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
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=5, 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.