| import numpy as np |
| from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, average_precision_score |
| import pandas as pd |
| import matplotlib.pyplot as plt |
| import seaborn as sns |
|
|
| def evaluate_metrics(labels, probs, target_cols, threshold=0.5): |
| """ |
| Evaluate metrics for each label |
| |
| Parameters: |
| ----------- |
| labels : numpy.ndarray |
| Ground truth labels (0 or 1) |
| probs : numpy.ndarray |
| Prediction probabilities |
| target_cols : list |
| List of target disease names |
| threshold : float |
| Threshold for converting probabilities to binary predictions |
| |
| Returns: |
| -------- |
| pandas.DataFrame |
| Metrics for each label |
| """ |
| |
| preds = (probs >= threshold).astype(int) |
| |
| |
| metrics_dict = { |
| 'Disease': target_cols, |
| 'Positive_Samples': [], |
| 'Accuracy': [], |
| 'Precision': [], |
| 'Recall': [], |
| 'F1': [], |
| 'AUC-ROC': [], |
| 'AP': [] |
| } |
| |
| |
| for i in range(len(target_cols)): |
| metrics_dict['Positive_Samples'].append(np.sum(labels[:, i])) |
| metrics_dict['Accuracy'].append(accuracy_score(labels[:, i], preds[:, i])) |
| metrics_dict['Precision'].append(precision_score(labels[:, i], preds[:, i], zero_division=0)) |
| metrics_dict['Recall'].append(recall_score(labels[:, i], preds[:, i], zero_division=0)) |
| metrics_dict['F1'].append(f1_score(labels[:, i], preds[:, i], zero_division=0)) |
| metrics_dict['AUC-ROC'].append(roc_auc_score(labels[:, i], probs[:, i])) |
| metrics_dict['AP'].append(average_precision_score(labels[:, i], probs[:, i])) |
| |
| |
| metrics_df = pd.DataFrame(metrics_dict) |
| |
| |
| numeric_cols = ['Accuracy', 'Precision', 'Recall', 'F1', 'AUC-ROC', 'AP'] |
| metrics_df[numeric_cols] = metrics_df[numeric_cols].round(3) |
| |
| return metrics_df |
|
|
| def plot_metrics_heatmap(metrics_df, metric_cols=['Precision', 'Recall', 'F1', 'AUC-ROC', 'AP']): |
| """ |
| Plot a heatmap of evaluation metrics |
| |
| Parameters: |
| ----------- |
| metrics_df : pandas.DataFrame |
| Output from evaluate_metrics function |
| metric_cols : list |
| Metrics to display in the heatmap |
| """ |
| plt.figure(figsize=(12, len(target_cols)//2)) |
| |
| |
| heatmap_data = metrics_df[metric_cols].values |
| |
| |
| sns.heatmap(heatmap_data, |
| annot=True, |
| fmt='.3f', |
| cmap='YlOrRd', |
| xticklabels=metric_cols, |
| yticklabels=metrics_df['Disease'], |
| vmin=0, |
| vmax=1) |
| |
| plt.title('Evaluation Metrics Heatmap') |
| plt.tight_layout() |
| plt.show() |
