parse_results.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "domain = 'food'\n",
    "df = pd.read_csv('~/' + domain + '.csv', skiprows=[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>buffer</th>\n",
       "      <th>mode</th>\n",
       "      <th>parent</th>\n",
       "      <th>family</th>\n",
       "      <th>iterations</th>\n",
       "      <th>precision</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>reattach</td>\n",
       "      <td>poincare</td>\n",
       "      <td>fasttext</td>\n",
       "      <td>1</td>\n",
       "      <td>0.3333</td>\n",
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       "      <th>2</th>\n",
       "      <td>5</td>\n",
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       "      <td>fasttext</td>\n",
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       "      <td>fasttext</td>\n",
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      "text/plain": [
       "   buffer      mode    parent    family  iterations  precision  recall  \\\n",
       "0       5  reattach  poincare  fasttext           1     0.3333  0.1890   \n",
       "1       5  reattach  poincare  fasttext           2     0.3318  0.1878   \n",
       "2       5  reattach  poincare  fasttext           3     0.3318  0.1878   \n",
       "3       5  reattach  poincare  fasttext           4     0.3318  0.1878   \n",
       "4       5  reattach  poincare  fasttext           5     0.3318  0.1878   \n",
       "\n",
       "       f1     f_m  \n",
       "0  0.2413  0.2087  \n",
       "1  0.2398  0.2085  \n",
       "2  0.2398  0.2085  \n",
       "3  0.2398  0.2085  \n",
       "4  0.2398  0.2085  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "buffer = 10\n",
    "mode = 'reattach'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.loc[(df['buffer'] == buffer) & (df['mode'] == mode)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
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       "      <th>60</th>\n",
       "      <td>10</td>\n",
       "      <td>reattach</td>\n",
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       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>10</td>\n",
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       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>10</td>\n",
       "      <td>reattach</td>\n",
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       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>10</td>\n",
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       "      <td>0.3318</td>\n",
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       "      <td>0.2398</td>\n",
       "      <td>0.2085</td>\n",
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       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>10</td>\n",
       "      <td>reattach</td>\n",
       "      <td>poincare</td>\n",
       "      <td>fasttext</td>\n",
       "      <td>5</td>\n",
       "      <td>0.3304</td>\n",
       "      <td>0.1865</td>\n",
       "      <td>0.2384</td>\n",
       "      <td>0.2083</td>\n",
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       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>10</td>\n",
       "      <td>reattach</td>\n",
       "      <td>poincare</td>\n",
       "      <td>fasttext</td>\n",
       "      <td>6</td>\n",
       "      <td>0.3285</td>\n",
       "      <td>0.1853</td>\n",
       "      <td>0.2369</td>\n",
       "      <td>0.2080</td>\n",
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       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>10</td>\n",
       "      <td>reattach</td>\n",
       "      <td>poincare</td>\n",
       "      <td>fasttext</td>\n",
       "      <td>7</td>\n",
       "      <td>0.3255</td>\n",
       "      <td>0.1834</td>\n",
       "      <td>0.2346</td>\n",
       "      <td>0.2085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>10</td>\n",
       "      <td>reattach</td>\n",
       "      <td>poincare</td>\n",
       "      <td>fasttext</td>\n",
       "      <td>8</td>\n",
       "      <td>0.3180</td>\n",
       "      <td>0.1790</td>\n",
       "      <td>0.2290</td>\n",
       "      <td>0.2079</td>\n",
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       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>10</td>\n",
       "      <td>reattach</td>\n",
       "      <td>poincare</td>\n",
       "      <td>fasttext</td>\n",
       "      <td>9</td>\n",
       "      <td>0.3161</td>\n",
       "      <td>0.1777</td>\n",
       "      <td>0.2275</td>\n",
       "      <td>0.2084</td>\n",
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       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>10</td>\n",
       "      <td>reattach</td>\n",
       "      <td>poincare</td>\n",
       "      <td>fasttext</td>\n",
       "      <td>10</td>\n",
       "      <td>0.3146</td>\n",
       "      <td>0.1764</td>\n",
       "      <td>0.2261</td>\n",
       "      <td>0.2083</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "    buffer      mode    parent    family  iterations  precision  recall  \\\n",
       "60      10  reattach  poincare  fasttext           1     0.3326  0.1884   \n",
       "61      10  reattach  poincare  fasttext           2     0.3318  0.1878   \n",
       "62      10  reattach  poincare  fasttext           3     0.3318  0.1878   \n",
       "63      10  reattach  poincare  fasttext           4     0.3318  0.1878   \n",
       "64      10  reattach  poincare  fasttext           5     0.3304  0.1865   \n",
       "65      10  reattach  poincare  fasttext           6     0.3285  0.1853   \n",
       "66      10  reattach  poincare  fasttext           7     0.3255  0.1834   \n",
       "67      10  reattach  poincare  fasttext           8     0.3180  0.1790   \n",
       "68      10  reattach  poincare  fasttext           9     0.3161  0.1777   \n",
       "69      10  reattach  poincare  fasttext          10     0.3146  0.1764   \n",
       "\n",
       "        f1     f_m  \n",
       "60  0.2405  0.2087  \n",
       "61  0.2398  0.2085  \n",
       "62  0.2398  0.2085  \n",
       "63  0.2398  0.2085  \n",
       "64  0.2384  0.2083  \n",
       "65  0.2369  0.2080  \n",
       "66  0.2346  0.2085  \n",
       "67  0.2290  0.2079  \n",
       "68  0.2275  0.2084  \n",
       "69  0.2261  0.2083  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_both = df.loc[(df['parent'] == 'poincare') & (df['family'] == 'fasttext')]\n",
    "df_both"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
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       "      <td>reattach</td>\n",
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       "      <th>73</th>\n",
       "      <td>10</td>\n",
       "      <td>reattach</td>\n",
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       "      <td>-</td>\n",
       "      <td>4</td>\n",
       "      <td>0.3318</td>\n",
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       "      <td>0.2398</td>\n",
       "      <td>0.2085</td>\n",
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       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>10</td>\n",
       "      <td>reattach</td>\n",
       "      <td>poincare</td>\n",
       "      <td>-</td>\n",
       "      <td>5</td>\n",
       "      <td>0.3300</td>\n",
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       "      <th>75</th>\n",
       "      <td>10</td>\n",
       "      <td>reattach</td>\n",
       "      <td>poincare</td>\n",
       "      <td>-</td>\n",
       "      <td>6</td>\n",
       "      <td>0.3285</td>\n",
       "      <td>0.1853</td>\n",
       "      <td>0.2369</td>\n",
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       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>10</td>\n",
       "      <td>reattach</td>\n",
       "      <td>poincare</td>\n",
       "      <td>-</td>\n",
       "      <td>7</td>\n",
       "      <td>0.3274</td>\n",
       "      <td>0.1840</td>\n",
       "      <td>0.2356</td>\n",
       "      <td>0.2078</td>\n",
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       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>10</td>\n",
       "      <td>reattach</td>\n",
       "      <td>poincare</td>\n",
       "      <td>-</td>\n",
       "      <td>8</td>\n",
       "      <td>0.3239</td>\n",
       "      <td>0.1802</td>\n",
       "      <td>0.2316</td>\n",
       "      <td>0.2052</td>\n",
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       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>10</td>\n",
       "      <td>reattach</td>\n",
       "      <td>poincare</td>\n",
       "      <td>-</td>\n",
       "      <td>9</td>\n",
       "      <td>0.3273</td>\n",
       "      <td>0.1821</td>\n",
       "      <td>0.2340</td>\n",
       "      <td>0.2044</td>\n",
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       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>10</td>\n",
       "      <td>reattach</td>\n",
       "      <td>poincare</td>\n",
       "      <td>-</td>\n",
       "      <td>10</td>\n",
       "      <td>0.3265</td>\n",
       "      <td>0.1815</td>\n",
       "      <td>0.2333</td>\n",
       "      <td>0.2053</td>\n",
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      ],
      "text/plain": [
       "    buffer      mode    parent family  iterations  precision  recall      f1  \\\n",
       "70      10  reattach  poincare      -           1     0.3326  0.1884  0.2405   \n",
       "71      10  reattach  poincare      -           2     0.3318  0.1878  0.2398   \n",
       "72      10  reattach  poincare      -           3     0.3318  0.1878  0.2398   \n",
       "73      10  reattach  poincare      -           4     0.3318  0.1878  0.2398   \n",
       "74      10  reattach  poincare      -           5     0.3300  0.1865  0.2383   \n",
       "75      10  reattach  poincare      -           6     0.3285  0.1853  0.2369   \n",
       "76      10  reattach  poincare      -           7     0.3274  0.1840  0.2356   \n",
       "77      10  reattach  poincare      -           8     0.3239  0.1802  0.2316   \n",
       "78      10  reattach  poincare      -           9     0.3273  0.1821  0.2340   \n",
       "79      10  reattach  poincare      -          10     0.3265  0.1815  0.2333   \n",
       "\n",
       "       f_m  \n",
       "70  0.2087  \n",
       "71  0.2085  \n",
       "72  0.2085  \n",
       "73  0.2085  \n",
       "74  0.2085  \n",
       "75  0.2078  \n",
       "76  0.2078  \n",
       "77  0.2052  \n",
       "78  0.2044  \n",
       "79  0.2053  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_parent = df.loc[(df['parent'] == 'poincare') & (df['family'] == '-')]\n",
    "df_parent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
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       "      <th>buffer</th>\n",
       "      <th>mode</th>\n",
       "      <th>parent</th>\n",
       "      <th>family</th>\n",
       "      <th>iterations</th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1</th>\n",
       "      <th>f_m</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>10</td>\n",
       "      <td>reattach</td>\n",
       "      <td>-</td>\n",
       "      <td>fasttext</td>\n",
       "      <td>1</td>\n",
       "      <td>0.3311</td>\n",
       "      <td>0.1871</td>\n",
       "      <td>0.2391</td>\n",
       "      <td>0.2083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>10</td>\n",
       "      <td>reattach</td>\n",
       "      <td>-</td>\n",
       "      <td>fasttext</td>\n",
       "      <td>2</td>\n",
       "      <td>0.3311</td>\n",
       "      <td>0.1871</td>\n",
       "      <td>0.2391</td>\n",
       "      <td>0.2083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>10</td>\n",
       "      <td>reattach</td>\n",
       "      <td>-</td>\n",
       "      <td>fasttext</td>\n",
       "      <td>3</td>\n",
       "      <td>0.3292</td>\n",
       "      <td>0.1859</td>\n",
       "      <td>0.2376</td>\n",
       "      <td>0.2074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>10</td>\n",
       "      <td>reattach</td>\n",
       "      <td>-</td>\n",
       "      <td>fasttext</td>\n",
       "      <td>4</td>\n",
       "      <td>0.3292</td>\n",
       "      <td>0.1859</td>\n",
       "      <td>0.2376</td>\n",
       "      <td>0.2074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>10</td>\n",
       "      <td>reattach</td>\n",
       "      <td>-</td>\n",
       "      <td>fasttext</td>\n",
       "      <td>5</td>\n",
       "      <td>0.3270</td>\n",
       "      <td>0.1840</td>\n",
       "      <td>0.2355</td>\n",
       "      <td>0.2068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>10</td>\n",
       "      <td>reattach</td>\n",
       "      <td>-</td>\n",
       "      <td>fasttext</td>\n",
       "      <td>6</td>\n",
       "      <td>0.3225</td>\n",
       "      <td>0.1815</td>\n",
       "      <td>0.2323</td>\n",
       "      <td>0.2065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>10</td>\n",
       "      <td>reattach</td>\n",
       "      <td>-</td>\n",
       "      <td>fasttext</td>\n",
       "      <td>7</td>\n",
       "      <td>0.3221</td>\n",
       "      <td>0.1808</td>\n",
       "      <td>0.2316</td>\n",
       "      <td>0.2070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>10</td>\n",
       "      <td>reattach</td>\n",
       "      <td>-</td>\n",
       "      <td>fasttext</td>\n",
       "      <td>8</td>\n",
       "      <td>0.3146</td>\n",
       "      <td>0.1764</td>\n",
       "      <td>0.2261</td>\n",
       "      <td>0.2064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>10</td>\n",
       "      <td>reattach</td>\n",
       "      <td>-</td>\n",
       "      <td>fasttext</td>\n",
       "      <td>9</td>\n",
       "      <td>0.3145</td>\n",
       "      <td>0.1758</td>\n",
       "      <td>0.2255</td>\n",
       "      <td>0.2062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>10</td>\n",
       "      <td>reattach</td>\n",
       "      <td>-</td>\n",
       "      <td>fasttext</td>\n",
       "      <td>10</td>\n",
       "      <td>0.3141</td>\n",
       "      <td>0.1752</td>\n",
       "      <td>0.2249</td>\n",
       "      <td>0.2061</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    buffer      mode parent    family  iterations  precision  recall      f1  \\\n",
       "80      10  reattach      -  fasttext           1     0.3311  0.1871  0.2391   \n",
       "81      10  reattach      -  fasttext           2     0.3311  0.1871  0.2391   \n",
       "82      10  reattach      -  fasttext           3     0.3292  0.1859  0.2376   \n",
       "83      10  reattach      -  fasttext           4     0.3292  0.1859  0.2376   \n",
       "84      10  reattach      -  fasttext           5     0.3270  0.1840  0.2355   \n",
       "85      10  reattach      -  fasttext           6     0.3225  0.1815  0.2323   \n",
       "86      10  reattach      -  fasttext           7     0.3221  0.1808  0.2316   \n",
       "87      10  reattach      -  fasttext           8     0.3146  0.1764  0.2261   \n",
       "88      10  reattach      -  fasttext           9     0.3145  0.1758  0.2255   \n",
       "89      10  reattach      -  fasttext          10     0.3141  0.1752  0.2249   \n",
       "\n",
       "       f_m  \n",
       "80  0.2083  \n",
       "81  0.2083  \n",
       "82  0.2074  \n",
       "83  0.2074  \n",
       "84  0.2068  \n",
       "85  0.2065  \n",
       "86  0.2070  \n",
       "87  0.2064  \n",
       "88  0.2062  \n",
       "89  0.2061  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_family = df.loc[(df['parent'] == '-') & (df['family'] == 'fasttext')]\n",
    "df_family"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Buffer:  | Mode: \n",
    "\n",
    "metric = 'precision'\n",
    "plt.plot(df_both['iterations'], df_both[metric], label='Both')\n",
    "plt.plot(df_parent['iterations'], df_parent[metric], alpha=0.7, label='Only Parent')\n",
    "plt.plot(df_family['iterations'], df_family[metric], alpha=0.7, label='Only Family')\n",
    "plt.legend(loc='lower right')\n",
    "plt.xlabel('Number of iterations')\n",
    "plt.ylabel(metric)\n",
    "plt.title('Buffer: ' + str(buffer) + ' | Mode: ' + mode + ' | ' + metric)\n",
    "# plt.axis([0, 10, 0.364, 0.369])\n",
    "plt.savefig('b5_mr.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "taxi_py3",
   "language": "python",
   "name": "taxi_py3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}