Add test to make sure the main metrics class works

tests/main.py

@@ -0,0 +1,8 @@
+from test_metrics import run_test_metrics
+
+def main():
+    """ Runs generation and analysis steps back-to-back. """
+    run_test_metrics()
+
+if __name__ == "__main__":
+    main()

tests/test_metrics.py

@@ -0,0 +1,37 @@
+"""
+This module contains test functions for calibration metrics.
+
+The main test function generates synthetic data and tests the CalibrationMetrics class.
+"""
+
+import numpy as np
+from calzone.metrics import CalibrationMetrics
+
+def run_test_metrics():
+    """
+    Test function for calibration metrics.
+    
+    Generates synthetic binary classification data and tests the CalibrationMetrics class.
+    The test includes:
+    - Generating random binary labels using binomial distribution
+    - Creating probability predictions using beta distribution
+    - Testing the CalibrationMetrics class with the generated data
+    Notice that the test is not exhaustive and doesn't test for correctness of the metrics.
+    Returns:
+        None
+    
+    Raises:
+        AssertionError: If the results from CalibrationMetrics are not in dictionary format
+    """
+    # Generate sample data
+    np.random.seed(123)
+    n_samples = 1000
+    y_true = np.random.binomial(1, 0.3, n_samples)
+    y_proba = np.zeros((n_samples,2))
+    y_proba[:, 1] = np.random.beta(0.5, 0.5, (n_samples, ))
+    y_proba[:, 0] = 1 - y_proba[:, 1]  # Ensure probabilities sum to 1
+
+    # Test CalibrationMetrics class
+    metrics = CalibrationMetrics(class_to_calculate=1)
+    results = metrics.calculate_metrics(y_true, y_proba, metrics='all')
+    assert isinstance(results, dict)