diff --git a/Extra/plot_fig1.ipynb b/Extra/plot_fig1.ipynb new file mode 100644 index 0000000..6e62a04 --- /dev/null +++ b/Extra/plot_fig1.ipynb @@ -0,0 +1,664 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "\n", + "import arviz as az\n", + "import cmasher as cmr\n", + "import matplotlib.cm as cm\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import seaborn as sns\n", + "import xgi\n", + "\n", + "import fig_settings as fs\n", + "from lcs import *\n", + "\n", + "import matplotlib as mpl\n", + "import matplotlib.colors as mcolors\n", + "from cycler import cycler\n", + "\n", + "\n", + "# Set the global default size of the axis labels\n", + "plt.rcParams['axes.labelsize'] = 20\n", + "# Set the global default size of the tick labels\n", + "plt.rcParams['xtick.labelsize'] = 15\n", + "plt.rcParams['ytick.labelsize'] = 15\n", + "plt.rcParams['axes.titlesize'] = 25\n", + "plt.rcParams['legend.fontsize'] = 15\n", + "plt.rcParams['xtick.major.size'] = 7 # length in points\n" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [], + "source": [ + "#pallete = [\"#F8B195\",\"#355C7D\",\"#F67280\",\"#C06C84\",\"#6C5B7B\"]\n", + "pallete = [\"#355C7D\",\"#F67280\",\"#F8B195\",\"#C06C84\",\"#6C5B7B\"]\n", + "mpl.rcParams['axes.prop_cycle'] = mpl.cycler(color= pallete)\n", + "cmap = mcolors.LinearSegmentedColormap.from_list('my_cmap',[pallete[0],pallete[1]])" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [], + "source": [ + "fs.set_fonts()" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/willthompson/miniconda3/envs/complex_inference/lib/python3.11/site-packages/xgi/drawing/draw.py:386: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n", + " node_collection = ax.scatter(\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "el = zkc(format=\"edgelist\")\n", + "H = xgi.Hypergraph(el)\n", + "A = zkc()\n", + "n = A.shape[0]\n", + "\n", + "i = 13\n", + "t = 56\n", + "\n", + "gamma = 0.2\n", + "b = 0.07\n", + "contagion_function = lambda nu, b: 1 - (1 - b) ** nu\n", + "c = contagion_function(np.arange(n), b)\n", + "x0 = np.zeros(n)\n", + "x0[0] = 1\n", + "\n", + "x = contagion_process(A, gamma, c, x0, tmin=0, tmax=100, random_seed=2)\n", + "\n", + "infected_color = 'C1' \n", + "susceptible_color = \"white\"\n", + "subgraph_color = \"black\"\n", + "graph_color = (0.1, 0.1, 0.1, 0.1)\n", + "subgraph_node_lc = \"black\"\n", + "graph_node_lc = (0.3, 0.3, 0.3)\n", + "\n", + "sg = H.nodes.memberships(i)\n", + "nbrs = H.nodes.neighbors(i)\n", + "nbrs.add(i)\n", + "\n", + "pos = xgi.pca_transform(xgi.pairwise_spring_layout(H, seed=5, k=0.3))\n", + "node_fc = [infected_color if x[t, i] else susceptible_color for i in H.nodes]\n", + "node_ec = [subgraph_node_lc if n in nbrs else graph_node_lc for n in H.nodes]\n", + "node_fc[12] = 'C0'\n", + "\n", + "dyad_color = [subgraph_color if e in sg else graph_color for e in H.edges]\n", + "\n", + "plt.figure(figsize=(4, 3))\n", + "\n", + "\n", + "xgi.draw(\n", + " H,\n", + " pos=pos,\n", + " node_size=7.5,\n", + " node_fc=node_fc,\n", + " dyad_color=dyad_color,\n", + " node_ec=node_ec,\n", + " node_lw=0.5,\n", + ")\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig(\"Figures/Fig1/zkc_network.svg\", dpi=1000)\n", + "plt.savefig(\"Figures/Fig1/zkc_network.png\", dpi=1000)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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eHtamTZv01FNPaXBwUA888EDqtdraWrW0tOj06dNp7xkaGlIgEFBzc3NW+woGg6qqqsqqACgs51xWJdO53rlk1QP66KOPtGnTJoVCIb388sv605/+lPb6Y489pt27d2vbtm3q6OjQ9u3bdeHCBR09elTd3d2pnhIASJJcFkZHR52kjOXYsWPOOef6+vrc2rVrnc/nc+Fw2O3du9fduXPnvvcTi8Xm3c98BYCt5Pc3FostuG3WM6FLITkTOhcePBxgUcnmSgauhgdghgACYIYAAmDG0zel52p4wB7PhgdQkQggAGYIIABmCCAAZgggAGYIIABmPD0MD5Sz+Yav57tkKNf35fteC/SAAJghgACYIYAAmCGAAJghgACYIYAAmGEYHqgguQ61F3L4Pps7mtIDAmCGAAJghgACYIYAAmCGAAJghgACYIZheGABud6UPdchcS9etV4s9IAAmCGAAJghgACYIYAAmCGAAJghgACYYRgeWEA5DYvnelV7Ma6ivx/0gACYIYAAmCGAAJghgACYIYAAmCGAAJipuGH4cns2Nryv1FfD56Pc/sfpAQEwQwABMEMAATBDAAEwQwABMEMAATBTccPwQKGV29B2Lqymr9ADAmCGAAJghgACYIYAAmCGAAJghgACYIYAAmCm4uYBLYY5GyitYsyR8dptY+bbZ75PvpgPPSAAZgggAGYIIABmsg6gw4cPa8OGDQqFQqqtrVVTU5MOHTqkRCKR2ub48eNqampSTU2NIpGI9u3bl/Y6AEg5BFBvb69Wr16tQ4cO6eTJk/rc5z6nb33rW+rp6ZEkDQ4OaseOHWpra9PAwIA6Ojq0Z88eHThwoOCVB1DeqlyWp9w/+ugjrVixIrXsnFNjY6NWrFihP//5z2pra5NzTufOnUtt09nZqf7+fl2/fl1Lly5dcB9TU1MKBoOKxWIKBALZVA8ouMUwCjafXEfB7uf7m3UP6JPhI/2/citXrtTMzIyGh4cVjUbTtolGo5qcnNT58+ez3d2cqqqqMpZyUinHUemccxmLlz6zHOV8Enp2dlZXrlzRSy+9pBs3bqi7u1ujo6NKJBIKhUJp20YiEUnSyMhIfrUFUFFynoj47LPP6t1339WKFSs0MDCgJ554Qu+++64kqa6uLm3b5HIsFpvzs+LxuOLxeGp5amoq12oBKCM594COHDmiN998Uzt37tSWLVt07NixVPdxyZL0XEuuz/TToqenR8FgMFXC4XCu1QJQRnIOoHXr1mnz5s06ePCgPv/5z6urq0v19fWSpFu3bqVte/PmTUlSMBic87O6uroUi8VSZXx8PNdqASgjBZmI2NDQoGvXrqmhoUHV1dX3nOsZGxuTJDU2Ns75fr/fr0AgkFYAVL6sAmhyclK3b99OW5dIJHTmzBmtWrVKtbW1amlp0enTp9O2GRoaUiAQUHNzc/41BlAxsjoJfenSJb3wwgtqb2/X+vXrdfv2bf3sZz/TX/7yF73xxhuSpN27d2vbtm3q6OjQ9u3bdeHCBR09elTd3d3y+XxZVS7TT7ZKGaqslONYzHKdMlGM+UP5fK4Zl4WrV6+6zs5O9/jjj7tgMOhqampcS0uLe+edd9K26+vrc2vXrnU+n8+Fw2G3d+9ed+fOnfveTywWc5IyFsAr5vs/Lcb/cLE+N599ZiqxWGzBz856JnQpJGdCZ+LBKmORWgw9IE/NhAaAQiGAAJghgACYIYAAmPH0TelzuR1HpdzmwEt1LfWJ1oX2Od/nFuN98yn132mh/RXjTgrFaLckekAAzBBAAMwQQADMEEAAzBBAAMwQQADMeHoYPhdeGr7OR6mH6MvpRvjlVNdMyukYsv1fXOhazk+iBwTADAEEwAwBBMAMAQTADAEEwIwnR8GSZ9Z5QOHcyqVdvFbPYtTHa8dYanMdf3Ld/YzWevKWrP/4xz94OCFQ5sbHx/Xwww/Pu40nAyiRSGhiYkLLly8vq/kSAD7u+UxPTysUCqm6ev6zPJ4MIACLAyehAZghgACYIYAAmCGAAJghgACYIYAAmCGAAJj5L7opwZWlVqbQAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot matrices\n", + "plt.figure(figsize=(7, 3))\n", + "plt.imshow(x.T, cmap=cm.Greys, vmin=0, vmax=1, interpolation=\"none\")\n", + "plt.yticks([0, 10, 20, 30])\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig(\"Figures/Fig1/x.svg\", dpi=1000)\n", + "plt.savefig(\"Figures/Fig1/x.png\", dpi=1000)\n", + "plt.show()\n", + "\n", + "plt.figure(figsize=(3, 3))\n", + "plt.imshow(A, vmin=0, vmax=1, cmap=cm.Greys, interpolation=\"none\")\n", + "plt.xticks([0, 10, 20, 30])\n", + "plt.yticks([0, 10, 20, 30])\n", + "ax = plt.gca()\n", + "ax.xaxis.tick_top()\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig(\"Figures/Fig1/a.svg\", dpi=1000)\n", + "plt.savefig(\"Figures/Fig1/a.png\", dpi=1000)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "with open(\"Data/zkc_infer_contagion_functions.json\") as file:\n", + " data = json.load(file)\n", + "\n", + "A = np.array(data[\"A\"], dtype=float)\n", + "c1 = np.array(data[\"c1\"], dtype=float)\n", + "c2 = np.array(data[\"c2\"], dtype=float)\n", + "x1 = np.array(data[\"x1\"], dtype=int)\n", + "x2 = np.array(data[\"x2\"], dtype=int)\n", + "A1_samples = np.array(data[\"A1-samples\"], dtype=int)\n", + "A2_samples = np.array(data[\"A2-samples\"], dtype=int)\n", + "gamma1_samples = np.array(data[\"gamma1-samples\"], dtype=float)\n", + "gamma2_samples = np.array(data[\"gamma2-samples\"], dtype=float)\n", + "c1_samples = np.array(data[\"c1-samples\"], dtype=float)\n", + "c2_samples = np.array(data[\"c2-samples\"], dtype=float)\n", + "l1 = np.array(data[\"l1\"], dtype=float)\n", + "l2 = np.array(data[\"l2\"], dtype=float)\n", + "\n", + "# import cmasher as cmr\n", + "# cmap = cmr.ember\n", + "colors = pallete\n", + "\n", + "kmax = np.max(degrees(A))\n", + "n = A.shape[0]\n", + "\n", + "nus = np.arange(0, n, 1)\n", + "\n", + "plt.figure(figsize=(4, 3))\n", + "\n", + "# simple contagion\n", + "c1_mean = c1_samples.mean(axis=0)\n", + "plt.plot(nus, c1, \"-\", color='C0', label=\"Simple contagion\")\n", + "# plt.scatter(nus, c1_mean, linewidth=0.5, color=colors[2])\n", + "\n", + "err_c1 = np.zeros((2, n))\n", + "c1_mode = np.zeros(n)\n", + "for i in range(n):\n", + " interval = az.hdi(c1_samples[:, i], hdi_prob=0.95)\n", + " x, y = interval\n", + " err_c1[0, i] = max(c1_mean[i] - x, 0)\n", + " err_c1[1, i] = max(y - c1_mean[i], 0)\n", + "plt.errorbar(nus, c1_mean, err_c1, color='C0', fmt=\"o\")\n", + "\n", + "# threshold contagion, tau=2\n", + "c2_mean = c2_samples.mean(axis=0)\n", + "plt.plot(nus, c2, \"-\", color='C1', label=\"Complex contagion\")\n", + "# plt.scatter(nus, c2_mean, linewidth=0.5, color=colors[1])\n", + "\n", + "err_c2 = np.zeros((2, n))\n", + "c2_mode = np.zeros(n)\n", + "for i in range(n):\n", + " interval = az.hdi(c2_samples[:, i], alpha=0.05, roundto=4)\n", + " x, y = interval\n", + " err_c2[0, i] = max(c2_mean[i] - x, 0)\n", + " err_c2[1, i] = max(y - c2_mean[i], 0)\n", + "plt.errorbar(nus, c2_mean, err_c2, color='C1', fmt=\"o\")\n", + "\n", + "plt.xticks(np.arange(0, n, 5))\n", + "plt.xlabel(r\"$\\nu$\")\n", + "plt.ylabel(r\"$c(\\nu)$\")\n", + "\n", + "\n", + "plt.xlim([0, kmax + 2.5])\n", + "plt.ylim([0, 1])\n", + "\n", + "plt.legend(loc=\"upper left\")\n", + "\n", + "sns.despine()\n", + "plt.tight_layout()\n", + "\n", + "\n", + "plt.savefig(\"Figures/Fig1/zkc_infer_contagion_function.svg\", dpi=1000)\n", + "plt.savefig(\"Figures/Fig1/zkc_infer_contagion_function.png\", dpi=1000)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "with open(\"Data/zkc_infer_vs_tmax.json\") as file:\n", + " data = json.load(file)\n", + "\n", + "#colors = [\"steelblue\", \"darksalmon\", \"mediumseagreen\"]\n", + "colors = pallete\n", + "\n", + "tmax = data[\"tmax\"]\n", + "sps = np.array(data[\"sps\"], dtype=float)\n", + "ps = np.array(data[\"ps\"], dtype=float)\n", + "fce = np.array(data[\"fce\"], dtype=float)\n", + "\n", + "fig = plt.figure(figsize=(4, 3))\n", + "\n", + "#plt.semilogx(tmax, sps[0].mean(axis=1), color=colors[2], label=\"Simple contagion\")\n", + "plt.semilogx(tmax, sps[0].mean(axis=1), color='C0', label=\"Simple contagion\")\n", + "plt.semilogx(tmax, sps[1].mean(axis=1), color='C1', label=\"Complex contagion\")\n", + "plt.fill_between(\n", + " tmax,\n", + " sps[0].mean(axis=1) - sps[0].std(axis=1),\n", + " sps[0].mean(axis=1) + sps[0].std(axis=1),\n", + " alpha=0.3,\n", + " color='C0',\n", + ")\n", + "plt.fill_between(\n", + " tmax,\n", + " sps[1].mean(axis=1) - sps[1].std(axis=1),\n", + " sps[1].mean(axis=1) + sps[1].std(axis=1),\n", + " alpha=0.3,\n", + " color='C1',\n", + ")\n", + "plt.ylabel(\"F-Score\")\n", + "plt.xlabel(r\"$t_{max}$\")\n", + "\n", + "plt.legend(loc=\"upper left\")\n", + "sns.despine()\n", + "\n", + "plt.savefig(\"Figures/Fig1/zkc_infer_vs_tmax.svg\", dpi=1000)\n", + "plt.savefig(\"Figures/Fig1/zkc_infer_vs_tmax.png\", dpi=1000)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "with open(\"Data/zkc_frac_vs_beta.json\") as file:\n", + " data = json.load(file)\n", + "beta = np.array(data[\"beta\"], dtype=float)\n", + "frac = np.array(data[\"fraction\"], dtype=float)\n", + "ps = np.array(data[\"ps\"], dtype=float)\n", + "sps = np.array(data[\"sps\"], dtype=float)\n", + "fce = np.array(data[\"fce\"], dtype=float)\n", + "\n", + "#cmap = cmr.gem\n", + "cmap = cmap\n", + "\n", + "sps_summary = sps.mean(axis=2)\n", + "\n", + "fig = plt.figure(figsize=(4, 3))\n", + "\n", + "c = plt.imshow(\n", + " to_imshow_orientation(sps_summary),\n", + " extent=(min(frac), max(frac), min(beta), max(beta)),\n", + " aspect=\"auto\",\n", + " cmap=cmap,\n", + " vmin=0,\n", + " vmax=1,\n", + ")\n", + "plt.xlabel(r\"$f$\")\n", + "plt.ylabel(r\"$\\beta$\")\n", + "\n", + "plt.xticks([0, 0.5, 1], [0, 0.5, 1])\n", + "plt.yticks([0, 0.25, 0.5, 0.75, 1], [0, 0.25, 0.5, 0.75, 1])\n", + "\n", + "cbar = plt.colorbar(c)\n", + "cbar.set_label(r\"F-Score\", fontsize=12, rotation=270, labelpad=15)\n", + "plt.tight_layout()\n", + "\n", + "plt.savefig(\"Figures/Fig1/zkc_frac_vs_beta.svg\", dpi=1000)\n", + "plt.savefig(\"Figures/Fig1/zkc_frac_vs_beta.png\", dpi=1000)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/willthompson/miniconda3/envs/complex_inference/lib/python3.11/site-packages/xgi/drawing/draw.py:386: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n", + " node_collection = ax.scatter(\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ((ax1, ax2),(ax3, ax4)) = plt.subplots(2,2,figsize=(8,6), sharey=False, sharex=False)\n", + "\n", + "\"\"\"\n", + "Panel 1: Network Viz\n", + "\"\"\"\n", + "el = zkc(format=\"edgelist\")\n", + "H = xgi.Hypergraph(el)\n", + "A = zkc()\n", + "n = A.shape[0]\n", + "\n", + "i = 13\n", + "t = 56\n", + "\n", + "gamma = 0.2\n", + "b = 0.07\n", + "contagion_function = lambda nu, b: 1 - (1 - b) ** nu\n", + "c = contagion_function(np.arange(n), b)\n", + "x0 = np.zeros(n)\n", + "x0[0] = 1\n", + "\n", + "x = contagion_process(A, gamma, c, x0, tmin=0, tmax=100, random_seed=2)\n", + "\n", + "infected_color = 'C1' \n", + "susceptible_color = \"white\"\n", + "subgraph_color = \"black\"\n", + "graph_color = (0.1, 0.1, 0.1, 0.1)\n", + "subgraph_node_lc = \"black\"\n", + "graph_node_lc = (0.3, 0.3, 0.3)\n", + "\n", + "sg = H.nodes.memberships(i)\n", + "nbrs = H.nodes.neighbors(i)\n", + "nbrs.add(i)\n", + "\n", + "pos = xgi.pca_transform(xgi.pairwise_spring_layout(H, seed=5, k=0.3))\n", + "node_fc = [infected_color if x[t, i] else susceptible_color for i in H.nodes]\n", + "node_ec = [subgraph_node_lc if n in nbrs else graph_node_lc for n in H.nodes]\n", + "node_fc[12] = 'C0'\n", + "\n", + "dyad_color = [subgraph_color if e in sg else graph_color for e in H.edges]\n", + "\n", + "\n", + "\n", + "xgi.draw(\n", + " H,\n", + " pos=pos,\n", + " node_size=7.5,\n", + " node_fc=node_fc,\n", + " dyad_color=dyad_color,\n", + " node_ec=node_ec,\n", + " node_lw=0.5,\n", + " ax = ax1\n", + ")\n", + "\n", + "# plt.savefig(\"Figures/Fig1/zkc_network.svg\", dpi=1000)\n", + "# plt.savefig(\"Figures/Fig1/zkc_network.png\", dpi=1000)\n", + "\n", + "\n", + "\"\"\"\n", + "Panel 2: \n", + "\"\"\"\n", + "\n", + "\n", + "with open(\"Data/zkc_infer_contagion_functions.json\") as file:\n", + " data = json.load(file)\n", + "\n", + "A = np.array(data[\"A\"], dtype=float)\n", + "c1 = np.array(data[\"c1\"], dtype=float)\n", + "c2 = np.array(data[\"c2\"], dtype=float)\n", + "x1 = np.array(data[\"x1\"], dtype=int)\n", + "x2 = np.array(data[\"x2\"], dtype=int)\n", + "A1_samples = np.array(data[\"A1-samples\"], dtype=int)\n", + "A2_samples = np.array(data[\"A2-samples\"], dtype=int)\n", + "gamma1_samples = np.array(data[\"gamma1-samples\"], dtype=float)\n", + "gamma2_samples = np.array(data[\"gamma2-samples\"], dtype=float)\n", + "c1_samples = np.array(data[\"c1-samples\"], dtype=float)\n", + "c2_samples = np.array(data[\"c2-samples\"], dtype=float)\n", + "l1 = np.array(data[\"l1\"], dtype=float)\n", + "l2 = np.array(data[\"l2\"], dtype=float)\n", + "\n", + "# import cmasher as cmr\n", + "# cmap = cmr.ember\n", + "\n", + "kmax = np.max(degrees(A))\n", + "n = A.shape[0]\n", + "\n", + "nus = np.arange(0, n, 1)\n", + "\n", + "# simple contagion\n", + "c1_mean = c1_samples.mean(axis=0)\n", + "ax2.plot(nus, c1, \"-\", color='C0', label=\"Simple contagion\")\n", + "# ax2.scatter(nus, c1_mean, linewidth=0.5, color=colors[2])\n", + "\n", + "err_c1 = np.zeros((2, n))\n", + "c1_mode = np.zeros(n)\n", + "for i in range(n):\n", + " interval = az.hdi(c1_samples[:, i], hdi_prob=0.95)\n", + " x, y = interval\n", + " err_c1[0, i] = max(c1_mean[i] - x, 0)\n", + " err_c1[1, i] = max(y - c1_mean[i], 0)\n", + "ax2.errorbar(nus, c1_mean, err_c1, color='C0', fmt=\"o\")\n", + "\n", + "# threshold contagion, tau=2\n", + "c2_mean = c2_samples.mean(axis=0)\n", + "ax2.plot(nus, c2, \"-\", color='C1', label=\"Complex contagion\")\n", + "# ax2.scatter(nus, c2_mean, linewidth=0.5, color=colors[1])\n", + "\n", + "err_c2 = np.zeros((2, n))\n", + "c2_mode = np.zeros(n)\n", + "for i in range(n):\n", + " interval = az.hdi(c2_samples[:, i], alpha=0.05, roundto=4)\n", + " x, y = interval\n", + " err_c2[0, i] = max(c2_mean[i] - x, 0)\n", + " err_c2[1, i] = max(y - c2_mean[i], 0)\n", + "ax2.errorbar(nus, c2_mean, err_c2, color='C1', fmt=\"o\")\n", + "\n", + "ax2.set_xticks(np.arange(0, n, 5))\n", + "ax2.set_xlabel(r\"$\\nu$\")\n", + "ax2.set_ylabel(r\"$c(\\nu)$\")\n", + "\n", + "\n", + "ax2.set_xlim([0, kmax + 2.5])\n", + "ax2.set_ylim([0, 1])\n", + "\n", + "ax2.legend(loc=\"upper left\")\n", + "\n", + "sns.despine()\n", + "\n", + "\n", + "# # ax2.savefig(\"Figures/Fig1/zkc_infer_contagion_function.svg\", dpi=1000)\n", + "# # ax2.savefig(\"Figures/Fig1/zkc_infer_contagion_function.png\", dpi=1000)\n", + "# ax2.show()\n", + "\n", + "with open(\"Data/zkc_infer_vs_tmax.json\") as file:\n", + " data = json.load(file)\n", + "\n", + "#colors = [\"steelblue\", \"darksalmon\", \"mediumseagreen\"]\n", + "colors = pallete\n", + "\n", + "tmax = data[\"tmax\"]\n", + "sps = np.array(data[\"sps\"], dtype=float)\n", + "ps = np.array(data[\"ps\"], dtype=float)\n", + "fce = np.array(data[\"fce\"], dtype=float)\n", + "\n", + "\n", + "#ax3.semilogx(tmax, sps[0].mean(axis=1), color=colors[2], label=\"Simple contagion\")\n", + "ax3.semilogx(tmax, sps[0].mean(axis=1), color='C0', label=\"Simple contagion\")\n", + "ax3.semilogx(tmax, sps[1].mean(axis=1), color='C1', label=\"Complex contagion\")\n", + "ax3.fill_between(\n", + " tmax,\n", + " sps[0].mean(axis=1) - sps[0].std(axis=1),\n", + " sps[0].mean(axis=1) + sps[0].std(axis=1),\n", + " alpha=0.3,\n", + " color='C0',\n", + ")\n", + "ax3.fill_between(\n", + " tmax,\n", + " sps[1].mean(axis=1) - sps[1].std(axis=1),\n", + " sps[1].mean(axis=1) + sps[1].std(axis=1),\n", + " alpha=0.3,\n", + " color='C1',\n", + ")\n", + "ax3.set_ylabel(\"F-Score\")\n", + "ax3.set_xlabel(r\"$t_{max}$\")\n", + "\n", + "ax3.legend(loc=\"upper left\")\n", + "sns.despine()\n", + "\n", + "\n", + "\n", + "with open(\"Data/zkc_frac_vs_beta.json\") as file:\n", + " data = json.load(file)\n", + "beta = np.array(data[\"beta\"], dtype=float)\n", + "frac = np.array(data[\"fraction\"], dtype=float)\n", + "ps = np.array(data[\"ps\"], dtype=float)\n", + "sps = np.array(data[\"sps\"], dtype=float)\n", + "fce = np.array(data[\"fce\"], dtype=float)\n", + "\n", + "#cmap = cmr.gem\n", + "cmap = cmap\n", + "\n", + "sps_summary = sps.mean(axis=2)\n", + "\n", + "\n", + "c = ax4.imshow(\n", + " to_imshow_orientation(sps_summary),\n", + " extent=(min(frac), max(frac), min(beta), max(beta)),\n", + " aspect=\"auto\",\n", + " cmap=cmap,\n", + " vmin=0,\n", + " vmax=1,\n", + ")\n", + "ax4.set_xlabel(r\"$f$\")\n", + "ax4.set_ylabel(r\"$\\beta$\")\n", + "\n", + "ax4.set_xticks([0, 0.5, 1], [0, 0.5, 1])\n", + "ax4.set_yticks([0, 0.25, 0.5, 0.75, 1], [0, 0.25, 0.5, 0.75, 1])\n", + "\n", + "cbar = plt.colorbar(c,ax = ax4)\n", + "cbar.set_label(r\"F-Score\", fontsize=12, rotation=270, labelpad=15)\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "hyper", + "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 +} diff --git a/Extra/plot_fig2.ipynb b/Extra/plot_fig2.ipynb new file mode 100644 index 0000000..3abb144 --- /dev/null +++ b/Extra/plot_fig2.ipynb @@ -0,0 +1,436 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "\n", + "import cmasher as cmr\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import seaborn as sns\n", + "import xgi\n", + "\n", + "import fig_settings as fs\n", + "cmap = fs.cmap\n", + "\n", + "\n", + "from lcs import *" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fs.set_fonts({\"font.family\": \"sans-serif\"})\n", + "#cmap = cmr.gem" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "models = [\"Erdos-Renyi\", \"SBM\", \"Watts-Strogatz\", \"CM\", \"clustered_network\"]\n", + "cfs = [\n", + " \"SIS\",\n", + " r\"Threshold, $\\tau=2$\",\n", + " r\"Threshold, $\\tau=3$\",\n", + "]\n", + "keys = [\"p\", \"epsilon\", \"p\", \"alpha\", \"size\"]\n", + "titles = [\"Erdös-Rényi\", \"SBM\", \"Small-World\", \"Power-law CM\", \"Clustered\"]\n", + "labels = [r\"$p$\", r\"$\\epsilon$\", r\"$p$\", r\"$\\alpha$\", r\"$s$\"]\n", + "xticks = [\n", + " [0, 0.5, 1],\n", + " [0, 0.5, 1],\n", + " [-6, -4, -2, 0],\n", + " [1.5, 2, 2.5, 3, 3.5, 4],\n", + " [1, 7, 13, 19],\n", + "]\n", + "xticklabels = [\n", + " [\"0\", \"0.5\", \"1\"],\n", + " [\"0\", \"0.5\", \"1\"],\n", + " [r\"$10^{-6}$\", r\"$10^{-4}$\", r\"$10^{-2}$\", r\"$10^{0}$\"],\n", + " [\"1.5\", \"2\", \"2.5\", \"3\", \"3.5\", \"4\"],\n", + " [\"1\", \"7\", \"13\", \"19\"],\n", + "]\n", + "convert_to_log = [False, False, True, False, False]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, axes = plt.subplots(len(cfs), len(models), figsize=(14, 8))\n", + "for i, m in enumerate(models):\n", + " with open(f\"Data/{m.lower()}.json\") as file:\n", + " data = json.load(file)\n", + " var = np.array(data[keys[i]], dtype=float)\n", + " b = np.array(data[\"beta\"], dtype=float)\n", + " sps = np.array(data[\"sps\"], dtype=float)\n", + "\n", + " if convert_to_log[i]:\n", + " var = np.log10(var)\n", + "\n", + " for j, cf in enumerate(cfs):\n", + " sps_summary = sps[j].mean(axis=2).T\n", + " im = axes[j, i].imshow(\n", + " to_imshow_orientation(sps_summary),\n", + " extent=(min(var), max(var), min(b), max(b)),\n", + " vmin=0,\n", + " vmax=1,\n", + " aspect=\"auto\",\n", + " cmap=cmap,\n", + " )\n", + " axes[j, i].set_xlim([min(var), max(var)])\n", + " axes[j, i].set_ylim([min(b), max(b)])\n", + " axes[j, i].set_xticks(xticks[i], xticklabels[i])\n", + " axes[j, i].set_yticks([0, 0.5, 1], [0, 0.5, 1])\n", + "\n", + " if j == 0:\n", + " axes[j, i].set_title(titles[i])\n", + " if i == 0:\n", + " axes[j, i].set_ylabel(f\"{cfs[j]}\\n\" + r\"$\\beta$\")\n", + " if j == len(cfs) - 1:\n", + " axes[j, i].set_xlabel(labels[i], fontsize=16)\n", + "fig.subplots_adjust(bottom=0.15, top=0.95, left=0.1, right=0.8, wspace=0.3, hspace=0.3)\n", + "cbar_ax = fig.add_axes([0.82, 0.15, 0.02, 0.8])\n", + "cbar = fig.colorbar(im, cax=cbar_ax)\n", + "cbar.set_label(r\"F-Score\", fontsize=16, rotation=270, labelpad=25)\n", + "# plt.tight_layout()\n", + "\n", + "plt.savefig(\"Figures/Fig2/generative_models_sps.png\", dpi=1000)\n", + "plt.savefig(\"Figures/Fig2/generative_models_sps.pdf\", dpi=1000)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def visualize_networks(i, ax):\n", + " n = 50\n", + " match i:\n", + " case 0:\n", + " A = erdos_renyi(n, 0.1, seed=0)\n", + " e = [(i, j) for i, j in nx.Graph(A).edges]\n", + " case 1:\n", + " A = sbm(n, 10, 0.9, seed=0)\n", + " e = [(i, j) for i, j in nx.Graph(A).edges]\n", + " case 2:\n", + " A = watts_strogatz(n, 6, 0.03, seed=0)\n", + " e = [(i, j) for i, j in nx.Graph(A).edges]\n", + " case 3:\n", + " A = truncated_power_law_configuration(n, 2, 20, 3, seed=0)\n", + " e = [(i, j) for i, j in nx.Graph(A).edges]\n", + " case 4:\n", + " k = 2 # each node belongs to two cliques\n", + " clique_size = 4\n", + " k1 = k * np.ones(n)\n", + " num_cliques = round(sum(k1) / clique_size)\n", + " k2 = clique_size * np.ones(num_cliques)\n", + " A = clustered_network(k1, k2, seed=0)\n", + " e = [(i, j) for i, j in nx.Graph(A).edges]\n", + "\n", + " H = xgi.Hypergraph(e)\n", + "\n", + " node_size = 3\n", + " dyad_lw = 0.5\n", + " node_lw = 0.5\n", + "\n", + " match i:\n", + " case 0:\n", + " pos = xgi.pairwise_spring_layout(H, seed=2)\n", + " case 1:\n", + " pos = xgi.pca_transform(xgi.pairwise_spring_layout(H, seed=2))\n", + " case 2:\n", + " pos = xgi.circular_layout(H)\n", + " case 3:\n", + " # sorted_nodes = [n for n, _ in sorted(H.nodes.degree.asdict().items(), key=lambda d: d[1])]\n", + " # Hnew = xgi.Hypergraph()\n", + " # Hnew.add_nodes_from(sorted_nodes)\n", + " # Hnew.add_edges_from(e)\n", + " # pos = xgi.circular_layout(Hnew)\n", + " pos = xgi.pairwise_spring_layout(H, seed=2)\n", + " case 4:\n", + " pos = xgi.pairwise_spring_layout(H, seed=2)\n", + " xgi.draw(H, ax=ax, pos=pos, node_size=node_size, node_lw=node_lw, dyad_lw=dyad_lw)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, axes = plt.subplots(len(cfs) + 1, len(models), figsize=(8, 6))\n", + "for i, m in enumerate(models):\n", + " with open(f\"Data/{m.lower()}.json\") as file:\n", + " data = json.load(file)\n", + " var = np.array(data[keys[i]], dtype=float)\n", + " b = np.array(data[\"beta\"], dtype=float)\n", + " sps = np.array(data[\"sps\"], dtype=float)\n", + "\n", + " if convert_to_log[i]:\n", + " var = np.log10(var)\n", + "\n", + " for j, cf in enumerate(cfs):\n", + " sps_summary = sps[j].mean(axis=2).T\n", + " im = axes[j + 1, i].imshow(\n", + " to_imshow_orientation(sps_summary),\n", + " extent=(min(var), max(var), min(b), max(b)),\n", + " vmin=0,\n", + " vmax=1,\n", + " aspect=\"auto\",\n", + " cmap=cmap,\n", + " )\n", + " axes[j + 1, i].set_xlim([min(var), max(var)])\n", + " axes[j + 1, i].set_ylim([min(b), max(b)])\n", + " axes[j + 1, i].set_xticks(xticks[i], xticklabels[i])\n", + " axes[j + 1, i].set_yticks([0, 0.5, 1], [0, 0.5, 1])\n", + "\n", + " if i == 0:\n", + " axes[j + 1, i].set_ylabel(f\"{cfs[j]}\\n\" + r\"$\\beta$\")\n", + "\n", + " if j + 1 == len(cfs):\n", + " axes[j + 1, i].set_xlabel(labels[i], fontsize=16)\n", + "\n", + "fig.subplots_adjust(bottom=0.15, top=0.95, left=0.1, right=0.8, wspace=0.3, hspace=0.3)\n", + "cbar_ax = fig.add_axes([0.82, 0.15, 0.02, 0.8])\n", + "cbar = fig.colorbar(im, cax=cbar_ax)\n", + "cbar.set_label(r\"F-Score\", fontsize=16, rotation=270, labelpad=25)\n", + "\n", + "for i, m in enumerate(models):\n", + " visualize_networks(i, axes[0, i])\n", + " axes[0, i].set_title(titles[i])\n", + "\n", + "plt.savefig(\"Figures/Fig2/generative_models_sps.png\", dpi=1000)\n", + "plt.savefig(\"Figures/Fig2/generative_models_sps.pdf\", dpi=1000)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(len(cfs) + 1, len(models), figsize=(14, 8))\n", + "for i, m in enumerate(models):\n", + " with open(f\"Data/{m.lower()}.json\") as file:\n", + " data = json.load(file)\n", + " var = np.array(data[keys[i]], dtype=float)\n", + " b = np.array(data[\"beta\"], dtype=float)\n", + " sps = np.array(data[\"sps\"], dtype=float)\n", + "\n", + " if convert_to_log[i]:\n", + " var = np.log10(var)\n", + "\n", + " for j, cf in enumerate(cfs):\n", + " sps_summary = sps[j].mean(axis=2).T\n", + " im = axes[j + 1, i].imshow(\n", + " to_imshow_orientation(sps_summary),\n", + " extent=(min(var), max(var), min(b), max(b)),\n", + " vmin=0,\n", + " vmax=1,\n", + " aspect=\"auto\",\n", + " cmap=cmap,\n", + " )\n", + " axes[j + 1, i].set_xlim([min(var), max(var)])\n", + " axes[j + 1, i].set_ylim([min(b), max(b)])\n", + " axes[j + 1, i].set_xticks(xticks[i], xticklabels[i])\n", + " axes[j + 1, i].set_yticks([0, 0.5, 1], [0, 0.5, 1])\n", + "\n", + " if i == 0:\n", + " axes[j + 1, i].set_ylabel(f\"{cfs[j]}\\n\" + r\"$\\beta$\")\n", + "\n", + " if j + 1 == len(cfs):\n", + " axes[j + 1, i].set_xlabel(labels[i], fontsize=16)\n", + "\n", + "fig.subplots_adjust(bottom=0.15, top=0.95, left=0.1, right=0.8, wspace=0.3, hspace=0.3)\n", + "cbar_ax = fig.add_axes([0.82, 0.15, 0.02, 0.8])\n", + "cbar = fig.colorbar(im, cax=cbar_ax)\n", + "cbar.set_label(r\"F-Score\", fontsize=16, rotation=270, labelpad=25)\n", + "\n", + "for i, m in enumerate(models):\n", + " visualize_networks(i, axes[0, i])\n", + " axes[0, i].set_title(titles[i])\n", + "\n", + "\n", + "plt.savefig(\"Figures/Fig2/generative_models_sps.png\", dpi=1000)\n", + "plt.savefig(\"Figures/Fig2/generative_models_sps.pdf\", dpi=1000)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(len(cfs), len(models), figsize=(14, 8))\n", + "for i, m in enumerate(models):\n", + " with open(f\"Data/{m.lower()}.json\") as file:\n", + " data = json.load(file)\n", + " var = np.array(data[keys[i]], dtype=float)\n", + " b = np.array(data[\"beta\"], dtype=float)\n", + " fce = np.array(data[\"fce\"], dtype=float)\n", + "\n", + " if convert_to_log[i]:\n", + " var = np.log10(var)\n", + "\n", + " for j, cf in enumerate(cfs):\n", + " fce_summary = fce[j].mean(axis=2).T\n", + " im = axes[j, i].imshow(\n", + " to_imshow_orientation(fce_summary),\n", + " extent=(min(var), max(var), min(b), max(b)),\n", + " vmin=0,\n", + " vmax=1,\n", + " aspect=\"auto\",\n", + " cmap=cmap,\n", + " )\n", + " axes[j, i].set_xlim([min(var), max(var)])\n", + " axes[j, i].set_ylim([min(b), max(b)])\n", + " axes[j, i].set_xticks(xticks[i], xticklabels[i])\n", + " axes[j, i].set_yticks([0, 0.5, 1], [0, 0.5, 1])\n", + "\n", + " if j == 0:\n", + " axes[j, i].set_title(titles[i])\n", + " if i == 0:\n", + " axes[j, i].set_ylabel(f\"{cfs[j]}\\n\" + r\"$\\beta$\")\n", + " if j == len(cfs) - 1:\n", + " axes[j, i].set_xlabel(labels[i], fontsize=16)\n", + "fig.subplots_adjust(bottom=0.15, top=0.95, left=0.1, right=0.8, wspace=0.3, hspace=0.3)\n", + "cbar_ax = fig.add_axes([0.82, 0.15, 0.02, 0.8])\n", + "cbar = fig.colorbar(im, cax=cbar_ax)\n", + "cbar.set_label(r\"FCE\", fontsize=16, rotation=270, labelpad=25)\n", + "\n", + "plt.savefig(\"Figures/Fig2/generative_models_fce.png\", dpi=1000)\n", + "plt.savefig(\"Figures/Fig2/generative_models_fce.pdf\", dpi=1000)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "n = 50\n", + "\n", + "A = erdos_renyi(n, 0.1, seed=0)\n", + "e1 = [(i, j) for i, j in nx.Graph(A).edges]\n", + "\n", + "A = sbm(n, 10, 0.9, seed=0)\n", + "e2 = [(i, j) for i, j in nx.Graph(A).edges]\n", + "\n", + "A = watts_strogatz(n, 6, 0.03, seed=0)\n", + "e3 = [(i, j) for i, j in nx.Graph(A).edges]\n", + "\n", + "A = truncated_power_law_configuration(n, 2, 20, 3, seed=0)\n", + "e4 = [(i, j) for i, j in nx.Graph(A).edges]\n", + "\n", + "k = 2 # each node belongs to two cliques\n", + "clique_size = 4\n", + "k1 = k * np.ones(n)\n", + "num_cliques = round(sum(k1) / clique_size)\n", + "k2 = clique_size * np.ones(num_cliques)\n", + "A = clustered_network(k1, k2, seed=0)\n", + "e5 = [(i, j) for i, j in nx.Graph(A).edges]\n", + "\n", + "H1 = xgi.Hypergraph(e1)\n", + "H2 = xgi.Hypergraph(e2)\n", + "H3 = xgi.Hypergraph(e3)\n", + "H4 = xgi.Hypergraph(e4)\n", + "H5 = xgi.Hypergraph(e5)\n", + "\n", + "node_size = 2\n", + "dyad_lw = 0.5\n", + "node_lw = 0.5\n", + "\n", + "plt.figure()\n", + "plt.subplot(151)\n", + "pos1 = xgi.pairwise_spring_layout(H1, seed=2)\n", + "xgi.draw(H1, pos=pos1, node_size=node_size, node_lw=node_lw, dyad_lw=dyad_lw)\n", + "\n", + "plt.subplot(152)\n", + "pos2 = xgi.pca_transform(xgi.pairwise_spring_layout(H2, seed=2))\n", + "xgi.draw(H2, pos=pos2, node_size=node_size, node_lw=node_lw, dyad_lw=dyad_lw)\n", + "\n", + "plt.subplot(153)\n", + "pos3 = xgi.circular_layout(H3)\n", + "xgi.draw(H3, pos=pos3, node_size=node_size, node_lw=node_lw, dyad_lw=dyad_lw)\n", + "\n", + "plt.subplot(154)\n", + "pos4 = xgi.pairwise_spring_layout(H4, seed=2)\n", + "xgi.draw(H4, pos=pos4, node_size=node_size, node_lw=node_lw, dyad_lw=dyad_lw)\n", + "\n", + "plt.subplot(155)\n", + "pos5 = xgi.pairwise_spring_layout(H5, seed=2)\n", + "xgi.draw(H5, pos=pos5, node_size=node_size, node_lw=node_lw, dyad_lw=dyad_lw)\n", + "plt.savefig(\"test2.png\", dpi=1000)" + ] + } + ], + 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clip-path="url(#pfac6842263)" style="fill: none; stroke: #1a1a1a; stroke-opacity: 0.1; stroke-width: 1.5; stroke-linecap: square"/> + + +" clip-path="url(#pfac6842263)" style="fill: none; stroke: #1a1a1a; stroke-opacity: 0.1; stroke-width: 1.5; stroke-linecap: square"/> + + +" clip-path="url(#pfac6842263)" style="fill: none; stroke: #1a1a1a; stroke-opacity: 0.1; stroke-width: 1.5; stroke-linecap: square"/> - - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - + diff --git a/Figures/Fig2/generative_models_sps.pdf b/Figures/Fig2/generative_models_sps.pdf index e40acd5..e157740 100644 Binary files a/Figures/Fig2/generative_models_sps.pdf and b/Figures/Fig2/generative_models_sps.pdf differ diff --git a/Figures/Fig2/generative_models_sps.png b/Figures/Fig2/generative_models_sps.png index 68e47e1..59eaf58 100644 Binary files a/Figures/Fig2/generative_models_sps.png and b/Figures/Fig2/generative_models_sps.png differ diff --git a/fig_settings.py b/fig_settings.py index c2843be..d3fbd6a 100644 --- a/fig_settings.py +++ b/fig_settings.py @@ -6,9 +6,31 @@ """ import os - import matplotlib.pylab as pylab import matplotlib.pyplot as plt +import matplotlib as mpl +import cmasher as cmr + + +#color styling +def set_colors(n_colors = 2): + global cmap + global pallette + cmap = 'cmr.redshift' + qualitative_cmap = cmr.get_sub_cmap(cmap, 0.2, 0.8, N=n_colors) + + pallette = qualitative_cmap.colors + mpl.rcParams['axes.prop_cycle'] = mpl.cycler(color= pallette) + + +def set_fontsize(): + plt.rcParams['axes.labelsize'] = 30 + # Set the global default size of the tick labels + plt.rcParams['xtick.labelsize'] = 15 + plt.rcParams['ytick.labelsize'] = 15 + plt.rcParams['axes.titlesize'] = 25 + plt.rcParams['legend.fontsize'] = 25 + def set_fonts(extra_params={}): @@ -52,7 +74,6 @@ def fig_size(frac_width, frac_height, n_cols=1, n_rows=1): return (width, height) - def get_formats(): return ["eps", "jpg", "pdf", "png", "tif"] diff --git a/plot_fig1.ipynb b/plot_fig1.ipynb deleted file mode 100644 index 7d61c77..0000000 --- a/plot_fig1.ipynb +++ /dev/null @@ -1,319 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import json\n", - "\n", - "import arviz as az\n", - "import cmasher as cmr\n", - "import matplotlib.cm as cm\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import seaborn as sns\n", - "import xgi\n", - "\n", - "import fig_settings as fs\n", - "from lcs import *" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fs.set_fonts()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "el = zkc(format=\"edgelist\")\n", - "H = xgi.Hypergraph(el)\n", - "A = zkc()\n", - "n = A.shape[0]\n", - "\n", - "i = 13\n", - "t = 56\n", - "\n", - "gamma = 0.2\n", - "b = 0.07\n", - "contagion_function = lambda nu, b: 1 - (1 - b) ** nu\n", - "c = contagion_function(np.arange(n), b)\n", - "x0 = np.zeros(n)\n", - "x0[0] = 1\n", - "\n", - "x = contagion_process(A, gamma, c, x0, tmin=0, tmax=100, random_seed=2)\n", - "\n", - "infected_color = \"darkred\"\n", - "susceptible_color = \"white\"\n", - "subgraph_color = \"black\"\n", - "graph_color = (0.1, 0.1, 0.1, 0.1)\n", - "subgraph_node_lc = \"black\"\n", - "graph_node_lc = (0.3, 0.3, 0.3)\n", - "\n", - "sg = H.nodes.memberships(i)\n", - "nbrs = H.nodes.neighbors(i)\n", - "nbrs.add(i)\n", - "\n", - "pos = xgi.pca_transform(xgi.pairwise_spring_layout(H, seed=5, k=0.3))\n", - "node_fc = [infected_color if x[t, i] else susceptible_color for i in H.nodes]\n", - "node_ec = [subgraph_node_lc if n in nbrs else graph_node_lc for n in H.nodes]\n", - "node_fc[12] = \"royalblue\"\n", - "\n", - "dyad_color = [subgraph_color if e in sg else graph_color for e in H.edges]\n", - "\n", - "plt.figure(figsize=(4, 3))\n", - "\n", - "\n", - "xgi.draw(\n", - " H,\n", - " pos=pos,\n", - " node_size=7.5,\n", - " node_fc=node_fc,\n", - " dyad_color=dyad_color,\n", - " node_ec=node_ec,\n", - " node_lw=1,\n", - ")\n", - "\n", - "plt.tight_layout()\n", - "plt.savefig(\"Figures/Fig1/zkc_network.svg\", dpi=1000)\n", - "plt.savefig(\"Figures/Fig1/zkc_network.png\", dpi=1000)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Plot matrices\n", - "plt.figure(figsize=(7, 3))\n", - "plt.imshow(x.T, cmap=cm.Greys, vmin=0, vmax=1, interpolation=\"none\")\n", - "plt.yticks([0, 10, 20, 30])\n", - "\n", - "plt.tight_layout()\n", - "plt.savefig(\"Figures/Fig1/x.svg\", dpi=1000)\n", - "plt.savefig(\"Figures/Fig1/x.png\", dpi=1000)\n", - "plt.show()\n", - "\n", - "plt.figure(figsize=(3, 3))\n", - "plt.imshow(A, vmin=0, vmax=1, cmap=cm.Greys, interpolation=\"none\")\n", - "plt.xticks([0, 10, 20, 30])\n", - "plt.yticks([0, 10, 20, 30])\n", - "ax = plt.gca()\n", - "ax.xaxis.tick_top()\n", - "\n", - "plt.tight_layout()\n", - "plt.savefig(\"Figures/Fig1/a.svg\", dpi=1000)\n", - "plt.savefig(\"Figures/Fig1/a.png\", dpi=1000)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "with open(\"Data/zkc_infer_contagion_functions.json\") as file:\n", - " data = json.load(file)\n", - "\n", - "A = np.array(data[\"A\"], dtype=float)\n", - "c1 = np.array(data[\"c1\"], dtype=float)\n", - "c2 = np.array(data[\"c2\"], dtype=float)\n", - "x1 = np.array(data[\"x1\"], dtype=int)\n", - "x2 = np.array(data[\"x2\"], dtype=int)\n", - "A1_samples = np.array(data[\"A1-samples\"], dtype=int)\n", - "A2_samples = np.array(data[\"A2-samples\"], dtype=int)\n", - "gamma1_samples = np.array(data[\"gamma1-samples\"], dtype=float)\n", - "gamma2_samples = np.array(data[\"gamma2-samples\"], dtype=float)\n", - "c1_samples = np.array(data[\"c1-samples\"], dtype=float)\n", - "c2_samples = np.array(data[\"c2-samples\"], dtype=float)\n", - "l1 = np.array(data[\"l1\"], dtype=float)\n", - "l2 = np.array(data[\"l2\"], dtype=float)\n", - "\n", - "# import cmasher as cmr\n", - "# cmap = cmr.ember\n", - "colors = [\"steelblue\", \"darksalmon\", \"mediumseagreen\"]\n", - "\n", - "kmax = np.max(degrees(A))\n", - "n = A.shape[0]\n", - "\n", - "nus = np.arange(0, n, 1)\n", - "\n", - "plt.figure(figsize=(4, 3))\n", - "\n", - "# simple contagion\n", - "c1_mean = c1_samples.mean(axis=0)\n", - "plt.plot(nus, c1, \"-\", color=colors[0], label=\"Simple contagion\")\n", - "# plt.scatter(nus, c1_mean, linewidth=0.5, color=colors[0])\n", - "\n", - "err_c1 = np.zeros((2, n))\n", - "c1_mode = np.zeros(n)\n", - "for i in range(n):\n", - " interval = az.hdi(c1_samples[:, i], hdi_prob=0.95)\n", - " x, y = interval\n", - " err_c1[0, i] = max(c1_mean[i] - x, 0)\n", - " err_c1[1, i] = max(y - c1_mean[i], 0)\n", - "plt.errorbar(nus, c1_mean, err_c1, color=colors[0], fmt=\"o\")\n", - "\n", - "# threshold contagion, tau=2\n", - "c2_mean = c2_samples.mean(axis=0)\n", - "plt.plot(nus, c2, \"-\", color=colors[1], label=\"Complex contagion\")\n", - "# plt.scatter(nus, c2_mean, linewidth=0.5, color=colors[1])\n", - "\n", - "err_c2 = np.zeros((2, n))\n", - "c2_mode = np.zeros(n)\n", - "for i in range(n):\n", - " interval = az.hdi(c2_samples[:, i], alpha=0.05, roundto=4)\n", - " x, y = interval\n", - " err_c2[0, i] = max(c2_mean[i] - x, 0)\n", - " err_c2[1, i] = max(y - c2_mean[i], 0)\n", - "plt.errorbar(nus, c2_mean, err_c2, color=colors[1], fmt=\"o\")\n", - "\n", - "plt.xticks(np.arange(0, n, 5))\n", - "plt.xlabel(r\"$\\nu$\")\n", - "plt.ylabel(r\"$c(\\nu)$\")\n", - "\n", - "\n", - "plt.xlim([0, kmax + 2.5])\n", - "plt.ylim([0, 1])\n", - "\n", - "plt.legend(loc=\"upper left\")\n", - "\n", - "sns.despine()\n", - "plt.tight_layout()\n", - "\n", - "\n", - "plt.savefig(\"Figures/Fig1/zkc_infer_contagion_function.svg\", dpi=1000)\n", - "plt.savefig(\"Figures/Fig1/zkc_infer_contagion_function.png\", dpi=1000)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "with open(\"Data/zkc_infer_vs_tmax.json\") as file:\n", - " data = json.load(file)\n", - "\n", - "colors = [\"steelblue\", \"darksalmon\", \"mediumseagreen\"]\n", - "\n", - "tmax = data[\"tmax\"]\n", - "sps = np.array(data[\"sps\"], dtype=float)\n", - "ps = np.array(data[\"ps\"], dtype=float)\n", - "fce = np.array(data[\"fce\"], dtype=float)\n", - "\n", - "fig = plt.figure(figsize=(4, 3))\n", - "\n", - "plt.semilogx(tmax, sps[0].mean(axis=1), color=colors[0], label=\"Simple contagion\")\n", - "plt.semilogx(tmax, sps[1].mean(axis=1), color=colors[1], label=\"Complex contagion\")\n", - "plt.fill_between(\n", - " tmax,\n", - " sps[0].mean(axis=1) - sps[0].std(axis=1),\n", - " sps[0].mean(axis=1) + sps[0].std(axis=1),\n", - " alpha=0.3,\n", - " color=colors[0],\n", - ")\n", - "plt.fill_between(\n", - " tmax,\n", - " sps[1].mean(axis=1) - sps[1].std(axis=1),\n", - " sps[1].mean(axis=1) + sps[1].std(axis=1),\n", - " alpha=0.3,\n", - " color=colors[1],\n", - ")\n", - "plt.ylabel(\"F-Score\")\n", - "plt.xlabel(r\"$t_{max}$\")\n", - "\n", - "plt.legend(loc=\"upper left\")\n", - "sns.despine()\n", - "\n", - "plt.savefig(\"Figures/Fig1/zkc_infer_vs_tmax.svg\", dpi=1000)\n", - "plt.savefig(\"Figures/Fig1/zkc_infer_vs_tmax.png\", dpi=1000)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "with open(\"Data/zkc_frac_vs_beta.json\") as file:\n", - " data = json.load(file)\n", - "beta = np.array(data[\"beta\"], dtype=float)\n", - "frac = np.array(data[\"fraction\"], dtype=float)\n", - "ps = np.array(data[\"ps\"], dtype=float)\n", - "sps = np.array(data[\"sps\"], dtype=float)\n", - "fce = np.array(data[\"fce\"], dtype=float)\n", - "\n", - "cmap = cmr.gem\n", - "\n", - "sps_summary = sps.mean(axis=2)\n", - "\n", - "fig = plt.figure(figsize=(4, 3))\n", - "\n", - "c = plt.imshow(\n", - " to_imshow_orientation(sps_summary),\n", - " extent=(min(frac), max(frac), min(beta), max(beta)),\n", - " aspect=\"auto\",\n", - " cmap=cmap,\n", - " vmin=0,\n", - " vmax=1,\n", - ")\n", - "plt.xlabel(r\"$f$\")\n", - "plt.ylabel(r\"$\\beta$\")\n", - "\n", - "plt.xticks([0, 0.5, 1], [0, 0.5, 1])\n", - "plt.yticks([0, 0.25, 0.5, 0.75, 1], [0, 0.25, 0.5, 0.75, 1])\n", - "\n", - "cbar = plt.colorbar(c)\n", - "cbar.set_label(r\"F-Score\", fontsize=12, rotation=270, labelpad=15)\n", - "plt.tight_layout()\n", - "\n", - "plt.savefig(\"Figures/Fig1/zkc_frac_vs_beta.svg\", dpi=1000)\n", - "plt.savefig(\"Figures/Fig1/zkc_frac_vs_beta.png\", dpi=1000)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "hyper", - "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": "3.10.0" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/plot_fig1.py b/plot_fig1.py new file mode 100644 index 0000000..d319b4a --- /dev/null +++ b/plot_fig1.py @@ -0,0 +1,204 @@ + +import json +import arviz as az +import matplotlib.pyplot as plt +import numpy as np +import seaborn as sns +import xgi +import fig_settings as fs +from lcs import * + + + +fs.set_fonts() +fs.set_colors() +fs.set_fontsize() +cmap = fs.cmap + +fig, ((ax1, ax2),(ax3, ax4)) = plt.subplots(2,2,figsize=(16,12), sharey=False, sharex=False) + +""" +Panel 1: Network Viz +""" +el = zkc(format="edgelist") +H = xgi.Hypergraph(el) +A = zkc() +n = A.shape[0] + +i = 13 +t = 56 + +gamma = 0.2 +b = 0.07 +contagion_function = lambda nu, b: 1 - (1 - b) ** nu +c = contagion_function(np.arange(n), b) +x0 = np.zeros(n) +x0[0] = 1 + +x = contagion_process(A, gamma, c, x0, tmin=0, tmax=100, random_seed=2) + +infected_color = 'C0' +susceptible_color = "white" +subgraph_color = "black" +graph_color = (0.1, 0.1, 0.1, 0.1) +subgraph_node_lc = "black" +graph_node_lc = (0.3, 0.3, 0.3) + +sg = H.nodes.memberships(i) +nbrs = H.nodes.neighbors(i) +nbrs.add(i) + +pos = xgi.pca_transform(xgi.pairwise_spring_layout(H, seed=5, k=0.3)) +node_fc = [infected_color if x[t, i] else susceptible_color for i in H.nodes] +node_ec = [subgraph_node_lc if n in nbrs else graph_node_lc for n in H.nodes] +node_fc[12] = 'C1' + +dyad_color = [subgraph_color if e in sg else graph_color for e in H.edges] + + + +xgi.draw( + H, + pos=pos, + node_size=7.5, + node_fc=node_fc, + dyad_color=dyad_color, + node_ec=node_ec, + node_lw=0.5, + ax = ax1 +) + + +""" +Panel 2: +""" + + +with open("Data/zkc_infer_contagion_functions.json") as file: + data = json.load(file) +A = np.array(data["A"], dtype=float) +c1 = np.array(data["c1"], dtype=float) +c2 = np.array(data["c2"], dtype=float) +x1 = np.array(data["x1"], dtype=int) +x2 = np.array(data["x2"], dtype=int) +A1_samples = np.array(data["A1-samples"], dtype=int) +A2_samples = np.array(data["A2-samples"], dtype=int) +gamma1_samples = np.array(data["gamma1-samples"], dtype=float) +gamma2_samples = np.array(data["gamma2-samples"], dtype=float) +c1_samples = np.array(data["c1-samples"], dtype=float) +c2_samples = np.array(data["c2-samples"], dtype=float) +l1 = np.array(data["l1"], dtype=float) +l2 = np.array(data["l2"], dtype=float) + +kmax = np.max(degrees(A)) +n = A.shape[0] + +nus = np.arange(0, n, 1) + +# simple contagion +c1_mean = c1_samples.mean(axis=0) +ax2.plot(nus, c1, "-", color='C0', label="Simple contagion") + +err_c1 = np.zeros((2, n)) +c1_mode = np.zeros(n) +for i in range(n): + interval = az.hdi(c1_samples[:, i], hdi_prob=0.95) + x, y = interval + err_c1[0, i] = max(c1_mean[i] - x, 0) + err_c1[1, i] = max(y - c1_mean[i], 0) +ax2.errorbar(nus, c1_mean, err_c1, color='C0', fmt="o") + +# threshold contagion, tau=2 +c2_mean = c2_samples.mean(axis=0) +ax2.plot(nus, c2, "-", color='C1', label="Complex contagion") + +err_c2 = np.zeros((2, n)) +c2_mode = np.zeros(n) +for i in range(n): + interval = az.hdi(c2_samples[:, i], alpha=0.05, roundto=4) + x, y = interval + err_c2[0, i] = max(c2_mean[i] - x, 0) + err_c2[1, i] = max(y - c2_mean[i], 0) +ax2.errorbar(nus, c2_mean, err_c2, color='C1', fmt="o") + +ax2.set_xticks(np.arange(0, n, 5)) +ax2.set_xlabel(r"$\nu$") +ax2.set_ylabel(r"$c(\nu)$") + + +ax2.set_xlim([0, kmax + 2.5]) +ax2.set_ylim([0, 1]) + +ax2.legend(loc="upper left") + +sns.despine() + + +with open("Data/zkc_infer_vs_tmax.json") as file: + data = json.load(file) + + +tmax = data["tmax"] +sps = np.array(data["sps"], dtype=float) +ps = np.array(data["ps"], dtype=float) +fce = np.array(data["fce"], dtype=float) + + +ax3.semilogx(tmax, sps[0].mean(axis=1), color='C0', label="Simple contagion") +ax3.semilogx(tmax, sps[1].mean(axis=1), color='C1', label="Complex contagion") +ax3.fill_between( + tmax, + sps[0].mean(axis=1) - sps[0].std(axis=1), + sps[0].mean(axis=1) + sps[0].std(axis=1), + alpha=0.3, + color='C0', +) +ax3.fill_between( + tmax, + sps[1].mean(axis=1) - sps[1].std(axis=1), + sps[1].mean(axis=1) + sps[1].std(axis=1), + alpha=0.3, + color='C1', +) +ax3.set_ylabel("F-Score") +ax3.set_xlabel(r"$t_{max}$") + +ax3.legend(loc="upper left") +sns.despine() + + + +with open("Data/zkc_frac_vs_beta.json") as file: + data = json.load(file) +beta = np.array(data["beta"], dtype=float) +frac = np.array(data["fraction"], dtype=float) +ps = np.array(data["ps"], dtype=float) +sps = np.array(data["sps"], dtype=float) +fce = np.array(data["fce"], dtype=float) + +cmap = cmap + +sps_summary = sps.mean(axis=2) + + +c = ax4.imshow( + to_imshow_orientation(sps_summary), + extent=(min(frac), max(frac), min(beta), max(beta)), + aspect="auto", + cmap=cmap, + vmin=0, + vmax=1, +) +ax4.set_xlabel(r"$f$") +ax4.set_ylabel(r"$\beta$") + +ax4.set_xticks([0, 0.5, 1], [0, 0.5, 1]) +ax4.set_yticks([0, 0.25, 0.5, 0.75, 1], [0, 0.25, 0.5, 0.75, 1]) + + +cbar_ax = fig.add_axes([0.92, 0.11, 0.02, 0.35]) # x, y, width, height +cbar = plt.colorbar(c, cax=cbar_ax) +cbar.set_label(r"F-Score", fontsize=12, rotation=270, labelpad=15) + +plt.savefig("Figures/Fig1/figure1_4panel.png", dpi=1000) +plt.savefig("Figures/Fig1/figure1_4panel.pdf", dpi=1000) diff --git a/plot_fig2.ipynb b/plot_fig2.ipynb deleted file mode 100644 index c14104a..0000000 --- a/plot_fig2.ipynb +++ /dev/null @@ -1,400 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import json\n", - "\n", - "import cmasher as cmr\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import seaborn as sns\n", - "import xgi\n", - "\n", - "import fig_settings as fs\n", - "from lcs import *" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fs.set_fonts({\"font.family\": \"sans-serif\"})\n", - "cmap = cmr.gem" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "models = [\"Erdos-Renyi\", \"SBM\", \"Watts-Strogatz\", \"CM\", \"clustered_network\"]\n", - "cfs = [\n", - " \"SIS\",\n", - " r\"Threshold, $\\tau=2$\",\n", - " r\"Threshold, $\\tau=3$\",\n", - "]\n", - "keys = [\"p\", \"epsilon\", \"p\", \"alpha\", \"size\"]\n", - "titles = [\"Erdös-Rényi\", \"SBM\", \"Small-World\", \"Power-law CM\", \"Clustered\"]\n", - "labels = [r\"$p$\", r\"$\\epsilon$\", r\"$p$\", r\"$\\alpha$\", r\"$s$\"]\n", - "xticks = [\n", - " [0, 0.5, 1],\n", - " [0, 0.5, 1],\n", - " [-6, -4, -2, 0],\n", - " [1.5, 2, 2.5, 3, 3.5, 4],\n", - " [1, 7, 13, 19],\n", - "]\n", - "xticklabels = [\n", - " [\"0\", \"0.5\", \"1\"],\n", - " [\"0\", \"0.5\", \"1\"],\n", - " [r\"$10^{-6}$\", r\"$10^{-4}$\", r\"$10^{-2}$\", r\"$10^{0}$\"],\n", - " [\"1.5\", \"2\", \"2.5\", \"3\", \"3.5\", \"4\"],\n", - " [\"1\", \"7\", \"13\", \"19\"],\n", - "]\n", - "convert_to_log = [False, False, True, False, False]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axes = plt.subplots(len(cfs), len(models), figsize=(14, 8))\n", - "for i, m in enumerate(models):\n", - " with open(f\"Data/{m.lower()}.json\") as file:\n", - " data = json.load(file)\n", - " var = np.array(data[keys[i]], dtype=float)\n", - " b = np.array(data[\"beta\"], dtype=float)\n", - " sps = np.array(data[\"sps\"], dtype=float)\n", - "\n", - " if convert_to_log[i]:\n", - " var = np.log10(var)\n", - "\n", - " for j, cf in enumerate(cfs):\n", - " sps_summary = sps[j].mean(axis=2).T\n", - " im = axes[j, i].imshow(\n", - " to_imshow_orientation(sps_summary),\n", - " extent=(min(var), max(var), min(b), max(b)),\n", - " vmin=0,\n", - " vmax=1,\n", - " aspect=\"auto\",\n", - " cmap=cmap,\n", - " )\n", - " axes[j, i].set_xlim([min(var), max(var)])\n", - " axes[j, i].set_ylim([min(b), max(b)])\n", - " axes[j, i].set_xticks(xticks[i], xticklabels[i])\n", - " axes[j, i].set_yticks([0, 0.5, 1], [0, 0.5, 1])\n", - "\n", - " if j == 0:\n", - " axes[j, i].set_title(titles[i])\n", - " if i == 0:\n", - " axes[j, i].set_ylabel(f\"{cfs[j]}\\n\" + r\"$\\beta$\")\n", - " if j == len(cfs) - 1:\n", - " axes[j, i].set_xlabel(labels[i], fontsize=16)\n", - "fig.subplots_adjust(bottom=0.15, top=0.95, left=0.1, right=0.8, wspace=0.3, hspace=0.3)\n", - "cbar_ax = fig.add_axes([0.82, 0.15, 0.02, 0.8])\n", - "cbar = fig.colorbar(im, cax=cbar_ax)\n", - "cbar.set_label(r\"F-Score\", fontsize=16, rotation=270, labelpad=25)\n", - "# plt.tight_layout()\n", - "\n", - "plt.savefig(\"Figures/Fig2/generative_models_sps.png\", dpi=1000)\n", - "plt.savefig(\"Figures/Fig2/generative_models_sps.pdf\", dpi=1000)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def visualize_networks(i, ax):\n", - " n = 50\n", - " match i:\n", - " case 0:\n", - " A = erdos_renyi(n, 0.1, seed=0)\n", - " e = [(i, j) for i, j in nx.Graph(A).edges]\n", - " case 1:\n", - " A = sbm(n, 10, 0.9, seed=0)\n", - " e = [(i, j) for i, j in nx.Graph(A).edges]\n", - " case 2:\n", - " A = watts_strogatz(n, 6, 0.03, seed=0)\n", - " e = [(i, j) for i, j in nx.Graph(A).edges]\n", - " case 3:\n", - " A = truncated_power_law_configuration(n, 2, 20, 3, seed=0)\n", - " e = [(i, j) for i, j in nx.Graph(A).edges]\n", - " case 4:\n", - " k = 2 # each node belongs to two cliques\n", - " clique_size = 4\n", - " k1 = k * np.ones(n)\n", - " num_cliques = round(sum(k1) / clique_size)\n", - " k2 = clique_size * np.ones(num_cliques)\n", - " A = clustered_network(k1, k2, seed=0)\n", - " e = [(i, j) for i, j in nx.Graph(A).edges]\n", - "\n", - " H = xgi.Hypergraph(e)\n", - "\n", - " node_size = 3\n", - " dyad_lw = 0.5\n", - " node_lw = 0.5\n", - "\n", - " match i:\n", - " case 0:\n", - " pos = xgi.pairwise_spring_layout(H, seed=2)\n", - " case 1:\n", - " pos = xgi.pca_transform(xgi.pairwise_spring_layout(H, seed=2))\n", - " case 2:\n", - " pos = xgi.circular_layout(H)\n", - " case 3:\n", - " # sorted_nodes = [n for n, _ in sorted(H.nodes.degree.asdict().items(), key=lambda d: d[1])]\n", - " # Hnew = xgi.Hypergraph()\n", - " # Hnew.add_nodes_from(sorted_nodes)\n", - " # Hnew.add_edges_from(e)\n", - " # pos = xgi.circular_layout(Hnew)\n", - " pos = xgi.pairwise_spring_layout(H, seed=2)\n", - " case 4:\n", - " pos = xgi.pairwise_spring_layout(H, seed=2)\n", - " xgi.draw(H, ax=ax, pos=pos, node_size=node_size, node_lw=node_lw, dyad_lw=dyad_lw)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axes = plt.subplots(len(cfs) + 1, len(models), figsize=(8, 6))\n", - "for i, m in enumerate(models):\n", - " with open(f\"Data/{m.lower()}.json\") as file:\n", - " data = json.load(file)\n", - " var = np.array(data[keys[i]], dtype=float)\n", - " b = np.array(data[\"beta\"], dtype=float)\n", - " sps = np.array(data[\"sps\"], dtype=float)\n", - "\n", - " if convert_to_log[i]:\n", - " var = np.log10(var)\n", - "\n", - " for j, cf in enumerate(cfs):\n", - " sps_summary = sps[j].mean(axis=2).T\n", - " im = axes[j + 1, i].imshow(\n", - " to_imshow_orientation(sps_summary),\n", - " extent=(min(var), max(var), min(b), max(b)),\n", - " vmin=0,\n", - " vmax=1,\n", - " aspect=\"auto\",\n", - " cmap=cmap,\n", - " )\n", - " axes[j + 1, i].set_xlim([min(var), max(var)])\n", - " axes[j + 1, i].set_ylim([min(b), max(b)])\n", - " axes[j + 1, i].set_xticks(xticks[i], xticklabels[i])\n", - " axes[j + 1, i].set_yticks([0, 0.5, 1], [0, 0.5, 1])\n", - "\n", - " if i == 0:\n", - " axes[j + 1, i].set_ylabel(f\"{cfs[j]}\\n\" + r\"$\\beta$\")\n", - "\n", - " if j + 1 == len(cfs):\n", - " axes[j + 1, i].set_xlabel(labels[i], fontsize=16)\n", - "\n", - "fig.subplots_adjust(bottom=0.15, top=0.95, left=0.1, right=0.8, wspace=0.3, hspace=0.3)\n", - "cbar_ax = fig.add_axes([0.82, 0.15, 0.02, 0.8])\n", - "cbar = fig.colorbar(im, cax=cbar_ax)\n", - "cbar.set_label(r\"F-Score\", fontsize=16, rotation=270, labelpad=25)\n", - "\n", - "for i, m in enumerate(models):\n", - " visualize_networks(i, axes[0, i])\n", - " axes[0, i].set_title(titles[i])\n", - "\n", - "plt.savefig(\"Figures/Fig2/generative_models_sps.png\", dpi=1000)\n", - "plt.savefig(\"Figures/Fig2/generative_models_sps.pdf\", dpi=1000)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axes = plt.subplots(len(cfs) + 1, len(models), figsize=(14, 8))\n", - "for i, m in enumerate(models):\n", - " with open(f\"Data/{m.lower()}.json\") as file:\n", - " data = json.load(file)\n", - " var = np.array(data[keys[i]], dtype=float)\n", - " b = np.array(data[\"beta\"], dtype=float)\n", - " sps = np.array(data[\"sps\"], dtype=float)\n", - "\n", - " if convert_to_log[i]:\n", - " var = np.log10(var)\n", - "\n", - " for j, cf in enumerate(cfs):\n", - " sps_summary = sps[j].mean(axis=2).T\n", - " im = axes[j + 1, i].imshow(\n", - " to_imshow_orientation(sps_summary),\n", - " extent=(min(var), max(var), min(b), max(b)),\n", - " vmin=0,\n", - " vmax=1,\n", - " aspect=\"auto\",\n", - " cmap=cmap,\n", - " )\n", - " axes[j + 1, i].set_xlim([min(var), max(var)])\n", - " axes[j + 1, i].set_ylim([min(b), max(b)])\n", - " axes[j + 1, i].set_xticks(xticks[i], xticklabels[i])\n", - " axes[j + 1, i].set_yticks([0, 0.5, 1], [0, 0.5, 1])\n", - "\n", - " if i == 0:\n", - " axes[j + 1, i].set_ylabel(f\"{cfs[j]}\\n\" + r\"$\\beta$\")\n", - "\n", - " if j + 1 == len(cfs):\n", - " axes[j + 1, i].set_xlabel(labels[i], fontsize=16)\n", - "\n", - "fig.subplots_adjust(bottom=0.15, top=0.95, left=0.1, right=0.8, wspace=0.3, hspace=0.3)\n", - "cbar_ax = fig.add_axes([0.82, 0.15, 0.02, 0.8])\n", - "cbar = fig.colorbar(im, cax=cbar_ax)\n", - "cbar.set_label(r\"F-Score\", fontsize=16, rotation=270, labelpad=25)\n", - "\n", - "for i, m in enumerate(models):\n", - " visualize_networks(i, axes[0, i])\n", - " axes[0, i].set_title(titles[i])\n", - "\n", - "\n", - "plt.savefig(\"Figures/Fig2/generative_models_sps.png\", dpi=1000)\n", - "plt.savefig(\"Figures/Fig2/generative_models_sps.pdf\", dpi=1000)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axes = plt.subplots(len(cfs), len(models), figsize=(14, 8))\n", - "for i, m in enumerate(models):\n", - " with open(f\"Data/{m.lower()}.json\") as file:\n", - " data = json.load(file)\n", - " var = np.array(data[keys[i]], dtype=float)\n", - " b = np.array(data[\"beta\"], dtype=float)\n", - " fce = np.array(data[\"fce\"], dtype=float)\n", - "\n", - " if convert_to_log[i]:\n", - " var = np.log10(var)\n", - "\n", - " for j, cf in enumerate(cfs):\n", - " fce_summary = fce[j].mean(axis=2).T\n", - " im = axes[j, i].imshow(\n", - " to_imshow_orientation(fce_summary),\n", - " extent=(min(var), max(var), min(b), max(b)),\n", - " vmin=0,\n", - " vmax=1,\n", - " aspect=\"auto\",\n", - " cmap=cmap,\n", - " )\n", - " axes[j, i].set_xlim([min(var), max(var)])\n", - " axes[j, i].set_ylim([min(b), max(b)])\n", - " axes[j, i].set_xticks(xticks[i], xticklabels[i])\n", - " axes[j, i].set_yticks([0, 0.5, 1], [0, 0.5, 1])\n", - "\n", - " if j == 0:\n", - " axes[j, i].set_title(titles[i])\n", - " if i == 0:\n", - " axes[j, i].set_ylabel(f\"{cfs[j]}\\n\" + r\"$\\beta$\")\n", - " if j == len(cfs) - 1:\n", - " axes[j, i].set_xlabel(labels[i], fontsize=16)\n", - "fig.subplots_adjust(bottom=0.15, top=0.95, left=0.1, right=0.8, wspace=0.3, hspace=0.3)\n", - "cbar_ax = fig.add_axes([0.82, 0.15, 0.02, 0.8])\n", - "cbar = fig.colorbar(im, cax=cbar_ax)\n", - "cbar.set_label(r\"FCE\", fontsize=16, rotation=270, labelpad=25)\n", - "\n", - "plt.savefig(\"Figures/Fig2/generative_models_fce.png\", dpi=1000)\n", - "plt.savefig(\"Figures/Fig2/generative_models_fce.pdf\", dpi=1000)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "n = 50\n", - "\n", - "A = erdos_renyi(n, 0.1, seed=0)\n", - "e1 = [(i, j) for i, j in nx.Graph(A).edges]\n", - "\n", - "A = sbm(n, 10, 0.9, seed=0)\n", - "e2 = [(i, j) for i, j in nx.Graph(A).edges]\n", - "\n", - "A = watts_strogatz(n, 6, 0.03, seed=0)\n", - "e3 = [(i, j) for i, j in nx.Graph(A).edges]\n", - "\n", - "A = truncated_power_law_configuration(n, 2, 20, 3, seed=0)\n", - "e4 = [(i, j) for i, j in nx.Graph(A).edges]\n", - "\n", - "k = 2 # each node belongs to two cliques\n", - "clique_size = 4\n", - "k1 = k * np.ones(n)\n", - "num_cliques = round(sum(k1) / clique_size)\n", - "k2 = clique_size * np.ones(num_cliques)\n", - "A = clustered_network(k1, k2, seed=0)\n", - "e5 = [(i, j) for i, j in nx.Graph(A).edges]\n", - "\n", - "H1 = xgi.Hypergraph(e1)\n", - "H2 = xgi.Hypergraph(e2)\n", - "H3 = xgi.Hypergraph(e3)\n", - "H4 = xgi.Hypergraph(e4)\n", - "H5 = xgi.Hypergraph(e5)\n", - "\n", - "node_size = 2\n", - "dyad_lw = 0.5\n", - "node_lw = 0.5\n", - "\n", - "plt.figure()\n", - "plt.subplot(151)\n", - "pos1 = xgi.pairwise_spring_layout(H1, seed=2)\n", - "xgi.draw(H1, pos=pos1, node_size=node_size, node_lw=node_lw, dyad_lw=dyad_lw)\n", - "\n", - "plt.subplot(152)\n", - "pos2 = xgi.pca_transform(xgi.pairwise_spring_layout(H2, seed=2))\n", - "xgi.draw(H2, pos=pos2, node_size=node_size, node_lw=node_lw, dyad_lw=dyad_lw)\n", - "\n", - "plt.subplot(153)\n", - "pos3 = xgi.circular_layout(H3)\n", - "xgi.draw(H3, pos=pos3, node_size=node_size, node_lw=node_lw, dyad_lw=dyad_lw)\n", - "\n", - "plt.subplot(154)\n", - "pos4 = xgi.pairwise_spring_layout(H4, seed=2)\n", - "xgi.draw(H4, pos=pos4, node_size=node_size, node_lw=node_lw, dyad_lw=dyad_lw)\n", - "\n", - "plt.subplot(155)\n", - "pos5 = xgi.pairwise_spring_layout(H5, seed=2)\n", - "xgi.draw(H5, pos=pos5, node_size=node_size, node_lw=node_lw, dyad_lw=dyad_lw)\n", - "plt.savefig(\"test2.png\", dpi=1000)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "hyper", - "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": "3.10.0" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/plot_fig2.py b/plot_fig2.py new file mode 100644 index 0000000..a4865bb --- /dev/null +++ b/plot_fig2.py @@ -0,0 +1,130 @@ + +import json + +import matplotlib.pyplot as plt +import numpy as np +import xgi + +import fig_settings as fs +from lcs import * + +fs.set_fonts() +fs.set_colors() +fs.set_fonts({"font.family": "sans-serif"}) +cmap = fs.cmap + + +models = ["Erdos-Renyi", "SBM", "Watts-Strogatz", "CM", "clustered_network"] +cfs = [ + "SIS", + r"Threshold, $\tau=2$", + r"Threshold, $\tau=3$", +] +keys = ["p", "epsilon", "p", "alpha", "size"] +titles = ["Erdös-Rényi", "SBM", "Small-World", "Power-law CM", "Clustered"] +labels = [r"$p$", r"$\epsilon$", r"$p$", r"$\alpha$", r"$s$"] +xticks = [ + [0, 0.5, 1], + [0, 0.5, 1], + [-6, -4, -2, 0], + [1.5, 2, 2.5, 3, 3.5, 4], + [1, 7, 13, 19], +] +xticklabels = [ + ["0", "0.5", "1"], + ["0", "0.5", "1"], + [r"$10^{-6}$", r"$10^{-4}$", r"$10^{-2}$", r"$10^{0}$"], + ["1.5", "2", "2.5", "3", "3.5", "4"], + ["1", "7", "13", "19"], +] +convert_to_log = [False, False, True, False, False] + +def visualize_networks(i, ax): + n = 50 + match i: + case 0: + A = erdos_renyi(n, 0.1, seed=0) + e = [(i, j) for i, j in nx.Graph(A).edges] + case 1: + A = sbm(n, 10, 0.9, seed=0) + e = [(i, j) for i, j in nx.Graph(A).edges] + case 2: + A = watts_strogatz(n, 6, 0.03, seed=0) + e = [(i, j) for i, j in nx.Graph(A).edges] + case 3: + A = truncated_power_law_configuration(n, 2, 20, 3, seed=0) + e = [(i, j) for i, j in nx.Graph(A).edges] + case 4: + k = 2 # each node belongs to two cliques + clique_size = 4 + k1 = k * np.ones(n) + num_cliques = round(sum(k1) / clique_size) + k2 = clique_size * np.ones(num_cliques) + A = clustered_network(k1, k2, seed=0) + e = [(i, j) for i, j in nx.Graph(A).edges] + + H = xgi.Hypergraph(e) + + node_size = 3 + dyad_lw = 0.5 + node_lw = 0.5 + + match i: + case 0: + pos = xgi.pairwise_spring_layout(H, seed=2) + case 1: + pos = xgi.pca_transform(xgi.pairwise_spring_layout(H, seed=2)) + case 2: + pos = xgi.circular_layout(H) + case 3: + pos = xgi.pairwise_spring_layout(H, seed=2) + case 4: + pos = xgi.pairwise_spring_layout(H, seed=2) + xgi.draw(H, ax=ax, pos=pos, node_size=node_size, node_lw=node_lw, dyad_lw=dyad_lw) + + + +fig, axes = plt.subplots(len(cfs) + 1, len(models), figsize=(14, 8)) +for i, m in enumerate(models): + with open(f"Data/{m.lower()}.json") as file: + data = json.load(file) + var = np.array(data[keys[i]], dtype=float) + b = np.array(data["beta"], dtype=float) + sps = np.array(data["sps"], dtype=float) + + if convert_to_log[i]: + var = np.log10(var) + + for j, cf in enumerate(cfs): + sps_summary = sps[j].mean(axis=2).T + im = axes[j + 1, i].imshow( + to_imshow_orientation(sps_summary), + extent=(min(var), max(var), min(b), max(b)), + vmin=0, + vmax=1, + aspect="auto", + cmap=cmap, + ) + axes[j + 1, i].set_xlim([min(var), max(var)]) + axes[j + 1, i].set_ylim([min(b), max(b)]) + axes[j + 1, i].set_xticks(xticks[i], xticklabels[i]) + axes[j + 1, i].set_yticks([0, 0.5, 1], [0, 0.5, 1]) + + if i == 0: + axes[j + 1, i].set_ylabel(f"{cfs[j]}\n" + r"$\beta$") + + if j + 1 == len(cfs): + axes[j + 1, i].set_xlabel(labels[i], fontsize=16) + +fig.subplots_adjust(bottom=0.15, top=0.95, left=0.1, right=0.8, wspace=0.3, hspace=0.3) +cbar_ax = fig.add_axes([0.82, 0.15, 0.02, 0.8]) +cbar = fig.colorbar(im, cax=cbar_ax) +cbar.set_label(r"F-Score", fontsize=16, rotation=270, labelpad=25) + +for i, m in enumerate(models): + visualize_networks(i, axes[0, i]) + axes[0, i].set_title(titles[i]) + + +plt.savefig("Figures/Fig2/generative_models_sps.png", dpi=1000) +plt.savefig("Figures/Fig2/generative_models_sps.pdf", dpi=1000) \ No newline at end of file