add pipeline test

plotsandgraphs/binary_classifier.py

@@ -354,16 +354,16 @@ def plot_roc_curve(
     return fig
 
 
-def plot_calibration_curve(y_prob: np.ndarray, y_true: np.ndarray, n_bins=10, save_fig_path=None) -> Figure:
+def plot_calibration_curve(y_true: np.ndarray, y_score: np.ndarray, n_bins=10, save_fig_path=None) -> Figure:
     """
     Creates calibration plot for a binary classifier and calculates the ECE.
 
     Parameters
     ----------
-    y_prob : np.ndarray
-        The output probabilities of the classifier. Between 0 and 1.
     y_true : np.ndarray
         The actual labels of the data. Either 0 or 1.
+    y_score : np.ndarray
+        The output probabilities of the classifier. Between 0 and 1.
     n_bins : int
         The number of bins to use for the calibration curve.
     save_fig_path : str, optional
@@ -376,13 +376,13 @@ def plot_calibration_curve(y_prob: np.ndarray, y_true: np.ndarray, n_bins=10, sa
     ece : float
         The expected calibration error.
     """
-    prob_true, prob_pred = calibration_curve(y_true, y_prob, n_bins=n_bins, strategy="uniform")
+    prob_true, prob_pred = calibration_curve(y_true, y_score, n_bins=n_bins, strategy="uniform")
 
     # Find the number of samples in each bin
-    bin_counts = np.histogram(y_prob, bins=n_bins, range=(0, 1))[0]
+    bin_counts = np.histogram(y_score, bins=n_bins, range=(0, 1))[0]
 
     # Calculate the weighted absolute difference (ECE)
-    ece = np.abs(prob_pred - prob_true) * (bin_counts / len(y_prob))
+    ece = np.abs(prob_pred - prob_true) * (bin_counts / len(y_score))
     ece = ece.sum().round(2)
 
     fig = plt.figure(figsize=(5, 5))

tests/test_pipeline.py

@@ -0,0 +1,23 @@
+from pathlib import Path
+from typing import Tuple
+import numpy as np
+import pytest
+
+from plotsandgraphs import pipeline
+
+
+from .utils import random_data_binary_classifier
+
+TEST_RESULTS_PATH = Path("tests/test_results/pipeline")
+
+def test_binary_classification_pipeline(random_data_binary_classifier):
+    """
+    Test binary classification pipeline.
+
+    Parameters
+    ----------
+    random_data_binary_classifier : Tuple[np.ndarray, np.ndarray]
+        The simulated data.
+    """
+    y_true, y_score = random_data_binary_classifier
+    pipeline.binary_classifier(y_true, y_score, save_fig_path=TEST_RESULTS_PATH / "pipeline.png")

tests/utils.py

@@ -0,0 +1,25 @@
+from typing import Tuple
+import numpy as np
+import pytest
+
+
+@pytest.fixture(scope="module")
+def random_data_binary_classifier() -> Tuple[np.ndarray, np.ndarray]:
+    """
+    Create random data for binary classifier tests.
+
+    Returns
+    -------
+    Tuple[np.ndarray, np.ndarray]
+        The simulated data. y_true, y_score
+    """
+    # create some data
+    n_samples = 1000
+    y_true = np.random.choice(
+        [0, 1], n_samples, p=[0.4, 0.6]
+    )  # the true class labels 0 or 1, with class imbalance 40:60
+
+    y_score = np.zeros(y_true.shape)  # a model's probability of class 1 predictions
+    y_score[y_true == 1] = np.random.beta(1, 0.6, y_score[y_true == 1].shape)
+    y_score[y_true == 0] = np.random.beta(0.5, 1, y_score[y_true == 0].shape)
+    return y_true, y_score