spark用户画像代码

以下是一个用于创建Spark用户画像的代码示例：

// 引入需要的包
import org.apache.spark.ml.classification.LogisticRegression
import org.apache.spark.ml.linalg.Vector
import org.apache.spark.sql.Row
import org.apache.spark.ml.feature.{Tokenizer, HashingTF}
import org.apache.spark.ml.{Pipeline, PipelineModel}
import org.apache.spark.ml.evaluation.BinaryClassificationEvaluator
import org.apache.spark.ml.tuning.{CrossValidator, ParamGridBuilder}
import org.apache.spark.sql.SQLContext

// 创建SparkSession和SQLContext
val spark = SparkSession.builder().appName("UserPortrait").getOrCreate()
val sqlContext = spark.sqlContext

// 读取数据
val data = spark.read.format("csv").option("header", "true").load("/path/to/data.csv")

// 特征工程
val tokenizer = new Tokenizer().setInputCol("text").setOutputCol("words")
val hashingTF = new HashingTF().setInputCol(tokenizer.getOutputCol).setOutputCol("features")
val lr = new LogisticRegression().setLabelCol("label").setFeaturesCol(hashingTF.getOutputCol)

// 构建Pipeline
val pipeline = new Pipeline().setStages(Array(tokenizer, hashingTF, lr))

// 划分训练集和测试集
val Array(trainingData, testData) = data.randomSplit(Array(0.8, 0.2))

// 训练模型
val model = pipeline.fit(trainingData)

// 进行预测
val predictions = model.transform(testData)

// 评估模型
val evaluator = new BinaryClassificationEvaluator().setLabelCol("label").setRawPredictionCol("prediction")
val accuracy = evaluator.evaluate(predictions)

// 参数网格和交叉验证
val paramGrid = new ParamGridBuilder().addGrid(lr.regParam, Array(0.01, 0.1, 1.0)).build()
val crossValidator = new CrossValidator().setEstimator(pipeline).setEvaluator(evaluator).setEstimatorParamMaps(paramGrid).setNumFolds(3)
val cvModel = crossValidator.fit(data)

// 保存模型到磁盘
model.save("/portrait/sparkML-LRmodel")
pipeline.save("/portrait/sparkML-LRpipeline")

请注意，这只是一个示例代码，具体的实现可能会因数据和需求的不同而有所变化。在实际使用时，您需要根据具体情况进行调整和修改。