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Posted to reviews@spark.apache.org by GitBox <gi...@apache.org> on 2020/10/12 08:25:58 UTC
[GitHub] [spark] zhengruifeng edited a comment on pull request #30013: [SPARK-32455][ML][Follow-Up] LogisticRegressionModel prediction optimization
zhengruifeng edited a comment on pull request #30013:
URL: https://github.com/apache/spark/pull/30013#issuecomment-706965136
test in commit 27eab00:
```
import scala.util.Random
import org.apache.spark.ml.linalg._
import org.apache.spark.ml.classification._
import org.apache.spark.ml.regression._
import org.apache.spark.sql.functions._
import org.apache.spark.storage.StorageLevel
val df = spark.read.option("numFeatures", "2000").format("libsvm").load("/data1/Datasets/epsilon/epsilon_normalized.t").withColumn("aftcensor", (col("label")+1)/2).withColumn("aftlabel", (col("label")+2)/2).withColumn("label", (col("label")+1)/2).limit(100)
df.persist(StorageLevel.MEMORY_AND_DISK)
df.count
val vec = df.select("features").head.getAs[Vector](0)
val lor = new LogisticRegression().setMaxIter(1).setThreshold(0.1)
val lorm = lor.fit(df)
lorm.getThreshold
lorm.predict(vec)
```
results:
// master
scala> val lorm = lor.fit(df)
20/10/12 15:47:23 WARN LogisticRegressionModel: **_threshold=0.5, _rawThreshold=0.0**
lorm: org.apache.spark.ml.classification.LogisticRegressionModel = LogisticRegressionModel: uid=logreg_4c79066a563d, numClasses=2, numFeatures=2000
scala> **lorm.getThreshold**
res9: Double = **0.1**
scala> lorm.predict(vec)
20/10/12 15:47:29 WARN LogisticRegressionModel: **_threshold=0.5, _rawThreshold=0.0**
res10: Double = 0.0
The `_threshold` and `_rawThreshold` here are incorrect.
// this PR
scala> lorm.predict(vec)
20/10/12 16:01:09 WARN LogisticRegressionModel: _threshold=0.1, _rawThreshold=-2.197224577336219
res3: Double = 0.0
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