Github user witgo commented on a diff in the pull request: https://github.com/apache/spark/pull/7621#discussion_r35433813 --- Diff: mllib/src/main/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifier.scala --- @@ -0,0 +1,130 @@ +/* + * 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.ml.classification + +import breeze.linalg.{argmax => Bargmax} + +import org.apache.spark.annotation.Experimental +import org.apache.spark.ml.{PredictionModel, Predictor} +import org.apache.spark.ml.param.ParamMap +import org.apache.spark.ml.util.Identifiable +import org.apache.spark.ml.regression.MultilayerPerceptronParams +import org.apache.spark.mllib.ann.{FeedForwardTrainer, FeedForwardTopology} +import org.apache.spark.mllib.linalg.{Vectors, Vector} +import org.apache.spark.mllib.regression.LabeledPoint +import org.apache.spark.sql.DataFrame + +/** + * :: Experimental :: + * Label to vector converter. + */ +@Experimental +private object LabelConverter { + + /** + * Encodes a label as a vector. + * Returns a vector of given length with zeroes at all positions + * and value 1.0 at the position that corresponds to the label. + * + * @param labeledPoint labeled point + * @param labelCount total number of labels + * @return vector encoding of a label + */ + def apply(labeledPoint: LabeledPoint, labelCount: Int): (Vector, Vector) = { + val output = Array.fill(labelCount){0.0} + output(labeledPoint.label.toInt) = 1.0 + (labeledPoint.features, Vectors.dense(output)) + } + + /** + * Converts a vector to a label. + * Returns the position of the maximal element of a vector. + * + * @param output label encoded with a vector + * @return label + */ + def apply(output: Vector): Double = { + Bargmax(output.toBreeze.toDenseVector).toDouble + } +} + +/** + * :: Experimental :: + * Classifier trainer based on the Multilayer Perceptron. + * Each layer has sigmoid activation function, output layer has softmax. + * Number of inputs has to be equal to the size of feature vectors. + * Number of outputs has to be equal to the total number of labels. + * + */ +@Experimental +class MultilayerPerceptronClassifier (override val uid: String) + extends Predictor[Vector, MultilayerPerceptronClassifier, MultilayerPerceptronClassifierModel] + with MultilayerPerceptronParams { + + override def copy(extra: ParamMap): MultilayerPerceptronClassifier = defaultCopy(extra) + + def this() = this(Identifiable.randomUID("mlpc")) + + /** + * Train a model using the given dataset and parameters. + * Developers can implement this instead of [[fit()]] to avoid dealing with schema validation + * and copying parameters into the model. + * + * @param dataset Training dataset + * @return Fitted model + */ + override protected def train(dataset: DataFrame): MultilayerPerceptronClassifierModel = { + val labels = getLayers.last.toInt + val lpData = extractLabeledPoints(dataset) + val data = lpData.map(lp => LabelConverter(lp, labels)) + val myLayers = getLayers.map(_.toInt) + val topology = FeedForwardTopology.multiLayerPerceptron(myLayers, true) + val FeedForwardTrainer = new FeedForwardTrainer(topology, myLayers(0), myLayers.last) + FeedForwardTrainer.LBFGSOptimizer.setConvergenceTol(getTol).setNumIterations(getMaxIter) + FeedForwardTrainer.setStackSize(getBlockSize) + val mlpModel = FeedForwardTrainer.train(data) + new MultilayerPerceptronClassifierModel(uid, myLayers, mlpModel.weights()) + } +} + +/** + * :: Experimental :: + * Classifier model based on the Multilayer Perceptron. + * Each layer has sigmoid activation function, output layer has softmax. + */ +@Experimental +class MultilayerPerceptronClassifierModel private[ml] (override val uid: String, + layers: Array[Int], + weights: Vector) + extends PredictionModel[Vector, MultilayerPerceptronClassifierModel] + with Serializable { --- End diff -- ```with Saveable```?
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