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 multiclass 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
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 : {arraylike, 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 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 : estimator

fit
(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
y : numpy array, shape (n_samples,)
Target values
coef_init : array, shape (n_classes, n_features)
The initial coefficients to warmstart the optimization.
intercept_init : array, shape (n_classes,)
The initial intercept to warmstart the optimization.
sample_weight : arraylike, 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 : {arraylike, 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 : arraylike, 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 : {arraylike, 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 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: X : arraylike, shape = (n_samples, n_features)
Test samples.
y : arraylike, shape = (n_samples) or (n_samples, n_outputs)
True labels for X.
sample_weight : arraylike, 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 L1regularized models can be much more memory and storageefficient than the usual numpy.ndarray representation.The
intercept_
member is not converted.Returns: self : 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.
