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config.cpp
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config.cpp
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/*!
* Copyright (c) 2016 Microsoft Corporation. All rights reserved.
* Licensed under the MIT License. See LICENSE file in the project root for license information.
*/
#include <LightGBM/config.h>
#include <LightGBM/utils/common.h>
#include <LightGBM/utils/log.h>
#include <LightGBM/utils/random.h>
#include <limits>
namespace LightGBM {
void Config::KV2Map(std::unordered_map<std::string, std::string>& params, const char* kv) {
std::vector<std::string> tmp_strs = Common::Split(kv, '=');
if (tmp_strs.size() == 2) {
std::string key = Common::RemoveQuotationSymbol(Common::Trim(tmp_strs[0]));
std::string value = Common::RemoveQuotationSymbol(Common::Trim(tmp_strs[1]));
if (!Common::CheckASCII(key) || !Common::CheckASCII(value)) {
Log::Fatal("Do not support non-ascii characters in config.");
}
if (key.size() > 0) {
auto value_search = params.find(key);
if (value_search == params.end()) { // not set
params.emplace(key, value);
} else {
Log::Warning("%s is set=%s, %s=%s will be ignored. Current value: %s=%s",
key.c_str(), value_search->second.c_str(), key.c_str(), value.c_str(),
key.c_str(), value_search->second.c_str());
}
}
} else {
Log::Warning("Unknown parameter %s", kv);
}
}
std::unordered_map<std::string, std::string> Config::Str2Map(const char* parameters) {
std::unordered_map<std::string, std::string> params;
auto args = Common::Split(parameters, " \t\n\r");
for (auto arg : args) {
KV2Map(params, Common::Trim(arg).c_str());
}
ParameterAlias::KeyAliasTransform(¶ms);
return params;
}
void GetBoostingType(const std::unordered_map<std::string, std::string>& params, std::string* boosting) {
std::string value;
if (Config::GetString(params, "boosting", &value)) {
std::transform(value.begin(), value.end(), value.begin(), Common::tolower);
if (value == std::string("gbdt") || value == std::string("gbrt")) {
*boosting = "gbdt";
} else if (value == std::string("dart")) {
*boosting = "dart";
} else if (value == std::string("goss")) {
*boosting = "goss";
} else if (value == std::string("rf") || value == std::string("random_forest")) {
*boosting = "rf";
} else {
Log::Fatal("Unknown boosting type %s", value.c_str());
}
}
}
void GetObjectiveType(const std::unordered_map<std::string, std::string>& params, std::string* objective) {
std::string value;
if (Config::GetString(params, "objective", &value)) {
std::transform(value.begin(), value.end(), value.begin(), Common::tolower);
*objective = value;
}
}
void GetMetricType(const std::unordered_map<std::string, std::string>& params, std::vector<std::string>* metric) {
std::string value;
if (Config::GetString(params, "metric", &value)) {
// clear old metrics
metric->clear();
// to lower
std::transform(value.begin(), value.end(), value.begin(), Common::tolower);
// split
std::vector<std::string> metrics = Common::Split(value.c_str(), ',');
// remove duplicate
std::unordered_set<std::string> metric_sets;
for (auto& met : metrics) {
std::transform(met.begin(), met.end(), met.begin(), Common::tolower);
if (metric_sets.count(met) <= 0) {
metric_sets.insert(met);
}
}
for (auto& met : metric_sets) {
metric->push_back(met);
}
metric->shrink_to_fit();
}
// add names of objective function if not providing metric
if (metric->empty() && value.size() == 0) {
if (Config::GetString(params, "objective", &value)) {
std::transform(value.begin(), value.end(), value.begin(), Common::tolower);
metric->push_back(value);
}
}
}
void GetTaskType(const std::unordered_map<std::string, std::string>& params, TaskType* task) {
std::string value;
if (Config::GetString(params, "task", &value)) {
std::transform(value.begin(), value.end(), value.begin(), Common::tolower);
if (value == std::string("train") || value == std::string("training")) {
*task = TaskType::kTrain;
} else if (value == std::string("predict") || value == std::string("prediction")
|| value == std::string("test")) {
*task = TaskType::kPredict;
} else if (value == std::string("convert_model")) {
*task = TaskType::kConvertModel;
} else if (value == std::string("refit") || value == std::string("refit_tree")) {
*task = TaskType::KRefitTree;
} else {
Log::Fatal("Unknown task type %s", value.c_str());
}
}
}
void GetDeviceType(const std::unordered_map<std::string, std::string>& params, std::string* device_type) {
std::string 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";
} else if (value == std::string("gpu")) {
*device_type = "gpu";
} else {
Log::Fatal("Unknown device type %s", value.c_str());
}
}
}
void GetTreeLearnerType(const std::unordered_map<std::string, std::string>& params, std::string* tree_learner) {
std::string value;
if (Config::GetString(params, "tree_learner", &value)) {
std::transform(value.begin(), value.end(), value.begin(), Common::tolower);
if (value == std::string("serial")) {
*tree_learner = "serial";
} else if (value == std::string("feature") || value == std::string("feature_parallel")) {
*tree_learner = "feature";
} else if (value == std::string("data") || value == std::string("data_parallel")) {
*tree_learner = "data";
} else if (value == std::string("voting") || value == std::string("voting_parallel")) {
*tree_learner = "voting";
} else {
Log::Fatal("Unknown tree learner type %s", value.c_str());
}
}
}
void Config::Set(const std::unordered_map<std::string, std::string>& params) {
// generate seeds by seed.
if (GetInt(params, "seed", &seed)) {
Random rand(seed);
int int_max = std::numeric_limits<short>::max();
data_random_seed = static_cast<int>(rand.NextShort(0, int_max));
bagging_seed = static_cast<int>(rand.NextShort(0, int_max));
drop_seed = static_cast<int>(rand.NextShort(0, int_max));
feature_fraction_seed = static_cast<int>(rand.NextShort(0, int_max));
}
GetTaskType(params, &task);
GetBoostingType(params, &boosting);
GetMetricType(params, &metric);
GetObjectiveType(params, &objective);
GetDeviceType(params, &device_type);
GetTreeLearnerType(params, &tree_learner);
GetMembersFromString(params);
// sort eval_at
std::sort(eval_at.begin(), eval_at.end());
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();
if (verbosity == 1) {
LightGBM::Log::ResetLogLevel(LightGBM::LogLevel::Info);
} else if (verbosity == 0) {
LightGBM::Log::ResetLogLevel(LightGBM::LogLevel::Warning);
} else if (verbosity >= 2) {
LightGBM::Log::ResetLogLevel(LightGBM::LogLevel::Debug);
} else {
LightGBM::Log::ResetLogLevel(LightGBM::LogLevel::Fatal);
}
}
bool CheckMultiClassObjective(const std::string& objective) {
return (objective == std::string("multiclass")
|| objective == std::string("multiclassova")
|| objective == std::string("softmax")
|| objective == std::string("multiclass_ova")
|| objective == std::string("ova")
|| objective == std::string("ovr"));
}
void Config::CheckParamConflict() {
// check if objective, metric, and num_class match
int num_class_check = num_class;
bool objective_custom = objective == std::string("none") || objective == std::string("null")
|| objective == std::string("custom") || objective == std::string("na");
bool objective_type_multiclass = CheckMultiClassObjective(objective) || (objective_custom && num_class_check > 1);
if (objective_type_multiclass) {
if (num_class_check <= 1) {
Log::Fatal("Number of classes should be specified and greater than 1 for multiclass training");
}
} else {
if (task == TaskType::kTrain && num_class_check != 1) {
Log::Fatal("Number of classes must be 1 for non-multiclass training");
}
}
for (std::string metric_type : metric) {
bool metric_custom_or_none = metric_type == std::string("none") || metric_type == std::string("null")
|| metric_type == std::string("custom") || metric_type == std::string("na");
bool metric_type_multiclass = (CheckMultiClassObjective(metric_type)
|| metric_type == std::string("multi_logloss")
|| metric_type == std::string("multi_error")
|| (metric_custom_or_none && num_class_check > 1));
if ((objective_type_multiclass && !metric_type_multiclass)
|| (!objective_type_multiclass && metric_type_multiclass)) {
Log::Fatal("Multiclass objective and metrics don't match");
}
}
if (num_machines > 1) {
is_parallel = true;
} else {
is_parallel = false;
tree_learner = "serial";
}
bool is_single_tree_learner = tree_learner == std::string("serial");
if (is_single_tree_learner) {
is_parallel = false;
num_machines = 1;
}
if (is_single_tree_learner || tree_learner == std::string("feature")) {
is_parallel_find_bin = false;
} else if (tree_learner == std::string("data")
|| tree_learner == std::string("voting")) {
is_parallel_find_bin = true;
if (histogram_pool_size >= 0
&& tree_learner == std::string("data")) {
Log::Warning("Histogram LRU queue was enabled (histogram_pool_size=%f).\n"
"Will disable this to reduce communication costs",
histogram_pool_size);
// Change pool size to -1 (no limit) when using data parallel to reduce communication costs
histogram_pool_size = -1;
}
}
// Check max_depth and num_leaves
if (max_depth > 0) {
int full_num_leaves = static_cast<int>(std::pow(2, max_depth));
if (full_num_leaves > num_leaves
&& num_leaves == kDefaultNumLeaves) {
Log::Warning("Accuracy may be bad since you didn't set num_leaves and 2^max_depth > num_leaves");
}
num_leaves = std::min(num_leaves, 2 << max_depth);
}
}
std::string Config::ToString() const {
std::stringstream str_buf;
str_buf << "[boosting: " << boosting << "]\n";
str_buf << "[objective: " << objective << "]\n";
str_buf << "[metric: " << Common::Join(metric, ",") << "]\n";
str_buf << "[tree_learner: " << tree_learner << "]\n";
str_buf << "[device_type: " << device_type << "]\n";
str_buf << SaveMembersToString();
return str_buf.str();
}
} // namespace LightGBM