sklearn.linear_model.PassiveAggressiveRegressor

class sklearn.linear_model.PassiveAggressiveRegressor(C=1.0, fit_intercept=True, max_iter=None, tol=None, 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.

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.

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(random_state=0)
>>> regr.fit(X, y)
PassiveAggressiveRegressor(C=1.0, average=False, epsilon=0.1,
              fit_intercept=True, loss='epsilon_insensitive',
              max_iter=None, n_iter=None, random_state=0, shuffle=True,
              tol=None, 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, 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.

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.