1 files changed, 199 insertions, 0 deletions

diff --git a/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/src/DistancePlot.ipynb b/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/src/DistancePlot.ipynb
new file mode 100644
index 00000000..44842cf4
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/src/DistancePlot.ipynb
@@ -0,0 +1,199 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os, sys\n",
+    "lib_path = os.path.abspath(os.path.join('..','..', '..', 'utils'))\n",
+    "sys.path.append(lib_path)\n",
+    "import glob\n",
+    "import matplotlib.pyplot as plt\n",
+    "from GraphType import GraphCollection\n",
+    "import DistributionMetrics as metrics\n",
+    "from GraphType import GraphStat\n",
+    "import readCSV as reader\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def getModels(folderName, numberOfModels):\n",
+    "    filenames = reader.readmultiplefiles(folderName, numberOfModels, False)\n",
+    "    graphStats = [GraphStat(filename) for filename in filenames]\n",
+    "    return graphStats"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# read models\n",
+    "domain = 'ecore'\n",
+    "rep = getModels('../input/{}/MPC_REP/'.format(domain), 1)[0]\n",
+    "na_rep = getModels('../input/{}/NA_REP/'.format(domain), 1)[0]\n",
+    "od_rep = getModels('../input/{}/OUT_DEGREE_REP/'.format(domain), 1)[0]\n",
+    "rep.na = na_rep.na\n",
+    "rep.out_d = od_rep.out_d\n",
+    "\n",
+    "human_models = getModels('../input/{}/Human/'.format(domain), 304)\n",
+    "folder = '../output/distances/{}/'.format(domain)\n",
+    "mkdir(folder)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if domain == 'yakindumm':\n",
+    "    rep.nodeTypeStat = {'Entry': 0.04257802080554814, 'Choice': 0.1267671379034409, 'State': 0.1596092291277674, 'Transition': 0.6138636969858629, 'Statechart': 0.010136036276340358, 'Region': 0.04467858095492131, 'Exit': 0.0018338223526273673, 'FinalState': 0.0005334755934915977}\n",
+    "elif domain == 'ecpre':\n",
+    "    rep.nodeTypeStat = {'EAttribute': 0.23539778449144008, 'EClass': 0.30996978851963747, 'EReference': 0.33081570996978854, 'EPackage': 0.012789526686807653, 'EAnnotation': 0.002517623363544813, 'EEnumLiteral': 0.07275931520644502, 'EEnum': 0.013645518630412891, 'EDataType': 0.004028197381671702, 'EParameter': 0.005941591137965764, 'EGenericType': 0.002014098690835851, 'EOperation': 0.009415911379657605, 'ETypeParameter': 0.0007049345417925478}\n",
+    "node_types = sorted(rep.nodeTypeStat.keys())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_diagram(name, models, rep, metric, distance_metric):\n",
+    "    model_metrics = list(map(metric, models))\n",
+    "    rep = metric(rep)\n",
+    "    distances= list(map(lambda m: distance_metric(m,rep), model_metrics))\n",
+    "    plt.figure()\n",
+    "    plt.title('{} for {}'.format(name, domain))\n",
+    "    plt.hist(distances)\n",
+    "    plt.savefig('{}/{}.png'.format(folder, name), dpi = 500)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def mpc(model):\n",
+    "    return model.mpc\n",
+    "def na(model):\n",
+    "    return model.na\n",
+    "def od(model):\n",
+    "    return model.out_d\n",
+    "\n",
+    "def nt(model):\n",
+    "    nd_dict = model.nodeTypeStat\n",
+    "    dist = []\n",
+    "    for key in node_types:\n",
+    "        dist.append(nd_dict.get(key, 0))\n",
+    "    return dist\n",
+    "\n",
+    "def mkdir(path):\n",
+    "    if not os.path.exists(path):\n",
+    "        os.makedirs(path)\n",
+    "\n",
+    "def ks_distance(s1, s2):\n",
+    "    value, p = metrics.ks_distance(s1, s2)\n",
+    "    return value"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "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"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "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": [
+    "plot_diagram('MPC', human_models, rep, mpc, ks_distance)\n",
+    "plot_diagram('Node_Activity', human_models, rep, na, ks_distance)\n",
+    "plot_diagram('Out_Degree', human_models, rep, od, ks_distance)\n",
+    "plot_diagram('Node_Type', human_models, rep, nt, metrics.manual_ks)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}