Create catboost_regressor.py

Author: AHuzail

machine_learning/catboost_regressor.py

@@ -0,0 +1,83 @@
+# CatBoost Classifier Example
+import numpy as np
+from matplotlib import pyplot as plt
+from sklearn.datasets import load_iris
+from sklearn.metrics import ConfusionMatrixDisplay
+from sklearn.model_selection import train_test_split
+from catboost import CatBoostClassifier
+
+
+def data_handling(data: dict) -> tuple:
+    """
+    Extracts the features and target values from the provided dataset.
+
+    Args:
+        data (dict): A dictionary containing the dataset's features and targets.
+
+    Returns:
+        tuple: A tuple with features and targets.
+
+    Example:
+    >>> data_handling({'data':'[5.1, 3.5, 1.4, 0.2]', 'target': [0]})
+    ('[5.1, 3.5, 1.4, 0.2]', [0])
+    """
+    return data["data"], data["target"]
+
+
+def catboost(features: np.ndarray, target: np.ndarray) -> CatBoostClassifier:
+    """
+    Trains a CatBoostClassifier using the provided features and target.
+
+    Args:
+        features (np.ndarray): The input features for training the classifier.
+        target (np.ndarray): The target labels corresponding to the features.
+
+    Returns:
+        CatBoostClassifier: A trained CatBoost classifier.
+
+    Example:
+    >>> catboost(np.array([[5.1, 3.6, 1.4, 0.2]]), np.array([0]))
+    CatBoostClassifier(...)
+    """
+    classifier = CatBoostClassifier(verbose=0)  # Suppressing verbose output
+    classifier.fit(features, target)
+    return classifier
+
+
+def main() -> None:
+    """
+    Demonstrates the training and evaluation of a CatBoost classifier
+    on the Iris dataset, displaying a confusion matrix of the results.
+
+    The dataset is split into training and testing sets, the model is
+    trained on the training data, and then evaluated on the test data.
+    A normalized confusion matrix is displayed.
+    """
+
+    # Load the Iris dataset
+    iris = load_iris()
+    features, targets = data_handling(iris)
+    x_train, x_test, y_train, y_test = train_test_split(
+        features, targets, test_size=0.25
+    )
+
+    # Train a CatBoost classifier
+    catboost_classifier = catboost(x_train, y_train)
+
+    # Display the confusion matrix for the test data
+    ConfusionMatrixDisplay.from_estimator(
+        catboost_classifier,
+        x_test,
+        y_test,
+        display_labels=iris["target_names"],
+        cmap="Blues",
+        normalize="true",
+    )
+    plt.title("Normalized Confusion Matrix - IRIS Dataset")
+    plt.show()
+
+
+if __name__ == "__main__":
+    import doctest
+    doctest.testmod(verbose=True)
+    main()