Perceptron#

classsklearn.linear_model.Perceptron(*,penalty=None,alpha=0.0001,l1_ratio=0.15,fit_intercept=True,max_iter=1000,tol=0.001,shuffle=True,verbose=0,eta0=1.0,n_jobs=None,random_state=0,early_stopping=False,validation_fraction=0.1,n_iter_no_change=5,class_weight=None,warm_start=False)[source]#

Linear perceptron classifier.

The implementation is a wrapper aroundSGDClassifierby fixing theloss andlearning_rate parameters as:

SGDClassifier(loss="perceptron",learning_rate="constant")

Other available parameters are described below and are forwarded toSGDClassifier.

Read more in theUser Guide.

Parameters:

penalty{‘l2’,’l1’,’elasticnet’}, default=None

The penalty (aka regularization term) to be used.

alphafloat, default=0.0001

Constant that multiplies the regularization term if regularization isused.

l1_ratiofloat, default=0.15

The Elastic Net mixing parameter, with0<=l1_ratio<=1.l1_ratio=0 corresponds to L2 penalty,l1_ratio=1 to L1.Only used ifpenalty='elasticnet'.

Added in version 0.24.

fit_interceptbool, default=True

Whether the intercept should be estimated or not. If False, thedata is assumed to be already centered.

max_iterint, default=1000

The maximum number of passes over the training data (aka epochs).It only impacts the behavior in thefit method, and not thepartial_fit method.

Added in version 0.19.

tolfloat or None, default=1e-3

The stopping criterion. If it is not None, the iterations will stopwhen (loss > previous_loss - tol).

Added in version 0.19.

shufflebool, default=True

Whether or not the training data should be shuffled after each epoch.

verboseint, default=0

The verbosity level.

eta0float, default=1

Constant by which the updates are multiplied.

n_jobsint, default=None

The number of CPUs to use to do the OVA (One Versus All, formulti-class problems) computation.None means 1 unless in ajoblib.parallel_backend context.-1 means using all processors. SeeGlossaryfor more details.

random_stateint, RandomState instance or None, default=0

Used to shuffle the training data, whenshuffle is set toTrue. Pass an int for reproducible output across multiplefunction calls.SeeGlossary.

early_stoppingbool, default=False

Whether to use early stopping to terminate training when validationscore is not improving. If set to True, it will automatically set asidea stratified fraction of training data as validation and terminatetraining when validation score is not improving by at leasttol forn_iter_no_change consecutive epochs.

Added in version 0.20.

validation_fractionfloat, default=0.1

The proportion of training data to set aside as validation set forearly stopping. Must be between 0 and 1.Only used if early_stopping is True.

Added in version 0.20.

n_iter_no_changeint, default=5

Number of iterations with no improvement to wait before early stopping.

Added in version 0.20.

class_weightdict, {class_label: weight} or “balanced”, default=None

Preset for the class_weight fit parameter.

Weights associated with classes. If not given, all classesare supposed to have weight one.

The “balanced” mode uses the values of y to automatically adjustweights inversely proportional to class frequencies in the input dataasn_samples/(n_classes*np.bincount(y)).

warm_startbool, default=False

When set to True, reuse the solution of the previous call to fit asinitialization, otherwise, just erase the previous solution. Seethe Glossary.

Attributes:

classes_ndarray of shape (n_classes,)

The unique classes labels.

coef_ndarray of shape (1, n_features) if n_classes == 2 else (n_classes, n_features)

Weights assigned to the features.

intercept_ndarray of shape (1,) if n_classes == 2 else (n_classes,)

Constants in decision function.

n_features_in_int

Number of features seen duringfit.

Added in version 0.24.

feature_names_in_ndarray of shape (n_features_in_,)

Names of features seen duringfit. Defined only whenXhas feature names that are all strings.

Added in version 1.0.

n_iter_int

The actual number of iterations to reach the stopping criterion.For multiclass fits, it is the maximum over every binary fit.

t_int

Number of weight updates performed during training.Same as(n_iter_*n_samples+1).

See also

sklearn.linear_model.SGDClassifier

Linear classifiers (SVM, logistic regression, etc.) with SGD training.

Notes

Perceptron is a classification algorithm which shares the sameunderlying implementation withSGDClassifier. In fact,Perceptron() is equivalent toSGDClassifier(loss="perceptron",eta0=1,learning_rate="constant",penalty=None).

References

https://en.wikipedia.org/wiki/Perceptron and references therein.

Examples

>>>fromsklearn.datasetsimportload_digits>>>fromsklearn.linear_modelimportPerceptron>>>X,y=load_digits(return_X_y=True)>>>clf=Perceptron(tol=1e-3,random_state=0)>>>clf.fit(X,y)Perceptron()>>>clf.score(X,y)0.939...

decision_function(X)[source]#

Predict confidence scores for samples.

The confidence score for a sample is proportional to the signeddistance of that sample to the hyperplane.

Parameters:

X{array-like, sparse matrix} of shape (n_samples, n_features)

The data matrix for which we want to get the confidence scores.

Returns:

scoresndarray of shape (n_samples,) or (n_samples, n_classes)

Confidence scores per(n_samples,n_classes) combination. In thebinary case, confidence score forself.classes_[1] where >0 meansthis class would be predicted.

densify()[source]#

Convert coefficient matrix to dense array format.

Converts thecoef_ member (back) to a numpy.ndarray. This is thedefault format ofcoef_ and is required for fitting, so callingthis method is only required on models that have previously beensparsified; otherwise, it is a no-op.

Returns:

self

Fitted estimator.

fit(X,y,coef_init=None,intercept_init=None,sample_weight=None)[source]#

Fit linear model with Stochastic Gradient Descent.

Parameters:

X{array-like, sparse matrix}, shape (n_samples, n_features)

Training data.

yndarray of shape (n_samples,)

Target values.

coef_initndarray of shape (n_classes, n_features), default=None

The initial coefficients to warm-start the optimization.

intercept_initndarray of shape (n_classes,), default=None

The initial intercept to warm-start the optimization.

sample_weightarray-like, shape (n_samples,), default=None

Weights applied to individual samples.If not provided, uniform weights are assumed. These weights willbe multiplied with class_weight (passed through theconstructor) if class_weight is specified.

Returns:

selfobject

Returns an instance of self.

get_metadata_routing()[source]#

Get metadata routing of this object.

Please checkUser Guide on how the routingmechanism works.

Returns:

routingMetadataRequest

AMetadataRequest encapsulatingrouting information.

get_params(deep=True)[source]#

Get parameters for this estimator.

Parameters:

deepbool, default=True

If True, will return the parameters for this estimator andcontained subobjects that are estimators.

Returns:

paramsdict

Parameter names mapped to their values.

partial_fit(X,y,classes=None,sample_weight=None)[source]#

Perform one epoch of stochastic gradient descent on given samples.

Internally, this method usesmax_iter=1. Therefore, it is notguaranteed that a minimum of the cost function is reached after callingit once. Matters such as objective convergence, early stopping, andlearning rate adjustments should be handled by the user.

Parameters:

X{array-like, sparse matrix}, shape (n_samples, n_features)

Subset of the training data.

yndarray of shape (n_samples,)

Subset of the target values.

classesndarray of shape (n_classes,), default=None

Classes across all calls to partial_fit.Can be obtained by vianp.unique(y_all), where y_all is thetarget vector of the entire dataset.This argument is required for the first call to partial_fitand can be omitted in the subsequent calls.Note that y doesn’t need to contain all labels inclasses.

sample_weightarray-like, shape (n_samples,), default=None

Weights applied to individual samples.If not provided, uniform weights are assumed.

Returns:

selfobject

Returns an instance of self.

predict(X)[source]#

Predict class labels for samples in X.

Parameters:

X{array-like, sparse matrix} of shape (n_samples, n_features)

The data matrix for which we want to get the predictions.

Returns:

y_predndarray of shape (n_samples,)

Vector containing the class labels for each sample.

score(X,y,sample_weight=None)[source]#

Returnaccuracy on provided data and labels.

In multi-label classification, this is the subset accuracywhich is a harsh metric since you require for each sample thateach label set be correctly predicted.

Parameters:

Xarray-like of shape (n_samples, n_features)

Test samples.

yarray-like of shape (n_samples,) or (n_samples, n_outputs)

True labels forX.

sample_weightarray-like of shape (n_samples,), default=None

Sample weights.

Returns:

scorefloat

Mean accuracy ofself.predict(X) w.r.t.y.

set_fit_request(*,coef_init:bool|None|str='$UNCHANGED$',intercept_init:bool|None|str='$UNCHANGED$',sample_weight:bool|None|str='$UNCHANGED$')→Perceptron[source]#

Configure whether metadata should be requested to be passed to thefit method.

Note that this method is only relevant when this estimator is used as asub-estimator within ameta-estimator and metadata routing is enabledwithenable_metadata_routing=True (seesklearn.set_config).Please check theUser Guide on how the routingmechanism 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 theexisting request. This allows you to change the request for someparameters and not others.

Added in version 1.3.

Parameters:

coef_initstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing forcoef_init parameter infit.

intercept_initstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing forintercept_init parameter infit.

sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing forsample_weight 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 asPipeline). The latter haveparameters of the form<component>__<parameter> so that it’spossible to update each component of a nested object.

Parameters:

**paramsdict

Estimator parameters.

Returns:

selfestimator instance

Estimator instance.

set_partial_fit_request(*,classes:bool|None|str='$UNCHANGED$',sample_weight:bool|None|str='$UNCHANGED$')→Perceptron[source]#

Configure whether metadata should be requested to be passed to thepartial_fit method.

Note that this method is only relevant when this estimator is used as asub-estimator within ameta-estimator and metadata routing is enabledwithenable_metadata_routing=True (seesklearn.set_config).Please check theUser Guide on how the routingmechanism 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 theexisting request. This allows you to change the request for someparameters and not others.

Added in version 1.3.

Parameters:

classesstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing forclasses parameter inpartial_fit.

sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing forsample_weight parameter inpartial_fit.

Returns:

selfobject

The updated object.

set_score_request(*,sample_weight:bool|None|str='$UNCHANGED$')→Perceptron[source]#

Configure whether metadata should be requested to be passed to thescore method.

Note that this method is only relevant when this estimator is used as asub-estimator within ameta-estimator and metadata routing is enabledwithenable_metadata_routing=True (seesklearn.set_config).Please check theUser Guide on how the routingmechanism 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 theexisting request. This allows you to change the request for someparameters and not others.

Added in version 1.3.

Parameters:

sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing forsample_weight parameter inscore.

Returns:

selfobject

The updated object.

sparsify()[source]#

Convert coefficient matrix to sparse format.

Converts thecoef_ member to a scipy.sparse matrix, which forL1-regularized models can be much more memory- and storage-efficientthan the usual numpy.ndarray representation.

Theintercept_ member is not converted.

Returns:

self

Fitted estimator.

Notes

For non-sparse models, i.e. when there are not many zeros incoef_,this may actuallyincrease memory usage, so use this method withcare. A rule of thumb is that the number of zero elements, which canbe computed with(coef_==0).sum(), must be more than 50% for thisto provide significant benefits.

After calling this method, further fitting with the partial_fitmethod (if any) will not work until you call densify.

Gallery examples#

Out-of-core classification of text documents

Out-of-core classification of text documents