Switched from classmethod to staticmethod where applicable and logged column names

ivanmkc · ivanmkc · commit 9e66508dbf36 · 2021-05-04T15:50:50.000-04:00
diff --git a/google/cloud/aiplatform/datasets/tabular_dataset.py b/google/cloud/aiplatform/datasets/tabular_dataset.py
@@ -85,10 +85,8 @@ def column_names(self) -> List[str]:
 
         raise RuntimeError("No valid CSV or BigQuery datasource found.")
 
-    @classmethod
-    def _retrieve_gcs_source_columns(
-        cls, project: str, gcs_csv_file_path: str
-    ) -> List[str]:
+    @staticmethod
+    def _retrieve_gcs_source_columns(project: str, gcs_csv_file_path: str) -> List[str]:
         """Retrieve the columns from a comma-delimited CSV file stored on Google Cloud Storage
 
         Example Usage:
@@ -139,7 +137,7 @@ def _retrieve_gcs_source_columns(
             header_line = line[:first_new_line_index]
 
             # Split to make it an iterable
-            header_line = header_line.split("\n")
+            header_line = header_line.split("\n")[:1]
 
             csv_reader = csv.reader(header_line, delimiter=",")
         except:
@@ -149,8 +147,8 @@ def _retrieve_gcs_source_columns(
 
         return next(csv_reader)
 
-    @classmethod
-    def _retrieve_bq_source_columns(cls, project: str, bq_table_uri: str) -> List[str]:
+    @staticmethod
+    def _retrieve_bq_source_columns(project: str, bq_table_uri: str) -> List[str]:
         """Retrieve the columns from a table on Google BigQuery
 
         Example Usage:
diff --git a/google/cloud/aiplatform/training_jobs.py b/google/cloud/aiplatform/training_jobs.py
@@ -2918,11 +2918,23 @@ def _run(
         training_task_definition = schema.training_job.definition.automl_tabular
 
         if self._column_transformations is None:
-            column_transformations = [
-                {"auto": {"column_name": column_name}}
+            _LOGGER.info(
+                "No column transformations provided, so now retrieving columns from dataset in order to set default column transformations."
+            )
+
+            column_names = [
+                column_name
                 for column_name in dataset.column_names
                 if column_name != target_column
             ]
+            column_transformations = [
+                {"auto": {"column_name": column_name}} for column_name in column_names
+            ]
+
+            _LOGGER.info(
+                "The column transformation of type 'auto' was set for the following columns: %s."
+                % column_names
+            )
         else:
             column_transformations = self._column_transformations
 
