revert default seasonality

src/gluonts/model/evaluation.py

@@ -23,9 +23,10 @@
 
 from gluonts.dataset import DataEntry
 from gluonts.dataset.split import TestData
-from gluonts.model import Forecast, Predictor
 from gluonts.ev.ts_stats import seasonal_error
 from gluonts.itertools import batcher, prod
+from gluonts.model import Forecast, Predictor
+from gluonts.time_feature.seasonality import get_seasonality
 
 logger = logging.getLogger(__name__)
 
@@ -60,7 +61,7 @@ def _get_data_batch(
     input_batch: List[DataEntry],
     label_batch: List[DataEntry],
     forecast_batch: List[Forecast],
-    seasonality: int = 1,
+    seasonality: Optional[int] = None,
     mask_invalid_label: bool = True,
     allow_nan_forecast: bool = False,
 ) -> ChainMap:
@@ -74,11 +75,18 @@ def _get_data_batch(
 
     seasonal_error_values = []
     for input_ in input_batch:
+        seasonality_entry = seasonality
+        if seasonality_entry is None:
+            seasonality_entry = get_seasonality(input_["start"].freqstr)
         input_target = input_["target"]
         if mask_invalid_label:
             input_target = np.ma.masked_invalid(input_target)
         seasonal_error_values.append(
-            seasonal_error(input_target, seasonality=seasonality, time_axis=-1)
+            seasonal_error(
+                input_target,
+                seasonality=seasonality_entry,
+                time_axis=-1,
+            )
         )
     other_data["seasonal_error"] = np.array(seasonal_error_values)
 
@@ -96,7 +104,7 @@ def evaluate_forecasts_raw(
     batch_size: int = 100,
     mask_invalid_label: bool = True,
     allow_nan_forecast: bool = False,
-    seasonality: int = 1
+    seasonality: Optional[int] = None,
 ) -> dict:
     """
     Evaluate ``forecasts`` by comparing them with ``test_data``, according
@@ -181,7 +189,7 @@ def evaluate_forecasts(
     batch_size: int = 100,
     mask_invalid_label: bool = True,
     allow_nan_forecast: bool = False,
-    seasonality: int = 1
+    seasonality: Optional[int] = None,
 ) -> pd.DataFrame:
     """
     Evaluate ``forecasts`` by comparing them with ``test_data``, according
@@ -235,7 +243,7 @@ def evaluate_model(
     batch_size: int = 100,
     mask_invalid_label: bool = True,
     allow_nan_forecast: bool = False,
-    seasonality: int = 1
+    seasonality: Optional[int] = None,
 ) -> pd.DataFrame:
     """
     Evaluate ``model`` when applied to ``test_data``, according