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Classes
SGDRegressor

SGDRegressor

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.

Read more in the User Guide.

Python Reference (opens in a new tab)

Constructors

constructor()

Signature

new SGDRegressor(opts?: object): SGDRegressor;

Parameters

NameTypeDescription
opts?object-
opts.alpha?numberConstant that multiplies the regularization term. The higher the value, the stronger the regularization. Also used to compute the learning rate when set to learning\_rate is set to ‘optimal’. Default Value 0.0001
opts.average?number | booleanWhen set to true, computes the averaged SGD weights across all updates and stores the result in the coef\_ attribute. If set to an int greater than 1, averaging will begin once the total number of samples seen reaches average. So average=10 will begin averaging after seeing 10 samples. Default Value false
opts.early_stopping?booleanWhether 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 returned by the score method is not improving by at least tol for n\_iter\_no\_change consecutive epochs. Default Value false
opts.epsilon?numberEpsilon in the epsilon-insensitive loss functions; only if loss is ‘huber’, ‘epsilon_insensitive’, or ‘squared_epsilon_insensitive’. For ‘huber’, determines the threshold at which it becomes less important to get the prediction exactly right. For epsilon-insensitive, any differences between the current prediction and the correct label are ignored if they are less than this threshold. Default Value 0.1
opts.eta0?numberThe initial learning rate for the ‘constant’, ‘invscaling’ or ‘adaptive’ schedules. The default value is 0.01. Default Value 0.01
opts.fit_intercept?booleanWhether the intercept should be estimated or not. If false, the data is assumed to be already centered. Default Value true
opts.l1_ratio?numberThe Elastic Net mixing parameter, with 0 <= l1_ratio <= 1. l1_ratio=0 corresponds to L2 penalty, l1_ratio=1 to L1. Only used if penalty is ‘elasticnet’. Default Value 0.15
opts.learning_rate?stringThe learning rate schedule: Default Value 'invscaling'
opts.loss?stringThe loss function to be used. The possible values are ‘squared_error’, ‘huber’, ‘epsilon_insensitive’, or ‘squared_epsilon_insensitive’ The ‘squared_error’ refers to the ordinary least squares fit. ‘huber’ modifies ‘squared_error’ to focus less on getting outliers correct by switching from squared to linear loss past a distance of epsilon. ‘epsilon_insensitive’ ignores errors less than epsilon and is linear past that; this is the loss function used in SVR. ‘squared_epsilon_insensitive’ is the same but becomes squared loss past a tolerance of epsilon. More details about the losses formulas can be found in the User Guide. Default Value 'squared_error'
opts.max_iter?numberThe maximum number of passes over the training data (aka epochs). It only impacts the behavior in the fit method, and not the partial\_fit method. Default Value 1000
opts.n_iter_no_change?numberNumber of iterations with no improvement to wait before stopping fitting. Convergence is checked against the training loss or the validation loss depending on the early\_stopping parameter. Default Value 5
opts.penalty?"l1" | "l2" | "elasticnet"The penalty (aka regularization term) to be used. Defaults to ‘l2’ which is the standard regularizer for linear SVM models. ‘l1’ and ‘elasticnet’ might bring sparsity to the model (feature selection) not achievable with ‘l2’. No penalty is added when set to undefined. Default Value 'l2'
opts.power_t?numberThe exponent for inverse scaling learning rate. Default Value 0.25
opts.random_state?numberUsed for shuffling the data, when shuffle is set to true. Pass an int for reproducible output across multiple function calls. See Glossary.
opts.shuffle?booleanWhether or not the training data should be shuffled after each epoch. Default Value true
opts.tol?numberThe stopping criterion. If it is not undefined, training will stop when (loss > best_loss - tol) for n\_iter\_no\_change consecutive epochs. Convergence is checked against the training loss or the validation loss depending on the early\_stopping parameter. Default Value 0.001
opts.validation_fraction?numberThe 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. Default Value 0.1
opts.verbose?numberThe verbosity level. Default Value 0
opts.warm_start?booleanWhen set to true, reuse the solution of the previous call to fit as initialization, otherwise, just erase the previous solution. See the Glossary. Repeatedly calling fit or partial_fit when warm_start is true can result in a different solution than when calling fit a single time because of the way the data is shuffled. If a dynamic learning rate is used, the learning rate is adapted depending on the number of samples already seen. Calling fit resets this counter, while partial\_fit will result in increasing the existing counter. Default Value false

Returns

SGDRegressor

Defined in: generated/linear_model/SGDRegressor.ts:29 (opens in a new tab)

Properties

_isDisposed

boolean = false

Defined in: generated/linear_model/SGDRegressor.ts:27 (opens in a new tab)

_isInitialized

boolean = false

Defined in: generated/linear_model/SGDRegressor.ts:26 (opens in a new tab)

_py

PythonBridge

Defined in: generated/linear_model/SGDRegressor.ts:25 (opens in a new tab)

id

string

Defined in: generated/linear_model/SGDRegressor.ts:22 (opens in a new tab)

opts

any

Defined in: generated/linear_model/SGDRegressor.ts:23 (opens in a new tab)

Accessors

coef_

Weights assigned to the features.

Signature

coef_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/linear_model/SGDRegressor.ts:509 (opens in a new tab)

feature_names_in_

Names of features seen during fit. Defined only when X has feature names that are all strings.

Signature

feature_names_in_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/linear_model/SGDRegressor.ts:628 (opens in a new tab)

intercept_

The intercept term.

Signature

intercept_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/linear_model/SGDRegressor.ts:532 (opens in a new tab)

n_features_in_

Number of features seen during fit.

Signature

n_features_in_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/linear_model/SGDRegressor.ts:603 (opens in a new tab)

n_iter_

The actual number of iterations before reaching the stopping criterion.

Signature

n_iter_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/linear_model/SGDRegressor.ts:557 (opens in a new tab)

py

Signature

py(): PythonBridge;

Returns

PythonBridge

Defined in: generated/linear_model/SGDRegressor.ts:171 (opens in a new tab)

Signature

py(pythonBridge: PythonBridge): void;

Parameters

NameType
pythonBridgePythonBridge

Returns

void

Defined in: generated/linear_model/SGDRegressor.ts:175 (opens in a new tab)

t_

Number of weight updates performed during training. Same as (n\_iter\_ \* n\_samples + 1).

Signature

t_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/linear_model/SGDRegressor.ts:580 (opens in a new tab)

Methods

densify()

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.

Signature

densify(opts: object): Promise<any>;

Parameters

NameType
optsobject

Returns

Promise<any>

Defined in: generated/linear_model/SGDRegressor.ts:263 (opens in a new tab)

dispose()

Disposes of the underlying Python resources.

Once dispose() is called, the instance is no longer usable.

Signature

dispose(): Promise<void>;

Returns

Promise<void>

Defined in: generated/linear_model/SGDRegressor.ts:244 (opens in a new tab)

fit()

Fit linear model with Stochastic Gradient Descent.

Signature

fit(opts: object): Promise<any>;

Parameters

NameTypeDescription
optsobject-
opts.X?anyTraining data.
opts.coef_init?ArrayLikeThe initial coefficients to warm-start the optimization.
opts.intercept_init?ArrayLikeThe initial intercept to warm-start the optimization.
opts.sample_weight?ArrayLikeWeights applied to individual samples (1. for unweighted).
opts.y?ArrayLikeTarget values.

Returns

Promise<any>

Defined in: generated/linear_model/SGDRegressor.ts:289 (opens in a new tab)

init()

Initializes the underlying Python resources.

This instance is not usable until the Promise returned by init() resolves.

Signature

init(py: PythonBridge): Promise<void>;

Parameters

NameType
pyPythonBridge

Returns

Promise<void>

Defined in: generated/linear_model/SGDRegressor.ts:184 (opens in a new tab)

partial_fit()

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.

Signature

partial_fit(opts: object): Promise<any>;

Parameters

NameTypeDescription
optsobject-
opts.X?anySubset of training data.
opts.sample_weight?ArrayLikeWeights applied to individual samples. If not provided, uniform weights are assumed.
opts.y?any[]Subset of target values.

Returns

Promise<any>

Defined in: generated/linear_model/SGDRegressor.ts:352 (opens in a new tab)

predict()

Predict using the linear model.

Signature

predict(opts: object): Promise<any>;

Parameters

NameTypeDescription
optsobject-
opts.X?anyInput data.

Returns

Promise<any>

Defined in: generated/linear_model/SGDRegressor.ts:397 (opens in a new tab)

score()

Return the coefficient of determination of the prediction.

The coefficient of determination \(R^2\) is defined as \((1 - \frac{u}{v})\), where \(u\) is the residual sum of squares ((y\_true \- y\_pred)\*\* 2).sum() and \(v\) is the total sum of squares ((y\_true \- y\_true.mean()) \*\* 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a \(R^2\) score of 0.0.

Signature

score(opts: object): Promise<number>;

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLike[]Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape (n\_samples, n\_samples\_fitted), where n\_samples\_fitted is the number of samples used in the fitting for the estimator.
opts.sample_weight?ArrayLikeSample weights.
opts.y?ArrayLikeTrue values for X.

Returns

Promise<number>

Defined in: generated/linear_model/SGDRegressor.ts:432 (opens in a new tab)

sparsify()

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.

Signature

sparsify(opts: object): Promise<any>;

Parameters

NameType
optsobject

Returns

Promise<any>

Defined in: generated/linear_model/SGDRegressor.ts:483 (opens in a new tab)