Source code for getml.hyperopt.load_hyperopt

# Copyright 2022 The SQLNet Company GmbH
#
# This file is licensed under the Elastic License 2.0 (ELv2).
# Refer to the LICENSE.txt file in the root of the repository
# for details.
#


"""Loads a hyperparameter optimization object from the getML engine into Python."""

from getml.data import Placeholder
from getml.pipeline import Pipeline
from getml.predictors import LinearRegression
from getml.pipeline.helpers2 import _make_dummy


from .hyperopt import (
    GaussianHyperparameterSearch,
    LatinHypercubeSearch,
    RandomSearch,
    _get_json_obj,
)


[docs]def load_hyperopt(name): """Loads a hyperparameter optimization object from the getML engine into Python. Args: name (str): The name of the hyperopt to be loaded. Returns: A :class:`~getml.hyperopt.GaussianHyperparameterSearch` that is a handler for the pipeline signified by name. Note: Not supported in the getML community edition. """ # This will be overwritten by .refresh(...) anyway dummy_pipeline = _make_dummy("123456") dummy_param_space = {"predictors": [{"reg_lambda": [0.0, 1.0]}]} json_obj = _get_json_obj(name) if json_obj["type_"] == "GaussianHyperparameterSearch": return GaussianHyperparameterSearch( param_space=dummy_param_space, pipeline=dummy_pipeline )._parse_json_obj(json_obj) if json_obj["type_"] == "LatinHypercubeSearch": return LatinHypercubeSearch( param_space=dummy_param_space, pipeline=dummy_pipeline )._parse_json_obj(json_obj) if json_obj["type_"] == "RandomSearch": return RandomSearch( param_space=dummy_param_space, pipeline=dummy_pipeline )._parse_json_obj(json_obj) raise ValueError("Unknown type: '" + json_obj["type_"] + "'!")