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[SPARK-19281][FOLLOWUP][ML] Minor fix for PySpark FPGrowth. #18089

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[SPARK-19281][FOLLOWUP][ML] Minor fix for PySpark FPGrowth. #18089

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21 changes: 11 additions & 10 deletions python/pyspark/ml/fpm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -23,17 +23,17 @@
__all__ = ["FPGrowth", "FPGrowthModel"]


class HasSupport(Params):
class HasMinSupport(Params):
"""
Mixin for param support.
Mixin for param minSupport.
"""

minSupport = Param(
Params._dummy(),
"minSupport",
"""Minimal support level of the frequent pattern. [0.0, 1.0].
Any pattern that appears more than (minSupport * size-of-the-dataset)
times will be output""",
"Minimal support level of the frequent pattern. [0.0, 1.0]. " +
"Any pattern that appears more than (minSupport * size-of-the-dataset) " +
"times will be output in the frequent itemsets.",
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Note there will be \n in the generated Python API docs if using """.

Before this PR:
image

After this PR:
image

typeConverter=TypeConverters.toFloat)

def setMinSupport(self, value):
Expand All @@ -49,16 +49,17 @@ def getMinSupport(self):
return self.getOrDefault(self.minSupport)


class HasConfidence(Params):
class HasMinConfidence(Params):
"""
Mixin for param confidence.
Mixin for param minConfidence.
"""

minConfidence = Param(
Params._dummy(),
"minConfidence",
"""Minimal confidence for generating Association Rule. [0.0, 1.0]
Note that minConfidence has no effect during fitting.""",
"Minimal confidence for generating Association Rule. [0.0, 1.0]. " +
"minConfidence will not affect the mining for frequent itemsets, " +
"but will affect the association rules generation.",
typeConverter=TypeConverters.toFloat)

def setMinConfidence(self, value):
Expand Down Expand Up @@ -126,7 +127,7 @@ def associationRules(self):


class FPGrowth(JavaEstimator, HasItemsCol, HasPredictionCol,
HasSupport, HasConfidence, JavaMLWritable, JavaMLReadable):
HasMinSupport, HasMinConfidence, JavaMLWritable, JavaMLReadable):
"""
.. note:: Experimental

Expand Down