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Quantile Transform

The quantile transform computes the q-th quantile of the data in the sliding window. The quantile is computed using the numpy.quantile function. The transform can be combined with the SlidingWindow to compute the quantile of the data in a sliding window. We can use this transform to compute the median of the data in a sliding window by setting q=0.5.

Bases: Transform

Compute the q-th quantile of the values.

__call__(signal_window, q, method='linear', where=lambda : not np.isnan(x))

Compute the q-th quantile of the values in x.

Parameters:

Name Type Description Default
signal_window ndarray

The array to compute the q-th quantile of.

 required
q Union[float, float_]

The quantile to compute. q belongs to [0, 1].

 required
method str

The method to use when computing the quantile. Default is 'linear'. See numpy.quantile for more information.

 'linear'
where Callable[[Union[int, float, int_, float_]], Union[bool, bool_]]

where: 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_, int_]

A scalar value representing the q-th quantile of the signal.

Examples

25th percentile

import numpy as np
import autonfeat as aft

# 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)

# Create transform
tf = aft.QuantileTransform()

# Get featurizer
featurizer = window.use(tf)

# Get features
features = featurizer(x, q=0.25)

# Print features
print(window)
print(tf)
print(features)

Median

import numpy as np
import autonfeat as aft

# 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)

# Create transform
tf = aft.QuantileTransform()

# Get featurizer
featurizer = window.use(tf)

# Get features
features = featurizer(x, q=0.5)

# Print features
print(window)
print(tf)
print(features)

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