spark GLMClassificationModel 源码

spark GLMClassificationModel 代码

文件路径：/mllib/src/main/scala/org/apache/spark/mllib/classification/impl/GLMClassificationModel.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.
 */

package org.apache.spark.mllib.classification.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.{Row, SparkSession}

/**
 * Helper class for import/export of GLM classification models.
 */
private[classification] object GLMClassificationModel {

  object SaveLoadV1_0 {

    def thisFormatVersion: String = "1.0"

    /** Model data for import/export */
    case class Data(weights: Vector, intercept: Double, threshold: Option[Double])

    /**
     * Helper method for saving GLM classification model metadata and data.
     * @param modelClass  String name for model class, to be saved with metadata
     * @param numClasses  Number of classes label can take, to be saved with metadata
     */
    def save(
        sc: SparkContext,
        path: String,
        modelClass: String,
        numFeatures: Int,
        numClasses: Int,
        weights: Vector,
        intercept: Double,
        threshold: Option[Double]): Unit = {
      val spark = SparkSession.builder().sparkContext(sc).getOrCreate()

      // Create JSON metadata.
      val metadata = compact(render(
        ("class" -> modelClass) ~ ("version" -> thisFormatVersion) ~
        ("numFeatures" -> numFeatures) ~ ("numClasses" -> numClasses)))
      sc.parallelize(Seq(metadata), 1).saveAsTextFile(Loader.metadataPath(path))

      // Create Parquet data.
      val data = Data(weights, intercept, threshold)
      spark.createDataFrame(Seq(data)).repartition(1).write.parquet(Loader.dataPath(path))
    }

    /**
     * Helper method for loading GLM classification model data.
     *
     * NOTE: Callers of this method should check numClasses, numFeatures on their own.
     *
     * @param modelClass  String name for model class (used for error messages)
     */
    def loadData(sc: SparkContext, path: String, modelClass: String): Data = {
      val dataPath = Loader.dataPath(path)
      val spark = SparkSession.builder().sparkContext(sc).getOrCreate()
      val dataRDD = spark.read.parquet(dataPath)
      val dataArray = dataRDD.select("weights", "intercept", "threshold").take(1)
      assert(dataArray.length == 1, s"Unable to load $modelClass data from: $dataPath")
      val data = dataArray(0)
      assert(data.size == 3, s"Unable to load $modelClass data from: $dataPath")
      val (weights, intercept) = data match {
        case Row(weights: Vector, intercept: Double, _) =>
          (weights, intercept)
      }
      val threshold = if (data.isNullAt(2)) {
        None
      } else {
        Some(data.getDouble(2))
      }
      Data(weights, intercept, threshold)
    }
  }
}

相关信息

spark 源码目录

相关文章

spark ArrayWrappers 源码

spark InMemoryStore 源码

spark KVIndex 源码

spark KVStore 源码

spark KVStoreIterator 源码

spark KVStoreSerializer 源码

spark KVStoreView 源码

spark KVTypeInfo 源码

spark LevelDB 源码

spark LevelDBIterator 源码