multipers.data package

Submodules

multipers.data.MOL2 module

multipers.data.UCR module

multipers.data.UCR.get(dataset='UCR/Coffee', test=False, DATASET_PATH='/user/dloiseau/home/Datasets/', dim=3, delay=1, skip=1)
Parameters:

  • dataset (str)

  • test (bool)

  • DATASET_PATH (str)

multipers.data.UCR.get_test(*args, **kwargs)
multipers.data.UCR.get_train(*args, **kwargs)

multipers.data.graphs module

class multipers.data.graphs.Graph2SimplexTrees(filtrations=[], delayed=False, num_collapses=100, progress=False)

Bases: BaseEstimator, TransformerMixin

Transforms a list of networkx graphs into a list of simplextree multi

Usual Filtrations

  • “cc” closeness centrality

  • “geodesic” if the graph provides data to compute it, e.g., BZR, COX2, PROTEINS

  • “degree”

  • “ricciCurvature” the ricci curvature

  • “fiedler” the square of the fiedler vector

_sklearn_auto_wrap_output_keys = {'transform'}
fit(X, y=None)
transform(X)
Parameters:

X (list[Graph])

Parameters:

progress (bool)

multipers.data.graphs._check_installed(dataset)
Parameters:

dataset (str)

multipers.data.graphs.compute_cc(graphs, progress=1)
Parameters:

graphs (list[Graph])

multipers.data.graphs.compute_degree(graphs, progress=1)
Parameters:

graphs (list[Graph])

multipers.data.graphs.compute_fiedler(graphs, progress=1)
Parameters:

graphs (list[Graph])

multipers.data.graphs.compute_filtration(dataset, filtration='ALL', **kwargs)
Parameters:

  • dataset (str)

  • filtration (str)

multipers.data.graphs.compute_geodesic(graphs, progress=1)
Parameters:

graphs (list[Graph])

multipers.data.graphs.compute_hks(graphs, t, progress=1)
Parameters:

  • graphs (list[Graph])

  • t (float)

multipers.data.graphs.compute_intrinsic(graphs, progress=1, nowarning=False)
Parameters:

graphs (list[Graph])

multipers.data.graphs.compute_ricci(graphs, alpha=0.5, progress=1)
Parameters:

graphs (list[Graph])

multipers.data.graphs.get(dataset, filtration=None)
Parameters:

  • dataset (str)

  • filtration (str | None)

multipers.data.graphs.get_from_file(dataset)
Parameters:

dataset (str)

multipers.data.graphs.get_from_file_old(dataset, label='lb')
Parameters:

dataset (str)

multipers.data.graphs.get_graphs(dataset, N='')
Parameters:

  • dataset (str)

  • N (int | str)

Return type:

tuple[list[Graph], list[int]]

multipers.data.graphs.reset_graphs(dataset, N=None)
Parameters:

dataset (str)

multipers.data.graphs.set_graphs(graphs, labels, dataset, N='')
Parameters:

  • graphs (list[Graph])

  • labels (list)

  • dataset (str)

  • N (int | str)

multipers.data.immuno_regions module

multipers.data.immuno_regions.get(DATASET_PATH='/user/dloiseau/home/Datasets/')
multipers.data.immuno_regions.get_immuno(i=1, DATASET_PATH='/user/dloiseau/home/Datasets/')

multipers.data.minimal_presentation_to_st_bf module

multipers.data.pytorch2simplextree module

class multipers.data.pytorch2simplextree.Torch2SimplexTree(filtrations=[])

Bases: BaseEstimator, TransformerMixin

WARNING : build in progress PyTorch Data-like to simplextree.

Input

Class having pos, edges, faces methods

Filtrations

  • Geodesic (geodesic rips)

  • eccentricity

_sklearn_auto_wrap_output_keys = {'transform'}
fit(X, y=None)
mp = <module 'multipers' from '/home/dloiseau/micromamba/envs/312/lib/python3.12/site-packages/multipers/__init__.py'>
transform(X)
Parameters:

X (list[Graph])

Parameters:

filtrations (Iterable[str])

multipers.data.pytorch2simplextree.modelnet2graphs(version='10', print_flag=False, labels_only=False, a=0, b=10, weight_flag=False)

load modelnet 10 or 40 and convert to graphs

multipers.data.pytorch2simplextree.modelnet2pts2gs(train_dataset, test_dataset, nbr_size=8, exp_flag=True, labels_only=False, n=100, n_jobs=1, random=False)
multipers.data.pytorch2simplextree.torch_geometric_2nx(dataset, labels_only=False, print_flag=False, weight_flag=False)
Parameters:

  • dataset

  • labels_only – return labels only

  • print_flag

  • weight_flag – whether computing distance as weights or not

Returns:

multipers.data.shape3d module

multipers.data.shape3d.get(dataset, num_graph=0, seed=0, node_per_graph=0)
Parameters:

dataset (str)

multipers.data.shape3d.get_(dataset, dataset_num=None, num_sample=0, DATASET_PATH='/user/dloiseau/home/Datasets/')
Parameters:

  • dataset (str)

  • dataset_num (int | None)

  • num_sample (int)

multipers.data.shape3d.get_ModelNet(dataset, num_graph, seed)
multipers.data.shape3d.load_modelnet(version='10', sample_points=False, reset=False, remove_faces=False)
Parameters:

reset (bool)

multipers.data.synthetic module

multipers.data.synthetic.get_orbit5k(num_pts=1000, num_data=5000)
multipers.data.synthetic.noisy_annulus(n1=1000, n2=200, r1=1, r2=2, dim=2, center=None, **kwargs)

Generates a noisy annulus dataset.

Parameters

r1float.

Lower radius of the annulus.

r2float.

Upper radius of the annulus.

n1int

Number of points in the annulus.

n2int

Number of points in the square.

dimint

Dimension of the annulus.

center: list or array

center of the annulus.

Returns

numpy array

Dataset. size : (n1+n2) x dim

Parameters:

  • n1 (int)

  • n2 (int)

  • r1 (float)

  • r2 (float)

  • dim (int)

  • center (ndarray | list | None)

Return type:

ndarray

multipers.data.synthetic.orbit(n=1000, r=1.0, x0=[])
Parameters:

  • n (int)

  • r (float)

multipers.data.synthetic.three_annulus(num_pts=500, num_outliers=500)
Parameters:

  • num_pts (int)

  • num_outliers (int)

Module contents