neuraxle.steps.features¶

Module-level documentation for neuraxle.steps.features. Here is an inheritance diagram, including dependencies to other base modules of Neuraxle:

Featurization Steps¶

You can find here steps that featurize your data.

Classes

`Cheap3DTo2DTransformer`()	Prebuild class to featurize 3D data into 2D data for simple classification or regression, for instance.
`FFTPeakBinWithValue`()	Compute peak fft bins (int), and their magnitudes’ value (float), to concatenate them.

class neuraxle.steps.features.FFTPeakBinWithValue[source]¶

Bases: neuraxle.union.FeatureUnion

Compute peak fft bins (int), and their magnitudes’ value (float), to concatenate them. This is intended to be used only after a NumpyFFT absolute step.

__init__()[source]¶: Create a feature union. :param steps_as_tuple: the NamedStepsList of steps to process in parallel and to join. :param joiner: What will be used to join the features. NumpyConcatenateInnerFeatures() is used by default. :param n_jobs: The number of jobs for the parallelized joblib.Parallel loop in fit and in transform. :param backend: The type of parallelization to do with joblib.Parallel. Possible values: “loky”, “multiprocessing”, “threading”, “dask” if you use dask, and more.

_abc_impl = <_abc_data object>¶

class neuraxle.steps.features.Cheap3DTo2DTransformer[source]¶

Bases: neuraxle.steps.flow.ChooseOneOrManyStepsOf

Prebuild class to featurize 3D data into 2D data for simple classification or regression, for instance.

You can enable, or disable features using hyperparams :

step = Cheap3DTo2DTransformer().set_hyperparams(hyperparams={
    'FFT__enabled': True,
    'NumpyMean__enabled': True,
    'NumpyMedian__enabled': True,
    'NumpyMin__enabled': True,
    'NumpyMax__enabled': True
})