protoclass.extraction.SemiQuantificationExtraction

class protoclass.extraction.SemiQuantificationExtraction(base_modality, random_state=None)

Enhancement signal extraction from temporal modality.

Parameters:

base_modality : object

The base modality on which the normalization will be applied. The base modality should inherate from TemporalModality class.

Attributes

base_modality	(object) The base modality on which the normalization will be applied. The base modality should inherate from TemporalModality class.
roi_data_	(ndarray, shape flexible) Corresponds to the index to consider in order to fit the data.

Methods

fit(modality[, ground_truth, cat])	Find the parameters needed to apply the extraction.
load_from_pickles(filename)	Function to load a normalization object.
save_to_pickles(filename)	Function to save a normalizatio object using pickles.
transform(modality[, ground_truth, cat])	Extract the data from the given modality.

__init__(base_modality, random_state=None)

Methods

__init__(base_modality[, random_state])
fit(modality[, ground_truth, cat])	Find the parameters needed to apply the extraction.
load_from_pickles(filename)	Function to load a normalization object.
save_to_pickles(filename)	Function to save a normalizatio object using pickles.
transform(modality[, ground_truth, cat])	Extract the data from the given modality.

fit(modality, ground_truth=None, cat=None)

Find the parameters needed to apply the extraction.

Parameters:

modality : object of type TemporalModality

The modality object of interest.

ground-truth : object of type GTModality or None

The ground-truth of GTModality. If None, the whole data will be considered.

cat : str or None

String corresponding at the ground-truth of interest. Cannot be None if ground-truth is not None.

load_from_pickles(filename)

Function to load a normalization object.

Parameters:

filename : str

Filename to the pickle file. The extension should be .p.

Returns:

bpp : object

Returns the loaded object.

save_to_pickles(filename)

Function to save a normalizatio object using pickles.

Parameters:

filename : str

Filename to the pickle file. The extension should be .p.

Returns:

None

transform(modality, ground_truth=None, cat=None)

Extract the data from the given modality.

Parameters:

modality : object of type TemporalModality

The modality object of interest.

ground-truth : object of type GTModality or None

The ground-truth of GTModality. If None, the whole data will be considered.

cat : str or None

String corresponding at the ground-truth of interest. Cannot be None if ground-truth is not None.

Returns

——

data : ndarray, shape (n_sample, n_feature)

A matrix containing the features extracted. The number of samples is equal to the number of positive label in the ground-truth. The feature will be the following (wash-in, wash-out, AUC, tau, relative enhancement)