GitLab

sklearn compatibility update- renames .feature_importance_ to .featur…

minor typo fixes

* suppprt pandas categorical

* refine logic

* make default=auto

* fix train/valid categorical codes

* add test

* unify set _predictor

* fix tests

* fix warning

* support feature_name=int

* src & callbacks

* lgb.Booster and utils

* cv

* wip lgb.Dataset

* lgb.Dataset

* lgb.Predictor

* lgb.train

* typos

* add flags to facilitate macosx compilation

* fix basic_string template error with clang

* most unfortunate mode of development

* fixup tests

* last test

* roxygen

* roxygen v5.x.x

* add compat.py

* unify types

* range/xrange -> range_

* move argc_ to compat.py; add is_numeric error type

* use simplejson for json

* move json to compat.py

* move pandas to compat.py

* move sklearn to compat.py

* remove unused function

* fix 'unify types'

* argc_ (lambda -> def)

* add pmml to test

* refine pmml.py

* use ~n instead of -n-1

* change map to list comprehension

* fix check

* fix 'use ~n instead of -n-1'

* fix exception

http://research.microsoft.com/en-us/um/people/zhang/INRIA/Publis/Tutorial-Estim/node24.html

* fix typo

* fix hessians to approximate hessians with Gaussian function

* fix ApproximateHessianWithGaussian

* take fabs of gradient

* use atan(1) to calculate pi

* fix pi

* Add L1 objective function

* fix hessians

* update

* Fix to avoid issue #176 for R package installation

* Provide installation step using devtools

Removing the string to decimal conversion for float values

* Add Huber loss

https://en.wikipedia.org/wiki/Huber_loss

* update

* update docs/Parameters.md

* update

We dont need it we aren't doing any computation with
float values. We print out whatever values are
read from the LightGBM_model.txt as a string.

* use handle is not None for _is_constructed

* sort imports; clean code; move FAQ to docs

* finish R's c_api

* clean code

* fix sizeof pointer in 32bit system.

* add predictor class

* add Dataset class

* format code

* add booster

* add type check for expose function

* add a simple callback

* add all callbacks

* finish the basic training logic

* update docs

* add an simple training interface

* add basic test

* adapt the changes in c_api

* add test for Dataset

* add test for custom obj/eval functions

* fix python test

* fix bug in metadata init

* fix R CMD check

* fix reset parameter

* redefine CVBooster

* env.model won't be None

* update env.params

file as input and output a directory with pmml files. Each file can be given as input to
pmml consumers. You can test the files using jpmml-evaluator. Please refer to this the
data mining group's website for the pmml specifcation (http://dmg.org/pmml/pmml-v4-3.html).
Also refer to the README.md file in the pmml directory to see how to test the pmml output
with jpmml-evaluator.