[SPARK-10138] [ML] move setters to MultilayerPerceptronClassifier and add Java test suite
Otherwise, setters do not return self type. jkbradley avulanov Author: Xiangrui Meng <meng@databricks.com> Closes #8342 from mengxr/SPARK-10138.
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@ -42,9 +42,6 @@ private[ml] trait MultilayerPerceptronParams extends PredictorParams
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ParamValidators.arrayLengthGt(1)
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)
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/** @group setParam */
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def setLayers(value: Array[Int]): this.type = set(layers, value)
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/** @group getParam */
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final def getLayers: Array[Int] = $(layers)
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@ -61,33 +58,9 @@ private[ml] trait MultilayerPerceptronParams extends PredictorParams
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"it is adjusted to the size of this data. Recommended size is between 10 and 1000",
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ParamValidators.gt(0))
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/** @group setParam */
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def setBlockSize(value: Int): this.type = set(blockSize, value)
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/** @group getParam */
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final def getBlockSize: Int = $(blockSize)
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/**
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* Set the maximum number of iterations.
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* Default is 100.
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* @group setParam
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*/
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def setMaxIter(value: Int): this.type = set(maxIter, value)
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/**
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* Set the convergence tolerance of iterations.
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* Smaller value will lead to higher accuracy with the cost of more iterations.
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* Default is 1E-4.
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* @group setParam
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*/
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def setTol(value: Double): this.type = set(tol, value)
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/**
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* Set the seed for weights initialization.
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* @group setParam
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*/
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def setSeed(value: Long): this.type = set(seed, value)
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setDefault(maxIter -> 100, tol -> 1e-4, layers -> Array(1, 1), blockSize -> 128)
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}
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@ -136,6 +109,33 @@ class MultilayerPerceptronClassifier(override val uid: String)
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def this() = this(Identifiable.randomUID("mlpc"))
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/** @group setParam */
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def setLayers(value: Array[Int]): this.type = set(layers, value)
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/** @group setParam */
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def setBlockSize(value: Int): this.type = set(blockSize, value)
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/**
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* Set the maximum number of iterations.
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* Default is 100.
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* @group setParam
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*/
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def setMaxIter(value: Int): this.type = set(maxIter, value)
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/**
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* Set the convergence tolerance of iterations.
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* Smaller value will lead to higher accuracy with the cost of more iterations.
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* Default is 1E-4.
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* @group setParam
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*/
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def setTol(value: Double): this.type = set(tol, value)
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/**
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* Set the seed for weights initialization.
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* @group setParam
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*/
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def setSeed(value: Long): this.type = set(seed, value)
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override def copy(extra: ParamMap): MultilayerPerceptronClassifier = defaultCopy(extra)
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/**
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@ -0,0 +1,74 @@
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/*
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* Licensed to the Apache Software Foundation (ASF) under one or more
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* contributor license agreements. See the NOTICE file distributed with
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* this work for additional information regarding copyright ownership.
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* The ASF licenses this file to You under the Apache License, Version 2.0
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* (the "License"); you may not use this file except in compliance with
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* the License. You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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package org.apache.spark.ml.classification;
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import java.io.Serializable;
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import java.util.Arrays;
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import org.junit.After;
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import org.junit.Assert;
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import org.junit.Before;
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import org.junit.Test;
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import org.apache.spark.api.java.JavaSparkContext;
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import org.apache.spark.mllib.linalg.Vectors;
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import org.apache.spark.mllib.regression.LabeledPoint;
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import org.apache.spark.sql.DataFrame;
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import org.apache.spark.sql.Row;
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import org.apache.spark.sql.SQLContext;
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public class JavaMultilayerPerceptronClassifierSuite implements Serializable {
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private transient JavaSparkContext jsc;
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private transient SQLContext sqlContext;
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@Before
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public void setUp() {
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jsc = new JavaSparkContext("local", "JavaLogisticRegressionSuite");
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sqlContext = new SQLContext(jsc);
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}
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@After
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public void tearDown() {
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jsc.stop();
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jsc = null;
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sqlContext = null;
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}
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@Test
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public void testMLPC() {
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DataFrame dataFrame = sqlContext.createDataFrame(
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jsc.parallelize(Arrays.asList(
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new LabeledPoint(0.0, Vectors.dense(0.0, 0.0)),
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new LabeledPoint(1.0, Vectors.dense(0.0, 1.0)),
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new LabeledPoint(1.0, Vectors.dense(1.0, 0.0)),
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new LabeledPoint(0.0, Vectors.dense(1.0, 1.0)))),
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LabeledPoint.class);
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MultilayerPerceptronClassifier mlpc = new MultilayerPerceptronClassifier()
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.setLayers(new int[] {2, 5, 2})
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.setBlockSize(1)
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.setSeed(11L)
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.setMaxIter(100);
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MultilayerPerceptronClassificationModel model = mlpc.fit(dataFrame);
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DataFrame result = model.transform(dataFrame);
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Row[] predictionAndLabels = result.select("prediction", "label").collect();
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for (Row r: predictionAndLabels) {
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Assert.assertEquals((int) r.getDouble(0), (int) r.getDouble(1));
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}
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}
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}
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