diff options
8 files changed, 143 insertions, 80 deletions
diff --git a/data/mllib/sample_lda_libsvm_data.txt b/data/mllib/sample_lda_libsvm_data.txt new file mode 100644 index 0000000000..bf118d7d5b --- /dev/null +++ b/data/mllib/sample_lda_libsvm_data.txt @@ -0,0 +1,12 @@ +0 1:1 2:2 3:6 4:0 5:2 6:3 7:1 8:1 9:0 10:0 11:3 +1 1:1 2:3 3:0 4:1 5:3 6:0 7:0 8:2 9:0 10:0 11:1 +2 1:1 2:4 3:1 4:0 5:0 6:4 7:9 8:0 9:1 10:2 11:0 +3 1:2 2:1 3:0 4:3 5:0 6:0 7:5 8:0 9:2 10:3 11:9 +4 1:3 2:1 3:1 4:9 5:3 6:0 7:2 8:0 9:0 10:1 11:3 +5 1:4 2:2 3:0 4:3 5:4 6:5 7:1 8:1 9:1 10:4 11:0 +6 1:2 2:1 3:0 4:3 5:0 6:0 7:5 8:0 9:2 10:2 11:9 +7 1:1 2:1 3:1 4:9 5:2 6:1 7:2 8:0 9:0 10:1 11:3 +8 1:4 2:4 3:0 4:3 5:4 6:2 7:1 8:3 9:0 10:0 11:0 +9 1:2 2:8 3:2 4:0 5:3 6:0 7:2 8:0 9:2 10:7 11:2 +10 1:1 2:1 3:1 4:9 5:0 6:2 7:2 8:0 9:0 10:3 11:3 +11 1:4 2:1 3:0 4:0 5:4 6:5 7:1 8:3 9:0 10:1 11:0 diff --git a/docs/ml-clustering.md b/docs/ml-clustering.md index 876a280c4c..0d69bf67df 100644 --- a/docs/ml-clustering.md +++ b/docs/ml-clustering.md @@ -109,8 +109,13 @@ Refer to the [Java API docs](api/java/org/apache/spark/ml/clustering/LDA.html) f {% include_example java/org/apache/spark/examples/ml/JavaLDAExample.java %} </div> -</div> +<div data-lang="python" markdown="1"> + +Refer to the [Python API docs](api/python/pyspark.ml.html#pyspark.ml.clustering.LDA) for more details. +{% include_example python/ml/lda_example.py %} +</div> +</div> ## Bisecting k-means diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaAFTSurvivalRegressionExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaAFTSurvivalRegressionExample.java index 2c2aa6df47..b0115756cf 100644 --- a/examples/src/main/java/org/apache/spark/examples/ml/JavaAFTSurvivalRegressionExample.java +++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaAFTSurvivalRegressionExample.java @@ -31,6 +31,13 @@ import org.apache.spark.sql.SparkSession; import org.apache.spark.sql.types.*; // $example off$ +/** + * An example demonstrating AFTSurvivalRegression. + * Run with + * <pre> + * bin/run-example ml.JavaAFTSurvivalRegressionExample + * </pre> + */ public class JavaAFTSurvivalRegressionExample { public static void main(String[] args) { SparkSession spark = SparkSession diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaLDAExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaLDAExample.java index 1c52f37867..7102ddd801 100644 --- a/examples/src/main/java/org/apache/spark/examples/ml/JavaLDAExample.java +++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaLDAExample.java @@ -17,26 +17,15 @@ package org.apache.spark.examples.ml; // $example on$ -import java.util.regex.Pattern; - -import org.apache.spark.api.java.JavaRDD; -import org.apache.spark.api.java.function.Function; import org.apache.spark.ml.clustering.LDA; import org.apache.spark.ml.clustering.LDAModel; -import org.apache.spark.mllib.linalg.Vector; -import org.apache.spark.mllib.linalg.VectorUDT; -import org.apache.spark.mllib.linalg.Vectors; import org.apache.spark.sql.Dataset; import org.apache.spark.sql.Row; import org.apache.spark.sql.SparkSession; -import org.apache.spark.sql.catalyst.expressions.GenericRow; -import org.apache.spark.sql.types.Metadata; -import org.apache.spark.sql.types.StructField; -import org.apache.spark.sql.types.StructType; // $example off$ /** - * An example demonstrating LDA + * An example demonstrating LDA. * Run with * <pre> * bin/run-example ml.JavaLDAExample @@ -44,53 +33,37 @@ import org.apache.spark.sql.types.StructType; */ public class JavaLDAExample { - // $example on$ - private static class ParseVector implements Function<String, Row> { - private static final Pattern separator = Pattern.compile(" "); - - @Override - public Row call(String line) { - String[] tok = separator.split(line); - double[] point = new double[tok.length]; - for (int i = 0; i < tok.length; ++i) { - point[i] = Double.parseDouble(tok[i]); - } - Vector[] points = {Vectors.dense(point)}; - return new GenericRow(points); - } - } - public static void main(String[] args) { - - String inputFile = "data/mllib/sample_lda_data.txt"; - - // Parses the arguments + // Creates a SparkSession SparkSession spark = SparkSession .builder() .appName("JavaLDAExample") .getOrCreate(); - // Loads data - JavaRDD<Row> points = spark.read().text(inputFile).javaRDD().map(new ParseVector()); - StructField[] fields = {new StructField("features", new VectorUDT(), false, Metadata.empty())}; - StructType schema = new StructType(fields); - Dataset<Row> dataset = spark.createDataFrame(points, schema); + // $example on$ + // Loads data. + Dataset<Row> dataset = spark.read().format("libsvm") + .load("data/mllib/sample_lda_libsvm_data.txt"); - // Trains a LDA model - LDA lda = new LDA() - .setK(10) - .setMaxIter(10); + // Trains a LDA model. + LDA lda = new LDA().setK(10).setMaxIter(10); LDAModel model = lda.fit(dataset); - System.out.println(model.logLikelihood(dataset)); - System.out.println(model.logPerplexity(dataset)); - - // Shows the result + double ll = model.logLikelihood(dataset); + double lp = model.logPerplexity(dataset); + System.out.println("The lower bound on the log likelihood of the entire corpus: " + ll); + System.out.println("The upper bound bound on perplexity: " + lp); + + // Describe topics. Dataset<Row> topics = model.describeTopics(3); + System.out.println("The topics described by their top-weighted terms:"); topics.show(false); - model.transform(dataset).show(false); + + // Shows the result. + Dataset<Row> transformed = model.transform(dataset); + transformed.show(false); + // $example off$ spark.stop(); } - // $example off$ } diff --git a/examples/src/main/python/ml/aft_survival_regression.py b/examples/src/main/python/ml/aft_survival_regression.py index 0ee01fd825..9879679829 100644 --- a/examples/src/main/python/ml/aft_survival_regression.py +++ b/examples/src/main/python/ml/aft_survival_regression.py @@ -17,19 +17,26 @@ from __future__ import print_function -from pyspark import SparkContext -from pyspark.sql import SQLContext # $example on$ from pyspark.ml.regression import AFTSurvivalRegression from pyspark.mllib.linalg import Vectors # $example off$ +from pyspark.sql import SparkSession + +""" +An example demonstrating aft survival regression. +Run with: + bin/spark-submit examples/src/main/python/ml/aft_survival_regression.py +""" if __name__ == "__main__": - sc = SparkContext(appName="AFTSurvivalRegressionExample") - sqlContext = SQLContext(sc) + spark = SparkSession \ + .builder \ + .appName("PythonAFTSurvivalRegressionExample") \ + .getOrCreate() # $example on$ - training = sqlContext.createDataFrame([ + training = spark.createDataFrame([ (1.218, 1.0, Vectors.dense(1.560, -0.605)), (2.949, 0.0, Vectors.dense(0.346, 2.158)), (3.627, 0.0, Vectors.dense(1.380, 0.231)), @@ -48,4 +55,4 @@ if __name__ == "__main__": model.transform(training).show(truncate=False) # $example off$ - sc.stop() + spark.stop() diff --git a/examples/src/main/python/ml/lda_example.py b/examples/src/main/python/ml/lda_example.py new file mode 100644 index 0000000000..6ca56adf3c --- /dev/null +++ b/examples/src/main/python/ml/lda_example.py @@ -0,0 +1,64 @@ +# +# 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. +# + + +from __future__ import print_function + +# $example on$ +from pyspark.ml.clustering import LDA +# $example off$ +from pyspark.sql import SparkSession + + +""" +An example demonstrating LDA. +Run with: + bin/spark-submit examples/src/main/python/ml/lda_example.py +""" + + +if __name__ == "__main__": + # Creates a SparkSession + spark = SparkSession \ + .builder \ + .appName("PythonKMeansExample") \ + .getOrCreate() + + # $example on$ + # Loads data. + dataset = spark.read.format("libsvm").load("data/mllib/sample_lda_libsvm_data.txt") + + # Trains a LDA model. + lda = LDA(k=10, maxIter=10) + model = lda.fit(dataset) + + ll = model.logLikelihood(dataset) + lp = model.logPerplexity(dataset) + print("The lower bound on the log likelihood of the entire corpus: " + str(ll)) + print("The upper bound bound on perplexity: " + str(lp)) + + # Describe topics. + topics = model.describeTopics(3) + print("The topics described by their top-weighted terms:") + topics.show(truncate=False) + + # Shows the result + transformed = model.transform(dataset) + transformed.show(truncate=False) + # $example off$ + + spark.stop() diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/AFTSurvivalRegressionExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/AFTSurvivalRegressionExample.scala index 2b224d50a0..b44304d810 100644 --- a/examples/src/main/scala/org/apache/spark/examples/ml/AFTSurvivalRegressionExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/ml/AFTSurvivalRegressionExample.scala @@ -25,7 +25,11 @@ import org.apache.spark.mllib.linalg.Vectors import org.apache.spark.sql.SparkSession /** - * An example for AFTSurvivalRegression. + * An example demonstrating AFTSurvivalRegression. + * Run with + * {{{ + * bin/run-example ml.AFTSurvivalRegressionExample + * }}} */ object AFTSurvivalRegressionExample { diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/LDAExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/LDAExample.scala index c2920f6a5d..22b3b0e3ad 100644 --- a/examples/src/main/scala/org/apache/spark/examples/ml/LDAExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/ml/LDAExample.scala @@ -20,57 +20,48 @@ package org.apache.spark.examples.ml // scalastyle:off println // $example on$ import org.apache.spark.ml.clustering.LDA -import org.apache.spark.mllib.linalg.{Vectors, VectorUDT} -import org.apache.spark.sql.{Row, SparkSession} -import org.apache.spark.sql.types.{StructField, StructType} // $example off$ +import org.apache.spark.sql.SparkSession /** - * An example demonstrating a LDA of ML pipeline. + * An example demonstrating LDA. * Run with * {{{ * bin/run-example ml.LDAExample * }}} */ object LDAExample { - - final val FEATURES_COL = "features" - def main(args: Array[String]): Unit = { - - val input = "data/mllib/sample_lda_data.txt" - // Creates a Spark context and a SQL context + // Creates a SparkSession val spark = SparkSession .builder .appName(s"${this.getClass.getSimpleName}") .getOrCreate() // $example on$ - // Loads data - val rowRDD = spark.read.text(input).rdd.filter(_.nonEmpty) - .map(_.split(" ").map(_.toDouble)).map(Vectors.dense).map(Row(_)) - val schema = StructType(Array(StructField(FEATURES_COL, new VectorUDT, false))) - val dataset = spark.createDataFrame(rowRDD, schema) + // Loads data. + val dataset = spark.read.format("libsvm") + .load("data/mllib/sample_lda_libsvm_data.txt") - // Trains a LDA model - val lda = new LDA() - .setK(10) - .setMaxIter(10) - .setFeaturesCol(FEATURES_COL) + // Trains a LDA model. + val lda = new LDA().setK(10).setMaxIter(10) val model = lda.fit(dataset) - val transformed = model.transform(dataset) val ll = model.logLikelihood(dataset) val lp = model.logPerplexity(dataset) + println(s"The lower bound on the log likelihood of the entire corpus: $ll") + println(s"The upper bound bound on perplexity: $lp") - // describeTopics + // Describe topics. val topics = model.describeTopics(3) - - // Shows the result + println("The topics described by their top-weighted terms:") topics.show(false) - transformed.show(false) + // Shows the result. + val transformed = model.transform(dataset) + transformed.show(false) // $example off$ + spark.stop() } } |