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+
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+/windows/LightGBM.VC.db

CMakeLists.txt

+cmake_minimum_required(VERSION 2.8)
+
+PROJECT(LightGBM)
+
+OPTION(USE_MPI "MPI based parallel learning" OFF)
+
+if(USE_MPI)
+  find_package(MPI REQUIRED)
+  ADD_DEFINITIONS(-DUSE_MPI)
+  MESSAGE(${MPI_LIBRARIES})
+  MESSAGE(${MPI_CXX_LIBRARIES})
+else()
+  ADD_DEFINITIONS(-DUSE_SOCKET)
+endif()
+
+if(UNIX)
+  SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wall -fopenmp  -pthread -O2 -std=c++11")
+endif()
+
+SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wall -fopenmp -O2 -std=c++11")
+
+SET(LightGBM_HEADER_DIR ${PROJECT_SOURCE_DIR}/include)
+SET(EXECUTABLE_OUTPUT_PATH ${PROJECT_SOURCE_DIR})
+
+ADD_SUBDIRECTORY(src)
+

README.md

+LightGBM, Light Gradient Boosting
+==========
+
+
+
+
+LightGBM is a gradient boosting framework that using histogram based tree learning algorithm. It can outperform existing boosting tools on both learning speed and accuracy. Our [experiments](https://github.com/Microsoft/LightGBM/wiki/Experiments#comparison-experiment) shows it is about 6x faster than xgboost with much better prediction accuracy.
+
+LightGBM can be run on multiple machines, Our [experiments](https://github.com/Microsoft/LightGBM/wiki/Experiments#parallel-experiment) shows it can perform linear speed up in parallel learning. 
+
+* [**Wiki**](https://github.com/Microsoft/LightGBM/wiki)
+* [**Installation**](https://github.com/Microsoft/LightGBM/wiki/Installation-Guide) 
+* [**Quick Start**](https://github.com/Microsoft/LightGBM/wiki/Quick-Start)
+* [**Feature Highlight**](https://github.com/Microsoft/LightGBM/wiki/Feature-Highlight) 
+* [**Parallel Learning Guide**](https://github.com/Microsoft/LightGBM/wiki/Parallel-Learning-Guide) 
+* [**Parameters**](https://github.com/Microsoft/LightGBM/wiki/Parameters) 
+* [**Examples**](https://github.com/Microsoft/LightGBM/tree/master/examples)
+
+
+
+
+
+

examples/README.md

+Examples
+=====================
+
+You can learn how to use LightGBM by these examples. If you have any questions, please refer to our [wiki](https://github.com/Microsoft/LightGBM/wiki). 
+

examples/binary_classification/README.md

+Binary Classification Example
+=====================
+Here is an example for LightGBM to run binary classification task.
+
+***You should copy executable file to this folder first.***
+
+#### Training
+
+For windows, by running following command in this folder:
+```
+LightGBM.exe config=train.conf
+```
+
+
+For linux, by running following command in this folder:
+```
+./LightGBM config=train.conf
+```
+
+#### Prediction
+
+You should finish training first.
+
+For windows, by running following command in this folder:
+```
+LightGBM.exe config=predict.conf
+```
+
+For linux, by running following command in this folder:
+```
+./LightGBM config=predict.conf
+```
+
+

examples/binary_classification/binary.test.weight

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examples/binary_classification/predict.conf

+
+task = predict
+
+data = binary.test
+
+input_model= LightGBM_model.txt
+

examples/binary_classification/train.conf

+# task type, support train and predict
+task = train
+
+# boosting type, support gbdt for now, alias: boosting, boost
+boosting_type = gbdt
+
+# application type, support following application
+# regression , regression task
+# binary , binary classification task
+# lambdarank , lambdarank task
+# alias: application, app
+objective = binary
+
+# eval metrics, support multi metric, delimite by ',' , support following metrics
+# l1 
+# l2 , default metric for regression
+# ndcg , default metric for lambdarank
+# auc 
+# binary_logloss , default metric for binary
+# binary_error
+metric = binary_logloss,auc
+
+# frequence for metric output
+metric_freq = 1
+
+# true if need output metric for training data, alias: tranining_metric, train_metric
+is_training_metric = true
+
+# number of bins for feature bucket, 255 is a recommend setting, it can save memories, and also has good accuracy. 
+max_bin = 255
+
+# training data
+# if exsting weight file, should name to "binary.train.weight"
+# alias: train_data, train
+data = binary.train
+
+# validation data, support multi validation data, separated by ','
+# if exsting weight file, should name to "binary.test.weight"
+# alias: valid, test, test_data, 
+valid_data = binary.test
+
+# number of trees(iterations), alias: num_tree, num_iteration, num_iterations, num_round, num_rounds
+num_trees = 100
+
+# shrinkage rate , alias: shrinkage_rate
+learning_rate = 0.1
+
+# number of leaves for one tree, alias: num_leaf
+num_leaves = 63
+
+# type of tree learner, support following types:
+# serial , single machine version
+# feature , use feature parallel to train
+# data , use data parallel to train
+# voting , use voting based parallel to train
+# alias: tree
+tree_learner = serial
+
+# number of threads for multi-threading. One thread will use one CPU, defalut is setted to #cpu. 
+# num_threads = 8
+
+# feature sub-sample, will random select 80% feature to train on each iteration 
+# alias: sub_feature
+feature_fraction = 0.8
+
+# Support bagging (data sub-sample), will perform bagging every 5 iterations
+bagging_freq = 5
+
+# Bagging farction, will random select 80% data on bagging
+# alias: sub_row
+bagging_fraction = 0.8
+
+# minimal number data for one leaf, use this to deal with over-fit
+# alias : min_data_per_leaf, min_data
+min_data_in_leaf = 50
+
+# minimal sum hessians for one leaf, use this to deal with over-fit
+min_sum_hessian_in_leaf = 5.0
+
+# save memory and faster speed for sparse feature, alias: is_sparse
+is_enable_sparse = true
+
+# when data is bigger than memory size, set this to true. otherwise set false will have faster speed
+# alias: two_round_loading, two_round
+use_two_round_loading = false
+
+# true if need to save data to binary file and application will auto load data from binary file next time
+# alias: is_save_binary, save_binary
+is_save_binary_file = false
+
+# output model file
+output_model = LightGBM_model.txt
+
+# support continuous train from trained gbdt model
+# input_model= trained_model.txt
+
+# output prediction file for predict task
+# output_result= prediction.txt
+
+# support continuous train from initial score file
+# input_init_score= init_score.txt
+
+
+# number of machines in parallel training, alias: num_machine
+num_machines = 1
+
+# local listening port in parallel training, alias: local_port
+local_listen_port = 12400
+
+# machines list file for parallel training, alias: mlist
+machine_list_file = mlist.txt

examples/lambdarank/README.md

+LambdaRank Example
+=====================
+Here is an example for LightGBM to run lambdarank task.
+
+***You should copy executable file to this folder first.***
+
+#### Training
+
+For windows, by running following command in this folder:
+```
+LightGBM.exe config=train.conf
+```
+
+
+For linux, by running following command in this folder:
+```
+./LightGBM config=train.conf
+```
+
+#### Prediction
+
+You should finish training first.
+
+For windows, by running following command in this folder:
+```
+LightGBM.exe config=predict.conf
+```
+
+For linux, by running following command in this folder:
+```
+./LightGBM config=predict.conf
+```
+

examples/lambdarank/predict.conf

+
+task = predict
+
+data = rank.test
+
+input_model= LightGBM_model.txt
+

examples/lambdarank/rank.test.query

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+20
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+17
+16
+15
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+15
+14
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+17
+15
+14
+16
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+19
+16
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+18
+11
+13
+14
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examples/lambdarank/train.conf

+# task type, support train and predict
+task = train
+
+# boosting type, support gbdt for now, alias: boosting, boost
+boosting_type = gbdt
+
+# application type, support following application
+# regression , regression task
+# binary , binary classification task
+# lambdarank , lambdarank task
+# alias: application, app
+objective = lambdarank
+
+# eval metrics, support multi metric, delimite by ',' , support following metrics
+# l1 
+# l2 , default metric for regression
+# ndcg , default metric for lambdarank
+# auc 
+# binary_logloss , default metric for binary
+# binary_error
+metric = ndcg
+
+# evaluation position for ndcg metric, alias : ndcg_at
+ndcg_eval_at = 1,3,5
+
+# frequence for metric output
+metric_freq = 1
+
+# true if need output metric for training data, alias: tranining_metric, train_metric
+is_training_metric = true
+
+# number of bins for feature bucket, 255 is a recommend setting, it can save memories, and also has good accuracy. 
+max_bin = 255
+
+# training data
+# if exsting weight file, should name to "rank.train.weight"
+# if exsting query file, should name to "rank.train.query"
+# alias: train_data, train
+data = rank.train
+
+# validation data, support multi validation data, separated by ','
+# if exsting weight file, should name to "rank.test.weight"
+# if exsting query file, should name to "rank.test.query"
+# alias: valid, test, test_data, 
+valid_data = rank.test
+
+# number of trees(iterations), alias: num_tree, num_iteration, num_iterations, num_round, num_rounds
+num_trees = 100
+
+# shrinkage rate , alias: shrinkage_rate
+learning_rate = 0.1
+
+# number of leaves for one tree, alias: num_leaf
+num_leaves = 31
+
+# type of tree learner, support following types:
+# serial , single machine version
+# feature , use feature parallel to train
+# data , use data parallel to train
+# voting , use voting based parallel to train
+# alias: tree
+tree_learner = serial
+
+# number of threads for multi-threading. One thread will use one CPU, defalut is setted to #cpu. 
+# num_threads = 8
+
+# feature sub-sample, will random select 80% feature to train on each iteration 
+# alias: sub_feature
+feature_fraction = 1.0
+
+# Support bagging (data sub-sample), will perform bagging every 5 iterations
+bagging_freq = 1
+
+# Bagging farction, will random select 80% data on bagging
+# alias: sub_row
+bagging_fraction = 0.9
+
+# minimal number data for one leaf, use this to deal with over-fit
+# alias : min_data_per_leaf, min_data
+min_data_in_leaf = 50
+
+# minimal sum hessians for one leaf, use this to deal with over-fit
+min_sum_hessian_in_leaf = 5.0
+
+# save memory and faster speed for sparse feature, alias: is_sparse
+is_enable_sparse = true
+
+# when data is bigger than memory size, set this to true. otherwise set false will have faster speed
+# alias: two_round_loading, two_round
+use_two_round_loading = false
+
+# true if need to save data to binary file and application will auto load data from binary file next time
+# alias: is_save_binary, save_binary
+is_save_binary_file = false
+
+# output model file
+output_model = LightGBM_model.txt
+
+# support continuous train from trained gbdt model
+# input_model= trained_model.txt
+
+# output prediction file for predict task
+# output_result= prediction.txt
+
+# support continuous train from initial score file
+# input_init_score= init_score.txt
+
+
+# number of machines in parallel training, alias: num_machine
+num_machines = 1
+
+# local listening port in parallel training, alias: local_port
+local_listen_port = 12400
+
+# machines list file for parallel training, alias: mlist
+machine_list_file = mlist.txt

examples/parallel_learning/README.md

+Parallel Learning Example
+=====================
+Here is an example for LightGBM to perform parallel learning for 2 machines.
+
+1. Edit mlist.txt , write the ip of these 2 machines that you want to run application on. 
+
+  ```
+  machine1_ip 12400
+  machine2_ip 12400
+  ```
+  
+2. Copy this folder and executable file to these 2 machines that you want to run application on.
+3. Run command in this folder on both 2 machines:
+
+  For windows:```LightGBM.exe config=train.conf```
+  
+  For linux:```./LightGBM config=train.conf```
+
+This parallel learning example is based on socket. LightGBM also support parallel learning based on mpi.
+
+For more details about the usage of parallel learning, please refer to [this]().