def test_onset_wk():
adl = udata.ActualDataLoader("./data")
aidx, adata = adl.get()
target = udata.Target("onset_wk")
y, Xs, yi = target.get_training_data(adl, [], None, 222222)
# Check if all onset weeks of same season are the same
seasons = [u.epiweek_to_season(i[0]) for i in yi]
df = pd.DataFrame({
"epiweek": [i[0] for i in yi],
"season": [u.epiweek_to_season(i[0]) for i in yi],
"region": [i[1] for i in yi],
"onset_wk": y
})
for name, group in df.groupby(["season", "region"]):
assert group["onset_wk"].isnull().all() or len(set(
group["onset_wk"])) == 1
# Random checks
onset_map = [[1997, "nat", 7], [2007, "nat", 12]]
for item in onset_map:
predicted_onset = df[(df["season"] == item[0]) &
(df["region"] == item[1])].iloc[0, :]["onset_wk"]
assert int(predicted_onset) == item[-1]
def test_peak():
adl = udata.ActualDataLoader("./data")
aidx, adata = adl.get()
target = udata.Target("peak")
y, Xs, yi = target.get_training_data(adl, [], None, 222222)
assert all(aidx["epiweek"] == [i[0] for i in yi])
assert all(aidx["region"] == [i[1] for i in yi])
assert all(adata <= y)
def test_peak_wk():
adl = udata.ActualDataLoader("./data")
aidx, adata = adl.get()
target = udata.Target("peak_wk")
y, Xs, yi = target.get_training_data(adl, [], None, 222222)
assert all(aidx["epiweek"] == [i[0] for i in yi])
assert all(aidx["region"] == [i[1] for i in yi])
assert all([
u.epiweek_to_season(a) == u.epiweek_to_season(b)
for a, b in zip([i[0] for i in yi], aidx["epiweek"])
])
import utils.misc as u
import losses
import os
from tqdm import tqdm
data_dir = snakemake.input.data_dir
EXP_NAME = snakemake.config["EXP_NAME"]
exp_dir = os.path.join(data_dir, "processed", EXP_NAME)
TEST_SPLIT_THRESH = snakemake.config["TEST_SPLIT_THRESH"][EXP_NAME]
COMPONENTS = [udata.Component(exp_dir, name) for name in u.available_models(exp_dir)]
ACTUAL_DL = udata.ActualDataLoader(data_dir)
REGIONS = ["nat", *[f"hhs{i}" for i in range(1, 11)], None]
TARGETS = [udata.Target(t) for t in [1, 2, 3, 4, "peak", "peak_wk", "onset_wk"]]
# Entry point
for target in tqdm(TARGETS):
output_dir = u.ensure_dir(f"./results/{EXP_NAME}/{target.name}")
for idx, cmp in enumerate(COMPONENTS):
eval_df = {
"region": [],
"score": []
}
for region in REGIONS:
y, Xs, yi = target.get_testing_data(ACTUAL_DL, COMPONENTS, region, TEST_SPLIT_THRESH)
y_one_hot = udists.actual_to_one_hot(y, bins=target.bins)
output = Xs[idx]