[PYTHON] Explication de tous les paramètres lightGBM (en route)

Contenu

J'expliquerai tous les paramètres de lightGBM en gros. Comme il y a beaucoup de contenu, je vais le traduire lentement sur plusieurs jours. Je mettrai à jour les détails dans un article séparé de temps à autre. Si vous faites une erreur, j'apprécierais que vous la signaliez. Le github officiel de lightGBM est ici

Le format de description de base est par défaut = par défaut, type = type, options = options, contraintes = contraintes

Paramètres de base

-- config, default = "", type = string, alias: config_file

--Définir le chemin du fichier

-- tâche, par défaut = train '', type = enum, options: train, prédire '', convert_model, refit, alias: `` type_tâche ''

-- train, alias: formation

--prédire '', alias: prédiction, test``

-- convert_model, Convertit le fichier modèle au format if-else. Pour plus d'informations, consultez Paramètres IO

-- refit, refit avec de nouvelles données, alias: refit_tree

--Revenir

-- régression, perte L2, alias: regression_l2, l2, mean_squared_error, mse, l2_root, root_mean_squared_error, `` rmse ''

-- regression_l1, perte L1, alias: l1, mean_absolute_error, mae

  -  ``huber``, [Huber loss](https://en.wikipedia.org/wiki/Huber_loss>)

  -  ``fair``, [Fair loss](https://www.kaggle.com/c/allstate-claims-severity/discussion/24520)

  -  ``poisson``, [Poisson regression](https://en.wikipedia.org/wiki/Poisson_regression)

  -  ``quantile``, [Quantile regression](https://en.wikipedia.org/wiki/Quantile_regression)

  -  ``mape``, [MAPE loss](https://en.wikipedia.org/wiki/Mean_absolute_percentage_error): ``mean_absolute_percentage_error``

-- gamma, régression gamma avec log-link Exemple d'utilisation: cas où la fréquence de couverture d'assurance est modélisée, et autres cas où la distribution gamma est suivie distribué par gamma

-- tweedie, régression Tweedie avec log-link. Exemple d'utilisation: Modélisation de la perte totale d'assurance et autres cas suivant la distribution de tweedie [tweedie-distribué](https://en.wikipedia.org/wiki/ Tweedie_distribution # Occurrence_and_applications)

--Dichotomie

-- binaire, binaire perte de journal (ou régression logistique)

--Label doit être 0 ou 1; [0,1] Voir Entropie croisée (https://en.wikipedia.org/wiki/Cross_entropy) pour les probabilités générales des étiquettes

-- multiclass, Softmax, Alias: softmax

-- multiclassova, One-vs-All, Alias: multiclass_ova, ʻova`` , ʻOvr``

  -  ``num_class`` should be set as well

-Application croisée d'entropie

-- cross_entropy, fonction objective de l'entropie croisée (le poids est arbitraire), alias: xentropy

-- cross_entropy_lambda, autre paramétrage de l'entropie croisée, alias: xentlambda

  -  label is anything in interval [0, 1]

--Application de classement

-- lambdarank, lambdarank. label_gain (livre de définition) (Explication à partir de la page) a des étiquettes entières et pondère toute valeur de l'étiquette afin qu'elle soit inférieure au nombre d'éléments dans label_gain.

-- rank_xendcg, XE_NDCG_MART Fonction d'objectif de rang, alias: xendcg, xe_ndcg, xe_ndcg_mart, `` xendcg_mart '' ''

-- rank_xendcg Le calcul est rapide et le comportement est similaire à lambdarank.

-- boosting, default = gbdt, type = enum, options: gbdt, rf, dart, goss, alias: boosting_type '', `` booster ''

-- gbdt, amplification de gradient typique, également connue sous le nom de: gbrt

-- rf, arbre aléatoire, alias: random_forest

-- data, default = "", type = string, alias: train, train_data, train_data_file, data_filename

-- valid, default = "", type = string, alias: test, valid_data, valid_data_file, test_data, test_data_file` `,` `noms_fichiers_valides

-- num_iterations, par défaut = 100 '', type = int, alias: num_iteration, n_iter, num_tree, num_trees, num_round, num_rounds, num_boost_round, n_estimators, Contraintes: num_iterations> = 0``

--Nombre de boosting

-- learning_rate, default = 0.1, type = double, alias: shrinkage_rate, ```eta, contrainte: learning_rate> 0.0``

-- dart affecte les poids normalisés des `` arbres tombés ''.

-- num_leaves, default = 31, type = int, alias: num_leaf, max_leaves, max_leaf, contraintes: `` 1 <num_leaves <= 131072` ''

-Nombre maximum de feuilles dans un arbre.

-- tree_learner, default = serial '', type = enum, options: serial, feature, data, vote, alias: tree , tree_type, tree_learner_type``

-- fonctionnalité, fonctionnalité apprenant en arbre parallèle, alias: feature_parallel

-- data, apprenant d'arbre parallèle de données, alias: data_parallel

--Nombre de threads utilisés pour LightGBM

--Dans OpenMP, `` 0 '' signifie le nombre par défaut de threads.

--Task Manager et d'autres outils de surveillance du processeur peuvent montrer que tous les cœurs ne sont pas utilisés. ** C'est normal **

--Spécifiez le périphérique utilisé pour l'apprentissage des arbres. Vous pouvez accélérer en utilisant le GPU.

--Par défaut, cette graine n'est pas utilisée en raison des valeurs par défaut des autres graines.

-Cette graine a une priorité moindre que les autres graines. Autrement dit, si vous spécifiez explicitement une autre graine, cette graine sera écrasée.

Paramètres de contrôle d'apprentissage

--Seul `` cpu '' peut être utilisé

--Il est recommandé d'appliquer ce paramètre dans les cas suivants:

-- num_threads est grand, par exemple > 20

--Je veux réduire le coût de la mémoire

--Seul `` cpu '' peut être utilisé

--Il est recommandé d'appliquer ce paramètre dans les cas suivants:

--Lorsque vous souhaitez accélérer en utilisant une petite valeur bagging_fraction '' ou goss ''

--Taille maximale du cache de l'histogramme historique (unité Mo)

-- <= 0 signifie illimité.

-- min_data_in_leaf, default = 20 '', type = int, alias: min_data_per_leaf, min_data, min_child_samples, contraintes: min_data_in_leaf> = 0``

--Nombre minimum de données pour une feuille. Utilisé pour traiter le surajustement.

-Somme minimale de hesian dans une feuille. Similaire à `` min_data_in_leaf '', il est utilisé pour gérer le surajustement.

-- bagging_fraction, default = 1.0, type = double, alias: sub_row, subsample, bagging, contraintes: `` 0.0 <bagging_fraction <= 1.0` ''

-- pos_bagging_fraction, default = 1.0, type = double, alias: pos_sub_row, pos_subsample, pos_bagging, contraintes: `` 0.0 <pos_bagging_fraction <= 1.0` ''

--Utiliser uniquement avec `` binaire ''.

--Doit être utilisé avec `` neg_bagging_fraction ''.

--Si vous le définissez sur `` 1.0 '', il sera invalide.

-- neg_bagging_fraction, default = 1.0, type = double, alias: neg_sub_row, neg_subsample, neg_bagging, contraintes: `` 0.0 <neg_bagging_fraction <= 1.0` ''

--Utiliser uniquement avec `` binaire ''.

--Utiliser avec `` pos_bagging_fraction ''.

--Si vous le définissez sur `` 1.0 '', il sera invalide.

-- bagging_freq, par défaut = 0, type = int, alias: subsample_freq

-- bagging_seed, par défaut = 3 '', type = int, alias: bagging_fraction_seed``

--Bagging des semences aléatoires

-- feature_fraction, default = 1.0, type = double, alias: sub_feature, colsample_bytree, contrainte: 0.0 <feature_fraction <= 1.0

--Peut être utilisé pour accélérer la formation.

--Peut être utilisé comme contre-mesure contre le surajustement.

-- feature_fraction_bynode, default = 1.0, type = double, alias: sub_feature_bynode, colsample_bynode, contrainte: 0.0 <feature_fraction_bynode <= 1.0

--Peut être utilisé comme contre-mesure contre le surajustement.

--Si `` true '', lightGBM sélectionnera un seul seuil aléatoire pour chaque fonctionnalité lors de l'évaluation des fractionnements de nœuds.

--ʻearly_stopping_round``, default = `` 0``, type = int, alias: ʻearly_stopping_rounds,` ʻearly_stopping, n_iter_no_change

--Au dernier tour de `ʻearly_stopping_round``, arrêtez l'entraînement si les performances ne s'améliorent pas.

-- <= 0 signifie invalide.

--Si vous souhaitez n'utiliser que la première évaluation de l'arrêt prématuré, définissez ceci sur `` vrai ''.

-- max_delta_step, par défaut = 0.0 '', type = double, alias: max_tree_output, max_leaf_output``

-- <= 0 signifie illimité.

-- lambda_l1, par défaut = 0.0, type = double, alias: reg_alpha, limite: lambda_l1> = 0.0

-- lambda_l2, par défaut = 0.0, type = double, alias: reg_lambda, lambda, limite: lambda_l2> = 0.0

-- min_gain_to_split, par défaut = 0.0, type = double, alias: min_split_gain, limite: min_gain_to_split> = 0.0

--Gain minimum lors du fractionnement (gain)

-- drop_rate, default = 0.1, type = double, alias: rate_drop, contrainte: 0.0 <= drop_rate <= 1.0

IO Parameters

Dataset Parameters


-  ``max_bin`` , default = ``255``, type = int, constraints: ``max_bin > 1``

   -  max number of bins that feature values will be bucketed in

   -  small number of bins may reduce training accuracy but may increase general power (deal with over-fitting)

   -  LightGBM will auto compress memory according to ``max_bin``. For example, LightGBM will use ``uint8_t`` for feature value if ``max_bin=255``

-  ``max_bin_by_feature`` , default = ``None``, type = multi-int

   -  max number of bins for each feature

   -  if not specified, will use ``max_bin`` for all features

-  ``min_data_in_bin`` , default = ``3``, type = int, constraints: ``min_data_in_bin > 0``

   -  minimal number of data inside one bin

   -  use this to avoid one-data-one-bin (potential over-fitting)

-  ``bin_construct_sample_cnt`` , default = ``200000``, type = int, aliases: ``subsample_for_bin``, constraints: ``bin_construct_sample_cnt > 0``

   -  number of data that sampled to construct histogram bins

   -  setting this to larger value will give better training result, but will increase data loading time

   -  set this to larger value if data is very sparse

-  ``data_random_seed`` , default = ``1``, type = int, aliases: ``data_seed``

   -  random seed for sampling data to construct histogram bins

-  ``is_enable_sparse`` , default = ``true``, type = bool, aliases: ``is_sparse``, ``enable_sparse``, ``sparse``

   -  used to enable/disable sparse optimization

-  ``enable_bundle`` , default = ``true``, type = bool, aliases: ``is_enable_bundle``, ``bundle``

   -  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>`__

   -  **Note**: disabling this may cause the slow training speed for sparse datasets

-  ``use_missing`` , default = ``true``, type = bool

   -  set this to ``false`` to disable the special handle of missing value

-  ``zero_as_missing`` , default = ``false``, type = bool

   -  set this to ``true`` to treat all zero as missing values (including the unshown values in LibSVM / sparse matrices)

   -  set this to ``false`` to use ``na`` for representing missing values

-  ``feature_pre_filter`` , default = ``true``, type = bool

   -  set this to ``true`` to pre-filter the unsplittable features by ``min_data_in_leaf``

   -  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

   -  **Note**: setting this to ``false`` may slow down the training

-  ``pre_partition`` , default = ``false``, type = bool, aliases: ``is_pre_partition``

   -  used for parallel learning (excluding the ``feature_parallel`` mode)

   -  ``true`` if training data are pre-partitioned, and different machines use different partitions

-  ``two_round`` , default = ``false``, type = bool, aliases: ``two_round_loading``, ``use_two_round_loading``

   -  set this to ``true`` if data file is too big to fit in memory

   -  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

   -  **Note**: works only in case of loading data directly from file

-  ``header`` , default = ``false``, type = bool, aliases: ``has_header``

   -  set this to ``true`` if input data has header

   -  **Note**: works only in case of loading data directly from file

-  ``label_column`` , default = ``""``, type = int or string, aliases: ``label``

   -  used to specify the label column

   -  use number for index, e.g. ``label=0`` means column\_0 is the label

   -  add a prefix ``name:`` for column name, e.g. ``label=name:is_click``

   -  **Note**: works only in case of loading data directly from file

-  ``weight_column`` , default = ``""``, type = int or string, aliases: ``weight``

   -  used to specify the weight column

   -  use number for index, e.g. ``weight=0`` means column\_0 is the weight

   -  add a prefix ``name:`` for column name, e.g. ``weight=name:weight``

   -  **Note**: works only in case of loading data directly from file

   -  **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``

-  ``group_column`` , default = ``""``, type = int or string, aliases: ``group``, ``group_id``, ``query_column``, ``query``, ``query_id``

   -  used to specify the query/group id column

   -  use number for index, e.g. ``query=0`` means column\_0 is the query id

   -  add a prefix ``name:`` for column name, e.g. ``query=name:query_id``

   -  **Note**: works only in case of loading data directly from file

   -  **Note**: data should be grouped by query\_id

   -  **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``

-  ``ignore_column`` , default = ``""``, type = multi-int or string, aliases: ``ignore_feature``, ``blacklist``

   -  used to specify some ignoring columns in training

   -  use number for index, e.g. ``ignore_column=0,1,2`` means column\_0, column\_1 and column\_2 will be ignored

   -  add a prefix ``name:`` for column name, e.g. ``ignore_column=name:c1,c2,c3`` means c1, c2 and c3 will be ignored

   -  **Note**: works only in case of loading data directly from file

   -  **Note**: index starts from ``0`` and it doesn't count the label column when passing type is ``int``

   -  **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

-  ``categorical_feature`` , default = ``""``, type = multi-int or string, aliases: ``cat_feature``, ``categorical_column``, ``cat_column``

   -  used to specify categorical features

   -  use number for index, e.g. ``categorical_feature=0,1,2`` means column\_0, column\_1 and column\_2 are categorical features

   -  add a prefix ``name:`` for column name, e.g. ``categorical_feature=name:c1,c2,c3`` means c1, c2 and c3 are categorical features

   -  **Note**: only supports categorical with ``int`` type (not applicable for data represented as pandas DataFrame in Python-package)

   -  **Note**: index starts from ``0`` and it doesn't count the label column when passing type is ``int``

   -  **Note**: all values should be less than ``Int32.MaxValue`` (2147483647)

   -  **Note**: using large values could be memory consuming. Tree decision rule works best when categorical features are presented by consecutive integers starting from zero

   -  **Note**: all negative values will be treated as **missing values**

   -  **Note**: the output cannot be monotonically constrained with respect to a categorical feature

-  ``forcedbins_filename`` , default = ``""``, type = string

   -  path to a ``.json`` file that specifies bin upper bounds for some or all features

   -  ``.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)

   -  see `this file <https://github.com/microsoft/LightGBM/tree/master/examples/regression/forced_bins.json>`__ as an example

-  ``save_binary`` , default = ``false``, type = bool, aliases: ``is_save_binary``, ``is_save_binary_file``

   -  if ``true``, LightGBM will save the dataset (including validation data) to a binary file. This speed ups the data loading for the next time

   -  **Note**: ``init_score`` is not saved in binary file

   -  **Note**: can be used only in CLI version; for language-specific packages you can use the correspondent function

Predict Parameters

Convert Parameters


-  ``convert_model_language`` , default = ``""``, type = string

   -  used only in ``convert_model`` task

   -  only ``cpp`` is supported yet; for conversion model to other languages consider using `m2cgen <https://github.com/BayesWitnesses/m2cgen>`__ utility

   -  if ``convert_model_language`` is set and ``task=train``, the model will be also converted

   -  **Note**: can be used only in CLI version

-  ``convert_model`` , default = ``gbdt_prediction.cpp``, type = string, aliases: ``convert_model_file``

   -  used only in ``convert_model`` task

   -  output filename of converted model

   -  **Note**: can be used only in CLI version

Objective Parameters
--------------------

-  ``objective_seed`` , default = ``5``, type = int

   -  used only in ``rank_xendcg`` objective

   -  random seed for objectives, if random process is needed

-  ``num_class`` , default = ``1``, type = int, aliases: ``num_classes``, constraints: ``num_class > 0``

   -  used only in ``multi-class`` classification application

-  ``is_unbalance`` , default = ``false``, type = bool, aliases: ``unbalance``, ``unbalanced_sets``

   -  used only in ``binary`` and ``multiclassova`` applications

   -  set this to ``true`` if training data are unbalanced

   -  **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

   -  **Note**: this parameter cannot be used at the same time with ``scale_pos_weight``, choose only **one** of them

-  ``scale_pos_weight`` , default = ``1.0``, type = double, constraints: ``scale_pos_weight > 0.0``

   -  used only in ``binary`` and ``multiclassova`` applications

   -  weight of labels with positive class

   -  **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

   -  **Note**: this parameter cannot be used at the same time with ``is_unbalance``, choose only **one** of them

-  ``sigmoid`` , default = ``1.0``, type = double, constraints: ``sigmoid > 0.0``

   -  used only in ``binary`` and ``multiclassova`` classification and in ``lambdarank`` applications

   -  parameter for the sigmoid function

-  ``boost_from_average`` , default = ``true``, type = bool

   -  used only in ``regression``, ``binary``, ``multiclassova`` and ``cross-entropy`` applications

   -  adjusts initial score to the mean of labels for faster convergence

-  ``reg_sqrt`` , default = ``false``, type = bool

   -  used only in ``regression`` application

   -  used to fit ``sqrt(label)`` instead of original values and prediction result will be also automatically converted to ``prediction^2``

   -  might be useful in case of large-range labels

-  ``alpha`` , default = ``0.9``, type = double, constraints: ``alpha > 0.0``

   -  used only in ``huber`` and ``quantile`` ``regression`` applications

   -  parameter for `Huber loss <https://en.wikipedia.org/wiki/Huber_loss>`__ and `Quantile regression <https://en.wikipedia.org/wiki/Quantile_regression>`__

-  ``fair_c`` , default = ``1.0``, type = double, constraints: ``fair_c > 0.0``

   -  used only in ``fair`` ``regression`` application

   -  parameter for `Fair loss <https://www.kaggle.com/c/allstate-claims-severity/discussion/24520>`__

-  ``poisson_max_delta_step`` , default = ``0.7``, type = double, constraints: ``poisson_max_delta_step > 0.0``

   -  used only in ``poisson`` ``regression`` application

   -  parameter for `Poisson regression <https://en.wikipedia.org/wiki/Poisson_regression>`__ to safeguard optimization

-  ``tweedie_variance_power`` , default = ``1.5``, type = double, constraints: ``1.0 <= tweedie_variance_power < 2.0``

   -  used only in ``tweedie`` ``regression`` application

   -  used to control the variance of the tweedie distribution

   -  set this closer to ``2`` to shift towards a **Gamma** distribution

   -  set this closer to ``1`` to shift towards a **Poisson** distribution

-  ``lambdarank_truncation_level`` , default = ``20``, type = int, constraints: ``lambdarank_truncation_level > 0``

   -  used only in ``lambdarank`` application

   -  used for truncating the max DCG, 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>`__

-  ``lambdarank_norm`` , default = ``true``, type = bool

   -  used only in ``lambdarank`` application

   -  set this to ``true`` to normalize the lambdas for different queries, and improve the performance for unbalanced data

   -  set this to ``false`` to enforce the original lambdarank algorithm

-  ``label_gain`` , default = ``0,1,3,7,15,31,63,...,2^30-1``, type = multi-double

   -  used only in ``lambdarank`` application

   -  relevant gain for labels. For example, the gain of label ``2`` is ``3`` in case of default label gains

   -  separate by ``,``

Metric Parameters
-----------------

-  ``metric`` , default = ``""``, type = multi-enum, aliases: ``metrics``, ``metric_types``

   -  metric(s) to be evaluated on the evaluation set(s)

      -  ``""`` (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)

      -  ``"None"`` (string, **not** a ``None`` value) means that no metric will be registered, aliases: ``na``, ``null``, ``custom``

      -  ``l1``, absolute loss, aliases: ``mean_absolute_error``, ``mae``, ``regression_l1``

      -  ``l2``, square loss, aliases: ``mean_squared_error``, ``mse``, ``regression_l2``, ``regression``

      -  ``rmse``, root square loss, aliases: ``root_mean_squared_error``, ``l2_root``

      -  ``quantile``, `Quantile regression <https://en.wikipedia.org/wiki/Quantile_regression>`__

      -  ``mape``, `MAPE loss <https://en.wikipedia.org/wiki/Mean_absolute_percentage_error>`__, aliases: ``mean_absolute_percentage_error``

      -  ``huber``, `Huber loss <https://en.wikipedia.org/wiki/Huber_loss>`__

      -  ``fair``, `Fair loss <https://www.kaggle.com/c/allstate-claims-severity/discussion/24520>`__

      -  ``poisson``, negative log-likelihood for `Poisson regression <https://en.wikipedia.org/wiki/Poisson_regression>`__

      -  ``gamma``, negative log-likelihood for **Gamma** regression

      -  ``gamma_deviance``, residual deviance for **Gamma** regression

      -  ``tweedie``, negative log-likelihood for **Tweedie** regression

      -  ``ndcg``, `NDCG <https://en.wikipedia.org/wiki/Discounted_cumulative_gain#Normalized_DCG>`__, aliases: ``lambdarank``, ``rank_xendcg``, ``xendcg``, ``xe_ndcg``, ``xe_ndcg_mart``, ``xendcg_mart``

      -  ``map``, `MAP <https://makarandtapaswi.wordpress.com/2012/07/02/intuition-behind-average-precision-and-map/>`__, aliases: ``mean_average_precision``

      -  ``auc``, `AUC <https://en.wikipedia.org/wiki/Receiver_operating_characteristic#Area_under_the_curve>`__

      -  ``binary_logloss``, `log loss <https://en.wikipedia.org/wiki/Cross_entropy>`__, aliases: ``binary``

      -  ``binary_error``, for one sample: ``0`` for correct classification, ``1`` for error classification

      -  ``auc_mu``, `AUC-mu <http://proceedings.mlr.press/v97/kleiman19a/kleiman19a.pdf>`__

      -  ``multi_logloss``, log loss for multi-class classification, aliases: ``multiclass``, ``softmax``, ``multiclassova``, ``multiclass_ova``, ``ova``, ``ovr``

      -  ``multi_error``, error rate for multi-class classification

      -  ``cross_entropy``, cross-entropy (with optional linear weights), aliases: ``xentropy``

      -  ``cross_entropy_lambda``, "intensity-weighted" cross-entropy, aliases: ``xentlambda``

      -  ``kullback_leibler``, `Kullback-Leibler divergence <https://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence>`__, aliases: ``kldiv``

   -  support multiple metrics, separated by ``,``

-  ``metric_freq`` , default = ``1``, type = int, aliases: ``output_freq``, constraints: ``metric_freq > 0``

   -  frequency for metric output

   -  **Note**: can be used only in CLI version

-  ``is_provide_training_metric`` , default = ``false``, type = bool, aliases: ``training_metric``, ``is_training_metric``, ``train_metric``

   -  set this to ``true`` to output metric result over training dataset

   -  **Note**: can be used only in CLI version

-  ``eval_at`` , default = ``1,2,3,4,5``, type = multi-int, aliases: ``ndcg_eval_at``, ``ndcg_at``, ``map_eval_at``, ``map_at``

   -  used only with ``ndcg`` and ``map`` metrics

   -  `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 ``,``

-  ``multi_error_top_k`` , default = ``1``, type = int, constraints: ``multi_error_top_k > 0``

   -  used only with ``multi_error`` metric

   -  threshold for top-k multi-error metric

   -  the error on each sample is ``0`` if the true class is among the top ``multi_error_top_k`` predictions, and ``1`` otherwise

      -  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

   -  when ``multi_error_top_k=1`` this is equivalent to the usual multi-error metric

-  ``auc_mu_weights`` , default = ``None``, type = multi-double

   -  used only with ``auc_mu`` metric

   -  list representing flattened matrix (in row-major order) giving loss weights for classification errors

   -  list should have ``n * n`` elements, where ``n`` is the number of classes

   -  the matrix co-ordinate ``[i, j]`` should correspond to the ``i * n + j``-th element of the list

   -  if not specified, will use equal weights for all classes

Network Parameters
------------------

-  ``num_machines`` , default = ``1``, type = int, aliases: ``num_machine``, constraints: ``num_machines > 0``

   -  the number of machines for parallel learning application

   -  this parameter is needed to be set in both **socket** and **mpi** versions

-  ``local_listen_port`` , default = ``12400``, type = int, aliases: ``local_port``, ``port``, constraints: ``local_listen_port > 0``

   -  TCP listen port for local machines

   -  **Note**: don't forget to allow this port in firewall settings before training

-  ``time_out`` , default = ``120``, type = int, constraints: ``time_out > 0``

   -  socket time-out in minutes

-  ``machine_list_filename`` , default = ``""``, type = string, aliases: ``machine_list_file``, ``machine_list``, ``mlist``

   -  path of file that lists machines for this parallel learning application

   -  each line contains one IP and one port for one machine. The format is ``ip port`` (space as a separator)

-  ``machines`` , default = ``""``, type = string, aliases: ``workers``, ``nodes``

   -  list of machines in the following format: ``ip1:port1,ip2:port2``

GPU Parameters
--------------

-  ``gpu_platform_id`` , default = ``-1``, type = int

   -  OpenCL platform ID. Usually each GPU vendor exposes one OpenCL platform

   -  ``-1`` means the system-wide default platform

   -  **Note**: refer to `GPU Targets <./GPU-Targets.rst#query-opencl-devices-in-your-system>`__ for more details

-  ``gpu_device_id`` , default = ``-1``, type = int

   -  OpenCL device ID in the specified platform. Each GPU in the selected platform has a unique device ID

   -  ``-1`` means the default device in the selected platform

   -  **Note**: refer to `GPU Targets <./GPU-Targets.rst#query-opencl-devices-in-your-system>`__ for more details

-  ``gpu_use_dp`` , default = ``false``, type = bool

   -  set this to ``true`` to use double precision math on GPU (by default single precision is used)

.. end params list

Others
------

Continued Training with Input Score

LightGBM supports continued training with initial scores. It uses an additional file to store these initial scores, like the following:

::

0.5
-0.1
0.9
...

It means the initial score of the first data row is 0.5, second is -0.1, and so on. The initial score file corresponds with data file line by line, and has per score per line.

And if the name of data file is train.txt, the initial score file should be named as train.txt.init and placed in the same folder as the data file. In this case, LightGBM will auto load initial score file if it exists.

Weight Data


LightGBM supports weighted training. It uses an additional file to store weight data, like the following:

::

    1.0
    0.5
    0.8
    ...

It means the weight of the first data row is ``1.0``, second is ``0.5``, and so on.
The weight file corresponds with data file line by line, and has per weight per line.

And if the name of data file is ``train.txt``, the weight file should be named as ``train.txt.weight`` and placed in the same folder as the data file.
In this case, LightGBM will load the weight file automatically if it exists.

Also, you can include weight column in your data file. Please refer to the ``weight_column`` `parameter <#weight_column>`__ in above.

Query Data
~~~~~~~~~~

For learning to rank, it needs query information for training data.
LightGBM uses an additional file to store query data, like the following:

::

    27
    18
    67
    ...

It means first ``27`` lines samples belong to one query and next ``18`` lines belong to another, and so on.

**Note**: data should be ordered by the query.

If the name of data file is ``train.txt``, the query file should be named as ``train.txt.query`` and placed in the same folder as the data file.
In this case, LightGBM will load the query file automatically if it exists.

Also, you can include query/group id column in your data file. Please refer to the ``group_column`` `parameter <#group_column>`__ in above.

.. _Laurae++ Interactive Documentation: https://sites.google.com/view/lauraepp/parameters

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