798 lines
28 KiB
Python
798 lines
28 KiB
Python
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"""
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This module implements multioutput regression and classification.
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The estimators provided in this module are meta-estimators: they require
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a base estimator to be provided in their constructor. The meta-estimator
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extends single output estimators to multioutput estimators.
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"""
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# Author: Tim Head <betatim@gmail.com>
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# Author: Hugo Bowne-Anderson <hugobowne@gmail.com>
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# Author: Chris Rivera <chris.richard.rivera@gmail.com>
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# Author: Michael Williamson
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# Author: James Ashton Nichols <james.ashton.nichols@gmail.com>
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#
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# License: BSD 3 clause
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import numpy as np
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import scipy.sparse as sp
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from joblib import Parallel, delayed
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from abc import ABCMeta, abstractmethod
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from .base import BaseEstimator, clone, MetaEstimatorMixin
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from .base import RegressorMixin, ClassifierMixin, is_classifier
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from .model_selection import cross_val_predict
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from .utils import check_array, check_X_y, check_random_state
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from .utils.metaestimators import if_delegate_has_method
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from .utils.validation import (check_is_fitted, has_fit_parameter,
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_check_fit_params, _deprecate_positional_args)
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from .utils.multiclass import check_classification_targets
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from .utils import deprecated
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__all__ = ["MultiOutputRegressor", "MultiOutputClassifier",
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"ClassifierChain", "RegressorChain"]
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def _fit_estimator(estimator, X, y, sample_weight=None, **fit_params):
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estimator = clone(estimator)
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if sample_weight is not None:
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estimator.fit(X, y, sample_weight=sample_weight, **fit_params)
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else:
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estimator.fit(X, y, **fit_params)
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return estimator
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def _partial_fit_estimator(estimator, X, y, classes=None, sample_weight=None,
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first_time=True):
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if first_time:
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estimator = clone(estimator)
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if sample_weight is not None:
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if classes is not None:
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estimator.partial_fit(X, y, classes=classes,
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sample_weight=sample_weight)
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else:
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estimator.partial_fit(X, y, sample_weight=sample_weight)
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else:
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if classes is not None:
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estimator.partial_fit(X, y, classes=classes)
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else:
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estimator.partial_fit(X, y)
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return estimator
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class _MultiOutputEstimator(BaseEstimator, MetaEstimatorMixin,
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metaclass=ABCMeta):
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@abstractmethod
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@_deprecate_positional_args
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def __init__(self, estimator, *, n_jobs=None):
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self.estimator = estimator
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self.n_jobs = n_jobs
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@if_delegate_has_method('estimator')
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def partial_fit(self, X, y, classes=None, sample_weight=None):
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"""Incrementally fit the model to data.
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Fit a separate model for each output variable.
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Parameters
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----------
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X : (sparse) array-like, shape (n_samples, n_features)
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Data.
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y : (sparse) array-like, shape (n_samples, n_outputs)
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Multi-output targets.
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classes : list of numpy arrays, shape (n_outputs)
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Each array is unique classes for one output in str/int
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Can be obtained by via
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``[np.unique(y[:, i]) for i in range(y.shape[1])]``, where y is the
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target matrix of the entire dataset.
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This argument is required for the first call to partial_fit
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and can be omitted in the subsequent calls.
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Note that y doesn't need to contain all labels in `classes`.
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sample_weight : array-like of shape (n_samples,), default=None
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Sample weights. If None, then samples are equally weighted.
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Only supported if the underlying regressor supports sample
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weights.
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Returns
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-------
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self : object
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"""
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X, y = check_X_y(X, y,
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multi_output=True,
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accept_sparse=True)
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if y.ndim == 1:
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raise ValueError("y must have at least two dimensions for "
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"multi-output regression but has only one.")
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if (sample_weight is not None and
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not has_fit_parameter(self.estimator, 'sample_weight')):
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raise ValueError("Underlying estimator does not support"
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" sample weights.")
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first_time = not hasattr(self, 'estimators_')
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self.estimators_ = Parallel(n_jobs=self.n_jobs)(
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delayed(_partial_fit_estimator)(
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self.estimators_[i] if not first_time else self.estimator,
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X, y[:, i],
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classes[i] if classes is not None else None,
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sample_weight, first_time) for i in range(y.shape[1]))
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return self
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def fit(self, X, y, sample_weight=None, **fit_params):
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""" Fit the model to data.
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Fit a separate model for each output variable.
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Parameters
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----------
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X : (sparse) array-like, shape (n_samples, n_features)
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Data.
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y : (sparse) array-like, shape (n_samples, n_outputs)
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Multi-output targets. An indicator matrix turns on multilabel
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estimation.
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sample_weight : array-like of shape (n_samples,), default=None
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Sample weights. If None, then samples are equally weighted.
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Only supported if the underlying regressor supports sample
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weights.
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**fit_params : dict of string -> object
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Parameters passed to the ``estimator.fit`` method of each step.
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Returns
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-------
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self : object
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"""
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if not hasattr(self.estimator, "fit"):
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raise ValueError("The base estimator should implement"
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" a fit method")
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X, y = self._validate_data(X, y, multi_output=True, accept_sparse=True)
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if is_classifier(self):
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check_classification_targets(y)
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if y.ndim == 1:
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raise ValueError("y must have at least two dimensions for "
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"multi-output regression but has only one.")
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if (sample_weight is not None and
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not has_fit_parameter(self.estimator, 'sample_weight')):
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raise ValueError("Underlying estimator does not support"
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" sample weights.")
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fit_params_validated = _check_fit_params(X, fit_params)
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self.estimators_ = Parallel(n_jobs=self.n_jobs)(
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delayed(_fit_estimator)(
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self.estimator, X, y[:, i], sample_weight,
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**fit_params_validated)
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for i in range(y.shape[1]))
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return self
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def predict(self, X):
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"""Predict multi-output variable using a model
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trained for each target variable.
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Parameters
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----------
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X : (sparse) array-like, shape (n_samples, n_features)
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Data.
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Returns
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-------
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y : (sparse) array-like, shape (n_samples, n_outputs)
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Multi-output targets predicted across multiple predictors.
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Note: Separate models are generated for each predictor.
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"""
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check_is_fitted(self)
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if not hasattr(self.estimator, "predict"):
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raise ValueError("The base estimator should implement"
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" a predict method")
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X = check_array(X, accept_sparse=True)
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y = Parallel(n_jobs=self.n_jobs)(
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delayed(e.predict)(X)
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for e in self.estimators_)
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return np.asarray(y).T
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def _more_tags(self):
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return {'multioutput_only': True}
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class MultiOutputRegressor(RegressorMixin, _MultiOutputEstimator):
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"""Multi target regression
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This strategy consists of fitting one regressor per target. This is a
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simple strategy for extending regressors that do not natively support
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multi-target regression.
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.. versionadded:: 0.18
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Parameters
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----------
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estimator : estimator object
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An estimator object implementing :term:`fit` and :term:`predict`.
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n_jobs : int or None, optional (default=None)
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The number of jobs to run in parallel for :meth:`fit`.
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``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
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``-1`` means using all processors. See :term:`Glossary <n_jobs>`
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for more details.
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When individual estimators are fast to train or predict
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using `n_jobs>1` can result in slower performance due
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to the overhead of spawning processes.
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.. versionchanged:: v0.20
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`n_jobs` default changed from 1 to None
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Attributes
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----------
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estimators_ : list of ``n_output`` estimators
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Estimators used for predictions.
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Examples
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--------
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>>> import numpy as np
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>>> from sklearn.datasets import load_linnerud
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>>> from sklearn.multioutput import MultiOutputRegressor
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>>> from sklearn.linear_model import Ridge
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>>> X, y = load_linnerud(return_X_y=True)
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>>> clf = MultiOutputRegressor(Ridge(random_state=123)).fit(X, y)
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>>> clf.predict(X[[0]])
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array([[176..., 35..., 57...]])
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"""
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@_deprecate_positional_args
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def __init__(self, estimator, *, n_jobs=None):
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super().__init__(estimator, n_jobs=n_jobs)
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@if_delegate_has_method('estimator')
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def partial_fit(self, X, y, sample_weight=None):
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"""Incrementally fit the model to data.
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Fit a separate model for each output variable.
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Parameters
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----------
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X : (sparse) array-like, shape (n_samples, n_features)
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Data.
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y : (sparse) array-like, shape (n_samples, n_outputs)
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Multi-output targets.
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sample_weight : array-like of shape (n_samples,), default=None
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Sample weights. If None, then samples are equally weighted.
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Only supported if the underlying regressor supports sample
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weights.
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Returns
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-------
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self : object
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"""
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super().partial_fit(
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X, y, sample_weight=sample_weight)
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class MultiOutputClassifier(ClassifierMixin, _MultiOutputEstimator):
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"""Multi target classification
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This strategy consists of fitting one classifier per target. This is a
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simple strategy for extending classifiers that do not natively support
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multi-target classification
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Parameters
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----------
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estimator : estimator object
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An estimator object implementing :term:`fit`, :term:`score` and
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:term:`predict_proba`.
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n_jobs : int or None, optional (default=None)
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The number of jobs to use for the computation.
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It does each target variable in y in parallel.
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``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
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``-1`` means using all processors. See :term:`Glossary <n_jobs>`
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for more details.
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.. versionchanged:: v0.20
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`n_jobs` default changed from 1 to None
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Attributes
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----------
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classes_ : array, shape = (n_classes,)
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Class labels.
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estimators_ : list of ``n_output`` estimators
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Estimators used for predictions.
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Examples
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--------
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>>> import numpy as np
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>>> from sklearn.datasets import make_multilabel_classification
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>>> from sklearn.multioutput import MultiOutputClassifier
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>>> from sklearn.neighbors import KNeighborsClassifier
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>>> X, y = make_multilabel_classification(n_classes=3, random_state=0)
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>>> clf = MultiOutputClassifier(KNeighborsClassifier()).fit(X, y)
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>>> clf.predict(X[-2:])
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array([[1, 1, 0], [1, 1, 1]])
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"""
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@_deprecate_positional_args
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def __init__(self, estimator, *, n_jobs=None):
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super().__init__(estimator, n_jobs=n_jobs)
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def fit(self, X, Y, sample_weight=None, **fit_params):
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"""Fit the model to data matrix X and targets Y.
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Parameters
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----------
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X : {array-like, sparse matrix} of shape (n_samples, n_features)
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The input data.
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Y : array-like of shape (n_samples, n_classes)
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The target values.
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sample_weight : array-like of shape (n_samples,) or None
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Sample weights. If None, then samples are equally weighted.
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Only supported if the underlying classifier supports sample
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weights.
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**fit_params : dict of string -> object
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Parameters passed to the ``estimator.fit`` method of each step.
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Returns
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-------
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self : object
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"""
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super().fit(X, Y, sample_weight, **fit_params)
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self.classes_ = [estimator.classes_ for estimator in self.estimators_]
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return self
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@property
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def predict_proba(self):
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"""Probability estimates.
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Returns prediction probabilities for each class of each output.
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This method will raise a ``ValueError`` if any of the
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estimators do not have ``predict_proba``.
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Parameters
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----------
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X : array-like, shape (n_samples, n_features)
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Data
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Returns
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-------
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p : array of shape (n_samples, n_classes), or a list of n_outputs \
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such arrays if n_outputs > 1.
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The class probabilities of the input samples. The order of the
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classes corresponds to that in the attribute :term:`classes_`.
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.. versionchanged:: 0.19
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This function now returns a list of arrays where the length of
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the list is ``n_outputs``, and each array is (``n_samples``,
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``n_classes``) for that particular output.
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"""
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check_is_fitted(self)
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||
|
|
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, 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)
|
||
|
|
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}, shape (n_samples, n_features)
|
||
|
|
The input data.
|
||
|
|
Y : array-like, shape (n_samples, n_classes)
|
||
|
|
The target values.
|
||
|
|
**fit_params : dict of string -> object
|
||
|
|
Parameters passed to the `fit` method of each step.
|
||
|
|
|
||
|
|
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 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}, shape (n_samples, n_features)
|
||
|
|
The input data.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
Y_pred : array-like, 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 <classifierchain>`.
|
||
|
|
|
||
|
|
.. 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', 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,
|
||
|
|
- :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 <random_state>`.
|
||
|
|
|
||
|
|
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.
|
||
|
|
|
||
|
|
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}, shape (n_samples, n_features)
|
||
|
|
The input data.
|
||
|
|
Y : array-like, 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}, 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
|
||
|
|
|
||
|
|
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 <regressorchain>`.
|
||
|
|
|
||
|
|
.. 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', 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,
|
||
|
|
- :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 <random_state>`.
|
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|
|
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||
|
|
Attributes
|
||
|
|
----------
|
||
|
|
estimators_ : list
|
||
|
|
A list of clones of base_estimator.
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||
|
|
|
||
|
|
order_ : list
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||
|
|
The order of labels in the classifier chain.
|
||
|
|
|
||
|
|
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}, shape (n_samples, n_features)
|
||
|
|
The input data.
|
||
|
|
Y : array-like, 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.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
self : object
|
||
|
|
"""
|
||
|
|
super().fit(X, Y, **fit_params)
|
||
|
|
return self
|
||
|
|
|
||
|
|
def _more_tags(self):
|
||
|
|
return {'multioutput_only': True}
|
||
|
|
|
||
|
|
|
||
|
|
# TODO: remove in 0.24
|
||
|
|
@deprecated("MultiOutputEstimator is deprecated in version "
|
||
|
|
"0.22 and will be removed in version 0.24.")
|
||
|
|
class MultiOutputEstimator(_MultiOutputEstimator):
|
||
|
|
pass
|