Pythonでロジスティック回帰を実装するには、`LogisticRegression`クラスを使用。

現時点では、デフォルトのパラメータをそのまま使用。モデルの作成と学習は1行で実行可能：

```python
logistic_regression = LogisticRegression().fit(X_train, y_train)
```

この章で使用するデータセットは**ポルトガルの銀行機関**から提供されたもので、電話によるマーケティングキャンペーンの情報を含む。目的は、顧客の個人情報、財務情報、連絡先情報、過去のマーケティング活動の結果に基づき、**定期預金に申し込むかどうか**を予測すること。
  
データはすでに前処理済みで、モデルにそのまま入力可能。

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

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

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

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

    def test_X_train_is_correct(self):
        import user_code
        import pandas as pd
        from sklearn.model_selection import train_test_split

        df = pd.read_csv(
            'https://codefinity-content-media.s3.eu-west-1.amazonaws.com/b71ff7ac-3932-41d2-a4d8-060e24b00129/marketing_bank.csv')
        X = df.drop('deposit', axis=1).values
        y = df['deposit'].values
        expected_value, _, _, _ = train_test_split(X, y, test_size=0.2, random_state=42)

        variable = 'X_train'
        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:
            if actual_value.shape != expected_value.shape:
                failure_message = f"The shape of `{variable}` is incorrect."
            else:
                for i in range(actual_value.shape[0]):
                    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}` to contain `{expected_element}` at index [{i}, {idx}], but got `{actual_element}`."
                    break

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

    def test_X_test_is_correct(self):
        import user_code
        import pandas as pd
        from sklearn.model_selection import train_test_split

        df = pd.read_csv(
            'https://codefinity-content-media.s3.eu-west-1.amazonaws.com/b71ff7ac-3932-41d2-a4d8-060e24b00129/marketing_bank.csv')
        X = df.drop('deposit', axis=1).values
        y = df['deposit'].values
        _, expected_value, _, _ = train_test_split(X, y, test_size=0.2, random_state=42)

        variable = 'X_test'
        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:
            if actual_value.shape != expected_value.shape:
                failure_message = f"The shape of `{variable}` is incorrect."
            else:
                for i in range(actual_value.shape[0]):
                    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}` to contain `{expected_element}` at index [{i}, {idx}], but got `{actual_element}`."
                    break

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

    def test_y_train_is_correct(self):
        import user_code
        import pandas as pd
        from sklearn.model_selection import train_test_split

        df = pd.read_csv(
            'https://codefinity-content-media.s3.eu-west-1.amazonaws.com/b71ff7ac-3932-41d2-a4d8-060e24b00129/marketing_bank.csv')
        X = df.drop('deposit', axis=1).values
        y = df['deposit'].values
        _, _, expected_value, _ = train_test_split(X, y, test_size=0.2, random_state=42)

        variable = 'y_train'
        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_y_test_is_correct(self):
        import user_code
        import pandas as pd
        from sklearn.model_selection import train_test_split

        df = pd.read_csv(
            'https://codefinity-content-media.s3.eu-west-1.amazonaws.com/b71ff7ac-3932-41d2-a4d8-060e24b00129/marketing_bank.csv')
        X = df.drop('deposit', axis=1).values
        y = df['deposit'].values
        _, _, _, expected_value = train_test_split(X, y, test_size=0.2, random_state=42)

        variable = 'y_test'
        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_lr_is_declared(self):
        import user_code

        variable = 'lr'
        _dynamic_test(
            self,
            hasattr(user_code, variable),
            f"The `{variable}` variable is declared.",
            f"Expected `{variable}` to be declared."
        )

    def test_lr_is_correct(self):
        import user_code
        import pandas as pd
        from sklearn.linear_model import LogisticRegression
        from sklearn.model_selection import train_test_split

        df = pd.read_csv(
            'https://codefinity-content-media.s3.eu-west-1.amazonaws.com/b71ff7ac-3932-41d2-a4d8-060e24b00129/marketing_bank.csv')
        X = df.drop('deposit', axis=1)
        y = df['deposit']
        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

        expected_value = LogisticRegression().fit(X_train, y_train)

        variable = 'lr'
        actual_value = getattr(user_code, variable, None)
        condition = False
        if actual_value is None:
            failure_message = f"The `{variable}` variable is not declared."
        elif isinstance(actual_value, LogisticRegression):
            if np.array_equal(actual_value.coef_, expected_value.coef_):
                condition = True
                failure_message = None
            else:
                idx, expected_element, actual_element = get_first_differing_index(expected_value.coef_[0], actual_value.coef_[0])
                failure_message = f"Expected the coefficients of the model to contain `{expected_element}` at index {idx}, but got `{actual_element}`."
        else:
            failure_message = f"`{variable}` is not a `LogisticRegression`."

        _dynamic_test(
            self,
            condition,
            f"`{variable}` is a correctly fitted `LogisticRegression`.",
            failure_message
        )

    def test_test_accuracy_declared(self):
        import user_code

        variable = 'test_accuracy'
        _dynamic_test(
            self,
            hasattr(user_code, variable),
            f"The `{variable}` variable is declared.",
            f"Expected `{variable}` to be declared."
        )

    def test_test_accuracy_is_correct(self):
        import user_code
        expected_value = 0.8083296014330497

        variable = 'test_accuracy'
        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, float):
            condition = actual_value == expected_value
            failure_message = f"Expected `{variable}` to be `{expected_value}`, but got `{actual_value}`."
        else:
            condition = False
            failure_message = f"`{variable}` is not a `float`."

        _dynamic_test(
            self,
            condition,
            f"`{variable}` is correct.",
            failure_message
        )


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

test_main.py

Pythonを使用したカテゴリカルな結果を予測するための分類手法を紹介します。分類モデルの構築、トレーニング、評価、決定境界の解釈、一般的なアルゴリズムの実世界データセットへの適用に焦点を当てます。


チャレンジ：ロジスティック回帰の実装

解答