class_docstr: 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:`PassiveAggressive.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 : int, default=0
      The verbosity level.
  
  loss : str, 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_ : ndarray of shape (1, n_features) if n_classes == 2 else             (n_classes, n_features)
      Weights assigned to the features.
  
  intercept_ : ndarray of shape (1,) if n_classes == 2 else (n_classes,)
      Constants in decision function.
  
  n_features_in_ : int
      Number of features seen during :term:`fit`.
  
      .. versionadded:: 0.24
  
  feature_names_in_ : ndarray of shape (`n_features_in_`,)
      Names of features seen during :term:`fit`. Defined only when `X`
      has feature names that are all strings.
  
      .. versionadded:: 1.0
  
  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_ : ndarray of shape (n_classes,)
      The unique classes labels.
  
  t_ : int
      Number of weight updates performed during training.
      Same as ``(n_iter_ * n_samples + 1)``.
  
  loss_function_ : callable
      Loss function used by the algorithm.
  
  See Also
  --------
  SGDClassifier : Incrementally trained logistic regression.
  Perceptron : Linear perceptron classifier.
  
  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)
  
  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]
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docstr: Fit linear model with Passive Aggressive algorithm.
  
  Parameters
  ----------
  X : {array-like, sparse matrix} of shape (n_samples, n_features)
      Training data.
  
  y : array-like of shape (n_samples,)
      Target values.
  
  coef_init : ndarray of shape (n_classes, n_features)
      The initial coefficients to warm-start the optimization.
  
  intercept_init : ndarray of shape (n_classes,)
      The initial intercept to warm-start the optimization.
  
  Returns
  -------
  self : object
      Fitted estimator.
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code: 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 : array-like of shape (n_samples,)
          Target values.
  
      coef_init : ndarray of shape (n_classes, n_features)
          The initial coefficients to warm-start the optimization.
  
      intercept_init : ndarray of shape (n_classes,)
          The initial intercept to warm-start the optimization.
  
      Returns
      -------
      self : object
          Fitted estimator.
      """
      self._more_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,
      )
  
file_path: catboost/contrib/python/scikit-learn/py3/sklearn/linear_model/_passive_aggressive.py
imports: from numbers import Real
  from ..base import _fit_context
  from ..utils._param_validation import Interval, StrOptions
  from ._stochastic_gradient import DEFAULT_EPSILON, BaseSGDClassifier, BaseSGDRegressor
label_desc: Use image node_id 3 for calling the PassiveAggressiveClassifier obj's underlying member method code with example usage: obj.fit(X, y, coef_init, intercept_init) and returns: self