Nixtla/mlforecast - CodSpeed

mlforecast

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build(deps): bump cryptography from 48.0.1 to 50.0.0 Bumps [cryptography](https://github.com/pyca/cryptography) from 48.0.1 to 50.0.0. - [Changelog](https://github.com/pyca/cryptography/blob/main/CHANGELOG.rst) - [Commits](https://github.com/pyca/cryptography/compare/48.0.1...50.0.0) --- updated-dependencies: - dependency-name: cryptography dependency-version: 50.0.0 dependency-type: indirect ... Signed-off-by: dependabot[bot] <support@github.com>

dependabot/uv/cryptography-50.0.0

12 days ago

no-mistakes(review): Reject duplicate rows in pooled TimeSeries.update timestamp check

fix/tier-a-settings

14 days ago

no-mistakes(document): Format new test hunks with pinned ruff 0.15.18

refactor/lag-target-transform-scope

14 days ago

no-mistakes(document): Wrap over-long line in new snapshot restore test

refactor/pooled-snapshot-views

14 days ago

test: build the benchmark statics without the 1k-series panel `statics` derived the column names from the `series` fixture, so the many-series benchmarks also built the unrelated 1.25M-row panel. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01C9961UnEfaPugEEdjHLAoB

perf/vectorize-grouped-array

18 days ago

fix: train LightGBMCV boosters on features_order_, harden categorical_feature carry-over - build the lgb.Dataset from ts.features_order_ so the CV boosters see the same features that predict and MLForecast.fit use (id column as a static feature, groupby lag transforms, positional categorical_feature) - warn when params carries a categorical_feature alias, since LightGBM ignores it once the Dataset is constructed - from_cv tolerates CV objects pickled before categorical_feature existed and copies the list instead of sharing it - _LGBMRegressor.fit takes categorical_feature as an explicit keyword Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01K3e3v5YVqjboL2Cw4C14py

lgbcv-categorical

18 days ago

feat: categorical_feature in LightGBMCV, carried over by MLForecast.from_cv LightGBMCV builds the lgb.Dataset before the boosters see params, so categorical_feature in params was silently ignored. Add it as an argument of setup/fit forwarded to lgb.Dataset, and have from_cv return an LGBMRegressor subclass that keeps it as an attribute (outside get_params, preserved through __sklearn_clone__) and applies it in fit. Requires scikit-learn>=1.3. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01UTU4n15Z3py2kzx61nKZ9i

lgbcv-categorical

18 days ago

build(deps): bump anyio from 4.12.1 to 4.14.2 Bumps [anyio](https://github.com/agronholm/anyio) from 4.12.1 to 4.14.2. - [Release notes](https://github.com/agronholm/anyio/releases) - [Commits](https://github.com/agronholm/anyio/compare/4.12.1...4.14.2) --- updated-dependencies: - dependency-name: anyio dependency-version: 4.14.2 dependency-type: indirect ... Signed-off-by: dependabot[bot] <support@github.com>

dependabot/uv/anyio-4.14.2

18 days ago

Latest Branches

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build(deps): bump cryptography from 48.0.1 to 50.0.0#746

12 days ago

26fd21e

dependabot/uv/cryptography-50.0.0

0%

fix: stop MLForecast settings from being silently dropped or overridden#745

14 days ago

3af40fa

fix/tier-a-settings

0%

refactor: deduplicate scope setup in lag transforms and fitted take in target transforms#744

14 days ago

d1e26c4

refactor/lag-target-transform-scope

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