def test_runoff_example(self, tmp_path): cfg = Config(Path("tests/testconfigs/config_runoff.yml")) create_and_assign_temp_run_path_to_config(cfg, tmp_path) cfg._cfg["data_path"] = Path("data/ALL_dynamic_ds.nc") cfg._cfg["static_data_path"] = Path("data/camels_static.nc") cfg._cfg["static_inputs"] = ["p_mean", "pet_mean", "area", "gauge_elev"] cfg._cfg["n_epochs"] = 3 ds, static = load_data(cfg) # select subset of 3 basins basins = [1001, 2001, 2002] ds = ds.sel(station_id=basins) static = static.sel(station_id=basins) trainer = Trainer(cfg, ds, static_data=static) self.check_loaded_data( cfg, trainer, data=ds.sel(time=slice(cfg.train_start_date, cfg.train_end_date)), ) losses = trainer.train_and_validate() tester = Tester(cfg, ds, static_data=static) preds = tester.run_test() return losses, preds
def main(args): reader = load_data(args) # Run oracle scores = [] validator = KFoldValidator(num_folds=args.folds, verbose=args.verbose) for item in reader.scored_items(): best_params, best_params_score = validator.train([item]) scores.append(best_params_score) if args.verbose: print(item.name, best_params, best_params_score, sep='\t') if args.summarize: print('all', '---', statistics.mean(scores), sep='\t')
def test_kenya_vci_example(self, tmp_path): cfg = Config(Path("tests/testconfigs/config.yml")) create_and_assign_temp_run_path_to_config(cfg, tmp_path) cfg._cfg["data_path"] = Path("data/kenya.nc") cfg._cfg["n_epochs"] = 3 ds, static = load_data(cfg) trainer = Trainer(cfg, ds, static_data=static) self.check_loaded_data( cfg, trainer, data=ds.sel(time=slice(cfg.train_start_date, cfg.train_end_date)), ) losses = trainer.train_and_validate() tester = Tester(cfg, ds, static_data=static) preds = tester.run_test() return losses, preds
def test_file_list(self): data = load_data(["fibers.trk", "data.nii"]) self.assertEqual(data, ["fibers.trk", "data.nii"])
def test_single_file(self): data = load_data(["fibers.trk"]) self.assertEqual(data, ["fibers.trk"])
def begin(self): super(FiberTrackingHook, self).begin() self.test_set = run.load_data(self.test_set)
dirs[int(raw_input('enter your choice: '))]) print("Loading params from: " + path) args = np.load(os.path.join(path, 'args.pkl')) return args, path def init_model(): model = VRAE(args.rnn_size, args.rnn_size, args.n_features, args.latent_size, num_drivers, batch_size=args.batch_size) model.create_gradientfunctions(x_train, t_train, x_valid, t_valid) return model def load_epoch(model, e): model.load_parameters(os.path.join(path, str(e))) def forward(model, batch_num=0): return model.encoder(x_train[batch_num * model.batch_size:(batch_num + 1) * model.batch_size].transpose(1, 0, 2)) args, path = load_params() x_train, t_train, x_valid, t_valid = load_data(args) num_drivers = np.max(t_train) + 1 model = init_model()