sklearn.linear_model.Perceptron

class sklearn.linear_model.Perceptron(penalty=None, alpha=0.0001, fit_intercept=True, n_iter=5, shuffle=True, verbose=0, eta0=1.0, n_jobs=1, random_state=0, class_weight=None, warm_start=False)[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.

n_iter : int, optional

The number of passes over the training data (aka epochs). Defaults to 5.

shuffle : bool, optional, default True

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

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.

verbose : integer, optional

The verbosity level

n_jobs : integer, optional

The number of CPUs to use to do the OVA (One Versus All, for multi-class problems) computation. -1 means ‘all CPUs’. Defaults to 1.

eta0 : double

Constant by which the updates are multiplied. Defaults to 1.

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.

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.

See also

SGDClassifier

Notes

Perceptron and SGDClassifier share the same underlying implementation. 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.

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, n_iter=5, shuffle=True, verbose=0, eta0=1.0, n_jobs=1, random_state=0, class_weight=None, warm_start=False)[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, 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, 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.