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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