lale.lib.autogen.ransac_regressor module

class lale.lib.autogen.ransac_regressor.RANSACRegressor(*, min_samples=None, residual_threshold=None, is_data_valid=None, is_model_valid=None, max_trials=100, max_skips=inf, stop_n_inliers=inf, stop_score=inf, stop_probability=0.99, loss='absolute_error', random_state=None, estimator=None)

Bases: PlannedIndividualOp

Combined schema for expected data and hyperparameters.

This documentation is auto-generated from JSON schemas.

Parameters

  • min_samples (union type, default None) –

    Minimum number of samples chosen randomly from original data

    • float, >=0.0 for optimizer, <=1.0 for optimizer, uniform distribution

    • or None

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

    Maximum residual for a data sample to be classified as an inlier

    • float

    • or None

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

    This function is called with the randomly selected data before the model is fitted to it: is_data_valid(X, y)

    • callable

    • or None

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

    This function is called with the estimated model and the randomly selected data: is_model_valid(model, X, y)

    • callable

    • or None

  • max_trials (integer, >=100 for optimizer, <=101 for optimizer, uniform distribution, default 100) – Maximum number of iterations for random sample selection.

  • max_skips (union type, default inf) –

    Maximum number of iterations that can be skipped due to finding zero inliers or invalid data defined by is_data_valid or invalid models defined by is_model_valid

    • integer, not for optimizer

    • or inf

  • stop_n_inliers (union type, default inf) –

    Stop iteration if at least this number of inliers are found.

    • integer, not for optimizer

    • or inf

  • stop_score (float, not for optimizer, default inf) – Stop iteration if score is greater equal than this threshold.

  • stop_probability (float, not for optimizer, default 0.99) – RANSAC iteration stops if at least one outlier-free set of the training data is sampled in RANSAC

  • loss (union type, default 'absolute_error') –

    String inputs, “absolute_error” and “squared_error” are supported which find the absolute error and squared error per sample respectively

    • callable, not for optimizer

    • or ‘absolute_error’ or ‘squared_error’

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

    The generator used to initialize the centers

    • integer

    • or numpy.random.RandomState

    • or None

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

    Base estimator object which implements the following methods: * fit(X, y): Fit model to given training data and target values

    • dict

    • or None

Notes

constraint-1 : any type

constraint-2 : 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 (union type) –

    Training data.

    • array of items : Any

    • or array of items : array of items : float

  • y (union type) –

    Target values.

    • array of items : float

    • or array of items : array of items : float

  • sample_weight (array, optional of items : float) – Individual weights for each sample raises error if sample_weight is passed and base_estimator fit method does not support it.

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 – Returns predicted values.

  • array of items : float

  • or array of items : array of items : float

Return type

union type