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Added JavaDeveloperApiExample.java and fixed other issues: Made devel…
…oper API private[spark] for now. Added constructors Java can understand to specialized Param types.
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examples/src/main/java/org/apache/spark/examples/ml/JavaDeveloperApiExample.java
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| /* | ||
| * 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. | ||
| */ | ||
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| package org.apache.spark.examples.ml; | ||
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| import java.util.List; | ||
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| import com.google.common.collect.Lists; | ||
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| import org.apache.spark.SparkConf; | ||
| import org.apache.spark.api.java.JavaRDD; | ||
| import org.apache.spark.api.java.JavaSparkContext; | ||
| import org.apache.spark.ml.classification.Classifier; | ||
| import org.apache.spark.ml.classification.ClassificationModel; | ||
| import org.apache.spark.ml.param.IntParam; | ||
| import org.apache.spark.ml.param.ParamMap; | ||
| import org.apache.spark.ml.param.Params; | ||
| import org.apache.spark.ml.param.Params$; | ||
| import org.apache.spark.mllib.linalg.BLAS; | ||
| import org.apache.spark.mllib.linalg.Vector; | ||
| import org.apache.spark.mllib.linalg.Vectors; | ||
| import org.apache.spark.mllib.regression.LabeledPoint; | ||
| import org.apache.spark.sql.DataFrame; | ||
| import org.apache.spark.sql.Row; | ||
| import org.apache.spark.sql.SQLContext; | ||
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| /** | ||
| * A simple example demonstrating how to write your own learning algorithm using Estimator, | ||
| * Transformer, and other abstractions. | ||
| * This mimics {@link org.apache.spark.ml.classification.LogisticRegression}. | ||
| * | ||
| * Run with | ||
| * <pre> | ||
| * bin/run-example ml.JavaDeveloperApiExample | ||
| * </pre> | ||
| */ | ||
| public class JavaDeveloperApiExample { | ||
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| public static void main(String[] args) throws Exception { | ||
| SparkConf conf = new SparkConf().setAppName("JavaDeveloperApiExample"); | ||
| JavaSparkContext jsc = new JavaSparkContext(conf); | ||
| SQLContext jsql = new SQLContext(jsc); | ||
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| // Prepare training data. | ||
| List<LabeledPoint> localTraining = Lists.newArrayList( | ||
| new LabeledPoint(1.0, Vectors.dense(0.0, 1.1, 0.1)), | ||
| new LabeledPoint(0.0, Vectors.dense(2.0, 1.0, -1.0)), | ||
| new LabeledPoint(0.0, Vectors.dense(2.0, 1.3, 1.0)), | ||
| new LabeledPoint(1.0, Vectors.dense(0.0, 1.2, -0.5))); | ||
| DataFrame training = jsql.applySchema(jsc.parallelize(localTraining), LabeledPoint.class); | ||
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| // Create a LogisticRegression instance. This instance is an Estimator. | ||
| MyJavaLogisticRegression lr = new MyJavaLogisticRegression(); | ||
| // Print out the parameters, documentation, and any default values. | ||
| System.out.println("MyJavaLogisticRegression parameters:\n" + lr.explainParams() + "\n"); | ||
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| // We may set parameters using setter methods. | ||
| lr.setMaxIter(10); | ||
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| // Learn a LogisticRegression model. This uses the parameters stored in lr. | ||
| MyJavaLogisticRegressionModel model = lr.fit(training); | ||
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| // Prepare test data. | ||
| List<LabeledPoint> localTest = Lists.newArrayList( | ||
| new LabeledPoint(1.0, Vectors.dense(-1.0, 1.5, 1.3)), | ||
| new LabeledPoint(0.0, Vectors.dense(3.0, 2.0, -0.1)), | ||
| new LabeledPoint(1.0, Vectors.dense(0.0, 2.2, -1.5))); | ||
| DataFrame test = jsql.applySchema(jsc.parallelize(localTest), LabeledPoint.class); | ||
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| // Make predictions on test documents. cvModel uses the best model found (lrModel). | ||
| DataFrame results = model.transform(test); | ||
| double sumPredictions = 0; | ||
| for (Row r : results.select("features", "label", "prediction").collect()) { | ||
| sumPredictions += r.getDouble(2); | ||
| } | ||
| if (sumPredictions != 0.0) { | ||
| throw new Exception("MyJavaLogisticRegression predicted something other than 0," + | ||
| " even though all weights are 0!"); | ||
| } | ||
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| jsc.stop(); | ||
| } | ||
| } | ||
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| /** | ||
| * Example of defining a type of {@link Classifier}. | ||
| * | ||
| * NOTE: This is private since it is an example. In practice, you may not want it to be private. | ||
| */ | ||
| class MyJavaLogisticRegression | ||
| extends Classifier<Vector, MyJavaLogisticRegression, MyJavaLogisticRegressionModel> | ||
| implements Params { | ||
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| /** | ||
| * Param for max number of iterations | ||
| * <p/> | ||
| * NOTE: The usual way to add a parameter to a model or algorithm is to include: | ||
| * - val myParamName: ParamType | ||
| * - def getMyParamName | ||
| * - def setMyParamName | ||
| */ | ||
| IntParam maxIter = new IntParam(this, "maxIter", "max number of iterations"); | ||
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| int getMaxIter() { return (int)get(maxIter); } | ||
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| public MyJavaLogisticRegression() { | ||
| setMaxIter(100); | ||
| } | ||
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| // The parameter setter is in this class since it should return type MyJavaLogisticRegression. | ||
| MyJavaLogisticRegression setMaxIter(int value) { | ||
| return (MyJavaLogisticRegression)set(maxIter, value); | ||
| } | ||
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| // This method is used by fit(). | ||
| // In Java, we have to make it public since Java does not understand Scala's protected modifier. | ||
| public MyJavaLogisticRegressionModel train(DataFrame dataset, ParamMap paramMap) { | ||
| // Extract columns from data using helper method. | ||
| JavaRDD<LabeledPoint> oldDataset = extractLabeledPoints(dataset, paramMap).toJavaRDD(); | ||
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| // Do learning to estimate the weight vector. | ||
| int numFeatures = oldDataset.take(1).get(0).features().size(); | ||
| Vector weights = Vectors.zeros(numFeatures); // Learning would happen here. | ||
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| // Create a model, and return it. | ||
| return new MyJavaLogisticRegressionModel(this, paramMap, weights); | ||
| } | ||
| } | ||
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| /** | ||
| * Example of defining a type of {@link ClassificationModel}. | ||
| * | ||
| * NOTE: This is private since it is an example. In practice, you may not want it to be private. | ||
| */ | ||
| class MyJavaLogisticRegressionModel | ||
| extends ClassificationModel<Vector, MyJavaLogisticRegressionModel> implements Params { | ||
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| private MyJavaLogisticRegression parent_; | ||
| public MyJavaLogisticRegression parent() { return parent_; } | ||
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| private ParamMap fittingParamMap_; | ||
| public ParamMap fittingParamMap() { return fittingParamMap_; } | ||
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| private Vector weights_; | ||
| public Vector weights() { return weights_; } | ||
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| public MyJavaLogisticRegressionModel( | ||
| MyJavaLogisticRegression parent_, | ||
| ParamMap fittingParamMap_, | ||
| Vector weights_) { | ||
| this.parent_ = parent_; | ||
| this.fittingParamMap_ = fittingParamMap_; | ||
| this.weights_ = weights_; | ||
| } | ||
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| // This uses the default implementation of transform(), which reads column "features" and outputs | ||
| // columns "prediction" and "rawPrediction." | ||
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| // This uses the default implementation of predict(), which chooses the label corresponding to | ||
| // the maximum value returned by [[predictRaw()]]. | ||
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| /** | ||
| * Raw prediction for each possible label. | ||
| * The meaning of a "raw" prediction may vary between algorithms, but it intuitively gives | ||
| * a measure of confidence in each possible label (where larger = more confident). | ||
| * This internal method is used to implement [[transform()]] and output [[rawPredictionCol]]. | ||
| * | ||
| * @return vector where element i is the raw prediction for label i. | ||
| * This raw prediction may be any real number, where a larger value indicates greater | ||
| * confidence for that label. | ||
| * | ||
| * In Java, we have to make this method public since Java does not understand Scala's protected | ||
| * modifier. | ||
| */ | ||
| public Vector predictRaw(Vector features) { | ||
| double margin = BLAS.dot(features, weights_); | ||
| // There are 2 classes (binary classification), so we return a length-2 vector, | ||
| // where index i corresponds to class i (i = 0, 1). | ||
| return Vectors.dense(-margin, margin); | ||
| } | ||
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| /** | ||
| * Number of classes the label can take. 2 indicates binary classification. | ||
| */ | ||
| public int numClasses() { return 2; } | ||
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| /** | ||
| * Create a copy of the model. | ||
| * The copy is shallow, except for the embedded paramMap, which gets a deep copy. | ||
| * <p/> | ||
| * This is used for the defaul implementation of [[transform()]]. | ||
| * | ||
| * In Java, we have to make this method public since Java does not understand Scala's protected | ||
| * modifier. | ||
| */ | ||
| public MyJavaLogisticRegressionModel copy() { | ||
| MyJavaLogisticRegressionModel m = | ||
| new MyJavaLogisticRegressionModel(parent_, fittingParamMap_, weights_); | ||
| Params$.MODULE$.inheritValues(this.paramMap(), this, m); | ||
| return m; | ||
| } | ||
| } |
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