Lightwood

Features:
* Simpler and better Json AI 826
* Compute & log time per phase 828

Bug fixes: -

Other:
* Remove `anomaly_error_rate` arg in favor of `fixed_confidence` 825

Full Changelog: https://github.com/mindsdb/lightwood/compare/v22.1.4.0...v22.2.1.0

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 to `horizon` (803)
* Set `allow_incomplete_history` to `True` by default (818)

Full Changelog
https://github.com/mindsdb/lightwood/compare/v1.9.0...v22.1.4.0

Features:
* Improved T+N forecast bounds (788)
* Optimized classifier ICP block for confidence estimation (798)

Bug fixes:
* Fixed initialization issues in confidence normalizer (788)
* Fixed no analysis mode (+ parameter to specify this in a problem definition, 791)
* Fixed temporal delta estimation for ungrouped series (792)

Other
* Add original query index column in output (used internally in MindsDB, 794)
* Streamlined `explain()` arg passing 797

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
* Make image encoder optional (778)
* Revamp notebooks test docs (764)

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` or `inf` 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
* Update README.md example (731)
* Separate branch for docs (740)
* Docs for image and audio encoders; LightGBM and LinearRegression mixers (721, 722)

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()` to `sktime` 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)