eland/ml/ml_model.py [267:382]:
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        es_if_exists: Optional[str] = None,
        es_compress_model_definition: bool = True,
    ) -> "MLModel":
        """
        Transform and serialize a trained 3rd party model into Elasticsearch.
        This model can then be used for inference in the Elastic Stack.

        Parameters
        ----------
        es_client: Elasticsearch client argument(s)
            - elasticsearch-py parameters or
            - elasticsearch-py instance

        model_id: str
            The unique identifier of the trained inference model in Elasticsearch.

        model: An instance of a supported python model. We support the following model types:
            - sklearn.tree.DecisionTreeClassifier
                - NOTE: When calculating the probabilities of a given classification label, Elasticsearch utilizes
                        softMax. SKLearn instead normalizes the results. We try to account for this during model
                        serialization, but probabilities may be slightly different in the predictions.
            - sklearn.tree.DecisionTreeRegressor
            - sklearn.ensemble.RandomForestRegressor
            - sklearn.ensemble.RandomForestClassifier
            - lightgbm.LGBMRegressor
                - Categorical fields are expected to already be processed
                - Only the following objectives are supported
                    - "regression"
                    - "regression_l1"
                    - "huber"
                    - "fair"
                    - "quantile"
                    - "mape"
            - lightgbm.LGBMClassifier
                - Categorical fields are expected to already be processed
                - Only the following objectives are supported
                    - "binary"
                    - "multiclass"
                    - "multiclassova"
            - xgboost.XGBClassifier
                - only the following objectives are supported:
                    - "binary:logistic"
                    - "multi:softmax"
                    - "multi:softprob"
            - xgboost.XGBRanker
                - only the following objectives are supported:
                    - "rank:map"
                    - "rank:ndcg"
                    - "rank:pairwise"
            - xgboost.XGBRegressor
                - only the following objectives are supported:
                    - "reg:squarederror"
                    - "reg:linear"
                    - "reg:squaredlogerror"
                    - "reg:logistic"
                    - "reg:pseudohubererror"

        feature_names: List[str]
            Names of the features (required)

        classification_labels: List[str]
            Labels of the classification targets

        classification_weights: List[str]
            Weights of the classification targets

        es_if_exists: {'fail', 'replace'} default 'fail'
            How to behave if model already exists

            - fail: Raise a Value Error
            - replace: Overwrite existing model

        es_compress_model_definition: bool
            If True will use 'compressed_definition' which uses gzipped
            JSON instead of raw JSON to reduce the amount of data sent
            over the wire in HTTP requests. Defaults to 'True'.

        Examples
        --------
        >>> from sklearn import datasets
        >>> from sklearn.tree import DecisionTreeClassifier
        >>> from eland.ml import MLModel

        >>> # Train model
        >>> training_data = datasets.make_classification(n_features=5, random_state=0)
        >>> test_data = [[-50.1, 0.2, 0.3, -0.5, 1.0], [1.6, 2.1, -10, 50, -1.0]]
        >>> classifier = DecisionTreeClassifier()
        >>> classifier = classifier.fit(training_data[0], training_data[1])

        >>> # Get some test results
        >>> classifier.predict(test_data)
        array([0, 1])

        >>> # Serialise the model to Elasticsearch
        >>> feature_names = ["f0", "f1", "f2", "f3", "f4"]
        >>> model_id = "test_decision_tree_classifier"
        >>> es_model = MLModel.import_model(
        ...   'http://localhost:9200',
        ...   model_id=model_id,
        ...   model=classifier,
        ...   feature_names=feature_names,
        ...   es_if_exists='replace'
        ... )

        >>> # Get some test results from Elasticsearch model
        >>> es_model.predict(test_data)
        array([0, 1])

        >>> # Delete model from Elasticsearch
        >>> es_model.delete_model()
        """

        return cls._import_model(
            es_client=es_client,
            model_id=model_id,
            model=model,
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eland/ml/ml_model.py [403:463]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
        es_if_exists: Optional[str] = None,
        es_compress_model_definition: bool = True,
    ) -> "MLModel":
        """
        Transform and serialize a trained 3rd party model into Elasticsearch.
        This model can then be used as a learning_to_rank rescorer in the Elastic Stack.

        Parameters
        ----------
        es_client: Elasticsearch client argument(s)
            - elasticsearch-py parameters or
            - elasticsearch-py instance

        model_id: str
            The unique identifier of the trained inference model in Elasticsearch.

        model: An instance of a supported python model. We support the following model types for LTR prediction:
            - sklearn.tree.DecisionTreeRegressor
            - sklearn.ensemble.RandomForestRegressor
            - xgboost.XGBRanker
                - only the following objectives are supported:
                    - "rank:map"
                    - "rank:ndcg"
                    - "rank:pairwise"
            - xgboost.XGBRegressor
                - only the following objectives are supported:
                    - "reg:squarederror"
                    - "reg:linear"
                    - "reg:squaredlogerror"
                    - "reg:logistic"
                    - "reg:pseudohubererror"
            - lightgbm.LGBMRegressor
                - Categorical fields are expected to already be processed
                - Only the following objectives are supported
                    - "regression"
                    - "regression_l1"
                    - "huber"
                    - "fair"
                    - "quantile"
                    - "mape"

        ltr_model_config: LTRModelConfig
            The LTR model configuration is used to configure feature extractors for the LTR model.
            Feature names are automatically inferred from the feature extractors.

        es_if_exists: {'fail', 'replace'} default 'fail'
            How to behave if model already exists

            - fail: Raise a Value Error
            - replace: Overwrite existing model

        es_compress_model_definition: bool
            If True will use 'compressed_definition' which uses gzipped
            JSON instead of raw JSON to reduce the amount of data sent
            over the wire in HTTP requests. Defaults to 'True'.
        """

        return cls._import_model(
            es_client=es_client,
            model_id=model_id,
            model=model,
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