Releases: mindsdb/lightwood
Releases · mindsdb/lightwood
Release 22.7.2.0
Release 22.6.1.2
Release v22.5.1.0
Release v22.4.1.0
Lightwood 22.4.1.0 changelog:
Features: -
Bug fixes:
Other
- #850 - Restore Windows CI
Full Changelog:
Release 22.2.1.0
Lightwood 22.2.1.0 changelog:
Features:
Bug fixes: -
Other:
- Remove
anomaly_error_rate
arg in favor offixed_confidence
#825
Full Changelog: v22.1.4.0...v22.2.1.0
Release 22.1.4.0
Lightwood 22.1.4.0 changelog:
Moving forward, our release versioning schema will follow this format:
[year's last two digits].[month].[week].[patch]
(note: other MindsDB repositories will also switch to this)
Features:
- ConfStats block (#800): provides calibration insights for the lightwood predictor
- Temperature scaling block (#795, experimental & non-default): alternative to ICP block for confidence estimation
- Improved documentation pages (#806)
- Replaced default encoder for time series forecasting tasks (from RNN to simple MA features, #805)
- Explicit detrend and deseasonalize options for sktime mixer (#812)
Bug fixes:
- Updated
update model
tutorial (#774) - Fix forecast horizon lower bound (#801)
- Handle empty input when predicting (#811)
Other
- Rename
nr_predictions
parameter tohorizon
(#803) - Set
allow_incomplete_history
toTrue
by default (#818)
Full Changelog
Release 1.9.0
Release 1.8.0
Lightwood 1.8.0 changelog:
Features:
- SkTime mixer 2.0 (#758, #767)
- Improve time aim feature (#763)
- Improved OHE and binary encoders, standardized a few more (#755, #785)
- Streamlined predictor.adjust signature (#762)
- Add precision, recall, f1 (#776)
Bug fixes:
- Do not drop single-group-by column (#761, #756)
- OH and Binary Encoders weighting fix (#769)
- LGBM array mixer does not modify the datasource (#771)
- Fixes missing torchvision import (#784)
Other
Release 1.7.0
Lightwood 1.7.0 changelog:
Features:
- Simplified type mapping in Json AI (#724)
- Setter for neural mixer # epochs (#737)
- Improved
nan
handling (#720) - Drop columns with no information (#736)
- LightGBM mixer supports weights (#749)
- Improved OneHot and Binary encoders' logic around weights (#749)
- New accuracy function lookup hierarchy (#754)
- Better warning logs when
nan
orinf
values are encountered (#754)
Bug fixes:
- Fixed LightGBM error on CPU (#726)
- Cast TS group by values to string to avoid TypeError (#727)
- Check target values when transforming time series if task requires them (#747)
- Streamline encode/decode in
TsArrayNumericEncoder
(#748) target_weights
argument is now used properly (#749)- Use custom R2 accuracy to account for edge cases (#754)
- Fixed target dropping behavior (#754)
Other
Release 1.6.0
Lightwood 1.6.0 changelog:
Many thanks to our community contributors for this release!
@MichaelLantz @mrandri19 @ongspxm @vaithak
Features:
- SHAP analysis block (#679, @mrandri19)
- Disable
GlobalFeatureImportance
when we have too many columns (#681, @ongspxm; #698) - Added cleaner support for file path data types (image, audio, video) (#675)
- Add
partial_fit()
tosktime
mixer (#689) - Add
ModeEnsemble
(#692, @mrandri19) - Add weighted
MeanEnsembler
(#680, @vaithak)
Bug fixes:
- Normalized column importance range (#690)
- Fix ensemble supports_proba in calibrate.py (#694, @mrandri19)
- Remove self-referential import (#696)
- Make a integration test for time_aim (#685, @MichaelLantz)
- Fix for various datasets (#700)
Other
- Improve logging for analysis blocks (#677; @MichaelLantz)
- Custom block example:
LabelEncoder
(#663) - Implement ShapleyValues analysis (#679)
- Move array/TS normalizers to generic helpers (#702)