from pathlib import Path
from camelsml import evaluate, load_config
cfg = load_config("run_config.txt")
cfg["val_basin_file"] = Path(
f"gb_split/split_seed_19970204/basins_validation.txt")
for k in range(2, 5):
runs = list(Path(f"/work/bernharl/train_us_val_gb/{k}").glob("*"))
print(runs)
if len(runs) != 1:
raise RuntimeError(
f"Amount of runs per cross val should be 1, not {len(runs)}")
cfg["run_dir"] = runs[0]
cfg["train_basin_file"] = Path(
f"cv/cross_validation_seed_19970204/{k}/basins_train.txt")
cfg["eval_dir"] = Path(f"/work/bernharl/train_us_val_gb/val_gb/{k}")
for i in range(1, cfg["epochs"] + 1):
evaluate(cfg, split="val", epoch=i)
from pathlib import Path
from camelsml import load_config, train
cfg = load_config(cfg_file="run_config.txt", device="cuda:0", num_workers=60)
cfg["test_basin_file"] = Path(
"/home/bernhard/git/Master-Thesis/runs/combined_dataset/train_gb_val_us/cv/cross_validation_seed_19970204/basins_test.txt"
)
for i in range(0, 5):
cfg["run_dir"] = Path(f"{i}/")
cfg["train_basin_file"] = Path(
f"/home/bernhard/git/Master-Thesis/runs/combined_dataset/train_gb_val_us/cv/cross_validation_seed_19970204/{i}/basins_train.txt"
)
cfg["val_basin_file"] = Path(
f"/home/bernhard/git/Master-Thesis/runs/combined_dataset/train_gb_val_us/cv/cross_validation_seed_19970204/{i}/basins_val.txt"
)
train(cfg)
from pathlib import Path
import pickle
import random
import numpy as np
from camelsml import permutation_test, load_config
permutation_folder = Path("permutation")
permutation_folder.mkdir(exist_ok=True)
cfg = load_config("run_config.txt", device="cuda:0", num_workers=24)
np.random.seed(cfg["seed"])
random.seed(cfg["seed"])
for i in range(0, 1):
save_path = permutation_folder / f"{i}"
save_path.mkdir(exist_ok=True)
cv_dir = list((Path().absolute() / f"{i}").glob("*"))
if len(cv_dir) != 1:
raise RuntimeError(f"cv_dir must contain only one run")
else:
cv_dir = cv_dir[0]
cfg["run_dir"] = cv_dir
cfg["train_basin_file"] = Path(
f"/home/bernhard/git/Master-Thesis/runs/correlation_reduction/cross_validation/cross_validation_seed_19970204/{i}/basins_train.txt"
)
cfg["val_basin_file"] = Path(
f"/home/bernhard/git/Master-Thesis/runs/correlation_reduction/cross_validation/cross_validation_seed_19970204/{i}/basins_val.txt"
)
with open(save_path / "i_list.pickle", "wb") as outfile:
pickle.dump(permutation_test(cfg, k=2), outfile)
from pathlib import Path
import numpy as np
from camelsml import (
split_basins,
cross_validation_split,
load_config,
combine_cv_datasets,
)
cfg = load_config(cfg_file="../camels_root_info.txt")
cv_folder_us = Path(
"/home/bernhard/git/Master-Thesis/runs/combined_dataset/train_us_val_gb/cv"
)
cv_folder_gb = Path(
"/home/bernhard/git/Master-Thesis/runs/combined_dataset/train_gb_val_us/cv"
)
store_folder = Path("/home/bernhard/git/Master-Thesis/runs/combined_dataset/mixed/cv")
combine_cv_datasets(
cv_folder_1=cv_folder_us,
cv_folder_2=cv_folder_gb,
k=5,
seed=19970204,
normalize=True,
store_folder=store_folder,
dataset=cfg["dataset"],
timeseries=cfg["timeseries"],
from time import sleep
from pathlib import Path
from camelsml import evaluate, load_config
cfg = load_config("run_config.txt", device="cuda:0")
cfg["val_basin_file"] = Path(
f"gb_split/split_seed_19970204/basins_validation.txt")
for k in range(3, 4):
runs = list(Path(f"{k}").glob("*"))
print(runs)
if len(runs) != 1:
raise RuntimeError(
f"Amount of runs per cross val should be 1, not {len(runs)}")
cfg["run_dir"] = runs[0]
cfg["train_basin_file"] = Path(
f"cv/cross_validation_seed_19970204/{k}/basins_train.txt")
cfg["eval_dir"] = Path(f"val_gb/{k}")
if k == 0:
start = 25
else:
start = 1
for i in range(start, cfg["epochs"] + 1):
evaluate(cfg, split="val", epoch=i)
from pathlib import Path
from camelsml import load_config, train, split_basins
cfg = load_config("training_runs/test/test.txt",
device="cuda:0",
num_workers=24)
"""split_basins(
cfg["camels_root"],
"/home/bernhard/git/ealstm_regional_modeling_camels_gb/data/basin_list.txt",
split=[0.67, 0.33],
store_folder="training_runs",
seed=1010,
)"""
train(cfg)