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Fix model_dir NameError, optimize sunrise/sunset caching, replace assert with ValueError #3

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Merged
bartzbeielstein merged 3 commits into from
Feb 11, 2026
Merged

Fix model_dir NameError, optimize sunrise/sunset caching, replace assert with ValueError #3

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5b9a3f6
Initial plan
Copilot Feb 11, 2026
f44ec6d
Apply code fixes: NameError, performance optimization, and code quali…
Copilot Feb 11, 2026
4bea9b7
Remove unnecessary int() conversion in missing_count calculation
Copilot Feb 11, 2026
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46 changes: 30 additions & 16 deletions src/spotforecast2_safe/processing/n2n_predict_with_covariates.py
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Original file line number Diff line number Diff line change
Expand Up @@ -278,17 +278,29 @@ def _get_day_night_features(

extended_index = pd.date_range(start=start, end=cov_end, freq=freq, tz=timezone)

sunrise_hour = [
sun(location.observer, date=date, tzinfo=location.timezone)["sunrise"]
for date in extended_index
]
sunset_hour = [
sun(location.observer, date=date, tzinfo=location.timezone)["sunset"]
for date in extended_index
]
# Cache sunrise and sunset times per unique calendar date to avoid
# recomputing them for every timestamp in the extended_index.
normalized_dates = extended_index.normalize()
unique_dates = normalized_dates.unique()

sunrise_map = {}
sunset_map = {}
for d in unique_dates:
s = sun(location.observer, date=d, tzinfo=location.timezone)
sunrise_map[d] = s["sunrise"]
sunset_map[d] = s["sunset"]

sunrise_series = pd.Series(
[sunrise_map[d] for d in normalized_dates],
index=extended_index,
)
sunset_series = pd.Series(
[sunset_map[d] for d in normalized_dates],
index=extended_index,
)

sunrise_hour = pd.Series(sunrise_hour, index=extended_index).dt.round("h").dt.hour
sunset_hour = pd.Series(sunset_hour, index=extended_index).dt.round("h").dt.hour
sunrise_hour = sunrise_series.dt.round("h").dt.hour
sunset_hour = sunset_series.dt.round("h").dt.hour

sun_light_features = pd.DataFrame(
{
Expand Down Expand Up @@ -444,7 +456,8 @@ def _create_interaction_features(

transformer_poly = PolynomialFeatures(
degree=degree, interaction_only=True, include_bias=False
).set_output(transform="pandas")
)
transformer_poly = transformer_poly.set_output(transform="pandas")

weather_window_cols = [
col
Expand Down Expand Up @@ -725,8 +738,7 @@ def n2n_predict_with_covariates(
# Set default model_dir if not provided
if model_dir is None:
from spotforecast2_safe.data.fetch_data import get_cache_home

model_dir = get_cache_home() / "forecasters"
model_dir = get_cache_home() / "forecasters"

# Input Validation
if forecast_horizon <= 0:
Expand Down Expand Up @@ -888,9 +900,11 @@ def n2n_predict_with_covariates(
axis=1,
)

assert (
sum(exogenous_features.isnull().sum()) == 0
), "Missing values in exogenous features"
missing_count = exogenous_features.isnull().sum().sum()
if missing_count != 0:
raise ValueError(
f"Missing values in exogenous features: {missing_count} missing entries"
)

# Apply cyclical encoding
exogenous_features = _apply_cyclical_encoding(
Expand Down
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