pyspark使用XGboost训练模型实例

0、获取下载对应spark版本Xgoobst Jar的方法

 使用xgboost模型有三个依赖需要添加或配置：

（1）xgboost4j.jar https://mvnrepository.com/artifact/ml.dmlc/xgboost4j

（2）xgboost4j-spark.jar https://mvnrepository.com/artifact/ml.dmlc/xgboost4j-spark

  (3) sparkxgb.zip  这个github还可以，里面也有代码实践例子https://github.com/MachineLP/Spark-/tree/master/pyspark-xgboost

其他参考：去年同月份(2022-08)有一个xgboost的博文，记录了下载最新xgboost.jar的方法，

pyspark使用xgboost做模型训练_sparkxbg包_Just Jump的博客-CSDN博客

还有scala版本，配置pom的方法：Xgboost安装、使用和算法原理理解_xgboost 文档_Just Jump的博客-CSDN博客

1、这是一个跑通的代码实例，使用的是泰坦尼克生还数据，分类模型。

这里使用了Pipeline来封装特征处理和模型训练步骤，保存为pipelineModel。

注意这里加载xgboost依赖的jar包和zip包的方法。

#这是用 pipeline 包装了XGBOOST的例子。 此路通！

import os
import sys
import time
import pandas as pd
import numpy as np
import pyspark.sql.types as typ
import pyspark.ml.feature as ft
from pyspark.sql.functions import isnan, isnull

from pyspark.sql.types import StructType, StructField

from pyspark.sql.types import *
from pyspark.ml.feature import StringIndexer, VectorAssembler
from pyspark.ml import Pipeline
from pyspark.sql.functions import col
from pyspark.sql import SparkSession

os.environ['PYSPARK_PYTHON'] = 'Python3.7/bin/python'
os.environ['PYSPARK_SUBMIT_ARGS'] = '--jars xgboost4j-spark-0.90.jar,xgboost4j-0.90.jar pyspark-shell'

spark = SparkSession \
        .builder \
        .appName("PySpark XGBOOST Titanic") \
        .config('spark.driver.allowMultipleContexts', 'true') \
        .config('spark.pyspark.python', 'Python3.7/bin/python') \
        .config('spark.yarn.dist.archives', 'hdfs://ns62007/user/dmc_adm/_PYSPARK_ENV/Python3.7.zip#Python3.7') \
        .config('spark.executorEnv.PYSPARK_PYTHON', 'Python3.7/bin/python') \
        .config('spark.sql.autoBroadcastJoinThreshold', '-1') \
        .enableHiveSupport() \
        .getOrCreate()

spark.sparkContext.addPyFile("sparkxgb.zip")

schema = StructType(
  [StructField("PassengerId", DoubleType()),
    StructField("Survived", DoubleType()),
    StructField("Pclass", DoubleType()),
    StructField("Name", StringType()),
    StructField("Sex", StringType()),
    StructField("Age", DoubleType()),
    StructField("SibSp", DoubleType()),
    StructField("Parch", DoubleType()),
    StructField("Ticket", StringType()),
    StructField("Fare", DoubleType()),
    StructField("Cabin", StringType()),
    StructField("Embarked", StringType())
  ])

upload_file = "titanic/train.csv"
hdfs_path = "hdfs://tmp/gao/dev_data/dmb_upload_data/"
file_path = os.path.join(hdfs_path, upload_file.split("/")[-1])

df_raw = spark\
  .read\
  .option("header", "true")\
  .schema(schema)\
  .csv(file_path)

df_raw.show(20)
df = df_raw.na.fill(0)

sexIndexer = StringIndexer()\
  .setInputCol("Sex")\
  .setOutputCol("SexIndex")\
  .setHandleInvalid("keep")

cabinIndexer = StringIndexer()\
  .setInputCol("Cabin")\
  .setOutputCol("CabinIndex")\
  .setHandleInvalid("keep")

embarkedIndexer = StringIndexer()\
  .setInputCol("Embarked")\
  .setHandleInvalid("keep")

# .setOutputCol("EmbarkedIndex")\

vectorAssembler = VectorAssembler()\
  .setInputCols(["Pclass", "Age", "SibSp", "Parch", "Fare"])\
  .setOutputCol("features")


from sparkxgb import XGBoostClassifier
xgboost = XGBoostClassifier(
    maxDepth=3,
    missing=float(0.0),
    featuresCol="features",
    labelCol="Survived"
)

pipeline = Pipeline(stages=[vectorAssembler, xgboost])


trainDF, testDF = df.randomSplit([0.8, 0.2], seed=24)
trainDF.show(2)
model = pipeline.fit(trainDF)

print (88888888888888888888)
model.transform(testDF).select(col("PassengerId"), col("Survived"), col("prediction")).show()
print (9999999999999999999)

# Write model/classifier
model.write().overwrite().save(os.path.join(hdfs_path,"xgboost_class_test"))

from pyspark.ml import PipelineModel
model1 = PipelineModel.load(os.path.join(hdfs_path,"xgboost_class_test"))
model1.transform(testDF).show()

这是执行结果：

2、当然也可以不用pipeline封装，直接训练xgboost模型并保存。这也是跑通的例子。

# Train a xgboost model
from pyspark.ml.feature import VectorAssembler, StringIndexer, OneHotEncoder, StandardScaler
from pyspark.ml import Pipeline
# spark.sparkContext.addPyFile("sparkxgb.zip") # read xgboost pyspark client lib
from sparkxgb import XGBoostClassifier

assembler = VectorAssembler(
    inputCols=[ 'Pclass',
 'Age',
 'SibSp',
 'Parch',
 'Fare'  ],
    outputCol="features", handleInvalid="skip")

xgboost = XGBoostClassifier(
    maxDepth=3,
    missing=float(0.0),
    featuresCol="features", 
    labelCol="Survived")

# pipeline = Pipeline(stages=[assembler, xgboost])
# trained_model = pipeline.fit(data)

trainDF, testDF = data.randomSplit([0.8, 0.2], seed=24)
trainDF.show(2)

td = assembler.transform(trainDF)
td.cache()
td.show()
trained_raw_model = xgboost.fit(td)

# save distribute and native model
distrib_model_path = os.path.join(hdfs_path, 'distribute_xgboost_model')
native_model_path = "./native_xgboost_model.xgboost"
# 保存分布式的训练模型
trained_raw_model.write().overwrite().save(distrib_model_path)
# 保存模型到本地磁盘  # save trained model to local disk
trained_raw_model.nativeBooster.saveModel(native_model_path)
print(trained_raw_model.nativeBooster.gamma)

# model predict 
result = trained_raw_model.transform(assembler.transform(testDF))
# result.select(["Survived", "rawPrediction", "probability", "prediction"]).show()
result.show()

# evaluate model
from pyspark.ml.evaluation import MulticlassClassificationEvaluator, BinaryClassificationEvaluator
evaluator = BinaryClassificationEvaluator(metricName="areaUnderROC")\
            .setLabelCol("Survived")
print(evaluator.evaluate(result))

# 加载已经训练好的XGB模型(分布式的)
from sparkxgb import XGBoostClassifier,XGBoostClassificationModel
model1= XGBoostClassificationModel().load(distrib_model_path)
model1.transform(td).show()

这是运行结果：

Done.