b68674fe64
Enhance MLFlowRepository and Activities classes with new methods and metrics - Added `check_artifact_exists` method to MLFlowRepository for verifying artifact presence in the MLflow Model Registry. - Implemented `get_prediction_data` method in MLFlowRepository to retrieve prediction data from models. - Updated Activities class to integrate ModelMetrics for improved metrics handling. - Enhanced tests for artifact existence checks and prediction data retrieval, ensuring robust coverage for new functionalities. - Updated various workflows to include `transform_table_name` in input data for better data handling.
99 lines
3.4 KiB
Python
99 lines
3.4 KiB
Python
from temporalio import workflow
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with workflow.unsafe.imports_passed_through():
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from datetime import timedelta
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from typing import Any
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from sientia_do.temporal.policies import retry_policy
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from laborious.activities.activities import Activities
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from sientia_do.temporal.constants import DATETIME_FORMAT_WITH_TZ
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@workflow.defn(name='drift')
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class Drift:
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@workflow.run
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async def run(self, input_data: dict[str, Any]):
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"""
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Execute the drift workflow.
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This method orchestrates the complete drift process by:
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1. Loading data using the provided custom SQL query
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2. Preparing prediction configuration and filters
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3. Delegating to the PredictionProcess workflow for ML operations
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"""
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metadata = {
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'metadata': {
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'schedule_name': input_data['schedule_name'],
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'model_name': input_data['model_name'],
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'model_id': input_data['model_id'],
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'workflow_name': 'drift',
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}
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}
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gathering_query = f"""
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SELECT *
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FROM {input_data['schema']}.{input_data['source_table_name']}
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WHERE
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model_id = {input_data['model_id']} AND
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timestamp > NOW() - INTERVAL '{input_data['interval']} minutes'
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ORDER BY timestamp ASC
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"""
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target_data_handler = workflow.start_local_activity_method(
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Activities.load_custom_query,
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{
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**metadata,
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'query': gathering_query,
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'datetime_columns': ['timestamp', 'created_at'],
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},
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retry_policy=retry_policy,
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start_to_close_timeout=timedelta(seconds=300),
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)
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reference_data_handler = workflow.start_local_activity_method(
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Activities.get_reference_data,
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{
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**metadata,
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'model_name': input_data['model_name']
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},
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retry_policy=retry_policy,
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start_to_close_timeout=timedelta(seconds=300),
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)
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target_data = await target_data_handler
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reference_data = await reference_data_handler
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if not target_data:
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return
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drift_data = await workflow.execute_local_activity_method(
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Activities.calculate_drift,
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{
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**metadata,
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'target_data': target_data,
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'reference_data': reference_data,
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'model_name': input_data['model_name'],
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'model_id': input_data['model_id'],
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'target_name': input_data['target_name'],
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'drift_metrics': input_data['drift_metrics'],
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'chunk_period': input_data.get('chunk_period', 'min'),
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},
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retry_policy=retry_policy,
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start_to_close_timeout=timedelta(seconds=300),
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)
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if drift_data:
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await workflow.execute_activity_method(
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Activities.export_data_to_postgres,
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{
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**metadata,
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'data': drift_data,
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'schema': input_data['schema'],
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'table_name': input_data['target_table_name'],
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'timestamp_conversion': {'column': 'timestamp', 'format': DATETIME_FORMAT_WITH_TZ},
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},
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retry_policy=retry_policy,
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start_to_close_timeout=timedelta(seconds=300),
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) |