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env-llmeval/lib/python3.10/site-packages/sklearn/covariance/_elliptic_envelope.py

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+# Author: Virgile Fritsch <[email protected]>
+#
+# License: BSD 3 clause
+
+from numbers import Real
+
+import numpy as np
+
+from ..base import OutlierMixin, _fit_context
+from ..metrics import accuracy_score
+from ..utils._param_validation import Interval
+from ..utils.validation import check_is_fitted
+from ._robust_covariance import MinCovDet
+
+
+class EllipticEnvelope(OutlierMixin, MinCovDet):
+    """An object for detecting outliers in a Gaussian distributed dataset.
+
+    Read more in the :ref:`User Guide <outlier_detection>`.
+
+    Parameters
+    ----------
+    store_precision : bool, default=True
+        Specify if the estimated precision is stored.
+
+    assume_centered : bool, default=False
+        If True, the support of robust location and covariance estimates
+        is computed, and a covariance estimate is recomputed from it,
+        without centering the data.
+        Useful to work with data whose mean is significantly equal to
+        zero but is not exactly zero.
+        If False, the robust location and covariance are directly computed
+        with the FastMCD algorithm without additional treatment.
+
+    support_fraction : float, default=None
+        The proportion of points to be included in the support of the raw
+        MCD estimate. If None, the minimum value of support_fraction will
+        be used within the algorithm: `(n_samples + n_features + 1) / 2 * n_samples`.
+        Range is (0, 1).
+
+    contamination : float, default=0.1
+        The amount of contamination of the data set, i.e. the proportion
+        of outliers in the data set. Range is (0, 0.5].
+
+    random_state : int, RandomState instance or None, default=None
+        Determines the pseudo random number generator for shuffling
+        the data. Pass an int for reproducible results across multiple function
+        calls. See :term:`Glossary <random_state>`.
+
+    Attributes
+    ----------
+    location_ : ndarray of shape (n_features,)
+        Estimated robust location.
+
+    covariance_ : ndarray of shape (n_features, n_features)
+        Estimated robust covariance matrix.
+
+    precision_ : ndarray of shape (n_features, n_features)
+        Estimated pseudo inverse matrix.
+        (stored only if store_precision is True)
+
+    support_ : ndarray of shape (n_samples,)
+        A mask of the observations that have been used to compute the
+        robust estimates of location and shape.
+
+    offset_ : float
+        Offset used to define the decision function from the raw scores.
+        We have the relation: ``decision_function = score_samples - offset_``.
+        The offset depends on the contamination parameter and is defined in
+        such a way we obtain the expected number of outliers (samples with
+        decision function < 0) in training.
+
+        .. versionadded:: 0.20
+
+    raw_location_ : ndarray of shape (n_features,)
+        The raw robust estimated location before correction and re-weighting.
+
+    raw_covariance_ : ndarray of shape (n_features, n_features)
+        The raw robust estimated covariance before correction and re-weighting.
+
+    raw_support_ : ndarray of shape (n_samples,)
+        A mask of the observations that have been used to compute
+        the raw robust estimates of location and shape, before correction
+        and re-weighting.
+
+    dist_ : ndarray of shape (n_samples,)
+        Mahalanobis distances of the training set (on which :meth:`fit` is
+        called) observations.
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
+    See Also
+    --------
+    EmpiricalCovariance : Maximum likelihood covariance estimator.
+    GraphicalLasso : Sparse inverse covariance estimation
+        with an l1-penalized estimator.
+    LedoitWolf : LedoitWolf Estimator.
+    MinCovDet : Minimum Covariance Determinant
+        (robust estimator of covariance).
+    OAS : Oracle Approximating Shrinkage Estimator.
+    ShrunkCovariance : Covariance estimator with shrinkage.
+
+    Notes
+    -----
+    Outlier detection from covariance estimation may break or not
+    perform well in high-dimensional settings. In particular, one will
+    always take care to work with ``n_samples > n_features ** 2``.
+
+    References
+    ----------
+    .. [1] Rousseeuw, P.J., Van Driessen, K. "A fast algorithm for the
+       minimum covariance determinant estimator" Technometrics 41(3), 212
+       (1999)
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from sklearn.covariance import EllipticEnvelope
+    >>> true_cov = np.array([[.8, .3],
+    ...                      [.3, .4]])
+    >>> X = np.random.RandomState(0).multivariate_normal(mean=[0, 0],
+    ...                                                  cov=true_cov,
+    ...                                                  size=500)
+    >>> cov = EllipticEnvelope(random_state=0).fit(X)
+    >>> # predict returns 1 for an inlier and -1 for an outlier
+    >>> cov.predict([[0, 0],
+    ...              [3, 3]])
+    array([ 1, -1])
+    >>> cov.covariance_
+    array([[0.7411..., 0.2535...],
+           [0.2535..., 0.3053...]])
+    >>> cov.location_
+    array([0.0813... , 0.0427...])
+    """
+
+    _parameter_constraints: dict = {
+        **MinCovDet._parameter_constraints,
+        "contamination": [Interval(Real, 0, 0.5, closed="right")],
+    }
+
+    def __init__(
+        self,
+        *,
+        store_precision=True,
+        assume_centered=False,
+        support_fraction=None,
+        contamination=0.1,
+        random_state=None,
+    ):
+        super().__init__(
+            store_precision=store_precision,
+            assume_centered=assume_centered,
+            support_fraction=support_fraction,
+            random_state=random_state,
+        )
+        self.contamination = contamination
+
+    @_fit_context(prefer_skip_nested_validation=True)
+    def fit(self, X, y=None):
+        """Fit the EllipticEnvelope model.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            Training data.
+
+        y : Ignored
+            Not used, present for API consistency by convention.
+
+        Returns
+        -------
+        self : object
+            Returns the instance itself.
+        """
+        super().fit(X)
+        self.offset_ = np.percentile(-self.dist_, 100.0 * self.contamination)
+        return self
+
+    def decision_function(self, X):
+        """Compute the decision function of the given observations.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            The data matrix.
+
+        Returns
+        -------
+        decision : ndarray of shape (n_samples,)
+            Decision function of the samples.
+            It is equal to the shifted Mahalanobis distances.
+            The threshold for being an outlier is 0, which ensures a
+            compatibility with other outlier detection algorithms.
+        """
+        check_is_fitted(self)
+        negative_mahal_dist = self.score_samples(X)
+        return negative_mahal_dist - self.offset_
+
+    def score_samples(self, X):
+        """Compute the negative Mahalanobis distances.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            The data matrix.
+
+        Returns
+        -------
+        negative_mahal_distances : array-like of shape (n_samples,)
+            Opposite of the Mahalanobis distances.
+        """
+        check_is_fitted(self)
+        return -self.mahalanobis(X)
+
+    def predict(self, X):
+        """
+        Predict labels (1 inlier, -1 outlier) of X according to fitted model.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            The data matrix.
+
+        Returns
+        -------
+        is_inlier : ndarray of shape (n_samples,)
+            Returns -1 for anomalies/outliers and +1 for inliers.
+        """
+        values = self.decision_function(X)
+        is_inlier = np.full(values.shape[0], -1, dtype=int)
+        is_inlier[values >= 0] = 1
+
+        return is_inlier
+
+    def score(self, X, y, sample_weight=None):
+        """Return the mean accuracy on the given test data and labels.
+
+        In multi-label classification, this is the subset accuracy
+        which is a harsh metric since you require for each sample that
+        each label set be correctly predicted.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            Test samples.
+
+        y : array-like of shape (n_samples,) or (n_samples, n_outputs)
+            True labels for X.
+
+        sample_weight : array-like of shape (n_samples,), default=None
+            Sample weights.
+
+        Returns
+        -------
+        score : float
+            Mean accuracy of self.predict(X) w.r.t. y.
+        """
+        return accuracy_score(y, self.predict(X), sample_weight=sample_weight)

env-llmeval/lib/python3.10/site-packages/sklearn/covariance/tests/test_covariance.py

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+# Author: Alexandre Gramfort <[email protected]>
+#         Gael Varoquaux <[email protected]>
+#         Virgile Fritsch <[email protected]>
+#
+# License: BSD 3 clause
+
+import numpy as np
+import pytest
+
+from sklearn import datasets
+from sklearn.covariance import (
+    OAS,
+    EmpiricalCovariance,
+    LedoitWolf,
+    ShrunkCovariance,
+    empirical_covariance,
+    ledoit_wolf,
+    ledoit_wolf_shrinkage,
+    oas,
+    shrunk_covariance,
+)
+from sklearn.covariance._shrunk_covariance import _ledoit_wolf
+from sklearn.utils._testing import (
+    assert_allclose,
+    assert_almost_equal,
+    assert_array_almost_equal,
+    assert_array_equal,
+)
+
+from .._shrunk_covariance import _oas
+
+X, _ = datasets.load_diabetes(return_X_y=True)
+X_1d = X[:, 0]
+n_samples, n_features = X.shape
+
+
+def test_covariance():
+    # Tests Covariance module on a simple dataset.
+    # test covariance fit from data
+    cov = EmpiricalCovariance()
+    cov.fit(X)
+    emp_cov = empirical_covariance(X)
+    assert_array_almost_equal(emp_cov, cov.covariance_, 4)
+    assert_almost_equal(cov.error_norm(emp_cov), 0)
+    assert_almost_equal(cov.error_norm(emp_cov, norm="spectral"), 0)
+    assert_almost_equal(cov.error_norm(emp_cov, norm="frobenius"), 0)
+    assert_almost_equal(cov.error_norm(emp_cov, scaling=False), 0)
+    assert_almost_equal(cov.error_norm(emp_cov, squared=False), 0)
+    with pytest.raises(NotImplementedError):
+        cov.error_norm(emp_cov, norm="foo")
+    # Mahalanobis distances computation test
+    mahal_dist = cov.mahalanobis(X)
+    assert np.amin(mahal_dist) > 0
+
+    # test with n_features = 1
+    X_1d = X[:, 0].reshape((-1, 1))
+    cov = EmpiricalCovariance()
+    cov.fit(X_1d)
+    assert_array_almost_equal(empirical_covariance(X_1d), cov.covariance_, 4)
+    assert_almost_equal(cov.error_norm(empirical_covariance(X_1d)), 0)
+    assert_almost_equal(cov.error_norm(empirical_covariance(X_1d), norm="spectral"), 0)
+
+    # test with one sample
+    # Create X with 1 sample and 5 features
+    X_1sample = np.arange(5).reshape(1, 5)
+    cov = EmpiricalCovariance()
+    warn_msg = "Only one sample available. You may want to reshape your data array"
+    with pytest.warns(UserWarning, match=warn_msg):
+        cov.fit(X_1sample)
+
+    assert_array_almost_equal(cov.covariance_, np.zeros(shape=(5, 5), dtype=np.float64))
+
+    # test integer type
+    X_integer = np.asarray([[0, 1], [1, 0]])
+    result = np.asarray([[0.25, -0.25], [-0.25, 0.25]])
+    assert_array_almost_equal(empirical_covariance(X_integer), result)
+
+    # test centered case
+    cov = EmpiricalCovariance(assume_centered=True)
+    cov.fit(X)
+    assert_array_equal(cov.location_, np.zeros(X.shape[1]))
+
+
+@pytest.mark.parametrize("n_matrices", [1, 3])
+def test_shrunk_covariance_func(n_matrices):
+    """Check `shrunk_covariance` function."""
+
+    n_features = 2
+    cov = np.ones((n_features, n_features))
+    cov_target = np.array([[1, 0.5], [0.5, 1]])
+
+    if n_matrices > 1:
+        cov = np.repeat(cov[np.newaxis, ...], n_matrices, axis=0)
+        cov_target = np.repeat(cov_target[np.newaxis, ...], n_matrices, axis=0)
+
+    cov_shrunk = shrunk_covariance(cov, 0.5)
+    assert_allclose(cov_shrunk, cov_target)
+
+
+def test_shrunk_covariance():
+    """Check consistency between `ShrunkCovariance` and `shrunk_covariance`."""
+
+    # Tests ShrunkCovariance module on a simple dataset.
+    # compare shrunk covariance obtained from data and from MLE estimate
+    cov = ShrunkCovariance(shrinkage=0.5)
+    cov.fit(X)
+    assert_array_almost_equal(
+        shrunk_covariance(empirical_covariance(X), shrinkage=0.5), cov.covariance_, 4
+    )
+
+    # same test with shrinkage not provided
+    cov = ShrunkCovariance()
+    cov.fit(X)
+    assert_array_almost_equal(
+        shrunk_covariance(empirical_covariance(X)), cov.covariance_, 4
+    )
+
+    # same test with shrinkage = 0 (<==> empirical_covariance)
+    cov = ShrunkCovariance(shrinkage=0.0)
+    cov.fit(X)
+    assert_array_almost_equal(empirical_covariance(X), cov.covariance_, 4)
+
+    # test with n_features = 1
+    X_1d = X[:, 0].reshape((-1, 1))
+    cov = ShrunkCovariance(shrinkage=0.3)
+    cov.fit(X_1d)
+    assert_array_almost_equal(empirical_covariance(X_1d), cov.covariance_, 4)
+
+    # test shrinkage coeff on a simple data set (without saving precision)
+    cov = ShrunkCovariance(shrinkage=0.5, store_precision=False)
+    cov.fit(X)
+    assert cov.precision_ is None
+
+
+def test_ledoit_wolf():
+    # Tests LedoitWolf module on a simple dataset.
+    # test shrinkage coeff on a simple data set
+    X_centered = X - X.mean(axis=0)
+    lw = LedoitWolf(assume_centered=True)
+    lw.fit(X_centered)
+    shrinkage_ = lw.shrinkage_
+
+    score_ = lw.score(X_centered)
+    assert_almost_equal(
+        ledoit_wolf_shrinkage(X_centered, assume_centered=True), shrinkage_
+    )
+    assert_almost_equal(
+        ledoit_wolf_shrinkage(X_centered, assume_centered=True, block_size=6),
+        shrinkage_,
+    )
+    # compare shrunk covariance obtained from data and from MLE estimate
+    lw_cov_from_mle, lw_shrinkage_from_mle = ledoit_wolf(
+        X_centered, assume_centered=True
+    )
+    assert_array_almost_equal(lw_cov_from_mle, lw.covariance_, 4)
+    assert_almost_equal(lw_shrinkage_from_mle, lw.shrinkage_)
+    # compare estimates given by LW and ShrunkCovariance
+    scov = ShrunkCovariance(shrinkage=lw.shrinkage_, assume_centered=True)
+    scov.fit(X_centered)
+    assert_array_almost_equal(scov.covariance_, lw.covariance_, 4)
+
+    # test with n_features = 1
+    X_1d = X[:, 0].reshape((-1, 1))
+    lw = LedoitWolf(assume_centered=True)
+    lw.fit(X_1d)
+    lw_cov_from_mle, lw_shrinkage_from_mle = ledoit_wolf(X_1d, assume_centered=True)
+    assert_array_almost_equal(lw_cov_from_mle, lw.covariance_, 4)
+    assert_almost_equal(lw_shrinkage_from_mle, lw.shrinkage_)
+    assert_array_almost_equal((X_1d**2).sum() / n_samples, lw.covariance_, 4)
+
+    # test shrinkage coeff on a simple data set (without saving precision)
+    lw = LedoitWolf(store_precision=False, assume_centered=True)
+    lw.fit(X_centered)
+    assert_almost_equal(lw.score(X_centered), score_, 4)
+    assert lw.precision_ is None
+
+    # Same tests without assuming centered data
+    # test shrinkage coeff on a simple data set
+    lw = LedoitWolf()
+    lw.fit(X)
+    assert_almost_equal(lw.shrinkage_, shrinkage_, 4)
+    assert_almost_equal(lw.shrinkage_, ledoit_wolf_shrinkage(X))
+    assert_almost_equal(lw.shrinkage_, ledoit_wolf(X)[1])
+    assert_almost_equal(
+        lw.shrinkage_, _ledoit_wolf(X=X, assume_centered=False, block_size=10000)[1]
+    )
+    assert_almost_equal(lw.score(X), score_, 4)
+    # compare shrunk covariance obtained from data and from MLE estimate
+    lw_cov_from_mle, lw_shrinkage_from_mle = ledoit_wolf(X)
+    assert_array_almost_equal(lw_cov_from_mle, lw.covariance_, 4)
+    assert_almost_equal(lw_shrinkage_from_mle, lw.shrinkage_)
+    # compare estimates given by LW and ShrunkCovariance
+    scov = ShrunkCovariance(shrinkage=lw.shrinkage_)
+    scov.fit(X)
+    assert_array_almost_equal(scov.covariance_, lw.covariance_, 4)
+
+    # test with n_features = 1
+    X_1d = X[:, 0].reshape((-1, 1))
+    lw = LedoitWolf()
+    lw.fit(X_1d)
+    assert_allclose(
+        X_1d.var(ddof=0),
+        _ledoit_wolf(X=X_1d, assume_centered=False, block_size=10000)[0],
+    )
+    lw_cov_from_mle, lw_shrinkage_from_mle = ledoit_wolf(X_1d)
+    assert_array_almost_equal(lw_cov_from_mle, lw.covariance_, 4)
+    assert_almost_equal(lw_shrinkage_from_mle, lw.shrinkage_)
+    assert_array_almost_equal(empirical_covariance(X_1d), lw.covariance_, 4)
+
+    # test with one sample
+    # warning should be raised when using only 1 sample
+    X_1sample = np.arange(5).reshape(1, 5)
+    lw = LedoitWolf()
+
+    warn_msg = "Only one sample available. You may want to reshape your data array"
+    with pytest.warns(UserWarning, match=warn_msg):
+        lw.fit(X_1sample)
+
+    assert_array_almost_equal(lw.covariance_, np.zeros(shape=(5, 5), dtype=np.float64))
+
+    # test shrinkage coeff on a simple data set (without saving precision)
+    lw = LedoitWolf(store_precision=False)
+    lw.fit(X)
+    assert_almost_equal(lw.score(X), score_, 4)
+    assert lw.precision_ is None
+
+
+def _naive_ledoit_wolf_shrinkage(X):
+    # A simple implementation of the formulas from Ledoit & Wolf
+
+    # The computation below achieves the following computations of the
+    # "O. Ledoit and M. Wolf, A Well-Conditioned Estimator for
+    # Large-Dimensional Covariance Matrices"
+    # beta and delta are given in the beginning of section 3.2
+    n_samples, n_features = X.shape
+    emp_cov = empirical_covariance(X, assume_centered=False)
+    mu = np.trace(emp_cov) / n_features
+    delta_ = emp_cov.copy()
+    delta_.flat[:: n_features + 1] -= mu
+    delta = (delta_**2).sum() / n_features
+    X2 = X**2
+    beta_ = (
+        1.0
+        / (n_features * n_samples)
+        * np.sum(np.dot(X2.T, X2) / n_samples - emp_cov**2)
+    )
+
+    beta = min(beta_, delta)
+    shrinkage = beta / delta
+    return shrinkage
+
+
+def test_ledoit_wolf_small():
+    # Compare our blocked implementation to the naive implementation
+    X_small = X[:, :4]
+    lw = LedoitWolf()
+    lw.fit(X_small)
+    shrinkage_ = lw.shrinkage_
+
+    assert_almost_equal(shrinkage_, _naive_ledoit_wolf_shrinkage(X_small))
+
+
+def test_ledoit_wolf_large():
+    # test that ledoit_wolf doesn't error on data that is wider than block_size
+    rng = np.random.RandomState(0)
+    # use a number of features that is larger than the block-size
+    X = rng.normal(size=(10, 20))
+    lw = LedoitWolf(block_size=10).fit(X)
+    # check that covariance is about diagonal (random normal noise)
+    assert_almost_equal(lw.covariance_, np.eye(20), 0)
+    cov = lw.covariance_
+
+    # check that the result is consistent with not splitting data into blocks.
+    lw = LedoitWolf(block_size=25).fit(X)
+    assert_almost_equal(lw.covariance_, cov)
+
+
+@pytest.mark.parametrize(
+    "ledoit_wolf_fitting_function", [LedoitWolf().fit, ledoit_wolf_shrinkage]
+)
+def test_ledoit_wolf_empty_array(ledoit_wolf_fitting_function):
+    """Check that we validate X and raise proper error with 0-sample array."""
+    X_empty = np.zeros((0, 2))
+    with pytest.raises(ValueError, match="Found array with 0 sample"):
+        ledoit_wolf_fitting_function(X_empty)
+
+
+def test_oas():
+    # Tests OAS module on a simple dataset.
+    # test shrinkage coeff on a simple data set
+    X_centered = X - X.mean(axis=0)
+    oa = OAS(assume_centered=True)
+    oa.fit(X_centered)
+    shrinkage_ = oa.shrinkage_
+    score_ = oa.score(X_centered)
+    # compare shrunk covariance obtained from data and from MLE estimate
+    oa_cov_from_mle, oa_shrinkage_from_mle = oas(X_centered, assume_centered=True)
+    assert_array_almost_equal(oa_cov_from_mle, oa.covariance_, 4)
+    assert_almost_equal(oa_shrinkage_from_mle, oa.shrinkage_)
+    # compare estimates given by OAS and ShrunkCovariance
+    scov = ShrunkCovariance(shrinkage=oa.shrinkage_, assume_centered=True)
+    scov.fit(X_centered)
+    assert_array_almost_equal(scov.covariance_, oa.covariance_, 4)
+
+    # test with n_features = 1
+    X_1d = X[:, 0:1]
+    oa = OAS(assume_centered=True)
+    oa.fit(X_1d)
+    oa_cov_from_mle, oa_shrinkage_from_mle = oas(X_1d, assume_centered=True)
+    assert_array_almost_equal(oa_cov_from_mle, oa.covariance_, 4)
+    assert_almost_equal(oa_shrinkage_from_mle, oa.shrinkage_)
+    assert_array_almost_equal((X_1d**2).sum() / n_samples, oa.covariance_, 4)
+
+    # test shrinkage coeff on a simple data set (without saving precision)
+    oa = OAS(store_precision=False, assume_centered=True)
+    oa.fit(X_centered)
+    assert_almost_equal(oa.score(X_centered), score_, 4)
+    assert oa.precision_ is None
+
+    # Same tests without assuming centered data--------------------------------
+    # test shrinkage coeff on a simple data set
+    oa = OAS()
+    oa.fit(X)
+    assert_almost_equal(oa.shrinkage_, shrinkage_, 4)
+    assert_almost_equal(oa.score(X), score_, 4)
+    # compare shrunk covariance obtained from data and from MLE estimate
+    oa_cov_from_mle, oa_shrinkage_from_mle = oas(X)
+    assert_array_almost_equal(oa_cov_from_mle, oa.covariance_, 4)
+    assert_almost_equal(oa_shrinkage_from_mle, oa.shrinkage_)
+    # compare estimates given by OAS and ShrunkCovariance
+    scov = ShrunkCovariance(shrinkage=oa.shrinkage_)
+    scov.fit(X)
+    assert_array_almost_equal(scov.covariance_, oa.covariance_, 4)
+
+    # test with n_features = 1
+    X_1d = X[:, 0].reshape((-1, 1))
+    oa = OAS()
+    oa.fit(X_1d)
+    oa_cov_from_mle, oa_shrinkage_from_mle = oas(X_1d)
+    assert_array_almost_equal(oa_cov_from_mle, oa.covariance_, 4)
+    assert_almost_equal(oa_shrinkage_from_mle, oa.shrinkage_)
+    assert_array_almost_equal(empirical_covariance(X_1d), oa.covariance_, 4)
+
+    # test with one sample
+    # warning should be raised when using only 1 sample
+    X_1sample = np.arange(5).reshape(1, 5)
+    oa = OAS()
+    warn_msg = "Only one sample available. You may want to reshape your data array"
+    with pytest.warns(UserWarning, match=warn_msg):
+        oa.fit(X_1sample)
+
+    assert_array_almost_equal(oa.covariance_, np.zeros(shape=(5, 5), dtype=np.float64))
+
+    # test shrinkage coeff on a simple data set (without saving precision)
+    oa = OAS(store_precision=False)
+    oa.fit(X)
+    assert_almost_equal(oa.score(X), score_, 4)
+    assert oa.precision_ is None
+
+    # test function _oas without assuming centered data
+    X_1f = X[:, 0:1]
+    oa = OAS()
+    oa.fit(X_1f)
+    # compare shrunk covariance obtained from data and from MLE estimate
+    _oa_cov_from_mle, _oa_shrinkage_from_mle = _oas(X_1f)
+    assert_array_almost_equal(_oa_cov_from_mle, oa.covariance_, 4)
+    assert_almost_equal(_oa_shrinkage_from_mle, oa.shrinkage_)
+    assert_array_almost_equal((X_1f**2).sum() / n_samples, oa.covariance_, 4)
+
+
+def test_EmpiricalCovariance_validates_mahalanobis():
+    """Checks that EmpiricalCovariance validates data with mahalanobis."""
+    cov = EmpiricalCovariance().fit(X)
+
+    msg = f"X has 2 features, but \\w+ is expecting {X.shape[1]} features as input"
+    with pytest.raises(ValueError, match=msg):
+        cov.mahalanobis(X[:, :2])

env-llmeval/lib/python3.10/site-packages/sklearn/covariance/tests/test_elliptic_envelope.py

@@ -0,0 +1,52 @@
+"""
+Testing for Elliptic Envelope algorithm (sklearn.covariance.elliptic_envelope).
+"""
+
+import numpy as np
+import pytest
+
+from sklearn.covariance import EllipticEnvelope
+from sklearn.exceptions import NotFittedError
+from sklearn.utils._testing import (
+    assert_almost_equal,
+    assert_array_almost_equal,
+    assert_array_equal,
+)
+
+
+def test_elliptic_envelope(global_random_seed):
+    rnd = np.random.RandomState(global_random_seed)
+    X = rnd.randn(100, 10)
+    clf = EllipticEnvelope(contamination=0.1)
+    with pytest.raises(NotFittedError):
+        clf.predict(X)
+    with pytest.raises(NotFittedError):
+        clf.decision_function(X)
+    clf.fit(X)
+    y_pred = clf.predict(X)
+    scores = clf.score_samples(X)
+    decisions = clf.decision_function(X)
+
+    assert_array_almost_equal(scores, -clf.mahalanobis(X))
+    assert_array_almost_equal(clf.mahalanobis(X), clf.dist_)
+    assert_almost_equal(
+        clf.score(X, np.ones(100)), (100 - y_pred[y_pred == -1].size) / 100.0
+    )
+    assert sum(y_pred == -1) == sum(decisions < 0)
+
+
+def test_score_samples():
+    X_train = [[1, 1], [1, 2], [2, 1]]
+    clf1 = EllipticEnvelope(contamination=0.2).fit(X_train)
+    clf2 = EllipticEnvelope().fit(X_train)
+    assert_array_equal(
+        clf1.score_samples([[2.0, 2.0]]),
+        clf1.decision_function([[2.0, 2.0]]) + clf1.offset_,
+    )
+    assert_array_equal(
+        clf2.score_samples([[2.0, 2.0]]),
+        clf2.decision_function([[2.0, 2.0]]) + clf2.offset_,
+    )
+    assert_array_equal(
+        clf1.score_samples([[2.0, 2.0]]), clf2.score_samples([[2.0, 2.0]])
+    )

env-llmeval/lib/python3.10/site-packages/sklearn/covariance/tests/test_graphical_lasso.py

@@ -0,0 +1,286 @@
+""" Test the graphical_lasso module.
+"""
+import sys
+from io import StringIO
+
+import numpy as np
+import pytest
+from numpy.testing import assert_allclose
+from scipy import linalg
+
+from sklearn import datasets
+from sklearn.covariance import (
+    GraphicalLasso,
+    GraphicalLassoCV,
+    empirical_covariance,
+    graphical_lasso,
+)
+from sklearn.datasets import make_sparse_spd_matrix
+from sklearn.utils import check_random_state
+from sklearn.utils._testing import (
+    _convert_container,
+    assert_array_almost_equal,
+    assert_array_less,
+)
+
+
+def test_graphical_lassos(random_state=1):
+    """Test the graphical lasso solvers.
+
+    This checks is unstable for some random seeds where the covariance found with "cd"
+    and "lars" solvers are different (4 cases / 100 tries).
+    """
+    # Sample data from a sparse multivariate normal
+    dim = 20
+    n_samples = 100
+    random_state = check_random_state(random_state)
+    prec = make_sparse_spd_matrix(dim, alpha=0.95, random_state=random_state)
+    cov = linalg.inv(prec)
+    X = random_state.multivariate_normal(np.zeros(dim), cov, size=n_samples)
+    emp_cov = empirical_covariance(X)
+
+    for alpha in (0.0, 0.1, 0.25):
+        covs = dict()
+        icovs = dict()
+        for method in ("cd", "lars"):
+            cov_, icov_, costs = graphical_lasso(
+                emp_cov, return_costs=True, alpha=alpha, mode=method
+            )
+            covs[method] = cov_
+            icovs[method] = icov_
+            costs, dual_gap = np.array(costs).T
+            # Check that the costs always decrease (doesn't hold if alpha == 0)
+            if not alpha == 0:
+                # use 1e-12 since the cost can be exactly 0
+                assert_array_less(np.diff(costs), 1e-12)
+        # Check that the 2 approaches give similar results
+        assert_allclose(covs["cd"], covs["lars"], atol=5e-4)
+        assert_allclose(icovs["cd"], icovs["lars"], atol=5e-4)
+
+    # Smoke test the estimator
+    model = GraphicalLasso(alpha=0.25).fit(X)
+    model.score(X)
+    assert_array_almost_equal(model.covariance_, covs["cd"], decimal=4)
+    assert_array_almost_equal(model.covariance_, covs["lars"], decimal=4)
+
+    # For a centered matrix, assume_centered could be chosen True or False
+    # Check that this returns indeed the same result for centered data
+    Z = X - X.mean(0)
+    precs = list()
+    for assume_centered in (False, True):
+        prec_ = GraphicalLasso(assume_centered=assume_centered).fit(Z).precision_
+        precs.append(prec_)
+    assert_array_almost_equal(precs[0], precs[1])
+
+
+def test_graphical_lasso_when_alpha_equals_0():
+    """Test graphical_lasso's early return condition when alpha=0."""
+    X = np.random.randn(100, 10)
+    emp_cov = empirical_covariance(X, assume_centered=True)
+
+    model = GraphicalLasso(alpha=0, covariance="precomputed").fit(emp_cov)
+    assert_allclose(model.precision_, np.linalg.inv(emp_cov))
+
+    _, precision = graphical_lasso(emp_cov, alpha=0)
+    assert_allclose(precision, np.linalg.inv(emp_cov))
+
+
+@pytest.mark.parametrize("mode", ["cd", "lars"])
+def test_graphical_lasso_n_iter(mode):
+    X, _ = datasets.make_classification(n_samples=5_000, n_features=20, random_state=0)
+    emp_cov = empirical_covariance(X)
+
+    _, _, n_iter = graphical_lasso(
+        emp_cov, 0.2, mode=mode, max_iter=2, return_n_iter=True
+    )
+    assert n_iter == 2
+
+
+def test_graphical_lasso_iris():
+    # Hard-coded solution from R glasso package for alpha=1.0
+    # (need to set penalize.diagonal to FALSE)
+    cov_R = np.array(
+        [
+            [0.68112222, 0.0000000, 0.265820, 0.02464314],
+            [0.00000000, 0.1887129, 0.000000, 0.00000000],
+            [0.26582000, 0.0000000, 3.095503, 0.28697200],
+            [0.02464314, 0.0000000, 0.286972, 0.57713289],
+        ]
+    )
+    icov_R = np.array(
+        [
+            [1.5190747, 0.000000, -0.1304475, 0.0000000],
+            [0.0000000, 5.299055, 0.0000000, 0.0000000],
+            [-0.1304475, 0.000000, 0.3498624, -0.1683946],
+            [0.0000000, 0.000000, -0.1683946, 1.8164353],
+        ]
+    )
+    X = datasets.load_iris().data
+    emp_cov = empirical_covariance(X)
+    for method in ("cd", "lars"):
+        cov, icov = graphical_lasso(emp_cov, alpha=1.0, return_costs=False, mode=method)
+        assert_array_almost_equal(cov, cov_R)
+        assert_array_almost_equal(icov, icov_R)
+
+
+def test_graph_lasso_2D():
+    # Hard-coded solution from Python skggm package
+    # obtained by calling `quic(emp_cov, lam=.1, tol=1e-8)`
+    cov_skggm = np.array([[3.09550269, 1.186972], [1.186972, 0.57713289]])
+
+    icov_skggm = np.array([[1.52836773, -3.14334831], [-3.14334831, 8.19753385]])
+    X = datasets.load_iris().data[:, 2:]
+    emp_cov = empirical_covariance(X)
+    for method in ("cd", "lars"):
+        cov, icov = graphical_lasso(emp_cov, alpha=0.1, return_costs=False, mode=method)
+        assert_array_almost_equal(cov, cov_skggm)
+        assert_array_almost_equal(icov, icov_skggm)
+
+
+def test_graphical_lasso_iris_singular():
+    # Small subset of rows to test the rank-deficient case
+    # Need to choose samples such that none of the variances are zero
+    indices = np.arange(10, 13)
+
+    # Hard-coded solution from R glasso package for alpha=0.01
+    cov_R = np.array(
+        [
+            [0.08, 0.056666662595, 0.00229729713223, 0.00153153142149],
+            [0.056666662595, 0.082222222222, 0.00333333333333, 0.00222222222222],
+            [0.002297297132, 0.003333333333, 0.00666666666667, 0.00009009009009],
+            [0.001531531421, 0.002222222222, 0.00009009009009, 0.00222222222222],
+        ]
+    )
+    icov_R = np.array(
+        [
+            [24.42244057, -16.831679593, 0.0, 0.0],
+            [-16.83168201, 24.351841681, -6.206896552, -12.5],
+            [0.0, -6.206896171, 153.103448276, 0.0],
+            [0.0, -12.499999143, 0.0, 462.5],
+        ]
+    )
+    X = datasets.load_iris().data[indices, :]
+    emp_cov = empirical_covariance(X)
+    for method in ("cd", "lars"):
+        cov, icov = graphical_lasso(
+            emp_cov, alpha=0.01, return_costs=False, mode=method
+        )
+        assert_array_almost_equal(cov, cov_R, decimal=5)
+        assert_array_almost_equal(icov, icov_R, decimal=5)
+
+
+def test_graphical_lasso_cv(random_state=1):
+    # Sample data from a sparse multivariate normal
+    dim = 5
+    n_samples = 6
+    random_state = check_random_state(random_state)
+    prec = make_sparse_spd_matrix(dim, alpha=0.96, random_state=random_state)
+    cov = linalg.inv(prec)
+    X = random_state.multivariate_normal(np.zeros(dim), cov, size=n_samples)
+    # Capture stdout, to smoke test the verbose mode
+    orig_stdout = sys.stdout
+    try:
+        sys.stdout = StringIO()
+        # We need verbose very high so that Parallel prints on stdout
+        GraphicalLassoCV(verbose=100, alphas=5, tol=1e-1).fit(X)
+    finally:
+        sys.stdout = orig_stdout
+
+
+@pytest.mark.parametrize("alphas_container_type", ["list", "tuple", "array"])
+def test_graphical_lasso_cv_alphas_iterable(alphas_container_type):
+    """Check that we can pass an array-like to `alphas`.
+
+    Non-regression test for:
+    https://github.com/scikit-learn/scikit-learn/issues/22489
+    """
+    true_cov = np.array(
+        [
+            [0.8, 0.0, 0.2, 0.0],
+            [0.0, 0.4, 0.0, 0.0],
+            [0.2, 0.0, 0.3, 0.1],
+            [0.0, 0.0, 0.1, 0.7],
+        ]
+    )
+    rng = np.random.RandomState(0)
+    X = rng.multivariate_normal(mean=[0, 0, 0, 0], cov=true_cov, size=200)
+    alphas = _convert_container([0.02, 0.03], alphas_container_type)
+    GraphicalLassoCV(alphas=alphas, tol=1e-1, n_jobs=1).fit(X)
+
+
+@pytest.mark.parametrize(
+    "alphas,err_type,err_msg",
+    [
+        ([-0.02, 0.03], ValueError, "must be > 0"),
+        ([0, 0.03], ValueError, "must be > 0"),
+        (["not_number", 0.03], TypeError, "must be an instance of float"),
+    ],
+)
+def test_graphical_lasso_cv_alphas_invalid_array(alphas, err_type, err_msg):
+    """Check that if an array-like containing a value
+    outside of (0, inf] is passed to `alphas`, a ValueError is raised.
+    Check if a string is passed, a TypeError is raised.
+    """
+    true_cov = np.array(
+        [
+            [0.8, 0.0, 0.2, 0.0],
+            [0.0, 0.4, 0.0, 0.0],
+            [0.2, 0.0, 0.3, 0.1],
+            [0.0, 0.0, 0.1, 0.7],
+        ]
+    )
+    rng = np.random.RandomState(0)
+    X = rng.multivariate_normal(mean=[0, 0, 0, 0], cov=true_cov, size=200)
+
+    with pytest.raises(err_type, match=err_msg):
+        GraphicalLassoCV(alphas=alphas, tol=1e-1, n_jobs=1).fit(X)
+
+
+def test_graphical_lasso_cv_scores():
+    splits = 4
+    n_alphas = 5
+    n_refinements = 3
+    true_cov = np.array(
+        [
+            [0.8, 0.0, 0.2, 0.0],
+            [0.0, 0.4, 0.0, 0.0],
+            [0.2, 0.0, 0.3, 0.1],
+            [0.0, 0.0, 0.1, 0.7],
+        ]
+    )
+    rng = np.random.RandomState(0)
+    X = rng.multivariate_normal(mean=[0, 0, 0, 0], cov=true_cov, size=200)
+    cov = GraphicalLassoCV(cv=splits, alphas=n_alphas, n_refinements=n_refinements).fit(
+        X
+    )
+
+    cv_results = cov.cv_results_
+    # alpha and one for each split
+
+    total_alphas = n_refinements * n_alphas + 1
+    keys = ["alphas"]
+    split_keys = [f"split{i}_test_score" for i in range(splits)]
+    for key in keys + split_keys:
+        assert key in cv_results
+        assert len(cv_results[key]) == total_alphas
+
+    cv_scores = np.asarray([cov.cv_results_[key] for key in split_keys])
+    expected_mean = cv_scores.mean(axis=0)
+    expected_std = cv_scores.std(axis=0)
+
+    assert_allclose(cov.cv_results_["mean_test_score"], expected_mean)
+    assert_allclose(cov.cv_results_["std_test_score"], expected_std)
+
+
+# TODO(1.5): remove in 1.5
+def test_graphical_lasso_cov_init_deprecation():
+    """Check that we raise a deprecation warning if providing `cov_init` in
+    `graphical_lasso`."""
+    rng, dim, n_samples = np.random.RandomState(0), 20, 100
+    prec = make_sparse_spd_matrix(dim, alpha=0.95, random_state=0)
+    cov = linalg.inv(prec)
+    X = rng.multivariate_normal(np.zeros(dim), cov, size=n_samples)
+
+    emp_cov = empirical_covariance(X)
+    with pytest.warns(FutureWarning, match="cov_init parameter is deprecated"):
+        graphical_lasso(emp_cov, alpha=0.1, cov_init=emp_cov)

env-llmeval/lib/python3.10/site-packages/sklearn/covariance/tests/test_robust_covariance.py

@@ -0,0 +1,171 @@
+# Author: Alexandre Gramfort <[email protected]>
+#         Gael Varoquaux <[email protected]>
+#         Virgile Fritsch <[email protected]>
+#
+# License: BSD 3 clause
+
+import itertools
+
+import numpy as np
+import pytest
+
+from sklearn import datasets
+from sklearn.covariance import MinCovDet, empirical_covariance, fast_mcd
+from sklearn.utils._testing import assert_array_almost_equal
+
+X = datasets.load_iris().data
+X_1d = X[:, 0]
+n_samples, n_features = X.shape
+
+
+def test_mcd(global_random_seed):
+    # Tests the FastMCD algorithm implementation
+    # Small data set
+    # test without outliers (random independent normal data)
+    launch_mcd_on_dataset(100, 5, 0, 0.02, 0.1, 75, global_random_seed)
+    # test with a contaminated data set (medium contamination)
+    launch_mcd_on_dataset(100, 5, 20, 0.3, 0.3, 65, global_random_seed)
+    # test with a contaminated data set (strong contamination)
+    launch_mcd_on_dataset(100, 5, 40, 0.1, 0.1, 50, global_random_seed)
+
+    # Medium data set
+    launch_mcd_on_dataset(1000, 5, 450, 0.1, 0.1, 540, global_random_seed)
+
+    # Large data set
+    launch_mcd_on_dataset(1700, 5, 800, 0.1, 0.1, 870, global_random_seed)
+
+    # 1D data set
+    launch_mcd_on_dataset(500, 1, 100, 0.02, 0.02, 350, global_random_seed)
+
+
+def test_fast_mcd_on_invalid_input():
+    X = np.arange(100)
+    msg = "Expected 2D array, got 1D array instead"
+    with pytest.raises(ValueError, match=msg):
+        fast_mcd(X)
+
+
+def test_mcd_class_on_invalid_input():
+    X = np.arange(100)
+    mcd = MinCovDet()
+    msg = "Expected 2D array, got 1D array instead"
+    with pytest.raises(ValueError, match=msg):
+        mcd.fit(X)
+
+
+def launch_mcd_on_dataset(
+    n_samples, n_features, n_outliers, tol_loc, tol_cov, tol_support, seed
+):
+    rand_gen = np.random.RandomState(seed)
+    data = rand_gen.randn(n_samples, n_features)
+    # add some outliers
+    outliers_index = rand_gen.permutation(n_samples)[:n_outliers]
+    outliers_offset = 10.0 * (rand_gen.randint(2, size=(n_outliers, n_features)) - 0.5)
+    data[outliers_index] += outliers_offset
+    inliers_mask = np.ones(n_samples).astype(bool)
+    inliers_mask[outliers_index] = False
+
+    pure_data = data[inliers_mask]
+    # compute MCD by fitting an object
+    mcd_fit = MinCovDet(random_state=seed).fit(data)
+    T = mcd_fit.location_
+    S = mcd_fit.covariance_
+    H = mcd_fit.support_
+    # compare with the estimates learnt from the inliers
+    error_location = np.mean((pure_data.mean(0) - T) ** 2)
+    assert error_location < tol_loc
+    error_cov = np.mean((empirical_covariance(pure_data) - S) ** 2)
+    assert error_cov < tol_cov
+    assert np.sum(H) >= tol_support
+    assert_array_almost_equal(mcd_fit.mahalanobis(data), mcd_fit.dist_)
+
+
+def test_mcd_issue1127():
+    # Check that the code does not break with X.shape = (3, 1)
+    # (i.e. n_support = n_samples)
+    rnd = np.random.RandomState(0)
+    X = rnd.normal(size=(3, 1))
+    mcd = MinCovDet()
+    mcd.fit(X)
+
+
+def test_mcd_issue3367(global_random_seed):
+    # Check that MCD completes when the covariance matrix is singular
+    # i.e. one of the rows and columns are all zeros
+    rand_gen = np.random.RandomState(global_random_seed)
+
+    # Think of these as the values for X and Y -> 10 values between -5 and 5
+    data_values = np.linspace(-5, 5, 10).tolist()
+    # Get the cartesian product of all possible coordinate pairs from above set
+    data = np.array(list(itertools.product(data_values, data_values)))
+
+    # Add a third column that's all zeros to make our data a set of point
+    # within a plane, which means that the covariance matrix will be singular
+    data = np.hstack((data, np.zeros((data.shape[0], 1))))
+
+    # The below line of code should raise an exception if the covariance matrix
+    # is singular. As a further test, since we have points in XYZ, the
+    # principle components (Eigenvectors) of these directly relate to the
+    # geometry of the points. Since it's a plane, we should be able to test
+    # that the Eigenvector that corresponds to the smallest Eigenvalue is the
+    # plane normal, specifically [0, 0, 1], since everything is in the XY plane
+    # (as I've set it up above). To do this one would start by:
+    #
+    #     evals, evecs = np.linalg.eigh(mcd_fit.covariance_)
+    #     normal = evecs[:, np.argmin(evals)]
+    #
+    # After which we need to assert that our `normal` is equal to [0, 0, 1].
+    # Do note that there is floating point error associated with this, so it's
+    # best to subtract the two and then compare some small tolerance (e.g.
+    # 1e-12).
+    MinCovDet(random_state=rand_gen).fit(data)
+
+
+def test_mcd_support_covariance_is_zero():
+    # Check that MCD returns a ValueError with informative message when the
+    # covariance of the support data is equal to 0.
+    X_1 = np.array([0.5, 0.1, 0.1, 0.1, 0.957, 0.1, 0.1, 0.1, 0.4285, 0.1])
+    X_1 = X_1.reshape(-1, 1)
+    X_2 = np.array([0.5, 0.3, 0.3, 0.3, 0.957, 0.3, 0.3, 0.3, 0.4285, 0.3])
+    X_2 = X_2.reshape(-1, 1)
+    msg = (
+        "The covariance matrix of the support data is equal to 0, try to "
+        "increase support_fraction"
+    )
+    for X in [X_1, X_2]:
+        with pytest.raises(ValueError, match=msg):
+            MinCovDet().fit(X)
+
+
+def test_mcd_increasing_det_warning(global_random_seed):
+    # Check that a warning is raised if we observe increasing determinants
+    # during the c_step. In theory the sequence of determinants should be
+    # decreasing. Increasing determinants are likely due to ill-conditioned
+    # covariance matrices that result in poor precision matrices.
+
+    X = [
+        [5.1, 3.5, 1.4, 0.2],
+        [4.9, 3.0, 1.4, 0.2],
+        [4.7, 3.2, 1.3, 0.2],
+        [4.6, 3.1, 1.5, 0.2],
+        [5.0, 3.6, 1.4, 0.2],
+        [4.6, 3.4, 1.4, 0.3],
+        [5.0, 3.4, 1.5, 0.2],
+        [4.4, 2.9, 1.4, 0.2],
+        [4.9, 3.1, 1.5, 0.1],
+        [5.4, 3.7, 1.5, 0.2],
+        [4.8, 3.4, 1.6, 0.2],
+        [4.8, 3.0, 1.4, 0.1],
+        [4.3, 3.0, 1.1, 0.1],
+        [5.1, 3.5, 1.4, 0.3],
+        [5.7, 3.8, 1.7, 0.3],
+        [5.4, 3.4, 1.7, 0.2],
+        [4.6, 3.6, 1.0, 0.2],
+        [5.0, 3.0, 1.6, 0.2],
+        [5.2, 3.5, 1.5, 0.2],
+    ]
+
+    mcd = MinCovDet(support_fraction=0.5, random_state=global_random_seed)
+    warn_msg = "Determinant has increased"
+    with pytest.warns(RuntimeWarning, match=warn_msg):
+        mcd.fit(X)

env-llmeval/lib/python3.10/site-packages/sklearn/feature_selection/_base.py

@@ -0,0 +1,266 @@
+"""Generic feature selection mixin"""
+
+# Authors: G. Varoquaux, A. Gramfort, L. Buitinck, J. Nothman
+# License: BSD 3 clause
+
+import warnings
+from abc import ABCMeta, abstractmethod
+from operator import attrgetter
+
+import numpy as np
+from scipy.sparse import csc_matrix, issparse
+
+from ..base import TransformerMixin
+from ..utils import (
+    _is_pandas_df,
+    _safe_indexing,
+    check_array,
+    safe_sqr,
+)
+from ..utils._set_output import _get_output_config
+from ..utils._tags import _safe_tags
+from ..utils.validation import _check_feature_names_in, check_is_fitted
+
+
+class SelectorMixin(TransformerMixin, metaclass=ABCMeta):
+    """
+    Transformer mixin that performs feature selection given a support mask
+
+    This mixin provides a feature selector implementation with `transform` and
+    `inverse_transform` functionality given an implementation of
+    `_get_support_mask`.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from sklearn.datasets import load_iris
+    >>> from sklearn.base import BaseEstimator
+    >>> from sklearn.feature_selection import SelectorMixin
+    >>> class FeatureSelector(SelectorMixin, BaseEstimator):
+    ...    def fit(self, X, y=None):
+    ...        self.n_features_in_ = X.shape[1]
+    ...        return self
+    ...    def _get_support_mask(self):
+    ...        mask = np.zeros(self.n_features_in_, dtype=bool)
+    ...        mask[:2] = True  # select the first two features
+    ...        return mask
+    >>> X, y = load_iris(return_X_y=True)
+    >>> FeatureSelector().fit_transform(X, y).shape
+    (150, 2)
+    """
+
+    def get_support(self, indices=False):
+        """
+        Get a mask, or integer index, of the features selected.
+
+        Parameters
+        ----------
+        indices : bool, default=False
+            If True, the return value will be an array of integers, rather
+            than a boolean mask.
+
+        Returns
+        -------
+        support : array
+            An index that selects the retained features from a feature vector.
+            If `indices` is False, this is a boolean array of shape
+            [# input features], in which an element is True iff its
+            corresponding feature is selected for retention. If `indices` is
+            True, this is an integer array of shape [# output features] whose
+            values are indices into the input feature vector.
+        """
+        mask = self._get_support_mask()
+        return mask if not indices else np.where(mask)[0]
+
+    @abstractmethod
+    def _get_support_mask(self):
+        """
+        Get the boolean mask indicating which features are selected
+
+        Returns
+        -------
+        support : boolean array of shape [# input features]
+            An element is True iff its corresponding feature is selected for
+            retention.
+        """
+
+    def transform(self, X):
+        """Reduce X to the selected features.
+
+        Parameters
+        ----------
+        X : array of shape [n_samples, n_features]
+            The input samples.
+
+        Returns
+        -------
+        X_r : array of shape [n_samples, n_selected_features]
+            The input samples with only the selected features.
+        """
+        # Preserve X when X is a dataframe and the output is configured to
+        # be pandas.
+        output_config_dense = _get_output_config("transform", estimator=self)["dense"]
+        preserve_X = output_config_dense != "default" and _is_pandas_df(X)
+
+        # note: we use _safe_tags instead of _get_tags because this is a
+        # public Mixin.
+        X = self._validate_data(
+            X,
+            dtype=None,
+            accept_sparse="csr",
+            force_all_finite=not _safe_tags(self, key="allow_nan"),
+            cast_to_ndarray=not preserve_X,
+            reset=False,
+        )
+        return self._transform(X)
+
+    def _transform(self, X):
+        """Reduce X to the selected features."""
+        mask = self.get_support()
+        if not mask.any():
+            warnings.warn(
+                (
+                    "No features were selected: either the data is"
+                    " too noisy or the selection test too strict."
+                ),
+                UserWarning,
+            )
+            if hasattr(X, "iloc"):
+                return X.iloc[:, :0]
+            return np.empty(0, dtype=X.dtype).reshape((X.shape[0], 0))
+        return _safe_indexing(X, mask, axis=1)
+
+    def inverse_transform(self, X):
+        """Reverse the transformation operation.
+
+        Parameters
+        ----------
+        X : array of shape [n_samples, n_selected_features]
+            The input samples.
+
+        Returns
+        -------
+        X_r : array of shape [n_samples, n_original_features]
+            `X` with columns of zeros inserted where features would have
+            been removed by :meth:`transform`.
+        """
+        if issparse(X):
+            X = X.tocsc()
+            # insert additional entries in indptr:
+            # e.g. if transform changed indptr from [0 2 6 7] to [0 2 3]
+            # col_nonzeros here will be [2 0 1] so indptr becomes [0 2 2 3]
+            it = self.inverse_transform(np.diff(X.indptr).reshape(1, -1))
+            col_nonzeros = it.ravel()
+            indptr = np.concatenate([[0], np.cumsum(col_nonzeros)])
+            Xt = csc_matrix(
+                (X.data, X.indices, indptr),
+                shape=(X.shape[0], len(indptr) - 1),
+                dtype=X.dtype,
+            )
+            return Xt
+
+        support = self.get_support()
+        X = check_array(X, dtype=None)
+        if support.sum() != X.shape[1]:
+            raise ValueError("X has a different shape than during fitting.")
+
+        if X.ndim == 1:
+            X = X[None, :]
+        Xt = np.zeros((X.shape[0], support.size), dtype=X.dtype)
+        Xt[:, support] = X
+        return Xt
+
+    def get_feature_names_out(self, input_features=None):
+        """Mask feature names according to selected features.
+
+        Parameters
+        ----------
+        input_features : array-like of str or None, default=None
+            Input features.
+
+            - If `input_features` is `None`, then `feature_names_in_` is
+              used as feature names in. If `feature_names_in_` is not defined,
+              then the following input feature names are generated:
+              `["x0", "x1", ..., "x(n_features_in_ - 1)"]`.
+            - If `input_features` is an array-like, then `input_features` must
+              match `feature_names_in_` if `feature_names_in_` is defined.
+
+        Returns
+        -------
+        feature_names_out : ndarray of str objects
+            Transformed feature names.
+        """
+        check_is_fitted(self)
+        input_features = _check_feature_names_in(self, input_features)
+        return input_features[self.get_support()]
+
+
+def _get_feature_importances(estimator, getter, transform_func=None, norm_order=1):
+    """
+    Retrieve and aggregate (ndim > 1)  the feature importances
+    from an estimator. Also optionally applies transformation.
+
+    Parameters
+    ----------
+    estimator : estimator
+        A scikit-learn estimator from which we want to get the feature
+        importances.
+
+    getter : "auto", str or callable
+        An attribute or a callable to get the feature importance. If `"auto"`,
+        `estimator` is expected to expose `coef_` or `feature_importances`.
+
+    transform_func : {"norm", "square"}, default=None
+        The transform to apply to the feature importances. By default (`None`)
+        no transformation is applied.
+
+    norm_order : int, default=1
+        The norm order to apply when `transform_func="norm"`. Only applied
+        when `importances.ndim > 1`.
+
+    Returns
+    -------
+    importances : ndarray of shape (n_features,)
+        The features importances, optionally transformed.
+    """
+    if isinstance(getter, str):
+        if getter == "auto":
+            if hasattr(estimator, "coef_"):
+                getter = attrgetter("coef_")
+            elif hasattr(estimator, "feature_importances_"):
+                getter = attrgetter("feature_importances_")
+            else:
+                raise ValueError(
+                    "when `importance_getter=='auto'`, the underlying "
+                    f"estimator {estimator.__class__.__name__} should have "
+                    "`coef_` or `feature_importances_` attribute. Either "
+                    "pass a fitted estimator to feature selector or call fit "
+                    "before calling transform."
+                )
+        else:
+            getter = attrgetter(getter)
+    elif not callable(getter):
+        raise ValueError("`importance_getter` has to be a string or `callable`")
+
+    importances = getter(estimator)
+
+    if transform_func is None:
+        return importances
+    elif transform_func == "norm":
+        if importances.ndim == 1:
+            importances = np.abs(importances)
+        else:
+            importances = np.linalg.norm(importances, axis=0, ord=norm_order)
+    elif transform_func == "square":
+        if importances.ndim == 1:
+            importances = safe_sqr(importances)
+        else:
+            importances = safe_sqr(importances).sum(axis=0)
+    else:
+        raise ValueError(
+            "Valid values for `transform_func` are "
+            + "None, 'norm' and 'square'. Those two "
+            + "transformation are only supported now"
+        )
+
+    return importances

env-llmeval/lib/python3.10/site-packages/sklearn/feature_selection/_from_model.py

@@ -0,0 +1,522 @@
+# Authors: Gilles Louppe, Mathieu Blondel, Maheshakya Wijewardena
+# License: BSD 3 clause
+
+from copy import deepcopy
+from numbers import Integral, Real
+
+import numpy as np
+
+from ..base import BaseEstimator, MetaEstimatorMixin, _fit_context, clone
+from ..exceptions import NotFittedError
+from ..utils._param_validation import HasMethods, Interval, Options
+from ..utils._tags import _safe_tags
+from ..utils.metadata_routing import (
+    MetadataRouter,
+    MethodMapping,
+    _routing_enabled,
+    process_routing,
+)
+from ..utils.metaestimators import available_if
+from ..utils.validation import _num_features, check_is_fitted, check_scalar
+from ._base import SelectorMixin, _get_feature_importances
+
+
+def _calculate_threshold(estimator, importances, threshold):
+    """Interpret the threshold value"""
+
+    if threshold is None:
+        # determine default from estimator
+        est_name = estimator.__class__.__name__
+        is_l1_penalized = hasattr(estimator, "penalty") and estimator.penalty == "l1"
+        is_lasso = "Lasso" in est_name
+        is_elasticnet_l1_penalized = "ElasticNet" in est_name and (
+            (hasattr(estimator, "l1_ratio_") and np.isclose(estimator.l1_ratio_, 1.0))
+            or (hasattr(estimator, "l1_ratio") and np.isclose(estimator.l1_ratio, 1.0))
+        )
+        if is_l1_penalized or is_lasso or is_elasticnet_l1_penalized:
+            # the natural default threshold is 0 when l1 penalty was used
+            threshold = 1e-5
+        else:
+            threshold = "mean"
+
+    if isinstance(threshold, str):
+        if "*" in threshold:
+            scale, reference = threshold.split("*")
+            scale = float(scale.strip())
+            reference = reference.strip()
+
+            if reference == "median":
+                reference = np.median(importances)
+            elif reference == "mean":
+                reference = np.mean(importances)
+            else:
+                raise ValueError("Unknown reference: " + reference)
+
+            threshold = scale * reference
+
+        elif threshold == "median":
+            threshold = np.median(importances)
+
+        elif threshold == "mean":
+            threshold = np.mean(importances)
+
+        else:
+            raise ValueError(
+                "Expected threshold='mean' or threshold='median' got %s" % threshold
+            )
+
+    else:
+        threshold = float(threshold)
+
+    return threshold
+
+
+def _estimator_has(attr):
+    """Check if we can delegate a method to the underlying estimator.
+
+    First, we check the fitted `estimator_` if available, otherwise we check the
+    unfitted `estimator`. We raise the original `AttributeError` if `attr` does
+    not exist. This function is used together with `available_if`.
+    """
+
+    def check(self):
+        if hasattr(self, "estimator_"):
+            getattr(self.estimator_, attr)
+        else:
+            getattr(self.estimator, attr)
+
+        return True
+
+    return check
+
+
+class SelectFromModel(MetaEstimatorMixin, SelectorMixin, BaseEstimator):
+    """Meta-transformer for selecting features based on importance weights.
+
+    .. versionadded:: 0.17
+
+    Read more in the :ref:`User Guide <select_from_model>`.
+
+    Parameters
+    ----------
+    estimator : object
+        The base estimator from which the transformer is built.
+        This can be both a fitted (if ``prefit`` is set to True)
+        or a non-fitted estimator. The estimator should have a
+        ``feature_importances_`` or ``coef_`` attribute after fitting.
+        Otherwise, the ``importance_getter`` parameter should be used.
+
+    threshold : str or float, default=None
+        The threshold value to use for feature selection. Features whose
+        absolute importance value is greater or equal are kept while the others
+        are discarded. If "median" (resp. "mean"), then the ``threshold`` value
+        is the median (resp. the mean) of the feature importances. A scaling
+        factor (e.g., "1.25*mean") may also be used. If None and if the
+        estimator has a parameter penalty set to l1, either explicitly
+        or implicitly (e.g, Lasso), the threshold used is 1e-5.
+        Otherwise, "mean" is used by default.
+
+    prefit : bool, default=False
+        Whether a prefit model is expected to be passed into the constructor
+        directly or not.
+        If `True`, `estimator` must be a fitted estimator.
+        If `False`, `estimator` is fitted and updated by calling
+        `fit` and `partial_fit`, respectively.
+
+    norm_order : non-zero int, inf, -inf, default=1
+        Order of the norm used to filter the vectors of coefficients below
+        ``threshold`` in the case where the ``coef_`` attribute of the
+        estimator is of dimension 2.
+
+    max_features : int, callable, default=None
+        The maximum number of features to select.
+
+        - If an integer, then it specifies the maximum number of features to
+          allow.
+        - If a callable, then it specifies how to calculate the maximum number of
+          features allowed by using the output of `max_features(X)`.
+        - If `None`, then all features are kept.
+
+        To only select based on ``max_features``, set ``threshold=-np.inf``.
+
+        .. versionadded:: 0.20
+        .. versionchanged:: 1.1
+           `max_features` accepts a callable.
+
+    importance_getter : str or callable, default='auto'
+        If 'auto', uses the feature importance either through a ``coef_``
+        attribute or ``feature_importances_`` attribute of estimator.
+
+        Also accepts a string that specifies an attribute name/path
+        for extracting feature importance (implemented with `attrgetter`).
+        For example, give `regressor_.coef_` in case of
+        :class:`~sklearn.compose.TransformedTargetRegressor`  or
+        `named_steps.clf.feature_importances_` in case of
+        :class:`~sklearn.pipeline.Pipeline` with its last step named `clf`.
+
+        If `callable`, overrides the default feature importance getter.
+        The callable is passed with the fitted estimator and it should
+        return importance for each feature.
+
+        .. versionadded:: 0.24
+
+    Attributes
+    ----------
+    estimator_ : estimator
+        The base estimator from which the transformer is built. This attribute
+        exist only when `fit` has been called.
+
+        - If `prefit=True`, it is a deep copy of `estimator`.
+        - If `prefit=False`, it is a clone of `estimator` and fit on the data
+          passed to `fit` or `partial_fit`.
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`. Only defined if the
+        underlying estimator exposes such an attribute when fit.
+
+        .. versionadded:: 0.24
+
+    max_features_ : int
+        Maximum number of features calculated during :term:`fit`. Only defined
+        if the ``max_features`` is not `None`.
+
+        - If `max_features` is an `int`, then `max_features_ = max_features`.
+        - If `max_features` is a callable, then `max_features_ = max_features(X)`.
+
+        .. versionadded:: 1.1
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
+    threshold_ : float
+        The threshold value used for feature selection.
+
+    See Also
+    --------
+    RFE : Recursive feature elimination based on importance weights.
+    RFECV : Recursive feature elimination with built-in cross-validated
+        selection of the best number of features.
+    SequentialFeatureSelector : Sequential cross-validation based feature
+        selection. Does not rely on importance weights.
+
+    Notes
+    -----
+    Allows NaN/Inf in the input if the underlying estimator does as well.
+
+    Examples
+    --------
+    >>> from sklearn.feature_selection import SelectFromModel
+    >>> from sklearn.linear_model import LogisticRegression
+    >>> 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]
+    >>> selector = SelectFromModel(estimator=LogisticRegression()).fit(X, y)
+    >>> selector.estimator_.coef_
+    array([[-0.3252...,  0.8345...,  0.4976...]])
+    >>> selector.threshold_
+    0.55249...
+    >>> selector.get_support()
+    array([False,  True, False])
+    >>> selector.transform(X)
+    array([[-1.34],
+           [-0.02],
+           [-0.48],
+           [ 1.48]])
+
+    Using a callable to create a selector that can use no more than half
+    of the input features.
+
+    >>> def half_callable(X):
+    ...     return round(len(X[0]) / 2)
+    >>> half_selector = SelectFromModel(estimator=LogisticRegression(),
+    ...                                 max_features=half_callable)
+    >>> _ = half_selector.fit(X, y)
+    >>> half_selector.max_features_
+    2
+    """
+
+    _parameter_constraints: dict = {
+        "estimator": [HasMethods("fit")],
+        "threshold": [Interval(Real, None, None, closed="both"), str, None],
+        "prefit": ["boolean"],
+        "norm_order": [
+            Interval(Integral, None, -1, closed="right"),
+            Interval(Integral, 1, None, closed="left"),
+            Options(Real, {np.inf, -np.inf}),
+        ],
+        "max_features": [Interval(Integral, 0, None, closed="left"), callable, None],
+        "importance_getter": [str, callable],
+    }
+
+    def __init__(
+        self,
+        estimator,
+        *,
+        threshold=None,
+        prefit=False,
+        norm_order=1,
+        max_features=None,
+        importance_getter="auto",
+    ):
+        self.estimator = estimator
+        self.threshold = threshold
+        self.prefit = prefit
+        self.importance_getter = importance_getter
+        self.norm_order = norm_order
+        self.max_features = max_features
+
+    def _get_support_mask(self):
+        estimator = getattr(self, "estimator_", self.estimator)
+        max_features = getattr(self, "max_features_", self.max_features)
+
+        if self.prefit:
+            try:
+                check_is_fitted(self.estimator)
+            except NotFittedError as exc:
+                raise NotFittedError(
+                    "When `prefit=True`, `estimator` is expected to be a fitted "
+                    "estimator."
+                ) from exc
+        if callable(max_features):
+            # This branch is executed when `transform` is called directly and thus
+            # `max_features_` is not set and we fallback using `self.max_features`
+            # that is not validated
+            raise NotFittedError(
+                "When `prefit=True` and `max_features` is a callable, call `fit` "
+                "before calling `transform`."
+            )
+        elif max_features is not None and not isinstance(max_features, Integral):
+            raise ValueError(
+                f"`max_features` must be an integer. Got `max_features={max_features}` "
+                "instead."
+            )
+
+        scores = _get_feature_importances(
+            estimator=estimator,
+            getter=self.importance_getter,
+            transform_func="norm",
+            norm_order=self.norm_order,
+        )
+        threshold = _calculate_threshold(estimator, scores, self.threshold)
+        if self.max_features is not None:
+            mask = np.zeros_like(scores, dtype=bool)
+            candidate_indices = np.argsort(-scores, kind="mergesort")[:max_features]
+            mask[candidate_indices] = True
+        else:
+            mask = np.ones_like(scores, dtype=bool)
+        mask[scores < threshold] = False
+        return mask
+
+    def _check_max_features(self, X):
+        if self.max_features is not None:
+            n_features = _num_features(X)
+
+            if callable(self.max_features):
+                max_features = self.max_features(X)
+            else:  # int
+                max_features = self.max_features
+
+            check_scalar(
+                max_features,
+                "max_features",
+                Integral,
+                min_val=0,
+                max_val=n_features,
+            )
+            self.max_features_ = max_features
+
+    @_fit_context(
+        # SelectFromModel.estimator is not validated yet
+        prefer_skip_nested_validation=False
+    )
+    def fit(self, X, y=None, **fit_params):
+        """Fit the SelectFromModel meta-transformer.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            The training input samples.
+
+        y : array-like of shape (n_samples,), default=None
+            The target values (integers that correspond to classes in
+            classification, real numbers in regression).
+
+        **fit_params : dict
+            - If `enable_metadata_routing=False` (default):
+
+                Parameters directly passed to the `partial_fit` method of the
+                sub-estimator. They are ignored if `prefit=True`.
+
+            - If `enable_metadata_routing=True`:
+
+                Parameters safely routed to the `partial_fit` method of the
+                sub-estimator. They are ignored if `prefit=True`.
+
+                .. versionchanged:: 1.4
+                    See :ref:`Metadata Routing User Guide <metadata_routing>` for
+                    more details.
+
+        Returns
+        -------
+        self : object
+            Fitted estimator.
+        """
+        self._check_max_features(X)
+
+        if self.prefit:
+            try:
+                check_is_fitted(self.estimator)
+            except NotFittedError as exc:
+                raise NotFittedError(
+                    "When `prefit=True`, `estimator` is expected to be a fitted "
+                    "estimator."
+                ) from exc
+            self.estimator_ = deepcopy(self.estimator)
+        else:
+            if _routing_enabled():
+                routed_params = process_routing(self, "fit", **fit_params)
+                self.estimator_ = clone(self.estimator)
+                self.estimator_.fit(X, y, **routed_params.estimator.fit)
+            else:
+                # TODO(SLEP6): remove when metadata routing cannot be disabled.
+                self.estimator_ = clone(self.estimator)
+                self.estimator_.fit(X, y, **fit_params)
+
+        if hasattr(self.estimator_, "feature_names_in_"):
+            self.feature_names_in_ = self.estimator_.feature_names_in_
+        else:
+            self._check_feature_names(X, reset=True)
+
+        return self
+
+    @property
+    def threshold_(self):
+        """Threshold value used for feature selection."""
+        scores = _get_feature_importances(
+            estimator=self.estimator_,
+            getter=self.importance_getter,
+            transform_func="norm",
+            norm_order=self.norm_order,
+        )
+        return _calculate_threshold(self.estimator, scores, self.threshold)
+
+    @available_if(_estimator_has("partial_fit"))
+    @_fit_context(
+        # SelectFromModel.estimator is not validated yet
+        prefer_skip_nested_validation=False
+    )
+    def partial_fit(self, X, y=None, **partial_fit_params):
+        """Fit the SelectFromModel meta-transformer only once.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            The training input samples.
+
+        y : array-like of shape (n_samples,), default=None
+            The target values (integers that correspond to classes in
+            classification, real numbers in regression).
+
+        **partial_fit_params : dict
+            - If `enable_metadata_routing=False` (default):
+
+                Parameters directly passed to the `partial_fit` method of the
+                sub-estimator.
+
+            - If `enable_metadata_routing=True`:
+
+                Parameters passed to the `partial_fit` method of the
+                sub-estimator. They are ignored if `prefit=True`.
+
+                .. versionchanged:: 1.4
+                    `**partial_fit_params` are routed to the sub-estimator, if
+                    `enable_metadata_routing=True` is set via
+                    :func:`~sklearn.set_config`, which allows for aliasing.
+
+                See :ref:`Metadata Routing User Guide <metadata_routing>` for
+                more details.
+
+        Returns
+        -------
+        self : object
+            Fitted estimator.
+        """
+        first_call = not hasattr(self, "estimator_")
+
+        if first_call:
+            self._check_max_features(X)
+
+        if self.prefit:
+            if first_call:
+                try:
+                    check_is_fitted(self.estimator)
+                except NotFittedError as exc:
+                    raise NotFittedError(
+                        "When `prefit=True`, `estimator` is expected to be a fitted "
+                        "estimator."
+                    ) from exc
+                self.estimator_ = deepcopy(self.estimator)
+            return self
+
+        if first_call:
+            self.estimator_ = clone(self.estimator)
+        if _routing_enabled():
+            routed_params = process_routing(self, "partial_fit", **partial_fit_params)
+            self.estimator_ = clone(self.estimator)
+            self.estimator_.partial_fit(X, y, **routed_params.estimator.partial_fit)
+        else:
+            # TODO(SLEP6): remove when metadata routing cannot be disabled.
+            self.estimator_.partial_fit(X, y, **partial_fit_params)
+
+        if hasattr(self.estimator_, "feature_names_in_"):
+            self.feature_names_in_ = self.estimator_.feature_names_in_
+        else:
+            self._check_feature_names(X, reset=first_call)
+
+        return self
+
+    @property
+    def n_features_in_(self):
+        """Number of features seen during `fit`."""
+        # For consistency with other estimators we raise a AttributeError so
+        # that hasattr() fails if the estimator isn't fitted.
+        try:
+            check_is_fitted(self)
+        except NotFittedError as nfe:
+            raise AttributeError(
+                "{} object has no n_features_in_ attribute.".format(
+                    self.__class__.__name__
+                )
+            ) from nfe
+
+        return self.estimator_.n_features_in_
+
+    def get_metadata_routing(self):
+        """Get metadata routing of this object.
+
+        Please check :ref:`User Guide <metadata_routing>` on how the routing
+        mechanism works.
+
+        .. versionadded:: 1.4
+
+        Returns
+        -------
+        routing : MetadataRouter
+            A :class:`~sklearn.utils.metadata_routing.MetadataRouter` encapsulating
+            routing information.
+        """
+        router = MetadataRouter(owner=self.__class__.__name__).add(
+            estimator=self.estimator,
+            method_mapping=MethodMapping()
+            .add(callee="partial_fit", caller="partial_fit")
+            .add(callee="fit", caller="fit"),
+        )
+        return router
+
+    def _more_tags(self):
+        return {"allow_nan": _safe_tags(self.estimator, key="allow_nan")}

env-llmeval/lib/python3.10/site-packages/sklearn/feature_selection/_mutual_info.py

@@ -0,0 +1,514 @@
+# Author: Nikolay Mayorov <[email protected]>
+# License: 3-clause BSD
+
+from numbers import Integral
+
+import numpy as np
+from scipy.sparse import issparse
+from scipy.special import digamma
+
+from ..metrics.cluster import mutual_info_score
+from ..neighbors import KDTree, NearestNeighbors
+from ..preprocessing import scale
+from ..utils import check_random_state
+from ..utils._param_validation import Interval, StrOptions, validate_params
+from ..utils.multiclass import check_classification_targets
+from ..utils.validation import check_array, check_X_y
+
+
+def _compute_mi_cc(x, y, n_neighbors):
+    """Compute mutual information between two continuous variables.
+
+    Parameters
+    ----------
+    x, y : ndarray, shape (n_samples,)
+        Samples of two continuous random variables, must have an identical
+        shape.
+
+    n_neighbors : int
+        Number of nearest neighbors to search for each point, see [1]_.
+
+    Returns
+    -------
+    mi : float
+        Estimated mutual information in nat units. If it turned out to be
+        negative it is replaced by 0.
+
+    Notes
+    -----
+    True mutual information can't be negative. If its estimate by a numerical
+    method is negative, it means (providing the method is adequate) that the
+    mutual information is close to 0 and replacing it by 0 is a reasonable
+    strategy.
+
+    References
+    ----------
+    .. [1] A. Kraskov, H. Stogbauer and P. Grassberger, "Estimating mutual
+           information". Phys. Rev. E 69, 2004.
+    """
+    n_samples = x.size
+
+    x = x.reshape((-1, 1))
+    y = y.reshape((-1, 1))
+    xy = np.hstack((x, y))
+
+    # Here we rely on NearestNeighbors to select the fastest algorithm.
+    nn = NearestNeighbors(metric="chebyshev", n_neighbors=n_neighbors)
+
+    nn.fit(xy)
+    radius = nn.kneighbors()[0]
+    radius = np.nextafter(radius[:, -1], 0)
+
+    # KDTree is explicitly fit to allow for the querying of number of
+    # neighbors within a specified radius
+    kd = KDTree(x, metric="chebyshev")
+    nx = kd.query_radius(x, radius, count_only=True, return_distance=False)
+    nx = np.array(nx) - 1.0
+
+    kd = KDTree(y, metric="chebyshev")
+    ny = kd.query_radius(y, radius, count_only=True, return_distance=False)
+    ny = np.array(ny) - 1.0
+
+    mi = (
+        digamma(n_samples)
+        + digamma(n_neighbors)
+        - np.mean(digamma(nx + 1))
+        - np.mean(digamma(ny + 1))
+    )
+
+    return max(0, mi)
+
+
+def _compute_mi_cd(c, d, n_neighbors):
+    """Compute mutual information between continuous and discrete variables.
+
+    Parameters
+    ----------
+    c : ndarray, shape (n_samples,)
+        Samples of a continuous random variable.
+
+    d : ndarray, shape (n_samples,)
+        Samples of a discrete random variable.
+
+    n_neighbors : int
+        Number of nearest neighbors to search for each point, see [1]_.
+
+    Returns
+    -------
+    mi : float
+        Estimated mutual information in nat units. If it turned out to be
+        negative it is replaced by 0.
+
+    Notes
+    -----
+    True mutual information can't be negative. If its estimate by a numerical
+    method is negative, it means (providing the method is adequate) that the
+    mutual information is close to 0 and replacing it by 0 is a reasonable
+    strategy.
+
+    References
+    ----------
+    .. [1] B. C. Ross "Mutual Information between Discrete and Continuous
+       Data Sets". PLoS ONE 9(2), 2014.
+    """
+    n_samples = c.shape[0]
+    c = c.reshape((-1, 1))
+
+    radius = np.empty(n_samples)
+    label_counts = np.empty(n_samples)
+    k_all = np.empty(n_samples)
+    nn = NearestNeighbors()
+    for label in np.unique(d):
+        mask = d == label
+        count = np.sum(mask)
+        if count > 1:
+            k = min(n_neighbors, count - 1)
+            nn.set_params(n_neighbors=k)
+            nn.fit(c[mask])
+            r = nn.kneighbors()[0]
+            radius[mask] = np.nextafter(r[:, -1], 0)
+            k_all[mask] = k
+        label_counts[mask] = count
+
+    # Ignore points with unique labels.
+    mask = label_counts > 1
+    n_samples = np.sum(mask)
+    label_counts = label_counts[mask]
+    k_all = k_all[mask]
+    c = c[mask]
+    radius = radius[mask]
+
+    kd = KDTree(c)
+    m_all = kd.query_radius(c, radius, count_only=True, return_distance=False)
+    m_all = np.array(m_all)
+
+    mi = (
+        digamma(n_samples)
+        + np.mean(digamma(k_all))
+        - np.mean(digamma(label_counts))
+        - np.mean(digamma(m_all))
+    )
+
+    return max(0, mi)
+
+
+def _compute_mi(x, y, x_discrete, y_discrete, n_neighbors=3):
+    """Compute mutual information between two variables.
+
+    This is a simple wrapper which selects a proper function to call based on
+    whether `x` and `y` are discrete or not.
+    """
+    if x_discrete and y_discrete:
+        return mutual_info_score(x, y)
+    elif x_discrete and not y_discrete:
+        return _compute_mi_cd(y, x, n_neighbors)
+    elif not x_discrete and y_discrete:
+        return _compute_mi_cd(x, y, n_neighbors)
+    else:
+        return _compute_mi_cc(x, y, n_neighbors)
+
+
+def _iterate_columns(X, columns=None):
+    """Iterate over columns of a matrix.
+
+    Parameters
+    ----------
+    X : ndarray or csc_matrix, shape (n_samples, n_features)
+        Matrix over which to iterate.
+
+    columns : iterable or None, default=None
+        Indices of columns to iterate over. If None, iterate over all columns.
+
+    Yields
+    ------
+    x : ndarray, shape (n_samples,)
+        Columns of `X` in dense format.
+    """
+    if columns is None:
+        columns = range(X.shape[1])
+
+    if issparse(X):
+        for i in columns:
+            x = np.zeros(X.shape[0])
+            start_ptr, end_ptr = X.indptr[i], X.indptr[i + 1]
+            x[X.indices[start_ptr:end_ptr]] = X.data[start_ptr:end_ptr]
+            yield x
+    else:
+        for i in columns:
+            yield X[:, i]
+
+
+def _estimate_mi(
+    X,
+    y,
+    discrete_features="auto",
+    discrete_target=False,
+    n_neighbors=3,
+    copy=True,
+    random_state=None,
+):
+    """Estimate mutual information between the features and the target.
+
+    Parameters
+    ----------
+    X : array-like or sparse matrix, shape (n_samples, n_features)
+        Feature matrix.
+
+    y : array-like of shape (n_samples,)
+        Target vector.
+
+    discrete_features : {'auto', bool, array-like}, default='auto'
+        If bool, then determines whether to consider all features discrete
+        or continuous. If array, then it should be either a boolean mask
+        with shape (n_features,) or array with indices of discrete features.
+        If 'auto', it is assigned to False for dense `X` and to True for
+        sparse `X`.
+
+    discrete_target : bool, default=False
+        Whether to consider `y` as a discrete variable.
+
+    n_neighbors : int, default=3
+        Number of neighbors to use for MI estimation for continuous variables,
+        see [1]_ and [2]_. Higher values reduce variance of the estimation, but
+        could introduce a bias.
+
+    copy : bool, default=True
+        Whether to make a copy of the given data. If set to False, the initial
+        data will be overwritten.
+
+    random_state : int, RandomState instance or None, default=None
+        Determines random number generation for adding small noise to
+        continuous variables in order to remove repeated values.
+        Pass an int for reproducible results across multiple function calls.
+        See :term:`Glossary <random_state>`.
+
+    Returns
+    -------
+    mi : ndarray, shape (n_features,)
+        Estimated mutual information between each feature and the target in
+        nat units. A negative value will be replaced by 0.
+
+    References
+    ----------
+    .. [1] A. Kraskov, H. Stogbauer and P. Grassberger, "Estimating mutual
+           information". Phys. Rev. E 69, 2004.
+    .. [2] B. C. Ross "Mutual Information between Discrete and Continuous
+           Data Sets". PLoS ONE 9(2), 2014.
+    """
+    X, y = check_X_y(X, y, accept_sparse="csc", y_numeric=not discrete_target)
+    n_samples, n_features = X.shape
+
+    if isinstance(discrete_features, (str, bool)):
+        if isinstance(discrete_features, str):
+            if discrete_features == "auto":
+                discrete_features = issparse(X)
+            else:
+                raise ValueError("Invalid string value for discrete_features.")
+        discrete_mask = np.empty(n_features, dtype=bool)
+        discrete_mask.fill(discrete_features)
+    else:
+        discrete_features = check_array(discrete_features, ensure_2d=False)
+        if discrete_features.dtype != "bool":
+            discrete_mask = np.zeros(n_features, dtype=bool)
+            discrete_mask[discrete_features] = True
+        else:
+            discrete_mask = discrete_features
+
+    continuous_mask = ~discrete_mask
+    if np.any(continuous_mask) and issparse(X):
+        raise ValueError("Sparse matrix `X` can't have continuous features.")
+
+    rng = check_random_state(random_state)
+    if np.any(continuous_mask):
+        X = X.astype(np.float64, copy=copy)
+        X[:, continuous_mask] = scale(
+            X[:, continuous_mask], with_mean=False, copy=False
+        )
+
+        # Add small noise to continuous features as advised in Kraskov et. al.
+        means = np.maximum(1, np.mean(np.abs(X[:, continuous_mask]), axis=0))
+        X[:, continuous_mask] += (
+            1e-10
+            * means
+            * rng.standard_normal(size=(n_samples, np.sum(continuous_mask)))
+        )
+
+    if not discrete_target:
+        y = scale(y, with_mean=False)
+        y += (
+            1e-10
+            * np.maximum(1, np.mean(np.abs(y)))
+            * rng.standard_normal(size=n_samples)
+        )
+
+    mi = [
+        _compute_mi(x, y, discrete_feature, discrete_target, n_neighbors)
+        for x, discrete_feature in zip(_iterate_columns(X), discrete_mask)
+    ]
+
+    return np.array(mi)
+
+
+@validate_params(
+    {
+        "X": ["array-like", "sparse matrix"],
+        "y": ["array-like"],
+        "discrete_features": [StrOptions({"auto"}), "boolean", "array-like"],
+        "n_neighbors": [Interval(Integral, 1, None, closed="left")],
+        "copy": ["boolean"],
+        "random_state": ["random_state"],
+    },
+    prefer_skip_nested_validation=True,
+)
+def mutual_info_regression(
+    X, y, *, discrete_features="auto", n_neighbors=3, copy=True, random_state=None
+):
+    """Estimate mutual information for a continuous target variable.
+
+    Mutual information (MI) [1]_ between two random variables is a non-negative
+    value, which measures the dependency between the variables. It is equal
+    to zero if and only if two random variables are independent, and higher
+    values mean higher dependency.
+
+    The function relies on nonparametric methods based on entropy estimation
+    from k-nearest neighbors distances as described in [2]_ and [3]_. Both
+    methods are based on the idea originally proposed in [4]_.
+
+    It can be used for univariate features selection, read more in the
+    :ref:`User Guide <univariate_feature_selection>`.
+
+    Parameters
+    ----------
+    X : array-like or sparse matrix, shape (n_samples, n_features)
+        Feature matrix.
+
+    y : array-like of shape (n_samples,)
+        Target vector.
+
+    discrete_features : {'auto', bool, array-like}, default='auto'
+        If bool, then determines whether to consider all features discrete
+        or continuous. If array, then it should be either a boolean mask
+        with shape (n_features,) or array with indices of discrete features.
+        If 'auto', it is assigned to False for dense `X` and to True for
+        sparse `X`.
+
+    n_neighbors : int, default=3
+        Number of neighbors to use for MI estimation for continuous variables,
+        see [2]_ and [3]_. Higher values reduce variance of the estimation, but
+        could introduce a bias.
+
+    copy : bool, default=True
+        Whether to make a copy of the given data. If set to False, the initial
+        data will be overwritten.
+
+    random_state : int, RandomState instance or None, default=None
+        Determines random number generation for adding small noise to
+        continuous variables in order to remove repeated values.
+        Pass an int for reproducible results across multiple function calls.
+        See :term:`Glossary <random_state>`.
+
+    Returns
+    -------
+    mi : ndarray, shape (n_features,)
+        Estimated mutual information between each feature and the target in
+        nat units.
+
+    Notes
+    -----
+    1. The term "discrete features" is used instead of naming them
+       "categorical", because it describes the essence more accurately.
+       For example, pixel intensities of an image are discrete features
+       (but hardly categorical) and you will get better results if mark them
+       as such. Also note, that treating a continuous variable as discrete and
+       vice versa will usually give incorrect results, so be attentive about
+       that.
+    2. True mutual information can't be negative. If its estimate turns out
+       to be negative, it is replaced by zero.
+
+    References
+    ----------
+    .. [1] `Mutual Information
+           <https://en.wikipedia.org/wiki/Mutual_information>`_
+           on Wikipedia.
+    .. [2] A. Kraskov, H. Stogbauer and P. Grassberger, "Estimating mutual
+           information". Phys. Rev. E 69, 2004.
+    .. [3] B. C. Ross "Mutual Information between Discrete and Continuous
+           Data Sets". PLoS ONE 9(2), 2014.
+    .. [4] L. F. Kozachenko, N. N. Leonenko, "Sample Estimate of the Entropy
+           of a Random Vector", Probl. Peredachi Inf., 23:2 (1987), 9-16
+
+    Examples
+    --------
+    >>> from sklearn.datasets import make_regression
+    >>> from sklearn.feature_selection import mutual_info_regression
+    >>> X, y = make_regression(
+    ...     n_samples=50, n_features=3, n_informative=1, noise=1e-4, random_state=42
+    ... )
+    >>> mutual_info_regression(X, y)
+    array([0.1..., 2.6...  , 0.0...])
+    """
+    return _estimate_mi(X, y, discrete_features, False, n_neighbors, copy, random_state)
+
+
+@validate_params(
+    {
+        "X": ["array-like", "sparse matrix"],
+        "y": ["array-like"],
+        "discrete_features": [StrOptions({"auto"}), "boolean", "array-like"],
+        "n_neighbors": [Interval(Integral, 1, None, closed="left")],
+        "copy": ["boolean"],
+        "random_state": ["random_state"],
+    },
+    prefer_skip_nested_validation=True,
+)
+def mutual_info_classif(
+    X, y, *, discrete_features="auto", n_neighbors=3, copy=True, random_state=None
+):
+    """Estimate mutual information for a discrete target variable.
+
+    Mutual information (MI) [1]_ between two random variables is a non-negative
+    value, which measures the dependency between the variables. It is equal
+    to zero if and only if two random variables are independent, and higher
+    values mean higher dependency.
+
+    The function relies on nonparametric methods based on entropy estimation
+    from k-nearest neighbors distances as described in [2]_ and [3]_. Both
+    methods are based on the idea originally proposed in [4]_.
+
+    It can be used for univariate features selection, read more in the
+    :ref:`User Guide <univariate_feature_selection>`.
+
+    Parameters
+    ----------
+    X : {array-like, sparse matrix} of shape (n_samples, n_features)
+        Feature matrix.
+
+    y : array-like of shape (n_samples,)
+        Target vector.
+
+    discrete_features : 'auto', bool or array-like, default='auto'
+        If bool, then determines whether to consider all features discrete
+        or continuous. If array, then it should be either a boolean mask
+        with shape (n_features,) or array with indices of discrete features.
+        If 'auto', it is assigned to False for dense `X` and to True for
+        sparse `X`.
+
+    n_neighbors : int, default=3
+        Number of neighbors to use for MI estimation for continuous variables,
+        see [2]_ and [3]_. Higher values reduce variance of the estimation, but
+        could introduce a bias.
+
+    copy : bool, default=True
+        Whether to make a copy of the given data. If set to False, the initial
+        data will be overwritten.
+
+    random_state : int, RandomState instance or None, default=None
+        Determines random number generation for adding small noise to
+        continuous variables in order to remove repeated values.
+        Pass an int for reproducible results across multiple function calls.
+        See :term:`Glossary <random_state>`.
+
+    Returns
+    -------
+    mi : ndarray, shape (n_features,)
+        Estimated mutual information between each feature and the target in
+        nat units.
+
+    Notes
+    -----
+    1. The term "discrete features" is used instead of naming them
+       "categorical", because it describes the essence more accurately.
+       For example, pixel intensities of an image are discrete features
+       (but hardly categorical) and you will get better results if mark them
+       as such. Also note, that treating a continuous variable as discrete and
+       vice versa will usually give incorrect results, so be attentive about
+       that.
+    2. True mutual information can't be negative. If its estimate turns out
+       to be negative, it is replaced by zero.
+
+    References
+    ----------
+    .. [1] `Mutual Information
+           <https://en.wikipedia.org/wiki/Mutual_information>`_
+           on Wikipedia.
+    .. [2] A. Kraskov, H. Stogbauer and P. Grassberger, "Estimating mutual
+           information". Phys. Rev. E 69, 2004.
+    .. [3] B. C. Ross "Mutual Information between Discrete and Continuous
+           Data Sets". PLoS ONE 9(2), 2014.
+    .. [4] L. F. Kozachenko, N. N. Leonenko, "Sample Estimate of the Entropy
+           of a Random Vector:, Probl. Peredachi Inf., 23:2 (1987), 9-16
+
+    Examples
+    --------
+    >>> from sklearn.datasets import make_classification
+    >>> from sklearn.feature_selection import mutual_info_classif
+    >>> X, y = make_classification(
+    ...     n_samples=100, n_features=10, n_informative=2, n_clusters_per_class=1,
+    ...     shuffle=False, random_state=42
+    ... )
+    >>> mutual_info_classif(X, y)
+    array([0.58..., 0.10..., 0.19..., 0.09... , 0.        ,
+           0.     , 0.     , 0.     , 0.      , 0.        ])
+    """
+    check_classification_targets(y)
+    return _estimate_mi(X, y, discrete_features, True, n_neighbors, copy, random_state)

env-llmeval/lib/python3.10/site-packages/sklearn/feature_selection/_rfe.py

@@ -0,0 +1,792 @@
+# Authors: Alexandre Gramfort <[email protected]>
+#          Vincent Michel <[email protected]>
+#          Gilles Louppe <[email protected]>
+#
+# License: BSD 3 clause
+
+"""Recursive feature elimination for feature ranking"""
+
+from numbers import Integral
+
+import numpy as np
+from joblib import effective_n_jobs
+
+from ..base import BaseEstimator, MetaEstimatorMixin, _fit_context, clone, is_classifier
+from ..metrics import check_scoring
+from ..model_selection import check_cv
+from ..model_selection._validation import _score
+from ..utils._param_validation import HasMethods, Interval, RealNotInt
+from ..utils.metadata_routing import (
+    _raise_for_unsupported_routing,
+    _RoutingNotSupportedMixin,
+)
+from ..utils.metaestimators import _safe_split, available_if
+from ..utils.parallel import Parallel, delayed
+from ..utils.validation import check_is_fitted
+from ._base import SelectorMixin, _get_feature_importances
+
+
+def _rfe_single_fit(rfe, estimator, X, y, train, test, scorer):
+    """
+    Return the score for a fit across one fold.
+    """
+    X_train, y_train = _safe_split(estimator, X, y, train)
+    X_test, y_test = _safe_split(estimator, X, y, test, train)
+    return rfe._fit(
+        X_train,
+        y_train,
+        lambda estimator, features: _score(
+            # TODO(SLEP6): pass score_params here
+            estimator,
+            X_test[:, features],
+            y_test,
+            scorer,
+            score_params=None,
+        ),
+    ).scores_
+
+
+def _estimator_has(attr):
+    """Check if we can delegate a method to the underlying estimator.
+
+    First, we check the fitted `estimator_` if available, otherwise we check the
+    unfitted `estimator`. We raise the original `AttributeError` if `attr` does
+    not exist. This function is used together with `available_if`.
+    """
+
+    def check(self):
+        if hasattr(self, "estimator_"):
+            getattr(self.estimator_, attr)
+        else:
+            getattr(self.estimator, attr)
+
+        return True
+
+    return check
+
+
+class RFE(_RoutingNotSupportedMixin, SelectorMixin, MetaEstimatorMixin, BaseEstimator):
+    """Feature ranking with recursive feature elimination.
+
+    Given an external estimator that assigns weights to features (e.g., the
+    coefficients of a linear model), the goal of recursive feature elimination
+    (RFE) is to select features by recursively considering smaller and smaller
+    sets of features. First, the estimator is trained on the initial set of
+    features and the importance of each feature is obtained either through
+    any specific attribute or callable.
+    Then, the least important features are pruned from current set of features.
+    That procedure is recursively repeated on the pruned set until the desired
+    number of features to select is eventually reached.
+
+    Read more in the :ref:`User Guide <rfe>`.
+
+    Parameters
+    ----------
+    estimator : ``Estimator`` instance
+        A supervised learning estimator with a ``fit`` method that provides
+        information about feature importance
+        (e.g. `coef_`, `feature_importances_`).
+
+    n_features_to_select : int or float, default=None
+        The number of features to select. If `None`, half of the features are
+        selected. If integer, the parameter is the absolute number of features
+        to select. If float between 0 and 1, it is the fraction of features to
+        select.
+
+        .. versionchanged:: 0.24
+           Added float values for fractions.
+
+    step : int or float, default=1
+        If greater than or equal to 1, then ``step`` corresponds to the
+        (integer) number of features to remove at each iteration.
+        If within (0.0, 1.0), then ``step`` corresponds to the percentage
+        (rounded down) of features to remove at each iteration.
+
+    verbose : int, default=0
+        Controls verbosity of output.
+
+    importance_getter : str or callable, default='auto'
+        If 'auto', uses the feature importance either through a `coef_`
+        or `feature_importances_` attributes of estimator.
+
+        Also accepts a string that specifies an attribute name/path
+        for extracting feature importance (implemented with `attrgetter`).
+        For example, give `regressor_.coef_` in case of
+        :class:`~sklearn.compose.TransformedTargetRegressor`  or
+        `named_steps.clf.feature_importances_` in case of
+        class:`~sklearn.pipeline.Pipeline` with its last step named `clf`.
+
+        If `callable`, overrides the default feature importance getter.
+        The callable is passed with the fitted estimator and it should
+        return importance for each feature.
+
+        .. versionadded:: 0.24
+
+    Attributes
+    ----------
+    classes_ : ndarray of shape (n_classes,)
+        The classes labels. Only available when `estimator` is a classifier.
+
+    estimator_ : ``Estimator`` instance
+        The fitted estimator used to select features.
+
+    n_features_ : int
+        The number of selected features.
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`. Only defined if the
+        underlying estimator exposes such an attribute when fit.
+
+        .. versionadded:: 0.24
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
+    ranking_ : ndarray of shape (n_features,)
+        The feature ranking, such that ``ranking_[i]`` corresponds to the
+        ranking position of the i-th feature. Selected (i.e., estimated
+        best) features are assigned rank 1.
+
+    support_ : ndarray of shape (n_features,)
+        The mask of selected features.
+
+    See Also
+    --------
+    RFECV : Recursive feature elimination with built-in cross-validated
+        selection of the best number of features.
+    SelectFromModel : Feature selection based on thresholds of importance
+        weights.
+    SequentialFeatureSelector : Sequential cross-validation based feature
+        selection. Does not rely on importance weights.
+
+    Notes
+    -----
+    Allows NaN/Inf in the input if the underlying estimator does as well.
+
+    References
+    ----------
+
+    .. [1] Guyon, I., Weston, J., Barnhill, S., & Vapnik, V., "Gene selection
+           for cancer classification using support vector machines",
+           Mach. Learn., 46(1-3), 389--422, 2002.
+
+    Examples
+    --------
+    The following example shows how to retrieve the 5 most informative
+    features in the Friedman #1 dataset.
+
+    >>> from sklearn.datasets import make_friedman1
+    >>> from sklearn.feature_selection import RFE
+    >>> from sklearn.svm import SVR
+    >>> X, y = make_friedman1(n_samples=50, n_features=10, random_state=0)
+    >>> estimator = SVR(kernel="linear")
+    >>> selector = RFE(estimator, n_features_to_select=5, step=1)
+    >>> selector = selector.fit(X, y)
+    >>> selector.support_
+    array([ True,  True,  True,  True,  True, False, False, False, False,
+           False])
+    >>> selector.ranking_
+    array([1, 1, 1, 1, 1, 6, 4, 3, 2, 5])
+    """
+
+    _parameter_constraints: dict = {
+        "estimator": [HasMethods(["fit"])],
+        "n_features_to_select": [
+            None,
+            Interval(RealNotInt, 0, 1, closed="right"),
+            Interval(Integral, 0, None, closed="neither"),
+        ],
+        "step": [
+            Interval(Integral, 0, None, closed="neither"),
+            Interval(RealNotInt, 0, 1, closed="neither"),
+        ],
+        "verbose": ["verbose"],
+        "importance_getter": [str, callable],
+    }
+
+    def __init__(
+        self,
+        estimator,
+        *,
+        n_features_to_select=None,
+        step=1,
+        verbose=0,
+        importance_getter="auto",
+    ):
+        self.estimator = estimator
+        self.n_features_to_select = n_features_to_select
+        self.step = step
+        self.importance_getter = importance_getter
+        self.verbose = verbose
+
+    @property
+    def _estimator_type(self):
+        return self.estimator._estimator_type
+
+    @property
+    def classes_(self):
+        """Classes labels available when `estimator` is a classifier.
+
+        Returns
+        -------
+        ndarray of shape (n_classes,)
+        """
+        return self.estimator_.classes_
+
+    @_fit_context(
+        # RFE.estimator is not validated yet
+        prefer_skip_nested_validation=False
+    )
+    def fit(self, X, y, **fit_params):
+        """Fit the RFE model and then the underlying estimator on the selected features.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            The training input samples.
+
+        y : array-like of shape (n_samples,)
+            The target values.
+
+        **fit_params : dict
+            Additional parameters passed to the `fit` method of the underlying
+            estimator.
+
+        Returns
+        -------
+        self : object
+            Fitted estimator.
+        """
+        _raise_for_unsupported_routing(self, "fit", **fit_params)
+        return self._fit(X, y, **fit_params)
+
+    def _fit(self, X, y, step_score=None, **fit_params):
+        # Parameter step_score controls the calculation of self.scores_
+        # step_score is not exposed to users
+        # and is used when implementing RFECV
+        # self.scores_ will not be calculated when calling _fit through fit
+
+        X, y = self._validate_data(
+            X,
+            y,
+            accept_sparse="csc",
+            ensure_min_features=2,
+            force_all_finite=False,
+            multi_output=True,
+        )
+
+        # Initialization
+        n_features = X.shape[1]
+        if self.n_features_to_select is None:
+            n_features_to_select = n_features // 2
+        elif isinstance(self.n_features_to_select, Integral):  # int
+            n_features_to_select = self.n_features_to_select
+        else:  # float
+            n_features_to_select = int(n_features * self.n_features_to_select)
+
+        if 0.0 < self.step < 1.0:
+            step = int(max(1, self.step * n_features))
+        else:
+            step = int(self.step)
+
+        support_ = np.ones(n_features, dtype=bool)
+        ranking_ = np.ones(n_features, dtype=int)
+
+        if step_score:
+            self.scores_ = []
+
+        # Elimination
+        while np.sum(support_) > n_features_to_select:
+            # Remaining features
+            features = np.arange(n_features)[support_]
+
+            # Rank the remaining features
+            estimator = clone(self.estimator)
+            if self.verbose > 0:
+                print("Fitting estimator with %d features." % np.sum(support_))
+
+            estimator.fit(X[:, features], y, **fit_params)
+
+            # Get importance and rank them
+            importances = _get_feature_importances(
+                estimator,
+                self.importance_getter,
+                transform_func="square",
+            )
+            ranks = np.argsort(importances)
+
+            # for sparse case ranks is matrix
+            ranks = np.ravel(ranks)
+
+            # Eliminate the worse features
+            threshold = min(step, np.sum(support_) - n_features_to_select)
+
+            # Compute step score on the previous selection iteration
+            # because 'estimator' must use features
+            # that have not been eliminated yet
+            if step_score:
+                self.scores_.append(step_score(estimator, features))
+            support_[features[ranks][:threshold]] = False
+            ranking_[np.logical_not(support_)] += 1
+
+        # Set final attributes
+        features = np.arange(n_features)[support_]
+        self.estimator_ = clone(self.estimator)
+        self.estimator_.fit(X[:, features], y, **fit_params)
+
+        # Compute step score when only n_features_to_select features left
+        if step_score:
+            self.scores_.append(step_score(self.estimator_, features))
+        self.n_features_ = support_.sum()
+        self.support_ = support_
+        self.ranking_ = ranking_
+
+        return self
+
+    @available_if(_estimator_has("predict"))
+    def predict(self, X):
+        """Reduce X to the selected features and predict using the estimator.
+
+        Parameters
+        ----------
+        X : array of shape [n_samples, n_features]
+            The input samples.
+
+        Returns
+        -------
+        y : array of shape [n_samples]
+            The predicted target values.
+        """
+        check_is_fitted(self)
+        return self.estimator_.predict(self.transform(X))
+
+    @available_if(_estimator_has("score"))
+    def score(self, X, y, **fit_params):
+        """Reduce X to the selected features and return the score of the estimator.
+
+        Parameters
+        ----------
+        X : array of shape [n_samples, n_features]
+            The input samples.
+
+        y : array of shape [n_samples]
+            The target values.
+
+        **fit_params : dict
+            Parameters to pass to the `score` method of the underlying
+            estimator.
+
+            .. versionadded:: 1.0
+
+        Returns
+        -------
+        score : float
+            Score of the underlying base estimator computed with the selected
+            features returned by `rfe.transform(X)` and `y`.
+        """
+        check_is_fitted(self)
+        return self.estimator_.score(self.transform(X), y, **fit_params)
+
+    def _get_support_mask(self):
+        check_is_fitted(self)
+        return self.support_
+
+    @available_if(_estimator_has("decision_function"))
+    def decision_function(self, X):
+        """Compute the decision function of ``X``.
+
+        Parameters
+        ----------
+        X : {array-like or sparse matrix} of shape (n_samples, n_features)
+            The input samples. Internally, it will be converted to
+            ``dtype=np.float32`` and if a sparse matrix is provided
+            to a sparse ``csr_matrix``.
+
+        Returns
+        -------
+        score : array, shape = [n_samples, n_classes] or [n_samples]
+            The decision function of the input samples. The order of the
+            classes corresponds to that in the attribute :term:`classes_`.
+            Regression and binary classification produce an array of shape
+            [n_samples].
+        """
+        check_is_fitted(self)
+        return self.estimator_.decision_function(self.transform(X))
+
+    @available_if(_estimator_has("predict_proba"))
+    def predict_proba(self, X):
+        """Predict class probabilities for X.
+
+        Parameters
+        ----------
+        X : {array-like or sparse matrix} of shape (n_samples, n_features)
+            The input samples. Internally, it will be converted to
+            ``dtype=np.float32`` and if a sparse matrix is provided
+            to a sparse ``csr_matrix``.
+
+        Returns
+        -------
+        p : array of shape (n_samples, n_classes)
+            The class probabilities of the input samples. The order of the
+            classes corresponds to that in the attribute :term:`classes_`.
+        """
+        check_is_fitted(self)
+        return self.estimator_.predict_proba(self.transform(X))
+
+    @available_if(_estimator_has("predict_log_proba"))
+    def predict_log_proba(self, X):
+        """Predict class log-probabilities for X.
+
+        Parameters
+        ----------
+        X : array of shape [n_samples, n_features]
+            The input samples.
+
+        Returns
+        -------
+        p : array of shape (n_samples, n_classes)
+            The class log-probabilities of the input samples. The order of the
+            classes corresponds to that in the attribute :term:`classes_`.
+        """
+        check_is_fitted(self)
+        return self.estimator_.predict_log_proba(self.transform(X))
+
+    def _more_tags(self):
+        tags = {
+            "poor_score": True,
+            "requires_y": True,
+            "allow_nan": True,
+        }
+
+        # Adjust allow_nan if estimator explicitly defines `allow_nan`.
+        if hasattr(self.estimator, "_get_tags"):
+            tags["allow_nan"] = self.estimator._get_tags()["allow_nan"]
+
+        return tags
+
+
+class RFECV(RFE):
+    """Recursive feature elimination with cross-validation to select features.
+
+    The number of features selected is tuned automatically by fitting an :class:`RFE`
+    selector on the different cross-validation splits (provided by the `cv` parameter).
+    The performance of the :class:`RFE` selector are evaluated using `scorer` for
+    different number of selected features and aggregated together. Finally, the scores
+    are averaged across folds and the number of features selected is set to the number
+    of features that maximize the cross-validation score.
+    See glossary entry for :term:`cross-validation estimator`.
+
+    Read more in the :ref:`User Guide <rfe>`.
+
+    Parameters
+    ----------
+    estimator : ``Estimator`` instance
+        A supervised learning estimator with a ``fit`` method that provides
+        information about feature importance either through a ``coef_``
+        attribute or through a ``feature_importances_`` attribute.
+
+    step : int or float, default=1
+        If greater than or equal to 1, then ``step`` corresponds to the
+        (integer) number of features to remove at each iteration.
+        If within (0.0, 1.0), then ``step`` corresponds to the percentage
+        (rounded down) of features to remove at each iteration.
+        Note that the last iteration may remove fewer than ``step`` features in
+        order to reach ``min_features_to_select``.
+
+    min_features_to_select : int, default=1
+        The minimum number of features to be selected. This number of features
+        will always be scored, even if the difference between the original
+        feature count and ``min_features_to_select`` isn't divisible by
+        ``step``.
+
+        .. versionadded:: 0.20
+
+    cv : int, cross-validation generator or an iterable, default=None
+        Determines the cross-validation splitting strategy.
+        Possible inputs for cv are:
+
+        - None, to use the default 5-fold cross-validation,
+        - integer, to specify the number of folds.
+        - :term:`CV splitter`,
+        - An iterable yielding (train, test) splits as arrays of indices.
+
+        For integer/None inputs, if ``y`` is binary or multiclass,
+        :class:`~sklearn.model_selection.StratifiedKFold` is used. If the
+        estimator is a classifier or if ``y`` is neither binary nor multiclass,
+        :class:`~sklearn.model_selection.KFold` is used.
+
+        Refer :ref:`User Guide <cross_validation>` for the various
+        cross-validation strategies that can be used here.
+
+        .. versionchanged:: 0.22
+            ``cv`` default value of None changed from 3-fold to 5-fold.
+
+    scoring : str, callable or None, default=None
+        A string (see model evaluation documentation) or
+        a scorer callable object / function with signature
+        ``scorer(estimator, X, y)``.
+
+    verbose : int, default=0
+        Controls verbosity of output.
+
+    n_jobs : int or None, default=None
+        Number of cores to run in parallel while fitting across folds.
+        ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
+        ``-1`` means using all processors. See :term:`Glossary <n_jobs>`
+        for more details.
+
+        .. versionadded:: 0.18
+
+    importance_getter : str or callable, default='auto'
+        If 'auto', uses the feature importance either through a `coef_`
+        or `feature_importances_` attributes of estimator.
+
+        Also accepts a string that specifies an attribute name/path
+        for extracting feature importance.
+        For example, give `regressor_.coef_` in case of
+        :class:`~sklearn.compose.TransformedTargetRegressor`  or
+        `named_steps.clf.feature_importances_` in case of
+        :class:`~sklearn.pipeline.Pipeline` with its last step named `clf`.
+
+        If `callable`, overrides the default feature importance getter.
+        The callable is passed with the fitted estimator and it should
+        return importance for each feature.
+
+        .. versionadded:: 0.24
+
+    Attributes
+    ----------
+    classes_ : ndarray of shape (n_classes,)
+        The classes labels. Only available when `estimator` is a classifier.
+
+    estimator_ : ``Estimator`` instance
+        The fitted estimator used to select features.
+
+    cv_results_ : dict of ndarrays
+        A dict with keys:
+
+        split(k)_test_score : ndarray of shape (n_subsets_of_features,)
+            The cross-validation scores across (k)th fold.
+
+        mean_test_score : ndarray of shape (n_subsets_of_features,)
+            Mean of scores over the folds.
+
+        std_test_score : ndarray of shape (n_subsets_of_features,)
+            Standard deviation of scores over the folds.
+
+        .. versionadded:: 1.0
+
+    n_features_ : int
+        The number of selected features with cross-validation.
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`. Only defined if the
+        underlying estimator exposes such an attribute when fit.
+
+        .. versionadded:: 0.24
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
+    ranking_ : narray of shape (n_features,)
+        The feature ranking, such that `ranking_[i]`
+        corresponds to the ranking
+        position of the i-th feature.
+        Selected (i.e., estimated best)
+        features are assigned rank 1.
+
+    support_ : ndarray of shape (n_features,)
+        The mask of selected features.
+
+    See Also
+    --------
+    RFE : Recursive feature elimination.
+
+    Notes
+    -----
+    The size of all values in ``cv_results_`` is equal to
+    ``ceil((n_features - min_features_to_select) / step) + 1``,
+    where step is the number of features removed at each iteration.
+
+    Allows NaN/Inf in the input if the underlying estimator does as well.
+
+    References
+    ----------
+
+    .. [1] Guyon, I., Weston, J., Barnhill, S., & Vapnik, V., "Gene selection
+           for cancer classification using support vector machines",
+           Mach. Learn., 46(1-3), 389--422, 2002.
+
+    Examples
+    --------
+    The following example shows how to retrieve the a-priori not known 5
+    informative features in the Friedman #1 dataset.
+
+    >>> from sklearn.datasets import make_friedman1
+    >>> from sklearn.feature_selection import RFECV
+    >>> from sklearn.svm import SVR
+    >>> X, y = make_friedman1(n_samples=50, n_features=10, random_state=0)
+    >>> estimator = SVR(kernel="linear")
+    >>> selector = RFECV(estimator, step=1, cv=5)
+    >>> selector = selector.fit(X, y)
+    >>> selector.support_
+    array([ True,  True,  True,  True,  True, False, False, False, False,
+           False])
+    >>> selector.ranking_
+    array([1, 1, 1, 1, 1, 6, 4, 3, 2, 5])
+    """
+
+    _parameter_constraints: dict = {
+        **RFE._parameter_constraints,
+        "min_features_to_select": [Interval(Integral, 0, None, closed="neither")],
+        "cv": ["cv_object"],
+        "scoring": [None, str, callable],
+        "n_jobs": [None, Integral],
+    }
+    _parameter_constraints.pop("n_features_to_select")
+
+    def __init__(
+        self,
+        estimator,
+        *,
+        step=1,
+        min_features_to_select=1,
+        cv=None,
+        scoring=None,
+        verbose=0,
+        n_jobs=None,
+        importance_getter="auto",
+    ):
+        self.estimator = estimator
+        self.step = step
+        self.importance_getter = importance_getter
+        self.cv = cv
+        self.scoring = scoring
+        self.verbose = verbose
+        self.n_jobs = n_jobs
+        self.min_features_to_select = min_features_to_select
+
+    @_fit_context(
+        # RFECV.estimator is not validated yet
+        prefer_skip_nested_validation=False
+    )
+    def fit(self, X, y, groups=None):
+        """Fit the RFE model and automatically tune the number of selected features.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            Training vector, where `n_samples` is the number of samples and
+            `n_features` is the total number of features.
+
+        y : array-like of shape (n_samples,)
+            Target values (integers for classification, real numbers for
+            regression).
+
+        groups : array-like of shape (n_samples,) or None, default=None
+            Group labels for the samples used while splitting the dataset into
+            train/test set. Only used in conjunction with a "Group" :term:`cv`
+            instance (e.g., :class:`~sklearn.model_selection.GroupKFold`).
+
+            .. versionadded:: 0.20
+
+        Returns
+        -------
+        self : object
+            Fitted estimator.
+        """
+        _raise_for_unsupported_routing(self, "fit", groups=groups)
+        X, y = self._validate_data(
+            X,
+            y,
+            accept_sparse="csr",
+            ensure_min_features=2,
+            force_all_finite=False,
+            multi_output=True,
+        )
+
+        # Initialization
+        cv = check_cv(self.cv, y, classifier=is_classifier(self.estimator))
+        scorer = check_scoring(self.estimator, scoring=self.scoring)
+        n_features = X.shape[1]
+
+        if 0.0 < self.step < 1.0:
+            step = int(max(1, self.step * n_features))
+        else:
+            step = int(self.step)
+
+        # Build an RFE object, which will evaluate and score each possible
+        # feature count, down to self.min_features_to_select
+        rfe = RFE(
+            estimator=self.estimator,
+            n_features_to_select=self.min_features_to_select,
+            importance_getter=self.importance_getter,
+            step=self.step,
+            verbose=self.verbose,
+        )
+
+        # Determine the number of subsets of features by fitting across
+        # the train folds and choosing the "features_to_select" parameter
+        # that gives the least averaged error across all folds.
+
+        # Note that joblib raises a non-picklable error for bound methods
+        # even if n_jobs is set to 1 with the default multiprocessing
+        # backend.
+        # This branching is done so that to
+        # make sure that user code that sets n_jobs to 1
+        # and provides bound methods as scorers is not broken with the
+        # addition of n_jobs parameter in version 0.18.
+
+        if effective_n_jobs(self.n_jobs) == 1:
+            parallel, func = list, _rfe_single_fit
+        else:
+            parallel = Parallel(n_jobs=self.n_jobs)
+            func = delayed(_rfe_single_fit)
+
+        scores = parallel(
+            func(rfe, self.estimator, X, y, train, test, scorer)
+            for train, test in cv.split(X, y, groups)
+        )
+
+        scores = np.array(scores)
+        scores_sum = np.sum(scores, axis=0)
+        scores_sum_rev = scores_sum[::-1]
+        argmax_idx = len(scores_sum) - np.argmax(scores_sum_rev) - 1
+        n_features_to_select = max(
+            n_features - (argmax_idx * step), self.min_features_to_select
+        )
+
+        # Re-execute an elimination with best_k over the whole set
+        rfe = RFE(
+            estimator=self.estimator,
+            n_features_to_select=n_features_to_select,
+            step=self.step,
+            importance_getter=self.importance_getter,
+            verbose=self.verbose,
+        )
+
+        rfe.fit(X, y)
+
+        # Set final attributes
+        self.support_ = rfe.support_
+        self.n_features_ = rfe.n_features_
+        self.ranking_ = rfe.ranking_
+        self.estimator_ = clone(self.estimator)
+        self.estimator_.fit(self._transform(X), y)
+
+        # reverse to stay consistent with before
+        scores_rev = scores[:, ::-1]
+        self.cv_results_ = {}
+        self.cv_results_["mean_test_score"] = np.mean(scores_rev, axis=0)
+        self.cv_results_["std_test_score"] = np.std(scores_rev, axis=0)
+
+        for i in range(scores.shape[0]):
+            self.cv_results_[f"split{i}_test_score"] = scores_rev[i]
+
+        return self

env-llmeval/lib/python3.10/site-packages/sklearn/feature_selection/_sequential.py

@@ -0,0 +1,300 @@
+"""
+Sequential feature selection
+"""
+from numbers import Integral, Real
+
+import numpy as np
+
+from ..base import BaseEstimator, MetaEstimatorMixin, _fit_context, clone, is_classifier
+from ..metrics import get_scorer_names
+from ..model_selection import check_cv, cross_val_score
+from ..utils._param_validation import HasMethods, Interval, RealNotInt, StrOptions
+from ..utils._tags import _safe_tags
+from ..utils.metadata_routing import _RoutingNotSupportedMixin
+from ..utils.validation import check_is_fitted
+from ._base import SelectorMixin
+
+
+class SequentialFeatureSelector(
+    _RoutingNotSupportedMixin, SelectorMixin, MetaEstimatorMixin, BaseEstimator
+):
+    """Transformer that performs Sequential Feature Selection.
+
+    This Sequential Feature Selector adds (forward selection) or
+    removes (backward selection) features to form a feature subset in a
+    greedy fashion. At each stage, this estimator chooses the best feature to
+    add or remove based on the cross-validation score of an estimator. In
+    the case of unsupervised learning, this Sequential Feature Selector
+    looks only at the features (X), not the desired outputs (y).
+
+    Read more in the :ref:`User Guide <sequential_feature_selection>`.
+
+    .. versionadded:: 0.24
+
+    Parameters
+    ----------
+    estimator : estimator instance
+        An unfitted estimator.
+
+    n_features_to_select : "auto", int or float, default="auto"
+        If `"auto"`, the behaviour depends on the `tol` parameter:
+
+        - if `tol` is not `None`, then features are selected while the score
+          change does not exceed `tol`.
+        - otherwise, half of the features are selected.
+
+        If integer, the parameter is the absolute number of features to select.
+        If float between 0 and 1, it is the fraction of features to select.
+
+        .. versionadded:: 1.1
+           The option `"auto"` was added in version 1.1.
+
+        .. versionchanged:: 1.3
+           The default changed from `"warn"` to `"auto"` in 1.3.
+
+    tol : float, default=None
+        If the score is not incremented by at least `tol` between two
+        consecutive feature additions or removals, stop adding or removing.
+
+        `tol` can be negative when removing features using `direction="backward"`.
+        It can be useful to reduce the number of features at the cost of a small
+        decrease in the score.
+
+        `tol` is enabled only when `n_features_to_select` is `"auto"`.
+
+        .. versionadded:: 1.1
+
+    direction : {'forward', 'backward'}, default='forward'
+        Whether to perform forward selection or backward selection.
+
+    scoring : str or callable, default=None
+        A single str (see :ref:`scoring_parameter`) or a callable
+        (see :ref:`scoring`) to evaluate the predictions on the test set.
+
+        NOTE that when using a custom scorer, it should return a single
+        value.
+
+        If None, the estimator's score method is used.
+
+    cv : int, cross-validation generator or an iterable, default=None
+        Determines the cross-validation splitting strategy.
+        Possible inputs for cv are:
+
+        - None, to use the default 5-fold cross validation,
+        - integer, to specify the number of folds in a `(Stratified)KFold`,
+        - :term:`CV splitter`,
+        - An iterable yielding (train, test) splits as arrays of indices.
+
+        For integer/None inputs, if the estimator is a classifier and ``y`` is
+        either binary or multiclass,
+        :class:`~sklearn.model_selection.StratifiedKFold` is used. In all other
+        cases, :class:`~sklearn.model_selection.KFold` is used. These splitters
+        are instantiated with `shuffle=False` so the splits will be the same
+        across calls.
+
+        Refer :ref:`User Guide <cross_validation>` for the various
+        cross-validation strategies that can be used here.
+
+    n_jobs : int, default=None
+        Number of jobs to run in parallel. When evaluating a new feature to
+        add or remove, the cross-validation procedure is parallel over the
+        folds.
+        ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
+        ``-1`` means using all processors. See :term:`Glossary <n_jobs>`
+        for more details.
+
+    Attributes
+    ----------
+    n_features_in_ : int
+        Number of features seen during :term:`fit`. Only defined if the
+        underlying estimator exposes such an attribute when fit.
+
+        .. versionadded:: 0.24
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
+    n_features_to_select_ : int
+        The number of features that were selected.
+
+    support_ : ndarray of shape (n_features,), dtype=bool
+        The mask of selected features.
+
+    See Also
+    --------
+    GenericUnivariateSelect : Univariate feature selector with configurable
+        strategy.
+    RFE : Recursive feature elimination based on importance weights.
+    RFECV : Recursive feature elimination based on importance weights, with
+        automatic selection of the number of features.
+    SelectFromModel : Feature selection based on thresholds of importance
+        weights.
+
+    Examples
+    --------
+    >>> from sklearn.feature_selection import SequentialFeatureSelector
+    >>> from sklearn.neighbors import KNeighborsClassifier
+    >>> from sklearn.datasets import load_iris
+    >>> X, y = load_iris(return_X_y=True)
+    >>> knn = KNeighborsClassifier(n_neighbors=3)
+    >>> sfs = SequentialFeatureSelector(knn, n_features_to_select=3)
+    >>> sfs.fit(X, y)
+    SequentialFeatureSelector(estimator=KNeighborsClassifier(n_neighbors=3),
+                              n_features_to_select=3)
+    >>> sfs.get_support()
+    array([ True, False,  True,  True])
+    >>> sfs.transform(X).shape
+    (150, 3)
+    """
+
+    _parameter_constraints: dict = {
+        "estimator": [HasMethods(["fit"])],
+        "n_features_to_select": [
+            StrOptions({"auto"}),
+            Interval(RealNotInt, 0, 1, closed="right"),
+            Interval(Integral, 0, None, closed="neither"),
+        ],
+        "tol": [None, Interval(Real, None, None, closed="neither")],
+        "direction": [StrOptions({"forward", "backward"})],
+        "scoring": [None, StrOptions(set(get_scorer_names())), callable],
+        "cv": ["cv_object"],
+        "n_jobs": [None, Integral],
+    }
+
+    def __init__(
+        self,
+        estimator,
+        *,
+        n_features_to_select="auto",
+        tol=None,
+        direction="forward",
+        scoring=None,
+        cv=5,
+        n_jobs=None,
+    ):
+        self.estimator = estimator
+        self.n_features_to_select = n_features_to_select
+        self.tol = tol
+        self.direction = direction
+        self.scoring = scoring
+        self.cv = cv
+        self.n_jobs = n_jobs
+
+    @_fit_context(
+        # SequentialFeatureSelector.estimator is not validated yet
+        prefer_skip_nested_validation=False
+    )
+    def fit(self, X, y=None):
+        """Learn the features to select from X.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            Training vectors, where `n_samples` is the number of samples and
+            `n_features` is the number of predictors.
+
+        y : array-like of shape (n_samples,), default=None
+            Target values. This parameter may be ignored for
+            unsupervised learning.
+
+        Returns
+        -------
+        self : object
+            Returns the instance itself.
+        """
+        tags = self._get_tags()
+        X = self._validate_data(
+            X,
+            accept_sparse="csc",
+            ensure_min_features=2,
+            force_all_finite=not tags.get("allow_nan", True),
+        )
+        n_features = X.shape[1]
+
+        if self.n_features_to_select == "auto":
+            if self.tol is not None:
+                # With auto feature selection, `n_features_to_select_` will be updated
+                # to `support_.sum()` after features are selected.
+                self.n_features_to_select_ = n_features - 1
+            else:
+                self.n_features_to_select_ = n_features // 2
+        elif isinstance(self.n_features_to_select, Integral):
+            if self.n_features_to_select >= n_features:
+                raise ValueError("n_features_to_select must be < n_features.")
+            self.n_features_to_select_ = self.n_features_to_select
+        elif isinstance(self.n_features_to_select, Real):
+            self.n_features_to_select_ = int(n_features * self.n_features_to_select)
+
+        if self.tol is not None and self.tol < 0 and self.direction == "forward":
+            raise ValueError("tol must be positive when doing forward selection")
+
+        cv = check_cv(self.cv, y, classifier=is_classifier(self.estimator))
+
+        cloned_estimator = clone(self.estimator)
+
+        # the current mask corresponds to the set of features:
+        # - that we have already *selected* if we do forward selection
+        # - that we have already *excluded* if we do backward selection
+        current_mask = np.zeros(shape=n_features, dtype=bool)
+        n_iterations = (
+            self.n_features_to_select_
+            if self.n_features_to_select == "auto" or self.direction == "forward"
+            else n_features - self.n_features_to_select_
+        )
+
+        old_score = -np.inf
+        is_auto_select = self.tol is not None and self.n_features_to_select == "auto"
+        for _ in range(n_iterations):
+            new_feature_idx, new_score = self._get_best_new_feature_score(
+                cloned_estimator, X, y, cv, current_mask
+            )
+            if is_auto_select and ((new_score - old_score) < self.tol):
+                break
+
+            old_score = new_score
+            current_mask[new_feature_idx] = True
+
+        if self.direction == "backward":
+            current_mask = ~current_mask
+
+        self.support_ = current_mask
+        self.n_features_to_select_ = self.support_.sum()
+
+        return self
+
+    def _get_best_new_feature_score(self, estimator, X, y, cv, current_mask):
+        # Return the best new feature and its score to add to the current_mask,
+        # i.e. return the best new feature and its score to add (resp. remove)
+        # when doing forward selection (resp. backward selection).
+        # Feature will be added if the current score and past score are greater
+        # than tol when n_feature is auto,
+        candidate_feature_indices = np.flatnonzero(~current_mask)
+        scores = {}
+        for feature_idx in candidate_feature_indices:
+            candidate_mask = current_mask.copy()
+            candidate_mask[feature_idx] = True
+            if self.direction == "backward":
+                candidate_mask = ~candidate_mask
+            X_new = X[:, candidate_mask]
+            scores[feature_idx] = cross_val_score(
+                estimator,
+                X_new,
+                y,
+                cv=cv,
+                scoring=self.scoring,
+                n_jobs=self.n_jobs,
+            ).mean()
+        new_feature_idx = max(scores, key=lambda feature_idx: scores[feature_idx])
+        return new_feature_idx, scores[new_feature_idx]
+
+    def _get_support_mask(self):
+        check_is_fitted(self)
+        return self.support_
+
+    def _more_tags(self):
+        return {
+            "allow_nan": _safe_tags(self.estimator, key="allow_nan"),
+        }

env-llmeval/lib/python3.10/site-packages/sklearn/feature_selection/_univariate_selection.py

@@ -0,0 +1,1161 @@
+"""Univariate features selection."""
+
+# Authors: V. Michel, B. Thirion, G. Varoquaux, A. Gramfort, E. Duchesnay.
+#          L. Buitinck, A. Joly
+# License: BSD 3 clause
+
+
+import warnings
+from numbers import Integral, Real
+
+import numpy as np
+from scipy import special, stats
+from scipy.sparse import issparse
+
+from ..base import BaseEstimator, _fit_context
+from ..preprocessing import LabelBinarizer
+from ..utils import as_float_array, check_array, check_X_y, safe_mask, safe_sqr
+from ..utils._param_validation import Interval, StrOptions, validate_params
+from ..utils.extmath import row_norms, safe_sparse_dot
+from ..utils.validation import check_is_fitted
+from ._base import SelectorMixin
+
+
+def _clean_nans(scores):
+    """
+    Fixes Issue #1240: NaNs can't be properly compared, so change them to the
+    smallest value of scores's dtype. -inf seems to be unreliable.
+    """
+    # XXX where should this function be called? fit? scoring functions
+    # themselves?
+    scores = as_float_array(scores, copy=True)
+    scores[np.isnan(scores)] = np.finfo(scores.dtype).min
+    return scores
+
+
+######################################################################
+# Scoring functions
+
+
+# The following function is a rewriting of scipy.stats.f_oneway
+# Contrary to the scipy.stats.f_oneway implementation it does not
+# copy the data while keeping the inputs unchanged.
+def f_oneway(*args):
+    """Perform a 1-way ANOVA.
+
+    The one-way ANOVA tests the null hypothesis that 2 or more groups have
+    the same population mean. The test is applied to samples from two or
+    more groups, possibly with differing sizes.
+
+    Read more in the :ref:`User Guide <univariate_feature_selection>`.
+
+    Parameters
+    ----------
+    *args : {array-like, sparse matrix}
+        Sample1, sample2... The sample measurements should be given as
+        arguments.
+
+    Returns
+    -------
+    f_statistic : float
+        The computed F-value of the test.
+    p_value : float
+        The associated p-value from the F-distribution.
+
+    Notes
+    -----
+    The ANOVA test has important assumptions that must be satisfied in order
+    for the associated p-value to be valid.
+
+    1. The samples are independent
+    2. Each sample is from a normally distributed population
+    3. The population standard deviations of the groups are all equal. This
+       property is known as homoscedasticity.
+
+    If these assumptions are not true for a given set of data, it may still be
+    possible to use the Kruskal-Wallis H-test (`scipy.stats.kruskal`_) although
+    with some loss of power.
+
+    The algorithm is from Heiman[2], pp.394-7.
+
+    See ``scipy.stats.f_oneway`` that should give the same results while
+    being less efficient.
+
+    References
+    ----------
+    .. [1] Lowry, Richard.  "Concepts and Applications of Inferential
+           Statistics". Chapter 14.
+           http://vassarstats.net/textbook
+
+    .. [2] Heiman, G.W.  Research Methods in Statistics. 2002.
+    """
+    n_classes = len(args)
+    args = [as_float_array(a) for a in args]
+    n_samples_per_class = np.array([a.shape[0] for a in args])
+    n_samples = np.sum(n_samples_per_class)
+    ss_alldata = sum(safe_sqr(a).sum(axis=0) for a in args)
+    sums_args = [np.asarray(a.sum(axis=0)) for a in args]
+    square_of_sums_alldata = sum(sums_args) ** 2
+    square_of_sums_args = [s**2 for s in sums_args]
+    sstot = ss_alldata - square_of_sums_alldata / float(n_samples)
+    ssbn = 0.0
+    for k, _ in enumerate(args):
+        ssbn += square_of_sums_args[k] / n_samples_per_class[k]
+    ssbn -= square_of_sums_alldata / float(n_samples)
+    sswn = sstot - ssbn
+    dfbn = n_classes - 1
+    dfwn = n_samples - n_classes
+    msb = ssbn / float(dfbn)
+    msw = sswn / float(dfwn)
+    constant_features_idx = np.where(msw == 0.0)[0]
+    if np.nonzero(msb)[0].size != msb.size and constant_features_idx.size:
+        warnings.warn("Features %s are constant." % constant_features_idx, UserWarning)
+    f = msb / msw
+    # flatten matrix to vector in sparse case
+    f = np.asarray(f).ravel()
+    prob = special.fdtrc(dfbn, dfwn, f)
+    return f, prob
+
+
+@validate_params(
+    {
+        "X": ["array-like", "sparse matrix"],
+        "y": ["array-like"],
+    },
+    prefer_skip_nested_validation=True,
+)
+def f_classif(X, y):
+    """Compute the ANOVA F-value for the provided sample.
+
+    Read more in the :ref:`User Guide <univariate_feature_selection>`.
+
+    Parameters
+    ----------
+    X : {array-like, sparse matrix} of shape (n_samples, n_features)
+        The set of regressors that will be tested sequentially.
+
+    y : array-like of shape (n_samples,)
+        The target vector.
+
+    Returns
+    -------
+    f_statistic : ndarray of shape (n_features,)
+        F-statistic for each feature.
+
+    p_values : ndarray of shape (n_features,)
+        P-values associated with the F-statistic.
+
+    See Also
+    --------
+    chi2 : Chi-squared stats of non-negative features for classification tasks.
+    f_regression : F-value between label/feature for regression tasks.
+
+    Examples
+    --------
+    >>> from sklearn.datasets import make_classification
+    >>> from sklearn.feature_selection import f_classif
+    >>> X, y = make_classification(
+    ...     n_samples=100, n_features=10, n_informative=2, n_clusters_per_class=1,
+    ...     shuffle=False, random_state=42
+    ... )
+    >>> f_statistic, p_values = f_classif(X, y)
+    >>> f_statistic
+    array([2.2...e+02, 7.0...e-01, 1.6...e+00, 9.3...e-01,
+           5.4...e+00, 3.2...e-01, 4.7...e-02, 5.7...e-01,
+           7.5...e-01, 8.9...e-02])
+    >>> p_values
+    array([7.1...e-27, 4.0...e-01, 1.9...e-01, 3.3...e-01,
+           2.2...e-02, 5.7...e-01, 8.2...e-01, 4.5...e-01,
+           3.8...e-01, 7.6...e-01])
+    """
+    X, y = check_X_y(X, y, accept_sparse=["csr", "csc", "coo"])
+    args = [X[safe_mask(X, y == k)] for k in np.unique(y)]
+    return f_oneway(*args)
+
+
+def _chisquare(f_obs, f_exp):
+    """Fast replacement for scipy.stats.chisquare.
+
+    Version from https://github.com/scipy/scipy/pull/2525 with additional
+    optimizations.
+    """
+    f_obs = np.asarray(f_obs, dtype=np.float64)
+
+    k = len(f_obs)
+    # Reuse f_obs for chi-squared statistics
+    chisq = f_obs
+    chisq -= f_exp
+    chisq **= 2
+    with np.errstate(invalid="ignore"):
+        chisq /= f_exp
+    chisq = chisq.sum(axis=0)
+    return chisq, special.chdtrc(k - 1, chisq)
+
+
+@validate_params(
+    {
+        "X": ["array-like", "sparse matrix"],
+        "y": ["array-like"],
+    },
+    prefer_skip_nested_validation=True,
+)
+def chi2(X, y):
+    """Compute chi-squared stats between each non-negative feature and class.
+
+    This score can be used to select the `n_features` features with the
+    highest values for the test chi-squared statistic from X, which must
+    contain only **non-negative features** such as booleans or frequencies
+    (e.g., term counts in document classification), relative to the classes.
+
+    Recall that the chi-square test measures dependence between stochastic
+    variables, so using this function "weeds out" the features that are the
+    most likely to be independent of class and therefore irrelevant for
+    classification.
+
+    Read more in the :ref:`User Guide <univariate_feature_selection>`.
+
+    Parameters
+    ----------
+    X : {array-like, sparse matrix} of shape (n_samples, n_features)
+        Sample vectors.
+
+    y : array-like of shape (n_samples,)
+        Target vector (class labels).
+
+    Returns
+    -------
+    chi2 : ndarray of shape (n_features,)
+        Chi2 statistics for each feature.
+
+    p_values : ndarray of shape (n_features,)
+        P-values for each feature.
+
+    See Also
+    --------
+    f_classif : ANOVA F-value between label/feature for classification tasks.
+    f_regression : F-value between label/feature for regression tasks.
+
+    Notes
+    -----
+    Complexity of this algorithm is O(n_classes * n_features).
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from sklearn.feature_selection import chi2
+    >>> X = np.array([[1, 1, 3],
+    ...               [0, 1, 5],
+    ...               [5, 4, 1],
+    ...               [6, 6, 2],
+    ...               [1, 4, 0],
+    ...               [0, 0, 0]])
+    >>> y = np.array([1, 1, 0, 0, 2, 2])
+    >>> chi2_stats, p_values = chi2(X, y)
+    >>> chi2_stats
+    array([15.3...,  6.5       ,  8.9...])
+    >>> p_values
+    array([0.0004..., 0.0387..., 0.0116... ])
+    """
+
+    # XXX: we might want to do some of the following in logspace instead for
+    # numerical stability.
+    # Converting X to float allows getting better performance for the
+    # safe_sparse_dot call made below.
+    X = check_array(X, accept_sparse="csr", dtype=(np.float64, np.float32))
+    if np.any((X.data if issparse(X) else X) < 0):
+        raise ValueError("Input X must be non-negative.")
+
+    # Use a sparse representation for Y by default to reduce memory usage when
+    # y has many unique classes.
+    Y = LabelBinarizer(sparse_output=True).fit_transform(y)
+    if Y.shape[1] == 1:
+        Y = Y.toarray()
+        Y = np.append(1 - Y, Y, axis=1)
+
+    observed = safe_sparse_dot(Y.T, X)  # n_classes * n_features
+
+    if issparse(observed):
+        # convert back to a dense array before calling _chisquare
+        # XXX: could _chisquare be reimplement to accept sparse matrices for
+        # cases where both n_classes and n_features are large (and X is
+        # sparse)?
+        observed = observed.toarray()
+
+    feature_count = X.sum(axis=0).reshape(1, -1)
+    class_prob = Y.mean(axis=0).reshape(1, -1)
+    expected = np.dot(class_prob.T, feature_count)
+
+    return _chisquare(observed, expected)
+
+
+@validate_params(
+    {
+        "X": ["array-like", "sparse matrix"],
+        "y": ["array-like"],
+        "center": ["boolean"],
+        "force_finite": ["boolean"],
+    },
+    prefer_skip_nested_validation=True,
+)
+def r_regression(X, y, *, center=True, force_finite=True):
+    """Compute Pearson's r for each features and the target.
+
+    Pearson's r is also known as the Pearson correlation coefficient.
+
+    Linear model for testing the individual effect of each of many regressors.
+    This is a scoring function to be used in a feature selection procedure, not
+    a free standing feature selection procedure.
+
+    The cross correlation between each regressor and the target is computed
+    as::
+
+        E[(X[:, i] - mean(X[:, i])) * (y - mean(y))] / (std(X[:, i]) * std(y))
+
+    For more on usage see the :ref:`User Guide <univariate_feature_selection>`.
+
+    .. versionadded:: 1.0
+
+    Parameters
+    ----------
+    X : {array-like, sparse matrix} of shape (n_samples, n_features)
+        The data matrix.
+
+    y : array-like of shape (n_samples,)
+        The target vector.
+
+    center : bool, default=True
+        Whether or not to center the data matrix `X` and the target vector `y`.
+        By default, `X` and `y` will be centered.
+
+    force_finite : bool, default=True
+        Whether or not to force the Pearson's R correlation to be finite.
+        In the particular case where some features in `X` or the target `y`
+        are constant, the Pearson's R correlation is not defined. When
+        `force_finite=False`, a correlation of `np.nan` is returned to
+        acknowledge this case. When `force_finite=True`, this value will be
+        forced to a minimal correlation of `0.0`.
+
+        .. versionadded:: 1.1
+
+    Returns
+    -------
+    correlation_coefficient : ndarray of shape (n_features,)
+        Pearson's R correlation coefficients of features.
+
+    See Also
+    --------
+    f_regression: Univariate linear regression tests returning f-statistic
+        and p-values.
+    mutual_info_regression: Mutual information for a continuous target.
+    f_classif: ANOVA F-value between label/feature for classification tasks.
+    chi2: Chi-squared stats of non-negative features for classification tasks.
+
+    Examples
+    --------
+    >>> from sklearn.datasets import make_regression
+    >>> from sklearn.feature_selection import r_regression
+    >>> X, y = make_regression(
+    ...     n_samples=50, n_features=3, n_informative=1, noise=1e-4, random_state=42
+    ... )
+    >>> r_regression(X, y)
+    array([-0.15...,  1.        , -0.22...])
+    """
+    X, y = check_X_y(X, y, accept_sparse=["csr", "csc", "coo"], dtype=np.float64)
+    n_samples = X.shape[0]
+
+    # Compute centered values
+    # Note that E[(x - mean(x))*(y - mean(y))] = E[x*(y - mean(y))], so we
+    # need not center X
+    if center:
+        y = y - np.mean(y)
+        # TODO: for Scipy <= 1.10, `isspmatrix(X)` returns `True` for sparse arrays.
+        # Here, we check the output of the `.mean` operation that returns a `np.matrix`
+        # for sparse matrices while a `np.array` for dense and sparse arrays.
+        # We can reconsider using `isspmatrix` when the minimum version is
+        # SciPy >= 1.11
+        X_means = X.mean(axis=0)
+        X_means = X_means.getA1() if isinstance(X_means, np.matrix) else X_means
+        # Compute the scaled standard deviations via moments
+        X_norms = np.sqrt(row_norms(X.T, squared=True) - n_samples * X_means**2)
+    else:
+        X_norms = row_norms(X.T)
+
+    correlation_coefficient = safe_sparse_dot(y, X)
+    with np.errstate(divide="ignore", invalid="ignore"):
+        correlation_coefficient /= X_norms
+        correlation_coefficient /= np.linalg.norm(y)
+
+    if force_finite and not np.isfinite(correlation_coefficient).all():
+        # case where the target or some features are constant
+        # the correlation coefficient(s) is/are set to the minimum (i.e. 0.0)
+        nan_mask = np.isnan(correlation_coefficient)
+        correlation_coefficient[nan_mask] = 0.0
+    return correlation_coefficient
+
+
+@validate_params(
+    {
+        "X": ["array-like", "sparse matrix"],
+        "y": ["array-like"],
+        "center": ["boolean"],
+        "force_finite": ["boolean"],
+    },
+    prefer_skip_nested_validation=True,
+)
+def f_regression(X, y, *, center=True, force_finite=True):
+    """Univariate linear regression tests returning F-statistic and p-values.
+
+    Quick linear model for testing the effect of a single regressor,
+    sequentially for many regressors.
+
+    This is done in 2 steps:
+
+    1. The cross correlation between each regressor and the target is computed
+       using :func:`r_regression` as::
+
+           E[(X[:, i] - mean(X[:, i])) * (y - mean(y))] / (std(X[:, i]) * std(y))
+
+    2. It is converted to an F score and then to a p-value.
+
+    :func:`f_regression` is derived from :func:`r_regression` and will rank
+    features in the same order if all the features are positively correlated
+    with the target.
+
+    Note however that contrary to :func:`f_regression`, :func:`r_regression`
+    values lie in [-1, 1] and can thus be negative. :func:`f_regression` is
+    therefore recommended as a feature selection criterion to identify
+    potentially predictive feature for a downstream classifier, irrespective of
+    the sign of the association with the target variable.
+
+    Furthermore :func:`f_regression` returns p-values while
+    :func:`r_regression` does not.
+
+    Read more in the :ref:`User Guide <univariate_feature_selection>`.
+
+    Parameters
+    ----------
+    X : {array-like, sparse matrix} of shape (n_samples, n_features)
+        The data matrix.
+
+    y : array-like of shape (n_samples,)
+        The target vector.
+
+    center : bool, default=True
+        Whether or not to center the data matrix `X` and the target vector `y`.
+        By default, `X` and `y` will be centered.
+
+    force_finite : bool, default=True
+        Whether or not to force the F-statistics and associated p-values to
+        be finite. There are two cases where the F-statistic is expected to not
+        be finite:
+
+        - when the target `y` or some features in `X` are constant. In this
+          case, the Pearson's R correlation is not defined leading to obtain
+          `np.nan` values in the F-statistic and p-value. When
+          `force_finite=True`, the F-statistic is set to `0.0` and the
+          associated p-value is set to `1.0`.
+        - when a feature in `X` is perfectly correlated (or
+          anti-correlated) with the target `y`. In this case, the F-statistic
+          is expected to be `np.inf`. When `force_finite=True`, the F-statistic
+          is set to `np.finfo(dtype).max` and the associated p-value is set to
+          `0.0`.
+
+        .. versionadded:: 1.1
+
+    Returns
+    -------
+    f_statistic : ndarray of shape (n_features,)
+        F-statistic for each feature.
+
+    p_values : ndarray of shape (n_features,)
+        P-values associated with the F-statistic.
+
+    See Also
+    --------
+    r_regression: Pearson's R between label/feature for regression tasks.
+    f_classif: ANOVA F-value between label/feature for classification tasks.
+    chi2: Chi-squared stats of non-negative features for classification tasks.
+    SelectKBest: Select features based on the k highest scores.
+    SelectFpr: Select features based on a false positive rate test.
+    SelectFdr: Select features based on an estimated false discovery rate.
+    SelectFwe: Select features based on family-wise error rate.
+    SelectPercentile: Select features based on percentile of the highest
+        scores.
+
+    Examples
+    --------
+    >>> from sklearn.datasets import make_regression
+    >>> from sklearn.feature_selection import f_regression
+    >>> X, y = make_regression(
+    ...     n_samples=50, n_features=3, n_informative=1, noise=1e-4, random_state=42
+    ... )
+    >>> f_statistic, p_values = f_regression(X, y)
+    >>> f_statistic
+    array([1.2...+00, 2.6...+13, 2.6...+00])
+    >>> p_values
+    array([2.7..., 1.5..., 1.0...])
+    """
+    correlation_coefficient = r_regression(
+        X, y, center=center, force_finite=force_finite
+    )
+    deg_of_freedom = y.size - (2 if center else 1)
+
+    corr_coef_squared = correlation_coefficient**2
+
+    with np.errstate(divide="ignore", invalid="ignore"):
+        f_statistic = corr_coef_squared / (1 - corr_coef_squared) * deg_of_freedom
+        p_values = stats.f.sf(f_statistic, 1, deg_of_freedom)
+
+    if force_finite and not np.isfinite(f_statistic).all():
+        # case where there is a perfect (anti-)correlation
+        # f-statistics can be set to the maximum and p-values to zero
+        mask_inf = np.isinf(f_statistic)
+        f_statistic[mask_inf] = np.finfo(f_statistic.dtype).max
+        # case where the target or some features are constant
+        # f-statistics would be minimum and thus p-values large
+        mask_nan = np.isnan(f_statistic)
+        f_statistic[mask_nan] = 0.0
+        p_values[mask_nan] = 1.0
+    return f_statistic, p_values
+
+
+######################################################################
+# Base classes
+
+
+class _BaseFilter(SelectorMixin, BaseEstimator):
+    """Initialize the univariate feature selection.
+
+    Parameters
+    ----------
+    score_func : callable
+        Function taking two arrays X and y, and returning a pair of arrays
+        (scores, pvalues) or a single array with scores.
+    """
+
+    _parameter_constraints: dict = {"score_func": [callable]}
+
+    def __init__(self, score_func):
+        self.score_func = score_func
+
+    @_fit_context(prefer_skip_nested_validation=True)
+    def fit(self, X, y=None):
+        """Run score function on (X, y) and get the appropriate features.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            The training input samples.
+
+        y : array-like of shape (n_samples,) or None
+            The target values (class labels in classification, real numbers in
+            regression). If the selector is unsupervised then `y` can be set to `None`.
+
+        Returns
+        -------
+        self : object
+            Returns the instance itself.
+        """
+        if y is None:
+            X = self._validate_data(X, accept_sparse=["csr", "csc"])
+        else:
+            X, y = self._validate_data(
+                X, y, accept_sparse=["csr", "csc"], multi_output=True
+            )
+
+        self._check_params(X, y)
+        score_func_ret = self.score_func(X, y)
+        if isinstance(score_func_ret, (list, tuple)):
+            self.scores_, self.pvalues_ = score_func_ret
+            self.pvalues_ = np.asarray(self.pvalues_)
+        else:
+            self.scores_ = score_func_ret
+            self.pvalues_ = None
+
+        self.scores_ = np.asarray(self.scores_)
+
+        return self
+
+    def _check_params(self, X, y):
+        pass
+
+    def _more_tags(self):
+        return {"requires_y": True}
+
+
+######################################################################
+# Specific filters
+######################################################################
+class SelectPercentile(_BaseFilter):
+    """Select features according to a percentile of the highest scores.
+
+    Read more in the :ref:`User Guide <univariate_feature_selection>`.
+
+    Parameters
+    ----------
+    score_func : callable, default=f_classif
+        Function taking two arrays X and y, and returning a pair of arrays
+        (scores, pvalues) or a single array with scores.
+        Default is f_classif (see below "See Also"). The default function only
+        works with classification tasks.
+
+        .. versionadded:: 0.18
+
+    percentile : int, default=10
+        Percent of features to keep.
+
+    Attributes
+    ----------
+    scores_ : array-like of shape (n_features,)
+        Scores of features.
+
+    pvalues_ : array-like of shape (n_features,)
+        p-values of feature scores, None if `score_func` returned only scores.
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
+    See Also
+    --------
+    f_classif : ANOVA F-value between label/feature for classification tasks.
+    mutual_info_classif : Mutual information for a discrete target.
+    chi2 : Chi-squared stats of non-negative features for classification tasks.
+    f_regression : F-value between label/feature for regression tasks.
+    mutual_info_regression : Mutual information for a continuous target.
+    SelectKBest : Select features based on the k highest scores.
+    SelectFpr : Select features based on a false positive rate test.
+    SelectFdr : Select features based on an estimated false discovery rate.
+    SelectFwe : Select features based on family-wise error rate.
+    GenericUnivariateSelect : Univariate feature selector with configurable
+        mode.
+
+    Notes
+    -----
+    Ties between features with equal scores will be broken in an unspecified
+    way.
+
+    This filter supports unsupervised feature selection that only requests `X` for
+    computing the scores.
+
+    Examples
+    --------
+    >>> from sklearn.datasets import load_digits
+    >>> from sklearn.feature_selection import SelectPercentile, chi2
+    >>> X, y = load_digits(return_X_y=True)
+    >>> X.shape
+    (1797, 64)
+    >>> X_new = SelectPercentile(chi2, percentile=10).fit_transform(X, y)
+    >>> X_new.shape
+    (1797, 7)
+    """
+
+    _parameter_constraints: dict = {
+        **_BaseFilter._parameter_constraints,
+        "percentile": [Interval(Real, 0, 100, closed="both")],
+    }
+
+    def __init__(self, score_func=f_classif, *, percentile=10):
+        super().__init__(score_func=score_func)
+        self.percentile = percentile
+
+    def _get_support_mask(self):
+        check_is_fitted(self)
+
+        # Cater for NaNs
+        if self.percentile == 100:
+            return np.ones(len(self.scores_), dtype=bool)
+        elif self.percentile == 0:
+            return np.zeros(len(self.scores_), dtype=bool)
+
+        scores = _clean_nans(self.scores_)
+        threshold = np.percentile(scores, 100 - self.percentile)
+        mask = scores > threshold
+        ties = np.where(scores == threshold)[0]
+        if len(ties):
+            max_feats = int(len(scores) * self.percentile / 100)
+            kept_ties = ties[: max_feats - mask.sum()]
+            mask[kept_ties] = True
+        return mask
+
+    def _more_tags(self):
+        return {"requires_y": False}
+
+
+class SelectKBest(_BaseFilter):
+    """Select features according to the k highest scores.
+
+    Read more in the :ref:`User Guide <univariate_feature_selection>`.
+
+    Parameters
+    ----------
+    score_func : callable, default=f_classif
+        Function taking two arrays X and y, and returning a pair of arrays
+        (scores, pvalues) or a single array with scores.
+        Default is f_classif (see below "See Also"). The default function only
+        works with classification tasks.
+
+        .. versionadded:: 0.18
+
+    k : int or "all", default=10
+        Number of top features to select.
+        The "all" option bypasses selection, for use in a parameter search.
+
+    Attributes
+    ----------
+    scores_ : array-like of shape (n_features,)
+        Scores of features.
+
+    pvalues_ : array-like of shape (n_features,)
+        p-values of feature scores, None if `score_func` returned only scores.
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
+    See Also
+    --------
+    f_classif: ANOVA F-value between label/feature for classification tasks.
+    mutual_info_classif: Mutual information for a discrete target.
+    chi2: Chi-squared stats of non-negative features for classification tasks.
+    f_regression: F-value between label/feature for regression tasks.
+    mutual_info_regression: Mutual information for a continuous target.
+    SelectPercentile: Select features based on percentile of the highest
+        scores.
+    SelectFpr : Select features based on a false positive rate test.
+    SelectFdr : Select features based on an estimated false discovery rate.
+    SelectFwe : Select features based on family-wise error rate.
+    GenericUnivariateSelect : Univariate feature selector with configurable
+        mode.
+
+    Notes
+    -----
+    Ties between features with equal scores will be broken in an unspecified
+    way.
+
+    This filter supports unsupervised feature selection that only requests `X` for
+    computing the scores.
+
+    Examples
+    --------
+    >>> from sklearn.datasets import load_digits
+    >>> from sklearn.feature_selection import SelectKBest, chi2
+    >>> X, y = load_digits(return_X_y=True)
+    >>> X.shape
+    (1797, 64)
+    >>> X_new = SelectKBest(chi2, k=20).fit_transform(X, y)
+    >>> X_new.shape
+    (1797, 20)
+    """
+
+    _parameter_constraints: dict = {
+        **_BaseFilter._parameter_constraints,
+        "k": [StrOptions({"all"}), Interval(Integral, 0, None, closed="left")],
+    }
+
+    def __init__(self, score_func=f_classif, *, k=10):
+        super().__init__(score_func=score_func)
+        self.k = k
+
+    def _check_params(self, X, y):
+        if not isinstance(self.k, str) and self.k > X.shape[1]:
+            warnings.warn(
+                f"k={self.k} is greater than n_features={X.shape[1]}. "
+                "All the features will be returned."
+            )
+
+    def _get_support_mask(self):
+        check_is_fitted(self)
+
+        if self.k == "all":
+            return np.ones(self.scores_.shape, dtype=bool)
+        elif self.k == 0:
+            return np.zeros(self.scores_.shape, dtype=bool)
+        else:
+            scores = _clean_nans(self.scores_)
+            mask = np.zeros(scores.shape, dtype=bool)
+
+            # Request a stable sort. Mergesort takes more memory (~40MB per
+            # megafeature on x86-64).
+            mask[np.argsort(scores, kind="mergesort")[-self.k :]] = 1
+            return mask
+
+    def _more_tags(self):
+        return {"requires_y": False}
+
+
+class SelectFpr(_BaseFilter):
+    """Filter: Select the pvalues below alpha based on a FPR test.
+
+    FPR test stands for False Positive Rate test. It controls the total
+    amount of false detections.
+
+    Read more in the :ref:`User Guide <univariate_feature_selection>`.
+
+    Parameters
+    ----------
+    score_func : callable, default=f_classif
+        Function taking two arrays X and y, and returning a pair of arrays
+        (scores, pvalues).
+        Default is f_classif (see below "See Also"). The default function only
+        works with classification tasks.
+
+    alpha : float, default=5e-2
+        Features with p-values less than `alpha` are selected.
+
+    Attributes
+    ----------
+    scores_ : array-like of shape (n_features,)
+        Scores of features.
+
+    pvalues_ : array-like of shape (n_features,)
+        p-values of feature scores.
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
+    See Also
+    --------
+    f_classif : ANOVA F-value between label/feature for classification tasks.
+    chi2 : Chi-squared stats of non-negative features for classification tasks.
+    mutual_info_classif: Mutual information for a discrete target.
+    f_regression : F-value between label/feature for regression tasks.
+    mutual_info_regression : Mutual information for a continuous target.
+    SelectPercentile : Select features based on percentile of the highest
+        scores.
+    SelectKBest : Select features based on the k highest scores.
+    SelectFdr : Select features based on an estimated false discovery rate.
+    SelectFwe : Select features based on family-wise error rate.
+    GenericUnivariateSelect : Univariate feature selector with configurable
+        mode.
+
+    Examples
+    --------
+    >>> from sklearn.datasets import load_breast_cancer
+    >>> from sklearn.feature_selection import SelectFpr, chi2
+    >>> X, y = load_breast_cancer(return_X_y=True)
+    >>> X.shape
+    (569, 30)
+    >>> X_new = SelectFpr(chi2, alpha=0.01).fit_transform(X, y)
+    >>> X_new.shape
+    (569, 16)
+    """
+
+    _parameter_constraints: dict = {
+        **_BaseFilter._parameter_constraints,
+        "alpha": [Interval(Real, 0, 1, closed="both")],
+    }
+
+    def __init__(self, score_func=f_classif, *, alpha=5e-2):
+        super().__init__(score_func=score_func)
+        self.alpha = alpha
+
+    def _get_support_mask(self):
+        check_is_fitted(self)
+
+        return self.pvalues_ < self.alpha
+
+
+class SelectFdr(_BaseFilter):
+    """Filter: Select the p-values for an estimated false discovery rate.
+
+    This uses the Benjamini-Hochberg procedure. ``alpha`` is an upper bound
+    on the expected false discovery rate.
+
+    Read more in the :ref:`User Guide <univariate_feature_selection>`.
+
+    Parameters
+    ----------
+    score_func : callable, default=f_classif
+        Function taking two arrays X and y, and returning a pair of arrays
+        (scores, pvalues).
+        Default is f_classif (see below "See Also"). The default function only
+        works with classification tasks.
+
+    alpha : float, default=5e-2
+        The highest uncorrected p-value for features to keep.
+
+    Attributes
+    ----------
+    scores_ : array-like of shape (n_features,)
+        Scores of features.
+
+    pvalues_ : array-like of shape (n_features,)
+        p-values of feature scores.
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
+    See Also
+    --------
+    f_classif : ANOVA F-value between label/feature for classification tasks.
+    mutual_info_classif : Mutual information for a discrete target.
+    chi2 : Chi-squared stats of non-negative features for classification tasks.
+    f_regression : F-value between label/feature for regression tasks.
+    mutual_info_regression : Mutual information for a continuous target.
+    SelectPercentile : Select features based on percentile of the highest
+        scores.
+    SelectKBest : Select features based on the k highest scores.
+    SelectFpr : Select features based on a false positive rate test.
+    SelectFwe : Select features based on family-wise error rate.
+    GenericUnivariateSelect : Univariate feature selector with configurable
+        mode.
+
+    References
+    ----------
+    https://en.wikipedia.org/wiki/False_discovery_rate
+
+    Examples
+    --------
+    >>> from sklearn.datasets import load_breast_cancer
+    >>> from sklearn.feature_selection import SelectFdr, chi2
+    >>> X, y = load_breast_cancer(return_X_y=True)
+    >>> X.shape
+    (569, 30)
+    >>> X_new = SelectFdr(chi2, alpha=0.01).fit_transform(X, y)
+    >>> X_new.shape
+    (569, 16)
+    """
+
+    _parameter_constraints: dict = {
+        **_BaseFilter._parameter_constraints,
+        "alpha": [Interval(Real, 0, 1, closed="both")],
+    }
+
+    def __init__(self, score_func=f_classif, *, alpha=5e-2):
+        super().__init__(score_func=score_func)
+        self.alpha = alpha
+
+    def _get_support_mask(self):
+        check_is_fitted(self)
+
+        n_features = len(self.pvalues_)
+        sv = np.sort(self.pvalues_)
+        selected = sv[
+            sv <= float(self.alpha) / n_features * np.arange(1, n_features + 1)
+        ]
+        if selected.size == 0:
+            return np.zeros_like(self.pvalues_, dtype=bool)
+        return self.pvalues_ <= selected.max()
+
+
+class SelectFwe(_BaseFilter):
+    """Filter: Select the p-values corresponding to Family-wise error rate.
+
+    Read more in the :ref:`User Guide <univariate_feature_selection>`.
+
+    Parameters
+    ----------
+    score_func : callable, default=f_classif
+        Function taking two arrays X and y, and returning a pair of arrays
+        (scores, pvalues).
+        Default is f_classif (see below "See Also"). The default function only
+        works with classification tasks.
+
+    alpha : float, default=5e-2
+        The highest uncorrected p-value for features to keep.
+
+    Attributes
+    ----------
+    scores_ : array-like of shape (n_features,)
+        Scores of features.
+
+    pvalues_ : array-like of shape (n_features,)
+        p-values of feature scores.
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
+    See Also
+    --------
+    f_classif : ANOVA F-value between label/feature for classification tasks.
+    chi2 : Chi-squared stats of non-negative features for classification tasks.
+    f_regression : F-value between label/feature for regression tasks.
+    SelectPercentile : Select features based on percentile of the highest
+        scores.
+    SelectKBest : Select features based on the k highest scores.
+    SelectFpr : Select features based on a false positive rate test.
+    SelectFdr : Select features based on an estimated false discovery rate.
+    GenericUnivariateSelect : Univariate feature selector with configurable
+        mode.
+
+    Examples
+    --------
+    >>> from sklearn.datasets import load_breast_cancer
+    >>> from sklearn.feature_selection import SelectFwe, chi2
+    >>> X, y = load_breast_cancer(return_X_y=True)
+    >>> X.shape
+    (569, 30)
+    >>> X_new = SelectFwe(chi2, alpha=0.01).fit_transform(X, y)
+    >>> X_new.shape
+    (569, 15)
+    """
+
+    _parameter_constraints: dict = {
+        **_BaseFilter._parameter_constraints,
+        "alpha": [Interval(Real, 0, 1, closed="both")],
+    }
+
+    def __init__(self, score_func=f_classif, *, alpha=5e-2):
+        super().__init__(score_func=score_func)
+        self.alpha = alpha
+
+    def _get_support_mask(self):
+        check_is_fitted(self)
+
+        return self.pvalues_ < self.alpha / len(self.pvalues_)
+
+
+######################################################################
+# Generic filter
+######################################################################
+
+
+# TODO this class should fit on either p-values or scores,
+# depending on the mode.
+class GenericUnivariateSelect(_BaseFilter):
+    """Univariate feature selector with configurable strategy.
+
+    Read more in the :ref:`User Guide <univariate_feature_selection>`.
+
+    Parameters
+    ----------
+    score_func : callable, default=f_classif
+        Function taking two arrays X and y, and returning a pair of arrays
+        (scores, pvalues). For modes 'percentile' or 'kbest' it can return
+        a single array scores.
+
+    mode : {'percentile', 'k_best', 'fpr', 'fdr', 'fwe'}, default='percentile'
+        Feature selection mode. Note that the `'percentile'` and `'kbest'`
+        modes are supporting unsupervised feature selection (when `y` is `None`).
+
+    param : "all", float or int, default=1e-5
+        Parameter of the corresponding mode.
+
+    Attributes
+    ----------
+    scores_ : array-like of shape (n_features,)
+        Scores of features.
+
+    pvalues_ : array-like of shape (n_features,)
+        p-values of feature scores, None if `score_func` returned scores only.
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
+    See Also
+    --------
+    f_classif : ANOVA F-value between label/feature for classification tasks.
+    mutual_info_classif : Mutual information for a discrete target.
+    chi2 : Chi-squared stats of non-negative features for classification tasks.
+    f_regression : F-value between label/feature for regression tasks.
+    mutual_info_regression : Mutual information for a continuous target.
+    SelectPercentile : Select features based on percentile of the highest
+        scores.
+    SelectKBest : Select features based on the k highest scores.
+    SelectFpr : Select features based on a false positive rate test.
+    SelectFdr : Select features based on an estimated false discovery rate.
+    SelectFwe : Select features based on family-wise error rate.
+
+    Examples
+    --------
+    >>> from sklearn.datasets import load_breast_cancer
+    >>> from sklearn.feature_selection import GenericUnivariateSelect, chi2
+    >>> X, y = load_breast_cancer(return_X_y=True)
+    >>> X.shape
+    (569, 30)
+    >>> transformer = GenericUnivariateSelect(chi2, mode='k_best', param=20)
+    >>> X_new = transformer.fit_transform(X, y)
+    >>> X_new.shape
+    (569, 20)
+    """
+
+    _selection_modes: dict = {
+        "percentile": SelectPercentile,
+        "k_best": SelectKBest,
+        "fpr": SelectFpr,
+        "fdr": SelectFdr,
+        "fwe": SelectFwe,
+    }
+
+    _parameter_constraints: dict = {
+        **_BaseFilter._parameter_constraints,
+        "mode": [StrOptions(set(_selection_modes.keys()))],
+        "param": [Interval(Real, 0, None, closed="left"), StrOptions({"all"})],
+    }
+
+    def __init__(self, score_func=f_classif, *, mode="percentile", param=1e-5):
+        super().__init__(score_func=score_func)
+        self.mode = mode
+        self.param = param
+
+    def _make_selector(self):
+        selector = self._selection_modes[self.mode](score_func=self.score_func)
+
+        # Now perform some acrobatics to set the right named parameter in
+        # the selector
+        possible_params = selector._get_param_names()
+        possible_params.remove("score_func")
+        selector.set_params(**{possible_params[0]: self.param})
+
+        return selector
+
+    def _more_tags(self):
+        return {"preserves_dtype": [np.float64, np.float32]}
+
+    def _check_params(self, X, y):
+        self._make_selector()._check_params(X, y)
+
+    def _get_support_mask(self):
+        check_is_fitted(self)
+
+        selector = self._make_selector()
+        selector.pvalues_ = self.pvalues_
+        selector.scores_ = self.scores_
+        return selector._get_support_mask()

env-llmeval/lib/python3.10/site-packages/sklearn/feature_selection/_variance_threshold.py

@@ -0,0 +1,136 @@
+# Author: Lars Buitinck
+# License: 3-clause BSD
+from numbers import Real
+
+import numpy as np
+
+from ..base import BaseEstimator, _fit_context
+from ..utils._param_validation import Interval
+from ..utils.sparsefuncs import mean_variance_axis, min_max_axis
+from ..utils.validation import check_is_fitted
+from ._base import SelectorMixin
+
+
+class VarianceThreshold(SelectorMixin, BaseEstimator):
+    """Feature selector that removes all low-variance features.
+
+    This feature selection algorithm looks only at the features (X), not the
+    desired outputs (y), and can thus be used for unsupervised learning.
+
+    Read more in the :ref:`User Guide <variance_threshold>`.
+
+    Parameters
+    ----------
+    threshold : float, default=0
+        Features with a training-set variance lower than this threshold will
+        be removed. The default is to keep all features with non-zero variance,
+        i.e. remove the features that have the same value in all samples.
+
+    Attributes
+    ----------
+    variances_ : array, shape (n_features,)
+        Variances of individual features.
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
+    See Also
+    --------
+    SelectFromModel: Meta-transformer for selecting features based on
+        importance weights.
+    SelectPercentile : Select features according to a percentile of the highest
+        scores.
+    SequentialFeatureSelector : Transformer that performs Sequential Feature
+        Selection.
+
+    Notes
+    -----
+    Allows NaN in the input.
+    Raises ValueError if no feature in X meets the variance threshold.
+
+    Examples
+    --------
+    The following dataset has integer features, two of which are the same
+    in every sample. These are removed with the default setting for threshold::
+
+        >>> from sklearn.feature_selection import VarianceThreshold
+        >>> X = [[0, 2, 0, 3], [0, 1, 4, 3], [0, 1, 1, 3]]
+        >>> selector = VarianceThreshold()
+        >>> selector.fit_transform(X)
+        array([[2, 0],
+               [1, 4],
+               [1, 1]])
+    """
+
+    _parameter_constraints: dict = {
+        "threshold": [Interval(Real, 0, None, closed="left")]
+    }
+
+    def __init__(self, threshold=0.0):
+        self.threshold = threshold
+
+    @_fit_context(prefer_skip_nested_validation=True)
+    def fit(self, X, y=None):
+        """Learn empirical variances from X.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix}, shape (n_samples, n_features)
+            Data from which to compute variances, where `n_samples` is
+            the number of samples and `n_features` is the number of features.
+
+        y : any, default=None
+            Ignored. This parameter exists only for compatibility with
+            sklearn.pipeline.Pipeline.
+
+        Returns
+        -------
+        self : object
+            Returns the instance itself.
+        """
+        X = self._validate_data(
+            X,
+            accept_sparse=("csr", "csc"),
+            dtype=np.float64,
+            force_all_finite="allow-nan",
+        )
+
+        if hasattr(X, "toarray"):  # sparse matrix
+            _, self.variances_ = mean_variance_axis(X, axis=0)
+            if self.threshold == 0:
+                mins, maxes = min_max_axis(X, axis=0)
+                peak_to_peaks = maxes - mins
+        else:
+            self.variances_ = np.nanvar(X, axis=0)
+            if self.threshold == 0:
+                peak_to_peaks = np.ptp(X, axis=0)
+
+        if self.threshold == 0:
+            # Use peak-to-peak to avoid numeric precision issues
+            # for constant features
+            compare_arr = np.array([self.variances_, peak_to_peaks])
+            self.variances_ = np.nanmin(compare_arr, axis=0)
+
+        if np.all(~np.isfinite(self.variances_) | (self.variances_ <= self.threshold)):
+            msg = "No feature in X meets the variance threshold {0:.5f}"
+            if X.shape[0] == 1:
+                msg += " (X contains only one sample)"
+            raise ValueError(msg.format(self.threshold))
+
+        return self
+
+    def _get_support_mask(self):
+        check_is_fitted(self)
+
+        return self.variances_ > self.threshold
+
+    def _more_tags(self):
+        return {"allow_nan": True}

env-llmeval/lib/python3.10/site-packages/sklearn/feature_selection/tests/test_base.py

@@ -0,0 +1,153 @@
+import numpy as np
+import pytest
+from numpy.testing import assert_array_equal
+
+from sklearn.base import BaseEstimator
+from sklearn.feature_selection._base import SelectorMixin
+from sklearn.utils.fixes import CSC_CONTAINERS
+
+
+class StepSelector(SelectorMixin, BaseEstimator):
+    """Retain every `step` features (beginning with 0).
+
+    If `step < 1`, then no features are selected.
+    """
+
+    def __init__(self, step=2):
+        self.step = step
+
+    def fit(self, X, y=None):
+        X = self._validate_data(X, accept_sparse="csc")
+        return self
+
+    def _get_support_mask(self):
+        mask = np.zeros(self.n_features_in_, dtype=bool)
+        if self.step >= 1:
+            mask[:: self.step] = True
+        return mask
+
+
+support = [True, False] * 5
+support_inds = [0, 2, 4, 6, 8]
+X = np.arange(20).reshape(2, 10)
+Xt = np.arange(0, 20, 2).reshape(2, 5)
+Xinv = X.copy()
+Xinv[:, 1::2] = 0
+y = [0, 1]
+feature_names = list("ABCDEFGHIJ")
+feature_names_t = feature_names[::2]
+feature_names_inv = np.array(feature_names)
+feature_names_inv[1::2] = ""
+
+
+def test_transform_dense():
+    sel = StepSelector()
+    Xt_actual = sel.fit(X, y).transform(X)
+    Xt_actual2 = StepSelector().fit_transform(X, y)
+    assert_array_equal(Xt, Xt_actual)
+    assert_array_equal(Xt, Xt_actual2)
+
+    # Check dtype matches
+    assert np.int32 == sel.transform(X.astype(np.int32)).dtype
+    assert np.float32 == sel.transform(X.astype(np.float32)).dtype
+
+    # Check 1d list and other dtype:
+    names_t_actual = sel.transform([feature_names])
+    assert_array_equal(feature_names_t, names_t_actual.ravel())
+
+    # Check wrong shape raises error
+    with pytest.raises(ValueError):
+        sel.transform(np.array([[1], [2]]))
+
+
+@pytest.mark.parametrize("csc_container", CSC_CONTAINERS)
+def test_transform_sparse(csc_container):
+    X_sp = csc_container(X)
+    sel = StepSelector()
+    Xt_actual = sel.fit(X_sp).transform(X_sp)
+    Xt_actual2 = sel.fit_transform(X_sp)
+    assert_array_equal(Xt, Xt_actual.toarray())
+    assert_array_equal(Xt, Xt_actual2.toarray())
+
+    # Check dtype matches
+    assert np.int32 == sel.transform(X_sp.astype(np.int32)).dtype
+    assert np.float32 == sel.transform(X_sp.astype(np.float32)).dtype
+
+    # Check wrong shape raises error
+    with pytest.raises(ValueError):
+        sel.transform(np.array([[1], [2]]))
+
+
+def test_inverse_transform_dense():
+    sel = StepSelector()
+    Xinv_actual = sel.fit(X, y).inverse_transform(Xt)
+    assert_array_equal(Xinv, Xinv_actual)
+
+    # Check dtype matches
+    assert np.int32 == sel.inverse_transform(Xt.astype(np.int32)).dtype
+    assert np.float32 == sel.inverse_transform(Xt.astype(np.float32)).dtype
+
+    # Check 1d list and other dtype:
+    names_inv_actual = sel.inverse_transform([feature_names_t])
+    assert_array_equal(feature_names_inv, names_inv_actual.ravel())
+
+    # Check wrong shape raises error
+    with pytest.raises(ValueError):
+        sel.inverse_transform(np.array([[1], [2]]))
+
+
+@pytest.mark.parametrize("csc_container", CSC_CONTAINERS)
+def test_inverse_transform_sparse(csc_container):
+    X_sp = csc_container(X)
+    Xt_sp = csc_container(Xt)
+    sel = StepSelector()
+    Xinv_actual = sel.fit(X_sp).inverse_transform(Xt_sp)
+    assert_array_equal(Xinv, Xinv_actual.toarray())
+
+    # Check dtype matches
+    assert np.int32 == sel.inverse_transform(Xt_sp.astype(np.int32)).dtype
+    assert np.float32 == sel.inverse_transform(Xt_sp.astype(np.float32)).dtype
+
+    # Check wrong shape raises error
+    with pytest.raises(ValueError):
+        sel.inverse_transform(np.array([[1], [2]]))
+
+
+def test_get_support():
+    sel = StepSelector()
+    sel.fit(X, y)
+    assert_array_equal(support, sel.get_support())
+    assert_array_equal(support_inds, sel.get_support(indices=True))
+
+
+def test_output_dataframe():
+    """Check output dtypes for dataframes is consistent with the input dtypes."""
+    pd = pytest.importorskip("pandas")
+
+    X = pd.DataFrame(
+        {
+            "a": pd.Series([1.0, 2.4, 4.5], dtype=np.float32),
+            "b": pd.Series(["a", "b", "a"], dtype="category"),
+            "c": pd.Series(["j", "b", "b"], dtype="category"),
+            "d": pd.Series([3.0, 2.4, 1.2], dtype=np.float64),
+        }
+    )
+
+    for step in [2, 3]:
+        sel = StepSelector(step=step).set_output(transform="pandas")
+        sel.fit(X)
+
+        output = sel.transform(X)
+        for name, dtype in output.dtypes.items():
+            assert dtype == X.dtypes[name]
+
+    # step=0 will select nothing
+    sel0 = StepSelector(step=0).set_output(transform="pandas")
+    sel0.fit(X, y)
+
+    msg = "No features were selected"
+    with pytest.warns(UserWarning, match=msg):
+        output0 = sel0.transform(X)
+
+    assert_array_equal(output0.index, X.index)
+    assert output0.shape == (X.shape[0], 0)

env-llmeval/lib/python3.10/site-packages/sklearn/feature_selection/tests/test_chi2.py

@@ -0,0 +1,93 @@
+"""
+Tests for chi2, currently the only feature selection function designed
+specifically to work with sparse matrices.
+"""
+
+import warnings
+
+import numpy as np
+import pytest
+import scipy.stats
+
+from sklearn.feature_selection import SelectKBest, chi2
+from sklearn.feature_selection._univariate_selection import _chisquare
+from sklearn.utils._testing import assert_array_almost_equal, assert_array_equal
+from sklearn.utils.fixes import COO_CONTAINERS, CSR_CONTAINERS
+
+# Feature 0 is highly informative for class 1;
+# feature 1 is the same everywhere;
+# feature 2 is a bit informative for class 2.
+X = [[2, 1, 2], [9, 1, 1], [6, 1, 2], [0, 1, 2]]
+y = [0, 1, 2, 2]
+
+
+def mkchi2(k):
+    """Make k-best chi2 selector"""
+    return SelectKBest(chi2, k=k)
+
+
+@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
+def test_chi2(csr_container):
+    # Test Chi2 feature extraction
+
+    chi2 = mkchi2(k=1).fit(X, y)
+    chi2 = mkchi2(k=1).fit(X, y)
+    assert_array_equal(chi2.get_support(indices=True), [0])
+    assert_array_equal(chi2.transform(X), np.array(X)[:, [0]])
+
+    chi2 = mkchi2(k=2).fit(X, y)
+    assert_array_equal(sorted(chi2.get_support(indices=True)), [0, 2])
+
+    Xsp = csr_container(X, dtype=np.float64)
+    chi2 = mkchi2(k=2).fit(Xsp, y)
+    assert_array_equal(sorted(chi2.get_support(indices=True)), [0, 2])
+    Xtrans = chi2.transform(Xsp)
+    assert_array_equal(Xtrans.shape, [Xsp.shape[0], 2])
+
+    # == doesn't work on scipy.sparse matrices
+    Xtrans = Xtrans.toarray()
+    Xtrans2 = mkchi2(k=2).fit_transform(Xsp, y).toarray()
+    assert_array_almost_equal(Xtrans, Xtrans2)
+
+
+@pytest.mark.parametrize("coo_container", COO_CONTAINERS)
+def test_chi2_coo(coo_container):
+    # Check that chi2 works with a COO matrix
+    # (as returned by CountVectorizer, DictVectorizer)
+    Xcoo = coo_container(X)
+    mkchi2(k=2).fit_transform(Xcoo, y)
+    # if we got here without an exception, we're safe
+
+
+@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
+def test_chi2_negative(csr_container):
+    # Check for proper error on negative numbers in the input X.
+    X, y = [[0, 1], [-1e-20, 1]], [0, 1]
+    for X in (X, np.array(X), csr_container(X)):
+        with pytest.raises(ValueError):
+            chi2(X, y)
+
+
+def test_chi2_unused_feature():
+    # Unused feature should evaluate to NaN
+    # and should issue no runtime warning
+    with warnings.catch_warnings(record=True) as warned:
+        warnings.simplefilter("always")
+        chi, p = chi2([[1, 0], [0, 0]], [1, 0])
+        for w in warned:
+            if "divide by zero" in repr(w):
+                raise AssertionError("Found unexpected warning %s" % w)
+    assert_array_equal(chi, [1, np.nan])
+    assert_array_equal(p[1], np.nan)
+
+
+def test_chisquare():
+    # Test replacement for scipy.stats.chisquare against the original.
+    obs = np.array([[2.0, 2.0], [1.0, 1.0]])
+    exp = np.array([[1.5, 1.5], [1.5, 1.5]])
+    # call SciPy first because our version overwrites obs
+    chi_scp, p_scp = scipy.stats.chisquare(obs, exp)
+    chi_our, p_our = _chisquare(obs, exp)
+
+    assert_array_almost_equal(chi_scp, chi_our)
+    assert_array_almost_equal(p_scp, p_our)

env-llmeval/lib/python3.10/site-packages/sklearn/feature_selection/tests/test_feature_select.py

@@ -0,0 +1,1017 @@
+"""
+Todo: cross-check the F-value with stats model
+"""
+import itertools
+import warnings
+
+import numpy as np
+import pytest
+from numpy.testing import assert_allclose
+from scipy import sparse, stats
+
+from sklearn.datasets import load_iris, make_classification, make_regression
+from sklearn.feature_selection import (
+    GenericUnivariateSelect,
+    SelectFdr,
+    SelectFpr,
+    SelectFwe,
+    SelectKBest,
+    SelectPercentile,
+    chi2,
+    f_classif,
+    f_oneway,
+    f_regression,
+    mutual_info_classif,
+    mutual_info_regression,
+    r_regression,
+)
+from sklearn.utils import safe_mask
+from sklearn.utils._testing import (
+    _convert_container,
+    assert_almost_equal,
+    assert_array_almost_equal,
+    assert_array_equal,
+    ignore_warnings,
+)
+from sklearn.utils.fixes import CSR_CONTAINERS
+
+##############################################################################
+# Test the score functions
+
+
+def test_f_oneway_vs_scipy_stats():
+    # Test that our f_oneway gives the same result as scipy.stats
+    rng = np.random.RandomState(0)
+    X1 = rng.randn(10, 3)
+    X2 = 1 + rng.randn(10, 3)
+    f, pv = stats.f_oneway(X1, X2)
+    f2, pv2 = f_oneway(X1, X2)
+    assert np.allclose(f, f2)
+    assert np.allclose(pv, pv2)
+
+
+def test_f_oneway_ints():
+    # Smoke test f_oneway on integers: that it does raise casting errors
+    # with recent numpys
+    rng = np.random.RandomState(0)
+    X = rng.randint(10, size=(10, 10))
+    y = np.arange(10)
+    fint, pint = f_oneway(X, y)
+
+    # test that is gives the same result as with float
+    f, p = f_oneway(X.astype(float), y)
+    assert_array_almost_equal(f, fint, decimal=4)
+    assert_array_almost_equal(p, pint, decimal=4)
+
+
+@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
+def test_f_classif(csr_container):
+    # Test whether the F test yields meaningful results
+    # on a simple simulated classification problem
+    X, y = make_classification(
+        n_samples=200,
+        n_features=20,
+        n_informative=3,
+        n_redundant=2,
+        n_repeated=0,
+        n_classes=8,
+        n_clusters_per_class=1,
+        flip_y=0.0,
+        class_sep=10,
+        shuffle=False,
+        random_state=0,
+    )
+
+    F, pv = f_classif(X, y)
+    F_sparse, pv_sparse = f_classif(csr_container(X), y)
+    assert (F > 0).all()
+    assert (pv > 0).all()
+    assert (pv < 1).all()
+    assert (pv[:5] < 0.05).all()
+    assert (pv[5:] > 1.0e-4).all()
+    assert_array_almost_equal(F_sparse, F)
+    assert_array_almost_equal(pv_sparse, pv)
+
+
+@pytest.mark.parametrize("center", [True, False])
+def test_r_regression(center):
+    X, y = make_regression(
+        n_samples=2000, n_features=20, n_informative=5, shuffle=False, random_state=0
+    )
+
+    corr_coeffs = r_regression(X, y, center=center)
+    assert (-1 < corr_coeffs).all()
+    assert (corr_coeffs < 1).all()
+
+    sparse_X = _convert_container(X, "sparse")
+
+    sparse_corr_coeffs = r_regression(sparse_X, y, center=center)
+    assert_allclose(sparse_corr_coeffs, corr_coeffs)
+
+    # Testing against numpy for reference
+    Z = np.hstack((X, y[:, np.newaxis]))
+    correlation_matrix = np.corrcoef(Z, rowvar=False)
+    np_corr_coeffs = correlation_matrix[:-1, -1]
+    assert_array_almost_equal(np_corr_coeffs, corr_coeffs, decimal=3)
+
+
+@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
+def test_f_regression(csr_container):
+    # Test whether the F test yields meaningful results
+    # on a simple simulated regression problem
+    X, y = make_regression(
+        n_samples=200, n_features=20, n_informative=5, shuffle=False, random_state=0
+    )
+
+    F, pv = f_regression(X, y)
+    assert (F > 0).all()
+    assert (pv > 0).all()
+    assert (pv < 1).all()
+    assert (pv[:5] < 0.05).all()
+    assert (pv[5:] > 1.0e-4).all()
+
+    # with centering, compare with sparse
+    F, pv = f_regression(X, y, center=True)
+    F_sparse, pv_sparse = f_regression(csr_container(X), y, center=True)
+    assert_allclose(F_sparse, F)
+    assert_allclose(pv_sparse, pv)
+
+    # again without centering, compare with sparse
+    F, pv = f_regression(X, y, center=False)
+    F_sparse, pv_sparse = f_regression(csr_container(X), y, center=False)
+    assert_allclose(F_sparse, F)
+    assert_allclose(pv_sparse, pv)
+
+
+def test_f_regression_input_dtype():
+    # Test whether f_regression returns the same value
+    # for any numeric data_type
+    rng = np.random.RandomState(0)
+    X = rng.rand(10, 20)
+    y = np.arange(10).astype(int)
+
+    F1, pv1 = f_regression(X, y)
+    F2, pv2 = f_regression(X, y.astype(float))
+    assert_allclose(F1, F2, 5)
+    assert_allclose(pv1, pv2, 5)
+
+
+def test_f_regression_center():
+    # Test whether f_regression preserves dof according to 'center' argument
+    # We use two centered variates so we have a simple relationship between
+    # F-score with variates centering and F-score without variates centering.
+    # Create toy example
+    X = np.arange(-5, 6).reshape(-1, 1)  # X has zero mean
+    n_samples = X.size
+    Y = np.ones(n_samples)
+    Y[::2] *= -1.0
+    Y[0] = 0.0  # have Y mean being null
+
+    F1, _ = f_regression(X, Y, center=True)
+    F2, _ = f_regression(X, Y, center=False)
+    assert_allclose(F1 * (n_samples - 1.0) / (n_samples - 2.0), F2)
+    assert_almost_equal(F2[0], 0.232558139)  # value from statsmodels OLS
+
+
+@pytest.mark.parametrize(
+    "X, y, expected_corr_coef, force_finite",
+    [
+        (
+            # A feature in X is constant - forcing finite
+            np.array([[2, 1], [2, 0], [2, 10], [2, 4]]),
+            np.array([0, 1, 1, 0]),
+            np.array([0.0, 0.32075]),
+            True,
+        ),
+        (
+            # The target y is constant - forcing finite
+            np.array([[5, 1], [3, 0], [2, 10], [8, 4]]),
+            np.array([0, 0, 0, 0]),
+            np.array([0.0, 0.0]),
+            True,
+        ),
+        (
+            # A feature in X is constant - not forcing finite
+            np.array([[2, 1], [2, 0], [2, 10], [2, 4]]),
+            np.array([0, 1, 1, 0]),
+            np.array([np.nan, 0.32075]),
+            False,
+        ),
+        (
+            # The target y is constant - not forcing finite
+            np.array([[5, 1], [3, 0], [2, 10], [8, 4]]),
+            np.array([0, 0, 0, 0]),
+            np.array([np.nan, np.nan]),
+            False,
+        ),
+    ],
+)
+def test_r_regression_force_finite(X, y, expected_corr_coef, force_finite):
+    """Check the behaviour of `force_finite` for some corner cases with `r_regression`.
+
+    Non-regression test for:
+    https://github.com/scikit-learn/scikit-learn/issues/15672
+    """
+    with warnings.catch_warnings():
+        warnings.simplefilter("error", RuntimeWarning)
+        corr_coef = r_regression(X, y, force_finite=force_finite)
+    np.testing.assert_array_almost_equal(corr_coef, expected_corr_coef)
+
+
+@pytest.mark.parametrize(
+    "X, y, expected_f_statistic, expected_p_values, force_finite",
+    [
+        (
+            # A feature in X is constant - forcing finite
+            np.array([[2, 1], [2, 0], [2, 10], [2, 4]]),
+            np.array([0, 1, 1, 0]),
+            np.array([0.0, 0.2293578]),
+            np.array([1.0, 0.67924985]),
+            True,
+        ),
+        (
+            # The target y is constant - forcing finite
+            np.array([[5, 1], [3, 0], [2, 10], [8, 4]]),
+            np.array([0, 0, 0, 0]),
+            np.array([0.0, 0.0]),
+            np.array([1.0, 1.0]),
+            True,
+        ),
+        (
+            # Feature in X correlated with y - forcing finite
+            np.array([[0, 1], [1, 0], [2, 10], [3, 4]]),
+            np.array([0, 1, 2, 3]),
+            np.array([np.finfo(np.float64).max, 0.845433]),
+            np.array([0.0, 0.454913]),
+            True,
+        ),
+        (
+            # Feature in X anti-correlated with y - forcing finite
+            np.array([[3, 1], [2, 0], [1, 10], [0, 4]]),
+            np.array([0, 1, 2, 3]),
+            np.array([np.finfo(np.float64).max, 0.845433]),
+            np.array([0.0, 0.454913]),
+            True,
+        ),
+        (
+            # A feature in X is constant - not forcing finite
+            np.array([[2, 1], [2, 0], [2, 10], [2, 4]]),
+            np.array([0, 1, 1, 0]),
+            np.array([np.nan, 0.2293578]),
+            np.array([np.nan, 0.67924985]),
+            False,
+        ),
+        (
+            # The target y is constant - not forcing finite
+            np.array([[5, 1], [3, 0], [2, 10], [8, 4]]),
+            np.array([0, 0, 0, 0]),
+            np.array([np.nan, np.nan]),
+            np.array([np.nan, np.nan]),
+            False,
+        ),
+        (
+            # Feature in X correlated with y - not forcing finite
+            np.array([[0, 1], [1, 0], [2, 10], [3, 4]]),
+            np.array([0, 1, 2, 3]),
+            np.array([np.inf, 0.845433]),
+            np.array([0.0, 0.454913]),
+            False,
+        ),
+        (
+            # Feature in X anti-correlated with y - not forcing finite
+            np.array([[3, 1], [2, 0], [1, 10], [0, 4]]),
+            np.array([0, 1, 2, 3]),
+            np.array([np.inf, 0.845433]),
+            np.array([0.0, 0.454913]),
+            False,
+        ),
+    ],
+)
+def test_f_regression_corner_case(
+    X, y, expected_f_statistic, expected_p_values, force_finite
+):
+    """Check the behaviour of `force_finite` for some corner cases with `f_regression`.
+
+    Non-regression test for:
+    https://github.com/scikit-learn/scikit-learn/issues/15672
+    """
+    with warnings.catch_warnings():
+        warnings.simplefilter("error", RuntimeWarning)
+        f_statistic, p_values = f_regression(X, y, force_finite=force_finite)
+    np.testing.assert_array_almost_equal(f_statistic, expected_f_statistic)
+    np.testing.assert_array_almost_equal(p_values, expected_p_values)
+
+
+def test_f_classif_multi_class():
+    # Test whether the F test yields meaningful results
+    # on a simple simulated classification problem
+    X, y = make_classification(
+        n_samples=200,
+        n_features=20,
+        n_informative=3,
+        n_redundant=2,
+        n_repeated=0,
+        n_classes=8,
+        n_clusters_per_class=1,
+        flip_y=0.0,
+        class_sep=10,
+        shuffle=False,
+        random_state=0,
+    )
+
+    F, pv = f_classif(X, y)
+    assert (F > 0).all()
+    assert (pv > 0).all()
+    assert (pv < 1).all()
+    assert (pv[:5] < 0.05).all()
+    assert (pv[5:] > 1.0e-4).all()
+
+
+def test_select_percentile_classif():
+    # Test whether the relative univariate feature selection
+    # gets the correct items in a simple classification problem
+    # with the percentile heuristic
+    X, y = make_classification(
+        n_samples=200,
+        n_features=20,
+        n_informative=3,
+        n_redundant=2,
+        n_repeated=0,
+        n_classes=8,
+        n_clusters_per_class=1,
+        flip_y=0.0,
+        class_sep=10,
+        shuffle=False,
+        random_state=0,
+    )
+
+    univariate_filter = SelectPercentile(f_classif, percentile=25)
+    X_r = univariate_filter.fit(X, y).transform(X)
+    X_r2 = (
+        GenericUnivariateSelect(f_classif, mode="percentile", param=25)
+        .fit(X, y)
+        .transform(X)
+    )
+    assert_array_equal(X_r, X_r2)
+    support = univariate_filter.get_support()
+    gtruth = np.zeros(20)
+    gtruth[:5] = 1
+    assert_array_equal(support, gtruth)
+
+
+@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
+def test_select_percentile_classif_sparse(csr_container):
+    # Test whether the relative univariate feature selection
+    # gets the correct items in a simple classification problem
+    # with the percentile heuristic
+    X, y = make_classification(
+        n_samples=200,
+        n_features=20,
+        n_informative=3,
+        n_redundant=2,
+        n_repeated=0,
+        n_classes=8,
+        n_clusters_per_class=1,
+        flip_y=0.0,
+        class_sep=10,
+        shuffle=False,
+        random_state=0,
+    )
+    X = csr_container(X)
+    univariate_filter = SelectPercentile(f_classif, percentile=25)
+    X_r = univariate_filter.fit(X, y).transform(X)
+    X_r2 = (
+        GenericUnivariateSelect(f_classif, mode="percentile", param=25)
+        .fit(X, y)
+        .transform(X)
+    )
+    assert_array_equal(X_r.toarray(), X_r2.toarray())
+    support = univariate_filter.get_support()
+    gtruth = np.zeros(20)
+    gtruth[:5] = 1
+    assert_array_equal(support, gtruth)
+
+    X_r2inv = univariate_filter.inverse_transform(X_r2)
+    assert sparse.issparse(X_r2inv)
+    support_mask = safe_mask(X_r2inv, support)
+    assert X_r2inv.shape == X.shape
+    assert_array_equal(X_r2inv[:, support_mask].toarray(), X_r.toarray())
+    # Check other columns are empty
+    assert X_r2inv.nnz == X_r.nnz
+
+
+##############################################################################
+# Test univariate selection in classification settings
+
+
+def test_select_kbest_classif():
+    # Test whether the relative univariate feature selection
+    # gets the correct items in a simple classification problem
+    # with the k best heuristic
+    X, y = make_classification(
+        n_samples=200,
+        n_features=20,
+        n_informative=3,
+        n_redundant=2,
+        n_repeated=0,
+        n_classes=8,
+        n_clusters_per_class=1,
+        flip_y=0.0,
+        class_sep=10,
+        shuffle=False,
+        random_state=0,
+    )
+
+    univariate_filter = SelectKBest(f_classif, k=5)
+    X_r = univariate_filter.fit(X, y).transform(X)
+    X_r2 = (
+        GenericUnivariateSelect(f_classif, mode="k_best", param=5)
+        .fit(X, y)
+        .transform(X)
+    )
+    assert_array_equal(X_r, X_r2)
+    support = univariate_filter.get_support()
+    gtruth = np.zeros(20)
+    gtruth[:5] = 1
+    assert_array_equal(support, gtruth)
+
+
+def test_select_kbest_all():
+    # Test whether k="all" correctly returns all features.
+    X, y = make_classification(
+        n_samples=20, n_features=10, shuffle=False, random_state=0
+    )
+
+    univariate_filter = SelectKBest(f_classif, k="all")
+    X_r = univariate_filter.fit(X, y).transform(X)
+    assert_array_equal(X, X_r)
+    # Non-regression test for:
+    # https://github.com/scikit-learn/scikit-learn/issues/24949
+    X_r2 = (
+        GenericUnivariateSelect(f_classif, mode="k_best", param="all")
+        .fit(X, y)
+        .transform(X)
+    )
+    assert_array_equal(X_r, X_r2)
+
+
+@pytest.mark.parametrize("dtype_in", [np.float32, np.float64])
+def test_select_kbest_zero(dtype_in):
+    # Test whether k=0 correctly returns no features.
+    X, y = make_classification(
+        n_samples=20, n_features=10, shuffle=False, random_state=0
+    )
+    X = X.astype(dtype_in)
+
+    univariate_filter = SelectKBest(f_classif, k=0)
+    univariate_filter.fit(X, y)
+    support = univariate_filter.get_support()
+    gtruth = np.zeros(10, dtype=bool)
+    assert_array_equal(support, gtruth)
+    with pytest.warns(UserWarning, match="No features were selected"):
+        X_selected = univariate_filter.transform(X)
+    assert X_selected.shape == (20, 0)
+    assert X_selected.dtype == dtype_in
+
+
+def test_select_heuristics_classif():
+    # Test whether the relative univariate feature selection
+    # gets the correct items in a simple classification problem
+    # with the fdr, fwe and fpr heuristics
+    X, y = make_classification(
+        n_samples=200,
+        n_features=20,
+        n_informative=3,
+        n_redundant=2,
+        n_repeated=0,
+        n_classes=8,
+        n_clusters_per_class=1,
+        flip_y=0.0,
+        class_sep=10,
+        shuffle=False,
+        random_state=0,
+    )
+
+    univariate_filter = SelectFwe(f_classif, alpha=0.01)
+    X_r = univariate_filter.fit(X, y).transform(X)
+    gtruth = np.zeros(20)
+    gtruth[:5] = 1
+    for mode in ["fdr", "fpr", "fwe"]:
+        X_r2 = (
+            GenericUnivariateSelect(f_classif, mode=mode, param=0.01)
+            .fit(X, y)
+            .transform(X)
+        )
+        assert_array_equal(X_r, X_r2)
+        support = univariate_filter.get_support()
+        assert_allclose(support, gtruth)
+
+
+##############################################################################
+# Test univariate selection in regression settings
+
+
+def assert_best_scores_kept(score_filter):
+    scores = score_filter.scores_
+    support = score_filter.get_support()
+    assert_allclose(np.sort(scores[support]), np.sort(scores)[-support.sum() :])
+
+
+def test_select_percentile_regression():
+    # Test whether the relative univariate feature selection
+    # gets the correct items in a simple regression problem
+    # with the percentile heuristic
+    X, y = make_regression(
+        n_samples=200, n_features=20, n_informative=5, shuffle=False, random_state=0
+    )
+
+    univariate_filter = SelectPercentile(f_regression, percentile=25)
+    X_r = univariate_filter.fit(X, y).transform(X)
+    assert_best_scores_kept(univariate_filter)
+    X_r2 = (
+        GenericUnivariateSelect(f_regression, mode="percentile", param=25)
+        .fit(X, y)
+        .transform(X)
+    )
+    assert_array_equal(X_r, X_r2)
+    support = univariate_filter.get_support()
+    gtruth = np.zeros(20)
+    gtruth[:5] = 1
+    assert_array_equal(support, gtruth)
+    X_2 = X.copy()
+    X_2[:, np.logical_not(support)] = 0
+    assert_array_equal(X_2, univariate_filter.inverse_transform(X_r))
+    # Check inverse_transform respects dtype
+    assert_array_equal(
+        X_2.astype(bool), univariate_filter.inverse_transform(X_r.astype(bool))
+    )
+
+
+def test_select_percentile_regression_full():
+    # Test whether the relative univariate feature selection
+    # selects all features when '100%' is asked.
+    X, y = make_regression(
+        n_samples=200, n_features=20, n_informative=5, shuffle=False, random_state=0
+    )
+
+    univariate_filter = SelectPercentile(f_regression, percentile=100)
+    X_r = univariate_filter.fit(X, y).transform(X)
+    assert_best_scores_kept(univariate_filter)
+    X_r2 = (
+        GenericUnivariateSelect(f_regression, mode="percentile", param=100)
+        .fit(X, y)
+        .transform(X)
+    )
+    assert_array_equal(X_r, X_r2)
+    support = univariate_filter.get_support()
+    gtruth = np.ones(20)
+    assert_array_equal(support, gtruth)
+
+
+def test_select_kbest_regression():
+    # Test whether the relative univariate feature selection
+    # gets the correct items in a simple regression problem
+    # with the k best heuristic
+    X, y = make_regression(
+        n_samples=200,
+        n_features=20,
+        n_informative=5,
+        shuffle=False,
+        random_state=0,
+        noise=10,
+    )
+
+    univariate_filter = SelectKBest(f_regression, k=5)
+    X_r = univariate_filter.fit(X, y).transform(X)
+    assert_best_scores_kept(univariate_filter)
+    X_r2 = (
+        GenericUnivariateSelect(f_regression, mode="k_best", param=5)
+        .fit(X, y)
+        .transform(X)
+    )
+    assert_array_equal(X_r, X_r2)
+    support = univariate_filter.get_support()
+    gtruth = np.zeros(20)
+    gtruth[:5] = 1
+    assert_array_equal(support, gtruth)
+
+
+def test_select_heuristics_regression():
+    # Test whether the relative univariate feature selection
+    # gets the correct items in a simple regression problem
+    # with the fpr, fdr or fwe heuristics
+    X, y = make_regression(
+        n_samples=200,
+        n_features=20,
+        n_informative=5,
+        shuffle=False,
+        random_state=0,
+        noise=10,
+    )
+
+    univariate_filter = SelectFpr(f_regression, alpha=0.01)
+    X_r = univariate_filter.fit(X, y).transform(X)
+    gtruth = np.zeros(20)
+    gtruth[:5] = 1
+    for mode in ["fdr", "fpr", "fwe"]:
+        X_r2 = (
+            GenericUnivariateSelect(f_regression, mode=mode, param=0.01)
+            .fit(X, y)
+            .transform(X)
+        )
+        assert_array_equal(X_r, X_r2)
+        support = univariate_filter.get_support()
+        assert_array_equal(support[:5], np.ones((5,), dtype=bool))
+        assert np.sum(support[5:] == 1) < 3
+
+
+def test_boundary_case_ch2():
+    # Test boundary case, and always aim to select 1 feature.
+    X = np.array([[10, 20], [20, 20], [20, 30]])
+    y = np.array([[1], [0], [0]])
+    scores, pvalues = chi2(X, y)
+    assert_array_almost_equal(scores, np.array([4.0, 0.71428571]))
+    assert_array_almost_equal(pvalues, np.array([0.04550026, 0.39802472]))
+
+    filter_fdr = SelectFdr(chi2, alpha=0.1)
+    filter_fdr.fit(X, y)
+    support_fdr = filter_fdr.get_support()
+    assert_array_equal(support_fdr, np.array([True, False]))
+
+    filter_kbest = SelectKBest(chi2, k=1)
+    filter_kbest.fit(X, y)
+    support_kbest = filter_kbest.get_support()
+    assert_array_equal(support_kbest, np.array([True, False]))
+
+    filter_percentile = SelectPercentile(chi2, percentile=50)
+    filter_percentile.fit(X, y)
+    support_percentile = filter_percentile.get_support()
+    assert_array_equal(support_percentile, np.array([True, False]))
+
+    filter_fpr = SelectFpr(chi2, alpha=0.1)
+    filter_fpr.fit(X, y)
+    support_fpr = filter_fpr.get_support()
+    assert_array_equal(support_fpr, np.array([True, False]))
+
+    filter_fwe = SelectFwe(chi2, alpha=0.1)
+    filter_fwe.fit(X, y)
+    support_fwe = filter_fwe.get_support()
+    assert_array_equal(support_fwe, np.array([True, False]))
+
+
+@pytest.mark.parametrize("alpha", [0.001, 0.01, 0.1])
+@pytest.mark.parametrize("n_informative", [1, 5, 10])
+def test_select_fdr_regression(alpha, n_informative):
+    # Test that fdr heuristic actually has low FDR.
+    def single_fdr(alpha, n_informative, random_state):
+        X, y = make_regression(
+            n_samples=150,
+            n_features=20,
+            n_informative=n_informative,
+            shuffle=False,
+            random_state=random_state,
+            noise=10,
+        )
+
+        with warnings.catch_warnings(record=True):
+            # Warnings can be raised when no features are selected
+            # (low alpha or very noisy data)
+            univariate_filter = SelectFdr(f_regression, alpha=alpha)
+            X_r = univariate_filter.fit(X, y).transform(X)
+            X_r2 = (
+                GenericUnivariateSelect(f_regression, mode="fdr", param=alpha)
+                .fit(X, y)
+                .transform(X)
+            )
+
+        assert_array_equal(X_r, X_r2)
+        support = univariate_filter.get_support()
+        num_false_positives = np.sum(support[n_informative:] == 1)
+        num_true_positives = np.sum(support[:n_informative] == 1)
+
+        if num_false_positives == 0:
+            return 0.0
+        false_discovery_rate = num_false_positives / (
+            num_true_positives + num_false_positives
+        )
+        return false_discovery_rate
+
+    # As per Benjamini-Hochberg, the expected false discovery rate
+    # should be lower than alpha:
+    # FDR = E(FP / (TP + FP)) <= alpha
+    false_discovery_rate = np.mean(
+        [single_fdr(alpha, n_informative, random_state) for random_state in range(100)]
+    )
+    assert alpha >= false_discovery_rate
+
+    # Make sure that the empirical false discovery rate increases
+    # with alpha:
+    if false_discovery_rate != 0:
+        assert false_discovery_rate > alpha / 10
+
+
+def test_select_fwe_regression():
+    # Test whether the relative univariate feature selection
+    # gets the correct items in a simple regression problem
+    # with the fwe heuristic
+    X, y = make_regression(
+        n_samples=200, n_features=20, n_informative=5, shuffle=False, random_state=0
+    )
+
+    univariate_filter = SelectFwe(f_regression, alpha=0.01)
+    X_r = univariate_filter.fit(X, y).transform(X)
+    X_r2 = (
+        GenericUnivariateSelect(f_regression, mode="fwe", param=0.01)
+        .fit(X, y)
+        .transform(X)
+    )
+    assert_array_equal(X_r, X_r2)
+    support = univariate_filter.get_support()
+    gtruth = np.zeros(20)
+    gtruth[:5] = 1
+    assert_array_equal(support[:5], np.ones((5,), dtype=bool))
+    assert np.sum(support[5:] == 1) < 2
+
+
+def test_selectkbest_tiebreaking():
+    # Test whether SelectKBest actually selects k features in case of ties.
+    # Prior to 0.11, SelectKBest would return more features than requested.
+    Xs = [[0, 1, 1], [0, 0, 1], [1, 0, 0], [1, 1, 0]]
+    y = [1]
+    dummy_score = lambda X, y: (X[0], X[0])
+    for X in Xs:
+        sel = SelectKBest(dummy_score, k=1)
+        X1 = ignore_warnings(sel.fit_transform)([X], y)
+        assert X1.shape[1] == 1
+        assert_best_scores_kept(sel)
+
+        sel = SelectKBest(dummy_score, k=2)
+        X2 = ignore_warnings(sel.fit_transform)([X], y)
+        assert X2.shape[1] == 2
+        assert_best_scores_kept(sel)
+
+
+def test_selectpercentile_tiebreaking():
+    # Test if SelectPercentile selects the right n_features in case of ties.
+    Xs = [[0, 1, 1], [0, 0, 1], [1, 0, 0], [1, 1, 0]]
+    y = [1]
+    dummy_score = lambda X, y: (X[0], X[0])
+    for X in Xs:
+        sel = SelectPercentile(dummy_score, percentile=34)
+        X1 = ignore_warnings(sel.fit_transform)([X], y)
+        assert X1.shape[1] == 1
+        assert_best_scores_kept(sel)
+
+        sel = SelectPercentile(dummy_score, percentile=67)
+        X2 = ignore_warnings(sel.fit_transform)([X], y)
+        assert X2.shape[1] == 2
+        assert_best_scores_kept(sel)
+
+
+def test_tied_pvalues():
+    # Test whether k-best and percentiles work with tied pvalues from chi2.
+    # chi2 will return the same p-values for the following features, but it
+    # will return different scores.
+    X0 = np.array([[10000, 9999, 9998], [1, 1, 1]])
+    y = [0, 1]
+
+    for perm in itertools.permutations((0, 1, 2)):
+        X = X0[:, perm]
+        Xt = SelectKBest(chi2, k=2).fit_transform(X, y)
+        assert Xt.shape == (2, 2)
+        assert 9998 not in Xt
+
+        Xt = SelectPercentile(chi2, percentile=67).fit_transform(X, y)
+        assert Xt.shape == (2, 2)
+        assert 9998 not in Xt
+
+
+def test_scorefunc_multilabel():
+    # Test whether k-best and percentiles works with multilabels with chi2.
+
+    X = np.array([[10000, 9999, 0], [100, 9999, 0], [1000, 99, 0]])
+    y = [[1, 1], [0, 1], [1, 0]]
+
+    Xt = SelectKBest(chi2, k=2).fit_transform(X, y)
+    assert Xt.shape == (3, 2)
+    assert 0 not in Xt
+
+    Xt = SelectPercentile(chi2, percentile=67).fit_transform(X, y)
+    assert Xt.shape == (3, 2)
+    assert 0 not in Xt
+
+
+def test_tied_scores():
+    # Test for stable sorting in k-best with tied scores.
+    X_train = np.array([[0, 0, 0], [1, 1, 1]])
+    y_train = [0, 1]
+
+    for n_features in [1, 2, 3]:
+        sel = SelectKBest(chi2, k=n_features).fit(X_train, y_train)
+        X_test = sel.transform([[0, 1, 2]])
+        assert_array_equal(X_test[0], np.arange(3)[-n_features:])
+
+
+def test_nans():
+    # Assert that SelectKBest and SelectPercentile can handle NaNs.
+    # First feature has zero variance to confuse f_classif (ANOVA) and
+    # make it return a NaN.
+    X = [[0, 1, 0], [0, -1, -1], [0, 0.5, 0.5]]
+    y = [1, 0, 1]
+
+    for select in (
+        SelectKBest(f_classif, k=2),
+        SelectPercentile(f_classif, percentile=67),
+    ):
+        ignore_warnings(select.fit)(X, y)
+        assert_array_equal(select.get_support(indices=True), np.array([1, 2]))
+
+
+def test_invalid_k():
+    X = [[0, 1, 0], [0, -1, -1], [0, 0.5, 0.5]]
+    y = [1, 0, 1]
+
+    msg = "k=4 is greater than n_features=3. All the features will be returned."
+    with pytest.warns(UserWarning, match=msg):
+        SelectKBest(k=4).fit(X, y)
+    with pytest.warns(UserWarning, match=msg):
+        GenericUnivariateSelect(mode="k_best", param=4).fit(X, y)
+
+
+def test_f_classif_constant_feature():
+    # Test that f_classif warns if a feature is constant throughout.
+
+    X, y = make_classification(n_samples=10, n_features=5)
+    X[:, 0] = 2.0
+    with pytest.warns(UserWarning):
+        f_classif(X, y)
+
+
+def test_no_feature_selected():
+    rng = np.random.RandomState(0)
+
+    # Generate random uncorrelated data: a strict univariate test should
+    # rejects all the features
+    X = rng.rand(40, 10)
+    y = rng.randint(0, 4, size=40)
+    strict_selectors = [
+        SelectFwe(alpha=0.01).fit(X, y),
+        SelectFdr(alpha=0.01).fit(X, y),
+        SelectFpr(alpha=0.01).fit(X, y),
+        SelectPercentile(percentile=0).fit(X, y),
+        SelectKBest(k=0).fit(X, y),
+    ]
+    for selector in strict_selectors:
+        assert_array_equal(selector.get_support(), np.zeros(10))
+        with pytest.warns(UserWarning, match="No features were selected"):
+            X_selected = selector.transform(X)
+        assert X_selected.shape == (40, 0)
+
+
+def test_mutual_info_classif():
+    X, y = make_classification(
+        n_samples=100,
+        n_features=5,
+        n_informative=1,
+        n_redundant=1,
+        n_repeated=0,
+        n_classes=2,
+        n_clusters_per_class=1,
+        flip_y=0.0,
+        class_sep=10,
+        shuffle=False,
+        random_state=0,
+    )
+
+    # Test in KBest mode.
+    univariate_filter = SelectKBest(mutual_info_classif, k=2)
+    X_r = univariate_filter.fit(X, y).transform(X)
+    X_r2 = (
+        GenericUnivariateSelect(mutual_info_classif, mode="k_best", param=2)
+        .fit(X, y)
+        .transform(X)
+    )
+    assert_array_equal(X_r, X_r2)
+    support = univariate_filter.get_support()
+    gtruth = np.zeros(5)
+    gtruth[:2] = 1
+    assert_array_equal(support, gtruth)
+
+    # Test in Percentile mode.
+    univariate_filter = SelectPercentile(mutual_info_classif, percentile=40)
+    X_r = univariate_filter.fit(X, y).transform(X)
+    X_r2 = (
+        GenericUnivariateSelect(mutual_info_classif, mode="percentile", param=40)
+        .fit(X, y)
+        .transform(X)
+    )
+    assert_array_equal(X_r, X_r2)
+    support = univariate_filter.get_support()
+    gtruth = np.zeros(5)
+    gtruth[:2] = 1
+    assert_array_equal(support, gtruth)
+
+
+def test_mutual_info_regression():
+    X, y = make_regression(
+        n_samples=100,
+        n_features=10,
+        n_informative=2,
+        shuffle=False,
+        random_state=0,
+        noise=10,
+    )
+
+    # Test in KBest mode.
+    univariate_filter = SelectKBest(mutual_info_regression, k=2)
+    X_r = univariate_filter.fit(X, y).transform(X)
+    assert_best_scores_kept(univariate_filter)
+    X_r2 = (
+        GenericUnivariateSelect(mutual_info_regression, mode="k_best", param=2)
+        .fit(X, y)
+        .transform(X)
+    )
+    assert_array_equal(X_r, X_r2)
+    support = univariate_filter.get_support()
+    gtruth = np.zeros(10)
+    gtruth[:2] = 1
+    assert_array_equal(support, gtruth)
+
+    # Test in Percentile mode.
+    univariate_filter = SelectPercentile(mutual_info_regression, percentile=20)
+    X_r = univariate_filter.fit(X, y).transform(X)
+    X_r2 = (
+        GenericUnivariateSelect(mutual_info_regression, mode="percentile", param=20)
+        .fit(X, y)
+        .transform(X)
+    )
+    assert_array_equal(X_r, X_r2)
+    support = univariate_filter.get_support()
+    gtruth = np.zeros(10)
+    gtruth[:2] = 1
+    assert_array_equal(support, gtruth)
+
+
+def test_dataframe_output_dtypes():
+    """Check that the output datafarme dtypes are the same as the input.
+
+    Non-regression test for gh-24860.
+    """
+    pd = pytest.importorskip("pandas")
+
+    X, y = load_iris(return_X_y=True, as_frame=True)
+    X = X.astype(
+        {
+            "petal length (cm)": np.float32,
+            "petal width (cm)": np.float64,
+        }
+    )
+    X["petal_width_binned"] = pd.cut(X["petal width (cm)"], bins=10)
+
+    column_order = X.columns
+
+    def selector(X, y):
+        ranking = {
+            "sepal length (cm)": 1,
+            "sepal width (cm)": 2,
+            "petal length (cm)": 3,
+            "petal width (cm)": 4,
+            "petal_width_binned": 5,
+        }
+        return np.asarray([ranking[name] for name in column_order])
+
+    univariate_filter = SelectKBest(selector, k=3).set_output(transform="pandas")
+    output = univariate_filter.fit_transform(X, y)
+
+    assert_array_equal(
+        output.columns, ["petal length (cm)", "petal width (cm)", "petal_width_binned"]
+    )
+    for name, dtype in output.dtypes.items():
+        assert dtype == X.dtypes[name]
+
+
+@pytest.mark.parametrize(
+    "selector",
+    [
+        SelectKBest(k=4),
+        SelectPercentile(percentile=80),
+        GenericUnivariateSelect(mode="k_best", param=4),
+        GenericUnivariateSelect(mode="percentile", param=80),
+    ],
+)
+def test_unsupervised_filter(selector):
+    """Check support for unsupervised feature selection for the filter that could
+    require only `X`.
+    """
+    rng = np.random.RandomState(0)
+    X = rng.randn(10, 5)
+
+    def score_func(X, y=None):
+        return np.array([1, 1, 1, 1, 0])
+
+    selector.set_params(score_func=score_func)
+    selector.fit(X)
+    X_trans = selector.transform(X)
+    assert_allclose(X_trans, X[:, :4])
+    X_trans = selector.fit_transform(X)
+    assert_allclose(X_trans, X[:, :4])

env-llmeval/lib/python3.10/site-packages/sklearn/feature_selection/tests/test_from_model.py

@@ -0,0 +1,684 @@
+import re
+import warnings
+from unittest.mock import Mock
+
+import numpy as np
+import pytest
+
+from sklearn import datasets
+from sklearn.base import BaseEstimator
+from sklearn.cross_decomposition import CCA, PLSCanonical, PLSRegression
+from sklearn.datasets import make_friedman1
+from sklearn.decomposition import PCA
+from sklearn.ensemble import HistGradientBoostingClassifier, RandomForestClassifier
+from sklearn.exceptions import NotFittedError
+from sklearn.feature_selection import SelectFromModel
+from sklearn.linear_model import (
+    ElasticNet,
+    ElasticNetCV,
+    Lasso,
+    LassoCV,
+    LinearRegression,
+    LogisticRegression,
+    PassiveAggressiveClassifier,
+    SGDClassifier,
+)
+from sklearn.pipeline import make_pipeline
+from sklearn.svm import LinearSVC
+from sklearn.utils._testing import (
+    MinimalClassifier,
+    assert_allclose,
+    assert_array_almost_equal,
+    assert_array_equal,
+    skip_if_32bit,
+)
+
+
+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",
+    [
+        (
+            data.shape[1] + 1,
+            ValueError,
+            "max_features ==",
+        ),
+        (
+            lambda X: 1.5,
+            TypeError,
+            "max_features must be an instance of int, not float.",
+        ),
+        (
+            lambda X: data.shape[1] + 1,
+            ValueError,
+            "max_features ==",
+        ),
+        (
+            lambda X: -1,
+            ValueError,
+            "max_features ==",
+        ),
+    ],
+)
+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)
+
+
+@pytest.mark.parametrize(
+    "estimator",
+    [
+        Lasso(alpha=0.1, random_state=42),
+        LassoCV(random_state=42),
+        ElasticNet(l1_ratio=1, random_state=42),
+        ElasticNetCV(l1_ratio=[1], random_state=42),
+    ],
+)
+def test_coef_default_threshold(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,
+    )
+
+    # For the Lasso and related models, the threshold defaults to 1e-5
+    transformer = SelectFromModel(estimator=estimator)
+    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(dual="auto", 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)
+
+    name = type(model).__name__
+    err_msg = (
+        f"This {name} instance is not fitted yet. Call 'fit' with "
+        "appropriate arguments before using this estimator."
+    )
+    with pytest.raises(NotFittedError, 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],
+        [ValueError, "max_features == 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_")
+
+
+def test_from_model_estimator_attribute_error():
+    """Check that we raise the proper AttributeError when the estimator
+    does not implement the `partial_fit` method, which is decorated with
+    `available_if`.
+
+    Non-regression test for:
+    https://github.com/scikit-learn/scikit-learn/issues/28108
+    """
+    # `LinearRegression` does not implement 'partial_fit' and should raise an
+    # AttributeError
+    from_model = SelectFromModel(estimator=LinearRegression())
+
+    outer_msg = "This 'SelectFromModel' has no attribute 'partial_fit'"
+    inner_msg = "'LinearRegression' object has no attribute 'partial_fit'"
+    with pytest.raises(AttributeError, match=outer_msg) as exec_info:
+        from_model.fit(data, y).partial_fit(data)
+    assert isinstance(exec_info.value.__cause__, AttributeError)
+    assert inner_msg in str(exec_info.value.__cause__)

env-llmeval/lib/python3.10/site-packages/sklearn/feature_selection/tests/test_mutual_info.py

@@ -0,0 +1,254 @@
+import numpy as np
+import pytest
+
+from sklearn.feature_selection import mutual_info_classif, mutual_info_regression
+from sklearn.feature_selection._mutual_info import _compute_mi
+from sklearn.utils import check_random_state
+from sklearn.utils._testing import (
+    assert_allclose,
+    assert_array_equal,
+)
+from sklearn.utils.fixes import CSR_CONTAINERS
+
+
+def test_compute_mi_dd():
+    # In discrete case computations are straightforward and can be done
+    # by hand on given vectors.
+    x = np.array([0, 1, 1, 0, 0])
+    y = np.array([1, 0, 0, 0, 1])
+
+    H_x = H_y = -(3 / 5) * np.log(3 / 5) - (2 / 5) * np.log(2 / 5)
+    H_xy = -1 / 5 * np.log(1 / 5) - 2 / 5 * np.log(2 / 5) - 2 / 5 * np.log(2 / 5)
+    I_xy = H_x + H_y - H_xy
+
+    assert_allclose(_compute_mi(x, y, x_discrete=True, y_discrete=True), I_xy)
+
+
+def test_compute_mi_cc(global_dtype):
+    # For two continuous variables a good approach is to test on bivariate
+    # normal distribution, where mutual information is known.
+
+    # Mean of the distribution, irrelevant for mutual information.
+    mean = np.zeros(2)
+
+    # Setup covariance matrix with correlation coeff. equal 0.5.
+    sigma_1 = 1
+    sigma_2 = 10
+    corr = 0.5
+    cov = np.array(
+        [
+            [sigma_1**2, corr * sigma_1 * sigma_2],
+            [corr * sigma_1 * sigma_2, sigma_2**2],
+        ]
+    )
+
+    # True theoretical mutual information.
+    I_theory = np.log(sigma_1) + np.log(sigma_2) - 0.5 * np.log(np.linalg.det(cov))
+
+    rng = check_random_state(0)
+    Z = rng.multivariate_normal(mean, cov, size=1000).astype(global_dtype, copy=False)
+
+    x, y = Z[:, 0], Z[:, 1]
+
+    # Theory and computed values won't be very close
+    # We here check with a large relative tolerance
+    for n_neighbors in [3, 5, 7]:
+        I_computed = _compute_mi(
+            x, y, x_discrete=False, y_discrete=False, n_neighbors=n_neighbors
+        )
+        assert_allclose(I_computed, I_theory, rtol=1e-1)
+
+
+def test_compute_mi_cd(global_dtype):
+    # To test define a joint distribution as follows:
+    # p(x, y) = p(x) p(y | x)
+    # X ~ Bernoulli(p)
+    # (Y | x = 0) ~ Uniform(-1, 1)
+    # (Y | x = 1) ~ Uniform(0, 2)
+
+    # Use the following formula for mutual information:
+    # I(X; Y) = H(Y) - H(Y | X)
+    # Two entropies can be computed by hand:
+    # H(Y) = -(1-p)/2 * ln((1-p)/2) - p/2*log(p/2) - 1/2*log(1/2)
+    # H(Y | X) = ln(2)
+
+    # Now we need to implement sampling from out distribution, which is
+    # done easily using conditional distribution logic.
+
+    n_samples = 1000
+    rng = check_random_state(0)
+
+    for p in [0.3, 0.5, 0.7]:
+        x = rng.uniform(size=n_samples) > p
+
+        y = np.empty(n_samples, global_dtype)
+        mask = x == 0
+        y[mask] = rng.uniform(-1, 1, size=np.sum(mask))
+        y[~mask] = rng.uniform(0, 2, size=np.sum(~mask))
+
+        I_theory = -0.5 * (
+            (1 - p) * np.log(0.5 * (1 - p)) + p * np.log(0.5 * p) + np.log(0.5)
+        ) - np.log(2)
+
+        # Assert the same tolerance.
+        for n_neighbors in [3, 5, 7]:
+            I_computed = _compute_mi(
+                x, y, x_discrete=True, y_discrete=False, n_neighbors=n_neighbors
+            )
+            assert_allclose(I_computed, I_theory, rtol=1e-1)
+
+
+def test_compute_mi_cd_unique_label(global_dtype):
+    # Test that adding unique label doesn't change MI.
+    n_samples = 100
+    x = np.random.uniform(size=n_samples) > 0.5
+
+    y = np.empty(n_samples, global_dtype)
+    mask = x == 0
+    y[mask] = np.random.uniform(-1, 1, size=np.sum(mask))
+    y[~mask] = np.random.uniform(0, 2, size=np.sum(~mask))
+
+    mi_1 = _compute_mi(x, y, x_discrete=True, y_discrete=False)
+
+    x = np.hstack((x, 2))
+    y = np.hstack((y, 10))
+    mi_2 = _compute_mi(x, y, x_discrete=True, y_discrete=False)
+
+    assert_allclose(mi_1, mi_2)
+
+
+# We are going test that feature ordering by MI matches our expectations.
+def test_mutual_info_classif_discrete(global_dtype):
+    X = np.array(
+        [[0, 0, 0], [1, 1, 0], [2, 0, 1], [2, 0, 1], [2, 0, 1]], dtype=global_dtype
+    )
+    y = np.array([0, 1, 2, 2, 1])
+
+    # Here X[:, 0] is the most informative feature, and X[:, 1] is weakly
+    # informative.
+    mi = mutual_info_classif(X, y, discrete_features=True)
+    assert_array_equal(np.argsort(-mi), np.array([0, 2, 1]))
+
+
+def test_mutual_info_regression(global_dtype):
+    # We generate sample from multivariate normal distribution, using
+    # transformation from initially uncorrelated variables. The zero
+    # variables after transformation is selected as the target vector,
+    # it has the strongest correlation with the variable 2, and
+    # the weakest correlation with the variable 1.
+    T = np.array([[1, 0.5, 2, 1], [0, 1, 0.1, 0.0], [0, 0.1, 1, 0.1], [0, 0.1, 0.1, 1]])
+    cov = T.dot(T.T)
+    mean = np.zeros(4)
+
+    rng = check_random_state(0)
+    Z = rng.multivariate_normal(mean, cov, size=1000).astype(global_dtype, copy=False)
+    X = Z[:, 1:]
+    y = Z[:, 0]
+
+    mi = mutual_info_regression(X, y, random_state=0)
+    assert_array_equal(np.argsort(-mi), np.array([1, 2, 0]))
+    # XXX: should mutual_info_regression be fixed to avoid
+    # up-casting float32 inputs to float64?
+    assert mi.dtype == np.float64
+
+
+def test_mutual_info_classif_mixed(global_dtype):
+    # Here the target is discrete and there are two continuous and one
+    # discrete feature. The idea of this test is clear from the code.
+    rng = check_random_state(0)
+    X = rng.rand(1000, 3).astype(global_dtype, copy=False)
+    X[:, 1] += X[:, 0]
+    y = ((0.5 * X[:, 0] + X[:, 2]) > 0.5).astype(int)
+    X[:, 2] = X[:, 2] > 0.5
+
+    mi = mutual_info_classif(X, y, discrete_features=[2], n_neighbors=3, random_state=0)
+    assert_array_equal(np.argsort(-mi), [2, 0, 1])
+    for n_neighbors in [5, 7, 9]:
+        mi_nn = mutual_info_classif(
+            X, y, discrete_features=[2], n_neighbors=n_neighbors, random_state=0
+        )
+        # Check that the continuous values have an higher MI with greater
+        # n_neighbors
+        assert mi_nn[0] > mi[0]
+        assert mi_nn[1] > mi[1]
+        # The n_neighbors should not have any effect on the discrete value
+        # The MI should be the same
+        assert mi_nn[2] == mi[2]
+
+
+@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
+def test_mutual_info_options(global_dtype, csr_container):
+    X = np.array(
+        [[0, 0, 0], [1, 1, 0], [2, 0, 1], [2, 0, 1], [2, 0, 1]], dtype=global_dtype
+    )
+    y = np.array([0, 1, 2, 2, 1], dtype=global_dtype)
+    X_csr = csr_container(X)
+
+    for mutual_info in (mutual_info_regression, mutual_info_classif):
+        with pytest.raises(ValueError):
+            mutual_info(X_csr, y, discrete_features=False)
+        with pytest.raises(ValueError):
+            mutual_info(X, y, discrete_features="manual")
+        with pytest.raises(ValueError):
+            mutual_info(X_csr, y, discrete_features=[True, False, True])
+        with pytest.raises(IndexError):
+            mutual_info(X, y, discrete_features=[True, False, True, False])
+        with pytest.raises(IndexError):
+            mutual_info(X, y, discrete_features=[1, 4])
+
+        mi_1 = mutual_info(X, y, discrete_features="auto", random_state=0)
+        mi_2 = mutual_info(X, y, discrete_features=False, random_state=0)
+        mi_3 = mutual_info(X_csr, y, discrete_features="auto", random_state=0)
+        mi_4 = mutual_info(X_csr, y, discrete_features=True, random_state=0)
+        mi_5 = mutual_info(X, y, discrete_features=[True, False, True], random_state=0)
+        mi_6 = mutual_info(X, y, discrete_features=[0, 2], random_state=0)
+
+        assert_allclose(mi_1, mi_2)
+        assert_allclose(mi_3, mi_4)
+        assert_allclose(mi_5, mi_6)
+
+        assert not np.allclose(mi_1, mi_3)
+
+
+@pytest.mark.parametrize("correlated", [True, False])
+def test_mutual_information_symmetry_classif_regression(correlated, global_random_seed):
+    """Check that `mutual_info_classif` and `mutual_info_regression` are
+    symmetric by switching the target `y` as `feature` in `X` and vice
+    versa.
+
+    Non-regression test for:
+    https://github.com/scikit-learn/scikit-learn/issues/23720
+    """
+    rng = np.random.RandomState(global_random_seed)
+    n = 100
+    d = rng.randint(10, size=n)
+
+    if correlated:
+        c = d.astype(np.float64)
+    else:
+        c = rng.normal(0, 1, size=n)
+
+    mi_classif = mutual_info_classif(
+        c[:, None], d, discrete_features=[False], random_state=global_random_seed
+    )
+
+    mi_regression = mutual_info_regression(
+        d[:, None], c, discrete_features=[True], random_state=global_random_seed
+    )
+
+    assert mi_classif == pytest.approx(mi_regression)
+
+
+def test_mutual_info_regression_X_int_dtype(global_random_seed):
+    """Check that results agree when X is integer dtype and float dtype.
+
+    Non-regression test for Issue #26696.
+    """
+    rng = np.random.RandomState(global_random_seed)
+    X = rng.randint(100, size=(100, 10))
+    X_float = X.astype(np.float64, copy=True)
+    y = rng.randint(100, size=100)
+
+    expected = mutual_info_regression(X_float, y, random_state=global_random_seed)
+    result = mutual_info_regression(X, y, random_state=global_random_seed)
+    assert_allclose(result, expected)

env-llmeval/lib/python3.10/site-packages/sklearn/feature_selection/tests/test_rfe.py

@@ -0,0 +1,615 @@
+"""
+Testing Recursive feature elimination
+"""
+
+from operator import attrgetter
+
+import numpy as np
+import pytest
+from numpy.testing import assert_allclose, assert_array_almost_equal, assert_array_equal
+
+from sklearn.base import BaseEstimator, ClassifierMixin
+from sklearn.compose import TransformedTargetRegressor
+from sklearn.cross_decomposition import CCA, PLSCanonical, PLSRegression
+from sklearn.datasets import load_iris, make_friedman1
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.feature_selection import RFE, RFECV
+from sklearn.impute import SimpleImputer
+from sklearn.linear_model import LinearRegression, LogisticRegression
+from sklearn.metrics import get_scorer, make_scorer, zero_one_loss
+from sklearn.model_selection import GroupKFold, cross_val_score
+from sklearn.pipeline import make_pipeline
+from sklearn.preprocessing import StandardScaler
+from sklearn.svm import SVC, SVR, LinearSVR
+from sklearn.utils import check_random_state
+from sklearn.utils._testing import ignore_warnings
+from sklearn.utils.fixes import CSR_CONTAINERS
+
+
+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 0.0
+
+    def get_params(self, deep=True):
+        return {"foo_param": self.foo_param}
+
+    def set_params(self, **params):
+        return self
+
+    def _more_tags(self):
+        return {"allow_nan": True}
+
+
+def test_rfe_features_importance():
+    generator = check_random_state(0)
+    iris = load_iris()
+    # Add some irrelevant features. Random seed is set to make sure that
+    # irrelevant features are always irrelevant.
+    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())
+
+
+@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
+def test_rfe(csr_container):
+    generator = check_random_state(0)
+    iris = load_iris()
+    # Add some irrelevant features. Random seed is set to make sure that
+    # irrelevant features are always irrelevant.
+    X = np.c_[iris.data, generator.normal(size=(len(iris.data), 6))]
+    X_sparse = csr_container(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_fit_score_params():
+    # Make sure RFE passes the metadata down to fit and score methods of the
+    # underlying estimator
+    class TestEstimator(BaseEstimator, ClassifierMixin):
+        def fit(self, X, y, prop=None):
+            if prop is None:
+                raise ValueError("fit: prop cannot be None")
+            self.svc_ = SVC(kernel="linear").fit(X, y)
+            self.coef_ = self.svc_.coef_
+            return self
+
+        def score(self, X, y, prop=None):
+            if prop is None:
+                raise ValueError("score: prop cannot be None")
+            return self.svc_.score(X, y)
+
+    X, y = load_iris(return_X_y=True)
+    with pytest.raises(ValueError, match="fit: prop cannot be None"):
+        RFE(estimator=TestEstimator()).fit(X, y)
+    with pytest.raises(ValueError, match="score: prop cannot be None"):
+        RFE(estimator=TestEstimator()).fit(X, y, prop="foo").score(X, y)
+
+    RFE(estimator=TestEstimator()).fit(X, y, prop="foo").score(X, y, prop="foo")
+
+
+def test_rfe_percent_n_features():
+    # test that the results are the same
+    generator = check_random_state(0)
+    iris = load_iris()
+    # Add some irrelevant features. Random seed is set to make sure that
+    # irrelevant features are always irrelevant.
+    X = np.c_[iris.data, generator.normal(size=(len(iris.data), 6))]
+    y = iris.target
+    # there are 10 features in the data. We select 40%.
+    clf = SVC(kernel="linear")
+    rfe_num = RFE(estimator=clf, n_features_to_select=4, step=0.1)
+    rfe_num.fit(X, y)
+
+    rfe_perc = RFE(estimator=clf, n_features_to_select=0.4, step=0.1)
+    rfe_perc.fit(X, y)
+
+    assert_array_equal(rfe_perc.ranking_, rfe_num.ranking_)
+    assert_array_equal(rfe_perc.support_, rfe_num.support_)
+
+
+def test_rfe_mockclassifier():
+    generator = check_random_state(0)
+    iris = load_iris()
+    # Add some irrelevant features. Random seed is set to make sure that
+    # irrelevant features are always irrelevant.
+    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
+
+
+@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
+def test_rfecv(csr_container):
+    generator = check_random_state(0)
+    iris = load_iris()
+    # Add some irrelevant features. Random seed is set to make sure that
+    # irrelevant features are always irrelevant.
+    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:
+
+    for key in rfecv.cv_results_.keys():
+        assert len(rfecv.cv_results_[key]) == 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 = csr_container(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 cv_results_
+    def test_scorer(estimator, X, y):
+        return 1.0
+
+    rfecv = RFECV(estimator=SVC(kernel="linear"), step=1, scoring=test_scorer)
+    rfecv.fit(X, y)
+
+    # 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)
+
+    for key in rfecv.cv_results_.keys():
+        assert len(rfecv.cv_results_[key]) == 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 = csr_container(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=0.2)
+    X_sparse = csr_container(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:
+
+    for key in rfecv.cv_results_.keys():
+        assert len(rfecv.cv_results_[key]) == X.shape[1]
+
+    assert len(rfecv.ranking_) == X.shape[1]
+
+
+def test_rfecv_verbose_output():
+    # Check verbose=1 is producing an output.
+    import sys
+    from io import StringIO
+
+    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_cv_results_size(global_random_seed):
+    generator = check_random_state(global_random_seed)
+    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
+
+        for key in rfecv.cv_results_.keys():
+            assert len(rfecv.cv_results_[key]) == 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() > 0.7
+
+
+def test_rfe_min_step(global_random_seed):
+    n_features = 10
+    X, y = make_friedman1(
+        n_samples=50, n_features=n_features, random_state=global_random_seed
+    )
+    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(global_random_seed):
+    # 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(global_random_seed)
+        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 each score in 'cv_results_' 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(global_random_seed)
+        X = generator.normal(size=(100, n_features))
+        y = generator.rand(100).round()
+        rfecv = RFECV(estimator=SVC(kernel="linear"), step=step)
+        rfecv.fit(X, y)
+
+        for key in rfecv.cv_results_.keys():
+            assert len(rfecv.cv_results_[key]) == formula1(
+                n_features, n_features_to_select, step
+            )
+            assert len(rfecv.cv_results_[key]) == formula2(
+                n_features, n_features_to_select, step
+            )
+
+
+def test_rfe_cv_n_jobs(global_random_seed):
+    generator = check_random_state(global_random_seed)
+    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_cv_results_ = rfecv.cv_results_
+
+    rfecv.set_params(n_jobs=2)
+    rfecv.fit(X, y)
+    assert_array_almost_equal(rfecv.ranking_, rfecv_ranking)
+
+    assert rfecv_cv_results_.keys() == rfecv.cv_results_.keys()
+    for key in rfecv_cv_results_.keys():
+        assert rfecv_cv_results_[key] == pytest.approx(rfecv.cv_results_[key])
+
+
+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(dual="auto", random_state=0)
+
+    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),
+    ],
+)
+@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(dual="auto")
+    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
+
+
+def test_rfecv_std_and_mean(global_random_seed):
+    generator = check_random_state(global_random_seed)
+    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)
+    n_split_keys = len(rfecv.cv_results_) - 2
+    split_keys = [f"split{i}_test_score" for i in range(n_split_keys)]
+
+    cv_scores = np.asarray([rfecv.cv_results_[key] for key in split_keys])
+    expected_mean = np.mean(cv_scores, axis=0)
+    expected_std = np.std(cv_scores, axis=0)
+
+    assert_allclose(rfecv.cv_results_["mean_test_score"], expected_mean)
+    assert_allclose(rfecv.cv_results_["std_test_score"], expected_std)
+
+
+@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)
+
+
+@pytest.mark.parametrize("ClsRFE", [RFE, RFECV])
+def test_pipeline_with_nans(ClsRFE):
+    """Check that RFE works with pipeline that accept nans.
+
+    Non-regression test for gh-21743.
+    """
+    X, y = load_iris(return_X_y=True)
+    X[0, 0] = np.nan
+
+    pipe = make_pipeline(
+        SimpleImputer(),
+        StandardScaler(),
+        LogisticRegression(),
+    )
+
+    fs = ClsRFE(
+        estimator=pipe,
+        importance_getter="named_steps.logisticregression.coef_",
+    )
+    fs.fit(X, y)
+
+
+@pytest.mark.parametrize("ClsRFE", [RFE, RFECV])
+@pytest.mark.parametrize("PLSEstimator", [CCA, PLSCanonical, PLSRegression])
+def test_rfe_pls(ClsRFE, PLSEstimator):
+    """Check the behaviour of RFE 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)
+    selector = ClsRFE(estimator, step=1).fit(X, y)
+    assert selector.score(X, y) > 0.5
+
+
+def test_rfe_estimator_attribute_error():
+    """Check that we raise the proper AttributeError when the estimator
+    does not implement the `decision_function` method, which is decorated with
+    `available_if`.
+
+    Non-regression test for:
+    https://github.com/scikit-learn/scikit-learn/issues/28108
+    """
+    iris = load_iris()
+
+    # `LinearRegression` does not implement 'decision_function' and should raise an
+    # AttributeError
+    rfe = RFE(estimator=LinearRegression())
+
+    outer_msg = "This 'RFE' has no attribute 'decision_function'"
+    inner_msg = "'LinearRegression' object has no attribute 'decision_function'"
+    with pytest.raises(AttributeError, match=outer_msg) as exec_info:
+        rfe.fit(iris.data, iris.target).decision_function(iris.data)
+    assert isinstance(exec_info.value.__cause__, AttributeError)
+    assert inner_msg in str(exec_info.value.__cause__)

env-llmeval/lib/python3.10/site-packages/sklearn/feature_selection/tests/test_sequential.py

@@ -0,0 +1,323 @@
+import numpy as np
+import pytest
+from numpy.testing import assert_array_equal
+
+from sklearn.cluster import KMeans
+from sklearn.datasets import make_blobs, make_classification, make_regression
+from sklearn.ensemble import HistGradientBoostingRegressor
+from sklearn.feature_selection import SequentialFeatureSelector
+from sklearn.linear_model import LinearRegression
+from sklearn.model_selection import LeaveOneGroupOut, cross_val_score
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.pipeline import make_pipeline
+from sklearn.preprocessing import StandardScaler
+from sklearn.utils.fixes import CSR_CONTAINERS
+
+
+def test_bad_n_features_to_select():
+    n_features = 5
+    X, y = make_regression(n_features=n_features)
+    sfs = SequentialFeatureSelector(LinearRegression(), n_features_to_select=n_features)
+    with pytest.raises(ValueError, match="n_features_to_select must be < n_features"):
+        sfs.fit(X, y)
+
+
+@pytest.mark.parametrize("direction", ("forward", "backward"))
+@pytest.mark.parametrize("n_features_to_select", (1, 5, 9, "auto"))
+def test_n_features_to_select(direction, n_features_to_select):
+    # Make sure n_features_to_select is respected
+
+    n_features = 10
+    X, y = make_regression(n_features=n_features, random_state=0)
+    sfs = SequentialFeatureSelector(
+        LinearRegression(),
+        n_features_to_select=n_features_to_select,
+        direction=direction,
+        cv=2,
+    )
+    sfs.fit(X, y)
+
+    if n_features_to_select == "auto":
+        n_features_to_select = n_features // 2
+
+    assert sfs.get_support(indices=True).shape[0] == n_features_to_select
+    assert sfs.n_features_to_select_ == n_features_to_select
+    assert sfs.transform(X).shape[1] == n_features_to_select
+
+
+@pytest.mark.parametrize("direction", ("forward", "backward"))
+def test_n_features_to_select_auto(direction):
+    """Check the behaviour of `n_features_to_select="auto"` with different
+    values for the parameter `tol`.
+    """
+
+    n_features = 10
+    tol = 1e-3
+    X, y = make_regression(n_features=n_features, random_state=0)
+    sfs = SequentialFeatureSelector(
+        LinearRegression(),
+        n_features_to_select="auto",
+        tol=tol,
+        direction=direction,
+        cv=2,
+    )
+    sfs.fit(X, y)
+
+    max_features_to_select = n_features - 1
+
+    assert sfs.get_support(indices=True).shape[0] <= max_features_to_select
+    assert sfs.n_features_to_select_ <= max_features_to_select
+    assert sfs.transform(X).shape[1] <= max_features_to_select
+    assert sfs.get_support(indices=True).shape[0] == sfs.n_features_to_select_
+
+
+@pytest.mark.parametrize("direction", ("forward", "backward"))
+def test_n_features_to_select_stopping_criterion(direction):
+    """Check the behaviour stopping criterion for feature selection
+    depending on the values of `n_features_to_select` and `tol`.
+
+    When `direction` is `'forward'`, select a new features at random
+    among those not currently selected in selector.support_,
+    build a new version of the data that includes all the features
+    in selector.support_ + this newly selected feature.
+    And check that the cross-validation score of the model trained on
+    this new dataset variant is lower than the model with
+    the selected forward selected features or at least does not improve
+    by more than the tol margin.
+
+    When `direction` is `'backward'`, instead of adding a new feature
+    to selector.support_, try to remove one of those selected features at random
+    And check that the cross-validation score is either decreasing or
+    not improving by more than the tol margin.
+    """
+
+    X, y = make_regression(n_features=50, n_informative=10, random_state=0)
+
+    tol = 1e-3
+
+    sfs = SequentialFeatureSelector(
+        LinearRegression(),
+        n_features_to_select="auto",
+        tol=tol,
+        direction=direction,
+        cv=2,
+    )
+    sfs.fit(X, y)
+    selected_X = sfs.transform(X)
+
+    rng = np.random.RandomState(0)
+
+    added_candidates = list(set(range(X.shape[1])) - set(sfs.get_support(indices=True)))
+    added_X = np.hstack(
+        [
+            selected_X,
+            (X[:, rng.choice(added_candidates)])[:, np.newaxis],
+        ]
+    )
+
+    removed_candidate = rng.choice(list(range(sfs.n_features_to_select_)))
+    removed_X = np.delete(selected_X, removed_candidate, axis=1)
+
+    plain_cv_score = cross_val_score(LinearRegression(), X, y, cv=2).mean()
+    sfs_cv_score = cross_val_score(LinearRegression(), selected_X, y, cv=2).mean()
+    added_cv_score = cross_val_score(LinearRegression(), added_X, y, cv=2).mean()
+    removed_cv_score = cross_val_score(LinearRegression(), removed_X, y, cv=2).mean()
+
+    assert sfs_cv_score >= plain_cv_score
+
+    if direction == "forward":
+        assert (sfs_cv_score - added_cv_score) <= tol
+        assert (sfs_cv_score - removed_cv_score) >= tol
+    else:
+        assert (added_cv_score - sfs_cv_score) <= tol
+        assert (removed_cv_score - sfs_cv_score) <= tol
+
+
+@pytest.mark.parametrize("direction", ("forward", "backward"))
+@pytest.mark.parametrize(
+    "n_features_to_select, expected",
+    (
+        (0.1, 1),
+        (1.0, 10),
+        (0.5, 5),
+    ),
+)
+def test_n_features_to_select_float(direction, n_features_to_select, expected):
+    # Test passing a float as n_features_to_select
+    X, y = make_regression(n_features=10)
+    sfs = SequentialFeatureSelector(
+        LinearRegression(),
+        n_features_to_select=n_features_to_select,
+        direction=direction,
+        cv=2,
+    )
+    sfs.fit(X, y)
+    assert sfs.n_features_to_select_ == expected
+
+
+@pytest.mark.parametrize("seed", range(10))
+@pytest.mark.parametrize("direction", ("forward", "backward"))
+@pytest.mark.parametrize(
+    "n_features_to_select, expected_selected_features",
+    [
+        (2, [0, 2]),  # f1 is dropped since it has no predictive power
+        (1, [2]),  # f2 is more predictive than f0 so it's kept
+    ],
+)
+def test_sanity(seed, direction, n_features_to_select, expected_selected_features):
+    # Basic sanity check: 3 features, only f0 and f2 are correlated with the
+    # target, f2 having a stronger correlation than f0. We expect f1 to be
+    # dropped, and f2 to always be selected.
+
+    rng = np.random.RandomState(seed)
+    n_samples = 100
+    X = rng.randn(n_samples, 3)
+    y = 3 * X[:, 0] - 10 * X[:, 2]
+
+    sfs = SequentialFeatureSelector(
+        LinearRegression(),
+        n_features_to_select=n_features_to_select,
+        direction=direction,
+        cv=2,
+    )
+    sfs.fit(X, y)
+    assert_array_equal(sfs.get_support(indices=True), expected_selected_features)
+
+
+@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
+def test_sparse_support(csr_container):
+    # Make sure sparse data is supported
+
+    X, y = make_regression(n_features=10)
+    X = csr_container(X)
+    sfs = SequentialFeatureSelector(
+        LinearRegression(), n_features_to_select="auto", cv=2
+    )
+    sfs.fit(X, y)
+    sfs.transform(X)
+
+
+def test_nan_support():
+    # Make sure nans are OK if the underlying estimator supports nans
+
+    rng = np.random.RandomState(0)
+    n_samples, n_features = 40, 4
+    X, y = make_regression(n_samples, n_features, random_state=0)
+    nan_mask = rng.randint(0, 2, size=(n_samples, n_features), dtype=bool)
+    X[nan_mask] = np.nan
+    sfs = SequentialFeatureSelector(
+        HistGradientBoostingRegressor(), n_features_to_select="auto", cv=2
+    )
+    sfs.fit(X, y)
+    sfs.transform(X)
+
+    with pytest.raises(ValueError, match="Input X contains NaN"):
+        # LinearRegression does not support nans
+        SequentialFeatureSelector(
+            LinearRegression(), n_features_to_select="auto", cv=2
+        ).fit(X, y)
+
+
+def test_pipeline_support():
+    # Make sure that pipelines can be passed into SFS and that SFS can be
+    # passed into a pipeline
+
+    n_samples, n_features = 50, 3
+    X, y = make_regression(n_samples, n_features, random_state=0)
+
+    # pipeline in SFS
+    pipe = make_pipeline(StandardScaler(), LinearRegression())
+    sfs = SequentialFeatureSelector(pipe, n_features_to_select="auto", cv=2)
+    sfs.fit(X, y)
+    sfs.transform(X)
+
+    # SFS in pipeline
+    sfs = SequentialFeatureSelector(
+        LinearRegression(), n_features_to_select="auto", cv=2
+    )
+    pipe = make_pipeline(StandardScaler(), sfs)
+    pipe.fit(X, y)
+    pipe.transform(X)
+
+
+@pytest.mark.parametrize("n_features_to_select", (2, 3))
+def test_unsupervised_model_fit(n_features_to_select):
+    # Make sure that models without classification labels are not being
+    # validated
+
+    X, y = make_blobs(n_features=4)
+    sfs = SequentialFeatureSelector(
+        KMeans(n_init=1),
+        n_features_to_select=n_features_to_select,
+    )
+    sfs.fit(X)
+    assert sfs.transform(X).shape[1] == n_features_to_select
+
+
+@pytest.mark.parametrize("y", ("no_validation", 1j, 99.9, np.nan, 3))
+def test_no_y_validation_model_fit(y):
+    # Make sure that other non-conventional y labels are not accepted
+
+    X, clusters = make_blobs(n_features=6)
+    sfs = SequentialFeatureSelector(
+        KMeans(),
+        n_features_to_select=3,
+    )
+
+    with pytest.raises((TypeError, ValueError)):
+        sfs.fit(X, y)
+
+
+def test_forward_neg_tol_error():
+    """Check that we raise an error when tol<0 and direction='forward'"""
+    X, y = make_regression(n_features=10, random_state=0)
+    sfs = SequentialFeatureSelector(
+        LinearRegression(),
+        n_features_to_select="auto",
+        direction="forward",
+        tol=-1e-3,
+    )
+
+    with pytest.raises(ValueError, match="tol must be positive"):
+        sfs.fit(X, y)
+
+
+def test_backward_neg_tol():
+    """Check that SequentialFeatureSelector works negative tol
+
+    non-regression test for #25525
+    """
+    X, y = make_regression(n_features=10, random_state=0)
+    lr = LinearRegression()
+    initial_score = lr.fit(X, y).score(X, y)
+
+    sfs = SequentialFeatureSelector(
+        lr,
+        n_features_to_select="auto",
+        direction="backward",
+        tol=-1e-3,
+    )
+    Xr = sfs.fit_transform(X, y)
+    new_score = lr.fit(Xr, y).score(Xr, y)
+
+    assert 0 < sfs.get_support().sum() < X.shape[1]
+    assert new_score < initial_score
+
+
+def test_cv_generator_support():
+    """Check that no exception raised when cv is generator
+
+    non-regression test for #25957
+    """
+    X, y = make_classification(random_state=0)
+
+    groups = np.zeros_like(y, dtype=int)
+    groups[y.size // 2 :] = 1
+
+    cv = LeaveOneGroupOut()
+    splits = cv.split(X, y, groups=groups)
+
+    knc = KNeighborsClassifier(n_neighbors=5)
+
+    sfs = SequentialFeatureSelector(knc, n_features_to_select=5, cv=splits)
+    sfs.fit(X, y)

env-llmeval/lib/python3.10/site-packages/sklearn/feature_selection/tests/test_variance_threshold.py

@@ -0,0 +1,72 @@
+import numpy as np
+import pytest
+
+from sklearn.feature_selection import VarianceThreshold
+from sklearn.utils._testing import assert_array_equal
+from sklearn.utils.fixes import BSR_CONTAINERS, CSC_CONTAINERS, CSR_CONTAINERS
+
+data = [[0, 1, 2, 3, 4], [0, 2, 2, 3, 5], [1, 1, 2, 4, 0]]
+
+data2 = [[-0.13725701]] * 10
+
+
+@pytest.mark.parametrize(
+    "sparse_container", [None] + BSR_CONTAINERS + CSC_CONTAINERS + CSR_CONTAINERS
+)
+def test_zero_variance(sparse_container):
+    # Test VarianceThreshold with default setting, zero variance.
+    X = data if sparse_container is None else sparse_container(data)
+    sel = VarianceThreshold().fit(X)
+    assert_array_equal([0, 1, 3, 4], sel.get_support(indices=True))
+
+
+def test_zero_variance_value_error():
+    # Test VarianceThreshold with default setting, zero variance, error cases.
+    with pytest.raises(ValueError):
+        VarianceThreshold().fit([[0, 1, 2, 3]])
+    with pytest.raises(ValueError):
+        VarianceThreshold().fit([[0, 1], [0, 1]])
+
+
+@pytest.mark.parametrize("sparse_container", [None] + CSR_CONTAINERS)
+def test_variance_threshold(sparse_container):
+    # Test VarianceThreshold with custom variance.
+    X = data if sparse_container is None else sparse_container(data)
+    X = VarianceThreshold(threshold=0.4).fit_transform(X)
+    assert (len(data), 1) == X.shape
+
+
+@pytest.mark.skipif(
+    np.var(data2) == 0,
+    reason=(
+        "This test is not valid for this platform, "
+        "as it relies on numerical instabilities."
+    ),
+)
+@pytest.mark.parametrize(
+    "sparse_container", [None] + BSR_CONTAINERS + CSC_CONTAINERS + CSR_CONTAINERS
+)
+def test_zero_variance_floating_point_error(sparse_container):
+    # Test that VarianceThreshold(0.0).fit eliminates features that have
+    # the same value in every sample, even when floating point errors
+    # cause np.var not to be 0 for the feature.
+    # See #13691
+    X = data2 if sparse_container is None else sparse_container(data2)
+    msg = "No feature in X meets the variance threshold 0.00000"
+    with pytest.raises(ValueError, match=msg):
+        VarianceThreshold().fit(X)
+
+
+@pytest.mark.parametrize(
+    "sparse_container", [None] + BSR_CONTAINERS + CSC_CONTAINERS + CSR_CONTAINERS
+)
+def test_variance_nan(sparse_container):
+    arr = np.array(data, dtype=np.float64)
+    # add single NaN and feature should still be included
+    arr[0, 0] = np.nan
+    # make all values in feature NaN and feature should be rejected
+    arr[:, 1] = np.nan
+
+    X = arr if sparse_container is None else sparse_container(arr)
+    sel = VarianceThreshold().fit(X)
+    assert_array_equal([0, 3, 4], sel.get_support(indices=True))

env-llmeval/lib/python3.10/site-packages/sklearn/impute/__init__.py

@@ -0,0 +1,24 @@
+"""Transformers for missing value imputation"""
+import typing
+
+from ._base import MissingIndicator, SimpleImputer
+from ._knn import KNNImputer
+
+if typing.TYPE_CHECKING:
+    # Avoid errors in type checkers (e.g. mypy) for experimental estimators.
+    # TODO: remove this check once the estimator is no longer experimental.
+    from ._iterative import IterativeImputer  # noqa
+
+__all__ = ["MissingIndicator", "SimpleImputer", "KNNImputer"]
+
+
+# TODO: remove this check once the estimator is no longer experimental.
+def __getattr__(name):
+    if name == "IterativeImputer":
+        raise ImportError(
+            f"{name} is experimental and the API might change without any "
+            "deprecation cycle. To use it, you need to explicitly import "
+            "enable_iterative_imputer:\n"
+            "from sklearn.experimental import enable_iterative_imputer"
+        )
+    raise AttributeError(f"module {__name__} has no attribute {name}")

env-llmeval/lib/python3.10/site-packages/sklearn/impute/_base.py

@@ -0,0 +1,1075 @@
+# Authors: Nicolas Tresegnie <[email protected]>
+#          Sergey Feldman <[email protected]>
+# License: BSD 3 clause
+
+import numbers
+import warnings
+from collections import Counter
+from functools import partial
+
+import numpy as np
+import numpy.ma as ma
+from scipy import sparse as sp
+
+from ..base import BaseEstimator, TransformerMixin, _fit_context
+from ..utils import _is_pandas_na, is_scalar_nan
+from ..utils._mask import _get_mask
+from ..utils._param_validation import MissingValues, StrOptions
+from ..utils.fixes import _mode
+from ..utils.sparsefuncs import _get_median
+from ..utils.validation import FLOAT_DTYPES, _check_feature_names_in, check_is_fitted
+
+
+def _check_inputs_dtype(X, missing_values):
+    if _is_pandas_na(missing_values):
+        # Allow using `pd.NA` as missing values to impute numerical arrays.
+        return
+    if X.dtype.kind in ("f", "i", "u") and not isinstance(missing_values, numbers.Real):
+        raise ValueError(
+            "'X' and 'missing_values' types are expected to be"
+            " both numerical. Got X.dtype={} and "
+            " type(missing_values)={}.".format(X.dtype, type(missing_values))
+        )
+
+
+def _most_frequent(array, extra_value, n_repeat):
+    """Compute the most frequent value in a 1d array extended with
+    [extra_value] * n_repeat, where extra_value is assumed to be not part
+    of the array."""
+    # Compute the most frequent value in array only
+    if array.size > 0:
+        if array.dtype == object:
+            # scipy.stats.mode is slow with object dtype array.
+            # Python Counter is more efficient
+            counter = Counter(array)
+            most_frequent_count = counter.most_common(1)[0][1]
+            # tie breaking similarly to scipy.stats.mode
+            most_frequent_value = min(
+                value
+                for value, count in counter.items()
+                if count == most_frequent_count
+            )
+        else:
+            mode = _mode(array)
+            most_frequent_value = mode[0][0]
+            most_frequent_count = mode[1][0]
+    else:
+        most_frequent_value = 0
+        most_frequent_count = 0
+
+    # Compare to array + [extra_value] * n_repeat
+    if most_frequent_count == 0 and n_repeat == 0:
+        return np.nan
+    elif most_frequent_count < n_repeat:
+        return extra_value
+    elif most_frequent_count > n_repeat:
+        return most_frequent_value
+    elif most_frequent_count == n_repeat:
+        # tie breaking similarly to scipy.stats.mode
+        return min(most_frequent_value, extra_value)
+
+
+class _BaseImputer(TransformerMixin, BaseEstimator):
+    """Base class for all imputers.
+
+    It adds automatically support for `add_indicator`.
+    """
+
+    _parameter_constraints: dict = {
+        "missing_values": [MissingValues()],
+        "add_indicator": ["boolean"],
+        "keep_empty_features": ["boolean"],
+    }
+
+    def __init__(
+        self, *, missing_values=np.nan, add_indicator=False, keep_empty_features=False
+    ):
+        self.missing_values = missing_values
+        self.add_indicator = add_indicator
+        self.keep_empty_features = keep_empty_features
+
+    def _fit_indicator(self, X):
+        """Fit a MissingIndicator."""
+        if self.add_indicator:
+            self.indicator_ = MissingIndicator(
+                missing_values=self.missing_values, error_on_new=False
+            )
+            self.indicator_._fit(X, precomputed=True)
+        else:
+            self.indicator_ = None
+
+    def _transform_indicator(self, X):
+        """Compute the indicator mask.'
+
+        Note that X must be the original data as passed to the imputer before
+        any imputation, since imputation may be done inplace in some cases.
+        """
+        if self.add_indicator:
+            if not hasattr(self, "indicator_"):
+                raise ValueError(
+                    "Make sure to call _fit_indicator before _transform_indicator"
+                )
+            return self.indicator_.transform(X)
+
+    def _concatenate_indicator(self, X_imputed, X_indicator):
+        """Concatenate indicator mask with the imputed data."""
+        if not self.add_indicator:
+            return X_imputed
+
+        if sp.issparse(X_imputed):
+            # sp.hstack may result in different formats between sparse arrays and
+            # matrices; specify the format to keep consistent behavior
+            hstack = partial(sp.hstack, format=X_imputed.format)
+        else:
+            hstack = np.hstack
+
+        if X_indicator is None:
+            raise ValueError(
+                "Data from the missing indicator are not provided. Call "
+                "_fit_indicator and _transform_indicator in the imputer "
+                "implementation."
+            )
+
+        return hstack((X_imputed, X_indicator))
+
+    def _concatenate_indicator_feature_names_out(self, names, input_features):
+        if not self.add_indicator:
+            return names
+
+        indicator_names = self.indicator_.get_feature_names_out(input_features)
+        return np.concatenate([names, indicator_names])
+
+    def _more_tags(self):
+        return {"allow_nan": is_scalar_nan(self.missing_values)}
+
+
+class SimpleImputer(_BaseImputer):
+    """Univariate imputer for completing missing values with simple strategies.
+
+    Replace missing values using a descriptive statistic (e.g. mean, median, or
+    most frequent) along each column, or using a constant value.
+
+    Read more in the :ref:`User Guide <impute>`.
+
+    .. versionadded:: 0.20
+       `SimpleImputer` replaces the previous `sklearn.preprocessing.Imputer`
+       estimator which is now removed.
+
+    Parameters
+    ----------
+    missing_values : int, float, str, np.nan, None or pandas.NA, default=np.nan
+        The placeholder for the missing values. All occurrences of
+        `missing_values` will be imputed. For pandas' dataframes with
+        nullable integer dtypes with missing values, `missing_values`
+        can be set to either `np.nan` or `pd.NA`.
+
+    strategy : str, default='mean'
+        The imputation strategy.
+
+        - If "mean", then replace missing values using the mean along
+          each column. Can only be used with numeric data.
+        - If "median", then replace missing values using the median along
+          each column. Can only be used with numeric data.
+        - If "most_frequent", then replace missing using the most frequent
+          value along each column. Can be used with strings or numeric data.
+          If there is more than one such value, only the smallest is returned.
+        - If "constant", then replace missing values with fill_value. Can be
+          used with strings or numeric data.
+
+        .. versionadded:: 0.20
+           strategy="constant" for fixed value imputation.
+
+    fill_value : str or numerical value, default=None
+        When strategy == "constant", `fill_value` is used to replace all
+        occurrences of missing_values. For string or object data types,
+        `fill_value` must be a string.
+        If `None`, `fill_value` will be 0 when imputing numerical
+        data and "missing_value" for strings or object data types.
+
+    copy : bool, default=True
+        If True, a copy of X will be created. If False, imputation will
+        be done in-place whenever possible. Note that, in the following cases,
+        a new copy will always be made, even if `copy=False`:
+
+        - If `X` is not an array of floating values;
+        - If `X` is encoded as a CSR matrix;
+        - If `add_indicator=True`.
+
+    add_indicator : bool, default=False
+        If True, a :class:`MissingIndicator` transform will stack onto output
+        of the imputer's transform. This allows a predictive estimator
+        to account for missingness despite imputation. If a feature has no
+        missing values at fit/train time, the feature won't appear on
+        the missing indicator even if there are missing values at
+        transform/test time.
+
+    keep_empty_features : bool, default=False
+        If True, features that consist exclusively of missing values when
+        `fit` is called are returned in results when `transform` is called.
+        The imputed value is always `0` except when `strategy="constant"`
+        in which case `fill_value` will be used instead.
+
+        .. versionadded:: 1.2
+
+    Attributes
+    ----------
+    statistics_ : array of shape (n_features,)
+        The imputation fill value for each feature.
+        Computing statistics can result in `np.nan` values.
+        During :meth:`transform`, features corresponding to `np.nan`
+        statistics will be discarded.
+
+    indicator_ : :class:`~sklearn.impute.MissingIndicator`
+        Indicator used to add binary indicators for missing values.
+        `None` if `add_indicator=False`.
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
+    See Also
+    --------
+    IterativeImputer : Multivariate imputer that estimates values to impute for
+        each feature with missing values from all the others.
+    KNNImputer : Multivariate imputer that estimates missing features using
+        nearest samples.
+
+    Notes
+    -----
+    Columns which only contained missing values at :meth:`fit` are discarded
+    upon :meth:`transform` if strategy is not `"constant"`.
+
+    In a prediction context, simple imputation usually performs poorly when
+    associated with a weak learner. However, with a powerful learner, it can
+    lead to as good or better performance than complex imputation such as
+    :class:`~sklearn.impute.IterativeImputer` or :class:`~sklearn.impute.KNNImputer`.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from sklearn.impute import SimpleImputer
+    >>> imp_mean = SimpleImputer(missing_values=np.nan, strategy='mean')
+    >>> imp_mean.fit([[7, 2, 3], [4, np.nan, 6], [10, 5, 9]])
+    SimpleImputer()
+    >>> X = [[np.nan, 2, 3], [4, np.nan, 6], [10, np.nan, 9]]
+    >>> print(imp_mean.transform(X))
+    [[ 7.   2.   3. ]
+     [ 4.   3.5  6. ]
+     [10.   3.5  9. ]]
+
+    For a more detailed example see
+    :ref:`sphx_glr_auto_examples_impute_plot_missing_values.py`.
+    """
+
+    _parameter_constraints: dict = {
+        **_BaseImputer._parameter_constraints,
+        "strategy": [StrOptions({"mean", "median", "most_frequent", "constant"})],
+        "fill_value": "no_validation",  # any object is valid
+        "copy": ["boolean"],
+    }
+
+    def __init__(
+        self,
+        *,
+        missing_values=np.nan,
+        strategy="mean",
+        fill_value=None,
+        copy=True,
+        add_indicator=False,
+        keep_empty_features=False,
+    ):
+        super().__init__(
+            missing_values=missing_values,
+            add_indicator=add_indicator,
+            keep_empty_features=keep_empty_features,
+        )
+        self.strategy = strategy
+        self.fill_value = fill_value
+        self.copy = copy
+
+    def _validate_input(self, X, in_fit):
+        if self.strategy in ("most_frequent", "constant"):
+            # If input is a list of strings, dtype = object.
+            # Otherwise ValueError is raised in SimpleImputer
+            # with strategy='most_frequent' or 'constant'
+            # because the list is converted to Unicode numpy array
+            if isinstance(X, list) and any(
+                isinstance(elem, str) for row in X for elem in row
+            ):
+                dtype = object
+            else:
+                dtype = None
+        else:
+            dtype = FLOAT_DTYPES
+
+        if not in_fit and self._fit_dtype.kind == "O":
+            # Use object dtype if fitted on object dtypes
+            dtype = self._fit_dtype
+
+        if _is_pandas_na(self.missing_values) or is_scalar_nan(self.missing_values):
+            force_all_finite = "allow-nan"
+        else:
+            force_all_finite = True
+
+        try:
+            X = self._validate_data(
+                X,
+                reset=in_fit,
+                accept_sparse="csc",
+                dtype=dtype,
+                force_all_finite=force_all_finite,
+                copy=self.copy,
+            )
+        except ValueError as ve:
+            if "could not convert" in str(ve):
+                new_ve = ValueError(
+                    "Cannot use {} strategy with non-numeric data:\n{}".format(
+                        self.strategy, ve
+                    )
+                )
+                raise new_ve from None
+            else:
+                raise ve
+
+        if in_fit:
+            # Use the dtype seen in `fit` for non-`fit` conversion
+            self._fit_dtype = X.dtype
+
+        _check_inputs_dtype(X, self.missing_values)
+        if X.dtype.kind not in ("i", "u", "f", "O"):
+            raise ValueError(
+                "SimpleImputer does not support data with dtype "
+                "{0}. Please provide either a numeric array (with"
+                " a floating point or integer dtype) or "
+                "categorical data represented either as an array "
+                "with integer dtype or an array of string values "
+                "with an object dtype.".format(X.dtype)
+            )
+
+        if sp.issparse(X) and self.missing_values == 0:
+            # missing_values = 0 not allowed with sparse data as it would
+            # force densification
+            raise ValueError(
+                "Imputation not possible when missing_values "
+                "== 0 and input is sparse. Provide a dense "
+                "array instead."
+            )
+
+        if self.strategy == "constant":
+            if in_fit and self.fill_value is not None:
+                fill_value_dtype = type(self.fill_value)
+                err_msg = (
+                    f"fill_value={self.fill_value!r} (of type {fill_value_dtype!r}) "
+                    f"cannot be cast to the input data that is {X.dtype!r}. Make sure "
+                    "that both dtypes are of the same kind."
+                )
+            elif not in_fit:
+                fill_value_dtype = self.statistics_.dtype
+                err_msg = (
+                    f"The dtype of the filling value (i.e. {fill_value_dtype!r}) "
+                    f"cannot be cast to the input data that is {X.dtype!r}. Make sure "
+                    "that the dtypes of the input data is of the same kind between "
+                    "fit and transform."
+                )
+            else:
+                # By default, fill_value=None, and the replacement is always
+                # compatible with the input data
+                fill_value_dtype = X.dtype
+
+            # Make sure we can safely cast fill_value dtype to the input data dtype
+            if not np.can_cast(fill_value_dtype, X.dtype, casting="same_kind"):
+                raise ValueError(err_msg)
+
+        return X
+
+    @_fit_context(prefer_skip_nested_validation=True)
+    def fit(self, X, y=None):
+        """Fit the imputer on `X`.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix}, shape (n_samples, n_features)
+            Input data, where `n_samples` is the number of samples and
+            `n_features` is the number of features.
+
+        y : Ignored
+            Not used, present here for API consistency by convention.
+
+        Returns
+        -------
+        self : object
+            Fitted estimator.
+        """
+        X = self._validate_input(X, in_fit=True)
+
+        # default fill_value is 0 for numerical input and "missing_value"
+        # otherwise
+        if self.fill_value is None:
+            if X.dtype.kind in ("i", "u", "f"):
+                fill_value = 0
+            else:
+                fill_value = "missing_value"
+        else:
+            fill_value = self.fill_value
+
+        if sp.issparse(X):
+            self.statistics_ = self._sparse_fit(
+                X, self.strategy, self.missing_values, fill_value
+            )
+        else:
+            self.statistics_ = self._dense_fit(
+                X, self.strategy, self.missing_values, fill_value
+            )
+
+        return self
+
+    def _sparse_fit(self, X, strategy, missing_values, fill_value):
+        """Fit the transformer on sparse data."""
+        missing_mask = _get_mask(X, missing_values)
+        mask_data = missing_mask.data
+        n_implicit_zeros = X.shape[0] - np.diff(X.indptr)
+
+        statistics = np.empty(X.shape[1])
+
+        if strategy == "constant":
+            # for constant strategy, self.statistics_ is used to store
+            # fill_value in each column
+            statistics.fill(fill_value)
+        else:
+            for i in range(X.shape[1]):
+                column = X.data[X.indptr[i] : X.indptr[i + 1]]
+                mask_column = mask_data[X.indptr[i] : X.indptr[i + 1]]
+                column = column[~mask_column]
+
+                # combine explicit and implicit zeros
+                mask_zeros = _get_mask(column, 0)
+                column = column[~mask_zeros]
+                n_explicit_zeros = mask_zeros.sum()
+                n_zeros = n_implicit_zeros[i] + n_explicit_zeros
+
+                if len(column) == 0 and self.keep_empty_features:
+                    # in case we want to keep columns with only missing values.
+                    statistics[i] = 0
+                else:
+                    if strategy == "mean":
+                        s = column.size + n_zeros
+                        statistics[i] = np.nan if s == 0 else column.sum() / s
+
+                    elif strategy == "median":
+                        statistics[i] = _get_median(column, n_zeros)
+
+                    elif strategy == "most_frequent":
+                        statistics[i] = _most_frequent(column, 0, n_zeros)
+
+        super()._fit_indicator(missing_mask)
+
+        return statistics
+
+    def _dense_fit(self, X, strategy, missing_values, fill_value):
+        """Fit the transformer on dense data."""
+        missing_mask = _get_mask(X, missing_values)
+        masked_X = ma.masked_array(X, mask=missing_mask)
+
+        super()._fit_indicator(missing_mask)
+
+        # Mean
+        if strategy == "mean":
+            mean_masked = np.ma.mean(masked_X, axis=0)
+            # Avoid the warning "Warning: converting a masked element to nan."
+            mean = np.ma.getdata(mean_masked)
+            mean[np.ma.getmask(mean_masked)] = 0 if self.keep_empty_features else np.nan
+
+            return mean
+
+        # Median
+        elif strategy == "median":
+            median_masked = np.ma.median(masked_X, axis=0)
+            # Avoid the warning "Warning: converting a masked element to nan."
+            median = np.ma.getdata(median_masked)
+            median[np.ma.getmaskarray(median_masked)] = (
+                0 if self.keep_empty_features else np.nan
+            )
+
+            return median
+
+        # Most frequent
+        elif strategy == "most_frequent":
+            # Avoid use of scipy.stats.mstats.mode due to the required
+            # additional overhead and slow benchmarking performance.
+            # See Issue 14325 and PR 14399 for full discussion.
+
+            # To be able access the elements by columns
+            X = X.transpose()
+            mask = missing_mask.transpose()
+
+            if X.dtype.kind == "O":
+                most_frequent = np.empty(X.shape[0], dtype=object)
+            else:
+                most_frequent = np.empty(X.shape[0])
+
+            for i, (row, row_mask) in enumerate(zip(X[:], mask[:])):
+                row_mask = np.logical_not(row_mask).astype(bool)
+                row = row[row_mask]
+                if len(row) == 0 and self.keep_empty_features:
+                    most_frequent[i] = 0
+                else:
+                    most_frequent[i] = _most_frequent(row, np.nan, 0)
+
+            return most_frequent
+
+        # Constant
+        elif strategy == "constant":
+            # for constant strategy, self.statistcs_ is used to store
+            # fill_value in each column
+            return np.full(X.shape[1], fill_value, dtype=X.dtype)
+
+    def transform(self, X):
+        """Impute all missing values in `X`.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix}, shape (n_samples, n_features)
+            The input data to complete.
+
+        Returns
+        -------
+        X_imputed : {ndarray, sparse matrix} of shape \
+                (n_samples, n_features_out)
+            `X` with imputed values.
+        """
+        check_is_fitted(self)
+
+        X = self._validate_input(X, in_fit=False)
+        statistics = self.statistics_
+
+        if X.shape[1] != statistics.shape[0]:
+            raise ValueError(
+                "X has %d features per sample, expected %d"
+                % (X.shape[1], self.statistics_.shape[0])
+            )
+
+        # compute mask before eliminating invalid features
+        missing_mask = _get_mask(X, self.missing_values)
+
+        # Decide whether to keep missing features
+        if self.strategy == "constant" or self.keep_empty_features:
+            valid_statistics = statistics
+            valid_statistics_indexes = None
+        else:
+            # same as np.isnan but also works for object dtypes
+            invalid_mask = _get_mask(statistics, np.nan)
+            valid_mask = np.logical_not(invalid_mask)
+            valid_statistics = statistics[valid_mask]
+            valid_statistics_indexes = np.flatnonzero(valid_mask)
+
+            if invalid_mask.any():
+                invalid_features = np.arange(X.shape[1])[invalid_mask]
+                # use feature names warning if features are provided
+                if hasattr(self, "feature_names_in_"):
+                    invalid_features = self.feature_names_in_[invalid_features]
+                warnings.warn(
+                    "Skipping features without any observed values:"
+                    f" {invalid_features}. At least one non-missing value is needed"
+                    f" for imputation with strategy='{self.strategy}'."
+                )
+                X = X[:, valid_statistics_indexes]
+
+        # Do actual imputation
+        if sp.issparse(X):
+            if self.missing_values == 0:
+                raise ValueError(
+                    "Imputation not possible when missing_values "
+                    "== 0 and input is sparse. Provide a dense "
+                    "array instead."
+                )
+            else:
+                # if no invalid statistics are found, use the mask computed
+                # before, else recompute mask
+                if valid_statistics_indexes is None:
+                    mask = missing_mask.data
+                else:
+                    mask = _get_mask(X.data, self.missing_values)
+                indexes = np.repeat(
+                    np.arange(len(X.indptr) - 1, dtype=int), np.diff(X.indptr)
+                )[mask]
+
+                X.data[mask] = valid_statistics[indexes].astype(X.dtype, copy=False)
+        else:
+            # use mask computed before eliminating invalid mask
+            if valid_statistics_indexes is None:
+                mask_valid_features = missing_mask
+            else:
+                mask_valid_features = missing_mask[:, valid_statistics_indexes]
+            n_missing = np.sum(mask_valid_features, axis=0)
+            values = np.repeat(valid_statistics, n_missing)
+            coordinates = np.where(mask_valid_features.transpose())[::-1]
+
+            X[coordinates] = values
+
+        X_indicator = super()._transform_indicator(missing_mask)
+
+        return super()._concatenate_indicator(X, X_indicator)
+
+    def inverse_transform(self, X):
+        """Convert the data back to the original representation.
+
+        Inverts the `transform` operation performed on an array.
+        This operation can only be performed after :class:`SimpleImputer` is
+        instantiated with `add_indicator=True`.
+
+        Note that `inverse_transform` can only invert the transform in
+        features that have binary indicators for missing values. If a feature
+        has no missing values at `fit` time, the feature won't have a binary
+        indicator, and the imputation done at `transform` time won't be
+        inverted.
+
+        .. versionadded:: 0.24
+
+        Parameters
+        ----------
+        X : array-like of shape \
+                (n_samples, n_features + n_features_missing_indicator)
+            The imputed data to be reverted to original data. It has to be
+            an augmented array of imputed data and the missing indicator mask.
+
+        Returns
+        -------
+        X_original : ndarray of shape (n_samples, n_features)
+            The original `X` with missing values as it was prior
+            to imputation.
+        """
+        check_is_fitted(self)
+
+        if not self.add_indicator:
+            raise ValueError(
+                "'inverse_transform' works only when "
+                "'SimpleImputer' is instantiated with "
+                "'add_indicator=True'. "
+                f"Got 'add_indicator={self.add_indicator}' "
+                "instead."
+            )
+
+        n_features_missing = len(self.indicator_.features_)
+        non_empty_feature_count = X.shape[1] - n_features_missing
+        array_imputed = X[:, :non_empty_feature_count].copy()
+        missing_mask = X[:, non_empty_feature_count:].astype(bool)
+
+        n_features_original = len(self.statistics_)
+        shape_original = (X.shape[0], n_features_original)
+        X_original = np.zeros(shape_original)
+        X_original[:, self.indicator_.features_] = missing_mask
+        full_mask = X_original.astype(bool)
+
+        imputed_idx, original_idx = 0, 0
+        while imputed_idx < len(array_imputed.T):
+            if not np.all(X_original[:, original_idx]):
+                X_original[:, original_idx] = array_imputed.T[imputed_idx]
+                imputed_idx += 1
+                original_idx += 1
+            else:
+                original_idx += 1
+
+        X_original[full_mask] = self.missing_values
+        return X_original
+
+    def _more_tags(self):
+        return {
+            "allow_nan": _is_pandas_na(self.missing_values) or is_scalar_nan(
+                self.missing_values
+            )
+        }
+
+    def get_feature_names_out(self, input_features=None):
+        """Get output feature names for transformation.
+
+        Parameters
+        ----------
+        input_features : array-like of str or None, default=None
+            Input features.
+
+            - If `input_features` is `None`, then `feature_names_in_` is
+              used as feature names in. If `feature_names_in_` is not defined,
+              then the following input feature names are generated:
+              `["x0", "x1", ..., "x(n_features_in_ - 1)"]`.
+            - If `input_features` is an array-like, then `input_features` must
+              match `feature_names_in_` if `feature_names_in_` is defined.
+
+        Returns
+        -------
+        feature_names_out : ndarray of str objects
+            Transformed feature names.
+        """
+        check_is_fitted(self, "n_features_in_")
+        input_features = _check_feature_names_in(self, input_features)
+        non_missing_mask = np.logical_not(_get_mask(self.statistics_, np.nan))
+        names = input_features[non_missing_mask]
+        return self._concatenate_indicator_feature_names_out(names, input_features)
+
+
+class MissingIndicator(TransformerMixin, BaseEstimator):
+    """Binary indicators for missing values.
+
+    Note that this component typically should not be used in a vanilla
+    :class:`~sklearn.pipeline.Pipeline` consisting of transformers and a
+    classifier, but rather could be added using a
+    :class:`~sklearn.pipeline.FeatureUnion` or
+    :class:`~sklearn.compose.ColumnTransformer`.
+
+    Read more in the :ref:`User Guide <impute>`.
+
+    .. versionadded:: 0.20
+
+    Parameters
+    ----------
+    missing_values : int, float, str, np.nan or None, default=np.nan
+        The placeholder for the missing values. All occurrences of
+        `missing_values` will be imputed. For pandas' dataframes with
+        nullable integer dtypes with missing values, `missing_values`
+        should be set to `np.nan`, since `pd.NA` will be converted to `np.nan`.
+
+    features : {'missing-only', 'all'}, default='missing-only'
+        Whether the imputer mask should represent all or a subset of
+        features.
+
+        - If `'missing-only'` (default), the imputer mask will only represent
+          features containing missing values during fit time.
+        - If `'all'`, the imputer mask will represent all features.
+
+    sparse : bool or 'auto', default='auto'
+        Whether the imputer mask format should be sparse or dense.
+
+        - If `'auto'` (default), the imputer mask will be of same type as
+          input.
+        - If `True`, the imputer mask will be a sparse matrix.
+        - If `False`, the imputer mask will be a numpy array.
+
+    error_on_new : bool, default=True
+        If `True`, :meth:`transform` will raise an error when there are
+        features with missing values that have no missing values in
+        :meth:`fit`. This is applicable only when `features='missing-only'`.
+
+    Attributes
+    ----------
+    features_ : ndarray of shape (n_missing_features,) or (n_features,)
+        The features indices which will be returned when calling
+        :meth:`transform`. They are computed during :meth:`fit`. If
+        `features='all'`, `features_` is equal to `range(n_features)`.
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
+    See Also
+    --------
+    SimpleImputer : Univariate imputation of missing values.
+    IterativeImputer : Multivariate imputation of missing values.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from sklearn.impute import MissingIndicator
+    >>> X1 = np.array([[np.nan, 1, 3],
+    ...                [4, 0, np.nan],
+    ...                [8, 1, 0]])
+    >>> X2 = np.array([[5, 1, np.nan],
+    ...                [np.nan, 2, 3],
+    ...                [2, 4, 0]])
+    >>> indicator = MissingIndicator()
+    >>> indicator.fit(X1)
+    MissingIndicator()
+    >>> X2_tr = indicator.transform(X2)
+    >>> X2_tr
+    array([[False,  True],
+           [ True, False],
+           [False, False]])
+    """
+
+    _parameter_constraints: dict = {
+        "missing_values": [MissingValues()],
+        "features": [StrOptions({"missing-only", "all"})],
+        "sparse": ["boolean", StrOptions({"auto"})],
+        "error_on_new": ["boolean"],
+    }
+
+    def __init__(
+        self,
+        *,
+        missing_values=np.nan,
+        features="missing-only",
+        sparse="auto",
+        error_on_new=True,
+    ):
+        self.missing_values = missing_values
+        self.features = features
+        self.sparse = sparse
+        self.error_on_new = error_on_new
+
+    def _get_missing_features_info(self, X):
+        """Compute the imputer mask and the indices of the features
+        containing missing values.
+
+        Parameters
+        ----------
+        X : {ndarray, sparse matrix} of shape (n_samples, n_features)
+            The input data with missing values. Note that `X` has been
+            checked in :meth:`fit` and :meth:`transform` before to call this
+            function.
+
+        Returns
+        -------
+        imputer_mask : {ndarray, sparse matrix} of shape \
+        (n_samples, n_features)
+            The imputer mask of the original data.
+
+        features_with_missing : ndarray of shape (n_features_with_missing)
+            The features containing missing values.
+        """
+        if not self._precomputed:
+            imputer_mask = _get_mask(X, self.missing_values)
+        else:
+            imputer_mask = X
+
+        if sp.issparse(X):
+            imputer_mask.eliminate_zeros()
+
+            if self.features == "missing-only":
+                n_missing = imputer_mask.getnnz(axis=0)
+
+            if self.sparse is False:
+                imputer_mask = imputer_mask.toarray()
+            elif imputer_mask.format == "csr":
+                imputer_mask = imputer_mask.tocsc()
+        else:
+            if not self._precomputed:
+                imputer_mask = _get_mask(X, self.missing_values)
+            else:
+                imputer_mask = X
+
+            if self.features == "missing-only":
+                n_missing = imputer_mask.sum(axis=0)
+
+            if self.sparse is True:
+                imputer_mask = sp.csc_matrix(imputer_mask)
+
+        if self.features == "all":
+            features_indices = np.arange(X.shape[1])
+        else:
+            features_indices = np.flatnonzero(n_missing)
+
+        return imputer_mask, features_indices
+
+    def _validate_input(self, X, in_fit):
+        if not is_scalar_nan(self.missing_values):
+            force_all_finite = True
+        else:
+            force_all_finite = "allow-nan"
+        X = self._validate_data(
+            X,
+            reset=in_fit,
+            accept_sparse=("csc", "csr"),
+            dtype=None,
+            force_all_finite=force_all_finite,
+        )
+        _check_inputs_dtype(X, self.missing_values)
+        if X.dtype.kind not in ("i", "u", "f", "O"):
+            raise ValueError(
+                "MissingIndicator does not support data with "
+                "dtype {0}. Please provide either a numeric array"
+                " (with a floating point or integer dtype) or "
+                "categorical data represented either as an array "
+                "with integer dtype or an array of string values "
+                "with an object dtype.".format(X.dtype)
+            )
+
+        if sp.issparse(X) and self.missing_values == 0:
+            # missing_values = 0 not allowed with sparse data as it would
+            # force densification
+            raise ValueError(
+                "Sparse input with missing_values=0 is "
+                "not supported. Provide a dense "
+                "array instead."
+            )
+
+        return X
+
+    def _fit(self, X, y=None, precomputed=False):
+        """Fit the transformer on `X`.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            Input data, where `n_samples` is the number of samples and
+            `n_features` is the number of features.
+            If `precomputed=True`, then `X` is a mask of the input data.
+
+        precomputed : bool
+            Whether the input data is a mask.
+
+        Returns
+        -------
+        imputer_mask : {ndarray, sparse matrix} of shape (n_samples, \
+        n_features)
+            The imputer mask of the original data.
+        """
+        if precomputed:
+            if not (hasattr(X, "dtype") and X.dtype.kind == "b"):
+                raise ValueError("precomputed is True but the input data is not a mask")
+            self._precomputed = True
+        else:
+            self._precomputed = False
+
+        # Need not validate X again as it would have already been validated
+        # in the Imputer calling MissingIndicator
+        if not self._precomputed:
+            X = self._validate_input(X, in_fit=True)
+        else:
+            # only create `n_features_in_` in the precomputed case
+            self._check_n_features(X, reset=True)
+
+        self._n_features = X.shape[1]
+
+        missing_features_info = self._get_missing_features_info(X)
+        self.features_ = missing_features_info[1]
+
+        return missing_features_info[0]
+
+    @_fit_context(prefer_skip_nested_validation=True)
+    def fit(self, X, y=None):
+        """Fit the transformer on `X`.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            Input data, where `n_samples` is the number of samples and
+            `n_features` is the number of features.
+
+        y : Ignored
+            Not used, present for API consistency by convention.
+
+        Returns
+        -------
+        self : object
+            Fitted estimator.
+        """
+        self._fit(X, y)
+
+        return self
+
+    def transform(self, X):
+        """Generate missing values indicator for `X`.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            The input data to complete.
+
+        Returns
+        -------
+        Xt : {ndarray, sparse matrix} of shape (n_samples, n_features) \
+        or (n_samples, n_features_with_missing)
+            The missing indicator for input data. The data type of `Xt`
+            will be boolean.
+        """
+        check_is_fitted(self)
+
+        # Need not validate X again as it would have already been validated
+        # in the Imputer calling MissingIndicator
+        if not self._precomputed:
+            X = self._validate_input(X, in_fit=False)
+        else:
+            if not (hasattr(X, "dtype") and X.dtype.kind == "b"):
+                raise ValueError("precomputed is True but the input data is not a mask")
+
+        imputer_mask, features = self._get_missing_features_info(X)
+
+        if self.features == "missing-only":
+            features_diff_fit_trans = np.setdiff1d(features, self.features_)
+            if self.error_on_new and features_diff_fit_trans.size > 0:
+                raise ValueError(
+                    "The features {} have missing values "
+                    "in transform but have no missing values "
+                    "in fit.".format(features_diff_fit_trans)
+                )
+
+            if self.features_.size < self._n_features:
+                imputer_mask = imputer_mask[:, self.features_]
+
+        return imputer_mask
+
+    @_fit_context(prefer_skip_nested_validation=True)
+    def fit_transform(self, X, y=None):
+        """Generate missing values indicator for `X`.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            The input data to complete.
+
+        y : Ignored
+            Not used, present for API consistency by convention.
+
+        Returns
+        -------
+        Xt : {ndarray, sparse matrix} of shape (n_samples, n_features) \
+        or (n_samples, n_features_with_missing)
+            The missing indicator for input data. The data type of `Xt`
+            will be boolean.
+        """
+        imputer_mask = self._fit(X, y)
+
+        if self.features_.size < self._n_features:
+            imputer_mask = imputer_mask[:, self.features_]
+
+        return imputer_mask
+
+    def get_feature_names_out(self, input_features=None):
+        """Get output feature names for transformation.
+
+        Parameters
+        ----------
+        input_features : array-like of str or None, default=None
+            Input features.
+
+            - If `input_features` is `None`, then `feature_names_in_` is
+              used as feature names in. If `feature_names_in_` is not defined,
+              then the following input feature names are generated:
+              `["x0", "x1", ..., "x(n_features_in_ - 1)"]`.
+            - If `input_features` is an array-like, then `input_features` must
+              match `feature_names_in_` if `feature_names_in_` is defined.
+
+        Returns
+        -------
+        feature_names_out : ndarray of str objects
+            Transformed feature names.
+        """
+        check_is_fitted(self, "n_features_in_")
+        input_features = _check_feature_names_in(self, input_features)
+        prefix = self.__class__.__name__.lower()
+        return np.asarray(
+            [
+                f"{prefix}_{feature_name}"
+                for feature_name in input_features[self.features_]
+            ],
+            dtype=object,
+        )
+
+    def _more_tags(self):
+        return {
+            "allow_nan": True,
+            "X_types": ["2darray", "string"],
+            "preserves_dtype": [],
+        }

env-llmeval/lib/python3.10/site-packages/sklearn/impute/_iterative.py

@@ -0,0 +1,906 @@
+import warnings
+from collections import namedtuple
+from numbers import Integral, Real
+from time import time
+
+import numpy as np
+from scipy import stats
+
+from ..base import _fit_context, clone
+from ..exceptions import ConvergenceWarning
+from ..preprocessing import normalize
+from ..utils import (
+    _safe_assign,
+    _safe_indexing,
+    check_array,
+    check_random_state,
+    is_scalar_nan,
+)
+from ..utils._mask import _get_mask
+from ..utils._param_validation import HasMethods, Interval, StrOptions
+from ..utils.metadata_routing import _RoutingNotSupportedMixin
+from ..utils.validation import FLOAT_DTYPES, _check_feature_names_in, check_is_fitted
+from ._base import SimpleImputer, _BaseImputer, _check_inputs_dtype
+
+_ImputerTriplet = namedtuple(
+    "_ImputerTriplet", ["feat_idx", "neighbor_feat_idx", "estimator"]
+)
+
+
+def _assign_where(X1, X2, cond):
+    """Assign X2 to X1 where cond is True.
+
+    Parameters
+    ----------
+    X1 : ndarray or dataframe of shape (n_samples, n_features)
+        Data.
+
+    X2 : ndarray of shape (n_samples, n_features)
+        Data to be assigned.
+
+    cond : ndarray of shape (n_samples, n_features)
+        Boolean mask to assign data.
+    """
+    if hasattr(X1, "mask"):  # pandas dataframes
+        X1.mask(cond=cond, other=X2, inplace=True)
+    else:  # ndarrays
+        X1[cond] = X2[cond]
+
+
+class IterativeImputer(_RoutingNotSupportedMixin, _BaseImputer):
+    """Multivariate imputer that estimates each feature from all the others.
+
+    A strategy for imputing missing values by modeling each feature with
+    missing values as a function of other features in a round-robin fashion.
+
+    Read more in the :ref:`User Guide <iterative_imputer>`.
+
+    .. versionadded:: 0.21
+
+    .. note::
+
+      This estimator is still **experimental** for now: the predictions
+      and the API might change without any deprecation cycle. To use it,
+      you need to explicitly import `enable_iterative_imputer`::
+
+        >>> # explicitly require this experimental feature
+        >>> from sklearn.experimental import enable_iterative_imputer  # noqa
+        >>> # now you can import normally from sklearn.impute
+        >>> from sklearn.impute import IterativeImputer
+
+    Parameters
+    ----------
+    estimator : estimator object, default=BayesianRidge()
+        The estimator to use at each step of the round-robin imputation.
+        If `sample_posterior=True`, the estimator must support
+        `return_std` in its `predict` method.
+
+    missing_values : int or np.nan, default=np.nan
+        The placeholder for the missing values. All occurrences of
+        `missing_values` will be imputed. For pandas' dataframes with
+        nullable integer dtypes with missing values, `missing_values`
+        should be set to `np.nan`, since `pd.NA` will be converted to `np.nan`.
+
+    sample_posterior : bool, default=False
+        Whether to sample from the (Gaussian) predictive posterior of the
+        fitted estimator for each imputation. Estimator must support
+        `return_std` in its `predict` method if set to `True`. Set to
+        `True` if using `IterativeImputer` for multiple imputations.
+
+    max_iter : int, default=10
+        Maximum number of imputation rounds to perform before returning the
+        imputations computed during the final round. A round is a single
+        imputation of each feature with missing values. The stopping criterion
+        is met once `max(abs(X_t - X_{t-1}))/max(abs(X[known_vals])) < tol`,
+        where `X_t` is `X` at iteration `t`. Note that early stopping is only
+        applied if `sample_posterior=False`.
+
+    tol : float, default=1e-3
+        Tolerance of the stopping condition.
+
+    n_nearest_features : int, default=None
+        Number of other features to use to estimate the missing values of
+        each feature column. Nearness between features is measured using
+        the absolute correlation coefficient between each feature pair (after
+        initial imputation). To ensure coverage of features throughout the
+        imputation process, the neighbor features are not necessarily nearest,
+        but are drawn with probability proportional to correlation for each
+        imputed target feature. Can provide significant speed-up when the
+        number of features is huge. If `None`, all features will be used.
+
+    initial_strategy : {'mean', 'median', 'most_frequent', 'constant'}, \
+            default='mean'
+        Which strategy to use to initialize the missing values. Same as the
+        `strategy` parameter in :class:`~sklearn.impute.SimpleImputer`.
+
+    fill_value : str or numerical value, default=None
+        When `strategy="constant"`, `fill_value` is used to replace all
+        occurrences of missing_values. For string or object data types,
+        `fill_value` must be a string.
+        If `None`, `fill_value` will be 0 when imputing numerical
+        data and "missing_value" for strings or object data types.
+
+        .. versionadded:: 1.3
+
+    imputation_order : {'ascending', 'descending', 'roman', 'arabic', \
+            'random'}, default='ascending'
+        The order in which the features will be imputed. Possible values:
+
+        - `'ascending'`: From features with fewest missing values to most.
+        - `'descending'`: From features with most missing values to fewest.
+        - `'roman'`: Left to right.
+        - `'arabic'`: Right to left.
+        - `'random'`: A random order for each round.
+
+    skip_complete : bool, default=False
+        If `True` then features with missing values during :meth:`transform`
+        which did not have any missing values during :meth:`fit` will be
+        imputed with the initial imputation method only. Set to `True` if you
+        have many features with no missing values at both :meth:`fit` and
+        :meth:`transform` time to save compute.
+
+    min_value : float or array-like of shape (n_features,), default=-np.inf
+        Minimum possible imputed value. Broadcast to shape `(n_features,)` if
+        scalar. If array-like, expects shape `(n_features,)`, one min value for
+        each feature. The default is `-np.inf`.
+
+        .. versionchanged:: 0.23
+           Added support for array-like.
+
+    max_value : float or array-like of shape (n_features,), default=np.inf
+        Maximum possible imputed value. Broadcast to shape `(n_features,)` if
+        scalar. If array-like, expects shape `(n_features,)`, one max value for
+        each feature. The default is `np.inf`.
+
+        .. versionchanged:: 0.23
+           Added support for array-like.
+
+    verbose : int, default=0
+        Verbosity flag, controls the debug messages that are issued
+        as functions are evaluated. The higher, the more verbose. Can be 0, 1,
+        or 2.
+
+    random_state : int, RandomState instance or None, default=None
+        The seed of the pseudo random number generator to use. Randomizes
+        selection of estimator features if `n_nearest_features` is not `None`,
+        the `imputation_order` if `random`, and the sampling from posterior if
+        `sample_posterior=True`. Use an integer for determinism.
+        See :term:`the Glossary <random_state>`.
+
+    add_indicator : bool, default=False
+        If `True`, a :class:`MissingIndicator` transform will stack onto output
+        of the imputer's transform. This allows a predictive estimator
+        to account for missingness despite imputation. If a feature has no
+        missing values at fit/train time, the feature won't appear on
+        the missing indicator even if there are missing values at
+        transform/test time.
+
+    keep_empty_features : bool, default=False
+        If True, features that consist exclusively of missing values when
+        `fit` is called are returned in results when `transform` is called.
+        The imputed value is always `0` except when
+        `initial_strategy="constant"` in which case `fill_value` will be
+        used instead.
+
+        .. versionadded:: 1.2
+
+    Attributes
+    ----------
+    initial_imputer_ : object of type :class:`~sklearn.impute.SimpleImputer`
+        Imputer used to initialize the missing values.
+
+    imputation_sequence_ : list of tuples
+        Each tuple has `(feat_idx, neighbor_feat_idx, estimator)`, where
+        `feat_idx` is the current feature to be imputed,
+        `neighbor_feat_idx` is the array of other features used to impute the
+        current feature, and `estimator` is the trained estimator used for
+        the imputation. Length is `self.n_features_with_missing_ *
+        self.n_iter_`.
+
+    n_iter_ : int
+        Number of iteration rounds that occurred. Will be less than
+        `self.max_iter` if early stopping criterion was reached.
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
+    n_features_with_missing_ : int
+        Number of features with missing values.
+
+    indicator_ : :class:`~sklearn.impute.MissingIndicator`
+        Indicator used to add binary indicators for missing values.
+        `None` if `add_indicator=False`.
+
+    random_state_ : RandomState instance
+        RandomState instance that is generated either from a seed, the random
+        number generator or by `np.random`.
+
+    See Also
+    --------
+    SimpleImputer : Univariate imputer for completing missing values
+        with simple strategies.
+    KNNImputer : Multivariate imputer that estimates missing features using
+        nearest samples.
+
+    Notes
+    -----
+    To support imputation in inductive mode we store each feature's estimator
+    during the :meth:`fit` phase, and predict without refitting (in order)
+    during the :meth:`transform` phase.
+
+    Features which contain all missing values at :meth:`fit` are discarded upon
+    :meth:`transform`.
+
+    Using defaults, the imputer scales in :math:`\\mathcal{O}(knp^3\\min(n,p))`
+    where :math:`k` = `max_iter`, :math:`n` the number of samples and
+    :math:`p` the number of features. It thus becomes prohibitively costly when
+    the number of features increases. Setting
+    `n_nearest_features << n_features`, `skip_complete=True` or increasing `tol`
+    can help to reduce its computational cost.
+
+    Depending on the nature of missing values, simple imputers can be
+    preferable in a prediction context.
+
+    References
+    ----------
+    .. [1] `Stef van Buuren, Karin Groothuis-Oudshoorn (2011). "mice:
+        Multivariate Imputation by Chained Equations in R". Journal of
+        Statistical Software 45: 1-67.
+        <https://www.jstatsoft.org/article/view/v045i03>`_
+
+    .. [2] `S. F. Buck, (1960). "A Method of Estimation of Missing Values in
+        Multivariate Data Suitable for use with an Electronic Computer".
+        Journal of the Royal Statistical Society 22(2): 302-306.
+        <https://www.jstor.org/stable/2984099>`_
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from sklearn.experimental import enable_iterative_imputer
+    >>> from sklearn.impute import IterativeImputer
+    >>> imp_mean = IterativeImputer(random_state=0)
+    >>> imp_mean.fit([[7, 2, 3], [4, np.nan, 6], [10, 5, 9]])
+    IterativeImputer(random_state=0)
+    >>> X = [[np.nan, 2, 3], [4, np.nan, 6], [10, np.nan, 9]]
+    >>> imp_mean.transform(X)
+    array([[ 6.9584...,  2.       ,  3.        ],
+           [ 4.       ,  2.6000...,  6.        ],
+           [10.       ,  4.9999...,  9.        ]])
+
+    For a more detailed example see
+    :ref:`sphx_glr_auto_examples_impute_plot_missing_values.py` or
+    :ref:`sphx_glr_auto_examples_impute_plot_iterative_imputer_variants_comparison.py`.
+    """
+
+    _parameter_constraints: dict = {
+        **_BaseImputer._parameter_constraints,
+        "estimator": [None, HasMethods(["fit", "predict"])],
+        "sample_posterior": ["boolean"],
+        "max_iter": [Interval(Integral, 0, None, closed="left")],
+        "tol": [Interval(Real, 0, None, closed="left")],
+        "n_nearest_features": [None, Interval(Integral, 1, None, closed="left")],
+        "initial_strategy": [
+            StrOptions({"mean", "median", "most_frequent", "constant"})
+        ],
+        "fill_value": "no_validation",  # any object is valid
+        "imputation_order": [
+            StrOptions({"ascending", "descending", "roman", "arabic", "random"})
+        ],
+        "skip_complete": ["boolean"],
+        "min_value": [None, Interval(Real, None, None, closed="both"), "array-like"],
+        "max_value": [None, Interval(Real, None, None, closed="both"), "array-like"],
+        "verbose": ["verbose"],
+        "random_state": ["random_state"],
+    }
+
+    def __init__(
+        self,
+        estimator=None,
+        *,
+        missing_values=np.nan,
+        sample_posterior=False,
+        max_iter=10,
+        tol=1e-3,
+        n_nearest_features=None,
+        initial_strategy="mean",
+        fill_value=None,
+        imputation_order="ascending",
+        skip_complete=False,
+        min_value=-np.inf,
+        max_value=np.inf,
+        verbose=0,
+        random_state=None,
+        add_indicator=False,
+        keep_empty_features=False,
+    ):
+        super().__init__(
+            missing_values=missing_values,
+            add_indicator=add_indicator,
+            keep_empty_features=keep_empty_features,
+        )
+
+        self.estimator = estimator
+        self.sample_posterior = sample_posterior
+        self.max_iter = max_iter
+        self.tol = tol
+        self.n_nearest_features = n_nearest_features
+        self.initial_strategy = initial_strategy
+        self.fill_value = fill_value
+        self.imputation_order = imputation_order
+        self.skip_complete = skip_complete
+        self.min_value = min_value
+        self.max_value = max_value
+        self.verbose = verbose
+        self.random_state = random_state
+
+    def _impute_one_feature(
+        self,
+        X_filled,
+        mask_missing_values,
+        feat_idx,
+        neighbor_feat_idx,
+        estimator=None,
+        fit_mode=True,
+    ):
+        """Impute a single feature from the others provided.
+
+        This function predicts the missing values of one of the features using
+        the current estimates of all the other features. The `estimator` must
+        support `return_std=True` in its `predict` method for this function
+        to work.
+
+        Parameters
+        ----------
+        X_filled : ndarray
+            Input data with the most recent imputations.
+
+        mask_missing_values : ndarray
+            Input data's missing indicator matrix.
+
+        feat_idx : int
+            Index of the feature currently being imputed.
+
+        neighbor_feat_idx : ndarray
+            Indices of the features to be used in imputing `feat_idx`.
+
+        estimator : object
+            The estimator to use at this step of the round-robin imputation.
+            If `sample_posterior=True`, the estimator must support
+            `return_std` in its `predict` method.
+            If None, it will be cloned from self._estimator.
+
+        fit_mode : boolean, default=True
+            Whether to fit and predict with the estimator or just predict.
+
+        Returns
+        -------
+        X_filled : ndarray
+            Input data with `X_filled[missing_row_mask, feat_idx]` updated.
+
+        estimator : estimator with sklearn API
+            The fitted estimator used to impute
+            `X_filled[missing_row_mask, feat_idx]`.
+        """
+        if estimator is None and fit_mode is False:
+            raise ValueError(
+                "If fit_mode is False, then an already-fitted "
+                "estimator should be passed in."
+            )
+
+        if estimator is None:
+            estimator = clone(self._estimator)
+
+        missing_row_mask = mask_missing_values[:, feat_idx]
+        if fit_mode:
+            X_train = _safe_indexing(
+                _safe_indexing(X_filled, neighbor_feat_idx, axis=1),
+                ~missing_row_mask,
+                axis=0,
+            )
+            y_train = _safe_indexing(
+                _safe_indexing(X_filled, feat_idx, axis=1),
+                ~missing_row_mask,
+                axis=0,
+            )
+            estimator.fit(X_train, y_train)
+
+        # if no missing values, don't predict
+        if np.sum(missing_row_mask) == 0:
+            return X_filled, estimator
+
+        # get posterior samples if there is at least one missing value
+        X_test = _safe_indexing(
+            _safe_indexing(X_filled, neighbor_feat_idx, axis=1),
+            missing_row_mask,
+            axis=0,
+        )
+        if self.sample_posterior:
+            mus, sigmas = estimator.predict(X_test, return_std=True)
+            imputed_values = np.zeros(mus.shape, dtype=X_filled.dtype)
+            # two types of problems: (1) non-positive sigmas
+            # (2) mus outside legal range of min_value and max_value
+            # (results in inf sample)
+            positive_sigmas = sigmas > 0
+            imputed_values[~positive_sigmas] = mus[~positive_sigmas]
+            mus_too_low = mus < self._min_value[feat_idx]
+            imputed_values[mus_too_low] = self._min_value[feat_idx]
+            mus_too_high = mus > self._max_value[feat_idx]
+            imputed_values[mus_too_high] = self._max_value[feat_idx]
+            # the rest can be sampled without statistical issues
+            inrange_mask = positive_sigmas & ~mus_too_low & ~mus_too_high
+            mus = mus[inrange_mask]
+            sigmas = sigmas[inrange_mask]
+            a = (self._min_value[feat_idx] - mus) / sigmas
+            b = (self._max_value[feat_idx] - mus) / sigmas
+
+            truncated_normal = stats.truncnorm(a=a, b=b, loc=mus, scale=sigmas)
+            imputed_values[inrange_mask] = truncated_normal.rvs(
+                random_state=self.random_state_
+            )
+        else:
+            imputed_values = estimator.predict(X_test)
+            imputed_values = np.clip(
+                imputed_values, self._min_value[feat_idx], self._max_value[feat_idx]
+            )
+
+        # update the feature
+        _safe_assign(
+            X_filled,
+            imputed_values,
+            row_indexer=missing_row_mask,
+            column_indexer=feat_idx,
+        )
+        return X_filled, estimator
+
+    def _get_neighbor_feat_idx(self, n_features, feat_idx, abs_corr_mat):
+        """Get a list of other features to predict `feat_idx`.
+
+        If `self.n_nearest_features` is less than or equal to the total
+        number of features, then use a probability proportional to the absolute
+        correlation between `feat_idx` and each other feature to randomly
+        choose a subsample of the other features (without replacement).
+
+        Parameters
+        ----------
+        n_features : int
+            Number of features in `X`.
+
+        feat_idx : int
+            Index of the feature currently being imputed.
+
+        abs_corr_mat : ndarray, shape (n_features, n_features)
+            Absolute correlation matrix of `X`. The diagonal has been zeroed
+            out and each feature has been normalized to sum to 1. Can be None.
+
+        Returns
+        -------
+        neighbor_feat_idx : array-like
+            The features to use to impute `feat_idx`.
+        """
+        if self.n_nearest_features is not None and self.n_nearest_features < n_features:
+            p = abs_corr_mat[:, feat_idx]
+            neighbor_feat_idx = self.random_state_.choice(
+                np.arange(n_features), self.n_nearest_features, replace=False, p=p
+            )
+        else:
+            inds_left = np.arange(feat_idx)
+            inds_right = np.arange(feat_idx + 1, n_features)
+            neighbor_feat_idx = np.concatenate((inds_left, inds_right))
+        return neighbor_feat_idx
+
+    def _get_ordered_idx(self, mask_missing_values):
+        """Decide in what order we will update the features.
+
+        As a homage to the MICE R package, we will have 4 main options of
+        how to order the updates, and use a random order if anything else
+        is specified.
+
+        Also, this function skips features which have no missing values.
+
+        Parameters
+        ----------
+        mask_missing_values : array-like, shape (n_samples, n_features)
+            Input data's missing indicator matrix, where `n_samples` is the
+            number of samples and `n_features` is the number of features.
+
+        Returns
+        -------
+        ordered_idx : ndarray, shape (n_features,)
+            The order in which to impute the features.
+        """
+        frac_of_missing_values = mask_missing_values.mean(axis=0)
+        if self.skip_complete:
+            missing_values_idx = np.flatnonzero(frac_of_missing_values)
+        else:
+            missing_values_idx = np.arange(np.shape(frac_of_missing_values)[0])
+        if self.imputation_order == "roman":
+            ordered_idx = missing_values_idx
+        elif self.imputation_order == "arabic":
+            ordered_idx = missing_values_idx[::-1]
+        elif self.imputation_order == "ascending":
+            n = len(frac_of_missing_values) - len(missing_values_idx)
+            ordered_idx = np.argsort(frac_of_missing_values, kind="mergesort")[n:]
+        elif self.imputation_order == "descending":
+            n = len(frac_of_missing_values) - len(missing_values_idx)
+            ordered_idx = np.argsort(frac_of_missing_values, kind="mergesort")[n:][::-1]
+        elif self.imputation_order == "random":
+            ordered_idx = missing_values_idx
+            self.random_state_.shuffle(ordered_idx)
+        return ordered_idx
+
+    def _get_abs_corr_mat(self, X_filled, tolerance=1e-6):
+        """Get absolute correlation matrix between features.
+
+        Parameters
+        ----------
+        X_filled : ndarray, shape (n_samples, n_features)
+            Input data with the most recent imputations.
+
+        tolerance : float, default=1e-6
+            `abs_corr_mat` can have nans, which will be replaced
+            with `tolerance`.
+
+        Returns
+        -------
+        abs_corr_mat : ndarray, shape (n_features, n_features)
+            Absolute correlation matrix of `X` at the beginning of the
+            current round. The diagonal has been zeroed out and each feature's
+            absolute correlations with all others have been normalized to sum
+            to 1.
+        """
+        n_features = X_filled.shape[1]
+        if self.n_nearest_features is None or self.n_nearest_features >= n_features:
+            return None
+        with np.errstate(invalid="ignore"):
+            # if a feature in the neighborhood has only a single value
+            # (e.g., categorical feature), the std. dev. will be null and
+            # np.corrcoef will raise a warning due to a division by zero
+            abs_corr_mat = np.abs(np.corrcoef(X_filled.T))
+        # np.corrcoef is not defined for features with zero std
+        abs_corr_mat[np.isnan(abs_corr_mat)] = tolerance
+        # ensures exploration, i.e. at least some probability of sampling
+        np.clip(abs_corr_mat, tolerance, None, out=abs_corr_mat)
+        # features are not their own neighbors
+        np.fill_diagonal(abs_corr_mat, 0)
+        # needs to sum to 1 for np.random.choice sampling
+        abs_corr_mat = normalize(abs_corr_mat, norm="l1", axis=0, copy=False)
+        return abs_corr_mat
+
+    def _initial_imputation(self, X, in_fit=False):
+        """Perform initial imputation for input `X`.
+
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            Input data, where `n_samples` is the number of samples and
+            `n_features` is the number of features.
+
+        in_fit : bool, default=False
+            Whether function is called in :meth:`fit`.
+
+        Returns
+        -------
+        Xt : ndarray of shape (n_samples, n_features)
+            Input data, where `n_samples` is the number of samples and
+            `n_features` is the number of features.
+
+        X_filled : ndarray of shape (n_samples, n_features)
+            Input data with the most recent imputations.
+
+        mask_missing_values : ndarray of shape (n_samples, n_features)
+            Input data's missing indicator matrix, where `n_samples` is the
+            number of samples and `n_features` is the number of features,
+            masked by non-missing features.
+
+        X_missing_mask : ndarray, shape (n_samples, n_features)
+            Input data's mask matrix indicating missing datapoints, where
+            `n_samples` is the number of samples and `n_features` is the
+            number of features.
+        """
+        if is_scalar_nan(self.missing_values):
+            force_all_finite = "allow-nan"
+        else:
+            force_all_finite = True
+
+        X = self._validate_data(
+            X,
+            dtype=FLOAT_DTYPES,
+            order="F",
+            reset=in_fit,
+            force_all_finite=force_all_finite,
+        )
+        _check_inputs_dtype(X, self.missing_values)
+
+        X_missing_mask = _get_mask(X, self.missing_values)
+        mask_missing_values = X_missing_mask.copy()
+        if self.initial_imputer_ is None:
+            self.initial_imputer_ = SimpleImputer(
+                missing_values=self.missing_values,
+                strategy=self.initial_strategy,
+                fill_value=self.fill_value,
+                keep_empty_features=self.keep_empty_features,
+            ).set_output(transform="default")
+            X_filled = self.initial_imputer_.fit_transform(X)
+        else:
+            X_filled = self.initial_imputer_.transform(X)
+
+        valid_mask = np.flatnonzero(
+            np.logical_not(np.isnan(self.initial_imputer_.statistics_))
+        )
+
+        if not self.keep_empty_features:
+            # drop empty features
+            Xt = X[:, valid_mask]
+            mask_missing_values = mask_missing_values[:, valid_mask]
+        else:
+            # mark empty features as not missing and keep the original
+            # imputation
+            mask_missing_values[:, valid_mask] = True
+            Xt = X
+
+        return Xt, X_filled, mask_missing_values, X_missing_mask
+
+    @staticmethod
+    def _validate_limit(limit, limit_type, n_features):
+        """Validate the limits (min/max) of the feature values.
+
+        Converts scalar min/max limits to vectors of shape `(n_features,)`.
+
+        Parameters
+        ----------
+        limit: scalar or array-like
+            The user-specified limit (i.e, min_value or max_value).
+        limit_type: {'max', 'min'}
+            Type of limit to validate.
+        n_features: int
+            Number of features in the dataset.
+
+        Returns
+        -------
+        limit: ndarray, shape(n_features,)
+            Array of limits, one for each feature.
+        """
+        limit_bound = np.inf if limit_type == "max" else -np.inf
+        limit = limit_bound if limit is None else limit
+        if np.isscalar(limit):
+            limit = np.full(n_features, limit)
+        limit = check_array(limit, force_all_finite=False, copy=False, ensure_2d=False)
+        if not limit.shape[0] == n_features:
+            raise ValueError(
+                f"'{limit_type}_value' should be of "
+                f"shape ({n_features},) when an array-like "
+                f"is provided. Got {limit.shape}, instead."
+            )
+        return limit
+
+    @_fit_context(
+        # IterativeImputer.estimator is not validated yet
+        prefer_skip_nested_validation=False
+    )
+    def fit_transform(self, X, y=None):
+        """Fit the imputer on `X` and return the transformed `X`.
+
+        Parameters
+        ----------
+        X : array-like, shape (n_samples, n_features)
+            Input data, where `n_samples` is the number of samples and
+            `n_features` is the number of features.
+
+        y : Ignored
+            Not used, present for API consistency by convention.
+
+        Returns
+        -------
+        Xt : array-like, shape (n_samples, n_features)
+            The imputed input data.
+        """
+        self.random_state_ = getattr(
+            self, "random_state_", check_random_state(self.random_state)
+        )
+
+        if self.estimator is None:
+            from ..linear_model import BayesianRidge
+
+            self._estimator = BayesianRidge()
+        else:
+            self._estimator = clone(self.estimator)
+
+        self.imputation_sequence_ = []
+
+        self.initial_imputer_ = None
+
+        X, Xt, mask_missing_values, complete_mask = self._initial_imputation(
+            X, in_fit=True
+        )
+
+        super()._fit_indicator(complete_mask)
+        X_indicator = super()._transform_indicator(complete_mask)
+
+        if self.max_iter == 0 or np.all(mask_missing_values):
+            self.n_iter_ = 0
+            return super()._concatenate_indicator(Xt, X_indicator)
+
+        # Edge case: a single feature. We return the initial ...
+        if Xt.shape[1] == 1:
+            self.n_iter_ = 0
+            return super()._concatenate_indicator(Xt, X_indicator)
+
+        self._min_value = self._validate_limit(self.min_value, "min", X.shape[1])
+        self._max_value = self._validate_limit(self.max_value, "max", X.shape[1])
+
+        if not np.all(np.greater(self._max_value, self._min_value)):
+            raise ValueError("One (or more) features have min_value >= max_value.")
+
+        # order in which to impute
+        # note this is probably too slow for large feature data (d > 100000)
+        # and a better way would be good.
+        # see: https://goo.gl/KyCNwj and subsequent comments
+        ordered_idx = self._get_ordered_idx(mask_missing_values)
+        self.n_features_with_missing_ = len(ordered_idx)
+
+        abs_corr_mat = self._get_abs_corr_mat(Xt)
+
+        n_samples, n_features = Xt.shape
+        if self.verbose > 0:
+            print("[IterativeImputer] Completing matrix with shape %s" % (X.shape,))
+        start_t = time()
+        if not self.sample_posterior:
+            Xt_previous = Xt.copy()
+            normalized_tol = self.tol * np.max(np.abs(X[~mask_missing_values]))
+        for self.n_iter_ in range(1, self.max_iter + 1):
+            if self.imputation_order == "random":
+                ordered_idx = self._get_ordered_idx(mask_missing_values)
+
+            for feat_idx in ordered_idx:
+                neighbor_feat_idx = self._get_neighbor_feat_idx(
+                    n_features, feat_idx, abs_corr_mat
+                )
+                Xt, estimator = self._impute_one_feature(
+                    Xt,
+                    mask_missing_values,
+                    feat_idx,
+                    neighbor_feat_idx,
+                    estimator=None,
+                    fit_mode=True,
+                )
+                estimator_triplet = _ImputerTriplet(
+                    feat_idx, neighbor_feat_idx, estimator
+                )
+                self.imputation_sequence_.append(estimator_triplet)
+
+            if self.verbose > 1:
+                print(
+                    "[IterativeImputer] Ending imputation round "
+                    "%d/%d, elapsed time %0.2f"
+                    % (self.n_iter_, self.max_iter, time() - start_t)
+                )
+
+            if not self.sample_posterior:
+                inf_norm = np.linalg.norm(Xt - Xt_previous, ord=np.inf, axis=None)
+                if self.verbose > 0:
+                    print(
+                        "[IterativeImputer] Change: {}, scaled tolerance: {} ".format(
+                            inf_norm, normalized_tol
+                        )
+                    )
+                if inf_norm < normalized_tol:
+                    if self.verbose > 0:
+                        print("[IterativeImputer] Early stopping criterion reached.")
+                    break
+                Xt_previous = Xt.copy()
+        else:
+            if not self.sample_posterior:
+                warnings.warn(
+                    "[IterativeImputer] Early stopping criterion not reached.",
+                    ConvergenceWarning,
+                )
+        _assign_where(Xt, X, cond=~mask_missing_values)
+
+        return super()._concatenate_indicator(Xt, X_indicator)
+
+    def transform(self, X):
+        """Impute all missing values in `X`.
+
+        Note that this is stochastic, and that if `random_state` is not fixed,
+        repeated calls, or permuted input, results will differ.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            The input data to complete.
+
+        Returns
+        -------
+        Xt : array-like, shape (n_samples, n_features)
+             The imputed input data.
+        """
+        check_is_fitted(self)
+
+        X, Xt, mask_missing_values, complete_mask = self._initial_imputation(
+            X, in_fit=False
+        )
+
+        X_indicator = super()._transform_indicator(complete_mask)
+
+        if self.n_iter_ == 0 or np.all(mask_missing_values):
+            return super()._concatenate_indicator(Xt, X_indicator)
+
+        imputations_per_round = len(self.imputation_sequence_) // self.n_iter_
+        i_rnd = 0
+        if self.verbose > 0:
+            print("[IterativeImputer] Completing matrix with shape %s" % (X.shape,))
+        start_t = time()
+        for it, estimator_triplet in enumerate(self.imputation_sequence_):
+            Xt, _ = self._impute_one_feature(
+                Xt,
+                mask_missing_values,
+                estimator_triplet.feat_idx,
+                estimator_triplet.neighbor_feat_idx,
+                estimator=estimator_triplet.estimator,
+                fit_mode=False,
+            )
+            if not (it + 1) % imputations_per_round:
+                if self.verbose > 1:
+                    print(
+                        "[IterativeImputer] Ending imputation round "
+                        "%d/%d, elapsed time %0.2f"
+                        % (i_rnd + 1, self.n_iter_, time() - start_t)
+                    )
+                i_rnd += 1
+
+        _assign_where(Xt, X, cond=~mask_missing_values)
+
+        return super()._concatenate_indicator(Xt, X_indicator)
+
+    def fit(self, X, y=None):
+        """Fit the imputer on `X` and return self.
+
+        Parameters
+        ----------
+        X : array-like, shape (n_samples, n_features)
+            Input data, where `n_samples` is the number of samples and
+            `n_features` is the number of features.
+
+        y : Ignored
+            Not used, present for API consistency by convention.
+
+        Returns
+        -------
+        self : object
+            Fitted estimator.
+        """
+        self.fit_transform(X)
+        return self
+
+    def get_feature_names_out(self, input_features=None):
+        """Get output feature names for transformation.
+
+        Parameters
+        ----------
+        input_features : array-like of str or None, default=None
+            Input features.
+
+            - If `input_features` is `None`, then `feature_names_in_` is
+              used as feature names in. If `feature_names_in_` is not defined,
+              then the following input feature names are generated:
+              `["x0", "x1", ..., "x(n_features_in_ - 1)"]`.
+            - If `input_features` is an array-like, then `input_features` must
+              match `feature_names_in_` if `feature_names_in_` is defined.
+
+        Returns
+        -------
+        feature_names_out : ndarray of str objects
+            Transformed feature names.
+        """
+        check_is_fitted(self, "n_features_in_")
+        input_features = _check_feature_names_in(self, input_features)
+        names = self.initial_imputer_.get_feature_names_out(input_features)
+        return self._concatenate_indicator_feature_names_out(names, input_features)

env-llmeval/lib/python3.10/site-packages/sklearn/impute/_knn.py

@@ -0,0 +1,401 @@
+# Authors: Ashim Bhattarai <[email protected]>
+#          Thomas J Fan <[email protected]>
+# License: BSD 3 clause
+
+from numbers import Integral
+
+import numpy as np
+
+from ..base import _fit_context
+from ..metrics import pairwise_distances_chunked
+from ..metrics.pairwise import _NAN_METRICS
+from ..neighbors._base import _get_weights
+from ..utils import is_scalar_nan
+from ..utils._mask import _get_mask
+from ..utils._param_validation import Hidden, Interval, StrOptions
+from ..utils.validation import FLOAT_DTYPES, _check_feature_names_in, check_is_fitted
+from ._base import _BaseImputer
+
+
+class KNNImputer(_BaseImputer):
+    """Imputation for completing missing values using k-Nearest Neighbors.
+
+    Each sample's missing values are imputed using the mean value from
+    `n_neighbors` nearest neighbors found in the training set. Two samples are
+    close if the features that neither is missing are close.
+
+    Read more in the :ref:`User Guide <knnimpute>`.
+
+    .. versionadded:: 0.22
+
+    Parameters
+    ----------
+    missing_values : int, float, str, np.nan or None, default=np.nan
+        The placeholder for the missing values. All occurrences of
+        `missing_values` will be imputed. For pandas' dataframes with
+        nullable integer dtypes with missing values, `missing_values`
+        should be set to np.nan, since `pd.NA` will be converted to np.nan.
+
+    n_neighbors : int, default=5
+        Number of neighboring samples to use for imputation.
+
+    weights : {'uniform', 'distance'} or callable, default='uniform'
+        Weight function used in prediction.  Possible values:
+
+        - 'uniform' : uniform weights. All points in each neighborhood are
+          weighted equally.
+        - 'distance' : weight points by the inverse of their distance.
+          in this case, closer neighbors of a query point will have a
+          greater influence than neighbors which are further away.
+        - callable : a user-defined function which accepts an
+          array of distances, and returns an array of the same shape
+          containing the weights.
+
+    metric : {'nan_euclidean'} or callable, default='nan_euclidean'
+        Distance metric for searching neighbors. Possible values:
+
+        - 'nan_euclidean'
+        - callable : a user-defined function which conforms to the definition
+          of ``_pairwise_callable(X, Y, metric, **kwds)``. The function
+          accepts two arrays, X and Y, and a `missing_values` keyword in
+          `kwds` and returns a scalar distance value.
+
+    copy : bool, default=True
+        If True, a copy of X will be created. If False, imputation will
+        be done in-place whenever possible.
+
+    add_indicator : bool, default=False
+        If True, a :class:`MissingIndicator` transform will stack onto the
+        output of the imputer's transform. This allows a predictive estimator
+        to account for missingness despite imputation. If a feature has no
+        missing values at fit/train time, the feature won't appear on the
+        missing indicator even if there are missing values at transform/test
+        time.
+
+    keep_empty_features : bool, default=False
+        If True, features that consist exclusively of missing values when
+        `fit` is called are returned in results when `transform` is called.
+        The imputed value is always `0`.
+
+        .. versionadded:: 1.2
+
+    Attributes
+    ----------
+    indicator_ : :class:`~sklearn.impute.MissingIndicator`
+        Indicator used to add binary indicators for missing values.
+        ``None`` if add_indicator is False.
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
+    See Also
+    --------
+    SimpleImputer : Univariate imputer for completing missing values
+        with simple strategies.
+    IterativeImputer : Multivariate imputer that estimates values to impute for
+        each feature with missing values from all the others.
+
+    References
+    ----------
+    * `Olga Troyanskaya, Michael Cantor, Gavin Sherlock, Pat Brown, Trevor
+      Hastie, Robert Tibshirani, David Botstein and Russ B. Altman, Missing
+      value estimation methods for DNA microarrays, BIOINFORMATICS Vol. 17
+      no. 6, 2001 Pages 520-525.
+      <https://academic.oup.com/bioinformatics/article/17/6/520/272365>`_
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from sklearn.impute import KNNImputer
+    >>> X = [[1, 2, np.nan], [3, 4, 3], [np.nan, 6, 5], [8, 8, 7]]
+    >>> imputer = KNNImputer(n_neighbors=2)
+    >>> imputer.fit_transform(X)
+    array([[1. , 2. , 4. ],
+           [3. , 4. , 3. ],
+           [5.5, 6. , 5. ],
+           [8. , 8. , 7. ]])
+
+    For a more detailed example see
+    :ref:`sphx_glr_auto_examples_impute_plot_missing_values.py`.
+    """
+
+    _parameter_constraints: dict = {
+        **_BaseImputer._parameter_constraints,
+        "n_neighbors": [Interval(Integral, 1, None, closed="left")],
+        "weights": [StrOptions({"uniform", "distance"}), callable, Hidden(None)],
+        "metric": [StrOptions(set(_NAN_METRICS)), callable],
+        "copy": ["boolean"],
+    }
+
+    def __init__(
+        self,
+        *,
+        missing_values=np.nan,
+        n_neighbors=5,
+        weights="uniform",
+        metric="nan_euclidean",
+        copy=True,
+        add_indicator=False,
+        keep_empty_features=False,
+    ):
+        super().__init__(
+            missing_values=missing_values,
+            add_indicator=add_indicator,
+            keep_empty_features=keep_empty_features,
+        )
+        self.n_neighbors = n_neighbors
+        self.weights = weights
+        self.metric = metric
+        self.copy = copy
+
+    def _calc_impute(self, dist_pot_donors, n_neighbors, fit_X_col, mask_fit_X_col):
+        """Helper function to impute a single column.
+
+        Parameters
+        ----------
+        dist_pot_donors : ndarray of shape (n_receivers, n_potential_donors)
+            Distance matrix between the receivers and potential donors from
+            training set. There must be at least one non-nan distance between
+            a receiver and a potential donor.
+
+        n_neighbors : int
+            Number of neighbors to consider.
+
+        fit_X_col : ndarray of shape (n_potential_donors,)
+            Column of potential donors from training set.
+
+        mask_fit_X_col : ndarray of shape (n_potential_donors,)
+            Missing mask for fit_X_col.
+
+        Returns
+        -------
+        imputed_values: ndarray of shape (n_receivers,)
+            Imputed values for receiver.
+        """
+        # Get donors
+        donors_idx = np.argpartition(dist_pot_donors, n_neighbors - 1, axis=1)[
+            :, :n_neighbors
+        ]
+
+        # Get weight matrix from distance matrix
+        donors_dist = dist_pot_donors[
+            np.arange(donors_idx.shape[0])[:, None], donors_idx
+        ]
+
+        weight_matrix = _get_weights(donors_dist, self.weights)
+
+        # fill nans with zeros
+        if weight_matrix is not None:
+            weight_matrix[np.isnan(weight_matrix)] = 0.0
+
+        # Retrieve donor values and calculate kNN average
+        donors = fit_X_col.take(donors_idx)
+        donors_mask = mask_fit_X_col.take(donors_idx)
+        donors = np.ma.array(donors, mask=donors_mask)
+
+        return np.ma.average(donors, axis=1, weights=weight_matrix).data
+
+    @_fit_context(prefer_skip_nested_validation=True)
+    def fit(self, X, y=None):
+        """Fit the imputer on X.
+
+        Parameters
+        ----------
+        X : array-like shape of (n_samples, n_features)
+            Input data, where `n_samples` is the number of samples and
+            `n_features` is the number of features.
+
+        y : Ignored
+            Not used, present here for API consistency by convention.
+
+        Returns
+        -------
+        self : object
+            The fitted `KNNImputer` class instance.
+        """
+        # Check data integrity and calling arguments
+        if not is_scalar_nan(self.missing_values):
+            force_all_finite = True
+        else:
+            force_all_finite = "allow-nan"
+
+        X = self._validate_data(
+            X,
+            accept_sparse=False,
+            dtype=FLOAT_DTYPES,
+            force_all_finite=force_all_finite,
+            copy=self.copy,
+        )
+
+        self._fit_X = X
+        self._mask_fit_X = _get_mask(self._fit_X, self.missing_values)
+        self._valid_mask = ~np.all(self._mask_fit_X, axis=0)
+
+        super()._fit_indicator(self._mask_fit_X)
+
+        return self
+
+    def transform(self, X):
+        """Impute all missing values in X.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            The input data to complete.
+
+        Returns
+        -------
+        X : array-like of shape (n_samples, n_output_features)
+            The imputed dataset. `n_output_features` is the number of features
+            that is not always missing during `fit`.
+        """
+
+        check_is_fitted(self)
+        if not is_scalar_nan(self.missing_values):
+            force_all_finite = True
+        else:
+            force_all_finite = "allow-nan"
+        X = self._validate_data(
+            X,
+            accept_sparse=False,
+            dtype=FLOAT_DTYPES,
+            force_all_finite=force_all_finite,
+            copy=self.copy,
+            reset=False,
+        )
+
+        mask = _get_mask(X, self.missing_values)
+        mask_fit_X = self._mask_fit_X
+        valid_mask = self._valid_mask
+
+        X_indicator = super()._transform_indicator(mask)
+
+        # Removes columns where the training data is all nan
+        if not np.any(mask):
+            # No missing values in X
+            if self.keep_empty_features:
+                Xc = X
+                Xc[:, ~valid_mask] = 0
+            else:
+                Xc = X[:, valid_mask]
+
+            # Even if there are no missing values in X, we still concatenate Xc
+            # with the missing value indicator matrix, X_indicator.
+            # This is to ensure that the output maintains consistency in terms
+            # of columns, regardless of whether missing values exist in X or not.
+            return super()._concatenate_indicator(Xc, X_indicator)
+
+        row_missing_idx = np.flatnonzero(mask.any(axis=1))
+
+        non_missing_fix_X = np.logical_not(mask_fit_X)
+
+        # Maps from indices from X to indices in dist matrix
+        dist_idx_map = np.zeros(X.shape[0], dtype=int)
+        dist_idx_map[row_missing_idx] = np.arange(row_missing_idx.shape[0])
+
+        def process_chunk(dist_chunk, start):
+            row_missing_chunk = row_missing_idx[start : start + len(dist_chunk)]
+
+            # Find and impute missing by column
+            for col in range(X.shape[1]):
+                if not valid_mask[col]:
+                    # column was all missing during training
+                    continue
+
+                col_mask = mask[row_missing_chunk, col]
+                if not np.any(col_mask):
+                    # column has no missing values
+                    continue
+
+                (potential_donors_idx,) = np.nonzero(non_missing_fix_X[:, col])
+
+                # receivers_idx are indices in X
+                receivers_idx = row_missing_chunk[np.flatnonzero(col_mask)]
+
+                # distances for samples that needed imputation for column
+                dist_subset = dist_chunk[dist_idx_map[receivers_idx] - start][
+                    :, potential_donors_idx
+                ]
+
+                # receivers with all nan distances impute with mean
+                all_nan_dist_mask = np.isnan(dist_subset).all(axis=1)
+                all_nan_receivers_idx = receivers_idx[all_nan_dist_mask]
+
+                if all_nan_receivers_idx.size:
+                    col_mean = np.ma.array(
+                        self._fit_X[:, col], mask=mask_fit_X[:, col]
+                    ).mean()
+                    X[all_nan_receivers_idx, col] = col_mean
+
+                    if len(all_nan_receivers_idx) == len(receivers_idx):
+                        # all receivers imputed with mean
+                        continue
+
+                    # receivers with at least one defined distance
+                    receivers_idx = receivers_idx[~all_nan_dist_mask]
+                    dist_subset = dist_chunk[dist_idx_map[receivers_idx] - start][
+                        :, potential_donors_idx
+                    ]
+
+                n_neighbors = min(self.n_neighbors, len(potential_donors_idx))
+                value = self._calc_impute(
+                    dist_subset,
+                    n_neighbors,
+                    self._fit_X[potential_donors_idx, col],
+                    mask_fit_X[potential_donors_idx, col],
+                )
+                X[receivers_idx, col] = value
+
+        # process in fixed-memory chunks
+        gen = pairwise_distances_chunked(
+            X[row_missing_idx, :],
+            self._fit_X,
+            metric=self.metric,
+            missing_values=self.missing_values,
+            force_all_finite=force_all_finite,
+            reduce_func=process_chunk,
+        )
+        for chunk in gen:
+            # process_chunk modifies X in place. No return value.
+            pass
+
+        if self.keep_empty_features:
+            Xc = X
+            Xc[:, ~valid_mask] = 0
+        else:
+            Xc = X[:, valid_mask]
+
+        return super()._concatenate_indicator(Xc, X_indicator)
+
+    def get_feature_names_out(self, input_features=None):
+        """Get output feature names for transformation.
+
+        Parameters
+        ----------
+        input_features : array-like of str or None, default=None
+            Input features.
+
+            - If `input_features` is `None`, then `feature_names_in_` is
+              used as feature names in. If `feature_names_in_` is not defined,
+              then the following input feature names are generated:
+              `["x0", "x1", ..., "x(n_features_in_ - 1)"]`.
+            - If `input_features` is an array-like, then `input_features` must
+              match `feature_names_in_` if `feature_names_in_` is defined.
+
+        Returns
+        -------
+        feature_names_out : ndarray of str objects
+            Transformed feature names.
+        """
+        check_is_fitted(self, "n_features_in_")
+        input_features = _check_feature_names_in(self, input_features)
+        names = input_features[self._valid_mask]
+        return self._concatenate_indicator_feature_names_out(names, input_features)

env-llmeval/lib/python3.10/site-packages/sklearn/impute/tests/test_base.py

@@ -0,0 +1,107 @@
+import numpy as np
+import pytest
+
+from sklearn.impute._base import _BaseImputer
+from sklearn.impute._iterative import _assign_where
+from sklearn.utils._mask import _get_mask
+from sklearn.utils._testing import _convert_container, assert_allclose
+
+
+@pytest.fixture
+def data():
+    X = np.random.randn(10, 2)
+    X[::2] = np.nan
+    return X
+
+
+class NoFitIndicatorImputer(_BaseImputer):
+    def fit(self, X, y=None):
+        return self
+
+    def transform(self, X, y=None):
+        return self._concatenate_indicator(X, self._transform_indicator(X))
+
+
+class NoTransformIndicatorImputer(_BaseImputer):
+    def fit(self, X, y=None):
+        mask = _get_mask(X, value_to_mask=np.nan)
+        super()._fit_indicator(mask)
+        return self
+
+    def transform(self, X, y=None):
+        return self._concatenate_indicator(X, None)
+
+
+class NoPrecomputedMaskFit(_BaseImputer):
+    def fit(self, X, y=None):
+        self._fit_indicator(X)
+        return self
+
+    def transform(self, X):
+        return self._concatenate_indicator(X, self._transform_indicator(X))
+
+
+class NoPrecomputedMaskTransform(_BaseImputer):
+    def fit(self, X, y=None):
+        mask = _get_mask(X, value_to_mask=np.nan)
+        self._fit_indicator(mask)
+        return self
+
+    def transform(self, X):
+        return self._concatenate_indicator(X, self._transform_indicator(X))
+
+
+def test_base_imputer_not_fit(data):
+    imputer = NoFitIndicatorImputer(add_indicator=True)
+    err_msg = "Make sure to call _fit_indicator before _transform_indicator"
+    with pytest.raises(ValueError, match=err_msg):
+        imputer.fit(data).transform(data)
+    with pytest.raises(ValueError, match=err_msg):
+        imputer.fit_transform(data)
+
+
+def test_base_imputer_not_transform(data):
+    imputer = NoTransformIndicatorImputer(add_indicator=True)
+    err_msg = (
+        "Call _fit_indicator and _transform_indicator in the imputer implementation"
+    )
+    with pytest.raises(ValueError, match=err_msg):
+        imputer.fit(data).transform(data)
+    with pytest.raises(ValueError, match=err_msg):
+        imputer.fit_transform(data)
+
+
+def test_base_no_precomputed_mask_fit(data):
+    imputer = NoPrecomputedMaskFit(add_indicator=True)
+    err_msg = "precomputed is True but the input data is not a mask"
+    with pytest.raises(ValueError, match=err_msg):
+        imputer.fit(data)
+    with pytest.raises(ValueError, match=err_msg):
+        imputer.fit_transform(data)
+
+
+def test_base_no_precomputed_mask_transform(data):
+    imputer = NoPrecomputedMaskTransform(add_indicator=True)
+    err_msg = "precomputed is True but the input data is not a mask"
+    imputer.fit(data)
+    with pytest.raises(ValueError, match=err_msg):
+        imputer.transform(data)
+    with pytest.raises(ValueError, match=err_msg):
+        imputer.fit_transform(data)
+
+
+@pytest.mark.parametrize("X1_type", ["array", "dataframe"])
+def test_assign_where(X1_type):
+    """Check the behaviour of the private helpers `_assign_where`."""
+    rng = np.random.RandomState(0)
+
+    n_samples, n_features = 10, 5
+    X1 = _convert_container(rng.randn(n_samples, n_features), constructor_name=X1_type)
+    X2 = rng.randn(n_samples, n_features)
+    mask = rng.randint(0, 2, size=(n_samples, n_features)).astype(bool)
+
+    _assign_where(X1, X2, mask)
+
+    if X1_type == "dataframe":
+        X1 = X1.to_numpy()
+    assert_allclose(X1[mask], X2[mask])

env-llmeval/lib/python3.10/site-packages/sklearn/impute/tests/test_common.py

@@ -0,0 +1,220 @@
+import numpy as np
+import pytest
+
+from sklearn.experimental import enable_iterative_imputer  # noqa
+from sklearn.impute import IterativeImputer, KNNImputer, SimpleImputer
+from sklearn.utils._testing import (
+    assert_allclose,
+    assert_allclose_dense_sparse,
+    assert_array_equal,
+)
+from sklearn.utils.fixes import CSR_CONTAINERS
+
+
+def imputers():
+    return [IterativeImputer(tol=0.1), KNNImputer(), SimpleImputer()]
+
+
+def sparse_imputers():
+    return [SimpleImputer()]
+
+
+# ConvergenceWarning will be raised by the IterativeImputer
+@pytest.mark.filterwarnings("ignore::sklearn.exceptions.ConvergenceWarning")
+@pytest.mark.parametrize("imputer", imputers(), ids=lambda x: x.__class__.__name__)
+def test_imputation_missing_value_in_test_array(imputer):
+    # [Non Regression Test for issue #13968] Missing value in test set should
+    # not throw an error and return a finite dataset
+    train = [[1], [2]]
+    test = [[3], [np.nan]]
+    imputer.set_params(add_indicator=True)
+    imputer.fit(train).transform(test)
+
+
+# ConvergenceWarning will be raised by the IterativeImputer
+@pytest.mark.filterwarnings("ignore::sklearn.exceptions.ConvergenceWarning")
+@pytest.mark.parametrize("marker", [np.nan, -1, 0])
+@pytest.mark.parametrize("imputer", imputers(), ids=lambda x: x.__class__.__name__)
+def test_imputers_add_indicator(marker, imputer):
+    X = np.array(
+        [
+            [marker, 1, 5, marker, 1],
+            [2, marker, 1, marker, 2],
+            [6, 3, marker, marker, 3],
+            [1, 2, 9, marker, 4],
+        ]
+    )
+    X_true_indicator = np.array(
+        [
+            [1.0, 0.0, 0.0, 1.0],
+            [0.0, 1.0, 0.0, 1.0],
+            [0.0, 0.0, 1.0, 1.0],
+            [0.0, 0.0, 0.0, 1.0],
+        ]
+    )
+    imputer.set_params(missing_values=marker, add_indicator=True)
+
+    X_trans = imputer.fit_transform(X)
+    assert_allclose(X_trans[:, -4:], X_true_indicator)
+    assert_array_equal(imputer.indicator_.features_, np.array([0, 1, 2, 3]))
+
+    imputer.set_params(add_indicator=False)
+    X_trans_no_indicator = imputer.fit_transform(X)
+    assert_allclose(X_trans[:, :-4], X_trans_no_indicator)
+
+
+# ConvergenceWarning will be raised by the IterativeImputer
+@pytest.mark.filterwarnings("ignore::sklearn.exceptions.ConvergenceWarning")
+@pytest.mark.parametrize("marker", [np.nan, -1])
+@pytest.mark.parametrize(
+    "imputer", sparse_imputers(), ids=lambda x: x.__class__.__name__
+)
+@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
+def test_imputers_add_indicator_sparse(imputer, marker, csr_container):
+    X = csr_container(
+        [
+            [marker, 1, 5, marker, 1],
+            [2, marker, 1, marker, 2],
+            [6, 3, marker, marker, 3],
+            [1, 2, 9, marker, 4],
+        ]
+    )
+    X_true_indicator = csr_container(
+        [
+            [1.0, 0.0, 0.0, 1.0],
+            [0.0, 1.0, 0.0, 1.0],
+            [0.0, 0.0, 1.0, 1.0],
+            [0.0, 0.0, 0.0, 1.0],
+        ]
+    )
+    imputer.set_params(missing_values=marker, add_indicator=True)
+
+    X_trans = imputer.fit_transform(X)
+    assert_allclose_dense_sparse(X_trans[:, -4:], X_true_indicator)
+    assert_array_equal(imputer.indicator_.features_, np.array([0, 1, 2, 3]))
+
+    imputer.set_params(add_indicator=False)
+    X_trans_no_indicator = imputer.fit_transform(X)
+    assert_allclose_dense_sparse(X_trans[:, :-4], X_trans_no_indicator)
+
+
+# ConvergenceWarning will be raised by the IterativeImputer
+@pytest.mark.filterwarnings("ignore::sklearn.exceptions.ConvergenceWarning")
+@pytest.mark.parametrize("imputer", imputers(), ids=lambda x: x.__class__.__name__)
+@pytest.mark.parametrize("add_indicator", [True, False])
+def test_imputers_pandas_na_integer_array_support(imputer, add_indicator):
+    # Test pandas IntegerArray with pd.NA
+    pd = pytest.importorskip("pandas")
+    marker = np.nan
+    imputer = imputer.set_params(add_indicator=add_indicator, missing_values=marker)
+
+    X = np.array(
+        [
+            [marker, 1, 5, marker, 1],
+            [2, marker, 1, marker, 2],
+            [6, 3, marker, marker, 3],
+            [1, 2, 9, marker, 4],
+        ]
+    )
+    # fit on numpy array
+    X_trans_expected = imputer.fit_transform(X)
+
+    # Creates dataframe with IntegerArrays with pd.NA
+    X_df = pd.DataFrame(X, dtype="Int16", columns=["a", "b", "c", "d", "e"])
+
+    # fit on pandas dataframe with IntegerArrays
+    X_trans = imputer.fit_transform(X_df)
+
+    assert_allclose(X_trans_expected, X_trans)
+
+
+@pytest.mark.parametrize("imputer", imputers(), ids=lambda x: x.__class__.__name__)
+@pytest.mark.parametrize("add_indicator", [True, False])
+def test_imputers_feature_names_out_pandas(imputer, add_indicator):
+    """Check feature names out for imputers."""
+    pd = pytest.importorskip("pandas")
+    marker = np.nan
+    imputer = imputer.set_params(add_indicator=add_indicator, missing_values=marker)
+
+    X = np.array(
+        [
+            [marker, 1, 5, 3, marker, 1],
+            [2, marker, 1, 4, marker, 2],
+            [6, 3, 7, marker, marker, 3],
+            [1, 2, 9, 8, marker, 4],
+        ]
+    )
+    X_df = pd.DataFrame(X, columns=["a", "b", "c", "d", "e", "f"])
+    imputer.fit(X_df)
+
+    names = imputer.get_feature_names_out()
+
+    if add_indicator:
+        expected_names = [
+            "a",
+            "b",
+            "c",
+            "d",
+            "f",
+            "missingindicator_a",
+            "missingindicator_b",
+            "missingindicator_d",
+            "missingindicator_e",
+        ]
+        assert_array_equal(expected_names, names)
+    else:
+        expected_names = ["a", "b", "c", "d", "f"]
+        assert_array_equal(expected_names, names)
+
+
+@pytest.mark.parametrize("keep_empty_features", [True, False])
+@pytest.mark.parametrize("imputer", imputers(), ids=lambda x: x.__class__.__name__)
+def test_keep_empty_features(imputer, keep_empty_features):
+    """Check that the imputer keeps features with only missing values."""
+    X = np.array([[np.nan, 1], [np.nan, 2], [np.nan, 3]])
+    imputer = imputer.set_params(
+        add_indicator=False, keep_empty_features=keep_empty_features
+    )
+
+    for method in ["fit_transform", "transform"]:
+        X_imputed = getattr(imputer, method)(X)
+        if keep_empty_features:
+            assert X_imputed.shape == X.shape
+        else:
+            assert X_imputed.shape == (X.shape[0], X.shape[1] - 1)
+
+
+@pytest.mark.parametrize("imputer", imputers(), ids=lambda x: x.__class__.__name__)
+@pytest.mark.parametrize("missing_value_test", [np.nan, 1])
+def test_imputation_adds_missing_indicator_if_add_indicator_is_true(
+    imputer, missing_value_test
+):
+    """Check that missing indicator always exists when add_indicator=True.
+
+    Non-regression test for gh-26590.
+    """
+    X_train = np.array([[0, np.nan], [1, 2]])
+
+    # Test data where missing_value_test variable can be set to np.nan or 1.
+    X_test = np.array([[0, missing_value_test], [1, 2]])
+
+    imputer.set_params(add_indicator=True)
+    imputer.fit(X_train)
+
+    X_test_imputed_with_indicator = imputer.transform(X_test)
+    assert X_test_imputed_with_indicator.shape == (2, 3)
+
+    imputer.set_params(add_indicator=False)
+    imputer.fit(X_train)
+    X_test_imputed_without_indicator = imputer.transform(X_test)
+    assert X_test_imputed_without_indicator.shape == (2, 2)
+
+    assert_allclose(
+        X_test_imputed_with_indicator[:, :-1], X_test_imputed_without_indicator
+    )
+    if np.isnan(missing_value_test):
+        expected_missing_indicator = [1, 0]
+    else:
+        expected_missing_indicator = [0, 0]
+
+    assert_allclose(X_test_imputed_with_indicator[:, -1], expected_missing_indicator)

env-llmeval/lib/python3.10/site-packages/sklearn/impute/tests/test_impute.py

@@ -0,0 +1,1754 @@
+import io
+import re
+import warnings
+from itertools import product
+
+import numpy as np
+import pytest
+from scipy import sparse
+from scipy.stats import kstest
+
+from sklearn import tree
+from sklearn.datasets import load_diabetes
+from sklearn.dummy import DummyRegressor
+from sklearn.exceptions import ConvergenceWarning
+
+# make IterativeImputer available
+from sklearn.experimental import enable_iterative_imputer  # noqa
+from sklearn.impute import IterativeImputer, KNNImputer, MissingIndicator, SimpleImputer
+from sklearn.impute._base import _most_frequent
+from sklearn.linear_model import ARDRegression, BayesianRidge, RidgeCV
+from sklearn.model_selection import GridSearchCV
+from sklearn.pipeline import Pipeline, make_union
+from sklearn.random_projection import _sparse_random_matrix
+from sklearn.utils._testing import (
+    _convert_container,
+    assert_allclose,
+    assert_allclose_dense_sparse,
+    assert_array_almost_equal,
+    assert_array_equal,
+)
+from sklearn.utils.fixes import (
+    BSR_CONTAINERS,
+    COO_CONTAINERS,
+    CSC_CONTAINERS,
+    CSR_CONTAINERS,
+    LIL_CONTAINERS,
+)
+
+
+def _assert_array_equal_and_same_dtype(x, y):
+    assert_array_equal(x, y)
+    assert x.dtype == y.dtype
+
+
+def _assert_allclose_and_same_dtype(x, y):
+    assert_allclose(x, y)
+    assert x.dtype == y.dtype
+
+
+def _check_statistics(
+    X, X_true, strategy, statistics, missing_values, sparse_container
+):
+    """Utility function for testing imputation for a given strategy.
+
+    Test with dense and sparse arrays
+
+    Check that:
+        - the statistics (mean, median, mode) are correct
+        - the missing values are imputed correctly"""
+
+    err_msg = "Parameters: strategy = %s, missing_values = %s, sparse = {0}" % (
+        strategy,
+        missing_values,
+    )
+
+    assert_ae = assert_array_equal
+
+    if X.dtype.kind == "f" or X_true.dtype.kind == "f":
+        assert_ae = assert_array_almost_equal
+
+    # Normal matrix
+    imputer = SimpleImputer(missing_values=missing_values, strategy=strategy)
+    X_trans = imputer.fit(X).transform(X.copy())
+    assert_ae(imputer.statistics_, statistics, err_msg=err_msg.format(False))
+    assert_ae(X_trans, X_true, err_msg=err_msg.format(False))
+
+    # Sparse matrix
+    imputer = SimpleImputer(missing_values=missing_values, strategy=strategy)
+    imputer.fit(sparse_container(X))
+    X_trans = imputer.transform(sparse_container(X.copy()))
+
+    if sparse.issparse(X_trans):
+        X_trans = X_trans.toarray()
+
+    assert_ae(imputer.statistics_, statistics, err_msg=err_msg.format(True))
+    assert_ae(X_trans, X_true, err_msg=err_msg.format(True))
+
+
+@pytest.mark.parametrize("strategy", ["mean", "median", "most_frequent", "constant"])
+@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
+def test_imputation_shape(strategy, csr_container):
+    # Verify the shapes of the imputed matrix for different strategies.
+    X = np.random.randn(10, 2)
+    X[::2] = np.nan
+
+    imputer = SimpleImputer(strategy=strategy)
+    X_imputed = imputer.fit_transform(csr_container(X))
+    assert X_imputed.shape == (10, 2)
+    X_imputed = imputer.fit_transform(X)
+    assert X_imputed.shape == (10, 2)
+
+    iterative_imputer = IterativeImputer(initial_strategy=strategy)
+    X_imputed = iterative_imputer.fit_transform(X)
+    assert X_imputed.shape == (10, 2)
+
+
+@pytest.mark.parametrize("strategy", ["mean", "median", "most_frequent"])
+def test_imputation_deletion_warning(strategy):
+    X = np.ones((3, 5))
+    X[:, 0] = np.nan
+    imputer = SimpleImputer(strategy=strategy).fit(X)
+
+    with pytest.warns(UserWarning, match="Skipping"):
+        imputer.transform(X)
+
+
+@pytest.mark.parametrize("strategy", ["mean", "median", "most_frequent"])
+def test_imputation_deletion_warning_feature_names(strategy):
+    pd = pytest.importorskip("pandas")
+
+    missing_values = np.nan
+    feature_names = np.array(["a", "b", "c", "d"], dtype=object)
+    X = pd.DataFrame(
+        [
+            [missing_values, missing_values, 1, missing_values],
+            [4, missing_values, 2, 10],
+        ],
+        columns=feature_names,
+    )
+
+    imputer = SimpleImputer(strategy=strategy).fit(X)
+
+    # check SimpleImputer returning feature name attribute correctly
+    assert_array_equal(imputer.feature_names_in_, feature_names)
+
+    # ensure that skipped feature warning includes feature name
+    with pytest.warns(
+        UserWarning, match=r"Skipping features without any observed values: \['b'\]"
+    ):
+        imputer.transform(X)
+
+
+@pytest.mark.parametrize("strategy", ["mean", "median", "most_frequent", "constant"])
+@pytest.mark.parametrize("csc_container", CSC_CONTAINERS)
+def test_imputation_error_sparse_0(strategy, csc_container):
+    # check that error are raised when missing_values = 0 and input is sparse
+    X = np.ones((3, 5))
+    X[0] = 0
+    X = csc_container(X)
+
+    imputer = SimpleImputer(strategy=strategy, missing_values=0)
+    with pytest.raises(ValueError, match="Provide a dense array"):
+        imputer.fit(X)
+
+    imputer.fit(X.toarray())
+    with pytest.raises(ValueError, match="Provide a dense array"):
+        imputer.transform(X)
+
+
+def safe_median(arr, *args, **kwargs):
+    # np.median([]) raises a TypeError for numpy >= 1.10.1
+    length = arr.size if hasattr(arr, "size") else len(arr)
+    return np.nan if length == 0 else np.median(arr, *args, **kwargs)
+
+
+def safe_mean(arr, *args, **kwargs):
+    # np.mean([]) raises a RuntimeWarning for numpy >= 1.10.1
+    length = arr.size if hasattr(arr, "size") else len(arr)
+    return np.nan if length == 0 else np.mean(arr, *args, **kwargs)
+
+
+@pytest.mark.parametrize("csc_container", CSC_CONTAINERS)
+def test_imputation_mean_median(csc_container):
+    # Test imputation using the mean and median strategies, when
+    # missing_values != 0.
+    rng = np.random.RandomState(0)
+
+    dim = 10
+    dec = 10
+    shape = (dim * dim, dim + dec)
+
+    zeros = np.zeros(shape[0])
+    values = np.arange(1, shape[0] + 1)
+    values[4::2] = -values[4::2]
+
+    tests = [
+        ("mean", np.nan, lambda z, v, p: safe_mean(np.hstack((z, v)))),
+        ("median", np.nan, lambda z, v, p: safe_median(np.hstack((z, v)))),
+    ]
+
+    for strategy, test_missing_values, true_value_fun in tests:
+        X = np.empty(shape)
+        X_true = np.empty(shape)
+        true_statistics = np.empty(shape[1])
+
+        # Create a matrix X with columns
+        #    - with only zeros,
+        #    - with only missing values
+        #    - with zeros, missing values and values
+        # And a matrix X_true containing all true values
+        for j in range(shape[1]):
+            nb_zeros = (j - dec + 1 > 0) * (j - dec + 1) * (j - dec + 1)
+            nb_missing_values = max(shape[0] + dec * dec - (j + dec) * (j + dec), 0)
+            nb_values = shape[0] - nb_zeros - nb_missing_values
+
+            z = zeros[:nb_zeros]
+            p = np.repeat(test_missing_values, nb_missing_values)
+            v = values[rng.permutation(len(values))[:nb_values]]
+
+            true_statistics[j] = true_value_fun(z, v, p)
+
+            # Create the columns
+            X[:, j] = np.hstack((v, z, p))
+
+            if 0 == test_missing_values:
+                # XXX unreached code as of v0.22
+                X_true[:, j] = np.hstack(
+                    (v, np.repeat(true_statistics[j], nb_missing_values + nb_zeros))
+                )
+            else:
+                X_true[:, j] = np.hstack(
+                    (v, z, np.repeat(true_statistics[j], nb_missing_values))
+                )
+
+            # Shuffle them the same way
+            np.random.RandomState(j).shuffle(X[:, j])
+            np.random.RandomState(j).shuffle(X_true[:, j])
+
+        # Mean doesn't support columns containing NaNs, median does
+        if strategy == "median":
+            cols_to_keep = ~np.isnan(X_true).any(axis=0)
+        else:
+            cols_to_keep = ~np.isnan(X_true).all(axis=0)
+
+        X_true = X_true[:, cols_to_keep]
+
+        _check_statistics(
+            X, X_true, strategy, true_statistics, test_missing_values, csc_container
+        )
+
+
+@pytest.mark.parametrize("csc_container", CSC_CONTAINERS)
+def test_imputation_median_special_cases(csc_container):
+    # Test median imputation with sparse boundary cases
+    X = np.array(
+        [
+            [0, np.nan, np.nan],  # odd: implicit zero
+            [5, np.nan, np.nan],  # odd: explicit nonzero
+            [0, 0, np.nan],  # even: average two zeros
+            [-5, 0, np.nan],  # even: avg zero and neg
+            [0, 5, np.nan],  # even: avg zero and pos
+            [4, 5, np.nan],  # even: avg nonzeros
+            [-4, -5, np.nan],  # even: avg negatives
+            [-1, 2, np.nan],  # even: crossing neg and pos
+        ]
+    ).transpose()
+
+    X_imputed_median = np.array(
+        [
+            [0, 0, 0],
+            [5, 5, 5],
+            [0, 0, 0],
+            [-5, 0, -2.5],
+            [0, 5, 2.5],
+            [4, 5, 4.5],
+            [-4, -5, -4.5],
+            [-1, 2, 0.5],
+        ]
+    ).transpose()
+    statistics_median = [0, 5, 0, -2.5, 2.5, 4.5, -4.5, 0.5]
+
+    _check_statistics(
+        X, X_imputed_median, "median", statistics_median, np.nan, csc_container
+    )
+
+
+@pytest.mark.parametrize("strategy", ["mean", "median"])
+@pytest.mark.parametrize("dtype", [None, object, str])
+def test_imputation_mean_median_error_invalid_type(strategy, dtype):
+    X = np.array([["a", "b", 3], [4, "e", 6], ["g", "h", 9]], dtype=dtype)
+    msg = "non-numeric data:\ncould not convert string to float:"
+    with pytest.raises(ValueError, match=msg):
+        imputer = SimpleImputer(strategy=strategy)
+        imputer.fit_transform(X)
+
+
+@pytest.mark.parametrize("strategy", ["mean", "median"])
+@pytest.mark.parametrize("type", ["list", "dataframe"])
+def test_imputation_mean_median_error_invalid_type_list_pandas(strategy, type):
+    X = [["a", "b", 3], [4, "e", 6], ["g", "h", 9]]
+    if type == "dataframe":
+        pd = pytest.importorskip("pandas")
+        X = pd.DataFrame(X)
+    msg = "non-numeric data:\ncould not convert string to float:"
+    with pytest.raises(ValueError, match=msg):
+        imputer = SimpleImputer(strategy=strategy)
+        imputer.fit_transform(X)
+
+
+@pytest.mark.parametrize("strategy", ["constant", "most_frequent"])
+@pytest.mark.parametrize("dtype", [str, np.dtype("U"), np.dtype("S")])
+def test_imputation_const_mostf_error_invalid_types(strategy, dtype):
+    # Test imputation on non-numeric data using "most_frequent" and "constant"
+    # strategy
+    X = np.array(
+        [
+            [np.nan, np.nan, "a", "f"],
+            [np.nan, "c", np.nan, "d"],
+            [np.nan, "b", "d", np.nan],
+            [np.nan, "c", "d", "h"],
+        ],
+        dtype=dtype,
+    )
+
+    err_msg = "SimpleImputer does not support data"
+    with pytest.raises(ValueError, match=err_msg):
+        imputer = SimpleImputer(strategy=strategy)
+        imputer.fit(X).transform(X)
+
+
+@pytest.mark.parametrize("csc_container", CSC_CONTAINERS)
+def test_imputation_most_frequent(csc_container):
+    # Test imputation using the most-frequent strategy.
+    X = np.array(
+        [
+            [-1, -1, 0, 5],
+            [-1, 2, -1, 3],
+            [-1, 1, 3, -1],
+            [-1, 2, 3, 7],
+        ]
+    )
+
+    X_true = np.array(
+        [
+            [2, 0, 5],
+            [2, 3, 3],
+            [1, 3, 3],
+            [2, 3, 7],
+        ]
+    )
+
+    # scipy.stats.mode, used in SimpleImputer, doesn't return the first most
+    # frequent as promised in the doc but the lowest most frequent. When this
+    # test will fail after an update of scipy, SimpleImputer will need to be
+    # updated to be consistent with the new (correct) behaviour
+    _check_statistics(X, X_true, "most_frequent", [np.nan, 2, 3, 3], -1, csc_container)
+
+
+@pytest.mark.parametrize("marker", [None, np.nan, "NAN", "", 0])
+def test_imputation_most_frequent_objects(marker):
+    # Test imputation using the most-frequent strategy.
+    X = np.array(
+        [
+            [marker, marker, "a", "f"],
+            [marker, "c", marker, "d"],
+            [marker, "b", "d", marker],
+            [marker, "c", "d", "h"],
+        ],
+        dtype=object,
+    )
+
+    X_true = np.array(
+        [
+            ["c", "a", "f"],
+            ["c", "d", "d"],
+            ["b", "d", "d"],
+            ["c", "d", "h"],
+        ],
+        dtype=object,
+    )
+
+    imputer = SimpleImputer(missing_values=marker, strategy="most_frequent")
+    X_trans = imputer.fit(X).transform(X)
+
+    assert_array_equal(X_trans, X_true)
+
+
+@pytest.mark.parametrize("dtype", [object, "category"])
+def test_imputation_most_frequent_pandas(dtype):
+    # Test imputation using the most frequent strategy on pandas df
+    pd = pytest.importorskip("pandas")
+
+    f = io.StringIO("Cat1,Cat2,Cat3,Cat4\n,i,x,\na,,y,\na,j,,\nb,j,x,")
+
+    df = pd.read_csv(f, dtype=dtype)
+
+    X_true = np.array(
+        [["a", "i", "x"], ["a", "j", "y"], ["a", "j", "x"], ["b", "j", "x"]],
+        dtype=object,
+    )
+
+    imputer = SimpleImputer(strategy="most_frequent")
+    X_trans = imputer.fit_transform(df)
+
+    assert_array_equal(X_trans, X_true)
+
+
+@pytest.mark.parametrize("X_data, missing_value", [(1, 0), (1.0, np.nan)])
+def test_imputation_constant_error_invalid_type(X_data, missing_value):
+    # Verify that exceptions are raised on invalid fill_value type
+    X = np.full((3, 5), X_data, dtype=float)
+    X[0, 0] = missing_value
+
+    fill_value = "x"
+    err_msg = f"fill_value={fill_value!r} (of type {type(fill_value)!r}) cannot be cast"
+    with pytest.raises(ValueError, match=re.escape(err_msg)):
+        imputer = SimpleImputer(
+            missing_values=missing_value, strategy="constant", fill_value=fill_value
+        )
+        imputer.fit_transform(X)
+
+
+def test_imputation_constant_integer():
+    # Test imputation using the constant strategy on integers
+    X = np.array([[-1, 2, 3, -1], [4, -1, 5, -1], [6, 7, -1, -1], [8, 9, 0, -1]])
+
+    X_true = np.array([[0, 2, 3, 0], [4, 0, 5, 0], [6, 7, 0, 0], [8, 9, 0, 0]])
+
+    imputer = SimpleImputer(missing_values=-1, strategy="constant", fill_value=0)
+    X_trans = imputer.fit_transform(X)
+
+    assert_array_equal(X_trans, X_true)
+
+
+@pytest.mark.parametrize("array_constructor", CSR_CONTAINERS + [np.asarray])
+def test_imputation_constant_float(array_constructor):
+    # Test imputation using the constant strategy on floats
+    X = np.array(
+        [
+            [np.nan, 1.1, 0, np.nan],
+            [1.2, np.nan, 1.3, np.nan],
+            [0, 0, np.nan, np.nan],
+            [1.4, 1.5, 0, np.nan],
+        ]
+    )
+
+    X_true = np.array(
+        [[-1, 1.1, 0, -1], [1.2, -1, 1.3, -1], [0, 0, -1, -1], [1.4, 1.5, 0, -1]]
+    )
+
+    X = array_constructor(X)
+
+    X_true = array_constructor(X_true)
+
+    imputer = SimpleImputer(strategy="constant", fill_value=-1)
+    X_trans = imputer.fit_transform(X)
+
+    assert_allclose_dense_sparse(X_trans, X_true)
+
+
+@pytest.mark.parametrize("marker", [None, np.nan, "NAN", "", 0])
+def test_imputation_constant_object(marker):
+    # Test imputation using the constant strategy on objects
+    X = np.array(
+        [
+            [marker, "a", "b", marker],
+            ["c", marker, "d", marker],
+            ["e", "f", marker, marker],
+            ["g", "h", "i", marker],
+        ],
+        dtype=object,
+    )
+
+    X_true = np.array(
+        [
+            ["missing", "a", "b", "missing"],
+            ["c", "missing", "d", "missing"],
+            ["e", "f", "missing", "missing"],
+            ["g", "h", "i", "missing"],
+        ],
+        dtype=object,
+    )
+
+    imputer = SimpleImputer(
+        missing_values=marker, strategy="constant", fill_value="missing"
+    )
+    X_trans = imputer.fit_transform(X)
+
+    assert_array_equal(X_trans, X_true)
+
+
+@pytest.mark.parametrize("dtype", [object, "category"])
+def test_imputation_constant_pandas(dtype):
+    # Test imputation using the constant strategy on pandas df
+    pd = pytest.importorskip("pandas")
+
+    f = io.StringIO("Cat1,Cat2,Cat3,Cat4\n,i,x,\na,,y,\na,j,,\nb,j,x,")
+
+    df = pd.read_csv(f, dtype=dtype)
+
+    X_true = np.array(
+        [
+            ["missing_value", "i", "x", "missing_value"],
+            ["a", "missing_value", "y", "missing_value"],
+            ["a", "j", "missing_value", "missing_value"],
+            ["b", "j", "x", "missing_value"],
+        ],
+        dtype=object,
+    )
+
+    imputer = SimpleImputer(strategy="constant")
+    X_trans = imputer.fit_transform(df)
+
+    assert_array_equal(X_trans, X_true)
+
+
+@pytest.mark.parametrize("X", [[[1], [2]], [[1], [np.nan]]])
+def test_iterative_imputer_one_feature(X):
+    # check we exit early when there is a single feature
+    imputer = IterativeImputer().fit(X)
+    assert imputer.n_iter_ == 0
+    imputer = IterativeImputer()
+    imputer.fit([[1], [2]])
+    assert imputer.n_iter_ == 0
+    imputer.fit([[1], [np.nan]])
+    assert imputer.n_iter_ == 0
+
+
+def test_imputation_pipeline_grid_search():
+    # Test imputation within a pipeline + gridsearch.
+    X = _sparse_random_matrix(100, 100, density=0.10)
+    missing_values = X.data[0]
+
+    pipeline = Pipeline(
+        [
+            ("imputer", SimpleImputer(missing_values=missing_values)),
+            ("tree", tree.DecisionTreeRegressor(random_state=0)),
+        ]
+    )
+
+    parameters = {"imputer__strategy": ["mean", "median", "most_frequent"]}
+
+    Y = _sparse_random_matrix(100, 1, density=0.10).toarray()
+    gs = GridSearchCV(pipeline, parameters)
+    gs.fit(X, Y)
+
+
+def test_imputation_copy():
+    # Test imputation with copy
+    X_orig = _sparse_random_matrix(5, 5, density=0.75, random_state=0)
+
+    # copy=True, dense => copy
+    X = X_orig.copy().toarray()
+    imputer = SimpleImputer(missing_values=0, strategy="mean", copy=True)
+    Xt = imputer.fit(X).transform(X)
+    Xt[0, 0] = -1
+    assert not np.all(X == Xt)
+
+    # copy=True, sparse csr => copy
+    X = X_orig.copy()
+    imputer = SimpleImputer(missing_values=X.data[0], strategy="mean", copy=True)
+    Xt = imputer.fit(X).transform(X)
+    Xt.data[0] = -1
+    assert not np.all(X.data == Xt.data)
+
+    # copy=False, dense => no copy
+    X = X_orig.copy().toarray()
+    imputer = SimpleImputer(missing_values=0, strategy="mean", copy=False)
+    Xt = imputer.fit(X).transform(X)
+    Xt[0, 0] = -1
+    assert_array_almost_equal(X, Xt)
+
+    # copy=False, sparse csc => no copy
+    X = X_orig.copy().tocsc()
+    imputer = SimpleImputer(missing_values=X.data[0], strategy="mean", copy=False)
+    Xt = imputer.fit(X).transform(X)
+    Xt.data[0] = -1
+    assert_array_almost_equal(X.data, Xt.data)
+
+    # copy=False, sparse csr => copy
+    X = X_orig.copy()
+    imputer = SimpleImputer(missing_values=X.data[0], strategy="mean", copy=False)
+    Xt = imputer.fit(X).transform(X)
+    Xt.data[0] = -1
+    assert not np.all(X.data == Xt.data)
+
+    # Note: If X is sparse and if missing_values=0, then a (dense) copy of X is
+    # made, even if copy=False.
+
+
+def test_iterative_imputer_zero_iters():
+    rng = np.random.RandomState(0)
+
+    n = 100
+    d = 10
+    X = _sparse_random_matrix(n, d, density=0.10, random_state=rng).toarray()
+    missing_flag = X == 0
+    X[missing_flag] = np.nan
+
+    imputer = IterativeImputer(max_iter=0)
+    X_imputed = imputer.fit_transform(X)
+    # with max_iter=0, only initial imputation is performed
+    assert_allclose(X_imputed, imputer.initial_imputer_.transform(X))
+
+    # repeat but force n_iter_ to 0
+    imputer = IterativeImputer(max_iter=5).fit(X)
+    # transformed should not be equal to initial imputation
+    assert not np.all(imputer.transform(X) == imputer.initial_imputer_.transform(X))
+
+    imputer.n_iter_ = 0
+    # now they should be equal as only initial imputation is done
+    assert_allclose(imputer.transform(X), imputer.initial_imputer_.transform(X))
+
+
+def test_iterative_imputer_verbose():
+    rng = np.random.RandomState(0)
+
+    n = 100
+    d = 3
+    X = _sparse_random_matrix(n, d, density=0.10, random_state=rng).toarray()
+    imputer = IterativeImputer(missing_values=0, max_iter=1, verbose=1)
+    imputer.fit(X)
+    imputer.transform(X)
+    imputer = IterativeImputer(missing_values=0, max_iter=1, verbose=2)
+    imputer.fit(X)
+    imputer.transform(X)
+
+
+def test_iterative_imputer_all_missing():
+    n = 100
+    d = 3
+    X = np.zeros((n, d))
+    imputer = IterativeImputer(missing_values=0, max_iter=1)
+    X_imputed = imputer.fit_transform(X)
+    assert_allclose(X_imputed, imputer.initial_imputer_.transform(X))
+
+
+@pytest.mark.parametrize(
+    "imputation_order", ["random", "roman", "ascending", "descending", "arabic"]
+)
+def test_iterative_imputer_imputation_order(imputation_order):
+    rng = np.random.RandomState(0)
+    n = 100
+    d = 10
+    max_iter = 2
+    X = _sparse_random_matrix(n, d, density=0.10, random_state=rng).toarray()
+    X[:, 0] = 1  # this column should not be discarded by IterativeImputer
+
+    imputer = IterativeImputer(
+        missing_values=0,
+        max_iter=max_iter,
+        n_nearest_features=5,
+        sample_posterior=False,
+        skip_complete=True,
+        min_value=0,
+        max_value=1,
+        verbose=1,
+        imputation_order=imputation_order,
+        random_state=rng,
+    )
+    imputer.fit_transform(X)
+    ordered_idx = [i.feat_idx for i in imputer.imputation_sequence_]
+
+    assert len(ordered_idx) // imputer.n_iter_ == imputer.n_features_with_missing_
+
+    if imputation_order == "roman":
+        assert np.all(ordered_idx[: d - 1] == np.arange(1, d))
+    elif imputation_order == "arabic":
+        assert np.all(ordered_idx[: d - 1] == np.arange(d - 1, 0, -1))
+    elif imputation_order == "random":
+        ordered_idx_round_1 = ordered_idx[: d - 1]
+        ordered_idx_round_2 = ordered_idx[d - 1 :]
+        assert ordered_idx_round_1 != ordered_idx_round_2
+    elif "ending" in imputation_order:
+        assert len(ordered_idx) == max_iter * (d - 1)
+
+
+@pytest.mark.parametrize(
+    "estimator", [None, DummyRegressor(), BayesianRidge(), ARDRegression(), RidgeCV()]
+)
+def test_iterative_imputer_estimators(estimator):
+    rng = np.random.RandomState(0)
+
+    n = 100
+    d = 10
+    X = _sparse_random_matrix(n, d, density=0.10, random_state=rng).toarray()
+
+    imputer = IterativeImputer(
+        missing_values=0, max_iter=1, estimator=estimator, random_state=rng
+    )
+    imputer.fit_transform(X)
+
+    # check that types are correct for estimators
+    hashes = []
+    for triplet in imputer.imputation_sequence_:
+        expected_type = (
+            type(estimator) if estimator is not None else type(BayesianRidge())
+        )
+        assert isinstance(triplet.estimator, expected_type)
+        hashes.append(id(triplet.estimator))
+
+    # check that each estimator is unique
+    assert len(set(hashes)) == len(hashes)
+
+
+def test_iterative_imputer_clip():
+    rng = np.random.RandomState(0)
+    n = 100
+    d = 10
+    X = _sparse_random_matrix(n, d, density=0.10, random_state=rng).toarray()
+
+    imputer = IterativeImputer(
+        missing_values=0, max_iter=1, min_value=0.1, max_value=0.2, random_state=rng
+    )
+
+    Xt = imputer.fit_transform(X)
+    assert_allclose(np.min(Xt[X == 0]), 0.1)
+    assert_allclose(np.max(Xt[X == 0]), 0.2)
+    assert_allclose(Xt[X != 0], X[X != 0])
+
+
+def test_iterative_imputer_clip_truncnorm():
+    rng = np.random.RandomState(0)
+    n = 100
+    d = 10
+    X = _sparse_random_matrix(n, d, density=0.10, random_state=rng).toarray()
+    X[:, 0] = 1
+
+    imputer = IterativeImputer(
+        missing_values=0,
+        max_iter=2,
+        n_nearest_features=5,
+        sample_posterior=True,
+        min_value=0.1,
+        max_value=0.2,
+        verbose=1,
+        imputation_order="random",
+        random_state=rng,
+    )
+    Xt = imputer.fit_transform(X)
+    assert_allclose(np.min(Xt[X == 0]), 0.1)
+    assert_allclose(np.max(Xt[X == 0]), 0.2)
+    assert_allclose(Xt[X != 0], X[X != 0])
+
+
+def test_iterative_imputer_truncated_normal_posterior():
+    #  test that the values that are imputed using `sample_posterior=True`
+    #  with boundaries (`min_value` and `max_value` are not None) are drawn
+    #  from a distribution that looks gaussian via the Kolmogorov Smirnov test.
+    #  note that starting from the wrong random seed will make this test fail
+    #  because random sampling doesn't occur at all when the imputation
+    #  is outside of the (min_value, max_value) range
+    rng = np.random.RandomState(42)
+
+    X = rng.normal(size=(5, 5))
+    X[0][0] = np.nan
+
+    imputer = IterativeImputer(
+        min_value=0, max_value=0.5, sample_posterior=True, random_state=rng
+    )
+
+    imputer.fit_transform(X)
+    # generate multiple imputations for the single missing value
+    imputations = np.array([imputer.transform(X)[0][0] for _ in range(100)])
+
+    assert all(imputations >= 0)
+    assert all(imputations <= 0.5)
+
+    mu, sigma = imputations.mean(), imputations.std()
+    ks_statistic, p_value = kstest((imputations - mu) / sigma, "norm")
+    if sigma == 0:
+        sigma += 1e-12
+    ks_statistic, p_value = kstest((imputations - mu) / sigma, "norm")
+    # we want to fail to reject null hypothesis
+    # null hypothesis: distributions are the same
+    assert ks_statistic < 0.2 or p_value > 0.1, "The posterior does appear to be normal"
+
+
+@pytest.mark.parametrize("strategy", ["mean", "median", "most_frequent"])
+def test_iterative_imputer_missing_at_transform(strategy):
+    rng = np.random.RandomState(0)
+    n = 100
+    d = 10
+    X_train = rng.randint(low=0, high=3, size=(n, d))
+    X_test = rng.randint(low=0, high=3, size=(n, d))
+
+    X_train[:, 0] = 1  # definitely no missing values in 0th column
+    X_test[0, 0] = 0  # definitely missing value in 0th column
+
+    imputer = IterativeImputer(
+        missing_values=0, max_iter=1, initial_strategy=strategy, random_state=rng
+    ).fit(X_train)
+    initial_imputer = SimpleImputer(missing_values=0, strategy=strategy).fit(X_train)
+
+    # if there were no missing values at time of fit, then imputer will
+    # only use the initial imputer for that feature at transform
+    assert_allclose(
+        imputer.transform(X_test)[:, 0], initial_imputer.transform(X_test)[:, 0]
+    )
+
+
+def test_iterative_imputer_transform_stochasticity():
+    rng1 = np.random.RandomState(0)
+    rng2 = np.random.RandomState(1)
+    n = 100
+    d = 10
+    X = _sparse_random_matrix(n, d, density=0.10, random_state=rng1).toarray()
+
+    # when sample_posterior=True, two transforms shouldn't be equal
+    imputer = IterativeImputer(
+        missing_values=0, max_iter=1, sample_posterior=True, random_state=rng1
+    )
+    imputer.fit(X)
+
+    X_fitted_1 = imputer.transform(X)
+    X_fitted_2 = imputer.transform(X)
+
+    # sufficient to assert that the means are not the same
+    assert np.mean(X_fitted_1) != pytest.approx(np.mean(X_fitted_2))
+
+    # when sample_posterior=False, and n_nearest_features=None
+    # and imputation_order is not random
+    # the two transforms should be identical even if rng are different
+    imputer1 = IterativeImputer(
+        missing_values=0,
+        max_iter=1,
+        sample_posterior=False,
+        n_nearest_features=None,
+        imputation_order="ascending",
+        random_state=rng1,
+    )
+
+    imputer2 = IterativeImputer(
+        missing_values=0,
+        max_iter=1,
+        sample_posterior=False,
+        n_nearest_features=None,
+        imputation_order="ascending",
+        random_state=rng2,
+    )
+    imputer1.fit(X)
+    imputer2.fit(X)
+
+    X_fitted_1a = imputer1.transform(X)
+    X_fitted_1b = imputer1.transform(X)
+    X_fitted_2 = imputer2.transform(X)
+
+    assert_allclose(X_fitted_1a, X_fitted_1b)
+    assert_allclose(X_fitted_1a, X_fitted_2)
+
+
+def test_iterative_imputer_no_missing():
+    rng = np.random.RandomState(0)
+    X = rng.rand(100, 100)
+    X[:, 0] = np.nan
+    m1 = IterativeImputer(max_iter=10, random_state=rng)
+    m2 = IterativeImputer(max_iter=10, random_state=rng)
+    pred1 = m1.fit(X).transform(X)
+    pred2 = m2.fit_transform(X)
+    # should exclude the first column entirely
+    assert_allclose(X[:, 1:], pred1)
+    # fit and fit_transform should both be identical
+    assert_allclose(pred1, pred2)
+
+
+def test_iterative_imputer_rank_one():
+    rng = np.random.RandomState(0)
+    d = 50
+    A = rng.rand(d, 1)
+    B = rng.rand(1, d)
+    X = np.dot(A, B)
+    nan_mask = rng.rand(d, d) < 0.5
+    X_missing = X.copy()
+    X_missing[nan_mask] = np.nan
+
+    imputer = IterativeImputer(max_iter=5, verbose=1, random_state=rng)
+    X_filled = imputer.fit_transform(X_missing)
+    assert_allclose(X_filled, X, atol=0.02)
+
+
+@pytest.mark.parametrize("rank", [3, 5])
+def test_iterative_imputer_transform_recovery(rank):
+    rng = np.random.RandomState(0)
+    n = 70
+    d = 70
+    A = rng.rand(n, rank)
+    B = rng.rand(rank, d)
+    X_filled = np.dot(A, B)
+    nan_mask = rng.rand(n, d) < 0.5
+    X_missing = X_filled.copy()
+    X_missing[nan_mask] = np.nan
+
+    # split up data in half
+    n = n // 2
+    X_train = X_missing[:n]
+    X_test_filled = X_filled[n:]
+    X_test = X_missing[n:]
+
+    imputer = IterativeImputer(
+        max_iter=5, imputation_order="descending", verbose=1, random_state=rng
+    ).fit(X_train)
+    X_test_est = imputer.transform(X_test)
+    assert_allclose(X_test_filled, X_test_est, atol=0.1)
+
+
+def test_iterative_imputer_additive_matrix():
+    rng = np.random.RandomState(0)
+    n = 100
+    d = 10
+    A = rng.randn(n, d)
+    B = rng.randn(n, d)
+    X_filled = np.zeros(A.shape)
+    for i in range(d):
+        for j in range(d):
+            X_filled[:, (i + j) % d] += (A[:, i] + B[:, j]) / 2
+    # a quarter is randomly missing
+    nan_mask = rng.rand(n, d) < 0.25
+    X_missing = X_filled.copy()
+    X_missing[nan_mask] = np.nan
+
+    # split up data
+    n = n // 2
+    X_train = X_missing[:n]
+    X_test_filled = X_filled[n:]
+    X_test = X_missing[n:]
+
+    imputer = IterativeImputer(max_iter=10, verbose=1, random_state=rng).fit(X_train)
+    X_test_est = imputer.transform(X_test)
+    assert_allclose(X_test_filled, X_test_est, rtol=1e-3, atol=0.01)
+
+
+def test_iterative_imputer_early_stopping():
+    rng = np.random.RandomState(0)
+    n = 50
+    d = 5
+    A = rng.rand(n, 1)
+    B = rng.rand(1, d)
+    X = np.dot(A, B)
+    nan_mask = rng.rand(n, d) < 0.5
+    X_missing = X.copy()
+    X_missing[nan_mask] = np.nan
+
+    imputer = IterativeImputer(
+        max_iter=100, tol=1e-2, sample_posterior=False, verbose=1, random_state=rng
+    )
+    X_filled_100 = imputer.fit_transform(X_missing)
+    assert len(imputer.imputation_sequence_) == d * imputer.n_iter_
+
+    imputer = IterativeImputer(
+        max_iter=imputer.n_iter_, sample_posterior=False, verbose=1, random_state=rng
+    )
+    X_filled_early = imputer.fit_transform(X_missing)
+    assert_allclose(X_filled_100, X_filled_early, atol=1e-7)
+
+    imputer = IterativeImputer(
+        max_iter=100, tol=0, sample_posterior=False, verbose=1, random_state=rng
+    )
+    imputer.fit(X_missing)
+    assert imputer.n_iter_ == imputer.max_iter
+
+
+def test_iterative_imputer_catch_warning():
+    # check that we catch a RuntimeWarning due to a division by zero when a
+    # feature is constant in the dataset
+    X, y = load_diabetes(return_X_y=True)
+    n_samples, n_features = X.shape
+
+    # simulate that a feature only contain one category during fit
+    X[:, 3] = 1
+
+    # add some missing values
+    rng = np.random.RandomState(0)
+    missing_rate = 0.15
+    for feat in range(n_features):
+        sample_idx = rng.choice(
+            np.arange(n_samples), size=int(n_samples * missing_rate), replace=False
+        )
+        X[sample_idx, feat] = np.nan
+
+    imputer = IterativeImputer(n_nearest_features=5, sample_posterior=True)
+    with warnings.catch_warnings():
+        warnings.simplefilter("error", RuntimeWarning)
+        X_fill = imputer.fit_transform(X, y)
+    assert not np.any(np.isnan(X_fill))
+
+
+@pytest.mark.parametrize(
+    "min_value, max_value, correct_output",
+    [
+        (0, 100, np.array([[0] * 3, [100] * 3])),
+        (None, None, np.array([[-np.inf] * 3, [np.inf] * 3])),
+        (-np.inf, np.inf, np.array([[-np.inf] * 3, [np.inf] * 3])),
+        ([-5, 5, 10], [100, 200, 300], np.array([[-5, 5, 10], [100, 200, 300]])),
+        (
+            [-5, -np.inf, 10],
+            [100, 200, np.inf],
+            np.array([[-5, -np.inf, 10], [100, 200, np.inf]]),
+        ),
+    ],
+    ids=["scalars", "None-default", "inf", "lists", "lists-with-inf"],
+)
+def test_iterative_imputer_min_max_array_like(min_value, max_value, correct_output):
+    # check that passing scalar or array-like
+    # for min_value and max_value in IterativeImputer works
+    X = np.random.RandomState(0).randn(10, 3)
+    imputer = IterativeImputer(min_value=min_value, max_value=max_value)
+    imputer.fit(X)
+
+    assert isinstance(imputer._min_value, np.ndarray) and isinstance(
+        imputer._max_value, np.ndarray
+    )
+    assert (imputer._min_value.shape[0] == X.shape[1]) and (
+        imputer._max_value.shape[0] == X.shape[1]
+    )
+
+    assert_allclose(correct_output[0, :], imputer._min_value)
+    assert_allclose(correct_output[1, :], imputer._max_value)
+
+
+@pytest.mark.parametrize(
+    "min_value, max_value, err_msg",
+    [
+        (100, 0, "min_value >= max_value."),
+        (np.inf, -np.inf, "min_value >= max_value."),
+        ([-5, 5], [100, 200, 0], "_value' should be of shape"),
+    ],
+)
+def test_iterative_imputer_catch_min_max_error(min_value, max_value, err_msg):
+    # check that passing scalar or array-like
+    # for min_value and max_value in IterativeImputer works
+    X = np.random.random((10, 3))
+    imputer = IterativeImputer(min_value=min_value, max_value=max_value)
+    with pytest.raises(ValueError, match=err_msg):
+        imputer.fit(X)
+
+
+@pytest.mark.parametrize(
+    "min_max_1, min_max_2",
+    [([None, None], [-np.inf, np.inf]), ([-10, 10], [[-10] * 4, [10] * 4])],
+    ids=["None-vs-inf", "Scalar-vs-vector"],
+)
+def test_iterative_imputer_min_max_array_like_imputation(min_max_1, min_max_2):
+    # Test that None/inf and scalar/vector give the same imputation
+    X_train = np.array(
+        [
+            [np.nan, 2, 2, 1],
+            [10, np.nan, np.nan, 7],
+            [3, 1, np.nan, 1],
+            [np.nan, 4, 2, np.nan],
+        ]
+    )
+    X_test = np.array(
+        [[np.nan, 2, np.nan, 5], [2, 4, np.nan, np.nan], [np.nan, 1, 10, 1]]
+    )
+    imputer1 = IterativeImputer(
+        min_value=min_max_1[0], max_value=min_max_1[1], random_state=0
+    )
+    imputer2 = IterativeImputer(
+        min_value=min_max_2[0], max_value=min_max_2[1], random_state=0
+    )
+    X_test_imputed1 = imputer1.fit(X_train).transform(X_test)
+    X_test_imputed2 = imputer2.fit(X_train).transform(X_test)
+    assert_allclose(X_test_imputed1[:, 0], X_test_imputed2[:, 0])
+
+
+@pytest.mark.parametrize("skip_complete", [True, False])
+def test_iterative_imputer_skip_non_missing(skip_complete):
+    # check the imputing strategy when missing data are present in the
+    # testing set only.
+    # taken from: https://github.com/scikit-learn/scikit-learn/issues/14383
+    rng = np.random.RandomState(0)
+    X_train = np.array([[5, 2, 2, 1], [10, 1, 2, 7], [3, 1, 1, 1], [8, 4, 2, 2]])
+    X_test = np.array([[np.nan, 2, 4, 5], [np.nan, 4, 1, 2], [np.nan, 1, 10, 1]])
+    imputer = IterativeImputer(
+        initial_strategy="mean", skip_complete=skip_complete, random_state=rng
+    )
+    X_test_est = imputer.fit(X_train).transform(X_test)
+    if skip_complete:
+        # impute with the initial strategy: 'mean'
+        assert_allclose(X_test_est[:, 0], np.mean(X_train[:, 0]))
+    else:
+        assert_allclose(X_test_est[:, 0], [11, 7, 12], rtol=1e-4)
+
+
+@pytest.mark.parametrize("rs_imputer", [None, 1, np.random.RandomState(seed=1)])
+@pytest.mark.parametrize("rs_estimator", [None, 1, np.random.RandomState(seed=1)])
+def test_iterative_imputer_dont_set_random_state(rs_imputer, rs_estimator):
+    class ZeroEstimator:
+        def __init__(self, random_state):
+            self.random_state = random_state
+
+        def fit(self, *args, **kgards):
+            return self
+
+        def predict(self, X):
+            return np.zeros(X.shape[0])
+
+    estimator = ZeroEstimator(random_state=rs_estimator)
+    imputer = IterativeImputer(random_state=rs_imputer)
+    X_train = np.zeros((10, 3))
+    imputer.fit(X_train)
+    assert estimator.random_state == rs_estimator
+
+
+@pytest.mark.parametrize(
+    "X_fit, X_trans, params, msg_err",
+    [
+        (
+            np.array([[-1, 1], [1, 2]]),
+            np.array([[-1, 1], [1, -1]]),
+            {"features": "missing-only", "sparse": "auto"},
+            "have missing values in transform but have no missing values in fit",
+        ),
+        (
+            np.array([["a", "b"], ["c", "a"]], dtype=str),
+            np.array([["a", "b"], ["c", "a"]], dtype=str),
+            {},
+            "MissingIndicator does not support data with dtype",
+        ),
+    ],
+)
+def test_missing_indicator_error(X_fit, X_trans, params, msg_err):
+    indicator = MissingIndicator(missing_values=-1)
+    indicator.set_params(**params)
+    with pytest.raises(ValueError, match=msg_err):
+        indicator.fit(X_fit).transform(X_trans)
+
+
+def _generate_missing_indicator_cases():
+    missing_values_dtypes = [(0, np.int32), (np.nan, np.float64), (-1, np.int32)]
+    arr_types = (
+        [np.array]
+        + CSC_CONTAINERS
+        + CSR_CONTAINERS
+        + COO_CONTAINERS
+        + LIL_CONTAINERS
+        + BSR_CONTAINERS
+    )
+    return [
+        (arr_type, missing_values, dtype)
+        for arr_type, (missing_values, dtype) in product(
+            arr_types, missing_values_dtypes
+        )
+        if not (missing_values == 0 and arr_type is not np.array)
+    ]
+
+
+@pytest.mark.parametrize(
+    "arr_type, missing_values, dtype", _generate_missing_indicator_cases()
+)
+@pytest.mark.parametrize(
+    "param_features, n_features, features_indices",
+    [("missing-only", 3, np.array([0, 1, 2])), ("all", 3, np.array([0, 1, 2]))],
+)
+def test_missing_indicator_new(
+    missing_values, arr_type, dtype, param_features, n_features, features_indices
+):
+    X_fit = np.array([[missing_values, missing_values, 1], [4, 2, missing_values]])
+    X_trans = np.array([[missing_values, missing_values, 1], [4, 12, 10]])
+    X_fit_expected = np.array([[1, 1, 0], [0, 0, 1]])
+    X_trans_expected = np.array([[1, 1, 0], [0, 0, 0]])
+
+    # convert the input to the right array format and right dtype
+    X_fit = arr_type(X_fit).astype(dtype)
+    X_trans = arr_type(X_trans).astype(dtype)
+    X_fit_expected = X_fit_expected.astype(dtype)
+    X_trans_expected = X_trans_expected.astype(dtype)
+
+    indicator = MissingIndicator(
+        missing_values=missing_values, features=param_features, sparse=False
+    )
+    X_fit_mask = indicator.fit_transform(X_fit)
+    X_trans_mask = indicator.transform(X_trans)
+
+    assert X_fit_mask.shape[1] == n_features
+    assert X_trans_mask.shape[1] == n_features
+
+    assert_array_equal(indicator.features_, features_indices)
+    assert_allclose(X_fit_mask, X_fit_expected[:, features_indices])
+    assert_allclose(X_trans_mask, X_trans_expected[:, features_indices])
+
+    assert X_fit_mask.dtype == bool
+    assert X_trans_mask.dtype == bool
+    assert isinstance(X_fit_mask, np.ndarray)
+    assert isinstance(X_trans_mask, np.ndarray)
+
+    indicator.set_params(sparse=True)
+    X_fit_mask_sparse = indicator.fit_transform(X_fit)
+    X_trans_mask_sparse = indicator.transform(X_trans)
+
+    assert X_fit_mask_sparse.dtype == bool
+    assert X_trans_mask_sparse.dtype == bool
+    assert X_fit_mask_sparse.format == "csc"
+    assert X_trans_mask_sparse.format == "csc"
+    assert_allclose(X_fit_mask_sparse.toarray(), X_fit_mask)
+    assert_allclose(X_trans_mask_sparse.toarray(), X_trans_mask)
+
+
+@pytest.mark.parametrize(
+    "arr_type",
+    CSC_CONTAINERS + CSR_CONTAINERS + COO_CONTAINERS + LIL_CONTAINERS + BSR_CONTAINERS,
+)
+def test_missing_indicator_raise_on_sparse_with_missing_0(arr_type):
+    # test for sparse input and missing_value == 0
+
+    missing_values = 0
+    X_fit = np.array([[missing_values, missing_values, 1], [4, missing_values, 2]])
+    X_trans = np.array([[missing_values, missing_values, 1], [4, 12, 10]])
+
+    # convert the input to the right array format
+    X_fit_sparse = arr_type(X_fit)
+    X_trans_sparse = arr_type(X_trans)
+
+    indicator = MissingIndicator(missing_values=missing_values)
+
+    with pytest.raises(ValueError, match="Sparse input with missing_values=0"):
+        indicator.fit_transform(X_fit_sparse)
+
+    indicator.fit_transform(X_fit)
+    with pytest.raises(ValueError, match="Sparse input with missing_values=0"):
+        indicator.transform(X_trans_sparse)
+
+
+@pytest.mark.parametrize("param_sparse", [True, False, "auto"])
+@pytest.mark.parametrize(
+    "arr_type, missing_values",
+    [(np.array, 0)]
+    + list(
+        product(
+            CSC_CONTAINERS
+            + CSR_CONTAINERS
+            + COO_CONTAINERS
+            + LIL_CONTAINERS
+            + BSR_CONTAINERS,
+            [np.nan],
+        )
+    ),
+)
+def test_missing_indicator_sparse_param(arr_type, missing_values, param_sparse):
+    # check the format of the output with different sparse parameter
+    X_fit = np.array([[missing_values, missing_values, 1], [4, missing_values, 2]])
+    X_trans = np.array([[missing_values, missing_values, 1], [4, 12, 10]])
+    X_fit = arr_type(X_fit).astype(np.float64)
+    X_trans = arr_type(X_trans).astype(np.float64)
+
+    indicator = MissingIndicator(missing_values=missing_values, sparse=param_sparse)
+    X_fit_mask = indicator.fit_transform(X_fit)
+    X_trans_mask = indicator.transform(X_trans)
+
+    if param_sparse is True:
+        assert X_fit_mask.format == "csc"
+        assert X_trans_mask.format == "csc"
+    elif param_sparse == "auto" and missing_values == 0:
+        assert isinstance(X_fit_mask, np.ndarray)
+        assert isinstance(X_trans_mask, np.ndarray)
+    elif param_sparse is False:
+        assert isinstance(X_fit_mask, np.ndarray)
+        assert isinstance(X_trans_mask, np.ndarray)
+    else:
+        if sparse.issparse(X_fit):
+            assert X_fit_mask.format == "csc"
+            assert X_trans_mask.format == "csc"
+        else:
+            assert isinstance(X_fit_mask, np.ndarray)
+            assert isinstance(X_trans_mask, np.ndarray)
+
+
+def test_missing_indicator_string():
+    X = np.array([["a", "b", "c"], ["b", "c", "a"]], dtype=object)
+    indicator = MissingIndicator(missing_values="a", features="all")
+    X_trans = indicator.fit_transform(X)
+    assert_array_equal(X_trans, np.array([[True, False, False], [False, False, True]]))
+
+
+@pytest.mark.parametrize(
+    "X, missing_values, X_trans_exp",
+    [
+        (
+            np.array([["a", "b"], ["b", "a"]], dtype=object),
+            "a",
+            np.array([["b", "b", True, False], ["b", "b", False, True]], dtype=object),
+        ),
+        (
+            np.array([[np.nan, 1.0], [1.0, np.nan]]),
+            np.nan,
+            np.array([[1.0, 1.0, True, False], [1.0, 1.0, False, True]]),
+        ),
+        (
+            np.array([[np.nan, "b"], ["b", np.nan]], dtype=object),
+            np.nan,
+            np.array([["b", "b", True, False], ["b", "b", False, True]], dtype=object),
+        ),
+        (
+            np.array([[None, "b"], ["b", None]], dtype=object),
+            None,
+            np.array([["b", "b", True, False], ["b", "b", False, True]], dtype=object),
+        ),
+    ],
+)
+def test_missing_indicator_with_imputer(X, missing_values, X_trans_exp):
+    trans = make_union(
+        SimpleImputer(missing_values=missing_values, strategy="most_frequent"),
+        MissingIndicator(missing_values=missing_values),
+    )
+    X_trans = trans.fit_transform(X)
+    assert_array_equal(X_trans, X_trans_exp)
+
+
+@pytest.mark.parametrize("imputer_constructor", [SimpleImputer, IterativeImputer])
+@pytest.mark.parametrize(
+    "imputer_missing_values, missing_value, err_msg",
+    [
+        ("NaN", np.nan, "Input X contains NaN"),
+        ("-1", -1, "types are expected to be both numerical."),
+    ],
+)
+def test_inconsistent_dtype_X_missing_values(
+    imputer_constructor, imputer_missing_values, missing_value, err_msg
+):
+    # regression test for issue #11390. Comparison between incoherent dtype
+    # for X and missing_values was not raising a proper error.
+    rng = np.random.RandomState(42)
+    X = rng.randn(10, 10)
+    X[0, 0] = missing_value
+
+    imputer = imputer_constructor(missing_values=imputer_missing_values)
+
+    with pytest.raises(ValueError, match=err_msg):
+        imputer.fit_transform(X)
+
+
+def test_missing_indicator_no_missing():
+    # check that all features are dropped if there are no missing values when
+    # features='missing-only' (#13491)
+    X = np.array([[1, 1], [1, 1]])
+
+    mi = MissingIndicator(features="missing-only", missing_values=-1)
+    Xt = mi.fit_transform(X)
+
+    assert Xt.shape[1] == 0
+
+
+@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
+def test_missing_indicator_sparse_no_explicit_zeros(csr_container):
+    # Check that non missing values don't become explicit zeros in the mask
+    # generated by missing indicator when X is sparse. (#13491)
+    X = csr_container([[0, 1, 2], [1, 2, 0], [2, 0, 1]])
+
+    mi = MissingIndicator(features="all", missing_values=1)
+    Xt = mi.fit_transform(X)
+
+    assert Xt.getnnz() == Xt.sum()
+
+
+@pytest.mark.parametrize("imputer_constructor", [SimpleImputer, IterativeImputer])
+def test_imputer_without_indicator(imputer_constructor):
+    X = np.array([[1, 1], [1, 1]])
+    imputer = imputer_constructor()
+    imputer.fit(X)
+
+    assert imputer.indicator_ is None
+
+
+@pytest.mark.parametrize(
+    "arr_type",
+    CSC_CONTAINERS + CSR_CONTAINERS + COO_CONTAINERS + LIL_CONTAINERS + BSR_CONTAINERS,
+)
+def test_simple_imputation_add_indicator_sparse_matrix(arr_type):
+    X_sparse = arr_type([[np.nan, 1, 5], [2, np.nan, 1], [6, 3, np.nan], [1, 2, 9]])
+    X_true = np.array(
+        [
+            [3.0, 1.0, 5.0, 1.0, 0.0, 0.0],
+            [2.0, 2.0, 1.0, 0.0, 1.0, 0.0],
+            [6.0, 3.0, 5.0, 0.0, 0.0, 1.0],
+            [1.0, 2.0, 9.0, 0.0, 0.0, 0.0],
+        ]
+    )
+
+    imputer = SimpleImputer(missing_values=np.nan, add_indicator=True)
+    X_trans = imputer.fit_transform(X_sparse)
+
+    assert sparse.issparse(X_trans)
+    assert X_trans.shape == X_true.shape
+    assert_allclose(X_trans.toarray(), X_true)
+
+
+@pytest.mark.parametrize(
+    "strategy, expected", [("most_frequent", "b"), ("constant", "missing_value")]
+)
+def test_simple_imputation_string_list(strategy, expected):
+    X = [["a", "b"], ["c", np.nan]]
+
+    X_true = np.array([["a", "b"], ["c", expected]], dtype=object)
+
+    imputer = SimpleImputer(strategy=strategy)
+    X_trans = imputer.fit_transform(X)
+
+    assert_array_equal(X_trans, X_true)
+
+
+@pytest.mark.parametrize(
+    "order, idx_order",
+    [("ascending", [3, 4, 2, 0, 1]), ("descending", [1, 0, 2, 4, 3])],
+)
+def test_imputation_order(order, idx_order):
+    # regression test for #15393
+    rng = np.random.RandomState(42)
+    X = rng.rand(100, 5)
+    X[:50, 1] = np.nan
+    X[:30, 0] = np.nan
+    X[:20, 2] = np.nan
+    X[:10, 4] = np.nan
+
+    with pytest.warns(ConvergenceWarning):
+        trs = IterativeImputer(max_iter=1, imputation_order=order, random_state=0).fit(
+            X
+        )
+        idx = [x.feat_idx for x in trs.imputation_sequence_]
+        assert idx == idx_order
+
+
+@pytest.mark.parametrize("missing_value", [-1, np.nan])
+def test_simple_imputation_inverse_transform(missing_value):
+    # Test inverse_transform feature for np.nan
+    X_1 = np.array(
+        [
+            [9, missing_value, 3, -1],
+            [4, -1, 5, 4],
+            [6, 7, missing_value, -1],
+            [8, 9, 0, missing_value],
+        ]
+    )
+
+    X_2 = np.array(
+        [
+            [5, 4, 2, 1],
+            [2, 1, missing_value, 3],
+            [9, missing_value, 7, 1],
+            [6, 4, 2, missing_value],
+        ]
+    )
+
+    X_3 = np.array(
+        [
+            [1, missing_value, 5, 9],
+            [missing_value, 4, missing_value, missing_value],
+            [2, missing_value, 7, missing_value],
+            [missing_value, 3, missing_value, 8],
+        ]
+    )
+
+    X_4 = np.array(
+        [
+            [1, 1, 1, 3],
+            [missing_value, 2, missing_value, 1],
+            [2, 3, 3, 4],
+            [missing_value, 4, missing_value, 2],
+        ]
+    )
+
+    imputer = SimpleImputer(
+        missing_values=missing_value, strategy="mean", add_indicator=True
+    )
+
+    X_1_trans = imputer.fit_transform(X_1)
+    X_1_inv_trans = imputer.inverse_transform(X_1_trans)
+
+    X_2_trans = imputer.transform(X_2)  # test on new data
+    X_2_inv_trans = imputer.inverse_transform(X_2_trans)
+
+    assert_array_equal(X_1_inv_trans, X_1)
+    assert_array_equal(X_2_inv_trans, X_2)
+
+    for X in [X_3, X_4]:
+        X_trans = imputer.fit_transform(X)
+        X_inv_trans = imputer.inverse_transform(X_trans)
+        assert_array_equal(X_inv_trans, X)
+
+
+@pytest.mark.parametrize("missing_value", [-1, np.nan])
+def test_simple_imputation_inverse_transform_exceptions(missing_value):
+    X_1 = np.array(
+        [
+            [9, missing_value, 3, -1],
+            [4, -1, 5, 4],
+            [6, 7, missing_value, -1],
+            [8, 9, 0, missing_value],
+        ]
+    )
+
+    imputer = SimpleImputer(missing_values=missing_value, strategy="mean")
+    X_1_trans = imputer.fit_transform(X_1)
+    with pytest.raises(
+        ValueError, match=f"Got 'add_indicator={imputer.add_indicator}'"
+    ):
+        imputer.inverse_transform(X_1_trans)
+
+
+@pytest.mark.parametrize(
+    "expected,array,dtype,extra_value,n_repeat",
+    [
+        # array of object dtype
+        ("extra_value", ["a", "b", "c"], object, "extra_value", 2),
+        (
+            "most_frequent_value",
+            ["most_frequent_value", "most_frequent_value", "value"],
+            object,
+            "extra_value",
+            1,
+        ),
+        ("a", ["min_value", "min_valuevalue"], object, "a", 2),
+        ("min_value", ["min_value", "min_value", "value"], object, "z", 2),
+        # array of numeric dtype
+        (10, [1, 2, 3], int, 10, 2),
+        (1, [1, 1, 2], int, 10, 1),
+        (10, [20, 20, 1], int, 10, 2),
+        (1, [1, 1, 20], int, 10, 2),
+    ],
+)
+def test_most_frequent(expected, array, dtype, extra_value, n_repeat):
+    assert expected == _most_frequent(
+        np.array(array, dtype=dtype), extra_value, n_repeat
+    )
+
+
+@pytest.mark.parametrize(
+    "initial_strategy", ["mean", "median", "most_frequent", "constant"]
+)
+def test_iterative_imputer_keep_empty_features(initial_strategy):
+    """Check the behaviour of the iterative imputer with different initial strategy
+    and keeping empty features (i.e. features containing only missing values).
+    """
+    X = np.array([[1, np.nan, 2], [3, np.nan, np.nan]])
+
+    imputer = IterativeImputer(
+        initial_strategy=initial_strategy, keep_empty_features=True
+    )
+    X_imputed = imputer.fit_transform(X)
+    assert_allclose(X_imputed[:, 1], 0)
+    X_imputed = imputer.transform(X)
+    assert_allclose(X_imputed[:, 1], 0)
+
+
+def test_iterative_imputer_constant_fill_value():
+    """Check that we propagate properly the parameter `fill_value`."""
+    X = np.array([[-1, 2, 3, -1], [4, -1, 5, -1], [6, 7, -1, -1], [8, 9, 0, -1]])
+
+    fill_value = 100
+    imputer = IterativeImputer(
+        missing_values=-1,
+        initial_strategy="constant",
+        fill_value=fill_value,
+        max_iter=0,
+    )
+    imputer.fit_transform(X)
+    assert_array_equal(imputer.initial_imputer_.statistics_, fill_value)
+
+
+@pytest.mark.parametrize("keep_empty_features", [True, False])
+def test_knn_imputer_keep_empty_features(keep_empty_features):
+    """Check the behaviour of `keep_empty_features` for `KNNImputer`."""
+    X = np.array([[1, np.nan, 2], [3, np.nan, np.nan]])
+
+    imputer = KNNImputer(keep_empty_features=keep_empty_features)
+
+    for method in ["fit_transform", "transform"]:
+        X_imputed = getattr(imputer, method)(X)
+        if keep_empty_features:
+            assert X_imputed.shape == X.shape
+            assert_array_equal(X_imputed[:, 1], 0)
+        else:
+            assert X_imputed.shape == (X.shape[0], X.shape[1] - 1)
+
+
+def test_simple_impute_pd_na():
+    pd = pytest.importorskip("pandas")
+
+    # Impute pandas array of string types.
+    df = pd.DataFrame({"feature": pd.Series(["abc", None, "de"], dtype="string")})
+    imputer = SimpleImputer(missing_values=pd.NA, strategy="constant", fill_value="na")
+    _assert_array_equal_and_same_dtype(
+        imputer.fit_transform(df), np.array([["abc"], ["na"], ["de"]], dtype=object)
+    )
+
+    # Impute pandas array of string types without any missing values.
+    df = pd.DataFrame({"feature": pd.Series(["abc", "de", "fgh"], dtype="string")})
+    imputer = SimpleImputer(fill_value="ok", strategy="constant")
+    _assert_array_equal_and_same_dtype(
+        imputer.fit_transform(df), np.array([["abc"], ["de"], ["fgh"]], dtype=object)
+    )
+
+    # Impute pandas array of integer types.
+    df = pd.DataFrame({"feature": pd.Series([1, None, 3], dtype="Int64")})
+    imputer = SimpleImputer(missing_values=pd.NA, strategy="constant", fill_value=-1)
+    _assert_allclose_and_same_dtype(
+        imputer.fit_transform(df), np.array([[1], [-1], [3]], dtype="float64")
+    )
+
+    # Use `np.nan` also works.
+    imputer = SimpleImputer(missing_values=np.nan, strategy="constant", fill_value=-1)
+    _assert_allclose_and_same_dtype(
+        imputer.fit_transform(df), np.array([[1], [-1], [3]], dtype="float64")
+    )
+
+    # Impute pandas array of integer types with 'median' strategy.
+    df = pd.DataFrame({"feature": pd.Series([1, None, 2, 3], dtype="Int64")})
+    imputer = SimpleImputer(missing_values=pd.NA, strategy="median")
+    _assert_allclose_and_same_dtype(
+        imputer.fit_transform(df), np.array([[1], [2], [2], [3]], dtype="float64")
+    )
+
+    # Impute pandas array of integer types with 'mean' strategy.
+    df = pd.DataFrame({"feature": pd.Series([1, None, 2], dtype="Int64")})
+    imputer = SimpleImputer(missing_values=pd.NA, strategy="mean")
+    _assert_allclose_and_same_dtype(
+        imputer.fit_transform(df), np.array([[1], [1.5], [2]], dtype="float64")
+    )
+
+    # Impute pandas array of float types.
+    df = pd.DataFrame({"feature": pd.Series([1.0, None, 3.0], dtype="float64")})
+    imputer = SimpleImputer(missing_values=pd.NA, strategy="constant", fill_value=-2.0)
+    _assert_allclose_and_same_dtype(
+        imputer.fit_transform(df), np.array([[1.0], [-2.0], [3.0]], dtype="float64")
+    )
+
+    # Impute pandas array of float types with 'median' strategy.
+    df = pd.DataFrame({"feature": pd.Series([1.0, None, 2.0, 3.0], dtype="float64")})
+    imputer = SimpleImputer(missing_values=pd.NA, strategy="median")
+    _assert_allclose_and_same_dtype(
+        imputer.fit_transform(df),
+        np.array([[1.0], [2.0], [2.0], [3.0]], dtype="float64"),
+    )
+
+
+def test_missing_indicator_feature_names_out():
+    """Check that missing indicator return the feature names with a prefix."""
+    pd = pytest.importorskip("pandas")
+
+    missing_values = np.nan
+    X = pd.DataFrame(
+        [
+            [missing_values, missing_values, 1, missing_values],
+            [4, missing_values, 2, 10],
+        ],
+        columns=["a", "b", "c", "d"],
+    )
+
+    indicator = MissingIndicator(missing_values=missing_values).fit(X)
+    feature_names = indicator.get_feature_names_out()
+    expected_names = ["missingindicator_a", "missingindicator_b", "missingindicator_d"]
+    assert_array_equal(expected_names, feature_names)
+
+
+def test_imputer_lists_fit_transform():
+    """Check transform uses object dtype when fitted on an object dtype.
+
+    Non-regression test for #19572.
+    """
+
+    X = [["a", "b"], ["c", "b"], ["a", "a"]]
+    imp_frequent = SimpleImputer(strategy="most_frequent").fit(X)
+    X_trans = imp_frequent.transform([[np.nan, np.nan]])
+    assert X_trans.dtype == object
+    assert_array_equal(X_trans, [["a", "b"]])
+
+
+@pytest.mark.parametrize("dtype_test", [np.float32, np.float64])
+def test_imputer_transform_preserves_numeric_dtype(dtype_test):
+    """Check transform preserves numeric dtype independent of fit dtype."""
+    X = np.asarray(
+        [[1.2, 3.4, np.nan], [np.nan, 1.2, 1.3], [4.2, 2, 1]], dtype=np.float64
+    )
+    imp = SimpleImputer().fit(X)
+
+    X_test = np.asarray([[np.nan, np.nan, np.nan]], dtype=dtype_test)
+    X_trans = imp.transform(X_test)
+    assert X_trans.dtype == dtype_test
+
+
+@pytest.mark.parametrize("array_type", ["array", "sparse"])
+@pytest.mark.parametrize("keep_empty_features", [True, False])
+def test_simple_imputer_constant_keep_empty_features(array_type, keep_empty_features):
+    """Check the behaviour of `keep_empty_features` with `strategy='constant'.
+    For backward compatibility, a column full of missing values will always be
+    fill and never dropped.
+    """
+    X = np.array([[np.nan, 2], [np.nan, 3], [np.nan, 6]])
+    X = _convert_container(X, array_type)
+    fill_value = 10
+    imputer = SimpleImputer(
+        strategy="constant",
+        fill_value=fill_value,
+        keep_empty_features=keep_empty_features,
+    )
+
+    for method in ["fit_transform", "transform"]:
+        X_imputed = getattr(imputer, method)(X)
+        assert X_imputed.shape == X.shape
+        constant_feature = (
+            X_imputed[:, 0].toarray() if array_type == "sparse" else X_imputed[:, 0]
+        )
+        assert_array_equal(constant_feature, fill_value)
+
+
+@pytest.mark.parametrize("array_type", ["array", "sparse"])
+@pytest.mark.parametrize("strategy", ["mean", "median", "most_frequent"])
+@pytest.mark.parametrize("keep_empty_features", [True, False])
+def test_simple_imputer_keep_empty_features(strategy, array_type, keep_empty_features):
+    """Check the behaviour of `keep_empty_features` with all strategies but
+    'constant'.
+    """
+    X = np.array([[np.nan, 2], [np.nan, 3], [np.nan, 6]])
+    X = _convert_container(X, array_type)
+    imputer = SimpleImputer(strategy=strategy, keep_empty_features=keep_empty_features)
+
+    for method in ["fit_transform", "transform"]:
+        X_imputed = getattr(imputer, method)(X)
+        if keep_empty_features:
+            assert X_imputed.shape == X.shape
+            constant_feature = (
+                X_imputed[:, 0].toarray() if array_type == "sparse" else X_imputed[:, 0]
+            )
+            assert_array_equal(constant_feature, 0)
+        else:
+            assert X_imputed.shape == (X.shape[0], X.shape[1] - 1)
+
+
+def test_simple_imputer_constant_fill_value_casting():
+    """Check that we raise a proper error message when we cannot cast the fill value
+    to the input data type. Otherwise, check that the casting is done properly.
+
+    Non-regression test for:
+    https://github.com/scikit-learn/scikit-learn/issues/28309
+    """
+    # cannot cast fill_value at fit
+    fill_value = 1.5
+    X_int64 = np.array([[1, 2, 3], [2, 3, 4]], dtype=np.int64)
+    imputer = SimpleImputer(
+        strategy="constant", fill_value=fill_value, missing_values=2
+    )
+    err_msg = f"fill_value={fill_value!r} (of type {type(fill_value)!r}) cannot be cast"
+    with pytest.raises(ValueError, match=re.escape(err_msg)):
+        imputer.fit(X_int64)
+
+    # cannot cast fill_value at transform
+    X_float64 = np.array([[1, 2, 3], [2, 3, 4]], dtype=np.float64)
+    imputer.fit(X_float64)
+    err_msg = (
+        f"The dtype of the filling value (i.e. {imputer.statistics_.dtype!r}) "
+        "cannot be cast"
+    )
+    with pytest.raises(ValueError, match=re.escape(err_msg)):
+        imputer.transform(X_int64)
+
+    # check that no error is raised when having the same kind of dtype
+    fill_value_list = [np.float64(1.5), 1.5, 1]
+    X_float32 = X_float64.astype(np.float32)
+
+    for fill_value in fill_value_list:
+        imputer = SimpleImputer(
+            strategy="constant", fill_value=fill_value, missing_values=2
+        )
+        X_trans = imputer.fit_transform(X_float32)
+        assert X_trans.dtype == X_float32.dtype

env-llmeval/lib/python3.10/site-packages/sklearn/impute/tests/test_knn.py

@@ -0,0 +1,547 @@
+import numpy as np
+import pytest
+
+from sklearn import config_context
+from sklearn.impute import KNNImputer
+from sklearn.metrics.pairwise import nan_euclidean_distances, pairwise_distances
+from sklearn.neighbors import KNeighborsRegressor
+from sklearn.utils._testing import assert_allclose
+
+
+@pytest.mark.parametrize("weights", ["uniform", "distance"])
+@pytest.mark.parametrize("n_neighbors", range(1, 6))
+def test_knn_imputer_shape(weights, n_neighbors):
+    # Verify the shapes of the imputed matrix for different weights and
+    # number of neighbors.
+    n_rows = 10
+    n_cols = 2
+    X = np.random.rand(n_rows, n_cols)
+    X[0, 0] = np.nan
+
+    imputer = KNNImputer(n_neighbors=n_neighbors, weights=weights)
+    X_imputed = imputer.fit_transform(X)
+    assert X_imputed.shape == (n_rows, n_cols)
+
+
+@pytest.mark.parametrize("na", [np.nan, -1])
+def test_knn_imputer_default_with_invalid_input(na):
+    # Test imputation with default values and invalid input
+
+    # Test with inf present
+    X = np.array(
+        [
+            [np.inf, 1, 1, 2, na],
+            [2, 1, 2, 2, 3],
+            [3, 2, 3, 3, 8],
+            [na, 6, 0, 5, 13],
+            [na, 7, 0, 7, 8],
+            [6, 6, 2, 5, 7],
+        ]
+    )
+    with pytest.raises(ValueError, match="Input X contains (infinity|NaN)"):
+        KNNImputer(missing_values=na).fit(X)
+
+    # Test with inf present in matrix passed in transform()
+    X = np.array(
+        [
+            [np.inf, 1, 1, 2, na],
+            [2, 1, 2, 2, 3],
+            [3, 2, 3, 3, 8],
+            [na, 6, 0, 5, 13],
+            [na, 7, 0, 7, 8],
+            [6, 6, 2, 5, 7],
+        ]
+    )
+
+    X_fit = np.array(
+        [
+            [0, 1, 1, 2, na],
+            [2, 1, 2, 2, 3],
+            [3, 2, 3, 3, 8],
+            [na, 6, 0, 5, 13],
+            [na, 7, 0, 7, 8],
+            [6, 6, 2, 5, 7],
+        ]
+    )
+    imputer = KNNImputer(missing_values=na).fit(X_fit)
+    with pytest.raises(ValueError, match="Input X contains (infinity|NaN)"):
+        imputer.transform(X)
+
+    # Test with missing_values=0 when NaN present
+    imputer = KNNImputer(missing_values=0, n_neighbors=2, weights="uniform")
+    X = np.array(
+        [
+            [np.nan, 0, 0, 0, 5],
+            [np.nan, 1, 0, np.nan, 3],
+            [np.nan, 2, 0, 0, 0],
+            [np.nan, 6, 0, 5, 13],
+        ]
+    )
+    msg = "Input X contains NaN"
+    with pytest.raises(ValueError, match=msg):
+        imputer.fit(X)
+
+    X = np.array(
+        [
+            [0, 0],
+            [np.nan, 2],
+        ]
+    )
+
+
+@pytest.mark.parametrize("na", [np.nan, -1])
+def test_knn_imputer_removes_all_na_features(na):
+    X = np.array(
+        [
+            [1, 1, na, 1, 1, 1.0],
+            [2, 3, na, 2, 2, 2],
+            [3, 4, na, 3, 3, na],
+            [6, 4, na, na, 6, 6],
+        ]
+    )
+    knn = KNNImputer(missing_values=na, n_neighbors=2).fit(X)
+
+    X_transform = knn.transform(X)
+    assert not np.isnan(X_transform).any()
+    assert X_transform.shape == (4, 5)
+
+    X_test = np.arange(0, 12).reshape(2, 6)
+    X_transform = knn.transform(X_test)
+    assert_allclose(X_test[:, [0, 1, 3, 4, 5]], X_transform)
+
+
+@pytest.mark.parametrize("na", [np.nan, -1])
+def test_knn_imputer_zero_nan_imputes_the_same(na):
+    # Test with an imputable matrix and compare with different missing_values
+    X_zero = np.array(
+        [
+            [1, 0, 1, 1, 1.0],
+            [2, 2, 2, 2, 2],
+            [3, 3, 3, 3, 0],
+            [6, 6, 0, 6, 6],
+        ]
+    )
+
+    X_nan = np.array(
+        [
+            [1, na, 1, 1, 1.0],
+            [2, 2, 2, 2, 2],
+            [3, 3, 3, 3, na],
+            [6, 6, na, 6, 6],
+        ]
+    )
+
+    X_imputed = np.array(
+        [
+            [1, 2.5, 1, 1, 1.0],
+            [2, 2, 2, 2, 2],
+            [3, 3, 3, 3, 1.5],
+            [6, 6, 2.5, 6, 6],
+        ]
+    )
+
+    imputer_zero = KNNImputer(missing_values=0, n_neighbors=2, weights="uniform")
+
+    imputer_nan = KNNImputer(missing_values=na, n_neighbors=2, weights="uniform")
+
+    assert_allclose(imputer_zero.fit_transform(X_zero), X_imputed)
+    assert_allclose(
+        imputer_zero.fit_transform(X_zero), imputer_nan.fit_transform(X_nan)
+    )
+
+
+@pytest.mark.parametrize("na", [np.nan, -1])
+def test_knn_imputer_verify(na):
+    # Test with an imputable matrix
+    X = np.array(
+        [
+            [1, 0, 0, 1],
+            [2, 1, 2, na],
+            [3, 2, 3, na],
+            [na, 4, 5, 5],
+            [6, na, 6, 7],
+            [8, 8, 8, 8],
+            [16, 15, 18, 19],
+        ]
+    )
+
+    X_imputed = np.array(
+        [
+            [1, 0, 0, 1],
+            [2, 1, 2, 8],
+            [3, 2, 3, 8],
+            [4, 4, 5, 5],
+            [6, 3, 6, 7],
+            [8, 8, 8, 8],
+            [16, 15, 18, 19],
+        ]
+    )
+
+    imputer = KNNImputer(missing_values=na)
+    assert_allclose(imputer.fit_transform(X), X_imputed)
+
+    # Test when there is not enough neighbors
+    X = np.array(
+        [
+            [1, 0, 0, na],
+            [2, 1, 2, na],
+            [3, 2, 3, na],
+            [4, 4, 5, na],
+            [6, 7, 6, na],
+            [8, 8, 8, na],
+            [20, 20, 20, 20],
+            [22, 22, 22, 22],
+        ]
+    )
+
+    # Not enough neighbors, use column mean from training
+    X_impute_value = (20 + 22) / 2
+    X_imputed = np.array(
+        [
+            [1, 0, 0, X_impute_value],
+            [2, 1, 2, X_impute_value],
+            [3, 2, 3, X_impute_value],
+            [4, 4, 5, X_impute_value],
+            [6, 7, 6, X_impute_value],
+            [8, 8, 8, X_impute_value],
+            [20, 20, 20, 20],
+            [22, 22, 22, 22],
+        ]
+    )
+
+    imputer = KNNImputer(missing_values=na)
+    assert_allclose(imputer.fit_transform(X), X_imputed)
+
+    # Test when data in fit() and transform() are different
+    X = np.array([[0, 0], [na, 2], [4, 3], [5, 6], [7, 7], [9, 8], [11, 16]])
+
+    X1 = np.array([[1, 0], [3, 2], [4, na]])
+
+    X_2_1 = (0 + 3 + 6 + 7 + 8) / 5
+    X1_imputed = np.array([[1, 0], [3, 2], [4, X_2_1]])
+
+    imputer = KNNImputer(missing_values=na)
+    assert_allclose(imputer.fit(X).transform(X1), X1_imputed)
+
+
+@pytest.mark.parametrize("na", [np.nan, -1])
+def test_knn_imputer_one_n_neighbors(na):
+    X = np.array([[0, 0], [na, 2], [4, 3], [5, na], [7, 7], [na, 8], [14, 13]])
+
+    X_imputed = np.array([[0, 0], [4, 2], [4, 3], [5, 3], [7, 7], [7, 8], [14, 13]])
+
+    imputer = KNNImputer(n_neighbors=1, missing_values=na)
+
+    assert_allclose(imputer.fit_transform(X), X_imputed)
+
+
+@pytest.mark.parametrize("na", [np.nan, -1])
+def test_knn_imputer_all_samples_are_neighbors(na):
+    X = np.array([[0, 0], [na, 2], [4, 3], [5, na], [7, 7], [na, 8], [14, 13]])
+
+    X_imputed = np.array([[0, 0], [6, 2], [4, 3], [5, 5.5], [7, 7], [6, 8], [14, 13]])
+
+    n_neighbors = X.shape[0] - 1
+    imputer = KNNImputer(n_neighbors=n_neighbors, missing_values=na)
+
+    assert_allclose(imputer.fit_transform(X), X_imputed)
+
+    n_neighbors = X.shape[0]
+    imputer_plus1 = KNNImputer(n_neighbors=n_neighbors, missing_values=na)
+    assert_allclose(imputer_plus1.fit_transform(X), X_imputed)
+
+
+@pytest.mark.parametrize("na", [np.nan, -1])
+def test_knn_imputer_weight_uniform(na):
+    X = np.array([[0, 0], [na, 2], [4, 3], [5, 6], [7, 7], [9, 8], [11, 10]])
+
+    # Test with "uniform" weight (or unweighted)
+    X_imputed_uniform = np.array(
+        [[0, 0], [5, 2], [4, 3], [5, 6], [7, 7], [9, 8], [11, 10]]
+    )
+
+    imputer = KNNImputer(weights="uniform", missing_values=na)
+    assert_allclose(imputer.fit_transform(X), X_imputed_uniform)
+
+    # Test with "callable" weight
+    def no_weight(dist):
+        return None
+
+    imputer = KNNImputer(weights=no_weight, missing_values=na)
+    assert_allclose(imputer.fit_transform(X), X_imputed_uniform)
+
+    # Test with "callable" uniform weight
+    def uniform_weight(dist):
+        return np.ones_like(dist)
+
+    imputer = KNNImputer(weights=uniform_weight, missing_values=na)
+    assert_allclose(imputer.fit_transform(X), X_imputed_uniform)
+
+
+@pytest.mark.parametrize("na", [np.nan, -1])
+def test_knn_imputer_weight_distance(na):
+    X = np.array([[0, 0], [na, 2], [4, 3], [5, 6], [7, 7], [9, 8], [11, 10]])
+
+    # Test with "distance" weight
+    nn = KNeighborsRegressor(metric="euclidean", weights="distance")
+    X_rows_idx = [0, 2, 3, 4, 5, 6]
+    nn.fit(X[X_rows_idx, 1:], X[X_rows_idx, 0])
+    knn_imputed_value = nn.predict(X[1:2, 1:])[0]
+
+    # Manual calculation
+    X_neighbors_idx = [0, 2, 3, 4, 5]
+    dist = nan_euclidean_distances(X[1:2, :], X, missing_values=na)
+    weights = 1 / dist[:, X_neighbors_idx].ravel()
+    manual_imputed_value = np.average(X[X_neighbors_idx, 0], weights=weights)
+
+    X_imputed_distance1 = np.array(
+        [[0, 0], [manual_imputed_value, 2], [4, 3], [5, 6], [7, 7], [9, 8], [11, 10]]
+    )
+
+    # NearestNeighbor calculation
+    X_imputed_distance2 = np.array(
+        [[0, 0], [knn_imputed_value, 2], [4, 3], [5, 6], [7, 7], [9, 8], [11, 10]]
+    )
+
+    imputer = KNNImputer(weights="distance", missing_values=na)
+    assert_allclose(imputer.fit_transform(X), X_imputed_distance1)
+    assert_allclose(imputer.fit_transform(X), X_imputed_distance2)
+
+    # Test with weights = "distance" and n_neighbors=2
+    X = np.array(
+        [
+            [na, 0, 0],
+            [2, 1, 2],
+            [3, 2, 3],
+            [4, 5, 5],
+        ]
+    )
+
+    # neighbors are rows 1, 2, the nan_euclidean_distances are:
+    dist_0_1 = np.sqrt((3 / 2) * ((1 - 0) ** 2 + (2 - 0) ** 2))
+    dist_0_2 = np.sqrt((3 / 2) * ((2 - 0) ** 2 + (3 - 0) ** 2))
+    imputed_value = np.average([2, 3], weights=[1 / dist_0_1, 1 / dist_0_2])
+
+    X_imputed = np.array(
+        [
+            [imputed_value, 0, 0],
+            [2, 1, 2],
+            [3, 2, 3],
+            [4, 5, 5],
+        ]
+    )
+
+    imputer = KNNImputer(n_neighbors=2, weights="distance", missing_values=na)
+    assert_allclose(imputer.fit_transform(X), X_imputed)
+
+    # Test with varying missingness patterns
+    X = np.array(
+        [
+            [1, 0, 0, 1],
+            [0, na, 1, na],
+            [1, 1, 1, na],
+            [0, 1, 0, 0],
+            [0, 0, 0, 0],
+            [1, 0, 1, 1],
+            [10, 10, 10, 10],
+        ]
+    )
+
+    # Get weights of donor neighbors
+    dist = nan_euclidean_distances(X, missing_values=na)
+    r1c1_nbor_dists = dist[1, [0, 2, 3, 4, 5]]
+    r1c3_nbor_dists = dist[1, [0, 3, 4, 5, 6]]
+    r1c1_nbor_wt = 1 / r1c1_nbor_dists
+    r1c3_nbor_wt = 1 / r1c3_nbor_dists
+
+    r2c3_nbor_dists = dist[2, [0, 3, 4, 5, 6]]
+    r2c3_nbor_wt = 1 / r2c3_nbor_dists
+
+    # Collect donor values
+    col1_donor_values = np.ma.masked_invalid(X[[0, 2, 3, 4, 5], 1]).copy()
+    col3_donor_values = np.ma.masked_invalid(X[[0, 3, 4, 5, 6], 3]).copy()
+
+    # Final imputed values
+    r1c1_imp = np.ma.average(col1_donor_values, weights=r1c1_nbor_wt)
+    r1c3_imp = np.ma.average(col3_donor_values, weights=r1c3_nbor_wt)
+    r2c3_imp = np.ma.average(col3_donor_values, weights=r2c3_nbor_wt)
+
+    X_imputed = np.array(
+        [
+            [1, 0, 0, 1],
+            [0, r1c1_imp, 1, r1c3_imp],
+            [1, 1, 1, r2c3_imp],
+            [0, 1, 0, 0],
+            [0, 0, 0, 0],
+            [1, 0, 1, 1],
+            [10, 10, 10, 10],
+        ]
+    )
+
+    imputer = KNNImputer(weights="distance", missing_values=na)
+    assert_allclose(imputer.fit_transform(X), X_imputed)
+
+    X = np.array(
+        [
+            [0, 0, 0, na],
+            [1, 1, 1, na],
+            [2, 2, na, 2],
+            [3, 3, 3, 3],
+            [4, 4, 4, 4],
+            [5, 5, 5, 5],
+            [6, 6, 6, 6],
+            [na, 7, 7, 7],
+        ]
+    )
+
+    dist = pairwise_distances(
+        X, metric="nan_euclidean", squared=False, missing_values=na
+    )
+
+    # Calculate weights
+    r0c3_w = 1.0 / dist[0, 2:-1]
+    r1c3_w = 1.0 / dist[1, 2:-1]
+    r2c2_w = 1.0 / dist[2, (0, 1, 3, 4, 5)]
+    r7c0_w = 1.0 / dist[7, 2:7]
+
+    # Calculate weighted averages
+    r0c3 = np.average(X[2:-1, -1], weights=r0c3_w)
+    r1c3 = np.average(X[2:-1, -1], weights=r1c3_w)
+    r2c2 = np.average(X[(0, 1, 3, 4, 5), 2], weights=r2c2_w)
+    r7c0 = np.average(X[2:7, 0], weights=r7c0_w)
+
+    X_imputed = np.array(
+        [
+            [0, 0, 0, r0c3],
+            [1, 1, 1, r1c3],
+            [2, 2, r2c2, 2],
+            [3, 3, 3, 3],
+            [4, 4, 4, 4],
+            [5, 5, 5, 5],
+            [6, 6, 6, 6],
+            [r7c0, 7, 7, 7],
+        ]
+    )
+
+    imputer_comp_wt = KNNImputer(missing_values=na, weights="distance")
+    assert_allclose(imputer_comp_wt.fit_transform(X), X_imputed)
+
+
+def test_knn_imputer_callable_metric():
+    # Define callable metric that returns the l1 norm:
+    def custom_callable(x, y, missing_values=np.nan, squared=False):
+        x = np.ma.array(x, mask=np.isnan(x))
+        y = np.ma.array(y, mask=np.isnan(y))
+        dist = np.nansum(np.abs(x - y))
+        return dist
+
+    X = np.array([[4, 3, 3, np.nan], [6, 9, 6, 9], [4, 8, 6, 9], [np.nan, 9, 11, 10.0]])
+
+    X_0_3 = (9 + 9) / 2
+    X_3_0 = (6 + 4) / 2
+    X_imputed = np.array(
+        [[4, 3, 3, X_0_3], [6, 9, 6, 9], [4, 8, 6, 9], [X_3_0, 9, 11, 10.0]]
+    )
+
+    imputer = KNNImputer(n_neighbors=2, metric=custom_callable)
+    assert_allclose(imputer.fit_transform(X), X_imputed)
+
+
+@pytest.mark.parametrize("working_memory", [None, 0])
+@pytest.mark.parametrize("na", [-1, np.nan])
+# Note that we use working_memory=0 to ensure that chunking is tested, even
+# for a small dataset. However, it should raise a UserWarning that we ignore.
+@pytest.mark.filterwarnings("ignore:adhere to working_memory")
+def test_knn_imputer_with_simple_example(na, working_memory):
+    X = np.array(
+        [
+            [0, na, 0, na],
+            [1, 1, 1, na],
+            [2, 2, na, 2],
+            [3, 3, 3, 3],
+            [4, 4, 4, 4],
+            [5, 5, 5, 5],
+            [6, 6, 6, 6],
+            [na, 7, 7, 7],
+        ]
+    )
+
+    r0c1 = np.mean(X[1:6, 1])
+    r0c3 = np.mean(X[2:-1, -1])
+    r1c3 = np.mean(X[2:-1, -1])
+    r2c2 = np.mean(X[[0, 1, 3, 4, 5], 2])
+    r7c0 = np.mean(X[2:-1, 0])
+
+    X_imputed = np.array(
+        [
+            [0, r0c1, 0, r0c3],
+            [1, 1, 1, r1c3],
+            [2, 2, r2c2, 2],
+            [3, 3, 3, 3],
+            [4, 4, 4, 4],
+            [5, 5, 5, 5],
+            [6, 6, 6, 6],
+            [r7c0, 7, 7, 7],
+        ]
+    )
+
+    with config_context(working_memory=working_memory):
+        imputer_comp = KNNImputer(missing_values=na)
+        assert_allclose(imputer_comp.fit_transform(X), X_imputed)
+
+
+@pytest.mark.parametrize("na", [-1, np.nan])
+@pytest.mark.parametrize("weights", ["uniform", "distance"])
+def test_knn_imputer_not_enough_valid_distances(na, weights):
+    # Samples with needed feature has nan distance
+    X1 = np.array([[na, 11], [na, 1], [3, na]])
+    X1_imputed = np.array([[3, 11], [3, 1], [3, 6]])
+
+    knn = KNNImputer(missing_values=na, n_neighbors=1, weights=weights)
+    assert_allclose(knn.fit_transform(X1), X1_imputed)
+
+    X2 = np.array([[4, na]])
+    X2_imputed = np.array([[4, 6]])
+    assert_allclose(knn.transform(X2), X2_imputed)
+
+
+@pytest.mark.parametrize("na", [-1, np.nan])
+def test_knn_imputer_drops_all_nan_features(na):
+    X1 = np.array([[na, 1], [na, 2]])
+    knn = KNNImputer(missing_values=na, n_neighbors=1)
+    X1_expected = np.array([[1], [2]])
+    assert_allclose(knn.fit_transform(X1), X1_expected)
+
+    X2 = np.array([[1, 2], [3, na]])
+    X2_expected = np.array([[2], [1.5]])
+    assert_allclose(knn.transform(X2), X2_expected)
+
+
+@pytest.mark.parametrize("working_memory", [None, 0])
+@pytest.mark.parametrize("na", [-1, np.nan])
+def test_knn_imputer_distance_weighted_not_enough_neighbors(na, working_memory):
+    X = np.array([[3, na], [2, na], [na, 4], [5, 6], [6, 8], [na, 5]])
+
+    dist = pairwise_distances(
+        X, metric="nan_euclidean", squared=False, missing_values=na
+    )
+
+    X_01 = np.average(X[3:5, 1], weights=1 / dist[0, 3:5])
+    X_11 = np.average(X[3:5, 1], weights=1 / dist[1, 3:5])
+    X_20 = np.average(X[3:5, 0], weights=1 / dist[2, 3:5])
+    X_50 = np.average(X[3:5, 0], weights=1 / dist[5, 3:5])
+
+    X_expected = np.array([[3, X_01], [2, X_11], [X_20, 4], [5, 6], [6, 8], [X_50, 5]])
+
+    with config_context(working_memory=working_memory):
+        knn_3 = KNNImputer(missing_values=na, n_neighbors=3, weights="distance")
+        assert_allclose(knn_3.fit_transform(X), X_expected)
+
+        knn_4 = KNNImputer(missing_values=na, n_neighbors=4, weights="distance")
+        assert_allclose(knn_4.fit_transform(X), X_expected)
+
+
+@pytest.mark.parametrize("na, allow_nan", [(-1, False), (np.nan, True)])
+def test_knn_tags(na, allow_nan):
+    knn = KNNImputer(missing_values=na)
+    assert knn._get_tags()["allow_nan"] == allow_nan