[ENH] Partially replace pd.Int64Index with pd.Index (#2339)

Replaces `pd.Int64Index` with `pd.Index`, and `VALID_INDEX_TYPES` checks with dedicated functions in `sktime.utils.validation.series`.

sktime/datatypes/_series/_check.py

@@ -40,6 +40,8 @@
 import numpy as np
 import pandas as pd
 
+from sktime.utils.validation.series import is_in_valid_index_types
+
 VALID_INDEX_TYPES = (pd.Int64Index, pd.RangeIndex, pd.PeriodIndex, pd.DatetimeIndex)
 
 # whether the checks insist on freq attribute is set
@@ -69,7 +71,7 @@ def ret(valid, msg, metadata, return_metadata):
     metadata["is_univariate"] = len(obj.columns) < 2
 
     # check whether the time index is of valid type
-    if not type(index) in VALID_INDEX_TYPES:
+    if not is_in_valid_index_types(index):
         msg = (
             f"{type(index)} is not supported for {var_name}, use "
             f"one of {VALID_INDEX_TYPES} instead."
@@ -131,7 +133,7 @@ def ret(valid, msg, metadata, return_metadata):
         return ret(False, msg, None, return_metadata)
 
     # check whether the time index is of valid type
-    if not type(index) in VALID_INDEX_TYPES:
+    if not is_in_valid_index_types(index):
         msg = (
             f"{type(index)} is not supported for {var_name}, use "
             f"one of {VALID_INDEX_TYPES} instead."
@@ -214,7 +216,7 @@ def _index_equally_spaced(index):
     -------
     equally_spaced: bool - whether index is equally spaced
     """
-    if not isinstance(index, VALID_INDEX_TYPES):
+    if not is_in_valid_index_types(index):
         raise TypeError(f"index must be one of {VALID_INDEX_TYPES}")
 
     # empty and single element indices are equally spaced

sktime/forecasting/base/_fh.py

@@ -20,12 +20,13 @@
     is_int,
     is_timedelta_or_date_offset,
 )
-from sktime.utils.validation.series import VALID_INDEX_TYPES
+from sktime.utils.validation.series import (
+    VALID_INDEX_TYPES,
+    is_in_valid_absolute_index_types,
+    is_in_valid_index_types,
+    is_in_valid_relative_index_types,
+)
 
-RELATIVE_TYPES = (pd.Int64Index, pd.RangeIndex, pd.TimedeltaIndex)
-ABSOLUTE_TYPES = (pd.Int64Index, pd.RangeIndex, pd.DatetimeIndex, pd.PeriodIndex)
-assert set(RELATIVE_TYPES).issubset(VALID_INDEX_TYPES)
-assert set(ABSOLUTE_TYPES).issubset(VALID_INDEX_TYPES)
 VALID_FORECASTING_HORIZON_TYPES = (int, list, np.ndarray, pd.Index)
 
 DELEGATED_METHODS = (
@@ -95,19 +96,20 @@ def _check_values(values: Union[VALID_FORECASTING_HORIZON_TYPES]) -> pd.Index:
     # isinstance() does not work here, because index types inherit from each
     # other,
     # hence we check for type equality here
-    if type(values) in VALID_INDEX_TYPES:
+    if is_in_valid_index_types(values):
         pass
 
-    # convert single integer to pandas index, no further checks needed
+    # convert single integer or timedelta or dateoffset
+    # to pandas index, no further checks needed
     elif is_int(values):
-        return pd.Int64Index([values], dtype=int)
+        values = pd.Index([values], dtype=int)
 
     elif is_timedelta_or_date_offset(values):
-        return pd.Index([values])
+        values = pd.Index([values])
 
     # convert np.array or list to pandas index
     elif is_array(values) and array_is_int(values):
-        values = pd.Int64Index(values, dtype=int)
+        values = pd.Index(values, dtype=int)
 
     elif is_array(values) and array_is_timedelta_or_date_offset(values):
         values = pd.Index(values)
@@ -181,17 +183,17 @@ def __init__(
         # types inherit from each other, hence we check for type equality
         error_msg = f"`values` type is not compatible with `is_relative={is_relative}`."
         if is_relative is None:
-            if type(values) in RELATIVE_TYPES:
+            if is_in_valid_relative_index_types(values):
                 is_relative = True
-            elif type(values) in ABSOLUTE_TYPES:
+            elif is_in_valid_absolute_index_types(values):
                 is_relative = False
             else:
                 raise TypeError(f"{type(values)} is not a supported fh index type")
         if is_relative:
-            if not type(values) in RELATIVE_TYPES:
+            if not is_in_valid_relative_index_types(values):
                 raise TypeError(error_msg)
         else:
-            if not type(values) in ABSOLUTE_TYPES:
+            if not is_in_valid_absolute_index_types(values):
                 raise TypeError(error_msg)
 
         self._values = values

sktime/forecasting/base/adapters/_statsmodels.py

@@ -47,7 +47,7 @@ def _fit(self, y, X=None, fh=None):
         """
         # statsmodels does not support the pd.Int64Index as required,
         # so we coerce them here to pd.RangeIndex
-        if isinstance(y, pd.Series) and type(y.index) == pd.Int64Index:
+        if isinstance(y, pd.Series) and y.index.is_integer():
             y, X = _coerce_int_to_range_index(y, X)
         self._fit_forecaster(y, X)
         return self
@@ -113,7 +113,7 @@ def _coerce_int_to_range_index(y, X=None):
         np.testing.assert_array_equal(y.index, new_index)
     except AssertionError:
         raise ValueError(
-            "Coercion of pd.Int64Index to pd.RangeIndex "
+            "Coercion of integer pd.Index to pd.RangeIndex "
             "failed. Please provide `y_train` with a "
             "pd.RangeIndex."
         )

sktime/forecasting/base/tests/test_fh.py

@@ -31,7 +31,11 @@
     _get_intervals_count_and_unit,
     _shift,
 )
-from sktime.utils.validation.series import VALID_INDEX_TYPES
+from sktime.utils.validation.series import (
+    VALID_INDEX_TYPES,
+    is_in_valid_index_types,
+    is_integer_index,
+)
 
 
 def _assert_index_equal(a, b):
@@ -49,7 +53,10 @@ def test_fh(index_type, fh_type, is_relative, steps):
     """Testing ForecastingHorizon conversions."""
     # generate data
     y = make_forecasting_problem(index_type=index_type)
-    assert isinstance(y.index, INDEX_TYPE_LOOKUP.get(index_type))
+    if index_type == "int":
+        assert is_integer_index(y.index)
+    else:
+        assert isinstance(y.index, INDEX_TYPE_LOOKUP.get(index_type))
 
     # split data
     y_train, y_test = temporal_train_test_split(y, test_size=10)
@@ -59,12 +66,15 @@ def test_fh(index_type, fh_type, is_relative, steps):
 
     # generate fh
     fh = _make_fh(cutoff, steps, fh_type, is_relative)
-    assert isinstance(fh.to_pandas(), INDEX_TYPE_LOOKUP.get(fh_type))
+    if fh_type == "int":
+        assert is_integer_index(fh.to_pandas())
+    else:
+        assert isinstance(fh.to_pandas(), INDEX_TYPE_LOOKUP.get(fh_type))
 
     # get expected outputs
     if isinstance(steps, int):
         steps = np.array([steps])
-    fh_relative = pd.Int64Index(steps).sort_values()
+    fh_relative = pd.Index(steps).sort_values()
     fh_absolute = y.index[np.where(y.index == cutoff)[0] + steps].sort_values()
     fh_indexer = fh_relative - 1
     fh_oos = fh.to_pandas()[fh_relative > 0]
@@ -102,7 +112,7 @@ def test_fh(index_type, fh_type, is_relative, steps):
 
 
 def test_fh_method_delegation():
-    """Test ForecastinHorizon delegated methods."""
+    """Test ForecastingHorizon delegated methods."""
     fh = ForecastingHorizon(1)
     for method in DELEGATED_METHODS:
         assert hasattr(fh, method)
@@ -125,10 +135,7 @@ def test_check_fh_values_bad_input_types(arg):
         ForecastingHorizon(arg)
 
 
-DUPLICATE_INPUT_ARGS = (
-    np.array([1, 2, 2]),
-    [3, 3, 1],
-)
+DUPLICATE_INPUT_ARGS = (np.array([1, 2, 2]), [3, 3, 1])
 
 
 @pytest.mark.parametrize("arg", DUPLICATE_INPUT_ARGS)
@@ -139,7 +146,7 @@ def test_check_fh_values_duplicate_input_values(arg):
 
 
 GOOD_ABSOLUTE_INPUT_ARGS = (
-    pd.Int64Index([1, 2, 3]),
+    pd.Index([1, 2, 3]),
     pd.period_range("2000-01-01", periods=3, freq="D"),
     pd.date_range("2000-01-01", periods=3, freq="M"),
     np.array([1, 2, 3]),
@@ -151,8 +158,9 @@ def test_check_fh_values_duplicate_input_values(arg):
 @pytest.mark.parametrize("arg", GOOD_ABSOLUTE_INPUT_ARGS)
 def test_check_fh_absolute_values_input_conversion_to_pandas_index(arg):
     """Test conversion of absolute horizons to pandas index."""
-    output = ForecastingHorizon(arg, is_relative=False).to_pandas()
-    assert type(output) in VALID_INDEX_TYPES
+    assert is_in_valid_index_types(
+        ForecastingHorizon(arg, is_relative=False).to_pandas()
+    )
 
 
 GOOD_RELATIVE_INPUT_ARGS = [
@@ -212,7 +220,7 @@ def test_coerce_duration_to_int(duration):
     ret = _coerce_duration_to_int(duration, freq=_get_freq(duration))
 
     # check output type is always integer
-    assert type(ret) in (pd.Int64Index, np.integer, int)
+    assert (type(ret) in (np.integer, int)) or is_integer_index(ret)
 
     # check result
     if isinstance(duration, pd.Index):

sktime/forecasting/tests/_config.py

@@ -107,7 +107,7 @@
 ]
 
 INDEX_TYPE_LOOKUP = {
-    "int": pd.Int64Index,
+    "int": pd.Index,
     "range": pd.RangeIndex,
     "datetime": pd.DatetimeIndex,
     "period": pd.PeriodIndex,

sktime/utils/_testing/series.py

@@ -58,7 +58,7 @@ def _make_index(n_timepoints, index_type=None):
 
     elif index_type == "int":
         start = 3
-        return pd.Int64Index(np.arange(start, start + n_timepoints))
+        return pd.Index(np.arange(start, start + n_timepoints), dtype=int)
 
     else:
         raise ValueError(f"index_class: {index_type} is not supported")

sktime/utils/datetime.py

@@ -11,7 +11,7 @@
 import numpy as np
 import pandas as pd
 
-from sktime.utils.validation.series import check_time_index
+from sktime.utils.validation.series import check_time_index, is_integer_index
 
 
 def _coerce_duration_to_int(duration, freq=None):
@@ -37,7 +37,7 @@ def _coerce_duration_to_int(duration, freq=None):
         duration[0], pd.tseries.offsets.BaseOffset
     ):
         count = _get_intervals_count_and_unit(freq)[0]
-        return pd.Int64Index([d.n / count for d in duration])
+        return pd.Index([d.n / count for d in duration], dtype=int)
     elif isinstance(duration, (pd.Timedelta, pd.TimedeltaIndex)):
         count, unit = _get_intervals_count_and_unit(freq)
         # integer conversion only works reliably with non-ambiguous units (
@@ -100,7 +100,7 @@ def _shift(x, by=1):
         Shifted time point
     """
     assert isinstance(x, (pd.Period, pd.Timestamp, int, np.integer)), type(x)
-    assert isinstance(by, (int, np.integer, pd.Int64Index)), type(by)
+    assert isinstance(by, (int, np.integer)) or is_integer_index(by), type(by)
     if isinstance(x, pd.Timestamp):
         if not hasattr(x, "freq") or x.freq is None:
             raise ValueError("No `freq` information available")

sktime/utils/validation/series.py

@@ -25,6 +25,28 @@
     pd.DatetimeIndex,
     pd.TimedeltaIndex,
 )
+RELATIVE_INDEX_TYPES = (pd.RangeIndex, pd.TimedeltaIndex)
+ABSOLUTE_INDEX_TYPES = (pd.RangeIndex, pd.DatetimeIndex, pd.PeriodIndex)
+assert set(RELATIVE_INDEX_TYPES).issubset(VALID_INDEX_TYPES)
+assert set(ABSOLUTE_INDEX_TYPES).issubset(VALID_INDEX_TYPES)
+
+
+def is_integer_index(x) -> bool:
+    """Check that the input is an integer pd.Index."""
+    return isinstance(x, pd.Index) and x.is_integer()
+
+
+def is_in_valid_index_types(x) -> bool:
+    """Check that the input type belongs to the valid index types."""
+    return isinstance(x, VALID_INDEX_TYPES) or is_integer_index(x)
+
+
+def is_in_valid_relative_index_types(x) -> bool:
+    return isinstance(x, RELATIVE_INDEX_TYPES) or is_integer_index(x)
+
+
+def is_in_valid_absolute_index_types(x) -> bool:
+    return isinstance(x, ABSOLUTE_INDEX_TYPES) or is_integer_index(x)
 
 
 def _check_is_univariate(y, var_name="input"):
@@ -185,7 +207,7 @@ def check_time_index(
 
     # We here check for type equality because isinstance does not
     # work reliably because index types inherit from each other.
-    if not type(index) in VALID_INDEX_TYPES:
+    if not is_in_valid_index_types(index):
         raise NotImplementedError(
             f"{type(index)} is not supported for {var_name}, use "
             f"one of {VALID_INDEX_TYPES} instead."
@@ -194,7 +216,7 @@ def check_time_index(
     if enforce_index_type and type(index) is not enforce_index_type:
         raise NotImplementedError(
             f"{type(index)} is not supported for {var_name}, use "
-            f"type: {enforce_index_type} instead."
+            f"type: {enforce_index_type} or integer pd.Index instead."
         )
 
     # Check time index is ordered in time
@@ -274,11 +296,6 @@ def check_equal_time_index(*ys, mode="equal"):
             raise ValueError(msg)
 
 
-def _is_int_index(index):
-    """Check if index type is one of pd.RangeIndex or pd.Int64Index."""
-    return type(index) in (pd.Int64Index, pd.RangeIndex)
-
-
 def check_consistent_index_type(a, b):
     """Check that two indices have consistent types.
 
@@ -299,8 +316,8 @@ def check_consistent_index_type(a, b):
         "series have the same index type."
     )
 
-    if _is_int_index(a):
-        if not _is_int_index(b):
+    if is_integer_index(a):
+        if not is_integer_index(b):
             raise TypeError(msg)
 
     else:

sktime/utils/validation/tests/test_forecasting.py

@@ -12,7 +12,7 @@
 
 from sktime.utils.validation.forecasting import check_fh
 
-empty_input = (np.array([]), [], pd.Int64Index([]))
+empty_input = (np.array([], dtype=int), [], pd.Index([], dtype=int))
 
 
 @pytest.mark.parametrize("arg", empty_input)