dummy.py

# Author: Mathieu Blondel <mathieu@mblondel.org>
#         Arnaud Joly <a.joly@ulg.ac.be>
#         Maheshakya Wijewardena <maheshakya.10@cse.mrt.ac.lk>
# License: BSD 3 clause

import warnings
import numpy as np
import scipy.sparse as sp

from .base import BaseEstimator, ClassifierMixin, RegressorMixin
from .base import MultiOutputMixin
from .utils import check_random_state
from .utils.validation import _num_samples
from .utils.validation import check_array
from .utils.validation import check_consistent_length
from .utils.validation import check_is_fitted, _check_sample_weight
from .utils.random import _random_choice_csc
from .utils.stats import _weighted_percentile
from .utils.multiclass import class_distribution
from .utils.validation import _deprecate_positional_args


class DummyClassifier(MultiOutputMixin, ClassifierMixin, BaseEstimator):
    """
    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>`.

    .. versionadded:: 0.13

    Parameters
    ----------
    strategy : {"stratified", "most_frequent", "prior", "uniform", \
            "constant"}, default="prior"
        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

          .. versionchanged:: 0.24
             The default value of `strategy` has changed to "prior" in version
             0.24.

    random_state : int, RandomState instance or None, default=None
        Controls the randomness to generate the predictions when
        ``strategy='stratified'`` or ``strategy='uniform'``.
        Pass an int for reproducible output across multiple function calls.
        See :term:`Glossary <random_state>`.

    constant : int or str or array-like of shape (n_outputs,)
        The explicit constant as predicted by the "constant" strategy. This
        parameter is useful only for the "constant" strategy.

    Attributes
    ----------
    classes_ : ndarray of shape (n_classes,) or list thereof
        Class labels for each output.

    n_classes_ : int or list of int
        Number of label for each output.

    class_prior_ : ndarray of shape (n_classes,) or list thereof
        Probability of each class for each output.

    n_outputs_ : int
        Number of outputs.

    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.

    Examples
    --------
    >>> import numpy as np
    >>> from sklearn.dummy import DummyClassifier
    >>> X = np.array([-1, 1, 1, 1])
    >>> y = np.array([0, 1, 1, 1])
    >>> dummy_clf = DummyClassifier(strategy="most_frequent")
    >>> dummy_clf.fit(X, y)
    DummyClassifier(strategy='most_frequent')
    >>> dummy_clf.predict(X)
    array([1, 1, 1, 1])
    >>> dummy_clf.score(X, y)
    0.75
    """
    @_deprecate_positional_args
    def __init__(self, *, strategy="prior", 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 of shape (n_samples, n_features)
            Training data.

        y : array-like of shape (n_samples,) or (n_samples, n_outputs)
            Target values.

        sample_weight : array-like of shape (n_samples,), default=None
            Sample weights.

        Returns
        -------
        self : object
        """
        allowed_strategies = ("most_frequent", "stratified", "uniform",
                              "constant", "prior")

        if self.strategy not in allowed_strategies:
            raise ValueError("Unknown strategy type: %s, expected one of %s."
                             % (self.strategy, allowed_strategies))

        self._strategy = self.strategy

        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.asarray(y)
            y = np.atleast_1d(y)

        if y.ndim == 1:
            y = np.reshape(y, (-1, 1))

        self.n_outputs_ = y.shape[1]

        self.n_features_in_ = None  # No input validation is done for X

        check_consistent_length(X, y)

        if sample_weight is not None:
            sample_weight = _check_sample_weight(sample_weight, X)

        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":
            for k in range(self.n_outputs_):
                if not any(constant[k][0] == c for c in self.classes_[k]):
                    # Checking in case of constant strategy if the constant
                    # provided by the user is in y.
                    err_msg = ("The constant target value must be present in "
                               "the training data. You provided constant={}. "
                               "Possible values are: {}."
                               .format(self.constant, list(self.classes_[k])))
                    raise ValueError(err_msg)

        if self.n_outputs_ == 1:
            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 of shape (n_samples, n_features)
            Test data.

        Returns
        -------
        y : array-like of shape (n_samples,) or (n_samples, n_outputs)
            Predicted target values for X.
        """
        check_is_fitted(self)

        # numpy random_state expects Python int and not long as size argument
        # under Windows
        n_samples = _num_samples(X)
        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:
                y = np.ravel(y)

        return y

    def predict_proba(self, X):
        """
        Return probability estimates for the test vectors X.

        Parameters
        ----------
        X : array-like of shape (n_samples, n_features)
            Test data.

        Returns
        -------
        P : ndarray of shape (n_samples, n_classes) or list thereof
            Returns the probability of the sample for each class in
            the model, where classes are ordered arithmetically, for each
            output.
        """
        check_is_fitted(self)

        # numpy random_state expects Python int and not long as size argument
        # under Windows
        n_samples = _num_samples(X)
        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]

        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)
                out = out.astype(np.float64)

            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:
            P = P[0]

        return P

    def predict_log_proba(self, X):
        """
        Return log probability estimates for the test vectors X.

        Parameters
        ----------
        X : {array-like, object with finite length or shape}
            Training data, requires length = n_samples

        Returns
        -------
        P : ndarray of shape (n_samples, n_classes) or list thereof
            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]

    def _more_tags(self):
        return {
            'poor_score': True, 'no_validation': True,
            '_xfail_checks': {
                'check_methods_subset_invariance':
                'fails for the predict method',
                'check_methods_sample_order_invariance':
                'fails for the predict method'
            }
        }

    def score(self, X, y, sample_weight=None):
        """Returns the mean accuracy on the given test data and labels.

        In multi-label classification, this is the subset accuracy
        which is a harsh metric since you require for each sample that
        each label set be correctly predicted.

        Parameters
        ----------
        X : None or array-like of shape (n_samples, n_features)
            Test samples. Passing None as test samples gives the same result
            as passing real test samples, since DummyClassifier
            operates independently of the sampled observations.

        y : array-like of shape (n_samples,) or (n_samples, n_outputs)
            True labels for X.

        sample_weight : array-like of shape (n_samples,), default=None
            Sample weights.

        Returns
        -------
        score : float
            Mean accuracy of self.predict(X) wrt. y.

        """
        if X is None:
            X = np.zeros(shape=(len(y), 1))
        return super().score(X, y, sample_weight)


class DummyRegressor(MultiOutputMixin, RegressorMixin, BaseEstimator):
    """
    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>`.

    .. versionadded:: 0.13

    Parameters
    ----------
    strategy : {"mean", "median", "quantile", "constant"}, default="mean"
        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-like of shape (n_outputs,), default=None
        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], default=None
        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_ : ndarray of shape (1, n_outputs)
        Mean or median or quantile of the training targets or constant value
        given by the user.

    n_outputs_ : int
        Number of outputs.

    Examples
    --------
    >>> import numpy as np
    >>> from sklearn.dummy import DummyRegressor
    >>> X = np.array([1.0, 2.0, 3.0, 4.0])
    >>> y = np.array([2.0, 3.0, 5.0, 10.0])
    >>> dummy_regr = DummyRegressor(strategy="mean")
    >>> dummy_regr.fit(X, y)
    DummyRegressor()
    >>> dummy_regr.predict(X)
    array([5., 5., 5., 5.])
    >>> dummy_regr.score(X, y)
    0.0
    """
    @_deprecate_positional_args
    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 of shape (n_samples, n_features)
            Training data.

        y : array-like of shape (n_samples,) or (n_samples, n_outputs)
            Target values.

        sample_weight : array-like of shape (n_samples,), default=None
            Sample weights.

        Returns
        -------
        self : object
        """
        allowed_strategies = ("mean", "median", "quantile", "constant")
        if self.strategy not in allowed_strategies:
            raise ValueError("Unknown strategy type: %s, expected one of %s."
                             % (self.strategy, allowed_strategies))

        y = check_array(y, ensure_2d=False)
        self.n_features_in_ = None  # No input validation is done for X
        if len(y) == 0:
            raise ValueError("y must not be empty.")

        if y.ndim == 1:
            y = np.reshape(y, (-1, 1))
        self.n_outputs_ = y.shape[1]

        check_consistent_length(X, y, sample_weight)

        if sample_weight is not None:
            sample_weight = _check_sample_weight(sample_weight, X)

        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.n_outputs_ != 1 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, return_std=False):
        """
        Perform classification on test vectors X.

        Parameters
        ----------
        X : array-like of shape (n_samples, n_features)
            Test data.

        return_std : bool, default=False
            Whether to return the standard deviation of posterior prediction.
            All zeros in this case.

            .. versionadded:: 0.20

        Returns
        -------
        y : array-like of shape (n_samples,) or (n_samples, n_outputs)
            Predicted target values for X.

        y_std : array-like of shape (n_samples,) or (n_samples, n_outputs)
            Standard deviation of predictive distribution of query points.
        """
        check_is_fitted(self)
        n_samples = _num_samples(X)

        y = np.full((n_samples, self.n_outputs_), self.constant_,
                    dtype=np.array(self.constant_).dtype)
        y_std = np.zeros((n_samples, self.n_outputs_))

        if self.n_outputs_ == 1:
            y = np.ravel(y)
            y_std = np.ravel(y_std)

        return (y, y_std) if return_std else y

    def _more_tags(self):
        return {'poor_score': True, 'no_validation': True}

    def score(self, X, y, sample_weight=None):
        """Returns the coefficient of determination R^2 of the prediction.

        The coefficient R^2 is defined as (1 - u/v), where u is the residual
        sum of squares ((y_true - y_pred) ** 2).sum() and v is the total
        sum of squares ((y_true - y_true.mean()) ** 2).sum().
        The best possible score is 1.0 and it can be negative (because the
        model can be arbitrarily worse). A constant model that always
        predicts the expected value of y, disregarding the input features,
        would get a R^2 score of 0.0.

        Parameters
        ----------
        X : None or array-like of shape (n_samples, n_features)
            Test samples. Passing None as test samples gives the same result
            as passing real test samples, since DummyRegressor
            operates independently of the sampled observations.

        y : array-like of shape (n_samples,) or (n_samples, n_outputs)
            True values for X.

        sample_weight : array-like of shape (n_samples,), default=None
            Sample weights.

        Returns
        -------
        score : float
            R^2 of self.predict(X) wrt. y.
        """
        if X is None:
            X = np.zeros(shape=(len(y), 1))
        return super().score(X, y, sample_weight)