sklearn.linear_model
.PassiveAggressiveRegressor¶

class
sklearn.linear_model.
PassiveAggressiveRegressor
(C=1.0, fit_intercept=True, n_iter=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: C : float
Maximum step size (regularization). Defaults to 1.0.
epsilon : float
If the difference between the current prediction and the correct label is below this threshold, the model is not updated.
fit_intercept : bool
Whether the intercept should be estimated or not. If False, the data is assumed to be already centered. Defaults to True.
n_iter : int, optional
The number of passes over the training data (aka epochs). Defaults to 5.
shuffle : bool, default=True
Whether or not the training data should be shuffled after each epoch.
random_state : int seed, RandomState instance, or None (default)
The seed of the pseudo random number generator to use when shuffling the data.
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.
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
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.
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)
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, n_iter=5, shuffle=True, verbose=0, loss='epsilon_insensitive', epsilon=0.1, random_state=None, warm_start=False, average=False)[source]¶

densify
()[source]¶ 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)[source]¶ 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)[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 : {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)[source]¶ 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)[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 regression sum of squares ((y_true  y_pred) ** 2).sum() and v is the residual sum of squares ((y_true  y_true.mean()) ** 2).sum(). 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.
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.

sparsify
()[source]¶ 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.
