8d34228d7b
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 - Removed outdated sections related to failed tests and their causes, streamlining the report.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.
597 lines
21 KiB
Python
597 lines
21 KiB
Python
"""
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End-to-end tests for the Drift workflow.
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The drift suite drives the **real** ``sientia_model.analytics.drift_analysis.DriftAnalysis``
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analyzer (no mocking). Each scenario exercises the full pipeline:
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laborious_data (Postgres)
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-> load_custom_query
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-> calculate_drift (DriftAnalysis univariate + multivariate)
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-> export_data_to_postgres (sientia_data.drift_metrics)
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Coverage focus:
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- Happy path persists every column required by ``sientia_data.drift_metrics``
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with a valid reference dataset downloaded from MLflow.
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- 30% fallback path activates when the MLflow reference is unavailable and
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emits the ``MODEL_METRICS_REFERENCE_DATA_WARNING`` notification.
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- Empty target data short-circuits the workflow without persisting anything.
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- Invalid ``chunk_period`` is rejected by ``calculate_drift``.
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- ``chunk_period='s'`` preserves second-level precision in
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``chunk_start_date``.
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Tests assert behavioral / structural properties (column presence, NOT NULL
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constraints, business-key invariants) rather than exact numeric values, since
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those depend on the real analyzer implementation and synthetic data.
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"""
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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, patch
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import pandas as pd
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import pytest
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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 e2e.helpers import (
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insert_target_data_for_drift,
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load_scenario_input,
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make_workflow_id,
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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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EXPECTED_DRIFT_COLUMNS = [
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'id',
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'model_id',
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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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'accurate',
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'timestamp',
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'created_at',
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]
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# Columns the DDL marks as NOT NULL. ``feature`` and ``timestamp`` are
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# nullable in the production schema (multivariate rows do not bind to a
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# single feature; ``timestamp`` is allowed to be empty when upstream data has
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# no usable instant).
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NON_NULL_DRIFT_COLUMNS = {
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'id',
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'model_id',
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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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'accurate',
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'created_at',
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}
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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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input_data.update(overrides)
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return input_data
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def _recent_minute_timestamps(count: int, offset_minutes: int = 6) -> list[str]:
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"""
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Build ``count`` consecutive UTC minute timestamps placed in the recent past.
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The Drift workflow filters target rows with ``timestamp > NOW() - INTERVAL``,
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so timestamps must be recent for tests to retrieve any data. Snapping to
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minute precision keeps the helper deterministic regardless of clock skew.
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Args:
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- count (int): How many consecutive minute timestamps to generate.
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- offset_minutes (int): Minutes ago for the EARLIEST generated timestamp.
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Return:
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list[str]: ISO strings with ``+0000`` offset, one per minute.
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"""
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base = datetime.now(timezone.utc).replace(second=0, microsecond=0) - timedelta(
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minutes=offset_minutes
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)
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return [
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(base + timedelta(minutes=i)).strftime('%Y-%m-%d %H:%M:%S%z') for i in range(count)
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]
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def _configure_reference_csv(mlflow_repository_stub, reference_rows: pd.DataFrame) -> None:
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"""
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Wire ``mlflow_repository_stub`` so ``get_reference_data`` returns
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``reference_rows`` by writing them to ``dst_path/evaluation_data.csv``.
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Args:
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- mlflow_repository_stub: External MLflow repository fixture.
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- reference_rows (pd.DataFrame): Rows to expose as the production reference.
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"""
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def _download(run_id: str, artifact_path: str, dst_path: str, metadata=None):
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target = Path(dst_path) / 'evaluation_data.csv'
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reference_rows.to_csv(target, index=False)
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mlflow_repository_stub._client.get_model_version_by_alias.return_value = MagicMock(
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run_id='fake-reference-run'
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)
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mlflow_repository_stub.download_artifacts.side_effect = _download
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def _force_reference_unavailable(mlflow_repository_stub) -> None:
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"""Make ``get_reference_data`` return ``None`` by failing alias resolution."""
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mlflow_repository_stub._client.get_model_version_by_alias.side_effect = Exception(
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'no production alias registered'
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)
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def _select_drift_rows(postgres_engine, model_id: int) -> list[dict]:
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"""Read every persisted drift row for ``model_id`` ordered by chunk/feature/method."""
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with postgres_engine.connect() as conn:
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rows = (
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conn.execute(
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text(
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'SELECT * FROM sientia_data.drift_metrics '
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'WHERE model_id = :m '
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'ORDER BY chunk_index, feature, method'
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),
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{'m': str(model_id)},
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)
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.mappings()
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.all()
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)
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return [dict(row) for row in rows]
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def _assert_required_columns_populated(rows: list[dict]) -> None:
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"""Validate column presence and NOT NULL constraints on every row."""
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assert rows, 'expected at least one drift row to be persisted'
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seen_columns = set(rows[0].keys())
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for column in EXPECTED_DRIFT_COLUMNS:
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assert column in seen_columns, f'Missing drift column in postgres: {column}'
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for row in rows:
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for column in NON_NULL_DRIFT_COLUMNS:
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assert row[column] is not None, f"Column '{column}' is NULL in {row}"
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assert 'p_value' not in row, 'p_value must not be persisted to drift_metrics'
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@pytest.mark.asyncio
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@pytest.mark.integration
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async def test_drift_happy_path_persists_all_columns_with_reference_data(
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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.1.1: Happy path with reference data downloaded from MLflow.
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Drives the full pipeline against the real ``DriftAnalysis``. Asserts:
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- One row is persisted per ``(chunk_index, feature, method)`` combination
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plus the multivariate row block, with every column required by
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``sientia_data.drift_metrics`` populated.
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- The three default univariate methods are forwarded to the analyzer.
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- ``model_id`` and ``timestamp`` are stamped by the activity (not by the
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analyzer); ``timestamp`` equals ``max(target_data.timestamp)`` and is
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identical on every persisted row.
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- ``chunk_start_date`` / ``chunk_end_date`` are persisted as ISO text so
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the analyzer's nanosecond-precision boundaries survive the ``text``
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column type.
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- ``accurate=True`` because the reference dataset was available.
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"""
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client = temporal_test_env.client
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model_id = 411
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target_timestamps = _recent_minute_timestamps(count=10)
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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': [10.0 + i * 0.1 for i in range(10)],
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'sensor_2': [20.0 + i * 0.5 for i in range(10)],
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},
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)
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reference_df = pd.DataFrame(
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{
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'timestamp': [
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f'2023-12-31 11:{minute:02d}:00+00:00' for minute in range(10)
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],
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'sensor_1': [9.0 + i * 0.05 for i in range(10)],
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'sensor_2': [18.0 + i * 0.25 for i in range(10)],
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}
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)
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_configure_reference_csv(mlflow_repository_stub, reference_df)
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input_data = _drift_input(model_id)
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await start_and_await_workflow(
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client, Drift.run, input_data, make_workflow_id('test-drift-happy-path')
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)
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rows = _select_drift_rows(postgres_engine, model_id)
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exported_csv_path = '/tmp/test_drift_happy_path_exported.csv'
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pd.DataFrame(rows).to_csv(exported_csv_path, index=False)
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print(
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f'\n[test_drift_happy_path] Exported drift dataframe '
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f'({len(rows)} rows) -> {exported_csv_path}'
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)
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_assert_required_columns_populated(rows)
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# The activity drops the target column from the feature list, so only
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# ``sensor_2`` participates in univariate analysis (``sensor_1`` is the
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# configured target). Multivariate produces one row per chunk regardless.
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univariate_rows = [r for r in rows if r['feature'] != 'multivariate']
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multivariate_rows = [r for r in rows if r['feature'] == 'multivariate']
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assert univariate_rows, 'expected univariate drift rows for non-target features'
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assert multivariate_rows, 'expected one multivariate drift row per chunk'
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# All three default methods must reach the analyzer.
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assert {r['method'] for r in univariate_rows} == set(DEFAULT_DRIFT_METHODS)
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assert all(r['method'] == 'multivariate' for r in multivariate_rows)
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assert {r['feature'] for r in univariate_rows} == {'sensor_2'}
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# ``timestamp`` is stamped uniformly with ``max(target_data.timestamp)``.
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expected_timestamp = pd.to_datetime(max(target_timestamps), utc=True)
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persisted_timestamps = {pd.to_datetime(r['timestamp'], utc=True) for r in rows}
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assert len(persisted_timestamps) == 1, (
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'timestamp must be uniform across all drift rows '
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f'(got {len(persisted_timestamps)} distinct values)'
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)
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assert pd.Timestamp(persisted_timestamps.pop()) == expected_timestamp, (
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'timestamp must equal max(target_data.timestamp)'
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)
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# ``model_id`` is stamped by ``calculate_drift`` (not produced by the analyzer).
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assert all(r['model_id'] == str(model_id) for r in rows), (
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'model_id must be stamped on every drift row'
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)
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# Reference path → accurate=True.
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assert all(r['accurate'] is True for r in rows), (
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'reference path should mark all rows as accurate'
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)
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# ISO text serialization preserves ordering between start/end of each chunk.
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for row in rows:
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assert 'T' in row['chunk_start_date'], (
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f"chunk_start_date should be ISO text, got {row['chunk_start_date']!r}"
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)
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assert 'T' in row['chunk_end_date'], (
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f"chunk_end_date should be ISO text, got {row['chunk_end_date']!r}"
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)
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assert row['chunk_start_date'] <= row['chunk_end_date'], (
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f'chunk_start_date must precede chunk_end_date '
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f"(start={row['chunk_start_date']}, end={row['chunk_end_date']})"
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)
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@pytest.mark.asyncio
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@pytest.mark.integration
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async def test_drift_uses_30pct_fallback_when_reference_unavailable(
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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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notification_inserts,
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):
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"""
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Scenario D.1.2: ``get_reference_data`` returns ``None`` (production alias
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missing), so ``calculate_drift`` falls back to using the first 30% of
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target rows as reference. Persisted rows must report ``accurate=False``
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and a ``MODEL_METRICS_REFERENCE_DATA_WARNING`` notification must be
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emitted to mongo.
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"""
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client = temporal_test_env.client
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model_id = 412
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target_timestamps = _recent_minute_timestamps(count=10)
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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': [10.0 + i * 0.1 for i in range(10)],
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'sensor_2': [20.0 + i * 0.5 for i in range(10)],
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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)
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await start_and_await_workflow(
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client, Drift.run, input_data, make_workflow_id('test-drift-fallback')
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)
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rows = _select_drift_rows(postgres_engine, model_id)
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_assert_required_columns_populated(rows)
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assert all(r['accurate'] is False for r in rows), (
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'fallback path must mark all rows as inaccurate'
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)
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fallback_warnings = [
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call
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for call in notification_inserts.call_args_list
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if call.args
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and isinstance(call.args[0], dict)
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and call.args[0].get('notification_id') == 'MODEL_METRICS_REFERENCE_DATA_WARNING'
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]
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assert len(fallback_warnings) >= 1, 'expected reference fallback warning notification'
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@pytest.mark.asyncio
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@pytest.mark.integration
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async def test_drift_empty_target_data_short_circuits_workflow(
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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.3.1: When ``load_custom_query`` returns no rows the workflow
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must return early without invoking the analyzer or writing any drift rows.
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"""
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client = temporal_test_env.client
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model_id = 431
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with postgres_engine.begin() as conn:
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conn.execute(text(f'DELETE FROM sientia_data.laborious_data WHERE model_id = {model_id}'))
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_force_reference_unavailable(mlflow_repository_stub)
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input_data = _drift_input(model_id)
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await start_and_await_workflow(
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client, Drift.run, input_data, make_workflow_id('test-drift-empty-target')
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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_invalid_chunk_period_raises_value_error(
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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.4.2: ``calculate_drift`` validates ``chunk_period`` and rejects
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anything other than ``min`` / ``s``. The workflow must surface the
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``ValueError`` and persist nothing.
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"""
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client = temporal_test_env.client
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model_id = 442
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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, 2.0, 3.0, 4.0, 5.0],
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'sensor_2': [10.0, 20.0, 30.0, 40.0, 50.0],
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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='hour')
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with pytest.raises(Exception) as excinfo:
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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-bad-chunk-period'),
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)
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# Temporal wraps the activity ValueError in WorkflowFailureError; the
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# message may live on ``.message`` or ``str(exc)`` depending on the SDK
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# error class, so walk the cause chain looking for the guard text.
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cause_descriptions = []
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current: BaseException | None = excinfo.value
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while current is not None:
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cause_descriptions.append(
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getattr(current, 'message', None) or str(current) or repr(current)
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)
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current = current.__cause__
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assert any('Invalid chunk period' in msg for msg in cause_descriptions), (
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f'Expected ValueError about chunk period in chain, got: {cause_descriptions}'
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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_empty_merge_skips_export_without_insufficient_notification(
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|
temporal_test_env: WorkflowEnvironment,
|
|
temporal_worker_drift: Worker,
|
|
test_activities: Activities,
|
|
postgres_engine,
|
|
mlflow_repository_stub,
|
|
):
|
|
"""
|
|
Scenario D.4.3a: When the analyzer returns an empty merged frame (no metric rows),
|
|
``calculate_drift`` yields ``[]``; the workflow skips export. Real insufficient-data
|
|
cases are signaled by ``DriftInsufficientDataError`` inside ``sientia_model``, not by
|
|
empty output alone.
|
|
"""
|
|
client = temporal_test_env.client
|
|
model_id = 444
|
|
|
|
target_timestamps = _recent_minute_timestamps(count=5)
|
|
insert_target_data_for_drift(
|
|
postgres_engine,
|
|
model_id=model_id,
|
|
timestamps=target_timestamps,
|
|
variables_values={
|
|
'sensor_1': [1.0 + i * 0.1 for i in range(5)],
|
|
'sensor_2': [10.0 + i * 0.5 for i in range(5)],
|
|
},
|
|
)
|
|
_force_reference_unavailable(mlflow_repository_stub)
|
|
|
|
input_data = _drift_input(model_id, chunk_period='min')
|
|
empty_merge = pd.DataFrame(
|
|
columns=[
|
|
'timestamp',
|
|
'feature',
|
|
'method',
|
|
'value',
|
|
'alert',
|
|
'chunk_index',
|
|
'chunk_start_date',
|
|
'chunk_end_date',
|
|
'threshold',
|
|
'drift_type',
|
|
]
|
|
)
|
|
with patch.object(ModelMetrics, 'get_drift_metrics', return_value=empty_merge):
|
|
await start_and_await_workflow(
|
|
client,
|
|
Drift.run,
|
|
input_data,
|
|
make_workflow_id('test-drift-empty-merge'),
|
|
)
|
|
|
|
with postgres_engine.connect() as conn:
|
|
count = conn.execute(
|
|
text('SELECT COUNT(*) FROM sientia_data.drift_metrics WHERE model_id = :m'),
|
|
{'m': str(model_id)},
|
|
).scalar()
|
|
assert count == 0
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
@pytest.mark.integration
|
|
async def test_drift_chunk_period_seconds_sufficient_data_preserves_seconds_in_chunk_start_date(
|
|
temporal_test_env: WorkflowEnvironment,
|
|
temporal_worker_drift: Worker,
|
|
test_activities: Activities,
|
|
postgres_engine,
|
|
mlflow_repository_stub,
|
|
):
|
|
"""
|
|
Scenario D.4.3b: With enough sub-minute samples and ``chunk_period='s'``, drift rows
|
|
persist and ``chunk_start_date`` keeps second-level precision (incl. second=30).
|
|
|
|
``DriftAnalysis._chunk_dataframe`` is patched to skip empty ``pd.Grouper(freq='s')``
|
|
buckets so sparse seconds between samples do not flood the pipeline with NaT rows;
|
|
the durable fix belongs in ``sientia_model``.
|
|
"""
|
|
client = temporal_test_env.client
|
|
model_id = 443
|
|
|
|
base = datetime.now(timezone.utc).replace(second=0, microsecond=0) - timedelta(minutes=6)
|
|
target_timestamps = []
|
|
sensor_1_vals = []
|
|
sensor_2_vals = []
|
|
for minute_offset in range(6):
|
|
t0 = base + timedelta(minutes=minute_offset)
|
|
t1 = t0 + timedelta(seconds=30)
|
|
target_timestamps.append(t0.strftime('%Y-%m-%d %H:%M:%S%z'))
|
|
target_timestamps.append(t1.strftime('%Y-%m-%d %H:%M:%S%z'))
|
|
v0 = 1.0 + minute_offset * 0.1
|
|
v1 = v0 + 0.05
|
|
sensor_1_vals.extend([v0, v1])
|
|
sensor_2_vals.extend([10.0 + v0, 10.0 + v1])
|
|
|
|
insert_target_data_for_drift(
|
|
postgres_engine,
|
|
model_id=model_id,
|
|
timestamps=target_timestamps,
|
|
variables_values={
|
|
'sensor_1': sensor_1_vals,
|
|
'sensor_2': sensor_2_vals,
|
|
},
|
|
)
|
|
_force_reference_unavailable(mlflow_repository_stub)
|
|
|
|
input_data = _drift_input(model_id, chunk_period='s')
|
|
with patch.object(DriftAnalysis, '_chunk_dataframe', _chunk_dataframe_skip_empty_groups):
|
|
await start_and_await_workflow(
|
|
client,
|
|
Drift.run,
|
|
input_data,
|
|
make_workflow_id('test-drift-chunk-seconds-sufficient'),
|
|
)
|
|
|
|
with postgres_engine.connect() as conn:
|
|
rows = (
|
|
conn.execute(
|
|
text(
|
|
'SELECT chunk_start_date FROM sientia_data.drift_metrics '
|
|
'WHERE model_id = :m ORDER BY chunk_index'
|
|
),
|
|
{'m': str(model_id)},
|
|
)
|
|
.mappings()
|
|
.all()
|
|
)
|
|
|
|
assert rows, 'expected at least one drift row to be persisted'
|
|
seconds_present = {pd.Timestamp(r['chunk_start_date']).second for r in rows}
|
|
assert 30 in seconds_present, (
|
|
f'expected at least one chunk_start_date with seconds=30, got {seconds_present}'
|
|
)
|