import unittest
import importlib
import numpy as np


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

    def test_dbscan_is_correct(self):
        import user_code
        from sklearn.cluster import DBSCAN

        variable = 'dbscan'
        actual_value = getattr(user_code, variable, None)
        if actual_value is None:
            condition = False
            failure_message = f"The `{variable}` variable is not declared."
        elif isinstance(actual_value, DBSCAN):
            condition = actual_value.eps == 0.3 and actual_value.min_samples == 6
            failure_message = f"Expected `{variable}` to be a `DBSCAN` with `eps=0.3` and `min_samples=6`, but got `eps={actual_value.eps}` and `min_samples={actual_value.min_samples}`."
        else:
            condition = False
            failure_message = f"`{variable}` is not a `DBSCAN`."

        _dynamic_test(
            self,
            condition,
            "`dbscan` is a `DBSCAN` with `eps=0.3`, `min_samples=6`, and `random_state=42`.",
            failure_message
        )

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

    def test_labels_is_correct(self):
        import user_code

        from sklearn.datasets import make_blobs
        from sklearn.cluster import DBSCAN
        from sklearn.preprocessing import StandardScaler

        data, _ = make_blobs(n_samples=300, centers=3, cluster_std=0.5, random_state=42)
        transformation_matrix = np.array([[0.6, -0.6], [0.4, 0.8]])
        data = np.dot(data, transformation_matrix)
        data = StandardScaler().fit_transform(data)
        dbscan = DBSCAN(eps=0.3, min_samples=6)
        expected_value = dbscan.fit_predict(data)

        variable = 'labels'
        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_cluster_points_variable_is_declared(self):
        import user_code
        _dynamic_test(
            self,
            hasattr(user_code, 'cluster_points'),
            "The `cluster_points` variable is declared.",
            "Expected `cluster_points` to be declared."
        )

    def test_clusters_is_correct(self):
        import user_code

        from sklearn.datasets import make_blobs
        from sklearn.cluster import DBSCAN
        from sklearn.preprocessing import StandardScaler

        data, _ = make_blobs(n_samples=300, centers=3, cluster_std=0.5, random_state=42)
        transformation_matrix = np.array([[0.6, -0.6], [0.4, 0.8]])
        data = np.dot(data, transformation_matrix)
        data = StandardScaler().fit_transform(data)
        dbscan = DBSCAN(eps=0.3, min_samples=6)
        labels = dbscan.fit_predict(data)
        unique_labels = set(labels)
        clusters = []

        for label in unique_labels:
            cluster_points = data[labels == label]
            clusters.append(cluster_points)

        expected_value = clusters

        variable = 'clusters'
        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, list):
            failure_message = f"`{variable}` is not a `list`."
        elif not actual_value:
            failure_message = f"`{variable}` is empty."
        elif all(np.array_equal(a, b) for a, b in zip(actual_value, expected_value)):
            condition = True
            failure_message = None
        elif len(actual_value) != 3:
            failure_message = f"Expected `{variable}` to contain `3` arrays, but got `{len(actual_value)}`."
        else:
            for i in range(len(actual_value)):
                if isinstance(actual_value[i], np.ndarray):
                    if np.array_equal(actual_value[i], expected_value[i]):
                        continue
                    idx, expected_element, actual_element = get_first_differing_index(expected_value[i], actual_value[i])
                    failure_message = f"Expected `{variable}[{i}]` to contain `{expected_element}` at index {idx}, but got `{actual_element}`."
                else:
                    failure_message = f"`{variable}[{i}]` is not a `numpy.ndarray`."
                break

        _dynamic_test(
            self,
            condition,
            "`cluster_points` are correctly selected from `data` using `labels`.",
            failure_message
        )


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

test_main.py

Entdecken Sie die Kraft verborgener Muster mit unüberwachtem Lernen. Beherrschen Sie die einflussreichsten Clustering-Algorithmen, darunter K-Means, hierarchisches Clustering, DBSCAN und Gaußsche Mischmodelle. Lernen Sie, die Clusterqualität mit WSS- und Silhouette-Werten zu bewerten, verschiedene Distanzmaße zu handhaben und robuste Lösungen auf realen Datensätzen zu implementieren. Entwickeln Sie Fähigkeiten zur Kundensegmentierung und zur Entdeckung von Strukturen in nicht gelabelten Daten mit Scikit-learn.

Herausforderung: Implementierung von DBSCAN

Lösung

Herausforderung: Implementierung von DBSCAN

Lösung