Exp_3_One_Hot_Encoding.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "02c24288",
   "metadata": {},
   "source": [
    "# One Hot Encoding of Text in NLP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6f9cfb01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Thomas',\n",
       " 'Jefferson',\n",
       " 'began',\n",
       " 'building',\n",
       " 'Monticello',\n",
       " 'at',\n",
       " 'the',\n",
       " 'age',\n",
       " 'of',\n",
       " '26.']"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "sentence = \"\"\"Thomas Jefferson began building Monticello at the age of 26.\"\"\" \n",
    "sentence.split()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "59a34c5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Thomas',\n",
       " 'Jefferson',\n",
       " 'began',\n",
       " 'building',\n",
       " 'Monticello',\n",
       " 'at',\n",
       " 'the',\n",
       " 'age',\n",
       " 'of',\n",
       " '26.']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "token_sequence = str.split(sentence)\n",
    "token_sequence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2bc4d246",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['26.',\n",
       " 'Jefferson',\n",
       " 'Monticello',\n",
       " 'Thomas',\n",
       " 'age',\n",
       " 'at',\n",
       " 'began',\n",
       " 'building',\n",
       " 'of',\n",
       " 'the']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vocab = sorted(set(token_sequence)) \n",
    "vocab"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7254d04a",
   "metadata": {},
   "source": [
    "# What is the difference between vocab and the output of sentence.split()."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ea4b0060",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['26.', 'Jefferson', 'Monticello', 'Thomas', 'age', 'at', 'began', 'building', 'of', 'the']\n"
     ]
    }
   ],
   "source": [
    "print(vocab)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d0c7378",
   "metadata": {},
   "source": [
    "# why 26. ? What does it mean? is it a floting point number or age.?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8271b069",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'26., Jefferson, Monticello, Thomas, age, at, began, building, of, the'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "', '.join(vocab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "128d7cfb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n"
     ]
    }
   ],
   "source": [
    "num_tokens = len(token_sequence)\n",
    "print(num_tokens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0030687e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n"
     ]
    }
   ],
   "source": [
    "vocab_size = len(vocab)\n",
    "print(vocab_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b4f8e587",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0]]\n"
     ]
    }
   ],
   "source": [
    "onehot_vectors = np.zeros((num_tokens,vocab_size), int)\n",
    "print(onehot_vectors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b6ba773b",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i, word in enumerate(token_sequence):\n",
    "    onehot_vectors[i,vocab.index(word)]=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b63f4261",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n",
       "       [0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0, 1, 0, 0],\n",
       "       [0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 1, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 1],\n",
       "       [0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n",
       "       [1, 0, 0, 0, 0, 0, 0, 0, 0, 0]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "onehot_vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fa6dcfce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'26. Jefferson Monticello Thomas age at began building of the'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "' '.join(vocab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8ea4254d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n",
       "       [0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0, 1, 0, 0],\n",
       "       [0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 1, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 1],\n",
       "       [0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n",
       "       [1, 0, 0, 0, 0, 0, 0, 0, 0, 0]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "onehot_vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "78ba951e",
   "metadata": {},
   "outputs": [],
   "source": [
    "l = [\"eat\", \"sleep\", \"repeat\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "19b99efc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0, 'eat'), (1, 'sleep'), (2, 'repeat')]\n",
      "[(5, 'eat'), (6, 'sleep'), (7, 'repeat')]\n"
     ]
    }
   ],
   "source": [
    "print (list(enumerate(l)))\n",
    "print (list(enumerate(l, 5)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a3133d78",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "eat 0\n",
      "sleep 1\n",
      "repeat 2\n"
     ]
    }
   ],
   "source": [
    "for count, ele in enumerate(l):\n",
    "    print (ele,count)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "971f534e",
   "metadata": {},
   "source": [
    "# Improving readability using pandas dataframes which convert 1D arrays in to series of lists, dictionaries and numbers of tables with labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "389fb047",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df = pd.DataFrame(onehot_vectors, columns = vocab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3937328b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#df[df==0] = ''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "41440664",
   "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",
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       "\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>26.</th>\n",
       "      <th>Jefferson</th>\n",
       "      <th>Monticello</th>\n",
       "      <th>Thomas</th>\n",
       "      <th>age</th>\n",
       "      <th>at</th>\n",
       "      <th>began</th>\n",
       "      <th>building</th>\n",
       "      <th>of</th>\n",
       "      <th>the</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   26.  Jefferson  Monticello  Thomas  age  at  began  building  of  the\n",
       "0    0          0           0       1    0   0      0         0   0    0\n",
       "1    0          1           0       0    0   0      0         0   0    0\n",
       "2    0          0           0       0    0   0      1         0   0    0\n",
       "3    0          0           0       0    0   0      0         1   0    0\n",
       "4    0          0           1       0    0   0      0         0   0    0\n",
       "5    0          0           0       0    0   1      0         0   0    0\n",
       "6    0          0           0       0    0   0      0         0   0    1\n",
       "7    0          0           0       0    1   0      0         0   0    0\n",
       "8    0          0           0       0    0   0      0         0   1    0\n",
       "9    1          0           0       0    0   0      0         0   0    0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0a7ea14",
   "metadata": {},
   "source": [
    "# Each Row represents a vector for a single word and all UNIQUE for 10 Words\n",
    "# Each column represents a word in your corpus. words in the document.\n",
    "# Location of '1' in a column indicates a vocabulary word that was present at the position in the sentence or document.\n",
    "# Check the above statemet,..\n",
    "# ALL BUT ONE - One-Hot - 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "fc1b7547",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "26.           0\n",
      "Jefferson     0\n",
      "Monticello    0\n",
      "Thomas        0\n",
      "age           0\n",
      "at            0\n",
      "began         1\n",
      "building      0\n",
      "of            0\n",
      "the           0\n",
      "Name: 2, dtype: int32\n"
     ]
    }
   ],
   "source": [
    "s = df.iloc[2]\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d88e3a74",
   "metadata": {},
   "source": [
    "# Number representation for the word 'Began' as a vector"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f492b4c9",
   "metadata": {},
   "source": [
    "# One Hot encoding vectors retain all the details such as word order, grammar and order of the original sentence"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5bcd7580",
   "metadata": {},
   "source": [
    "# suppose there is corpus of 3000 books with 3500 sentences and 15 words per sentence.\n",
    "# Calculate the size of the one-hot encoding vector."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9b044a8b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "157500000\n"
     ]
    }
   ],
   "source": [
    "num_rows = 3000*3500*15\n",
    "print(num_rows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f5a3d567",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "157.5\n",
      "Tera Bytes\n"
     ]
    }
   ],
   "source": [
    "num_bytes = num_rows * 1000000\n",
    "print(num_bytes/1e12)\n",
    "print('Tera Bytes')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07b9bc96",
   "metadata": {},
   "source": [
    "# Not a good way of representation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cc8df344",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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