def test_test_gages(self):
data_model = GagesModel.load_datamodel(self.config_data.data_path["Temp"],
data_source_file_name='test_data_source.txt',
stat_file_name='test_Statistics.json', flow_file_name='test_flow.npy',
forcing_file_name='test_forcing.npy', attr_file_name='test_attr.npy',
f_dict_file_name='test_dictFactorize.json',
var_dict_file_name='test_dictAttribute.json',
t_s_dict_file_name='test_dictTimeSpace.json')
with torch.cuda.device(1):
data_models = GagesModel.every_model(data_model)
obs_lst = []
pred_lst = []
for i in range(len(data_models)):
print("\n", "Testing model", str(i + 1), ":\n")
pred, obs = master_test(data_models[i])
basin_area = data_models[i].data_source.read_attr(data_models[i].t_s_dict["sites_id"], ['area_gages2'],
is_return_dict=False)
mean_prep = data_models[i].data_source.read_attr(data_models[i].t_s_dict["sites_id"], ['p_mean'],
is_return_dict=False)
pred = _basin_norm(pred, basin_area, mean_prep, to_norm=False)
obs = _basin_norm(obs, basin_area, mean_prep, to_norm=False)
obs_lst.append(obs.flatten())
pred_lst.append(pred.flatten())
preds = np.array(pred_lst)
obss = np.array(obs_lst)
plot_we_need(data_model, obss, preds, id_col="id", lon_col="lon", lat_col="lat")
def test_train_camels(self):
data_model = GagesModel.load_datamodel(self.config_data.data_path["Temp"],
data_source_file_name='data_source.txt',
stat_file_name='Statistics.json', flow_file_name='flow.npy',
forcing_file_name='forcing.npy', attr_file_name='attr.npy',
f_dict_file_name='dictFactorize.json',
var_dict_file_name='dictAttribute.json',
t_s_dict_file_name='dictTimeSpace.json')
with torch.cuda.device(1):
data_models = GagesModel.every_model(data_model)
for i in range(len(data_models)):
print("\n", "Training model", str(i + 1), ":\n")
model, train_loss, valid_loss = master_train(data_models[i], valid_size=0.2)
fig = plot_loss_early_stop(train_loss, valid_loss)
out_dir = self.config_data.data_path["Out"]
fig.savefig(os.path.join(out_dir, 'loss_plot.png'), bbox_inches='tight')
