tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tutorial to the use of `cdg` \n",
    "\n",
    "[Daniele Zambon](https://dzambon.github.io) (`daniele.zambon@usi.ch`)  \n",
    "_Università della Svizzera italiana_     \n",
    "Last Update: 16/09/2019   \n",
    "\n",
    "## Install the package\n",
    "\n",
    "You can get it from [GitHub](https://github.com/dzambon/cdg)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "notebook prepared with cdg version: 2.1\n"
     ]
    }
   ],
   "source": [
    "import cdg\n",
    "print(\"notebook prepared with cdg version:\", cdg.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Demo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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<cdg.graph.graph.Graph object at 0x7f69b377b880>, <cdg.graph.graph.Graph object at 0x7f69b377b4c0>, <cdg.graph.graph.Graph object at 0x7f69b377bca0>, <cdg.graph.graph.Graph object at 0x7f69b377bdf0>, <cdg.graph.graph.Graph object at 0x7f69b377be50>, <cdg.graph.graph.Graph object at 0x7f69b377b700>, <cdg.graph.graph.Graph object at 0x7f69b377be80>, <cdg.graph.graph.Graph object at 0x7f69b377b9a0>, <cdg.graph.graph.Graph object at 0x7f69b377b940>, <cdg.graph.graph.Graph object at 0x7f69b377b280>, <cdg.graph.graph.Graph object at 0x7f69b377bd60>])])\n"
     ]
    }
   ],
   "source": [
    "# Generate synthetic graphs from a predefined model\n",
    "#   - 500 graphs from class 0\n",
    "#   - 50 graphs from class 8\n",
    "from cdg.graph import DelaunayGraphs, convert\n",
    "no_nodes = 5\n",
    "no_graphs = {0: 500, 8: 50}\n",
    "model = DelaunayGraphs()\n",
    "G = model.get(seed_points=no_nodes, classes=list(no_graphs.keys()),\n",
    "              no_graphs=no_graphs, sigma=.3, include_seed_graph=False)\n",
    "\n",
    "print(G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'grakel'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-6-5f91242ac2a2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mcdg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgraph\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdistance\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mGraphEditDistanceNX\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0mged\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGraphEditDistanceNX\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode_cost\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'euclidean'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0mGnx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconvert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mG\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mG\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformat_in\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'cdg'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformat_out\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'nx'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m \u001b[0mG_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mG_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGnx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m50\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mGnx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m50\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/phd/cdg/cdg/graph/conversion.py\u001b[0m in \u001b[0;36mconvert\u001b[0;34m(graphs, format_in, format_out, directed, label_ndim, **kwargs)\u001b[0m\n\u001b[1;32m     56\u001b[0m     \u001b[0;32massert\u001b[0m \u001b[0mformat_in\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mGRAPH_FORMATS_IMPORT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'format_in={} is not in {}, '\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mformat_in\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mGRAPH_FORMATS_IMPORT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m     \u001b[0;32massert\u001b[0m \u001b[0mformat_out\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mGRAPH_FORMATS_EXPORT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'format_out={} is not in {}, '\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mformat_out\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mGRAPH_FORMATS_EXPORT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 58\u001b[0;31m     \u001b[0;32massert\u001b[0m \u001b[0mformat_in\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0minfer_format\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgraphs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'provided graphs are not in format format_in.'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     59\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mformat_in\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mformat_out\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/phd/cdg/cdg/graph/conversion.py\u001b[0m in \u001b[0;36minfer_format\u001b[0;34m(graphs)\u001b[0m\n\u001b[1;32m     18\u001b[0m     \u001b[0;34m:\u001b[0m\u001b[0;32mreturn\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m     '''\n\u001b[0;32m---> 20\u001b[0;31m     \u001b[0;32mimport\u001b[0m \u001b[0mgrakel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     21\u001b[0m     \u001b[0;32mimport\u001b[0m \u001b[0mnetworkx\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'grakel'"
     ]
    }
   ],
   "source": [
    "# Generate synthetic graphs from a predefined model\n",
    "#   - 500 graphs from class 0\n",
    "#   - 50 graphs from class 8\n",
    "from cdg.graph import DelaunayGraphs, convert\n",
    "no_nodes = 5\n",
    "no_graphs = {0: 500, 8: 50}\n",
    "model = DelaunayGraphs()\n",
    "G = model.get(seed_points=no_nodes, classes=list(no_graphs.keys()),\n",
    "              no_graphs=no_graphs, sigma=.3, include_seed_graph=False)\n",
    "\n",
    "# Convert them\n",
    "from cdg.graph.distance import GraphEditDistanceNX\n",
    "ged = GraphEditDistanceNX(node_cost='euclidean', n_jobs=2)\n",
    "Gnx = convert(G[0] + G[8], format_in='cdg', format_out='nx')\n",
    "G_train, G_test = Gnx[:50], Gnx[50:]\n",
    "\n",
    "from cdg.embedding import MultiDimensionalScaling\n",
    "mds = MultiDimensionalScaling(emb_dim=2, nprot=5)\n",
    "mds.fit(graphs=G_train, dist_fun=ged.get_measure_fun(verbose=True))\n",
    "x = mds.transform(G_test)\n",
    "\n",
    "from cdg.changedetection import GaussianCusum\n",
    "cdt = GaussianCusum(window_size=5, arl=20)\n",
    "cdt.fit(x[:100])\n",
    "y, g = cdt.predict(x, reset=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot(g, 'b', label=\"cumulative sum\")\n",
    "plt.plot(g*0. + cdt.threshold, '--k', label=\"threshold\")\n",
    "plt.plot([450]*2, [g.min(), g.max()], '--r', label=\"change\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Change detection \n",
    "\n",
    "Let's generate a sample sequence with a change simulated at time `N_change`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": [
    "import numpy as np\n",
    "N = 400\n",
    "N_train = 100\n",
    "N_change = 320\n",
    "alpha = 0.01\n",
    "\n",
    "x = np.random.normal(size=(N, 1))\n",
    "x[N_change:] += 1.\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure()\n",
    "plt.scatter(np.arange(N), x, c='k', label=\"x(t)\")\n",
    "plt.plot([N_change]*2, [x.min(), x.max()], '--r', label=\"N_change\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Change-detection test\n",
    "\n",
    "Under the assumption that the process is Gaussian and i.i.d. one can use `GaussianCusum`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from cdg.changedetection import GaussianCusum\n",
    "cdt = GaussianCusum(arl=round(1./alpha))\n",
    "cdt.fit(x[:N_train])\n",
    "y, g = cdt.predict(x, reset=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One could also proceed iterative time-step by time-step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "An alarm is raised at time 33\n",
      "An alarm is raised at time 326\n",
      "An alarm is raised at time 354\n",
      "An alarm is raised at time 361\n",
      "An alarm is raised at time 398\n"
     ]
    }
   ],
   "source": [
    "cdt.reset()\n",
    "cdt.fit(x[:N_train])\n",
    "for t in range(N):\n",
    "    alarm, _ = cdt.iterate(x[t:t + 1])\n",
    "    if alarm:\n",
    "        print(\"An alarm is raised at time {}\".format(t))\n",
    "        cdt.reset()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One the above assumption is not appropriate, one can:\n",
    "\n",
    "* use a windowing approach with `GaussianCusum(window_size=50)` that exploits the central limit theorem;\n",
    "* use the nonparametric `TwoSidedCusum`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Change-point method\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Identified change-point: [320] with p-value: [4.4086956307864966e-13]\n"
     ]
    }
   ],
   "source": [
    "from cdg.changedetection import MuCPM\n",
    "cpm = MuCPM()\n",
    "cpm.predict(x, alpha=alpha, margin=20)\n",
    "print(\"Identified change-point:\", cpm.cps_fwer, 'with p-value:', cpm.pvals_fwer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data sets\n",
    "\n",
    "### Load a data set\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data set not there...\n"
     ]
    }
   ],
   "source": [
    "from cdg.graph import DataSet, isdataset\n",
    "path = './dataset/delaunay'\n",
    "if isdataset(path):\n",
    "    dataset = DataSet.load_dataset(path=path, name='MyDataSet')   \n",
    "else:\n",
    "    print('Data set not there...')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Generate Delaunay's triangulation graphs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cdg.Graph[no_nodes: 7, nf_dim: 2, ef_dim: 0](\n",
      "---Adjacency---\n",
      "[[0. 0. 1. 0. 1. 1. 1.]\n",
      " [0. 0. 1. 1. 1. 0. 1.]\n",
      " [1. 1. 0. 1. 0. 1. 1.]\n",
      " [0. 1. 1. 0. 0. 1. 0.]\n",
      " [1. 1. 0. 0. 0. 0. 1.]\n",
      " [1. 0. 1. 1. 0. 0. 0.]\n",
      " [1. 1. 1. 0. 1. 0. 0.]]\n",
      "---NodeFeatures---\n",
      "[[8.84106585 2.46721861]\n",
      " [1.1429479  5.13683779]\n",
      " [7.65771102 2.30314049]\n",
      " [1.99294489 1.42906562]\n",
      " [6.69154097 9.50241862]\n",
      " [7.91662357 1.26672989]\n",
      " [7.99898016 3.16394143]])\n"
     ]
    }
   ],
   "source": [
    "from cdg.graph import DelaunayGraphs \n",
    "no_nodes = 7\n",
    "no_graphs = 100\n",
    "model = DelaunayGraphs()\n",
    "G = model.get(seed_points=no_nodes, classes=[0, 8], \n",
    "              no_graphs=no_graphs, sigma=.3, include_seed_graph=False) \n",
    "print(G[0][0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Generate graphs from the stochastic block model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cdg.Graph[no_nodes: 7, nf_dim: 0, ef_dim: 0](\n",
      "---Adjacency---\n",
      "[[0 0 0 0 0 1 0]\n",
      " [0 0 1 0 1 0 0]\n",
      " [0 1 0 0 0 0 0]\n",
      " [0 0 0 0 0 1 1]\n",
      " [0 1 0 0 0 0 0]\n",
      " [1 0 0 1 0 0 1]\n",
      " [0 0 0 1 0 1 0]])\n"
     ]
    }
   ],
   "source": [
    "from cdg.graph import StochasticBlockModel \n",
    "no_nodes = 7\n",
    "no_graphs = 100\n",
    "model = StochasticBlockModel(\n",
    "            communities=[list(range(no_nodes//2)), list(range(no_nodes//2, no_nodes))],\n",
    "            prob_matrix=[[.8, .2], [.2, .6]])\n",
    "\n",
    "G = model.get(no_graphs)\n",
    "print(G[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Generate graphs from the degree-corrected stochastic block model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cdg.Graph[no_nodes: 7, nf_dim: 0, ef_dim: 1](\n",
      "---Adjacency---\n",
      "[[1 1 0 0 0 0 1]\n",
      " [1 1 1 0 0 0 0]\n",
      " [0 1 1 0 0 0 0]\n",
      " [0 0 0 1 0 0 1]\n",
      " [0 0 0 0 1 1 1]\n",
      " [0 0 0 0 1 1 0]\n",
      " [1 0 0 1 1 0 1]]\n",
      "---EdgeFeatures---\n",
      "[[1 2 0 0 0 0 1]\n",
      " [2 1 1 0 0 0 0]\n",
      " [0 1 1 0 0 0 0]\n",
      " [0 0 0 1 0 0 1]\n",
      " [0 0 0 0 1 1 1]\n",
      " [0 0 0 0 1 1 0]\n",
      " [1 0 0 1 1 0 1]])\n"
     ]
    }
   ],
   "source": [
    "from cdg.graph import DegreeCorrectedStochasticBlockModel\n",
    "no_nodes = 7\n",
    "no_graphs = 100\n",
    "model = DegreeCorrectedStochasticBlockModel(\n",
    "    communities=[list(range(no_nodes // 2)), list(range(no_nodes // 2, no_nodes))],\n",
    "    prob_matrix=[[.8, .2], [.2, .6]],\n",
    "    delta=[0.1, 0.02])\n",
    "\n",
    "G = model.get(no_graphs)\n",
    "print(G[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Store a data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Is data set there? True\n"
     ]
    }
   ],
   "source": [
    "# The path needs to be present\n",
    "path = './data/my-del'\n",
    "# Create graphs\n",
    "no_nodes = 7\n",
    "no_graphs = 100\n",
    "model = DelaunayGraphs()\n",
    "G = model.get(seed_points=no_nodes, classes=[0, 8], \n",
    "              no_graphs=no_graphs, sigma=.3, include_seed_graph=False) \n",
    "labels = np.array([0] * no_graphs + [8] * no_graphs)\n",
    "# Create data set and store it\n",
    "dataset = DataSet(graphs=G[0]+G[8], labels=labels, name='my-delaunay', store=path)\n",
    "\n",
    "print(\"Is data set there?\", isdataset(path))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Dynamic model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from cdg.graph import StochasticBlockModel, DynamicsGenerator\n",
    "no_nodes = 8\n",
    "no_graphs = 100\n",
    "alpha = .5\n",
    "\n",
    "base_model = StochasticBlockModel(communities=[list(range(no_nodes))], prob_matrix=[[.3]])\n",
    "#getter = lambda : base_model.get(no_graphs=1)[0]\n",
    "#dyn_model = DynamicsGenerator(alpha=alpha, getter=getter)\n",
    "#G = dyn_model.get(no_graphs=no_graphs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Simulations\n",
    "\n",
    "### Run a simulation of CPM\n",
    "Collect a data set\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "prot. sel. annealing: 100%|██████████| 3/3 [00:00<00:00, 336.12it/s]\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.5s finished\n",
      "prot. sel. annealing: 100%|██████████| 3/3 [00:00<00:00, 343.82it/s]\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.5s finished\n",
      "prot. sel. annealing: 100%|██████████| 3/3 [00:00<00:00, 378.78it/s]\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.5s finished\n",
      "prot. sel. annealing: 100%|██████████| 3/3 [00:00<00:00, 250.84it/s]\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.5s finished\n",
      "prot. sel. annealing: 100%|██████████| 3/3 [00:00<00:00, 456.90it/s]\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.5s finished\n",
      "prot. sel. annealing: 100%|██████████| 3/3 [00:00<00:00, 290.10it/s]\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.5s finished\n",
      "prot. sel. annealing: 100%|██████████| 3/3 [00:00<00:00, 239.75it/s]\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.5s finished\n",
      "prot. sel. annealing: 100%|██████████| 3/3 [00:00<00:00, 283.60it/s]\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.5s finished\n",
      "prot. sel. annealing: 100%|██████████| 3/3 [00:00<00:00, 305.83it/s]\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.5s finished\n",
      "prot. sel. annealing: 100%|██████████| 3/3 [00:00<00:00, 331.74it/s]\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.5s finished\n"
     ]
    }
   ],
   "source": [
    "from cdg.graph import StochasticBlockModel, DataSet, FrobeniusGraphDistance\n",
    "# Generate graphs\n",
    "no_nodes = 7\n",
    "no_graphs_class_0, no_graphs_class_1 = 100, 50\n",
    "model0 = StochasticBlockModel(communities=[list(range(no_nodes))],\n",
    "                              prob_matrix=[[.2]])\n",
    "model1 = StochasticBlockModel(communities=[list(range(no_nodes))],\n",
    "                              prob_matrix=[[.3]])\n",
    "graphs  = model0.get(no_graphs=no_graphs_class_0)\n",
    "graphs += model1.get(no_graphs=no_graphs_class_1)\n",
    "y = [0] * no_graphs_class_0 + [1] * no_graphs_class_1\n",
    "# Choose a distance\n",
    "frob_distance = FrobeniusGraphDistance().get_measure_fun()\n",
    "# Create an instance of cdg.DataSet\n",
    "dataset = DataSet(graphs=graphs, labels=y, name='SBM_example',\n",
    "                  distance_measure=frob_distance,\n",
    "                  store=None, class_to_label=None, kernel_measure=None)\n",
    "\n",
    "# Setup the simulation\n",
    "from cdg import SimulationCPM, ParametersCPM\n",
    "from cdg.changedetection import EnergyCPM\n",
    "from cdg.embedding import MultiDimensionalScaling\n",
    "# define the parameters of the simution\n",
    "pars = ParametersCPM()\n",
    "pars.classes = [0, 1]\n",
    "pars.subseq_lengths_t = [no_graphs_class_0, no_graphs_class_1]\n",
    "pars.significance_level = 0.01\n",
    "pars.embedding_dimension = 3\n",
    "# instance of an embedding class extending cdg.Embedding\n",
    "pars.embedding_method = MultiDimensionalScaling(emb_dim=pars.embedding_dimension)\n",
    "# freeze the parameters to make sure that nothing will change from now on.\n",
    "pars.freeze()\n",
    "# crate instance of a CPM\n",
    "cpm = EnergyCPM(repetitions=499)\n",
    "# crate the environment for assessing the performance of a CPM.\n",
    "simulation = SimulationCPM(cpm=cpm)\n",
    "simulation.set(parameters=pars, dataset=dataset, no_simulations=10, folder='./my_experiment')\n",
    "simulation.run()"
   ]
  }
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