SIENTIAPDE-1222

Refactor datetime index handling in MLFlow and MLFlowRepository

- Moved the detect_and_parse_datetime_index method from MLFlow to MLFlowRepository for better organization and reusability.
- Updated the method to include enhanced logging and error handling for invalid datetime formats.
- Adjusted the transform method in MLFlowRepository to utilize the new datetime index parsing logic.
- Added unit tests for both valid and invalid datetime index cases to ensure robustness.

tests/laborious/activities/test_mlflow.py

@@ -1,8 +1,9 @@
+from datetime import datetime
 from unittest.mock import ANY, MagicMock, patch
 
 import numpy as np
-from pandas import DataFrame
-from pytest import fixture, mark
+from pandas import DataFrame, Timestamp
+from pytest import fixture, mark, raises
 from laborious.activities.mlflow import MLFlow
 from sientia_do.notifications.models import NotificationLevel
 
@@ -58,7 +59,7 @@ metadata = {
 @mark.asyncio
 @patch("laborious.activities.mlflow.DataFrame")
 @patch("laborious.activities.mlflow.max")
-async def test_request_transform(mock_max, mock_dataframe, mlflow):
+async def test_request_transform_success(mock_max, mock_dataframe, mlflow):
     mock_max.return_value = '2024-01-02'
     # Mock input data
     input_data = {

tests/laborious/utils/repository/test_model_repository.py

@@ -2,7 +2,8 @@ from unittest.mock import ANY, MagicMock, call, patch
 import numpy as np
 from pandas import DataFrame
 import pytest
-from laborious.utils.repository import model_repository
+from datetime import datetime, timezone
+from pandas import Timestamp
 from laborious.utils.repository.model_repository import MLFlowRepository
 
 
@@ -22,18 +23,113 @@ def mlflow_repository():
         return repo
 
 
+metadata = {
+    "metadata": {
+        "model_id": "test_model",
+        "model_name": "test_model",
+        "workflow_name": "test_workflow",
+        "schema_name": "test_schedule",
+    },
+}
+
+
+invalid_cases = [
+    (
+        {
+            'value': {
+                '2024-01-01 12:00:00': 1,
+                2024: 2
+            }
+        }
+    ),
+    (
+        {
+            'value': {
+                '2024-01-01': 1,
+                '2024-01-02': 2
+            }
+        }
+    ),
+    (
+        {
+            'value': {
+                1: 1,
+                2: 2
+            }
+        }
+    )
+]
+
+
+@pytest.mark.parametrize("data", invalid_cases)
+def test_detect_and_parse_datetime_index_error_cases(mlflow_repository, data):
+    input_data = DataFrame(
+        data
+    )
+
+    with pytest.raises(ValueError) as e:
+        mlflow_repository.detect_and_parse_datetime_index(
+            input_data, metadata['metadata'])
+
+        assert str(e) == "Index must be all timestamp like column. Valid formats are: pandas Timestamp, datetime, string in format %Y-%m-%d %H:%M:%S"
+
+
+valid_cases = [
+    (
+        {
+            'value': {
+                '2024-01-01 12:00:00+0000': 1,
+                '2024-01-02 12:00:00+0000': 2
+            }
+        }, ['2024-01-01 12:00:00+0000', '2024-01-02 12:00:00+0000']
+    ),
+    (
+        {
+            'value': {
+                datetime(2025, 1, 1, 12, 0, 0, tzinfo=timezone.utc): 1,
+                datetime(2025, 1, 2, 12, 0, 0, tzinfo=timezone.utc): 2
+            }
+        }, ['2025-01-01 12:00:00+0000', '2025-01-02 12:00:00+0000']
+    ),
+    (
+        {
+            'value': {
+                Timestamp(2026, 1, 1, 12, 0, 0, tzinfo=timezone.utc): 1,
+                Timestamp(2026, 1, 2, 12, 0, 0, tzinfo=timezone.utc): 2
+            }
+        }, ['2026-01-01 12:00:00+0000', '2026-01-02 12:00:00+0000']
+    ),
+]
+
+
+@pytest.mark.parametrize("data,expected", valid_cases)
+def test_detect_and_parse_datetime_index_valid_format(mlflow_repository, data, expected):
+    input_data = DataFrame(data)
+
+    response = mlflow_repository.detect_and_parse_datetime_index(
+        input_data, metadata['metadata'])
+
+    assert response.index.tolist() == expected
+
+
 def test_transform_success(mlflow_repository):
     data = 'data'
     model_name = 'model'
 
-    output = mlflow_repository.transform(model_name, data, 1)
+    mlflow_repository.detect_and_parse_datetime_index = MagicMock()
+
+    output = mlflow_repository.transform(
+        model_name, data, {}, metadata['metadata'])
 
     mlflow_repository.model_serving.get_cached_transform.assert_called_once_with(
-        model_name, data, 1)
+        model_name, data, 0, 'sklearn', False, 'model', 'predict')
+
+    mlflow_repository.detect_and_parse_datetime_index.assert_called_once_with(
+        mlflow_repository.model_serving.get_cached_transform.return_value, metadata['metadata'])
 
     assert output == {
         'success': True,
-        'content': mlflow_repository.model_serving.get_cached_transform.return_value.to_dict.return_value
+        'content': mlflow_repository.detect_and_parse_datetime_index.return_value.to_dict.return_value
     }
 
 
@@ -44,10 +140,11 @@ def test_transform_error(mlflow_repository):
     mlflow_repository.model_serving.get_cached_transform.side_effect = Exception(
         'error')
 
-    output = mlflow_repository.transform(model_name, data, 1)
+    output = mlflow_repository.transform(
+        model_name, data, {}, metadata['metadata'])
 
     mlflow_repository.model_serving.get_cached_transform.assert_called_once_with(
-        model_name, data, 1)
+        model_name, data, 0, 'sklearn', False, 'model', 'predict')
 
     assert output == {
         'success': False,