106 lines
3.6 KiB
Python
106 lines
3.6 KiB
Python
#!/usr/bin/env python3
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import numpy as np
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import igraph as ig
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import dionysus as d
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import multiprocessing
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# from dask.distributed import Client
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from zigzag import sliding_windows, zigzag_network
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from wrcf import wrcf_diagram
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from sliced_wasserstein import diagram_array, SW_approx
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import dill
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def remove_inf(dgm):
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"""Remove infinite points in a persistence diagram.
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:param dgm: Diagram
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:return: the same diagram without the infinite points.
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"""
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res = d.Diagram()
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for p in dgm:
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if p.death != np.inf:
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res.append(p)
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return res
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## Global parameters
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N_WINDOWS = 40
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## Computations
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ZIGZAG_PERS = True
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WRCF_PERS = True
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SW_KERNEL = True
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BOTTLENECK_DIST = True
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if __name__=="__main__":
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print("Loading SocioPatterns dataset...", end="", flush=True)
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g = ig.read("data/sociopatterns/infectious/infectious.graphml")
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del g.es["id"]
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# print(g.summary())
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print("done.")
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print("Temporal partitioning...", end="", flush=True)
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wins = sliding_windows(g, 1/N_WINDOWS)
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print("done.")
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pool = multiprocessing.Pool(processes=multiprocessing.cpu_count())
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if ZIGZAG_PERS:
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print("Zigzag persistence...", end="", flush=True)
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zz_dgms = pool.map(zigzag_network, wins)
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dill.dump(zz_dgms, open("sociopatterns/zz_dgms.dill", "wb"))
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print("done, saved.")
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if WRCF_PERS:
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print("WRCF...", end="", flush=True)
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## Collapse each subnetwork into a static graph: the weight is the
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## number of appearances of each edge
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for w in wins:
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w.es["time"] = np.repeat(1, len(w.es["time"]))
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w.simplify(combine_edges="sum")
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w.es["weight"] = w.es["time"]
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del w.es["time"]
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wrcf_dgms = pool.map(wrcf_diagram, wins)
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dill.dump(wrcf_dgms, open("sociopatterns/wrcf_dgms.dill", "wb"))
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print("done.")
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pool.terminate()
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if ZIGZAG_PERS and SW_KERNEL:
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print("Sliced Wasserstein Kernel (zigzag)...", end="", flush=True)
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zz_dgms1 = [dgm[1] for dgm in zz_dgms if len(dgm) > 1]
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zz_gram1 = np.array([[SW_approx(zz_dgms1[i], zz_dgms1[j], 10)
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for i in range(len(zz_dgms1))] for j in range(len(zz_dgms1))])
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dill.dump(zz_gram1, open("sociopatterns/zz_gram1.dill", "wb"))
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print("done, saved.")
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if WRCF_PERS and SW_KERNEL:
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print("Sliced Wasserstein Kernel (WRCF)...", end="", flush=True)
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wrcf_dgms1 = [dgm[1] for dgm in wrcf_dgms if len(dgm) > 1]
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wrcf_gram1 = np.array([[SW_approx(wrcf_dgms1[i], wrcf_dgms1[j], 10)
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for i in range(len(wrcf_dgms1))] for j in range(len(wrcf_dgms1))])
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dill.dump(wrcf_gram1, open("sociopatterns/wrcf_gram1.dill", "wb"))
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print("done, saved.")
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if ZIGZAG_PERS and BOTTLENECK_DIST:
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print("Bottleneck distance (zigzag)...", end="", flush=True)
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zz_dgms1 = list(map(remove_inf, zz_dgms1))
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zz_distmat = np.array([[d.bottleneck_distance(zz_dgms1[i], zz_dgms1[j])
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for i in range(len(zz_dgms1))] for j in range(len(zz_dgms1))])
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dill.dump(zz_distmat, open("sociopatterns/zz_distmat.dill", "wb"))
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print("done, saved.")
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if WRCF_PERS and BOTTLENECK_DIST:
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print("Bottleneck distance (WRCF)...", end="", flush=True)
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wrcf_dgms1 = list(map(remove_inf, wrcf_dgms1))
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wrcf_distmat = np.array([[d.bottleneck_distance(wrcf_dgms1[i], wrcf_dgms1[j])
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for i in range(len(wrcf_dgms1))] for j in range(len(wrcf_dgms1))])
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dill.dump(wrcf_distmat, open("sociopatterns/wrcf_distmat.dill", "wb"))
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print("done, saved.")
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