a88a15c60a
SIENTIAPDE-1273 Enhance security analysis and SQL injection handling - Added skip for potential SQL injection false positives in Bandit configuration. - Updated validate.sh to use the pyproject.toml configuration for Bandit security analysis. - Refactored code to replace ensure_dataframe utility with direct DataFrame usage in multiple activities, improving clarity and reducing dependencies. - Removed the deprecated dataframe_utils module to streamline the codebase.
355 lines
14 KiB
Python
355 lines
14 KiB
Python
from temporalio import activity, workflow
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with workflow.unsafe.imports_passed_through():
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import time
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import traceback
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import warnings
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from typing import Any
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import numpy as np
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from pandas import DataFrame, Index, to_datetime
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from sientia.ModelAnalysis import ModelAnalysis
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from sientia_do.notifications.handlers import CoreNotificationHandler as NotificationHandler
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from sientia_do.notifications.models import NotificationLevel
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from sientia_do.observability.logger import Logger
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from sientia_do.observability.metrics_controller import MetricsController
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from sientia_do.observability.sientia_monitoring import SientiaMonitoring
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from sientia_do.temporal.constants import DATETIME_FORMAT, DATETIME_FORMAT_WITH_TZ
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from laborious import metrics
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warnings.filterwarnings('ignore', category=RuntimeWarning, message='Degrees of freedom <= 0')
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warnings.filterwarnings(
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'ignore', category=RuntimeWarning, message='invalid value encountered in scalar divide'
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)
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class ModelMetrics(SientiaMonitoring):
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"""
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Metrics activities for the Laborious system.
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This class provides activities for writing metrics to the Prometheus monitoring system.
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"""
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def __init__(
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self,
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logger: Logger,
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notification_handler: NotificationHandler,
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metrics_controller: MetricsController,
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):
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SientiaMonitoring.__init__(self, logger, notification_handler, metrics_controller)
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def close(self) -> None:
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"""
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Close the model metrics activity and clean up resources.
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"""
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SientiaMonitoring.shutdown(self)
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def __del__(self):
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self.close()
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async def get_drift_metrics(
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self,
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reference_data: DataFrame,
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target_data: DataFrame,
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target_name: str,
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reference_columns: Index,
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drift_metrics: list[str],
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chunk_period: str,
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metadata: dict[str, Any],
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) -> DataFrame:
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"""
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Calculate univariate drift metrics for a model.
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Args:
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model_analysis (ModelAnalysis): Model analysis object
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reference_data (DataFrame): Reference data
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target_data (DataFrame): Target data
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reference_columns (list[str]): Reference columns
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drift_metrics (list[str]): Drift metrics
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metadata (dict[str, Any]): Workflow execution metadata
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"""
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config = {
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'target': target_name,
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'prediction': 'prediction',
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'timestamp': 'timestamp',
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'features': reference_columns,
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}
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model_analysis = ModelAnalysis(config=config)
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self.debug(
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f'Reference data: Size {reference_data.shape} \n{reference_data.head(5).to_string()}',
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metadata,
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)
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self.debug(
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f'Target data: Size {target_data.shape} \n{target_data.head(5).to_string()}', metadata
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)
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core_labels = self.get_core_labels(metadata, operation_type='detect_univariate_drift')
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start_time = time.time()
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try:
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univariate_drift = model_analysis.detect_univariate_drift(
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reference_df=reference_data,
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analysis_df=target_data,
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features=reference_columns,
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timestamp_col=config['timestamp'],
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methods=drift_metrics,
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chunk_period=chunk_period,
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)
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except Exception as e:
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self.error(f'Error detecting univariate drift: {e}', metadata)
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await self.emit_metric(
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metric_object=metrics.MODEL_ANALYZE_ERROR_COUNT, tags=core_labels
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)
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raise e
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await self.observe_lag(start_time, metrics.MODEL_ANALYZE_LAG, core_labels)
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await self.emit_metric(metric_object=metrics.MODEL_ANALYZE_COUNT, tags=core_labels)
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core_labels = self.get_core_labels(metadata, operation_type='detect_multivariate_drift')
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start_time = time.time()
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try:
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multivariate_drift = model_analysis.detect_multivariate_drift(
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reference_df=reference_data,
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analysis_df=target_data,
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features=reference_columns,
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timestamp_col=config['timestamp'],
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chunk_period=chunk_period,
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)
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except Exception as e:
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self.error(f'Error detecting multivariate drift: {e}', metadata)
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await self.emit_metric(
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metric_object=metrics.MODEL_ANALYZE_ERROR_COUNT, tags=core_labels
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)
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raise e
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await self.observe_lag(start_time, metrics.MODEL_ANALYZE_LAG, core_labels)
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await self.emit_metric(metric_object=metrics.MODEL_ANALYZE_COUNT, tags=core_labels)
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start_time = time.time()
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core_labels = self.get_core_labels(metadata, operation_type='get_drift_metrics_dataframe')
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try:
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drift_df = model_analysis.get_drift_metrics_dataframe(
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univariate_drift=univariate_drift,
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multivariate_drift=multivariate_drift,
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)
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except Exception as e:
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self.error(f'Error getting drift metrics: {e}', metadata)
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await self.emit_metric(
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metric_object=metrics.MODEL_ANALYZE_ERROR_COUNT, tags=core_labels
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)
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raise e
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await self.observe_lag(start_time, metrics.MODEL_ANALYZE_LAG, core_labels)
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await self.emit_metric(metric_object=metrics.MODEL_ANALYZE_COUNT, tags=core_labels)
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self.debug(
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f'Drift dataframe: Size {drift_df.shape} \n{drift_df.head(5).to_string()}', metadata
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)
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return drift_df
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@activity.defn(name='calculate_drift')
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async def calculate_drift(self, input_data: dict[str, Any]) -> list[dict]:
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"""
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Calculate drift metrics for a model.
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Args:
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input_data (dict[str, Any]): Input data containing:
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- metadata (dict): Workflow execution metadata
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- model_name (str): Name of the MLFlow model to calculate drift for
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- reference_data (pd.DataFrame): Reference data for the model
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- target_data (pd.DataFrame): Target data for calculating drift
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- target_name (str): Name of the target column
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- drift_metrics (list[str]): List of drift metrics to calculate
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"""
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metadata = input_data['metadata']
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model_name = input_data['model_name']
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model_id = input_data['model_id']
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reference_raw_data = input_data['reference_data']
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target_data = DataFrame(input_data['target_data'])
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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['chunk_period']
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if chunk_period not in ['min', 's']:
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self.error(f'Invalid chunk period: {chunk_period}', metadata)
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raise ValueError(f'Invalid chunk period: {chunk_period}, must be "min" or "s"')
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self.info(f'Calculating drift for model {model_name}', metadata)
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target_data = target_data.pivot(index='timestamp', columns='variable', values='value')
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target_data['timestamp'] = target_data.index
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target_data['timestamp'] = to_datetime(target_data['timestamp'])
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target_data['timestamp'] = target_data['timestamp'].dt.strftime(DATETIME_FORMAT)
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target_data = target_data.reset_index(drop=True)
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target_data.dropna(inplace=True)
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if reference_raw_data is not None:
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self.info('Using reference data', metadata)
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reference_data = DataFrame(reference_raw_data)
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accurate = True
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else:
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# Get 30% first rows of target_data
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self.warning('Using 30% first rows of target data as reference data', metadata)
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target_data.sort_values(by='timestamp', ascending=True, inplace=True)
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reference_data = target_data.head(int(len(target_data) * 0.3))
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accurate = False
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await self.send_notification_async(
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metadata=metadata,
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notification_id='MODEL_METRICS_REFERENCE_DATA_WARNING',
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message='Using 30% first rows of target data as reference data',
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block='model_metrics',
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level=NotificationLevel.WARNING,
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attachment_content=reference_data.to_csv(),
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)
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reference_columns = reference_data.drop(
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columns=[target_name, 'timestamp', 'target', 'prediction'], errors='ignore'
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).columns
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try:
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drift_df = await self.get_drift_metrics(
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reference_data=reference_data,
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target_data=target_data,
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target_name=target_name,
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reference_columns=reference_columns,
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drift_metrics=drift_metrics,
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chunk_period=chunk_period,
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metadata=metadata,
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)
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except Exception as e:
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self.error(f'Error getting drift metrics: {e}', metadata)
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await self.send_notification_async(
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metadata=metadata,
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notification_id='MODEL_METRICS_GET_DRIFT_METRICS_ERROR',
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message=f'Error getting drift metrics: {e}',
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block='model_metrics',
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level=NotificationLevel.ERROR,
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attachment_content=traceback.format_exc(),
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)
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return []
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if drift_df.empty:
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self.warning('No drift metrics found', metadata)
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return []
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# Drop unnecessary columns
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drift_df.drop(columns=['p_value'], inplace=True)
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# Extract timestamps only until minutes
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if chunk_period == 'min':
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target_timestamps = target_data['timestamp'].apply(lambda x: x[:16])
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else:
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target_timestamps = target_data['timestamp']
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# Drop rows where timestamp is not in target data, to avoid save drift from reference
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drift_df = drift_df[drift_df['timestamp'].isin(target_timestamps)]
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if drift_df.empty:
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self.warning(
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'No drift metrics found after dropping rows where timestamp is not in target data',
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metadata,
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)
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return []
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# Rename columns to match database columns
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drift_df.rename(
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columns={
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'metric': 'method',
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'statistic': 'value',
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},
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inplace=True,
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)
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# Drop duplicates
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drift_df.drop_duplicates(
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subset=['timestamp', 'method', 'feature'], keep='first', inplace=True
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)
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drift_df['model_id'] = model_id
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drift_df['accurate'] = accurate
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drift_df['timestamp'] = to_datetime(drift_df['timestamp'])
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drift_df['timestamp'] = drift_df['timestamp'].dt.tz_localize('UTC')
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drift_df['timestamp'] = drift_df['timestamp'].dt.strftime(DATETIME_FORMAT_WITH_TZ)
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self.debug(
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f'Drift dataframe: Size {drift_df.shape} \n{drift_df.head(5).to_string()}', metadata
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)
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self.debug(f'Drift dataframe: {drift_df.head(5).to_string()}', metadata)
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return drift_df.to_dict(orient='records')
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@activity.defn(name='calculate_simple_metrics')
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async def calculate_simple_metrics(self, input_data: dict[str, Any]) -> list[dict]:
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"""
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Calculate simple metrics for a model. Metrics available are:
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- rmse
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- mse
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- mae
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- r2
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- accuracy
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- precision
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- recall
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- f1
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Args:
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input_data (dict[str, Any]): Input data containing:
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- metadata (dict): Workflow execution metadata
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- model_id (str): ID of the MLFlow model
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- target_data (pd.DataFrame): Target data for calculating metrics, containing target and prediction columns
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- metrics (list[str]): List of metrics to calculate
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Returns:
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dict[Hashable, Any]: Dictionary containing the calculated metrics
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"""
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metadata = input_data['metadata']
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model_id = input_data['model_id']
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target_data = DataFrame(input_data['target_data'])
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metrics = input_data['metrics']
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interval_minutes = input_data['interval_minutes']
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data_size = target_data.shape[0]
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output_data = []
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diff = target_data['target'] - target_data['prediction']
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diff_squared = diff**2
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self.info(f'Calculating simple metrics for model {model_id}: {metrics}', metadata)
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for metric in metrics:
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if metric == 'rmse':
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output_data.append({'metric': 'rmse', 'value': np.sqrt(np.mean(diff_squared))})
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elif metric == 'mse':
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output_data.append({'metric': 'mse', 'value': np.mean(diff_squared)})
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elif metric == 'mae':
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output_data.append({'metric': 'mae', 'value': np.mean(np.abs(diff))})
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elif metric == 'r2':
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y_true = target_data['target']
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y_mean = np.mean(y_true)
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ss_res = np.sum(diff_squared)
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ss_tot = np.sum((y_true - y_mean) ** 2)
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# Evita divisão por zero
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if ss_tot == 0:
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r2_score = 0.0
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else:
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r2_score = 1 - (ss_res / ss_tot)
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output_data.append({'metric': 'r2', 'value': r2_score})
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data = DataFrame(output_data)
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data['model_id'] = model_id
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data['timestamp'] = target_data['timestamp'].max()
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data['data_size'] = data_size
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data['interval_minutes'] = interval_minutes
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self.debug(
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f'Simple metrics dataframe: Size {data.shape} \n{data.head(5).to_string()}', metadata
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)
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return data.to_dict(orient='records')
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