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/*
* 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.
*/
package org.apache.spark.mllib.regression.impl
import org.json4s.JsonDSL._
import org.json4s.jackson.JsonMethods._
import org.apache.spark.SparkContext
import org.apache.spark.mllib.linalg.Vector
import org.apache.spark.mllib.util.Loader
import org.apache.spark.sql.{DataFrame, Row, SQLContext}
/**
* Helper methods for import/export of GLM regression models.
*/
private[regression] object GLMRegressionModel {
object SaveLoadV1_0 {
def thisFormatVersion: String = "1.0"
/** Model data for model import/export */
case class Data(weights: Vector, intercept: Double)
/**
* Helper method for saving GLM regression model metadata and data.
* @param modelClass String name for model class, to be saved with metadata
*/
def save(
sc: SparkContext,
path: String,
modelClass: String,
weights: Vector,
intercept: Double): Unit = {
val sqlContext = SQLContext.getOrCreate(sc)
import sqlContext.implicits._
// Create JSON metadata.
val metadata = compact(render(
("class" -> modelClass) ~ ("version" -> thisFormatVersion) ~
("numFeatures" -> weights.size)))
sc.parallelize(Seq(metadata), 1).saveAsTextFile(Loader.metadataPath(path))
// Create Parquet data.
val data = Data(weights, intercept)
val dataRDD: DataFrame = sc.parallelize(Seq(data), 1).toDF()
// TODO: repartition with 1 partition after SPARK-5532 gets fixed
dataRDD.write.parquet(Loader.dataPath(path))
}
/**
* Helper method for loading GLM regression model data.
* @param modelClass String name for model class (used for error messages)
* @param numFeatures Number of features, to be checked against loaded data.
* The length of the weights vector should equal numFeatures.
*/
def loadData(sc: SparkContext, path: String, modelClass: String, numFeatures: Int): Data = {
val datapath = Loader.dataPath(path)
val sqlContext = SQLContext.getOrCreate(sc)
val dataRDD = sqlContext.read.parquet(datapath)
val dataArray = dataRDD.select("weights", "intercept").take(1)
assert(dataArray.length == 1, s"Unable to load $modelClass data from: $datapath")
val data = dataArray(0)
assert(data.size == 2, s"Unable to load $modelClass data from: $datapath")
data match {
case Row(weights: Vector, intercept: Double) =>
assert(weights.size == numFeatures, s"Expected $numFeatures features, but" +
s" found ${weights.size} features when loading $modelClass weights from $datapath")
Data(weights, intercept)
}
}
}
}
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