content: # 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)
  