Quick Start
===========

This is a quick start guide for LightGBM CLI version.

Follow the `Installation Guide <./Installation-Guide.rst>`__ to install LightGBM first.

**List of other helpful links**

-  `Parameters <./Parameters.rst>`__

-  `Parameters Tuning <./Parameters-Tuning.rst>`__

-  `Python-package Quick Start <./Python-Intro.rst>`__

-  `Python API <./Python-API.rst>`__

Training Data Format
--------------------

LightGBM supports input data files with `CSV`_, `TSV`_ and `LibSVM`_ formats.

Files could be both with and without headers.

Label column could be specified both by index and by name.

Some columns could be ignored.

Categorical Feature Support
~~~~~~~~~~~~~~~~~~~~~~~~~~~

LightGBM can use categorical features directly (without one-hot encoding).
The experiment on `Expo data`_ shows about 8x speed-up compared with one-hot encoding.

For the setting details, please refer to `Parameters <./Parameters.rst>`__.

Weight and Query/Group Data
~~~~~~~~~~~~~~~~~~~~~~~~~~~

LightGBM also supports weighted training, it needs an additional `weight data <./Parameters.rst#io-parameters>`__.
And it needs an additional `query data <./Parameters.rst#io-parameters>`_ for ranking task.

Also, weight and query data could be specified as columns in training data in the same manner as label.

Parameter Quick Look
--------------------

The parameter format is ``key1=value1 key2=value2 ...``.
Parameters can be set both in config file and command line.

Some important parameters:

-  ``config``, default=\ ``""``, type=string, alias=\ ``config_file``

   -  path to config file

-  ``task``, default=\ ``train``, type=enum, options=\ ``train``, ``predict``, ``convert_model``

   -  ``train``, alias=\ ``training``, for training

   -  ``predict``, alias=\ ``prediction``, ``test``, for prediction

   -  ``convert_model``, for converting model file into if-else format, see more information in `IO Parameters <./Parameters.rst#io-parameters>`__

-  ``application``, default=\ ``regression``, type=enum,
   options=\ ``regression``, ``regression_l1``, ``huber``, ``fair``, ``poisson``, ``quantile``, ``mape``, ``gammma``, ``tweedie``,
   ``binary``, ``multiclass``, ``multiclassova``, ``xentropy``, ``xentlambda``, ``lambdarank``,
   alias=\ ``objective``, ``app``

   -  regression application

      -  ``regression_l2``, L2 loss, alias=\ ``regression``, ``mean_squared_error``, ``mse``, ``l2_root``, ``root_mean_squared_error``, ``rmse``

      -  ``regression_l1``, L1 loss, alias=\ ``mean_absolute_error``, ``mae``

      -  ``huber``, `Huber loss`_

      -  ``fair``, `Fair loss`_

      -  ``poisson``, `Poisson regression`_

      -  ``quantile``, `Quantile regression`_

      -  ``mape``, `MAPE loss`_, alias=\ ``mean_absolute_percentage_error``

      -  ``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`_

      -  ``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`_

   -  ``binary``, binary `log loss`_ classification application

   -  multi-class classification application

      -  ``multiclass``, `softmax`_ objective function, alias=\ ``softmax``

      -  ``multiclassova``, `One-vs-All`_ binary objective function, alias=\ ``multiclass_ova``, ``ova``, ``ovr``

      -  ``num_class`` should be set as well

   -  cross-entropy application

      -  ``xentropy``, objective function for cross-entropy (with optional linear weights), alias=\ ``cross_entropy``

      -  ``xentlambda``, alternative parameterization of cross-entropy, alias=\ ``cross_entropy_lambda``

      -  the label is anything in interval [0, 1]

   -  ``lambdarank``, `lambdarank`_ application

      -  the label should be ``int`` type in lambdarank tasks, and larger number represent the higher relevance (e.g. 0:bad, 1:fair, 2:good, 3:perfect)

      -  ``label_gain`` can be used to set the gain(weight) of ``int`` label

      -  all values in ``label`` must be smaller than number of elements in ``label_gain``

-  ``boosting``, default=\ ``gbdt``, type=enum,
   options=\ ``gbdt``, ``rf``, ``dart``, ``goss``,
   alias=\ ``boost``, ``boosting_type``

   -  ``gbdt``, traditional Gradient Boosting Decision Tree

   -  ``rf``, Random Forest

   -  ``dart``, `Dropouts meet Multiple Additive Regression Trees`_

   -  ``goss``, Gradient-based One-Side Sampling

-  ``data``, default=\ ``""``, type=string, alias=\ ``train``, ``train_data``

   -  training data, LightGBM will train from this data

-  ``valid``, default=\ ``""``, type=multi-string, alias=\ ``test``, ``valid_data``, ``test_data``

   -  validation/test data, LightGBM will output metrics for these data

   -  support multi validation data, separate by ``,``

-  ``num_iterations``, default=\ ``100``, type=int,
   alias=\ ``num_iteration``, ``num_tree``, ``num_trees``, ``num_round``, ``num_rounds``, ``num_boost_round``, ``n_estimators``

   -  number of boosting iterations

-  ``learning_rate``, default=\ ``0.1``, type=double, alias=\ ``shrinkage_rate``

   -  shrinkage rate

-  ``num_leaves``, default=\ ``31``, type=int, alias=\ ``num_leaf``

   -  number of leaves in one tree

-  ``tree_learner``, default=\ ``serial``, type=enum, options=\ ``serial``, ``feature``, ``data``, ``voting``, alias=\ ``tree``

   -  ``serial``, single machine tree learner

   -  ``feature``, alias=\ ``feature_parallel``, feature parallel tree learner

   -  ``data``, alias=\ ``data_parallel``, data parallel tree learner

   -  ``voting``, alias=\ ``voting_parallel``, voting parallel tree learner

   -  refer to `Parallel Learning Guide <./Parallel-Learning-Guide.rst>`__ to get more details

-  ``num_threads``, default=\ ``OpenMP_default``, type=int, alias=\ ``num_thread``, ``nthread``

   -  number of threads for LightGBM

   -  for the best speed, set this to the number of **real CPU cores**,
      not the number of threads (most CPU using `hyper-threading`_ to generate 2 threads per CPU core)

   -  for parallel learning, should not use full CPU cores since this will cause poor performance for the network

-  ``max_depth``, default=\ ``-1``, type=int

   -  limit the max depth for tree model.
      This is used to deal with over-fitting when ``#data`` is small.
      Tree still grows by leaf-wise

   -  ``< 0`` means no limit

-  ``min_data_in_leaf``, default=\ ``20``, type=int, alias=\ ``min_data_per_leaf`` , ``min_data``, ``min_child_samples``

   -  minimal number of data in one leaf. Can be used this to deal with over-fitting

-  ``min_sum_hessian_in_leaf``, default=\ ``1e-3``, type=double,
   alias=\ ``min_sum_hessian_per_leaf``, ``min_sum_hessian``, ``min_hessian``, ``min_child_weight``

   -  minimal sum hessian in one leaf. Like ``min_data_in_leaf``, it can be used to deal with over-fitting

For all parameters, please refer to `Parameters <./Parameters.rst>`__.

Run LightGBM
------------

For Windows:

::

    lightgbm.exe config=your_config_file other_args ...

For Unix:

::

    ./lightgbm config=your_config_file other_args ...

Parameters can be set both in config file and command line, and the parameters in command line have higher priority than in config file.
For example, following command line will keep ``num_trees=10`` and ignore the same parameter in config file.

::

    ./lightgbm config=train.conf num_trees=10

Examples
--------

-  `Binary Classification <https://github.com/Microsoft/LightGBM/tree/master/examples/binary_classification>`__

-  `Regression <https://github.com/Microsoft/LightGBM/tree/master/examples/regression>`__

-  `Lambdarank <https://github.com/Microsoft/LightGBM/tree/master/examples/lambdarank>`__

-  `Parallel Learning <https://github.com/Microsoft/LightGBM/tree/master/examples/parallel_learning>`__

.. _CSV: https://en.wikipedia.org/wiki/Comma-separated_values

.. _TSV: https://en.wikipedia.org/wiki/Tab-separated_values

.. _LibSVM: https://www.csie.ntu.edu.tw/~cjlin/libsvm/

.. _Expo data: http://stat-computing.org/dataexpo/2009/

.. _Huber loss: https://en.wikipedia.org/wiki/Huber_loss

.. _Fair loss: https://www.kaggle.com/c/allstate-claims-severity/discussion/24520

.. _Poisson regression: https://en.wikipedia.org/wiki/Poisson_regression

.. _Quantile regression: https://en.wikipedia.org/wiki/Quantile_regression

.. _MAPE loss: https://en.wikipedia.org/wiki/Mean_absolute_percentage_error

.. _log loss: https://en.wikipedia.org/wiki/Cross_entropy

.. _softmax: https://en.wikipedia.org/wiki/Softmax_function

.. _One-vs-All: https://en.wikipedia.org/wiki/Multiclass_classification#One-vs.-rest

.. _lambdarank: https://papers.nips.cc/paper/2971-learning-to-rank-with-nonsmooth-cost-functions.pdf

.. _Dropouts meet Multiple Additive Regression Trees: https://arxiv.org/abs/1505.01866

.. _hyper-threading: https://en.wikipedia.org/wiki/Hyper-threading

.. _gamma-distributed: https://en.wikipedia.org/wiki/Gamma_distribution#Applications

.. _tweedie-distributed: https://en.wikipedia.org/wiki/Tweedie_distribution#Applications
