From c1080b6fddb22d84694da2453e46a03fbc041576 Mon Sep 17 00:00:00 2001
From: DB Tsai <dbt@netflix.com>
Date: Thu, 14 May 2015 01:26:08 -0700
Subject: [PATCH] [SPARK-7568] [ML] ml.LogisticRegression doesn't output the
right prediction
The difference is because we previously don't fit the intercept in Spark 1.3. Here, we change the input `String` so that the probability of instance 6 can be classified as `1.0` without any ambiguity.
with lambda = 0.001 in current LOR implementation, the prediction is
```
(4, spark i j k) --> prob=[0.1596407738787411,0.8403592261212589], prediction=1.0
(5, l m n) --> prob=[0.8378325685476612,0.16216743145233883], prediction=0.0
(6, spark hadoop spark) --> prob=[0.0692663313297627,0.9307336686702373], prediction=1.0
(7, apache hadoop) --> prob=[0.9821575333444208,0.01784246665557917], prediction=0.0
```
and the training accuracy is
```
(0, a b c d e spark) --> prob=[0.0021342419881406746,0.9978657580118594], prediction=1.0
(1, b d) --> prob=[0.9959176174854043,0.004082382514595685], prediction=0.0
(2, spark f g h) --> prob=[0.0014541569986711233,0.9985458430013289], prediction=1.0
(3, hadoop mapreduce) --> prob=[0.9982978367343561,0.0017021632656438518], prediction=0.0
```
Author: DB Tsai <dbt@netflix.com>
Closes #6109 from dbtsai/lor-example and squashes the following commits:
ac63ce4 [DB Tsai] first commit
---
.../examples/ml/JavaSimpleTextClassificationPipeline.java | 4 ++--
.../src/main/python/ml/simple_text_classification_pipeline.py | 4 ++--
.../spark/examples/ml/SimpleTextClassificationPipeline.scala | 4 ++--
3 files changed, 6 insertions(+), 6 deletions(-)
diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleTextClassificationPipeline.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleTextClassificationPipeline.java
index ef1ec103a8..54738813d0 100644
--- a/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleTextClassificationPipeline.java
+++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleTextClassificationPipeline.java
@@ -66,7 +66,7 @@ public class JavaSimpleTextClassificationPipeline {
.setOutputCol("features");
LogisticRegression lr = new LogisticRegression()
.setMaxIter(10)
- .setRegParam(0.01);
+ .setRegParam(0.001);
Pipeline pipeline = new Pipeline()
.setStages(new PipelineStage[] {tokenizer, hashingTF, lr});
@@ -77,7 +77,7 @@ public class JavaSimpleTextClassificationPipeline {
List<Document> localTest = Lists.newArrayList(
new Document(4L, "spark i j k"),
new Document(5L, "l m n"),
- new Document(6L, "mapreduce spark"),
+ new Document(6L, "spark hadoop spark"),
new Document(7L, "apache hadoop"));
DataFrame test = jsql.createDataFrame(jsc.parallelize(localTest), Document.class);
diff --git a/examples/src/main/python/ml/simple_text_classification_pipeline.py b/examples/src/main/python/ml/simple_text_classification_pipeline.py
index fab21f003b..b4f06bf888 100644
--- a/examples/src/main/python/ml/simple_text_classification_pipeline.py
+++ b/examples/src/main/python/ml/simple_text_classification_pipeline.py
@@ -48,7 +48,7 @@ if __name__ == "__main__":
# Configure an ML pipeline, which consists of tree stages: tokenizer, hashingTF, and lr.
tokenizer = Tokenizer(inputCol="text", outputCol="words")
hashingTF = HashingTF(inputCol=tokenizer.getOutputCol(), outputCol="features")
- lr = LogisticRegression(maxIter=10, regParam=0.01)
+ lr = LogisticRegression(maxIter=10, regParam=0.001)
pipeline = Pipeline(stages=[tokenizer, hashingTF, lr])
# Fit the pipeline to training documents.
@@ -58,7 +58,7 @@ if __name__ == "__main__":
Document = Row("id", "text")
test = sc.parallelize([(4, "spark i j k"),
(5, "l m n"),
- (6, "mapreduce spark"),
+ (6, "spark hadoop spark"),
(7, "apache hadoop")]) \
.map(lambda x: Document(*x)).toDF()
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
index 6772efd2c5..1324b066c3 100644
--- a/examples/src/main/scala/org/apache/spark/examples/ml/SimpleTextClassificationPipeline.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/ml/SimpleTextClassificationPipeline.scala
@@ -64,7 +64,7 @@ object SimpleTextClassificationPipeline {
.setOutputCol("features")
val lr = new LogisticRegression()
.setMaxIter(10)
- .setRegParam(0.01)
+ .setRegParam(0.001)
val pipeline = new Pipeline()
.setStages(Array(tokenizer, hashingTF, lr))
@@ -75,7 +75,7 @@ object SimpleTextClassificationPipeline {
val test = sc.parallelize(Seq(
Document(4L, "spark i j k"),
Document(5L, "l m n"),
- Document(6L, "mapreduce spark"),
+ Document(6L, "spark hadoop spark"),
Document(7L, "apache hadoop")))
// Make predictions on test documents.
--
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