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SIENTIAPDE-1646

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Refactor ModelMetrics to utilize DriftAnalysis for drift detection

- Replaced ModelAnalysis with DriftAnalysis in the ModelMetrics class to enhance drift detection capabilities.
- Updated method signatures and documentation to reflect the changes in target_name and return values.
- Adjusted data handling to ensure compatibility with the new analysis methods and improved clarity in the drift metrics dataframe preparation.
This commit is contained in:
vitor-aignosi
2026-05-08 16:39:12 -03:00
parent c9ac25ea80
commit ee9c71cbbb
5 changed files with 209 additions and 229 deletions
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docs/E2E_TEST_REPORT.md Normal file
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# E2E test run report
**Date:** 2026-05-08
**Command:** `source venv/bin/activate && rtk pytest e2e/ -v --tb=short`
**Environment:** Linux, Python 3.11.15, pytest 9.0.3
## Summary
| Metric | Count |
|--------|------:|
| Collected | 43 |
| **Passed** | **37** |
| **Failed** | **6** |
Full pytest output (compressed by `rtk`) was written to:
`~/.local/share/rtk/tee/1778268193_pytest.log`
---
## Failed tests (6)
1. `e2e/test_drift.py::test_drift_happy_path_persists_all_columns_with_reference_data`
2. `e2e/test_drift.py::test_drift_uses_30pct_fallback_when_reference_unavailable`
3. `e2e/test_drift.py::test_drift_chunk_period_seconds_preserves_seconds_in_chunk_start_date`
4. `e2e/test_predictions_batch_prediction_process.py::test_scenario_2_1_3_input_gate_triggers_repeat`
5. `e2e/test_predictions_batch_prediction_process.py::test_scenario_2_2_3_transform_gate_triggers_repeat`
6. `e2e/test_predictions_batch_prediction_process.py::test_scenario_2_3_3_predict_gate_triggers_repeat`
**Follow-up:** Jensen–Shannon NULLs for the drift happy-path scenario are fully traced (histogram out-of-range + `density=True`, vs NannyML’s leftover bin) in [DRIFT_JS_NULL_INVESTIGATION.md](./DRIFT_JS_NULL_INVESTIGATION.md).
---
## Failure group A — Drift: `drift_metrics.value` NOT NULL (2 tests)
### Error
`psycopg2.errors.NotNullViolation`: null value in column `value` of relation `sientia_data.drift_metrics` violates not-null constraint.
Example failing row (from logs): `feature=sensor_2`, `method=jensen_shannon`, `value=null`, with `kolmogorov_smirnov` / `wasserstein` populated for the same chunk.
The bulk INSERT built by `Activities.export_data_to_postgres` includes parameters such as `'value__4': None` for `jensen_shannon` on a given chunk.
### Root cause
`calculate_drift` (real `DriftAnalysis` + `ModelMetrics.get_drift_metrics`) can emit **NaN / missing** values for some metric methods (here **Jensen–Shannon**) on some features/chunks. Pandas/SQLAlchemy turns that into SQL `NULL`, while the E2E schema (mirroring production) defines:
```sql
value numeric NOT NULL
```
in `e2e/db_schema.sql` for `sientia_data.drift_metrics`.
### Recommended fixes (pick one consistent with product rules)
1. **Application layer (preferred if NULLs are never valid in production):** Before export, sanitize the drift dataframe — e.g. drop rows where `value` is null/NaN, or replace with a defined sentinel (only if product agrees), or skip emitting that method row when the statistic is undefined.
2. **Analytics layer:** Harden the Jensen–Shannon (and similar) paths so they always return a finite float for the supported inputs, or explicitly map “undefined” to an agreed numeric convention.
3. **Schema (only if product allows missing metrics):** Align DDL with reality by making `value` nullable — **only** if production and downstream consumers already expect missing metrics; the E2E comment in `test_drift.py` suggests `feature`/`timestamp` nullable cases exist, but `value` is still listed in `NON_NULL_DRIFT_COLUMNS`.
### Tests affected
- `test_drift_happy_path_persists_all_columns_with_reference_data`
- `test_drift_uses_30pct_fallback_when_reference_unavailable`
---
## Failure group B — Drift: chunk period seconds (1 test)
### Error
`AssertionError: expected at least one drift row to be persisted` (`e2e/test_drift.py:493`).
The workflow run completed without failing the test via `WorkflowFailureError`, but **no rows** were found in `sientia_data.drift_metrics` for the model.
### Likely cause
In `Drift.run`, persistence runs only when `if drift_data:` is truthy (`laborious/workflows/drift.py`). An **empty** drift result skips `export_data_to_postgres`, so the table stays empty.
Probable reasons:
- With **`chunk_period='s'`** and only **three** target rows (30 s spacing), `calculate_drift` / `DriftAnalysis` may produce **no output rows** (insufficient data per chunk or internal filters).
- Less likely here: time-window mismatch — timestamps are built from `datetime.now(UTC)` with `interval: 60` minutes from `drift_base.json`, so data should still fall in the window.
### Recommended fixes
1. **Test data:** Increase the number of second-spaced points (and/or span multiple chunk boundaries) so the analyzer reliably emits at least one chunk row.
2. **Product code:** If sub-minute chunking is required to always produce metrics when any data exists, adjust `ModelMetrics` / `DriftAnalysis` integration for small-N second buckets.
3. **Diagnostics:** Run the same scenario with `pytest -s` and confirm logs for “empty `drift_data`” vs export errors.
Solução a ser aplicada:
Dividir em dois testes:
1. Teste com dados suficientes para produzir pelo menos uma linha de drift
2. Teste com dados insuficientes para produzir pelo menos uma linha de drift, mas ja esperando os erros e validando que nao foi persistido nada
### Test affected
- `test_drift_chunk_period_seconds_preserves_seconds_in_chunk_start_date`
---
## Failure group C — Prediction REPEAT path: unique constraint on predictions (3 tests)
### Error
`psycopg2.errors.UniqueViolation`: duplicate key value violates unique constraint **`unique_model_id_timestamp`** on `sientia_data.predictions` (`model_id`, `timestamp`).
### Root cause
REPEAT is handled by `Activities.repeat_last_prediction` (registered from `sientia_do.temporal.activities.postgres_sync.Postgres`, via `Storage` inheritance in `laborious/activities/storage.py`). The E2E tests seed a prior row with a **fixed** timestamp:
```python
# e2e/test_predictions_batch_prediction_process.py — insert_sample_prediction
VALUES ({model_id}, '2024-01-01 12:00:00+00:00', ...)
```
The helper `assert_repeat` expects **two** rows with the same `(model_id, prediction, prediction_confidence, prediction_status)` but compares **only** those columns — not `timestamp` (`e2e/helpers.py`). So the intended behavior is: **duplicate business payload**, not necessarily **duplicate primary unique key** `(model_id, timestamp)`.
If `repeat_last_prediction` **INSERT**s a copy using the **same** `timestamp` as the last prediction, Postgres correctly rejects the second insert.
### Recommended fixes
1. **Implement REPEAT as UPSERT:** Use `ON CONFLICT (model_id, timestamp) DO UPDATE` (or the project’s existing `export_data_to_postgres` `on_conflict` pattern used in `FormatAndExportPrediction`) when writing the repeated prediction — if the product definition of REPEAT is “refresh same logical slot.”
2. **Insert with the current batch timestamp:** Copy numeric/status fields from the last row but set `timestamp` to the **new** batch instant (e.g. the workflow’s `last_timestamp` / slice timestamp). This matches `assert_repeat`, which does not assert on `timestamp`.
3. **Test-only change (weakest):** Relax assertions or change seed data — only if production behavior is “duplicate key is expected” (unlikely).
Solução a ser aplicada:
Alterar o teste para usar o timestamp da ultima execucao do batch, ao inves do timestamp do primeiro batch.
2. **Insert with the current batch timestamp:** Copy numeric/status fields from the last row but set `timestamp` to the **new** batch instant (e.g. the workflow’s `last_timestamp` / slice timestamp). This matches `assert_repeat`, which does not assert on `timestamp`.
### Tests affected
- `test_scenario_2_1_3_input_gate_triggers_repeat`
- `test_scenario_2_2_3_transform_gate_triggers_repeat`
- `test_scenario_2_3_3_predict_gate_triggers_repeat`
---
## Passing areas (sanity check)
All scenarios in `test_child_workflows_e2e.py`, `test_minimal_retrain.py`, `test_minio_offload.py`, `test_predictions_batch_format_export.py`, `test_predictions_batch_main_workflow.py` (except the three REPEAT cases above), and `test_simple_metrics.py` **passed** in this run.
---
## Suggested order of work
1. Fix **drift `value` NULL** — unblocks two high-value drift E2Es and may clarify the chunk-seconds scenario if exports start succeeding consistently.
2. Fix **`repeat_last_prediction` uniqueness** — unblocks three prediction-process E2Es; implementation likely lives in **`sientia_do`** Postgres activities, not in this repo.
3. Revisit **`test_drift_chunk_period_seconds`** data volume / expectations after drift export is stable.

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docs/sientia_model_drift_jensen_shannon_change.md Normal file
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# Specification: `sientia_model` — Jensen–Shannon drift (`DriftAnalysis`)
This document describes what **`sientia_model.analytics.drift_analysis.DriftAnalysis`** should change so downstream consumers (e.g. Laborious `calculate_drift` → Postgres `drift_metrics.value NOT NULL`) no longer receive **NaN** for Jensen–Shannon on valid finite data.
## Scope
- **File:** `sientia_model/analytics/drift_analysis.py`
- **Method:** `_jensen_shannon_distance(self, ref: np.ndarray, cur: np.ndarray, bins: int = 20) -> float`
- **Callers:** `detect_univariate_drift` uses this for the `jensen_shannon` method; results are written to `value` in the drift dataframe.
## Problem
The current implementation:
1. Builds bin edges from **`ref`** only: `np.histogram(ref, bins=bins, density=True)`.
2. Builds the chunk histogram with **`density=True`** on the same edges: `np.histogram(cur, bins=edges, density=True)`.
When **every** value in **`cur`** falls **outside** the closed support implied by those edges (typical case: production chunk drifted above the reference max or below the reference min), NumPy yields **all-zero counts** for `cur`. With **`density=True`**, normalization does **0/0**, producing **NaN** for the whole histogram, which propagates to **`float('nan')`** in `detect_univariate_drift` → **SQL NULL** where `value` is `NOT NULL`.
This appears in Laborious when:
- `model_config.target` excludes the main target column from univariate features, so another feature (e.g. `sensor_2`) is compared chunk-by-chunk against the full reference series for that feature.
- Reference and current ranges do not overlap for some chunks (strong drift or different scaling).
Other methods (`kolmogorov_smirnov`, `wasserstein`) do not use this histogram+density path, so they can stay finite while **Jensen–Shannon** alone becomes null.
## Required behavior
1. **Finite output** for finite `ref` and `cur` after removing non-finite values, whenever both sides have **at least one** usable sample.
2. **Explicit handling of out-of-range chunk mass:** probability mass from `cur` that does not fall into any bin defined from `ref` must still be represented (so the chunk distribution sums to 1), analogous to NannyML’s continuous JS approach (tail / “leftover” mass).
3. **Missing values:** drop `NaN` from `ref` and `cur` before computing. If either side is **empty** after that, return **`float('nan')`** (callers may filter or map; schema may still forbid null — product decision outside this spec).
## Recommended algorithm (replace current body)
1. `ref = np.asarray(ref, float); cur = np.asarray(cur, float)`.
2. `ref = ref[~np.isnan(ref)]; cur = cur[~np.isnan(cur)]`.
3. If `ref.size == 0` or `cur.size == 0`: return `float('nan')`.
4. `hist_ref, edges = np.histogram(ref, bins=bins)` — **counts**, not `density=True`.
5. `p = hist_ref.astype(float) / ref.size` (reference bin probabilities).
6. `hist_cur, _ = np.histogram(cur, bins=edges)`; `q = hist_cur.astype(float) / cur.size`.
7. `leftover = 1.0 - float(np.sum(q))`. If `leftover > 1e-15` (tolerance for float noise), append **`leftover`** to `q` and **`0.0`** to `p` so both remain proper discrete distributions over the same extended support.
8. Apply small smoothing (existing module constant `EPSILON` is fine): add `EPSILON` to `p` and `q`, renormalize each to sum 1.
9. `m = 0.5 * (p + q)`; compute symmetric JS via KL terms as today, e.g. `inner = 0.5 * (sum(p*log(p/m)) + sum(q*log(q/m)))`.
10. Return `sqrt(max(inner, 0.0))` to guard against tiny negative `inner` from floating-point error.
## Non-goals / notes
- **Numerical parity** with the old `density=True` implementation is not required; parity with **NannyML** or **scipy** JS is desirable but optional. The priority is **finite, interpretable** drift when the chunk is outside the reference histogram range.
- **Multivariate** drift in the same file is unchanged by this spec.
- **Tests** in `sientia_model` should cover: (a) chunk entirely above reference max, (b) entirely below reference min, (c) overlapping range, (d) `ref` or `cur` all-NaN after cleaning.
## Reference (external)
- NannyML continuous JS uses count-based bin probabilities and a **leftover** mass bin; see `ContinuousJensenShannonDistance` in `nannyml/drift/univariate/methods.py` (`_calculate`, `leftover = 1 - np.sum(...)`).
- Historical NaN-in-reference issue: [NannyML#339](https://github.com/NannyML/nannyml/issues/339) / [#340](https://github.com/NannyML/nannyml/pull/340) (orthogonal to out-of-range mass, but relevant for input cleaning).

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e2e/conftest.py
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@@ -1,7 +1,5 @@
"""Pytest configuration and fixtures for E2E tests."""
import asyncio
import time
from concurrent.futures import ThreadPoolExecutor
from pathlib import Path
from unittest.mock import MagicMock
@@ -16,7 +14,6 @@ from testcontainers.postgres import PostgresContainer
from temporalio.testing import WorkflowEnvironment
from temporalio.worker import Worker
from e2e.opc_test_server import OpcE2ETestServer
from laborious.activities.activities import Activities
from laborious.workflows.drift import Drift
from laborious.workflows.minimal_retrain import MinimalRetrain
@@ -464,140 +461,3 @@ async def temporal_worker_minimal_retrain(temporal_test_env, test_activities):
yield worker
def _connect_activities_to_opc_server(activities: Activities, server_url: str) -> None:
"""
Initialize OPC repositories and block until the E2E server session is ready.
Runs synchronously (typically via ``asyncio.to_thread``) so the asyncua test
server event loop is not blocked during ``Client.connect()``.
Args:
activities (Activities): Worker activities under test.
server_url (str): ``opc.tcp://`` URL from ``OpcE2ETestServer``.
"""
activities.init_opc()
repo = activities.opc_repository['1']
deadline = time.monotonic() + 30.0
while time.monotonic() < deadline:
if repo._session_ready.is_set():
return
connected, _ = repo.connect()
if connected:
return
time.sleep(0.5)
raise RuntimeError(f'Could not connect OpcRepository to OPC E2E server at {server_url}')
def _build_e2e_opc_config(server_url: str) -> dict[str, dict]:
"""
OPC server config for E2E Activities pointing at an in-process asyncua server.
Args:
server_url (str): ``opc.tcp://`` endpoint from ``OpcE2ETestServer``.
Return:
dict: ``opc_config`` payload for ``Activities`` (server id ``1``).
"""
return {
'1': {
'id': '1',
'server_name': 'e2e_opcua',
'url': server_url,
'server_uri': server_url,
'cert_path': None,
'private_key_path': None,
'server_cert_path': None,
'reconnection_interval': 0,
}
}
@pytest_asyncio.fixture
async def opc_e2e_server():
"""In-process asyncua server with writable prediction/confidence nodes."""
server = OpcE2ETestServer()
await server.start()
await asyncio.sleep(0.5)
try:
yield server
finally:
await server.stop()
@pytest_asyncio.fixture(scope='function')
async def test_activities_real_opc(
postgres_container,
minio_container,
mock_logger,
notification_handler,
metrics_controller,
mlflow_repository_stub,
plugin_store_stub,
pi_web_api_client_stub,
opc_e2e_server: OpcE2ETestServer,
):
"""Activities with real OpcRepository connected to the in-process OPC UA server."""
minio_client = minio_container.get_client()
if not minio_client.bucket_exists('test-bucket'):
minio_client.make_bucket('test-bucket')
minio_port = minio_container.get_exposed_port(9000)
activities = Activities(
postgres_config={
'host': 'localhost',
'port': int(postgres_container.get_exposed_port(5432)),
'user': postgres_container.username,
'password': postgres_container.password,
'dbname': postgres_container.dbname,
'min_connections': 1,
'max_connections': 5,
},
plugin_store=plugin_store_stub,
minio_config={
'endpoint_url': f'localhost:{minio_port}',
'access_key': 'minioadmin',
'secret_key': 'minioadmin',
'default_bucket': 'test-bucket',
'retention_hours': 24,
'secure': False,
},
opc_config=_build_e2e_opc_config(opc_e2e_server.url),
pi_web_api_config={
'base_url': 'http://localhost:8080',
'auth_type': 'bearer',
'auth_token': 'test_token',
},
logger=mock_logger,
notification_handler=notification_handler,
metrics_controller=metrics_controller,
mlflow_repository=mlflow_repository_stub,
)
activities.pi_web_api_client = pi_web_api_client_stub
await asyncio.to_thread(_connect_activities_to_opc_server, activities, opc_e2e_server.url)
try:
yield activities
finally:
await asyncio.to_thread(_teardown_real_opc_activities, activities)
def _teardown_real_opc_activities(activities: Activities) -> None:
"""Disconnect OPC sessions and shut down activities (sync, for asyncio.to_thread)."""
for opc_repo in activities.opc_repository.values():
opc_repo.disconnect()
activities.shutdown()
@pytest_asyncio.fixture(scope='function')
async def temporal_worker_real_opc(temporal_test_env, test_activities_real_opc):
"""Temporal worker using real OpcRepository against the in-process OPC UA server."""
with ThreadPoolExecutor(max_workers=32) as activity_executor:
async with Worker(
temporal_test_env.client,
task_queue='test-queue',
workflows=[PredictionsBatch, PredictionProcess, FormatAndExportPrediction],
activities=_worker_activity_list(test_activities_real_opc),
activity_executor=activity_executor,
) as worker:
yield worker

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e2e/scenarios.md
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@@ -10,27 +10,6 @@ It is a functional reference of scenario behavior, inputs, and expected outcomes
- Tests run under `e2e/` and are marked with `@pytest.mark.integration`.
- PostgreSQL and MinIO are provisioned with testcontainers.
- `test_minio_offload.py` uses real MinIO I/O; other scenario suites may use stubs/mocks for optional outputs.
- Real OPC UA scenarios use `@pytest.mark.opc` and an in-process asyncua server (`e2e/test_opc_real_server.py`).
### Local validation
Use the existing project virtualenv and the shared `validate` script for unit/quality gates; run E2E separately (Docker required).
```bash
source ./venv/bin/activate
# Auto-fix + static checks (no pytest)
validate --fix --project-name=laborious
# Full unit + quality gate
validate --project-name=laborious
# E2E (integration)
pytest e2e/ --override-ini testpaths=e2e -m integration
# E2E (real OPC server only)
pytest e2e/test_opc_real_server.py --override-ini testpaths=e2e -m opc
```
---
@@ -106,15 +85,11 @@ Source: `e2e/test_predictions_batch_prediction_process.py`
- Workflow exits without export.
#### 2.1.3 REPEAT with history
**Summary**: Prior prediction is reused; outcome depends on batch vs history timestamp.
**Summary**: Prior prediction is reused.
**Description**:
- Input gate returns `REPEAT`.
- `repeat_last_prediction` inserts a row using ``last_timestamp`` from the batch payload (max timestamp in `laborious_data` for the query), not the copied row’s timestamp.
**Tests**:
- **Collision**: batch `last_timestamp` equals the historical prediction row’s `timestamp` → Postgres `unique_model_id_timestamp` violation; workflow fails; still one row.
- **Distinct batch time**: laborious rows are stamped later than the historical prediction → second row inserted; same prediction fields as the first (see `assert_repeat`).
- `repeat_last_prediction` path is executed using existing historical row.
#### 2.1.4 REPEAT without history
**Summary**: Repeat requested but no previous prediction exists.
@@ -137,8 +112,6 @@ Source: `e2e/test_predictions_batch_prediction_process.py`
#### 2.2.3 REPEAT on transform response error
**Summary**: Transform response error triggers repeat-last-prediction path.
**Tests**: Same timestamp collision vs distinct batch timestamp as §2.1.3 (`*_fails` / `*_inserts_second_row`).
#### 2.2.4 STOP on transform content NaN
**Summary**: Content gate (`NAN_VALUES`) blocks on all-NaN transform payload.
@@ -153,8 +126,6 @@ Source: `e2e/test_predictions_batch_prediction_process.py`
#### 2.3.3 REPEAT on predict response error
**Summary**: Predict response error routes to repeat-last-prediction.
**Tests**: Same timestamp collision vs distinct batch timestamp as §2.1.3 (`*_fails` / `*_inserts_second_row`).
### 2.4.1 Priority Conflict Resolution
**Summary**: Deterministic selection when multiple filters produce different flags.
@@ -216,30 +187,6 @@ Source: `e2e/test_predictions_batch_format_export.py`
- Workflow completes.
- Prediction persisted with PI error confidence and descriptive comment.
#### 3.2.4 OPC session / channel error (confidence 14)
**Summary**: Tier-1 `BadSessionIdInvalid` (or equivalent session error) degrades the prediction without failing the workflow.
**Sources**:
- Mock: `e2e/test_predictions_batch_format_export.py::test_scenario_3_2_4_opc_session_bad_mock`
- Real server: `e2e/test_opc_real_server.py::test_scenario_3_2_4_opc_session_bad_real_server` (`@pytest.mark.opc`)
**Expected Outcome**:
- Workflow completes.
- `prediction_confidence` is 14.
- Comments contain `OPC UA session/channel error: BadSessionIdInvalid`.
#### 3.2.5 OPC write blocked during reconnect (confidence 14)
**Summary**: While reconnect holds the repository connection lock, writes fail fast with `reconnect_in_progress`.
**Sources**:
- Mock: `e2e/test_predictions_batch_format_export.py::test_scenario_3_2_5_opc_reconnect_in_progress_mock`
- Real server: `e2e/test_opc_real_server.py::test_scenario_3_2_5_opc_write_blocked_during_reconnect_real_server` (`@pytest.mark.opc`)
**Expected Outcome**:
- Workflow completes.
- `prediction_confidence` is 14.
- Comments contain `OPC UA reconnect in progress`.
### 3.3.1 Combined Optional Outputs (PI + OPC)
**Summary**: Both external output channels are enabled together.
@@ -349,33 +296,15 @@ first 30% of target rows as reference.
- The workflow surfaces the `ValueError` ("Invalid chunk period: ...").
- No rows are persisted.
#### D.4.3a Insufficient drift metrics while target has rows
**Summary**: Laborious raises when the merged drift table is empty but the
target window is non-empty (`Insufficient drift data:` + notification
`MODEL_METRICS_DRIFT_INSUFFICIENT_DATA`).
**Description**:
- The e2e patches `ModelMetrics.get_drift_metrics` to return an empty
DataFrame, simulating a ``sientia_model`` path that emits no rows.
**Expected Outcome**:
- Workflow fails; no rows in `sientia_data.drift_metrics`.
#### D.4.3b `chunk_period='s'` preserves seconds in `chunk_start_date`
**Summary**: Target data includes sub-minute spacing across several minutes;
the activity uses `chunk_period='s'`.
#### D.4.3 `chunk_period='s'` preserves seconds in `chunk_start_date`
**Summary**: Target data spans two minutes with samples at second-30
boundaries; the activity is configured with `chunk_period='s'`.
**Expected Outcome**:
- At least one persisted `chunk_start_date` carries `seconds=30`, proving
that the analyzer chunked at sub-minute granularity and the ISO-text
serialization preserved the boundary.
**Note**: The e2e patches `DriftAnalysis._chunk_dataframe` to **skip empty**
`pd.Grouper(freq='s')` buckets. The stock implementation iterates every
second between min/max timestamps, producing empty chunks and NaT rows that
`calculate_drift` filters away entirely. The durable fix belongs in
`sientia_model`.
---
## 6. Simple Metrics Workflow Scenarios

13
tests/conftest.py
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@@ -3,19 +3,6 @@ import os
from sientia_do.temporal.activities.postgres_sync import Postgres
def _noop_postgres_del(_self):
"""
Unit tests use MagicMock metrics controllers; postgres_sync.Postgres.__del__ calls
close() during GC and triggers async shutdown. Explicit ``close()`` is covered in tests.
"""
return None
Postgres.__del__ = _noop_postgres_del # type: ignore[method-assign]
from sientia_do.temporal.activities.postgres_sync import Postgres
def _noop_postgres_del(_self):
"""
Unit tests use MagicMock metrics controllers; postgres_sync.Postgres.__del__ calls