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| import pandas as pd | |
| import numpy as np | |
| import os | |
| from omnibin import generate_binary_classification_report, ColorScheme | |
| # Define paths | |
| RESULTS_DIR = os.path.join(os.path.dirname(__file__), "results") | |
| # Ensure results directory exists | |
| os.makedirs(RESULTS_DIR, exist_ok=True) | |
| # Generate random data | |
| data = pd.DataFrame({ | |
| 'y_true': (y:=np.random.choice([0,1],1000,p:=[.9,.1])), | |
| 'y_pred': np.where( | |
| y, | |
| np.random.beta(3,1.5,1000)*.9+.1, # Positive cases: less skewed towards 1.0 | |
| np.random.beta(1.5,3,1000)*.9+.1 # Negative cases: less skewed towards 0.1 | |
| ) | |
| }) | |
| y_true = data['y_true'].values | |
| y_scores = data['y_pred'].values | |
| # Generate comprehensive classification report | |
| report_path = generate_binary_classification_report( | |
| y_true=y_true, | |
| y_scores=y_scores, | |
| output_path=os.path.join(RESULTS_DIR, "classification_report.pdf"), | |
| n_bootstrap=1000, | |
| random_seed=42, # Set a fixed random seed for reproducibility | |
| dpi=72, | |
| color_scheme=ColorScheme.DEFAULT | |
| ) | |
| print(f"Report generated and saved to: {report_path}") |