Quantile Function¶

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

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

Parameters:

Name	Type	Description	Default
`x`	`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_]]`	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 q-th quantile of the values in `x`.

Raises:

Type	Description
`ValueError`	If `q` is not in [0, 1].

Examples¶

25th percentile¶

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.quantile_tf)

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

# Print features
print(features)

Median¶

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.quantile_tf)

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

# Print features
print(features)

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