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author | Bryan Cutler <cutlerb@gmail.com> | 2016-07-14 09:12:46 +0100 |
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committer | Sean Owen <sowen@cloudera.com> | 2016-07-14 09:12:46 +0100 |
commit | e3f8a033679261aaee15bda0f970a1890411e743 (patch) | |
tree | fecc6121b1d5357c2214f710018de2a9ddea2786 /examples/src/main/scala | |
parent | 252d4f27f23b547777892bcea25a2cea62d8cbab (diff) | |
download | spark-e3f8a033679261aaee15bda0f970a1890411e743.tar.gz spark-e3f8a033679261aaee15bda0f970a1890411e743.tar.bz2 spark-e3f8a033679261aaee15bda0f970a1890411e743.zip |
[SPARK-16403][EXAMPLES] Cleanup to remove unused imports, consistent style, minor fixes
## What changes were proposed in this pull request?
Cleanup of examples, mostly from PySpark-ML to fix minor issues: unused imports, style consistency, pipeline_example is a duplicate, use future print funciton, and a spelling error.
* The "Pipeline Example" is duplicated by "Simple Text Classification Pipeline" in Scala, Python, and Java.
* "Estimator Transformer Param Example" is duplicated by "Simple Params Example" in Scala, Python and Java
* Synced random_forest_classifier_example.py with Scala by adding IndexToString label converted
* Synced train_validation_split.py (in Scala ModelSelectionViaTrainValidationExample) by adjusting data split, adding grid for intercept.
* RegexTokenizer was doing nothing in tokenizer_example.py and JavaTokenizerExample.java, synced with Scala version
## How was this patch tested?
local tests and run modified examples
Author: Bryan Cutler <cutlerb@gmail.com>
Closes #14081 from BryanCutler/examples-cleanup-SPARK-16403.
Diffstat (limited to 'examples/src/main/scala')
16 files changed, 24 insertions, 208 deletions
diff --git a/examples/src/main/scala/org/apache/spark/examples/BroadcastTest.scala b/examples/src/main/scala/org/apache/spark/examples/BroadcastTest.scala index a68fd0285f..86eed3867c 100644 --- a/examples/src/main/scala/org/apache/spark/examples/BroadcastTest.scala +++ b/examples/src/main/scala/org/apache/spark/examples/BroadcastTest.scala @@ -18,7 +18,6 @@ // scalastyle:off println package org.apache.spark.examples -import org.apache.spark.SparkConf import org.apache.spark.sql.SparkSession /** diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/GaussianMixtureExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/GaussianMixtureExample.scala index 2c2bf421bc..26095b46f5 100644 --- a/examples/src/main/scala/org/apache/spark/examples/ml/GaussianMixtureExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/ml/GaussianMixtureExample.scala @@ -33,8 +33,10 @@ import org.apache.spark.sql.SparkSession */ object GaussianMixtureExample { def main(args: Array[String]): Unit = { - // Creates a SparkSession - val spark = SparkSession.builder.appName(s"${this.getClass.getSimpleName}").getOrCreate() + val spark = SparkSession + .builder + .appName(s"${this.getClass.getSimpleName}") + .getOrCreate() // $example on$ // Loads data diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/IsotonicRegressionExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/IsotonicRegressionExample.scala index 7c5d3f2341..a840559d24 100644 --- a/examples/src/main/scala/org/apache/spark/examples/ml/IsotonicRegressionExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/ml/IsotonicRegressionExample.scala @@ -33,8 +33,6 @@ import org.apache.spark.sql.SparkSession object IsotonicRegressionExample { def main(args: Array[String]): Unit = { - - // Creates a SparkSession. val spark = SparkSession .builder .appName(s"${this.getClass.getSimpleName}") diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/KMeansExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/KMeansExample.scala index 2341b36db2..a1d19e138d 100644 --- a/examples/src/main/scala/org/apache/spark/examples/ml/KMeansExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/ml/KMeansExample.scala @@ -34,7 +34,6 @@ import org.apache.spark.sql.SparkSession object KMeansExample { def main(args: Array[String]): Unit = { - // Creates a SparkSession. val spark = SparkSession .builder .appName(s"${this.getClass.getSimpleName}") diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/ModelSelectionViaTrainValidationSplitExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/ModelSelectionViaTrainValidationSplitExample.scala index 75fef2922a..1cd2641f9a 100644 --- a/examples/src/main/scala/org/apache/spark/examples/ml/ModelSelectionViaTrainValidationSplitExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/ml/ModelSelectionViaTrainValidationSplitExample.scala @@ -46,6 +46,7 @@ object ModelSelectionViaTrainValidationSplitExample { val Array(training, test) = data.randomSplit(Array(0.9, 0.1), seed = 12345) val lr = new LinearRegression() + .setMaxIter(10) // We use a ParamGridBuilder to construct a grid of parameters to search over. // TrainValidationSplit will try all combinations of values and determine best model using diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/MultilayerPerceptronClassifierExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/MultilayerPerceptronClassifierExample.scala index e8a9b32da9..a39e3202ba 100644 --- a/examples/src/main/scala/org/apache/spark/examples/ml/MultilayerPerceptronClassifierExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/ml/MultilayerPerceptronClassifierExample.scala @@ -39,27 +39,33 @@ object MultilayerPerceptronClassifierExample { // Load the data stored in LIBSVM format as a DataFrame. val data = spark.read.format("libsvm") .load("data/mllib/sample_multiclass_classification_data.txt") + // Split the data into train and test val splits = data.randomSplit(Array(0.6, 0.4), seed = 1234L) val train = splits(0) val test = splits(1) + // specify layers for the neural network: // input layer of size 4 (features), two intermediate of size 5 and 4 // and output of size 3 (classes) val layers = Array[Int](4, 5, 4, 3) + // create the trainer and set its parameters val trainer = new MultilayerPerceptronClassifier() .setLayers(layers) .setBlockSize(128) .setSeed(1234L) .setMaxIter(100) + // train the model val model = trainer.fit(train) + // compute accuracy on the test set val result = model.transform(test) val predictionAndLabels = result.select("prediction", "label") val evaluator = new MulticlassClassificationEvaluator() .setMetricName("accuracy") + println("Accuracy: " + evaluator.evaluate(predictionAndLabels)) // $example off$ diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/NaiveBayesExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/NaiveBayesExample.scala index 7089a4bc87..3ae0623c4c 100644 --- a/examples/src/main/scala/org/apache/spark/examples/ml/NaiveBayesExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/ml/NaiveBayesExample.scala @@ -30,6 +30,7 @@ object NaiveBayesExample { .builder .appName("NaiveBayesExample") .getOrCreate() + // $example on$ // Load the data stored in LIBSVM format as a DataFrame. val data = spark.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt") diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/PipelineExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/PipelineExample.scala index b16692b1fa..12f8663b9c 100644 --- a/examples/src/main/scala/org/apache/spark/examples/ml/PipelineExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/ml/PipelineExample.scala @@ -54,7 +54,7 @@ object PipelineExample { .setOutputCol("features") val lr = new LogisticRegression() .setMaxIter(10) - .setRegParam(0.01) + .setRegParam(0.001) val pipeline = new Pipeline() .setStages(Array(tokenizer, hashingTF, lr)) @@ -74,7 +74,7 @@ object PipelineExample { val test = spark.createDataFrame(Seq( (4L, "spark i j k"), (5L, "l m n"), - (6L, "mapreduce spark"), + (6L, "spark hadoop spark"), (7L, "apache hadoop") )).toDF("id", "text") diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/QuantileDiscretizerExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/QuantileDiscretizerExample.scala index 2f7e217b8f..aedb9e7d3b 100644 --- a/examples/src/main/scala/org/apache/spark/examples/ml/QuantileDiscretizerExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/ml/QuantileDiscretizerExample.scala @@ -28,16 +28,16 @@ object QuantileDiscretizerExample { .builder .appName("QuantileDiscretizerExample") .getOrCreate() - import spark.implicits._ // $example on$ val data = Array((0, 18.0), (1, 19.0), (2, 8.0), (3, 5.0), (4, 2.2)) - var df = spark.createDataFrame(data).toDF("id", "hour") + val df = spark.createDataFrame(data).toDF("id", "hour") // $example off$ // Output of QuantileDiscretizer for such small datasets can depend on the number of // partitions. Here we force a single partition to ensure consistent results. // Note this is not necessary for normal use cases .repartition(1) + // $example on$ val discretizer = new QuantileDiscretizer() .setInputCol("hour") diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/RFormulaExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/RFormulaExample.scala index 9ea4920146..3498fa8a50 100644 --- a/examples/src/main/scala/org/apache/spark/examples/ml/RFormulaExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/ml/RFormulaExample.scala @@ -36,10 +36,12 @@ object RFormulaExample { (8, "CA", 12, 0.0), (9, "NZ", 15, 0.0) )).toDF("id", "country", "hour", "clicked") + val formula = new RFormula() .setFormula("clicked ~ country + hour") .setFeaturesCol("features") .setLabelCol("label") + val output = formula.fit(dataset).transform(dataset) output.select("features", "label").show() // $example off$ diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/SimpleParamsExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/SimpleParamsExample.scala deleted file mode 100644 index 29f1f50960..0000000000 --- a/examples/src/main/scala/org/apache/spark/examples/ml/SimpleParamsExample.scala +++ /dev/null @@ -1,104 +0,0 @@ -/* - * 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.ml - -import org.apache.spark.ml.classification.LogisticRegression -import org.apache.spark.ml.feature.LabeledPoint -import org.apache.spark.ml.linalg.{Vector, Vectors} -import org.apache.spark.ml.param.ParamMap -import org.apache.spark.sql.{Row, SparkSession} - -/** - * A simple example demonstrating ways to specify parameters for Estimators and Transformers. - * Run with - * {{{ - * bin/run-example ml.SimpleParamsExample - * }}} - */ -object SimpleParamsExample { - - def main(args: Array[String]) { - val spark = SparkSession - .builder - .appName("SimpleParamsExample") - .getOrCreate() - import spark.implicits._ - - // Prepare training data. - // We use LabeledPoint, which is a case class. Spark SQL can convert RDDs of case classes - // into DataFrames, where it uses the case class metadata to infer the schema. - val training = spark.createDataFrame(Seq( - LabeledPoint(1.0, Vectors.dense(0.0, 1.1, 0.1)), - LabeledPoint(0.0, Vectors.dense(2.0, 1.0, -1.0)), - LabeledPoint(0.0, Vectors.dense(2.0, 1.3, 1.0)), - LabeledPoint(1.0, Vectors.dense(0.0, 1.2, -0.5)))) - - // Create a LogisticRegression instance. This instance is an Estimator. - val lr = new LogisticRegression() - // Print out the parameters, documentation, and any default values. - println("LogisticRegression parameters:\n" + lr.explainParams() + "\n") - - // We may set parameters using setter methods. - lr.setMaxIter(10) - .setRegParam(0.01) - - // Learn a LogisticRegression model. This uses the parameters stored in lr. - val model1 = lr.fit(training) - // Since model1 is a Model (i.e., a Transformer produced by an Estimator), - // we can view the parameters it used during fit(). - // This prints the parameter (name: value) pairs, where names are unique IDs for this - // LogisticRegression instance. - println("Model 1 was fit using parameters: " + model1.parent.extractParamMap()) - - // We may alternatively specify parameters using a ParamMap, - // which supports several methods for specifying parameters. - val paramMap = ParamMap(lr.maxIter -> 20) - paramMap.put(lr.maxIter, 30) // Specify 1 Param. This overwrites the original maxIter. - paramMap.put(lr.regParam -> 0.1, lr.thresholds -> Array(0.5, 0.5)) // Specify multiple Params. - - // One can also combine ParamMaps. - val paramMap2 = ParamMap(lr.probabilityCol -> "myProbability") // Change output column name - val paramMapCombined = paramMap ++ paramMap2 - - // Now learn a new model using the paramMapCombined parameters. - // paramMapCombined overrides all parameters set earlier via lr.set* methods. - val model2 = lr.fit(training.toDF(), paramMapCombined) - println("Model 2 was fit using parameters: " + model2.parent.extractParamMap()) - - // Prepare test data. - val test = spark.createDataFrame(Seq( - LabeledPoint(1.0, Vectors.dense(-1.0, 1.5, 1.3)), - LabeledPoint(0.0, Vectors.dense(3.0, 2.0, -0.1)), - LabeledPoint(1.0, Vectors.dense(0.0, 2.2, -1.5)))) - - // Make predictions on test data using the Transformer.transform() method. - // LogisticRegressionModel.transform will only use the 'features' column. - // Note that model2.transform() outputs a 'myProbability' column instead of the usual - // 'probability' column since we renamed the lr.probabilityCol parameter previously. - model2.transform(test) - .select("features", "label", "myProbability", "prediction") - .collect() - .foreach { case Row(features: Vector, label: Double, prob: Vector, prediction: Double) => - println(s"($features, $label) -> prob=$prob, prediction=$prediction") - } - - spark.stop() - } -} -// scalastyle:on println diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/SimpleTextClassificationPipeline.scala b/examples/src/main/scala/org/apache/spark/examples/ml/SimpleTextClassificationPipeline.scala deleted file mode 100644 index 0b2a058bb6..0000000000 --- a/examples/src/main/scala/org/apache/spark/examples/ml/SimpleTextClassificationPipeline.scala +++ /dev/null @@ -1,93 +0,0 @@ -/* - * 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.ml - -import scala.beans.BeanInfo - -import org.apache.spark.ml.Pipeline -import org.apache.spark.ml.classification.LogisticRegression -import org.apache.spark.ml.feature.{HashingTF, Tokenizer} -import org.apache.spark.ml.linalg.Vector -import org.apache.spark.sql.{Row, SparkSession} - -@BeanInfo -case class LabeledDocument(id: Long, text: String, label: Double) - -@BeanInfo -case class Document(id: Long, text: String) - -/** - * A simple text classification pipeline that recognizes "spark" from input text. This is to show - * how to create and configure an ML pipeline. Run with - * {{{ - * bin/run-example ml.SimpleTextClassificationPipeline - * }}} - */ -object SimpleTextClassificationPipeline { - - def main(args: Array[String]) { - val spark = SparkSession - .builder - .appName("SimpleTextClassificationPipeline") - .getOrCreate() - import spark.implicits._ - - // Prepare training documents, which are labeled. - val training = spark.createDataFrame(Seq( - LabeledDocument(0L, "a b c d e spark", 1.0), - LabeledDocument(1L, "b d", 0.0), - LabeledDocument(2L, "spark f g h", 1.0), - LabeledDocument(3L, "hadoop mapreduce", 0.0))) - - // Configure an ML pipeline, which consists of three stages: tokenizer, hashingTF, and lr. - val tokenizer = new Tokenizer() - .setInputCol("text") - .setOutputCol("words") - val hashingTF = new HashingTF() - .setNumFeatures(1000) - .setInputCol(tokenizer.getOutputCol) - .setOutputCol("features") - val lr = new LogisticRegression() - .setMaxIter(10) - .setRegParam(0.001) - val pipeline = new Pipeline() - .setStages(Array(tokenizer, hashingTF, lr)) - - // Fit the pipeline to training documents. - val model = pipeline.fit(training.toDF()) - - // Prepare test documents, which are unlabeled. - val test = spark.createDataFrame(Seq( - Document(4L, "spark i j k"), - Document(5L, "l m n"), - Document(6L, "spark hadoop spark"), - Document(7L, "apache hadoop"))) - - // Make predictions on test documents. - model.transform(test.toDF()) - .select("id", "text", "probability", "prediction") - .collect() - .foreach { case Row(id: Long, text: String, prob: Vector, prediction: Double) => - println(s"($id, $text) --> prob=$prob, prediction=$prediction") - } - - spark.stop() - } -} -// scalastyle:on println diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/StopWordsRemoverExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/StopWordsRemoverExample.scala index fb1a43e962..a56de0856d 100644 --- a/examples/src/main/scala/org/apache/spark/examples/ml/StopWordsRemoverExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/ml/StopWordsRemoverExample.scala @@ -36,7 +36,7 @@ object StopWordsRemoverExample { .setOutputCol("filtered") val dataSet = spark.createDataFrame(Seq( - (0, Seq("I", "saw", "the", "red", "baloon")), + (0, Seq("I", "saw", "the", "red", "balloon")), (1, Seq("Mary", "had", "a", "little", "lamb")) )).toDF("id", "raw") diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/TfIdfExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/TfIdfExample.scala index 33b5daec59..97f6fcce15 100644 --- a/examples/src/main/scala/org/apache/spark/examples/ml/TfIdfExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/ml/TfIdfExample.scala @@ -40,13 +40,16 @@ object TfIdfExample { val tokenizer = new Tokenizer().setInputCol("sentence").setOutputCol("words") val wordsData = tokenizer.transform(sentenceData) + val hashingTF = new HashingTF() .setInputCol("words").setOutputCol("rawFeatures").setNumFeatures(20) + val featurizedData = hashingTF.transform(wordsData) // alternatively, CountVectorizer can also be used to get term frequency vectors val idf = new IDF().setInputCol("rawFeatures").setOutputCol("features") val idfModel = idf.fit(featurizedData) + val rescaledData = idfModel.transform(featurizedData) rescaledData.select("features", "label").take(3).foreach(println) // $example off$ diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/TokenizerExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/TokenizerExample.scala index 1c70dc700b..90d0faaf47 100644 --- a/examples/src/main/scala/org/apache/spark/examples/ml/TokenizerExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/ml/TokenizerExample.scala @@ -45,6 +45,7 @@ object TokenizerExample { val tokenized = tokenizer.transform(sentenceDataFrame) tokenized.select("words", "label").take(3).foreach(println) + val regexTokenized = regexTokenizer.transform(sentenceDataFrame) regexTokenized.select("words", "label").take(3).foreach(println) // $example off$ diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/Word2VecExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/Word2VecExample.scala index 9ac5623607..5c8bd19f20 100644 --- a/examples/src/main/scala/org/apache/spark/examples/ml/Word2VecExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/ml/Word2VecExample.scala @@ -45,6 +45,7 @@ object Word2VecExample { .setVectorSize(3) .setMinCount(0) val model = word2Vec.fit(documentDF) + val result = model.transform(documentDF) result.select("result").take(3).foreach(println) // $example off$ |