wehlutyk/eliot-intership-twitter-results.ipynb

## eliot-intership-twitter-results.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "%matplotlib inline\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('all_loss_data.csv')\n",
    "\n",
    "# dimxitotal is the effective total number of embedding dimensions\n",
    "data['dimxitotal'] = data.dimxiadj + data.dimxifeat - data.overlap\n",
    "data['dimxi reduction'] = data.dimxitotal.max() - data.dimxitotal\n",
    "# 'ref' is no no-overlap model, 'ov' is the overlap model\n",
    "data['model'] = list(map(lambda ov: 'ov' if ov > 0 else 'ref', data.overlap))\n",
    "\n",
    "# The dimxitotal = 32 row is common to both 'ov' and 'ref' models, so duplicate it\n",
    "dup_base = data[data.dimxitotal == 32].copy()\n",
    "dup_base.model = 'ov'\n",
    "data = data.append(dup_base, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>dimxiadj</th>\n",
       "      <th>dimxifeat</th>\n",
       "      <th>overlap</th>\n",
       "      <th>adj</th>\n",
       "      <th>feat</th>\n",
       "      <th>kl</th>\n",
       "      <th>reg</th>\n",
       "      <th>total loss</th>\n",
       "      <th>dimxitotal</th>\n",
       "      <th>dimxi reduction</th>\n",
       "      <th>model</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0.035126</td>\n",
       "      <td>0.541941</td>\n",
       "      <td>0.005931</td>\n",
       "      <td>0.003020</td>\n",
       "      <td>0.586018</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>ref</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>0.036918</td>\n",
       "      <td>0.550210</td>\n",
       "      <td>0.006178</td>\n",
       "      <td>0.002820</td>\n",
       "      <td>0.596126</td>\n",
       "      <td>28</td>\n",
       "      <td>4</td>\n",
       "      <td>ov</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "      <td>0.030521</td>\n",
       "      <td>0.554297</td>\n",
       "      <td>0.005961</td>\n",
       "      <td>0.002888</td>\n",
       "      <td>0.593667</td>\n",
       "      <td>24</td>\n",
       "      <td>8</td>\n",
       "      <td>ov</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>12</td>\n",
       "      <td>0.040553</td>\n",
       "      <td>0.558634</td>\n",
       "      <td>0.006304</td>\n",
       "      <td>0.002888</td>\n",
       "      <td>0.608379</td>\n",
       "      <td>20</td>\n",
       "      <td>12</td>\n",
       "      <td>ov</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>0.038805</td>\n",
       "      <td>0.564300</td>\n",
       "      <td>0.005792</td>\n",
       "      <td>0.003039</td>\n",
       "      <td>0.611936</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>ov</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0.046108</td>\n",
       "      <td>0.584731</td>\n",
       "      <td>0.006321</td>\n",
       "      <td>0.002895</td>\n",
       "      <td>0.640056</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>ref</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0.043175</td>\n",
       "      <td>0.572037</td>\n",
       "      <td>0.006533</td>\n",
       "      <td>0.002902</td>\n",
       "      <td>0.624647</td>\n",
       "      <td>20</td>\n",
       "      <td>12</td>\n",
       "      <td>ref</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0.055150</td>\n",
       "      <td>0.560946</td>\n",
       "      <td>0.006303</td>\n",
       "      <td>0.002822</td>\n",
       "      <td>0.625223</td>\n",
       "      <td>24</td>\n",
       "      <td>8</td>\n",
       "      <td>ref</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0.045141</td>\n",
       "      <td>0.551431</td>\n",
       "      <td>0.006043</td>\n",
       "      <td>0.002918</td>\n",
       "      <td>0.605534</td>\n",
       "      <td>28</td>\n",
       "      <td>4</td>\n",
       "      <td>ref</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0.035126</td>\n",
       "      <td>0.541941</td>\n",
       "      <td>0.005931</td>\n",
       "      <td>0.003020</td>\n",
       "      <td>0.586018</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>ov</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   dimxiadj  dimxifeat  overlap       adj      feat        kl       reg  \\\n",
       "0        16         16        0  0.035126  0.541941  0.005931  0.003020   \n",
       "1        16         16        4  0.036918  0.550210  0.006178  0.002820   \n",
       "2        16         16        8  0.030521  0.554297  0.005961  0.002888   \n",
       "3        16         16       12  0.040553  0.558634  0.006304  0.002888   \n",
       "4        16         16       16  0.038805  0.564300  0.005792  0.003039   \n",
       "5         8          8        0  0.046108  0.584731  0.006321  0.002895   \n",
       "6        10         10        0  0.043175  0.572037  0.006533  0.002902   \n",
       "7        12         12        0  0.055150  0.560946  0.006303  0.002822   \n",
       "8        14         14        0  0.045141  0.551431  0.006043  0.002918   \n",
       "9        16         16        0  0.035126  0.541941  0.005931  0.003020   \n",
       "\n",
       "   total loss  dimxitotal  dimxi reduction model  \n",
       "0    0.586018          32                0   ref  \n",
       "1    0.596126          28                4    ov  \n",
       "2    0.593667          24                8    ov  \n",
       "3    0.608379          20               12    ov  \n",
       "4    0.611936          16               16    ov  \n",
       "5    0.640056          16               16   ref  \n",
       "6    0.624647          20               12   ref  \n",
       "7    0.625223          24                8   ref  \n",
       "8    0.605534          28                4   ref  \n",
       "9    0.586018          32                0    ov  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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": [
    "for loss_type in ['adj', 'feat', 'total loss']:\n",
    "    plt.figure()\n",
    "    ax = sb.lineplot(x='dimxi reduction', y=loss_type,\n",
    "                     hue='model', style='model', markers=['o', 'o'],\n",
    "                     data=data)\n",
    "    ax.set_title(loss_type)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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