16_word_embeddings/01_using_pretrained_vectors.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports & Settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T01:57:57.304058Z",
     "start_time": "2020-06-21T01:57:57.302119Z"
    }
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T01:57:58.032600Z",
     "start_time": "2020-06-21T01:57:57.305226Z"
    }
   },
   "outputs": [],
   "source": [
    "from time import time\n",
    "from collections import Counter\n",
    "from pathlib import Path\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from numpy.linalg import norm\n",
    "from scipy.spatial.distance import cdist, cosine\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import FuncFormatter\n",
    "import seaborn as sns\n",
    "\n",
    "from gensim.models import Word2Vec, KeyedVectors\n",
    "from gensim.scripts.glove2word2vec import glove2word2vec\n",
    "\n",
    "from sklearn.decomposition import IncrementalPCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T01:57:58.035979Z",
     "start_time": "2020-06-21T01:57:58.033610Z"
    }
   },
   "outputs": [],
   "source": [
    "sns.set_style('white')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T01:57:58.043789Z",
     "start_time": "2020-06-21T01:57:58.037292Z"
    }
   },
   "outputs": [],
   "source": [
    "analogies_path = Path('data', 'analogies-en.txt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Convert GloVE Vectors to gensim format"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The various GloVE vectors are available [here](https://nlp.stanford.edu/projects/glove/). Download link for the [wikipedia](http://nlp.stanford.edu/data/glove.6B.zip) version. Unzip and store in `data/glove`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T01:57:58.052116Z",
     "start_time": "2020-06-21T01:57:58.044557Z"
    }
   },
   "outputs": [],
   "source": [
    "glove_path = Path('..', 'data', 'glove')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### WikiPedia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T01:57:58.060201Z",
     "start_time": "2020-06-21T01:57:58.053199Z"
    }
   },
   "outputs": [],
   "source": [
    "glove_wiki_file= glove_path / 'glove.6B.300d.txt'\n",
    "word2vec_wiki_file = glove_path / 'glove.wiki.gensim.txt'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T01:58:00.194708Z",
     "start_time": "2020-06-21T01:57:58.061566Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(400000, 300)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "glove2word2vec(glove_input_file=glove_wiki_file, word2vec_output_file=word2vec_wiki_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Twitter Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T01:58:00.197636Z",
     "start_time": "2020-06-21T01:58:00.195918Z"
    }
   },
   "outputs": [],
   "source": [
    "glove_twitter_file= glove_path / 'glove.twitter.27B.200d.txt'\n",
    "word2vec_twitter_file = glove_path / 'glove.twitter.gensim.txt'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T01:58:04.614752Z",
     "start_time": "2020-06-21T01:58:00.198547Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1193514, 200)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "glove2word2vec(glove_input_file=glove_twitter_file, word2vec_output_file=word2vec_twitter_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Common Crawl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T01:58:04.617401Z",
     "start_time": "2020-06-21T01:58:04.615555Z"
    }
   },
   "outputs": [],
   "source": [
    "glove_crawl_file= glove_path / 'glove.840B.300d.txt'\n",
    "word2vec_crawl_file = glove_path / 'glove.crawl.gensim.txt'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T01:58:42.639901Z",
     "start_time": "2020-06-21T01:58:04.618416Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2196017, 300)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "glove2word2vec(glove_input_file=glove_crawl_file, word2vec_output_file=word2vec_crawl_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T02:31:52.238086Z",
     "start_time": "2020-06-21T02:31:52.236331Z"
    }
   },
   "outputs": [],
   "source": [
    "results_path = Path('results', 'glove')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T01:58:42.645338Z",
     "start_time": "2020-06-21T01:58:42.641083Z"
    }
   },
   "outputs": [],
   "source": [
    "def eval_analogies(file_name, vocab=30000):\n",
    "    model = KeyedVectors.load_word2vec_format(file_name, binary=False)\n",
    "    accuracy = model.wv.accuracy(analogies_path,\n",
    "                                 restrict_vocab=vocab,\n",
    "                                 case_insensitive=True)\n",
    "    return (pd.DataFrame([[c['section'],\n",
    "                           len(c['correct']),\n",
    "                           len(c['incorrect'])] for c in accuracy],\n",
    "                         columns=['category', 'correct', 'incorrect'])\n",
    "            .assign(samples=lambda x: x.correct.add(x.incorrect))\n",
    "            .assign(average=lambda x: x.correct.div(x.samples))\n",
    "            .drop(['correct', 'incorrect'], axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T02:03:14.487707Z",
     "start_time": "2020-06-21T01:58:42.646544Z"
    }
   },
   "outputs": [],
   "source": [
    "result = eval_analogies(word2vec_twitter_file, vocab=100000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### twitter result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T02:07:46.052811Z",
     "start_time": "2020-06-21T02:03:14.488583Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>category</th>\n",
       "      <th>samples</th>\n",
       "      <th>average</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>capital-common-countries</td>\n",
       "      <td>462</td>\n",
       "      <td>0.701299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>capital-world</td>\n",
       "      <td>930</td>\n",
       "      <td>0.690323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>city-in-state</td>\n",
       "      <td>3644</td>\n",
       "      <td>0.350714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>currency</td>\n",
       "      <td>268</td>\n",
       "      <td>0.018657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>family</td>\n",
       "      <td>342</td>\n",
       "      <td>0.824561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>gram1-adjective-to-adverb</td>\n",
       "      <td>650</td>\n",
       "      <td>0.143077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>gram2-opposite</td>\n",
       "      <td>342</td>\n",
       "      <td>0.365497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>gram3-comparative</td>\n",
       "      <td>1260</td>\n",
       "      <td>0.757937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>gram4-superlative</td>\n",
       "      <td>930</td>\n",
       "      <td>0.686022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>gram5-present-participle</td>\n",
       "      <td>702</td>\n",
       "      <td>0.750712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>gram6-nationality-adjective</td>\n",
       "      <td>870</td>\n",
       "      <td>0.750575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>gram7-past-tense</td>\n",
       "      <td>1190</td>\n",
       "      <td>0.576471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>gram8-plural</td>\n",
       "      <td>1122</td>\n",
       "      <td>0.811052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>gram9-plural-verbs</td>\n",
       "      <td>600</td>\n",
       "      <td>0.655000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>total</td>\n",
       "      <td>13312</td>\n",
       "      <td>0.564228</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       category  samples   average\n",
       "0      capital-common-countries      462  0.701299\n",
       "1                 capital-world      930  0.690323\n",
       "2                 city-in-state     3644  0.350714\n",
       "3                      currency      268  0.018657\n",
       "4                        family      342  0.824561\n",
       "5     gram1-adjective-to-adverb      650  0.143077\n",
       "6                gram2-opposite      342  0.365497\n",
       "7             gram3-comparative     1260  0.757937\n",
       "8             gram4-superlative      930  0.686022\n",
       "9      gram5-present-participle      702  0.750712\n",
       "10  gram6-nationality-adjective      870  0.750575\n",
       "11             gram7-past-tense     1190  0.576471\n",
       "12                 gram8-plural     1122  0.811052\n",
       "13           gram9-plural-verbs      600  0.655000\n",
       "14                        total    13312  0.564228"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter_result = eval_analogies(word2vec_twitter_file, vocab=100000)\n",
    "twitter_result.to_csv(glove_path / 'accuracy_twitter.csv', index=False)\n",
    "twitter_result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### wiki result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T02:13:42.769512Z",
     "start_time": "2020-06-21T02:07:46.053665Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>category</th>\n",
       "      <th>samples</th>\n",
       "      <th>average</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>capital-common-countries</td>\n",
       "      <td>506</td>\n",
       "      <td>0.948617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>capital-world</td>\n",
       "      <td>8372</td>\n",
       "      <td>0.964644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>city-in-state</td>\n",
       "      <td>4242</td>\n",
       "      <td>0.599953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>currency</td>\n",
       "      <td>752</td>\n",
       "      <td>0.174202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>family</td>\n",
       "      <td>506</td>\n",
       "      <td>0.881423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>gram1-adjective-to-adverb</td>\n",
       "      <td>992</td>\n",
       "      <td>0.225806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>gram2-opposite</td>\n",
       "      <td>756</td>\n",
       "      <td>0.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>gram3-comparative</td>\n",
       "      <td>1332</td>\n",
       "      <td>0.882132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>gram4-superlative</td>\n",
       "      <td>1056</td>\n",
       "      <td>0.746212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>gram5-present-participle</td>\n",
       "      <td>1056</td>\n",
       "      <td>0.699811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>gram6-nationality-adjective</td>\n",
       "      <td>1640</td>\n",
       "      <td>0.925000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>gram7-past-tense</td>\n",
       "      <td>1560</td>\n",
       "      <td>0.611538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>gram8-plural</td>\n",
       "      <td>1332</td>\n",
       "      <td>0.780781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>gram9-plural-verbs</td>\n",
       "      <td>870</td>\n",
       "      <td>0.585057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>total</td>\n",
       "      <td>24972</td>\n",
       "      <td>0.754445</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       category  samples   average\n",
       "0      capital-common-countries      506  0.948617\n",
       "1                 capital-world     8372  0.964644\n",
       "2                 city-in-state     4242  0.599953\n",
       "3                      currency      752  0.174202\n",
       "4                        family      506  0.881423\n",
       "5     gram1-adjective-to-adverb      992  0.225806\n",
       "6                gram2-opposite      756  0.285714\n",
       "7             gram3-comparative     1332  0.882132\n",
       "8             gram4-superlative     1056  0.746212\n",
       "9      gram5-present-participle     1056  0.699811\n",
       "10  gram6-nationality-adjective     1640  0.925000\n",
       "11             gram7-past-tense     1560  0.611538\n",
       "12                 gram8-plural     1332  0.780781\n",
       "13           gram9-plural-verbs      870  0.585057\n",
       "14                        total    24972  0.754445"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wiki_result = eval_analogies(word2vec_wiki_file, vocab=100000)\n",
    "wiki_result.to_csv(glove_path / 'accuracy_wiki.csv', index=False)\n",
    "wiki_result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Common Crawl result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T02:24:30.653243Z",
     "start_time": "2020-06-21T02:13:42.770441Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>category</th>\n",
       "      <th>samples</th>\n",
       "      <th>average</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>capital-common-countries</td>\n",
       "      <td>506</td>\n",
       "      <td>0.946640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>capital-world</td>\n",
       "      <td>4290</td>\n",
       "      <td>0.917483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>city-in-state</td>\n",
       "      <td>4242</td>\n",
       "      <td>0.706742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>currency</td>\n",
       "      <td>206</td>\n",
       "      <td>0.184466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>family</td>\n",
       "      <td>420</td>\n",
       "      <td>0.978571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>gram1-adjective-to-adverb</td>\n",
       "      <td>992</td>\n",
       "      <td>0.388105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>gram2-opposite</td>\n",
       "      <td>702</td>\n",
       "      <td>0.363248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>gram3-comparative</td>\n",
       "      <td>1332</td>\n",
       "      <td>0.876877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>gram4-superlative</td>\n",
       "      <td>1122</td>\n",
       "      <td>0.919786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>gram5-present-participle</td>\n",
       "      <td>1056</td>\n",
       "      <td>0.827652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>gram6-nationality-adjective</td>\n",
       "      <td>1406</td>\n",
       "      <td>0.948791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>gram7-past-tense</td>\n",
       "      <td>1560</td>\n",
       "      <td>0.621154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>gram8-plural</td>\n",
       "      <td>1332</td>\n",
       "      <td>0.864114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>gram9-plural-verbs</td>\n",
       "      <td>870</td>\n",
       "      <td>0.672414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>total</td>\n",
       "      <td>20036</td>\n",
       "      <td>0.779347</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       category  samples   average\n",
       "0      capital-common-countries      506  0.946640\n",
       "1                 capital-world     4290  0.917483\n",
       "2                 city-in-state     4242  0.706742\n",
       "3                      currency      206  0.184466\n",
       "4                        family      420  0.978571\n",
       "5     gram1-adjective-to-adverb      992  0.388105\n",
       "6                gram2-opposite      702  0.363248\n",
       "7             gram3-comparative     1332  0.876877\n",
       "8             gram4-superlative     1122  0.919786\n",
       "9      gram5-present-participle     1056  0.827652\n",
       "10  gram6-nationality-adjective     1406  0.948791\n",
       "11             gram7-past-tense     1560  0.621154\n",
       "12                 gram8-plural     1332  0.864114\n",
       "13           gram9-plural-verbs      870  0.672414\n",
       "14                        total    20036  0.779347"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crawl_result = eval_analogies(word2vec_crawl_file, vocab=100000)\n",
    "crawl_result.to_csv(glove_path / 'accuracy_crawl.csv', index=False)\n",
    "crawl_result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Combine & compare results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T02:24:30.657191Z",
     "start_time": "2020-06-21T02:24:30.654331Z"
    }
   },
   "outputs": [],
   "source": [
    "cat_dict = {'capital-common-countries':'Capitals',\n",
    "            'capital-world':'Capitals RoW',\n",
    "            'city-in-state':'City-State',\n",
    "            'currency':'Currency',\n",
    "            'family':'Famliy',\n",
    "            'gram1-adjective-to-adverb':'Adj-Adverb',\n",
    "            'gram2-opposite':'Opposite',\n",
    "            'gram3-comparative':'Comparative',\n",
    "            'gram4-superlative':'Superlative',\n",
    "            'gram5-present-participle':'Pres. Part.',\n",
    "            'gram6-nationality-adjective':'Nationality',\n",
    "            'gram7-past-tense':'Past Tense',\n",
    "            'gram8-plural':'Plural',\n",
    "            'gram9-plural-verbs':'Plural Verbs',\n",
    "            'total':'Total'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T02:24:30.674854Z",
     "start_time": "2020-06-21T02:24:30.658299Z"
    }
   },
   "outputs": [],
   "source": [
    "accuracy = (twitter_result.assign(glove='Twitter')\n",
    "            .append(wiki_result.assign(glove='Wiki'))\n",
    "            .append(crawl_result.assign(glove='Crawl')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T02:24:30.682077Z",
     "start_time": "2020-06-21T02:24:30.675739Z"
    }
   },
   "outputs": [],
   "source": [
    "accuracy.category = accuracy.category.replace(cat_dict)\n",
    "accuracy = accuracy.rename(columns=str.capitalize)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T02:33:07.347540Z",
     "start_time": "2020-06-21T02:33:07.344509Z"
    }
   },
   "outputs": [],
   "source": [
    "accuracy.to_csv(results_path / 'accuracy.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T02:33:18.648905Z",
     "start_time": "2020-06-21T02:33:18.645238Z"
    }
   },
   "outputs": [],
   "source": [
    "accuracy = pd.read_csv(results_path / 'accuracy.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T02:33:19.383976Z",
     "start_time": "2020-06-21T02:33:18.798060Z"
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(16, 4))\n",
    "sns.barplot(x='Category', y='Average', hue='Glove', data=accuracy, ax=ax)\n",
    "ax.set_title(f'Word Vector Accuracy by Glove Source: Twitter: {0.564228:.2%}, Wiki: {0.75444:.2%}, Crawl: {0.779347:.2%}')\n",
    "ax.set_ylim(0,1)\n",
    "ax.yaxis.set_major_formatter(FuncFormatter(lambda y, _: '{:.0%}'.format(y)))\n",
    "sns.despine()\n",
    "fig.tight_layout()\n",
    "fig.savefig(results_path / 'glove_accuracy', dpi=300);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize Embeddings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load GloVe Wiki Vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T02:25:45.392257Z",
     "start_time": "2020-06-21T02:24:31.312295Z"
    }
   },
   "outputs": [],
   "source": [
    "model = KeyedVectors.load_word2vec_format(word2vec_wiki_file, binary=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T02:30:37.835035Z",
     "start_time": "2020-06-21T02:25:45.393238Z"
    }
   },
   "outputs": [],
   "source": [
    "accuracy = model.accuracy(questions=str(analogies_path), restrict_vocab=100000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T02:30:37.881617Z",
     "start_time": "2020-06-21T02:30:37.836455Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100000, 300)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vectors = model.vectors[:100000]\n",
    "vectors /= norm(vectors, axis=1).reshape(-1, 1)\n",
    "vectors.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T02:30:37.902106Z",
     "start_time": "2020-06-21T02:30:37.882841Z"
    }
   },
   "outputs": [],
   "source": [
    "words = model.index2word[:100000]\n",
    "word2id = {w:i for i, w in enumerate(words)}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Project Embedding into 2D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T02:30:39.964321Z",
     "start_time": "2020-06-21T02:30:37.903497Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2.604632\n",
       "1    1.293812\n",
       "dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca = IncrementalPCA(n_components=2)\n",
    "\n",
    "vectors2D = pca.fit_transform(vectors)\n",
    "pd.Series(pca.explained_variance_ratio_).mul(100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot Analogy Examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T02:30:40.099820Z",
     "start_time": "2020-06-21T02:30:39.965236Z"
    }
   },
   "outputs": [],
   "source": [
    "results = pd.DataFrame()\n",
    "correct = incorrect = 0\n",
    "for section in accuracy:\n",
    "    correct += len(section['correct'])\n",
    "    incorrect += len(section['incorrect'])\n",
    "    df = pd.DataFrame(section['correct']).apply(lambda x: x.str.lower()).assign(section=section['section'])\n",
    "    results = pd.concat([results, df])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T02:30:40.104634Z",
     "start_time": "2020-06-21T02:30:40.100791Z"
    }
   },
   "outputs": [],
   "source": [
    "def find_most_similar_analogy(v):\n",
    "    \"\"\"Find analogy that most similar in 2D\"\"\"\n",
    "    v1 = vectors2D[v[1]] - vectors2D[v[0]]\n",
    "    v2 = vectors2D[v[3]] - vectors2D[v[2]]\n",
    "    idx, most_similar = None, np.inf\n",
    "    \n",
    "    for i in range(len(v1)):\n",
    "        similarity = cosine(v1[i], v2[i])\n",
    "        if similarity < most_similar:\n",
    "            idx = i\n",
    "            most_similar = similarity\n",
    "    return idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T02:30:40.114670Z",
     "start_time": "2020-06-21T02:30:40.105677Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_plot_lims(coordinates):\n",
    "    xlim, ylim = coordinates.agg(['min', 'max']).T.values\n",
    "    xrange, yrange = (xlim[1] - xlim[0]) * .1, (ylim[1] - ylim[0]) * .1\n",
    "    xlim[0], xlim[1] = xlim[0] - xrange, xlim[1] + xrange\n",
    "    ylim[0], ylim[1] = ylim[0] - yrange, ylim[1] + yrange\n",
    "    return xlim, ylim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T02:32:05.062750Z",
     "start_time": "2020-06-21T02:31:56.747116Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=3, ncols=4, figsize=(16, 9))\n",
    "axes = axes.flatten()\n",
    "fc = ec = 'darkgrey'\n",
    "for s, (section, result) in enumerate(results.groupby('section')):\n",
    "    if s > 11:\n",
    "        break\n",
    "        \n",
    "    df = result.drop('section', axis=1).apply(lambda x: x.map(word2id))\n",
    "    most_similar_idx = find_most_similar_analogy(df)\n",
    "    \n",
    "    best_analogy = result.iloc[most_similar_idx, :4].tolist()\n",
    "\n",
    "    analogy_idx = [words.index(word) for word in best_analogy]\n",
    "    best_analogy = [a.capitalize() for a in best_analogy]\n",
    "    \n",
    "    coords = pd.DataFrame(vectors2D[analogy_idx])  # xy array\n",
    "    \n",
    "    xlim, ylim = get_plot_lims(coords)\n",
    "    axes[s].set_xlim(xlim)\n",
    "    axes[s].set_ylim(ylim)\n",
    "\n",
    "    for i in [0, 2]:\n",
    "        axes[s].annotate(s=best_analogy[i], xy=coords.iloc[i+1], xytext=coords.iloc[i],\n",
    "                         arrowprops=dict(width=1,headwidth=5, headlength=5,\n",
    "                                         fc=fc, ec=ec, shrink=.1),\n",
    "                         fontsize=12)\n",
    "    \n",
    "        axes[s].annotate(best_analogy[i+1], xy=coords.iloc[i+1],\n",
    "                         xytext=coords.iloc[i+1],\n",
    "                         va='center', ha='center',\n",
    "                         fontsize=12, color='darkred' if i == 2 else 'k');\n",
    "\n",
    "    axes[s].axis('off')\n",
    "    title = ' '.join([s.capitalize()\n",
    "                      for s in section.split('-') if not s.startswith('gram')])\n",
    "    axes[s].set_title(title, fontsize=16)\n",
    "\n",
    "fig.suptitle('word2vec Embeddings | Analogy Examples', fontsize=18)\n",
    "fig.tight_layout()\n",
    "fig.subplots_adjust(top=.9);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:ml4t] *",
   "language": "python",
   "name": "conda-env-ml4t-py"
  },
  "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": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}