diff options
author | Xiangrui Meng <meng@databricks.com> | 2015-11-18 15:47:49 -0800 |
---|---|---|
committer | Xiangrui Meng <meng@databricks.com> | 2015-11-18 15:47:49 -0800 |
commit | 7e987de1770f4ab3d54bc05db8de0a1ef035941d (patch) | |
tree | 856cbb3cf219827d4022b40675e3b79300ed91e1 /mllib/src/test | |
parent | 5df08949f5d9e5b4b0e9c2db50c1b4eb93383de3 (diff) | |
download | spark-7e987de1770f4ab3d54bc05db8de0a1ef035941d.tar.gz spark-7e987de1770f4ab3d54bc05db8de0a1ef035941d.tar.bz2 spark-7e987de1770f4ab3d54bc05db8de0a1ef035941d.zip |
[SPARK-6787][ML] add read/write to estimators under ml.feature (1)
Add read/write support to the following estimators under spark.ml:
* CountVectorizer
* IDF
* MinMaxScaler
* StandardScaler (a little awkward because we store some params in spark.mllib model)
* StringIndexer
Added some necessary method for read/write. Maybe we should add `private[ml] trait DefaultParamsReadable` and `DefaultParamsWritable` to save some boilerplate code, though we still need to override `load` for Java compatibility.
jkbradley
Author: Xiangrui Meng <meng@databricks.com>
Closes #9798 from mengxr/SPARK-6787.
Diffstat (limited to 'mllib/src/test')
5 files changed, 129 insertions, 22 deletions
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/CountVectorizerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/CountVectorizerSuite.scala index e192fa4850..9c99990173 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/feature/CountVectorizerSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/feature/CountVectorizerSuite.scala @@ -18,14 +18,17 @@ package org.apache.spark.ml.feature import org.apache.spark.SparkFunSuite import org.apache.spark.ml.param.ParamsSuite +import org.apache.spark.ml.util.DefaultReadWriteTest import org.apache.spark.mllib.linalg.{Vector, Vectors} import org.apache.spark.mllib.util.MLlibTestSparkContext import org.apache.spark.mllib.util.TestingUtils._ import org.apache.spark.sql.Row -class CountVectorizerSuite extends SparkFunSuite with MLlibTestSparkContext { +class CountVectorizerSuite extends SparkFunSuite with MLlibTestSparkContext + with DefaultReadWriteTest { test("params") { + ParamsSuite.checkParams(new CountVectorizer) ParamsSuite.checkParams(new CountVectorizerModel(Array("empty"))) } @@ -164,4 +167,23 @@ class CountVectorizerSuite extends SparkFunSuite with MLlibTestSparkContext { assert(features ~== expected absTol 1e-14) } } + + test("CountVectorizer read/write") { + val t = new CountVectorizer() + .setInputCol("myInputCol") + .setOutputCol("myOutputCol") + .setMinDF(0.5) + .setMinTF(3.0) + .setVocabSize(10) + testDefaultReadWrite(t) + } + + test("CountVectorizerModel read/write") { + val instance = new CountVectorizerModel("myCountVectorizerModel", Array("a", "b", "c")) + .setInputCol("myInputCol") + .setOutputCol("myOutputCol") + .setMinTF(3.0) + val newInstance = testDefaultReadWrite(instance) + assert(newInstance.vocabulary === instance.vocabulary) + } } diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/IDFSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/IDFSuite.scala index 08f80af034..bc958c1585 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/feature/IDFSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/feature/IDFSuite.scala @@ -19,13 +19,14 @@ package org.apache.spark.ml.feature import org.apache.spark.SparkFunSuite import org.apache.spark.ml.param.ParamsSuite +import org.apache.spark.ml.util.DefaultReadWriteTest import org.apache.spark.mllib.feature.{IDFModel => OldIDFModel} import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vector, Vectors} import org.apache.spark.mllib.util.MLlibTestSparkContext import org.apache.spark.mllib.util.TestingUtils._ import org.apache.spark.sql.Row -class IDFSuite extends SparkFunSuite with MLlibTestSparkContext { +class IDFSuite extends SparkFunSuite with MLlibTestSparkContext with DefaultReadWriteTest { def scaleDataWithIDF(dataSet: Array[Vector], model: Vector): Array[Vector] = { dataSet.map { @@ -98,4 +99,20 @@ class IDFSuite extends SparkFunSuite with MLlibTestSparkContext { assert(x ~== y absTol 1e-5, "Transformed vector is different with expected vector.") } } + + test("IDF read/write") { + val t = new IDF() + .setInputCol("myInputCol") + .setOutputCol("myOutputCol") + .setMinDocFreq(5) + testDefaultReadWrite(t) + } + + test("IDFModel read/write") { + val instance = new IDFModel("myIDFModel", new OldIDFModel(Vectors.dense(1.0, 2.0))) + .setInputCol("myInputCol") + .setOutputCol("myOutputCol") + val newInstance = testDefaultReadWrite(instance) + assert(newInstance.idf === instance.idf) + } } diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/MinMaxScalerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/MinMaxScalerSuite.scala index c04dda41ee..09183fe65b 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/feature/MinMaxScalerSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/feature/MinMaxScalerSuite.scala @@ -18,12 +18,12 @@ package org.apache.spark.ml.feature import org.apache.spark.SparkFunSuite -import org.apache.spark.ml.util.MLTestingUtils +import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTestingUtils} import org.apache.spark.mllib.linalg.{Vector, Vectors} import org.apache.spark.mllib.util.MLlibTestSparkContext import org.apache.spark.sql.{Row, SQLContext} -class MinMaxScalerSuite extends SparkFunSuite with MLlibTestSparkContext { +class MinMaxScalerSuite extends SparkFunSuite with MLlibTestSparkContext with DefaultReadWriteTest { test("MinMaxScaler fit basic case") { val sqlContext = new SQLContext(sc) @@ -69,4 +69,25 @@ class MinMaxScalerSuite extends SparkFunSuite with MLlibTestSparkContext { } } } + + test("MinMaxScaler read/write") { + val t = new MinMaxScaler() + .setInputCol("myInputCol") + .setOutputCol("myOutputCol") + .setMax(1.0) + .setMin(-1.0) + testDefaultReadWrite(t) + } + + test("MinMaxScalerModel read/write") { + val instance = new MinMaxScalerModel( + "myMinMaxScalerModel", Vectors.dense(-1.0, 0.0), Vectors.dense(1.0, 10.0)) + .setInputCol("myInputCol") + .setOutputCol("myOutputCol") + .setMin(-1.0) + .setMax(1.0) + val newInstance = testDefaultReadWrite(instance) + assert(newInstance.originalMin === instance.originalMin) + assert(newInstance.originalMax === instance.originalMax) + } } diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/StandardScalerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/StandardScalerSuite.scala index 879a3ae875..49a4b2efe0 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/feature/StandardScalerSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/feature/StandardScalerSuite.scala @@ -19,12 +19,16 @@ package org.apache.spark.ml.feature import org.apache.spark.SparkFunSuite -import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vector, Vectors} +import org.apache.spark.ml.param.ParamsSuite +import org.apache.spark.ml.util.DefaultReadWriteTest +import org.apache.spark.mllib.feature +import org.apache.spark.mllib.linalg.{Vector, Vectors} import org.apache.spark.mllib.util.MLlibTestSparkContext import org.apache.spark.mllib.util.TestingUtils._ -import org.apache.spark.sql.{DataFrame, Row, SQLContext} +import org.apache.spark.sql.{DataFrame, Row} -class StandardScalerSuite extends SparkFunSuite with MLlibTestSparkContext{ +class StandardScalerSuite extends SparkFunSuite with MLlibTestSparkContext + with DefaultReadWriteTest { @transient var data: Array[Vector] = _ @transient var resWithStd: Array[Vector] = _ @@ -56,23 +60,29 @@ class StandardScalerSuite extends SparkFunSuite with MLlibTestSparkContext{ ) } - def assertResult(dataframe: DataFrame): Unit = { - dataframe.select("standarded_features", "expected").collect().foreach { + def assertResult(df: DataFrame): Unit = { + df.select("standardized_features", "expected").collect().foreach { case Row(vector1: Vector, vector2: Vector) => assert(vector1 ~== vector2 absTol 1E-5, "The vector value is not correct after standardization.") } } + test("params") { + ParamsSuite.checkParams(new StandardScaler) + val oldModel = new feature.StandardScalerModel(Vectors.dense(1.0), Vectors.dense(2.0)) + ParamsSuite.checkParams(new StandardScalerModel("empty", oldModel)) + } + test("Standardization with default parameter") { val df0 = sqlContext.createDataFrame(data.zip(resWithStd)).toDF("features", "expected") - val standardscaler0 = new StandardScaler() + val standardScaler0 = new StandardScaler() .setInputCol("features") - .setOutputCol("standarded_features") + .setOutputCol("standardized_features") .fit(df0) - assertResult(standardscaler0.transform(df0)) + assertResult(standardScaler0.transform(df0)) } test("Standardization with setter") { @@ -80,29 +90,49 @@ class StandardScalerSuite extends SparkFunSuite with MLlibTestSparkContext{ val df2 = sqlContext.createDataFrame(data.zip(resWithMean)).toDF("features", "expected") val df3 = sqlContext.createDataFrame(data.zip(data)).toDF("features", "expected") - val standardscaler1 = new StandardScaler() + val standardScaler1 = new StandardScaler() .setInputCol("features") - .setOutputCol("standarded_features") + .setOutputCol("standardized_features") .setWithMean(true) .setWithStd(true) .fit(df1) - val standardscaler2 = new StandardScaler() + val standardScaler2 = new StandardScaler() .setInputCol("features") - .setOutputCol("standarded_features") + .setOutputCol("standardized_features") .setWithMean(true) .setWithStd(false) .fit(df2) - val standardscaler3 = new StandardScaler() + val standardScaler3 = new StandardScaler() .setInputCol("features") - .setOutputCol("standarded_features") + .setOutputCol("standardized_features") .setWithMean(false) .setWithStd(false) .fit(df3) - assertResult(standardscaler1.transform(df1)) - assertResult(standardscaler2.transform(df2)) - assertResult(standardscaler3.transform(df3)) + assertResult(standardScaler1.transform(df1)) + assertResult(standardScaler2.transform(df2)) + assertResult(standardScaler3.transform(df3)) + } + + test("StandardScaler read/write") { + val t = new StandardScaler() + .setInputCol("myInputCol") + .setOutputCol("myOutputCol") + .setWithStd(false) + .setWithMean(true) + testDefaultReadWrite(t) + } + + test("StandardScalerModel read/write") { + val oldModel = new feature.StandardScalerModel( + Vectors.dense(1.0, 2.0), Vectors.dense(3.0, 4.0), false, true) + val instance = new StandardScalerModel("myStandardScalerModel", oldModel) + val newInstance = testDefaultReadWrite(instance) + assert(newInstance.std === instance.std) + assert(newInstance.mean === instance.mean) + assert(newInstance.getWithStd === instance.getWithStd) + assert(newInstance.getWithMean === instance.getWithMean) } } diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/StringIndexerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/StringIndexerSuite.scala index be37bfb438..749bfac747 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/feature/StringIndexerSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/feature/StringIndexerSuite.scala @@ -118,6 +118,23 @@ class StringIndexerSuite assert(indexerModel.transform(df).eq(df)) } + test("StringIndexer read/write") { + val t = new StringIndexer() + .setInputCol("myInputCol") + .setOutputCol("myOutputCol") + .setHandleInvalid("skip") + testDefaultReadWrite(t) + } + + test("StringIndexerModel read/write") { + val instance = new StringIndexerModel("myStringIndexerModel", Array("a", "b", "c")) + .setInputCol("myInputCol") + .setOutputCol("myOutputCol") + .setHandleInvalid("skip") + val newInstance = testDefaultReadWrite(instance) + assert(newInstance.labels === instance.labels) + } + test("IndexToString params") { val idxToStr = new IndexToString() ParamsSuite.checkParams(idxToStr) @@ -175,7 +192,7 @@ class StringIndexerSuite assert(outSchema("output").dataType === StringType) } - test("read/write") { + test("IndexToString read/write") { val t = new IndexToString() .setInputCol("myInputCol") .setOutputCol("myOutputCol") |