Expand source code 
# This is just a simple wrapper around sklearn decisiontree
# https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html

from sklearn.tree import DecisionTreeClassifier, export_text, DecisionTreeRegressor
from imodels.util.arguments import check_fit_arguments

from imodels.util.tree import compute_tree_complexity


class GreedyTreeClassifier(DecisionTreeClassifier):
    """Wrapper around sklearn greedy tree classifier
    """

    def fit(self, X, y, feature_names=None, sample_weight=None, check_input=True):
        """Build a decision tree classifier from the training set (X, y).
        Parameters
        ----------
        X : {array-like, sparse matrix} of shape (n_samples, n_features)
            The training input samples. Internally, it will be converted to
            ``dtype=np.float32`` and if a sparse matrix is provided
            to a sparse ``csc_matrix``.
        y : array-like of shape (n_samples,) or (n_samples, n_outputs)
            The target values (class labels) as integers or strings.
        feature_names : array-like of shape (n_features)
            The names of the features
        sample_weight : array-like of shape (n_samples,), default=None
            Sample weights. If None, then samples are equally weighted. Splits
            that would create child nodes with net zero or negative weight are
            ignored while searching for a split in each node. Splits are also
            ignored if they would result in any single class carrying a
            negative weight in either child node.
        check_input : bool, default=True
            Allow to bypass several input checking.
            Don't use this parameter unless you know what you do.

        Returns
        -------
        self : DecisionTreeClassifier
            Fitted estimator.
        """
        X, y, feature_names = check_fit_arguments(self, X, y, feature_names)
        super().fit(X, y, sample_weight=sample_weight, check_input=check_input)
        self._set_complexity()

    def _set_complexity(self):
        """Set complexity as number of non-leaf nodes
        """
        self.complexity_ = compute_tree_complexity(self.tree_)

    def __str__(self):
        s = '> ------------------------------\n'
        s += '> Greedy CART Tree:\n'
        s += '> \tPrediction is made by looking at the value in the appropriate leaf of the tree\n'
        s += '> ------------------------------' + '\n'
        if hasattr(self, 'feature_names') and self.feature_names is not None:
            return s + export_text(self, feature_names=self.feature_names, show_weights=True)
        else:
            return s + export_text(self, show_weights=True)


class GreedyTreeRegressor(DecisionTreeRegressor):
    """Wrapper around sklearn greedy tree regressor
    """

    def fit(self, X, y, feature_names=None, sample_weight=None, check_input=True):
        """Build a decision tree regressor from the training set (X, y).
        Parameters
        ----------
        X : {array-like, sparse matrix} of shape (n_samples, n_features)
            The training input samples. Internally, it will be converted to
            ``dtype=np.float32`` and if a sparse matrix is provided
            to a sparse ``csc_matrix``.
        y : array-like of shape (n_samples,) or (n_samples, n_outputs)
            The target values (real numbers). Use ``dtype=np.float64`` and
            ``order='C'`` for maximum efficiency.
        sample_weight : array-like of shape (n_samples,), default=None
            Sample weights. If None, then samples are equally weighted. Splits
            that would create child nodes with net zero or negative weight are
            ignored while searching for a split in each node.
        check_input : bool, default=True
            Allow to bypass several input checking.
            Don't use this parameter unless you know what you do.
        Returns
        -------
        self : DecisionTreeRegressor
            Fitted estimator.
        """
        if feature_names is not None:
            self.feature_names = feature_names
        else:
            self.feature_names = ["X" + str(i + 1) for i in range(X.shape[1])]
        super().fit(X, y, sample_weight=sample_weight, check_input=check_input)
        self._set_complexity()

    def _set_complexity(self):
        """Set complexity as number of non-leaf nodes
        """
        self.complexity_ = compute_tree_complexity(self.tree_)

    def __str__(self):
        if hasattr(self, 'feature_names') and self.feature_names is not None:
            return 'GreedyTree:\n' + export_text(self, feature_names=self.feature_names, show_weights=True)
        else:
            return 'GreedyTree:\n' + export_text(self, show_weights=True)

Classes

class GreedyTreeClassifier (*, criterion='gini', splitter='best', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=None, random_state=None, max_leaf_nodes=None, min_impurity_decrease=0.0, class_weight=None, ccp_alpha=0.0, monotonic_cst=None)

Wrapper around sklearn greedy tree classifier

Expand source code 
class GreedyTreeClassifier(DecisionTreeClassifier):
    """Wrapper around sklearn greedy tree classifier
    """

    def fit(self, X, y, feature_names=None, sample_weight=None, check_input=True):
        """Build a decision tree classifier from the training set (X, y).
        Parameters
        ----------
        X : {array-like, sparse matrix} of shape (n_samples, n_features)
            The training input samples. Internally, it will be converted to
            ``dtype=np.float32`` and if a sparse matrix is provided
            to a sparse ``csc_matrix``.
        y : array-like of shape (n_samples,) or (n_samples, n_outputs)
            The target values (class labels) as integers or strings.
        feature_names : array-like of shape (n_features)
            The names of the features
        sample_weight : array-like of shape (n_samples,), default=None
            Sample weights. If None, then samples are equally weighted. Splits
            that would create child nodes with net zero or negative weight are
            ignored while searching for a split in each node. Splits are also
            ignored if they would result in any single class carrying a
            negative weight in either child node.
        check_input : bool, default=True
            Allow to bypass several input checking.
            Don't use this parameter unless you know what you do.

        Returns
        -------
        self : DecisionTreeClassifier
            Fitted estimator.
        """
        X, y, feature_names = check_fit_arguments(self, X, y, feature_names)
        super().fit(X, y, sample_weight=sample_weight, check_input=check_input)
        self._set_complexity()

    def _set_complexity(self):
        """Set complexity as number of non-leaf nodes
        """
        self.complexity_ = compute_tree_complexity(self.tree_)

    def __str__(self):
        s = '> ------------------------------\n'
        s += '> Greedy CART Tree:\n'
        s += '> \tPrediction is made by looking at the value in the appropriate leaf of the tree\n'
        s += '> ------------------------------' + '\n'
        if hasattr(self, 'feature_names') and self.feature_names is not None:
            return s + export_text(self, feature_names=self.feature_names, show_weights=True)
        else:
            return s + export_text(self, show_weights=True)

Ancestors

  • sklearn.tree._classes.DecisionTreeClassifier
  • sklearn.base.ClassifierMixin
  • sklearn.tree._classes.BaseDecisionTree
  • sklearn.base.MultiOutputMixin
  • sklearn.base.BaseEstimator
  • sklearn.utils._estimator_html_repr._HTMLDocumentationLinkMixin
  • sklearn.utils._metadata_requests._MetadataRequester

Subclasses

Methods

def fit(self, X, y, feature_names=None, sample_weight=None, check_input=True)

Build a decision tree classifier from the training set (X, y). Parameters

X : {array-like, sparse matrix} of shape (n_samples, n_features)
The training input samples. Internally, it will be converted to dtype=np.float32 and if a sparse matrix is provided to a sparse csc_matrix.
y : array-like of shape (n_samples,) or (n_samples, n_outputs)
The target values (class labels) as integers or strings.
feature_names : array-like of shape (n_features)
The names of the features
sample_weight : array-like of shape (n_samples,), default=None
Sample weights. If None, then samples are equally weighted. Splits that would create child nodes with net zero or negative weight are ignored while searching for a split in each node. Splits are also ignored if they would result in any single class carrying a negative weight in either child node.
check_input : bool, default=True
Allow to bypass several input checking. Don't use this parameter unless you know what you do.

Returns

self : DecisionTreeClassifier
Fitted estimator.
Expand source code 
def fit(self, X, y, feature_names=None, sample_weight=None, check_input=True):
    """Build a decision tree classifier from the training set (X, y).
    Parameters
    ----------
    X : {array-like, sparse matrix} of shape (n_samples, n_features)
        The training input samples. Internally, it will be converted to
        ``dtype=np.float32`` and if a sparse matrix is provided
        to a sparse ``csc_matrix``.
    y : array-like of shape (n_samples,) or (n_samples, n_outputs)
        The target values (class labels) as integers or strings.
    feature_names : array-like of shape (n_features)
        The names of the features
    sample_weight : array-like of shape (n_samples,), default=None
        Sample weights. If None, then samples are equally weighted. Splits
        that would create child nodes with net zero or negative weight are
        ignored while searching for a split in each node. Splits are also
        ignored if they would result in any single class carrying a
        negative weight in either child node.
    check_input : bool, default=True
        Allow to bypass several input checking.
        Don't use this parameter unless you know what you do.

    Returns
    -------
    self : DecisionTreeClassifier
        Fitted estimator.
    """
    X, y, feature_names = check_fit_arguments(self, X, y, feature_names)
    super().fit(X, y, sample_weight=sample_weight, check_input=check_input)
    self._set_complexity()
def set_fit_request(self: GreedyTreeClassifier, *, check_input: Union[bool, ForwardRef(None), str] = '$UNCHANGED$', feature_names: Union[bool, ForwardRef(None), str] = '$UNCHANGED$', sample_weight: Union[bool, ForwardRef(None), str] = '$UNCHANGED$') ‑> GreedyTreeClassifier

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see :func:sklearn.set_config). Please see :ref:User Guide <metadata_routing> on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to fit.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version: 1.3

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a :class:~sklearn.pipeline.Pipeline. Otherwise it has no effect.

Parameters

check_input : str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for check_input parameter in fit.
feature_names : str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for feature_names parameter in fit.
sample_weight : str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for sample_weight parameter in fit.

Returns

self : object
The updated object.
Expand source code 
def func(*args, **kw):
    """Updates the request for provided parameters

    This docstring is overwritten below.
    See REQUESTER_DOC for expected functionality
    """
    if not _routing_enabled():
        raise RuntimeError(
            "This method is only available when metadata routing is enabled."
            " You can enable it using"
            " sklearn.set_config(enable_metadata_routing=True)."
        )

    if self.validate_keys and (set(kw) - set(self.keys)):
        raise TypeError(
            f"Unexpected args: {set(kw) - set(self.keys)} in {self.name}. "
            f"Accepted arguments are: {set(self.keys)}"
        )

    # This makes it possible to use the decorated method as an unbound method,
    # for instance when monkeypatching.
    # https://github.com/scikit-learn/scikit-learn/issues/28632
    if instance is None:
        _instance = args[0]
        args = args[1:]
    else:
        _instance = instance

    # Replicating python's behavior when positional args are given other than
    # `self`, and `self` is only allowed if this method is unbound.
    if args:
        raise TypeError(
            f"set_{self.name}_request() takes 0 positional argument but"
            f" {len(args)} were given"
        )

    requests = _instance._get_metadata_request()
    method_metadata_request = getattr(requests, self.name)

    for prop, alias in kw.items():
        if alias is not UNCHANGED:
            method_metadata_request.add_request(param=prop, alias=alias)
    _instance._metadata_request = requests

    return _instance
def set_predict_proba_request(self: GreedyTreeClassifier, *, check_input: Union[bool, ForwardRef(None), str] = '$UNCHANGED$') ‑> GreedyTreeClassifier

Request metadata passed to the predict_proba method.

Note that this method is only relevant if enable_metadata_routing=True (see :func:sklearn.set_config). Please see :ref:User Guide <metadata_routing> on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to predict_proba if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to predict_proba.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version: 1.3

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a :class:~sklearn.pipeline.Pipeline. Otherwise it has no effect.

Parameters

check_input : str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for check_input parameter in predict_proba.

Returns

self : object
The updated object.
Expand source code 
def func(*args, **kw):
    """Updates the request for provided parameters

    This docstring is overwritten below.
    See REQUESTER_DOC for expected functionality
    """
    if not _routing_enabled():
        raise RuntimeError(
            "This method is only available when metadata routing is enabled."
            " You can enable it using"
            " sklearn.set_config(enable_metadata_routing=True)."
        )

    if self.validate_keys and (set(kw) - set(self.keys)):
        raise TypeError(
            f"Unexpected args: {set(kw) - set(self.keys)} in {self.name}. "
            f"Accepted arguments are: {set(self.keys)}"
        )

    # This makes it possible to use the decorated method as an unbound method,
    # for instance when monkeypatching.
    # https://github.com/scikit-learn/scikit-learn/issues/28632
    if instance is None:
        _instance = args[0]
        args = args[1:]
    else:
        _instance = instance

    # Replicating python's behavior when positional args are given other than
    # `self`, and `self` is only allowed if this method is unbound.
    if args:
        raise TypeError(
            f"set_{self.name}_request() takes 0 positional argument but"
            f" {len(args)} were given"
        )

    requests = _instance._get_metadata_request()
    method_metadata_request = getattr(requests, self.name)

    for prop, alias in kw.items():
        if alias is not UNCHANGED:
            method_metadata_request.add_request(param=prop, alias=alias)
    _instance._metadata_request = requests

    return _instance
def set_predict_request(self: GreedyTreeClassifier, *, check_input: Union[bool, ForwardRef(None), str] = '$UNCHANGED$') ‑> GreedyTreeClassifier

Request metadata passed to the predict method.

Note that this method is only relevant if enable_metadata_routing=True (see :func:sklearn.set_config). Please see :ref:User Guide <metadata_routing> on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to predict if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to predict.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version: 1.3

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a :class:~sklearn.pipeline.Pipeline. Otherwise it has no effect.

Parameters

check_input : str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for check_input parameter in predict.

Returns

self : object
The updated object.
Expand source code 
def func(*args, **kw):
    """Updates the request for provided parameters

    This docstring is overwritten below.
    See REQUESTER_DOC for expected functionality
    """
    if not _routing_enabled():
        raise RuntimeError(
            "This method is only available when metadata routing is enabled."
            " You can enable it using"
            " sklearn.set_config(enable_metadata_routing=True)."
        )

    if self.validate_keys and (set(kw) - set(self.keys)):
        raise TypeError(
            f"Unexpected args: {set(kw) - set(self.keys)} in {self.name}. "
            f"Accepted arguments are: {set(self.keys)}"
        )

    # This makes it possible to use the decorated method as an unbound method,
    # for instance when monkeypatching.
    # https://github.com/scikit-learn/scikit-learn/issues/28632
    if instance is None:
        _instance = args[0]
        args = args[1:]
    else:
        _instance = instance

    # Replicating python's behavior when positional args are given other than
    # `self`, and `self` is only allowed if this method is unbound.
    if args:
        raise TypeError(
            f"set_{self.name}_request() takes 0 positional argument but"
            f" {len(args)} were given"
        )

    requests = _instance._get_metadata_request()
    method_metadata_request = getattr(requests, self.name)

    for prop, alias in kw.items():
        if alias is not UNCHANGED:
            method_metadata_request.add_request(param=prop, alias=alias)
    _instance._metadata_request = requests

    return _instance
def set_score_request(self: GreedyTreeClassifier, *, sample_weight: Union[bool, ForwardRef(None), str] = '$UNCHANGED$') ‑> GreedyTreeClassifier

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see :func:sklearn.set_config). Please see :ref:User Guide <metadata_routing> on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to score.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version: 1.3

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a :class:~sklearn.pipeline.Pipeline. Otherwise it has no effect.

Parameters

sample_weight : str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for sample_weight parameter in score.

Returns

self : object
The updated object.
Expand source code 
def func(*args, **kw):
    """Updates the request for provided parameters

    This docstring is overwritten below.
    See REQUESTER_DOC for expected functionality
    """
    if not _routing_enabled():
        raise RuntimeError(
            "This method is only available when metadata routing is enabled."
            " You can enable it using"
            " sklearn.set_config(enable_metadata_routing=True)."
        )

    if self.validate_keys and (set(kw) - set(self.keys)):
        raise TypeError(
            f"Unexpected args: {set(kw) - set(self.keys)} in {self.name}. "
            f"Accepted arguments are: {set(self.keys)}"
        )

    # This makes it possible to use the decorated method as an unbound method,
    # for instance when monkeypatching.
    # https://github.com/scikit-learn/scikit-learn/issues/28632
    if instance is None:
        _instance = args[0]
        args = args[1:]
    else:
        _instance = instance

    # Replicating python's behavior when positional args are given other than
    # `self`, and `self` is only allowed if this method is unbound.
    if args:
        raise TypeError(
            f"set_{self.name}_request() takes 0 positional argument but"
            f" {len(args)} were given"
        )

    requests = _instance._get_metadata_request()
    method_metadata_request = getattr(requests, self.name)

    for prop, alias in kw.items():
        if alias is not UNCHANGED:
            method_metadata_request.add_request(param=prop, alias=alias)
    _instance._metadata_request = requests

    return _instance
class GreedyTreeRegressor (*, criterion='squared_error', splitter='best', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=None, random_state=None, max_leaf_nodes=None, min_impurity_decrease=0.0, ccp_alpha=0.0, monotonic_cst=None)

Wrapper around sklearn greedy tree regressor

Expand source code 
class GreedyTreeRegressor(DecisionTreeRegressor):
    """Wrapper around sklearn greedy tree regressor
    """

    def fit(self, X, y, feature_names=None, sample_weight=None, check_input=True):
        """Build a decision tree regressor from the training set (X, y).
        Parameters
        ----------
        X : {array-like, sparse matrix} of shape (n_samples, n_features)
            The training input samples. Internally, it will be converted to
            ``dtype=np.float32`` and if a sparse matrix is provided
            to a sparse ``csc_matrix``.
        y : array-like of shape (n_samples,) or (n_samples, n_outputs)
            The target values (real numbers). Use ``dtype=np.float64`` and
            ``order='C'`` for maximum efficiency.
        sample_weight : array-like of shape (n_samples,), default=None
            Sample weights. If None, then samples are equally weighted. Splits
            that would create child nodes with net zero or negative weight are
            ignored while searching for a split in each node.
        check_input : bool, default=True
            Allow to bypass several input checking.
            Don't use this parameter unless you know what you do.
        Returns
        -------
        self : DecisionTreeRegressor
            Fitted estimator.
        """
        if feature_names is not None:
            self.feature_names = feature_names
        else:
            self.feature_names = ["X" + str(i + 1) for i in range(X.shape[1])]
        super().fit(X, y, sample_weight=sample_weight, check_input=check_input)
        self._set_complexity()

    def _set_complexity(self):
        """Set complexity as number of non-leaf nodes
        """
        self.complexity_ = compute_tree_complexity(self.tree_)

    def __str__(self):
        if hasattr(self, 'feature_names') and self.feature_names is not None:
            return 'GreedyTree:\n' + export_text(self, feature_names=self.feature_names, show_weights=True)
        else:
            return 'GreedyTree:\n' + export_text(self, show_weights=True)

Ancestors

  • sklearn.tree._classes.DecisionTreeRegressor
  • sklearn.base.RegressorMixin
  • sklearn.tree._classes.BaseDecisionTree
  • sklearn.base.MultiOutputMixin
  • sklearn.base.BaseEstimator
  • sklearn.utils._estimator_html_repr._HTMLDocumentationLinkMixin
  • sklearn.utils._metadata_requests._MetadataRequester

Methods

def fit(self, X, y, feature_names=None, sample_weight=None, check_input=True)

Build a decision tree regressor from the training set (X, y). Parameters

X : {array-like, sparse matrix} of shape (n_samples, n_features)
The training input samples. Internally, it will be converted to dtype=np.float32 and if a sparse matrix is provided to a sparse csc_matrix.
y : array-like of shape (n_samples,) or (n_samples, n_outputs)
The target values (real numbers). Use dtype=np.float64 and order='C' for maximum efficiency.
sample_weight : array-like of shape (n_samples,), default=None
Sample weights. If None, then samples are equally weighted. Splits that would create child nodes with net zero or negative weight are ignored while searching for a split in each node.
check_input : bool, default=True
Allow to bypass several input checking. Don't use this parameter unless you know what you do.

Returns

self : DecisionTreeRegressor
Fitted estimator.
Expand source code 
def fit(self, X, y, feature_names=None, sample_weight=None, check_input=True):
    """Build a decision tree regressor from the training set (X, y).
    Parameters
    ----------
    X : {array-like, sparse matrix} of shape (n_samples, n_features)
        The training input samples. Internally, it will be converted to
        ``dtype=np.float32`` and if a sparse matrix is provided
        to a sparse ``csc_matrix``.
    y : array-like of shape (n_samples,) or (n_samples, n_outputs)
        The target values (real numbers). Use ``dtype=np.float64`` and
        ``order='C'`` for maximum efficiency.
    sample_weight : array-like of shape (n_samples,), default=None
        Sample weights. If None, then samples are equally weighted. Splits
        that would create child nodes with net zero or negative weight are
        ignored while searching for a split in each node.
    check_input : bool, default=True
        Allow to bypass several input checking.
        Don't use this parameter unless you know what you do.
    Returns
    -------
    self : DecisionTreeRegressor
        Fitted estimator.
    """
    if feature_names is not None:
        self.feature_names = feature_names
    else:
        self.feature_names = ["X" + str(i + 1) for i in range(X.shape[1])]
    super().fit(X, y, sample_weight=sample_weight, check_input=check_input)
    self._set_complexity()
def set_fit_request(self: GreedyTreeRegressor, *, check_input: Union[bool, ForwardRef(None), str] = '$UNCHANGED$', feature_names: Union[bool, ForwardRef(None), str] = '$UNCHANGED$', sample_weight: Union[bool, ForwardRef(None), str] = '$UNCHANGED$') ‑> GreedyTreeRegressor

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see :func:sklearn.set_config). Please see :ref:User Guide <metadata_routing> on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to fit.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version: 1.3

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a :class:~sklearn.pipeline.Pipeline. Otherwise it has no effect.

Parameters

check_input : str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for check_input parameter in fit.
feature_names : str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for feature_names parameter in fit.
sample_weight : str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for sample_weight parameter in fit.

Returns

self : object
The updated object.
Expand source code 
def func(*args, **kw):
    """Updates the request for provided parameters

    This docstring is overwritten below.
    See REQUESTER_DOC for expected functionality
    """
    if not _routing_enabled():
        raise RuntimeError(
            "This method is only available when metadata routing is enabled."
            " You can enable it using"
            " sklearn.set_config(enable_metadata_routing=True)."
        )

    if self.validate_keys and (set(kw) - set(self.keys)):
        raise TypeError(
            f"Unexpected args: {set(kw) - set(self.keys)} in {self.name}. "
            f"Accepted arguments are: {set(self.keys)}"
        )

    # This makes it possible to use the decorated method as an unbound method,
    # for instance when monkeypatching.
    # https://github.com/scikit-learn/scikit-learn/issues/28632
    if instance is None:
        _instance = args[0]
        args = args[1:]
    else:
        _instance = instance

    # Replicating python's behavior when positional args are given other than
    # `self`, and `self` is only allowed if this method is unbound.
    if args:
        raise TypeError(
            f"set_{self.name}_request() takes 0 positional argument but"
            f" {len(args)} were given"
        )

    requests = _instance._get_metadata_request()
    method_metadata_request = getattr(requests, self.name)

    for prop, alias in kw.items():
        if alias is not UNCHANGED:
            method_metadata_request.add_request(param=prop, alias=alias)
    _instance._metadata_request = requests

    return _instance
def set_predict_request(self: GreedyTreeRegressor, *, check_input: Union[bool, ForwardRef(None), str] = '$UNCHANGED$') ‑> GreedyTreeRegressor

Request metadata passed to the predict method.

Note that this method is only relevant if enable_metadata_routing=True (see :func:sklearn.set_config). Please see :ref:User Guide <metadata_routing> on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to predict if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to predict.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version: 1.3

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a :class:~sklearn.pipeline.Pipeline. Otherwise it has no effect.

Parameters

check_input : str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for check_input parameter in predict.

Returns

self : object
The updated object.
Expand source code 
def func(*args, **kw):
    """Updates the request for provided parameters

    This docstring is overwritten below.
    See REQUESTER_DOC for expected functionality
    """
    if not _routing_enabled():
        raise RuntimeError(
            "This method is only available when metadata routing is enabled."
            " You can enable it using"
            " sklearn.set_config(enable_metadata_routing=True)."
        )

    if self.validate_keys and (set(kw) - set(self.keys)):
        raise TypeError(
            f"Unexpected args: {set(kw) - set(self.keys)} in {self.name}. "
            f"Accepted arguments are: {set(self.keys)}"
        )

    # This makes it possible to use the decorated method as an unbound method,
    # for instance when monkeypatching.
    # https://github.com/scikit-learn/scikit-learn/issues/28632
    if instance is None:
        _instance = args[0]
        args = args[1:]
    else:
        _instance = instance

    # Replicating python's behavior when positional args are given other than
    # `self`, and `self` is only allowed if this method is unbound.
    if args:
        raise TypeError(
            f"set_{self.name}_request() takes 0 positional argument but"
            f" {len(args)} were given"
        )

    requests = _instance._get_metadata_request()
    method_metadata_request = getattr(requests, self.name)

    for prop, alias in kw.items():
        if alias is not UNCHANGED:
            method_metadata_request.add_request(param=prop, alias=alias)
    _instance._metadata_request = requests

    return _instance
def set_score_request(self: GreedyTreeRegressor, *, sample_weight: Union[bool, ForwardRef(None), str] = '$UNCHANGED$') ‑> GreedyTreeRegressor

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see :func:sklearn.set_config). Please see :ref:User Guide <metadata_routing> on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to score.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version: 1.3

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a :class:~sklearn.pipeline.Pipeline. Otherwise it has no effect.

Parameters

sample_weight : str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for sample_weight parameter in score.

Returns

self : object
The updated object.
Expand source code 
def func(*args, **kw):
    """Updates the request for provided parameters

    This docstring is overwritten below.
    See REQUESTER_DOC for expected functionality
    """
    if not _routing_enabled():
        raise RuntimeError(
            "This method is only available when metadata routing is enabled."
            " You can enable it using"
            " sklearn.set_config(enable_metadata_routing=True)."
        )

    if self.validate_keys and (set(kw) - set(self.keys)):
        raise TypeError(
            f"Unexpected args: {set(kw) - set(self.keys)} in {self.name}. "
            f"Accepted arguments are: {set(self.keys)}"
        )

    # This makes it possible to use the decorated method as an unbound method,
    # for instance when monkeypatching.
    # https://github.com/scikit-learn/scikit-learn/issues/28632
    if instance is None:
        _instance = args[0]
        args = args[1:]
    else:
        _instance = instance

    # Replicating python's behavior when positional args are given other than
    # `self`, and `self` is only allowed if this method is unbound.
    if args:
        raise TypeError(
            f"set_{self.name}_request() takes 0 positional argument but"
            f" {len(args)} were given"
        )

    requests = _instance._get_metadata_request()
    method_metadata_request = getattr(requests, self.name)

    for prop, alias in kw.items():
        if alias is not UNCHANGED:
            method_metadata_request.add_request(param=prop, alias=alias)
    _instance._metadata_request = requests

    return _instance