import sklearn.metrics
from sklearn.linear_model import RidgeClassifier

from autosklearn.pipeline.components.feature_preprocessing.kernel_pca import KernelPCA
from autosklearn.pipeline.util import (
    PreprocessingTestCase,
    _test_preprocessing,
    get_dataset,
)

import unittest


class KernelPCAComponentTest(PreprocessingTestCase):
    def test_default_configuration(self):
        transformation, original = _test_preprocessing(
            KernelPCA, dataset="digits", train_size_maximum=2000
        )
        self.assertEqual(transformation.shape[0], original.shape[0])
        self.assertFalse((transformation == 0).all())

    def test_default_configuration_sparse(self):
        transformation, original = _test_preprocessing(
            KernelPCA, make_sparse=True, dataset="digits"
        )
        self.assertEqual(transformation.shape[0], original.shape[0])
        self.assertFalse((transformation == 0).all())

    def test_default_configuration_classify(self):
        for i in range(5):
            X_train, Y_train, X_test, Y_test = get_dataset(
                dataset="digits", make_sparse=False, train_size_maximum=1000
            )
            configuration_space = KernelPCA.get_hyperparameter_search_space()
            default = configuration_space.get_default_configuration()
            preprocessor = KernelPCA(
                random_state=1,
                **{
                    hp_name: default[hp_name]
                    for hp_name in default
                    if default[hp_name] is not None
                },
            )
            preprocessor.fit(X_train, Y_train)
            X_train_trans = preprocessor.transform(X_train)
            X_test_trans = preprocessor.transform(X_test)

            # fit a classifier on top
            classifier = RidgeClassifier(random_state=1)
            predictor = classifier.fit(X_train_trans, Y_train)
            predictions = predictor.predict(X_test_trans)
            accuracy = sklearn.metrics.accuracy_score(predictions, Y_test)
            self.assertAlmostEqual(accuracy, 0.6775407779171895)

    @unittest.skip("Always returns float64")
    def test_preprocessing_dtype(self):
        super(KernelPCAComponentTest, self)._test_preprocessing_dtype(KernelPCA)
