refactor: update input dataset and training parameters

- Renamed columns in `input_dataset.csv` from `feature_a`, `feature_b`, and `target` to `Counter`, `Rollout`, and `Square` for better clarity.
- Updated the `run_training_test.py` script to reflect the new column names in the workflow input, ensuring consistency in data processing.
- Added `date_column` parameter to the workflow input for improved data handling.

input_dataset.csv

@@ -1,4 +1,4 @@
-timestamp,feature_a,feature_b,target
+timestamp,Counter,Rollout,Square
 2026-01-01 00:00:00,1.0,2.0,5.1
 2026-01-01 00:01:00,2.0,2.0,5.9
 2026-01-01 00:02:00,2.0,3.0,8.3

scripts/run_training_test.py

@@ -87,6 +87,7 @@ request_data = {
     'data_model_kwargs': {},
     'model_kwargs': {},
     'opt_params': {},
+    'date_column': 'timestamp',
     'model_metadata': {'schemas': {'components': {'schemas': {}}}},
 }
 
@@ -133,8 +134,8 @@ TH, TN, TQ = TEMPORAL_HOST, TEMPORAL_NAMESPACE, TRAIN_TASK_QUEUE
 
 _workflow_input = {
     'experiment_run_id': EXPERIMENT_RUN_ID,
-    'variable_columns': ['feature_a', 'feature_b'],
-    'target_variable': 'target',
+    'variable_columns': ['Counter', 'Rollout'],
+    'target_variable': 'Square',
     'bucket_name': MINIO_BUCKET,
     'file_name': OBJECT_NAME,
     'line_separator': ',',
@@ -142,12 +143,13 @@ _workflow_input = {
     'train_size': 80,
     'shuffle': True,
     'random_state': 42,
-    'model_name': 'test-runtime-linear-regression-model',
+    'model_name': 'test-runtime',
     'model_type': 'linear_regression',
     'model_id': EXPERIMENT_RUN_ID,
     'data_model_kwargs': {},
     'model_kwargs': {},
     'opt_params': {},
+    'date_column': 'timestamp',
 }
 
 c = await client.Client.connect(