def main():
result_wandb = convert_args(sys.argv)
with open("config.json", "w+") as f:
r = requests.get(os.environ["BASE_CONFIG_PATH"])
f.write(r.text)
the_config = make_config(result_wandb, "config.json")
print(the_config)
train_function("PyTorch", the_config)
def loop_through(data_dir: str,
interrmittent_gcs: bool = False,
use_transfer: bool = True,
start_index: int = 0,
end_index: int = 25) -> None:
"""
Function that makes and executes a set of config files
This is since we have over 9k files and.
"""
if not os.path.exists("model_save"):
os.mkdir("model_save")
sorted_dir_list = sorted(os.listdir(data_dir))
# total = len(sorted_dir_list)
for i in range(start_index, end_index):
file_name = sorted_dir_list[i]
station_id_gage = file_name.split("_flow.csv")[0]
res = split_on_letter(station_id_gage)
gage_id = res[0]
station_id = res[1]
file_path_name = os.path.join(data_dir, file_name)
print("Training on: " + file_path_name)
correct_file = None
if use_transfer and len(os.listdir("model_save")) > 1:
weight_files = filter(lambda x: x.endswith(".pth"),
os.listdir("model_save"))
paths = []
for weight_file in weight_files:
paths.append(os.path.join("model_save", weight_file))
correct_file = max(paths, key=os.path.getctime)
print(correct_file)
config = make_config_file(file_path_name, gage_id, station_id,
correct_file)
extension = ".json"
file_name_json = station_id + "config_f" + extension
with open(file_name_json, "w+") as f:
json.dump(config, f)
try:
train_function("PyTorch", config)
except Exception as e:
print("An exception occured for: " + file_name_json)
traceback.print_exc()
print(e)
def test_decoder_multi_step(self):
if "save_path" in self.model_params:
del self.model_params["save_path"]
forecast_model = train_function("PyTorch", self.model_params)
t = torch.Tensor([3, 4, 5]).repeat(1, 336, 1)
output = simple_decode(forecast_model.model,
torch.ones(1, 5, 3),
336,
t,
output_len=3)
# We want to check for leakage
self.assertFalse(3 in output[:, :, 0])
def test_multivariate_single_step(self):
# dumb error fixes
if "save_path" in self.model_params3:
del self.model_params["save_path"]
t = torch.Tensor([3, 6, 5]).repeat(1, 100, 1)
forecast_model3 = train_function("PyTorch", self.model_params3)
output = simple_decode(forecast_model3.model,
torch.ones(1, 5, 3),
100,
t,
output_len=3,
multi_targets=2)
self.assertFalse(3 in output)
self.assertFalse(6 in output)
temp_training_data_dir = data_dir / "temp"
temp_training_data_dir.mkdir(exist_ok=True)
query = "region_identifier=='{}'"
for region in italian_regions:
region_df, dataset_length, file_path = format_corona_data(
df.query(query.format(region)), region, temp_training_data_dir)
sweep_id = wandb.sweep(generate_wandb_sweep_config(
f"Multivariate TS sweep new cases, mobility, weather -- {region}"),
project="covid-forecast")
wandb.agent(
sweep_id, lambda: train_function(
"PyTorch",
generate_training_config(
str(file_path),
feature_columns=[
'retail_recreation', 'grocery_pharmacy', 'parks',
'transit_stations', 'workplaces', 'residential',
'avg_temperature', 'min_temperature',
'max_temperature', 'relative_humidity',
'specific_humidity', 'pressure', "new_cases"
],
target_column=["new_cases"],
df_len=dataset_length)))
logger.info("done")
shutil.rmtree(temp_training_data_dir)
shutil.rmtree("wandb")
shutil.rmtree("model_save")