prototypes/Multi-dimensional graph scaling.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>f.name</th>\n",
       "      <th>m.name</th>\n",
       "      <th>r.lineNumber</th>\n",
       "      <th>hit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>duration</td>\n",
       "      <td>getPrincipalFee</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>duration</td>\n",
       "      <td>extend</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>duration</td>\n",
       "      <td>extend</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>duration</td>\n",
       "      <td>&lt;init&gt;</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>dailyRate</td>\n",
       "      <td>getPrincipalFee</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      f.name           m.name  r.lineNumber  hit\n",
       "0   duration  getPrincipalFee            49    1\n",
       "1   duration           extend            24    1\n",
       "2   duration           extend            24    1\n",
       "3   duration           <init>            18    1\n",
       "4  dailyRate  getPrincipalFee            49    1"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import py2neo\n",
    "import pandas as pd\n",
    "\n",
    "graph = py2neo.Graph()\n",
    "\n",
    "data = graph.data(\"MATCH (f:Field)<-[:DECLARES]-(c:Class)-[:DECLARES]->(m:Method), (m)-[r:READS|WRITES]->(f) RETURN m.name, f.name, r.lineNumber\")\n",
    "df = pd.DataFrame(data)\n",
    "df['hit'] = 1\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>f.name</th>\n",
       "      <th>customer</th>\n",
       "      <th>dailyRate</th>\n",
       "      <th>date</th>\n",
       "      <th>duration</th>\n",
       "      <th>fees</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>m.name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>&lt;init&gt;</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>addFee</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>extend</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>extendForWeek</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>getAdditionalFees</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>getPrincipalFee</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "f.name             customer  dailyRate  date  duration  fees\n",
       "m.name                                                      \n",
       "<init>                  1.0        1.0   1.0       1.0   1.0\n",
       "addFee                  0.0        0.0   0.0       0.0   1.0\n",
       "extend                  0.0        0.0   0.0       1.0   0.0\n",
       "extendForWeek           1.0        1.0   1.0       0.0   0.0\n",
       "getAdditionalFees       0.0        0.0   0.0       0.0   1.0\n",
       "getPrincipalFee         1.0        1.0   0.0       1.0   0.0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix = df.drop_duplicates().pivot(index=\"m.name\", columns=\"f.name\", values=\"hit\").fillna(0)\n",
    "matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.        ,  0.5527864 ,  0.5527864 ,  0.22540333,  0.5527864 ],\n",
       "       [ 0.5527864 ,  0.        ,  1.        ,  1.        ,  0.        ],\n",
       "       [ 0.5527864 ,  1.        ,  0.        ,  1.        ,  1.        ],\n",
       "       [ 0.22540333,  1.        ,  1.        ,  0.        ,  1.        ],\n",
       "       [ 0.5527864 ,  0.        ,  1.        ,  1.        ,  0.        ]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics.pairwise import cosine_distances\n",
    "\n",
    "dissimilarity_matrix = cosine_distances(matrix)\n",
    "dissimilarity_matrix[:5,:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.01921689,  0.12978066],\n",
       "       [ 0.3465834 , -0.43509102],\n",
       "       [-0.5699487 , -0.06500858],\n",
       "       [ 0.09208679,  0.49470377],\n",
       "       [ 0.3478405 , -0.43414008]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.manifold import MDS\n",
    "\n",
    "# uses a fixed seed for random_state for reproducibility\n",
    "model = MDS(dissimilarity='precomputed', random_state=0)\n",
    "dissimilarity_2d = model.fit_transform(dissimilarity_matrix)\n",
    "dissimilarity_2d[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x194680820f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(8,8))\n",
    "x = dissimilarity_2d[:,0]\n",
    "y = dissimilarity_2d[:,1]\n",
    "plt.scatter(x, y);"
   ]
  }
 ],
 "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": "3.6.4"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}