# Author: Mathieu Blondel # Arnaud Joly # Maheshakya Wijewardena # License: BSD 3 clause from __future__ import division import warnings import numpy as np import scipy.sparse as sp from .base import BaseEstimator, ClassifierMixin, RegressorMixin from .utils import check_random_state from .utils.validation import check_array from .utils.validation import check_consistent_length from .utils.validation import check_is_fitted from .utils.random import random_choice_csc from .utils.stats import _weighted_percentile from .utils.multiclass import class_distribution class DummyClassifier(BaseEstimator, ClassifierMixin): """ DummyClassifier is a classifier that makes predictions using simple rules. This classifier is useful as a simple baseline to compare with other (real) classifiers. Do not use it for real problems. Read more in the :ref:`User Guide `. Parameters ---------- strategy : str, default="stratified" Strategy to use to generate predictions. * "stratified": generates predictions by respecting the training set's class distribution. * "most_frequent": always predicts the most frequent label in the training set. * "prior": always predicts the class that maximizes the class prior (like "most_frequent") and ``predict_proba`` returns the class prior. * "uniform": generates predictions uniformly at random. * "constant": always predicts a constant label that is provided by the user. This is useful for metrics that evaluate a non-majority class .. versionadded:: 0.17 Dummy Classifier now supports prior fitting strategy using parameter *prior*. random_state : int seed, RandomState instance, or None (default) The seed of the pseudo random number generator to use. constant : int or str or array of shape = [n_outputs] The explicit constant as predicted by the "constant" strategy. This parameter is useful only for the "constant" strategy. Attributes ---------- classes_ : array or list of array of shape = [n_classes] Class labels for each output. n_classes_ : array or list of array of shape = [n_classes] Number of label for each output. class_prior_ : array or list of array of shape = [n_classes] Probability of each class for each output. n_outputs_ : int, Number of outputs. outputs_2d_ : bool, True if the output at fit is 2d, else false. sparse_output_ : bool, True if the array returned from predict is to be in sparse CSC format. Is automatically set to True if the input y is passed in sparse format. """ def __init__(self, strategy="stratified", random_state=None, constant=None): self.strategy = strategy self.random_state = random_state self.constant = constant def fit(self, X, y, sample_weight=None): """Fit the random classifier. Parameters ---------- X : {array-like, sparse matrix}, shape = [n_samples, n_features] Training vectors, where n_samples is the number of samples and n_features is the number of features. y : array-like, shape = [n_samples] or [n_samples, n_outputs] Target values. sample_weight : array-like of shape = [n_samples], optional Sample weights. Returns ------- self : object Returns self. """ if self.strategy not in ("most_frequent", "stratified", "uniform", "constant", "prior"): raise ValueError("Unknown strategy type.") if self.strategy == "uniform" and sp.issparse(y): y = y.toarray() warnings.warn('A local copy of the target data has been converted ' 'to a numpy array. Predicting on sparse target data ' 'with the uniform strategy would not save memory ' 'and would be slower.', UserWarning) self.sparse_output_ = sp.issparse(y) if not self.sparse_output_: y = np.atleast_1d(y) self.output_2d_ = y.ndim == 2 if y.ndim == 1: y = np.reshape(y, (-1, 1)) self.n_outputs_ = y.shape[1] if self.strategy == "constant": if self.constant is None: raise ValueError("Constant target value has to be specified " "when the constant strategy is used.") else: constant = np.reshape(np.atleast_1d(self.constant), (-1, 1)) if constant.shape[0] != self.n_outputs_: raise ValueError("Constant target value should have " "shape (%d, 1)." % self.n_outputs_) (self.classes_, self.n_classes_, self.class_prior_) = class_distribution(y, sample_weight) if (self.strategy == "constant" and any(constant[k] not in self.classes_[k] for k in range(self.n_outputs_))): # Checking in case of constant strategy if the constant # provided by the user is in y. raise ValueError("The constant target value must be " "present in training data") if self.n_outputs_ == 1 and not self.output_2d_: self.n_classes_ = self.n_classes_[0] self.classes_ = self.classes_[0] self.class_prior_ = self.class_prior_[0] return self def predict(self, X): """Perform classification on test vectors X. Parameters ---------- X : {array-like, sparse matrix}, shape = [n_samples, n_features] Input vectors, where n_samples is the number of samples and n_features is the number of features. Returns ------- y : array, shape = [n_samples] or [n_samples, n_outputs] Predicted target values for X. """ check_is_fitted(self, 'classes_') X = check_array(X, accept_sparse=['csr', 'csc', 'coo']) # numpy random_state expects Python int and not long as size argument # under Windows n_samples = int(X.shape[0]) rs = check_random_state(self.random_state) n_classes_ = self.n_classes_ classes_ = self.classes_ class_prior_ = self.class_prior_ constant = self.constant if self.n_outputs_ == 1: # Get same type even for self.n_outputs_ == 1 n_classes_ = [n_classes_] classes_ = [classes_] class_prior_ = [class_prior_] constant = [constant] # Compute probability only once if self.strategy == "stratified": proba = self.predict_proba(X) if self.n_outputs_ == 1: proba = [proba] if self.sparse_output_: class_prob = None if self.strategy in ("most_frequent", "prior"): classes_ = [np.array([cp.argmax()]) for cp in class_prior_] elif self.strategy == "stratified": class_prob = class_prior_ elif self.strategy == "uniform": raise ValueError("Sparse target prediction is not " "supported with the uniform strategy") elif self.strategy == "constant": classes_ = [np.array([c]) for c in constant] y = random_choice_csc(n_samples, classes_, class_prob, self.random_state) else: if self.strategy in ("most_frequent", "prior"): y = np.tile([classes_[k][class_prior_[k].argmax()] for k in range(self.n_outputs_)], [n_samples, 1]) elif self.strategy == "stratified": y = np.vstack(classes_[k][proba[k].argmax(axis=1)] for k in range(self.n_outputs_)).T elif self.strategy == "uniform": ret = [classes_[k][rs.randint(n_classes_[k], size=n_samples)] for k in range(self.n_outputs_)] y = np.vstack(ret).T elif self.strategy == "constant": y = np.tile(self.constant, (n_samples, 1)) if self.n_outputs_ == 1 and not self.output_2d_: y = np.ravel(y) return y def predict_proba(self, X): """ Return probability estimates for the test vectors X. Parameters ---------- X : {array-like, sparse matrix}, shape = [n_samples, n_features] Input vectors, where n_samples is the number of samples and n_features is the number of features. Returns ------- P : array-like or list of array-lke of shape = [n_samples, n_classes] Returns the probability of the sample for each class in the model, where classes are ordered arithmetically, for each output. """ check_is_fitted(self, 'classes_') X = check_array(X, accept_sparse=['csr', 'csc', 'coo']) # numpy random_state expects Python int and not long as size argument # under Windows n_samples = int(X.shape[0]) rs = check_random_state(self.random_state) n_classes_ = self.n_classes_ classes_ = self.classes_ class_prior_ = self.class_prior_ constant = self.constant if self.n_outputs_ == 1 and not self.output_2d_: # Get same type even for self.n_outputs_ == 1 n_classes_ = [n_classes_] classes_ = [classes_] class_prior_ = [class_prior_] constant = [constant] P = [] for k in range(self.n_outputs_): if self.strategy == "most_frequent": ind = class_prior_[k].argmax() out = np.zeros((n_samples, n_classes_[k]), dtype=np.float64) out[:, ind] = 1.0 elif self.strategy == "prior": out = np.ones((n_samples, 1)) * class_prior_[k] elif self.strategy == "stratified": out = rs.multinomial(1, class_prior_[k], size=n_samples) elif self.strategy == "uniform": out = np.ones((n_samples, n_classes_[k]), dtype=np.float64) out /= n_classes_[k] elif self.strategy == "constant": ind = np.where(classes_[k] == constant[k]) out = np.zeros((n_samples, n_classes_[k]), dtype=np.float64) out[:, ind] = 1.0 P.append(out) if self.n_outputs_ == 1 and not self.output_2d_: P = P[0] return P def predict_log_proba(self, X): """ Return log probability estimates for the test vectors X. Parameters ---------- X : {array-like, sparse matrix}, shape = [n_samples, n_features] Input vectors, where n_samples is the number of samples and n_features is the number of features. Returns ------- P : array-like or list of array-like of shape = [n_samples, n_classes] Returns the log probability of the sample for each class in the model, where classes are ordered arithmetically for each output. """ proba = self.predict_proba(X) if self.n_outputs_ == 1: return np.log(proba) else: return [np.log(p) for p in proba] class DummyRegressor(BaseEstimator, RegressorMixin): """ DummyRegressor is a regressor that makes predictions using simple rules. This regressor is useful as a simple baseline to compare with other (real) regressors. Do not use it for real problems. Read more in the :ref:`User Guide `. Parameters ---------- strategy : str Strategy to use to generate predictions. * "mean": always predicts the mean of the training set * "median": always predicts the median of the training set * "quantile": always predicts a specified quantile of the training set, provided with the quantile parameter. * "constant": always predicts a constant value that is provided by the user. constant : int or float or array of shape = [n_outputs] The explicit constant as predicted by the "constant" strategy. This parameter is useful only for the "constant" strategy. quantile : float in [0.0, 1.0] The quantile to predict using the "quantile" strategy. A quantile of 0.5 corresponds to the median, while 0.0 to the minimum and 1.0 to the maximum. Attributes ---------- constant_ : float or array of shape [n_outputs] Mean or median or quantile of the training targets or constant value given by the user. n_outputs_ : int, Number of outputs. outputs_2d_ : bool, True if the output at fit is 2d, else false. """ def __init__(self, strategy="mean", constant=None, quantile=None): self.strategy = strategy self.constant = constant self.quantile = quantile def fit(self, X, y, sample_weight=None): """Fit the random regressor. Parameters ---------- X : {array-like, sparse matrix}, shape = [n_samples, n_features] Training vectors, where n_samples is the number of samples and n_features is the number of features. y : array-like, shape = [n_samples] or [n_samples, n_outputs] Target values. sample_weight : array-like of shape = [n_samples], optional Sample weights. Returns ------- self : object Returns self. """ if self.strategy not in ("mean", "median", "quantile", "constant"): raise ValueError("Unknown strategy type: %s, expected " "'mean', 'median', 'quantile' or 'constant'" % self.strategy) y = check_array(y, ensure_2d=False) if len(y) == 0: raise ValueError("y must not be empty.") self.output_2d_ = y.ndim == 2 if y.ndim == 1: y = np.reshape(y, (-1, 1)) self.n_outputs_ = y.shape[1] check_consistent_length(X, y, sample_weight) if self.strategy == "mean": self.constant_ = np.average(y, axis=0, weights=sample_weight) elif self.strategy == "median": if sample_weight is None: self.constant_ = np.median(y, axis=0) else: self.constant_ = [_weighted_percentile(y[:, k], sample_weight, percentile=50.) for k in range(self.n_outputs_)] elif self.strategy == "quantile": if self.quantile is None or not np.isscalar(self.quantile): raise ValueError("Quantile must be a scalar in the range " "[0.0, 1.0], but got %s." % self.quantile) percentile = self.quantile * 100.0 if sample_weight is None: self.constant_ = np.percentile(y, axis=0, q=percentile) else: self.constant_ = [_weighted_percentile(y[:, k], sample_weight, percentile=percentile) for k in range(self.n_outputs_)] elif self.strategy == "constant": if self.constant is None: raise TypeError("Constant target value has to be specified " "when the constant strategy is used.") self.constant = check_array(self.constant, accept_sparse=['csr', 'csc', 'coo'], ensure_2d=False, ensure_min_samples=0) if self.output_2d_ and self.constant.shape[0] != y.shape[1]: raise ValueError( "Constant target value should have " "shape (%d, 1)." % y.shape[1]) self.constant_ = self.constant self.constant_ = np.reshape(self.constant_, (1, -1)) return self def predict(self, X): """ Perform classification on test vectors X. Parameters ---------- X : {array-like, sparse matrix}, shape = [n_samples, n_features] Input vectors, where n_samples is the number of samples and n_features is the number of features. Returns ------- y : array, shape = [n_samples] or [n_samples, n_outputs] Predicted target values for X. """ check_is_fitted(self, "constant_") X = check_array(X, accept_sparse=['csr', 'csc', 'coo']) n_samples = X.shape[0] y = np.ones((n_samples, 1)) * self.constant_ if self.n_outputs_ == 1 and not self.output_2d_: y = np.ravel(y) return y