SGDClassifierWrapper Class

SGD Classifier Wrapper Class.

Wrapper around SGD Classifier to support predict probabilities on loss functions other than log loss and modified huber loss. This breaks partial_fit on loss functions other than log and modified_huber since the calibrated model does not support partial_fit.

Read more at: http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDClassifier.html.

Initialize SGD Classifier Wrapper Model.

Constructor

SGDClassifierWrapper(random_state=None, n_jobs=1, **kwargs)

Parameters

Name	Description
random_state	int or <xref:np.random.RandomState> RandomState instance or None, optional (default=None) 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. Default value: None
n_jobs	int Number of parallel threads. Default value: 1
kwargs Required	Other parameters.

Methods

fit	Fit function for SGD Classifier Wrapper Model.
get_model	Return SGD Classifier Wrapper Model. Else returns None
get_params	Return parameters for SGD Classifier Wrapper Model.
partial_fit	Return partial fit result.
predict	Prediction function for SGD Classifier Wrapper Model.
predict_proba	Prediction class probabilities for X for SGD Classifier Wrapper model.
set_score_request	Request metadata passed to the score method. Note that this method is only relevant if enable_metadata_routing=True (see <xref:sklearn.set_config>). Please see User Guide on how the routing mechanism works. The options for each parameter are: True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided. False: metadata is not requested and the meta-estimator will not pass it to score. 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 <xref:sklearn.pipeline.Pipeline>. Otherwise it has no effect.

fit

Fit function for SGD Classifier Wrapper Model.

fit(X, y, **kwargs)

Parameters

Name	Description
X Required	ndarray Input data.
y Required	ndarray Input target values.
kwargs Required	Other parameters.

Returns

Type	Description
	Returns an instance of inner SGDClassifier model.

get_model

Return SGD Classifier Wrapper Model.

Else returns None

get_model()

Returns

Type	Description
	Returns the fitted model if fit method has been called.

get_params

Return parameters for SGD Classifier Wrapper Model.

get_params(deep=True)

Parameters

Name	Description
deep	bool If True, will return the parameters for this estimator and contained subobjects that are estimators. Default value: True

Returns

Type	Description
	parameters for SGD Classifier Wrapper Model.

partial_fit

Return partial fit result.

partial_fit(X, y, **kwargs)

Parameters

Name	Description
X Required	ndarray Input data.
y Required	ndarray Input target values.
kwargs Required	Other parameters.

Returns

Type	Description
	Returns an instance of inner SGDClassifier model.

predict

Prediction function for SGD Classifier Wrapper Model.

predict(X)

Parameters

Name	Description
X Required	ndarray Input data.

Returns

Type	Description
	Prediction values from SGD Classifier Wrapper model.

predict_proba

Prediction class probabilities for X for SGD Classifier Wrapper model.

predict_proba(X)

Parameters

Name	Description
X Required	ndarray Input data.

Returns

Type	Description
	Prediction probability values from SGD Classifier Wrapper model.

set_score_request

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see <xref:sklearn.set_config>). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

• True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.

• False: metadata is not requested and the meta-estimator will not pass it to score.

• 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

<xref:sklearn.pipeline.Pipeline>. Otherwise it has no effect.

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') -> SGDClassifierWrapper

Parameters

Name	Description
sample_weight Required	str, True, False or None, default=sklearn.utils.metadata_routing.UNCHANGED Metadata routing for sample_weight parameter in score.

Keyword-Only Parameters

Name	Description
sample_weight	Default value: $UNCHANGED$

Returns

Type	Description
object	self – The updated object.