In this challenge, build and evaluate a model using both the train-test split and cross-validation on the preprocessed **penguins dataset**.

The following functions will be useful:

* `cross_val_score()` from `sklearn.model_selection`;
* `train_test_split()` from `sklearn.model_selection`;
* `.fit()` and `.score()` methods of the model.


import unittest
import pandas as pd
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 TestKNNWithCVAndSplit(unittest.TestCase):

    @classmethod
    def setUpClass(cls):
        cls.df = pd.read_csv(
            'https://codefinity-content-media.s3.eu-west-1.amazonaws.com/a65bbc96-309e-4df9-a790-a1eb8c815a1c/penguins_pipelined.csv'
        )
        cls.X, cls.y = cls.df.drop('species', axis=1), cls.df['species']
        import user_code
        cls.uc = user_code  # Ð¾ÑÑÐºÑÑÑÑÑÑ: model, scores, X_train, X_test, y_train, y_test

    def test_model_is_knn_with_4_neighbors(self):
        from sklearn.neighbors import KNeighborsClassifier
        uc = self.uc
        cond = isinstance(getattr(uc, 'model', None), KNeighborsClassifier) and \
               getattr(uc.model, 'n_neighbors', None) == 4
        _dynamic_test(
            self,
            cond,
            "Model is KNeighborsClassifier with n_neighbors=4",
            "Model must be KNeighborsClassifier with n_neighbors=4"
        )

    def test_cross_val_scores_len_and_cv3(self):
        # ÐÐµÑÐµÐ²ÑÑÑÑÐ¼Ð¾, ÑÐ¾ Ð¾ÑÑÐ¸Ð¼Ð°Ð½Ð¾ ÑÑÐ²Ð½Ð¾ 3 Ð¾ÑÑÐ½ÐºÐ¸ Ñ Ð²Ð¾Ð½Ð¸ ÑÑÐ¼ÑÑÐ½Ñ Ð· cv=3 Ð´Ð»Ñ 4-NN
        from sklearn.model_selection import cross_val_score
        from sklearn.neighbors import KNeighborsClassifier
        uc = self.uc
        scores = getattr(uc, 'scores', None)
        try:
            ok_len = hasattr(scores, '__len__') and len(scores) == 3
            ref = cross_val_score(KNeighborsClassifier(n_neighbors=4), self.X, self.y, cv=3)
            ok_mean_close = np.isfinite(np.mean(scores)).item() and np.isclose(np.mean(scores), np.mean(ref), rtol=0, atol=1e-6)
            cond = ok_len and ok_mean_close
        except Exception:
            cond = False
        _dynamic_test(
            self,
            cond,
            "Cross-validation produced 3-fold scores with cv=3",
            "Cross-validation must use cv=3 and produce 3 scores"
        )

    def test_train_test_split_shapes(self):
        uc = self.uc
        has_vars = all(hasattr(uc, name) for name in ['X_train', 'X_test', 'y_train', 'y_test'])
        if has_vars:
            n_total = len(self.X)
            n_test = len(uc.X_test)
            # ÐÐ¾Ð¿ÑÑÐºÐ°ÑÐ¼Ð¾ Ð¿Ð¾ÑÐ¸Ð±ÐºÑ Ð¾ÐºÑÑÐ³Ð»ÐµÐ½Ð½Ñ Ð¿ÑÐ¸ test_size=0.33
            expected_test = int(round(0.33 * n_total))
            cond = uc.X_train.shape[0] + uc.X_test.shape[0] == n_total and \
                   uc.y_train.shape[0] + uc.y_test.shape[0] == n_total and \
                   abs(n_test - expected_test) <= 2
        else:
            cond = False
        _dynamic_test(
            self,
            cond,
            "Data was split into train and test with approximately 33% for test",
            "Data must be split into train and test with approximately 33% for test"
        )

    def test_model_fitted_and_predicts_on_test(self):
        uc = self.uc
        try:
            y_pred = uc.model.predict(uc.X_test)
            cond = isinstance(y_pred, (np.ndarray, list)) and len(y_pred) == len(uc.y_test)
        except Exception:
            cond = False
        _dynamic_test(
            self,
            cond,
            "Model is fitted and can predict on X_test",
            "Model must be fitted and able to predict on X_test"
        )

    def test_test_score_available(self):
        uc = self.uc
        try:
            score = uc.model.score(uc.X_test, uc.y_test)
            cond = isinstance(score, (float, np.floating)) and np.isfinite(score)
        except Exception:
            cond = False
        _dynamic_test(
            self,
            cond,
            "Test score is available via model.score(X_test, y_test)",
            "Test score must be computed via model.score(X_test, y_test)"
        )

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


test_code.py

Machine learning drives modern technological innovation across all industries. Embark on a comprehensive introduction to predictive modeling by mastering foundational algorithmic concepts utilizing Scikit-Learn. Participants will construct robust classifiers, evaluate performance metrics, ultimately culminating in the development of a complete predictive project.

Challenge: Evaluating the Model with Cross-Validation

Solution