multipers.data package
Submodules
multipers.data.MOL2 module
multipers.data.UCR module
- multipers.data.UCR.get(dataset='UCR/Coffee', test=False, DATASET_PATH='/home/runner/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,TransformerMixinTransforms 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='/home/runner/Datasets/')
- multipers.data.immuno_regions.get_immuno(i=1, DATASET_PATH='/home/runner/Datasets/')
multipers.data.minimal_presentation_to_st_bf module
multipers.data.pytorch2simplextree module
multipers.data.shape3d module
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)