spark FMRegressorExample 源码
spark FMRegressorExample 代码
文件路径：/examples/src/main/scala/org/apache/spark/examples/ml/FMRegressorExample.scala
/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
// scalastyle:off println
package org.apache.spark.examples.ml
// $example on$
import org.apache.spark.ml.Pipeline
import org.apache.spark.ml.evaluation.RegressionEvaluator
import org.apache.spark.ml.feature.MinMaxScaler
import org.apache.spark.ml.regression.{FMRegressionModel, FMRegressor}
// $example off$
import org.apache.spark.sql.SparkSession
object FMRegressorExample {
def main(args: Array[String]): Unit = {
val spark = SparkSession
.builder
.appName("FMRegressorExample")
.getOrCreate()
// $example on$
// Load and parse the data file, converting it to a DataFrame.
val data = spark.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")
// Scale features.
val featureScaler = new MinMaxScaler()
.setInputCol("features")
.setOutputCol("scaledFeatures")
.fit(data)
// Split the data into training and test sets (30% held out for testing).
val Array(trainingData, testData) = data.randomSplit(Array(0.7, 0.3))
// Train a FM model.
val fm = new FMRegressor()
.setLabelCol("label")
.setFeaturesCol("scaledFeatures")
.setStepSize(0.001)
// Create a Pipeline.
val pipeline = new Pipeline()
.setStages(Array(featureScaler, fm))
// Train model.
val model = pipeline.fit(trainingData)
// Make predictions.
val predictions = model.transform(testData)
// Select example rows to display.
predictions.select("prediction", "label", "features").show(5)
// Select (prediction, true label) and compute test error.
val evaluator = new RegressionEvaluator()
.setLabelCol("label")
.setPredictionCol("prediction")
.setMetricName("rmse")
val rmse = evaluator.evaluate(predictions)
println(s"Root Mean Squared Error (RMSE) on test data = $rmse")
val fmModel = model.stages(1).asInstanceOf[FMRegressionModel]
println(s"Factors: ${fmModel.factors} Linear: ${fmModel.linear} " +
s"Intercept: ${fmModel.intercept}")
// $example off$
spark.stop()
}
}
// scalastyle:on println
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