[SPARK-28985][PYTHON][ML][FOLLOW-UP] Add _AFTSurvivalRegressionParams
### What changes were proposed in this pull request?
Adds
```python
_AFTSurvivalRegressionParams(HasFeaturesCol, HasLabelCol, HasPredictionCol,
HasMaxIter, HasTol, HasFitIntercept,
HasAggregationDepth): ...
```
with related Params and uses it to replace `HasFitIntercept`, `HasMaxIter`, `HasTol` and `HasAggregationDepth` in `AFTSurvivalRegression` base classes and `JavaPredictionModel,` in `AFTSurvivalRegressionModel` base classes.
### Why are the changes needed?
Previous work (#25776) on [SPARK-28985](https://issues.apache.org/jira/browse/SPARK-28985) replaced `JavaEstimator`, `HasFeaturesCol`, `HasLabelCol`, `HasPredictionCol` in `AFTSurvivalRegression` and `JavaModel` in `AFTSurvivalRegressionModel` with newly added `JavaPredictor`:
e97b55d322/python/pyspark/ml/wrapper.py (L377)
and `JavaPredictionModel`
e97b55d322/python/pyspark/ml/wrapper.py (L405)
respectively.
This however is inconsistent with Scala counterpart where both classes extend private `AFTSurvivalRegressionBase`
eb037a8180/mllib/src/main/scala/org/apache/spark/ml/regression/AFTSurvivalRegression.scala (L48-L50)
This preserves some of the existing inconsistencies (variables as defined in [the official example](https://github.com/apache/spark/blob/master/examples/src/main/python/ml/aft_survival_regression.p))
```
from pyspark.ml.regression import AFTSurvivalRegression, AFTSurvivalRegressionModel
from pyspark.ml.param.shared import HasMaxIter, HasTol, HasFitIntercept, HasAggregationDepth
from pyspark.ml.param import Param
issubclass(AFTSurvivalRegressionModel, HasMaxIter)
# False
hasattr(model, "maxIter") and isinstance(model.maxIter, Param)
# True
issubclass(AFTSurvivalRegressionModel, HasTol)
# False
hasattr(model, "tol") and isinstance(model.tol, Param)
# True
```
and can cause problems in the future, if Predictor / PredictionModel API changes (unlike [`IsotonicRegression`](https://github.com/apache/spark/pull/26023), current implementation is technically speaking correct, though incomplete).
### Does this PR introduce any user-facing change?
Yes, it adds a number of base classes to `AFTSurvivalRegressionModel`. These change purely additive and have negligible potential for breaking existing code (and none, compared to changes already made in #25776). Additionally affected API hasn't been released in the current form yet.
### How was this patch tested?
- Existing unit tests.
- Manual testing.
CC huaxingao, zhengruifeng
Closes #26024 from zero323/SPARK-28985-FOLLOW-UP-aftsurival-regression.
Authored-by: zero323 <mszymkiewicz@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
This commit is contained in:
zero323
Sean Owen
parent
228b1ea96c
commit
df22535bbd
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@ -1480,9 +1480,56 @@ class GBTRegressionModel(TreeEnsembleModel, JavaPredictionModel, JavaMLWritable,
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return self._call_java("evaluateEachIteration", dataset, loss)
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class _AFTSurvivalRegressionParams(HasFeaturesCol, HasLabelCol, HasPredictionCol,
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HasMaxIter, HasTol, HasFitIntercept,
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HasAggregationDepth):
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"""
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Params for :py:class:`AFTSurvivalRegression` and :py:class:`AFTSurvivalRegressionModel`.
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.. versionadded:: 3.0.0
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"""
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censorCol = Param(
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Params._dummy(), "censorCol",
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"censor column name. The value of this column could be 0 or 1. " +
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"If the value is 1, it means the event has occurred i.e. " +
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"uncensored; otherwise censored.", typeConverter=TypeConverters.toString)
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quantileProbabilities = Param(
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Params._dummy(), "quantileProbabilities",
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"quantile probabilities array. Values of the quantile probabilities array " +
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"should be in the range (0, 1) and the array should be non-empty.",
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typeConverter=TypeConverters.toListFloat)
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quantilesCol = Param(
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Params._dummy(), "quantilesCol",
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"quantiles column name. This column will output quantiles of " +
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"corresponding quantileProbabilities if it is set.",
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typeConverter=TypeConverters.toString)
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@since("1.6.0")
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def getCensorCol(self):
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"""
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Gets the value of censorCol or its default value.
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"""
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return self.getOrDefault(self.censorCol)
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@since("1.6.0")
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def getQuantileProbabilities(self):
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"""
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Gets the value of quantileProbabilities or its default value.
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"""
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return self.getOrDefault(self.quantileProbabilities)
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@since("1.6.0")
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def getQuantilesCol(self):
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"""
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Gets the value of quantilesCol or its default value.
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"""
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return self.getOrDefault(self.quantilesCol)
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@inherit_doc
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class AFTSurvivalRegression(JavaPredictor, HasFitIntercept, HasMaxIter, HasTol,
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HasAggregationDepth, JavaMLWritable, JavaMLReadable):
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class AFTSurvivalRegression(JavaEstimator, _AFTSurvivalRegressionParams,
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JavaMLWritable, JavaMLReadable):
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"""
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Accelerated Failure Time (AFT) Model Survival Regression
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@ -1529,20 +1576,6 @@ class AFTSurvivalRegression(JavaPredictor, HasFitIntercept, HasMaxIter, HasTol,
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.. versionadded:: 1.6.0
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"""
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censorCol = Param(Params._dummy(), "censorCol",
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"censor column name. The value of this column could be 0 or 1. " +
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"If the value is 1, it means the event has occurred i.e. " +
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"uncensored; otherwise censored.", typeConverter=TypeConverters.toString)
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quantileProbabilities = \
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Param(Params._dummy(), "quantileProbabilities",
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"quantile probabilities array. Values of the quantile probabilities array " +
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"should be in the range (0, 1) and the array should be non-empty.",
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typeConverter=TypeConverters.toListFloat)
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quantilesCol = Param(Params._dummy(), "quantilesCol",
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"quantiles column name. This column will output quantiles of " +
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"corresponding quantileProbabilities if it is set.",
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typeConverter=TypeConverters.toString)
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@keyword_only
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def __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction",
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fitIntercept=True, maxIter=100, tol=1E-6, censorCol="censor",
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@ -1588,13 +1621,6 @@ class AFTSurvivalRegression(JavaPredictor, HasFitIntercept, HasMaxIter, HasTol,
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"""
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return self._set(censorCol=value)
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@since("1.6.0")
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def getCensorCol(self):
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"""
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Gets the value of censorCol or its default value.
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"""
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return self.getOrDefault(self.censorCol)
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@since("1.6.0")
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def setQuantileProbabilities(self, value):
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"""
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@ -1602,13 +1628,6 @@ class AFTSurvivalRegression(JavaPredictor, HasFitIntercept, HasMaxIter, HasTol,
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"""
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return self._set(quantileProbabilities=value)
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@since("1.6.0")
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def getQuantileProbabilities(self):
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"""
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Gets the value of quantileProbabilities or its default value.
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"""
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return self.getOrDefault(self.quantileProbabilities)
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@since("1.6.0")
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def setQuantilesCol(self, value):
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"""
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@ -1616,15 +1635,9 @@ class AFTSurvivalRegression(JavaPredictor, HasFitIntercept, HasMaxIter, HasTol,
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"""
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return self._set(quantilesCol=value)
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@since("1.6.0")
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def getQuantilesCol(self):
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"""
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Gets the value of quantilesCol or its default value.
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"""
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return self.getOrDefault(self.quantilesCol)
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class AFTSurvivalRegressionModel(JavaPredictionModel, JavaMLWritable, JavaMLReadable):
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class AFTSurvivalRegressionModel(JavaModel, _AFTSurvivalRegressionParams,
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JavaMLWritable, JavaMLReadable):
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"""
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Model fitted by :class:`AFTSurvivalRegression`.
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