apply suggestions from PR review

darts/dataprocessing/encoders/encoders.py

@@ -884,7 +884,7 @@ def __init__(
             BoxCox(). The transformers will be fitted on the training dataset when calling calling `model.fit()`.
             The training, validation and inference datasets are then transformed equally.
         Supported time zone:
-            optionally, apply a time zone conversion with keyword 'tz'. This converts the time zone-naive index to a
+            Optionally, apply a time zone conversion with keyword 'tz'. This converts the time zone-naive index to a
             timezone `'tz'` before applying the `'cyclic'` or `'datetime_attribute'` temporal encoders.
 
         An example of a valid `add_encoders` dict for hourly data:

darts/tests/utils/test_timeseries_generation.py

@@ -358,18 +358,25 @@ def helper_routine(idx, attr, vals_exp, **kwargs):
             np.testing.assert_array_almost_equal(vals_act, vals_exp)
 
         # no pd.DatetimeIndex
-        with pytest.raises(ValueError):
+        with pytest.raises(ValueError) as err:
             helper_routine(
                 pd.RangeIndex(start=0, stop=len(idx)), "h", vals_exp=np.arange(len(idx))
             )
+        assert str(err.value).startswith(
+            "`time_index` must be a pandas `DatetimeIndex`"
+        )
 
         # invalid attribute
-        with pytest.raises(ValueError):
+        with pytest.raises(ValueError) as err:
             helper_routine(idx, "h", vals_exp=np.arange(len(idx)))
+        assert str(err.value).startswith(
+            "attribute `h` needs to be an attribute of pd.DatetimeIndex."
+        )
 
         # no time zone aware index
-        with pytest.raises(ValueError):
+        with pytest.raises(ValueError) as err:
             helper_routine(idx.tz_localize("UTC"), "h", vals_exp=np.arange(len(idx)))
+        assert "`time_index` must be time zone naive." == str(err.value)
 
         # ===> datetime attribute
         # hour

darts/utils/timeseries_generation.py

@@ -547,7 +547,7 @@ def holidays_timeseries(
     Parameters
     ----------
     time_index
-        Either a `pd.DatetimeIndex` or a `TimeSeries` for which to generate the holidays/
+        Either a `pd.DatetimeIndex` or a `TimeSeries` for which to generate the holidays.
     country_code
         The country ISO code.
     prov
@@ -561,7 +561,7 @@ def holidays_timeseries(
         Extend the time_index by add_length, should match or exceed forecasting window.
         Set only one of until and add_length.
     column_name
-        Optionally, the name of the value column for the returned TimeSeries/
+        Optionally, the name of the value column for the returned TimeSeries.
     dtype
         The desired NumPy dtype (np.float32 or np.float64) for the resulting series.
     tz