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"""Multioutput regression and classification.
The estimators provided in this module are meta-estimators: they require
a base estimator to be provided in their constructor. The meta-estimator
extends single output estimators to multioutput estimators.
"""
# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
import warnings
from abc import ABCMeta, abstractmethod
from numbers import Integral
import numpy as np
import scipy.sparse as sp
from sklearn.base import (
BaseEstimator,
ClassifierMixin,
MetaEstimatorMixin,
RegressorMixin,
_fit_context,
clone,
is_classifier,
)
from sklearn.model_selection import cross_val_predict
from sklearn.utils import Bunch, check_random_state, get_tags
from sklearn.utils._param_validation import HasMethods, Hidden, StrOptions
from sklearn.utils._response import _get_response_values
from sklearn.utils._sparse import _align_api_if_sparse
from sklearn.utils._user_interface import _print_elapsed_time
from sklearn.utils.metadata_routing import (
MetadataRouter,
MethodMapping,
_raise_for_params,
_routing_enabled,
process_routing,
)
from sklearn.utils.metaestimators import available_if
from sklearn.utils.multiclass import check_classification_targets
from sklearn.utils.parallel import Parallel, delayed
from sklearn.utils.validation import (
_check_method_params,
_check_response_method,
check_is_fitted,
has_fit_parameter,
validate_data,
)
__all__ = [
"ClassifierChain",
"MultiOutputClassifier",
"MultiOutputRegressor",
"RegressorChain",
]
def _fit_estimator(estimator, X, y, sample_weight=None, **fit_params):
estimator = clone(estimator)
if sample_weight is not None:
estimator.fit(X, y, sample_weight=sample_weight, **fit_params)
else:
estimator.fit(X, y, **fit_params)
return estimator
def _partial_fit_estimator(
estimator, X, y, classes=None, partial_fit_params=None, first_time=True
):
partial_fit_params = {} if partial_fit_params is None else partial_fit_params
if first_time:
estimator = clone(estimator)
if classes is not None:
estimator.partial_fit(X, y, classes=classes, **partial_fit_params)
else:
estimator.partial_fit(X, y, **partial_fit_params)
return estimator
def _available_if_estimator_has(attr):
"""Return a function to check if the sub-estimator(s) has(have) `attr`.
Helper for Chain implementations.
"""
def _check(self):
if hasattr(self, "estimators_"):
return all(hasattr(est, attr) for est in self.estimators_)
if hasattr(self.estimator, attr):
return True
return False
return available_if(_check)
class _MultiOutputEstimator(MetaEstimatorMixin, BaseEstimator, metaclass=ABCMeta):
_parameter_constraints: dict = {
"estimator": [HasMethods(["fit", "predict"])],
"n_jobs": [Integral, None],
}
@abstractmethod
def __init__(self, estimator, *, n_jobs=None):
self.estimator = estimator
self.n_jobs = n_jobs
@_available_if_estimator_has("partial_fit")
@_fit_context(
# MultiOutput*.estimator is not validated yet
prefer_skip_nested_validation=False
)
def partial_fit(self, X, y, classes=None, sample_weight=None, **partial_fit_params):
"""Incrementally fit a separate model for each class output.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input data.
y : {array-like, sparse matrix} of shape (n_samples, n_outputs)
Multi-output targets.
classes : list of ndarray of shape (n_outputs,), default=None
Each array is unique classes for one output in str/int.
Can be obtained via
``[np.unique(y[:, i]) for i in range(y.shape[1])]``, where `y`
is the target matrix of the entire dataset.
This argument is required for the first call to partial_fit
and can be omitted in the subsequent calls.
Note that `y` doesn't need to contain all labels in `classes`.
sample_weight : array-like of shape (n_samples,), default=None
Sample weights. If `None`, then samples are equally weighted.
Only supported if the underlying regressor supports sample
weights.
**partial_fit_params : dict of str -> object
Parameters passed to the ``estimator.partial_fit`` method of each
sub-estimator.
Only available if `enable_metadata_routing=True`. See the
:ref:`User Guide <metadata_routing>`.
.. versionadded:: 1.3
Returns
-------
self : object
Returns a fitted instance.
"""
_raise_for_params(partial_fit_params, self, "partial_fit")
first_time = not hasattr(self, "estimators_")
y = validate_data(self, X="no_validation", y=y, multi_output=True)
if y.ndim == 1:
raise ValueError(
"y must have at least two dimensions for "
"multi-output regression but has only one."
)
if _routing_enabled():
if sample_weight is not None:
partial_fit_params["sample_weight"] = sample_weight
routed_params = process_routing(
self,
"partial_fit",
**partial_fit_params,
)
else:
if sample_weight is not None and not has_fit_parameter(
self.estimator, "sample_weight"
):
raise ValueError(
"Underlying estimator does not support sample weights."
)
if sample_weight is not None:
routed_params = Bunch(
estimator=Bunch(partial_fit=Bunch(sample_weight=sample_weight))
)
else:
routed_params = Bunch(estimator=Bunch(partial_fit=Bunch()))
self.estimators_ = Parallel(n_jobs=self.n_jobs)(
delayed(_partial_fit_estimator)(
self.estimators_[i] if not first_time else self.estimator,
X,
y[:, i],
classes[i] if classes is not None else None,
partial_fit_params=routed_params.estimator.partial_fit,
first_time=first_time,
)
for i in range(y.shape[1])
)
if first_time and hasattr(self.estimators_[0], "n_features_in_"):
self.n_features_in_ = self.estimators_[0].n_features_in_
if first_time and hasattr(self.estimators_[0], "feature_names_in_"):
self.feature_names_in_ = self.estimators_[0].feature_names_in_
return self
@_fit_context(
# MultiOutput*.estimator is not validated yet
prefer_skip_nested_validation=False
)
def fit(self, X, y, sample_weight=None, **fit_params):
"""Fit the model to data, separately for each output variable.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input data.
y : {array-like, sparse matrix} of shape (n_samples, n_outputs)
Multi-output targets. An indicator matrix turns on multilabel
estimation.
sample_weight : array-like of shape (n_samples,), default=None
Sample weights. If `None`, then samples are equally weighted.
Only supported if the underlying regressor supports sample
weights.
**fit_params : dict of string -> object
Parameters passed to the ``estimator.fit`` method of each step.
.. versionadded:: 0.23
Returns
-------
self : object
Returns a fitted instance.
"""
if not hasattr(self.estimator, "fit"):
raise ValueError("The base estimator should implement a fit method")
y = validate_data(self, X="no_validation", y=y, multi_output=True)
if is_classifier(self):
check_classification_targets(y)
if y.ndim == 1:
raise ValueError(
"y must have at least two dimensions for "
"multi-output regression but has only one."
)
if _routing_enabled():
if sample_weight is not None:
fit_params["sample_weight"] = sample_weight
routed_params = process_routing(
self,
"fit",
**fit_params,
)
else:
if sample_weight is not None and not has_fit_parameter(
self.estimator, "sample_weight"
):
raise ValueError(
"Underlying estimator does not support sample weights."
)
fit_params_validated = _check_method_params(X, params=fit_params)
routed_params = Bunch(estimator=Bunch(fit=fit_params_validated))
if sample_weight is not None:
routed_params.estimator.fit["sample_weight"] = sample_weight
self.estimators_ = Parallel(n_jobs=self.n_jobs)(
delayed(_fit_estimator)(
self.estimator, X, y[:, i], **routed_params.estimator.fit
)
for i in range(y.shape[1])
)
if hasattr(self.estimators_[0], "n_features_in_"):
self.n_features_in_ = self.estimators_[0].n_features_in_
if hasattr(self.estimators_[0], "feature_names_in_"):
self.feature_names_in_ = self.estimators_[0].feature_names_in_
return self
def predict(self, X):
"""Predict multi-output variable using model for each target variable.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input data.
Returns
-------
y : {array-like, sparse matrix} of shape (n_samples, n_outputs)
Multi-output targets predicted across multiple predictors.
Note: Separate models are generated for each predictor.
"""
check_is_fitted(self)
if not hasattr(self.estimators_[0], "predict"):
raise ValueError("The base estimator should implement a predict method")
y = Parallel(n_jobs=self.n_jobs)(
delayed(e.predict)(X) for e in self.estimators_
)
return np.asarray(y).T
def __sklearn_tags__(self):
tags = super().__sklearn_tags__()
tags.input_tags.sparse = get_tags(self.estimator).input_tags.sparse
tags.target_tags.single_output = False
tags.target_tags.multi_output = True
return tags
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:: 1.3
Returns
-------
routing : MetadataRouter
A :class:`~sklearn.utils.metadata_routing.MetadataRouter` encapsulating
routing information.
"""
router = MetadataRouter(owner=self).add(
estimator=self.estimator,
method_mapping=MethodMapping()
.add(caller="partial_fit", callee="partial_fit")
.add(caller="fit", callee="fit"),
)
return router
class MultiOutputRegressor(RegressorMixin, _MultiOutputEstimator):
"""Multi target regression.
This strategy consists of fitting one regressor per target. This is a
simple strategy for extending regressors that do not natively support
multi-target regression.
.. versionadded:: 0.18
Parameters
----------
estimator : estimator object
An estimator object implementing :term:`fit` and :term:`predict`.
n_jobs : int or None, optional (default=None)
The number of jobs to run in parallel.
:meth:`fit`, :meth:`predict` and :meth:`partial_fit` (if supported
by the passed estimator) will be parallelized for each target.
When individual estimators are fast to train or predict,
using ``n_jobs > 1`` can result in slower performance due
to the parallelism overhead.
``None`` means `1` unless in a :obj:`joblib.parallel_backend` context.
``-1`` means using all available processes / threads.
See :term:`Glossary <n_jobs>` for more details.
.. versionchanged:: 0.20
`n_jobs` default changed from `1` to `None`.
Attributes
----------
estimators_ : list of ``n_output`` estimators
Estimators used for predictions.
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`. Only defined if the
underlying estimators expose such an attribute when fit.
.. versionadded:: 1.0
See Also
--------
RegressorChain : A multi-label model that arranges regressions into a
chain.
MultiOutputClassifier : Classifies each output independently rather than
chaining.
Examples
--------
>>> import numpy as np
>>> from sklearn.datasets import load_linnerud
>>> from sklearn.multioutput import MultiOutputRegressor
>>> from sklearn.linear_model import Ridge
>>> X, y = load_linnerud(return_X_y=True)
>>> regr = MultiOutputRegressor(Ridge(random_state=123)).fit(X, y)
>>> regr.predict(X[[0]])
array([[176, 35.1, 57.1]])
"""
def __init__(self, estimator, *, n_jobs=None):
super().__init__(estimator, n_jobs=n_jobs)
@_available_if_estimator_has("partial_fit")
def partial_fit(self, X, y, sample_weight=None, **partial_fit_params):
"""Incrementally fit the model to data, for each output variable.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input data.
y : {array-like, sparse matrix} of shape (n_samples, n_outputs)
Multi-output targets.
sample_weight : array-like of shape (n_samples,), default=None
Sample weights. If `None`, then samples are equally weighted.
Only supported if the underlying regressor supports sample
weights.
**partial_fit_params : dict of str -> object
Parameters passed to the ``estimator.partial_fit`` method of each
sub-estimator.
Only available if `enable_metadata_routing=True`. See the
:ref:`User Guide <metadata_routing>`.
.. versionadded:: 1.3
Returns
-------
self : object
Returns a fitted instance.
"""
super().partial_fit(X, y, sample_weight=sample_weight, **partial_fit_params)
class MultiOutputClassifier(ClassifierMixin, _MultiOutputEstimator):
"""Multi target classification.
This strategy consists of fitting one classifier per target. This is a
simple strategy for extending classifiers that do not natively support
multi-target classification.
Parameters
----------
estimator : estimator object
An estimator object implementing :term:`fit` and :term:`predict`.
A :term:`predict_proba` method will be exposed only if `estimator` implements
it.
n_jobs : int or None, optional (default=None)
The number of jobs to run in parallel.
:meth:`fit`, :meth:`predict` and :meth:`partial_fit` (if supported
by the passed estimator) will be parallelized for each target.
When individual estimators are fast to train or predict,
using ``n_jobs > 1`` can result in slower performance due
to the parallelism overhead.
``None`` means `1` unless in a :obj:`joblib.parallel_backend` context.
``-1`` means using all available processes / threads.
See :term:`Glossary <n_jobs>` for more details.
.. versionchanged:: 0.20
`n_jobs` default changed from `1` to `None`.
Attributes
----------
classes_ : ndarray of shape (n_classes,)
Class labels.
estimators_ : list of ``n_output`` estimators
Estimators used for predictions.
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`. Only defined if the
underlying estimators expose such an attribute when fit.
.. versionadded:: 1.0
See Also
--------
ClassifierChain : A multi-label model that arranges binary classifiers
into a chain.
MultiOutputRegressor : Fits one regressor per target variable.
Examples
--------
>>> import numpy as np
>>> from sklearn.datasets import make_multilabel_classification
>>> from sklearn.multioutput import MultiOutputClassifier
>>> from sklearn.linear_model import LogisticRegression
>>> X, y = make_multilabel_classification(n_classes=3, random_state=0)
>>> clf = MultiOutputClassifier(LogisticRegression()).fit(X, y)
>>> clf.predict(X[-2:])
array([[1, 1, 1],
[1, 0, 1]])
"""
def __init__(self, estimator, *, n_jobs=None):
super().__init__(estimator, n_jobs=n_jobs)
def fit(self, X, Y, sample_weight=None, **fit_params):
"""Fit the model to data matrix X and targets Y.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input data.
Y : array-like of shape (n_samples, n_classes)
The target values.
sample_weight : array-like of shape (n_samples,), default=None
Sample weights. If `None`, then samples are equally weighted.
Only supported if the underlying classifier supports sample
weights.
**fit_params : dict of string -> object
Parameters passed to the ``estimator.fit`` method of each step.
.. versionadded:: 0.23
Returns
-------
self : object
Returns a fitted instance.
"""
super().fit(X, Y, sample_weight=sample_weight, **fit_params)
self.classes_ = [estimator.classes_ for estimator in self.estimators_]
return self
def _check_predict_proba(self):
if hasattr(self, "estimators_"):
# raise an AttributeError if `predict_proba` does not exist for
# each estimator
[getattr(est, "predict_proba") for est in self.estimators_]
return True
# raise an AttributeError if `predict_proba` does not exist for the
# unfitted estimator
getattr(self.estimator, "predict_proba")
return True
@available_if(_check_predict_proba)
def predict_proba(self, X):
"""Return prediction probabilities for each class of each output.
This method will raise a ``ValueError`` if any of the
estimators do not have ``predict_proba``.
Parameters
----------
X : array-like of shape (n_samples, n_features)
The input data.
Returns
-------
p : array of shape (n_samples, n_classes), or a list of n_outputs \
such arrays if n_outputs > 1.
The class probabilities of the input samples. The order of the
classes corresponds to that in the attribute :term:`classes_`.
.. versionchanged:: 0.19
This function now returns a list of arrays where the length of
the list is ``n_outputs``, and each array is (``n_samples``,
``n_classes``) for that particular output.
"""
check_is_fitted(self)
results = [estimator.predict_proba(X) for estimator in self.estimators_]
return results
def score(self, X, y):
"""Return the mean accuracy on the given test data and labels.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Test samples.
y : array-like of shape (n_samples, n_outputs)
True values for X.
Returns
-------
scores : float
Mean accuracy of predicted target versus true target.
"""
check_is_fitted(self)
n_outputs_ = len(self.estimators_)
if y.ndim == 1:
raise ValueError(
"y must have at least two dimensions for "
"multi target classification but has only one"
)
if y.shape[1] != n_outputs_:
raise ValueError(
"The number of outputs of Y for fit {0} and"
" score {1} should be same".format(n_outputs_, y.shape[1])
)
y_pred = self.predict(X)
return np.mean(np.all(y == y_pred, axis=1))
def __sklearn_tags__(self):
tags = super().__sklearn_tags__()
# FIXME
tags._skip_test = True
return tags
def _available_if_base_estimator_has(attr):
"""Return a function to check if `base_estimator` or `estimators_` has `attr`.
Helper for Chain implementations.
"""
def _check(self):
return hasattr(self._get_estimator(), attr) or all(
hasattr(est, attr) for est in self.estimators_
)
return available_if(_check)
class _BaseChain(BaseEstimator, metaclass=ABCMeta):
_parameter_constraints: dict = {
"base_estimator": [
HasMethods(["fit", "predict"]),
StrOptions({"deprecated"}),
],
"estimator": [
HasMethods(["fit", "predict"]),
Hidden(None),
],
"order": ["array-like", StrOptions({"random"}), None],
"cv": ["cv_object", StrOptions({"prefit"})],
"random_state": ["random_state"],
"verbose": ["boolean"],
}
# TODO(1.9): Remove base_estimator
def __init__(
self,
estimator=None,
*,
order=None,
cv=None,
random_state=None,
verbose=False,
base_estimator="deprecated",
):
self.estimator = estimator
self.base_estimator = base_estimator
self.order = order
self.cv = cv
self.random_state = random_state
self.verbose = verbose
# TODO(1.9): This is a temporary getter method to validate input wrt deprecation.
# It was only included to avoid relying on the presence of self.estimator_
def _get_estimator(self):
"""Get and validate estimator."""
if self.estimator is not None and (self.base_estimator != "deprecated"):
raise ValueError(
"Both `estimator` and `base_estimator` are provided. You should only"
" pass `estimator`. `base_estimator` as a parameter is deprecated in"
" version 1.7, and will be removed in version 1.9."
)
if self.base_estimator != "deprecated":
warning_msg = (
"`base_estimator` as an argument was deprecated in 1.7 and will be"
" removed in 1.9. Use `estimator` instead."
)
warnings.warn(warning_msg, FutureWarning)
return self.base_estimator
else:
return self.estimator
def _log_message(self, *, estimator_idx, n_estimators, processing_msg):
if not self.verbose:
return None
return f"({estimator_idx} of {n_estimators}) {processing_msg}"
def _get_predictions(self, X, *, output_method):
"""Get predictions for each model in the chain."""
check_is_fitted(self)
X = validate_data(self, X, accept_sparse=True, reset=False)
Y_output_chain = np.zeros((X.shape[0], len(self.estimators_)))
Y_feature_chain = np.zeros((X.shape[0], len(self.estimators_)))
# `RegressorChain` does not have a `chain_method_` parameter so we
# default to "predict"
chain_method = getattr(self, "chain_method_", "predict")
hstack = sp.hstack if sp.issparse(X) else np.hstack
for chain_idx, estimator in enumerate(self.estimators_):
previous_predictions = Y_feature_chain[:, :chain_idx]
# if `X` is a scipy sparse dok_array, we convert it to a sparse
# coo_array format before hstacking, it's faster; see
# https://github.com/scipy/scipy/issues/20060#issuecomment-1937007039:
if sp.issparse(X) and not sp.isspmatrix(X) and X.format == "dok":
X = sp.coo_array(X)
X_aug = hstack((X, previous_predictions))
feature_predictions, _ = _get_response_values(
estimator,
X_aug,
response_method=chain_method,
)
Y_feature_chain[:, chain_idx] = feature_predictions
output_predictions, _ = _get_response_values(
estimator,
X_aug,
response_method=output_method,
)
Y_output_chain[:, chain_idx] = output_predictions
inv_order = np.empty_like(self.order_)
inv_order[self.order_] = np.arange(len(self.order_))
Y_output = Y_output_chain[:, inv_order]
return _align_api_if_sparse(Y_output)
@abstractmethod
def fit(self, X, Y, **fit_params):
"""Fit the model to data matrix X and targets Y.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input data.
Y : array-like of shape (n_samples, n_classes)
The target values.
**fit_params : dict of string -> object
Parameters passed to the `fit` method of each step.
.. versionadded:: 0.23
Returns
-------
self : object
Returns a fitted instance.
"""
X, Y = validate_data(self, X, Y, multi_output=True, accept_sparse=True)
random_state = check_random_state(self.random_state)
self.order_ = self.order
if isinstance(self.order_, tuple):
self.order_ = np.array(self.order_)
if self.order_ is None:
self.order_ = np.array(range(Y.shape[1]))
elif isinstance(self.order_, str):
if self.order_ == "random":
self.order_ = random_state.permutation(Y.shape[1])
elif sorted(self.order_) != list(range(Y.shape[1])):
raise ValueError("invalid order")
self.estimators_ = [clone(self._get_estimator()) for _ in range(Y.shape[1])]
if self.cv is None:
Y_pred_chain = Y[:, self.order_]
if sp.issparse(X):
X_aug = sp.hstack((X, Y_pred_chain), format="lil")
X_aug = X_aug.tocsr()
else:
X_aug = np.hstack((X, Y_pred_chain))
elif sp.issparse(X):
# TODO: remove this condition check when the minimum supported scipy version
# doesn't support sparse matrices anymore
if not sp.isspmatrix(X):
# if `X` is a scipy sparse dok_array, we convert it to a sparse
# coo_array format before hstacking, it's faster; see
# https://github.com/scipy/scipy/issues/20060#issuecomment-1937007039:
if X.format == "dok":
X = sp.coo_array(X)
# in case that `X` is a sparse array we create `Y_pred_chain` as a
# sparse array format:
Y_pred_chain = sp.coo_array((X.shape[0], Y.shape[1]))
else:
Y_pred_chain = sp.coo_matrix((X.shape[0], Y.shape[1]))
X_aug = sp.hstack((X, Y_pred_chain), format="lil")
else:
Y_pred_chain = np.zeros((X.shape[0], Y.shape[1]))
X_aug = np.hstack((X, Y_pred_chain))
del Y_pred_chain
if _routing_enabled():
routed_params = process_routing(self, "fit", **fit_params)
else:
routed_params = Bunch(estimator=Bunch(fit=fit_params))
if hasattr(self, "chain_method"):
chain_method = _check_response_method(
self._get_estimator(),
self.chain_method,
).__name__
self.chain_method_ = chain_method
else:
# `RegressorChain` does not have a `chain_method` parameter
chain_method = "predict"
for chain_idx, estimator in enumerate(self.estimators_):
message = self._log_message(
estimator_idx=chain_idx + 1,
n_estimators=len(self.estimators_),
processing_msg=f"Processing order {self.order_[chain_idx]}",
)
y = Y[:, self.order_[chain_idx]]
with _print_elapsed_time("Chain", message):
estimator.fit(
X_aug[:, : (X.shape[1] + chain_idx)],
y,
**routed_params.estimator.fit,
)
if self.cv is not None and chain_idx < len(self.estimators_) - 1:
col_idx = X.shape[1] + chain_idx
cv_result = cross_val_predict(
self._get_estimator(),
X_aug[:, :col_idx],
y=y,
cv=self.cv,
method=chain_method,
)
# `predict_proba` output is 2D, we use only output for classes[-1]
if cv_result.ndim > 1:
cv_result = cv_result[:, 1]
if sp.issparse(X_aug):
X_aug[:, col_idx] = np.expand_dims(cv_result, 1)
else:
X_aug[:, col_idx] = cv_result
return self
def predict(self, X):
"""Predict on the data matrix X using the ClassifierChain model.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input data.
Returns
-------
Y_pred : array-like of shape (n_samples, n_classes)
The predicted values.
"""
return self._get_predictions(X, output_method="predict")
def __sklearn_tags__(self):
tags = super().__sklearn_tags__()
tags.input_tags.sparse = get_tags(self._get_estimator()).input_tags.sparse
return tags
class ClassifierChain(MetaEstimatorMixin, ClassifierMixin, _BaseChain):
"""A multi-label model that arranges binary classifiers into a chain.
Each model makes a prediction in the order specified by the chain using
all of the available features provided to the model plus the predictions
of models that are earlier in the chain.
For an example of how to use ``ClassifierChain`` and benefit from its
ensemble, see
:ref:`ClassifierChain on a yeast dataset
<sphx_glr_auto_examples_multioutput_plot_classifier_chain_yeast.py>` example.
Read more in the :ref:`User Guide <classifierchain>`.
.. versionadded:: 0.19
Parameters
----------
estimator : estimator
The base estimator from which the classifier chain is built.
order : array-like of shape (n_outputs,) or 'random', default=None
If `None`, the order will be determined by the order of columns in
the label matrix Y.::
order = [0, 1, 2, ..., Y.shape[1] - 1]
The order of the chain can be explicitly set by providing a list of
integers. For example, for a chain of length 5.::
order = [1, 3, 2, 4, 0]
means that the first model in the chain will make predictions for
column 1 in the Y matrix, the second model will make predictions
for column 3, etc.
If order is `random` a random ordering will be used.
cv : int, cross-validation generator or an iterable, default=None
Determines whether to use cross validated predictions or true
labels for the results of previous estimators in the chain.
Possible inputs for cv are:
- None, to use true labels when fitting,
- integer, to specify the number of folds in a (Stratified)KFold,
- :term:`CV splitter`,
- an iterable yielding (train, test) splits as arrays of indices.
chain_method : {'predict', 'predict_proba', 'predict_log_proba', \
'decision_function'} or list of such str's, default='predict'
Prediction method to be used by estimators in the chain for
the 'prediction' features of previous estimators in the chain.
- if `str`, name of the method;
- if a list of `str`, provides the method names in order of
preference. The method used corresponds to the first method in
the list that is implemented by `base_estimator`.
.. versionadded:: 1.5
random_state : int, RandomState instance or None, optional (default=None)
If ``order='random'``, determines random number generation for the
chain order.
In addition, it controls the random seed given at each `base_estimator`
at each chaining iteration. Thus, it is only used when `base_estimator`
exposes a `random_state`.
Pass an int for reproducible output across multiple function calls.
See :term:`Glossary <random_state>`.
verbose : bool, default=False
If True, chain progress is output as each model is completed.
.. versionadded:: 1.2
base_estimator : estimator, default="deprecated"
Use `estimator` instead.
.. deprecated:: 1.7
`base_estimator` is deprecated and will be removed in 1.9.
Use `estimator` instead.
Attributes
----------
classes_ : list
A list of arrays of length ``len(estimators_)`` containing the
class labels for each estimator in the chain.
estimators_ : list
A list of clones of base_estimator.
order_ : list
The order of labels in the classifier chain.
chain_method_ : str
Prediction method used by estimators in the chain for the prediction
features.
n_features_in_ : int
Number of features seen during :term:`fit`. Only defined if the
underlying `base_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
See Also
--------
RegressorChain : Equivalent for regression.
MultiOutputClassifier : Classifies each output independently rather than
chaining.
References
----------
Jesse Read, Bernhard Pfahringer, Geoff Holmes, Eibe Frank, "Classifier
Chains for Multi-label Classification", 2009.
Examples
--------