diff --git a/AAAAsindy_analysis_with_metrics.csv b/AAAAsindy_analysis_with_metrics.csv
new file mode 100644
index 00000000..0ad115b8
--- /dev/null
+++ b/AAAAsindy_analysis_with_metrics.csv
@@ -0,0 +1,267 @@
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diff --git a/all_scores.csv b/all_scores.csv
deleted file mode 100644
index 558f68fc..00000000
--- a/all_scores.csv
+++ /dev/null
@@ -1,306 +0,0 @@
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-295,296.0,104.0,64.67,53.23,53.17
-296,297.0,104.0,71.28,72.76,72.72
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-298,299.0,96.0,80.76,81.67,81.68
-299,300.0,103.0,86.96,82.35,82.35
-300,301.0,104.0,67.92,62.75,62.77
-301,302.0,104.0,56.77,50.4,50.39
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diff --git a/analysis/check_age b/analysis/check_age
new file mode 100644
index 00000000..3127007b
--- /dev/null
+++ b/analysis/check_age
@@ -0,0 +1,42 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+import numpy as np
+
+file_path = '/Users/martynaplomecka/closedloop_rl/data/eckstein2022/SLCNinfo_Share.csv'
+df = pd.read_csv(file_path)
+
+conditions = [
+    (df['ID'] >= 17) & (df['ID'] <= 221),
+    (df['ID'] >= 265) & (df['ID'] <= 300)
+]
+choices = ['CATEGORY 1', 'CATEGORY 2']
+df['Category'] = np.select(conditions, choices, default='CATEGORY 3')
+
+print("\nCategory counts:")
+print(df['Category'].value_counts())
+
+output_path = file_path.replace('.csv', '_with_categories.csv')
+df.to_csv(output_path, index=False)
+print(f"Updated file saved: {output_path}")
+
+# ID vs Age (years)
+plt.figure(figsize=(12, 8))
+
+colors = {'CATEGORY 1': 'red', 'CATEGORY 2': 'blue', 'CATEGORY 3': 'green'}
+for category in ['CATEGORY 1', 'CATEGORY 2', 'CATEGORY 3']:
+    mask = df['Category'] == category
+    if mask.any():
+        plt.scatter(df[mask]['ID'], df[mask]['age - years'], 
+                   c=colors[category], label=category, alpha=0.7, s=30)
+
+plt.xlabel('ID')
+plt.ylabel('Age (years)')
+plt.title('Subject ID vs Age by Category')
+plt.legend()
+plt.grid(True, alpha=0.3)
+plt.tight_layout()
+plt.savefig('id_vs_age_plot.png', dpi=300, bbox_inches='tight')
+plt.close() 
+
+print(f"\nID range: {df['ID'].min()} - {df['ID'].max()}")
+print(f"Age range: {df['age - years'].min()} - {df['age - years'].max()} years")
diff --git a/analysis/finetuning_SEQ/all_results.json b/analysis/finetuning_SEQ/all_results.json
deleted file mode 100644
index 8d6a5875..00000000
--- a/analysis/finetuning_SEQ/all_results.json
+++ /dev/null
@@ -1,9366 +0,0 @@
-{
-  "results": [
-    {
-      "dataset": "data_128p_0.csv",
-      "best_params": {
-        "dropout": 0.31905877051758935,
-        "learning_rate": 0.0003638666906537081,
-        "hidden_size": 22,
-        "embedding_size": 9,
-        "n_steps": 62
-      },
-      "best_val_loss": 0.3835820792793909,
-      "final_train_loss": 0.3158555030822754,
-      "sindy_bic": 4.447191774137533,
-      "sindy_ll": -1.865074178983563,
-      "participant_equations": {
-        "0.0": {
-          "x_learning_rate_reward": {
-            "equation": "SINDy(differentiation_method=FiniteDifference(axis=-2), discrete_time=True,\n      feature_library=PolynomialLibrary(),\n      feature_names=['x_learning_rate_reward', 'c_reward', 'c_value_reward'],\n      optimizer=SR3(nu=1, threshold=0.05, thresholder='weighted_l1',\n                    thresholds=array([[0.  , 0.05, 0.05, 0.05, 0.1 , 0.1 , 0.1 , 0.1 , 0.1 , 0.1 ]]),\n                    verbose=True))",
-            "coefficients": [
-              0.8089341211556266,
-              0.11340760680137527,
-              0.20601336749385474,
-              -0.32382421805123096,
-              0.0,
-              0.09821141523925904,
-              0.0,
-              0.0,
-              0.0,
-              0.0
-            ],
-            "feature_names": [
-              "x_learning_rate_reward",
-              "c_reward",
-              "c_value_reward"
-            ]
-          },
-          "x_value_reward_not_chosen": {
-            "equation": "SINDy(differentiation_method=FiniteDifference(axis=-2), discrete_time=True,\n      feature_library=PolynomialLibrary(),\n      feature_names=['x_value_reward_not_chosen', 'dummy'],\n      optimizer=SR3(nu=1, threshold=0.05, thresholder='weighted_l1',\n                    thresholds=array([[0.  , 0.05, 0.05, 0.1 , 0.1 , 0.1 ]]),\n                    verbose=True))",
-            "coefficients": [
-              1.2809159282752134,
-              0.3455758166847831,
-              0.0,
-              0.0,
-              0.0,
-              0.0
-            ],
-            "feature_names": [
-              "x_value_reward_not_chosen",
-              "dummy"
-            ]
-          },
-          "x_value_choice_chosen": {
-            "equation": "SINDy(differentiation_method=FiniteDifference(axis=-2), discrete_time=True,\n      feature_library=PolynomialLibrary(),\n      feature_names=['x_value_choice_chosen', 'dummy'],\n      optimizer=SR3(nu=1, threshold=0.05, thresholder='weighted_l1',\n                    thresholds=array([[0.  , 0.05, 0.05, 0.1 , 0.1 , 0.1 ]]),\n                    verbose=True))",
-            "coefficients": [
-              0.9564620474980643,
-              -0.5499402818875253,
-              0.0,
-              0.5742904581020674,
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\ No newline at end of file
diff --git a/analysis/finetuning_SEQ/bic_violin_plot.png b/analysis/finetuning_SEQ/bic_violin_plot.png
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diff --git a/analysis/finetuning_SEQ/model_data_128p_0.pt b/analysis/finetuning_SEQ/model_data_128p_0.pt
deleted file mode 100644
index df9f0a67..00000000
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diff --git a/analysis/finetuning_SEQ/result_data_128p_0.json b/analysis/finetuning_SEQ/result_data_128p_0.json
deleted file mode 100644
index 7ae4eb16..00000000
--- a/analysis/finetuning_SEQ/result_data_128p_0.json
+++ /dev/null
@@ -1,9362 +0,0 @@
-{
-  "dataset": "data_128p_0.csv",
-  "best_params": {
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-    "learning_rate": 0.0003638666906537081,
-    "hidden_size": 22,
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-    "n_steps": 62
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-  "sindy_bic": 4.447191774137533,
-  "sindy_ll": -1.865074178983563,
-  "participant_equations": {
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-          "c_reward",
-          "c_value_reward"
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-      "x_value_reward_not_chosen": {
-        "equation": "SINDy(differentiation_method=FiniteDifference(axis=-2), discrete_time=True,\n      feature_library=PolynomialLibrary(),\n      feature_names=['x_value_reward_not_chosen', 'dummy'],\n      optimizer=SR3(nu=1, threshold=0.05, thresholder='weighted_l1',\n                    thresholds=array([[0.  , 0.05, 0.05, 0.1 , 0.1 , 0.1 ]]),\n                    verbose=True))",
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-        "equation": "SINDy(differentiation_method=FiniteDifference(axis=-2), discrete_time=True,\n      feature_library=PolynomialLibrary(),\n      feature_names=['x_learning_rate_reward', 'c_reward', 'c_value_reward'],\n      optimizer=SR3(nu=1, threshold=0.05, thresholder='weighted_l1',\n                    thresholds=array([[0.  , 0.05, 0.05, 0.05, 0.1 , 0.1 , 0.1 , 0.1 , 0.1 , 0.1 ]]),\n                    verbose=True))",
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-        "equation": "SINDy(differentiation_method=FiniteDifference(axis=-2), discrete_time=True,\n      feature_library=PolynomialLibrary(),\n      feature_names=['x_learning_rate_reward', 'c_reward', 'c_value_reward'],\n      optimizer=SR3(nu=1, threshold=0.05, thresholder='weighted_l1',\n                    thresholds=array([[0.  , 0.05, 0.05, 0.05, 0.1 , 0.1 , 0.1 , 0.1 , 0.1 , 0.1 ]]),\n                    verbose=True))",
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-        "feature_names": [
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-          "c_reward",
-          "c_value_reward"
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-      },
-      "x_value_reward_not_chosen": {
-        "equation": "SINDy(differentiation_method=FiniteDifference(axis=-2), discrete_time=True,\n      feature_library=PolynomialLibrary(),\n      feature_names=['x_value_reward_not_chosen', 'dummy'],\n      optimizer=SR3(nu=1, threshold=0.05, thresholder='weighted_l1',\n                    thresholds=array([[0.  , 0.05, 0.05, 0.1 , 0.1 , 0.1 ]]),\n                    verbose=True))",
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-  }
-}
\ No newline at end of file
diff --git a/analysis/finetuning_SEQ/sindy_bic_comparison.png b/analysis/finetuning_SEQ/sindy_bic_comparison.png
deleted file mode 100644
index e3c96246..00000000
Binary files a/analysis/finetuning_SEQ/sindy_bic_comparison.png and /dev/null differ
diff --git a/analysis/participants2.py b/analysis/participants2.py
new file mode 100644
index 00000000..c4a5ac62
--- /dev/null
+++ b/analysis/participants2.py
@@ -0,0 +1,112 @@
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+from matplotlib.colors import Normalize
+from matplotlib.cm import ScalarMappable
+import matplotlib.gridspec as gridspec
+
+df = pd.read_csv('AAAAsindy_analysis_with_metrics.csv')
+
+df = df.rename(columns={'slcn_age - years': 'age'})
+
+behavioral_measures = ['switch_rate', 'stay_after_reward', 'perseveration', 'avg_reward']
+output_metrics = ['bic_spice', 'aic_spice'] #whatever, updtae here
+
+fig = plt.figure(figsize=(15, 12))
+outer_grid = gridspec.GridSpec(2, 1, height_ratios=[1, 1], hspace=0.3)
+
+#  colormap for age
+cmap = plt.cm.viridis
+norm = Normalize(vmin=df['age'].min(), vmax=df['age'].max())
+
+cbar_ax = fig.add_axes([0.92, 0.15, 0.02, 0.7])
+sm = ScalarMappable(norm=norm, cmap=cmap)
+sm.set_array([])
+cbar = fig.colorbar(sm, cax=cbar_ax)
+cbar.set_label('Age (years)', fontsize=12)
+
+# Function to calculate correlation and p-value
+def calculate_correlation(x, y):
+    mask = ~np.isnan(x) & ~np.isnan(y)
+    if sum(mask) < 3:  # Need at least 3 data points for correlation
+        return "Insufficient data"
+    
+    corr = np.corrcoef(x[mask], y[mask])[0, 1]
+    return f"r = {corr:.3f}"
+
+# through output metrics
+for i, metric in enumerate(output_metrics):
+    inner_grid = gridspec.GridSpecFromSubplotSpec(1, len(behavioral_measures), 
+                                             subplot_spec=outer_grid[i], wspace=0.3)
+    
+    for j, behavior in enumerate(behavioral_measures):
+        ax = plt.Subplot(fig, inner_grid[j])
+        
+        # Filter out NaN values for this specific pair
+        valid_data = df.dropna(subset=[behavior, metric, 'age'])
+        
+        # Only proceed if we have enough data points
+        if len(valid_data) > 2:
+            # Create scatter plot
+            scatter = ax.scatter(valid_data[behavior], valid_data[metric], 
+                       c=valid_data['age'], cmap=cmap, norm=norm, 
+                       alpha=0.7, edgecolors='w', linewidth=0.5)
+            
+            corr_text = calculate_correlation(valid_data[behavior], valid_data[metric])
+            
+            #  regression line
+            sns.regplot(x=behavior, y=metric, data=valid_data, 
+                       scatter=False, ci=None, line_kws={'color': 'red'}, ax=ax)
+            
+            ax.text(0.05, 0.95, corr_text, transform=ax.transAxes, 
+                   fontsize=10, verticalalignment='top', 
+                   bbox=dict(boxstyle='round', facecolor='white', alpha=0.7))
+        else:
+            ax.text(0.5, 0.5, "Insufficient data", transform=ax.transAxes, 
+                   fontsize=12, ha='center')
+        
+        ax.set_title(f"{behavior.replace('_', ' ').title()}", fontsize=12)
+        ax.set_xlabel(behavior.replace('_', ' ').title(), fontsize=10)
+        
+        if j == 0:  # Only add y-label to the leftmost subplot
+            if metric == 'bic_spice':
+                ax.set_ylabel('BIC (Spice Model)', fontsize=10)
+            else:
+                ax.set_ylabel('AIC (Spice Model)', fontsize=10)
+        
+        fig.add_subplot(ax)
+    
+    row_title = "Behavioral Measures vs BIC (Spice Model)" if metric == 'bic_spice' else "Behavioral Measures vs AIC (Spice Model)"
+    fig.text(0.5, 0.98 - i*0.48, row_title, ha='center', fontsize=16, fontweight='bold')
+
+import os
+os.makedirs('analysis/plots', exist_ok=True)
+plt.savefig('analysis/plots/behavioral_vs_bic_aic_scatter.png', dpi=300, bbox_inches='tight')
+plt.close()
+
+
+
+# second plot for correlation matrix
+plt.figure(figsize=(10, 8))
+
+# Select columns for correlation
+selected_columns = behavioral_measures + output_metrics + ['age']
+corr_df = df[selected_columns].dropna()
+
+corr_matrix = corr_df.corr()
+
+sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', vmin=-1, vmax=1, fmt='.2f', linewidths=0.5)
+plt.title('Correlation Matrix of Behavioral Measures, Model Metrics, and Age', fontsize=14)
+plt.tight_layout()
+plt.savefig('analysis/plots/correlation_matrix.png', dpi=300)
+plt.close()
+
+print("\nsig correlations with BIC and AIC:")
+for behavior in behavioral_measures:
+    for metric in output_metrics:
+        valid_data = df.dropna(subset=[behavior, metric])
+        if len(valid_data) > 2:
+            corr = np.corrcoef(valid_data[behavior], valid_data[metric])[0, 1]
+            if abs(corr) > 0.3:  # Only show correlations stronger than 0.3
+                print(f"{behavior} vs {metric}: r = {corr:.3f}")
\ No newline at end of file
diff --git a/analysis/participants3.py b/analysis/participants3.py
new file mode 100644
index 00000000..c51bf8a4
--- /dev/null
+++ b/analysis/participants3.py
@@ -0,0 +1,171 @@
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+import os
+from scipy import stats
+
+os.makedirs('analysis/plots', exist_ok=True)
+
+df = pd.read_csv('AAAAsindy_analysis_with_metrics.csv')
+
+df = df.rename(columns={'slcn_age - years': 'age'})
+
+#  model metrics to analyze
+model_metrics = [
+    'nll_spice', 
+    'nll_rnn', 
+    'trial_likelihood_spice', 
+    'trial_likelihood_rnn', 
+    'bic_spice', 
+    'aic_spice'
+]
+
+metric_names = {
+    'nll_spice': 'Negative Log-Likelihood (SPICE)',
+    'nll_rnn': 'Negative Log-Likelihood (RNN)',
+    'trial_likelihood_spice': 'Trial Likelihood (SPICE)',
+    'trial_likelihood_rnn': 'Trial Likelihood (RNN)',
+    'bic_spice': 'BIC (SPICE)',
+    'aic_spice': 'AIC (SPICE)'
+}
+
+def calculate_correlation(x, y):
+    mask = ~np.isnan(x) & ~np.isnan(y)
+    if sum(mask) < 3:  # Need at least 3 data points for correlation
+        return "Insufficient data", 1.0
+    
+    r, p = stats.pearsonr(x[mask], y[mask])
+    significance = ""
+    if p < 0.001:
+        significance = "***"
+    elif p < 0.01:
+        significance = "**"
+    elif p < 0.05:
+        significance = "*"
+    
+    corr_text = f"r = {r:.3f}{significance}"
+    return corr_text, p
+
+plt.figure(figsize=(18, 12))
+
+for i, metric in enumerate(model_metrics):
+    ax = plt.subplot(2, 3, i+1)
+    
+    # Drop rows with NaN values for this metric or age
+    valid_data = df.dropna(subset=[metric, 'age'])
+    
+    # Only proceed if we have enough data points
+    if len(valid_data) > 2:
+        sns.scatterplot(x='age', y=metric, data=valid_data, 
+                        alpha=0.7, edgecolor='w', s=80, ax=ax)
+        
+        sns.regplot(x='age', y=metric, data=valid_data, 
+                    scatter=False, ci=95, line_kws={'color': 'red'}, ax=ax)
+        
+        corr_text, p_value = calculate_correlation(valid_data['age'], valid_data[metric])
+        
+        # Add correlation text
+        ax.text(0.05, 0.95, corr_text, transform=ax.transAxes, 
+                fontsize=12, verticalalignment='top', 
+                bbox=dict(boxstyle='round', facecolor='white', alpha=0.7))
+        
+        # Add linear equation if the correlation is significant
+        if p_value < 0.05:
+            # Calculate linear regression
+            slope, intercept, r_value, _, _ = stats.linregress(
+                valid_data['age'].dropna(), valid_data[metric].dropna())
+            equation = f"y = {slope:.3f}x + {intercept:.3f}"
+            ax.text(0.05, 0.87, equation, transform=ax.transAxes, 
+                    fontsize=10, verticalalignment='top',
+                    bbox=dict(boxstyle='round', facecolor='white', alpha=0.7))
+    else:
+        ax.text(0.5, 0.5, "Insufficient data", transform=ax.transAxes, 
+                fontsize=12, ha='center')
+    
+    # Set titles and labels
+    ax.set_title(metric_names[metric], fontsize=14)
+    ax.set_xlabel('Age (years)', fontsize=12)
+    ax.set_ylabel(metric_names[metric], fontsize=12)
+    
+    # Add grid
+    ax.grid(True, linestyle='--', alpha=0.7)
+
+plt.tight_layout()
+plt.savefig('analysis/plots/age_vs_model_metrics.png', dpi=300, bbox_inches='tight')
+
+for metric in model_metrics:
+    plt.figure(figsize=(8, 6))
+    
+    valid_data = df.dropna(subset=[metric, 'age'])
+    
+    if len(valid_data) > 2:
+        # Create scatter plot
+        sns.scatterplot(x='age', y=metric, data=valid_data, 
+                        alpha=0.7, edgecolor='w', s=100)
+        
+        # Add regression line with confidence interval
+        sns.regplot(x='age', y=metric, data=valid_data, 
+                    scatter=False, ci=95, line_kws={'color': 'red'})
+        
+        corr_text, p_value = calculate_correlation(valid_data['age'], valid_data[metric])
+        
+        # Add correlation text
+        plt.text(0.05, 0.95, corr_text, transform=plt.gca().transAxes, 
+                fontsize=12, verticalalignment='top', 
+                bbox=dict(boxstyle='round', facecolor='white', alpha=0.7))
+        
+        # Add linear equation if significant
+        if p_value < 0.05:
+            slope, intercept, r_value, _, _ = stats.linregress(
+                valid_data['age'].dropna(), valid_data[metric].dropna())
+            equation = f"y = {slope:.3f}x + {intercept:.3f}"
+            plt.text(0.05, 0.87, equation, transform=plt.gca().transAxes, 
+                    fontsize=10, verticalalignment='top',
+                    bbox=dict(boxstyle='round', facecolor='white', alpha=0.7))
+        
+        # Add jitter to points if there are many overlapping points
+        if len(valid_data) > 20:
+     
+            pass  # Tfis is for proper indentation
+            
+        # Add data points count
+        plt.text(0.05, 0.79, f"n = {len(valid_data)}", transform=plt.gca().transAxes, 
+                fontsize=10, verticalalignment='top',
+                bbox=dict(boxstyle='round', facecolor='white', alpha=0.7))
+    else:
+        plt.text(0.5, 0.5, "Insufficient data", transform=plt.gca().transAxes, 
+                fontsize=12, ha='center')
+    
+    plt.title(f"Age vs {metric_names[metric]}", fontsize=14)
+    plt.xlabel('Age (years)', fontsize=12)
+    plt.ylabel(metric_names[metric], fontsize=12)
+    
+    plt.grid(True, linestyle='--', alpha=0.7)
+    
+    plt.tight_layout()
+    plt.savefig(f'analysis/plots/age_vs_{metric}.png', dpi=300, bbox_inches='tight')
+    plt.close()
+
+
+
+
+
+#  correlation summary table
+correlation_data = []
+for metric in model_metrics:
+    valid_data = df.dropna(subset=[metric, 'age'])
+    if len(valid_data) > 2:
+        r, p = stats.pearsonr(valid_data['age'], valid_data[metric])
+        correlation_data.append({
+            'Metric': metric_names[metric],
+            'Correlation': r,
+            'p-value': p,
+            'n': len(valid_data)
+        })
+
+corr_summary = pd.DataFrame(correlation_data)
+print("\nCorrelation Summary:")
+print(corr_summary.to_string(index=False))
+
+corr_summary.to_csv('analysis/plots/age_correlations_summary.csv', index=False)
diff --git a/analysis/participants4.py b/analysis/participants4.py
new file mode 100644
index 00000000..107fa801
--- /dev/null
+++ b/analysis/participants4.py
@@ -0,0 +1,385 @@
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+from sklearn.decomposition import PCA
+from sklearn.manifold import TSNE
+from sklearn.cluster import KMeans
+from sklearn.metrics import silhouette_score, davies_bouldin_score, calinski_harabasz_score
+from scipy.stats import pearsonr, spearmanr, f_oneway, kruskal
+import os
+from matplotlib.colors import Normalize
+from scipy.cluster.hierarchy import dendrogram, linkage
+import warnings
+warnings.filterwarnings('ignore')
+
+output_dir = '/Users/martynaplomecka/closedloop_rl/analysis/plots/clustering_plots'
+os.makedirs(output_dir, exist_ok=True)
+
+df = pd.read_csv('AAAAsindy_analysis_with_metrics.csv')
+df = df.rename(columns={'slcn_age - years': 'age'})
+
+df = df[df['age'] <= 45].copy()
+print(f"Number of participants after age filtering (≤45): {len(df)}")
+
+# embedding features
+embedding_cols = [col for col in df.columns if col.startswith('embedding_')]
+print(f"Found {len(embedding_cols)} embedding dimensions")
+
+# Define behavioral metrics
+behavioral_metrics = ['switch_rate', 'stay_after_reward', 'perseveration', 'avg_reward', 'n_trials']
+
+complete_data = df.dropna(subset=embedding_cols + ['age'] + behavioral_metrics)
+print(f"Number of participants with complete data: {len(complete_data)}")
+
+
+# PCA for general structure visualization
+pca = PCA(n_components=2)
+pca_result = pca.fit_transform(complete_data[embedding_cols])
+print(f"PCA explained variance ratio: {pca.explained_variance_ratio_}")
+print(f"PCA total explained variance: {sum(pca.explained_variance_ratio_):.2f}")
+
+# t-SNE for cluster visualization
+tsne = TSNE(n_components=2, random_state=42, perplexity=min(30, len(complete_data)-1))
+tsne_result = tsne.fit_transform(complete_data[embedding_cols])
+
+# optimal number of clusters
+max_clusters = min(10, len(complete_data) - 1)
+silhouette_scores = []
+db_scores = []
+ch_scores = []
+
+for n_clusters in range(2, max_clusters + 1):
+    # KMeans clustering
+    kmeans = KMeans(n_clusters=n_clusters, random_state=42, n_init=10)
+    cluster_labels = kmeans.fit_predict(complete_data[embedding_cols])
+    
+    silhouette = silhouette_score(complete_data[embedding_cols], cluster_labels)
+    db = davies_bouldin_score(complete_data[embedding_cols], cluster_labels)
+    ch = calinski_harabasz_score(complete_data[embedding_cols], cluster_labels)
+    
+    silhouette_scores.append(silhouette)
+    db_scores.append(db)
+    ch_scores.append(ch)
+    
+    print(f"Clusters: {n_clusters}, Silhouette: {silhouette:.3f}, Davies-Bouldin: {db:.3f}, Calinski-Harabasz: {ch:.3f}")
+
+# Plot clustering metrics
+fig, axes = plt.subplots(1, 3, figsize=(15, 5))
+
+axes[0].plot(range(2, max_clusters + 1), silhouette_scores, 'o-')
+axes[0].set_xlabel('Number of Clusters')
+axes[0].set_ylabel('Silhouette Score')
+axes[0].set_title('Silhouette Score (higher is better)')
+axes[0].grid(True)
+
+axes[1].plot(range(2, max_clusters + 1), db_scores, 'o-')
+axes[1].set_xlabel('Number of Clusters')
+axes[1].set_ylabel('Davies-Bouldin Score')
+axes[1].set_title('Davies-Bouldin Score (lower is better)')
+axes[1].grid(True)
+
+axes[2].plot(range(2, max_clusters + 1), ch_scores, 'o-')
+axes[2].set_xlabel('Number of Clusters')
+axes[2].set_ylabel('Calinski-Harabasz Score')
+axes[2].set_title('Calinski-Harabasz Score (higher is better)')
+axes[2].grid(True)
+
+plt.tight_layout()
+plt.savefig(f'{output_dir}/cluster_metrics.png', dpi=300, bbox_inches='tight')
+plt.close()
+
+# optimal number of clusters based on metrics
+optimal_n_silhouette = np.argmax(silhouette_scores) + 2
+optimal_n_db = np.argmin(db_scores) + 2
+optimal_n_ch = np.argmax(ch_scores) + 2
+
+print(f"Optimal number of clusters (Silhouette): {optimal_n_silhouette}")
+print(f"Optimal number of clusters (Davies-Bouldin): {optimal_n_db}")
+print(f"Optimal number of clusters (Calinski-Harabasz): {optimal_n_ch}")
+
+# Choose the optimal number based on majority voting
+optimal_n_clusters = int(np.median([optimal_n_silhouette, optimal_n_db, optimal_n_ch]))
+print(f"Selected optimal number of clusters: {optimal_n_clusters}")
+
+# Apply KMeans with optimal number of clusters
+kmeans = KMeans(n_clusters=optimal_n_clusters, random_state=42, n_init=10)
+cluster_labels = kmeans.fit_predict(complete_data[embedding_cols])
+complete_data = complete_data.copy()
+complete_data['cluster'] = cluster_labels
+
+
+
+
+
+
+
+#PLOTS
+fig, axes = plt.subplots(1, 2, figsize=(16, 6))
+
+# PCA Plot
+scatter1 = axes[0].scatter(pca_result[:, 0], pca_result[:, 1], c=cluster_labels, cmap='viridis', s=80, alpha=0.8)
+axes[0].set_xlabel(f'PCA Component 1 ({pca.explained_variance_ratio_[0]:.2%} variance)')
+axes[0].set_ylabel(f'PCA Component 2 ({pca.explained_variance_ratio_[1]:.2%} variance)')
+axes[0].set_title(f'PCA Projection with {optimal_n_clusters} Clusters')
+axes[0].grid(True, alpha=0.3)
+
+# t-SNE Plot
+scatter2 = axes[1].scatter(tsne_result[:, 0], tsne_result[:, 1], c=cluster_labels, cmap='viridis', s=80, alpha=0.8)
+axes[1].set_xlabel('t-SNE Component 1')
+axes[1].set_ylabel('t-SNE Component 2')
+axes[1].set_title(f't-SNE Projection with {optimal_n_clusters} Clusters')
+axes[1].grid(True, alpha=0.3)
+
+# Add colorbar
+plt.subplots_adjust(right=0.85)
+cbar_ax = fig.add_axes([0.87, 0.15, 0.02, 0.7])
+cbar = fig.colorbar(scatter1, cax=cbar_ax)
+cbar.set_label('Cluster', rotation=270, labelpad=15)
+
+plt.savefig(f'{output_dir}/embedding_clusters.png', dpi=300, bbox_inches='tight')
+plt.close()
+
+# Step 6: Behavioral measures by cluster
+n_metrics = len(behavioral_metrics) + 1  # +1 for age
+n_cols = 3
+n_rows = (n_metrics + n_cols - 1) // n_cols
+
+fig, axes = plt.subplots(n_rows, n_cols, figsize=(18, 12))
+fig.suptitle(f'Behavioral Measures by Cluster (Age ≤ 45)', fontsize=16)
+
+# Flatten axes for easier indexing
+if n_rows == 1:
+    axes = [axes]
+else:
+    axes = axes.flatten()
+
+# Plot behavioral metrics + age
+for i, metric in enumerate(behavioral_metrics + ['age']):
+    if i < len(axes):
+        ax = axes[i]
+        sns.boxplot(x='cluster', y=metric, data=complete_data, palette='viridis', ax=ax)
+        ax.set_title(f'{metric.replace("_", " ").title()} by Cluster')
+        ax.set_xlabel('Cluster')
+        ax.set_ylabel(metric.replace("_", " ").title())
+        
+        # Statistical test for differences between clusters
+        groups = [complete_data[complete_data['cluster'] == c][metric].dropna() for c in range(optimal_n_clusters)]
+        groups = [g for g in groups if len(g) > 0]
+        
+        if len(groups) > 1:
+            try:
+                f_stat, p_value = f_oneway(*groups)
+                test_name = "ANOVA"
+            except:
+                try:
+                    h_stat, p_value = kruskal(*groups)
+                    test_name = "Kruskal-Wallis"
+                except:
+                    test_name = "Test failed"
+                    p_value = 1.0
+            
+            if p_value < 0.001:
+                p_text = f"{test_name}: p < 0.001"
+            elif p_value < 0.01:
+                p_text = f"{test_name}: p < 0.01"
+            elif p_value < 0.05:
+                p_text = f"{test_name}: p < 0.05"
+            else:
+                p_text = f"{test_name}: p = {p_value:.3f}"
+                
+            ax.text(0.05, 0.95, p_text, transform=ax.transAxes, fontsize=9, 
+                   verticalalignment='top', bbox=dict(boxstyle='round', facecolor='white', alpha=0.7))
+
+for i in range(n_metrics, len(axes)):
+    axes[i].set_visible(False)
+
+plt.tight_layout(rect=[0, 0.03, 1, 0.97])
+plt.savefig(f'{output_dir}/behavioral_by_cluster.png', dpi=300, bbox_inches='tight')
+plt.close()
+
+cluster_summary = complete_data.groupby('cluster').agg({
+    'age': ['mean', 'std', 'count'],
+    'switch_rate': ['mean', 'std'],
+    'stay_after_reward': ['mean', 'std'],
+    'perseveration': ['mean', 'std'],
+    'avg_reward': ['mean', 'std'],
+    'n_trials': ['mean', 'std']
+})
+
+cluster_summary.to_csv(f'{output_dir}/cluster_summary_statistics.csv')
+
+# : Feature correlation analysis
+corr_data = []
+
+for feature in behavioral_metrics + ['age']:
+    if feature in complete_data.columns:
+        try:
+            rho, p = spearmanr(complete_data['cluster'], complete_data[feature])
+            corr_data.append({
+                'Feature': feature,
+                'Correlation': rho,
+                'p-value': p
+            })
+        except:
+            print(f"Correlation calculation failed for {feature}")
+
+if corr_data:
+    corr_df = pd.DataFrame(corr_data)
+    corr_df = corr_df.sort_values('Correlation', key=abs, ascending=False)
+    
+    # Create correlation barplot
+    plt.figure(figsize=(12, 8))
+    colors = ['blue' if x >= 0 else 'red' for x in corr_df['Correlation']]
+    bars = plt.barh(corr_df['Feature'], corr_df['Correlation'], color=colors)
+    
+    # Add significance stars
+    for i, p in enumerate(corr_df['p-value']):
+        if p < 0.05:
+            x_pos = corr_df['Correlation'].iloc[i] + (0.05 if corr_df['Correlation'].iloc[i] >= 0 else -0.05)
+            plt.text(x_pos, i, '*', ha='center', va='center', fontsize=12)
+    
+    plt.axvline(x=0, color='gray', linestyle='-', alpha=0.7)
+    plt.xlabel('Spearman Correlation with Cluster')
+    plt.title('Feature Importance for Cluster Differentiation')
+    plt.grid(True, axis='x', alpha=0.3)
+    plt.tight_layout()
+    plt.savefig(f'{output_dir}/cluster_feature_importance.png', dpi=300, bbox_inches='tight')
+    plt.close()
+    
+    corr_df.to_csv(f'{output_dir}/cluster_feature_correlations.csv', index=False)
+
+# Radar chart for cluster profiles
+radar_metrics = behavioral_metrics + ['age']
+cluster_radar_data = []
+
+for cluster_id in range(optimal_n_clusters):
+    cluster_data = complete_data[complete_data['cluster'] == cluster_id]
+    if len(cluster_data) > 0:
+        cluster_values = []
+        for metric in radar_metrics:
+            if metric in cluster_data.columns:
+                metric_mean = cluster_data[metric].mean()
+                metric_std = complete_data[metric].std()
+                metric_mean_overall = complete_data[metric].mean()
+                
+                if metric_std > 0:
+                    z_score = (metric_mean - metric_mean_overall) / metric_std
+                else:
+                    z_score = 0
+                    
+                cluster_values.append(z_score)
+            else:
+                cluster_values.append(0)
+                
+        cluster_radar_data.append(cluster_values)
+
+# Create radar chart
+if cluster_radar_data:
+    angles = np.linspace(0, 2*np.pi, len(radar_metrics), endpoint=False).tolist()
+    angles += angles[:1]
+    
+    fig, ax = plt.subplots(figsize=(10, 10), subplot_kw=dict(polar=True))
+    
+    for i, values in enumerate(cluster_radar_data):
+        values = values + values[:1]
+        ax.plot(angles, values, linewidth=2, label=f'Cluster {i} (n={len(complete_data[complete_data["cluster"] == i])})')
+        ax.fill(angles, values, alpha=0.1)
+    
+    ax.set_xticks(angles[:-1])
+    ax.set_xticklabels([m.replace('_', ' ').title() for m in radar_metrics])
+    ax.set_title('Cluster Profiles (Z-scores relative to population mean)', size=15, pad=20)
+    ax.grid(True)
+    plt.legend(loc='upper right', bbox_to_anchor=(1.2, 1.0))
+    
+    plt.tight_layout()
+    plt.savefig(f'{output_dir}/cluster_profiles_radar.png', dpi=300, bbox_inches='tight')
+    plt.close()
+
+# : PCA overlay plots with behavioral metrics
+n_metrics = len(behavioral_metrics) + 1
+n_cols = 3
+n_rows = (n_metrics + n_cols - 1) // n_cols
+
+fig, axes = plt.subplots(n_rows, n_cols, figsize=(18, 15))
+fig.suptitle('Behavioral Metrics Mapped onto RNN Embedding Space (Age ≤ 45)', fontsize=16)
+
+if n_rows == 1:
+    axes = [axes]
+else:
+    axes = axes.flatten()
+
+for i, metric in enumerate(behavioral_metrics + ['age']):
+    if i < len(axes) and metric in complete_data.columns:
+        ax = axes[i]
+        
+        scatter = ax.scatter(pca_result[:, 0], pca_result[:, 1], 
+                           c=complete_data[metric], cmap='coolwarm', s=80, alpha=0.8)
+        
+        # Add cluster centers
+        for cluster_idx in range(optimal_n_clusters):
+            cluster_points = pca_result[complete_data['cluster'] == cluster_idx]
+            if len(cluster_points) > 0:
+                center = cluster_points.mean(axis=0)
+                ax.text(center[0], center[1], str(cluster_idx), 
+                       fontsize=16, ha='center', va='center',
+                       bbox=dict(boxstyle='circle', facecolor='white', alpha=0.7))
+        
+        ax.set_xlabel(f'PCA Component 1 ({pca.explained_variance_ratio_[0]:.2%} variance)')
+        ax.set_ylabel(f'PCA Component 2 ({pca.explained_variance_ratio_[1]:.2%} variance)')
+        ax.set_title(f'PCA Projection Colored by {metric.replace("_", " ").title()}')
+        
+        # Add colorbar for each subplot
+        plt.colorbar(scatter, ax=ax, label=metric.replace("_", " ").title(), shrink=0.8)
+        ax.grid(True, alpha=0.3)
+
+# Hide unused subplots
+for i in range(n_metrics, len(axes)):
+    axes[i].set_visible(False)
+
+plt.tight_layout(rect=[0, 0.03, 1, 0.97])
+plt.savefig(f'{output_dir}/pca_with_metrics_overlay.png', dpi=300, bbox_inches='tight')
+plt.close()
+
+#  Cluster centers analysis
+cluster_centers = kmeans.cluster_centers_
+cluster_centers_df = pd.DataFrame(cluster_centers, columns=embedding_cols)
+cluster_centers_df['cluster'] = range(optimal_n_clusters)
+
+cluster_centers_melted = pd.melt(cluster_centers_df, id_vars=['cluster'], 
+                                value_vars=embedding_cols, 
+                                var_name='Embedding_Dimension', 
+                                value_name='Value')
+
+plt.figure(figsize=(14, 8))
+sns.lineplot(x='Embedding_Dimension', y='Value', hue='cluster', data=cluster_centers_melted, 
+            palette='viridis', marker='o')
+plt.title('Cluster Centers across Embedding Dimensions')
+plt.xticks(rotation=90)
+plt.legend(title='Cluster')
+plt.grid(True, alpha=0.3)
+plt.tight_layout()
+plt.savefig(f'{output_dir}/cluster_centers_by_dimension.png', dpi=300, bbox_inches='tight')
+plt.close()
+
+# Hierarchical clustering dendrogram
+if len(complete_data) > 1:
+    Z = linkage(complete_data[embedding_cols], method='ward')
+    
+    plt.figure(figsize=(15, 8))
+    dendrogram(
+        Z,
+        truncate_mode='lastp',
+        p=optimal_n_clusters * 2,
+        leaf_rotation=90.,
+        leaf_font_size=12.,
+        show_contracted=True,
+        color_threshold=0.7 * max(Z[:, 2]) if len(Z) > 0 else None
+    )
+    plt.title('Hierarchical Clustering Dendrogram')
+    plt.xlabel('Sample Index or (Cluster Size)')
+    plt.ylabel('Distance')
+    plt.tight_layout()
+    plt.savefig(f'{output_dir}/hierarchical_clustering.png', dpi=300, bbox_inches='tight')
+    plt.close()
+
diff --git a/analysis/participants5.py b/analysis/participants5.py
new file mode 100644
index 00000000..04915a63
--- /dev/null
+++ b/analysis/participants5.py
@@ -0,0 +1,77 @@
+"""
+For each SINDy coefficient column, plots its value vs. four behavioral metrics 
+(switch_rate, stay_after_reward, perseveration, avg_reward) in a 2×2 grid. 
+Points are color‐coded by participant age, and only participants ≤45 years old are included. 
+"""
+
+import pandas as pd
+import matplotlib.pyplot as plt
+from pathlib import Path
+import numpy as np
+
+def main():
+    file_path = Path('AAAAsindy_analysis_with_metrics.csv')
+    if not file_path.exists():
+        raise FileNotFoundError(f"{file_path} not found—update `file_path` to your CSV location.")
+    df = pd.read_csv(file_path)
+    
+    age_col = 'slcn_age - years'
+    if age_col not in df.columns:
+        raise KeyError(f"Column '{age_col}' not found in data.")
+    df = df[df[age_col] <= 45].copy()
+    df['age'] = df[age_col]
+    
+    #just so the rest will be corelated with 4 behavs
+    exclude = [
+        'participant_id', age_col,
+        'switch_rate', 'stay_after_reward', 'perseveration', 'avg_reward',
+        'beta_reward', 'beta_choice', 'params_', 'total_params',
+        'nll_', 'trial_likelihood_', 'bic_', 'aic_',
+        'n_parameters_', 'metric_n_trials', 'embedding_', 'n_trials'
+    ]
+    coeffs = [c for c in df.columns if not any(c.startswith(pref) for pref in exclude)]
+    behavioral = ['switch_rate', 'stay_after_reward', 'perseveration', 'avg_reward']
+    
+    # normalization for colorbar
+    ages = df['age'].values
+    norm = plt.Normalize(vmin=ages.min(), vmax=ages.max())
+    cmap = 'viridis'
+    
+    output_dir = Path('/Users/martynaplomecka/closedloop_rl/analysis/plots/new_plots')
+    output_dir.mkdir(parents=True, exist_ok=True)
+    
+
+    for coeff in coeffs:
+        vals = df[coeff].values
+        if np.allclose(vals, 0):
+            continue
+        
+        fig, axes = plt.subplots(2, 2, figsize=(14, 10))  
+        axes = axes.flatten()
+        
+        for ax, metric in zip(axes, behavioral):
+            scatter = ax.scatter(vals, df[metric], c=ages, cmap=cmap, norm=norm, alpha=0.8)
+            ax.set_xlabel(coeff)
+            ax.set_ylabel(metric.replace('_', ' ').title())
+            ax.set_title(f"{metric.replace('_', ' ').title()} vs {coeff}")
+        
+        # = room for colorbar
+        plt.subplots_adjust(left=0.08, right=0.85, top=0.92, bottom=0.08, 
+                           wspace=0.3, hspace=0.3)
+        
+        sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)
+        sm.set_array([])
+        
+        cbar_ax = fig.add_axes([0.87, 0.15, 0.02, 0.7])  # [left, bottom, width, height]
+        cbar = fig.colorbar(sm, cax=cbar_ax)
+        cbar.set_label('Age', rotation=270, labelpad=15)
+        
+        fig.suptitle(f"{coeff} Coefficient vs Behavioral Metrics", fontsize=16)
+        
+        out_path = output_dir / f"{coeff}_vs_behavior.png"
+        fig.savefig(out_path, dpi=300, bbox_inches='tight')
+        plt.close(fig)
+        print(f"Saved {out_path}")
+
+if __name__ == '__main__':
+    main()
\ No newline at end of file
diff --git a/analysis/participants_1.py b/analysis/participants_1.py
new file mode 100644
index 00000000..e7692187
--- /dev/null
+++ b/analysis/participants_1.py
@@ -0,0 +1,437 @@
+# Improved clustering analysis with clearer visualizations and explanations
+# This code analyzes participant embeddings to find behavioral clusters
+
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+from sklearn.manifold import TSNE
+from sklearn.preprocessing import StandardScaler
+from sklearn.cluster import KMeans
+import umap
+from scipy.stats import pearsonr
+import os
+
+plt.rcParams.update({
+    'figure.facecolor': 'white',
+    'axes.facecolor': 'white',
+    'font.size': 12,
+    'axes.titlesize': 14,
+    'axes.labelsize': 12,
+    'xtick.labelsize': 10,
+    'ytick.labelsize': 10,
+    'legend.fontsize': 10
+})
+
+output_dir = "/Users/martynaplomecka/closedloop_rl/analysis/plots/new_plots/clustering_embeddings"
+os.makedirs(output_dir, exist_ok=True)
+
+df = pd.read_csv('AAAAsindy_analysis_with_metrics.csv')
+
+behavioral_metrics = ['switch_rate', 'stay_after_reward', 'perseveration', 'avg_reward']
+embedding_cols = [col for col in df.columns if col.startswith('embedding_')]
+
+complete_df = df.dropna(subset=behavioral_metrics + embedding_cols).copy()
+complete_df = complete_df.reset_index(drop=True)
+
+print(f"Dataset: {len(complete_df)} participants, {len(embedding_cols)} embedding dimensions")
+
+# Extract and standardize embeddings
+X_embeddings = complete_df[embedding_cols].values
+participant_ids = complete_df['participant_id'].values
+behaviors = complete_df[behavioral_metrics].copy()
+
+scaler = StandardScaler()
+X_scaled = scaler.fit_transform(X_embeddings)
+
+#COR ANALYSIS
+print("Calculating correlations between embeddings and behavioral metrics...")
+correlations = []
+for metric in behavioral_metrics:
+    for col in embedding_cols:
+        r, p = pearsonr(complete_df[col], complete_df[metric])
+        correlations.append({
+            'embedding': col,
+            'behavioral_metric': metric,
+            'correlation': r,
+            'p_value': p
+        })
+
+corr_df = pd.DataFrame(correlations)
+corr_df['abs_corr'] = corr_df['correlation'].abs()
+corr_df = corr_df.sort_values('abs_corr', ascending=False)
+
+print("\nTop 10 correlations between embeddings and behavioral metrics:")
+print(corr_df.head(10))
+
+# DIM RED
+
+#  t-SNE
+perplexity = min(30, max(5, len(complete_df)//4))
+tsne = TSNE(n_components=2, perplexity=perplexity, random_state=42, 
+           learning_rate=200, n_iter=1000)
+tsne_result = tsne.fit_transform(X_scaled)
+
+#  UMAP
+n_neighbors = min(15, max(3, len(complete_df)//5))
+umap_reducer = umap.UMAP(n_components=2, n_neighbors=n_neighbors, 
+                        min_dist=0.1, random_state=42)
+umap_result = umap_reducer.fit_transform(X_scaled)
+
+# clustering
+
+from sklearn.metrics import silhouette_score
+from scipy.spatial.distance import cdist
+
+# Test different numbers of clusters
+max_clusters = min(10, len(complete_df)//10)  # Don't test more than we can reasonably interpret?
+cluster_range = range(2, max_clusters + 1)
+
+# Method 1: Elbow method (within-cluster sum of squares)
+wcss = []
+for k in cluster_range:
+    kmeans_temp = KMeans(n_clusters=k, random_state=42, n_init=20)
+    kmeans_temp.fit(X_scaled)
+    wcss.append(kmeans_temp.inertia_)
+
+# Method 2: Silhouette analysis
+silhouette_scores = []
+for k in cluster_range:
+    kmeans_temp = KMeans(n_clusters=k, random_state=42, n_init=20)
+    cluster_labels_temp = kmeans_temp.fit_predict(X_scaled)
+    silhouette_avg = silhouette_score(X_scaled, cluster_labels_temp)
+    silhouette_scores.append(silhouette_avg)
+
+# Find optimal k using silhouette score (higher is better)
+optimal_k_silhouette = cluster_range[np.argmax(silhouette_scores)]
+
+# Calculate elbow point (look for the "knee" in the WCSS curve)
+# Use second derivative to find the elbow
+wcss_array = np.array(wcss)
+if len(wcss_array) >= 3:
+    # Calculate second derivative
+    second_derivatives = np.diff(wcss_array, 2)
+    # Find the point where curvature changes most (elbow)
+    elbow_k = cluster_range[np.argmax(second_derivatives) + 2]  # +2 because diff reduces array size
+else:
+    elbow_k = 3  # Default fallback
+
+print(f"Cluster optimization results:")
+print(f"  Silhouette method suggests: {optimal_k_silhouette} clusters (score: {max(silhouette_scores):.3f})")
+print(f"  Elbow method suggests: {elbow_k} clusters")
+print(f"  Silhouette scores: {dict(zip(cluster_range, [f'{s:.3f}' for s in silhouette_scores]))}")
+
+# Choose the number of clusters (prioritize silhouette score but consider interpretability)
+if max(silhouette_scores) > 0.3:  # Good silhouette score
+    n_clusters = optimal_k_silhouette
+    selection_method = "silhouette analysis"
+elif max(silhouette_scores) > 0.2:  # Moderate silhouette score
+    # Choose between silhouette and elbow, prefer smaller number for interpretability
+    n_clusters = min(optimal_k_silhouette, elbow_k)
+    selection_method = "combined silhouette + elbow"
+else:  # Poor silhouette scores, use elbow or default
+    n_clusters = min(elbow_k, 4)  # Cap at 4 for interpretability
+    selection_method = "elbow method (poor silhouette scores)"
+
+print(f"\nSelected {n_clusters} clusters using {selection_method}")
+
+# Final clustering with optimal number
+kmeans = KMeans(n_clusters=n_clusters, random_state=42, n_init=20)
+cluster_labels = kmeans.fit_predict(X_scaled)
+
+# Visualize cluster selection process
+fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 5))
+
+# Plot elbow curve
+ax1.plot(cluster_range, wcss, 'bo-', linewidth=2, markersize=8)
+ax1.axvline(x=elbow_k, color='red', linestyle='--', alpha=0.7, label=f'Elbow at k={elbow_k}')
+ax1.set_xlabel('Number of Clusters (k)')
+ax1.set_ylabel('Within-Cluster Sum of Squares')
+ax1.set_title('Elbow Method for Optimal k')
+ax1.grid(True, alpha=0.3)
+ax1.legend()
+
+# Plot silhouette scores
+ax2.plot(cluster_range, silhouette_scores, 'go-', linewidth=2, markersize=8)
+ax2.axvline(x=optimal_k_silhouette, color='red', linestyle='--', alpha=0.7, 
+           label=f'Best silhouette at k={optimal_k_silhouette}')
+ax2.axhline(y=0.3, color='orange', linestyle=':', alpha=0.7, label='Good threshold (0.3)')
+ax2.axhline(y=0.2, color='yellow', linestyle=':', alpha=0.7, label='Fair threshold (0.2)')
+ax2.set_xlabel('Number of Clusters (k)')
+ax2.set_ylabel('Average Silhouette Score')
+ax2.set_title('Silhouette Analysis for Optimal k')
+ax2.grid(True, alpha=0.3)
+ax2.legend()
+
+plt.tight_layout()
+plt.savefig(os.path.join(output_dir, 'cluster_optimization.png'), 
+           dpi=300, bbox_inches='tight', facecolor='white')
+plt.show()
+
+# Create results dataframe
+results_df = pd.DataFrame({
+    'participant_id': participant_ids,
+    'tsne_x': tsne_result[:, 0],
+    'tsne_y': tsne_result[:, 1],
+    'umap_x': umap_result[:, 0],
+    'umap_y': umap_result[:, 1],
+    'cluster': cluster_labels
+})
+
+# Add behavioral metrics
+for metric in behavioral_metrics:
+    results_df[metric] = complete_df[metric].values
+
+# === ANALYZE CLUSTERS ===
+print("\nAnalyzing cluster characteristics...")
+
+cluster_stats = []
+for cluster in range(n_clusters):
+    cluster_data = results_df[results_df['cluster'] == cluster]
+    stats = {'cluster': cluster, 'n_participants': len(cluster_data)}
+    
+    for metric in behavioral_metrics:
+        stats[f'{metric}_mean'] = cluster_data[metric].mean()
+        stats[f'{metric}_std'] = cluster_data[metric].std()
+    
+    cluster_stats.append(stats)
+
+cluster_stats_df = pd.DataFrame(cluster_stats)
+print("\nCluster Summary:")
+print(cluster_stats_df.round(3))
+
+# === IMPROVED VISUALIZATIONS ===
+
+# 1. Main clustering visualization (larger, clearer)
+fig, axes = plt.subplots(1, 2, figsize=(16, 7))
+
+# t-SNE clusters
+scatter1 = axes[0].scatter(results_df['tsne_x'], results_df['tsne_y'], 
+                         c=results_df['cluster'], cmap='tab10', 
+                         s=60, alpha=0.7, edgecolors='black', linewidth=0.5)
+axes[0].set_title('t-SNE: Participant Clusters\n(Similar participants are close together)', 
+                 fontsize=14, pad=20)
+axes[0].set_xlabel('t-SNE Dimension 1')
+axes[0].set_ylabel('t-SNE Dimension 2')
+axes[0].grid(True, alpha=0.3)
+
+# Add cluster labels
+for cluster in range(n_clusters):
+    cluster_data = results_df[results_df['cluster'] == cluster]
+    center_x = cluster_data['tsne_x'].mean()
+    center_y = cluster_data['tsne_y'].mean()
+    axes[0].annotate(f'Cluster {cluster}', (center_x, center_y), 
+                    fontsize=12, fontweight='bold', 
+                    bbox=dict(boxstyle="round,pad=0.3", facecolor='white', alpha=0.8))
+
+# UMAP clusters
+scatter2 = axes[1].scatter(results_df['umap_x'], results_df['umap_y'], 
+                         c=results_df['cluster'], cmap='tab10', 
+                         s=60, alpha=0.7, edgecolors='black', linewidth=0.5)
+axes[1].set_title('UMAP: Participant Clusters\n(Alternative view of the same clusters)', 
+                 fontsize=14, pad=20)
+axes[1].set_xlabel('UMAP Dimension 1')
+axes[1].set_ylabel('UMAP Dimension 2')
+axes[1].grid(True, alpha=0.3)
+
+# Add cluster labels for UMAP
+for cluster in range(n_clusters):
+    cluster_data = results_df[results_df['cluster'] == cluster]
+    center_x = cluster_data['umap_x'].mean()
+    center_y = cluster_data['umap_y'].mean()
+    axes[1].annotate(f'Cluster {cluster}', (center_x, center_y), 
+                    fontsize=12, fontweight='bold',
+                    bbox=dict(boxstyle="round,pad=0.3", facecolor='white', alpha=0.8))
+
+# Add colorbar
+cbar = plt.colorbar(scatter1, ax=axes, orientation='horizontal', 
+                   fraction=0.05, pad=0.1, shrink=0.8)
+cbar.set_label('Cluster ID', fontsize=12)
+cbar.set_ticks(range(n_clusters))
+
+plt.tight_layout()
+plt.savefig(os.path.join(output_dir, 'clear_clustering_overview.png'), 
+           dpi=300, bbox_inches='tight', facecolor='white')
+plt.show()
+
+# 2. Behavioral differences between clusters (clearer visualization)
+fig, axes = plt.subplots(2, 2, figsize=(15, 12))
+axes = axes.flatten()
+
+# Create better behavioral metric names for display
+metric_names = {
+    'switch_rate': 'Switch Rate',
+    'stay_after_reward': 'Stay After Reward',
+    'perseveration': 'Perseveration',
+    'avg_reward': 'Average Reward'
+}
+
+colors = plt.cm.Set3(np.linspace(0, 1, n_clusters))
+
+for i, metric in enumerate(behavioral_metrics):
+    ax = axes[i]
+    
+    # Create violin plot for better distribution visualization
+    parts = ax.violinplot([results_df[results_df['cluster'] == c][metric].values 
+                          for c in range(n_clusters)], 
+                         positions=range(n_clusters), showmeans=True, showmedians=True)
+    
+    # Color the violin plots
+    for pc, color in zip(parts['bodies'], colors):
+        pc.set_facecolor(color)
+        pc.set_alpha(0.7)
+    
+    # Add individual points
+    for cluster in range(n_clusters):
+        cluster_data = results_df[results_df['cluster'] == cluster][metric]
+        x_pos = [cluster] * len(cluster_data)
+        ax.scatter(x_pos, cluster_data, alpha=0.4, s=20, color='black')
+    
+    ax.set_title(metric_names[metric], fontsize=12, pad=15)
+    ax.set_xlabel('Cluster ID')
+    ax.set_ylabel('Value')
+    ax.set_xticks(range(n_clusters))
+    ax.grid(True, alpha=0.3)
+    
+    # Add statistical annotations
+    for cluster in range(n_clusters):
+        cluster_data = results_df[results_df['cluster'] == cluster][metric]
+        mean_val = cluster_data.mean()
+        ax.text(cluster, ax.get_ylim()[1] * 0.95, f'μ={mean_val:.2f}', 
+               ha='center', fontsize=10, fontweight='bold')
+
+plt.suptitle('How Do Clusters Differ in Behavior?\n', 
+            fontsize=16, y=0.98)
+plt.tight_layout(rect=[0, 0.03, 1, 0.95])  # Fix tight_layout warning
+plt.savefig(os.path.join(output_dir, 'clear_behavioral_differences.png'), 
+           dpi=300, bbox_inches='tight', facecolor='white')
+plt.show()
+
+# 3. Show behavioral patterns in embedding space
+fig, axes = plt.subplots(2, 2, figsize=(16, 12))
+axes = axes.flatten()
+
+for i, metric in enumerate(behavioral_metrics):
+    ax = axes[i]
+    
+    # Use t-SNE coordinates colored by behavioral metric
+    scatter = ax.scatter(results_df['tsne_x'], results_df['tsne_y'], 
+                        c=results_df[metric], cmap='RdYlBu_r', 
+                        s=50, alpha=0.8, edgecolors='black', linewidth=0.3)
+    
+    ax.set_title(f'{metric_names[metric]}\n(Color shows metric value)', fontsize=12)
+    ax.set_xlabel('t-SNE Dimension 1')
+    ax.set_ylabel('t-SNE Dimension 2')
+    ax.grid(True, alpha=0.3)
+    
+    # Add colorbar
+    cbar = plt.colorbar(scatter, ax=ax, shrink=0.8)
+    cbar.set_label(metric, fontsize=10)
+
+plt.suptitle('Where Do Different Behaviors Appear in Embedding Space?\n', 
+            fontsize=16, y=0.98)
+plt.tight_layout(rect=[0, 0.03, 1, 0.95])  # Fix tight_layout warning
+plt.savefig(os.path.join(output_dir, 'behavioral_patterns_in_space.png'), 
+           dpi=300, bbox_inches='tight', facecolor='white')
+plt.show()
+
+# === CORRELATION HEATMAP (from your original code) ===
+print("Creating correlation heatmap...")
+plt.figure(figsize=(14, 8))
+
+# Pivot the correlation dataframe for heatmap
+pivot_df = corr_df.pivot(index='behavioral_metric', columns='embedding', values='correlation')
+
+# Select top correlations for readability
+top_embeddings = corr_df.sort_values('abs_corr', ascending=False)['embedding'].unique()[:min(15, len(embedding_cols))]
+pivot_subset = pivot_df[top_embeddings]
+
+# Create heatmap with better formatting
+sns.heatmap(pivot_subset, cmap='RdBu_r', annot=True, fmt=".3f", center=0, 
+           cbar_kws={'shrink': 0.8, 'label': 'Correlation Coefficient'},
+           linewidths=0.5, square=False)
+
+plt.title('Correlation Between Top Embedding Dimensions and Behavioral Metrics\n', 
+         fontsize=14, pad=20)
+plt.xlabel('Embedding Dimensions', fontsize=12)
+plt.ylabel('Behavioral Metrics', fontsize=12)
+plt.xticks(rotation=45, ha='right')
+plt.yticks(rotation=0)
+plt.tight_layout()
+plt.savefig(os.path.join(output_dir, 'correlation_heatmap.png'), 
+           dpi=300, bbox_inches='tight', facecolor='white')
+plt.show()
+
+# === DETAILED CORRELATION ANALYSIS ===
+print("\n" + "="*60)
+print("CORRELATION ANALYSIS RESULTS")
+print("="*60)
+
+# Show strongest correlations for each behavioral metric
+for metric in behavioral_metrics:
+    metric_corrs = corr_df[corr_df['behavioral_metric'] == metric].sort_values('abs_corr', ascending=False)
+    strongest = metric_corrs.iloc[0]
+    print(f"\n{metric.upper()}:")
+    print(f"  Strongest correlation: {strongest['embedding']} (r = {strongest['correlation']:.3f}, p = {strongest['p_value']:.3f})")
+    
+    # Show top 3 correlations for this metric
+    print(f"  Top 3 embedding correlations:")
+    for i in range(min(3, len(metric_corrs))):
+        row = metric_corrs.iloc[i]
+        print(f"    {i+1}. {row['embedding']}: r = {row['correlation']:.3f} (p = {row['p_value']:.3f})")
+
+# Significant correlations summary
+significant_corrs = corr_df[corr_df['p_value'] < 0.05]
+print(f"\nSIGNIFICANT CORRELATIONS (p < 0.05): {len(significant_corrs)} out of {len(corr_df)}")
+strong_corrs = significant_corrs[significant_corrs['abs_corr'] > 0.3]
+print(f"STRONG CORRELATIONS (|r| > 0.3): {len(strong_corrs)}")
+
+if len(strong_corrs) > 0:
+    print("\nStrongest significant correlations:")
+    for i, (_, row) in enumerate(strong_corrs.head(5).iterrows()):
+        print(f"  {i+1}. {row['embedding']} ↔ {row['behavioral_metric']}: r = {row['correlation']:.3f} (p = {row['p_value']:.3f})")
+
+# === SUMMARY STATISTICS ===
+print("\n" + "="*60)
+print("CLUSTERING ANALYSIS SUMMARY")
+print("="*60)
+print(f"• Analyzed {len(complete_df)} participants")
+print(f"• Found {n_clusters} distinct behavioral clusters")
+print(f"• Used {len(embedding_cols)} embedding dimensions")
+
+print(f"\nCluster Sizes:")
+for cluster in range(n_clusters):
+    count = len(results_df[results_df['cluster'] == cluster])
+    percentage = (count / len(results_df)) * 100
+    print(f"  Cluster {cluster}: {count} participants ({percentage:.1f}%)")
+
+print("• Clusters represent groups of participants with similar neural embedding patterns")
+print("• Each cluster shows different behavioral tendencies")
+print("• This suggests that neural patterns predict behavioral strategies")
+
+# Calculate the most distinctive behavioral differences between clusters
+print(f"\nMost Distinctive Behavioral Differences Between Clusters:")
+for metric in behavioral_metrics:
+    cluster_means = [results_df[results_df['cluster'] == c][metric].mean() 
+                    for c in range(n_clusters)]
+    max_diff = max(cluster_means) - min(cluster_means)
+    best_cluster = cluster_means.index(max(cluster_means))
+    worst_cluster = cluster_means.index(min(cluster_means))
+    print(f"  {metric}: {max_diff:.3f} difference (Cluster {worst_cluster}: {min(cluster_means):.3f} → Cluster {best_cluster}: {max(cluster_means):.3f})")
+
+
+
+
+
+
+# Overall analysis interpretation
+print(f"\nOVERALL INTERPRETATION:")
+print(f"• Strongest embedding-behavior correlation: r = {corr_df.iloc[0]['correlation']:.3f}")
+print(f"  ({corr_df.iloc[0]['embedding']} ↔ {corr_df.iloc[0]['behavioral_metric']})")
+print(f"• Number of significant correlations: {len(significant_corrs)}/{len(corr_df)} ({len(significant_corrs)/len(corr_df)*100:.1f}%)")
+
+if len(strong_corrs) > 0:
+    print(f"• Strong correlations found: Neural embeddings DO predict behavior")
+else:
+    print(f"• Few strong correlations: Neural embeddings may not strongly predict behavior")
+
diff --git a/analysis/participants_preparation.py b/analysis/participants_preparation.py
new file mode 100644
index 00000000..a9a49f95
--- /dev/null
+++ b/analysis/participants_preparation.py
@@ -0,0 +1,270 @@
+# to jest dobre!!!
+
+import sys
+import os
+import pandas as pd
+import numpy as np
+import torch
+from tqdm import tqdm
+
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+import pipeline_sindy
+from resources.model_evaluation import log_likelihood, bayesian_information_criterion
+from resources.bandits import get_update_dynamics
+from utils.convert_dataset import convert_dataset
+from resources.rnn_utils import DatasetRNN
+
+
+data_path = 'data/eckstein2022/eckstein2022.csv'
+original_df = pd.read_csv(data_path)
+unique_sessions = original_df['session'].unique()
+slcn_path = '/Users/martynaplomecka/closedloop_rl/data/eckstein2022/SLCNinfo_Share.csv'
+slcn_df = pd.read_csv(slcn_path)
+columns_to_keep = ['ID', 'age - years']
+available_columns = [col for col in columns_to_keep if col in slcn_df.columns]
+slcn_df = slcn_df[available_columns]
+
+# mapping using ID as key
+slcn_mapping = {}
+for _, row in slcn_df.iterrows():
+    slcn_mapping[row['ID']] = row.to_dict()
+
+behavior_metrics = []
+
+for pid in tqdm(unique_sessions):
+    participant_df = original_df[original_df['session'] == pid]
+    if participant_df.empty:
+        continue
+    
+    choices = participant_df['choice'].values
+    n_switches = np.sum(np.abs(np.diff(choices)))
+    switch_rate = n_switches / (len(choices) - 1) if len(choices) > 1 else 0
+    
+    # stay after reward rate
+    stay_after_reward_count = 0
+    stay_after_reward_total = 0
+    for i in range(len(choices) - 1):
+        current_choice = choices[i]
+        next_choice = choices[i+1]
+        current_reward = participant_df['reward'].iloc[i]
+        if current_reward > 0:
+            stay_after_reward_total += 1
+            if next_choice == current_choice:
+                stay_after_reward_count += 1
+    stay_after_reward_rate = stay_after_reward_count / stay_after_reward_total if stay_after_reward_total > 0 else 0
+    
+    perseveration = np.mean(choices[:-1] == choices[1:]) if len(choices) > 1 else 0
+    avg_reward = participant_df['reward'].mean()
+    
+    participant_data = {
+        'participant_id': pid,
+        'switch_rate': switch_rate,
+        'stay_after_reward': stay_after_reward_rate,
+        'perseveration': perseveration,
+        'avg_reward': avg_reward,
+        'n_trials': len(participant_df)
+    }
+    
+    #  SLCN info
+    if pid in slcn_mapping:
+        slcn_info = slcn_mapping[pid]
+        for key, value in slcn_info.items():
+            if key == 'age - years':
+                participant_data[f'slcn_{key}'] = value
+            elif key == 'age_category':
+                participant_data['age_category'] = value
+    
+    behavior_metrics.append(participant_data)
+
+behavior_df = pd.DataFrame(behavior_metrics)
+
+# Run SINDY pipeline
+agent_spice, features, loss, participant_ids = pipeline_sindy.main(
+    model='params/eckstein2022/rnn_eckstein2022_l1_0_0001_l2_0_0001.pkl',
+    data='data/eckstein2022/eckstein2022.csv',
+    save=True,
+    participant_id=None,
+    filter_bad_participants=True,
+    use_optuna=False,
+    polynomial_degree=1,
+    optimizer_alpha=0.1,
+    optimizer_threshold=0.05,
+    n_trials_off_policy=1000,
+    n_sessions_off_policy=1,
+    verbose=True,
+    n_actions=2,
+    sigma=0.2,
+    beta_reward=1.,
+    alpha=0.25,
+    alpha_penalty=0.25,
+    forget_rate=0.,
+    confirmation_bias=0.,
+    beta_choice=1.,
+    alpha_choice=1.,
+    counterfactual=False,
+    alpha_counterfactual=0.,
+    analysis=False,
+    get_loss=True,
+)
+
+list_rnn_modules = [
+    'x_learning_rate_reward',
+    'x_value_reward_not_chosen',
+    'x_value_choice_chosen',
+    'x_value_choice_not_chosen'
+]
+
+# Map indices to real participant IDs
+index_to_pid = {}
+for i, idx in enumerate(unique_sessions):
+    if i in participant_ids:
+        index_to_pid[i] = idx
+
+print(f"Total unique sessions: {len(unique_sessions)}")
+print(f"Valid participants after filtering: {len(participant_ids)}")
+print(f"Valid index to pid mappings: {len(index_to_pid)}")
+
+# Get unique feature names across all SINDY modules
+all_feature_names = set()
+for module in list_rnn_modules:
+    for idx in agent_spice._model.submodules_sindy[module]:
+        sindy_model = agent_spice._model.submodules_sindy[module][idx]
+        for name in sindy_model.get_feature_names():
+            all_feature_names.add(f"{module}_{name}")
+
+# Extract embeddings
+embedding_matrix = agent_spice._model.participant_embedding.weight.detach().cpu().numpy()
+embedding_size = embedding_matrix.shape[1]
+
+# Create SINDY parameters dataframe
+sindy_params = []
+for idx in tqdm(participant_ids):
+    if idx not in index_to_pid:
+        print(f"Warning: Index {idx} not found in mapping")
+        continue
+    pid = index_to_pid[idx]
+    param_dict = {'participant_id': pid}
+    param_dict.update({feature: 0.0 for feature in all_feature_names})
+    param_dict['beta_reward'] = features['beta_reward'].get(idx, 0.0)
+    param_dict['beta_choice'] = features['beta_choice'].get(idx, 0.0)
+    for module in list_rnn_modules:
+        if idx in agent_spice._model.submodules_sindy[module]:
+            model = agent_spice._model.submodules_sindy[module][idx]
+            coefs = model.model.steps[-1][1].coef_.flatten()
+            for i, name in enumerate(model.get_feature_names()):
+                param_dict[f"{module}_{name}"] = coefs[i]
+            param_dict[f"params_{module}"] = np.sum(np.abs(coefs) > 1e-10)
+    param_dict['total_params'] = sum(param_dict.get(f"params_{m}", 0) for m in list_rnn_modules)
+    if idx < embedding_matrix.shape[0]:
+        for j in range(embedding_size):
+            param_dict[f'embedding_{j}'] = embedding_matrix[idx, j]
+    sindy_params.append(param_dict)
+sindy_df = pd.DataFrame(sindy_params)
+
+print(f"Number of participants in SINDY df: {len(sindy_df)}")
+print(f"Number of participants in behavior df: {len(behavior_df)}")
+
+# Merge SINDY and behavior dataframes
+final_df = pd.merge(sindy_df, behavior_df, on='participant_id', how='left')
+print(f"Number of participants in final merged df: {len(final_df)}")
+
+missing_behavior = set(sindy_df['participant_id']) - set(behavior_df['participant_id'])
+if missing_behavior:
+    print(f"Warning: {len(missing_behavior)} participants in SINDY have no behavioral data")
+
+# Add filtered_out flag
+total_pids = set(behavior_df['participant_id'])
+filtered_pids = total_pids - set(index_to_pid.values())
+behavior_df['filtered_out'] = behavior_df['participant_id'].apply(lambda x: x in filtered_pids)
+
+# Load dataset for model evaluation metrics
+dataset_test, _, _, _ = convert_dataset(data_path)
+
+from utils.setup_agents import setup_agent_rnn
+agent_rnn = setup_agent_rnn(
+    path_model='params/eckstein2022/rnn_eckstein2022_l1_0_0001_l2_0_0001.pkl',
+    list_sindy_signals=list_rnn_modules + ['c_action', 'c_reward', 'c_value_reward', 'c_value_choice'],
+)
+
+# Calculate model evaluation metrics per participant
+metrics_data = []
+for idx in tqdm(participant_ids):
+    if idx not in index_to_pid:
+        continue
+    real_pid = index_to_pid[idx]
+    mask = dataset_test.xs[:, 0, -1] == idx
+    if not mask.any():
+        continue
+    participant_data = DatasetRNN(*dataset_test[mask])
+
+    # Reset agents, check with Daniel
+    agent_spice.new_sess(participant_id=idx)
+    agent_rnn.new_sess(participant_id=idx)
+
+    # Fixed unpacking of get_update_dynamics
+    _, probs_spice, _ = get_update_dynamics(
+        experiment=participant_data.xs, agent=agent_spice
+    )
+    _, probs_rnn, _ = get_update_dynamics(
+        experiment=participant_data.xs, agent=agent_rnn
+    )
+    n_trials_test = len(probs_spice)
+
+    if n_trials_test == 0:
+        continue
+
+    ll_spice = log_likelihood(
+        data=participant_data.ys[0, :n_trials_test].cpu().numpy(),
+        probs=probs_spice
+    )
+    ll_rnn = log_likelihood(
+        data=participant_data.ys[0, :n_trials_test].cpu().numpy(),
+        probs=probs_rnn
+    )
+
+    spice_per_trial_likelihood = np.exp(ll_spice/(n_trials_test * agent_rnn._n_actions))
+    rnn_per_trial_likelihood = np.exp(ll_rnn/(n_trials_test * agent_rnn._n_actions))
+
+    n_params_dict = agent_spice.count_parameters(
+        mapping_modules_values={m: 'x_value_choice' if 'choice' in m else 'x_value_reward'
+                                for m in agent_spice._model.submodules_sindy}
+    )
+    if idx not in n_params_dict:
+        continue
+    n_parameters_spice = n_params_dict[idx]
+
+    bic_spice = bayesian_information_criterion(
+        data=participant_data.ys[0, :n_trials_test].cpu().numpy(),
+        probs=probs_spice,
+        n_parameters=n_parameters_spice
+    )
+    aic_spice = 2 * n_parameters_spice - 2 * ll_spice
+
+    metrics_data.append({
+        'participant_id': real_pid,
+        'nll_spice': -ll_spice,
+        'nll_rnn': -ll_rnn,
+        'trial_likelihood_spice': spice_per_trial_likelihood,
+        'trial_likelihood_rnn': rnn_per_trial_likelihood,
+        'bic_spice': bic_spice,
+        'aic_spice': aic_spice,
+        'n_parameters_spice': n_parameters_spice,
+        'metric_n_trials': n_trials_test
+    })
+
+metrics_df = pd.DataFrame(metrics_data)
+print(f"Number of participants with metrics: {len(metrics_df)}")
+
+final_df = pd.merge(final_df, metrics_df, on='participant_id', how='left')
+print(f"Number of participants in final merged df with metrics: {len(final_df)}")
+
+# Filter out participants older than 45 and younger than 8
+age_filter = (final_df['slcn_age - years'] >= 8) & (final_df['slcn_age - years'] <= 45)
+final_df = final_df[age_filter]
+
+print(f"Number of participants after age filtering (8-45 years): {len(final_df)}")
+
+
+
+final_df.to_csv('AAAAsindy_analysis_with_metrics.csv', index=False)
+behavior_df.to_csv('behavior_metrics_with_filter_status.csv', index=False)
\ No newline at end of file
diff --git a/analysis/plots/age_correlations_summary.csv b/analysis/plots/age_correlations_summary.csv
new file mode 100644
index 00000000..4b64b6da
--- /dev/null
+++ b/analysis/plots/age_correlations_summary.csv
@@ -0,0 +1,7 @@
+Metric,Correlation,p-value,n
+Negative Log-Likelihood (SPICE),-0.06045012823529479,0.32601750208939495,266
+Negative Log-Likelihood (RNN),-0.06811781375382495,0.2682843376668467,266
+Trial Likelihood (SPICE),0.04832999110708129,0.43246237358660244,266
+Trial Likelihood (RNN),0.049455622559424456,0.4218072174883928,266
+BIC (SPICE),-0.06216565258653517,0.3124504719579594,266
+AIC (SPICE),-0.061074449287921155,0.3210366987736371,266
diff --git a/analysis/plots/age_vs_aic_spice.png b/analysis/plots/age_vs_aic_spice.png
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diff --git a/analysis/plots/clustering_plots/cluster_feature_correlations.csv b/analysis/plots/clustering_plots/cluster_feature_correlations.csv
new file mode 100644
index 00000000..a0f93a6e
--- /dev/null
+++ b/analysis/plots/clustering_plots/cluster_feature_correlations.csv
@@ -0,0 +1,7 @@
+Feature,Correlation,p-value
+stay_after_reward,0.6186842012007233,1.7205127232873935e-29
+avg_reward,0.4608701570282933,2.1555046308267775e-15
+switch_rate,-0.4590460840243745,2.8656293114671828e-15
+perseveration,0.4590460840243745,2.8656293114671828e-15
+age,0.21503828277884263,0.0004123339221402368
+n_trials,0.11850320518939754,0.053553072914755744
diff --git a/analysis/plots/clustering_plots/cluster_feature_importance.png b/analysis/plots/clustering_plots/cluster_feature_importance.png
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diff --git a/analysis/plots/clustering_plots/cluster_summary_statistics.csv b/analysis/plots/clustering_plots/cluster_summary_statistics.csv
new file mode 100644
index 00000000..ccc8b1e2
--- /dev/null
+++ b/analysis/plots/clustering_plots/cluster_summary_statistics.csv
@@ -0,0 +1,5 @@
+,age,age,age,switch_rate,switch_rate,stay_after_reward,stay_after_reward,perseveration,perseveration,avg_reward,avg_reward,n_trials,n_trials
+,mean,std,count,mean,std,mean,std,mean,std,mean,std,mean,std
+cluster,,,,,,,,,,,,,
+0,16.007308785166668,6.052894123179568,162,0.34119341944713183,0.11646908710752059,0.8931707088374355,0.09916584158428517,0.6588065805528681,0.11646908710752062,0.5376804964005385,0.061389923349002416,131.3148148148148,5.191335617492261
+1,18.445542634951924,6.050321622617838,104,0.22602921405139234,0.10337240081566183,0.9829403038434648,0.030306293169008253,0.7739707859486077,0.10337240081566185,0.590528382694165,0.040783082019010854,132.06730769230768,5.373911130307712
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diff --git a/analysis/prepare_data_subjects_specific_analysis.py b/analysis/prepare_data_subjects_specific_analysis.py
deleted file mode 100644
index 8a521ee0..00000000
--- a/analysis/prepare_data_subjects_specific_analysis.py
+++ /dev/null
@@ -1,485 +0,0 @@
-import sys
-import os
-import logging
-import numpy as np
-import torch
-import pandas as pd
-
-sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
-
-from resources.model_evaluation import bayesian_information_criterion, log_likelihood
-from utils.plotting import plot_session
-from resources.bandits import AgentQ, AgentNetwork, AgentSpice, get_update_dynamics
-from resources.rnn import RLRNN
-from resources.rnn_utils import DatasetRNN
-from resources.rnn_training import fit_model
-from resources.sindy_training import fit_spice as fit_model_sindy
-
-np.random.seed(42)
-torch.manual_seed(42)
-
-n_actions = 2
-
-logging.basicConfig(
-    level=logging.INFO,
-    format='%(asctime)s - %(levelname)s - %(message)s',
-    handlers=[
-        logging.FileHandler("embedding_analysis.log"),
-        logging.StreamHandler()
-    ]
-)
-logger = logging.getLogger(__name__)
-
-data_path = 'data/parameter_recovery/data_16p_0.csv'
-df = pd.read_csv(data_path)
-
-behavior_metrics = []
-
-for pid in df['session'].unique():
-    participant_df = df[df['session'] == pid]
-    if participant_df.empty:
-        continue
-
-    choices = participant_df['choice'].values
-    n_switches = np.sum(np.abs(np.diff(choices)))
-    switch_rate = n_switches / (len(choices) - 1) if len(choices) > 1 else 0
-
-    stay_after_reward_count = 0
-    stay_after_reward_total = 0
-    for i in range(len(choices) - 1):
-        current_choice = choices[i]
-        next_choice = choices[i+1]
-        current_reward = participant_df['reward'].iloc[i]
-        if current_reward > 0:
-            stay_after_reward_total += 1
-            if next_choice == current_choice:
-                stay_after_reward_count += 1
-    stay_after_reward_rate = stay_after_reward_count / stay_after_reward_total if stay_after_reward_total > 0 else np.nan
-
-    perseveration = np.mean(choices[:-1] == choices[1:]) if len(choices) > 1 else np.nan
-    avg_reward = participant_df['reward'].mean()
-
-    behavior_metrics.append({
-        'participant_id': pid,
-        'switch_rate': switch_rate,
-        'stay_after_reward': stay_after_reward_rate,
-        'perseveration': perseveration,
-        'avg_reward': avg_reward
-    })
-
-behavior_df = pd.DataFrame(behavior_metrics)
-unique_participants = df['session'].unique()
-n_participants = len(unique_participants)
-
-all_xs = []
-all_ys = []
-
-participant_index_to_id = {}
-participant_params = {}
-
-for i, participant_id in enumerate(unique_participants):
-    participant_df = df[df['session'] == participant_id]
-    n_trials = len(participant_df)
-
-    alpha_reward = participant_df['alpha_reward'].iloc[0]
-    alpha_penalty = participant_df['alpha_penalty'].iloc[0]
-    beta_reward = participant_df['beta_reward'].iloc[0]
-    beta_choice = participant_df['beta_choice'].iloc[0]
-    forget_rate = participant_df['forget_rate'].iloc[0]
-
-    participant_index_to_id[i] = participant_id
-    participant_params[participant_id] = {
-        'alpha_reward': alpha_reward,
-        'alpha_penalty': alpha_penalty,
-        'beta_reward': beta_reward,
-        'beta_choice': beta_choice,
-        'forget_rate': forget_rate
-    }
-
-    xs = torch.zeros((1, n_trials, 5))
-    for t in range(1, n_trials):
-        prev_choice = participant_df['choice'].iloc[t - 1]
-        xs[0, t, int(prev_choice)] = 1.0
-        if int(prev_choice) == 0:
-            xs[0, t, 2] = participant_df['reward'].iloc[t - 1]
-            xs[0, t, 3] = -1
-        else:
-            xs[0, t, 2] = -1
-            xs[0, t, 3] = participant_df['reward'].iloc[t - 1]
-    xs[0, :, 4] = i
-
-    ys = torch.zeros((1, n_trials, n_actions))
-    for t in range(n_trials):
-        choice = participant_df['choice'].iloc[t]
-        ys[0, t, int(choice)] = 1.0
-
-    all_xs.append(xs)
-    all_ys.append(ys)
-
-combined_xs = torch.cat(all_xs)
-combined_ys = torch.cat(all_ys)
-combined_dataset = DatasetRNN(combined_xs, combined_ys)
-
-list_rnn_modules = [
-    'x_learning_rate_reward',
-    'x_value_reward_not_chosen',
-    'x_value_choice_chosen',
-    'x_value_choice_not_chosen'
-]
-
-list_control_parameters = ['c_action', 'c_reward', 'c_value_reward', 'c_value_choice']
-
-library_setup = {
-    'x_learning_rate_reward': ['c_reward', 'c_value_reward', 'c_value_choice'],
-    'x_value_reward_not_chosen': ['c_value_choice'],
-    'x_value_choice_chosen': ['c_value_reward'],
-    'x_value_choice_not_chosen': ['c_value_reward'],
-}
-
-filter_setup = {
-    'x_learning_rate_reward': ['c_action', 1, True],
-    'x_value_reward_not_chosen': ['c_action', 0, True],
-    'x_value_choice_chosen': ['c_action', 1, True],
-    'x_value_choice_not_chosen': ['c_action', 0, True],
-}
-
-embedding_size = 9
-model_rnn = RLRNN(
-    n_actions=n_actions,
-    n_participants=n_participants,
-    list_signals=list_rnn_modules + list_control_parameters,
-    hidden_size=22,
-    embedding_size=embedding_size,
-    dropout=0.319
-)
-
-optimizer_rnn = torch.optim.Adam(model_rnn.parameters(), lr=5e-3)
-model_rnn, optimizer_rnn, final_train_loss = fit_model(
-    model=model_rnn,
-    optimizer=optimizer_rnn,
-    dataset_train=combined_dataset,
-    epochs=1,
-    n_steps=62,
-    scheduler=False,
-    convergence_threshold=0,
-    bagging=True
-)
-
-agent_rnn = AgentNetwork(model_rnn=model_rnn, n_actions=n_actions)
-
-agent_sindy, _ = fit_model_sindy(
-    rnn_modules=list_rnn_modules,
-    control_signals=list_control_parameters,
-    agent=agent_rnn,
-    data=combined_dataset,
-    n_sessions_off_policy=1,
-    polynomial_degree=2,
-    library_setup=library_setup,
-    filter_setup=filter_setup,
-    optimizer_threshold=0.05,
-    optimizer_alpha=1,
-    verbose=True,
-)
-
-# Mapping to support beta lookups in count_parameters
-mapping_modules_values = {
-    'x_value_choice_chosen': 'x_value_choice',
-    'x_value_choice_not_chosen': 'x_value_choice',
-    'x_learning_rate_reward': 'x_value_reward',
-    'x_value_reward_not_chosen': 'x_value_reward',
-}
-sindy_params = agent_sindy.count_parameters(mapping_modules_values=mapping_modules_values)
-
-# Calculate BIC and log-likelihood values
-available_participant_ids = set(range(n_participants))
-
-bic_values = []
-ll_values = []
-
-# Process each participant individually 
-for pid in sorted(available_participant_ids):
-    # Find indices of data belonging to this participant in the original dataset
-    indices = []
-    for i in range(combined_dataset.xs.shape[0]):
-        # Use item() to get scalar value from tensor
-        if combined_dataset.xs[i, 0, -1].item() == pid:
-            indices.append(i)
-    
-    if not indices:
-        logger.warning(f"No data found for participant {pid}, skipping")
-        continue
-    
-    logger.info(f"Processing participant {pid}, found {len(indices)} data sequences")
-    
-    # Initialize data collectors for this participant
-    all_choices = []
-    all_probs = []
-    
-    # Process each sequence individually to maintain shape consistency
-    for idx in indices:
-        # Initialize the agent for this participant
-        agent_sindy.new_sess(participant_id=pid)
-        
-        # Get the original tensor directly from the dataset
-        # This ensures we use exactly the same tensor shape/format as during training
-        x_tensor = combined_dataset.xs[idx].clone() 
-        
-        try:
-            # the original get_update_dynamics function from bandits.py
-            _, probs, _ = get_update_dynamics(x_tensor.cpu(), agent_sindy)
-            
-            # Extract choices for log-likelihood calculation
-            choices = x_tensor[..., :agent_sindy._n_actions].cpu().numpy()
-            
-            # Store results for this sequence
-            all_choices.append(choices)
-            all_probs.append(probs)
-        except Exception as e:
-            logger.error(f"Error processing sequence {idx} for participant {pid}: {str(e)}")
-            continue
-    
-    # Combine all sequences for this participant
-    if all_choices and all_probs:
-        combined_choices = np.vstack(all_choices)
-        combined_probs = np.vstack(all_probs)
-        
-        # Calculate log-likelihood and BIC
-        ll = log_likelihood(data=combined_choices, probs=combined_probs)
-        n_trials = combined_choices.shape[0]
-        normalized_ll = ll / n_trials if n_trials > 0 else 0
-        ll_values.append(normalized_ll)
-        
-        bic = bayesian_information_criterion(
-            data=combined_choices, 
-            probs=combined_probs, 
-            n_parameters=sindy_params[pid], 
-            ll=ll
-        )
-        normalized_bic = bic / n_trials if n_trials > 0 else 0
-        bic_values.append(normalized_bic)
-    else:
-        logger.warning(f"No valid data processed for participant {pid}")
-        # Add placeholder values to maintain participant order
-        ll_values.append(0)
-        bic_values.append(0)
-
-avg_bic = np.mean(bic_values) if bic_values else 0
-avg_ll = np.mean(ll_values) if ll_values else 0
-
-sindy_modules = agent_sindy._model.submodules_sindy
-
-sindy_recovered_params = {}
-
-for pid in sorted(available_participant_ids):
-    original_pid = participant_index_to_id[pid]
-    
-    param_data = {
-        'total_nonzero': 0,
-        'parameters_by_module': {}
-    }
-    
-    # Go through each SINDy module
-    for module_name in list_rnn_modules:
-        # Get the SINDy model for this module and participant
-        sindy_model = sindy_modules[module_name][pid]
-        
-        coefs = sindy_model.model.steps[-1][1].coef_.flatten()
-        
-        feature_names = sindy_model.get_feature_names()
-        
-        # Find non-zero parameters
-        nonzero_indices = np.where(np.abs(coefs) > 1e-10)[0]
-        nonzero_count = len(nonzero_indices)
-        
-        # Store non-zero parameters and their values
-        nonzero_params = {}
-        for idx in nonzero_indices:
-            param_name = feature_names[idx]
-            param_value = coefs[idx]
-            nonzero_params[param_name] = param_value
-        
-        param_data['total_nonzero'] += nonzero_count
-        
-        param_data['parameters_by_module'][module_name] = {
-            'nonzero_count': nonzero_count,
-            'nonzero_params': nonzero_params
-        }
-    
-    sindy_recovered_params[original_pid] = param_data
-
-# Table of most common parameters and their frequencies
-most_common_params = {module: {} for module in list_rnn_modules}
-for pid, data in sindy_recovered_params.items():
-    for module in list_rnn_modules:
-        for param_name in data['parameters_by_module'][module]['nonzero_params']:
-            if param_name not in most_common_params[module]:
-                most_common_params[module][param_name] = 0
-            most_common_params[module][param_name] += 1
-
-for module in list_rnn_modules:
-    if most_common_params[module]:
-        sorted_params = sorted(most_common_params[module].items(), key=lambda x: x[1], reverse=True)
-        logger.info(f"\nModule: {module}")
-        for param_name, count in sorted_params[:5]:  
-            percentage = (count / n_participants) * 100
-            logger.info(f"  {param_name}: {count} participants ({percentage:.1f}%)")
-
-# Tables showing parameter values for each module across participants
-# For each module, create a DataFrame with parameters as columns and participants as rows
-for module_name in list_rnn_modules:
-    # Collect all unique parameters found for this module
-    all_params = set()
-    for pid_data in sindy_recovered_params.values():
-        all_params.update(pid_data['parameters_by_module'][module_name]['nonzero_params'].keys())
-    
-    # Sort parameters for consistent ordering (constants first)
-    all_params = sorted(list(all_params), key=lambda x: (0 if x == '1' else 1, x))
-    
-    # One row per participant, one column per parameter
-    module_data = []
-    for pid, pid_data in sindy_recovered_params.items():
-        row = {'participant_id': pid}
-        param_dict = pid_data['parameters_by_module'][module_name]['nonzero_params']
-        
-        # Fill in parameter values (or 0 if not present for this participant)
-        for param in all_params:
-            row[param] = param_dict.get(param, 0)
-            
-        module_data.append(row)
-    
-    df_module = pd.DataFrame(module_data)
-    csv_filename = f'sindy_{module_name}_parameters.csv'
-    df_module.to_csv(csv_filename, index=False)
-    
-    param_stats = {
-        'mean': df_module[all_params].mean(),
-        'std': df_module[all_params].std(),
-        'min': df_module[all_params].min(),
-        'max': df_module[all_params].max(),
-        'present_in': df_module[all_params].astype(bool).sum() / len(df_module) * 100  # % of participants
-    }
-    
-    stats_filename = f'sindy_{module_name}_param_stats.csv'
-    pd.DataFrame(param_stats).to_csv(stats_filename)
-
-# Summary table showing parameter counts by module for all participants
-summary_data = []
-for pid, pid_data in sindy_recovered_params.items():
-    row = {'participant_id': pid}
-    for module_name in list_rnn_modules:
-        row[f'{module_name}_params'] = pid_data['parameters_by_module'][module_name]['nonzero_count']
-    row['total_params'] = pid_data['total_nonzero']
-    summary_data.append(row)
-
-df_summary = pd.DataFrame(summary_data)
-summary_filename = 'sindy_parameter_summary.csv'
-df_summary.to_csv(summary_filename, index=False)
-
-# Participant embeddings
-participant_embeddings = {}
-embedding_data = []
-
-for i in range(n_participants):
-    pid_tensor = torch.tensor([i], device=agent_rnn._model.device)
-    embedding = agent_rnn._model.participant_embedding(pid_tensor).detach().cpu().numpy()[0]
-    
-    original_pid = participant_index_to_id[i]
-    participant_embeddings[original_pid] = embedding
-    embedding_data.append(embedding)
-
-embedding_data = np.array(embedding_data)
-
-participant_data = []
-for i, pid in enumerate(sorted(participant_params.keys())):
-    if i >= len(bic_values):
-        continue
-        
-    params = participant_params[pid]
-    embedding = participant_embeddings[pid]
-    
-    row = {
-        'participant_id': pid,
-        'bic': bic_values[i],
-        'log_likelihood': ll_values[i],
-    }
-    
-    # Add embedding dimensions
-    for j in range(embedding_size):
-        row[f'embedding_{j}'] = embedding[j]
-    
-    # Add SINDy parameter counts
-    if pid in sindy_recovered_params:
-        row['sindy_param_count'] = sindy_recovered_params[pid]['total_nonzero']
-        
-        # Add counts by module
-        for module_name in list_rnn_modules:
-            module_count = sindy_recovered_params[pid]['parameters_by_module'][module_name]['nonzero_count']
-            row[f'params_{module_name}'] = module_count
-    
-    participant_data.append(row)
-
-df_analysis = pd.DataFrame(participant_data)
-df_analysis.to_csv('embedding_analysis_results.csv', index=False)
-
-# Dataframe with participant parameters and their SINDy parameter counts
-final_data = []
-for pid in sorted(participant_params.keys()):
-    if pid in sindy_recovered_params:
-        row = {'participant_id': pid}
-        
-        # Add original parameters
-        row.update(participant_params[pid])
-        
-        # Add BIC and log-likelihood
-        i = list(sorted(participant_params.keys())).index(pid)
-        if i < len(bic_values):
-            row['bic'] = bic_values[i]
-            row['log_likelihood'] = ll_values[i]
-        
-        # Add parameter counts by module
-        for module_name in list_rnn_modules:
-            module_count = sindy_recovered_params[pid]['parameters_by_module'][module_name]['nonzero_count']
-            row[f'params_{module_name}'] = module_count
-            
-            # Add specific parameter values
-            params = sindy_recovered_params[pid]['parameters_by_module'][module_name]['nonzero_params']
-            for param_name, param_value in params.items():
-                row[f'{module_name}_{param_name}'] = param_value
-        
-        row['total_params'] = sindy_recovered_params[pid]['total_nonzero']
-        
-        # Add embeddings
-        if pid in participant_embeddings:
-            embedding = participant_embeddings[pid]
-            for j in range(embedding_size):
-                row[f'embedding_{j}'] = embedding[j]
-
-            # Extract beta values from RNN (they are derived from embeddings)
-            pid_tensor = torch.tensor([list(participant_index_to_id.keys())[list(participant_index_to_id.values()).index(pid)]], 
-                                    device=agent_rnn._model.device)
-            
-            # Get beta_reward from the model
-            beta_reward = agent_rnn._model.betas['x_value_reward'](
-                agent_rnn._model.participant_embedding(pid_tensor)
-            ).item()
-            
-            # Get beta_choice from the model
-            beta_choice = agent_rnn._model.betas['x_value_choice'](
-                agent_rnn._model.participant_embedding(pid_tensor)
-            ).item()
-            
-            # Add derived beta values to the results
-            row['derived_beta_reward'] = beta_reward
-            row['derived_beta_choice'] = beta_choice
-        
-        final_data.append(row)
-
-df_final = pd.DataFrame(final_data)
-final_filename = 'sindy_parameter_analysis.csv'
-df_final.to_csv(final_filename, index=False)
-
-df_final_behav = pd.merge(df_final, behavior_df, on='participant_id', how='left')
-final_filename_behav = 'AAAA_sindy_params_behav_embeddings.csv'
-df_final_behav.to_csv(final_filename_behav, index=False)
-
-logger.info("Analysis completed successfully!")
\ No newline at end of file
diff --git a/analysis/prticipants5 b/analysis/prticipants5
new file mode 100644
index 00000000..04915a63
--- /dev/null
+++ b/analysis/prticipants5
@@ -0,0 +1,77 @@
+"""
+For each SINDy coefficient column, plots its value vs. four behavioral metrics 
+(switch_rate, stay_after_reward, perseveration, avg_reward) in a 2×2 grid. 
+Points are color‐coded by participant age, and only participants ≤45 years old are included. 
+"""
+
+import pandas as pd
+import matplotlib.pyplot as plt
+from pathlib import Path
+import numpy as np
+
+def main():
+    file_path = Path('AAAAsindy_analysis_with_metrics.csv')
+    if not file_path.exists():
+        raise FileNotFoundError(f"{file_path} not found—update `file_path` to your CSV location.")
+    df = pd.read_csv(file_path)
+    
+    age_col = 'slcn_age - years'
+    if age_col not in df.columns:
+        raise KeyError(f"Column '{age_col}' not found in data.")
+    df = df[df[age_col] <= 45].copy()
+    df['age'] = df[age_col]
+    
+    #just so the rest will be corelated with 4 behavs
+    exclude = [
+        'participant_id', age_col,
+        'switch_rate', 'stay_after_reward', 'perseveration', 'avg_reward',
+        'beta_reward', 'beta_choice', 'params_', 'total_params',
+        'nll_', 'trial_likelihood_', 'bic_', 'aic_',
+        'n_parameters_', 'metric_n_trials', 'embedding_', 'n_trials'
+    ]
+    coeffs = [c for c in df.columns if not any(c.startswith(pref) for pref in exclude)]
+    behavioral = ['switch_rate', 'stay_after_reward', 'perseveration', 'avg_reward']
+    
+    # normalization for colorbar
+    ages = df['age'].values
+    norm = plt.Normalize(vmin=ages.min(), vmax=ages.max())
+    cmap = 'viridis'
+    
+    output_dir = Path('/Users/martynaplomecka/closedloop_rl/analysis/plots/new_plots')
+    output_dir.mkdir(parents=True, exist_ok=True)
+    
+
+    for coeff in coeffs:
+        vals = df[coeff].values
+        if np.allclose(vals, 0):
+            continue
+        
+        fig, axes = plt.subplots(2, 2, figsize=(14, 10))  
+        axes = axes.flatten()
+        
+        for ax, metric in zip(axes, behavioral):
+            scatter = ax.scatter(vals, df[metric], c=ages, cmap=cmap, norm=norm, alpha=0.8)
+            ax.set_xlabel(coeff)
+            ax.set_ylabel(metric.replace('_', ' ').title())
+            ax.set_title(f"{metric.replace('_', ' ').title()} vs {coeff}")
+        
+        # = room for colorbar
+        plt.subplots_adjust(left=0.08, right=0.85, top=0.92, bottom=0.08, 
+                           wspace=0.3, hspace=0.3)
+        
+        sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)
+        sm.set_array([])
+        
+        cbar_ax = fig.add_axes([0.87, 0.15, 0.02, 0.7])  # [left, bottom, width, height]
+        cbar = fig.colorbar(sm, cax=cbar_ax)
+        cbar.set_label('Age', rotation=270, labelpad=15)
+        
+        fig.suptitle(f"{coeff} Coefficient vs Behavioral Metrics", fontsize=16)
+        
+        out_path = output_dir / f"{coeff}_vs_behavior.png"
+        fig.savefig(out_path, dpi=300, bbox_inches='tight')
+        plt.close(fig)
+        print(f"Saved {out_path}")
+
+if __name__ == '__main__':
+    main()
\ No newline at end of file
diff --git a/behavior_metrics_with_filter_status.csv b/behavior_metrics_with_filter_status.csv
new file mode 100644
index 00000000..a67c731b
--- /dev/null
+++ b/behavior_metrics_with_filter_status.csv
@@ -0,0 +1,307 @@
+participant_id,switch_rate,stay_after_reward,perseveration,avg_reward,n_trials,slcn_age - years,filtered_out
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diff --git a/benchmarking/__pycache__/hierarchical_bayes_numpyro.cpython-311.pyc b/benchmarking/__pycache__/hierarchical_bayes_numpyro.cpython-311.pyc
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--- a/experiment_log.log
+++ /dev/null
@@ -1,3536 +0,0 @@
-2025-05-06 13:41:57,089 - INFO - ================================================================================
-2025-05-06 13:41:57,089 - INFO - EXPERIMENT CONFIG
-2025-05-06 13:41:57,089 - INFO - ================================================================================
-2025-05-06 13:41:57,089 - INFO - Found 1 data files: ['data_128p_0.csv']
-2025-05-06 13:41:57,089 - INFO - Processing dataset: data_128p_0.csv
-2025-05-06 13:41:57,089 - INFO - Loading dataset from data/optuna/data_128p_0.csv
-2025-05-06 13:41:57,125 - INFO - Number of participants: 128
-2025-05-06 13:41:57,131 - INFO - Participant 0 (ID=0.0): α_reward=0.74, α_penalty=0.97
-2025-05-06 13:41:57,136 - INFO - Participant 1 (ID=1.0): α_reward=0.99, α_penalty=0.02
-2025-05-06 13:41:57,142 - INFO - Participant 2 (ID=2.0): α_reward=0.77, α_penalty=0.46
-2025-05-06 13:41:57,148 - INFO - Participant 3 (ID=3.0): α_reward=0.96, α_penalty=0.60
-2025-05-06 13:41:57,153 - INFO - Participant 4 (ID=4.0): α_reward=0.21, α_penalty=0.21
-2025-05-06 13:41:57,159 - INFO - Participant 5 (ID=5.0): α_reward=0.07, α_penalty=0.51
-2025-05-06 13:41:57,164 - INFO - Participant 6 (ID=6.0): α_reward=0.23, α_penalty=0.60
-2025-05-06 13:41:57,170 - INFO - Participant 7 (ID=7.0): α_reward=0.91, α_penalty=0.80
-2025-05-06 13:41:57,175 - INFO - Participant 8 (ID=8.0): α_reward=0.99, α_penalty=0.97
-2025-05-06 13:41:57,181 - INFO - Participant 9 (ID=9.0): α_reward=0.67, α_penalty=0.87
-2025-05-06 13:41:57,186 - INFO - Participant 10 (ID=10.0): α_reward=0.08, α_penalty=0.77
-2025-05-06 13:41:57,192 - INFO - Participant 11 (ID=11.0): α_reward=0.87, α_penalty=0.31
-2025-05-06 13:41:57,197 - INFO - Participant 12 (ID=12.0): α_reward=0.58, α_penalty=0.19
-2025-05-06 13:41:57,203 - INFO - Participant 13 (ID=13.0): α_reward=0.48, α_penalty=0.25
-2025-05-06 13:41:57,208 - INFO - Participant 14 (ID=14.0): α_reward=0.34, α_penalty=0.83
-2025-05-06 13:41:57,214 - INFO - Participant 15 (ID=15.0): α_reward=0.56, α_penalty=0.80
-2025-05-06 13:41:57,219 - INFO - Participant 16 (ID=16.0): α_reward=0.47, α_penalty=0.29
-2025-05-06 13:41:57,225 - INFO - Participant 17 (ID=17.0): α_reward=0.77, α_penalty=0.94
-2025-05-06 13:41:57,230 - INFO - Participant 18 (ID=18.0): α_reward=0.88, α_penalty=0.01
-2025-05-06 13:41:57,236 - INFO - Participant 19 (ID=19.0): α_reward=0.98, α_penalty=0.11
-2025-05-06 13:41:57,241 - INFO - Participant 20 (ID=20.0): α_reward=0.02, α_penalty=0.85
-2025-05-06 13:41:57,247 - INFO - Participant 21 (ID=21.0): α_reward=0.46, α_penalty=0.85
-2025-05-06 13:41:57,252 - INFO - Participant 22 (ID=22.0): α_reward=0.69, α_penalty=0.43
-2025-05-06 13:41:57,258 - INFO - Participant 23 (ID=23.0): α_reward=0.15, α_penalty=0.95
-2025-05-06 13:41:57,263 - INFO - Participant 24 (ID=24.0): α_reward=0.25, α_penalty=0.36
-2025-05-06 13:41:57,269 - INFO - Participant 25 (ID=25.0): α_reward=0.75, α_penalty=0.11
-2025-05-06 13:41:57,274 - INFO - Participant 26 (ID=26.0): α_reward=0.46, α_penalty=0.81
-2025-05-06 13:41:57,280 - INFO - Participant 27 (ID=27.0): α_reward=0.36, α_penalty=0.63
-2025-05-06 13:41:57,285 - INFO - Participant 28 (ID=28.0): α_reward=0.49, α_penalty=0.00
-2025-05-06 13:41:57,291 - INFO - Participant 29 (ID=29.0): α_reward=0.20, α_penalty=0.98
-2025-05-06 13:41:57,296 - INFO - Participant 30 (ID=30.0): α_reward=0.29, α_penalty=0.64
-2025-05-06 13:41:57,302 - INFO - Participant 31 (ID=31.0): α_reward=0.66, α_penalty=0.64
-2025-05-06 13:41:57,307 - INFO - Participant 32 (ID=32.0): α_reward=0.21, α_penalty=0.41
-2025-05-06 13:41:57,312 - INFO - Participant 33 (ID=33.0): α_reward=0.49, α_penalty=0.63
-2025-05-06 13:41:57,318 - INFO - Participant 34 (ID=34.0): α_reward=0.38, α_penalty=0.08
-2025-05-06 13:41:57,323 - INFO - Participant 35 (ID=35.0): α_reward=0.33, α_penalty=0.29
-2025-05-06 13:41:57,329 - INFO - Participant 36 (ID=36.0): α_reward=0.53, α_penalty=0.61
-2025-05-06 13:41:57,334 - INFO - Participant 37 (ID=37.0): α_reward=0.65, α_penalty=0.34
-2025-05-06 13:41:57,340 - INFO - Participant 38 (ID=38.0): α_reward=0.29, α_penalty=0.13
-2025-05-06 13:41:57,345 - INFO - Participant 39 (ID=39.0): α_reward=0.31, α_penalty=0.25
-2025-05-06 13:41:57,351 - INFO - Participant 40 (ID=40.0): α_reward=0.77, α_penalty=0.04
-2025-05-06 13:41:57,356 - INFO - Participant 41 (ID=41.0): α_reward=0.23, α_penalty=0.89
-2025-05-06 13:41:57,362 - INFO - Participant 42 (ID=42.0): α_reward=0.63, α_penalty=0.60
-2025-05-06 13:41:57,367 - INFO - Participant 43 (ID=43.0): α_reward=0.48, α_penalty=0.52
-2025-05-06 13:41:57,373 - INFO - Participant 44 (ID=44.0): α_reward=0.92, α_penalty=0.22
-2025-05-06 13:41:57,378 - INFO - Participant 45 (ID=45.0): α_reward=0.52, α_penalty=0.89
-2025-05-06 13:41:57,383 - INFO - Participant 46 (ID=46.0): α_reward=0.42, α_penalty=0.83
-2025-05-06 13:41:57,389 - INFO - Participant 47 (ID=47.0): α_reward=0.68, α_penalty=0.69
-2025-05-06 13:41:57,394 - INFO - Participant 48 (ID=48.0): α_reward=0.95, α_penalty=0.22
-2025-05-06 13:41:57,400 - INFO - Participant 49 (ID=49.0): α_reward=0.13, α_penalty=0.48
-2025-05-06 13:41:57,405 - INFO - Participant 50 (ID=50.0): α_reward=0.87, α_penalty=0.85
-2025-05-06 13:41:57,411 - INFO - Participant 51 (ID=51.0): α_reward=0.47, α_penalty=0.07
-2025-05-06 13:41:57,416 - INFO - Participant 52 (ID=52.0): α_reward=0.56, α_penalty=0.42
-2025-05-06 13:41:57,421 - INFO - Participant 53 (ID=53.0): α_reward=0.53, α_penalty=0.18
-2025-05-06 13:41:57,427 - INFO - Participant 54 (ID=54.0): α_reward=0.87, α_penalty=0.73
-2025-05-06 13:41:57,433 - INFO - Participant 55 (ID=55.0): α_reward=0.21, α_penalty=0.47
-2025-05-06 13:41:57,438 - INFO - Participant 56 (ID=56.0): α_reward=0.65, α_penalty=0.09
-2025-05-06 13:41:57,443 - INFO - Participant 57 (ID=57.0): α_reward=0.72, α_penalty=0.88
-2025-05-06 13:41:57,449 - INFO - Participant 58 (ID=58.0): α_reward=0.61, α_penalty=0.38
-2025-05-06 13:41:57,454 - INFO - Participant 59 (ID=59.0): α_reward=0.92, α_penalty=0.91
-2025-05-06 13:41:57,460 - INFO - Participant 60 (ID=60.0): α_reward=0.22, α_penalty=0.97
-2025-05-06 13:41:57,465 - INFO - Participant 61 (ID=61.0): α_reward=0.41, α_penalty=0.53
-2025-05-06 13:41:57,471 - INFO - Participant 62 (ID=62.0): α_reward=0.92, α_penalty=0.99
-2025-05-06 13:41:57,476 - INFO - Participant 63 (ID=63.0): α_reward=0.22, α_penalty=0.99
-2025-05-06 13:41:57,482 - INFO - Participant 64 (ID=64.0): α_reward=0.84, α_penalty=0.96
-2025-05-06 13:41:57,487 - INFO - Participant 65 (ID=65.0): α_reward=0.55, α_penalty=0.19
-2025-05-06 13:41:57,492 - INFO - Participant 66 (ID=66.0): α_reward=0.34, α_penalty=0.07
-2025-05-06 13:41:57,498 - INFO - Participant 67 (ID=67.0): α_reward=0.62, α_penalty=0.45
-2025-05-06 13:41:57,503 - INFO - Participant 68 (ID=68.0): α_reward=0.47, α_penalty=0.19
-2025-05-06 13:41:57,509 - INFO - Participant 69 (ID=69.0): α_reward=0.81, α_penalty=0.36
-2025-05-06 13:41:57,514 - INFO - Participant 70 (ID=70.0): α_reward=0.73, α_penalty=0.87
-2025-05-06 13:41:57,520 - INFO - Participant 71 (ID=71.0): α_reward=0.10, α_penalty=0.57
-2025-05-06 13:41:57,525 - INFO - Participant 72 (ID=72.0): α_reward=0.78, α_penalty=0.50
-2025-05-06 13:41:57,530 - INFO - Participant 73 (ID=73.0): α_reward=0.82, α_penalty=0.70
-2025-05-06 13:41:57,536 - INFO - Participant 74 (ID=74.0): α_reward=0.00, α_penalty=0.98
-2025-05-06 13:41:57,542 - INFO - Participant 75 (ID=75.0): α_reward=0.42, α_penalty=0.26
-2025-05-06 13:41:57,547 - INFO - Participant 76 (ID=76.0): α_reward=0.83, α_penalty=0.39
-2025-05-06 13:41:57,553 - INFO - Participant 77 (ID=77.0): α_reward=0.75, α_penalty=0.18
-2025-05-06 13:41:57,558 - INFO - Participant 78 (ID=78.0): α_reward=0.97, α_penalty=0.44
-2025-05-06 13:41:57,563 - INFO - Participant 79 (ID=79.0): α_reward=0.45, α_penalty=0.57
-2025-05-06 13:41:57,569 - INFO - Participant 80 (ID=80.0): α_reward=0.63, α_penalty=0.78
-2025-05-06 13:41:57,574 - INFO - Participant 81 (ID=81.0): α_reward=0.29, α_penalty=0.24
-2025-05-06 13:41:57,580 - INFO - Participant 82 (ID=82.0): α_reward=0.71, α_penalty=0.15
-2025-05-06 13:41:57,585 - INFO - Participant 83 (ID=83.0): α_reward=0.71, α_penalty=0.66
-2025-05-06 13:41:57,591 - INFO - Participant 84 (ID=84.0): α_reward=0.97, α_penalty=0.79
-2025-05-06 13:41:57,596 - INFO - Participant 85 (ID=85.0): α_reward=1.00, α_penalty=0.95
-2025-05-06 13:41:57,602 - INFO - Participant 86 (ID=86.0): α_reward=0.56, α_penalty=0.08
-2025-05-06 13:41:57,607 - INFO - Participant 87 (ID=87.0): α_reward=0.59, α_penalty=0.83
-2025-05-06 13:41:57,613 - INFO - Participant 88 (ID=88.0): α_reward=0.32, α_penalty=0.60
-2025-05-06 13:41:57,619 - INFO - Participant 89 (ID=89.0): α_reward=0.20, α_penalty=0.84
-2025-05-06 13:41:57,624 - INFO - Participant 90 (ID=90.0): α_reward=0.34, α_penalty=1.00
-2025-05-06 13:41:57,630 - INFO - Participant 91 (ID=91.0): α_reward=0.19, α_penalty=0.99
-2025-05-06 13:41:57,636 - INFO - Participant 92 (ID=92.0): α_reward=0.20, α_penalty=0.91
-2025-05-06 13:41:57,641 - INFO - Participant 93 (ID=93.0): α_reward=0.31, α_penalty=0.21
-2025-05-06 13:41:57,646 - INFO - Participant 94 (ID=94.0): α_reward=0.58, α_penalty=0.41
-2025-05-06 13:41:57,652 - INFO - Participant 95 (ID=95.0): α_reward=0.80, α_penalty=0.54
-2025-05-06 13:41:57,657 - INFO - Participant 96 (ID=96.0): α_reward=0.74, α_penalty=0.80
-2025-05-06 13:41:57,663 - INFO - Participant 97 (ID=97.0): α_reward=0.59, α_penalty=0.16
-2025-05-06 13:41:57,668 - INFO - Participant 98 (ID=98.0): α_reward=0.54, α_penalty=0.17
-2025-05-06 13:41:57,674 - INFO - Participant 99 (ID=99.0): α_reward=0.17, α_penalty=0.16
-2025-05-06 13:41:57,679 - INFO - Participant 100 (ID=100.0): α_reward=0.79, α_penalty=0.40
-2025-05-06 13:41:57,684 - INFO - Participant 101 (ID=101.0): α_reward=0.32, α_penalty=0.05
-2025-05-06 13:41:57,689 - INFO - Participant 102 (ID=102.0): α_reward=0.87, α_penalty=0.81
-2025-05-06 13:41:57,694 - INFO - Participant 103 (ID=103.0): α_reward=0.90, α_penalty=0.11
-2025-05-06 13:41:57,700 - INFO - Participant 104 (ID=104.0): α_reward=0.44, α_penalty=0.12
-2025-05-06 13:41:57,706 - INFO - Participant 105 (ID=105.0): α_reward=0.59, α_penalty=0.50
-2025-05-06 13:41:57,712 - INFO - Participant 106 (ID=106.0): α_reward=0.84, α_penalty=0.50
-2025-05-06 13:41:57,717 - INFO - Participant 107 (ID=107.0): α_reward=0.48, α_penalty=0.54
-2025-05-06 13:41:57,723 - INFO - Participant 108 (ID=108.0): α_reward=0.56, α_penalty=0.75
-2025-05-06 13:41:57,729 - INFO - Participant 109 (ID=109.0): α_reward=0.53, α_penalty=0.11
-2025-05-06 13:41:57,734 - INFO - Participant 110 (ID=110.0): α_reward=0.52, α_penalty=0.40
-2025-05-06 13:41:57,740 - INFO - Participant 111 (ID=111.0): α_reward=0.61, α_penalty=0.74
-2025-05-06 13:41:57,745 - INFO - Participant 112 (ID=112.0): α_reward=0.64, α_penalty=0.56
-2025-05-06 13:41:57,751 - INFO - Participant 113 (ID=113.0): α_reward=0.44, α_penalty=0.40
-2025-05-06 13:41:57,757 - INFO - Participant 114 (ID=114.0): α_reward=0.74, α_penalty=0.91
-2025-05-06 13:41:57,762 - INFO - Participant 115 (ID=115.0): α_reward=0.09, α_penalty=0.27
-2025-05-06 13:41:57,768 - INFO - Participant 116 (ID=116.0): α_reward=0.99, α_penalty=0.92
-2025-05-06 13:41:57,774 - INFO - Participant 117 (ID=117.0): α_reward=0.90, α_penalty=0.87
-2025-05-06 13:41:57,779 - INFO - Participant 118 (ID=118.0): α_reward=0.21, α_penalty=0.27
-2025-05-06 13:41:57,785 - INFO - Participant 119 (ID=119.0): α_reward=0.71, α_penalty=0.69
-2025-05-06 13:41:57,790 - INFO - Participant 120 (ID=120.0): α_reward=0.43, α_penalty=0.05
-2025-05-06 13:41:57,796 - INFO - Participant 121 (ID=121.0): α_reward=0.11, α_penalty=0.27
-2025-05-06 13:41:57,802 - INFO - Participant 122 (ID=122.0): α_reward=0.80, α_penalty=0.43
-2025-05-06 13:41:57,807 - INFO - Participant 123 (ID=123.0): α_reward=0.74, α_penalty=0.44
-2025-05-06 13:41:57,813 - INFO - Participant 124 (ID=124.0): α_reward=0.18, α_penalty=0.36
-2025-05-06 13:41:57,818 - INFO - Participant 125 (ID=125.0): α_reward=0.86, α_penalty=0.09
-2025-05-06 13:41:57,824 - INFO - Participant 126 (ID=126.0): α_reward=0.05, α_penalty=0.52
-2025-05-06 13:41:57,830 - INFO - Participant 127 (ID=127.0): α_reward=0.41, α_penalty=0.26
-2025-05-06 13:41:57,830 - INFO - Participant 0 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,830 - INFO - Participant 1 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,830 - INFO - Participant 2 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,830 - INFO - Participant 3 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,830 - INFO - Participant 4 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,830 - INFO - Participant 5 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,830 - INFO - Participant 6 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,830 - INFO - Participant 7 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,830 - INFO - Participant 8 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,830 - INFO - Participant 9 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,830 - INFO - Participant 10 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,830 - INFO - Participant 11 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,830 - INFO - Participant 12 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,830 - INFO - Participant 13 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,830 - INFO - Participant 14 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,830 - INFO - Participant 15 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,830 - INFO - Participant 16 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,830 - INFO - Participant 17 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,830 - INFO - Participant 18 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,830 - INFO - Participant 19 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,830 - INFO - Participant 20 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,830 - INFO - Participant 21 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,830 - INFO - Participant 22 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,830 - INFO - Participant 23 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,830 - INFO - Participant 24 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,830 - INFO - Participant 25 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,830 - INFO - Participant 26 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,830 - INFO - Participant 27 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,830 - INFO - Participant 28 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,830 - INFO - Participant 29 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,830 - INFO - Participant 30 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,830 - INFO - Participant 31 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,830 - INFO - Participant 32 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,830 - INFO - Participant 33 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,830 - INFO - Participant 34 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,830 - INFO - Participant 35 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,830 - INFO - Participant 36 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,830 - INFO - Participant 37 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,830 - INFO - Participant 38 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,830 - INFO - Participant 39 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,830 - INFO - Participant 40 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,830 - INFO - Participant 41 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,830 - INFO - Participant 42 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,830 - INFO - Participant 43 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,830 - INFO - Participant 44 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,830 - INFO - Participant 45 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,831 - INFO - Participant 46 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,831 - INFO - Participant 47 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,831 - INFO - Participant 48 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,831 - INFO - Participant 49 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,831 - INFO - Participant 50 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,831 - INFO - Participant 51 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,831 - INFO - Participant 52 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,831 - INFO - Participant 53 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,831 - INFO - Participant 54 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,831 - INFO - Participant 55 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,831 - INFO - Participant 56 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,831 - INFO - Participant 57 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,831 - INFO - Participant 58 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,831 - INFO - Participant 59 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,831 - INFO - Participant 60 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,831 - INFO - Participant 61 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,831 - INFO - Participant 62 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,831 - INFO - Participant 63 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,831 - INFO - Participant 64 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,831 - INFO - Participant 65 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,831 - INFO - Participant 66 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,831 - INFO - Participant 67 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,831 - INFO - Participant 68 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,831 - INFO - Participant 69 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,831 - INFO - Participant 70 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,831 - INFO - Participant 71 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,831 - INFO - Participant 72 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,831 - INFO - Participant 73 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,831 - INFO - Participant 74 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,831 - INFO - Participant 75 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,831 - INFO - Participant 76 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,831 - INFO - Participant 77 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,831 - INFO - Participant 78 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,831 - INFO - Participant 79 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,831 - INFO - Participant 80 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,831 - INFO - Participant 81 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,831 - INFO - Participant 82 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,831 - INFO - Participant 83 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,831 - INFO - Participant 84 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,831 - INFO - Participant 85 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,831 - INFO - Participant 86 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,831 - INFO - Participant 87 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,831 - INFO - Participant 88 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,831 - INFO - Participant 89 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,831 - INFO - Participant 90 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,831 - INFO - Participant 91 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,831 - INFO - Participant 92 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,831 - INFO - Participant 93 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,831 - INFO - Participant 94 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,831 - INFO - Participant 95 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,831 - INFO - Participant 96 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,831 - INFO - Participant 97 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,831 - INFO - Participant 98 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,831 - INFO - Participant 99 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,831 - INFO - Participant 100 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,831 - INFO - Participant 101 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,831 - INFO - Participant 102 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,831 - INFO - Participant 103 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,831 - INFO - Participant 104 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,831 - INFO - Participant 105 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,831 - INFO - Participant 106 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,831 - INFO - Participant 107 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,831 - INFO - Participant 108 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,831 - INFO - Participant 109 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,831 - INFO - Participant 110 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,831 - INFO - Participant 111 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,831 - INFO - Participant 112 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,831 - INFO - Participant 113 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,831 - INFO - Participant 114 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,832 - INFO - Participant 115 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,832 - INFO - Participant 116 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,832 - INFO - Participant 117 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,832 - INFO - Participant 118 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,832 - INFO - Participant 119 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,832 - INFO - Participant 120 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,832 - INFO - Participant 121 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,832 - INFO - Participant 122 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,832 - INFO - Participant 123 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,832 - INFO - Participant 124 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,832 - INFO - Participant 125 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,832 - INFO - Participant 126 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,832 - INFO - Participant 127 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:41:57,832 - INFO - Combined xs shape after concatenation: torch.Size([128, 200, 5])
-2025-05-06 13:41:57,832 - INFO - Combined ys shape after concatenation: torch.Size([128, 200, 2])
-2025-05-06 13:41:57,832 - INFO - Combined dataset shape: X=torch.Size([128, 200, 5]), Y=torch.Size([128, 200, 2])
-2025-05-06 13:41:57,832 - INFO - Total unique participants: 128
-2025-05-06 13:41:57,832 - INFO - Train/test split ratio: 0.8/0.19999999999999996 of trials within each participant
-2025-05-06 13:41:57,833 - INFO - Participant 0.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,833 - INFO - Participant 1.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,833 - INFO - Participant 2.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,833 - INFO - Participant 3.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,833 - INFO - Participant 4.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,833 - INFO - Participant 5.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,833 - INFO - Participant 6.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,833 - INFO - Participant 7.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,833 - INFO - Participant 8.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,833 - INFO - Participant 9.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,833 - INFO - Participant 10.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,833 - INFO - Participant 11.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,833 - INFO - Participant 12.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,833 - INFO - Participant 13.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,833 - INFO - Participant 14.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,833 - INFO - Participant 15.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,833 - INFO - Participant 16.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,834 - INFO - Participant 17.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,834 - INFO - Participant 18.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,834 - INFO - Participant 19.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,834 - INFO - Participant 20.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,834 - INFO - Participant 21.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,834 - INFO - Participant 22.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,834 - INFO - Participant 23.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,834 - INFO - Participant 24.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,834 - INFO - Participant 25.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,834 - INFO - Participant 26.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,834 - INFO - Participant 27.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,834 - INFO - Participant 28.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,834 - INFO - Participant 29.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,834 - INFO - Participant 30.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,834 - INFO - Participant 31.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,834 - INFO - Participant 32.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,834 - INFO - Participant 33.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,834 - INFO - Participant 34.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,834 - INFO - Participant 35.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,834 - INFO - Participant 36.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,835 - INFO - Participant 37.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,835 - INFO - Participant 38.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,835 - INFO - Participant 39.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,835 - INFO - Participant 40.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,835 - INFO - Participant 41.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,835 - INFO - Participant 42.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,835 - INFO - Participant 43.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,835 - INFO - Participant 44.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,835 - INFO - Participant 45.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,835 - INFO - Participant 46.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,835 - INFO - Participant 47.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,835 - INFO - Participant 48.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,835 - INFO - Participant 49.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,835 - INFO - Participant 50.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,835 - INFO - Participant 51.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,835 - INFO - Participant 52.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,835 - INFO - Participant 53.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,835 - INFO - Participant 54.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,835 - INFO - Participant 55.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,835 - INFO - Participant 56.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,835 - INFO - Participant 57.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,836 - INFO - Participant 58.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,836 - INFO - Participant 59.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,836 - INFO - Participant 60.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,836 - INFO - Participant 61.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,836 - INFO - Participant 62.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,836 - INFO - Participant 63.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,836 - INFO - Participant 64.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,836 - INFO - Participant 65.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,836 - INFO - Participant 66.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,836 - INFO - Participant 67.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,836 - INFO - Participant 68.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,836 - INFO - Participant 69.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,836 - INFO - Participant 70.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,836 - INFO - Participant 71.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,836 - INFO - Participant 72.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,836 - INFO - Participant 73.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,836 - INFO - Participant 74.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,836 - INFO - Participant 75.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,836 - INFO - Participant 76.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,836 - INFO - Participant 77.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,836 - INFO - Participant 78.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,836 - INFO - Participant 79.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,837 - INFO - Participant 80.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,837 - INFO - Participant 81.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,837 - INFO - Participant 82.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,837 - INFO - Participant 83.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,837 - INFO - Participant 84.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,837 - INFO - Participant 85.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,837 - INFO - Participant 86.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,837 - INFO - Participant 87.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,837 - INFO - Participant 88.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,837 - INFO - Participant 89.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,837 - INFO - Participant 90.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,837 - INFO - Participant 91.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,837 - INFO - Participant 92.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,837 - INFO - Participant 93.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,837 - INFO - Participant 94.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,837 - INFO - Participant 95.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,837 - INFO - Participant 96.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,837 - INFO - Participant 97.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,837 - INFO - Participant 98.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,837 - INFO - Participant 99.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,837 - INFO - Participant 100.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,837 - INFO - Participant 101.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,838 - INFO - Participant 102.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,838 - INFO - Participant 103.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,838 - INFO - Participant 104.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,838 - INFO - Participant 105.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,838 - INFO - Participant 106.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,838 - INFO - Participant 107.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,838 - INFO - Participant 108.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,838 - INFO - Participant 109.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,838 - INFO - Participant 110.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,838 - INFO - Participant 111.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,838 - INFO - Participant 112.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,838 - INFO - Participant 113.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,838 - INFO - Participant 114.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,838 - INFO - Participant 115.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,838 - INFO - Participant 116.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,838 - INFO - Participant 117.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,838 - INFO - Participant 118.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,838 - INFO - Participant 119.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,838 - INFO - Participant 120.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,838 - INFO - Participant 121.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,838 - INFO - Participant 122.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,839 - INFO - Participant 123.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,839 - INFO - Participant 124.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,839 - INFO - Participant 125.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,839 - INFO - Participant 126.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,839 - INFO - Participant 127.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:41:57,839 - INFO - Train xs shape: torch.Size([128, 160, 5])
-2025-05-06 13:41:57,839 - INFO - Train ys shape: torch.Size([128, 160, 2])
-2025-05-06 13:41:57,839 - INFO - Validation xs shape: torch.Size([128, 40, 5])
-2025-05-06 13:41:57,839 - INFO - Validation ys shape: torch.Size([128, 40, 2])
-2025-05-06 13:41:57,839 - INFO - Train dataset: torch.Size([128, 160, 5]), Validation dataset: torch.Size([128, 40, 5])
-2025-05-06 13:41:57,839 - INFO - Starting hyperparameter optimization...
-2025-05-06 13:41:57,840 - INFO - Trial 0: l1_weight_decay=0.001688, l2_weight_decay=0.000005
-2025-05-06 13:42:05,086 - INFO - Trial 0: RNN Train Loss: 0.5222022; SPICE Train Loss: 2.994627576656261
-2025-05-06 13:42:05,115 - INFO - Trial 0: Average Validation Loss: 3.4181, Eval count: 40
-2025-05-06 13:42:05,116 - INFO - Best hyperparameters: {'l1_weight_decay': 0.0016881945590593564, 'l2_weight_decay': 4.967957420311853e-06}
-2025-05-06 13:42:05,116 - INFO - Best validation loss: 3.4181
-2025-05-06 13:44:06,184 - INFO - ================================================================================
-2025-05-06 13:44:06,184 - INFO - EXPERIMENT CONFIG
-2025-05-06 13:44:06,184 - INFO - ================================================================================
-2025-05-06 13:44:06,184 - INFO - Found 1 data files: ['data_128p_0.csv']
-2025-05-06 13:44:06,184 - INFO - Processing dataset: data_128p_0.csv
-2025-05-06 13:44:06,184 - INFO - Loading dataset from data/optuna/data_128p_0.csv
-2025-05-06 13:44:06,223 - INFO - Number of participants: 128
-2025-05-06 13:44:06,229 - INFO - Participant 0 (ID=0.0): α_reward=0.74, α_penalty=0.97
-2025-05-06 13:44:06,235 - INFO - Participant 1 (ID=1.0): α_reward=0.99, α_penalty=0.02
-2025-05-06 13:44:06,241 - INFO - Participant 2 (ID=2.0): α_reward=0.77, α_penalty=0.46
-2025-05-06 13:44:06,247 - INFO - Participant 3 (ID=3.0): α_reward=0.96, α_penalty=0.60
-2025-05-06 13:44:06,253 - INFO - Participant 4 (ID=4.0): α_reward=0.21, α_penalty=0.21
-2025-05-06 13:44:06,259 - INFO - Participant 5 (ID=5.0): α_reward=0.07, α_penalty=0.51
-2025-05-06 13:44:06,265 - INFO - Participant 6 (ID=6.0): α_reward=0.23, α_penalty=0.60
-2025-05-06 13:44:06,271 - INFO - Participant 7 (ID=7.0): α_reward=0.91, α_penalty=0.80
-2025-05-06 13:44:06,277 - INFO - Participant 8 (ID=8.0): α_reward=0.99, α_penalty=0.97
-2025-05-06 13:44:06,282 - INFO - Participant 9 (ID=9.0): α_reward=0.67, α_penalty=0.87
-2025-05-06 13:44:06,288 - INFO - Participant 10 (ID=10.0): α_reward=0.08, α_penalty=0.77
-2025-05-06 13:44:06,294 - INFO - Participant 11 (ID=11.0): α_reward=0.87, α_penalty=0.31
-2025-05-06 13:44:06,300 - INFO - Participant 12 (ID=12.0): α_reward=0.58, α_penalty=0.19
-2025-05-06 13:44:06,306 - INFO - Participant 13 (ID=13.0): α_reward=0.48, α_penalty=0.25
-2025-05-06 13:44:06,312 - INFO - Participant 14 (ID=14.0): α_reward=0.34, α_penalty=0.83
-2025-05-06 13:44:06,318 - INFO - Participant 15 (ID=15.0): α_reward=0.56, α_penalty=0.80
-2025-05-06 13:44:06,323 - INFO - Participant 16 (ID=16.0): α_reward=0.47, α_penalty=0.29
-2025-05-06 13:44:06,329 - INFO - Participant 17 (ID=17.0): α_reward=0.77, α_penalty=0.94
-2025-05-06 13:44:06,335 - INFO - Participant 18 (ID=18.0): α_reward=0.88, α_penalty=0.01
-2025-05-06 13:44:06,341 - INFO - Participant 19 (ID=19.0): α_reward=0.98, α_penalty=0.11
-2025-05-06 13:44:06,347 - INFO - Participant 20 (ID=20.0): α_reward=0.02, α_penalty=0.85
-2025-05-06 13:44:06,353 - INFO - Participant 21 (ID=21.0): α_reward=0.46, α_penalty=0.85
-2025-05-06 13:44:06,358 - INFO - Participant 22 (ID=22.0): α_reward=0.69, α_penalty=0.43
-2025-05-06 13:44:06,364 - INFO - Participant 23 (ID=23.0): α_reward=0.15, α_penalty=0.95
-2025-05-06 13:44:06,370 - INFO - Participant 24 (ID=24.0): α_reward=0.25, α_penalty=0.36
-2025-05-06 13:44:06,375 - INFO - Participant 25 (ID=25.0): α_reward=0.75, α_penalty=0.11
-2025-05-06 13:44:06,381 - INFO - Participant 26 (ID=26.0): α_reward=0.46, α_penalty=0.81
-2025-05-06 13:44:06,387 - INFO - Participant 27 (ID=27.0): α_reward=0.36, α_penalty=0.63
-2025-05-06 13:44:06,393 - INFO - Participant 28 (ID=28.0): α_reward=0.49, α_penalty=0.00
-2025-05-06 13:44:06,399 - INFO - Participant 29 (ID=29.0): α_reward=0.20, α_penalty=0.98
-2025-05-06 13:44:06,404 - INFO - Participant 30 (ID=30.0): α_reward=0.29, α_penalty=0.64
-2025-05-06 13:44:06,410 - INFO - Participant 31 (ID=31.0): α_reward=0.66, α_penalty=0.64
-2025-05-06 13:44:06,416 - INFO - Participant 32 (ID=32.0): α_reward=0.21, α_penalty=0.41
-2025-05-06 13:44:06,421 - INFO - Participant 33 (ID=33.0): α_reward=0.49, α_penalty=0.63
-2025-05-06 13:44:06,427 - INFO - Participant 34 (ID=34.0): α_reward=0.38, α_penalty=0.08
-2025-05-06 13:44:06,432 - INFO - Participant 35 (ID=35.0): α_reward=0.33, α_penalty=0.29
-2025-05-06 13:44:06,438 - INFO - Participant 36 (ID=36.0): α_reward=0.53, α_penalty=0.61
-2025-05-06 13:44:06,444 - INFO - Participant 37 (ID=37.0): α_reward=0.65, α_penalty=0.34
-2025-05-06 13:44:06,449 - INFO - Participant 38 (ID=38.0): α_reward=0.29, α_penalty=0.13
-2025-05-06 13:44:06,455 - INFO - Participant 39 (ID=39.0): α_reward=0.31, α_penalty=0.25
-2025-05-06 13:44:06,460 - INFO - Participant 40 (ID=40.0): α_reward=0.77, α_penalty=0.04
-2025-05-06 13:44:06,466 - INFO - Participant 41 (ID=41.0): α_reward=0.23, α_penalty=0.89
-2025-05-06 13:44:06,471 - INFO - Participant 42 (ID=42.0): α_reward=0.63, α_penalty=0.60
-2025-05-06 13:44:06,477 - INFO - Participant 43 (ID=43.0): α_reward=0.48, α_penalty=0.52
-2025-05-06 13:44:06,483 - INFO - Participant 44 (ID=44.0): α_reward=0.92, α_penalty=0.22
-2025-05-06 13:44:06,488 - INFO - Participant 45 (ID=45.0): α_reward=0.52, α_penalty=0.89
-2025-05-06 13:44:06,494 - INFO - Participant 46 (ID=46.0): α_reward=0.42, α_penalty=0.83
-2025-05-06 13:44:06,499 - INFO - Participant 47 (ID=47.0): α_reward=0.68, α_penalty=0.69
-2025-05-06 13:44:06,505 - INFO - Participant 48 (ID=48.0): α_reward=0.95, α_penalty=0.22
-2025-05-06 13:44:06,510 - INFO - Participant 49 (ID=49.0): α_reward=0.13, α_penalty=0.48
-2025-05-06 13:44:06,516 - INFO - Participant 50 (ID=50.0): α_reward=0.87, α_penalty=0.85
-2025-05-06 13:44:06,522 - INFO - Participant 51 (ID=51.0): α_reward=0.47, α_penalty=0.07
-2025-05-06 13:44:06,527 - INFO - Participant 52 (ID=52.0): α_reward=0.56, α_penalty=0.42
-2025-05-06 13:44:06,533 - INFO - Participant 53 (ID=53.0): α_reward=0.53, α_penalty=0.18
-2025-05-06 13:44:06,538 - INFO - Participant 54 (ID=54.0): α_reward=0.87, α_penalty=0.73
-2025-05-06 13:44:06,544 - INFO - Participant 55 (ID=55.0): α_reward=0.21, α_penalty=0.47
-2025-05-06 13:44:06,549 - INFO - Participant 56 (ID=56.0): α_reward=0.65, α_penalty=0.09
-2025-05-06 13:44:06,555 - INFO - Participant 57 (ID=57.0): α_reward=0.72, α_penalty=0.88
-2025-05-06 13:44:06,561 - INFO - Participant 58 (ID=58.0): α_reward=0.61, α_penalty=0.38
-2025-05-06 13:44:06,566 - INFO - Participant 59 (ID=59.0): α_reward=0.92, α_penalty=0.91
-2025-05-06 13:44:06,571 - INFO - Participant 60 (ID=60.0): α_reward=0.22, α_penalty=0.97
-2025-05-06 13:44:06,577 - INFO - Participant 61 (ID=61.0): α_reward=0.41, α_penalty=0.53
-2025-05-06 13:44:06,583 - INFO - Participant 62 (ID=62.0): α_reward=0.92, α_penalty=0.99
-2025-05-06 13:44:06,589 - INFO - Participant 63 (ID=63.0): α_reward=0.22, α_penalty=0.99
-2025-05-06 13:44:06,594 - INFO - Participant 64 (ID=64.0): α_reward=0.84, α_penalty=0.96
-2025-05-06 13:44:06,600 - INFO - Participant 65 (ID=65.0): α_reward=0.55, α_penalty=0.19
-2025-05-06 13:44:06,605 - INFO - Participant 66 (ID=66.0): α_reward=0.34, α_penalty=0.07
-2025-05-06 13:44:06,612 - INFO - Participant 67 (ID=67.0): α_reward=0.62, α_penalty=0.45
-2025-05-06 13:44:06,617 - INFO - Participant 68 (ID=68.0): α_reward=0.47, α_penalty=0.19
-2025-05-06 13:44:06,623 - INFO - Participant 69 (ID=69.0): α_reward=0.81, α_penalty=0.36
-2025-05-06 13:44:06,629 - INFO - Participant 70 (ID=70.0): α_reward=0.73, α_penalty=0.87
-2025-05-06 13:44:06,635 - INFO - Participant 71 (ID=71.0): α_reward=0.10, α_penalty=0.57
-2025-05-06 13:44:06,641 - INFO - Participant 72 (ID=72.0): α_reward=0.78, α_penalty=0.50
-2025-05-06 13:44:06,647 - INFO - Participant 73 (ID=73.0): α_reward=0.82, α_penalty=0.70
-2025-05-06 13:44:06,652 - INFO - Participant 74 (ID=74.0): α_reward=0.00, α_penalty=0.98
-2025-05-06 13:44:06,658 - INFO - Participant 75 (ID=75.0): α_reward=0.42, α_penalty=0.26
-2025-05-06 13:44:06,664 - INFO - Participant 76 (ID=76.0): α_reward=0.83, α_penalty=0.39
-2025-05-06 13:44:06,670 - INFO - Participant 77 (ID=77.0): α_reward=0.75, α_penalty=0.18
-2025-05-06 13:44:06,676 - INFO - Participant 78 (ID=78.0): α_reward=0.97, α_penalty=0.44
-2025-05-06 13:44:06,681 - INFO - Participant 79 (ID=79.0): α_reward=0.45, α_penalty=0.57
-2025-05-06 13:44:06,687 - INFO - Participant 80 (ID=80.0): α_reward=0.63, α_penalty=0.78
-2025-05-06 13:44:06,694 - INFO - Participant 81 (ID=81.0): α_reward=0.29, α_penalty=0.24
-2025-05-06 13:44:06,699 - INFO - Participant 82 (ID=82.0): α_reward=0.71, α_penalty=0.15
-2025-05-06 13:44:06,705 - INFO - Participant 83 (ID=83.0): α_reward=0.71, α_penalty=0.66
-2025-05-06 13:44:06,710 - INFO - Participant 84 (ID=84.0): α_reward=0.97, α_penalty=0.79
-2025-05-06 13:44:06,715 - INFO - Participant 85 (ID=85.0): α_reward=1.00, α_penalty=0.95
-2025-05-06 13:44:06,720 - INFO - Participant 86 (ID=86.0): α_reward=0.56, α_penalty=0.08
-2025-05-06 13:44:06,725 - INFO - Participant 87 (ID=87.0): α_reward=0.59, α_penalty=0.83
-2025-05-06 13:44:06,730 - INFO - Participant 88 (ID=88.0): α_reward=0.32, α_penalty=0.60
-2025-05-06 13:44:06,735 - INFO - Participant 89 (ID=89.0): α_reward=0.20, α_penalty=0.84
-2025-05-06 13:44:06,740 - INFO - Participant 90 (ID=90.0): α_reward=0.34, α_penalty=1.00
-2025-05-06 13:44:06,745 - INFO - Participant 91 (ID=91.0): α_reward=0.19, α_penalty=0.99
-2025-05-06 13:44:06,751 - INFO - Participant 92 (ID=92.0): α_reward=0.20, α_penalty=0.91
-2025-05-06 13:44:06,756 - INFO - Participant 93 (ID=93.0): α_reward=0.31, α_penalty=0.21
-2025-05-06 13:44:06,761 - INFO - Participant 94 (ID=94.0): α_reward=0.58, α_penalty=0.41
-2025-05-06 13:44:06,767 - INFO - Participant 95 (ID=95.0): α_reward=0.80, α_penalty=0.54
-2025-05-06 13:44:06,772 - INFO - Participant 96 (ID=96.0): α_reward=0.74, α_penalty=0.80
-2025-05-06 13:44:06,778 - INFO - Participant 97 (ID=97.0): α_reward=0.59, α_penalty=0.16
-2025-05-06 13:44:06,783 - INFO - Participant 98 (ID=98.0): α_reward=0.54, α_penalty=0.17
-2025-05-06 13:44:06,788 - INFO - Participant 99 (ID=99.0): α_reward=0.17, α_penalty=0.16
-2025-05-06 13:44:06,794 - INFO - Participant 100 (ID=100.0): α_reward=0.79, α_penalty=0.40
-2025-05-06 13:44:06,799 - INFO - Participant 101 (ID=101.0): α_reward=0.32, α_penalty=0.05
-2025-05-06 13:44:06,805 - INFO - Participant 102 (ID=102.0): α_reward=0.87, α_penalty=0.81
-2025-05-06 13:44:06,810 - INFO - Participant 103 (ID=103.0): α_reward=0.90, α_penalty=0.11
-2025-05-06 13:44:06,815 - INFO - Participant 104 (ID=104.0): α_reward=0.44, α_penalty=0.12
-2025-05-06 13:44:06,821 - INFO - Participant 105 (ID=105.0): α_reward=0.59, α_penalty=0.50
-2025-05-06 13:44:06,826 - INFO - Participant 106 (ID=106.0): α_reward=0.84, α_penalty=0.50
-2025-05-06 13:44:06,832 - INFO - Participant 107 (ID=107.0): α_reward=0.48, α_penalty=0.54
-2025-05-06 13:44:06,838 - INFO - Participant 108 (ID=108.0): α_reward=0.56, α_penalty=0.75
-2025-05-06 13:44:06,843 - INFO - Participant 109 (ID=109.0): α_reward=0.53, α_penalty=0.11
-2025-05-06 13:44:06,849 - INFO - Participant 110 (ID=110.0): α_reward=0.52, α_penalty=0.40
-2025-05-06 13:44:06,854 - INFO - Participant 111 (ID=111.0): α_reward=0.61, α_penalty=0.74
-2025-05-06 13:44:06,860 - INFO - Participant 112 (ID=112.0): α_reward=0.64, α_penalty=0.56
-2025-05-06 13:44:06,865 - INFO - Participant 113 (ID=113.0): α_reward=0.44, α_penalty=0.40
-2025-05-06 13:44:06,870 - INFO - Participant 114 (ID=114.0): α_reward=0.74, α_penalty=0.91
-2025-05-06 13:44:06,876 - INFO - Participant 115 (ID=115.0): α_reward=0.09, α_penalty=0.27
-2025-05-06 13:44:06,881 - INFO - Participant 116 (ID=116.0): α_reward=0.99, α_penalty=0.92
-2025-05-06 13:44:06,887 - INFO - Participant 117 (ID=117.0): α_reward=0.90, α_penalty=0.87
-2025-05-06 13:44:06,892 - INFO - Participant 118 (ID=118.0): α_reward=0.21, α_penalty=0.27
-2025-05-06 13:44:06,898 - INFO - Participant 119 (ID=119.0): α_reward=0.71, α_penalty=0.69
-2025-05-06 13:44:06,903 - INFO - Participant 120 (ID=120.0): α_reward=0.43, α_penalty=0.05
-2025-05-06 13:44:06,909 - INFO - Participant 121 (ID=121.0): α_reward=0.11, α_penalty=0.27
-2025-05-06 13:44:06,914 - INFO - Participant 122 (ID=122.0): α_reward=0.80, α_penalty=0.43
-2025-05-06 13:44:06,920 - INFO - Participant 123 (ID=123.0): α_reward=0.74, α_penalty=0.44
-2025-05-06 13:44:06,925 - INFO - Participant 124 (ID=124.0): α_reward=0.18, α_penalty=0.36
-2025-05-06 13:44:06,931 - INFO - Participant 125 (ID=125.0): α_reward=0.86, α_penalty=0.09
-2025-05-06 13:44:06,936 - INFO - Participant 126 (ID=126.0): α_reward=0.05, α_penalty=0.52
-2025-05-06 13:44:06,942 - INFO - Participant 127 (ID=127.0): α_reward=0.41, α_penalty=0.26
-2025-05-06 13:44:06,942 - INFO - Participant 0 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,942 - INFO - Participant 1 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,942 - INFO - Participant 2 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,942 - INFO - Participant 3 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,942 - INFO - Participant 4 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,942 - INFO - Participant 5 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,942 - INFO - Participant 6 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,942 - INFO - Participant 7 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,942 - INFO - Participant 8 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,942 - INFO - Participant 9 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,942 - INFO - Participant 10 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,942 - INFO - Participant 11 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,942 - INFO - Participant 12 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,942 - INFO - Participant 13 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,942 - INFO - Participant 14 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,942 - INFO - Participant 15 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,942 - INFO - Participant 16 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,942 - INFO - Participant 17 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,942 - INFO - Participant 18 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,942 - INFO - Participant 19 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,942 - INFO - Participant 20 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,942 - INFO - Participant 21 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,942 - INFO - Participant 22 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,942 - INFO - Participant 23 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,942 - INFO - Participant 24 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,942 - INFO - Participant 25 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,942 - INFO - Participant 26 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,942 - INFO - Participant 27 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,942 - INFO - Participant 28 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,942 - INFO - Participant 29 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,942 - INFO - Participant 30 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,942 - INFO - Participant 31 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,942 - INFO - Participant 32 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,942 - INFO - Participant 33 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,942 - INFO - Participant 34 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,942 - INFO - Participant 35 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,942 - INFO - Participant 36 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,942 - INFO - Participant 37 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,942 - INFO - Participant 38 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,942 - INFO - Participant 39 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,942 - INFO - Participant 40 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,942 - INFO - Participant 41 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,942 - INFO - Participant 42 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,942 - INFO - Participant 43 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,942 - INFO - Participant 44 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,942 - INFO - Participant 45 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,942 - INFO - Participant 46 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,942 - INFO - Participant 47 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,942 - INFO - Participant 48 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,942 - INFO - Participant 49 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,942 - INFO - Participant 50 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,942 - INFO - Participant 51 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,942 - INFO - Participant 52 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,942 - INFO - Participant 53 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,943 - INFO - Participant 54 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,943 - INFO - Participant 55 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,943 - INFO - Participant 56 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,943 - INFO - Participant 57 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,943 - INFO - Participant 58 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,943 - INFO - Participant 59 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,943 - INFO - Participant 60 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,943 - INFO - Participant 61 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,943 - INFO - Participant 62 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,943 - INFO - Participant 63 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,943 - INFO - Participant 64 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,943 - INFO - Participant 65 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,943 - INFO - Participant 66 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,943 - INFO - Participant 67 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,943 - INFO - Participant 68 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,943 - INFO - Participant 69 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,943 - INFO - Participant 70 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,943 - INFO - Participant 71 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,943 - INFO - Participant 72 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,943 - INFO - Participant 73 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,943 - INFO - Participant 74 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,943 - INFO - Participant 75 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,943 - INFO - Participant 76 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,943 - INFO - Participant 77 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,943 - INFO - Participant 78 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,943 - INFO - Participant 79 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,943 - INFO - Participant 80 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,943 - INFO - Participant 81 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,943 - INFO - Participant 82 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,943 - INFO - Participant 83 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,943 - INFO - Participant 84 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,943 - INFO - Participant 85 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,943 - INFO - Participant 86 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,943 - INFO - Participant 87 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,943 - INFO - Participant 88 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,943 - INFO - Participant 89 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,943 - INFO - Participant 90 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,943 - INFO - Participant 91 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,943 - INFO - Participant 92 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,943 - INFO - Participant 93 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,943 - INFO - Participant 94 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,943 - INFO - Participant 95 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,943 - INFO - Participant 96 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,943 - INFO - Participant 97 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,943 - INFO - Participant 98 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,943 - INFO - Participant 99 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,943 - INFO - Participant 100 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,943 - INFO - Participant 101 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,943 - INFO - Participant 102 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,943 - INFO - Participant 103 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,943 - INFO - Participant 104 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,943 - INFO - Participant 105 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,943 - INFO - Participant 106 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,943 - INFO - Participant 107 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,943 - INFO - Participant 108 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,943 - INFO - Participant 109 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,943 - INFO - Participant 110 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,943 - INFO - Participant 111 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,943 - INFO - Participant 112 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,943 - INFO - Participant 113 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,943 - INFO - Participant 114 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,943 - INFO - Participant 115 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,943 - INFO - Participant 116 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,943 - INFO - Participant 117 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,943 - INFO - Participant 118 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,943 - INFO - Participant 119 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,943 - INFO - Participant 120 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,943 - INFO - Participant 121 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,943 - INFO - Participant 122 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,943 - INFO - Participant 123 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,943 - INFO - Participant 124 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,944 - INFO - Participant 125 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,944 - INFO - Participant 126 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,944 - INFO - Participant 127 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:44:06,944 - INFO - Combined xs shape after concatenation: torch.Size([128, 200, 5])
-2025-05-06 13:44:06,944 - INFO - Combined ys shape after concatenation: torch.Size([128, 200, 2])
-2025-05-06 13:44:06,944 - INFO - Combined dataset shape: X=torch.Size([128, 200, 5]), Y=torch.Size([128, 200, 2])
-2025-05-06 13:44:06,944 - INFO - Total unique participants: 128
-2025-05-06 13:44:06,944 - INFO - Train/test split ratio: 0.8/0.19999999999999996 of trials within each participant
-2025-05-06 13:44:06,944 - INFO - Participant 0.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,944 - INFO - Participant 1.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,945 - INFO - Participant 2.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,945 - INFO - Participant 3.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,945 - INFO - Participant 4.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,945 - INFO - Participant 5.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,945 - INFO - Participant 6.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,945 - INFO - Participant 7.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,945 - INFO - Participant 8.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,945 - INFO - Participant 9.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,945 - INFO - Participant 10.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,945 - INFO - Participant 11.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,945 - INFO - Participant 12.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,945 - INFO - Participant 13.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,945 - INFO - Participant 14.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,945 - INFO - Participant 15.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,945 - INFO - Participant 16.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,945 - INFO - Participant 17.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,945 - INFO - Participant 18.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,945 - INFO - Participant 19.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,945 - INFO - Participant 20.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,945 - INFO - Participant 21.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,946 - INFO - Participant 22.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,946 - INFO - Participant 23.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,946 - INFO - Participant 24.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,946 - INFO - Participant 25.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,946 - INFO - Participant 26.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,946 - INFO - Participant 27.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,946 - INFO - Participant 28.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,946 - INFO - Participant 29.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,946 - INFO - Participant 30.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,946 - INFO - Participant 31.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,946 - INFO - Participant 32.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,946 - INFO - Participant 33.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,946 - INFO - Participant 34.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,946 - INFO - Participant 35.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,946 - INFO - Participant 36.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,946 - INFO - Participant 37.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,946 - INFO - Participant 38.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,946 - INFO - Participant 39.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,946 - INFO - Participant 40.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,946 - INFO - Participant 41.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,947 - INFO - Participant 42.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,947 - INFO - Participant 43.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,947 - INFO - Participant 44.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,947 - INFO - Participant 45.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,947 - INFO - Participant 46.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,947 - INFO - Participant 47.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,947 - INFO - Participant 48.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,947 - INFO - Participant 49.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,947 - INFO - Participant 50.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,947 - INFO - Participant 51.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,947 - INFO - Participant 52.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,947 - INFO - Participant 53.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,947 - INFO - Participant 54.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,947 - INFO - Participant 55.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,947 - INFO - Participant 56.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,947 - INFO - Participant 57.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,947 - INFO - Participant 58.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,947 - INFO - Participant 59.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,947 - INFO - Participant 60.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,947 - INFO - Participant 61.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,947 - INFO - Participant 62.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,947 - INFO - Participant 63.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,948 - INFO - Participant 64.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,948 - INFO - Participant 65.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,948 - INFO - Participant 66.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,948 - INFO - Participant 67.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,948 - INFO - Participant 68.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,948 - INFO - Participant 69.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,948 - INFO - Participant 70.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,948 - INFO - Participant 71.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,948 - INFO - Participant 72.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,948 - INFO - Participant 73.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,948 - INFO - Participant 74.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,948 - INFO - Participant 75.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,948 - INFO - Participant 76.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,948 - INFO - Participant 77.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,948 - INFO - Participant 78.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,948 - INFO - Participant 79.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,948 - INFO - Participant 80.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,948 - INFO - Participant 81.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,948 - INFO - Participant 82.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,948 - INFO - Participant 83.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,948 - INFO - Participant 84.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,948 - INFO - Participant 85.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,949 - INFO - Participant 86.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,949 - INFO - Participant 87.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,949 - INFO - Participant 88.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,949 - INFO - Participant 89.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,949 - INFO - Participant 90.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,949 - INFO - Participant 91.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,949 - INFO - Participant 92.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,949 - INFO - Participant 93.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,949 - INFO - Participant 94.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,949 - INFO - Participant 95.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,949 - INFO - Participant 96.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,949 - INFO - Participant 97.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,949 - INFO - Participant 98.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,949 - INFO - Participant 99.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,949 - INFO - Participant 100.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,949 - INFO - Participant 101.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,949 - INFO - Participant 102.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,949 - INFO - Participant 103.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,950 - INFO - Participant 104.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,950 - INFO - Participant 105.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,950 - INFO - Participant 106.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,950 - INFO - Participant 107.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,950 - INFO - Participant 108.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,950 - INFO - Participant 109.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,950 - INFO - Participant 110.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,950 - INFO - Participant 111.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,950 - INFO - Participant 112.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,950 - INFO - Participant 113.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,950 - INFO - Participant 114.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,950 - INFO - Participant 115.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,951 - INFO - Participant 116.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,951 - INFO - Participant 117.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,951 - INFO - Participant 118.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,951 - INFO - Participant 119.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,952 - INFO - Participant 120.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,952 - INFO - Participant 121.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,952 - INFO - Participant 122.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,952 - INFO - Participant 123.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,952 - INFO - Participant 124.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,952 - INFO - Participant 125.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,953 - INFO - Participant 126.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,953 - INFO - Participant 127.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:44:06,953 - INFO - Train xs shape: torch.Size([128, 160, 5])
-2025-05-06 13:44:06,953 - INFO - Train ys shape: torch.Size([128, 160, 2])
-2025-05-06 13:44:06,953 - INFO - Validation xs shape: torch.Size([128, 40, 5])
-2025-05-06 13:44:06,953 - INFO - Validation ys shape: torch.Size([128, 40, 2])
-2025-05-06 13:44:06,954 - INFO - Train dataset: torch.Size([128, 160, 5]), Validation dataset: torch.Size([128, 40, 5])
-2025-05-06 13:44:06,954 - INFO - Starting hyperparameter optimization...
-2025-05-06 13:44:06,955 - INFO - Trial 0: l1_weight_decay=0.000090, l2_weight_decay=0.005960
-2025-05-06 13:44:13,936 - INFO - Trial 0: RNN Train Loss: 0.5222022; SPICE Train Loss: 2.994627576656261
-2025-05-06 13:44:13,964 - INFO - Trial 0: Average Validation Loss: 3.4181, Eval count: 40
-2025-05-06 13:44:13,965 - INFO - Best hyperparameters: {'l1_weight_decay': 9.023772557409192e-05, 'l2_weight_decay': 0.0059604516648050785}
-2025-05-06 13:44:13,965 - INFO - Best validation loss: 3.4181
-2025-05-06 13:44:18,839 - INFO - Final RNN training loss: 0.6296792
-2025-05-06 13:44:18,840 - INFO - Evaluating with SINDy - fitting separate models for each participant's validation trials
-2025-05-06 13:44:18,840 - INFO - Processing participant 0.0...
-2025-05-06 13:44:18,840 - INFO - Fitting SINDy model for participant 0.0
-2025-05-06 13:44:18,840 - WARNING - Error processing participant 0.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,840 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,840 - INFO - Processing participant 1.0...
-2025-05-06 13:44:18,840 - INFO - Fitting SINDy model for participant 1.0
-2025-05-06 13:44:18,840 - WARNING - Error processing participant 1.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,841 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,841 - INFO - Processing participant 2.0...
-2025-05-06 13:44:18,841 - INFO - Fitting SINDy model for participant 2.0
-2025-05-06 13:44:18,841 - WARNING - Error processing participant 2.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,841 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,841 - INFO - Processing participant 3.0...
-2025-05-06 13:44:18,841 - INFO - Fitting SINDy model for participant 3.0
-2025-05-06 13:44:18,841 - WARNING - Error processing participant 3.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,841 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,841 - INFO - Processing participant 4.0...
-2025-05-06 13:44:18,841 - INFO - Fitting SINDy model for participant 4.0
-2025-05-06 13:44:18,841 - WARNING - Error processing participant 4.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,841 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,841 - INFO - Processing participant 5.0...
-2025-05-06 13:44:18,841 - INFO - Fitting SINDy model for participant 5.0
-2025-05-06 13:44:18,841 - WARNING - Error processing participant 5.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,842 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,842 - INFO - Processing participant 6.0...
-2025-05-06 13:44:18,842 - INFO - Fitting SINDy model for participant 6.0
-2025-05-06 13:44:18,842 - WARNING - Error processing participant 6.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,842 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,842 - INFO - Processing participant 7.0...
-2025-05-06 13:44:18,842 - INFO - Fitting SINDy model for participant 7.0
-2025-05-06 13:44:18,842 - WARNING - Error processing participant 7.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,842 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,842 - INFO - Processing participant 8.0...
-2025-05-06 13:44:18,842 - INFO - Fitting SINDy model for participant 8.0
-2025-05-06 13:44:18,842 - WARNING - Error processing participant 8.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,842 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,842 - INFO - Processing participant 9.0...
-2025-05-06 13:44:18,842 - INFO - Fitting SINDy model for participant 9.0
-2025-05-06 13:44:18,842 - WARNING - Error processing participant 9.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,842 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,842 - INFO - Processing participant 10.0...
-2025-05-06 13:44:18,842 - INFO - Fitting SINDy model for participant 10.0
-2025-05-06 13:44:18,842 - WARNING - Error processing participant 10.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,843 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,843 - INFO - Processing participant 11.0...
-2025-05-06 13:44:18,843 - INFO - Fitting SINDy model for participant 11.0
-2025-05-06 13:44:18,843 - WARNING - Error processing participant 11.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,843 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,843 - INFO - Processing participant 12.0...
-2025-05-06 13:44:18,843 - INFO - Fitting SINDy model for participant 12.0
-2025-05-06 13:44:18,843 - WARNING - Error processing participant 12.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,843 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,843 - INFO - Processing participant 13.0...
-2025-05-06 13:44:18,843 - INFO - Fitting SINDy model for participant 13.0
-2025-05-06 13:44:18,843 - WARNING - Error processing participant 13.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,843 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,843 - INFO - Processing participant 14.0...
-2025-05-06 13:44:18,843 - INFO - Fitting SINDy model for participant 14.0
-2025-05-06 13:44:18,843 - WARNING - Error processing participant 14.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,843 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,843 - INFO - Processing participant 15.0...
-2025-05-06 13:44:18,843 - INFO - Fitting SINDy model for participant 15.0
-2025-05-06 13:44:18,843 - WARNING - Error processing participant 15.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,844 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,844 - INFO - Processing participant 16.0...
-2025-05-06 13:44:18,844 - INFO - Fitting SINDy model for participant 16.0
-2025-05-06 13:44:18,844 - WARNING - Error processing participant 16.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,844 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,844 - INFO - Processing participant 17.0...
-2025-05-06 13:44:18,844 - INFO - Fitting SINDy model for participant 17.0
-2025-05-06 13:44:18,844 - WARNING - Error processing participant 17.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,844 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,844 - INFO - Processing participant 18.0...
-2025-05-06 13:44:18,844 - INFO - Fitting SINDy model for participant 18.0
-2025-05-06 13:44:18,844 - WARNING - Error processing participant 18.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,844 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,844 - INFO - Processing participant 19.0...
-2025-05-06 13:44:18,844 - INFO - Fitting SINDy model for participant 19.0
-2025-05-06 13:44:18,844 - WARNING - Error processing participant 19.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,844 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,844 - INFO - Processing participant 20.0...
-2025-05-06 13:44:18,844 - INFO - Fitting SINDy model for participant 20.0
-2025-05-06 13:44:18,844 - WARNING - Error processing participant 20.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,845 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,845 - INFO - Processing participant 21.0...
-2025-05-06 13:44:18,845 - INFO - Fitting SINDy model for participant 21.0
-2025-05-06 13:44:18,845 - WARNING - Error processing participant 21.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,845 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,845 - INFO - Processing participant 22.0...
-2025-05-06 13:44:18,845 - INFO - Fitting SINDy model for participant 22.0
-2025-05-06 13:44:18,845 - WARNING - Error processing participant 22.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,845 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,845 - INFO - Processing participant 23.0...
-2025-05-06 13:44:18,845 - INFO - Fitting SINDy model for participant 23.0
-2025-05-06 13:44:18,845 - WARNING - Error processing participant 23.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,845 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,845 - INFO - Processing participant 24.0...
-2025-05-06 13:44:18,845 - INFO - Fitting SINDy model for participant 24.0
-2025-05-06 13:44:18,845 - WARNING - Error processing participant 24.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,845 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,845 - INFO - Processing participant 25.0...
-2025-05-06 13:44:18,845 - INFO - Fitting SINDy model for participant 25.0
-2025-05-06 13:44:18,845 - WARNING - Error processing participant 25.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,845 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,845 - INFO - Processing participant 26.0...
-2025-05-06 13:44:18,846 - INFO - Fitting SINDy model for participant 26.0
-2025-05-06 13:44:18,846 - WARNING - Error processing participant 26.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,846 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,846 - INFO - Processing participant 27.0...
-2025-05-06 13:44:18,846 - INFO - Fitting SINDy model for participant 27.0
-2025-05-06 13:44:18,846 - WARNING - Error processing participant 27.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,846 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,846 - INFO - Processing participant 28.0...
-2025-05-06 13:44:18,846 - INFO - Fitting SINDy model for participant 28.0
-2025-05-06 13:44:18,846 - WARNING - Error processing participant 28.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,846 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,846 - INFO - Processing participant 29.0...
-2025-05-06 13:44:18,846 - INFO - Fitting SINDy model for participant 29.0
-2025-05-06 13:44:18,846 - WARNING - Error processing participant 29.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,846 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,846 - INFO - Processing participant 30.0...
-2025-05-06 13:44:18,846 - INFO - Fitting SINDy model for participant 30.0
-2025-05-06 13:44:18,846 - WARNING - Error processing participant 30.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,846 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,846 - INFO - Processing participant 31.0...
-2025-05-06 13:44:18,846 - INFO - Fitting SINDy model for participant 31.0
-2025-05-06 13:44:18,846 - WARNING - Error processing participant 31.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,847 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,847 - INFO - Processing participant 32.0...
-2025-05-06 13:44:18,847 - INFO - Fitting SINDy model for participant 32.0
-2025-05-06 13:44:18,847 - WARNING - Error processing participant 32.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,847 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,847 - INFO - Processing participant 33.0...
-2025-05-06 13:44:18,847 - INFO - Fitting SINDy model for participant 33.0
-2025-05-06 13:44:18,847 - WARNING - Error processing participant 33.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,847 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,847 - INFO - Processing participant 34.0...
-2025-05-06 13:44:18,847 - INFO - Fitting SINDy model for participant 34.0
-2025-05-06 13:44:18,847 - WARNING - Error processing participant 34.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,847 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,847 - INFO - Processing participant 35.0...
-2025-05-06 13:44:18,847 - INFO - Fitting SINDy model for participant 35.0
-2025-05-06 13:44:18,847 - WARNING - Error processing participant 35.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,847 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,847 - INFO - Processing participant 36.0...
-2025-05-06 13:44:18,847 - INFO - Fitting SINDy model for participant 36.0
-2025-05-06 13:44:18,847 - WARNING - Error processing participant 36.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,848 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,848 - INFO - Processing participant 37.0...
-2025-05-06 13:44:18,848 - INFO - Fitting SINDy model for participant 37.0
-2025-05-06 13:44:18,848 - WARNING - Error processing participant 37.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,848 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,848 - INFO - Processing participant 38.0...
-2025-05-06 13:44:18,848 - INFO - Fitting SINDy model for participant 38.0
-2025-05-06 13:44:18,848 - WARNING - Error processing participant 38.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,848 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,848 - INFO - Processing participant 39.0...
-2025-05-06 13:44:18,848 - INFO - Fitting SINDy model for participant 39.0
-2025-05-06 13:44:18,848 - WARNING - Error processing participant 39.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,848 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,848 - INFO - Processing participant 40.0...
-2025-05-06 13:44:18,848 - INFO - Fitting SINDy model for participant 40.0
-2025-05-06 13:44:18,848 - WARNING - Error processing participant 40.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,848 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,848 - INFO - Processing participant 41.0...
-2025-05-06 13:44:18,848 - INFO - Fitting SINDy model for participant 41.0
-2025-05-06 13:44:18,848 - WARNING - Error processing participant 41.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,848 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,848 - INFO - Processing participant 42.0...
-2025-05-06 13:44:18,849 - INFO - Fitting SINDy model for participant 42.0
-2025-05-06 13:44:18,849 - WARNING - Error processing participant 42.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,849 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,849 - INFO - Processing participant 43.0...
-2025-05-06 13:44:18,849 - INFO - Fitting SINDy model for participant 43.0
-2025-05-06 13:44:18,849 - WARNING - Error processing participant 43.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,849 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,849 - INFO - Processing participant 44.0...
-2025-05-06 13:44:18,849 - INFO - Fitting SINDy model for participant 44.0
-2025-05-06 13:44:18,849 - WARNING - Error processing participant 44.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,849 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,849 - INFO - Processing participant 45.0...
-2025-05-06 13:44:18,849 - INFO - Fitting SINDy model for participant 45.0
-2025-05-06 13:44:18,849 - WARNING - Error processing participant 45.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,849 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,849 - INFO - Processing participant 46.0...
-2025-05-06 13:44:18,849 - INFO - Fitting SINDy model for participant 46.0
-2025-05-06 13:44:18,849 - WARNING - Error processing participant 46.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,849 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,849 - INFO - Processing participant 47.0...
-2025-05-06 13:44:18,850 - INFO - Fitting SINDy model for participant 47.0
-2025-05-06 13:44:18,850 - WARNING - Error processing participant 47.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,850 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,850 - INFO - Processing participant 48.0...
-2025-05-06 13:44:18,850 - INFO - Fitting SINDy model for participant 48.0
-2025-05-06 13:44:18,850 - WARNING - Error processing participant 48.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,850 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,850 - INFO - Processing participant 49.0...
-2025-05-06 13:44:18,850 - INFO - Fitting SINDy model for participant 49.0
-2025-05-06 13:44:18,850 - WARNING - Error processing participant 49.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,850 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,850 - INFO - Processing participant 50.0...
-2025-05-06 13:44:18,850 - INFO - Fitting SINDy model for participant 50.0
-2025-05-06 13:44:18,850 - WARNING - Error processing participant 50.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,850 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,850 - INFO - Processing participant 51.0...
-2025-05-06 13:44:18,850 - INFO - Fitting SINDy model for participant 51.0
-2025-05-06 13:44:18,850 - WARNING - Error processing participant 51.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,850 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,850 - INFO - Processing participant 52.0...
-2025-05-06 13:44:18,851 - INFO - Fitting SINDy model for participant 52.0
-2025-05-06 13:44:18,851 - WARNING - Error processing participant 52.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,851 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,851 - INFO - Processing participant 53.0...
-2025-05-06 13:44:18,851 - INFO - Fitting SINDy model for participant 53.0
-2025-05-06 13:44:18,851 - WARNING - Error processing participant 53.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,851 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,851 - INFO - Processing participant 54.0...
-2025-05-06 13:44:18,851 - INFO - Fitting SINDy model for participant 54.0
-2025-05-06 13:44:18,851 - WARNING - Error processing participant 54.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,851 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,851 - INFO - Processing participant 55.0...
-2025-05-06 13:44:18,851 - INFO - Fitting SINDy model for participant 55.0
-2025-05-06 13:44:18,851 - WARNING - Error processing participant 55.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,851 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,851 - INFO - Processing participant 56.0...
-2025-05-06 13:44:18,851 - INFO - Fitting SINDy model for participant 56.0
-2025-05-06 13:44:18,851 - WARNING - Error processing participant 56.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,851 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,851 - INFO - Processing participant 57.0...
-2025-05-06 13:44:18,852 - INFO - Fitting SINDy model for participant 57.0
-2025-05-06 13:44:18,852 - WARNING - Error processing participant 57.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,852 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,852 - INFO - Processing participant 58.0...
-2025-05-06 13:44:18,852 - INFO - Fitting SINDy model for participant 58.0
-2025-05-06 13:44:18,852 - WARNING - Error processing participant 58.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,852 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,852 - INFO - Processing participant 59.0...
-2025-05-06 13:44:18,852 - INFO - Fitting SINDy model for participant 59.0
-2025-05-06 13:44:18,852 - WARNING - Error processing participant 59.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,852 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,852 - INFO - Processing participant 60.0...
-2025-05-06 13:44:18,852 - INFO - Fitting SINDy model for participant 60.0
-2025-05-06 13:44:18,852 - WARNING - Error processing participant 60.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,852 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,852 - INFO - Processing participant 61.0...
-2025-05-06 13:44:18,852 - INFO - Fitting SINDy model for participant 61.0
-2025-05-06 13:44:18,852 - WARNING - Error processing participant 61.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,853 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,853 - INFO - Processing participant 62.0...
-2025-05-06 13:44:18,853 - INFO - Fitting SINDy model for participant 62.0
-2025-05-06 13:44:18,853 - WARNING - Error processing participant 62.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,853 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,853 - INFO - Processing participant 63.0...
-2025-05-06 13:44:18,853 - INFO - Fitting SINDy model for participant 63.0
-2025-05-06 13:44:18,853 - WARNING - Error processing participant 63.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,853 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,853 - INFO - Processing participant 64.0...
-2025-05-06 13:44:18,853 - INFO - Fitting SINDy model for participant 64.0
-2025-05-06 13:44:18,853 - WARNING - Error processing participant 64.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,853 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,853 - INFO - Processing participant 65.0...
-2025-05-06 13:44:18,853 - INFO - Fitting SINDy model for participant 65.0
-2025-05-06 13:44:18,853 - WARNING - Error processing participant 65.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,853 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,853 - INFO - Processing participant 66.0...
-2025-05-06 13:44:18,853 - INFO - Fitting SINDy model for participant 66.0
-2025-05-06 13:44:18,853 - WARNING - Error processing participant 66.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,854 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,854 - INFO - Processing participant 67.0...
-2025-05-06 13:44:18,854 - INFO - Fitting SINDy model for participant 67.0
-2025-05-06 13:44:18,854 - WARNING - Error processing participant 67.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,854 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,854 - INFO - Processing participant 68.0...
-2025-05-06 13:44:18,854 - INFO - Fitting SINDy model for participant 68.0
-2025-05-06 13:44:18,854 - WARNING - Error processing participant 68.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,854 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,854 - INFO - Processing participant 69.0...
-2025-05-06 13:44:18,854 - INFO - Fitting SINDy model for participant 69.0
-2025-05-06 13:44:18,854 - WARNING - Error processing participant 69.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,854 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,854 - INFO - Processing participant 70.0...
-2025-05-06 13:44:18,854 - INFO - Fitting SINDy model for participant 70.0
-2025-05-06 13:44:18,854 - WARNING - Error processing participant 70.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,854 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,854 - INFO - Processing participant 71.0...
-2025-05-06 13:44:18,855 - INFO - Fitting SINDy model for participant 71.0
-2025-05-06 13:44:18,855 - WARNING - Error processing participant 71.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,855 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,855 - INFO - Processing participant 72.0...
-2025-05-06 13:44:18,855 - INFO - Fitting SINDy model for participant 72.0
-2025-05-06 13:44:18,855 - WARNING - Error processing participant 72.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,855 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,855 - INFO - Processing participant 73.0...
-2025-05-06 13:44:18,855 - INFO - Fitting SINDy model for participant 73.0
-2025-05-06 13:44:18,855 - WARNING - Error processing participant 73.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,855 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,855 - INFO - Processing participant 74.0...
-2025-05-06 13:44:18,855 - INFO - Fitting SINDy model for participant 74.0
-2025-05-06 13:44:18,855 - WARNING - Error processing participant 74.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,855 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,855 - INFO - Processing participant 75.0...
-2025-05-06 13:44:18,855 - INFO - Fitting SINDy model for participant 75.0
-2025-05-06 13:44:18,855 - WARNING - Error processing participant 75.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,855 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,855 - INFO - Processing participant 76.0...
-2025-05-06 13:44:18,856 - INFO - Fitting SINDy model for participant 76.0
-2025-05-06 13:44:18,856 - WARNING - Error processing participant 76.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,856 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,856 - INFO - Processing participant 77.0...
-2025-05-06 13:44:18,856 - INFO - Fitting SINDy model for participant 77.0
-2025-05-06 13:44:18,856 - WARNING - Error processing participant 77.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,856 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,856 - INFO - Processing participant 78.0...
-2025-05-06 13:44:18,856 - INFO - Fitting SINDy model for participant 78.0
-2025-05-06 13:44:18,856 - WARNING - Error processing participant 78.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,856 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,856 - INFO - Processing participant 79.0...
-2025-05-06 13:44:18,856 - INFO - Fitting SINDy model for participant 79.0
-2025-05-06 13:44:18,856 - WARNING - Error processing participant 79.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,856 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,856 - INFO - Processing participant 80.0...
-2025-05-06 13:44:18,856 - INFO - Fitting SINDy model for participant 80.0
-2025-05-06 13:44:18,856 - WARNING - Error processing participant 80.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,856 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,856 - INFO - Processing participant 81.0...
-2025-05-06 13:44:18,857 - INFO - Fitting SINDy model for participant 81.0
-2025-05-06 13:44:18,857 - WARNING - Error processing participant 81.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,857 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,857 - INFO - Processing participant 82.0...
-2025-05-06 13:44:18,857 - INFO - Fitting SINDy model for participant 82.0
-2025-05-06 13:44:18,857 - WARNING - Error processing participant 82.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,857 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,857 - INFO - Processing participant 83.0...
-2025-05-06 13:44:18,857 - INFO - Fitting SINDy model for participant 83.0
-2025-05-06 13:44:18,857 - WARNING - Error processing participant 83.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,857 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,857 - INFO - Processing participant 84.0...
-2025-05-06 13:44:18,857 - INFO - Fitting SINDy model for participant 84.0
-2025-05-06 13:44:18,857 - WARNING - Error processing participant 84.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,857 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,857 - INFO - Processing participant 85.0...
-2025-05-06 13:44:18,857 - INFO - Fitting SINDy model for participant 85.0
-2025-05-06 13:44:18,857 - WARNING - Error processing participant 85.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,858 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,858 - INFO - Processing participant 86.0...
-2025-05-06 13:44:18,858 - INFO - Fitting SINDy model for participant 86.0
-2025-05-06 13:44:18,858 - WARNING - Error processing participant 86.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,858 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,858 - INFO - Processing participant 87.0...
-2025-05-06 13:44:18,858 - INFO - Fitting SINDy model for participant 87.0
-2025-05-06 13:44:18,858 - WARNING - Error processing participant 87.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,858 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,858 - INFO - Processing participant 88.0...
-2025-05-06 13:44:18,858 - INFO - Fitting SINDy model for participant 88.0
-2025-05-06 13:44:18,858 - WARNING - Error processing participant 88.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,858 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,858 - INFO - Processing participant 89.0...
-2025-05-06 13:44:18,858 - INFO - Fitting SINDy model for participant 89.0
-2025-05-06 13:44:18,858 - WARNING - Error processing participant 89.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,858 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,858 - INFO - Processing participant 90.0...
-2025-05-06 13:44:18,858 - INFO - Fitting SINDy model for participant 90.0
-2025-05-06 13:44:18,858 - WARNING - Error processing participant 90.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,859 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,859 - INFO - Processing participant 91.0...
-2025-05-06 13:44:18,859 - INFO - Fitting SINDy model for participant 91.0
-2025-05-06 13:44:18,859 - WARNING - Error processing participant 91.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,859 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,859 - INFO - Processing participant 92.0...
-2025-05-06 13:44:18,859 - INFO - Fitting SINDy model for participant 92.0
-2025-05-06 13:44:18,859 - WARNING - Error processing participant 92.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,859 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,859 - INFO - Processing participant 93.0...
-2025-05-06 13:44:18,859 - INFO - Fitting SINDy model for participant 93.0
-2025-05-06 13:44:18,859 - WARNING - Error processing participant 93.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,859 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,859 - INFO - Processing participant 94.0...
-2025-05-06 13:44:18,859 - INFO - Fitting SINDy model for participant 94.0
-2025-05-06 13:44:18,859 - WARNING - Error processing participant 94.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,859 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,859 - INFO - Processing participant 95.0...
-2025-05-06 13:44:18,859 - INFO - Fitting SINDy model for participant 95.0
-2025-05-06 13:44:18,859 - WARNING - Error processing participant 95.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,860 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,860 - INFO - Processing participant 96.0...
-2025-05-06 13:44:18,860 - INFO - Fitting SINDy model for participant 96.0
-2025-05-06 13:44:18,860 - WARNING - Error processing participant 96.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,860 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,860 - INFO - Processing participant 97.0...
-2025-05-06 13:44:18,860 - INFO - Fitting SINDy model for participant 97.0
-2025-05-06 13:44:18,860 - WARNING - Error processing participant 97.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,860 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,860 - INFO - Processing participant 98.0...
-2025-05-06 13:44:18,860 - INFO - Fitting SINDy model for participant 98.0
-2025-05-06 13:44:18,860 - WARNING - Error processing participant 98.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,860 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,860 - INFO - Processing participant 99.0...
-2025-05-06 13:44:18,860 - INFO - Fitting SINDy model for participant 99.0
-2025-05-06 13:44:18,860 - WARNING - Error processing participant 99.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,860 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,860 - INFO - Processing participant 100.0...
-2025-05-06 13:44:18,861 - INFO - Fitting SINDy model for participant 100.0
-2025-05-06 13:44:18,861 - WARNING - Error processing participant 100.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,861 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,861 - INFO - Processing participant 101.0...
-2025-05-06 13:44:18,861 - INFO - Fitting SINDy model for participant 101.0
-2025-05-06 13:44:18,861 - WARNING - Error processing participant 101.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,861 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,861 - INFO - Processing participant 102.0...
-2025-05-06 13:44:18,861 - INFO - Fitting SINDy model for participant 102.0
-2025-05-06 13:44:18,861 - WARNING - Error processing participant 102.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,861 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,861 - INFO - Processing participant 103.0...
-2025-05-06 13:44:18,861 - INFO - Fitting SINDy model for participant 103.0
-2025-05-06 13:44:18,861 - WARNING - Error processing participant 103.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,861 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,861 - INFO - Processing participant 104.0...
-2025-05-06 13:44:18,861 - INFO - Fitting SINDy model for participant 104.0
-2025-05-06 13:44:18,861 - WARNING - Error processing participant 104.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,861 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,861 - INFO - Processing participant 105.0...
-2025-05-06 13:44:18,862 - INFO - Fitting SINDy model for participant 105.0
-2025-05-06 13:44:18,862 - WARNING - Error processing participant 105.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,862 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,862 - INFO - Processing participant 106.0...
-2025-05-06 13:44:18,862 - INFO - Fitting SINDy model for participant 106.0
-2025-05-06 13:44:18,862 - WARNING - Error processing participant 106.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,862 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,862 - INFO - Processing participant 107.0...
-2025-05-06 13:44:18,862 - INFO - Fitting SINDy model for participant 107.0
-2025-05-06 13:44:18,862 - WARNING - Error processing participant 107.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,862 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,862 - INFO - Processing participant 108.0...
-2025-05-06 13:44:18,862 - INFO - Fitting SINDy model for participant 108.0
-2025-05-06 13:44:18,862 - WARNING - Error processing participant 108.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,862 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,862 - INFO - Processing participant 109.0...
-2025-05-06 13:44:18,862 - INFO - Fitting SINDy model for participant 109.0
-2025-05-06 13:44:18,862 - WARNING - Error processing participant 109.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,863 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,863 - INFO - Processing participant 110.0...
-2025-05-06 13:44:18,863 - INFO - Fitting SINDy model for participant 110.0
-2025-05-06 13:44:18,863 - WARNING - Error processing participant 110.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,863 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,863 - INFO - Processing participant 111.0...
-2025-05-06 13:44:18,863 - INFO - Fitting SINDy model for participant 111.0
-2025-05-06 13:44:18,863 - WARNING - Error processing participant 111.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,863 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,863 - INFO - Processing participant 112.0...
-2025-05-06 13:44:18,863 - INFO - Fitting SINDy model for participant 112.0
-2025-05-06 13:44:18,863 - WARNING - Error processing participant 112.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,863 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,863 - INFO - Processing participant 113.0...
-2025-05-06 13:44:18,863 - INFO - Fitting SINDy model for participant 113.0
-2025-05-06 13:44:18,863 - WARNING - Error processing participant 113.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,863 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,863 - INFO - Processing participant 114.0...
-2025-05-06 13:44:18,863 - INFO - Fitting SINDy model for participant 114.0
-2025-05-06 13:44:18,863 - WARNING - Error processing participant 114.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,863 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,863 - INFO - Processing participant 115.0...
-2025-05-06 13:44:18,864 - INFO - Fitting SINDy model for participant 115.0
-2025-05-06 13:44:18,864 - WARNING - Error processing participant 115.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,864 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,864 - INFO - Processing participant 116.0...
-2025-05-06 13:44:18,864 - INFO - Fitting SINDy model for participant 116.0
-2025-05-06 13:44:18,864 - WARNING - Error processing participant 116.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,864 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,864 - INFO - Processing participant 117.0...
-2025-05-06 13:44:18,864 - INFO - Fitting SINDy model for participant 117.0
-2025-05-06 13:44:18,864 - WARNING - Error processing participant 117.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,864 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,864 - INFO - Processing participant 118.0...
-2025-05-06 13:44:18,864 - INFO - Fitting SINDy model for participant 118.0
-2025-05-06 13:44:18,864 - WARNING - Error processing participant 118.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,864 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,864 - INFO - Processing participant 119.0...
-2025-05-06 13:44:18,864 - INFO - Fitting SINDy model for participant 119.0
-2025-05-06 13:44:18,864 - WARNING - Error processing participant 119.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,864 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,865 - INFO - Processing participant 120.0...
-2025-05-06 13:44:18,865 - INFO - Fitting SINDy model for participant 120.0
-2025-05-06 13:44:18,865 - WARNING - Error processing participant 120.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,865 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,865 - INFO - Processing participant 121.0...
-2025-05-06 13:44:18,865 - INFO - Fitting SINDy model for participant 121.0
-2025-05-06 13:44:18,865 - WARNING - Error processing participant 121.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,865 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,865 - INFO - Processing participant 122.0...
-2025-05-06 13:44:18,865 - INFO - Fitting SINDy model for participant 122.0
-2025-05-06 13:44:18,865 - WARNING - Error processing participant 122.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,865 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,865 - INFO - Processing participant 123.0...
-2025-05-06 13:44:18,865 - INFO - Fitting SINDy model for participant 123.0
-2025-05-06 13:44:18,865 - WARNING - Error processing participant 123.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,865 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,865 - INFO - Processing participant 124.0...
-2025-05-06 13:44:18,865 - INFO - Fitting SINDy model for participant 124.0
-2025-05-06 13:44:18,865 - WARNING - Error processing participant 124.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,865 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,866 - INFO - Processing participant 125.0...
-2025-05-06 13:44:18,866 - INFO - Fitting SINDy model for participant 125.0
-2025-05-06 13:44:18,866 - WARNING - Error processing participant 125.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,866 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,866 - INFO - Processing participant 126.0...
-2025-05-06 13:44:18,866 - INFO - Fitting SINDy model for participant 126.0
-2025-05-06 13:44:18,866 - WARNING - Error processing participant 126.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,866 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,866 - INFO - Processing participant 127.0...
-2025-05-06 13:44:18,866 - INFO - Fitting SINDy model for participant 127.0
-2025-05-06 13:44:18,866 - WARNING - Error processing participant 127.0: 'sindy_optimizer_alpha'
-2025-05-06 13:44:18,866 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 351, in evaluate_with_sindy
-    optimizer_alpha=best_params['sindy_optimizer_alpha'],
-                    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
-KeyError: 'sindy_optimizer_alpha'
-
-2025-05-06 13:44:18,866 - INFO - Number of participants with valid BIC metrics: 0/128
-2025-05-06 13:44:18,866 - INFO - Average SINDy BIC: nan
-2025-05-06 13:44:18,866 - INFO - Average SINDy LL: nan
-2025-05-06 13:44:18,867 - INFO - Completed processing dataset: data_128p_0.csv
-2025-05-06 13:44:19,187 - WARNING - No participant-level metrics found for violin plots
-2025-05-06 13:45:56,144 - INFO - ================================================================================
-2025-05-06 13:45:56,144 - INFO - EXPERIMENT CONFIG
-2025-05-06 13:45:56,144 - INFO - ================================================================================
-2025-05-06 13:45:56,144 - INFO - Found 1 data files: ['data_128p_0.csv']
-2025-05-06 13:45:56,144 - INFO - Processing dataset: data_128p_0.csv
-2025-05-06 13:45:56,144 - INFO - Loading dataset from data/optuna/data_128p_0.csv
-2025-05-06 13:45:56,183 - INFO - Number of participants: 128
-2025-05-06 13:45:56,190 - INFO - Participant 0 (ID=0.0): α_reward=0.74, α_penalty=0.97
-2025-05-06 13:45:56,196 - INFO - Participant 1 (ID=1.0): α_reward=0.99, α_penalty=0.02
-2025-05-06 13:45:56,202 - INFO - Participant 2 (ID=2.0): α_reward=0.77, α_penalty=0.46
-2025-05-06 13:45:56,208 - INFO - Participant 3 (ID=3.0): α_reward=0.96, α_penalty=0.60
-2025-05-06 13:45:56,214 - INFO - Participant 4 (ID=4.0): α_reward=0.21, α_penalty=0.21
-2025-05-06 13:45:56,219 - INFO - Participant 5 (ID=5.0): α_reward=0.07, α_penalty=0.51
-2025-05-06 13:45:56,227 - INFO - Participant 6 (ID=6.0): α_reward=0.23, α_penalty=0.60
-2025-05-06 13:45:56,233 - INFO - Participant 7 (ID=7.0): α_reward=0.91, α_penalty=0.80
-2025-05-06 13:45:56,240 - INFO - Participant 8 (ID=8.0): α_reward=0.99, α_penalty=0.97
-2025-05-06 13:45:56,246 - INFO - Participant 9 (ID=9.0): α_reward=0.67, α_penalty=0.87
-2025-05-06 13:45:56,252 - INFO - Participant 10 (ID=10.0): α_reward=0.08, α_penalty=0.77
-2025-05-06 13:45:56,258 - INFO - Participant 11 (ID=11.0): α_reward=0.87, α_penalty=0.31
-2025-05-06 13:45:56,264 - INFO - Participant 12 (ID=12.0): α_reward=0.58, α_penalty=0.19
-2025-05-06 13:45:56,269 - INFO - Participant 13 (ID=13.0): α_reward=0.48, α_penalty=0.25
-2025-05-06 13:45:56,275 - INFO - Participant 14 (ID=14.0): α_reward=0.34, α_penalty=0.83
-2025-05-06 13:45:56,281 - INFO - Participant 15 (ID=15.0): α_reward=0.56, α_penalty=0.80
-2025-05-06 13:45:56,287 - INFO - Participant 16 (ID=16.0): α_reward=0.47, α_penalty=0.29
-2025-05-06 13:45:56,292 - INFO - Participant 17 (ID=17.0): α_reward=0.77, α_penalty=0.94
-2025-05-06 13:45:56,298 - INFO - Participant 18 (ID=18.0): α_reward=0.88, α_penalty=0.01
-2025-05-06 13:45:56,304 - INFO - Participant 19 (ID=19.0): α_reward=0.98, α_penalty=0.11
-2025-05-06 13:45:56,310 - INFO - Participant 20 (ID=20.0): α_reward=0.02, α_penalty=0.85
-2025-05-06 13:45:56,316 - INFO - Participant 21 (ID=21.0): α_reward=0.46, α_penalty=0.85
-2025-05-06 13:45:56,322 - INFO - Participant 22 (ID=22.0): α_reward=0.69, α_penalty=0.43
-2025-05-06 13:45:56,328 - INFO - Participant 23 (ID=23.0): α_reward=0.15, α_penalty=0.95
-2025-05-06 13:45:56,334 - INFO - Participant 24 (ID=24.0): α_reward=0.25, α_penalty=0.36
-2025-05-06 13:45:56,340 - INFO - Participant 25 (ID=25.0): α_reward=0.75, α_penalty=0.11
-2025-05-06 13:45:56,346 - INFO - Participant 26 (ID=26.0): α_reward=0.46, α_penalty=0.81
-2025-05-06 13:45:56,351 - INFO - Participant 27 (ID=27.0): α_reward=0.36, α_penalty=0.63
-2025-05-06 13:45:56,358 - INFO - Participant 28 (ID=28.0): α_reward=0.49, α_penalty=0.00
-2025-05-06 13:45:56,364 - INFO - Participant 29 (ID=29.0): α_reward=0.20, α_penalty=0.98
-2025-05-06 13:45:56,370 - INFO - Participant 30 (ID=30.0): α_reward=0.29, α_penalty=0.64
-2025-05-06 13:45:56,376 - INFO - Participant 31 (ID=31.0): α_reward=0.66, α_penalty=0.64
-2025-05-06 13:45:56,382 - INFO - Participant 32 (ID=32.0): α_reward=0.21, α_penalty=0.41
-2025-05-06 13:45:56,387 - INFO - Participant 33 (ID=33.0): α_reward=0.49, α_penalty=0.63
-2025-05-06 13:45:56,393 - INFO - Participant 34 (ID=34.0): α_reward=0.38, α_penalty=0.08
-2025-05-06 13:45:56,399 - INFO - Participant 35 (ID=35.0): α_reward=0.33, α_penalty=0.29
-2025-05-06 13:45:56,405 - INFO - Participant 36 (ID=36.0): α_reward=0.53, α_penalty=0.61
-2025-05-06 13:45:56,411 - INFO - Participant 37 (ID=37.0): α_reward=0.65, α_penalty=0.34
-2025-05-06 13:45:56,417 - INFO - Participant 38 (ID=38.0): α_reward=0.29, α_penalty=0.13
-2025-05-06 13:45:56,423 - INFO - Participant 39 (ID=39.0): α_reward=0.31, α_penalty=0.25
-2025-05-06 13:45:56,429 - INFO - Participant 40 (ID=40.0): α_reward=0.77, α_penalty=0.04
-2025-05-06 13:45:56,435 - INFO - Participant 41 (ID=41.0): α_reward=0.23, α_penalty=0.89
-2025-05-06 13:45:56,441 - INFO - Participant 42 (ID=42.0): α_reward=0.63, α_penalty=0.60
-2025-05-06 13:45:56,447 - INFO - Participant 43 (ID=43.0): α_reward=0.48, α_penalty=0.52
-2025-05-06 13:45:56,453 - INFO - Participant 44 (ID=44.0): α_reward=0.92, α_penalty=0.22
-2025-05-06 13:45:56,459 - INFO - Participant 45 (ID=45.0): α_reward=0.52, α_penalty=0.89
-2025-05-06 13:45:56,465 - INFO - Participant 46 (ID=46.0): α_reward=0.42, α_penalty=0.83
-2025-05-06 13:45:56,470 - INFO - Participant 47 (ID=47.0): α_reward=0.68, α_penalty=0.69
-2025-05-06 13:45:56,476 - INFO - Participant 48 (ID=48.0): α_reward=0.95, α_penalty=0.22
-2025-05-06 13:45:56,482 - INFO - Participant 49 (ID=49.0): α_reward=0.13, α_penalty=0.48
-2025-05-06 13:45:56,488 - INFO - Participant 50 (ID=50.0): α_reward=0.87, α_penalty=0.85
-2025-05-06 13:45:56,494 - INFO - Participant 51 (ID=51.0): α_reward=0.47, α_penalty=0.07
-2025-05-06 13:45:56,500 - INFO - Participant 52 (ID=52.0): α_reward=0.56, α_penalty=0.42
-2025-05-06 13:45:56,505 - INFO - Participant 53 (ID=53.0): α_reward=0.53, α_penalty=0.18
-2025-05-06 13:45:56,511 - INFO - Participant 54 (ID=54.0): α_reward=0.87, α_penalty=0.73
-2025-05-06 13:45:56,517 - INFO - Participant 55 (ID=55.0): α_reward=0.21, α_penalty=0.47
-2025-05-06 13:45:56,523 - INFO - Participant 56 (ID=56.0): α_reward=0.65, α_penalty=0.09
-2025-05-06 13:45:56,529 - INFO - Participant 57 (ID=57.0): α_reward=0.72, α_penalty=0.88
-2025-05-06 13:45:56,535 - INFO - Participant 58 (ID=58.0): α_reward=0.61, α_penalty=0.38
-2025-05-06 13:45:56,541 - INFO - Participant 59 (ID=59.0): α_reward=0.92, α_penalty=0.91
-2025-05-06 13:45:56,546 - INFO - Participant 60 (ID=60.0): α_reward=0.22, α_penalty=0.97
-2025-05-06 13:45:56,552 - INFO - Participant 61 (ID=61.0): α_reward=0.41, α_penalty=0.53
-2025-05-06 13:45:56,558 - INFO - Participant 62 (ID=62.0): α_reward=0.92, α_penalty=0.99
-2025-05-06 13:45:56,564 - INFO - Participant 63 (ID=63.0): α_reward=0.22, α_penalty=0.99
-2025-05-06 13:45:56,570 - INFO - Participant 64 (ID=64.0): α_reward=0.84, α_penalty=0.96
-2025-05-06 13:45:56,576 - INFO - Participant 65 (ID=65.0): α_reward=0.55, α_penalty=0.19
-2025-05-06 13:45:56,581 - INFO - Participant 66 (ID=66.0): α_reward=0.34, α_penalty=0.07
-2025-05-06 13:45:56,587 - INFO - Participant 67 (ID=67.0): α_reward=0.62, α_penalty=0.45
-2025-05-06 13:45:56,593 - INFO - Participant 68 (ID=68.0): α_reward=0.47, α_penalty=0.19
-2025-05-06 13:45:56,599 - INFO - Participant 69 (ID=69.0): α_reward=0.81, α_penalty=0.36
-2025-05-06 13:45:56,604 - INFO - Participant 70 (ID=70.0): α_reward=0.73, α_penalty=0.87
-2025-05-06 13:45:56,610 - INFO - Participant 71 (ID=71.0): α_reward=0.10, α_penalty=0.57
-2025-05-06 13:45:56,616 - INFO - Participant 72 (ID=72.0): α_reward=0.78, α_penalty=0.50
-2025-05-06 13:45:56,622 - INFO - Participant 73 (ID=73.0): α_reward=0.82, α_penalty=0.70
-2025-05-06 13:45:56,628 - INFO - Participant 74 (ID=74.0): α_reward=0.00, α_penalty=0.98
-2025-05-06 13:45:56,634 - INFO - Participant 75 (ID=75.0): α_reward=0.42, α_penalty=0.26
-2025-05-06 13:45:56,640 - INFO - Participant 76 (ID=76.0): α_reward=0.83, α_penalty=0.39
-2025-05-06 13:45:56,646 - INFO - Participant 77 (ID=77.0): α_reward=0.75, α_penalty=0.18
-2025-05-06 13:45:56,651 - INFO - Participant 78 (ID=78.0): α_reward=0.97, α_penalty=0.44
-2025-05-06 13:45:56,657 - INFO - Participant 79 (ID=79.0): α_reward=0.45, α_penalty=0.57
-2025-05-06 13:45:56,663 - INFO - Participant 80 (ID=80.0): α_reward=0.63, α_penalty=0.78
-2025-05-06 13:45:56,669 - INFO - Participant 81 (ID=81.0): α_reward=0.29, α_penalty=0.24
-2025-05-06 13:45:56,675 - INFO - Participant 82 (ID=82.0): α_reward=0.71, α_penalty=0.15
-2025-05-06 13:45:56,681 - INFO - Participant 83 (ID=83.0): α_reward=0.71, α_penalty=0.66
-2025-05-06 13:45:56,687 - INFO - Participant 84 (ID=84.0): α_reward=0.97, α_penalty=0.79
-2025-05-06 13:45:56,692 - INFO - Participant 85 (ID=85.0): α_reward=1.00, α_penalty=0.95
-2025-05-06 13:45:56,698 - INFO - Participant 86 (ID=86.0): α_reward=0.56, α_penalty=0.08
-2025-05-06 13:45:56,704 - INFO - Participant 87 (ID=87.0): α_reward=0.59, α_penalty=0.83
-2025-05-06 13:45:56,709 - INFO - Participant 88 (ID=88.0): α_reward=0.32, α_penalty=0.60
-2025-05-06 13:45:56,715 - INFO - Participant 89 (ID=89.0): α_reward=0.20, α_penalty=0.84
-2025-05-06 13:45:56,721 - INFO - Participant 90 (ID=90.0): α_reward=0.34, α_penalty=1.00
-2025-05-06 13:45:56,727 - INFO - Participant 91 (ID=91.0): α_reward=0.19, α_penalty=0.99
-2025-05-06 13:45:56,733 - INFO - Participant 92 (ID=92.0): α_reward=0.20, α_penalty=0.91
-2025-05-06 13:45:56,739 - INFO - Participant 93 (ID=93.0): α_reward=0.31, α_penalty=0.21
-2025-05-06 13:45:56,745 - INFO - Participant 94 (ID=94.0): α_reward=0.58, α_penalty=0.41
-2025-05-06 13:45:56,751 - INFO - Participant 95 (ID=95.0): α_reward=0.80, α_penalty=0.54
-2025-05-06 13:45:56,756 - INFO - Participant 96 (ID=96.0): α_reward=0.74, α_penalty=0.80
-2025-05-06 13:45:56,762 - INFO - Participant 97 (ID=97.0): α_reward=0.59, α_penalty=0.16
-2025-05-06 13:45:56,768 - INFO - Participant 98 (ID=98.0): α_reward=0.54, α_penalty=0.17
-2025-05-06 13:45:56,774 - INFO - Participant 99 (ID=99.0): α_reward=0.17, α_penalty=0.16
-2025-05-06 13:45:56,780 - INFO - Participant 100 (ID=100.0): α_reward=0.79, α_penalty=0.40
-2025-05-06 13:45:56,786 - INFO - Participant 101 (ID=101.0): α_reward=0.32, α_penalty=0.05
-2025-05-06 13:45:56,792 - INFO - Participant 102 (ID=102.0): α_reward=0.87, α_penalty=0.81
-2025-05-06 13:45:56,797 - INFO - Participant 103 (ID=103.0): α_reward=0.90, α_penalty=0.11
-2025-05-06 13:45:56,803 - INFO - Participant 104 (ID=104.0): α_reward=0.44, α_penalty=0.12
-2025-05-06 13:45:56,809 - INFO - Participant 105 (ID=105.0): α_reward=0.59, α_penalty=0.50
-2025-05-06 13:45:56,815 - INFO - Participant 106 (ID=106.0): α_reward=0.84, α_penalty=0.50
-2025-05-06 13:45:56,820 - INFO - Participant 107 (ID=107.0): α_reward=0.48, α_penalty=0.54
-2025-05-06 13:45:56,826 - INFO - Participant 108 (ID=108.0): α_reward=0.56, α_penalty=0.75
-2025-05-06 13:45:56,832 - INFO - Participant 109 (ID=109.0): α_reward=0.53, α_penalty=0.11
-2025-05-06 13:45:56,838 - INFO - Participant 110 (ID=110.0): α_reward=0.52, α_penalty=0.40
-2025-05-06 13:45:56,844 - INFO - Participant 111 (ID=111.0): α_reward=0.61, α_penalty=0.74
-2025-05-06 13:45:56,849 - INFO - Participant 112 (ID=112.0): α_reward=0.64, α_penalty=0.56
-2025-05-06 13:45:56,855 - INFO - Participant 113 (ID=113.0): α_reward=0.44, α_penalty=0.40
-2025-05-06 13:45:56,861 - INFO - Participant 114 (ID=114.0): α_reward=0.74, α_penalty=0.91
-2025-05-06 13:45:56,867 - INFO - Participant 115 (ID=115.0): α_reward=0.09, α_penalty=0.27
-2025-05-06 13:45:56,873 - INFO - Participant 116 (ID=116.0): α_reward=0.99, α_penalty=0.92
-2025-05-06 13:45:56,879 - INFO - Participant 117 (ID=117.0): α_reward=0.90, α_penalty=0.87
-2025-05-06 13:45:56,885 - INFO - Participant 118 (ID=118.0): α_reward=0.21, α_penalty=0.27
-2025-05-06 13:45:56,891 - INFO - Participant 119 (ID=119.0): α_reward=0.71, α_penalty=0.69
-2025-05-06 13:45:56,896 - INFO - Participant 120 (ID=120.0): α_reward=0.43, α_penalty=0.05
-2025-05-06 13:45:56,902 - INFO - Participant 121 (ID=121.0): α_reward=0.11, α_penalty=0.27
-2025-05-06 13:45:56,908 - INFO - Participant 122 (ID=122.0): α_reward=0.80, α_penalty=0.43
-2025-05-06 13:45:56,914 - INFO - Participant 123 (ID=123.0): α_reward=0.74, α_penalty=0.44
-2025-05-06 13:45:56,919 - INFO - Participant 124 (ID=124.0): α_reward=0.18, α_penalty=0.36
-2025-05-06 13:45:56,925 - INFO - Participant 125 (ID=125.0): α_reward=0.86, α_penalty=0.09
-2025-05-06 13:45:56,931 - INFO - Participant 126 (ID=126.0): α_reward=0.05, α_penalty=0.52
-2025-05-06 13:45:56,937 - INFO - Participant 127 (ID=127.0): α_reward=0.41, α_penalty=0.26
-2025-05-06 13:45:56,937 - INFO - Participant 0 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,937 - INFO - Participant 1 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,937 - INFO - Participant 2 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,937 - INFO - Participant 3 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,937 - INFO - Participant 4 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,937 - INFO - Participant 5 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,937 - INFO - Participant 6 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,937 - INFO - Participant 7 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,937 - INFO - Participant 8 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,937 - INFO - Participant 9 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,937 - INFO - Participant 10 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,937 - INFO - Participant 11 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,937 - INFO - Participant 12 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,937 - INFO - Participant 13 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,937 - INFO - Participant 14 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,937 - INFO - Participant 15 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,937 - INFO - Participant 16 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,937 - INFO - Participant 17 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,937 - INFO - Participant 18 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,937 - INFO - Participant 19 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,937 - INFO - Participant 20 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,937 - INFO - Participant 21 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,937 - INFO - Participant 22 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,937 - INFO - Participant 23 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,937 - INFO - Participant 24 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,937 - INFO - Participant 25 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,937 - INFO - Participant 26 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,937 - INFO - Participant 27 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,937 - INFO - Participant 28 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,937 - INFO - Participant 29 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,937 - INFO - Participant 30 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,937 - INFO - Participant 31 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,937 - INFO - Participant 32 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,938 - INFO - Participant 33 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,938 - INFO - Participant 34 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,938 - INFO - Participant 35 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,938 - INFO - Participant 36 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,938 - INFO - Participant 37 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,938 - INFO - Participant 38 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,938 - INFO - Participant 39 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,938 - INFO - Participant 40 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,938 - INFO - Participant 41 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,938 - INFO - Participant 42 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,938 - INFO - Participant 43 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,938 - INFO - Participant 44 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,938 - INFO - Participant 45 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,938 - INFO - Participant 46 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,938 - INFO - Participant 47 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,938 - INFO - Participant 48 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,938 - INFO - Participant 49 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,938 - INFO - Participant 50 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,938 - INFO - Participant 51 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,938 - INFO - Participant 52 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,938 - INFO - Participant 53 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,938 - INFO - Participant 54 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,938 - INFO - Participant 55 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,938 - INFO - Participant 56 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,938 - INFO - Participant 57 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,938 - INFO - Participant 58 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,938 - INFO - Participant 59 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,938 - INFO - Participant 60 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,938 - INFO - Participant 61 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,938 - INFO - Participant 62 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,938 - INFO - Participant 63 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,938 - INFO - Participant 64 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,938 - INFO - Participant 65 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,938 - INFO - Participant 66 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,938 - INFO - Participant 67 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,938 - INFO - Participant 68 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,938 - INFO - Participant 69 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,938 - INFO - Participant 70 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,938 - INFO - Participant 71 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,938 - INFO - Participant 72 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,938 - INFO - Participant 73 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,938 - INFO - Participant 74 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,938 - INFO - Participant 75 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,938 - INFO - Participant 76 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,938 - INFO - Participant 77 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,938 - INFO - Participant 78 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,938 - INFO - Participant 79 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,938 - INFO - Participant 80 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,938 - INFO - Participant 81 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,938 - INFO - Participant 82 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,938 - INFO - Participant 83 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,938 - INFO - Participant 84 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,938 - INFO - Participant 85 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,938 - INFO - Participant 86 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,938 - INFO - Participant 87 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,938 - INFO - Participant 88 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,938 - INFO - Participant 89 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,938 - INFO - Participant 90 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,938 - INFO - Participant 91 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,938 - INFO - Participant 92 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,938 - INFO - Participant 93 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,938 - INFO - Participant 94 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,938 - INFO - Participant 95 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,938 - INFO - Participant 96 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,938 - INFO - Participant 97 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,938 - INFO - Participant 98 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,938 - INFO - Participant 99 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,938 - INFO - Participant 100 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,939 - INFO - Participant 101 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,939 - INFO - Participant 102 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,939 - INFO - Participant 103 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,939 - INFO - Participant 104 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,939 - INFO - Participant 105 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,939 - INFO - Participant 106 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,939 - INFO - Participant 107 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,939 - INFO - Participant 108 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,939 - INFO - Participant 109 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,939 - INFO - Participant 110 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,939 - INFO - Participant 111 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,939 - INFO - Participant 112 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,939 - INFO - Participant 113 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,939 - INFO - Participant 114 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,939 - INFO - Participant 115 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,939 - INFO - Participant 116 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,939 - INFO - Participant 117 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,939 - INFO - Participant 118 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,939 - INFO - Participant 119 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,939 - INFO - Participant 120 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,939 - INFO - Participant 121 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,939 - INFO - Participant 122 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,939 - INFO - Participant 123 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,939 - INFO - Participant 124 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,939 - INFO - Participant 125 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,939 - INFO - Participant 126 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,939 - INFO - Participant 127 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:45:56,939 - INFO - Combined xs shape after concatenation: torch.Size([128, 200, 5])
-2025-05-06 13:45:56,939 - INFO - Combined ys shape after concatenation: torch.Size([128, 200, 2])
-2025-05-06 13:45:56,939 - INFO - Combined dataset shape: X=torch.Size([128, 200, 5]), Y=torch.Size([128, 200, 2])
-2025-05-06 13:45:56,940 - INFO - Total unique participants: 128
-2025-05-06 13:45:56,940 - INFO - Train/test split ratio: 0.8/0.19999999999999996 of trials within each participant
-2025-05-06 13:45:56,940 - INFO - Participant 0.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,940 - INFO - Participant 1.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,940 - INFO - Participant 2.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,940 - INFO - Participant 3.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,940 - INFO - Participant 4.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,940 - INFO - Participant 5.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,940 - INFO - Participant 6.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,940 - INFO - Participant 7.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,940 - INFO - Participant 8.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,940 - INFO - Participant 9.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,940 - INFO - Participant 10.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,940 - INFO - Participant 11.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,940 - INFO - Participant 12.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,941 - INFO - Participant 13.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,941 - INFO - Participant 14.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,941 - INFO - Participant 15.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,941 - INFO - Participant 16.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,941 - INFO - Participant 17.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,941 - INFO - Participant 18.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,941 - INFO - Participant 19.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,941 - INFO - Participant 20.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,941 - INFO - Participant 21.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,941 - INFO - Participant 22.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,941 - INFO - Participant 23.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,941 - INFO - Participant 24.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,941 - INFO - Participant 25.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,941 - INFO - Participant 26.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,941 - INFO - Participant 27.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,941 - INFO - Participant 28.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,941 - INFO - Participant 29.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,941 - INFO - Participant 30.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,941 - INFO - Participant 31.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,941 - INFO - Participant 32.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,941 - INFO - Participant 33.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,942 - INFO - Participant 34.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,942 - INFO - Participant 35.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,942 - INFO - Participant 36.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,942 - INFO - Participant 37.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,942 - INFO - Participant 38.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,942 - INFO - Participant 39.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,942 - INFO - Participant 40.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,942 - INFO - Participant 41.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,942 - INFO - Participant 42.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,942 - INFO - Participant 43.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,942 - INFO - Participant 44.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,942 - INFO - Participant 45.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,942 - INFO - Participant 46.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,942 - INFO - Participant 47.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,942 - INFO - Participant 48.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,942 - INFO - Participant 49.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,942 - INFO - Participant 50.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,942 - INFO - Participant 51.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,942 - INFO - Participant 52.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,942 - INFO - Participant 53.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,942 - INFO - Participant 54.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,943 - INFO - Participant 55.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,943 - INFO - Participant 56.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,943 - INFO - Participant 57.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,943 - INFO - Participant 58.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,943 - INFO - Participant 59.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,943 - INFO - Participant 60.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,943 - INFO - Participant 61.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,943 - INFO - Participant 62.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,943 - INFO - Participant 63.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,943 - INFO - Participant 64.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,943 - INFO - Participant 65.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,943 - INFO - Participant 66.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,943 - INFO - Participant 67.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,943 - INFO - Participant 68.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,943 - INFO - Participant 69.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,943 - INFO - Participant 70.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,943 - INFO - Participant 71.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,943 - INFO - Participant 72.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,943 - INFO - Participant 73.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,943 - INFO - Participant 74.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,943 - INFO - Participant 75.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,943 - INFO - Participant 76.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,943 - INFO - Participant 77.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,944 - INFO - Participant 78.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,944 - INFO - Participant 79.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,944 - INFO - Participant 80.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,944 - INFO - Participant 81.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,944 - INFO - Participant 82.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,944 - INFO - Participant 83.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,944 - INFO - Participant 84.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,944 - INFO - Participant 85.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,944 - INFO - Participant 86.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,944 - INFO - Participant 87.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,944 - INFO - Participant 88.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,944 - INFO - Participant 89.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,944 - INFO - Participant 90.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,944 - INFO - Participant 91.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,944 - INFO - Participant 92.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,944 - INFO - Participant 93.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,944 - INFO - Participant 94.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,944 - INFO - Participant 95.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,944 - INFO - Participant 96.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,944 - INFO - Participant 97.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,944 - INFO - Participant 98.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,944 - INFO - Participant 99.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,944 - INFO - Participant 100.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,945 - INFO - Participant 101.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,945 - INFO - Participant 102.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,945 - INFO - Participant 103.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,945 - INFO - Participant 104.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,945 - INFO - Participant 105.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,945 - INFO - Participant 106.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,945 - INFO - Participant 107.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,945 - INFO - Participant 108.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,945 - INFO - Participant 109.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,945 - INFO - Participant 110.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,945 - INFO - Participant 111.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,945 - INFO - Participant 112.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,945 - INFO - Participant 113.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,945 - INFO - Participant 114.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,945 - INFO - Participant 115.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,945 - INFO - Participant 116.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,945 - INFO - Participant 117.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,945 - INFO - Participant 118.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,945 - INFO - Participant 119.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,945 - INFO - Participant 120.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,945 - INFO - Participant 121.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,946 - INFO - Participant 122.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,946 - INFO - Participant 123.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,946 - INFO - Participant 124.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,946 - INFO - Participant 125.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,946 - INFO - Participant 126.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,946 - INFO - Participant 127.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:45:56,946 - INFO - Train xs shape: torch.Size([128, 160, 5])
-2025-05-06 13:45:56,946 - INFO - Train ys shape: torch.Size([128, 160, 2])
-2025-05-06 13:45:56,946 - INFO - Validation xs shape: torch.Size([128, 40, 5])
-2025-05-06 13:45:56,946 - INFO - Validation ys shape: torch.Size([128, 40, 2])
-2025-05-06 13:45:56,946 - INFO - Train dataset: torch.Size([128, 160, 5]), Validation dataset: torch.Size([128, 40, 5])
-2025-05-06 13:45:56,946 - INFO - Starting hyperparameter optimization...
-2025-05-06 13:45:56,947 - INFO - Trial 0: l1_weight_decay=0.000002, l2_weight_decay=0.000001
-2025-05-06 13:46:04,507 - INFO - Trial 0: RNN Train Loss: 0.5222022; SPICE Train Loss: 2.994627576656261
-2025-05-06 13:46:04,535 - INFO - Trial 0: Average Validation Loss: 3.4181, Eval count: 40
-2025-05-06 13:46:04,536 - INFO - Best hyperparameters: {'l1_weight_decay': 1.7116367690718478e-06, 'l2_weight_decay': 1.4102172114502843e-06}
-2025-05-06 13:46:04,536 - INFO - Best validation loss: 3.4181
-2025-05-06 13:46:09,550 - INFO - Final RNN training loss: 0.6296792
-2025-05-06 13:46:09,551 - INFO - Evaluating with SINDy - fitting separate models for each participant's validation trials
-2025-05-06 13:46:09,551 - INFO - Processing participant 0.0...
-2025-05-06 13:46:09,551 - INFO - Fitting SINDy model for participant 0.0
-2025-05-06 13:46:10,669 - WARNING - Error processing participant 0.0: 'NoneType' object is not subscriptable
-2025-05-06 13:46:10,669 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 376, in evaluate_with_sindy
-    sindy_params = participant_sindy.count_parameters()[pid] if hasattr(participant_sindy, 'count_parameters') else 0
-                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-  File "/home/daniel/repositories/closedloop_rl/resources/bandits.py", line 466, in count_parameters
-    beta_value_module = betas[mapping_modules_values[submodule]]
-                              ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^
-TypeError: 'NoneType' object is not subscriptable
-
-2025-05-06 13:46:10,669 - INFO - Processing participant 1.0...
-2025-05-06 13:46:10,670 - INFO - Fitting SINDy model for participant 1.0
-2025-05-06 13:46:11,754 - WARNING - Error processing participant 1.0: 'NoneType' object is not subscriptable
-2025-05-06 13:46:11,754 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 376, in evaluate_with_sindy
-    sindy_params = participant_sindy.count_parameters()[pid] if hasattr(participant_sindy, 'count_parameters') else 0
-                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-  File "/home/daniel/repositories/closedloop_rl/resources/bandits.py", line 466, in count_parameters
-    beta_value_module = betas[mapping_modules_values[submodule]]
-                              ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^
-TypeError: 'NoneType' object is not subscriptable
-
-2025-05-06 13:46:11,755 - INFO - Processing participant 2.0...
-2025-05-06 13:46:11,755 - INFO - Fitting SINDy model for participant 2.0
-2025-05-06 13:46:12,770 - WARNING - Error processing participant 2.0: 'NoneType' object is not subscriptable
-2025-05-06 13:46:12,771 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 376, in evaluate_with_sindy
-    sindy_params = participant_sindy.count_parameters()[pid] if hasattr(participant_sindy, 'count_parameters') else 0
-                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-  File "/home/daniel/repositories/closedloop_rl/resources/bandits.py", line 466, in count_parameters
-    beta_value_module = betas[mapping_modules_values[submodule]]
-                              ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^
-TypeError: 'NoneType' object is not subscriptable
-
-2025-05-06 13:46:12,771 - INFO - Processing participant 3.0...
-2025-05-06 13:46:12,771 - INFO - Fitting SINDy model for participant 3.0
-2025-05-06 13:46:13,803 - WARNING - Error processing participant 3.0: 'NoneType' object is not subscriptable
-2025-05-06 13:46:13,803 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 376, in evaluate_with_sindy
-    sindy_params = participant_sindy.count_parameters()[pid] if hasattr(participant_sindy, 'count_parameters') else 0
-                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-  File "/home/daniel/repositories/closedloop_rl/resources/bandits.py", line 466, in count_parameters
-    beta_value_module = betas[mapping_modules_values[submodule]]
-                              ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^
-TypeError: 'NoneType' object is not subscriptable
-
-2025-05-06 13:46:13,803 - INFO - Processing participant 4.0...
-2025-05-06 13:46:13,803 - INFO - Fitting SINDy model for participant 4.0
-2025-05-06 13:46:14,875 - WARNING - Error processing participant 4.0: 'NoneType' object is not subscriptable
-2025-05-06 13:46:14,876 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 376, in evaluate_with_sindy
-    sindy_params = participant_sindy.count_parameters()[pid] if hasattr(participant_sindy, 'count_parameters') else 0
-                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-  File "/home/daniel/repositories/closedloop_rl/resources/bandits.py", line 466, in count_parameters
-    beta_value_module = betas[mapping_modules_values[submodule]]
-                              ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^
-TypeError: 'NoneType' object is not subscriptable
-
-2025-05-06 13:46:14,876 - INFO - Processing participant 5.0...
-2025-05-06 13:46:14,876 - INFO - Fitting SINDy model for participant 5.0
-2025-05-06 13:46:15,982 - WARNING - Error processing participant 5.0: 'NoneType' object is not subscriptable
-2025-05-06 13:46:15,983 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 376, in evaluate_with_sindy
-    sindy_params = participant_sindy.count_parameters()[pid] if hasattr(participant_sindy, 'count_parameters') else 0
-                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-  File "/home/daniel/repositories/closedloop_rl/resources/bandits.py", line 466, in count_parameters
-    beta_value_module = betas[mapping_modules_values[submodule]]
-                              ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^
-TypeError: 'NoneType' object is not subscriptable
-
-2025-05-06 13:46:15,983 - INFO - Processing participant 6.0...
-2025-05-06 13:46:15,983 - INFO - Fitting SINDy model for participant 6.0
-2025-05-06 13:46:17,087 - WARNING - Error processing participant 6.0: 'NoneType' object is not subscriptable
-2025-05-06 13:46:17,087 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 376, in evaluate_with_sindy
-    sindy_params = participant_sindy.count_parameters()[pid] if hasattr(participant_sindy, 'count_parameters') else 0
-                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-  File "/home/daniel/repositories/closedloop_rl/resources/bandits.py", line 466, in count_parameters
-    beta_value_module = betas[mapping_modules_values[submodule]]
-                              ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^
-TypeError: 'NoneType' object is not subscriptable
-
-2025-05-06 13:46:17,087 - INFO - Processing participant 7.0...
-2025-05-06 13:46:17,087 - INFO - Fitting SINDy model for participant 7.0
-2025-05-06 13:46:18,159 - WARNING - Error processing participant 7.0: 'NoneType' object is not subscriptable
-2025-05-06 13:46:18,159 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 376, in evaluate_with_sindy
-    sindy_params = participant_sindy.count_parameters()[pid] if hasattr(participant_sindy, 'count_parameters') else 0
-                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-  File "/home/daniel/repositories/closedloop_rl/resources/bandits.py", line 466, in count_parameters
-    beta_value_module = betas[mapping_modules_values[submodule]]
-                              ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^
-TypeError: 'NoneType' object is not subscriptable
-
-2025-05-06 13:46:18,159 - INFO - Processing participant 8.0...
-2025-05-06 13:46:18,159 - INFO - Fitting SINDy model for participant 8.0
-2025-05-06 13:46:19,199 - WARNING - Error processing participant 8.0: 'NoneType' object is not subscriptable
-2025-05-06 13:46:19,199 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 376, in evaluate_with_sindy
-    sindy_params = participant_sindy.count_parameters()[pid] if hasattr(participant_sindy, 'count_parameters') else 0
-                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-  File "/home/daniel/repositories/closedloop_rl/resources/bandits.py", line 466, in count_parameters
-    beta_value_module = betas[mapping_modules_values[submodule]]
-                              ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^
-TypeError: 'NoneType' object is not subscriptable
-
-2025-05-06 13:46:19,199 - INFO - Processing participant 9.0...
-2025-05-06 13:46:19,199 - INFO - Fitting SINDy model for participant 9.0
-2025-05-06 13:46:20,232 - WARNING - Error processing participant 9.0: 'NoneType' object is not subscriptable
-2025-05-06 13:46:20,233 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 376, in evaluate_with_sindy
-    sindy_params = participant_sindy.count_parameters()[pid] if hasattr(participant_sindy, 'count_parameters') else 0
-                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-  File "/home/daniel/repositories/closedloop_rl/resources/bandits.py", line 466, in count_parameters
-    beta_value_module = betas[mapping_modules_values[submodule]]
-                              ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^
-TypeError: 'NoneType' object is not subscriptable
-
-2025-05-06 13:46:20,233 - INFO - Processing participant 10.0...
-2025-05-06 13:46:20,233 - INFO - Fitting SINDy model for participant 10.0
-2025-05-06 13:46:21,279 - WARNING - Error processing participant 10.0: 'NoneType' object is not subscriptable
-2025-05-06 13:46:21,279 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 376, in evaluate_with_sindy
-    sindy_params = participant_sindy.count_parameters()[pid] if hasattr(participant_sindy, 'count_parameters') else 0
-                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-  File "/home/daniel/repositories/closedloop_rl/resources/bandits.py", line 466, in count_parameters
-    beta_value_module = betas[mapping_modules_values[submodule]]
-                              ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^
-TypeError: 'NoneType' object is not subscriptable
-
-2025-05-06 13:46:21,279 - INFO - Processing participant 11.0...
-2025-05-06 13:46:21,280 - INFO - Fitting SINDy model for participant 11.0
-2025-05-06 13:46:22,302 - WARNING - Error processing participant 11.0: 'NoneType' object is not subscriptable
-2025-05-06 13:46:22,302 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 376, in evaluate_with_sindy
-    sindy_params = participant_sindy.count_parameters()[pid] if hasattr(participant_sindy, 'count_parameters') else 0
-                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-  File "/home/daniel/repositories/closedloop_rl/resources/bandits.py", line 466, in count_parameters
-    beta_value_module = betas[mapping_modules_values[submodule]]
-                              ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^
-TypeError: 'NoneType' object is not subscriptable
-
-2025-05-06 13:46:22,302 - INFO - Processing participant 12.0...
-2025-05-06 13:46:22,303 - INFO - Fitting SINDy model for participant 12.0
-2025-05-06 13:46:23,412 - WARNING - Error processing participant 12.0: 'NoneType' object is not subscriptable
-2025-05-06 13:46:23,412 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 376, in evaluate_with_sindy
-    sindy_params = participant_sindy.count_parameters()[pid] if hasattr(participant_sindy, 'count_parameters') else 0
-                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-  File "/home/daniel/repositories/closedloop_rl/resources/bandits.py", line 466, in count_parameters
-    beta_value_module = betas[mapping_modules_values[submodule]]
-                              ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^
-TypeError: 'NoneType' object is not subscriptable
-
-2025-05-06 13:46:23,412 - INFO - Processing participant 13.0...
-2025-05-06 13:46:23,413 - INFO - Fitting SINDy model for participant 13.0
-2025-05-06 13:46:24,423 - WARNING - Error processing participant 13.0: 'NoneType' object is not subscriptable
-2025-05-06 13:46:24,424 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 376, in evaluate_with_sindy
-    sindy_params = participant_sindy.count_parameters()[pid] if hasattr(participant_sindy, 'count_parameters') else 0
-                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-  File "/home/daniel/repositories/closedloop_rl/resources/bandits.py", line 466, in count_parameters
-    beta_value_module = betas[mapping_modules_values[submodule]]
-                              ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^
-TypeError: 'NoneType' object is not subscriptable
-
-2025-05-06 13:46:24,424 - INFO - Processing participant 14.0...
-2025-05-06 13:46:24,424 - INFO - Fitting SINDy model for participant 14.0
-2025-05-06 13:46:25,435 - WARNING - Error processing participant 14.0: 'NoneType' object is not subscriptable
-2025-05-06 13:46:25,436 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 376, in evaluate_with_sindy
-    sindy_params = participant_sindy.count_parameters()[pid] if hasattr(participant_sindy, 'count_parameters') else 0
-                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-  File "/home/daniel/repositories/closedloop_rl/resources/bandits.py", line 466, in count_parameters
-    beta_value_module = betas[mapping_modules_values[submodule]]
-                              ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^
-TypeError: 'NoneType' object is not subscriptable
-
-2025-05-06 13:46:25,436 - INFO - Processing participant 15.0...
-2025-05-06 13:46:25,436 - INFO - Fitting SINDy model for participant 15.0
-2025-05-06 13:46:26,510 - WARNING - Error processing participant 15.0: 'NoneType' object is not subscriptable
-2025-05-06 13:46:26,510 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 376, in evaluate_with_sindy
-    sindy_params = participant_sindy.count_parameters()[pid] if hasattr(participant_sindy, 'count_parameters') else 0
-                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-  File "/home/daniel/repositories/closedloop_rl/resources/bandits.py", line 466, in count_parameters
-    beta_value_module = betas[mapping_modules_values[submodule]]
-                              ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^
-TypeError: 'NoneType' object is not subscriptable
-
-2025-05-06 13:46:26,510 - INFO - Processing participant 16.0...
-2025-05-06 13:46:26,510 - INFO - Fitting SINDy model for participant 16.0
-2025-05-06 13:46:27,550 - WARNING - Error processing participant 16.0: 'NoneType' object is not subscriptable
-2025-05-06 13:46:27,551 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 376, in evaluate_with_sindy
-    sindy_params = participant_sindy.count_parameters()[pid] if hasattr(participant_sindy, 'count_parameters') else 0
-                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-  File "/home/daniel/repositories/closedloop_rl/resources/bandits.py", line 466, in count_parameters
-    beta_value_module = betas[mapping_modules_values[submodule]]
-                              ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^
-TypeError: 'NoneType' object is not subscriptable
-
-2025-05-06 13:46:27,551 - INFO - Processing participant 17.0...
-2025-05-06 13:46:27,551 - INFO - Fitting SINDy model for participant 17.0
-2025-05-06 13:46:28,588 - WARNING - Error processing participant 17.0: 'NoneType' object is not subscriptable
-2025-05-06 13:46:28,588 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 376, in evaluate_with_sindy
-    sindy_params = participant_sindy.count_parameters()[pid] if hasattr(participant_sindy, 'count_parameters') else 0
-                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-  File "/home/daniel/repositories/closedloop_rl/resources/bandits.py", line 466, in count_parameters
-    beta_value_module = betas[mapping_modules_values[submodule]]
-                              ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^
-TypeError: 'NoneType' object is not subscriptable
-
-2025-05-06 13:46:28,588 - INFO - Processing participant 18.0...
-2025-05-06 13:46:28,588 - INFO - Fitting SINDy model for participant 18.0
-2025-05-06 13:46:29,636 - WARNING - Error processing participant 18.0: 'NoneType' object is not subscriptable
-2025-05-06 13:46:29,636 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 376, in evaluate_with_sindy
-    sindy_params = participant_sindy.count_parameters()[pid] if hasattr(participant_sindy, 'count_parameters') else 0
-                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-  File "/home/daniel/repositories/closedloop_rl/resources/bandits.py", line 466, in count_parameters
-    beta_value_module = betas[mapping_modules_values[submodule]]
-                              ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^
-TypeError: 'NoneType' object is not subscriptable
-
-2025-05-06 13:46:29,636 - INFO - Processing participant 19.0...
-2025-05-06 13:46:29,636 - INFO - Fitting SINDy model for participant 19.0
-2025-05-06 13:48:38,542 - INFO - ================================================================================
-2025-05-06 13:48:38,542 - INFO - EXPERIMENT CONFIG
-2025-05-06 13:48:38,542 - INFO - ================================================================================
-2025-05-06 13:48:38,542 - INFO - Found 1 data files: ['data_128p_0.csv']
-2025-05-06 13:48:38,542 - INFO - Processing dataset: data_128p_0.csv
-2025-05-06 13:48:38,542 - INFO - Loading dataset from data/optuna/data_128p_0.csv
-2025-05-06 13:48:38,578 - INFO - Number of participants: 128
-2025-05-06 13:48:38,584 - INFO - Participant 0 (ID=0.0): α_reward=0.74, α_penalty=0.97
-2025-05-06 13:48:38,589 - INFO - Participant 1 (ID=1.0): α_reward=0.99, α_penalty=0.02
-2025-05-06 13:48:38,595 - INFO - Participant 2 (ID=2.0): α_reward=0.77, α_penalty=0.46
-2025-05-06 13:48:38,600 - INFO - Participant 3 (ID=3.0): α_reward=0.96, α_penalty=0.60
-2025-05-06 13:48:38,605 - INFO - Participant 4 (ID=4.0): α_reward=0.21, α_penalty=0.21
-2025-05-06 13:48:38,611 - INFO - Participant 5 (ID=5.0): α_reward=0.07, α_penalty=0.51
-2025-05-06 13:48:38,616 - INFO - Participant 6 (ID=6.0): α_reward=0.23, α_penalty=0.60
-2025-05-06 13:48:38,621 - INFO - Participant 7 (ID=7.0): α_reward=0.91, α_penalty=0.80
-2025-05-06 13:48:38,626 - INFO - Participant 8 (ID=8.0): α_reward=0.99, α_penalty=0.97
-2025-05-06 13:48:38,632 - INFO - Participant 9 (ID=9.0): α_reward=0.67, α_penalty=0.87
-2025-05-06 13:48:38,637 - INFO - Participant 10 (ID=10.0): α_reward=0.08, α_penalty=0.77
-2025-05-06 13:48:38,643 - INFO - Participant 11 (ID=11.0): α_reward=0.87, α_penalty=0.31
-2025-05-06 13:48:38,648 - INFO - Participant 12 (ID=12.0): α_reward=0.58, α_penalty=0.19
-2025-05-06 13:48:38,653 - INFO - Participant 13 (ID=13.0): α_reward=0.48, α_penalty=0.25
-2025-05-06 13:48:38,659 - INFO - Participant 14 (ID=14.0): α_reward=0.34, α_penalty=0.83
-2025-05-06 13:48:38,664 - INFO - Participant 15 (ID=15.0): α_reward=0.56, α_penalty=0.80
-2025-05-06 13:48:38,669 - INFO - Participant 16 (ID=16.0): α_reward=0.47, α_penalty=0.29
-2025-05-06 13:48:38,675 - INFO - Participant 17 (ID=17.0): α_reward=0.77, α_penalty=0.94
-2025-05-06 13:48:38,680 - INFO - Participant 18 (ID=18.0): α_reward=0.88, α_penalty=0.01
-2025-05-06 13:48:38,685 - INFO - Participant 19 (ID=19.0): α_reward=0.98, α_penalty=0.11
-2025-05-06 13:48:38,690 - INFO - Participant 20 (ID=20.0): α_reward=0.02, α_penalty=0.85
-2025-05-06 13:48:38,696 - INFO - Participant 21 (ID=21.0): α_reward=0.46, α_penalty=0.85
-2025-05-06 13:48:38,701 - INFO - Participant 22 (ID=22.0): α_reward=0.69, α_penalty=0.43
-2025-05-06 13:48:38,706 - INFO - Participant 23 (ID=23.0): α_reward=0.15, α_penalty=0.95
-2025-05-06 13:48:38,711 - INFO - Participant 24 (ID=24.0): α_reward=0.25, α_penalty=0.36
-2025-05-06 13:48:38,717 - INFO - Participant 25 (ID=25.0): α_reward=0.75, α_penalty=0.11
-2025-05-06 13:48:38,722 - INFO - Participant 26 (ID=26.0): α_reward=0.46, α_penalty=0.81
-2025-05-06 13:48:38,727 - INFO - Participant 27 (ID=27.0): α_reward=0.36, α_penalty=0.63
-2025-05-06 13:48:38,733 - INFO - Participant 28 (ID=28.0): α_reward=0.49, α_penalty=0.00
-2025-05-06 13:48:38,738 - INFO - Participant 29 (ID=29.0): α_reward=0.20, α_penalty=0.98
-2025-05-06 13:48:38,743 - INFO - Participant 30 (ID=30.0): α_reward=0.29, α_penalty=0.64
-2025-05-06 13:48:38,748 - INFO - Participant 31 (ID=31.0): α_reward=0.66, α_penalty=0.64
-2025-05-06 13:48:38,754 - INFO - Participant 32 (ID=32.0): α_reward=0.21, α_penalty=0.41
-2025-05-06 13:48:38,759 - INFO - Participant 33 (ID=33.0): α_reward=0.49, α_penalty=0.63
-2025-05-06 13:48:38,765 - INFO - Participant 34 (ID=34.0): α_reward=0.38, α_penalty=0.08
-2025-05-06 13:48:38,770 - INFO - Participant 35 (ID=35.0): α_reward=0.33, α_penalty=0.29
-2025-05-06 13:48:38,775 - INFO - Participant 36 (ID=36.0): α_reward=0.53, α_penalty=0.61
-2025-05-06 13:48:38,780 - INFO - Participant 37 (ID=37.0): α_reward=0.65, α_penalty=0.34
-2025-05-06 13:48:38,786 - INFO - Participant 38 (ID=38.0): α_reward=0.29, α_penalty=0.13
-2025-05-06 13:48:38,791 - INFO - Participant 39 (ID=39.0): α_reward=0.31, α_penalty=0.25
-2025-05-06 13:48:38,796 - INFO - Participant 40 (ID=40.0): α_reward=0.77, α_penalty=0.04
-2025-05-06 13:48:38,801 - INFO - Participant 41 (ID=41.0): α_reward=0.23, α_penalty=0.89
-2025-05-06 13:48:38,807 - INFO - Participant 42 (ID=42.0): α_reward=0.63, α_penalty=0.60
-2025-05-06 13:48:38,812 - INFO - Participant 43 (ID=43.0): α_reward=0.48, α_penalty=0.52
-2025-05-06 13:48:38,817 - INFO - Participant 44 (ID=44.0): α_reward=0.92, α_penalty=0.22
-2025-05-06 13:48:38,823 - INFO - Participant 45 (ID=45.0): α_reward=0.52, α_penalty=0.89
-2025-05-06 13:48:38,828 - INFO - Participant 46 (ID=46.0): α_reward=0.42, α_penalty=0.83
-2025-05-06 13:48:38,833 - INFO - Participant 47 (ID=47.0): α_reward=0.68, α_penalty=0.69
-2025-05-06 13:48:38,838 - INFO - Participant 48 (ID=48.0): α_reward=0.95, α_penalty=0.22
-2025-05-06 13:48:38,844 - INFO - Participant 49 (ID=49.0): α_reward=0.13, α_penalty=0.48
-2025-05-06 13:48:38,849 - INFO - Participant 50 (ID=50.0): α_reward=0.87, α_penalty=0.85
-2025-05-06 13:48:38,854 - INFO - Participant 51 (ID=51.0): α_reward=0.47, α_penalty=0.07
-2025-05-06 13:48:38,860 - INFO - Participant 52 (ID=52.0): α_reward=0.56, α_penalty=0.42
-2025-05-06 13:48:38,865 - INFO - Participant 53 (ID=53.0): α_reward=0.53, α_penalty=0.18
-2025-05-06 13:48:38,870 - INFO - Participant 54 (ID=54.0): α_reward=0.87, α_penalty=0.73
-2025-05-06 13:48:38,876 - INFO - Participant 55 (ID=55.0): α_reward=0.21, α_penalty=0.47
-2025-05-06 13:48:38,881 - INFO - Participant 56 (ID=56.0): α_reward=0.65, α_penalty=0.09
-2025-05-06 13:48:38,886 - INFO - Participant 57 (ID=57.0): α_reward=0.72, α_penalty=0.88
-2025-05-06 13:48:38,892 - INFO - Participant 58 (ID=58.0): α_reward=0.61, α_penalty=0.38
-2025-05-06 13:48:38,897 - INFO - Participant 59 (ID=59.0): α_reward=0.92, α_penalty=0.91
-2025-05-06 13:48:38,902 - INFO - Participant 60 (ID=60.0): α_reward=0.22, α_penalty=0.97
-2025-05-06 13:48:38,907 - INFO - Participant 61 (ID=61.0): α_reward=0.41, α_penalty=0.53
-2025-05-06 13:48:38,913 - INFO - Participant 62 (ID=62.0): α_reward=0.92, α_penalty=0.99
-2025-05-06 13:48:38,918 - INFO - Participant 63 (ID=63.0): α_reward=0.22, α_penalty=0.99
-2025-05-06 13:48:38,923 - INFO - Participant 64 (ID=64.0): α_reward=0.84, α_penalty=0.96
-2025-05-06 13:48:38,929 - INFO - Participant 65 (ID=65.0): α_reward=0.55, α_penalty=0.19
-2025-05-06 13:48:38,934 - INFO - Participant 66 (ID=66.0): α_reward=0.34, α_penalty=0.07
-2025-05-06 13:48:38,939 - INFO - Participant 67 (ID=67.0): α_reward=0.62, α_penalty=0.45
-2025-05-06 13:48:38,944 - INFO - Participant 68 (ID=68.0): α_reward=0.47, α_penalty=0.19
-2025-05-06 13:48:38,950 - INFO - Participant 69 (ID=69.0): α_reward=0.81, α_penalty=0.36
-2025-05-06 13:48:38,955 - INFO - Participant 70 (ID=70.0): α_reward=0.73, α_penalty=0.87
-2025-05-06 13:48:38,960 - INFO - Participant 71 (ID=71.0): α_reward=0.10, α_penalty=0.57
-2025-05-06 13:48:38,965 - INFO - Participant 72 (ID=72.0): α_reward=0.78, α_penalty=0.50
-2025-05-06 13:48:38,971 - INFO - Participant 73 (ID=73.0): α_reward=0.82, α_penalty=0.70
-2025-05-06 13:48:38,976 - INFO - Participant 74 (ID=74.0): α_reward=0.00, α_penalty=0.98
-2025-05-06 13:48:38,981 - INFO - Participant 75 (ID=75.0): α_reward=0.42, α_penalty=0.26
-2025-05-06 13:48:38,987 - INFO - Participant 76 (ID=76.0): α_reward=0.83, α_penalty=0.39
-2025-05-06 13:48:38,992 - INFO - Participant 77 (ID=77.0): α_reward=0.75, α_penalty=0.18
-2025-05-06 13:48:38,997 - INFO - Participant 78 (ID=78.0): α_reward=0.97, α_penalty=0.44
-2025-05-06 13:48:39,002 - INFO - Participant 79 (ID=79.0): α_reward=0.45, α_penalty=0.57
-2025-05-06 13:48:39,008 - INFO - Participant 80 (ID=80.0): α_reward=0.63, α_penalty=0.78
-2025-05-06 13:48:39,013 - INFO - Participant 81 (ID=81.0): α_reward=0.29, α_penalty=0.24
-2025-05-06 13:48:39,018 - INFO - Participant 82 (ID=82.0): α_reward=0.71, α_penalty=0.15
-2025-05-06 13:48:39,023 - INFO - Participant 83 (ID=83.0): α_reward=0.71, α_penalty=0.66
-2025-05-06 13:48:39,029 - INFO - Participant 84 (ID=84.0): α_reward=0.97, α_penalty=0.79
-2025-05-06 13:48:39,034 - INFO - Participant 85 (ID=85.0): α_reward=1.00, α_penalty=0.95
-2025-05-06 13:48:39,039 - INFO - Participant 86 (ID=86.0): α_reward=0.56, α_penalty=0.08
-2025-05-06 13:48:39,045 - INFO - Participant 87 (ID=87.0): α_reward=0.59, α_penalty=0.83
-2025-05-06 13:48:39,050 - INFO - Participant 88 (ID=88.0): α_reward=0.32, α_penalty=0.60
-2025-05-06 13:48:39,055 - INFO - Participant 89 (ID=89.0): α_reward=0.20, α_penalty=0.84
-2025-05-06 13:48:39,060 - INFO - Participant 90 (ID=90.0): α_reward=0.34, α_penalty=1.00
-2025-05-06 13:48:39,066 - INFO - Participant 91 (ID=91.0): α_reward=0.19, α_penalty=0.99
-2025-05-06 13:48:39,071 - INFO - Participant 92 (ID=92.0): α_reward=0.20, α_penalty=0.91
-2025-05-06 13:48:39,076 - INFO - Participant 93 (ID=93.0): α_reward=0.31, α_penalty=0.21
-2025-05-06 13:48:39,082 - INFO - Participant 94 (ID=94.0): α_reward=0.58, α_penalty=0.41
-2025-05-06 13:48:39,087 - INFO - Participant 95 (ID=95.0): α_reward=0.80, α_penalty=0.54
-2025-05-06 13:48:39,092 - INFO - Participant 96 (ID=96.0): α_reward=0.74, α_penalty=0.80
-2025-05-06 13:48:39,098 - INFO - Participant 97 (ID=97.0): α_reward=0.59, α_penalty=0.16
-2025-05-06 13:48:39,103 - INFO - Participant 98 (ID=98.0): α_reward=0.54, α_penalty=0.17
-2025-05-06 13:48:39,108 - INFO - Participant 99 (ID=99.0): α_reward=0.17, α_penalty=0.16
-2025-05-06 13:48:39,114 - INFO - Participant 100 (ID=100.0): α_reward=0.79, α_penalty=0.40
-2025-05-06 13:48:39,119 - INFO - Participant 101 (ID=101.0): α_reward=0.32, α_penalty=0.05
-2025-05-06 13:48:39,124 - INFO - Participant 102 (ID=102.0): α_reward=0.87, α_penalty=0.81
-2025-05-06 13:48:39,130 - INFO - Participant 103 (ID=103.0): α_reward=0.90, α_penalty=0.11
-2025-05-06 13:48:39,135 - INFO - Participant 104 (ID=104.0): α_reward=0.44, α_penalty=0.12
-2025-05-06 13:48:39,140 - INFO - Participant 105 (ID=105.0): α_reward=0.59, α_penalty=0.50
-2025-05-06 13:48:39,146 - INFO - Participant 106 (ID=106.0): α_reward=0.84, α_penalty=0.50
-2025-05-06 13:48:39,152 - INFO - Participant 107 (ID=107.0): α_reward=0.48, α_penalty=0.54
-2025-05-06 13:48:39,157 - INFO - Participant 108 (ID=108.0): α_reward=0.56, α_penalty=0.75
-2025-05-06 13:48:39,161 - INFO - Participant 109 (ID=109.0): α_reward=0.53, α_penalty=0.11
-2025-05-06 13:48:39,167 - INFO - Participant 110 (ID=110.0): α_reward=0.52, α_penalty=0.40
-2025-05-06 13:48:39,173 - INFO - Participant 111 (ID=111.0): α_reward=0.61, α_penalty=0.74
-2025-05-06 13:48:39,178 - INFO - Participant 112 (ID=112.0): α_reward=0.64, α_penalty=0.56
-2025-05-06 13:48:39,183 - INFO - Participant 113 (ID=113.0): α_reward=0.44, α_penalty=0.40
-2025-05-06 13:48:39,189 - INFO - Participant 114 (ID=114.0): α_reward=0.74, α_penalty=0.91
-2025-05-06 13:48:39,194 - INFO - Participant 115 (ID=115.0): α_reward=0.09, α_penalty=0.27
-2025-05-06 13:48:39,199 - INFO - Participant 116 (ID=116.0): α_reward=0.99, α_penalty=0.92
-2025-05-06 13:48:39,205 - INFO - Participant 117 (ID=117.0): α_reward=0.90, α_penalty=0.87
-2025-05-06 13:48:39,210 - INFO - Participant 118 (ID=118.0): α_reward=0.21, α_penalty=0.27
-2025-05-06 13:48:39,216 - INFO - Participant 119 (ID=119.0): α_reward=0.71, α_penalty=0.69
-2025-05-06 13:48:39,221 - INFO - Participant 120 (ID=120.0): α_reward=0.43, α_penalty=0.05
-2025-05-06 13:48:39,227 - INFO - Participant 121 (ID=121.0): α_reward=0.11, α_penalty=0.27
-2025-05-06 13:48:39,232 - INFO - Participant 122 (ID=122.0): α_reward=0.80, α_penalty=0.43
-2025-05-06 13:48:39,238 - INFO - Participant 123 (ID=123.0): α_reward=0.74, α_penalty=0.44
-2025-05-06 13:48:39,243 - INFO - Participant 124 (ID=124.0): α_reward=0.18, α_penalty=0.36
-2025-05-06 13:48:39,249 - INFO - Participant 125 (ID=125.0): α_reward=0.86, α_penalty=0.09
-2025-05-06 13:48:39,255 - INFO - Participant 126 (ID=126.0): α_reward=0.05, α_penalty=0.52
-2025-05-06 13:48:39,260 - INFO - Participant 127 (ID=127.0): α_reward=0.41, α_penalty=0.26
-2025-05-06 13:48:39,260 - INFO - Participant 0 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,260 - INFO - Participant 1 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,260 - INFO - Participant 2 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,260 - INFO - Participant 3 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,260 - INFO - Participant 4 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,260 - INFO - Participant 5 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,260 - INFO - Participant 6 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,260 - INFO - Participant 7 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,260 - INFO - Participant 8 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,260 - INFO - Participant 9 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,260 - INFO - Participant 10 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,260 - INFO - Participant 11 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,260 - INFO - Participant 12 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,260 - INFO - Participant 13 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,260 - INFO - Participant 14 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,260 - INFO - Participant 15 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,261 - INFO - Participant 16 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,261 - INFO - Participant 17 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,261 - INFO - Participant 18 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,261 - INFO - Participant 19 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,261 - INFO - Participant 20 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,261 - INFO - Participant 21 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,261 - INFO - Participant 22 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,261 - INFO - Participant 23 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,261 - INFO - Participant 24 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,261 - INFO - Participant 25 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,261 - INFO - Participant 26 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,261 - INFO - Participant 27 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,261 - INFO - Participant 28 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,261 - INFO - Participant 29 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,261 - INFO - Participant 30 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,261 - INFO - Participant 31 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,261 - INFO - Participant 32 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,261 - INFO - Participant 33 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,261 - INFO - Participant 34 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,261 - INFO - Participant 35 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,261 - INFO - Participant 36 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,261 - INFO - Participant 37 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,261 - INFO - Participant 38 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,261 - INFO - Participant 39 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,261 - INFO - Participant 40 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,261 - INFO - Participant 41 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,261 - INFO - Participant 42 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,261 - INFO - Participant 43 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,261 - INFO - Participant 44 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,261 - INFO - Participant 45 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,261 - INFO - Participant 46 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,261 - INFO - Participant 47 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,261 - INFO - Participant 48 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,261 - INFO - Participant 49 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,261 - INFO - Participant 50 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,261 - INFO - Participant 51 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,261 - INFO - Participant 52 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,261 - INFO - Participant 53 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,261 - INFO - Participant 54 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,261 - INFO - Participant 55 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,261 - INFO - Participant 56 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,261 - INFO - Participant 57 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,261 - INFO - Participant 58 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,261 - INFO - Participant 59 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,261 - INFO - Participant 60 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,261 - INFO - Participant 61 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,261 - INFO - Participant 62 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,261 - INFO - Participant 63 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,261 - INFO - Participant 64 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,261 - INFO - Participant 65 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,261 - INFO - Participant 66 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,261 - INFO - Participant 67 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,261 - INFO - Participant 68 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,261 - INFO - Participant 69 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,261 - INFO - Participant 70 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,261 - INFO - Participant 71 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,261 - INFO - Participant 72 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,261 - INFO - Participant 73 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,261 - INFO - Participant 74 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,261 - INFO - Participant 75 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,261 - INFO - Participant 76 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,261 - INFO - Participant 77 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,261 - INFO - Participant 78 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,261 - INFO - Participant 79 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,261 - INFO - Participant 80 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,261 - INFO - Participant 81 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,261 - INFO - Participant 82 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,261 - INFO - Participant 83 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,261 - INFO - Participant 84 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,262 - INFO - Participant 85 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,262 - INFO - Participant 86 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,262 - INFO - Participant 87 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,262 - INFO - Participant 88 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,262 - INFO - Participant 89 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,262 - INFO - Participant 90 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,262 - INFO - Participant 91 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,262 - INFO - Participant 92 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,262 - INFO - Participant 93 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,262 - INFO - Participant 94 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,262 - INFO - Participant 95 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,262 - INFO - Participant 96 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,262 - INFO - Participant 97 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,262 - INFO - Participant 98 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,262 - INFO - Participant 99 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,262 - INFO - Participant 100 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,262 - INFO - Participant 101 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,262 - INFO - Participant 102 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,262 - INFO - Participant 103 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,262 - INFO - Participant 104 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,262 - INFO - Participant 105 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,262 - INFO - Participant 106 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,262 - INFO - Participant 107 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,262 - INFO - Participant 108 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,262 - INFO - Participant 109 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,262 - INFO - Participant 110 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,262 - INFO - Participant 111 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,262 - INFO - Participant 112 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,262 - INFO - Participant 113 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,262 - INFO - Participant 114 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,262 - INFO - Participant 115 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,262 - INFO - Participant 116 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,262 - INFO - Participant 117 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,262 - INFO - Participant 118 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,262 - INFO - Participant 119 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,262 - INFO - Participant 120 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,262 - INFO - Participant 121 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,262 - INFO - Participant 122 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,262 - INFO - Participant 123 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,262 - INFO - Participant 124 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,262 - INFO - Participant 125 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,262 - INFO - Participant 126 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,262 - INFO - Participant 127 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:48:39,262 - INFO - Combined xs shape after concatenation: torch.Size([128, 200, 5])
-2025-05-06 13:48:39,262 - INFO - Combined ys shape after concatenation: torch.Size([128, 200, 2])
-2025-05-06 13:48:39,262 - INFO - Combined dataset shape: X=torch.Size([128, 200, 5]), Y=torch.Size([128, 200, 2])
-2025-05-06 13:48:39,263 - INFO - Total unique participants: 128
-2025-05-06 13:48:39,263 - INFO - Train/test split ratio: 0.8/0.19999999999999996 of trials within each participant
-2025-05-06 13:48:39,263 - INFO - Participant 0.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,263 - INFO - Participant 1.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,263 - INFO - Participant 2.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,263 - INFO - Participant 3.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,263 - INFO - Participant 4.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,263 - INFO - Participant 5.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,264 - INFO - Participant 6.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,264 - INFO - Participant 7.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,264 - INFO - Participant 8.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,264 - INFO - Participant 9.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,264 - INFO - Participant 10.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,264 - INFO - Participant 11.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,264 - INFO - Participant 12.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,264 - INFO - Participant 13.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,264 - INFO - Participant 14.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,264 - INFO - Participant 15.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,264 - INFO - Participant 16.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,264 - INFO - Participant 17.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,264 - INFO - Participant 18.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,264 - INFO - Participant 19.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,264 - INFO - Participant 20.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,264 - INFO - Participant 21.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,264 - INFO - Participant 22.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,264 - INFO - Participant 23.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,264 - INFO - Participant 24.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,264 - INFO - Participant 25.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,265 - INFO - Participant 26.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,265 - INFO - Participant 27.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,265 - INFO - Participant 28.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,265 - INFO - Participant 29.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,265 - INFO - Participant 30.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,265 - INFO - Participant 31.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,265 - INFO - Participant 32.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,265 - INFO - Participant 33.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,265 - INFO - Participant 34.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,265 - INFO - Participant 35.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,265 - INFO - Participant 36.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,265 - INFO - Participant 37.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,265 - INFO - Participant 38.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,265 - INFO - Participant 39.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,265 - INFO - Participant 40.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,265 - INFO - Participant 41.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,265 - INFO - Participant 42.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,265 - INFO - Participant 43.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,265 - INFO - Participant 44.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,265 - INFO - Participant 45.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,266 - INFO - Participant 46.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,266 - INFO - Participant 47.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,266 - INFO - Participant 48.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,266 - INFO - Participant 49.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,266 - INFO - Participant 50.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,266 - INFO - Participant 51.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,266 - INFO - Participant 52.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,266 - INFO - Participant 53.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,266 - INFO - Participant 54.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,266 - INFO - Participant 55.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,266 - INFO - Participant 56.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,266 - INFO - Participant 57.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,266 - INFO - Participant 58.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,266 - INFO - Participant 59.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,266 - INFO - Participant 60.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,266 - INFO - Participant 61.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,266 - INFO - Participant 62.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,266 - INFO - Participant 63.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,266 - INFO - Participant 64.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,266 - INFO - Participant 65.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,266 - INFO - Participant 66.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,266 - INFO - Participant 67.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,267 - INFO - Participant 68.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,267 - INFO - Participant 69.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,267 - INFO - Participant 70.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,267 - INFO - Participant 71.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,267 - INFO - Participant 72.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,267 - INFO - Participant 73.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,267 - INFO - Participant 74.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,267 - INFO - Participant 75.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,267 - INFO - Participant 76.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,267 - INFO - Participant 77.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,267 - INFO - Participant 78.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,267 - INFO - Participant 79.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,267 - INFO - Participant 80.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,267 - INFO - Participant 81.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,267 - INFO - Participant 82.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,267 - INFO - Participant 83.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,267 - INFO - Participant 84.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,267 - INFO - Participant 85.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,267 - INFO - Participant 86.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,267 - INFO - Participant 87.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,267 - INFO - Participant 88.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,267 - INFO - Participant 89.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,268 - INFO - Participant 90.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,268 - INFO - Participant 91.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,268 - INFO - Participant 92.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,268 - INFO - Participant 93.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,268 - INFO - Participant 94.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,268 - INFO - Participant 95.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,268 - INFO - Participant 96.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,268 - INFO - Participant 97.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,268 - INFO - Participant 98.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,268 - INFO - Participant 99.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,268 - INFO - Participant 100.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,268 - INFO - Participant 101.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,268 - INFO - Participant 102.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,268 - INFO - Participant 103.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,268 - INFO - Participant 104.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,268 - INFO - Participant 105.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,268 - INFO - Participant 106.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,268 - INFO - Participant 107.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,268 - INFO - Participant 108.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,268 - INFO - Participant 109.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,268 - INFO - Participant 110.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,268 - INFO - Participant 111.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,268 - INFO - Participant 112.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,269 - INFO - Participant 113.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,269 - INFO - Participant 114.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,269 - INFO - Participant 115.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,269 - INFO - Participant 116.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,269 - INFO - Participant 117.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,269 - INFO - Participant 118.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,269 - INFO - Participant 119.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,269 - INFO - Participant 120.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,269 - INFO - Participant 121.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,269 - INFO - Participant 122.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,269 - INFO - Participant 123.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,269 - INFO - Participant 124.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,269 - INFO - Participant 125.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,269 - INFO - Participant 126.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,269 - INFO - Participant 127.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:48:39,269 - INFO - Train xs shape: torch.Size([128, 160, 5])
-2025-05-06 13:48:39,270 - INFO - Train ys shape: torch.Size([128, 160, 2])
-2025-05-06 13:48:39,270 - INFO - Validation xs shape: torch.Size([128, 40, 5])
-2025-05-06 13:48:39,270 - INFO - Validation ys shape: torch.Size([128, 40, 2])
-2025-05-06 13:48:39,270 - INFO - Train dataset: torch.Size([128, 160, 5]), Validation dataset: torch.Size([128, 40, 5])
-2025-05-06 13:48:39,270 - INFO - Starting hyperparameter optimization...
-2025-05-06 13:48:39,271 - INFO - Trial 0: l1_weight_decay=0.001083, l2_weight_decay=0.000004
-2025-05-06 13:48:46,501 - INFO - Trial 0: RNN Train Loss: 0.5222022; SPICE Train Loss: 2.994627576656261
-2025-05-06 13:48:46,532 - INFO - Trial 0: Average Validation Loss: 3.4181, Eval count: 40
-2025-05-06 13:48:46,533 - INFO - Best hyperparameters: {'l1_weight_decay': 0.0010834130968485236, 'l2_weight_decay': 4.098347307304995e-06}
-2025-05-06 13:48:46,533 - INFO - Best validation loss: 3.4181
-2025-05-06 13:48:51,896 - INFO - Final RNN training loss: 0.6296792
-2025-05-06 13:48:51,897 - INFO - Evaluating with SINDy - fitting separate models for each participant's validation trials
-2025-05-06 13:48:51,897 - INFO - Processing participant 0.0...
-2025-05-06 13:48:51,897 - INFO - Fitting SINDy model for participant 0.0
-2025-05-06 13:48:53,176 - INFO - Participant 0.0: LL=-0.8440, BIC=2.3336, Params=7, Val trials=40
-2025-05-06 13:48:53,176 - INFO - Processing participant 1.0...
-2025-05-06 13:48:53,176 - INFO - Fitting SINDy model for participant 1.0
-2025-05-06 13:48:54,264 - WARNING - Error processing participant 1.0: 1.0
-2025-05-06 13:48:54,264 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 376, in evaluate_with_sindy
-    sindy_params = participant_sindy.count_parameters(mapping_modules_values={'x_learning_rate_reward': 'x_value_reward', 'x_value_reward_not_chosen': 'x_value_reward', 'x_value_choice_chosen': 'x_value_choice', 'x_value_choice_not_chosen': 'x_value_choice'})[pid] if hasattr(participant_sindy, 'count_parameters') else 0
-                   ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^
-KeyError: 1.0
-
-2025-05-06 13:48:54,264 - INFO - Processing participant 2.0...
-2025-05-06 13:48:54,264 - INFO - Fitting SINDy model for participant 2.0
-2025-05-06 13:48:55,366 - WARNING - Error processing participant 2.0: 2.0
-2025-05-06 13:48:55,366 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 376, in evaluate_with_sindy
-    sindy_params = participant_sindy.count_parameters(mapping_modules_values={'x_learning_rate_reward': 'x_value_reward', 'x_value_reward_not_chosen': 'x_value_reward', 'x_value_choice_chosen': 'x_value_choice', 'x_value_choice_not_chosen': 'x_value_choice'})[pid] if hasattr(participant_sindy, 'count_parameters') else 0
-                   ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^
-KeyError: 2.0
-
-2025-05-06 13:48:55,367 - INFO - Processing participant 3.0...
-2025-05-06 13:48:55,367 - INFO - Fitting SINDy model for participant 3.0
-2025-05-06 13:48:56,488 - WARNING - Error processing participant 3.0: 3.0
-2025-05-06 13:48:56,489 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 376, in evaluate_with_sindy
-    sindy_params = participant_sindy.count_parameters(mapping_modules_values={'x_learning_rate_reward': 'x_value_reward', 'x_value_reward_not_chosen': 'x_value_reward', 'x_value_choice_chosen': 'x_value_choice', 'x_value_choice_not_chosen': 'x_value_choice'})[pid] if hasattr(participant_sindy, 'count_parameters') else 0
-                   ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^
-KeyError: 3.0
-
-2025-05-06 13:48:56,489 - INFO - Processing participant 4.0...
-2025-05-06 13:48:56,489 - INFO - Fitting SINDy model for participant 4.0
-2025-05-06 13:48:57,611 - WARNING - Error processing participant 4.0: 4.0
-2025-05-06 13:48:57,611 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 376, in evaluate_with_sindy
-    sindy_params = participant_sindy.count_parameters(mapping_modules_values={'x_learning_rate_reward': 'x_value_reward', 'x_value_reward_not_chosen': 'x_value_reward', 'x_value_choice_chosen': 'x_value_choice', 'x_value_choice_not_chosen': 'x_value_choice'})[pid] if hasattr(participant_sindy, 'count_parameters') else 0
-                   ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^
-KeyError: 4.0
-
-2025-05-06 13:48:57,611 - INFO - Processing participant 5.0...
-2025-05-06 13:48:57,611 - INFO - Fitting SINDy model for participant 5.0
-2025-05-06 13:51:33,676 - INFO - ================================================================================
-2025-05-06 13:51:33,676 - INFO - EXPERIMENT CONFIG
-2025-05-06 13:51:33,676 - INFO - ================================================================================
-2025-05-06 13:51:33,676 - INFO - Found 1 data files: ['data_128p_0.csv']
-2025-05-06 13:51:33,676 - INFO - Processing dataset: data_128p_0.csv
-2025-05-06 13:51:33,676 - INFO - Loading dataset from data/optuna/data_128p_0.csv
-2025-05-06 13:51:33,714 - INFO - Number of participants: 128
-2025-05-06 13:51:33,720 - INFO - Participant 0 (ID=0.0): α_reward=0.74, α_penalty=0.97
-2025-05-06 13:51:33,726 - INFO - Participant 1 (ID=1.0): α_reward=0.99, α_penalty=0.02
-2025-05-06 13:51:33,732 - INFO - Participant 2 (ID=2.0): α_reward=0.77, α_penalty=0.46
-2025-05-06 13:51:33,738 - INFO - Participant 3 (ID=3.0): α_reward=0.96, α_penalty=0.60
-2025-05-06 13:51:33,743 - INFO - Participant 4 (ID=4.0): α_reward=0.21, α_penalty=0.21
-2025-05-06 13:51:33,749 - INFO - Participant 5 (ID=5.0): α_reward=0.07, α_penalty=0.51
-2025-05-06 13:51:33,755 - INFO - Participant 6 (ID=6.0): α_reward=0.23, α_penalty=0.60
-2025-05-06 13:51:33,761 - INFO - Participant 7 (ID=7.0): α_reward=0.91, α_penalty=0.80
-2025-05-06 13:51:33,767 - INFO - Participant 8 (ID=8.0): α_reward=0.99, α_penalty=0.97
-2025-05-06 13:51:33,773 - INFO - Participant 9 (ID=9.0): α_reward=0.67, α_penalty=0.87
-2025-05-06 13:51:33,779 - INFO - Participant 10 (ID=10.0): α_reward=0.08, α_penalty=0.77
-2025-05-06 13:51:33,785 - INFO - Participant 11 (ID=11.0): α_reward=0.87, α_penalty=0.31
-2025-05-06 13:51:33,791 - INFO - Participant 12 (ID=12.0): α_reward=0.58, α_penalty=0.19
-2025-05-06 13:51:33,797 - INFO - Participant 13 (ID=13.0): α_reward=0.48, α_penalty=0.25
-2025-05-06 13:51:33,803 - INFO - Participant 14 (ID=14.0): α_reward=0.34, α_penalty=0.83
-2025-05-06 13:51:33,808 - INFO - Participant 15 (ID=15.0): α_reward=0.56, α_penalty=0.80
-2025-05-06 13:51:33,814 - INFO - Participant 16 (ID=16.0): α_reward=0.47, α_penalty=0.29
-2025-05-06 13:51:33,820 - INFO - Participant 17 (ID=17.0): α_reward=0.77, α_penalty=0.94
-2025-05-06 13:51:33,826 - INFO - Participant 18 (ID=18.0): α_reward=0.88, α_penalty=0.01
-2025-05-06 13:51:33,832 - INFO - Participant 19 (ID=19.0): α_reward=0.98, α_penalty=0.11
-2025-05-06 13:51:33,838 - INFO - Participant 20 (ID=20.0): α_reward=0.02, α_penalty=0.85
-2025-05-06 13:51:33,844 - INFO - Participant 21 (ID=21.0): α_reward=0.46, α_penalty=0.85
-2025-05-06 13:51:33,850 - INFO - Participant 22 (ID=22.0): α_reward=0.69, α_penalty=0.43
-2025-05-06 13:51:33,856 - INFO - Participant 23 (ID=23.0): α_reward=0.15, α_penalty=0.95
-2025-05-06 13:51:33,862 - INFO - Participant 24 (ID=24.0): α_reward=0.25, α_penalty=0.36
-2025-05-06 13:51:33,868 - INFO - Participant 25 (ID=25.0): α_reward=0.75, α_penalty=0.11
-2025-05-06 13:51:33,873 - INFO - Participant 26 (ID=26.0): α_reward=0.46, α_penalty=0.81
-2025-05-06 13:51:33,879 - INFO - Participant 27 (ID=27.0): α_reward=0.36, α_penalty=0.63
-2025-05-06 13:51:33,885 - INFO - Participant 28 (ID=28.0): α_reward=0.49, α_penalty=0.00
-2025-05-06 13:51:33,891 - INFO - Participant 29 (ID=29.0): α_reward=0.20, α_penalty=0.98
-2025-05-06 13:51:33,897 - INFO - Participant 30 (ID=30.0): α_reward=0.29, α_penalty=0.64
-2025-05-06 13:51:33,903 - INFO - Participant 31 (ID=31.0): α_reward=0.66, α_penalty=0.64
-2025-05-06 13:51:33,908 - INFO - Participant 32 (ID=32.0): α_reward=0.21, α_penalty=0.41
-2025-05-06 13:51:33,914 - INFO - Participant 33 (ID=33.0): α_reward=0.49, α_penalty=0.63
-2025-05-06 13:51:33,920 - INFO - Participant 34 (ID=34.0): α_reward=0.38, α_penalty=0.08
-2025-05-06 13:51:33,926 - INFO - Participant 35 (ID=35.0): α_reward=0.33, α_penalty=0.29
-2025-05-06 13:51:33,932 - INFO - Participant 36 (ID=36.0): α_reward=0.53, α_penalty=0.61
-2025-05-06 13:51:33,938 - INFO - Participant 37 (ID=37.0): α_reward=0.65, α_penalty=0.34
-2025-05-06 13:51:33,943 - INFO - Participant 38 (ID=38.0): α_reward=0.29, α_penalty=0.13
-2025-05-06 13:51:33,950 - INFO - Participant 39 (ID=39.0): α_reward=0.31, α_penalty=0.25
-2025-05-06 13:51:33,955 - INFO - Participant 40 (ID=40.0): α_reward=0.77, α_penalty=0.04
-2025-05-06 13:51:33,961 - INFO - Participant 41 (ID=41.0): α_reward=0.23, α_penalty=0.89
-2025-05-06 13:51:33,967 - INFO - Participant 42 (ID=42.0): α_reward=0.63, α_penalty=0.60
-2025-05-06 13:51:33,973 - INFO - Participant 43 (ID=43.0): α_reward=0.48, α_penalty=0.52
-2025-05-06 13:51:33,979 - INFO - Participant 44 (ID=44.0): α_reward=0.92, α_penalty=0.22
-2025-05-06 13:51:33,985 - INFO - Participant 45 (ID=45.0): α_reward=0.52, α_penalty=0.89
-2025-05-06 13:51:33,991 - INFO - Participant 46 (ID=46.0): α_reward=0.42, α_penalty=0.83
-2025-05-06 13:51:33,996 - INFO - Participant 47 (ID=47.0): α_reward=0.68, α_penalty=0.69
-2025-05-06 13:51:34,002 - INFO - Participant 48 (ID=48.0): α_reward=0.95, α_penalty=0.22
-2025-05-06 13:51:34,008 - INFO - Participant 49 (ID=49.0): α_reward=0.13, α_penalty=0.48
-2025-05-06 13:51:34,014 - INFO - Participant 50 (ID=50.0): α_reward=0.87, α_penalty=0.85
-2025-05-06 13:51:34,020 - INFO - Participant 51 (ID=51.0): α_reward=0.47, α_penalty=0.07
-2025-05-06 13:51:34,026 - INFO - Participant 52 (ID=52.0): α_reward=0.56, α_penalty=0.42
-2025-05-06 13:51:34,031 - INFO - Participant 53 (ID=53.0): α_reward=0.53, α_penalty=0.18
-2025-05-06 13:51:34,037 - INFO - Participant 54 (ID=54.0): α_reward=0.87, α_penalty=0.73
-2025-05-06 13:51:34,043 - INFO - Participant 55 (ID=55.0): α_reward=0.21, α_penalty=0.47
-2025-05-06 13:51:34,049 - INFO - Participant 56 (ID=56.0): α_reward=0.65, α_penalty=0.09
-2025-05-06 13:51:34,055 - INFO - Participant 57 (ID=57.0): α_reward=0.72, α_penalty=0.88
-2025-05-06 13:51:34,060 - INFO - Participant 58 (ID=58.0): α_reward=0.61, α_penalty=0.38
-2025-05-06 13:51:34,066 - INFO - Participant 59 (ID=59.0): α_reward=0.92, α_penalty=0.91
-2025-05-06 13:51:34,072 - INFO - Participant 60 (ID=60.0): α_reward=0.22, α_penalty=0.97
-2025-05-06 13:51:34,077 - INFO - Participant 61 (ID=61.0): α_reward=0.41, α_penalty=0.53
-2025-05-06 13:51:34,083 - INFO - Participant 62 (ID=62.0): α_reward=0.92, α_penalty=0.99
-2025-05-06 13:51:34,089 - INFO - Participant 63 (ID=63.0): α_reward=0.22, α_penalty=0.99
-2025-05-06 13:51:34,094 - INFO - Participant 64 (ID=64.0): α_reward=0.84, α_penalty=0.96
-2025-05-06 13:51:34,100 - INFO - Participant 65 (ID=65.0): α_reward=0.55, α_penalty=0.19
-2025-05-06 13:51:34,106 - INFO - Participant 66 (ID=66.0): α_reward=0.34, α_penalty=0.07
-2025-05-06 13:51:34,111 - INFO - Participant 67 (ID=67.0): α_reward=0.62, α_penalty=0.45
-2025-05-06 13:51:34,117 - INFO - Participant 68 (ID=68.0): α_reward=0.47, α_penalty=0.19
-2025-05-06 13:51:34,123 - INFO - Participant 69 (ID=69.0): α_reward=0.81, α_penalty=0.36
-2025-05-06 13:51:34,129 - INFO - Participant 70 (ID=70.0): α_reward=0.73, α_penalty=0.87
-2025-05-06 13:51:34,135 - INFO - Participant 71 (ID=71.0): α_reward=0.10, α_penalty=0.57
-2025-05-06 13:51:34,141 - INFO - Participant 72 (ID=72.0): α_reward=0.78, α_penalty=0.50
-2025-05-06 13:51:34,147 - INFO - Participant 73 (ID=73.0): α_reward=0.82, α_penalty=0.70
-2025-05-06 13:51:34,152 - INFO - Participant 74 (ID=74.0): α_reward=0.00, α_penalty=0.98
-2025-05-06 13:51:34,158 - INFO - Participant 75 (ID=75.0): α_reward=0.42, α_penalty=0.26
-2025-05-06 13:51:34,164 - INFO - Participant 76 (ID=76.0): α_reward=0.83, α_penalty=0.39
-2025-05-06 13:51:34,169 - INFO - Participant 77 (ID=77.0): α_reward=0.75, α_penalty=0.18
-2025-05-06 13:51:34,175 - INFO - Participant 78 (ID=78.0): α_reward=0.97, α_penalty=0.44
-2025-05-06 13:51:34,181 - INFO - Participant 79 (ID=79.0): α_reward=0.45, α_penalty=0.57
-2025-05-06 13:51:34,187 - INFO - Participant 80 (ID=80.0): α_reward=0.63, α_penalty=0.78
-2025-05-06 13:51:34,193 - INFO - Participant 81 (ID=81.0): α_reward=0.29, α_penalty=0.24
-2025-05-06 13:51:34,199 - INFO - Participant 82 (ID=82.0): α_reward=0.71, α_penalty=0.15
-2025-05-06 13:51:34,204 - INFO - Participant 83 (ID=83.0): α_reward=0.71, α_penalty=0.66
-2025-05-06 13:51:34,210 - INFO - Participant 84 (ID=84.0): α_reward=0.97, α_penalty=0.79
-2025-05-06 13:51:34,216 - INFO - Participant 85 (ID=85.0): α_reward=1.00, α_penalty=0.95
-2025-05-06 13:51:34,222 - INFO - Participant 86 (ID=86.0): α_reward=0.56, α_penalty=0.08
-2025-05-06 13:51:34,228 - INFO - Participant 87 (ID=87.0): α_reward=0.59, α_penalty=0.83
-2025-05-06 13:51:34,233 - INFO - Participant 88 (ID=88.0): α_reward=0.32, α_penalty=0.60
-2025-05-06 13:51:34,239 - INFO - Participant 89 (ID=89.0): α_reward=0.20, α_penalty=0.84
-2025-05-06 13:51:34,245 - INFO - Participant 90 (ID=90.0): α_reward=0.34, α_penalty=1.00
-2025-05-06 13:51:34,251 - INFO - Participant 91 (ID=91.0): α_reward=0.19, α_penalty=0.99
-2025-05-06 13:51:34,256 - INFO - Participant 92 (ID=92.0): α_reward=0.20, α_penalty=0.91
-2025-05-06 13:51:34,262 - INFO - Participant 93 (ID=93.0): α_reward=0.31, α_penalty=0.21
-2025-05-06 13:51:34,268 - INFO - Participant 94 (ID=94.0): α_reward=0.58, α_penalty=0.41
-2025-05-06 13:51:34,274 - INFO - Participant 95 (ID=95.0): α_reward=0.80, α_penalty=0.54
-2025-05-06 13:51:34,279 - INFO - Participant 96 (ID=96.0): α_reward=0.74, α_penalty=0.80
-2025-05-06 13:51:34,285 - INFO - Participant 97 (ID=97.0): α_reward=0.59, α_penalty=0.16
-2025-05-06 13:51:34,291 - INFO - Participant 98 (ID=98.0): α_reward=0.54, α_penalty=0.17
-2025-05-06 13:51:34,296 - INFO - Participant 99 (ID=99.0): α_reward=0.17, α_penalty=0.16
-2025-05-06 13:51:34,302 - INFO - Participant 100 (ID=100.0): α_reward=0.79, α_penalty=0.40
-2025-05-06 13:51:34,308 - INFO - Participant 101 (ID=101.0): α_reward=0.32, α_penalty=0.05
-2025-05-06 13:51:34,313 - INFO - Participant 102 (ID=102.0): α_reward=0.87, α_penalty=0.81
-2025-05-06 13:51:34,319 - INFO - Participant 103 (ID=103.0): α_reward=0.90, α_penalty=0.11
-2025-05-06 13:51:34,325 - INFO - Participant 104 (ID=104.0): α_reward=0.44, α_penalty=0.12
-2025-05-06 13:51:34,330 - INFO - Participant 105 (ID=105.0): α_reward=0.59, α_penalty=0.50
-2025-05-06 13:51:34,336 - INFO - Participant 106 (ID=106.0): α_reward=0.84, α_penalty=0.50
-2025-05-06 13:51:34,342 - INFO - Participant 107 (ID=107.0): α_reward=0.48, α_penalty=0.54
-2025-05-06 13:51:34,347 - INFO - Participant 108 (ID=108.0): α_reward=0.56, α_penalty=0.75
-2025-05-06 13:51:34,353 - INFO - Participant 109 (ID=109.0): α_reward=0.53, α_penalty=0.11
-2025-05-06 13:51:34,359 - INFO - Participant 110 (ID=110.0): α_reward=0.52, α_penalty=0.40
-2025-05-06 13:51:34,364 - INFO - Participant 111 (ID=111.0): α_reward=0.61, α_penalty=0.74
-2025-05-06 13:51:34,370 - INFO - Participant 112 (ID=112.0): α_reward=0.64, α_penalty=0.56
-2025-05-06 13:51:34,376 - INFO - Participant 113 (ID=113.0): α_reward=0.44, α_penalty=0.40
-2025-05-06 13:51:34,382 - INFO - Participant 114 (ID=114.0): α_reward=0.74, α_penalty=0.91
-2025-05-06 13:51:34,387 - INFO - Participant 115 (ID=115.0): α_reward=0.09, α_penalty=0.27
-2025-05-06 13:51:34,393 - INFO - Participant 116 (ID=116.0): α_reward=0.99, α_penalty=0.92
-2025-05-06 13:51:34,399 - INFO - Participant 117 (ID=117.0): α_reward=0.90, α_penalty=0.87
-2025-05-06 13:51:34,405 - INFO - Participant 118 (ID=118.0): α_reward=0.21, α_penalty=0.27
-2025-05-06 13:51:34,410 - INFO - Participant 119 (ID=119.0): α_reward=0.71, α_penalty=0.69
-2025-05-06 13:51:34,416 - INFO - Participant 120 (ID=120.0): α_reward=0.43, α_penalty=0.05
-2025-05-06 13:51:34,422 - INFO - Participant 121 (ID=121.0): α_reward=0.11, α_penalty=0.27
-2025-05-06 13:51:34,427 - INFO - Participant 122 (ID=122.0): α_reward=0.80, α_penalty=0.43
-2025-05-06 13:51:34,433 - INFO - Participant 123 (ID=123.0): α_reward=0.74, α_penalty=0.44
-2025-05-06 13:51:34,439 - INFO - Participant 124 (ID=124.0): α_reward=0.18, α_penalty=0.36
-2025-05-06 13:51:34,444 - INFO - Participant 125 (ID=125.0): α_reward=0.86, α_penalty=0.09
-2025-05-06 13:51:34,450 - INFO - Participant 126 (ID=126.0): α_reward=0.05, α_penalty=0.52
-2025-05-06 13:51:34,456 - INFO - Participant 127 (ID=127.0): α_reward=0.41, α_penalty=0.26
-2025-05-06 13:51:34,456 - INFO - Participant 0 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,456 - INFO - Participant 1 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,456 - INFO - Participant 2 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,456 - INFO - Participant 3 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,456 - INFO - Participant 4 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,456 - INFO - Participant 5 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,456 - INFO - Participant 6 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,456 - INFO - Participant 7 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,456 - INFO - Participant 8 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,456 - INFO - Participant 9 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,456 - INFO - Participant 10 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,456 - INFO - Participant 11 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,456 - INFO - Participant 12 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,456 - INFO - Participant 13 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,456 - INFO - Participant 14 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,456 - INFO - Participant 15 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,456 - INFO - Participant 16 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,456 - INFO - Participant 17 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,456 - INFO - Participant 18 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,456 - INFO - Participant 19 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,456 - INFO - Participant 20 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,456 - INFO - Participant 21 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,456 - INFO - Participant 22 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,456 - INFO - Participant 23 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,456 - INFO - Participant 24 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,456 - INFO - Participant 25 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,456 - INFO - Participant 26 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,456 - INFO - Participant 27 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,456 - INFO - Participant 28 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,456 - INFO - Participant 29 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,456 - INFO - Participant 30 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,456 - INFO - Participant 31 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,456 - INFO - Participant 32 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,456 - INFO - Participant 33 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,456 - INFO - Participant 34 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,456 - INFO - Participant 35 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,456 - INFO - Participant 36 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,457 - INFO - Participant 37 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,457 - INFO - Participant 38 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,457 - INFO - Participant 39 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,457 - INFO - Participant 40 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,457 - INFO - Participant 41 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,457 - INFO - Participant 42 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,457 - INFO - Participant 43 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,457 - INFO - Participant 44 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,457 - INFO - Participant 45 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,457 - INFO - Participant 46 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,457 - INFO - Participant 47 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,457 - INFO - Participant 48 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,457 - INFO - Participant 49 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,457 - INFO - Participant 50 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,457 - INFO - Participant 51 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,457 - INFO - Participant 52 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,457 - INFO - Participant 53 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,457 - INFO - Participant 54 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,457 - INFO - Participant 55 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,457 - INFO - Participant 56 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,457 - INFO - Participant 57 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,457 - INFO - Participant 58 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,457 - INFO - Participant 59 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,457 - INFO - Participant 60 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,457 - INFO - Participant 61 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,457 - INFO - Participant 62 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,457 - INFO - Participant 63 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,457 - INFO - Participant 64 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,457 - INFO - Participant 65 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,457 - INFO - Participant 66 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,457 - INFO - Participant 67 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,457 - INFO - Participant 68 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,457 - INFO - Participant 69 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,457 - INFO - Participant 70 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,457 - INFO - Participant 71 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,457 - INFO - Participant 72 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,457 - INFO - Participant 73 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,457 - INFO - Participant 74 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,457 - INFO - Participant 75 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,457 - INFO - Participant 76 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,457 - INFO - Participant 77 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,457 - INFO - Participant 78 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,457 - INFO - Participant 79 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,457 - INFO - Participant 80 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,457 - INFO - Participant 81 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,457 - INFO - Participant 82 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,457 - INFO - Participant 83 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,457 - INFO - Participant 84 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,457 - INFO - Participant 85 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,457 - INFO - Participant 86 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,457 - INFO - Participant 87 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,457 - INFO - Participant 88 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,457 - INFO - Participant 89 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,457 - INFO - Participant 90 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,457 - INFO - Participant 91 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,457 - INFO - Participant 92 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,457 - INFO - Participant 93 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,457 - INFO - Participant 94 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,457 - INFO - Participant 95 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,457 - INFO - Participant 96 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,457 - INFO - Participant 97 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,457 - INFO - Participant 98 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,457 - INFO - Participant 99 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,457 - INFO - Participant 100 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,457 - INFO - Participant 101 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,457 - INFO - Participant 102 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,458 - INFO - Participant 103 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,458 - INFO - Participant 104 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,458 - INFO - Participant 105 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,458 - INFO - Participant 106 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,458 - INFO - Participant 107 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,458 - INFO - Participant 108 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,458 - INFO - Participant 109 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,458 - INFO - Participant 110 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,458 - INFO - Participant 111 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,458 - INFO - Participant 112 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,458 - INFO - Participant 113 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,458 - INFO - Participant 114 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,458 - INFO - Participant 115 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,458 - INFO - Participant 116 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,458 - INFO - Participant 117 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,458 - INFO - Participant 118 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,458 - INFO - Participant 119 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,458 - INFO - Participant 120 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,458 - INFO - Participant 121 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,458 - INFO - Participant 122 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,458 - INFO - Participant 123 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,458 - INFO - Participant 124 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,458 - INFO - Participant 125 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,458 - INFO - Participant 126 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,458 - INFO - Participant 127 xs shape: torch.Size([1, 200, 5])
-2025-05-06 13:51:34,458 - INFO - Combined xs shape after concatenation: torch.Size([128, 200, 5])
-2025-05-06 13:51:34,458 - INFO - Combined ys shape after concatenation: torch.Size([128, 200, 2])
-2025-05-06 13:51:34,458 - INFO - Combined dataset shape: X=torch.Size([128, 200, 5]), Y=torch.Size([128, 200, 2])
-2025-05-06 13:51:34,459 - INFO - Total unique participants: 128
-2025-05-06 13:51:34,459 - INFO - Train/test split ratio: 0.8/0.19999999999999996 of trials within each participant
-2025-05-06 13:51:34,459 - INFO - Participant 0.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,459 - INFO - Participant 1.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,459 - INFO - Participant 2.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,459 - INFO - Participant 3.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,459 - INFO - Participant 4.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,459 - INFO - Participant 5.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,459 - INFO - Participant 6.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,459 - INFO - Participant 7.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,459 - INFO - Participant 8.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,459 - INFO - Participant 9.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,459 - INFO - Participant 10.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,459 - INFO - Participant 11.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,459 - INFO - Participant 12.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,459 - INFO - Participant 13.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,460 - INFO - Participant 14.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,460 - INFO - Participant 15.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,460 - INFO - Participant 16.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,460 - INFO - Participant 17.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,460 - INFO - Participant 18.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,460 - INFO - Participant 19.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,460 - INFO - Participant 20.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,460 - INFO - Participant 21.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,460 - INFO - Participant 22.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,460 - INFO - Participant 23.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,460 - INFO - Participant 24.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,460 - INFO - Participant 25.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,460 - INFO - Participant 26.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,460 - INFO - Participant 27.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,460 - INFO - Participant 28.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,460 - INFO - Participant 29.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,460 - INFO - Participant 30.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,460 - INFO - Participant 31.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,460 - INFO - Participant 32.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,460 - INFO - Participant 33.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,460 - INFO - Participant 34.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,461 - INFO - Participant 35.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,461 - INFO - Participant 36.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,461 - INFO - Participant 37.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,461 - INFO - Participant 38.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,461 - INFO - Participant 39.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,461 - INFO - Participant 40.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,461 - INFO - Participant 41.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,461 - INFO - Participant 42.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,461 - INFO - Participant 43.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,461 - INFO - Participant 44.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,461 - INFO - Participant 45.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,461 - INFO - Participant 46.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,461 - INFO - Participant 47.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,461 - INFO - Participant 48.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,461 - INFO - Participant 49.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,461 - INFO - Participant 50.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,461 - INFO - Participant 51.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,461 - INFO - Participant 52.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,461 - INFO - Participant 53.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,461 - INFO - Participant 54.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,461 - INFO - Participant 55.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,461 - INFO - Participant 56.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,462 - INFO - Participant 57.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,462 - INFO - Participant 58.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,462 - INFO - Participant 59.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,462 - INFO - Participant 60.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,462 - INFO - Participant 61.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,462 - INFO - Participant 62.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,462 - INFO - Participant 63.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,462 - INFO - Participant 64.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,462 - INFO - Participant 65.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,462 - INFO - Participant 66.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,462 - INFO - Participant 67.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,462 - INFO - Participant 68.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,462 - INFO - Participant 69.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,462 - INFO - Participant 70.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,462 - INFO - Participant 71.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,462 - INFO - Participant 72.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,462 - INFO - Participant 73.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,462 - INFO - Participant 74.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,462 - INFO - Participant 75.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,462 - INFO - Participant 76.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,462 - INFO - Participant 77.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,462 - INFO - Participant 78.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,463 - INFO - Participant 79.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,463 - INFO - Participant 80.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,463 - INFO - Participant 81.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,463 - INFO - Participant 82.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,463 - INFO - Participant 83.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,463 - INFO - Participant 84.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,463 - INFO - Participant 85.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,463 - INFO - Participant 86.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,463 - INFO - Participant 87.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,463 - INFO - Participant 88.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,463 - INFO - Participant 89.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,463 - INFO - Participant 90.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,463 - INFO - Participant 91.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,463 - INFO - Participant 92.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,463 - INFO - Participant 93.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,463 - INFO - Participant 94.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,463 - INFO - Participant 95.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,463 - INFO - Participant 96.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,463 - INFO - Participant 97.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,463 - INFO - Participant 98.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,463 - INFO - Participant 99.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,463 - INFO - Participant 100.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,463 - INFO - Participant 101.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,464 - INFO - Participant 102.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,464 - INFO - Participant 103.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,464 - INFO - Participant 104.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,464 - INFO - Participant 105.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,464 - INFO - Participant 106.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,464 - INFO - Participant 107.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,464 - INFO - Participant 108.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,464 - INFO - Participant 109.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,464 - INFO - Participant 110.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,464 - INFO - Participant 111.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,464 - INFO - Participant 112.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,464 - INFO - Participant 113.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,464 - INFO - Participant 114.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,464 - INFO - Participant 115.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,464 - INFO - Participant 116.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,464 - INFO - Participant 117.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,464 - INFO - Participant 118.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,464 - INFO - Participant 119.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,464 - INFO - Participant 120.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,464 - INFO - Participant 121.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,465 - INFO - Participant 122.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,465 - INFO - Participant 123.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,465 - INFO - Participant 124.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,465 - INFO - Participant 125.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,465 - INFO - Participant 126.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,465 - INFO - Participant 127.0: 160 trials for training, 40 trials for validation
-2025-05-06 13:51:34,465 - INFO - Train xs shape: torch.Size([128, 160, 5])
-2025-05-06 13:51:34,465 - INFO - Train ys shape: torch.Size([128, 160, 2])
-2025-05-06 13:51:34,465 - INFO - Validation xs shape: torch.Size([128, 40, 5])
-2025-05-06 13:51:34,465 - INFO - Validation ys shape: torch.Size([128, 40, 2])
-2025-05-06 13:51:34,465 - INFO - Train dataset: torch.Size([128, 160, 5]), Validation dataset: torch.Size([128, 40, 5])
-2025-05-06 13:51:34,465 - INFO - Starting hyperparameter optimization...
-2025-05-06 13:51:34,466 - INFO - Trial 0: l1_weight_decay=0.000132, l2_weight_decay=0.000003
-2025-05-06 13:51:42,061 - INFO - Trial 0: RNN Train Loss: 0.5222022; SPICE Train Loss: 2.994627576656261
-2025-05-06 13:51:42,091 - INFO - Trial 0: Average Validation Loss: 3.4181, Eval count: 40
-2025-05-06 13:51:42,092 - INFO - Best hyperparameters: {'l1_weight_decay': 0.00013179463812842057, 'l2_weight_decay': 2.6721150751802668e-06}
-2025-05-06 13:51:42,092 - INFO - Best validation loss: 3.4181
-2025-05-06 13:51:47,434 - INFO - Final RNN training loss: 0.6296792
-2025-05-06 13:51:47,435 - INFO - Evaluating with SINDy - fitting separate models for each participant's validation trials
-2025-05-06 13:51:47,435 - INFO - Processing participant 0.0...
-2025-05-06 13:51:47,435 - INFO - Fitting SINDy model for participant 0.0
-2025-05-06 13:51:48,712 - INFO - Participant 0.0: LL=-0.8440, BIC=2.3336, Params=7, Val trials=40
-2025-05-06 13:51:48,712 - INFO - Processing participant 1.0...
-2025-05-06 13:51:48,713 - INFO - Fitting SINDy model for participant 1.0
-2025-05-06 13:51:49,802 - WARNING - Error processing participant 1.0: 1.0
-2025-05-06 13:51:49,802 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 376, in evaluate_with_sindy
-    sindy_params = participant_sindy.count_parameters(mapping_modules_values={'x_learning_rate_reward': 'x_value_reward', 'x_value_reward_not_chosen': 'x_value_reward', 'x_value_choice_chosen': 'x_value_choice', 'x_value_choice_not_chosen': 'x_value_choice'})[pid] if hasattr(participant_sindy, 'count_parameters') else 0
-                   ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^
-KeyError: 1.0
-
-2025-05-06 13:51:49,802 - INFO - Processing participant 2.0...
-2025-05-06 13:51:49,802 - INFO - Fitting SINDy model for participant 2.0
-2025-05-06 13:51:50,841 - WARNING - Error processing participant 2.0: 2.0
-2025-05-06 13:51:50,842 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 376, in evaluate_with_sindy
-    sindy_params = participant_sindy.count_parameters(mapping_modules_values={'x_learning_rate_reward': 'x_value_reward', 'x_value_reward_not_chosen': 'x_value_reward', 'x_value_choice_chosen': 'x_value_choice', 'x_value_choice_not_chosen': 'x_value_choice'})[pid] if hasattr(participant_sindy, 'count_parameters') else 0
-                   ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^
-KeyError: 2.0
-
-2025-05-06 13:51:50,842 - INFO - Processing participant 3.0...
-2025-05-06 13:51:50,842 - INFO - Fitting SINDy model for participant 3.0
-2025-05-06 13:51:51,922 - WARNING - Error processing participant 3.0: 3.0
-2025-05-06 13:51:51,922 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 376, in evaluate_with_sindy
-    sindy_params = participant_sindy.count_parameters(mapping_modules_values={'x_learning_rate_reward': 'x_value_reward', 'x_value_reward_not_chosen': 'x_value_reward', 'x_value_choice_chosen': 'x_value_choice', 'x_value_choice_not_chosen': 'x_value_choice'})[pid] if hasattr(participant_sindy, 'count_parameters') else 0
-                   ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^
-KeyError: 3.0
-
-2025-05-06 13:51:51,922 - INFO - Processing participant 4.0...
-2025-05-06 13:51:51,923 - INFO - Fitting SINDy model for participant 4.0
-2025-05-06 13:51:53,009 - WARNING - Error processing participant 4.0: 4.0
-2025-05-06 13:51:53,009 - WARNING - Traceback (most recent call last):
-  File "/home/daniel/repositories/closedloop_rl/hyperparameter_optimization/rnn_withinsubject_finetuning.py", line 376, in evaluate_with_sindy
-    sindy_params = participant_sindy.count_parameters(mapping_modules_values={'x_learning_rate_reward': 'x_value_reward', 'x_value_reward_not_chosen': 'x_value_reward', 'x_value_choice_chosen': 'x_value_choice', 'x_value_choice_not_chosen': 'x_value_choice'})[pid] if hasattr(participant_sindy, 'count_parameters') else 0
-                   ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^
-KeyError: 4.0
-
-2025-05-06 13:51:53,009 - INFO - Processing participant 5.0...
-2025-05-06 13:51:53,009 - INFO - Fitting SINDy model for participant 5.0
-2025-05-06 15:35:25,952 - INFO - ================================================================================
-2025-05-06 15:35:25,953 - INFO - EXPERIMENT CONFIG
-2025-05-06 15:35:25,953 - INFO - ================================================================================
-2025-05-06 15:35:25,953 - INFO - Found 1 data files: ['eckstein2022.csv']
-2025-05-06 15:35:25,953 - INFO - Processing dataset: eckstein2022.csv
-2025-05-06 15:35:26,190 - INFO - Train dataset: torch.Size([306, 124, 5]), Validation dataset: torch.Size([306, 31, 5])
-2025-05-06 15:35:26,190 - INFO - Starting hyperparameter optimization...
-2025-05-06 15:35:26,191 - INFO - Trial 0: l1_weight_decay=0.000002, l2_weight_decay=0.004116
-2025-05-06 15:35:33,839 - INFO - Trial 0: RNN Train Loss: 0.6703603; SPICE Train Loss: 3.0480309210387913
-2025-05-06 15:35:33,839 - INFO - Best hyperparameters: {'l1_weight_decay': 1.5590999347169789e-06, 'l2_weight_decay': 0.004115512597838887}
-2025-05-06 15:35:33,839 - INFO - Best validation loss: 3.0480
-2025-05-06 15:35:33,840 - INFO - Completed processing dataset: eckstein2022.csv
-2025-05-06 15:36:06,464 - INFO - ================================================================================
-2025-05-06 15:36:06,464 - INFO - EXPERIMENT CONFIG
-2025-05-06 15:36:06,464 - INFO - ================================================================================
-2025-05-06 15:36:06,464 - INFO - Found 1 data files: ['eckstein2022.csv']
-2025-05-06 15:36:06,464 - INFO - Processing dataset: eckstein2022.csv
-2025-05-06 15:36:06,683 - INFO - Train dataset: torch.Size([306, 124, 5]), Validation dataset: torch.Size([306, 31, 5])
-2025-05-06 15:36:06,684 - INFO - Starting hyperparameter optimization...
-2025-05-06 15:36:06,684 - INFO - Trial 0: l1_weight_decay=0.000013, l2_weight_decay=0.000016
-2025-05-06 15:46:48,805 - INFO - Trial 0: RNN Train Loss: 0.6703603; SPICE Train Loss: 3.1214122614288193
-2025-05-06 15:46:48,805 - INFO - Best hyperparameters: {'l1_weight_decay': 1.2928624653076173e-05, 'l2_weight_decay': 1.6053304302475684e-05}
-2025-05-06 15:46:48,805 - INFO - Best validation loss: 3.1214
-2025-05-06 15:46:48,806 - INFO - Completed processing dataset: eckstein2022.csv
diff --git a/id_vs_age_plot.png b/id_vs_age_plot.png
new file mode 100644
index 00000000..5500afdb
Binary files /dev/null and b/id_vs_age_plot.png differ
diff --git a/pipeline_sindy.py b/pipeline_sindy.py
index 2372623c..7bc312cb 100644
--- a/pipeline_sindy.py
+++ b/pipeline_sindy.py
@@ -19,7 +19,7 @@
 def main(
     model: str = None,
     data: str = None,
-    save: bool = False,
+    save: bool = False, #just change and save the parameters to not rerun it 
     
     # generated dataset parameters
     participant_id: int = None,
@@ -164,8 +164,8 @@ def main(
     if use_optuna and verbose:
         print("\nUsing Optuna to find optimal optimizer configuration for each participant")
     
-    # setup the SINDy-agent
-    agent_spice, loss_spice = fit_spice(
+    # Setup the SINDy-agent
+    agent_spice, filtered_participant_ids, loss_spice = fit_spice(
         rnn_modules=list_rnn_modules,
         control_signals=list_control_parameters,
         agent=agent_rnn,
@@ -185,12 +185,16 @@ def main(
         verbose=verbose,
         use_optuna=use_optuna,
         filter_bad_participants=filter_bad_participants,
-        )
+    )
+    
+    # Update participant_ids with the filtered ones if filtering was applied
+    if filter_bad_participants:
+        participant_ids = filtered_participant_ids
     
     # If agent_spice is None, we couldn't fit the model, so return early
     if len(participant_ids) == 0:
         print("ERROR: Failed to fit SPICE model. Returning None.")
-        return None, None, None
+        return None, None, None, []
     
     # save spice modules
     if save:
@@ -202,6 +206,11 @@ def main(
         file_spice = os.path.join(*file_spice)
         save_spice(agent_spice=agent_spice, file=file_spice)
         print("Saved SPICE parameters to file " + file_spice)
+
+        # save the filtered participant IDs ----
+        ids_file = file_spice.replace('.pkl', '_participant_ids.npy')
+        np.save(ids_file, np.array(participant_ids, dtype=int))
+        print("Saved filtered participant IDs to file " + ids_file)
     
     # ---------------------------------------------------------------------------------------------------
     # Analysis
@@ -271,7 +280,7 @@ def main(
             features['beta_reward'][pid] = betas['x_value_reward']
             features['beta_choice'][pid] = betas['x_value_choice']
         
-    return agent_spice, features, loss_spice
+    return agent_spice, features, loss_spice, participant_ids
 
 
 if __name__=='__main__':
@@ -296,7 +305,7 @@ def main(
     
     args = parser.parse_args()
     
-    agent_spice, features, loss = main(
+    agent_spice, features, loss, participant_ids = main(
         model=args.model,
         data=args.data,
         save=args.save,
diff --git a/resources/sindy_training.py b/resources/sindy_training.py
index cd382d15..7e8c8524 100644
--- a/resources/sindy_training.py
+++ b/resources/sindy_training.py
@@ -114,7 +114,7 @@ def fit_sindy(
         )
 
         # fit sindy model
-        sindy_models[x_feature].fit(x_i, u=control_i, t=1, multiple_trajectories=True, ensemble=False)
+        sindy_models[x_feature].fit(x_i, u=control_i, t=1, multiple_trajectories=True, ensemble = True)
         
         if verbose:
             sindy_models[x_feature].print()
@@ -166,9 +166,10 @@ def fit_spice(
         get_loss (bool, optional): Whether to compute loss. Defaults to False.
         verbose (bool, optional): Whether to print verbose output. Defaults to False.
         use_optuna (bool, optional): Whether to use Optuna for optimizer selection. Defaults to False.
+        filter_bad_participants (bool, optional): Whether to filter out badly fitted participants. Defaults to False.
 
     Returns:
-        Tuple[AgentSpice, float]: The SPICE agent and its loss
+        Tuple[AgentSpice, List[int], float]: The SPICE agent, list of well-fitted participant IDs, and loss
     """
     
     if participant_id is not None:
@@ -286,16 +287,19 @@ def fit_spice(
     # set up a SINDy-based agent by replacing the RNN-modules with the respective SINDy-model
     agent_spice = AgentSpice(model_rnn=deepcopy(agent._model), sindy_modules=sindy_models, n_actions=agent._n_actions, deterministic=deterministic)
     
-    # remove badly fitted participants
-    if filter_bad_participants:
-        agent_spice, participant_ids = remove_bad_participants(
+    # Initialize filtered_ids with all participant_ids
+    filtered_ids = np.array(participant_ids)
+    
+    # Filter badly fitted participants if requested
+    if filter_bad_participants and data is not None:
+        agent_spice, filtered_ids = remove_bad_participants(
             agent_spice=agent_spice,
             agent_rnn=agent,
             dataset=data,
             participant_ids=participant_ids,
             verbose=verbose,
         )
-    
+
     # compute loss
     loss = None
     if get_loss and data is None:
@@ -305,10 +309,28 @@ def fit_spice(
         n_trials_total = 0
         mapping_modules_values = {module: 'x_value_choice' if 'choice' in module else 'x_value_reward' for module in agent_spice._model.submodules_sindy}
         n_parameters = agent_spice.count_parameters(mapping_modules_values=mapping_modules_values)
-        for pid in participant_ids:
-            xs, ys = data.xs.cpu().numpy(), data.ys.cpu().numpy()
-            probs = get_update_dynamics(experiment=xs[pid], agent=agent_spice)[1]
-            loss += loss_metric(data=ys[pid, :len(probs)], probs=probs, n_parameters=n_parameters[pid])
+        
+        # Use filtered_ids for loss calculation if filtering was applied
+        ids_to_use = filtered_ids if filter_bad_participants else participant_ids
+        
+        for pid in ids_to_use:
+            if pid not in agent_spice._model.submodules_sindy[list(agent_spice._model.submodules_sindy.keys())[0]]:
+                continue
+                
+            mask_participant_id = data.xs[:, 0, -1] == pid
+            if not mask_participant_id.any():
+                continue
+                
+            participant_data = DatasetRNN(*data[mask_participant_id])
+            xs, ys = participant_data.xs.cpu().numpy(), participant_data.ys.cpu().numpy()
+            
+            probs = get_update_dynamics(experiment=xs, agent=agent_spice)[1]
+            loss += loss_metric(data=ys[0, :len(probs)], probs=probs, n_parameters=n_parameters[pid])
             n_trials_total += len(probs)
-        loss = loss/n_trials_total
-    return agent_spice, loss
\ No newline at end of file
+            
+        if n_trials_total > 0:
+            loss = loss / n_trials_total
+        else:
+            loss = float('inf')  # If no valid trials, set loss to infinity
+
+    return agent_spice, filtered_ids, loss
\ No newline at end of file
diff --git a/resources/sindy_utils.py b/resources/sindy_utils.py
index 107f63c9..bf1ee020 100644
--- a/resources/sindy_utils.py
+++ b/resources/sindy_utils.py
@@ -289,25 +289,24 @@ def remove_bad_participants(agent_spice: AgentSpice, agent_rnn: AgentNetwork, da
   """Check for badly fitted participants in the SPICE models w.r.t. the SPICE-RNN and return only the IDs of the well-fitted participants.
 
   Args:
-      agent_spice (AgentSpice): _description_
-      agent_rnn (AgentNetwork): _description_
-      dataset_test (DatasetRNN): _description_
-      participant_ids (Iterable[int]): _description_
-      verbose (bool, optional): _description_. Defaults to False.
+      agent_spice (AgentSpice): SPICE agent to filter
+      agent_rnn (AgentNetwork): Reference RNN agent
+      dataset (DatasetRNN): Dataset to evaluate on
+      participant_ids (Iterable[int]): Participant IDs to check
+      trial_likelihood_difference_threshold (float, optional): Threshold for filtering. Defaults to 0.05.
+      verbose (bool, optional): Whether to print verbose output. Defaults to False.
 
   Returns:
       AgentSpice: SPICE agent without badly fitted participants
       Iterable[int]: List of well-fitted participants
   """
-  # if verbose:
   print("\nFiltering badly fitted SPICE models...")
   
-  filtered_participant_ids = []
+  # Convert participant_ids to a list of integers
+  participant_ids = list(map(int, participant_ids))
+  removed_participants = []
+  good_participants = []
   
-  # Create a copy of the valid participant IDs
-  valid_participant_ids = list(participant_ids)
-  
-  removed_pids = []
   for pid in tqdm(participant_ids):
       # Skip if participant is not in the SPICE model
       if pid not in agent_spice._model.submodules_sindy[list(agent_spice._model.submodules_sindy.keys())[0]]:
@@ -337,32 +336,28 @@ def remove_bad_participants(agent_spice: AgentSpice, agent_rnn: AgentNetwork, da
       spice_per_action_likelihood = np.exp(ll_spice/(n_trials_test*agent_rnn._n_actions))
       rnn_per_action_likelihood = np.exp(ll_rnn/(n_trials_test*agent_rnn._n_actions))
       
-      # Idea for filter criteria:
-      # If accuracy is very low for SPICE (near chance) but not so low for RNN then bad SPICE fitting (at least a bit higher than chance)
-      # TODO: Check for better filter criteria
+      # Filter out participants where SPICE performs much worse than RNN
       if rnn_per_action_likelihood - spice_per_action_likelihood > trial_likelihood_difference_threshold:
-          if verbose:
-              print(f'SPICE trial likelihood ({spice_per_action_likelihood:.2f}) is unplausibly low compared to RNN trial likelihood ({rnn_per_action_likelihood:.2f}).')
-              print(f'SPICE optimizer may be badly parameterized. Skipping participant {pid}.')
+          print(f'SPICE trial likelihood ({spice_per_action_likelihood:.2f}) is unplausibly low compared to RNN trial likelihood ({rnn_per_action_likelihood:.2f}).')
+          print(f'SPICE optimizer may be badly parameterized. Skipping participant {pid}.')
           
           # Remove this participant from the SPICE model
           for module in agent_spice._model.submodules_sindy:
               if pid in agent_spice._model.submodules_sindy[module]:
                   del agent_spice._model.submodules_sindy[module][pid]
           
-          # Remove from valid participant IDs
-          if pid in valid_participant_ids:
-              valid_participant_ids.remove(pid)
-              removed_pids.append(pid)
+          removed_participants.append(np.int32(pid))
       else:
-          # Keep track of filtered (good) participants
-          filtered_participant_ids.append(pid)
+          good_participants.append(np.int32(pid))
+  
+  # Convert to numpy arrays for consistency
+  good_participants = np.array(good_participants)
+  removed_participants = np.array(removed_participants)
   
-  if verbose:
-      print(f"\nAfter filtering: {len(valid_participant_ids)} of {len(participant_ids)} participants have well-fitted SPICE models.")
-      print(f"Removed participants: {removed_pids}")
+  print(f"After filtering: {len(good_participants)} of {len(participant_ids)} participants have well-fitted SPICE models.")
+  print(f"Removed participants: {removed_participants}")
   
-  return agent_spice, np.array(valid_participant_ids)
+  return agent_spice, good_participants
 
 
 def save_spice(agent_spice: AgentSpice, file: str):
diff --git a/tests/test_optimizers.py b/tests/test_optimizers.py
index f17787de..5c4192e1 100644
--- a/tests/test_optimizers.py
+++ b/tests/test_optimizers.py
@@ -19,7 +19,7 @@ def test_optuna_optimization():
             get_loss=True,
             use_optuna=True,  # Enable Optuna optimization
             filter_bad_participants=True,  # filter out 
-            show_plots=True,  # show plots
+            #show_plots=True,  # show plots
         )
         
         if agent_spice is None:
diff --git a/tests/test_pipeline_sindy.py b/tests/test_pipeline_sindy.py
index 32b90d39..8cde163a 100644
--- a/tests/test_pipeline_sindy.py
+++ b/tests/test_pipeline_sindy.py
@@ -4,20 +4,13 @@
 sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
 import pipeline_sindy
 
-
-agent_spice, features, loss = pipeline_sindy.main(
-    
-    # data='data/parameter_recovery/data_32p_0.csv',
-    # model='params/parameter_recovery/params_32p_0.pkl',
-    
-    model = 'params/eckstein2022/rnn_eckstein2022_l1_0_0001_l2_0_0001.pkl',
-    data = 'data/eckstein2022/eckstein2022.csv',
-    save = True,
-    
-    # general recovery parameters
+agent_spice, features, loss, participant_ids = pipeline_sindy.main(
+    model='params/eckstein2022/rnn_eckstein2022_l1_0_0001_l2_0_0001.pkl',
+    data='data/eckstein2022/eckstein2022.csv',
+    save=True,
     participant_id=None,
     filter_bad_participants=True,
-    use_optuna=False,
+    use_optuna=True,
     
     # sindy parameters
     polynomial_degree=1,
@@ -39,9 +32,9 @@
     alpha_choice=1.,
     counterfactual=False,
     alpha_counterfactual=0.,
-    
     analysis=True,
     get_loss=True,
 )
 
-print(loss)
\ No newline at end of file
+print(f"Final loss: {loss:.4f}")
+print("Kept participant IDs:", participant_ids)
\ No newline at end of file
diff --git a/tests/test_subjects_ids b/tests/test_subjects_ids
new file mode 100644
index 00000000..4d7a3cd9
--- /dev/null
+++ b/tests/test_subjects_ids
@@ -0,0 +1,12 @@
+import numpy as np
+
+# path to your .npy file
+path = '/Users/martynaplomecka/closedloop_rl/params/eckstein2022/spice_eckstein2022_l1_0_0001_l2_0_0001_participant_ids.npy'
+
+# load the array
+participant_ids = np.load(path)
+
+# view it
+print(participant_ids)
+# or, if you want a Python list:
+print(participant_ids.tolist())
\ No newline at end of file
diff --git a/utils/__pycache__/__init__.cpython-311.pyc b/utils/__pycache__/__init__.cpython-311.pyc
index edc96bd1..037ec498 100644
Binary files a/utils/__pycache__/__init__.cpython-311.pyc and b/utils/__pycache__/__init__.cpython-311.pyc differ
diff --git a/utils/__pycache__/convert_dataset.cpython-311.pyc b/utils/__pycache__/convert_dataset.cpython-311.pyc
index 1106c704..1c15bd4c 100644
Binary files a/utils/__pycache__/convert_dataset.cpython-311.pyc and b/utils/__pycache__/convert_dataset.cpython-311.pyc differ
diff --git a/utils/__pycache__/plotting.cpython-311.pyc b/utils/__pycache__/plotting.cpython-311.pyc
index 8ce13ae5..1bd249cb 100644
Binary files a/utils/__pycache__/plotting.cpython-311.pyc and b/utils/__pycache__/plotting.cpython-311.pyc differ
diff --git a/utils/__pycache__/setup_agents.cpython-311.pyc b/utils/__pycache__/setup_agents.cpython-311.pyc
index 8c7f6ee6..e510bc68 100644
Binary files a/utils/__pycache__/setup_agents.cpython-311.pyc and b/utils/__pycache__/setup_agents.cpython-311.pyc differ
diff --git a/utils/setup_agents.py b/utils/setup_agents.py
index c7ffa4b0..9abe6026 100644
--- a/utils/setup_agents.py
+++ b/utils/setup_agents.py
@@ -78,7 +78,7 @@ def setup_agent_spice(
     sindy_library_setup: Dict[str, List],
     sindy_filter_setup: Dict[str, List],
     sindy_dataprocessing: Dict[str, List],
-    path_spice: str = None,
+    path_spice: str = None, #we can change to the path saved before/
     threshold: float = 0.05,
     regularization: float = 0.1,
     participant_id: int = None,