content: # Authors: Rob Zinkov, Mathieu Blondel
  # License: BSD 3 clause
  
  from ..utils.validation import _deprecate_positional_args
  from ._stochastic_gradient import BaseSGDClassifier
  from ._stochastic_gradient import BaseSGDRegressor
  from ._stochastic_gradient import DEFAULT_EPSILON
  
  
  class PassiveAggressiveClassifier(BaseSGDClassifier):
      """Passive Aggressive Classifier
  
      Read more in the :ref:`User Guide <passive_aggressive>`.
  
      Parameters
      ----------
  
      C : float, default=1.0
          Maximum step size (regularization). Defaults to 1.0.
  
      fit_intercept : bool, default=True
          Whether the intercept should be estimated or not. If False, the
          data is assumed to be already centered.
  
      max_iter : int, default=1000
          The maximum number of passes over the training data (aka epochs).
          It only impacts the behavior in the ``fit`` method, and not the
          :meth:`partial_fit` method.
  
          .. versionadded:: 0.19
  
      tol : float or None, default=1e-3
          The stopping criterion. If it is not None, the iterations will stop
          when (loss > previous_loss - tol).
  
          .. versionadded:: 0.19
  
      early_stopping : bool, default=False
          Whether to use early stopping to terminate training when validation.
          score is not improving. If set to True, it will automatically set aside
          a stratified fraction of training data as validation and terminate
          training when validation score is not improving by at least tol for
          n_iter_no_change consecutive epochs.
  
          .. versionadded:: 0.20
  
      validation_fraction : float, default=0.1
          The proportion of training data to set aside as validation set for
          early stopping. Must be between 0 and 1.
          Only used if early_stopping is True.
  
          .. versionadded:: 0.20
  
      n_iter_no_change : int, default=5
          Number of iterations with no improvement to wait before early stopping.
  
          .. versionadded:: 0.20
  
      shuffle : bool, default=True
          Whether or not the training data should be shuffled after each epoch.
  
      verbose : integer, default=0
          The verbosity level
  
      loss : string, default="hinge"
          The loss function to be used:
          hinge: equivalent to PA-I in the reference paper.
          squared_hinge: equivalent to PA-II in the reference paper.
  
      n_jobs : int or None, default=None
          The number of CPUs to use to do the OVA (One Versus All, for
          multi-class problems) computation.
          ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
          ``-1`` means using all processors. See :term:`Glossary <n_jobs>`
          for more details.
  
      random_state : int, RandomState instance, default=None
          Used to shuffle the training data, when ``shuffle`` is set to
          ``True``. Pass an int for reproducible output across multiple
          function calls.
          See :term:`Glossary <random_state>`.
  
      warm_start : bool, default=False
          When set to True, reuse the solution of the previous call to fit as
          initialization, otherwise, just erase the previous solution.
          See :term:`the Glossary <warm_start>`.
  
          Repeatedly calling fit or partial_fit when warm_start is True can
          result in a different solution than when calling fit a single time
          because of the way the data is shuffled.
  
      class_weight : dict, {class_label: weight} or "balanced" or None, \
              default=None
          Preset for the class_weight fit parameter.
  
          Weights associated with classes. If not given, all classes
          are supposed to have weight one.
  
          The "balanced" mode uses the values of y to automatically adjust
          weights inversely proportional to class frequencies in the input data
          as ``n_samples / (n_classes * np.bincount(y))``
  
          .. versionadded:: 0.17
             parameter *class_weight* to automatically weight samples.
  
      average : bool or int, default=False
          When set to True, computes the averaged SGD weights and stores the
          result in the ``coef_`` attribute. If set to an int greater than 1,
          averaging will begin once the total number of samples seen reaches
          average. So average=10 will begin averaging after seeing 10 samples.
  
          .. versionadded:: 0.19
             parameter *average* to use weights averaging in SGD
  
      Attributes
      ----------
      coef_ : array, shape = [1, n_features] if n_classes == 2 else [n_classes,\
              n_features]
          Weights assigned to the features.
  
      intercept_ : array, shape = [1] if n_classes == 2 else [n_classes]
          Constants in decision function.
  
      n_iter_ : int
          The actual number of iterations to reach the stopping criterion.
          For multiclass fits, it is the maximum over every binary fit.
  
      classes_ : array of shape (n_classes,)
          The unique classes labels.
  
      t_ : int
          Number of weight updates performed during training.
          Same as ``(n_iter_ * n_samples)``.
  
      loss_function_ : callable
          Loss function used by the algorithm.
  
      Examples
      --------
      >>> from sklearn.linear_model import PassiveAggressiveClassifier
      >>> from sklearn.datasets import make_classification
  
      >>> X, y = make_classification(n_features=4, random_state=0)
      >>> clf = PassiveAggressiveClassifier(max_iter=1000, random_state=0,
      ... tol=1e-3)
      >>> clf.fit(X, y)
      PassiveAggressiveClassifier(random_state=0)
      >>> print(clf.coef_)
      [[0.26642044 0.45070924 0.67251877 0.64185414]]
      >>> print(clf.intercept_)
      [1.84127814]
      >>> print(clf.predict([[0, 0, 0, 0]]))
      [1]
  
      See Also
      --------
      SGDClassifier
      Perceptron
  
      References
      ----------
      Online Passive-Aggressive Algorithms
      <http://jmlr.csail.mit.edu/papers/volume7/crammer06a/crammer06a.pdf>
      K. Crammer, O. Dekel, J. Keshat, S. Shalev-Shwartz, Y. Singer - JMLR (2006)
  
      """
      @_deprecate_positional_args
      def __init__(self, *, C=1.0, fit_intercept=True, max_iter=1000, tol=1e-3,
                   early_stopping=False, validation_fraction=0.1,
                   n_iter_no_change=5, shuffle=True, verbose=0, loss="hinge",
                   n_jobs=None, random_state=None, warm_start=False,
                   class_weight=None, average=False):
          super().__init__(
              penalty=None,
              fit_intercept=fit_intercept,
              max_iter=max_iter,
              tol=tol,
              early_stopping=early_stopping,
              validation_fraction=validation_fraction,
              n_iter_no_change=n_iter_no_change,
              shuffle=shuffle,
              verbose=verbose,
              random_state=random_state,
              eta0=1.0,
              warm_start=warm_start,
              class_weight=class_weight,
              average=average,
              n_jobs=n_jobs)
  
          self.C = C
          self.loss = loss
  
      def partial_fit(self, X, y, classes=None):
          """Fit linear model with Passive Aggressive algorithm.
  
          Parameters
          ----------
          X : {array-like, sparse matrix} of shape (n_samples, n_features)
              Subset of the training data
  
          y : numpy array of shape [n_samples]
              Subset of the target values
  
          classes : array, shape = [n_classes]
              Classes across all calls to partial_fit.
              Can be obtained by via `np.unique(y_all)`, where y_all is the
              target vector of the entire dataset.
              This argument is required for the first call to partial_fit
              and can be omitted in the subsequent calls.
              Note that y doesn't need to contain all labels in `classes`.
  
          Returns
          -------
          self : returns an instance of self.
          """
          self._validate_params(for_partial_fit=True)
          if self.class_weight == 'balanced':
              raise ValueError("class_weight 'balanced' is not supported for "
                               "partial_fit. For 'balanced' weights, use "
                               "`sklearn.utils.compute_class_weight` with "
                               "`class_weight='balanced'`. In place of y you "
                               "can use a large enough subset of the full "
                               "training set target to properly estimate the "
                               "class frequency distributions. Pass the "
                               "resulting weights as the class_weight "
                               "parameter.")
          lr = "pa1" if self.loss == "hinge" else "pa2"
          return self._partial_fit(X, y, alpha=1.0, C=self.C,
                                   loss="hinge", learning_rate=lr, max_iter=1,
                                   classes=classes, sample_weight=None,
                                   coef_init=None, intercept_init=None)
  
      def fit(self, X, y, coef_init=None, intercept_init=None):
          """Fit linear model with Passive Aggressive algorithm.
  
          Parameters
          ----------
          X : {array-like, sparse matrix} of shape (n_samples, n_features)
              Training data
  
          y : numpy array of shape [n_samples]
              Target values
  
          coef_init : array, shape = [n_classes,n_features]
              The initial coefficients to warm-start the optimization.
  
          intercept_init : array, shape = [n_classes]
              The initial intercept to warm-start the optimization.
  
          Returns
          -------
          self : returns an instance of self.
          """
          self._validate_params()
          lr = "pa1" if self.loss == "hinge" else "pa2"
          return self._fit(X, y, alpha=1.0, C=self.C,
                           loss="hinge", learning_rate=lr,
                           coef_init=coef_init, intercept_init=intercept_init)
  
  
  class PassiveAggressiveRegressor(BaseSGDRegressor):
      """Passive Aggressive Regressor
  
      Read more in the :ref:`User Guide <passive_aggressive>`.
  
      Parameters
      ----------
  
      C : float, default=1.0
          Maximum step size (regularization). Defaults to 1.0.
  
      fit_intercept : bool, default=True
          Whether the intercept should be estimated or not. If False, the
          data is assumed to be already centered. Defaults to True.
  
      max_iter : int, default=1000
          The maximum number of passes over the training data (aka epochs).
          It only impacts the behavior in the ``fit`` method, and not the
          :meth:`partial_fit` method.
  
          .. versionadded:: 0.19
  
      tol : float or None, default=1e-3
          The stopping criterion. If it is not None, the iterations will stop
          when (loss > previous_loss - tol).
  
          .. versionadded:: 0.19
  
      early_stopping : bool, default=False
          Whether to use early stopping to terminate training when validation.
          score is not improving. If set to True, it will automatically set aside
          a fraction of training data as validation and terminate
          training when validation score is not improving by at least tol for
          n_iter_no_change consecutive epochs.
  
          .. versionadded:: 0.20
  
      validation_fraction : float, default=0.1
          The proportion of training data to set aside as validation set for
          early stopping. Must be between 0 and 1.
          Only used if early_stopping is True.
  
          .. versionadded:: 0.20
  
      n_iter_no_change : int, default=5
          Number of iterations with no improvement to wait before early stopping.
  
          .. versionadded:: 0.20
  
      shuffle : bool, default=True
          Whether or not the training data should be shuffled after each epoch.
  
      verbose : integer, default=0
          The verbosity level
  
      loss : string, default="epsilon_insensitive"
          The loss function to be used:
          epsilon_insensitive: equivalent to PA-I in the reference paper.
          squared_epsilon_insensitive: equivalent to PA-II in the reference
          paper.
  
      epsilon : float, default=0.1
          If the difference between the current prediction and the correct label
          is below this threshold, the model is not updated.
  
      random_state : int, RandomState instance, default=None
          Used to shuffle the training data, when ``shuffle`` is set to
          ``True``. Pass an int for reproducible output across multiple
          function calls.
          See :term:`Glossary <random_state>`.
  
      warm_start : bool, default=False
          When set to True, reuse the solution of the previous call to fit as
          initialization, otherwise, just erase the previous solution.
          See :term:`the Glossary <warm_start>`.
  
          Repeatedly calling fit or partial_fit when warm_start is True can
          result in a different solution than when calling fit a single time
          because of the way the data is shuffled.
  
      average : bool or int, default=False
          When set to True, computes the averaged SGD weights and stores the
          result in the ``coef_`` attribute. If set to an int greater than 1,
          averaging will begin once the total number of samples seen reaches
          average. So average=10 will begin averaging after seeing 10 samples.
  
          .. versionadded:: 0.19
             parameter *average* to use weights averaging in SGD
  
      Attributes
      ----------
      coef_ : array, shape = [1, n_features] if n_classes == 2 else [n_classes,\
              n_features]
          Weights assigned to the features.
  
      intercept_ : array, shape = [1] if n_classes == 2 else [n_classes]
          Constants in decision function.
  
      n_iter_ : int
          The actual number of iterations to reach the stopping criterion.
  
      t_ : int
          Number of weight updates performed during training.
          Same as ``(n_iter_ * n_samples)``.
  
      Examples
      --------
      >>> from sklearn.linear_model import PassiveAggressiveRegressor
      >>> from sklearn.datasets import make_regression
  
      >>> X, y = make_regression(n_features=4, random_state=0)
      >>> regr = PassiveAggressiveRegressor(max_iter=100, random_state=0,
      ... tol=1e-3)
      >>> regr.fit(X, y)
      PassiveAggressiveRegressor(max_iter=100, random_state=0)
      >>> print(regr.coef_)
      [20.48736655 34.18818427 67.59122734 87.94731329]
      >>> print(regr.intercept_)
      [-0.02306214]
      >>> print(regr.predict([[0, 0, 0, 0]]))
      [-0.02306214]
  
      See Also
      --------
      SGDRegressor
  
      References
      ----------
      Online Passive-Aggressive Algorithms
      <http://jmlr.csail.mit.edu/papers/volume7/crammer06a/crammer06a.pdf>
      K. Crammer, O. Dekel, J. Keshat, S. Shalev-Shwartz, Y. Singer - JMLR (2006)
  
      """
      @_deprecate_positional_args
      def __init__(self, *, C=1.0, fit_intercept=True, max_iter=1000, tol=1e-3,
                   early_stopping=False, validation_fraction=0.1,
                   n_iter_no_change=5, shuffle=True, verbose=0,
                   loss="epsilon_insensitive", epsilon=DEFAULT_EPSILON,
                   random_state=None, warm_start=False,
                   average=False):
          super().__init__(
              penalty=None,
              l1_ratio=0,
              epsilon=epsilon,
              eta0=1.0,
              fit_intercept=fit_intercept,
              max_iter=max_iter,
              tol=tol,
              early_stopping=early_stopping,
              validation_fraction=validation_fraction,
              n_iter_no_change=n_iter_no_change,
              shuffle=shuffle,
              verbose=verbose,
              random_state=random_state,
              warm_start=warm_start,
              average=average)
          self.C = C
          self.loss = loss
  
      def partial_fit(self, X, y):
          """Fit linear model with Passive Aggressive algorithm.
  
          Parameters
          ----------
          X : {array-like, sparse matrix} of shape (n_samples, n_features)
              Subset of training data
  
          y : numpy array of shape [n_samples]
              Subset of target values
  
          Returns
          -------
          self : returns an instance of self.
          """
          self._validate_params(for_partial_fit=True)
          lr = "pa1" if self.loss == "epsilon_insensitive" else "pa2"
          return self._partial_fit(X, y, alpha=1.0, C=self.C,
                                   loss="epsilon_insensitive",
                                   learning_rate=lr, max_iter=1,
                                   sample_weight=None,
                                   coef_init=None, intercept_init=None)
  
      def fit(self, X, y, coef_init=None, intercept_init=None):
          """Fit linear model with Passive Aggressive algorithm.
  
          Parameters
          ----------
          X : {array-like, sparse matrix} of shape (n_samples, n_features)
              Training data
  
          y : numpy array of shape [n_samples]
              Target values
  
          coef_init : array, shape = [n_features]
              The initial coefficients to warm-start the optimization.
  
          intercept_init : array, shape = [1]
              The initial intercept to warm-start the optimization.
  
          Returns
          -------
          self : returns an instance of self.
          """
          self._validate_params()
          lr = "pa1" if self.loss == "epsilon_insensitive" else "pa2"
          return self._fit(X, y, alpha=1.0, C=self.C,
                           loss="epsilon_insensitive",
                           learning_rate=lr,
                           coef_init=coef_init,
                           intercept_init=intercept_init)
  