sklearn.linear_model.Perceptron

class sklearn.linear_model.Perceptron(penalty=None, alpha=0.0001, fit_intercept=True, max_iter=None, tol=None, 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, n_iter=None) [source]

Read more in the User Guide.

Parameters:

penalty : None, ‘l2’ or ‘l1’ or ‘elasticnet’ The penalty (aka regularization term) to be used. Defaults to None. alpha : float Constant that multiplies the regularization term if regularization is used. Defaults to 0.0001 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, optional, default True Whether or not the training data should be shuffled after each epoch. verbose : integer, optional The verbosity level eta0 : double Constant by which the updates are multiplied. Defaults to 1. n_jobs : int or None, optional (default=None) The number of CPUs to use to do the OVA (One Versus All, for multi-class problems) computation. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details. 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. early_stopping : bool, 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_fraction : float, 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_change : int, default=5 Number of iterations with no improvement to wait before early stopping. New in version 0.20. class_weight : dict, {class_label: weight} or “balanced” or None, optional 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_start : bool, optional When set to True, reuse the solution of the previous call to fit as initialization, otherwise, just erase the previous solution. See the Glossary. 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. For multiclass fits, it is the maximum over every binary fit.

See also

SGDClassifier

Notes

Perceptron is a classification algorithm which shares the same underlying implementation with SGDClassifier. In fact, Perceptron() is equivalent to SGDClassifier(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=1e-3, random_state=0)
>>> clf.fit(X, y)
Perceptron(alpha=0.0001, class_weight=None, early_stopping=False, eta0=1.0,
      fit_intercept=True, max_iter=None, n_iter=None, n_iter_no_change=5,
      n_jobs=None, penalty=None, random_state=0, shuffle=True, tol=0.001,
      validation_fraction=0.1, verbose=0, warm_start=False)
>>> clf.score(X, y) 
0.946...

Methods

decision_function(X)	Predict confidence scores for samples.
densify()	Convert coefficient matrix to dense array format.
fit(X, y[, coef_init, intercept_init, …])	Fit linear model with Stochastic Gradient Descent.
get_params([deep])	Get parameters for this estimator.
partial_fit(X, y[, classes, sample_weight])	Fit linear model with Stochastic Gradient Descent.
predict(X)	Predict class labels for samples in X.
score(X, y[, sample_weight])	Returns the mean accuracy on the given test data and labels.
set_params(*args, **kwargs)
sparsify()	Convert coefficient matrix to sparse format.

__init__(penalty=None, alpha=0.0001, fit_intercept=True, max_iter=None, tol=None, 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, n_iter=None) [source]

decision_function(X) [source]

Predict confidence scores for samples.

The confidence score for a sample is the signed distance of that sample to the hyperplane.

Parameters:	X : array_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() [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, sample_weight=None) [source]

Fit linear model with Stochastic Gradient Descent.

Parameters:

X : {array-like, sparse matrix}, shape (n_samples, n_features) Training data y : numpy array, shape (n_samples,) Target values coef_init : array, shape (n_classes, n_features) The initial coefficients to warm-start the optimization. intercept_init : array, shape (n_classes,) The initial intercept to warm-start the optimization. sample_weight : array-like, shape (n_samples,), optional 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(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.

loss_function

DEPRECATED: Attribute loss_function was deprecated in version 0.19 and will be removed in 0.21. Use loss_function_ instead

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

Fit linear model with Stochastic Gradient Descent.

Parameters:

X : {array-like, sparse matrix}, shape (n_samples, n_features) Subset of the training data y : numpy array, shape (n_samples,) Subset of the target values classes : array, shape (n_classes,) 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 in classes. sample_weight : array-like, shape (n_samples,), optional Weights applied to individual samples. If not provided, uniform weights are assumed.

Returns:

self : returns an instance of self.

predict(X) [source]

Predict class labels for samples in X.

Parameters:	X : array_like or sparse matrix, shape (n_samples, n_features) Samples.
Returns:	C : array, shape [n_samples] Predicted class label per sample.

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

Returns the mean accuracy on the given test data and labels.

In multi-label 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:	X : array-like, shape = (n_samples, n_features) Test samples. y : array-like, shape = (n_samples) or (n_samples, n_outputs) True labels for X. sample_weight : array-like, shape = [n_samples], optional Sample weights.
Returns:	score : float Mean accuracy 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.

Examples using sklearn.linear_model.Perceptron