sklearn.linear_model
.Perceptron¶

class
sklearn.linear_model.
Perceptron
(penalty=None, alpha=0.0001, 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]¶ Read more in the User 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 is used.
 fit_interceptbool, default=True
Whether the intercept should be estimated or not. If False, the data 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 the
fit
method, and not thepartial_fit
method.New in version 0.19.
 tolfloat, default=1e3
The stopping criterion. If it is not None, the iterations will stop when (loss > previous_loss  tol).
New 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
 eta0double, 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, for multiclass problems) computation.
None
means 1 unless in ajoblib.parallel_backend
context.1
means using all processors. See Glossary for more details. 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. 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 stratified 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.
 class_weightdict, {class_label: weight} or “balanced”, default=None
Preset for the class_weight fit parameter.
Weights associated with classes. If not given, all classes are supposed to have weight one.
The “balanced” mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as
n_samples / (n_classes * np.bincount(y))
 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.
 Attributes
 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_iter_int
The actual number of iterations to reach the stopping criterion. For multiclass fits, it is the maximum over every binary fit.
 classes_ndarray of shape (n_classes,)
The unique classes labels.
 t_int
Number of weight updates performed during training. Same as
(n_iter_ * n_samples)
.
See also
Notes
Perceptron
is a classification algorithm which shares the same underlying 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
>>> from sklearn.datasets import load_digits >>> from sklearn.linear_model import Perceptron >>> X, y = load_digits(return_X_y=True) >>> clf = Perceptron(tol=1e3, random_state=0) >>> clf.fit(X, y) Perceptron() >>> clf.score(X, y) 0.939...
Methods
decision_function
(self, X)Predict confidence scores for samples.
densify
(self)Convert coefficient matrix to dense array format.
fit
(self, X, y[, coef_init, intercept_init, …])Fit linear model with Stochastic Gradient Descent.
get_params
(self[, deep])Get parameters for this estimator.
partial_fit
(self, X, y[, classes, sample_weight])Perform one epoch of stochastic gradient descent on given samples.
predict
(self, X)Predict class labels for samples in X.
score
(self, X, y[, sample_weight])Return the mean accuracy on the given test data and labels.
set_params
(self, \*\*kwargs)Set and validate the parameters of estimator.
sparsify
(self)Convert coefficient matrix to sparse format.

__init__
(self, penalty=None, alpha=0.0001, 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]¶ Initialize self. See help(type(self)) for accurate signature.

decision_function
(self, X)[source]¶ Predict confidence scores for samples.
The confidence score for a sample is the signed distance of that sample to the hyperplane.
 Parameters
 Xarray_like or sparse matrix, shape (n_samples, n_features)
Samples.
 Returns
 array, shape=(n_samples,) if n_classes == 2 else (n_samples, n_classes)
Confidence scores per (sample, class) combination. In the binary case, confidence score for self.classes_[1] where >0 means this class would be predicted.

densify
(self)[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
Fitted estimator.

fit
(self, X, y, coef_init=None, intercept_init=None, sample_weight=None)[source]¶ Fit linear model with Stochastic Gradient Descent.
 Parameters
 X{arraylike, 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 warmstart the optimization.
 intercept_initndarray of shape (n_classes,), default=None
The initial intercept to warmstart the optimization.
 sample_weightarraylike, shape (n_samples,), default=None
Weights applied to individual samples. If not provided, uniform weights are assumed. These weights will be multiplied with class_weight (passed through the constructor) if class_weight is specified.
 Returns
 self :
Returns an instance of self.

get_params
(self, 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
 paramsmapping of string to any
Parameter names mapped to their values.

partial_fit
(self, X, y, classes=None, sample_weight=None)[source]¶ Perform one epoch of stochastic gradient descent on given samples.
Internally, this method uses
max_iter = 1
. Therefore, it is not guaranteed that a minimum of the cost function is reached after calling it once. Matters such as objective convergence and early stopping should be handled by the user. Parameters
 X{arraylike, 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 via
np.unique(y_all)
, where y_all is the target vector of the entire dataset. This argument is required for the first call to partial_fit and can be omitted in the subsequent calls. Note that y doesn’t need to contain all labels inclasses
. sample_weightarraylike, shape (n_samples,), default=None
Weights applied to individual samples. If not provided, uniform weights are assumed.
 Returns
 self :
Returns an instance of self.

predict
(self, X)[source]¶ Predict class labels for samples in X.
 Parameters
 Xarray_like or sparse matrix, shape (n_samples, n_features)
Samples.
 Returns
 Carray, shape [n_samples]
Predicted class label per sample.

score
(self, X, y, sample_weight=None)[source]¶ Return the mean accuracy on the given test data and labels.
In multilabel classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.
 Parameters
 Xarraylike of shape (n_samples, n_features)
Test samples.
 yarraylike of shape (n_samples,) or (n_samples, n_outputs)
True labels for X.
 sample_weightarraylike of shape (n_samples,), default=None
Sample weights.
 Returns
 scorefloat
Mean accuracy of self.predict(X) wrt. y.

set_params
(self, **kwargs)[source]¶ Set and validate the parameters of estimator.
 Parameters
 **kwargsdict
Estimator parameters.
 Returns
 selfobject
Estimator instance.

sparsify
(self)[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
Fitted 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.