`sklearn.linear_model`.PassiveAggressiveRegressor¶

class sklearn.linear_model.PassiveAggressiveRegressor(C=1.0, fit_intercept=True, max_iter=None, tol=None, 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, n_iter=None)[source]¶

Passive Aggressive Regressor

See also

SGDRegressor

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(C=1.0, average=False, early_stopping=False,
              epsilon=0.1, fit_intercept=True, loss='epsilon_insensitive',
              max_iter=100, n_iter=None, n_iter_no_change=5,
              random_state=0, shuffle=True, tol=0.001,
              validation_fraction=0.1, verbose=0, warm_start=False)
>>> 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`()	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)	Fit linear model with Passive Aggressive algorithm.
`predict`(X)	Predict using the linear model
`score`(X, y[, sample_weight])	Returns the coefficient of determination R^2 of the prediction.
`set_params`(args, *kwargs)	Set the parameters of this estimator.
`sparsify`()	Convert coefficient matrix to sparse format.

__init__(C=1.0, fit_intercept=True, max_iter=None, tol=None, 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, n_iter=None)[source]¶: Initialize self. See help(type(self)) for accurate signature.

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:	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_features] The initial coefficients to warm-start the optimization. intercept_init : array, shape = [1] 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:	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)[source]¶

Fit linear model with Passive Aggressive algorithm.

Parameters:	X : {array-like, sparse matrix}, shape = [n_samples, n_features] Subset of training data y : numpy array of shape [n_samples] Subset of target values
Returns:	self : returns an instance of self.

predict(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(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:

X : array-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.
y : array-like, shape = (n_samples) or (n_samples, n_outputs): True values for X.
sample_weight : array-like, shape = [n_samples], optional: Sample weights.

Returns:

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

set_params(*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()[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.

sklearn.linear_model.PassiveAggressiveRegressor¶

`sklearn.linear_model`.PassiveAggressiveRegressor¶