import unittest
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)

class TestUserCode(unittest.TestCase):

    def test_model_exists(self):
        import user_code
        _dynamic_test(
            self,
            hasattr(user_code, "model"),
            "The `model` estimator is created.",
            "Expected a variable named `model` to be defined."
        )

    def test_model_is_fitted(self):
        import user_code
        condition = hasattr(user_code.model, "coef_")
        _dynamic_test(
            self,
            condition,
            "The estimator is fitted using `fit()`.",
            "Expected the estimator to be fitted before prediction."
        )

    def test_predictions_exist(self):
        import user_code
        _dynamic_test(
            self,
            hasattr(user_code, "y_pred"),
            "The `y_pred` variable is defined.",
            "Expected `y_pred` to be defined."
        )

    def test_predictions_shape(self):
        import user_code
        expected_shape = (2,)
        actual_shape = user_code.y_pred.shape
        _dynamic_test(
            self,
            actual_shape == expected_shape,
            "The predictions have the correct shape.",
            f"Expected predictions of shape {expected_shape}, but got {actual_shape}."
        )

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: Apply the Estimator API

Ratkaisu

Challenge: Apply the Estimator API

Ratkaisu