def _train_cross_valid(self, _skiptrain=False):
df = DataFeeder(*(self.traindata + [self.traingold]))
splitter = SplitIdxIterator(df.size,
split=self.validsplits,
random=self.validrandom,
folds=self.validsplits)
err = []
verr = []
c = 0
for splitidxs in splitter:
trainf = self.buildtrainfun(self.model)
validf = self.getvalidfun(self.model)
tf, vf = df.isplit(splitidxs, df_randoms=(True, False))
tf.numbats(self.numbats)
vf.batsize = tf.batsize
serr, sverr = self.trainloop(trainf=self.getbatchloop(trainf, tf),
validf=self.getbatchloop(validf, vf),
_skiptrain=_skiptrain)
err.append(serr)
verr.append(sverr)
self.resetmodel(self.model)
err = np.asarray(err)
avgerr = np.mean(err, axis=0)
verr = np.asarray(verr)
avgverr = np.mean(verr, axis=0)
self.tt.tock("done")
return avgerr, avgverr, err, verr
def _train_cross_valid(self):
df = DataFeeder(*(self.traindata + [self.traingold]))
splitter = SplitIdxIterator(df.size, split=self.validsplits, random=self.validrandom, folds=self.validsplits)
err = []
verr = []
c = 0
for splitidxs in splitter:
trainf = self.buildtrainfun(self.model)
validf = self.buildvalidfun(self.model)
tf, vf = df.isplit(splitidxs)
serr, sverr = self.trainloop(
trainf=self.getbatchloop(trainf, tf.numbats(self.numbats)),
validf=self.getbatchloop(validf, vf))
err.append(serr)
verr.append(sverr)
self.resetmodel(self.model)
err = np.asarray(err)
avgerr = np.mean(err, axis=0)
verr = np.asarray(verr)
avgverr = np.mean(verr, axis=0)
self.tt.tock("done")
return avgerr, avgverr, err, verr
