Combined Preprocessing (Preprocess)

The Preprocess class provides a way of efficiently applying multiple preprocessing transformations to provided input observation sequences.

For further information, please see the preprocessing tutorial notebook.

Example

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import numpy as np
from sequentia.preprocessing import Preprocess

# Create some sample data
X = [np.random.random((20 * i, 3)) for i in range(1, 4)]

# Create the Preprocess object
pre = Preprocess()
pre.trim_zeros()
pre.center()
pre.standardize()
pre.filtrate(n=5, method='median')
pre.downsample(n=5, method='decimate')
pre.fft()

# View a summary of the preprocessing steps
pre.summary()

# Transform the data applying transformations in order
X = pre.transform(X)

API reference

class sequentia.preprocessing.Preprocess[source]

Efficiently applies multiple preprocessing transformations to the provided input observation sequence(s).

trim_zeros(self)[source]

Trim zero-observations from the input observation sequence(s).

center(self)[source]

Centers an observation sequence (or multiple sequences) by centering observations around the mean.

standardize(self)[source]

Standardizes an observation sequence (or multiple sequences) by transforming observations so that they have zero mean and unit variance.

downsample(self, n, method='decimate')[source]

Downsamples an observation sequence (or multiple sequences) by either:

  • Decimating the next \(n-1\) observations
  • Averaging the current observation with the next \(n-1\) observations
Parameters:
n: int

Downsample factor.

method: {‘decimate’, ‘average’}

The downsampling method.

fft(self)[source]

Applies a Discrete Fourier Transform to the input observation sequence(s).

filtrate(self, n, method='median')[source]

Applies a median or mean filter to the input observation sequence(s).

Parameters:
n: int

Window size.

method: {‘median’, ‘mean’}

The filtering method.

transform(self, X)[source]

Applies the preprocessing transformations to the provided input observation sequence(s).

Parameters:
X: numpy.ndarray or List[numpy.ndarray]

An individual observation sequence or a list of multiple observation sequences.

Returns:
transformed: numpy.ndarray or List[numpy.ndarray]

The input observation sequence(s) with preprocessing transformations applied in order.

summary(self)[source]

Displays an ordered summary of the preprocessing transformations.