config.h 63.2 KB
Newer Older
1
2
3
4
5
/*!
 * Copyright (c) 2016 Microsoft Corporation. All rights reserved.
 * Licensed under the MIT License. See LICENSE file in the project root for license information.
 *
 * \note
6
7
8
9
 * - desc and descl2 fields must be written in reStructuredText format;
 * - nested sections can be placed only at the bottom of parent's section;
 * - [doc-only] tag indicates that only documentation for this param should be generated and all other actions are performed manually;
 * - [no-save] tag indicates that this param should not be saved into a model text representation.
10
 */
Guolin Ke's avatar
Guolin Ke committed
11
12
13
#ifndef LIGHTGBM_CONFIG_H_
#define LIGHTGBM_CONFIG_H_

14
15
16
17
18
#include <LightGBM/export.h>
#include <LightGBM/meta.h>
#include <LightGBM/utils/common.h>
#include <LightGBM/utils/log.h>

Guolin Ke's avatar
Guolin Ke committed
19
20
#include <string>
#include <algorithm>
Guolin Ke's avatar
Guolin Ke committed
21
#include <memory>
22
23
24
#include <unordered_map>
#include <unordered_set>
#include <vector>
Guolin Ke's avatar
Guolin Ke committed
25
26
27

namespace LightGBM {

Guolin Ke's avatar
Guolin Ke committed
28
29
/*! \brief Types of tasks */
enum TaskType {
30
  kTrain, kPredict, kConvertModel, KRefitTree, kSaveBinary
Guolin Ke's avatar
Guolin Ke committed
31
};
32
const int kDefaultNumLeaves = 31;
Guolin Ke's avatar
Guolin Ke committed
33

Guolin Ke's avatar
Guolin Ke committed
34
struct Config {
Nikita Titov's avatar
Nikita Titov committed
35
 public:
Guolin Ke's avatar
Guolin Ke committed
36
  std::string ToString() const;
Guolin Ke's avatar
Guolin Ke committed
37
38
39
40
  /*!
  * \brief Get string value by specific name of key
  * \param params Store the key and value for params
  * \param name Name of key
Hui Xue's avatar
Hui Xue committed
41
  * \param out Value will assign to out if key exists
Guolin Ke's avatar
Guolin Ke committed
42
43
  * \return True if key exists
  */
Guolin Ke's avatar
Guolin Ke committed
44
  inline static bool GetString(
Guolin Ke's avatar
Guolin Ke committed
45
46
47
48
49
50
51
    const std::unordered_map<std::string, std::string>& params,
    const std::string& name, std::string* out);

  /*!
  * \brief Get int value by specific name of key
  * \param params Store the key and value for params
  * \param name Name of key
Hui Xue's avatar
Hui Xue committed
52
  * \param out Value will assign to out if key exists
Guolin Ke's avatar
Guolin Ke committed
53
54
  * \return True if key exists
  */
Guolin Ke's avatar
Guolin Ke committed
55
  inline static bool GetInt(
Guolin Ke's avatar
Guolin Ke committed
56
57
58
59
    const std::unordered_map<std::string, std::string>& params,
    const std::string& name, int* out);

  /*!
60
  * \brief Get double value by specific name of key
Guolin Ke's avatar
Guolin Ke committed
61
62
  * \param params Store the key and value for params
  * \param name Name of key
Hui Xue's avatar
Hui Xue committed
63
  * \param out Value will assign to out if key exists
Guolin Ke's avatar
Guolin Ke committed
64
65
  * \return True if key exists
  */
Guolin Ke's avatar
Guolin Ke committed
66
  inline static bool GetDouble(
Guolin Ke's avatar
Guolin Ke committed
67
    const std::unordered_map<std::string, std::string>& params,
68
    const std::string& name, double* out);
Guolin Ke's avatar
Guolin Ke committed
69
70
71
72
73

  /*!
  * \brief Get bool value by specific name of key
  * \param params Store the key and value for params
  * \param name Name of key
Hui Xue's avatar
Hui Xue committed
74
  * \param out Value will assign to out if key exists
Guolin Ke's avatar
Guolin Ke committed
75
76
  * \return True if key exists
  */
Guolin Ke's avatar
Guolin Ke committed
77
  inline static bool GetBool(
Guolin Ke's avatar
Guolin Ke committed
78
79
    const std::unordered_map<std::string, std::string>& params,
    const std::string& name, bool* out);
80

Guolin Ke's avatar
Guolin Ke committed
81
  static void KV2Map(std::unordered_map<std::string, std::string>* params, const char* kv);
82
  static std::unordered_map<std::string, std::string> Str2Map(const char* parameters);
Guolin Ke's avatar
Guolin Ke committed
83

Guolin Ke's avatar
Guolin Ke committed
84
  #pragma region Parameters
85

Guolin Ke's avatar
Guolin Ke committed
86
87
  #pragma region Core Parameters

88
  // [no-save]
Guolin Ke's avatar
Guolin Ke committed
89
  // [doc-only]
90
91
  // alias = config_file
  // desc = path of config file
92
  // desc = **Note**: can be used only in CLI version
Guolin Ke's avatar
Guolin Ke committed
93
94
  std::string config = "";

95
  // [no-save]
Guolin Ke's avatar
Guolin Ke committed
96
  // [doc-only]
97
98
99
100
101
102
  // type = enum
  // default = train
  // options = train, predict, convert_model, refit
  // alias = task_type
  // desc = ``train``, for training, aliases: ``training``
  // desc = ``predict``, for prediction, aliases: ``prediction``, ``test``
Nikita Titov's avatar
Nikita Titov committed
103
  // desc = ``convert_model``, for converting model file into if-else format, see more information in `Convert Parameters <#convert-parameters>`__
104
  // desc = ``refit``, for refitting existing models with new data, aliases: ``refit_tree``
105
  // desc = ``save_binary``, load train (and validation) data then save dataset to binary file. Typical usage: ``save_binary`` first, then run multiple ``train`` tasks in parallel using the saved binary file
Guolin Ke's avatar
Guolin Ke committed
106
  // desc = **Note**: can be used only in CLI version; for language-specific packages you can use the correspondent functions
Guolin Ke's avatar
Guolin Ke committed
107
108
109
  TaskType task = TaskType::kTrain;

  // [doc-only]
110
  // type = enum
111
  // options = regression, regression_l1, huber, fair, poisson, quantile, mape, gamma, tweedie, binary, multiclass, multiclassova, cross_entropy, cross_entropy_lambda, lambdarank, rank_xendcg
112
113
  // alias = objective_type, app, application
  // desc = regression application
Guolin Ke's avatar
Guolin Ke committed
114
115
  // descl2 = ``regression``, L2 loss, aliases: ``regression_l2``, ``l2``, ``mean_squared_error``, ``mse``, ``l2_root``, ``root_mean_squared_error``, ``rmse``
  // descl2 = ``regression_l1``, L1 loss, aliases: ``l1``, ``mean_absolute_error``, ``mae``
116
117
118
119
120
  // descl2 = ``huber``, `Huber loss <https://en.wikipedia.org/wiki/Huber_loss>`__
  // descl2 = ``fair``, `Fair loss <https://www.kaggle.com/c/allstate-claims-severity/discussion/24520>`__
  // descl2 = ``poisson``, `Poisson regression <https://en.wikipedia.org/wiki/Poisson_regression>`__
  // descl2 = ``quantile``, `Quantile regression <https://en.wikipedia.org/wiki/Quantile_regression>`__
  // descl2 = ``mape``, `MAPE loss <https://en.wikipedia.org/wiki/Mean_absolute_percentage_error>`__, aliases: ``mean_absolute_percentage_error``
121
  // descl2 = ``gamma``, Gamma regression with log-link. It might be useful, e.g., for modeling insurance claims severity, or for any target that might be `gamma-distributed <https://en.wikipedia.org/wiki/Gamma_distribution#Occurrence_and_applications>`__
122
  // descl2 = ``tweedie``, Tweedie regression with log-link. It might be useful, e.g., for modeling total loss in insurance, or for any target that might be `tweedie-distributed <https://en.wikipedia.org/wiki/Tweedie_distribution#Occurrence_and_applications>`__
123
124
125
  // desc = binary classification application
  // descl2 = ``binary``, binary `log loss <https://en.wikipedia.org/wiki/Cross_entropy>`__ classification (or logistic regression)
  // descl2 = requires labels in {0, 1}; see ``cross-entropy`` application for general probability labels in [0, 1]
126
127
128
129
130
  // desc = multi-class classification application
  // descl2 = ``multiclass``, `softmax <https://en.wikipedia.org/wiki/Softmax_function>`__ objective function, aliases: ``softmax``
  // descl2 = ``multiclassova``, `One-vs-All <https://en.wikipedia.org/wiki/Multiclass_classification#One-vs.-rest>`__ binary objective function, aliases: ``multiclass_ova``, ``ova``, ``ovr``
  // descl2 = ``num_class`` should be set as well
  // desc = cross-entropy application
Guolin Ke's avatar
Guolin Ke committed
131
132
  // descl2 = ``cross_entropy``, objective function for cross-entropy (with optional linear weights), aliases: ``xentropy``
  // descl2 = ``cross_entropy_lambda``, alternative parameterization of cross-entropy, aliases: ``xentlambda``
133
  // descl2 = label is anything in interval [0, 1]
134
  // desc = ranking application
135
  // descl2 = ``lambdarank``, `lambdarank <https://papers.nips.cc/paper/2971-learning-to-rank-with-nonsmooth-cost-functions.pdf>`__ objective. `label_gain <#label_gain>`__ can be used to set the gain (weight) of ``int`` label and all values in ``label`` must be smaller than number of elements in ``label_gain``
136
137
  // descl2 = ``rank_xendcg``, `XE_NDCG_MART <https://arxiv.org/abs/1911.09798>`__ ranking objective function, aliases: ``xendcg``, ``xe_ndcg``, ``xe_ndcg_mart``, ``xendcg_mart``
  // descl2 = ``rank_xendcg`` is faster than and achieves the similar performance as ``lambdarank``
138
  // descl2 = label should be ``int`` type, and larger number represents the higher relevance (e.g. 0:bad, 1:fair, 2:good, 3:perfect)
Guolin Ke's avatar
Guolin Ke committed
139
140
141
  std::string objective = "regression";

  // [doc-only]
142
143
  // type = enum
  // alias = boosting_type, boost
144
  // options = gbdt, rf, dart, goss
145
146
  // desc = ``gbdt``, traditional Gradient Boosting Decision Tree, aliases: ``gbrt``
  // desc = ``rf``, Random Forest, aliases: ``random_forest``
147
148
  // desc = ``dart``, `Dropouts meet Multiple Additive Regression Trees <https://arxiv.org/abs/1505.01866>`__
  // desc = ``goss``, Gradient-based One-Side Sampling
Nikita Titov's avatar
Nikita Titov committed
149
  // descl2 = **Note**: internally, LightGBM uses ``gbdt`` mode for the first ``1 / learning_rate`` iterations
Guolin Ke's avatar
Guolin Ke committed
150
151
  std::string boosting = "gbdt";

152
  // desc = fit piecewise linear gradient boosting tree
153
154
155
  // descl2 = tree splits are chosen in the usual way, but the model at each leaf is linear instead of constant
  // descl2 = the linear model at each leaf includes all the numerical features in that leaf's branch
  // descl2 = categorical features are used for splits as normal but are not used in the linear models
156
  // descl2 = missing values should not be encoded as ``0``. Use ``np.nan`` for Python, ``NA`` for the CLI, and ``NA``, ``NA_real_``, or ``NA_integer_`` for R
157
  // descl2 = it is recommended to rescale data before training so that features have similar mean and standard deviation
158
159
160
  // descl2 = **Note**: only works with CPU and ``serial`` tree learner
  // descl2 = **Note**: ``regression_l1`` objective is not supported with linear tree boosting
  // descl2 = **Note**: setting ``linear_tree=true`` significantly increases the memory use of LightGBM
161
  // descl2 = **Note**: if you specify ``monotone_constraints``, constraints will be enforced when choosing the split points, but not when fitting the linear models on leaves
162
163
  bool linear_tree = false;

164
  // alias = train, train_data, train_data_file, data_filename
165
  // desc = path of training data, LightGBM will train from this data
166
  // desc = **Note**: can be used only in CLI version
Guolin Ke's avatar
Guolin Ke committed
167
168
  std::string data = "";

169
  // alias = test, valid_data, valid_data_file, test_data, test_data_file, valid_filenames
170
  // default = ""
171
  // desc = path(s) of validation/test data, LightGBM will output metrics for these data
172
  // desc = support multiple validation data, separated by ``,``
173
  // desc = **Note**: can be used only in CLI version
Guolin Ke's avatar
Guolin Ke committed
174
175
  std::vector<std::string> valid;

176
  // alias = num_iteration, n_iter, num_tree, num_trees, num_round, num_rounds, num_boost_round, n_estimators
177
178
179
  // check = >=0
  // desc = number of boosting iterations
  // desc = **Note**: internally, LightGBM constructs ``num_class * num_iterations`` trees for multi-class classification problems
Guolin Ke's avatar
Guolin Ke committed
180
  int num_iterations = 100;
Guolin Ke's avatar
Guolin Ke committed
181

182
  // alias = shrinkage_rate, eta
183
  // check = >0.0
184
185
  // desc = shrinkage rate
  // desc = in ``dart``, it also affects on normalization weights of dropped trees
Guolin Ke's avatar
Guolin Ke committed
186
187
  double learning_rate = 0.1;

188
  // default = 31
189
  // alias = num_leaf, max_leaves, max_leaf
190
  // check = >1
191
  // check = <=131072
192
  // desc = max number of leaves in one tree
Guolin Ke's avatar
Guolin Ke committed
193
194
195
  int num_leaves = kDefaultNumLeaves;

  // [doc-only]
196
197
  // type = enum
  // options = serial, feature, data, voting
198
  // alias = tree, tree_type, tree_learner_type
199
200
201
202
  // desc = ``serial``, single machine tree learner
  // desc = ``feature``, feature parallel tree learner, aliases: ``feature_parallel``
  // desc = ``data``, data parallel tree learner, aliases: ``data_parallel``
  // desc = ``voting``, voting parallel tree learner, aliases: ``voting_parallel``
203
  // desc = refer to `Distributed Learning Guide <./Parallel-Learning-Guide.rst>`__ to get more details
Guolin Ke's avatar
Guolin Ke committed
204
205
  std::string tree_learner = "serial";

206
  // alias = num_thread, nthread, nthreads, n_jobs
Guolin Ke's avatar
Guolin Ke committed
207
  // desc = number of threads for LightGBM
208
209
210
211
  // desc = ``0`` means default number of threads in OpenMP
  // desc = for the best speed, set this to the number of **real CPU cores**, not the number of threads (most CPUs use `hyper-threading <https://en.wikipedia.org/wiki/Hyper-threading>`__ to generate 2 threads per CPU core)
  // desc = do not set it too large if your dataset is small (for instance, do not use 64 threads for a dataset with 10,000 rows)
  // desc = be aware a task manager or any similar CPU monitoring tool might report that cores not being fully utilized. **This is normal**
212
  // desc = for distributed learning, do not use all CPU cores because this will cause poor performance for the network communication
213
  // desc = **Note**: please **don't** change this during training, especially when running multiple jobs simultaneously by external packages, otherwise it may cause undesirable errors
Guolin Ke's avatar
Guolin Ke committed
214
215
216
  int num_threads = 0;

  // [doc-only]
217
218
  // type = enum
  // options = cpu, gpu
219
  // alias = device
220
221
222
223
  // 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
  // desc = **Note**: refer to `Installation Guide <./Installation-Guide.rst#build-gpu-version>`__ to build LightGBM with GPU support
Guolin Ke's avatar
Guolin Ke committed
224
225
226
  std::string device_type = "cpu";

  // [doc-only]
227
  // alias = random_seed, random_state
228
229
230
231
  // default = None
  // desc = this seed is used to generate other seeds, e.g. ``data_random_seed``, ``feature_fraction_seed``, etc.
  // desc = by default, this seed is unused in favor of default values of other seeds
  // desc = this seed has lower priority in comparison with other seeds, which means that it will be overridden, if you set other seeds explicitly
Guolin Ke's avatar
Guolin Ke committed
232
233
  int seed = 0;

Guolin Ke's avatar
Guolin Ke committed
234
235
236
237
238
  // desc = used only with ``cpu`` device type
  // desc = setting this to ``true`` should ensure the stable results when using the same data and the same parameters (and different ``num_threads``)
  // desc = when you use the different seeds, different LightGBM versions, the binaries compiled by different compilers, or in different systems, the results are expected to be different
  // desc = you can `raise issues <https://github.com/microsoft/LightGBM/issues>`__ in LightGBM GitHub repo when you meet the unstable results
  // desc = **Note**: setting this to ``true`` may slow down the training
239
  // desc = **Note**: to avoid potential instability due to numerical issues, please set ``force_col_wise=true`` or ``force_row_wise=true`` when setting ``deterministic=true``
Guolin Ke's avatar
Guolin Ke committed
240
241
  bool deterministic = false;

Guolin Ke's avatar
Guolin Ke committed
242
243
244
245
  #pragma endregion

  #pragma region Learning Control Parameters

246
247
248
249
  // desc = used only with ``cpu`` device type
  // desc = set this to ``true`` to force col-wise histogram building
  // desc = enabling this is recommended when:
  // descl2 = the number of columns is large, or the total number of bins is large
Nikita Titov's avatar
Nikita Titov committed
250
  // descl2 = ``num_threads`` is large, e.g. ``> 20``
251
252
253
  // descl2 = you want to reduce memory cost
  // desc = **Note**: when both ``force_col_wise`` and ``force_row_wise`` are ``false``, LightGBM will firstly try them both, and then use the faster one. To remove the overhead of testing set the faster one to ``true`` manually
  // desc = **Note**: this parameter cannot be used at the same time with ``force_row_wise``, choose only one of them
254
255
  bool force_col_wise = false;

256
257
258
259
  // desc = used only with ``cpu`` device type
  // desc = set this to ``true`` to force row-wise histogram building
  // desc = enabling this is recommended when:
  // descl2 = the number of data points is large, and the total number of bins is relatively small
Nikita Titov's avatar
Nikita Titov committed
260
  // descl2 = ``num_threads`` is relatively small, e.g. ``<= 16``
261
262
263
264
  // descl2 = you want to use small ``bagging_fraction`` or ``goss`` boosting to speed up
  // desc = **Note**: setting this to ``true`` will double the memory cost for Dataset object. If you have not enough memory, you can try setting ``force_col_wise=true``
  // desc = **Note**: when both ``force_col_wise`` and ``force_row_wise`` are ``false``, LightGBM will firstly try them both, and then use the faster one. To remove the overhead of testing set the faster one to ``true`` manually
  // desc = **Note**: this parameter cannot be used at the same time with ``force_col_wise``, choose only one of them
265
266
  bool force_row_wise = false;

267
268
269
270
271
  // alias = hist_pool_size
  // desc = max cache size in MB for historical histogram
  // desc = ``< 0`` means no limit
  double histogram_pool_size = -1.0;

272
  // 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
273
  // desc = ``<= 0`` means no limit
Guolin Ke's avatar
Guolin Ke committed
274
275
276
  int max_depth = -1;

  // alias = min_data_per_leaf, min_data, min_child_samples
277
278
  // check = >=0
  // desc = minimal number of data in one leaf. Can be used to deal with over-fitting
279
  // desc = **Note**: this is an approximation based on the Hessian, so occasionally you may observe splits which produce leaf nodes that have less than this many observations
Guolin Ke's avatar
Guolin Ke committed
280
281
  int min_data_in_leaf = 20;

282
283
284
  // 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
Guolin Ke's avatar
Guolin Ke committed
285
286
  double min_sum_hessian_in_leaf = 1e-3;

287
288
289
290
291
292
293
  // 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
Guolin Ke's avatar
Guolin Ke committed
294
295
  double bagging_fraction = 1.0;

Guolin Ke's avatar
Guolin Ke committed
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
  // alias = pos_sub_row, pos_subsample, pos_bagging
  // check = >0.0
  // check = <=1.0
  // desc = used only in ``binary`` application
  // desc = used for imbalanced binary classification problem, will randomly sample ``#pos_samples * pos_bagging_fraction`` positive samples in bagging
  // desc = should be used together with ``neg_bagging_fraction``
  // desc = set this to ``1.0`` to disable
  // desc = **Note**: to enable this, you need to set ``bagging_freq`` and ``neg_bagging_fraction`` as well
  // desc = **Note**: if both ``pos_bagging_fraction`` and ``neg_bagging_fraction`` are set to ``1.0``,  balanced bagging is disabled
  // desc = **Note**: if balanced bagging is enabled, ``bagging_fraction`` will be ignored
  double pos_bagging_fraction = 1.0;

  // alias = neg_sub_row, neg_subsample, neg_bagging
  // check = >0.0
  // check = <=1.0
  // desc = used only in ``binary`` application
  // desc = used for imbalanced binary classification problem, will randomly sample ``#neg_samples * neg_bagging_fraction`` negative samples in bagging
  // desc = should be used together with ``pos_bagging_fraction``
  // desc = set this to ``1.0`` to disable
  // desc = **Note**: to enable this, you need to set ``bagging_freq`` and ``pos_bagging_fraction`` as well
  // desc = **Note**: if both ``pos_bagging_fraction`` and ``neg_bagging_fraction`` are set to ``1.0``,  balanced bagging is disabled
  // desc = **Note**: if balanced bagging is enabled, ``bagging_fraction`` will be ignored
  double neg_bagging_fraction = 1.0;

320
321
  // alias = subsample_freq
  // desc = frequency for bagging
322
  // desc = ``0`` means disable bagging; ``k`` means perform bagging at every ``k`` iteration. Every ``k``-th iteration, LightGBM will randomly select ``bagging_fraction * 100 %`` of the data to use for the next ``k`` iterations
323
  // desc = **Note**: to enable bagging, ``bagging_fraction`` should be set to value smaller than ``1.0`` as well
Guolin Ke's avatar
Guolin Ke committed
324
325
326
327
328
329
330
  int bagging_freq = 0;

  // alias = bagging_fraction_seed
  // desc = random seed for bagging
  int bagging_seed = 3;

  // alias = sub_feature, colsample_bytree
331
332
  // check = >0.0
  // check = <=1.0
333
  // desc = LightGBM will randomly select a subset of features on each iteration (tree) if ``feature_fraction`` is smaller than ``1.0``. For example, if you set it to ``0.8``, LightGBM will select 80% of features before training each tree
334
335
  // desc = can be used to speed up training
  // desc = can be used to deal with over-fitting
Guolin Ke's avatar
Guolin Ke committed
336
337
  double feature_fraction = 1.0;

338
339
340
  // alias = sub_feature_bynode, colsample_bynode
  // check = >0.0
  // check = <=1.0
341
  // desc = LightGBM will randomly select a subset of features on each tree node if ``feature_fraction_bynode`` is smaller than ``1.0``. For example, if you set it to ``0.8``, LightGBM will select 80% of features at each tree node
342
343
344
345
346
  // desc = can be used to deal with over-fitting
  // desc = **Note**: unlike ``feature_fraction``, this cannot speed up training
  // desc = **Note**: if both ``feature_fraction`` and ``feature_fraction_bynode`` are smaller than ``1.0``, the final fraction of each node is ``feature_fraction * feature_fraction_bynode``
  double feature_fraction_bynode = 1.0;

347
  // desc = random seed for ``feature_fraction``
Guolin Ke's avatar
Guolin Ke committed
348
349
  int feature_fraction_seed = 2;

350
351
  // desc = use extremely randomized trees
  // desc = if set to ``true``, when evaluating node splits LightGBM will check only one randomly-chosen threshold for each feature
352
  // desc = can be used to speed up training
353
354
355
356
357
358
  // desc = can be used to deal with over-fitting
  bool extra_trees = false;

  // desc = random seed for selecting thresholds when ``extra_trees`` is true
  int extra_seed = 6;

359
  // alias = early_stopping_rounds, early_stopping, n_iter_no_change
360
361
  // desc = will stop training if one metric of one validation data doesn't improve in last ``early_stopping_round`` rounds
  // desc = ``<= 0`` means disable
362
  // desc = can be used to speed up training
Guolin Ke's avatar
Guolin Ke committed
363
364
  int early_stopping_round = 0;

365
  // desc = LightGBM allows you to provide multiple evaluation metrics. Set this to ``true``, if you want to use only the first metric for early stopping
366
367
  bool first_metric_only = false;

368
369
370
371
  // 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``
Guolin Ke's avatar
Guolin Ke committed
372
373
  double max_delta_step = 0.0;

374
375
376
  // alias = reg_alpha
  // check = >=0.0
  // desc = L1 regularization
Guolin Ke's avatar
Guolin Ke committed
377
378
  double lambda_l1 = 0.0;

379
  // alias = reg_lambda, lambda
380
  // check = >=0.0
Guolin Ke's avatar
Guolin Ke committed
381
382
383
  // desc = L2 regularization
  double lambda_l2 = 0.0;

384
  // check = >=0.0
385
  // desc = linear tree regularization, corresponds to the parameter ``lambda`` in Eq. 3 of `Gradient Boosting with Piece-Wise Linear Regression Trees <https://arxiv.org/pdf/1802.05640.pdf>`__
386
387
  double linear_lambda = 0.0;

388
389
390
  // alias = min_split_gain
  // check = >=0.0
  // desc = the minimal gain to perform split
391
  // desc = can be used to speed up training
Guolin Ke's avatar
Guolin Ke committed
392
393
  double min_gain_to_split = 0.0;

394
  // alias = rate_drop
395
396
397
  // check = >=0.0
  // check = <=1.0
  // desc = used only in ``dart``
398
  // desc = dropout rate: a fraction of previous trees to drop during the dropout
Guolin Ke's avatar
Guolin Ke committed
399
400
  double drop_rate = 0.1;

401
  // desc = used only in ``dart``
402
  // desc = max number of dropped trees during one boosting iteration
403
  // desc = ``<=0`` means no limit
Guolin Ke's avatar
Guolin Ke committed
404
405
  int max_drop = 50;

406
407
408
  // check = >=0.0
  // check = <=1.0
  // desc = used only in ``dart``
409
  // desc = probability of skipping the dropout procedure during a boosting iteration
Guolin Ke's avatar
Guolin Ke committed
410
411
  double skip_drop = 0.5;

412
413
  // desc = used only in ``dart``
  // desc = set this to ``true``, if you want to use xgboost dart mode
Guolin Ke's avatar
Guolin Ke committed
414
415
  bool xgboost_dart_mode = false;

416
417
  // desc = used only in ``dart``
  // desc = set this to ``true``, if you want to use uniform drop
Guolin Ke's avatar
Guolin Ke committed
418
419
  bool uniform_drop = false;

420
421
  // desc = used only in ``dart``
  // desc = random seed to choose dropping models
Guolin Ke's avatar
Guolin Ke committed
422
423
  int drop_seed = 4;

424
425
426
427
  // check = >=0.0
  // check = <=1.0
  // desc = used only in ``goss``
  // desc = the retain ratio of large gradient data
Guolin Ke's avatar
Guolin Ke committed
428
429
  double top_rate = 0.2;

430
431
432
433
  // check = >=0.0
  // check = <=1.0
  // desc = used only in ``goss``
  // desc = the retain ratio of small gradient data
Guolin Ke's avatar
Guolin Ke committed
434
435
  double other_rate = 0.1;

436
437
  // check = >0
  // desc = minimal number of data per categorical group
Guolin Ke's avatar
Guolin Ke committed
438
439
  int min_data_per_group = 100;

440
441
  // check = >0
  // desc = used for the categorical features
442
443
  // desc = limit number of split points considered for categorical features. See `the documentation on how LightGBM finds optimal splits for categorical features <./Features.rst#optimal-split-for-categorical-features>`_ for more details
  // desc = can be used to speed up training
Guolin Ke's avatar
Guolin Ke committed
444
445
  int max_cat_threshold = 32;

446
447
  // check = >=0.0
  // desc = used for the categorical features
448
  // desc = L2 regularization in categorical split
449
  double cat_l2 = 10.0;
Guolin Ke's avatar
Guolin Ke committed
450

451
452
453
454
  // 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;
455

456
457
  // 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
Guolin Ke's avatar
Guolin Ke committed
458
459
460
  int max_cat_to_onehot = 4;

  // alias = topk
461
  // check = >0
462
  // desc = used only in ``voting`` tree learner, refer to `Voting parallel <./Parallel-Learning-Guide.rst#choose-appropriate-parallel-algorithm>`__
463
  // desc = set this to larger value for more accurate result, but it will slow down the training speed
Guolin Ke's avatar
Guolin Ke committed
464
465
466
  int top_k = 20;

  // type = multi-int
467
468
469
470
471
  // 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
Guolin Ke's avatar
Guolin Ke committed
472
  std::vector<int8_t> monotone_constraints;
Guolin Ke's avatar
Guolin Ke committed
473

Nikita Titov's avatar
Nikita Titov committed
474
  // type = enum
475
  // alias = monotone_constraining_method, mc_method
476
  // options = basic, intermediate, advanced
477
478
479
  // desc = used only if ``monotone_constraints`` is set
  // desc = monotone constraints method
  // descl2 = ``basic``, the most basic monotone constraints method. It does not slow the library at all, but over-constrains the predictions
480
481
  // descl2 = ``intermediate``, a `more advanced method <https://hal.archives-ouvertes.fr/hal-02862802/document>`__, which may slow the library very slightly. However, this method is much less constraining than the basic method and should significantly improve the results
  // descl2 = ``advanced``, an `even more advanced method <https://hal.archives-ouvertes.fr/hal-02862802/document>`__, which may slow the library. However, this method is even less constraining than the intermediate method and should again significantly improve the results
482
483
  std::string monotone_constraints_method = "basic";

484
485
486
  // alias = monotone_splits_penalty, ms_penalty, mc_penalty
  // check = >=0.0
  // desc = used only if ``monotone_constraints`` is set
487
  // desc = `monotone penalty <https://hal.archives-ouvertes.fr/hal-02862802/document>`__: a penalization parameter X forbids any monotone splits on the first X (rounded down) level(s) of the tree. The penalty applied to monotone splits on a given depth is a continuous, increasing function the penalization parameter
488
489
490
  // desc = if ``0.0`` (the default), no penalization is applied
  double monotone_penalty = 0.0;

Guolin Ke's avatar
Guolin Ke committed
491
  // type = multi-double
492
  // alias = feature_contrib, fc, fp, feature_penalty
Guolin Ke's avatar
Guolin Ke committed
493
494
495
496
  // default = None
  // desc = used to control feature's split gain, will use ``gain[i] = max(0, feature_contri[i]) * gain[i]`` to replace the split gain of i-th feature
  // desc = you need to specify all features in order
  std::vector<double> feature_contri;
497

498
499
500
501
  // 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
502
  // desc = **Note**: the forced split logic will be ignored, if the split makes gain worse
503
  // desc = see `this file <https://github.com/microsoft/LightGBM/tree/master/examples/binary_classification/forced_splits.json>`__ as an example
Guolin Ke's avatar
Guolin Ke committed
504
505
  std::string forcedsplits_filename = "";

Guolin Ke's avatar
Guolin Ke committed
506
507
508
509
510
511
  // check = >=0.0
  // check = <=1.0
  // desc = decay rate of ``refit`` task, will use ``leaf_output = refit_decay_rate * old_leaf_output + (1.0 - refit_decay_rate) * new_leaf_output`` to refit trees
  // desc = used only in ``refit`` task in CLI version or as argument in ``refit`` function in language-specific package
  double refit_decay_rate = 0.9;

512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
  // check = >=0.0
  // desc = cost-effective gradient boosting multiplier for all penalties
  double cegb_tradeoff = 1.0;

  // check = >=0.0
  // desc = cost-effective gradient-boosting penalty for splitting a node
  double cegb_penalty_split = 0.0;

  // type = multi-double
  // default = 0,0,...,0
  // desc = cost-effective gradient boosting penalty for using a feature
  // desc = applied per data point
  std::vector<double> cegb_penalty_feature_lazy;

  // type = multi-double
  // default = 0,0,...,0
  // desc = cost-effective gradient boosting penalty for using a feature
  // desc = applied once per forest
530
  std::vector<double> cegb_penalty_feature_coupled;
531

Belinda Trotta's avatar
Belinda Trotta committed
532
533
534
535
536
  // check = >= 0.0
  // desc = controls smoothing applied to tree nodes
  // desc = helps prevent overfitting on leaves with few samples
  // desc = if set to zero, no smoothing is applied
  // desc = if ``path_smooth > 0`` then ``min_data_in_leaf`` must be at least ``2``
537
  // desc = larger values give stronger regularization
Belinda Trotta's avatar
Belinda Trotta committed
538
539
540
541
  // descl2 = the weight of each node is ``(n / path_smooth) * w + w_p / (n / path_smooth + 1)``, where ``n`` is the number of samples in the node, ``w`` is the optimal node weight to minimise the loss (approximately ``-sum_gradients / sum_hessians``), and ``w_p`` is the weight of the parent node
  // descl2 = note that the parent output ``w_p`` itself has smoothing applied, unless it is the root node, so that the smoothing effect accumulates with the tree depth
  double path_smooth = 0;

542
543
544
545
  // desc = controls which features can appear in the same branch
  // desc = by default interaction constraints are disabled, to enable them you can specify
  // descl2 = for CLI, lists separated by commas, e.g. ``[0,1,2],[2,3]``
  // descl2 = for Python-package, list of lists, e.g. ``[[0, 1, 2], [2, 3]]``
546
  // descl2 = for R-package, list of character or numeric vectors, e.g. ``list(c("var1", "var2", "var3"), c("var3", "var4"))`` or ``list(c(1L, 2L, 3L), c(3L, 4L))``. Numeric vectors should use 1-based indexing, where ``1L`` is the first feature, ``2L`` is the second feature, etc
547
548
549
  // desc = any two features can only appear in the same branch only if there exists a constraint containing both features
  std::string interaction_constraints = "";

550
551
  // alias = verbose
  // desc = controls the level of LightGBM's verbosity
552
  // desc = ``< 0``: Fatal, ``= 0``: Error (Warning), ``= 1``: Info, ``> 1``: Debug
553
554
  int verbosity = 1;

555
  // [no-save]
556
557
558
559
560
561
562
  // alias = model_input, model_in
  // desc = filename of input model
  // desc = for ``prediction`` task, this model will be applied to prediction data
  // desc = for ``train`` task, training will be continued from this model
  // desc = **Note**: can be used only in CLI version
  std::string input_model = "";

563
  // [no-save]
564
565
566
567
568
  // alias = model_output, model_out
  // desc = filename of output model in training
  // desc = **Note**: can be used only in CLI version
  std::string output_model = "LightGBM_model.txt";

569
570
571
572
573
  // desc = the feature importance type in the saved model file
  // desc = ``0``: count-based feature importance (numbers of splits are counted); ``1``: gain-based feature importance (values of gain are counted)
  // desc = **Note**: can be used only in CLI version
  int saved_feature_importance_type = 0;

574
  // [no-save]
575
576
577
578
579
580
581
582
583
584
585
586
  // alias = save_period
  // desc = frequency of saving model file snapshot
  // desc = set this to positive value to enable this function. For example, the model file will be snapshotted at each iteration if ``snapshot_freq=1``
  // desc = **Note**: can be used only in CLI version
  int snapshot_freq = -1;

  #pragma endregion

  #pragma region IO Parameters

  #pragma region Dataset Parameters

587
588
589
590
  // check = >1
  // desc = max number of bins that feature values will be bucketed in
  // desc = small number of bins may reduce training accuracy but may increase general power (deal with over-fitting)
  // desc = LightGBM will auto compress memory according to ``max_bin``. For example, LightGBM will use ``uint8_t`` for feature value if ``max_bin=255``
591
  int max_bin = 255;
Guolin Ke's avatar
Guolin Ke committed
592

Belinda Trotta's avatar
Belinda Trotta committed
593
594
595
596
  // type = multi-int
  // default = None
  // desc = max number of bins for each feature
  // desc = if not specified, will use ``max_bin`` for all features
597
  std::vector<int32_t> max_bin_by_feature;
Belinda Trotta's avatar
Belinda Trotta committed
598

599
600
601
  // check = >0
  // desc = minimal number of data inside one bin
  // desc = use this to avoid one-data-one-bin (potential over-fitting)
Guolin Ke's avatar
Guolin Ke committed
602
603
  int min_data_in_bin = 3;

604
605
  // alias = subsample_for_bin
  // check = >0
606
607
  // desc = number of data that sampled to construct feature discrete bins
  // desc = setting this to larger value will give better training result, but may increase data loading time
608
  // desc = set this to larger value if data is very sparse
609
  // desc = **Note**: don't set this to small values, otherwise, you may encounter unexpected errors and poor accuracy
610
611
  int bin_construct_sample_cnt = 200000;

612
  // alias = data_seed
613
  // desc = random seed for sampling data to construct histogram bins
Guolin Ke's avatar
Guolin Ke committed
614
  int data_random_seed = 1;
Guolin Ke's avatar
Guolin Ke committed
615

616
617
618
  // alias = is_sparse, enable_sparse, sparse
  // desc = used to enable/disable sparse optimization
  bool is_enable_sparse = true;
Guolin Ke's avatar
Guolin Ke committed
619

620
621
622
623
624
625
626
627
  // alias = is_enable_bundle, bundle
  // desc = set this to ``false`` to disable Exclusive Feature Bundling (EFB), which is described in `LightGBM: A Highly Efficient Gradient Boosting Decision Tree <https://papers.nips.cc/paper/6907-lightgbm-a-highly-efficient-gradient-boosting-decision-tree>`__
  // desc = **Note**: disabling this may cause the slow training speed for sparse datasets
  bool enable_bundle = true;

  // desc = set this to ``false`` to disable the special handle of missing value
  bool use_missing = true;

628
  // desc = set this to ``true`` to treat all zero as missing values (including the unshown values in LibSVM / sparse matrices)
629
630
631
  // desc = set this to ``false`` to use ``na`` for representing missing values
  bool zero_as_missing = false;

632
  // desc = set this to ``true`` (the default) to tell LightGBM to ignore the features that are unsplittable based on ``min_data_in_leaf``
633
634
635
636
637
  // desc = as dataset object is initialized only once and cannot be changed after that, you may need to set this to ``false`` when searching parameters with ``min_data_in_leaf``, otherwise features are filtered by ``min_data_in_leaf`` firstly if you don't reconstruct dataset object
  // desc = **Note**: setting this to ``false`` may slow down the training
  bool feature_pre_filter = true;

  // alias = is_pre_partition
638
  // desc = used for distributed learning (excluding the ``feature_parallel`` mode)
639
640
641
  // desc = ``true`` if training data are pre-partitioned, and different machines use different partitions
  bool pre_partition = false;

642
643
644
  // alias = two_round_loading, use_two_round_loading
  // desc = set this to ``true`` if data file is too big to fit in memory
  // desc = by default, LightGBM will map data file to memory and load features from memory. This will provide faster data loading speed, but may cause run out of memory error when the data file is very big
645
  // desc = **Note**: works only in case of loading data directly from file
Guolin Ke's avatar
Guolin Ke committed
646
647
648
  bool two_round = false;

  // alias = has_header
649
  // desc = set this to ``true`` if input data has header
650
  // desc = **Note**: works only in case of loading data directly from file
Guolin Ke's avatar
Guolin Ke committed
651
652
  bool header = false;

653
654
655
656
657
  // type = int or string
  // alias = label
  // desc = used to specify the label column
  // desc = use number for index, e.g. ``label=0`` means column\_0 is the label
  // desc = add a prefix ``name:`` for column name, e.g. ``label=name:is_click``
658
  // desc = **Note**: works only in case of loading data directly from file
Guolin Ke's avatar
Guolin Ke committed
659
  std::string label_column = "";
Guolin Ke's avatar
Guolin Ke committed
660

661
662
663
664
665
  // type = int or string
  // alias = weight
  // desc = used to specify the weight column
  // desc = use number for index, e.g. ``weight=0`` means column\_0 is the weight
  // desc = add a prefix ``name:`` for column name, e.g. ``weight=name:weight``
666
  // desc = **Note**: works only in case of loading data directly from file
667
  // desc = **Note**: index starts from ``0`` and it doesn't count the label column when passing type is ``int``, e.g. when label is column\_0, and weight is column\_1, the correct parameter is ``weight=0``
Guolin Ke's avatar
Guolin Ke committed
668
  std::string weight_column = "";
Guolin Ke's avatar
Guolin Ke committed
669

670
671
672
673
674
  // type = int or string
  // alias = group, group_id, query_column, query, query_id
  // desc = used to specify the query/group id column
  // desc = use number for index, e.g. ``query=0`` means column\_0 is the query id
  // desc = add a prefix ``name:`` for column name, e.g. ``query=name:query_id``
675
  // desc = **Note**: works only in case of loading data directly from file
676
  // desc = **Note**: data should be grouped by query\_id, for more information, see `Query Data <#query-data>`__
677
  // desc = **Note**: index starts from ``0`` and it doesn't count the label column when passing type is ``int``, e.g. when label is column\_0 and query\_id is column\_1, the correct parameter is ``query=0``
Guolin Ke's avatar
Guolin Ke committed
678
  std::string group_column = "";
Guolin Ke's avatar
Guolin Ke committed
679

680
  // type = multi-int or string
Guolin Ke's avatar
Guolin Ke committed
681
  // alias = ignore_feature, blacklist
682
683
684
685
686
  // desc = used to specify some ignoring columns in training
  // desc = use number for index, e.g. ``ignore_column=0,1,2`` means column\_0, column\_1 and column\_2 will be ignored
  // desc = add a prefix ``name:`` for column name, e.g. ``ignore_column=name:c1,c2,c3`` means c1, c2 and c3 will be ignored
  // desc = **Note**: works only in case of loading data directly from file
  // desc = **Note**: index starts from ``0`` and it doesn't count the label column when passing type is ``int``
687
  // desc = **Note**: despite the fact that specified columns will be completely ignored during the training, they still should have a valid format allowing LightGBM to load file successfully
Guolin Ke's avatar
Guolin Ke committed
688
  std::string ignore_column = "";
689

690
691
692
693
694
  // type = multi-int or string
  // alias = cat_feature, categorical_column, cat_column
  // desc = used to specify categorical features
  // desc = use number for index, e.g. ``categorical_feature=0,1,2`` means column\_0, column\_1 and column\_2 are categorical features
  // desc = add a prefix ``name:`` for column name, e.g. ``categorical_feature=name:c1,c2,c3`` means c1, c2 and c3 are categorical features
695
  // desc = **Note**: only supports categorical with ``int`` type (not applicable for data represented as pandas DataFrame in Python-package)
696
697
  // desc = **Note**: index starts from ``0`` and it doesn't count the label column when passing type is ``int``
  // desc = **Note**: all values should be less than ``Int32.MaxValue`` (2147483647)
698
  // desc = **Note**: using large values could be memory consuming. Tree decision rule works best when categorical features are presented by consecutive integers starting from zero
699
  // desc = **Note**: all negative values will be treated as **missing values**
700
  // desc = **Note**: the output cannot be monotonically constrained with respect to a categorical feature
Guolin Ke's avatar
Guolin Ke committed
701
702
  std::string categorical_feature = "";

703
704
705
706
707
  // desc = path to a ``.json`` file that specifies bin upper bounds for some or all features
  // desc = ``.json`` file should contain an array of objects, each containing the word ``feature`` (integer feature index) and ``bin_upper_bound`` (array of thresholds for binning)
  // desc = see `this file <https://github.com/microsoft/LightGBM/tree/master/examples/regression/forced_bins.json>`__ as an example
  std::string forcedbins_filename = "";

708
  // [no-save]
709
710
711
712
713
714
715
716
717
718
  // alias = is_save_binary, is_save_binary_file
  // desc = if ``true``, LightGBM will save the dataset (including validation data) to a binary file. This speed ups the data loading for the next time
  // desc = **Note**: ``init_score`` is not saved in binary file
  // desc = **Note**: can be used only in CLI version; for language-specific packages you can use the correspondent function
  bool save_binary = false;

  #pragma endregion

  #pragma region Predict Parameters

719
720
721
722
723
724
  // [no-save]
  // desc = used only in ``prediction`` task
  // desc = used to specify from which iteration to start the prediction
  // desc = ``<= 0`` means from the first iteration
  int start_iteration_predict = 0;

725
  // [no-save]
726
727
728
729
730
  // desc = used only in ``prediction`` task
  // desc = used to specify how many trained iterations will be used in prediction
  // desc = ``<= 0`` means no limit
  int num_iteration_predict = -1;

731
  // [no-save]
732
733
734
735
  // alias = is_predict_raw_score, predict_rawscore, raw_score
  // desc = used only in ``prediction`` task
  // desc = set this to ``true`` to predict only the raw scores
  // desc = set this to ``false`` to predict transformed scores
Guolin Ke's avatar
Guolin Ke committed
736
737
  bool predict_raw_score = false;

738
  // [no-save]
739
740
741
  // alias = is_predict_leaf_index, leaf_index
  // desc = used only in ``prediction`` task
  // desc = set this to ``true`` to predict with leaf index of all trees
Guolin Ke's avatar
Guolin Ke committed
742
743
  bool predict_leaf_index = false;

744
  // [no-save]
745
746
  // alias = is_predict_contrib, contrib
  // desc = used only in ``prediction`` task
747
  // desc = set this to ``true`` to estimate `SHAP values <https://arxiv.org/abs/1706.06060>`__, which represent how each feature contributes to each prediction
748
  // desc = produces ``#features + 1`` values where the last value is the expected value of the model output over the training data
749
  // desc = **Note**: if you want to get more explanation for your model's predictions using SHAP values like SHAP interaction values, you can install `shap package <https://github.com/slundberg/shap>`__
Nikita Titov's avatar
Nikita Titov committed
750
  // desc = **Note**: unlike the shap package, with ``predict_contrib`` we return a matrix with an extra column, where the last column is the expected value
751
  // desc = **Note**: this feature is not implemented for linear trees
Guolin Ke's avatar
Guolin Ke committed
752
753
  bool predict_contrib = false;

754
  // [no-save]
755
  // desc = used only in ``prediction`` task
756
757
758
759
760
  // desc = control whether or not LightGBM raises an error when you try to predict on data with a different number of features than the training data
  // desc = if ``false`` (the default), a fatal error will be raised if the number of features in the dataset you predict on differs from the number seen during training
  // desc = if ``true``, LightGBM will attempt to predict on whatever data you provide. This is dangerous because you might get incorrect predictions, but you could use it in situations where it is difficult or expensive to generate some features and you are very confident that they were never chosen for splits in the model
  // desc = **Note**: be very careful setting this parameter to ``true``
  bool predict_disable_shape_check = false;
Guolin Ke's avatar
Guolin Ke committed
761

762
  // [no-save]
763
764
  // desc = used only in ``prediction`` task
  // desc = if ``true``, will use early-stopping to speed up the prediction. May affect the accuracy
765
  bool pred_early_stop = false;
766

767
  // [no-save]
768
769
  // desc = used only in ``prediction`` task
  // desc = the frequency of checking early-stopping prediction
770
  int pred_early_stop_freq = 10;
Guolin Ke's avatar
Guolin Ke committed
771

772
  // [no-save]
773
774
  // desc = used only in ``prediction`` task
  // desc = the threshold of margin in early-stopping prediction
Guolin Ke's avatar
Guolin Ke committed
775
  double pred_early_stop_margin = 10.0;
Guolin Ke's avatar
Guolin Ke committed
776

777
  // [no-save]
778
  // alias = predict_result, prediction_result, predict_name, prediction_name, pred_name, name_pred
779
  // desc = used only in ``prediction`` task
780
781
782
783
784
785
786
  // desc = filename of prediction result
  // desc = **Note**: can be used only in CLI version
  std::string output_result = "LightGBM_predict_result.txt";

  #pragma endregion

  #pragma region Convert Parameters
787

788
  // [no-save]
789
  // desc = used only in ``convert_model`` task
790
  // desc = only ``cpp`` is supported yet; for conversion model to other languages consider using `m2cgen <https://github.com/BayesWitnesses/m2cgen>`__ utility
791
  // desc = if ``convert_model_language`` is set and ``task=train``, the model will be also converted
792
  // desc = **Note**: can be used only in CLI version
Guolin Ke's avatar
Guolin Ke committed
793
794
  std::string convert_model_language = "";

795
  // [no-save]
796
797
798
  // alias = convert_model_file
  // desc = used only in ``convert_model`` task
  // desc = output filename of converted model
799
  // desc = **Note**: can be used only in CLI version
Guolin Ke's avatar
Guolin Ke committed
800
801
  std::string convert_model = "gbdt_prediction.cpp";

802
  #pragma endregion
Guolin Ke's avatar
Guolin Ke committed
803

804
805
  #pragma endregion

Guolin Ke's avatar
Guolin Ke committed
806
807
  #pragma region Objective Parameters

808
  // desc = used only in ``rank_xendcg`` objective
809
810
811
  // desc = random seed for objectives, if random process is needed
  int objective_seed = 5;

812
813
814
815
  // check = >0
  // alias = num_classes
  // desc = used only in ``multi-class`` classification application
  int num_class = 1;
Guolin Ke's avatar
Guolin Ke committed
816

817
  // alias = unbalance, unbalanced_sets
818
  // desc = used only in ``binary`` and ``multiclassova`` applications
819
  // desc = set this to ``true`` if training data are unbalanced
820
  // desc = **Note**: while enabling this should increase the overall performance metric of your model, it will also result in poor estimates of the individual class probabilities
821
822
  // desc = **Note**: this parameter cannot be used at the same time with ``scale_pos_weight``, choose only **one** of them
  bool is_unbalance = false;
Guolin Ke's avatar
Guolin Ke committed
823

824
  // check = >0.0
825
  // desc = used only in ``binary`` and ``multiclassova`` applications
826
  // desc = weight of labels with positive class
827
  // desc = **Note**: while enabling this should increase the overall performance metric of your model, it will also result in poor estimates of the individual class probabilities
828
829
  // desc = **Note**: this parameter cannot be used at the same time with ``is_unbalance``, choose only **one** of them
  double scale_pos_weight = 1.0;
Guolin Ke's avatar
Guolin Ke committed
830

831
832
833
834
  // check = >0.0
  // desc = used only in ``binary`` and ``multiclassova`` classification and in ``lambdarank`` applications
  // desc = parameter for the sigmoid function
  double sigmoid = 1.0;
Guolin Ke's avatar
Guolin Ke committed
835

836
  // desc = used only in ``regression``, ``binary``, ``multiclassova`` and ``cross-entropy`` applications
837
  // desc = adjusts initial score to the mean of labels for faster convergence
Guolin Ke's avatar
Guolin Ke committed
838
839
  bool boost_from_average = true;

840
841
842
843
  // desc = used only in ``regression`` application
  // desc = used to fit ``sqrt(label)`` instead of original values and prediction result will be also automatically converted to ``prediction^2``
  // desc = might be useful in case of large-range labels
  bool reg_sqrt = false;
Guolin Ke's avatar
Guolin Ke committed
844

845
846
847
848
  // check = >0.0
  // desc = used only in ``huber`` and ``quantile`` ``regression`` applications
  // desc = parameter for `Huber loss <https://en.wikipedia.org/wiki/Huber_loss>`__ and `Quantile regression <https://en.wikipedia.org/wiki/Quantile_regression>`__
  double alpha = 0.9;
Guolin Ke's avatar
Guolin Ke committed
849

850
851
852
853
  // check = >0.0
  // desc = used only in ``fair`` ``regression`` application
  // desc = parameter for `Fair loss <https://www.kaggle.com/c/allstate-claims-severity/discussion/24520>`__
  double fair_c = 1.0;
Guolin Ke's avatar
Guolin Ke committed
854

855
856
857
858
  // check = >0.0
  // desc = used only in ``poisson`` ``regression`` application
  // desc = parameter for `Poisson regression <https://en.wikipedia.org/wiki/Poisson_regression>`__ to safeguard optimization
  double poisson_max_delta_step = 0.7;
Guolin Ke's avatar
Guolin Ke committed
859

860
861
862
863
864
865
866
  // check = >=1.0
  // check = <2.0
  // desc = used only in ``tweedie`` ``regression`` application
  // desc = used to control the variance of the tweedie distribution
  // desc = set this closer to ``2`` to shift towards a **Gamma** distribution
  // desc = set this closer to ``1`` to shift towards a **Poisson** distribution
  double tweedie_variance_power = 1.5;
Guolin Ke's avatar
Guolin Ke committed
867

868
869
  // check = >0
  // desc = used only in ``lambdarank`` application
Nikita Titov's avatar
Nikita Titov committed
870
871
  // desc = controls the number of top-results to focus on during training, refer to "truncation level" in the Sec. 3 of `LambdaMART paper <https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/MSR-TR-2010-82.pdf>`__
  // desc = this parameter is closely related to the desirable cutoff ``k`` in the metric **NDCG@k** that we aim at optimizing the ranker for. The optimal setting for this parameter is likely to be slightly higher than ``k`` (e.g., ``k + 3``) to include more pairs of documents to train on, but perhaps not too high to avoid deviating too much from the desired target metric **NDCG@k**
872
  int lambdarank_truncation_level = 30;
Guolin Ke's avatar
Guolin Ke committed
873

874
875
  // desc = used only in ``lambdarank`` application
  // desc = set this to ``true`` to normalize the lambdas for different queries, and improve the performance for unbalanced data
876
877
  // desc = set this to ``false`` to enforce the original lambdarank algorithm
  bool lambdarank_norm = true;
878

879
880
881
882
883
884
885
  // type = multi-double
  // default = 0,1,3,7,15,31,63,...,2^30-1
  // desc = used only in ``lambdarank`` application
  // desc = relevant gain for labels. For example, the gain of label ``2`` is ``3`` in case of default label gains
  // desc = separate by ``,``
  std::vector<double> label_gain;

Guolin Ke's avatar
Guolin Ke committed
886
887
888
  #pragma endregion

  #pragma region Metric Parameters
889

Guolin Ke's avatar
Guolin Ke committed
890
  // [doc-only]
891
892
893
  // alias = metrics, metric_types
  // default = ""
  // type = multi-enum
894
  // desc = metric(s) to be evaluated on the evaluation set(s)
895
  // descl2 = ``""`` (empty string or not specified) means that metric corresponding to specified ``objective`` will be used (this is possible only for pre-defined objective functions, otherwise no evaluation metric will be added)
896
  // descl2 = ``"None"`` (string, **not** a ``None`` value) means that no metric will be registered, aliases: ``na``, ``null``, ``custom``
897
898
  // descl2 = ``l1``, absolute loss, aliases: ``mean_absolute_error``, ``mae``, ``regression_l1``
  // descl2 = ``l2``, square loss, aliases: ``mean_squared_error``, ``mse``, ``regression_l2``, ``regression``
899
  // descl2 = ``rmse``, root square loss, aliases: ``root_mean_squared_error``, ``l2_root``
900
901
902
903
904
905
906
907
  // descl2 = ``quantile``, `Quantile regression <https://en.wikipedia.org/wiki/Quantile_regression>`__
  // descl2 = ``mape``, `MAPE loss <https://en.wikipedia.org/wiki/Mean_absolute_percentage_error>`__, aliases: ``mean_absolute_percentage_error``
  // descl2 = ``huber``, `Huber loss <https://en.wikipedia.org/wiki/Huber_loss>`__
  // descl2 = ``fair``, `Fair loss <https://www.kaggle.com/c/allstate-claims-severity/discussion/24520>`__
  // descl2 = ``poisson``, negative log-likelihood for `Poisson regression <https://en.wikipedia.org/wiki/Poisson_regression>`__
  // descl2 = ``gamma``, negative log-likelihood for **Gamma** regression
  // descl2 = ``gamma_deviance``, residual deviance for **Gamma** regression
  // descl2 = ``tweedie``, negative log-likelihood for **Tweedie** regression
908
  // descl2 = ``ndcg``, `NDCG <https://en.wikipedia.org/wiki/Discounted_cumulative_gain#Normalized_DCG>`__, aliases: ``lambdarank``, ``rank_xendcg``, ``xendcg``, ``xe_ndcg``, ``xe_ndcg_mart``, ``xendcg_mart``
909
910
  // descl2 = ``map``, `MAP <https://makarandtapaswi.wordpress.com/2012/07/02/intuition-behind-average-precision-and-map/>`__, aliases: ``mean_average_precision``
  // descl2 = ``auc``, `AUC <https://en.wikipedia.org/wiki/Receiver_operating_characteristic#Area_under_the_curve>`__
911
  // descl2 = ``average_precision``, `average precision score <https://scikit-learn.org/stable/modules/generated/sklearn.metrics.average_precision_score.html>`__
912
913
  // descl2 = ``binary_logloss``, `log loss <https://en.wikipedia.org/wiki/Cross_entropy>`__, aliases: ``binary``
  // descl2 = ``binary_error``, for one sample: ``0`` for correct classification, ``1`` for error classification
Belinda Trotta's avatar
Belinda Trotta committed
914
  // descl2 = ``auc_mu``, `AUC-mu <http://proceedings.mlr.press/v97/kleiman19a/kleiman19a.pdf>`__
915
916
  // descl2 = ``multi_logloss``, log loss for multi-class classification, aliases: ``multiclass``, ``softmax``, ``multiclassova``, ``multiclass_ova``, ``ova``, ``ovr``
  // descl2 = ``multi_error``, error rate for multi-class classification
Guolin Ke's avatar
Guolin Ke committed
917
918
919
  // descl2 = ``cross_entropy``, cross-entropy (with optional linear weights), aliases: ``xentropy``
  // descl2 = ``cross_entropy_lambda``, "intensity-weighted" cross-entropy, aliases: ``xentlambda``
  // descl2 = ``kullback_leibler``, `Kullback-Leibler divergence <https://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence>`__, aliases: ``kldiv``
920
  // desc = support multiple metrics, separated by ``,``
Guolin Ke's avatar
Guolin Ke committed
921
922
  std::vector<std::string> metric;

923
  // [no-save]
924
  // check = >0
Guolin Ke's avatar
Guolin Ke committed
925
926
  // alias = output_freq
  // desc = frequency for metric output
927
  // desc = **Note**: can be used only in CLI version
Guolin Ke's avatar
Guolin Ke committed
928
929
  int metric_freq = 1;

930
  // [no-save]
931
932
  // alias = training_metric, is_training_metric, train_metric
  // desc = set this to ``true`` to output metric result over training dataset
933
  // desc = **Note**: can be used only in CLI version
934
  bool is_provide_training_metric = false;
935

936
937
  // type = multi-int
  // default = 1,2,3,4,5
938
  // alias = ndcg_eval_at, ndcg_at, map_eval_at, map_at
939
  // desc = used only with ``ndcg`` and ``map`` metrics
940
  // desc = `NDCG <https://en.wikipedia.org/wiki/Discounted_cumulative_gain#Normalized_DCG>`__ and `MAP <https://makarandtapaswi.wordpress.com/2012/07/02/intuition-behind-average-precision-and-map/>`__ evaluation positions, separated by ``,``
Guolin Ke's avatar
Guolin Ke committed
941
  std::vector<int> eval_at;
Guolin Ke's avatar
Guolin Ke committed
942

Belinda Trotta's avatar
Belinda Trotta committed
943
944
945
946
947
948
949
950
  // check = >0
  // desc = used only with ``multi_error`` metric
  // desc = threshold for top-k multi-error metric
  // desc = the error on each sample is ``0`` if the true class is among the top ``multi_error_top_k`` predictions, and ``1`` otherwise
  // descl2 = more precisely, the error on a sample is ``0`` if there are at least ``num_classes - multi_error_top_k`` predictions strictly less than the prediction on the true class
  // desc = when ``multi_error_top_k=1`` this is equivalent to the usual multi-error metric
  int multi_error_top_k = 1;

Belinda Trotta's avatar
Belinda Trotta committed
951
952
953
954
955
956
957
958
959
  // type = multi-double
  // default = None
  // desc = used only with ``auc_mu`` metric
  // desc = list representing flattened matrix (in row-major order) giving loss weights for classification errors
  // desc = list should have ``n * n`` elements, where ``n`` is the number of classes
  // desc = the matrix co-ordinate ``[i, j]`` should correspond to the ``i * n + j``-th element of the list
  // desc = if not specified, will use equal weights for all classes
  std::vector<double> auc_mu_weights;

Guolin Ke's avatar
Guolin Ke committed
960
961
962
963
  #pragma endregion

  #pragma region Network Parameters

964
965
  // check = >0
  // alias = num_machine
966
  // desc = the number of machines for distributed learning application
967
  // desc = this parameter is needed to be set in both **socket** and **mpi** versions
Guolin Ke's avatar
Guolin Ke committed
968
  int num_machines = 1;
Guolin Ke's avatar
Guolin Ke committed
969

970
  // check = >0
971
  // default = 12400 (random for Dask-package)
972
973
974
  // alias = local_port, port
  // desc = TCP listen port for local machines
  // desc = **Note**: don't forget to allow this port in firewall settings before training
Guolin Ke's avatar
Guolin Ke committed
975
  int local_listen_port = 12400;
Guolin Ke's avatar
Guolin Ke committed
976

977
978
979
  // check = >0
  // desc = socket time-out in minutes
  int time_out = 120;
Guolin Ke's avatar
Guolin Ke committed
980

981
  // alias = machine_list_file, machine_list, mlist
982
  // desc = path of file that lists machines for this distributed learning application
983
  // desc = each line contains one IP and one port for one machine. The format is ``ip port`` (space as a separator)
984
  // desc = **Note**: can be used only in CLI version
Guolin Ke's avatar
Guolin Ke committed
985
  std::string machine_list_filename = "";
Guolin Ke's avatar
Guolin Ke committed
986

987
988
  // alias = workers, nodes
  // desc = list of machines in the following format: ``ip1:port1,ip2:port2``
989
  std::string machines = "";
Guolin Ke's avatar
Guolin Ke committed
990

Guolin Ke's avatar
Guolin Ke committed
991
992
993
994
  #pragma endregion

  #pragma region GPU Parameters

995
996
  // desc = OpenCL platform ID. Usually each GPU vendor exposes one OpenCL platform
  // desc = ``-1`` means the system-wide default platform
997
  // desc = **Note**: refer to `GPU Targets <./GPU-Targets.rst#query-opencl-devices-in-your-system>`__ for more details
Guolin Ke's avatar
Guolin Ke committed
998
999
  int gpu_platform_id = -1;

1000
1001
  // desc = OpenCL device ID in the specified platform. Each GPU in the selected platform has a unique device ID
  // desc = ``-1`` means the default device in the selected platform
1002
  // desc = **Note**: refer to `GPU Targets <./GPU-Targets.rst#query-opencl-devices-in-your-system>`__ for more details
Guolin Ke's avatar
Guolin Ke committed
1003
1004
  int gpu_device_id = -1;

1005
1006
  // desc = set this to ``true`` to use double precision math on GPU (by default single precision is used)
  // desc = **Note**: can be used only in OpenCL implementation, in CUDA implementation only double precision is currently supported
Guolin Ke's avatar
Guolin Ke committed
1007
1008
  bool gpu_use_dp = false;

1009
1010
1011
1012
1013
  // check = >0
  // desc = number of GPUs
  // desc = **Note**: can be used only in CUDA implementation
  int num_gpu = 1;

Guolin Ke's avatar
Guolin Ke committed
1014
1015
1016
  #pragma endregion

  #pragma endregion
Guolin Ke's avatar
Guolin Ke committed
1017

1018
1019
  size_t file_load_progress_interval_bytes = size_t(10) * 1024 * 1024 * 1024;

Guolin Ke's avatar
Guolin Ke committed
1020
  bool is_parallel = false;
1021
  bool is_data_based_parallel = false;
Guolin Ke's avatar
Guolin Ke committed
1022
  LIGHTGBM_EXPORT void Set(const std::unordered_map<std::string, std::string>& params);
jcipar's avatar
jcipar committed
1023
1024
  static const std::unordered_map<std::string, std::string>& alias_table();
  static const std::unordered_set<std::string>& parameter_set();
Belinda Trotta's avatar
Belinda Trotta committed
1025
  std::vector<std::vector<double>> auc_mu_weights_matrix;
1026
  std::vector<std::vector<int>> interaction_constraints_vector;
1027

Nikita Titov's avatar
Nikita Titov committed
1028
 private:
Guolin Ke's avatar
Guolin Ke committed
1029
  void CheckParamConflict();
Guolin Ke's avatar
Guolin Ke committed
1030
1031
  void GetMembersFromString(const std::unordered_map<std::string, std::string>& params);
  std::string SaveMembersToString() const;
Belinda Trotta's avatar
Belinda Trotta committed
1032
  void GetAucMuWeights();
1033
  void GetInteractionConstraints();
Guolin Ke's avatar
Guolin Ke committed
1034
1035
};

Guolin Ke's avatar
Guolin Ke committed
1036
inline bool Config::GetString(
Guolin Ke's avatar
Guolin Ke committed
1037
1038
  const std::unordered_map<std::string, std::string>& params,
  const std::string& name, std::string* out) {
1039
  if (params.count(name) > 0 && !params.at(name).empty()) {
Guolin Ke's avatar
Guolin Ke committed
1040
1041
1042
1043
1044
1045
    *out = params.at(name);
    return true;
  }
  return false;
}

Guolin Ke's avatar
Guolin Ke committed
1046
inline bool Config::GetInt(
Guolin Ke's avatar
Guolin Ke committed
1047
1048
  const std::unordered_map<std::string, std::string>& params,
  const std::string& name, int* out) {
1049
  if (params.count(name) > 0 && !params.at(name).empty()) {
1050
    if (!Common::AtoiAndCheck(params.at(name).c_str(), out)) {
1051
      Log::Fatal("Parameter %s should be of type int, got \"%s\"",
Guolin Ke's avatar
Guolin Ke committed
1052
                 name.c_str(), params.at(name).c_str());
1053
    }
Guolin Ke's avatar
Guolin Ke committed
1054
1055
1056
1057
1058
    return true;
  }
  return false;
}

Guolin Ke's avatar
Guolin Ke committed
1059
inline bool Config::GetDouble(
Guolin Ke's avatar
Guolin Ke committed
1060
  const std::unordered_map<std::string, std::string>& params,
1061
  const std::string& name, double* out) {
1062
  if (params.count(name) > 0 && !params.at(name).empty()) {
1063
    if (!Common::AtofAndCheck(params.at(name).c_str(), out)) {
1064
      Log::Fatal("Parameter %s should be of type double, got \"%s\"",
Guolin Ke's avatar
Guolin Ke committed
1065
                 name.c_str(), params.at(name).c_str());
1066
    }
Guolin Ke's avatar
Guolin Ke committed
1067
1068
1069
1070
1071
    return true;
  }
  return false;
}

Guolin Ke's avatar
Guolin Ke committed
1072
inline bool Config::GetBool(
Guolin Ke's avatar
Guolin Ke committed
1073
1074
  const std::unordered_map<std::string, std::string>& params,
  const std::string& name, bool* out) {
1075
  if (params.count(name) > 0 && !params.at(name).empty()) {
Guolin Ke's avatar
Guolin Ke committed
1076
    std::string value = params.at(name);
Guolin Ke's avatar
Guolin Ke committed
1077
    std::transform(value.begin(), value.end(), value.begin(), Common::tolower);
1078
    if (value == std::string("false") || value == std::string("-")) {
Guolin Ke's avatar
Guolin Ke committed
1079
      *out = false;
1080
    } else if (value == std::string("true") || value == std::string("+")) {
Guolin Ke's avatar
Guolin Ke committed
1081
      *out = true;
1082
    } else {
1083
      Log::Fatal("Parameter %s should be \"true\"/\"+\" or \"false\"/\"-\", got \"%s\"",
Guolin Ke's avatar
Guolin Ke committed
1084
                 name.c_str(), params.at(name).c_str());
Guolin Ke's avatar
Guolin Ke committed
1085
1086
1087
1088
1089
1090
1091
1092
1093
1094
    }
    return true;
  }
  return false;
}

struct ParameterAlias {
  static void KeyAliasTransform(std::unordered_map<std::string, std::string>* params) {
    std::unordered_map<std::string, std::string> tmp_map;
    for (const auto& pair : *params) {
jcipar's avatar
jcipar committed
1095
1096
      auto alias = Config::alias_table().find(pair.first);
      if (alias != Config::alias_table().end()) {  // found alias
Guolin Ke's avatar
Guolin Ke committed
1097
        auto alias_set = tmp_map.find(alias->second);
1098
1099
        if (alias_set != tmp_map.end()) {  // alias already set
                                           // set priority by length & alphabetically to ensure reproducible behavior
wxchan's avatar
wxchan committed
1100
1101
          if (alias_set->second.size() < pair.first.size() ||
            (alias_set->second.size() == pair.first.size() && alias_set->second < pair.first)) {
1102
            Log::Warning("%s is set with %s=%s, %s=%s will be ignored. Current value: %s=%s",
Guolin Ke's avatar
Guolin Ke committed
1103
1104
                         alias->second.c_str(), alias_set->second.c_str(), params->at(alias_set->second).c_str(),
                         pair.first.c_str(), pair.second.c_str(), alias->second.c_str(), params->at(alias_set->second).c_str());
wxchan's avatar
wxchan committed
1105
          } else {
1106
            Log::Warning("%s is set with %s=%s, will be overridden by %s=%s. Current value: %s=%s",
Guolin Ke's avatar
Guolin Ke committed
1107
1108
                         alias->second.c_str(), alias_set->second.c_str(), params->at(alias_set->second).c_str(),
                         pair.first.c_str(), pair.second.c_str(), alias->second.c_str(), pair.second.c_str());
wxchan's avatar
wxchan committed
1109
1110
            tmp_map[alias->second] = pair.first;
          }
1111
        } else {  // alias not set
wxchan's avatar
wxchan committed
1112
1113
          tmp_map.emplace(alias->second, pair.first);
        }
jcipar's avatar
jcipar committed
1114
      } else if (Config::parameter_set().find(pair.first) == Config::parameter_set().end()) {
wxchan's avatar
wxchan committed
1115
        Log::Warning("Unknown parameter: %s", pair.first.c_str());
Guolin Ke's avatar
Guolin Ke committed
1116
1117
1118
      }
    }
    for (const auto& pair : tmp_map) {
wxchan's avatar
wxchan committed
1119
      auto alias = params->find(pair.first);
1120
      if (alias == params->end()) {  // not find
wxchan's avatar
wxchan committed
1121
1122
1123
        params->emplace(pair.first, params->at(pair.second));
        params->erase(pair.second);
      } else {
Guolin Ke's avatar
Guolin Ke committed
1124
1125
1126
        Log::Warning("%s is set=%s, %s=%s will be ignored. Current value: %s=%s",
                     pair.first.c_str(), alias->second.c_str(), pair.second.c_str(), params->at(pair.second).c_str(),
                     pair.first.c_str(), alias->second.c_str());
Guolin Ke's avatar
Guolin Ke committed
1127
1128
1129
1130
1131
      }
    }
  }
};

1132
1133
1134
1135
1136
1137
1138
1139
1140
1141
1142
1143
1144
1145
1146
1147
1148
1149
inline std::string ParseObjectiveAlias(const std::string& type) {
  if (type == std::string("regression") || type == std::string("regression_l2")
    || type == std::string("mean_squared_error") || type == std::string("mse") || type == std::string("l2")
    || type == std::string("l2_root") || type == std::string("root_mean_squared_error") || type == std::string("rmse")) {
    return "regression";
  } else if (type == std::string("regression_l1") || type == std::string("mean_absolute_error")
    || type == std::string("l1") || type == std::string("mae")) {
    return "regression_l1";
  } else if (type == std::string("multiclass") || type == std::string("softmax")) {
    return "multiclass";
  } else if (type == std::string("multiclassova") || type == std::string("multiclass_ova") || type == std::string("ova") || type == std::string("ovr")) {
    return "multiclassova";
  } else if (type == std::string("xentropy") || type == std::string("cross_entropy")) {
    return "cross_entropy";
  } else if (type == std::string("xentlambda") || type == std::string("cross_entropy_lambda")) {
    return "cross_entropy_lambda";
  } else if (type == std::string("mean_absolute_percentage_error") || type == std::string("mape")) {
    return "mape";
1150
1151
1152
  } else if (type == std::string("rank_xendcg") || type == std::string("xendcg") || type == std::string("xe_ndcg")
             || type == std::string("xe_ndcg_mart") || type == std::string("xendcg_mart")) {
    return "rank_xendcg";
1153
1154
1155
1156
1157
1158
1159
1160
1161
1162
1163
1164
1165
1166
1167
  } else if (type == std::string("none") || type == std::string("null") || type == std::string("custom") || type == std::string("na")) {
    return "custom";
  }
  return type;
}

inline std::string ParseMetricAlias(const std::string& type) {
  if (type == std::string("regression") || type == std::string("regression_l2") || type == std::string("l2") || type == std::string("mean_squared_error") || type == std::string("mse")) {
    return "l2";
  } else if (type == std::string("l2_root") || type == std::string("root_mean_squared_error") || type == std::string("rmse")) {
    return "rmse";
  } else if (type == std::string("regression_l1") || type == std::string("l1") || type == std::string("mean_absolute_error") || type == std::string("mae")) {
    return "l1";
  } else if (type == std::string("binary_logloss") || type == std::string("binary")) {
    return "binary_logloss";
1168
1169
  } else if (type == std::string("ndcg") || type == std::string("lambdarank") || type == std::string("rank_xendcg")
             || type == std::string("xendcg") || type == std::string("xe_ndcg") || type == std::string("xe_ndcg_mart") || type == std::string("xendcg_mart")) {
1170
1171
1172
1173
1174
1175
1176
1177
1178
1179
1180
1181
1182
1183
1184
1185
1186
1187
1188
    return "ndcg";
  } else if (type == std::string("map") || type == std::string("mean_average_precision")) {
    return "map";
  } else if (type == std::string("multi_logloss") || type == std::string("multiclass") || type == std::string("softmax") || type == std::string("multiclassova") || type == std::string("multiclass_ova") || type == std::string("ova") || type == std::string("ovr")) {
    return "multi_logloss";
  } else if (type == std::string("xentropy") || type == std::string("cross_entropy")) {
    return "cross_entropy";
  } else if (type == std::string("xentlambda") || type == std::string("cross_entropy_lambda")) {
    return "cross_entropy_lambda";
  } else if (type == std::string("kldiv") || type == std::string("kullback_leibler")) {
    return "kullback_leibler";
  } else if (type == std::string("mean_absolute_percentage_error") || type == std::string("mape")) {
    return "mape";
  } else if (type == std::string("none") || type == std::string("null") || type == std::string("custom") || type == std::string("na")) {
    return "custom";
  }
  return type;
}

Guolin Ke's avatar
Guolin Ke committed
1189
1190
}   // namespace LightGBM

Belinda Trotta's avatar
Belinda Trotta committed
1191
#endif   // LightGBM_CONFIG_H_