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

Read more in the User Guide.

Parameters:

C : float Maximum step size (regularization). Defaults to 1.0. fit_intercept : bool Whether the intercept should be estimated or not. If False, the data is assumed to be already centered. Defaults to True. max_iter : int, optional 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. Defaults to 5. Defaults to 1000 from 0.21, or if tol is not None. New in version 0.19. tol : float or None, optional The stopping criterion. If it is not None, the iterations will stop when (loss > previous_loss - tol). Defaults to None. Defaults to 1e-3 from 0.21. 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 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: epsilon_insensitive: equivalent to PA-I in the reference paper. squared_epsilon_insensitive: equivalent to PA-II in the reference paper. epsilon : float If the difference between the current prediction and the correct label is below this threshold, the model is not updated. 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. 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 n_iter : int, optional The number of passes over the training data (aka epochs). Defaults to None. Deprecated, will be removed in 0.21. Changed in version 0.19: Deprecated

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.

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)
>>> 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=None,
              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)
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]

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.

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.