def __new__(cls,
data=None,
measure=orange.MeasureAttribute_relief(k=20, m=50),
threshold=0.0):
if data is None:
self = object.__new__(cls)
return self
else:
self = cls(measure=measure, threshold=threshold)
return self(data)
def __new__(cls,
data=None,
measure=orange.MeasureAttribute_relief(k=20, m=50),
margin=0):
if data is None:
self = object.__new__(cls)
return self
else:
self = cls(measure=measure, margin=margin)
return self(data)
def select_relief(data,
measure=orange.MeasureAttribute_relief(k=20, m=50),
margin=0):
"""Iteratively remove the worst scored feature until no feature
has a score below the margin. The filter procedure was originally
designed for measures such as Relief, which are context dependent,
i.e., removal of features may change the scores of other remaining
features. The score is thus recomputed in each iteration.
:param data: a data table
:type data: :obj:`Orange.data.Table`
:param measure: a feature scorer
:type measure: :obj:`Orange.feature.scoring.Score`
:param margin: margin for removal
:type margin: float
"""
measl = score_all(data, measure)
while len(data.domain.attributes) > 0 and measl[-1][1] < margin:
data = select_best_n(data, measl, len(data.domain.attributes) - 1)
measl = score_all(data, measure)
return data
def __init__(self,
measure=orange.MeasureAttribute_relief(k=20, m=50),
margin=0):
self.measure = measure
self.margin = margin
def __init__(self,
measure=orange.MeasureAttribute_relief(k=20, m=50),
n=5):
self.measure = measure
self.n = n
def __init__(self, measure=orange.MeasureAttribute_relief(k=20, m=50), \
threshold=0.0):
self.measure = measure
self.threshold = threshold