安装 sparkxgboost
时间: 2023-11-22 09:49:44 浏览: 146
安装sparkxgboost需要以下步骤:
1.下载xgboost4j-spark jar包,可以从官网下载或者使用以下命令从maven中央库下载:
```shell
wget https://repo1.maven.org/maven2/ml/dmlc/xgboost4j-spark/0.90/xgboost4j-spark-0.90.jar
```
2.将jar包添加到Spark的classpath中,可以通过以下命令将jar包复制到Spark的lib目录下:
```shell
hadoop dfs -get s3://bucket/xgboost4j-spark-0.7.jar /usr/lib/spark/lib/
```
3.在Spark应用程序中导入xgboost4j-spark包并使用它,例如:
```python
from pyspark.ml.feature import VectorAssembler
from pyspark.ml import Pipeline
from pyspark.ml.classification import GBTClassifier
from pyspark.ml.evaluation import MulticlassClassificationEvaluator
from pyspark.ml.tuning import CrossValidator, ParamGridBuilder
from xgboost import XGBoostEstimator
# 创建XGBoostEstimator
xgboost = XGBoostEstimator(
featuresCol="features",
labelCol="label",
predictionCol="prediction"
)
# 创建Pipeline
pipeline = Pipeline(stages=[xgboost])
# 创建ParamGridBuilder
paramGrid = ParamGridBuilder() \
.addGrid(xgboost.maxDepth, [2, 4, 6]) \
.addGrid(xgboost.eta, [0.1, 0.6, 1.0]) \
.build()
# 创建CrossValidator
crossval = CrossValidator(
estimator=pipeline,
estimatorParamMaps=paramGrid,
evaluator=MulticlassClassificationEvaluator(),
numFolds=3
)
# 训练模型
model = crossval.fit(train)
# 预测
predictions = model.transform(test)
# 评估
evaluator = MulticlassClassificationEvaluator(
labelCol="label", predictionCol="prediction", metricName="accuracy")
accuracy = evaluator.evaluate(predictions)
print("Test Error = %g " % (1.0 - accuracy))
```