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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() |