<commit_before>###############################
Using XGBoost PySpark Estimator
###############################
Starting from version 1.7.0, xgboost supports pyspark estimator APIs.  The feature is
still experimental and not yet ready for production use.

*****************
SparkXGBRegressor
*****************

SparkXGBRegressor is a PySpark ML estimator. It implements the XGBoost classification
algorithm based on XGBoost python library, and it can be used in PySpark Pipeline
and PySpark ML meta algorithms like CrossValidator/TrainValidationSplit/OneVsRest.

We can create a `SparkXGBRegressor` estimator like:

.. code-block:: python

  from xgboost.spark import SparkXGBRegressor
  spark_reg_estimator = SparkXGBRegressor(num_workers=2, max_depth=5)


The above snippet create an spark estimator which can fit on a spark dataset,
and return a spark model that can transform a spark dataset and generate dataset
with prediction column. We can set almost all of xgboost sklearn estimator parameters
as `SparkXGBRegressor` parameters, but some parameter such as `nthread` is forbidden
in spark estimator, and some parameters are replaced with pyspark specific parameters
such as `weight_col`, `validation_indicator_col`, `use_gpu`, for details please see
`SparkXGBRegressor` doc.

The following code snippet shows how to train a spark xgboost regressor model,
first we need to prepare a training dataset as a spark dataframe contains
"features" and "label" column, the "features" column must be `pyspark.ml.linalg.Vector`
type or spark array type.

.. code-block:: python

  xgb_regressor_model = xgb_regressor.fit(train_spark_dataframe)


The following code snippet shows how to predict test data using a spark xgboost regressor model,
first we need to prepare a test dataset as a spark dataframe contains
"features" and "label" column, the "features" column must be `pyspark.ml.linalg.Vector`
type or spark array type.

.. code-block:: python

  transformed_test_spark_dataframe = xgb_regressor.predict(test_spark_dataframe)


The above snippet code returns a `transformed_test_spark_dataframe` that contains the input
dataset columns and an appended column "prediction" representing the prediction results.


******************
SparkXGBClassifier
******************


`SparkXGBClassifier` estimator has similar API with `SparkXGBRegressor`, but it has some
pyspark classifier specific params, e.g. `raw_prediction_col` and `probability_col` parameters.
Correspondingly, by default, `SparkXGBClassifierModel` transforming test dataset will
generate result dataset with 3 new columns:

- "prediction": represents the predicted label.
- "raw_prediction": represents the output margin values.
- "probability": represents the prediction probability on each label.
<commit_msg>[pyspark][doc] add more doc for pyspark (#8271)<commit_after>################################
Distributed XGBoost with PySpark
################################

Starting from version 1.7.0, xgboost supports pyspark estimator APIs.

.. note::

   The feature is still experimental and not yet ready for production use.

.. contents::
  :backlinks: none
  :local:

*************************
XGBoost PySpark Estimator
*************************

SparkXGBRegressor
=================

SparkXGBRegressor is a PySpark ML estimator. It implements the XGBoost classification
algorithm based on XGBoost python library, and it can be used in PySpark Pipeline
and PySpark ML meta algorithms like CrossValidator/TrainValidationSplit/OneVsRest.

We can create a `SparkXGBRegressor` estimator like:

.. code-block:: python

  from xgboost.spark import SparkXGBRegressor
  spark_reg_estimator = SparkXGBRegressor(
    features_col="features",
    label_col="label",
    num_workers=2,
  )


The above snippet creates a spark estimator which can fit on a spark dataset,
and return a spark model that can transform a spark dataset and generate dataset
with prediction column. We can set almost all of xgboost sklearn estimator parameters
as `SparkXGBRegressor` parameters, but some parameter such as `nthread` is forbidden
in spark estimator, and some parameters are replaced with pyspark specific parameters
such as `weight_col`, `validation_indicator_col`, `use_gpu`, for details please see
`SparkXGBRegressor` doc.

The following code snippet shows how to train a spark xgboost regressor model,
first we need to prepare a training dataset as a spark dataframe contains
"label" column and "features" column(s), the "features" column(s) must be `pyspark.ml.linalg.Vector`
type or spark array type or a list of feature column names.


.. code-block:: python

  xgb_regressor_model = xgb_regressor.fit(train_spark_dataframe)


The following code snippet shows how to predict test data using a spark xgboost regressor model,
first we need to prepare a test dataset as a spark dataframe contains
"features" and "label" column, the "features" column must be `pyspark.ml.linalg.Vector`
type or spark array type.

.. code-block:: python

  transformed_test_spark_dataframe = xgb_regressor.predict(test_spark_dataframe)


The above snippet code returns a `transformed_test_spark_dataframe` that contains the input
dataset columns and an appended column "prediction" representing the prediction results.

SparkXGBClassifier
==================

`SparkXGBClassifier` estimator has similar API with `SparkXGBRegressor`, but it has some
pyspark classifier specific params, e.g. `raw_prediction_col` and `probability_col` parameters.
Correspondingly, by default, `SparkXGBClassifierModel` transforming test dataset will
generate result dataset with 3 new columns:
- "prediction": represents the predicted label.
- "raw_prediction": represents the output margin values.
- "probability": represents the prediction probability on each label.


***************************
XGBoost PySpark GPU support
***************************

XGBoost PySpark supports GPU training and prediction. To enable GPU support, you first need
to install the xgboost and cudf packages. Then you can set `use_gpu` parameter to `True`.

Below tutorial will show you how to train a model with XGBoost PySpark GPU on Spark
standalone cluster.


Write your PySpark application
==============================

.. code-block:: python

  from xgboost.spark import SparkXGBRegressor
  spark = SparkSession.builder.getOrCreate()

  # read data into spark dataframe
  train_data_path = "xxxx/train"
  train_df = spark.read.parquet(data_path)

  test_data_path = "xxxx/test"
  test_df = spark.read.parquet(test_data_path)

  # assume the label column is named "class"
  label_name = "class"

  # get a list with feature column names
  feature_names = [x.name for x in train_df.schema if x.name != label]

  # create a xgboost pyspark regressor estimator and set use_gpu=True
  regressor = SparkXGBRegressor(
    features_col=feature_names,
    label_col=label_name,
    num_workers=2,
    use_gpu=True,
  )

  # train and return the model
  model = regressor.fit(train_df)

  # predict on test data
  predict_df = model.transform(test_df)
  predict_df.show()

Prepare the necessary packages
==============================

We recommend using Conda or Virtualenv to manage python dependencies
in PySpark. Please refer to
`How to Manage Python Dependencies in PySpark <https://www.databricks.com/blog/2020/12/22/how-to-manage-python-dependencies-in-pyspark.html>`_.

.. code-block:: bash

  conda create -y -n xgboost-env -c conda-forge conda-pack python=3.9
  conda activate xgboost-env
  pip install xgboost
  pip install cudf
  conda pack -f -o xgboost-env.tar.gz


Submit the PySpark application
==============================

Assuming you have configured your Spark cluster with GPU support, if not yet, please
refer to `spark standalone configuration with GPU support <https://nvidia.github.io/spark-rapids/docs/get-started/getting-started-on-prem.html#spark-standalone-cluster>`_.

.. code-block:: bash

  export PYSPARK_DRIVER_PYTHON=python
  export PYSPARK_PYTHON=./environment/bin/python

  spark-submit \
    --master spark://<master-ip>:7077 \
    --conf spark.executor.resource.gpu.amount=1 \
    --conf spark.task.resource.gpu.amount=1 \
    --archives xgboost-env.tar.gz#environment \
    xgboost_app.py


Model Persistence
=================

Similar to standard PySpark ml estimators, one can persist and reuse the model with `save`
and `load` methods:

.. code-block:: python

  regressor = SparkXGBRegressor()
  model = regressor.fit(train_df)
  # save the model
  model.save("/tmp/xgboost-pyspark-model")
  # load the model
  model2 = SparkXGBRankerModel.load("/tmp/xgboost-pyspark-model")

To export the underlying booster model used by XGBoost:

.. code-block:: python

  regressor = SparkXGBRegressor()
  model = regressor.fit(train_df)
  # the same booster object returned by xgboost.train
  booster: xgb.Booster = model.get_booster()
  booster.predict(...)
  booster.save_model("model.json")

This booster is shared by other Python interfaces and can be used by other language
bindings like the C and R packages. Lastly, one can extract a booster file directly from
saved spark estimator without going through the getter:

.. code-block:: python

  import xgboost as xgb
  bst = xgb.Booster()
  bst.load_model("/tmp/xgboost-pyspark-model/model/part-00000")

Accelerate the whole pipeline of xgboost pyspark
================================================

With `RAPIDS Accelerator for Apache Spark <https://nvidia.github.io/spark-rapids/>`_,
you can accelerate the whole pipeline (ETL, Train, Transform) for xgboost pyspark
without any code change by leveraging GPU.

Below is a simple example submit command for enabling GPU acceleration:

.. code-block:: bash

  export PYSPARK_DRIVER_PYTHON=python
  export PYSPARK_PYTHON=./environment/bin/python

  spark-submit \
    --master spark://<master-ip>:7077 \
    --conf spark.executor.resource.gpu.amount=1 \
    --conf spark.task.resource.gpu.amount=1 \
    --packages com.nvidia:rapids-4-spark_2.12:22.08.0 \
    --conf spark.plugins=com.nvidia.spark.SQLPlugin \
    --conf spark.sql.execution.arrow.maxRecordsPerBatch=1000000 \
    --archives xgboost-env.tar.gz#environment \
    xgboost_app.py

{'fraction_non_alphanumeric': 0.11467033506927385, 'fraction_numerical': 0.0058956470472634375, 'mean_word_length': 4.86628242074928, 'pattern_counts': {'":': 6, '<': 8, '<?xml version=': 0, '>': 8, 'https://': 3, 'lorem ipsum': 0, 'www.': 1, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '2249153', 'n_tokens_mistral': 3109, 'n_tokens_neox': 2873, 'n_words': 1065}