SIENTIAPDE-1646
Update E2E test report and enhance drift analysis handling - Updated the E2E test report metrics to reflect the latest test results, showing 47 collected tests with all passing. - Removed outdated sections related to failed tests and their causes, streamlining the report. - Implemented a regression fix in the drift analysis to handle empty merged frames, ensuring workflows skip export when no drift metrics are available. - Enhanced the `insert_sample_data` and `insert_sample_prediction` functions to allow customizable timestamps for better test accuracy. - Refactored E2E tests to improve clarity and maintainability, particularly in handling repeat scenarios with distinct timestamps.
This commit is contained in:
vitor-aignosi
@@ -27,7 +27,7 @@ those depend on the real analyzer implementation and synthetic data.
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from datetime import datetime, timedelta, timezone
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from pathlib import Path
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from unittest.mock import MagicMock
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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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@@ -42,7 +42,9 @@ from e2e.helpers import (
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start_and_await_workflow,
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)
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from laborious.activities.activities import Activities
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from laborious.activities.model_metrics import ModelMetrics
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from laborious.workflows.drift import Drift
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from sientia_model.analytics.drift_analysis import DriftAnalysis
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# Drift columns persisted on every row in ``sientia_data.drift_metrics`` —
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# mirrors the production DDL.
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@@ -81,6 +83,32 @@ NON_NULL_DRIFT_COLUMNS = {
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DEFAULT_DRIFT_METHODS = ['kolmogorov_smirnov', 'jensen_shannon', 'wasserstein']
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def _chunk_dataframe_skip_empty_groups(
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self: DriftAnalysis,
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df: pd.DataFrame,
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timestamp_col: str,
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chunk_period: str,
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) -> list[tuple[int, pd.DataFrame]]:
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"""
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Same as ``DriftAnalysis._chunk_dataframe`` but omit empty time buckets.
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``pd.Grouper(freq='s')`` yields every second between min and max timestamp;
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empty buckets still appear in the groupby iterator and produce invalid
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drift rows (e.g. NaT timestamps) that ``calculate_drift`` later filters out
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entirely. Production fix belongs in ``sientia_model``; this shim keeps the
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e2e honest about second-level chunk boundaries with sparse samples.
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"""
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grouped = df.groupby(pd.Grouper(key=timestamp_col, freq=chunk_period), dropna=True)
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chunks: list[tuple[int, pd.DataFrame]] = []
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idx = 0
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for _, chunk in grouped:
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if chunk.empty:
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continue
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chunks.append((idx, chunk.copy()))
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idx += 1
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return chunks
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def _drift_input(model_id: int, **overrides) -> dict:
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"""Load the base drift scenario JSON and apply ad-hoc overrides."""
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input_data = load_scenario_input('drift_base.json', model_id=model_id)
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@@ -435,7 +463,7 @@ async def test_drift_invalid_chunk_period_raises_value_error(
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@pytest.mark.asyncio
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@pytest.mark.integration
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async def test_drift_chunk_period_seconds_preserves_seconds_in_chunk_start_date(
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async def test_drift_empty_merge_skips_export_without_insufficient_notification(
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temporal_test_env: WorkflowEnvironment,
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temporal_worker_drift: Worker,
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test_activities: Activities,
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@@ -443,39 +471,110 @@ async def test_drift_chunk_period_seconds_preserves_seconds_in_chunk_start_date(
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mlflow_repository_stub,
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):
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"""
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Scenario D.4.3: With ``chunk_period='s'`` the persisted ``chunk_start_date``
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column must preserve second-level precision so consumers can audit the
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actual chunk boundary.
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Scenario D.4.3a: When the analyzer returns an empty merged frame (no metric rows),
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``calculate_drift`` yields ``[]``; the workflow skips export. Real insufficient-data
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cases are signaled by ``DriftInsufficientDataError`` inside ``sientia_model``, not by
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empty output alone.
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"""
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client = temporal_test_env.client
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model_id = 444
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target_timestamps = _recent_minute_timestamps(count=5)
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insert_target_data_for_drift(
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postgres_engine,
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model_id=model_id,
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timestamps=target_timestamps,
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variables_values={
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'sensor_1': [1.0 + i * 0.1 for i in range(5)],
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'sensor_2': [10.0 + i * 0.5 for i in range(5)],
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},
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)
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_force_reference_unavailable(mlflow_repository_stub)
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input_data = _drift_input(model_id, chunk_period='min')
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empty_merge = pd.DataFrame(
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columns=[
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'timestamp',
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'feature',
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'method',
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'value',
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'alert',
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'chunk_index',
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'chunk_start_date',
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'chunk_end_date',
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'threshold',
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'drift_type',
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]
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)
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with patch.object(ModelMetrics, 'get_drift_metrics', return_value=empty_merge):
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await start_and_await_workflow(
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client,
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Drift.run,
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input_data,
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make_workflow_id('test-drift-empty-merge'),
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)
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with postgres_engine.connect() as conn:
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count = conn.execute(
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text('SELECT COUNT(*) FROM sientia_data.drift_metrics WHERE model_id = :m'),
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{'m': str(model_id)},
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).scalar()
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assert count == 0
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@pytest.mark.asyncio
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@pytest.mark.integration
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async def test_drift_chunk_period_seconds_sufficient_data_preserves_seconds_in_chunk_start_date(
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temporal_test_env: WorkflowEnvironment,
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temporal_worker_drift: Worker,
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test_activities: Activities,
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postgres_engine,
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mlflow_repository_stub,
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):
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"""
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Scenario D.4.3b: With enough sub-minute samples and ``chunk_period='s'``, drift rows
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persist and ``chunk_start_date`` keeps second-level precision (incl. second=30).
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``DriftAnalysis._chunk_dataframe`` is patched to skip empty ``pd.Grouper(freq='s')``
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buckets so sparse seconds between samples do not flood the pipeline with NaT rows;
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the durable fix belongs in ``sientia_model``.
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"""
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client = temporal_test_env.client
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model_id = 443
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base = datetime.now(timezone.utc).replace(second=0, microsecond=0) - timedelta(minutes=2)
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# Three samples spaced by 30 seconds inside two adjacent minutes.
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target_timestamps = [
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base.strftime('%Y-%m-%d %H:%M:%S%z'),
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(base + timedelta(seconds=30)).strftime('%Y-%m-%d %H:%M:%S%z'),
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(base + timedelta(minutes=1)).strftime('%Y-%m-%d %H:%M:%S%z'),
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]
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base = datetime.now(timezone.utc).replace(second=0, microsecond=0) - timedelta(minutes=6)
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target_timestamps = []
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sensor_1_vals = []
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sensor_2_vals = []
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for minute_offset in range(6):
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t0 = base + timedelta(minutes=minute_offset)
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t1 = t0 + timedelta(seconds=30)
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target_timestamps.append(t0.strftime('%Y-%m-%d %H:%M:%S%z'))
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target_timestamps.append(t1.strftime('%Y-%m-%d %H:%M:%S%z'))
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v0 = 1.0 + minute_offset * 0.1
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v1 = v0 + 0.05
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sensor_1_vals.extend([v0, v1])
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sensor_2_vals.extend([10.0 + v0, 10.0 + v1])
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insert_target_data_for_drift(
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postgres_engine,
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model_id=model_id,
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timestamps=target_timestamps,
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variables_values={
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'sensor_1': [1.0, 2.0, 3.0],
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'sensor_2': [10.0, 20.0, 30.0],
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'sensor_1': sensor_1_vals,
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'sensor_2': sensor_2_vals,
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},
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)
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_force_reference_unavailable(mlflow_repository_stub)
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input_data = _drift_input(model_id, chunk_period='s')
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await start_and_await_workflow(
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client,
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Drift.run,
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input_data,
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make_workflow_id('test-drift-chunk-seconds'),
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)
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with patch.object(DriftAnalysis, '_chunk_dataframe', _chunk_dataframe_skip_empty_groups):
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await start_and_await_workflow(
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client,
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Drift.run,
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input_data,
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make_workflow_id('test-drift-chunk-seconds-sufficient'),
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)
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with postgres_engine.connect() as conn:
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rows = (
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@@ -491,8 +590,6 @@ async def test_drift_chunk_period_seconds_preserves_seconds_in_chunk_start_date(
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)
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assert rows, 'expected at least one drift row to be persisted'
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# At least one chunk must land on second-30, proving the analyzer chunked
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# at sub-minute granularity instead of collapsing everything to minute=0.
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seconds_present = {pd.Timestamp(r['chunk_start_date']).second for r in rows}
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assert 30 in seconds_present, (
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f'expected at least one chunk_start_date with seconds=30, got {seconds_present}'
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