def get_log_normalized_dls(train, test, bs=1024):
"""Get lognormalized DataLoaders from train and test DataFrames.
Parameters
----------
train : DataFrame
Training data.
test : DataFrame
Test data.
bs : int
Batch size.
Returns
-------
(DataLoader, DataLoader)
Train and test DataLoaders.
"""
train, test = log_normalize(train, test)
train_x = train
test_x = test
train_y = train_x # y = x since we are building and AE
test_y = test_x
train_ds = TensorDataset(torch.tensor(train_x.values, dtype=torch.float),
torch.tensor(train_y.values, dtype=torch.float))
valid_ds = TensorDataset(torch.tensor(test_x.values, dtype=torch.float),
torch.tensor(test_y.values, dtype=torch.float))
train_dl, valid_dl = get_data(train_ds, valid_ds, bs)
return train_dl, valid_dl
def db_from_df(train, test, bs=1024):
# Create TensorDatasets
train_ds = TensorDataset(torch.tensor(train.values),
torch.tensor(train.values))
valid_ds = TensorDataset(torch.tensor(test.values),
torch.tensor(test.values))
# Create DataLoaders
train_dl, valid_dl = get_data(train_ds, valid_ds, bs=bs)
# Return DataBunch
return basic_data.DataBunch(train_dl, valid_dl)
x = train[variables].values
x_scaled = StandardScaler().fit_transform(x)
train[variables] = x_scaled
x = test[variables].values
x_scaled = StandardScaler().fit_transform(x)
test[variables] = x_scaled
# Create TensorDatasets
train_ds = TensorDataset(torch.tensor(train.values, dtype=torch.float),
torch.tensor(train.values, dtype=torch.float))
valid_ds = TensorDataset(torch.tensor(test.values, dtype=torch.float),
torch.tensor(test.values, dtype=torch.float))
# Create DataLoaders
train_dl, valid_dl = get_data(train_ds, valid_ds, bs=bs)
# Return DataBunch
db = basic_data.DataBunch(train_dl, valid_dl)
loss_func = nn.MSELoss()
bn_wd = False # Don't use weight decay for batchnorm layers
true_wd = True # wd will be used for all optimizers
def train_model(model, epochs, lr, wd, module_string, ct, path):
plt.close('all')
learn = basic_train.Learner(data=db,
model=model,
loss_func=loss_func,