Deprecate ImportedMLModel in favor of MLModel.import_model()

docs/source/examples/introduction_to_eland_webinar.ipynb

@@ -2495,10 +2495,10 @@
     }
    ],
    "source": [
-    "from eland.ml import ImportedMLModel\n",
+    "from eland.ml import MLModel\n",
     "\n",
     "# Serialize the scikit-learn model into Elasticsearch\n",
-    "ed_classifier = ImportedMLModel(\n",
+    "ed_classifier = MLModel.import_model(\n",
     "  es_client=es,\n",
     "  model_id=\"wine-classifier\",\n",
     "  model=sk_classifier,\n",

docs/source/reference/api/eland.ml.ImportedMLModel.predict.rst

@@ -1,6 +0,0 @@
-eland.ml.ImportedMLModel.predict
-================================
-
-.. currentmodule:: eland.ml
-
-.. automethod:: ImportedMLModel.predict

docs/source/reference/api/eland.ml.ImportedMLModel.rst

@@ -1,6 +0,0 @@
-eland.ml.ImportedMLModel
-========================
-
-.. currentmodule:: eland.ml
-
-.. autoclass:: ImportedMLModel

docs/source/reference/api/eland.ml.MLModel.import_model.rst

@@ -0,0 +1,6 @@
+eland.ml.MLModel.import_model
+=============================
+
+.. currentmodule:: eland.ml
+
+.. automethod:: MLModel.import_model

docs/source/reference/api/eland.ml.MLModel.predict.rst

@@ -0,0 +1,6 @@
+eland.ml.MLModel.predict
+========================
+
+.. currentmodule:: eland.ml
+
+.. automethod:: MLModel.predict

docs/source/reference/ml.rst

@@ -27,19 +27,20 @@ Constructor
 .. autosummary::
    :toctree: api/
 
-   ImportedMLModel
+   MLModel
 
 Predictions
 ^^^^^^^^^^^
 .. autosummary::
    :toctree: api/
 
-   ImportedMLModel.predict
+   MLModel.predict
 
 Manage Models
 ^^^^^^^^^^^^^
 .. autosummary::
    :toctree: api/
 
+   MLModel.import_model
    MLModel.exists_model
    MLModel.delete_model

eland/ml/__init__.py

@@ -15,8 +15,7 @@
 #  specific language governing permissions and limitations
 #  under the License.
 
-from eland.ml.ml_model import MLModel
+from eland.ml.ml_model import MLModel, ImportedMLModel
-from eland.ml.imported_ml_model import ImportedMLModel
 
 __all__ = [
     "MLModel",

eland/ml/common.py

@@ -0,0 +1,19 @@
+#  Licensed to Elasticsearch B.V. under one or more contributor
+#  license agreements. See the NOTICE file distributed with
+#  this work for additional information regarding copyright
+#  ownership. Elasticsearch B.V. licenses this file to you under
+#  the Apache License, Version 2.0 (the "License"); you may
+#  not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+# 	http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing,
+#  software distributed under the License is distributed on an
+#  "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+#  KIND, either express or implied.  See the License for the
+#  specific language governing permissions and limitations
+#  under the License.
+
+TYPE_CLASSIFICATION = "classification"
+TYPE_REGRESSION = "regression"

eland/ml/imported_ml_model.py

@@ -1,321 +0,0 @@
-#  Licensed to Elasticsearch B.V. under one or more contributor
-#  license agreements. See the NOTICE file distributed with
-#  this work for additional information regarding copyright
-#  ownership. Elasticsearch B.V. licenses this file to you under
-#  the Apache License, Version 2.0 (the "License"); you may
-#  not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-# 	http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing,
-#  software distributed under the License is distributed on an
-#  "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-#  KIND, either express or implied.  See the License for the
-#  specific language governing permissions and limitations
-#  under the License.
-
-from typing import Union, List, Optional, Tuple, TYPE_CHECKING, cast, Dict, Any
-
-import numpy as np  # type: ignore
-
-from .ml_model import MLModel
-from .transformers import get_model_transformer
-from ..common import es_version
-import warnings
-
-
-if TYPE_CHECKING:
-    from elasticsearch import Elasticsearch  # type: ignore # noqa: F401
-
-    # Try importing each ML lib separately so mypy users don't have to
-    # have both installed to use type-checking.
-    try:
-        from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor  # type: ignore # noqa: F401
-        from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor  # type: ignore # noqa: F401
-    except ImportError:
-        pass
-    try:
-        from xgboost import XGBRegressor, XGBClassifier  # type: ignore # noqa: F401
-    except ImportError:
-        pass
-    try:
-        from lightgbm import LGBMRegressor, LGBMClassifier  # type: ignore # noqa: F401
-    except ImportError:
-        pass
-
-
-class ImportedMLModel(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
-        - 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.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
-
-    overwrite: **DEPRECATED** - bool
-        Delete and overwrite existing model (if exists)
-
-    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 ImportedMLModel
-
-    >>> # 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 = ImportedMLModel('localhost', model_id, classifier, 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()
-
-    """
-
-    def __init__(
-        self,
-        es_client: Union[str, List[str], Tuple[str, ...], "Elasticsearch"],
-        model_id: str,
-        model: Union[
-            "DecisionTreeClassifier",
-            "DecisionTreeRegressor",
-            "RandomForestRegressor",
-            "RandomForestClassifier",
-            "XGBClassifier",
-            "XGBRegressor",
-            "LGBMRegressor",
-            "LGBMClassifier",
-        ],
-        feature_names: List[str],
-        classification_labels: Optional[List[str]] = None,
-        classification_weights: Optional[List[float]] = None,
-        es_if_exists: Optional[str] = None,
-        overwrite: Optional[bool] = None,
-        es_compress_model_definition: bool = True,
-    ):
-        super().__init__(es_client, model_id)
-
-        self._feature_names = feature_names
-
-        transformer = get_model_transformer(
-            model,
-            feature_names=feature_names,
-            classification_labels=classification_labels,
-            classification_weights=classification_weights,
-        )
-        self._model_type = transformer.model_type
-        serializer = transformer.transform()
-
-        # Verify if both parameters are given
-        if overwrite is not None and es_if_exists is not None:
-            raise ValueError(
-                "Using 'overwrite' and 'es_if_exists' together is invalid, use only 'es_if_exists'"
-            )
-
-        if overwrite is not None:
-            warnings.warn(
-                "'overwrite' parameter is deprecated, use 'es_if_exists' instead",
-                DeprecationWarning,
-                stacklevel=2,
-            )
-            es_if_exists = "replace" if overwrite else "fail"
-        elif es_if_exists is None:
-            es_if_exists = "fail"
-
-        if es_if_exists not in ("fail", "replace"):
-            raise ValueError("'es_if_exists' must be either 'fail' or 'replace'")
-        elif es_if_exists == "fail":
-            if self.exists_model():
-                raise ValueError(
-                    f"Trained machine learning model {model_id} already exists"
-                )
-        elif es_if_exists == "replace":
-            self.delete_model()
-
-        body: Dict[str, Any] = {
-            "input": {"field_names": feature_names},
-        }
-        # 'inference_config' is required in 7.8+ but isn't available in <=7.7
-        if es_version(self._client) >= (7, 8):
-            body["inference_config"] = {self._model_type: {}}
-
-        if es_compress_model_definition:
-            body["compressed_definition"] = serializer.serialize_and_compress_model()
-        else:
-            body["definition"] = serializer.serialize_model()
-
-        self._client.ml.put_trained_model(
-            model_id=self._model_id,
-            body=body,
-        )
-
-    def predict(self, X: Union[List[float], List[List[float]]]) -> np.ndarray:
-        """
-        Make a prediction using a trained model stored in Elasticsearch.
-
-        Parameters for this method are not yet fully compatible with standard sklearn.predict.
-
-        Parameters
-        ----------
-        X: list or list of lists of type float
-            Input feature vector - TODO support DataFrame and other formats
-
-        Returns
-        -------
-        y: np.ndarray of dtype float for regressors or int for classifiers
-
-        Examples
-        --------
-        >>> from sklearn import datasets
-        >>> from xgboost import XGBRegressor
-        >>> from eland.ml import ImportedMLModel
-
-        >>> # Train model
-        >>> training_data = datasets.make_classification(n_features=6, random_state=0)
-        >>> test_data = [[-1, -2, -3, -4, -5, -6], [10, 20, 30, 40, 50, 60]]
-        >>> regressor = XGBRegressor(objective='reg:squarederror')
-        >>> regressor = regressor.fit(training_data[0], training_data[1])
-
-        >>> # Get some test results
-        >>> regressor.predict(np.array(test_data))
-        array([0.06062475, 0.9990102 ], dtype=float32)
-
-        >>> # Serialise the model to Elasticsearch
-        >>> feature_names = ["f0", "f1", "f2", "f3", "f4", "f5"]
-        >>> model_id = "test_xgb_regressor"
-        >>> es_model = ImportedMLModel('localhost', model_id, regressor, feature_names, es_if_exists='replace')
-
-        >>> # Get some test results from Elasticsearch model
-        >>> es_model.predict(test_data)
-        array([0.0606248 , 0.99901026], dtype=float32)
-
-        >>> # Delete model from Elasticsearch
-        >>> es_model.delete_model()
-
-        """
-        docs = []
-        if isinstance(X, list):
-            # Is it a list of floats?
-            if all(isinstance(i, float) for i in X):
-                features = cast(List[List[float]], [X])
-            else:
-                features = cast(List[List[float]], X)
-            for i in features:
-                doc = {"_source": dict(zip(self._feature_names, i))}
-                docs.append(doc)
-        else:
-            raise NotImplementedError(f"Prediction for type {type(X)}, not supported")
-
-        # field_mappings -> field_map in ES 7.7
-        field_map_name = (
-            "field_map" if es_version(self._client) >= (7, 7) else "field_mappings"
-        )
-
-        results = self._client.ingest.simulate(
-            body={
-                "pipeline": {
-                    "processors": [
-                        {
-                            "inference": {
-                                "model_id": self._model_id,
-                                "inference_config": {self._model_type: {}},
-                                field_map_name: {},
-                            }
-                        }
-                    ]
-                },
-                "docs": docs,
-            }
-        )
-
-        # Unpack results into an array. Errors can be present
-        # within the response without a non-2XX HTTP status code.
-        y = []
-        for res in results["docs"]:
-            if "error" in res:
-                raise RuntimeError(
-                    f"Failed to run prediction for model ID {self._model_id!r}",
-                    res["error"],
-                )
-
-            y.append(res["doc"]["_source"]["ml"]["inference"]["predicted_value"])
-
-        # Return results as np.ndarray of float32 or int (consistent with sklearn/xgboost)
-        if self._model_type == MLModel.TYPE_CLASSIFICATION:
-            dt = np.int
-        else:
-            dt = np.float32
-        return np.asarray(y, dtype=dt)

eland/ml/ml_model.py

@@ -15,8 +15,33 @@
 #  specific language governing permissions and limitations
 #  under the License.
 
-import elasticsearch
+from typing import List, Union, cast, Optional, Dict, TYPE_CHECKING, Any, Tuple
-from eland.common import ensure_es_client
+import warnings
+import numpy as np  # type: ignore
+import elasticsearch  # type: ignore
+from .common import TYPE_REGRESSION, TYPE_CLASSIFICATION
+from .transformers import get_model_transformer
+from eland.common import ensure_es_client, es_version
+from eland.utils import deprecated_api
+
+if TYPE_CHECKING:
+    from elasticsearch import Elasticsearch  # noqa: F401
+
+    # Try importing each ML lib separately so mypy users don't have to
+    # have both installed to use type-checking.
+    try:
+        from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor  # type: ignore # noqa: F401
+        from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor  # type: ignore # noqa: F401
+    except ImportError:
+        pass
+    try:
+        from xgboost import XGBRegressor, XGBClassifier  # type: ignore # noqa: F401
+    except ImportError:
+        pass
+    try:
+        from lightgbm import LGBMRegressor, LGBMClassifier  # type: ignore # noqa: F401
+    except ImportError:
+        pass
 
 
 class MLModel:
@@ -24,16 +49,17 @@ class MLModel:
     A machine learning model managed by Elasticsearch.
     (See https://www.elastic.co/guide/en/elasticsearch/reference/master/put-inference.html)
 
-    These models can be created by Elastic ML, or transformed from supported python formats such as scikit-learn or
+    These models can be created by Elastic ML, or transformed from supported Python formats
-    xgboost and imported into Elasticsearch.
+    such as scikit-learn or xgboost and imported into Elasticsearch.
 
-    The methods for this class attempt to mirror standard python classes.
+    The methods for this class attempt to mirror standard Python classes.
     """
 
-    TYPE_CLASSIFICATION = "classification"
+    def __init__(
-    TYPE_REGRESSION = "regression"
+        self,
-
+        es_client: Union[str, List[str], Tuple[str, ...], "Elasticsearch"],
-    def __init__(self, es_client, model_id: str):
+        model_id: str,
+    ):
         """
         Parameters
         ----------
@@ -46,6 +72,331 @@ class MLModel:
         """
         self._client = ensure_es_client(es_client)
         self._model_id = model_id
+        self._trained_model_config_cache: Optional[Dict[str, Any]] = None
+
+    def predict(
+        self, X: Union[np.ndarray, List[float], List[List[float]]]
+    ) -> np.ndarray:
+        """
+        Make a prediction using a trained model stored in Elasticsearch.
+
+        Parameters for this method are not yet fully compatible with standard sklearn.predict.
+
+        Parameters
+        ----------
+        X: Input feature vector.
+           Must be either a numpy ndarray or a list or list of lists
+           of type float. TODO: support DataFrame and other formats
+
+        Returns
+        -------
+        y: np.ndarray of dtype float for regressors or int for classifiers
+
+        Examples
+        --------
+        >>> from sklearn import datasets
+        >>> from xgboost import XGBRegressor
+        >>> from eland.ml import ImportedMLModel
+
+        >>> # Train model
+        >>> training_data = datasets.make_classification(n_features=6, random_state=0)
+        >>> test_data = [[-1, -2, -3, -4, -5, -6], [10, 20, 30, 40, 50, 60]]
+        >>> regressor = XGBRegressor(objective='reg:squarederror')
+        >>> regressor = regressor.fit(training_data[0], training_data[1])
+
+        >>> # Get some test results
+        >>> regressor.predict(np.array(test_data))
+        array([0.06062475, 0.9990102 ], dtype=float32)
+
+        >>> # Serialise the model to Elasticsearch
+        >>> feature_names = ["f0", "f1", "f2", "f3", "f4", "f5"]
+        >>> model_id = "test_xgb_regressor"
+        >>> es_model = MLModel.import_model('localhost', model_id, regressor, feature_names, es_if_exists='replace')
+
+        >>> # Get some test results from Elasticsearch model
+        >>> es_model.predict(test_data)
+        array([0.0606248 , 0.99901026], dtype=float32)
+
+        >>> # Delete model from Elasticsearch
+        >>> es_model.delete_model()
+        """
+        docs = []
+        if isinstance(X, np.ndarray):
+
+            def to_list_or_float(x: Any) -> Union[List[Any], float]:
+                if isinstance(x, np.ndarray):
+                    return [to_list_or_float(i) for i in x.tolist()]
+                elif isinstance(x, list):
+                    return [to_list_or_float(i) for i in x]
+                return float(x)
+
+            X = to_list_or_float(X)
+
+        # Is it a list of floats?
+        if isinstance(X, list) and all(isinstance(i, (float, int)) for i in X):
+            features = cast(List[List[float]], [X])
+        # If not a list of lists of floats then we error out.
+        elif isinstance(X, list) and all(
+            [
+                isinstance(i, list) and all([isinstance(ix, (float, int)) for ix in i])
+                for i in X
+            ]
+        ):
+            features = cast(List[List[float]], X)
+        else:
+            raise NotImplementedError(
+                f"Prediction for type {type(X)}, not supported: {X!r}"
+            )
+
+        for i in features:
+            doc = {"_source": dict(zip(self.feature_names, i))}
+            docs.append(doc)
+
+        # field_mappings -> field_map in ES 7.7
+        field_map_name = (
+            "field_map" if es_version(self._client) >= (7, 7) else "field_mappings"
+        )
+
+        results = self._client.ingest.simulate(
+            body={
+                "pipeline": {
+                    "processors": [
+                        {
+                            "inference": {
+                                "model_id": self._model_id,
+                                "inference_config": {self.model_type: {}},
+                                field_map_name: {},
+                            }
+                        }
+                    ]
+                },
+                "docs": docs,
+            }
+        )
+
+        # Unpack results into an array. Errors can be present
+        # within the response without a non-2XX HTTP status code.
+        y = []
+        for res in results["docs"]:
+            if "error" in res:
+                raise RuntimeError(
+                    f"Failed to run prediction for model ID {self._model_id!r}",
+                    res["error"],
+                )
+
+            y.append(res["doc"]["_source"]["ml"]["inference"][self.results_field])
+
+        # Return results as np.ndarray of float32 or int (consistent with sklearn/xgboost)
+        if self.model_type == TYPE_CLASSIFICATION:
+            dt = np.int
+        else:
+            dt = np.float32
+        return np.asarray(y, dtype=dt)
+
+    @property
+    def model_type(self) -> str:
+        inference_config = self._trained_model_config["inference_config"]
+        if "classification" in inference_config:
+            return TYPE_CLASSIFICATION
+        elif "regression" in inference_config:
+            return TYPE_REGRESSION
+        raise ValueError("Unable to determine 'model_type' for MLModel")
+
+    @property
+    def feature_names(self) -> List[str]:
+        return list(self._trained_model_config["input"]["field_names"])
+
+    @property
+    def results_field(self) -> str:
+        return cast(
+            str,
+            self._trained_model_config["inference_config"][self.model_type][
+                "results_field"
+            ],
+        )
+
+    @classmethod
+    def import_model(
+        cls,
+        es_client: Union[str, List[str], Tuple[str, ...], "Elasticsearch"],
+        model_id: str,
+        model: Union[
+            "DecisionTreeClassifier",
+            "DecisionTreeRegressor",
+            "RandomForestRegressor",
+            "RandomForestClassifier",
+            "XGBClassifier",
+            "XGBRegressor",
+            "LGBMRegressor",
+            "LGBMClassifier",
+        ],
+        feature_names: List[str],
+        classification_labels: Optional[List[str]] = None,
+        classification_weights: Optional[List[float]] = None,
+        es_if_exists: Optional[str] = None,
+        overwrite: Optional[bool] = 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
+            - 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.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
+
+        overwrite: **DEPRECATED** - bool
+            Delete and overwrite existing model (if exists)
+
+        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(
+        ...   'localhost',
+        ...   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()
+        """
+        es_client = ensure_es_client(es_client)
+        transformer = get_model_transformer(
+            model,
+            feature_names=feature_names,
+            classification_labels=classification_labels,
+            classification_weights=classification_weights,
+        )
+        serializer = transformer.transform()
+        model_type = transformer.model_type
+
+        # Verify if both parameters are given
+        if overwrite is not None and es_if_exists is not None:
+            raise ValueError(
+                "Using 'overwrite' and 'es_if_exists' together is invalid, use only 'es_if_exists'"
+            )
+
+        if overwrite is not None:
+            warnings.warn(
+                "'overwrite' parameter is deprecated, use 'es_if_exists' instead",
+                DeprecationWarning,
+                stacklevel=2,
+            )
+            es_if_exists = "replace" if overwrite else "fail"
+        elif es_if_exists is None:
+            es_if_exists = "fail"
+
+        ml_model = MLModel(
+            es_client=es_client,
+            model_id=model_id,
+        )
+        if es_if_exists not in ("fail", "replace"):
+            raise ValueError("'es_if_exists' must be either 'fail' or 'replace'")
+        elif es_if_exists == "fail":
+            if ml_model.exists_model():
+                raise ValueError(
+                    f"Trained machine learning model {model_id} already exists"
+                )
+        elif es_if_exists == "replace":
+            ml_model.delete_model()
+
+        body: Dict[str, Any] = {
+            "input": {"field_names": feature_names},
+        }
+        # 'inference_config' is required in 7.8+ but isn't available in <=7.7
+        if es_version(es_client) >= (7, 8):
+            body["inference_config"] = {model_type: {}}
+
+        if es_compress_model_definition:
+            body["compressed_definition"] = serializer.serialize_and_compress_model()
+        else:
+            body["definition"] = serializer.serialize_model()
+
+        ml_model._client.ml.put_trained_model(
+            model_id=model_id,
+            body=body,
+        )
+        return ml_model
 
     def delete_model(self) -> None:
         """
@@ -69,3 +420,18 @@ class MLModel:
         except elasticsearch.NotFoundError:
             return False
         return True
+
+    @property
+    def _trained_model_config(self) -> Dict[str, Any]:
+        """Lazily loads an ML models 'trained_model_config' information"""
+        if self._trained_model_config_cache is None:
+            resp = self._client.ml.get_trained_models(model_id=self._model_id)
+            if resp["count"] > 1:
+                raise ValueError(f"Model ID {self._model_id!r} wasn't unambiguous")
+            elif resp["count"] == 0:
+                raise ValueError(f"Model with Model ID {self._model_id!r} wasn't found")
+            self._trained_model_config_cache = resp["trained_model_configs"][0]
+        return self._trained_model_config_cache
+
+
+ImportedMLModel = deprecated_api("MLModel.import_model()")(MLModel.import_model)

eland/ml/transformers/__init__.py

@@ -37,7 +37,7 @@ def get_model_transformer(model: Any, **kwargs: Any) -> ModelTransformer:
             return transformer(model, **kwargs)
 
     raise NotImplementedError(
-        f"ML model of type {type(model)}, not currently implemented"
+        f"Importing ML models of type {type(model)}, not currently implemented"
     )
 
 

eland/ml/transformers/lightgbm.py

@@ -18,7 +18,7 @@
 from typing import Optional, List, Dict, Any, Type
 from .base import ModelTransformer
 from .._model_serializer import Ensemble, Tree, TreeNode
-from ..ml_model import MLModel
+from ..common import TYPE_CLASSIFICATION, TYPE_REGRESSION
 from .._optional import import_optional_dependency
 
 import_optional_dependency("lightgbm", on_version="warn")
@@ -201,7 +201,7 @@ class LGBMRegressorTransformer(LGBMForestTransformer):
 
     @property
     def model_type(self) -> str:
-        return MLModel.TYPE_REGRESSION
+        return TYPE_REGRESSION
 
 
 class LGBMClassifierTransformer(LGBMForestTransformer):
@@ -244,7 +244,7 @@ class LGBMClassifierTransformer(LGBMForestTransformer):
 
     @property
     def model_type(self) -> str:
-        return MLModel.TYPE_CLASSIFICATION
+        return TYPE_CLASSIFICATION
 
     def is_objective_supported(self) -> bool:
         return self._objective in {

eland/ml/transformers/sklearn.py

@@ -18,7 +18,7 @@
 import numpy as np  # type: ignore
 from typing import Optional, Sequence, Union, Dict, Any, Type, Tuple
 from .base import ModelTransformer
-from ..ml_model import MLModel
+from ..common import TYPE_CLASSIFICATION, TYPE_REGRESSION
 from .._optional import import_optional_dependency
 from .._model_serializer import Ensemble, Tree, TreeNode
 
@@ -148,9 +148,9 @@ class SKLearnDecisionTreeTransformer(SKLearnTransformer):
     @property
     def model_type(self) -> str:
         return (
-            MLModel.TYPE_REGRESSION
+            TYPE_REGRESSION
             if isinstance(self._model, DecisionTreeRegressor)
-            else MLModel.TYPE_CLASSIFICATION
+            else TYPE_CLASSIFICATION
         )
 
 
@@ -223,7 +223,7 @@ class SKLearnForestRegressorTransformer(SKLearnForestTransformer):
 
     @property
     def model_type(self) -> str:
-        return MLModel.TYPE_REGRESSION
+        return TYPE_REGRESSION
 
 
 class SKLearnForestClassifierTransformer(SKLearnForestTransformer):
@@ -247,7 +247,7 @@ class SKLearnForestClassifierTransformer(SKLearnForestTransformer):
 
     @property
     def model_type(self) -> str:
-        return MLModel.TYPE_CLASSIFICATION
+        return TYPE_CLASSIFICATION
 
 
 _MODEL_TRANSFORMERS: Dict[type, Type[ModelTransformer]] = {

eland/ml/transformers/xgboost.py

@@ -20,7 +20,7 @@ from typing import Optional, List, Dict, Any, Type
 from .base import ModelTransformer
 import pandas as pd  # type: ignore
 from .._model_serializer import Ensemble, Tree, TreeNode
-from ..ml_model import MLModel
+from ..common import TYPE_CLASSIFICATION, TYPE_REGRESSION
 from .._optional import import_optional_dependency
 
 import_optional_dependency("xgboost", on_version="warn")
@@ -207,7 +207,7 @@ class XGBoostRegressorTransformer(XGBoostForestTransformer):
 
     @property
     def model_type(self) -> str:
-        return MLModel.TYPE_REGRESSION
+        return TYPE_REGRESSION
 
 
 class XGBoostClassifierTransformer(XGBoostForestTransformer):
@@ -253,7 +253,7 @@ class XGBoostClassifierTransformer(XGBoostForestTransformer):
 
     @property
     def model_type(self) -> str:
-        return MLModel.TYPE_CLASSIFICATION
+        return TYPE_CLASSIFICATION
 
 
 _MODEL_TRANSFORMERS: Dict[type, Type[ModelTransformer]] = {

eland/tests/ml/test_imported_ml_model_pytest.py

@@ -18,7 +18,7 @@
 import pytest
 import numpy as np
 
-from eland.ml import ImportedMLModel
+from eland.ml import MLModel
 from eland.tests import ES_TEST_CLIENT, ES_VERSION
 
 
@@ -71,7 +71,7 @@ def skip_if_multiclass_classifition():
 
 
 def random_rows(data, size):
-    return data[np.random.randint(data.shape[0], size=size), :].tolist()
+    return data[np.random.randint(data.shape[0], size=size), :]
 
 
 def check_prediction_equality(es_model, py_model, test_data):
@@ -84,7 +84,7 @@ def check_prediction_equality(es_model, py_model, test_data):
 class TestImportedMLModel:
     @requires_no_ml_extras
     def test_import_ml_model_when_dependencies_are_not_available(self):
-        from eland.ml import MLModel, ImportedMLModel  # noqa: F401
+        from eland.ml import MLModel  # noqa: F401
 
     @requires_sklearn
     def test_unpack_and_raise_errors_in_ingest_simulate(self, mocker):
@@ -98,7 +98,7 @@ class TestImportedMLModel:
         model_id = "test_decision_tree_classifier"
         test_data = [[0.1, 0.2, 0.3, -0.5, 1.0], [1.6, 2.1, -10, 50, -1.0]]
 
-        es_model = ImportedMLModel(
+        es_model = MLModel.import_model(
             ES_TEST_CLIENT,
             model_id,
             classifier,
@@ -142,7 +142,7 @@ class TestImportedMLModel:
         feature_names = ["f0", "f1", "f2", "f3", "f4"]
         model_id = "test_decision_tree_classifier"
 
-        es_model = ImportedMLModel(
+        es_model = MLModel.import_model(
             ES_TEST_CLIENT,
             model_id,
             classifier,
@@ -171,7 +171,7 @@ class TestImportedMLModel:
         feature_names = ["f0", "f1", "f2", "f3", "f4"]
         model_id = "test_decision_tree_regressor"
 
-        es_model = ImportedMLModel(
+        es_model = MLModel.import_model(
             ES_TEST_CLIENT,
             model_id,
             regressor,
@@ -199,7 +199,7 @@ class TestImportedMLModel:
         feature_names = ["f0", "f1", "f2", "f3", "f4"]
         model_id = "test_random_forest_classifier"
 
-        es_model = ImportedMLModel(
+        es_model = MLModel.import_model(
             ES_TEST_CLIENT,
             model_id,
             classifier,
@@ -227,7 +227,7 @@ class TestImportedMLModel:
         feature_names = ["f0", "f1", "f2", "f3", "f4"]
         model_id = "test_random_forest_regressor"
 
-        es_model = ImportedMLModel(
+        es_model = MLModel.import_model(
             ES_TEST_CLIENT,
             model_id,
             regressor,
@@ -265,7 +265,7 @@ class TestImportedMLModel:
         feature_names = ["f0", "f1", "f2", "f3", "f4"]
         model_id = "test_xgb_classifier"
 
-        es_model = ImportedMLModel(
+        es_model = MLModel.import_model(
             ES_TEST_CLIENT,
             model_id,
             classifier,
@@ -305,7 +305,7 @@ class TestImportedMLModel:
         feature_names = ["feature0", "feature1", "feature2", "feature3", "feature4"]
         model_id = "test_xgb_classifier"
 
-        es_model = ImportedMLModel(
+        es_model = MLModel.import_model(
             ES_TEST_CLIENT, model_id, classifier, feature_names, es_if_exists="replace"
         )
         # Get some test results
@@ -337,7 +337,7 @@ class TestImportedMLModel:
         feature_names = ["f0", "f1", "f2", "f3", "f4"]
         model_id = "test_xgb_regressor"
 
-        es_model = ImportedMLModel(
+        es_model = MLModel.import_model(
             ES_TEST_CLIENT,
             model_id,
             regressor,
@@ -368,7 +368,7 @@ class TestImportedMLModel:
         feature_names = ["f0"]
         model_id = "test_xgb_regressor"
 
-        es_model = ImportedMLModel(
+        es_model = MLModel.import_model(
             ES_TEST_CLIENT, model_id, regressor, feature_names, es_if_exists="replace"
         )
 
@@ -405,7 +405,7 @@ class TestImportedMLModel:
         feature_names = ["Column_0", "Column_1", "Column_2", "Column_3", "Column_4"]
         model_id = "test_lgbm_regressor"
 
-        es_model = ImportedMLModel(
+        es_model = MLModel.import_model(
             ES_TEST_CLIENT,
             model_id,
             regressor,
@@ -446,7 +446,7 @@ class TestImportedMLModel:
         feature_names = ["Column_0", "Column_1", "Column_2", "Column_3", "Column_4"]
         model_id = "test_lgbm_classifier"
 
-        es_model = ImportedMLModel(
+        es_model = MLModel.import_model(
             ES_TEST_CLIENT,
             model_id,
             classifier,
@@ -480,7 +480,7 @@ class TestImportedMLModel:
 
         match = "Using 'overwrite' and 'es_if_exists' together is invalid, use only 'es_if_exists'"
         with pytest.raises(ValueError, match=match):
-            ImportedMLModel(
+            MLModel.import_model(
                 ES_TEST_CLIENT,
                 model_id,
                 regressor,
@@ -507,7 +507,7 @@ class TestImportedMLModel:
 
         match = "'overwrite' parameter is deprecated, use 'es_if_exists' instead"
         with pytest.warns(DeprecationWarning, match=match):
-            ImportedMLModel(
+            MLModel.import_model(
                 ES_TEST_CLIENT,
                 model_id,
                 regressor,
@@ -536,7 +536,7 @@ class TestImportedMLModel:
         )
         with pytest.raises(ValueError, match=match_error):
             with pytest.warns(DeprecationWarning, match=match_warning):
-                ImportedMLModel(
+                MLModel.import_model(
                     ES_TEST_CLIENT,
                     model_id,
                     regressor,
@@ -560,7 +560,7 @@ class TestImportedMLModel:
         # If both overwrite and es_if_exists is given.
         match = f"Trained machine learning model {model_id} already exists"
         with pytest.raises(ValueError, match=match):
-            ImportedMLModel(
+            MLModel.import_model(
                 ES_TEST_CLIENT,
                 model_id,
                 regressor,

noxfile.py

@@ -34,7 +34,7 @@ SOURCE_FILES = (
 # Whenever type-hints are completed on a file it should
 # be added here so that this file will continue to be checked
 # by mypy. Errors from other files are ignored.
-TYPED_FILES = {
+TYPED_FILES = (
     "eland/actions.py",
     "eland/arithmetics.py",
     "eland/common.py",
@@ -46,13 +46,13 @@ TYPED_FILES = {
     "eland/utils.py",
     "eland/ml/__init__.py",
     "eland/ml/_model_serializer.py",
-    "eland/ml/imported_ml_model.py",
+    "eland/ml/ml_model.py",
     "eland/ml/transformers/__init__.py",
     "eland/ml/transformers/base.py",
     "eland/ml/transformers/lightgbm.py",
     "eland/ml/transformers/sklearn.py",
     "eland/ml/transformers/xgboost.py",
-}
+)
 
 
 @nox.session(reuse_venv=True)