* use gain for sklearn feature_importances_
`gain` is a better feature importance criteria than the currently used `weight`
* added importance_type to class
* fixed test
* white space
* fix variable name
* fix deprecation warning
* fix exp array
* white spaces
* Enable running objectives with 0 GPU.
* Enable 0 GPU for objectives.
* Add doc for GPU objectives.
* Fix some objectives defaulted to running on all GPUs.
* Make C++ unit tests run and pass on Windows
* Fix logic for external memory. The letter ':' is part of drive letter,
so remove the drive letter before splitting on ':'.
* Cosmetic syntax changes to keep MSVC happy.
* Fix lint
* Add Windows guard
* Fix#3342 and h2oai/h2o4gpu#625: Save predictor parameters in model file
This allows pickled models to retain predictor attributes, such as
'predictor' (whether to use CPU or GPU) and 'n_gpu' (number of GPUs
to use). Related: h2oai/h2o4gpu#625Closes#3342.
TODO. Write a test.
* Fix lint
* Do not load GPU predictor into CPU-only XGBoost
* Add a test for pickling GPU predictors
* Make sample data big enough to pass multi GPU test
* Update test_gpu_predictor.cu
* Fix#3747: Add coef_ and intercept_ as properties of sklearn wrapper
Scikit-learn expects linear learners to expose `coef_` and `intercept_`
as properties.
Closes#3747.
* Fix lint
* Clean up logic for converting tree_method to updater sequence
* Use C++11 enum class for extra safety
Compiler will give warnings if switch statements don't handle all
possible values of C++11 enum class.
Also allow enum class to be used as DMLC parameter.
* Fix compiler error + lint
* Address reviewer comment
* Better docstring for DECLARE_FIELD_ENUM_CLASS
* Fix lint
* Add C++ test to see if tree_method is recognized
* Fix clang-tidy error
* Add test_learner.h to R package
* Update comments
* Fix lint error
* add back train method but mark as deprecated
* add back train method but mark as deprecated
* add back train method but mark as deprecated
* add back train method but mark as deprecated
* fix scalastyle error
* fix scalastyle error
* fix scalastyle error
* fix scalastyle error
* documenting tracker
* Make it a separate note
The `save_model()` and `load_model()` method only saves the part of the model
that's common to all language interfaces and do not preserve Python-specific
attributes, such as `feature_names`. More crucially, label encoder is not
preserved either; this is needed for the scikit-learn wrapper, since you may
have string labels.
Fix: Explicitly recommend pickling as the way to save scikit-learn model
objects.
* Multi-GPU support in GPUPredictor.
- GPUPredictor is multi-GPU
- removed DeviceMatrix, as it has been made obsolete by using HostDeviceVector in DMatrix
* Replaced pointers with spans in GPUPredictor.
* Added a multi-GPU predictor test.
* Fix multi-gpu test.
* Fix n_rows < n_gpus.
* Reinitialize shards when GPUSet is changed.
* Tests range of data.
* Remove commented code.
* Remove commented code.
* Enable auto-locking of issues closed long ago
Issues that were closed more than 90 days ago will be locked automatically so
that no additional comments would be allowed. We will use a bot to do
this: https://probot.github.io/apps/lock/
Background: As a maintainer, I often see people leaving comments to old issue
posts that were closed long ago. Those comments are hard to discover and assist
with, since they get buried under list of other active issues.
With the change, users who want to follow up with an old issue would be asked
to file a new issue.
* Exempt `feature-request` from auto locking
* Disable comment to avoid triggering notification
* add back train method but mark as deprecated
* add back train method but mark as deprecated
* add back train method but mark as deprecated
* add back train method but mark as deprecated
* fix scalastyle error
* fix scalastyle error
* fix scalastyle error
* fix scalastyle error
* temp
* add method for classifier and regressor
* update tutorial
* address the comments
* update
A privilege escalation vulnerability (CVE-2017-15288) has been
identified in the Scala compilation daemon. See
https://nvd.nist.gov/vuln/detail/CVE-2017-15288
Fix: Upgrade Scala to 2.11.12.
**Symptom** Apple Clang's implementation of `std::shuffle` expects doesn't work
correctly when it is run with the random bit generator for R package:
```cpp
CustomGlobalRandomEngine::result_type
CustomGlobalRandomEngine::operator()() {
return static_cast<result_type>(
std::floor(unif_rand() * CustomGlobalRandomEngine::max()));
}
```
Minimial reproduction of failure (compile using Apple Clang 10.0):
```cpp
std::vector<int> feature_set(100);
std::iota(feature_set.begin(), feature_set.end(), 0);
// initialize with 0, 1, 2, 3, ..., 99
std::shuffle(feature_set.begin(), feature_set.end(), common::GlobalRandom());
// This returns 0, 1, 2, ..., 99, so content didn't get shuffled at all!!!
```
Note that this bug is platform-dependent; it does not appear when GCC or
upstream LLVM Clang is used.
**Diagnosis** Apple Clang's `std::shuffle` expects 32-bit integer
inputs, whereas `CustomGlobalRandomEngine::operator()` produces 64-bit
integers.
**Fix** Have `CustomGlobalRandomEngine::operator()` produce 32-bit integers.
Closes#3523.
