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https://github.com/elastic/eland.git
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1040160451
* Fix bugs with field mapping: 1. If no permission to call _mapping, return readable error 2. If index is wildcard, fix issues with user warnings * Fixing lint issues * Removing trailing backslashes in doc * Remove pandas/matplotlib deprecation warning This warning is due to a conflict between pandas/matplotlib that may be resolved in a later version. For now, ignore the warning so CI works.
98 lines
3.2 KiB
Python
98 lines
3.2 KiB
Python
# Licensed to Elasticsearch B.V. under one or more contributor
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# license agreements. See the NOTICE file distributed with
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# this work for additional information regarding copyright
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# ownership. Elasticsearch B.V. licenses this file to you under
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# the Apache License, Version 2.0 (the "License"); you may
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# not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing,
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# software distributed under the License is distributed on an
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# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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# KIND, either express or implied. See the License for the
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# specific language governing permissions and limitations
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# under the License.
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import inspect
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from typing import Any, Dict, Type
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from .base import ModelTransformer
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__all__ = ["get_model_transformer"]
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_MODEL_TRANSFORMERS: Dict[type, Type[ModelTransformer]] = {}
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def get_model_transformer(model: Any, **kwargs: Any) -> ModelTransformer:
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"""Creates a ModelTransformer for a given model or raises an exception if one is not available"""
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for model_type, transformer in _MODEL_TRANSFORMERS.items():
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if isinstance(model, model_type):
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# Filter out kwargs that aren't applicable to the specific 'ModelTransformer'
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accepted_kwargs = {
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param for param in inspect.signature(transformer.__init__).parameters
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}
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kwargs = {k: v for k, v in kwargs.items() if k in accepted_kwargs}
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return transformer(model, **kwargs)
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raise NotImplementedError(
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f"Importing ML models of type {type(model)}, not currently implemented"
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)
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try:
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from .sklearn import _MODEL_TRANSFORMERS as _SKLEARN_MODEL_TRANSFORMERS
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from .sklearn import (
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SKLearnDecisionTreeTransformer,
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SKLearnForestClassifierTransformer,
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SKLearnForestRegressorTransformer,
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SKLearnForestTransformer,
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SKLearnTransformer,
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)
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__all__ += [
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"SKLearnDecisionTreeTransformer",
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"SKLearnForestClassifierTransformer",
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"SKLearnForestRegressorTransformer",
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"SKLearnForestTransformer",
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"SKLearnTransformer",
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]
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_MODEL_TRANSFORMERS.update(_SKLEARN_MODEL_TRANSFORMERS)
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except ImportError:
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pass
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try:
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from .xgboost import _MODEL_TRANSFORMERS as _XGBOOST_MODEL_TRANSFORMERS
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from .xgboost import (
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XGBoostClassifierTransformer,
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XGBoostForestTransformer,
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XGBoostRegressorTransformer,
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)
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__all__ += [
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"XGBoostClassifierTransformer",
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"XGBoostForestTransformer",
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"XGBoostRegressorTransformer",
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]
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_MODEL_TRANSFORMERS.update(_XGBOOST_MODEL_TRANSFORMERS)
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except ImportError:
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pass
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try:
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from .lightgbm import _MODEL_TRANSFORMERS as _LIGHTGBM_MODEL_TRANSFORMERS
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from .lightgbm import (
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LGBMClassifierTransformer,
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LGBMForestTransformer,
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LGBMRegressorTransformer,
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)
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__all__ += [
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"LGBMForestTransformer",
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"LGBMRegressorTransformer",
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"LGBMClassifierTransformer",
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]
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_MODEL_TRANSFORMERS.update(_LIGHTGBM_MODEL_TRANSFORMERS)
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except ImportError:
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pass
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