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 depen(self, rows):
mod = rforest(
self.train,
self.test,
tunings=rows # n_est, max_feat, mss, msl
,
smoteit=True)
g = _Abcd(before=Bugs(self.test), after=mod, show=False)[-1]
return g
def __init__(
self, train, test, clusters, prune=False, B=0.33, verbose=False):
self.train = createTbl(train, isBin=True)
self.test = createTbl(test, isBin=True)
self.pred = rforest(self.train, self.test, smoteit=True, duplicate=True)
self.clusters = clusters
self.Prune = prune
self.B = B
self.mask = self.fWeight()
self.write = verbose
def main(self):
train, test = run(dataName='ant').categorize()
train_DF = createTbl(train[-1], isBin=True)
test_DF = createTbl(test[-1], isBin=True)
before = rforest(train=train_DF, test=test_DF)
for _ in xrange(1):
clstr = [c for c in self.nodes(train_DF._rows)]
newTbl = patches(train=train[-1],
test=test[-1],
clusters=clstr).deltasCSVWriter(name=self.name)
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 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 depen(self, rows): mod = rforest(self.train, self.test, tunings=rows, smoteit=True) prec = ABCD(before=Bugs(self.test), after=mod).all()[2] pdpf = ABCD(before=Bugs(self.test), after=mod).all()[:2] return prec
#! /Users/rkrsn/miniconda/bin/python
#! /Users/rkrsn/miniconda/bin/python
def depen(self, rows):
mod = rforest(self.train, self.test, tunings=rows, smoteit=True)
prec = ABCD(before=Bugs(self.test), after=mod).all()[2]
pdpf = ABCD(before=Bugs(self.test), after=mod).all()[:2]
return prec
def depen(self, rows):
mod = rforest(self.train, self.test
, tunings = rows # n_est, max_feat, mss, msl
, smoteit = True)
g = _Abcd(before = Bugs(self.test), after = mod, show = False)[-1]
return g