Upload evaluate.py

evaluate.py

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+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
+    """
+    # Convert probabilities to binary predictions
+    preds = (probs >= threshold).astype(int)
+    
+    # Dictionary to store metrics
+    metrics_dict = {
+        'Disease': target_cols,
+        'Positive_Samples': [],
+        'Accuracy': [],
+        'Precision': [],
+        'Recall': [],
+        'F1': [],
+        'AUC-ROC': [],
+        'AP': []  # Average Precision
+    }
+    
+    # Calculate metrics for each label
+    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]))
+    
+    # Convert to DataFrame
+    metrics_df = pd.DataFrame(metrics_dict)
+    
+    # Round numerical values to 3 decimal places
+    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))
+    
+    # Prepare data for heatmap
+    heatmap_data = metrics_df[metric_cols].values
+    
+    # Draw heatmap
+    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()