def before_after(self):
bef, aft = [], []
for _ in xrange(self.reps):
predRows = []
train_DF = createTbl(self.train[self._n], isBin=True)
test_df = createTbl(self.test[self._n], isBin=True)
actual = Bugs(test_df)
before = self.pred(train_DF, test_df,
tunings=self.tunedParams,
smoteit=True)
for predicted, row in zip(before, test_df._rows):
tmp = row.cells
tmp[-2] = predicted
if predicted > 0:
predRows.append(tmp)
predTest = clone(test_df, rows=predRows)
if predRows:
newTab = treatments2(
train=self.train[self._n],
test=self.test[self._n],
test_df=predTest,
extent=self.extent,
far=False,
infoPrune=self.infoPrune,
Prune=self.Prune).main()
else:
newTab = treatments2(
train=self.train[
self._n],
test=self.test[
self._n],
far=False,
extent=self.extent,
infoPrune=self.infoPrune,
Prune=self.Prune).main()
after = self.pred(train_DF, newTab,
tunings=self.tunedParams,
smoteit=True)
bef.append(sum(before))
aft.append(sum(after))
return bef, aft
def before_after(self):
bef, aft = [], []
for _ in xrange(self.reps):
predRows = []
train_DF = createTbl(self.train[self._n], isBin=True)
test_df = createTbl(self.test[self._n], isBin=True)
actual = Bugs(test_df)
before = self.pred(train_DF,
test_df,
tunings=self.tunedParams,
smoteit=True)
for predicted, row in zip(before, test_df._rows):
tmp = row.cells
tmp[-2] = predicted
if predicted > 0:
predRows.append(tmp)
predTest = clone(test_df, rows=predRows)
if predRows:
newTab = treatments2(train=self.train[self._n],
test=self.test[self._n],
test_df=predTest,
extent=self.extent,
far=False,
infoPrune=self.infoPrune,
Prune=self.Prune).main()
else:
newTab = treatments2(train=self.train[self._n],
test=self.test[self._n],
far=False,
extent=self.extent,
infoPrune=self.infoPrune,
Prune=self.Prune).main()
after = self.pred(train_DF,
newTab,
tunings=self.tunedParams,
smoteit=True)
bef.append(sum(before))
aft.append(sum(after))
return bef, aft
def go(self):
for _ in xrange(self.reps):
predRows = []
train_DF = createTbl(self.train[self._n][-2:], isBin=True)
test_df = createTbl(self.test[self._n], isBin=True)
actual = Bugs(test_df)
before = self.pred(train_DF,
test_df,
tunings=self.tunedParams,
smoteit=True)
for predicted, row in zip(before, test_df._rows):
tmp = row.cells
tmp[-2] = predicted
if predicted > 0:
predRows.append(tmp)
predTest = clone(test_df, rows=predRows)
if predRows:
newTab = treatments2(train=self.train[self._n][-2:],
test=self.test[self._n],
test_df=predTest,
extent=self.extent,
far=False,
smote=True,
resample=False,
infoPrune=self.infoPrune,
Prune=self.Prune).main()
else:
newTab = treatments2(train=self.train[self._n][-2:],
test=self.test[self._n],
far=False,
smote=True,
resample=False,
extent=self.extent,
infoPrune=self.infoPrune,
Prune=self.Prune).main()
after = self.pred(train_DF,
newTab,
tunings=self.tunedParams,
smoteit=True)
self.out_pred.append(_Abcd(before=actual, after=before))
delta = cliffs(lst1=Bugs(predTest), lst2=after).delta()
self.out.append(delta)
if self.extent == 0:
append = 'Base'
else:
if self.Prune:
append = str(self.extent) + '_iP(' + str(
int(self.infoPrune *
100)) + r'%)' if not self.fSelect else str(
self.extent) + '_w_iP(' + str(
int(self.infoPrune * 100)) + r'%)'
else:
append = str(self.extent) if not self.fSelect else str(
self.extent) + '_w'
self.out.insert(0, self.dataName + '_' + append)
self.out_pred.insert(0, self.dataName)
print(self.out)
def go(self):
for _ in xrange(self.reps):
predRows = []
train_DF = createTbl(self.train[self._n][-2:], isBin=True)
test_df = createTbl(self.test[self._n], isBin=True)
actual = Bugs(test_df)
before = self.pred(train_DF, test_df,
tunings=self.tunedParams,
smoteit=True)
for predicted, row in zip(before, test_df._rows):
tmp = row.cells
tmp[-2] = predicted
if predicted > 0:
predRows.append(tmp)
predTest = clone(test_df, rows=predRows)
if predRows:
newTab = treatments2(
train=self.train[self._n][-2:],
test=self.test[self._n],
test_df=predTest,
extent=self.extent,
far=False,
smote=True,
resample=False,
infoPrune=self.infoPrune,
Prune=self.Prune).main()
else:
newTab = treatments2(
train=self.train[
self._n][-2:],
test=self.test[
self._n],
far=False,
smote=True,
resample=False,
extent=self.extent,
infoPrune=self.infoPrune,
Prune=self.Prune).main()
after = self.pred(train_DF, newTab,
tunings=self.tunedParams,
smoteit=True)
self.out_pred.append(_Abcd(before=actual, after=before))
delta = cliffs(lst1=Bugs(predTest), lst2=after).delta()
self.out.append(delta)
if self.extent == 0:
append = 'Base'
else:
if self.Prune:
append = str(
self.extent) + '_iP(' + str(
int(self.infoPrune * 100)) + r'%)' if not self.fSelect else str(
self.extent) + '_w_iP(' + str(
int(self.infoPrune * 100)) + r'%)'
else:
append = str(
self.extent) if not self.fSelect else str(
self.extent) + '_w'
self.out.insert(0, self.dataName + '_' + append)
self.out_pred.insert(0, self.dataName)
print(self.out)
