smoke-test-ml-pipeline

name

smoke-test-ml-pipeline

description

Owns the smoke test contract for an ML experiment: a small, diagnostic-by-construction pytest that fits the experiment's learner on a portion of the real `data/` source and predicts on a *disjoint* portion that deliberately carries **no pre-history buffer**. The assertion is structural — the number of predictions must equal the number of rows in the predict grid. A pipeline that loads-then-features-then-splits will silently drop the cold-start rows of the predict slice and the test will fail with a row-count mismatch; a pipeline that marks X early and references upstream history nodes from feature steps will pass trivially. The smoke test is the executable proof of the X-marker placement rule from `build-ml-pipeline`. TRIGGER when: `test-ml-pipeline` has dispatched here to write the smoke test for an approved experiment; `pytest tests/smoke/` is failing on row count; the user asks "why is the smoke test failing?"; a pipeline edit in `build-ml-pipeline` needs an executable proof; an experiment script changes the pipeline shape and the matching smoke test needs revisiting. SKIP when: the design note does not exist or is not yet approved (route to `iterate-ml-experiment`); the user is asking about a regression test or schema invariant (route to `regression-test-ml-pipeline` / `distribution-test-ml-pipeline` once those exist); the question is the *interpretation* of CV metrics, not predict-time correctness (route to `evaluate-ml-pipeline`). HOW TO USE: read the matching experiment's `journal/NN_*.md` and `experiments/NN_*.py` first to understand the pipeline's source binding (what env-dict keys does `build_learner` expect?). Then construct two env-dicts from the **real `data/` source** — a train env and a predict env — such that the predict env carries *only the rows we want predictions for* and *no pre-history buffer*. The hard assertion is that the prediction count matches the predict-env row count exactly. The soft assertion is that the smoke set's MAE is within `3 × CV_mean` (or the task-appropriate analogue). **Do not write the design note or run CV — that's other skills' job.**

Smoke Test ML Pipeline

The minimal pytest that catches the "load → featurize → split" anti-pattern at iteration time, before it reaches production.

Stop conditions — read before anything else

• No smoke test without an approved design note + script. The pairing rule from test-ml-pipeline is hard: tests/smoke/test_NN_<short_name>.py exists only when journal/NN_<short_name>.md is at least approved and experiments/NN_<short_name>.py exists with the matching stem.
• Symbol from memory is forbidden. Any skrub / scikit-learn name you write in the smoke test must come from a Skill(python-api) / Skill(python-api) call in this turn. The smoke test is a small file but it imports the predicting-package API surface; the same memory-forbidden rule applies.
• Don't shrink the assertion. The hard assertion is exact row-count equality. Not "approximately equal", not "at least 80% of expected rows". A row-count mismatch is the failure mode the smoke test exists to catch. Loosening the assertion silently reintroduces the bug.
• Don't synthesize the fixture. The smoke test reads the real data/ source. Synthetic fixtures look fine but skip the loaders that actually break in production.
• No wrappers, no NaN-handling, no eval_mode hacks. If the smoke test only passes after wrapping the predictor or conditioning on eval_mode, the pipeline is wrong. Route back to build-ml-pipeline and fix the X-marker placement. Wrappers paper over the failure mode; they don't solve it.
• The smoke test uses only the predicting package's API. For a SkrubLearner produced by build-ml-pipeline that means skrub's fit / predict / (optionally score) plus sklearn.metrics for any metric the soft assertion uses. Do not import skore (or any other tracking / reporting library) in the test file. The smoke test must be runnable in any environment that can import skrub + import sklearn — the skore Project is a side artifact, not a test dependency. Soft-assertion baselines (CV-mean MAE, etc.) are hardcoded from the design note's Status.headline with a comment pointing to the design note; update by hand when the experiment's headline number changes.
• Don't filter warnings. No @pytest.mark.filterwarnings(...), no warnings.filterwarnings(...) in the test body, no filterwarnings = [...] in pytest.ini / pyproject.toml — unless the user explicitly asks. See python-code-style § Stop conditions.

Pre-flight — emit this checklist as visible text before any test code

Pre-flight (smoke-test-ml-pipeline):
- [ ] Tier 1 mandatory libs importable: pytest + sklearn + skrub
      (per `data-science-python-stack` § "Tier 1"). **Not skore** —
      see the Stop conditions; the smoke test is intentionally
      portable to any skrub-capable environment
- [ ] Skill(python-api) consulted for skrub / sklearn symbols used in
      the test: <symbols, or "none">
      Evidence: Read scratch/api/<lib>/<version>/<topic>.md (this turn)
                | Write scratch/api/<lib>/<version>/<topic>.md (this turn)
                | "n/a — test only uses symbols already present in
                  src/<pkg>/ (build_learner / load_training_table / etc.)"
      "Read python-api SKILL.md" alone is NOT evidence.
- [ ] `journal/NN_<short_name>.md` read this turn (frozen sections:
      Question, Method) so the test asserts what the experiment claims
- [ ] `experiments/NN_<short_name>.py` skimmed this turn for the env-dict
      keys `build_learner` consumes (`data_dir` / `start` + `end` /
      `raw_frame` / etc.)
- [ ] `src/<pkg>/data.py` skimmed this turn for the loader signature
      (so the predict-env construction matches the loader's expectations)
- [ ] Test category & stem decided: `tests/smoke/test_NN_<short_name>.py`
- [ ] Predict-grid size decided: smallest window that still triggers
      the failure mode (default: a single horizon-length slice; for
      time series, the most recent N steps such that the target is
      *just* observable for assertion)
- [ ] Hard assertion wired: `len(predictions) == n_predict_grid_rows`
- [ ] Soft assertion wired (or explicitly skipped): smoke MAE within
      `3 × CV_MEAN_HARDCODED_FROM_PLAN` (or task-appropriate
      analogue). Value is a literal pulled from the matching
      `journal/NN_<short_name>.md` § Status.headline; the test does
      not import `skore` / read the project store at runtime.

What the smoke test asserts

Two assertions, two severities:

Hard — the row-count check

assert len(predictions) == n_predict_grid_rows

This is the structural-correctness assertion. It is a binary pass/fail and it is the whole point of the smoke test. A correctly built pipeline (per build-ml-pipeline's X-marker rule) satisfies this trivially. A pipeline that loads-then-features- then-splits will fail it because predict-time featurization on the predict env runs with no pre-history buffer and silently drops cold-start rows.

n_predict_grid_rows is the count of rows the predict env claims to want predictions for — typically the number of target-time rows in the predict-time grid. If the pipeline's source binding is a directory of raw files, it's the row count of the supervised frame derived from the predict env at predict time (usable via build_supervised_frame(predict_dir)).

Soft — the metric-vs-CV gap

smoke_mae = mean_absolute_error(y_true, predictions)
assert smoke_mae < 3 * cv_mae_mean, (
    f"smoke MAE {smoke_mae:.0f} is more than 3× the CV mean "
    f"({cv_mae_mean:.0f}); predictions may be NaN-poisoned even "
    f"though the count matches."
)

The metric gap catches the second-order failure mode: the prediction count is right, but the values are garbage because some features are NaN at predict time (e.g. an encoder hasn't seen a new category, a lag is null because the upstream history reference wasn't wired correctly). The 3× bound is a starting heuristic; adjust per task. The smoke window is a single seasonal slice, so the bound has to be loose enough that a legitimate hard-season window doesn't trip it.

The soft assertion is opt-out, not opt-in: skip it only if the task has no obvious metric-vs-CV comparator (e.g. the smoke fixture deliberately has no ground truth). If you skip it, leave a comment on why in the test file.

The diagnostic-by-construction property

The fixture is built specifically to fail on the buggy shape and pass on the correct one. This is the single most important property of the smoke test; if you take the fixture construction shortcut and it doesn't have this property, the test is worthless.

Concretely, the predict-time env-dict carries only the rows we want predictions for, with no pre-history buffer beyond what predict-time-known features absolutely require. Two consequences:

• Late-mark_as_X pipeline: features are computed inside the graph from the predict env's data alone. Backward lags / rolling windows / target shifts have NaN at the cold-start rows. The pre-marker drop_nulls (or the model's NaN intolerance) drops those rows. len(predictions) < n_predict_grid_rows. Test fails.
• Early-mark_as_X pipeline: the marker lands on the predict-grid node (Layer 2 of build-ml-pipeline's rule 2); history-dependent features take the upstream history DataOp as an additional apply_func argument. At predict time, the history node resolves to the full available history (bound from the same source the train env uses), and the join in each feature step produces real values for every row in the predict grid. len(predictions) == n_predict_grid_rows. Test passes.

The two outcomes are deterministic. The smoke test cannot be "flaky" — if the row count is off by one, the pipeline is wrong.

For the predict-grid size: smallest is best. Use the smallest predict window that is still an honest predict-time grid. A single horizon-length slice (e.g. one day for a t+24 model) is enough to expose the failure; anything larger only hides it behind volume.

Fixture construction — data/ is the source

The fixture reads from the real data/ source, not from a synthetic generator and not from a checked-in fixture file. The loaders the experiment uses are the loaders the smoke test must exercise. Synthetic fixtures defeat the purpose.

Construction depends on the experiment's source binding (read experiments/NN_*.py to find out which env-dict keys build_learner consumes), but the shape is always the same:

1. Identify the predict-grid time bounds (predict_start, predict_end). For time series, the most recent horizon-equivalent window of the data.
2. Identify the train env. The cleanest choice is all data strictly before predict_start - HORIZON (embargo equal to the forecast horizon). For tabular IID, just exclude the rows in the predict grid.
3. Build two env-dicts:
   • train_env: whatever shape the experiment uses for its fit binding, restricted to data before the embargo.
   • predict_env: the predict-grid description, with no additional history padding (this is the diagnostic property; if you pad, the test passes spuriously).
4. Compute n_predict_grid_rows independently of the prediction — the count comes from the supervised representation of the predict env (not from the prediction itself).
5. Compute y_true from the supervised representation of the predict env (the soft assertion's ground truth).

The fixture must not write derived files to data/holdout/, data/train/, etc. Those are workspace-level artifacts owned by the project's setup script(s); the smoke test fixture is ephemeral. Use tmp_path (the pytest-built-in temporary directory fixture) when the experiment's source binding requires on-disk inputs.

Three common source-binding shapes — the smoke fixture has to match whichever the experiment uses:

Binding shape	Predict env construction	n_predict_grid_rows
Directory of raw files — build_learner binds a data_dir-style var; the loader globs / reads files from it.	Write a tiny temp dir with the time-sliced raw files inside the test (use the tmp_path pytest built-in). Bind it as data_dir.	The row count of the supervised representation of the predict env (e.g. len(build_supervised_frame(predict_dir)) in load-forecasting), known a priori from the slice.
Predict-grid + raw-history sources — the early-mark shape from build-ml-pipeline rule 2: predict_grid plus history_source / weather_source / etc. as separate vars.	Build the in-memory predict_grid value (a list of timestamps, a panel-key grid, …) and the source identifiers. No file write needed.	len(predict_grid).
Materialized (X, y) IID — build_learner binds X and y directly (or a single data env-dict mapping to {"X": ..., "y": ...}).	Hold out a small subset of rows from the materialized X (and the matching y) before fit; train_env gets the rest, predict_env gets the held-out subset.	len(predict_subset).

For the second shape (predict-grid + raw-history sources), the three layers — sources → predict-grid + alignment + mark_as_X → features after (with history as an upstream reference) — are described in build-ml-pipeline § "Common patterns" rule 2, with a full worked example (drawn from this workspace's 01_baseline pipeline) in python-api/references/pre_mark_alignment.md. Read that reference before constructing the predict env for an early-mark pipeline.

IID flat-table problems — what the smoke test still buys you

For pipelines with no cross-row dependencies (per-row math, stateful encoders that learn at fit and apply per-row at predict, no lags / rolling / joins-with-history), the smoke test reduces to "fit on the train subset, predict on the held-out subset, assert len(predictions) == len(predict_subset)".

The diagnostic-by-construction property does not apply — there are no cross-row reaches for the test to break, so the hard assertion will pass on a correctly-built pipeline and on a buggy one. What the smoke test still catches in the IID case:

• Loader bugs that drop or duplicate rows on a smaller input than CV used.
• Shape mismatches between learner.predict(env)'s output and the predict-env row count (e.g. an estimator that returns (N, 2) predictions when the test only checks len(...)).
• Accidental NaN-poisoning when an encoder has never seen a category present in the predict subset (the soft assertion on smoke-MAE-vs-CV-mean catches this; keep it on).

Treat the IID smoke test as a sanity check, not a CV-replacement. The CV-replacement role is what the test plays for cross-row pipelines, where the diagnostic-by- construction property is the load-bearing guarantee.

The standard pytest shape

One test function per smoke test file. The function name mirrors the experiment stem so pytest output is self-explanatory.

"""Smoke test for `experiments/NN_<short_name>.py`."""

# stdlib + numpy first
import pytest

from <pkg> import PROJECT_ROOT
from <pkg>.pipeline import build_learner
# additional imports per the experiment's binding shape

DATA_DIR = PROJECT_ROOT / "data"


@pytest.fixture
def train_predict_envs(tmp_path):
    """Build a (train_env, predict_env, n_predict_grid_rows, y_true) tuple.

    Diagnostic by construction: predict_env carries only the
    rows we want predictions for, with no pre-history padding.
    """
    # ... per-experiment fixture construction ...
    return train_env, predict_env, n_predict_grid_rows, y_true


def test_NN_<short_name>(train_predict_envs):
    """Predict-time replay must produce one prediction per predict-grid row."""
    train_env, predict_env, n_predict_grid_rows, y_true = train_predict_envs

    learner = build_learner()
    learner.fit(train_env)
    predictions = learner.predict(predict_env)

    # HARD: structural correctness.
    assert len(predictions) == n_predict_grid_rows, (
        f"got {len(predictions)} predictions for "
        f"{n_predict_grid_rows} predict-grid rows — pipeline is "
        f"dropping cold-start rows; check `mark_as_X` placement "
        f"and that history-dependent features reference an "
        f"upstream history node, not a per-slice computation."
    )

    # SOFT: predictions are not NaN-poisoned.
    from sklearn.metrics import mean_absolute_error
    smoke_mae = mean_absolute_error(y_true, predictions)
    # CV_MAE_MEAN is hardcoded at the top of the file from
    # `journal/NN_<short_name>.md` § Status.headline. The smoke test
    # uses only the predicting package's API (skrub/sklearn) —
    # no skore import, so it runs anywhere skrub does.
    assert smoke_mae < 3 * CV_MAE_MEAN, (
        f"smoke MAE {smoke_mae:.0f} > 3 × CV mean "
        f"({CV_MAE_MEAN:.0f}) — predictions may be NaN-poisoned."
    )

tmp_path is the pytest built-in for a per-test temporary directory; use it whenever the experiment's source binding requires on-disk inputs.

Failure semantics

A failing smoke test is a pipeline-shape problem, not a metric problem.

• Hard-assertion failure (row count) → the pipeline is broken. Re-enter build-ml-pipeline, audit the X-marker placement and the history-dependent feature steps. Don't tune the model; don't loosen the assertion; don't add a wrapper. Fix the shape.
• Soft-assertion failure (metric way off) → the predictions exist but are garbage on the smoke window. Most common cause: an upstream history node isn't being correctly resolved at predict time, so a lag column is silently NaN. Inspect learner.skb.full_report() and look for nodes whose value at predict time doesn't match what fit time saw.
• Failure blocks done status. iterate-ml-experiment § 4 refuses to flip an experiment to done until the matching smoke test passes. The CV report can land in the skore Project before the smoke test passes (CV is independent of predict-time binding), but the experiment row in JOURNAL.md stays approved until smoke passes.

What this skill does NOT do

• Run pytest. Test execution is the user's call (or CI's).
• Write the design note or the experiment script. Those are iterate-ml-experiment and organize-ml-workspace / build-ml-pipeline.
• Touch the skore Project. The smoke test does not call project.put — it's a pre-flight check, not a metric artifact. CV metrics come from evaluate-ml-pipeline.
• Define what "good metrics" mean. The hard assertion is structural; the soft assertion is a sanity bound, not a performance target. Performance judgment is the user's, per iterate-ml-experiment's rule that the user judges results.

Companion skills

• test-ml-pipeline — the router that dispatched here. Owns layout and pairing.
• build-ml-pipeline — owns the X-marker placement rule the smoke test asserts. Smoke-test failure typically routes back here for a pipeline-shape fix.
• iterate-ml-experiment — owns the iteration loop. Requires the smoke test to pass before an experiment can flip to done.
• evaluate-ml-pipeline — owns CV. The smoke test fills the predict-time-binding gap CV doesn't cover. The soft assertion's CV-mean baseline is hardcoded in the smoke test from the matching design note's Status.headline (which evaluate-ml-pipeline ultimately fills in after the run); the test does not import skore at runtime.
• python-api / python-api — symbol references for the predicting-package APIs the smoke test uses. Consult before naming any imported function in the test body. python-api is not a smoke-test dependency — see the "no skore import" Stop condition above. Cache hits first: check scratch/api/<lib>/<version>/ before WebSearching; cache new findings back there (per python-api Shape 0/3).
• data-science-python-stack — declares pytest as a Tier 1 mandatory dependency for any workspace using this skill.
• python-code-style — must be invoked after writing or editing tests/smoke/test_NN_*.py. Running pixi run ruff check directly without invoking this skill silently drops the NumPyDoc docstring convention the stack expects: ruff's D-rules pass on a one-line summary, but only the skill body teaches the parameter-shape-in-type-slot and the section layout (Parameters / Returns / Notes) the test fixture + test function should use.