# sklearn.linear_model.PassiveAggressiveRegressor¶

class sklearn.linear_model.PassiveAggressiveRegressor(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='epsilon_insensitive', epsilon=0.1, random_state=None, warm_start=False, average=False)[source]

Passive Aggressive Regressor

Read more in the User Guide.

Parameters
Cfloat

Maximum step size (regularization). Defaults to 1.0.

fit_interceptbool

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

max_iterint, 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 the partial_fit method.

New in version 0.19.

tolfloat or None, optional (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 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, optional

The verbosity level

lossstring, optional

The loss function to be used: epsilon_insensitive: equivalent to PA-I in the reference paper. squared_epsilon_insensitive: equivalent to PA-II in the reference paper.

epsilonfloat

If the difference between the current prediction and the correct label is below this threshold, the model is not updated.

random_stateint, 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_startbool, 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.

averagebool 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.

t_int

Number of weight updates performed during training. Same as (n_iter_ * n_samples).

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 PassiveAggressiveRegressor
>>> from sklearn.datasets import make_regression

>>> X, y = make_regression(n_features=4, random_state=0)
>>> regr = PassiveAggressiveRegressor(max_iter=100, random_state=0,
... tol=1e-3)
>>> regr.fit(X, y)
PassiveAggressiveRegressor(max_iter=100, random_state=0)
>>> print(regr.coef_)
[20.48736655 34.18818427 67.59122734 87.94731329]
>>> print(regr.intercept_)
[-0.02306214]
>>> print(regr.predict([[0, 0, 0, 0]]))
[-0.02306214]


Methods

 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) Fit linear model with Passive Aggressive algorithm. predict(self, X) Predict using the linear model score(self, X, y[, sample_weight]) Returns the coefficient of determination R^2 of the prediction. set_params(self, \*args, \*\*kwargs) Set the parameters of this estimator. 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='epsilon_insensitive', epsilon=0.1, random_state=None, warm_start=False, average=False)[source]

Initialize self. See help(type(self)) for accurate signature.

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

Fit linear model with Passive Aggressive algorithm.

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

Training data

ynumpy array of shape [n_samples]

Target values

coef_initarray, shape = [n_features]

The initial coefficients to warm-start the optimization.

intercept_initarray, shape = [1]

The initial intercept to warm-start the optimization.

Returns
selfreturns an instance of self.
get_params(self, deep=True)[source]

Get parameters for this estimator.

Parameters
deepboolean, optional

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

Returns
paramsmapping of string to any

Parameter names mapped to their values.

partial_fit(self, X, y)[source]

Fit linear model with Passive Aggressive algorithm.

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

Subset of training data

ynumpy array of shape [n_samples]

Subset of target values

Returns
selfreturns an instance of self.
predict(self, X)[source]

Predict using the linear model

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

Predicted target values per element in X.

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

Returns the coefficient of determination R^2 of the prediction.

The coefficient R^2 is defined as (1 - u/v), where u is the residual sum of squares ((y_true - y_pred) ** 2).sum() and v is the total sum of squares ((y_true - y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a R^2 score of 0.0.

Parameters
Xarray-like, shape = (n_samples, n_features)

Test samples. For some estimators this may be a precomputed kernel matrix instead, shape = (n_samples, n_samples_fitted], where n_samples_fitted is the number of samples used in the fitting for the estimator.

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

True values for X.

sample_weightarray-like, shape = [n_samples], optional

Sample weights.

Returns
scorefloat

R^2 of self.predict(X) wrt. y.

Notes

The R2 score used when calling score on a regressor will use multioutput='uniform_average' from version 0.23 to keep consistent with r2_score. This will influence the score method of all the multioutput regressors (except for MultiOutputRegressor). To specify the default value manually and avoid the warning, please either call r2_score directly or make a custom scorer with make_scorer (the built-in scorer 'r2' uses multioutput='uniform_average').

set_params(self, *args, **kwargs)[source]

Set the parameters of this estimator.

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

Returns
self
sparsify(self)[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
selfestimator

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.