import unittest
import importlib
import numpy as np
from sklearn.datasets import load_diabetes
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score

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

class TestRegressionMetrics(unittest.TestCase):
    @classmethod
    def setUpClass(cls):
        cls.user = importlib.import_module("user_code")

        data = load_diabetes()
        X_train, X_test, y_train, y_test = train_test_split(
            data.data, data.target, test_size=0.3, random_state=42
        )

        model = LinearRegression()
        model.fit(X_train, y_train)
        y_pred = model.predict(X_test)

        cls.true_metrics = {
            "mse": mean_squared_error(y_test, y_pred),
            "rmse": np.sqrt(mean_squared_error(y_test, y_pred)),
            "mae": mean_absolute_error(y_test, y_pred),
            "r2": r2_score(y_test, y_pred),
            "mean_cv_r2": cross_val_score(model, data.data, data.target, cv=5, scoring="r2").mean(),
        }

    def test_mse(self):
        cond = abs(self.user.mse - self.true_metrics["mse"]) < 1e-6
        _dynamic_test(self, cond, "MSE computed correctly.", "MSE is incorrect.")

    def test_rmse(self):
        cond = abs(self.user.rmse - self.true_metrics["rmse"]) < 1e-6
        _dynamic_test(self, cond, "RMSE computed correctly.", "RMSE is incorrect.")

    def test_mae(self):
        cond = abs(self.user.mae - self.true_metrics["mae"]) < 1e-6
        _dynamic_test(self, cond, "MAE computed correctly.", "MAE is incorrect.")

    def test_r2(self):
        cond = abs(self.user.r2 - self.true_metrics["r2"]) < 1e-6
        _dynamic_test(self, cond, "R² computed correctly.", "R² is incorrect.")

    def test_cross_val(self):
        cond = abs(self.user.mean_cv_r2 - self.true_metrics["mean_cv_r2"]) < 1e-6
        _dynamic_test(self, cond, "Cross-validation mean R² computed correctly.", "Cross-validation mean R² is incorrect.")

if __name__ == "__main__":
    unittest.main(argv=["first-arg-is-ignored"], exit=False)

test_main.py

Pythonを用いた適切なパフォーマンス指標による教師あり学習モデルの評価に焦点を当てています。回帰および分類の結果を評価する方法、モデルを客観的に比較する方法、指標値を解釈して情報に基づいたモデリングの意思決定を行う方法について説明します。


チャレンジ：回帰指標

解答