def get_predictions(df, idxs=(0, -1)):
data = torch.tensor(df[idxs[0]:idxs[1]].values, dtype=torch.float)
pred = model(data).detach().numpy()
data = data.detach().numpy()
data_df = pd.DataFrame(data, columns=df.columns)
pred_df = pd.DataFrame(pred, columns=df.columns)
# Unnormalize
unnormalized_data_df = utils.custom_unnormalize(data_df)
unnormalized_pred_df = utils.custom_unnormalize(pred_df)
# Handle variables with discrete distributions
unnormalized_pred_df['N90Constituents'] = unnormalized_pred_df['N90Constituents'].round()
uniques = unnormalized_data_df['ActiveArea'].unique()
utils.round_to_input(unnormalized_pred_df, uniques, 'ActiveArea')
data = unnormalized_data_df.values
pred = unnormalized_pred_df.values
return data, pred, unnormalized_data_df, unnormalized_pred_df
data = torch.tensor(test[idxs[0]:idxs[1]].values, dtype=torch.float)
pred = model(data).detach().numpy()
data = data.detach().numpy()
data_df = pd.DataFrame(data, columns=test.columns)
pred_df = pd.DataFrame(pred, columns=test.columns)
# Unnormalize
unnormalized_data_df = utils.custom_unnormalize(data_df)
unnormalized_pred_df = utils.custom_unnormalize(pred_df)
# Handle variables with discrete distributions
unnormalized_pred_df['N90Constituents'] = unnormalized_pred_df[
'N90Constituents'].round()
uniques = unnormalized_data_df['ActiveArea'].unique()
utils.round_to_input(unnormalized_pred_df, uniques, 'ActiveArea')
data = unnormalized_data_df.values
pred = unnormalized_pred_df.values
# Residuals
residuals = (pred - data) / data
diff = (pred - data)
for kk, key in enumerate(test.keys()):
if key in diff_list:
curr_residuals = diff[:, kk]
if key == 'AverageLArQF':
curr_residuals = curr_residuals[
np.abs(curr_residuals) < 1000]
limits = (-1000, 1000)