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Posted to issues@spark.apache.org by "Sean Owen (JIRA)" <ji...@apache.org> on 2017/09/07 14:54:02 UTC
[jira] [Resolved] (SPARK-21919) inconsistent behavior of
AFTsurvivalRegression algorithm
[ https://issues.apache.org/jira/browse/SPARK-21919?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Sean Owen resolved SPARK-21919.
-------------------------------
Resolution: Duplicate
> inconsistent behavior of AFTsurvivalRegression algorithm
> --------------------------------------------------------
>
> Key: SPARK-21919
> URL: https://issues.apache.org/jira/browse/SPARK-21919
> Project: Spark
> Issue Type: Bug
> Components: ML, PySpark
> Affects Versions: 2.2.0
> Environment: Spark Version: 2.2.0
> Cluster setup: Standalone single node
> Python version: 3.5.2
> Reporter: Ashish Chopra
>
> Took the direct example from spark ml documentation.
> {code}
> 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)),
> (0.273, 1.0, Vectors.dense(0.520, 1.151)),
> (4.199, 0.0, Vectors.dense(0.795, -0.226))], ["label", "censor",
> "features"])
> quantileProbabilities = [0.3, 0.6]
> aft = AFTSurvivalRegression(quantileProbabilities=quantileProbabilities,
> quantilesCol="quantiles")
> #aft = AFTSurvivalRegression()
> model = aft.fit(training)
>
> # Print the coefficients, intercept and scale parameter for AFT survival regression
> print("Coefficients: " + str(model.coefficients))
> print("Intercept: " + str(model.intercept))
> print("Scale: " + str(model.scale))
> model.transform(training).show(truncate=False)
> {code}
> result is:
> Coefficients: [-0.496304411053,0.198452172529]
> Intercept: 2.6380898963056327
> Scale: 1.5472363533632303
> ||label||censor||features ||prediction || quantiles ||
> |1.218|1.0 |[1.56,-0.605] |5.718985621018951 | [1.160322990805951,4.99546058340675]|
> |2.949|0.0 |[0.346,2.158] |18.07678210850554 |[3.66759199449632,15.789837303662042]|
> |3.627|0.0 |[1.38,0.231] |7.381908879359964 |[1.4977129086101573,6.4480027195054905]|
> |0.273|1.0 |[0.52,1.151] |13.577717814884505|[2.754778414791513,11.859962351993202]|
> |4.199|0.0 |[0.795,-0.226]|9.013087597344805 |[1.828662187733188,7.8728164067854856]|
> But if we change the value of all labels as label + 20. as:
> {code}
> training = spark.createDataFrame([
> (21.218, 1.0, Vectors.dense(1.560, -0.605)),
> (22.949, 0.0, Vectors.dense(0.346, 2.158)),
> (23.627, 0.0, Vectors.dense(1.380, 0.231)),
> (20.273, 1.0, Vectors.dense(0.520, 1.151)),
> (24.199, 0.0, Vectors.dense(0.795, -0.226))], ["label", "censor",
> "features"])
> quantileProbabilities = [0.3, 0.6]
> aft = AFTSurvivalRegression(quantileProbabilities=quantileProbabilities,
> quantilesCol="quantiles")
> #aft = AFTSurvivalRegression()
> model = aft.fit(training)
>
> # Print the coefficients, intercept and scale parameter for AFT survival regression
> print("Coefficients: " + str(model.coefficients))
> print("Intercept: " + str(model.intercept))
> print("Scale: " + str(model.scale))
> model.transform(training).show(truncate=False)
> {code}
> result changes to:
> Coefficients: [23.9932020748,3.18105314757]
> Intercept: 7.35052273751137
> Scale: 7698609960.724161
> ||label ||censor||features ||prediction ||quantiles||
> |21.218|1.0 |[1.56,-0.605] |4.0912442688237169E18|[0.0,0.0]|
> |22.949|0.0 |[0.346,2.158] |6.011158613411288E9 |[0.0,0.0]|
> |23.627|0.0 |[1.38,0.231] |7.7835948690311181E17|[0.0,0.0]|
> |20.273|1.0 |[0.52,1.151] |1.5880852723124176E10|[0.0,0.0]|
> |24.199|0.0 |[0.795,-0.226]|1.4590190884193677E11|[0.0,0.0]|
> Can someone please explain this exponential blow up in prediction, as per my understanding prediction in AFT is a prediction of the time when the failure event will occur, not able to understand why it will change exponentially against the value of the label.
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