fixed dataset.utils bug

src/enfobench/__version__.py

@@ -1 +1 @@
-__version__ = "0.3.3"
+__version__ = "0.3.4"

src/enfobench/dataset/utils.py

@@ -8,7 +8,7 @@ def create_perfect_forecasts_from_covariates(
     *,
     horizon: pd.Timedelta,
     step: pd.Timedelta,
-    **kwargs,
+    start: pd.Timestamp | None = None,
 ) -> pd.DataFrame:
     """Create forecasts from covariates.
 
@@ -19,29 +19,31 @@ def create_perfect_forecasts_from_covariates(
         past_covariates: The external covariates.
         horizon: The forecast horizon.
         step: The step size between forecasts.
+        start: The start date of the forecast. If None, the first date of the covariates is used.
 
     Returns:
         The external forecast dataframe.
     """
-    start = kwargs.get("start", past_covariates.index[0])
+    start = start or past_covariates.index[0]
     last_date = past_covariates.index[-1]
 
     forecasts = []
     while start + horizon <= last_date:
         forecast = past_covariates.loc[(past_covariates.index > start) & (past_covariates.index <= start + horizon)]
-        forecast.insert(0, "cutoff_date", start)
         forecast.rename_axis("timestamp", inplace=True)
         forecast.reset_index(inplace=True)
+        forecast["cutoff_date"] = start.isoformat()  # pd.concat fails if cutoff_date is a Timestamp
 
         if len(forecast) == 0:
             warnings.warn(
                 f"Covariates not found for {start} - {start + horizon}, cannot make forecast at step {start}",
                 UserWarning,
                 stacklevel=2,
             )
-
-        forecasts.append(forecast)
+        else:
+            forecasts.append(forecast)
         start += step
 
-    forecast_df = pd.concat(forecasts, ignore_index=True)
+    forecast_df = pd.concat(forecasts, ignore_index=False)
+    forecast_df["cutoff_date"] = pd.to_datetime(forecast_df["cutoff_date"])  # convert back to Timestamp
     return forecast_df

src/enfobench/evaluation/utils.py

@@ -29,10 +29,14 @@ def periods_in_duration(target: pd.DatetimeIndex, duration: timedelta | pd.Timed
 
     first_delta = target[1] - target[0]
     last_delta = target[-1] - target[-2]
-    assert first_delta == last_delta, "Season length is not constant"
+    if first_delta != last_delta:
+        msg = f"Season length is not constant: '{first_delta}' != '{last_delta}'"
+        raise ValueError(msg)
 
     periods = duration / first_delta
-    assert periods.is_integer(), "Season length is not a multiple of the frequency"
+    if not periods.is_integer():
+        msg = f"Season length '{duration}' is not a multiple of the frequency '{first_delta}'"
+        raise ValueError(msg)
     return int(periods)
 
 

tests/test_dataset/test_utils.py

@@ -0,0 +1,26 @@
+import numpy as np
+import pandas as pd
+
+from enfobench.dataset.utils import create_perfect_forecasts_from_covariates
+
+
+def test_create_perfect_forecasts_from_covariates():
+    index = pd.date_range(start="2020-01-01", end="2020-10-02 13:54:00", freq="1H")
+    past_covariates = pd.DataFrame(
+        index=index,
+        data=np.random.rand(len(index), 2),
+        columns=["covariate_1", "covariate_2"],
+    )
+
+    future_covariates = create_perfect_forecasts_from_covariates(
+        past_covariates,
+        start=pd.Timestamp("2020-01-01"),
+        step=pd.Timedelta("1D"),
+        horizon=pd.Timedelta("7D"),
+    )
+
+    assert isinstance(future_covariates, pd.DataFrame)
+    assert "covariate_1" in future_covariates.columns
+    assert "covariate_2" in future_covariates.columns
+    assert "timestamp" in future_covariates.columns
+    assert "cutoff_date" in future_covariates.columns