linear_model.PassiveAggressiveClassifier()

class sklearn.linear_model.PassiveAggressiveClassifier(C=1.0, fit_intercept=True, n_iter=5, shuffle=True, verbose=0, loss='hinge', n_jobs=1, random_state=None, warm_start=False, class_weight=None) [source]

Passive Aggressive Classifier

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. n_iter : int, optional The number of passes over the training data (aka epochs). Defaults to 5. shuffle : bool, default=True Whether or not the training data should be shuffled after each epoch. random_state : int seed, RandomState instance, or None (default) The seed of the pseudo random number generator to use when shuffling the data. verbose : integer, optional The verbosity level n_jobs : integer, optional The number of CPUs to use to do the OVA (One Versus All, for multi-class problems) computation. -1 means ?all CPUs?. Defaults to 1. loss : string, optional 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. 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. 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.
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.

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)

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])	Returns the mean accuracy on the given test data and labels.
`set_params`(\args, \\*kwargs)
`sparsify`()	Convert coefficient matrix to sparse format.

__init__(C=1.0, fit_intercept=True, n_iter=5, shuffle=True, verbose=0, loss='hinge', n_jobs=1, random_state=None, warm_start=False, class_weight=None) [source]

decision_function(X) [source]

Predict confidence scores for samples.

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

Parameters:

Parameters:	X : {array-like, 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.

X : {array-like, 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: estimator :

fit(X, y, coef_init=None, intercept_init=None) [source]

Fit linear model with Passive Aggressive algorithm.

Parameters:

Parameters:	X : {array-like, 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 warm-start the optimization. intercept_init : array, shape = [n_classes] The initial intercept to warm-start the optimization.
Returns:	self : returns an instance of self.

X : {array-like, 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 warm-start the optimization.

intercept_init : array, shape = [n_classes]

The initial intercept to warm-start the optimization.

Returns:

self : returns an instance of self.

get_params(deep=True) [source]

Get parameters for this estimator.

Parameters:

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.

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(X, y, classes=None) [source]

Fit linear model with Passive Aggressive algorithm.

Parameters:

Parameters:	X : {array-like, 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 in `classes`.
Returns:	self : returns an instance of self.

X : {array-like, 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 in classes.

Returns:

self : returns an instance of self.

predict(X) [source]

Predict class labels for samples in X.

Parameters:

Parameters:	X : {array-like, sparse matrix}, shape = [n_samples, n_features] Samples.
Returns:	C : array, shape = [n_samples] Predicted class label per sample.

X : {array-like, sparse matrix}, shape = [n_samples, n_features]

Samples.

Returns:

C : array, shape = [n_samples]

Predicted class label per sample.

score(X, y, sample_weight=None) [source]

Returns 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:

Parameters:	X : array-like, shape = (n_samples, n_features) Test samples. y : array-like, shape = (n_samples) or (n_samples, n_outputs) True labels for X. sample_weight : array-like, shape = [n_samples], optional Sample weights.
Returns:	score : float Mean accuracy of self.predict(X) wrt. y.

X : array-like, shape = (n_samples, n_features)

Test samples.

y : array-like, shape = (n_samples) or (n_samples, n_outputs)

True labels for X.

sample_weight : array-like, shape = [n_samples], optional

Sample weights.

Returns:

score : float

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

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: 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.