Cosine_similarity_example.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "text_1 = \"I love playing football with my friends\"\n",
    "text_2 = \"I hate waching and playing basketball\"\n",
    "text_3 = \"When I was a kid I was playing football with my friends every day all the evening\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['I love playing football with my friends',\n",
       " 'I hate waching and playing basketball',\n",
       " 'When I was a kid I was playing football with my friends every day all the evening']"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "texts = [text_1, text_2, text_3]\n",
    "texts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
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       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>all</th>\n",
       "      <th>and</th>\n",
       "      <th>basketball</th>\n",
       "      <th>day</th>\n",
       "      <th>evening</th>\n",
       "      <th>every</th>\n",
       "      <th>football</th>\n",
       "      <th>friends</th>\n",
       "      <th>hate</th>\n",
       "      <th>kid</th>\n",
       "      <th>love</th>\n",
       "      <th>my</th>\n",
       "      <th>playing</th>\n",
       "      <th>the</th>\n",
       "      <th>waching</th>\n",
       "      <th>was</th>\n",
       "      <th>when</th>\n",
       "      <th>with</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>text_1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>text_2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>text_2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        all  and  basketball  day  evening  every  football  friends  hate  \\\n",
       "text_1    0    0           0    0        0      0         1        1     0   \n",
       "text_2    0    1           1    0        0      0         0        0     1   \n",
       "text_2    1    0           0    1        1      1         1        1     0   \n",
       "\n",
       "        kid  love  my  playing  the  waching  was  when  with  \n",
       "text_1    0     1   1        1    0        0    0     0     1  \n",
       "text_2    0     0   0        1    0        1    0     0     0  \n",
       "text_2    1     0   1        1    1        0    2     1     1  "
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "## Firstly let's count the words using the CountVectorizer\n",
    "count_vectorizer = CountVectorizer(stop_words='english')\n",
    "count_vectorizer = CountVectorizer()\n",
    "matrix = count_vectorizer.fit_transform(texts)\n",
    "\n",
    "## we can create a dataframe to represent the number of the words in every sentence\n",
    "table = matrix.todense()\n",
    "df = pd.DataFrame(table, \n",
    "                  columns=count_vectorizer.get_feature_names(), \n",
    "                  index=['text_1', 'text_2', 'text_2'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Text_1</th>\n",
       "      <th>Text_2</th>\n",
       "      <th>Text_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>text_1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.464102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>text_2</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.358899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>text_3</th>\n",
       "      <td>3.464102</td>\n",
       "      <td>4.358899</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Text_1    Text_2    Text_3\n",
       "text_1  0.000000  3.000000  3.464102\n",
       "text_2  3.000000  0.000000  4.358899\n",
       "text_3  3.464102  4.358899  0.000000"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Compute the Euclidean distance of these sentences\n",
    "from scipy.spatial import distance\n",
    "\n",
    "matrix = distance.cdist(df, df, 'euclidean')\n",
    "\n",
    "df_eucl = pd.DataFrame(matrix, \n",
    "                  columns= [\"Text_1\", \"Text_2\", \"Text_3\"],\n",
    "                  index=['text_1', 'text_2', 'text_3'])\n",
    "df_eucl\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Text 1</th>\n",
       "      <th>Text 2</th>\n",
       "      <th>Text 3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Text 1</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.182574</td>\n",
       "      <td>0.510310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Text 2</th>\n",
       "      <td>0.182574</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.111803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Text 3</th>\n",
       "      <td>0.510310</td>\n",
       "      <td>0.111803</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Text 1    Text 2    Text 3\n",
       "Text 1  1.000000  0.182574  0.510310\n",
       "Text 2  0.182574  1.000000  0.111803\n",
       "Text 3  0.510310  0.111803  1.000000"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Finally we can compute the Cosine Similarity \n",
    "\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "values = cosine_similarity(df, df)\n",
    "df = pd.DataFrame(values, columns=[\"Text 1\", \"Text 2\", \"Text 3\"], index = [\"Text 1\", \"Text 2\", \"Text 3\"])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## As we can see the higher the similarity we are getting closer to 1 lower closer to 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Draw a graph in order to represent the Euclidean distance and cosine similarity\n",
    "doc_1 = [10,12]\n",
    "doc_2 = [7,5]\n",
    "df = pd.DataFrame([doc_1, doc_2], columns=(\"Data\", \"Science\"), index=[\"Doc 1\", \"Doc 2\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Data</th>\n",
       "      <th>Science</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Doc 1</th>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Doc 2</th>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "       Data  Science\n",
       "Doc 1    10       12\n",
       "Doc 2     7        5"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10,5))\n",
    "ax.scatter(df.Data, df.Science)\n",
    "\n",
    "for i, txt in enumerate(df.index):\n",
    "    ax.annotate(txt, (df.Data[i], df.Science[i]))\n",
    "\n",
    "\n",
    "plt.title(\"Euclidean distance\", fontsize=20) \n",
    "plt.plot(df.Data, df.Science, label=\"Euclidean Distance\", color=\"r\")\n",
    "\n",
    "\n",
    "plt.xticks(np.arange(0,16))\n",
    "plt.yticks(np.arange(0,16))\n",
    "\n",
    "plt.xlabel(\"Word: Data\", fontsize=15)\n",
    "plt.ylabel(\"Word: Science\", fontsize=15)\n",
    "plt.legend()\n",
    "\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,5))\n",
    "ax.scatter(df.Data, df.Science)\n",
    "\n",
    "for i, txt in enumerate(df.index):\n",
    "    ax.annotate(txt, (df.Data[i], df.Science[i]))\n",
    "\n",
    "plt.title(\"Euclidean distance\", fontsize=20) \n",
    "\n",
    "line_2 = plt.plot([0,7], [0,5])\n",
    "line_1 = plt.plot([0,10], [0,12])\n",
    "\n",
    "\n",
    "plt.xticks(np.arange(0,16))\n",
    "plt.yticks(np.arange(0,16))\n",
    "\n",
    "plt.xlabel(\"Word: Data\", fontsize=15)\n",
    "plt.ylabel(\"Word: Science\", fontsize=15)\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "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>Data</th>\n",
       "      <th>Science</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Doc 1</th>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Doc 2</th>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Doc_3</th>\n",
       "      <td>11</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Data  Science\n",
       "Doc 1    10       12\n",
       "Doc 2     7        5\n",
       "Doc_3    11       18"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "doc_1 = [10,12]\n",
    "doc_2 = [7,5]\n",
    "doc_3 = [11,18]\n",
    "df = pd.DataFrame([doc_1, doc_2, doc_3], columns=(\"Data\", \"Science\"), index=[\"Doc 1\", \"Doc 2\", \"Doc_3\"])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,5))\n",
    "ax.scatter(df.Data, df.Science)\n",
    "\n",
    "for i, txt in enumerate(df.index):\n",
    "    ax.annotate(txt, (df.Data[i], df.Science[i]))\n",
    "\n",
    "\n",
    "plt.title(\"Euclidean distance\", fontsize=20) \n",
    "\n",
    "line_2 = plt.plot([0,7], [0,5])\n",
    "line_1 = plt.plot([0,10], [0,12])\n",
    "line_3 = plt.plot([0,11],[0,18])\n",
    "\n",
    "\n",
    "plt.xticks(np.arange(0,26))\n",
    "plt.yticks(np.arange(0,26))\n",
    "\n",
    "plt.xlabel(\"Word: Data\", fontsize=15)\n",
    "plt.ylabel(\"Word: Science\", fontsize=15)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.7 (tensorflow)",
   "language": "python",
   "name": "tensorflow"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}