fix(#514): simplify public-facing types by inlining type-aliases

src/test_benchmark.py

@@ -11,7 +11,7 @@
 import pytest
 from iterpy.iter import Iter
 from timeseriesflattener.aggregators import Aggregator, MaxAggregator, MeanAggregator
-from timeseriesflattener.feature_specs.meta import LookDistance, ValueFrame
+from timeseriesflattener.feature_specs.meta import ValueFrame
 from timeseriesflattener.feature_specs.prediction_times import PredictionTimeFrame
 from timeseriesflattener.feature_specs.predictor import PredictorSpec
 from timeseriesflattener.flattener import Flattener
@@ -50,7 +50,7 @@ def _generate_benchmark_dataset(
     n_features: int,
     n_observations_per_pred_time: int,
     aggregations: Sequence[Literal["max", "mean"]],
-    lookbehinds: Sequence[LookDistance | tuple[LookDistance, LookDistance]],
+    lookbehinds: Sequence[dt.timedelta | tuple[dt.timedelta, dt.timedelta]],
 ) -> BenchmarkDataset:
     pred_time_df = PredictionTimeFrame(
         init_df=pl.LazyFrame(

src/timeseriesflattener/_intermediary_frames.py

@@ -1,22 +1,16 @@
 from __future__ import annotations
 
 from dataclasses import InitVar, dataclass
-from typing import TYPE_CHECKING
+from typing import TYPE_CHECKING, Union
 
 import polars as pl
 
 from ._frame_validator import _validate_col_name_columns_exist
-from .feature_specs.default_column_names import (
-    default_prediction_time_uuid_col_name,
-    default_timestamp_col_name,
-)
 from .frame_utilities.anyframe_to_lazyframe import _anyframe_to_lazyframe
 
 if TYPE_CHECKING:
     from collections.abc import Sequence
 
-    from .feature_specs.meta import ValueType
-
 if TYPE_CHECKING:
     import datetime as dt
 
@@ -27,8 +21,8 @@ class TimeMaskedFrame:
 
     init_df: pl.LazyFrame
     value_col_names: Sequence[str]
-    timestamp_col_name: str = default_timestamp_col_name
-    prediction_time_uuid_col_name: str = default_prediction_time_uuid_col_name
+    timestamp_col_name: str = "timestamp"
+    prediction_time_uuid_col_name: str = "prediction_time_uuid"
     validate_cols_exist: bool = True
 
     def __post_init__(self):
@@ -47,12 +41,12 @@ def collect(self) -> pl.DataFrame:
 class AggregatedValueFrame:
     df: pl.LazyFrame
     value_col_name: str
-    prediction_time_uuid_col_name: str = default_prediction_time_uuid_col_name
+    prediction_time_uuid_col_name: str = "prediction_time_uuid"
 
     def __post_init__(self):
         _validate_col_name_columns_exist(obj=self)
 
-    def fill_nulls(self, fallback: ValueType) -> AggregatedValueFrame:
+    def fill_nulls(self, fallback: Union[int, float, str, None]) -> AggregatedValueFrame:
         filled = self.df.with_columns(
             pl.col(self.value_col_name)
             .fill_null(fallback)
@@ -76,7 +70,7 @@ class TimeDeltaFrame:
     df: pl.LazyFrame
     value_col_names: Sequence[str]
     value_timestamp_col_name: str
-    prediction_time_uuid_col_name: str = default_prediction_time_uuid_col_name
+    prediction_time_uuid_col_name: str = "prediction_time_uuid"
     timedelta_col_name: str = "time_from_prediction_to_value"
 
     def __post_init__(self):

src/timeseriesflattener/feature_specs/meta.py

@@ -1,31 +1,15 @@
 from __future__ import annotations
 
 import datetime as dt
-from collections.abc import Sequence
 from dataclasses import InitVar, dataclass
-from typing import TYPE_CHECKING, Literal, Union
+from typing import Literal, Union
 
 import pandas as pd
 import polars as pl
 
-from timeseriesflattener.feature_specs.default_column_names import default_entity_id_col_name
-
 from .._frame_validator import _validate_col_name_columns_exist
 from ..frame_utilities.anyframe_to_lazyframe import _anyframe_to_lazyframe
 
-if TYPE_CHECKING:
-    from typing_extensions import TypeAlias
-
-
-ValueType = Union[int, float, str, None]
-InitDF_T = Union[pl.LazyFrame, pl.DataFrame, pd.DataFrame]
-
-
-LookDistance = dt.timedelta
-
-
-LookDistances: TypeAlias = Sequence[Union[LookDistance, tuple[LookDistance, LookDistance]]]
-
 
 @dataclass
 class ValueFrame:
@@ -37,12 +21,14 @@ class ValueFrame:
         Additional columns containing the values of the time series. The name of the columns will be used for feature naming.
     """
 
-    init_df: InitVar[InitDF_T]
-    entity_id_col_name: str = default_entity_id_col_name
+    init_df: InitVar[Union[pl.LazyFrame, pl.DataFrame, pd.DataFrame]]
+    entity_id_col_name: str = "entity_id"
     value_timestamp_col_name: str = "timestamp"
     coerce_to_lazy: InitVar[bool] = True
 
-    def __post_init__(self, init_df: InitDF_T, coerce_to_lazy: bool):
+    def __post_init__(
+        self, init_df: Union[pl.LazyFrame, pl.DataFrame, pd.DataFrame], coerce_to_lazy: bool
+    ):
         if coerce_to_lazy:
             self.df = _anyframe_to_lazyframe(init_df)
         else:
@@ -63,8 +49,8 @@ def collect(self) -> pl.DataFrame:
 
 @dataclass(frozen=True)
 class LookPeriod:
-    first: LookDistance
-    last: LookDistance
+    first: dt.timedelta
+    last: dt.timedelta
 
     def __post_init__(self):
         if self.first >= self.last:
@@ -74,11 +60,11 @@ def __post_init__(self):
 
 
 def _lookdistance_to_normalised_lookperiod(
-    lookdistance: LookDistance | tuple[LookDistance, LookDistance],
+    lookdistance: dt.timedelta | tuple[dt.timedelta, dt.timedelta],
     direction: Literal["ahead", "behind"],
 ) -> LookPeriod:
     is_ahead = direction == "ahead"
-    if isinstance(lookdistance, LookDistance):
+    if isinstance(lookdistance, dt.timedelta):
         return LookPeriod(
             first=dt.timedelta(days=0) if is_ahead else -lookdistance,
             last=lookdistance if is_ahead else dt.timedelta(0),

src/timeseriesflattener/feature_specs/outcome.py

@@ -1,12 +1,13 @@
 from __future__ import annotations
 
+import datetime as dt
 from dataclasses import InitVar, dataclass
-from typing import TYPE_CHECKING
+from typing import TYPE_CHECKING, Union
 
 import polars as pl
 
 from .._frame_validator import _validate_col_name_columns_exist
-from .meta import LookDistances, ValueFrame, ValueType, _lookdistance_to_normalised_lookperiod
+from .meta import ValueFrame, _lookdistance_to_normalised_lookperiod
 
 if TYPE_CHECKING:
     from collections.abc import Sequence
@@ -20,12 +21,14 @@ class OutcomeSpec:
     """Specification for an outcome. If your outcome is binary/boolean, you can use BooleanOutcomeSpec instead."""
 
     value_frame: ValueFrame
-    lookahead_distances: InitVar[LookDistances]
+    lookahead_distances: InitVar[Sequence[Union[dt.timedelta, tuple[dt.timedelta, dt.timedelta]]]]
     aggregators: Sequence[Aggregator]
-    fallback: ValueType
+    fallback: Union[int, float, str, None]
     column_prefix: str = "outc"
 
-    def __post_init__(self, lookahead_distances: LookDistances):
+    def __post_init__(
+        self, lookahead_distances: Sequence[Union[dt.timedelta, tuple[dt.timedelta, dt.timedelta]]]
+    ):
         self.normalised_lookperiod = [
             _lookdistance_to_normalised_lookperiod(lookdistance=lookdistance, direction="ahead")
             for lookdistance in lookahead_distances
@@ -47,7 +50,7 @@ class BooleanOutcomeSpec:
     """
 
     init_frame: InitVar[TimestampValueFrame]
-    lookahead_distances: LookDistances
+    lookahead_distances: Sequence[Union[dt.timedelta, tuple[dt.timedelta, dt.timedelta]]]
     aggregators: Sequence[Aggregator]
     output_name: str
     column_prefix: str = "outc"

src/timeseriesflattener/feature_specs/prediction_times.py

@@ -1,23 +1,17 @@
 from __future__ import annotations
 
 from dataclasses import InitVar, dataclass
-from typing import TYPE_CHECKING
+from typing import TYPE_CHECKING, Union
 
+import pandas as pd
 import polars as pl
 
 from .._frame_validator import _validate_col_name_columns_exist
 from ..frame_utilities.anyframe_to_lazyframe import _anyframe_to_lazyframe
-from .default_column_names import (
-    default_entity_id_col_name,
-    default_pred_time_col_name,
-    default_prediction_time_uuid_col_name,
-)
 
 if TYPE_CHECKING:
     from collections.abc import Sequence
 
-    from .meta import InitDF_T
-
 
 @dataclass
 class PredictionTimeFrame:
@@ -28,13 +22,15 @@ class PredictionTimeFrame:
         timestamp_col_name: The name of the column containing the timestamps for when to make a prediction.
     """
 
-    init_df: InitVar[InitDF_T]
-    entity_id_col_name: str = default_entity_id_col_name
-    timestamp_col_name: str = default_pred_time_col_name
-    prediction_time_uuid_col_name: str = default_prediction_time_uuid_col_name
+    init_df: InitVar[Union[pl.LazyFrame, pl.DataFrame, pd.DataFrame]]
+    entity_id_col_name: str = "entity_id"
+    timestamp_col_name: str = "pred_timestamp"
+    prediction_time_uuid_col_name: str = "prediction_time_uuid"
     coerce_to_lazy: InitVar[bool] = True
 
-    def __post_init__(self, init_df: InitDF_T, coerce_to_lazy: bool):
+    def __post_init__(
+        self, init_df: Union[pl.LazyFrame, pl.DataFrame, pd.DataFrame], coerce_to_lazy: bool
+    ):
         if coerce_to_lazy:
             self.df = _anyframe_to_lazyframe(init_df)
         else:

src/timeseriesflattener/feature_specs/predictor.py

@@ -1,10 +1,11 @@
 from __future__ import annotations
 
+import datetime as dt
 from dataclasses import InitVar, dataclass
-from typing import TYPE_CHECKING
+from typing import TYPE_CHECKING, Union
 
 from .._frame_validator import _validate_col_name_columns_exist
-from .meta import LookDistances, ValueFrame, ValueType, _lookdistance_to_normalised_lookperiod
+from .meta import ValueFrame, _lookdistance_to_normalised_lookperiod
 
 if TYPE_CHECKING:
     from collections.abc import Sequence
@@ -25,12 +26,14 @@ class PredictorSpec:
     """
 
     value_frame: ValueFrame
-    lookbehind_distances: InitVar[LookDistances]
+    lookbehind_distances: InitVar[Sequence[Union[dt.timedelta, tuple[dt.timedelta, dt.timedelta]]]]
     aggregators: Sequence[Aggregator]
-    fallback: ValueType
+    fallback: Union[int, float, str, None]
     column_prefix: str = "pred"
 
-    def __post_init__(self, lookbehind_distances: LookDistances):
+    def __post_init__(
+        self, lookbehind_distances: Sequence[Union[dt.timedelta, tuple[dt.timedelta, dt.timedelta]]]
+    ):
         self.normalised_lookperiod = [
             _lookdistance_to_normalised_lookperiod(lookdistance=lookdistance, direction="behind")
             for lookdistance in lookbehind_distances

src/timeseriesflattener/feature_specs/static.py

@@ -1,25 +1,22 @@
 from __future__ import annotations
 
 from dataclasses import InitVar, dataclass
-from typing import TYPE_CHECKING
+from typing import TYPE_CHECKING, Union
 
+import pandas as pd
 import polars as pl
 
 from .._frame_validator import _validate_col_name_columns_exist
 from ..frame_utilities.anyframe_to_lazyframe import _anyframe_to_lazyframe
-from .default_column_names import default_entity_id_col_name
-
-if TYPE_CHECKING:
-    from .meta import InitDF_T, ValueType
 
 
 @dataclass
 class StaticFrame:
-    init_df: InitVar[InitDF_T]
+    init_df: InitVar[Union[pl.LazyFrame, pl.DataFrame, pd.DataFrame]]
 
-    entity_id_col_name: str = default_entity_id_col_name
+    entity_id_col_name: str = "entity_id"
 
-    def __post_init__(self, init_df: InitDF_T):
+    def __post_init__(self, init_df: Union[pl.LazyFrame, pl.DataFrame, pd.DataFrame]):
         self.df = _anyframe_to_lazyframe(init_df)
         _validate_col_name_columns_exist(obj=self)
         self.value_col_names = [col for col in self.df.columns if col != self.entity_id_col_name]
@@ -41,4 +38,4 @@ class StaticSpec:
 
     value_frame: StaticFrame
     column_prefix: str
-    fallback: ValueType
+    fallback: Union[int, float, str, None]

src/timeseriesflattener/feature_specs/timedelta.py

@@ -1,10 +1,10 @@
 from __future__ import annotations
 
 from dataclasses import dataclass
-from typing import TYPE_CHECKING, Literal
+from typing import TYPE_CHECKING, Literal, Union
 
 from .._frame_validator import _validate_col_name_columns_exist
-from .meta import ValueFrame, ValueType
+from .meta import ValueFrame
 
 if TYPE_CHECKING:
     import polars as pl
@@ -15,7 +15,7 @@
 @dataclass
 class TimeDeltaSpec:
     init_frame: TimestampValueFrame
-    fallback: ValueType
+    fallback: Union[int, float, str, None]
     output_name: str
     column_prefix: str = "pred"
     time_format: Literal["seconds", "minutes", "hours", "days", "years"] = "days"

src/timeseriesflattener/feature_specs/timestamp_frame.py

@@ -1,16 +1,13 @@
 from __future__ import annotations
 
 from dataclasses import InitVar, dataclass
-from typing import TYPE_CHECKING
+from typing import Union
 
+import pandas as pd
 import polars as pl
 
 from .._frame_validator import _validate_col_name_columns_exist
 from ..frame_utilities.anyframe_to_lazyframe import _anyframe_to_lazyframe
-from .default_column_names import default_entity_id_col_name
-
-if TYPE_CHECKING:
-    from .meta import InitDF_T
 
 
 @dataclass
@@ -22,11 +19,11 @@ class TimestampValueFrame:
         value_timestamp_col_name: The name of the column containing the timestamps. Must be a string, and the column's values must be datetimes.
     """
 
-    init_df: InitVar[InitDF_T]
-    entity_id_col_name: str = default_entity_id_col_name
+    init_df: InitVar[Union[pl.LazyFrame, pl.DataFrame, pd.DataFrame]]
+    entity_id_col_name: str = "entity_id"
     value_timestamp_col_name: str = "timestamp"
 
-    def __post_init__(self, init_df: InitDF_T):
+    def __post_init__(self, init_df: Union[pl.LazyFrame, pl.DataFrame, pd.DataFrame]):
         self.df = _anyframe_to_lazyframe(init_df)
         _validate_col_name_columns_exist(obj=self)