Data Sparsity Function¶

The data sparsity function computes the ratio of invalid values in a sliding window to the total number of values in the window. See n-valid for more details on how valid and invalid values are computed. It can be coupled with the SlidingWindow abstraction to compute the data sparsity feature of a time series. It can be defined as:

\[ \text{sparsity} = \frac{N_{invalid}}{N_{total}} \]

where \(N_{invalid}\) is the number of invalid values in a window \(W\) and \(N_{total}\) is the total number of values in \(W\).

Compute the data sparsity of the array x.

Parameters:

Name	Type	Description	Default
`x`	`ndarray`	The array to compute the data sparsity of.	required
`where`	`Callable[[Union[int, float, int_, float_]], Union[bool, bool_]]`	A function that takes a value and returns `True` or `False`. Default is `lambda x: not np.isnan(x)` i.e. a measurement is valid if it is not a `NaN` value.	`lambda : not numpy.isnan(x)`

Returns:

Type	Description
`Union[float, float_]`	The data sparsity of measurements in `x`.

Raises:

Type	Description
`DivideByZeroError`	If `x` is empty.

Examples¶

import numpy as np
import autonfeat as aft
import autonfeat.functional as F

# Random data
n_samples = 100
x = np.random.rand(n_samples)

# Create sliding window
ws = 10
ss = 10
window = aft.SlidingWindow(window_size=ws, step_size=ss)

# Get featurizer
featurizer = window.use(F.data_sparsity_tf)

# Get features
features = featurizer(x)

# Print features
print(features)

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