python3-sklearn 0.19.1-3 / usr / lib / python3 / dist-packages / sklearn / multioutput.py

"""
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 <betatim@gmail.com>
# Author: Hugo Bowne-Anderson <hugobowne@gmail.com>
# Author: Chris Rivera <chris.richard.rivera@gmail.com>
# Author: Michael Williamson
# Author: James Ashton Nichols <james.ashton.nichols@gmail.com>
#
# License: BSD 3 clause

import numpy as np
import scipy.sparse as sp
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.fixes import parallel_helper
from .utils.metaestimators import if_delegate_has_method
from .utils.validation import check_is_fitted, has_fit_parameter
from .utils.multiclass import check_classification_targets
from .externals.joblib import Parallel, delayed
from .externals import six

__all__ = ["MultiOutputRegressor", "MultiOutputClassifier", "ClassifierChain"]


def _fit_estimator(estimator, X, y, sample_weight=None):
    estimator = clone(estimator)
    if sample_weight is not None:
        estimator.fit(X, y, sample_weight=sample_weight)
    else:
        estimator.fit(X, y)
    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(six.with_metaclass(ABCMeta, BaseEstimator,
                                              MetaEstimatorMixin)):
    @abstractmethod
    def __init__(self, estimator, n_jobs=1):
        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 : (sparse) array-like, shape (n_samples, n_features)
            Data.

        y : (sparse) array-like, shape (n_samples, n_outputs)
            Multi-output targets.

        classes : list of numpy arrays, 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, shape = (n_samples) or None
            Sample weights. If None, then samples are equally weighted.
            Only supported if the underlying regressor supports sample
            weights.

        Returns
        -------
        self : object
            Returns self.
        """
        X, y = check_X_y(X, y,
                         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 the model to data.
        Fit a separate model for each output variable.

        Parameters
        ----------
        X : (sparse) array-like, shape (n_samples, n_features)
            Data.

        y : (sparse) array-like, shape (n_samples, n_outputs)
            Multi-output targets. An indicator matrix turns on multilabel
            estimation.

        sample_weight : array-like, shape = (n_samples) or None
            Sample weights. If None, then samples are equally weighted.
            Only supported if the underlying regressor supports sample
            weights.

        Returns
        -------
        self : object
            Returns self.
        """

        if not hasattr(self.estimator, "fit"):
            raise ValueError("The base estimator should implement a fit method")

        X, y = check_X_y(X, y,
                         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.")

        self.estimators_ = Parallel(n_jobs=self.n_jobs)(
            delayed(_fit_estimator)(
                self.estimator, X, y[:, i], sample_weight)
            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 : (sparse) array-like, shape (n_samples, n_features)
            Data.

        Returns
        -------
        y : (sparse) array-like, shape (n_samples, n_outputs)
            Multi-output targets predicted across multiple predictors.
            Note: Separate models are generated for each predictor.
        """
        check_is_fitted(self, 'estimators_')
        if not hasattr(self.estimator, "predict"):
            raise ValueError("The base estimator should implement a predict method")

        X = check_array(X, accept_sparse=True)

        y = Parallel(n_jobs=self.n_jobs)(
            delayed(parallel_helper)(e, 'predict', X)
            for e in self.estimators_)

        return np.asarray(y).T


class MultiOutputRegressor(MultiOutputEstimator, RegressorMixin):
    """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.

    Parameters
    ----------
    estimator : estimator object
        An estimator object implementing `fit` and `predict`.

    n_jobs : int, optional, default=1
        The number of jobs to run in parallel for `fit`. If -1,
        then the number of jobs is set to the number of cores.
        When individual estimators are fast to train or predict
        using `n_jobs>1` can result in slower performance due
        to the overhead of spawning processes.
    """

    def __init__(self, estimator, n_jobs=1):
        super(MultiOutputRegressor, self).__init__(estimator, 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 : (sparse) array-like, shape (n_samples, n_features)
            Data.

        y : (sparse) array-like, shape (n_samples, n_outputs)
            Multi-output targets.

        sample_weight : array-like, shape = (n_samples) or None
            Sample weights. If None, then samples are equally weighted.
            Only supported if the underlying regressor supports sample
            weights.

        Returns
        -------
        self : object
            Returns self.
        """
        super(MultiOutputRegressor, self).partial_fit(
            X, y, sample_weight=sample_weight)

    def score(self, X, y, sample_weight=None):
        """Returns the coefficient of determination R^2 of the prediction.

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

        Notes
        -----
        R^2 is calculated by weighting all the targets equally using
        `multioutput='uniform_average'`.

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

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

        sample_weight : array-like, shape [n_samples], optional
            Sample weights.

        Returns
        -------
        score : float
            R^2 of self.predict(X) wrt. y.
        """
        # XXX remove in 0.19 when r2_score default for multioutput changes
        from .metrics import r2_score
        return r2_score(y, self.predict(X), sample_weight=sample_weight,
                        multioutput='uniform_average')


class MultiOutputClassifier(MultiOutputEstimator, ClassifierMixin):
    """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 `fit`, `score` and `predict_proba`.

    n_jobs : int, optional, default=1
        The number of jobs to use for the computation. If -1 all CPUs are used.
        If 1 is given, no parallel computing code is used at all, which is
        useful for debugging. For n_jobs below -1, (n_cpus + 1 + n_jobs) are
        used. Thus for n_jobs = -2, all CPUs but one are used.
        The number of jobs to use for the computation.
        It does each target variable in y in parallel.

    Attributes
    ----------
    estimators_ : list of ``n_output`` estimators
        Estimators used for predictions.
    """

    def __init__(self, estimator, n_jobs=1):
        super(MultiOutputClassifier, self).__init__(estimator, n_jobs)

    def predict_proba(self, X):
        """Probability estimates.
        Returns prediction probabilities for each class of each output.

        Parameters
        ----------
        X : array-like, 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 `classes_`.
        """
        check_is_fitted(self, 'estimators_')
        if not hasattr(self.estimator, "predict_proba"):
            raise ValueError("The base estimator should implement"
                             "predict_proba method")

        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, shape [n_samples, n_features]
            Test samples

        y : array-like, shape [n_samples, n_outputs]
            True values for X

        Returns
        -------
        scores : float
            accuracy_score of self.predict(X) versus y
        """
        check_is_fitted(self, 'estimators_')
        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))


class ClassifierChain(BaseEstimator, ClassifierMixin, MetaEstimatorMixin):
    """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.

    Parameters
    ----------
    base_estimator : estimator
        The base estimator from which the classifier chain is built.

    order : array-like, shape=[n_outputs] or 'random', optional
        By default 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, optional (
    default=None)
        Determines whether to use cross validated predictions or true
        labels for the results of previous estimators in the chain.
        If cv is None the true labels are used when fitting. Otherwise
        possible inputs for cv are:
            * integer, to specify the number of folds in a (Stratified)KFold,
            * An object to be used as a cross-validation generator.
            * An iterable yielding train, test splits.

    random_state : int, RandomState instance or None, optional (default=None)
        If int, random_state is the seed used by the random number generator;
        If RandomState instance, random_state is the random number generator;
        If None, the random number generator is the RandomState instance used
        by `np.random`.

        The random number generator is used to generate random chain orders.

    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.

    References
    ----------
    Jesse Read, Bernhard Pfahringer, Geoff Holmes, Eibe Frank, "Classifier
    Chains for Multi-label Classification", 2009.

    """
    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

    def fit(self, X, Y):
        """Fit the model to data matrix X and targets Y.

        Parameters
        ----------
        X : {array-like, sparse matrix}, shape (n_samples, n_features)
            The input data.
        Y : array-like, shape (n_samples, n_classes)
            The target values.

        Returns
        -------
        self : object
            Returns self.
        """
        X, Y = check_X_y(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 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])]

        self.classes_ = []

        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)
            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

            self.classes_.append(estimator.classes_)
        return self

    def predict(self, X):
        """Predict on the data matrix X using the ClassifierChain model.

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

        Returns
        -------
        Y_pred : array-like, shape (n_samples, n_classes)
            The predicted values.

        """
        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

    @if_delegate_has_method('base_estimator')
    def predict_proba(self, X):
        """Predict probability estimates.

        Parameters
        ----------
        X : {array-like, sparse matrix}, shape (n_samples, n_features)

        Returns
        -------
        Y_prob : array-like, 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, shape (n_samples, n_features)

        Returns
        -------
        Y_decision : array-like, 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