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Commit dc57e974 authored by Nikita Titov's avatar Nikita Titov Committed by Guolin Ke
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[docs] generate parameters description from config file. Stage 2 (#1413) 

* fixed gpu test according new config file

* refine Learning Control Parameters section

* added missed check for length consistency of valid and valid init scores

* added missed aliases for core params
parent a39c848e
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Showing with 105 additions and 90 deletions
.travis/test.sh
View file @ dc57e974
......@@ -86,7 +86,7 @@ fi
if [[ ${TASK} == "gpu" ]]; then
conda install --yes -c conda-forge boost=1.63.0
if [[ ${METHOD} == "pip" ]]; then
sed -i 's/const std::string kDefaultDevice = "cpu";/const std::string kDefaultDevice = "gpu";/' ../include/LightGBM/config.h
sed -i 's/std::string device_type = "cpu";/std::string device_type = "gpu";/' ../include/LightGBM/config.h
cd $TRAVIS_BUILD_DIR/python-package && python setup.py sdist || exit -1
cd $TRAVIS_BUILD_DIR/python-package/dist && pip install lightgbm-$LGB_VER.tar.gz -v --install-option=--gpu --install-option="--boost-root=$HOME/miniconda/envs/test-env/" --install-option="--opencl-include-dir=$AMDAPPSDK/include/" || exit -1
cd $TRAVIS_BUILD_DIR && pytest tests/python_package_test || exit -1
......@@ -103,7 +103,7 @@ if [[ ${TASK} == "mpi" ]]; then
cmake -DUSE_MPI=ON ..
elif [[ ${TASK} == "gpu" ]]; then
cmake -DUSE_GPU=ON -DBOOST_ROOT="$HOME/miniconda/envs/test-env/" -DOpenCL_INCLUDE_DIR=$AMDAPPSDK/include/ ..
sed -i 's/const std::string kDefaultDevice = "cpu";/const std::string kDefaultDevice = "gpu";/' ../include/LightGBM/config.h
sed -i 's/std::string device_type = "cpu";/std::string device_type = "gpu";/' ../include/LightGBM/config.h
else
cmake ..
fi
......
include/LightGBM/config.h
View file @ dc57e974
......@@ -75,12 +75,13 @@ public:
static std::unordered_map<std::string, std::string> Str2Map(const char* parameters);
#pragma region Parameters
#pragma region Core Parameters
// [doc-only]
// alias = config_file
// desc = path of config file
// desc = **Note**: only can be used in CLI version
// desc = **Note**: can be used only in CLI version
std::string config = "";
// [doc-only]
......@@ -92,7 +93,7 @@ public:
// desc = ``predict``, for prediction, aliases: ``prediction``, ``test``
// desc = ``convert_model``, for converting model file into if-else format, see more information in `IO Parameters <#io-parameters>`__
// desc = ``refit``, for refitting existing models with new data, aliases: ``refit_tree``
// desc = **Note**: only can be used in CLI version
// desc = **Note**: can be used only in CLI version
TaskType task = TaskType::kTrain;
// [doc-only]
......@@ -124,13 +125,12 @@ public:
// descl2 = all values in ``label`` must be smaller than number of elements in ``label_gain``
std::string objective = "regression";
// [doc-only]
// type = enum
// alias = boosting_type, boost
// options = gbdt, rf, dart, goss
// desc = ``gbdt``, traditional Gradient Boosting Decision Tree
// desc = ``rf``, Random Forest
// options = gbdt, gbrt, rf, random_forest, dart, goss
// desc = ``gbdt``, traditional Gradient Boosting Decision Tree, aliases: ``gbrt``
// desc = ``rf``, Random Forest, aliases: ``random_forest``
// desc = ``dart``, `Dropouts meet Multiple Additive Regression Trees <https://arxiv.org/abs/1505.01866>`__
// desc = ``goss``, Gradient-based One-Side Sampling
std::string boosting = "gbdt";
......@@ -153,7 +153,7 @@ public:
int num_iterations = 100;
// alias = shrinkage_rate
// check = >0
// check = >0.0
// desc = shrinkage rate
// desc = in ``dart``, it also affects on normalization weights of dropped trees
double learning_rate = 0.1;
......@@ -187,6 +187,7 @@ public:
// [doc-only]
// type = enum
// options = cpu, gpu
// alias = device
// desc = device for the tree learning, you can use GPU to achieve the faster learning
// desc = **Note**: it is recommended to use the smaller ``max_bin`` (e.g. 63) to get the better speed up
// desc = **Note**: for the faster speed, GPU uses 32-bit float point to sum up by default, so this may affect the accuracy for some tasks. You can set ``gpu_use_dp=true`` to enable 64-bit float point, but it will slow down the training
......@@ -203,147 +204,159 @@ public:
#pragma region Learning Control Parameters
// desc=limit the max depth for tree model. This is used to deal with over-fitting when #data is small. Tree still grows by leaf-wise
// desc=< 0 means no limit
// desc = limit the max depth for tree model. This is used to deal with over-fitting when ``#data`` is small. Tree still grows leaf-wise
// desc = ``< 0`` means no limit
int max_depth = -1;
// alias = min_data_per_leaf, min_data, min_child_samples
// check=>=0
// desc=minimal number of data in one leaf. Can be used to deal with over-fitting
// check = >=0
// desc = minimal number of data in one leaf. Can be used to deal with over-fitting
int min_data_in_leaf = 20;
// alias=min_sum_hessian_per_leaf,min_sum_hessian,min_hessian,min_child_weight
// check >=0
// desc=minimal sum hessian in one leaf. Like min_data_in_leaf,it can be used to deal with over-fitting
// alias = min_sum_hessian_per_leaf, min_sum_hessian, min_hessian, min_child_weight
// check = >=0.0
// desc = minimal sum hessian in one leaf. Like ``min_data_in_leaf``, it can be used to deal with over-fitting
double min_sum_hessian_in_leaf = 1e-3;
// alias=sub_row,subsample,bagging
// check=>0
// check=<=1.0
// desc = like feature_fraction, but this will randomly select part of data without resampling
// desc=can be used to speed up training
// desc=can be used to deal with over-fitting
// desc=**Note**: To enable bagging,bagging_freq should be set to a non zero value as well
// alias = sub_row, subsample, bagging
// check = >0.0
// check = <=1.0
// desc = like ``feature_fraction``, but this will randomly select part of data without resampling
// desc = can be used to speed up training
// desc = can be used to deal with over-fitting
// desc = **Note**: to enable bagging, ``bagging_freq`` should be set to a non zero value as well
double bagging_fraction = 1.0;
// alias=subsample_freq
// desc=frequency for bagging,0 means disable bagging. k means will perform bagging at every k iteration
// desc=**Note**: to enable bagging,bagging_fraction should be set as well
// alias = subsample_freq
// desc = frequency for bagging
// desc = ``0`` means disable bagging; ``k`` means perform bagging at every ``k`` iteration
// desc = **Note**: to enable bagging, ``bagging_fraction`` should be set to value smaller than ``1.0`` as well
int bagging_freq = 0;
// alias = bagging_fraction_seed
// desc = random seed for bagging
int bagging_seed = 3;
// alias = sub_feature, colsample_bytree
// check=>0
// check=<=1.0
// desc=LightGBM will randomly select part of features on each iteration if feature_fraction smaller than 1.0. For example, if set to 0.8, will select 80 % features before training each tree
// desc=can be used to speed up training
// desc=can be used to deal with over-fitting
// check = >0.0
// check = <=1.0
// desc = LightGBM will randomly select part of features on each iteration if ``feature_fraction`` smaller than ``1.0``. For example, if you set it to ``0.8``, LightGBM will select 80% of features before training each tree
// desc = can be used to speed up training
// desc = can be used to deal with over-fitting
double feature_fraction = 1.0;
// desc=random seed for feature_fraction
// desc = random seed for ``feature_fraction``
int feature_fraction_seed = 2;
// alias=early_stopping_rounds,early_stopping
// desc=will stop training if one metric of one validation data doesn't improve in last early_stopping_round rounds
// desc=enable when greater than 0
// alias = early_stopping_rounds, early_stopping
// desc = will stop training if one metric of one validation data doesn't improve in last ``early_stopping_round`` rounds
// desc = ``<= 0`` means disable
int early_stopping_round = 0;
// alias=max_tree_output,max_leaf_output
// desc=Used to limit the max output of tree leaves
// desc=when <= 0,there is not constraint
// desc=the final max output of leaves is learning_rate*max_delta_step
// alias = max_tree_output, max_leaf_output
// desc = used to limit the max output of tree leaves
// desc = ``<= 0`` means no constraint
// desc = the final max output of leaves is ``learning_rate * max_delta_step``
double max_delta_step = 0.0;
// alias=reg_alpha
// check=>=0
// desc=L1 regularization
// alias = reg_alpha
// check = >=0.0
// desc = L1 regularization
double lambda_l1 = 0.0;
// alias = reg_lambda
// check=>=0
// check = >=0.0
// desc = L2 regularization
double lambda_l2 = 0.0;
// alias=min_split_gain
// desc=the minimal gain to perform split
// alias = min_split_gain
// check = >=0.0
// desc = the minimal gain to perform split
double min_gain_to_split = 0.0;
// check=>=0
// check=<=1.0
// desc=only used in dart
// check = >=0.0
// check = <=1.0
// desc = used only in ``dart``
// desc = dropout rate
double drop_rate = 0.1;
// desc=only used in dart,max number of dropped trees on one iteration
// desc=<=0 means no limit
// desc = used only in ``dart``
// desc = max number of dropped trees on one iteration
// desc = ``<=0`` means no limit
int max_drop = 50;
// check=>=0
// check=<=1.0
// desc=only used in dart,probability of skipping drop
// check = >=0.0
// check = <=1.0
// desc = used only in ``dart``
// desc = probability of skipping drop
double skip_drop = 0.5;
// desc=only used in dart,set this to true if want to use xgboost dart mode
// desc = used only in ``dart``
// desc = set this to ``true``, if you want to use xgboost dart mode
bool xgboost_dart_mode = false;
// desc=only used in dart,set this to true if want to use uniform drop
// desc = used only in ``dart``
// desc = set this to ``true``, if you want to use uniform drop
bool uniform_drop = false;
// desc=only used in dart,random seed to choose dropping models
// desc = used only in ``dart``
// desc = random seed to choose dropping models
int drop_seed = 4;
// check=>=0
// check=<=1.0
// desc=only used in goss,the retain ratio of large gradient data
// check = >=0.0
// check = <=1.0
// desc = used only in ``goss``
// desc = the retain ratio of large gradient data
double top_rate = 0.2;
// check=>=0
// check=<=1.0
// desc=only used in goss,the retain ratio of small gradient data
// check = >=0.0
// check = <=1.0
// desc = used only in ``goss``
// desc = the retain ratio of small gradient data
double other_rate = 0.1;
// check=>0
// desc=min number of data per categorical group
// check = >0
// desc = minimal number of data per categorical group
int min_data_per_group = 100;
// check=>0
// desc=use for the categorical features
// desc=limit the max threshold points in categorical features
// check = >0
// desc = used for the categorical features
// desc = limit the max threshold points in categorical features
int max_cat_threshold = 32;
// check=>=0
// desc=L2 regularization in categorcial split
double cat_l2 = 10;
// check = >=0.0
// desc = used for the categorical features
// desc = L2 regularization in categorcial split
double cat_l2 = 10.0;
// check=>=0
// desc=used for the categorical features
// desc=this can reduce the effect of noises in categorical features,especially for categories with few data
double cat_smooth = 10;
// check = >=0.0
// desc = used for the categorical features
// desc = this can reduce the effect of noises in categorical features, especially for categories with few data
double cat_smooth = 10.0;
// check=>0
// desc=when number of categories of one feature smaller than or equal to max_cat_to_onehot,one-vs-other split algorithm will be used
// check = >0
// desc = when number of categories of one feature smaller than or equal to ``max_cat_to_onehot``, one-vs-other split algorithm will be used
int max_cat_to_onehot = 4;
// alias = topk
// desc=used in `Voting parallel <./Parallel-Learning-Guide.rst#choose-appropriate-parallel-algorithm>`__
// desc=set this to larger value for more accurate result,but it will slow down the training speed
// check = >0
// desc = used in `Voting parallel <./Parallel-Learning-Guide.rst#choose-appropriate-parallel-algorithm>`__
// desc = set this to larger value for more accurate result, but it will slow down the training speed
int top_k = 20;
// type = multi-int
// alias = mc,monotone_constraint
// default=none
// desc=used for constraints of monotonic features
// desc=1 means increasing,-1 means decreasing,0 means non-constraint
// desc=you need to specify all features in order. For example,mc=-1,0,1 means the decreasing for 1st feature,non-constraint for 2nd feature and increasing for the 3rd feature
// alias = mc, monotone_constraint
// default = None
// desc = used for constraints of monotonic features
// desc = ``1`` means increasing, ``-1`` means decreasing, ``0`` means non-constraint
// desc = you need to specify all features in order. For example, ``mc=-1,0,1`` means decreasing for 1st feature, non-constraint for 2nd feature and increasing for the 3rd feature
std::vector<int8_t> monotone_constraints;
// alias=forced_splits_filename,forced_splits_file,forced_splits
// desc = path to a.json file that specifies splits to force at the top of every decision tree before best - first learning commences
// desc=.json file can be arbitrarily nested,and each split contains feature,threshold fields,as well as left and right fields representing subsplits.Categorical splits are forced in a one - hot fashion, with left representing the split containing the feature value and right representing other values
// desc=see `this file <https://github.com/Microsoft/LightGBM/tree/master/examples/binary_classification/forced_splits.json>`__ as an example
// alias = fs, forced_splits_filename, forced_splits_file, forced_splits
// desc = path to a ``.json`` file that specifies splits to force at the top of every decision tree before best-first learning commences
// desc = ``.json`` file can be arbitrarily nested, and each split contains ``feature``, ``threshold`` fields, as well as ``left`` and ``right`` fields representing subsplits
// desc = categorical splits are forced in a one-hot fashion, with ``left`` representing the split containing the feature value and ``right`` representing other values
// desc = see `this file <https://github.com/Microsoft/LightGBM/tree/master/examples/binary_classification/forced_splits.json>`__ as an example
std::string forcedsplits_filename = "";
#pragma endregion
......
src/io/config.cpp
View file @ dc57e974
......@@ -52,7 +52,7 @@ void GetBoostingType(const std::unordered_map<std::string, std::string>& params,
*boosting = "dart";
} else if (value == std::string("goss")) {
*boosting = "goss";
} else if (value == std::string("rf") || value == std::string("randomforest")) {
} else if (value == std::string("rf") || value == std::string("random_forest")) {
*boosting = "rf";
} else {
Log::Fatal("Unknown boosting type %s", value.c_str());
......@@ -120,7 +120,7 @@ void GetTaskType(const std::unordered_map<std::string, std::string>& params, Tas
void GetDeviceType(const std::unordered_map<std::string, std::string>& params, std::string* device_type) {
std::string value;
if (Config::GetString(params, "device", &value)) {
if (Config::GetString(params, "device_type", &value)) {
std::transform(value.begin(), value.end(), value.begin(), Common::tolower);
if (value == std::string("cpu")) {
*device_type = "cpu";
......@@ -174,6 +174,7 @@ void Config::Set(const std::unordered_map<std::string, std::string>& params) {
if (valid_data_initscores.size() == 0 && valid.size() > 0) {
valid_data_initscores = std::vector<std::string>(valid.size(), "");
}
CHECK(valid.size() == valid_data_initscores.size());
// check for conflicts
CheckParamConflict();
......
src/io/config_auto.cpp
View file @ dc57e974
......@@ -30,6 +30,7 @@ std::unordered_map<std::string, std::string> Config::alias_table({
{"num_thread", "num_threads"},
{"nthread", "num_threads"},
{"nthreads", "num_threads"},
{"device", "device_type"},
{"random_seed", "seed"},
{"min_data_per_leaf", "min_data_in_leaf"},
{"min_data", "min_data_in_leaf"},
......
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