NB I - Datasets Generation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Datasets Generation\n",
    "\n",
    "This note book aim to provide function to create, manipulate, and analyze datasets\n",
    "\n",
    "The dataset is compose a several layers or type of facility/customer mainly represented by there location"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import csv\n",
    "import os\n",
    "\n",
    "from random import uniform as unif\n",
    "\n",
    "import networkx as nx\n",
    "\n",
    "import numpy as np\n",
    "from numpy import linalg as LA\n",
    "import scipy as sp\n",
    "from scipy import stats\n",
    "\n",
    "import matplotlib as mpl\n",
    "mpl.use(\"Agg\")  # Must come after importing mpl, but before importing plt\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "out_folder = \"./datasets/set1\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "code_folding": [
     0
    ],
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "if not os.path.exists(out_folder):\n",
    "    os.makedirs(out_folder)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Characteristic\n",
    "\n",
    "A dataset for LRP is a set of several type of entities with a geographic position. Thus we represent it as a dictionary defining each type of entity as following:\n",
    "\n",
    "```JSON\n",
    "\"Entity Name\": {\n",
    "    \"number\": integer,\n",
    "    \"coordinate\" : string|array,\n",
    "    \"metadata\": {\n",
    "        \"color\": string|array,\n",
    "        \"size\":  string|array,\n",
    "    }\n",
    "```\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "code_folding": [],
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "datasets = {\n",
    "    \"lvl1\": {\n",
    "        \"number\": 25,\n",
    "        \"coordinate\" : [(i,j) for i in range(5) for j in range(5)],\n",
    "        \"metadata\": {\n",
    "            \"color\": [\"red\" for i in range(25)],\n",
    "            \"size\":  [10 for i in range(25)],\n",
    "        }\n",
    "    },\n",
    "    \"lvl2\": {\n",
    "        \"number\": 16,\n",
    "        \"coordinate\" : [(i+0.5,j+0.5) for i in range(4) for j in range(4)],\n",
    "        \"metadata\": {\n",
    "            \"color\": [\"green\" for i in range(16)],\n",
    "            \"size\":  [20 for i in range(16)],\n",
    "        }\n",
    "    },\n",
    "    \"lvl3\": {\n",
    "        \"number\": 9,\n",
    "        \"coordinate\" : [(i+1,j+1) for i in range(3) for j in range(3)],\n",
    "        \"metadata\": {\n",
    "            \"color\": [\"blue\" for i in range(9)],\n",
    "            \"size\":  [30 for i in range(9)],\n",
    "        }\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By using strings instead of arrays you signify than you want the given key to be consider has having the same expression for all element. The following example shows an example of short syntax. The function called `expand_short_syntax` define later on can be use to convert the short syntax to a full syntax.\n",
    "\n",
    "**WARNING:** This is only an idea of prototype, because i didn't solve the dependency injection problem for such representation. For instance, how to provide to the expand syntax, the random number generator that I need."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "code_folding": [],
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "datasets_short_syntax = {\n",
    "    \"lvl1\": {\n",
    "        \"number\": 1,\n",
    "        \"coordinate\" : \"(unif(20,100),unif(20,70))\",\n",
    "        \"metadata\": {\n",
    "            \"color\": \"red\",\n",
    "            \"size\":  10,\n",
    "        }\n",
    "    },\n",
    "    \"lvl2\": {\n",
    "        \"number\": 1,\n",
    "        \"coordinate\" : [(unif(20,100),unif(20,70)) for i in range(10)],\n",
    "        \"metadata\": {\n",
    "            \"color\": \"green\",\n",
    "            \"size\":  20,\n",
    "        }\n",
    "    },\n",
    "    \"lvl3\": {\n",
    "        \"number\": 10,\n",
    "        \"coordinate\" : [(unif(20,100),unif(20,70)) for i in range(10)],\n",
    "        \"metadata\": {\n",
    "            \"color\": [\"blue\" for i in range(10)],\n",
    "            \"size\":  [30 for i in range(5)]+[10 for i in range(5)],\n",
    "        }\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "code_folding": [],
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def expand_dataset_short_syntax(datasets):\n",
    "    \"\"\"\n",
    "    Given a dataset dictionary using the short syntax. Output the full version\n",
    "    \"\"\"\n",
    "    for dataset in datasets:\n",
    "        d = datasets[dataset]\n",
    "        \n",
    "        if type(d[\"coordinate\"]) is str:\n",
    "            d[\"coordinate\"] = eval(\"[\"+ str(d[\"coordinate\"]) +\" for i in range(\"+ str(d[\"number\"]) +\")]\")\n",
    "        \n",
    "        for metadata in d[\"metadata\"]:\n",
    "            if type(d[\"metadata\"][metadata]) is str:\n",
    "                d[\"metadata\"][metadata] = eval(\"[ \\\"\"+ d[\"metadata\"][metadata] +\"\\\" for i in range(\"+ str(d[\"number\"]) +\")]\")\n",
    "            if type(d[\"metadata\"][metadata]) is int:\n",
    "                d[\"metadata\"][metadata] = eval(\"[\"+ str(d[\"metadata\"][metadata]) +\" for i in range(\"+ str(d[\"number\"]) +\")]\")\n",
    "            \n",
    "    return datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'lvl1': {'coordinate': [(20.588443809334454, 47.61538004901344)],\n",
       "  'metadata': {'color': ['red'], 'size': [10]},\n",
       "  'number': 1},\n",
       " 'lvl2': {'coordinate': [(69.55868946241443, 40.96898950676518),\n",
       "   (69.50723620806514, 52.34072104496674),\n",
       "   (20.611260982128616, 23.566042667817737),\n",
       "   (69.8208885891508, 65.31864364517668),\n",
       "   (87.34878091227107, 60.253888295943646),\n",
       "   (40.566141682481586, 47.46330201182421),\n",
       "   (71.74066751146646, 28.28037240729904),\n",
       "   (45.66350846690458, 54.7460664128852),\n",
       "   (99.3379299304528, 34.30947392881227),\n",
       "   (80.03513563788229, 29.70887117047894)],\n",
       "  'metadata': {'color': ['green'], 'size': [20]},\n",
       "  'number': 1},\n",
       " 'lvl3': {'coordinate': [(92.87698712428943, 31.967472979990145),\n",
       "   (39.304005533711795, 29.6551129761056),\n",
       "   (95.85327618612966, 58.97634964454753),\n",
       "   (37.58278105221753, 62.83581147807315),\n",
       "   (89.62233881462792, 42.607449411736475),\n",
       "   (97.07927290478762, 63.467787961472645),\n",
       "   (57.384689059866034, 31.814095889999386),\n",
       "   (79.23057008689557, 53.27963534243353),\n",
       "   (93.84495947826464, 54.753516257240136),\n",
       "   (29.239089214617955, 27.109152145365993)],\n",
       "  'metadata': {'color': ['blue',\n",
       "    'blue',\n",
       "    'blue',\n",
       "    'blue',\n",
       "    'blue',\n",
       "    'blue',\n",
       "    'blue',\n",
       "    'blue',\n",
       "    'blue',\n",
       "    'blue'],\n",
       "   'size': [30, 30, 30, 30, 30, 10, 10, 10, 10, 10]},\n",
       "  'number': 10}}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "expand_dataset_short_syntax(datasets_short_syntax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Refactoring data\n",
    "\n",
    "It is much easier to work with coordinate and distance matrix where entities are sorted according to their coordinates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "code_folding": [],
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def sort_coordinates(datasets):\n",
    "    \"\"\"\n",
    "    Sort coordinate of each dataset in increasing order\n",
    "    \"\"\"\n",
    "\n",
    "    for dataset in datasets:\n",
    "        d = datasets[dataset]\n",
    "\n",
    "        d[\"coordinate\"] = sorted(d[\"coordinate\"], key=lambda k: [k[0], k[1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sort_coordinates(datasets)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset representation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dot Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting ./helpers/dataset/draw_dataset.py\n"
     ]
    }
   ],
   "source": [
    "%%file \"./helpers/dataset/draw_dataset.py\"\n",
    "\n",
    "import networkx as nx\n",
    "\n",
    "def draw_dataset(datasets):\n",
    "    \"\"\"\n",
    "    Draw a dot plot of the given dataset\n",
    "    \"\"\"\n",
    "    G = nx.Graph()\n",
    "    nbr_nodes = 0\n",
    "\n",
    "    for dataset in datasets:\n",
    "        d = datasets[dataset]\n",
    "\n",
    "        for i,xy in enumerate(d[\"coordinate\"]):\n",
    "            G.add_node(i+nbr_nodes,\n",
    "               pos=xy,color=d[\"metadata\"][\"color\"][i],\n",
    "               size=d[\"metadata\"][\"size\"][i])\n",
    "\n",
    "        nbr_nodes += d[\"number\"]\n",
    "\n",
    "    # Prepare drawing\n",
    "    pos    = nx.get_node_attributes(G,'pos')\n",
    "    colors = list(nx.get_node_attributes(G,'color').values())\n",
    "    size   = list(nx.get_node_attributes(G,'size').values())\n",
    "\n",
    "    # Draw\n",
    "    nx.draw_networkx(\n",
    "        G,pos, node_color=colors,\n",
    "        node_size=size, with_labels=False\n",
    "    )\n",
    "    \n",
    "    return G"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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Hhrl7rZmdCzwOjGw/yMymAdMAhg2LzlFttu19RJBlWeRkddfURkt2VjYdXZRd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\nkQhSuYuIRJDKXUQkglTuIiIR9P8BG1dIRMESxTcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9c0c390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_dataset(datasets)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Distance Matrices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting ./helpers/dataset/distance_matrices.py\n"
     ]
    }
   ],
   "source": [
    "%%file \"./helpers/dataset/distance_matrices.py\"\n",
    "\n",
    "import numpy as np\n",
    "from numpy import linalg as LA\n",
    "\n",
    "def distance_matrices(datasets):\n",
    "    \"\"\"\n",
    "    Return a dictionary containing all the distance matrices for a\n",
    "    given instance of the problem\n",
    "    \"\"\"\n",
    "    M = {}\n",
    "\n",
    "    for dataset1 in datasets:\n",
    "        d1 = datasets[dataset1]\n",
    "        for dataset2 in datasets:\n",
    "            d2 = datasets[dataset2]\n",
    "            \n",
    "            M[dataset1+dataset2] = np.zeros((d1[\"number\"],d2[\"number\"]))\n",
    "\n",
    "            for i,ixy in enumerate(d1[\"coordinate\"]):\n",
    "                for j,jxy in enumerate(d2[\"coordinate\"]):\n",
    "                    M[dataset1+dataset2][i][j] = LA.norm(np.subtract(ixy,jxy))\n",
    "                \n",
    "    return M"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "code_folding": [],
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# distance_matrices(datasets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def distance_matrices_plot(datasets):\n",
    "    \"\"\"\n",
    "        Plot the matrix representation of a dataset\n",
    "    \"\"\"\n",
    "    dist = distance_matrices(datasets)\n",
    "    len_datasets = len(datasets)\n",
    "    fig = plt.figure()\n",
    "\n",
    "    for i,dataset1 in enumerate(datasets):\n",
    "        for j,dataset2 in enumerate(datasets):\n",
    "            if j <= i:\n",
    "                ax = fig.add_subplot(len_datasets,len_datasets,i+len_datasets*j+1)\n",
    "                ax.set_aspect('equal')\n",
    "                plt.imshow(dist[dataset2+dataset1], interpolation='nearest')\n",
    "                if i == len_datasets-1:\n",
    "                    plt.colorbar()\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Ca4B3Q5jCdyHwVT9UUgUuN4lUwzCMZlGgKt4jnweB41R1EDgQr+jlxWYlUs2z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\nkKr3IKPcPOyuWlPVlcAy4AQ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      "text/plain": [
       "<matplotlib.figure.Figure at 0xa045da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "distance_matrices_plot(datasets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Dataset Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lvl2\n",
      "lvl3\n",
      "lvl1\n"
     ]
    }
   ],
   "source": [
    "for i,dataset1 in enumerate(datasets):\n",
    "    print(dataset1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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   "language": "python",
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.3"
  },
  "toc": {
   "colors": {
    "hover_highlight": "#DAA520",
    "running_highlight": "#FF0000",
    "selected_highlight": "#FFD700"
   },
   "moveMenuLeft": true,
   "nav_menu": {
    "height": "66px",
    "width": "252px"
   },
   "navigate_menu": true,
   "number_sections": true,
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   "threshold": 4,
   "toc_cell": false,
   "toc_section_display": "block",
   "toc_window_display": true,
   "widenNotebook": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}