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Posted to reviews@spark.apache.org by zhengruifeng <gi...@git.apache.org> on 2017/03/22 08:59:41 UTC
[GitHub] spark pull request #17384: [SPARK-20056][ML] IsotonicRegression support Nume...
GitHub user zhengruifeng opened a pull request:
https://github.com/apache/spark/pull/17384
[SPARK-20056][ML] IsotonicRegression support Numeric features
## What changes were proposed in this pull request?
IsotonicRegression support Numeric features
## How was this patch tested?
existing tests and added tests
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/zhengruifeng/spark iso_num_feacol
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/spark/pull/17384.patch
To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:
This closes #17384
----
commit fbfb7f44db03cb50096cb03848a5cb9c015a9dc7
Author: Zheng RuiFeng <ru...@foxmail.com>
Date: 2017-03-22T08:55:27Z
revert domain
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[GitHub] spark issue #17384: [SPARK-20056][ML] IsotonicRegression support Numeric fea...
Posted by AmplabJenkins <gi...@git.apache.org>.
Github user AmplabJenkins commented on the issue:
https://github.com/apache/spark/pull/17384
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[GitHub] spark pull request #17384: [SPARK-20056][ML] IsotonicRegression support Nume...
Posted by zhengruifeng <gi...@git.apache.org>.
Github user zhengruifeng closed the pull request at:
https://github.com/apache/spark/pull/17384
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[GitHub] spark pull request #17384: [SPARK-20056][ML] IsotonicRegression support Nume...
Posted by BenFradet <gi...@git.apache.org>.
Github user BenFradet commented on a diff in the pull request:
https://github.com/apache/spark/pull/17384#discussion_r109133953
--- Diff: mllib/src/test/scala/org/apache/spark/ml/regression/IsotonicRegressionSuite.scala ---
@@ -189,6 +191,47 @@ class IsotonicRegressionSuite
assert(expected.predictions === actual.predictions)
}
}
+
+ test("Besides VectorUDT, should support all NumericType features, and not support other types") {
+ val df = generateIsotonicInput(Seq(1, 2, 3))
+ val ir = new IsotonicRegression()
+
+ val expected = ir.fit(df)
+ val expectedPrediction =
+ expected.transform(df).select("prediction").map(_.getDouble(0)).collect()
+
+ val types =
+ Seq(ShortType, LongType, IntegerType, FloatType, ByteType, DoubleType, DecimalType(10, 0))
+
+ types.foreach { t =>
+ val df2 = df.select(col("label"), col("features").cast(t), col("weight"))
+
+ val actual = ir.fit(df2)
+ assert(expected.boundaries === actual.boundaries)
+ assert(expected.predictions === actual.predictions)
+
+ val actualPrediction =
+ actual.transform(df2).select("prediction").map(_.getDouble(0)).collect()
+ assert(expectedPrediction === actualPrediction)
+ }
+
+ val dfWithStringFeatures =
+ df.select(col("label"), col("features").cast(StringType), col("weight"))
+
+ val thrown = intercept[IllegalArgumentException] {
+ ir.fit(dfWithStringFeatures)
+ }
+ assert(thrown.getMessage.contains(
+ "Column features must be of type NumericType or VectorUDT," +
+ " but was actually of type StringType"))
+
+ val thrown2 = intercept[IllegalArgumentException] {
+ expected.transform(dfWithStringFeatures)
+ }
+ assert(thrown2.getMessage.contains(
+ "Column features must be of type NumericType or VectorUDT," +
+ " but was actually of type StringType"))
+ }
--- End diff --
My bad, skimmed too quickly over the tests and it looked very similar to what I had done in the past.
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[GitHub] spark issue #17384: [SPARK-20056][ML] IsotonicRegression support Numeric fea...
Posted by AmplabJenkins <gi...@git.apache.org>.
Github user AmplabJenkins commented on the issue:
https://github.com/apache/spark/pull/17384
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[GitHub] spark issue #17384: [SPARK-20056][ML] IsotonicRegression support Numeric fea...
Posted by AmplabJenkins <gi...@git.apache.org>.
Github user AmplabJenkins commented on the issue:
https://github.com/apache/spark/pull/17384
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[GitHub] spark issue #17384: [SPARK-20056][ML] IsotonicRegression support Numeric fea...
Posted by SparkQA <gi...@git.apache.org>.
Github user SparkQA commented on the issue:
https://github.com/apache/spark/pull/17384
**[Test build #75044 has started](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/75044/testReport)** for PR 17384 at commit [`647ff46`](https://github.com/apache/spark/commit/647ff461ee5ac1e36e6d9a83ca62271403ad01ab).
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[GitHub] spark pull request #17384: [SPARK-20056][ML] IsotonicRegression support Nume...
Posted by BenFradet <gi...@git.apache.org>.
Github user BenFradet commented on a diff in the pull request:
https://github.com/apache/spark/pull/17384#discussion_r108224092
--- Diff: mllib/src/test/scala/org/apache/spark/ml/regression/IsotonicRegressionSuite.scala ---
@@ -189,6 +191,47 @@ class IsotonicRegressionSuite
assert(expected.predictions === actual.predictions)
}
}
+
+ test("Besides VectorUDT, should support all NumericType features, and not support other types") {
+ val df = generateIsotonicInput(Seq(1, 2, 3))
+ val ir = new IsotonicRegression()
+
+ val expected = ir.fit(df)
+ val expectedPrediction =
+ expected.transform(df).select("prediction").map(_.getDouble(0)).collect()
+
+ val types =
+ Seq(ShortType, LongType, IntegerType, FloatType, ByteType, DoubleType, DecimalType(10, 0))
+
+ types.foreach { t =>
+ val df2 = df.select(col("label"), col("features").cast(t), col("weight"))
+
+ val actual = ir.fit(df2)
+ assert(expected.boundaries === actual.boundaries)
+ assert(expected.predictions === actual.predictions)
+
+ val actualPrediction =
+ actual.transform(df2).select("prediction").map(_.getDouble(0)).collect()
+ assert(expectedPrediction === actualPrediction)
+ }
+
+ val dfWithStringFeatures =
+ df.select(col("label"), col("features").cast(StringType), col("weight"))
+
+ val thrown = intercept[IllegalArgumentException] {
+ ir.fit(dfWithStringFeatures)
+ }
+ assert(thrown.getMessage.contains(
+ "Column features must be of type NumericType or VectorUDT," +
+ " but was actually of type StringType"))
+
+ val thrown2 = intercept[IllegalArgumentException] {
+ expected.transform(dfWithStringFeatures)
+ }
+ assert(thrown2.getMessage.contains(
+ "Column features must be of type NumericType or VectorUDT," +
+ " but was actually of type StringType"))
+ }
--- End diff --
Why not use [MLTestingUtil's checkNumericTypes](https://github.com/apache/spark/blob/master/mllib/src/test/scala/org/apache/spark/ml/util/MLTestingUtils.scala#L41-L88)?
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[GitHub] spark issue #17384: [SPARK-20056][ML] IsotonicRegression support Numeric fea...
Posted by SparkQA <gi...@git.apache.org>.
Github user SparkQA commented on the issue:
https://github.com/apache/spark/pull/17384
**[Test build #75041 has started](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/75041/testReport)** for PR 17384 at commit [`fbfb7f4`](https://github.com/apache/spark/commit/fbfb7f44db03cb50096cb03848a5cb9c015a9dc7).
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[GitHub] spark issue #17384: [SPARK-20056][ML] IsotonicRegression support Numeric fea...
Posted by AmplabJenkins <gi...@git.apache.org>.
Github user AmplabJenkins commented on the issue:
https://github.com/apache/spark/pull/17384
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[GitHub] spark issue #17384: [SPARK-20056][ML] IsotonicRegression support Numeric fea...
Posted by SparkQA <gi...@git.apache.org>.
Github user SparkQA commented on the issue:
https://github.com/apache/spark/pull/17384
**[Test build #75041 has finished](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/75041/testReport)** for PR 17384 at commit [`fbfb7f4`](https://github.com/apache/spark/commit/fbfb7f44db03cb50096cb03848a5cb9c015a9dc7).
* This patch **fails Spark unit tests**.
* This patch merges cleanly.
* This patch adds no public classes.
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[GitHub] spark issue #17384: [SPARK-20056][ML] IsotonicRegression support Numeric fea...
Posted by MLnick <gi...@git.apache.org>.
Github user MLnick commented on the issue:
https://github.com/apache/spark/pull/17384
For the other algos we've done something similar - but aimed to allowing any numeric type for `label` and `weight` columns. I'm not totally against this here, but I think it falls into a slightly different "category" - i.e. supporting different numeric types for input features.
Overall I think it's more natural to enforce a double feature column (if you think about it, a feature vector column is implicitly enforced to be double as we don't support arbitrary numeric types for vectors).
I'm not convinced this solves any major pain point.
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[GitHub] spark issue #17384: [SPARK-20056][ML] IsotonicRegression support Numeric fea...
Posted by AmplabJenkins <gi...@git.apache.org>.
Github user AmplabJenkins commented on the issue:
https://github.com/apache/spark/pull/17384
Merged build finished. Test FAILed.
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[GitHub] spark pull request #17384: [SPARK-20056][ML] IsotonicRegression support Nume...
Posted by zhengruifeng <gi...@git.apache.org>.
Github user zhengruifeng commented on a diff in the pull request:
https://github.com/apache/spark/pull/17384#discussion_r109074112
--- Diff: mllib/src/test/scala/org/apache/spark/ml/regression/IsotonicRegressionSuite.scala ---
@@ -189,6 +191,47 @@ class IsotonicRegressionSuite
assert(expected.predictions === actual.predictions)
}
}
+
+ test("Besides VectorUDT, should support all NumericType features, and not support other types") {
+ val df = generateIsotonicInput(Seq(1, 2, 3))
+ val ir = new IsotonicRegression()
+
+ val expected = ir.fit(df)
+ val expectedPrediction =
+ expected.transform(df).select("prediction").map(_.getDouble(0)).collect()
+
+ val types =
+ Seq(ShortType, LongType, IntegerType, FloatType, ByteType, DoubleType, DecimalType(10, 0))
+
+ types.foreach { t =>
+ val df2 = df.select(col("label"), col("features").cast(t), col("weight"))
+
+ val actual = ir.fit(df2)
+ assert(expected.boundaries === actual.boundaries)
+ assert(expected.predictions === actual.predictions)
+
+ val actualPrediction =
+ actual.transform(df2).select("prediction").map(_.getDouble(0)).collect()
+ assert(expectedPrediction === actualPrediction)
+ }
+
+ val dfWithStringFeatures =
+ df.select(col("label"), col("features").cast(StringType), col("weight"))
+
+ val thrown = intercept[IllegalArgumentException] {
+ ir.fit(dfWithStringFeatures)
+ }
+ assert(thrown.getMessage.contains(
+ "Column features must be of type NumericType or VectorUDT," +
+ " but was actually of type StringType"))
+
+ val thrown2 = intercept[IllegalArgumentException] {
+ expected.transform(dfWithStringFeatures)
+ }
+ assert(thrown2.getMessage.contains(
+ "Column features must be of type NumericType or VectorUDT," +
+ " but was actually of type StringType"))
+ }
--- End diff --
`checkNumericTypes` only test `fit` method on `labelCol` and `weightCol`.
`IsotonicRegressor` is the only algorithm which need to test both `fit` and `transform` on `featureCol`. So I prefer not to use `checkNumericTypes`. Thanks for your review
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[GitHub] spark pull request #17384: [SPARK-20056][ML] IsotonicRegression support Nume...
Posted by zhengruifeng <gi...@git.apache.org>.
Github user zhengruifeng commented on a diff in the pull request:
https://github.com/apache/spark/pull/17384#discussion_r109074309
--- Diff: mllib/src/main/scala/org/apache/spark/ml/regression/IsotonicRegression.scala ---
@@ -84,7 +84,7 @@ private[regression] trait IsotonicRegressionBase extends Params with HasFeatures
val extract = udf { v: Vector => v(idx) }
extract(col($(featuresCol)))
} else {
- col($(featuresCol))
+ col($(featuresCol)).cast(DoubleType)
--- End diff --
Precision loss may happen here. But I think we can not avoid it now, for we use `mllib.IsotonicRegression` and the input dataframe is of type `input: RDD[(Double, Double, Double)])`
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[GitHub] spark issue #17384: [SPARK-20056][ML] IsotonicRegression support Numeric fea...
Posted by SparkQA <gi...@git.apache.org>.
Github user SparkQA commented on the issue:
https://github.com/apache/spark/pull/17384
**[Test build #75042 has started](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/75042/testReport)** for PR 17384 at commit [`26ff8d8`](https://github.com/apache/spark/commit/26ff8d8e147d9317c8208022a528a3529071fef9).
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[GitHub] spark issue #17384: [SPARK-20056][ML] IsotonicRegression support Numeric fea...
Posted by SparkQA <gi...@git.apache.org>.
Github user SparkQA commented on the issue:
https://github.com/apache/spark/pull/17384
**[Test build #75042 has finished](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/75042/testReport)** for PR 17384 at commit [`26ff8d8`](https://github.com/apache/spark/commit/26ff8d8e147d9317c8208022a528a3529071fef9).
* This patch **fails Spark unit tests**.
* This patch merges cleanly.
* This patch adds no public classes.
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[GitHub] spark pull request #17384: [SPARK-20056][ML] IsotonicRegression support Nume...
Posted by felixcheung <gi...@git.apache.org>.
Github user felixcheung commented on a diff in the pull request:
https://github.com/apache/spark/pull/17384#discussion_r108046716
--- Diff: mllib/src/main/scala/org/apache/spark/ml/regression/IsotonicRegression.scala ---
@@ -84,7 +84,7 @@ private[regression] trait IsotonicRegressionBase extends Params with HasFeatures
val extract = udf { v: Vector => v(idx) }
extract(col($(featuresCol)))
} else {
- col($(featuresCol))
+ col($(featuresCol)).cast(DoubleType)
--- End diff --
would there be precision loss by doing this?
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[GitHub] spark issue #17384: [SPARK-20056][ML] IsotonicRegression support Numeric fea...
Posted by SparkQA <gi...@git.apache.org>.
Github user SparkQA commented on the issue:
https://github.com/apache/spark/pull/17384
**[Test build #75044 has finished](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/75044/testReport)** for PR 17384 at commit [`647ff46`](https://github.com/apache/spark/commit/647ff461ee5ac1e36e6d9a83ca62271403ad01ab).
* This patch passes all tests.
* This patch merges cleanly.
* This patch adds no public classes.
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[GitHub] spark issue #17384: [SPARK-20056][ML] IsotonicRegression support Numeric fea...
Posted by zhengruifeng <gi...@git.apache.org>.
Github user zhengruifeng commented on the issue:
https://github.com/apache/spark/pull/17384
@MLnick Agree. I will close this PR.
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[GitHub] spark issue #17384: [SPARK-20056][ML] IsotonicRegression support Numeric fea...
Posted by AmplabJenkins <gi...@git.apache.org>.
Github user AmplabJenkins commented on the issue:
https://github.com/apache/spark/pull/17384
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