import unittest
import numpy as np
from sklearn.model_selection import GridSearchCV

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_train_test_split_exists(self):
        import user_code
        condition = (
            hasattr(user_code, "X_train") and hasattr(user_code, "X_test")
            and hasattr(user_code, "y_train") and hasattr(user_code, "y_test")
        )
        _dynamic_test(
            self,
            condition,
            "Training and test splits are created.",
            "Expected X_train, X_test, y_train, and y_test to be defined."
        )

    def test_cross_val_score_used(self):
        import user_code
        _dynamic_test(
            self,
            hasattr(user_code, "cv_scores") and hasattr(user_code, "cv_mean_score"),
            "Cross-validation scores are computed.",
            "Expected cross-validation scores to be computed."
        )

    def test_cv_mean_score_range(self):
        import user_code
        score = user_code.cv_mean_score
        _dynamic_test(
            self,
            0.0 <= score <= 1.0,
            "The mean cross-validation score is within a valid range.",
            f"Expected `cv_mean_score` to be between 0 and 1, but got {score}."
        )

    def test_grid_search_exists(self):
        import user_code
        _dynamic_test(
            self,
            isinstance(user_code.grid_search, GridSearchCV),
            "The `grid_search` object is created.",
            "Expected `grid_search` to be an instance of `GridSearchCV`."
        )

    def test_best_params_exist(self):
        import user_code
        _dynamic_test(
            self,
            isinstance(user_code.best_params, dict) and len(user_code.best_params) > 0,
            "The best parameters are extracted.",
            "Expected `best_params` to be a non-empty dictionary."
        )

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

test_main.py

Master the scikit-learn library by learning its API, core abstractions, and engineering patterns. Focus on syntax, structure, and workflow to confidently build, compose, and inspect machine learning pipelines using scikit-learn.

Explore the foundational Estimator API, understand fit/predict/transform, and learn the design philosophy behind scikit-learn’s consistent interface.

Dive into transformers, preprocessing objects, and how to build robust data pipelines using scikit-learn’s modular approach.

Master scikit-learn’s pipeline and composition tools to create maintainable, reproducible workflows.

Leverage scikit-learn’s model selection and evaluation tools for robust, reproducible workflows.

Learn to inspect, configure, and refactor scikit-learn objects for robust, maintainable code. Avoid common anti-patterns.

Challenge: Model Selection Workflow

Solution

Challenge: Model Selection Workflow

Solution