Python-package Introduction
===========================

This document gives a basic walk-through of LightGBM Python-package.

**List of other helpful links**

-  `Python Examples <https://github.com/microsoft/LightGBM/tree/master/examples/python-guide>`__

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

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

Install
-------

The preferred way to install LightGBM is via pip:

::

    pip install lightgbm

Refer to `Python-package`_ folder for the detailed installation guide.

To verify your installation, try to ``import lightgbm`` in Python:

::

    import lightgbm as lgb

Data Interface
--------------

The LightGBM Python module can load data from:

-  LibSVM (zero-based) / TSV / CSV format text file

-  NumPy 2D array(s), pandas DataFrame, H2O DataTable's Frame, SciPy sparse matrix

-  LightGBM binary file

-  LightGBM ``Sequence`` object(s)

The data is stored in a ``Dataset`` object.

Many of the examples in this page use functionality from ``numpy``. To run the examples, be sure to import ``numpy`` in your session.

.. code:: python

    import numpy as np

**To load a LibSVM (zero-based) text file or a LightGBM binary file into Dataset:**

.. code:: python

    train_data = lgb.Dataset('train.svm.bin')

**To load a numpy array into Dataset:**

.. code:: python

    rng = np.random.default_rng()
    data = rng.uniform(size=(500, 10))  # 500 entities, each contains 10 features
    label = rng.integers(low=0, high=2, size=(500, ))  # binary target
    train_data = lgb.Dataset(data, label=label)

**To load a scipy.sparse.csr\_matrix array into Dataset:**

.. code:: python

    import scipy
    csr = scipy.sparse.csr_matrix((dat, (row, col)))
    train_data = lgb.Dataset(csr)

**Load from Sequence objects:**

We can implement ``Sequence`` interface to read binary files. The following example shows reading HDF5 file with ``h5py``.

.. code:: python

    import h5py

    class HDFSequence(lgb.Sequence):
        def __init__(self, hdf_dataset, batch_size):
            self.data = hdf_dataset
            self.batch_size = batch_size

        def __getitem__(self, idx):
            return self.data[idx]

        def __len__(self):
            return len(self.data)

    f = h5py.File('train.hdf5', 'r')
    train_data = lgb.Dataset(HDFSequence(f['X'], 8192), label=f['Y'][:])

Features of using ``Sequence`` interface:

- Data sampling uses random access, thus does not go through the whole dataset
- Reading data in batch, thus saves memory when constructing ``Dataset`` object
- Supports creating ``Dataset`` from multiple data files

Please refer to ``Sequence`` `API doc <./Python-API.rst#data-structure-api>`__.

`dataset_from_multi_hdf5.py <https://github.com/microsoft/LightGBM/blob/master/examples/python-guide/dataset_from_multi_hdf5.py>`__ is a detailed example.

**Saving Dataset into a LightGBM binary file will make loading faster:**

.. code:: python

    train_data = lgb.Dataset('train.svm.txt')
    train_data.save_binary('train.bin')

**Create validation data:**

.. code:: python

    validation_data = train_data.create_valid('validation.svm')

or

.. code:: python

    validation_data = lgb.Dataset('validation.svm', reference=train_data)

In LightGBM, the validation data should be aligned with training data.

**Specific feature names and categorical features:**

.. code:: python

    train_data = lgb.Dataset(data, label=label, feature_name=['c1', 'c2', 'c3'], categorical_feature=['c3'])

LightGBM can use categorical features as input directly.
It doesn't need to convert to one-hot encoding, and is much faster than one-hot encoding (about 8x speed-up).

**Note**: You should convert your categorical features to ``int`` type before you construct ``Dataset``.

**Weights can be set when needed:**

.. code:: python

    rng = np.random.default_rng()
    w = rng.uniform(size=(500, ))
    train_data = lgb.Dataset(data, label=label, weight=w)

or

.. code:: python

    train_data = lgb.Dataset(data, label=label)
    rng = np.random.default_rng()
    w = rng.uniform(size=(500, ))
    train_data.set_weight(w)

And you can use ``Dataset.set_init_score()`` to set initial score, and ``Dataset.set_group()`` to set group/query data for ranking tasks.

**Memory efficient usage:**

The ``Dataset`` object in LightGBM is very memory-efficient, it only needs to save discrete bins.
However, Numpy/Array/Pandas object is memory expensive.
If you are concerned about your memory consumption, you can save memory by:

1. Set ``free_raw_data=True`` (default is ``True``) when constructing the ``Dataset``

2. Explicitly set ``raw_data=None`` after the ``Dataset`` has been constructed

3. Call ``gc``

Setting Parameters
------------------

LightGBM can use a dictionary to set `Parameters <./Parameters.rst>`__.
For instance:

-  Booster parameters:

   .. code:: python

       param = {'num_leaves': 31, 'objective': 'binary'}
       param['metric'] = 'auc'

-  You can also specify multiple eval metrics:

   .. code:: python

       param['metric'] = ['auc', 'binary_logloss']

Training
--------

Training a model requires a parameter list and data set:

.. code:: python

    num_round = 10
    bst = lgb.train(param, train_data, num_round, valid_sets=[validation_data])

After training, the model can be saved:

.. code:: python

    bst.save_model('model.txt')

The trained model can also be dumped to JSON format:

.. code:: python

    json_model = bst.dump_model()

A saved model can be loaded:

.. code:: python

    bst = lgb.Booster(model_file='model.txt')  # init model

CV
--

Training with 5-fold CV:

.. code:: python

    lgb.cv(param, train_data, num_round, nfold=5)

Early Stopping
--------------

If you have a validation set, you can use early stopping to find the optimal number of boosting rounds.
Early stopping requires at least one set in ``valid_sets``. If there is more than one, it will use all of them except the training data:

.. code:: python

    bst = lgb.train(param, train_data, num_round, valid_sets=valid_sets, callbacks=[lgb.early_stopping(stopping_rounds=5)])
    bst.save_model('model.txt', num_iteration=bst.best_iteration)

The model will train until the validation score stops improving.
Validation score needs to improve at least every ``stopping_rounds`` to continue training.

The index of iteration that has the best performance will be saved in the ``best_iteration`` field if early stopping logic is enabled by setting ``early_stopping`` callback.
Note that ``train()`` will return a model from the best iteration.

This works with both metrics to minimize (L2, log loss, etc.) and to maximize (NDCG, AUC, etc.).
Note that if you specify more than one evaluation metric, all of them will be used for early stopping.
However, you can change this behavior and make LightGBM check only the first metric for early stopping by passing ``first_metric_only=True`` in ``early_stopping`` callback constructor.

Prediction
----------

A model that has been trained or loaded can perform predictions on datasets:

.. code:: python

    # 7 entities, each contains 10 features
    rng = np.random.default_rng()
    data = rng.uniform(size=(7, 10))
    ypred = bst.predict(data)

If early stopping is enabled during training, you can get predictions from the best iteration with ``bst.best_iteration``:

.. code:: python

    ypred = bst.predict(data, num_iteration=bst.best_iteration)

.. _Python-package: https://github.com/microsoft/LightGBM/tree/master/python-package