Remove deprecated options and aliases

README.md

@@ -153,7 +153,7 @@ to be serialized and used as an inference model in Elasticsearch
 
 ```python
 >>> from xgboost import XGBClassifier
->>> from eland.ml import ImportedMLModel
+>>> from eland.ml import MLModel
 
 # Train and exercise an XGBoost ML model locally
 >>> xgb_model = XGBClassifier(booster="gbtree")
@@ -163,7 +163,7 @@ to be serialized and used as an inference model in Elasticsearch
 [0 1 1 0 1 0 0 0 1 0]
 
 # Import the model into Elasticsearch
->>> es_model = ImportedMLModel(
+>>> es_model = MLModel(
     es_client="localhost:9200",
     model_id="xgb-classifier",
     model=xgb_model,

docs/sphinx/reference/api/eland.DataFrame.es_info.rst

@@ -3,8 +3,4 @@ eland.DataFrame.es_info
 
 .. currentmodule:: eland
 
-.. warning::
-    Previously this method was named ``info_es()``.
-    DataFrame.info_es() is deprecated, use DataFrame.es_info() instead.
-
 .. automethod:: DataFrame.es_info

docs/sphinx/reference/api/eland.Series.es_info.rst

@@ -3,8 +3,4 @@ eland.Series.es_info
 
 .. currentmodule:: eland
 
-.. warning::
-    Previously this method was named ``info_es()``.
-    Series.info_es() is deprecated, use Series.es_info() instead.
-
 .. automethod:: Series.es_info

eland/__init__.py

@@ -27,7 +27,7 @@
 )
 from .common import SortOrder
 from .dataframe import DataFrame
-from .etl import csv_to_eland, eland_to_pandas, pandas_to_eland, read_csv, read_es
+from .etl import csv_to_eland, eland_to_pandas, pandas_to_eland
 from .index import Index
 from .ndframe import NDFrame
 from .series import Series
@@ -40,7 +40,5 @@
     "pandas_to_eland",
     "eland_to_pandas",
     "csv_to_eland",
-    "read_csv",
-    "read_es",
     "SortOrder",
 ]

eland/dataframe.py

@@ -39,7 +39,7 @@
 from eland.groupby import DataFrameGroupBy
 from eland.ndframe import NDFrame
 from eland.series import Series
-from eland.utils import deprecated_api, is_valid_attr_name
+from eland.utils import is_valid_attr_name
 
 
 class DataFrame(NDFrame):
@@ -628,10 +628,6 @@ def es_info(self):
 
         return buf.getvalue()
 
-    @deprecated_api("eland.DataFrame.es_info()")
-    def info_es(self):
-        return self.es_info()
-
     def es_match(
         self,
         text: str,

eland/etl.py

@@ -27,37 +27,6 @@
 from eland import DataFrame
 from eland.common import DEFAULT_CHUNK_SIZE, ensure_es_client
 from eland.field_mappings import FieldMappings, verify_mapping_compatibility
-from eland.utils import deprecated_api
-
-
-@deprecated_api("eland.DataFrame()")
-def read_es(
-    es_client: Union[str, List[str], Tuple[str, ...], Elasticsearch],
-    es_index_pattern: str,
-) -> DataFrame:
-    """
-    Utility method to create an eland.Dataframe from an Elasticsearch index_pattern.
-    (Similar to pandas.read_csv, but source data is an Elasticsearch index rather than
-    a csv file)
-
-    Parameters
-    ----------
-    es_client: Elasticsearch client argument(s)
-        - elasticsearch-py parameters or
-        - elasticsearch-py instance
-    es_index_pattern: str
-        Elasticsearch index pattern
-
-    Returns
-    -------
-    eland.DataFrame
-
-    See Also
-    --------
-    eland.pandas_to_eland: Create an eland.Dataframe from pandas.DataFrame
-    eland.eland_to_pandas: Create a pandas.Dataframe from eland.DataFrame
-    """
-    return DataFrame(es_client=es_client, es_index_pattern=es_index_pattern)
 
 
 def pandas_to_eland(
@@ -552,8 +521,3 @@ def csv_to_eland(  # type: ignore
 
     # Now create an eland.DataFrame that references the new index
     return DataFrame(es_client, es_index_pattern=es_dest_index)
-
-
-@deprecated_api("eland.csv_to_eland()")
-def read_csv(*args, **kwargs) -> DataFrame:  # type: ignore
-    return csv_to_eland(*args, **kwargs)

eland/index.py

@@ -17,8 +17,6 @@
 
 from typing import TYPE_CHECKING, Optional, TextIO
 
-from eland.utils import deprecated_api
-
 if TYPE_CHECKING:
     from .query_compiler import QueryCompiler
 
@@ -95,7 +93,3 @@ def es_info(self, buf: TextIO) -> None:
         buf.write("Index:\n")
         buf.write(f" es_index_field: {self.es_index_field}\n")
         buf.write(f" is_source_field: {self.is_source_field}\n")
-
-    @deprecated_api("eland.Index.es_info()")
-    def info_es(self, buf: TextIO) -> None:
-        self.es_info(buf)

eland/ml/__init__.py

@@ -15,9 +15,6 @@
 #  specific language governing permissions and limitations
 #  under the License.
 
-from eland.ml.ml_model import ImportedMLModel, MLModel
+from eland.ml.ml_model import ImportedMLModel, MLModel  # noqa: F401
 
-__all__ = [
-    "MLModel",
-    "ImportedMLModel",
-]
+__all__ = ["MLModel"]

eland/ml/ml_model.py

@@ -15,7 +15,6 @@
 #  specific language governing permissions and limitations
 #  under the License.
 
-import warnings
 from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union, cast
 
 import elasticsearch
@@ -105,7 +104,7 @@ def predict(
         --------
         >>> from sklearn import datasets
         >>> from xgboost import XGBRegressor
-        >>> from eland.ml import ImportedMLModel
+        >>> from eland.ml import MLModel
 
         >>> # Train model
         >>> training_data = datasets.make_classification(n_features=6, random_state=0)
@@ -254,7 +253,6 @@ def import_model(
         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":
         """
@@ -318,9 +316,6 @@ def import_model(
             - 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
@@ -370,20 +365,7 @@ def import_model(
         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:
+        if es_if_exists is None:
             es_if_exists = "fail"
 
         ml_model = MLModel(

eland/series.py

@@ -59,7 +59,7 @@
     ScriptFilter,
 )
 from eland.ndframe import NDFrame
-from eland.utils import deprecated_api, to_list
+from eland.utils import to_list
 
 if TYPE_CHECKING:  # type: ignore
     from elasticsearch import Elasticsearch  # noqa: F401
@@ -712,10 +712,6 @@ def es_info(self) -> str:
 
         return buf.getvalue()
 
-    @deprecated_api("eland.Series.es_info()")
-    def info_es(self) -> str:
-        return self.es_info()
-
     def __add__(self, right):
         """
         Return addition of series and right, element-wise (binary operator add).

tests/ml/test_imported_ml_model_pytest.py → tests/ml/test_ml_model_pytest.py

@@ -80,7 +80,7 @@ def check_prediction_equality(es_model, py_model, test_data):
     np.testing.assert_almost_equal(test_results, es_results, decimal=2)
 
 
-class TestImportedMLModel:
+class TestMLModel:
     @requires_no_ml_extras
     def test_import_ml_model_when_dependencies_are_not_available(self):
         from eland.ml import MLModel  # noqa: F401
@@ -239,6 +239,17 @@ def test_random_forest_regressor(self, compress_model_definition):
             es_model, regressor, random_rows(training_data[0], 20)
         )
 
+        match = f"Trained machine learning model {model_id} already exists"
+        with pytest.raises(ValueError, match=match):
+            MLModel.import_model(
+                ES_TEST_CLIENT,
+                model_id,
+                regressor,
+                feature_names,
+                es_if_exists="fail",
+                es_compress_model_definition=compress_model_definition,
+            )
+
         # Clean up
         es_model.delete_model()
 
@@ -460,110 +471,3 @@ 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):
-            MLModel.import_model(
-                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):
-            MLModel.import_model(
-                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):
-                MLModel.import_model(
-                    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):
-            MLModel.import_model(
-                ES_TEST_CLIENT,
-                model_id,
-                regressor,
-                feature_names,
-                es_if_exists="fail",
-                es_compress_model_definition=compress_model_definition,
-            )