sklearn.linear_model.PassiveAggressiveRegressor¶
- class sklearn.linear_model.PassiveAggressiveRegressor(*, C=1.0, fit_intercept=True, max_iter=1000, tol=0.001, 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)[source]¶
Passive Aggressive Regressor.
Read more in the User Guide.
- Parameters:
- Cfloat, default=1.0
Maximum step size (regularization). Defaults to 1.0.
- fit_interceptbool, default=True
Whether the intercept should be estimated or not. If False, the data is assumed to be already centered. Defaults to True.
- max_iterint, default=1000
The maximum number of passes over the training data (aka epochs). It only impacts the behavior in the
fit
method, and not thepartial_fit
method.New in version 0.19.
- tolfloat or None, default=1e-3
The stopping criterion. If it is not None, the iterations will stop when (loss > previous_loss - tol).
New in version 0.19.
- early_stoppingbool, 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_fractionfloat, 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_changeint, default=5
Number of iterations with no improvement to wait before early stopping.
New in version 0.20.
- shufflebool, default=True
Whether or not the training data should be shuffled after each epoch.
- verboseint, default=0
The verbosity level.
- lossstr, default=”epsilon_insensitive”
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.
- epsilonfloat, default=0.1
If the difference between the current prediction and the correct label is below this threshold, the model is not updated.
- random_stateint, RandomState instance, default=None
Used to shuffle the training data, when
shuffle
is set toTrue
. Pass an int for reproducible output across multiple function calls. See Glossary.- warm_startbool, default=False
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.
- averagebool or int, default=False
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.
- n_features_in_int
Number of features seen during fit.
New in version 0.24.
- feature_names_in_ndarray of shape (
n_features_in_
,) Names of features seen during fit. Defined only when
X
has feature names that are all strings.New in version 1.0.
- n_iter_int
The actual number of iterations to reach the stopping criterion.
- t_int
Number of weight updates performed during training. Same as
(n_iter_ * n_samples + 1)
.
See also
SGDRegressor
Linear model fitted by minimizing a regularized empirical loss with SGD.
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, ... tol=1e-3) >>> regr.fit(X, y) PassiveAggressiveRegressor(max_iter=100, random_state=0) >>> 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 metadata routing of this object.
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])Return the coefficient of determination of the prediction.
set_fit_request
(*[, coef_init, intercept_init])Request metadata passed to the
fit
method.set_params
(**params)Set the parameters of this estimator.
set_partial_fit_request
(*[, sample_weight])Request metadata passed to the
partial_fit
method.set_score_request
(*[, sample_weight])Request metadata passed to the
score
method.sparsify
()Convert coefficient matrix to sparse format.
- 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 no-op.- Returns:
- self
Fitted estimator.
- fit(X, y, coef_init=None, intercept_init=None)[source]¶
Fit linear model with Passive Aggressive algorithm.
- Parameters:
- X{array-like, sparse matrix} of shape (n_samples, n_features)
Training data.
- ynumpy array of shape [n_samples]
Target values.
- coef_initarray, shape = [n_features]
The initial coefficients to warm-start the optimization.
- intercept_initarray, shape = [1]
The initial intercept to warm-start the optimization.
- Returns:
- selfobject
Fitted estimator.
- get_metadata_routing()[source]¶
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
- Returns:
- routingMetadataRequest
A
MetadataRequest
encapsulating routing information.
- get_params(deep=True)[source]¶
Get parameters for this estimator.
- Parameters:
- deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns:
- paramsdict
Parameter names mapped to their values.
- partial_fit(X, y)[source]¶
Fit linear model with Passive Aggressive algorithm.
- Parameters:
- X{array-like, sparse matrix} of shape (n_samples, n_features)
Subset of training data.
- ynumpy array of shape [n_samples]
Subset of target values.
- Returns:
- selfobject
Fitted estimator.
- predict(X)[source]¶
Predict using the linear model.
- Parameters:
- X{array-like, sparse matrix}, shape (n_samples, n_features)
Input data.
- Returns:
- ndarray of shape (n_samples,)
Predicted target values per element in X.
- score(X, y, sample_weight=None)[source]¶
Return the coefficient of determination of the prediction.
The coefficient of determination \(R^2\) is defined as \((1 - \frac{u}{v})\), 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 ofy
, disregarding the input features, would get a \(R^2\) score of 0.0.- Parameters:
- Xarray-like of shape (n_samples, n_features)
Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape
(n_samples, n_samples_fitted)
, wheren_samples_fitted
is the number of samples used in the fitting for the estimator.- yarray-like of shape (n_samples,) or (n_samples, n_outputs)
True values for
X
.- sample_weightarray-like of shape (n_samples,), default=None
Sample weights.
- Returns:
- scorefloat
\(R^2\) of
self.predict(X)
w.r.t.y
.
Notes
The \(R^2\) score used when calling
score
on a regressor usesmultioutput='uniform_average'
from version 0.23 to keep consistent with default value ofr2_score
. This influences thescore
method of all the multioutput regressors (except forMultiOutputRegressor
).
- set_fit_request(*, coef_init: bool | None | str = '$UNCHANGED$', intercept_init: bool | None | str = '$UNCHANGED$') PassiveAggressiveRegressor [source]¶
Request metadata passed to the
fit
method.Note that this method is only relevant if
enable_metadata_routing=True
(seesklearn.set_config
). Please see User Guide on how the routing mechanism works.The options for each parameter are:
True
: metadata is requested, and passed tofit
if provided. The request is ignored if metadata is not provided.False
: metadata is not requested and the meta-estimator will not pass it tofit
.None
: metadata is not requested, and the meta-estimator will raise an error if the user provides it.str
: metadata should be passed to the meta-estimator with this given alias instead of the original name.
The default (
sklearn.utils.metadata_routing.UNCHANGED
) retains the existing request. This allows you to change the request for some parameters and not others.New in version 1.3.
Note
This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a
Pipeline
. Otherwise it has no effect.- Parameters:
- coef_initstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for
coef_init
parameter infit
.- intercept_initstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for
intercept_init
parameter infit
.
- Returns:
- selfobject
The updated object.
- set_params(**params)[source]¶
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as
Pipeline
). The latter have parameters of the form<component>__<parameter>
so that it’s possible to update each component of a nested object.- Parameters:
- **paramsdict
Estimator parameters.
- Returns:
- selfestimator instance
Estimator instance.
- set_partial_fit_request(*, sample_weight: bool | None | str = '$UNCHANGED$') PassiveAggressiveRegressor [source]¶
Request metadata passed to the
partial_fit
method.Note that this method is only relevant if
enable_metadata_routing=True
(seesklearn.set_config
). Please see User Guide on how the routing mechanism works.The options for each parameter are:
True
: metadata is requested, and passed topartial_fit
if provided. The request is ignored if metadata is not provided.False
: metadata is not requested and the meta-estimator will not pass it topartial_fit
.None
: metadata is not requested, and the meta-estimator will raise an error if the user provides it.str
: metadata should be passed to the meta-estimator with this given alias instead of the original name.
The default (
sklearn.utils.metadata_routing.UNCHANGED
) retains the existing request. This allows you to change the request for some parameters and not others.New in version 1.3.
Note
This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a
Pipeline
. Otherwise it has no effect.- Parameters:
- sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for
sample_weight
parameter inpartial_fit
.
- Returns:
- selfobject
The updated object.
- set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') PassiveAggressiveRegressor [source]¶
Request metadata passed to the
score
method.Note that this method is only relevant if
enable_metadata_routing=True
(seesklearn.set_config
). Please see User Guide on how the routing mechanism works.The options for each parameter are:
True
: metadata is requested, and passed toscore
if provided. The request is ignored if metadata is not provided.False
: metadata is not requested and the meta-estimator will not pass it toscore
.None
: metadata is not requested, and the meta-estimator will raise an error if the user provides it.str
: metadata should be passed to the meta-estimator with this given alias instead of the original name.
The default (
sklearn.utils.metadata_routing.UNCHANGED
) retains the existing request. This allows you to change the request for some parameters and not others.New in version 1.3.
Note
This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a
Pipeline
. Otherwise it has no effect.- Parameters:
- sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for
sample_weight
parameter inscore
.
- Returns:
- selfobject
The updated object.
- 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
Fitted 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.