docs

splicemachine/features/feature_store.py

@@ -25,6 +25,7 @@
 class FeatureStore:
     def __init__(self, splice_ctx: PySpliceContext) -> None:
         self.splice_ctx = splice_ctx
+        self.mlflow_ctx = None
         self.feature_sets = []  # Cache of newly created feature sets
 
     def register_splice_context(self, splice_ctx: PySpliceContext) -> None:
@@ -166,7 +167,7 @@ def get_feature_vector(self, features: List[Union[str, Feature]],
         Gets a feature vector given a list of Features and primary key values for their corresponding Feature Sets
 
         :param features: List of str Feature names or Features
-        :param join_key_values: (dict) join key vals to get the proper Feature values formatted as {join_key_column_name: join_key_value}
+        :param join_key_values: (dict) join key values to get the proper Feature values formatted as {join_key_column_name: join_key_value}
         :param return_sql: Whether to return the SQL needed to get the vector or the values themselves. Default False
         :return: Pandas Dataframe or str (SQL statement)
         """
@@ -735,12 +736,16 @@ def __log_mlflow_results(self, name, rounds, mlflow_results):
         :param name: MLflow run name
         :param rounds: Number of rounds of feature elimination that were run
         :param mlflow_results: The params / metrics to log
-        :return:
         """
-        with self.mlflow_ctx.start_run(run_name=name):
+        try:
+            if self.mlflow_ctx.active_run():
+                self.mlflow_ctx.start_run(run_name=name)
             for r in range(rounds):
                 with self.mlflow_ctx.start_run(run_name=f'Round {r}', nested=True):
                     self.mlflow_ctx.log_metrics(mlflow_results[r])
+        finally:
+            self.mlflow_ctx.end_run()
+
 
     def __prune_features_for_elimination(self, features) -> List[Feature]:
         """

splicemachine/spark/context.py

@@ -124,16 +124,17 @@ def replaceDataframeSchema(self, dataframe, schema_table_name):
 
     def fileToTable(self, file_path, schema_table_name, primary_keys=None, drop_table=False, **pandas_args):
         """
-        Load a file from the local filesystem and create a new table (or recreate an existing table), and load the data
-        from the file into the new table
+        Load a file from the local filesystem or from a remote location and create a new table
+        (or recreate an existing table), and load the data from the file into the new table. Any file_path that can be
+        read by pandas should work here.
 
         :param file_path: The local file to load
         :param schema_table_name: The schema.table name
         :param primary_keys: List[str] of primary keys for the table. Default None
         :param drop_table: Whether or not to drop the table. If this is False and the table already exists, the
             function will fail. Default False
         :param pandas_args: Extra parameters to be passed into the pd.read_csv function. Any parameters accepted
-        in pd.read_csv will work here
+            in pd.read_csv will work here
         :return: None
         """
         import pandas as pd