lale.lib.sklearn.sgd_regressor module

class lale.lib.sklearn.sgd_regressor.SGDRegressor(*, loss='squared_error', penalty='l2', alpha=0.0001, l1_ratio=0.15, fit_intercept=True, max_iter=1000, tol=None, shuffle=True, verbose=0, epsilon=0.1, random_state=None, learning_rate='invscaling', eta0=0.01, power_t=0.25, early_stopping=False, validation_fraction=0.1, n_iter_no_change=5, warm_start=False, average=False)

Bases: PlannedIndividualOp

SGD regressor from scikit-learn uses linear regressors (SVM, logistic regression, a.o.) with stochastic gradient descent training.

This documentation is auto-generated from JSON schemas.

Parameters

  • loss (‘squared_error’, ‘huber’, ‘epsilon_insensitive’, or ‘squared_epsilon_insensitive’, default ‘squared_error’) – 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 scikit-learn User Guide.

  • penalty (‘elasticnet’, ‘l1’, or ‘l2’, default ‘l2’) – The penalty (aka regularization term) to be used. Defaults to ‘l2’

  • alpha (float, >=1e-10 for optimizer, <=1.0 for optimizer, loguniform distribution, default 0.0001) – Constant that multiplies the regularization term. Defaults to 0.0001

  • l1_ratio (float, >=1e-09 for optimizer, <=1.0 for optimizer, loguniform distribution, default 0.15) – The Elastic Net mixing parameter, with 0 <= l1_ratio <= 1.

  • fit_intercept (boolean, default True) – Whether the intercept should be estimated or not. If False, the

  • max_iter (integer, >=5 for optimizer, <=1000 for optimizer, uniform distribution, default 1000) – The maximum number of passes over the training data (aka epochs).

  • tol (union type, default None) –

    The stopping criterion. If it is not None, the iterations will stop

    • float, >=1e-08 for optimizer, <=0.01 for optimizer

    • or None

  • shuffle (boolean, default True) – Whether or not the training data should be shuffled after each epoch.

  • verbose (integer, not for optimizer, default 0) – The verbosity level.

  • epsilon (float, >=1e-08 for optimizer, <=1.35 for optimizer, loguniform distribution, default 0.1) – Epsilon in the epsilon-insensitive loss functions; only if loss is

  • random_state (union type, not for optimizer, default None) –

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

    • integer

    • or numpy.random.RandomState

    • or None

  • learning_rate (‘optimal’, ‘constant’, ‘invscaling’, or ‘adaptive’, default ‘invscaling’) –

    The learning rate schedule:

    See also constraint-1.

  • eta0 (float, >=0.01 for optimizer, <=1.0 for optimizer, loguniform distribution, default 0.01) –

    The initial learning rate for the ‘constant’, ‘invscaling’ or

    See also constraint-1.

  • power_t (float, >=1e-05 for optimizer, <=1.0 for optimizer, uniform distribution, default 0.25) – The exponent for inverse scaling learning rate [default 0.5].

  • early_stopping (boolean, not for optimizer, default False) – Whether to use early stopping to terminate training when validation

  • validation_fraction (float, >=0.0, <=1.0, not for optimizer, default 0.1) – The proportion of training data to set aside as validation set for

  • n_iter_no_change (integer, >=5 for optimizer, <=10 for optimizer, not for optimizer, default 5) – Number of iterations with no improvement to wait before early stopping.

  • warm_start (boolean, not for optimizer, default False) – When set to True, reuse the solution of the previous call to fit as

  • average (union type, not for optimizer, default False) –

    When set to True, computes the averaged SGD weights and stores the result in the coef_ attribute.

    • boolean

    • or integer, not for optimizer

Notes

constraint-1 : union type

eta0 must be greater than 0 if the learning_rate is not ‘optimal’.

  • learning_rate : ‘optimal’

  • or eta0 : float, >0.0

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

Train the operator.

Note: The fit method is not available until this operator is trainable.

Once this method is available, it will have the following signature:

Parameters

  • X (array of items : array of items : float) –

  • y (array of items : float) –

  • coef_init (array, optional of items : float) – The initial coefficients to warm-start the optimization.

  • intercept_init (array, optional of items : float) – The initial intercept to warm-start the optimization.

  • sample_weight (union type, optional, default None) –

    Weights applied to individual samples.

    • array of items : float

    • or None

      Uniform weights.

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

Incremental fit to train train the operator on a batch of samples.

Note: The partial_fit method is not available until this operator is trainable.

Once this method is available, it will have the following signature:

Parameters

  • X (array of items : array of items : float) –

  • y (array of items : float) –

  • classes (array, optional of items : float) –

  • sample_weight (union type, optional, default None) –

    Weights applied to individual samples.

    • array of items : float

    • or None

      Uniform weights.

predict(X, **predict_params)

Make predictions.

Note: The predict method is not available until this operator is trained.

Once this method is available, it will have the following signature:

Parameters

X (array of items : array of items : float) –

Returns

result

Return type

array of items : float