New functionality
- We've finally implemented MVS sampling for GPU training. Switched default bootstrap algorithm to MVS for RMSE loss function while training on GPU
- Implemented near-zero cost model deserialization from memory blob. Currently, if your model doesn't use categorical features CTR counters and text features you can deserialize model from, for example, memory-mapped file.
- Added ability to load trained models from binary string or file-like stream. To load model from bytes string use
load_model(blob=b'....'), to deserialize form file-like stream useload_model(stream=gzip.open('model.cbm.gz', 'rb')) - Fixed auto-learning rate estimation params for GPU
- Supported beta parameter for QuerySoftMax function on CPU and GPU
New losses and metrics
- New loss function
RMSEWithUncertainty- it allows to estimate data uncertainty for trained regression models. The trained model will give you a two-element vector for each object with the first element as regression model prediction and the second element as an estimation of data uncertainty for that prediction.
Speedups
- Major speedups for CPU training: kdd98 -9%, higgs -18%, msrank -28%. We would like to recognize Intel software engineering team’s contributions to Catboost project. This was mutually beneficial activity, and we look forward to continuing joint cooperation.
Bugfixes:
- Fixed CatBoost model export as Python code
- Fixed AUC metric creation
- Add text features to
model.feature_names_. Issue #1314 - Allow models, trained on datasets with NaN values (Min treatment) and without NaNs in
model_sum()or as the base model ininit_model=. Issue #1271