "id": "c925a3d8", "metadata": {}, "outputs": [ @@ -692,8 +727,8 @@ " \n", " \n", " \n", - " arm\n", - " arm_name\n", + " treatment\n", + " treatment_name\n", " count\n", " mean\n", " median\n", @@ -752,11 +787,11 @@ "" ], "text/plain": [ - " arm arm_name count mean median \\\n", - "0 0 none 517 6585.891792 6123.187067 \n", - "1 1 tech_support_only 462 15104.111534 14483.719320 \n", - "2 2 discount_only 477 12247.935953 10454.932500 \n", - "3 3 discount_plus_support 544 26784.124649 23560.252890 \n", + " treatment treatment_name count mean median \\\n", + "0 0 none 517 6585.891792 6123.187067 \n", + "1 1 tech_support_only 462 15104.111534 14483.719320 \n", + "2 2 discount_only 477 12247.935953 10454.932500 \n", + "3 3 discount_plus_support 544 26784.124649 23560.252890 \n", "\n", " std min max \n", "0 3363.163462 -616.572451 21445.05937 \n", @@ -770,7 +805,7 @@ }, { "data": { - "image/png": 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mXIdh27Zt8uyzz8oNN9wgCxculAULFkj16tXl1VdflQMHDpi1NXLkSLnjjjvk3Xffldq1a6tgaNCgQXLdddep5yKwmDFjhjFAQcCDoAmvP2LECPX+sPLykCFDZMeOHU446qRhChMRERGRF8nOzpa0tDTVScfIAmAk4cqVKzJz5kzVaYecnBzVMUcn2lS9evXU/drzcGUfawMgeAgMDJS2bduqQGDfvn027xtec9myZWqVcMB+jh07Vo2MYLTh2LFj0rt3bzVSocFoAkY9MPLQpEkT4/19+vRRoyraGgYYTUDQ8Pzzz6v7GjRoIJs3b1b7ifu//PJLOXz4sHz22WfGdtq3b68CIgQzH3zwgc3vhyxjAEFERETkRTB6gE46nD9/Xk6ePCmnTp2SH3/80RhgQHR0dKHgQeuwazAqUbZsWTUigOBBg+di1W9bITjRggeoVKmSus3IyFC3GA3QAgvs97///it//vmn2X5b2s+YmBh1axpgYL9B20+kKlWoUEG9FwQymttvv11ef/11SUlJUStRk+MYQBARERF5mV9++UVee+01OXHihISHh6ur8dpKw1oKE+63xLSDr9Ge66jQ0FCz3/3987Pl8/Ly1G1iYqJKRUIdBBY2q1mzprRo0cJsv6+1nwXbL1j/cPHiRRVAWIKRmYoVK9rxrsgjAwicVMhTwxAaoshbbrlFFcAgJ86Sy5cvq4Kfn3/+WZ183bt3V7mApifVpk2bVF7d2bNnpU6dOmr4rHXr1ja1odm7d68MGDDAWJikQa4h8giRs4c/EOw3ipKqcGEzIiIiKiG4ao86gs6dO6v6APSX0JdBITICC082atQoFfSg+BkjDBhNwegE0o4cFRkZKbVq1VKpWJb6muyf+VgRNQphVq9eLVOnTpVPPvlEfcgY4io4lKUZPny4nD59Wp188+bNk59++kkmT55sfByFMqNHj1aV/1988YUKHFBkdPz4cavbMA0ennnmGWPkbBqAIC8PQ2EoMlq6dKmKqrHfts5aQERERGStv/76S/U10LepUaOGCh5ACx4KXsn3JOhXYeYo1DwgeABczIWCfS1boZ4DheBId8IMTNrP9u3bVcqXVkROPhBAIEjA7AHo0GOKLgzBzZ07V+Lj41VhTEH79++XXbt2qanGMESF4ACFQCicQR4goDOPqByV+nXr1lWjD9h25cqVVreB3DlU9qOav2rVqoX2A0Nv6enpKqfu+uuvV4VBGI1AkGJP0RERERGRNbR6BfQ70DlG7QNmOsIMR4D+iadCsfOGDRtUnwtF04sWLVLZGwiCtDoJe913331qlAEXeHEBGReUUTyNC8VIXdLpdE57H6Wd2wMIVMujkMY0vSgqKkoaNWoku3fvLrQ9piRDgQwCA9OIEyceolpEr+jAm7YHiHS19oprQ/vjw/bvvfeeSl8qCO1j5MS0GEfL80ORDhEREVFJQN0A1oDARc+nn35apX0DMiLQl0E/x1NhligUQiPrBGlYmHJ1ypQpauYnR/cbdRxI48L6FgiunnjiCXUxGlPY4mIy+VANBEYaIDY21ux+RIraY6bwx1JwWwyBYbYADFuh847Of8FZB0zbK64NLYhZt26d+rd2a6patWrqxxQWTkFAgVoIIiIiopKCxdfwY+nC7LUcOXKk0H1bt24tdJ89a0BYeg4u4Jq+JrI6Fi9eXGi7nj17FvkcQJ/L0r4XvA/pSyguL0hbh4J8JIDQhqu0PDhNcHCwJCcnW9y+4Lba9sgHxEqKRbWn1SYU14Y98EezatUqNSdzuXLl7GoDOYueMOyofSaODiVaQ/u8srL14mfjscdztDb8PeC4lXauPG/Id/C8IVvg/9Fafj9u2SksedYeZ9QXaLUYnojnjajPEZk6+N61VG+CY2TtZ+j2AEJLAUIthGk6EL4kLM2IhG0sFVdjewxdIQjQ2iv4uNZecW3YAgcbuXXI4cMwIhYrsRcWcSk405M7YU7pkpYXlz8qFB8XJ37ZtgUQhoREdYt5pP0z2WktTecN+R6eN2Qtba0CTljiGl999ZXFSWYKQhaGNh2rJyvN501WVpaq8cUsWEWxdIHdIwMILZXowoULaiYBDX6vX79+oe2RmoQCZlMIBjD3L9KUkIaEIADPN4XftcVMimvDlg4/lo7/+uuv1e2jjz4qjkBxDxZgcTdEpvifOaZCu9Z8y86QHhIqGHysHBsrIbGFF7u5lsygYDkropa3D6tdq8T2kTzvvCHfwfOGbO0A/ffff+rfuGDoyVe8fQVmTEJdanHw/2JPXqQNF3xx/pT28yYwMFD1t7UL7qawSrjV7YibYdYlLBSCSnwtgEAdw8GDBy0WL6O+APP7YgpWFBEBZlQCFM3gpGjWrJm6r2/fvsbnoX0tMi6uDWth3Yjvv/9eFTJhHQlHYd+dtZCLM6ATWNL7k3f1yyY4SCchFk7mazEE5c+mgC8sTzpupZ0rzhvyPTxvyBqYrETr/OGW03KWPNQUaKtAezMtDas0nzcBAQHqbwjft5aCPVsCK7cHEBgqQaCADj1qB1Bcg8p5jBIg6sUHjvUVsDgI3iwq9xEgjBgxQg2poWYAsw/06tXLOMKA6bswNzIi5vbt28vatWtVatD06dPV49a0URwUVm/cuFEFEZjBCSsfarR9JSIiIiLyNW6fxhWwBsT999+vCpAfeughFSFhwQ+k9GBWJEzthc66Fh1h1WpU42ONhhdeeEEFCab5edgeFfgff/yx9O7dW80DjIp/bdpWa9ooDtKWAOtA4PVMf7R9JSIiIiLyNW4fgQAEDFg5Gj8FWZq2C0Np8+fPv2abGE3AT1GsacN0YRL8mMLid0REREREpY1HjEAQEREREZF3YABBRERERETelcJERERE5IgLl9MlJa3wGk+uEBUeJBXLcvY5Kj0YQBAREZHXBw/PzNoqWfriV0wuCcG6AFk49g6nBBFYA2vGjBlqvYsvvvhCtm7dKt5i7969ar0FVy4oN27cOHWsPvzwQ5e9JjGAICIiIi+HkQcEDw90uk4quHgk4OLldPlsy1G1D84chXjsscfk4YcfFm/Sv39/Ffx4w4rU5BiOQBAREZFPQPBQtUKE+ILw8HD1Q+SJWERNRERE5Abx8fHy9NNPS9OmTdV6VBs2bDA+tmDBArnjjjuMv69fv166d+8ujRs3lnbt2qnFcbOz/7/m448//pBHH31UtXXbbbfJK6+8IhkZGeoxLMq7YsUKueuuu9TzcYu1sjQ7d+5UqVNnz54t8r6BAweqRX9feuklNcKABXlffPFFuXLlinoc28L48eNVWpG18L569uwpN910k3q/CxcuNK4ajddGu99995307dtXbrzxRrXNp59+arGtZ599VgYNGmR234kTJ1QbR48etXqfqHgMIIiIiIhcLCcnR4YMGSKXL1+WVatWybx589QiupYcPnxYLbY7bNgw1ZnGYrlffvmlvPfee+rxM2fOqIVxK1asqDrXCD62b98uU6ZMUY/PnDlTdcyfe+45FaQgNQoBCIIKW2D78uXLy5o1a2T27NmyZcsWYxu//vqrukWAMWHCBKvbe/nll+XBBx+Ur776Sp5//nl1DLC/ppAW9dRTT8mmTZukY8eOauFfvOeCsGbXrl271CLEpgEKgqbrrrvOpvdK18YUJiIiIiIX++2339RV8e+//15q1Khh7ChbWgQXV+L9/PykatWqUqVKFfWDjnZERH661meffSbR0dEqsAgMzO/aTZs2Tfbv369GCDDagFGBe+65Rz1Wq1Yt1eaSJUtU4GGtevXqyciRI41ttGnTRr0GVKhQQd1GRkaqn+Kg2Hrp0qUyYMAAY60H2kxKSlLByfDhw43bYmSlU6dO6t8jRoyQjz76SA4cOCDVq1c3a7NDhw4qwEEw8uSTT0peXp4KtIYOHWr1eyTrcASCiIiIyMX++ecfKVOmjDF4gIYNG0pISEihbZGyhNSk+++/X3WkJ02aJImJiarDrbV1ww03GIMHuPXWW1V6FFJ49Hq9NG/e3KzNli1bSkJCgvqxVp06dcx+R6BgmkZlC+z/pUuXLO4X9hf7ralbt67ZawK2KQjvH+lQCBpgx44d6nV69Ohh1z5S0RhAEBEREbkYRhRwhbwg0yBAExwcLB988IGa1hXpPqdOnVIpPUgXKuo5plf6LdFeu6jnanUIpoKCgsRZbNkvS69b1PP79Okjx48fl7/++kuNRCDgQqBGzsUAgoiIiMjFMNqQmppqVtyLwEArSjb1008/ydtvvy2NGjVS6TgIJpDis3HjRmNq0cGDB806/UiNQsExrt7rdDq1RoOpPXv2qLQjdK7xOJi+NvalJCHVCD+W9gv7YzoyYwu8X4zWoF4CNRqoiyDnYwBBRERE5GKtWrWSJk2ayJgxY+T333+XP//8U/3b379w1wwd6nfeeUcVHaN4GFfXt23bpjrK2voLKMbGzEu4+r579255/fXXVRoT6iQwajF//nz5+uuv5fTp06qGYPXq1WqtCYyEXH/99RIWFqZqIv7991/55Zdf5P3337f5PaENvD72xRqPP/64KiDHvmC/UOCNQAn7a00dRVEwCoF2kQ6GOg1yPhZRExERkU/Aom7e8poIFN59911V7IyOPDq7KPzFqsoFYVpWzJq0fPlymTt3rtoWBcPadKmVKlVSj6H4GEXYGFXo1q2bseAZU6uWLVtWTcOKugPUTqCO4oEHHlCPI8jAc/E4ntegQQMZO3asmhbVFngfmBkKQcTixYut2h7pSStXrlQF4JUrV5YnnnhCBRaOuPvuu2Xq1KnqWAQEBDjUFlnmZygqiYxcClceAFONuVt6erocOnRIDa/iakJJunL8hBwYOVrqPvOkhFapYtNzM86dk+ML35Umc2ZLRF3zwi7y7fOGfAfPG7JFZmam6pzGxsaqTrLWObxwOV2embVVrUbtDsG6AFk49g6nrkRN9sMoTZcuXVQak1ZojvQunD8IvkprUJGZmSknT56U2rVrWyzWt6UvyhEIIiIi8mrouKMDn5Jm34xAjooKD2Lw4AGw/gMW1ENKFGau0oIHcj4GEEREROQTQQRHADwDiruLW0xu8ODBZms9OANqL5DWhcABtRRUchhAEBEREZHToD4DK0BfS1RUlNOPOGap0ha2o5LFAIKIiIiInCY8PFz9kO/iNK5ERERERGQ1BhBERERERGQ1BhBERERERGQ1BhBERERERGQ1BhBERERERGQ1zsJEREREXi8n+aLkpqe65bUDwiIlsEwFt7w2kTswgCAiIiKvDx7OLB4uhhz3rETtFxgk1Z+a75Qgon79+jJjxgz577//5IsvvpCtW7eKt9i7d68YDAZp0aKFU9o7e/asdOrUST744ANp1aqVU9r0ZefOnVPrYHTv3r3EX4sBBBEREXk1jDwgeIhu00cCo8q79LVzUi5J0va1ah+cOQrx2GOPycMPPyzepH///ir4cVYAQbYZO3asVK1alQEEERERkbUQPOhiqvjEAeNibOTJWERNRERE5Abx8fHy9NNPS9OmTaV9+/ayYcMG42MLFiyQO+64w/j7+vXr1ZXlxo0bS7t27WT69OmSnf3/KVt//PGHPProo6qt2267TV555RXJyMhQj+Xm5sqKFSvkrrvuUs/H7ccff2x87s6dO1XqFFKGirpv4MCB8sYbb8hLL72kRhiaNWsmL774oly5ckU9jm1h/PjxMm7cOKveP7YbOXKkvPrqq6q91q1by8yZM83eV8HtsR/Xuq+441QcHEeMpOA43nLLLTJs2DCVGgQ4FnifO3fuNG5f8L7i3pO2PfazR48ectNNN8kDDzyg0r9M4fGePXuqx3EeLFy4UH2Opm28++670qZNG5Xm1bt3b9m1a5dKezM9b0oKAwgiIiIiF8vJyZEhQ4bI5cuXZdWqVTJv3jxZtmyZxW0PHz4sEydOVJ3Z7777Tl577TX58ssv5b333lOPnzlzRh555BGpWLGifPrppyr42L59u0yZMkU9jg4sOqDPPfecClKQGoWONYIKW2D78uXLy5o1a2T27NmyZcsWYxu//vqrukWAMWHCBKvb3Lx5s1y4cEE++eQTmTZtmuo4Y9/sUdxxKg466E8++aQKHL766iv13hA84D3Zwpr3hM/kqaeeUh3+OnXqqJQ1fI6A13355ZflwQcfVPvx/PPPq3MDzzGF565cuVLeeustef/991XQc/fdd6vPp6SxBoKIiIjIxX777Tc5evSofP/991KjRg11H+oHevXqVWhbXHH28/NT+e1VqlRRP+hQRkREqMc/++wziY6OVh3mwMD8rh06riioxQgBRhtwZfyee+5Rj9WqVUu1uWTJEhV4WKtevXrq6rrWBq5+4zWgQoX8+o/IyEj1Y62oqCgVjISGhsr111+vOt7obI8ePVpsVdxxKg6OFQI6BGJoo3r16qpznpCQYNN+RFnxnoYOHapGIGDq1KmyY8cO9Tni+C5dulQGDBhgrIHBsU5KSlJtDh8+3NgGRkrwmWh0Op2EhIRIuXLlpKRxBIKIiIjIxf755x8pU6aMMXiAhg0bqg5gQUjFwdXl+++/X6WrTJo0SRITE1XHUmvrhhtuMAYPcOutt6r0qBMnToher5fmzZubtdmyZUvVMbalc4wr5aYQKNiSHmQJUnTQ0dbgfWJ/T548aXNbxR2n4uDzwKgQOvRIPcKV/927dxvTs5z5nlqZzCqFjv+NN96oPkfs76VLlyx+XmgDn6emZs2a4i4MIIiIiIhcDFfK8/LyCt1vGgRogoOD1VSmSFlBWsupU6dU+ouWWmPpORpMq2qJ9tpFPVfLtzcVFBQkzobOs6X9CggIsDoVzNrjZI1Ro0apqXNfeOEFdewQTPTp06fIQCnXwnGy5j0VPO5ox9/f36bPy1KwWWoCCByQ+fPnq6jx5ptvlieeeMKYA2YJhpZQtIP8NERjyO/TioQ0mzZtkm7duqkIEEOBGCa0tQ0NilpwRcCe/SDXSD9zVq4cP2HzT8bVoigiIiJXQ98iNTVVpTFp0OHVipJN/fTTT/L2229Lo0aNVOoLOslIZdm4caN6HGksBw8eNOvMIjUKxbR169ZVHdqCRbp79uxRaUe46q51eE1fG/viCn///bfZfiMlClfva9euXWhb7GfB43P69Gmrj1NxcHUfxecxMTHy0EMPqf4p6ieOHz+u6iusPU5/W/Ge/vzzT+O/EZzgORhFQo0Jfix9Xnh90xErd3J7DQSKelavXq0KQypXrqzyuzB8hCIfS5EuTgR01FFgkpKSogp10tPTZdasWepx5JAhx2zMmDEqNw+FJDiJUMCCPyJr2tDgw3vmmWcsXiGwtg0qOf5Bwer26Nx5drfRbNECCa3iG1P+ERGR90AKS5MmTVR/BZ1WXJ3G1W5chS4IHcd33nlH5fIjNSc5OVm2bdumUmO0XHh0ltHO4MGDVRrM66+/rtKY8BxcjUdnGHUSmJ0IBc/oeyHfHiMhyNMPCwtTNRG48o5OOYpybYU20NnGRdayZcta9RwsmIeLsKjFwHOxn8j/N00B0uBCM/p1KCzGe8ct0n5wwdia41Qc7PM333wjmZmZqu+IzwKjGQiykL6FqXVRG7Fy5UpjXQKK33EMbX1PqK1AoFCtWjVZvHix6lNiNiZ4/PHHZe7cuaoGA31ZzAyFwAifI9LG8L4swf7htTG7F/rUPhtAIOJavny5Gi7q2LGjug8HDKMRqGDXiktMIzhMUYVIUgsGME0WAg78EVSqVEkVnnTu3FkGDRpkXFQDz8OHjW2taQPDYQhkPvroI/VHhRPE1v2gkhdcPkaue2G45GVn2fzcrAuX5OyatZKbkVki+0ZERK6HRd285TXROcU0nCh2xgw8SEfBDEDoABaEaVlRhIs+E/pJ2LZDhw7G6VLR78Bj6Lsg8wIdXmRiaAXPmFoVnWNMw4r8enR+UR+gdVjR4cZz8Tie16BBA9V/evbZZ216T3gf2hV7dIqtgaAAxwJ1C+gco/+G2g1LMK3poUOH1DFDXw0zDqGTrhVyF3ecioNjhH7km2++qY4NRhGwfwimtEJsBGavvfaa3HvvvaoGAccWwYat7wkjHLjojFmeEEh++OGHqnhbO464iI6+K14LwQAydBBYXEu/fv3U54bjhOwba9PA7OFnKCrZygUQUfXt21e+/fZbs2EdHFR03LXpxzT4UHEwtanCtCAEBx4fdteuXVXRCU4URGkanEQISJDaVFwb+MPBiALmUsZVAXywODmOHDli9X6gDVtpQ1m4MuBuGEnBHyiGV3E1oSQhlejAyNFS95knXToSgPSl4wvflSZzZktEXfOiMPL884Z8B88bsgWuDKNzGhsbqzrJWgcpJ/minFk8XK1G7Q5+gUFS/an5Tl2JujRAfw0BEzrPJQ3BAM4fBBUl2bEeV8x7wkxRGB3BiJFpIbUr4P2jkBt9bkv1E7b0Rd06AoEhFsAXgSlEYNpjps6fP19oW0RoGJKLi4tTHX/8z6jgsI1pe8W1oU2/tW7dOvVv7daW/SAiIiLXQccdHfjc9FS3HPaAsEgGD1SquDWA0IqOC9Y6oIreUn4XtrdUF4Hts7KyVGRVVHt43Jo2rN1vR9uwBINBCIDcTftcXFEUrn1mWdl68XPg2NkKr6e9vr8HHHNf4MrzhnwHzxuyBf4fqyVO4Na0UNUvopwERpT8/PdFsTQbT2mFjA8s6HYtyPTAZ1jwcywpqC9BPey1YAE+pEE5wlDMe9LqanHr6nMGr4fXxfeupfpe7HfBeg6PDCC04ROk/5gOpeALwlLxDLaxNI0WtkfKBDrwWnsFH9faK64Na/fb0TYswfy+SAHxFK6YgSEvLn9kKD4uTvzsqGWwlyEhUd1iKM8/kx1eZ3LVzB3kW3jekLW0aSwduWBHJQupOVi87lqQ7YHMDdOLiSUJdaqm6e2WYFYqR/dl0qRJ6raodjDD0759+665TUnB3wxqR0zXkrB3ql63BhBaGhBW6DOdlgq/W1q0A6lJP/zwg9l96MijyBlpSjgR0YHH803hd62wubg2rOGMNizB7AGmKwq6CyJT/M8cRVaWAjlnSg8JFVSXVI6NlZDYkp0xwFRmULCcFVF5gGG1rVtghjznvCHfwfOGbO0AaUXGuGho7dVSci1XrYZsC/TdUCRd2s+bwMBA1efWLrqbOnbsmPXtiBuhyh9V7Tt37jQGEKhjwFzGmO6qIKy5gBkCML2YtvoeZkMCFE/jhGjWrJm6D8XZGrTfokULq9qwhjPasAT770nFp+gElvT+5F0deQoO0kmIhZO5pBiCdMYvOU865r7AFecN+R6eN2QNzGyjdf5wW5LFsORbtHSh0nzeBAQEqL8hfN9aKqK2JbBy60JyGCZBoIDO+JYtW9QiHSNGjFBRYpcuXdSHffHiReMQD2Y5QoCAbTCDE9Z8wFARpizTRhgw/zHm8MWUW5ipAdNtIS0I03xZ20ZxnNEGEREREZE3cvtK1FiQDfPkotgG07ciOlq2bJlK58GMRm3btjWuIIjICAtpYNENBARY7KR9+/YyefJkY3vYHnPmIveud+/eqnOPuYi19RqsaaM4zmiDiIiIiMgbuX0lagQMWDkaPwWhg266/oJWfIIV/a4FIwH4KYo1bWjuu+8+9eNIG0REREREvsLtIxBEREREROQ93D4CQUREROSoS2mJkpJ1xS0HMio4QsqHe9asQ0QliQEEEREReX3w8MKmKZKdW3iNJlcICgiSt+5+xaYg4ty5c7J//37p3r27w68/cOBAqVq1qsycOVNKo8uXL6vp9U1n4CxpO3fulEGDBqlJgJByX9owgCAiIiKvhpEHBA+9G3aVCi4eCbiYlihfHPpW7YMtAcTYsWNVp98ZAURphxk3z54969IAorRjAEFEREQ+AcFDbCSnUy9tDAaDu3eh1GERNREREZELIeUIC9B+8cUXcscdd0h2drbMnj1b2rVrJ02bNpUHHnhAfv31V7PnYN2pRx99VD1+2223ySuvvKJWctekpaXJ+PHj1cK5WNR23Lhxkp6ebvU+nTp1Sh5//HH1XLwG/m06E2b9+vVl3bp1Zs8xvW/BggVqOv533nlHWrVqpfYD+3PlyhWz7T/66CP1/ho3biz33HOPSgEytW3bNvU49gFT88+YMcO4HpjWBmbBvP3229XjL774ojqOOJ54zFpYK+ypp55S+4r3jGUFtFXOtc8I65S99NJL6r1g/S+8lun70SB9Cosjmz4fHnzwQZk1a5b4IgYQRERERC6EzjY6yHfffbesWbNGdbS3b9+uOqzoDON+dG7RmYYzZ86odacqVqwon376qXo+tp8yZYqxzc2bN6vH0aFHSg/W0Fq6dKnV+zRy5Ei1GO7atWvl888/VysWP/fccza9rz///FMFPsuXL1eBxO7du9VaWabwHu+991758ssvpUOHDuo19u3bpx77/vvv5emnn5aOHTuq94H3h/eBfTO1evVqFURgTS6swYXjheNZMOgqCjr66NxjQeOVK1eq/cXCxVjc2DRAWLFihZQvX159RgjwEOzgvoKwv+XKlVPvSXPy5En5/fffpU+fPuKLmMJERERE5ELR0dFqwdyQkBBJTU2Vr7/+WtavXy8NGzZUjw8ePFgOHz6sFtZF5/Szzz5Tz8FCuYGB+V23adOmqSJszU033SQjRoxQ/65Ro4a0adNG/vrrL6v36d9//1UjG6jLwL7htU6cOCF5eXkqmLB2od233npLBSIwadIkeeKJJ1Q7derUUfdhba2HH35Y/XvUqFFq5GDVqlXqCv+SJUvkzjvvlGeeeUY9Xrt2bZWe9Oyzz8qxY8ekXr166n4EIBjB0OA4Yp8rVKhg1X4iAAkLC1PBDIIIQEDSqVMnFQRo+4fX04KXWrVqqWNqesw1+Ey0oEjbd3ye2Edtn30NRyCIiIiI3OTgwYPqtn///uoquvbzzTffqDQb+Oeff+SGG24wBg9w6623qqv1GnRwTZUpU8Ys9ac4CD7ef/99ldKD0Q+MaCAtx9rgQdsHLXgABAXa/mvQvim8V+1x3GrP0bRs2bJQGzVr1hRHoK0bb7zRGDwAgg8ELKavowU9msjISJVuZglGGpAGduDAARX0fPXVVxYXIvYVHIEgIiIicnMBMGoDwsPDzR7TOu+mgUNRAgICHNoPXHXv2rWr/PTTT/Lbb7+pK/KLFi1SV9KRxlNQTk5OofswCmAqNze30L4VfC/YRnufloqhMQJS8HkYcSiJomu8lul7MA0wilOvXj1p0qSJChwQuF26dEl69OghvoojEERERERuct1116lb5ODjyrr2gxoArUAZnVOMVGgdcq1eAAXYWVlZDu9DQkKCvPrqq6LX69VVc+T7oyOMfUKKEaBjbVofcPr06ULtIO8fKVkaLd2nUaNGZnUSprANRlcARdBaPYRmz5496rZu3brXTJ2yBV4H+2E6moAOP97TtV6nOH369FEF1d9++6107txZoqKixFcxgCAiIiJyMYw2oJgXaTGYUQizKm3dulUVTKP4+d1331W1DFp6ExZLwzZIa0JxMgqlkcYUHBzs8L4g3QkF2xMnTpRDhw6pffjkk09U0IBUH7j55ptVcTUeRzCD4uWCV+gx69OYMWNUGtD//vc/FZR069ZN1VVoULS8YcMGFWxghiLM9IQCcRgyZIhKnVq4cKF6/Mcff5SpU6eq43Otjj3qGS5cuKD22xqYLQqzVo0ePVrVmmCGq+eff17Kli3r0Loc3bt3l+TkZBX49e7dW3wZU5iIiIjIJ2BRN295zX79+qnF5Hr27Kk67yg+RtExOqAIHKZPn27shKKuADMFYWSgV69eqsOPjnnB2YnshfQgBC3o0GOqWEwPi4JuFDVrQQwCBvxgilXM9oQOd3x8vFk7sbGx6nlIh0LaEqZpRaF0wfeNmYwQZKDGAoXiuIW77rpL5syZo1KnEERgZiOkAWGK1WvBMcGIDLZFAGJah2EJVo5G4TaOpzYbEwqk8bsjowYRERFq5AGjNmjPl/kZuPqGR9CG9ExnFXAXXEHAFQZ8CSCqL0lXjp+QAyNHS91nnpTQKlXEVTLOnZPjC9+VJnNmS0Rd8yIp8vzzhnwHzxuyBXLLcQUeHVV0orXc+ktpifLCpilqNWp3CAoIkrfufsWmlah9DaaWxRS0GEW5VuoQ1nVwR3Ex0r9w/qB+wtF6kWvB+hEoBNdmxPIkeP8Y2UGxuKU6Elv6ohyBICIiIq+Gjjs68ClZhRf5coWo4IhSHTyQqNoHXETD2g9IL/N1DCCIiIjI66EDz058YUhNQjrQtWC15b59+4ovwKrRpsXmGi3hBjNKobPvbO+99566uo+aDYyQ+ToGEEREREQ+CjULXbp0ueY2MTExDr/OsGHD1M+1oGC6pKGA2VJ2PoIKzFhVcKpcZ/nkk0+kNGEAQUREROSjUCuCn9JCK/q+Vg0EOY7TuBIRERERkdUYQBARERERkdUYQBARERERkdUYQBARERERkdUYQBARERERkdU4CxMRERF5vayLF0WfkuqW19ZFRUpwhQpueW0id2AAQURERF4fPOx7drjkZWW75fX9g4Ok2TvzGURQqcEAgoiIiLwaRh4QPFS7v48EVyzv0tfOunBJzq5Zq/aBoxBUWjCAICIiIp+A4CG0ShXxBvXr15fp06fL119/Lfv27ZOoqCjp16+fPPfcc8Zttm3bJgsXLpSjR4+qFZS7d+8uI0aMMC6GZk0bP/74oyxYsECOHTsmlSpVUm0888wzEhQU5Jb3Tb6BRdREREREbjBr1izp3bu3fPPNNzJgwADV0d+9e7d67Pvvv5enn35aOnbsKOvWrZMpU6bIxo0bZeTIkVa38fPPP8sLL7wgDzzwgAoyXnnlFdm0aZOMHj2anzc5hAEEERERkRv06tVL7r33Xqlevbo89dRTagQBIwmwZMkSufPOO9VoQe3ataVTp04qANiyZYsaTbCmjcWLF6vgAaMSNWrUkLZt26pA5Ntvv5WzZ8/yMye7MYWJiIiIyA3q1q1r9ntkZKTo9Xr173/++UelG5lq2bKl8bF69eoV28bBgwfljz/+kDVr1hgfNxgM6vb48eNSrVq1Enlf5PsYQBARERG5gaU6BK2Dr92aysvLU7eBgYFWtYHthwwZolKcCqrAaWfJAUxhIiIiIvIwKJDWUpE0e/bssTjqUJTrrrtOTp48KTVr1jT+xMfHy+uvvy5paWklst9UOjCAICIiIvIwGDnYvHmzmoUJQQBmU5o6darcfvvtVgcQTzzxhHz33Xfy9ttvqzZ+++03GT9+vKSmpnIEgrw7hQnDazixP//8c3VC33LLLTJp0iRVDGTJ5cuXZdq0aWpmAT8/P5UfOGbMGAkNDTVugxkGMAsBCoTq1KkjY8eOldatWzu1jYSEBHnttddk+/btaqjwtttuk3Hjxqkp0oiIiMj1sCaDr7zmXXfdJXPmzJFFixapIKJcuXLSo0cPGT58uNVtdO3aVebOnSvvvvuuKqiOjo6WO+64Q0aNGlUi+0ylh9sDCPxRrF69WmbOnCmVK1eW2bNnq6h7w4YNFvP68IeTkZEhK1askJSUFJkwYYKkp6eracxgx44danoyBARt2rRRhUNDhw6V9evXGyN2Z7SBadFycnLk/fffVwEEZjV49tlnzQqVyPOln7F/FoqA0BCvmW+ciMiX6aIi1WrQWNDNHfDa2AdbHDlypNB9W7duNfu9W7du6seRNu6++271Q+QzAUR2drYsX75cRcKY5xgQKbdr104N2yHSNrV//37ZtWuXmgdZ68i/+uqrKuDAvMi4+r906VLp3LmzDBo0SD2OkQM8b+XKlWpbZ7SBoANt4KpAw4YN1TYIMDDVWlJSkorwybP5BwWr26Nz5znUTrNFCxhEEBG5GVaAbvbOfLUatDsgeOAq1FSauDWAOHz4sCriMU0NwvzFjRo1UougFAwgUDyEWQNMc/8wpRnSkPbu3auG6lBwhFQiU61atVIBibPawAqQWBESIxLalGpffvmlmqcZ+0+eL7h8jFz3wnDJy86ye8gaV7pyMzKdvm9ERGQ7dODZiScqBQEEZgKA2NhYs/srVqxofMzU+fPnC22LNCdc8Y+Li1MjA0hFQipUUe05ow1sj5Qr1Gq0aNFCBR94fNWqVeLvb39dOlKh8NruhvQu09uSlJmZ3wHPytaLX5Z9nXm7RUaIn0TY99xsvXH//T3gM/MErjxvyHfwvCFbZGVlmU1zmpubywNIVuF5I+rvBbXH+N7VpgQueIzQp/X4AEL7H0fBWofg4GBJTk62uL2lughsjy8VrTNqqT087qw2cIAPHTokTZs2ValP+ECQeoUUpo8//lgiIuzrlGLhF7TrKU6dOlXir5EXlx+UxcfFiZ+dowHuYEhIVLeY1cI/kx1mV5835Ht43pC1tDUQtP8nE9miNJ83WVlZqn73xIkTRW5jqY/scQEEUoG0Wgjt39obNJ0RyXR7bFsQtg8LC1OdfK29go9r7TmjDczQhNEGTKmmBQuY3QBTq6GI+tFHH7XjaIjodDrjypLuhCAL/zOvVauWxc/BmdJDQgUlYJVjYyUk1nzUx5NlBgULyq+RthZWu5a7d8cjuPK8Id/B84Zsgf8X//fff+rf+P+1tVdLiXDxF+dPaT9vAgMDpUaNGsb+rqljx45Z3464kZZKdOHCBfVmNPgdC6gUhLSiH374wew+dPRRuIwUIqQhIQjA803hd216VWe0gToKdBxNRxrKlCmj7jt9+rTdxwMnNF7bU6ATWNL7k3c1cAwO0kmIhZPZUxmCdHI5MkDO5yZLaKbtU/iF6kIkNrKi+CJXnDfke3jekDWQJoz/V2qpFgEBATxwZBUt3a00nzf+/v7qB9+3phfuNbYEVm4NIBo0aKA64Tt37jQGEKhBOHjwoAwYMKDQ9lgj4o033lCddKymCJgNCZo3b67eeLNmzdR9ffv2NT4P7aNWwVltIAj55ptvjJEsoHYBa0b07NmzxI4XeY4LuVfkg3tiRA59IGJn1tm8blN8NoggIioJGKkv7WkoRPbS6my1vyNHuDWAQJ4VAgV06LFAStWqVdU6EOigd+nSRUWLiYmJEhkZqSKlJk2aqM79iBEjZPLkyepAoJC5V69extGBwYMHqylVMZNT+/btZe3ataquYPr06epxZ7SBbZctW6bWgnj++efVfW+99ZYKJu677z63HU9ynSxDjrrtXrmVVK1m3YqgmotpifLFoW8lQ88ZnIiIbIErx+gTXLp0SXWCcBGyNKejkPXQp9QCz9I2AmG4OkkPsmmQaeOM9+/2heSwqBsKOiZOnKgKmDFCgM45vhhwRb9Tp04yY8YM1THHlwRWrcaibY888ojqsGPaVSzLrmnbtq1aIRoL1KGwGTUFqE/Qpm11RhtIdcLidwh20AaGgzA6gfvwxUalR0xQlMRGcvVxIiKXfe/GxMjFixdVZwiBBJE1MOsQ+puoAXBkxkxvFh0dXWiWUZcGEJgKVbta7yhEQVj1GT8FVatWrdAqi/jimD9//jXbxAgBforijDYQTCCoICIiItfRRhyQ+uyMVAwqPRM2YPYhnDelcaIPnU7n1JEXuwIIzDZ02223qVEBrNhs7ZRPRERERM6AzpClQlAiS7R1D5B5wvPGTQEEUoqw8vKoUaNU/mH37t1VMNG4cWMn7BJ5m4xz5+xekTn9DCZDJVvEpV5wqH7Cl2eAIiIiIg8NIO699171g1SmL774QgUTWEANtQIIJDATUfny5Z2/t+SRwcO+p4c53I5/kPdM4eru4OH5ja843A5ngCIiIiK3FFGjDuKpp55SP3///bfMnDlTFRbPmTNHpTlhlWbMekS+Sxt5qHZ/HwmuWN7u4CG4fIyT98w3aSMPvRt2lQrh5Wx+PmeAIiIiIrfPwoRF1TAC8f3336s1HNq0aSMdO3aUbdu2yUMPPSRjxoyxe2Vm8h4IHkKrVHH3bniV/1Li7X4OggfO/kREREReE0BgETYEDV999ZVaUh7rNwwcOFClL2mrS2N9B9RILFq0iAEEkYmggPxJBxbsfN/hNoiIiIi8IoC46667VBU7ZmCaOnWqtG7d2uJ2derUkVOnTjm6j0Q+JSasrDzX6lHJzs22O3hAG0REREReE0C8/PLLqlC6uEXTnnnmGfVDROYYABAREZG3smspvu+++06tAGnJ4cOH5Z577nF0v4iIiIiIyJtHIFAsbTAY1L937dolu3fvlsTExELb/fjjj3LmzBnn7iVREeJTsiVDn39e2ipU5yeVo1hLQERERFQiAcTnn3+uCqexhDx+pkyZUmgbLcDo0aOHTTtBZG/wMGqdY8HqG/dVZxBBREREVBIBxMSJE6VPnz4qSHjkkUdk0qRJauE4U/7+/hIVFSXXXXedLftAZBdt5KFn42gpH2FbOc+lKzny1Z9Jdo9eEBEREZVWVve6UDDdsmVL9e8PPvhAbrjhBgkPDy/JfSOyCoIHpiIREREReVgAsX79eunQoYOULVtWzp07p36upVevXs7YPyIiIiIi8sYAYty4cfLZZ5+pAAL/vhbUSDCAICIiIiIqxQHEli1bpEKFCsZ/E/mCc0n2LeYWl+oneVlhTt8fIiIiIp8JIKpWrWrx35qcnBy5cuWKREdHO2/viEpIUED+EiiLfrlgbwsi0l6SY3Odul9EREREPrkSNYKFxYsXS82aNdWicTt37pThw4dLSkqKKrSeP3++lClTxvl7S+Qk5cID5am2FSU7N8+u5/9zLl5+PR0o2Tn8SIiIiKh0sWslagQIixYtUgEDTJs2TY08jB8/Xv7991958803nb2fRCUSRGD2Jnt+yoRw+lciIiIqnewKIL755hsZOXKkPPzww3L8+HE5evSoPP300zJo0CAZMWKEbN261fl7SkRERERE3hlAXLhwQZo0aaL+vW3bNrWAXPv27dXvlStXltTUVOfuJREREREReW8AUbFiRTl79qz6N0YbGjZsKOXKlVO/79+/XwURRERERETke+wKIHr06CEzZsyQxx9/XPbu3St9+vRR90+fPl0WLFigCquJiIiIiMj32DUL0wsvvCBhYWGye/duefHFF6V///7q/j///FMee+wxVQ9BRERERES+x64AAitNP/nkk+rH1CeffOKs/SIiIiIiIl8JIACF0jt27JD09HQxGApPadmrVy9H942IiIiIiHwhgPjll1/UwnEZGRlFjlAwgCAiIiIi8j12BRBYKK5OnTpq4bhKlSqpaVyJiIiIiMj32RVAYPG4hQsXSosWLZy/R0RERERE5LHsGjqoUqWKXLlyxfl7Q0REREREvhdAYPald955x7iYHBERERERlQ52pTBt2LBBzp8/L3feeadagTokJKRQEfUPP/zgrH0kIiIiIiJvDiAqV66sfoiIiIiIqHSxK4CYMWOG03YgLy9P3n77bfn888/V2hK33HKLTJo0SapXr25x+8uXL8u0adPk559/ViMd3bt3lzFjxkhoaKhxm02bNsmCBQtUihVmixo7dqy0bt3aqW3o9XqZP3++rF+/Xu33jTfeKBMmTJCGDRs67dgQEREREXkah+ZfxWxMH3zwgbzxxhsqpWnPnj02F1djNqfVq1fL1KlT1UrWCCiGDBki2dnZFrfH+hOnT5+WFStWyLx58+Snn36SyZMnGx/H4najR4+Wfv36yRdffKE6/UOHDlX76sw2sP26devktddek7Vr16pUrieeeEIFE0REREREvsquEQh08jFKgI4zVqHGVfy7775bBQP//vuvrFq1yqoUJwQJy5cvl1GjRknHjh3VfXPnzpV27drJ5s2bpUePHmbb79+/X3bt2iUbN26UunXrqvteffVVFXCMHDlSrUmxdOlS6dy5swwaNEg9jpEDPG/lypVqW2e0cebMGfXeFy9erPYVMKKBxfP++usvs5EKIk/0X0q8Xc8L1YVIbGRFp+8PERER+XgAgUABhdToNKPj36ZNG3U/rto/++yzKgiYNWtWse0cPnxY0tLSzDrcUVFR0qhRI9m9e3ehAAIjHBUqVDB2/KFly5YqgNm7d6907dpV9u3bJ+PGjTN7XqtWrVRA4qw2tm/fLpGRkdK+fXuz/d66davVx5DIHYICgtTtgp3v293GvG5TGEQQERGVYnYFELj6jjSgPn36SG5urvF+5P/jfqQ0WSM+Pv8qaGxsrNn9FStWND5mCmlSBbcNCgqS6OhoiYuLk5SUFElPTy80+mHanjPaOHnypKrRQECxZMkS1SaCHgQdpoGJrTCag9d2t4yMDLPba8nMzFS3Wdl68cvKElfS0tz0OTmSrXfpS0uOOu91kpuTazwG3iDcP1SeuPkhyc61/YAlZFyWr49tkcupSVImIMKh84aI5w3Zi981xPOmZGhZRSUWQFy6dKnIYmGkAKETbsuXADrwpoKDgyU5Odni9gW31bbPysoyduQstYfHndUG6jxQQ4GRGBRfY/Rh0aJF0r9/f5UaFRMTI/ZAYfahQ4fEU5w6darYbfLi8oOq+Lg48ct2bQARp8ptAiQhIUHExXFXmqr1CZHLyUny7+nTUhroszONAXRGSKpD5w0RzxtyFL9riOeN81nqIzstgKhZs6YqPL7tttsKPYb6AjxuDW39CFxJNl1LAh110xmRTLe3VFyN7cPCwlQnX2uv4ONae85oIzAwUAURSNXSRhzw7w4dOqiia9RT2EOn00m9evXE3RBk4Yu5Vq1aFj8HU+khoXIEU/vGxkpIrIun9k3MFvnjggrYKkXadSrb7UJW/hX8smWipYaV57u3C7pyUeSSSO3ataVWdDWHzhsinjdkL37XEM+bknHs2DGrt7Wr1/XII4+oImpcMb/99tvVcAeuyO/cuVMVRResHyiKlkp04cIFqVGjhvF+/F6/fv1C2yOtqOACdejoJyUlqRQjpCEhCMDzTeF3jIw4sw0EEabpSghMkNbkyOrcOI54bU+BTmBx+5N3NfALDtJJyNXgy1W0IFkXGChBOusiZmcJDAhQtwGBAYUWUvRVwfr8Y4z3e63zwprzhojnDTmK3zXE88a5rE1fsnsa1759+8oLL7ygpjHF1KXImcIMRrgK/9hjj8lDDz1kVTsNGjSQiIgIFXhokP508OBBtR5EQbgPdQgIVkxHPKB58+bqjTdr1sx4nwbtt2jRwqlt5OTkyJ9//ml8HKlPmJ3J2tEXIiIiIiJvZHfeBwKHe+65R3W0cTUesxI1adJEXcG3Jc9qwIABquga6yhUrVpVZs+era7wd+nSRRVoJyYmqrZx1RPto3M/YsQItQ4DCo4xEoLpU7XRgcGDB6s1G1DUjFmSUPCNuoLp06erx53RBgIJpG9heldM64r3jEXlAgIC5N5777X3kBIRERER+V4A8fXXX6sF3w4cOKCuwgM69+iUY+QB6yfYArM2oZ2JEyeqq/i4ur9s2TJVD4B0oE6dOqmVr++77z41OoBVq6dMmaLSqFCvgGlXx48fb2yvbdu2anE3FDhjRAQ1BVivQUs3ckYbgFWqEfg899xzar/x/rGoHgIhIiIiIiIp7QEERgNefPFF+fbbb9WV+u7du0v58uVV+hJSgjASMWzYMHUFfubMmVbvAK7aY/0I/BRUrVo1OXIEJbr/DwWzuNp/LRhNwE9RnNEGUq8wgmG6gjURERERka+zOoBYvXq1WvdgwoQJKu2oYKEFAgyMTODKPVJ87r///pLYXyIiIiIiciOri6jXr18v/fr1k4EDB1qs0sZIwsMPPywPPPCAmsqUiIiIiIhKcQCBxaNQUFycdu3ayT///OPofhERERERkTenMGHhljJlyhS7XdmyZSUtLc3R/aJSJD4lWzL0Bpufdy6p8IKAREREROQhAQSKpZGmVBx/f3+1LZG1wcOodWccOlhBAXYtZ0JERERErlwHgsgZtJGHno2jpXxEoF3BQ7lwnsZERERErmJTzwtTlmL60mu5cuWKo/tEpRCCh8pRQe7eDSIiIiJyVgCBBd6guPSk8PBwNY0rERERERGV4gDiww8/LNk9ISIiIiIij8fkcSIHJKX6yX/xGTY/LzgoQMqXY8oWEREReR8GEER20Pnnp/Jt3eUvW3edsusYjnqiLoMIIiIi8joMIIjsEBUioqtzQG4tc6OUr1jVpucmpejlx98SJCs7l8eeiIiIvA4DCCI7+QVlSXSkcBSBiIiIShWuwEVERERERFZjAEFERERERFZjAEFERERERFZjDQSRm1xIyLbreZwCloiIiNyJAQSRi+l0fur206/P2d0Gp4AlIiIid2EAQeRiZSJ18kCPWNHr89eSsAWngCUiIiJ3YwBB5KYggoiIiMgbsYiaiIiIiIisxhEIKrUu669ItiHHrucm5mU4fX+IiIiIvAEDCCq1wcOycz843E6gP/+EiIiIqHRh74dKJW3k4dao6yUqMMzm5+uvXJEr+36XqGq2P5eIiIjImzGAoFINwUNZXbjNz9P750pulu2zKBERERF5OxZRExERERGR1RhAEBERERGR1RhAEBERERGR1RhAEBERERGR1RhAEBERERGR1RhAEBERERGR1TiNK1Ep45d8SUSfZfPzgtISJSbbvpW7iYiIyHe4PYDIy8uTt99+Wz7//HNJTU2VW265RSZNmiTVq1e3uP3ly5dl2rRp8vPPP4ufn590795dxowZI6GhocZtNm3aJAsWLJCzZ89KnTp1ZOzYsdK6dWuntmHqq6++ktGjR8uWLVukWrVqTj0+RM4OHkI+e9Ou5+LMHo1/JF0UKVeDHwwREVEp5fYAYuHChbJ69WqZOXOmVK5cWWbPni1DhgyRDRs2SFBQUKHthw8fLhkZGbJixQpJSUmRCRMmSHp6usyaNUs9vmPHDtWZR0DQpk0bWbNmjQwdOlTWr18vdevWdVobmv/++09effVVlxwrIo1f0kXxCwyw+YD4J11Qt/qbbxdDRLRNz01LOCtlD+0W0WfygyAiIirF3BpAZGdny/Lly2XUqFHSsWNHdd/cuXOlXbt2snnzZunRo4fZ9vv375ddu3bJxo0bjR15dN4RcIwcOVIqVaokS5culc6dO8ugQYPU4xg5wPNWrlyptnVGG6ajJwg0brjhBhV0EJU0v/Qr6jbox08lJDDR7nYM0RXEEF7GpufkZCbb/XpERETkO9waQBw+fFjS0tLMUoOioqKkUaNGsnv37kIBxJ49e6RChQpmowAtW7ZUaUh79+6Vrl27yr59+2TcuHFmz2vVqpUKSJzVhmbx4sWi1+vlueeeYwBBrpGXX4OQU7+FZMeG29dGoM7m4IGIiIjIIwKI+Ph4dRsbG2t2f8WKFY2PmTp//nyhbZHmFB0dLXFxcSodCalISIUqqj1ntAF//PGHGj1BehPaJHIlQ2ikGMqU40EnIiKi0hVAoA4BCtY6BAcHS3JyssXtLdVFYPusrCzJzMwssj087qw2EGAg7Qo/tWrVcloAYTAYVNvupn0u2u21aMcrK1svflePj61pbKDPyZFsvbhMTk7+i+Xk5kiOn+0zC+Xk5qpbPdq5+h5cIUd/dQQCx8uFr5v/mvnvOSsr2+J5ast5Q8TzhuzF7xrieVMy0A9FRo7HBxAhISHqFh0h7d+AjrrpjEim21vqNGH7sLAw1cnX2iv4uNaeM9rADE61a9eWfv36iTMhHerQoUPiKU6dOlXsNnlx+aMy8XFx4pdtewARp1L6AyQhIUHEhbHTZUP+i2HEySB2dMSvdqATEhPFX++6yCcjBQcsXFKSk8U/yLXFzDkpyYKxu3Pnzkm6/pBD5w0RzxtyFL9riOeN81m6yO5xAYSWSnThwgWpUeP/p4XE7/Xr1y+0PdKKfvjhB7P70NFPSkpSKUZIQ0IQgOebwu8ojnZWG2vXrlUHuGnTpur33KtXo1Gz8dRTT6kfe+h0OqlXr554wtUdfDFjdMVSIGcqPSRUjuC4xsZKSKx52pdVErNF/rggMTExUinSdaejvz5Z5GJ+zU3ZQNtrCfSBgZIkIjHlyokuxnWpRJf1mHkpR6LKlJFKlWybRclRV/zzA5YqVapIzXoNHTpviHjekL34XUM8b0rGsWPHrN7WrQFEgwYNJCIiQnbu3GkMIHBF+ODBgzJgwIBC22ONiDfeeENOnz4tNWvWVPdhRiVo3ry5GnZp1qyZuq9v377G56H9Fi1aOK2NgsXUBw4cULMxLVmyRK6//nq7jwdeG8GLp0AnsLj9ybs6chQcpJOQq6M3ttACXV1goATprIt6nSHQoMu/DQiUwEDb/wwMAflTqOoCdVZH684QqMO+5qh9duXrqte+Om1scHDQNc8La84bIp435Ch+1xDPG+eyNn3J7QEEOkAIFNChL1eunFStWlWtA4FRgi5duqgr+4mJiRIZGalSj5o0aaI69yNGjJDJkyerPGwsOterVy/j6MDgwYPVmg2Yyal9+/ZqtABpQdOnT1ePO6MNLfDQaMXVuDKLEQwiIiIiIl/l7+4dwKJu999/v0ycOFEeeughCQgIkGXLlql0HsyK1LZtW7VmgxYZYdVqrPb8yCOPyAsvvKA6+AgENNj+tddek48//lh69+6tplfFdKvatK3OaIOIiIiIqLRy+0rUCBiQ/oOfgtDJP3IEGfb/D7ny8+fPv2abGE3AT1Gc0UbBNSIK7icRERERkS9yewBB5M2yk1FKbTu/oEDRleFibkREROR9GEAQ2cHvakHxxW0/2338qvW9j0EEEREReR0GEET2/OGEh0uFDu3EcHVxNVvkXLkiSQf+EEO27QvYeYILaQkiif9aXFQwPvOShCadlZDM/1/XRROqC5HYyIou2ksiIiIqKQwgiOz94wm3ff0Ibxbon/918cmfX8m5o5uK3vBs0Q/N6zaFQQQREZGXYwBBRFYJD8oPmO5rdLdklys8kpCVla1WJMeiglgrwtTFtET5+cDXknHuqGRFqeXHbeYfHCK6clX4aREREbkZAwgiskn58HJiiMxfM8VUpi5TsoPSpXJEBbVui9kXTeplGf1vosi/c+Q/B4539acXMIggIiJyMwYQRFTi/PX6/H806yzlK9m+nkpOyiVJ2r5W8rIynb9zREREZBMGEETkOhHlRBfDNCQiIiJvxgCCiGzin3RB8izcH5CVLaGpCRKQECR+BWogglISeZSJiIh8BAMIIrLy20KnboJ+/Mziw6h6iMI/9lt+zLQNIiIi8l4MIIjIKobwMpLd8QGRnKv1DAXo9XpJTEiQcjExotOZBwrJmcmy5d/d0i8imkebiIjIyzGAICKbgoii5GVnS1aGXvKiYsQQZJ7CpNf5S8rV1buJiIjIu/m7eweIiIiIiMh7MIAgIiIiIiKrMYAgIiIiIiKrMYAgIiIiIiKrsYiayA2CAgzin3ZZ/IIMNj3PLz1VRDgVKhEREbkPAwjyapf1VyTbkGPz8xL06Ii7h78+Q+pUyBX560ebnxuQGykit4kE8E+XiIiI3IO9EPLq4GHZuR8caiPQz/VTi/rn5Qc8WVXqS0BMRZuem3vFT+QPEUNoRAntHREREdG1MYAgr6WNPNwadb1EBYbZFTxEBhrXSHa5vOAw8b/GugqWGHKR8mT7iAsRERGRszCAIK+H4KGsLtzdu0E+Sp94TvKyMu1+vn9wiOjKVXHqPhEREbkTAwgiomsED2cWDXP4+FR/egGDCCIi8hkMIIi80IVkpDLlufx1g3UiFaJKz+zP2shDdJs+EhhV3ubn56RckqTtax0awSAiIvI0DCCIvEjQ1ZrvT7a7rw5i9L06twUR2Qln3ZJGhOBBF8M0JCIiIvX/RR4GclSiLlICU/IkODjL5ueeS8rmB2CDMuF+0u+2QMnOdf1hS0ozyNa/ciVL7/rX9tMFq9uLX86zuw2mERERETkHAwhySFxSliyp2VtkB4IH+64OQ1BA6UmLcUYQUdoERsVIhZ7DxaC3PUhlGhEREZFzMYAgh2Tq8/Pwu9UOkMqVy9odPJQL56lIxQcRRERE5H7stZFTxIT4SeWoIB5NIiIiIh/HvBEiIiIiIrIaAwgiIiIiIrIaU5iIyClrUOToDZKQGii5OoME6swfT870l6wsrhZORETkCxhAENnJPytNJNeO+VSz0314DQqt0LngXK+hItJJjtRLtP/1A4MkJixavJG961c4wtG1L4iIiIrCAILIzuAh8u8fHTt2/ld75D6yBkVObo6kJidLZJkyEhhg/tVyPiVTth8KlG//+Z/8dCbZ7n147tbBXhVEOGP9Ckdw7QsiIvLZACIvL0/efvtt+fzzzyU1NVVuueUWmTRpklSvXt3i9pcvX5Zp06bJzz//LH5+ftK9e3cZM2aMhIbiKme+TZs2yYIFC+Ts2bNSp04dGTt2rLRu3dqpbRw9elRmz54tBw4cEH9/f7Xf48aNkypVeNXP510deciqXE9yg8Jse2pGhqQePS7RQf9/rvnCGhT6HJEAfa6UjRTRBRbcLgQhhrSr2VIqly2c/lScpMxU+fX0LsnOyS4161c4whlrX+gTz9n9fI5+EBH5No8IIBYuXCirV6+WmTNnSuXKlVWnfMiQIbJhwwYJCio8Nejw4cMlIyNDVqxYISkpKTJhwgRJT0+XWbNmqcd37Ngho0ePVgFBmzZtZM2aNTJ06FBZv3691K1b1yltIAAZPHiwNGvWTD788EPJzs5W+4/9/uKLLyQ4OP/KI/k2BA+GYNty+3NzRHJs70P7hDIhURITVrrmbvDG9SsQPJxZNMyhNjj6QUTku9weQKDjvXz5chk1apR07NhR3Td37lxp166dbN68WXr06GG2/f79+2XXrl2yceNGYzDw6quvqo77yJEjpVKlSrJ06VLp3LmzDBo0SD2OkQM8b+XKlWpbZ7Txww8/qIDj9ddfl5AQXF0VFfjgPezbt89spIKIyJtoIw/RbfpIYFR5m57Llb+JiHyf2y8FHj58WNLS0sw63FFRUdKoUSPZvXt3oe337NkjFSpUMHb8oWXLlioNae/evSodylIHvlWrVsb2nNEGHsPIiRY8ANKYACMaRETeDsGDLqaKTT+2BhxEROR93D4CER8fr25jY2PN7q9YsaLxMVPnz58vtC3SnKKjoyUuLk513jEygFSootpzRhvVqlVTP6aWLFmiAgrUQhARERER+SK3BxCoQ4CCtQ6oIUhOTra4vaW6CGyflZUlmZmZRbaHx53VRkGog1i1apVMnDhRypUrJ/YwGAwqcPGUz0S7vRbteOhzciVb79oC15wcvXH2nxy/a00t6nwBKGRQtdS5knf139bCCJd6bk4uFk8QX5Gbk2N2awpvVd3q9ZKdXXQhdlH0qNC+mvKYmWlbQXJOdv4xxt91ro1/X/qr3wVZ2XrJtfF13cmR9+zo+7b1tW35viHiOUP24neNdf1QZON4RQChpQChY2CaDoSOqemMSKbbY9uCsH1YWJixeLngNqbtOaMN04M9b948WbRokTz99NMycOBAsZder5dDhw6Jpzh16lSx25w7F6duk1NSRHfetZ34y4b8zglGjAzi2uAlKDtNMJloelqa6LNtrIi+2jlLTk0Rv1zfCSA0llL4UtMxZW0ZSUhMkICrwYAtUvVp6hYjhFlBtnWI/dMSBWXuJ0+elNxE2zqpAcnxEoWR0rg4yUvxngDCkffs6Pu297Wt+b4h4jlDjuJ3zbVZusDukQGElkp04cIFqVGjhvF+/F6/fv1C2yOtCAXMptDRT0pKUilGSENCEIDnm8LvKI52Vhtah3/8+PHy9ddfq9tHH33UoWOh0+mkXr164glROv7AatWqZTGIM2W4gkj1jJSJipJKlVw724y/PlnkYn7NTNlA165yHJARIHJBJCw8XPKCbZzGNSBArqA7HRklAWUixVdg5AHBAz6PgEDzr5ZcXf5tTLkYqVTO9hEIXUaSSHL+90WliAo2PTfncrCkHhSpXiZIAsvZNnVujgThZaVybKwEljVPafRk2nuuXbu26CrVsvn5+vOhkvCbfe/b1te25fuGiOcM2YvfNcU7duyYWMvtAUSDBg0kIiJCdu7caQwg0Ak5ePCgDBgwoND2qC9444035PTp01KzZk11H2ZUgubNm6uhF0ytivv69u1rfB7ab9GihdPaAEzx+v3338ubb76p1pFwFF4XgYunwP/Mi9sfbbRGFxggQTrrolZnCTTk90qxaFlggQ5rSfO/ulBaQECA8d/WMlwttg8IDBBd4NWetQ9B8FDwfQUGGFR3PFCnk6Ag2+du0OUEGq+MhITYNkVyTniEpGLE59vFYq+Q8AgJtPF13UkfpFPvGaOtwXZ8p2RdHQ0ODtKJzsb3be9rW/N9Q8RzhhzF75qiWZu+5BEBBDoECBTQoUftQNWqVdV0qBgl6NKli8oxT0xMlMjISPU/pCZNmqjO/YgRI2Ty5MmqZgCLzvXq1cs4OoD1GbBmA2Zyat++vaxdu1alBk2fPl097ow21q1bp6aBRRCBGZwuXrxofE/avhKR9y/mhtWkvXEtB8hOOOvS59nTBuotkDKFUQ8ELlyEjojI87k9gNAWdcvJyVEFyCi8wwjBsmXLVEoPVoHu1KmTzJgxQ+677z4VHWHV6ilTpsgjjzyiroB37dpVpRBp2rZtK6+99pqaZhVrSiAtaPHixcZpW53RBtKWAOtA4MeUtq9E5Bm8NQCwF4IeuPjlPKe0U9KvjXoLpExpuAgdEZFn84gAAmkgWPUZPwVhqtQjR46Y3RcTEyPz58+/ZpsYTcBPURxtA4vfERH54qiLIyMvtr42ZnpCsTbqLQIykyVp+1rjQnZEROSZPCKAICIi3xl1seW1MU0sZnpCsXZAmu/VBBER+SIGEERuor+CuZhs5xcYKIHhrp11ioiIiEjDAILIxfz8sSaCSPKBA3a3UaFDBwYRRERE5BYMIIhc/UcXGiJlbmoihryryzPbIC8jU64cPyYGC6s9+7qsrHA5k5ArGXasCB0a5Cex0a6dZpiIiMhXMYAgcscfXqh90/z63rrV1klM9ZNjRzvJG0exCrVtK1Fr5g2qziCCiIjICRhAkJJx7pzkZuTPfIKpdPPi4iU9JFTyilnPIjP+vNceQf+sNJFc20cBICATS2WRq2Tn5C9uc2djndQuX8am515MzZEv9iRJRjYWsyMiIiJHMYAgFTzse3pYoSNhPnmuZfHB5USq91CFvd4WPET+/aPjDfnZvqoy2a9shL/ElmUqEhERkTt5V6+PSoQ28lDt/j4SXLG82bzswUHXnlYxMCVPZEeWBEZEeNenc3XkIatyPckNCrOvDT9/MQSFOne/fNyFZIwC5Nn8vITU/BEIIiIicj8GEGSE4CG0ShXxy8oSv+wsCYmtLCHB116JNjgYBa1nvfYoIngwBHNK1JIWlD/xlHyy3d7i7xCzdoiIiMh9GEAQUYkrE+4n/W4LlGz7Sk4kLfuK/HXhb4kOb+vsXSMiIiIbMYAgIpcFEfYKzjKI7nKmJGXZXryelGl7yhQREREVjQEEEXm8AP/8r6pfT+20a/0IkZslWc2cde2UPCIiIioeAwgi8njhujBpW6ul5ObZXkNx9nK2xMWJ6HNL6yoaREREzsUAgoi8JoiwR1gg1+wgIiJyJk5iT0REREREVuMIBBHRNSSkJ0l2TrbdxygoMEhiwqJ5jImIyGcwgCAiukbw8PaO9x0+Ps/dOphBBBER+QwGEERERdBGHtrWbCnRIZE2H6ekzFT59fQuh0YwiIiIPA0DCCKiYiB4iAkrx+PkItkJ7lnd3j84RHTlqtj1XH3iOcnLynTLaxMRuRoDCCIi8gh+uvx1Oi5+Oc9t+1D96QU2d+QRPJxZNMwtr01E5A4MIIiIyCMERsVIhZ7DxaDPcvlr56RckqTta+0aRdCeE92mjwRGlXfpaxMRuQMDCCIi8qggwlsheNDFcASBiHwfAwgiKhWOXkix+TmXM5MlKyu8RPaHiIjIWzGAICKfFqwLULdf7sTXXbqNz9aJSCdJrZctMfYthE1ERORzGEAQkU+LLRMmPVumS5Y+1+bnpqT7y29HgiTQj6MQpYU9M0C5a9YoZ8wAxdmfiMgeDCCIvJD+yhW7n+sXGCiB4eGlLoiwx8UUA0pcnb4/5JszQGltuJIzZoDi7E9EZCsGEOTVovS5EpSRKv42Xl0OyEwVb+Tnn5+Ok3zggEPtVOjQodQFEY64kIxAIs/m5yVn+ktGRhk5k5ArGZm2zywUGuQnsdFBNj+PXD8DFIIHdxSAOzIDFGd/IiJ7MYAgr+WXcUX6oTD2wh4HGvEXbxIYGiJlbmoihjzb03EgLyNTrhw/JoYcXlW3RlB+vCafbLf3eIWKSAd543i6HfUX+eYNqs4gwkU4AxQRkXUYQJDX8svL79QlVagpQaFl7GjAXwxB6OCJ1wUR9tI7dU98X5lwP+l3W6Bk2xevSVr2Ffkj/rC0q9VKrWZti4upOfLFniTJyMboB5UG3lh/QUSlEwMI8no5umDRBTMdh0ouiLBXcJZBgi+nScUof4kJd20qUlxStkPBB9OnXMdb6y+IqPRiAEFE5GMQPDz/wRmH22H6VOmov3BkFMNdszj5pyWK/vwpyQqxb0SWs08ROYYBBBGRB/svMdvu5/RuES0VIm3/mndn+lRpHTlxR/2FM0Y+3DGLU87leCnzy2JJcLAdzj5FZD8GEEREHihIl1/gv2DzBbvbqFouSGIivOdrniMn3jXy4egsTvauX6GPP65uw1r2lLCYWK/Zb2eMfHDND/IUbv8/S15enrz99tvy+eefS2pqqtxyyy0yadIkqV69usXtL1++LNOmTZOff/5Z/Pz8pHv37jJmzBgJDf3/YthNmzbJggUL5OzZs1KnTh0ZO3astG7d2uVteJNEXaQEpuRJcHCWZGdnSxyWGUjMlqBiLuSdS7L96mhBl/VXJNtg+yw3qTlpUtHhVy+d7F1HwpE1JHLS0hya/am0rV+Bjv9zXSpKtt726WO1AMTR4MGe0Q/Iyr0i0eHWVZ7j+yYxO1mCr1yU86kBXjty4q2cMfJhTwpUTmqCnP98pkOvG1i2iujsCCC8dd0NrvlBnsTtAcTChQtl9erVMnPmTKlcubLMnj1bhgwZIhs2bJAgC73X4cOHS0ZGhqxYsUJSUlJkwoQJkp6eLrNmzVKP79ixQ0aPHq06823atJE1a9bI0KFDZf369VK3bl2XteFN4pKyZEnN3iI7cBVK+x9BgMgf1l/5DArwtzt4WHbuB7ueWz47R+7DnnrZVKzevo6EPWtIIHi4+NNPdr+mI6/tzdw1euCM0Y96122R4OA0659wUdSaGZj2NjRYL7Fl7Vv8j7wrBapsh4ckICzKpudkZesl7sJFKRtZTkrTuhtc84M8iVsDCFx5Wr58uYwaNUo6duyo7ps7d660a9dONm/eLD169DDbfv/+/bJr1y7ZuHGjsSP/6quvqoBj5MiRUqlSJVm6dKl07txZBg0apB7HyAGet3LlSrWtq9rwJplXr3B2qx0glSuXFX1OjiQkJEhMTIzoAgOtCh7Khdt3KmkjD7dGXS9RgbZ1GLCAnFzcI8EBQcLrjSW/joS2hkR2UpLFkYTcnFwxpGdIbkCqSODVBRQKjHhE1K0n/nZMQ+vt61ckZdm3cKEuQCdRIRF2PTcl84roc+2cuNdPZEDbYLumr/0vKV22HwqQmyo1kesqFT8iq9fnSGJCgpSLiZFTiXo5cVwk5+oUzeTZ3FX8nZuZJYYU+17TlCPT5iJ40MVUcWnRujNe2xt5a9qW3kv32ysCiMOHD0taWppZalBUVJQ0atRIdu/eXSiA2LNnj1SoUMHYaYeWLVuqFKK9e/dK165dZd++fTJu3Diz57Vq1UoFJK5qo1u3buKNYkL8pHJUkGSjz5EuUikyUIJ0rilGRPBQVmfblWVbV58mx9aRyLFy9OLyNR4LCA+36/W9df2KAP/8r9hfT+20u43eje62OYhA8PDFwU3iDllZ+Du+WcqGlpGYsAirLiTpdZlSLjRaEoLsW2yP3McbF99z17S5zipaL01T9npr2pbeS/fbawKI+Ph4dRsba57DWLFiReNjps6fP19oW6Q5RUdHS1xcnEolQhoRUqGKas8VbdhDr9eLwWCQP/74Q1wtO0Avz/esLMH+OZLrl4XkJakSVVHEzyC54vhVnmuJCQuVYdF3SYD446KnTfzKRol/lQclwi9ADPZP1U82qFK/4TUeNR0HKuIDsfdzMohUrd9IxN/f/jbcIFqi5MGyPe0cITNIrsGgUvQyDbb9HeqCddLn5h4S4IeD5doDZjD4SXZeoAT550jileLfObYIiy4nqdkiFaJi5Pme+c+19T1DmTJ+6rssITNTks5k2PkOyBsYQivKwYv4jO35nANEOoww/8qyhZ9IXDKupiS59nUdeW1DmPi3Gy5xx86JHD8nXsMgar8NWPjVxu8yPzzDkCdxf58Q8TuR35xBZP++feLp+x1oyJXU0/Hid/aSuBL6orgY7vEBBGoIoGCtQ3BwsCQnJ1vc3lJdBLbPysqSzMzMItvD465qwx7aB2btB+dc+Vfys/LcdzrYn7Cgc+zLmLyLffXEXs2RZB53JgIhiHDHc53xfCLf5K3/v3Rwvw3eud9lcnIkJFAnroQ+qFcEECFXF4DBELb2b0An3NJsRtgG2xaE7cPCwlQHXmuv4ONae65owx5Nmza163lERERERK7k1ulrtDSgCxfMZ/rA75YKkZFWVHBbdOSTkpJUihFSiNCBv1Z7rmiDiIiIiMhXuTWAaNCggURERMjOnf9fYIgahIMHD6r1IArCfahDOH36tPE+zIYEzZs3V8MuzZo1M96nQfstWrRwWRtERERERL7KrQEE6ggGDBggb7zxhmzZskXNyjRixAh1hb9Lly6Sm5srFy9eNNYlNGnSRHXusQ2KjbFeAxad69Wrl3F0YPDgwfLNN9/I+++/L8ePH5fXX39dDh06JI888ohL2yAiIiIi8kV+Bkz940YIEubMmSPr1q1TgYK2EnW1atXUKtCdOnWSGTNmyH33YckwUesTTJkyRX755RdVr4BpV8ePH2+sXQAs+IYF6jBKUK9ePbUonOlUsa5qg4iIiIjI17g9gCAiIiIiIu/h1hQmIiIiIiLyLgwgiIiIiIjIagwgiIiIiIjIagwgiIiIiIjIagwgiIiIiIjIagwgiIiIiIjIagwgyExeXp7Mnz9f2rVrJzfffLM88cQTcubMGR4lH5WUlKTWXWnfvr1aHPGhhx6SPXv2GB//7bff1BosWDwRa51ggUVTWVlZaj0UrJHStGlTefHFFyUxMdFsG2e0QZ7r5MmT6nPDWj4aLLyJRULxHXLHHXfIBx98YPP3jDPaIM+C9ZW6desmjRs3lu7du8umTZuMj2HdpyeffFJ9D7Vt21beeusttU6UqY8++kitDXXTTTdJ//795eDBg2aPO6MN8iw5OTkyb948uf3229X3zMMPPyy///678XF+17gR1oEg0ixYsMDQqlUrw48//mg4dOiQ4bHHHjN06dLFkJWVxYPkgwYPHmzo0aOHYffu3YYTJ04YpkyZYrjpppsMx48fNxw7dszQuHFjw5w5c9S/33vvPUOjRo0M//vf/4zPHzdunKFz587q+QcOHDD06tXL8PDDDxsfd0Yb5Lmys7MN9913n+H66683rF27Vt2XmJiovkPGjx+vPvM1a9aocwC31n7POKMN8izr169Xf/urVq0ynD592rBw4UJDgwYNDPv27VPnET67oUOHGo4cOWL4/vvvDS1btjTMmzfP+Px169ap76Yvv/zScPToUcPo0aPVNgkJCepxZ7RBnmf+/PmGNm3aGH755RfDqVOnDBMmTDA0b97ccP78eX7XuBkDCDLC/3ibNm1q+Oijj4z3JScnqy/cDRs28Ej5GHwZo+O3Z88e4315eXmqM//WW28ZXn75ZcP9999v9pyRI0eqjhrEx8erDsC2bduMjyMIQZvoFIAz2iDP9eabbxoGDRpkFkAsXrzY0LZtW4NerzfbDp07a79nnNEGeQ58r9x+++2GmTNnmt2P7wF81vjMbrzxRkNSUpLxsU8++cTQrFkzY0CIz/711183Po5zo0OHDur54Iw2yPP07NnTMGPGDOPvqamp6vvmu+++43eNmzGFiYwOHz4saWlpKpVEExUVJY0aNZLdu3fzSPmYsmXLypIlS1Q6gcbPz0/9pKSkqFQm03MBbr31Vtm7dy8uPKhb7T5N7dq1pVKlSsbzxRltkGfC5/Ppp5/KzJkzze7HZ96yZUsJDAw03ofP99SpU3Lp0iWrvmec0QZ5Vprbf//9J/fcc4/Z/cuWLVMpR/i8b7jhBilTpozZ533lyhWVopKQkKA+e9PPG+dGixYtzM4ZR9sgzxMTEyM//vijSk9DOhq+c4KCgqRBgwb8rnEzBhBkFB8fr25jY2PNjkrFihWNj5HvQIerQ4cO6stY891338np06dVXjk+88qVKxc6FzIyMuTy5cty/vx5FYQEBwcXeb44ow3yPAgwx4wZIxMnTiz0fVHUZw5xcXFWfc84ow3yrAAC0tPT5fHHH1ed+L59+8rWrVvV/TxnqCgTJkwQnU6n6lZwsWvu3Lmq9qlGjRo8b9yMAQQZoVMHph1KQOcOha7k2/bt2yfjx4+XLl26SMeOHSUzM7PQuaD9np2drc6Xgo8XPF+c0QZ5nsmTJ6uCxoJXlIv6zLUAEZ+pNd8zzmiDPAdGAWDs2LHSo0cPWb58ubRp00aeeeYZNckCzxkqyrFjxyQyMlLeeecdNfqACTlGjRqlRpV43rjX/48PU6kXEhJi7Nhp/wb8Dzk0NLTUHx9f9sMPP6gvZcxe8sYbbxg7YzgXTGm/43zAOVLw8YLnizPaIM+bSQfpIhs2bLD4uKXPVOvUh4WFWfU944w2yHPgCjJg9KF3797q3w0bNlQzIL3//vs2fd4Ft7HnnCmqDfIsGG3ErHwrVqxQqWaAUQgEFQsWLOB3jZtxBIKMtHSACxcumB0V/I6cdPJNq1atkmHDhqlp8hYvXmy80ovzwdK5gP8Z44oQUkwwDWzB/yGbni/OaIM8y9q1a1U+OUapMAqBH3jllVdkyJAh6jO19JkDPlNrvmec0QZ5Du0zuf76683ur1evnspt5zlDlhw4cED0er1ZnR5gSnCk2vK8cS8GEGSEoqSIiAjZuXOnWa4zrhLdcsstPFI+aPXq1TJ16lQ1t/acOXPMUkJwxWfXrl1m2+/YsUONUvj7+0vz5s3VXPxaIbSW64y6Bu18cUYb5FkwQrVx40Y1EqH9wPDhw2X69Onqc8PnaTr/Pj5zFMejINKa7xlntEGeA8XN4eHhqkNo6p9//lG57PjM8NlpqU7a543n4LPGZ47P3vTzxvoAGAkzPWccbYM8i1YHdeTIkULnTa1atfhd427ungaKPAvm68e82D/88IPZ3OqYY5t8C6ZLveGGGwzPPvus4cKFC2Y/KSkphn/++Uc9Pnv2bDUX/7Jlywqt4YApWe+44w7Djh07jGs4DBgwwPi4M9ogz2c6jeulS5cMt9xyi2Hs2LFqrn3cjzUcMAe/td8zzmiDPMs777yjpt7FdKum60Dg7z4zM1NNH/3444+rz1JbwwFrfWg+/fRTNU0vzgFtDQesA6Kt4eCMNsiz5ObmGh566CFD165dDb/99pvh5MmThrlz5xoaNmxo+P333/ld42YMIMhMTk6Omif71ltvNdx8882GJ554wnDmzBkeJR+0aNEi1fGz9IOOG/z0009qoTnMr44v8W+++casjbS0NLWwT4sWLdQPggEsAmbKGW2Q9wQQgEDwgQceUJ855v//8MMPbf6ecUYb5FmWL1+uLhbgogLm90cn33RdGixsiUARa4BgLRp0IE1hIcr27durIKB///6GgwcPmj3ujDbIs2Bdj8mTJxs6duyoAtAHH3zQsHPnTuPj/K5xHz/8x92jIERERERE5B1YA0FERERERFZjAEFERERERFZjAEFERERERFZjAEFERERERFZjAEFERERERFZjAEFERERERFZjAEFERERERFZjAEFERERERFYLtH5TIiKiaxs4cKDs2rXL7D4/Pz8JCwuTWrVqySOPPCL33nsvDyMRkRdjAEFERE7VqFEjeeWVV4y/5+bmSnx8vKxYsULGjBkj0dHR0qFDBx51IiIvxQCCiIicKiIiQm6++eZC97dv315at24t69atYwBBROTFWANBREQuERwcLEFBQSqlCfLy8mTJkiVy5513yo033ih33XWXfPjhh8btFy9erO5PTk42awcjGTfccIMkJCSo38+dOycjR46Uli1bSpMmTVSa1MGDB43bnz17VurXry+bNm2S4cOHS9OmTdW2EydOlPT0dON22GbBggVmr4Xfcb+pPXv2yIABA9RroZ2xY8dKYmKik48WEZHnYgBBREROZTAYJCcnx/iTlZUlJ06ckPHjx0taWpqxBmLy5Mkyf/586dmzpwoWunbtKq+99pq888476vF77rlHPX/z5s1m7X/zzTfStm1biYmJUR33fv36yd9//y0vv/yyvPnmmyowefjhh+X48eNmz0NaVdWqVWXhwoXy+OOPy5o1a2TRokU2vbfdu3fLo48+KiEhIfLWW2/JSy+9pGo+Bg0aJJmZmQ4fOyIib8AUJiIicip0sjFCYAqjDtdff73MmzdPbr/9djl58qR89tlnauRg6NChahsEBdju3Xfflf79+6vO/i233CJff/219O3bV23z77//yh9//CFz585Vv69cuVKSkpLk448/VttrqVLdunVTr4UARYO6C4wWAFKptm/fLtu2bZMXX3zR6veGAKV27dpqHwMCAtR9GIno3r27rF27VgUuRES+jiMQRETkVAgecHUfP7jaj8ABMzDhij1GGWDHjh1qpOKOO+4wG63A7xix2Lt3r9oOoxMISC5evGgcfUCNBbaD3377TRo2bCiVKlUytuHv76+CiP/9739m+1WwLqNy5cpmKUzFycjIkAMHDqhAxHSUpXr16lK3bl0VkBARlQYcgSAiIqcKDw+Xxo0bG3/HFXoEAo899pgqoC5XrpwaNQBcubfk/Pnz6hYBx9SpU1X9AtKEEECgVgIpRIB2Tp8+XWjEw7TTrwkNDTV7DIEGAgFrpaSkqPSopUuXqh9LNR5ERKUBAwgiIipR5cuXl0mTJsnzzz8v06dPV2lAUVFRxhQkBBwFValSRd1GRkaq0QYEELfeeqscPXpU1Tpo8DgKmTE9rCUo2rYFppw1ZTpCgf1EihVqICwFPgUDFCIiX8UUJiIiKnEYSWjXrp2qZ0DRcYsWLdT9ly9fVqMV2g+KolG7oI1QAIquf//9d1XngMACAYMG/0Y9BeoSTNv58ssvVQqVVqdgDaRGaSMfmn379pk9jjUuUBBu+lrXXXedmq1p586dDh4lIiLvwACCiIhcAjMW6XQ6mTZtmtSrV0+lNWE04b333lM1EQgQRo8erYII1ExoEHhg8blPP/1UzcykTQMLGA1AWhFuN27cqGoi0Camg0VQYYuOHTuqFKlPPvlEtYN9QXqUKRR9//rrr6rw+qeffpKtW7fKkCFD1PZFpVEREfkaBhBEROQSderUkYEDB8qRI0dUsDBjxgwZPHiw6rCjE46pXDF70vLly81GDgIDA1XKENKLEHSYQvE0no8ZmDAt7FNPPaVmaUKqFIIKW2CaWcwQNWvWLLVeRFhYWKEZmjBT1LJly9TK2tgGqVPY1/fff9/i4nlERL7Iz2BLBRkREREREZVqHIEgIiIiIiKrMYAgIiIiIiKrMYAgIiIiIiKrMYAgIiIiIiIGEERERERE5HwcgSAiIiIiIqsxgCAiIiIiIqsxgCAiIiIiIqsxgCAiIiIiIqsxgCAiIiIiIqsxgCAiIiIiIqsxgCAiIiIiIrHW/wE4yK77NX5rMgAAAABJRU5ErkJggg==", + "image/png": 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", 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" ] @@ -780,27 +815,27 @@ } ], "source": [ - "arm_revenue = (\n", + "treatment_revenue = (\n", " policy_df\n", - " .groupby(['arm', 'arm_name'])[outcome]\n", + " .groupby(['treatment', 'treatment_name'])[outcome]\n", " .agg(['count', 'mean', 'median', 'std', 'min', 'max'])\n", " .reset_index()\n", ")\n", - "display(arm_revenue)\n", + "display(treatment_revenue)\n", "\n", "plt.figure(figsize=(8, 4))\n", "\n", "sns.histplot(\n", " data=policy_df,\n", " x=outcome,\n", - " hue='arm_name',\n", + " hue='treatment_name',\n", " bins=40,\n", " element='step',\n", " stat='density',\n", " common_norm=False\n", ")\n", "\n", - "plt.title('Revenue density by arm')\n", + "plt.title('Revenue density by treatment')\n", "plt.tight_layout()\n", "plt.show()" ] @@ -860,39 +895,6 @@ "We also check whether propensity scores are severely skewed. If $\\hat{e}_a(x)$ is very close to zero for some customers under some treatment, those observations receive excessively large weights, which can destabilize estimation." ] }, - { - "cell_type": "markdown", - "id": "pl2exzwbceb", - "metadata": {}, - "source": [ - "### Data Split\n", - "\n", - "Using the same data for policy learning and evaluation leads to overestimation. To prevent this, we split the data into train and test sets. Policies are learned on the train set and evaluated using AIPW scores computed on the test set.\n", - "\n", - "The same split is used for propensity estimation in the positivity check." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "n7ziuiv612q", - "metadata": {}, - "outputs": [], - "source": [ - "idx = np.arange(len(policy_df))\n", - "train_idx, test_idx = train_test_split(\n", - " idx,\n", - " test_size=0.5,\n", - " stratify=A,\n", - " random_state=SEED,\n", - ")\n", - "\n", - "X_train = X[train_idx]\n", - "X_test = X[test_idx]\n", - "A_train = A[train_idx]\n", - "A_test = A[test_idx]" - ] - }, { "cell_type": "code", "execution_count": 8, @@ -920,7 +922,7 @@ " \n", " \n", " \n", - " arm\n", + " treatment\n", " mean_propensity\n", " min_propensity\n", " p01_propensity\n", @@ -965,7 +967,7 @@ "" ], "text/plain": [ - " arm mean_propensity min_propensity p01_propensity \\\n", + " treatment mean_propensity min_propensity p01_propensity \\\n", "0 none 0.263941 0.057600 0.066831 \n", "1 tech_support_only 0.229863 0.126582 0.156283 \n", "2 discount_only 0.237934 0.125638 0.141322 \n", @@ -993,7 +995,7 @@ "e_hat_raw = multi_propensity.predict_proba(X_test)\n", "\n", "propensity_summary = pd.DataFrame({\n", - " 'arm': ARM_LABELS,\n", + " 'treatment': TREATMENT_LABELS,\n", " 'mean_propensity': e_hat_raw.mean(axis=0),\n", " 'min_propensity': e_hat_raw.min(axis=0),\n", " 'p01_propensity': np.quantile(e_hat_raw, 0.01, axis=0),\n", @@ -1020,7 +1022,7 @@ } ], "source": [ - "prop_df = pd.DataFrame(e_hat_raw, columns=ARM_LABELS)\n", + "prop_df = pd.DataFrame(e_hat_raw, columns=TREATMENT_LABELS)\n", "prop_long = prop_df.melt(var_name='treatment', value_name='propensity')\n", "\n", "fig, ax = plt.subplots(figsize=(8, 4))\n", @@ -1060,7 +1062,31 @@ "id": "f4edd2d8", "metadata": {}, "source": [ - "## Policy Learning Methods\n\nWe compare three policy learning approaches.\n\n- Plug-in Policy (DRLearner)\n\n First estimates per-customer CATE, then selects the treatment with the highest expected net benefit.\n\n This is a flexible approach with no constraints on policy form, but final performance depends heavily on the quality of CATE estimation.\n\n- DRPolicyTree\n\n DRPolicyTree learns a policy by directly maximizing the AIPW score.\n\n The policy structure is constrained to a shallow decision tree, making results interpretable as if-then rules. This is useful when policy interpretability and explainability matter.\n\n- DRPolicyForest\n\n DRPolicyForest also directly maximizes the AIPW score but uses a forest rather than a single tree.\n\n A single tree has a simple structure but can exhibit high variance. A forest averages across many trees, reducing variance and learning heterogeneous treatment effects more stably.\n\n However, the resulting policy is harder to interpret as a single clear if-then rule set.\n\nAll three policies are learned on the train set and compared using the same test set. It is important to use separate data for learning and evaluation, as using the training data for evaluation leads to overestimation." + "## Policy Learning Methods\n", + "\n", + "We compare three policy learning approaches.\n", + "\n", + "- Plug-in Policy (DRLearner)\n", + "\n", + " First estimates per-customer CATE, then selects the treatment with the highest expected net benefit.\n", + "\n", + " This is a flexible approach with no constraints on policy form, but final performance depends heavily on the quality of CATE estimation.\n", + "\n", + "- DRPolicyTree\n", + "\n", + " DRPolicyTree learns a policy by directly maximizing the AIPW score.\n", + "\n", + " The policy structure is constrained to a shallow decision tree, making results interpretable as decision rules. This is useful when policy interpretability and explainability matter.\n", + "\n", + "- DRPolicyForest\n", + "\n", + " DRPolicyForest also directly maximizes the AIPW score but uses a forest rather than a single tree.\n", + "\n", + " A single tree has a simple structure but can exhibit high variance. A forest averages across many trees, reducing variance and learning heterogeneous treatment effects more stably.\n", + "\n", + " However, the resulting policy is harder to interpret as a single clear decision rule set.\n", + "\n", + "All three policies are learned on the train set and compared using the same test set. It is important to use separate data for learning and evaluation, as using the training data for evaluation leads to overestimation." ] }, { @@ -1095,12 +1121,12 @@ "\n", "This approach is intuitive and flexible, but it depends heavily on the quality of CATE estimation. The learned policy is therefore evaluated on a separate test set using AIPW value.\n", "\n", - "> **Note** One might ask \"why not just select the arm with the highest $\\hat\\Gamma_{i,a}$ for each customer?\" However, AIPW scores have very high variance at the individual observation level, so a policy built by pointwise argmax overfits to noise. AIPW scores must always be used in aggregated form — for example, summed across observations for policy evaluation (value estimation, cost curves, etc.), averaged within nodes for DRPolicyTree/Forest, or used as pseudo-outcomes for regression in DRLearner." + "> **Note** One might ask \"why not just select the treatment with the highest $\\hat\\Gamma_{i,a}$ for each customer?\" However, AIPW scores have very high variance at the individual observation level, so a policy built by pointwise argmax overfits to noise. AIPW scores must always be used in aggregated form — for example, summed across observations for policy evaluation (value estimation, cost curves, etc.), averaged within nodes for DRPolicyTree/Forest, or used as pseudo-outcomes for regression in DRLearner." ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 10, "id": "debd92a9", "metadata": { "execution": { @@ -1138,7 +1164,7 @@ ")\n", "dr_cate.fit(Y_net_train, A_train, X=X_train)\n", "\n", - "# returns E[Y_net(a) - Y_net(0) | X] for each arm relative to none (baseline)\n", + "# returns E[Y_net(a) - Y_net(0) | X] for each treatment relative to none (baseline)\n", "cate_vs_none = dr_cate.const_marginal_effect(X_test)\n", "plugin_arm_values = np.column_stack([\n", " np.zeros(len(X_test)), # none: baseline, relative effect = 0\n", @@ -1149,7 +1175,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 11, "id": "33437c2c", "metadata": { "execution": { @@ -1170,18 +1196,18 @@ "Name: proportion, dtype: float64" ] }, - "execution_count": 13, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "pd.Series(pi_plugin).map(ARM_NAMES).value_counts(normalize=True).reindex(ARM_LABELS).fillna(0)" + "pd.Series(pi_plugin).map(TREATMENT_NAMES).value_counts(normalize=True).reindex(TREATMENT_LABELS).fillna(0)" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 12, "id": "33ktqy8zrqm", "metadata": {}, "outputs": [ @@ -1209,7 +1235,7 @@ " Size\n", " Employee Count\n", " IT Spend\n", - " assigned_arm\n", + " assigned_treatment\n", " τ_tech\n", " τ_disc\n", " τ_both\n", @@ -1311,7 +1337,7 @@ "" ], "text/plain": [ - " Size Employee Count IT Spend assigned_arm τ_tech τ_disc \\\n", + " Size Employee Count IT Spend assigned_treatment τ_tech τ_disc \\\n", "0 96204 142 17655 none -6140 -1419 \n", "1 25390 178 7971 none -14556 -4252 \n", "2 66555 279 19899 none -17629 -2896 \n", @@ -1334,7 +1360,7 @@ "8 9048 " ] }, - "execution_count": 14, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -1346,7 +1372,7 @@ "sample_ids = np.concatenate([none_idx, tech_idx, both_idx])\n", "\n", "df_plugin_sample = pd.DataFrame(X_test[sample_ids], columns=covariates)[['Size', 'Employee Count', 'IT Spend']]\n", - "df_plugin_sample['assigned_arm'] = pd.Series(pi_plugin[sample_ids]).map(ARM_NAMES).values\n", + "df_plugin_sample['assigned_treatment'] = pd.Series(pi_plugin[sample_ids]).map(TREATMENT_NAMES).values\n", "df_plugin_sample[['τ_tech', 'τ_disc', 'τ_both']] = np.round(cate_vs_none[sample_ids], 0).astype(int)\n", "df_plugin_sample.index = pd.RangeIndex(len(df_plugin_sample))\n", "df_plugin_sample" @@ -1371,7 +1397,7 @@ "\n", "In practice, policy interpretability is often as important as performance. Shallow decision tree-based policies are frequently used in operations for the following reasons:\n", "\n", - "- **Stakeholder communication**: the intuitive if-then branching structure makes policies easy to explain and share.\n", + "- **Stakeholder communication**: the intuitive decision rule branching structure makes policies easy to explain and share.\n", "- **Fairness review**: the explicit structure of who receives which treatment makes it easy to audit for bias.\n", "- **Operational stability**: simple rule-based policies are more manageable in production than complex models.\n", "\n", @@ -1384,12 +1410,12 @@ "\n", "Policy Tree restricts $\\Pi$ to shallow decision trees. With a small depth, the resulting policy is human-readable. We use `DRPolicyTree` from `econml`.\n", "\n", - "To prevent unstable value estimation in small leaves under multi-arm settings, `min_samples_leaf` is used to ensure leaves are large enough." + "To prevent unstable value estimation in small leaves under multi-treatment settings, `min_samples_leaf` is used to ensure leaves are large enough." ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 13, "id": "6c987a8b", "metadata": { "execution": { @@ -1410,7 +1436,7 @@ "Name: proportion, dtype: float64" ] }, - "execution_count": 15, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -1439,12 +1465,12 @@ "dr_policy_tree.fit(Y_net_train, A_train, X=X_train)\n", "pi_tree = dr_policy_tree.predict(X_test).astype(int).ravel()\n", "\n", - "pd.Series(pi_tree).map(ARM_NAMES).value_counts(normalize=True).reindex(ARM_LABELS).fillna(0)\n" + "pd.Series(pi_tree).map(TREATMENT_NAMES).value_counts(normalize=True).reindex(TREATMENT_LABELS).fillna(0)\n" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 14, "id": "64529830", "metadata": { "execution": { @@ -1457,7 +1483,7 @@ "outputs": [ { "data": { - "image/png": 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", "text/plain": [ "
" ] @@ -1468,8 +1494,8 @@ ], "source": [ "fig, ax = plt.subplots(figsize=(13, 7))\n", - "dr_policy_tree.plot(feature_names=covariates, treatment_names=ARM_LABELS, ax=ax)\n", - "ax.set_title('4-arm DRPolicyTree')\n", + "dr_policy_tree.plot(feature_names=covariates, treatment_names=TREATMENT_LABELS, ax=ax)\n", + "ax.set_title('4-way DRPolicyTree')\n", "plt.tight_layout()" ] }, @@ -1501,7 +1527,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 15, "id": "d7939447", "metadata": { "execution": { @@ -1522,7 +1548,7 @@ "Name: proportion, dtype: float64" ] }, - "execution_count": 17, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -1553,7 +1579,7 @@ "dr_policy_forest.fit(Y_net_train, A_train, X=X_train)\n", "pi_forest = dr_policy_forest.predict(X_test).astype(int).ravel()\n", "\n", - "pd.Series(pi_forest).map(ARM_NAMES).value_counts(normalize=True).reindex(ARM_LABELS).fillna(0)\n" + "pd.Series(pi_forest).map(TREATMENT_NAMES).value_counts(normalize=True).reindex(TREATMENT_LABELS).fillna(0)\n" ] }, { @@ -1592,43 +1618,55 @@ "id": "5261c54f", "metadata": {}, "source": [ - "The AIPW (Augmented Inverse Probability Weighting) score is defined as:\n\n$$\n\\hat\\Gamma_{i,a} = \\hat\\mu_a(X_i) + \\frac{\\mathbf{1}[A_i=a]}{\\hat e_a(X_i)}\\bigl(Y_i^{net} - \\hat\\mu_a(X_i)\\bigr)\n$$\n\n- $\\hat\\mu_a(X_i)$: predicted $E[Y^{net}(a)\\mid X_i]$ from the outcome model\n- $\\hat e_a(X_i)$: predicted $P(A_i=a\\mid X_i)$ from the propensity model\n- The second term corrects the difference between observed and predicted values via IPW.\n\nThis structure ensures unbiasedness holds as long as either the outcome model or the propensity model is correctly specified. This property is called doubly robust.\n\n- If the outcome model is correct: the expected value of the IPW correction term approaches zero, so $\\hat\\mu_a(X_i)$ contributes directly.\n- If the propensity model is correct: the IPW correction term removes the bias of the outcome model.\n\nThis score is used to estimate the value of each learned policy." + "The AIPW (Augmented Inverse Probability Weighting) score is defined as:\n", + "\n", + "$$\n", + "\\hat\\Gamma_{i,a} = \\hat\\mu_a(X_i) + \\frac{\\mathbf{1}[A_i=a]}{\\hat e_a(X_i)}\\bigl(Y_i^{net} - \\hat\\mu_a(X_i)\\bigr)\n", + "$$\n", + "\n", + "- $\\hat\\mu_a(X_i)$: predicted $E[Y^{net}(a)\\mid X_i]$ from the outcome model\n", + "- $\\hat e_a(X_i)$: predicted $P(A_i=a\\mid X_i)$ from the propensity model\n", + "- The second term corrects the difference between observed and predicted values via IPW.\n", + "\n", + "This structure ensures unbiasedness holds as long as either the outcome model or the propensity model is correctly specified. This property is called doubly robust.\n", + "\n", + "- If the outcome model is correct: the expected value of the IPW correction term approaches zero, so $\\hat\\mu_a(X_i)$ contributes directly.\n", + "- If the propensity model is correct: the IPW correction term removes the bias of the outcome model.\n", + "\n", + "This score is used to estimate the value of each learned policy." ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 16, "id": "c51d8c52", "metadata": {}, "outputs": [], "source": [ - "Y_net_train = Y_net[train_idx]\n", - "Y_net_test = Y_net[test_idx]\n", - "\n", "e_hat_net = np.clip(e_hat_raw, 0.02, 0.98)\n", "e_hat_net = e_hat_net / e_hat_net.sum(axis=1, keepdims=True)\n", "\n", "gamma_net = np.zeros((len(X_test), 4))\n", "mu_hat_net = np.zeros((len(X_test), 4))\n", "\n", - "for arm_id in range(4):\n", + "for treatment_id in range(4):\n", " outcome_model = RandomForestRegressor(\n", " n_estimators=400,\n", " min_samples_leaf=20,\n", " random_state=SEED,\n", " n_jobs=1,\n", " )\n", - " outcome_model.fit(X_train[A_train == arm_id], Y_net_train[A_train == arm_id])\n", + " outcome_model.fit(X_train[A_train == treatment_id], Y_net_train[A_train == treatment_id])\n", " mu_a = outcome_model.predict(X_test)\n", - " observed_a = (A_test == arm_id).astype(float)\n", + " observed_a = (A_test == treatment_id).astype(float)\n", "\n", - " gamma_net[:, arm_id] = mu_a + observed_a / e_hat_net[:, arm_id] * (Y_net_test - mu_a)\n", - " mu_hat_net[:, arm_id] = mu_a" + " gamma_net[:, treatment_id] = mu_a + observed_a / e_hat_net[:, treatment_id] * (Y_net_test - mu_a)\n", + " mu_hat_net[:, treatment_id] = mu_a" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 17, "id": "0349d9ab", "metadata": {}, "outputs": [ @@ -1806,7 +1844,7 @@ "4 3378.748035 5743.802939 2988.217314 " ] }, - "execution_count": 11, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -1818,12 +1856,12 @@ " {\n", " 'sample': pd.DataFrame({\n", " 'sample_id': sample_ids,\n", - " 'actual_arm': pd.Series(A_test[sample_ids]).map(ARM_NAMES).to_numpy(),\n", + " 'actual_arm': pd.Series(A_test[sample_ids]).map(TREATMENT_NAMES).to_numpy(),\n", " 'observed_net_outcome': Y_net_test[sample_ids],\n", " }),\n", - " 'e_hat': pd.DataFrame(e_hat_net[sample_ids], columns=ARM_LABELS),\n", - " 'mu_hat': pd.DataFrame(mu_hat_net[sample_ids], columns=ARM_LABELS),\n", - " 'gamma': pd.DataFrame(gamma_net[sample_ids], columns=ARM_LABELS),\n", + " 'e_hat': pd.DataFrame(e_hat_net[sample_ids], columns=TREATMENT_LABELS),\n", + " 'mu_hat': pd.DataFrame(mu_hat_net[sample_ids], columns=TREATMENT_LABELS),\n", + " 'gamma': pd.DataFrame(gamma_net[sample_ids], columns=TREATMENT_LABELS),\n", " },\n", " axis=1,\n", ")\n", @@ -1878,7 +1916,7 @@ " \n", " \n", " 4\n", - " plugin_drlearner_4arm\n", + " plugin_drlearner_4treatment\n", " 11810.377712\n", " 11283.561563\n", " 12337.193861\n", @@ -1889,7 +1927,7 @@ " \n", " \n", " 6\n", - " dr_policy_forest_4arm\n", + " dr_policy_forest_4treatment\n", " 11625.627540\n", " 11102.022589\n", " 12149.232491\n", @@ -1900,7 +1938,7 @@ " \n", " \n", " 5\n", - " dr_policy_tree_4arm\n", + " dr_policy_tree_4treatment\n", " 11148.462369\n", " 10700.609230\n", " 11596.315508\n", @@ -1958,14 +1996,14 @@ "" ], "text/plain": [ - " policy value ci_lower ci_upper \\\n", - "4 plugin_drlearner_4arm 11810.377712 11283.561563 12337.193861 \n", - "6 dr_policy_forest_4arm 11625.627540 11102.022589 12149.232491 \n", - "5 dr_policy_tree_4arm 11148.462369 10700.609230 11596.315508 \n", - "1 all_tech_support_only 10526.523876 9833.034751 11220.013001 \n", - "3 all_discount_plus_support 10136.708882 9439.445941 10833.971824 \n", - "2 all_discount_only 7545.079205 6993.536621 8096.621788 \n", - "0 all_none 7249.345467 6955.447432 7543.243502 \n", + " policy value ci_lower ci_upper \\\n", + "4 plugin_drlearner_4treatment 11810.377712 11283.561563 12337.193861 \n", + "6 dr_policy_forest_4treatment 11625.627540 11102.022589 12149.232491 \n", + "5 dr_policy_tree_4treatment 11148.462369 10700.609230 11596.315508 \n", + "1 all_tech_support_only 10526.523876 9833.034751 11220.013001 \n", + "3 all_discount_plus_support 10136.708882 9439.445941 10833.971824 \n", + "2 all_discount_only 7545.079205 6993.536621 8096.621788 \n", + "0 all_none 7249.345467 6955.447432 7543.243502 \n", "\n", " rate_none rate_tech_support_only rate_discount_only \\\n", "4 0.092 0.500 0.035 \n", @@ -1993,10 +2031,10 @@ ], "source": [ "policy_assignments = {\n", - " **{f'all_{arm_name}': np.full(len(X_test), arm_id) for arm_id, arm_name in ARM_NAMES.items()},\n", - " 'plugin_drlearner_4arm': pi_plugin,\n", - " 'dr_policy_tree_4arm': pi_tree,\n", - " 'dr_policy_forest_4arm': pi_forest,\n", + " **{f'all_{treatment_name}': np.full(len(X_test), treatment_id) for treatment_id, treatment_name in TREATMENT_NAMES.items()},\n", + " 'plugin_drlearner_4treatment': pi_plugin,\n", + " 'dr_policy_tree_4treatment': pi_tree,\n", + " 'dr_policy_forest_4treatment': pi_forest,\n", "}\n", "\n", "eval_rows = []\n", @@ -2009,15 +2047,15 @@ " 'value': scores.mean(),\n", " 'value_se': scores.std(ddof=1) / np.sqrt(len(scores)),\n", " }\n", - " for arm_id, arm_name in ARM_NAMES.items():\n", - " row[f'rate_{arm_name}'] = np.mean(pi == arm_id)\n", + " for treatment_id, treatment_name in TREATMENT_NAMES.items():\n", + " row[f'rate_{treatment_name}'] = np.mean(pi == treatment_id)\n", " eval_rows.append(row)\n", "\n", "policy_eval = pd.DataFrame(eval_rows)\n", "policy_eval['ci_lower'] = policy_eval['value'] - 1.96 * policy_eval['value_se']\n", "policy_eval['ci_upper'] = policy_eval['value'] + 1.96 * policy_eval['value_se']\n", "\n", - "display_cols = ['policy', 'value', 'ci_lower', 'ci_upper'] + [f'rate_{n}' for n in ARM_LABELS]\n", + "display_cols = ['policy', 'value', 'ci_lower', 'ci_upper'] + [f'rate_{n}' for n in TREATMENT_LABELS]\n", "policy_eval.sort_values('value', ascending=False)[display_cols]" ] }, @@ -2029,7 +2067,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -2042,15 +2080,15 @@ "fig, axes = plt.subplots(1, 2, figsize=(15, 5))\n", "plot_df = policy_eval.sort_values('value', ascending=False)\n", "sns.barplot(data=plot_df, x='value', y='policy', ax=axes[0], color='seagreen')\n", - "axes[0].set_title('4-arm net policy value')\n", + "axes[0].set_title('4-way net policy value')\n", "axes[0].set_xlabel('AIPW-estimated net value')\n", "axes[0].set_ylabel('')\n", "\n", - "arm_rate_cols = [f'rate_{name}' for name in ARM_LABELS]\n", - "rate_df = policy_eval.set_index('policy')[arm_rate_cols]\n", - "rate_df.columns = ARM_LABELS\n", - "rate_df.loc[['plugin_drlearner_4arm', 'dr_policy_tree_4arm', 'dr_policy_forest_4arm']].plot(kind='barh', stacked=True, ax=axes[1], colormap='tab20')\n", - "axes[1].set_title('Assigned arm share')\n", + "treatment_rate_cols = [f'rate_{name}' for name in TREATMENT_LABELS]\n", + "rate_df = policy_eval.set_index('policy')[treatment_rate_cols]\n", + "rate_df.columns = TREATMENT_LABELS\n", + "rate_df.loc[['plugin_drlearner_4treatment', 'dr_policy_tree_4treatment', 'dr_policy_forest_4treatment']].plot(kind='barh', stacked=True, ax=axes[1], colormap='tab20')\n", + "axes[1].set_title('Assigned treatment share')\n", "axes[1].set_xlabel('Share')\n", "axes[1].set_ylabel('')\n", "axes[1].legend(loc='center left', bbox_to_anchor=(1.0, 0.5))\n", @@ -2086,7 +2124,7 @@ "source": [ "### Estimating Per-Customer Expected Costs\n", "\n", - "To construct cost curves, we first estimate $E[C(a) \\mid X_i]$ per customer. Since actual costs are observed only for the treatment the customer received, we fit a separate cost regression model on the training data for each arm and apply it to the test set." + "To construct cost curves, we first estimate $E[C(a) \\mid X_i]$ per customer. Since actual costs are observed only for the treatment the customer received, we fit a separate cost regression model on the training data for each treatment and apply it to the test set." ] }, { @@ -2107,22 +2145,22 @@ } ], "source": [ - "# Estimate E[C(a) | X] per arm\n", - "# c_hat_test[i, a] = predicted cost for customer i under arm a\n", - "c_true_by_arm = {1: c_tech, 2: c_disc, 3: c_tech + c_disc}\n", + "# Estimate E[C(a) | X] per treatment\n", + "# c_hat_test[i, a] = predicted cost for customer i under treatment a\n", + "c_true_by_treatment = {1: c_tech, 2: c_disc, 3: c_tech + c_disc}\n", "\n", - "c_hat_test = np.zeros((len(X_test), 4)) # arm 0 → cost = 0\n", - "for arm_id in [1, 2, 3]:\n", - " mask_train = (A_train == arm_id)\n", + "c_hat_test = np.zeros((len(X_test), 4)) # treatment 0 → cost = 0\n", + "for treatment_id in [1, 2, 3]:\n", + " mask_train = (A_train == treatment_id)\n", " mdl = RandomForestRegressor(n_estimators=300, min_samples_leaf=20,\n", " random_state=SEED, n_jobs=1)\n", - " mdl.fit(X_train[mask_train], c_true_by_arm[arm_id][train_idx[mask_train]])\n", - " c_hat_test[:, arm_id] = np.maximum(mdl.predict(X_test), 200.0)\n", + " mdl.fit(X_train[mask_train], c_true_by_treatment[treatment_id][train_idx[mask_train]])\n", + " c_hat_test[:, treatment_id] = np.maximum(mdl.predict(X_test), 200.0)\n", "\n", "print(\"Estimated E[C(a)|X] — test set (mean ± std):\")\n", - "for arm_id, arm_name in ARM_NAMES.items():\n", - " if arm_id > 0:\n", - " print(f\" {arm_name:25s}: {c_hat_test[:, arm_id].mean():8,.0f} ± {c_hat_test[:, arm_id].std():6,.0f}\")" + "for treatment_id, treatment_name in TREATMENT_NAMES.items():\n", + " if treatment_id > 0:\n", + " print(f\" {treatment_name:25s}: {c_hat_test[:, treatment_id].mean():8,.0f} ± {c_hat_test[:, treatment_id].std():6,.0f}\")" ] }, { @@ -2162,7 +2200,7 @@ "source": [ "def policy_cost_curve(pi_eval, gamma_matrix, c_hat_matrix):\n", " \"\"\"\n", - " Generate cost curves using per-arm estimated customer costs.\n", + " Generate cost curves using per-treatment estimated customer costs.\n", " c_hat_matrix[i, a] = Ê[C(a) | X_i]\n", "\n", " x = cumulative average treatment cost per customer (gross)\n", @@ -2267,4 +2305,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} diff --git a/book/who_should_be_treated/who_should_be_treated_ko.ipynb b/book/who_should_be_treated/who_should_be_treated_ko.ipynb index 335872f..1d986f9 100644 --- a/book/who_should_be_treated/who_should_be_treated_ko.ipynb +++ b/book/who_should_be_treated/who_should_be_treated_ko.ipynb @@ -11,7 +11,7 @@ "\n", "인과추론의 많은 문제는 평균 처치 효과(ATE)에 집중합니다. 예를 들어 “프로모션을 진행할 것인가, 하지 않을 것인가?”라는 질문은 모든 고객에게 동일한 프로모션을 제공하는 정책과 아무에게도 제공하지 않는 정책을 비교하는 문제입니다.\n", "\n", - "하지만 예산이 제한되어 있고 그 안에서 최대 효과를 내야 한다면 이야기가 달라집니다. 프로모션 효과가 고객마다 크게 다르다면, 모두에게 동일하게 제공하는 전략이 최선이 아닐 수 있습니다.\n", + "하지만 예산이 제한되어 있고 그 안에서 최대 효과를 내야 한다면 이야기가 달라집니다. 프로모션 효과가 고객마다 다르다면, 모두에게 동일하게 제공하는 전략이 최선이 아닐 수 있습니다.\n", "\n", "- 이미 구매할 고객(always-converters)에게 불필요한 비용을 쓰게 되고,\n", "- 마케팅에 부정적으로 반응하는 고객(sleeping dogs) 때문에 오히려 손실이 발생하며,\n", @@ -19,9 +19,9 @@ "\n", "따라서 핵심 질문은 다음과 같습니다.\n", "\n", - "_\"어떤 고객에게 집중할 때 순이익이 가장 커질까?\"_\n", + "_\"어떤 고객을 대상으로 해야 순이익이 가장 커질까?\"_\n", "\n", - "정책학습(policy learning)은 가능한 처치 규칙들을 탐색하면서 전체 평균 순수익을 최대화하는 방법을 학습합니다. 즉, “누구를 처치해야 하는가?”라는 질문에 답하는 접근입니다." + "이번에는 가능한 처치 규칙들을 탐색해 전체 평균 순수익을 근사적으로 최대화하는 방법을 배웁니다. 다시 말해, “누구를 처치해야 하는가?”라는 질문에 답하는 문제를 다룹니다. 우리는 이를 정책학습(policy learning)이라고 부릅니다." ] }, { @@ -34,7 +34,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 52, "id": "fc2741f9", "metadata": { "execution": { @@ -59,7 +59,6 @@ "import numpy as np\n", "import pandas as pd\n", "import seaborn as sns\n", - "from scipy import stats\n", "from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor\n", "from sklearn.model_selection import train_test_split\n", "\n", @@ -81,15 +80,15 @@ "source": [ "## 정책학습 문제 정의\n", "\n", - "한 소프트웨어 판매 회사는 할인이나 기술지원 같은 인센티브가 고객의 소프트웨어 구매를 실제로 늘리는지, 그리고 어떤 고객에게 더 효과적인지 알고 싶어합니다.\n", + "한 소프트웨어 판매 회사는 할인이나 기술지원 같은 프로모션이 고객의 소프트웨어 구매를 실제로 늘리는지, 그리고 어떤 고객에게 더 효과적인지 알고 싶어합니다.\n", "\n", - "이상적으로는 고객마다 서로 다른 인센티브를 무작위로 배정하는 실험(RCT)을 수행하는 것이 가장 좋지만 실제 비즈니스 환경에서는 비용 문제, 영업 전략, 대형 고객을 놓칠 위험 등으로 인해 무작위 실험을 수행하기 어렵습니다. 따라서 이번 데이터는 무작위 실험 데이터가 아닌 관찰 데이터(observational data)입니다.\n", + "고객마다 서로 다른 프로모션을 무작위로 배정하는 실험(RCT)을 수행하는 것이 가장 이상적이지만 실제 비즈니스 환경에서는 비용 문제, 영업 전략, 대형 고객을 놓칠 위험 등으로 인해 무작위 실험을 수행하기 어렵습니다. 따라서 이번 데이터는 무작위 실험 데이터가 아닌 관찰 데이터입니다.\n", "\n", "데이터에는 약 2,000명의 고객 정보가 포함되어 있으며, 다음과 같은 변수들로 구성됩니다.\n", "\n", "- 고객 특성: 각 고객의 산업 분야, 규모, 매출, 기술 프로필에 대한 세부 정보.\n", - "- 개입: 고객에게 제공된 인센티브에 대한 정보.\n", - "- 결과: 인센티브 제공 이후 1년 동안의 소프트웨어 구매 금액\n", + "- 개입: 고객에게 제공된 프로모션에 대한 정보.\n", + "- 결과: 프로모션 제공 이후 1년 동안의 소프트웨어 구매 금액\n", "\n", "| 변수명 | 타입 | 설명 |\n", "| --------------- | -- | -------------------------------------------------------------- |\n", @@ -121,7 +120,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 53, "id": "477aed7a", "metadata": { "execution": { @@ -171,8 +170,8 @@ " Tech Support\n", " Discount\n", " Revenue\n", - " arm\n", - " arm_name\n", + " treatment\n", + " treatment_name\n", " \n", " \n", " \n", @@ -268,22 +267,22 @@ "3 0 0 0 0 30288 \n", "4 0 0 1 0 25930 \n", "\n", - " Employee Count PC Count Size Tech Support Discount Revenue arm \\\n", - "0 26 26 152205 0 1 17688.36300 2 \n", - "1 107 70 159038 0 1 14981.43559 2 \n", - "2 10 7 264935 1 1 32917.13894 3 \n", - "3 40 39 77522 1 1 14773.76855 3 \n", - "4 37 43 91446 1 1 17098.69823 3 \n", + " Employee Count PC Count Size Tech Support Discount Revenue \\\n", + "0 26 26 152205 0 1 17688.36300 \n", + "1 107 70 159038 0 1 14981.43559 \n", + "2 10 7 264935 1 1 32917.13894 \n", + "3 40 39 77522 1 1 14773.76855 \n", + "4 37 43 91446 1 1 17098.69823 \n", "\n", - " arm_name \n", - "0 discount_only \n", - "1 discount_only \n", - "2 discount_plus_support \n", - "3 discount_plus_support \n", - "4 discount_plus_support " + " treatment treatment_name \n", + "0 2 discount_only \n", + "1 2 discount_only \n", + "2 3 discount_plus_support \n", + "3 3 discount_plus_support \n", + "4 3 discount_plus_support " ] }, - "execution_count": 2, + "execution_count": 53, "metadata": {}, "output_type": "execute_result" } @@ -304,29 +303,29 @@ "]\n", "outcome = 'Revenue'\n", "\n", - "ARM_NAMES = {\n", + "TREATMENT_NAMES = {\n", " 0: 'none',\n", " 1: 'tech_support_only',\n", " 2: 'discount_only',\n", " 3: 'discount_plus_support',\n", "}\n", - "ARM_LABELS = [ARM_NAMES[i] for i in range(4)]\n", + "TREATMENT_LABELS = [TREATMENT_NAMES[i] for i in range(4)]\n", "\n", "required_columns = covariates + ['Tech Support', 'Discount', outcome]\n", "for col in required_columns:\n", " data[col] = pd.to_numeric(data[col], errors='coerce')\n", "\n", "policy_df = data[required_columns].dropna().copy()\n", - "policy_df['arm'] = (\n", + "policy_df['treatment'] = (\n", " 2 * policy_df['Discount'].astype(int)\n", " + policy_df['Tech Support'].astype(int)\n", ").astype(int)\n", - "policy_df['arm_name'] = policy_df['arm'].map(ARM_NAMES)\n", + "policy_df['treatment_name'] = policy_df['treatment'].map(TREATMENT_NAMES)\n", "\n", "n = len(policy_df)\n", "X = policy_df[covariates].to_numpy()\n", "Y = policy_df[outcome].to_numpy(dtype=float)\n", - "A = policy_df['arm'].to_numpy(dtype=int)\n", + "A = policy_df['treatment'].to_numpy(dtype=int)\n", "\n", "print(policy_df.shape)\n", "policy_df.head()\n" @@ -339,7 +338,7 @@ "source": [ "### 비용 설정\n", "\n", - "실제 운영 환경에서는 인센티브 효과뿐 아니라 비용도 함께 고려해야 합니다. 또한 같은 인센티브라도 고객 특성에 따라 비용이 달라질 수 있습니다.\n", + "수익 극대화를 위해서는 매출과 함께 비용도 고려해야 합니다. 또한 동일한 프로모션이라도 고객 특성에 따라 비용이 달라질 수 있습니다.\n", "\n", "예를 들어 기술지원은 직원 수가 많은 기업일수록 더 많은 지원 시간이 필요할 수 있고, 할인 역시 규모가 큰 고객일수록 더 큰 할인 폭을 요구할 수 있습니다.\n", "\n", @@ -365,12 +364,12 @@ "Y_i^{net} = Y_i - C_i(A_i)\n", "$$\n", "\n", - "이후 $Y_i^{net}$을 outcome으로 사용해 AIPW score를 계산하면, $\\hat\\Gamma_{i,a}$는 자연스럽게 $E[Y(a) - C(a) \\mid X_i]$를 추정합니다. 따라서 AIPW 기반 정책학습과 평가가 자동으로 비용을 반영합니다." + "결과적으로 AIPW score가 순이익을 기준으로 계산되므로, 비용을 고려한 정책 최적화와 평가가 가능해집니다." ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 54, "id": "5174ddd2", "metadata": { "execution": { @@ -402,7 +401,7 @@ " \n", " \n", " \n", - " arm\n", + " treatment\n", " mean_cost\n", " std_cost\n", " mean_revenue\n", @@ -447,7 +446,7 @@ "" ], "text/plain": [ - " arm mean_cost std_cost mean_revenue \\\n", + " treatment mean_cost std_cost mean_revenue \\\n", "0 none 0.000000 0.000000 6585.891792 \n", "1 tech_support_only 5037.815894 4917.641358 15104.111534 \n", "2 discount_only 5181.570334 3043.343172 12247.935953 \n", @@ -460,7 +459,7 @@ "3 14118.841353 " ] }, - "execution_count": 3, + "execution_count": 54, "metadata": {}, "output_type": "execute_result" } @@ -494,7 +493,7 @@ "Y_net = Y - C_obs\n", "\n", "pd.DataFrame({\n", - " 'arm': ARM_LABELS,\n", + " 'treatment': TREATMENT_LABELS,\n", " 'mean_cost': [C_obs[A == a].mean() if (A == a).sum() > 0 else 0.0 for a in range(4)],\n", " 'std_cost': [C_obs[A == a].std() if (A == a).sum() > 0 else 0.0 for a in range(4)],\n", " 'mean_revenue': [Y[A == a].mean() for a in range(4)],\n", @@ -502,6 +501,50 @@ "})" ] }, + { + "cell_type": "markdown", + "id": "dc34dc79", + "metadata": {}, + "source": [ + "### 데이터 분할\n", + "\n", + "정책학습과 평가를 같은 데이터로 수행하면 과대추정이 발생할 수 있습니다. 따라서 데이터를 train/test로 나눠 학습과 평가를 분리합니다." + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "1272f0ea", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train: 1000 \n", + "Test: 1000\n" + ] + } + ], + "source": [ + "idx = np.arange(len(policy_df))\n", + "train_idx, test_idx = train_test_split(\n", + " idx,\n", + " test_size=0.5,\n", + " stratify=A,\n", + " random_state=SEED,\n", + ")\n", + "\n", + "X_train = X[train_idx]\n", + "X_test = X[test_idx]\n", + "A_train = A[train_idx]\n", + "A_test = A[test_idx]\n", + "Y_net_train = Y_net[train_idx]\n", + "Y_net_test = Y_net[test_idx]\n", + "\n", + "print(f\"Train: {len(train_idx)} \\nTest: {len(test_idx)}\")" + ] + }, { "cell_type": "markdown", "id": "024e5803", @@ -519,7 +562,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 57, "id": "3d0f329e", "metadata": {}, "outputs": [ @@ -548,7 +591,7 @@ " share\n", " \n", " \n", - " arm\n", + " treatment\n", " \n", " \n", " \n", @@ -580,7 +623,7 @@ ], "text/plain": [ " count share\n", - "arm \n", + "treatment \n", "none 517 0.2585\n", "tech_support_only 462 0.2310\n", "discount_only 477 0.2385\n", @@ -592,10 +635,10 @@ } ], "source": [ - "arm_counts = policy_df['arm_name'].value_counts().reindex(ARM_LABELS).rename_axis('arm').to_frame('count')\n", - "arm_counts['share'] = arm_counts['count'] / len(policy_df)\n", + "treatment_counts = policy_df['treatment_name'].value_counts().reindex(TREATMENT_LABELS).rename_axis('treatment').to_frame('count')\n", + "treatment_counts['share'] = treatment_counts['count'] / len(policy_df)\n", "\n", - "display(arm_counts)" + "display(treatment_counts)" ] }, { @@ -617,7 +660,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 58, "id": "d6488c62", "metadata": {}, "outputs": [ @@ -633,15 +676,15 @@ } ], "source": [ - "arm_covariate_means = (\n", + "treatment_covariate_means = (\n", " policy_df\n", - " .groupby('arm_name')[covariates]\n", + " .groupby('treatment_name')[covariates]\n", " .mean()\n", - " .reindex(ARM_LABELS)\n", + " .reindex(TREATMENT_LABELS)\n", ")\n", "\n", "plt.figure(figsize=(10, 5))\n", - "sns.heatmap(arm_covariate_means.T, annot=True, fmt='.2f', cmap='YlGnBu', cbar_kws={'label': 'Mean'})\n", + "sns.heatmap(treatment_covariate_means.T, annot=True, fmt='.2f', cmap='YlGnBu', cbar_kws={'label': 'Mean'})\n", "plt.xlabel('Treatments')\n", "plt.ylabel('Covariate')\n", "plt.title('Mean customer characteristics by treatments')\n", @@ -667,7 +710,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 59, "id": "c925a3d8", "metadata": {}, "outputs": [ @@ -692,8 +735,8 @@ " \n", " \n", " \n", - " arm\n", - " arm_name\n", + " treatment\n", + " treatment_name\n", " count\n", " mean\n", " median\n", @@ -752,11 +795,11 @@ "" ], "text/plain": [ - " arm arm_name count mean median \\\n", - "0 0 none 517 6585.891792 6123.187067 \n", - "1 1 tech_support_only 462 15104.111534 14483.719320 \n", - "2 2 discount_only 477 12247.935953 10454.932500 \n", - "3 3 discount_plus_support 544 26784.124649 23560.252890 \n", + " treatment treatment_name count mean median \\\n", + "0 0 none 517 6585.891792 6123.187067 \n", + "1 1 tech_support_only 462 15104.111534 14483.719320 \n", + "2 2 discount_only 477 12247.935953 10454.932500 \n", + "3 3 discount_plus_support 544 26784.124649 23560.252890 \n", "\n", " std min max \n", "0 3363.163462 -616.572451 21445.05937 \n", @@ -770,7 +813,7 @@ }, { "data": { - "image/png": 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mXIdh27Zt8uyzz8oNN9wgCxculAULFkj16tXl1VdflQMHDpi1NXLkSLnjjjvk3Xffldq1a6tgaNCgQXLdddep5yKwmDFjhjFAQcCDoAmvP2LECPX+sPLykCFDZMeOHU446qRhChMRERGRF8nOzpa0tDTVScfIAmAk4cqVKzJz5kzVaYecnBzVMUcn2lS9evXU/drzcGUfawMgeAgMDJS2bduqQGDfvn027xtec9myZWqVcMB+jh07Vo2MYLTh2LFj0rt3bzVSocFoAkY9MPLQpEkT4/19+vRRoyraGgYYTUDQ8Pzzz6v7GjRoIJs3b1b7ifu//PJLOXz4sHz22WfGdtq3b68CIgQzH3zwgc3vhyxjAEFERETkRTB6gE46nD9/Xk6ePCmnTp2SH3/80RhgQHR0dKHgQeuwazAqUbZsWTUigOBBg+di1W9bITjRggeoVKmSus3IyFC3GA3QAgvs97///it//vmn2X5b2s+YmBh1axpgYL9B20+kKlWoUEG9FwQymttvv11ef/11SUlJUStRk+MYQBARERF5mV9++UVee+01OXHihISHh6ur8dpKw1oKE+63xLSDr9Ge66jQ0FCz3/3987Pl8/Ly1G1iYqJKRUIdBBY2q1mzprRo0cJsv6+1nwXbL1j/cPHiRRVAWIKRmYoVK9rxrsgjAwicVMhTwxAaoshbbrlFFcAgJ86Sy5cvq4Kfn3/+WZ183bt3V7mApifVpk2bVF7d2bNnpU6dOmr4rHXr1ja1odm7d68MGDDAWJikQa4h8giRs4c/EOw3ipKqcGEzIiIiKiG4ao86gs6dO6v6APSX0JdBITICC082atQoFfSg+BkjDBhNwegE0o4cFRkZKbVq1VKpWJb6muyf+VgRNQphVq9eLVOnTpVPPvlEfcgY4io4lKUZPny4nD59Wp188+bNk59++kkmT55sfByFMqNHj1aV/1988YUKHFBkdPz4cavbMA0ennnmGWPkbBqAIC8PQ2EoMlq6dKmKqrHfts5aQERERGStv/76S/U10LepUaOGCh5ACx4KXsn3JOhXYeYo1DwgeABczIWCfS1boZ4DheBId8IMTNrP9u3bVcqXVkROPhBAIEjA7AHo0GOKLgzBzZ07V+Lj41VhTEH79++XXbt2qanGMESF4ACFQCicQR4goDOPqByV+nXr1lWjD9h25cqVVreB3DlU9qOav2rVqoX2A0Nv6enpKqfu+uuvV4VBGI1AkGJP0RERERGRNbR6BfQ70DlG7QNmOsIMR4D+iadCsfOGDRtUnwtF04sWLVLZGwiCtDoJe913331qlAEXeHEBGReUUTyNC8VIXdLpdE57H6Wd2wMIVMujkMY0vSgqKkoaNWoku3fvLrQ9piRDgQwCA9OIEyceolpEr+jAm7YHiHS19oprQ/vjw/bvvfeeSl8qCO1j5MS0GEfL80ORDhEREVFJQN0A1oDARc+nn35apX0DMiLQl0E/x1NhligUQiPrBGlYmHJ1ypQpauYnR/cbdRxI48L6FgiunnjiCXUxGlPY4mIy+VANBEYaIDY21ux+RIraY6bwx1JwWwyBYbYADFuh847Of8FZB0zbK64NLYhZt26d+rd2a6patWrqxxQWTkFAgVoIIiIiopKCxdfwY+nC7LUcOXKk0H1bt24tdJ89a0BYeg4u4Jq+JrI6Fi9eXGi7nj17FvkcQJ/L0r4XvA/pSyguL0hbh4J8JIDQhqu0PDhNcHCwJCcnW9y+4Lba9sgHxEqKRbWn1SYU14Y98EezatUqNSdzuXLl7GoDOYueMOyofSaODiVaQ/u8srL14mfjscdztDb8PeC4lXauPG/Id/C8IVvg/9Fafj9u2SksedYeZ9QXaLUYnojnjajPEZk6+N61VG+CY2TtZ+j2AEJLAUIthGk6EL4kLM2IhG0sFVdjewxdIQjQ2iv4uNZecW3YAgcbuXXI4cMwIhYrsRcWcSk405M7YU7pkpYXlz8qFB8XJ37ZtgUQhoREdYt5pP0z2WktTecN+R6eN2Qtba0CTljiGl999ZXFSWYKQhaGNh2rJyvN501WVpaq8cUsWEWxdIHdIwMILZXowoULaiYBDX6vX79+oe2RmoQCZlMIBjD3L9KUkIaEIADPN4XftcVMimvDlg4/lo7/+uuv1e2jjz4qjkBxDxZgcTdEpvifOaZCu9Z8y86QHhIqGHysHBsrIbGFF7u5lsygYDkropa3D6tdq8T2kTzvvCHfwfOGbO0A/ffff+rfuGDoyVe8fQVmTEJdanHw/2JPXqQNF3xx/pT28yYwMFD1t7UL7qawSrjV7YibYdYlLBSCSnwtgEAdw8GDBy0WL6O+APP7YgpWFBEBZlQCFM3gpGjWrJm6r2/fvsbnoX0tMi6uDWth3Yjvv/9eFTJhHQlHYd+dtZCLM6ATWNL7k3f1yyY4SCchFk7mazEE5c+mgC8sTzpupZ0rzhvyPTxvyBqYrETr/OGW03KWPNQUaKtAezMtDas0nzcBAQHqbwjft5aCPVsCK7cHEBgqQaCADj1qB1Bcg8p5jBIg6sUHjvUVsDgI3iwq9xEgjBgxQg2poWYAsw/06tXLOMKA6bswNzIi5vbt28vatWtVatD06dPV49a0URwUVm/cuFEFEZjBCSsfarR9JSIiIiLyNW6fxhWwBsT999+vCpAfeughFSFhwQ+k9GBWJEzthc66Fh1h1WpU42ONhhdeeEEFCab5edgeFfgff/yx9O7dW80DjIp/bdpWa9ooDtKWAOtA4PVMf7R9JSIiIiLyNW4fgQAEDFg5Gj8FWZq2C0Np8+fPv2abGE3AT1GsacN0YRL8mMLid0REREREpY1HjEAQEREREZF3YABBRERERETelcJERERE5IgLl9MlJa3wGk+uEBUeJBXLcvY5Kj0YQBAREZHXBw/PzNoqWfriV0wuCcG6AFk49g6nBBFYA2vGjBlqvYsvvvhCtm7dKt5i7969ar0FVy4oN27cOHWsPvzwQ5e9JjGAICIiIi+HkQcEDw90uk4quHgk4OLldPlsy1G1D84chXjsscfk4YcfFm/Sv39/Ffx4w4rU5BiOQBAREZFPQPBQtUKE+ILw8HD1Q+SJWERNRERE5Abx8fHy9NNPS9OmTdV6VBs2bDA+tmDBArnjjjuMv69fv166d+8ujRs3lnbt2qnFcbOz/7/m448//pBHH31UtXXbbbfJK6+8IhkZGeoxLMq7YsUKueuuu9TzcYu1sjQ7d+5UqVNnz54t8r6BAweqRX9feuklNcKABXlffPFFuXLlinoc28L48eNVWpG18L569uwpN910k3q/CxcuNK4ajddGu99995307dtXbrzxRrXNp59+arGtZ599VgYNGmR234kTJ1QbR48etXqfqHgMIIiIiIhcLCcnR4YMGSKXL1+WVatWybx589QiupYcPnxYLbY7bNgw1ZnGYrlffvmlvPfee+rxM2fOqIVxK1asqDrXCD62b98uU6ZMUY/PnDlTdcyfe+45FaQgNQoBCIIKW2D78uXLy5o1a2T27NmyZcsWYxu//vqrukWAMWHCBKvbe/nll+XBBx+Ur776Sp5//nl1DLC/ppAW9dRTT8mmTZukY8eOauFfvOeCsGbXrl271CLEpgEKgqbrrrvOpvdK18YUJiIiIiIX++2339RV8e+//15q1Khh7ChbWgQXV+L9/PykatWqUqVKFfWDjnZERH661meffSbR0dEqsAgMzO/aTZs2Tfbv369GCDDagFGBe+65Rz1Wq1Yt1eaSJUtU4GGtevXqyciRI41ttGnTRr0GVKhQQd1GRkaqn+Kg2Hrp0qUyYMAAY60H2kxKSlLByfDhw43bYmSlU6dO6t8jRoyQjz76SA4cOCDVq1c3a7NDhw4qwEEw8uSTT0peXp4KtIYOHWr1eyTrcASCiIiIyMX++ecfKVOmjDF4gIYNG0pISEihbZGyhNSk+++/X3WkJ02aJImJiarDrbV1ww03GIMHuPXWW1V6FFJ49Hq9NG/e3KzNli1bSkJCgvqxVp06dcx+R6BgmkZlC+z/pUuXLO4X9hf7ralbt67ZawK2KQjvH+lQCBpgx44d6nV69Ohh1z5S0RhAEBEREbkYRhRwhbwg0yBAExwcLB988IGa1hXpPqdOnVIpPUgXKuo5plf6LdFeu6jnanUIpoKCgsRZbNkvS69b1PP79Okjx48fl7/++kuNRCDgQqBGzsUAgoiIiMjFMNqQmppqVtyLwEArSjb1008/ydtvvy2NGjVS6TgIJpDis3HjRmNq0cGDB806/UiNQsExrt7rdDq1RoOpPXv2qLQjdK7xOJi+NvalJCHVCD+W9gv7YzoyYwu8X4zWoF4CNRqoiyDnYwBBRERE5GKtWrWSJk2ayJgxY+T333+XP//8U/3b379w1wwd6nfeeUcVHaN4GFfXt23bpjrK2voLKMbGzEu4+r579255/fXXVRoT6iQwajF//nz5+uuv5fTp06qGYPXq1WqtCYyEXH/99RIWFqZqIv7991/55Zdf5P3337f5PaENvD72xRqPP/64KiDHvmC/UOCNQAn7a00dRVEwCoF2kQ6GOg1yPhZRExERkU/Aom7e8poIFN59911V7IyOPDq7KPzFqsoFYVpWzJq0fPlymTt3rtoWBcPadKmVKlVSj6H4GEXYGFXo1q2bseAZU6uWLVtWTcOKugPUTqCO4oEHHlCPI8jAc/E4ntegQQMZO3asmhbVFngfmBkKQcTixYut2h7pSStXrlQF4JUrV5YnnnhCBRaOuPvuu2Xq1KnqWAQEBDjUFlnmZygqiYxcClceAFONuVt6erocOnRIDa/iakJJunL8hBwYOVrqPvOkhFapYtNzM86dk+ML35Umc2ZLRF3zwi7y7fOGfAfPG7JFZmam6pzGxsaqTrLWObxwOV2embVVrUbtDsG6AFk49g6nrkRN9sMoTZcuXVQak1ZojvQunD8IvkprUJGZmSknT56U2rVrWyzWt6UvyhEIIiIi8mrouKMDn5Jm34xAjooKD2Lw4AGw/gMW1ENKFGau0oIHcj4GEEREROQTQQRHADwDiruLW0xu8ODBZms9OANqL5DWhcABtRRUchhAEBEREZHToD4DK0BfS1RUlNOPOGap0ha2o5LFAIKIiIiInCY8PFz9kO/iNK5ERERERGQ1BhBERERERGQ1BhBERERERGQ1BhBERERERGQ1BhBERERERGQ1zsJEREREXi8n+aLkpqe65bUDwiIlsEwFt7w2kTswgCAiIiKvDx7OLB4uhhz3rETtFxgk1Z+a75Qgon79+jJjxgz577//5IsvvpCtW7eKt9i7d68YDAZp0aKFU9o7e/asdOrUST744ANp1aqVU9r0ZefOnVPrYHTv3r3EX4sBBBEREXk1jDwgeIhu00cCo8q79LVzUi5J0va1ah+cOQrx2GOPycMPPyzepH///ir4cVYAQbYZO3asVK1alQEEERERkbUQPOhiqvjEAeNibOTJWERNRERE5Abx8fHy9NNPS9OmTaV9+/ayYcMG42MLFiyQO+64w/j7+vXr1ZXlxo0bS7t27WT69OmSnf3/KVt//PGHPProo6qt2267TV555RXJyMhQj+Xm5sqKFSvkrrvuUs/H7ccff2x87s6dO1XqFFKGirpv4MCB8sYbb8hLL72kRhiaNWsmL774oly5ckU9jm1h/PjxMm7cOKveP7YbOXKkvPrqq6q91q1by8yZM83eV8HtsR/Xuq+441QcHEeMpOA43nLLLTJs2DCVGgQ4FnifO3fuNG5f8L7i3pO2PfazR48ectNNN8kDDzyg0r9M4fGePXuqx3EeLFy4UH2Opm28++670qZNG5Xm1bt3b9m1a5dKezM9b0oKAwgiIiIiF8vJyZEhQ4bI5cuXZdWqVTJv3jxZtmyZxW0PHz4sEydOVJ3Z7777Tl577TX58ssv5b333lOPnzlzRh555BGpWLGifPrppyr42L59u0yZMkU9jg4sOqDPPfecClKQGoWONYIKW2D78uXLy5o1a2T27NmyZcsWYxu//vqrukWAMWHCBKvb3Lx5s1y4cEE++eQTmTZtmuo4Y9/sUdxxKg466E8++aQKHL766iv13hA84D3Zwpr3hM/kqaeeUh3+OnXqqJQ1fI6A13355ZflwQcfVPvx/PPPq3MDzzGF565cuVLeeustef/991XQc/fdd6vPp6SxBoKIiIjIxX777Tc5evSofP/991KjRg11H+oHevXqVWhbXHH28/NT+e1VqlRRP+hQRkREqMc/++wziY6OVh3mwMD8rh06riioxQgBRhtwZfyee+5Rj9WqVUu1uWTJEhV4WKtevXrq6rrWBq5+4zWgQoX8+o/IyEj1Y62oqCgVjISGhsr111+vOt7obI8ePVpsVdxxKg6OFQI6BGJoo3r16qpznpCQYNN+RFnxnoYOHapGIGDq1KmyY8cO9Tni+C5dulQGDBhgrIHBsU5KSlJtDh8+3NgGRkrwmWh0Op2EhIRIuXLlpKRxBIKIiIjIxf755x8pU6aMMXiAhg0bqg5gQUjFwdXl+++/X6WrTJo0SRITE1XHUmvrhhtuMAYPcOutt6r0qBMnToher5fmzZubtdmyZUvVMbalc4wr5aYQKNiSHmQJUnTQ0dbgfWJ/T548aXNbxR2n4uDzwKgQOvRIPcKV/927dxvTs5z5nlqZzCqFjv+NN96oPkfs76VLlyx+XmgDn6emZs2a4i4MIIiIiIhcDFfK8/LyCt1vGgRogoOD1VSmSFlBWsupU6dU+ouWWmPpORpMq2qJ9tpFPVfLtzcVFBQkzobOs6X9CggIsDoVzNrjZI1Ro0apqXNfeOEFdewQTPTp06fIQCnXwnGy5j0VPO5ox9/f36bPy1KwWWoCCByQ+fPnq6jx5ptvlieeeMKYA2YJhpZQtIP8NERjyO/TioQ0mzZtkm7duqkIEEOBGCa0tQ0NilpwRcCe/SDXSD9zVq4cP2HzT8bVoigiIiJXQ98iNTVVpTFp0OHVipJN/fTTT/L2229Lo0aNVOoLOslIZdm4caN6HGksBw8eNOvMIjUKxbR169ZVHdqCRbp79uxRaUe46q51eE1fG/viCn///bfZfiMlClfva9euXWhb7GfB43P69Gmrj1NxcHUfxecxMTHy0EMPqf4p6ieOHz+u6iusPU5/W/Ge/vzzT+O/EZzgORhFQo0Jfix9Xnh90xErd3J7DQSKelavXq0KQypXrqzyuzB8hCIfS5EuTgR01FFgkpKSogp10tPTZdasWepx5JAhx2zMmDEqNw+FJDiJUMCCPyJr2tDgw3vmmWcsXiGwtg0qOf5Bwer26Nx5drfRbNECCa3iG1P+ERGR90AKS5MmTVR/BZ1WXJ3G1W5chS4IHcd33nlH5fIjNSc5OVm2bdumUmO0XHh0ltHO4MGDVRrM66+/rtKY8BxcjUdnGHUSmJ0IBc/oeyHfHiMhyNMPCwtTNRG48o5OOYpybYU20NnGRdayZcta9RwsmIeLsKjFwHOxn8j/N00B0uBCM/p1KCzGe8ct0n5wwdia41Qc7PM333wjmZmZqu+IzwKjGQiykL6FqXVRG7Fy5UpjXQKK33EMbX1PqK1AoFCtWjVZvHix6lNiNiZ4/PHHZe7cuaoGA31ZzAyFwAifI9LG8L4swf7htTG7F/rUPhtAIOJavny5Gi7q2LGjug8HDKMRqGDXiktMIzhMUYVIUgsGME0WAg78EVSqVEkVnnTu3FkGDRpkXFQDz8OHjW2taQPDYQhkPvroI/VHhRPE1v2gkhdcPkaue2G45GVn2fzcrAuX5OyatZKbkVki+0ZERK6HRd285TXROcU0nCh2xgw8SEfBDEDoABaEaVlRhIs+E/pJ2LZDhw7G6VLR78Bj6Lsg8wIdXmRiaAXPmFoVnWNMw4r8enR+UR+gdVjR4cZz8Tie16BBA9V/evbZZ216T3gf2hV7dIqtgaAAxwJ1C+gco/+G2g1LMK3poUOH1DFDXw0zDqGTrhVyF3ecioNjhH7km2++qY4NRhGwfwimtEJsBGavvfaa3HvvvaoGAccWwYat7wkjHLjojFmeEEh++OGHqnhbO464iI6+K14LwQAydBBYXEu/fv3U54bjhOwba9PA7OFnKCrZygUQUfXt21e+/fZbs2EdHFR03LXpxzT4UHEwtanCtCAEBx4fdteuXVXRCU4URGkanEQISJDaVFwb+MPBiALmUsZVAXywODmOHDli9X6gDVtpQ1m4MuBuGEnBHyiGV3E1oSQhlejAyNFS95knXToSgPSl4wvflSZzZktEXfOiMPL884Z8B88bsgWuDKNzGhsbqzrJWgcpJ/minFk8XK1G7Q5+gUFS/an5Tl2JujRAfw0BEzrPJQ3BAM4fBBUl2bEeV8x7wkxRGB3BiJFpIbUr4P2jkBt9bkv1E7b0Rd06AoEhFsAXgSlEYNpjps6fP19oW0RoGJKLi4tTHX/8z6jgsI1pe8W1oU2/tW7dOvVv7daW/SAiIiLXQccdHfjc9FS3HPaAsEgGD1SquDWA0IqOC9Y6oIreUn4XtrdUF4Hts7KyVGRVVHt43Jo2rN1vR9uwBINBCIDcTftcXFEUrn1mWdl68XPg2NkKr6e9vr8HHHNf4MrzhnwHzxuyBf4fqyVO4Na0UNUvopwERpT8/PdFsTQbT2mFjA8s6HYtyPTAZ1jwcywpqC9BPey1YAE+pEE5wlDMe9LqanHr6nMGr4fXxfeupfpe7HfBeg6PDCC04ROk/5gOpeALwlLxDLaxNI0WtkfKBDrwWnsFH9faK64Na/fb0TYswfy+SAHxFK6YgSEvLn9kKD4uTvzsqGWwlyEhUd1iKM8/kx1eZ3LVzB3kW3jekLW0aSwduWBHJQupOVi87lqQ7YHMDdOLiSUJdaqm6e2WYFYqR/dl0qRJ6raodjDD0759+665TUnB3wxqR0zXkrB3ql63BhBaGhBW6DOdlgq/W1q0A6lJP/zwg9l96MijyBlpSjgR0YHH803hd62wubg2rOGMNizB7AGmKwq6CyJT/M8cRVaWAjlnSg8JFVSXVI6NlZDYkp0xwFRmULCcFVF5gGG1rVtghjznvCHfwfOGbO0AaUXGuGho7dVSci1XrYZsC/TdUCRd2s+bwMBA1efWLrqbOnbsmPXtiBuhyh9V7Tt37jQGEKhjwFzGmO6qIKy5gBkCML2YtvoeZkMCFE/jhGjWrJm6D8XZGrTfokULq9qwhjPasAT770nFp+gElvT+5F0deQoO0kmIhZO5pBiCdMYvOU865r7AFecN+R6eN2QNzGyjdf5wW5LFsORbtHSh0nzeBAQEqL8hfN9aKqK2JbBy60JyGCZBoIDO+JYtW9QiHSNGjFBRYpcuXdSHffHiReMQD2Y5QoCAbTCDE9Z8wFARpizTRhgw/zHm8MWUW5ipAdNtIS0I03xZ20ZxnNEGEREREZE3cvtK1FiQDfPkotgG07ciOlq2bJlK58GMRm3btjWuIIjICAtpYNENBARY7KR9+/YyefJkY3vYHnPmIveud+/eqnOPuYi19RqsaaM4zmiDiIiIiMgbuX0lagQMWDkaPwWhg266/oJWfIIV/a4FIwH4KYo1bWjuu+8+9eNIG0REREREvsLtIxBEREREROQ93D4CQUREROSoS2mJkpJ1xS0HMio4QsqHe9asQ0QliQEEEREReX3w8MKmKZKdW3iNJlcICgiSt+5+xaYg4ty5c7J//37p3r27w68/cOBAqVq1qsycOVNKo8uXL6vp9U1n4CxpO3fulEGDBqlJgJByX9owgCAiIiKvhpEHBA+9G3aVCi4eCbiYlihfHPpW7YMtAcTYsWNVp98ZAURphxk3z54969IAorRjAEFEREQ+AcFDbCSnUy9tDAaDu3eh1GERNREREZELIeUIC9B+8cUXcscdd0h2drbMnj1b2rVrJ02bNpUHHnhAfv31V7PnYN2pRx99VD1+2223ySuvvKJWctekpaXJ+PHj1cK5WNR23Lhxkp6ebvU+nTp1Sh5//HH1XLwG/m06E2b9+vVl3bp1Zs8xvW/BggVqOv533nlHWrVqpfYD+3PlyhWz7T/66CP1/ho3biz33HOPSgEytW3bNvU49gFT88+YMcO4HpjWBmbBvP3229XjL774ojqOOJ54zFpYK+ypp55S+4r3jGUFtFXOtc8I65S99NJL6r1g/S+8lun70SB9Cosjmz4fHnzwQZk1a5b4IgYQRERERC6EzjY6yHfffbesWbNGdbS3b9+uOqzoDON+dG7RmYYzZ86odacqVqwon376qXo+tp8yZYqxzc2bN6vH0aFHSg/W0Fq6dKnV+zRy5Ei1GO7atWvl888/VysWP/fccza9rz///FMFPsuXL1eBxO7du9VaWabwHu+991758ssvpUOHDuo19u3bpx77/vvv5emnn5aOHTuq94H3h/eBfTO1evVqFURgTS6swYXjheNZMOgqCjr66NxjQeOVK1eq/cXCxVjc2DRAWLFihZQvX159RgjwEOzgvoKwv+XKlVPvSXPy5En5/fffpU+fPuKLmMJERERE5ELR0dFqwdyQkBBJTU2Vr7/+WtavXy8NGzZUjw8ePFgOHz6sFtZF5/Szzz5Tz8FCuYGB+V23adOmqSJszU033SQjRoxQ/65Ro4a0adNG/vrrL6v36d9//1UjG6jLwL7htU6cOCF5eXkqmLB2od233npLBSIwadIkeeKJJ1Q7derUUfdhba2HH35Y/XvUqFFq5GDVqlXqCv+SJUvkzjvvlGeeeUY9Xrt2bZWe9Oyzz8qxY8ekXr166n4EIBjB0OA4Yp8rVKhg1X4iAAkLC1PBDIIIQEDSqVMnFQRo+4fX04KXWrVqqWNqesw1+Ey0oEjbd3ye2Edtn30NRyCIiIiI3OTgwYPqtn///uoquvbzzTffqDQb+Oeff+SGG24wBg9w6623qqv1GnRwTZUpU8Ys9ac4CD7ef/99ldKD0Q+MaCAtx9rgQdsHLXgABAXa/mvQvim8V+1x3GrP0bRs2bJQGzVr1hRHoK0bb7zRGDwAgg8ELKavowU9msjISJVuZglGGpAGduDAARX0fPXVVxYXIvYVHIEgIiIicnMBMGoDwsPDzR7TOu+mgUNRAgICHNoPXHXv2rWr/PTTT/Lbb7+pK/KLFi1SV9KRxlNQTk5OofswCmAqNze30L4VfC/YRnufloqhMQJS8HkYcSiJomu8lul7MA0wilOvXj1p0qSJChwQuF26dEl69OghvoojEERERERuct1116lb5ODjyrr2gxoArUAZnVOMVGgdcq1eAAXYWVlZDu9DQkKCvPrqq6LX69VVc+T7oyOMfUKKEaBjbVofcPr06ULtIO8fKVkaLd2nUaNGZnUSprANRlcARdBaPYRmz5496rZu3brXTJ2yBV4H+2E6moAOP97TtV6nOH369FEF1d9++6107txZoqKixFcxgCAiIiJyMYw2oJgXaTGYUQizKm3dulUVTKP4+d1331W1DFp6ExZLwzZIa0JxMgqlkcYUHBzs8L4g3QkF2xMnTpRDhw6pffjkk09U0IBUH7j55ptVcTUeRzCD4uWCV+gx69OYMWNUGtD//vc/FZR069ZN1VVoULS8YcMGFWxghiLM9IQCcRgyZIhKnVq4cKF6/Mcff5SpU6eq43Otjj3qGS5cuKD22xqYLQqzVo0ePVrVmmCGq+eff17Kli3r0Loc3bt3l+TkZBX49e7dW3wZU5iIiIjIJ2BRN295zX79+qnF5Hr27Kk67yg+RtExOqAIHKZPn27shKKuADMFYWSgV69eqsOPjnnB2YnshfQgBC3o0GOqWEwPi4JuFDVrQQwCBvxgilXM9oQOd3x8vFk7sbGx6nlIh0LaEqZpRaF0wfeNmYwQZKDGAoXiuIW77rpL5syZo1KnEERgZiOkAWGK1WvBMcGIDLZFAGJah2EJVo5G4TaOpzYbEwqk8bsjowYRERFq5AGjNmjPl/kZuPqGR9CG9ExnFXAXXEHAFQZ8CSCqL0lXjp+QAyNHS91nnpTQKlXEVTLOnZPjC9+VJnNmS0Rd8yIp8vzzhnwHzxuyBXLLcQUeHVV0orXc+ktpifLCpilqNWp3CAoIkrfufsWmlah9DaaWxRS0GEW5VuoQ1nVwR3Ex0r9w/qB+wtF6kWvB+hEoBNdmxPIkeP8Y2UGxuKU6Elv6ohyBICIiIq+Gjjs68ClZhRf5coWo4IhSHTyQqNoHXETD2g9IL/N1DCCIiIjI66EDz058YUhNQjrQtWC15b59+4ovwKrRpsXmGi3hBjNKobPvbO+99566uo+aDYyQ+ToGEEREREQ+CjULXbp0ueY2MTExDr/OsGHD1M+1oGC6pKGA2VJ2PoIKzFhVcKpcZ/nkk0+kNGEAQUREROSjUCuCn9JCK/q+Vg0EOY7TuBIRERERkdUYQBARERERkdUYQBARERERkdUYQBARERERkdUYQBARERERkdU4CxMRERF5vayLF0WfkuqW19ZFRUpwhQpueW0id2AAQURERF4fPOx7drjkZWW75fX9g4Ok2TvzGURQqcEAgoiIiLwaRh4QPFS7v48EVyzv0tfOunBJzq5Zq/aBoxBUWjCAICIiIp+A4CG0ShXxBvXr15fp06fL119/Lfv27ZOoqCjp16+fPPfcc8Zttm3bJgsXLpSjR4+qFZS7d+8uI0aMMC6GZk0bP/74oyxYsECOHTsmlSpVUm0888wzEhQU5Jb3Tb6BRdREREREbjBr1izp3bu3fPPNNzJgwADV0d+9e7d67Pvvv5enn35aOnbsKOvWrZMpU6bIxo0bZeTIkVa38fPPP8sLL7wgDzzwgAoyXnnlFdm0aZOMHj2anzc5hAEEERERkRv06tVL7r33Xqlevbo89dRTagQBIwmwZMkSufPOO9VoQe3ataVTp04qANiyZYsaTbCmjcWLF6vgAaMSNWrUkLZt26pA5Ntvv5WzZ8/yMye7MYWJiIiIyA3q1q1r9ntkZKTo9Xr173/++UelG5lq2bKl8bF69eoV28bBgwfljz/+kDVr1hgfNxgM6vb48eNSrVq1Enlf5PsYQBARERG5gaU6BK2Dr92aysvLU7eBgYFWtYHthwwZolKcCqrAaWfJAUxhIiIiIvIwKJDWUpE0e/bssTjqUJTrrrtOTp48KTVr1jT+xMfHy+uvvy5paWklst9UOjCAICIiIvIwGDnYvHmzmoUJQQBmU5o6darcfvvtVgcQTzzxhHz33Xfy9ttvqzZ+++03GT9+vKSmpnIEgrw7hQnDazixP//8c3VC33LLLTJp0iRVDGTJ5cuXZdq0aWpmAT8/P5UfOGbMGAkNDTVugxkGMAsBCoTq1KkjY8eOldatWzu1jYSEBHnttddk+/btaqjwtttuk3Hjxqkp0oiIiMj1sCaDr7zmXXfdJXPmzJFFixapIKJcuXLSo0cPGT58uNVtdO3aVebOnSvvvvuuKqiOjo6WO+64Q0aNGlUi+0ylh9sDCPxRrF69WmbOnCmVK1eW2bNnq6h7w4YNFvP68IeTkZEhK1askJSUFJkwYYKkp6eracxgx44danoyBARt2rRRhUNDhw6V9evXGyN2Z7SBadFycnLk/fffVwEEZjV49tlnzQqVyPOln7F/FoqA0BCvmW+ciMiX6aIi1WrQWNDNHfDa2AdbHDlypNB9W7duNfu9W7du6seRNu6++271Q+QzAUR2drYsX75cRcKY5xgQKbdr104N2yHSNrV//37ZtWuXmgdZ68i/+uqrKuDAvMi4+r906VLp3LmzDBo0SD2OkQM8b+XKlWpbZ7SBoANt4KpAw4YN1TYIMDDVWlJSkorwybP5BwWr26Nz5znUTrNFCxhEEBG5GVaAbvbOfLUatDsgeOAq1FSauDWAOHz4sCriMU0NwvzFjRo1UougFAwgUDyEWQNMc/8wpRnSkPbu3auG6lBwhFQiU61atVIBibPawAqQWBESIxLalGpffvmlmqcZ+0+eL7h8jFz3wnDJy86ye8gaV7pyMzKdvm9ERGQ7dODZiScqBQEEZgKA2NhYs/srVqxofMzU+fPnC22LNCdc8Y+Li1MjA0hFQipUUe05ow1sj5Qr1Gq0aNFCBR94fNWqVeLvb39dOlKh8NruhvQu09uSlJmZ3wHPytaLX5Z9nXm7RUaIn0TY99xsvXH//T3gM/MErjxvyHfwvCFbZGVlmU1zmpubywNIVuF5I+rvBbXH+N7VpgQueIzQp/X4AEL7H0fBWofg4GBJTk62uL2lughsjy8VrTNqqT087qw2cIAPHTokTZs2ValP+ECQeoUUpo8//lgiIuzrlGLhF7TrKU6dOlXir5EXlx+UxcfFiZ+dowHuYEhIVLeY1cI/kx1mV5835Ht43pC1tDUQtP8nE9miNJ83WVlZqn73xIkTRW5jqY/scQEEUoG0Wgjt39obNJ0RyXR7bFsQtg8LC1OdfK29go9r7TmjDczQhNEGTKmmBQuY3QBTq6GI+tFHH7XjaIjodDrjypLuhCAL/zOvVauWxc/BmdJDQgUlYJVjYyUk1nzUx5NlBgULyq+RthZWu5a7d8cjuPK8Id/B84Zsgf8X//fff+rf+P+1tVdLiXDxF+dPaT9vAgMDpUaNGsb+rqljx45Z3464kZZKdOHCBfVmNPgdC6gUhLSiH374wew+dPRRuIwUIqQhIQjA803hd216VWe0gToKdBxNRxrKlCmj7jt9+rTdxwMnNF7bU6ATWNL7k3c1cAwO0kmIhZPZUxmCdHI5MkDO5yZLaKbtU/iF6kIkNrKi+CJXnDfke3jekDWQJoz/V2qpFgEBATxwZBUt3a00nzf+/v7qB9+3phfuNbYEVm4NIBo0aKA64Tt37jQGEKhBOHjwoAwYMKDQ9lgj4o033lCddKymCJgNCZo3b67eeLNmzdR9ffv2NT4P7aNWwVltIAj55ptvjJEsoHYBa0b07NmzxI4XeY4LuVfkg3tiRA59IGJn1tm8blN8NoggIioJGKkv7WkoRPbS6my1vyNHuDWAQJ4VAgV06LFAStWqVdU6EOigd+nSRUWLiYmJEhkZqSKlJk2aqM79iBEjZPLkyepAoJC5V69extGBwYMHqylVMZNT+/btZe3ataquYPr06epxZ7SBbZctW6bWgnj++efVfW+99ZYKJu677z63HU9ynSxDjrrtXrmVVK1m3YqgmotpifLFoW8lQ88ZnIiIbIErx+gTXLp0SXWCcBGyNKejkPXQp9QCz9I2AmG4OkkPsmmQaeOM9+/2heSwqBsKOiZOnKgKmDFCgM45vhhwRb9Tp04yY8YM1THHlwRWrcaibY888ojqsGPaVSzLrmnbtq1aIRoL1KGwGTUFqE/Qpm11RhtIdcLidwh20AaGgzA6gfvwxUalR0xQlMRGcvVxIiKXfe/GxMjFixdVZwiBBJE1MOsQ+puoAXBkxkxvFh0dXWiWUZcGEJgKVbta7yhEQVj1GT8FVatWrdAqi/jimD9//jXbxAgBforijDYQTCCoICIiItfRRhyQ+uyMVAwqPRM2YPYhnDelcaIPnU7n1JEXuwIIzDZ02223qVEBrNhs7ZRPRERERM6AzpClQlAiS7R1D5B5wvPGTQEEUoqw8vKoUaNU/mH37t1VMNG4cWMn7BJ5m4xz5+xekTn9DCZDJVvEpV5wqH7Cl2eAIiIiIg8NIO699171g1SmL774QgUTWEANtQIIJDATUfny5Z2/t+SRwcO+p4c53I5/kPdM4eru4OH5ja843A5ngCIiIiK3FFGjDuKpp55SP3///bfMnDlTFRbPmTNHpTlhlWbMekS+Sxt5qHZ/HwmuWN7u4CG4fIyT98w3aSMPvRt2lQrh5Wx+PmeAIiIiIrfPwoRF1TAC8f3336s1HNq0aSMdO3aUbdu2yUMPPSRjxoyxe2Vm8h4IHkKrVHH3bniV/1Li7X4OggfO/kREREReE0BgETYEDV999ZVaUh7rNwwcOFClL2mrS2N9B9RILFq0iAEEkYmggPxJBxbsfN/hNoiIiIi8IoC46667VBU7ZmCaOnWqtG7d2uJ2derUkVOnTjm6j0Q+JSasrDzX6lHJzs22O3hAG0REREReE0C8/PLLqlC6uEXTnnnmGfVDROYYABAREZG3smspvu+++06tAGnJ4cOH5Z577nF0v4iIiIiIyJtHIFAsbTAY1L937dolu3fvlsTExELb/fjjj3LmzBnn7iVREeJTsiVDn39e2ipU5yeVo1hLQERERFQiAcTnn3+uCqexhDx+pkyZUmgbLcDo0aOHTTtBZG/wMGqdY8HqG/dVZxBBREREVBIBxMSJE6VPnz4qSHjkkUdk0qRJauE4U/7+/hIVFSXXXXedLftAZBdt5KFn42gpH2FbOc+lKzny1Z9Jdo9eEBEREZVWVve6UDDdsmVL9e8PPvhAbrjhBgkPDy/JfSOyCoIHpiIREREReVgAsX79eunQoYOULVtWzp07p36upVevXs7YPyIiIiIi8sYAYty4cfLZZ5+pAAL/vhbUSDCAICIiIiIqxQHEli1bpEKFCsZ/E/mCc0n2LeYWl+oneVlhTt8fIiIiIp8JIKpWrWrx35qcnBy5cuWKREdHO2/viEpIUED+EiiLfrlgbwsi0l6SY3Odul9EREREPrkSNYKFxYsXS82aNdWicTt37pThw4dLSkqKKrSeP3++lClTxvl7S+Qk5cID5am2FSU7N8+u5/9zLl5+PR0o2Tn8SIiIiKh0sWslagQIixYtUgEDTJs2TY08jB8/Xv7991958803nb2fRCUSRGD2Jnt+yoRw+lciIiIqnewKIL755hsZOXKkPPzww3L8+HE5evSoPP300zJo0CAZMWKEbN261fl7SkRERERE3hlAXLhwQZo0aaL+vW3bNrWAXPv27dXvlStXltTUVOfuJREREREReW8AUbFiRTl79qz6N0YbGjZsKOXKlVO/79+/XwURRERERETke+wKIHr06CEzZsyQxx9/XPbu3St9+vRR90+fPl0WLFigCquJiIiIiMj32DUL0wsvvCBhYWGye/duefHFF6V///7q/j///FMee+wxVQ9BRERERES+x64AAitNP/nkk+rH1CeffOKs/SIiIiIiIl8JIACF0jt27JD09HQxGApPadmrVy9H942IiIiIiHwhgPjll1/UwnEZGRlFjlAwgCAiIiIi8j12BRBYKK5OnTpq4bhKlSqpaVyJiIiIiMj32RVAYPG4hQsXSosWLZy/R0RERERE5LHsGjqoUqWKXLlyxfl7Q0REREREvhdAYPald955x7iYHBERERERlQ52pTBt2LBBzp8/L3feeadagTokJKRQEfUPP/zgrH0kIiIiIiJvDiAqV66sfoiIiIiIqHSxK4CYMWOG03YgLy9P3n77bfn888/V2hK33HKLTJo0SapXr25x+8uXL8u0adPk559/ViMd3bt3lzFjxkhoaKhxm02bNsmCBQtUihVmixo7dqy0bt3aqW3o9XqZP3++rF+/Xu33jTfeKBMmTJCGDRs67dgQEREREXkah+ZfxWxMH3zwgbzxxhsqpWnPnj02F1djNqfVq1fL1KlT1UrWCCiGDBki2dnZFrfH+hOnT5+WFStWyLx58+Snn36SyZMnGx/H4najR4+Wfv36yRdffKE6/UOHDlX76sw2sP26devktddek7Vr16pUrieeeEIFE0REREREvsquEQh08jFKgI4zVqHGVfy7775bBQP//vuvrFq1yqoUJwQJy5cvl1GjRknHjh3VfXPnzpV27drJ5s2bpUePHmbb79+/X3bt2iUbN26UunXrqvteffVVFXCMHDlSrUmxdOlS6dy5swwaNEg9jpEDPG/lypVqW2e0cebMGfXeFy9erPYVMKKBxfP++usvs5EKIk/0X0q8Xc8L1YVIbGRFp+8PERER+XgAgUABhdToNKPj36ZNG3U/rto/++yzKgiYNWtWse0cPnxY0tLSzDrcUVFR0qhRI9m9e3ehAAIjHBUqVDB2/KFly5YqgNm7d6907dpV9u3bJ+PGjTN7XqtWrVRA4qw2tm/fLpGRkdK+fXuz/d66davVx5DIHYICgtTtgp3v293GvG5TGEQQERGVYnYFELj6jjSgPn36SG5urvF+5P/jfqQ0WSM+Pv8qaGxsrNn9FStWND5mCmlSBbcNCgqS6OhoiYuLk5SUFElPTy80+mHanjPaOHnypKrRQECxZMkS1SaCHgQdpoGJrTCag9d2t4yMDLPba8nMzFS3Wdl68cvKElfS0tz0OTmSrXfpS0uOOu91kpuTazwG3iDcP1SeuPkhyc61/YAlZFyWr49tkcupSVImIMKh84aI5w3Zi981xPOmZGhZRSUWQFy6dKnIYmGkAKETbsuXADrwpoKDgyU5Odni9gW31bbPysoyduQstYfHndUG6jxQQ4GRGBRfY/Rh0aJF0r9/f5UaFRMTI/ZAYfahQ4fEU5w6darYbfLi8oOq+Lg48ct2bQARp8ptAiQhIUHExXFXmqr1CZHLyUny7+nTUhroszONAXRGSKpD5w0RzxtyFL9riOeN81nqIzstgKhZs6YqPL7tttsKPYb6AjxuDW39CFxJNl1LAh110xmRTLe3VFyN7cPCwlQnX2uv4ONae85oIzAwUAURSNXSRhzw7w4dOqiia9RT2EOn00m9evXE3RBk4Yu5Vq1aFj8HU+khoXIEU/vGxkpIrIun9k3MFvnjggrYKkXadSrb7UJW/hX8smWipYaV57u3C7pyUeSSSO3ataVWdDWHzhsinjdkL37XEM+bknHs2DGrt7Wr1/XII4+oImpcMb/99tvVcAeuyO/cuVMVRResHyiKlkp04cIFqVGjhvF+/F6/fv1C2yOtqOACdejoJyUlqRQjpCEhCMDzTeF3jIw4sw0EEabpSghMkNbkyOrcOI54bU+BTmBx+5N3NfALDtJJyNXgy1W0IFkXGChBOusiZmcJDAhQtwGBAYUWUvRVwfr8Y4z3e63zwprzhojnDTmK3zXE88a5rE1fsnsa1759+8oLL7ygpjHF1KXImcIMRrgK/9hjj8lDDz1kVTsNGjSQiIgIFXhokP508OBBtR5EQbgPdQgIVkxHPKB58+bqjTdr1sx4nwbtt2jRwqlt5OTkyJ9//ml8HKlPmJ3J2tEXIiIiIiJvZHfeBwKHe+65R3W0cTUesxI1adJEXcG3Jc9qwIABquga6yhUrVpVZs+era7wd+nSRRVoJyYmqrZx1RPto3M/YsQItQ4DCo4xEoLpU7XRgcGDB6s1G1DUjFmSUPCNuoLp06erx53RBgIJpG9heldM64r3jEXlAgIC5N5777X3kBIRERER+V4A8fXXX6sF3w4cOKCuwgM69+iUY+QB6yfYArM2oZ2JEyeqq/i4ur9s2TJVD4B0oE6dOqmVr++77z41OoBVq6dMmaLSqFCvgGlXx48fb2yvbdu2anE3FDhjRAQ1BVivQUs3ckYbgFWqEfg899xzar/x/rGoHgIhIiIiIiIp7QEERgNefPFF+fbbb9WV+u7du0v58uVV+hJSgjASMWzYMHUFfubMmVbvAK7aY/0I/BRUrVo1OXIEJbr/DwWzuNp/LRhNwE9RnNEGUq8wgmG6gjURERERka+zOoBYvXq1WvdgwoQJKu2oYKEFAgyMTODKPVJ87r///pLYXyIiIiIiciOri6jXr18v/fr1k4EDB1qs0sZIwsMPPywPPPCAmsqUiIiIiIhKcQCBxaNQUFycdu3ayT///OPofhERERERkTenMGHhljJlyhS7XdmyZSUtLc3R/aJSJD4lWzL0Bpufdy6p8IKAREREROQhAQSKpZGmVBx/f3+1LZG1wcOodWccOlhBAXYtZ0JERERErlwHgsgZtJGHno2jpXxEoF3BQ7lwnsZERERErmJTzwtTlmL60mu5cuWKo/tEpRCCh8pRQe7eDSIiIiJyVgCBBd6guPSk8PBwNY0rERERERGV4gDiww8/LNk9ISIiIiIij8fkcSIHJKX6yX/xGTY/LzgoQMqXY8oWEREReR8GEER20Pnnp/Jt3eUvW3edsusYjnqiLoMIIiIi8joMIIjsEBUioqtzQG4tc6OUr1jVpucmpejlx98SJCs7l8eeiIiIvA4DCCI7+QVlSXSkcBSBiIiIShWuwEVERERERFZjAEFERERERFZjAEFERERERFZjDQSRm1xIyLbreZwCloiIiNyJAQSRi+l0fur206/P2d0Gp4AlIiIid2EAQeRiZSJ18kCPWNHr89eSsAWngCUiIiJ3YwBB5KYggoiIiMgbsYiaiIiIiIisxhEIKrUu669ItiHHrucm5mU4fX+IiIiIvAEDCCq1wcOycz843E6gP/+EiIiIqHRh74dKJW3k4dao6yUqMMzm5+uvXJEr+36XqGq2P5eIiIjImzGAoFINwUNZXbjNz9P750pulu2zKBERERF5OxZRExERERGR1RhAEBERERGR1RhAEBERERGR1RhAEBERERGR1RhAEBERERGR1RhAEBERERGR1TiNK1Ep45d8SUSfZfPzgtISJSbbvpW7iYiIyHe4PYDIy8uTt99+Wz7//HNJTU2VW265RSZNmiTVq1e3uP3ly5dl2rRp8vPPP4ufn590795dxowZI6GhocZtNm3aJAsWLJCzZ89KnTp1ZOzYsdK6dWuntmHqq6++ktGjR8uWLVukWrVqTj0+RM4OHkI+e9Ou5+LMHo1/JF0UKVeDHwwREVEp5fYAYuHChbJ69WqZOXOmVK5cWWbPni1DhgyRDRs2SFBQUKHthw8fLhkZGbJixQpJSUmRCRMmSHp6usyaNUs9vmPHDtWZR0DQpk0bWbNmjQwdOlTWr18vdevWdVobmv/++09effVVlxwrIo1f0kXxCwyw+YD4J11Qt/qbbxdDRLRNz01LOCtlD+0W0WfygyAiIirF3BpAZGdny/Lly2XUqFHSsWNHdd/cuXOlXbt2snnzZunRo4fZ9vv375ddu3bJxo0bjR15dN4RcIwcOVIqVaokS5culc6dO8ugQYPU4xg5wPNWrlyptnVGG6ajJwg0brjhBhV0EJU0v/Qr6jbox08lJDDR7nYM0RXEEF7GpufkZCbb/XpERETkO9waQBw+fFjS0tLMUoOioqKkUaNGsnv37kIBxJ49e6RChQpmowAtW7ZUaUh79+6Vrl27yr59+2TcuHFmz2vVqpUKSJzVhmbx4sWi1+vlueeeYwBBrpGXX4OQU7+FZMeG29dGoM7m4IGIiIjIIwKI+Ph4dRsbG2t2f8WKFY2PmTp//nyhbZHmFB0dLXFxcSodCalISIUqqj1ntAF//PGHGj1BehPaJHIlQ2ikGMqU40EnIiKi0hVAoA4BCtY6BAcHS3JyssXtLdVFYPusrCzJzMwssj087qw2EGAg7Qo/tWrVcloAYTAYVNvupn0u2u21aMcrK1svflePj61pbKDPyZFsvbhMTk7+i+Xk5kiOn+0zC+Xk5qpbPdq5+h5cIUd/dQQCx8uFr5v/mvnvOSsr2+J5ast5Q8TzhuzF7xrieVMy0A9FRo7HBxAhISHqFh0h7d+AjrrpjEim21vqNGH7sLAw1cnX2iv4uNaeM9rADE61a9eWfv36iTMhHerQoUPiKU6dOlXsNnlx+aMy8XFx4pdtewARp1L6AyQhIUHEhbHTZUP+i2HEySB2dMSvdqATEhPFX++6yCcjBQcsXFKSk8U/yLXFzDkpyYKxu3Pnzkm6/pBD5w0RzxtyFL9riOeN81m6yO5xAYSWSnThwgWpUeP/p4XE7/Xr1y+0PdKKfvjhB7P70NFPSkpSKUZIQ0IQgOebwu8ojnZWG2vXrlUHuGnTpur33KtXo1Gz8dRTT6kfe+h0OqlXr554wtUdfDFjdMVSIGcqPSRUjuC4xsZKSKx52pdVErNF/rggMTExUinSdaejvz5Z5GJ+zU3ZQNtrCfSBgZIkIjHlyokuxnWpRJf1mHkpR6LKlJFKlWybRclRV/zzA5YqVapIzXoNHTpviHjekL34XUM8b0rGsWPHrN7WrQFEgwYNJCIiQnbu3GkMIHBF+ODBgzJgwIBC22ONiDfeeENOnz4tNWvWVPdhRiVo3ry5GnZp1qyZuq9v377G56H9Fi1aOK2NgsXUBw4cULMxLVmyRK6//nq7jwdeG8GLp0AnsLj9ybs6chQcpJOQq6M3ttACXV1goATprIt6nSHQoMu/DQiUwEDb/wwMAflTqOoCdVZH684QqMO+5qh9duXrqte+Om1scHDQNc8La84bIp435Ch+1xDPG+eyNn3J7QEEOkAIFNChL1eunFStWlWtA4FRgi5duqgr+4mJiRIZGalSj5o0aaI69yNGjJDJkyerPGwsOterVy/j6MDgwYPVmg2Yyal9+/ZqtABpQdOnT1ePO6MNLfDQaMXVuDKLEQwiIiIiIl/l7+4dwKJu999/v0ycOFEeeughCQgIkGXLlql0HsyK1LZtW7VmgxYZYdVqrPb8yCOPyAsvvKA6+AgENNj+tddek48//lh69+6tplfFdKvatK3OaIOIiIiIqLRy+0rUCBiQ/oOfgtDJP3IEGfb/D7ny8+fPv2abGE3AT1Gc0UbBNSIK7icRERERkS9yewBB5M2yk1FKbTu/oEDRleFibkREROR9GEAQ2cHvakHxxW0/2338qvW9j0EEEREReR0GEET2/OGEh0uFDu3EcHVxNVvkXLkiSQf+EEO27QvYeYILaQkiif9aXFQwPvOShCadlZDM/1/XRROqC5HYyIou2ksiIiIqKQwgiOz94wm3ff0Ibxbon/918cmfX8m5o5uK3vBs0Q/N6zaFQQQREZGXYwBBRFYJD8oPmO5rdLdklys8kpCVla1WJMeiglgrwtTFtET5+cDXknHuqGRFqeXHbeYfHCK6clX4aREREbkZAwgiskn58HJiiMxfM8VUpi5TsoPSpXJEBbVui9kXTeplGf1vosi/c+Q/B4539acXMIggIiJyMwYQRFTi/PX6/H806yzlK9m+nkpOyiVJ2r5W8rIynb9zREREZBMGEETkOhHlRBfDNCQiIiJvxgCCiGzin3RB8izcH5CVLaGpCRKQECR+BWogglISeZSJiIh8BAMIIrLy20KnboJ+/Mziw6h6iMI/9lt+zLQNIiIi8l4MIIjIKobwMpLd8QGRnKv1DAXo9XpJTEiQcjExotOZBwrJmcmy5d/d0i8imkebiIjIyzGAICKbgoii5GVnS1aGXvKiYsQQZJ7CpNf5S8rV1buJiIjIu/m7eweIiIiIiMh7MIAgIiIiIiKrMYAgIiIiIiKrMYAgIiIiIiKrsYiayA2CAgzin3ZZ/IIMNj3PLz1VRDgVKhEREbkPAwjyapf1VyTbkGPz8xL06Ii7h78+Q+pUyBX560ebnxuQGykit4kE8E+XiIiI3IO9EPLq4GHZuR8caiPQz/VTi/rn5Qc8WVXqS0BMRZuem3vFT+QPEUNoRAntHREREdG1MYAgr6WNPNwadb1EBYbZFTxEBhrXSHa5vOAw8b/GugqWGHKR8mT7iAsRERGRszCAIK+H4KGsLtzdu0E+Sp94TvKyMu1+vn9wiOjKVXHqPhEREbkTAwgiomsED2cWDXP4+FR/egGDCCIi8hkMIIi80IVkpDLlufx1g3UiFaJKz+zP2shDdJs+EhhV3ubn56RckqTtax0awSAiIvI0DCCIvEjQ1ZrvT7a7rw5i9L06twUR2Qln3ZJGhOBBF8M0JCIiIvX/RR4GclSiLlICU/IkODjL5ueeS8rmB2CDMuF+0u+2QMnOdf1hS0ozyNa/ciVL7/rX9tMFq9uLX86zuw2mERERETkHAwhySFxSliyp2VtkB4IH+64OQ1BA6UmLcUYQUdoERsVIhZ7DxaC3PUhlGhEREZFzMYAgh2Tq8/Pwu9UOkMqVy9odPJQL56lIxQcRRERE5H7stZFTxIT4SeWoIB5NIiIiIh/HvBEiIiIiIrIaAwgiIiIiIrIaU5iIyClrUOToDZKQGii5OoME6swfT870l6wsrhZORETkCxhAENnJPytNJNeO+VSz0314DQqt0LngXK+hItJJjtRLtP/1A4MkJixavJG961c4wtG1L4iIiIrCAILIzuAh8u8fHTt2/ld75D6yBkVObo6kJidLZJkyEhhg/tVyPiVTth8KlG//+Z/8dCbZ7n147tbBXhVEOGP9Ckdw7QsiIvLZACIvL0/efvtt+fzzzyU1NVVuueUWmTRpklSvXt3i9pcvX5Zp06bJzz//LH5+ftK9e3cZM2aMhIbiKme+TZs2yYIFC+Ts2bNSp04dGTt2rLRu3dqpbRw9elRmz54tBw4cEH9/f7Xf48aNkypVeNXP510deciqXE9yg8Jse2pGhqQePS7RQf9/rvnCGhT6HJEAfa6UjRTRBRbcLgQhhrSr2VIqly2c/lScpMxU+fX0LsnOyS4161c4whlrX+gTz9n9fI5+EBH5No8IIBYuXCirV6+WmTNnSuXKlVWnfMiQIbJhwwYJCio8Nejw4cMlIyNDVqxYISkpKTJhwgRJT0+XWbNmqcd37Ngho0ePVgFBmzZtZM2aNTJ06FBZv3691K1b1yltIAAZPHiwNGvWTD788EPJzs5W+4/9/uKLLyQ4OP/KI/k2BA+GYNty+3NzRHJs70P7hDIhURITVrrmbvDG9SsQPJxZNMyhNjj6QUTku9weQKDjvXz5chk1apR07NhR3Td37lxp166dbN68WXr06GG2/f79+2XXrl2yceNGYzDw6quvqo77yJEjpVKlSrJ06VLp3LmzDBo0SD2OkQM8b+XKlWpbZ7Txww8/qIDj9ddfl5AQXF0VFfjgPezbt89spIKIyJtoIw/RbfpIYFR5m57Llb+JiHyf2y8FHj58WNLS0sw63FFRUdKoUSPZvXt3oe337NkjFSpUMHb8oWXLlioNae/evSodylIHvlWrVsb2nNEGHsPIiRY8ANKYACMaRETeDsGDLqaKTT+2BhxEROR93D4CER8fr25jY2PN7q9YsaLxMVPnz58vtC3SnKKjoyUuLk513jEygFSootpzRhvVqlVTP6aWLFmiAgrUQhARERER+SK3BxCoQ4CCtQ6oIUhOTra4vaW6CGyflZUlmZmZRbaHx53VRkGog1i1apVMnDhRypUrJ/YwGAwqcPGUz0S7vRbteOhzciVb79oC15wcvXH2nxy/a00t6nwBKGRQtdS5knf139bCCJd6bk4uFk8QX5Gbk2N2awpvVd3q9ZKdXXQhdlH0qNC+mvKYmWlbQXJOdv4xxt91ro1/X/qr3wVZ2XrJtfF13cmR9+zo+7b1tW35viHiOUP24neNdf1QZON4RQChpQChY2CaDoSOqemMSKbbY9uCsH1YWJixeLngNqbtOaMN04M9b948WbRokTz99NMycOBAsZder5dDhw6Jpzh16lSx25w7F6duk1NSRHfetZ34y4b8zglGjAzi2uAlKDtNMJloelqa6LNtrIi+2jlLTk0Rv1zfCSA0llL4UtMxZW0ZSUhMkICrwYAtUvVp6hYjhFlBtnWI/dMSBWXuJ0+elNxE2zqpAcnxEoWR0rg4yUvxngDCkffs6Pu297Wt+b4h4jlDjuJ3zbVZusDukQGElkp04cIFqVGjhvF+/F6/fv1C2yOtCAXMptDRT0pKUilGSENCEIDnm8LvKI52Vhtah3/8+PHy9ddfq9tHH33UoWOh0+mkXr164glROv7AatWqZTGIM2W4gkj1jJSJipJKlVw724y/PlnkYn7NTNlA165yHJARIHJBJCw8XPKCbZzGNSBArqA7HRklAWUixVdg5AHBAz6PgEDzr5ZcXf5tTLkYqVTO9hEIXUaSSHL+90WliAo2PTfncrCkHhSpXiZIAsvZNnVujgThZaVybKwEljVPafRk2nuuXbu26CrVsvn5+vOhkvCbfe/b1te25fuGiOcM2YvfNcU7duyYWMvtAUSDBg0kIiJCdu7caQwg0Ak5ePCgDBgwoND2qC9444035PTp01KzZk11H2ZUgubNm6uhF0ytivv69u1rfB7ab9GihdPaAEzx+v3338ubb76p1pFwFF4XgYunwP/Mi9sfbbRGFxggQTrrolZnCTTk90qxaFlggQ5rSfO/ulBaQECA8d/WMlwttg8IDBBd4NWetQ9B8FDwfQUGGFR3PFCnk6Ag2+du0OUEGq+MhITYNkVyTniEpGLE59vFYq+Q8AgJtPF13UkfpFPvGaOtwXZ8p2RdHQ0ODtKJzsb3be9rW/N9Q8RzhhzF75qiWZu+5BEBBDoECBTQoUftQNWqVdV0qBgl6NKli8oxT0xMlMjISPU/pCZNmqjO/YgRI2Ty5MmqZgCLzvXq1cs4OoD1GbBmA2Zyat++vaxdu1alBk2fPl097ow21q1bp6aBRRCBGZwuXrxofE/avhKR9y/mhtWkvXEtB8hOOOvS59nTBuotkDKFUQ8ELlyEjojI87k9gNAWdcvJyVEFyCi8wwjBsmXLVEoPVoHu1KmTzJgxQ+677z4VHWHV6ilTpsgjjzyiroB37dpVpRBp2rZtK6+99pqaZhVrSiAtaPHixcZpW53RBtKWAOtA4MeUtq9E5Bm8NQCwF4IeuPjlPKe0U9KvjXoLpExpuAgdEZFn84gAAmkgWPUZPwVhqtQjR46Y3RcTEyPz58+/ZpsYTcBPURxtA4vfERH54qiLIyMvtr42ZnpCsTbqLQIykyVp+1rjQnZEROSZPCKAICIi3xl1seW1MU0sZnpCsXZAmu/VBBER+SIGEERuor+CuZhs5xcYKIHhrp11ioiIiEjDAILIxfz8sSaCSPKBA3a3UaFDBwYRRERE5BYMIIhc/UcXGiJlbmoihryryzPbIC8jU64cPyYGC6s9+7qsrHA5k5ArGXasCB0a5Cex0a6dZpiIiMhXMYAgcscfXqh90/z63rrV1klM9ZNjRzvJG0exCrVtK1Fr5g2qziCCiIjICRhAkJJx7pzkZuTPfIKpdPPi4iU9JFTyilnPIjP+vNceQf+sNJFc20cBICATS2WRq2Tn5C9uc2djndQuX8am515MzZEv9iRJRjYWsyMiIiJHMYAgFTzse3pYoSNhPnmuZfHB5USq91CFvd4WPET+/aPjDfnZvqoy2a9shL/ElmUqEhERkTt5V6+PSoQ28lDt/j4SXLG82bzswUHXnlYxMCVPZEeWBEZEeNenc3XkIatyPckNCrOvDT9/MQSFOne/fNyFZIwC5Nn8vITU/BEIIiIicj8GEGSE4CG0ShXxy8oSv+wsCYmtLCHB116JNjgYBa1nvfYoIngwBHNK1JIWlD/xlHyy3d7i7xCzdoiIiMh9GEAQUYkrE+4n/W4LlGz7Sk4kLfuK/HXhb4kOb+vsXSMiIiIbMYAgIpcFEfYKzjKI7nKmJGXZXryelGl7yhQREREVjQEEEXm8AP/8r6pfT+20a/0IkZslWc2cde2UPCIiIioeAwgi8njhujBpW6ul5ObZXkNx9nK2xMWJ6HNL6yoaREREzsUAgoi8JoiwR1gg1+wgIiJyJk5iT0REREREVuMIBBHRNSSkJ0l2TrbdxygoMEhiwqJ5jImIyGcwgCAiukbw8PaO9x0+Ps/dOphBBBER+QwGEERERdBGHtrWbCnRIZE2H6ekzFT59fQuh0YwiIiIPA0DCCKiYiB4iAkrx+PkItkJ7lnd3j84RHTlqtj1XH3iOcnLynTLaxMRuRoDCCIi8gh+uvx1Oi5+Oc9t+1D96QU2d+QRPJxZNMwtr01E5A4MIIiIyCMERsVIhZ7DxaDPcvlr56RckqTta+0aRdCeE92mjwRGlXfpaxMRuQMDCCIi8qggwlsheNDFcASBiHwfAwgiKhWOXkix+TmXM5MlKyu8RPaHiIjIWzGAICKfFqwLULdf7sTXXbqNz9aJSCdJrZctMfYthE1ERORzGEAQkU+LLRMmPVumS5Y+1+bnpqT7y29HgiTQj6MQpYU9M0C5a9YoZ8wAxdmfiMgeDCCIvJD+yhW7n+sXGCiB4eGlLoiwx8UUA0pcnb4/5JszQGltuJIzZoDi7E9EZCsGEOTVovS5EpSRKv42Xl0OyEwVb+Tnn5+Ok3zggEPtVOjQodQFEY64kIxAIs/m5yVn+ktGRhk5k5ArGZm2zywUGuQnsdFBNj+PXD8DFIIHdxSAOzIDFGd/IiJ7MYAgr+WXcUX6oTD2wh4HGvEXbxIYGiJlbmoihjzb03EgLyNTrhw/JoYcXlW3RlB+vCafbLf3eIWKSAd543i6HfUX+eYNqs4gwkU4AxQRkXUYQJDX8svL79QlVagpQaFl7GjAXwxB6OCJ1wUR9tI7dU98X5lwP+l3W6Bk2xevSVr2Ffkj/rC0q9VKrWZti4upOfLFniTJyMboB5UG3lh/QUSlEwMI8no5umDRBTMdh0ouiLBXcJZBgi+nScUof4kJd20qUlxStkPBB9OnXMdb6y+IqPRiAEFE5GMQPDz/wRmH22H6VOmov3BkFMNdszj5pyWK/vwpyQqxb0SWs08ROYYBBBGRB/svMdvu5/RuES0VIm3/mndn+lRpHTlxR/2FM0Y+3DGLU87leCnzy2JJcLAdzj5FZD8GEEREHihIl1/gv2DzBbvbqFouSGIivOdrniMn3jXy4egsTvauX6GPP65uw1r2lLCYWK/Zb2eMfHDND/IUbv8/S15enrz99tvy+eefS2pqqtxyyy0yadIkqV69usXtL1++LNOmTZOff/5Z/Pz8pHv37jJmzBgJDf3/YthNmzbJggUL5OzZs1KnTh0ZO3astG7d2uVteJNEXaQEpuRJcHCWZGdnSxyWGUjMlqBiLuSdS7L96mhBl/VXJNtg+yw3qTlpUtHhVy+d7F1HwpE1JHLS0hya/am0rV+Bjv9zXSpKtt726WO1AMTR4MGe0Q/Iyr0i0eHWVZ7j+yYxO1mCr1yU86kBXjty4q2cMfJhTwpUTmqCnP98pkOvG1i2iujsCCC8dd0NrvlBnsTtAcTChQtl9erVMnPmTKlcubLMnj1bhgwZIhs2bJAgC73X4cOHS0ZGhqxYsUJSUlJkwoQJkp6eLrNmzVKP79ixQ0aPHq06823atJE1a9bI0KFDZf369VK3bl2XteFN4pKyZEnN3iI7cBVK+x9BgMgf1l/5DArwtzt4WHbuB7ueWz47R+7DnnrZVKzevo6EPWtIIHi4+NNPdr+mI6/tzdw1euCM0Y96122R4OA0659wUdSaGZj2NjRYL7Fl7Vv8j7wrBapsh4ckICzKpudkZesl7sJFKRtZTkrTuhtc84M8iVsDCFx5Wr58uYwaNUo6duyo7ps7d660a9dONm/eLD169DDbfv/+/bJr1y7ZuHGjsSP/6quvqoBj5MiRUqlSJVm6dKl07txZBg0apB7HyAGet3LlSrWtq9rwJplXr3B2qx0glSuXFX1OjiQkJEhMTIzoAgOtCh7Khdt3KmkjD7dGXS9RgbZ1GLCAnFzcI8EBQcLrjSW/joS2hkR2UpLFkYTcnFwxpGdIbkCqSODVBRQKjHhE1K0n/nZMQ+vt61ckZdm3cKEuQCdRIRF2PTcl84roc+2cuNdPZEDbYLumr/0vKV22HwqQmyo1kesqFT8iq9fnSGJCgpSLiZFTiXo5cVwk5+oUzeTZ3FX8nZuZJYYU+17TlCPT5iJ40MVUcWnRujNe2xt5a9qW3kv32ysCiMOHD0taWppZalBUVJQ0atRIdu/eXSiA2LNnj1SoUMHYaYeWLVuqFKK9e/dK165dZd++fTJu3Diz57Vq1UoFJK5qo1u3buKNYkL8pHJUkGSjz5EuUikyUIJ0rilGRPBQVmfblWVbV58mx9aRyLFy9OLyNR4LCA+36/W9df2KAP/8r9hfT+20u43eje62OYhA8PDFwU3iDllZ+Du+WcqGlpGYsAirLiTpdZlSLjRaEoLsW2yP3McbF99z17S5zipaL01T9npr2pbeS/fbawKI+Ph4dRsba57DWLFiReNjps6fP19oW6Q5RUdHS1xcnEolQhoRUqGKas8VbdhDr9eLwWCQP/74Q1wtO0Avz/esLMH+OZLrl4XkJakSVVHEzyC54vhVnmuJCQuVYdF3SYD446KnTfzKRol/lQclwi9ADPZP1U82qFK/4TUeNR0HKuIDsfdzMohUrd9IxN/f/jbcIFqi5MGyPe0cITNIrsGgUvQyDbb9HeqCddLn5h4S4IeD5doDZjD4SXZeoAT550jileLfObYIiy4nqdkiFaJi5Pme+c+19T1DmTJ+6rssITNTks5k2PkOyBsYQivKwYv4jO35nANEOoww/8qyhZ9IXDKupiS59nUdeW1DmPi3Gy5xx86JHD8nXsMgar8NWPjVxu8yPzzDkCdxf58Q8TuR35xBZP++feLp+x1oyJXU0/Hid/aSuBL6orgY7vEBBGoIoGCtQ3BwsCQnJ1vc3lJdBLbPysqSzMzMItvD465qwx7aB2btB+dc+Vfys/LcdzrYn7Cgc+zLmLyLffXEXs2RZB53JgIhiHDHc53xfCLf5K3/v3Rwvw3eud9lcnIkJFAnroQ+qFcEECFXF4DBELb2b0An3NJsRtgG2xaE7cPCwlQHXmuv4ONae65owx5Nmza163lERERERK7k1ulrtDSgCxfMZ/rA75YKkZFWVHBbdOSTkpJUihFSiNCBv1Z7rmiDiIiIiMhXuTWAaNCggURERMjOnf9fYIgahIMHD6r1IArCfahDOH36tPE+zIYEzZs3V8MuzZo1M96nQfstWrRwWRtERERERL7KrQEE6ggGDBggb7zxhmzZskXNyjRixAh1hb9Lly6Sm5srFy9eNNYlNGnSRHXusQ2KjbFeAxad69Wrl3F0YPDgwfLNN9/I+++/L8ePH5fXX39dDh06JI888ohL2yAiIiIi8kV+Bkz940YIEubMmSPr1q1TgYK2EnW1atXUKtCdOnWSGTNmyH33YckwUesTTJkyRX755RdVr4BpV8ePH2+sXQAs+IYF6jBKUK9ePbUonOlUsa5qg4iIiIjI17g9gCAiIiIiIu/h1hQmIiIiIiLyLgwgiIiIiIjIagwgiIiIiIjIagwgiIiIiIjIagwgiIiIiIjIagwgiIiIiIjIagwgyExeXp7Mnz9f2rVrJzfffLM88cQTcubMGR4lH5WUlKTWXWnfvr1aHPGhhx6SPXv2GB//7bff1BosWDwRa51ggUVTWVlZaj0UrJHStGlTefHFFyUxMdFsG2e0QZ7r5MmT6nPDWj4aLLyJRULxHXLHHXfIBx98YPP3jDPaIM+C9ZW6desmjRs3lu7du8umTZuMj2HdpyeffFJ9D7Vt21beeusttU6UqY8++kitDXXTTTdJ//795eDBg2aPO6MN8iw5OTkyb948uf3229X3zMMPPyy///678XF+17gR1oEg0ixYsMDQqlUrw48//mg4dOiQ4bHHHjN06dLFkJWVxYPkgwYPHmzo0aOHYffu3YYTJ04YpkyZYrjpppsMx48fNxw7dszQuHFjw5w5c9S/33vvPUOjRo0M//vf/4zPHzdunKFz587q+QcOHDD06tXL8PDDDxsfd0Yb5Lmys7MN9913n+H66683rF27Vt2XmJiovkPGjx+vPvM1a9aocwC31n7POKMN8izr169Xf/urVq0ynD592rBw4UJDgwYNDPv27VPnET67oUOHGo4cOWL4/vvvDS1btjTMmzfP+Px169ap76Yvv/zScPToUcPo0aPVNgkJCepxZ7RBnmf+/PmGNm3aGH755RfDqVOnDBMmTDA0b97ccP78eX7XuBkDCDLC/3ibNm1q+Oijj4z3JScnqy/cDRs28Ej5GHwZo+O3Z88e4315eXmqM//WW28ZXn75ZcP9999v9pyRI0eqjhrEx8erDsC2bduMjyMIQZvoFIAz2iDP9eabbxoGDRpkFkAsXrzY0LZtW4NerzfbDp07a79nnNEGeQ58r9x+++2GmTNnmt2P7wF81vjMbrzxRkNSUpLxsU8++cTQrFkzY0CIz/711183Po5zo0OHDur54Iw2yPP07NnTMGPGDOPvqamp6vvmu+++43eNmzGFiYwOHz4saWlpKpVEExUVJY0aNZLdu3fzSPmYsmXLypIlS1Q6gcbPz0/9pKSkqFQm03MBbr31Vtm7dy8uPKhb7T5N7dq1pVKlSsbzxRltkGfC5/Ppp5/KzJkzze7HZ96yZUsJDAw03ofP99SpU3Lp0iWrvmec0QZ5Vprbf//9J/fcc4/Z/cuWLVMpR/i8b7jhBilTpozZ533lyhWVopKQkKA+e9PPG+dGixYtzM4ZR9sgzxMTEyM//vijSk9DOhq+c4KCgqRBgwb8rnEzBhBkFB8fr25jY2PNjkrFihWNj5HvQIerQ4cO6stY891338np06dVXjk+88qVKxc6FzIyMuTy5cty/vx5FYQEBwcXeb44ow3yPAgwx4wZIxMnTiz0fVHUZw5xcXFWfc84ow3yrAAC0tPT5fHHH1ed+L59+8rWrVvV/TxnqCgTJkwQnU6n6lZwsWvu3Lmq9qlGjRo8b9yMAQQZoVMHph1KQOcOha7k2/bt2yfjx4+XLl26SMeOHSUzM7PQuaD9np2drc6Xgo8XPF+c0QZ5nsmTJ6uCxoJXlIv6zLUAEZ+pNd8zzmiDPAdGAWDs2LHSo0cPWb58ubRp00aeeeYZNckCzxkqyrFjxyQyMlLeeecdNfqACTlGjRqlRpV43rjX/48PU6kXEhJi7Nhp/wb8Dzk0NLTUHx9f9sMPP6gvZcxe8sYbbxg7YzgXTGm/43zAOVLw8YLnizPaIM+bSQfpIhs2bLD4uKXPVOvUh4WFWfU944w2yHPgCjJg9KF3797q3w0bNlQzIL3//vs2fd4Ft7HnnCmqDfIsGG3ErHwrVqxQqWaAUQgEFQsWLOB3jZtxBIKMtHSACxcumB0V/I6cdPJNq1atkmHDhqlp8hYvXmy80ovzwdK5gP8Z44oQUkwwDWzB/yGbni/OaIM8y9q1a1U+OUapMAqBH3jllVdkyJAh6jO19JkDPlNrvmec0QZ5Du0zuf76683ur1evnspt5zlDlhw4cED0er1ZnR5gSnCk2vK8cS8GEGSEoqSIiAjZuXOnWa4zrhLdcsstPFI+aPXq1TJ16lQ1t/acOXPMUkJwxWfXrl1m2+/YsUONUvj7+0vz5s3VXPxaIbSW64y6Bu18cUYb5FkwQrVx40Y1EqH9wPDhw2X69Onqc8PnaTr/Pj5zFMejINKa7xlntEGeA8XN4eHhqkNo6p9//lG57PjM8NlpqU7a543n4LPGZ47P3vTzxvoAGAkzPWccbYM8i1YHdeTIkULnTa1atfhd427ungaKPAvm68e82D/88IPZ3OqYY5t8C6ZLveGGGwzPPvus4cKFC2Y/KSkphn/++Uc9Pnv2bDUX/7Jlywqt4YApWe+44w7Djh07jGs4DBgwwPi4M9ogz2c6jeulS5cMt9xyi2Hs2LFqrn3cjzUcMAe/td8zzmiDPMs777yjpt7FdKum60Dg7z4zM1NNH/3444+rz1JbwwFrfWg+/fRTNU0vzgFtDQesA6Kt4eCMNsiz5ObmGh566CFD165dDb/99pvh5MmThrlz5xoaNmxo+P333/ld42YMIMhMTk6Omif71ltvNdx8882GJ554wnDmzBkeJR+0aNEi1fGz9IOOG/z0009qoTnMr44v8W+++casjbS0NLWwT4sWLdQPggEsAmbKGW2Q9wQQgEDwgQceUJ855v//8MMPbf6ecUYb5FmWL1+uLhbgogLm90cn33RdGixsiUARa4BgLRp0IE1hIcr27durIKB///6GgwcPmj3ujDbIs2Bdj8mTJxs6duyoAtAHH3zQsHPnTuPj/K5xHz/8x92jIERERERE5B1YA0FERERERFZjAEFERERERFZjAEFERERERFZjAEFERERERFZjAEFERERERFZjAEFERERERFZjAEFERERERFZjAEFERERERFYLtH5TIiKiaxs4cKDs2rXL7D4/Pz8JCwuTWrVqySOPPCL33nsvDyMRkRdjAEFERE7VqFEjeeWVV4y/5+bmSnx8vKxYsULGjBkj0dHR0qFDBx51IiIvxQCCiIicKiIiQm6++eZC97dv315at24t69atYwBBROTFWANBREQuERwcLEFBQSqlCfLy8mTJkiVy5513yo033ih33XWXfPjhh8btFy9erO5PTk42awcjGTfccIMkJCSo38+dOycjR46Uli1bSpMmTVSa1MGDB43bnz17VurXry+bNm2S4cOHS9OmTdW2EydOlPT0dON22GbBggVmr4Xfcb+pPXv2yIABA9RroZ2xY8dKYmKik48WEZHnYgBBREROZTAYJCcnx/iTlZUlJ06ckPHjx0taWpqxBmLy5Mkyf/586dmzpwoWunbtKq+99pq888476vF77rlHPX/z5s1m7X/zzTfStm1biYmJUR33fv36yd9//y0vv/yyvPnmmyowefjhh+X48eNmz0NaVdWqVWXhwoXy+OOPy5o1a2TRokU2vbfdu3fLo48+KiEhIfLWW2/JSy+9pGo+Bg0aJJmZmQ4fOyIib8AUJiIicip0sjFCYAqjDtdff73MmzdPbr/9djl58qR89tlnauRg6NChahsEBdju3Xfflf79+6vO/i233CJff/219O3bV23z77//yh9//CFz585Vv69cuVKSkpLk448/VttrqVLdunVTr4UARYO6C4wWAFKptm/fLtu2bZMXX3zR6veGAKV27dpqHwMCAtR9GIno3r27rF27VgUuRES+jiMQRETkVAgecHUfP7jaj8ABMzDhij1GGWDHjh1qpOKOO+4wG63A7xix2Lt3r9oOoxMISC5evGgcfUCNBbaD3377TRo2bCiVKlUytuHv76+CiP/9739m+1WwLqNy5cpmKUzFycjIkAMHDqhAxHSUpXr16lK3bl0VkBARlQYcgSAiIqcKDw+Xxo0bG3/HFXoEAo899pgqoC5XrpwaNQBcubfk/Pnz6hYBx9SpU1X9AtKEEECgVgIpRIB2Tp8+XWjEw7TTrwkNDTV7DIEGAgFrpaSkqPSopUuXqh9LNR5ERKUBAwgiIipR5cuXl0mTJsnzzz8v06dPV2lAUVFRxhQkBBwFValSRd1GRkaq0QYEELfeeqscPXpU1Tpo8DgKmTE9rCUo2rYFppw1ZTpCgf1EihVqICwFPgUDFCIiX8UUJiIiKnEYSWjXrp2qZ0DRcYsWLdT9ly9fVqMV2g+KolG7oI1QAIquf//9d1XngMACAYMG/0Y9BeoSTNv58ssvVQqVVqdgDaRGaSMfmn379pk9jjUuUBBu+lrXXXedmq1p586dDh4lIiLvwACCiIhcAjMW6XQ6mTZtmtSrV0+lNWE04b333lM1EQgQRo8erYII1ExoEHhg8blPP/1UzcykTQMLGA1AWhFuN27cqGoi0Camg0VQYYuOHTuqFKlPPvlEtYN9QXqUKRR9//rrr6rw+qeffpKtW7fKkCFD1PZFpVEREfkaBhBEROQSderUkYEDB8qRI0dUsDBjxgwZPHiw6rCjE46pXDF70vLly81GDgIDA1XKENKLEHSYQvE0no8ZmDAt7FNPPaVmaUKqFIIKW2CaWcwQNWvWLLVeRFhYWKEZmjBT1LJly9TK2tgGqVPY1/fff9/i4nlERL7Iz2BLBRkREREREZVqHIEgIiIiIiKrMYAgIiIiIiKrMYAgIiIiIiKrMYAgIiIiIiIGEERERERE5HwcgSAiIiIiIqsxgCAiIiIiIqsxgCAiIiIiIqsxgCAiIiIiIqsxgCAiIiIiIqsxgCAiIiIiIqsxgCAiIiIiIrHW/wE4yK77NX5rMgAAAABJRU5ErkJggg==", + "image/png": 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", "text/plain": [ "
" ] @@ -780,27 +823,27 @@ } ], "source": [ - "arm_revenue = (\n", + "treatment_revenue = (\n", " policy_df\n", - " .groupby(['arm', 'arm_name'])[outcome]\n", + " .groupby(['treatment', 'treatment_name'])[outcome]\n", " .agg(['count', 'mean', 'median', 'std', 'min', 'max'])\n", " .reset_index()\n", ")\n", - "display(arm_revenue)\n", + "display(treatment_revenue)\n", "\n", "plt.figure(figsize=(8, 4))\n", "\n", "sns.histplot(\n", " data=policy_df,\n", " x=outcome,\n", - " hue='arm_name',\n", + " hue='treatment_name',\n", " bins=40,\n", " element='step',\n", " stat='density',\n", " common_norm=False\n", ")\n", "\n", - "plt.title('Revenue density by arm')\n", + "plt.title('Revenue density by treatment')\n", "plt.tight_layout()\n", "plt.show()" ] @@ -819,9 +862,7 @@ "source": [ "Revenue 평균은 `none` 약 6,586, `discount_only` 약 12,248, `tech_support_only` 약 15,104, `discount_plus_support` 약 26,784로 나타납니다.\n", "\n", - "앞서 확인했듯이 규모가 큰 고객일수록 더 강한 개입을 받는 경향이 있기 때문에 평균 차이를 그대로 정책을 평가하면 안 됩니다. 즉, Revenue 차이에는 고객 특성의 영향과 실제 처치 효과가 함께 섞여 있습니다.\n", - "\n", - "따라서 단순 평균 비교만으로 정책을 판단하기보다는, 이후 AIPW 기반 정책 평가를 통해 비교해야 합니다." + "앞서 확인했듯이 규모가 큰 고객일수록 더 강한 개입을 받는 경향이 있기 때문에 평균 차이를 그대로 정책을 평가하면 안 됩니다. 즉, Revenue 차이에는 고객 특성의 영향과 실제 처치 효과가 함께 섞여 있습니다." ] }, { @@ -860,42 +901,9 @@ "추가로 propensity score가 극단적으로 치우쳐 있지 않은지도 확인합니다. 만약 특정 고객군에서 어떤 처치의 $e_a(x)$ 값이 0에 매우 가까우면, 일부 관측치에 지나치게 큰 가중치가 부여되어 추정이 불안정해질 수 있습니다." ] }, - { - "cell_type": "markdown", - "id": "pl2exzwbceb", - "metadata": {}, - "source": [ - "### 데이터 분할\n", - "\n", - "정책학습과 평가에 동일한 데이터를 사용하면 과대추정이 발생합니다. 이를 방지하기 위해 데이터를 train/test로 분리합니다. 정책은 train set으로 학습하고, 평가는 test set의 AIPW score로 수행합니다.\n", - "\n", - "Positivity 확인을 위한 propensity 추정도 동일한 분할을 사용합니다." - ] - }, { "cell_type": "code", - "execution_count": 7, - "id": "n7ziuiv612q", - "metadata": {}, - "outputs": [], - "source": [ - "idx = np.arange(len(policy_df))\n", - "train_idx, test_idx = train_test_split(\n", - " idx,\n", - " test_size=0.5,\n", - " stratify=A,\n", - " random_state=SEED,\n", - ")\n", - "\n", - "X_train = X[train_idx]\n", - "X_test = X[test_idx]\n", - "A_train = A[train_idx]\n", - "A_test = A[test_idx]" - ] - }, - { - "cell_type": "code", - "execution_count": 8, + "execution_count": 60, "id": "9be31313", "metadata": {}, "outputs": [ @@ -920,7 +928,7 @@ " \n", " \n", " \n", - " arm\n", + " treatment\n", " mean_propensity\n", " min_propensity\n", " p01_propensity\n", @@ -965,7 +973,7 @@ "" ], "text/plain": [ - " arm mean_propensity min_propensity p01_propensity \\\n", + " treatment mean_propensity min_propensity p01_propensity \\\n", "0 none 0.263941 0.057600 0.066831 \n", "1 tech_support_only 0.229863 0.126582 0.156283 \n", "2 discount_only 0.237934 0.125638 0.141322 \n", @@ -993,7 +1001,7 @@ "e_hat_raw = multi_propensity.predict_proba(X_test)\n", "\n", "propensity_summary = pd.DataFrame({\n", - " 'arm': ARM_LABELS,\n", + " 'treatment': TREATMENT_LABELS,\n", " 'mean_propensity': e_hat_raw.mean(axis=0),\n", " 'min_propensity': e_hat_raw.min(axis=0),\n", " 'p01_propensity': np.quantile(e_hat_raw, 0.01, axis=0),\n", @@ -1004,7 +1012,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 61, "id": "fl52yekx049", "metadata": {}, "outputs": [ @@ -1020,7 +1028,7 @@ } ], "source": [ - "prop_df = pd.DataFrame(e_hat_raw, columns=ARM_LABELS)\n", + "prop_df = pd.DataFrame(e_hat_raw, columns=TREATMENT_LABELS)\n", "prop_long = prop_df.melt(var_name='treatment', value_name='propensity')\n", "\n", "fig, ax = plt.subplots(figsize=(8, 4))\n", @@ -1060,7 +1068,31 @@ "id": "f4edd2d8", "metadata": {}, "source": [ - "## 정책학습 방법론\n\n세 가지 정책학습 방법을 비교합니다.\n\n- Plug-in Policy (DRLearner)\n\n 먼저 고객별 CATE를 추정한 뒤, 기대 순이익(net benefit)이 가장 큰 처치를 선택합니다.\n\n 정책 형태에 제약이 없는 유연한 접근이지만, 최종 성능은 CATE 추정 품질에 크게 영향을 받습니다.\n\n- DRPolicyTree\n\n DRPolicyTree는 AIPW score를 직접 최대화하도록 정책을 학습합니다.\n\n 정책 구조를 얕은 decision tree로 제한하기 때문에, 결과를 if-then 규칙 형태로 해석할 수 있습니다. 따라서 정책 해석과 설명이 중요한 상황에서 유용합니다.\n\n- DRPolicyForest\n\n DRPolicyForest 역시 AIPW score를 직접 최대화하지만, 단일 트리 대신 forest 구조를 사용합니다.\n\n 단일 tree는 구조가 단순하지만 분산이 커질 수 있습니다. 반면 forest는 여러 트리의 결과를 평균적으로 활용하기 때문에 분산을 줄이면서 더 안정적으로 이질적인 처치 효과를 학습할 수 있습니다.\n\n 대신 최종 정책을 하나의 명확한 if-then 규칙 형태로 해석하기는 더 어렵습니다.\n\n세 정책은 모두 train set에서 학습하고, 동일한 test set에서 정책 가치를 비교합니다. 학습에 사용한 데이터로 정책을 평가하면 과대추정이 발생하므로, 학습과 평가에 서로 다른 데이터를 사용하는 것이 중요합니다." + "## 정책학습 방법론\n", + "\n", + "세 가지 정책학습 방법을 비교합니다.\n", + "\n", + "- Plug-in Policy (DRLearner)\n", + "\n", + " 먼저 고객별 CATE를 추정한 뒤, 기대 순이익(net benefit)이 가장 큰 처치를 선택합니다.\n", + "\n", + " 정책 형태에 제약이 없는 유연한 접근이지만, 최종 성능은 CATE 추정 품질에 크게 영향을 받습니다.\n", + "\n", + "- DRPolicyTree\n", + "\n", + " DRPolicyTree는 AIPW score를 직접 최대화하도록 정책을 학습합니다.\n", + "\n", + " 정책 구조를 얕은 decision tree로 제한하기 때문에, 결과를 조건 분기 규칙으로 해석할 수 있습니다. 따라서 정책 해석과 설명이 중요한 상황에서 유용합니다.\n", + "\n", + "- DRPolicyForest\n", + "\n", + " DRPolicyForest 역시 AIPW score를 직접 최대화하지만, 단일 트리 대신 forest 구조를 사용합니다.\n", + "\n", + " 단일 tree는 구조가 단순하지만 분산이 커질 수 있습니다. 반면 forest는 여러 트리의 결과를 평균적으로 활용하기 때문에 분산을 줄이면서 더 안정적으로 이질적인 처치 효과를 학습할 수 있습니다.\n", + "\n", + " 대신 최종 정책을 하나의 명확한 조건 분기 규칙 형태로 해석하기는 더 어렵습니다.\n", + "\n", + "세 정책은 모두 train set에서 학습하고, 동일한 test set에서 정책 가치를 비교합니다. 학습에 사용한 데이터로 정책을 평가하면 과대추정이 발생하므로, 학습과 평가에 서로 다른 데이터를 사용하는 것이 중요합니다." ] }, { @@ -1095,12 +1127,12 @@ "\n", "이 방식은 직관적이고 유연하지만, CATE 추정 품질에 크게 영향을 받습니다. 따라서 학습된 정책은 별도의 test set에서 AIPW value로 평가합니다.\n", "\n", - "> **NOTE** \"각 고객의 $\\hat\\Gamma_{i,a}$ 중 가장 큰 arm을 선택하면 되지 않나?\"라고 생각할 수 있습니다. 그러나 AIPW score는 개별 관측치 수준에서 분산이 매우 크기 때문에, pointwise argmax로 만든 정책은 노이즈에 과적합됩니다. AIPW score는 반드시 집계된 형태로 사용해야 합니다. 정책 평가(value 추정, 비용 곡선 등)처럼 여러 관측치를 합산하거나, DRPolicyTree/Forest처럼 노드 단위 평균을 최적화하거나, DRLearner처럼 pseudo-outcome으로 회귀하는 방식이 그 예입니다." + "> **NOTE** \"각 고객의 $\\hat\\Gamma_{i,a}$ 중 가장 큰 처치를 선택하면 되지 않나?\"라고 생각할 수 있습니다. 그러나 AIPW score는 개별 관측치 수준에서 분산이 매우 크기 때문에, pointwise argmax로 만든 정책은 노이즈에 과적합됩니다. AIPW score는 반드시 집계된 형태로 사용해야 합니다. 정책 평가(value 추정, 비용 곡선 등)처럼 여러 관측치를 합산하거나, DRPolicyTree/Forest처럼 노드 단위 평균을 최적화하거나, DRLearner처럼 pseudo-outcome으로 회귀하는 방식이 그 예입니다." ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 62, "id": "debd92a9", "metadata": { "execution": { @@ -1140,16 +1172,16 @@ "\n", "# none을 baseline으로, 나머지 처치별 E[Y_net(a) - Y_net(0) | X] 반환\n", "cate_vs_none = dr_cate.const_marginal_effect(X_test)\n", - "plugin_arm_values = np.column_stack([\n", + "plugin_treatment_values = np.column_stack([\n", " np.zeros(len(X_test)), # none: baseline, 상대효과 = 0\n", " cate_vs_none,\n", "])\n", - "pi_plugin = np.argmax(plugin_arm_values, axis=1)" + "pi_plugin = np.argmax(plugin_treatment_values, axis=1)" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 63, "id": "33437c2c", "metadata": { "execution": { @@ -1170,18 +1202,18 @@ "Name: proportion, dtype: float64" ] }, - "execution_count": 13, + "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "pd.Series(pi_plugin).map(ARM_NAMES).value_counts(normalize=True).reindex(ARM_LABELS).fillna(0)" + "pd.Series(pi_plugin).map(TREATMENT_NAMES).value_counts(normalize=True).reindex(TREATMENT_LABELS).fillna(0)" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 64, "id": "33ktqy8zrqm", "metadata": {}, "outputs": [ @@ -1209,7 +1241,7 @@ " Size\n", " Employee Count\n", " IT Spend\n", - " assigned_arm\n", + " assigned_treatment\n", " τ_tech\n", " τ_disc\n", " τ_both\n", @@ -1311,7 +1343,7 @@ "" ], "text/plain": [ - " Size Employee Count IT Spend assigned_arm τ_tech τ_disc \\\n", + " Size Employee Count IT Spend assigned_treatment τ_tech τ_disc \\\n", "0 96204 142 17655 none -6140 -1419 \n", "1 25390 178 7971 none -14556 -4252 \n", "2 66555 279 19899 none -17629 -2896 \n", @@ -1334,7 +1366,7 @@ "8 9048 " ] }, - "execution_count": 14, + "execution_count": 64, "metadata": {}, "output_type": "execute_result" } @@ -1346,7 +1378,7 @@ "sample_ids = np.concatenate([none_idx, tech_idx, both_idx])\n", "\n", "df_plugin_sample = pd.DataFrame(X_test[sample_ids], columns=covariates)[['Size', 'Employee Count', 'IT Spend']]\n", - "df_plugin_sample['assigned_arm'] = pd.Series(pi_plugin[sample_ids]).map(ARM_NAMES).values\n", + "df_plugin_sample['assigned_treatment'] = pd.Series(pi_plugin[sample_ids]).map(TREATMENT_NAMES).values\n", "df_plugin_sample[['τ_tech', 'τ_disc', 'τ_both']] = np.round(cate_vs_none[sample_ids], 0).astype(int)\n", "df_plugin_sample.index = pd.RangeIndex(len(df_plugin_sample))\n", "df_plugin_sample" @@ -1389,7 +1421,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 65, "id": "6c987a8b", "metadata": { "execution": { @@ -1410,7 +1442,7 @@ "Name: proportion, dtype: float64" ] }, - "execution_count": 15, + "execution_count": 65, "metadata": {}, "output_type": "execute_result" } @@ -1439,12 +1471,12 @@ "dr_policy_tree.fit(Y_net_train, A_train, X=X_train)\n", "pi_tree = dr_policy_tree.predict(X_test).astype(int).ravel()\n", "\n", - "pd.Series(pi_tree).map(ARM_NAMES).value_counts(normalize=True).reindex(ARM_LABELS).fillna(0)\n" + "pd.Series(pi_tree).map(TREATMENT_NAMES).value_counts(normalize=True).reindex(TREATMENT_LABELS).fillna(0)\n" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 66, "id": "64529830", "metadata": { "execution": { @@ -1457,7 +1489,7 @@ "outputs": [ { "data": { - "image/png": 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73F00wi3ocVVm36WCzqebTKr12Cz8CymkzqF9Kk+LKOLmTbQqRQsuixJFyvCWB542h+GG/vbuZ6uKtLbhCvFkXDMTM9wCIwAAAFgzWKoNAABgDfkt9rNuLujl5smzNtgsT7Zr2bXqNgsD7WeffK4t9vGSB1ScLHbttavio/C7+jH6rWLJmJ4rfUoeebRLcLdql+kY6Ky8xHxXsWiXsyDOVvR9uvRR1xo8qvgOjS15XNFk1LUq+zx+V9W4uooShXqg+E79E5vqAr13wq/rl+iPrmrQWqA392+tR0vudZV/dplJZS/ogNCh7roHhA5z8yPaHIvRZMQt0mILqWzhX3q1YgAAAKwaKgQBAADWkD1D+2la/B/dWHC1q4CzVlpbGdjCNptL0FYHPn3Bsa49tkOwk3YO7OpWEFZo5e9ru8COGl3yiKbG/1B732a6KWu4qzzMV37lZawCr3/WLXqk5F49XfKoazveLbin/pd+sTv/2ox+LhQ8M6+LPPKqQ6CTTkstn/NwdRyZ0kXFySINKOztKhJtFeQbsoaopa+1O79XZn/dX3Sbzsk7yc27aKse25yGZufgrjo77QLdVjTYBZi22rCtSLzkyscAAABYdZ6k9asAAACsgB0yzB6er4UTS9hXdcyCRQvSLsq4qq6Hgnoi59g0tbwu280nCQAAsCK0DAMAAAAAAACNCIEgAAAAAAAA0IgwhyAAAMB65vKMXnU9BAAAAKzHqBAEAAAAAAAAGhEqBAEAQJ04L+9kdU/roX1CB9TZI/B22Wu6q3i4Qgqpd+ZA7RTsUHneQ8V3K5KMVC7c8WP0O91c0LPa9WOK2Rpter7pG+pf0FM/Rb+rdn6ZytQ97X86IfU0/RObqoeK79If8d+U5cnWMaknuNV4V+SZkif0VvgVPZg7tvK0d8Kva1zJaOUlF6iFt5VOSztbnYJ7rfT2r2ibTL+Ca9122QrFFUbmjFJb3wbVFpy5qfA6NfE2XWb14tz4bD1YfLd+itlt+bRzoKPOTb9YWd5sJZIJPVf6lN4Iv6ySZJG28e+gHumXqYWvVbXbsBWLr82/SFdl9NWWgW1qvJ9oMqLHS0bp4/D7CqtU7X2b67z0S7Sxf9Nql/st+rP6FV6rMU0mVZ52ycKz3DgrJJRURGFdnXG99gkdqB+j3+qxkgc0Pf6v0jzpOjh0hLqlnrHchTyWNebXy17ShNJnFz2GLXVy6pnaI7Tvcq9blizVmQu6KqqoDgodwaIyAABglREIAgCARq2Nt53uzX2i8veiRJEeLblXb4Vf1aGhoytP3zawg55p+mq1sOaq/PN1aupZ7vd+WcOq3e740jF6L/yWjkjpopJkifoXXqc9gvvqhqyhmh2f6QI0Wyn4kJQjlzm2n6M/6NnS0WribVJ52t+xvzSq+E4NzrpDm/g302eRjzSs8CY9lPuMC+RWxoq2yfwZ+00Ds25bZgDntrVsrL6LfqX9Qgcv8zLDim7SJr7N9HDuM4omo7qtaLDuLb5VvTJv1ktlz+mVsom6IWuwNvJtojGlj+rGgmt0Z84jCnqC7vq/x37RbYWDNSvx33K36emSR/VL7EfdknOfsj05Glc6Wv0LemlU7lMKeIIuvHw/8pYeKL7DhYdV3Z3zWLXf7y+6Tf8lZmjP4H5amFiggYV9dW7axdo/dIgbh4XAGZ5MHZXatcaxLGvMUyKf64mSB3VT1nBt5ttSU6Kfu8ewla+te0yXdd0UT6p7vGyVaQAAgNVByzAAAFhlFljcW3RrtdOuWHieXit70QUvz5aMdlVXJy84Umcs6OKq7pZVLfhB+J1qlXsX5Z1R+fuv0Z/UM/8SnbrgKHd7H1a5bFVW3dVt/uE1flk1XG1ctLC7/Aqoc3Cf5V7uvuLbtI1/ex2QcthS502N/aGxJU/omozrleZJ0y/RH1SaLNXZaRe6gGsD/0Y6IuU4VyW2LBZM3lk0TEenVA+bZsanu4q6hOJuH3vkceP1atlVarW15DZZcFmaLFH7RSFVTazS7q2yV7VXcL9lXsa2PdOTpVPSzlbIk6IMb6YOTTlaP0W/d+d/FHnPVUxu6t9Cfo9fp6Weq7zEfH0XneLO/zT8oYYU3qiT085c4TaEFdapqWerqbeZu61jU0/SguQ8zYyXB2sPl9yjl0qfd5V9y/Nl5FM3rqsy+sjn8Wl2fJZ2DXTWQSmHu9+tQnL34F6u4rEmyxvz/MRcdU09RZv7t3LVhR2Du2sD34b6JfbDSm8vAADAqqBCEAAArLIDUw5zlU3nJS9TwBNwbbEz4tO0d/AAfRR5V2+GJ2lg1u1q6WvlQr3eBZdpz+C+2jqwfa3vY158rmvtPC/tUleB9lvsZw0q7Kum3ubaZonbae5rWa3ibVXclj1KTX3Nl1uF9W10ir6JfqkHcp6q8Xyr4Ds69Xht6G/vfrfwLqCAC5IqWAvuf4npy7yPe4tv0cEpR6iZt6U+jrxXefrOwV21qX9zXZ1/gbsNCwStpTWnShXhqqhpm/6I/aZUT5rb33/FfldzbwudnHaWdgvu4c63ysfbiobo8oyero05quoVdxVSPalLVVB+HvmoMmi0gDPFk1J5nm2Tx+PVjPh0dZTc4zwq52kX8N1aNHC522Gtxkvej21Da18b93uXlG76X/ol+j76zTJvI5aMufbmM9LOq9yvViFZtUrSqhynRCdrv+BBNd7G8sZ8SMpR1X6fGZ+hf+N/q72vfH+szPYCAACsCioEAQDAKtvev7ObS+3LyGfudwuFOgf3Vro3Qx2Cu2to9t0uDMxLLHCVWxbMzE/MW6n7sPbOTX1buPDRArWtA9vpwNBhrgpxbbAwsDbz+h2bcqKb/25J30W/1j/xqeqScnLlaVv5Lbj0aGzJ465NdVrsH1cdGEmGa7z9N8peVmGiQMeldFvqPAuirLV0cNbtGtfkdTdn351Fw10r8eqoaZtiimoL/9Y6J+0iPZI7Tl1ST9bwwpv0e+xXd/4DRbdr39CB2iqw7Urdl80X+EnkA1cxaazS7sXS5/Rv7G+3f8aUPqZwsqxy/2R7c1w4trK+iUzR/UW3q0fapa5duLaP73vhN10oeWDo8BrPt7klRxT2l18+HZV6fI2Xqe2Y58Rn6eaCXm6OQntur8x1AQAAVhVHGgAAYJVZu+MBoUNdaGdVYx+E39YVmb0qq74eL35AX0UnK8eb60K9pJJuoYaVMTc+y80JZ+3CFeLJhKuSW/qys3V5/rk13s71mUOWqihcFbagxK+xn3Rd5k01nv9G2SQ3x1yGN6PyNPv5xqwherj4Hk0qG68NfRu7/Tap7IUab/+Z0ic0POtueT1Lf3ZrYVm6J0PbBnZ0v+8bOsi1UL8Tfk3n+C9a7tirLpqxTWCHyqq9ZW2T3bZ9Vdg7dIALy6zqblr8bze/3WUZ1RclWR4L0u4rHumqEQdmjdTG/k3c6dY+W6ZS9S/s6Z4jtljHRr72rrV4VU0qHa8nSx7U+RlXuH29Mt4IT9LhKcdWq+isYPtvaGE/1/o9IGukq35cVT9Ev9Hwwptd1ex56Zeu8u0AAACsLAJBAACwWixsuWTh2foi+qn8Hp928O/iTrcwpihZqIdyx7rFEGy+u9PyFi/SUZWtOmvVaBUKkvmVPzfxNleHQCf1yRpQeZpVGfpqaHSwluGnq6wauzZ8Gv5AOwY6uJCzplbTLyKfuKCoKqt6s6BrSPadlac9XjxKm/m3WOo2Pgl/4KoDL80/e9Ftxt1KtxaI3pH9sAukrF26Kp/HatVWfFi35KIZK9omC/9sBebOocXzKUYUUVBBvR9+y1Ulds871p0edgt0JPVn7HfdmfPwUvdhC5bYIhw21luzH1BulRbneYm5Ojx0nLqnnVc5f6ItymIh8sqyIPqe4lvdwh03Z9263MVQamLPLWtL75O5+PlWwRb6GFDQ21U09ki/fLWq+KwK1ObU/F/6xUu1EAMAAKxttAwDAIDV0tLXWlv6t3Hhxv6hQ13VoLEw0O+aKn1uUYnHSx5QcbK4WvBXwRZomBz5xLXD2kIWb4VfqTxvn9AB+i72lVtowcIem2+tT/7lem05C3KsTVatuLW/vLVzSVPjf8qW+7DFMaqyqsh+Bde4Sj7bBpu/7vXwSzpqiQVDzElpp+vZpq+6YNO+Lsm41s3dZz9b4LlrcA+9F3nTVZdZyGr77avIZO0V2m+Nb1NRolAPFN/p5oaMJ+OuJfyX6I+uarB/1gg90/SVynFaG7e1vdYUBtp1BxT2dq2wg7JGVgsDzYeRtzWsqJ8LAosTRXqw+E43v6AturGyHi8Z5RYjuSX7vpUOA41tX2tv26XmZJwbn6ObCq7TkSlddFHG1asVBn4R+VSjiu9Q36yBhIEAAKBOUCEIAABWm83vZ4twWChU4bS0c9xppy841s0d2CHYSTsHdtW/salSqPr1z0jr4VpJz8jropbe1u52bOVa08rXRjdkDnZBz13FwxRUivYPHawTU0+vk0dudmKm9vDuW+N5c+IzXZXdkq2mIU9IvTMH6uHiu3V30Qg187XU+emXa5fgbu58q/qzdt4bs4Zp28AOy73/Q1KOdCv/3l10ixYm89TK20Y9M29aKoRcE9tk4VdxssiFeVbh1863oW7IGuJC4BWxVZ1vLujpqhKtJdmq7qyy0J4PFWxev9FNJurYlJM0Jz5bFyw8zVVS7hToqOszB1eGy7Vl8w6+VPacm//v4oXVVxEemH1brQJG2xdNvE2XOv218EQXclvlon1V2DHQ0VWvWjXlfUUja7WozbjSpxRXXIMLrq92+qlpZ7tVkQEAANY2T9I+WgYAAFgBO2SYPTxfCyeWNJh99XbZa3q+9Gndm/tEXQ8FqDUL2gMK6qKMqypPyzk2TS2vy17pEBUAADROtAwDAAAAAAAAjQiBIAAAqJ1kwzxy+C8xXd3mH65vIlPqeijAcpUlS91z1RZ0WYr9bdL3AwAAaomWYQAAUCvJWFLznyjSvIcK2WNAPdPsvEw17Z4hj5+WYQAAsGIN8HN+AACwVvikzINS3HcA9exv80D+NgEAQO1RIQgAAGotmUiqeHJYC54sUtkfUSVj7Dygrnj8UspmATXpnqH03ULyeKkOBAAAtUMgCAAAVrp1mLZEoP7gbxIAAKwsAkEAAIC1aO7cuRozZoz+/vtv7bvvvjr88MMVDAbZ51VEIhG98sor+uCDD9S+fXudcsopatasGfsIAABgLSEQBAAAWAsSiYQLAkeMGOHCraFDh6pjx47s6+X44osv1KtXL82fP1/XXXedCwY9HtpgAQAA1jQCQQAAgDXsv//+U9++ffXJJ5+4UOvaa69Veno6+7kWiouLNXz4cI0dO1Z77rmnBg0apNatW7PvAAAA1iACQQAAgDUkmUzqhRdecCFWRkaG+77XXnuxf1fBhx9+6EJVCwjte5cuXagWBAAAWEMIBAEAANbQXIE33HCD3n33XRde9enTR1lZWezb1VBQUOBC1QkTJmj//ffXgAED1Lx5c/YpAADAaiIQBAAAWE22IEb//v3l8/l0880366CDDmKfrkFvvfWWbrzxRsXjcfXr109HHHEE+xcAAGA1EAgCAACsogULFrgA8NVXX9Vhhx3mwqomTZqwP9fSvr7pppv0+uuvu0DQAsLc3Fz2NQAAwCogEAQAAFgFb7/9tgulYrEYVWvrcI7GimrMQCDgWogPOOCAdXX3AAAADQaBIAAAwEooLCzU4MGDNX78eDevnVUItmjRgn24Ds2ZM8fN1/jee++pa9eubr7GzMxMHgMAAIBaIhAEAACopY8//tiFTxYK2sq3FkZ5PB72Xx1VC1ooa4uO2OItFtLusccePBYAAAC1QCAIAACwAsXFxRoxYoTGjBmjzp07u/CpTZs27Ld6YMaMGS6k/eyzz3TqqafqmmuuUXp6el0PCwAAoF4jEAQAAFiOL7/8Ur169dK8efN07bXX6pRTTpHX62Wf1SOJRMKFtRbaNm/eXEOGDFHHjh3relgAAAD1FkezAAAANSgrK9PQoUN1+umnu5Bp4sSJOu200wgD6yELaO2xmTBhgpo2beoes2HDhikcDtf10AAAAOolKgQBAACW8N1336lnz56aPn26rrjiCp111lny+Xzsp/VAPB7XY489pttuu00bbrihC3V32GGHuh4WAABAvUKFIAAAwCKRSMQFSSeffLJSU1P1wgsv6NxzzyUMXI9YcGuPmT12KSkp7rG844473GMLAACAclQIAgAASPrll19cVeAff/yhiy66SD169FAgEGDfrMei0ageeOAB3Xfffdp8881dG/GWW25Z18MCAACoc1QIAgCARi0Wi+n+++/XCSec4BanGDdunC6++GLCwAbAAt1LLrlEzz77rHucjz/+eBcQ2s8AAACNGRWCAACg0frzzz/dCsI//PCD/ve//+nSSy9VMBis62FhLbCW4bvuuksPPfSQtt9+eze34CabbMK+BgAAjRIVggAAoNGxSkBbeKJLly4qKCjQmDFjdPXVVxMGNmAW9Npj/PTTTys/P1/HHXecHn/8cfdcAAAAaGyoEAQAAI3KtGnT1Lt3b33xxRc644wzdNVVV7kFRNB4lJaW6tZbb9WTTz6p3XbbTYMHD9YGG2xQ18MCAABYZwgEAQBAo5BMJvXMM8+4hSVyc3M1ZMgQderUqa6HhTr02WefqU+fPsrLy3Ot4yeddJI8Hg+PCQAAaPAIBAEAQIM3a9Ys9e3bVx999JG6deum6667ThkZGXU9LNQDRUVFLiS2hUf22msvDRo0SK1atarrYQEAAKxVBIIAAKBBVwVOnDhRAwcOdG3BFvbss88+dT0s1EPvv/++C43Lysp0/fXX69hjj6VaEAAANFgEggAAoEGaN2+ebrzxRr399ts65phjXMiTnZ1d18NCPbZw4UIXHr/00ks66KCD1L9/fzVr1qyuhwUAALDGEQgCAIAG57XXXlO/fv3k9Xp188036+CDD67rIWE98vrrr7vnj7npppt02GGH1fWQAAAA1igCQQAA0KAqvAYMGKBJkybpkEMOcRVeTZo0qethYT00f/58Fwq++eabOuqoo3TDDTcoJyenrocFAACwRhAIAgCABuG9995zbcHhcNi1CluIw4qxWN05KK192ELmUCjk2on3228/dioAAFjvEQgCAID1fpXYwYMH6/nnn3cLhlho07Jly7oeFhqQ2bNnuwVHPvzwQ51wwgnq3bs3q1QDAID1GoEgAABYb3366afq06ePaxW27xbWUBWItVUtOG7cOA0ZMsS1DlsI3blzZ3Y2AABYLxEIAgCA9U5JSYluvfVWjR49Wp06dXLhTLt27ep6WGgEpk+f7ioEJ0+erNNPP11XX3210tLS6npYAAAAK4VAEAAArFe++uor9erVy7VxXnPNNTrttNPcasLAupJIJFwYfcstt6hVq1YaOnSodtllFx4AAACw3uDoGQAArBdssZDhw4fr1FNPVW5uriZMmKDu3bsTBmKdswD6jDPOcM9Bey5aKD1ixAj3HAUAAFgfUCEIAADqvR9++EE9e/bUP//8o8svv1znnHOOfD5fXQ8LUCwW0yOPPKI777xTG2+8sasW3G677dgzAACgXqNCEAAA1FvRaFR33XWXTjrpJAWDQY0fP17nnXceYSDqDb/frx49erhVrgOBgLp16+aes/bcBQAAqK+oEAQAAPXSb7/95qoCf/31V1144YW64IILXOAC1FeRSET333+/+9pqq61cteAWW2xR18MCAABYChWCAACgXonH4xo1apS6du3qApZnn31Wl156KWEg6j2rYr3sssv0zDPPuPkE7Tn84IMPuuc0AABAfUKFIAAAqDemTp2q3r1765tvvtG5557rwpVQKFTXwwJWmgWCd9xxh5tfcKeddnLVgjbHIAAAQH1AIAgAAOpcIpHQ6NGjdeutt6ply5YaMmSIOnToUNfDAlbblClT1KtXL82ZM0fXXHONW5HYVikGAACoSwSCAACgTk2fPt1VBU6ePFmnn366rr76aqWlpfGooMEoKSnRLbfcoqeeekqdOnVygXfbtm3relgAAKARIxAEAAB1IplM6rnnntPgwYOVk5Pjvnfu3JlHAw3Wp59+6sLvgoIC9/2EE06Qx+Op62EBAIBGiEAQAACsc7Nnz9b111+vDz74wIUiFo5kZGTwSKDBKywsdBWCzz//vPbdd18NGDDAtckDAACsSwSCAABgnVYFvvTSSy4EscVCBg4cqP32249HAI3Ou+++qxtuuMGtpG3fjzrqKKoFAQDAOkMgCAAA1on58+frpptu0htvvOHCD6sQzM3NZe+j0crLy3Ph+Msvv6xDDz3U/X00adKkrocFAAAaAQJBAACw1lkI2K9fP7easIUehx9+OHsdWOTVV191fxe2+rAFhAcddBD7BgAArFUEggAAYK3Jz893AYe1CR944IG6+eab1axZM/Y4sIR58+a51uF33nlHxx57rPr27avs7Gz2EwAAWCsIBAEAwFrx/vvvu7bg0tJS991CDlZUBZY/x+bEiRPd3JppaWkaNGiQ9t57b3YZAABY4wgEAQDAGlVUVKRhw4bp2Wef1V577eVCjVatWrGXgVqaOXOmqxD8+OOP1a1bN1133XWswg0AANYoAkEAALDGfP755+rdu7dbLKFnz54uzKAqEFi1asFnnnnGheu2+M7QoUO12267sSsBAMAa4V0zNwMAABozawu2NsczzjhDbdq00YsvvqiTTz6ZMBBYRRak29+Q/S21bt1a3bt31+DBg1VWVsY+BQAAq40KQQAAsFq+/vpr9erVy7U5XnXVVS4UtNVSAawZtjr3E088oVtvvVVt27Z1VYM77rgjuxcAAKwyjtYBAMAqiUQiLqA49dRTlZWVpRdeeEFnnXUWYSCwhlnAbn9bEyZMcHMJWuXgyJEj3d8gAADAqqBCEAAArLSffvrJzRE4depUXXrppTr33HPl9/vZk8BaFovF9NBDD+nuu+9W+/btNXz4cG299dbsdwAAsFKoEAQAALUWjUZdEHHiiSe6qqXnnntO559/PmEgsI5Y8H7BBRe4vz2bZ/CEE07QPffc4/42AQAAaosKQQAAUCt//PGHrrvuOv3yyy/q0aOHLrroIgWDQfYeUEesZdjCwFGjRmmbbbZxcwtuttlmPB4AAGCFqBAEAADLFY/H9fDDD6tLly5uNeGxY8fqiiuuIAwE6pgF8ldeeaX7mywuLnZ/o4888oj7mwUAAFgeKgQBAMAy/fPPP24FYVtJ+Oyzz9bll1+ulJQU9hhQz5SVlem2227T448/rp133llDhw7VRhttVNfDAgAA9RSBIAAAWEoikdCYMWM0YsQINWvWzIULHTt2ZE8B9dwXX3zhQvz58+fr2muv1SmnnMLK3wAAYCkEggAAoJr//vtPffr00aeffurCBAsV0tPT2UvAesLah231YWsl3mOPPTRo0CC1adOmrocFAADqEQJBAADgJJNJjR8/XoMHD1ZGRob7vueee7J3gPXUhx9+qL59+7qA0L7bHIO2MjEAAACBIAAA0Jw5c3TjjTfq3XffVdeuXdW7d29lZWWxZ4D1XEFBgasQnDBhgvbff38NGDBAzZs3r+thAQCAOkYgCABAI/fKK6+of//+8vv9uvnmm3XggQfW9ZAArGFvvfWWC/1tBeJ+/frpiCOOYB8DANCIEQgCANBILViwwAWBr732mg4//HAXFjRp0qSuhwVgLf7N33TTTXr99df5mwcAoJEjEAQAoBF6++23dcMNN1AtBDTCuUIrqoIDgQBVwQAANFIEggAANCLMJwagYt5Q+1Dgvffec/OG2srimZmZ7BwAABoJAkEAABqJjz76yL3pZ8VRAFVXFrdFRywMZGVxAAAaDwJBAAAaOAsAhw8frrFjx2qPPfZwb/7btGlT18MCUE/MmDHDfVjw2Wef6ZRTTtG1116r9PT0uh4WAABYiwgEAQBowL744gv16tVL8+fPd2/y7c2+1+ut62EBqGcSiYTGjBmjESNGqFmzZho6dKg6duxY18MCAABrCe8IAABogMrKyjRkyBB1795dLVu21MSJE3XaaacRBgKokX1QYK8REyZMcIHg6aefrmHDhrnXEgAA0PBQIQgAQAPz3Xff6brrrnNtgFdeeaXOPPNM+Xy+uh4WgPVEPB7XY489pttuu00bbLCBCwZ32GGHuh4WAABYg6gQBACggYhEIu4NfLdu3dz8Xy+88ILOOeccwkAAK8U+QDj33HPda0hqaqpOPvlk3X777e41BgAANAxUCAIA0AD88ssvrirwzz//1EUXXaQePXooEAjU9bAArOei0ageeOAB3Xfffdpss81cteBWW21V18MCAACriQpBAADWY7FYzL1RP+GEE5RMJjVu3DhdfPHFhIEA1gj7YOGSSy7Rs88+61qJ7bXGAkJ77QEAAOsvKgQBAFhPWTVgz5499eOPP+q8885zb9qDwWBdDwtAA2Utw3fddZceeughbbfddm4l4k033bSuhwUAAFYBFYIAAKxnEomEm/C/S5cuKiws1JgxY3TVVVcRBgJYq+wDh6uvvlpPP/20CgoK3GuQvRbZaxIAAFi/UCEIAGi0brzxRr300kvuZ2t/s7mybAL9Cl9//bXqm2nTpql379764osvdMYZZ7ggsOqYAWBdKC0t1a233qonn3xSu+22mwYPHuxWJF4Txo8frz59+tT42vbOO+8oNzd3udc/4IADXHB55JFHrpHxAADQEBEIAgAg6eWXX3Zvbu3NZn1k8wOOHTtWw4cPd2+GhwwZok6dOtX1sAA0cp999pkL7/Ly8tSrVy+ddNJJ8ng8qx0Ijho1Sq+99toqXZ9AEACAFaNlGACA5bwp7datm04//XRXAfPpp5+6N5oWHla9zGGHHVb5+zfffKOTTz5ZHTt2dNUpr7zyymrv35kzZ+p///ufbrrpJh111FF68cUXCQMB1Au77767e02y1yarurbXqlmzZq31D0hsMSV7jd1ll13UuXNnDRo0qMbL2muwvUbba/LRRx+tF154odo8rOeee657fT/44IP1xBNPrNVxAwBQnxAIAgCwHBbwWWvue++9595QLo+9CT7nnHNchYxVzQwYMED9+/fXl19+ucpveidMmODexP7222968MEH3W1mZGTwmAGoN+w1yV6brKrPXqssHLTXLnsNWxteffVVt6K6rXb81Vdf6f7773fzGk6ZMmWptubrrrvOVVbb67BVMNprslUzFhcXu9frDh066KOPPnIBowWCEydOXCtjBgCgviEQBABgOdLS0nTIIYe474FAYLn7yqpktt12W3Xt2lV+v99VrtjP1uq7subNm6eLL77YrSK8//77a9KkSdpnn314rADUW/vuu697rbLXLHvtspXP7bVsVfzzzz/uQ5iqXxVzvtproS2m1K5dO82dO9cFf+np6Zo9e/ZStxMKhfTss8+6eVetEtACRJt24f3333ev0xdddJFbLGWzzTbTWWedtUqv1wAArI/8dT0AAADqsxYtWtT6sv/9959biKRqJWE8Hnch4cpWv1h7sNfr1d133+1a2QBgfZCdna0RI0a4161+/fq5akF7Pas6tUJtbLTRRsucQ9BWNbb7+PDDD9WsWTNts802rhpxyYpEW5Tkqaee0r333uvCSVs46vjjj9e1116rGTNmuKruqq/Xdrs5OTmruOUAAKxfCAQBAFiOJSfHt5DO3lRWsNazCi1btnQVMvfcc0/laVax4vP5arWP7bas7c7mKDz00EPdm+gmTZrw+ABY71hltbXj2uvY5Zdf7oLBG264YY0EbrYAVH5+vt59911XvW1B4K677rrU5QoLC7Vw4ULdeeedLuyzKSAsGNxiiy3c6/Xmm2/uWpsrLFiwQOFweLXHBwDA+oCWYQAAVkL79u3dSsSRSETTpk3Tc889V3meveG1hUfefPNN9+bTWt5OO+20WrWg2RtbmyvQ5rKyN7t33HEHYSCA9VrTpk1dGGfVfB988IF7jbT5WFdXQUGBm8LBWn5tLkC7fQv/qn5YY0pKStwiJ2+99Zb7cMcqvu27hZL77befaze2CkK7nv18/vnnu/ECANAYEAgCALASrrnmGs2ZM8etannppZe6OQIrbLDBBm6S+4ceesjNVWVhoFX6XXjhhcu8PXsT26dPH11wwQWu7c3myLI3zUtWJgLA+shey4455hg3t+DWW2/tQjd7zSsqKlrl27SKQ6vm69Spk3uNtSrAvfbayy1oUpVVAY4cOVK33Xabm9P1lFNOcV/WzpyVlaVHHnnEhYV2XRvjlltu6aoYAQBoDDzJtbX8FwAAWC6rJuzdu7erdrHvJ5xwAkEggAbL3nZYVfXgwYNdlZ59tw9XAADAukcgCADAOmZtbLfccotrVbMKlyFDhqht27Y8DgAahenTp7sPQSZPnqzTTz9dV199tZsLEAAArDsEggAArENTpkxRr169XNuxrXR56qmnuoVKAKAxsXlWR48e7eZMtdbeoUOHurZeAACwbvAOBACAdcBWrhw2bJibV9Am2p84caKrjCEMBNAY2WvfGWec4Vb5zc3NdR+ODB8+nFV+AQBYR6gQBABgLfv+++/Vs2dP/fvvv24y/HPOOUc+n4/9DgCS4vG4Hn74YbfC70YbbeQ+PNluu+3YNwAArEVUCAIAsJZEIhHdcccd6tatm1JSUjR+/Hidd955hIEAUIV9QNKjRw89//zzCgaDOumkk3TXXXcpGo2ynwAAWEuoEAQAYC349ddfXVXg77//rgsvvFDnn3++AoEA+xoAVvBByv333+++ttxyS1ctuMUWW7DPAABYw6gQBABgDYrFYho1apSOP/549/MzzzyjSy65hDAQAGrBKgQvu+wy99pp4WDXrl3da6q1FQMAgDWHCkEAANaQqVOnuhWEv/32W5177rnuTW0oFGL/AsAqLsZk0y488sgj2mmnnTRkyBC1b9+efQkAwBpAIAgAwGpKJBJ68sknNXLkSLVs2VJDhw7VLrvswn4FgDVgypQp6t27t2bPnq1rrrnGrdbOCu0AAKweAkEAAFbDtGnT1KdPH02ePFndu3fXVVddpbS0NPYpAKxBJSUluvXWWzV69Gh16tRJgwcPVrt27djHAACsIgJBAABWQTKZ1Lhx41wLW05Ojntz2rlzZ/YlAKxFn376qfsQZuHChe77CSecII/Hwz4HAGAlEQgCALCSrG2tb9+++vDDD92bUWtly8jIYD8CwDpQVFTkPox57rnntM8++2jgwIFuugYAAFB7BIIAAKxEVeCLL77o3nzaYiH2fb/99mP/AUAdeO+993T99de71YhvuOEGHXXUUVQLAgBQSwSCAADUwvz589WvXz+9+eab7k2nvfm0VmEAQN3Jy8tzH85MmjRJhx56qG666SY1adKEhwQAgBUgEAQAYAXeeOMN3Xjjje5ne7N52GGHsc8AoB559dVX3euzrT5888036+CDD67rIQEAUK8RCAIAsAz5+fkaMGCAXnrpJR100EHq37+/mjVrxv4CgHpo3rx57sObt99+W8ccc4xrJ87Ozq7rYQEAUC8RCAIAUIP333/fvZksLS117cH25pKVLAGg/s/1OnHiRNdGnJqaqkGDBrmFRwAAQHUEggAALLF65dChQzVu3Djttdde7s1kq1at2EcAsB6ZNWuWWw3+o48+0kknnaSePXuyGjwAAFUQCAIAsMhnn32mPn36uEnqe/furRNPPJGqQABYj6sFn3nmGQ0bNky5ubkaMmSIOnXqVNfDAgCgXvDW9QAAAKhr1hZs7WVnnnmm2rZtqxdffNFVlNAiDADrL3sNP/nkk91reps2bXTGGWe413p7zQcAoLGjQhAA0Kh9/fXX6tWrl2bOnKmrr75a3bt3d6tUAgAajkQioSeeeEIjR45U69at3dQQO++8c10PCwCAOsM7HgBAoxSJRHTLLbfo1FNPdatQTpgwwVUIEgYCQMNjr+1nnXWWXnjhBWVlZbnX/ltvvdX9LwAAoDGiQhAA0Oj8+OOPripw6tSpuuyyy3TOOefI7/fX9bAAAOtALBbTQw89pLvvvlvt27d3cwxus8027HsAQKNChSAAoNGIRqPuDaDND+jz+fTcc8+pR48ehIEA0IjYB0AXXHCB+x9glYO2gJT9b7D/EQAANBZUCAIAGoXff/9dPXv21C+//KLzzz9fF154oYLBYF0PCwBQh6xl+N5779WoUaO01VZbafjw4dpss814TAAADR4VggCABi0ej7vWsC5duqisrExjx47V5ZdfThgIAHD/C6644gr3v8FWH7b/FQ8//LD73wEAQENGhSAAoMH6+++/3VyB33zzjc4++2z3pi8UCtX1sAAA9ZB9aHTHHXfo0UcfdSsQ20rEG220UV0PCwCAtYJAEADQ4CQSCT399NMaMWKEmjdv7t7UdezYsa6HBQBYD3z55Zfuw6R58+bp2muv1SmnnMIK9ACABodAEADQoMyYMUN9+vTRZ599plNPPVXXXHON0tPT63pYAID1SHFxsftQacyYMercubMGDx6sNm3a1PWwAABYYwgEAQANQjKZ1PPPP+/etGVmZrrve+65Z10PCwCwHvv444/dh0xFRUXue9euXeXxeOp6WAAArDYCQQDAem/27Nm64YYb9P7777s3a/amzUJBAABWV0FBgYYMGaLx48dr//33180336wWLVqwYwEA6zUCQQDAel0V+PLLL7s3Z4FAQAMGDNABBxxQ18MCADRAb7/9tm688UbFYjH169dPRxxxRF0PCQCAVUYgCABYLy1YsEA33XSTXn/9dfemzN6k5ebm1vWwAAAN/H+PfQj16quv6rDDDnPBYJMmTep6WAAArDQCQQDAeuett95yAWA8HqdKAwCwzr3yyivq37+/fD6fq04/8MADeRQAAOsVAkEAwHo1j9PAgQM1ceJEN4+TvQlr3rx5XQ8LANAIzZ07181f++6776pLly5u/tqsrKy6HhYAALVCIAgAWC98+OGH6tu3r4qLi3X99dfruOOOY6VHAECdz2X7wgsvaNCgQcrIyHDf99prLx4VAEC9RyAIAKjXioqKNHz4cD3zzDPac8893Zut1q1b1/WwAACo9N9//7kPrT755BOdfPLJuu6665Sens4eAgDUWwSCAIB6a/Lkyerdu7ebxN3eXNmbLI/HU9fDAgBgKYlEQmPGjNGIESPUtGlTDR06VLvuuit7CgBQL3nregAAACyprKxMgwcP1hlnnKFWrVq5OQNPOeUUwkAAQL3l9Xp12mmnuf9ZLVu2VPfu3TVkyBD3Pw0AgPqGCkEAQL3y7bffqmfPnpoxY4auuuoqFwraKo4AAKwv4vG4Hn/8cd12221q27atm/pihx12qOthAQBQiQpBAEC9EIlE3Bsnawu2idknTJigs88+mzAQALDesQ+yzjnnHLfgiM0l2K1bN/c/zv7XAQBQH1AhCACoc7/88oubI/Cvv/7SxRdfrPPOO09+v7+uhwUAwGqLRqN68MEHdc8992jTTTd11YJbbbUVexYAUKeoEAQA1JlYLKb77rtPJ5xwgvt93LhxuvDCCwkDAQANRiAQ0EUXXaTnnntOyWTS/c+z/332PxAAgLpChSAAoE78+eefbq7AH3/8UT169HCVgcFgkEcDANBgWcuwVQqOGjVK2267rYYNG+aqBgEAWNeoEAQArPOJ1h999FEdd9xxKioq0tixY3XllVcSBgIAGjz74Mv+59n/vsLCQnXp0kWPPfaYEolEXQ8NANDIUCEIAFhn/v33X/Xu3VtTpkzRmWee6d4UpaSk8AgAABqd0tJSt9CIrUa86667asiQIdpggw3qelgAgEaCQBAAsNbZnElWDWETqTdp0sS96dltt93Y8wCARu/zzz93H5bl5eW5BbZOPvlkeTyeRr9fAABrF4EgAGCtmjlzpvr27auPP/5Y3bp1c292MjIy2OsAACxiU2jYh2bPPPOM9tprLw0cOFCtW7dm/wAA1hoCQQDAWqsKnDBhgntTk56erkGDBmnvvfdmbwMAsAwffPCBrr/+epWUlLjvxx57LNWCAIC1gkAQALDGzZ07VzfeeKPeeecdt3iIVQhmZWWxpwEAWIH8/Hz3IdrEiRN14IEH6uabb1azZs3YbwCANYpAEACwRr3yyivq37+/fD6fexNz0EEHsYcBAFhJb775pvtwzVYgvummm3T44YezDwEAawyBIABgjbDJ0C0AtEDw0EMPdW9ebAERAACwahYsWOD+n77++us68sgjdcMNNyg3N5fdCQBYbQSCAIDVZq3B9iYlGo26agZ708IKiQAArJk5eV9++WX3oVswGNSAAQO0//77s2sBAKuFQBAAsMoKCws1ePBgjR8/Xvvtt597s9KyZUv2KAAAa9js2bPdh2/vv/++jj/+ePXu3VuZmZnsZwDAKiEQBACskk8++UR9+vRRQUGB+25vTqgKBABg7VYLPvfccxoyZIhbrMu+d+7cmV0OAFhpBIIAgJVSXFysW265RU8//bR23313VyHYtm1b9iIAAOvIjBkzXIXg559/rtNOO03XXHON0tLS2P8AgFojEAQA1NqXX37p3oDMnTtX1157rU455RR5vV72IAAA65itPmwfzo0YMUItWrRw1YIdO3bkcQAA1Arv4gAAKxQOhzVs2DCdfvrpatq0qSZMmOAqEggDAQCoG/Y/2P4vT5w40f1vtp/tf7X9zwYAYEWoEAQALNd3332nXr166d9//9WVV16ps846Sz6fj70GAEA9EY/H9cgjj+iOO+7Qhhtu6ILB7bffvq6HBQCox6gQBADUKBKJuDcWJ598slJSUvTCCy/o3HPPJQwEAKCesQ/qzjvvPPe/2v5nd+vWzf0Pt//lAADUhApBAMBSfv31V/Xs2VO///67LrzwQp1//vkKBALsKQAA6rloNKoHHnhA9913nzbffHNXLbjlllvW9bAAAPUMFYIAgEqxWMy9iTj++OPdz88++6wuueQSwkAAANYT9gGe/e+2/+H2v9z+p48aNcr9DABABSoEAQDOX3/95eYK/P777/W///1Pl156qYLBIHsHAID1lLUM33nnnXr44Ye1ww47aOjQoWrfvn1dDwsAUA8QCAJAI5dIJPTkk0/q1ltvVevWrd2bhZ133rmuhwUAANaQr776yn3oN3v2bF111VXq3r27W6UYANB4EQgCQCM2bdo09enTR5MnT3ZvDq6++mqlpqbW9bAAAMAaVlJSopEjR7oPAXfbbTcNHjxYG2ywAfsZABopAkEAaISSyaSbW8iqAXNzc92bgt13372uhwUAANayTz/9VH379lVeXp569+6tE088UR6Ph/0OAI0MgSAANDLWLmRVgR999JFOOukkt5pwRkZGXQ8LAACsI0VFRe5DwXHjxmnvvffWoEGD1LJlS/Y/ADQiBIIA0IiqAl988UUNHDhQoVDIHfzvu+++dT0sAABQR95//31XLRgOh3X99dfrmGOOoVoQABoJAkEAaATmz5+vfv366c0339TRRx/tDvpzcnLqelgAAKCOLVy4UAMGDNCkSZN08MEHq3///mratGldDwsAsJYRCAJAA/f666+7MNDYQf6hhx5a10MCAAD1+Hjh5ptv1iGHHFLXQwIArEUEggDQQPGJPwAAWNmOghtvvFFvvfWW6yi44YYblJ2dzU4EgAaIQBAAGvicQHYwbwf1rCAIAABqO+ewtRGnpqa6uYeZcxgAGh4CQQBoQFg1EAAArAmzZs1yHy5+9NFHOvHEE9WrVy9lZGSwcwGggfDW9QAAACsvLy9PY8eOrXbap59+6ioBX375Zfep/oMPPqiWLVuyewEAwEpr1aqVHnroIXdMYccWtgLxZ599Vu0yY8aMccckAID1D4EgAKyHhg0bprvuusv9XFJS4g7WzzrrLLVr104vvfSSTjrpJFqEAQDAarHpRuyYwlqI27ZtqzPPPNO1EJeWlrrz7Vhk+PDh7GUAWA/RMgwA65kff/xRxx9/vJv0e6uttnItPLNnz9bVV1+t008/XV4vn/UAAIA1K5FI6Mknn9Stt96q1q1ba8iQIfr555/dh5Ljx4/XNttswy4HgPUI7xoBYD2b6Hvo0KFq3769pk2bptNOO025ubmaMGGCzjjjDMJAAACwVtgHjlYhaMccOTk57hhkxowZ7pjEjk3sGAUAsP6gQhAA1iNvv/22LrroIrVp00Zz587VZZdd5lqF7VP7lJSUuh4eAABo4MrKylwr8eOPP64777xTzZs313///af77rtPBxxwQF0PDwBQSwSCALCeiEQi2nvvvbVw4UJXFbjxxhtrwYIF7iA8GAzqiy++kM/nq+thAgCABioej2vXXXd1xyT24WSTJk00depUd2xiVYMffvihOyYBANR//roeAACgdr7//nt3wB0IBNSiRQs1a9ZMO++8s1tIZPvttycMBAAAa5V98PjYY4+5Y5Lp06e76UtscbPi4mJ3jGKnd+jQgUcBANYDVAgCwHrWphMKhVhBGAAA1Bs2f2A4HGb6EgBYjxAIAgAAAAAAAI0IqwwDqymWjLEPgXqMv1EAwJKSiTg7BWjA+BsHVow5BIHVkEgm9H34az1V9Kh+j/6iWDLK/gTqCb8noM0DW+m0jLO1Y6iDvB4+AwMAlCv55mUVf/GcYvP/lQgHgYbD65O/6YZK3/UEpe9yTF2PBqjXaBkGVsO8+BydPftERUUQCNRXAQX0aMtxauZrUddDAQDUsWQipvBfX2rB01fV9VAArGVNTh2p0CYd5fFSBwXUhHIJYBVZNeBHpe8RBgL1nAX29rdKBS8AwIKB0h/fZkcAjYD9rRMGAstGVA6sMo/yEvPZf5LiCxNa8ECJSidHlChJypvpVdoeAeWemyZfplex2XFNP2uhNng6V77c+v85xPy7i5WMJNXsqozK02LzE5p3S5HKvo/JmyJlHZ+qnFNSK88vmFSm/KdK3b4IbeVXs6szFGjnU9l3Uc3qWVDt9t20kx6p/RtNq51e+k1Us64uULunchRo5Ss/7duo8h4oUeTfuLzpHmUeEVLOGak1rjJc9kNUMy8vkCe4+LT0fUNq3mvxdjRW5X+rS+8zAEDjkyjm+G1VLCxL6IGvSzT5v4hKokllhrzao21A5+6Y5n6eXRzXWZMW6uljc5WbUn+P97q/mKe5JQlVPZSaeEITBX0eLShN6M4vi/XlzKj8Xmm/DYO6pGO6/N7yCz/5fYkm/Fam0lhSu7QK6OpOGZXburzrxhNJPfhNid6cGlYkIW2e69OlHdPVPqfmt+O3fF6kN/4Ky1dlNw7cJ1MdWlc5yMMK8bcOLB+BILAaEkqy/yTNublQvmZetX0kR75sr6Kz4po3vEhzbi5S6xFZ8rf0aeNXq4dfa0P4j5gKJ5Yp58w0+Zut/IFovCihBfeWqOjVsDKPDlU7b06/QgU39mnD8bmKTY9rVq9C+Vt4lXFgSKVfRpQ3qkStRmQp2N6nvMdKNPv6QrV9JFspOwSqbXs8P6EZ5+cr96zFYaI7vSChuUOL7Em1+LQFCc3uW6imF6cp45CQYv8lXLjozfQou2vq0tv/W0ypHQNqNSxrpbe9oeNvFQBQKcnx26q4+aNCNUv16pEjc5Sd4tWsoriGf1akmz8q0ogDs9Qy3adXu639470/8mKa+FuZztw+Tc3SVu54rzia0IzChJ7rmqsmqUtf94YPCrVxtk/Pd81VUTSp694p0LM/l+nUbVM16Y8yvfpXWHcekq0mKV6NnFykwZ8UacQBWSu87ou/hzVlVlQPHZmjzKDHhYO23x49KqfGcf62IKZenTN0wMbVj0exkvhbB5aLQBDAagv/FFPz6zNdGGisuq3ppenKf75MyWRSsdkJTT9loTZ4PldFr4e18ImSxVdOSMmI1OqWLKV2CKjks4jyHilR9L+EAm28anJBulJ3CSzzvpPRpIrfi6hgYpmi0+PKODQkb4ZHRW+GNW9kUY3XWVY4Ob37QqXvE1TaPtU/fbXqPNvGloMy5Q16FNzEr6yuKSp8ucwFgoWvhpVxcEihLctfUq0ysuDFPFdNmLpj9bHPu61YKdv7lXlYSvXThxe528gfXVp5mgWraZ0Dyjy8/LKBDXxK2yuo8HcxqevS44/8Gq8cAwAAwJr007yYrt8z04WBplVGeZXb87+UH+/NLk7olIkLXSD2+l9hPfHD4uO9RFKKxKVbDsxSh1YBfTYjoke+LdF/RQm1yfTqgp3TXcXdskTjSb33b8QFgdML4zp0k5Aygh5XcWfBXE1qCid/mx93IWJNYeDP86L6Jz+u2w7KctWCIb9HQ/bLdGN3t/dnWF22SFHbzPIujos6pKvr83muMtKqA5d33WmFcfdzctGXFRwGfcve1r8XxrVlU47pAKxdvMoAWG3pB4U0b0SRyr4OKWUnv0LbBBRs71fza5ZuVbU224pW22Q86SrgPCGPUnbxuwq3Of0L1aJ/pgsHSydHNfuGQrUdla1A2+pHTfHChPLHlrowLriBT1nHpbgwzxMsb+mwcM2+Vobdj7+5r7xSr4rov3H5mnoqA08T3Min/Gfj5ef/E1fKzosPYj0+jwJtvYr+Ha8WCJZOiar0y6g2eKr6p8EFE8pcG3FWl5RqgWDKNgH3VTX8tH2ScVDN7SK2/2JzPZp2Wp5reU7dLagmF5S3bQMAAKyOgzYOacRnRfp6dkg7tfRrm2YB1/J6ze5LH++dsm2q+zLWLtv3/UKFfB7t0tLvqt/6f1io/tYC2yqgyf9FdcP7hRp1RHZl2FahMJzQ2J9KXWXeBlk+HbdFivbZMOhCN3Nw+5D7qi27b2vnveT1fBfSbZjlU4+d0rR9i4B+XRB3FX6jfyjVa3+F3UQjh7QP6awdyrfDAr/2OYvHZ63CWSGPC+9mFieWe92jNwvpo2kRHT8+z4WB2SGPbj8ou8Yx/rUwrnhSuv+rYv0wN+YC2JO2TtERm1b/MBkAVheBIIDV1uyqdBXvGFDxe2HNGxZWojip4GY+F0aldlj2XCfzby92c+61vj3bzYlnFXfp+4WUtlv5ddI6B10LbOFrYTU5N63adS2Ey3+6TOkHBNXkwvRVahFekoWBNUmWJuVJqT7/nIWYdrpJlCblXfL8FI+SZdVbkqwyMvvElGrBYmRqzAWbbe7LXm5XQyKS1NybC+Xxlc9fuNQYE0n5mnqV1imgzCNTlCi2FuRizRtapJaDaCEGAACr56pO6dqxZUDv/RPWsL/CKo4mtVmuTxfsnLbcue1u/6LYzT94+8Hlx3sv/1Gm/TYKabc25dfp3C6ojq0Deu3PsM7dqfrx3j8FcT39U5kO2CioC3dJX+kW4SXZvIFbNvGrx85paprq1Yu/l6nXu4V65KhsFz7+PD+mHVr49eTROZpTklCf9wqUGvC4tl+bNzDFX/14L8XnUVk8ucLrxhJy4ecZ26cqJ8Wrh74pUd/3C/TwkTmV4WYFazfesYVf3bZJVb+mfn03J+YC06ygV3ttwByCANYcAkEAq83j9VRW5FnLiFXGWdWbzbPX7omcGtdyWPhUqUomR10QVhGmWWtx2ddRlXwYqbycVRF6Q0sf/KRsF1Dbh7NVML5MM85aqJRdAso6JqSUDgF3sFn0VtgFjjXZaFKTldu+FClZVv20ZDgpb1r5uG389nu188uS8qR6qrUdl/0UU4ubMitPS4STmjOgSE2vTHeLrcQWVJlAsApblGV2v0JX/dhqZJa8VW63coxej1rfsjj486b51KRHmv67KN+FidbqDAAAsKq8Hk9lRZ4d7/2dH3cLbPR6r1BPHJ1T49JdT/1Q6ioA7zssuzJMs9bir2dH9eG0xcd78WRSIf/Sx3vbNQ/o4SOyNf63MrdgibUVH7N5yIVrdrz31tSwCxxrMumkpY/3Ttq6+oeqJ2yVqpf/CLuFQCyYszbe/+2U5rbVKhK7bpni2p8t1Ev1exSOVT/eszDQTl/RdYd8UqRzdkxT64xF7ca7pOmNqeX3u0e76ttt29ah1eLqQdvmQzYJ6v1/wwSCANYoAkEAq6VkckRz+hdpw2dz5E33uoMzaxdudmWGm9vPwsFA++qVd0Vvh7VwTKna3JElf5PFn/TawiSZx6So6cXp1ebRqwjelmRz+TW7JkO5FyRU9EpY8xYFgG3uyVbGQSH3tSYEN/a7BT5s0RFfRvl4I267yl9CAxv7FPmnvH24IsSMzki4BUYq99MHYdcGXXWV5civMcVmxDV3QHmLcsUh5oxz89XsynQ3/vAvMc3qXaD0vYJqenm6PEt8Ml11FeT8Z0uVe06avKHyy1jbsFUU2hcAAMCqspWF+39YpGe75ig9UH68Z+3CV+6W4eb2s7bZqu205u2/wxrzU6nuODir2px9tjDJMZun6OIOi4/3bIGStEDNxzib5Pp1TacMXbBzQq/8uTgAvOeQbB3UPuS+auv5X0q1SY5fO1eZrzCaSLpAb8Nsr+vWsGq+ivn9rHW34vjMWoKtYrFD6/Lf88oSKggn3Xa7yy3nulYxGKuYUNCFq+Vf1r68pE9nRNychEdutrhF2OZfXLKSEABWFxNLAVgttoquL8ujucOK3aIeFSvp5j9TKvmk0PbVP3co/SaqebcUqcUNGQpuWv28zENDbtGRsu+i7pPn8K8x/Xd+vko+WfwJck0spMs+KVXtnsxxi5nY/a5JtphHaGu/W4E4UZZ0bb4FL5S58ZqMw0Iqei2ssp+iLoTLe7hE/qZehbZZvH1lP8aUsl317XUrEL/e1FUs2perprS5DB8uDzRjc+KadV2Bm1uw2dUZywwD3T7I9Kj47bBbkMXmGozNjWvBqBK3yIrNaQgAALCqdmgRcPPlDfu0WNMLyo/38sMJPfNTqewwY/sW1Y9xvpkd1S2fF+mGPTO0aW7182xBEKuc+25O+fHer/NjOv/VfH0yY/nHexlBm0sv1bXkXtohXb5VeCdr1Yl3flnsFgKxxTtG/1CikmhSndtaVV7AzQv4wNclisSTmlEY1wu/lunARSv92rjH/Vymf/Pjrn343inF7jrN03wrvK7d/uPfl2rOovt99LtSpfo82r750gupWLB495Rit38SyaQLY9/5O6zDN2XFYQBrFhWCAFaLtcu2vjNLeY+VaubVBUoUJFxra8pOATc3oIV1iaJ4tXn0bAGNuUOKXHhW8dFpzmmpyjk9Tc2uTdf8O4sVnZlwIVfOqalLrci7LPZpdVqntTO3ii10Mv+2Ik07KU+eYPk8fhWLlqTtWr54x9zBRa6S0Fb6tRWJqwZxMduefVfuyLVgYliJwqTyx5S6rwo2r2LLAVmVKynbqsmunXhYlubfU6x/u+S5V/f0/cvnVwQAAFgd1u575yFZeuy7Ul39doEKwglXsbZTy4CbG9DCuiIrY1vkie9LXLXckE+LXEBWURt32rapOn27NF27e7oL5mYWJpQZKp9n77BNan+816ntqh3vWUtvPFmii17LV0ks6eYTHH5Alhu/uf3gLN39ZbFOeqF88Q9byOOErcrHddRmIVcReN07BeXz/LX0q++e5Quq2L5Y3nWv2C1do74u0cWv5yscl7ZqWn6/Nseg6flOgVqme3VVpwzXQmzzJQ7/rEjzSxJqme5Tz84Zrn0aANYkT9I+lgGw0mLJmB4vHKVxRaPZe0A9d2LG6Tozs4f8Hj4HA4DGbv5TVyr85+d1PQwAa1lo005qetpt7GdgGWgZBgAAAAAAABoRAkEAAAAAAACgESEQBAAAAAAAABoRAkEAAAAAAACgESEQBNAoRGfFNXX/+YotSKg+KP0mqqkHznfjqhD5J6aZl+fr78Pna9opeSp8razadfKeLNG/xy/Q30fM1+zrCxTPqx/bAgAAUBdmFcW1/1PztaC0fhwTPfdLqVuFuKqSaFIDPy7UMeMW6NjnFujeKcWKJxav6/nRtIjOfGmhDh87X+e/ulA/z4vWwcgBNEYEggCwjsULEpo7tEiqcuyajCY1u3ehUjoEtNFLTdSsV4YW3F2ish/KDwoLJpWp6NWwWt+ZrQ2fbyJPqkdzBxfx2AEAANSxcCypB78p0b1TSpY6b+TkIhcKjjkuRw8clq0ps6Ia+1P5h77/5MdcWHhJxzS9dFITHbZJSH3eK1RxtH4EnAAaNn9dDwBA47Dg4RIVvVamZEQKbOxTk/PTlLJNQMlkUvlPlarorYhicxPyBKWMA0Nqekm6u960k/OUdUKKCieFFZsVV8pOAeWelab5dxYrMjWm4OZ+teiXKX9TrwvZPAEp8ndckT9iCmzoU9PL0pWybWCp8UT+jWvBXcUK/xqTN9OjrK4pyj4+tfy8v2KaN7JY0b/j8mZ7lLZXUE16pMnj81S7jbLvoprVs/qnwBXaPZYjf0tfjefNG16kjINDyh9dWnla6ddRJYqSyjkt1d1P6o4BpR8UVOHLYaVsF3BhYFaXFAXalt9m04vS9W/XPMVmx5d5PwAAAGvTw9+U6LW/yhRJSBtn+XT+Lmnapln58d1TP5bqrb8jmlucUNAnHbhxSJd0LD++O3lCnk7YKkWTfg9rVnFcO7UM6Kwd0nTnF8Wamh/T5rl+9ds7U01TvRr6aZECXunv/Lj+WBDThtk+XdYxXds2X/r47t/8uO6aUqxf58eUGfSo65YpOn6r8uO7v/JiGjm52N1OdsijvTYIqsdOafJ5qx/ffTcnqp7v1nx899hROWqZvvRx16Vv5Kttpk9Hbx7SzKLFYV5pLKn3/onovsOylR7wKj0gdd8uVaO+KdFp26Xqjb/C6tA6oF1bB93lu2yZqom/h/XRtKgO3SS0mo8OACwfgSCAta50SlRFb4bV9sEcebM8WvhYqebfVay29+Wo+N2IC/ta3Z6lQCufyn6KauZlBUrfN6iU7csP9IpeC6v1yCzJI00/Z6Fm9ytU61uy5Gvi0czLC1QwvlRNzis/wCx8LayW/TOVultA+ePKNLtPodqNzqk2nkRpUrOuKVDm0SG1HJyp6Iy4ZvctlC/L64K6ebcXuxAw+64Uxeck9N8lBUrdKaC0zuUHaxVSdgho41ebrtS+KJhQpmRMLtyrGghG/427ALNq6BjcyKeiNyLl5/8TV7D94gNQX67X7UsLPwkEAQDAumaVbm9ODevBI3KUFfLose9KddeXxbrvsBy9+09Ek/4I6/aDstQqw6ef5kV12RsF2nfDoLZvUX5899pfYY08KEsej3TOpIXq90GhbjkwS01SPbr8zQKN/6VU5+2cXnnZ/ntnarc2AY37ucxV0Y0+pvrxXWk0qWveKdDRm4U0eN9MzSiKq+97hcoKeXVw+5Bu/6LYhYB3HZKiOSUJXfJ6gXZqEVDndtWP73ZoEdCr3Vbu+G7QfplqnubTY9+VVAsEZxTEFU9KG2cvPobbKNvnLmNVhf8UxNU+u/pb8o2yfPo7PyaJQBDA2kUgCGCts6q/xMKECieVKW2PoHLOSlXuOWnuvLTdgy5Y8zfzuvn9kmHJm+ZRbN7ig6mMI0LyNSmf4SC4qV/BjX0KbFB+YGXXjc1afNn0vYPuPkz2ySkqGF+m0slRhbZd/HJX8mlEHr+U2718DMGN/co+IUUFL5a5QNAT9Kj004gC7XxK3dmvDZ7JkWeJT49XhVU05o8tVZv7spVcPHVMZUhpbcDV9lvIo0RZcvH5KUucn+JRctH5AAAA61LQKy0MJzTpjzLt0Taos3ZI1Tk7lh9b7d426IK1ZmleN79fOCalBTyaV2WuvyM2DalJavnx3aa5fheabZBVfnxn151VvPiye28Q1B6LgruTt0nR+F/LNHlmVNs2W3x89+mMiPxeqfv25WPYONvvqhBf/L3MBYJBn8ddpl2mTzu38uuZLjnyWhq5BlgYWBOrELQxBap84Bta9HNZLKnSmJSyxDvykL/8PABY2wgEAax1VunXvE+Gq45b+GSpvFle5XRPVdYxKUomklrwQLEL7azqLbiFT7JjoCrHQb7sxdOderySN6PKwZu3+mX9i1pq3WU9HvmbexVfYiGR2OyEYnMS+ueoBZWnWUDnyyy/3RY3ZijvkRItuKfYtTFbtWGzK9Plb179YK/s+6ib968mbR/Orla5lwgnNWdAkZpeme62c8nFTbw1hHvJcNKFo25bUj3u92rnly0dIgIAAKwLVunXZ48MTfitTE/+UKqsoFfdt0/VMZunKJFM6oGvizX5v6hyU73aItdXfnhX5VAmO7T4+M4+d80IepZ1eKe2GdWP75ovChqrml2c0JzihI56tvrxXWao/HZv3CtDj3xbonumFGtuScJVG165W/pSYd73c6Lq/V7Nx3cPH5ldY8vwsqT4PYol5BYRqWhNDlvJoKTUgMeFgRaWVmW/N+f4DsA6QCAIYK1z89y18qn1yGwXjBW/H9G8IUVK7RBQ/rOlShQmtcHYXHkt9Eom9e/RedVvYCUyr3iVykILG2Nz4vK3qL5+klUjWvuttTBXXi/fqhOT7jqRP+Nqcn66vFd4FJ0e17wRRVowqkQt+mYuFXRuNKlJrcYV+TWm2Iy45g4oXwik4iB3xrn5Lmy0eRWjT8Xd/VdUI1o7cGBRm7BVRUb+iSu1w6Lx5iWUKEhWayMGAABYV2YXx1078MiDsl376/v/RjTk0yJ1aBXQsz+XqjCS1NguuUr1lx/fHT2u+vHdynykWbWy0MLGOSVxtUivfnxn1Yjtc3yuhblCflnCBXB2nT/z4jp/53RdsZtH0wviGvF5kUZ9XaK+e2YuFXROOql2x3cr0i7L5yoE/8mPa5Pc8rfeNodh20yvq1i0KkZrG67KLmutzQCwtrHKMIC1LvxzTLN7FbiFPLwhj3zZHvdxhAWAFgZa+67HV94Wm/dAiRLFSSXLF9ddacXvhlX6bVTJWFILnyp1R5upu1U/qErtHFB8fmLRfH5JV603u1eh8h4tdWHc/DuKtfDxErfyr2tV9nvc/IKrw803+HpTFyDaV7snciorCTMOCil154CrElz4eKm7X1uwpPjtiDIPLZ8/JuPQkArGlbl9aPtp/r3FbkXiJasWAQAA1oWf58XU690Ct5BHyO9xC3VY+GUBoIWB9rN1x9rcfg98XaLiaFKrunjuu/+E9e3sqGKJpJ76odSFibu1qX5817ltQPNLE65i0S5nFYS93ivUo9+VutbgO74s1uPflygaT7pWZb/X4+YXXJtsX+yzQdAtIlIYTrgQdfQPpW41YXNw+6C++C+iT6dH3Jhf+LVUeeGE9mi39IIpALCmUSEIYK1L3y/kqttmXV2gRFHCtdLaysAWttlcgrY68D/HLnDtsamdgkrdNaDo1FWbTDllx4Br9438EVdwM59aDc9ywWM8f/FlfBletbolS/PvLVHeoyXuo5G0PYNqenH5xNUt+mW4UPDfLnnuvNROAeWeU75C3dri8XvUcniW5t9epH+6lMmX5VGTS9IqF1bJPCrkKgJnXWf7MKnUHf1q0TdjrY4JAABgWfbbKOSq2a5+u0BFkYRrpe23V6YL287ZIc2tDnzscwuU5veoU9ugdm0d0NSFq3Z8t2OLgGv3/SMvrs1yfRp+QJYL26oc3ikj6NUtB2Tp3q9K9Oi3Ja4Nec92QV28aGXjfntl6I4vitXl+Tx3Xqc2AZ2z49o9vjNXdUrX3V+W6IyXFroOkUPah3TKNuX3axWCN+6dqQe/LtHNH8fdgiJD98t0KxIDwNrmSVr9NoCVFkvG9HjhKI0rGs3eqycsWLQFTJpdRVCG6k7MOF1nZvaQ38pRAQCN2vynrlT4z8/rehioJQsWbQGTqzpxfIeVE9q0k5qedhu7DVgGPnoAAAAAAAAAGhECQQAAAAAAAKARoXcKQIPRvBetJAAAAA1Jr84c3wHA2kCFIAAAAAAAANCIUCEI1BPTTs5Tbo80ZRyw8iuvrSmFr5Vp3vBieUJSy4GZCm0f0IJ7i1X8fkTJcFKhrfxqekm6gpuUv3SU/RDVzMsL3EIeFdL3DS1VqVf6TdStMNzuqRwFWvnKT/s2qrwHShT5Ny5vukeZR4SUc0aqPB7PUuOKzU9o3i1FKvs+Jm+KlHV8qnJOWbwq3MxrCxT+LlrtI462o3IU2KD8voytnzTrukL5m3pXqpJwebcd+Sum+feUKPJbzO2z9H1Cyr0gTd5g+TZM656n+NyEVGWTNprYRJ5F51dl21f4RliexUN2j0Fqh8U7NxFOatZVBco6PqXyebJwdIkWPlVa7baSESl1l4Bajcha6n7Cf8S04K5ihf+MyxssXwG6yQVpS43JHpf/zl+olkOylLpT+UrHidKk5t9TrJIPI7Jl8mw16GZXpcubXr5zSj6PKG9UiaL/xeVv7lXueWlK37vm53PRO2EtfKJUsdlxt9p09kmpyjo2pfx+IsnlPu+WNY7YvIT+uyDfbX/uuWnKOXXtrxwIAGjcTp6Qpx47pemAjevu+O21P8s0/LNihfzSwH0y1aF1lQOzRUb/UKLHvy+Vv8rxzCUd0nXkZikqDCd0z1clmvxfRPGEtF0Lvy7tkK5WGT6N/LxIb/4drnZbZTHpqM1CurpThkqiSY2cXKTJ/0Vlh3CHtg/p/J3T5LNlfJcQTyT14DclenNqWJGEtHmuT5d2TFf7nOpvSf8rjOuC1/L12FE5bsXiJX0zO+pWNn7qmBw3xpWRH07ootfy1XfPDG3TrPz4xvw8L6p7ppToz4UxZYe8OnWbVB2zRUrluB/6tkRv/BVWOC5t19yvqzulq3la+X2/9HuZnvm5VHmlSW3exOf262ZNan6bPWVWVKO+Ltb0goTSgx4duWlIZ2y/+Ph37E+lmvBrmYqiSW2Y5dOFu6Rp+xYBfTcnqp7vFlS7rdiiQ8w3Tmlaq21/4ddSjfulTHllCbVO9+nMHVK174blz9tYIqn7virR23+H3e3utUFQV+yarhR/+bi+nxPVXVOKNS0/7laTtnHZytFLeuL7Er3yZ1hjj8utcQxnTVqo2cXxyt9teVPbp9fvmaEDq/wNReJJXfBqvvbZMKizdkirdhuJZFI3fVikrZr6deq25cd6l7+Rr5/nx9Qqw6snjq75vgEsjUAQQDWBdl61e6L8H+n8e4sVmRpXu8dy5M3waOHjpZpzU2Hl+eHfYkrtGFCrYUsHTxXiBQm3+q8SVU5bkNDsvoVqenGaMg4JKfZfQrN6Fsib6VF216VDnDn9ChXc2KcNx+cqNj2uWb0K5W/hVcaB5QcOFsi1ui1LKdssPrBbUv7YMpV9FVXGwSt3wL6s205Gk24cmUeG1GpYpuJ5Cc2+sVB5D5Wo6UXpShQnFJuR0IbP5brAa0VsX1pQuaxAODot7vZj+KeYdPzi03NOT3NfFcq+j2r29YVqcmH1g6eKMc/uWaDsbqlqNTJFicKkZl5doPxxZco5bfF+T0aSmjuwUMnqx/+aN6JIiZKkC3bNnJuKtOCBEreqs4Wj9txocWOmUncPqPSzqGb3K9QGT/rlb1n9YD3yZ0zzhhep5bAspe4YcCHlzIvzFdzMp5RtA24fLu95t7xxbPxqU828In+F+xsAgIakXdbyg5DfFsR1zg5pOmVRgFLVyMnFLoB5/KgcBXwe3f1lsW78sFCjDs9xK/tWXd333X/CundKiQuxyq9b5ELBMcflqDCcVN/3CzX2pzKdtt3S9/Pi72EXiD10ZI4ygx4XDt78UZEePar8/7mxUHLEZ0UqjCRr3I6CcEJDPylSouazl+uX+TEN/qRQ/xVVOSiVtKA0oeveLXRB5pGbZrnLXfVWgTbN9Wnb5gE9/G2JvpoV1QOHl4/71slFGvFZsYYfkKUP/rX9UayB+2VppxZ+Tfoj7MLKx47OUW5K9eO/hWUJ9X2vQD07Z2i/DYOaXZzQJW/kq12Wz4Vhn86IaPyvZbrj4Cy1TPe6n/u8X6gXjs/VDi0CerXb4uAvvyyh81/N11k71u7Dz0+mR/TE96UaeVCWC2BtP/d+r1Bjj/O7YPOx70r107yoHjkyRz6v3ONyz5RiF/rafdllL+mYpoM3Dunj6RHd9FGhu2zrKoHsD3OjGv1DaY0hbgULeau6bXKRZhQm3P6oyu77n4LFwWGF+aUJ3fp5kT6dEXWBYIU7Dsl2wfjTP1X/kBzA8hEIAmvQnMGF8oY8anb14gOnGectVObRKco8OqT8p0pV9FZEsbkJV1VngZZVPq2oWtAq9/KfLq0MRMp+imrBvSWK/h2Xr5nXVdbVFCRZ9dX0sxbWOFYL8VJ2WHaAZpqcn6ZkTG6bLOBKFCXlzV78Tz7ya1yhLZf/MmLBj4Vw+aMX/4OOzoorrXNAmYeXf/Jq1XZpewUV/i4mddVSlWoWgrUclOkq76xKLKtrigpfLnP7Lzoz7sKh4GbLHkf456iKXi1T+n5Lf5K5PMu7bXsMg+19yjk9VR6fR/7mPredRW+Wp2jh38ofm9qEgRbURf5e9r4M/xrTrF4FLrSzasllSZQlNXdQkZr0SKuspqvKE/Co3ehceVLkPom2sNbCP19O9U/xLVxL2Tngnl+V27sg4Sr2NhibI19G+Ta16JPh9o8pmFimjINCSutcvo/te5t7s+XNXHr7g5v6teH4JvKmeZSMJ5XIT7gKTKsUXdHzbkXjAABgZQ3+uFAhv8eFHxXOe2Whjt48RUdvFtJTP5bqrb8jmlucUNAnF95c0jF9hdWCFQFFRVBngYuFaX/nx9UszasztkutsbLQKqiskqomw/bPcuHQyvptfkzHLqp4W5L9B7UqrMxQ+f/Vrlum6NxX8lUWS1ZWiC0OY4p1094ZLkQqjSX13j8R3XdYttIDXqUHpO7bpWrUNyU1BoLTCuMuyEsu+rIiQtufFZ77pVQv/lbmxnLL58U1jnX4Z0U6uH1Io39cueDnw2lh3fVliQv9Bn5cVO281/8Ku3DpqM3K98/WzQK67/BsNUnxuupACzKH7Z/pHjNzWcd0zSkuPx5779+IDt0kpA6tyh8T28cW5Nl+6bJl9f2dk+LV+OObKC3gcZ0r+eGkq8jMDi3qLCmIu9Mrwk7bP6FlFEDe9kWxtm/h12Gb1PyYLqlz24CePjZXqQGPqwa0+071exRcVMn52l9lunzX9Mow77yd0nT5m/mugvP9aREXUFbc1z4bhlwV4BtTwzpz+/IPoIsiCQ37tMg9d2yf1IYFoLafLBCuWlFqj9UfeXHt1LL689yef+e8vFDHbp6igjDHfcCaQCAIrEGZh6W4SqamlyVd+BKZGnOVXRkHBFX8bkSFk8JqdXuWa5u1UG/mZQVK3zeolO1rf2AXmxvXrGsL1fTSNBdAhX+OuWo7a9Fc8nasMssqplaVBV3WwrpwbKlrBfWkedRqSGa1qrbYXI+mnZbngqXU3YKu/dS3KAQqmFDmgp2sLinVAkGrtqtacWeBWOnkqDIOWjqwi/4bl6+pR74qQWRwI5/yn41XVvBZsGT7IPJ7zFUO5p6VprQ9ym/LgqK5Q4rUrGeGil4Pu3bS2lrebQfa+KpVRtoBXMnHUYU2LX9ZdW3Efum/S/LdcyCwoc8FdTU91pG/4lJcWnB/scp+iLltzT4pRZlHlB94+dt6tcHTufKmelTwXNkyx2uhsYWQmUcu++DQbsNMP3uhC/xSdvQrvUqYXPJpxFUZtrknW4UvLL4v2x4LN0s+iij/uTLXypu2pz3e6ZXPhbRdg5rVu8AFuP5WXjXpkS7vZp6ax5HmceHetBPzXPVo1kkpCm7sX+HzbkXjAABgZR22aYpu+rBQl3VMugq5qQtjLpw5YKOg3v0n4qq+bj8oy7WnWqh32RsF2nfDoGvlrK25JXFd+3ahLrUqq/Yh195o1XTN07xL3Y61ZFatBltdVuE1uyThWkYt/LRQ6PBNUnTKtinyejy6ae/Fx3bmo+kRtcv0VgsDzQNfFWv3tgF1XNSSPKMgrnhS2jh7cWq1UbZPM4sSCseSLmStysLVj6ZFdPz4PBd2WRB2+0HZlefvv1HIBUoVYduSJvxW5tpZLWhb2UBw++YWiOXI7/UsFQj+uiCmNhleDfioUF/OirrKPgtrN97Yr7/zYyqOJt02jfhsoQoiCe3cMuCCMmPh3ZL7ybZteuHS1W2mIgw88tkFKo3ZNgfd7RkLmi1oO/3FhZVh4ND9s9yYq7Iqyy9nRl3LdG3ZB8GpgfJQ+tI3Clwge3GHNGWneF2YN780qfbZi6OBjbJ8isSlmUVx/ZsfV/uc6smkPc5/L1y8jRbgHrFpigsOaxMIWihplajn7ZxWraLQ/k6sdfvWA7N0xxfVQ+GMgEdPHp2jrJBX382hGwRYE1hUBFiDUnb2uyqnks/K/xFaAJW2d1DeDK/Sdg+q9d3ZLgy0IMTaMV0oMm/ZFV81sQrD0BY+Fz5acJKyXUAZh4VU8OKyg6LVZYHexq83UZP/pWnWdQVujrhkIilfU6+rBGv7YI6rBovNSmietQdbcDM1pvyxpWrec/nz9dl8cXP6F7oAyOYGXFKyNClPSvUDIU/I405350el0NZ+Nb0oTRuOy1X2yakulLWqOjPv9iKlHxhyragra0W3XXm5RFLzby924WXOWYtade1Abku/mvfN0Abjcl3way3GVrW51D4oSrpgzlp57X6aXpbu5iYs/qj8eWSVcBVB3rLEixLKH1+m3P8t3Spckzb3Z7sWbAuubZuMVR/Ov6PYjdlOrzbGwqRr9bYAuu2obLW5P0eRP+NacF/5wVqiIOkCYNtHGz6fq6yuqZp9fflzZVl82R73vGrzYLar+ls4pnSFz7sVjQMAgJW1c0u/0gMeffZfpLJibO8NgsoIerV726DuPiTbhYHWWhqOlYc680pX7vjtrakRbdHU58JHq4barnlAh20S0ou/r73jtwoLyhLaoUV5BZzN7XbDnpma+HuZnv9l6fu2efKe/rHUzR9X1YzCuAt6zq4yn5tVCNqchBaiVggt+tmqC5dkYZ5V0lkwN+mkJi4A7Pt+gWtXNk1TvS6grImFtDa/nrXbrgqrzlsyWKtg7cmv/hl2Ie/zXXNdUGaViNYCa23Qdi2b99Daba2tOhxPavAn5ce7e7UL6tW/wi5os5Dr5T/K9G9B3F1meSac0MQFetML4rrzy/JjmGg8qS2b+PXgEdl6tVsTnbF9mvp9UOied0vO03fiVikuzFtZm+f69cbJTXTbQVl69LtSvfN32D2OpmqwafNRVswXaecvGXra41zxGNs2Wyt3t21qV62oRfvTHurDNwlVmxtw0MdFOmuHVLXNXLo00gJmCwMBrDlUCAJrkH36lnFoyIV2VkVW9HZYzXtlVoZGCx4odpVwvlyvglv4yns0VrLiPTYrrrIfY/rnqAWVp9lthzavoa11dlwzzq35E7SWQzJrXZlorZsm67gUFb5SppKPI8o+MVWtb1lcIedNK6+A+++ifLf4xZwBRWp6ZbrbVgtAa9yW2XE3z5wtaNFqZFaNoZe1tyaXOF61qjALU421qdpXBWudtrZdC9OsDdfmJ1xRKGnsOvNGLv7E2Kr/lnfbFe298cKE5g4sUmx2Qq3vzJK/WfmBii2SUVX2CakqfDms0i+jyjyy+kFOaoeAUjss/oTcFgTJOCSo4vfDSt+rdm3Oxe9EFGjprVwApFaPacjjKjpn/C+/fDuGFCn71FQFN6qh3diGkZCrxLNFRLzpci3Mc4cVqdlVdr5H6fsH3JyAJvPQkArGl6r086gCXWrud7FA24Q28yv7xBQVvRKutlhMTc87q9Jc3jgAAFiV4zdr+7TQbo+2QbewQq89MitDige+LnaLZuSmerVFrq/88G0lj99mFcf149yYjnp28fGb3fbmNSw+YS3D575c8/HbkP0yl1uZuGS78Wnbpur07dJ0x8GLjzO2bOrX8Vum6INpEZ24dWrlWB7+pkQv/hHWgH2XXpjklT/KtGubQLWgxkIiC/msrbai5bMiCLMqxKXG/kmRztkxrXLeuYt2SXNtp1bttke7ZR/vWLXhgI+KdOWu6a56b8mAbHkLV+zQPKBhByx7rmsT9FooHHCtsGbX1kF1ahN01YwWWtoW2QIcFZVs5+6Y5h4fmzvxoPYht0iHBVkWnO23YUi7tg64uQaX93yzVuk2mbawR5rbL1fuJhcM7tIqoM1yy58TJ2+T6gLa9/4Nq+uW5Y+ThY0/zYstVdVZ1ZILkFy1W4arSjUV4e2OLQM6fNOQ3v4n7Mbr9nOVENOC74rH0R5nC/yqsstaMG7VgzY34d2HZi0zzK2JVd1a62/VVuEnF80/WNs2aACrj0AQWMMsEJxx9kKVfBotr+DbpfzPLO/BElfdtMHY8tZPaxf49+i8mm/EssLo4l8T+Yv/QVtrcFqngFoOWHxwY1WGnho+MLOW4Y0mNVnlbbHKMZtLrmL1V2PjssU/rJos/9lS5Z6TVhncWNuwVfpZ9VZsRlxzB5QHbBWjt3Cy2ZXpLmQL/xJz7aUWeDW9PF2eJT55rGBtpFYRZhVwFXPGWdAXaF++Xy2gq1jht3KMkfIAq/itsFvs4t9j8yqrEW0w4d9javdw9TYLa79ecsGR5d22ic6Iu8o1W/DEzZe3KKQ0+c+Xunn8Uneu3hpd0wrD1qZr21i11bf8fmp/YFX8YUTpVcLLZc2JaKs9t7kvu7IF2z3P/FKyJOlWU478EnNtuhXnze5doJzuaW7OR3dalU/8k3asvejXwEa+as9Zxx07Lv2OqejdsApfKlPrkdlLPa9W9Lyz1uvljQMAgFVhgeDZkxa6xQospNilZflxhi18YRVkY7vkujnX7Pjt6HE1H79Z1hKtkpvYPG0VrDW4U5uABuy7+PhtXknCtYYuyVqGrYJuVdTUbmyVbj/MjbmAqYJV5QUXhUMWuPX7sNC1xd5zaLZb3XZJFh5amFeVLYZhFYL/5Me1yaIQy+ZHbJvprbztquaUJFwVXQXbdvuquvJxTayld0ZRXAOWaPW1UO7K3dJdKLe8hStWxNpff1tQvfvDAlIbqW2LjdGCzwoVuZldwlptLcysCFYtHD15wkL3fFqSrdRrC7g8dER2ZRBmVYEZi473bJGRqvdjbN/4qwRttoiJVVkuuWBJVUsuQGLG/Vzq5uXrvcfiD8ntvjODXjd3ZNNUj3scKwJfa5VOsdAyw+tawl/6o/pBnl3WVlv+YFrYhYVnT8qv3H5bNdiC74ePzHbPxyXZ8/7neTEN2Kd6qGlB/PySZGVobhWItqL0r/NjGrL/8kNdAKuGmltgDQu09im0jV/z7y524aB9CmgsDLQ55SwwS5QmlfdAiRLFyaVDlEWLbJR8EnEBkoU4ha8sXu7V5nsr/Sqm4g/DrjLQQqmZl+er4KU133IS2s6vhU+XujnwbCzW0pkoTLg2YV+mR8Vvh5X3SIk7z+Y2XDCqxG2zValt/HpTF0baV7snyg/M2j6c7cLA2JzyIM1aQm0BlmWFgRX7wtp2bREVWzTDWpELXihzFWjGKtvm32mrIcfcAhWFr4cV/tHmIwyp1YgsbfzK4nFkHhZyC5EsGQYuy/Ju2+YmtG2w4KrFwMxqYaCxikG7rlVBun03usRdpyJYqyYp93wp+y7qHtOSyREVvxNW5uG1WxHZ3pzYvH0p2y//Mx6b189a2hfcX+KqOC1Inn9fsdsvbr7JNxbvK/vyBKySNEs5VjXY3q/Qtn7Nt8ehOOECTFskp2KlZ1ttufjdsEqnRNw22EI40elxN7/fklK28Sv8a9w9Z+2y1oJdMK7ULb6zoufdisYBAMCqsKq1bZr53bxmFuZUHL9ZGGihjOVbpVGrFixxc8pVDf4qbJDlc6u5WtBic6/ZfHAVDtgopK9mxdyCCRY2WQuuLdrw0jpoGbb2zke+LdH7/4bdMcPP86J64bcyHbFp+f/OIZ8Wubbiew7NqjEMtDkIpxcm3Dx8VVlAus8GQbeISGE44arybJVZa4Ve1sIWj39fqjnFcbePrGU11edZ6nZrCrheP7mpC0nt64mjy4/jLHBaMgxcFfZ4W2Bqbaz22HwxM6LJM6OuOtDaxm0F3Ie+KXGVicXRhB79tsRtiy2kYoHVte8UuJDLAixbkdieL3vWUPFolX9FkaRb0deCM5un0n4+etFiJlad+vwvpa49unwxkzK3InLndov3j1WZWhC3smwfWqXhp9Mjbhu/nBnRm1MjOnLRc+DQTVL0+Pclbg6//HBCD31b6uY0tDZre4z/K0y41mALdO05/O3sqDvfqk9frfLYXLt7hlqke93PNYWBbhvmRV3QuuRqxLb4zsvdym/Hvqxa8tRtUwkDgbWICkFgLcg4LMXNpWdz+1WwSrq5Q4v0z7ELXHiU2imo1F0Dik61TySrH8xY6+28kcX6p0ueAq297nZslVxji1m0HJzpqrjmDSt2LbVW2War3a5pWcenuPBy5jUFbs6+0FZ+tRq5uLrM2mrn31Osf7vkuVeT9P2DanLhihd3KJgYdgFp/phS91UhtWN55WNF+27Fgigt+mdq/m1FmnZSnqvOs7kGK6r5LFS0Ofhm9S50q9VaBZmFWBbMrvb2L+e2LcyyduTiBWEXjFYNMNuOynHz3i2Il5S3UJckXYtxq+FZbj5JM6tngfwtvWp2VYZrL7f9Nnd4keLzEy6cszZnmx+yNqyC1Cr8bEGR5bE3N7ZaswWVtpiHJ9XjFrzJObt28w7a886C2elnLnQVjLZqc+755de1BUWa98koX/16Ztzto5YDM+VvUf442ONpIak9Z2z7bJGQBfeVuHDSql5zz0urDPVW9Lxb3jgAAFhV1qo49NOiaoHWOTukudOOfW6B0vwedWobdC2WFtosefxmKwxbBViX5/PUOsNaH0N69a/y4zdrDx28X6YLz4Z9WqwUv1wb5+k1rMa7pllb8vV7ZrgwbugnRW4+PVsN2AIdC6Xe/zeigFc68YXqlY+2Kq1Vos0sTrh59JrUMLXLVZ3SdfeXJTrjpYWuou6Q9iGdUqUS8fBn5le2rF6xW7pGfV2ii1/Pd1VktrLv8AOyamwvXpfa5/h1y4FZLuy1hSxyUzy6bvcMNz5jPz/0bYkufC3fhcH2+Nu2GNuHVi13/qsL3TZZWGcLYlRUSI7+ocStUG1Vi7adww/I1F1fFuvY58rcoiq2kvVJW5cHgtaWbHq/W6iiaNIt5GGXtxWdK9hjse+GK1/TY23iNnekPf8Gfpxwgdz1e2VUtp+ftX2qC2ltG20xEat6rFhJ2+YqLB93ie6ZUqwW6T712zuzsvV7Rao+B9w2FCXcfJEA6p4naR8TAVhpsWRMjxeO0rii0Q1m71lVl61U2+6J3LoeCrDKZl6R71a8tsrGCidmnK4zM3vIb2W6AIBGbf5TVyr85+dqKF77s0xP/1TqKqyAxqqmv4PQpp3U9LTb6nRcQH1GNA8AAAAAAAA0IgSCAKqJTk/o78Pnu7nggPVJ5J+Ye+6WfV99YnDjcc1OAAA0TNMLEq41c8pMjt/Q+Fz+Rr5r1wewcuidAlZZUik2gV8DknlYivsC1kfBjfyV804uqfxvlRkyAACSJ9CwjnUO2zTFfQGN1R2HZNd4uiew9ufoBNZnVAgCq/zH49POoV3Zf8B6YKdQR/c3CwBo3JLxmIIb71LXwwCwDgQ33tn9zQOoGYEgsIq8Hq+2CW6vo9OOl5c/JaBesr9N+xu1v1X7mwUANG4en1/puxzrFhsA0HDZ37j9rdvfPICascowsBpskW6Px6P8+EL9Fv1Z0WSU/QnUEwFPQFsEtla2L6fybxUAgGQiIY/Xq1jef4rNnapkIs5OARoIj9cnf/P28ue2qfxbB1AzAkFgDbLQAeteIpnQN998o0gkog4dOijgD9TpwxCNRTXlyykKhULaaaedCKLqCAEgAKA2OH6rn5KLju/C4Yg6duwgfx0f38ViUX3J8V29x/EfUHvUzwJrEP+A6sadt9+pBx98UE899ZSCgaDqmo3BwsDTTz9d5513nq688sq6HhIAAFgGjt/qp9sXHd+NHj1agXpwfGdj4PgOQENC/SyA9donn3yiBx54QFdccYV23nln1Re77LKLLr/8cjc2GyMAAABW/vjOjqnqC47vADQktAwDWG/NmzdPxx57rLbccks99NBD8tazOUISiYTOPfdc/f7775owYYKaNWtW10MCAACo1zi+A4B1o369ewaAlQjbevbs6X4ePnx4vQsDjY3JxlYxVvsOAACAmnF8BwDrTv17Bw0AtWAVgR9//LFGjBhRryvvmjdv7sb40Ucf6eGHH67r4QAAANT74zv7QLW+H9/ZGDm+A7A+IxAEsN75+uuvdfvtt6tHjx7aY489VN/tueeebqw2ZlstDwAAAMs+vrNjp/pur7324vgOwHqNOQQBrFfy8/N13HHHqWXLlnryyScVCAS0PohGo+revbvmzJnj5hPMysqq6yEBAADUCxzfAcC6R4UggPVGMpnU9ddfr6KiIt16663rTRhobKw25sLCQrcNti0AAACN3fp+fHfLLbdwfAdgvUQgCGC98fTTT+uNN97Q4MGD1bZtW61vbMyDBg3S66+/rrFjx9b1cAAAAOrN8Z0dI62Px3ft2rXj+A7AeolAEMB64ZdfftHQoUN12mmn6eCDD9b66pBDDtGpp57qQk3bJgAAgMaq6vGdHSOtrzi+A7A+Yg5BAPVecXGxjj/+eIVCIT377LPu+/osHA7rxBNPdPMKPv/880pLS6vrIQEAAKxTHN8BQN2iQhBAvTdw4EDNnj1bt91223ofBhrbBtuWmTNnasCAAXU9HAAAgHWO4zsAqFsEggDqtYkTJ2r8+PHq16+fNtlkEzUUm266qdsm27YXX3yxrocDAACwzjTk47sbb7yR4zsA6wVahgHUW1OnTlXXrl3dnIHDhw9XQ1xV77rrrtNbb72lF154QRtvvHFdDwkAAGCt4vgOAOoHAkEA9VIkElG3bt1UUlLiPmVNT09XQ1RUVOTmR7Tts5WHg8FgXQ8JAABgreD4DgDqD1qGAdRLVhH4+++/6/bbb2+wYaDJyMhw8wn+9ttvGjFiRF0PBwAAYK3h+A4A6g8CQQD1jrXQPvnkk+rVq5e23nprNXTbbLONevbsqSeeeEJvv/12XQ8HAABgjeP4DgDqF1qGAdQrtvLuscceq1133VV33323PB6PGgObT/Diiy/WlClTNGHCBLVu3bquhwQAALBGcHzH8R2A+odAEEC9EYvF1L17d3fQaKFYTk6OGpOFCxfquOOOU5s2bVy1oN/vr+shAQAArBaO78qP79q2bavHH3+c47v/t3cf4E1WXRzA/9lpmnQCZYsy3ANxK+6tyHLAhzhw4MC9UFQcCLJREAUF2YJsRUFFwYkoiKKoKMjedGXvfM+5oaV7QEva5v/z6YNt2rw39x0578m59xJRjcEhw0RUY0hF4G+//Ybhw4fHXTJQyGseNmwYfv31V7z11luxbg4RERHRYWN8F43v1qxZw/iOiGoUJgSJqEZYsWIF3nnnHTz88MNo164d4tUZZ5yBhx56CG+//bbqEyIiIqLaivFdFOM7IqqJOGSYiGIuMzNTzRvYunVrTJgwAVptfH9WEQqF0KtXL2zcuBELFy5Eenp6rJtEREREdEjxXatWrVR8p9Pp4roHGd8RUU0T33fdRBRz4XAYTz/9tPp3yJAhcZ8MFBIwDx06VAWOsvqw9A0RERFRbYzvJKaJ92SgYHxHRDUNE4JEFFMTJ07Ed999p5KB9evX5944oEGDBhg8eDC+/fZbvP/+++wXIiIiqnXxncQyjO8OYnxHRDUJE4JEFDOyeMbIkSNxzz334IILLuCeKOLCCy/E3XffjREjRqjFVoiIiIhqU3zXvn37WDenxmF8R0Q1BecQJKKYsNvt6NSpk/rUeNq0aTAYDNwTJQgEAujRowf279+PBQsWICkpif1ERERENRLju4phfEdENQErBInoiItEInj++efhcDgwfPhwJgPLIIlSqRCUAPuFF15QfUdERERUU+M7iVkY35Uf30kfMb4jolhiQpCIjrhZs2bhs88+w4ABA9C0aVPugXJIH0lfLVmyBB9++CH7i4iIiGqcmTNnqvjutddeY3xXAc2aNWN8R0QxxYQgER1R69evx8CBA9G9e3dcddVV7P0Kuvrqq9GtWzcVZEsfEhEREdUUf//9N+O7Q8D4johiiXMIEtER43a7ceONN0Kv12P27NkwmUzs/Urwer246aabEAqFMGfOHFgsFvYfERERxTy+69q1qxoGKyMZzGYz90glML4jolhhhSARHTEy7HXnzp1q5TkmAytPAuxRo0apPpRKQSIiIqKaEN/t2rVLxXdMBlYe4zsiihUmBInoiPj4448xd+5cvPjii2jZsiV7/RBJ38niIlIhuGjRIvYjERERxTy+k9iE8d2hk76TBVkY3xHRkcQhw0RU7bZs2YJOnTrhsssuw9ChQ6HRaNjrh7mK35NPPolly5Zh/vz5OOqoo9ifREREdEQxvqtajO+I6EhjQpCIqpXf71eLYTidTsybNw9Wq5U9XgWkP7t06aL6U1b1MxqN7FciIiI6IhjfVV9817lzZ9hsNsZ3RFTtOGSYiKrVsGHD8M8//6h5ZZgMrDrSlyNGjFB9O3z48Cp8ZiIiIqKyMb6rvvhOYmbGd0R0JDAhSETV5quvvsLkyZPx9NNP48QTT2RPV7GTTjoJTz31FCZNmqSGDxMRERFVN8Z31R/fydQwjO+IqLpxyDARVYvdu3ejY8eOOP300zF27FjOG1iN883cf//9WLNmDRYuXIiGDRtW16aIiIgozjG+OzIY3xHRkcCEIBFVuWAwiNtvvx3bt2/HggULkJqayl6uRtnZ2Sr52rx5c1WRqdPp2N9ERERUpRjfHVlZWVlqUT7Gd0RUXThkmIiqnFQE/vLLL2puOyYDq5/0scwnuHr1atX3RERERFWN8d2RlZaWpmJpxndEVF2YECSiKvXjjz+qgPGhhx7CGWecwd49QqSv+/Tpo/p+5cqV7HciIiKqMozvYuPMM8/Egw8+yPiOiKoFhwwTUZUObbjhhhvQsmVLTJw4kUNXj7BQKIQ777wTmzZtUvMJyifLRERERIeD8V3s47s77rgDmzdvZnxHRFWKFYJEVCXC4TCeeeYZFbQMGTKEycAYkLkDhw4dikAggL59+6oJqYmIiIgOFeO7mhHfDRs2jPEdEVU5JgSJqEpMmjQJ33zzDQYPHoyMjAz2aoxI38s++Prrr9U+ISIiIjpU77//PuO7GoDxHRFVByYEieiwrV27Vk16fNddd+HCCy9kj8bYRRddhF69eql9IvuGiIiIqLIkhpBFyxjf1QyM74ioqnEOQSI6LA6HA506dUJ6ejqmT58Og8HAHq0B/H4/evTogezsbMyfPx82my3WTSIiIqJaFt/JfMQS3xmNxlg3iRjfEVEVY4UgER0ymaPuhRdeQG5urqpGYzKw5pDAXT7Vl4Tgiy++yPkEiYiIqMLx3fPPP4+cnBwVSzAZWHMwviOiqsSEIBEdstmzZ2Px4sV49dVX0axZM/ZkDSP7RPbNp59+ijlz5sS6OURERFQLfPjhh1iyZAkGDBjA+K4GYnxHRFWFCUEiOiT//POPChRvueUWXHPNNezFGuraa69V+0j21b///hvr5hAREVENj+9ee+01xnc1HOM7IqoKnEOQiCrN4/HgxhtvhFarVVWCZrOZvVjD99dNN92k/l/2V0JCQqybRERERDUM47vahfEdER0uVggSUaXJJ8fbt2/HyJEjmQysBSQBOGrUKGzbtg0DBw6MdXOIiIioBmJ8V/viO4nFGd8R0aFiQpCIKuWTTz5RVWaymEirVq3Ye7WE7CuZIFzmBZI5BYmIiIiKxncSKzC+qz1at27N+I6IDhmHDBNRhW3duhWdOnXCJZdcgmHDhkGj0bD3atmqgU888QS+/vprLFiwgBOFExEREeO7Wo7xHREdKiYEiahC/H4/unfvDrvdjvnz58NqtbLnaiGn06mSuikpKZgxYwaMRmOsm0REREQxwviubmB8R0SHgkOGiahCRowYgfXr16t/mQysvWTfyXwzf//9t/qXiIiI4hfju7qB8R0RHQomBImoXMuXL8f777+PJ598EieffDJ7rJaTfSj7cuLEiWr4MBEREcUfxnd1L76TqWEY3xFRRXHIMBGVac+ePbjhhhvQtm1bvP3225w3sA7NN3Pffffht99+w8KFC5GRkRHrJhEREdERwviubmJ8R0SVwYQgEZUqFArhjjvuwJYtW9QiFGlpaeytOiQrKwsdO3ZEixYtMGnSJOh0ulg3iYiIiKoZ47u6jfEdEVUUhwwTUamkInDVqlUYPnw4k4F1kCR4Zd/KPpZ9TURERHUf47u6jfEdEVUUE4JEVKKffvoJb731Fh588EGceeaZ7KU66qyzzlL7WPb1zz//HOvmEBERUTVifBc/8d0DDzzA+I6IysQhw0RUDIcaxBcOHSIiIqr7GN/FF8Z3RFQeVggSUbHJiJ999lkEAgEMGzaM88rFAZk7UPa1z+fDc889p44BIiIiqjsY38UfxndEVB4mBImokMmTJ2P58uUYPHgwV56NI7LKsOzzZcuWqWOAiIiI6g5ZPIzxXfxhfEdEZWFCkIjy/f7776pSrFevXrjooovYM3Hm4osvxp133qmOgT/++CPWzSEiIqIqsHbtWrWIGOO7+I3v7rjjDsZ3RFQM5xAkIsXpdKJTp05ISUnBjBkzYDQa2TNxyO/3o3v37rDb7Zg/fz6sVmusm0RERESHyOFwoHPnzozv4pzEd926dVPHA+M7IsrDCkEiUvPKvPjii8jOzsbIkSOZDIxjkgiWYyAzMxP9+/fnfIJERES1FOM7KhjfjRo1ivEdERXChCARYc6cOfjkk0/w6quvolmzZuyRONe8eXN1LCxatAhz586NdXOIiIjoEOO7Tz/9lPEdKYzviKgoJgSJ4ty///6LAQMG4Oabb8a1114b6+ZQDXHdddfhpptuUjcRGzdujHVziIiIqBIY31Fp8d2NN97I+I6IFM4hSBTHvF6vCgrE7NmzkZCQEOsmUQ3i8XjU8aHVatXxYTabY90kIiIiqsD7t3yoJxjfUUnHR9euXaHT6RjfEcU5VggSxbGBAwdi27Ztas44JgOpKDkm5NjYsmULBg0axA4iIiKqBeQ9m/EdlRXfyXyCjO+IiAlBojglc8rMmjULzz//PFq3bh3r5lAN1aZNG/Tr1w8zZ87E4sWLY90cIiIiqkB8J+/djO+oNIzviEhwyDBRHJJPjTt16oQLL7wQI0aMgEajiXWTqIavUvjYY4/hu+++w/z587nwDBERUQ2O79q3b68q/BnfUVkY3xERE4JEccbv96NHjx7Izs5WyR2bzRbrJlEt4HA41E1Geno6pk+fDoPBEOsmERERUYH47n//+x9ycnIY31GFMb4jim8cMkwUZ+QT47/++ktVBjIZSBUlx4ocO+vWrVPzzhAREVHNwfiODje+k3+JKL4wIUgUR77++mtMnDgRTzzxBE455ZRYN4dqGTlmHn/8cbz33nv45ptvYt0cIiIiYnxHVRTfTZgwgfEdUZzhkGGiOLFnzx507NhRvem/88470Gr5eQBVXjgcRu/evfHHH39g4cKFaNCgAbuRiIgoRhjfUVXHdwsWLEBGRgY7ligOMCFIFAdCoRDuvPNObNq0SSVx0tLSYt0kqsWysrJwww03oGXLlqriVKfTxbpJREREcYfxHVUlxndE8YclQkRxQCoCf/rpJwwbNozJQDpsklCWY2nlypUYP348e5SIiCgGGN9RdcV348aNY+cSxQEmBInquFWrVmHMmDF48MEHcfbZZ8e6OVRHnHPOOXjggQfw5ptvqmOMiIiIjpyff/5ZxXfyXsz4jqoyvrv//vsxevRoxndEcYBDhonqsOzsbHTq1AnNmjXDpEmToNfrY90kqkOCwSBuv/12bN++Xc03k5qaGusmERERxUV8J/NCN2/enPEdVTnGd0TxgxWCRHVUJBLBs88+C6/Xq8r/mQykqibH1PDhw9Ux9txzz6ljjoiIiKo/vvP5fIzvqFowviOKH0wIEtVRU6ZMwbJly/D666+jYcOGsW4O1VFybA0aNAhfffUVpk6dGuvmEBER1WmM7+hIYHxHFB+YECSqg/744w8MHToUd9xxBy655JJYN4fquEsvvVQNHR4yZAjWrVsX6+YQERHV6fhO3nMZ39GRiO9uu+02xndEdRjnECSqY5xOJ7p06QKr1YqZM2fCaDTGukkUB/x+P7p166aOv3nz5qnjj4iIiKqGvL927twZNpuN8R0dMYzviOo2VggS1bF5Zfr374/9+/dj5MiRTAbSESOJZznm9u3bh5deeonzCRIREVVxfJeZmcn4jo54fDdixAjGd0R1FBOCRHWIVGYtWrQIr7zyCo466qhYN4fijBxzcux9/PHHmD9/fqybQ0REVCfMnTuX8R3FTIsWLRjfEdVRHDJMVEds3LgRXbt2xXXXXYfXXnst1s2hOCYrDn/66afqBqZly5axbg4REVGtju9kKpjrr7+e8R3FlKxuvXjxYsZ3RHUIE4JEdYDX68VNN92EUCiEOXPmwGKxxLpJFMfcbjduvPFG6PV6fPjhhzCbzbFuEhERUa3D+I5qWnwnxQcGg4HxHVEdwSHDRHXAoEGDsGXLFowaNYrJQIo5SUjLfIKbNm3C4MGDY90cIiKiWh3fyXsqP+ylWGN8R1T3MCFIVMstWbJErTbXr18/tGnTJtbNIVKOPfZYNXR4xowZ+Oyzz9grRERElSBDMyW+k/dSeU8lqgmOO+44xndEdQiHDBPVYtu2bUPnzp1x/vnnq+pAjUYT6yYRFVoV8ZFHHsEPP/yABQsWoGnTpuwdIiKicjC+o5qM8R1R3cGEIFEtFQgE0KNHD+zfvx8LFy6EzWaLdZOIirHb7ejUqRPq16+PadOmqXlniIiIqPz4Tj5MS0pKYldRjcP4jqhu4JBholpKKgLXrVun/mUykGoquZEZMWIE/vjjD7z55puxbg4REVGtiO9k3kAmA6k2xHdvvPFGrJtDRIeICUGiWujbb7/Fe++9h8cffxynnHJKrJtDVKbTTjsNjz76KMaPH4/vvvuOvUVERFSCb775RsV3jz32GE499VT2EdWK+O7dd99lfEdUS3HIMFEts3fvXnTs2BEnnXQSxo0bB62WeX2q+cLhMO655x789ddfaoi7DCEmIiKiwvHdiSeeqD5AY3xHtQHjO6LajQlBolokFAqhV69e2Lhxo0qqpKenx7pJRBWWmZmpbnZat26NCRMm8GaHiIiI8R3VkfiuVatWmDhxIuM7olqEpUVEtYh8Yrxy5UoMHTqUyUCqdSSBLcfuihUr1PASIiIiYnxHdSO++/HHH9W9ChHVHkwIEtUSq1atwujRo3H//ffj3HPPjXVziA6JHLv33XefmoB69erV7EUiIkK8x3ey6Ja8NzK+o9pKjt3evXurY5nxHVHtwSHDRLVATk4OOnXqhCZNmmDy5MnQ6/WxbhLRIQsGg7jtttuwa9cuzJ8/HykpKexNIiKKy/hOhlpKfDdlyhTGd1Tr47uePXuq+G7BggWM74hqAVYIEtVwkUgEzz33HDweD4YNG8ZgkWo9SWgPHz4cLpcL/fr1U8c4ERFRPJH3vmeffRZer1e9J/LDXqor8Z3b7WZ8R1RLMCFIVMNNmzYNX375JQYOHIhGjRrFujlEVUKO5UGDBmHp0qWYMWMGe5WIiOIuvvvqq68Y31Gd0rhxY3VMS3w3ffr0WDeHiMrBhCBRDfbnn39i8ODBanjlZZddFuvmEFUpOaZlaIkkBv/66y/2LhERxYV169ap+E7eAxnfUV1z+eWXq2P79ddfZ3xHVMNxDkGiGsrpdKJr165ITEzEzJkzYTQaY90koirn9/txyy23qOEl8+bNU8c7ERFRXY/vLBYLZs2axfiO6iTGd0S1AysEiWqoV155BXv37sWIESMYLFKdJYnukSNHqmNdjnkiIqK6PG/gyy+/rN7z5L2PH/ZSXSXHttzDML4jqtmYECSqgWTl1YULF6qgsUWLFrFuDlG1kmP8pZdeUivSyRcREVFdJO9xH330EeM7igtHH3004zuiGo5DholqmP/++w9dunTBNddco+ZWI4oXstrikiVLMHfuXBxzzDGxbg4REVGV2bhxoxoqzPiO4k3fvn3x2WefMb4jqoGYECSqQXw+H2666SYEAgH1pinzyxDFC5fLpW6WTCYTPvzwQ/UvERFRXYnvZF41mS+X8R3FE8Z3RDUXhwwT1SCy4tymTZvUvDIMFineyIIio0aNUlWyQ4YMiXVziIiIqoSstirxnbzHMb6jeMP4jqjmYkKQqIb4/PPPMX36dDVs8rjjjot1c4hiQo59GVoybdo0fPHFF9wLRERU6+O7GTNmML6juMb4jqhm4pBhohpgx44d6NSpE84991y88cYb0Gg0sW4SUUxXYXz44Yfx448/qgnYmzRpwr1BRES1zvbt29G5c2fGd0QH4ruHHnoIK1euZHxHVEMwIUgUYzJfYM+ePbF371715piUlBTrJhHFXG5urrqJatCgAaZOnQqDwRDrJhEREVUqvrv11luxb98+zJ8/H8nJyew9insS30kRREZGBuM7ohqAQ4aJYmz06NH4/fffMWLECCYDiQ6QG6fhw4dj7dq1GDNmDPuFiIhqlTfffFPFd/JexmQgUfH4Tu6BiCi2mBAkiqHvv/8e48ePx6OPPorTTjuN+4KogLZt26pzY9y4cfjhhx/YN0REVCt89913+fGdvJcR0UGnn366OjfkHJF7ISKKHQ4ZJooRGULSsWNHHH/88Xj33Xeh1TI/T1RUOBzG3XffjfXr12PhwoWoV68eO4mIiGosxndE5WN8R1QzMCFIFMM3wX/++UclOdLT07kfiEqxf/9+lTyXFeqYPCciopoc391111358R0/xCIqP7479thj8d5777E4gigGWJJEFAOS1JAhkEOHDmUykKgcckMl54oMK5GAkYiIqKbGdytWrFDvWUwGEpVNzpEhQ4aoeyLGd0SxwYQg0RH2yy+/4I033kDv3r1x7rnnsv+JKuC8885T58yoUaOwZs0a9hkREdUoq1evzo/v5D2LiMp3/vnn495772V8RxQjHDJMdATl5OSgc+fOaNiwIaZOnQq9Xs/+J6qgYDCIW2+9FXv37sX8+fO5aiMREdWo+C4jIwPTpk1jfEdUCYFAAD179sSePXuwYMECxndERxArBImOkEgkgueffx4ulwvDhw9nsEhUSZJAHzFiBBwOhzqX5JwiIiKKJXkv6tevH5xOp3qP4oe9RJVjMBjUvZGcQ4zviI4sJgSJjpAZM2bgiy++wMCBA9G4cWP2O9EhkHNn0KBB+Pzzz/HBBx+wD4mIKObx3dKlS9V7E+M7okPTpEkTvPbaayq+k3OKiI4MJgSJjoC//vpLBYpSDn/55Zezz4kOg5xDMnRYzqm///6bfUlERDGN7+Q9ifEd0eG58sor0aNHD7z++uuM74iOEM4hSFTNZIhwly5dkJCQgFmzZsFkMrHPiQ6Tz+fDLbfcAq/Xi3nz5sFisbBPiYjoiGF8R1Q98d3NN9+s/p07dy4SExPZzUTViBWCRNXs1VdfVYsgjBw5kslAoioiiXWZq0kmoJZzjIiI6Eh65ZVXGN8RVUN8J/dMEt8NGDCA/UtUzZgQJKpGslKWrIbav39/HH300exroip0zDHHqHNLKgQXLlzIviUioiMW38kX4zui6onvXnzxRcZ3REcAhwwTVZNNmzapocJXXXWVmguDiKrHM888oyahlsQgE+9ERFSd/vvvP3Tt2lXNdzZ48GB2NlE1efrpp9WCjIzviKoPE4JE1Ty/Gee/IDoy8zjJPIIyT6fRaGSXExFRtcV3Ho9HJSk4vxlR9XE6nSr5zviOqPpwyDBRNRg6dCg2btyo5sBgsEhUveQcGzVqFP79918MGTKE3U1ERNVC3mMkvpP3HMZ3RNXLarWqeynGd0TVhwlBoiq2dOlSTJ06FX379sXxxx/P/iU6AuRck3NOzj05B4mIiKqSvLdMmzaN8R3REXTCCSeoqWEY3xFVDw4ZJqpCO3fuRKdOnXDWWWdh9OjR0Gg07F+iIyQSiaBPnz74+eef1SIjjRo1Yt8TEdFhY3xHFNv47sEHH8SqVavUYj6NGzfm7iCqIkwIElWRYDCInj17Yvfu3erNKjk5mX1LdITl5OSgc+fOaNiwofo0Wa/Xcx8QEdFhxXe33nor9uzZw/iOKIbxnRRdyIe9jO+Iqg6HDBNVEakI/O233zB8+HAmA4liJCUlBcOGDVPn4pgxY7gfiIjosOO7tWvXMr4jinF8J/dYEt/JOUlEVYMJQaIq8MMPP2DcuHF45JFHcPrpp7NPiWKoXbt2ePjhh/HOO+9gxYoV3BdERHRIGN8R1bz4Tu65GN8RVQ0OGSY6TPv371cl7G3atMF7770HrZZ5dqJYC4fDuOuuu9TKdDKfYHp6eqybREREtSy+69ixo4rvJkyYwPiOqAYIhUIqvtuwYYMawl+vXr1YN4moVmPmgugwkw6y8pVMdjtkyBAGi0Q1hCTm5ZyUc/Tpp59W/xIREVUE4zuimkmn02Ho0KH55yjjO6LDw4Qg0WGQT4y///57lXjgJ1RENUv9+vXVufndd99h4sSJsW4OERHVEjLiQ9475D1E3kuIqOaQc3Lw4MHqHJV7MSI6dEwIEh2iX3/9FaNGjcK9996L888/n/1IVANdcMEF6hwdOXKkOmeJiIjKsmbNmvz4Tt5DiKjmad++Pe655x51rjK+Izp0nEOQ6BDY7XY1b2CDBg0wdepUGAwG9iNRDRUIBNCzZ0/s3btXzTeTlJQU6yYREVENlJubi86dOzO+I6ol8d2tt96Kffv2Mb4jOkSsECSqJJkv8Pnnn4fD4cDw4cOZDCSq4SRhP2zYMHXOyrkr5zAREVFB8t7wwgsvqPcKec/gh71ENZuco3IvJoUajO+IDg0TgkSVNHPmTHz22Wd47bXX0KRJE/YfUS3QtGlTdc7KuTtr1qxYN4eIiGqYDz74ID++k/cMIqpd8Z3coxFR5TAhSFQJf//9NwYOHIgePXrgyiuvZN8R1SJyzv7vf/9T5/D69etj3RwiIqpB8d2gQYPUewTjO6La5aqrrkL37t1VfCfnMhFVHOcQJKogt9uNrl27wmg04sMPP4TJZGLfEdUyPp8PN910E4LBIObMmQOLxRLrJhERUQxJfNelSxcV382ePZvxHVEt5PV6cfPNN6t5BefOncv4jqiCWCFIVEGvvvoqdu/erVYrZTKQqHaSc1fO4Z07d2LAgAGxbg4REcUY4zui2s9sNqv4bteuXYzviCqBCUGiCvjoo48wb948vPjiizjmmGPYZ0S1WMuWLdW5LJ8gf/zxx7FuDhERxcjChQvz4zt5byCi2kvOYVkYiPEdUcVxyDBROTZv3ozOnTvjiiuuwJAhQ9hfRHVkNcmnn34aS5cuxYIFC3DUUUfFuklERBSD+O7yyy9X8Z1Go2H/E9WB+O6pp57Cl19+yfiOqAKYECQqg9/vR7du3eByudSnTVarlf1FVEc4nU41L2hiYqJamU7mjyIioviI72655RY1fyDjO6K6F9/JvKBy38b4jqhsHDJMVIahQ4fin3/+UXNSMBlIVLfIOT1ixAh1jg8bNizWzSEioiNEKgL//fdf9R7A+I6obpFzWu7dGN8RlY8JQaJSSKn5lClT8Mwzz+CEE05gPxHVQSeeeKIaOjx58mR89dVXsW4OEREdgfhu6tSp6tov7wFEVPfIuS1DhxnfEZWNQ4aJSiArVHXq1Ant2rXDW2+9xXlliOr4fDMPPPAAfvnlFzXBfMOGDWPdJCIiqsb47vTTT8fYsWMZ3xHV8fju/vvvx5o1a9R8go0aNYp1k4hqHCYEiYoIBoO47bbbsHPnTvXmkZKSwj4iquOys7PVTWLTpk3Vp8l6vT7WTSIiomqI73bs2KHiu9TUVPYvUR3H+I6obBwyTFSEVAT++uuvGD58OJOBRHFCbgzlnJcqQakaISKiumXMmDGqUkiu9UwGEsVffCf3eERUGBOCRAWsWLECb7/9Nh566CE1XJiI4scZZ5yhzn1JCP7444+xbg4REVVhfPfOO++oa7xc64kofsg536dPH3WPx/iOqDAOGSY6IDMzEx07dkSrVq0wYcIE6HQ69g1RnAmFQujVqxc2btyIjz76CGlpabFuEhERVUF817JlS0ycOJHxHVGcxnd33nkn/vvvPzVfdHp6eqybRFQjsEKQCEA4HFarCcubxZAhQxgsEsUp+SBg6NCh6log1wS5NhARUe2P7+Tazg97ieJTwfiub9++jO+IDmBCkAjA+++/j2+//VYlAxs0aMA+IYpjcg0YPHgwvvnmG0yaNCnWzSEiokMkFYES38k1nfEdUXzLyMjA66+/ruI7ufcjIiYEifDbb79hxIgRuOeee9C+fXv2CBHhwgsvxN13360mol67di17hIioFsZ3I0eOxF133aWu6UREF110kZoaRu79GN8RcQ5BinN2ux2dOnVC/fr1MW3aNBgMhlg3iYhqiEAggB49eqj5pxYsWACbzRbrJhERUSXiu3r16mH69OmM74gon9/vV/FdVlYW4zuKexwyTHFF5o24+eab1dLzkUgEL7zwggoapQqIyUAiKkiuCXJtyM3NVdcKuWbItUOuIXItISKimsHlcuH555+H2+1mfEdEZTIajapCMCcnR103JL7Lu4bIv0TxhAlBiitbt25VQ0h8Ph8+/PBDLFmyBK+99hqaNm0a66YRUQ3UrFkzDBgwAIsXL8bs2bPVtUOuIdu2bYt104iI6IDVq1era7RUdM+aNUvFd6+++qq6hhMRlRbfybVC7gmlWlCuIfLBL1E8YUKQ4so///yj/tVqtSoR2L17d1x11VWxbhYR1WBXX301unXrpgJHuXYUvJYQEVHs/fvvv7BYLHA6nRg4cCBuueUWXHPNNbFuFhHVYHKNkGuF3BNKZaBcQ+RaQhRPNBGpkSWKE2PGjFFzBaalpUGv1+PNN99U3x977LG46aabYt08Iqph5NPi9evX49Zbb8VDDz2khgrLp8jyfZ8+fWLdPCIiAvDMM89g48aNasiwTqfDlClTsHTpUrRr1w7HHHMM+4iICvnvv/9UZfEVV1yBnj17IhwOw2w2o3Xr1molYqJ4wQpBiitS1SOJwB07dqBNmza4/vrr8fHHHyM1NTXWTSOiGkiuDXKNkGvFcccdp64dcg3hJ8hERDWHXJMdDoe6Rl966aXo0KEDXnrpJZUkJCIqSq4N/fv3V/HdJZdcoqaCkQpjjgCheMMKQYor7du3x969e9UNvZSFy7Lz8qmQ1WqNddOIqIaSAHHq1KmYOHGiqj4JBoPIyMjAN998E+umERHFPancPuWUU9S1OTk5WSUGO3bsiAcffJBzCBJRmXPLjx07FgsXLlT3grLQpCwoJ3NFS6UxUTxgQpDihoyOlwofSQb27t0bd955J2w2W6ybRUS1hASKkyZNwrhx49QN6F9//QWNRhPrZhERxbW///5bJQCFVPtIIpDDhImoMsOHZVqpTz75RH3/0UcfqemkiOIBE4IUV2To39lnn40GDRrEuilEVEtJlfHKlSvVkDQiIootWQzgscceU4nAU089lbuDiA7Jr7/+qioGR44cicTERPYixQUmBImIiIiIiIiIiOIIFxUhIiIiIiIiIiKKI3rU4vngwghDp+GEn0S1VSgSghZazsN2CCLBCDR6zl9HVFPxHK1kf0UiQDgk/1dNe4SIjjwNoNUxzitFJBIGwmFe94hq/XVOC42mdtba1cqEYCgSRGZoP77wfIrtwa0qqUBEtYsk85vqm+OKhGuRrqsHnaZWXo6OuEgogog/gtzFHnjW+dX/E1HNojFqkHCiEcnXJKj/1+iYvC9PKHs7fJvXIBL0HZF9RETVT6M3wdSiLfRpzdjdBUTCYWi0WgT3bIR/x5+IhPzsH6JaSqMzwtjkBBgats4/t2uTWjeHoHySsiu0Ew/vuwvOiCPWzSGiw2TV2DC6/kQ01DWqtZ+sHOmqo8137YPv32Csm0JE5TC11qPFhPqs5i1HzqfD4F41j8cTUR1lObMrUq55ItbNqFH3s9mznoX3n29j3RQiqiLmNu2ResugWnc/W7tae6Cg+lP3QiYDieoISex/4l6ACIeJVag60LXKx2QgUS0hiXs5Z+XcpZIFs3cwGUhUx7l/notg9s5YN6NGiIRD8G9azWQgUR3j/edb+Df/os7x2qTWJQS1Gi22BjbFuhlEVIXknNZyPtDyhQHfRlYGEtUmvv+C6tyl4iKhIPxbfmXXEMUB/5Y16pyPe5EwfFt/i/tuIKqLfBLTyNygtUitnLQrhNqVdaXCArtD2N49B83mpkKfFpuctIyUz5nmgfMTH0LOCAzNtEjrnYiE0wzqcedSH/YNckJjPPg3yTcnIPVOC8L+CLLGuuD62o+ILwLTcXqk90mE8ZhaeTrVCDynK9NZrDSqTfaEduPenO6YlDoXqdq0WDcHH3nm4JfAT3gpaUiJj492DsHu0E68ljxKff+y/Rn8GVhb6He88KKn5W7cmNDjiLS51gvynC1dhHMG1mG7nSF0X5iDuV1SkZYQm3gvHIlg0loPFm/0wRUI46hkPXq3teC0jGi8l8cXjODxL+3oeqwZl7YwxaStdV10flBeD9UCBEHOGVjX1ITrnVi5w4/xv7qx0xlCfYsW95xmQftm0WvappwgRq9y4Z+sEJJNGtx4XAI6H2uOWVvrpKCc27Vr3mhmMCguOT7ywbnEh4ZDk6BvrIXzMx/2PGdH06mp0Kdr4fsnCNv1JtR7zFrsb7Pfc8O/KYSmk1KgtWqQM9mDvS850HRKakxeCxFReXwRHz70TMFczwc4zXBGib/znW8ZvvJ9hhP0J+f/rH/S4EK/M8/zAZb7luJac2d2OhHVeAv+8WL5Vh/GXJWEBhYtFm3w4fmvHZjXNRXGA4v9bLOH8PoKJ/7cH0TXY2PdYiKiQ/NfdhAvfefAi+fbcE4TA37cEUD/bx2YeoMeNqMWT3/lwEXNjXj9kiTscobU90YdcF0rJgXjGROCcShrghvOJV5E/IChhQ5pvS0wn2BQVXO50z1wLvUjuC+squOsl5lU9ZvY1i0bSTea4VjkQ3B3CObTDEi9w4LMN13wbwrC2FqPBv1tKqG273UnNAbAvzkE/4YgDM11SH84EeYTC38iK/xbQ8ga7YJvfRBamwZJXcxI7poQfey/IPaPcCGwOQRtsgaWC4xIu9dSbMVG79oAdj9jL/H1SuJOn6Er9LOQPYyUngkwNIv+3HatGVnj3PD/G4Q+3Qj/+iCsV5b8CbH0VyQIaE0ahF1hhJ0RaJNr3eh7orgwzT0BX3qXIAA/mula4A5LbxxrOEFd72Z7puMb/1LsD++DEUZcaLoMdyf2UX93T3Y3dDDfiM98i7A3tBsnG05Dd8sdGO96E1uDm3CMvjWesvVHmjYdbzhfhwEGbA1txn/BDWiqa457Ex/GcYYTi7Vne2gr3nWNxobgerWgzvXmLuiQ0FU9tjn4H952jcC20GbYNMk4x3gBbrPcq1bkLmhdYC1esT9T4usdkzIJ9XUZxX7eN/chNNI1wdWmDtgd3lViJeNk93hcZeqgtl+STcENmOmegmHJY2HRWCq4B4goVib86saS/7zwh4EWSTr0Pt2CE+pF473p6zxYutmPfa6wuiG8rIUJfc6IxnvdFmTjxuPMWPSvD7tdIVVNd8cpFrz5swubcoNonapH//Y2pCdoVSLNoAU254awISuI5sk6PHxGIk6sXzze25obwujVLqzPDMJm1KDLsWZ0PS4h/0Z2xE8u9TxSuXJBMyPuPc0CnbZwvLd2bwDPLCs53pt0fQoyEgtfLzu1MeOalmYk6DWqCtDhj8Bq1CAvjJS29F1mR4+TEpDprl3DvIjoIF7vgIX/enF5CxPObRod4ib/jr0qWSUD/9gXgCcYwf2nR6+rUi0t18eP//UyIRjnmBCMM57VATi/8KHJuynQJmmQM8mDzNEuNHk7Ba5lfpXsazgqCYaGOnj/DGDXw3YkXmSE+eQDQ2mX+NBoRJKqhN3eKwd7+jvQaFgSdGka7HrEDvs8D9LuiQaUjiU+ZLxsQ8JZBuTO9mLPcw40nZZSqD1hTwS7n7TD1sGEjIE2BHaEsKefA7okLaxXmLB/lEslAZNHmxHaG8bOPnY1rNdyrrHwqj6nGNBicXqF+yG1p6VYQlESe8ZjdCpQ9m0IQZPgR/YkD6ADrBcbkdLLAq1Ro5KRcn+eM9OD7PFuaCwaNBxkO4y9QkTV4bfAaiz3fYFRKe/CpknCB55JKhk3LOVtfOdfhi98izAgaRQydA2xPvAnnrU/jPONF+F4Q7RC7ivfEryWNEKV/j+c0wuvO/rjlaRhSNWk4Tn7I1jkmYfbEu9Rv/ulbwmesb2M0w1nYaF3NgY4nsM7KdMKtccT8eBF+5MqKfe8bSB2hXbgNUc/2LRJuNh0Bca5RuFs4wV43Twa+8N78Yy9D04ynIYzjecWep4TDadgVvriSvXF87bXkK6rjw/ck4olBEOREIY7B+B2y73qsdISgpIMleRlc/3Rldo2ER15q3cH8MUmH969NgVJJo0aNitDxd6+OgXLtvhVpdyoy5PQ0KrDn/sDePhzu6ocOblBNN5b8p8PIy5PgkYD9FqUg/7fODDssiSkJWjwyBd2zPvbg3vaJub/7svtbTirsQGz//LiueUOTLuhcLznCUTw5Fd2dGhlwsCLbNjhDKHfcgeSTFpccbQJo352qSTg6CvN2OsOo89ndpzWwJB/Y5vnlAYGLL6l4vGeVqNBgh5YtsWHAd87VSLw+fOt+YnGJjYtZnRKVQnDOX97q6DniehI4/Uu6p+sIM5sZMSzy+yq4rmhVYt72yailUGjZhySD28Kfsgi/7vdwQ9C4h3LmuKMVP2Fc8JwLPKqqruUOxJUMlBYzjGi0ZhklQwMZoUR8QFaiwbB/QcvFNZrTdClaaFL1cLYUo/E9kZVZadN1KqkXHD3wd+VxyznGaHRa5DczQyNUQPPT4FC7XGv8EOjjyboNAYNjC30SL7RDPtH0aBM/c0KP9zfB9Tw3GazUoolAw+Xb0MQe15yIOW2BOgb6BDOjcDYSofES00qgdloaBI8PweQPc5d6O+SOpvR4rM0pN1twe6n7Qjs5NyWRDWJAUbkhnPwuXeRqt7rnnCHSgaKdsZz8HryGJUMzA5nwQcfEjQWZIb35//95aZrkaJNQ4o2FS30LXGusT2a6JrBok3ECYZTsC+8O/93zzG2x1nG86DX6NHF3A1GjVHN1VfQKv8K6KHHzZaeMGgMaK5vgRvMN2KJ96NoezVG9TsrA98jUWPFeymziiUDD5UkA0sjiVJ5XReYLin1d9YG1mBLaBM6m7tVSXuIqHoZtUCOL4xFG7zYnBPCHackqGSgOKeJEWOuTFbJwCxPGL4gYDFosN9zMIa7tqVJzYOVataiZaoe7ZsZ0SxJh0SDViXldrsO/q48dl5TI/RaDbqdYFZDcX/aVTjeW7HDD70W6HmyBQadBi2S9aoK8aN/o/Ge/I38zvfbA6qCb1bnlGLJwMNxflMjPu+WhhcusOK1H5z4fW+0fVajViUDiaj24vUuyu6LqGkSup2QgLldU9Hl2AQ8v9yOnY4QTq6vVx/wTP7dDX8ogi25QXy8wQsf5yaPe6wQjDNS6Vf/OSvsC7zImeqBNkmrhs4m3WBGJBxB1jiXStqphF8bXXTu3wLz/+oKDI3VaKGSdPm0hX9X3+TgsA2NRgN9fS1CWYU/hQjuCSO4N4wt12fl/ywSAXS26PM2eNGK7IluZL3lUsOYpdqw3mOJ0NcvPCTE+3sAe551lPiam0xILjZkOI9ruQ/7hrmQ0j0BKT2iw1Z0KVo0fiP54MtqplPJwswxbqQ/lHjw56ZoG5M6meH41Av3934k3xR9DiKKvRMMJ+Nx63P41LsAH3qmqkq8mxN64mrzDQhHwpjsGqeSdpLwa6lrI8sbIFzgIpakLXAdgFYl6Qp+X/B3ZThuwetdura+SjQWtC+8R1X+/S/r+vyfyXPYNNEK46esL2K6eyImuN5CZnifqja8P/GxYsm8PwO/Y4Dj2RJf8xvJE0ocMlya3wO/4nvf1xiRMq7M35Ok6iWmK2HVFp9XlYhqHqn0e+48q7o5nPqHB0lGLXqenIAbWpvVQhvj1rjw084AUhO0aJOqi4Z7BWK4ZJO2UBWJJOnyvy+yNEQTa+F4Tyayl0RjQXtcYex1hXH9h4XjPduBWOrFC6yY+Jsbb612YZ87rKoNHzsrEfUtheM3SeQ9u7zkeG/CdcnFhgznyZsvUCbXP7uxH8u3+vOrIYmoduP17uB17pKjDDj1wKJJVx1jwrz1HqzcGVCLhwy6OEldY+f97UWLFB2uOtqE+f+wMjreMSEYZ4J7QtA31KHRiGSEfRG1Uu7+QU4ktDMg90MPwo4Ims1MhTZBo4bObu2QXfgJKvEhaqhAZaEkG4N7Q9A3KFyUqq+nhfFonRrCnP93uVKdGFF/498YUqv/ah/VILA9hP1Dncga70aDfrZiic6jFlVuBc/syW7YZ3vR4DmrqmTM498ShPMLv6r8y2+/P1pdKWQBEXNbA5I6HpyANRKAmv+QiGqOfaE9aKBriFeTR6hFNX7wf41RzkE41dAOCzwfwhlx4L3UmTBrEtT1rkd2hyLPUPFzumBloSQb94X2or62QaHfSdPWQ3Pd0WoIcx57OFe1Tf5mU2gjbk/sjfs0j2JnaDvGOIeqef0et/UrluickbYIVeEb31Jkhffhruyb1PeBSABBBFXSMm8bwUgQP/t/wKtq+DQR1QZ7XCFVATji8mQ1d97XW/0YtMKJdg0N+PAvj5pLb2bn6FBZuf51mF043qtMRFOwslCSjXvdITRILBzv1bNocXSKTg1hzpPrDavqFPmbjdkh9G6biEfP0mC7PYShK50Yv8aNfufbit34L7q54vHe2NUudZN892kHYzqpjpE5DImobuD1LuqoZB0CRUYAh9WnNxF13ZP/ffPKgx92j1/jQps0poPiHYcMxxnfX0Hs6WtXC3lIhZsuWaPSwpIAlGSgDN+V+fFkbj8ZIht2RVSy61C4lvng+S2ASDCCnOkeFV0mnFV4+EfCuQaEMsOqYlF+T4Yq7+nrQPb7Hmi0GmS+4ULOZDcigYgaqgy9Rs0veLjs872wz/Gi0ZtJhZKBQmvVqrkQc+d4EAlFVF/lTHWrhUeE6SQ9cmZ4ENgWUu3K+UASqeEqH8pMRIfnn+BfeMXeVy3kYdKYkKRJVkN2EzQJKhko/6+DTs3tN9k9Dq6IC0Ec2gVPVuhdF/hNJc/meKZDAw1ON55V6HfOMJyL7HCmqliU35MKwlccfTHD8z60Gi3Gu97ATPdklZSToco6jV5VNVanB61PqvkIJfknX7dYblerDBdMOEqiMowwWurbVGtbiKjq/LU/uliGLORhkqlbTBo1ZFcSgJIMlP+XojmZ22/cGjdcgUixG8mKkvn5ftsTQDAcwfQ/PCqZeFbjwjHRuU0MyPSEVcWi/J5UEPZd7sD7az1qnr83VrnUULZAKKKGKsvwY5lf8HCd3ECP+eu9+Gt/AKFwRM2r+PveoJq3kIjqBl7voq5rZcKyzT6s3uVXH7TIolLyAYtMmSAV2U9+acdXm+VD6Ah+3RPAxxt8anEnim9MCceZxItN8G8JYfcTdoSdYTWUVlYGlmRbai+LWh14S8csNXdgwtlGJJxpQGBTUNJgld6W+VSDGu7r3xBSc/I1HJKkEo+h3IO/o7Nq0XBYEjLHupH9vlulqC3nG5H+YHRoboP+VpUU3No5Wz2WcLYBqb0Of1huznS3SnrufLBAYwDU72tF4kWywEmSWjBE2i99YbvOjOTu0QtmUlez+ttdT9oR8URgOk6PhiOSCw2nJqLYO990MbaFtuBF+xNwhp1qKK2sDCzJth6WXmp14FuzOqq5A9sZz0Zbw5lqBeFDuNzhJMOpmOaeiE2hDTha1wovJQ1RlYe5OHiNkeG2LycNw0T3WMxwv6+GHZ9lPB93Jz6oHn/K2l8lBW/P7gwNtGhnOBs9Enoh1vaGdqlh1UVXOyaimuvio0zYkhvCE1/a4fSH1VDa/hfYVLKt1ykWtTpwxzlZsOg1OLuJEWc2MmBTzqHFe6c2MKjhvhuyQ2iVqsOQS5NU4rFghCVz9Q27NAljf3Hj/d/cahiy3KQ+eGBl4/4XWPHGzy50nputHju7sQG9Tj38eE+GCOecHlELiuT6IqpKcehlNjSx8XpGVFfwehclC4o8d75VXWd3OUNoZNVhwEU2NDgwlYL8/5jVLlWBnWHR4ZEzEot9eEPxRxORcQK1TL/Mx7DatzLWzaAySGJRhtjWe5zzTVH52pnOxmvpI9lV5ZCK1KwPnNj3TsnzJ1FsSGJRFjB5wPo4dwEVU/8+G9K6W9XCWVRYJBSAe83HyP10GLumlpLEokzo//jZjPeobMnXPglL2w7Q6OJ77sZIKAjHsvFw/jAt1k2hSuL1jspjPe9W2C65Fxpd7am7Y0kTERERERERERFRHGFCkIiIiIiIiIiIKI7UnlpGqlVkLj4ionjwiLVvrJtARBQTfc9lvEdE8YHXO6qL4j4huK1bNlLvtcB6aexWG3Ms8WL/EBc0JiBjgA0J7QpP7hncE0LmGBe8a4OADkg4w6AW3Si6iIXn14BaLKTp9BQYGkYnD/WsDiBrvAuB7WFoE2VxDBNSbkuARqMp929LI6vu7uydg4xBSUg4LToPSDAzjP3DnPD+HoTWLAtvJCCle/HJoGXlXs9PAbXASGVFwhHsfcmpFvFI+d/B53Yu9WHfoOichXmSb05A6p0WhH0RZL/nViseR/zRRUnSH0rMX6nY91cAmWPcaqEV6Z+kDiak3Gopsx3ynLsft6vFRYoeN/J8u55yoMWitIO/748ga6wLrq/9iPiii5Ck90mE8ZjKn34hexhZY91w/+hX9b3Wi01I7W1RK0YL90q/WgwlsDsMXaoGyd0SkHR9dDGUzddkFnkhUH3SaEwSzCcWns9l9zN2eNcWXm014gVS77YgpcfBvpcpSHc/7YA+XVsoCbytZzZC+8JqZek8Ry1Mw66n7PD/FYS+oRZNp6RW+vVT1bonuxt6Wu7FhaZLY9a1X3qXYLRrCEww4VnbAJxmbIf7s3tif3gftAUOoGlpC2EocJL7I348mXsfzjVeiO6WO4o97yz3FCz1fYp3U2fm/+wr32eY7Z6G7EgWGmgbooflTpxtvKDM9pW0HWfYgQnut7Da/xPCCOF4/Um4J/EhNNA1VI9/5v0YCzwfHthOBrol3I7zTBeVuZ1/An+hv+MpfFBgdV/Z9kTXWPzg/xq+iA9t9MfhrsQ+aKE/Rj2+OfifasfG4D8wwoTzTBfiDst9MB7opw/dU9WKxrKK8qmG0/GA9Qm1OEhJ3nIOw1e+z9W6y3ny9kceacML9sdxvblroWNmZ2g7xrnewN+BdWoF56vNN6Cb5fZi21gXWItX7M8U+lkQsoiBBnPTP8fL9mfwZ2Btoce98KKn5W7cmNAD/wU34D3XaLXqsRFGtWjMnZb78o+LyvT7Ct+3+MAzCXvDu5GiSUXHhJtwjbljod8p7fWWtL9+8f+EwY7+8MGHx63Px/ScIiIiIiKqiLhPCNYUhqalJ0gkCSar9DablaoWFdg30In9w13IeMVWKFEkC3lIkif/Zzlh7OlnR71nrEi82IjgnjB29cmFoakO1stMZf5taSL+CPYNcCDiK9LG/g4YW+jQfF4qgttD2N3XAX0Dbf52JImWM8WD3A88KqFZWSrhONwJz4qASqgV5PsnCNv1JtR7rPin1Nnj3PD8EkCjN5KhS9cic4QTe553oPGbySrBuKefA6l3WWC91oTAtjB2PZILQzOdWmm4JIFtIdVXvj+DQNcC/RKJwLXUj/1vuOQuvnAb3nPDvymEppNSoLVqkDPZg70vOQ4pIbZ/sBNhZwRNJ6YABmDvK06ViG3Qz4ZQbljtB5VUPsMI758B7HrUDlMrveqzFovTC7V3z3MO6FK0xZKBouHgwgnbnA88KvGa1Lnw0vS5M73w/hKA9YqD/RV2hRHcEUbzOalq9eqCGr+RrBLguTM8lX7tVHc11jbF2NQp6v/dYRd2hXdgUuoctRpwaSa43lIrCJ9bwmN/Bf7Ah55pSCvw95I8G+96EwOT3sAx+lb40f8dBjtewnups5CmTa/Udsa6Rqhk3diUyTBoDHjXNQavO17EiJTxWO1fiSnud9Uqw610x2J1YKXaTkNdE7XdouRc/Nq/VCXUApKhL2Ca+z1sCW3CmJRJSNRYMdMzWT3X26lTEIgE8IqjL640XYf+tsHIDWdjkONF9Te9Eh/A595FWOpbjEHJb6p+fNs5AiOdA/Fy0tASX6ckFR+19kX7UhJZO0Lb1OIp64N/4voCFz9JmvW3P4VLTFfiBdsglWB7LvdRNNY1K5YUO9FwCmalL87/3h7OxeO5vfG/hGiitX/S4EK/P8/zAZb7luJac+fo67U/g04Jt+BV8wg4Iw68YH8CC72zVbKwMv2+ObhR9UVf28tqZel/g3/jJcfTSNfWx1nG88p8vaXtr9ONZ6nXJkl2ip1uC7Jx72kWXNoidh/yLtnoxZAfXTDpgQEX2tCuUfEVHFfu8GP8r27sdoaRmqBBtxMScH2r6PtrKBzBdR9moeDntmlmLaZ3jMYM323zqxV9d7tCaJ6kw71tE3F6wwMfzoYjePsXN77c7EMwDFzQzIhHz0yEWV/ygjaLNngx/Q8PcrxhHFdPjyfOsqJpUvRDgW32kFr1d31WUC0acslRJtx3ugV6rUadB9P+8OCTjT44/RE0S9Kid9tEnJZRufhOnufprxxIt2jzK2/k9b/7qxtfbPLBHwZap+rw0BmJODolGvv9tT+AMavdagXlRIMGHVqbcOtJ0Q9y/aEIxq524eutfvhCERyXrkefdok4JrXk251vt/kwaa0nfz/cdFwCOraJ7oc9rhDGrHJh7b4gdBrgjIYGtSJysika1zz1pR1r9wXUish5xl+TgmYH+q8s8hrf+82Nz//zwRcCTqqvxxNnJ6K+Jfq3w1Y61WO6AiFU3rG03x3GTfOzYS7wko5P12PE5ckYsdKJLzYXDtC9QeD6ViY8UcKiL1tygxjxkwv/ZAWRYtLi9lMScPUxB+O8nh9lY587XOhYXHhjGozSIQdIn9+3OBcXNjfijlMs+PhfL8b+4lLbfeuqJJxQL74XEDkSast176vNPkz5w6POLbmm3Xz8wfPtjkU56ud5ZMlTOTeeP9+KcAQY8ZOz0HP5Q0ATqxZTbkiFLxg9n5Zt8amfywrlcs1IOnCuFlXW+VXQkB+d2OkIYdQVyer7cCSirpefbPDBGYjglAZ6PHxGIhpaD23F8t/2BDBujRtb7dFr2bUtTbjt5GjRjsMXxlu/uPHTTj9CYeCkBno81O7gtir6Ggr3WeFzVXiCEbz5sws/7vCrfj65gR6PnHnwWlTW+01lyLVUtl/S4lMlvQ+Iqb+7seAfr2qjbFOuYalmbbnvAwXJMSXHVkHSn4EwMLtzKqb87q7wNbPg8SDXzvuW5Krj7a5TLfjficWLoGqTOpEQ3DvQoSqk6j1xcMftuCcHtg5m2DqYkDtdkhl+BPeFVRWZJKmkQqu8asG8xEVe4kYSLFKdFdgcgq6eVlXalVRZKBV92+8ofPAVTLSYT6n4iRT2RKBN0iDlTgu0Zg1glio2M/YNLXxh3D/EqZIyudMOJlok2dN8Xhq0lmjwFs6NIBICdMmacv+2NFnj3DC3Nag+KFgxKAmyjNds0Bo1qvItqYsZjk+8+QnBXQ/lwtBEp/ZHcFcFMo9FkoE7euUgqaMZYXvxRbH964OwXlnym6BruQ/pDyeqbYu0+xKxtWs2/JuCKlkVyo72CSJQAY/6MpYcOPvWB7G7r11VyEmbCvXLW254/wgg9bYElQAsKK23BZEg1DEqyTJJ6GmLVHdWRNgbgfvHABq/lZyfaEu9MwG7HrYj/EhYJXwlURuRyr8Di4drdCWf5Y4FPpXcbPDSwaRyaXwbgsiZ4kbjscnqWMr/+V8BOBd7VbK50O//Ez0/iiYDqXqMdAyESWNS1V95Hs25B1ebO+AqUwfM9kzHN/6lquJOqqouNF2GuxP7lFstKJV7cz0z8hN16wN/YqJ7LLaFNiNNWw+3JNxWYvJoX2gP+uQUr9gTLyYNVkmhsmwI/aOev6xkoFR3bQptwEmG04o95gw78aZzMDqYu+B7//L8n+8KbUc4ElYVfXJ+aKCBHoZCVYgV3U4EEfzPcges2uj5c725Cx7JvQu+iBeZ4X3oktAdrfXHqcfOMJ6DZrrm+Dv4R4kJQanwkwSm9Kck8wq63dJbVdDJ/pVEqSviRLI2GpjKdo7SHY2bEm6FTqNDuq4+LjZdgeW+L9Tjkgy8ztwZjXRN1PeSJLw9u4vaP/V1GYW2I8m2LaHNaKU/tsR++De4Hq/a++KmhB7ICheuNP7Z/wMMMKB7wh0qiG2sa4pByW/ApCn84UFJ3naNxAn6k3Gp+epij20KbsBM9xQMSx4LiyYa5L2TOg0mmKPBctiuEnJJmpT8/qhov+8J78ZV5utVEk+0MRyPUwxtVXWiJATLer1l7S+iPE2TtJjSoeQP/XK9YfT/1oEBF9lwRiMj/twfwKNf2NEqVa8SWHKDI7HIJzenQVtkNMffmUG8/J1D3TRd3NyIH3cG8NxyO965JhktkvUquSXPN/G6FHWj+Mp3Try12lViMmjVLj/Gr3Fj6KVJODpFh0lr3Xj+awcmXp+stvva9w6c3cSIwZfYkO2N4Ikv7aj/txe3nJCAj/71Ycl/PvW3ja1afLbJp9ox9YZUpCdU/L1/5p9e/LIngCuOPhjDyXOv3h3Ae9elwGbUqOSgvI73r09RN+T9vnaoGzC5ed7mCOORz3NVEu6i5ia896sbm3JCmHR9CqxGDSb/7sFL3zpU0qCojdlBDPzeiZcvtOHMRgbVt08vc6C+RYvzmhrx0rdOtErVYVanVARCEQz8wYnhK1145cLodV+SaCMvP7SE14Tf3PhldwDjrom+xuE/OTH0RxeGXJqU/9yyj0tK8MhjcmM+uUP02leQ3GwXvOGWBMnY1W6VZChKXtOzyx24+hgThl+WhHX7gqpvm9p0OKm+Aa5AGDscYczpkoq0MvapHF9b7AfvCTq0NquvS6YXGZVCcX3dk/NNEiqDL0nCqRkGbMgK4sHPc9U5dmJ9gzpnCxr5k1Mdf3Kd02k1ha4R2+0h9beSuBLjfo2eT29ckayuP5I8lGvZm1dG46Wiyjq/Cp47n/3nw8n1D95Ezfnbi4X/ejHw4iQck6LD+2s9ePIru7reFkySV0SWJ6zOtwfbWXDl0SbsdIbxzDI7bCYNuhyboBL1kkCbfH0KDDqNSqi9+K1DfehQ0ddQ3rkqJq91q7ZM65gCnUaDoT86MWKlC4MuSSr3/aYinP4wxv7ixuKNPnRoZarw+4B8WLX4P5/ah5I8ln0q12B5zynvfaCgjEQdFt9y8EN/6dM+n+fi/CZG1LNoK3zNXFbkeDgqWa+e99EvclEX1ImEoO1qs6q4Sn84Ao1Bo5I9kuywXmqEa5kfjkU+NByVpIbCqqqph+1IvMgI88kVfxMP7gth91MOpD9kUckz319BVV2mr68t9jz6DF2haqzDoU3QFKvWcn3nh7HVwV1nX+BVCSep3iqa1MtLBm65LgsRD5B4iVEl9Cryt0W5V/jh/T2akHLM9+b/PLA1BF26ptAQZuNROuR+ePCiI8lCfX0dsie5K50QlKq6plNT1DBf79rCJ568Nt+GEDQJfmRP8qgh1daLjUjpZVHJSUmOaSSRmufAhziB7SEYj9arYb+Zo1zIlMq+sAx1NsNybsmfruibaNFsRqraJ/Y5B1+/SL7FrJLMMvS6KI1OoxJzOTM9ajivxqJBw0HlJ+KKiRxIXJoLPzdCQGBXGMbWOljaG7HnGUd0uaAwkPaARVUIFiQVodkT3GjwkjV/qHFZMt90qSHg0l95wu6IGqYt1afOz6LDsfP4/wlCowd29slV56GhuQ5p91oqdb5RxV1mvlpVQt0TeVhVq20JblLVTe2Nl+I7/zJ84VuEAUmjkKFrqJJ6z9ofxvnGi3C84eQKb2N/aJ8aHnmP5SGVdPon+Bdec/RTFVUnFHkeSTYVrAKrLKlU00OPZ3L7qNfRVNcct1vuzW+vtGWi+y28kjRcVWkVNdY1DFeYr0U9bUahhGBb45loqW+NJ3Lvg1alATV4wvp8qYnHsrbzjO2lQt+v9H+nqhwlCXal+fpCj+0K7cDW0GYcrSueDBSdzbeoBO3vgV+LPSaJPvlvnmcmprjHI0FjwYu2QeqxhrrGhSrq5Fq4MvA9jta3VN9vD21RCcM8MlTYpklSbSmaENwc+k8lSt93vaMSaEnaZHQy34zLzdeqxxtpm2B86gyYNQn4yDun0N9uCK5Hc/3Rqo9W+L9Rv3O9uTM6JNyIsvwWWI1fA6swLmV6iY9LNWeHhK7qufPIc4uHcu5Ur+Mk/an5CezK9PvZxvPVVx5JLspw5nMTLyz39Za1v6hqDPzeAZNeUyiJdc+nOdFEQysTpq/zYOlmP/a5wjDqgMtamNDnjMRyq2akgmXGn578G1ZJnEngvzk3pG4MbjspocSbq5IqDPLIze0pDSr33rbHHVaVL1KNkffhnbyV6w+EUFKR1zpVXywZKL7Z6kO7hgb1msX5TY04tYEBSzb6cN/peiz5z6tukvMSOPecZsEjX+SqapmiN6xygyY3YMemR9/b5ebqo3+z8fveoLph32YP48xG0dBDSHOkv4XdF0bPkxLyq+GubWnGuF/c+DcriPQmpVeoFCQVHos3etWNZkHbHKEDfRP90hbYrsMfUcnJkDymJhs40K4DZXq921pUZaQcP5LQkurF5AMVJUXtdoVxfWszzmoc3f7x9Qxom2HA2r0B9W+SUYM7T7Go6kr5kuNPbpbFLmcI7mBEJXErS6oDJekpiVY57oRUGe11hfMTdZtzQvn7pSg5Po5NK78iKdMTVgnMl9pb86t9ClqzJ6D6p8eJCSrhIvv88hZGVf0kCcF/MqPnRVnJQKmw3JAdqnRlKMXfda9lqh7zuqbBYtCocyDXF1bndmIJRRgrdvixfItffQggx2ZBkgx67Qcnbmhlzq+GW77Fp86hJrbocX5f20R0nZeNTTnB/MriPOWdX2K3M6Q+LJF+l37KI2268bgEtEnLu2YmYN56j/oA49wKXvfyt+EK4dwmBlzTMnpTJ9fSC5oasXZvEF2OjV7fpIrPdqDKscuxZtz1aS68wYh6vyjvNVT0XJVKcLnyqLciTd71VlOh95uK6PlRjqpIvLCZsVLvA/L+1LmNOX+fPtAuEV3mZqvjUq7HZb0PlEWqHRN0mhI/JMks5ZpZ2vFQl9SJhKC5rV7N/ybzqiW2N6kEhSRGtFYtLOcYVUWevp4WwaxoBZUkyYL7K5eUkgpDUxudSj6qbZ5kgPVqE+wfeY9ooiNnugeub/xo/GY0SSjJz9yZHjR+Ozl6Mpei+YI0hPaHVeJUEjwyvLaifyukIk6SZhmDbSrpWlDEEymcdJMT06RRP88jycBDpZJWpSSupOpRhlMnXmpCg5eNCO2NvsZIwK3mCky8wIicqR4Yj9FBa9Mia7xbJcuilXQRVTFa76lEWC83wb8xhD3P22E8WgfbdcWrW3TW0oOiirw+Sbomd5XKSR92P21HkwkpMDSueL9IItLczoDsd92o97RVXQGlck/I3ISRANS8gZJ8TTjLAO+vAex5yQlDCx0sZx680NrnelXyUIYVl8ezJoDAphAyBhZOYO4f5UTiZSY13FjOt0Jkdx2rV9W2Mkzb8ZFXDSFvOjFZJcupap2sbwuLJhGr/D/iXFN7NU/eucb2SNRa0c54Dk4wnIJ0bT1kh7PU/GaSVMoM76/UNmSIZEtdG5V8FMcbTsJlpquxxPtRsYTg4ZJUnVSqSRIwVZuutvGyoy9GJ09UCciRztdUNVpe5VtBn3s/UcmdTuZb8F2BZGBeFZwMH73VcheO1Z+okoVvOoegia55/px8eaSSsKztFLTM9znmeGagn+21Yo/tDe3GK/a+qipT+qwkUtlXHqn062Duii98n+Al+9N4I2WCSggWbO941xvYEdqKR63Pqp/JvIFFq/Tke69MBlqEO+JUybXOCbegtb4//gyuxUDHC7Bqk3CO8QJYtaUvGuCIOFRC9G5LH9yVOkslF1+2P41kbVqZ8+jJHI8dzTep5GNRawNr1FDpfraBJf7tsOR34Im41bBfSYYXHWpckX7Pb3/Yjlcdz+IYfWtcYLxE/ays11uR/UWH5+qWZlXV9fAZEVUZITd1ctNy6VFGLNvix6INPoy6PEkNnZKb24c/t+Oi5kacXIkb1H3uEJ760oGHzrCopNhfmdHqKKkOK/o8RSsMDpcMgW3fzIhnljnUzZckvx443ZKfXJLKD1cgopIBMlxTbj4faGdRFRnyu0WH/8pzSBJNKjEyPREcXaBy46gknRrSJAksqWgoSCpF2hYY+iU33k1sWnXDI8khGT4qN0GSTJDtXtDUgE4Hhvf1PLnw0CxJoskQOqmaqQh3IIJBPzjxzLlWVXUhQ4PzyE2XDFOTG3p5bckmDUZdHr1OyHDdrseaMeonlxrOLO2S789tasx/DVIZOfNPj2q7JB8GXVzyB7BycytfeSTJKa/jwmaJSDBoMPhAtV6e77b7C+0jee5+yx34NzuIBola3HGyRVUWlkf2lezfXc4whv6YA7s/rBKQkrQV/+WE1I3uO7+48Me+oEpo3ny8WSVd1bYzgyoxevvHOXD4wyoxI1VGRZN+435x4ZwmBlWFWhIZpiiVhgUTLkcl6/D5Jn/+a5QkdZ/PclWb1XDB0yz554ecQ2+tdqvqQtkXdHjq+nVPyDkj1Wgy5F3OXTmui1aaybQHUg13T1tLicloSRRJMrFgQqfodTHvs4/tjlCxhGB555ckKwd878S9bS3qHC2YAJJkZMHtaA7ErFKxiLJDxWKksrhgdbEkKn/aGcDlR0fP15fa24pdf5ratGr76zODZb6Goso6V286PgEvfuPADbOzVVJNhmEfHCJd+vtNRY2/Nlldm15fUXhkY3nvA1IpL5XreWSocJJJoxKhUrle1vtAaeSaN2+9F+9em1xsLYXSrpllHQ91SZ0Y0yc71XqVSSXtIqEInF/68hN3kvTJGufClo5ZaiEIx2JvfqVVZQR3h+BdF8SW67Pyvxwfe1UCqtjv7gkV+r2CX1JhdyhkYQqZu04q+hqNSFLDcmVevr2vOpH+WCJ0qdoy+0eq5ST5lHK7Bc4v/RX+WyGfYEs1WPL/EmA8qngOWe45i95jSoKq4PDSito/wqkWv8j7Ko8Mi5Z56WxXmlTiUOb/k6HckjQVaQ8mwthSh53352LH3TkwtY4mj7U2Ddzf+uH5OQDbNWaV5JR59mQxEvvC4jfMVUHaJ9tJ6mRW7XR/X3i+sIpo8KxVJVtlSLpUulrOi1605PU4FngRygqrn2n0GpXwk35xfHwwYSfng+NTrxrSXRGORV41HLtgMtTxmQ/BnWGk3FryfAnShw3622BopFPHXfKNCWo+Sc+qQzv2qWxyfl9qukol7UKREL7xfZmfuJNE0WTXONya1VEtjvCld7Ea7hqu5AVwX2g3/g6uw/+yrs//WuL9GPvDe0v43T2Ffq/g15+B38vdVseEm/G0rT8ydI3U4hg3JNyI+toGWBNYhQ89U1VFX0lDTLeHtmKWZ4pKiGk1xa9psoCEzMN3ouFU6DV6XGS6HCcbTsNXviXFfres7eSRvp3iehfvukbjOdurhRbfEH8EfsWTuQ/gNMMZ6JP4FA6HDBmW6s9rzZ3U3Hwr/d/nPyYLnEhC64/gbxiU9KZK/gqpbpP5/QqSIc2y6EdRpxra4dXkESp5Jn1ziuF0XGK8Ej/4vi63bTIMXYYJX5fQWbWxtf5YlSyWasHSyL6Suflk8ZGSyPyHMidhaYk56Q+peJQFRX4J/KT64FD6XeaVfCr3ATWH5HO2Aaoik2KvbYZezQn0487oe6TcKEgCzWrU4pwmRoy5MlndFMuNpS8Yvcnc76nch7xLN/nRJl2nbsIlISIVUTJ08qN/q+f9vyCZuyjVrMFrF9nwWbc0DL3Upoa2/rwr+npNOg1OqKdXVTgfdEpVN0Uyv5LcPEkCS6pnZL4nuVGR/5ehVv5gdC4oUfAGTubzypsTqShPIAJzkapB+V4qUPJI1eCnN6ep4akyLEuGyBUlQ/8kkSE36A0SK3YOjfrJqapOZKhgUVLhJ1UpMzqmYNHNaWruwn5f29VQL7khl+qop85JxJJb0vD21clYvtWHTzYU3m9SWSJ9e/dpFjy9zK7mfSqLJANl+KxUZl5yVPGbSpk37Jutftx/ejQRGghF5+2TRK3MQ9Xt+ATVB3KjXh6HT6asgJojccTlSWpIoMx3KMPhhCRWT22gV0OzZ3dJVZVPb61yqySpkKHQkrAdfWWSGmYpu/C55Y78alOxwxHC8q1+VeFYGtn/CUVu9qVCLW//y/3ysWl69Dvfql6jJJ/6LnOoCh1VpfW9E3eckpBfwUOHp65f9/JIgl/OTUnKfL3Fjw/WFb6myHkhx941xxSvWpRr3ox1HnWtkaRpHpkrdeofHux1hdR1UOZnlcSV9FNR5Z1fMu2CVOvJdaco2c6cvz3YnBtU16NJv3vgDUXU+Xs45LlkaK58mNH12OIxmswVKK9b5oOtyGsoqLxzVRKwknSe2zUVC7qmqvlWX/42GlOV9X5TUSVVJ1fkfUD2Y9FkpHp/qsT7QEnXcdle0Q/HyrpmlnU81CV1okJQSEJwx505cK8IqGGU5gOlrFJNFXZE0GxmdKinvGFu7ZBd8pPooKqsClaf5ZGhwZazDch49eAnhlJlWMJ9p6qCOqrAKrOHSxaLkFVfJcnTeFwy9AfmZpO584I7Qtj3ajSIyGvtjrtyUe+xROgztNg/woUm7yVHh5aKQAQ6q6bcv5WKufzt7w3DtzYA/99BNeRV/X4A2POsHSk9LUhsb1SJqJAznJ848m8OwVBgiGlF1Xvcqr4qyr8lCOcXfqTdffAEluGreYuRyryRqb0s+c8p8x2G34ioxKAkjgvub0UH1c9VSSoWZZi2zIGY38ZANIlXWaHsMOo9kQhtYrSf3T/51RBkWSjG8VFpr+fgtzLXowwdL21YdEGRYATuH/wqAV2Qa6kP/v+C2NoxOz9ZLQeQ798gmk5IQe5cqcjUI6HA0HRZDKe0uRnp8ElCsE/Onfg5sAJ6jQ6n6E9XP5/qflctvvBe6kyVIJLrX4/sDiU+hxY6BHHwALJHDg7PT9PWRzvD2Xgu6dX8n0mVoa6Ez5RkOOqMAivlVtbHnrk4Sn+MmtOtYHWfJAcl2ZkVyVTJRSHVbpIAkmGrUvUn1V4P5d6pHgtGQvDDp373jeQJKlEpFYYF6TSypm7x61RZ23khaZBKtMmKsnvCuzAk+S01rLlopeJ7rjG4O/HBYkNZK2OI4yWcbGhbaPVb2UdWjS1/WKxUDDbXtcCQAnPtiWa6FmrY8GmIJipzwtlwROyFhhHnWeVfgaxwFq40X5f/Mz/8hVZ1Lk1TfXOVlCtIBqBI4rk0K3zfqCRkSSseByNBNS/hq0kjCv18T2iXWkRkWPLb+VWFAQTU8PK8SsjK9Lu85mHOAWpotMwHWNKnxRQbsi+uOsakbl7Pa2JUC2T0PS96zMuNwLg1LlVJkZqgRZtUXfQz3krej8lwLZkz7foPs/J/Js/d+sBQsIIkAXLXJyXPEyTVZ2VV6BQddifDM2U4ltzU51WTSTWCzCH18b8+nNnIiPtOLzwMUIbBLvrXp4ZWyRC5p8624p01bgxaEcZZjQzqRkUm1c9L7hW8Oc27IZaKt6LkZqvojazcbMnvSmJLbp7m35iqhi5LddidpySouaV6nXrwOiND9YatdKH7CQnocVLJHxIWXCzglPoGNTxRzZdVYPL4Qn36g1Nto9GByfOlevLzTT6s2hVAIBzBz7sCamJ7IXMuysIEMq/XdQcWZclLbAmpaPx0gxffb/erSpiS/JcdVHNzHZOiR7/zrIUq5uRGXebyWr0roJJ3eYuTXH60SX3lkdckiQy5IS86jO+Zr6KLj+RVXT17rlUds1KBmVcBJYlXOcYk6SvJ0HYND1ZOy2T6Vx5jxNdbfSoh0e/8wpVDkgzoNDdb9WneDb+85jMbG8pM1sn+l/1dkBxHkmgS0q8FyVBJGU4s+0ESUdL2gguQ0OGp69e9vAUf8s4vqba96XgzPt3oQ/cCCzJIJWTH1tGEZVEyNFeqay8vMsT5wdMT8fYvLty/JFcd13Kuf7vNr+bnLKqs80uS7fKvzO1ZErnOeYJyTjtU/8v8dfKBjU1WXSrHNbMOFrlc0cKUP2+d9FX/bxzqfUGuMQWv1bJvJvzqxkcbfHj1ooMLhpR3jShIEqWlnauSDJT5WWW7eYt1PHZWIjrNyVbXRdnHpb3fFDXtD7ca1p5HPqyQ611pJOFd1vuAfFhRdDvq/UmvUfu2Iu8DBUkFvcwD+NZVJc8r+WkJ18xf9wTKPB7qkjqTEJRqJNMJemSOccF2lSk/uJdkoCRE5IN/WaAjZ7IbYVd0eGWx55CqrR/8au5BSfY5Pj1YXSFDUnM+8ML1rQ+W841qHjwZ9inVU6m3l/4J3OGSisc9z0ZXg5VVhQsmVWQodIvPDpZzy5DobV2z0WRCspovUV6vLGAhc+ul3pGgqrrk/2WxlfL+tticiJ8XLhvffGUmMgYlIeG06BuC6Xi9WnBFFvAI7grBPt+LtHuqr1/yyLBw+zwPdCkaNSQ3sCOMnKlu2K6NXhDssz0I5UZQv58VEVdEDZeWeRRlwQtZ7TjrXTdy53hUxZzMhShzAybdVLUBjukkPXJmeJBwugH6hlrkzvEi7AhXKClXVNY7bnWcpj1oUYuISII2qaNJJXwt5xqw+1kvnMt8aqEP37ognEt8qP/swYut/Mx4vL5CSU8ZQi0TSxjbFL5MNByaVKyqU5Kw9ftGtyPtcixyoeHrNtXPubM8as5BaR9VD6mmO1Z/gkqGSHIw7/onyUBJmMg8dDKEdJZ7MlwRV6HEX54mumb4yf+DmnswK7wfS32f5j8mQz/neT9Qi2zI3GuSCJNElFRxdbPcXqWvZV94Dz53LcKLttdVld4Czyy4I26caTgXF6deUeh3pQ3yurtboouY3Gy5Nf+xb3xfYap7PN5Nnam+P9N4nloU5YLAxThRf6pKnv7i/wk3Jx/8mzx5C6mUtp1RzkHICWdhcNJbxarYfvavUMN3JXEoSa/DcZz+JDUcWSr2Gmgb4mPvHDjCDpxpPFf1ibRLEoYPJj5RLKF1mekq9benGc9APW19THSNVe0pacirpO8muMaoY+B4/Ulqbr9vfV+VuiJxQecbL8Z090S1InBH881qEZZlvs/QJ/HJUv9Gqk1lOyXZFNoIWfqlpb5NoZ/L65eh8e+738F9iY+qY3ui621VjSiViZXpd5mn8nVHf7UQj5wvVPPIjfGdi3KwYkdA3RienhF9H5IFJmS45MzOqerGQD7k6DC75A95JT8m1Xh5cn0Hby5kiJysRPnqRQffz2T11pKmIJKbGqlUOxQlDbsbvcpVqF2qrdqDcwjK4h5SGSRzbgkZDhVSFREaNVSudZqu0AT8DyzJVb8vc02lJ2jUUKu8mxqpZDHroBb+KKpFsk79bh6pAJFJ/I9O1mGvO6Qq9fLmlRKyunDBFS1lMvrZf3vx3HnWMofKFl0s4Kmv7PgvJ4iOc7Lzk26yGZl/cMJ1KdjrDqsb1TyyT+RL+kfaV6zv5LED1z+p0pPht3krlwr5/ZISA0IqXgZ851TD7fJW9yy4+ItM8i+ve9w1yYWGL0ryz6QDLiwwgb0Md8ub67CgokOP5cZUXo/0b568vJz0hBzzkjAueGMrw75l/8sN8vtr3eh6nDm/8sZ/oK8Kbvubbf5CiduStEjRYfq6aLVf3nyVMhxO9r+Y+7dHJUkLDiuXhKy0Q5JVme5IfmJJqgrl5lkSybIYAR2aunzdk2SMrEAtq2GXdm5KW/7aH8SrBxbuKUqOa6lUlXOyoH2esDre85JsW3NDeMNfcqJTzvnSzq+lm33quWRIc7R9EXWeynEufSFTOHRqY1Jzs+adyx/86UGbCszpWdLwa1m049lldpXEk7lfC74uOddl8SkZpipJLPlQpiKvoaiyztVnz7Oq46rgtSjvKaQtZb3fFCUJ35JW+S2N9HVZ7wPq/ckeQrtG0d/P9oZh90VUAlbmcizrfaAkP+4IoLFVV+IxUdo1s7zjoS6pMwlBYb3ajP2vO9XcfnmkOkyG2sqQYRnCmnC2EQlnGhDYJB+bFv6EQRY+kIq6LZ2zYWikVc8jq6gKGW4r86hJAmb/YJcaJiuLi5Q2bLKqeH4JqAVMpFBDXkMeSQwetTCt/AVJhtiQOdqFrR290CZrVDIw+eaKJbykKlGqDCtSsdfgZRsyRzqx7eZs1VZZhEL6p7rp07XIGJik9kv2RLfaxzL/X3L36GtM7W1B5jAXtt2UrQbIy2IyafdHP1GQRTIyBtjU30miVFZftnU0q8Si8K4NqD5oOinlsOa+k4VKJDm760m7mldRhiY3HJFcaBGWiqr3ZCL2D5ch8NnQyjF4jVkle4UMEa7/tBU5UzzYP8yl5s2UxGHesGIR2BVSfVYSGYZsvdyIlFujF0RJ7Mpw8vzq0gqSas2skBs7H8hViUCZT7DhkCSVvKXqI8OE33C+rhIkeXpYeqmfyZBhmTuwnfFstDWcia3BTUUvf7jNci/edo3AbdmdkaFtpJ5nqTe6OIjMV/eCbSAmu8djtGswjDDjEtMVaoXbqtbTcjdCMs9N7gNqnjiZT/DlpCFqTsTDIdVv8nxjnMOQE8lGQ21jtThIXuJptnuaGnY9JmVSmc8jC5384P9arax7V/ZNhR4blzpDreocQggD7c8Xeux/ljvVcOjKkHkDvREPXrQ/qf6VFXQHJI9QFXKfeT/G7vBOZPuy8K3vy/y/kaTeiJTxuNJ0vaoIlKSh68AcgY9b++X/3sv2Z1Bfm4EHrI+rlXXvTLwfo51D1Kq6UuX5iPWZcuffE1LlJ0OV33OPUQlIOc6k4u5cU3SBjpJIQvk87UUlPrY3tEs9Z9Hhu3KjLvM0vut6E72yb1IVrxcaL0V3S7QitLx+H+scoZLNMt+gJJll9eZxzlHqK88V5utKXIGbjjypDpNhszKX1FUtD37IKzcvkhiStyUZ8jj5d7eqGCl6cyBkmM8P2/1qOJRUNEklSp5LjzLhg3VeNdG6DIuSm66nv7KrSr3byxhmWRVkIvlnl3vVDbJMpL5uf1Ct2CuVY3lzXK3d60L/C2ww6jVqXqO8/pA5v2Ro61tXJqORVauqaWReJEkkiKuOMas+kZs4uTl877foEKmiN9Di6pYmvPqdExcfZVTVOpKIlBU6ZTt2f0Ql/yQRIdVrcjM25Xd3fmXO/PVezFnvxZtXHKyaqyhZJbKgESudKpkmK1nm9Y8MoZZ2SNWKVLjIJPAn1zeohIa0SYbtyST78tpl5U+pNBIn1deroXVSMdMwUasek3n2SppbSubIk8ocWcQhr/8KJkeln1PMWrWqcNEbbXnOceuiQ8nkRl0We1i3L4Anzy6+yENRMgRU9rusiCzzhMmw7vd/c6vXnWjQqiTsmNUu9dzyeqQi76vNPgy9LElVPsqQPUlMPHm2Vd2gjl7lxhmNpG90+YnM7Y6w6q+ySOJUqqmkr289MUEdW19u9ufPubjHFcaiDS68folNrfI56y+PqmCUdl5xdOHqbjl3ZH/JIgh06OrydU9e17AfQyopeF0rE/7NCmH2Xx61WESedfsDah7T0haykepGqTYsSp5HEp9S4Sv98uYqlxr6X9LzlHV+yRBqOa/yyLXkp53+/Dn15PyQauMhl0bPEdlO3urwlSXDm6XvbzxOPowo3veDVjiR5ZVkYJK6ZlT0NRRVdNXnoueq/L1Un77c3qaGYctcg7JokawY/ef+st9vDkd57wOyDen/MxoaUT9Ri7GrXaoyUq5zcr0r632gJHJsndSg5P2UW8o1U46Fso6HukQTKTjpRC3RL/MxrPatRF3hWOJF7gwPmk4peal2orqkpOO9nelsvJY+Mqbtqg1k6HXWB07se+fgnGm13ZfeJZjrmVGsKo+oNronuxt6Wu4ttKBK/ftsSOtuLbYgF8koiADcaz5G7qfDSpw3SSYin94xJX/4qEzeLj/bmBOERa9Rk4tLVYksZiFDbQuusClDnmS4p1Q9yc2M3Awv/s+bf4P0256AmmtKquTMeqhJ9u8+1VLiULXKKLqqZ0mkwkxuLmRlWVnps/uJ5vwhXTKfndyUyY2HVH2cmqFXw0Jl/rC8ZJwsmiFJKZnzTm6o84apysT0cqP01Raf+lup3JN5p/LmYpJhfLKSbF4lx6fS1nUeVW0iz/HEWVY0PVCJItUrMmn9xpyQqkq6rIVRza8kybEb52UhxyuLHxR+XXIzd1GBqrmKKHojKKsDy4IgktSQ1ZjlZrtPu0Q0P1C5Jv0iK0XKDZzMRyarsN5yvFklT+SWZtofHnXjKnNQFf1b2ZYkuqRq79XvHGqxhrx5FvNc19KMs5sY1LyNUnVX8HCQ177wxjS1HUlUfrohusCBPL+sbFqwmq4sUv3z3m9uNS+hJDDObGTAo2clqkVThMzp9uFfHmS6w6raSub/uvjAHFay4qUkA2Q1aKkslEnw5fhIOvC3st+kimfp/9KKrVQtw/okeZlX8SM30jKP1z9ZITVhv1RJ5h2HUrEzbo1bDQuXFZVlPkFZ1baklZVLSgheMj1TJTMKLpyQfO2TsLTtAI0uvkeMREJBOJaNh/OHaXF13ZNFe97+xa0q+CS53+0Es5rPMI9c12S+utISLtfMzFQVqEVXypVKPZm64JfdAXW+ShXh/acfvO7JsP2MRG1+BWFZ51dBRRNAcn2VCu+vt/lVUk4SU4+ckVjqSuZlkeu0PL/sg4LOaGhQQ2Fv+zgHBkkCF3nqGR1T1QclZb2Goud5WeeqrKwrw62l76TYWPpWrpl5K6CX9X5TGXL8Sl4zbx+U9z4g19gZ67wqgazmTMzQq+Rc3tDmst4H5DiT6u6Cw5b7LrOr94OSPrT4u4xrZqG+LyEh+OgXuWql+v8VSFRbz7sVtkvuhUZXe+rumBCsAZgQpHjChOChY0KQqGZjQrDqEoK1VUVujInqOiYEDy0hWFvxukfx6NE6khDkGL4aIrA9rFbV9ayu/MqzRLXFzkdykTmi8JL3RDvD23FL5jX41b+anUG1ksxHKcfwvhJW3iYiIiIiqolqT+qyDrNdbVZfRHVd4zfq3rwLdPhzH8oXUW12uvEszEqPzrlJtN0eVqtKDrjw4MqQRPFAhviN/YUf/MYjXvcoXmzJDeK+Jblqag6pEKztmBAkIiIiIqoCMidWwXmxiOKJzOUlXxRfeN2jeHJUsr7ElaNrKw4ZrsFkRdjavt3Azti8htom5AwjZC9huTCiWmpPaFdcbbemYT9Uvd2hndXwrEQ10y5nKK62S0Txi9c7imesEKyg/SOccH5xYOn2EBAJApoCCxK1WFy1WeLMt12IeCKo93jJq/FUl6rcrn2+B+4fA2g4uPgy6JWx69FcJJxlRMr/ii81X1ds75GDjEE26E6o3hz9tm7ZSL3XAuulh79kPNUeY50j8LXvC/X/IYQQRBAmHDwGqnqo4/uut+GJePCA9fEqfd5Ybvf3wK942f405qR/jn2hPeiTcwfGpc5AirbmLRxQVf2wJ7Qb9+Z0x6TUuUjVpiGefeKZj1WBH9E/aXCsm0JxSFZg/GJzNAYNhYFgGIVWx63qSgVZddITiJS6ImR1idV2iajm4PWO6MhiQrCCJEGWlyRzfuVD9ng3ms2svhvBcG4EmhgMSa/K7YZyI0Ckap6rrgvb2VFUfSQxlJcc+sb3Faa6x+Pd1JnVtj17JBcGHPkL2JHabn1dRo2eLy5W/V+XSZ9G+IZGMSIJsrwk2VebfRj/qxszO1VfDJrri8AYgzFEsdouEdUcvN4RHVlMCFYRxxIvHB/7oNED/k0hNHjJBlMbHTLfccPzo18lxiwXGJF2XyK0CRpEIhHkTvfAudSP4L6wSsJZLzMhvU8icqa54Vwa/SQ4sD2Eek9bseO2HKQ9nIicyW6E3UBSRxNMx+uR9Y4boewwEtubUO+ZRGi0GoSd4VK3K+10fu6DoYkOruV+aMyA7RozUntZim230YjCC0DI4znTPcUPogwdmk5KKfQzqaZUvxsGtvXIRrPpqQjuDyNzjAve3wLQmDSwXWlCyu0J0OgO9McsLxwLvAg5IjC20qHeo4kwHh09RANbQ6pS0LchBH09Leo9kQjzyYZy94tnTQBZY10I7ApDn6aB9RozUrpHKw03XZKJRm8lwXxC9HmyJ7nh+zOIhkOSov202Ad9Ix1cX/ugS9EipWcCbNea8yvtrNea4PzMp/o/oa0B6U9YoU+LRrKu7/xqXwV2hqGvr0VKjwRYr4hWZMnrkOf1rPJDm6QFgtG27n7cjvSHEmG7ruy5VwK7Q2q/e9cE1HFjOc+ItHst0CZqy9y/hfbPlz5kveVCs9mpqv/z9q/v7yAyBhxeRSfVPl96l+Az38fQQ48toU14xvYSWura4H33O1jl/xFyITnHeAHuSLwPZk2COl9ne6bjG/9S7A/vgxFGXGi6DHcn9sFs9zR87VuqnndnaDsesj6NB3Nuw72JD+MD92R44MY1po5ooz8ek9zvICecjXNN7fFw4jPQarRwhZ2lblfaucz3ORrrmuA7/3KYYMbl5mvQw9Kr2HYHJI8o9Brl8Tme6SUm98akTCr2c2nHO66RWBVYCavGivbGy0qtnJvmnqDaFoAfzXQtcIelN441nJDft3M9M5AZ3ocmuma4O/EhnGA4GeFIGPO8H+Az7yK4Ig4cpWuJXon3o7X+OPV3HTMvwZCkt/Kf5wP3JKwP/omXkoYcVj8U9YbzdRhgwNbQZvwX3ICmuuZqXx1nOLHcasHo3xpVsnl/aB9Gu4bg3+DfsGgScYqhrXoe2W9lCUaCGO96Az/6vwOgwTH6VurvGuuaFnrNeQr2yz3Z3XC56Vp85ftMHUcnG9riQesTqn3SR1/6FiND1wg/+L5GkjYFtyT0xOXma9Xz2MO5mOwej5/9K9RxdorhdPRKfABp2nRVDTrGORQt9C3xe+AX3G3po46dMMLond0D41KLH0dEsbRkoxcfb/BBrwU25YTwUnsb2qTp8M4vbvy4049IBLigmRH3nZ6IBH005pq+zoOlm/3Y5wrDqAMua2FCnzMSMe0PN5ZuisaC2x0hPH2OFbd9nIOHz0zE5LVuuANAxzYmHF9Pr54/2xtG+2YmPHNuIrQaDZz+cKnblXZ+vsmHJjYdlm/1w6wDrmlpRq9TLcW2O+LywjGoPC5tLiojUYdJ1xeOQcWjX+Ti5PoGrN4dwKbcIJradHjojESc0iAa863dG8D4NW5szg0hxaxBx9Zm3HicGRqNBq+vcCJBH10k4Y/9AdS3aG+NloAAABmQSURBVHHXqRZc1Dwax23NDWH0ahfWZwZhM2rQ5Vgzuh5Xd0exENUkvN7xekdViwnBKiTJpAYvW5FwphEaA7D3JYdKyDV5P0X9u2+gUyXE6j9lhWuZH45FPjQclQRDQx28fwaw62E7Ei8yIuVWCwLbo0lCqUqUBFAkAPj+CKLp9FS1nd2P2ZFwpgFNxiUjmBXBzt45SLzMCMtZRux73VnqdoV3TVAlkZovSIVndQB7+jpgubD4douSx+WrIiT5FdgRyk+wRcIR7HnODtNxejT7IBVhexh7XnQABiC1pwXOT32wz/Yg4/UkGI/RIWeKB3tecKDp1OhFT9rZcKgNhmY67B/hQuYoF5pMKH5BLGrfICfS7rfAeokJ/o1B7HzYDstZBhhbln/oe9cGkXKqAUd9lAbv70Hs6WuHvrEOCadFg0lpc8ZgG/T1darP9w1wqCSq55cA9r3iQIOXbUg4y6D+du/zDmgTNarfo88dQJN3U1QCWWvVquRkwxEHk5OliQQj2P2UHeZTDKpCNeKNYO9AJ/YNdiHjFVuZ+9fU6uBrliTx/pEu1VbLmdE2SXI6tRcD2ngliZe+1pfR1ngm9DBgiOMlVRE1JuV99e9I50C86xqDh6xP4Tv/MnzhW4QBSaOQoWuI9YE/8az9YZxvvAg3WW7FzvD2/ESRJJICCOCv4B8qkSLbed7+GNoazsSI5HHIjmTh8ZzeuNB4mVqpVZJMpW1X/B5cg7OM52Fq6gL8FliNVxx9ca7xwmLbLUoel6+KkmSgJJnGp8yAP+LDa45+Jf6etGG57wuMSnkXNk0SPvBMwruu0RiW8rZ6bJzrDTyf9BpO0p+mElQDHM9iQupsLPDMUn/3gm0gGuuaqYTsi/YnMTr5fdTT1S+3fYfaDyX50rcEz9hexumGs7DQOxsDHM/hnZRpqIyp7ndVwvNF2+twRZx4wf64StRda+5U5t8t832G/0Ib8E7qdJWYHOsagWnu9/C07aUKbXep71O8aBuMdF19dewMdwzIT4KuC67FiYZTMS3tI/wV/B2v2Puioa4xTjKchsGO/jBpzOo4k0S47KeB9ucxOHmM+tvd4Z24Vt8JT1pfQBgh7AnvLpacJKpJ/twfxMvtrTizsREGLfDStw5V0/r+dSnq34E/ODFmlQtPnWPFsi1+LNrgw6jLk9DQqsOf+wN4+HM7LmpuxK0nWbDdEVaVelKps9sZQiAM/LEviOkdU9V2Hltqx5mNDBh3TTKyPBH0XpyDy1oY1WqLkkwrbbtizZ4gzmtqxIKuqSpZ13eZAxc2K77douRx+aqMTzd6MeyyJDS26jDqZxfeXOXCe9emqITek1/a8ciZibj6GBM25Ybw/HIHdFqgy7HROGjxRh+GXpqE4+vZMOV3D0b85MIFTY1qVcknv7KjQysTBl5kww5nCP2WO5Bk0uKKozkdC9GRwOsdr3dUdViYX4WkECLxQpOqxJMhoO7vA0h7KBE6mxa6JC3SelvgXOJDxB+B5RwjGo1JVsnAYFYYER+gtWhUFV1pkm82Q2vURBNSOsB2g1klk4zNdSpRFdwTRigrXOZ21U63apB8Y7QyTxKIunQNAtuqdxJnqTwLbAmpCkitWQN9Ax1Sb7fA8VH002CpTLR1NMPUWq/aJRV19Z+zqgpDYb3cCGOL6GOJFxsrvFiJJDddX/ng/skPfVMdjvo4tULJQKGrp4lWMBo0SDjdoJJqri99B/fH/xJgPEqv9ptU6EkiLpgZhnOJV7XRcq5RtVf2l62DCY5PvPl/aznbqKoOZf9VhiQpQ3vCSH84WvGpS9UivY8F7m/9+YuSVGT/ak3RfnR96Vff+9YHVaWjtJnikxkJONd0oarockbsWBn4HvckPgSr1gabNgm3W3rjK98SBCJ+tDOeg9eTx6hkYHY4Cz74kKCxIDO8v9Tn72i+GUaNEScbToMOOlxtvgGJWquqSJMkzb7wHuSEs8rcrkjUWHFDwo3QaXQqgZiqScfO0LYq7QvZ1g/+b9HdcqdqgySbbrbcVuLvSuItN5yDz72LVJVd94Q7VDJQLPctRXvTJar6TKofrzBfp5JXeuiw1LcYNyX0QHP90dBr9LjO3Fkl1L73L6tQG6uyH84xtlfJRWlHF3M3tZ9+CfxUqecwaIxYF/gN3/uXRxO5ye+WmwzM+ztZrONL72LsD+/FQ4lPVTgZKLok/A/N9EfBorHgdsu9KlGaFc5Uj6Vp6qFbwu0waAxqH0jC9Bvfl2p7fwR/U5WISdpkWLSJuM/6GDaG/sHm0Mb8577EdKX6W0kcEtV0UtF2YXOTqsSz+yL4fnsAD7VLhM2kVcmq3m0tWPKfD/5QBOc0MWLMlckqGZjlCcMXBCwGDfZ7So9Bbz7ODKNOg9MyDJCBBTe0NsNq1KJ5sg6NbTrscYXVc5W1XWE1anDjcQnQaTUqgZi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u+9PIhxpeeKOGZN+lsmSpUjwp1S4f8oQUTpZWhpihpc5PUVmybI3HBQAAgHIEggAAAGvJtoEddGVGX71aNl7PlT7pKvFOTu2hw1OOVSKZ0OPFD7jQLsebq818WyqppBJKVl6/atWcLcaR7smo9nvVy7b0ta782ePxqLG3qQsaq5qbmO0q/05bcHTlaXYbmZ5M9/M1GTfoqZJH9HDxPZqfmOuqDS9Iv0KNfU2r3c5P0e81qLBPjdt8R/bDS7UM7xc62H1V2Cd0oKuc/CzykdvusKqHe+FkeThqLCy036ufX6ZUT2qN9w8AAIBVRyAIAACwlsyNz1YzXwsNzB7pQq1PIu/r9qIh2inQXuNLn1NRslAP5Y5RiifVzSnYLe+YJW7Bs9L3VbWy0MLGufE5rjW3qkbeJtrI1861MFcoSOS7sdl1psb/1Bnp5+l8z+VucY+7i0a4BT2uzOy3VND5dKOJKz02C/9CCqlTaN/K0yKKuHkTrUrRgsuiRJEyvOWBp81huJG/nfvZqiKtbbhCPBnXzMQMt8AIAAAA1g6WagMAAFhLfov9rJsKert58qwNNsuT7Vp2rbrNwkD72Sefa4t9vOQBFSeLXXvt6vgo/K5+jH6rWDKm50ufkkce7RrcvdplOgQ6KS8x31Us2uUsiLMVfZ8ufdS1Bo8qvkNjSh5XNBl1rco+j99VNa6pokShHii+U//EprpA753w6/ol+qOrGrQW6C382+jRkntd5Z9dZmLZizowdJi77oGhw938iDbHYjQZcYu02EIqW/qXXq0YAAAAq4cKQQAAgLVkr9D+mhb/RzcUXOUq4KyV1lYGtrDN5hK01YG7LzjOtce2D3bULoHd3ArCCq36fW0f2EmjSx7R1PgfaufbXDdmDXeVh/nKr7yMVeANyLpFj5Tcq6dLHnVtx7sH99L/0i9y51+T0d+FgmfkdZZHXrUPdFS31PI5D9fEUSmdVZws0sDCPq4i0VZBvj5riJr7Wrrze2cO0P1Ft+nsvJPdvIu26rHNaWh2Ce6ms9LO121Fg12AaasN24rES658DAAAgNXnSVq/CgAAwArYIcPs4flaOKGEfVXLLFi0IO3CjCtreyioI3KOS1Pza7PdfJIAAAArQsswAAAAAAAA0IAQCAIAAAAAAAANCHMIAgAAbGAuy+hd20MAAADABowKQQAAAAAAAKABoUIQAADUinPzuqpHWk/tGzqw1h6Bt8te013FwxVSSH0yB2nnYPvK8x4qvluRZKRy4Y4fo9/ppoJe1a4fU8zWaNMLjd/QgIJe+in6XbXzy1SmHmn/04mp3fRPbKoeKr5Lf8R/U5YnW8emnuhW412RZ0ue0FvhV/Vg7pjK094Jv66xJaOVl1ygZt4W6pZ2ljoG917l7V/RNpn+Bde47bIViiuMzBml1r621RacubHwWjXyNl5m9eLc+Gw9WHy3forZbfm0S6CDzkm/SFnebCWSCT1f+pTeCL+ikmSRtvXvqJ7pl6qZr0W127AVi6/Jv1BXZvTTVoFta7yfaDKix0tG6ePw+wqrVO18W+jc9Iu1iX+zapf7Lfqz+hdeo2caTaw87eKFZ7pxVkgoqYjCuirjOu0bOkg/Rr/VYyUPaHr8X6V50nVI6Eidknr6chfyWNaYXy97WeNLn1v0GDZX19QztGdov+VetyxZqjMWdFFUUR0cOpJFZQAAwGojEAQAAA1aK28b3Zv7ROXvRYkiPVpyr94KT9JhoWMqT98usKOebTypWlhzZf55Oi31TPd7/6xh1W53XOkzei/8lo5M6aySZIkGFF6rPYP76fqsoZodn+kCNFsp+NCUo5Y5tp+jP+i50tFq5G1Uedrfsb80qvhODc66Q5v6N9enkY80rPBGPZT7rAvkVsWKtsn8GftNg7JuW2YA57a1bIy+i36l/UOHLPMyw4pu1Ka+zfVw7rOKJqO6rWiw7i2+Vb0zb9LLZc/r1bIJuj5rsDb2bapnSh/VDQVX686cRxT0BN31f4/9otsKB2tW4r/lbtPTJY/ql9iPuiXnPmV7cjS2dLQGFPTWqNynFPAEXXj5fuQtPVB8hwsPq7o757Fqv99fdJv+S8zQXsH9tTCxQIMK++mctIt0QOhQNw4LgTM8mTo6tUuNY1nWmL+MfKYnSh7UjVnDtblvK30Z/cw9hi18rd1juqzrpnhS3eNlq0wDAACsCVqGAQDAarPA4t6iW6uddvnCc/Va2UsueHmuZLSruuq64CidvqCzq7pbVrXgB+F3qlXuXZh3euXvv0Z/Uq/8i3XagqPd7X1Y5bJVWXXXKfOPqPHLquFWxoULe8ivgDoF913u5e4rvk3b+nfQgSmHL3Xe1NgfGlPyhK7OuE5pnjT9Ev1BpclSnZV2gQu42vo31pEpx7sqsWWxYPLOomE6JqV62DQzPt1V1CUUd/vYI48br1fLrlJbWUtukwWXpckStVsUUtXEKu3eKpukvYP7L/Mytu2ZniydmnaWQp4UZXgzdVjKMfop+r07/6PIe65icjP/lvJ7/OqWeo7yEvP1XfRLd/7k8IcaUniDuqadscJtCCus01LPUmNvE3dbx6WerAXJeZoZLw/WHi65Ry+XvuAq+5bni8hkN64rM/rK5/FpdnyWdgt00sEpR7jfrUJyj+DeruKxJssb8/zEXHVJPVVb+Ld21YUdgnuorW8j/RL7YZW3FwAAYHVQIQgAAFbbQSmHu8qmc5OXKuAJuLbYGfFp2id4oD6KvKs3wxM1KOt2Nfe1cKFen4JLtVdwP20T2GGl72NefK5r7Tw37RJXgfZb7GfdXNhPjb1Nte0St9PU17xaxdvquC17lBr7mi63Cuvb6Jf6JvqFHsh5qsbzrYLvmNQTtJG/nfvdwruAAi5IqmAtuP8lpi/zPu4tvkWHpBypJt7m+jjyXuXpuwR302b+LXRV/vnuNiwQtJbWnCpVhKujpm36I/abUj1pbn//FftdTb3N1DXtTO0e3NOdb5WPtxUN0WUZvVwbc1TVK+4qpHpSl6qg/CzyUWXQaAFniiel8jzbJo/Hqxnx6eogucd5VM7TLuC7tWjQcrfDWo2XvB/bhpa+Vu73zimn6H/pF+v76DfLvI1YMubam09PO7dyv1qFZNUqSaty/DI6RfsHD67xNpY35kNTjq72+8z4DP0b/1vtfOX7Y1W2FwAAYHVQIQgAAFbbDv5d3FxqX0Q+db9bKNQpuI/SvRlqH9xDQ7PvdmFgXmKBq9yyYGZ+Yt4q3Ye1d27m29KFjxaobRPYXgeFDndViOuChYErM6/fcSknufnvlvRd9Gv9E5+qzildK0/b2m/BpUdjSh53barTYv+46sBIMlzj7b9R9ooKEwU6PuWUpc6zIMpaSwdn3a6xjV53c/bdWTTctRKviZq2KaaotvRvo7PTLtQjuWPVObWrhhfeqN9jv7rzHyi6XfuFDtLWge1W6b5svsBPIh+4ikljlXYvlT6vf2N/u/3zTOljCifLKvdPtjfHhWOr6pvIl7q/6Hb1TLvEtQuv7OP7XvhNF0oeFDqixvNtbskRhQPkl09Hp55Q42VWdsxz4rN0U0FvN0ehPbdX5boAAACriyMNAACw2qzd8cDQYS60s6qxD8Jv6/LM3pVVX48XP6CvolOU4811oV5SSbdQw6qYG5/l5oSzduEK8WTCVcktfdnZuiz/nBpv57rMIUtVFK4OW1Di19hPujbzxhrPf6NsoptjLsObUXma/XxD1hA9XHyPJpaN00a+Tdx+m1j2Yo23/2zpExqedbe8nqU/u7WwLN2Toe0CO7nf9wsd7Fqo3wm/prP9Fy537FUXzdg2sGNl1d6ytslu274q7BM60IVlVnU3Lf63m9/u0ozqi5IsjwVp9xWPdNWIg7JGahP/pu50a58tU6kGFPZyzxFbrGNjXzvXWry6JpaO05MlD+q8jMvdvl4Vb4Qn6oiU46pVdFaw/Te0sL9r/R6YNdJVP66uH6LfaHjhTa5q9tz0S1b7dgAAAFYVgSAAAFgjFrZcvPAsfR6dLL/Hpx39u7rTLYwpShbqodwxbjEEm++uW97iRTqqslVnrRqtQkEyv/LnRt6mah/oqL5ZAytPsypDXw2NDtYy/HSVVWPXhcnhD7RToL0LOWtqNf088okLiqqyqjcLuoZk31l52uPFo7S5f8ulbuOT8AeuOvCS/LMW3WbcrXRrgegd2Q+7QMrapavyeaxWbcWHdUsumrGibbLwz1Zg7hRaPJ9iRBEFFdT74bdcVWKPvOPc6WG3QEdSf8Z+1505Dy91H7ZgiS3CYWO9NfsB5VZpcZ6XmKsjQserR9q5lfMn2qIsFiKvKgui7ym+1S3ccVPWrctdDKUm9tyytvS+mYufbxVsoY+BBX1cRWPP9MvWqIrPqkBtTs3/pV+0VAsxAADAukbLMAAAWCPNfS21lX9bF24cEDrMVQ0aCwP9rqnS5xaVeLzkARUni6sFfxVsgYYpkU9cO6wtZPFW+NXK8/YNHajvYl+5hRYs7LH51vrmX6bXlrMgx7pk1Yrb+MtbO5c0Nf6nbLkPWxyjKquK7F9wtavks22w+eteD7+so5dYMMScnNZdzzWe5IJN+7o44xo3d5/9bIHnbsE99V7kTVddZiGr7bevIlO0d2j/tb5NRYlCPVB8p5sbMp6Mu5bwX6I/uqrBAVkj9GzjVyvHaW3c1vZaUxho1x1Y2Me1wt6cNbJaGGg+jLytYUX9XRBYnCjSg8V3uvkFbdGNVfV4ySi3GMkt2fetchhobPtaelsvNSfj3Pgc3VhwrY5K6awLM65aozDw88hkjSq+Q/2yBhEGAgCAWkGFIAAAWGM2v58twmGhUIVuaWe707ovOM7NHdg+2FG7BHbTv7GpUqj69U9P6+laSU/P66zm3pbudmzlWtPC10rXZw52Qc9dxcMUVIoOCB2ik1K718ojNzsxU3t696vxvDnxma7KbslW05AnpD6Zg/Rw8d26u2iEmvia67z0y7RrcHd3vlX9WTvvDVnDtF1gx+Xe/6EpR7mVf+8uukULk3lq4W2lXpk3LhVCro1tsvCrOFnkwjyr8Gvj20jXZw1xIfCK2KrONxX0clWJ1pJsVXdWWWjPhwo2r9/oRhN0XMrJmhOfrfMXdnOVlDsHOui6zMGV4fLKsnkHXy573s3/d9HC6qsID8q+baUCRtsXjbyNlzr9tfAEF3Jb5aJ9Vdgp0MFVr1o15X1FI1dqUZuxpU8prrgGF1xX7fTT0s5yqyIDAACsa56kfbQMAACwAnbIMHt4vhZOKKk3++rtstf0QunTujf3idoeCrDSLGgPKKgLM66sPC3nuDQ1vzZ7lUNUAADQMNEyDAAAAAAAADQgBIIAAGDlJOvnkcN/iek6Zf4R+ibyZW0PBViusmSpe67agi5Lsb9N+n4AAMBKomUYAACslGQsqflPFGneQ4XsMaCOaXJuphr3yJDHT8swAABYsXr4OT8AAFgnfFLmwSnuO4A69rd5EH+bAABg5VEhCAAAVloykVTxlLAWPFmksj+iSsbYeUBt8fillM0DatQjQ+m7h+TxUh0IAABWDoEgAABY5dZh2hKBuoO/SQAAsKoIBAEAANahuXPn6plnntHff/+t/fbbT0cccYSCwSD7vIpIJKJXX31VH3zwgdq1a6dTTz1VTZo0YR8BAACsIwSCAAAA60AikXBB4IgRI1y4NXToUHXo0IF9vRyff/65evfurfnz5+vaa691waDHQxssAADA2kYgCAAAsJb9999/6tevnz755BMXal1zzTVKT09nP6+E4uJiDR8+XGPGjNFee+2lm2++WS1btmTfAQAArEUEggAAAGtJMpnUiy++6EKsjIwM933vvfdm/66GDz/80IWqFhDa986dO1MtCAAAsJYQCAIAAKyluQKvv/56vfvuuy686tu3r7Kysti3a6CgoMCFquPHj9cBBxyggQMHqmnTpuxTAACANUQgCAAAsIZsQYwBAwbI5/Pppptu0sEHH8w+XYveeust3XDDDYrH4+rfv7+OPPJI9i8AAMAaIBAEAABYTQsWLHAB4KRJk3T44Ye7sKpRo0bsz3W0r2+88Ua9/vrrLhC0gDA3N5d9DQAAsBoIBAEAAFbD22+/7UKpWCxG1dp6nKOxohozEAi4FuIDDzxwfd09AABAvUEgCAAAsAoKCws1ePBgjRs3zs1rZxWCzZo1Yx+uR3PmzHHzNb733nvq0qWLm68xMzOTxwAAAGAlEQgCAACspI8//tiFTxYK2sq3FkZ5PB72Xy1VC1ooa4uO2OItFtLuueeePBYAAAArgUAQAABgBYqLizVixAg988wz6tSpkwufWrVqxX6rA2bMmOFC2k8//VSnnXaarr76aqWnp9f2sAAAAOo0AkEAAIDl+OKLL9S7d2/NmzdP11xzjU499VR5vV72WR2SSCRcWGuhbdOmTTVkyBB16NChtocFAABQZ3E0CwAAUIOysjINHTpU3bt3dyHThAkT1K1bN8LAOsgCWntsxo8fr8aNG7vHbNiwYQqHw7U9NAAAgDqJCkEAAIAlfPfdd+rVq5emT5+uyy+/XGeeeaZ8Ph/7aQMQj8f12GOP6bbbbtNGG23kQt0dd9yxtocFAABQp1AhCAAAsEgkEnFBUteuXZWamqoXX3xR55xzDmHgBsSCW3vM7LFLSUlxj+Udd9zhHlsAAACUo0IQAABA0i+//OKqAv/44w9deOGF6tmzpwKBAPtmAxaNRvXAAw/ovvvu0xZbbOHaiLfaaqvaHhYAAECto0IQAAA0aLFYTPfff79OPPFEtzjF2LFjddFFFxEG1gMW6F588cV67rnn3ON8wgknuIDQfgYAAGjIqBAEAAAN1p9//ulWEP7hhx/0v//9T5dccomCwWBtDwvrgLUM33XXXXrooYe0ww47uLkFN910U/Y1AABokKgQBAAADY5VAtrCE507d1ZBQYGeeeYZXXXVVYSB9ZgFvfYYP/3008rPz9fxxx+vxx9/3D0XAAAAGhoqBAEAQIMybdo09enTR59//rlOP/10XXnllW4BETQcpaWluvXWW/Xkk09q99131+DBg9W2bdvaHhYAAMB6QyAIAAAahGQyqWeffdYtLJGbm6shQ4aoY8eOtT0s1KJPP/1Uffv2VV5enmsdP/nkk+XxeHhMAABAvUcgCAAA6r1Zs2apX79++uijj3TKKafo2muvVUZGRm0PC3VAUVGRC4lt4ZG9995bN998s1q0aFHbwwIAAFinCAQBAEC9rgqcMGGCBg0a5NqCLezZd999a3tYqIPef/99FxqXlZXpuuuu03HHHUe1IAAAqLcIBAEAQL00b9483XDDDXr77bd17LHHupAnOzu7toeFOmzhwoUuPH755Zd18MEHa8CAAWrSpEltDwsAAGCtIxAEAAD1zmuvvab+/fvL6/Xqpptu0iGHHFLbQ8IG5PXXX3fPH3PjjTfq8MMPr+0hAQAArFUEggAAoF5VeA0cOFATJ07UoYce6iq8GjVqVNvDwgZo/vz5LhR88803dfTRR+v6669XTk5ObQ8LAABgrSAQBAAA9cJ7773n2oLD4bBrFbYQhxVjsaZzUFr7sIXMoVDItRPvv//+7FQAALDBIxAEAAAb/CqxgwcP1gsvvOAWDLHQpnnz5rU9LNQjs2fPdguOfPjhhzrxxBPVp08fVqkGAAAbNAJBAACwwZo8ebL69u3rWoXtu4U1VAViXVULjh07VkOGDHGtwxZCd+rUiZ0NAAA2SASCAABgg1NSUqJbb71Vo0ePVseOHV0406ZNm9oeFhqA6dOnuwrBKVOmqHv37rrqqquUlpZW28MCAABYJQSCAABgg/LVV1+pd+/ero3z6quvVrdu3dxqwsD6kkgkXBh9yy23qEWLFho6dKh23XVXHgAAALDB4OgZAABsEGyxkOHDh+u0005Tbm6uxo8frx49ehAGYr2zAPr00093z0F7LlooPWLECPccBQAA2BBQIQgAAOq8H374Qb169dI///yjyy67TGeffbZ8Pl9tDwtQLBbTI488ojvvvFObbLKJqxbcfvvt2TMAAKBOo0IQAADUWdFoVHfddZdOPvlkBYNBjRs3Tueeey5hIOoMv9+vnj17ulWuA4GATjnlFPectecuAABAXUWFIAAAqJN+++03VxX466+/6oILLtD555/vAhegropEIrr//vvd19Zbb+2qBbfccsvaHhYAAMBSqBAEAAB1Sjwe16hRo9SlSxcXsDz33HO65JJLCANR51kV66WXXqpnn33WzSdoz+EHH3zQPacBAADqEioEAQBAnTF16lT16dNH33zzjc455xwXroRCodoeFrDKLBC844473PyCO++8s6sWtDkGAQAA6gICQQAAUOsSiYRGjx6tW2+9Vc2bN9eQIUPUvn372h4WsMa+/PJL9e7dW3PmzNHVV1/tViS2VYoBAABqE4EgAACoVdOnT3dVgVOmTFH37t111VVXKS0tjUcF9UZJSYluueUWPfXUU+rYsaMLvFu3bl3bwwIAAA0YgSAAAKgVyWRSzz//vAYPHqycnBz3vVOnTjwaqLcmT57swu+CggL3/cQTT5TH46ntYQEAgAaIQBAAAKx3s2fP1nXXXacPPvjAhSIWjmRkZPBIoN4rLCx0FYIvvPCC9ttvPw0cONC1yQMAAKxPBIIAAGC9VgW+/PLLLgSxxUIGDRqk/fffn0cADc67776r66+/3q2kbd+PPvpoqgUBAMB6QyAIAADWi/nz5+vGG2/UG2+84cIPqxDMzc1l76PBysvLc+H4K6+8osMOO8z9fTRq1Ki2hwUAABoAAkEAALDOWQjYv39/t5qwhR5HHHEEex1YZNKkSe7vwlYftoDw4IMPZt8AAIB1ikAQAACsM/n5+S7gsDbhgw46SDfddJOaNGnCHgeWMG/ePNc6/M477+i4445Tv379lJ2dzX4CAADrBIEgAABYJ95//33XFlxaWuq+W8jBiqrA8ufYnDBhgptbMy0tTTfffLP22WcfdhkAAFjrCAQBAMBaVVRUpGHDhum5557T3nvv7UKNFi1asJeBlTRz5kxXIfjxxx/rlFNO0bXXXssq3AAAYK0iEAQAAGvNZ599pj59+rjFEnr16uXCDKoCgdWrFnz22WdduG6L7wwdOlS77747uxIAAKwV3rVzMwAAoCGztmBrczz99NPVqlUrvfTSS+ratSthILCaLEi3vyH7W2rZsqV69OihwYMHq6ysjH0KAADWGBWCAABgjXz99dfq3bu3a3O88sorXShoq6UCWDtsde4nnnhCt956q1q3bu2qBnfaaSd2LwAAWG0crQMAgNUSiURcQHHaaacpKytLL774os4880zCQGAts4Dd/rbGjx/v5hK0ysGRI0e6v0EAAIDVQYUgAABYZT/99JObI3Dq1Km65JJLdM4558jv97MngXUsFovpoYce0t1336127dpp+PDh2mabbdjvAABglVAhCAAAVlo0GnVBxEknneSqlp5//nmdd955hIHAemLB+/nnn+/+9myewRNPPFH33HOP+9sEAABYWVQIAgCAlfLHH3/o2muv1S+//KKePXvqwgsvVDAYZO8BtcRahi0MHDVqlLbddls3t+Dmm2/O4wEAAFaICkEAALBc8XhcDz/8sDp37uxWEx4zZowuv/xywkCgllkgf8UVV7i/yeLiYvc3+sgjj7i/WQAAgOWhQhAAACzTP//841YQtpWEzzrrLF122WVKSUlhjwF1TFlZmW677TY9/vjj2mWXXTR06FBtvPHGtT0sAABQRxEIAgCApSQSCT3zzDMaMWKEmjRp4sKFDh06sKeAOu7zzz93If78+fN1zTXX6NRTT2XlbwAAsBQCQQAAUM1///2nvn37avLkyS5MsFAhPT2dvQRsIKx92FYftlbiPffcUzfffLNatWpV28MCAAB1CIEgAABwksmkxo0bp8GDBysjI8N932uvvdg7wAbqww8/VL9+/VxAaN9tjkFbmRgAAIBAEAAAaM6cObrhhhv07rvvqkuXLurTp4+ysrLYM8AGrqCgwFUIjh8/XgcccIAGDhyopk2b1vawAABALSMQBACggXv11Vc1YMAA+f1+3XTTTTrooINqe0gA1rK33nrLhf62AnH//v115JFHso8BAGjACAQBAGigFixY4ILA1157TUcccYQLCxo1alTbwwKwDv/mb7zxRr3++uv8zQMA0MARCAIA0AC9/fbbuv7666kWAhrgXKEVVcGBQICqYAAAGigCQQAAGhDmEwNQMW+ofSjw3nvvuXlDbWXxzMxMdg4AAA0EgSAAAA3ERx995N70s+IogKori9uiIxYGsrI4AAANB4EgAAD1nAWAw4cP15gxY7Tnnnu6N/+tWrWq7WEBqCNmzJjhPiz49NNPdeqpp+qaa65Renp6bQ8LAACsQwSCAADUY59//rl69+6t+fPnuzf59mbf6/XW9rAA1DGJRELPPPOMRowYoSZNmmjo0KHq0KFDbQ8LAACsI7wjAACgHiorK9OQIUPUo0cPNW/eXBMmTFC3bt0IAwHUyD4osNeI8ePHu0Cwe/fuGjZsmHstAQAA9Q8VggAA1DPfffedrr32WtcGeMUVV+iMM86Qz+er7WEB2EDE43E99thjuu2229S2bVsXDO644461PSwAALAWUSEIAEA9EYlE3Bv4U045xc3/9eKLL+rss88mDASwSuwDhHPOOce9hqSmpqpr1666/fbb3WsMAACoH6gQBACgHvjll19cVeCff/6pCy+8UD179lQgEKjtYQHYwEWjUT3wwAO67777tPnmm7tqwa233rq2hwUAANYQFYIAAGzAYrGYe6N+4oknKplMauzYsbrooosIAwGsFfbBwsUXX6znnnvOtRLba40FhPbaAwAANlxUCAIAsIGyasBevXrpxx9/1LnnnuvetAeDwdoeFoB6ylqG77rrLj300EPafvvt3UrEm222WW0PCwAArAYqBAEA2MAkEgk34X/nzp1VWFioZ555RldeeSVhIIB1yj5wuOqqq/T000+roKDAvQbZa5G9JgEAgA0LFYIAgAbrhhtu0Msvv+x+tvY3myvLJtCv8PXXX6uumTZtmvr06aPPP/9cp59+ugsCq44ZANaH0tJS3XrrrXryySe1++67a/DgwW5F4rVh3Lhx6tu3b42vbe+8845yc3OXe/0DDzzQBZdHHXXUWhkPAAD1EYEgAACSXnnlFffm1t5s1kU2P+CYMWM0fPhw92Z4yJAh6tixY20PC0AD9+mnn7rwLi8vT71799bJJ58sj8ezxoHgqFGj9Nprr63W9QkEAQBYMVqGAQBYzpvSU045Rd27d3cVMJMnT3ZvNC08rHqZww8/vPL3b775Rl27dlWHDh1cdcqrr766xvt35syZ+t///qcbb7xRRx99tF566SXCQAB1wh577OFek+y1yaqu7bVq1qxZ6/wDEltMyV5jd911V3Xq1Ek333xzjZe112B7jbbX5GOOOUYvvvhitXlYzznnHPf6fsghh+iJJ55Yp+MGAKAuIRAEAGA5LOCz1tz33nvPvaFcHnsTfPbZZ7sKGauaGThwoAYMGKAvvvhitd/0jh8/3r2J/e233/Tggw+628zIyOAxA1Bn2GuSvTZZVZ+9Vlk4aK9d9hq2LkyaNMmtqG6rHX/11Ve6//773byGX3755VJtzddee62rrLbXYatgtNdkq2YsLi52r9ft27fXRx995AJGCwQnTJiwTsYMAEBdQyAIAMBypKWl6dBDD3XfA4HAcveVVclst9126tKli/x+v6tcsZ+t1XdVzZs3TxdddJFbRfiAAw7QxIkTte+++/JYAaiz9ttvP/daZa9Z9tplK5/ba9nq+Oeff9yHMFW/KuZ8tddCW0ypTZs2mjt3rgv+0tPTNXv27KVuJxQK6bnnnnPzrloloAWINu3C+++/716nL7zwQrdYyuabb64zzzxztV6vAQDYEPlrewAAANRlzZo1W+nL/vfff24hkqqVhPF43IWEq1r9Yu3BXq9Xd999t2tlA4ANQXZ2tkaMGOFet/r37++qBe31rOrUCitj4403XuYcgraqsd3Hhx9+qCZNmmjbbbd11YhLViTaoiRPPfWU7r33XhdO2sJRJ5xwgq655hrNmDHDVXVXfb22283JyVnNLQcAYMNCIAgAwHIsOTm+hXT2prKCtZ5VaN68uauQueeeeypPs4oVn8+3UvvYbsva7myOwsMOO8y9iW7UqBGPD4ANjlVWWzuuvY5ddtllLhi8/vrr10rgZgtA5efn691333XV2xYE7rbbbktdrrCwUAsXLtSdd97pwj6bAsKCwS233NK9Xm+xxRautbnCggULFA6H13h8AABsCGgZBgBgFbRr186tRByJRDRt2jQ9//zzlefZG15beOTNN990bz6t5a1bt24r1YJmb2xtrkCby8re7N5xxx2EgQA2aI0bN3ZhnFXzffDBB+410uZjXVMFBQVuCgdr+bW5AO32Lfyr+mGNKSkpcYucvPXWW+7DHav4tu8WSu6///6u3dgqCO169vN5553nxgsAQENAIAgAwCq4+uqrNWfOHLeq5SWXXOLmCKzQtm1bN8n9Qw895OaqsjDQKv0uuOCCZd6evYnt27evzj//fNf2ZnNk2ZvmJSsTAWBDZK9lxx57rJtbcJtttnGhm73mFRUVrfZtWsWhVfN17NjRvcZaFeDee+/tFjSpyqoAR44cqdtuu83N6Xrqqae6L2tnzsrK0iOPPOLCQruujXGrrbZyVYwAADQEnuS6Wv4LAAAsl1UT9unTx1W72PcTTzyRIBBAvWVvO6yqevDgwa5Kz77bhysAAGD9IxAEAGA9sza2W265xbWqWYXLkCFD1Lp1ax4HAA3C9OnT3YcgU6ZMUffu3XXVVVe5uQABAMD6QyAIAMB69OWXX6p3796u7dhWujzttNPcQiUA0JDYPKujR492c6Zaa+/QoUNdWy8AAFg/eAcCAMB6YCtXDhs2zM0raBPtT5gwwVXGEAYCaIjste/00093q/zm5ua6D0eGDx/OKr8AAKwnVAgCALCOff/99+rVq5f+/fdfNxn+2WefLZ/Px34HAEnxeFwPP/ywW+F34403dh+ebL/99uwbAADWISoEAQBYRyKRiO644w6dcsopSklJ0bhx43TuuecSBgJAFfYBSc+ePfXCCy8oGAzq5JNP1l133aVoNMp+AgBgHaFCEACAdeDXX391VYG///67LrjgAp133nkKBALsawBYwQcp999/v/vaaqutXLXglltuyT4DAGAto0IQAIC1KBaLadSoUTrhhBPcz88++6wuvvhiwkAAWAlWIXjppZe6104LB7t06eJeU62tGAAArD1UCAIAsJZMnTrVrSD87bff6pxzznFvakOhEPsXAFZzMSabduGRRx7RzjvvrCFDhqhdu3bsSwAA1gICQQAA1lAikdCTTz6pkSNHqnnz5ho6dKh23XVX9isArAVffvml+vTpo9mzZ+vqq692q7WzQjsAAGuGQBAAgDUwbdo09e3bV1OmTFGPHj105ZVXKi0tjX0KAGtRSUmJbr31Vo0ePVodO3bU4MGD1aZNG/YxAACriUAQAIDVkEwmNXbsWNfClpOT496cdurUiX0JAOvQ5MmT3YcwCxcudN9PPPFEeTwe9jkAAKuIQBAAgFVkbWv9+vXThx9+6N6MWitbRkYG+xEA1oOioiL3Yczzzz+vfffdV4MGDXLTNQAAgJVHIAgAwCpUBb700kvuzactFmLf999/f/YfANSC9957T9ddd51bjfj666/X0UcfTbUgAAAriUAQAICVMH/+fPXv319vvvmme9Npbz6tVRgAUHvy8vLchzMTJ07UYYcdphtvvFGNGjXiIQEAYAUIBAEAWIE33nhDN9xwg/vZ3mwefvjh7DMAqEMmTZrkXp9t9eGbbrpJhxxySG0PCQCAOo1AEACAZcjPz9fAgQP18ssv6+CDD9aAAQPUpEkT9hcA1EHz5s1zH968/fbbOvbYY107cXZ2dm0PCwCAOolAEACAGrz//vvuzWRpaalrD7Y3l6xkCQB1f67XCRMmuDbi1NRU3XzzzW7hEQAAUB2BIAAAS6xeOXToUI0dO1Z77723ezPZokUL9hEAbEBmzZrlVoP/6KOPdPLJJ6tXr16sBg8AQBUEggAALPLpp5+qb9++bpL6Pn366KSTTqIqEAA24GrBZ599VsOGDVNubq6GDBmijh071vawAACoE7y1PQAAAGqbtQVbe9kZZ5yh1q1b66WXXnIVJbQIA8CGy17Du3bt6l7TW7VqpdNPP9291ttrPgAADR0VggCABu3rr79W7969NXPmTF111VXq0aOHW6USAFB/JBIJPfHEExo5cqRatmzppobYZZddantYAADUGt7xAAAapEgkoltuuUWnnXaaW4Vy/PjxrkKQMBAA6h97bT/zzDP14osvKisry73233rrre5/AQAADREVggCABufHH390VYFTp07VpZdeqrPPPlt+v7+2hwUAWA9isZgeeugh3X333WrXrp2bY3Dbbbdl3wMAGhQqBAEADUY0GnVvAG1+QJ/Pp+eff149e/YkDASABsQ+ADr//PPd/wCrHLQFpOx/g/2PAACgoaBCEADQIPz+++/q1auXfvnlF5133nm64IILFAwGa3tYAIBaZC3D9957r0aNGqWtt95aw4cP1+abb85jAgCo96gQBADUa/F43LWGde7cWWVlZRozZowuu+wywkAAgPtfcPnll7v/Dbb6sP2vePjhh93/DgAA6jMqBAEA9dbff//t5gr85ptvdNZZZ7k3faFQqLaHBQCog+xDozvuuEOPPvqoW4HYViLeeOONa3tYAACsEwSCAIB6J5FI6Omnn9aIESPUtGlT96auQ4cOtT0sAMAG4IsvvnAfJs2bN0/XXHONTj31VFagBwDUOwSCAIB6ZcaMGerbt68+/fRTnXbaabr66quVnp5e28MCAGxAiouL3YdKzzzzjDp16qTBgwerVatWtT0sAADWGgJBAEC9kEwm9cILL7g3bZmZme77XnvtVdvDAgBswD7++GP3IVNRUZH73qVLF3k8ntoeFgAAa4xAEACwwZs9e7auv/56vf/+++7Nmr1ps1AQAIA1VVBQoCFDhmjcuHE64IADdNNNN6lZs2bsWADABo1AEACwQVcFvvLKK+7NWSAQ0MCBA3XggQfW9rAAAPXQ22+/rRtuuEGxWEz9+/fXkUceWdtDAgBgtREIAgA2SAsWLNCNN96o119/3b0pszdpubm5tT0sAEA9/99jH0JNmjRJhx9+uAsGGzVqVNvDAgBglREIAgA2OG+99ZYLAOPxOFUaAID17tVXX9WAAQPk8/lcdfpBBx3EowAA2KAQCAIANqh5nAYNGqQJEya4eZzsTVjTpk1re1gAgAZo7ty5bv7ad999V507d3bz12ZlZdX2sAAAWCkEggCADcKHH36ofv36qbi4WNddd52OP/54VnoEANT6XLYvvviibr75ZmVkZLjve++9N48KAKDOIxAEANRpRUVFGj58uJ599lnttdde7s1Wy5Yta3tYAABU+u+//9yHVp988om6du2qa6+9Vunp6ewhAECdRSAIAKizpkyZoj59+rhJ3O3Nlb3J8ng8tT0sAACWkkgk9Mwzz2jEiBFq3Lixhg4dqt122409BQCok7y1PQAAAJZUVlamwYMH6/TTT1eLFi3cnIGnnnoqYSAAoM7yer3q1q2b+5/VvHlz9ejRQ0OGDHH/0wAAqGuoEAQA1CnffvutevXqpRkzZujKK690oaCt4ggAwIYiHo/r8ccf12233abWrVu7qS923HHH2h4WAACVqBAEANQJkUjEvXGytmCbmH38+PE666yzCAMBABsc+yDr7LPPdguO2FyCp5xyivsfZ//rAACoC6gQBADUul9++cXNEfjXX3/poosu0rnnniu/31/bwwIAYI1Fo1E9+OCDuueee7TZZpu5asGtt96aPQsAqFVUCAIAak0sFtN9992nE0880f0+duxYXXDBBYSBAIB6IxAI6MILL9Tzzz+vZDLp/ufZ/z77HwgAQG2hQhAAUCv+/PNPN1fgjz/+qJ49e7rKwGAwyKMBAKi3rGXYKgVHjRql7bbbTsOGDXNVgwAArG9UCAIA1vtE648++qiOP/54FRUVacyYMbriiisIAwEA9Z598GX/8+x/X2FhoTp37qzHHntMiUSitocGAGhgqBAEAKw3//77r/r06aMvv/xSZ5xxhntTlJKSwiMAAGhwSktL3UIjthrxbrvtpiFDhqht27a1PSwAQANBIAgAWOdsziSrhrCJ1Bs1auTe9Oy+++7seQBAg/fZZ5+5D8vy8vLcAltdu3aVx+Np8PsFALBuEQgCANapmTNnql+/fvr44491yimnuDc7GRkZ7HUAABaxKTTsQ7Nnn31We++9twYNGqSWLVuyfwAA6wyBIABgnVUFjh8/3r2pSU9P180336x99tmHvQ0AwDJ88MEHuu6661RSUuK+H3fccVQLAgDWCQJBAMBaN3fuXN1www1655133OIhViGYlZXFngYAYAXy8/Pdh2gTJkzQQQcdpJtuuklNmjRhvwEA1ioCQQDAWvXqq69qwIAB8vl87k3MwQcfzB4GAGAVvfnmm+7DNVuB+MYbb9QRRxzBPgQArDUEggCAtcImQ7cA0ALBww47zL15sQVEAADA6lmwYIH7f/r666/rqKOO0vXXX6/c3Fx2JwBgjREIAgDWmLUG25uUaDTqqhnsTQsrJAIAsHbm5H3llVfch27BYFADBw7UAQccwK4FAKwRAkEAwGorLCzU4MGDNW7cOO2///7uzUrz5s3ZowAArGWzZ892H769//77OuGEE9SnTx9lZmaynwEAq4VAEACwWj755BP17dtXBQUF7ru9OaEqEACAdVst+Pzzz2vIkCFusS773qlTJ3Y5AGCVEQgCAFZJcXGxbrnlFj399NPaY489XIVg69at2YsAAKwnM2bMcBWCn332mbp166arr75aaWlp7H8AwEojEAQArLQvvvjCvQGZO3eurrnmGp166qnyer3sQQAA1jNbfdg+nBsxYoSaNWvmqgU7dOjA4wAAWCm8iwMArFA4HNawYcPUvXt3NW7cWOPHj3cVCYSBAADUDvsfbP+XJ0yY4P4328/2v9r+ZwMAsCJUCAIAluu7775T79699e+//+qKK67QmWeeKZ/Px14DAKCOiMfjeuSRR3THHXdoo402csHgDjvsUNvDAgDUYVQIAgBqFIlE3BuLrl27KiUlRS+++KLOOeccwkAAAOoY+6Du3HPPdf+r7X/2Kaec4v6H2/9yAABqQoUgAGApv/76q3r16qXff/9dF1xwgc477zwFAgH2FAAAdVw0GtUDDzyg++67T1tssYWrFtxqq61qe1gAgDqGCkEAQKVYLObeRJxwwgnu5+eee04XX3wxYSAAABsI+wDP/nfb/3D7X27/00eNGuV+BgCgAhWCAADnr7/+cnMFfv/99/rf//6nSy65RMFgkL0DAMAGylqG77zzTj388MPacccdNXToULVr1662hwUAqAMIBAGggUskEnryySd16623qmXLlu7Nwi677FLbwwIAAGvJV1995T70mz17tq688kr16NHDrVIMAGi4CAQBoAGbNm2a+vbtqylTprg3B1dddZVSU1Nre1gAAGAtKykp0ciRI92HgLvvvrsGDx6stm3bsp8BoIEiEASABiiZTLq5hawaMDc3170p2GOPPWp7WAAAYB2bPHmy+vXrp7y8PPXp00cnnXSSPB4P+x0AGhgCQQBoYKxdyKoCP/roI5188sluNeGMjIzaHhYAAFhPioqK3IeCY8eO1T777KObb75ZzZs3Z/8DQANCIAgADagq8KWXXtKgQYMUCoXcwf9+++1X28MCAAC15P3333fVguFwWNddd52OPfZYqgUBoIEgEASABmD+/Pnq37+/3nzzTR1zzDHuoD8nJ6e2hwUAAGrZwoULNXDgQE2cOFGHHHKIBgwYoMaNG9f2sAAA6xiBIADUc6+//roLA40d5B922GG1PSQAAFCHjxduuukmHXroobU9JADAOkQgCAD1FJ/4AwCAVe0ouOGGG/TWW2+5joLrr79e2dnZ7EQAqIcIBAGgns8JZAfzdlDPCoIAAGBl5xy2NuLU1FQ39zBzDgNA/UMgCAD1CKsGAgCAtWHWrFnuw8WPPvpIJ510knr37q2MjAx2LgDUE97aHgAAYNXl5eVpzJgx1U6bPHmyqwR85ZVX3Kf6Dz74oJo3b87uBQAAq6xFixZ66KGH3DGFHVvYCsSffvpptcs888wz7pgEALDhIRAEgA3QsGHDdNddd7mfS0pK3MH6mWeeqTZt2ujll1/WySefTIswAABYIzbdiB1TWAtx69atdcYZZ7gW4tLSUne+HYsMHz6cvQwAGyBahgFgA/Pjjz/qhBNOcJN+b7311q6FZ/bs2brqqqvUvXt3eb181gMAANauRCKhJ598UrfeeqtatmypIUOG6Oeff3YfSo4bN07bbrstuxwANiC8awSADWyi76FDh6pdu3aaNm2aunXrptzcXI0fP16nn346YSAAAFgn7ANHqxC0Y46cnBx3DDJjxgx3TGLHJnaMAgDYcFAhCAAbkLffflsXXnihWrVqpblz5+rSSy91rcL2qX1KSkptDw8AANRzZWVlrpX48ccf15133qmmTZvqv//+03333acDDzywtocHAFhJBIIAsIGIRCLaZ599tHDhQlcVuMkmm2jBggXuIDwYDOrzzz+Xz+er7WECAIB6Kh6Pa7fddnPHJPbhZKNGjTR16lR3bGJVgx9++KE7JgEA1H3+2h4AAGDlfP/99+6AOxAIqFmzZmrSpIl22WUXt5DIDjvsQBgIAADWKfvg8bHHHnPHJNOnT3fTl9jiZsXFxe4YxU5v3749jwIAbACoEASADaxNJxQKsYIwAACoM2z+wHA4zPQlALABIRAEAAAAAAAAGhBWGQbWUCwZYx8CdRh/owCAJSUTcXYKUI/xNw6sGHMIAmsgkUzo+/DXeqroUf0e/UWxZJT9CdQRfk9AWwS2VreMs7RTqL28Hj4DAwCUK/nmFRV//rxi8/+VCAeB+sPrk7/xRkrf7USl73psbY8GqNNoGQbWwLz4HJ01+yRFRRAI1FUBBfRo87Fq4mtW20MBANSyZCKm8F9faMHTV9b2UACsY41OG6nQph3k8VIHBdSEcglgNVk14Eel7xEGAnWcBfb2t0oFLwDAgoHSH99mRwANgP2tEwYCy0ZUDqw2j/IS89l/kuILE1rwQIlKp0SUKEnKm+lV2p4B5Z6TJl+mV7HZcU0/c6HaPp0rX27d/xxi/t3FSkaSanJlRuVpsfkJzbulSGXfx+RNkbJOSFXOqamV5xdMLFP+U6VuX4S29qvJVRkKtPGp7LuoZvUqqHb7btpJj9TujcbVTi/9JqpZVxWozVM5CrTwlZ/2bVR5D5Qo8m9c3nSPMo8MKef01BpXGS77IaqZlxXIE1x8Wvp+ITXtvXg7Gqryv9Wl9xkAoOFJFHP8tjoWliX0wNclmvJfRCXRpDJDXu3ZOqBzdkpzP88ujuvMiQv19HG5yk2pu8d7PV7K09yShKoeSk04sZGCPo8WlCZ05xfF+mJmVH6vtP9GQV3cIV1+b/mFn/y+RON/K1NpLKldWwR0VceMym1d3nXjiaQe/KZEb04NK5KQtsj16ZIO6WqXU/Pb8Vs+K9Ibf4Xlq7IbB+2bqfYtqxzkYYX4WweWj0AQWAMJJdl/kubcVChfE69aP5IjX7ZX0VlxzRtepDk3FanliCz5m/u0yaTq4de6EP4jpsIJZco5I03+Jqt+IBovSmjBvSUqmhRW5jGhaufN6V+o4CY+bTQuV7Hpcc3qXSh/M68yDgqp9IuI8kaVqMWILAXb+ZT3WIlmX1eo1o9kK2XHQLVtj+cnNOO8fOWeuThMdKcXJDR3aJE9qRaftiCh2f0K1fiiNGUcGlLsv4QLF72ZHmV3SV16+3+LKbVDQC2GZa3yttd3/K0CAColOX5bHTd9VKgmqV49clSOslO8mlUU1/BPi3TTR0UacVCWmqf7NOmUdX+890deTBN+K9MZO6SpSdqqHe8VRxOaUZjQ811y1Sh16ete/0GhNsn26YUuuSqKJnXtOwV67ucynbZdqib+UaZJf4V156HZapTi1cgpRRr8SZFGHJi1wuu+9HtYX86K6qGjcpQZ9Lhw0Pbbo0fn1DjO3xbE1LtThg7cpPrxKFYRf+vAchEIAlhj4Z9ianpdpgsDjVW3Nb4kXfkvlCmZTCo2O6Hppy5U2xdyVfR6WAufKFl85YSUjEgtbslSavuASj6NKO+REkX/SyjQyqtG56crddfAMu87GU2q+L2ICiaUKTo9rozDQvJmeFT0ZljzRhbVeJ1lhZPTeyxU+r5Bpe1b/dNXq86zbWx+c6a8QY+Cm/qV1SVFha+UuUCwcFJYGYeEFNqq/CXVKiMLXspz1YSpO1Uf+7zbipWyg1+Zh6dUP314kbuN/NGlladZsJrWKaDMI8ovG2jrU9reQYW/i0ldlh5/5Nd45RgAAADWpp/mxXTdXpkuDDQtMsqr3F74pfx4b3ZxQqdOWOgCsdf/CuuJHxYf7yWSUiQu3XJQltq3COjTGRE98m2J/itKqFWmV+fvku4q7pYlGk/qvX8jLgicXhjXYZuGlBH0uIo7C+ZqUlM4+dv8uAsRawoDf54X1T/5cd12cJarFgz5PRqyf6Ybu7u9P8PqvGWKWmeWd3Fc2D5dXV7Ic5WRVh24vOtOK4y7n5OLvqzgMOhb9rb+vTCurRpzTAdg3eJVBsAaSz84pHkjilT2dUgpO/sV2jagYDu/ml69dKuqtdlWtNom40lXAecJeZSyq99VuM0ZUKhmAzJdOFg6JarZ1xeq9ahsBVpXP2qKFyaUP6bUhXHBtj5lHZ/iwjxPsLylw8I1+1oVdj/+pr7ySr0qov/G5WvsqQw8TXBjn/Kfi5ef/09cKbssPoj1+DwKtPYq+ne8WiBY+mVUpV9E1fap6p8GF4wvc23EWZ1TqgWCKdsG3FfV8NP2ScbBNbeL2P6LzfVoWrc81/KcuntQjc4vb9sGAABYEwdvEtKIT4v09eyQdm7u17ZNAq7l9eo9lj7eO3W7VPdlrF223/uFCvk82rW531W/DfiwUAOsBbZFQFP+i+r69ws16sjsyrCtQmE4oTE/lbrKvLZZPh2/ZYr23SjoQjdzSLuQ+1pZdt/Wznvx6/kupNsoy6eeO6dph2YB/bog7ir8Rv9Qqtf+CruJRg5tF9KZO5ZvhwV+7XIWj89ahbNCHhfezSxOLPe6x2we0kfTIjphXJ4LA7NDHt1+cHaNY/xrYVzxpHT/V8X6YW7MBbAnb5OiIzer/mEyAKwpAkEAa6zJlekq3img4vfCmjcsrERxUsHNfS6MSm2/7LlO5t9e7Obca3l7tpsTzyru0vcPKW338uukdQq6FtjC18JqdE5atetaCJf/dJnSDwyq0QXpq9UivCQLA2uSLE3Kk1J9/jkLMe10kyhNyrvk+SkeJcuqtyRZZWT2SSnVgsXI1JgLNlvdl73croZEJKm5NxXK4yufv3CpMSaS8jX2Kq1jQJlHpShRbC3IxZo3tEjNb6aFGAAArJkrO6Zrp+YBvfdPWMP+Cqs4mtTmuT6dv0vacue2u/3zYjf/4O2HlB/vvfJHmfbfOKTdW5Vfp1OboDq0DOi1P8M6Z+fqx3v/FMT19E9lOnDjoC7YNX2VW4SXZPMGbtXIr567pKlxqlcv/V6m3u8W6pGjs134+PP8mHZs5teTx+RoTklCfd8rUGrA49p+bd7AFH/1470Un0dl8eQKrxtLyIWfp++QqpwUrx76pkT93i/Qw0flVIabFazdeKdmfp2ybar6N/bruzkxF5hmBb3auy1zCAJYewgEAawxj9dTWZFnLSNWGWdVbzbPXpsncmpcy2HhU6UqmRJ1QVhFmGatxWVfR1XyYaTyclZF6A0tffCTsn1ArR/OVsG4Ms04c6FSdg0o69iQUtoH3MFm0VthFzjWZOOJjVZt+1KkZFn105LhpLxp5eO28dvv1c4vS8qT6qnWdlz2U0zNbsysPC0RTmrOwCI1viLdLbYSW1BlAsEqbFGW2f0LXfVji5FZ8la53coxej1qecvi4M+b5lOjnmn678J8FyZaqzMAAMDq8no8lRV5drz3d37cLbDR+71CPXFMTo1Ldz31Q6mrALzv8OzKMM1ai7+eHdWH0xYf78WTSYX8Sx/vbd80oIePzNa438rcgiXWVnzsFiEXrtnx3ltTwy5wrMnEk5c+3jt5m+ofqp64dape+SPsFgKxYM7aeP+3c5rbVqtI7LJVimt/tlAv1e9ROFb9eM/CQDt9Rdcd8kmRzt4pTS0zFrUb75qmN6aW3++ebapvt21b+xaLqwdtmw/dNKj3/w0TCAJYqwgEAayRkikRzRlQpI2ey5E33esOzqxduMkVGW5uPwsHA+2qV94VvR3WwmdK1eqOLPkbLf6k1xYmyTw2RY0vSq82j15F8LYkm8uvydUZyj0/oaJXw5q3KABsdU+2Mg4Oua+1IbiJ3y3wYYuO+DLKxxtx21X+EhrYxKfIP+XtwxUhZnRGwi0wUrmfPgi7NuiqqyxHfo0pNiOuuQPLW5QrDjFnnJOvJleku/GHf4lpVp8Cpe8dVOPL0uVZ4pPpqqsg5z9Xqtyz0+QNlV/G2oatotC+AAAAVpetLDzgwyI91yVH6YHy4z1rF75i9ww3t5+1zVZtpzVv/x3WMz+V6o5DsqrN2WcLkxy7RYouar/4eM8WKEkL1HyMs2muX1d3zND5uyT06p+LA8B7Ds3Wwe1C7mtlvfBLqTbN8WuXKvMVRhNJF+htlO113RpWzVcxv5+17lYcn1lLsFUstm9Z/nteWUIF4aTbbne55VzXKgZjFRMKunC1/Mval5c0eUbEzUl41OaLW4Rt/sUlKwkBYE0xsRSANWKr6PqyPJo7rNgt6lGxkm7+s6WSTwrtUP1zh9Jvopp3S5GaXZ+h4GbVz8s8LOQWHSn7Luo+eQ7/GtN/5+Wr5JPFnyDXxEK67JNT1ebJHLeYid3v2mSLeYS28bsViBNlSdfmW/BimRuvyTg8pKLXwir7KepCuLyHS+Rv7FVo28XbV/ZjTCnbV99etwLx641dxaJ9uWpKm8vw4fJAMzYnrlnXFri5BZtclbHMMNDtg0yPit8OuwVZbK7B2Ny4FowqcYus2JyGAAAAq2vHZgE3X96wycWaXlB+vJcfTujZn0plhxk7NKt+jPPN7Khu+axI1++Voc1yq59nC4JY5dx3c8qP936dH9N5k/L1yYzlH+9lBG0uvVTXkntJ+3T5VuOdrFUn3vlFsVsIxBbvGP1DiUqiSXVqbVV5ATcv4ANflygST2pGYVwv/lqmgxat9GvjHvtzmf7Nj7v24Xu/LHbXaZrmW+F17fYf/75Ucxbd76PflSrV59EOTZdeSMWCxbu/LHb7J5FMujD2nb/DOmIzVhwGsHZRIQhgjVi7bMs7s5T3WKlmXlWgREHCtbam7BxwcwNaWJcoilebR88W0Jg7pMiFZxUfneZ0S1VO9zQ1uSZd8+8sVnRmwoVcOaelLrUi77LYp9VpHdfN3Cq20Mn824o07eQ8eYLl8/hVLFqStlv54h1zBxe5SkJb6ddWJK4axMVse/ZbtSPXgglhJQqTyn+m1H1VsHkVmw/MqlxJ2VZNdu3Ew7I0/55i/ds5z726px9QPr8iAADAmrB23zsPzdJj35XqqrcLVBBOuIq1nZsH3NyAFtYVWRnbIk98X+Kq5YZMLnIBWUVtXLftUtV9+zRds0e6C+ZmFiaUGSqfZ+/wTVf+eK9j69U73rOW3niyRBe+lq+SWNLNJzj8wCw3fnP7IVm6+4tinfxi+eIftpDHiVuXj+vozUOuIvDadwrK5/lr7le/vcoXVLF9sbzrXr57ukZ9XaKLXs9XOC5t3bj8fm2OQdPrnQI1T/fqyo4ZroXY5ksc/mmR5pck1Dzdp16dMlz7NACsTZ6kfSwDYJXFkjE9XjhKY4tGs/eAOu6kjO46I7On/B4+BwOAhm7+U1co/OdntT0MAOtYaLOOatztNvYzsAy0DAMAAAAAAAANCIEgAAAAAAAA0IAQCAIAAAAAAAANCIEgAAAAAAAA0IAQCAJoEKKz4pp6wHzFFiRUF5R+E9XUg+a7cVWI/BPTzMvy9fcR8zXt1DwVvlZW7Tp5T5bo3xMW6O8j52v2dQWK59WNbQEAAKgNs4riOuCp+VpQWjeOiZ7/pdStQlxVSTSpQR8X6tixC3Tc8wt075fFiicWr+v50bSIznh5oY4YM1/nTVqon+dFa2HkABoiAkEAWM/iBQnNHVokVTl2TUaTmt2nUCntA9r45UZq0jtDC+4uUdkP5QeFBRPLVDQprJZ3ZmujFxrJk+rR3MFFPHYAAAC1LBxL6sFvSnTvlyVLnTdySpELBZ85PkcPHJ6tL2dFNean8g99/8mPubDw4g5pevnkRjp805D6vleo4mjdCDgB1G/+2h4AgIZhwcMlKnqtTMmIFNjEp0bnpSll24CSyaTynypV0VsRxeYm5AlKGQeF1PjidHe9aV3zlHViigonhhWbFVfKzgHlnpmm+XcWKzI1puAWfjXrnyl/Y68L2TwBKfJ3XJE/Ygps5FPjS9OVsl1gqfFE/o1rwV3FCv8akzfTo6wuKco+IbX8vL9imjeyWNG/4/Jme5S2d1CNeqbJ4/NUu42y76Ka1av6p8AV2jyWI39zX43nzRtepIxDQsofXVp5WunXUSWKksrpluruJ3WngNIPDqrwlbBStg+4MDCrc4oCrctvs/GF6fq3S55is+PLvB8AAIB16eFvSvTaX2WKJKRNsnw6b9c0bduk/PjuqR9L9dbfEc0tTijokw7aJKSLO5Qf33Udn6cTt07RxN/DmlUc187NAzpzxzTd+XmxpubHtEWuX/33yVTjVK+GTi5SwCv9nR/XHwti2ijbp0s7pGu7pksf3/2bH9ddXxbr1/kxZQY96rJVik7Yuvz47q+8mEZOKXa3kx3yaO+2QfXcOU0+b/Xju+/mRNXr3ZqP7x47OkfN05c+7rrkjXy1zvTpmC1Cmlm0OMwrjSX13j8R3Xd4ttIDXqUHpB7bp2rUNyXqtn2q3vgrrPYtA9qtZdBdvvNWqZrwe1gfTYvqsE1Da/joAMDyEQgCWOdKv4yq6M2wWj+YI2+WRwsfK9X8u4rV+r4cFb8bcWFfi9uzFGjhU9lPUc28tEDp+wWVskP5gV7Ra2G1HJkleaTpZy/U7P6FanlLlnyNPJp5WYEKxpWq0bnlB5iFr4XVfECmUncPKH9smWb3LVSb0TnVxpMoTWrW1QXKPCak5oMzFZ0R1+x+hfJleV1QN+/2YhcCZt+VovichP67uECpOweU1qn8YK1Cyo4BbTKp8Srti4LxZUrG5MK9qoFg9N+4CzCrho7BjX0qeiNSfv4/cQXbLT4A9eV63b608JNAEAAArG9W6fbm1LAePDJHWSGPHvuuVHd9Uaz7Ds/Ru/9ENPGPsG4/OEstMnz6aV5Ul75RoP02CmqHZuXHd6/9FdbIg7Pk8UhnT1yo/h8U6paDstQo1aPL3izQuF9Kde4u6ZWXHbBPpnZvFdDYn8tcFd3oY6sf35VGk7r6nQIds3lIg/fL1IyiuPq9V6iskFeHtAvp9s+LXQh416EpmlOS0MWvF2jnZgF1alP9+G7HZgFNOmXVju9u3j9TTdN8euy7kmqB4IyCuOJJaZPsxcdwG2f73GWsqvCfgrjaZVd/S75xlk9/58ckEQgCWLcIBAGsc1b1l1iYUOHEMqXtGVTOmanKPTvNnZe2R9AFa/4mXje/XzIsedM8is1bfDCVcWRIvkblMxwEN/MruIlPgbblB1Z23disxZdN3yfo7sNkd01RwbgylU6JKrTd4pe7kskRefxSbo/yMQQ38Sv7xBQVvFTmAkFP0KPSyREF2viUuotfbZ/NkWeJT49Xh1U05o8pVav7spVcPHVMZUhpbcDV9lvIo0RZcvH5KUucn+JRctH5AAAA61PQKy0MJzTxjzLt2TqoM3dM1dk7lR9b7dE66IK1JmleN79fOCalBTyaV2WuvyM3C6lRavnx3Wa5fheatc0qP76z684qXnzZfdoGteei4K7rtika92uZpsyMarsmi4/vJs+IyO+VeuxQPoZNsv2uCvGl38tcIBj0edxl2mT6tEsLv57tnCOvpZFrgYWBNbEKQRtToMoHvqFFP5fFkiqNSSlLvCMP+cvPA4B1jUAQwDpnlX5N+2a46riFT5bKm+VVTo9UZR2bomQiqQUPFLvQzqreglv6JDsGqnIc5MtePN2pxyt5M6ocvHmrX9a/qKXWXdbjkb+pV/ElFhKJzU4oNiehf45eUHmaBXS+zPLbbXZDhvIeKdGCe4pdG7NVGza5Il3+ptUP9sq+j7p5/2rS+uHsapV7iXBScwYWqfEV6W47l1zcxFtDuJcMJ1046rYl1eN+r3Z+2dIhIgAAwPpglX5998zQ+N/K9OQPpcoKetVjh1Qdu0WKEsmkHvi6WFP+iyo31astc33lh3dVDmWyQ4uP7+xz14ygZ1mHd2qdUf34rumioLGq2cUJzSlO6Ojnqh/fZYbKb/eGvTP0yLcluufLYs0tSbhqwyt2T18qzPt+TlR93qv5+O7ho7JrbBlelhS/R7GE3CIiFa3JYSsZlJQa8Lgw0MLSquz3phzfAVgPCAQBrHNunrsWPrUcme2CseL3I5o3pEip7QPKf65UicKk2o7JlddCr2RS/x6TV/0GViHzilepLLSwMTYnLn+z6usnWTWitd9aC3Pl9fKtOjHprhP5M65G56XLe7lH0elxzRtRpAWjStSsX+ZSQefGExut1Lgiv8YUmxHX3IHlC4FUHOTOOCffhY02r2L0qbi7/4pqRGsHDixqE7aqyMg/caW2XzTevIQSBclqbcQAAADry+ziuGsHHnlwtmt/ff/fiIZMLlL7FgE993OpCiNJjemcq1R/+fHdMWOrH9+tykeaVSsLLWycUxJXs/Tqx3dWjdgux+damCvklyVcAGfX+TMvrvN2Sdflu3s0vSCuEZ8VadTXJeq3V+ZSQefEk1fu+G5F2mT5XIXgP/lxbZpb/tbb5jBsnel1FYtWxWhtw1XZZa21GQDWNVYZBrDOhX+OaXbvAreQhzfkkS/b4z6OsADQwkBr3/X4ytti8x4oUaI4qWT54rqrrPjdsEq/jSoZS2rhU6XuaDN19+oHVamdAorPTyyazy/pqvVm9y5U3qOlLoybf0exFj5e4lb+da3Kfo+bX3BNuPkGX2/sAkT7avNETmUlYcbBIaXuEnBVggsfL3X3awuWFL8dUeZh5fPHZBwWUsHYMrcPbT/Nv7fYrUi8ZNUiAADA+vDzvJh6v1vgFvII+T1uoQ4LvywAtDDQfrbuWJvb74GvS1QcTWp1F89995+wvp0dVSyR1FM/lLowcfdW1Y/vOrUOaH5pwlUs2uWsgrD3e4V69LtS1xp8xxfFevz7EkXjSdeq7Pd63PyC65Lti33bBt0iIoXhhAtRR/9Q6lYTNoe0C+rz/yKaPD3ixvzir6XKCye0Z5ulF0wBgLWNCkEA61z6/iFX3TbrqgIlihKuldZWBrawzeYStNWB/zlugWuPTe0YVOpuAUWnrt5kyik7BVy7b+SPuIKb+9RieJYLHuP5iy/jy/CqxS1Zmn9vifIeLXEfjaTtFVTji8onrm7WP8OFgv92znPnpXYMKPfs8hXq1hWP36Pmw7M0//Yi/dO5TL4sjxpdnFa5sErm0SFXETjrWtuHSaXu5FezfhnrdEwAAADLsv/GIVfNdtXbBSqKJFwrbf+9M13YdvaOaW514OOeX6A0v0cdWwe1W8uApi5cveO7nZoFXLvvH3lxbZ7r0/ADs1zYVuXwThlBr245MEv3flWiR78tcW3Ie7UJ6qJFKxv33ztDd3xerM4v5LnzOrYK6Oyd1u3xnbmyY7ru/qJEp7+80HWIHNoupFO3Lb9fqxC8YZ9MPfh1iW76OO4WFBm6f6ZbkRgA1jVP0uq3AayyWDKmxwtHaWzRaPZeHWHBoi1g0uRKgjJUd1JGd52R2VN+K0cFADRo85+6QuE/P6vtYWAlWbBoC5hc2ZHjO6ya0GYd1bjbbew2YBn46AEAAAAAAABoQAgEAQAAAAAAgAaE3ikA9UbT3rSSAAAA1Ce9O3F8BwDrAhWCAAAAAAAAQANChSBQR0zrmqfcnmnKOHDVV15bWwpfK9O84cXyhKTmgzIV2iGgBfcWq/j9iJLhpEJb+9X44nQFNy1/6Sj7IaqZlxW4hTwqpO8XWqpSr/SbqFthuM1TOQq08JWf9m1UeQ+UKPJvXN50jzKPDCnn9FR5PJ6lxhWbn9C8W4pU9n1M3hQp64RU5Zy6eFW4mdcUKPxdtNpHHK1H5SjQtvy+jK2fNOvaQvkbe1epknB5tx35K6b595Qo8lvM7bP0fUPKPT9N3mD5Nkzrkaf43IRUZZM2ntBInkXnV2XbV/hGWJ7FQ3aPQWr7xTs3EU5q1pUFyjohpfJ5snB0iRY+VVrttpIRKXXXgFqMyFrqfsJ/xLTgrmKF/4zLGyxfAbrR+WlLjckel//OW6jmQ7KUunP5SseJ0qTm31Oskg8jsmXybDXoJlemy5tevnNKPosob1SJov/F5W/qVe65aUrfp+bnc9E7YS18olSx2XG32nT2yanKOi6l/H4iyeU+75Y1jti8hP47P99tf+45aco5bd2vHAgAaNi6js9Tz53TdOAmtXf89tqfZRr+abFCfmnQvplq37LKgdkio38o0ePfl8pf5Xjm4vbpOmrzFBWGE7rnqxJN+S+ieELavplfl7RPV4sMn0Z+VqQ3/w5Xu62ymHT05iFd1TFDJdGkRk4p0pT/orJDuMPahXTeLmny2TK+S4gnknrwmxK9OTWsSELaItenSzqkq11O9bek/xXGdf5r+Xrs6By3YvGSvpkddSsbP3VsjhvjqsgPJ3Tha/nqt1eGtm1Sfnxjfp4X1T1flujPhTFlh7w6bdtUHbtlSuW4H/q2RG/8FVY4Lm3f1K+rOqaraVr5fb/8e5me/blUeaVJbdHI5/br5o1qfpv95ayoRn1drOkFCaUHPTpqs5BO32Hx8e+Yn0o1/tcyFUWT2ijLpwt2TdMOzQL6bk5Uvd4tqHZbsUWHmG+c2niltv3FX0s19pcy5ZUl1DLdpzN2TNV+G5U/b2OJpO77qkRv/x12t7t326Au3y1dKf7ycX0/J6q7vizWtPy4W03axmUrRy/pie9L9OqfYY05PrfGMZw5caFmF8crf7flTW2fXrdXhg6q8jcUiSd1/qR87btRUGfumFbtNhLJpG78sEhbN/brtO3Kj/UueyNfP8+PqUWGV08cU/N9A1gagSCAagJtvGrzRPk/0vn3FisyNa42j+XIm+HRwsdLNefGwsrzw7/FlNohoBbDlg6eKsQLEm71XyWqnLYgodn9CtX4ojRlHBpS7L+EZvUqkDfTo+wuS4c4c/oXKriJTxuNy1VselyzehfK38yrjIPKDxwskGtxW5ZStl18YLek/DFlKvsqqoxDVu2AfVm3nYwm3TgyjwqpxbBMxfMSmn1DofIeKlHjC9OVKE4oNiOhjZ7PdYHXiti+tKByWYFwdFrc7cfwTzHphMWn53RPc18Vyr6PavZ1hWp0QfWDp4oxz+5VoOxTUtViZIoShUnNvKpA+WPLlNNt8X5PRpKaO6hQyerH/5o3okiJkqQLds2cG4u04IESt6qzhaP23Gh2Q6ZS9wio9NOoZvcvVNsn/fI3r36wHvkzpnnDi9R8WJZSdwq4kHLmRfkKbu5TynYBtw+X97xb3jg2mdRYMy/PX+H+BgCgPmmTtfwg5LcFcZ29Y5pOXRSgVDVySrELYB4/OkcBn0d3f1GsGz4s1KgjctzKvlVX9333n7Du/bLEhVjl1y1yoeAzx+eoMJxUv/cLNeanMnXbfun7een3sAvEHjoqR5lBjwsHb/qoSI8eXf7/3FgoOeLTIhVGkjVuR0E4oaGfFClR89nL9cv8mAZ/Uqj/iqoclEpaUJrQte8WuiDzqM2y3OWufKtAm+X6tF3TgB7+tkRfzYrqgSPKx33rlCKN+LRYww/M0gf/2v4o1qD9s7RzM78m/hF2YeVjx+QoN6X68d/CsoT6vVegXp0ytP9GQc0uTujiN/LVJsvnwrDJMyIa92uZ7jgkS83Tve7nvu8X6sUTcrVjs4AmnbI4+MsvS+i8Sfk6c6eV+/Dzk+kRPfF9qUYenOUCWNvPfd4r1Jjj/S7YfOy7Uv00L6pHjsqRzyv3uNzzZbELfe2+7LIXd0jTIZuE9PH0iG78qNBdtmWVQPaHuVGN/qG0xhC3goW8Vd02pUgzChNuf1Rl9/1PweLgsML80oRu/axIk2dEXSBY4Y5Ds10w/vRP1T8kB7B8BILAWjRncKG8IY+aXLX4wGnGuQuVeUyKMo8JKf+pUhW9FVFsbsJV1VmgZZVPK6oWtMq9/KdLKwORsp+iWnBviaJ/x+Vr4nWVdTUFSVZ9Nf3MhTWO1UK8lB2XHaCZRuelKRmT2yYLuBJFSXmzF/+Tj/waV2ir5b+MWPBjIVz+6MX/oKOz4krrFFDmEeWfvFq1XdreQYW/i0ldtFSlmoVgzW/OdJV3ViWW1SVFha+Uuf0XnRl34VBw82WPI/xzVEWTypS+/9KfZC7P8m7bHsNgO59yuqfK4/PI39TntrPozfIULfxb+WOzMmGgBXWRv5e9L8O/xjSrd4EL7axaclkSZUnNvblIjXqmVVbTVeUJeNRmdK48KXKfRFtYa+GfL6f6p/gWrqXsEnDPr8rtXZBwFXttx+TIl1G+Tc36Zrj9YwomlCnj4JDSOpXvY/ve6t5seTOX3v7gZn5tNK6RvGkeJeNJJfITrgLTKkVX9Lxb0TgAAFhVgz8uVMjvceFHhXNfXahjtkjRMZuH9NSPpXrr74jmFicU9MmFNxd3SF9htWBFQFER1FngYmHa3/lxNUnz6vTtU2usLLQKKqukqsmwA7JcOLSqfpsf03GLKt6WZP9BrQorM1T+f7XLVik659V8lcWSlRVii8OYYt24T4YLkUpjSb33T0T3HZ6t9IBX6QGpx/apGvVNSY2B4LTCuAvykou+rIjQ9meF538p1Uu/lbmx3PJZcY1jHf5pkQ5pF9LoH1ct+PlwWlh3fVHiQr9BHxdVO+/1v8IuXDp68/L9s02TgO47IluNUryuOtCCzGEHZLrHzFzaIV1zisuPx977N6LDNg2pfYvyx8T2sQV5tl86b1V9f+ekeDXuhEZKC3hc50p+OOkqMrNDizpLCuLu9Iqw0/ZPaBkFkLd9Xqwdmvl1+KY1P6ZL6tQ6oKePy1VqwOOqAe2+U/0eBRdVcr72V5ku2y29Msw7d+c0XfZmvqvgfH9axAWUFfe170YhVwX4xtSwztih/APookhCwyYXueeO7ZOVYQGo7ScLhKtWlNpj9UdeXDs3r/48t+ff2a8s1HFbpKggzHEfsDYQCAJrUebhKa6SqfGlSRe+RKbGXGVXxoFBFb8bUeHEsFrcnuXaZi3Um3lpgdL3Cyplh5U/sIvNjWvWNYVqfEmaC6DCP8dctZ21aC55O1aZZRVTq8uCLmthXTim1LWCetI8ajEks1pVW2yuR9O65blgKXX3oGs/9S0KgQrGl7lgJ6tzSrVA0KrtqlbcWSBWOiWqjIOXDuyi/8bla+yRr0oQGdzYp/zn4pUVfBYs2T6I/B5zlYO5Z6Ypbc/y27KgaO6QIjXplaGi18OunXRlLe+2A6181Soj7QCu5OOoQpuVv6y6NmK/9N/F+e45ENjI54K6mh7ryF9xKS4tuL9YZT/E3LZmn5yizCPLD7z8rb1q+3SuvKkeFTxftszxWmhsIWTmUcs+OLTbMNPPWugCv5Sd/EqvEiaXTI64KsNW92Sr8MXF92XbY+FmyUcR5T9f5lp50/ayxzu98rmQtltQs/oUuADX38KrRj3T5d3cU/M40jwu3Jt2Up6rHs06OUXBTfwrfN6taBwAAKyqwzdL0Y0fFurSDklXITd1YcyFMwduHNS7/0Rc1dftB2e59lQL9S59o0D7bRR0rZwra25JXNe8XahLrMqqXci1N1o1XdM071K3Yy2ZVavB1pRVeM0uSbiWUQs/LRQ6YtMUnbpdirwej27cZ/GxnfloekRtMr3VwkDzwFfF2qN1QB0WtSTPKIgrnpQ2yV6cWm2c7dPMooTCsaQLWauycPWjaRGdMC7PhV0WhN1+cHbl+QdsHHKBUkXYtqTxv5W5dlYL2lY1ENyhqQViOfJ7PUsFgr8uiKlVhlcDPyrUF7OirrLPwtpNNvHr7/yYiqNJt00jPl2ogkhCuzQPuKDMWHi35H6ybZteuHR1m6kIA496boFKY7bNQXd7xoJmC9q6v7SwMgwcekCWG3NVVmX5xcyoa5leWfZBcGqgPJS+5I0CF8he1D5N2SleF+bNL02qXfbiaGDjLJ8icWlmUVz/5sfVLqd6MmmP898LF2+jBbhHbpbigsOVCQQtlLRK1HN3SatWUWh/J9a6fetBWbrj8+qhcEbAoyePyVFWyKvv5tANAqwNLCoCrEUpu/hdlVPJp+X/CC2AStsnKG+GV2l7BNXy7mwXBloQYu2YLhSZt+yKr5pYhWFoS58LHy04Sdk+oIzDQyp4adlB0ZqyQG+T1xup0f/SNOvaAjdHXDKRlK+x11WCtX4wx1WDxWYlNM/agy24mRpT/phSNe21/Pn6bL64OQMKXQBkcwMuKVmalCel+oGQJ+Rxp7vzo1JoG78aX5imjcbmKrtrqgtlrarOzLu9SOkHhVwr6qpa0W1XXi6R1Pzbi114mXPmolZdO5Dbyq+m/TLUdmyuC36txdiqNpfaB0VJF8xZK6/dT+NL093chMUflT+PrBKuIshblnhRQvnjypT7v6VbhWvS6v5s14JtwbVtk7Hqw/l3FLsx2+nVxliYdK3eFkC3HpWtVvfnKPJnXAvuKz9YSxQkXQBs+2ijF3KV1SVVs68rf64siy/b455XrR7MdlV/C58pXeHzbkXjAABgVe3S3K/0gEef/heprBjbp21QGUGv9mgd1N2HZrsw0FpLw7HyUGde6aodv701NaItG/tc+GjVUNs3DejwTUN66fd1d/xWYUFZQjs2K6+As7ndrt8rUxN+L9MLvyx93zZP3tM/lrr546qaURh3Qc9ZVeZzswpBm5PQQtQKoUU/W3XhkizMs0o6C+YmntzIBYD93i9w7cqmcarXBZQ1sZDW5tezdtvVYdV5SwZrFaw9edKfYRfyvtAl1wVlVoloLbDWBm3XsnkPrd3W2qrD8aQGf1J+vLt3m6Am/RV2QZuFXK/8UaZ/C+LuMssz/sRGLtCbXhDXnV+UH8NE40lt1civB4/M1qRTGun0HdLU/4NC97xbcp6+k7ZOcWHeqtoi1683ujbSbQdn6dHvSvXO32H3OJqqwabNR1kxX6Sdv2ToaY9zxWNs22yt3Kdsu3LVilq0P+2hPmLTULW5AW/+uEhn7piq1plLl0ZawGxhIIC1hwpBYC2yT98yDgu50M6qyIreDqtp78zK0GjBA8WuEs6X61VwS195j8YqVrzHZsVV9mNM/xy9oPI0u+3QFjW0tc6Oa8Y5NX+C1nxI5kpXJlrrpsk6PkWFr5ap5OOIsk9KVctbFlfIedPKK+D+uzDfLX4xZ2CRGl+R7rbVAtAat2V23M0zZwtatBiZVWPoZe2tySWOV60qzMJUY22q9lXBWqetbdfCNGvDtfkJVxRKGrvOvJGLPzG26r/l3XZFe2+8MKG5g4oUm51Qyzuz5G9SfqBii2RUlX1iqgpfCav0i6gyj6p+kJPaPqDU9os/IbcFQTIODar4/bDS9165NufidyIKNPdWLgCyUo9pyOMqOmf8L798O4YUKfu0VAU3rqHd2IaRkKvEs0VEvOlyLcxzhxWpyZV2vkfpBwTcnIAm87CQCsaVqvSzqAKda+53sUDbhDb3K/ukFBW9Gq62WExNzzur0lzeOAAAWJ3jN2v7tNBuz9ZBt7BC7z0zK0OKB74udotm5KZ6tWWur/zwbRWP32YVx/Xj3JiOfm7x8Zvd9hY1LD5hLcPnvFLz8duQ/TOXW5m4ZLtxt+1S1X37NN1xyOLjjK0a+3XCVin6YFpEJ22TWjmWh78p0Ut/hDVwv6UXJnn1jzLt1ipQLaixkMhCPmurrWj5rAjCrApxqbF/UqSzd0qrnHfuwl3TXNupVbvt2WbZxztWbTjwoyJdsVu6q95bMiBb3sIVOzYNaNiBy57r2gS9FgoHXCus2a1lUB1bBV01o4WWtkW2AEdFJds5O6W5x8fmTjy4Xcgt0mFBlgVn+28U0m4tA26uweU936xVulWmLeyR5vbLFbvLBYO7tgho89zy50TXbVNdQPvev2F12ar8cbKw8ad5saWqOqtacgGSK3fPcFWppiK83al5QEdsFtLb/4TdeN1+rhJiWvBd8Tja42yBX1V2WQvGrXrQ5ia8+7CsZYa5NbGqW2v9rdoq/OSi+QdXtg0awJojEATWMgsEZ5y1UCWTo+UVfLuW/5nlPVjiqpvajilv/bR2gX+Pyav5RiwrjC7+NZG/+B+0tQandQyo+cDFBzdWZeip4QMzaxneeGKj1d4WqxyzueQqVn81Ni5b/MOqyfKfK1Xu2WmVwY21DVuln1VvxWbENXdgecBWMXoLJ5tcke5CtvAvMddeaoFX48vS5Vnik8cK1kZqFWFWAVcxZ5wFfYF25fvVArqKFX4rxxgpD7CK3wq7xS7+PS6vshrRBhP+PaY2D1dvs7D26yUXHFnebZvojLirXLMFT9x8eYtCSpP/Qqmbxy91l+qt0TWtMGxturaNVVt9y+9n5Q+sij+MKL1KeLmsORFttedW92VXtmC755lfSpYk3WrKkV9irk234rzZfQqU0yPNzfnoTqvyiX/SjrUX/RrY2FftOeu4Y8el3zEVvRtW4ctlajkye6nn1Yqed9Z6vbxxAACwOiwQPGviQrdYgYUUuzYvP86whS+sgmxM51w355odvx0ztubjN8taolVyE5unrYK1BndsFdDA/RYfv80rSbjW0CVZy7BV0K2OmtqNrdLth7kxFzBVsKq84KJwyAK3/h8WurbYew7LdqvbLsnCQwvzqrLFMKxC8J/8uDZdFGLZ/IitM72Vt13VnJKEq6KrYNtuX1VXPq6JtfTOKIpr4BKtvhbKXbF7ugvllrdwxYpY++tvC6p3f1hAaiO1bbExWvBZoSI3s0tYq62FmRXBqoWjXccvdM+nJdlKvbaAy0NHZlcGYVYVmLHoeM8WGal6P8b2jb9K0GaLmFiV5ZILllS15AIkZuzPpW5evj57Lv6Q3O47M+h1c0c2TvW4x7Ei8LVW6RQLLTO8riX85T+qH+TZZW215Q+mhV1YeNbE/Mrtt1WDLfh++Khs93xckj3vf54X08B9q4eaFsTPL0lWhuZWgWgrSv86P6YhByw/1AWweqi5BdayQEufQtv6Nf/uYhcO2qeAxsJAm1POArNEaVJ5D5QoUZxcOkRZtMhGyScRFyBZiFP46uLlXm2+t9KvYir+MOwqAy2UmnlZvgpeXvstJ6Ht/Vr4dKmbA8/GYi2dicKEaxP2ZXpU/HZYeY+UuPNsbsMFo0rcNluV2iavN3ZhpH21eaL8wKz1w9kuDIzNKQ/SrCXUFmBZVhhYsS+sbdcWUbFFM6wVueDFMleBZqyybf6dthpyzC1QUfh6WOEfbT7CkFqMyNImry4eR+bhIbcQyZJh4LIs77ZtbkLbBguumg3KrBYGGqsYtOtaFaTbd6NL3HUqgrVqknLPl7Lvou4xLZkSUfE7YWUesXIrItubE5u3L2WH5X/GY/P6WUv7gvtLXBWnBcnz7yt2+8XNN/nG4n1lX56AVZJmKceqBtv5FdrOr/n2OBQnXIBpi+RUrPRsqy0XvxtW6ZcRtw22EE50etzN77eklG39Cv8ad89Zu6y1YBeMLXWL76zoebeicQAAsDqsam3bJn43r5mFORXHbxYGWihj+VZp1KoFS9ycclWDvwpts3xuNVcLWmzuNZsPrsKBG4f01ayYWzDBwiZrwbVFG15eDy3D1t75yLclev/fsDtm+HleVC/+VqYjNyv/3zlkcpFrK77nsKwaw0Cbg3B6YcLNw1eVBaT7tg26RUQKwwlXlWerzFor9LIWtnj8+1LNKY67fWQtq6k+z1K3W1PA9XrXxi4kta8njik/jrPAackwcHXY422BqbWx2mPz+cyIpsyMuupAaxu3FXAf+qbEVSYWRxN69NsSty22kIoFVte8U+BCLguwbEVie77sVUPFo1X+FUWSbkVfC85snkr7+ZhFi5lYdeoLv5S69ujyxUzK3IrIndos3j9WZWpB3KqyfWiVhpOnR9w2fjEzojenRnTUoufAYZum6PHvS9wcfvnhhB76ttTNaWht1vYY/1eYcK3BFujac/jb2VF3vlWfTqry2FyzR4aapXvdzzWFgW4b5kVd0LrkasS2+M4rp5Tfjn1ZteRp26USBgLrEBWCwDqQcXiKm0vP5varYJV0c4cW6Z/jFrjwKLVjUKm7BRSdap9IVj+YsdbbeSOL9U/nPAVaet3t2Cq5xhazaD4401VxzRtW7FpqrbLNVrtd27JOSHHh5cyrC9ycfaGt/WoxcnF1mbXVzr+nWP92znOvJukHBNXoghUv7lAwIewC0vxnSt1XhdQO5ZWPFe27FQuiNBuQqfm3FWnayXmuOs/mGqyo5rNQ0ebgm9Wn0K1WaxVkFmJZMLvG27+c27Ywy9qRixeEXTBaNcBsPSrHzXu3IF5S3kJdknQtxi2GZ7n5JM2sXgXyN/eqyZUZrr3c9tvc4UWKz0+4cM7anG1+yJVhFaRW4WcLiiyPvbmx1ZotqLTFPDypHrfgTc5ZKzfvoD3vLJidfsZCV8Foqzbnnld+XVtQpGnfjPLVr2fG3T5qPihT/mblj4M9nhaS2nPGts8WCVlwX4kLJ63qNffctMpQb0XPu+WNAwCA1WWtikMnF1ULtM7eMc2ddtzzC5Tm96hj66BrsbTQZsnjN1th2CrAOr+Qp5YZ1voY0qS/yo/frD108P6ZLjwbNrlYKX65Ns7uNazGu7ZZW/J1e2W4MG7oJ0VuPj1bDdgCHQul3v83ooBXOunF6pWPtiqtVaLNLE64efQa1TC1y5Ud03X3FyU6/eWFrqLu0HYhnVqlEvGIZ+dXtqxevnu6Rn1dootez3dVZLay7/ADs2psL16f2uX4dctBWS7stYUsclM8unaPDDc+Yz8/9G2JLngt34XB9vjbthjbh1Ytd96khW6bLKyzBTEqKiRH/1DiVqi2qkXbzuEHZuquL4p13PNlblEVW8n65G3KA0FrSzZ93i1UUTTpFvKwy9uKzhXssdhvo1Wv6bE2cZs70p5/gz5OuEDuur0zKtvPz9wh1YW0to22mIhVPVaspG1zFZaPu0T3fFmsZuk+9d8ns7L1e0WqPgfcNhQl3HyRAGqfJ2kfEwFYZbFkTI8XjtLYotH1Zu9ZVZetVNvmidzaHgqw2mZenu9WvLbKxgonZXTXGZk95bcyXQBAgzb/qSsU/vMz1Rev/Vmmp38qdRVWQENV099BaLOOatzttlodF1CXEc0DAAAAAAAADQiBIIBqotMT+vuI+W4uOGBDEvkn5p67Zd9XnxjceFyzEwAA9dP0goRrzfxyJsdvaHgueyPftesDWDX0TgGrLakUm8CvHsk8PMV9ARui4Mb+ynknl1T+t8oMGQAAyROoX8c6h2+W4r6AhuqOQ7NrPN0TWPdzdAIbMioEgdX+4/Fpl9Bu7D9gA7BzqIP7mwUANGzJeEzBTXat7WEAWA+Cm+zi/uYB1IxAEFhNXo9X2wZ30DFpJ8jLnxJQJ9nfpv2N2t+q/c0CABo2j8+v9F2Pc4sNAKi/7G/c/tbtbx5AzVhlGFgDtki3x+NRfnyhfov+rGgyyv4E6oiAJ6AtA9so25dT+bcKAEAykZDH61Us7z/F5k5VMhFnpwD1hMfrk79pO/lzW1X+rQOoGYEgsBZZ6ID1L5FM6JtvvlEkElH79u0V8Adq9WGIxqL68osvFQqFtPPOOxNE1RICQADAyuD4rW5KLjq+C4cj6tChvfy1fHwXi0X1Bcd3dR7Hf8DKo34WWIv4B1Q77rz9Tj344IN66qmnFAwEVdtsDBYGdu/eXeeee66uuOKK2h4SAABYBo7f6qbbFx3fjR49WoE6cHxnY+D4DkB9Qv0sgA3aJ598ogceeECXX365dtllF9UVu+66qy677DI3NhsjAAAAVv34zo6p6gqO7wDUJ7QMA9hgzZs3T8cdd5y22morPfTQQ/LWsTlCEomEzjnnHP3+++8aP368mjRpUttDAgAAqNM4vgOA9aNuvXsGgFUI23r16uV+Hj58eJ0LA42NycZWMVb7DgAAgJpxfAcA60/dewcNACvBKgI//vhjjRgxok5X3jVt2tSN8aOPPtLDDz9c28MBAACo88d39oFqXT++szFyfAdgQ0YgCGCD8/XXX+v2229Xz549teeee6qu22uvvdxYbcy2Wh4AAACWfXxnx0513d57783xHYANGnMIAtig5Ofn6/jjj1fz5s315JNPKhAIaEMQjUbVo0cPzZkzx80nmJWVVdtDAgAAqBM4vgOA9Y8KQQAbjGQyqeuuu05FRUW69dZbN5gw0NhYbcyFhYVuG2xbAAAAGroN/fjulltu4fgOwAaJQBDABuPpp5/WG2+8ocGDB6t169ba0NiYb775Zr3++usaM2ZMbQ8HAACgzhzf2THShnh816ZNG47vAGyQCAQBbBB++eUXDR06VN26ddMhhxyiDdWhhx6q0047zYWatk0AAAANVdXjOztG2lBxfAdgQ8QcggDqvOLiYp1wwgkKhUJ67rnn3PcNWTgc1kknneTmFXzhhReUlpZW20MCAABYrzi+A4DaRYUggDpv0KBBmj17tm677bYNPgw0tg22LTNnztTAgQNrezgAAADrHcd3AFC7CAQB1GkTJkzQuHHj1L9/f2266aaqLzbbbDO3TbZtL730Um0PBwAAYL2pz8d3N9xwA8d3ADYItAwDqLOmTp2qLl26uDkDhw8frvq4qt61116rt956Sy+++KI22WST2h4SAADAOsXxHQDUDQSCAOqkSCSiU045RSUlJe5T1vT0dNVHRUVFbn5E2z5beTgYDNb2kAAAANYJju8AoO6gZRhAnWQVgb///rtuv/32ehsGmoyMDDef4G+//aYRI0bU9nAAAADWGY7vAKDuIBAE8P/27gO8yaqLA/g/O02TTqBsUcC9EHHj3oqAOOBDHLgV9wBFxYEgGwVRUJAtCAooCioKTkRBFCcKskeBruyd7zk3dG9oSdv8fz59sE2b9+a+I+c9OffeOkeG0M6YMQMDBgzAMcccg4bu2GOPRf/+/TF9+nR88cUXsW4OERERUY1jfEdEVLdwyDAR1Smy8m7Xrl3RqVMnjB8/HhqNBvFA5hO8//77sWbNGixcuBDNmjWLdZOIiIiIagTjO8Z3RFT3MCFIRHVGMBhEnz59VNAoSbGUlBTEk9zcXHTr1g3NmzdX1YJ6vT7WTSIiIiI6KIzvovFdixYtMG3aNMZ3RFRncMgwEdUZUhH466+/YtSoUXGXDBTymkeOHIlffvkFr7/+eqybQ0RERHTQGN9F47u1a9cyviOiOoUJQSKqE1auXIk333wTDz74IDp27Ih4deqpp+KBBx7AG2+8ofqEiIiIqL5ifBfF+I6I6iIOGSaimMvKylLzBrZv3x6TJ0+GVhvfn1WEQiH07dsXGzduxKJFi5Cenh7rJhEREREdUHzXrl07Fd/pdLq47kHGd0RU18T3XTcRxVw4HMaTTz6p/h0+fHjcJwOFBMwjRoxQgaOsPix9Q0RERFQf4zuJaeI9GSgY3xFRXcOEIBHF1JQpU/Dtt9+qZGDjxo25N/Zr0qQJhg0bhm+++QbvvPMO+4WIiIjqXXwnsQzju0KM74ioLmFCkIhiRhbPGDNmDO68806cc8453BMlnHvuubjjjjswevRotdgKERERUX2K7zp37hzr5tQ5jO+IqK7gHIJEFBN2ux3dunVTnxrPnDkTBoOBe6IMgUAAvXv3xr59+7Bw4UIkJSWxn4iIiKhOYnxXNYzviKguYIUgER1ykUgEzzzzDBwOB0aNGsVkYAUkUSoVghJgP/vss6rviIiIiOpqfCcxC+O7yuM76SPGd0QUS0wIEtEhN3fuXHz66acYPHgwWrZsyT1QCekj6aulS5fivffeY38RERFRnTNnzhwV37388suM76qgVatWjO+IKKaYECSiQ2r9+vUYMmQIevXqhcsuu4y9X0WXX345evbsqYJs6UMiIiKiuuLvv/9mfHcAGN8RUSxxDkEiOmTcbjeuu+466PV6zJs3DyaTib1fDV6vF9dffz1CoRDmz58Pi8XC/iMiIqKYx3c9evRQw2BlJIPZbOYeqQbGd0QUK6wQJKJDRoa97ty5U608x2Rg9UmAPXbsWNWHUilIREREVBfiu127dqn4jsnA6mN8R0SxwoQgER0SH330Ed5//30899xzaNu2LXv9AEnfyeIiUiG4ePFi9iMRERHFPL6T2ITx3YGTvpMFWRjfEdGhxCHDRFTrtmzZgm7duuGiiy7CiBEjoNFo2OsHuYrf448/juXLl2PBggU47LDD2J9ERER0SDG+q1mM74joUGNCkIhqld/vV4thOJ1OfPDBB7BarezxGiD9ee2116r+lFX9jEYj+5WIiIgOCcZ3tRffde/eHTabjfEdEdU6Dhkmolo1cuRI/PPPP2peGSYDa4705ejRo1Xfjho1qgafmYiIiKhijO9qL76TmJnxHREdCkwIElGt+fLLLzFt2jQ8+eSTOO6449jTNez444/HE088galTp6rhw0RERES1jfFd7cd3MjUM4zsiqm0cMkxEtWL37t3o2rUrTjnlFEyYMIHzBtbifDP33nsv1q5di0WLFqFp06a1tSkiIiKKc4zvDg3Gd0R0KDAhSEQ1LhgM4pZbbsH27duxcOFCpKamspdrUU5Ojkq+tm7dWlVk6nQ69jcRERHVKMZ3h1Z2drZalI/xHRHVFg4ZJqIaJxWBP//8s5rbjsnA2id9LPMJrlmzRvU9ERERUU1jfHdopaWlqVia8R0R1RYmBImoRv3www8qYHzggQdw6qmnsncPEenrfv36qb5ftWoV+52IiIhqDOO72OjUqRPuv/9+xndEVCs4ZJiIanRowzXXXIO2bdtiypQpHLp6iIVCIdx2223YtGmTmk9QPlkmIiIiOhiM72If3916663YvHkz4zsiqlGsECSiGhEOh9G/f38VtAwfPpzJwBiQuQNHjBiBQCCAAQMGqAmpiYiIiA4U47u6Ed+NHDmS8R0R1TgmBImoRkydOhVff/01hg0bhoyMDPZqjEjfyz746quv1D4hIiIiOlDvvPMO47s6gPEdEdUGJgSJ6KCtW7dOTXp8++2349xzz2WPxth5552Hvn37qn0i+4aIiIiouiSGkEXLGN/VDYzviKimcQ5BIjooDocD3bp1Q3p6OmbNmgWDwcAerQP8fj969+6NnJwcLFiwADabLdZNIiIionoW38l8xBLfGY3GWDeJGN8RUQ1jhSARHTCZo+7ZZ59FXl6eqkZjMrDukMBdPtWXhOBzzz3H+QSJiIioyvHdM888g9zcXBVLMBlYdzC+I6KaxIQgER2wefPmYcmSJXjppZfQqlUr9mQdI/tE9s0nn3yC+fPnx7o5REREVA+89957WLp0KQYPHsz4rg5ifEdENYUJQSI6IP/8848KFG+88UZcccUV7MU66sorr1T7SPbVv//+G+vmEBERUR2P715++WXGd3Uc4zsiqgmcQ5CIqs3j8eC6666DVqtVVYJms5m9WMf31/XXX6/+X/ZXQkJCrJtEREREdQzju/qF8R0RHSxWCBJRtcknx9u3b8eYMWOYDKwHJAE4duxYbNu2DUOGDIl1c4iIiKgOYnxX/+I7icUZ3xHRgWJCkIiq5eOPP1ZVZrKYSLt27dh79YTsK5kgXOYFkjkFiYiIiErGdxIrML6rP9q3b8/4jogOGIcME1GVbd26Fd26dcMFF1yAkSNHQqPRsPfq2aqBjz32GL766issXLiQE4UTERER47t6jvEdER0oJgSJqEr8fj969eoFu92OBQsWwGq1sufqIafTqZK6KSkpmD17NoxGY6ybRERERDHC+K5hYHxHRAeCQ4aJqEpGjx6N9evXq3+ZDKy/ZN/JfDN///23+peIiIjiF+O7hoHxHREdCCYEiahSK1aswDvvvIPHH38cJ5xwAnusnpN9KPtyypQpavgwERERxR/Gdw0vvpOpYRjfEVFVccgwEVUoMzMT11xzDTp06IA33niD8wY2oPlm7rnnHvz6669YtGgRMjIyYt0kIiIiOkQY3zVMjO+IqDqYECSicoVCIdx6663YsmWLWoQiLS2NvdWAZGdno2vXrmjTpg2mTp0KnU4X6yYRERFRLWN817AxviOiquKQYSIql1QErl69GqNGjWIysAGSBK/sW9nHsq+JiIio4WN817AxviOiqmJCkIjK9OOPP+L111/H/fffj06dOrGXGqjTTjtN7WPZ1z/99FOsm0NERES1iPFd/MR39913H+M7IqoQhwwTUSkcahBfOHSIiIio4WN8F18Y3xFRZVghSESlJiN+6qmnEAgEMHLkSM4rFwdk7kDZ1z6fD08//bQ6BoiIiKjhYHwXfxjfEVFlmBAkomKmTZuGFStWYNiwYVx5No7IKsOyz5cvX66OASIiImo4ZPEwxnfxh/EdEVWECUEiKvDbb7+pSrG+ffvivPPOY8/EmfPPPx+33XabOgZ+//33WDeHiIiIasC6devUImKM7+I3vrv11lsZ3xFRKZxDkIgUp9OJbt26ISUlBbNnz4bRaGTPxCG/349evXrBbrdjwYIFsFqtsW4SERERHSCHw4Hu3bszvotzEt/17NlTHQ+M74goHysEiUjNK/Pcc88hJycHY8aMYTIwjkkiWI6BrKwsDBo0iPMJEhER1VOM76hofDd27FjGd0RUDBOCRIT58+fj448/xksvvYRWrVqxR+Jc69at1bGwePFivP/++7FuDhERER1gfPfJJ58wviOF8R0RlcSEIFGc+/fffzF48GDccMMNuPLKK2PdHKojrrrqKlx//fXqJmLjxo2xbg4RERFVA+M7Ki++u+666xjfEZHCOQSJ4pjX61VBgZg3bx4SEhJi3SSqQzwejzo+tFqtOj7MZnOsm0RERERVeP+WD/UE4zsq6/jo0aMHdDod4zuiOMcKQaI4NmTIEGzbtk3NGcdkIJUkx4QcG1u2bMHQoUPZQURERPWAvGczvqOK4juZT5DxHRExIUgUp2ROmblz5+KZZ55B+/btY90cqqOOPPJIDBw4EHPmzMGSJUti3RwiIiKqQnwn792M76g8jO+ISHDIMFEckk+Nu3XrhnPPPRejR4+GRqOJdZOojq9S+Mgjj+Dbb7/FggULuPAMERFRHY7vOnfurCr8Gd9RRRjfERETgkRxxu/3o3fv3sjJyVHJHZvNFusmUT3gcDjUTUZ6ejpmzZoFg8EQ6yYRERFRkfjuf//7H3JzcxnfUZUxviOKbxwyTBRn5BPjv/76S1UGMhlIVSXHihw7f/zxh5p3hoiIiOoOxnd0sPGd/EtE8YUJQaI48tVXX2HKlCl47LHHcOKJJ8a6OVTPyDHz6KOP4u2338bXX38d6+YQERER4zuqofhu8uTJjO+I4gyHDBPFiczMTHTt2lW96b/55pvQavl5AFVfOBzG3Xffjd9//x2LFi1CkyZN2I1EREQxwviOajq+W7hwITIyMtixRHGACUGiOBAKhXDbbbdh06ZNKomTlpYW6yZRPZadnY1rrrkGbdu2VRWnOp0u1k0iIiKKO4zvqCYxviOKPywRIooDUhH4448/YuTIkUwG0kGThLIcS6tWrcKkSZPYo0RERDHA+I5qK76bOHEiO5coDjAhSNTArV69GuPHj8f999+P008/PdbNoQbijDPOwH333YfXXntNHWNERER06Pz0008qvpP3YsZ3VJPx3b333otx48YxviOKAxwyTNSA5eTkoFu3bmjVqhWmTp0KvV4f6yZRAxIMBnHLLbdg+/btar6Z1NTUWDeJiIgoLuI7mRe6devWjO+oxjG+I4ofrBAkaqAikQieeuopeL1eVf7PZCDVNDmmRo0apY6xp59+Wh1zREREVPvxnc/nY3xHtYLxHVH8YEKQqIGaPn06li9fjldeeQVNmzaNdXOogZJja+jQofjyyy8xY8aMWDeHiIioQWN8R4cC4zui+MCEIFED9Pvvv2PEiBG49dZbccEFF8S6OdTAXXjhhWro8PDhw/HHH3/EujlEREQNOr6T91zGd3Qo4rubb76Z8R1RA8Y5BIkaGKfTiWuvvRZWqxVz5syB0WiMdZMoDvj9fvTs2VMdfx988IE6/oiIiKhmyPtr9+7dYbPZGN/RIcP4jqhhY4UgUQObV2bQoEHYt28fxowZw2QgHTKSeJZjbu/evXj++ec5nyAREVENx3dZWVmM7+iQx3ejR49mfEfUQDEhSNSASGXW4sWL8eKLL+Kwww6LdXMozsgxJ8feRx99hAULFsS6OURERA3C+++/z/iOYqZNmzaM74gaKA4ZJmogNm7ciB49euCqq67Cyy+/HOvmUByTFYc/+eQTdQPTtm3bWDeHiIioXsd3MhXM1VdfzfiOYkpWt16yZAnjO6IGhAlBogbA6/Xi+uuvRygUwvz582GxWGLdJIpjbrcb1113HfR6Pd577z2YzeZYN4mIiKjeYXxHdS2+k+IDg8HA+I6ogeCQYaIGYOjQodiyZQvGjh3LZCDFnCSkZT7BTZs2YdiwYbFuDhERUb2O7+Q9lR/2UqwxviNqeJgQJKrnli5dqlabGzhwII488shYN4dIOeqoo9TQ4dmzZ+PTTz9lrxAREVWDDM2U+E7eS+U9laguOProoxnfETUgHDJMVI9t27YN3bt3x9lnn62qAzUaTaybRFRsVcSHHnoI33//PRYuXIiWLVuyd4iIiCrB+I7qMsZ3RA0HE4JE9VQgEEDv3r2xb98+LFq0CDabLdZNIirFbrejW7duaNy4MWbOnKnmnSEiIqLK4zv5MC0pKYldRXUO4zuihoFDhonqKakI/OOPP9S/TAZSXSU3MqNHj8bvv/+O1157LdbNISIiqhfxncwbyGQg1Yf47tVXX411c4joADEhSFQPffPNN3j77bfx6KOP4sQTT4x1c4gqdPLJJ+Phhx/GpEmT8O2337K3iIiIyvD111+r+O6RRx7BSSedxD6iehHfvfXWW4zviOopDhkmqmf27NmDrl274vjjj8fEiROh1TKvT3VfOBzGnXfeib/++ksNcZchxERERFQ8vjvuuOPUB2iM76g+YHxHVL8xIUhUj4RCIfTt2xcbN25USZX09PRYN4moyrKystTNTvv27TF58mTe7BARETG+owYS37Vr1w5TpkxhfEdUj7C0iKgekU+MV61ahREjRjAZSPWOJLDl2F25cqUaXkJERESM76hhxHc//PCDulchovqDCUGiemL16tUYN24c7r33Xpx55pmxbg7RAZFj95577lETUK9Zs4a9SEREiPf4ThbdkvdGxndUX8mxe/fdd6tjmfEdUf3BIcNE9UBubi66deuGFi1aYNq0adDr9bFuEtEBCwaDuPnmm7Fr1y4sWLAAKSkp7E0iIorL+E6GWkp8N336dMZ3VO/juz59+qj4buHChYzviOoBVggS1XGRSARPP/00PB4PRo4cyWCR6j1JaI8aNQoulwsDBw5UxzgREVE8kfe+p556Cl6vV70n8sNeaijxndvtZnxHVE8wIUhUx82cORNffPEFhgwZgmbNmsW6OUQ1Qo7loUOHYtmyZZg9ezZ7lYiI4i6++/LLLxnfUYPSvHlzdUxLfDdr1qxYN4eIKsGEIFEd9ueff2LYsGFqeOVFF10U6+YQ1Sg5pmVoiSQG//rrL/YuERHFhT/++EPFd/IeyPiOGpqLL75YHduvvPIK4zuiOo5zCBLVUU6nEz169EBiYiLmzJkDo9EY6yYR1Ti/348bb7xRDS/54IMP1PFORETU0OM7i8WCuXPnMr6jBonxHVH9wApBojrqxRdfxJ49ezB69GgGi9RgSaJ7zJgx6liXY56IiKghzxv4wgsvqPc8ee/jh73UUMmxLfcwjO+I6jYmBInqIFl5ddGiRSpobNOmTaybQ1Sr5Bh//vnn1Yp08kVERNQQyXvchx9+yPiO4sLhhx/O+I6ojuOQYaI65r///sO1116LK664Qs2tRhQvZLXFpUuX4v3338cRRxwR6+YQERHVmI0bN6qhwozvKN4MGDAAn376KeM7ojqICUGiOsTn8+H6669HIBBQb5oyvwxRvHC5XOpmyWQy4b333lP/EhERNZT4TuZVk/lyGd9RPGF8R1R3ccgwUR0iK85t2rRJzSvDYJHijSwoMnbsWFUlO3z48Fg3h4iIqEbIaqsS38l7HOM7ijeM74jqLiYEieqIzz77DLNmzVLDJo8++uhYN4coJuTYl6ElM2fOxOeff869QERE9T6+mz17NuM7imuM74jqJg4ZJqoDduzYgW7duuHMM8/Eq6++Co1GE+smEcV0FcYHH3wQP/zwg5qAvUWLFtwbRERU72zfvh3du3dnfEe0P7574IEHsGrVKsZ3RHUEE4JEMSbzBfbp0wd79uxRb45JSUmxbhJRzOXl5ambqCZNmmDGjBkwGAyxbhIREVG14rubbroJe/fuxYIFC5CcnMzeo7gn8Z0UQWRkZDC+I6oDOGSYKMbGjRuH3377DaNHj2YykGg/uXEaNWoU1q1bh/Hjx7NfiIioXnnttddUfCfvZUwGEpWO7+QeiIhiiwlBohj67rvvMGnSJDz88MM4+eSTuS+IiujQoYM6NyZOnIjvv/+efUNERPXCt99+WxDfyXsZERU65ZRT1Lkh54jcCxFR7HDIMFGMyBCSrl274phjjsFbb70FrZb5eaKSwuEw7rjjDqxfvx6LFi1Co0aN2ElERFRnMb4jqhzjO6K6gQlBohi+Cf7zzz8qyZGens79QFSOffv2qeS5rFDH5DkREdXl+O72228viO/4IRZR5fHdUUcdhbfffpvFEUQxwJIkohiQpIYMgRwxYgSTgUSVkBsqOVdkWIkEjERERHU1vlu5cqV6z2IykKhico4MHz5c3RMxviOKDSYEiQ6xn3/+Ga+++iruvvtunHnmmex/oio466yz1DkzduxYrF27ln1GRER1ypo1awriO3nPIqLKnX322bjrrrsY3xHFCIcMEx1Cubm56N69O5o2bYoZM2ZAr9ez/4mqKBgM4qabbsKePXuwYMECrtpIRER1Kr7LyMjAzJkzGd8RVUMgEECfPn2QmZmJhQsXMr4jOoRYIUh0iEQiETzzzDNwuVwYNWoUg0WiapIE+ujRo+FwONS5JOcUERFRLMl70cCBA+F0OtV7FD/sJaoeg8Gg7o3kHGJ8R3RoMSFIdIjMnj0bn3/+OYYMGYLmzZuz34kOgJw7Q4cOxWeffYZ3332XfUhERDGP75YtW6bemxjfER2YFi1a4OWXX1bxnZxTRHRoMCFIdAj89ddfKlCUcviLL76YfU50EOQckqHDck79/fff7EsiIoppfCfvSYzviA7OpZdeit69e+OVV15hfEd0iHAOQaJaJkOEr732WiQkJGDu3LkwmUzsc6KD5PP5cOONN8Lr9eKDDz6AxWJhnxIR0SHD+I6oduK7G264Qf37/vvvIzExkd1MVItYIUhUy1566SW1CMKYMWOYDCSqIZJYl7maZAJqOceIiIgOpRdffJHxHVEtxHdyzyTx3eDBg9m/RLWMCUGiWiQrZclqqIMGDcLhhx/OviaqQUcccYQ6t6RCcNGiRexbIiI6ZPGdfDG+I6qd+O65555jfEd0CHDIMFEt2bRpkxoqfNlll6m5MIiodvTv319NQi2JQSbeiYioNv3333/o0aOHmu9s2LBh7GyiWvLkk0+qBRkZ3xHVHiYEiWp5fjPOf0F0aOZxknkEZZ5Oo9HILiciolqL7zwej0pScH4zotrjdDpV8p3xHVHt4ZBholowYsQIbNy4Uc2BwWCRqHbJOTZ27Fj8+++/GD58OLubiIhqhbzHSHwn7zmM74hql9VqVfdSjO+Iag8TgkQ1bNmyZZgxYwYGDBiAY445hv1LdAjIuSbnnJx7cg4SERHVJHlvmTlzJuM7okPo2GOPVVPDML4jqh0cMkxUg3bu3Ilu3brhtNNOw7hx46DRaNi/RIdIJBJBv3798NNPP6lFRpo1a8a+JyKig8b4jii28d3999+P1atXq8V8mjdvzt1BVEOYECSqIcFgEH369MHu3bvVm1VycjL7lugQy83NRffu3dG0aVP1abJer+c+ICKig4rvbrrpJmRmZjK+I4phfCdFF/JhL+M7oprDIcNENUQqAn/99VeMGjWKyUCiGElJScHIkSPVuTh+/HjuByIiOuj4bt26dYzviGIc38k9lsR3ck4SUc1gQpCoBnz//feYOHEiHnroIZxyyinsU6IY6tixIx588EG8+eabWLlyJfcFEREdEMZ3RHUvvpN7LsZ3RDWDQ4aJDtK+fftUCfuRRx6Jt99+G1ot8+xEsRYOh3H77berlelkPsH09PRYN4mIiOpZfNe1a1cV302ePJnxHVEdEAqFVHy3YcMGNYS/UaNGsW4SUb3GzAXRQSYdZOUrmex2+PDhDBaJ6ghJzMs5Kefok08+qf4lIiKqCsZ3RHWTTqfDiBEjCs5RxndEB4cJQaKDIJ8Yf/fddyrxwE+oiOqWxo0bq3Pz22+/xZQpU2LdHCIiqidkxIe8d8h7iLyXEFHdIefksGHD1Dkq92JEdOCYECQ6QL/88gvGjh2Lu+66C2effTb7kagOOuecc9Q5OmbMGHXOEhERVWTt2rUF8Z28hxBR3dO5c2fceeed6lxlfEd04DiHINEBsNvtat7AJk2aYMaMGTAYDOxHojoqEAigT58+2LNnj5pvJikpKdZNIiKiOigvLw/du3dnfEdUT+K7m266CXv37mV8R3SAWCFIVE0yX+AzzzwDh8OBUaNGMRlIVMdJwn7kyJHqnJVzV85hIiKiouS94dlnn1XvFfKewQ97ieo2OUflXkwKNRjfER0YJgSJqmnOnDn49NNP8fLLL6NFixbsP6J6oGXLluqclXN37ty5sW4OERHVMe+++25BfCfvGURUv+I7uUcjouphQpCoGv7++28MGTIEvXv3xqWXXsq+I6pH5Jz93//+p87h9evXx7o5RERUh+K7oUOHqvcIxndE9ctll12GXr16qfhOzmUiqjrOIUhURW63Gz169IDRaMR7770Hk8nEviOqZ3w+H66//noEg0HMnz8fFosl1k0iIqIYkvju2muvVfHdvHnzGN8R1UNerxc33HCDmlfw/fffZ3xHVEWsECSqopdeegm7d+9Wq5UyGUhUP8m5K+fwzp07MXjw4Fg3h4iIYozxHVH9ZzabVXy3a9cuxndE1cCEIFEVfPjhh/jggw/w3HPP4YgjjmCfEdVjbdu2VeeyfIL80Ucfxbo5REQUI4sWLSqI7+S9gYjqLzmHZWEgxndEVcchw0SV2Lx5M7p3745LLrkEw4cPZ38RNZDVJJ988kksW7YMCxcuxGGHHRbrJhERUQziu4svvljFdxqNhv1P1ADiuyeeeAJffPEF4zuiKmBCkKgCfr8fPXv2hMvlUp82Wa1W9hdRA+F0OtW8oImJiWplOpk/ioiI4iO+u/HGG9X8gYzviBpefCfzgsp9G+M7oopxyDBRBUaMGIF//vlHzUnBZCBRwyLn9OjRo9U5PnLkyFg3h4iIDhGpCPz333/VewDjO6KGRc5puXdjfEdUOSYEicohpebTp09H//79ceyxx7KfiBqg4447Tg0dnjZtGr788stYN4eIiA5BfDdjxgx17Zf3ACJqeOTclqHDjO+IKsYhw0RlkBWqunXrho4dO+L111/nvDJEDXy+mfvuuw8///yzmmC+adOmsW4SERHVYnx3yimnYMKECYzviBp4fHfvvfdi7dq1aj7BZs2axbpJRHUOE4JEJQSDQdx8883YuXOnevNISUlhHxE1cDk5OeomsWXLlurTZL1eH+smERFRLcR3O3bsUPFdamoq+5eogWN8R1QxDhkmKkEqAn/55ReMGjWKyUCiOCE3hnLOS5WgVI0QEVHDMn78eFUpJNd6JgOJ4i++k3s8IiqOCUGiIlauXIk33ngDDzzwgBouTETx49RTT1XnviQEf/jhh1g3h4iIajC+e/PNN9U1Xq71RBQ/5Jzv16+fusdjfEdUHIcME+2XlZWFrl27ol27dpg8eTJ0Oh37hijOhEIh9O3bFxs3bsSHH36ItLS0WDeJiIhqIL5r27YtpkyZwviOKE7ju9tuuw3//fefmi86PT091k0iqhNYIUgEIBwOq9WE5c1i+PDhDBaJ4pR8EDBixAh1LZBrglwbiIio/sd3cm3nh71E8alofDdgwADGd0T7MSFIBOCdd97BN998o5KBTZo0YZ8QxTG5BgwbNgxff/01pk6dGuvmEBHRAZKKQInv5JrO+I4ovmVkZOCVV15R8Z3c+xERE4JE+PXXXzF69Gjceeed6Ny5M3uEiHDuuefijjvuUBNRr1u3jj1CRFQP47sxY8bg9ttvV9d0IqLzzjtPTQ0j936M74g4hyDFObvdjm7duqFx48aYOXMmDAZDrJtERHVEIBBA79691fxTCxcuhM1mi3WTiIioGvFdo0aNMGvWLMZ3RFTA7/er+C47O5vxHcU9DhmmuCLzRtxwww1q6flIJIJnn31WBY1SBcRkIBEVJdcEuTbk5eWpa4VcM+TaIdcQuZYQEVHd4HK58Mwzz8DtdjO+I6IKGY1GVSGYm5urrhsS3+VfQ+RfonjChCDFla1bt6ohJD6fD++99x6WLl2Kl19+GS1btox104ioDmrVqhUGDx6MJUuWYN68eeraIdeQbdu2xbppRES035o1a9Q1Wiq6586dq+K7l156SV3DiYjKi+/kWiH3hFItKNcQ+eCXKJ4wIUhx5Z9//lH/arValQjs1asXLrvsslg3i4jqsMsvvxw9e/ZUgaNcO4peS4iIKPb+/fdfWCwWOJ1ODBkyBDfeeCOuuOKKWDeLiOowuUbItULuCaUyUK4hci0hiieaiNTIEsWJ8ePHq7kC09LSoNfr8dprr6nvjzrqKFx//fWxbh4R1THyafH69etx00034YEHHlBDheVTZPm+X79+sW4eEREB6N+/PzZu3KiGDOt0OkyfPh3Lli1Dx44dccQRR7CPiKiY//77T1UWX3LJJejTpw/C4TDMZjPat2+vViImihesEKS4IlU9kgjcsWMHjjzySFx99dX46KOPkJqaGuumEVEdJNcGuUbIteLoo49W1w65hvATZCKiukOuyQ6HQ12jL7zwQnTp0gXPP/+8ShISEZUk14ZBgwap+O6CCy5QU8FIhTFHgFC8YYUgxZXOnTtjz5496oZeysJl2Xn5VMhqtca6aURUR0mAOGPGDEyZMkVVnwSDQWRkZODrr7+OddOIiOKeVG6feOKJ6tqcnJysEoNdu3bF/fffzzkEiajCueUnTJiARYsWqXtBWWhSFpSTuaKl0pgoHjAhSHFDRsdLhY8kA++++27cdtttsNlssW4WEdUTEihOnToVEydOVDegf/31FzQaTaybRUQU1/7++2+VABRS7SOJQA4TJqLqDB+WaaU+/vhj9f2HH36oppMiigdMCFJckaF/p59+Opo0aRLrphBRPSVVxqtWrVJD0oiIKLZkMYBHHnlEJQJPOukk7g4iOiC//PKLqhgcM2YMEhMT2YsUF5gQJCIiIiIiIiIiiiNcVISIiIiIiIiIiCiO6FGP54MLIwydhhN+EtVXoUgIWmg5D9sBiAQj0Og5fx1RXcVztJr9FYkA4ZD8Xy3tESI69DSAVsc4rxyRSBgIh3ndI6r31zktNJr6WWtXLxOCoUgQWaF9+NzzCbYHt6qkAhHVL5LMb6lvjUsSrkS6rhF0mnp5OTrkIqEIIv4I8pZ44PnDr/6fiOoWjVGDhOOMSL4iQf2/RsfkfWVCOdvh27wWkaDvkOwjIqp9Gr0JpjYdoE9rxe4uIhIOQ6PVIpi5Ef4dfyIS8rN/iOopjc4IY4tjYWjavuDcrk/q3RyC8knKrtBOPLj3djgjjlg3h4gOklVjw7jGU9BU16zefrJyqKuONt++F75/g7FuChFVwtRejzaTG7OatxK5n4yEe/UHPJ6IGihLpx5IueKxWDejTt3P5sx9Ct5/vol1U4iohpiP7IzUG4fWu/vZ+tXa/QXVn7gXMRlI1EBIYv9j90JEOEysStWBrtU+JgOJ6glJ3Ms5K+culS2Ys4PJQKIGzv3T+wjm7Ix1M+qESDgE/6Y1TAYSNTDef76Bf/PP6hyvT+pdQlCr0WJrYFOsm0FENUjOaS3nA61cGPBtZGUgUX3i+y+ozl0qLRIKwr/lF3YNURzwb1mrzvm4FwnDt/XXuO8GoobIJzGNzA1aj9TLSbtCqF9ZVyousDuE7b1y0er9VOjTYpOTlpHyuTM9cH7sQ8gZgaGVFml3JyLhZIN63LnMh71DndAYC/8m+YYEpN5mQdgfQfYEF1xf+RHxRWA6Wo/0fokwHlEvT6c6ged0dTqLlUb1SWZoN+7K7YWpqe8jVZsW6+bgQ898/Bz4Ec8nDS/z8XHO4dgd2omXk8eq71+w98efgXXFfscLL/pY7sB1Cb0PSZvrvSDP2fJFOGdgA7bbGUKvRbl4/9pUpCXEJt4LRyKYus6DJRt9cAXCOCxZj7s7WHByRjTey+cLRvDoF3b0OMqMC9uYYtLWhi46Pyivh2oBgiDnDGxo6sL1Tqza4cekX9zY6QyhsUWLO0+2oHOr6DVtU24Q41a78E92CMkmDa47OgHdjzLHrK0NUlDO7fo1bzQzGBSXHB/64FzqQ9MRSdA318L5qQ+ZT9vRckYq9Ola+P4Jwna1CY0esZb625y33fBvCqHl1BRorRrkTvNgz/MOtJyeGpPXQkRUGV/Eh/c80/G+512cbDi1zN/51rccX/o+xbH6Ewp+NihpWLHf+cDzLlb4luFKc3d2OhHVeQv/8WLFVh/GX5aEJhYtFm/w4ZmvHPigRyqM+xf72WYP4ZWVTvy5L4geR8W6xUREB+a/nCCe/9aB58624YwWBvywI4BB3zgw4xo9bEYtnvzSgfNaG/HKBUnY5Qyp74064Kp2TArGMyYE41D2ZDecS72I+AFDGx3S7rbAfKxBVc3lzfLAucyP4N6wqo6zXmRS1W9iW88cJF1nhmOxD8HdIZhPNiD1VguyXnPBvykIY3s9mgyyqYTa3lec0BgA/+YQ/BuCMLTWIf3BRJiPK/6JrPBvDSF7nAu+9UFobRokXWtGco+E6GP/BbFvtAuBzSFokzWwnGNE2l2WUis2etcFsLu/vczXK4k7fYau2M9C9jBS+iTA0Cr6c9uVZmRPdMP/bxD6dCP864OwXlr2J8TSX5EgoDVpEHaFEXZGoE2ud6PvieLCTPdkfOFdigD8aKVrg1std+Mow7HqejfPMwtf+5dhX3gvjDDiXNNFuCOxn/q7O3N6oov5OnzqW4w9od04wXAyelluxSTXa9ga3IQj9O3xhG0Q0rTpeNX5CgwwYGtoM/4LbkBLXWvclfggjjYcV6o920Nb8ZZrHDYE16sFda42X4suCT3UY5uD/+EN12hsC22GTZOMM4zn4GbLXWpF7qL+CKzDi/b+Zb7e8SlT0ViXUernA/IeQDNdC1xu6oLd4V1lVjJOc0/CZaYuavtl2RTcgDnu6RiZPAEWjaWKe4CIYmXyL24s/c8Lfxhok6TD3adYcGyjaLw36w8Plm32Y68rrG4IL2pjQr9To/Fez4U5uO5oMxb/68NuV0hV0916ogWv/eTCprwg2qfqMaizDekJWpVIM2iBzXkhbMgOonWyDg+emojjGpeO97bmhTBujQvrs4KwGTW49igzehydUHAjO/pHl3oeqVw5p5URd51sgU5bPN5btyeA/svLjvemXp2CjMTi18tuR5pxRVszEvQaVQXo8EdgNWqQH0ZKWwYst6P38QnIctevYV5EVIjXO2DRv15c3MaEM1tGh7jJvxMuS1bJwN/3BuAJRnDvKdHrqlRLy/Xxo3+9TAjGOSYE44xnTQDOz31o8VYKtEka5E71IGucCy3eSIFruV8l+5qOTYKhqQ7ePwPY9aAdiecZYT5h/1DapT40G52kKmG3981F5iAHmo1Mgi5Ng10P2WH/wIO0O6MBpWOpDxkv2JBwmgF587zIfNqBljNTirUn7Ilg9+N22LqYkDHEhsCOEDIHOqBL0sJ6iQn7xrpUEjB5nBmhPWHs7GdXw3otZxqLr+pzogFtlqRXuR9S+1hKJRQlsWc8QqcCZd+GEDQJfuRM9QA6wHq+ESl9LdAaNSoZKffnuXM8yJnkhsaiQdOhtoPYK0RUG34NrMEK3+cYm/IWbJokvOuZqpJxI1PewLf+5fjctxiDk8YiQ9cU6wN/4in7gzjbeB6OMUQr5L70LcXLSaNV6f+DuX3ximMQXkwaiVRNGp62P4TFng9wc+Kd6ne/8C1Ff9sLOMVwGhZ552Gw42m8mTKzWHs8EQ+esz+uknLP2IZgV2gHXnYMhE2bhPNNl2CiayxON56DV8zjsC+8B/3t/XC84WR0Mp5Z7HmOM5yIuelLqtUXz9heRrquMd51Ty2VEAxFQhjlHIxbLHepx8pLCEoyVJKXrfWHV2vbRHTordkdwOebfHjryhQkmTRq2KwMFXvj8hQs3+JXlXJjL05CU6sOf+4L4MHP7Kpy5IQm0Xhv6X8+jL44CRoN0HdxLgZ97cDIi5KQlqDBQ5/b8cHfHtzZIbHgd1/obMNpzQ2Y95cXT69wYOY1xeM9TyCCx7+0o0s7E4acZ8MOZwgDVziQZNLiksNNGPuTSyUBx11qxh53GP0+tePkJoaCG9t8JzYxYMmNVY/3tBoNEvTA8i0+DP7OqRKBz5xtLUg0trBpMbtbqkoYzv/bWwM9T0SHGq93Uf9kB9GpmRFPLberiuemVi3u6pCIdgaNmnFIPrwp+iGL/O92Bz8IiXcsa4ozUvUXzg3Dsdirqu5Sbk1QyUBhOcOIZuOTVTIwmB1GxAdoLRoE9xVeKKxXmqBL00KXqoWxrR6JnY2qyk6bqFVJueDuwt+VxyxnGaHRa5Dc0wyNUQPPj4Fi7XGv9EOjjyboNAYNjG30SL7ODPuH0aBM/c1KP9zfBdTw3FZzU0olAw+Wb0MQmc87kHJzAvRNdAjnRWBsp0PihSaVwGw2IgmenwLImegu9ndJ3c1o82ka0u6wYPeTdgR2cm5LorrEACPywrn4zLtYVe/1SrhVJQNFR+MZeCV5vEoG5oSz4YMPCRoLssL7Cv7+YtOVSNGmIUWbijb6tjjT2BktdK1g0SbiWMOJ2BveXfC7Zxg74zTjWdBr9LjW3BNGjVHN1VfUav9K6KHHDZY+MGgMaK1vg2vM12Gp98NoezVG9TurAt8hUWPF2ylzSyUDD5QkA8sjiVJ5XeeYLij3d9YF1mJLaBO6m3vWSHuIqHYZtUCuL4zFG7zYnBvCrScmqGSgOKOFEeMvTVbJwGxPGL4gYDFosM9TGMNd2dak5sFKNWvRNlWPzq2MaJWkQ6JBq5Jyu12FvyuPndXSCL1Wg57HmtVQ3B93FY/3Vu7wQ68F+pxggUGnQZtkvapC/PDfaLwnfyO/8932gKrgm9s9pVQy8GCc3dKIz3qm4dlzrHj5eyd+2xNtn9WoVclAIqq/eL2LsvsiapqEnscm4P0eqbj2qAQ8s8KOnY4QTmisVx/wTPvNDX8ogi15QXy0wQsf5yaPe6wQjDNS6df4aSvsC73IneGBNkmrhs4mXWNGJBxB9kSXStqphN+Ruujcv0Xm/9UVGRqr0UIl6Qpoi/+uvkXhsA2NRgN9Yy1C2cU/hQhmhhHcE8aWq7MLfhaJADpb9HmbPGdFzhQ3sl93qWHMUm3Y6JFE6BsXHxLi/S2AzKccZb7mFpOTSw0Zzuda4cPekS6k9EpASu/osBVdihbNX00ufFmtdCpZmDXejfQHEgt/boq2MambGY5PvHB/50fy9dHnIKLYO9ZwAh61Po1PvAvxnmeGqsS7IaEPLjdfg3AkjGmuiSppJwm/trojZXkDhItcxJK0Ra4D0KokXdHvi/6uDMcter1L1zZWicai9oYzVeXf/7KvLviZPIdNE60wfsL6HGa5p2Cy63VkhfeqasN7Ex8plcz7M/AbBjueKvM1v5o8ucwhw+X5LfALvvN9hdEpEyv8PUmqXmC6FFZt6XlViajukUq/p8+yqpvDGb97kGTUos8JCbimvVkttDFxrQs/7gwgNUGLI1N10XCvSAyXbNIWqyKRJF3B9yWWhmhhLR7vyUT2kmgsKtMVxh5XGFe/Vzzes+2PpZ47x4opv7rx+hoX9rrDqtrwkdMS0dhSPH6TRN5TK8qO9yZflVxqyHC+/PkCZXL905v7sWKrv6AakojqN17vCq9zFxxmwEn7F0267AgTPljvwaqdAbV4yNDzk9Q19oO/vWiTosNlh5uw4B9WRsc7JgTjTDAzBH1THZqNTkbYF1Er5e4b6kRCRwPy3vMg7Iig1ZxUaBM0aujs1i45xZ+gGh+ihopUFkqyMbgnBH2T4kWp+kZaGA/XqSHMBX+XJ9WJEfU3/o0htfqv9mENAttD2DfCiexJbjQZaCuV6DxscfVW8MyZ5oZ9nhdNnraqSsZ8/i1BOD/3q8q/gvb7o9WVQhYQMXcwIKlr4QSskQDU/IdEVHfsDWWiia4pXkoerRbV+N7/FcY6h+IkQ0cs9LwHZ8SBt1PnwKxJUNe73jldSjxD1c/popWFkmzcG9qDxtomxX4nTdsIrXWHqyHM+ezhPNU2+ZtNoY24JfFu3KN5GDtD2zHeOULN6/eobWCpROfstMWoCV/7liE7vBe351yvvg9EAggiqJKW+dsIRoL4yf89XlLDp4moPsh0hVQF4OiLk9XceV9t9WPoSic6NjXgvb88ai69Od2jQ2Xl+tdlXvF4rzoRTdHKQkk27nGH0CSxeLzXyKLF4Sk6NYQ5X543rKpT5G825oRwd4dEPHyaBtvtIYxY5cSktW4MPNtW6sZ/8Q1Vj/cmrHGpm+Q7Ti6M6aQ6RuYwJKKGgde7qMOSdQiUGAEcVp/eRNR1T/73tUsLP+yetNaFI9OYDop3HDIcZ3x/BZE5wK4W8pAKN12yRqWFJQEoyUAZvivz48ncfjJENuyKqGTXgXAt98HzawCRYAS5szwqukw4rfjwj4QzDQhlhVXFovyeDFXOHOBAzjseaLQaZL3qQu40NyKBiBqqDL1GzS94sOwLvLDP96LZa0nFkoFCa9WquRDz5nsQCUVUX+XOcKuFR4TpeD1yZ3sQ2BZS7cp9VxKp4RofykxEB+ef4F940T5ALeRh0piQpElWQ3YTNAkqGSj/r4NOze03zT0RrogLQRzYBU9W6P0j8KtKns33zIIGGpxiPK3Y75xqOBM54SxVsSi/JxWELzoGYLbnHWg1WkxyvYo57mkqKSdDlXUavapqrE33Wx9X8xFK8k++brTcolYZLppwlERlGGG01R9Zq20hoprz177oYhmykIdJpm4xadSQXUkASjJQ/l+K5mRuv4lr3XAFIqVuJKtK5uf7NTOAYDiCWb97VDLxtObFY6IzWxiQ5QmrikX5PakgHLDCgXfWedQ8f6+udqmhbIFQRA1VluHHMr/gwTqhiR4L1nvx174AQuGImlfxtz1BNW8hETUMvN5FXdXOhOWbfVizy68+aJFFpeQDFpkyQSqyH//Cji83y4fQEfySGcBHG3xqcSeKb0wJx5nE803wbwlh92N2hJ1hNZRWVgaWZFtqX4taHXhL12w1d2DC6UYkdDIgsCkoabBqb8t8kkEN9/VvCKk5+ZoOT1KJx1Be4e/orFo0HZmErAlu5LzjVilqy9lGpN8fHZrbZJBVJQW3ds9RjyWcbkBq34Mflps7y62SnjvvL9IYAI0HWJF4nixwkqQWDJH2S1/YrjIjuVf0gpnUw6z+dtfjdkQ8EZiO1qPp6ORiw6mJKPbONp2PbaEteM7+GJxhpxpKKysDS7Ktt6WvWh34puyuau7AjsbT0cHQSa0gfACXOxxvOAkz3VOwKbQBh+va4fmk4aryMA+F1xgZbvtC0khMcU/AbPc7atjxacazcUfi/erxJ6yDVFLwlpzu0ECLjobT0TuhL2JtT2iXGlZdcrVjIqq7zj/MhC15ITz2hR1Of1gNpR10jk0l2/qeaFGrA3ednw2LXoPTWxjRqZkBm3IPLN47qYlBDffdkBNCu1Qdhl+YpBKPRSMsmatv5IVJmPCzG+/86lbDkOUm9f79KxsPOseKV39yofv7Oeqx05sb0Pekg4/3ZIhw7ikRtaBIni+iqhRHXGRDCxuvZ0QNBa93UbKgyNNnW9V1dpczhGZWHQafZ0OT/VMpyP+PX+NSFdgZFh0eOjWx1Ic3FH80ERknUM8MzHoEa3yrYt0MqoAkFmWIbaNHOd8UVa6j6XS8nD6GXVUJqUjNfteJvW+WPX8SxYYkFmUBk/usj3IXUCmN77EhrZdVLZxFxUVCAbjXfoS8T0aya+opSSzKhP6Pns54jyqWfOXjsHToAo0uvudujISCcCyfBOf3M2PdFKomXu+oMtazboLtgrug0dWfujuWNBEREREREREREcURJgSJiIiIiIiIiIjiSP2pZaR6RebiIyKKBw9ZB8S6CUREMTHgTMZ7RBQfeL2jhijuE4LbeuYg9S4LrBfGbrUxx1Iv9g13QWMCMgbbkNCx+OSewcwQssa74F0XBHRAwqkGtehGyUUsPL8E1GIhLWelwNA0OnmoZ00A2ZNcCGwPQ5soi2OYkHJzAjQaTaV/Wx5ZdXfn3bnIGJqEhJOj84AEs8LYN9IJ729BaM2y8EYCUnqVngxaVu71/BhQC4xUVyQcwZ7nnWoRj5T/FT63c5kPe4dG5yzMl3xDAlJvsyDsiyDnbbda8Tjijy5Kkv5AYsFKxb6/Asga71YLrUj/JHUxIeUmS4XtkOfc/ahdLS5S8riR59v1hANtFqcV/r4/guwJLri+8iPiiy5Ckt4vEcYjqn/6hexhZE9ww/2DX9X3Ws83IfVui1oxWrhX+dViKIHdYehSNUjumYCkq6OLoWy+IqvEC4Hqk2bjk2A+rvh8Lrv72+FdV3y11YgXSL3DgpTehX0vU5DuftIBfbq2WBJ4W58chPaG1crS+Q5blIZdT9jh/ysIfVMtWk5Prfbrp5p1Z05P9LHchXNNF8asa7/wLsU413CYYMJTtsE42dgR9+b0wb7wXmiLHEAz0xbBUOQk90f8eDzvHpxpPBe9LLeWet657ulY5vsEb6XOKfjZl75PMc89EzmRbDTRNkVvy2043XhOhe0razvOsAOT3a9jjf9HhBHCMfrjcWfiA2iia6oe/9T7ERZ63tu/nQz0TLgFZ5nOq3A7/wT+wiDHE3i3yOq+su0prgn43v8VfBEfjtQfjdsT+6GN/gj1+Obgf6odG4P/wAgTzjKdi1st98C4v5/ec89QKxrLKsonGU7BfdbH1OIgZXndORJf+j5T6y7ny98f+aQNz9ofxdXmHsWOmZ2h7ZjoehV/B/5QKzhfbr4GPS23lNrGH4F1eNHev9jPgpBFDDR4P/0zvGDvjz8D64o97oUXfSx34LqE3vgvuAFvu8apVY+NMKpFY26z3FNwXFSn31f6vsG7nqnYE96NFE0quiZcjyvMXYv9Tnmvt6z99bP/RwxzDIIPPjxqfSam5xQRERERUVXEfUKwrjC0LD9BIkkwWaW31dxUtajA3iFO7BvlQsaLtmKJIlnIQ5I8BT/LDSNzoB2N+luReL4RwcwwdvXLg6GlDtaLTBX+bXki/gj2DnYg4ivRxkEOGNvo0PqDVAS3h7B7gAP6JtqC7UgSLXe6B3nvelRCs7pUwnGUE56VAZVQK8r3TxC2q01o9EjpT6lzJrrh+TmAZq8mQ5euRdZoJzKfcaD5a8kqwZg50IHU2y2wXmlCYFsYux7Kg6GVTq00XJbAtpDqK9+fQaBHkX6JROBa5se+V11yF1+8DW+74d8UQsupKdBaNcid5sGe5x0HlBDbN8yJsDOCllNSAAOw50WnSsQ2GWhDKC+s9oNKKp9qhPfPAHY9bIepnV71WZsl6cXam/m0A7oUbalkoGg6rHjCNvddj0q8JnUvvjR93hwvvD8HYL2ksL/CrjCCO8JoPT9VrV5dVPNXk1UCPG+2p9qvnRqu5tqWmJA6Xf2/O+zCrvAOTE2dr1YDLs9k1+tqBeEzy3jsr8DveM8zE2lF/l6SZ5Ncr2FI0qs4Qt8OP/i/xTDH83g7dS7StOnV2s4E12iVrJuQMg0GjQFvucbjFcdzGJ0yCWv8qzDd/ZZaZbid7iisCaxS22mqa6G2W5Kci1/5l6mEWkAy9EXMdL+NLaFNGJ8yFYkaK+Z4pqnneiN1OgKRAF50DMClpqswyDYMeeEcDHU8p/6mb+J9+My7GMt8SzA0+TXVj284R2OMcwheSBpR5uuUpOLD1gHoXE4ia0dom1o8ZX3wT1xd5OInSbNB9idwgelSPGsbqhJsT+c9jOa6VqWSYscZTsTc9CUF39vDeXg07278LyGaaB2UNKzY73/geRcrfMtwpbl79PXa+6Nbwo14yTwazogDz9ofwyLvPJUsrE6/bw5uVH0xwPaCWln63+DfeN7xJNK1jXGa8awKX295++sU42nqtUmSnWKn58Ic3HWyBRe2id2HvEs3ejH8BxdMemDwuTZ0bFZ6BcdVO/yY9Isbu51hpCZo0PPYBFzdLvr+GgpHcNV72Sj6uW2aWYtZXaMxw7fb/GpF392uEFon6XBXh0Sc0nT/h7PhCN742Y0vNvsQDAPntDLi4U6JMOvLXtBm8QYvZv3uQa43jKMb6fHYaVa0TIp+KLDNHlKr/q7PDqpFQy44zIR7TrFAr9Wo82Dm7x58vNEHpz+CVkla3N0hESdnVC++k+d58ksH0i3agsobef1v/eLG55t88IeB9qk6PHBqIg5PicZ+f+0LYPwat1pBOdGgQZf2Jtx0fPSDXH8ogglrXPhqqx++UARHp+vRr2Mijkgt+3bnm20+TF3nKdgP1x+dgK5HRvdDpiuE8atdWLc3CJ0GOLWpQa2InGyKxjVPfGHHur0BtSJyvklXpKDV/v6riLzGt39147P/fPCFgOMb6/HY6YlobIn+7chVTvWYrkgIlX8s7XOHcf2CHJiLvKRj0vUYfXEyRq9y4vPNxQN0bxC4up0Jj5Wx6MuWvCBG/+jCP9lBpJi0uOXEBFx+RGGc1+fDHOx1h4sdi4uuS4NROmQ/6fN7luTh3NZG3HqiBR/968WEn11qu69floRjG8X3AiKHQn257n252Yfpv3vUuSXXtBuOKTzfbl2cq36eT5Y8lXPjmbOtCEeA0T86iz2XPwS0sGox/ZpU+ILR82n5Fp/6uaxQLteMpP3nakkVnV9FDf/BiZ2OEMZekqy+D0ci6nr58QYfnIEITmyix4OnJqKp9cBWLP81M4CJa93Yao9ey65sa8LNJ0SLdhy+MF7/2Y0fd/oRCgPHN9HjgY6F26rqayjeZ8XPVeEJRvDaTy78sMOv+vmEJno81KnwWlTR+011yLVUtl/W4lNlvQ+IGb+5sfAfr2qjbFOuYalmbaXvA0XJMSXHVlHSn4EwMK97Kqb/5q7yNbPo8SDXznuW5qnj7faTLPjfcaWLoOqTBpEQ3DPEoSqkGj1WuON23JkLWxczbF1MyJslyQw/gnvDqopMklRSoVVZtWB+4iI/cSMJFqnOCmwOQddIqyrtyqoslIq+7bcWP/iKJlrMJ1b9RAp7ItAmaZBymwVaswYwSxWbGXtHFL8w7hvuVEmZvJmFiRZJ9rT+IA1aSzR4C+dFEAkBumRNpX9bnuyJbpg7GFQfFK0YlARZxss2aI0aVfmWdK0Zjo+9BQnBXQ/kwdBCp/ZHcFcVMo8lkoE7+uYiqasZYXvpRbH964OwXlr2m6BrhQ/pDyaqbYu0exKxtUcO/JuCKlkVyon2CSJQAY/6MpYdOPvWB7F7gF1VyEmbivXL6254fw8g9eYElQAsKu1uCyJBqGNUkmWS0NOWqO6sirA3AvcPATR/Pbkg0ZZ6WwJ2PWhH+KGwSvhKojYilX/7Fw/X6Mo+yx0LfSq52eT5wqRyeXwbgsid7kbzCcnqWCr4+V8BOJd4VbK52O//Ez0/SiYDqXaMcQyBSWNS1V/5Hs69E5ebu+AyUxfM88zC1/5lquJOqqrONV2EOxL7VVotKJV773tmFyTq1gf+xBT3BGwLbUaathFuTLi5zOTR3lAm+uWWrtgTzyUNU0mhimwI/aOev6JkoFR3bQptwPGGk0s95gw78ZpzGLqYr8V3/hUFP98V2o5wJKwq+uT80EADPQzFqhCrup0IIvif5VZYtdHz52rztXgo73b4Il5khffi2oReaK8/Wj12qvEMtNK1xt/B38tMCEqFnyQwpT8lmVfULZa7VQWd7F9JlLoiTiRro4GpbOcw3eG4PuEm6DQ6pOsa43zTJVjh+1w9LsnAq8zd0UzXQn0vScJbcq5V+6exLqPYdiTZtiW0Ge30R5XZD/8G1+Ml+wBcn9Ab2eHilcY/+b+HAQb0SrhVBbHNdS0xNPlVmDTFPzwoyxuuMThWfwIuNF9e6rFNwQ2Y456OkckTYNFEg7w3U2fCBHM0WA7bVUIuSZNS0B9V7ffM8G5cZr5aJfHEkYZjcKKhg6pOlIRgRa+3ov1FlK9lkhbTu5T9oV+eN4xB3zgw+DwbTm1mxJ/7Anj4czvapepVAktucCQW+fiGNGhLjOb4OyuIF751qJum81sb8cPOAJ5eYcebVySjTbJeJbfk+aZclaJuFF/81onX17jKTAat3uXHpLVujLgwCYen6DB1nRvPfOXAlKuT1XZf/s6B01sYMewCG3K8ETz2hR2N//bixmMT8OG/Piz9z6f+trlVi083+VQ7ZlyTivSEqr/3z/nTi58zA7jk8MIYTp57ze4A3r4qBTajRiUH5XW8c3WKuiEf+JVD3YDJzfM2RxgPfZanknDntTbh7V/c2JQbwtSrU2A1ajDtNw+e/8ahkgYlbcwJYsh3Trxwrg2dmhlU3z653IHGFi3OamnE89840S5Vh7ndUhEIRTDkeydGrXLhxXOj131Joo25+MASXpN/dePn3QFMvCL6Gkf96MSIH1wYfmFSwXPLPi4rwSOPyY35tC7Ra19RcrNd9IZbEiQT1rhVkqEkeU1PrXDg8iNMGHVREv7YG1R929Kmw/GNDXAFwtjhCGP+talIq2CfyvG1xV54T9ClvVl9XTCrxKgUiuvrnpxvklAZdkESTsowYEN2EPd/lqfOseMaG9Q5W9SYH53q+JPrnE6rKXaN2G4Pqb+VxJWY+Ev0fHr1kmR1/ZHkoVzLXrs0Gi+VVNH5VfTc+fQ/H05oXHgTNf9vLxb968WQ85NwRIoO76zz4PEv7ep6WzRJXhXZnrA63+7vaMGlh5uw0xlG/+V22EwaXHtUgkrUSwJt2tUpMOg0KqH23DcO9aFDVV9DZeeqmLbOrdoys2sKdBoNRvzgxOhVLgy9IKnS95uqcPrDmPCzG0s2+tClnanK7wPyYdWS/3xqH0ryWPapXIPlPaey94GiMhJ1WHJj4Yf+0qf9PsvD2S2MaGTRVvmaubzE8XBYsl4978Of56EhaBAJQdvlZlVxlf5gBBqDRiV7JNlhvdAI13I/HIt9aDo2SQ2FVVVTD9qReJ4R5hOq/iYe3BvC7iccSH/AopJnvr+CqrpM31hb6nn0Gbpi1VgHQ5ugKVWt5frWD2O7wl1nX+hVCSep3iqZ1MtPBm65KhsRD5B4gVEl9KrytyW5V/rh/S2akHIs8Bb8PLA1BF26ptgQZuNhOuS9V3jRkWShvrEOOVPd1U4ISlVdyxkpapivd13xE09em29DCJoEP3KmetSQauv5RqT0tajkpCTHNJJIzbf/Q5zA9hCMh+vVsN+ssS5kSWVfWIY6m2E5s+xPV/QttGg1O1XtE/v8wtcvkm80qySzDL0uSaPTqMRc7hyPGs6rsWjQdGjlibhSIvsTl+biz40QENgVhrG9DpbORmT2d0SXCwoDafdZVIVgUVIRmjPZjSbPWwuGGlck6zWXGgIu/ZUv7I6oYdpSfer8NDocO5//nyA0emBnvzx1Hhpa65B2l6Va5xtV3UXmy1Ul1J2RB1W12pbgJlXd1Nl4Ib71L8fnvsUYnDQWGbqmKqn3lP1BnG08D8cYTqjyNvaF9qrhkXdaHlBJp3+Cf+Flx0BVUXVsieeRZFPRKrDqkko1PfTon9dPvY6Wuta4xXJXQXulLVPcr+PFpFGqSqukCa6RuMR8JRppM4olBDsYO6Gtvj0ey7sHWpUG1OAx6zPlJh4r2k5/2/PFvl/l/1ZVOUoS7FLz1cUe2xXaga2hzThcVzoZKLqbb1QJ2t8Cv5R6TBJ98t8HnjmY7p6EBI0Fz9mGqsea6poXq6iTa+GqwHc4XN9Wfb89tEUlDPPJUGGbJkm1pWRCcHPoP5Uofcf1pkqgJWmT0c18Ay42X6keb6ZtgUmps2HWJOBD7/xif7shuB6t9YerPlrp/1r9ztXm7uiScB0q8mtgDX4JrMbElFllPi7VnF0SeqjnzifPLR7IvU29juP1JxUksKvT76cbz1Zf+SS5KMOZz0w8t9LXW9H+opox5DsHTHpNsSTWnZ/kRhMN7UyY9YcHyzb7sdcVhlEHXNTGhH6nJlZaNSMVLLP/9BTcsEriTAL/zXkhdWNw8/EJZd5clVVhkE9ubk9sUr33tkx3WFW+SDVG/od38lau3x9CSUVe+1R9qWSg+HqrDx2bGtRrFme3NOKkJgYs3ejDPafosfQ/r7pJzk/g3HmyBQ99nqeqZUresMoNmtyAHZUefW+Xm6sP/83Bb3uC6oZ9mz2MTs2ioYeQ5kh/C7svjD7HJxRUw13Z1oyJP7vxb3YQ6S3Kr1ApSio8lmz0qhvNorY5Qvv7JvqlLbJdhz+ikpMheUxNNrC/XfvL9O7uYFGVkXL8SEJLqheT91eUlLTbFcbV7c04rXl0+8c0MqBDhgHr9gTUv0lGDW470aKqK+VLjj+5WRa7nCG4gxGVxK0uqQ6UpKckWuW4E1JltMcVLkjUbc4NFeyXkuT4OCqt8oqkLE9YJTCf72wtqPYpam1mQPVP7+MSVMJF9vnFbYyq+kkSgv9kRc+LipKBUmG5ISdU7cpQir/rXttUPT7okQaLQaPOgTxfWJ3biWUUYazc4ceKLX71IYAcm0VJMujl7524pp25oBpuxRafOoda2KLH+T0dEtHjgxxsyg0WVBbnq+z8ErudIfVhifS79FM+adN1RyfgyLT8a2YCPljvUR9gnFnF617BNlwhnNnCgCvaRm/q5Fp6Tksj1u0J4tqjotc3qeKz7a9yvPYoM27/JA/eYES9X1T2Gqp6rkoluFx51FuRJv96q6nS+01V9PkwV1UkntvKWK33AXl/6n6kuWCf3tcxEde+n6OOS7keV/Q+UBGpdkzQacr8kCSrnGtmecdDQ9IgEoLmDno1/5vMq5bY2aQSFJIY0Vq1sJxhVBV5+kZaBLOjFVSSJAvuq15SSioMTUfqVPJRbfN4A6yXm2D/0HtIEx25szxwfe1H89eiSUJJfubN8aD5G8nRk7kcrRemIbQvrBKnkuCR4bVV/VshFXGSNMsYZlNJ16IinkjxpJucmCaN+nk+SQYeKJW0KidxJVWPMpw68UITmrxgRGhP9DVGAm41V2DiOUbkzvDAeIQOWpsW2ZPcKlkWraSLqIrRRk8kwnqxCf6NIWQ+Y4fxcB1sV5WubtFZyw+KqvL6JOma3EMqJ33Y/aQdLSanwNC86v0iiUhzRwNy3nKj0ZNWdQWUyj0hcxNGAlDzBkryNeE0A7y/BJD5vBOGNjpYOhVeaO3ve1XyUIYVV8azNoDAphAyhhRPYO4b60TiRSY13FjOt2Jkdx2lV9W2Mkzb8aFXDSFvOSVZJcupZp2g7wCLJhGr/T/gTFNnNU/emcbOSNRa0dF4Bo41nIh0bSPkhLPV/GaSVMoK76vWNmSIZFvdkSr5KI4xHI+LTJdjqffDUgnBgyWpOqlUkyRgqjZdbeMFxwCMS56iEpBjnC+rarT8yreiPvN+rJI73cw34tsiycD8KjgZPnqT5XYcpT9OJQtfcw5HC13rgjn58kklYUXbKWq57zPM98zGQNvLpR7bE9qNF+0DVFWm9FlZpLKvMlLp18XcA5/7Psbz9ifxaspklRAs2t5JrlexI7QVD1ufUj+TeQNLVunJ916ZDLQEd8SpkmvdE25Ee/0g/BlchyGOZ2HVJuEM4zmwastfNMARcaiE6B2Wfrg9da5KLr5gfxLJ2rQK59GTOR67mq9XyceS1gXWqqHSA21DyvzbkclvwhNxq2G/kgwvOdS4Kv1e0P6wHS85nsIR+vY4x3iB+llFr7cq+4sOzuVtzaqq68FTI6oyQm7q5KblwsOMWL7Fj8UbfBh7cZIaOiU3tw9+Zsd5rY04oRo3qHvdITzxhQMPnGpRSbG/sqLVUVIdVvJ5SlYYHCwZAtu5lRH9lzvUzZckv+47xVKQXJLKD1cgopIBMlxTbj7v62hRFRnyuyWH/8pzSBJNKjGyPBEcXqRy47AknRrSJAksqWgoSipFOhQZ+iU33i1sWnXDI8khGT4qN0GSTJDtntPSgG77h/f1OaH40CxJoskQOqmaqQp3IIKh3zvR/0yrqrqQocH55KZLhqnJDb28tmSTBmMvjl4nZLhuj6PMGPujSw1nlnbJ92e2NBa8BqmMnPOnR7Vdkg9Dzy/7A1i5uZWvfJLklNdxbqtEJBg0GLa/Wi/ft9v9xfaRPPfAFQ78mxNEk0Qtbj3BoioLKyP7SvbvLmcYI37Ihd0fVglISdqK/3JD6kb3zZ9d+H1vUCU0bzjGrJKuattZQZUYveWjXDj8YZWYkSqjkkm/iT+7cEYLg6pCLYsMU5RKw6IJl8OSdfhsk7/gNUqSut+nearNarjgyZaC80POodfXuFV1oewLOjgN/bon5JyRajQZ8i7nrhzXJSvNZNoDqYa7s4OlzGS0JIokmVg0oVPyupj/2cd2R6hUQrCy80uSlYO/c+KuDhZ1jhZNAEkysuh2NPtjVqlYRMWhYilSWVy0ulgSlT/uDODiw6Pn6/OdbaWuPy1tWrX99VnBCl9DSRWdq9cfk4Dnvnbgmnk5Kqkmw7ALh0iX/35TVZOuTFbXpldWFh/ZWNn7gFTKS+V6PhkqnGTSqESoVK5X9D5QHrnmfbDei7euTC61lkJ518yKjoeGpEGM6ZOdar3MpJJ2kVAEzi98BYk7SfpkT3RhS9dstRCEY4m3oNKqOoK7Q/D+EcSWq7MLvhwfeVUCqtTvZoaK/V7RL6mwOxCyMIXMXScVfc1GJ6lhuTIv356XnEh/JBG6VG2F/SPVcpJ8SrnFAucX/ir/rZBPsKUaLPl/CTAeVjqHLPecJe8xJUFVdHhpVe0b7VSLX+R/VUaGRcu8dLZLTSpxKPP/yVBuSZqKtPsTYWyrw85787DjjlyY2keTx1qbBu5v/PD8FIDtCrNKcso8e7IYiX1R6RvmmiDtk+0kdTOrdrq/Kz5fWFU0ecqqkq0yJF0qXS1nRS9a8nocC70IZYfVzzR6jUr4Sb84PipM2Mn54PjEq4Z0V4VjsVcNxy6aDHV86kNwZxgpN5U9X4L0YZNBNhia6dRxl3xdgppP0rP6wI59qpic3xeaLlNJu1AkhK99XxQk7iRRNM01ETdld1WLI3zhXaKGu4areQHcG9qNv4N/4H/ZVxd8LfV+hH3hPWX8bmax3yv69Wfgt0q31TXhBjxpG4QMXTO1OMY1CdehsbYJ1gZW4z3PDFXRV9YQ0+2hrZjrma4SYlpN6WuaLCAh8/AdZzgJeo0e55kuxgmGk/Glb2mp361oO/mkb6e73sJbrnF42vZSscU3xO+BX/B43n042XAq+iU+gYMhQ4al+vNKczc1N98q/3cFj8kCJ5LQ+j34K4YmvaaSv0Kq22R+v6JkSLMs+lHSSYaOeCl5tEqeSd+caDgFFxgvxfe+ryptmwxDl2HCVyV0V21srz9KJYulWrA8sq9kbj5ZfKQsMv+hzElYXmJO+kMqHmVBkZ8DP6o+OJB+l3kln8i7T80h+bRtsKrIpNjrkKFXcwL9sDP6Hik3CpJAsxq1OKOFEeMvTVY3xXJj6QtGbzL3ear3Ie+yTX4cma5TN+GSEJGKKBk6+eG/tfP+X5TMXZRq1uDl82z4tGcaRlxoU0Nbf9oVfb0mnQbHNtKrKpx3u6WqmyKZX0luniSBJdUzMt+T3KjI/8tQK38wOheUKHoDJ/N55c+JVJInEIG5RNWgfC8VKPmkavCTG9LU8FQZliVD5EqSoX+SyJAb9CaJVTuHxv7oVFUnMlSwJKnwk6qU2V1TsPiGNDV34cCv7Gqol9yQS3XUE2ckYumNaXjj8mSs2OrDxxuK7zepLJG+veNkC55cblfzPlVEkoEyfFYqMy84rPRNpcwb9vVWP+49JZoIDYSi8/ZJolbmoep5TILqA7lRr4zDJ1NWQM2ROPriJDUkUOY7lOFwQhKrJzXRq6HZ865NVZVPr692qySpkKHQkrAdd2mSGmYpu/DpFY6CalOxwxHCiq1+VeFYHtn/CSVu9qVCLX//y/3yUWl6DDzbql6jJJ8GLHeoCh1VpfWdE7eemFBQwUMHp6Ff9/JJgl/OTUnKfLXFj3f/KH5NkfNCjr0rjihdtSjXvNl/eNS1RpKm+WSu1Bm/e7DHFVLXQZmfVRJX0k8lVXZ+ybQLUq0n152SZDvz//Zgc15QXY+m/uaBNxRR5+/BkOeSobnyYUaPo0rHaDJXoLxumQ+2Kq+hqMrOVUnAStL5/R6pWNgjVc23+sI30ZiqovebqiqrOrkq7wOyH0smI9X7UzXeB8q6jsv2Sn44VtE1s6LjoSFpEBWCQhKCO27LhXtlQA2jNO8vZZVqqrAjglZzokM95Q1za5ecsp9EB1VlVbT6LJ8MDbacbkDGS4WfGEqVYRn3naoK6rAiq8weLFksQlZ9lSRP84nJ0O+fm03mzgvuCGHvS9EgIr+1O27PQ6NHEqHP0GLfaBdavJ0cHVoqAhHorJpK/1Yq5gq2vycM37oA/H8H1ZBX9fsBIPMpO1L6WJDY2agSUSFnuCBx5N8cgqHIENOqavSoVX1VlX9LEM7P/Ui7o/AEluGr+YuRyryRqX0tBc8p8x2GX42oxKAkjovub0UH1c81SSoWZZi2zIFY0MZANIlXXaGcMBo9lghtYrSf3T/61RBkWSjG8WF5r6fwW5nrUYaOlzcsuqhIMAL3936VgC7KtcwH/39BbO2aU5CslgPI928QLSenIO99qcjUI6HI0HRZDKe8uRnp4ElCsF/ubfgpsBJ6jQ4n6k9RP5/hfkstvvB26hyVIJLrX++cLmU+hxY6BFF4ANkjhcPz07SN0dFwOp5OeqngZ1JlqCvjMyUZjjq7yEq51fWR530cpj9CzelWtLpPkoOS7MyOZKnkopBqN0kAybBVqfqTaq8H8m5TjwUjIfjhU7/7avJklaiUCsOidBpZU7f0daqi7TybNFQl2mRF2czwLgxPfl0Nay5Zqfi2azzuSLy/1FDW6hjueB4nGDoUW/1W9pFVYysYFisVg611bTC8yFx7opWujRo2fDKiicrccA4cEXuxYcT5VvtXIjucjUvNVxX8zA9/sVWdy9NS31ol5YqSASiSeC7PSt/XKglZ1orHwUhQzUv4UtLoYj/PDO1Si4iMTH6joKowgIAaXp5fCVmdfpfXPNI5WA2NlvkAy/q0mGJD9sVlR5jUzetZLYxqgYwBZ0WPebkRmLjWpSopUhO0ODJVF/2Mt5r3YzJcS+ZMu/q97IKfyXO33z8UrChJgNz+cdnzBEn1WUUVOiWH3cnwTBmOJTf1+dVkUo0gc0h99K8PnZoZcc8pxYcByjDYxf/61NAqGSL3xOlWvLnWjaErwzitmUHdqMik+vnJvaI3p/k3xFLxVpLcbJW8kZWbLfldSWzJzdOC61LV0GWpDrvtxAQ1t1TfkwqvMzJUb+QqF3odm4Dex5f9IWHRxQJObGxQwxPVfFlFJo8v1qffO9U2mu2fPF+qJz/b5MPqXQEEwhH8tCugJrYXMueiLEwg83pdtX9RlvzElpCKxk82ePHddr+qhCnLfzlBNTfXESl6DDzLWqxiTm7UZS6vNbsCKnmXvzjJxYeb1Fc+eU2SyJAb8pLD+Pp/GV18JL/q6qkzreqYlQrM/AooSbzKMSZJX0mGdmxaWDktk+lfeoQRX231qYTEwLOLVw5JMqDb+zmqT/Nv+OU1d2puqDBZJ/tf9ndRchxJoklIvxYlQyVlOLHsB0lESduLLkBCB6ehX/fyF3zIP7+k2vb6Y8z4ZKMPvYosyCCVkF3bRxOWJcnQXKmuvbjEEOf7T0nEGz+7cO/SPHVcy7n+zTa/mp+zpIrOL0m2y78yt2dZ5DrnCco57VD9L/PXyQc2Nll1qRJXzC0scrmkjalg3jrpq0FfO9T7glxjil6rZd9M/sWNDzf48NJ5hQuGVHaNKEoSpeWdq5IMlPlZZbv5i3U8cloius3PUddF2cflvd+UNPN3txrWnk8+rJDrXXkk4V3R+4B8WFFyO+r9Sa9R+7Yq7wNFSQW9zAP4+mVlzyv5SRnXzF8yAxUeDw1Jg0kISjWS6Vg9ssa7YLvMVBDcSzJQEiLywb8s0JE7zY2wKzq8stRzSNXW934196Ak+xyfFFZXyJDU3He9cH3jg+Vso5oHT4Z9SvVU6i3lfwJ3sKTiMfOp6Gqwsqpw0aSKDIVu82lhObcMid7WIwctJier+RLl9coCFjK3XuqtCaqqS/5fFlup7G9LzYn4WfGy8c2XZiFjaBISTo6+IZiO0asFV2QBj+CuEOwLvEi7s/b6JZ8MC7d/4IEuRaOG5AZ2hJE7ww3bldELgn2eB6G8CBoPtCLiiqjh0jKPoix4IasdZ7/lRt58j6qYk7kQZW7ApOtrNsAxHa9H7mwPEk4xQN9Ui7z5XoQd4Sol5UrKftOtjtO0+y1qERFJ0CZ1NamEr+VMA3Y/5YVzuU8t9OH7IwjnUh8aP1V4sZWfGY/RVynpKUOoZWIJ45HFLxNNRySVquqUJGzjAdHtSLsci11o+opN9XPeXI+ac1DaR7VDqumO0h+rkiGSHMy//kkyUBImMg+dDCGd654GV8RVLPGXr4WuFX70f6/mHswO78My3ycFj8nQzw+876pFNmTuNUmESSJKqrh6Wm6p0deyN5yJz1yL8ZztFVWlt9AzF+6IG50MZ+L81EuK/a60QV53L0t0EZMbLDcVPPa170vMcE/CW6lz1PedjGepRVHOCZyP4/QnqeTpz/4fcUNy4d/ky19IpbztjHUORW44G8OSXi9VxfaTf6UaviuJQ0l6HYyj9cer4chSsddE2xQfeefDEXagk/FM1SfSLkkY3p/4WKmE1kWmy9Tfnmw8FY20jTHFNUG1p6whr5K+m+war46BY/THq7n9vvF9We6KxEWdbTwfs9xT1IrAXc03qEVYlvs+Rb/Ex8v9G6k2le2UZVNoI2Tpl7b6I4v9XF6/DI1/x/0m7kl8WB3bU1xvqGpEqUysTr/LPJWvOAaphXjkfKG6R26Mb1uci5U7AurG8JSM6PuQLDAhwyXndE9VNwbyIUeXeWV/yCv5ManGy5fnK7y5kCFyshLlS+cVvp/J6q1lTUEkNzVSqXYgyhp2N261q1i7VFu1hXMIyuIeUhkkc24JGQ4VUhURGjVUrn2artgE/PctzVO/L3NNpSdo1FCr/JsaqWQx66AW/iipTbJO/W4+qQCRSfwPT9ZhjzukKvXy55USsrpw0RUtZTL6eX978fRZ1gqHypZcLOCJL+34LzeIrvNzCpJushmZf3DyVSnY4w6rG9V8sk/kS/pH2leq7+Sx/dc/qdKT4bf5K5cK+f2yEgNCKl4Gf+tUw+3yV/csuviLTPIvr3viFcnFhi9K8s+kA84tMoG9DHfLn+uwqJJDj+XGVF6P9G++/Lyc9IQc85IwLnpjK8O+Zf/LDfI769zocbS5oPLGv7+vim77623+YonbsrRJ0WHWH9Fqv/z5KmU4nOx/8f7fHpUkLTqsXBKy0g5JVmW5IwWJJakqlJtnSSTLYgR0YBrydU+SMbICtayGXd65KW35a18QL+1fuKckOa6lUlXOyaL2esLqeM9Psm3NC+FVf9mJTjnnyzu/lm32qeeSIc3R9kXUeSrHufSFTOHQ7UiTmps1/1x+908PjqzCnJ5lDb+WRTueWm5XSTyZ+7Xo65JzXRafkmGqksSSD2Wq8hpKquhcfeosqzquil6L8p9C2lLR+01JkvAta5Xf8khfV/Q+oN6f7CF0bBb9/RxvGHZfRCVgZS7Hit4HyvLDjgCaW3VlHhPlXTMrOx4akgaTEBTWy83Y94pTze2XT6rDZKitDBmWIawJpxuR0MmAwCb52LT4Jwyy8IFU1G3pngNDM616HllFVchwW5lHTRIw+4a51DBZWVykvGGTNcXzc0AtYCKFGvIa8kli8LBFaZUvSDLchqxxLmzt6oU2WaOSgck3VC3hJVWJUmVYlYq9Ji/YkDXGiW035Ki2yiIU0j+1TZ+uRcaQJLVfcqa41T6W+f+Se0VfY+rdFmSNdGHb9TlqgLwsJpN2b/QTBVkkI2OwTf2dJEpl9WVbV7NKLArvuoDqg5ZTUw5q7jtZqESSs7set6t5FWVoctPRycUWYamqRo8nYt8oGQKfA60cg1eYVbJXyBDhxk9akTvdg30jXWreTEkc5g8rFoFdIdVnZZFhyNaLjUi5KXpBlMSuDCcvqC6tIqnWzA65sfO+PJUIlPkEmw5PUslbqj0yTPhV5ysqQZKvt6Wv+pkMGZa5AzsaT0cHQydsDW4qefnDzZa78IZrNG7O6Y4MbTP1PMu80cVBZL66Z21DMM09CeNcw2CEGReYLlEr3Na0PpY7EJJ5bvLuU/PEyXyCLyQNV3MiHgypfpPnG+8cidxIDppqm6vFQfITT/PcM9Ww6/EpUyt8Hlno5Hv/V2pl3dtzri/22MTU2WpV5xBCGGJ/pthj/7PcpoZDV4fMG+iNePCc/XH1r6ygOzh5tKqQ+9T7EXaHdyLHl41vfF8U/I0k9UanTMKlpqtVRaAkDV375wh81Dqw4PdesPdHY20G7rM+qlbWvS3xXoxzDler6kqV50PW/pXOvyekyk+GKr/tHq8SkHKcScXdmaboAh1lkYTyWdrzynxsT2iXes6Sw3flRl3maXzL9Rr65lyvKl7PNV6IXpZoRWhl/T7BOVolm2W+QUkyy+rNE51j1Ve+S8xXlbkCNx16Uh0mw2ZlLqnL2hZ+yCs3L5IYkrclGfI47Te3qhgpeXMgZJjP99v9ajiUVDRJJUq+Cw8z4d0/vGqidRkWJTddT35pV5V6t1QwzLImyETyT63wqhtkmUj9j31BtWKvVI7lz3G1bo8Lg86xwajXqHmN8vtD5vySoa2vX5qMZlatqqaReZEkkSAuO8Ks+kRu4uTm8O1fo0OkSt5Ai8vbmvDSt06cf5hRVetIIlJW6JTt2P0RlfyTRIRUr8nN2PTf3AWVOQvWezF/vRevXVJYNVdVskpkUaNXOVUyTVayzO8fGUIt7ZCqFalwkUngT2hsUAkNaZMM25NJ9uW1y8qfUmkkjm+sV0PrpGKmaaJWPSbz7JU1t5TMkSeVObKIQ37/FU2OSj+nmLVqVeGSN9rynBP/iA4lkxt1Wezhj70BPH566UUeSpIhoLLfZUVkmSdMhnW/86tbve5Eg1YlYcevcannltcjFXlfbvZhxEVJqvJRhuxJYuLx063qBnXcajdObSZ9oytIZG53hFV/VUQSp1JNJX1903EJ6tj6YrO/YM7FTFcYize48MoFNrXK59y/PKqCUdp5yeHFq7vl3JH9JYsg0IFryNc9eV0jfwippOBV7Uz4NzuEeX951GIR+f7YF1DzmJa3kI1UN0q1YUnyPJL4lApf6ZfXVrvU0P+ynqei80uGUMt5lU+uJT/u9BfMqSfnh1QbD78weo7IdvJXh68uGd4sfX/d0fJhROm+H7rSiWyvJAOT1DWjqq+hpJKrPpc8V+Xvpfr0hc42NQxb5hqURYtkxeg/91X8fnMwKnsfkG1I/5/a1IjGiVpMWONSlZFynZPrXUXvA2WRY+v4JmXvp7xyrplyLFR0PDQkmkjRSSfqiYFZj2CNbxUaCsdSL/Jme9ByetlLtRM1JGUd7x1Np+Pl9DExbVd9IEOvs991Yu+bhXOm1XdfeJfifc/sUlV5RPXRnTk90cdyV7EFVRrfY0NaL2upBblIRkEE4F77EfI+GVnmvEkyEfmsrikFw0dl8nb52cbcICx6jZpcXKpKZDELGWpbdIVNGfIkwz2l6kluZuRmeMl/3oIbpF8zA2quKamSM+uhJtm/4yRLmUPVqqPkqp5lkQozubmQlWVlpc9ex5kLhnTJfHZyUyY3HlL1cVKGXg0LlfnD8pNxsmiGJKVkzju5oc4fpioT08uN0pdbfOpvpXJP5p3Kn4tJhvHJSrL5lRyfSFv/8KhqE3mOx06zouX+ShSpXpFJ6zfmhlRV0kVtjGp+JUmOXfdBNnK9svhB8dclN3PnFamaq4qSN4KyOrAsCCJJDVmNWW62+3VMROv9lWvSL7JSpNzAyXxksgrrjceYVfJEbmlm/u5RN64yB1XJv5VtSaJLqvZe+tahFmvIn2cx31VtzTi9hUHN2yhVd0UPB3nti65LU9uRROUnG6ILHMjzy8qmRavpKiLVP2//6lbzEkoCo1MzAx4+LVEtmiJkTrf3/vIgyx1W1VYy/9f5++ewkhUvJRkgq0FLZaFMgi/HR9L+v5X9JlU8y/6XVmqlahnWJ8nL/IofuZGWebz+yQ6pCfulSjL/OJSKnYlr3WpYuKyoLPMJyqq2Za2sXFZC8IJZWSqZUXThhOQrH4elQxdodPE9YiQSCsKxfBKc38+Mq+ueLNrzxs9uVcEnyf2ex5rVfIb55Lom89WVl3C5Yk6WqkAtuVKuVOrJ1AU/7w6o81WqCO89pfC6J8P2MxK1BRWEFZ1fRZVMAMn1VSq8v9rmV0k5SUw9dGpiuSuZV0Su0/L8sg+KOrWpQQ2FvfmjXBgkCVziqWd3TVUflFT0Gkqe5xWdq7Kyrgy3lr6TYmPpW7lm5q+AXtH7TXXI8St5zfx9UNn7gFxjZ//hVQlkNWdihl4l5/KHNlf0PiDHmVR3Fx22PGC5Xb0flPWhxd8VXDOL9X0ZCcGHP89TK9X/r0ii2nrWTbBdcBc0uvpTd8eEYB3AhCDFEyYEDxwTgkR1GxOCNZcQrK+qcmNM1NAxIXhgCcH6itc9ikcPN5CEIMfw1RGB7WG1qq5nTfVXniWqL3Y+lIes0cWXvCfaGd6OG7OuwC/+NewMqpdkPko5hveWsfI2EREREVFdVH9Slw2Y7XKz+iJq6Jq/2vDmXaCDn/tQvojqs1OMp2FuenTOTaLt9rBaVXLwuYUrQxLFAxniN+FnfvAbj3jdo3ixJS+Ie5bmqak5pEKwvmNCkIiIiIioBsicWEXnxSKKJzKXl3xRfOF1j+LJYcn6MleOrq84ZLgOkxVh6/t2Aztj8xrqm5AzjJC9jOXCiOqpzNCuuNpuXcN+qHm7Qztr4VmJ6qZdzlBcbZeI4hevdxTPWCFYRftGO+H8fP/S7SEgEgQ0RRYkarOkZrPEWW+4EPFE0OjRslfjqS01uV37Ag/cPwTQdFjpZdCrY9fDeUg4zYiU/5Vear6h2N47FxlDbdAdW7s5+m09c5B6lwXWCw9+yXiqPyY4R+Mr3+fq/0MIIYggTCg8Bmp6qOM7rjfgiXhwn/XRGn3eWG73t8AveMH+JOanf4a9oUz0y70VE1NnI0Vb9xYOqKl+yAztxl25vTA19X2katMQzz72LMDqwA8YlDQs1k2hOCQrMH6+ORqDhsJAMIxiq+PWdKWCrDrpCUTKXRGytsRqu0RUd/B6R3RoMSFYRZIgy0+SOb/0IWeSG63m1N6NYDgvAk0MhqTX5HZDeREgUjPP1dCF7ewoqj2SGMpPDn3t+xIz3JPwVuqcWtuePZIHAw79BexQbbexLqNOzxcXq/5vyKRPI3xDoxiRBFl+kuzLzT5M+sWNOd1qLwbN80VgjMEYolhtl4jqDl7viA4tJgRriGOpF46PfNDoAf+mEJo8b4PpSB2y3nTD84NfJcYs5xiRdk8itAkaRCIR5M3ywLnMj+DesErCWS8yIb1fInJnuuFcFv0kOLA9hEZPWrHj5lykPZiI3GluhN1AUlcTTMfokf2mG6GcMBI7m9CofyI0Wg3CznC525V2Oj/zwdBCB9cKPzRmwHaFGal9LaW222x08QUg5PHcWZ7SB1GGDi2nphT7mVRTqt8NA9t656DVrFQE94WRNd4F768BaEwa2C41IeWWBGh0+/tjrheOhV6EHBEY2+nQ6OFEGA+PHqKBrSFVKejbEIK+kRaNHkuE+QRDpfvFszaA7AkuBHaFoU/TwHqFGSm9opWGmy7IQrPXk2A+Nvo8OVPd8P0ZRNPhSdF+WuKDvpkOrq980KVokdInAbYrzQWVdtYrTXB+6lP9n9DBgPTHrNCnRSNZ17d+ta8CO8PQN9YipXcCrJdEK7Lkdcjzelb7oU3SAsFoW3c/akf6A4mwXVXx3CuB3SG1371rA+q4sZxlRNpdFmgTtRXu32L75wsfsl93odW8VNX/+fvX93cQGYMPrqKT6p8vvEvxqe8j6KHHltAm9Lc9j7a6I/GO+02s9v8AuZCcYTwHtybeA7MmQZ2v8zyz8LV/GfaF98III841XYQ7EvthnnsmvvItU8+7M7QdD1ifxP25N+OuxAfxrnsaPHDjClNXHKk/BlPdbyI3nIMzTZ3xYGJ/aDVauMLOcrcr7Vzu+wzNdS3wrX8FTDDjYvMV6G3pW2q7g5NHF3uN8vh8z6wyk3vjU6aW+rm0403XGKwOrIJVY0Vn40XlVs7NdE9WbQvAj1a6NrjVcjeOMhxb0Lfve2YjK7wXLXStcEfiAzjWcALCkTA+8L6LT72L4Yo4cJiuLfom3ov2+qPV33XNugDDk14veJ533VOxPvgnnk8aflD9UNKrzldggAFbQ5vxX3ADWupaq311tOG4SqsFo39rVMnmfaG9GOcajn+Df8OiScSJhg7qeWS/VSQYCWKS61X84P8WgAZH6Nupv2uua1nsNQGwp1IAABrdSURBVOcr2i935vTExaYr8aXvU3UcnWDogPutj6n2SR994VuCDF0zfO/7CknaFNyY0AcXm69Uz2MP52GaexJ+8q9Ux9mJhlPQN/E+pGnTVTXoeOcItNG3xW+Bn3GHpZ86dsII4+6c3piYWvo4IoqlpRu9+GiDD3otsCk3hOc723Bkmg5v/uzGDzv9iESAc1oZcc8piUjQR2OuWX94sGyzH3tdYRh1wEVtTOh3aiJm/u7Gsk3RWHC7I4Qnz7Di5o9y8WCnRExb54Y7AHQ90oRjGunV8+d4w+jcyoT+ZyZCq9HA6Q+Xu11p52ebfGhh02HFVj/MOuCKtmb0PclSarujLy4eg8rj0uaSMhJ1mHp18RhUPPx5Hk5obMCa3QFsyguipU2HB05NxIlNojHfuj0BTFrrxua8EFLMGnRtb8Z1R5uh0WjwykonEvTRRRJ+3xdAY4sWt59kwXmto3Hc1rwQxq1xYX1WEDajBtceZUaPoxvuKBaiuoTXO17vqGYxIViDJJnU5AUrEjoZoTEAe553qIRci3dS1L97hzhVQqzxE1a4lvvhWOxD07FJMDTVwftnALsetCPxPCNSbrIgsD2aJJSqREkARQKA7/cgWs5KVdvZ/YgdCZ0MaDExGcHsCHbenYvEi4ywnGbE3lec5W5XeNcGVRKp9cJUeNYEkDnAAcu5pbdbkjwuX1Uhya/AjlBBgi0SjiDzaTtMR+vR6t1UhO1hZD7nAAxAah8LnJ/4YJ/nQcYrSTAeoUPudA8yn3Wg5YzoRU/a2XSEDYZWOuwb7ULWWBdaTC59QSxp71An0u61wHqBCf6NQex80A7LaQYY21Z+6HvXBZFykgGHfZgG729BZA6wQ99ch4STo8GktDljmA36xjrV53sHO1QS1fNzAHtfdKDJCzYknGZQf7vnGQe0iRrV79HnDqDFWykqgay1alVysunowuRkeSLBCHY/YYf5RIOqUI14I9gzxIm9w1zIeNFW4f41tSt8zZIk3jfGpdpq6RRtkySnU/syoI1XkngZYH0BHYydoIcBwx3Pq4qo8SnvqH/HOIfgLdd4PGB9At/6l+Nz32IMThqLDF1TrA/8iafsD+Js43m43nITdoa3FySKJJEUQAB/BX9XiRTZzjP2R9DB0AmjkyciJ5KNR3PvxrnGi9RKrZJkKm+74rfgWpxmPAszUhfi18AavOgYgDON55babknyuHxVlSQDJck0KWU2/BEfXnYMLPP3pA0rfJ9jbMpbsGmS8K5nKt5yjcPIlDfUYxNdr+KZpJdxvP5klaAa7HgKk1PnYaFnrvq7Z21D0FzXSiVkn7M/jnHJ76CRrnGl7TvQfijLF76l6G97AacYTsMi7zwMdjyNN1NmojpmuN9SCc/nbK/AFXHiWfujKlF3pblbhX+33Pcp/gttwJups1RicoJrNGa638aTtuertN1lvk/wnG0Y0nWN1bEzyjG4IAn6R3AdjjOchJlpH+Kv4G940T4ATXXNcbzhZAxzDIJJY1bHmSTCZT8NsT+DYcnj1d/uDu/ElfpueNz6LMIIITO8u1Rykqgu+XNfEC90tqJTcyMMWuD5bxyqpvWdq1LUv0O+d2L8aheeOMOK5Vv8WLzBh7EXJ6GpVYc/9wXw4Gd2nNfaiJuOt2C7I6wq9aRSZ7czhEAY+H1vELO6pqrtPLLMjk7NDJh4RTKyPRHcvSQXF7UxqtUWJZlW3nbF2swgzmppxMIeqSpZN2C5A+e2Kr3dkuRx+aqOTzZ6MfKiJDS36jD2JxdeW+3C21emqITe41/Y8VCnRFx+hAmb8kJ4ZoUDOi1w7VHROGjJRh9GXJiEYxrZMP03D0b/6MI5LY1qVcnHv7SjSzsThpxnww5nCANXOJBk0uKSwzkdC9GhwOsdr3dUc1iYX4OkECLxXJOqxJMhoO7vAkh7IBE6mxa6JC3S7rbAudSHiD8CyxlGNBufrJKBwewwIj5Aa9GoKrryJN9ghtaoiSakdIDtGrNKJhlb61SiKpgZRig7XOF21U63apB8XbQyTxKIunQNAttqdxJnqTwLbAmpCkitWQN9Ex1Sb7HA8WH002CpTLR1NcPUXq/aJRV1jZ+2qgpDYb3YCGOb6GOJ5xurvFiJJDddX/rg/tEPfUsdDvsotUrJQKFrpIlWMBo0SDjFoJJqri98hfvjfwkwHqZX+00q9CQRF8wKw7nUq9poOdOo2iv7y9bFBMfH3oK/tZxuVFWHsv+qQ5KUocww0h+MVnzqUrVI72eB+xt/waIkVdm/WlO0H11f+NX3vvVBVekobab4ZEYCzjSdqyq6nBE7VgW+w52JD8CqtcGmTcItlrvxpW8pAhE/OhrPwCvJ41UyMCecDR98SNBYkBXeV+7zdzXfAKPGiBMMJ0MHHS43X4NErVVVpEmSZm84E7nh7Aq3KxI1VlyTcB10Gp1KIKZq0rEztK1G+0K29b3/G/Sy3KbaIMmmGyw3l/m7knjLC+fiM+9iVWXXK+FWlQwUK3zL0Nl0gao+k+rHS8xXqeSVHjos8y3B9Qm90Vp/OPQaPa4yd1cJte/8y6vUxprshzOMnVVyUdpxrbmn2k8/B36s1nMYNEb8EfgV3/lXRBO5yW9VmgzM/ztZrOML7xLsC+/BA4lPVDkZKK5N+B9a6Q+DRWPBLZa7VKI0O5ylHkvTNELPhFtg0BjUPpCE6de+L9T2fg/+qioRk7TJsGgTcY/1EWwM/YPNoY0Fz32B6VL1t5I4JKrrpKLt3NYmVYln90Xw3fYAHuiYCJtJq5JVd3ewYOl/PvhDEZzRwojxlyarZGC2JwxfELAYNNjnKT8GveFoM4w6DU7OMEAGFlzT3gyrUYvWyTo0t+mQ6Qqr56pou8Jq1OC6oxOg02pUAjE9QYNtjtqJQS9sY8LhKXqY9BqVsNxuj25n2WYfjknX46p2ZtWOdql69DouAR9vKIzxJOF5QhMD9FoNLj3cpPrU7o9g5Q6/qsTsc4IFBp0GbZL1qrLww38LYzwiql283pXG6x0dKFYI1iAZypovmBkNOnbekVfsdzQ6eSwMbaoG2RNd8PwYUEkd45G66Hx7FUwlp00ufH6NNpr4Kfhe/jdS+XaFLlVT4jFNleb6y53tQd7s0sM1dBlatKykWk+2LQuxbL02p+BnapPBCML+CEJZYeibFHl9Rk2xajmtrchjBo1a2KUqmo5MQu47Huwb7kTYEVEVmCqZVoVEnL6prmA4rfq+iVYlNfMZWhQ+h25/2yUhG8qJwHRc8VNLDRH+KVD4++kHlouXpJ0uTasSekWfWwT3VG//2i4zYfdTDqT7I2qItyQIVd9SXErTNir4/z2hTPXvw3l3FPsdSeTtCWciWZOKaa6JKmkki2rI8GJJAoUruJBI4iWfFlqV0Cr8XqP+trLtihRN8Xmz9BpdhdvNN98zGx94Zpf6eSNtBl5LmVzsZ/aIHUEE0EhbWKnXRNu0zOeV4b+PWp/GJ96FeM8zQyUQb0jooxKeOeEsHK0vPvQ2fyiuVB9m6JoXeyxD2wx7w3tQFQfaD2VppmtR8P8yXC5d21gleqtDkrhz3dMx1zNdVXUerT8e9yY+gtb6NhX+3fmmS+CLePGF71O8435D9cEtiXeroeJVaru2sO2NtE3Uv5JYFpKwloRp0ce3h7aovtdAU2yfJmgSkKRJVv0vx6ZUyRY9ZonqukYJhXFFpisaq9zxSfFYUMIBSdylmjWYuNaFH3cGkJqgxZGpumgIWsElJNlc+PxaTTSxVzQGlb+tbLtCtl3sMa0MYa789c3+w6O+SspI1GLyVWXHoKlF2iyJvfD+7cgw52a2wmuDaGbVFrSx9N9G/42+xjD2uMK4+r3Ca6T83FYkLiOi2sXrXWm83tGBYkKwluj2JwdbzkmBbn/ySSW+MsPQN9ci6zWXSlDJsM/8OQW3dilMlpVJc/DbxR8H/ppkld8DXelXkqUyZLb1olR1w6na5QqrhUek6lHXWIdQkerISCCC7ElupNx84ENY5XUHZQ5GNbeiFb4NQewd7ETuXC/Sbreo+lgZip0vlFf8k/Gi7RHB3WE1X2LB93vDxR6T/SPzBeoztAiWqGCU7yWRV+AA40Z5bkk6hn2RgqRgcEd0W5JYrg6Zg1GXrIFnlR+ur/xquDvFr6KHZPr+5OCklDmwaqPHhT/iV1V8TbXNMcn1GpwRB95OnVMwp2DvnC6VPH/lB31l2/37IC5g1yX8T31VRZImSQ1flWGiTXTRpFF2eG+ZvysrDsvvvJQ8Gr6ID9/7v8JY51CcZOioEmsyx2JRMt/gJaYr0USbgczQTlUxmU8q1w7XtytImkpSMp/MeVdbilZ2ytyGe0N70Hh/ci2ftEcEI8XbJK9RbApuwDUJPXBz4p3ICu3FZPfrmOAahVeSx1W47R2hbThWfyIuM3eBO+zCJ75FGOF4AbPTPor2gXySVGR7JRXdL3vCu9VxJm3ahI2lKlblcZkzUr4kgS3fS3WqkG3LwiGpmjT44T/QSzRR7BQ5aBtZoufrnG4pqopPSIWeJLOaW7Vq6KzDH8Gc7qkFcwp2mVdxDFqVc6Ky7f5R9mW0Sv53XIL6qgmSRPxpZ6D4tcgRRlqCpkqv8fAUHd66sjAJmecNw7e/ApKIDgFe76qM1zuqDIcM1xKZVy7hVAOyx7tV4ksSONnjXNj9lF09LslAmT9OihfCnghyJsrvRQoSVDIHoXxf09utzIFut+zn0iDsjj6XLIAiVXE5b3uiCUpnWM1tuG+EUz1uvcwE+yIf/JuCiIQiyJ3jUYmqolWQ1d6+3AC+5IR9gVfNYagqODWALin6nDIfoQy1lcckWej+KjoksWiSL2+eR7XH/ZMf7u/8qp358uZ41fyOsohLzkQXLGcZ1DBg6+UmtaCHe6Vf/a3n1wDsi33F/rYU6Xdn5f0u/ShDn1VC2RNRFYNZE9xqn+sPoOrQeqkJuTM80Cag0vkLKX7IENmTDadisnu8SpRIoutt1zi8ZH9KPS7JQJl3TSr3PBEPprknwhVxFSSwJJnmjrhqfLuVOdDtlnoejRHnmS5Wc9lJNZlUnM31zCjzd/8J/qXmptse2gqTxqSqzKRvpOLsQtOlar7FPwO/qUSbzKm31PshbNpkXGS+Qi3MsjW4SSW9PvYuUEOOZR5GIfMKrvR/o/5OFvuQRGOV21/NfvjWt1wN95V2yOIZklSTYchFSSWoVM7lt2Nd4GesC6wteFyqIye7JsAb8ajKOhlKLXMqVmaV/zsMcw5SSUQZdi7bkH+lQk+GUP8X+kclSmUY9xzPtFKJ5Q+8c7AntFstAjPVNRGnGc5CsjZ6oy4Jv0WeeQhFQljr/wk/+r/DhabLVOJZ5rCUuR4lySjHmswhKH2ev6hLqT7VSJ+6q9ynRLHU2KLDqc0MGL/GDVdAhgRHMG61C0+tiMaCkgyUqjep3PMEIpi4Vn4vouYKFDIHoXxf09utzIFut7pkAZX12UF8vMGLUDiCjTlBzP3Tg8sOr3x6gDNbGJDlCWPhP14EwxE1THrACgfeWVe6epGIah+vdxXj9Y4qw4RgLWr8jFUloLbfkottPXLUsNmmrySpIZyy6msoN4ItXbOx/aYchOwRtUhIYFO0GiLxfBM8qwPYcWdujW63Mgez3ZIkQSbDWLd0y1btyRhqU3PZbbsxB9t758odFpo8F10IQ1YcTr7RjMxnHNjSNQfeX4PIGJpUUE14IGTYcZMXbWo47JYuOdh+a65aDTipezTgS38oUa14vOXqbJVgs5ZY3Veq8fwbQtjaLUclVWVOQ9NRhUW1Miw480k7tvXMhcaiQaMB0Yom8/EGtQ9yJruxpUs2skY51TyOsop0eWxXmbDnOQfy3qs4oJR9mDHEphLK23pFX5NUJTZ+7sCq+yQhKK9R/iUq6jHrMyqtfl/uLbgtp4casvtc0itqCKasZpsXycVN2V1xb85NcETsKsEiyS1xtul8/BJYjYdz76zR7VbmYLZbkswv11J3GO7L7YNHcu9WFX/lbfMKc1c8Z38MN2RdgSnuN/CEbRBStGlqQQsZNiuVcv/L6aKSgYOShqn57rqbb8RFpssx2DEQvbO74CvfF2rBivzhu3cnPoTfA7+iV/bVeMv1mpp/sKqq2w/Hy8Ib7im4Kacr1gZWq3aUXB1YEmL3JT6Kz3wfo2f2VSrRdpHpsoLH70t8TCUhb8+5EX1yuquFRe5JfLjSbV9jvg4n6Dvg0by70TP7Snzu/RhP2war/S1zG55lPB9P5t2PO3N6qWHtJSsXj9Ifh0H2J3Fnbk/Vrw9aBxQ8JlWYm0Ib0Cenm0r+PWJ9Gu30R6nHHrUOVCsPP5B7G+7IvRE+ePG8bbia67EsnQxnqTkO+2R3UwlGorrumbOtKn1+y0e56PFBjqrSe+X8JDVEt++JFuR6I+g6Pxs3fZij5saTOfM25UZj0PMPM2H17gDu/CS3RrdbmYPZbnU0s+rwygVJ+GSjD9fMy1GLglzd3oRex1WeEJTKx5EXJuHbbX70eD8Ht3+ci7YpOrUKMxHFBq935eP1jiqjicg4gXpmYNYjWONbFetmUAPmWOpV8yW2nF58nq5823rmIPUuC6wX1u9EmlQZbr02Gy2nphQbDn2odTSdjpfTx8Rs+/WFGkr/rhN733TEuinUQMjKvNVZkbguuTOnJ/pY7sK5pgtLPfaFdyne98zGhNTpiLXG99iQ1svKOVrLEAkF4F77EfI+GXnodwwRHVLJVz4OS4cu0Ojie0RKJBSEY/kkOL+fGeumEFENs551E2wX3AWNrv7MzFd/WkpENUY+BwhsDsGxxAfzyYaYJgOJiIiIiIiI6NBiQpDqLde3fux9ufxKqeQbEpB6mwX1SdYEFxwfect9PGOwDQkdjQe9HRmKvfsJO7QWjRqaTfUIVzugKvov+C+eynuw3MfPMkXnK6xNj+fei22hzeU+PjNtkZqzsUHjOVtJ/3D2GqK4oKb+4AUx2hfsB6IGSVv/Ypp6mRC0aZnAICDxHCMSl6TXzjF2uVl9lUdWh64N6fclqq9DofX8NNQVsggBVYFaFKf+vdFQbByhb4+56Uti2v0jU96oted+K3VOuY9dZL5cfdUFumSes+XS6KBLaXYodwcRxYg61/kBQLQvkjN4HBI1QLqk+ndu17soVVZBPMPUOdbNIKIadLrpHHVuU8U0eg2s55gBjvAmqh90UOesxsBqkLJotFqY2nSENqn4YjFE1LDIOW5qc4o65+OdzC1mPuZCaExciIaoIdGYEmE+9oJ6NX9gvV1UREy3v4XF7g9gD+fFuilEdBCVgVdbrsXNSQe/Kmy8iIQjcHzlxd437Ahs52qnRHWVoaUOje9Lgu1cMzRVWGE1nifYDzn3wfnNNPi3rUMk4It1k4iohmgMJhhbnQhr51ugszaqdzfKtSUSDiG4dxOc381EYNd6tcASEdVPGp0BhmZHwXr2TdA3Phwabf2q3Ki3CUFptvznijgRjoRj3RwiqiatRotEjRUa+Y9zqVRLJBSBRqdByBlGJFgvL+FEDb6aV2fVFpyrVPnNsQwl5HsBUcOjbjUj4Xp3k1zbIuEgNFomSIkaikg9PafrbUKQiIiIiIiIiIiIqo8TORAREREREREREcURJgSJiIiIiIiIiIjiCBOCREREREREREREcYQJQSIiIiIiIiIiojjChCAREREREREREVEcYUKQiIiIiIiIiIgojjAhSEREREREREREFEeYECQiIiIiIiIiIoojTAgSERERERERERHFESYEiYiIiIiIiIiI4ggTgkRERERERERERHGECUEiIiIiIiIiIqI4woQgERERERERERFRHGFCkIiIiIiIiIiIKI4wIUhERERERERERBRHmBAkIiIiIiIiIiKKI0wIEhERERERERERxREmBImIiIiIiIiIiOIIE4JERERERERERERxhAlBIiIiIiIiIiKiOMKEIBERERERERERURxhQpCIiIiIiIiIiCiOMCFIREREREREREQUR5gQJCIiIiIiIiIiiiNMCBIREREREREREcURJgSJiIiIiIiIiIjiCBOCREREREREREREcYQJQSIiIiIiIiIiojjChCAREREREREREVEcYUKQiIiIiIiIiIgojjAhSEREREREREREFEeYECQiIiIiIiIiIoojTAgSERERERERERHFESYEiYiIiIiIiIiI4ggTgkRERERERERERHGECUEiIiIiIiIiIqI4woQgERERERERERFRHGFCkIiIiIiIiIiIKI4wIUhERERERERERBRHmBAkIiIiIiIiIiKKI0wIEhERERERERERxREmBImIiIiIiIiIiOIIE4JERERERERERERxhAlBIiIiIiIiIiKiOMKEIBERERERERERURxhQpCIiIiIiIiIiCiOMCFIREREREREREQUR5gQJCIiIiIiIiIiiiNMCBIREREREREREcURJgSJiIiIiIiIiIjiCBOCREREREREREREcYQJQSIiIiIiIiIiojjChCAREREREREREVEcYUKQiIiIiIiIiIgojjAhSEREREREREREFEeYECQiIiIiIiIiIoojTAgSERERERERERHFESYEiYiIiIiIiIiI4ggTgkRERERERERERHGECUEiIiIiIiIiIqI4woQgERERERERERFRHGFCkIiIiIiIiIiIKI4wIUhERERERERERBRHmBAkIiIiIiIiIiKKI0wIEhERERERERERxREmBImIiIiIiIiIiOIIE4JERERERERERERxhAlBIiIiIiIiIiKiOMKEIBERERERERERURxhQpCIiIiIiIiIiCiOMCFIREREREREREQUR5gQJCIiIiIiIiIiiiNMCBIREREREREREcURJgSJiIiIiIiIiIjiCBOCREREREREREREcYQJQSIiIiIiIiIiojjChCAREREREREREVEcYUKQiIiIiIiIiIgojjAhSEREREREREREFEeYECQiIiIiIiIiIoojTAgSERERERERERHFESYEiYiIiIiIiIiI4ggTgkRERERERERERHGECUEiIiIiIiIiIqI4woQgERERERERERFRHGFCkIiIiIiIiIiIKI4wIUhERERERERERBRHmBAkIiIiIiIiIiKKI0wIEhERERERERERxREmBImIiIiIiIiIiOIIE4JERERERERERERxhAlBIiIiIiIiIiIixI//A+uzP7tvI5puAAAAAElFTkSuQmCC", "text/plain": [ "
" ] @@ -1468,8 +1500,8 @@ ], "source": [ "fig, ax = plt.subplots(figsize=(13, 7))\n", - "dr_policy_tree.plot(feature_names=covariates, treatment_names=ARM_LABELS, ax=ax)\n", - "ax.set_title('4-arm DRPolicyTree')\n", + "dr_policy_tree.plot(feature_names=covariates, treatment_names=TREATMENT_LABELS, ax=ax)\n", + "ax.set_title('4-way DRPolicyTree')\n", "plt.tight_layout()" ] }, @@ -1501,7 +1533,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 67, "id": "d7939447", "metadata": { "execution": { @@ -1522,7 +1554,7 @@ "Name: proportion, dtype: float64" ] }, - "execution_count": 17, + "execution_count": 67, "metadata": {}, "output_type": "execute_result" } @@ -1553,7 +1585,7 @@ "dr_policy_forest.fit(Y_net_train, A_train, X=X_train)\n", "pi_forest = dr_policy_forest.predict(X_test).astype(int).ravel()\n", "\n", - "pd.Series(pi_forest).map(ARM_NAMES).value_counts(normalize=True).reindex(ARM_LABELS).fillna(0)\n" + "pd.Series(pi_forest).map(TREATMENT_NAMES).value_counts(normalize=True).reindex(TREATMENT_LABELS).fillna(0)\n" ] }, { @@ -1592,12 +1624,27 @@ "id": "1e2f1760", "metadata": {}, "source": [ - "AIPW(Augmented Inverse Probability Weighting) score는 다음과 같이 정의합니다.\n\n$$\n\\hat\\Gamma_{i,a} = \\hat\\mu_a(X_i) + \\frac{\\mathbf{1}[A_i=a]}{\\hat e_a(X_i)}\\bigl(Y_i^{net} - \\hat\\mu_a(X_i)\\bigr)\n$$\n\n- $\\hat\\mu_a(X_i)$: outcome model이 예측한 $E[Y^{net}(a)\\mid X_i]$\n- $\\hat e_a(X_i)$: propensity model이 예측한 $P(A_i=a\\mid X_i)$\n- 두 번째 항은 실제 관측값과 예측값의 차이를 IPW로 보정하는 역할을 합니다.\n\n이 구조 덕분에 outcome model과 propensity model 중 하나만 올바르게 추정되어도 불편성이 유지됩니다. 이를 doubly robust(이중 견고성)라고 부릅니다.\n\n- outcome model이 정확하면: IPW 보정항의 기댓값이 0에 가까워져 $\\hat\\mu_a(X_i)$가 직접 기여합니다.\n- propensity model이 정확하면: IPW 보정항이 outcome model의 편향을 제거합니다.\n\n이 score를 사용해 학습된 정책의 가치를 추정합니다." + "AIPW(Augmented Inverse Probability Weighting) score는 다음과 같이 정의합니다.\n", + "\n", + "$$\n", + "\\hat\\Gamma_{i,a} = \\hat\\mu_a(X_i) + \\frac{\\mathbf{1}[A_i=a]}{\\hat e_a(X_i)}\\bigl(Y_i^{net} - \\hat\\mu_a(X_i)\\bigr)\n", + "$$\n", + "\n", + "- $\\hat\\mu_a(X_i)$: outcome model이 예측한 $E[Y^{net}(a)\\mid X_i]$\n", + "- $\\hat e_a(X_i)$: propensity model이 예측한 $P(A_i=a\\mid X_i)$\n", + "- 두 번째 항은 실제 관측값과 예측값의 차이를 IPW로 보정하는 역할을 합니다.\n", + "\n", + "이 구조 덕분에 outcome model과 propensity model 중 하나만 올바르게 추정되어도 불편성이 유지됩니다. 이를 doubly robust(이중 견고성)라고 부릅니다.\n", + "\n", + "- outcome model이 정확하면, IPW 보정항의 기댓값이 0에 가까워져 $\\hat\\mu_a(X_i)$가 직접 기여합니다.\n", + "- propensity model이 정확하면, IPW 보정항이 outcome model의 편향을 제거합니다.\n", + "\n", + "이 score를 사용해 학습된 정책의 가치를 추정합니다." ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 68, "id": "9c3bfe27", "metadata": {}, "outputs": [], @@ -1611,24 +1658,24 @@ "gamma_net = np.zeros((len(X_test), 4))\n", "mu_hat_net = np.zeros((len(X_test), 4))\n", "\n", - "for arm_id in range(4):\n", + "for treatment_id in range(4):\n", " outcome_model = RandomForestRegressor(\n", " n_estimators=400,\n", " min_samples_leaf=20,\n", " random_state=SEED,\n", " n_jobs=1,\n", " )\n", - " outcome_model.fit(X_train[A_train == arm_id], Y_net_train[A_train == arm_id])\n", + " outcome_model.fit(X_train[A_train == treatment_id], Y_net_train[A_train == treatment_id])\n", " mu_a = outcome_model.predict(X_test)\n", - " observed_a = (A_test == arm_id).astype(float)\n", + " observed_a = (A_test == treatment_id).astype(float)\n", "\n", - " gamma_net[:, arm_id] = mu_a + observed_a / e_hat_net[:, arm_id] * (Y_net_test - mu_a)\n", - " mu_hat_net[:, arm_id] = mu_a" + " gamma_net[:, treatment_id] = mu_a + observed_a / e_hat_net[:, treatment_id] * (Y_net_test - mu_a)\n", + " mu_hat_net[:, treatment_id] = mu_a" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 69, "id": "0d014d17", "metadata": {}, "outputs": [ @@ -1661,7 +1708,7 @@ " \n", " \n", " sample_id\n", - " actual_arm\n", + " actual_treatment\n", " observed_net_outcome\n", " none\n", " tech_support_only\n", @@ -1774,7 +1821,7 @@ ], "text/plain": [ " sample e_hat \\\n", - " sample_id actual_arm observed_net_outcome none \n", + " sample_id actual_treatment observed_net_outcome none \n", "0 0 discount_plus_support 9254.558541 0.248633 \n", "1 1 tech_support_only 7702.861662 0.211087 \n", "2 2 tech_support_only 5062.184420 0.376570 \n", @@ -1806,7 +1853,7 @@ "4 3378.748035 5743.802939 2988.217314 " ] }, - "execution_count": 11, + "execution_count": 69, "metadata": {}, "output_type": "execute_result" } @@ -1818,12 +1865,12 @@ " {\n", " 'sample': pd.DataFrame({\n", " 'sample_id': sample_ids,\n", - " 'actual_arm': pd.Series(A_test[sample_ids]).map(ARM_NAMES).to_numpy(),\n", + " 'actual_treatment': pd.Series(A_test[sample_ids]).map(TREATMENT_NAMES).to_numpy(),\n", " 'observed_net_outcome': Y_net_test[sample_ids],\n", " }),\n", - " 'e_hat': pd.DataFrame(e_hat_net[sample_ids], columns=ARM_LABELS),\n", - " 'mu_hat': pd.DataFrame(mu_hat_net[sample_ids], columns=ARM_LABELS),\n", - " 'gamma': pd.DataFrame(gamma_net[sample_ids], columns=ARM_LABELS),\n", + " 'e_hat': pd.DataFrame(e_hat_net[sample_ids], columns=TREATMENT_LABELS),\n", + " 'mu_hat': pd.DataFrame(mu_hat_net[sample_ids], columns=TREATMENT_LABELS),\n", + " 'gamma': pd.DataFrame(gamma_net[sample_ids], columns=TREATMENT_LABELS),\n", " },\n", " axis=1,\n", ")\n", @@ -1833,7 +1880,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 70, "id": "14b616ad", "metadata": { "execution": { @@ -1878,7 +1925,7 @@ " \n", " \n", " 4\n", - " plugin_drlearner_4arm\n", + " plugin_drlearner_4treatment\n", " 11810.377712\n", " 11283.561563\n", " 12337.193861\n", @@ -1889,7 +1936,7 @@ " \n", " \n", " 6\n", - " dr_policy_forest_4arm\n", + " dr_policy_forest_4treatment\n", " 11625.627540\n", " 11102.022589\n", " 12149.232491\n", @@ -1900,7 +1947,7 @@ " \n", " \n", " 5\n", - " dr_policy_tree_4arm\n", + " dr_policy_tree_4treatment\n", " 11148.462369\n", " 10700.609230\n", " 11596.315508\n", @@ -1958,14 +2005,14 @@ "" ], "text/plain": [ - " policy value ci_lower ci_upper \\\n", - "4 plugin_drlearner_4arm 11810.377712 11283.561563 12337.193861 \n", - "6 dr_policy_forest_4arm 11625.627540 11102.022589 12149.232491 \n", - "5 dr_policy_tree_4arm 11148.462369 10700.609230 11596.315508 \n", - "1 all_tech_support_only 10526.523876 9833.034751 11220.013001 \n", - "3 all_discount_plus_support 10136.708882 9439.445941 10833.971824 \n", - "2 all_discount_only 7545.079205 6993.536621 8096.621788 \n", - "0 all_none 7249.345467 6955.447432 7543.243502 \n", + " policy value ci_lower ci_upper \\\n", + "4 plugin_drlearner_4treatment 11810.377712 11283.561563 12337.193861 \n", + "6 dr_policy_forest_4treatment 11625.627540 11102.022589 12149.232491 \n", + "5 dr_policy_tree_4treatment 11148.462369 10700.609230 11596.315508 \n", + "1 all_tech_support_only 10526.523876 9833.034751 11220.013001 \n", + "3 all_discount_plus_support 10136.708882 9439.445941 10833.971824 \n", + "2 all_discount_only 7545.079205 6993.536621 8096.621788 \n", + "0 all_none 7249.345467 6955.447432 7543.243502 \n", "\n", " rate_none rate_tech_support_only rate_discount_only \\\n", "4 0.092 0.500 0.035 \n", @@ -1986,17 +2033,17 @@ "0 0.000 " ] }, - "execution_count": 18, + "execution_count": 70, "metadata": {}, "output_type": "execute_result" } ], "source": [ "policy_assignments = {\n", - " **{f'all_{arm_name}': np.full(len(X_test), arm_id) for arm_id, arm_name in ARM_NAMES.items()},\n", - " 'plugin_drlearner_4arm': pi_plugin,\n", - " 'dr_policy_tree_4arm': pi_tree,\n", - " 'dr_policy_forest_4arm': pi_forest,\n", + " **{f'all_{treatment_name}': np.full(len(X_test), treatment_id) for treatment_id, treatment_name in TREATMENT_NAMES.items()},\n", + " 'plugin_drlearner_4treatment': pi_plugin,\n", + " 'dr_policy_tree_4treatment': pi_tree,\n", + " 'dr_policy_forest_4treatment': pi_forest,\n", "}\n", "\n", "eval_rows = []\n", @@ -2009,27 +2056,27 @@ " 'value': scores.mean(),\n", " 'value_se': scores.std(ddof=1) / np.sqrt(len(scores)),\n", " }\n", - " for arm_id, arm_name in ARM_NAMES.items():\n", - " row[f'rate_{arm_name}'] = np.mean(pi == arm_id)\n", + " for treatment_id, treatment_name in TREATMENT_NAMES.items():\n", + " row[f'rate_{treatment_name}'] = np.mean(pi == treatment_id)\n", " eval_rows.append(row)\n", "\n", "policy_eval = pd.DataFrame(eval_rows)\n", "policy_eval['ci_lower'] = policy_eval['value'] - 1.96 * policy_eval['value_se']\n", "policy_eval['ci_upper'] = policy_eval['value'] + 1.96 * policy_eval['value_se']\n", "\n", - "display_cols = ['policy', 'value', 'ci_lower', 'ci_upper'] + [f'rate_{n}' for n in ARM_LABELS]\n", + "display_cols = ['policy', 'value', 'ci_lower', 'ci_upper'] + [f'rate_{n}' for n in TREATMENT_LABELS]\n", "policy_eval.sort_values('value', ascending=False)[display_cols]" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 71, "id": "4814d1ed", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -2042,15 +2089,15 @@ "fig, axes = plt.subplots(1, 2, figsize=(15, 5))\n", "plot_df = policy_eval.sort_values('value', ascending=False)\n", "sns.barplot(data=plot_df, x='value', y='policy', ax=axes[0], color='seagreen')\n", - "axes[0].set_title('4-arm net policy value')\n", + "axes[0].set_title('4-way net policy value')\n", "axes[0].set_xlabel('AIPW-estimated net value')\n", "axes[0].set_ylabel('')\n", "\n", - "arm_rate_cols = [f'rate_{name}' for name in ARM_LABELS]\n", - "rate_df = policy_eval.set_index('policy')[arm_rate_cols]\n", - "rate_df.columns = ARM_LABELS\n", - "rate_df.loc[['plugin_drlearner_4arm', 'dr_policy_tree_4arm', 'dr_policy_forest_4arm']].plot(kind='barh', stacked=True, ax=axes[1], colormap='tab20')\n", - "axes[1].set_title('Assigned arm share')\n", + "treatment_rate_cols = [f'rate_{name}' for name in TREATMENT_LABELS]\n", + "rate_df = policy_eval.set_index('policy')[treatment_rate_cols]\n", + "rate_df.columns = TREATMENT_LABELS\n", + "rate_df.loc[['plugin_drlearner_4treatment', 'dr_policy_tree_4treatment', 'dr_policy_forest_4treatment']].plot(kind='barh', stacked=True, ax=axes[1], colormap='tab20')\n", + "axes[1].set_title('Assigned treatment share')\n", "axes[1].set_xlabel('Share')\n", "axes[1].set_ylabel('')\n", "axes[1].legend(loc='center left', bbox_to_anchor=(1.0, 0.5))\n", @@ -2091,7 +2138,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 72, "id": "a1z6z7eabyj", "metadata": {}, "outputs": [ @@ -2109,20 +2156,20 @@ "source": [ "# 처치별 E[C(a) | X] 추정\n", "# c_hat_test[i, a] = 고객 i가 처치 a를 받을 때의 예측 비용\n", - "c_true_by_arm = {1: c_tech, 2: c_disc, 3: c_tech + c_disc}\n", + "c_true_by_treatment = {1: c_tech, 2: c_disc, 3: c_tech + c_disc}\n", "\n", "c_hat_test = np.zeros((len(X_test), 4)) # 처치 0 → 비용 = 0\n", - "for arm_id in [1, 2, 3]:\n", - " mask_train = (A_train == arm_id)\n", + "for treatment_id in [1, 2, 3]:\n", + " mask_train = (A_train == treatment_id)\n", " mdl = RandomForestRegressor(n_estimators=300, min_samples_leaf=20,\n", " random_state=SEED, n_jobs=1)\n", - " mdl.fit(X_train[mask_train], c_true_by_arm[arm_id][train_idx[mask_train]])\n", - " c_hat_test[:, arm_id] = np.maximum(mdl.predict(X_test), 200.0)\n", + " mdl.fit(X_train[mask_train], c_true_by_treatment[treatment_id][train_idx[mask_train]])\n", + " c_hat_test[:, treatment_id] = np.maximum(mdl.predict(X_test), 200.0)\n", "\n", "print(\"Estimated E[C(a)|X] — test set (mean ± std):\")\n", - "for arm_id, arm_name in ARM_NAMES.items():\n", - " if arm_id > 0:\n", - " print(f\" {arm_name:25s}: {c_hat_test[:, arm_id].mean():8,.0f} ± {c_hat_test[:, arm_id].std():6,.0f}\")" + "for treatment_id, treatment_name in TREATMENT_NAMES.items():\n", + " if treatment_id > 0:\n", + " print(f\" {treatment_name:25s}: {c_hat_test[:, treatment_id].mean():8,.0f} ± {c_hat_test[:, treatment_id].std():6,.0f}\")" ] }, { @@ -2144,7 +2191,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 73, "id": "ttwi6dbqcfo", "metadata": {}, "outputs": [ @@ -2267,4 +2314,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +}