lale.lib.autogen.theil_sen_regressor module

class lale.lib.autogen.theil_sen_regressor.TheilSenRegressor(*, fit_intercept=True, copy_X=True, max_subpopulation=10000, n_subsamples=None, max_iter=300, tol=0.001, random_state=None, n_jobs=1, verbose=False)

Bases: PlannedIndividualOp

Combined schema for expected data and hyperparameters.

This documentation is auto-generated from JSON schemas.

Parameters

  • fit_intercept (boolean, default True) – Whether to calculate the intercept for this model

  • copy_X (boolean, default True) – If True, X will be copied; else, it may be overwritten.

  • max_subpopulation (integer, >=10000 for optimizer, <=10001 for optimizer, uniform distribution, default 10000) – Instead of computing with a set of cardinality ‘n choose k’, where n is the number of samples and k is the number of subsamples (at least number of features), consider only a stochastic subpopulation of a given maximal size if ‘n choose k’ is larger than max_subpopulation

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

    Number of samples to calculate the parameters

    • integer

    • or None

  • max_iter (integer, >=10 for optimizer, <=1000 for optimizer, uniform distribution, default 300) – Maximum number of iterations for the calculation of spatial median.

  • tol (float, >=1e-08 for optimizer, <=0.01 for optimizer, default 0.001) – Tolerance when calculating spatial median.

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

    A random number generator instance to define the state of the random permutations generator

    • integer

    • or numpy.random.RandomState

    • or None

  • n_jobs (union type, not for optimizer, default 1) –

    Number of CPUs to use during the cross validation

    • integer

    • or None

  • verbose (boolean, not for optimizer, default False) – Verbose mode when fitting the model.

Notes

constraint-1 : any type

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) – Training data

  • y (array of items : float) – Target values

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 (union type) –

Samples.

  • array of items : Any

  • or array of items : array of items : float

Returns

result – Returns predicted values.

Return type

array of items : float