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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 e6018af23f
commit 10c7e292b9
29 changed files with 843 additions and 1266 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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# 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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"""Pytest configuration and fixtures for E2E tests."""
from concurrent.futures import ThreadPoolExecutor
from typing import Any
from pathlib import Path
from unittest.mock import MagicMock
import pandas as pd
import pytest
import pytest_asyncio
from sqlalchemy import create_engine, text
from sqlalchemy import create_engine
from testcontainers.core.container import DockerContainer
from testcontainers.minio import MinioContainer
from testcontainers.postgres import PostgresContainer
from temporalio.testing import WorkflowEnvironment
from temporalio.worker import Worker
class _FakeModelAnalysis:
"""
Configurable double for ``sientia_model.analytics.model_analysis.ModelAnalysis``
used by E2E drift tests.
Replaces the real analyzer in ``sientia_model.analytics.model_analysis``
BEFORE the production import chain runs so
``laborious.activities.model_metrics`` resolves ``ModelAnalysis`` to this
class at import time. Keeps drift assertions stable across runs (the real
analyzer is data-dependent).
Tests configure responses through class-level attributes which are reset
between tests by ``reset_model_analysis_stub``:
- ``_drift_response_factory``: callable ``(univariate, multivariate) -> DataFrame``
controlling the consolidated drift dataframe seen by ``calculate_drift``.
- ``_univariate_side_effect`` / ``_multivariate_side_effect``: optional
side effects (Exception or callable) for the raw detection methods.
- ``_drift_metrics_dataframe_exception``: when set, raised by
``get_drift_metrics_dataframe`` to simulate analyzer failures.
"""
_drift_response_factory: Any = None
_univariate_side_effect: Any = None
_multivariate_side_effect: Any = None
_drift_metrics_dataframe_exception: Exception | None = None
last_instance: Any = None
def __init__(self, config):
self.config = config
type(self).last_instance = self
self.detect_univariate_drift_calls = []
self.detect_multivariate_drift_calls = []
self.get_drift_metrics_dataframe_calls = []
def detect_univariate_drift(self, **kwargs):
"""Record arguments and return ``{}`` unless ``_univariate_side_effect`` overrides it."""
self.detect_univariate_drift_calls.append(kwargs)
side_effect = type(self)._univariate_side_effect
if isinstance(side_effect, Exception):
raise side_effect
if callable(side_effect):
return side_effect(**kwargs)
return {}
def detect_multivariate_drift(self, **kwargs):
"""Record arguments and return ``{}`` unless ``_multivariate_side_effect`` overrides it."""
self.detect_multivariate_drift_calls.append(kwargs)
side_effect = type(self)._multivariate_side_effect
if isinstance(side_effect, Exception):
raise side_effect
if callable(side_effect):
return side_effect(**kwargs)
return {}
def get_drift_metrics_dataframe(self, univariate_drift, multivariate_drift):
"""Return the configured drift dataframe (copy) or raise the configured exception."""
self.get_drift_metrics_dataframe_calls.append(
{'univariate_drift': univariate_drift, 'multivariate_drift': multivariate_drift}
)
if type(self)._drift_metrics_dataframe_exception is not None:
raise type(self)._drift_metrics_dataframe_exception
factory = type(self)._drift_response_factory
if factory is None:
return pd.DataFrame()
result = factory(univariate_drift, multivariate_drift)
return result.copy() if isinstance(result, pd.DataFrame) else result
@classmethod
def reset(cls):
"""Clear all configured side effects and the last constructed instance."""
cls._drift_response_factory = None
cls._univariate_side_effect = None
cls._multivariate_side_effect = None
cls._drift_metrics_dataframe_exception = None
cls.last_instance = None
# Patch ``sientia_model.analytics.model_analysis.ModelAnalysis`` BEFORE the
# production import chain runs so drift E2E tests can drive deterministic
# analyzer outputs (the real implementation is data-dependent and would yield
# values that drift across runs). The patch is applied to the real installed
# module so that ``from laborious.activities.activities import Activities``
# resolves ``ModelAnalysis`` to ``_FakeModelAnalysis`` at import time.
import sientia_model.analytics.model_analysis as _sientia_model_analysis_module # noqa: E402
_sientia_model_analysis_module.ModelAnalysis = _FakeModelAnalysis
from laborious.activities.activities import Activities # noqa: E402
from laborious.activities.activities import Activities
from laborious.workflows.drift import Drift
from laborious.workflows.minimal_retrain import MinimalRetrain
from laborious.workflows.predictions_batch import PredictionsBatch
@@ -116,6 +27,11 @@ from sientia_do.notifications.handlers import CoreNotificationHandler
from sientia_do.observability.logger import Logger
from sientia_do.observability.metrics_controller import MetricsController
# Single source of truth for the test database schema. Mirrors the production
# DDL for ``sientia_data`` so any production change can be pasted directly into
# this file (see ``e2e/db_schema.sql``) without touching Python.
DB_SCHEMA_SQL_PATH = Path(__file__).parent / 'db_schema.sql'
@pytest_asyncio.fixture(scope='session')
def postgres_container():
@@ -153,113 +69,16 @@ def postgres_engine(postgres_container):
def _create_schema_and_tables(engine):
"""Create all schemas/tables required by workflow and activity paths."""
"""
Create all schemas/tables required by workflow and activity paths.
Loads the DDL from ``e2e/db_schema.sql`` (single source of truth that
mirrors the production schema). The SQL file is executed via the raw
DBAPI cursor so multi-statement DDL is supported.
"""
sql_text = DB_SCHEMA_SQL_PATH.read_text(encoding='utf-8')
with engine.begin() as conn:
conn.execute(text('CREATE SCHEMA IF NOT EXISTS predictions_schema'))
conn.execute(
text(
"""
CREATE TABLE IF NOT EXISTS predictions_schema.laborious_data (
id SERIAL NOT NULL,
model_id int4 NOT NULL,
variable text NOT NULL,
value numeric NULL,
"timestamp" timestamptz NOT NULL,
created_at timestamptz NOT NULL,
PRIMARY KEY (id)
);
"""
)
)
conn.execute(
text(
"""
CREATE TABLE IF NOT EXISTS predictions_schema.predictions (
id SERIAL NOT NULL,
model_id int4 NOT NULL,
prediction numeric NULL,
prediction_confidence numeric NOT NULL,
response_time numeric NOT NULL,
prediction_status text NOT NULL,
"timestamp" timestamptz NOT NULL,
created_at timestamptz DEFAULT CURRENT_TIMESTAMP NOT NULL,
comments text NULL,
PRIMARY KEY (id, created_at)
);
"""
)
)
conn.execute(
text(
"""
CREATE TABLE IF NOT EXISTS predictions_schema.transformed_data (
id SERIAL NOT NULL,
model_id int4 NOT NULL,
variable text NOT NULL,
value numeric NULL,
"timestamp" timestamptz NOT NULL,
created_at timestamptz DEFAULT CURRENT_TIMESTAMP NOT NULL,
PRIMARY KEY (id)
);
"""
)
)
conn.execute(
text(
"""
CREATE TABLE IF NOT EXISTS predictions_schema.drift (
id SERIAL NOT NULL,
model_id int4 NOT NULL,
feature text NOT NULL,
method text NOT NULL,
value numeric NULL,
drift bool NOT NULL,
chunk int4 NOT NULL,
"timestamp" timestamptz NOT NULL,
timestamp_end text NULL,
accurate bool NOT NULL,
created_at timestamptz DEFAULT CURRENT_TIMESTAMP NOT NULL,
updated_at timestamptz DEFAULT CURRENT_TIMESTAMP NOT NULL,
PRIMARY KEY (id)
);
"""
)
)
conn.execute(
text(
"""
CREATE TABLE IF NOT EXISTS predictions_schema.simple_metrics_data (
id SERIAL NOT NULL,
model_id int4 NOT NULL,
metric text NOT NULL,
value numeric NOT NULL,
"timestamp" timestamptz NOT NULL,
data_size int4 NOT NULL,
interval_minutes int4 NOT NULL,
created_at timestamptz DEFAULT CURRENT_TIMESTAMP NOT NULL,
PRIMARY KEY (id)
);
"""
)
)
conn.execute(
text(
"""
CREATE TABLE IF NOT EXISTS predictions_schema.retrain_reports (
id SERIAL NOT NULL,
model_id int4 NOT NULL,
model_name text NOT NULL,
"timestamp" text NOT NULL,
status text NOT NULL,
version text NULL,
mlflow_run_id text NULL,
mlflow_experiment_id text NULL,
created_at timestamptz DEFAULT CURRENT_TIMESTAMP NOT NULL,
PRIMARY KEY (id)
);
"""
)
)
conn.exec_driver_sql(sql_text)
@pytest_asyncio.fixture(autouse=True)
@@ -269,14 +88,6 @@ def setup_postgres_schema_and_tables(postgres_engine):
yield
@pytest.fixture(autouse=True)
def reset_model_analysis_stub():
"""Reset class-level state on the ModelAnalysis stub between tests."""
_FakeModelAnalysis.reset()
yield
_FakeModelAnalysis.reset()
@pytest_asyncio.fixture
def mock_logger():
"""Logger double with readable console output for E2E runs."""
@@ -650,36 +461,3 @@ async def temporal_worker_minimal_retrain(temporal_test_env, test_activities):
yield worker
@pytest.fixture
def model_analysis_stub():
"""
Expose the ``_FakeModelAnalysis`` class so drift tests can configure responses.
Use class-level attributes to control the analyzer outputs:
- ``stub.set_drift_dataframe(factory)`` to provide rows for ``calculate_drift``.
- ``stub.raise_on_get_drift_metrics_dataframe(exception)`` to simulate failures.
"""
class _Helper:
"""Thin convenience wrapper around _FakeModelAnalysis class state."""
cls = _FakeModelAnalysis
def set_drift_dataframe(self, factory):
self.cls._drift_response_factory = factory
def raise_on_get_drift_metrics_dataframe(self, exc: Exception):
self.cls._drift_metrics_dataframe_exception = exc
def set_univariate_side_effect(self, side_effect):
self.cls._univariate_side_effect = side_effect
def set_multivariate_side_effect(self, side_effect):
self.cls._multivariate_side_effect = side_effect
@property
def last_instance(self):
return self.cls.last_instance
return _Helper()

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@@ -0,0 +1,109 @@
-- =============================================================================
-- E2E test database schema for the ``sientia_data`` namespace.
--
-- Mirrors the production DDL one-to-one so any change in production can be
-- pasted directly into this file. The conftest fixture loads this SQL into the
-- testcontainers Postgres before each test run.
--
-- Notes on differences from production:
-- * Tables that are partitioned in production (e.g. ``simple_metrics``,
-- ``transformed_data``, ``drift_metrics``) are created as plain tables
-- here because the test suite does not exercise partition pruning.
-- * Indexes are intentionally omitted; tests rely on functional behavior,
-- not query plans.
-- =============================================================================
CREATE SCHEMA IF NOT EXISTS sientia_data;
-- -----------------------------------------------------------------------------
-- sientia_data.laborious_data
-- -----------------------------------------------------------------------------
CREATE TABLE IF NOT EXISTS sientia_data.laborious_data (
model_id int4 NOT NULL,
variable text NOT NULL,
value numeric NULL,
"timestamp" timestamptz NOT NULL,
created_at timestamptz DEFAULT CURRENT_TIMESTAMP NOT NULL,
CONSTRAINT unique_timestamp_variable
UNIQUE (model_id, "timestamp", variable)
);
-- -----------------------------------------------------------------------------
-- sientia_data.predictions
-- -----------------------------------------------------------------------------
CREATE TABLE IF NOT EXISTS sientia_data.predictions (
model_id int4 NOT NULL,
prediction numeric NULL,
prediction_confidence numeric NOT NULL,
response_time numeric NOT NULL,
prediction_status text NOT NULL,
"timestamp" timestamptz NOT NULL,
created_at timestamptz DEFAULT CURRENT_TIMESTAMP NOT NULL,
"comments" text NULL,
CONSTRAINT unique_model_id_timestamp
UNIQUE (model_id, "timestamp")
);
-- -----------------------------------------------------------------------------
-- sientia_data.transformed_data
-- Production: PARTITION BY RANGE (created_at). Tests use a plain table.
-- -----------------------------------------------------------------------------
CREATE TABLE IF NOT EXISTS sientia_data.transformed_data (
id SERIAL NOT NULL,
model_id int4 NOT NULL,
variable text NOT NULL,
value numeric NULL,
"timestamp" timestamptz NOT NULL,
created_at timestamptz DEFAULT CURRENT_TIMESTAMP NOT NULL,
PRIMARY KEY (id, created_at)
);
-- -----------------------------------------------------------------------------
-- sientia_data.drift_metrics
-- Production: PARTITION BY RANGE (created_at). Tests use a plain table.
-- -----------------------------------------------------------------------------
CREATE TABLE IF NOT EXISTS sientia_data.drift_metrics (
id SERIAL NOT NULL,
model_id text NOT NULL,
feature text NULL,
method text NOT NULL,
value numeric NOT NULL,
alert bool NOT NULL,
chunk_index int4 NOT NULL,
chunk_start_date text NOT NULL,
chunk_end_date text NOT NULL,
accurate bool NOT NULL,
"timestamp" timestamptz NULL,
created_at timestamptz DEFAULT CURRENT_TIMESTAMP NOT NULL,
PRIMARY KEY (id, created_at)
);
-- -----------------------------------------------------------------------------
-- sientia_data.simple_metrics
-- Production: PARTITION BY RANGE (created_at). Tests use a plain table.
-- -----------------------------------------------------------------------------
CREATE TABLE IF NOT EXISTS sientia_data.simple_metrics (
id SERIAL NOT NULL,
model_id text NOT NULL,
metric text NOT NULL,
value numeric NOT NULL,
"timestamp" timestamptz NULL,
data_size int4 NOT NULL,
interval_minutes int4 NOT NULL,
created_at timestamptz DEFAULT CURRENT_TIMESTAMP NOT NULL,
PRIMARY KEY (id, created_at)
);
-- -----------------------------------------------------------------------------
-- sientia_data.log_retrain
-- No primary key in production; all columns nullable.
-- -----------------------------------------------------------------------------
CREATE TABLE IF NOT EXISTS sientia_data.log_retrain (
mlflow_experiment_id int8 NULL,
mlflow_run_id text NULL,
model_id text NULL,
model_name text NULL,
status text NULL,
"timestamp" timestamptz NULL,
"version" text NULL
);

99
e2e/helpers.py
View File

@@ -9,7 +9,6 @@ from decimal import Decimal
from pathlib import Path
from typing import Any
import pandas as pd
from sqlalchemy import text
from sqlalchemy.engine import Engine
@@ -91,14 +90,14 @@ def insert_sample_data(postgres_engine: Engine, model_id: int, values: list[Any]
values: Per-sensor values; use string 'NULL' for SQL NULL.
"""
with postgres_engine.begin() as conn:
conn.execute(text(f'DELETE FROM predictions_schema.laborious_data WHERE model_id = {model_id}'))
conn.execute(text(f'DELETE FROM sientia_data.laborious_data WHERE model_id = {model_id}'))
values_sql = []
for i, value in enumerate(values):
values_sql.append(f"""
({model_id}, 'sensor_{i + 1}', {value}, '2024-01-01 12:00:00+00:00', '2024-01-01 12:00:00+00:00')
""")
insert_sql = f"""
INSERT INTO predictions_schema.laborious_data (model_id, variable, value, timestamp, created_at)
INSERT INTO sientia_data.laborious_data (model_id, variable, value, timestamp, created_at)
VALUES
{', '.join(values_sql)}
"""
@@ -130,7 +129,7 @@ def assert_prediction(
result_query = conn.execute(
text(
f'SELECT model_id, prediction, prediction_confidence, prediction_status, comments '
f'FROM predictions_schema.predictions WHERE model_id = {model_id} '
f'FROM sientia_data.predictions WHERE model_id = {model_id} '
f'ORDER BY created_at ASC'
)
)
@@ -159,7 +158,7 @@ def assert_continue(
result_query = conn.execute(
text(
f'SELECT model_id, prediction, prediction_confidence, prediction_status, comments '
f'FROM predictions_schema.predictions WHERE model_id = {model_id}'
f'FROM sientia_data.predictions WHERE model_id = {model_id}'
)
)
prediction_rows = result_query.fetchall()
@@ -179,7 +178,7 @@ def assert_stop(postgres_engine: Engine, model_id: int) -> None:
with postgres_engine.connect() as conn:
result_query = conn.execute(
text(f'SELECT COUNT(*) FROM predictions_schema.predictions WHERE model_id = {model_id}')
text(f'SELECT COUNT(*) FROM sientia_data.predictions WHERE model_id = {model_id}')
)
count = result_query.scalar()
assert count == 0, f'Expected no predictions, but found {count} records'
@@ -202,7 +201,7 @@ def assert_repeat(postgres_engine: Engine, model_id: int, last_prediction: tuple
result_query = conn.execute(
text(
f'SELECT model_id, prediction, prediction_confidence, prediction_status '
f'FROM predictions_schema.predictions WHERE model_id = {model_id} '
f'FROM sientia_data.predictions WHERE model_id = {model_id} '
f'ORDER BY created_at ASC'
)
)
@@ -255,97 +254,15 @@ def insert_target_data_for_drift(
with postgres_engine.begin() as conn:
conn.execute(
text(f'DELETE FROM predictions_schema.laborious_data WHERE model_id = {model_id}')
text(f'DELETE FROM sientia_data.laborious_data WHERE model_id = {model_id}')
)
if rows_sql:
conn.execute(
text(
'INSERT INTO predictions_schema.laborious_data '
'INSERT INTO sientia_data.laborious_data '
'(model_id, variable, value, "timestamp", created_at) VALUES '
+ ', '.join(rows_sql)
)
)
def build_drift_dataframe(
timestamps: list[str],
features: list[str],
methods: list[str],
statistic: float = 1.0,
drift_flags: dict[tuple[str, str], bool] | None = None,
include_multivariate: bool = True,
multivariate_value: float = 16.0,
multivariate_drift: bool = True,
extra_rows: list[dict[str, Any]] | None = None,
) -> Any:
"""
Build a deterministic dataframe matching the schema returned by
``sientia_model.analytics.model_analysis.ModelAnalysis.get_drift_metrics_dataframe``
so drift E2E tests can pin the exact rows persisted to PostgreSQL.
The output mirrors the analyzer's canonical schema:
``timestamp, feature, metric, statistic, p_value, alert, chunk_index,
chunk_start_date, chunk_end_date``. ``calculate_drift`` then renames
``alert -> drift``, ``chunk_index -> chunk``, ``chunk_end_date ->
timestamp_end`` and drops ``p_value`` / ``chunk_start_date``.
Args:
- timestamps: Truncated chunk start timestamps (``'2024-01-01 12:00'`` for ``min``).
- features: Univariate feature names (one row per feature/method/timestamp).
- methods: Univariate methods such as ``kolmogorov_smirnov``.
- statistic: Default univariate statistic value.
- drift_flags: Optional override of the ``alert`` flag per ``(feature, method)`` pair.
- include_multivariate: Whether to add a final multivariate row block.
- multivariate_value: Value placed on multivariate rows.
- multivariate_drift: Drift flag placed on multivariate rows.
- extra_rows: Additional pre-built rows to append (used for dedup/p_value tests).
Return:
pandas.DataFrame with columns: timestamp, feature, metric, statistic,
p_value, alert, chunk_index, chunk_start_date, chunk_end_date.
"""
rows: list[dict[str, Any]] = []
drift_flags = drift_flags or {}
# Synthetic chunk-end offset that mirrors the high-precision boundary
# (``...:59.999999999``) emitted by ``ModelAnalysis`` for minute chunks.
# Computing via ``Timedelta`` instead of string concatenation keeps the
# helper safe for both minute- and second-precision timestamps.
chunk_span = pd.Timedelta(seconds=59, nanoseconds=999999999)
for chunk_index, ts in enumerate(timestamps):
chunk_start = pd.Timestamp(ts)
chunk_end = chunk_start + chunk_span
for feature in features:
for method in methods:
rows.append(
{
'timestamp': chunk_start,
'feature': feature,
'metric': method,
'statistic': statistic,
'p_value': 0.5,
'alert': drift_flags.get((feature, method), False),
'chunk_index': chunk_index,
'chunk_start_date': chunk_start,
'chunk_end_date': chunk_end,
}
)
if include_multivariate:
rows.append(
{
'timestamp': chunk_start,
'feature': 'multivariate',
'metric': 'multivariate',
'statistic': multivariate_value,
'p_value': 0.0,
'alert': multivariate_drift,
'chunk_index': chunk_index,
'chunk_start_date': chunk_start,
'chunk_end_date': chunk_end,
}
)
if extra_rows:
rows.extend(extra_rows)
return pd.DataFrame(rows)

4
e2e/scenario_inputs/drift_base.json
View File

@@ -2,9 +2,9 @@
"schedule_name": "test-schedule",
"model_name": "test_model",
"model_id": "{{MODEL_ID}}",
"schema": "predictions_schema",
"schema": "sientia_data",
"source_table_name": "laborious_data",
"target_table_name": "drift",
"target_table_name": "drift_metrics",
"interval": 60,
"drift_metrics": ["kolmogorov_smirnov", "jensen_shannon", "wasserstein"],
"chunk_period": "min",

4
e2e/scenario_inputs/format_export_base.json
View File

@@ -2,8 +2,8 @@
"schedule_name": "test-schedule",
"model_name": "test_model",
"model_id": "{{MODEL_ID}}",
"query": "SELECT timestamp, variable, value, created_at FROM predictions_schema.laborious_data WHERE model_id = {{MODEL_ID}}",
"schema": "predictions_schema",
"query": "SELECT timestamp, variable, value, created_at FROM sientia_data.laborious_data WHERE model_id = {{MODEL_ID}}",
"schema": "sientia_data",
"table_name": "predictions",
"transform_table_name": "transformed_data",
"input_filters": {

4
e2e/scenario_inputs/main_happy_path.json
View File

@@ -10,8 +10,8 @@
"schedule_name": "test-schedule",
"model_name": "test_model",
"model_id": "{{MODEL_ID}}",
"query": "SELECT timestamp, variable, value, created_at FROM predictions_schema.laborious_data WHERE model_id = {{MODEL_ID}}",
"schema": "predictions_schema",
"query": "SELECT timestamp, variable, value, created_at FROM sientia_data.laborious_data WHERE model_id = {{MODEL_ID}}",
"schema": "sientia_data",
"table_name": "predictions",
"transform_table_name": "transformed_data",
"input_filters": {

4
e2e/scenario_inputs/main_invalid_datetime.json
View File

@@ -10,8 +10,8 @@
"schedule_name": "test-schedule",
"model_name": "test_model",
"model_id": "{{MODEL_ID}}",
"query": "SELECT timestamp, variable, value FROM predictions_schema.laborious_data WHERE model_id = {{MODEL_ID}}",
"schema": "predictions_schema",
"query": "SELECT timestamp, variable, value FROM sientia_data.laborious_data WHERE model_id = {{MODEL_ID}}",
"schema": "sientia_data",
"table_name": "predictions",
"transform_table_name": "transformed_data",
"input_filters": {

2
e2e/scenario_inputs/main_missing_required.json
View File

@@ -10,7 +10,7 @@
"schedule_name": "test-schedule",
"model_name": "test_model",
"model_id": "{{MODEL_ID}}",
"schema": "predictions_schema",
"schema": "sientia_data",
"table_name": "predictions",
"transform_table_name": "transformed_data"
}

2
e2e/scenario_inputs/main_sql_error.json
View File

@@ -11,7 +11,7 @@
"model_name": "test_model",
"model_id": "{{MODEL_ID}}",
"query": "SELECT * FROM nonexistent_table WHERE invalid_syntax =",
"schema": "predictions_schema",
"schema": "sientia_data",
"table_name": "predictions",
"transform_table_name": "transformed_data",
"input_filters": {

6
e2e/scenario_inputs/minimal_retrain_base.json
View File

@@ -2,9 +2,9 @@
"schedule_name": "test-schedule",
"model_name": "test_model",
"model_id": "{{MODEL_ID}}",
"query": "SELECT timestamp, variable, value, created_at FROM predictions_schema.laborious_data WHERE model_id = {{MODEL_ID}}",
"schema": "predictions_schema",
"table_name": "retrain_reports",
"query": "SELECT timestamp, variable, value, created_at FROM sientia_data.laborious_data WHERE model_id = {{MODEL_ID}}",
"schema": "sientia_data",
"table_name": "log_retrain",
"datetime_columns": ["timestamp", "created_at"],
"model_config": {
"target": "sensor_1"

2
e2e/scenario_inputs/minio_offload_load_query.json
View File

@@ -5,7 +5,7 @@
"model_id": "{{MODEL_ID}}",
"workflow_name": "predictions_batch"
},
"query": "SELECT timestamp, variable, value, created_at FROM predictions_schema.laborious_data WHERE model_id = {{MODEL_ID}}",
"query": "SELECT timestamp, variable, value, created_at FROM sientia_data.laborious_data WHERE model_id = {{MODEL_ID}}",
"model_name": "test_model",
"datetime_columns": ["timestamp", "created_at"]
}

4
e2e/scenario_inputs/minio_offload_workflow.json
View File

@@ -2,8 +2,8 @@
"schedule_name": "test-schedule",
"model_name": "test_model",
"model_id": "{{MODEL_ID}}",
"query": "SELECT timestamp, variable, value, created_at FROM predictions_schema.laborious_data WHERE model_id = {{MODEL_ID}}",
"schema": "predictions_schema",
"query": "SELECT timestamp, variable, value, created_at FROM sientia_data.laborious_data WHERE model_id = {{MODEL_ID}}",
"schema": "sientia_data",
"table_name": "predictions",
"transform_table_name": "transformed_data",
"input_filters": {

4
e2e/scenario_inputs/prediction_process_base.json
View File

@@ -2,8 +2,8 @@
"schedule_name": "test-schedule",
"model_name": "test_model",
"model_id": "{{MODEL_ID}}",
"query": "SELECT timestamp, variable, value, created_at FROM predictions_schema.laborious_data WHERE model_id = {{MODEL_ID}}",
"schema": "predictions_schema",
"query": "SELECT timestamp, variable, value, created_at FROM sientia_data.laborious_data WHERE model_id = {{MODEL_ID}}",
"schema": "sientia_data",
"table_name": "predictions",
"transform_table_name": "transformed_data",
"input_filters": {

4
e2e/scenario_inputs/simple_metrics_base.json
View File

@@ -2,10 +2,10 @@
"schedule_name": "test-schedule",
"model_name": "test_model",
"model_id": "{{MODEL_ID}}",
"schema": "predictions_schema",
"schema": "sientia_data",
"predictions_table_name": "predictions",
"data_table_name": "laborious_data",
"target_table_name": "simple_metrics_data",
"target_table_name": "simple_metrics",
"interval_minutes": 60,
"metrics": ["rmse", "mse", "mae", "r2"],
"model_config": {

106
e2e/scenarios.md
View File

@@ -232,81 +232,87 @@ Source: `e2e/test_minio_offload.py`
## 5. Drift Workflow Scenarios
Source: `e2e/test_drift.py`
The drift suite mocks `sientia.ModelAnalysis.ModelAnalysis` (not installed; see
`CODE_ISSUES.md` issue #1) through the controllable `_FakeModelAnalysis` stub
exposed by the `model_analysis_stub` fixture. The `mlflow_repository_stub`
provides the reference-data CSV via `download_artifacts`. Every scenario asserts
postgres rows in `predictions_schema.drift` against this canonical schema:
The drift suite drives the **real** `sientia_model.analytics.drift_analysis.DriftAnalysis`
analyzer (no stubs / mocks). Each scenario exercises the full pipeline:
`id, model_id, feature, method, value, drift, chunk, timestamp, timestamp_end, accurate, created_at, updated_at`.
```
laborious_data (Postgres) -> load_custom_query
-> calculate_drift (DriftAnalysis univariate + multivariate)
-> export_data_to_postgres (sientia_data.drift_metrics)
```
The `mlflow_repository_stub` provides the reference-data CSV via
`download_artifacts`, and tests assert postgres rows in
`sientia_data.drift_metrics` against this canonical schema:
`id, model_id, feature, method, value, alert, chunk_index, chunk_start_date, chunk_end_date, accurate, timestamp, created_at`.
Tests assert behavioral / structural properties (column presence, NOT NULL
constraints, business-key invariants like uniform `timestamp` and stamped
`model_id`) rather than exact numeric drift scores, since those depend on
the real analyzer implementation and the synthetic data fed in.
### 5.1 Happy paths
#### D.1.1 Full pipeline persists all columns with reference data
**Summary**: ModelAnalysis returns a deterministic drift dataframe; the
reference CSV is downloaded from the MLflow stub.
**Summary**: 10 minutes of target data are inserted; a 10-row reference CSV
is configured via the MLflow stub. The `DriftAnalysis` runs end-to-end.
**Expected Outcome**:
- One row per `(chunk, feature, method)` plus a `multivariate` block per chunk.
- Every drift column is populated and `accurate=True`.
- `timestamp_end` preserves the high-precision string (`HH:MM:59.999999999`).
- One row per `(chunk_index, feature, method)` plus a `multivariate` block
per chunk is persisted.
- Every column in the DDL is populated; `feature` is the only nullable column
per the new schema.
- `accurate=True` for every row (reference path).
- All three default univariate methods reach the analyzer.
- `model_id` is stamped as `text` and uniform across rows.
- `timestamp` equals `max(target_data.timestamp)` and is uniform across rows.
- `chunk_start_date` / `chunk_end_date` are persisted as ISO text and ordered.
- `p_value` is dropped before persistence.
- `drift` flags propagate per `(feature, method)` configuration.
#### D.1.2 30% fallback when reference data is unavailable
**Summary**: `get_reference_data` fails alias resolution and returns `None`;
`calculate_drift` uses the first 30% of target rows as reference.
**Summary**: MLflow alias resolution is forced to fail so
`get_reference_data` returns `None`; `calculate_drift` falls back to the
first 30% of target rows as reference.
**Expected Outcome**:
- Persisted rows carry `accurate=False`.
- All persisted rows carry `accurate=False`.
- A `MODEL_METRICS_REFERENCE_DATA_WARNING` notification is emitted to MongoDB.
### 5.2 Filtering / dedup invariants
#### D.2.1 Deduplication and `p_value` removal
**Summary**: ModelAnalysis returns duplicate `(timestamp, method, feature)` rows
plus a `p_value` column.
**Expected Outcome**:
- Duplicates are collapsed keeping the first occurrence.
- `p_value` is absent from the persisted rows.
#### D.2.2 Out-of-range timestamps filtered
**Summary**: Drift rows whose timestamps are not present in the target window
must be discarded before persistence.
### 5.3 Failure paths
### 5.2 Failure paths
#### D.3.1 Empty target data short-circuits the workflow
**Summary**: `load_custom_query` returns no rows; ModelAnalysis is never
instantiated and no drift rows are written.
**Summary**: `load_custom_query` returns no rows.
#### D.3.2 ModelAnalysis raises during dataframe assembly
**Summary**: `get_drift_metrics_dataframe` raises. The activity catches the
error, sends a `MODEL_METRICS_GET_DRIFT_METRICS_ERROR` notification, and the
workflow completes without persisting drift rows.
**Expected Outcome**:
- The workflow returns early and writes nothing to `sientia_data.drift_metrics`.
### 5.4 Configuration paths
#### D.4.1 Default drift metrics propagated to analyzer
**Summary**: Omitting `drift_metrics` defaults to
`['kolmogorov_smirnov', 'jensen_shannon', 'wasserstein']` and forwards the
exact list to `detect_univariate_drift`.
### 5.3 Configuration paths
#### D.4.2 Invalid `chunk_period` raises ValueError
**Summary**: Anything other than `min` / `s` is rejected by `calculate_drift`.
#### D.4.3 `chunk_period='s'` keeps seconds in timestamp filtering
**Summary**: Truncated `YYYY-MM-DD HH:MM` rows are filtered out when chunking
runs at second granularity.
**Expected Outcome**:
- The workflow surfaces the `ValueError` ("Invalid chunk period: ...").
- No rows are persisted.
#### 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.
---
## 6. Simple Metrics Workflow Scenarios
Source: `e2e/test_simple_metrics.py`
Validates `predictions_schema.simple_metrics_data` columns:
Validates `sientia_data.simple_metrics` columns:
`id, model_id, metric, value, timestamp, data_size, interval_minutes, created_at`.
Note: ``timestamp`` is now nullable per the new DDL and ``model_id`` is ``text``.
### 6.1 Happy paths
@@ -339,8 +345,10 @@ the workflow exits before `calculate_simple_metrics` and writes nothing.
Source: `e2e/test_minimal_retrain.py`
The MLflow registry is fully mocked (no real artifacts in test container).
Validates `predictions_schema.retrain_reports` columns:
`id, model_id, model_name, timestamp, status, version, mlflow_run_id, mlflow_experiment_id, created_at`.
Validates `sientia_data.log_retrain` columns:
`mlflow_experiment_id, mlflow_run_id, model_id, model_name, status, timestamp, version`.
Note: the new DDL drops the legacy ``id`` and ``created_at`` columns,
``mlflow_experiment_id`` is now ``int8`` and ``model_id`` is ``text``.
### 7.1 Happy path
@@ -350,7 +358,7 @@ the new version is promoted to the `production` alias.
**Expected Outcome**:
- Report row has success status, `version='7'`, `mlflow_run_id='retrain-run-id'`,
`mlflow_experiment_id='experiment-id'`.
`mlflow_experiment_id=4242` (`int8`).
- `mlflow.log_artifact` is called with the input CSV.
- `promote_to_alias` is called once with the resolved version and alias.

6
e2e/test_child_workflows_e2e.py
View File

@@ -26,7 +26,7 @@ async def test_format_and_export_prediction_default_path_e2e(
client = temporal_test_env.client
model_id = 401
with postgres_engine.begin() as conn:
conn.execute(text(f'DELETE FROM predictions_schema.predictions WHERE model_id = {model_id}'))
conn.execute(text(f'DELETE FROM sientia_data.predictions WHERE model_id = {model_id}'))
metadata = {
'metadata': {
@@ -44,7 +44,7 @@ async def test_format_and_export_prediction_default_path_e2e(
'timestamp': '2024-01-01 12:00:00+00:00',
'model_id': model_id,
'model_name': 'test_model',
'schema': 'predictions_schema',
'schema': 'sientia_data',
'table_name': 'predictions',
'transform_table_name': 'transformed_data',
'comment': 'e2e child workflow default path',
@@ -64,7 +64,7 @@ async def test_format_and_export_prediction_default_path_e2e(
row = conn.execute(
text(
f'SELECT prediction, prediction_confidence, prediction_status, comments '
f'FROM predictions_schema.predictions WHERE model_id = {model_id}'
f'FROM sientia_data.predictions WHERE model_id = {model_id}'
)
).fetchone()
assert row is not None

667
e2e/test_drift.py
View File

@@ -1,22 +1,31 @@
"""
End-to-end tests for the Drift workflow.
The drift suite drives the **real** ``sientia_model.analytics.drift_analysis.DriftAnalysis``
analyzer (no mocking). Each scenario exercises the full pipeline:
laborious_data (Postgres)
-> load_custom_query
-> calculate_drift (DriftAnalysis univariate + multivariate)
-> export_data_to_postgres (sientia_data.drift_metrics)
Coverage focus:
- Full pipeline persists drift rows with **all** columns expected by the
``predictions_schema.drift`` table (model_id, feature, method, value, drift,
chunk, timestamp, timestamp_end, accurate, plus DB-managed id/created_at/updated_at).
- ``get_reference_data`` happy path (CSV downloaded from MLflow stub) and
fallback path (30% of target data when reference is unavailable).
- ``calculate_drift`` invariants: ``p_value`` dropped, duplicates removed, rows
outside target timestamps filtered, default ``drift_metrics`` propagated.
- Failure paths: ``ModelAnalysis.get_drift_metrics_dataframe`` raises ⇒
workflow completes without writes; empty target data ⇒ workflow short-circuits;
invalid ``chunk_period`` ⇒ activity raises and workflow surfaces the error.
- Happy path persists every column required by ``sientia_data.drift_metrics``
with a valid reference dataset downloaded from MLflow.
- 30% fallback path activates when the MLflow reference is unavailable and
emits the ``MODEL_METRICS_REFERENCE_DATA_WARNING`` notification.
- Empty target data short-circuits the workflow without persisting anything.
- Invalid ``chunk_period`` is rejected by ``calculate_drift``.
- ``chunk_period='s'`` preserves second-level precision in
``chunk_start_date``.
Tests assert behavioral / structural properties (column presence, NOT NULL
constraints, business-key invariants) rather than exact numeric values, since
those depend on the real analyzer implementation and synthetic data.
"""
from datetime import datetime, timedelta, timezone
from decimal import Decimal
from pathlib import Path
from unittest.mock import MagicMock
@@ -27,7 +36,6 @@ from temporalio.testing import WorkflowEnvironment
from temporalio.worker import Worker
from e2e.helpers import (
build_drift_dataframe,
insert_target_data_for_drift,
load_scenario_input,
make_workflow_id,
@@ -36,21 +44,41 @@ from e2e.helpers import (
from laborious.activities.activities import Activities
from laborious.workflows.drift import Drift
# Drift columns that must be present (and non-null where required) on every row.
# Drift columns persisted on every row in ``sientia_data.drift_metrics`` —
# mirrors the production DDL.
EXPECTED_DRIFT_COLUMNS = [
'id',
'model_id',
'feature',
'method',
'value',
'drift',
'chunk',
'alert',
'chunk_index',
'chunk_start_date',
'chunk_end_date',
'accurate',
'timestamp',
'timestamp_end',
'created_at',
]
# Columns the DDL marks as NOT NULL. ``feature`` and ``timestamp`` are
# nullable in the production schema (multivariate rows do not bind to a
# single feature; ``timestamp`` is allowed to be empty when upstream data has
# no usable instant).
NON_NULL_DRIFT_COLUMNS = {
'id',
'model_id',
'method',
'value',
'alert',
'chunk_index',
'chunk_start_date',
'chunk_end_date',
'accurate',
'created_at',
'updated_at',
]
}
DEFAULT_DRIFT_METHODS = ['kolmogorov_smirnov', 'jensen_shannon', 'wasserstein']
def _drift_input(model_id: int, **overrides) -> dict:
@@ -60,42 +88,37 @@ def _drift_input(model_id: int, **overrides) -> dict:
return input_data
def _five_minute_window(offset_minutes: int = 6) -> tuple[list[str], list[str]]:
def _recent_minute_timestamps(count: int, offset_minutes: int = 6) -> list[str]:
"""
Build five consecutive UTC minute timestamps positioned in the recent past.
Build ``count`` consecutive UTC minute timestamps placed in the recent past.
The Drift workflow filters target rows with ``timestamp > NOW() - INTERVAL``,
so timestamps must be recent for tests to retrieve any data. We snap to
minute precision and back off ``offset_minutes`` minutes so all chunks land
well inside the default 60-minute interval defined in ``drift_base.json``.
so timestamps must be recent for tests to retrieve any data. Snapping to
minute precision keeps the helper deterministic regardless of clock skew.
Args:
- offset_minutes: How many minutes ago the most recent chunk should be.
- count (int): How many consecutive minute timestamps to generate.
- offset_minutes (int): Minutes ago for the EARLIEST generated timestamp.
Return:
Tuple ``(target_timestamps, chunk_timestamps)``:
- ``target_timestamps``: ISO strings with ``+0000`` used as ``timestamp``
and ``created_at`` columns when inserting target rows.
- ``chunk_timestamps``: ``YYYY-MM-DD HH:MM`` truncations matching what
``calculate_drift`` filters on for ``chunk_period='min'``.
list[str]: ISO strings with ``+0000`` offset, one per minute.
"""
base = datetime.now(timezone.utc).replace(second=0, microsecond=0) - timedelta(
minutes=offset_minutes
)
target_timestamps = [
(base + timedelta(minutes=i)).strftime('%Y-%m-%d %H:%M:%S%z') for i in range(5)
return [
(base + timedelta(minutes=i)).strftime('%Y-%m-%d %H:%M:%S%z') for i in range(count)
]
chunk_timestamps = [
(base + timedelta(minutes=i)).strftime('%Y-%m-%d %H:%M') for i in range(5)
]
return target_timestamps, chunk_timestamps
def _configure_reference_csv(mlflow_repository_stub, reference_rows: pd.DataFrame) -> None:
"""
Wire ``mlflow_repository_stub`` so ``get_reference_data`` returns
``reference_rows`` by writing them to ``dst_path/evaluation_data.csv``.
Args:
- mlflow_repository_stub: External MLflow repository fixture.
- reference_rows (pd.DataFrame): Rows to expose as the production reference.
"""
def _download(run_id: str, artifact_path: str, dst_path: str, metadata=None):
@@ -115,22 +138,34 @@ def _force_reference_unavailable(mlflow_repository_stub) -> None:
)
def _assert_drift_columns_complete(rows, *, expected_count: int) -> None:
"""Validate row count and that the canonical drift columns are populated."""
assert len(rows) == expected_count, (
f'Expected {expected_count} drift rows persisted, got {len(rows)}'
def _select_drift_rows(postgres_engine, model_id: int) -> list[dict]:
"""Read every persisted drift row for ``model_id`` ordered by chunk/feature/method."""
with postgres_engine.connect() as conn:
rows = (
conn.execute(
text(
'SELECT * FROM sientia_data.drift_metrics '
'WHERE model_id = :m '
'ORDER BY chunk_index, feature, method'
),
{'m': str(model_id)},
)
seen_columns = set(rows[0]._mapping.keys()) if rows else set()
.mappings()
.all()
)
return [dict(row) for row in rows]
def _assert_required_columns_populated(rows: list[dict]) -> None:
"""Validate column presence and NOT NULL constraints on every row."""
assert rows, 'expected at least one drift row to be persisted'
seen_columns = set(rows[0].keys())
for column in EXPECTED_DRIFT_COLUMNS:
assert column in seen_columns, f'Missing drift column in postgres: {column}'
for row in rows:
mapping = dict(row._mapping)
for column in EXPECTED_DRIFT_COLUMNS:
if column == 'value':
# ``value`` is nullable in the table; skip null check, only ensure key exists.
continue
assert mapping[column] is not None, f"Column '{column}' is NULL in {mapping}"
for column in NON_NULL_DRIFT_COLUMNS:
assert row[column] is not None, f"Column '{column}' is NULL in {row}"
assert 'p_value' not in row, 'p_value must not be persisted to drift_metrics'
@pytest.mark.asyncio
@@ -141,112 +176,111 @@ async def test_drift_happy_path_persists_all_columns_with_reference_data(
test_activities: Activities,
postgres_engine,
mlflow_repository_stub,
model_analysis_stub,
):
"""
Scenario D.1.1: Happy path with reference data downloaded from MLflow.
Validates the full drift pipeline produces one row per (chunk, feature, method)
combination plus the multivariate row block, with **all** columns required by
``predictions_schema.drift`` populated. Uses a deterministic mocked drift
dataframe so the postgres assertions remain stable across runs.
Drives the full pipeline against the real ``DriftAnalysis``. Asserts:
- One row is persisted per ``(chunk_index, feature, method)`` combination
plus the multivariate row block, with every column required by
``sientia_data.drift_metrics`` populated.
- The three default univariate methods are forwarded to the analyzer.
- ``model_id`` and ``timestamp`` are stamped by the activity (not by the
analyzer); ``timestamp`` equals ``max(target_data.timestamp)`` and is
identical on every persisted row.
- ``chunk_start_date`` / ``chunk_end_date`` are persisted as ISO text so
the analyzer's nanosecond-precision boundaries survive the ``text``
column type.
- ``accurate=True`` because the reference dataset was available.
"""
client = temporal_test_env.client
model_id = 411
target_timestamps, chunk_timestamps = _five_minute_window()
target_timestamps = _recent_minute_timestamps(count=10)
insert_target_data_for_drift(
postgres_engine,
model_id=model_id,
timestamps=target_timestamps,
variables_values={
'sensor_1': [10.0, 11.0, 12.0, 13.0, 14.0],
'sensor_2': [20.0, 21.0, 22.0, 23.0, 24.0],
'sensor_1': [10.0 + i * 0.1 for i in range(10)],
'sensor_2': [20.0 + i * 0.5 for i in range(10)],
},
)
reference_df = pd.DataFrame(
{
'timestamp': ['2023-12-31 11:00:00+00:00', '2023-12-31 11:01:00+00:00'],
'sensor_1': [9.5, 9.6],
'sensor_2': [19.5, 19.6],
'timestamp': [
f'2023-12-31 11:{minute:02d}:00+00:00' for minute in range(10)
],
'sensor_1': [9.0 + i * 0.05 for i in range(10)],
'sensor_2': [18.0 + i * 0.25 for i in range(10)],
}
)
_configure_reference_csv(mlflow_repository_stub, reference_df)
features = ['sensor_1', 'sensor_2']
methods = ['kolmogorov_smirnov', 'jensen_shannon', 'wasserstein']
drift_flags = {
('sensor_1', 'wasserstein'): True,
('sensor_2', 'wasserstein'): True,
('sensor_2', 'jensen_shannon'): True,
}
model_analysis_stub.set_drift_dataframe(
lambda univariate, multivariate: build_drift_dataframe(
timestamps=chunk_timestamps,
features=features,
methods=methods,
statistic=0.42,
drift_flags=drift_flags,
multivariate_value=15.5,
multivariate_drift=True,
)
)
input_data = _drift_input(model_id)
await start_and_await_workflow(
client, Drift.run, input_data, make_workflow_id('test-drift-happy-path')
)
with postgres_engine.connect() as conn:
rows = (
conn.execute(
text(
'SELECT * FROM predictions_schema.drift '
'WHERE model_id = :model_id ORDER BY chunk, feature, method'
),
{'model_id': model_id},
)
.mappings()
.all()
rows = _select_drift_rows(postgres_engine, model_id)
exported_csv_path = '/tmp/test_drift_happy_path_exported.csv'
pd.DataFrame(rows).to_csv(exported_csv_path, index=False)
print(
f'\n[test_drift_happy_path] Exported drift dataframe '
f'({len(rows)} rows) -> {exported_csv_path}'
)
expected_rows = len(chunk_timestamps) * (len(features) * len(methods) + 1) # univariate + multivariate
assert len(rows) == expected_rows
for column in EXPECTED_DRIFT_COLUMNS:
assert column in rows[0], f'Missing drift column in postgres: {column}'
_assert_required_columns_populated(rows)
for row in rows:
for column in EXPECTED_DRIFT_COLUMNS:
if column == 'value':
continue
assert row[column] is not None, f"Column '{column}' is NULL in {dict(row)}"
multivariate_rows = [r for r in rows if r['feature'] == 'multivariate']
# The activity drops the target column from the feature list, so only
# ``sensor_2`` participates in univariate analysis (``sensor_1`` is the
# configured target). Multivariate produces one row per chunk regardless.
univariate_rows = [r for r in rows if r['feature'] != 'multivariate']
multivariate_rows = [r for r in rows if r['feature'] == 'multivariate']
assert univariate_rows, 'expected univariate drift rows for non-target features'
assert multivariate_rows, 'expected one multivariate drift row per chunk'
assert len(multivariate_rows) == len(chunk_timestamps)
# All three default methods must reach the analyzer.
assert {r['method'] for r in univariate_rows} == set(DEFAULT_DRIFT_METHODS)
assert all(r['method'] == 'multivariate' for r in multivariate_rows)
assert all(r['drift'] is True for r in multivariate_rows)
assert all(Decimal(str(r['value'])) == Decimal('15.5') for r in multivariate_rows)
assert {r['feature'] for r in univariate_rows} == {'sensor_2'}
assert len(univariate_rows) == len(features) * len(methods) * len(chunk_timestamps)
assert {r['method'] for r in univariate_rows} == set(methods)
assert {r['feature'] for r in univariate_rows} == set(features)
# ``timestamp`` is stamped uniformly with ``max(target_data.timestamp)``.
expected_timestamp = pd.to_datetime(max(target_timestamps), utc=True)
persisted_timestamps = {pd.to_datetime(r['timestamp'], utc=True) for r in rows}
assert len(persisted_timestamps) == 1, (
'timestamp must be uniform across all drift rows '
f'(got {len(persisted_timestamps)} distinct values)'
)
assert pd.Timestamp(persisted_timestamps.pop()) == expected_timestamp, (
'timestamp must equal max(target_data.timestamp)'
)
drift_pairs = {(r['feature'], r['method']): r['drift'] for r in univariate_rows}
for (feature, method), expected_drift in drift_flags.items():
assert drift_pairs[(feature, method)] is expected_drift
# ``model_id`` is stamped by ``calculate_drift`` (not produced by the analyzer).
assert all(r['model_id'] == str(model_id) for r in rows), (
'model_id must be stamped on every drift row'
)
assert all(r['accurate'] is True for r in rows), 'reference path should mark rows as accurate'
# Reference path → accurate=True.
assert all(r['accurate'] is True for r in rows), (
'reference path should mark all rows as accurate'
)
assert all(
r['timestamp_end'].endswith(':59.999999999') and 'T' in r['timestamp_end']
for r in rows
), 'timestamp_end should preserve the high-precision ISO string from ModelAnalysis'
assert all('p_value' not in r for r in rows), 'p_value must be dropped before postgres'
# ISO text serialization preserves ordering between start/end of each chunk.
for row in rows:
assert 'T' in row['chunk_start_date'], (
f"chunk_start_date should be ISO text, got {row['chunk_start_date']!r}"
)
assert 'T' in row['chunk_end_date'], (
f"chunk_end_date should be ISO text, got {row['chunk_end_date']!r}"
)
assert row['chunk_start_date'] <= row['chunk_end_date'], (
f'chunk_start_date must precede chunk_end_date '
f"(start={row['chunk_start_date']}, end={row['chunk_end_date']})"
)
@pytest.mark.asyncio
@@ -257,60 +291,42 @@ async def test_drift_uses_30pct_fallback_when_reference_unavailable(
test_activities: Activities,
postgres_engine,
mlflow_repository_stub,
model_analysis_stub,
notification_inserts,
):
"""
Scenario D.1.2: ``get_reference_data`` returns ``None`` (production alias missing),
so ``calculate_drift`` falls back to using the first 30% of target rows as
reference. Persisted rows must report ``accurate=False`` and a warning
notification must be emitted to mongo.
Scenario D.1.2: ``get_reference_data`` returns ``None`` (production alias
missing), so ``calculate_drift`` falls back to using the first 30% of
target rows as reference. Persisted rows must report ``accurate=False``
and a ``MODEL_METRICS_REFERENCE_DATA_WARNING`` notification must be
emitted to mongo.
"""
client = temporal_test_env.client
model_id = 412
target_timestamps, chunk_timestamps = _five_minute_window()
target_timestamps = _recent_minute_timestamps(count=10)
insert_target_data_for_drift(
postgres_engine,
model_id=model_id,
timestamps=target_timestamps,
variables_values={
'sensor_1': [10.0, 10.1, 10.2, 10.3, 10.4],
'sensor_2': [20.0, 20.1, 20.2, 20.3, 20.4],
'sensor_1': [10.0 + i * 0.1 for i in range(10)],
'sensor_2': [20.0 + i * 0.5 for i in range(10)],
},
)
_force_reference_unavailable(mlflow_repository_stub)
model_analysis_stub.set_drift_dataframe(
lambda univariate, multivariate: build_drift_dataframe(
timestamps=chunk_timestamps,
features=['sensor_1'],
methods=['kolmogorov_smirnov'],
statistic=0.7,
include_multivariate=False,
)
)
input_data = _drift_input(model_id)
await start_and_await_workflow(
client, Drift.run, input_data, make_workflow_id('test-drift-fallback')
)
with postgres_engine.connect() as conn:
rows = (
conn.execute(
text('SELECT * FROM predictions_schema.drift WHERE model_id = :m'),
{'m': model_id},
)
.mappings()
.all()
)
rows = _select_drift_rows(postgres_engine, model_id)
_assert_required_columns_populated(rows)
assert len(rows) == len(chunk_timestamps)
assert all(r['accurate'] is False for r in rows), 'fallback path must mark rows as inaccurate'
assert all(r['feature'] == 'sensor_1' for r in rows)
assert all(r['accurate'] is False for r in rows), (
'fallback path must mark all rows as inaccurate'
)
fallback_warnings = [
call
@@ -322,168 +338,6 @@ async def test_drift_uses_30pct_fallback_when_reference_unavailable(
assert len(fallback_warnings) >= 1, 'expected reference fallback warning notification'
@pytest.mark.asyncio
@pytest.mark.integration
async def test_drift_drops_p_value_and_dedupes_by_timestamp_method_feature(
temporal_test_env: WorkflowEnvironment,
temporal_worker_drift: Worker,
test_activities: Activities,
postgres_engine,
mlflow_repository_stub,
model_analysis_stub,
):
"""
Scenario D.2.1: ModelAnalysis returns duplicate (timestamp, method, feature)
rows and a populated ``p_value`` column. ``calculate_drift`` must deduplicate
keeping the first occurrence and the persisted rows must not contain
``p_value``.
"""
client = temporal_test_env.client
model_id = 421
target_timestamps, chunk_timestamps = _five_minute_window()
insert_target_data_for_drift(
postgres_engine,
model_id=model_id,
timestamps=target_timestamps,
variables_values={
'sensor_1': [1.0, 2.0, 3.0, 4.0, 5.0],
'sensor_2': [10.0, 20.0, 30.0, 40.0, 50.0],
},
)
duplicates = [
# Re-emit the first chunk for sensor_1/kolmogorov_smirnov with a different value
# so we can prove the dedup keeps the FIRST row.
{
'timestamp': pd.Timestamp(chunk_timestamps[0]),
'feature': 'sensor_1',
'metric': 'kolmogorov_smirnov',
'statistic': 0.99,
'p_value': 0.02,
'alert': True,
'chunk_index': 0,
'chunk_start_date': pd.Timestamp(chunk_timestamps[0]),
'chunk_end_date': pd.Timestamp(f'{chunk_timestamps[0]}:59.999999999'),
}
]
model_analysis_stub.set_drift_dataframe(
lambda univariate, multivariate: build_drift_dataframe(
timestamps=chunk_timestamps,
features=['sensor_1'],
methods=['kolmogorov_smirnov'],
statistic=0.5,
include_multivariate=False,
extra_rows=duplicates,
)
)
_force_reference_unavailable(mlflow_repository_stub)
input_data = _drift_input(model_id)
await start_and_await_workflow(
client, Drift.run, input_data, make_workflow_id('test-drift-dedupe')
)
with postgres_engine.connect() as conn:
rows = (
conn.execute(
text(
'SELECT chunk, feature, method, value FROM predictions_schema.drift '
'WHERE model_id = :m ORDER BY chunk'
),
{'m': model_id},
)
.mappings()
.all()
)
assert len(rows) == len(chunk_timestamps), 'duplicates must be removed before persistence'
first_chunk_rows = [r for r in rows if r['chunk'] == 0]
assert len(first_chunk_rows) == 1
assert Decimal(str(first_chunk_rows[0]['value'])) == Decimal('0.5'), (
'dedup must keep the FIRST occurrence (statistic=0.5), not the duplicate (statistic=0.99)'
)
columns = set(rows[0].keys())
assert 'p_value' not in columns
@pytest.mark.asyncio
@pytest.mark.integration
async def test_drift_drops_rows_outside_target_timestamps(
temporal_test_env: WorkflowEnvironment,
temporal_worker_drift: Worker,
test_activities: Activities,
postgres_engine,
mlflow_repository_stub,
model_analysis_stub,
):
"""
Scenario D.2.2: Drift rows whose timestamps are not present in the target
data (e.g. came from the reference distribution) must be discarded so the
saved drift only reflects analysis chunks.
"""
client = temporal_test_env.client
model_id = 422
target_timestamps, chunk_timestamps = _five_minute_window()
insert_target_data_for_drift(
postgres_engine,
model_id=model_id,
timestamps=target_timestamps,
variables_values={
'sensor_1': [1.0, 2.0, 3.0, 4.0, 5.0],
'sensor_2': [10.0, 20.0, 30.0, 40.0, 50.0],
},
)
out_of_range_timestamp = pd.Timestamp('2099-12-31 23:59')
extra = [
{
'timestamp': out_of_range_timestamp,
'feature': 'sensor_1',
'metric': 'kolmogorov_smirnov',
'statistic': 0.5,
'p_value': 0.0,
'alert': True,
'chunk_index': 99,
'chunk_start_date': out_of_range_timestamp,
'chunk_end_date': pd.Timestamp('2099-12-31 23:59:59.999999999'),
}
]
model_analysis_stub.set_drift_dataframe(
lambda univariate, multivariate: build_drift_dataframe(
timestamps=chunk_timestamps,
features=['sensor_1'],
methods=['kolmogorov_smirnov'],
include_multivariate=False,
extra_rows=extra,
)
)
_force_reference_unavailable(mlflow_repository_stub)
input_data = _drift_input(model_id)
await start_and_await_workflow(
client, Drift.run, input_data, make_workflow_id('test-drift-tts-filter')
)
with postgres_engine.connect() as conn:
chunks = [
r[0]
for r in conn.execute(
text(
'SELECT chunk FROM predictions_schema.drift WHERE model_id = :m ORDER BY chunk'
),
{'m': model_id},
).all()
]
assert chunks == [0, 1, 2, 3, 4], 'out-of-range timestamps must be filtered out'
@pytest.mark.asyncio
@pytest.mark.integration
async def test_drift_empty_target_data_short_circuits_workflow(
@@ -492,33 +346,19 @@ async def test_drift_empty_target_data_short_circuits_workflow(
test_activities: Activities,
postgres_engine,
mlflow_repository_stub,
model_analysis_stub,
):
"""
Scenario D.3.1: When ``load_custom_query`` returns no rows the workflow must
return early without invoking ModelAnalysis or writing any drift rows.
Scenario D.3.1: When ``load_custom_query`` returns no rows the workflow
must return early without invoking the analyzer or writing any drift rows.
"""
client = temporal_test_env.client
model_id = 431
with postgres_engine.begin() as conn:
conn.execute(text(f'DELETE FROM predictions_schema.laborious_data WHERE model_id = {model_id}'))
conn.execute(text(f'DELETE FROM sientia_data.laborious_data WHERE model_id = {model_id}'))
_force_reference_unavailable(mlflow_repository_stub)
drift_calls = {'count': 0}
def _factory(univariate, multivariate):
drift_calls['count'] += 1
return build_drift_dataframe(
timestamps=['2024-01-01 12:00'],
features=['sensor_1'],
methods=['kolmogorov_smirnov'],
include_multivariate=False,
)
model_analysis_stub.set_drift_dataframe(_factory)
input_data = _drift_input(model_id)
await start_and_await_workflow(
client, Drift.run, input_data, make_workflow_id('test-drift-empty-target')
@@ -526,121 +366,10 @@ async def test_drift_empty_target_data_short_circuits_workflow(
with postgres_engine.connect() as conn:
count = conn.execute(
text('SELECT COUNT(*) FROM predictions_schema.drift WHERE model_id = :m'),
{'m': model_id},
text('SELECT COUNT(*) FROM sientia_data.drift_metrics WHERE model_id = :m'),
{'m': str(model_id)},
).scalar()
assert count == 0
assert drift_calls['count'] == 0, 'ModelAnalysis must not be invoked when target data is empty'
@pytest.mark.asyncio
@pytest.mark.integration
async def test_drift_model_analysis_failure_keeps_workflow_alive_no_writes(
temporal_test_env: WorkflowEnvironment,
temporal_worker_drift: Worker,
test_activities: Activities,
postgres_engine,
mlflow_repository_stub,
model_analysis_stub,
notification_inserts,
):
"""
Scenario D.3.2: ``ModelAnalysis.get_drift_metrics_dataframe`` raises. The
activity must catch the error, send an error notification and return ``[]``
so the workflow completes without persisting drift rows.
"""
client = temporal_test_env.client
model_id = 432
target_timestamps, _ = _five_minute_window()
insert_target_data_for_drift(
postgres_engine,
model_id=model_id,
timestamps=target_timestamps,
variables_values={
'sensor_1': [1.0, 2.0, 3.0, 4.0, 5.0],
'sensor_2': [10.0, 20.0, 30.0, 40.0, 50.0],
},
)
_force_reference_unavailable(mlflow_repository_stub)
model_analysis_stub.raise_on_get_drift_metrics_dataframe(
RuntimeError('drift analyzer crashed')
)
input_data = _drift_input(model_id)
await start_and_await_workflow(
client, Drift.run, input_data, make_workflow_id('test-drift-analyzer-error')
)
with postgres_engine.connect() as conn:
count = conn.execute(
text('SELECT COUNT(*) FROM predictions_schema.drift WHERE model_id = :m'),
{'m': model_id},
).scalar()
assert count == 0
error_notifications = [
call
for call in notification_inserts.call_args_list
if call.args
and isinstance(call.args[0], dict)
and call.args[0].get('notification_id') == 'MODEL_METRICS_GET_DRIFT_METRICS_ERROR'
]
assert len(error_notifications) >= 1
@pytest.mark.asyncio
@pytest.mark.integration
async def test_drift_default_drift_metrics_propagated_to_analyzer(
temporal_test_env: WorkflowEnvironment,
temporal_worker_drift: Worker,
test_activities: Activities,
postgres_engine,
mlflow_repository_stub,
model_analysis_stub,
):
"""
Scenario D.4.1: When ``drift_metrics`` is omitted from input the workflow
must default to ``['kolmogorov_smirnov', 'jensen_shannon', 'wasserstein']``
and forward exactly that list to ``ModelAnalysis.detect_univariate_drift``.
"""
client = temporal_test_env.client
model_id = 441
target_timestamps, chunk_timestamps = _five_minute_window()
insert_target_data_for_drift(
postgres_engine,
model_id=model_id,
timestamps=target_timestamps,
variables_values={
'sensor_1': [1.0, 2.0, 3.0, 4.0, 5.0],
'sensor_2': [10.0, 20.0, 30.0, 40.0, 50.0],
},
)
_force_reference_unavailable(mlflow_repository_stub)
model_analysis_stub.set_drift_dataframe(
lambda univariate, multivariate: build_drift_dataframe(
timestamps=chunk_timestamps,
features=['sensor_1'],
methods=['kolmogorov_smirnov'],
include_multivariate=False,
)
)
input_data = _drift_input(model_id)
input_data.pop('drift_metrics', None)
await start_and_await_workflow(
client, Drift.run, input_data, make_workflow_id('test-drift-default-methods')
)
instance = model_analysis_stub.last_instance
assert instance is not None, 'ModelAnalysis must have been instantiated'
assert len(instance.detect_univariate_drift_calls) == 1
forwarded_methods = instance.detect_univariate_drift_calls[0]['methods']
assert forwarded_methods == ['kolmogorov_smirnov', 'jensen_shannon', 'wasserstein']
@pytest.mark.asyncio
@@ -655,12 +384,12 @@ async def test_drift_invalid_chunk_period_raises_value_error(
"""
Scenario D.4.2: ``calculate_drift`` validates ``chunk_period`` and rejects
anything other than ``min`` / ``s``. The workflow must surface the
``ValueError`` to the caller and persist nothing.
``ValueError`` and persist nothing.
"""
client = temporal_test_env.client
model_id = 442
target_timestamps, _ = _five_minute_window()
target_timestamps = _recent_minute_timestamps(count=5)
insert_target_data_for_drift(
postgres_engine,
model_id=model_id,
@@ -682,8 +411,9 @@ async def test_drift_invalid_chunk_period_raises_value_error(
make_workflow_id('test-drift-bad-chunk-period'),
)
# Temporal wraps the activity ValueError in WorkflowFailureError; the message
# may live on ``.message`` or ``str(exc)`` depending on the SDK error class.
# Temporal wraps the activity ValueError in WorkflowFailureError; the
# message may live on ``.message`` or ``str(exc)`` depending on the SDK
# error class, so walk the cause chain looking for the guard text.
cause_descriptions = []
current: BaseException | None = excinfo.value
while current is not None:
@@ -697,42 +427,36 @@ async def test_drift_invalid_chunk_period_raises_value_error(
with postgres_engine.connect() as conn:
count = conn.execute(
text('SELECT COUNT(*) FROM predictions_schema.drift WHERE model_id = :m'),
{'m': model_id},
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_preserves_seconds_in_timestamp_column(
async def test_drift_chunk_period_seconds_preserves_seconds_in_chunk_start_date(
temporal_test_env: WorkflowEnvironment,
temporal_worker_drift: Worker,
test_activities: Activities,
postgres_engine,
mlflow_repository_stub,
model_analysis_stub,
):
"""
Scenario D.4.3: With ``chunk_period='s'`` the persisted ``timestamp``
column must preserve second-level precision instead of being flattened to
the start of the minute, and ``chunk_period`` must be propagated to the
analyzer so it actually chunks at second granularity.
Scenario D.4.3: With ``chunk_period='s'`` the persisted ``chunk_start_date``
column must preserve second-level precision so consumers can audit the
actual chunk boundary.
"""
client = temporal_test_env.client
model_id = 443
base = datetime.now(timezone.utc).replace(second=0, microsecond=0) - timedelta(minutes=2)
# Three samples spaced by 30 seconds inside two adjacent minutes.
target_timestamps = [
base.strftime('%Y-%m-%d %H:%M:%S%z'),
(base + timedelta(seconds=30)).strftime('%Y-%m-%d %H:%M:%S%z'),
(base + timedelta(minutes=1)).strftime('%Y-%m-%d %H:%M:%S%z'),
]
chunk_timestamps_full = [
base.strftime('%Y-%m-%d %H:%M:%S'),
(base + timedelta(seconds=30)).strftime('%Y-%m-%d %H:%M:%S'),
(base + timedelta(minutes=1)).strftime('%Y-%m-%d %H:%M:%S'),
]
insert_target_data_for_drift(
postgres_engine,
@@ -745,15 +469,6 @@ async def test_drift_chunk_period_seconds_preserves_seconds_in_timestamp_column(
)
_force_reference_unavailable(mlflow_repository_stub)
model_analysis_stub.set_drift_dataframe(
lambda univariate, multivariate: build_drift_dataframe(
timestamps=chunk_timestamps_full,
features=['sensor_1'],
methods=['kolmogorov_smirnov'],
include_multivariate=False,
)
)
input_data = _drift_input(model_id, chunk_period='s')
await start_and_await_workflow(
client,
@@ -766,21 +481,19 @@ async def test_drift_chunk_period_seconds_preserves_seconds_in_timestamp_column(
rows = (
conn.execute(
text(
'SELECT chunk, timestamp FROM predictions_schema.drift '
'WHERE model_id = :m ORDER BY chunk'
'SELECT chunk_start_date FROM sientia_data.drift_metrics '
'WHERE model_id = :m ORDER BY chunk_index'
),
{'m': model_id},
{'m': str(model_id)},
)
.mappings()
.all()
)
assert [r['chunk'] for r in rows] == [0, 1, 2]
seconds_present = {r['timestamp'].second for r in rows}
assert seconds_present == {0, 30}, (
f'expected seconds 0 and 30 to be preserved, got {seconds_present}'
assert rows, 'expected at least one drift row to be persisted'
# At least one chunk must land on second-30, proving the analyzer chunked
# at sub-minute granularity instead of collapsing everything to minute=0.
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}'
)
instance = model_analysis_stub.last_instance
assert instance is not None
assert instance.detect_univariate_drift_calls[0]['chunk_period'] == 's'

65
e2e/test_minimal_retrain.py
View File

@@ -7,9 +7,14 @@ test container; we only validate that the workflow:
- Loads training data via ``load_query_with_minio_offload``.
- Calls ``retrain_model`` with a payload pointing at MinIO.
- Calls ``update_production_model`` only when retrain succeeds.
- Persists ``retrain_reports`` rows with all required columns; success rows
carry the new ``version`` / ``mlflow_run_id`` / ``mlflow_experiment_id``
while failure rows leave them ``NULL``.
- Persists ``sientia_data.log_retrain`` rows with all required columns;
success rows carry the new ``version`` / ``mlflow_run_id`` /
``mlflow_experiment_id`` while failure rows leave them ``NULL``.
The production DDL drops the legacy ``id`` / ``created_at`` columns and
moves ``mlflow_experiment_id`` to ``int8`` and ``model_id`` to ``text``.
The stubs used here therefore emit ``experiment_id`` as an integer to fit
the new column type.
"""
from contextlib import contextmanager
@@ -30,16 +35,18 @@ from e2e.helpers import (
from laborious.activities.activities import Activities
from laborious.workflows.minimal_retrain import MinimalRetrain
# Columns defined by the production DDL for ``sientia_data.log_retrain``.
# The legacy ``retrain_reports`` table had ``id`` and ``created_at``; the new
# DDL drops both. ``mlflow_experiment_id`` is ``int8`` and ``model_id`` is
# ``text``.
EXPECTED_RETRAIN_REPORT_COLUMNS = [
'id',
'mlflow_experiment_id',
'mlflow_run_id',
'model_id',
'model_name',
'timestamp',
'status',
'timestamp',
'version',
'mlflow_run_id',
'mlflow_experiment_id',
'created_at',
]
@@ -106,7 +113,9 @@ def _configure_retrain_happy_path(mlflow_repository_stub) -> None:
def fake_start_run(**kwargs):
run_info = MagicMock()
run_info.run_id = 'retrain-run-id'
run_info.experiment_id = 'experiment-id'
# ``mlflow_experiment_id`` is ``int8`` in the new DDL, so we feed an
# integer-compatible id from the stubbed run info.
run_info.experiment_id = 4242
yield run_info
mlflow_repository_stub.start_run.side_effect = fake_start_run
@@ -125,10 +134,10 @@ async def test_minimal_retrain_happy_path_writes_success_report(
mlflow_repository_stub,
):
"""
Scenario MR.1.1: Retrain succeeds. ``retrain_reports`` must contain a
success row with version/mlflow_run_id/mlflow_experiment_id populated and
the registry must have been told to promote the new version to the
configured alias.
Scenario MR.1.1: Retrain succeeds. ``sientia_data.log_retrain`` must
contain a success row with version/mlflow_run_id/mlflow_experiment_id
populated and the registry must have been told to promote the new version
to the configured alias.
"""
client = temporal_test_env.client
model_id = 711
@@ -152,10 +161,10 @@ async def test_minimal_retrain_happy_path_writes_success_report(
rows = (
conn.execute(
text(
'SELECT * FROM predictions_schema.retrain_reports '
'SELECT * FROM sientia_data.log_retrain '
'WHERE model_id = :m'
),
{'m': model_id},
{'m': str(model_id)},
)
.mappings()
.all()
@@ -163,17 +172,19 @@ async def test_minimal_retrain_happy_path_writes_success_report(
assert len(rows) == 1
for column in EXPECTED_RETRAIN_REPORT_COLUMNS:
assert column in rows[0], f'Missing retrain report column: {column}'
assert column in rows[0], f'Missing log_retrain column: {column}'
row = rows[0]
assert row['model_id'] == model_id
# ``model_id`` is now ``text``; compare against the stringified id.
assert row['model_id'] == str(model_id)
assert row['model_name'] == 'test_model'
assert row['status'] == 'Model retrained successfully.'
assert row['version'] == '7'
assert row['mlflow_run_id'] == 'retrain-run-id'
assert row['mlflow_experiment_id'] == 'experiment-id'
# ``mlflow_experiment_id`` is now ``int8``; assert the integer value
# provided by the stubbed run info.
assert row['mlflow_experiment_id'] == 4242
assert row['timestamp'] is not None
assert row['created_at'] is not None
mlflow_repository_stub.promote_to_alias.assert_called_once()
promote_kwargs = mlflow_repository_stub.promote_to_alias.call_args.kwargs
@@ -220,10 +231,10 @@ async def test_minimal_retrain_failure_writes_report_without_version_columns(
rows = (
conn.execute(
text(
'SELECT * FROM predictions_schema.retrain_reports '
'SELECT * FROM sientia_data.log_retrain '
'WHERE model_id = :m'
),
{'m': model_id},
{'m': str(model_id)},
)
.mappings()
.all()
@@ -231,7 +242,7 @@ async def test_minimal_retrain_failure_writes_report_without_version_columns(
assert len(rows) == 1
row = rows[0]
assert row['model_id'] == model_id
assert row['model_id'] == str(model_id)
assert row['model_name'] == 'test_model'
assert 'training did not converge' in row['status']
assert row['version'] is None
@@ -275,10 +286,10 @@ async def test_minimal_retrain_missing_target_writes_failure_report(
row = (
conn.execute(
text(
'SELECT * FROM predictions_schema.retrain_reports '
'SELECT * FROM sientia_data.log_retrain '
'WHERE model_id = :m'
),
{'m': model_id},
{'m': str(model_id)},
)
.mappings()
.first()
@@ -313,7 +324,7 @@ async def test_minimal_retrain_no_training_data_does_not_persist_report(
model_id = 731
with postgres_engine.begin() as conn:
conn.execute(text(f'DELETE FROM predictions_schema.laborious_data WHERE model_id = {model_id}'))
conn.execute(text(f'DELETE FROM sientia_data.laborious_data WHERE model_id = {model_id}'))
_configure_retrain_happy_path(mlflow_repository_stub)
@@ -329,7 +340,7 @@ async def test_minimal_retrain_no_training_data_does_not_persist_report(
with postgres_engine.connect() as conn:
count = conn.execute(
text('SELECT COUNT(*) FROM predictions_schema.retrain_reports WHERE model_id = :m'),
{'m': model_id},
text('SELECT COUNT(*) FROM sientia_data.log_retrain WHERE model_id = :m'),
{'m': str(model_id)},
).scalar()
assert count == 0

12
e2e/test_minio_offload.py
View File

@@ -34,7 +34,7 @@ async def test_load_query_with_minio_offload_writes_object_to_bucket(
"""
model_id = 501
with postgres_engine.begin() as conn:
conn.execute(text(f'DELETE FROM predictions_schema.laborious_data WHERE model_id = {model_id}'))
conn.execute(text(f'DELETE FROM sientia_data.laborious_data WHERE model_id = {model_id}'))
insert_sample_data(postgres_engine, model_id, [1.0, 2.0])
scenario_input = load_scenario_input('minio_offload_load_query.json', model_id=model_id)
@@ -66,9 +66,9 @@ async def test_predictions_batch_with_minio_offload_path(
"""
model_id = 502
with postgres_engine.begin() as conn:
conn.execute(text(f'DELETE FROM predictions_schema.laborious_data WHERE model_id = {model_id}'))
conn.execute(text(f'DELETE FROM predictions_schema.predictions WHERE model_id = {model_id}'))
conn.execute(text(f'DELETE FROM predictions_schema.transformed_data WHERE model_id = {model_id}'))
conn.execute(text(f'DELETE FROM sientia_data.laborious_data WHERE model_id = {model_id}'))
conn.execute(text(f'DELETE FROM sientia_data.predictions WHERE model_id = {model_id}'))
conn.execute(text(f'DELETE FROM sientia_data.transformed_data WHERE model_id = {model_id}'))
insert_sample_data(postgres_engine, model_id, [10.0, 20.0, 30.0])
input_data = load_scenario_input('minio_offload_workflow.json', model_id=model_id)
@@ -83,7 +83,7 @@ async def test_predictions_batch_with_minio_offload_path(
with postgres_engine.connect() as conn:
count = conn.execute(
text(f'SELECT COUNT(*) FROM predictions_schema.predictions WHERE model_id = {model_id}')
text(f'SELECT COUNT(*) FROM sientia_data.predictions WHERE model_id = {model_id}')
).scalar()
assert count == 1
@@ -97,7 +97,7 @@ async def test_load_query_with_inline_payload_when_below_threshold(
"""Scenario 4.2.1: payload stays inline when threshold is high enough."""
model_id = 503
with postgres_engine.begin() as conn:
conn.execute(text(f'DELETE FROM predictions_schema.laborious_data WHERE model_id = {model_id}'))
conn.execute(text(f'DELETE FROM sientia_data.laborious_data WHERE model_id = {model_id}'))
insert_sample_data(postgres_engine, model_id, [1.0, 2.0])
scenario_input = load_scenario_input('minio_offload_load_query.json', model_id=model_id)

10
e2e/test_predictions_batch_format_export.py
View File

@@ -49,8 +49,8 @@ async def test_scenario_3_1_1_default_prediction_export(
model_id = 311
with postgres_engine.begin() as conn:
conn.execute(text(f"DELETE FROM predictions_schema.predictions WHERE model_id = {model_id}"))
conn.execute(text(f"DELETE FROM predictions_schema.transformed_data WHERE model_id = {model_id}"))
conn.execute(text(f"DELETE FROM sientia_data.predictions WHERE model_id = {model_id}"))
conn.execute(text(f"DELETE FROM sientia_data.transformed_data WHERE model_id = {model_id}"))
insert_sample_data(postgres_engine, model_id, ['NULL', 78.2])
input_data = get_base_input_data(model_id)
@@ -148,7 +148,7 @@ async def test_scenario_3_1_1_default_prediction_export(
with postgres_engine.connect() as conn:
tf_count = conn.execute(
text(f"SELECT COUNT(*) FROM predictions_schema.transformed_data WHERE model_id = {model_id}")
text(f"SELECT COUNT(*) FROM sientia_data.transformed_data WHERE model_id = {model_id}")
).scalar()
assert tf_count == 0, 'transform export must be skipped when path_flag is set'
@@ -407,7 +407,7 @@ async def test_scenario_3_1_5_export_without_transformed_data(
insert_sample_data(postgres_engine, model_id, [23.5, 78.2])
with postgres_engine.begin() as conn:
conn.execute(text(f"DELETE FROM predictions_schema.transformed_data WHERE model_id = {model_id}"))
conn.execute(text(f"DELETE FROM sientia_data.transformed_data WHERE model_id = {model_id}"))
input_data = get_base_input_data(model_id)
input_data['save_transform'] = False # Don't save transformed data
@@ -489,7 +489,7 @@ async def test_scenario_3_1_5_export_without_transformed_data(
with postgres_engine.connect() as conn:
result_query = conn.execute(
text(f"SELECT COUNT(*) FROM predictions_schema.transformed_data WHERE model_id = {model_id}")
text(f"SELECT COUNT(*) FROM sientia_data.transformed_data WHERE model_id = {model_id}")
)
count = result_query.scalar()
assert count == 0, f"Expected transform table to be empty, but found {count} records"

16
e2e/test_predictions_batch_main_workflow.py
View File

@@ -27,9 +27,9 @@ async def test_scenario_1_1_1_happy_path_complete_success(
client = temporal_test_env.client
with postgres_engine.begin() as conn:
conn.execute(text('DELETE FROM predictions_schema.laborious_data WHERE model_id = 123'))
conn.execute(text('DELETE FROM sientia_data.laborious_data WHERE model_id = 123'))
insert_sql = """
INSERT INTO predictions_schema.laborious_data (model_id, variable, value, timestamp, created_at)
INSERT INTO sientia_data.laborious_data (model_id, variable, value, timestamp, created_at)
VALUES
(123, 'sensor_1', 23.5, '2024-01-01 12:00:00+00:00', '2024-01-01 12:00:00+00:00'),
(123, 'sensor_2', 78.2, '2024-01-01 12:00:00+00:00', '2024-01-01 12:00:00+00:00'),
@@ -46,7 +46,7 @@ async def test_scenario_1_1_1_happy_path_complete_success(
make_workflow_id('test-predictions-batch'),
)
schema_name = 'predictions_schema'
schema_name = 'sientia_data'
with postgres_engine.connect() as conn:
result_query = conn.execute(
text(
@@ -101,7 +101,7 @@ async def test_scenario_1_2_1_sql_query_execution_error(
with postgres_engine.connect() as conn:
count = conn.execute(
text('SELECT COUNT(*) FROM predictions_schema.predictions WHERE model_id = 128')
text('SELECT COUNT(*) FROM sientia_data.predictions WHERE model_id = 128')
).scalar()
assert count == 0
@@ -131,7 +131,7 @@ async def test_scenario_1_2_2_missing_required_parameters(
with postgres_engine.connect() as conn:
count = conn.execute(
text('SELECT COUNT(*) FROM predictions_schema.predictions WHERE model_id = 129')
text('SELECT COUNT(*) FROM sientia_data.predictions WHERE model_id = 129')
).scalar()
assert count == 0
@@ -150,11 +150,11 @@ async def test_scenario_1_2_3_invalid_datetime_column_specification(
client = temporal_test_env.client
with postgres_engine.begin() as conn:
conn.execute(text('DELETE FROM predictions_schema.laborious_data WHERE model_id = 130'))
conn.execute(text('DELETE FROM sientia_data.laborious_data WHERE model_id = 130'))
conn.execute(
text(
"""
INSERT INTO predictions_schema.laborious_data (model_id, variable, value, timestamp, created_at)
INSERT INTO sientia_data.laborious_data (model_id, variable, value, timestamp, created_at)
VALUES (130, 'sensor_1', 23.5, '2024-01-01 12:00:00+00:00', '2024-01-01 12:00:00+00:00')
"""
)
@@ -174,7 +174,7 @@ async def test_scenario_1_2_3_invalid_datetime_column_specification(
with postgres_engine.connect() as conn:
count = conn.execute(
text('SELECT COUNT(*) FROM predictions_schema.predictions WHERE model_id = 130')
text('SELECT COUNT(*) FROM sientia_data.predictions WHERE model_id = 130')
).scalar()
assert count == 0

8
e2e/test_predictions_batch_prediction_process.py
View File

@@ -30,9 +30,9 @@ def get_base_input_data(model_id):
def insert_sample_prediction(postgres_engine, model_id):
with postgres_engine.begin() as conn:
conn.execute(text(f'DELETE FROM predictions_schema.predictions WHERE model_id = {model_id}'))
conn.execute(text(f'DELETE FROM sientia_data.predictions WHERE model_id = {model_id}'))
insert_sql = f"""
INSERT INTO predictions_schema.predictions (model_id, timestamp, prediction, prediction_confidence, prediction_status, comments, response_time)
INSERT INTO sientia_data.predictions (model_id, timestamp, prediction, prediction_confidence, prediction_status, comments, response_time)
VALUES
({model_id}, '2024-01-01 12:00:00+00:00', 10, 0, 'Good', '', 0.1)
"""
@@ -147,7 +147,7 @@ async def test_scenario_2_1_4_input_gate_repeat_without_prior_prediction(
model_id = 214
insert_sample_data(postgres_engine, model_id, ['NULL', 78.2])
with postgres_engine.begin() as conn:
conn.execute(text(f'DELETE FROM predictions_schema.predictions WHERE model_id = {model_id}'))
conn.execute(text(f'DELETE FROM sientia_data.predictions WHERE model_id = {model_id}'))
input_data = get_base_input_data(model_id)
input_data['input_filters']['SPECIFIC_VARIABLES_NULL_VALUES']['POLICY'] = 'REPEAT'
await start_and_await_workflow(
@@ -338,7 +338,7 @@ async def test_scenario_2_4_1_input_empty_data_stop(
client = temporal_test_env.client
model_id = 241
with postgres_engine.begin() as conn:
conn.execute(text(f'DELETE FROM predictions_schema.laborious_data WHERE model_id = {model_id}'))
conn.execute(text(f'DELETE FROM sientia_data.laborious_data WHERE model_id = {model_id}'))
input_data = get_base_input_data(model_id)
input_data['input_filters'] = {'EMPTY_DATA': {'POLICY': 'STOP', 'CONFIG': {}}}
await start_and_await_workflow(

45
e2e/test_simple_metrics.py
View File

@@ -4,7 +4,7 @@ End-to-end tests for the SimpleMetrics workflow.
Coverage focus:
- Happy path computes rmse/mse/mae/r2 from predictions joined against ``laborious_data``
and persists rows to ``predictions_schema.simple_metrics_data`` with all required columns.
and persists rows to ``sientia_data.simple_metrics`` with all required columns.
- Subset metric selection (only rmse) writes exactly the requested rows.
- Zero-variance target produces ``r2=0`` per division-by-zero guard.
- Empty join (no overlapping data) short-circuits without persisting anything.
@@ -23,6 +23,7 @@ from e2e.helpers import load_scenario_input, make_workflow_id, start_and_await_w
from laborious.activities.activities import Activities
from laborious.workflows.simple_metrics import SimpleMetrics
# Columns defined by the production DDL for ``sientia_data.simple_metrics``.
EXPECTED_SIMPLE_METRICS_COLUMNS = [
'id',
'model_id',
@@ -34,6 +35,11 @@ EXPECTED_SIMPLE_METRICS_COLUMNS = [
'created_at',
]
# ``timestamp`` is now nullable per the new DDL (production code may write it
# null when the upstream data has no usable instant); skip the non-null check
# for it while still validating presence.
NULLABLE_SIMPLE_METRICS_COLUMNS = {'timestamp'}
def _simple_metrics_input(model_id: int, **overrides) -> dict:
"""Load and override the simple-metrics base scenario."""
@@ -87,8 +93,8 @@ def _seed_predictions_and_targets(
)
with postgres_engine.begin() as conn:
conn.execute(text(f'DELETE FROM predictions_schema.predictions WHERE model_id = {model_id}'))
conn.execute(text(f'DELETE FROM predictions_schema.laborious_data WHERE model_id = {model_id}'))
conn.execute(text(f'DELETE FROM sientia_data.predictions WHERE model_id = {model_id}'))
conn.execute(text(f'DELETE FROM sientia_data.laborious_data WHERE model_id = {model_id}'))
# The SimpleMetrics SQL JOIN only filters ``predictions.model_id``; it does
# NOT filter ``laborious_data.model_id`` (see ``e2e/CODE_ISSUES.md`` issue
# SM-1). Without this cross-model cleanup, a previous test's target rows
@@ -96,7 +102,7 @@ def _seed_predictions_and_targets(
# whenever timestamps happened to overlap.
conn.execute(
text(
"DELETE FROM predictions_schema.laborious_data "
"DELETE FROM sientia_data.laborious_data "
"WHERE variable IN (:sensor_default, :target_name) "
"AND timestamp >= NOW() - INTERVAL '120 minutes'"
),
@@ -105,7 +111,7 @@ def _seed_predictions_and_targets(
if prediction_rows:
conn.execute(
text(
'INSERT INTO predictions_schema.predictions '
'INSERT INTO sientia_data.predictions '
'(model_id, prediction, prediction_confidence, response_time, '
'prediction_status, "timestamp", created_at) VALUES '
+ ', '.join(prediction_rows)
@@ -113,7 +119,7 @@ def _seed_predictions_and_targets(
)
conn.execute(
text(
'INSERT INTO predictions_schema.laborious_data '
'INSERT INTO sientia_data.laborious_data '
'(model_id, variable, value, "timestamp", created_at) VALUES '
+ ', '.join(target_rows)
)
@@ -132,8 +138,9 @@ async def test_simple_metrics_happy_path_persists_all_metrics_and_columns(
"""
Scenario S.1.1: rmse/mse/mae/r2 are calculated from a deterministic
prediction/target pair set and written one row per metric. Every column
expected by ``predictions_schema.simple_metrics_data`` must be populated and
the numerical values must match closed-form expectations.
expected by ``sientia_data.simple_metrics`` must be populated (except the
nullable ``timestamp`` column) and the numerical values must match
closed-form expectations.
"""
client = temporal_test_env.client
model_id = 511
@@ -169,10 +176,10 @@ async def test_simple_metrics_happy_path_persists_all_metrics_and_columns(
rows = (
conn.execute(
text(
'SELECT * FROM predictions_schema.simple_metrics_data '
'SELECT * FROM sientia_data.simple_metrics '
'WHERE model_id = :m ORDER BY metric'
),
{'m': model_id},
{'m': str(model_id)},
)
.mappings()
.all()
@@ -183,6 +190,8 @@ async def test_simple_metrics_happy_path_persists_all_metrics_and_columns(
assert column in rows[0], f'Missing simple_metrics column: {column}'
for row in rows:
for column in EXPECTED_SIMPLE_METRICS_COLUMNS:
if column in NULLABLE_SIMPLE_METRICS_COLUMNS:
continue
assert row[column] is not None, f"Column '{column}' is NULL in {dict(row)}"
by_metric = {row['metric']: row for row in rows}
@@ -198,7 +207,9 @@ async def test_simple_metrics_happy_path_persists_all_metrics_and_columns(
assert all(row['data_size'] == n for row in rows), 'data_size must equal target row count'
assert all(row['interval_minutes'] == 60 for row in rows)
assert all(row['model_id'] == model_id for row in rows)
# ``model_id`` is now ``text`` in the new DDL, so we compare with the
# stringified test id rather than the numeric value.
assert all(row['model_id'] == str(model_id) for row in rows)
@pytest.mark.asyncio
@@ -232,10 +243,10 @@ async def test_simple_metrics_subset_metrics_writes_only_requested_rows(
r[0]
for r in conn.execute(
text(
'SELECT metric FROM predictions_schema.simple_metrics_data '
'SELECT metric FROM sientia_data.simple_metrics '
'WHERE model_id = :m'
),
{'m': model_id},
{'m': str(model_id)},
).all()
]
assert metrics == ['rmse']
@@ -270,10 +281,10 @@ async def test_simple_metrics_zero_variance_target_returns_zero_r2(
with postgres_engine.connect() as conn:
r2_value = conn.execute(
text(
"SELECT value FROM predictions_schema.simple_metrics_data "
"SELECT value FROM sientia_data.simple_metrics "
"WHERE model_id = :m AND metric = 'r2'"
),
{'m': model_id},
{'m': str(model_id)},
).scalar()
assert r2_value is not None
assert Decimal(str(r2_value)) == Decimal('0'), f'expected r2=0, got {r2_value!r}'
@@ -314,9 +325,9 @@ async def test_simple_metrics_no_overlapping_data_short_circuits(
with postgres_engine.connect() as conn:
count = conn.execute(
text(
'SELECT COUNT(*) FROM predictions_schema.simple_metrics_data '
'SELECT COUNT(*) FROM sientia_data.simple_metrics '
'WHERE model_id = :m'
),
{'m': model_id},
{'m': str(model_id)},
).scalar()
assert count == 0, 'Empty target data must short-circuit and skip persistence'

71
laborious/activities/model_metrics.py
View File

@@ -15,7 +15,7 @@ with workflow.unsafe.imports_passed_through():
from sientia_do.observability.metrics_controller import MetricsController
from sientia_do.observability.sientia_monitoring import SientiaMonitoring
from sientia_do.temporal.constants import DATETIME_FORMAT_WITH_TZ
from sientia_model.analytics.model_analysis import ModelAnalysis
from sientia_model.analytics.drift_analysis import DriftAnalysis
from laborious import metrics
from laborious.utils.dataframe_debug import build_dataframe_debug_message
@@ -96,14 +96,16 @@ class ModelMetrics(SientiaMonitoring):
Args:
- reference_data (DataFrame): Baseline dataset representing expected behavior.
- target_data (DataFrame): Current analysis dataset to compare against reference.
- target_name (str): Target column name used by ``ModelAnalysis`` config.
- target_name (str): Target column name used by ``DriftAnalysis`` config.
- reference_columns (Index): Feature columns evaluated for drift.
- drift_metrics (list[str]): Enabled univariate methods.
- chunk_period (str): Time bucket granularity used by analysis methods.
- metadata (dict[str, Any]): Workflow metadata for logs and notifications.
Return:
DataFrame: Consolidated drift dataframe ready for downstream formatting/persistence.
DataFrame: Consolidated drift dataframe from ``get_drift_metrics_dataframe`` using
``method`` / ``value`` (and optional ``threshold``, ``drift_type``), ready for
activity-level formatting before Postgres export.
"""
config = {
@@ -113,7 +115,7 @@ class ModelMetrics(SientiaMonitoring):
'features': reference_columns,
}
model_analysis = ModelAnalysis(config=config)
drift_analysis = DriftAnalysis(config=config)
self._debug_dataframe(
f'Reference data: Size {reference_data.shape}', reference_data, metadata
@@ -124,7 +126,7 @@ class ModelMetrics(SientiaMonitoring):
core_labels = self.get_core_labels(metadata, operation_type='detect_univariate_drift')
start_time = time.time()
try:
univariate_drift = model_analysis.detect_univariate_drift(
univariate_drift = drift_analysis.detect_univariate_drift(
reference_df=reference_data,
analysis_df=target_data,
features=reference_columns,
@@ -142,7 +144,7 @@ class ModelMetrics(SientiaMonitoring):
core_labels = self.get_core_labels(metadata, operation_type='detect_multivariate_drift')
start_time = time.time()
try:
multivariate_drift = model_analysis.detect_multivariate_drift(
multivariate_drift = drift_analysis.detect_multivariate_drift(
reference_df=reference_data,
analysis_df=target_data,
features=reference_columns,
@@ -159,7 +161,7 @@ class ModelMetrics(SientiaMonitoring):
start_time = time.time()
core_labels = self.get_core_labels(metadata, operation_type='get_drift_metrics_dataframe')
try:
drift_df = model_analysis.get_drift_metrics_dataframe(
drift_df = drift_analysis.get_drift_metrics_dataframe(
univariate_drift=univariate_drift,
multivariate_drift=multivariate_drift,
)
@@ -179,12 +181,12 @@ class ModelMetrics(SientiaMonitoring):
"""
Parse ``series`` as datetime and return a TZ-naive UTC copy.
``sientia_model.analytics.model_analysis.ModelAnalysis`` preserves the
``sientia_model.analytics.drift_analysis.DriftAnalysis`` preserves the
timezone of the input dataframe in its outputs, while target rows
loaded from PostgreSQL come in with ``+00:00``. Forcing both sides of
a comparison to TZ-naive UTC keeps ``isin`` / ``floor`` operations
deterministic regardless of how the analyzer (or a test double)
constructs its timestamps.
deterministic regardless of how the analyzer constructs its
timestamps.
Args:
- series (Series): Input series containing datetime-parseable values.
@@ -228,7 +230,7 @@ class ModelMetrics(SientiaMonitoring):
target_data = target_data.pivot(index='timestamp', columns='variable', values='value')
target_data['timestamp'] = target_data.index
# Keep timestamps as datetime: ModelAnalysis._chunk_dataframe relies on
# Keep timestamps as datetime: DriftAnalysis._chunk_dataframe relies on
# ``pd.Grouper(freq=...)`` which rejects string timestamp columns.
target_data['timestamp'] = to_datetime(target_data['timestamp'])
target_data = target_data.reset_index(drop=True)
@@ -287,7 +289,7 @@ class ModelMetrics(SientiaMonitoring):
return []
# Defense-in-depth: drop chunks whose floored timestamp does not appear
# in the analysis window. ``ModelAnalysis`` already chunks only over
# in the analysis window. ``DriftAnalysis`` already chunks only over
# ``analysis_df`` so this only excludes rows injected by upstream
# callers that pre-merge reference data into the result.
target_floor = self._to_naive_utc(target_data['timestamp']).dt.floor(chunk_period)
@@ -301,38 +303,27 @@ class ModelMetrics(SientiaMonitoring):
)
return []
# Map ``sientia_model.analytics.model_analysis`` schema onto the drift
# table columns: ``metric -> method``, ``statistic -> value``,
# ``alert -> drift``, ``chunk_index -> chunk``,
# ``chunk_end_date -> timestamp_end``. ``p_value`` and
# ``chunk_start_date`` are not persisted.
drift_df = drift_df.rename(
columns={
'metric': 'method',
'statistic': 'value',
'alert': 'drift',
'chunk_index': 'chunk',
'chunk_end_date': 'timestamp_end',
}
)
drift_df.drop(columns=['p_value', 'chunk_start_date'], inplace=True, errors='ignore')
# Analyzer emits diagnostic columns that are not stored in ``sientia_data.drift_metrics``.
drift_df = drift_df.drop(columns=['threshold', 'drift_type'], errors='ignore')
# Drop duplicates
drift_df.drop_duplicates(
subset=['timestamp', 'method', 'feature'], keep='first', inplace=True
)
drift_df['model_id'] = model_id
drift_df['model_id'] = str(model_id)
drift_df['accurate'] = accurate
drift_df['timestamp'] = self._to_naive_utc(drift_df['timestamp'])
drift_df['timestamp'] = drift_df['timestamp'].dt.tz_localize('UTC')
drift_df['timestamp'] = drift_df['timestamp'].dt.strftime(DATETIME_FORMAT_WITH_TZ)
# ``timestamp`` is overridden with the most recent target instant so
# every persisted row shares a single business timestamp (the run's
# logical "now"), matching what downstream consumers expect.
latest_target_timestamp = self._to_naive_utc(target_data['timestamp']).max()
drift_df['timestamp'] = (
pd.Timestamp(latest_target_timestamp)
.tz_localize('UTC')
.strftime(DATETIME_FORMAT_WITH_TZ)
)
# ``timestamp_end`` may carry nanosecond precision (beyond
# ``timestamptz`` microseconds), so serialize as ISO text for the
# ``text`` Postgres column.
drift_df['timestamp_end'] = drift_df['timestamp_end'].apply(
# ``chunk_start_date`` / ``chunk_end_date`` may carry nanosecond
# precision (beyond ``timestamptz`` microseconds), so serialize as ISO
# text for the ``text`` Postgres columns.
for column in ('chunk_start_date', 'chunk_end_date'):
drift_df[column] = drift_df[column].apply(
lambda value: pd.Timestamp(value).isoformat() if pd.notna(value) else None
)

16
tests/conftest.py
View File

@@ -1,6 +1,4 @@
import os
import sys
from unittest.mock import MagicMock
from sientia_do.temporal.activities.postgres_sync import Postgres
@@ -60,13 +58,9 @@ class DummyMinioDataFramePayload:
"""
Pytest configuration file with global mocks for external dependencies.
This module mocks the 'sientia' module to avoid requiring its installation
during unit tests. The mock is registered in sys.modules before any test
imports are executed.
The historical ``sientia`` package is no longer imported by the codebase;
drift analysis lives in ``sientia_model.analytics.drift_analysis`` and is
imported lazily inside Temporal activities. No global module-level mock is
required here — unit tests that need to control ``DriftAnalysis`` outputs
should patch ``laborious.activities.model_metrics.DriftAnalysis`` directly.
"""
# Mock sientia module
sientia_mock = MagicMock()
sientia_mock.ModelAnalysis = MagicMock
sys.modules['sientia'] = sientia_mock
sys.modules['sientia.ModelAnalysis'] = MagicMock()

369
tests/laborious/activities/test_model_metrics.py
View File

@@ -1,8 +1,9 @@
from unittest.mock import ANY, MagicMock, patch
from pandas import DataFrame
from pandas import DataFrame, Timestamp
from pytest import fixture
from sientia_do.notifications.models import NotificationLevel
from sientia_do.temporal.constants import DATETIME_FORMAT_WITH_TZ
from laborious.activities.model_metrics import ModelMetrics
@@ -72,39 +73,28 @@ def test_calculate_drift_invalid_chunk_period(model_metrics_activity):
raise AssertionError('Expected ValueError')
@patch('laborious.activities.model_metrics.DataFrame')
@patch('laborious.activities.model_metrics.to_datetime')
def test_calculate_drift_with_reference_data(
mock_to_datetime, mock_dataframe, model_metrics_activity
):
# Arrange
mock_to_datetime.return_value.dt.strftime.return_value = '2023-05-26 11:12:27'
mock_to_datetime.return_value.dt.tz_localize.return_value.dt.strftime.return_value = (
'2023-05-26 11:12:27+00:00'
)
mock_drift_df = MagicMock()
mock_drift_df.empty = False
mock_drift_df.drop.return_value = mock_drift_df
mock_drift_df.__getitem__.return_value.isin.return_value = [True]
mock_drift_df.__getitem__.return_value = mock_drift_df
mock_drift_df.__getitem__.return_value.dt.tz_localize.return_value.dt.strftime.return_value = (
'2023-05-26 11:12:27+00:00'
)
mock_drift_df.rename.return_value = mock_drift_df
mock_drift_df.drop_duplicates.return_value = mock_drift_df
mock_drift_df.to_dict.return_value = [
def _sample_drift_metrics_df(ts: Timestamp) -> DataFrame:
"""Minimal analyzer-shaped dataframe (univariate row + columns the activity expects)."""
return DataFrame(
{
'method': 'ks_test',
'value': 0.5,
'feature': 'feature1',
'timestamp': '2023-05-26 11:12:27+00:00',
'model_id': 'test_model_id',
'accurate': True,
'timestamp': [ts],
'feature': ['feature1'],
'method': ['ks_test'],
'value': [0.5],
'alert': [False],
'chunk_index': [0],
'chunk_start_date': [ts],
'chunk_end_date': [ts],
'threshold': [0.1],
'drift_type': ['univariate'],
}
]
)
model_metrics_activity.get_drift_metrics = MagicMock(return_value=mock_drift_df)
def test_calculate_drift_with_reference_data(model_metrics_activity):
ts = Timestamp('2023-05-26 11:12:27')
drift_df = _sample_drift_metrics_df(ts)
model_metrics_activity.get_drift_metrics = MagicMock(return_value=drift_df)
reference_data = DataFrame(
{
@@ -114,20 +104,11 @@ def test_calculate_drift_with_reference_data(
}
)
mock_target_df = MagicMock()
mock_target_df.pivot.return_value = mock_target_df
mock_target_df.index = ['2023-05-26 11:12:27']
mock_target_df.reset_index.return_value = mock_target_df
mock_target_df.dropna.return_value = mock_target_df
mock_target_df.__getitem__.return_value.apply.return_value = ['2023-05-26 11:12:27']
mock_target_df.drop.return_value.columns = ['feature1']
mock_dataframe.return_value = mock_target_df
input_data = {
**metadata,
'model_name': 'test_model',
'model_id': 'test_model_id',
'reference_data': reference_data.to_dict(),
'reference_data': reference_data.to_dict('list'),
'target_data': {
'timestamp': ['2023-05-26 11:12:27'],
'variable': ['feature1'],
@@ -138,92 +119,40 @@ def test_calculate_drift_with_reference_data(
'chunk_period': 'min',
}
# Act
result = model_metrics_activity.calculate_drift(input_data)
# Assert
assert isinstance(result, list)
assert result == mock_drift_df.to_dict.return_value # type: ignore[comparison-overlap]
model_metrics_activity.info.assert_called()
model_metrics_activity.get_drift_metrics.assert_called_once()
# Verify transformations were called
mock_drift_df.drop.assert_called_once_with(
columns=['p_value', 'chunk_start_date'], inplace=True, errors='ignore'
)
mock_drift_df.__getitem__.assert_called()
mock_drift_df.rename.assert_called_once_with(
columns={
'metric': 'method',
'statistic': 'value',
'alert': 'drift',
'chunk_index': 'chunk',
'chunk_end_date': 'timestamp_end',
}
)
mock_drift_df.drop_duplicates.assert_called_once_with(
subset=['timestamp', 'method', 'feature'], keep='first', inplace=True
)
mock_drift_df.to_dict.assert_called_once_with(orient='records')
@patch('laborious.activities.model_metrics.DataFrame')
@patch('laborious.activities.model_metrics.to_datetime')
def test_calculate_drift_without_reference_data(
mock_to_datetime, mock_dataframe, model_metrics_activity
):
# Arrange
mock_to_datetime.return_value.dt.strftime.return_value = '2023-05-26 11:12:27'
mock_to_datetime.return_value.dt.tz_localize.return_value.dt.strftime.return_value = (
'2023-05-26 11:12:27+00:00'
)
mock_drift_df = MagicMock()
mock_drift_df.empty = False
mock_drift_df.drop.return_value = mock_drift_df
mock_drift_df.__getitem__.return_value.isin.return_value = [True]
mock_drift_df.__getitem__.return_value = mock_drift_df
mock_drift_df.__getitem__.return_value.dt.tz_localize.return_value.dt.strftime.return_value = (
'2023-05-26 11:12:27+00:00'
)
mock_drift_df.rename.return_value = mock_drift_df
mock_drift_df.drop_duplicates.return_value = mock_drift_df
mock_drift_df.to_dict.return_value = [
expected_timestamp = ts.tz_localize('UTC').strftime(DATETIME_FORMAT_WITH_TZ)
assert result == [
{
'timestamp': expected_timestamp,
'feature': 'feature1',
'method': 'ks_test',
'value': 0.5,
'feature': 'feature1',
'timestamp': '2023-05-26 11:12:27+00:00',
'alert': False,
'chunk_index': 0,
'chunk_start_date': ts.isoformat(),
'chunk_end_date': ts.isoformat(),
'model_id': 'test_model_id',
'accurate': False,
'accurate': True,
}
]
model_metrics_activity.info.assert_called()
model_metrics_activity.get_drift_metrics.assert_called_once()
model_metrics_activity.get_drift_metrics = MagicMock(return_value=mock_drift_df)
def test_calculate_drift_without_reference_data(model_metrics_activity):
# Ten rows so int(len * 0.3) >= 1 for the built-in reference slice.
ts_last = Timestamp('2023-05-26 11:12:36')
drift_df = _sample_drift_metrics_df(ts_last)
model_metrics_activity.get_drift_metrics = MagicMock(return_value=drift_df)
timestamps = [f'2023-05-26 11:12:{27 + i:02d}' for i in range(10)]
target_data_dict = {
'timestamp': ['2023-05-26 11:12:27', '2023-05-26 11:12:28', '2023-05-26 11:12:29'],
'variable': ['feature1', 'feature1', 'feature1'],
'value': [1.0, 2.0, 3.0],
'timestamp': timestamps,
'variable': ['feature1'] * 10,
'value': [float(i) for i in range(10)],
}
mock_target_df = MagicMock()
mock_target_df.pivot.return_value = mock_target_df
mock_target_df.index = ['2023-05-26 11:12:27', '2023-05-26 11:12:28', '2023-05-26 11:12:29']
mock_target_df.reset_index.return_value = mock_target_df
mock_target_df.dropna.return_value = mock_target_df
mock_target_df.sort_values.return_value = mock_target_df
mock_target_df.head.return_value = DataFrame(
{'timestamp': ['2023-05-26 11:12:27'], 'feature1': [1.0]}
)
mock_target_df.__getitem__.return_value.apply.return_value = [
'2023-05-26 11:12:27',
'2023-05-26 11:12:28',
'2023-05-26 11:12:29',
]
mock_target_df.drop.return_value.columns = ['feature1']
mock_dataframe.return_value = mock_target_df
mock_dataframe.side_effect = lambda x=None: mock_target_df if x is not None else mock_target_df
input_data = {
**metadata,
'model_name': 'test_model',
@@ -235,12 +164,23 @@ def test_calculate_drift_without_reference_data(
'chunk_period': 's',
}
# Act
result = model_metrics_activity.calculate_drift(input_data)
# Assert
assert isinstance(result, list)
assert result == mock_drift_df.to_dict.return_value
expected_timestamp = ts_last.tz_localize('UTC').strftime(DATETIME_FORMAT_WITH_TZ)
assert result == [
{
'timestamp': expected_timestamp,
'feature': 'feature1',
'method': 'ks_test',
'value': 0.5,
'alert': False,
'chunk_index': 0,
'chunk_start_date': ts_last.isoformat(),
'chunk_end_date': ts_last.isoformat(),
'model_id': 'test_model_id',
'accurate': False,
}
]
model_metrics_activity.warning.assert_called()
model_metrics_activity.send_notification.assert_called_once_with(
metadata=metadata['metadata'],
@@ -250,24 +190,6 @@ def test_calculate_drift_without_reference_data(
level=NotificationLevel.WARNING,
attachment_content=ANY,
)
# Verify transformations were called
mock_drift_df.drop.assert_called_once_with(
columns=['p_value', 'chunk_start_date'], inplace=True, errors='ignore'
)
mock_drift_df.__getitem__.assert_called()
mock_drift_df.rename.assert_called_once_with(
columns={
'metric': 'method',
'statistic': 'value',
'alert': 'drift',
'chunk_index': 'chunk',
'chunk_end_date': 'timestamp_end',
}
)
mock_drift_df.drop_duplicates.assert_called_once_with(
subset=['timestamp', 'method', 'feature'], keep='first', inplace=True
)
mock_drift_df.to_dict.assert_called_once_with(orient='records')
@patch('laborious.activities.model_metrics.DataFrame')
@@ -391,44 +313,18 @@ def test_calculate_drift_empty_after_timestamp_filter(
'No drift metrics found after dropping rows where timestamp is not in target data',
metadata['metadata'],
)
# When the timestamp filter empties the dataframe, the rename/drop pipeline
# is short-circuited, so neither ``drop`` nor ``rename`` should run.
# When the timestamp filter empties the dataframe, the post-filter
# pipeline is short-circuited, so neither ``drop`` nor ``rename`` runs
# (they wouldn't run anyway, as the activity preserves the lib's schema).
mock_drift_df.drop.assert_not_called()
mock_drift_df.rename.assert_not_called()
mock_drift_df.__getitem__.assert_called()
@patch('laborious.activities.model_metrics.DataFrame')
@patch('laborious.activities.model_metrics.to_datetime')
def test_calculate_drift_success_min(mock_to_datetime, mock_dataframe, model_metrics_activity):
# Arrange
mock_to_datetime.return_value.dt.strftime.return_value = '2023-05-26 11:12:27'
mock_to_datetime.return_value.dt.tz_localize.return_value.dt.strftime.return_value = (
'2023-05-26 11:12:27+00:00'
)
mock_drift_df = MagicMock()
mock_drift_df.empty = False
mock_drift_df.drop.return_value = mock_drift_df
mock_drift_df.__getitem__.return_value.isin.return_value = [True]
mock_drift_df.__getitem__.return_value = mock_drift_df
mock_drift_df.__getitem__.return_value.dt.tz_localize.return_value.dt.strftime.return_value = (
'2023-05-26 11:12:27+00:00'
)
mock_drift_df.rename.return_value = mock_drift_df
mock_drift_df.drop_duplicates.return_value = mock_drift_df
mock_drift_df.to_dict.return_value = [
{
'method': 'ks_test',
'value': 0.5,
'feature': 'feature1',
'timestamp': '2023-05-26 11:12:27+00:00',
'model_id': 'test_model_id',
'accurate': True,
}
]
model_metrics_activity.get_drift_metrics = MagicMock(return_value=mock_drift_df)
def test_calculate_drift_success_min(model_metrics_activity):
ts = Timestamp('2023-05-26 11:12:27')
drift_df = _sample_drift_metrics_df(ts)
model_metrics_activity.get_drift_metrics = MagicMock(return_value=drift_df)
reference_data = DataFrame(
{
@@ -438,20 +334,11 @@ def test_calculate_drift_success_min(mock_to_datetime, mock_dataframe, model_met
}
)
mock_target_df = MagicMock()
mock_target_df.pivot.return_value = mock_target_df
mock_target_df.index = ['2023-05-26 11:12:27']
mock_target_df.reset_index.return_value = mock_target_df
mock_target_df.dropna.return_value = mock_target_df
mock_target_df.__getitem__.return_value.isin.return_value = [True]
mock_target_df.drop.return_value.columns = ['feature1']
mock_dataframe.return_value = mock_target_df
input_data = {
**metadata,
'model_name': 'test_model',
'model_id': 'test_model_id',
'reference_data': reference_data.to_dict(),
'reference_data': reference_data.to_dict('list'),
'target_data': {
'timestamp': ['2023-05-26 11:12:27'],
'variable': ['feature1'],
@@ -462,65 +349,31 @@ def test_calculate_drift_success_min(mock_to_datetime, mock_dataframe, model_met
'chunk_period': 'min',
}
# Act
result = model_metrics_activity.calculate_drift(input_data)
# Assert
assert isinstance(result, list)
assert result == mock_drift_df.to_dict.return_value # type: ignore[comparison-overlap]
model_metrics_activity.info.assert_called()
model_metrics_activity.get_drift_metrics.assert_called_once()
# Verify transformations were called
mock_drift_df.drop.assert_called_once_with(
columns=['p_value', 'chunk_start_date'], inplace=True, errors='ignore'
)
mock_drift_df.__getitem__.assert_called()
mock_drift_df.rename.assert_called_once_with(
columns={
'metric': 'method',
'statistic': 'value',
'alert': 'drift',
'chunk_index': 'chunk',
'chunk_end_date': 'timestamp_end',
}
)
mock_drift_df.drop_duplicates.assert_called_once_with(
subset=['timestamp', 'method', 'feature'], keep='first', inplace=True
)
mock_drift_df.to_dict.assert_called_once_with(orient='records')
@patch('laborious.activities.model_metrics.DataFrame')
@patch('laborious.activities.model_metrics.to_datetime')
def test_calculate_drift_success_s(mock_to_datetime, mock_dataframe, model_metrics_activity):
# Arrange
mock_to_datetime.return_value.dt.strftime.return_value = '2023-05-26 11:12:27'
mock_to_datetime.return_value.dt.tz_localize.return_value.dt.strftime.return_value = (
'2023-05-26 11:12:27+00:00'
)
mock_drift_df = MagicMock()
mock_drift_df.empty = False
mock_drift_df.drop.return_value = mock_drift_df
mock_drift_df.__getitem__.return_value.isin.return_value = [True]
mock_drift_df.__getitem__.return_value = mock_drift_df
mock_drift_df.__getitem__.return_value.dt.tz_localize.return_value.dt.strftime.return_value = (
'2023-05-26 11:12:27+00:00'
)
mock_drift_df.rename.return_value = mock_drift_df
mock_drift_df.drop_duplicates.return_value = mock_drift_df
mock_drift_df.to_dict.return_value = [
expected_timestamp = ts.tz_localize('UTC').strftime(DATETIME_FORMAT_WITH_TZ)
assert result == [
{
'timestamp': expected_timestamp,
'feature': 'feature1',
'method': 'ks_test',
'value': 0.5,
'feature': 'feature1',
'timestamp': '2023-05-26 11:12:27+00:00',
'alert': False,
'chunk_index': 0,
'chunk_start_date': ts.isoformat(),
'chunk_end_date': ts.isoformat(),
'model_id': 'test_model_id',
'accurate': True,
}
]
model_metrics_activity.info.assert_called()
model_metrics_activity.get_drift_metrics.assert_called_once()
model_metrics_activity.get_drift_metrics = MagicMock(return_value=mock_drift_df)
def test_calculate_drift_success_s(model_metrics_activity):
ts = Timestamp('2023-05-26 11:12:27')
drift_df = _sample_drift_metrics_df(ts)
model_metrics_activity.get_drift_metrics = MagicMock(return_value=drift_df)
reference_data = DataFrame(
{
@@ -530,20 +383,11 @@ def test_calculate_drift_success_s(mock_to_datetime, mock_dataframe, model_metri
}
)
mock_target_df = MagicMock()
mock_target_df.pivot.return_value = mock_target_df
mock_target_df.index = ['2023-05-26 11:12:27']
mock_target_df.reset_index.return_value = mock_target_df
mock_target_df.dropna.return_value = mock_target_df
mock_target_df.__getitem__.return_value.isin.return_value = [True]
mock_target_df.drop.return_value.columns = ['feature1']
mock_dataframe.return_value = mock_target_df
input_data = {
**metadata,
'model_name': 'test_model',
'model_id': 'test_model_id',
'reference_data': reference_data.to_dict(),
'reference_data': reference_data.to_dict('list'),
'target_data': {
'timestamp': ['2023-05-26 11:12:27'],
'variable': ['feature1'],
@@ -554,32 +398,25 @@ def test_calculate_drift_success_s(mock_to_datetime, mock_dataframe, model_metri
'chunk_period': 's',
}
# Act
result = model_metrics_activity.calculate_drift(input_data)
# Assert
assert isinstance(result, list)
assert result == mock_drift_df.to_dict.return_value # type: ignore[comparison-overlap]
expected_timestamp = ts.tz_localize('UTC').strftime(DATETIME_FORMAT_WITH_TZ)
assert result == [
{
'timestamp': expected_timestamp,
'feature': 'feature1',
'method': 'ks_test',
'value': 0.5,
'alert': False,
'chunk_index': 0,
'chunk_start_date': ts.isoformat(),
'chunk_end_date': ts.isoformat(),
'model_id': 'test_model_id',
'accurate': True,
}
]
model_metrics_activity.info.assert_called()
model_metrics_activity.get_drift_metrics.assert_called_once()
# Verify transformations were called
mock_drift_df.drop.assert_called_once_with(
columns=['p_value', 'chunk_start_date'], inplace=True, errors='ignore'
)
mock_drift_df.__getitem__.assert_called()
mock_drift_df.rename.assert_called_once_with(
columns={
'metric': 'method',
'statistic': 'value',
'alert': 'drift',
'chunk_index': 'chunk',
'chunk_end_date': 'timestamp_end',
}
)
mock_drift_df.drop_duplicates.assert_called_once_with(
subset=['timestamp', 'method', 'feature'], keep='first', inplace=True
)
mock_drift_df.to_dict.assert_called_once_with(orient='records')
@patch('laborious.activities.model_metrics.DataFrame')
@@ -646,7 +483,7 @@ def test_calculate_drift_get_drift_metrics_error(
@patch('laborious.activities.model_metrics.to_datetime')
@patch('laborious.activities.model_metrics.time.time')
@patch('laborious.activities.model_metrics.ModelAnalysis')
@patch('laborious.activities.model_metrics.DriftAnalysis')
@patch('laborious.activities.model_metrics.metrics')
def test_get_drift_metrics_success(
mock_metrics, mock_model_analysis, mock_time, mock_to_datetime, model_metrics_activity
@@ -657,8 +494,8 @@ def test_get_drift_metrics_success(
mock_drift_df = DataFrame(
{
'timestamp': ['2023-05-26 11:12:27'],
'metric': ['ks_test'],
'statistic': [0.5],
'method': ['ks_test'],
'value': [0.5],
'feature': ['feature1'],
}
)
@@ -707,7 +544,7 @@ def test_get_drift_metrics_success(
@patch('laborious.activities.model_metrics.to_datetime')
@patch('laborious.activities.model_metrics.time.time')
@patch('laborious.activities.model_metrics.ModelAnalysis')
@patch('laborious.activities.model_metrics.DriftAnalysis')
@patch('laborious.activities.model_metrics.metrics')
def test_get_drift_metrics_univariate_error(
mock_metrics, mock_model_analysis, mock_time, mock_to_datetime, model_metrics_activity
@@ -764,7 +601,7 @@ def test_get_drift_metrics_univariate_error(
@patch('laborious.activities.model_metrics.to_datetime')
@patch('laborious.activities.model_metrics.time.time')
@patch('laborious.activities.model_metrics.ModelAnalysis')
@patch('laborious.activities.model_metrics.DriftAnalysis')
@patch('laborious.activities.model_metrics.metrics')
def test_get_drift_metrics_multivariate_error(
mock_metrics, mock_model_analysis, mock_time, mock_to_datetime, model_metrics_activity
@@ -810,7 +647,7 @@ def test_get_drift_metrics_multivariate_error(
@patch('laborious.activities.model_metrics.to_datetime')
@patch('laborious.activities.model_metrics.time.time')
@patch('laborious.activities.model_metrics.ModelAnalysis')
@patch('laborious.activities.model_metrics.DriftAnalysis')
@patch('laborious.activities.model_metrics.metrics')
def test_get_drift_metrics_dataframe_error(
mock_metrics, mock_model_analysis, mock_time, mock_to_datetime, model_metrics_activity

4
tests/laborious/utils/test_connectors_config.py
View File

@@ -125,6 +125,8 @@ def test_build_minio_config_with_env_vars():
environ['MINIO_SECRET_KEY'] = 'test-secret'
environ['MINIO_REGION_NAME'] = 'test-region'
environ['MINIO_DEFAULT_BUCKET'] = 'test-bucket'
# Isolate from IDE/CI env (e.g. VS Code may export MINIO_SECURE=true).
environ['MINIO_SECURE'] = 'false'
assert build_minio_config() == {
'endpoint_url': 'http://test-host',
'access_key': 'test-key',
@@ -141,6 +143,8 @@ def test_build_minio_config_with_defaults():
environ.pop('MINIO_SECRET_KEY', None)
environ.pop('MINIO_REGION_NAME', None)
environ.pop('MINIO_DEFAULT_BUCKET', None)
environ.pop('MINIO_SECURE', None)
environ.pop('MINIO_RETENTION_HOURS', None)
assert build_minio_config() == {
'endpoint_url': 'http://localhost:9000',
'access_key': 'minioadmin',