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SIENTIAPDE-1110

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Update dependencies and enhance logging in activities; bump version in requirements and values.yaml
This commit is contained in:
vitor-aignosi
2025-06-27 09:17:24 -03:00
parent cefef0b1e9
commit 98ea2a7f75
10 changed files with 87 additions and 59 deletions
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24
laborious/activities/mlflow.py
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@@ -6,9 +6,9 @@ from temporalio import activity, workflow
with workflow.unsafe.imports_passed_through():
from sientia_do.temporal.activities.base import BaseActivity
from sientia_do.notifications.handlers import NotificationHandler
from sientia_do.temporal.utils.logger import Logger
from laborious.utils.repository.model_repository import MLFlowRepository
from typing import Any
from logging import Logger
class MLFlow(BaseActivity):
@@ -36,13 +36,14 @@ class MLFlow(BaseActivity):
Returns:
dict[str, Any]: The transformed data.
"""
self.logger.info('Transforming data...')
metadata = input_data['metadata']
self.debug('Transforming data...', metadata)
data = DataFrame(input_data['data'])
model_name = input_data['model_name']
model_retention = input_data['model_retention']
self.logger.debug("Raw input data:")
self.logger.debug(data)
self.debug("Raw input data:", metadata)
self.debug(data, metadata)
# Sort by created_at in descending order and keep first occurrence of each variable/timestamp pair
data = data.sort_values('created_at', ascending=False).drop_duplicates(
@@ -56,14 +57,14 @@ class MLFlow(BaseActivity):
data.reset_index(inplace=True)
data.columns.name = None
self.logger.debug("Processed input data:")
self.logger.debug(data)
self.debug("Processed input data:", metadata)
self.debug(data, metadata)
response_data = self.model_monitoring_repository.transform(
model_name, data, model_retention)
self.logger.debug("Response data:")
self.logger.debug(response_data)
self.debug("Response data:", metadata)
self.debug(response_data, metadata)
return response_data
@@ -79,18 +80,19 @@ class MLFlow(BaseActivity):
Returns:
dict[str, Any]: The predicted data.
"""
self.logger.info('Predicting data...')
metadata = input_data['metadata']
self.debug('Predicting data...', metadata)
data = DataFrame(input_data['data'])
model_name = input_data['model_name']
model_retention = input_data['model_retention']
self.logger.debug(data)
self.debug(data, metadata)
data.replace(np.nan, None, inplace=True)
response_data = self.model_monitoring_repository.predict(
model_name, data, model_retention)
self.logger.debug(response_data)
self.debug(response_data, metadata)
return response_data