udpated attribute names as per review comments

Author: manu-sj

python/hsfs/core/vector_server.py

@@ -1001,9 +1001,7 @@ def apply_on_demand_transformations(
         _logger.debug("Applying On-Demand transformation functions.")
         for tf in self._on_demand_transformation_functions:
             if (
-                tf.hopsworks_udf.execution_mode.get_current_execution_mode(
-                    inference=True
-                )
+                tf.hopsworks_udf.execution_mode.get_current_execution_mode(online=True)
                 == UDFExecutionMode.PANDAS
             ):
                 # Check if feature provided as request parameter if not get it from retrieved feature vector.
@@ -1028,13 +1026,11 @@ def apply_on_demand_transformations(
                     for feature in tf.hopsworks_udf.transformation_features
                 ]
 
-            on_demand_feature = tf.hopsworks_udf.get_udf(inference=True)(
+            on_demand_feature = tf.hopsworks_udf.get_udf(online=True)(
                 *features
             )  # Get only python compatible UDF irrespective of engine
             if (
-                tf.hopsworks_udf.execution_mode.get_current_execution_mode(
-                    inference=True
-                )
+                tf.hopsworks_udf.execution_mode.get_current_execution_mode(online=True)
                 == UDFExecutionMode.PANDAS
             ):
                 rows[on_demand_feature.name] = on_demand_feature.values[0]
@@ -1046,9 +1042,7 @@ def apply_model_dependent_transformations(self, rows: Union[dict, pd.DataFrame])
         _logger.debug("Applying Model-Dependent transformation functions.")
         for tf in self.model_dependent_transformation_functions:
             if (
-                tf.hopsworks_udf.execution_mode.get_current_execution_mode(
-                    inference=True
-                )
+                tf.hopsworks_udf.execution_mode.get_current_execution_mode(online=True)
                 == UDFExecutionMode.PANDAS
             ):
                 features = [
@@ -1064,14 +1058,12 @@ def apply_model_dependent_transformations(self, rows: Union[dict, pd.DataFrame])
                     rows[feature]
                     for feature in tf.hopsworks_udf.transformation_features
                 ]
-            transformed_result = tf.hopsworks_udf.get_udf(inference=True)(
+            transformed_result = tf.hopsworks_udf.get_udf(online=True)(
                 *features
             )  # Get only python compatible UDF irrespective of engine
 
             if (
-                tf.hopsworks_udf.execution_mode.get_current_execution_mode(
-                    inference=True
-                )
+                tf.hopsworks_udf.execution_mode.get_current_execution_mode(online=True)
                 == UDFExecutionMode.PANDAS
             ):
                 if isinstance(transformed_result, pd.Series):

python/hsfs/engine/python.py

@@ -1263,7 +1263,7 @@ def _apply_transformation_function(
         self,
         transformation_functions: List[transformation_function.TransformationFunction],
         dataset: Union[pd.DataFrame, pl.DataFrame],
-        inference_mode: bool = False,
+        online_inference: bool = False,
     ) -> Union[pd.DataFrame, pl.DataFrame]:
         """
         Apply transformation function to the dataframe.
@@ -1304,7 +1304,7 @@ def _apply_transformation_function(
 
             if (
                 hopsworks_udf.execution_mode.get_current_execution_mode(
-                    inference=inference_mode
+                    online=online_inference
                 )
                 == UDFExecutionMode.PANDAS
             ):
@@ -1334,7 +1334,7 @@ def _apply_python_udf(
         # Raises
             `FeatureStoreException`: If any of the features mentioned in the transformation function is not present in the Feature View.
         """
-        udf = hopsworks_udf.get_udf(inference=False)
+        udf = hopsworks_udf.get_udf(online=False)
         if isinstance(dataframe, pd.DataFrame):
             if len(hopsworks_udf.return_types) > 1:
                 dataframe[hopsworks_udf.output_column_names] = dataframe.apply(
@@ -1398,7 +1398,7 @@ def _apply_pandas_udf(
         """
         if len(hopsworks_udf.return_types) > 1:
             dataframe[hopsworks_udf.output_column_names] = hopsworks_udf.get_udf(
-                inference=False
+                online=False
             )(
                 *(
                     [
@@ -1409,7 +1409,7 @@ def _apply_pandas_udf(
             )
         else:
             dataframe[hopsworks_udf.output_column_names[0]] = hopsworks_udf.get_udf(
-                inference=False
+                online=False
             )(
                 *(
                     [

python/hsfs/hopsworks_udf.py

@@ -42,10 +42,10 @@ class UDFExecutionMode(Enum):
     PYTHON = "python"
     PANDAS = "pandas"
 
-    def get_current_execution_mode(self, inference):
-        if self == UDFExecutionMode.DEFAULT and inference:
+    def get_current_execution_mode(self, online):
+        if self == UDFExecutionMode.DEFAULT and online:
             return UDFExecutionMode.PYTHON
-        elif self == UDFExecutionMode.DEFAULT and not inference:
+        elif self == UDFExecutionMode.DEFAULT and not online:
             return UDFExecutionMode.PANDAS
         else:
             return self
@@ -697,7 +697,7 @@ def update_return_type_one_hot(self):
             for _ in range(len(self.transformation_statistics.feature.unique_values))
         ]
 
-    def get_udf(self, inference: bool = False) -> Callable:
+    def get_udf(self, online: bool = False) -> Callable:
         """
         Function that checks the current engine type, execution type and returns the appropriate UDF.
 
@@ -719,10 +719,10 @@ def get_udf(self, inference: bool = False) -> Callable:
         """
 
         if (
-            self.execution_mode.get_current_execution_mode(inference)
+            self.execution_mode.get_current_execution_mode(online)
             == UDFExecutionMode.PANDAS
         ):
-            if engine.get_type() in ["python", "training"] or inference:
+            if engine.get_type() in ["python", "training"] or online:
                 return self.pandas_udf_wrapper()
             else:
                 from pyspark.sql.functions import pandas_udf
@@ -732,10 +732,10 @@ def get_udf(self, inference: bool = False) -> Callable:
                     returnType=self._create_pandas_udf_return_schema_from_list(),
                 )
         elif (
-            self.execution_mode.get_current_execution_mode(inference)
+            self.execution_mode.get_current_execution_mode(online)
             == UDFExecutionMode.PYTHON
         ):
-            if engine.get_type() in ["python", "training"] or inference:
+            if engine.get_type() in ["python", "training"] or online:
                 # Renaming into correct column names done within Python engine since a wrapper does not work for polars dataFrames.
                 return self.python_udf_wrapper(rename_outputs=False)
             else:

python/tests/test_hopswork_udf.py

@@ -874,7 +874,7 @@ def test(feature):
         pandas_wrapper_mocker = mocker.patch.object(test, "pandas_udf_wrapper")
         python_wrapper_mocker = mocker.patch.object(test, "python_udf_wrapper")
 
-        _ = test.get_udf(inference=False)
+        _ = test.get_udf(online=False)
 
         assert pandas_udf_mocker.call_count == 1
         assert python_udf_mocker.call_count == 0
@@ -893,7 +893,7 @@ def test(feature):
         pandas_wrapper_mocker = mocker.patch.object(test, "pandas_udf_wrapper")
         python_wrapper_mocker = mocker.patch.object(test, "python_udf_wrapper")
 
-        _ = test.get_udf(inference=True)
+        _ = test.get_udf(online=True)
 
         assert pandas_udf_mocker.call_count == 0
         assert python_udf_mocker.call_count == 0
@@ -912,7 +912,7 @@ def test(feature):
         pandas_wrapper_mocker = mocker.patch.object(test, "pandas_udf_wrapper")
         python_wrapper_mocker = mocker.patch.object(test, "python_udf_wrapper")
 
-        _ = test.get_udf(inference=False)
+        _ = test.get_udf(online=False)
 
         assert pandas_udf_mocker.call_count == 0
         assert python_udf_mocker.call_count == 1
@@ -931,7 +931,7 @@ def test(feature):
         pandas_wrapper_mocker = mocker.patch.object(test, "pandas_udf_wrapper")
         python_wrapper_mocker = mocker.patch.object(test, "python_udf_wrapper")
 
-        _ = test.get_udf(inference=True)
+        _ = test.get_udf(online=True)
 
         assert pandas_udf_mocker.call_count == 0
         assert python_udf_mocker.call_count == 0
@@ -950,7 +950,7 @@ def test(feature):
         pandas_wrapper_mocker = mocker.patch.object(test, "pandas_udf_wrapper")
         python_wrapper_mocker = mocker.patch.object(test, "python_udf_wrapper")
 
-        _ = test.get_udf(inference=False)
+        _ = test.get_udf(online=False)
 
         assert pandas_udf_mocker.call_count == 1
         assert python_udf_mocker.call_count == 0
@@ -969,7 +969,7 @@ def test(feature):
         pandas_wrapper_mocker = mocker.patch.object(test, "pandas_udf_wrapper")
         python_wrapper_mocker = mocker.patch.object(test, "python_udf_wrapper")
 
-        _ = test.get_udf(inference=True)
+        _ = test.get_udf(online=True)
 
         assert pandas_udf_mocker.call_count == 0
         assert python_udf_mocker.call_count == 0
@@ -988,7 +988,7 @@ def test(feature):
         pandas_wrapper_mocker = mocker.patch.object(test, "pandas_udf_wrapper")
         python_wrapper_mocker = mocker.patch.object(test, "python_udf_wrapper")
 
-        _ = test.get_udf(inference=False)
+        _ = test.get_udf(online=False)
 
         assert pandas_udf_mocker.call_count == 0
         assert python_udf_mocker.call_count == 0
@@ -1007,7 +1007,7 @@ def test(feature):
         pandas_wrapper_mocker = mocker.patch.object(test, "pandas_udf_wrapper")
         python_wrapper_mocker = mocker.patch.object(test, "python_udf_wrapper")
 
-        _ = test.get_udf(inference=True)
+        _ = test.get_udf(online=True)
 
         assert pandas_udf_mocker.call_count == 0
         assert python_udf_mocker.call_count == 0
@@ -1026,7 +1026,7 @@ def test(feature):
         pandas_wrapper_mocker = mocker.patch.object(test, "pandas_udf_wrapper")
         python_wrapper_mocker = mocker.patch.object(test, "python_udf_wrapper")
 
-        _ = test.get_udf(inference=False)
+        _ = test.get_udf(online=False)
 
         assert pandas_udf_mocker.call_count == 0
         assert python_udf_mocker.call_count == 0
@@ -1045,7 +1045,7 @@ def test(feature):
         pandas_wrapper_mocker = mocker.patch.object(test, "pandas_udf_wrapper")
         python_wrapper_mocker = mocker.patch.object(test, "python_udf_wrapper")
 
-        _ = test.get_udf(inference=True)
+        _ = test.get_udf(online=True)
 
         assert pandas_udf_mocker.call_count == 0
         assert python_udf_mocker.call_count == 0
@@ -1064,7 +1064,7 @@ def test(feature):
         pandas_wrapper_mocker = mocker.patch.object(test, "pandas_udf_wrapper")
         python_wrapper_mocker = mocker.patch.object(test, "python_udf_wrapper")
 
-        _ = test.get_udf(inference=False)
+        _ = test.get_udf(online=False)
 
         assert pandas_udf_mocker.call_count == 0
         assert python_udf_mocker.call_count == 0
@@ -1083,7 +1083,7 @@ def test(feature):
         pandas_wrapper_mocker = mocker.patch.object(test, "pandas_udf_wrapper")
         python_wrapper_mocker = mocker.patch.object(test, "python_udf_wrapper")
 
-        _ = test.get_udf(inference=True)
+        _ = test.get_udf(online=True)
 
         assert pandas_udf_mocker.call_count == 0
         assert python_udf_mocker.call_count == 0