v0.8.11

• topological_order no longer nondeterministic: the Kahn's-algorithm
  helpers in backend/ml_pipeline/_execution/graph_utils.py were seeded from
  a set of node ids, whose iteration order depends on insertion history —
  so topological_order(topological_order(nodes)) could return a different
  (still valid) order than topological_order(nodes), breaking idempotency
  and making execution order depend on how the node list happened to be
  built. The helpers now take a list[str] in deterministic order:
  topological_order passes input-list order (deduped via
  dict.fromkeys), and _collect_ancestors passes sorted() BFS results.
  Among simultaneously ready nodes the input-list order is preserved, so the
  result is deterministic and idempotent.

• SonarCloud PR analysis flagged Vite build output: all 46 open issues on
  PR #146 were in static/ml_canvas/assets/*.js — minified frontend build
  artifacts that SonarCloud's PR analysis picked up because the files changed
  in the PR. static/ml_canvas/** is now in sonar.exclusions in
  sonar-project.properties, so build output is no longer analyzed (the
  scanner job in .github/workflows/pr_check.yml reads this file directly).

• Drop-Rows canvas settings ignored (OC-13): the Drop Rows node's UI
  stores a 0–100% "drop rows missing more than X%" slider, but the converter
  sent keys the backend never read, so every canvas run silently ran as
  "drop any missing". DropMissingRows now accepts a missing_threshold
  percentage param (pandas + polars parity, mirroring DropMissingColumns):
  rows with more than X% of the subset missing are dropped, and the
  converter maps the checkbox / null / 0% to how="any". The
  DropMissingRowsArtifact gains the new field.

• Iterative Imputer canvas estimator choices ignored (OC-14): the
  Imputation node's UI emits lowercase estimator aliases (decision_tree,
  extra_trees, knn, bayesian_ridge) and the converter forwards them
  verbatim, but _build_iterative_estimator in
  preprocessing/imputation/_common.py matched only the exact documented
  strings (DecisionTree, ExtraTrees, KNeighbors) — so every canvas run
  silently fell back to BayesianRidge regardless of the user's choice. The
  alias is now normalized (lowercased, non-alphanumerics stripped) before
  dispatch, so both the UI values and the documented aliases resolve to the
  same regressor; unknown names still fall back to BayesianRidge. Added
  JSON-driven alias cases for the four UI values.

• MinMax/Robust scaler range controls ignored on canvas (OC-15): the
  Scaling node's UI stores the ranges as scalar fields
  (feature_range_min/feature_range_max, quantile_range_min/
  quantile_range_max), but the converter forwarded them verbatim while the
  backend reads tuple keys (feature_range/quantile_range) — so every
  canvas run silently used the defaults (0/1 and 25/75). The converter's
  scale_numeric_features branch now assembles the tuple keys from the
  scalar fields (with the same defaults); no backend change was needed.
  Added 4 vitest cases to pipelineConverter.test.ts.

• Alias Replacement punctuation mode was a silent no-op (OC-19): the
  Standardize Values node's UI offers a punctuation mode ("Removes common
  punctuation characters from text"), but the backend applier in
  preprocessing/cleaning/alias.py only handled the alias-mapping modes —
  punctuation resolved to an empty mapping, so the node did nothing. Both
  engine paths now strip string.punctuation only (case and spaces preserved,
  matching the UI wording; NaN passes through unchanged). Added JSON-driven
  cases plus a pandas/polars NaN-passthrough parity test.

• Value Replacement UI help text contradicted actual behavior (OC-20): the
  Standardize Values node's UI help text promised "If empty, applies to all
  compatible columns," but the backend no-ops on an empty columns list — the
  intended repo convention (user_picked_no_columns: unchecked boxes mean "do
  nothing for this node"). The help text now reads "Select columns to apply
  replacements to. If no columns are selected, this node does nothing." No
  backend change: the empty-columns no-op was already correct. Added a
  contract-locking test (test_apply_empty_columns_is_noop, pandas/polars)
  asserting empty columns + a mapping leaves data unchanged.

• select_from_model's max_features was Python-only (OC-53): the
  backend reads config.get("max_features") and passes it to sklearn's
  SelectFromModel, but the Feature Selection node's UI rendered only a
  threshold field for the select_from_model method — so the feature-count
  cap was unreachable from the canvas. The UI now offers an optional
  "Max Features" numeric input for that method (empty = no cap, matching the
  backend's None default). No converter change: the feature_selection
  branch already forwards node.data unchanged. Added 2 vitest cases to
  pipelineConverter.test.ts.

• Binning's "Precision (Decimals)" was UI-unreachable (OC-61): the
  backend (bucketing.py) reads config.get("precision", 3) and the canvas
  Binning node rendered a "Precision (Decimals)" input, but
  pipelineConverter.ts listed the GeneralBinning params explicitly and
  omitted precision, so the value was silently dropped. The converter now
  forwards precision (omitted when unset, matching the backend's default of
  3). Added 2 vitest cases to pipelineConverter.test.ts.

• CalibratedClassifierCV base estimator discarded during tuning (OC-66):
  the tuning engine builds the meta-estimator from
  model_calculator.default_params, which hardcoded
  estimator=LogisticRegression — so the user's base_estimator selection
  was silently ignored whenever the node was tuned (it only took effect in
  plain fit). CalibratedClassifierCalculator now routes the selection
  through the structural-tuning hook (same mechanism as the ensemble
  calculators): prepare_tuning_params captures base_estimator (flat or
  nested params shape) and default_params resolves it via the
  BASE_ESTIMATORS factory, with a warn-and-fallback to
  logistic_regression for unknown keys. No backend change: both the
  fixed-run and tuned paths already call prepare_tuning_params and exclude
  STRUCTURAL_TUNING_KEYS from the search space. Added unit tests
  (capture, resolution, fallback) and an integration test asserting the tuned
  pipeline fits a RandomForestClassifier inside CalibratedClassifierCV.

• KNN/Iterative imputers crashed on all-missing fitted columns (OC-16):
  sklearn's KNNImputer/IterativeImputer silently drop all-missing columns
  from transform() output when the column was all-missing at fit time,
  desyncing the artifact's columns list from the imputer's width and
  crashing _sklearn_transform_subset (IndexError in the polars branch,
  ValueError in the pandas branch). Both calculators now call the new
  drop_all_missing_columns helper in preprocessing/imputation/_common.py
  during fit: all-missing columns are dropped from the fit matrix (with a
  warning naming them) so the artifact stays in lockstep with the imputer's
  width, and fit returns an empty artifact when every configured column is
  all-missing (appliers pass that through as a no-op). Added 5 regression
  tests (parametrized over KNN + Iterative, pandas + polars).

• SimpleImputer polars mean/median crashed on all-null columns (OC-17):
  fitting a SimpleImputer with mean/median on an entirely-null column
  stored fill_values[col] = None in the polars path (polars mean()/
  median() over an all-null column returns None), and _apply_polars
  then called pl.col(col).fill_null(None) — which raises ValueError: must specify either a fill value or strategy. The pandas path already skipped
  None fills, so the engines diverged (pandas left the column all-null;
  polars crashed). _apply_polars now passes the column through unchanged
  when its fill value is None, mirroring _apply_pandas. Added 3 regression
  tests (polars no-crash for mean and median, plus a pandas/polars
  engine-parity test).

• Engine trusted config.nodes list order without verifying topological
  sort (OC-69): both predict_schemas and the engine's node loop iterated
  config.nodes as-is, but validate_no_cycles() only detects cycles — it
  never restores order. The canvas converter's BFS can emit an
  acyclic-but-misordered list (a merge node is enqueued when any parent is
  dequeued, not all), so a diamond merge fed by unequal-depth branches failed
  with a cryptic "Artifact not found" error and silent None schema
  degradation. A new public topological_order() in graph_utils.py (reusing
  the existing Kahn's-algorithm helpers) now re-sorts the node list at both
  consumer sites. Added 5 regression tests including a full engine e2e run of
  a misordered diamond.

• Multiclass splits missing a class emitted binary-only metrics + null
  curve points (OC-35): three places decided binary-vs-multiclass from the
  labels present in y_true instead of the model's trained label set, so a
  3-class model evaluated on a split containing only two classes was
  misclassified as binary — it gained unweighted precision/recall/f1
  keys, lost a computable log_loss (sklearn raised "2 vs 3. Please provide
  labels"), and emitted ROC/PR curve points with null coordinates (NaN from
  roc_curve on the absent class's all-zero one-vs-rest target). The binary
  gate in _add_binary_unweighted_metrics now resolves model.classes_ and
  is binary iff len(classes_) == 2 (falling back to the unique-label count
  only when the model exposes no classes_); log_loss is now called with
  labels=classes (the full trained label set); and the per-class curve loop
  in evaluate_classification_model skips any class absent from the split.
  Added 3 regression tests.