Repository: spark Updated Branches: refs/heads/branch-1.6 0665fb5ea -> 1dde97176
http://git-wip-us.apache.org/repos/asf/spark/blob/1dde9717/examples/src/main/scala/org/apache/spark/examples/mllib/MultiLabelMetricsExample.scala ---------------------------------------------------------------------- diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/MultiLabelMetricsExample.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/MultiLabelMetricsExample.scala new file mode 100644 index 0000000..4503c15 --- /dev/null +++ b/examples/src/main/scala/org/apache/spark/examples/mllib/MultiLabelMetricsExample.scala @@ -0,0 +1,69 @@ +/* + * 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. + */ + +// scalastyle:off println +package org.apache.spark.examples.mllib + +// $example on$ +import org.apache.spark.mllib.evaluation.MultilabelMetrics +import org.apache.spark.rdd.RDD +// $example off$ +import org.apache.spark.{SparkContext, SparkConf} + +object MultiLabelMetricsExample { + def main(args: Array[String]): Unit = { + val conf = new SparkConf().setAppName("MultiLabelMetricsExample") + val sc = new SparkContext(conf) + // $example on$ + val scoreAndLabels: RDD[(Array[Double], Array[Double])] = sc.parallelize( + Seq((Array(0.0, 1.0), Array(0.0, 2.0)), + (Array(0.0, 2.0), Array(0.0, 1.0)), + (Array.empty[Double], Array(0.0)), + (Array(2.0), Array(2.0)), + (Array(2.0, 0.0), Array(2.0, 0.0)), + (Array(0.0, 1.0, 2.0), Array(0.0, 1.0)), + (Array(1.0), Array(1.0, 2.0))), 2) + + // Instantiate metrics object + val metrics = new MultilabelMetrics(scoreAndLabels) + + // Summary stats + println(s"Recall = ${metrics.recall}") + println(s"Precision = ${metrics.precision}") + println(s"F1 measure = ${metrics.f1Measure}") + println(s"Accuracy = ${metrics.accuracy}") + + // Individual label stats + metrics.labels.foreach(label => + println(s"Class $label precision = ${metrics.precision(label)}")) + metrics.labels.foreach(label => println(s"Class $label recall = ${metrics.recall(label)}")) + metrics.labels.foreach(label => println(s"Class $label F1-score = ${metrics.f1Measure(label)}")) + + // Micro stats + println(s"Micro recall = ${metrics.microRecall}") + println(s"Micro precision = ${metrics.microPrecision}") + println(s"Micro F1 measure = ${metrics.microF1Measure}") + + // Hamming loss + println(s"Hamming loss = ${metrics.hammingLoss}") + + // Subset accuracy + println(s"Subset accuracy = ${metrics.subsetAccuracy}") + // $example off$ + } +} +// scalastyle:on println http://git-wip-us.apache.org/repos/asf/spark/blob/1dde9717/examples/src/main/scala/org/apache/spark/examples/mllib/MulticlassMetricsExample.scala ---------------------------------------------------------------------- diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/MulticlassMetricsExample.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/MulticlassMetricsExample.scala new file mode 100644 index 0000000..0904449 --- /dev/null +++ b/examples/src/main/scala/org/apache/spark/examples/mllib/MulticlassMetricsExample.scala @@ -0,0 +1,99 @@ +/* + * 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. + */ + +// scalastyle:off println +package org.apache.spark.examples.mllib + +// $example on$ +import org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS +import org.apache.spark.mllib.evaluation.MulticlassMetrics +import org.apache.spark.mllib.regression.LabeledPoint +import org.apache.spark.mllib.util.MLUtils +// $example off$ +import org.apache.spark.{SparkContext, SparkConf} + +object MulticlassMetricsExample { + + def main(args: Array[String]): Unit = { + val conf = new SparkConf().setAppName("MulticlassMetricsExample") + val sc = new SparkContext(conf) + + // $example on$ + // Load training data in LIBSVM format + val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_multiclass_classification_data.txt") + + // Split data into training (60%) and test (40%) + val Array(training, test) = data.randomSplit(Array(0.6, 0.4), seed = 11L) + training.cache() + + // Run training algorithm to build the model + val model = new LogisticRegressionWithLBFGS() + .setNumClasses(3) + .run(training) + + // Compute raw scores on the test set + val predictionAndLabels = test.map { case LabeledPoint(label, features) => + val prediction = model.predict(features) + (prediction, label) + } + + // Instantiate metrics object + val metrics = new MulticlassMetrics(predictionAndLabels) + + // Confusion matrix + println("Confusion matrix:") + println(metrics.confusionMatrix) + + // Overall Statistics + val precision = metrics.precision + val recall = metrics.recall // same as true positive rate + val f1Score = metrics.fMeasure + println("Summary Statistics") + println(s"Precision = $precision") + println(s"Recall = $recall") + println(s"F1 Score = $f1Score") + + // Precision by label + val labels = metrics.labels + labels.foreach { l => + println(s"Precision($l) = " + metrics.precision(l)) + } + + // Recall by label + labels.foreach { l => + println(s"Recall($l) = " + metrics.recall(l)) + } + + // False positive rate by label + labels.foreach { l => + println(s"FPR($l) = " + metrics.falsePositiveRate(l)) + } + + // F-measure by label + labels.foreach { l => + println(s"F1-Score($l) = " + metrics.fMeasure(l)) + } + + // Weighted stats + println(s"Weighted precision: ${metrics.weightedPrecision}") + println(s"Weighted recall: ${metrics.weightedRecall}") + println(s"Weighted F1 score: ${metrics.weightedFMeasure}") + println(s"Weighted false positive rate: ${metrics.weightedFalsePositiveRate}") + // $example off$ + } +} +// scalastyle:on println http://git-wip-us.apache.org/repos/asf/spark/blob/1dde9717/examples/src/main/scala/org/apache/spark/examples/mllib/RankingMetricsExample.scala ---------------------------------------------------------------------- diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/RankingMetricsExample.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/RankingMetricsExample.scala new file mode 100644 index 0000000..cffa03d --- /dev/null +++ b/examples/src/main/scala/org/apache/spark/examples/mllib/RankingMetricsExample.scala @@ -0,0 +1,110 @@ +/* + * 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. + */ + +// scalastyle:off println +package org.apache.spark.examples.mllib + +// $example on$ +import org.apache.spark.mllib.evaluation.{RegressionMetrics, RankingMetrics} +import org.apache.spark.mllib.recommendation.{ALS, Rating} +// $example off$ +import org.apache.spark.sql.SQLContext +import org.apache.spark.{SparkContext, SparkConf} + +object RankingMetricsExample { + def main(args: Array[String]) { + val conf = new SparkConf().setAppName("RankingMetricsExample") + val sc = new SparkContext(conf) + val sqlContext = new SQLContext(sc) + import sqlContext.implicits._ + // $example on$ + // Read in the ratings data + val ratings = sc.textFile("data/mllib/sample_movielens_data.txt").map { line => + val fields = line.split("::") + Rating(fields(0).toInt, fields(1).toInt, fields(2).toDouble - 2.5) + }.cache() + + // Map ratings to 1 or 0, 1 indicating a movie that should be recommended + val binarizedRatings = ratings.map(r => Rating(r.user, r.product, + if (r.rating > 0) 1.0 else 0.0)).cache() + + // Summarize ratings + val numRatings = ratings.count() + val numUsers = ratings.map(_.user).distinct().count() + val numMovies = ratings.map(_.product).distinct().count() + println(s"Got $numRatings ratings from $numUsers users on $numMovies movies.") + + // Build the model + val numIterations = 10 + val rank = 10 + val lambda = 0.01 + val model = ALS.train(ratings, rank, numIterations, lambda) + + // Define a function to scale ratings from 0 to 1 + def scaledRating(r: Rating): Rating = { + val scaledRating = math.max(math.min(r.rating, 1.0), 0.0) + Rating(r.user, r.product, scaledRating) + } + + // Get sorted top ten predictions for each user and then scale from [0, 1] + val userRecommended = model.recommendProductsForUsers(10).map { case (user, recs) => + (user, recs.map(scaledRating)) + } + + // Assume that any movie a user rated 3 or higher (which maps to a 1) is a relevant document + // Compare with top ten most relevant documents + val userMovies = binarizedRatings.groupBy(_.user) + val relevantDocuments = userMovies.join(userRecommended).map { case (user, (actual, + predictions)) => + (predictions.map(_.product), actual.filter(_.rating > 0.0).map(_.product).toArray) + } + + // Instantiate metrics object + val metrics = new RankingMetrics(relevantDocuments) + + // Precision at K + Array(1, 3, 5).foreach { k => + println(s"Precision at $k = ${metrics.precisionAt(k)}") + } + + // Mean average precision + println(s"Mean average precision = ${metrics.meanAveragePrecision}") + + // Normalized discounted cumulative gain + Array(1, 3, 5).foreach { k => + println(s"NDCG at $k = ${metrics.ndcgAt(k)}") + } + + // Get predictions for each data point + val allPredictions = model.predict(ratings.map(r => (r.user, r.product))).map(r => ((r.user, + r.product), r.rating)) + val allRatings = ratings.map(r => ((r.user, r.product), r.rating)) + val predictionsAndLabels = allPredictions.join(allRatings).map { case ((user, product), + (predicted, actual)) => + (predicted, actual) + } + + // Get the RMSE using regression metrics + val regressionMetrics = new RegressionMetrics(predictionsAndLabels) + println(s"RMSE = ${regressionMetrics.rootMeanSquaredError}") + + // R-squared + println(s"R-squared = ${regressionMetrics.r2}") + // $example off$ + } +} +// scalastyle:on println http://git-wip-us.apache.org/repos/asf/spark/blob/1dde9717/examples/src/main/scala/org/apache/spark/examples/mllib/RegressionMetricsExample.scala ---------------------------------------------------------------------- diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/RegressionMetricsExample.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/RegressionMetricsExample.scala new file mode 100644 index 0000000..47d4453 --- /dev/null +++ b/examples/src/main/scala/org/apache/spark/examples/mllib/RegressionMetricsExample.scala @@ -0,0 +1,67 @@ +/* + * 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. + */ +// scalastyle:off println + +package org.apache.spark.examples.mllib + +// $example on$ +import org.apache.spark.mllib.regression.LinearRegressionWithSGD +import org.apache.spark.mllib.evaluation.RegressionMetrics +import org.apache.spark.mllib.util.MLUtils +// $example off$ +import org.apache.spark.sql.SQLContext +import org.apache.spark.{SparkConf, SparkContext} + +object RegressionMetricsExample { + def main(args: Array[String]) : Unit = { + val conf = new SparkConf().setAppName("RegressionMetricsExample") + val sc = new SparkContext(conf) + val sqlContext = new SQLContext(sc) + // $example on$ + // Load the data + val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_linear_regression_data.txt").cache() + + // Build the model + val numIterations = 100 + val model = LinearRegressionWithSGD.train(data, numIterations) + + // Get predictions + val valuesAndPreds = data.map{ point => + val prediction = model.predict(point.features) + (prediction, point.label) + } + + // Instantiate metrics object + val metrics = new RegressionMetrics(valuesAndPreds) + + // Squared error + println(s"MSE = ${metrics.meanSquaredError}") + println(s"RMSE = ${metrics.rootMeanSquaredError}") + + // R-squared + println(s"R-squared = ${metrics.r2}") + + // Mean absolute error + println(s"MAE = ${metrics.meanAbsoluteError}") + + // Explained variance + println(s"Explained variance = ${metrics.explainedVariance}") + // $example off$ + } +} +// scalastyle:on println + --------------------------------------------------------------------- To unsubscribe, e-mail: commits-unsubscr...@spark.apache.org For additional commands, e-mail: commits-h...@spark.apache.org
