Github user vectorijk commented on a diff in the pull request:
https://github.com/apache/spark/pull/8926#discussion_r40864215
--- Diff: python/pyspark/ml/regression.py ---
@@ -609,6 +611,150 @@ class GBTRegressionModel(TreeEnsembleModels):
"""
+@inherit_doc
+class AFTSurvivalRegression(JavaEstimator, HasFeaturesCol, HasLabelCol,
HasPredictionCol,
+ HasFitIntercept, HasMaxIter, HasTol):
+ """
+ Accelerated Failure Time (AFT) Model Survival Regression
+
+ Fit a parametric AFT survival regression model based on the Weibull
distribution
+ of the survival time.
+
+ .. seealso:: `AFT Model
<https://en.wikipedia.org/wiki/Accelerated_failure_time_model>`_
+
+ >>> from pyspark.mllib.linalg import Vectors
+ >>> df = sqlContext.createDataFrame([
+ ... (1.0, Vectors.dense(1.0), 1.0),
+ ... (0.0, Vectors.sparse(1, [], []), 0.0)], ["label", "features",
"censor"])
+ >>> aftsr = AFTSurvivalRegression()
+ >>> model = aftsr.fit(df)
+ >>> model.transform(df).show()
+ +-----+---------+------+----------+
+ |label| features|censor|prediction|
+ +-----+---------+------+----------+
+ | 1.0| [1.0]| 1.0| 1.0|
+ | 0.0|(1,[],[])| 0.0| 1.0|
+ +-----+---------+------+----------+
+ ...
+
+ .. versionadded:: 1.6.0
+ """
+
+ # a placeholder to make it appear in the generated doc
+ censorCol = Param(Params._dummy(), "censorCol",
+ "censor column name. The value of this column could
be 0 or 1. " +
+ "If the value is 1, it means the event has occurred
i.e. " +
+ "uncensored; otherwise censored.")
+ quantileProbabilities = \
+ Param(Params._dummy(), "quantileProbabilities",
+ "quantile probabilities array. Values of the quantile
probabilities array " +
+ "should be in the range [0, 1] and the array should be
non-empty.")
+ quantilesCol = Param(Params._dummy(), "quantilesCol",
+ "quantiles column name. This column will output
quantiles of " +
+ "corresponding quantileProbabilities if it is
set.")
+
+ @keyword_only
+ def __init__(self, featuresCol="features", labelCol="label",
predictionCol="prediction",
+ fitIntercept=True, maxIter=100, tol=1E-6,
censorCol="censor",
+ quantileProbabilities=None, quantilesCol=None):
+ """
+ __init__(self, featuresCol="features", labelCol="label",
predictionCol="prediction", \
+ fitIntercept=True, maxIter=100, tol=1E-6,
censorCol="censor", \
+ quantileProbabilities=[0.01, 0.05, 0.1, 0.25, 0.5, 0.75,
0.9, 0.95, 0.99], \
+ quantilesCol=None):
+ """
+ super(AFTSurvivalRegression, self).__init__()
+ self._java_obj = self._new_java_obj(
+ "org.apache.spark.ml.regression.AFTSurvivalRegression",
self.uid)
+ #: Param for censor column name
+ self.censorCol = Param(self, "censorCol",
+ "censor column name. The value of this
column could be 0 or 1. " +
+ "If the value is 1, it means the event has
occurred i.e. " +
+ "uncensored; otherwise censored.")
+ #: Param for quantile probabilities array
+ self.quantileProbabilities = \
+ Param(self, "quantileProbabilities",
+ "quantile probabilities array. Values of the quantile
probabilities array " +
+ "should be in the range [0, 1] and the array should be
non-empty.")
+ #: Param for quantiles column name
+ self.quantilesCol = Param(self, "quantilesCol",
+ "quantiles column name. This column will
output quantiles of " +
+ "corresponding quantileProbabilities if
it is set.")
+ self._setDefault(censorCol="censor",
+ quantileProbabilities=[0.01, 0.05, 0.1, 0.25,
0.5, 0.75, 0.9, 0.95, 0.99])
+ kwargs = self.__init__._input_kwargs
+ self.setParams(**kwargs)
+
+ @keyword_only
+ @since("1.6.0")
+ def setParams(self, featuresCol="features", labelCol="label",
predictionCol="prediction",
+ fitIntercept=True, maxIter=100, tol=1E-6,
censorCol="censor",
+ quantileProbabilities=None, quantilesCol=None):
+ """
+ setParams(self, featuresCol="features", labelCol="label",
predictionCol="prediction", \
+ fitIntercept=True, maxIter=100, tol=1E-6,
censorCol="censor", \
+ quantileProbabilities=[0.01, 0.05, 0.1, 0.25, 0.5, 0.75,
0.9, 0.95, 0.99], \
+ quantilesCol=None):
+ """
+ kwargs = self.__init__._input_kwargs
--- End diff --
@mengxr @yanboliang changed that.
Plus, I added two methods in AFTSurvivalRegressionModel.
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