preprocessing.linearly_interpolate_ts

preprocessing.linearly_interpolate_ts

Linear interpolation transformer for time series data.

Classes

Name	Description
LinearlyInterpolateTS	Transformer that applies linear interpolation to time series data.

LinearlyInterpolateTS

preprocessing.linearly_interpolate_ts.LinearlyInterpolateTS(on_missing='raise')

Transformer that applies linear interpolation to time series data.

The transformer always runs y.interpolate(method="linear") first. The on_missing keyword then chooses how to handle any NaN that linear interpolation cannot bridge (typically the leading and trailing endpoints of the series).

Parameters

Name	Type	Description	Default
on_missing	OnMissing	Contract for residual NaN after linear interpolation. - "raise" (default, fail-safe): raise ValueError if any NaN remains. Refuses to silently embed imputed values disguised as measurements. - "ffill_bfill": explicit opt-in to endpoint forward-then-backward fill, so that leading and trailing NaNs are both bridged. - "passthrough": return the linearly interpolated series unchanged. The caller promises to handle the residual NaN downstream.	'raise'

Raises

Name	Type	Description
	ValueError	If on_missing is not one of the three accepted values, or if on_missing="raise" (the default) and any NaN remains after linear interpolation.

Examples

>>> import pandas as pd
>>> import numpy as np
>>> from spotforecast2_safe.preprocessing.linearly_interpolate_ts import LinearlyInterpolateTS

Interior gaps are bridged by linear interpolation under every mode; the default "raise" then succeeds because nothing remains:

>>> s = pd.Series([1.0, np.nan, 3.0])
>>> LinearlyInterpolateTS().fit_transform(s).tolist()
[1.0, 2.0, 3.0]

Endpoint NaNs are bridged only when the caller opts in explicitly:

>>> s = pd.Series([1.0, np.nan, 3.0, np.nan])
>>> LinearlyInterpolateTS(on_missing="ffill_bfill").fit_transform(s).tolist()
[1.0, 2.0, 3.0, 3.0]

"passthrough" lets the residual NaN survive for the caller to handle:

>>> out = LinearlyInterpolateTS(on_missing="passthrough").fit_transform(s)
>>> bool(out.isna().iloc[-1])
True

Methods

Name	Description
apply	Apply linear interpolation and dispatch on on_missing.
fit	Fitted transformer (no-op).
transform	Transform the input data by applying linear interpolation.

apply

preprocessing.linearly_interpolate_ts.LinearlyInterpolateTS.apply(y)

Apply linear interpolation and dispatch on on_missing.

Parameters

Name	Type	Description	Default
y	Union[pd.Series, pd.DataFrame]	Input Series or DataFrame.	required

Returns

Name	Type	Description
	Union[pd.Series, pd.DataFrame]	Union[pd.Series, pd.DataFrame]: The transformed data. For
	Union[pd.Series, pd.DataFrame]	on_missing="ffill_bfill" any residual endpoint NaN has
	Union[pd.Series, pd.DataFrame]	been forward- and back-filled; for "passthrough" it
	Union[pd.Series, pd.DataFrame]	survives; for "raise" (the default) the method raises
	Union[pd.Series, pd.DataFrame]	instead of returning a NaN-bearing result.

Raises

Name	Type	Description
	ValueError	If self.on_missing is not a recognized value, or if self.on_missing == "raise" and any NaN remains after linear interpolation.

fit

preprocessing.linearly_interpolate_ts.LinearlyInterpolateTS.fit(X, y=None)

Fitted transformer (no-op).

Parameters

Name	Type	Description	Default
X	Any	Input data.	required
y	Any	Ignored.	None

Returns

Name	Type	Description
self	LinearlyInterpolateTS	The fitted transformer.

transform

preprocessing.linearly_interpolate_ts.LinearlyInterpolateTS.transform(X)

Transform the input data by applying linear interpolation.

Parameters

Name	Type	Description	Default
X	Union[pd.Series, pd.DataFrame]	Input Series or DataFrame to interpolate.	required

Returns

Name	Type	Description
	Union[pd.Series, pd.DataFrame]	Union[pd.Series, pd.DataFrame]: Interpolated data, with
	Union[pd.Series, pd.DataFrame]	residual NaN handled according to self.on_missing.