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)[source]¶ Passive Aggressive Classifier
Read more in the User Guide.
Parameters:  C : float
Maximum step size (regularization). Defaults to 1.0.
 fit_intercept : bool, default=False
Whether the intercept should be estimated or not. If False, the data is assumed to be already centered.
 max_iter : int, optional (default=1000)
The maximum number of passes over the training data (aka epochs). It only impacts the behavior in the
fit
method, and not thepartial_fit
.New in version 0.19.
 tol : float or None, optional (default=1e3)
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 : integer, optional
The verbosity level
 loss : string, optional
The loss function to be used: hinge: equivalent to PAI in the reference paper. squared_hinge: equivalent to PAII in the reference paper.
 n_jobs : int or None, optional (default=None)
The number of CPUs to use to do the OVA (One Versus All, for multiclass problems) computation.
None
means 1 unless in ajoblib.parallel_backend
context.1
means using all processors. See Glossary for more details. random_state : int, RandomState instance or None, optional, default=None
The seed of the pseudo random number generator to use when shuffling the data. If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by
np.random
. warm_start : bool, optional
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, optional
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, optional
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_ : 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.
See also
References
Online PassiveAggressive Algorithms <http://jmlr.csail.mit.edu/papers/volume7/crammer06a/crammer06a.pdf> K. Crammer, O. Dekel, J. Keshat, S. ShalevShwartz, 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=1e3) >>> clf.fit(X, y) PassiveAggressiveClassifier(C=1.0, average=False, class_weight=None, early_stopping=False, fit_intercept=True, loss='hinge', max_iter=1000, n_iter_no_change=5, n_jobs=None, random_state=0, shuffle=True, tol=0.001, validation_fraction=0.1, verbose=0, warm_start=False) >>> 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
(self, X)Predict confidence scores for samples. densify
(self)Convert coefficient matrix to dense array format. fit
(self, X, y[, coef_init, intercept_init])Fit linear model with Passive Aggressive algorithm. get_params
(self[, deep])Get parameters for this estimator. partial_fit
(self, X, y[, classes])Fit linear model with Passive Aggressive algorithm. predict
(self, X)Predict class labels for samples in X. score
(self, X, y[, sample_weight])Returns the mean accuracy on the given test data and labels. set_params
(self, \*args, \*\*kwargs)sparsify
(self)Convert coefficient matrix to sparse format. 
__init__
(self, 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)[source]¶

decision_function
(self, X)[source]¶ Predict confidence scores for samples.
The confidence score for a sample is the signed distance of that sample to the hyperplane.
Parameters:  X : array_like or sparse matrix, shape (n_samples, n_features)
Samples.
Returns:  array, shape=(n_samples,) if n_classes == 2 else (n_samples, n_classes)
Confidence scores per (sample, class) combination. In the binary case, confidence score for self.classes_[1] where >0 means this class would be predicted.

densify
(self)[source]¶ Convert coefficient matrix to dense array format.
Converts the
coef_
member (back) to a numpy.ndarray. This is the default format ofcoef_
and is required for fitting, so calling this method is only required on models that have previously been sparsified; otherwise, it is a noop.Returns:  self : estimator

fit
(self, X, y, coef_init=None, intercept_init=None)[source]¶ Fit linear model with Passive Aggressive algorithm.
Parameters:  X : {arraylike, sparse matrix}, 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 warmstart the optimization.
 intercept_init : array, shape = [n_classes]
The initial intercept to warmstart the optimization.
Returns:  self : returns an instance of self.

get_params
(self, deep=True)[source]¶ Get parameters for this estimator.
Parameters:  deep : boolean, optional
If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:  params : mapping of string to any
Parameter names mapped to their values.

partial_fit
(self, X, y, classes=None)[source]¶ Fit linear model with Passive Aggressive algorithm.
Parameters:  X : {arraylike, sparse matrix}, 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 inclasses
.
Returns:  self : returns an instance of self.

predict
(self, X)[source]¶ Predict class labels for samples in X.
Parameters:  X : array_like or sparse matrix, shape (n_samples, n_features)
Samples.
Returns:  C : array, shape [n_samples]
Predicted class label per sample.

score
(self, X, y, sample_weight=None)[source]¶ Returns the mean accuracy on the given test data and labels.
In multilabel 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 : arraylike, shape = (n_samples, n_features)
Test samples.
 y : arraylike, shape = (n_samples) or (n_samples, n_outputs)
True labels for X.
 sample_weight : arraylike, shape = [n_samples], optional
Sample weights.
Returns:  score : float
Mean accuracy of self.predict(X) wrt. y.

sparsify
(self)[source]¶ Convert coefficient matrix to sparse format.
Converts the
coef_
member to a scipy.sparse matrix, which for L1regularized models can be much more memory and storageefficient than the usual numpy.ndarray representation.The
intercept_
member is not converted.Returns:  self : estimator
Notes
For nonsparse 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.