sklearn/base.py

"""Base classes for all estimators."""

# Author: Gael Varoquaux <gael.varoquaux@normalesup.org>
# License: BSD 3 clause

import copy
import functools
import inspect
import platform
import re
import warnings
from collections import defaultdict

import numpy as np

from . import __version__
from ._config import config_context, get_config
from .exceptions import InconsistentVersionWarning
from .utils import _IS_32BIT
from .utils._estimator_html_repr import _HTMLDocumentationLinkMixin, estimator_html_repr
from .utils._metadata_requests import _MetadataRequester, _routing_enabled
from .utils._param_validation import validate_parameter_constraints
from .utils._set_output import _SetOutputMixin
from .utils._tags import (
    _DEFAULT_TAGS,
)
from .utils.validation import (
    _check_feature_names_in,
    _check_y,
    _generate_get_feature_names_out,
    _get_feature_names,
    _is_fitted,
    _num_features,
    check_array,
    check_is_fitted,
    check_X_y,
)


def clone(estimator, *, safe=True):
    """Construct a new unfitted estimator with the same parameters.

    Clone does a deep copy of the model in an estimator
    without actually copying attached data. It returns a new estimator
    with the same parameters that has not been fitted on any data.

    .. versionchanged:: 1.3
        Delegates to `estimator.__sklearn_clone__` if the method exists.

    Parameters
    ----------
    estimator : {list, tuple, set} of estimator instance or a single \
            estimator instance
        The estimator or group of estimators to be cloned.
    safe : bool, default=True
        If safe is False, clone will fall back to a deep copy on objects
        that are not estimators. Ignored if `estimator.__sklearn_clone__`
        exists.

    Returns
    -------
    estimator : object
        The deep copy of the input, an estimator if input is an estimator.

    Notes
    -----
    If the estimator's `random_state` parameter is an integer (or if the
    estimator doesn't have a `random_state` parameter), an *exact clone* is
    returned: the clone and the original estimator will give the exact same
    results. Otherwise, *statistical clone* is returned: the clone might
    return different results from the original estimator. More details can be
    found in :ref:`randomness`.

    Examples
    --------
    >>> from sklearn.base import clone
    >>> from sklearn.linear_model import LogisticRegression
    >>> X = [[-1, 0], [0, 1], [0, -1], [1, 0]]
    >>> y = [0, 0, 1, 1]
    >>> classifier = LogisticRegression().fit(X, y)
    >>> cloned_classifier = clone(classifier)
    >>> hasattr(classifier, "classes_")
    True
    >>> hasattr(cloned_classifier, "classes_")
    False
    >>> classifier is cloned_classifier
    False
    """
    if hasattr(estimator, "__sklearn_clone__") and not inspect.isclass(estimator):
        return estimator.__sklearn_clone__()
    return _clone_parametrized(estimator, safe=safe)


def _clone_parametrized(estimator, *, safe=True):
    """Default implementation of clone. See :func:`sklearn.base.clone` for details."""

    estimator_type = type(estimator)
    if estimator_type is dict:
        return {k: clone(v, safe=safe) for k, v in estimator.items()}
    elif estimator_type in (list, tuple, set, frozenset):
        return estimator_type([clone(e, safe=safe) for e in estimator])
    elif not hasattr(estimator, "get_params") or isinstance(estimator, type):
        if not safe:
            return copy.deepcopy(estimator)
        else:
            if isinstance(estimator, type):
                raise TypeError(
                    "Cannot clone object. "
                    + "You should provide an instance of "
                    + "scikit-learn estimator instead of a class."
                )
            else:
                raise TypeError(
                    "Cannot clone object '%s' (type %s): "
                    "it does not seem to be a scikit-learn "
                    "estimator as it does not implement a "
                    "'get_params' method." % (repr(estimator), type(estimator))
                )

    klass = estimator.__class__
    new_object_params = estimator.get_params(deep=False)
    for name, param in new_object_params.items():
        new_object_params[name] = clone(param, safe=False)

    new_object = klass(**new_object_params)
    try:
        new_object._metadata_request = copy.deepcopy(estimator._metadata_request)
    except AttributeError:
        pass

    params_set = new_object.get_params(deep=False)

    # quick sanity check of the parameters of the clone
    for name in new_object_params:
        param1 = new_object_params[name]
        param2 = params_set[name]
        if param1 is not param2:
            raise RuntimeError(
                "Cannot clone object %s, as the constructor "
                "either does not set or modifies parameter %s" % (estimator, name)
            )

    # _sklearn_output_config is used by `set_output` to configure the output
    # container of an estimator.
    if hasattr(estimator, "_sklearn_output_config"):
        new_object._sklearn_output_config = copy.deepcopy(
            estimator._sklearn_output_config
        )
    return new_object


class BaseEstimator(_HTMLDocumentationLinkMixin, _MetadataRequester):
    """Base class for all estimators in scikit-learn.

    Inheriting from this class provides default implementations of:

    - setting and getting parameters used by `GridSearchCV` and friends;
    - textual and HTML representation displayed in terminals and IDEs;
    - estimator serialization;
    - parameters validation;
    - data validation;
    - feature names validation.

    Read more in the :ref:`User Guide <rolling_your_own_estimator>`.


    Notes
    -----
    All estimators should specify all the parameters that can be set
    at the class level in their ``__init__`` as explicit keyword
    arguments (no ``*args`` or ``**kwargs``).

    Examples
    --------
    >>> import numpy as np
    >>> from sklearn.base import BaseEstimator
    >>> class MyEstimator(BaseEstimator):
    ...     def __init__(self, *, param=1):
    ...         self.param = param
    ...     def fit(self, X, y=None):
    ...         self.is_fitted_ = True
    ...         return self
    ...     def predict(self, X):
    ...         return np.full(shape=X.shape[0], fill_value=self.param)
    >>> estimator = MyEstimator(param=2)
    >>> estimator.get_params()
    {'param': 2}
    >>> X = np.array([[1, 2], [2, 3], [3, 4]])
    >>> y = np.array([1, 0, 1])
    >>> estimator.fit(X, y).predict(X)
    array([2, 2, 2])
    >>> estimator.set_params(param=3).fit(X, y).predict(X)
    array([3, 3, 3])
    """

    @classmethod
    def _get_param_names(cls):
        """Get parameter names for the estimator"""
        # fetch the constructor or the original constructor before
        # deprecation wrapping if any
        init = getattr(cls.__init__, "deprecated_original", cls.__init__)
        if init is object.__init__:
            # No explicit constructor to introspect
            return []

        # introspect the constructor arguments to find the model parameters
        # to represent
        init_signature = inspect.signature(init)
        # Consider the constructor parameters excluding 'self'
        parameters = [
            p
            for p in init_signature.parameters.values()
            if p.name != "self" and p.kind != p.VAR_KEYWORD
        ]
        for p in parameters:
            if p.kind == p.VAR_POSITIONAL:
                raise RuntimeError(
                    "scikit-learn estimators should always "
                    "specify their parameters in the signature"
                    " of their __init__ (no varargs)."
                    " %s with constructor %s doesn't "
                    " follow this convention." % (cls, init_signature)
                )
        # Extract and sort argument names excluding 'self'
        return sorted([p.name for p in parameters])

    def get_params(self, deep=True):
        """
        Get parameters for this estimator.

        Parameters
        ----------
        deep : bool, default=True
            If True, will return the parameters for this estimator and
            contained subobjects that are estimators.

        Returns
        -------
        params : dict
            Parameter names mapped to their values.
        """
        out = dict()
        for key in self._get_param_names():
            value = getattr(self, key)
            if deep and hasattr(value, "get_params") and not isinstance(value, type):
                deep_items = value.get_params().items()
                out.update((key + "__" + k, val) for k, val in deep_items)
            out[key] = value
        return out

    def set_params(self, **params):
        """Set the parameters of this estimator.

        The method works on simple estimators as well as on nested objects
        (such as :class:`~sklearn.pipeline.Pipeline`). The latter have
        parameters of the form ``<component>__<parameter>`` so that it's
        possible to update each component of a nested object.

        Parameters
        ----------
        **params : dict
            Estimator parameters.

        Returns
        -------
        self : estimator instance
            Estimator instance.
        """
        if not params:
            # Simple optimization to gain speed (inspect is slow)
            return self
        valid_params = self.get_params(deep=True)

        nested_params = defaultdict(dict)  # grouped by prefix
        for key, value in params.items():
            key, delim, sub_key = key.partition("__")
            if key not in valid_params:
                local_valid_params = self._get_param_names()
                raise ValueError(
                    f"Invalid parameter {key!r} for estimator {self}. "
                    f"Valid parameters are: {local_valid_params!r}."
                )

            if delim:
                nested_params[key][sub_key] = value
            else:
                setattr(self, key, value)
                valid_params[key] = value

        for key, sub_params in nested_params.items():
            valid_params[key].set_params(**sub_params)

        return self

    def __sklearn_clone__(self):
        return _clone_parametrized(self)

    def __repr__(self, N_CHAR_MAX=700):
        # N_CHAR_MAX is the (approximate) maximum number of non-blank
        # characters to render. We pass it as an optional parameter to ease
        # the tests.

        from .utils._pprint import _EstimatorPrettyPrinter

        N_MAX_ELEMENTS_TO_SHOW = 30  # number of elements to show in sequences

        # use ellipsis for sequences with a lot of elements
        pp = _EstimatorPrettyPrinter(
            compact=True,
            indent=1,
            indent_at_name=True,
            n_max_elements_to_show=N_MAX_ELEMENTS_TO_SHOW,
        )

        repr_ = pp.pformat(self)

        # Use bruteforce ellipsis when there are a lot of non-blank characters
        n_nonblank = len("".join(repr_.split()))
        if n_nonblank > N_CHAR_MAX:
            lim = N_CHAR_MAX // 2  # apprx number of chars to keep on both ends
            regex = r"^(\s*\S){%d}" % lim
            # The regex '^(\s*\S){%d}' % n
            # matches from the start of the string until the nth non-blank
            # character:
            # - ^ matches the start of string
            # - (pattern){n} matches n repetitions of pattern
            # - \s*\S matches a non-blank char following zero or more blanks
            left_lim = re.match(regex, repr_).end()
            right_lim = re.match(regex, repr_[::-1]).end()

            if "\n" in repr_[left_lim:-right_lim]:
                # The left side and right side aren't on the same line.
                # To avoid weird cuts, e.g.:
                # categoric...ore',
                # we need to start the right side with an appropriate newline
                # character so that it renders properly as:
                # categoric...
                # handle_unknown='ignore',
                # so we add [^\n]*\n which matches until the next \n
                regex += r"[^\n]*\n"
                right_lim = re.match(regex, repr_[::-1]).end()

            ellipsis = "..."
            if left_lim + len(ellipsis) < len(repr_) - right_lim:
                # Only add ellipsis if it results in a shorter repr
                repr_ = repr_[:left_lim] + "..." + repr_[-right_lim:]

        return repr_

    def __getstate__(self):
        if getattr(self, "__slots__", None):
            raise TypeError(
                "You cannot use `__slots__` in objects inheriting from "
                "`sklearn.base.BaseEstimator`."
            )

        try:
            state = super().__getstate__()
            if state is None:
                # For Python 3.11+, empty instance (no `__slots__`,
                # and `__dict__`) will return a state equal to `None`.
                state = self.__dict__.copy()
        except AttributeError:
            # Python < 3.11
            state = self.__dict__.copy()

        if type(self).__module__.startswith("sklearn."):
            return dict(state.items(), _sklearn_version=__version__)
        else:
            return state

    def __setstate__(self, state):
        if type(self).__module__.startswith("sklearn."):
            pickle_version = state.pop("_sklearn_version", "pre-0.18")
            if pickle_version != __version__:
                warnings.warn(
                    InconsistentVersionWarning(
                        estimator_name=self.__class__.__name__,
                        current_sklearn_version=__version__,
                        original_sklearn_version=pickle_version,
                    ),
                )
        try:
            super().__setstate__(state)
        except AttributeError:
            self.__dict__.update(state)

    def _more_tags(self):
        return _DEFAULT_TAGS

    def _get_tags(self):
        collected_tags = {}
        for base_class in reversed(inspect.getmro(self.__class__)):
            if hasattr(base_class, "_more_tags"):
                # need the if because mixins might not have _more_tags
                # but might do redundant work in estimators
                # (i.e. calling more tags on BaseEstimator multiple times)
                more_tags = base_class._more_tags(self)
                collected_tags.update(more_tags)
        return collected_tags

    def _check_n_features(self, X, reset):
        """Set the `n_features_in_` attribute, or check against it.

        Parameters
        ----------
        X : {ndarray, sparse matrix} of shape (n_samples, n_features)
            The input samples.
        reset : bool
            If True, the `n_features_in_` attribute is set to `X.shape[1]`.
            If False and the attribute exists, then check that it is equal to
            `X.shape[1]`. If False and the attribute does *not* exist, then
            the check is skipped.
            .. note::
               It is recommended to call reset=True in `fit` and in the first
               call to `partial_fit`. All other methods that validate `X`
               should set `reset=False`.
        """
        try:
            n_features = _num_features(X)
        except TypeError as e:
            if not reset and hasattr(self, "n_features_in_"):
                raise ValueError(
                    "X does not contain any features, but "
                    f"{self.__class__.__name__} is expecting "
                    f"{self.n_features_in_} features"
                ) from e
            # If the number of features is not defined and reset=True,
            # then we skip this check
            return

        if reset:
            self.n_features_in_ = n_features
            return

        if not hasattr(self, "n_features_in_"):
            # Skip this check if the expected number of expected input features
            # was not recorded by calling fit first. This is typically the case
            # for stateless transformers.
            return

        if n_features != self.n_features_in_:
            raise ValueError(
                f"X has {n_features} features, but {self.__class__.__name__} "
                f"is expecting {self.n_features_in_} features as input."
            )

    def _check_feature_names(self, X, *, reset):
        """Set or check the `feature_names_in_` attribute.

        .. versionadded:: 1.0

        Parameters
        ----------
        X : {ndarray, dataframe} of shape (n_samples, n_features)
            The input samples.

        reset : bool
            Whether to reset the `feature_names_in_` attribute.
            If False, the input will be checked for consistency with
            feature names of data provided when reset was last True.
            .. note::
               It is recommended to call `reset=True` in `fit` and in the first
               call to `partial_fit`. All other methods that validate `X`
               should set `reset=False`.
        """

        if reset:
            feature_names_in = _get_feature_names(X)
            if feature_names_in is not None:
                self.feature_names_in_ = feature_names_in
            elif hasattr(self, "feature_names_in_"):
                # Delete the attribute when the estimator is fitted on a new dataset
                # that has no feature names.
                delattr(self, "feature_names_in_")
            return

        fitted_feature_names = getattr(self, "feature_names_in_", None)
        X_feature_names = _get_feature_names(X)

        if fitted_feature_names is None and X_feature_names is None:
            # no feature names seen in fit and in X
            return

        if X_feature_names is not None and fitted_feature_names is None:
            warnings.warn(
                f"X has feature names, but {self.__class__.__name__} was fitted without"
                " feature names"
            )
            return

        if X_feature_names is None and fitted_feature_names is not None:
            warnings.warn(
                "X does not have valid feature names, but"
                f" {self.__class__.__name__} was fitted with feature names"
            )
            return

        # validate the feature names against the `feature_names_in_` attribute
        if len(fitted_feature_names) != len(X_feature_names) or np.any(
            fitted_feature_names != X_feature_names
        ):
            message = (
                "The feature names should match those that were passed during fit.\n"
            )
            fitted_feature_names_set = set(fitted_feature_names)
            X_feature_names_set = set(X_feature_names)

            unexpected_names = sorted(X_feature_names_set - fitted_feature_names_set)
            missing_names = sorted(fitted_feature_names_set - X_feature_names_set)

            def add_names(names):
                output = ""
                max_n_names = 5
                for i, name in enumerate(names):
                    if i >= max_n_names:
                        output += "- ...\n"
                        break
                    output += f"- {name}\n"
                return output

            if unexpected_names:
                message += "Feature names unseen at fit time:\n"
                message += add_names(unexpected_names)

            if missing_names:
                message += "Feature names seen at fit time, yet now missing:\n"
                message += add_names(missing_names)

            if not missing_names and not unexpected_names:
                message += (
                    "Feature names must be in the same order as they were in fit.\n"
                )

            raise ValueError(message)

    def _validate_data(
        self,
        X="no_validation",
        y="no_validation",
        reset=True,
        validate_separately=False,
        cast_to_ndarray=True,
        **check_params,
    ):
        """Validate input data and set or check the `n_features_in_` attribute.

        Parameters
        ----------
        X : {array-like, sparse matrix, dataframe} of shape \
                (n_samples, n_features), default='no validation'
            The input samples.
            If `'no_validation'`, no validation is performed on `X`. This is
            useful for meta-estimator which can delegate input validation to
            their underlying estimator(s). In that case `y` must be passed and
            the only accepted `check_params` are `multi_output` and
            `y_numeric`.

        y : array-like of shape (n_samples,), default='no_validation'
            The targets.

            - If `None`, `check_array` is called on `X`. If the estimator's
              requires_y tag is True, then an error will be raised.
            - If `'no_validation'`, `check_array` is called on `X` and the
              estimator's requires_y tag is ignored. This is a default
              placeholder and is never meant to be explicitly set. In that case
              `X` must be passed.
            - Otherwise, only `y` with `_check_y` or both `X` and `y` are
              checked with either `check_array` or `check_X_y` depending on
              `validate_separately`.

        reset : bool, default=True
            Whether to reset the `n_features_in_` attribute.
            If False, the input will be checked for consistency with data
            provided when reset was last True.
            .. note::
               It is recommended to call reset=True in `fit` and in the first
               call to `partial_fit`. All other methods that validate `X`
               should set `reset=False`.

        validate_separately : False or tuple of dicts, default=False
            Only used if y is not None.
            If False, call validate_X_y(). Else, it must be a tuple of kwargs
            to be used for calling check_array() on X and y respectively.

            `estimator=self` is automatically added to these dicts to generate
            more informative error message in case of invalid input data.

        cast_to_ndarray : bool, default=True
            Cast `X` and `y` to ndarray with checks in `check_params`. If
            `False`, `X` and `y` are unchanged and only `feature_names_in_` and
            `n_features_in_` are checked.

        **check_params : kwargs
            Parameters passed to :func:`sklearn.utils.check_array` or
            :func:`sklearn.utils.check_X_y`. Ignored if validate_separately
            is not False.

            `estimator=self` is automatically added to these params to generate
            more informative error message in case of invalid input data.

        Returns
        -------
        out : {ndarray, sparse matrix} or tuple of these
            The validated input. A tuple is returned if both `X` and `y` are
            validated.
        """
        self._check_feature_names(X, reset=reset)

        if y is None and self._get_tags()["requires_y"]:
            raise ValueError(
                f"This {self.__class__.__name__} estimator "
                "requires y to be passed, but the target y is None."
            )

        no_val_X = isinstance(X, str) and X == "no_validation"
        no_val_y = y is None or isinstance(y, str) and y == "no_validation"

        if no_val_X and no_val_y:
            raise ValueError("Validation should be done on X, y or both.")

        default_check_params = {"estimator": self}
        check_params = {**default_check_params, **check_params}

        if not cast_to_ndarray:
            if not no_val_X and no_val_y:
                out = X
            elif no_val_X and not no_val_y:
                out = y
            else:
                out = X, y
        elif not no_val_X and no_val_y:
            out = check_array(X, input_name="X", **check_params)
        elif no_val_X and not no_val_y:
            out = _check_y(y, **check_params)
        else:
            if validate_separately:
                # We need this because some estimators validate X and y
                # separately, and in general, separately calling check_array()
                # on X and y isn't equivalent to just calling check_X_y()
                # :(
                check_X_params, check_y_params = validate_separately
                if "estimator" not in check_X_params:
                    check_X_params = {**default_check_params, **check_X_params}
                X = check_array(X, input_name="X", **check_X_params)
                if "estimator" not in check_y_params:
                    check_y_params = {**default_check_params, **check_y_params}
                y = check_array(y, input_name="y", **check_y_params)
            else:
                X, y = check_X_y(X, y, **check_params)
            out = X, y

        if not no_val_X and check_params.get("ensure_2d", True):
            self._check_n_features(X, reset=reset)

        return out

    def _validate_params(self):
        """Validate types and values of constructor parameters

        The expected type and values must be defined in the `_parameter_constraints`
        class attribute, which is a dictionary `param_name: list of constraints`. See
        the docstring of `validate_parameter_constraints` for a description of the
        accepted constraints.
        """
        validate_parameter_constraints(
            self._parameter_constraints,
            self.get_params(deep=False),
            caller_name=self.__class__.__name__,
        )

    @property
    def _repr_html_(self):
        """HTML representation of estimator.

        This is redundant with the logic of `_repr_mimebundle_`. The latter
        should be favorted in the long term, `_repr_html_` is only
        implemented for consumers who do not interpret `_repr_mimbundle_`.
        """
        if get_config()["display"] != "diagram":
            raise AttributeError(
                "_repr_html_ is only defined when the "
                "'display' configuration option is set to "
                "'diagram'"
            )
        return self._repr_html_inner

    def _repr_html_inner(self):
        """This function is returned by the @property `_repr_html_` to make
        `hasattr(estimator, "_repr_html_") return `True` or `False` depending
        on `get_config()["display"]`.
        """
        return estimator_html_repr(self)

    def _repr_mimebundle_(self, **kwargs):
        """Mime bundle used by jupyter kernels to display estimator"""
        output = {"text/plain": repr(self)}
        if get_config()["display"] == "diagram":
            output["text/html"] = estimator_html_repr(self)
        return output


class ClassifierMixin:
    """Mixin class for all classifiers in scikit-learn.

    This mixin defines the following functionality:

    - `_estimator_type` class attribute defaulting to `"classifier"`;
    - `score` method that default to :func:`~sklearn.metrics.accuracy_score`.
    - enforce that `fit` requires `y` to be passed through the `requires_y` tag.

    Read more in the :ref:`User Guide <rolling_your_own_estimator>`.

    Examples
    --------
    >>> import numpy as np
    >>> from sklearn.base import BaseEstimator, ClassifierMixin
    >>> # Mixin classes should always be on the left-hand side for a correct MRO
    >>> class MyEstimator(ClassifierMixin, BaseEstimator):
    ...     def __init__(self, *, param=1):
    ...         self.param = param
    ...     def fit(self, X, y=None):
    ...         self.is_fitted_ = True
    ...         return self
    ...     def predict(self, X):
    ...         return np.full(shape=X.shape[0], fill_value=self.param)
    >>> estimator = MyEstimator(param=1)
    >>> X = np.array([[1, 2], [2, 3], [3, 4]])
    >>> y = np.array([1, 0, 1])
    >>> estimator.fit(X, y).predict(X)
    array([1, 1, 1])
    >>> estimator.score(X, y)
    0.66...
    """

    _estimator_type = "classifier"

    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`.
        """
        from .metrics import accuracy_score

        return accuracy_score(y, self.predict(X), sample_weight=sample_weight)

    def _more_tags(self):
        return {"requires_y": True}


class RegressorMixin:
    """Mixin class for all regression estimators in scikit-learn.

    This mixin defines the following functionality:

    - `_estimator_type` class attribute defaulting to `"regressor"`;
    - `score` method that default to :func:`~sklearn.metrics.r2_score`.
    - enforce that `fit` requires `y` to be passed through the `requires_y` tag.

    Read more in the :ref:`User Guide <rolling_your_own_estimator>`.

    Examples
    --------
    >>> import numpy as np
    >>> from sklearn.base import BaseEstimator, RegressorMixin
    >>> # Mixin classes should always be on the left-hand side for a correct MRO
    >>> class MyEstimator(RegressorMixin, BaseEstimator):
    ...     def __init__(self, *, param=1):
    ...         self.param = param
    ...     def fit(self, X, y=None):
    ...         self.is_fitted_ = True
    ...         return self
    ...     def predict(self, X):
    ...         return np.full(shape=X.shape[0], fill_value=self.param)
    >>> estimator = MyEstimator(param=0)
    >>> X = np.array([[1, 2], [2, 3], [3, 4]])
    >>> y = np.array([-1, 0, 1])
    >>> estimator.fit(X, y).predict(X)
    array([0, 0, 0])
    >>> estimator.score(X, y)
    0.0
    """

    _estimator_type = "regressor"

    def score(self, X, y, sample_weight=None):
        """Return the coefficient of determination of the prediction.

        The coefficient of determination :math:`R^2` is defined as
        :math:`(1 - \\frac{u}{v})`, where :math:`u` is the residual
        sum of squares ``((y_true - y_pred)** 2).sum()`` and :math:`v`
        is the total sum of squares ``((y_true - y_true.mean()) ** 2).sum()``.
        The best possible score is 1.0 and it can be negative (because the
        model can be arbitrarily worse). A constant model that always predicts
        the expected value of `y`, disregarding the input features, would get
        a :math:`R^2` score of 0.0.

        Parameters
        ----------
        X : array-like of shape (n_samples, n_features)
            Test samples. For some estimators this may be a precomputed
            kernel matrix or a list of generic objects instead with shape
            ``(n_samples, n_samples_fitted)``, where ``n_samples_fitted``
            is the number of samples used in the fitting for the estimator.

        y : array-like of shape (n_samples,) or (n_samples, n_outputs)
            True values for `X`.

        sample_weight : array-like of shape (n_samples,), default=None
            Sample weights.

        Returns
        -------
        score : float
            :math:`R^2` of ``self.predict(X)`` w.r.t. `y`.

        Notes
        -----
        The :math:`R^2` score used when calling ``score`` on a regressor uses
        ``multioutput='uniform_average'`` from version 0.23 to keep consistent
        with default value of :func:`~sklearn.metrics.r2_score`.
        This influences the ``score`` method of all the multioutput
        regressors (except for
        :class:`~sklearn.multioutput.MultiOutputRegressor`).
        """

        from .metrics import r2_score

        y_pred = self.predict(X)
        return r2_score(y, y_pred, sample_weight=sample_weight)

    def _more_tags(self):
        return {"requires_y": True}


class ClusterMixin:
    """Mixin class for all cluster estimators in scikit-learn.

    - `_estimator_type` class attribute defaulting to `"clusterer"`;
    - `fit_predict` method returning the cluster labels associated to each sample.

    Examples
    --------
    >>> import numpy as np
    >>> from sklearn.base import BaseEstimator, ClusterMixin
    >>> class MyClusterer(ClusterMixin, BaseEstimator):
    ...     def fit(self, X, y=None):
    ...         self.labels_ = np.ones(shape=(len(X),), dtype=np.int64)
    ...         return self
    >>> X = [[1, 2], [2, 3], [3, 4]]
    >>> MyClusterer().fit_predict(X)
    array([1, 1, 1])
    """

    _estimator_type = "clusterer"

    def fit_predict(self, X, y=None, **kwargs):
        """
        Perform clustering on `X` and returns cluster labels.

        Parameters
        ----------
        X : array-like of shape (n_samples, n_features)
            Input data.

        y : Ignored
            Not used, present for API consistency by convention.

        **kwargs : dict
            Arguments to be passed to ``fit``.

            .. versionadded:: 1.4

        Returns
        -------
        labels : ndarray of shape (n_samples,), dtype=np.int64
            Cluster labels.
        """
        # non-optimized default implementation; override when a better
        # method is possible for a given clustering algorithm
        self.fit(X, **kwargs)
        return self.labels_

    def _more_tags(self):
        return {"preserves_dtype": []}


class BiclusterMixin:
    """Mixin class for all bicluster estimators in scikit-learn.

    This mixin defines the following functionality:

    - `biclusters_` property that returns the row and column indicators;
    - `get_indices` method that returns the row and column indices of a bicluster;
    - `get_shape` method that returns the shape of a bicluster;
    - `get_submatrix` method that returns the submatrix corresponding to a bicluster.

    Examples
    --------
    >>> import numpy as np
    >>> from sklearn.base import BaseEstimator, BiclusterMixin
    >>> class DummyBiClustering(BiclusterMixin, BaseEstimator):
    ...     def fit(self, X, y=None):
    ...         self.rows_ = np.ones(shape=(1, X.shape[0]), dtype=bool)
    ...         self.columns_ = np.ones(shape=(1, X.shape[1]), dtype=bool)
    ...         return self
    >>> X = np.array([[1, 1], [2, 1], [1, 0],
    ...               [4, 7], [3, 5], [3, 6]])
    >>> bicluster = DummyBiClustering().fit(X)
    >>> hasattr(bicluster, "biclusters_")
    True
    >>> bicluster.get_indices(0)
    (array([0, 1, 2, 3, 4, 5]), array([0, 1]))
    """

    @property
    def biclusters_(self):
        """Convenient way to get row and column indicators together.

        Returns the ``rows_`` and ``columns_`` members.
        """
        return self.rows_, self.columns_

    def get_indices(self, i):
        """Row and column indices of the `i`'th bicluster.

        Only works if ``rows_`` and ``columns_`` attributes exist.

        Parameters
        ----------
        i : int
            The index of the cluster.

        Returns
        -------
        row_ind : ndarray, dtype=np.intp
            Indices of rows in the dataset that belong to the bicluster.
        col_ind : ndarray, dtype=np.intp
            Indices of columns in the dataset that belong to the bicluster.
        """
        rows = self.rows_[i]
        columns = self.columns_[i]
        return np.nonzero(rows)[0], np.nonzero(columns)[0]

    def get_shape(self, i):
        """Shape of the `i`'th bicluster.

        Parameters
        ----------
        i : int
            The index of the cluster.

        Returns
        -------
        n_rows : int
            Number of rows in the bicluster.

        n_cols : int
            Number of columns in the bicluster.
        """
        indices = self.get_indices(i)
        return tuple(len(i) for i in indices)

    def get_submatrix(self, i, data):
        """Return the submatrix corresponding to bicluster `i`.

        Parameters
        ----------
        i : int
            The index of the cluster.
        data : array-like of shape (n_samples, n_features)
            The data.

        Returns
        -------
        submatrix : ndarray of shape (n_rows, n_cols)
            The submatrix corresponding to bicluster `i`.

        Notes
        -----
        Works with sparse matrices. Only works if ``rows_`` and
        ``columns_`` attributes exist.
        """
        from .utils.validation import check_array

        data = check_array(data, accept_sparse="csr")
        row_ind, col_ind = self.get_indices(i)
        return data[row_ind[:, np.newaxis], col_ind]


class TransformerMixin(_SetOutputMixin):
    """Mixin class for all transformers in scikit-learn.

    This mixin defines the following functionality:

    - a `fit_transform` method that delegates to `fit` and `transform`;
    - a `set_output` method to output `X` as a specific container type.

    If :term:`get_feature_names_out` is defined, then :class:`BaseEstimator` will
    automatically wrap `transform` and `fit_transform` to follow the `set_output`
    API. See the :ref:`developer_api_set_output` for details.

    :class:`OneToOneFeatureMixin` and
    :class:`ClassNamePrefixFeaturesOutMixin` are helpful mixins for
    defining :term:`get_feature_names_out`.

    Examples
    --------
    >>> import numpy as np
    >>> from sklearn.base import BaseEstimator, TransformerMixin
    >>> class MyTransformer(TransformerMixin, BaseEstimator):
    ...     def __init__(self, *, param=1):
    ...         self.param = param
    ...     def fit(self, X, y=None):
    ...         return self
    ...     def transform(self, X):
    ...         return np.full(shape=len(X), fill_value=self.param)
    >>> transformer = MyTransformer()
    >>> X = [[1, 2], [2, 3], [3, 4]]
    >>> transformer.fit_transform(X)
    array([1, 1, 1])
    """

    def fit_transform(self, X, y=None, **fit_params):
        """
        Fit to data, then transform it.

        Fits transformer to `X` and `y` with optional parameters `fit_params`
        and returns a transformed version of `X`.

        Parameters
        ----------
        X : array-like of shape (n_samples, n_features)
            Input samples.

        y :  array-like of shape (n_samples,) or (n_samples, n_outputs), \
                default=None
            Target values (None for unsupervised transformations).

        **fit_params : dict
            Additional fit parameters.

        Returns
        -------
        X_new : ndarray array of shape (n_samples, n_features_new)
            Transformed array.
        """
        # non-optimized default implementation; override when a better
        # method is possible for a given clustering algorithm

        # we do not route parameters here, since consumers don't route. But
        # since it's possible for a `transform` method to also consume
        # metadata, we check if that's the case, and we raise a warning telling
        # users that they should implement a custom `fit_transform` method
        # to forward metadata to `transform` as well.
        #
        # For that, we calculate routing and check if anything would be routed
        # to `transform` if we were to route them.
        if _routing_enabled():
            transform_params = self.get_metadata_routing().consumes(
                method="transform", params=fit_params.keys()
            )
            if transform_params:
                warnings.warn(
                    (
                        f"This object ({self.__class__.__name__}) has a `transform`"
                        " method which consumes metadata, but `fit_transform` does not"
                        " forward metadata to `transform`. Please implement a custom"
                        " `fit_transform` method to forward metadata to `transform` as"
                        " well. Alternatively, you can explicitly do"
                        " `set_transform_request`and set all values to `False` to"
                        " disable metadata routed to `transform`, if that's an option."
                    ),
                    UserWarning,
                )

        if y is None:
            # fit method of arity 1 (unsupervised transformation)
            return self.fit(X, **fit_params).transform(X)
        else:
            # fit method of arity 2 (supervised transformation)
            return self.fit(X, y, **fit_params).transform(X)


class OneToOneFeatureMixin:
    """Provides `get_feature_names_out` for simple transformers.

    This mixin assumes there's a 1-to-1 correspondence between input features
    and output features, such as :class:`~sklearn.preprocessing.StandardScaler`.

    Examples
    --------
    >>> import numpy as np
    >>> from sklearn.base import OneToOneFeatureMixin
    >>> class MyEstimator(OneToOneFeatureMixin):
    ...     def fit(self, X, y=None):
    ...         self.n_features_in_ = X.shape[1]
    ...         return self
    >>> X = np.array([[1, 2], [3, 4]])
    >>> MyEstimator().fit(X).get_feature_names_out()
    array(['x0', 'x1'], dtype=object)
    """

    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
            Same as input features.
        """
        check_is_fitted(self, "n_features_in_")
        return _check_feature_names_in(self, input_features)


class ClassNamePrefixFeaturesOutMixin:
    """Mixin class for transformers that generate their own names by prefixing.

    This mixin is useful when the transformer needs to generate its own feature
    names out, such as :class:`~sklearn.decomposition.PCA`. For example, if
    :class:`~sklearn.decomposition.PCA` outputs 3 features, then the generated feature
    names out are: `["pca0", "pca1", "pca2"]`.

    This mixin assumes that a `_n_features_out` attribute is defined when the
    transformer is fitted. `_n_features_out` is the number of output features
    that the transformer will return in `transform` of `fit_transform`.

    Examples
    --------
    >>> import numpy as np
    >>> from sklearn.base import ClassNamePrefixFeaturesOutMixin
    >>> class MyEstimator(ClassNamePrefixFeaturesOutMixin):
    ...     def fit(self, X, y=None):
    ...         self._n_features_out = X.shape[1]
    ...         return self
    >>> X = np.array([[1, 2], [3, 4]])
    >>> MyEstimator().fit(X).get_feature_names_out()
    array(['myestimator0', 'myestimator1'], dtype=object)
    """

    def get_feature_names_out(self, input_features=None):
        """Get output feature names for transformation.

        The feature names out will prefixed by the lowercased class name. For
        example, if the transformer outputs 3 features, then the feature names
        out are: `["class_name0", "class_name1", "class_name2"]`.

        Parameters
        ----------
        input_features : array-like of str or None, default=None
            Only used to validate feature names with the names seen in `fit`.

        Returns
        -------
        feature_names_out : ndarray of str objects
            Transformed feature names.
        """
        check_is_fitted(self, "_n_features_out")
        return _generate_get_feature_names_out(
            self, self._n_features_out, input_features=input_features
        )


class DensityMixin:
    """Mixin class for all density estimators in scikit-learn.

    This mixin defines the following functionality:

    - `_estimator_type` class attribute defaulting to `"DensityEstimator"`;
    - `score` method that default that do no-op.

    Examples
    --------
    >>> from sklearn.base import DensityMixin
    >>> class MyEstimator(DensityMixin):
    ...     def fit(self, X, y=None):
    ...         self.is_fitted_ = True
    ...         return self
    >>> estimator = MyEstimator()
    >>> hasattr(estimator, "score")
    True
    """

    _estimator_type = "DensityEstimator"

    def score(self, X, y=None):
        """Return the score of the model on the data `X`.

        Parameters
        ----------
        X : array-like of shape (n_samples, n_features)
            Test samples.

        y : Ignored
            Not used, present for API consistency by convention.

        Returns
        -------
        score : float
        """
        pass


class OutlierMixin:
    """Mixin class for all outlier detection estimators in scikit-learn.

    This mixin defines the following functionality:

    - `_estimator_type` class attribute defaulting to `outlier_detector`;
    - `fit_predict` method that default to `fit` and `predict`.

    Examples
    --------
    >>> import numpy as np
    >>> from sklearn.base import BaseEstimator, OutlierMixin
    >>> class MyEstimator(OutlierMixin):
    ...     def fit(self, X, y=None):
    ...         self.is_fitted_ = True
    ...         return self
    ...     def predict(self, X):
    ...         return np.ones(shape=len(X))
    >>> estimator = MyEstimator()
    >>> X = np.array([[1, 2], [2, 3], [3, 4]])
    >>> estimator.fit_predict(X)
    array([1., 1., 1.])
    """

    _estimator_type = "outlier_detector"

    def fit_predict(self, X, y=None, **kwargs):
        """Perform fit on X and returns labels for X.

        Returns -1 for outliers and 1 for inliers.

        Parameters
        ----------
        X : {array-like, sparse matrix} of shape (n_samples, n_features)
            The input samples.

        y : Ignored
            Not used, present for API consistency by convention.

        **kwargs : dict
            Arguments to be passed to ``fit``.

            .. versionadded:: 1.4

        Returns
        -------
        y : ndarray of shape (n_samples,)
            1 for inliers, -1 for outliers.
        """
        # we do not route parameters here, since consumers don't route. But
        # since it's possible for a `predict` method to also consume
        # metadata, we check if that's the case, and we raise a warning telling
        # users that they should implement a custom `fit_predict` method
        # to forward metadata to `predict` as well.
        #
        # For that, we calculate routing and check if anything would be routed
        # to `predict` if we were to route them.
        if _routing_enabled():
            transform_params = self.get_metadata_routing().consumes(
                method="predict", params=kwargs.keys()
            )
            if transform_params:
                warnings.warn(
                    (
                        f"This object ({self.__class__.__name__}) has a `predict` "
                        "method which consumes metadata, but `fit_predict` does not "
                        "forward metadata to `predict`. Please implement a custom "
                        "`fit_predict` method to forward metadata to `predict` as well."
                        "Alternatively, you can explicitly do `set_predict_request`"
                        "and set all values to `False` to disable metadata routed to "
                        "`predict`, if that's an option."
                    ),
                    UserWarning,
                )

        # override for transductive outlier detectors like LocalOulierFactor
        return self.fit(X, **kwargs).predict(X)


class MetaEstimatorMixin:
    """Mixin class for all meta estimators in scikit-learn.

    This mixin defines the following functionality:

    - define `_required_parameters` that specify the mandatory `estimator` parameter.

    Examples
    --------
    >>> from sklearn.base import MetaEstimatorMixin
    >>> from sklearn.datasets import load_iris
    >>> from sklearn.linear_model import LogisticRegression
    >>> class MyEstimator(MetaEstimatorMixin):
    ...     def __init__(self, *, estimator=None):
    ...         self.estimator = estimator
    ...     def fit(self, X, y=None):
    ...         if self.estimator is None:
    ...             self.estimator_ = LogisticRegression()
    ...         else:
    ...             self.estimator_ = self.estimator
    ...         return self
    >>> X, y = load_iris(return_X_y=True)
    >>> estimator = MyEstimator().fit(X, y)
    >>> estimator.estimator_
    LogisticRegression()
    """

    _required_parameters = ["estimator"]


class MultiOutputMixin:
    """Mixin to mark estimators that support multioutput."""

    def _more_tags(self):
        return {"multioutput": True}


class _UnstableArchMixin:
    """Mark estimators that are non-determinstic on 32bit or PowerPC"""

    def _more_tags(self):
        return {
            "non_deterministic": _IS_32BIT or platform.machine().startswith(
                ("ppc", "powerpc")
            )
        }


def is_classifier(estimator):
    """Return True if the given estimator is (probably) a classifier.

    Parameters
    ----------
    estimator : object
        Estimator object to test.

    Returns
    -------
    out : bool
        True if estimator is a classifier and False otherwise.

    Examples
    --------
    >>> from sklearn.base import is_classifier
    >>> from sklearn.svm import SVC, SVR
    >>> classifier = SVC()
    >>> regressor = SVR()
    >>> is_classifier(classifier)
    True
    >>> is_classifier(regressor)
    False
    """
    return getattr(estimator, "_estimator_type", None) == "classifier"


def is_regressor(estimator):
    """Return True if the given estimator is (probably) a regressor.

    Parameters
    ----------
    estimator : estimator instance
        Estimator object to test.

    Returns
    -------
    out : bool
        True if estimator is a regressor and False otherwise.

    Examples
    --------
    >>> from sklearn.base import is_regressor
    >>> from sklearn.svm import SVC, SVR
    >>> classifier = SVC()
    >>> regressor = SVR()
    >>> is_regressor(classifier)
    False
    >>> is_regressor(regressor)
    True
    """
    return getattr(estimator, "_estimator_type", None) == "regressor"


def is_outlier_detector(estimator):
    """Return True if the given estimator is (probably) an outlier detector.

    Parameters
    ----------
    estimator : estimator instance
        Estimator object to test.

    Returns
    -------
    out : bool
        True if estimator is an outlier detector and False otherwise.
    """
    return getattr(estimator, "_estimator_type", None) == "outlier_detector"


def _fit_context(*, prefer_skip_nested_validation):
    """Decorator to run the fit methods of estimators within context managers.

    Parameters
    ----------
    prefer_skip_nested_validation : bool
        If True, the validation of parameters of inner estimators or functions
        called during fit will be skipped.

        This is useful to avoid validating many times the parameters passed by the
        user from the public facing API. It's also useful to avoid validating
        parameters that we pass internally to inner functions that are guaranteed to
        be valid by the test suite.

        It should be set to True for most estimators, except for those that receive
        non-validated objects as parameters, such as meta-estimators that are given
        estimator objects.

    Returns
    -------
    decorated_fit : method
        The decorated fit method.
    """

    def decorator(fit_method):
        @functools.wraps(fit_method)
        def wrapper(estimator, *args, **kwargs):
            global_skip_validation = get_config()["skip_parameter_validation"]

            # we don't want to validate again for each call to partial_fit
            partial_fit_and_fitted = (
                fit_method.__name__ == "partial_fit" and _is_fitted(estimator)
            )

            if not global_skip_validation and not partial_fit_and_fitted:
                estimator._validate_params()

            with config_context(
                skip_parameter_validation=(
                    prefer_skip_nested_validation or global_skip_validation
                )
            ):
                return fit_method(estimator, *args, **kwargs)

        return wrapper

    return decorator