interval_sparsifier

Removes a given relative amount of samples from a signal.

class IntervalSparsifier(*kinetic_parameters: tuple[simba_ml.simulation.sparsifier.sparsifier.Sparsifier, Union[int, str]])

Bases: Sparsifier

A Sparsifier that sparsifies intervals with different Sparsifier.

The IntervalSparsifier takes sparsifiers and interval endings as arguments. The sparsifiers are applied to the according intervals.

Inits the IntervalSparsifier.

Parameters:

*kinetic_parameters – Pairs of (sparsifier, end_of_interval) where the end_of_interval is the last timestep, where the sparsifier should be applied. end_of_interval can either be represented explicit as an integer or relative to the length of the signal as a float.

Raises:

• ValueError – If interval endings are neither ints nor floats in range [0, 1]

• TypeError – If Sparsifiers are not of type Sparsifier.

Examples

>>> import pandas as pd
>>> from simba_ml.simulation import sparsifier
>>> signal = pd.DataFrame({"a": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]})
>>> sparsifier.interval_sparsifier.IntervalSparsifier(
...     (sparsifier.random_sample_sparsifier.RandomSampleSparsifier(0), 2),
...     (sparsifier.random_sample_sparsifier.RandomSampleSparsifier(1), 7),
...     (sparsifier.random_sample_sparsifier.RandomSampleSparsifier(0), 11),
... ).sparsify(signal).sort_index()
   a
2  3
3  4
4  5
5  6
6  7

>>> sparsifier.interval_sparsifier.IntervalSparsifier(
...     (sparsifier.random_sample_sparsifier.RandomSampleSparsifier(0),
...         0.2),
...     (sparsifier.random_sample_sparsifier.RandomSampleSparsifier(1),
...         0.5),
...     (sparsifier.random_sample_sparsifier.RandomSampleSparsifier(0),
...         1.0),
... ).sparsify(signal).sort_index()
   a
2  3
3  4
4  5

sparsify(signal: DataFrame) → DataFrame

Removes some (1-frac) samples chosen with a uniform random distributions.

Parameters:

signal – The signal to sparsify.

Returns:

The sparsified signal.

Return type:

DataFrame