def check_simple(df):
m = NeuralProphet()
folds = m.crossvalidation_split_df(df,
freq="D",
k=5,
fold_pct=0.1,
fold_overlap_pct=0.5)
assert all(
[70 + i * 5 == len(train) for i, (train, val) in enumerate(folds)])
assert all([10 == len(val) for (train, val) in folds])
def check_cv(df, freq, n_lags, n_forecasts, k, fold_pct, fold_overlap_pct):
m = NeuralProphet(
n_lags=n_lags,
n_forecasts=n_forecasts,
)
folds = m.crossvalidation_split_df(df, freq=freq, k=k, fold_pct=fold_pct, fold_overlap_pct=fold_overlap_pct)
total_samples = len(df) - m.n_lags + 2 - (2 * m.n_forecasts)
per_fold = int(fold_pct * total_samples)
not_overlap = per_fold - int(fold_overlap_pct * per_fold)
assert all([per_fold == len(val) - m.n_lags + 1 - m.n_forecasts for (train, val) in folds])
assert all(
[
total_samples - per_fold - (k - i - 1) * not_overlap == len(train) - m.n_lags + 1 - m.n_forecasts
for i, (train, val) in enumerate(folds)
]
)
