{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"width: 100%; overflow: hidden;\">\n",
    "    <div style=\"width: 150px; float: left;\"> <img src=\"https://raw.githubusercontent.com/DataForScience/Networks/master/data/D4Sci_logo_ball.png\" alt=\"Data For Science, Inc\" align=\"left\" border=\"0\" width=150px> </div>\n",
    "    <div style=\"float: left; margin-left: 10px;\"> \n",
    "        <h1>Graphs in NLP</h1>\n",
    "        <h1>Word Networks for Language Generation</h1>\n",
    "        <p>Bruno Gonçalves<br/>\n",
    "        <a href=\"http://www.data4sci.com/\">www.data4sci.com</a><br/>\n",
    "            @bgoncalves, @data4sci</p></div>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start by print out the versions of the libraries we're using for future reference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "from collections import Counter\n",
    "from pprint import pprint\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import gzip\n",
    "import zipfile\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "import nltk \n",
    "from nltk.corpus import stopwords\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt \n",
    "\n",
    "import os\n",
    "import re\n",
    "import tqdm as tq\n",
    "from tqdm import tqdm\n",
    "tqdm.pandas()\n",
    "\n",
    "import networkx as nx\n",
    "\n",
    "import sklearn\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "import watermark\n",
    "\n",
    "%load_ext watermark\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python implementation: CPython\n",
      "Python version       : 3.8.5\n",
      "IPython version      : 7.19.0\n",
      "\n",
      "Compiler    : Clang 10.0.0 \n",
      "OS          : Darwin\n",
      "Release     : 20.3.0\n",
      "Machine     : x86_64\n",
      "Processor   : i386\n",
      "CPU cores   : 16\n",
      "Architecture: 64bit\n",
      "\n",
      "Git hash: ae69fdf6632e9262d6c543a7940bbe63db46fb44\n",
      "\n",
      "networkx  : 2.5\n",
      "re        : 2.2.1\n",
      "seaborn   : 0.11.0\n",
      "pandas    : 1.1.3\n",
      "nltk      : 3.5\n",
      "tqdm      : 4.50.2\n",
      "json      : 2.0.9\n",
      "watermark : 2.1.0\n",
      "numpy     : 1.19.2\n",
      "matplotlib: 3.3.2\n",
      "sklearn   : 0.23.2\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%watermark -n -v -m -g -iv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load default figure style"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('./d4sci.mplstyle')\n",
    "colors = plt.rcParams['axes.prop_cycle'].by_key()['color']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extract 2-grams from data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extracting 2-grams from a piece of text is simple. Let us take a simple example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "text = 'Mary had a little lamb little lamb little lamb'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We extract individual words using a simple regex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "text_regex = re.compile(r\"[a-zA-Z]+\")\n",
    "words = text_regex.findall(text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And now we just have to combine neighboring words into 2-grams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Mary', 'had'),\n",
       " ('had', 'a'),\n",
       " ('a', 'little'),\n",
       " ('little', 'lamb'),\n",
       " ('lamb', 'little'),\n",
       " ('little', 'lamb'),\n",
       " ('lamb', 'little'),\n",
       " ('little', 'lamb')]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ngrams = []\n",
    "\n",
    "for i in range(len(words)-1):\n",
    "    ngrams.append((words[i], words[i+1]))\n",
    "    \n",
    "ngrams"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we can compute the weights and build out the edge_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>node_i</th>\n",
       "      <th>node_j</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mary</td>\n",
       "      <td>had</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>had</td>\n",
       "      <td>a</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a</td>\n",
       "      <td>little</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>little</td>\n",
       "      <td>lamb</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>lamb</td>\n",
       "      <td>little</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   node_i  node_j  weight\n",
       "0    Mary     had       1\n",
       "1     had       a       1\n",
       "2       a  little       1\n",
       "3  little    lamb       3\n",
       "4    lamb  little       2"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edge_list = pd.DataFrame([[edge[0], edge[1], weight] for edge, weight in Counter(ngrams).items()], \n",
    "                         columns=['node_i', 'node_j', 'weight'])\n",
    "\n",
    "edge_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fortunately, Google already did all this work for us..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Process Google Books 2-gram data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The full dataset is over 27Gb so I've already done this for you and saved it as a relatively small file (~500Mb)... You can download it from this [Google Drive link](https://drive.google.com/file/d/1vX5qi_GqiN4-PYTHqamWZAvbkUq0xFue/view?usp=sharing) (Save it under the repo's data folder or update the filename below) but just in case you want the code to process the original files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "filename = \"data/2-gram_full.dat.gz\"\n",
    "\n",
    "# If we don't have the processed file\n",
    "if not os.path.exists(filename):\n",
    "    edges = {}\n",
    "    \n",
    "    text = re.compile(r\"^[a-zA-Z]+$\")\n",
    "    \n",
    "    # Go through the list of 2-gram files from google books\n",
    "    for url in tqdm(open(\"data/2-grams.txt\", \"rt\"), total=100):\n",
    "        filename = os.path.basename(url.strip())\n",
    "        localfile = os.path.join('data', '2-grams', filename)\n",
    "        \n",
    "        # If we don't already have it, download it\n",
    "        # Depending on your internet connection this might take a while...\n",
    "        # and you should make sure that the \"2-grams\" directory exists\n",
    "        if not os.path.exists(localfile):\n",
    "            wget.download(url, out='data/2-grams')\n",
    "        \n",
    "        # Process it, line by line\n",
    "        with zipfile.ZipFile(localfile) as zf:\n",
    "            # About 3m per file in my MBP, about 6h total \n",
    "            with zf.open(filename[:-4], 'r') as fp:\n",
    "                for line in fp:\n",
    "                    fields = line.decode('utf8').strip().split()\n",
    "                    year = int(fields[2])\n",
    "\n",
    "                    word1 = fields[0].lower().strip()\n",
    "                    word2 = fields[1].lower().strip()\n",
    "                    count = int(fields[3])\n",
    "\n",
    "                    # Make sure we have two words with just letters\n",
    "                    if  text.match(word1) is not None and text.match(word2) is not None:\n",
    "                        edge = (word1, word2)\n",
    "\n",
    "                        edges[edge] = edges.get(edge, 0) + count\n",
    "    \n",
    "    new_edges = {}\n",
    "    \n",
    "    # Write down the edges\n",
    "    with gzip.open(filename, \"wt\") as fp:\n",
    "        print('node_i', 'node_j', 'weight', file=fp)\n",
    "        \n",
    "        for edge in tqdm(edges, total=len(edges)):\n",
    "            print(edge[0], edge[1], edges[edge], file=fp)\n",
    "            new_edges[edge] = edges[edge]\n",
    "    \n",
    "    del new_edges\n",
    "    del edges\n",
    "\n",
    "edges = pd.read_csv(filename, sep=' ')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have loaded our edges, we can start analyzing our network. First we check how many nodes we have. We can extract this information by combining the sets of nodes mentioned in the edge list. In total we have over 1.7M nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1720174\n"
     ]
    }
   ],
   "source": [
    "N = len(set(edges['node_i'].unique()) | set(edges['node_j'].unique()))\n",
    "print(N)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And over 70 million edges (2-grams)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "70387456"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edges.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In total, the English Google Books dataset contains 235 Billion word pairs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "235519098368"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edges['weight'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Node degrees"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we have a network, we can check what the in and out degree distributions look like. To get the in and out degrees we just need to count the number of unique in and out edges. Pandas makes this particularly easy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "degree_out = edges[['node_i', 'node_j']].groupby('node_i').count()\n",
    "degree_in = edges[['node_i', 'node_j']].groupby('node_j').count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conceptually, these are just dictionaries mapping each node to how many other nodes it is connected to"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>node_j</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>node_i</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>256131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aa</th>\n",
       "      <td>4163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aaa</th>\n",
       "      <td>603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aaaa</th>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aaaaa</th>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        node_j\n",
       "node_i        \n",
       "a       256131\n",
       "aa        4163\n",
       "aaa        603\n",
       "aaaa        46\n",
       "aaaaa       20"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "degree_out.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For visualization purposes we write a simple function to calculate the distribution from a sequence of values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prob_dist(values, resolution=1, normed=True):\n",
    "    counts = Counter((1+np.round(values.flatten()/resolution))*resolution)\n",
    "    \n",
    "    counts = pd.DataFrame(np.array(list(counts.items())))\n",
    "    counts = counts.sort_values(0)    \n",
    "    \n",
    "    if normed:\n",
    "        counts[1] /= counts[1].sum()\n",
    "    \n",
    "    return counts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The degree distributions are then"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "K_out = prob_dist(degree_out.values, 1)\n",
    "K_in = prob_dist(degree_in.values, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Which we can easily plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 3840x2640 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = K_out.plot(x=0, y=1, logy=True, logx=True, label=r'$k_{out}$')\n",
    "K_in.plot(x=0, y=1, logy=True, logx=True, ax=ax, label=r'$k_{in}$')\n",
    "ax.legend()\n",
    "ax.set_xlabel('k')\n",
    "ax.set_ylabel('P(k)')\n",
    "\n",
    "for label in ax.xaxis.get_majorticklabels():\n",
    "    label.set_y(-.01)\n",
    "\n",
    "plt.gcf().tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we note a few important points. \n",
    "\n",
    "- $P(k_{in})$ and $P(k_{out})$ are fairly similar. This is to be expected as one nodes outgoing edge must be another nodes incoming edge.\n",
    "- The distributions are power-law (ish) with a broad right tail. This implies that some nodes have huge degrees (almost 1 million) while most nodes have almost no connections\n",
    "- A few words have in/out-degree of 1. This likely corresponds to (unusual) typos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>node_i</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>node_j</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>aaaah</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aaaahs</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aaaai</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aaabab</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aaac</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zzril</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zztgx</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zzw</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zzzzz</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zzzzzz</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>858444 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        node_i\n",
       "node_j        \n",
       "aaaah        1\n",
       "aaaahs       1\n",
       "aaaai        1\n",
       "aaabab       1\n",
       "aaac         1\n",
       "...        ...\n",
       "zzril        1\n",
       "zztgx        1\n",
       "zzw          2\n",
       "zzzzz        2\n",
       "zzzzzz       1\n",
       "\n",
       "[858444 rows x 1 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "degree_in[degree_in['node_i'] <= 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>node_j</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>node_i</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>aaaai</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aaab</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aaaf</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aaah</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aaahed</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zzd</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zznl</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zzril</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zzw</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zzzzz</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>666522 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        node_j\n",
       "node_i        \n",
       "aaaai        1\n",
       "aaab         1\n",
       "aaaf         1\n",
       "aaah         1\n",
       "aaahed       1\n",
       "...        ...\n",
       "zzd          2\n",
       "zznl         1\n",
       "zzril        1\n",
       "zzw          1\n",
       "zzzzz        1\n",
       "\n",
       "[666522 rows x 1 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "degree_out[degree_out['node_j'] <= 2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The most likely degree for a word is 2:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    2.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "K_in.set_index(0).idxmax()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Which are also pretty uncommon..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
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       "      <th>aaad</th>\n",
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       "    <tr>\n",
       "      <th>aaalac</th>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>aabe</th>\n",
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       "    <tr>\n",
       "      <th>aaberge</th>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>aaboe</th>\n",
       "      <td>2</td>\n",
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <th>zyxin</th>\n",
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       "    <tr>\n",
       "      <th>zyzanski</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zyzzyva</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zzw</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
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       "      <th>zzzzz</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>222764 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          node_i\n",
       "node_j          \n",
       "aaad           2\n",
       "aaalac         2\n",
       "aabe           2\n",
       "aaberge        2\n",
       "aaboe          2\n",
       "...          ...\n",
       "zyxin          2\n",
       "zyzanski       2\n",
       "zyzzyva        2\n",
       "zzw            2\n",
       "zzzzz          2\n",
       "\n",
       "[222764 rows x 1 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "degree_in[degree_in['node_i'] == 2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On the other hand, words with the highest degree values are interesting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>122347</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        node_i\n",
       "node_j        \n",
       "and     585260\n",
       "in      360672\n",
       "of      325205\n",
       "to      262199\n",
       "is      239546\n",
       "was     217736\n",
       "or      182749\n",
       "the     179655\n",
       "as      161512\n",
       "for     159958\n",
       "with    157630\n",
       "on      152172\n",
       "a       142493\n",
       "are     138150\n",
       "that    137681\n",
       "had     137418\n",
       "at      137240\n",
       "by      129913\n",
       "has     126305\n",
       "from    122347"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "degree_in.sort_values('node_i', ascending=False).head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "  <tbody>\n",
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       "      <th>the</th>\n",
       "      <td>689422</td>\n",
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       "      <th>and</th>\n",
       "      <td>602357</td>\n",
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       "    <tr>\n",
       "      <th>of</th>\n",
       "      <td>555368</td>\n",
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       "    <tr>\n",
       "      <th>to</th>\n",
       "      <td>278294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>in</th>\n",
       "      <td>275767</td>\n",
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       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>256131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>by</th>\n",
       "      <td>229717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>or</th>\n",
       "      <td>201258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>as</th>\n",
       "      <td>185124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>with</th>\n",
       "      <td>184572</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        node_j\n",
       "node_i        \n",
       "the     689422\n",
       "and     602357\n",
       "of      555368\n",
       "to      278294\n",
       "in      275767\n",
       "a       256131\n",
       "by      229717\n",
       "or      201258\n",
       "as      185124\n",
       "with    184572"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "degree_out.sort_values('node_j', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In fact, we note that the words with the highers in/out degree tend to be the same as we confirm when we look at the Pearson correlation between in/out degree."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "node_info = degree_in.join(degree_out, how='outer').fillna(0).sort_values('node_i', ascending=False).copy()\n",
    "node_info.columns=['kin', 'kout']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "corr = node_info.corr()\n",
    "corr.rename(columns={'kin': r'$k_{in}$', \n",
    "                    'kout': r'$k_{out}$'}, inplace=True)\n",
    "corr.rename(index={'kin': r'$k_{in}$', \n",
    "                    'kout': r'$k_{out}$'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      "text/plain": [
       "<Figure size 3840x2640 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(corr.round(2), vmin=0, vmax=1, annot=True, cmap=plt.cm.inferno)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A correlation of 0.86 is pretty larger and we make it evern clearer with a linear regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm = LinearRegression()\n",
    "lm.fit(node_info[['kin']], node_info[['kout']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A slope of 1 is another pretty clear indication that the two degrees are strongly correlated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.00918683]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fa15800dfd0>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 3840x2640 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = node_info.plot.scatter(x='kin', y='kout')\n",
    "\n",
    "krange = np.array([node_info[['kin']].max(), node_info[['kin']].min()+1])\n",
    "pred = lm.predict(krange)\n",
    "ax.loglog(krange, pred, '-', color=colors[1], lw=2)\n",
    "\n",
    "ax.set_xlabel(r'$k_{in}$')\n",
    "ax.set_ylabel(r'$k_{out}$')\n",
    "\n",
    "for label in ax.xaxis.get_majorticklabels():\n",
    "    label.set_y(-.01)\n",
    "\n",
    "ax.legend(['Data', 'Fit y=%1.2f+%1.2f x' % (lm.intercept_[0], lm.coef_[0])])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at the most common words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>kin</th>\n",
       "      <th>kout</th>\n",
       "      <th>dist</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>and</th>\n",
       "      <td>585260.0</td>\n",
       "      <td>602357.0</td>\n",
       "      <td>839859.049513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>the</th>\n",
       "      <td>179655.0</td>\n",
       "      <td>689422.0</td>\n",
       "      <td>712445.515888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>of</th>\n",
       "      <td>325205.0</td>\n",
       "      <td>555368.0</td>\n",
       "      <td>643577.429257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>in</th>\n",
       "      <td>360672.0</td>\n",
       "      <td>275767.0</td>\n",
       "      <td>454017.323318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>to</th>\n",
       "      <td>262199.0</td>\n",
       "      <td>278294.0</td>\n",
       "      <td>382355.680011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>142493.0</td>\n",
       "      <td>256131.0</td>\n",
       "      <td>293099.546588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>or</th>\n",
       "      <td>182749.0</td>\n",
       "      <td>201258.0</td>\n",
       "      <td>271849.185331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is</th>\n",
       "      <td>239546.0</td>\n",
       "      <td>115148.0</td>\n",
       "      <td>265784.401386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>by</th>\n",
       "      <td>129913.0</td>\n",
       "      <td>229717.0</td>\n",
       "      <td>263907.725650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>as</th>\n",
       "      <td>161512.0</td>\n",
       "      <td>185124.0</td>\n",
       "      <td>245676.660511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>with</th>\n",
       "      <td>157630.0</td>\n",
       "      <td>184572.0</td>\n",
       "      <td>242722.145846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>was</th>\n",
       "      <td>217736.0</td>\n",
       "      <td>92856.0</td>\n",
       "      <td>236709.109314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for</th>\n",
       "      <td>159958.0</td>\n",
       "      <td>162214.0</td>\n",
       "      <td>227815.591126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>that</th>\n",
       "      <td>137681.0</td>\n",
       "      <td>171672.0</td>\n",
       "      <td>220062.112471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>from</th>\n",
       "      <td>122347.0</td>\n",
       "      <td>161662.0</td>\n",
       "      <td>202739.711584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>on</th>\n",
       "      <td>152172.0</td>\n",
       "      <td>120460.0</td>\n",
       "      <td>194079.698021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>at</th>\n",
       "      <td>137240.0</td>\n",
       "      <td>136259.0</td>\n",
       "      <td>193394.241592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>are</th>\n",
       "      <td>138150.0</td>\n",
       "      <td>92849.0</td>\n",
       "      <td>166452.273343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>had</th>\n",
       "      <td>137418.0</td>\n",
       "      <td>44787.0</td>\n",
       "      <td>144532.287372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>were</th>\n",
       "      <td>121297.0</td>\n",
       "      <td>69857.0</td>\n",
       "      <td>139974.864379</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           kin      kout           dist\n",
       "and   585260.0  602357.0  839859.049513\n",
       "the   179655.0  689422.0  712445.515888\n",
       "of    325205.0  555368.0  643577.429257\n",
       "in    360672.0  275767.0  454017.323318\n",
       "to    262199.0  278294.0  382355.680011\n",
       "a     142493.0  256131.0  293099.546588\n",
       "or    182749.0  201258.0  271849.185331\n",
       "is    239546.0  115148.0  265784.401386\n",
       "by    129913.0  229717.0  263907.725650\n",
       "as    161512.0  185124.0  245676.660511\n",
       "with  157630.0  184572.0  242722.145846\n",
       "was   217736.0   92856.0  236709.109314\n",
       "for   159958.0  162214.0  227815.591126\n",
       "that  137681.0  171672.0  220062.112471\n",
       "from  122347.0  161662.0  202739.711584\n",
       "on    152172.0  120460.0  194079.698021\n",
       "at    137240.0  136259.0  193394.241592\n",
       "are   138150.0   92849.0  166452.273343\n",
       "had   137418.0   44787.0  144532.287372\n",
       "were  121297.0   69857.0  139974.864379"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "topwords = node_info.copy()\n",
    "topwords['dist'] = np.sqrt(topwords['kin']**2+topwords['kout']**2)\n",
    "topwords.sort_values('dist', ascending=False).head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When we look at this list we note that these words appear to be stopwords. We confirm this by loading up a list of stopwords from NLTK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "stop_en = stopwords.words('english')\n",
    "overlap = set(stop_en) & set(node_info.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NLTK uses 179 stopwords, 153 of which are part of our dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NLTK words: 179\n",
      "Dataset: 153\n"
     ]
    }
   ],
   "source": [
    "print(\"NLTK words:\", len(stop_en))\n",
    "print(\"Dataset:\", len(overlap))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These words all have a pretty high mean degree, placing them well into the tail of our distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        vertical-align: top;\n",
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       "\n",
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       "        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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>kin</th>\n",
       "      <td>53011.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kout</th>\n",
       "      <td>56760.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            0\n",
       "kin   53011.0\n",
       "kout  56760.0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(node_info.loc[overlap].mean().round(0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But what about the 26 words we __don't__ see in our data?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{\"aren't\",\n",
       " \"couldn't\",\n",
       " \"didn't\",\n",
       " \"doesn't\",\n",
       " \"don't\",\n",
       " \"hadn't\",\n",
       " \"hasn't\",\n",
       " \"haven't\",\n",
       " \"isn't\",\n",
       " \"it's\",\n",
       " \"mightn't\",\n",
       " \"mustn't\",\n",
       " \"needn't\",\n",
       " \"shan't\",\n",
       " \"she's\",\n",
       " \"should've\",\n",
       " \"shouldn't\",\n",
       " \"that'll\",\n",
       " \"wasn't\",\n",
       " \"weren't\",\n",
       " \"won't\",\n",
       " \"wouldn't\",\n",
       " \"you'd\",\n",
       " \"you'll\",\n",
       " \"you're\",\n",
       " \"you've\"}"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing = set(stop_en) - set(node_info.index)\n",
    "missing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are all contractions of two words, so we see them popping up in our original 2-gram dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>node_i</th>\n",
       "      <th>node_j</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>35019650</th>\n",
       "      <td>aren</td>\n",
       "      <td>t</td>\n",
       "      <td>1841</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         node_i node_j  weight\n",
       "35019650   aren      t    1841"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edges[(edges['node_i'] == 'aren') & (edges['node_j'] == 't')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Edge weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>node_i</th>\n",
       "      <th>node_j</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8223473</th>\n",
       "      <td>of</td>\n",
       "      <td>the</td>\n",
       "      <td>3243999909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>973562</th>\n",
       "      <td>in</td>\n",
       "      <td>the</td>\n",
       "      <td>1753160117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9206022</th>\n",
       "      <td>to</td>\n",
       "      <td>the</td>\n",
       "      <td>1072859611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14931367</th>\n",
       "      <td>and</td>\n",
       "      <td>the</td>\n",
       "      <td>726576725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156926</th>\n",
       "      <td>on</td>\n",
       "      <td>the</td>\n",
       "      <td>645591979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8414488</th>\n",
       "      <td>to</td>\n",
       "      <td>be</td>\n",
       "      <td>506837334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17678123</th>\n",
       "      <td>for</td>\n",
       "      <td>the</td>\n",
       "      <td>500180793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105381</th>\n",
       "      <td>it</td>\n",
       "      <td>is</td>\n",
       "      <td>488758757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6835129</th>\n",
       "      <td>by</td>\n",
       "      <td>the</td>\n",
       "      <td>464390971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9439188</th>\n",
       "      <td>of</td>\n",
       "      <td>a</td>\n",
       "      <td>452357485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14047452</th>\n",
       "      <td>that</td>\n",
       "      <td>the</td>\n",
       "      <td>428132188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1807893</th>\n",
       "      <td>from</td>\n",
       "      <td>the</td>\n",
       "      <td>427141882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4526435</th>\n",
       "      <td>with</td>\n",
       "      <td>the</td>\n",
       "      <td>426489465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296975</th>\n",
       "      <td>at</td>\n",
       "      <td>the</td>\n",
       "      <td>376364433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>512126</th>\n",
       "      <td>in</td>\n",
       "      <td>a</td>\n",
       "      <td>340213039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10131914</th>\n",
       "      <td>as</td>\n",
       "      <td>a</td>\n",
       "      <td>284300647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3968437</th>\n",
       "      <td>it</td>\n",
       "      <td>was</td>\n",
       "      <td>272019810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16592992</th>\n",
       "      <td>is</td>\n",
       "      <td>the</td>\n",
       "      <td>241487811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17367007</th>\n",
       "      <td>is</td>\n",
       "      <td>a</td>\n",
       "      <td>237252182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018657</th>\n",
       "      <td>as</td>\n",
       "      <td>the</td>\n",
       "      <td>231078953</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         node_i node_j      weight\n",
       "8223473      of    the  3243999909\n",
       "973562       in    the  1753160117\n",
       "9206022      to    the  1072859611\n",
       "14931367    and    the   726576725\n",
       "156926       on    the   645591979\n",
       "8414488      to     be   506837334\n",
       "17678123    for    the   500180793\n",
       "105381       it     is   488758757\n",
       "6835129      by    the   464390971\n",
       "9439188      of      a   452357485\n",
       "14047452   that    the   428132188\n",
       "1807893    from    the   427141882\n",
       "4526435    with    the   426489465\n",
       "296975       at    the   376364433\n",
       "512126       in      a   340213039\n",
       "10131914     as      a   284300647\n",
       "3968437      it    was   272019810\n",
       "16592992     is    the   241487811\n",
       "17367007     is      a   237252182\n",
       "2018657      as    the   231078953"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edges.sort_values('weight', ascending=False, inplace=True)\n",
    "edges.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The distribution of edge weights is pretty broad spanning 9 orders of magnitude"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "Pw = prob_dist(edges['weight'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 3840x2640 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = Pw.plot(x=0, y=1, logy=True, logx=True, legend=None)\n",
    "ax.set_xlabel('w')\n",
    "ax.set_ylabel('P(w)')\n",
    "\n",
    "for label in ax.xaxis.get_majorticklabels():\n",
    "    label.set_y(-.01)\n",
    "\n",
    "plt.gcf().tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As in the case of the degree distributions, most of the edges have relatively small weights. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Google Books dataset contains books published as far back as the 1500s and with extensive coverage since 1800. If we set a weight threshold of 1000 (roughly 5 occurrences per year between 1800-2000, we have just 8.5 million edges left"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "edges_small = edges[edges['weight'] >= 1000].dropna().copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A reduction of 88% in size!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.12106054806129092"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edges_small.shape[0]/edges.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We save this reduced file for later analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.exists('data/2-gram.dat.gz'):\n",
    "    edges_small.to_csv('data/2-gram.dat.gz', sep=' ', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we can easily check that the weight distribution is simply truncated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "Pw_small = prob_dist(edges_small['weight'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3840x2640 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = Pw_small.plot(x=0, y=1, logy=True, logx=True, legend=None)\n",
    "ax.set_xlabel('w')\n",
    "ax.set_ylabel('P(w)')\n",
    "\n",
    "for label in ax.xaxis.get_majorticklabels():\n",
    "    label.set_y(-.01)\n",
    "\n",
    "plt.gcf().tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Node strength"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Continuing for now with the original file, we take a look at the node strenghts. Strenght is simply the weighted equivalent of the degree where each edge counts it's weight instead of just 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "strength_out = edges[['node_i', 'weight']].groupby(['node_i']).sum()\n",
    "strength_in = edges[['node_j', 'weight']].groupby(['node_j']).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The distribution is broad tailed as expected, but over a much wider range than the degree distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "S_out = prob_dist(strength_out.values, 1)\n",
    "S_in = prob_dist(strength_in.values, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3840x2640 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = S_out.plot(x=0, y=1, logy=True, logx=True, label=r'$s_{out}$')\n",
    "S_in.plot(x=0, y=1, logy=True, logx=True, ax=ax, label=r'$s_{in}$')\n",
    "ax.legend()\n",
    "ax.set_xlabel('s')\n",
    "ax.set_ylabel('P(s)')\n",
    "\n",
    "for label in ax.xaxis.get_majorticklabels():\n",
    "    label.set_y(-.01)\n",
    "\n",
    "plt.gcf().tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "strength_in.columns=['sin']\n",
    "strength_out.columns=['sout']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "node_info = node_info.join(strength_in, how='outer').fillna(1)\n",
    "node_info = node_info.join(strength_out, how='outer').fillna(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "corr = node_info.corr()\n",
    "corr.rename(columns={'kin': r'$k_{in}$', \n",
    "                    'sin': r'$s_{in}$',\n",
    "                    'kout': r'$k_{out}$',\n",
    "                    'sout': r'$s_{out}$'}, inplace=True)\n",
    "corr.rename(index={'kin': r'$k_{in}$', \n",
    "                    'sin': r'$s_{in}$',\n",
    "                    'kout': r'$k_{out}$',\n",
    "                    'sout': r'$s_{out}$'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 3840x2640 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(corr.round(2), vmin=0, vmax=1, annot=True, cmap=plt.cm.inferno)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fa1102ff1f0>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3840x2640 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = node_info.plot.scatter(x='sin', y='sout')\n",
    "\n",
    "srange = np.array([node_info[['sin']].max(), node_info[['sin']].min()+1])\n",
    "pred = lm.predict(srange)\n",
    "ax.loglog(srange, pred, '-', color=colors[1], lw=2)\n",
    "\n",
    "ax.set_xlabel(r'$s_{in}$')\n",
    "ax.set_ylabel(r'$s_{out}$')\n",
    "\n",
    "for label in ax.xaxis.get_majorticklabels():\n",
    "    label.set_y(-.01)\n",
    "\n",
    "ax.legend(['Data', 'Fit y=%1.2f+%1.2f x' % (lm.intercept_[0], lm.coef_[0])])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Walk"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We define a utility function to normalize the weights of each out going edges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "def normalize(x):\n",
    "    x['weight'] /= x['weight'].sum()\n",
    "    return x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And apply it to our edge list to generate a stochastic graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1202233/1202233 [12:31<00:00, 1599.50it/s]\n"
     ]
    }
   ],
   "source": [
    "graph = edges.groupby(['node_i'], as_index=False).progress_apply(normalize)\n",
    "graph.columns = ['node_i', 'node_j', 'weight']\n",
    "graph.set_index('node_i', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can perform our random walk starting with an arbitrarily chose word, say, \"you\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 20/20 [02:05<00:00,  6.29s/it]\n"
     ]
    }
   ],
   "source": [
    "Nsteps = 20\n",
    "current_word = 'you'\n",
    "\n",
    "text = [current_word]\n",
    "\n",
    "for step in tqdm(range(Nsteps), total=Nsteps):\n",
    "    # Get all the possible nearest neighbors\n",
    "    neighbors = graph.loc[[current_word]].copy()\n",
    "        \n",
    "    # Randomly select the next word\n",
    "    next_index = np.random.multinomial(1, neighbors['weight'].values.flatten(), size=1).argmax()\n",
    "    current_word = neighbors.iloc[next_index]['node_j']\n",
    "    \n",
    "    # Print the next word in the walk\n",
    "    text.append(current_word)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The text it generates is not exactly up to Shakespeare standards and it becomes increasingly weird as you demand more and more steps but a simple algorithm like this is already a good start towards a predictive writing system such as the one in your smartphone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "you too many other differences and particulars number of pennsylvania railroad and student couples found tu esto me of aklan and\n"
     ]
    }
   ],
   "source": [
    "print(\" \".join(text))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"width: 100%; overflow: hidden;\">\n",
    "     <img src=\"data/D4Sci_logo_full.png\" alt=\"Data For Science, Inc\" align=\"center\" border=\"0\" width=300px> \n",
    "</div>"
   ]
  }
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