catboost 0.5 on Python PyPI

Major Features And Improvements

In Python we added a new method eval_metrics: now it's possible for a given model to calculate specified metric values for each iteration on specified dataset.
One command-line binary for CPU and GPU: in CatBoost you can switch between CPU and GPU training by changing single parameter value task-type CPU or GPU (task_type 'CPU', 'GPU' in python bindings). Windows build still contains two binaries.
We have speed up the training up to 30% for datasets with a lot of objects.
Up to 10% speed-up of GPU implementation on Pascal cards

Cmdline:

Training parameter gradient-iterations renamed to leaf-estimation-iterations.
border option removed. If you want to specify border for binary classification mode you need to specify it in the following way: loss-function Logloss:Border=0.5
CTR parameters are changed:
- Removed priors, per-feature-priors, ctr-binarization;
- Added simple-ctr, combintations-ctr, per-feature-ctr;
  More details will be published in our documentation.

Python and R:

Training parameter gradient_iterations renamed to leaf_estimation_iterations.
border option removed. If you want to specify border for binary classification mode you need to specify it in the following way: loss_function='Logloss:Border=0.5'
CTR parameters are changed:
- Removed priors, per_feature_priors, ctr_binarization;
- Added simple_ctr, combintations_ctr, per_feature_ctr;
  More details will be published in our documentation.

As usual we are grateful to all who filed issues, asked and answered questions.