import unittest
import importlib
import numpy as np
import scipy.spatial.distance as dist
import scipy.cluster.hierarchy as sch


def _dynamic_test(test_case, condition, success_message, failure_message):
    if condition:
        test_case._testMethodName = success_message
        test_case.assertTrue(True, success_message)
    else:
        test_case._testMethodName = failure_message
        test_case.fail(failure_message)


def get_first_differing_index(expected_array, actual_array):
    for i, (val_1, val_2) in enumerate(zip(expected_array, actual_array)):
        if not np.array_equal(val_1, val_2):
            return i, expected_array[i], actual_array[i]

    if len(expected_array) > len(actual_array):
        return len(actual_array), expected_array[len(actual_array)], None
    else:
        return len(expected_array), None, actual_array[len(expected_array)]


class TestUserCode(unittest.TestCase):

    def test_distance_matrix_is_declared(self):
        import user_code
        _dynamic_test(
            self,
            hasattr(user_code, 'distance_matrix'),
            "The `distance_matrix` variable is declared.",
            "Expected `distance_matrix` to be declared."
        )

    def test_linkage_matrix_is_declared(self):
        import user_code
        _dynamic_test(
            self,
            hasattr(user_code, 'linkage_matrix'),
            "The `linkage_matrix` variable is declared.",
            "Expected `linkage_matrix` to be declared."
        )

    def test_distance_matrix_is_correct(self):
        import user_code
        from sklearn.datasets import make_blobs

        data, _ = make_blobs(n_samples=10, centers=3, cluster_std=1.0, random_state=42)
        expected_value = dist.pdist(data, metric='euclidean')

        variable = 'distance_matrix'
        actual_value = getattr(user_code, variable, None)
        condition = False
        if actual_value is None:
            failure_message = f"The `{variable}` variable is not declared."
        elif not isinstance(actual_value, np.ndarray):
            failure_message = f"`{variable}` is not a `numpy.ndarray`."
        elif actual_value.size == 0:
            failure_message = f"`{variable}` is empty."
        elif np.array_equal(actual_value, expected_value):
            condition = True
            failure_message = None
        else:
            idx, expected_element, actual_element = get_first_differing_index(expected_value, actual_value)
            failure_message = f"Expected `{variable}` to contain `{expected_element}` at index {idx}, but got `{actual_element}`."

        _dynamic_test(
            self,
            condition,
            f"`{variable}` contains the correct data.",
            failure_message
        )

    def test_linkage_matrix_is_correct(self):
        import user_code
        from sklearn.datasets import make_blobs

        data, _ = make_blobs(n_samples=10, centers=3, cluster_std=1.0, random_state=42)
        expected_distance = dist.pdist(data, metric='euclidean')
        expected_value = sch.linkage(expected_distance, method='single')

        variable = 'linkage_matrix'
        actual_value = getattr(user_code, variable, None)
        condition = False
        if actual_value is None:
            failure_message = f"The `{variable}` variable is not declared."
        elif not isinstance(actual_value, np.ndarray):
            failure_message = f"`{variable}` is not a `numpy.ndarray`."
        elif actual_value.size == 0:
            failure_message = f"`{variable}` is empty."
        elif np.array_equal(actual_value, expected_value):
            condition = True
            failure_message = None
        else:
            idx, expected_element, actual_element = get_first_differing_index(expected_value, actual_value)
            failure_message = f"Expected `{variable}` to contain `{expected_element}` at index {idx}, but got `{actual_element}`."

        _dynamic_test(
            self,
            condition,
            f"`{variable}` contains the correct data.",
            failure_message
        )


if __name__ == '__main__':
    unittest.main()

test_main.py

Découvrez la puissance des motifs cachés grâce à l'apprentissage non supervisé. Maîtrisez les algorithmes de regroupement les plus influents, notamment K-Means, le regroupement hiérarchique, DBSCAN et les modèles de mélanges gaussiens. Apprenez à évaluer la qualité des clusters à l'aide des scores WSS et Silhouette, à gérer diverses mesures de distance et à mettre en œuvre des solutions robustes sur des ensembles de données réels. Développez les compétences nécessaires pour segmenter les clients et identifier des structures dans des données non étiquetées avec Scikit-learn.

Défi : Mise en œuvre du regroupement hiérarchique

Solution

Défi : Mise en œuvre du regroupement hiérarchique

Solution