{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import networkx as nx\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "import scipy as sp\n",
    "import datetime as dt\n",
    "\n",
    "from ei_net import * \n",
    "from ce_net import * \n",
    "\n",
    "from collections import Counter\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "##########################################\n",
    "############ PLOTTING SETUP ##############\n",
    "EI_cmap = \"Greys\"\n",
    "where_to_save_pngs = \"../figs/pngs/\"\n",
    "where_to_save_pdfs = \"../figs/pdfs/\"\n",
    "save = True\n",
    "plt.rc('axes', axisbelow=True)\n",
    "##########################################\n",
    "##########################################"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The emergence of informative higher scales in complex networks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chapter 07: Effective Information Differences in Real Networks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The presence and informativeness of macroscales should vary across real networks, dependent on connectivity. Here we investigate the disposition toward causal emergence of real networks across different domains. We draw from the same set of networks analyzed in Chapter 04. The network sizes span up to 40,000 nodes, thus making it unfeasible to find the the best macroscales for each of them. Therefore, we focus specifically on the two categories that previously showed the greatest divergence in terms of the $EI$: biological and technological. Since we are interested in the general question of whether biological or technological networks show a greater disposition or propensity for causal emergence, we approximate causal emergence by calculating the causal emergence of sampled subgraphs of growing sizes. Each sample is found using a \"snowball sampling\" procedure, wherein a node is chosen randomly and then a weakly connected subgraph of a specified size is found around it. This subgraph is then analyzed using the previously described greedy algorithmic approach to find macro-nodes that maximized the $EI$ in each network. Each available network is sampled 20 times for each size taken from it. \n",
    "\n",
    "Here, we show how the causal emergence of these real networks differentiates as we increase the sampled subgraph size, in a sequence of 50, 100, 150, and finally 200 nodes per sample. Networks of these sizes previously provided ample  evidence of causal emergence in simulated networks. Comparing the two categories of real networks, we observe a significantly greater propensity for causal emergence in biological networks, and that this is more articulated the larger the samples are. Note that constructing a random null model of these networks (e.g., a configuration model) would tend to create networks with minimal or negligible causal emergence, as is the case for ER networks.\n",
    "\n",
    "That subsets of biological systems show a high disposition toward causal emergence is consistent, and even explanatory, of many long-standing hypotheses surrounding the existence of noise and degeneracy in biological systems. It also explains the difficulty of understanding how the causal structure of biological systems function, since they are cryptic by containing certainty at one level and uncertainty at another."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "________________________"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7.1 Sampling subgraphs to estimate causal emergence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def snowball_sample(G, n_seeds=1, n_total=0.2, n_waves=5):\n",
    "    \"\"\"\n",
    "    This function defines a procedure for \"snowball sampling\" a graph. The \n",
    "    algorithm starts from a (usually) single seed node, which it then expands\n",
    "    outward from, collecting the nodes in waves around it in a \"snowball\" \n",
    "    manner.\n",
    "    \n",
    "    Params\n",
    "    ------\n",
    "    G (nx.Graph): the graph to be sampled.\n",
    "    n_seeds (int): the number of seed nodes that the snowball sampling \n",
    "                   starts from.\n",
    "    n_total (float): the fraction of the total size that will be sampled.\n",
    "    n_waves (int): usually set high, this determines how many shells outward\n",
    "                   the snowballing will continue.\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    (V_s, E_s) (list, list): the nodelist and edgelist of the sampled graph.\n",
    "    \n",
    "    \"\"\"\n",
    "\n",
    "    nodes = list(G.nodes())\n",
    "    np.random.shuffle(nodes)\n",
    "    n_total = int(len(nodes)*n_total)\n",
    "    if n_total < 5: \n",
    "        n_total = 5\n",
    "    \n",
    "    V = [set()]*(n_waves+1)\n",
    "    \n",
    "    V_s = set()\n",
    "    E_s = set()\n",
    "    \n",
    "    for k in range(n_waves+1):\n",
    "        if k==0:\n",
    "            V[k] = set(nodes[:n_seeds])\n",
    "            nodes = set(nodes)\n",
    "        else:\n",
    "            for node_i in V[k-1]:\n",
    "                for node_j in G.neighbors(node_i):\n",
    "                    edge = (node_i,node_j) if node_i >= node_j else (node_j,node_i)\n",
    "                    E_s.add(edge)\n",
    "                    V[k].add(node_j)\n",
    "                    if len(V_s.union(V[k].intersection(nodes-V_s))) > n_total:\n",
    "                        V[k] = V[k].intersection(nodes-V_s)\n",
    "                        V_s = V_s.union(V[k])\n",
    "                        break\n",
    "                        \n",
    "            V[k] = V[k].intersection(nodes-V_s)\n",
    "\n",
    "        V_s = V_s.union(V[k])\n",
    "        \n",
    "    E_s = list(E_s)\n",
    "    return list(V_s), E_s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 504x403.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "G = nx.barabasi_albert_graph(40,1)\n",
    "samp,_ = snowball_sample(G,1)\n",
    "Gs = G.subgraph(samp).copy()\n",
    "\n",
    "mult=1.4\n",
    "fig,ax=plt.subplots(1,1,figsize=(5*mult,4*mult))\n",
    "pos = nx.kamada_kawai_layout(G)\n",
    "pos = nx.spring_layout(G,pos=pos,iterations=1)\n",
    "nx.draw_networkx_nodes(G,pos,node_color='gainsboro',node_size=350,\n",
    "                       edgecolors='#999999',linewidths=2.5,ax=ax)\n",
    "nx.draw_networkx_edges(G,pos,edge_color='#999999',alpha=0.7,width=3.5,ax=ax)\n",
    "xxx = [i for i in G.nodes() if i not in samp]\n",
    "labs = dict(zip(xxx,xxx))\n",
    "nx.draw_networkx_labels(G,pos,labels=labs,ax=ax,font_size=10,zorder=0)\n",
    "\n",
    "nodes = nx.draw_networkx_nodes(Gs,pos,node_color='#f5d13f',edgecolors='#333333',\n",
    "                       node_size=500,linewidths=2.5,ax=ax)\n",
    "edges = nx.draw_networkx_edges(Gs,pos,edge_color='#333333',alpha=0.7,width=3.5,ax=ax)\n",
    "\n",
    "labs = dict(zip(Gs.nodes(),[r\"$s_{%i}$\"%i for i in Gs.nodes()]))\n",
    "nx.draw_networkx_labels(Gs,pos,labels=labs,ax=ax,font_size=12)\n",
    "\n",
    "titl = \"{\"+ \", \".join(list(labs.values()))+\"}\" + r\"$\\in \\mathbf{S}$\"\n",
    "ax.set_title(titl,loc='center',pad=-320,fontsize=14)\n",
    "\n",
    "ax.set_axis_off()\n",
    "\n",
    "if save:\n",
    "    plt.savefig(where_to_save_pngs+\"Network_snowball.png\", dpi=425, bbox_inches='tight')\n",
    "    plt.savefig(where_to_save_pdfs+\"Network_snowball.pdf\", bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__________________"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7.2 Estimating Causal Emergence in Biological and Technological Networks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "json_data = open('../data/sampled_causalemergence.json',\"r\").read()\n",
    "consolidata = json.loads(json_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "type_mapping = {'brains':'Biological', \n",
    "                'citations':'Information', \n",
    "                'coauthorship':'Information', \n",
    "                'communication':'Social', \n",
    "                'computer':'Technological',\n",
    "                'humancontact':'Social', \n",
    "                'humansocial':'Social', \n",
    "                'hyperlink':'Information', \n",
    "                'infrastructure':'Technological',\n",
    "                'lexical':'Information', \n",
    "                'metabolic':'Biological', \n",
    "                'onlinesocial':'Social', \n",
    "                'power':'Technological', \n",
    "                'software':'Information',\n",
    "                'technological':'Technological', \n",
    "                'trophic':'Biological'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(1,1,figsize=(6,4))\n",
    "bio_noise = {50:[],100:[],150:[],200:[]}\n",
    "tec_noise = {50:[],100:[],150:[],200:[]}\n",
    "maxy = 0\n",
    "for name,net in consolidata.items():\n",
    "    col = net['Graph_Info']['Color']\n",
    "    if \"#\" not in net['Graph_Info']['Color']:\n",
    "        col = \"#\"+net['Graph_Info']['Color']\n",
    "    \n",
    "    \n",
    "    net_type = type_mapping[net['Graph_Info']['Type']]\n",
    "    yvals = net['CE']['CE']\n",
    "    xvals = np.array(net['CE']['N_sample'])[:len(yvals)]\n",
    "    if net_type=='Biological':\n",
    "        if max(yvals)>maxy:\n",
    "            maxy = max(yvals)\n",
    "        for yi,y in enumerate(yvals):\n",
    "            if y >= 0:\n",
    "                if xvals[yi] > 40:\n",
    "                    bio_noise[xvals[yi]].append(y)\n",
    "\n",
    "        xvals = xvals + np.random.uniform(-13,-3,len(yvals))            \n",
    "        ax.scatter(xvals, yvals, s=2, c=col,alpha=0.6,linewidths=0.15,edgecolors='k')\n",
    "\n",
    "    if net_type=='Technological':\n",
    "        if max(yvals)>maxy:\n",
    "            maxy = max(yvals)\n",
    "        for yi,y in enumerate(yvals):\n",
    "            if y >= 0:\n",
    "                if xvals[yi] > 40:\n",
    "                    tec_noise[xvals[yi]].append(y)\n",
    "        xvals = xvals + np.random.uniform(3,13,len(yvals))\n",
    "        ax.scatter(xvals, yvals, s=2, c=col,alpha=0.6,linewidths=0.15,edgecolors='k')\n",
    "\n",
    "plot_bio = [bio_noise[50], bio_noise[100], bio_noise[150], bio_noise[200]]\n",
    "plot_tec = [tec_noise[50], tec_noise[100], tec_noise[150], tec_noise[200]]\n",
    "\n",
    "parts = ax.violinplot(plot_bio, positions=[42, 92, 142, 192], \n",
    "                      showmeans=False, showmedians=False, \n",
    "                      showextrema=False, widths=15)\n",
    "ll = 0\n",
    "for i in range(len(parts['bodies'])):\n",
    "    pc = parts['bodies'][i]\n",
    "    pc.set_edgecolor(\"#ed4f44\")\n",
    "    pc.set_facecolor(\"#ed4f44\")\n",
    "    pc.set_alpha(0.5)\n",
    "    pc.set_linewidth(2.0)\n",
    "    ll += 1\n",
    "    if ll==2:\n",
    "        pc.set_label('Biological')\n",
    "\n",
    "parts = ax.violinplot(plot_tec, positions=[58, 108, 158, 208], \n",
    "                      showmeans=False, showmedians=False, \n",
    "                      showextrema=False, widths=15)\n",
    "ll = 0\n",
    "for i in range(len(parts['bodies'])):\n",
    "    pc = parts['bodies'][i]\n",
    "    pc.set_edgecolor('#00c6c5')\n",
    "    pc.set_facecolor('#00c6c5')\n",
    "    pc.set_alpha(0.5)\n",
    "    pc.set_linewidth(2.0)\n",
    "    ll += 1\n",
    "    if ll==2:\n",
    "        pc.set_label('Technological')\n",
    "        \n",
    "\n",
    "ax.hlines(1.6,138.8+50,162.325+50,color='k')\n",
    "ax.vlines(139.2+50,1.55,1.6,color='k')\n",
    "ax.vlines(162+50,1.55,1.6,color='k')\n",
    "ax.text(145.8+50,1.61,'***',fontsize=12)\n",
    "\n",
    "ax.hlines(1.4,138.8,162.325,color='k')\n",
    "ax.vlines(139.2,1.35,1.4,color='k')\n",
    "ax.vlines(162,1.35,1.4,color='k')\n",
    "ax.text(145.8,1.41,'***',fontsize=12)\n",
    "\n",
    "ax.hlines(1.4,138.8-50,162.325-50,color='k')\n",
    "ax.vlines(139.2-50,1.35,1.4,color='k')\n",
    "ax.vlines(162-50,1.35,1.4,color='k')\n",
    "ax.text(145.8-50,1.41,'***',fontsize=12)\n",
    "\n",
    "ax.hlines(1.44,138.8-100,162.325-100,color='k')\n",
    "ax.vlines(139.2-100,1.39,1.44,color='k')\n",
    "ax.vlines(162-100,1.39,1.44,color='k')\n",
    "ax.text(145.8-100,1.45,'***',fontsize=12)\n",
    "\n",
    "ax.scatter(-1,-1,alpha=0,label='  p < 1e-07 ***')\n",
    "\n",
    "ax.set_xticks([50,100,150,200])\n",
    "ax.grid(linewidth=2.5, color='#999999', alpha=0.2, linestyle='-')\n",
    "\n",
    "ax.set_xlim(30,220)\n",
    "ax.set_ylim(-maxy*0.02,maxy*1.3)\n",
    "ax.set_xlabel(r\"$N_s$\", fontsize=16)\n",
    "ax.set_ylabel(r\"Causal emergence\", fontsize=16)\n",
    "\n",
    "ax.legend(ncol=3,columnspacing=1.5)\n",
    "\n",
    "if save:\n",
    "    plt.savefig(where_to_save_pngs+\"SamplingCE.png\", dpi=425, bbox_inches='tight')\n",
    "    plt.savefig(where_to_save_pdfs+\"SamplingCE.pdf\", bbox_inches='tight')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "______________________"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## End of Chapter 07. In [Chapter 08](https://nbviewer.jupyter.org/github/jkbren/einet/blob/master/code/Chapter%2008%20-%20Miscellaneous.ipynb) we'll wrap up final details about causal emergence.\n",
    "_______________"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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