simplify and fix date splitting, update and consolidate tests

src/gluonts/dataset/split.py

@@ -76,11 +76,25 @@
 from typing import Generator, Optional, Tuple
 
 import pandas as pd
+from pandas.tseries.frequencies import to_offset
 
 from gluonts.dataset import Dataset, DataEntry
 from gluonts.dataset.field_names import FieldName
 
 
+def periods_between(
+    start: pd.Period,
+    end: pd.Period,
+) -> int:
+    """
+    Counts how many periods fit between ``start`` and ``end``
+    (inclusive).
+
+    The frequency is taken from ``start``.
+    """
+    return ((end - start).n // to_offset(start.freq).n) + 1
+
+
 def to_positive_slice(slice_: slice, length: int) -> slice:
     """
     Return an equivalent slice with positive bounds, given the
@@ -277,7 +291,7 @@ class OffsetSplitter(AbstractBaseSplitter):
     offset: int
 
     def training_entry(self, entry: DataEntry) -> DataEntry:
-        return TimeSeriesSlice(entry)[: self.offset]
+        return slice_data_entry(entry, slice(None, self.offset))
 
     def test_pair(
         self, entry: DataEntry, prediction_length: int, offset: int = 0
@@ -286,17 +300,19 @@ def test_pair(
         if self.offset < 0 and offset_ >= 0:
             offset_ += len(entry)
         if offset_ + prediction_length:
-            return (
-                TimeSeriesSlice(entry, prediction_length)[:offset_],
-                TimeSeriesSlice(entry, prediction_length)[
-                    offset_ : offset_ + prediction_length
-                ],
-            )
+            input_slice = slice(None, offset_)
+            label_slice = slice(offset_, offset_ + prediction_length)
         else:
-            return (
-                TimeSeriesSlice(entry, prediction_length)[:offset_],
-                TimeSeriesSlice(entry, prediction_length)[offset_:],
-            )
+            input_slice = slice(None, offset_)
+            label_slice = slice(offset_, None)
+        return (
+            slice_data_entry(
+                entry, input_slice, prediction_length=prediction_length
+            ),
+            slice_data_entry(
+                entry, label_slice, prediction_length=prediction_length
+            ),
+        )
 
 
 @dataclass
@@ -317,17 +333,22 @@ class DateSplitter(AbstractBaseSplitter):
     date: pd.Period
 
     def training_entry(self, entry: DataEntry) -> DataEntry:
-        return TimeSeriesSlice(entry)[: self.date]
+        length = periods_between(entry["start"], self.date)
+        return slice_data_entry(entry, slice(None, length))
 
     def test_pair(
         self, entry: DataEntry, prediction_length: int, offset: int = 0
     ) -> Tuple[DataEntry, DataEntry]:
-        date = self.date.asfreq(entry[FieldName.START].freq)
+        base = periods_between(entry["start"], self.date)
+        input_slice = slice(None, base + offset)
+        label_slice = slice(base + offset, base + offset + prediction_length)
         return (
-            TimeSeriesSlice(entry, prediction_length)[: date + offset],
-            TimeSeriesSlice(entry, prediction_length)[
-                date + (offset + 1) : date + (prediction_length + offset)
-            ],
+            slice_data_entry(
+                entry, input_slice, prediction_length=prediction_length
+            ),
+            slice_data_entry(
+                entry, label_slice, prediction_length=prediction_length
+            ),
         )