Github user mpjlu commented on a diff in the pull request:
https://github.com/apache/spark/pull/14597#discussion_r76624379
--- Diff: python/pyspark/mllib/feature.py ---
@@ -276,24 +276,64 @@ class ChiSqSelector(object):
"""
Creates a ChiSquared feature selector.
- :param numTopFeatures: number of features that selector will select.
-
>>> data = [
... LabeledPoint(0.0, SparseVector(3, {0: 8.0, 1: 7.0})),
... LabeledPoint(1.0, SparseVector(3, {1: 9.0, 2: 6.0})),
... LabeledPoint(1.0, [0.0, 9.0, 8.0]),
... LabeledPoint(2.0, [8.0, 9.0, 5.0])
... ]
- >>> model = ChiSqSelector(1).fit(sc.parallelize(data))
+ >>> model =
ChiSqSelector().setNumTopFeatures(1).fit(sc.parallelize(data))
+ >>> model.transform(SparseVector(3, {1: 9.0, 2: 6.0}))
+ SparseVector(1, {0: 6.0})
+ >>> model.transform(DenseVector([8.0, 9.0, 5.0]))
+ DenseVector([5.0])
+ >>> model =
ChiSqSelector().setPercentile(0.34).fit(sc.parallelize(data))
>>> model.transform(SparseVector(3, {1: 9.0, 2: 6.0}))
SparseVector(1, {0: 6.0})
>>> model.transform(DenseVector([8.0, 9.0, 5.0]))
DenseVector([5.0])
+ >>> data = [
+ ... LabeledPoint(0.0, SparseVector(4, {0: 8.0, 1: 7.0})),
+ ... LabeledPoint(1.0, SparseVector(4, {1: 9.0, 2: 6.0, 3: 4.0})),
+ ... LabeledPoint(1.0, [0.0, 9.0, 8.0, 4.0]),
+ ... LabeledPoint(2.0, [8.0, 9.0, 5.0, 9.0])
+ ... ]
+ >>> model = ChiSqSelector().setAlpha(0.1).fit(sc.parallelize(data))
+ >>> model.transform(DenseVector([1.0,2.0,3.0,4.0]))
+ DenseVector([4.0])
.. versionadded:: 1.4.0
"""
- def __init__(self, numTopFeatures):
- self.numTopFeatures = int(numTopFeatures)
+ def __init__(self):
+ self.param = 50
--- End diff --
Use three variables are clear. It needs another selectionType variable also.
In the fit function, according to the selection type, call different
functions. If you prefer that, I will change the code. I am ok for both
methods. Thanks.
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