import unittest
import importlib
import numpy as np
import pandas as pd


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)


class TestUserCode(unittest.TestCase):

    def test_age_column_has_no_missing_values(self):
        import user_code

        variable = 'data'
        actual_df = getattr(user_code, variable, None)
        condition = False
        if actual_df is None:
            failure_message = f"The `{variable}` variable is not declared."
        elif not isinstance(actual_df, pd.DataFrame):
            failure_message = f"`{variable}` is not a `DataFrame`."
        elif actual_df.empty:
            failure_message = f"`{variable}` is empty."
        elif 'Age' not in actual_df.columns:
            failure_message = f"`{variable}` has no 'Age' column."
        elif not actual_df['Age'].isnull().any():
            condition = True
            failure_message = None
        else:
            null_count = actual_df['Age'].isnull().sum()
            failure_message = f"Expected the 'Age' column to contain no missing values, but got `{null_count}`."

        _dynamic_test(
            self,
            condition,
            "The 'Age' column contains no missing values.",
            failure_message
        )

    def test_missing_values_are_replaced_correctly(self):
        import user_code

        expected_value = pd.Series([25, 30, 30, 35, 30]).astype(float)

        variable = 'data'
        actual_df = getattr(user_code, variable, None)
        condition = False
        if actual_df is None:
            failure_message = f"The `{variable}` variable is not declared."
        elif not isinstance(actual_df, pd.DataFrame):
            failure_message = f"`{variable}` is not a `DataFrame`."
        elif actual_df.empty:
            failure_message = f"`{variable}` is empty."
        elif 'Age' not in actual_df.columns:
            failure_message = f"`{variable}` has no 'Age' column."
        elif actual_df['Age'].equals(expected_value):
            condition = True
            failure_message = None
        else:
            failure_message = f"Expected `{variable}['Age']` to contain `{expected_value.values}`, but got `{actual_df['Age'].values}`."

        _dynamic_test(
            self,
            condition,
            "The missing values in the 'Age' column are correctly replaced with the mean.",
            failure_message
        )

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

    def test_city_encoder_is_onehotencoder(self):
        import user_code
        from sklearn.preprocessing import OneHotEncoder

        try:
            condition = isinstance(user_code.city_encoder, OneHotEncoder)
            failure_message = f"Expected `city_encoder` to be an instance of `OneHotEncoder`, but got `{type(user_code.city_encoder).__name__}`."
        except AttributeError:
            condition = False
            failure_message = "The `city_encoder` variable is not declared."

        _dynamic_test(
            self,
            condition,
            "The `city_encoder` is an instance of `OneHotEncoder`.",
            failure_message
        )

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

    def test_city_encoded_is_correct(self):
        import user_code
        from sklearn.preprocessing import OneHotEncoder
        from scipy.sparse import csr_matrix

        data = pd.DataFrame({
            'Age': [25, np.nan, 30, 35, np.nan],
            'City': ['New York', 'London', 'Paris', 'Berlin', 'London'],
            'Income': ['Low', 'Middle', 'High', 'High', 'Middle']
        })

        city_encoder = OneHotEncoder(drop='first')
        expected_value = city_encoder.fit_transform(data[['City']]).toarray()

        variable = 'city_encoded'
        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, csr_matrix):
            failure_message = f"`{variable}` is not a `scipy.sparse.csr_matrix`."
        elif not actual_value.size:
            failure_message = f"`{variable}` is empty."
        elif np.array_equal(actual_value.toarray(), expected_value):
            condition = True
            failure_message = None
        else:
            failure_message = f"Expected `{variable}` to contain `{expected_value}`, but got `{actual_value.toarray()}`."

        _dynamic_test(
            self,
            condition,
            f"The 'City' column is encoded correctly.",
            failure_message
        )

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

    def test_income_encoder_is_ordinalencoder(self):
        import user_code
        from sklearn.preprocessing import OrdinalEncoder

        try:
            condition = isinstance(user_code.income_encoder, OrdinalEncoder)
            failure_message = f"Expected `income_encoder` to be an instance of `OrdinalEncoder`, but got `{type(user_code.income_encoder).__name__}`."
        except AttributeError:
            condition = False
            failure_message = "The `income_encoder` variable is not declared."

        _dynamic_test(
            self,
            condition,
            "The `income_encoder` is an instance of `OrdinalEncoder`.",
            failure_message
        )

    def test_income_column_is_encoded_correctly(self):
        import user_code

        expected_value = pd.Series([0, 1, 2, 2, 1]).astype(float)

        variable = 'data'
        actual_df = getattr(user_code, variable, None)
        condition = False
        if actual_df is None:
            failure_message = f"The `{variable}` variable is not declared."
        elif not isinstance(actual_df, pd.DataFrame):
            failure_message = f"`{variable}` is not a `DataFrame`."
        elif actual_df.empty:
            failure_message = f"`{variable}` is empty."
        elif 'Income' not in actual_df.columns:
            failure_message = f"`{variable}` has no 'Income' column."
        elif actual_df['Income'].equals(expected_value):
            condition = True
            failure_message = None
        else:
            failure_message = f"Expected `{variable}['Income']` to contain `{expected_value.values}`, but got `{actual_df['Income'].values}`."

        _dynamic_test(
            self,
            condition,
            "The 'Income' column is encoded correctly.",
            failure_message
        )


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


test_main.py

Explora el poder de los patrones ocultos con el aprendizaje no supervisado. Domina los algoritmos de agrupamiento más influyentes, incluidos K-Means, Clustering Jerárquico, DBSCAN y Modelos de Mezcla Gaussiana. Aprende a evaluar la calidad de los clústeres utilizando WSS y puntuaciones de Silhouette, manejar diversas medidas de distancia e implementar soluciones robustas en conjuntos de datos del mundo real. Desarrolla habilidades para segmentar clientes y descubrir estructuras en datos no etiquetados utilizando Scikit-learn.

Desafío: Preprocesamiento del Conjunto de Datos

Solución

Desafío: Preprocesamiento del Conjunto de Datos

Solución