8.15.1.17. sklearn.linear_model.SGDClassifier

class sklearn.linear_model.SGDClassifier(loss='hinge', penalty='l2', alpha=0.0001, rho=0.85, fit_intercept=True, n_iter=5, shuffle=False, verbose=0, n_jobs=1, seed=0, learning_rate='optimal', eta0=0.0, power_t=0.5, class_weight=None, warm_start=False)

Linear model fitted by minimizing a regularized empirical loss with SGD.

SGD stands for Stochastic Gradient Descent: the gradient of the loss is estimated each sample at a time and the model is updated along the way with a decreasing strength schedule (aka learning rate).

The regularizer is a penalty added to the loss function that shrinks model parameters towards the zero vector using either the squared euclidean norm L2 or the absolute norm L1 or a combination of both (Elastic Net). If the parameter update crosses the 0.0 value because of the regularizer, the update is truncated to 0.0 to allow for learning sparse models and achieve online feature selection.

This implementation works with data represented as dense numpy arrays of floating point values for the features.

Parameters :

loss : str, ‘hinge’ or ‘log’ or ‘modified_huber’

The loss function to be used. Defaults to ‘hinge’. The hinge loss is a margin loss used by standard linear SVM models. The ‘log’ loss is the loss of logistic regression models and can be used for probability estimation in binary classifiers. ‘modified_huber’ is another smooth loss that brings tolerance to outliers.

penalty : str, ‘l2’ or ‘l1’ or ‘elasticnet’

The penalty (aka regularization term) to be used. Defaults to ‘l2’ which is the standard regularizer for linear SVM models. ‘l1’ and ‘elasticnet’ migh bring sparsity to the model (feature selection) not achievable with ‘l2’.

alpha : float

Constant that multiplies the regularization term. Defaults to 0.0001

rho : float

The Elastic Net mixing parameter, with 0 < rho <= 1. Defaults to 0.85.

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 :

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

seed: int, optional :

The seed of the pseudo random number generator to use when shuffling the data.

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.

learning_rate : string, optional

The learning rate: constant: eta = eta0 optimal: eta = 1.0/(t+t0) [default] invscaling: eta = eta0 / pow(t, power_t)

eta0 : double

The initial learning rate [default 0.01].

power_t : double

The exponent for inverse scaling learning rate [default 0.25].

class_weight : dict, {class_label

Preset for the class_weight fit parameter.

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

The “auto” mode uses the values of y to automatically adjust weights inversely proportional to class frequencies.

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 also

LinearSVC, LogisticRegression, Perceptron

Examples

>>> import numpy as np
>>> from sklearn import linear_model
>>> X = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]])
>>> Y = np.array([1, 1, 2, 2])
>>> clf = linear_model.SGDClassifier()
>>> clf.fit(X, Y)
... 
SGDClassifier(alpha=0.0001, class_weight=None, eta0=0.0,
        fit_intercept=True, learning_rate='optimal', loss='hinge',
        n_iter=5, n_jobs=1, penalty='l2', power_t=0.5, rho=0.85, seed=0,
        shuffle=False, verbose=0, warm_start=False)
>>> print clf.predict([[-0.8, -1]])
[1]

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.

Methods

decision_function(X)	Predict signed ‘distance’ to the hyperplane (aka confidence score)
fit(X, y[, coef_init, intercept_init, ...])	Fit linear model with Stochastic Gradient Descent.
fit_transform(X[, y])	Fit to data, then transform it
get_params([deep])	Get parameters for the estimator
partial_fit(X, y[, classes, class_weight, ...])	Fit linear model with Stochastic Gradient Descent.
predict(X)	Predict using the linear model
predict_proba(X)	Predict class membership probability
score(X, y)	Returns the mean accuracy on the given test data and labels.
set_params(**params)	Set the parameters of the estimator.
transform(X[, threshold])	Reduce X to its most important features.

__init__(loss='hinge', penalty='l2', alpha=0.0001, rho=0.85, fit_intercept=True, n_iter=5, shuffle=False, verbose=0, n_jobs=1, seed=0, learning_rate='optimal', eta0=0.0, power_t=0.5, class_weight=None, warm_start=False)

classes

DEPRECATED: to be removed in v0.12; use classes_ instead.

decision_function(X)

Predict signed ‘distance’ to the hyperplane (aka confidence score)

Parameters :

X : {array-like, sparse matrix}, shape = [n_samples, n_features]

Returns :

array, shape = [n_samples] if n_classes == 2 else [n_samples,n_classes] :

The signed ‘distances’ to the hyperplane(s).

fit(X, y, coef_init=None, intercept_init=None, class_weight=None, sample_weight=None)

Fit linear model with Stochastic Gradient Descent.

Parameters :

X : {array-like, sparse matrix}, shape = [n_samples, n_features]

Training data

y : numpy array of shape [n_samples]

Target values

coef_init : array, shape = [n_classes,n_features]

The initial coeffients 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.

Returns :

self : returns an instance of self.

fit_transform(X, y=None, **fit_params)

Fit to data, then transform it

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters :

X : numpy array of shape [n_samples, n_features]

Training set.

y : numpy array of shape [n_samples]

Target values.

Returns :

X_new : numpy array of shape [n_samples, n_features_new]

Transformed array.

Notes

This method just calls fit and transform consecutively, i.e., it is not an optimized implementation of fit_transform, unlike other transformers such as PCA.

get_params(deep=True)

Get parameters for the estimator

Parameters :

deep: boolean, optional :

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

partial_fit(X, y, classes=None, class_weight=None, sample_weight=None)

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 of 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)

Predict using the linear model

Parameters :

X : {array-like, sparse matrix}, shape = [n_samples, n_features]

Returns :

array, shape = [n_samples] :

Array containing the predicted class labels.

predict_proba(X)

Predict class membership probability

Parameters :

X : {array-like, sparse matrix}, shape = [n_samples, n_features]

Returns :

array, shape = [n_samples] if n_classes == 2 else [n_samples, :

n_classes] :

Contains the membership probabilities of the positive class.

score(X, y)

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

Parameters :

X : array-like, shape = [n_samples, n_features]

Training set.

y : array-like, shape = [n_samples]

Labels for X.

Returns :

z : float

set_params(**params)

Set the parameters of the estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns :self :

transform(X, threshold=None)

Reduce X to its most important features.

Parameters :

X : array or scipy sparse matrix of shape [n_samples, n_features]

The input samples.

threshold : string, float or None, optional (default=None)

The threshold value to use for feature selection. Features whose importance is greater or equal are kept while the others are discarded. If “median” (resp. “mean”), then the threshold value is the median (resp. the mean) of the feature importances. A scaling factor (e.g., “1.25*mean”) may also be used. If None and if available, the object attribute threshold is used. Otherwise, “mean” is used by default.

Returns :

X_r : array of shape [n_samples, n_selected_features]

The input samples with only the selected features.