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 
 
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.  
verboseinteger, default=0 

The verbosity level  
lossstring, 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_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.      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) 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(**kwargs) Set and validate the parameters of estimator.  
sparsify() Convert coefficient matrix to sparse format.    
decision_function(X) [source]
 
Predict confidence scores for samples. The confidence score for a sample is proportional to the signed distance of that sample to the hyperplane.  Parameters 
 
Xarray-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() [source]
 
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) [source]
 
Fit linear model with Passive Aggressive algorithm.  Parameters 
 
X{array-like, sparse matrix} of shape (n_samples, n_features) 

Training data  
ynumpy array of shape [n_samples] 

Target values  
coef_initarray, shape = [n_classes,n_features] 

The initial coefficients to warm-start the optimization.  
intercept_initarray, shape = [n_classes] 

The initial intercept to warm-start the optimization.    Returns 
 
selfreturns an instance of self. 
   
  
get_params(deep=True) [source]
 
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) [source]
 
Fit linear model with Passive Aggressive algorithm.  Parameters 
 
X{array-like, sparse matrix} of shape (n_samples, n_features) 

Subset of the training data  
ynumpy array of shape [n_samples] 

Subset of the target values  
classesarray, 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 
 
selfreturns an instance of self. 
   
  
predict(X) [source]
 
Predict class labels for samples in X.  Parameters 
 
Xarray-like or sparse matrix, shape (n_samples, n_features) 

Samples.    Returns 
 
Carray, shape [n_samples] 

Predicted class label per sample.     
  
score(X, y, sample_weight=None) [source]
 
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(**kwargs) [source]
 
Set and validate the parameters of estimator.  Parameters 
 
**kwargsdict 

Estimator parameters.    Returns 
 
selfobject 

Estimator instance.     
  
sparsify() [source]
 
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  

  Comparing various online solvers  

  Classification of text documents using sparse features