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:

Cfloat, default=1.0

Maximum step size (regularization). Defaults to 1.0.

fit_interceptbool, default=True

Whether the intercept should be estimated or not. If False, the data is assumed to be already centered.

max_iterint, 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 partial_fit method.

New in version 0.19.

tolfloat 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_stoppingbool, 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_fractionfloat, 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_changeint, default=5

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

New in version 0.20.

shufflebool, default=True

Whether or not the training data should be shuffled after each epoch.

verboseint, default=0

The verbosity level.

lossstr, 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_jobsint 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_stateint, 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_startbool, 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_weightdict, {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.

averagebool 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).

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_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_params(**params)	Set the parameters of this estimator.
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:

scoresndarray 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.

yarray-like of shape (n_samples,)

Target values.

coef_initndarray of shape (n_classes, n_features)

The initial coefficients to warm-start the optimization.

intercept_initndarray of shape (n_classes,)

The initial intercept to warm-start the optimization.

Returns:

selfobject

Fitted estimator.

get_params(deep=True)

Get parameters for this estimator.

Parameters:

deepbool, default=True

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

Returns:

paramsdict

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.

yarray-like of shape (n_samples,)

Subset of the target values.

classesndarray 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:

selfobject

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_predndarray 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:

Xarray-like of shape (n_samples, n_features)

Test samples.

yarray-like of shape (n_samples,) or (n_samples, n_outputs)

True labels for X.

sample_weightarray-like of shape (n_samples,), default=None

Sample weights.

Returns:

scorefloat

Mean accuracy of self.predict(X) wrt. y.

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:

**paramsdict

Estimator parameters.

Returns:

selfestimator instance

Estimator instance.

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