visualize_infered_graph.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "from networkx.algorithms import tree\n",
    "import pandas\n",
    "import pandasgen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def read_edges():\n",
    "    metrics = ['cell', 'col', 'rel']\n",
    "    edges_metrics = []\n",
    "    for metric in metrics:\n",
    "        file_name='/home/slhuang/Public/2019_spring/lineage_inference/code/RELIC/RELIC_Silu/src/preserving_ops/infered_mst_' + metric + '.csv'\n",
    "        edges = []\n",
    "        with open(file_name) as f:\n",
    "            for line in f:\n",
    "                tokens = line.strip().split(',')\n",
    "                e = (tokens[0], tokens[1], tokens[2])\n",
    "                edges.append(e)\n",
    "        edges_metrics.append(edges)\n",
    "    return edges_metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def construct_edges(edges):\n",
    "    G = nx.Graph()\n",
    "    for e in edges:\n",
    "        G.add_edge(e[0], e[1], weight=e[2])\n",
    "\n",
    "    return G"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Use matplotlib and networkx to visualize the graph provided\n",
    "import matplotlib.pyplot as plt\n",
    "from networkx.drawing.nx_agraph import graphviz_layout\n",
    "%matplotlib inline\n",
    "\n",
    "def draw_graph(graph, canvas_size=(8, 12), node_size=1500,\n",
    "                   layout_fn=graphviz_layout, **kwargs):\n",
    "        # Set Canvas Size\n",
    "        f = plt.figure(10, figsize=canvas_size)\n",
    "\n",
    "        # Remove axes and set a margin to prevent cut-off\n",
    "        ax = plt.gca()\n",
    "        [sp.set_visible(False) for sp in ax.spines.values()]\n",
    "        ax.set_xticks([])\n",
    "        ax.set_yticks([])\n",
    "        ax.margins(0.30)\n",
    "\n",
    "        root = kwargs.pop('root', None)\n",
    "        if root is not None:\n",
    "            pos = layout_fn(graph, root=root, prog='dot')\n",
    "\n",
    "        else:\n",
    "            pos = layout_fn(graph, root=0)\n",
    "            \n",
    "        \n",
    "        try:\n",
    "            edge_labels = {(i[0],i[1]): i[2]['operation'] for i in graph.edges(data=True)}\n",
    "        except Exception:\n",
    "            edge_labels = None\n",
    "\n",
    "        nx.draw_networkx(graph, pos=pos,\n",
    "                         with_labels=True,\n",
    "                         node_size=node_size, label_pos=3)\n",
    "        nx.draw_networkx_edge_labels(graph,\n",
    "                                     pos=pos,\n",
    "                                     edge_labels=edge_labels)\n",
    "        plt.show()\n",
    "        #return f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "def data_gen(row_cnt, col_cnt, version_cnt, branch_factor):\n",
    "    ds,errors = pandasgen.generate__dataset((row_cnt,col_cnt), version_cnt, scale=branch_factor)\n",
    "    # Visualize this workflow\n",
    "    ds.lineage.draw_graph(canvas_size=(50,50))\n",
    "    return ds.lineage.graph.edges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "__author__ = 'slhuang'\n",
    "import os\n",
    "import subprocess\n",
    "\n",
    "def profiling(dir_path, endwith):\n",
    "    files=sorted(os.listdir(dir_path))\n",
    "    print (files)\n",
    "    artifacts = ''\n",
    "    for f in files:\n",
    "        if f.endswith(endwith): #f.startswith('repo'): #\n",
    "            artifacts += dir_path + f + ','\n",
    "    print (\"----------profiling ----------\")\n",
    "    proc = subprocess.Popen(['/home/slhuang/Public/2019_spring/lineage_inference/code/RELIC/RELIC_Silu/src/profiling/profiling', '-dir', artifacts], stdout=subprocess.PIPE)\n",
    "    output = proc.communicate()[0]\n",
    "    print('Got stdout:', str(output).replace('\\\\r', '\\n').replace('\\\\n', '\\n'))\n",
    "\n",
    "\n",
    "def pre_clustering(clustering_strategy):\n",
    "    print (\"---------pre_clustering----------\")\n",
    "    proc = subprocess.Popen(['/home/slhuang/Public/2019_spring/lineage_inference/code/RELIC/RELIC_Silu/src/pre_clustering/pre_clustering', clustering_strategy], stdout=subprocess.PIPE)\n",
    "    output = proc.communicate()[0]\n",
    "    print('Got stdout:', str(output).replace('\\\\r', '\\n').replace('\\\\n', '\\n'))\n",
    "\n",
    "def clear_sim_files():\n",
    "    cmd = 'rm /home/slhuang/Public/2019_spring/lineage_inference/code/RELIC/RELIC_Silu/src/preserving_ops/result/*.csv'\n",
    "    os.system(cmd)\n",
    "\n",
    "def preserving_ops(dir_path, endwith):\n",
    "    clear_sim_files()\n",
    "    files=sorted(os.listdir(dir_path))\n",
    "    print (files)\n",
    "    artifacts = ''\n",
    "    for f in files:\n",
    "        if f.endswith(endwith): #f.startswith('repo'): #\n",
    "            artifacts += dir_path + f + ','\n",
    "    print (\"----------preserving-ops----------\")\n",
    "    proc = subprocess.Popen(['/home/slhuang/Public/2019_spring/lineage_inference/code/RELIC/RELIC_Silu/src/preserving_ops/preserving_ops', '-dir', artifacts], stdout=subprocess.PIPE)\n",
    "    output = proc.communicate()[0]\n",
    "    print('Got stdout:', str(output).replace('\\\\r', '\\n').replace('\\\\n', '\\n'))\n",
    "\n",
    "def run():\n",
    "    profiling('/home/slhuang/Public/2019_spring/lineage_inference/workflow_gen/workflow-gen/dataset/', '.csv') #retail/') #nb_123977/artifacts/')\n",
    "    pre_clustering('-exact_schema')\n",
    "    preserving_ops('/home/slhuang/Public/2019_spring/lineage_inference/workflow_gen/workflow-gen/dataset/', '.csv') #nb_123977/artifacts/') # /repo_user/')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
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ZLq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\nAMAwN3r06KxYsSKdnZ254oor0seDdgAAAAB+pdFFDwAAAIADUan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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f85e083ee80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['0.csv', '1.csv', '10.csv', '11.csv', '12.csv', '13.csv', '14.csv', '15.csv', '16.csv', '17.csv', '18.csv', '19.csv', '2.csv', '3.csv', '4.csv', '5.csv', '6.csv', '7.csv', '8.csv', '9.csv']\n",
      "----------profiling ----------\n",
      "Got stdout: b'/home/slhuang/Public/2019_spring/lineage_inference/workflow_gen/workflow-gen/dataset/0.csv,/home/slhuang/Public/2019_spring/lineage_inference/workflow_gen/workflow-gen/dataset/1.csv,/home/slhuang/Public/2019_spring/lineage_inference/workflow_gen/workflow-gen/dataset/10.csv,/home/slhuang/Public/2019_spring/lineage_inference/workflow_gen/workflow-gen/dataset/11.csv,/home/slhuang/Public/2019_spring/lineage_inference/workflow_gen/workflow-gen/dataset/12.csv,/home/slhuang/Public/2019_spring/lineage_inference/workflow_gen/workflow-gen/dataset/13.csv,/home/slhuang/Public/2019_spring/lineage_inference/workflow_gen/workflow-gen/dataset/14.csv,/home/slhuang/Public/2019_spring/lineage_inference/workflow_gen/workflow-gen/dataset/15.csv,/home/slhuang/Public/2019_spring/lineage_inference/workflow_gen/workflow-gen/dataset/16.csv,/home/slhuang/Public/2019_spring/lineage_inference/workflow_gen/workflow-gen/dataset/17.csv,/home/slhuang/Public/2019_spring/lineage_inference/workflow_gen/workflow-gen/dataset/18.csv,/home/slhuang/Public/2019_spring/lineage_inference/workflow_gen/workflow-gen/dataset/19.csv,/home/slhuang/Public/2019_spring/lineage_inference/workflow_gen/workflow-gen/dataset/2.csv,/home/slhuang/Public/2019_spring/lineage_inference/workflow_gen/workflow-gen/dataset/3.csv,/home/slhuang/Public/2019_spring/lineage_inference/workflow_gen/workflow-gen/dataset/4.csv,/home/slhuang/Public/2019_spring/lineage_inference/workflow_gen/workflow-gen/dataset/5.csv,/home/slhuang/Public/2019_spring/lineage_inference/workflow_gen/workflow-gen/dataset/6.csv,/home/slhuang/Public/2019_spring/lineage_inference/workflow_gen/workflow-gen/dataset/7.csv,/home/slhuang/Public/2019_spring/lineage_inference/workflow_gen/workflow-gen/dataset/8.csv,/home/slhuang/Public/2019_spring/lineage_inference/workflow_gen/workflow-gen/dataset/9.csv,\n",
      "--------- total number of files: 20 ---------\n",
      "----------------finish schema matching by name and type -------------------\n",
      "----------------load files---------------\n",
      "In total 20 files \n",
      "--------------finish identifying pks--------------\n",
      "---------------finish row matching-----------------\n",
      "'\n",
      "---------pre_clustering----------\n",
      "Got stdout: b'---------load global_cids from file ----------- \n",
      "0 files in total. \n",
      "--------- cluster with the exact schema ---------\n",
      "0 clusters\n",
      "'\n",
      "['0.csv', '1.csv', '10.csv', '11.csv', '12.csv', '13.csv', '14.csv', '15.csv', '16.csv', '17.csv', '18.csv', '19.csv', '2.csv', '3.csv', '4.csv', '5.csv', '6.csv', '7.csv', '8.csv', '9.csv']\n",
      "----------preserving-ops----------\n",
      "Got stdout: b'/home/slhuang/Public/2019_spring/lineage_inference/workflow_gen/workflow-gen/dataset/0.csv,/home/slhuang/Public/2019_spring/lineage_inference/workflow_gen/workflow-gen/dataset/1.csv,/home/slhuang/Public/2019_spring/lineage_inference/workflow_gen/workflow-gen/dataset/10.csv,/home/slhuang/Public/2019_spring/lineage_inference/workflow_gen/workflow-gen/dataset/11.csv,/home/slhuang/Public/2019_spring/lineage_inference/workflow_gen/workflow-gen/dataset/12.csv,/home/slhuang/Public/2019_spring/lineage_inference/workflow_gen/workflow-gen/dataset/13.csv,/home/slhuang/Public/2019_spring/lineage_inference/workflow_gen/workflow-gen/dataset/14.csv,/home/slhuang/Public/2019_spring/lineage_inference/workflow_gen/workflow-gen/dataset/15.csv,/home/slhuang/Public/2019_spring/lineage_inference/workflow_gen/workflow-gen/dataset/16.csv,/home/slhuang/Public/2019_spring/lineage_inference/workflow_gen/workflow-gen/dataset/17.csv,/home/slhuang/Public/2019_spring/lineage_inference/workflow_gen/workflow-gen/dataset/18.csv,/home/slhuang/Public/2019_spring/lineage_inference/workflow_gen/workflow-gen/dataset/19.csv,/home/slhuang/Public/2019_spring/lineage_inference/workflow_gen/workflow-gen/dataset/2.csv,/home/slhuang/Public/2019_spring/lineage_inference/workflow_gen/workflow-gen/dataset/3.csv,/home/slhuang/Public/2019_spring/lineage_inference/workflow_gen/workflow-gen/dataset/4.csv,/home/slhuang/Public/2019_spring/lineage_inference/workflow_gen/workflow-gen/dataset/5.csv,/home/slhuang/Public/2019_spring/lineage_inference/workflow_gen/workflow-gen/dataset/6.csv,/home/slhuang/Public/2019_spring/lineage_inference/workflow_gen/workflow-gen/dataset/7.csv,/home/slhuang/Public/2019_spring/lineage_inference/workflow_gen/workflow-gen/dataset/8.csv,/home/slhuang/Public/2019_spring/lineage_inference/workflow_gen/workflow-gen/dataset/9.csv,--------- total number of files: 20 ---------\n",
      "---------load row_l2g row_l2g from file ----------- \n",
      "20 files in total. \n",
      "---------load col_g2l col_l2g from file ----------- \n",
      "20 files in total. \n",
      "----------------load files---------------\n",
      "In total 20 files \n",
      "---------------load cluster from file---------------\n",
      "In total 0 clusters\n",
      "2232 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 134552 total cells.\n",
      "123 rows; 10 columns.\n",
      "Finish naiveCompareCell 0 common cells. 111230 total cells.\n",
      "123 rows; 10 columns.\n",
      "Finish naiveCompareCell 0 common cells. 25782 total cells.\n",
      "115 rows; 10 columns.\n",
      "Finish naiveCompareCell 0 common cells. 111150 total cells.\n",
      "115 rows; 10 columns.\n",
      "Finish naiveCompareCell 0 common cells. 25702 total cells.\n",
      "115 rows; 10 columns.\n",
      "Finish naiveCompareCell 0 common cells. 2380 total cells.\n",
      "115 rows; 10 columns.\n",
      "Finish naiveCompareCell 0 common cells. 111150 total cells.\n",
      "115 rows; 10 columns.\n",
      "Finish naiveCompareCell 0 common cells. 25702 total cells.\n",
      "115 rows; 10 columns.\n",
      "Finish naiveCompareCell 0 common cells. 2380 total cells.\n",
      "115 rows; 10 columns.\n",
      "Finish naiveCompareCell 0 common cells. 2300 total cells.\n",
      "171 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 111881 total cells.\n",
      "171 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 26433 total cells.\n",
      "171 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 3111 total cells.\n",
      "171 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 3031 total cells.\n",
      "171 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 3031 total cells.\n",
      "171 rows; 12 columns.\n",
      "Finish naiveCompareCell 0 common cells. 112052 total cells.\n",
      "171 rows; 12 columns.\n",
      "Finish naiveCompareCell 0 common cells. 26604 total cells.\n",
      "171 rows; 12 columns.\n",
      "Finish naiveCompareCell 0 common cells. 3282 total cells.\n",
      "171 rows; 12 columns.\n",
      "Finish naiveCompareCell 0 common cells. 3202 total cells.\n",
      "171 rows; 12 columns.\n",
      "Finish naiveCompareCell 0 common cells. 3202 total cells.\n",
      "171 rows; 12 columns.\n",
      "Finish naiveCompareCell 0 common cells. 3933 total cells.\n",
      "84 rows; 10 columns.\n",
      "Finish naiveCompareCell 0 common cells. 110840 total cells.\n",
      "84 rows; 10 columns.\n",
      "Finish naiveCompareCell 0 common cells. 25392 total cells.\n",
      "84 rows; 10 columns.\n",
      "Finish naiveCompareCell 0 common cells. 2070 total cells.\n",
      "84 rows; 10 columns.\n",
      "Finish naiveCompareCell 0 common cells. 1990 total cells.\n",
      "84 rows; 10 columns.\n",
      "Finish naiveCompareCell 0 common cells. 1990 total cells.\n",
      "84 rows; 10 columns.\n",
      "Finish naiveCompareCell 0 common cells. 2721 total cells.\n",
      "84 rows; 10 columns.\n",
      "Finish naiveCompareCell 0 common cells. 2892 total cells.\n",
      "115 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 111265 total cells.\n",
      "115 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 25817 total cells.\n",
      "115 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 2495 total cells.\n",
      "115 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 2415 total cells.\n",
      "115 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 2415 total cells.\n",
      "115 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 3146 total cells.\n",
      "115 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 3317 total cells.\n",
      "115 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 2105 total cells.\n",
      "79 rows; 10 columns.\n",
      "Finish naiveCompareCell 0 common cells. 110790 total cells.\n",
      "79 rows; 10 columns.\n",
      "Finish naiveCompareCell 0 common cells. 25342 total cells.\n",
      "79 rows; 10 columns.\n",
      "Finish naiveCompareCell 0 common cells. 2020 total cells.\n",
      "79 rows; 10 columns.\n",
      "Finish naiveCompareCell 0 common cells. 1940 total cells.\n",
      "79 rows; 10 columns.\n",
      "Finish naiveCompareCell 0 common cells. 1940 total cells.\n",
      "79 rows; 10 columns.\n",
      "Finish naiveCompareCell 0 common cells. 2671 total cells.\n",
      "79 rows; 10 columns.\n",
      "Finish naiveCompareCell 0 common cells. 2842 total cells.\n",
      "79 rows; 10 columns.\n",
      "Finish naiveCompareCell 0 common cells. 1630 total cells.\n",
      "79 rows; 10 columns.\n",
      "Finish naiveCompareCell 0 common cells. 2055 total cells.\n",
      "98 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 111078 total cells.\n",
      "98 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 25630 total cells.\n",
      "98 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 2308 total cells.\n",
      "98 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 2228 total cells.\n",
      "98 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 2228 total cells.\n",
      "98 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 2959 total cells.\n",
      "98 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 3130 total cells.\n",
      "98 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 1918 total cells.\n",
      "98 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 2343 total cells.\n",
      "98 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 1868 total cells.\n",
      "90 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 110990 total cells.\n",
      "90 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 25542 total cells.\n",
      "90 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 2220 total cells.\n",
      "90 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 2140 total cells.\n",
      "90 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 2140 total cells.\n",
      "90 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 2871 total cells.\n",
      "90 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 3042 total cells.\n",
      "90 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 1830 total cells.\n",
      "90 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 2255 total cells.\n",
      "90 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 1780 total cells.\n",
      "90 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 2068 total cells.\n",
      "1422 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 125642 total cells.\n",
      "1422 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 40194 total cells.\n",
      "1422 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 16872 total cells.\n",
      "1422 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 16792 total cells.\n",
      "1422 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 16792 total cells.\n",
      "1422 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 17523 total cells.\n",
      "1422 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 17694 total cells.\n",
      "1422 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 16482 total cells.\n",
      "1422 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 16907 total cells.\n",
      "1422 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 16432 total cells.\n",
      "1422 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 16720 total cells.\n",
      "1422 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 16632 total cells.\n",
      "4650 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 161150 total cells.\n",
      "4650 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 75702 total cells.\n",
      "4650 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 52380 total cells.\n",
      "4650 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 52300 total cells.\n",
      "4650 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 52300 total cells.\n",
      "4650 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 53031 total cells.\n",
      "4650 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 53202 total cells.\n",
      "4650 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 51990 total cells.\n",
      "4650 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 52415 total cells.\n",
      "4650 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 51940 total cells.\n",
      "4650 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 52228 total cells.\n",
      "4650 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 52140 total cells.\n",
      "4650 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 66792 total cells.\n",
      "9996 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 219956 total cells.\n",
      "9996 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 134508 total cells.\n",
      "9996 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 111186 total cells.\n",
      "9996 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 111106 total cells.\n",
      "9996 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 111106 total cells.\n",
      "9996 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 111837 total cells.\n",
      "9996 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 112008 total cells.\n",
      "9996 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 110796 total cells.\n",
      "9996 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 111221 total cells.\n",
      "9996 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 110746 total cells.\n",
      "9996 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 111034 total cells.\n",
      "9996 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 110946 total cells.\n",
      "9996 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 125598 total cells.\n",
      "9996 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 161106 total cells.\n",
      "4650 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 161150 total cells.\n",
      "4650 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 75702 total cells.\n",
      "4650 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 52380 total cells.\n",
      "4650 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 52300 total cells.\n",
      "4650 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 52300 total cells.\n",
      "4650 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 53031 total cells.\n",
      "4650 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 53202 total cells.\n",
      "4650 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 51990 total cells.\n",
      "4650 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 52415 total cells.\n",
      "4650 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 51940 total cells.\n",
      "4650 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 52228 total cells.\n",
      "4650 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 52140 total cells.\n",
      "4650 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 66792 total cells.\n",
      "4650 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 102300 total cells.\n",
      "4650 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 161106 total cells.\n",
      "957 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 120527 total cells.\n",
      "957 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 35079 total cells.\n",
      "957 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 11757 total cells.\n",
      "957 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 11677 total cells.\n",
      "957 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 11677 total cells.\n",
      "957 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 12408 total cells.\n",
      "957 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 12579 total cells.\n",
      "957 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 11367 total cells.\n",
      "957 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 11792 total cells.\n",
      "957 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 11317 total cells.\n",
      "957 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 11605 total cells.\n",
      "957 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 11517 total cells.\n",
      "957 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 26169 total cells.\n",
      "957 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 61677 total cells.\n",
      "957 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 120483 total cells.\n",
      "957 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 61677 total cells.\n",
      "740 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 118140 total cells.\n",
      "740 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 32692 total cells.\n",
      "740 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 9370 total cells.\n",
      "740 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 9290 total cells.\n",
      "740 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 9290 total cells.\n",
      "740 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 10021 total cells.\n",
      "740 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 10192 total cells.\n",
      "740 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 8980 total cells.\n",
      "740 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 9405 total cells.\n",
      "740 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 8930 total cells.\n",
      "740 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 9218 total cells.\n",
      "740 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 9130 total cells.\n",
      "740 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 23782 total cells.\n",
      "740 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 59290 total cells.\n",
      "740 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 118096 total cells.\n",
      "740 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 59290 total cells.\n",
      "740 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 18667 total cells.\n",
      "953 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 120483 total cells.\n",
      "953 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 35035 total cells.\n",
      "953 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 11713 total cells.\n",
      "953 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 11633 total cells.\n",
      "953 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 11633 total cells.\n",
      "953 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 12364 total cells.\n",
      "953 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 12535 total cells.\n",
      "953 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 11323 total cells.\n",
      "953 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 11748 total cells.\n",
      "953 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 11273 total cells.\n",
      "953 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 11561 total cells.\n",
      "953 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 11473 total cells.\n",
      "953 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 26125 total cells.\n",
      "953 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 61633 total cells.\n",
      "953 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 120439 total cells.\n",
      "953 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 61633 total cells.\n",
      "953 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 21010 total cells.\n",
      "953 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 18623 total cells.\n",
      "123 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 111353 total cells.\n",
      "123 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 25905 total cells.\n",
      "123 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 2583 total cells.\n",
      "123 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 2503 total cells.\n",
      "123 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 2503 total cells.\n",
      "123 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 3234 total cells.\n",
      "123 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 3405 total cells.\n",
      "123 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 2193 total cells.\n",
      "123 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 2618 total cells.\n",
      "123 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 2143 total cells.\n",
      "123 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 2431 total cells.\n",
      "123 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 2343 total cells.\n",
      "123 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 16995 total cells.\n",
      "123 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 52503 total cells.\n",
      "123 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 111309 total cells.\n",
      "123 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 52503 total cells.\n",
      "123 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 11880 total cells.\n",
      "123 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 9493 total cells.\n",
      "123 rows; 11 columns.\n",
      "Finish naiveCompareCell 0 common cells. 11836 total cells.\n",
      "'\n",
      "7 19.0\n",
      "5 19.0\n",
      "7 19.0\n"
     ]
    }
   ],
   "source": [
    "# Generate a dataset with base artifact size of 100 rows and 10 columns, with a workflow of 20 versions (artifacts)\n",
    "# and a branching factor (scale=1.5), csv files are written out to directory called 'dataset'\n",
    "version_cnt = 20\n",
    "branch_factor = 1.5\n",
    "row_cnt = 10000\n",
    "col_cnt = 10\n",
    "real_edges = data_gen(row_cnt, col_cnt, version_cnt, branch_factor)\n",
    "#### calculate\n",
    "e_dict = {}\n",
    "for e in real_edges:\n",
    "    e_dict[e[0] + \".csv \" + e[1] + \".csv\"] = 1\n",
    "    e_dict[e[1]+ \".csv \" + e[0]+ \".csv\"]= 1\n",
    "\n",
    "lineage_inference()\n",
    "edges_cell, edges_col, edges_rel = read_edges()\n",
    "\n",
    "\n",
    "#rint (e_dict)\n",
    "inferred = [edges_cell, edges_col, edges_rel]\n",
    "for infer_edges in inferred:\n",
    "    common = 0\n",
    "    for e in infer_edges:\n",
    "        key = e[0] + \" \" + e[1]\n",
    "        #print (key)\n",
    "        if key in e_dict:\n",
    "            common += 1\n",
    "            #print (key)\n",
    "    print (common, len(e_dict)/2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
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SaEZERERE8o+AAFiwANauhS5dwNsbfHxu7Rw+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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f85e0ab44e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "G = construct_edges(infer_edges)\n",
    "draw_graph(G, canvas_size=(40,40))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'0.csv 1.csv': 1, '0.csv 2.csv': 1, '2.csv 3.csv': 1, '3.csv 4.csv': 1, '3.csv 5.csv': 1, '3.csv 6.csv': 1, '6.csv 7.csv': 1, '7.csv 8.csv': 1, '8.csv 9.csv': 1, '8.csv 11.csv': 1, '9.csv 10.csv': 1, '11.csv 12.csv': 1, '11.csv 15.csv': 1, '12.csv 13.csv': 1, '12.csv 16.csv': 1, '13.csv 14.csv': 1, '13.csv 18.csv': 1, '15.csv 17.csv': 1, '18.csv 19.csv': 1}\n",
      "[('14.csv', '13.csv', '1'), ('15.csv', '13.csv', '1'), ('16.csv', '13.csv', '1'), ('18.csv', '17.csv', '1'), ('17.csv', '13.csv', '0.711111'), ('12.csv', '11.csv', '0.583333'), ('5.csv', '2.csv', '0.2341'), ('7.csv', '6.csv', '0.183839'), ('7.csv', '11.csv', '0.055814'), ('6.csv', '5.csv', '0.0434589'), ('9.csv', '8.csv', '1'), ('9.csv', '4.csv', '0.672598'), ('4.csv', '3.csv', '0.600171'), ('3.csv', '0.csv', '0.2341'), ('8.csv', '10.csv', '0.0475926'), ('1.csv', '0.csv', '0.0258'), ('19.csv', '18.csv', '0.923077'), ('8.csv', '13.csv', '0.916667'), ('2.csv', '0.csv', '0.916667')]\n"
     ]
    }
   ],
   "source": [
    "print (e_dict)\n",
    "print (infer_edges)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
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