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test(e2e): fix S.1.1 expected values to match RegressionMetrics rounding

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RegressionMetrics rounds every metric to 2 decimals (documented in
lib.md); the old test compared against unrounded closed-form values
with a 1e-6 tolerance, which the rewritten activity can no longer
satisfy. Round the expected values the same way before comparing.

Confirmed via real Docker-backed Postgres/MinIO/Mongo + Temporal test
environment: all 4 simple_metrics e2e scenarios pass.
This commit is contained in:
PedroHMCosme
2026-09-02 08:22:22 -03:00
parent 5997210118
commit a0b916999f
1 changed files with 6 additions and 5 deletions
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11
e2e/test_simple_metrics.py
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@@ -140,7 +140,8 @@ async def test_simple_metrics_happy_path_persists_all_metrics_and_columns(
prediction/target pair set and written one row per metric. Every column prediction/target pair set and written one row per metric. Every column
expected by ``sientia_data.simple_metrics`` must be populated (except the expected by ``sientia_data.simple_metrics`` must be populated (except the
nullable ``timestamp`` column) and the numerical values must match nullable ``timestamp`` column) and the numerical values must match
closed-form expectations. closed-form expectations, rounded to 2 decimals as ``RegressionMetrics``
persists them (see ``sientia_model/metrics/regression.py``).
""" """
client = temporal_test_env.client client = temporal_test_env.client
model_id = 511 model_id = 511
@@ -154,13 +155,13 @@ async def test_simple_metrics_happy_path_persists_all_metrics_and_columns(
] ]
diffs = [target - prediction for prediction, target in pairs] diffs = [target - prediction for prediction, target in pairs]
n = len(diffs) n = len(diffs)
expected_rmse = math.sqrt(sum(d * d for d in diffs) / n) expected_rmse = round(math.sqrt(sum(d * d for d in diffs) / n), 2)
expected_mse = sum(d * d for d in diffs) / n expected_mse = round(sum(d * d for d in diffs) / n, 2)
expected_mae = sum(abs(d) for d in diffs) / n expected_mae = round(sum(abs(d) for d in diffs) / n, 2)
target_mean = sum(t for _, t in pairs) / n target_mean = sum(t for _, t in pairs) / n
ss_res = sum((target - prediction) ** 2 for prediction, target in pairs) ss_res = sum((target - prediction) ** 2 for prediction, target in pairs)
ss_tot = sum((t - target_mean) ** 2 for _, t in pairs) ss_tot = sum((t - target_mean) ** 2 for _, t in pairs)
expected_r2 = 1.0 - (ss_res / ss_tot) expected_r2 = round(1.0 - (ss_res / ss_tot), 2)
_seed_predictions_and_targets(postgres_engine, model_id=model_id, pairs=pairs) _seed_predictions_and_targets(postgres_engine, model_id=model_id, pairs=pairs)