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Diffstat (limited to 'examples')
-rw-r--r-- | examples/src/main/python/mllib/dataset_example.py | 62 | ||||
-rw-r--r-- | examples/src/main/scala/org/apache/spark/examples/mllib/DatasetExample.scala | 121 |
2 files changed, 183 insertions, 0 deletions
diff --git a/examples/src/main/python/mllib/dataset_example.py b/examples/src/main/python/mllib/dataset_example.py new file mode 100644 index 0000000000..540dae785f --- /dev/null +++ b/examples/src/main/python/mllib/dataset_example.py @@ -0,0 +1,62 @@ +# +# 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. +# + +""" +An example of how to use SchemaRDD as a dataset for ML. Run with:: + bin/spark-submit examples/src/main/python/mllib/dataset_example.py +""" + +import os +import sys +import tempfile +import shutil + +from pyspark import SparkContext +from pyspark.sql import SQLContext +from pyspark.mllib.util import MLUtils +from pyspark.mllib.stat import Statistics + + +def summarize(dataset): + print "schema: %s" % dataset.schema().json() + labels = dataset.map(lambda r: r.label) + print "label average: %f" % labels.mean() + features = dataset.map(lambda r: r.features) + summary = Statistics.colStats(features) + print "features average: %r" % summary.mean() + +if __name__ == "__main__": + if len(sys.argv) > 2: + print >> sys.stderr, "Usage: dataset_example.py <libsvm file>" + exit(-1) + sc = SparkContext(appName="DatasetExample") + sqlCtx = SQLContext(sc) + if len(sys.argv) == 2: + input = sys.argv[1] + else: + input = "data/mllib/sample_libsvm_data.txt" + points = MLUtils.loadLibSVMFile(sc, input) + dataset0 = sqlCtx.inferSchema(points).setName("dataset0").cache() + summarize(dataset0) + tempdir = tempfile.NamedTemporaryFile(delete=False).name + os.unlink(tempdir) + print "Save dataset as a Parquet file to %s." % tempdir + dataset0.saveAsParquetFile(tempdir) + print "Load it back and summarize it again." + dataset1 = sqlCtx.parquetFile(tempdir).setName("dataset1").cache() + summarize(dataset1) + shutil.rmtree(tempdir) diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/DatasetExample.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/DatasetExample.scala new file mode 100644 index 0000000000..f8d83f4ec7 --- /dev/null +++ b/examples/src/main/scala/org/apache/spark/examples/mllib/DatasetExample.scala @@ -0,0 +1,121 @@ +/* + * 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.examples.mllib + +import java.io.File + +import com.google.common.io.Files +import scopt.OptionParser + +import org.apache.spark.{SparkConf, SparkContext} +import org.apache.spark.mllib.linalg.Vector +import org.apache.spark.mllib.regression.LabeledPoint +import org.apache.spark.mllib.stat.MultivariateOnlineSummarizer +import org.apache.spark.mllib.util.MLUtils +import org.apache.spark.rdd.RDD +import org.apache.spark.sql.{Row, SQLContext, SchemaRDD} + +/** + * An example of how to use [[org.apache.spark.sql.SchemaRDD]] as a Dataset for ML. Run with + * {{{ + * ./bin/run-example org.apache.spark.examples.mllib.DatasetExample [options] + * }}} + * If you use it as a template to create your own app, please use `spark-submit` to submit your app. + */ +object DatasetExample { + + case class Params( + input: String = "data/mllib/sample_libsvm_data.txt", + dataFormat: String = "libsvm") extends AbstractParams[Params] + + def main(args: Array[String]) { + val defaultParams = Params() + + val parser = new OptionParser[Params]("DatasetExample") { + head("Dataset: an example app using SchemaRDD as a Dataset for ML.") + opt[String]("input") + .text(s"input path to dataset") + .action((x, c) => c.copy(input = x)) + opt[String]("dataFormat") + .text("data format: libsvm (default), dense (deprecated in Spark v1.1)") + .action((x, c) => c.copy(input = x)) + checkConfig { params => + success + } + } + + parser.parse(args, defaultParams).map { params => + run(params) + }.getOrElse { + sys.exit(1) + } + } + + def run(params: Params) { + + val conf = new SparkConf().setAppName(s"DatasetExample with $params") + val sc = new SparkContext(conf) + val sqlContext = new SQLContext(sc) + import sqlContext._ // for implicit conversions + + // Load input data + val origData: RDD[LabeledPoint] = params.dataFormat match { + case "dense" => MLUtils.loadLabeledPoints(sc, params.input) + case "libsvm" => MLUtils.loadLibSVMFile(sc, params.input) + } + println(s"Loaded ${origData.count()} instances from file: ${params.input}") + + // Convert input data to SchemaRDD explicitly. + val schemaRDD: SchemaRDD = origData + println(s"Inferred schema:\n${schemaRDD.schema.prettyJson}") + println(s"Converted to SchemaRDD with ${schemaRDD.count()} records") + + // Select columns, using implicit conversion to SchemaRDD. + val labelsSchemaRDD: SchemaRDD = origData.select('label) + val labels: RDD[Double] = labelsSchemaRDD.map { case Row(v: Double) => v } + val numLabels = labels.count() + val meanLabel = labels.fold(0.0)(_ + _) / numLabels + println(s"Selected label column with average value $meanLabel") + + val featuresSchemaRDD: SchemaRDD = origData.select('features) + val features: RDD[Vector] = featuresSchemaRDD.map { case Row(v: Vector) => v } + val featureSummary = features.aggregate(new MultivariateOnlineSummarizer())( + (summary, feat) => summary.add(feat), + (sum1, sum2) => sum1.merge(sum2)) + println(s"Selected features column with average values:\n ${featureSummary.mean.toString}") + + val tmpDir = Files.createTempDir() + tmpDir.deleteOnExit() + val outputDir = new File(tmpDir, "dataset").toString + println(s"Saving to $outputDir as Parquet file.") + schemaRDD.saveAsParquetFile(outputDir) + + println(s"Loading Parquet file with UDT from $outputDir.") + val newDataset = sqlContext.parquetFile(outputDir) + + println(s"Schema from Parquet: ${newDataset.schema.prettyJson}") + val newFeatures = newDataset.select('features).map { case Row(v: Vector) => v } + val newFeaturesSummary = newFeatures.aggregate(new MultivariateOnlineSummarizer())( + (summary, feat) => summary.add(feat), + (sum1, sum2) => sum1.merge(sum2)) + println(s"Selected features column with average values:\n ${newFeaturesSummary.mean.toString}") + + sc.stop() + } + +} |