def __init__(self, data):
self.data = data
self.train = createTbl(data[-2:-1],
_smote=False,
isBin=True,
bugThres=1,
duplicate=True)
self.test = createTbl([data[-1]], isBin=True, bugThres=1)
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 __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 newTable(self, justDeltas=False):
if not self.bin:
oldRows = [r for r in self.test._rows if abs(r.cells[-2]) > 0]
else:
oldRows = self.test._rows
newRows = [self.patchIt(t) for t in oldRows]
if self.write:
self.deltasCSVWriter()
header = [h.name for h in self.test.headers[:-1]]
name = str(randint(0, 1e6))
with open('tmp0.csv', 'w') as csvfile:
writer = csv.writer(csvfile, delimiter=',')
writer.writerow(header)
for el in newRows:
writer.writerow(el + [0])
if justDeltas == False:
try:
new = createTbl(['tmp0.csv'])
rm('tmp0.csv')
return new
except:
set_trace()
else:
return self.change
def __init__(
self, train, test, clusters, prune=False, B=0.25
, verbose=False, config=False, models=False, pred=[], name=None):
if config or models:
self.train = createTbl(train, isBin=False)
self.test = createTbl(test, isBin=False)
else:
self.train = createTbl(train, isBin=True)
self.test = createTbl(test, isBin=True)
self.name = name
self.clusters = clusters
self.Prune = prune
self.B = B
self.mask = self.fWeight()
self.write = verbose
self.bin = config
self.pred = pred
self.change = []
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, 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 newTable(self):
oldRows = [r for r, p in zip(self.test._rows, self.pred) if p > 0]
newRows = [self.patchIt(t) for t in oldRows]
if self.write:
self.deltasCSVWriter()
header = [h.name for h in self.test.headers[:-1]]
with open('tmp.csv', 'w') as csvfile:
writer = csv.writer(csvfile, delimiter=',')
writer.writerow(header)
for el in newRows:
writer.writerow(el + [0])
return createTbl(['tmp.csv'])
def depen(self, row):
# My where2pred() takes data in string format. Ex:
# '../Data/ant/ant-1.6.csv'
self.where = defaults().update(
minSize=row[4], depthMin=int(
row[5]), depthMax=int(
row[6]), prune=row[7] > 0.5)
self.tree.infoPrune = row[1]
self.tree.m = int(row[2])
self.tree.n = int(row[3])
self.tree.prune = row[8] > 0.5
actual = Bugs(createTbl([self.test], isBin=True))
preds = where2prd(
self.train, [
self.test], tunings=[
self.where, self.tree], thresh=row[0])
return _Abcd(before=actual, after=preds, show=False)[-1]
def crossval(self, _s=True, k=2):
cv_acc = [' Accuracy']
cv_prec = [' Precision']
cv_sen = ['Sensitivity (Recall)']
cv_spec = [' Specificity']
cv_f = [' f']
cv_g = [' g']
for _ in xrange(k):
proj = self.explorer2()
data = createTbl(proj, isBin=False, _smote=False)
a, b, c, d, e, f = self.kFoldCrossVal(data, k=k, smote=_s)
cv_acc.extend(a)
cv_sen.extend(b)
cv_spec.extend(c)
cv_prec.extend(d)
cv_f.extend(e)
cv_g.extend(f)
return cv_acc, cv_sen, cv_spec, cv_prec, cv_f, cv_g
#! /Users/rkrsn/miniconda/bin/python
def __init__(self, data):
self.data = data
self.train = createTbl(data[:-1])
self.test = createTbl([data[-1]])
def __init__(self, data): self.data = data self.train = createTbl(data[:-1]) self.test = createTbl([data[-1]])
def test_smote(): dir = './Data/log.csv' Tbl = methods1.createTbl([dir], _smote=False) newTbl = methods1.createTbl([dir], _smote=True) set_trace() print(len(Tbl._rows), len(newTbl._rows))
