def eval(self, model):
res = []
for gr, fname in [(self.gr, self.trainf), (self.grv, self.valf), (self.grt, self.testf)]:
if gr is None:
res.append(None)
continue
ypred = model.predict(gr)['score']
res.append(ev.eval_rte(ypred, gr['score'], fname))
return tuple(res)
def eval(self, model):
res = []
for gr, fname in [(self.gr, self.trainf), (self.grv, self.valf),
(self.grt, self.testf)]:
if gr is None:
res.append(None)
continue
ypred = model.predict(gr)['score']
res.append(ev.eval_rte(ypred, gr['score'], fname))
return tuple(res)
def eval(self, model):
res = []
for gr, fname in [(self.gr, self.trainf), (self.grv, self.valf), (self.grt, self.testf)]:
if gr is None:
res.append(None)
continue
ypred = []
for ogr in self.sample_pairs(gr, batch_size=len(gr), shuffle=False, once=True):
ypred += list(model.predict(ogr)['score'])
ypred = np.array(ypred)
res.append(ev.eval_rte(ypred, gr['score'], fname))
return tuple(res)
def eval(self, model):
res = []
for gr, fname in [(self.gr, self.trainf), (self.grv, self.valf),
(self.grt, self.testf)]:
if gr is None:
res.append(None)
continue
ypred = []
for ogr in self.sample_pairs(gr,
batch_size=len(gr),
shuffle=False,
once=True):
ypred += list(model.predict(ogr)['score'])
ypred = np.array(ypred)
res.append(ev.eval_rte(ypred, gr['score'], fname))
return tuple(res)