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 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 depen(self, rows):
mod = CART(self.train, self.test, tunings=rows, smoteit=True)
g = _Abcd(before=Bugs(self.test), after=mod, show=False)[-1]
return g
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 go(self):
rseed(1)
for planner in ['xtrees', 'cart', 'HOW', 'baseln0', 'baseln1']:
out = [planner]
predRows = []
train_DF = createTbl(self.train[self._n], isBin=True)
test_df = createTbl(self.test[self._n], isBin=True)
actual = np.array(Bugs(test_df))
before = self.pred(train_DF,
test_df,
tunings=self.tunedParams,
smoteit=True)
base = lambda X: sorted(X)[-1] - sorted(X)[0]
newRows = lambda newTab: map(lambda Rows: Rows.cells[:-1], newTab.
_rows)
after = lambda newTab: self.pred(
train_DF, newTab, tunings=self.tunedParams, smoteit=True)
frac = lambda aft: sum([0 if a < 1 else 1 for a in aft]) / sum(
[0 if b < 1 else 1 for b in before])
predRows = [
row.cells for predicted, row in zip(
before,
createTbl(self.test[self._n], isBin=False)._rows)
if predicted > 0
]
predTest = genTable(test_df, rows=predRows)
for _ in xrange(self.reps):
"Apply Different Planners"
if planner == 'xtrees':
newTab = xtrees(train=self.train[-1],
test_DF=predTest,
bin=False,
majority=True).main()
elif planner == 'cart' or planner == 'CART':
newTab = xtrees(train=self.train[-1],
test_DF=predTest,
bin=False,
majority=False).main()
elif planner == 'HOW':
newTab = HOW(train=self.train[-1],
test=self.test[-1],
test_df=predTest).main()
elif planner == 'baseln0':
newTab = strawman(train=self.train[-1],
test=self.test[-1]).main()
elif planner == 'baseln1':
newTab = strawman(train=self.train[-1],
test=self.test[-1],
prune=True).main()
out.append(frac(after(newTab)))
self.logResults(out)
yield out