| /* |
| * 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.predictionio.examples.classification |
| |
| import org.apache.predictionio.controller.P2LAlgorithm |
| import org.apache.predictionio.controller.Params |
| |
| import org.apache.spark.mllib.classification.NaiveBayes |
| import org.apache.spark.mllib.classification.NaiveBayesModel |
| import org.apache.spark.mllib.linalg.Vectors |
| import org.apache.spark.SparkContext |
| |
| import grizzled.slf4j.Logger |
| |
| case class AlgorithmParams( |
| lambda: Double |
| ) extends Params |
| |
| // extends P2LAlgorithm because the MLlib's NaiveBayesModel doesn't contain RDD. |
| class NaiveBayesAlgorithm(val ap: AlgorithmParams) |
| extends P2LAlgorithm[PreparedData, NaiveBayesModel, Query, PredictedResult] { |
| |
| @transient lazy val logger = Logger[this.type] |
| |
| def train(sc: SparkContext, data: PreparedData): NaiveBayesModel = { |
| // MLLib NaiveBayes cannot handle empty training data. |
| require(data.labeledPoints.take(1).nonEmpty, |
| s"RDD[labeledPoints] in PreparedData cannot be empty." + |
| " Please check if DataSource generates TrainingData" + |
| " and Preparator generates PreparedData correctly.") |
| |
| NaiveBayes.train(data.labeledPoints, ap.lambda) |
| } |
| |
| def predict(model: NaiveBayesModel, query: Query): PredictedResult = { |
| val label = model.predict(Vectors.dense( |
| // MODIFIED |
| Array(query.featureA, query.featureB, query.featureC, query.featureD) |
| )) |
| PredictedResult(label) |
| } |
| |
| } |