Github user mpjlu commented on a diff in the pull request: https://github.com/apache/spark/pull/14597#discussion_r77137991 --- 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 -- Hi @srowen , use different fields for different value is not a problem, just need another selectionType field, and in the fit function, the code will be: if(selectionType == KBest) callMLlibFunc("fitChiSqSelectorKBest", self.numTopFeatures, data) elseif(selectionType == Percentile) callMLlibFunc("fitChiSqSelectorKPercentile", self.percentile, data) elseif(selecitonType == FPR) callMLlibFunc("fitChiSqSelectorFPR", self.alpha, data) Is that ok? Thanks.
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