def save(name,
ext,
target,
sep=",",
dec=".",
rem=[],
hidden=[100],
lr=0.1,
pat=5,
d=1,
l="mse",
act="tanh",
e=200,
bsize=100,
verb=0,
ts=.2):
ds = Dataset("{}.{}".format(name, ext), target, sep, dec, rem, testSize=ts)
# Split the data
Xtrain, Xvalid, yTrain, yValid = train_test_split(ds.Xtrain,
ds.ytrain.values,
test_size=0.1,
shuffle=True)
model = Regressor(hidden=hidden, lr=lr, pat=pat, delta=d, loss=l, act=act)
model.fit(Xtrain, yTrain, Xvalid, yValid, ep=e, bs=bsize, v=verb)
if verb > 0:
model.plot()
print("Metrics for", name, "data set:")
model.metrics(ds.Xtest, ds.ytest.values)
print("Saving model...")
model.save(name)
if verb > 0:
return ds
print()
