vitor-aignosi
503d9aa485
Update dependencies and refactor input filter handling for consistency - Updated sientia-dataops-library dependency version from 1.10.3 to 1.10.4 in requirements.txt. - Refactored input filter handling in the Gates class to read policy and config keys in a case-insensitive manner. - Updated test cases to ensure consistency in filter key naming conventions across various scenarios.
631 lines
21 KiB
Python
631 lines
21 KiB
Python
"""
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End-to-end tests for PredictionsBatch workflow - Prediction Process scenarios.
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"""
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import asyncio
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from datetime import datetime
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from decimal import Decimal
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from typing import Any
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from unittest.mock import MagicMock, patch
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import pandas as pd
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import pytest
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from pytz import timezone
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from sqlalchemy import text
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from temporalio.testing import WorkflowEnvironment
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from temporalio.worker import Worker
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from laborious.activities.activities import Activities
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from laborious.workflows.predictions_batch import PredictionsBatch
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base_input_data = {
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'schedule_name': 'test-schedule',
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'model_name': 'test_model',
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'model_id': 201,
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'query': 'SELECT timestamp, variable, value, created_at FROM predictions_schema.laborious_data WHERE model_id = 201',
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'schema': 'predictions_schema',
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'table_name': 'predictions',
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'transform_table_name': 'transformed_data',
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'input_filters': {
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'SPECIFIC_VARIABLES_NULL_VALUES': {
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'POLICY': 'CONTINUE', # Continue despite issues, not STOP
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'CONFIG': {'variables': ['sensor_1']},
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},
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},
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'mlflow_transform_filters': {
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'API_ERROR': {'POLICY': 'STOP', 'CONFIG': {}},
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},
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'mlflow_predict_filters': {
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'API_ERROR': {'POLICY': 'STOP', 'CONFIG': {}},
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},
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'path_priority': ['STOP', 'CONTINUE', 'REPEAT'],
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'opc_output_config': {},
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'pi_web_api_output_config': {},
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'save_transform': True,
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'prediction_store_policy': 'lts:1',
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'model_config': {
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'retention_minutes': 0,
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'transform_flavor': 'sklearn',
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'predict_flavor': 'sklearn',
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},
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'datetime_columns': ['timestamp', 'created_at'],
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}
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base_query = "SELECT timestamp, variable, value, created_at FROM predictions_schema.laborious_data WHERE model_id = {model_id}"
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def get_base_input_data(model_id):
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return {
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**base_input_data,
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'model_id': model_id,
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'query': base_query.format(model_id=model_id),
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}
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def insert_sample_data(postgres_engine, model_id, values: list[Any]):
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with postgres_engine.begin() as conn:
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conn.execute(text(f"DELETE FROM predictions_schema.laborious_data WHERE model_id = {model_id}"))
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# Insert data with some null values (quality issue)
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values_sql = []
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for i, value in enumerate(values):
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values_sql.append(f"""
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({model_id}, 'sensor_{i+1}', {value}, '2024-01-01 12:00:00+00:00', '2024-01-01 12:00:00+00:00')
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""")
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insert_sql = f"""
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INSERT INTO predictions_schema.laborious_data (model_id, variable, value, timestamp, created_at)
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VALUES
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{', '.join(values_sql)}
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"""
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conn.execute(text(insert_sql))
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def insert_sample_prediction(postgres_engine, model_id):
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with postgres_engine.begin() as conn:
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conn.execute(text(f"DELETE FROM predictions_schema.predictions WHERE model_id = {model_id}"))
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# Insert data with some null values (quality issue)
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insert_sql = f"""
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INSERT INTO predictions_schema.predictions (model_id, timestamp, prediction, prediction_confidence, prediction_status, comments, response_time)
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VALUES
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({model_id}, '2024-01-01 12:00:00+00:00', 10, 0, 'Good', '', 0.1)
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"""
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conn.execute(text(insert_sql))
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return (model_id, Decimal(10), Decimal(0), 'Good')
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async def start_and_await_workflow(client, input_data, workflow_id):
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handle = await client.start_workflow(
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PredictionsBatch.run,
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input_data,
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id=workflow_id,
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task_queue='test-queue',
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)
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print("[TEST] ✓ Workflow started")
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print("\n[TEST] 3. Waiting for workflow completion...")
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try:
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await asyncio.wait_for(handle.result(), timeout=60.0)
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print("[TEST] ✓ Workflow completed successfully")
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except asyncio.TimeoutError:
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pytest.fail("Workflow execution timed out after 60 seconds")
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def assert_continue(
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postgres_engine, model_id, prediction_confidence: Decimal = Decimal(2),
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comments: str = 'Input data with bad quality',
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):
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print("\n[TEST] 4. Verifying prediction was created despite warnings...")
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with postgres_engine.connect() as conn:
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result_query = conn.execute(
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text(f"SELECT model_id, prediction, prediction_confidence, prediction_status, comments FROM predictions_schema.predictions WHERE model_id = {model_id}")
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)
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prediction_rows = result_query.fetchall()
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assert len(prediction_rows) == 1, "Expected one prediction record despite warnings"
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# Assert prediction value is 0 and other fields
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row = prediction_rows[0]
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assert row[1] == 0, f"Expected prediction=0, got {row[1]}"
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assert row[2] == prediction_confidence, f"Expected prediction_confidence={prediction_confidence}, got {row[2]}"
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assert row[3] == 'Bad', f"Expected prediction_status='Bad', got {row[3]}"
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assert row[4] == comments, f"Expected comments='{comments}', got {row[4]}"
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def assert_stop(postgres_engine, model_id):
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print("\n[TEST] 4. Verifying no predictions were created...")
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with postgres_engine.connect() as conn:
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result_query = conn.execute(
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text(f"SELECT COUNT(*) FROM predictions_schema.predictions WHERE model_id = {model_id}")
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)
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count = result_query.scalar()
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assert count == 0, f"Expected no predictions, but found {count} records"
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def assert_repeat(postgres_engine, model_id, last_prediction: list):
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print("\n[TEST] 4. Verifying prediction was repeated...")
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with postgres_engine.connect() as conn:
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result_query = conn.execute(
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text(f'SELECT model_id, prediction, prediction_confidence, prediction_status FROM predictions_schema.predictions WHERE model_id = {model_id}')
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)
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prediction_rows = result_query.fetchall()
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print(prediction_rows)
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assert len(prediction_rows) == 2, "Expected two prediction records"
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assert prediction_rows[0] == last_prediction, f"Expected first prediction to be the same as the last prediction, got {prediction_rows[0]}, expected {last_prediction}"
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assert prediction_rows[1] == last_prediction, f"Expected second prediction to be the same as the last prediction, got {prediction_rows[1]}, expected {last_prediction}"
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@pytest.mark.asyncio
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@pytest.mark.integration
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async def test_scenario_2_1_1_input_gate_triggers_continue(
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temporal_test_env: WorkflowEnvironment,
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temporal_worker: Worker,
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test_activities: Activities,
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postgres_engine,
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):
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"""
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Scenario 2.1.1: Input Gate Triggers CONTINUE
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Description:
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Input gate determines data should use previous prediction.
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Expected Behavior:
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- input_gate returns path_flag='CONTINUE'
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- path_flag_handler calls export workflow with input data directly
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- MLFlow transform and predict skipped
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- Data exported as-is
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Assertions:
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- input_gate called
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- MLFlow operations NOT called
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- Export workflow called with original data
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- Workflow completes
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"""
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client = temporal_test_env.client
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model_id = 211
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print("\n[TEST] 1. Inserting test data...")
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insert_sample_data(postgres_engine, model_id, ['NULL', 78.2])
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print("[TEST] ✓ Data inserted successfully")
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input_data = get_base_input_data(model_id)
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print("\n[TEST] 2. Starting workflow with CONTINUE policy...")
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workflow_id = f'test-continue-policy-{datetime.now().timestamp()}'
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await start_and_await_workflow(client, input_data, workflow_id)
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assert_continue(postgres_engine, model_id)
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print("\n[TEST] ✓ All assertions passed!")
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@pytest.mark.asyncio
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@pytest.mark.integration
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async def test_scenario_2_1_2_input_gate_triggers_stop(
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temporal_test_env: WorkflowEnvironment,
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temporal_worker: Worker,
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test_activities: Activities,
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postgres_engine,
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):
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"""
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Scenario 2.1.2: Input Gate Triggers STOP
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Description:
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Input data quality gate fails with STOP policy.
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Expected Behavior:
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- input_gate returns path_flag='STOP'
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- path_flag_handler detects STOP
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- Workflow returns early without calling MLFlow
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- No prediction exported
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Assertions:
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- input_gate called
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- path_flag_handler returns True (early exit)
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- MLFlow transform NOT called
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- Export workflow NOT called
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- Workflow completes without error
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"""
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client = temporal_test_env.client
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model_id = 212
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print("\n[TEST] 1. Inserting test data...")
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insert_sample_data(postgres_engine, model_id, ['NULL', 78.2])
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print("[TEST] ✓ Data inserted successfully")
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input_data = get_base_input_data(model_id)
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input_data['input_filters']['SPECIFIC_VARIABLES_NULL_VALUES']['POLICY'] = 'STOP'
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print("\n[TEST] 2. Starting workflow that should stop at input gate...")
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workflow_id = f'test-input-stop-{datetime.now().timestamp()}'
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await start_and_await_workflow(client, input_data, workflow_id)
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assert_stop(postgres_engine, model_id)
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print("\n[TEST] ✓ All assertions passed!")
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@pytest.mark.asyncio
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@pytest.mark.integration
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async def test_scenario_2_1_3_input_gate_triggers_repeat(
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temporal_test_env: WorkflowEnvironment,
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temporal_worker: Worker,
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test_activities: Activities,
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postgres_engine,
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):
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"""
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Scenario 2.1.3: Input Gate Triggers REPEAT
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Description:
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Input gate determines data should repeat last prediction.
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Expected Behavior:
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- input_gate returns path_flag='REPEAT'
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- path_flag_handler calls repeat_last_prediction activity
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- MLFlow transform and predict skipped
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- Last prediction repeated and exported
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Assertions:
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- input_gate called
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- MLFlow operations NOT called
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- repeat_last_prediction activity called
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- Workflow completes
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"""
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client = temporal_test_env.client
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model_id = 213
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print("\n[TEST] 1. Inserting test data and previous prediction...")
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insert_sample_data(postgres_engine, model_id, ['NULL', 78.2])
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data = insert_sample_prediction(postgres_engine, model_id)
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print("[TEST] ✓ Data and previous prediction inserted")
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input_data = get_base_input_data(model_id)
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input_data['input_filters']['SPECIFIC_VARIABLES_NULL_VALUES']['POLICY'] = 'REPEAT'
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print("\n[TEST] 2. Starting workflow that should trigger REPEAT...")
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workflow_id = f'test-input-repeat-{datetime.now().timestamp()}'
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await start_and_await_workflow(client, input_data, workflow_id)
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assert_repeat(postgres_engine, model_id, data)
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print("\n[TEST] ✓ All assertions passed!")
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@pytest.fixture
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def bad_data_model(patch_mlflow):
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model = MagicMock(
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predict=MagicMock(
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side_effect=Exception("Bad data model")
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)
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)
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patch_mlflow.sklearn.load_model = MagicMock(return_value=model)
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return model
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@pytest.mark.asyncio
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@pytest.mark.integration
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async def test_scenario_2_2_1_transform_gate_triggers_continue(
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temporal_test_env: WorkflowEnvironment,
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temporal_worker: Worker,
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test_activities: Activities,
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postgres_engine,
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bad_data_model,
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):
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"""
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Scenario 2.2.1: Transform Gate Triggers CONTINUE
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Description:
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Transform response gate determines data should continue despite issues.
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Expected Behavior:
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- request_transform succeeds
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- mlflow_response_gate for transform returns path_flag='CONTINUE'
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- path_flag_handler calls export workflow with transform data
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- MLFlow predict skipped
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- Transform data exported as-is
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Assertions:
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- Transform completed
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- mlflow_response_gate called for transform
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- MLFlow predict NOT called
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- Export workflow called with transform data
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- Workflow completes
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"""
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client = temporal_test_env.client
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model_id = 221
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print("\n[TEST] 1. Inserting test data...")
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insert_sample_data(postgres_engine, model_id, [60.0, 78.2])
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input_data = get_base_input_data(model_id)
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input_data['mlflow_transform_filters']['API_ERROR']['POLICY'] = 'CONTINUE'
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print("\n[TEST] 2. Starting workflow that should trigger CONTINUE at transform gate...")
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workflow_id = f'test-transform-continue-{datetime.now().timestamp()}'
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await start_and_await_workflow(client, input_data, workflow_id)
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assert_continue(
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postgres_engine=postgres_engine,
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model_id=model_id,
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prediction_confidence=Decimal(10),
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comments='Unknown MLFlow API error',
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)
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print("\n[TEST] ✓ All assertions passed!")
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@pytest.mark.asyncio
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@pytest.mark.integration
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async def test_scenario_2_2_2_transform_gate_triggers_stop(
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temporal_test_env: WorkflowEnvironment,
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temporal_worker: Worker,
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test_activities: Activities,
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postgres_engine,
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bad_data_model,
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):
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"""
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Scenario 2.2.2: Transform Gate Triggers STOP
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Description:
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Transform response validation fails with STOP policy.
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Expected Behavior:
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- request_transform succeeds but response invalid
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- mlflow_response_gate for transform returns path_flag='STOP'
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- Workflow exits without calling predict or export
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Assertions:
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- Transform completed but validation failed
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- mlflow_response_gate called for transform
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- MLFlow predict NOT called
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- Export workflow NOT called
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- Workflow completes without error
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"""
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client = temporal_test_env.client
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model_id = 222
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print("\n[TEST] 1. Inserting test data...")
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insert_sample_data(postgres_engine, model_id, [60.0, 78.2])
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print("[TEST] ✓ Data inserted successfully")
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input_data = get_base_input_data(model_id)
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input_data['mlflow_transform_filters']['API_ERROR']['POLICY'] = 'STOP'
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print("\n[TEST] 2. Starting workflow that should trigger STOP at transform gate...")
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workflow_id = f'test-transform-stop-{datetime.now().timestamp()}'
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await start_and_await_workflow(client, input_data, workflow_id)
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assert_stop(postgres_engine, model_id)
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print("\n[TEST] ✓ All assertions passed!")
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|
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|
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@pytest.mark.asyncio
|
|
@pytest.mark.integration
|
|
async def test_scenario_2_2_3_transform_gate_triggers_repeat(
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temporal_test_env: WorkflowEnvironment,
|
|
temporal_worker: Worker,
|
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test_activities: Activities,
|
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postgres_engine,
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bad_data_model,
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):
|
|
"""
|
|
Scenario 2.2.3: Transform Gate Triggers REPEAT
|
|
|
|
Description:
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Transform response gate determines data should repeat last prediction.
|
|
|
|
Expected Behavior:
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- request_transform succeeds but response has issues
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- mlflow_response_gate for transform returns path_flag='REPEAT'
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- path_flag_handler calls repeat_last_prediction activity
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- MLFlow predict skipped
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- Last prediction repeated and exported
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|
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|
Assertions:
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- Transform completed but validation triggered REPEAT
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- mlflow_response_gate called for transform
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- MLFlow predict NOT called
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- repeat_last_prediction activity called
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- Workflow completes
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"""
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client = temporal_test_env.client
|
|
|
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model_id = 223
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|
|
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print("\n[TEST] 1. Inserting test data...")
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insert_sample_data(postgres_engine, model_id, [60.0, 78.2])
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data = insert_sample_prediction(postgres_engine, model_id)
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print("[TEST] ✓ Data inserted successfully")
|
|
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input_data = get_base_input_data(model_id)
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input_data['mlflow_transform_filters']['API_ERROR']['POLICY'] = 'REPEAT'
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|
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print("\n[TEST] 2. Starting workflow that should trigger REPEAT at transform gate...")
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workflow_id = f'test-transform-repeat-{datetime.now().timestamp()}'
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await start_and_await_workflow(client, input_data, workflow_id)
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assert_repeat(postgres_engine, model_id, data)
|
|
|
|
print("\n[TEST] ✓ All assertions passed!")
|
|
|
|
|
|
@pytest.fixture
|
|
def bad_predict_model(
|
|
patch_mlflow,
|
|
mock_mlflow_models
|
|
):
|
|
model = MagicMock(
|
|
predict=MagicMock(
|
|
side_effect=Exception("Bad predict model")
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|
)
|
|
)
|
|
|
|
def mock_sklearn_load_model(model_uri):
|
|
if 'data_model' in model_uri or 'transform' in model_uri.lower():
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|
return mock_mlflow_models['transform_model']
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|
return model
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|
patch_mlflow.sklearn = MagicMock()
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|
patch_mlflow.sklearn.load_model = MagicMock(side_effect=mock_sklearn_load_model)
|
|
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|
return model
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|
|
|
|
@pytest.mark.asyncio
|
|
@pytest.mark.integration
|
|
async def test_scenario_2_3_1_predict_gate_triggers_continue(
|
|
temporal_test_env: WorkflowEnvironment,
|
|
temporal_worker: Worker,
|
|
test_activities: Activities,
|
|
postgres_engine,
|
|
bad_predict_model,
|
|
):
|
|
"""
|
|
Scenario 2.3.1: Predict Gate Triggers CONTINUE
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|
|
|
Description:
|
|
Predict response gate determines data should continue despite issues.
|
|
|
|
Expected Behavior:
|
|
- request_predict succeeds
|
|
- mlflow_response_gate for predict returns path_flag='CONTINUE'
|
|
- path_flag_handler calls export workflow with predict data
|
|
- Prediction exported despite quality issues
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|
|
|
Assertions:
|
|
- Transform and predict completed
|
|
- mlflow_response_gate called for predict
|
|
- Export workflow called with predict data
|
|
- Workflow completes
|
|
"""
|
|
client = temporal_test_env.client
|
|
|
|
model_id = 231
|
|
|
|
print("\n[TEST] 1. Inserting test data...")
|
|
insert_sample_data(postgres_engine, model_id, [60.0, 78.2])
|
|
|
|
input_data = get_base_input_data(model_id)
|
|
input_data['mlflow_predict_filters']['API_ERROR']['POLICY'] = 'CONTINUE'
|
|
|
|
print("\n[TEST] 2. Starting workflow that should trigger CONTINUE at predict gate...")
|
|
workflow_id = f'test-predict-continue-{datetime.now().timestamp()}'
|
|
|
|
await start_and_await_workflow(client, input_data, workflow_id)
|
|
assert_continue(
|
|
postgres_engine=postgres_engine,
|
|
model_id=model_id,
|
|
prediction_confidence=Decimal(10),
|
|
comments='Unknown MLFlow API error',
|
|
)
|
|
|
|
print("\n[TEST] ✓ All assertions passed!")
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
@pytest.mark.integration
|
|
async def test_scenario_2_3_2_predict_gate_triggers_stop(
|
|
temporal_test_env: WorkflowEnvironment,
|
|
temporal_worker: Worker,
|
|
test_activities: Activities,
|
|
postgres_engine,
|
|
bad_predict_model,
|
|
):
|
|
"""
|
|
Scenario 2.3.2: Predict Gate Triggers STOP
|
|
|
|
Description:
|
|
Prediction validation fails with STOP policy.
|
|
|
|
Expected Behavior:
|
|
- request_predict succeeds but response invalid
|
|
- mlflow_response_gate for predict returns path_flag='STOP'
|
|
- Workflow exits without export
|
|
|
|
Assertions:
|
|
- Transform completed
|
|
- Predict completed but validation failed
|
|
- Export workflow NOT called
|
|
- Workflow completes without error
|
|
"""
|
|
client = temporal_test_env.client
|
|
|
|
model_id = 232
|
|
|
|
print("\n[TEST] 1. Inserting test data...")
|
|
insert_sample_data(postgres_engine, model_id, [23.5, 78.2])
|
|
print("[TEST] ✓ Data inserted successfully")
|
|
|
|
input_data = get_base_input_data(model_id)
|
|
input_data['mlflow_predict_filters']['API_ERROR']['POLICY'] = 'STOP'
|
|
|
|
print("\n[TEST] 2. Starting workflow that should stop at predict gate...")
|
|
workflow_id = f'test-predict-stop-{datetime.now().timestamp()}'
|
|
|
|
await start_and_await_workflow(client, input_data, workflow_id)
|
|
assert_stop(postgres_engine, model_id)
|
|
|
|
print("\n[TEST] ✓ All assertions passed!")
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
@pytest.mark.integration
|
|
async def test_scenario_2_3_3_predict_gate_triggers_repeat(
|
|
temporal_test_env: WorkflowEnvironment,
|
|
temporal_worker: Worker,
|
|
test_activities: Activities,
|
|
postgres_engine,
|
|
bad_predict_model,
|
|
):
|
|
"""
|
|
Scenario 2.3.3: Predict Gate Triggers REPEAT
|
|
|
|
Description:
|
|
Predict response gate determines data should repeat last prediction.
|
|
|
|
Expected Behavior:
|
|
- request_predict succeeds but response has issues
|
|
- mlflow_response_gate for predict returns path_flag='REPEAT'
|
|
- path_flag_handler calls repeat_last_prediction activity
|
|
- Last prediction repeated and exported
|
|
|
|
Assertions:
|
|
- Transform and predict completed but validation triggered REPEAT
|
|
- mlflow_response_gate called for predict
|
|
- repeat_last_prediction activity called
|
|
- Export workflow NOT called with current prediction
|
|
- Workflow completes
|
|
"""
|
|
client = temporal_test_env.client
|
|
|
|
model_id = 233
|
|
|
|
print("\n[TEST] 1. Inserting test data and previous prediction...")
|
|
insert_sample_data(postgres_engine, model_id, [23.5, 78.2])
|
|
data = insert_sample_prediction(postgres_engine, model_id)
|
|
|
|
print("[TEST] ✓ Data and previous prediction inserted")
|
|
|
|
input_data = get_base_input_data(model_id)
|
|
input_data['mlflow_predict_filters']['API_ERROR']['POLICY'] = 'REPEAT'
|
|
input_data['path_priority'] = ['REPEAT', 'STOP', 'CONTINUE'] # REPEAT first
|
|
|
|
print("\n[TEST] 2. Starting workflow that should trigger REPEAT at predict gate...")
|
|
workflow_id = f'test-predict-repeat-{datetime.now().timestamp()}'
|
|
|
|
await start_and_await_workflow(client, input_data, workflow_id)
|
|
assert_repeat(postgres_engine, model_id, data)
|
|
|
|
print("\n[TEST] ✓ All assertions passed!")
|