Giorgio
e7e4db264d
Reorganized flat 41-file directory into structured layout with: - scripts/ for Python analysis code with shared config.py - notebooks/ for Jupyter analysis notebooks - data/ split into raw/, metadata/, processed/ - docs/ with analysis summary, experimental design, and bimodal hypothesis tutorial - tasks/ with todo checklist and lessons learned - Comprehensive README, PLANNING.md, and .gitignore Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
97 lines
3.6 KiB
Python
97 lines
3.6 KiB
Python
import pandas as pd
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import numpy as np
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from sklearn.model_selection import train_test_split, cross_val_score
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from sklearn.ensemble import RandomForestClassifier
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from sklearn.linear_model import LogisticRegression
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from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
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from sklearn.preprocessing import StandardScaler
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from sklearn.impute import SimpleImputer
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import matplotlib.pyplot as plt
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import seaborn as sns
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from config import DATA_PROCESSED, FIGURES
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# Load data
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trained_distances = pd.read_csv(DATA_PROCESSED / 'trained_distances.csv')
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untrained_distances = pd.read_csv(DATA_PROCESSED / 'untrained_distances.csv')
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# Add group labels
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trained_distances['group'] = 'trained'
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untrained_distances['group'] = 'untrained'
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# Combine data
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combined_data = pd.concat([trained_distances, untrained_distances], ignore_index=True)
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combined_data = combined_data.dropna(subset=['group'])
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# Prepare features and target
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features = ['distance', 'n_flies', 'area_fly1', 'area_fly2']
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X = combined_data[features]
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y = combined_data['group']
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# Handle missing values in features
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imputer = SimpleImputer(strategy='mean')
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X_imputed = pd.DataFrame(imputer.fit_transform(X), columns=features)
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# Split data
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X_train, X_test, y_train, y_test = train_test_split(X_imputed, y, test_size=0.2, random_state=42)
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# Standardize features
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scaler = StandardScaler()
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X_train_scaled = scaler.fit_transform(X_train)
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X_test_scaled = scaler.transform(X_test)
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print("=== MACHINE LEARNING CLASSIFICATION ===")
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print(f"Training set size: {len(X_train)}")
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print(f"Testing set size: {len(X_test)}")
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# 1. Logistic Regression
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print("\n1. Logistic Regression:")
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lr_model = LogisticRegression(random_state=42)
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lr_model.fit(X_train_scaled, y_train)
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lr_predictions = lr_model.predict(X_test_scaled)
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lr_accuracy = accuracy_score(y_test, lr_predictions)
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print(f"Accuracy: {lr_accuracy:.4f}")
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print("\nClassification Report:")
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print(classification_report(y_test, lr_predictions))
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# 2. Random Forest
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print("\n2. Random Forest:")
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rf_model = RandomForestClassifier(n_estimators=100, random_state=42)
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rf_model.fit(X_train, y_train)
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rf_predictions = rf_model.predict(X_test)
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rf_accuracy = accuracy_score(y_test, rf_predictions)
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print(f"Accuracy: {rf_accuracy:.4f}")
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print("\nClassification Report:")
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print(classification_report(y_test, rf_predictions))
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# Feature importance
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print("\nFeature Importance (Random Forest):")
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feature_importance = pd.DataFrame({
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'feature': features,
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'importance': rf_model.feature_importances_
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}).sort_values('importance', ascending=False)
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print(feature_importance)
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# Confusion matrix for the best model
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best_model_name = "Random Forest" if rf_accuracy > lr_accuracy else "Logistic Regression"
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best_predictions = rf_predictions if rf_accuracy > lr_accuracy else lr_predictions
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plt.figure(figsize=(8, 6))
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cm = confusion_matrix(y_test, best_predictions)
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sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',
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xticklabels=['Trained', 'Untrained'],
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yticklabels=['Trained', 'Untrained'])
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plt.title(f'Confusion Matrix - {best_model_name}')
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plt.xlabel('Predicted')
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plt.ylabel('Actual')
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plt.tight_layout()
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plt.savefig(FIGURES / 'confusion_matrix.png', dpi=300, bbox_inches='tight')
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plt.show()
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# Cross-validation scores
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print("\n=== CROSS-VALIDATION SCORES ===")
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lr_cv_scores = cross_val_score(LogisticRegression(random_state=42), X_train_scaled, y_train, cv=5)
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rf_cv_scores = cross_val_score(RandomForestClassifier(n_estimators=100, random_state=42), X_train, y_train, cv=5)
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print(f"Logistic Regression CV Score: {lr_cv_scores.mean():.4f} (+/- {lr_cv_scores.std() * 2:.4f})")
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print(f"Random Forest CV Score: {rf_cv_scores.mean():.4f} (+/- {rf_cv_scores.std() * 2:.4f})")
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