import re
import pytest
import numpy as np
import warnings
from unittest.mock import Mock

from sklearn.utils._testing import assert_array_almost_equal
from sklearn.utils._testing import assert_array_equal
from sklearn.utils._testing import assert_allclose
from sklearn.utils._testing import skip_if_32bit
from sklearn.utils._testing import MinimalClassifier

from sklearn import datasets
from sklearn.cross_decomposition import CCA, PLSCanonical, PLSRegression
from sklearn.datasets import make_friedman1
from sklearn.exceptions import NotFittedError
from sklearn.linear_model import LogisticRegression, SGDClassifier, Lasso
from sklearn.svm import LinearSVC
from sklearn.feature_selection import SelectFromModel
from sklearn.ensemble import RandomForestClassifier, HistGradientBoostingClassifier
from sklearn.linear_model import PassiveAggressiveClassifier
from sklearn.base import BaseEstimator
from sklearn.pipeline import make_pipeline
from sklearn.decomposition import PCA


class NaNTag(BaseEstimator):
    def _more_tags(self):
        return {"allow_nan": True}


class NoNaNTag(BaseEstimator):
    def _more_tags(self):
        return {"allow_nan": False}


class NaNTagRandomForest(RandomForestClassifier):
    def _more_tags(self):
        return {"allow_nan": True}


iris = datasets.load_iris()
data, y = iris.data, iris.target
rng = np.random.RandomState(0)


def test_invalid_input():
    clf = SGDClassifier(
        alpha=0.1, max_iter=10, shuffle=True, random_state=None, tol=None
    )
    for threshold in ["gobbledigook", ".5 * gobbledigook"]:
        model = SelectFromModel(clf, threshold=threshold)
        model.fit(data, y)
        with pytest.raises(ValueError):
            model.transform(data)


def test_input_estimator_unchanged():
    # Test that SelectFromModel fits on a clone of the estimator.
    est = RandomForestClassifier()
    transformer = SelectFromModel(estimator=est)
    transformer.fit(data, y)
    assert transformer.estimator is est


@pytest.mark.parametrize(
    "max_features, err_type, err_msg",
    [
        (-1, ValueError, "max_features =="),
        (
            data.shape[1] + 1,
            ValueError,
            "max_features ==",
        ),
        (
            lambda X: 1.5,
            TypeError,
            "max_features(X) must be an instance of int, not float.",
        ),
        (
            "gobbledigook",
            TypeError,
            "'max_features' must be either an int or a callable",
        ),
        (
            "all",
            TypeError,
            "'max_features' must be either an int or a callable",
        ),
    ],
)
def test_max_features_error(max_features, err_type, err_msg):
    err_msg = re.escape(err_msg)
    clf = RandomForestClassifier(n_estimators=5, random_state=0)

    transformer = SelectFromModel(
        estimator=clf, max_features=max_features, threshold=-np.inf
    )
    with pytest.raises(err_type, match=err_msg):
        transformer.fit(data, y)


@pytest.mark.parametrize("max_features", [0, 2, data.shape[1], None])
def test_inferred_max_features_integer(max_features):
    """Check max_features_ and output shape for integer max_features."""
    clf = RandomForestClassifier(n_estimators=5, random_state=0)
    transformer = SelectFromModel(
        estimator=clf, max_features=max_features, threshold=-np.inf
    )
    X_trans = transformer.fit_transform(data, y)
    if max_features is not None:
        assert transformer.max_features_ == max_features
        assert X_trans.shape[1] == transformer.max_features_
    else:
        assert not hasattr(transformer, "max_features_")
        assert X_trans.shape[1] == data.shape[1]


@pytest.mark.parametrize(
    "max_features",
    [lambda X: 1, lambda X: X.shape[1], lambda X: min(X.shape[1], 10000)],
)
def test_inferred_max_features_callable(max_features):
    """Check max_features_ and output shape for callable max_features."""
    clf = RandomForestClassifier(n_estimators=5, random_state=0)
    transformer = SelectFromModel(
        estimator=clf, max_features=max_features, threshold=-np.inf
    )
    X_trans = transformer.fit_transform(data, y)
    assert transformer.max_features_ == max_features(data)
    assert X_trans.shape[1] == transformer.max_features_


@pytest.mark.parametrize("max_features", [lambda X: round(len(X[0]) / 2), 2])
def test_max_features_array_like(max_features):
    X = [
        [0.87, -1.34, 0.31],
        [-2.79, -0.02, -0.85],
        [-1.34, -0.48, -2.55],
        [1.92, 1.48, 0.65],
    ]
    y = [0, 1, 0, 1]

    clf = RandomForestClassifier(n_estimators=5, random_state=0)
    transformer = SelectFromModel(
        estimator=clf, max_features=max_features, threshold=-np.inf
    )
    X_trans = transformer.fit_transform(X, y)
    assert X_trans.shape[1] == transformer.max_features_


@pytest.mark.parametrize(
    "max_features",
    [lambda X: min(X.shape[1], 10000), lambda X: X.shape[1], lambda X: 1],
)
def test_max_features_callable_data(max_features):
    """Tests that the callable passed to `fit` is called on X."""
    clf = RandomForestClassifier(n_estimators=50, random_state=0)
    m = Mock(side_effect=max_features)
    transformer = SelectFromModel(estimator=clf, max_features=m, threshold=-np.inf)
    transformer.fit_transform(data, y)
    m.assert_called_with(data)


class FixedImportanceEstimator(BaseEstimator):
    def __init__(self, importances):
        self.importances = importances

    def fit(self, X, y=None):
        self.feature_importances_ = np.array(self.importances)


def test_max_features():
    # Test max_features parameter using various values
    X, y = datasets.make_classification(
        n_samples=1000,
        n_features=10,
        n_informative=3,
        n_redundant=0,
        n_repeated=0,
        shuffle=False,
        random_state=0,
    )
    max_features = X.shape[1]
    est = RandomForestClassifier(n_estimators=50, random_state=0)

    transformer1 = SelectFromModel(estimator=est, threshold=-np.inf)
    transformer2 = SelectFromModel(
        estimator=est, max_features=max_features, threshold=-np.inf
    )
    X_new1 = transformer1.fit_transform(X, y)
    X_new2 = transformer2.fit_transform(X, y)
    assert_allclose(X_new1, X_new2)

    # Test max_features against actual model.
    transformer1 = SelectFromModel(estimator=Lasso(alpha=0.025, random_state=42))
    X_new1 = transformer1.fit_transform(X, y)
    scores1 = np.abs(transformer1.estimator_.coef_)
    candidate_indices1 = np.argsort(-scores1, kind="mergesort")

    for n_features in range(1, X_new1.shape[1] + 1):
        transformer2 = SelectFromModel(
            estimator=Lasso(alpha=0.025, random_state=42),
            max_features=n_features,
            threshold=-np.inf,
        )
        X_new2 = transformer2.fit_transform(X, y)
        scores2 = np.abs(transformer2.estimator_.coef_)
        candidate_indices2 = np.argsort(-scores2, kind="mergesort")
        assert_allclose(
            X[:, candidate_indices1[:n_features]], X[:, candidate_indices2[:n_features]]
        )
    assert_allclose(transformer1.estimator_.coef_, transformer2.estimator_.coef_)


def test_max_features_tiebreak():
    # Test if max_features can break tie among feature importance
    X, y = datasets.make_classification(
        n_samples=1000,
        n_features=10,
        n_informative=3,
        n_redundant=0,
        n_repeated=0,
        shuffle=False,
        random_state=0,
    )
    max_features = X.shape[1]

    feature_importances = np.array([4, 4, 4, 4, 3, 3, 3, 2, 2, 1])
    for n_features in range(1, max_features + 1):
        transformer = SelectFromModel(
            FixedImportanceEstimator(feature_importances),
            max_features=n_features,
            threshold=-np.inf,
        )
        X_new = transformer.fit_transform(X, y)
        selected_feature_indices = np.where(transformer._get_support_mask())[0]
        assert_array_equal(selected_feature_indices, np.arange(n_features))
        assert X_new.shape[1] == n_features


def test_threshold_and_max_features():
    X, y = datasets.make_classification(
        n_samples=1000,
        n_features=10,
        n_informative=3,
        n_redundant=0,
        n_repeated=0,
        shuffle=False,
        random_state=0,
    )
    est = RandomForestClassifier(n_estimators=50, random_state=0)

    transformer1 = SelectFromModel(estimator=est, max_features=3, threshold=-np.inf)
    X_new1 = transformer1.fit_transform(X, y)

    transformer2 = SelectFromModel(estimator=est, threshold=0.04)
    X_new2 = transformer2.fit_transform(X, y)

    transformer3 = SelectFromModel(estimator=est, max_features=3, threshold=0.04)
    X_new3 = transformer3.fit_transform(X, y)
    assert X_new3.shape[1] == min(X_new1.shape[1], X_new2.shape[1])
    selected_indices = transformer3.transform(np.arange(X.shape[1])[np.newaxis, :])
    assert_allclose(X_new3, X[:, selected_indices[0]])


@skip_if_32bit
def test_feature_importances():
    X, y = datasets.make_classification(
        n_samples=1000,
        n_features=10,
        n_informative=3,
        n_redundant=0,
        n_repeated=0,
        shuffle=False,
        random_state=0,
    )

    est = RandomForestClassifier(n_estimators=50, random_state=0)
    for threshold, func in zip(["mean", "median"], [np.mean, np.median]):
        transformer = SelectFromModel(estimator=est, threshold=threshold)
        transformer.fit(X, y)
        assert hasattr(transformer.estimator_, "feature_importances_")

        X_new = transformer.transform(X)
        assert X_new.shape[1] < X.shape[1]
        importances = transformer.estimator_.feature_importances_

        feature_mask = np.abs(importances) > func(importances)
        assert_array_almost_equal(X_new, X[:, feature_mask])


def test_sample_weight():
    # Ensure sample weights are passed to underlying estimator
    X, y = datasets.make_classification(
        n_samples=100,
        n_features=10,
        n_informative=3,
        n_redundant=0,
        n_repeated=0,
        shuffle=False,
        random_state=0,
    )

    # Check with sample weights
    sample_weight = np.ones(y.shape)
    sample_weight[y == 1] *= 100

    est = LogisticRegression(random_state=0, fit_intercept=False)
    transformer = SelectFromModel(estimator=est)
    transformer.fit(X, y, sample_weight=None)
    mask = transformer._get_support_mask()
    transformer.fit(X, y, sample_weight=sample_weight)
    weighted_mask = transformer._get_support_mask()
    assert not np.all(weighted_mask == mask)
    transformer.fit(X, y, sample_weight=3 * sample_weight)
    reweighted_mask = transformer._get_support_mask()
    assert np.all(weighted_mask == reweighted_mask)


def test_coef_default_threshold():
    X, y = datasets.make_classification(
        n_samples=100,
        n_features=10,
        n_informative=3,
        n_redundant=0,
        n_repeated=0,
        shuffle=False,
        random_state=0,
    )

    # For the Lasso and related models, the threshold defaults to 1e-5
    transformer = SelectFromModel(estimator=Lasso(alpha=0.1, random_state=42))
    transformer.fit(X, y)
    X_new = transformer.transform(X)
    mask = np.abs(transformer.estimator_.coef_) > 1e-5
    assert_array_almost_equal(X_new, X[:, mask])


@skip_if_32bit
def test_2d_coef():
    X, y = datasets.make_classification(
        n_samples=1000,
        n_features=10,
        n_informative=3,
        n_redundant=0,
        n_repeated=0,
        shuffle=False,
        random_state=0,
        n_classes=4,
    )

    est = LogisticRegression()
    for threshold, func in zip(["mean", "median"], [np.mean, np.median]):
        for order in [1, 2, np.inf]:
            # Fit SelectFromModel a multi-class problem
            transformer = SelectFromModel(
                estimator=LogisticRegression(), threshold=threshold, norm_order=order
            )
            transformer.fit(X, y)
            assert hasattr(transformer.estimator_, "coef_")
            X_new = transformer.transform(X)
            assert X_new.shape[1] < X.shape[1]

            # Manually check that the norm is correctly performed
            est.fit(X, y)
            importances = np.linalg.norm(est.coef_, axis=0, ord=order)
            feature_mask = importances > func(importances)
            assert_array_almost_equal(X_new, X[:, feature_mask])


def test_partial_fit():
    est = PassiveAggressiveClassifier(
        random_state=0, shuffle=False, max_iter=5, tol=None
    )
    transformer = SelectFromModel(estimator=est)
    transformer.partial_fit(data, y, classes=np.unique(y))
    old_model = transformer.estimator_
    transformer.partial_fit(data, y, classes=np.unique(y))
    new_model = transformer.estimator_
    assert old_model is new_model

    X_transform = transformer.transform(data)
    transformer.fit(np.vstack((data, data)), np.concatenate((y, y)))
    assert_array_almost_equal(X_transform, transformer.transform(data))

    # check that if est doesn't have partial_fit, neither does SelectFromModel
    transformer = SelectFromModel(estimator=RandomForestClassifier())
    assert not hasattr(transformer, "partial_fit")


def test_calling_fit_reinitializes():
    est = LinearSVC(random_state=0)
    transformer = SelectFromModel(estimator=est)
    transformer.fit(data, y)
    transformer.set_params(estimator__C=100)
    transformer.fit(data, y)
    assert transformer.estimator_.C == 100


def test_prefit():
    # Test all possible combinations of the prefit parameter.

    # Passing a prefit parameter with the selected model
    # and fitting a unfit model with prefit=False should give same results.
    clf = SGDClassifier(alpha=0.1, max_iter=10, shuffle=True, random_state=0, tol=None)
    model = SelectFromModel(clf)
    model.fit(data, y)
    X_transform = model.transform(data)
    clf.fit(data, y)
    model = SelectFromModel(clf, prefit=True)
    assert_array_almost_equal(model.transform(data), X_transform)
    model.fit(data, y)
    assert model.estimator_ is not clf

    # Check that the model is rewritten if prefit=False and a fitted model is
    # passed
    model = SelectFromModel(clf, prefit=False)
    model.fit(data, y)
    assert_array_almost_equal(model.transform(data), X_transform)

    # Check that passing an unfitted estimator with `prefit=True` raises a
    # `ValueError`
    clf = SGDClassifier(alpha=0.1, max_iter=10, shuffle=True, random_state=0, tol=None)
    model = SelectFromModel(clf, prefit=True)
    err_msg = "When `prefit=True`, `estimator` is expected to be a fitted estimator."
    with pytest.raises(NotFittedError, match=err_msg):
        model.fit(data, y)
    with pytest.raises(NotFittedError, match=err_msg):
        model.partial_fit(data, y)
    with pytest.raises(NotFittedError, match=err_msg):
        model.transform(data)

    # Check that the internal parameters of prefitted model are not changed
    # when calling `fit` or `partial_fit` with `prefit=True`
    clf = SGDClassifier(alpha=0.1, max_iter=10, shuffle=True, tol=None).fit(data, y)
    model = SelectFromModel(clf, prefit=True)
    model.fit(data, y)
    assert_allclose(model.estimator_.coef_, clf.coef_)
    model.partial_fit(data, y)
    assert_allclose(model.estimator_.coef_, clf.coef_)


def test_prefit_max_features():
    """Check the interaction between `prefit` and `max_features`."""
    # case 1: an error should be raised at `transform` if `fit` was not called to
    # validate the attributes
    estimator = RandomForestClassifier(n_estimators=5, random_state=0)
    estimator.fit(data, y)
    model = SelectFromModel(estimator, prefit=True, max_features=lambda X: X.shape[1])

    err_msg = (
        "When `prefit=True` and `max_features` is a callable, call `fit` "
        "before calling `transform`."
    )
    with pytest.raises(NotFittedError, match=err_msg):
        model.transform(data)

    # case 2: `max_features` is not validated and different from an integer
    # FIXME: we cannot validate the upper bound of the attribute at transform
    # and we should force calling `fit` if we intend to force the attribute
    # to have such an upper bound.
    max_features = 2.5
    model.set_params(max_features=max_features)
    with pytest.raises(ValueError, match="`max_features` must be an integer"):
        model.transform(data)


def test_prefit_get_feature_names_out():
    """Check the interaction between prefit and the feature names."""
    clf = RandomForestClassifier(n_estimators=2, random_state=0)
    clf.fit(data, y)
    model = SelectFromModel(clf, prefit=True, max_features=1)

    # FIXME: the error message should be improved. Raising a `NotFittedError`
    # would be better since it would force to validate all class attribute and
    # create all the necessary fitted attribute
    err_msg = "Unable to generate feature names without n_features_in_"
    with pytest.raises(ValueError, match=err_msg):
        model.get_feature_names_out()

    model.fit(data, y)
    feature_names = model.get_feature_names_out()
    assert feature_names == ["x3"]


def test_threshold_string():
    est = RandomForestClassifier(n_estimators=50, random_state=0)
    model = SelectFromModel(est, threshold="0.5*mean")
    model.fit(data, y)
    X_transform = model.transform(data)

    # Calculate the threshold from the estimator directly.
    est.fit(data, y)
    threshold = 0.5 * np.mean(est.feature_importances_)
    mask = est.feature_importances_ > threshold
    assert_array_almost_equal(X_transform, data[:, mask])


def test_threshold_without_refitting():
    # Test that the threshold can be set without refitting the model.
    clf = SGDClassifier(alpha=0.1, max_iter=10, shuffle=True, random_state=0, tol=None)
    model = SelectFromModel(clf, threshold="0.1 * mean")
    model.fit(data, y)
    X_transform = model.transform(data)

    # Set a higher threshold to filter out more features.
    model.threshold = "1.0 * mean"
    assert X_transform.shape[1] > model.transform(data).shape[1]


def test_fit_accepts_nan_inf():
    # Test that fit doesn't check for np.inf and np.nan values.
    clf = HistGradientBoostingClassifier(random_state=0)

    model = SelectFromModel(estimator=clf)

    nan_data = data.copy()
    nan_data[0] = np.NaN
    nan_data[1] = np.Inf

    model.fit(data, y)


def test_transform_accepts_nan_inf():
    # Test that transform doesn't check for np.inf and np.nan values.
    clf = NaNTagRandomForest(n_estimators=100, random_state=0)
    nan_data = data.copy()

    model = SelectFromModel(estimator=clf)
    model.fit(nan_data, y)

    nan_data[0] = np.NaN
    nan_data[1] = np.Inf

    model.transform(nan_data)


def test_allow_nan_tag_comes_from_estimator():
    allow_nan_est = NaNTag()
    model = SelectFromModel(estimator=allow_nan_est)
    assert model._get_tags()["allow_nan"] is True

    no_nan_est = NoNaNTag()
    model = SelectFromModel(estimator=no_nan_est)
    assert model._get_tags()["allow_nan"] is False


def _pca_importances(pca_estimator):
    return np.abs(pca_estimator.explained_variance_)


@pytest.mark.parametrize(
    "estimator, importance_getter",
    [
        (
            make_pipeline(PCA(random_state=0), LogisticRegression()),
            "named_steps.logisticregression.coef_",
        ),
        (PCA(random_state=0), _pca_importances),
    ],
)
def test_importance_getter(estimator, importance_getter):
    selector = SelectFromModel(
        estimator, threshold="mean", importance_getter=importance_getter
    )
    selector.fit(data, y)
    assert selector.transform(data).shape[1] == 1


@pytest.mark.parametrize("PLSEstimator", [CCA, PLSCanonical, PLSRegression])
def test_select_from_model_pls(PLSEstimator):
    """Check the behaviour of SelectFromModel with PLS estimators.

    Non-regression test for:
    https://github.com/scikit-learn/scikit-learn/issues/12410
    """
    X, y = make_friedman1(n_samples=50, n_features=10, random_state=0)
    estimator = PLSEstimator(n_components=1)
    model = make_pipeline(SelectFromModel(estimator), estimator).fit(X, y)
    assert model.score(X, y) > 0.5


def test_estimator_does_not_support_feature_names():
    """SelectFromModel works with estimators that do not support feature_names_in_.

    Non-regression test for #21949.
    """
    pytest.importorskip("pandas")
    X, y = datasets.load_iris(as_frame=True, return_X_y=True)
    all_feature_names = set(X.columns)

    def importance_getter(estimator):
        return np.arange(X.shape[1])

    selector = SelectFromModel(
        MinimalClassifier(), importance_getter=importance_getter
    ).fit(X, y)

    # selector learns the feature names itself
    assert_array_equal(selector.feature_names_in_, X.columns)

    feature_names_out = set(selector.get_feature_names_out())
    assert feature_names_out < all_feature_names

    with warnings.catch_warnings():
        warnings.simplefilter("error", UserWarning)

        selector.transform(X.iloc[1:3])


@pytest.mark.parametrize(
    "error, err_msg, max_features",
    (
        [ValueError, "max_features == 10, must be <= 4", 10],
        [TypeError, "'max_features' must be either an int or a callable", "a"],
        [ValueError, r"max_features\(X\) == 5, must be <= 4", lambda x: x.shape[1] + 1],
    ),
)
def test_partial_fit_validate_max_features(error, err_msg, max_features):
    """Test that partial_fit from SelectFromModel validates `max_features`."""
    X, y = datasets.make_classification(
        n_samples=100,
        n_features=4,
        random_state=0,
    )

    with pytest.raises(error, match=err_msg):
        SelectFromModel(
            estimator=SGDClassifier(), max_features=max_features
        ).partial_fit(X, y, classes=[0, 1])


@pytest.mark.parametrize("as_frame", [True, False])
def test_partial_fit_validate_feature_names(as_frame):
    """Test that partial_fit from SelectFromModel validates `feature_names_in_`."""
    pytest.importorskip("pandas")
    X, y = datasets.load_iris(as_frame=as_frame, return_X_y=True)

    selector = SelectFromModel(estimator=SGDClassifier(), max_features=4).partial_fit(
        X, y, classes=[0, 1, 2]
    )
    if as_frame:
        assert_array_equal(selector.feature_names_in_, X.columns)
    else:
        assert not hasattr(selector, "feature_names_in_")
