SIENTIAPDE-1255: Refactor data quality gates to training focused metrics and repositories. This commit removes the data quality gates and filters, focusing on training-specific metrics and data repositories. It also updates the README to reflect these changes, including new training metrics and a streamlined data services section.
This commit is contained in:
Bruno Domingues
25
README.md
25
README.md
@@ -167,17 +167,18 @@ The Model Manager system uses a Temporal-based workflow architecture with clear
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#### **Data Services (`model_manager/utils/`)**
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- **Connectors Config**: Environment variable-based configuration management
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- **Repository**: Data access layer for MLFlow operations
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- `model_repository.py`: MLFlow model operations and retraining
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- **Filters**: Data quality validation and MLFlow response filtering
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- `conditional_filters.py`: Input data validation filters
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- `mlflow_filters.py`: MLFlow API response validation filters
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- **Repository**: Data access layer for training operations
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- `training_repository.py`: Training business logic and operations
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- **Models**: Data models and schemas
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- `train_model_params.py`: Training parameters model
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- `train_model_result.py`: Training result model
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- `experiment_status.py`: Experiment status enum
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- **Key Features**:
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- Environment variable-based configuration with sensible defaults
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- Connection pool management and optimization
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- Security credential management
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- Configuration validation and error handling
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- Support for MLFlow model flavors
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- Type-safe data models with validation
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### Data Flow Architecture
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@@ -814,10 +815,10 @@ The Model Manager system exposes comprehensive Prometheus metrics for operationa
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- Labels: `pod_id`, `model_name`, `pipeline_name`
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- Buckets: [0.01, 0.05, 0.1, 0.2, 0.5, 1.0, 2.0, 5.0, 10.0]
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### Data Quality Metrics
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- Filter pass/fail rates through notification system
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- MLFlow API response validation metrics
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- Data quality gate performance tracking
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### Training Metrics
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- Training success/failure rates through notification system
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- Model save performance metrics
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- Experiment status tracking
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## Configuration
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@@ -887,7 +888,7 @@ The project maintains **99%+ code coverage** with comprehensive unit and integra
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pytest tests/ --cov=model_manager --cov-report=term-missing --cov-report=xml --cov-report=html
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# Run specific test file
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pytest tests/activities/test_gates.py -v
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pytest tests/activities/test_training.py -v
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# Run with coverage visualization
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pytest tests/ --cov=model_manager --cov-report=xml
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@@ -1019,7 +1020,7 @@ model_manager/
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4. **Workflow Execution Failures**
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- Review activity error logs and notifications
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- Check data quality filter configurations
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- Check training parameter validation errors
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- Verify input data format and required fields
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### Debug Mode
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@@ -7,13 +7,12 @@ with workflow.unsafe.imports_passed_through():
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from sientia_do.observability.logger import Logger
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from model_manager.activities.experiment_tracking import ExperimentTracking
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from model_manager.activities.gates import Gates
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from model_manager.activities.minio import MinIO
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from model_manager.activities.mlflow import MLFlow
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from model_manager.activities.training import Training
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class Activities(ExperimentTracking, MLFlow, MinIO, Gates, Training):
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class Activities(ExperimentTracking, MLFlow, MinIO, Training):
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"""
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Main activities orchestrator for the Model Manager system.
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@@ -23,9 +22,8 @@ class Activities(ExperimentTracking, MLFlow, MinIO, Gates, Training):
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The class implements multiple inheritance to combine specialized functionality:
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- ExperimentTracking: ML experiment lifecycle tracking and database operations (extends Postgres)
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- MLFlow: Model inference and transformation operations
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- MLFlow: Model saving and artifact management operations
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- MinIO: Object storage operations (file upload/download/delete)
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- Gates: Data quality validation and filtering mechanisms
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- Training: ML model training operations (extends BaseActivity)
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Attributes:
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@@ -102,8 +100,6 @@ class Activities(ExperimentTracking, MLFlow, MinIO, Gates, Training):
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notification_handler=notification_handler,
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)
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Gates.__init__(self, logger=logger, notification_handler=notification_handler)
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Training.__init__(self, logger=logger, notification_handler=notification_handler)
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async def shutdown(self):
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@@ -1,583 +0,0 @@
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from temporalio import activity, workflow
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with workflow.unsafe.imports_passed_through():
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import traceback
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from typing import Any
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from pandas import DataFrame
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from sientia_do.formatters import create_sample_dict
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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.temporal.activities.base import BaseActivity
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from sientia_do.temporal.constants import DATETIME_FORMAT_WITH_TZ, now
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from model_manager import metrics
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from model_manager.utils.filters.conditional_filters import (
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filter_empty_data,
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filter_specific_variables_null_values,
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)
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from model_manager.utils.filters.mlflow_filters import api_error_filter, nan_values_filter
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# Input filter function mappings
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input_filter_functions = {
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'SPECIFIC_VARIABLES_NULL_VALUES': filter_specific_variables_null_values,
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'EMPTY_DATA': filter_empty_data,
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'path_confidence': {'STOP': -1, 'CONTINUE': 2, 'REPEAT': -1},
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}
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# MLFlow response filter function mappings
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mlflow_response_filter_functions = {
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'API_ERROR': api_error_filter,
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'path_confidence': {'STOP': -1, 'CONTINUE': 10, 'REPEAT': -1},
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}
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# MLFlow content filter function mappings
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mlflow_content_filter_functions = {
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'NAN_VALUES': nan_values_filter,
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'EMPTY_DATA': filter_empty_data,
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'path_confidence': {'STOP': -1, 'CONTINUE': 18, 'REPEAT': -1},
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}
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class Gates(BaseActivity):
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"""
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Data quality gates and filtering activities for the Model Manager system.
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This class implements comprehensive data quality validation and filtering
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mechanisms that can be applied at different stages of the prediction pipeline.
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It provides configurable filters with policy-based decision making to ensure
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data integrity and quality throughout the ML workflow.
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The class supports multiple filter types and implements a flexible policy
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system that can be configured for different validation requirements. Each
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filter returns a path decision (STOP, CONTINUE, REPEAT) along with confidence
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scores and detailed comments for monitoring and debugging.
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Attributes:
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input_filter_functions (dict): Mapping of input filter names to functions
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mlflow_response_filter_functions (dict): Mapping of MLFlow response filter names to functions
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mlflow_content_filter_functions (dict): Mapping of MLFlow content filter names to functions
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"""
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def __init__(self, logger: Logger, notification_handler: NotificationHandler):
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"""
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Initialize data quality gates with logging and notification capabilities.
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Args:
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logger: Logger instance for observability and debugging
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notification_handler: Notification handler for alerts and monitoring
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Raises:
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Exception: If BaseActivity initialization fails
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"""
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BaseActivity.__init__(self, logger, notification_handler, set_error_counter=True)
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@activity.defn(name='input_gate')
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async def input_gate(self, input_data: dict[str, Any]) -> tuple[str | None, int, str]:
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"""
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Apply input data quality filters and validation.
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This activity validates input data quality using configurable filters
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before proceeding with ML operations. It applies multiple filter types
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and returns a path decision based on the filter results and configured
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policies.
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The method implements a comprehensive filtering system that:
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1. Applies configured filters to input data
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2. Evaluates filter results against policy configurations
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3. Determines appropriate path decisions (STOP, CONTINUE, REPEAT)
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4. Provides confidence scores and detailed comments
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5. Handles errors gracefully with notification integration
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Args:
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input_data: Configuration and data for input validation
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Required keys:
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- metadata (dict): Workflow execution metadata
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- filters (dict): Filter configuration and policies
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- data (dict): Input data to validate
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- path_priority (list[str]): Priority order for path decisions
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Returns:
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tuple: (path_flag, confidence, comment)
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- path_flag (str | None): Decision path (STOP, CONTINUE, REPEAT, or None)
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- confidence (int): Confidence score for the decision
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- comment (str): Detailed explanation of the decision
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Raises:
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Exception: If filter execution fails or configuration is invalid
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"""
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metadata = input_data['metadata']
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self.info('Performing input gate...', metadata)
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filters = input_data['filters']
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data = DataFrame(input_data['data'])
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path_priority = input_data['path_priority']
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filter_output = []
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self.debug(f'Input data: {data.head(5).to_string()}', metadata)
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self.debug(f'Filters: {filters}', metadata)
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# Apply each configured filter
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for fil, config in filters.items():
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if fil not in input_filter_functions:
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self.error(f'Filter {fil} not found', metadata)
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continue
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try:
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if input_filter_functions[fil](data, config['config']): # type: ignore[operator]
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self.debug(f'Data not passed the input filter {fil}:{config}', metadata)
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filter_output.append(config['policy']) # type: ignore[index]
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except Exception as e: # noqa: BLE001
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trace = traceback.format_exc()
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self.send_notification(
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metadata=metadata,
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notification_id=f'INTPUT_GATE_ERROR__{fil}',
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message=f'Error in filter {fil}:{config}: \n {e}',
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block='input_gate',
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level=NotificationLevel.ERROR,
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attachment_content=trace,
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)
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for path_flag in path_priority:
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if path_flag in filter_output:
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self.info(f'Input gate result: {path_flag}', metadata)
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return (
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path_flag,
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input_filter_functions['path_confidence'][path_flag], # type: ignore[index]
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'Input data with bad quality',
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)
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self.info('Nothing was filtered by the input gate', metadata)
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return None, 0, ''
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@activity.defn(name='mlflow_response_gate')
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async def mlflow_response_gate(self, input_data: dict[str, Any]) -> tuple[str | None, int, str]:
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"""
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Validate MLFlow API response quality and integrity.
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This activity validates MLFlow API responses to ensure they meet quality
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standards before proceeding with further processing. It applies response-specific
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filters and determines appropriate path decisions based on response quality.
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The method implements response validation that:
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1. Applies MLFlow response-specific filters
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2. Evaluates API response quality and integrity
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3. Determines path decisions based on response validation results
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4. Provides confidence scores and detailed validation comments
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5. Handles API errors and response validation failures
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Args:
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input_data: Configuration and data for response validation
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Required keys:
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- metadata (dict): Workflow execution metadata
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- filters (dict): Response filter configuration and policies
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- data (dict): MLFlow API response data to validate
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- type (str): Type of MLFlow operation (transform, predict)
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- path_priority (list[str]): Priority order for path decisions
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Returns:
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tuple: (path_flag, confidence, comment)
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- path_flag (str | None): Decision path (STOP, CONTINUE, REPEAT, or None)
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- confidence (int): Confidence score for the decision
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- comment (str): Detailed explanation of the decision
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Raises:
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Exception: If response validation fails or configuration is invalid
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"""
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metadata = input_data['metadata']
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self.info('Performing mlflow response gate...', metadata)
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filters = input_data['filters']
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data = input_data['data']
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gate_type = input_data['type']
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path_priority = input_data['path_priority']
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filter_output = []
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self.debug(f'Input data: \n {create_sample_dict(data, max_items=5, max_depth=2)}', metadata)
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self.debug(f'Filters: {filters}', metadata)
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comments = []
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for fil, config in filters.items():
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if fil not in mlflow_response_filter_functions:
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self.error(f'Filter {fil} not found', metadata)
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continue
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try:
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if mlflow_response_filter_functions[fil](data, config): # type: ignore[operator]
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filter_output.append(config['policy']) # type: ignore[index]
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comments.append(data['content']['message'])
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self.send_notification(
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metadata=metadata,
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notification_id=f'{gate_type.upper()}_GATE_RESPONSE_FILTER__{fil}',
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message=data['content']['message'],
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block='mlflow_gate',
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level=NotificationLevel.ERROR,
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attachment_content=data['content']['traceback'],
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)
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except Exception as e: # noqa: BLE001
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trace = traceback.format_exc()
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self.send_notification(
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metadata=metadata,
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notification_id=f'MLFLOW_GATE_RESPONSE_FILTER__{fil}',
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message=f'Error in filter {fil}:{config}: \n {e}',
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block='mlflow_gate',
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level=NotificationLevel.ERROR,
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attachment_content=trace,
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)
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for path_flag in path_priority:
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if path_flag in filter_output:
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self.info(f'Mlflow response gate result: {path_flag}', metadata)
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return (
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path_flag,
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mlflow_response_filter_functions['path_confidence'][path_flag], # type: ignore[index]
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', '.join(comments),
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)
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self.info('Nothing was filtered by the mlflow response gate', metadata)
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return None, 0, ''
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@activity.defn(name='mlflow_content_gate')
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async def mlflow_content_gate(self, input_data: dict[str, Any]) -> tuple[str | None, int, str]:
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"""
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Validate MLFlow prediction content quality and integrity.
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This activity validates the content of MLFlow predictions to ensure they
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meet quality standards before export and persistence. It applies content-specific
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filters and determines appropriate path decisions based on content quality.
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The method implements content validation that:
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1. Applies MLFlow content-specific filters
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2. Evaluates prediction content quality and integrity
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3. Determines path decisions based on content validation results
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4. Provides confidence scores and detailed validation comments
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5. Handles content validation failures and quality issues
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Args:
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input_data: Configuration and data for content validation
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Required keys:
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- metadata (dict): Workflow execution metadata
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- filters (dict): Content filter configuration and policies
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- data (dict): MLFlow prediction content to validate
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- type (str): Type of MLFlow operation (transform, predict)
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- path_priority (list[str]): Priority order for path decisions
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Returns:
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tuple: (path_flag, confidence, comment)
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- path_flag (str | None): Decision path (STOP, CONTINUE, REPEAT, or None)
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- confidence (int): Confidence score for the decision
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- comment (str): Detailed explanation of the decision
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Raises:
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Exception: If content validation fails or configuration is invalid
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"""
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metadata = input_data['metadata']
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self.info('Performing mlflow content gate...', metadata)
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filters = input_data['filters']
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data = DataFrame(input_data['data'])
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gate_type = input_data['type']
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path_priority = input_data['path_priority']
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filter_output = []
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self.debug(f'Input data:\n {data.head(5).to_string()}', metadata)
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self.debug(f'Filters: \n {create_sample_dict(filters)}', metadata)
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for fil, config in filters.items():
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if fil not in mlflow_content_filter_functions:
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continue
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try:
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if mlflow_content_filter_functions[fil](data, config): # type: ignore[operator]
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filter_output.append(config['policy']) # type: ignore[index]
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self.send_notification(
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metadata=metadata,
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notification_id=f'{gate_type.upper()}_GATE_CONTENT_FILTER__{fil}',
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message=f'Data not passed the content filter {fil}:{config}',
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block='mlflow_gate',
|
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level=NotificationLevel.WARNING,
|
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attachment_content=data.to_string(),
|
||||
)
|
||||
except Exception as e: # noqa: BLE001
|
||||
trace = traceback.format_exc()
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self.send_notification(
|
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metadata=metadata,
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notification_id=f'MLFLOW_GATE_CONTENT_FILTER__{fil}',
|
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message=f'Error in filter {fil}:{config}: \n {e}',
|
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block='mlflow_gate',
|
||||
level=NotificationLevel.ERROR,
|
||||
attachment_content=trace,
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||||
)
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||||
|
||||
for path_flag in path_priority:
|
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if path_flag in filter_output:
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self.info(f'Mlflow content gate result: {path_flag}', metadata)
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||||
return (
|
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path_flag,
|
||||
mlflow_content_filter_functions['path_confidence'][path_flag], # type: ignore[index]
|
||||
'Transformed data not passed the content filter',
|
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)
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self.info('Nothing was filtered by the mlflow content gate', metadata)
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return None, 0, ''
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def get_prediction_store_policy(
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self, prediction_store_policy: str, metadata: dict[str, Any]
|
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) -> tuple[str, int]:
|
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"""
|
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Parse and validate prediction store policy configuration.
|
||||
|
||||
This method parses prediction store policy strings in the format 'type:value'
|
||||
and validates them against allowed policy types and values. It provides
|
||||
sensible defaults for invalid configurations and logs policy validation
|
||||
failures for operational monitoring.
|
||||
|
||||
Supported Policy Types:
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||||
- 'lts': Latest timestamp - sorts data by timestamp descending
|
||||
- 'erl': Earliest timestamp - sorts data by timestamp ascending
|
||||
|
||||
Args:
|
||||
prediction_store_policy (str): Policy string in format 'type:value'
|
||||
metadata (dict[str, Any]): Context metadata for logging and notifications
|
||||
|
||||
Returns:
|
||||
tuple[str, int]: (policy_type, policy_value)
|
||||
- policy_type (str): Validated policy type ('lts' or 'erl')
|
||||
- policy_value (int): Number of rows to retain
|
||||
"""
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||||
policy_elements = prediction_store_policy.split(':')
|
||||
|
||||
if len(policy_elements) < 2:
|
||||
self.error(
|
||||
f'Invalid prediction store policy: {prediction_store_policy}, using default policy',
|
||||
metadata,
|
||||
)
|
||||
return 'lts', 1
|
||||
|
||||
policy_type = policy_elements[0]
|
||||
policy_value = policy_elements[1]
|
||||
|
||||
# If the policy_type is not lts or erl, we use the default policy
|
||||
# If the policty_value is not a number or 0, we use the default policy
|
||||
if (
|
||||
policy_type not in ['lts', 'erl']
|
||||
or not policy_value.isdigit()
|
||||
or int(policy_value) == 0
|
||||
):
|
||||
self.error(
|
||||
f'Invalid prediction store policy: {prediction_store_policy}, using default policy',
|
||||
metadata,
|
||||
)
|
||||
return 'lts', 1
|
||||
|
||||
return policy_type, int(policy_value)
|
||||
|
||||
@activity.defn(name='format_prediction')
|
||||
async def format_prediction(self, input_data: dict[str, Any]) -> dict[Any, Any]:
|
||||
"""
|
||||
Format prediction data according to configured storage policies.
|
||||
|
||||
This method formats prediction data for storage and export operations.
|
||||
It applies timestamp-based sorting policies, adds metadata fields,
|
||||
and ensures data consistency before persistence. The method supports
|
||||
multiple storage policies for flexible data retention strategies.
|
||||
|
||||
Storage Policies:
|
||||
- 'lts:N': Latest timestamp - retains N most recent predictions
|
||||
- 'erl:N': Earliest timestamp - retains N oldest predictions
|
||||
|
||||
Args:
|
||||
input_data (dict): Input data containing:
|
||||
- data (dict[str, Any]): Raw prediction data to format
|
||||
- timestamp (str): Default timestamp if data lacks timestamp column
|
||||
- model_id (str): Unique identifier for the ML model
|
||||
- prediction_confidence (float): Confidence score for the prediction
|
||||
- prediction_store_policy (str): Storage policy in format 'type:value'
|
||||
|
||||
Returns:
|
||||
dict: Formatted prediction data ready for storage and export
|
||||
"""
|
||||
metadata = input_data['metadata']
|
||||
prediction_store_policy = input_data['prediction_store_policy']
|
||||
self.info('Formatting prediction...', metadata)
|
||||
|
||||
data = DataFrame(input_data['data'])
|
||||
|
||||
# Create timestamp column from index and reset index
|
||||
data['timestamp'] = data.index
|
||||
data = data.reset_index(drop=True)
|
||||
|
||||
self.debug(f'Prediction store policy: {prediction_store_policy}', metadata)
|
||||
self.debug(f'Prediction data: {data.head(5).to_string()}', metadata)
|
||||
|
||||
policy_type, policy_value = self.get_prediction_store_policy(
|
||||
prediction_store_policy, metadata
|
||||
)
|
||||
|
||||
# If data has no timestamp, we use the default timestamp and not sort the data
|
||||
self.info(
|
||||
f'Sorting data by timestamp and applying policy: {policy_type}:{policy_value}', metadata
|
||||
)
|
||||
|
||||
# If policy_type is lts, we need to sort the data by timestamp descending and take the first policy_value rows
|
||||
if policy_type == 'lts':
|
||||
self.debug('Sorting data by timestamp descending', metadata)
|
||||
data = data.sort_values(by='timestamp', ascending=False)
|
||||
# If policy_type is erl, we need to sort the data by timestamp ascending and take the first policy_value rows
|
||||
elif policy_type == 'erl':
|
||||
self.debug('Sorting data by timestamp ascending', metadata)
|
||||
data = data.sort_values(by='timestamp', ascending=True)
|
||||
else:
|
||||
self.error(f'Invalid policy type: {policy_type}, using default policy', metadata)
|
||||
raise ValueError(f'Invalid policy type: {policy_type}')
|
||||
|
||||
data = data.head(int(policy_value))
|
||||
|
||||
data['model_id'] = input_data['model_id']
|
||||
data['prediction_confidence'] = input_data['prediction_confidence']
|
||||
data['prediction_status'] = 'Good'
|
||||
data['comments'] = ''
|
||||
data = data.sort_values(by='timestamp', ascending=False)
|
||||
data = data.reset_index(drop=True)
|
||||
|
||||
self.info(f'Prediction formatted: {len(data)} rows', metadata)
|
||||
self.debug(f'Prediction data: {data.head(5).to_string()}', metadata)
|
||||
|
||||
return data.to_dict()
|
||||
|
||||
@activity.defn(name='format_default_prediction')
|
||||
async def format_default_prediction(self, input_data: dict[str, Any]) -> dict[Any, Any]:
|
||||
"""
|
||||
Create and format default prediction data for error conditions.
|
||||
|
||||
This method generates default prediction data when the main prediction
|
||||
pipeline encounters errors or quality issues. It creates a standardized
|
||||
data structure with zero values for predictions and useful metadata
|
||||
for operational monitoring and debugging.
|
||||
|
||||
The default prediction serves as a fallback mechanism to:
|
||||
1. Maintain data pipeline continuity during failures
|
||||
2. Provide operational visibility into prediction quality issues
|
||||
3. Enable downstream systems to handle error conditions gracefully
|
||||
4. Support debugging and troubleshooting efforts
|
||||
|
||||
Args:
|
||||
input_data (dict): Input data containing:
|
||||
- timestamp (str): Timestamp for the default prediction
|
||||
- model_id (str): Unique identifier for the ML model
|
||||
- prediction_confidence (float): Confidence score (typically low for errors)
|
||||
- comment (str): Error description or operational comment
|
||||
|
||||
Returns:
|
||||
dict: Formatted default prediction data with error indicators
|
||||
"""
|
||||
|
||||
metadata = input_data['metadata']
|
||||
self.debug('Formatting default prediction...', metadata)
|
||||
|
||||
data = DataFrame(
|
||||
{
|
||||
'prediction': [0],
|
||||
'response_time': [0],
|
||||
'timestamp': [input_data['timestamp']],
|
||||
'model_id': [input_data['model_id']],
|
||||
'prediction_confidence': [input_data['prediction_confidence']],
|
||||
'prediction_status': ['Bad'],
|
||||
'comments': [input_data['comment']],
|
||||
}
|
||||
)
|
||||
|
||||
self.info(f'Default prediction formatted: {data.size} rows', metadata)
|
||||
return data.to_dict()
|
||||
|
||||
@activity.defn(name='get_last_timestamp')
|
||||
async def get_last_timestamp(self, input_data: dict[str, Any]) -> str:
|
||||
"""
|
||||
Extract the most recent timestamp from prediction data.
|
||||
|
||||
This method analyzes prediction data to find the latest timestamp,
|
||||
enabling incremental processing and data continuity tracking.
|
||||
It handles empty datasets gracefully by returning the current time
|
||||
as a fallback timestamp.
|
||||
|
||||
The method is essential for:
|
||||
1. Incremental data processing workflows
|
||||
2. Data continuity validation
|
||||
3. Timestamp-based data loading optimization
|
||||
4. Workflow execution tracking
|
||||
|
||||
Args:
|
||||
input_data (dict): Input data containing:
|
||||
- data (dict[str, Any]): Prediction data to analyze
|
||||
|
||||
Returns:
|
||||
str: Formatted timestamp string in UTC with timezone
|
||||
"""
|
||||
metadata = input_data['metadata']
|
||||
|
||||
self.info('Getting last timestamp...', metadata)
|
||||
|
||||
data = DataFrame(input_data['data'])
|
||||
|
||||
self.debug(f'Input data: {data.head(5).to_string()}', metadata)
|
||||
|
||||
if data.empty:
|
||||
return now().strftime(DATETIME_FORMAT_WITH_TZ)
|
||||
|
||||
max_timestamp = max(data['timestamp'].values.tolist())
|
||||
|
||||
self.info(f'Last timestamp: {max_timestamp}', metadata)
|
||||
|
||||
return max_timestamp
|
||||
|
||||
@activity.defn(name='write_metrics')
|
||||
async def write_metrics(self, input_data: dict[str, Any]):
|
||||
"""
|
||||
Write prediction performance metrics to Prometheus monitoring system.
|
||||
|
||||
This method records comprehensive metrics for prediction operations,
|
||||
enabling operational monitoring, performance analysis, and alerting.
|
||||
It tracks prediction counts, confidence levels, and response times
|
||||
for each model and pipeline combination.
|
||||
|
||||
Metrics Recorded:
|
||||
1. Prediction Count: Incremental counter for successful predictions
|
||||
2. Confidence Monitor: Current confidence level for predictions
|
||||
3. Response Time Monitor: Histogram of prediction response times
|
||||
|
||||
Args:
|
||||
input_data (dict): Input data containing:
|
||||
- metadata (dict[str, Any]): Workflow execution metadata
|
||||
- prediction (dict[str, Any]): Prediction data with metrics
|
||||
|
||||
Raises:
|
||||
Exception: If metrics writing fails or configuration is invalid
|
||||
"""
|
||||
metadata = input_data['metadata']
|
||||
prediction = DataFrame(input_data['prediction'])
|
||||
prediction_confidence = prediction['prediction_confidence'].values[0]
|
||||
response_time = prediction['response_time'].values[0]
|
||||
|
||||
self.info(f'Writing metrics for model {metadata["model_name"]}', metadata)
|
||||
|
||||
metrics.PREDICTIONS_WRITTEN_COUNT.labels(
|
||||
pod_id=self.pod_id,
|
||||
model_name=metadata['model_name'],
|
||||
pipeline_name=metadata['workflow_name'],
|
||||
).inc()
|
||||
|
||||
metrics.PREDICTION_CONFIDENCE_MONITOR.labels(
|
||||
pod_id=self.pod_id,
|
||||
model_name=metadata['model_name'],
|
||||
pipeline_name=metadata['workflow_name'],
|
||||
).set(prediction_confidence)
|
||||
|
||||
metrics.PREDICTION_RESPONSE_TIME_MONITOR.labels(
|
||||
pod_id=self.pod_id,
|
||||
model_name=metadata['model_name'],
|
||||
pipeline_name=metadata['workflow_name'],
|
||||
).observe(response_time)
|
||||
|
||||
self.info(f'Metrics written for model {metadata["model_name"]}', metadata)
|
||||
@@ -1,44 +0,0 @@
|
||||
from pandas import DataFrame
|
||||
|
||||
|
||||
def filter_specific_variables_null_values(data: DataFrame, config: dict) -> bool:
|
||||
"""
|
||||
Filter to check if specific variables contain null values.
|
||||
|
||||
This function examines a DataFrame to determine if any of the specified variables
|
||||
contain null (NaN) values. It returns True if null values are found for any of
|
||||
the specified variables, False otherwise.
|
||||
|
||||
Args:
|
||||
data (DataFrame): The pandas DataFrame to be examined. Must contain columns
|
||||
named 'variable' and 'value'.
|
||||
config (dict): Configuration dictionary containing the following key:
|
||||
- variables (list): List of variable names to check for null values
|
||||
|
||||
Returns:
|
||||
bool: True if any of the specified variables contain null values,
|
||||
False if none of the specified variables contain null values.
|
||||
|
||||
"""
|
||||
return not data[data['variable'].isin(config['variables']) & data['value'].isna()].empty
|
||||
|
||||
|
||||
def filter_empty_data(data: DataFrame, _config: dict) -> bool:
|
||||
"""
|
||||
Filter to check if the DataFrame is empty.
|
||||
|
||||
This function determines whether the provided DataFrame contains any data.
|
||||
It's a simple utility function that can be used in conditional logic to
|
||||
handle cases where no data is available.
|
||||
|
||||
Args:
|
||||
data (DataFrame): The pandas DataFrame to be checked for emptiness.
|
||||
_config (dict): Configuration dictionary (unused in this function).
|
||||
The underscore prefix indicates this parameter is required for
|
||||
interface consistency but not used in the implementation.
|
||||
|
||||
Returns:
|
||||
bool: True if the DataFrame is empty (has no rows), False if it contains data.
|
||||
|
||||
"""
|
||||
return data.empty
|
||||
@@ -1,64 +0,0 @@
|
||||
import numpy as np
|
||||
from pandas import DataFrame
|
||||
|
||||
|
||||
def api_error_filter(response: dict, _config: dict) -> bool:
|
||||
"""
|
||||
Filter MLFlow API responses for error conditions.
|
||||
|
||||
This function analyzes MLFlow API responses to detect error conditions
|
||||
and determine if the response should be filtered out due to quality
|
||||
or reliability issues.
|
||||
|
||||
|
||||
Args:
|
||||
response: MLFlow API response data (dict)
|
||||
_config: Filter configuration dictionary
|
||||
Required keys:
|
||||
- error_codes (list, optional): List of error codes to detect
|
||||
- error_keywords (list, optional): List of error keywords to detect
|
||||
- check_structure (bool, optional): Whether to validate response structure
|
||||
|
||||
Returns:
|
||||
bool: True if data should be filtered (contains errors), False otherwise
|
||||
|
||||
"""
|
||||
if not response:
|
||||
return True
|
||||
|
||||
if not response['success']:
|
||||
return True
|
||||
|
||||
return False
|
||||
|
||||
|
||||
def nan_values_filter(predictions: DataFrame, _config: dict) -> bool:
|
||||
"""
|
||||
Filter data for NaN (Not a Number) values.
|
||||
|
||||
This function detects NaN values in MLFlow prediction results and
|
||||
determines if the data quality is sufficient for further processing
|
||||
or export operations.
|
||||
|
||||
Args:
|
||||
predictions: DataFrame containing prediction data to check for NaN values
|
||||
_config: Filter configuration dictionary
|
||||
Required keys:
|
||||
- max_nan_ratio (float, optional): Maximum allowed NaN value ratio (0.0 to 1.0)
|
||||
- max_nan_count (int, optional): Maximum allowed NaN value count
|
||||
- check_nested (bool, optional): Whether to check nested data structures
|
||||
|
||||
Returns:
|
||||
bool: True if data should be filtered (too many NaN values), False otherwise
|
||||
|
||||
"""
|
||||
data = (
|
||||
predictions.replace({None: np.nan})
|
||||
.infer_objects(copy=False)
|
||||
.drop(columns=['timestamp'], errors='ignore')
|
||||
)
|
||||
|
||||
if data.isna().all().all():
|
||||
return True
|
||||
|
||||
return False
|
||||
@@ -4,7 +4,6 @@ from pytest import mark
|
||||
|
||||
from model_manager.activities.activities import Activities
|
||||
from model_manager.activities.experiment_tracking import ExperimentTracking
|
||||
from model_manager.activities.gates import Gates
|
||||
from model_manager.activities.mlflow import MLFlow
|
||||
from model_manager.activities.training import Training
|
||||
|
||||
@@ -12,11 +11,9 @@ from model_manager.activities.training import Training
|
||||
@patch('model_manager.activities.activities.ExperimentTracking.__init__')
|
||||
@patch('model_manager.activities.activities.MLFlow.__init__')
|
||||
@patch('model_manager.activities.activities.MinIO.__init__')
|
||||
@patch('model_manager.activities.activities.Gates.__init__')
|
||||
@patch('model_manager.activities.activities.Training.__init__')
|
||||
def test___init__(
|
||||
mock_training_init,
|
||||
mock_gates_init,
|
||||
mock_minio_init,
|
||||
mock_mlflow_init,
|
||||
mock_experiment_tracking_init,
|
||||
@@ -59,7 +56,6 @@ def test___init__(
|
||||
assert isinstance(activities, Activities)
|
||||
assert isinstance(activities, ExperimentTracking)
|
||||
assert isinstance(activities, MLFlow)
|
||||
assert isinstance(activities, Gates)
|
||||
assert isinstance(activities, Training)
|
||||
|
||||
mock_experiment_tracking_init.assert_called_once_with(
|
||||
@@ -100,10 +96,6 @@ def test___init__(
|
||||
notification_handler=notification_handler,
|
||||
)
|
||||
|
||||
mock_gates_init.assert_called_once_with(
|
||||
ANY, logger=logger, notification_handler=notification_handler
|
||||
)
|
||||
|
||||
mock_training_init.assert_called_once_with(
|
||||
ANY, logger=logger, notification_handler=notification_handler
|
||||
)
|
||||
|
||||
@@ -1,630 +0,0 @@
|
||||
from unittest.mock import ANY, MagicMock, patch
|
||||
|
||||
from pytest import fixture, mark
|
||||
from sientia_do.notifications.models import NotificationLevel
|
||||
|
||||
from model_manager.activities.gates import Gates
|
||||
|
||||
|
||||
@fixture
|
||||
def gates_activity():
|
||||
gates = Gates(
|
||||
logger=MagicMock(),
|
||||
notification_handler=MagicMock(),
|
||||
)
|
||||
gates.error = MagicMock()
|
||||
gates.debug = MagicMock()
|
||||
gates.info = MagicMock()
|
||||
gates.warning = MagicMock()
|
||||
gates.critical = MagicMock()
|
||||
gates.send_notification = MagicMock()
|
||||
return gates
|
||||
|
||||
|
||||
metadata = {
|
||||
'metadata': {
|
||||
'model_id': 'test_model',
|
||||
'model_name': 'test_model',
|
||||
'workflow_name': 'test_workflow',
|
||||
'schema_name': 'test_schedule',
|
||||
},
|
||||
}
|
||||
|
||||
|
||||
@mark.asyncio
|
||||
async def test_input_gate_invalid_filter(gates_activity):
|
||||
# Arrange
|
||||
input_data = {
|
||||
**metadata,
|
||||
'filters': {'INVALID_FILTER': {'POLICY': 'STOP'}},
|
||||
'data': {'value': [1, 2, 3]},
|
||||
'path_priority': ['STOP', 'CONTINUE', 'REPEAT'],
|
||||
}
|
||||
|
||||
# Act
|
||||
result = await gates_activity.input_gate(input_data)
|
||||
|
||||
# Assert
|
||||
assert result == (None, 0, '')
|
||||
gates_activity.error.assert_called_once_with(
|
||||
'Filter INVALID_FILTER not found', metadata['metadata']
|
||||
)
|
||||
|
||||
|
||||
@mark.asyncio
|
||||
@patch('model_manager.activities.gates.input_filter_functions')
|
||||
async def test_input_gate_filter_exception(mock_input_filter_functions, gates_activity):
|
||||
# Arrange
|
||||
mock_input_filter_functions.__contains__.return_value = True
|
||||
mock_input_filter_functions.__getitem__.return_value = MagicMock(
|
||||
side_effect=Exception('Test error')
|
||||
)
|
||||
input_data = {
|
||||
**metadata,
|
||||
'filters': {'EMPTY_DATA': {'policy': 'STOP', 'config': {}}},
|
||||
'data': {'value': []},
|
||||
'path_priority': ['STOP', 'CONTINUE', 'REPEAT'],
|
||||
}
|
||||
|
||||
# Act
|
||||
result = await gates_activity.input_gate(input_data)
|
||||
|
||||
# Assert
|
||||
assert result == (None, 0, '')
|
||||
gates_activity.send_notification.assert_called_once_with(
|
||||
metadata=metadata['metadata'],
|
||||
notification_id='INTPUT_GATE_ERROR__EMPTY_DATA',
|
||||
message="Error in filter EMPTY_DATA:{'policy': 'STOP', 'config': {}}: \n Test error",
|
||||
block='input_gate',
|
||||
level=NotificationLevel.ERROR,
|
||||
attachment_content=ANY,
|
||||
)
|
||||
|
||||
|
||||
@mark.asyncio
|
||||
async def test_input_gate_no_filters(gates_activity):
|
||||
# Arrange
|
||||
input_data = {
|
||||
**metadata,
|
||||
'filters': {},
|
||||
'data': {'value': [1, 2, 3]},
|
||||
'path_priority': ['CONTINUE', 'STOP', 'REPEAT'],
|
||||
}
|
||||
|
||||
# Act
|
||||
result = await gates_activity.input_gate(input_data)
|
||||
|
||||
# Assert
|
||||
assert result == (None, 0, '')
|
||||
gates_activity.debug.assert_called()
|
||||
|
||||
|
||||
@mark.asyncio
|
||||
async def test_input_gate_with_filter(gates_activity):
|
||||
# Arrange
|
||||
input_data = {
|
||||
**metadata,
|
||||
'filters': {'EMPTY_DATA': {'policy': 'STOP', 'config': {}}},
|
||||
'data': {'value': []},
|
||||
'path_priority': ['STOP', 'CONTINUE', 'REPEAT'],
|
||||
}
|
||||
|
||||
# Act
|
||||
result = await gates_activity.input_gate(input_data)
|
||||
|
||||
# Assert
|
||||
assert result == ('STOP', -1, 'Input data with bad quality')
|
||||
gates_activity.debug.assert_called()
|
||||
|
||||
|
||||
@mark.asyncio
|
||||
async def test_input_gate_filter_returns_false(gates_activity):
|
||||
"""Test to cover line 129 branch when filter returns False (filter passes)."""
|
||||
# Arrange - Use data that will NOT trigger EMPTY_DATA filter (has data)
|
||||
input_data = {
|
||||
**metadata,
|
||||
'filters': {'EMPTY_DATA': {'policy': 'STOP', 'config': {}}},
|
||||
'data': {'value': [1, 2, 3, 4, 5]}, # Has data, filter returns False
|
||||
'path_priority': ['STOP', 'CONTINUE', 'REPEAT'],
|
||||
}
|
||||
|
||||
# Act
|
||||
result = await gates_activity.input_gate(input_data)
|
||||
|
||||
# Assert - Filter returns False, so no policy is added to filter_output
|
||||
assert result == (None, 0, '') # No filter triggered
|
||||
gates_activity.debug.assert_called()
|
||||
|
||||
|
||||
@mark.asyncio
|
||||
async def test_mlflow_response_gate_invalid_filter(gates_activity):
|
||||
# Arrange
|
||||
input_data = {
|
||||
**metadata,
|
||||
'filters': {'INVALID_FILTER': {'POLICY': 'STOP'}},
|
||||
'data': {'content': {'message': 'success'}},
|
||||
'type': 'test',
|
||||
'path_priority': ['STOP', 'CONTINUE', 'REPEAT'],
|
||||
}
|
||||
|
||||
# Act
|
||||
result = await gates_activity.mlflow_response_gate(input_data)
|
||||
|
||||
# Assert
|
||||
assert result == (None, 0, '')
|
||||
|
||||
|
||||
@mark.asyncio
|
||||
@patch('model_manager.activities.gates.mlflow_response_filter_functions')
|
||||
async def test_mlflow_response_gate_filter_exception(
|
||||
mock_mlflow_response_filter_functions, gates_activity
|
||||
):
|
||||
# Arrange
|
||||
mock_mlflow_response_filter_functions.__contains__.return_value = True
|
||||
mock_mlflow_response_filter_functions.__getitem__.return_value = MagicMock(
|
||||
side_effect=Exception('Test error')
|
||||
)
|
||||
input_data = {
|
||||
**metadata,
|
||||
'filters': {'INVALID_FILTER': {'POLICY': 'STOP'}},
|
||||
'data': {'content': {'message': 'success'}},
|
||||
'type': 'test',
|
||||
'path_priority': ['STOP', 'CONTINUE', 'REPEAT'],
|
||||
}
|
||||
|
||||
# Act
|
||||
result = await gates_activity.mlflow_response_gate(input_data)
|
||||
|
||||
# Assert
|
||||
assert result == (None, 0, '')
|
||||
gates_activity.send_notification.assert_called_once_with(
|
||||
metadata=metadata['metadata'],
|
||||
notification_id='MLFLOW_GATE_RESPONSE_FILTER__INVALID_FILTER',
|
||||
message="Error in filter INVALID_FILTER:{'POLICY': 'STOP'}: \n Test error",
|
||||
block='mlflow_gate',
|
||||
level=NotificationLevel.ERROR,
|
||||
attachment_content=ANY,
|
||||
)
|
||||
|
||||
|
||||
@mark.asyncio
|
||||
async def test_mlflow_response_gate_no_filters(gates_activity):
|
||||
# Arrange
|
||||
input_data = {
|
||||
**metadata,
|
||||
'filters': {},
|
||||
'data': {'content': {'message': 'success'}},
|
||||
'type': 'test',
|
||||
'path_priority': ['CONTINUE', 'STOP', 'REPEAT'],
|
||||
}
|
||||
|
||||
# Act
|
||||
result = await gates_activity.mlflow_response_gate(input_data)
|
||||
|
||||
# Assert
|
||||
assert result == (None, 0, '')
|
||||
gates_activity.debug.assert_called()
|
||||
|
||||
|
||||
@mark.asyncio
|
||||
async def test_mlflow_response_gate_with_filter(gates_activity):
|
||||
# Arrange
|
||||
input_data = {
|
||||
**metadata,
|
||||
'filters': {'API_ERROR': {'policy': 'STOP'}},
|
||||
'data': {
|
||||
'success': False,
|
||||
'content': {'message': 'API error occurred', 'traceback': 'error trace'},
|
||||
},
|
||||
'type': 'test',
|
||||
'path_priority': ['STOP', 'CONTINUE', 'REPEAT'],
|
||||
}
|
||||
|
||||
# Act
|
||||
result = await gates_activity.mlflow_response_gate(input_data)
|
||||
|
||||
# Assert
|
||||
assert result == ('STOP', -1, 'API error occurred')
|
||||
gates_activity.debug.assert_called()
|
||||
gates_activity.send_notification.assert_called()
|
||||
|
||||
|
||||
@mark.asyncio
|
||||
async def test_mlflow_response_gate_filter_returns_false(gates_activity):
|
||||
"""Test to cover line 208 branch when filter returns False (no API error)."""
|
||||
# Arrange - Use data that will NOT trigger API_ERROR filter (success=True)
|
||||
input_data = {
|
||||
**metadata,
|
||||
'filters': {'API_ERROR': {'policy': 'STOP'}},
|
||||
'data': {
|
||||
'success': True, # Success=True, filter returns False
|
||||
'content': {'message': 'Operation successful', 'result': 'data'},
|
||||
},
|
||||
'type': 'transform',
|
||||
'path_priority': ['STOP', 'CONTINUE', 'REPEAT'],
|
||||
}
|
||||
|
||||
# Act
|
||||
result = await gates_activity.mlflow_response_gate(input_data)
|
||||
|
||||
# Assert - Filter returns False, so no policy is added to filter_output
|
||||
assert result == (None, 0, '') # No filter triggered
|
||||
gates_activity.debug.assert_called()
|
||||
|
||||
|
||||
@mark.asyncio
|
||||
async def test_mlflow_content_gate_invalid_filter(gates_activity):
|
||||
# Arrange
|
||||
input_data = {
|
||||
**metadata,
|
||||
'filters': {'INVALID_FILTER': {'POLICY': 'STOP'}},
|
||||
'data': {'value': [1, 2, 3]},
|
||||
'type': 'test',
|
||||
'path_priority': ['STOP', 'CONTINUE', 'REPEAT'],
|
||||
}
|
||||
|
||||
# Act
|
||||
result = await gates_activity.mlflow_content_gate(input_data)
|
||||
|
||||
# Assert
|
||||
assert result == (None, 0, '')
|
||||
|
||||
|
||||
@mark.asyncio
|
||||
@patch('model_manager.activities.gates.mlflow_content_filter_functions')
|
||||
async def test_mlflow_content_gate_filter_exception(
|
||||
mock_mlflow_content_filter_functions, gates_activity
|
||||
):
|
||||
# Arrange
|
||||
mock_mlflow_content_filter_functions.__contains__.return_value = True
|
||||
mock_mlflow_content_filter_functions.__getitem__.return_value = MagicMock(
|
||||
side_effect=Exception('Test error')
|
||||
)
|
||||
input_data = {
|
||||
**metadata,
|
||||
'filters': {'API_ERROR': {'POLICY': 'STOP'}},
|
||||
'data': {
|
||||
'success': False,
|
||||
'content': {'message': 'API error occurred', 'traceback': 'error trace'},
|
||||
},
|
||||
'type': 'test',
|
||||
'path_priority': ['STOP', 'CONTINUE', 'REPEAT'],
|
||||
}
|
||||
|
||||
# Act
|
||||
result = await gates_activity.mlflow_content_gate(input_data)
|
||||
|
||||
# Assert
|
||||
assert result == (None, 0, '')
|
||||
gates_activity.debug.assert_called()
|
||||
gates_activity.send_notification.assert_called_once_with(
|
||||
metadata=metadata['metadata'],
|
||||
notification_id='MLFLOW_GATE_CONTENT_FILTER__API_ERROR',
|
||||
message="Error in filter API_ERROR:{'POLICY': 'STOP'}: \n Test error",
|
||||
block='mlflow_gate',
|
||||
level=NotificationLevel.ERROR,
|
||||
attachment_content=ANY,
|
||||
)
|
||||
|
||||
|
||||
@mark.asyncio
|
||||
async def test_mlflow_content_gate_no_filters(gates_activity):
|
||||
# Arrange
|
||||
input_data = {
|
||||
**metadata,
|
||||
'filters': {},
|
||||
'data': {'value': [1, 2, 3]},
|
||||
'type': 'test',
|
||||
'path_priority': ['CONTINUE', 'STOP', 'REPEAT'],
|
||||
}
|
||||
|
||||
# Act
|
||||
result = await gates_activity.mlflow_content_gate(input_data)
|
||||
|
||||
# Assert
|
||||
assert result == (None, 0, '')
|
||||
gates_activity.debug.assert_called()
|
||||
|
||||
|
||||
@mark.asyncio
|
||||
async def test_mlflow_content_gate_with_filter(gates_activity):
|
||||
# Arrange
|
||||
input_data = {
|
||||
**metadata,
|
||||
'filters': {'NAN_VALUES': {'policy': 'STOP', 'config': {}}},
|
||||
'data': {'value': [None, None, None]},
|
||||
'type': 'test',
|
||||
'path_priority': ['STOP', 'CONTINUE', 'REPEAT'],
|
||||
}
|
||||
|
||||
# Act
|
||||
result = await gates_activity.mlflow_content_gate(input_data)
|
||||
|
||||
# Assert
|
||||
assert result == ('STOP', -1, 'Transformed data not passed the content filter')
|
||||
gates_activity.debug.assert_called()
|
||||
gates_activity.send_notification.assert_called()
|
||||
|
||||
|
||||
@mark.asyncio
|
||||
async def test_mlflow_content_gate_filter_returns_false(gates_activity):
|
||||
"""Test to cover line 293 branch when filter returns False (no NaN values)."""
|
||||
# Arrange - Use data that will NOT trigger NAN_VALUES filter (no NaN)
|
||||
input_data = {
|
||||
**metadata,
|
||||
'filters': {'NAN_VALUES': {'policy': 'STOP', 'config': {}}},
|
||||
'data': {'value': [1.0, 2.0, 3.0, 4.0, 5.0]}, # All valid numbers, no NaN
|
||||
'type': 'predict',
|
||||
'path_priority': ['STOP', 'CONTINUE', 'REPEAT'],
|
||||
}
|
||||
|
||||
# Act
|
||||
result = await gates_activity.mlflow_content_gate(input_data)
|
||||
|
||||
# Assert - Filter returns False, so no policy is added to filter_output
|
||||
assert result == (None, 0, '') # No filter triggered
|
||||
gates_activity.debug.assert_called()
|
||||
|
||||
|
||||
def test_get_prediction_store_policy_invalid_policy(gates_activity):
|
||||
# Arrange
|
||||
prediction_store_policy = 'INVALID_POLICY'
|
||||
|
||||
# Act
|
||||
policy_type, policy_value = gates_activity.get_prediction_store_policy(
|
||||
prediction_store_policy, metadata
|
||||
)
|
||||
|
||||
# Assert
|
||||
assert policy_type == 'lts'
|
||||
assert policy_value == 1
|
||||
|
||||
|
||||
def test_get_prediction_store_policy_invalid_policy_value(gates_activity):
|
||||
# Arrange
|
||||
prediction_store_policy = 'abc:INVALID_VALUE'
|
||||
|
||||
# Act
|
||||
policy_type, policy_value = gates_activity.get_prediction_store_policy(
|
||||
prediction_store_policy, metadata
|
||||
)
|
||||
|
||||
# Assert
|
||||
assert policy_type == 'lts'
|
||||
assert policy_value == 1
|
||||
|
||||
|
||||
def test_get_prediction_store_policy_valid_policy_type(gates_activity):
|
||||
# Arrange
|
||||
prediction_store_policy = 'abc:1'
|
||||
|
||||
# Act
|
||||
policy_type, policy_value = gates_activity.get_prediction_store_policy(
|
||||
prediction_store_policy, metadata
|
||||
)
|
||||
|
||||
# Assert
|
||||
assert policy_type == 'lts'
|
||||
assert policy_value == 1
|
||||
|
||||
|
||||
def test_get_prediction_store_policy_valid_policy(gates_activity):
|
||||
# Arrange
|
||||
prediction_store_policy = 'erl:1'
|
||||
|
||||
# Act
|
||||
policy_type, policy_value = gates_activity.get_prediction_store_policy(
|
||||
prediction_store_policy, metadata
|
||||
)
|
||||
|
||||
# Assert
|
||||
assert policy_type == 'erl'
|
||||
assert policy_value == 1
|
||||
|
||||
|
||||
@mark.asyncio
|
||||
async def test_format_prediction_no_timestamp(gates_activity):
|
||||
# Arrange
|
||||
input_data = {
|
||||
**metadata,
|
||||
'data': {
|
||||
'prediction': {'2023-05-26 11:12:27': 1},
|
||||
'response_time': {'2023-05-26 11:12:27': 0.1},
|
||||
},
|
||||
'model_id': 'test_model',
|
||||
'prediction_confidence': 0.9,
|
||||
'prediction_store_policy': 'lts:1',
|
||||
}
|
||||
|
||||
# Act
|
||||
result = await gates_activity.format_prediction(input_data)
|
||||
|
||||
# Assert
|
||||
assert result['prediction'] == {0: 1}
|
||||
assert result['response_time'] == {0: ANY}
|
||||
assert result['timestamp'] == {0: '2023-05-26 11:12:27'}
|
||||
assert result['model_id'] == {0: 'test_model'}
|
||||
assert result['prediction_confidence'] == {0: 0.9}
|
||||
assert result['prediction_status'] == {0: 'Good'}
|
||||
assert result['comments'] == {0: ''}
|
||||
|
||||
|
||||
@mark.asyncio
|
||||
async def test_format_prediction_with_timestamp_erl(gates_activity):
|
||||
# Arrange
|
||||
input_data = {
|
||||
**metadata,
|
||||
'data': {
|
||||
'prediction': {
|
||||
'2023-05-26 11:12:27': 1,
|
||||
'2023-05-26 11:12:28': 2,
|
||||
'2023-05-26 11:12:29': 3,
|
||||
},
|
||||
'response_time': {
|
||||
'2023-05-26 11:12:27': 0.1,
|
||||
'2023-05-26 11:12:28': 0.2,
|
||||
'2023-05-26 11:12:29': 0.3,
|
||||
},
|
||||
},
|
||||
'model_id': 'test_model',
|
||||
'prediction_confidence': 0.9,
|
||||
'prediction_store_policy': 'erl:2',
|
||||
}
|
||||
|
||||
# Act
|
||||
result = await gates_activity.format_prediction(input_data)
|
||||
|
||||
# Assert
|
||||
assert result['prediction'] == {0: 2, 1: 1}
|
||||
assert result['response_time'] == {0: 0.2, 1: 0.1}
|
||||
assert result['timestamp'] == {0: '2023-05-26 11:12:28', 1: '2023-05-26 11:12:27'}
|
||||
assert result['model_id'] == {0: 'test_model', 1: 'test_model'}
|
||||
assert result['prediction_confidence'] == {0: 0.9, 1: 0.9}
|
||||
assert result['prediction_status'] == {0: 'Good', 1: 'Good'}
|
||||
assert result['comments'] == {0: '', 1: ''}
|
||||
|
||||
|
||||
@mark.asyncio
|
||||
async def test_format_prediction_with_timestamp_lts(gates_activity):
|
||||
# Arrange
|
||||
input_data = {
|
||||
**metadata,
|
||||
'data': {
|
||||
'prediction': {
|
||||
'2023-05-26 11:12:27': 1,
|
||||
'2023-05-26 11:12:28': 2,
|
||||
'2023-05-26 11:12:29': 3,
|
||||
},
|
||||
'response_time': {
|
||||
'2023-05-26 11:12:27': 0.1,
|
||||
'2023-05-26 11:12:28': 0.2,
|
||||
'2023-05-26 11:12:29': 0.3,
|
||||
},
|
||||
},
|
||||
'model_id': 'test_model',
|
||||
'prediction_confidence': 0.9,
|
||||
'prediction_store_policy': 'lts:2',
|
||||
}
|
||||
|
||||
# Act
|
||||
result = await gates_activity.format_prediction(input_data)
|
||||
|
||||
# Assert
|
||||
assert result['prediction'] == {0: 3, 1: 2}
|
||||
assert result['response_time'] == {0: 0.3, 1: 0.2}
|
||||
assert result['timestamp'] == {0: '2023-05-26 11:12:29', 1: '2023-05-26 11:12:28'}
|
||||
assert result['model_id'] == {0: 'test_model', 1: 'test_model'}
|
||||
assert result['prediction_confidence'] == {0: 0.9, 1: 0.9}
|
||||
assert result['prediction_status'] == {0: 'Good', 1: 'Good'}
|
||||
assert result['comments'] == {0: '', 1: ''}
|
||||
|
||||
|
||||
@mark.asyncio
|
||||
async def test_format_prediction_with_timestamp_invalid_policy(gates_activity):
|
||||
# Arrange
|
||||
input_data = {
|
||||
**metadata,
|
||||
'data': {
|
||||
'prediction': [1, 2, 3],
|
||||
'response_time': [0.1, 0.2, 0.3],
|
||||
'timestamp': ['2023-05-26 11:12:27', '2023-05-26 11:12:28', '2023-05-26 11:12:29'],
|
||||
},
|
||||
'model_id': 'test_model',
|
||||
'prediction_confidence': 0.9,
|
||||
'prediction_store_policy': 'lts:2',
|
||||
}
|
||||
gates_activity.get_prediction_store_policy = MagicMock(return_value=('invalid', 1))
|
||||
|
||||
try:
|
||||
await gates_activity.format_prediction(input_data)
|
||||
except ValueError as e:
|
||||
assert str(e) == 'Invalid policy type: invalid'
|
||||
else:
|
||||
raise AssertionError('Expected ValueError')
|
||||
|
||||
|
||||
@mark.asyncio
|
||||
async def test_format_default_prediction(gates_activity):
|
||||
# Arrange
|
||||
input_data = {
|
||||
**metadata,
|
||||
'timestamp': '2023-05-26 11:12:27',
|
||||
'model_id': 'test_model',
|
||||
'prediction_confidence': 0.1,
|
||||
'comment': 'Test comment',
|
||||
}
|
||||
|
||||
# Act
|
||||
result = await gates_activity.format_default_prediction(input_data)
|
||||
|
||||
# Assert
|
||||
assert result['prediction'] == {0: 0}
|
||||
assert result['response_time'] == {0: 0}
|
||||
assert result['timestamp'] == {0: '2023-05-26 11:12:27'}
|
||||
assert result['model_id'] == {0: 'test_model'}
|
||||
assert result['prediction_confidence'] == {0: 0.1}
|
||||
assert result['prediction_status'] == {0: 'Bad'}
|
||||
assert result['comments'] == {0: 'Test comment'}
|
||||
gates_activity.debug.assert_called()
|
||||
|
||||
|
||||
@mark.asyncio
|
||||
async def test_get_last_timestamp_with_data(gates_activity):
|
||||
# Arrange
|
||||
input_data = {**metadata, 'data': {'timestamp': ['2023-05-26 11:12:27', '2023-05-26 11:12:28']}}
|
||||
|
||||
# Act
|
||||
result = await gates_activity.get_last_timestamp(input_data)
|
||||
|
||||
# Assert
|
||||
assert result == '2023-05-26 11:12:28'
|
||||
|
||||
|
||||
@mark.asyncio
|
||||
async def test_get_last_timestamp_no_data(gates_activity):
|
||||
# Arrange
|
||||
input_data = {'data': {}, **metadata}
|
||||
|
||||
# Act
|
||||
result = await gates_activity.get_last_timestamp(input_data)
|
||||
|
||||
# Assert
|
||||
assert isinstance(result, str) # Should be a timestamp string
|
||||
assert len(result) > 0
|
||||
|
||||
|
||||
@mark.asyncio
|
||||
@patch('model_manager.activities.gates.metrics')
|
||||
async def test_write_metrics(mock_metrics, gates_activity):
|
||||
"""Test write_metrics method."""
|
||||
input_data = {
|
||||
**metadata,
|
||||
'prediction': {
|
||||
'prediction': [1, 2, 3],
|
||||
'prediction_confidence': [0.9, 0.8, 0.7],
|
||||
'response_time': [0.1, 0.2, 0.3],
|
||||
},
|
||||
}
|
||||
await gates_activity.write_metrics(input_data)
|
||||
mock_metrics.PREDICTIONS_WRITTEN_COUNT.labels.assert_called_once_with(
|
||||
pod_id=gates_activity.pod_id,
|
||||
model_name=metadata['metadata']['model_name'],
|
||||
pipeline_name=metadata['metadata']['workflow_name'],
|
||||
)
|
||||
mock_metrics.PREDICTIONS_WRITTEN_COUNT.labels.return_value.inc.assert_called_once_with()
|
||||
|
||||
mock_metrics.PREDICTION_CONFIDENCE_MONITOR.labels.assert_called_once_with(
|
||||
pod_id=gates_activity.pod_id,
|
||||
model_name=metadata['metadata']['model_name'],
|
||||
pipeline_name=metadata['metadata']['workflow_name'],
|
||||
)
|
||||
mock_metrics.PREDICTION_CONFIDENCE_MONITOR.labels.return_value.set.assert_called_once_with(0.9)
|
||||
|
||||
mock_metrics.PREDICTION_RESPONSE_TIME_MONITOR.labels.assert_called_once_with(
|
||||
pod_id=gates_activity.pod_id,
|
||||
model_name=metadata['metadata']['model_name'],
|
||||
pipeline_name=metadata['metadata']['workflow_name'],
|
||||
)
|
||||
mock_metrics.PREDICTION_RESPONSE_TIME_MONITOR.labels.return_value.observe.assert_called_once_with(
|
||||
0.1
|
||||
)
|
||||
@@ -1,37 +0,0 @@
|
||||
from pandas import DataFrame
|
||||
|
||||
from model_manager.utils.filters.conditional_filters import (
|
||||
filter_empty_data,
|
||||
filter_specific_variables_null_values,
|
||||
)
|
||||
|
||||
|
||||
def test_filter_specific_variables_null_values():
|
||||
assert (
|
||||
filter_specific_variables_null_values(
|
||||
DataFrame({'variable': ['variable1', 'variable2'], 'value': [1, 2]}),
|
||||
config={'variables': ['variable2']},
|
||||
)
|
||||
is False
|
||||
)
|
||||
|
||||
|
||||
def test_filter_specific_variables_null_values_with_null_values():
|
||||
assert (
|
||||
filter_specific_variables_null_values(
|
||||
DataFrame({'variable': ['variable1', 'variable2'], 'value': [1, None]}),
|
||||
config={'variables': ['variable2']},
|
||||
)
|
||||
is True
|
||||
)
|
||||
|
||||
|
||||
def test_filter_empty_data():
|
||||
assert filter_empty_data(DataFrame(), {}) is True
|
||||
|
||||
|
||||
def test_filter_empty_data_with_data():
|
||||
assert (
|
||||
filter_empty_data(DataFrame({'variable': ['variable1', 'variable2'], 'value': [1, 2]}), {})
|
||||
is False
|
||||
)
|
||||
@@ -1,23 +0,0 @@
|
||||
from pandas import DataFrame
|
||||
|
||||
from model_manager.utils.filters.mlflow_filters import api_error_filter, nan_values_filter
|
||||
|
||||
|
||||
def test_api_error_filter_invalid_response():
|
||||
assert api_error_filter(None, {})
|
||||
|
||||
|
||||
def test_api_error_filter_valid_response_fail():
|
||||
assert api_error_filter({'success': False}, {})
|
||||
|
||||
|
||||
def test_api_error_filter_valid_response_success():
|
||||
assert not api_error_filter({'success': True}, {})
|
||||
|
||||
|
||||
def test_nan_values_filter_all_nan_values():
|
||||
assert nan_values_filter(DataFrame({'variable': [None, None]}), {})
|
||||
|
||||
|
||||
def test_nan_values_filter_no_nan_values():
|
||||
assert not nan_values_filter(DataFrame({'variable': [1, 2]}), {})
|
||||
Reference in New Issue
Block a user