fix(components): remove default prediction column names in evaluation regression component to fix issues with bigquery data source

Signed-off-by: Jason Dai <jsndai@google.com>
PiperOrigin-RevId: 684488025

Author: jsondai

components/google-cloud/RELEASE.md

@@ -1,4 +1,5 @@
 ## Upcoming release
+* Remove default prediction column names in `v1.model_evaluation.regression_component` component to fix pipeline errors when using bigquery data source.
 
 ## Release 2.17.0
 * Fix Gemini batch prediction support to `v1.model_evaluation.autosxs_pipeline` after output schema change.

components/google-cloud/google_cloud_pipeline_components/container/v1/aiplatform/remote_runner.py

@@ -88,7 +88,7 @@ def write_to_artifact(executor_input, text):
       # Add URI Prefix
       # "https://[location]-aiplatform.googleapis.com/API_VERSION/": For AI Platform resource names, current version is defined in AIPLATFORM_API_VERSION.
       if RESOURCE_NAME_PATTERN.match(text):
-        location = re.findall(r'locations/([\w\-]+)', text)[0]
+        location = re.findall('locations/([\w\-]+)', text)[0]
         uri_with_prefix = f'https://{location}-aiplatform.googleapis.com/{AIPLATFORM_API_VERSION}/{text}'
         metadata.update({'resourceName': text})
 

components/google-cloud/google_cloud_pipeline_components/v1/hyperparameter_tuning_job/component.py

@@ -42,7 +42,7 @@ def hyperparameter_tuning_job(
     project: str = _placeholders.PROJECT_ID_PLACEHOLDER,
 ):
   # fmt: off
-  r"""Creates a Vertex AI hyperparameter tuning job and waits for it to
+  """Creates a Vertex AI hyperparameter tuning job and waits for it to
   complete.
 
   See [more information](https://cloud.google.com/vertex-ai/docs/training/using-hyperparameter-tuning).

components/google-cloud/google_cloud_pipeline_components/v1/model_evaluation/regression_component.py

@@ -36,7 +36,7 @@ def model_evaluation_regression(
     ground_truth_format: str = 'jsonl',
     ground_truth_gcs_source: List[str] = [],
     ground_truth_bigquery_source: str = '',
-    prediction_score_column: str = 'prediction.value',
+    prediction_score_column: str = '',
     dataflow_service_account: str = '',
     dataflow_disk_size_gb: int = 50,
     dataflow_machine_type: str = 'n1-standard-4',