multimodal.datasets package

Submodules

multimodal.datasets.base module

multimodal.datasets.base.load_dict(filename_)

multimodal.datasets.base.load_npz_X_y(filename_)

multimodal.datasets.base.save_dict(di_, filename_)

multimodal.datasets.base.save_npz_X_y(filename_, X, y)

multimodal.datasets.data_sample module

This module contains the DataSample class, MultiModalArray, MultiModalSparseArray, MultiModalSparseInfo and MultiModalData, class The DataSample class encapsulates a sample ‘s components nbL and nbEx numbers, MultiModalArray class inherit from numpy ndarray and contains a 2d data ndarray with the shape (n_samples, n_view_i * n_features_i)

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MultiModalSparseArray inherit from scipy sparce matrix with the shape (n_samples, n_view_i * n_features_i)

class multimodal.datasets.data_sample.DataSample(data=None, **kwargs)

Bases: dict

A DataSample instance

Example:

>>> from multimodal.datasets.base import load_dict
>>> from multimodal.tests.datasets.get_dataset_path import get_dataset_path
>>> from multimodal.datasets.data_sample import DataSample
>>> file = 'input_x_dic.pkl'
>>> data = load_dict(get_dataset_path(file))
>>> print(data.__class__)
<class 'dict'>
>>> s = DataSample(data)
>>> type(s.data)
<class 'multimodal.datasets.data_sample.MultiModalArray'>

• Input:

Parameters:

datadict

kwargsothers arguments

Attributes:

data{ array like} MultiModalArray

MultiModalArray

property data

MultiModalArray

class multimodal.datasets.data_sample.MultiModalArray(data, views_ind=None)

Bases: ndarray, MultiModalData

MultiModalArray inherit from numpy ndarray

Parameters:

datacan be

• dictionary of multiview array with shape = (n_samples, n_features) for multi-view

  for each view.

  {0: array([[]],

  1: array([[]], …}

• numpy array like with shape = (n_samples, n_features) for multi-view

  for each view.

  [[[…]],

  [[…]], …]

• {array like} with (n_samples, nviews * n_features) with ‘views_ind’ diferent to ‘None’

  for Multi-view input samples.

views_indarray-like (default= None ) if None

[0, n_features//2, n_features]) is constructed (2 views) Paramater specifying how to extract the data views from X:

• views_ind is a 1-D array of sorted integers, the entries indicate the limits of the slices used to extract the views, where view n is given by X[:, views_ind[n]:views_ind[n+1]].

Attributes:

views_indlist of views’ indice (may be None)

n_viewsint number of views

shapes_int: list of int numbers of feature for each views

:Example:

>>> from multimodal.datasets.base import load_dict

>>> from multimodal.tests.datasets.get_dataset_path import get_dataset_path

>>> from multimodal.datasets.data_sample import DataSample

>>> file = ‘input_x_dic.pkl’

>>> data = load_dict(get_dataset_path(file))

>>> print(data.__class__)

<class ‘dict’>

>>> multiviews = MultiModalArray(data)

>>> multiviews.shape

(120, 240)

>>> multiviews.shapes_int

[120, 120]

>>> multiviews.n_views

2

get_col(view, col)

get_row(view, row)

get_view(view)

set_view(view, data)

class multimodal.datasets.data_sample.MultiModalData

Bases: object

class multimodal.datasets.data_sample.MultiModalSparseArray(*arg, **kwargs)

Bases: csr_matrix, csc_matrix, MultiModalSparseInfo, MultiModalData

MultiModalArray inherit from numpy ndarray

Parameters:

datacan be

• dictionary of multiview array with shape = (n_samples, n_features) for multi-view

  for each view.

  {0: array([[]],

  1: array([[]], …}

• numpy array like with shape = (n_samples, n_features) for multi-view

  for each view.

  [[[…]],

  [[…]], …]

• {array like} with (n_samples, nviews * n_features) with ‘views_ind’ diferent to ‘None’

  for Multi-view input samples.

views_indarray-like (default= None ) if None

[0, n_features//2, n_features]) is constructed (2 views) Paramater specifying how to extract the data views from X:

• views_ind is a 1-D array of sorted integers, the entries indicate the limits of the slices used to extract the views, where view n is given by X[:, views_ind[n]:views_ind[n+1]].

Attributes:

views_indlist of views’ indice (may be None)

n_views : int number of views

shapes_int: list of int numbers of feature for each views

keys : name of key, where data come from a dictionary

:Example:

>>> from multimodal.datasets.base import load_dict

>>> from multimodal.tests.datasets.get_dataset_path import get_dataset_path

>>> from multimodal.datasets.data_sample import DataSample

>>> file = ‘input_x_dic.pkl’

>>> data = load_dict(get_dataset_path(file))

class multimodal.datasets.data_sample.MultiModalSparseInfo(data, views_ind=None)

Bases: object

Module contents