def main(self, mode='defect', justDeltas=False):
if mode == "defect":
train_DF = createTbl(self.train, isBin=False)
test_DF = createTbl(self.test, isBin=False)
before = rforest(train=train_DF, test=test_DF)
clstr = [c for c in self.nodes(train_DF._rows)]
return patches(train=self.train,
test=self.test,
clusters=clstr,
prune=self.prune,
pred=before).newTable(justDeltas=justDeltas)
elif mode == "models":
train_DF = createTbl(self.train, isBin=False)
test_DF = createTbl(self.test, isBin=False)
before = rforest(train=train_DF, test=test_DF)
clstr = [c for c in self.nodes(train_DF._rows)]
return patches(train=self.train,
test=self.test,
clusters=clstr,
prune=self.prune,
models=True,
pred=before).newTable(justDeltas=justDeltas)
elif mode == "config":
train_DF = createTbl(self.train, isBin=False)
test_DF = createTbl(self.test, isBin=False)
before = rforest2(train=train_DF, test=test_DF)
clstr = [c for c in self.nodes(train_DF._rows)]
return patches(train=self.train,
test=self.test,
clusters=clstr,
name=self.name,
prune=self.prune,
pred=before,
config=True).newTable(justDeltas=justDeltas)
def __init__(self,
train,
test,
clusters,
prune=False,
B=0.25,
verbose=False,
bin=False):
if bin:
self.train = createTbl(train, isBin=False)
self.test = createTbl(test, isBin=False)
else:
self.train = createTbl(train, isBin=False)
self.test = createTbl(test, isBin=True)
self.clusters = clusters
self.Prune = prune
self.B = B
self.mask = self.fWeight()
self.write = verbose
self.bin = bin
if bin:
self.pred = rforest2(self.train,
self.test,
smoteit=True,
duplicate=True)
else:
self.pred = rforest(self.train,
self.test,
smoteit=True,
duplicate=True)
def main(self, config=False):
if not config:
train_DF = createTbl(self.train, isBin=False)
test_DF = createTbl(self.test, isBin=False)
before = rforest(train=train_DF, test=test_DF)
clstr = [c for c in self.nodes(train_DF._rows)]
return patches(train=self.train,
test=self.test,
clusters=clstr,
prune=self.prune).newTable()
else:
train_DF = createTbl(self.train, isBin=False)
test_DF = createTbl(self.test, isBin=False)
before = rforest2(train=train_DF, test=test_DF)
clstr = [c for c in self.nodes(train_DF._rows)]
return patches(train=self.train,
test=self.test,
clusters=clstr,
prune=self.prune,
bin=True).newTable()
def __init__(
self, train, test, clusters, prune=False, B=0.25
, verbose=False, bin=False):
if bin:
self.train = createTbl(train, isBin=False)
self.test = createTbl(test, isBin=False)
else:
self.train = createTbl(train, isBin=False)
self.test = createTbl(test, isBin=True)
self.clusters = clusters
self.Prune = prune
self.B = B
self.mask = self.fWeight()
self.write = verbose
self.bin = bin
if bin:
self.pred = rforest2(self.train, self.test, smoteit=True, duplicate=True)
else:
self.pred = rforest(self.train, self.test, smoteit=True, duplicate=True)
def main(self, config=False):
if not config:
train_DF = createTbl(self.train, isBin=False)
test_DF = createTbl(self.test, isBin=False)
before = rforest(train=train_DF, test=test_DF)
clstr = [c for c in self.nodes(train_DF._rows)]
return patches(train=self.train,
test=self.test,
clusters=clstr,
prune=self.prune).newTable()
else:
train_DF = createTbl(self.train, isBin=False)
test_DF = createTbl(self.test, isBin=False)
before = rforest2(train=train_DF, test=test_DF)
clstr = [c for c in self.nodes(train_DF._rows)]
return patches(train=self.train,
test=self.test,
clusters=clstr,
prune=self.prune,
bin=True).newTable()
#! /Users/rkrsn/miniconda/bin/python
#! /Users/rkrsn/miniconda/bin/python
