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SIENTIAPDE-1231

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Update .gitignore and refactor metrics.py for improved logging and consistency

- Added coverage.xml to .gitignore to prevent tracking of coverage reports.
- Refactored metric labels in metrics.py for consistency in string formatting and improved readability.
- Enhanced logging messages in various activities to ensure uniformity in message formatting.
This commit is contained in:
Vitor Pimentel dos Santos
2025-10-15 16:00:18 -03:00
parent a5d2b0d3fd
commit ac795c7c53
39 changed files with 4122 additions and 2602 deletions
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46
laborious/workflows/predictions_batch.py
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@@ -1,14 +1,16 @@
from temporalio import workflow
with workflow.unsafe.imports_passed_through():
from laborious.activities.activities import Activities
from typing import Any
from sientia_do.temporal.policies import retry_policy
from datetime import timedelta
from typing import Any
from sientia_do.temporal.policies import retry_policy
from laborious.activities.activities import Activities
@workflow.defn(name="predictions_batch")
class PredictionsBatch():
@workflow.defn(name='predictions_batch')
class PredictionsBatch:
"""
Main batch prediction workflow for the Laborious system.
@@ -74,7 +76,7 @@ class PredictionsBatch():
'schedule_name': input_data['schedule_name'],
'model_name': input_data['model_name'],
'model_id': input_data['model_id'],
'workflow_name': 'predictions_batch'
'workflow_name': 'predictions_batch',
}
}
@@ -84,10 +86,10 @@ class PredictionsBatch():
{
**metadata,
'query': input_data['query'],
'datetime_columns': input_data.get('datetime_columns', [])
'datetime_columns': input_data.get('datetime_columns', []),
},
retry_policy=retry_policy,
start_to_close_timeout=timedelta(seconds=300)
start_to_close_timeout=timedelta(seconds=300),
)
# Prepare input for prediction_process workflow
@@ -98,28 +100,18 @@ class PredictionsBatch():
'table_name': input_data['table_name'],
'model_id': input_data['model_id'],
'model_name': input_data['model_name'],
'input_filters': input_data.get('input_filters', {
'EMPTY_DATA': {
'POLICY': 'STOP'
}
}),
'mlflow_transform_filters': input_data.get('mlflow_transform_filters', {
'API_ERROR': {
'POLICY': 'STOP'
}
}),
'mlflow_predict_filters': input_data.get('mlflow_predict_filters', {
'API_ERROR': {
'POLICY': 'STOP'
}
}),
'input_filters': input_data.get('input_filters', {'EMPTY_DATA': {'POLICY': 'STOP'}}),
'mlflow_transform_filters': input_data.get(
'mlflow_transform_filters', {'API_ERROR': {'POLICY': 'STOP'}}
),
'mlflow_predict_filters': input_data.get(
'mlflow_predict_filters', {'API_ERROR': {'POLICY': 'STOP'}}
),
'model_config': input_data.get('model_config', {}),
'path_priority': input_data.get('path_priority', ['STOP', 'CONTINUE', 'REPEAT']),
'opc_output_config': input_data.get('opc_output_config', {}),
'prediction_store_policy': input_data.get(
'prediction_store_policy', 'lts:1')
'prediction_store_policy': input_data.get('prediction_store_policy', 'lts:1'),
}
# Execute prediction process workflow
await workflow.execute_child_workflow(
'prediction_process', prediction_input)
await workflow.execute_child_workflow('prediction_process', prediction_input)