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"""Module for calculating and returning various machine learning evaluation metrics."""
from sklearn.metrics import accuracy_score, r2_score, confusion_matrix, mean_squared_error, precision_score, recall_score, f1_score
import numpy as np
import logging
# Configure logging for this module
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
def classification_metrics(y_true, y_pred):
"""Calculates common classification metrics.
Args:
y_true (array-like): True labels.
y_pred (array-like): Predicted labels.
Returns:
dict: A dictionary containing accuracy, precision, recall, F1-score, and confusion matrix.
"""
logging.info("Calculating classification metrics.")
try:
metrics = {
"Accuracy": accuracy_score(y_true, y_pred),
"Precision": precision_score(y_true, y_pred, average='weighted', zero_division=0),
"Recall": recall_score(y_true, y_pred, average='weighted', zero_division=0),
"F1 Score": f1_score(y_true, y_pred, average='weighted', zero_division=0),
"Confusion Matrix": confusion_matrix(y_true, y_pred).tolist()
}
logging.info("Classification metrics calculated successfully.")
return metrics
except Exception as e:
logging.error(f"Error calculating classification metrics: {e}", exc_info=True)
return {"error": f"Failed to calculate classification metrics: {e}"}
def regression_metrics(y_true, y_pred):
"""Calculates common regression metrics.
Args:
y_true (array-like): True target values.
y_pred (array-like): Predicted target values.
Returns:
dict: A dictionary containing R2 score, Mean Squared Error (MSE), and Root Mean Squared Error (RMSE).
"""
logging.info("Calculating regression metrics.")
try:
metrics = {
"R2 Score": r2_score(y_true, y_pred),
"MSE": mean_squared_error(y_true, y_pred),
"RMSE": np.sqrt(mean_squared_error(y_true, y_pred))
}
logging.info("Regression metrics calculated successfully.")
return metrics
except Exception as e:
logging.error(f"Error calculating regression metrics: {e}", exc_info=True)
return {"error": f"Failed to calculate regression metrics: {e}"}
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