neuraxle.steps.features

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

Inheritance diagram of neuraxle.steps.features

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 NamedTupleList 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
})
__init__()[source]

Create a feature union. :param steps_as_tuple: the NamedTupleList 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>