16_word_embeddings/02_evaluating_embeddings.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How to evaluate embeddings using linear algebra and analogies "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dimensions of the word and phrase vectors do not have an explicit meaning. However, the embeddings encode similar usage as proximity in the latent space in a way that carries over to semantic relationships. This results in the interesting properties that analogies can be expressed by adding and subtracting word vectors.\n",
    "\n",
    "Just as words can be used in different contexts, they can be related to other words in different ways, and these relationships correspond to different directions in the latent space. Accordingly, there are several types of analogies that the embeddings should reflect if the training data permits.\n",
    "\n",
    "The word2vec authors provide a list of several thousand relationships spanning aspects of geography, grammar and syntax, and family relationships to evaluate the quality of embedding vectors (see directory [analogies](data/analogies))."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T02:02:00.366112Z",
     "start_time": "2020-06-21T02:01:59.675597Z"
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   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from pathlib import Path\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": ""
    }
   },
   "source": [
    "### Settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T02:02:00.377200Z",
     "start_time": "2020-06-21T02:02:00.367549Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "sns.set_style('white')\n",
    "pd.set_option('float_format', '{:,.2f}'.format)\n",
    "np.random.seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T02:02:00.382459Z",
     "start_time": "2020-06-21T02:02:00.378877Z"
    }
   },
   "outputs": [],
   "source": [
    "analogy_path = Path('data', 'analogies-en.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T02:02:00.390451Z",
     "start_time": "2020-06-21T02:02:00.383658Z"
    }
   },
   "outputs": [],
   "source": [
    "def format_time(t):\n",
    "    m, s = divmod(t, 60)\n",
    "    h, m = divmod(m, 60)\n",
    "    return f'{h:02.0f}:{m:02.0f}:{s:02.0f}'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Evaluation: Analogies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T02:02:00.476793Z",
     "start_time": "2020-06-21T02:02:00.391463Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(analogy_path, header=None, names=['category'], squeeze=True)\n",
    "categories = df[df.str.startswith(':')]\n",
    "analogies = df[~df.str.startswith(':')].str.split(expand=True)\n",
    "analogies.columns = list('abcd')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T02:02:00.511818Z",
     "start_time": "2020-06-21T02:02:00.478472Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>: capital-common-countries</td>\n",
       "      <td>athens</td>\n",
       "      <td>greece</td>\n",
       "      <td>baghdad</td>\n",
       "      <td>iraq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>: capital-common-countries</td>\n",
       "      <td>athens</td>\n",
       "      <td>greece</td>\n",
       "      <td>bangkok</td>\n",
       "      <td>thailand</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>: capital-common-countries</td>\n",
       "      <td>athens</td>\n",
       "      <td>greece</td>\n",
       "      <td>beijing</td>\n",
       "      <td>china</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>: capital-common-countries</td>\n",
       "      <td>athens</td>\n",
       "      <td>greece</td>\n",
       "      <td>berlin</td>\n",
       "      <td>germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>: capital-common-countries</td>\n",
       "      <td>athens</td>\n",
       "      <td>greece</td>\n",
       "      <td>bern</td>\n",
       "      <td>switzerland</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "                       category       a       b        c            d\n",
       "1  : capital-common-countries    athens  greece  baghdad         iraq\n",
       "2  : capital-common-countries    athens  greece  bangkok     thailand\n",
       "3  : capital-common-countries    athens  greece  beijing        china\n",
       "4  : capital-common-countries    athens  greece   berlin      germany\n",
       "5  : capital-common-countries    athens  greece     bern  switzerland"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.concat([categories, analogies], axis=1)\n",
    "df.category = df.category.ffill()\n",
    "df = df[df['a'].notnull()]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T02:02:00.529477Z",
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    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "analogy_cnt = df.groupby('category').size().sort_values(ascending=False).to_frame('n')\n",
    "analogy_example = df.groupby('category').first()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T02:02:00.540665Z",
     "start_time": "2020-06-21T02:02:00.530425Z"
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n</th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>category</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>: capital-world</th>\n",
       "      <td>8556</td>\n",
       "      <td>abuja</td>\n",
       "      <td>nigeria</td>\n",
       "      <td>accra</td>\n",
       "      <td>ghana</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: city-in-state</th>\n",
       "      <td>4242</td>\n",
       "      <td>chicago</td>\n",
       "      <td>illinois</td>\n",
       "      <td>houston</td>\n",
       "      <td>texas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram6-nationality-adjective</th>\n",
       "      <td>1640</td>\n",
       "      <td>albania</td>\n",
       "      <td>albanian</td>\n",
       "      <td>argentina</td>\n",
       "      <td>argentinean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram7-past-tense</th>\n",
       "      <td>1560</td>\n",
       "      <td>dancing</td>\n",
       "      <td>danced</td>\n",
       "      <td>decreasing</td>\n",
       "      <td>decreased</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram8-plural</th>\n",
       "      <td>1332</td>\n",
       "      <td>banana</td>\n",
       "      <td>bananas</td>\n",
       "      <td>bird</td>\n",
       "      <td>birds</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram3-comparative</th>\n",
       "      <td>1332</td>\n",
       "      <td>bad</td>\n",
       "      <td>worse</td>\n",
       "      <td>big</td>\n",
       "      <td>bigger</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram4-superlative</th>\n",
       "      <td>1122</td>\n",
       "      <td>bad</td>\n",
       "      <td>worst</td>\n",
       "      <td>big</td>\n",
       "      <td>biggest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram5-present-participle</th>\n",
       "      <td>1056</td>\n",
       "      <td>code</td>\n",
       "      <td>coding</td>\n",
       "      <td>dance</td>\n",
       "      <td>dancing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram1-adjective-to-adverb</th>\n",
       "      <td>992</td>\n",
       "      <td>amazing</td>\n",
       "      <td>amazingly</td>\n",
       "      <td>apparent</td>\n",
       "      <td>apparently</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram9-plural-verbs</th>\n",
       "      <td>870</td>\n",
       "      <td>decrease</td>\n",
       "      <td>decreases</td>\n",
       "      <td>describe</td>\n",
       "      <td>describes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: currency</th>\n",
       "      <td>866</td>\n",
       "      <td>algeria</td>\n",
       "      <td>dinar</td>\n",
       "      <td>angola</td>\n",
       "      <td>kwanza</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram2-opposite</th>\n",
       "      <td>812</td>\n",
       "      <td>acceptable</td>\n",
       "      <td>unacceptable</td>\n",
       "      <td>aware</td>\n",
       "      <td>unaware</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: family</th>\n",
       "      <td>506</td>\n",
       "      <td>boy</td>\n",
       "      <td>girl</td>\n",
       "      <td>brother</td>\n",
       "      <td>sister</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: capital-common-countries</th>\n",
       "      <td>506</td>\n",
       "      <td>athens</td>\n",
       "      <td>greece</td>\n",
       "      <td>baghdad</td>\n",
       "      <td>iraq</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    n           a             b           c  \\\n",
       "category                                                                      \n",
       ": capital-world                  8556       abuja       nigeria       accra   \n",
       ": city-in-state                  4242     chicago      illinois     houston   \n",
       ": gram6-nationality-adjective    1640     albania      albanian   argentina   \n",
       ": gram7-past-tense               1560     dancing        danced  decreasing   \n",
       ": gram8-plural                   1332      banana       bananas        bird   \n",
       ": gram3-comparative              1332         bad         worse         big   \n",
       ": gram4-superlative              1122         bad         worst         big   \n",
       ": gram5-present-participle       1056        code        coding       dance   \n",
       ": gram1-adjective-to-adverb       992     amazing     amazingly    apparent   \n",
       ": gram9-plural-verbs              870    decrease     decreases    describe   \n",
       ": currency                        866     algeria         dinar      angola   \n",
       ": gram2-opposite                  812  acceptable  unacceptable       aware   \n",
       ": family                          506         boy          girl     brother   \n",
       ": capital-common-countries        506      athens        greece     baghdad   \n",
       "\n",
       "                                           d  \n",
       "category                                      \n",
       ": capital-world                        ghana  \n",
       ": city-in-state                        texas  \n",
       ": gram6-nationality-adjective    argentinean  \n",
       ": gram7-past-tense                 decreased  \n",
       ": gram8-plural                         birds  \n",
       ": gram3-comparative                   bigger  \n",
       ": gram4-superlative                  biggest  \n",
       ": gram5-present-participle           dancing  \n",
       ": gram1-adjective-to-adverb       apparently  \n",
       ": gram9-plural-verbs               describes  \n",
       ": currency                            kwanza  \n",
       ": gram2-opposite                     unaware  \n",
       ": family                              sister  \n",
       ": capital-common-countries              iraq  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analogy_cnt.join(analogy_example)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T02:03:02.440216Z",
     "start_time": "2020-06-21T02:03:02.125152Z"
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   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "analogy_cnt.join(analogy_example)['n'].sort_values().plot.barh(title='# Analogies by Category',\n",
    "                                                               figsize=(14, 6))\n",
    "sns.despine()\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "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": "3.7.7"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": true,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "47px",
    "left": "38px",
    "right": "1340px",
    "top": "66.5px",
    "width": "362px"
   },
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}