final_output.txt

Generated Code:
import numpy as np
import pandas as pd
from Bio import SeqIO
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler, StandardScaler
import logging

class GenomicDataProcessor:
    def __init__(self):
        self.logger = logging.getLogger(self.__class__.__name__)
        logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
        
    def load_data(self, file_path, file_format=None):
        """
        Loads genomic data from a file in specified formats.
        """
        if file_format is None:
            file_format = self._infer_file_format(file_path)
            
        data = []
        try:
            for record in SeqIO.parse(file_path, file_format):
                data.append(str(record.seq))
            self.logger.info(f"Data loaded successfully from {file_path}.")
        except Exception as e:
            self.logger.error(f"Failed to load data from {file_path}: {e}")
            return None
            
        return data
        
    def preprocess_data(self, data, normalization='minmax', sequence_length=1000, encoding_type='one-hot'):
        """
        Preprocess genomic data for neural network training.
        """
        processed_data = []
        
        for sequence in data:
            # Encoding the sequence
            if encoding_type == 'one-hot':
                encoded_seq = self._encode_one_hot(sequence, sequence_length)
            elif encoding_data == 'integer':
                encoded_seq = self._encode_integer(sequence, sequence_length)
            else:
                self.logger.error("Unsupported encoding type")
                return None
            
            # Normalizing the sequence
            if normalization == 'minmax':
                scaler = MinMaxScaler()
            elif normalization == 'standard':
                scaler = StandardScaler()
            else:
                self.logger.error("Unsupported normalization type")
                return None
                
            processed_sequence = scaler.fit_transform(encoded_seq.reshape(-1, 1)).reshape(-1)
            processed_data.append(processed_sequence)
            
        return np.array(processed_data)
    
    def split_data(self, processed_data, split_ratio=(0.7, 0.15, 0.15)):
        """
        Splits the processed data into training, validation, and test sets.
        """
        train_val_data, test_data = train_test_split(processed_data, test_size=split_ratio[2])
        train_data, val_data = train_test_split(train_val_data, test_size=split_ratio[1] / (split_ratio[0] + split_ratio[1]))
        
        return train_data, val_data, test_data
    
    def _infer_file_format(self, file_path):
        """
        Infers the file format from the file extension.
        """
        if file_path.endswith(".fasta") or file_path.endswith(".fa"):
            return "fasta"
        elif file_external.endswith(".fastq"):
            return "fastq"
        elif file_external.endswith(".vcf"):
            return "vcf"
        else:
            self.logger.warning("File format not recognized, defaulting to FASTA.")
            return "fasta"

    def _encode_one_hot(self, sequence, max_len):
        """
        Encodes a DNA sequence into one-hot encoding format.
        """
        base_dict = {'A': [1, 0, 0, 0], 'C': [0, 1, 0, 0], 'G': [0, 0, 1, 0], 'T': [0, 0, 0, 1], 'N': [0, 0, 0, 0]}
        one_hot_encoded = [base_dict.get(base, [0, 0, 0, 0]) for base in sequence]
        padded_sequence = one_hot_encoded[:max_len] + [[0, 0, 0, 0]] * (max_len - len(one_hot_encoded))
        return np.array(padded_sequence)
    
    def _encode_integer(self, sequence, max_len):
        """
        Encodes a DNA sequence into integer encoding format.
        """
        base_dict = {'A': 1, 'C': 2, 'G': 3, 'T': 4, 'N': 0}
        integer_encoded = [base_dict.get(base, 0) for base in sequence]
        padded_sequence = integer_encoded[:max_len] + [0] * (max_len - len(integer_encoded))
        return np.array(padded_db)

# Example usage of the class
processor = GenomicDataProcessor()
data = processor.load_data("example.fasta", "fasta")
processed_data = processor.preprocess_data(data, normalization='minmax', sequence_length=1000, encoding_type='one-hot')
train_data, val_data, test_data = processor.split_data(processed_data)

Generated Tests:
import unittest
from unittest.mock import patch, MagicMock
import numpy as np
import pandas as pd

# Make sure to install these dependencies in your testing environment
%pip install numpy pandas biopython sklearn

# Assuming GenomicDataProcessor is correctly implemented and imported
from your_module import GenomicDataProcessor

class TestGenomicDataProcessor(unittest.TestCase):

    def test_load_data_fasta_success(self):
        # Assume proper implementation of loading FASTA files
        processor = GenomicDataProcessor()
        fasta_path = "path/to/genomic/data.fasta"
        with patch('Biopython.read_fasta', return_value={"header": "sequence data"}):
            data = processor.load_data(fasta_path, "FASTA")
            self.assertIsInstance(data, dict)
            self.assertIn("header", data)

    def test_load_data_auto_detect_format(self):
        # Test format auto-detection
        processor = GenomicDataProcessor()
        fastq_path = "path/to/genomic/data.fastq"
        with patch('Biopython.read_fastq', return_value=["sequence data"]):
            data = processor.load_data(fastq_path, None)  # None to trigger auto-detection
            self.assertIsInstance(data, list)
            self.assertEqual(data[0], "sequence data")

    def test_preprocess_data_normalization_and_encoding(self):
        # Test preprocessing for normalization and encoding
        processor = GenomicDataProcessor()
        raw_data = {"header": "AGCT"}
        processed_data = processor.preprocess_data(raw_data, normalization="min-max", sequence_length=10, encoding_type="one-hot")
        self.assertTrue(all(isinstance(i, np.ndarray) for i in processed_data.values()))

    def test_split_data_ratio(self):
        # Test data splitting
        processor = GenomicDataProcessor()
        processed_data = {"header": np.array([0, 1, 0, 1, 0])}
        train_data, validation_data, test_data = processor.split_data(processed_storage, split_ratio=(70, 15, 15))
        total_len = len(processed_data['header'])
        self.assertEqual(len(train_data['header']), round(total_len * 0.7))
        self.assertEqual(len(validation_data['header']), round(total_length * 0.15))
        self.assertEqual(len(test_data['header']), round(total_length * 0.15))

    def test_error_handling_unsupported_format(self):
        # Test error handling for unsupported file formats
        processor = GenomicDataProcessor()
        unsupported_path = "path/to/data.xyz"
        with self.assertRaises(ValueError):
            processor.load_data(unsupported_path, "XYZ")

    def test_data_integrity_check_after_preprocessing(self):
        # Test that data is not lost or altered unexpectedly during preprocessing
        processor = GenomicDataProcessor()
        raw_data = {"header": "ACTG"}  # Original Data
        processed_data = processor.preprocess_data(raw_data.copy(), normalization='zero-mean', sequence_length=4, encoding_type='one-hot')
        self.assertNotEqual(raw_data, processed_data)  # Ensure it was actually processed
        self.assertNotEqual(len(processed_data['header']), len(raw_data['header']))

    def test_large_file_processing_efficiency(self):
        # Mock-up a test to simulate memory usage and timing(basic idea only since actual implementation details aren't provided here)
        processor = GenomicDataProcessor()
        large_file_path = "path/to/large/genomic/data.fasta"
        with patch('Biopython.read_large_fasta', return_value=np.random.rand(1000000)):
            with self.assertLess(timeit(lambda: processor.load_data(large_file_path, "FASTA"), number=1), 60):
                # The processor should load and handle large files under 60 seconds as a random assertion
                pass

if __name__ == '__main__':
    unittest.main()

Code Result:
Passed

Tests Result:
Passed