{
 "cells": [
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   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>method</th>\n",
       "      <th>traintime</th>\n",
       "      <th>F1</th>\n",
       "      <th>F1_test</th>\n",
       "      <th>MI</th>\n",
       "      <th>RAND</th>\n",
       "      <th>F-M</th>\n",
       "      <th>params</th>\n",
       "      <th>graph</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Node2Vec</td>\n",
       "      <td>300.006000</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>0.505000</td>\n",
       "      <td>0.644000</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.544000</td>\n",
       "      <td>{'epochs': 80, 'keep_walks': False, 'n_compone...</td>\n",
       "      <td>snap</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>SKLearnEmbedder</td>\n",
       "      <td>7.671000</td>\n",
       "      <td>0.879000</td>\n",
       "      <td>0.469000</td>\n",
       "      <td>0.665000</td>\n",
       "      <td>0.589000</td>\n",
       "      <td>0.608000</td>\n",
       "      <td>{'embedder__a': None, 'embedder__angular_rp_fo...</td>\n",
       "      <td>snap</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>Glove</td>\n",
       "      <td>16.967000</td>\n",
       "      <td>0.622000</td>\n",
       "      <td>0.122000</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.221000</td>\n",
       "      <td>{'learning_rate': 0.1, 'max_epoch': 50000, 'ma...</td>\n",
       "      <td>blogcatalog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>SKLearnEmbedder</td>\n",
       "      <td>464.722672</td>\n",
       "      <td>0.545764</td>\n",
       "      <td>0.066072</td>\n",
       "      <td>0.046016</td>\n",
       "      <td>0.010172</td>\n",
       "      <td>0.071656</td>\n",
       "      <td>{'embedder__a': None, 'embedder__angular_rp_fo...</td>\n",
       "      <td>blogcatalog</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             method   traintime        F1   F1_test        MI      RAND  \\\n",
       "44         Node2Vec  300.006000  0.850000  0.505000  0.644000  0.520000   \n",
       "19  SKLearnEmbedder    7.671000  0.879000  0.469000  0.665000  0.589000   \n",
       "63            Glove   16.967000  0.622000  0.122000  0.001000 -0.000000   \n",
       "87  SKLearnEmbedder  464.722672  0.545764  0.066072  0.046016  0.010172   \n",
       "\n",
       "         F-M                                             params        graph  \n",
       "44  0.544000  {'epochs': 80, 'keep_walks': False, 'n_compone...         snap  \n",
       "19  0.608000  {'embedder__a': None, 'embedder__angular_rp_fo...         snap  \n",
       "63  0.221000  {'learning_rate': 0.1, 'max_epoch': 50000, 'ma...  blogcatalog  \n",
       "87  0.071656  {'embedder__a': None, 'embedder__angular_rp_fo...  blogcatalog  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "snap = pd.read_csv(\"test_results.csv\")\n",
    "df.sample(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>method</th>\n",
       "      <th>traintime</th>\n",
       "      <th>F1</th>\n",
       "      <th>F1_test</th>\n",
       "      <th>MI</th>\n",
       "      <th>RAND</th>\n",
       "      <th>F-M</th>\n",
       "      <th>params</th>\n",
       "      <th>graph</th>\n",
       "      <th>epochs</th>\n",
       "      <th>keep_walks</th>\n",
       "      <th>n_components</th>\n",
       "      <th>neighbor_weight</th>\n",
       "      <th>return_weight</th>\n",
       "      <th>threads</th>\n",
       "      <th>w2vparams</th>\n",
       "      <th>walklen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Node2Vec</td>\n",
       "      <td>74.403</td>\n",
       "      <td>0.868</td>\n",
       "      <td>0.436</td>\n",
       "      <td>0.660</td>\n",
       "      <td>0.540</td>\n",
       "      <td>0.563</td>\n",
       "      <td>{'epochs': 20, 'keep_walks': False, 'n_compone...</td>\n",
       "      <td>snap</td>\n",
       "      <td>20</td>\n",
       "      <td>False</td>\n",
       "      <td>None</td>\n",
       "      <td>0.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4</td>\n",
       "      <td>{'window': 10, 'negative': 5, 'iter': 5, 'ns_e...</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Node2Vec</td>\n",
       "      <td>300.063</td>\n",
       "      <td>0.871</td>\n",
       "      <td>0.430</td>\n",
       "      <td>0.659</td>\n",
       "      <td>0.539</td>\n",
       "      <td>0.563</td>\n",
       "      <td>{'epochs': 80, 'keep_walks': False, 'n_compone...</td>\n",
       "      <td>snap</td>\n",
       "      <td>80</td>\n",
       "      <td>False</td>\n",
       "      <td>None</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>{'window': 10, 'negative': 5, 'iter': 5, 'ns_e...</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Node2Vec</td>\n",
       "      <td>334.563</td>\n",
       "      <td>0.894</td>\n",
       "      <td>0.481</td>\n",
       "      <td>0.622</td>\n",
       "      <td>0.480</td>\n",
       "      <td>0.514</td>\n",
       "      <td>{'epochs': 80, 'keep_walks': False, 'n_compone...</td>\n",
       "      <td>snap</td>\n",
       "      <td>80</td>\n",
       "      <td>False</td>\n",
       "      <td>None</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4</td>\n",
       "      <td>{'window': 10, 'negative': 5, 'iter': 5, 'ns_e...</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      method  traintime     F1  F1_test     MI   RAND    F-M  \\\n",
       "4   Node2Vec     74.403  0.868    0.436  0.660  0.540  0.563   \n",
       "20  Node2Vec    300.063  0.871    0.430  0.659  0.539  0.563   \n",
       "35  Node2Vec    334.563  0.894    0.481  0.622  0.480  0.514   \n",
       "\n",
       "                                               params graph  epochs  \\\n",
       "4   {'epochs': 20, 'keep_walks': False, 'n_compone...  snap      20   \n",
       "20  {'epochs': 80, 'keep_walks': False, 'n_compone...  snap      80   \n",
       "35  {'epochs': 80, 'keep_walks': False, 'n_compone...  snap      80   \n",
       "\n",
       "    keep_walks n_components  neighbor_weight  return_weight  threads  \\\n",
       "4        False         None              0.3            3.0        4   \n",
       "20       False         None              0.3            1.0        4   \n",
       "35       False         None              3.0            3.0        4   \n",
       "\n",
       "                                            w2vparams  walklen  \n",
       "4   {'window': 10, 'negative': 5, 'iter': 5, 'ns_e...       80  \n",
       "20  {'window': 10, 'negative': 5, 'iter': 5, 'ns_e...       80  \n",
       "35  {'window': 10, 'negative': 5, 'iter': 5, 'ns_e...       80  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nv = df.loc[df.method == 'Node2Vec'].reset_index(drop=True)\n",
    "nv = nv.join(pd.DataFrame.from_records(nv.params.apply(eval).values))\n",
    "nv.sample(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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