Class: 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.
Constructors
new SGDRegressor()
new SGDRegressor(
opts?):SGDRegressor
Parameters
| Parameter | Type | Description |
|---|---|---|
opts? | object | - |
opts.alpha? | number | Constant that multiplies the regularization term. The higher the value, the stronger the regularization. Also used to compute the learning rate when learning_rate is set to ‘optimal’. Values must be in the range \[0.0, inf). |
opts.average? | number | boolean | When 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. |
opts.early_stopping? | boolean | 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 returned by the score method is not improving by at least tol for n_iter_no_change consecutive epochs. |
opts.epsilon? | number | Epsilon 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. Values must be in the range \[0.0, inf). |
opts.eta0? | number | The initial learning rate for the ‘constant’, ‘invscaling’ or ‘adaptive’ schedules. The default value is 0.01. Values must be in the range \[0.0, inf). |
opts.fit_intercept? | boolean | Whether the intercept should be estimated or not. If false, the data is assumed to be already centered. |
opts.l1_ratio? | number | The 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’. Values must be in the range \[0.0, 1.0\]. |
opts.learning_rate? | string | The learning rate schedule: |
opts.loss? | string | The 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. |
opts.max_iter? | number | 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 method. Values must be in the range \[1, inf). |
opts.n_iter_no_change? | number | Number 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. Integer values must be in the range \[1, max_iter). |
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. |
opts.power_t? | number | The exponent for inverse scaling learning rate. Values must be in the range (-inf, inf). |
opts.random_state? | number | Used for shuffling the data, when shuffle is set to true. Pass an int for reproducible output across multiple function calls. See Glossary. |
opts.shuffle? | boolean | Whether or not the training data should be shuffled after each epoch. |
opts.tol? | number | The 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. Values must be in the range \[0.0, inf). |
opts.validation_fraction? | number | 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. Values must be in the range (0.0, 1.0). |
opts.verbose? | number | The verbosity level. Values must be in the range \[0, inf). |
opts.warm_start? | boolean | When 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. |
Returns SGDRegressor
Defined in generated/linear_model/SGDRegressor.ts:29
Properties
| Property | Type | Default value | Defined in |
|---|---|---|---|
_isDisposed | boolean | false | generated/linear_model/SGDRegressor.ts:27 |
_isInitialized | boolean | false | generated/linear_model/SGDRegressor.ts:26 |
_py | PythonBridge | undefined | generated/linear_model/SGDRegressor.ts:25 |
id | string | undefined | generated/linear_model/SGDRegressor.ts:22 |
opts | any | undefined | generated/linear_model/SGDRegressor.ts:23 |
Accessors
coef_
Get Signature
get coef_():
Promise<ArrayLike>
Weights assigned to the features.
Returns Promise<ArrayLike>
Defined in generated/linear_model/SGDRegressor.ts:628
feature_names_in_
Get Signature
get feature_names_in_():
Promise<ArrayLike>
Names of features seen during fit. Defined only when X has feature names that are all strings.
Returns Promise<ArrayLike>
Defined in generated/linear_model/SGDRegressor.ts:747
intercept_
Get Signature
get intercept_():
Promise<ArrayLike>
The intercept term.
Returns Promise<ArrayLike>
Defined in generated/linear_model/SGDRegressor.ts:651
n_features_in_
Get Signature
get n_features_in_():
Promise<number>
Number of features seen during fit.
Returns Promise<number>
Defined in generated/linear_model/SGDRegressor.ts:722
n_iter_
Get Signature
get n_iter_():
Promise<number>
The actual number of iterations before reaching the stopping criterion.
Returns Promise<number>
Defined in generated/linear_model/SGDRegressor.ts:676
py
Get Signature
get py():
PythonBridge
Returns PythonBridge
Set Signature
set py(
pythonBridge):void
Parameters
| Parameter | Type |
|---|---|
pythonBridge | PythonBridge |
Returns void
Defined in generated/linear_model/SGDRegressor.ts:171
t_
Get Signature
get t_():
Promise<number>
Number of weight updates performed during training. Same as (n_iter_ \* n_samples + 1).
Returns Promise<number>
Defined in generated/linear_model/SGDRegressor.ts:699
Methods
densify()
densify(
opts):Promise<any>
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.
Parameters
| Parameter | Type |
|---|---|
opts | object |
Returns Promise<any>
Defined in generated/linear_model/SGDRegressor.ts:242
dispose()
dispose():
Promise<void>
Disposes of the underlying Python resources.
Once dispose() is called, the instance is no longer usable.
Returns Promise<void>
Defined in generated/linear_model/SGDRegressor.ts:223
fit()
fit(
opts):Promise<any>
Fit linear model with Stochastic Gradient Descent.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.coef_init? | ArrayLike | The initial coefficients to warm-start the optimization. |
opts.intercept_init? | ArrayLike | The initial intercept to warm-start the optimization. |
opts.sample_weight? | ArrayLike | Weights applied to individual samples (1. for unweighted). |
opts.X? | any | Training data. |
opts.y? | ArrayLike | Target values. |
Returns Promise<any>
Defined in generated/linear_model/SGDRegressor.ts:268
get_metadata_routing()
get_metadata_routing(
opts):Promise<any>
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.routing? | any | A MetadataRequest encapsulating routing information. |
Returns Promise<any>
Defined in generated/linear_model/SGDRegressor.ts:322
init()
init(
py):Promise<void>
Initializes the underlying Python resources.
This instance is not usable until the Promise returned by init() resolves.
Parameters
| Parameter | Type |
|---|---|
py | PythonBridge |
Returns Promise<void>
Defined in generated/linear_model/SGDRegressor.ts:184
partial_fit()
partial_fit(
opts):Promise<any>
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.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.sample_weight? | ArrayLike | Weights applied to individual samples. If not provided, uniform weights are assumed. |
opts.X? | any | Subset of training data. |
opts.y? | any[] | Subset of target values. |
Returns Promise<any>
Defined in generated/linear_model/SGDRegressor.ts:358
predict()
predict(
opts):Promise<any>
Predict using the linear model.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | any | Input data. |
Returns Promise<any>
Defined in generated/linear_model/SGDRegressor.ts:400
score()
score(
opts):Promise<number>
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.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.sample_weight? | ArrayLike | Sample weights. |
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.y? | ArrayLike | True values for X. |
Returns Promise<number>
Defined in generated/linear_model/SGDRegressor.ts:434
set_fit_request()
set_fit_request(
opts):Promise<any>
Request metadata passed to the fit method.
Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see User Guide on how the routing mechanism works.
The options for each parameter are:
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.coef_init? | string | boolean | Metadata routing for coef_init parameter in fit. |
opts.intercept_init? | string | boolean | Metadata routing for intercept_init parameter in fit. |
opts.sample_weight? | string | boolean | Metadata routing for sample_weight parameter in fit. |
Returns Promise<any>
Defined in generated/linear_model/SGDRegressor.ts:480
set_partial_fit_request()
set_partial_fit_request(
opts):Promise<any>
Request metadata passed to the partial_fit method.
Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see User Guide on how the routing mechanism works.
The options for each parameter are:
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.sample_weight? | string | boolean | Metadata routing for sample_weight parameter in partial_fit. |
Returns Promise<any>
Defined in generated/linear_model/SGDRegressor.ts:526
set_score_request()
set_score_request(
opts):Promise<any>
Request metadata passed to the score method.
Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see User Guide on how the routing mechanism works.
The options for each parameter are:
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.sample_weight? | string | boolean | Metadata routing for sample_weight parameter in score. |
Returns Promise<any>
Defined in generated/linear_model/SGDRegressor.ts:564
sparsify()
sparsify(
opts):Promise<any>
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.
Parameters
| Parameter | Type |
|---|---|
opts | object |
Returns Promise<any>
Defined in generated/linear_model/SGDRegressor.ts:602