From 9b30da727a2b7ab980032026dd1f32f7236a4ab7 Mon Sep 17 00:00:00 2001 From: Will HW Thompson Date: Sun, 3 Dec 2023 13:32:22 -0500 Subject: [PATCH] Added clustering generative model (#13) * clustering file * bipartite test * test * clustering sweep * clustering data * changes to generative * test * bugfix to clustered unipartite * merge review changes * black and isort on genertive.py * added PR changes in generative * black and isort + PR --- Data/clustering.json | 1 + clustering.py | 132 ++++++++++++++++++++++++++++++++++++++ collect_clustering.py | 77 ++++++++++++++++++++++ lcs/generative.py | 29 ++++----- plot_graph_topology.ipynb | 112 ++++++++++++++++++++++++++++++++ 5 files changed, 334 insertions(+), 17 deletions(-) create mode 100644 Data/clustering.json create mode 100644 clustering.py create mode 100644 collect_clustering.py create mode 100644 plot_graph_topology.ipynb diff --git a/Data/clustering.json b/Data/clustering.json new file mode 100644 index 0000000..d7fedc4 --- 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0.9955734210464907, 0.00478046538999477], [0.8403651327079114, 0.9690657236221407, 0.5790969371645089, 0.8025270308355338, 0.7412075252902852, 0.8560956812412401, 0.969135500370174, 0.9835749823922333, 0.21047037515048328, 0.611821948637412], [0.9687188467151612, 0.7460370337062712, 0.9886235472956406, 0.017791820087577292, 0.005588381645772356, 0.934410260660472, 0.8057223992192561, 0.990912093154103, 0.5312487945855007, 0.7953302620339207], [0.9395318943816614, 0.9777205025897747, 0.09239366058095289, 0.6619270953005258, 0.7209137068954379, 0.1923817105546648, 0.9732921129129777, 0.7040731373996638, 0.9929465569961732, 0.010378188921624898]]]} \ No newline at end of file diff --git a/clustering.py b/clustering.py new file mode 100644 index 0000000..67b0a2e --- /dev/null +++ b/clustering.py @@ -0,0 +1,132 @@ +import json +import multiprocessing as mp +import os + +import numpy as np + +from lcs import * + + +def gen_clustered_clique_number(n, clique_number): + clique_size = n // clique_number # the number of nodes per clique + clique_membership = 0 # the number of cliques per node + + p_dist = delta_dist(clique_size) + g_dist = delta_dist(clique_membership) + A = clustered_unipartite(clique_number, n, p_dist, g_dist) + return A + + +def target_ipn(n, clique_number, gamma, c, mode, rho0, tmax, realizations): + x0 = np.zeros(n) + x0[random.sample(range(n), int(round(rho0 * n)))] = 1 + ipn = 0 + for _ in range(realizations): + A = gen_clustered_clique_number(n, clique_number) + x = contagion_process(A, gamma, c, x0, tmin=0, tmax=tmax) + ipn += infections_per_node(x, mode) / realizations + return ipn + + +def single_inference( + fname, gamma, c, b, rho0, A, tmax, p_c, p_rho, nsamples, burn_in, skip +): + n = np.size(A, axis=0) + x0 = np.zeros(n) + x0[random.sample(range(n), int(round(rho0 * n)))] = 1 + + x = contagion_process(A, gamma, c, x0, tmin=0, tmax=tmax) + p = beta(p_rho[0], p_rho[1]).rvs() + A0 = erdos_renyi(n, p) + samples = infer_adjacency_matrix( + x, A0, p_rho, p_c, nsamples=nsamples, burn_in=burn_in, skip=skip + ) + + # json dict + data = {} + data["gamma"] = gamma + data["c"] = c.tolist() + data["b"] = b + data["p-rho"] = p_rho.tolist() + data["p-c"] = p_c.tolist() + data["x"] = x.tolist() + data["A"] = A.tolist() + data["samples"] = samples.tolist() + + datastring = json.dumps(data) + + with open(fname, "w") as output_file: + output_file.write(datastring) + + +data_dir = "Data/clustering" +os.makedirs(data_dir, exist_ok=True) + +for f in os.listdir(data_dir): + os.remove(os.path.join(data_dir, f)) + +n = 100 +k = 6 + +n_processes = len(os.sched_getaffinity(0)) +print("n processes: ", n_processes) +realizations = 10 +clique_numbers = np.arange(1, 20) + +# MCMC parameters +burn_in = 100000 +nsamples = 100 +skip = 1500 +p_c = np.ones((2, n)) +p_rho = np.array([1, 1]) + +# contagion functions and parameters +cf1 = lambda nu, beta: 1 - (1 - beta) ** nu # simple contagion +cf2 = lambda nu, beta: beta * (nu >= 2) # complex contagion, tau=2 +cf3 = lambda nu, beta: beta * (nu >= 3) # complex contagion, tau=3 + +cfs = [cf1, cf2, cf3] + +rho0 = 1.0 +gamma = 0.1 +b = 0.04 +mode = "max" + +tmax = 1000 + +A = gen_clustered_clique_number(n, 3) + +arglist = [] +for clique_number in clique_numbers: + c = cfs[0](np.arange(n), b) + ipn = target_ipn(n, clique_number, gamma, c, mode, rho0, tmax, 1000) + for i, cf in enumerate(cfs): + if i != 0: + A = gen_clustered_clique_number(n, clique_number) + bscaled = fit_ipn(0.5, ipn, cf, gamma, A, rho0, tmax, mode) + else: + bscaled = b + c = cf(np.arange(n), bscaled) + print((clique_number, i), flush=True) + + for r in range(realizations): + A = gen_clustered_clique_number(n, clique_number) + arglist.append( + ( + f"{data_dir}/{clique_number}_{i}_{r}", + gamma, + c, + bscaled, + rho0, + A, + tmax, + p_c, + p_rho, + nsamples, + burn_in, + skip, + ) + ) + +with mp.Pool(processes=n_processes) as pool: + pool.starmap(single_inference, arglist) diff --git a/collect_clustering.py b/collect_clustering.py new file mode 100644 index 0000000..2bc525e --- /dev/null +++ b/collect_clustering.py @@ -0,0 +1,77 @@ +import json +import os + +import numpy as np + +from lcs import * + +plist = set() +clist = set() +rlist = set() +beta = [] +frac = [] + + +data_dir = "Data/clustering" + +for f in os.listdir(data_dir): + d = f.split(".json")[0].split("_") + try: + p = float(d[0]) + c = int(d[1]) + r = int(d[2]) + except: + p = float(d[0] + "-" + d[1]) + c = int(d[2]) + r = int(d[3]) + + plist.add(p) + clist.add(c) + rlist.add(r) + +clist = sorted(clist) +plist = sorted(plist) +rlist = sorted(rlist) + +c_dict = {c: i for i, c in enumerate(clist)} +p_dict = {p: i for i, p in enumerate(plist)} +r_dict = {r: i for i, r in enumerate(rlist)} + + +ps = np.zeros((len(clist), len(plist), len(rlist))) +sps = np.zeros((len(clist), len(plist), len(rlist))) + +for f in os.listdir(data_dir): + d = f.split(".json")[0].split("_") + try: + p = float(d[0]) + c = int(d[1]) + r = int(d[2]) + except: + p = float(d[0] + "-" + d[1]) + c = int(d[2]) + r = int(d[3]) + + i = c_dict[c] + j = p_dict[p] + k = r_dict[r] + + fname = os.path.join(data_dir, f) + + with open(fname, "r") as file: + data = json.loads(file.read()) + + A = np.array(data["A"]) + samples = np.array(data["samples"]) + + ps[i, j, k] = posterior_similarity(samples, A) + sps[i, j, k] = samplewise_posterior_similarity(samples, A) + +data = {} +data["clique_number"] = plist +data["sps"] = sps.tolist() +data["ps"] = ps.tolist() +datastring = json.dumps(data) + +with open("Data/clustering.json", "w") as output_file: + output_file.write(datastring) diff --git a/lcs/generative.py b/lcs/generative.py index aaeafe6..5cdd64c 100644 --- a/lcs/generative.py +++ b/lcs/generative.py @@ -51,14 +51,13 @@ def delta_dist(x_prime): return rv_discrete(name="custom", values=([x_prime], [1.0])) -def generate_hypergraph_bipartite_edge_list( - N_groups, N_inds, p_dist, g_dist, seed=None -): +def generate_bipartite_edge_list(n_groups, n_inds, p_dist, g_dist, seed=None): """ - generate_hypergraph_bipartite_edge_list(): generates a hypergraph in the style of Newman's model in "Community Structure in social and biological networks" + generate_bipartite_edge_list(): generates a hypergraph in the style of Newman's model in "Community Structure in social and biological networks" + inputs: - N_groups: the number of groups or cliques to create - N_inds: the number of individuals to create(may be less than this total) + n_groups: the number of groups or cliques to create + n_inds: the number of individuals to create(may be less than this total) p_dist: The distribution of clique sizes, must be from numpy.random g_dist: The distribution of number of cliques belonged to per individual seed: seed for pseudorandom number generator @@ -77,22 +76,18 @@ def generate_hypergraph_bipartite_edge_list( # generate chairs - for i in range(1, N_inds + 1): + for i in range(1, n_inds + 1): g_m = g_dist.rvs() + 1 # select the number of butts in clique i - butts.extend([i for _ in range(g_m)]) # add g_m butts to individuals + butts.extend([i] * g_m) # add g_m butts to individuals - for i in range(1, N_groups + 1): + for i in range(1, n_groups + 1): p_n = p_dist.rvs() # select the number of chairs in clique i p_n = int( - p_n if len(chairs) + p_n <= len(butts) else len(butts) - len(chairs) + p_n if i < n_groups else len(butts) - len(chairs) ) # pull a random length or select a length to make the two lists equal if we are bout to go over print(p_n) - chairs.extend( - [i for _ in range(int(p_n))] - ) # add p_n chairs belonging to clique i - chairs.extend([chairs[-1] for i in range(len(butts) - len(chairs))]) - breakpoint() - chairs = [chair + N_inds for chair in chairs] + chairs.extend([i] * p_n) # add p_n chairs belonging to clique i + chairs = [chair + n_inds for chair in chairs] # shuffle the lists rng.shuffle(chairs) @@ -119,7 +114,7 @@ def bipartite_graph(edge_list): def clustered_unipartite(n_groups, n_ind, my_p_dist, my_g_dist, **kwargs): - edge_list, vertex_attributes = generate_hypergraph_bipartite_edge_list( + edge_list, vertex_attributes = generate_bipartite_edge_list( n_groups, n_ind, my_p_dist, my_g_dist ) projected_nodes = [ diff --git a/plot_graph_topology.ipynb b/plot_graph_topology.ipynb new file mode 100644 index 0000000..c0ac947 --- /dev/null +++ b/plot_graph_topology.ipynb @@ -0,0 +1,112 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import seaborn as sns\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import json\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "ename": "FileNotFoundError", + "evalue": "[Errno 2] No such file or directory: 'Data/clustering.json'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m/users/w/t/wthomps3/CSDS/side_projects/modeling-and-inferring-complex-contagion/plot_graph_topology.ipynb Cell 2\u001b[0m line \u001b[0;36m3\n\u001b[1;32m 1\u001b[0m fname \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mData/clustering.json\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m----> 3\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mopen\u001b[39m(fname) \u001b[39mas\u001b[39;00m file:\n\u001b[1;32m 4\u001b[0m data \u001b[39m=\u001b[39m json\u001b[39m.\u001b[39mload(file)\n\u001b[1;32m 5\u001b[0m p \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39marray(data[\u001b[39m\"\u001b[39m\u001b[39mclique_number\u001b[39m\u001b[39m\"\u001b[39m], dtype\u001b[39m=\u001b[39m\u001b[39mfloat\u001b[39m)\n", + "File \u001b[0;32m~/anaconda3/envs/complex_inference/lib/python3.11/site-packages/IPython/core/interactiveshell.py:286\u001b[0m, in \u001b[0;36m_modified_open\u001b[0;34m(file, *args, **kwargs)\u001b[0m\n\u001b[1;32m 279\u001b[0m \u001b[39mif\u001b[39;00m file \u001b[39min\u001b[39;00m {\u001b[39m0\u001b[39m, \u001b[39m1\u001b[39m, \u001b[39m2\u001b[39m}:\n\u001b[1;32m 280\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m 281\u001b[0m \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mIPython won\u001b[39m\u001b[39m'\u001b[39m\u001b[39mt let you open fd=\u001b[39m\u001b[39m{\u001b[39;00mfile\u001b[39m}\u001b[39;00m\u001b[39m by default \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 282\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mas it is likely to crash IPython. If you know what you are doing, \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 283\u001b[0m \u001b[39m\"\u001b[39m\u001b[39myou can use builtins\u001b[39m\u001b[39m'\u001b[39m\u001b[39m open.\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 284\u001b[0m )\n\u001b[0;32m--> 286\u001b[0m \u001b[39mreturn\u001b[39;00m io_open(file, \u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n", + "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'Data/clustering.json'" + ] + } + ], + "source": [ + "fname = \"Data/clustering.json\"\n", + "\n", + "with open(fname) as file:\n", + " data = json.load(file)\n", + "p = np.array(data[\"clique_number\"], dtype=float)\n", + "ps = np.array(data[\"ps\"], dtype=float)\n", + "sps = np.array(data[\"sps\"], dtype=float)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'PS')" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "l = [\"Simple contagion\", \"Threshold contagion, tau=2\", \"Threshold contagion, tau=3\"]\n", + "\n", + "for i in range(sps.shape[0]):\n", + " ps_mean = np.mean(sps, axis=2)[i]\n", + " ps_std = np.std(sps, axis=2)[i]\n", + " plt.plot(p, ps_mean, \"^-\", label=l[i])\n", + " plt.fill_between(p, ps_mean - ps_std, ps_mean + ps_std, alpha=0.4)\n", + "plt.legend()\n", + "plt.xlabel(\"Clique Number\")\n", + "plt.ylabel(\"PS\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}