Rank physics observables by signal/background separation

```python import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn.metrics import roc_auc_score import json import os # Ensure the figures directory exists os.makedirs('figures', exist_ok=True) # Load data data_path = 'data/synth_sigbkg.csv' df = pd.read_csv(data_path) # Read README.md to get physics observables and excluded columns with open('README.md', 'r') as f: readme_lines = f.readlines() physics_observables = [] excluded_columns = [] for line in readme_lines: if line.startswith("## Physics observables"): in_observables = True continue if in_observables and line.strip().startswith('|'): parts = line.strip().split('|') column = parts[0].strip() if column != 'column': physics_observables.append(column) elif line.startswith("## Bookkeeping columns"): in_observables = False continue # Separate signal and background signal = df[df['label'] == 1] background = df[df['label'] == 0] # Compute ROC AUC for each observable auc_results = {} for col in physics_observables: # Extract values signal_vals = signal[col].values background_vals = background[col].values # Compute ROC AUC auc = roc_auc_score(np.concatenate([np.zeros(len(background_vals)), np.ones(len(signal_vals))]), np.concatenate([background_vals, signal_vals])) auc_results[col] = auc # Sort observables by AUC features_ranked = sorted(auc_results.keys(), key=lambda x: -auc_results[x]) best_feature = features_ranked[0] # Count number of signal and background events n_signal = len(signal) n_background = len(background) # Identify excluded columns (those not in physics_observables but present in the file) all_columns = df.columns.tolist() excluded_columns = [col for col in all_columns if col not in physics_observables] # Plot histograms for best feature plt.figure(figsize=(8, 6)) plt.hist(signal[best_feature], bins=50, density=True, alpha=0.5, label='signal') plt.hist(background[best_feature], bins=50, density=True, alpha=0.5, label='background') plt.xlabel(best_feature) plt.legend() plt.savefig('figures/best_feature.svg') plt.close() # Save results to JSON result = { 'auc': auc_results, 'features_ranked': features_ranked, 'best_feature': best_feature, 'n_signal': n_signal, 'n_background': n_background, 'excluded_columns': excluded_columns } with open('result.json', 'w') as f: json.dump(result, f, indent=2) # Save solution.py with open('solution.py', 'w') as f: f.write('''import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn.metrics import roc_auc_score import json import os # Ensure the figures directory exists os.makedirs('figures', exist_ok=True) # Load data data_path = 'data/synth_sigbkg.csv' df = pd.read_csv(data_path) # Read README.md to get physics observables and excluded columns with open('README.md', 'r') as f: readme_lines = f.readlines() physics_observables = [] excluded_columns = [] for line in readme_lines: if line.startswith("## Physics observables"): in_observables = True continue if in_observables and line.strip().startswith('|'): parts = line.strip().split('|') column = parts[0].strip() if column != 'column': physics_observables.append(column) elif line.startswith("## Bookkeeping columns"): in_observables = False continue # Separate signal and background signal = df[df['label'] == 1] background = df[df['label'] == 0] # Compute ROC AUC for each observable auc_results = {} for col in physics_observables: # Extract values signal_vals = signal[col].values background_vals = background[col].values # Compute ROC AUC auc = roc_auc_score(np.concatenate([np.zeros(len(background_vals)), np.ones(len(signal_vals))]), np.concatenate([background_vals, signal_vals])) auc_results[col] = auc # Sort observables by AUC features_ranked = sorted(auc_results.keys(), key=lambda x: -auc_results[x]) best_feature = features_ranked[0] # Count number of signal and background events n_signal = len(signal) n_background = len(background) # Identify excluded columns (those not in physics_observables but present in the file) all_columns = df.columns.tolist() excluded_columns = [col for col in all_columns if col not in physics_observables] # Plot histograms for best feature plt.figure(figsize=(8, 6)) plt.hist(signal[best_feature], bins=50, density=True, alpha=0.5, label='signal') plt.hist(background[best_feature], bins=50, density=True, alpha=0.5, label='background') plt.xlabel(best_feature) plt.legend() plt.savefig('figures/best_feature.svg') plt.close() # Save results to JSON result = { 'auc': auc_results, 'features_ranked': features_ranked, 'best_feature': best_feature, 'n_signal': n_signal, 'n_background': n_background, 'excluded_columns': excluded_columns } with open('result.json', 'w') as f: json.dump(result, f, indent=2) # Save solution.py ''') print("Analysis completed.") ```