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551 lines
20 KiB
551 lines
20 KiB
# Author: Mathieu Blondel <mathieu@mblondel.org>
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# Arnaud Joly <a.joly@ulg.ac.be>
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# Maheshakya Wijewardena <maheshakya.10@cse.mrt.ac.lk>
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# License: BSD 3 clause
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from __future__ import division
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import warnings
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import numpy as np
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import scipy.sparse as sp
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from .base import BaseEstimator, ClassifierMixin, RegressorMixin
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from .utils import check_random_state
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from .utils.validation import _num_samples
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from .utils.validation import check_array
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from .utils.validation import check_consistent_length
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from .utils.validation import check_is_fitted
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from .utils.random import random_choice_csc
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from .utils.stats import _weighted_percentile
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from .utils.multiclass import class_distribution
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class DummyClassifier(BaseEstimator, ClassifierMixin):
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"""
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DummyClassifier is a classifier that makes predictions using simple rules.
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This classifier is useful as a simple baseline to compare with other
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(real) classifiers. Do not use it for real problems.
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Read more in the :ref:`User Guide <dummy_estimators>`.
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Parameters
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----------
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strategy : str, default="stratified"
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Strategy to use to generate predictions.
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* "stratified": generates predictions by respecting the training
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set's class distribution.
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* "most_frequent": always predicts the most frequent label in the
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training set.
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* "prior": always predicts the class that maximizes the class prior
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(like "most_frequent") and ``predict_proba`` returns the class prior.
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* "uniform": generates predictions uniformly at random.
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* "constant": always predicts a constant label that is provided by
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the user. This is useful for metrics that evaluate a non-majority
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class
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.. versionadded:: 0.17
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Dummy Classifier now supports prior fitting strategy using
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parameter *prior*.
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random_state : int, RandomState instance or None, optional, default=None
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If int, random_state is the seed used by the random number generator;
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If RandomState instance, random_state is the random number generator;
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If None, the random number generator is the RandomState instance used
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by `np.random`.
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constant : int or str or array of shape = [n_outputs]
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The explicit constant as predicted by the "constant" strategy. This
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parameter is useful only for the "constant" strategy.
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Attributes
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----------
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classes_ : array or list of array of shape = [n_classes]
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Class labels for each output.
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n_classes_ : array or list of array of shape = [n_classes]
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Number of label for each output.
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class_prior_ : array or list of array of shape = [n_classes]
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Probability of each class for each output.
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n_outputs_ : int,
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Number of outputs.
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outputs_2d_ : bool,
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True if the output at fit is 2d, else false.
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sparse_output_ : bool,
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True if the array returned from predict is to be in sparse CSC format.
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Is automatically set to True if the input y is passed in sparse format.
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"""
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def __init__(self, strategy="stratified", random_state=None,
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constant=None):
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self.strategy = strategy
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self.random_state = random_state
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self.constant = constant
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def fit(self, X, y, sample_weight=None):
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"""Fit the random classifier.
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Parameters
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----------
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X : {array-like, object with finite length or shape}
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Training data, requires length = n_samples
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y : array-like, shape = [n_samples] or [n_samples, n_outputs]
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Target values.
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sample_weight : array-like of shape = [n_samples], optional
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Sample weights.
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Returns
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-------
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self : object
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"""
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allowed_strategies = ("most_frequent", "stratified", "uniform",
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"constant", "prior")
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if self.strategy not in allowed_strategies:
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raise ValueError("Unknown strategy type: %s, expected one of %s."
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% (self.strategy, allowed_strategies))
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if self.strategy == "uniform" and sp.issparse(y):
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y = y.toarray()
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warnings.warn('A local copy of the target data has been converted '
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'to a numpy array. Predicting on sparse target data '
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'with the uniform strategy would not save memory '
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'and would be slower.',
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UserWarning)
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self.sparse_output_ = sp.issparse(y)
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if not self.sparse_output_:
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y = np.atleast_1d(y)
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self.output_2d_ = y.ndim == 2
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if y.ndim == 1:
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y = np.reshape(y, (-1, 1))
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self.n_outputs_ = y.shape[1]
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check_consistent_length(X, y, sample_weight)
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if self.strategy == "constant":
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if self.constant is None:
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raise ValueError("Constant target value has to be specified "
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"when the constant strategy is used.")
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else:
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constant = np.reshape(np.atleast_1d(self.constant), (-1, 1))
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if constant.shape[0] != self.n_outputs_:
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raise ValueError("Constant target value should have "
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"shape (%d, 1)." % self.n_outputs_)
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(self.classes_,
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self.n_classes_,
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self.class_prior_) = class_distribution(y, sample_weight)
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if (self.strategy == "constant" and
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any(constant[k] not in self.classes_[k]
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for k in range(self.n_outputs_))):
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# Checking in case of constant strategy if the constant
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# provided by the user is in y.
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raise ValueError("The constant target value must be "
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"present in training data")
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if self.n_outputs_ == 1 and not self.output_2d_:
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self.n_classes_ = self.n_classes_[0]
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self.classes_ = self.classes_[0]
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self.class_prior_ = self.class_prior_[0]
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return self
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def predict(self, X):
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"""Perform classification on test vectors X.
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Parameters
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----------
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X : {array-like, object with finite length or shape}
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Training data, requires length = n_samples
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Returns
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-------
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y : array, shape = [n_samples] or [n_samples, n_outputs]
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Predicted target values for X.
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"""
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check_is_fitted(self, 'classes_')
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# numpy random_state expects Python int and not long as size argument
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# under Windows
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n_samples = _num_samples(X)
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rs = check_random_state(self.random_state)
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n_classes_ = self.n_classes_
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classes_ = self.classes_
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class_prior_ = self.class_prior_
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constant = self.constant
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if self.n_outputs_ == 1:
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# Get same type even for self.n_outputs_ == 1
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n_classes_ = [n_classes_]
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classes_ = [classes_]
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class_prior_ = [class_prior_]
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constant = [constant]
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# Compute probability only once
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if self.strategy == "stratified":
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proba = self.predict_proba(X)
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if self.n_outputs_ == 1:
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proba = [proba]
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if self.sparse_output_:
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class_prob = None
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if self.strategy in ("most_frequent", "prior"):
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classes_ = [np.array([cp.argmax()]) for cp in class_prior_]
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elif self.strategy == "stratified":
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class_prob = class_prior_
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elif self.strategy == "uniform":
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raise ValueError("Sparse target prediction is not "
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"supported with the uniform strategy")
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elif self.strategy == "constant":
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classes_ = [np.array([c]) for c in constant]
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y = random_choice_csc(n_samples, classes_, class_prob,
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self.random_state)
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else:
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if self.strategy in ("most_frequent", "prior"):
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y = np.tile([classes_[k][class_prior_[k].argmax()] for
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k in range(self.n_outputs_)], [n_samples, 1])
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elif self.strategy == "stratified":
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y = np.vstack(classes_[k][proba[k].argmax(axis=1)] for
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k in range(self.n_outputs_)).T
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elif self.strategy == "uniform":
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ret = [classes_[k][rs.randint(n_classes_[k], size=n_samples)]
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for k in range(self.n_outputs_)]
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y = np.vstack(ret).T
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elif self.strategy == "constant":
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y = np.tile(self.constant, (n_samples, 1))
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if self.n_outputs_ == 1 and not self.output_2d_:
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y = np.ravel(y)
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return y
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def predict_proba(self, X):
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"""
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Return probability estimates for the test vectors X.
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Parameters
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----------
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X : {array-like, object with finite length or shape}
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Training data, requires length = n_samples
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Returns
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-------
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P : array-like or list of array-lke of shape = [n_samples, n_classes]
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Returns the probability of the sample for each class in
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the model, where classes are ordered arithmetically, for each
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output.
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"""
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check_is_fitted(self, 'classes_')
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# numpy random_state expects Python int and not long as size argument
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# under Windows
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n_samples = _num_samples(X)
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rs = check_random_state(self.random_state)
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n_classes_ = self.n_classes_
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classes_ = self.classes_
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class_prior_ = self.class_prior_
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constant = self.constant
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if self.n_outputs_ == 1 and not self.output_2d_:
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# Get same type even for self.n_outputs_ == 1
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n_classes_ = [n_classes_]
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classes_ = [classes_]
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class_prior_ = [class_prior_]
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constant = [constant]
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P = []
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for k in range(self.n_outputs_):
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if self.strategy == "most_frequent":
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ind = class_prior_[k].argmax()
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out = np.zeros((n_samples, n_classes_[k]), dtype=np.float64)
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out[:, ind] = 1.0
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elif self.strategy == "prior":
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out = np.ones((n_samples, 1)) * class_prior_[k]
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elif self.strategy == "stratified":
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out = rs.multinomial(1, class_prior_[k], size=n_samples)
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elif self.strategy == "uniform":
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out = np.ones((n_samples, n_classes_[k]), dtype=np.float64)
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out /= n_classes_[k]
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elif self.strategy == "constant":
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ind = np.where(classes_[k] == constant[k])
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out = np.zeros((n_samples, n_classes_[k]), dtype=np.float64)
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out[:, ind] = 1.0
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P.append(out)
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if self.n_outputs_ == 1 and not self.output_2d_:
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P = P[0]
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return P
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def predict_log_proba(self, X):
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"""
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Return log probability estimates for the test vectors X.
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Parameters
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----------
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X : {array-like, object with finite length or shape}
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Training data, requires length = n_samples
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Returns
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-------
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P : array-like or list of array-like of shape = [n_samples, n_classes]
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Returns the log probability of the sample for each class in
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the model, where classes are ordered arithmetically for each
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output.
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"""
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proba = self.predict_proba(X)
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if self.n_outputs_ == 1:
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return np.log(proba)
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else:
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return [np.log(p) for p in proba]
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def score(self, X, y, sample_weight=None):
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"""Returns the mean accuracy on the given test data and labels.
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In multi-label classification, this is the subset accuracy
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which is a harsh metric since you require for each sample that
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each label set be correctly predicted.
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Parameters
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----------
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X : {array-like, None}
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Test samples with shape = (n_samples, n_features) or
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None. Passing None as test samples gives the same result
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as passing real test samples, since DummyClassifier
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operates independently of the sampled observations.
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y : array-like, shape = (n_samples) or (n_samples, n_outputs)
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True labels for X.
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sample_weight : array-like, shape = [n_samples], optional
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Sample weights.
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Returns
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-------
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score : float
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Mean accuracy of self.predict(X) wrt. y.
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"""
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if X is None:
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X = np.zeros(shape=(len(y), 1))
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return super(DummyClassifier, self).score(X, y, sample_weight)
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class DummyRegressor(BaseEstimator, RegressorMixin):
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"""
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DummyRegressor is a regressor that makes predictions using
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simple rules.
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This regressor is useful as a simple baseline to compare with other
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(real) regressors. Do not use it for real problems.
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Read more in the :ref:`User Guide <dummy_estimators>`.
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Parameters
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----------
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strategy : str
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Strategy to use to generate predictions.
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* "mean": always predicts the mean of the training set
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* "median": always predicts the median of the training set
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* "quantile": always predicts a specified quantile of the training set,
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provided with the quantile parameter.
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* "constant": always predicts a constant value that is provided by
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the user.
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constant : int or float or array of shape = [n_outputs]
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The explicit constant as predicted by the "constant" strategy. This
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parameter is useful only for the "constant" strategy.
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quantile : float in [0.0, 1.0]
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The quantile to predict using the "quantile" strategy. A quantile of
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0.5 corresponds to the median, while 0.0 to the minimum and 1.0 to the
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maximum.
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Attributes
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----------
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constant_ : float or array of shape [n_outputs]
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Mean or median or quantile of the training targets or constant value
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given by the user.
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n_outputs_ : int,
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Number of outputs.
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outputs_2d_ : bool,
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True if the output at fit is 2d, else false.
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"""
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def __init__(self, strategy="mean", constant=None, quantile=None):
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self.strategy = strategy
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self.constant = constant
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self.quantile = quantile
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def fit(self, X, y, sample_weight=None):
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"""Fit the random regressor.
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Parameters
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----------
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X : {array-like, object with finite length or shape}
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Training data, requires length = n_samples
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y : array-like, shape = [n_samples] or [n_samples, n_outputs]
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Target values.
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sample_weight : array-like of shape = [n_samples], optional
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Sample weights.
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Returns
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-------
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self : object
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"""
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allowed_strategies = ("mean", "median", "quantile", "constant")
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if self.strategy not in allowed_strategies:
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raise ValueError("Unknown strategy type: %s, expected one of %s."
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% (self.strategy, allowed_strategies))
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y = check_array(y, ensure_2d=False)
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if len(y) == 0:
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raise ValueError("y must not be empty.")
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self.output_2d_ = y.ndim == 2
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if y.ndim == 1:
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y = np.reshape(y, (-1, 1))
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self.n_outputs_ = y.shape[1]
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check_consistent_length(X, y, sample_weight)
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if self.strategy == "mean":
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self.constant_ = np.average(y, axis=0, weights=sample_weight)
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elif self.strategy == "median":
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if sample_weight is None:
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self.constant_ = np.median(y, axis=0)
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else:
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self.constant_ = [_weighted_percentile(y[:, k], sample_weight,
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percentile=50.)
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for k in range(self.n_outputs_)]
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elif self.strategy == "quantile":
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if self.quantile is None or not np.isscalar(self.quantile):
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raise ValueError("Quantile must be a scalar in the range "
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"[0.0, 1.0], but got %s." % self.quantile)
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percentile = self.quantile * 100.0
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if sample_weight is None:
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self.constant_ = np.percentile(y, axis=0, q=percentile)
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else:
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self.constant_ = [_weighted_percentile(y[:, k], sample_weight,
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percentile=percentile)
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for k in range(self.n_outputs_)]
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elif self.strategy == "constant":
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if self.constant is None:
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raise TypeError("Constant target value has to be specified "
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"when the constant strategy is used.")
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self.constant = check_array(self.constant,
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accept_sparse=['csr', 'csc', 'coo'],
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ensure_2d=False, ensure_min_samples=0)
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if self.output_2d_ and self.constant.shape[0] != y.shape[1]:
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raise ValueError(
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"Constant target value should have "
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"shape (%d, 1)." % y.shape[1])
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self.constant_ = self.constant
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self.constant_ = np.reshape(self.constant_, (1, -1))
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return self
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def predict(self, X, return_std=False):
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"""
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Perform classification on test vectors X.
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Parameters
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----------
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X : {array-like, object with finite length or shape}
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Training data, requires length = n_samples
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return_std : boolean, optional
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Whether to return the standard deviation of posterior prediction.
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All zeros in this case.
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Returns
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-------
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y : array, shape = [n_samples] or [n_samples, n_outputs]
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Predicted target values for X.
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y_std : array, shape = [n_samples] or [n_samples, n_outputs]
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Standard deviation of predictive distribution of query points.
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"""
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check_is_fitted(self, "constant_")
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n_samples = _num_samples(X)
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y = np.full((n_samples, self.n_outputs_), self.constant_,
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dtype=np.array(self.constant_).dtype)
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y_std = np.zeros((n_samples, self.n_outputs_))
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if self.n_outputs_ == 1 and not self.output_2d_:
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y = np.ravel(y)
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y_std = np.ravel(y_std)
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return (y, y_std) if return_std else y
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def score(self, X, y, sample_weight=None):
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"""Returns the coefficient of determination R^2 of the prediction.
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The coefficient R^2 is defined as (1 - u/v), where u is the residual
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sum of squares ((y_true - y_pred) ** 2).sum() and v is the total
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sum of squares ((y_true - y_true.mean()) ** 2).sum().
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The best possible score is 1.0 and it can be negative (because the
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model can be arbitrarily worse). A constant model that always
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predicts the expected value of y, disregarding the input features,
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would get a R^2 score of 0.0.
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Parameters
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----------
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X : {array-like, None}
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Test samples with shape = (n_samples, n_features) or None.
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For some estimators this may be a
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precomputed kernel matrix instead, shape = (n_samples,
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n_samples_fitted], where n_samples_fitted is the number of
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samples used in the fitting for the estimator.
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Passing None as test samples gives the same result
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as passing real test samples, since DummyRegressor
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operates independently of the sampled observations.
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y : array-like, shape = (n_samples) or (n_samples, n_outputs)
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True values for X.
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sample_weight : array-like, shape = [n_samples], optional
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Sample weights.
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Returns
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-------
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score : float
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R^2 of self.predict(X) wrt. y.
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"""
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if X is None:
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X = np.zeros(shape=(len(y), 1))
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return super(DummyRegressor, self).score(X, y, sample_weight)
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