lale.lib.lale.time_series_transformer module

class lale.lib.lale.time_series_transformer.CorrelationMatrix[source]

Bases: object

Calculate correlation coefficients matrix across all EEG channels.

apply(data)[source]
get_name()[source]
class lale.lib.lale.time_series_transformer.Eigenvalues[source]

Bases: object

Take eigenvalues of a matrix, and sort them by magnitude in order to make them useful as features (as they have no inherent order).

apply(data)[source]
get_name()[source]
class lale.lib.lale.time_series_transformer.FFT[source]

Bases: object

Apply Fast Fourier Transform to the last axis.

apply(data)[source]
get_name()[source]
class lale.lib.lale.time_series_transformer.FFTWithTimeFreqCorrelation(start, end, max_hz, scale_option)[source]

Bases: object

Combines FFT with time and frequency correlation, taking both correlation coefficients and eigenvalues.

apply(data)[source]
get_name()[source]
class lale.lib.lale.time_series_transformer.FreqCorrelation(start, end, scale_option, with_fft=False, with_corr=True, with_eigen=True)[source]

Bases: object

Correlation in the frequency domain. First take FFT with (start, end) slice options, then calculate correlation co-efficients on the FFT output, followed by calculating eigenvalues on the correlation co-efficients matrix. The output features are (fft, upper_right_diagonal(correlation_coefficients), eigenvalues) Features can be selected/omitted using the constructor arguments.

apply(data)[source]
get_name()[source]
class lale.lib.lale.time_series_transformer.Log10[source]

Bases: object

Apply Log10

apply(data)[source]
get_name()[source]
class lale.lib.lale.time_series_transformer.Magnitude[source]

Bases: object

Job: Take magnitudes of Complex data

apply(data)[source]
get_name()[source]
class lale.lib.lale.time_series_transformer.Pipeline(pipeline)[source]

Bases: object

A Pipeline is an object representing the data transformations to make on the input data, finally outputting extracted features. pipeline: List of transforms to apply one by one to the input data

apply(data)[source]
get_name()[source]
class lale.lib.lale.time_series_transformer.Resample(sample_rate)[source]

Bases: object

Resample time-series data.

apply(data)[source]
get_name()[source]
class lale.lib.lale.time_series_transformer.Slice(start, stop)[source]

Bases: object

Job: Take a slice of the data on the last axis. Note: Slice(x, y) works like a normal python slice, that is x to (y-1) will be taken.

apply(data)[source]
get_name()[source]
class lale.lib.lale.time_series_transformer.StandardizeFirst[source]

Bases: object

Scale across the first axis.

apply(data)[source]
get_name()[source]
class lale.lib.lale.time_series_transformer.StandardizeLast[source]

Bases: object

Scale across the last axis.

apply(data)[source]
get_name()[source]
class lale.lib.lale.time_series_transformer.TimeCorrelation(max_hz, scale_option, with_corr=True, with_eigen=True)[source]

Bases: object

Correlation in the time domain. First downsample the data, then calculate correlation co-efficients followed by calculating eigenvalues on the correlation co-efficients matrix. The output features are (upper_right_diagonal(correlation_coefficients), eigenvalues) Features can be selected/omitted using the constructor arguments.

apply(data)[source]
get_name()[source]
class lale.lib.lale.time_series_transformer.TimeFreqEigenVectors(*args, _lale_trained=False, _lale_impl=None, **kwargs)

Bases: TrainedIndividualOp

Combined schema for expected data and hyperparameters.

This documentation is auto-generated from JSON schemas.

Parameters

  • window_length (float, >=0.25 for optimizer, <=2 for optimizer, uniform distribution, default 1) – TODO

  • window_step (float, >=0.25 for optimizer, <=1 for optimizer, uniform distribution, default 0.5) – TODO

  • fft_min_freq (integer, not for optimizer, default 1) – TODO

  • fft_max_freq (integer, >=2 for optimizer, <=30 for optimizer, uniform distribution, default 24) – TODO

  • sampling_frequency (integer, not for optimizer, default 250) – TODO

transform(X, y=None)

Transform the data.

Parameters

  • X (array of items : array of items : array of items : float) – The input data to complete.

  • y (array) –

    • items : union type

      • integer

      • or string

Returns

result – The input data to complete.

Return type

array of items : array

class lale.lib.lale.time_series_transformer.seizure_type_data(seizure_type, data)

Bases: tuple

Create new instance of seizure_type_data(seizure_type, data)

data

Alias for field number 1

seizure_type

Alias for field number 0

lale.lib.lale.time_series_transformer.upper_right_triangle(matrix)[source]