/usr/lib/python3/dist-packages/sklearn/dummy.py is in python3-sklearn 0.17.0-4.
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# Arnaud Joly <a.joly@ulg.ac.be>
# Maheshakya Wijewardena <maheshakya.10@cse.mrt.ac.lk>
# 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.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 <dummy_estimators>`.
Parameters
----------
strategy : str
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.
"""
if not hasattr(self, "classes_"):
raise ValueError("DummyClassifier not fitted.")
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.
"""
if not hasattr(self, "classes_"):
raise ValueError("DummyClassifier not fitted.")
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 = np.ones(n_samples, dtype=int) * 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 <dummy_estimators>`.
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.
"""
if not hasattr(self, "constant_"):
raise ValueError("DummyRegressor not fitted.")
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
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