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Posted to issues@spark.apache.org by "Ashish Chopra (JIRA)" <ji...@apache.org> on 2017/09/05 06:25:00 UTC
[jira] [Created] (SPARK-21919) inconsistent behavior of
AFTsurvivalRegression algorithm
Ashish Chopra created SPARK-21919:
-------------------------------------
Summary: 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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