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Save predictions for a proper dataset

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Marcin
2019-05-18 14:42:35 +02:00
parent 7a33ea5d1f
commit 15c2d0663a
1 changed files with 12 additions and 8 deletions
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ulmfit/train_clas.py
+12 -8
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@@ -182,24 +182,28 @@ class CLSHyperParams(LMHyperParams):
if data_tst is None:
data_clas , _, data_tst = self.load_cls_data(bs)
dt = data_tst if mode == "test" else data_clas
else:
dt = data_tst
if learn is None:
learn = self.create_cls_learner(data_tst, drop_mult=0.3, metrics=self.get_metrics(True))
learn = self.create_cls_learner(dt, drop_mult=0.3, metrics=self.get_metrics(True))
learn.unfreeze()
learn.load(save_name)
probs, targets = learn.get_preds(ordered=True)
preds = np.argmax(probs.cpu().numpy(), axis=1)
if dump_preds:
with open(dump_preds, 'w') as f:
f.write('\n'.join([str(x) for x in preds]))
if mode == "test":
ds = data_tst.valid_dl
elif mode == "valid" or mode == "dev":
ds = data_clas.valid_dl
elif mode == "train":
ds = data_clas.valid_dl
ds = data_clas.train_dl
else:
raise AttributeError(f"Unrecognized mode {mode}, valid options: test, valid, train optionally dev==valid")
np.save(self.model_dir / f"preds-on-{mode}.npy", probs.cpu().numpy())
if mode in ["test", "valid", "dev"]:
probs, targets = learn.get_preds(ordered=True)
preds = np.argmax(probs.cpu().numpy(), axis=1)
if dump_preds:
with open(dump_preds, 'w') as f:
f.write('\n'.join([str(x) for x in preds]))
np.save(self.model_dir / f"preds-on-{mode}.npy", probs.cpu().numpy())
results = learn.validate(ds)
print(f"Model: {self.name}")
print(f"Validation on: {mode}")