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# `sklearn.linear_model`.PassiveAggressiveClassifier

class sklearn.linear_model.PassiveAggressiveClassifier(*, C=1.0, fit_intercept=True, max_iter=1000, tol=0.001, 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)

  Passive Aggressive Classifier.

  Read more in the User Guide.

  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 `PassiveAggressive.partial_fit` method.

      New in version 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).

      New in version 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.

      New in version 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.

      New in version 0.20.

    n_iter_no_change int, default=5

      Number of iterations with no improvement to wait before early stopping.

      New in version 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 `joblib.parallel_backend` context. `-1` means using all processors. See Glossary 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 Glossary.

    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 the Glossary.

      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))`.

      New in version 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.

      New in version 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 fit.

      New in version 0.24.

    feature_names_in_ndarray of shape (`n_features_in_`,)

      Names of features seen during fit. Defined only when `X` has feature names that are all strings.

      New in version 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]

  Methods

  `decision_function`(X) | Predict confidence scores for samples.
  `densify`() | Convert coefficient matrix to dense array format.
  `fit`(X, y[, coef_init, intercept_init]) | Fit linear model with Passive Aggressive algorithm.
  `get_metadata_routing`() | Get metadata routing of this object.
  `get_params`([deep]) | Get parameters for this estimator.
  `partial_fit`(X, y[, classes]) | Fit linear model with Passive Aggressive algorithm.
  `predict`(X) | Predict class labels for samples in X.
  `score`(X, y[, sample_weight]) | Return the mean accuracy on the given test data and labels.
  `set_fit_request`(*[, coef_init, intercept_init]) | Request metadata passed to the `fit` method.
  `set_params`(**params) | Set the parameters of this estimator.
  `set_partial_fit_request`(*[, classes]) | Request metadata passed to the `partial_fit` method.
  `set_score_request`(*[, sample_weight]) | Request metadata passed to the `score` method.
  `sparsify`() | Convert coefficient matrix to sparse format.

  decision_function(X)

    Predict confidence scores for samples.

    The confidence score for a sample is proportional to the signed distance of that sample to the hyperplane.

    Parameters:
      X{array-like, sparse matrix} of shape (n_samples, n_features)

        The data matrix for which we want to get the confidence scores.

    Returns:
      scores ndarray of shape (n_samples,) or (n_samples, n_classes)

        Confidence scores per `(n_samples, n_classes)` combination. In the binary case, confidence score for `self.classes_[1]` where >0 means this class would be predicted.

  densify()

    Convert coefficient matrix to dense array format.

    Converts the `coef_` member (back) to a numpy.ndarray. This is the default format of `coef_` and is required for fitting, so calling this method is only required on models that have previously been sparsified; otherwise, it is a no-op.

    Returns:
      self

        Fitted estimator.

  fit(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.

  get_metadata_routing()

    Get metadata routing of this object.

    Please check User Guide on how the routing mechanism works.

    Returns:
      routing MetadataRequest

        A `MetadataRequest` encapsulating routing information.

  get_params(deep=True)

    Get parameters for this estimator.

    Parameters:
      deep bool, default=True

        If True, will return the parameters for this estimator and contained subobjects that are estimators.

    Returns:
      params dict

        Parameter names mapped to their values.

  partial_fit(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 array-like of shape (n_samples,)

        Subset of the target values.

      classes ndarray of 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 object

        Fitted estimator.

  predict(X)

    Predict class labels for samples in X.

    Parameters:
      X{array-like, sparse matrix} of shape (n_samples, n_features)

        The data matrix for which we want to get the predictions.

    Returns:
      y_pred ndarray of shape (n_samples,)

        Vector containing the class labels for each sample.

  score(X, y, sample_weight=None)

    Return 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 array-like of shape (n_samples, n_features)

        Test samples.

      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)` w.r.t. `y`.

  set_fit_request(*, coef_init: bool | None | str = '$UNCHANGED$', intercept_init: bool | None | str = '$UNCHANGED$') → PassiveAggressiveClassifier

    Request metadata passed to the `fit` method.

    Note that this method is only relevant if `enable_metadata_routing=True` (see `sklearn.set_config`). Please see User Guide on how the routing mechanism works.

    The options for each parameter are:

    - `True`: metadata is requested, and passed to `fit` if provided. The request is ignored if metadata is not provided.

    - `False`: metadata is not requested and the meta-estimator will not pass it to `fit`.

    - `None`: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

    - `str`: metadata should be passed to the meta-estimator with this given alias instead of the original name.

    The default (`sklearn.utils.metadata_routing.UNCHANGED`) retains the existing request. This allows you to change the request for some parameters and not others.

    New in version 1.3.

    Note

    This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a `Pipeline`. Otherwise it has no effect.

    Parameters:
      coef_init str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

        Metadata routing for `coef_init` parameter in `fit`.

      intercept_init str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

        Metadata routing for `intercept_init` parameter in `fit`.

    Returns:
      self object

        The updated object.

  set_params(**params)

    Set the parameters of this estimator.

    The method works on simple estimators as well as on nested objects (such as `Pipeline`). The latter have parameters of the form `<component>__<parameter>` so that it’s possible to update each component of a nested object.

    Parameters:
      **params dict

        Estimator parameters.

    Returns:
      self estimator instance

        Estimator instance.

  set_partial_fit_request(*, classes: bool | None | str = '$UNCHANGED$') → PassiveAggressiveClassifier

    Request metadata passed to the `partial_fit` method.

    Note that this method is only relevant if `enable_metadata_routing=True` (see `sklearn.set_config`). Please see User Guide on how the routing mechanism works.

    The options for each parameter are:

    - `True`: metadata is requested, and passed to `partial_fit` if provided. The request is ignored if metadata is not provided.

    - `False`: metadata is not requested and the meta-estimator will not pass it to `partial_fit`.

    - `None`: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

    - `str`: metadata should be passed to the meta-estimator with this given alias instead of the original name.

    The default (`sklearn.utils.metadata_routing.UNCHANGED`) retains the existing request. This allows you to change the request for some parameters and not others.

    New in version 1.3.

    Note

    This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a `Pipeline`. Otherwise it has no effect.

    Parameters:
      classes str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

        Metadata routing for `classes` parameter in `partial_fit`.

    Returns:
      self object

        The updated object.

  set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → PassiveAggressiveClassifier

    Request metadata passed to the `score` method.

    Note that this method is only relevant if `enable_metadata_routing=True` (see `sklearn.set_config`). Please see User Guide on how the routing mechanism works.

    The options for each parameter are:

    - `True`: metadata is requested, and passed to `score` if provided. The request is ignored if metadata is not provided.

    - `False`: metadata is not requested and the meta-estimator will not pass it to `score`.

    - `None`: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

    - `str`: metadata should be passed to the meta-estimator with this given alias instead of the original name.

    The default (`sklearn.utils.metadata_routing.UNCHANGED`) retains the existing request. This allows you to change the request for some parameters and not others.

    New in version 1.3.

    Note

    This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a `Pipeline`. Otherwise it has no effect.

    Parameters:
      sample_weight str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

        Metadata routing for `sample_weight` parameter in `score`.

    Returns:
      self object

        The updated object.

  sparsify()

    Convert coefficient matrix to sparse format.

    Converts the `coef_` member to a scipy.sparse matrix, which for L1-regularized models can be much more memory- and storage-efficient than the usual numpy.ndarray representation.

    The `intercept_` member is not converted.

    Returns:
      self

        Fitted estimator.

    Notes

    For non-sparse models, i.e. when there are not many zeros in `coef_`, this may actually increase memory usage, so use this method with care. A rule of thumb is that the number of zero elements, which can be computed with `(coef_ == 0).sum()`, must be more than 50% for this to provide significant benefits.

    After calling this method, further fitting with the partial_fit method (if any) will not work until you call densify.

## Examples using `sklearn.linear_model.PassiveAggressiveClassifier`

Out-of-core classification of text documents

Out-of-core classification of text documents

Comparing various online solvers

Comparing various online solvers