diff options
Diffstat (limited to 'mllib/src/test')
5 files changed, 310 insertions, 23 deletions
diff --git a/mllib/src/test/java/org/apache/spark/ml/JavaPipelineSuite.java b/mllib/src/test/java/org/apache/spark/ml/JavaPipelineSuite.java index 56a9dbdd58..50995ffef9 100644 --- a/mllib/src/test/java/org/apache/spark/ml/JavaPipelineSuite.java +++ b/mllib/src/test/java/org/apache/spark/ml/JavaPipelineSuite.java @@ -65,7 +65,7 @@ public class JavaPipelineSuite { .setStages(new PipelineStage[] {scaler, lr}); PipelineModel model = pipeline.fit(dataset); model.transform(dataset).registerTempTable("prediction"); - DataFrame predictions = jsql.sql("SELECT label, score, prediction FROM prediction"); + DataFrame predictions = jsql.sql("SELECT label, probability, prediction FROM prediction"); predictions.collectAsList(); } } diff --git a/mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java b/mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java index f4ba23c445..26284023b0 100644 --- a/mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java +++ b/mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java @@ -18,17 +18,22 @@ package org.apache.spark.ml.classification; import java.io.Serializable; +import java.lang.Math; import java.util.List; import org.junit.After; import org.junit.Before; import org.junit.Test; +import org.apache.spark.api.java.JavaRDD; import org.apache.spark.api.java.JavaSparkContext; +import static org.apache.spark.mllib.classification.LogisticRegressionSuite.generateLogisticInputAsList; +import org.apache.spark.mllib.linalg.Vector; import org.apache.spark.mllib.regression.LabeledPoint; import org.apache.spark.sql.DataFrame; import org.apache.spark.sql.SQLContext; -import static org.apache.spark.mllib.classification.LogisticRegressionSuite.generateLogisticInputAsList; +import org.apache.spark.sql.Row; + public class JavaLogisticRegressionSuite implements Serializable { @@ -36,12 +41,17 @@ public class JavaLogisticRegressionSuite implements Serializable { private transient SQLContext jsql; private transient DataFrame dataset; + private transient JavaRDD<LabeledPoint> datasetRDD; + private double eps = 1e-5; + @Before public void setUp() { jsc = new JavaSparkContext("local", "JavaLogisticRegressionSuite"); jsql = new SQLContext(jsc); List<LabeledPoint> points = generateLogisticInputAsList(1.0, 1.0, 100, 42); - dataset = jsql.applySchema(jsc.parallelize(points, 2), LabeledPoint.class); + datasetRDD = jsc.parallelize(points, 2); + dataset = jsql.applySchema(datasetRDD, LabeledPoint.class); + dataset.registerTempTable("dataset"); } @After @@ -51,29 +61,88 @@ public class JavaLogisticRegressionSuite implements Serializable { } @Test - public void logisticRegression() { + public void logisticRegressionDefaultParams() { LogisticRegression lr = new LogisticRegression(); + assert(lr.getLabelCol().equals("label")); LogisticRegressionModel model = lr.fit(dataset); model.transform(dataset).registerTempTable("prediction"); - DataFrame predictions = jsql.sql("SELECT label, score, prediction FROM prediction"); + DataFrame predictions = jsql.sql("SELECT label, probability, prediction FROM prediction"); predictions.collectAsList(); + // Check defaults + assert(model.getThreshold() == 0.5); + assert(model.getFeaturesCol().equals("features")); + assert(model.getPredictionCol().equals("prediction")); + assert(model.getProbabilityCol().equals("probability")); } @Test public void logisticRegressionWithSetters() { + // Set params, train, and check as many params as we can. LogisticRegression lr = new LogisticRegression() .setMaxIter(10) - .setRegParam(1.0); + .setRegParam(1.0) + .setThreshold(0.6) + .setProbabilityCol("myProbability"); LogisticRegressionModel model = lr.fit(dataset); - model.transform(dataset, model.threshold().w(0.8)) // overwrite threshold - .registerTempTable("prediction"); - DataFrame predictions = jsql.sql("SELECT label, score, prediction FROM prediction"); - predictions.collectAsList(); + assert(model.fittingParamMap().apply(lr.maxIter()) == 10); + assert(model.fittingParamMap().apply(lr.regParam()).equals(1.0)); + assert(model.fittingParamMap().apply(lr.threshold()).equals(0.6)); + assert(model.getThreshold() == 0.6); + + // Modify model params, and check that the params worked. + model.setThreshold(1.0); + model.transform(dataset).registerTempTable("predAllZero"); + DataFrame predAllZero = jsql.sql("SELECT prediction, myProbability FROM predAllZero"); + for (Row r: predAllZero.collectAsList()) { + assert(r.getDouble(0) == 0.0); + } + // Call transform with params, and check that the params worked. + model.transform(dataset, model.threshold().w(0.0), model.probabilityCol().w("myProb")) + .registerTempTable("predNotAllZero"); + DataFrame predNotAllZero = jsql.sql("SELECT prediction, myProb FROM predNotAllZero"); + boolean foundNonZero = false; + for (Row r: predNotAllZero.collectAsList()) { + if (r.getDouble(0) != 0.0) foundNonZero = true; + } + assert(foundNonZero); + + // Call fit() with new params, and check as many params as we can. + LogisticRegressionModel model2 = lr.fit(dataset, lr.maxIter().w(5), lr.regParam().w(0.1), + lr.threshold().w(0.4), lr.probabilityCol().w("theProb")); + assert(model2.fittingParamMap().apply(lr.maxIter()) == 5); + assert(model2.fittingParamMap().apply(lr.regParam()).equals(0.1)); + assert(model2.fittingParamMap().apply(lr.threshold()).equals(0.4)); + assert(model2.getThreshold() == 0.4); + assert(model2.getProbabilityCol().equals("theProb")); } + @SuppressWarnings("unchecked") @Test - public void logisticRegressionFitWithVarargs() { + public void logisticRegressionPredictorClassifierMethods() { LogisticRegression lr = new LogisticRegression(); - lr.fit(dataset, lr.maxIter().w(10), lr.regParam().w(1.0)); + LogisticRegressionModel model = lr.fit(dataset); + assert(model.numClasses() == 2); + + model.transform(dataset).registerTempTable("transformed"); + DataFrame trans1 = jsql.sql("SELECT rawPrediction, probability FROM transformed"); + for (Row row: trans1.collect()) { + Vector raw = (Vector)row.get(0); + Vector prob = (Vector)row.get(1); + assert(raw.size() == 2); + assert(prob.size() == 2); + double probFromRaw1 = 1.0 / (1.0 + Math.exp(-raw.apply(1))); + assert(Math.abs(prob.apply(1) - probFromRaw1) < eps); + assert(Math.abs(prob.apply(0) - (1.0 - probFromRaw1)) < eps); + } + + DataFrame trans2 = jsql.sql("SELECT prediction, probability FROM transformed"); + for (Row row: trans2.collect()) { + double pred = row.getDouble(0); + Vector prob = (Vector)row.get(1); + double probOfPred = prob.apply((int)pred); + for (int i = 0; i < prob.size(); ++i) { + assert(probOfPred >= prob.apply(i)); + } + } } } diff --git a/mllib/src/test/java/org/apache/spark/ml/regression/JavaLinearRegressionSuite.java b/mllib/src/test/java/org/apache/spark/ml/regression/JavaLinearRegressionSuite.java new file mode 100644 index 0000000000..5bd616e74d --- /dev/null +++ b/mllib/src/test/java/org/apache/spark/ml/regression/JavaLinearRegressionSuite.java @@ -0,0 +1,89 @@ +/* + * 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.regression; + +import java.io.Serializable; +import java.util.List; + +import org.junit.After; +import org.junit.Before; +import org.junit.Test; + +import org.apache.spark.api.java.JavaRDD; +import org.apache.spark.api.java.JavaSparkContext; +import static org.apache.spark.mllib.classification.LogisticRegressionSuite + .generateLogisticInputAsList; +import org.apache.spark.mllib.regression.LabeledPoint; +import org.apache.spark.sql.DataFrame; +import org.apache.spark.sql.SQLContext; + + +public class JavaLinearRegressionSuite implements Serializable { + + private transient JavaSparkContext jsc; + private transient SQLContext jsql; + private transient DataFrame dataset; + private transient JavaRDD<LabeledPoint> datasetRDD; + + @Before + public void setUp() { + jsc = new JavaSparkContext("local", "JavaLinearRegressionSuite"); + jsql = new SQLContext(jsc); + List<LabeledPoint> points = generateLogisticInputAsList(1.0, 1.0, 100, 42); + datasetRDD = jsc.parallelize(points, 2); + dataset = jsql.applySchema(datasetRDD, LabeledPoint.class); + dataset.registerTempTable("dataset"); + } + + @After + public void tearDown() { + jsc.stop(); + jsc = null; + } + + @Test + public void linearRegressionDefaultParams() { + LinearRegression lr = new LinearRegression(); + assert(lr.getLabelCol().equals("label")); + LinearRegressionModel model = lr.fit(dataset); + model.transform(dataset).registerTempTable("prediction"); + DataFrame predictions = jsql.sql("SELECT label, prediction FROM prediction"); + predictions.collect(); + // Check defaults + assert(model.getFeaturesCol().equals("features")); + assert(model.getPredictionCol().equals("prediction")); + } + + @Test + public void linearRegressionWithSetters() { + // Set params, train, and check as many params as we can. + LinearRegression lr = new LinearRegression() + .setMaxIter(10) + .setRegParam(1.0); + LinearRegressionModel model = lr.fit(dataset); + assert(model.fittingParamMap().apply(lr.maxIter()) == 10); + assert(model.fittingParamMap().apply(lr.regParam()).equals(1.0)); + + // Call fit() with new params, and check as many params as we can. + LinearRegressionModel model2 = + lr.fit(dataset, lr.maxIter().w(5), lr.regParam().w(0.1), lr.predictionCol().w("thePred")); + assert(model2.fittingParamMap().apply(lr.maxIter()) == 5); + assert(model2.fittingParamMap().apply(lr.regParam()).equals(0.1)); + assert(model2.getPredictionCol().equals("thePred")); + } +} diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala index 33e40dc741..b3d1bfcfbe 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala @@ -20,44 +20,108 @@ package org.apache.spark.ml.classification import org.scalatest.FunSuite import org.apache.spark.mllib.classification.LogisticRegressionSuite.generateLogisticInput +import org.apache.spark.mllib.linalg.Vector import org.apache.spark.mllib.util.MLlibTestSparkContext -import org.apache.spark.sql.{SQLContext, DataFrame} +import org.apache.spark.mllib.util.TestingUtils._ +import org.apache.spark.sql.{DataFrame, Row, SQLContext} + class LogisticRegressionSuite extends FunSuite with MLlibTestSparkContext { @transient var sqlContext: SQLContext = _ @transient var dataset: DataFrame = _ + private val eps: Double = 1e-5 override def beforeAll(): Unit = { super.beforeAll() sqlContext = new SQLContext(sc) dataset = sqlContext.createDataFrame( - sc.parallelize(generateLogisticInput(1.0, 1.0, 100, 42), 2)) + sc.parallelize(generateLogisticInput(1.0, 1.0, nPoints = 100, seed = 42), 2)) } - test("logistic regression") { + test("logistic regression: default params") { val lr = new LogisticRegression + assert(lr.getLabelCol == "label") + assert(lr.getFeaturesCol == "features") + assert(lr.getPredictionCol == "prediction") + assert(lr.getRawPredictionCol == "rawPrediction") + assert(lr.getProbabilityCol == "probability") val model = lr.fit(dataset) model.transform(dataset) - .select("label", "prediction") + .select("label", "probability", "prediction", "rawPrediction") .collect() + assert(model.getThreshold === 0.5) + assert(model.getFeaturesCol == "features") + assert(model.getPredictionCol == "prediction") + assert(model.getRawPredictionCol == "rawPrediction") + assert(model.getProbabilityCol == "probability") } test("logistic regression with setters") { + // Set params, train, and check as many params as we can. val lr = new LogisticRegression() .setMaxIter(10) .setRegParam(1.0) + .setThreshold(0.6) + .setProbabilityCol("myProbability") val model = lr.fit(dataset) - model.transform(dataset, model.threshold -> 0.8) // overwrite threshold - .select("label", "score", "prediction") + assert(model.fittingParamMap.get(lr.maxIter) === Some(10)) + assert(model.fittingParamMap.get(lr.regParam) === Some(1.0)) + assert(model.fittingParamMap.get(lr.threshold) === Some(0.6)) + assert(model.getThreshold === 0.6) + + // Modify model params, and check that the params worked. + model.setThreshold(1.0) + val predAllZero = model.transform(dataset) + .select("prediction", "myProbability") .collect() + .map { case Row(pred: Double, prob: Vector) => pred } + assert(predAllZero.forall(_ === 0), + s"With threshold=1.0, expected predictions to be all 0, but only" + + s" ${predAllZero.count(_ === 0)} of ${dataset.count()} were 0.") + // Call transform with params, and check that the params worked. + val predNotAllZero = + model.transform(dataset, model.threshold -> 0.0, model.probabilityCol -> "myProb") + .select("prediction", "myProb") + .collect() + .map { case Row(pred: Double, prob: Vector) => pred } + assert(predNotAllZero.exists(_ !== 0.0)) + + // Call fit() with new params, and check as many params as we can. + val model2 = lr.fit(dataset, lr.maxIter -> 5, lr.regParam -> 0.1, lr.threshold -> 0.4, + lr.probabilityCol -> "theProb") + assert(model2.fittingParamMap.get(lr.maxIter).get === 5) + assert(model2.fittingParamMap.get(lr.regParam).get === 0.1) + assert(model2.fittingParamMap.get(lr.threshold).get === 0.4) + assert(model2.getThreshold === 0.4) + assert(model2.getProbabilityCol == "theProb") } - test("logistic regression fit and transform with varargs") { + test("logistic regression: Predictor, Classifier methods") { + val sqlContext = this.sqlContext val lr = new LogisticRegression - val model = lr.fit(dataset, lr.maxIter -> 10, lr.regParam -> 1.0) - model.transform(dataset, model.threshold -> 0.8, model.scoreCol -> "probability") - .select("label", "probability", "prediction") - .collect() + + val model = lr.fit(dataset) + assert(model.numClasses === 2) + + val threshold = model.getThreshold + val results = model.transform(dataset) + + // Compare rawPrediction with probability + results.select("rawPrediction", "probability").collect().map { + case Row(raw: Vector, prob: Vector) => + assert(raw.size === 2) + assert(prob.size === 2) + val probFromRaw1 = 1.0 / (1.0 + math.exp(-raw(1))) + assert(prob(1) ~== probFromRaw1 relTol eps) + assert(prob(0) ~== 1.0 - probFromRaw1 relTol eps) + } + + // Compare prediction with probability + results.select("prediction", "probability").collect().map { + case Row(pred: Double, prob: Vector) => + val predFromProb = prob.toArray.zipWithIndex.maxBy(_._1)._2 + assert(pred == predFromProb) + } } } diff --git a/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala new file mode 100644 index 0000000000..bbb44c3e2d --- /dev/null +++ b/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala @@ -0,0 +1,65 @@ +/* + * 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.regression + +import org.scalatest.FunSuite + +import org.apache.spark.mllib.classification.LogisticRegressionSuite.generateLogisticInput +import org.apache.spark.mllib.util.MLlibTestSparkContext +import org.apache.spark.sql.{DataFrame, SQLContext} + +class LinearRegressionSuite extends FunSuite with MLlibTestSparkContext { + + @transient var sqlContext: SQLContext = _ + @transient var dataset: DataFrame = _ + + override def beforeAll(): Unit = { + super.beforeAll() + sqlContext = new SQLContext(sc) + dataset = sqlContext.createDataFrame( + sc.parallelize(generateLogisticInput(1.0, 1.0, nPoints = 100, seed = 42), 2)) + } + + test("linear regression: default params") { + val lr = new LinearRegression + assert(lr.getLabelCol == "label") + val model = lr.fit(dataset) + model.transform(dataset) + .select("label", "prediction") + .collect() + // Check defaults + assert(model.getFeaturesCol == "features") + assert(model.getPredictionCol == "prediction") + } + + test("linear regression with setters") { + // Set params, train, and check as many as we can. + val lr = new LinearRegression() + .setMaxIter(10) + .setRegParam(1.0) + val model = lr.fit(dataset) + assert(model.fittingParamMap.get(lr.maxIter).get === 10) + assert(model.fittingParamMap.get(lr.regParam).get === 1.0) + + // Call fit() with new params, and check as many as we can. + val model2 = lr.fit(dataset, lr.maxIter -> 5, lr.regParam -> 0.1, lr.predictionCol -> "thePred") + assert(model2.fittingParamMap.get(lr.maxIter).get === 5) + assert(model2.fittingParamMap.get(lr.regParam).get === 0.1) + assert(model2.getPredictionCol == "thePred") + } +} |