""" This module implements 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. """ # Author: Tim Head # Author: Hugo Bowne-Anderson # Author: Chris Rivera # Author: Michael Williamson # Author: James Ashton Nichols # # License: BSD 3 clause from abc import ABCMeta, abstractmethod from numbers import Integral import numpy as np import scipy.sparse as sp from .base import ( BaseEstimator, ClassifierMixin, MetaEstimatorMixin, RegressorMixin, _fit_context, clone, is_classifier, ) from .model_selection import cross_val_predict from .utils import Bunch, _print_elapsed_time, check_random_state from .utils._param_validation import HasMethods, StrOptions from .utils.metadata_routing import ( MetadataRouter, MethodMapping, _raise_for_params, _routing_enabled, process_routing, ) from .utils.metaestimators import available_if from .utils.multiclass import check_classification_targets from .utils.parallel import Parallel, delayed from .utils.validation import _check_method_params, check_is_fitted, has_fit_parameter __all__ = [ "MultiOutputRegressor", "MultiOutputClassifier", "ClassifierChain", "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 `. .. 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 = self._validate_data(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 = self._validate_data(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 _more_tags(self): return {"multioutput_only": True} def get_metadata_routing(self): """Get metadata routing of this object. Please check :ref:`User Guide ` 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.__class__.__name__).add( estimator=self.estimator, method_mapping=MethodMapping() .add(callee="partial_fit", caller="partial_fit") .add(callee="fit", caller="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 ` 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..., 57...]]) """ 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 `. .. 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 ` 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 _more_tags(self): # FIXME return {"_skip_test": True} 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.base_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"])], "order": ["array-like", StrOptions({"random"}), None], "cv": ["cv_object", StrOptions({"prefit"})], "random_state": ["random_state"], "verbose": ["boolean"], } def __init__( self, base_estimator, *, order=None, cv=None, random_state=None, verbose=False ): self.base_estimator = base_estimator self.order = order self.cv = cv self.random_state = random_state self.verbose = verbose 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}" @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 = self._validate_data(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.base_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): Y_pred_chain = sp.lil_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)) 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.base_estimator, X_aug[:, :col_idx], y=y, cv=self.cv ) 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. """ check_is_fitted(self) X = self._validate_data(X, accept_sparse=True, reset=False) Y_pred_chain = np.zeros((X.shape[0], len(self.estimators_))) for chain_idx, estimator in enumerate(self.estimators_): previous_predictions = Y_pred_chain[:, :chain_idx] if sp.issparse(X): if chain_idx == 0: X_aug = X else: X_aug = sp.hstack((X, previous_predictions)) else: X_aug = np.hstack((X, previous_predictions)) Y_pred_chain[:, chain_idx] = estimator.predict(X_aug) inv_order = np.empty_like(self.order_) inv_order[self.order_] = np.arange(len(self.order_)) Y_pred = Y_pred_chain[:, inv_order] return Y_pred 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 ` example. Read more in the :ref:`User Guide `. .. versionadded:: 0.19 Parameters ---------- base_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. 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 `. verbose : bool, default=False If True, chain progress is output as each model is completed. .. versionadded:: 1.2 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. 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 -------- >>> from sklearn.datasets import make_multilabel_classification >>> from sklearn.linear_model import LogisticRegression >>> from sklearn.model_selection import train_test_split >>> from sklearn.multioutput import ClassifierChain >>> X, Y = make_multilabel_classification( ... n_samples=12, n_classes=3, random_state=0 ... ) >>> X_train, X_test, Y_train, Y_test = train_test_split( ... X, Y, random_state=0 ... ) >>> base_lr = LogisticRegression(solver='lbfgs', random_state=0) >>> chain = ClassifierChain(base_lr, order='random', random_state=0) >>> chain.fit(X_train, Y_train).predict(X_test) array([[1., 1., 0.], [1., 0., 0.], [0., 1., 0.]]) >>> chain.predict_proba(X_test) array([[0.8387..., 0.9431..., 0.4576...], [0.8878..., 0.3684..., 0.2640...], [0.0321..., 0.9935..., 0.0626...]]) """ @_fit_context( # ClassifierChain.base_estimator is not validated yet prefer_skip_nested_validation=False ) 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. Only available if `enable_metadata_routing=True`. See the :ref:`User Guide `. .. versionadded:: 1.3 Returns ------- self : object Class instance. """ _raise_for_params(fit_params, self, "fit") super().fit(X, Y, **fit_params) self.classes_ = [estimator.classes_ for estimator in self.estimators_] return self @_available_if_base_estimator_has("predict_proba") def predict_proba(self, X): """Predict probability estimates. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The input data. Returns ------- Y_prob : array-like of shape (n_samples, n_classes) The predicted probabilities. """ X = self._validate_data(X, accept_sparse=True, reset=False) Y_prob_chain = np.zeros((X.shape[0], len(self.estimators_))) Y_pred_chain = np.zeros((X.shape[0], len(self.estimators_))) for chain_idx, estimator in enumerate(self.estimators_): previous_predictions = Y_pred_chain[:, :chain_idx] if sp.issparse(X): X_aug = sp.hstack((X, previous_predictions)) else: X_aug = np.hstack((X, previous_predictions)) Y_prob_chain[:, chain_idx] = estimator.predict_proba(X_aug)[:, 1] Y_pred_chain[:, chain_idx] = estimator.predict(X_aug) inv_order = np.empty_like(self.order_) inv_order[self.order_] = np.arange(len(self.order_)) Y_prob = Y_prob_chain[:, inv_order] return Y_prob def predict_log_proba(self, X): """Predict logarithm of probability estimates. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The input data. Returns ------- Y_log_prob : array-like of shape (n_samples, n_classes) The predicted logarithm of the probabilities. """ return np.log(self.predict_proba(X)) @_available_if_base_estimator_has("decision_function") def decision_function(self, X): """Evaluate the decision_function of the models in the chain. Parameters ---------- X : array-like of shape (n_samples, n_features) The input data. Returns ------- Y_decision : array-like of shape (n_samples, n_classes) Returns the decision function of the sample for each model in the chain. """ X = self._validate_data(X, accept_sparse=True, reset=False) Y_decision_chain = np.zeros((X.shape[0], len(self.estimators_))) Y_pred_chain = np.zeros((X.shape[0], len(self.estimators_))) for chain_idx, estimator in enumerate(self.estimators_): previous_predictions = Y_pred_chain[:, :chain_idx] if sp.issparse(X): X_aug = sp.hstack((X, previous_predictions)) else: X_aug = np.hstack((X, previous_predictions)) Y_decision_chain[:, chain_idx] = estimator.decision_function(X_aug) Y_pred_chain[:, chain_idx] = estimator.predict(X_aug) inv_order = np.empty_like(self.order_) inv_order[self.order_] = np.arange(len(self.order_)) Y_decision = Y_decision_chain[:, inv_order] return Y_decision def get_metadata_routing(self): """Get metadata routing of this object. Please check :ref:`User Guide ` 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.__class__.__name__).add( estimator=self.base_estimator, method_mapping=MethodMapping().add(callee="fit", caller="fit"), ) return router def _more_tags(self): return {"_skip_test": True, "multioutput_only": True} class RegressorChain(MetaEstimatorMixin, RegressorMixin, _BaseChain): """A multi-label model that arranges regressions 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. Read more in the :ref:`User Guide `. .. versionadded:: 0.20 Parameters ---------- base_estimator : estimator The base estimator from which the regressor 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. 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 `. verbose : bool, default=False If True, chain progress is output as each model is completed. .. versionadded:: 1.2 Attributes ---------- estimators_ : list A list of clones of base_estimator. order_ : list The order of labels in the classifier chain. 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 -------- ClassifierChain : Equivalent for classification. MultiOutputRegressor : Learns each output independently rather than chaining. Examples -------- >>> from sklearn.multioutput import RegressorChain >>> from sklearn.linear_model import LogisticRegression >>> logreg = LogisticRegression(solver='lbfgs',multi_class='multinomial') >>> X, Y = [[1, 0], [0, 1], [1, 1]], [[0, 2], [1, 1], [2, 0]] >>> chain = RegressorChain(base_estimator=logreg, order=[0, 1]).fit(X, Y) >>> chain.predict(X) array([[0., 2.], [1., 1.], [2., 0.]]) """ @_fit_context( # RegressorChain.base_estimator is not validated yet prefer_skip_nested_validation=False ) 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 at each step of the regressor chain. .. versionadded:: 0.23 Returns ------- self : object Returns a fitted instance. """ super().fit(X, Y, **fit_params) return self def get_metadata_routing(self): """Get metadata routing of this object. Please check :ref:`User Guide ` 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.__class__.__name__).add( estimator=self.base_estimator, method_mapping=MethodMapping().add(callee="fit", caller="fit"), ) return router def _more_tags(self): return {"multioutput_only": True}