""" 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 import numpy as np import scipy.sparse as sp from joblib import Parallel from abc import ABCMeta, abstractmethod from .base import BaseEstimator, clone, MetaEstimatorMixin from .base import RegressorMixin, ClassifierMixin, is_classifier from .model_selection import cross_val_predict from .utils import check_array, check_X_y, check_random_state from .utils.metaestimators import if_delegate_has_method from .utils.validation import (check_is_fitted, has_fit_parameter, _check_fit_params, _deprecate_positional_args) from .utils.multiclass import check_classification_targets from .utils.fixes import delayed __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, sample_weight=None, first_time=True): if first_time: estimator = clone(estimator) if sample_weight is not None: if classes is not None: estimator.partial_fit(X, y, classes=classes, sample_weight=sample_weight) else: estimator.partial_fit(X, y, sample_weight=sample_weight) else: if classes is not None: estimator.partial_fit(X, y, classes=classes) else: estimator.partial_fit(X, y) return estimator class _MultiOutputEstimator(MetaEstimatorMixin, BaseEstimator, metaclass=ABCMeta): @abstractmethod @_deprecate_positional_args def __init__(self, estimator, *, n_jobs=None): self.estimator = estimator self.n_jobs = n_jobs @if_delegate_has_method('estimator') def partial_fit(self, X, y, classes=None, sample_weight=None): """Incrementally fit the model to data. Fit a separate model for each output variable. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) Data. y : {array-like, sparse matrix} of shape (n_samples, n_outputs) Multi-output targets. classes : list of ndarray of shape (n_outputs,) Each array is unique classes for one output in str/int Can be obtained by 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. Returns ------- self : object """ X, y = check_X_y(X, y, force_all_finite=False, multi_output=True, accept_sparse=True) if y.ndim == 1: raise ValueError("y must have at least two dimensions for " "multi-output regression but has only one.") if (sample_weight is not None and not has_fit_parameter(self.estimator, 'sample_weight')): raise ValueError("Underlying estimator does not support" " sample weights.") first_time = not hasattr(self, 'estimators_') 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, sample_weight, first_time) for i in range(y.shape[1])) return self def fit(self, X, y, sample_weight=None, **fit_params): """ Fit the model to data. Fit a separate model for each output variable. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) 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 """ if not hasattr(self.estimator, "fit"): raise ValueError("The base estimator should implement" " a fit method") X, y = self._validate_data(X, y, force_all_finite=False, multi_output=True, accept_sparse=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 (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_fit_params(X, fit_params) self.estimators_ = Parallel(n_jobs=self.n_jobs)( delayed(_fit_estimator)( self.estimator, X, y[:, i], sample_weight, **fit_params_validated) for i in range(y.shape[1])) return self def predict(self, X): """Predict multi-output variable using a model trained for each target variable. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) 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.estimator, "predict"): raise ValueError("The base estimator should implement" " a predict method") X = check_array(X, force_all_finite=False, accept_sparse=True) 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} 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. 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) >>> clf = MultiOutputRegressor(Ridge(random_state=123)).fit(X, y) >>> clf.predict(X[[0]]) array([[176..., 35..., 57...]]) """ @_deprecate_positional_args def __init__(self, estimator, *, n_jobs=None): super().__init__(estimator, n_jobs=n_jobs) @if_delegate_has_method('estimator') def partial_fit(self, X, y, sample_weight=None): """Incrementally fit the model to data. Fit a separate model for each output variable. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) 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. Returns ------- self : object """ super().partial_fit( X, y, sample_weight=sample_weight) 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`, :term:`score` and :term:`predict_proba`. 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. Examples -------- >>> import numpy as np >>> from sklearn.datasets import make_multilabel_classification >>> from sklearn.multioutput import MultiOutputClassifier >>> from sklearn.neighbors import KNeighborsClassifier >>> X, y = make_multilabel_classification(n_classes=3, random_state=0) >>> clf = MultiOutputClassifier(KNeighborsClassifier()).fit(X, y) >>> clf.predict(X[-2:]) array([[1, 1, 0], [1, 1, 1]]) """ @_deprecate_positional_args 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 """ super().fit(X, Y, sample_weight, **fit_params) self.classes_ = [estimator.classes_ for estimator in self.estimators_] return self @property def predict_proba(self): """Probability estimates. Returns 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) 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) if not all([hasattr(estimator, "predict_proba") for estimator in self.estimators_]): raise AttributeError("The base estimator should " "implement predict_proba method") return self._predict_proba def _predict_proba(self, X): results = [estimator.predict_proba(X) for estimator in self.estimators_] return results def score(self, X, y): """Returns 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 accuracy_score of self.predict(X) versus y """ 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} class _BaseChain(BaseEstimator, metaclass=ABCMeta): @_deprecate_positional_args def __init__(self, base_estimator, *, order=None, cv=None, random_state=None): self.base_estimator = base_estimator self.order = order self.cv = cv self.random_state = random_state @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 """ X, Y = self._validate_data(X, Y, multi_output=True, accept_sparse=True) random_state = check_random_state(self.random_state) check_array(X, accept_sparse=True) 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 for chain_idx, estimator in enumerate(self.estimators_): y = Y[:, self.order_[chain_idx]] estimator.fit(X_aug[:, :(X.shape[1] + chain_idx)], y, **fit_params) 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 = check_array(X, accept_sparse=True) 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. 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 `. 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. 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..., 0.0625...]]) 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. """ def fit(self, X, Y): """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. Returns ------- self : object """ super().fit(X, Y) self.classes_ = [estimator.classes_ for chain_idx, estimator in enumerate(self.estimators_)] return self @if_delegate_has_method('base_estimator') def predict_proba(self, X): """Predict probability estimates. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) Returns ------- Y_prob : array-like of shape (n_samples, n_classes) """ X = check_array(X, accept_sparse=True) 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 @if_delegate_has_method('base_estimator') 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) Returns ------- Y_decision : array-like of shape (n_samples, n_classes) Returns the decision function of the sample for each model in the chain. """ 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 _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 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 `. Attributes ---------- estimators_ : list A list of clones of base_estimator. order_ : list The order of labels in the classifier chain. 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.]]) See Also -------- ClassifierChain : Equivalent for classification. MultioutputRegressor : Learns each output independently rather than chaining. """ 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 """ super().fit(X, Y, **fit_params) return self def _more_tags(self): return {'multioutput_only': True}