"""
Testing Recursive feature elimination
"""

from operator import attrgetter
import pytest
import numpy as np
from numpy.testing import assert_array_almost_equal, assert_array_equal
from scipy import sparse

from sklearn.feature_selection import RFE, RFECV
from sklearn.datasets import load_iris, make_friedman1
from sklearn.metrics import zero_one_loss
from sklearn.svm import SVC, SVR, LinearSVR
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import GroupKFold
from sklearn.compose import TransformedTargetRegressor
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler

from sklearn.utils import check_random_state
from sklearn.utils._testing import ignore_warnings

from sklearn.metrics import make_scorer
from sklearn.metrics import get_scorer


class MockClassifier:
    """
    Dummy classifier to test recursive feature elimination
    """

    def __init__(self, foo_param=0):
        self.foo_param = foo_param

    def fit(self, X, y):
        assert len(X) == len(y)
        self.coef_ = np.ones(X.shape[1], dtype=np.float64)
        return self

    def predict(self, T):
        return T.shape[0]

    predict_proba = predict
    decision_function = predict
    transform = predict

    def score(self, X=None, y=None):
        return 0.

    def get_params(self, deep=True):
        return {'foo_param': self.foo_param}

    def set_params(self, **params):
        return self

    def _get_tags(self):
        return {}


def test_rfe_features_importance():
    generator = check_random_state(0)
    iris = load_iris()
    X = np.c_[iris.data, generator.normal(size=(len(iris.data), 6))]
    y = iris.target

    clf = RandomForestClassifier(n_estimators=20,
                                 random_state=generator, max_depth=2)
    rfe = RFE(estimator=clf, n_features_to_select=4, step=0.1)
    rfe.fit(X, y)
    assert len(rfe.ranking_) == X.shape[1]

    clf_svc = SVC(kernel="linear")
    rfe_svc = RFE(estimator=clf_svc, n_features_to_select=4, step=0.1)
    rfe_svc.fit(X, y)

    # Check if the supports are equal
    assert_array_equal(rfe.get_support(), rfe_svc.get_support())


def test_rfe():
    generator = check_random_state(0)
    iris = load_iris()
    X = np.c_[iris.data, generator.normal(size=(len(iris.data), 6))]
    X_sparse = sparse.csr_matrix(X)
    y = iris.target

    # dense model
    clf = SVC(kernel="linear")
    rfe = RFE(estimator=clf, n_features_to_select=4, step=0.1)
    rfe.fit(X, y)
    X_r = rfe.transform(X)
    clf.fit(X_r, y)
    assert len(rfe.ranking_) == X.shape[1]

    # sparse model
    clf_sparse = SVC(kernel="linear")
    rfe_sparse = RFE(estimator=clf_sparse, n_features_to_select=4, step=0.1)
    rfe_sparse.fit(X_sparse, y)
    X_r_sparse = rfe_sparse.transform(X_sparse)

    assert X_r.shape == iris.data.shape
    assert_array_almost_equal(X_r[:10], iris.data[:10])

    assert_array_almost_equal(rfe.predict(X), clf.predict(iris.data))
    assert rfe.score(X, y) == clf.score(iris.data, iris.target)
    assert_array_almost_equal(X_r, X_r_sparse.toarray())


def test_rfe_mockclassifier():
    generator = check_random_state(0)
    iris = load_iris()
    X = np.c_[iris.data, generator.normal(size=(len(iris.data), 6))]
    y = iris.target

    # dense model
    clf = MockClassifier()
    rfe = RFE(estimator=clf, n_features_to_select=4, step=0.1)
    rfe.fit(X, y)
    X_r = rfe.transform(X)
    clf.fit(X_r, y)
    assert len(rfe.ranking_) == X.shape[1]
    assert X_r.shape == iris.data.shape


def test_rfecv():
    generator = check_random_state(0)
    iris = load_iris()
    X = np.c_[iris.data, generator.normal(size=(len(iris.data), 6))]
    y = list(iris.target)   # regression test: list should be supported

    # Test using the score function
    rfecv = RFECV(estimator=SVC(kernel="linear"), step=1)
    rfecv.fit(X, y)
    # non-regression test for missing worst feature:
    assert len(rfecv.grid_scores_) == X.shape[1]
    assert len(rfecv.ranking_) == X.shape[1]
    X_r = rfecv.transform(X)

    # All the noisy variable were filtered out
    assert_array_equal(X_r, iris.data)

    # same in sparse
    rfecv_sparse = RFECV(estimator=SVC(kernel="linear"), step=1)
    X_sparse = sparse.csr_matrix(X)
    rfecv_sparse.fit(X_sparse, y)
    X_r_sparse = rfecv_sparse.transform(X_sparse)
    assert_array_equal(X_r_sparse.toarray(), iris.data)

    # Test using a customized loss function
    scoring = make_scorer(zero_one_loss, greater_is_better=False)
    rfecv = RFECV(estimator=SVC(kernel="linear"), step=1, scoring=scoring)
    ignore_warnings(rfecv.fit)(X, y)
    X_r = rfecv.transform(X)
    assert_array_equal(X_r, iris.data)

    # Test using a scorer
    scorer = get_scorer('accuracy')
    rfecv = RFECV(estimator=SVC(kernel="linear"), step=1, scoring=scorer)
    rfecv.fit(X, y)
    X_r = rfecv.transform(X)
    assert_array_equal(X_r, iris.data)

    # Test fix on grid_scores
    def test_scorer(estimator, X, y):
        return 1.0
    rfecv = RFECV(estimator=SVC(kernel="linear"), step=1, scoring=test_scorer)
    rfecv.fit(X, y)
    assert_array_equal(rfecv.grid_scores_, np.ones(len(rfecv.grid_scores_)))
    # In the event of cross validation score ties, the expected behavior of
    # RFECV is to return the FEWEST features that maximize the CV score.
    # Because test_scorer always returns 1.0 in this example, RFECV should
    # reduce the dimensionality to a single feature (i.e. n_features_ = 1)
    assert rfecv.n_features_ == 1

    # Same as the first two tests, but with step=2
    rfecv = RFECV(estimator=SVC(kernel="linear"), step=2)
    rfecv.fit(X, y)
    assert len(rfecv.grid_scores_) == 6
    assert len(rfecv.ranking_) == X.shape[1]
    X_r = rfecv.transform(X)
    assert_array_equal(X_r, iris.data)

    rfecv_sparse = RFECV(estimator=SVC(kernel="linear"), step=2)
    X_sparse = sparse.csr_matrix(X)
    rfecv_sparse.fit(X_sparse, y)
    X_r_sparse = rfecv_sparse.transform(X_sparse)
    assert_array_equal(X_r_sparse.toarray(), iris.data)

    # Verifying that steps < 1 don't blow up.
    rfecv_sparse = RFECV(estimator=SVC(kernel="linear"), step=.2)
    X_sparse = sparse.csr_matrix(X)
    rfecv_sparse.fit(X_sparse, y)
    X_r_sparse = rfecv_sparse.transform(X_sparse)
    assert_array_equal(X_r_sparse.toarray(), iris.data)


def test_rfecv_mockclassifier():
    generator = check_random_state(0)
    iris = load_iris()
    X = np.c_[iris.data, generator.normal(size=(len(iris.data), 6))]
    y = list(iris.target)   # regression test: list should be supported

    # Test using the score function
    rfecv = RFECV(estimator=MockClassifier(), step=1)
    rfecv.fit(X, y)
    # non-regression test for missing worst feature:
    assert len(rfecv.grid_scores_) == X.shape[1]
    assert len(rfecv.ranking_) == X.shape[1]


def test_rfecv_verbose_output():
    # Check verbose=1 is producing an output.
    from io import StringIO
    import sys
    sys.stdout = StringIO()

    generator = check_random_state(0)
    iris = load_iris()
    X = np.c_[iris.data, generator.normal(size=(len(iris.data), 6))]
    y = list(iris.target)

    rfecv = RFECV(estimator=SVC(kernel="linear"), step=1, verbose=1)
    rfecv.fit(X, y)

    verbose_output = sys.stdout
    verbose_output.seek(0)
    assert len(verbose_output.readline()) > 0


def test_rfecv_grid_scores_size():
    generator = check_random_state(0)
    iris = load_iris()
    X = np.c_[iris.data, generator.normal(size=(len(iris.data), 6))]
    y = list(iris.target)   # regression test: list should be supported

    # Non-regression test for varying combinations of step and
    # min_features_to_select.
    for step, min_features_to_select in [[2, 1], [2, 2], [3, 3]]:
        rfecv = RFECV(estimator=MockClassifier(), step=step,
                      min_features_to_select=min_features_to_select)
        rfecv.fit(X, y)

        score_len = np.ceil(
            (X.shape[1] - min_features_to_select) / step) + 1
        assert len(rfecv.grid_scores_) == score_len
        assert len(rfecv.ranking_) == X.shape[1]
        assert rfecv.n_features_ >= min_features_to_select


def test_rfe_estimator_tags():
    rfe = RFE(SVC(kernel='linear'))
    assert rfe._estimator_type == "classifier"
    # make sure that cross-validation is stratified
    iris = load_iris()
    score = cross_val_score(rfe, iris.data, iris.target)
    assert score.min() > .7


def test_rfe_min_step():
    n_features = 10
    X, y = make_friedman1(n_samples=50, n_features=n_features, random_state=0)
    n_samples, n_features = X.shape
    estimator = SVR(kernel="linear")

    # Test when floor(step * n_features) <= 0
    selector = RFE(estimator, step=0.01)
    sel = selector.fit(X, y)
    assert sel.support_.sum() == n_features // 2

    # Test when step is between (0,1) and floor(step * n_features) > 0
    selector = RFE(estimator, step=0.20)
    sel = selector.fit(X, y)
    assert sel.support_.sum() == n_features // 2

    # Test when step is an integer
    selector = RFE(estimator, step=5)
    sel = selector.fit(X, y)
    assert sel.support_.sum() == n_features // 2


def test_number_of_subsets_of_features():
    # In RFE, 'number_of_subsets_of_features'
    # = the number of iterations in '_fit'
    # = max(ranking_)
    # = 1 + (n_features + step - n_features_to_select - 1) // step
    # After optimization #4534, this number
    # = 1 + np.ceil((n_features - n_features_to_select) / float(step))
    # This test case is to test their equivalence, refer to #4534 and #3824

    def formula1(n_features, n_features_to_select, step):
        return 1 + ((n_features + step - n_features_to_select - 1) // step)

    def formula2(n_features, n_features_to_select, step):
        return 1 + np.ceil((n_features - n_features_to_select) / float(step))

    # RFE
    # Case 1, n_features - n_features_to_select is divisible by step
    # Case 2, n_features - n_features_to_select is not divisible by step
    n_features_list = [11, 11]
    n_features_to_select_list = [3, 3]
    step_list = [2, 3]
    for n_features, n_features_to_select, step in zip(
            n_features_list, n_features_to_select_list, step_list):
        generator = check_random_state(43)
        X = generator.normal(size=(100, n_features))
        y = generator.rand(100).round()
        rfe = RFE(estimator=SVC(kernel="linear"),
                  n_features_to_select=n_features_to_select, step=step)
        rfe.fit(X, y)
        # this number also equals to the maximum of ranking_
        assert (np.max(rfe.ranking_) ==
                formula1(n_features, n_features_to_select, step))
        assert (np.max(rfe.ranking_) ==
                formula2(n_features, n_features_to_select, step))

    # In RFECV, 'fit' calls 'RFE._fit'
    # 'number_of_subsets_of_features' of RFE
    # = the size of 'grid_scores' of RFECV
    # = the number of iterations of the for loop before optimization #4534

    # RFECV, n_features_to_select = 1
    # Case 1, n_features - 1 is divisible by step
    # Case 2, n_features - 1 is not divisible by step

    n_features_to_select = 1
    n_features_list = [11, 10]
    step_list = [2, 2]
    for n_features, step in zip(n_features_list, step_list):
        generator = check_random_state(43)
        X = generator.normal(size=(100, n_features))
        y = generator.rand(100).round()
        rfecv = RFECV(estimator=SVC(kernel="linear"), step=step)
        rfecv.fit(X, y)

        assert (rfecv.grid_scores_.shape[0] ==
                formula1(n_features, n_features_to_select, step))
        assert (rfecv.grid_scores_.shape[0] ==
                formula2(n_features, n_features_to_select, step))


def test_rfe_cv_n_jobs():
    generator = check_random_state(0)
    iris = load_iris()
    X = np.c_[iris.data, generator.normal(size=(len(iris.data), 6))]
    y = iris.target

    rfecv = RFECV(estimator=SVC(kernel='linear'))
    rfecv.fit(X, y)
    rfecv_ranking = rfecv.ranking_
    rfecv_grid_scores = rfecv.grid_scores_

    rfecv.set_params(n_jobs=2)
    rfecv.fit(X, y)
    assert_array_almost_equal(rfecv.ranking_, rfecv_ranking)
    assert_array_almost_equal(rfecv.grid_scores_, rfecv_grid_scores)


def test_rfe_cv_groups():
    generator = check_random_state(0)
    iris = load_iris()
    number_groups = 4
    groups = np.floor(np.linspace(0, number_groups, len(iris.target)))
    X = iris.data
    y = (iris.target > 0).astype(int)

    est_groups = RFECV(
        estimator=RandomForestClassifier(random_state=generator),
        step=1,
        scoring='accuracy',
        cv=GroupKFold(n_splits=2)
    )
    est_groups.fit(X, y, groups=groups)
    assert est_groups.n_features_ > 0


@pytest.mark.parametrize(
    'importance_getter',
    [attrgetter('regressor_.coef_'), 'regressor_.coef_'])
@pytest.mark.parametrize('selector, expected_n_features',
                         [(RFE, 5), (RFECV, 4)])
def test_rfe_wrapped_estimator(importance_getter, selector,
                               expected_n_features):
    # Non-regression test for
    # https://github.com/scikit-learn/scikit-learn/issues/15312
    X, y = make_friedman1(n_samples=50, n_features=10, random_state=0)
    estimator = LinearSVR()

    log_estimator = TransformedTargetRegressor(regressor=estimator,
                                               func=np.log,
                                               inverse_func=np.exp)

    selector = selector(log_estimator, importance_getter=importance_getter)
    sel = selector.fit(X, y)
    assert sel.support_.sum() == expected_n_features


@pytest.mark.parametrize(
    "importance_getter, err_type",
    [("auto", ValueError),
     ("random", AttributeError),
     (lambda x: x.importance, AttributeError),
     ([0], ValueError)]
)
@pytest.mark.parametrize("Selector", [RFE, RFECV])
def test_rfe_importance_getter_validation(importance_getter, err_type,
                                          Selector):
    X, y = make_friedman1(n_samples=50, n_features=10, random_state=42)
    estimator = LinearSVR()
    log_estimator = TransformedTargetRegressor(
        regressor=estimator, func=np.log, inverse_func=np.exp
    )

    with pytest.raises(err_type):
        model = Selector(log_estimator, importance_getter=importance_getter)
        model.fit(X, y)


@pytest.mark.parametrize("cv", [
    None,
    5
])
def test_rfe_allow_nan_inf_in_x(cv):
    iris = load_iris()
    X = iris.data
    y = iris.target

    # add nan and inf value to X
    X[0][0] = np.NaN
    X[0][1] = np.Inf

    clf = MockClassifier()
    if cv is not None:
        rfe = RFECV(estimator=clf, cv=cv)
    else:
        rfe = RFE(estimator=clf)
    rfe.fit(X, y)
    rfe.transform(X)


def test_w_pipeline_2d_coef_():
    pipeline = make_pipeline(StandardScaler(), LogisticRegression())

    data, y = load_iris(return_X_y=True)
    sfm = RFE(pipeline, n_features_to_select=2,
              importance_getter='named_steps.logisticregression.coef_')

    sfm.fit(data, y)
    assert sfm.transform(data).shape[1] == 2


@pytest.mark.parametrize('ClsRFE', [
    RFE,
    RFECV
    ])
def test_multioutput(ClsRFE):
    X = np.random.normal(size=(10, 3))
    y = np.random.randint(2, size=(10, 2))
    clf = RandomForestClassifier(n_estimators=5)
    rfe_test = ClsRFE(clf)
    rfe_test.fit(X, y)
