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

A comprehensive course for intermediate learners to master the key evaluation metrics used in machine learning, covering both supervised and unsupervised tasks. The course progresses from foundational concepts to practical Python implementations, interpretation of results, and a comparative overview of metric types and their trade-offs.

Explore the fundamental metrics used to evaluate classification models, including their definitions, formulas, Python implementations, and interpretation. Includes advanced metrics and model validation techniques relevant to classification.

Delve into the core metrics for evaluating regression models, including their mathematical foundations, Python implementations, and result interpretation. Includes advanced validation techniques relevant to regression.

Examine the key metrics for evaluating unsupervised learning models, including clustering, dimensionality reduction, and anomaly detection. Learn their mathematical foundations, Python implementations, and interpretation.

Challenge: Regression Metrics

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Challenge: Regression Metrics

Ratkaisu