PySpark MLlib does not have a built-in implementation of XGBoost, but you can use the XGBoost4j-Spark library to perform cross-validation and tune hyperparameters for XGBoost. Here are the steps to do so:
Here is an example code snippet that shows how to perform cross-validation and hyperparameter tuning for XGBoost using PySpark:
from pyspark.ml import Pipeline
from pyspark.ml.feature import VectorAssembler
from pyspark.ml.evaluation import BinaryClassificationEvaluator
from pyspark.ml.tuning import CrossValidator, ParamGridBuilder
from ml.dmlc.xgboost4j.scala.spark import XGBoostClassifier
# Load data into a PySpark DataFrame
data = spark.read.format("csv").option("header", "true").load("path/to/data.csv")
# Split data into training and testing sets
train, test = data.randomSplit([0.7, 0.3], seed=123)
# Define the XGBoost estimator with the desired hyperparameters to be tuned
xgb = XGBoostClassifier(objective="binary:logistic")
# Create a pipeline with the XGBoost estimator and any necessary data preprocessing steps
assembler = VectorAssembler(inputCols=["col1", "col2", "col3"], outputCol="features")
pipeline = Pipeline(stages=[assembler, xgb])
# Define the parameter grid containing the hyperparameters to be tuned and their possible values
paramGrid = ParamGridBuilder() \
.addGrid(xgb.maxDepth, [3, 5, 7]) \
.addGrid(xgb.eta, [0.1, 0.01, 0.001]) \
.build()
# Define your evaluation metric using PySpark's BinaryClassificationEvaluator
evaluator = BinaryClassificationEvaluator(labelCol="label", rawPredictionCol="rawPrediction", metricName="areaUnderROC")
# Use the CrossValidator class to perform cross-validation with the parameter grid, pipeline, and evaluation metric
cv = CrossValidator(estimator=pipeline, estimatorParamMaps=paramGrid, evaluator=evaluator, numFolds=5)
cvModel = cv.fit(train)
# Retrieve the best hyperparameters from the CrossValidator object and fit the final model using those hyperparameters
bestModel = cvModel.bestModel
bestParams = bestModel.stages[-1].extractParamMap()
xgb.setParams(**bestParams)
finalModel = pipeline.fit(train)
# Evaluate the final model on the testing set
predictions = finalModel.transform(test)
auc = evaluator.evaluate(predictions)
print("AUC on testing set: {:.4f}".format(auc))
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Asked: 2021-06-13 11:00:00 +0000
Seen: 10 times
Last updated: Jul 06 '21
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