Replace MLModel(overwrite) with es_if_exists

CONTRIBUTING.md

@@ -151,7 +151,8 @@ currently using a minimum version of PyCharm 2019.2.4.
     Tools\'-\>\'Docstring format\' to `numpy`
 -   Install development requirements. Open terminal in virtual
     environment and run `pip install -r requirements-dev.txt`
--   Setup Elasticsearch instance (assumes `localhost:9200`), and run
+-   Setup Elasticsearch instance with docker `ELASTICSEARCH_VERSION=elasticsearch:7.x-SNAPSHOT .ci/run-elasticsearch.sh` and check `http://localhost:9200`
+- Run
     `python -m eland.tests.setup_tests` to setup test environment -*note
     this modifies Elasticsearch indices*
 -   Install local `eland` module (required to execute notebook tests)

README.md

@@ -140,7 +140,7 @@ dtype: int64
 13057    20819.488281
 13058    18315.431274
 Length: 13059, dtype: float64
->>> print(s.info_es())
+>>> print(s.es_info())
 index_pattern: flights
 Index:
  index_field: _id

eland/ml/imported_ml_model.py

@@ -22,6 +22,7 @@
 from .ml_model import MLModel
 from .transformers import get_model_transformer
 from ..common import es_version
+import warnings
 
 
 if TYPE_CHECKING:
@@ -100,7 +101,13 @@ class ImportedMLModel(MLModel):
     classification_weights: List[str]
         Weights of the classification targets
 
-    overwrite: bool
+    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
@@ -127,7 +134,7 @@ class ImportedMLModel(MLModel):
     >>> # 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, overwrite=True)
+    >>> 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)
@@ -155,7 +162,8 @@ def __init__(
         feature_names: List[str],
         classification_labels: Optional[List[str]] = None,
         classification_weights: Optional[List[float]] = None,
-        overwrite: bool = False,
+        es_if_exists: Optional[str] = None,
+        overwrite: Optional[bool] = None,
         es_compress_model_definition: bool = True,
     ):
         super().__init__(es_client, model_id)
@@ -171,7 +179,30 @@ def __init__(
         self._model_type = transformer.model_type
         serializer = transformer.transform()
 
-        if overwrite:
+        # 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.check_existing_model():
+                raise ValueError(
+                    f"Trained machine learning model {model_id} already exists"
+                )
+        elif es_if_exists == "replace":
             self.delete_model()
 
         body: Dict[str, Any] = {
@@ -224,7 +255,7 @@ def predict(self, X: Union[List[float], List[List[float]]]) -> np.ndarray:
         >>> # 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, overwrite=True)
+        >>> 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)

eland/ml/ml_model.py

@@ -57,3 +57,15 @@ def delete_model(self) -> None:
             self._client.ml.delete_trained_model(model_id=self._model_id, ignore=(404,))
         except elasticsearch.NotFoundError:
             pass
+
+    def check_existing_model(self) -> bool:
+        """
+        Check If model exists in Elastic
+        """
+        try:
+            self._client.ml.get_trained_models(
+                model_id=self._model_id, include_model_definition=False
+            )
+        except elasticsearch.NotFoundError:
+            return False
+        return True

eland/tests/ml/test_imported_ml_model_pytest.py

@@ -103,7 +103,7 @@ def test_unpack_and_raise_errors_in_ingest_simulate(self, mocker):
             model_id,
             classifier,
             feature_names,
-            overwrite=True,
+            es_if_exists="replace",
             es_compress_model_definition=True,
         )
 
@@ -147,7 +147,7 @@ def test_decision_tree_classifier(self, compress_model_definition):
             model_id,
             classifier,
             feature_names,
-            overwrite=True,
+            es_if_exists="replace",
             es_compress_model_definition=compress_model_definition,
         )
 
@@ -176,7 +176,7 @@ def test_decision_tree_regressor(self, compress_model_definition):
             model_id,
             regressor,
             feature_names,
-            overwrite=True,
+            es_if_exists="replace",
             es_compress_model_definition=compress_model_definition,
         )
         # Get some test results
@@ -204,7 +204,7 @@ def test_random_forest_classifier(self, compress_model_definition):
             model_id,
             classifier,
             feature_names,
-            overwrite=True,
+            es_if_exists="replace",
             es_compress_model_definition=compress_model_definition,
         )
         # Get some test results
@@ -232,7 +232,7 @@ def test_random_forest_regressor(self, compress_model_definition):
             model_id,
             regressor,
             feature_names,
-            overwrite=True,
+            es_if_exists="replace",
             es_compress_model_definition=compress_model_definition,
         )
         # Get some test results
@@ -270,7 +270,7 @@ def test_xgb_classifier(self, compress_model_definition, multi_class):
             model_id,
             classifier,
             feature_names,
-            overwrite=True,
+            es_if_exists="replace",
             es_compress_model_definition=compress_model_definition,
         )
         # Get some test results
@@ -306,7 +306,7 @@ def test_xgb_classifier_objectives_and_booster(self, objective, booster):
         model_id = "test_xgb_classifier"
 
         es_model = ImportedMLModel(
-            ES_TEST_CLIENT, model_id, classifier, feature_names, overwrite=True
+            ES_TEST_CLIENT, model_id, classifier, feature_names, es_if_exists="replace"
         )
         # Get some test results
         check_prediction_equality(
@@ -342,7 +342,7 @@ def test_xgb_regressor(self, compress_model_definition, objective, booster):
             model_id,
             regressor,
             feature_names,
-            overwrite=True,
+            es_if_exists="replace",
             es_compress_model_definition=compress_model_definition,
         )
         # Get some test results
@@ -369,7 +369,7 @@ def test_predict_single_feature_vector(self):
         model_id = "test_xgb_regressor"
 
         es_model = ImportedMLModel(
-            ES_TEST_CLIENT, model_id, regressor, feature_names, overwrite=True
+            ES_TEST_CLIENT, model_id, regressor, feature_names, es_if_exists="replace"
         )
 
         # Single feature
@@ -410,7 +410,7 @@ def test_lgbm_regressor(self, compress_model_definition, objective, booster):
             model_id,
             regressor,
             feature_names,
-            overwrite=True,
+            es_if_exists="replace",
             es_compress_model_definition=compress_model_definition,
         )
         # Get some test results
@@ -451,7 +451,7 @@ def test_lgbm_classifier_objectives_and_booster(
             model_id,
             classifier,
             feature_names,
-            overwrite=True,
+            es_if_exists="replace",
             es_compress_model_definition=compress_model_definition,
         )
 
@@ -461,3 +461,110 @@ def test_lgbm_classifier_objectives_and_booster(
 
         # Clean up
         es_model.delete_model()
+
+    # If both overwrite and es_if_exists is given.
+    @requires_sklearn
+    @pytest.mark.parametrize("compress_model_definition", [True, False])
+    @pytest.mark.parametrize("es_if_exists", ["fail", "replace"])
+    @pytest.mark.parametrize("overwrite", [True, False])
+    def test_imported_mlmodel_bothparams(
+        self, compress_model_definition, es_if_exists, overwrite
+    ):
+        # Train model
+        training_data = datasets.make_regression(n_features=5)
+        regressor = RandomForestRegressor()
+        regressor.fit(training_data[0], training_data[1])
+
+        feature_names = ["f0", "f1", "f2", "f3", "f4"]
+        model_id = "test_random_forest_regressor"
+
+        match = "Using 'overwrite' and 'es_if_exists' together is invalid, use only 'es_if_exists'"
+        with pytest.raises(ValueError, match=match):
+            ImportedMLModel(
+                ES_TEST_CLIENT,
+                model_id,
+                regressor,
+                feature_names,
+                es_if_exists=es_if_exists,
+                overwrite=overwrite,
+                es_compress_model_definition=compress_model_definition,
+            )
+
+    # Deprecation warning for overwrite parameter
+    @requires_sklearn
+    @pytest.mark.parametrize("compress_model_definition", [True, False])
+    @pytest.mark.parametrize("overwrite", [True])
+    def test_imported_mlmodel_overwrite_true(
+        self, compress_model_definition, overwrite
+    ):
+        # Train model
+        training_data = datasets.make_regression(n_features=5)
+        regressor = RandomForestRegressor()
+        regressor.fit(training_data[0], training_data[1])
+
+        feature_names = ["f0", "f1", "f2", "f3", "f4"]
+        model_id = "test_random_forest_regressor"
+
+        match = "'overwrite' parameter is deprecated, use 'es_if_exists' instead"
+        with pytest.warns(DeprecationWarning, match=match):
+            ImportedMLModel(
+                ES_TEST_CLIENT,
+                model_id,
+                regressor,
+                feature_names,
+                overwrite=overwrite,
+                es_compress_model_definition=compress_model_definition,
+            )
+
+    @requires_sklearn
+    @pytest.mark.parametrize("compress_model_definition", [True, False])
+    @pytest.mark.parametrize("overwrite", [False])
+    def test_imported_mlmodel_overwrite_false(
+        self, compress_model_definition, overwrite
+    ):
+        # Train model
+        training_data = datasets.make_regression(n_features=5)
+        regressor = RandomForestRegressor()
+        regressor.fit(training_data[0], training_data[1])
+
+        feature_names = ["f0", "f1", "f2", "f3", "f4"]
+        model_id = "test_random_forest_regressor"
+
+        match_error = f"Trained machine learning model {model_id} already exists"
+        match_warning = (
+            "'overwrite' parameter is deprecated, use 'es_if_exists' instead"
+        )
+        with pytest.raises(ValueError, match=match_error):
+            with pytest.warns(DeprecationWarning, match=match_warning):
+                ImportedMLModel(
+                    ES_TEST_CLIENT,
+                    model_id,
+                    regressor,
+                    feature_names,
+                    overwrite=overwrite,
+                    es_compress_model_definition=compress_model_definition,
+                )
+
+    # Raise ValueError if Model exists when es_if_exists = 'fail'
+    @requires_sklearn
+    @pytest.mark.parametrize("compress_model_definition", [True, False])
+    def test_es_if_exists_fail(self, compress_model_definition):
+        # Train model
+        training_data = datasets.make_regression(n_features=5)
+        regressor = RandomForestRegressor()
+        regressor.fit(training_data[0], training_data[1])
+
+        feature_names = ["f0", "f1", "f2", "f3", "f4"]
+        model_id = "test_random_forest_regressor"
+
+        # 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(
+                ES_TEST_CLIENT,
+                model_id,
+                regressor,
+                feature_names,
+                es_if_exists="fail",
+                es_compress_model_definition=compress_model_definition,
+            )

eland/utils.py

@@ -36,6 +36,7 @@ def wrapped(*args: Any, **kwargs: Any) -> RT:
             warnings.warn(
                 f"{f.__name__} is deprecated, use {replace_with} instead",
                 DeprecationWarning,
+                stacklevel=2,
             )
             return f(*args, **kwargs)