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)¶ 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 1e3 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 PAI in the reference paper. squared_epsilon_insensitive: equivalent to PAII 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
References
Online PassiveAggressive Algorithms <http://jmlr.csail.mit.edu/papers/volume7/crammer06a/crammer06a.pdf> K. Crammer, O. Dekel, J. Keshat, S. ShalevShwartz, 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=1e3) >>> 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)¶ Initialize self. See help(type(self)) for accurate signature.

densify
()¶ Convert coefficient matrix to dense array format.
Converts the
coef_
member (back) to a numpy.ndarray. This is the default format ofcoef_
and is required for fitting, so calling this method is only required on models that have previously been sparsified; otherwise, it is a noop.Returns: self : estimator

fit
(X, y, coef_init=None, intercept_init=None)¶ Fit linear model with Passive Aggressive algorithm.
Parameters: X : {arraylike, 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 warmstart the optimization.
intercept_init : array, shape = [1]
The initial intercept to warmstart the optimization.
Returns: self : returns an instance of self.

get_params
(deep=True)¶ 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)¶ Fit linear model with Passive Aggressive algorithm.
Parameters: X : {arraylike, 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)¶ Predict using the linear model
Parameters: X : {arraylike, 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)¶ 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 : arraylike, 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 : arraylike, shape = (n_samples) or (n_samples, n_outputs)
True values for X.
sample_weight : arraylike, shape = [n_samples], optional
Sample weights.
Returns: score : float
R^2 of self.predict(X) wrt. y.

set_params
(*args, **kwargs)¶ 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
()¶ Convert coefficient matrix to sparse format.
Converts the
coef_
member to a scipy.sparse matrix, which for L1regularized models can be much more memory and storageefficient than the usual numpy.ndarray representation.The
intercept_
member is not converted.Returns: self : estimator Notes
For nonsparse 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.
