303 lines
9.4 KiB
Python
303 lines
9.4 KiB
Python
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# Author: Mathieu Blondel <mathieu@mblondel.org>
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# Arnaud Joly <a.joly@ulg.ac.be>
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# License: BSD Style.
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import numpy as np
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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.fixes import unique
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from .utils.validation import safe_asarray
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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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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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* "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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* "uniform": generates predictions uniformly at random.
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random_state: int seed, RandomState instance, or None (default)
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The seed of the pseudo random number generator to use.
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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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"""
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def __init__(self, strategy="stratified", random_state=None):
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self.strategy = strategy
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self.random_state = random_state
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def fit(self, X, y):
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"""Fit the random classifier.
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Parameters
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----------
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X : {array-like, sparse matrix}, shape = [n_samples, n_features]
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Training vectors, where n_samples is the number of samples
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and n_features is the number of features.
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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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Returns
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-------
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self : object
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Returns self.
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"""
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if self.strategy not in ("most_frequent", "stratified", "uniform"):
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raise ValueError("Unknown strategy type.")
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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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self.classes_ = []
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self.n_classes_ = []
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self.class_prior_ = []
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for k in xrange(self.n_outputs_):
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classes, y_k = unique(y[:, k], return_inverse=True)
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self.classes_.append(classes)
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self.n_classes_.append(classes.shape[0])
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self.class_prior_.append(np.bincount(y_k) / float(y_k.shape[0]))
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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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"""
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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, sparse matrix}, shape = [n_samples, n_features]
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Input vectors, where n_samples is the number of samples
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and n_features is the number of features.
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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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if not hasattr(self, "classes_"):
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raise ValueError("DummyClassifier not fitted.")
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X = safe_asarray(X)
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n_samples = X.shape[0]
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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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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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# 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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y = []
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for k in xrange(self.n_outputs_):
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if self.strategy == "most_frequent":
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ret = np.ones(n_samples, dtype=int) * class_prior_[k].argmax()
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elif self.strategy == "stratified":
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ret = proba[k].argmax(axis=1)
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elif self.strategy == "uniform":
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ret = rs.randint(n_classes_[k], size=n_samples)
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y.append(classes_[k][ret])
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y = np.vstack(y).T
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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, sparse matrix}, shape = [n_samples, n_features]
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Input vectors, where n_samples is the number of samples
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and n_features is the number of features.
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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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if not hasattr(self, "classes_"):
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raise ValueError("DummyClassifier not fitted.")
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X = safe_asarray(X)
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n_samples = X.shape[0]
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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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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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P = []
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for k in xrange(self.n_outputs_):
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if self.strategy == "most_frequent":
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ind = np.ones(n_samples, dtype=int) * 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 == "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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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, sparse matrix}, shape = [n_samples, n_features]
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Input vectors, where n_samples is the number of samples
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and n_features is the number of features.
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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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class DummyRegressor(BaseEstimator, RegressorMixin):
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"""
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DummyRegressor is a regressor that always predicts the mean of the training
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targets.
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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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Attributes
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----------
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`y_mean_` : float or array of shape [n_outputs]
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Mean of the training targets.
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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 fit(self, X, y):
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"""Fit the random regressor.
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Parameters
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----------
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X : {array-like, sparse matrix}, shape = [n_samples, n_features]
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Training vectors, where n_samples is the number of samples
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and n_features is the number of features.
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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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Returns
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-------
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self : object
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Returns self.
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"""
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y = safe_asarray(y)
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self.y_mean_ = np.reshape(np.mean(y, axis=0), (1, -1))
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self.n_outputs_ = np.size(self.y_mean_) # y.shape[1] is not safe
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self.output_2d_ = (y.ndim == 2)
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return self
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def predict(self, X):
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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, sparse matrix}, shape = [n_samples, n_features]
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Input vectors, where n_samples is the number of samples
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and n_features is the number of features.
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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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if not hasattr(self, "y_mean_"):
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raise ValueError("DummyRegressor not fitted.")
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X = safe_asarray(X)
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n_samples = X.shape[0]
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y = np.ones((n_samples, 1)) * self.y_mean_
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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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