fix: use Union in pydantic basemodels

Aarhus-Psychiatry-Research · May 17, 2024 · a80a9c2 · a80a9c2
1 parent fc46ef3
commit a80a9c2
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Showing 2 changed files with 10 additions and 10 deletions.
diff --git a/src/timeseriesflattener/v1/feature_specs/group_specs.py b/src/timeseriesflattener/v1/feature_specs/group_specs.py
@@ -1,7 +1,7 @@
 from __future__ import annotations
 import itertools
 from dataclasses import dataclass
-from typing import Dict, List, Sequence, Tuple
+from typing import Dict, List, Sequence, Tuple, Union
 
 import pandas as pd
 from timeseriesflattener.v1.aggregation_fns import AggregationFunType
@@ -40,10 +40,10 @@ class PredictorGroupSpec(BaseModel):
     """
 
     # Shared attributes from GroupSpec
-    lookbehind_days: Sequence[float | Tuple[float, float]]
+    lookbehind_days: Sequence[Union[float, Tuple[float, float]]]
     named_dataframes: Sequence[NamedDataframe]
     aggregation_fns: Sequence[AggregationFunType]
-    fallback: Sequence[int | float | str]
+    fallback: Sequence[Union[int, float, str]]
     prefix: str = "pred"
 
     def create_combinations(self) -> List[PredictorSpec]:
@@ -82,10 +82,10 @@ class OutcomeGroupSpec(BaseModel):
     prefix: str = "outc"
     named_dataframes: Sequence[NamedDataframe]
     aggregation_fns: Sequence[AggregationFunType]
-    fallback: Sequence[int | float | str]
+    fallback: Sequence[Union[int, float, str]]
 
     # Individual attributes
-    lookahead_days: Sequence[float | Tuple[float, float]]
+    lookahead_days: Sequence[Union[float, Tuple[float, float]]]
     incident: Sequence[bool]
 
     def create_combinations(self) -> List[OutcomeSpec]:

diff --git a/src/timeseriesflattener/v1/feature_specs/single_specs.py b/src/timeseriesflattener/v1/feature_specs/single_specs.py
@@ -1,6 +1,6 @@
 from __future__ import annotations
 from dataclasses import dataclass
-from typing import Tuple
+from typing import Tuple, Union
 
 import pandas as pd
 from timeseriesflattener.v1.aggregation_fns import AggregationFunType
@@ -117,9 +117,9 @@ class OutcomeSpec(BaseModel):
 
     timeseries_df: pd.DataFrame
     feature_base_name: str
-    lookahead_days: float | Tuple[float, float]
+    lookahead_days: Union[float, Tuple[float, float]]
     aggregation_fn: AggregationFunType
-    fallback: float | int
+    fallback: Union[float, int]
     incident: bool
     prefix: str = "outc"
 
@@ -171,8 +171,8 @@ class PredictorSpec(BaseModel):
     timeseries_df: pd.DataFrame
     feature_base_name: str
     aggregation_fn: AggregationFunType
-    fallback: float | int
-    lookbehind_days: float | Tuple[float, float]
+    fallback: Union[float, int]
+    lookbehind_days: Union[float, Tuple[float, float]]
     prefix: str = "pred"
 
     @property