l = round(random.uniform(1e-8, lrini),8)
if l not in lrs:
lrs.append(l)
print(lrs, len(lrs))
mse_train = []
mse_val = []
for i in range(300):
hp = {'epochs': 2000,
'batchsize': int(bs),
'lr': lrs[i],
'eta': eta}
print("Hyperparameters", hp)
data_dir = "./data/"
data = "reg"
loss = training.finetune(hp, model_design, (train_x, train_y), (test_x, test_y), data_dir, data, emb=False, reg=(train_yp, test_yp))
mse_train.append(np.mean(loss['train_loss']))
mse_val.append(np.mean(loss['val_loss']))
df = pd.DataFrame(lrs)
df['train_loss'] = mse_train
df['val_loss'] = mse_val
print("Random hparams search best result:")
print(df.loc[[df["val_loss"].idxmin()]])
lr = lrs[df["val_loss"].idxmin()]
print("Dataframe:", df)
df.to_csv("reg_lr.csv")
#print(len(x), len(y))
#print(splits)
train_x.index, train_y.index = np.arange(0, len(train_x)), np.arange(
0, len(train_y))
test_x.index, test_y.index = np.arange(0,
len(test_x)), np.arange(0, len(test_y))
print("train_x", train_x, test_x)
model_design = {'layersizes': [256]}
hp = {'epochs': 100000, 'batchsize': int(128), 'lr': 0.01}
print(hp)
print("TRAIN_TEST", train_x.shape, test_x.shape, "END")
data_dir = "./data/"
data = "embof"
tloss = training.finetune(hp, model_design, (train_x, train_y),
(test_x, test_y), data_dir, data)
#pd.DataFrame.from_dict(tloss).to_csv('res2_test.csv')
print(tloss)
train_loss = tloss['train_loss']
val_loss = tloss['val_loss']
pd.DataFrame({
"train_loss": train_loss,
"val_loss": val_loss
}).to_csv('OFres_vloss.csv')
test_x.index, test_y.index, yp_test.index = np.arange(
0, len(test_x)), np.arange(0, len(test_y)), np.arange(0, len(yp_test))
print("train_x", test_y, yp_test)
print("SIZES", train_x, train_y, yp_train)
model_design = {'layersizes': [128]}
hp = {'epochs': 10000, 'batchsize': int(128), 'lr': 0.01}
print(hp)
print("TRAIN_TEST", train_x.shape, test_x.shape, "END")
data_dir = "./data/"
data = "res2of"
tloss = training.finetune(hp,
model_design, (train_x, train_y), (test_x, test_y),
data_dir,
data,
res=2,
ypreles=(yp_train, yp_test))
#pd.DataFrame.from_dict(tloss).to_csv('res2_test.csv')
print(tloss)
train_loss = tloss['train_loss']
val_loss = tloss['val_loss']
pd.DataFrame({
"train_loss": train_loss,
"val_loss": val_loss
}).to_csv('OFres2_vloss.csv')
print(lrs, len(lrs))
mse_train = []
mse_val = []
print("trainshape", train_x.shape, train_y.to_frame().shape)
for i in range(300):
hp = {'epochs': 2000, 'batchsize': int(bs), 'lr': lrs[i]}
data_dir = "./data/"
data = "2res"
loss = training.finetune(hp,
model_design, (train_x, train_y.to_frame()),
(test_x, test_y.to_frame()),
data_dir,
data,
reg=None,
emb=False)
mse_train.append(np.mean(loss['train_loss']))
mse_val.append(np.mean(loss['val_loss']))
df = pd.DataFrame(lrs)
df['train_loss'] = mse_train
df['val_loss'] = mse_val
print("Random hparams search best result:")
print(df.loc[[df["val_loss"].idxmin()]])
lr = lrs[df["val_loss"].idxmin()]
print("Dataframe:", df)
df.to_csv("2res_lr.csv")
print("train_x", train_x, rtr)
model_design = {'layersizes': [[128], [128]]}
hp = {'epochs': 1000, 'batchsize': int(128), 'lr': 0.01, 'eta': 0.5}
print(hp)
print("TRAIN_TEST", train_x.shape, test_x.shape, "END")
data_dir = "./data/"
data = "embof"
tloss = training.finetune(hp,
model_design, (train_x, train_y), (test_x, test_y),
data_dir,
data,
reg=(yp_tr, yp_te),
raw=(rtr, rte),
emb=True,
sw=(swmn, swstd),
embtp=2,
qn=True)
#pd.DataFrame.from_dict(tloss).to_csv('res2_test.csv')
print(tloss)
train_loss = tloss['train_loss']
val_loss = tloss['val_loss']
pd.DataFrame({
"train_loss": train_loss,
"val_loss": val_loss
}).to_csv('OFemb_vloss.csv')
print(lrs, len(lrs))
mse_train = []
mse_val = []
for i in range(100):
hp = {'epochs': 500, 'batchsize': int(bs), 'lr': lrs[i], 'eta': eta}
data_dir = "./data/"
data = "emb2"
loss = training.finetune(hp,
model_design, (train_x, train_y),
(test_x, test_y),
data_dir,
data,
reg=(train_yp, test_yp),
raw=(train_xr, test_xr),
emb=True,
sw=(swmn, swstd),
embtp=2,
exp=2)
mse_train.append(np.mean(loss['train_loss']))
mse_val.append(np.mean(loss['val_loss']))
df = pd.DataFrame(lrs)
df['train_loss'] = mse_train
df['val_loss'] = mse_val
print("Random hparams search best result:")
print(df.loc[[df["val_loss"].idxmin()]])
lr = lrs[df["val_loss"].idxmin()]
print("Dataframe:", df)
