SIENTIAPDE-1579: Added local and validation flags to test script

docs/test-scenarios/12-angular-test-cv022-wit230.json

@@ -0,0 +1,29 @@
+{
+  "_description": "Cenário angular-test-01: CV022 WIT230 com lag e intervalo de datas",
+  "experimentName": "angular-test-01",
+  "username": "lucas.kou@aignosi.com.br",
+  "modelName": "Linear Regression",
+  "targetVariable": "03CV022/CORRENTE_N_M1_PV(Value)",
+  "variableColumns": ["303-WIT-230(Value)"],
+  "lagTrain": {"303-WIT-230(Value)": 3},
+  "lagVal": {"303-WIT-230(Value)": 0},
+  "remStaticWin": false,
+  "lowLim": {},
+  "uppLim": {},
+  "window": 0,
+  "useScaler": false,
+  "includeAr": false,
+  "trainSize": 80,
+  "shuffle": true,
+  "lineSeparator": ",",
+  "decimalSeparator": ".",
+  "removedIntervals": [],
+  "degree": 1,
+  "interactionOnly": false,
+  "nanTreatment": "drop",
+  "startDate": "01/05/2022",
+  "endDate": "31/07/2022",
+  "scalerName": "None",
+  "supportFilters": {},
+  "staticThreshold": null
+}

scripts/run_training_test.py

@@ -1,12 +1,19 @@
 #!/usr/bin/env python3
 """Utility script to trigger the training workflow end-to-end for testing.
 
-Steps performed:
+Steps performed (default):
 1. Upload the CSV test dataset to MinIO using the configured `mc` alias.
 2. Insert a new experiment_run record in Postgres and capture the generated ID.
 3. Trigger the Temporal `train_model` workflow with the correct payload.
 
-Prerequisites:
+Local diagnosis (--local / --validate-only):
+- --validate-only: Validates scenario parameters only (no MinIO, Postgres, Temporal).
+- --local: Runs the same training pipeline locally (validate + load CSV + train +
+  after_train_calculation). Use to get full Python tracebacks for debugging.
+  Does not upload to MinIO, insert DB, or start Temporal. By default skips
+  MLflow save; use --local-save-mlflow to also test saving to MLflow.
+
+Prerequisites (default flow):
 - `mc` CLI configured with alias defined in MINIO_ALIAS.
 - PostgreSQL accessible with credentials in environment variables or defaults.
 - Temporal server reachable without TLS on TEMPORAL_HOST / TEMPORAL_NAMESPACE.
@@ -23,6 +30,7 @@ import subprocess
 import sys
 import uuid
 from datetime import datetime, timedelta
+from io import BytesIO
 from pathlib import Path
 
 import psycopg2
@@ -220,6 +228,113 @@ async def trigger_temporal_workflow(workflow_input: dict) -> str:
     return workflow_id
 
 
+def _build_local_payload(request_data: dict, csv_path: Path) -> dict:
+    """Build workflow payload for local run (no real experiment_run_id)."""
+    return build_workflow_payload(
+        experiment_run_id=0,
+        file_name=csv_path.name,
+        request_data=request_data,
+    )
+
+
+def run_validate_only(scenario_name: str) -> dict:
+    """Validate scenario parameters only. No MinIO, Postgres, or Temporal.
+
+    Returns:
+        dict: {'success': bool, 'error': str | None, 'scenario': str}
+    """
+    from model_manager.utils.models.train_model_params import TrainModelParams
+
+    result = {'scenario': scenario_name, 'success': False, 'error': None}
+    try:
+        request_data = load_scenario(scenario_name)
+    except FileNotFoundError as exc:
+        result['error'] = str(exc)
+        return result
+
+    payload = _build_local_payload(request_data, Path('local.csv'))
+    try:
+        train_params = TrainModelParams.from_dict(payload)
+        train_params.validate_business_rules()
+        result['success'] = True
+    except (ValueError, TypeError, KeyError) as e:
+        result['error'] = str(e)
+    return result
+
+
+def run_local_pipeline(
+    scenario_name: str,
+    csv_path: Path,
+    save_mlflow: bool = False,
+) -> dict:
+    """Run the same training pipeline locally (validate + train + after_train).
+
+    Reads CSV from disk, runs TrainingRepository.train and after_train_calculation.
+    Optionally saves to MLflow if save_mlflow is True (requires MLflow env).
+
+    Returns:
+        dict: {'success': bool, 'error': str | None, 'scenario': str, ...}
+    """
+    from model_manager.utils.logger_helper import get_logger
+    from model_manager.utils.models.train_model_params import TrainModelParams
+    from model_manager.utils.repository.training_repository import TrainingRepository
+
+    result = {
+        'scenario': scenario_name,
+        'success': False,
+        'error': None,
+    }
+    try:
+        request_data = load_scenario(scenario_name)
+    except FileNotFoundError as exc:
+        result['error'] = str(exc)
+        return result
+
+    if not csv_path.is_absolute():
+        csv_path = PROJECT_ROOT / csv_path
+    _ensure_source_file(csv_path)
+    payload = _build_local_payload(request_data, csv_path)
+    try:
+        train_params = TrainModelParams.from_dict(payload)
+        train_params.validate_business_rules()
+    except (ValueError, TypeError, KeyError) as e:
+        result['error'] = f'Validation failed: {e}'
+        return result
+
+    logger = get_logger(__name__)
+    training_repository = TrainingRepository(logger)
+
+    with open(csv_path, 'rb') as f:
+        file_content = BytesIO(f.read())
+
+    try:
+        train_result = training_repository.train(file_content, train_params)
+        train_result = training_repository.after_train_calculation(
+            train_params, train_result
+        )
+    except Exception as e:
+        result['error'] = str(e)
+        raise  # re-raise so caller gets full traceback for diagnosis
+
+    if save_mlflow:
+        from model_manager.utils.connectors_config import build_mlflow_config
+        from model_manager.utils.repository.model_repository import ModelRepository
+
+        mlflow_config = build_mlflow_config()
+        model_repository = ModelRepository(
+            url=mlflow_config['url'],
+            username=mlflow_config['username'],
+            password=mlflow_config['password'],
+            logger=logger,
+        )
+        train_result = model_repository.save_model(train_result)
+
+    result['success'] = True
+    result['run_name'] = getattr(train_result, 'run_name', None)
+    result['run_dir'] = getattr(train_result, 'run_dir', None)
+    return result
+
+
 def parse_args() -> argparse.Namespace:
     """Parse command line arguments."""
     parser = argparse.ArgumentParser(
@@ -241,6 +356,15 @@ Examples:
 
   # Run all scenarios
   python scripts/run_training_test.py --all
+
+  # Validate scenario parameters only (no external services)
+  python scripts/run_training_test.py --scenario linear-regression-basic --validate-only
+
+  # Run training pipeline locally to diagnose errors (full traceback)
+  python scripts/run_training_test.py --scenario linear-regression-basic --local --csv docs/test-model-data.csv
+
+  # Local run and save to MLflow (requires MLflow env)
+  python scripts/run_training_test.py --scenario linear-regression-basic --local --local-save-mlflow
 """,
     )
     parser.add_argument(
@@ -268,6 +392,21 @@ Examples:
         action='store_true',
         help='Run all available test scenarios sequentially.',
     )
+    parser.add_argument(
+        '--validate-only',
+        action='store_true',
+        help='Only validate scenario parameters (no MinIO, Postgres, Temporal).',
+    )
+    parser.add_argument(
+        '--local',
+        action='store_true',
+        help='Run training pipeline locally (validate + train from CSV) to get full tracebacks.',
+    )
+    parser.add_argument(
+        '--local-save-mlflow',
+        action='store_true',
+        help='With --local, also save the model to MLflow (requires MLflow env).',
+    )
     return parser.parse_args()
 
 
@@ -405,12 +544,47 @@ def main() -> None:
         failed_count = sum(1 for r in results if not r['success'])
         sys.exit(1 if failed_count > 0 else 0)
 
+    # Validate-only mode: no external services
+    if args.validate_only:
+        if not args.scenario:
+            print('Error: --scenario is required with --validate-only.', file=sys.stderr)
+            sys.exit(1)
+        result = run_validate_only(args.scenario)
+        if result['success']:
+            print(f"Validation OK: {result['scenario']}")
+        else:
+            print(f"Validation failed: {result['error']}", file=sys.stderr)
+            sys.exit(1)
+        return
+
+    # Local pipeline: same code path as worker, full traceback on error
+    if args.local:
+        if not args.scenario:
+            print('Error: --scenario is required with --local.', file=sys.stderr)
+            sys.exit(1)
+        print(f'Running local pipeline: {args.scenario} (CSV: {args.csv})')
+        result = run_local_pipeline(
+            args.scenario,
+            args.csv,
+            save_mlflow=args.local_save_mlflow,
+        )
+        if not result['success']:
+            print(f"Error: {result['error']}", file=sys.stderr)
+            sys.exit(1)
+        out = {'scenario': result['scenario'], 'success': True}
+        if result.get('run_name') is not None:
+            out['run_name'] = result['run_name']
+        if result.get('run_dir') is not None:
+            out['run_dir'] = result['run_dir']
+        print(json.dumps(out, indent=2))
+        return
+
     # Require scenario argument if not listing or running all
     if not args.scenario:
         print('Error: --scenario or --all is required. Use --list to see available scenarios.', file=sys.stderr)
         sys.exit(1)
 
-    # Run single scenario
+    # Run single scenario (MinIO + Postgres + Temporal)
     print(f'Running scenario: {args.scenario}')
     result = run_single_scenario(args.scenario, args.csv)