def test_ealstm(self):
config_file = Path("tests") / "test_config/test_config_ealstm.yml"
cfg = read_config(config_file)
# check that initialisation works
model = EALSTM(cfg)
# trainer object
trainer = RegressionTrainer(cfg)
trainer.initialize_training()
trainer.train_and_validate()
# tester object
time_now = time.gmtime()
expt_name = cfg["experiment_name"]
run_dir = max([
p for p in Path("runs").glob(
f"{expt_name}_{time_now.tm_mday:02}{time_now.tm_mon:02}*")
])
assert run_dir.exists()
tester = Tester(cfg, run_dir)
epoch = int(
sorted(list(
run_dir.glob("*.pt")))[-1].name.split("epoch")[-1].replace(
".pt", ""))
tester.evaluate(epoch=epoch,
save_results=True,
metrics=cfg.get("metrics", []))
assert (run_dir / "test/model_epoch002/test_results.p").exists()
# analysis object (-> csv)
create_results_csv(run_dir=run_dir, epoch=epoch)
assert (run_dir / f"results_{run_dir.name}_E002.csv").exists
def eval_run(args: dict):
config_file = Path(args["run_dir"]) / "config.yml"
config = read_config(config_file)
start_evaluation(cfg=config,
run_dir=Path(args["run_dir"]),
epoch=args["epoch"])
def test_cudalstm(self):
config_file = Path("tests") / "test_config/test_config.yml"
cfg = read_config(config_file)
model = CudaLSTM(cfg)
# trainer object
trainer = RegressionTrainer(cfg)
trainer.initialize_training()
trainer.train_and_validate()
def start_run(args: dict):
config_file = Path(args["config_file"])
config = read_config(config_file)
print(f"### Run configurations for {config['experiment_name']}")
for key, val in config.items():
print(f"{key}: {val}")
start_training(config)
def test_base(self):
config_file = Path("tests") / "test_config/test_config.yml"
cfg = read_config(config_file)
model = BaseModel(cfg)
def test_base(self):
config_file = Path("tests") / "test_config/test_config.yml"
cfg = read_config(config_file)
trainer = BaseTrainer(cfg)
def test_cudalstm(self):
config_file = Path("tests") / "test_config/test_config.yml"
cfg = read_config(config_file)
trainer = RegressionTrainer(cfg)
all_dfs.append(valid_df)
# get the obs (ONCE)
obs = valid_df["obs"]
# rename the sim (for all)
all_dfs = [
df.rename(columns={
"sim": f"sim_E{epoch:03}"
}).drop(columns="obs") for epoch, df in zip(epochs, all_dfs)
]
# join all the simulations
df = all_dfs[0]
for d in all_dfs[1:]:
df = df.join(d)
df = df.join(obs)
outfile = run_dir / f"all_{run_dir.name}_results.csv"
print(f"** ALL Results written to: {outfile}")
df.to_csv(outfile)
if __name__ == "__main__":
args = get_args()
run_dir = Path(args["run_dir"])
assert run_dir.exists()
config_file = run_dir / "config.yml"
cfg = read_config(config_file)
analyse_all_epochs(cfg, run_dir)