euron.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer, ENGLISH_STOP_WORDS\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.cluster import KMeans, MiniBatchKMeans\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn.decomposition import TruncatedSVD \n",
    "from sklearn.preprocessing import normalize \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def parse_raw_message(raw_message):\n",
    "    lines = raw_message.split('\\n')\n",
    "    email = {}\n",
    "    message = ''\n",
    "    keys_to_extract = ['from', 'to']\n",
    "    for line in lines:\n",
    "        if ':' not in line:\n",
    "            message += line.strip()\n",
    "            email['body'] = message\n",
    "        else:\n",
    "            pairs = line.split(':')\n",
    "            key = pairs[0].lower()\n",
    "            val = pairs[1].strip()\n",
    "            if key in keys_to_extract:\n",
    "                email[key] = val\n",
    "    return email\n",
    "\n",
    "def parse_into_emails(messages):\n",
    "    emails = [parse_raw_message(message) for message in messages]\n",
    "    return {\n",
    "        'body': map_to_list(emails, 'body'), \n",
    "        'to': map_to_list(emails, 'to'), \n",
    "        'from_': map_to_list(emails, 'from')\n",
    "    }\n",
    "\n",
    "def map_to_list(emails, key):\n",
    "    results = []\n",
    "    for email in emails:\n",
    "        if key not in email:\n",
    "            results.append('')\n",
    "        else:\n",
    "            results.append(email[key])\n",
    "    return results\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "term frequency–inverse document frequency is a numerical statistic that is intended to reflect how important a word is to a document in a collection or corpus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def top_tfidf_feats(row, features, top_n=20):\n",
    "    topn_ids = np.argsort(row)[::-1][:top_n]\n",
    "    top_feats = [(features[i], row[i]) for i in topn_ids]\n",
    "    df = pd.DataFrame(top_feats, columns=['features', 'score'])\n",
    "    return df\n",
    "\n",
    "def top_feats_in_doc(X, features, row_id, top_n=25):\n",
    "    row = np.squeeze(X[row_id].toarray())\n",
    "    return top_tfidf_feats(row, features, top_n)\n",
    "\n",
    "def top_mean_feats(X, features, grp_ids=None, min_tfidf=0.1, top_n=25):\n",
    "    if grp_ids:\n",
    "        D = X[grp_ids].toarray()\n",
    "    else:\n",
    "        D = X.toarray()\n",
    "\n",
    "    D[D < min_tfidf] = 0\n",
    "    tfidf_means = np.mean(D, axis=0)\n",
    "    return top_tfidf_feats(tfidf_means, features, top_n)\n",
    "\n",
    "def top_feats_per_cluster(X, y, features, min_tfidf=0.1, top_n=25):\n",
    "    dfs = []\n",
    "\n",
    "    labels = np.unique(y)\n",
    "    for label in labels:\n",
    "        ids = np.where(y==label) \n",
    "        feats_df = top_mean_feats(X, features, ids, min_tfidf=min_tfidf, top_n=top_n)\n",
    "        feats_df.label = label\n",
    "        dfs.append(feats_df)\n",
    "    return dfs\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_tfidf_classfeats_h(dfs):\n",
    "    fig = plt.figure(figsize=(12, 9), facecolor=\"w\")\n",
    "    x = np.arange(len(dfs[0]))\n",
    "    for i, df in enumerate(dfs):\n",
    "        ax = fig.add_subplot(1, len(dfs), i+1)\n",
    "        ax.spines[\"top\"].set_visible(False)\n",
    "        ax.spines[\"right\"].set_visible(False)\n",
    "        ax.set_frame_on(False)\n",
    "        ax.get_xaxis().tick_bottom()\n",
    "        ax.get_yaxis().tick_left()\n",
    "        ax.set_xlabel(\"Tf-Idf Score\", labelpad=16, fontsize=14)\n",
    "        ax.set_title(\"cluster = \" + str(df.label), fontsize=16)\n",
    "        ax.ticklabel_format(axis='x', style='sci', scilimits=(-2,2))\n",
    "        ax.barh(x, df.score, align='center', color='#7530FF')\n",
    "        ax.set_yticks(x)\n",
    "        ax.set_ylim([-1, x[-1]+1])\n",
    "        yticks = ax.set_yticklabels(df.features)\n",
    "        plt.subplots_adjust(bottom=0.09, right=0.97, left=0.15, top=0.95, wspace=0.52)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "emails = pd.read_csv('E://data//split_emails.csv')\n",
    "# directory needs to be updated \n",
    "# Lets create a new frame with the data we need.\n",
    "email_df = pd.DataFrame(parse_into_emails(emails.message))\n",
    "\n",
    "# Drop emails with empty body, to or from_ columns. \n",
    "email_df.drop(email_df.query(\"body == '' | to == '' | from_ == ''\").index, inplace=True)\n",
    "#print(len(email_data.from_.unique())) # 1222 unique email addresses\n",
    "# print len(email_data.to.unique())  # 1593 unique email addresses\n",
    "# print email_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>body</th>\n",
       "      <th>to</th>\n",
       "      <th>from_</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Here is our forecast</td>\n",
       "      <td>tim.belden@enron.com</td>\n",
       "      <td>phillip.allen@enron.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Traveling to have a business meeting takes the...</td>\n",
       "      <td>john.lavorato@enron.com</td>\n",
       "      <td>phillip.allen@enron.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>test successful.  way to go!!!</td>\n",
       "      <td>leah.arsdall@enron.com</td>\n",
       "      <td>phillip.allen@enron.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Randy,Can you send me a schedule of the salary...</td>\n",
       "      <td>randall.gay@enron.com</td>\n",
       "      <td>phillip.allen@enron.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Greg,How about either next Tuesday or Thursday...</td>\n",
       "      <td>greg.piper@enron.com</td>\n",
       "      <td>phillip.allen@enron.com</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                body                       to  \\\n",
       "0                               Here is our forecast     tim.belden@enron.com   \n",
       "1  Traveling to have a business meeting takes the...  john.lavorato@enron.com   \n",
       "2                     test successful.  way to go!!!   leah.arsdall@enron.com   \n",
       "3  Randy,Can you send me a schedule of the salary...    randall.gay@enron.com   \n",
       "5  Greg,How about either next Tuesday or Thursday...     greg.piper@enron.com   \n",
       "\n",
       "                     from_  \n",
       "0  phillip.allen@enron.com  \n",
       "1  phillip.allen@enron.com  \n",
       "2  phillip.allen@enron.com  \n",
       "3  phillip.allen@enron.com  \n",
       "5  phillip.allen@enron.com  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "email_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>file</th>\n",
       "      <th>message</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>allen-p/_sent_mail/1.</td>\n",
       "      <td>Message-ID: &lt;18782981.1075855378110.JavaMail.e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>allen-p/_sent_mail/10.</td>\n",
       "      <td>Message-ID: &lt;15464986.1075855378456.JavaMail.e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>allen-p/_sent_mail/100.</td>\n",
       "      <td>Message-ID: &lt;24216240.1075855687451.JavaMail.e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>allen-p/_sent_mail/1000.</td>\n",
       "      <td>Message-ID: &lt;13505866.1075863688222.JavaMail.e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>allen-p/_sent_mail/1001.</td>\n",
       "      <td>Message-ID: &lt;30922949.1075863688243.JavaMail.e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>allen-p/_sent_mail/1002.</td>\n",
       "      <td>Message-ID: &lt;30965995.1075863688265.JavaMail.e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>allen-p/_sent_mail/1003.</td>\n",
       "      <td>Message-ID: &lt;16254169.1075863688286.JavaMail.e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>allen-p/_sent_mail/1004.</td>\n",
       "      <td>Message-ID: &lt;17189699.1075863688308.JavaMail.e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>allen-p/_sent_mail/101.</td>\n",
       "      <td>Message-ID: &lt;20641191.1075855687472.JavaMail.e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>allen-p/_sent_mail/102.</td>\n",
       "      <td>Message-ID: &lt;30795301.1075855687494.JavaMail.e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>allen-p/_sent_mail/103.</td>\n",
       "      <td>Message-ID: &lt;33076797.1075855687515.JavaMail.e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>allen-p/_sent_mail/104.</td>\n",
       "      <td>Message-ID: &lt;25459584.1075855687536.JavaMail.e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>allen-p/_sent_mail/105.</td>\n",
       "      <td>Message-ID: &lt;13116875.1075855687561.JavaMail.e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>allen-p/_sent_mail/106.</td>\n",
       "      <td>Message-ID: &lt;2707340.1075855687584.JavaMail.ev...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>allen-p/_sent_mail/107.</td>\n",
       "      <td>Message-ID: &lt;2465689.1075855687605.JavaMail.ev...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>allen-p/_sent_mail/108.</td>\n",
       "      <td>Message-ID: &lt;1115198.1075855687626.JavaMail.ev...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>allen-p/_sent_mail/109.</td>\n",
       "      <td>Message-ID: &lt;19773657.1075855687649.JavaMail.e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>allen-p/_sent_mail/11.</td>\n",
       "      <td>Message-ID: &lt;7391389.1075855378477.JavaMail.ev...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>allen-p/_sent_mail/110.</td>\n",
       "      <td>Message-ID: &lt;12759088.1075855687671.JavaMail.e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>allen-p/_sent_mail/111.</td>\n",
       "      <td>Message-ID: &lt;29177675.1075855687692.JavaMail.e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>allen-p/_sent_mail/112.</td>\n",
       "      <td>Message-ID: &lt;24729148.1075855687713.JavaMail.e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>allen-p/_sent_mail/113.</td>\n",
       "      <td>Message-ID: &lt;17610321.1075855687735.JavaMail.e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>allen-p/_sent_mail/114.</td>\n",
       "      <td>Message-ID: &lt;26575732.1075855687756.JavaMail.e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>allen-p/_sent_mail/115.</td>\n",
       "      <td>Message-ID: &lt;15294346.1075855687778.JavaMail.e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24</td>\n",
       "      <td>allen-p/_sent_mail/116.</td>\n",
       "      <td>Message-ID: &lt;25140503.1075855687800.JavaMail.e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25</td>\n",
       "      <td>allen-p/_sent_mail/117.</td>\n",
       "      <td>Message-ID: &lt;19034252.1075855687825.JavaMail.e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>26</td>\n",
       "      <td>allen-p/_sent_mail/118.</td>\n",
       "      <td>Message-ID: &lt;719350.1075855687850.JavaMail.eva...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27</td>\n",
       "      <td>allen-p/_sent_mail/119.</td>\n",
       "      <td>Message-ID: &lt;10523086.1075855687873.JavaMail.e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>28</td>\n",
       "      <td>allen-p/_sent_mail/12.</td>\n",
       "      <td>Message-ID: &lt;8572706.1075855378498.JavaMail.ev...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>29</td>\n",
       "      <td>allen-p/_sent_mail/120.</td>\n",
       "      <td>Message-ID: &lt;29665600.1075855687895.JavaMail.e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9970</th>\n",
       "      <td>9970</td>\n",
       "      <td>bass-e/_sent_mail/1024.</td>\n",
       "      <td>Message-ID: &lt;14436266.1075854730984.JavaMail.e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9971</th>\n",
       "      <td>9971</td>\n",
       "      <td>bass-e/_sent_mail/1025.</td>\n",
       "      <td>Message-ID: &lt;31782828.1075854731007.JavaMail.e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9972</th>\n",
       "      <td>9972</td>\n",
       "      <td>bass-e/_sent_mail/1026.</td>\n",
       "      <td>Message-ID: &lt;21002405.1075854731028.JavaMail.e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9973</th>\n",
       "      <td>9973</td>\n",
       "      <td>bass-e/_sent_mail/1027.</td>\n",
       "      <td>Message-ID: &lt;28358669.1075854731050.JavaMail.e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9974</th>\n",
       "      <td>9974</td>\n",
       "      <td>bass-e/_sent_mail/1028.</td>\n",
       "      <td>Message-ID: &lt;17008743.1075854731072.JavaMail.e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9975</th>\n",
       "      <td>9975</td>\n",
       "      <td>bass-e/_sent_mail/1029.</td>\n",
       "      <td>Message-ID: &lt;15753020.1075854731094.JavaMail.e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9976</th>\n",
       "      <td>9976</td>\n",
       "      <td>bass-e/_sent_mail/103.</td>\n",
       "      <td>Message-ID: &lt;22494944.1075854709158.JavaMail.e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9977</th>\n",
       "      <td>9977</td>\n",
       "      <td>bass-e/_sent_mail/1030.</td>\n",
       "      <td>Message-ID: &lt;16539833.1075854731116.JavaMail.e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9978</th>\n",
       "      <td>9978</td>\n",
       "      <td>bass-e/_sent_mail/1031.</td>\n",
       "      <td>Message-ID: &lt;20070752.1075854731138.JavaMail.e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9979</th>\n",
       "      <td>9979</td>\n",
       "      <td>bass-e/_sent_mail/1032.</td>\n",
       "      <td>Message-ID: &lt;6432003.1075854731161.JavaMail.ev...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9980</th>\n",
       "      <td>9980</td>\n",
       "      <td>bass-e/_sent_mail/1033.</td>\n",
       "      <td>Message-ID: &lt;23855039.1075854731182.JavaMail.e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9981</th>\n",
       "      <td>9981</td>\n",
       "      <td>bass-e/_sent_mail/1034.</td>\n",
       "      <td>Message-ID: &lt;23292901.1075854731204.JavaMail.e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9982</th>\n",
       "      <td>9982</td>\n",
       "      <td>bass-e/_sent_mail/1035.</td>\n",
       "      <td>Message-ID: &lt;4230720.1075854731226.JavaMail.ev...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9983</th>\n",
       "      <td>9983</td>\n",
       "      <td>bass-e/_sent_mail/1036.</td>\n",
       "      <td>Message-ID: &lt;3885684.1075854731249.JavaMail.ev...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9984</th>\n",
       "      <td>9984</td>\n",
       "      <td>bass-e/_sent_mail/1037.</td>\n",
       "      <td>Message-ID: &lt;9771073.1075854731272.JavaMail.ev...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9985</th>\n",
       "      <td>9985</td>\n",
       "      <td>bass-e/_sent_mail/1038.</td>\n",
       "      <td>Message-ID: &lt;7040082.1075854731297.JavaMail.ev...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9986</th>\n",
       "      <td>9986</td>\n",
       "      <td>bass-e/_sent_mail/1039.</td>\n",
       "      <td>Message-ID: &lt;9732572.1075854731328.JavaMail.ev...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9987</th>\n",
       "      <td>9987</td>\n",
       "      <td>bass-e/_sent_mail/104.</td>\n",
       "      <td>Message-ID: &lt;14743104.1075854709181.JavaMail.e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9988</th>\n",
       "      <td>9988</td>\n",
       "      <td>bass-e/_sent_mail/1040.</td>\n",
       "      <td>Message-ID: &lt;31048717.1075854731349.JavaMail.e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9989</th>\n",
       "      <td>9989</td>\n",
       "      <td>bass-e/_sent_mail/1041.</td>\n",
       "      <td>Message-ID: &lt;30821657.1075854731371.JavaMail.e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9990</th>\n",
       "      <td>9990</td>\n",
       "      <td>bass-e/_sent_mail/1042.</td>\n",
       "      <td>Message-ID: &lt;6282142.1075854731395.JavaMail.ev...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9991</th>\n",
       "      <td>9991</td>\n",
       "      <td>bass-e/_sent_mail/1043.</td>\n",
       "      <td>Message-ID: &lt;21726237.1075854731420.JavaMail.e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9992</th>\n",
       "      <td>9992</td>\n",
       "      <td>bass-e/_sent_mail/1044.</td>\n",
       "      <td>Message-ID: &lt;13540869.1075854731445.JavaMail.e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9993</th>\n",
       "      <td>9993</td>\n",
       "      <td>bass-e/_sent_mail/1045.</td>\n",
       "      <td>Message-ID: &lt;15234130.1075854731466.JavaMail.e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9994</th>\n",
       "      <td>9994</td>\n",
       "      <td>bass-e/_sent_mail/1046.</td>\n",
       "      <td>Message-ID: &lt;19710283.1075854731488.JavaMail.e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9995</th>\n",
       "      <td>9995</td>\n",
       "      <td>bass-e/_sent_mail/1047.</td>\n",
       "      <td>Message-ID: &lt;21376194.1075854731510.JavaMail.e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>9996</td>\n",
       "      <td>bass-e/_sent_mail/1048.</td>\n",
       "      <td>Message-ID: &lt;2401643.1075854731531.JavaMail.ev...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>9997</td>\n",
       "      <td>bass-e/_sent_mail/1049.</td>\n",
       "      <td>Message-ID: &lt;32002412.1075854731553.JavaMail.e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>9998</td>\n",
       "      <td>bass-e/_sent_mail/105.</td>\n",
       "      <td>Message-ID: &lt;21429983.1075854709202.JavaMail.e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>9999</td>\n",
       "      <td>bass-e/_sent_mail/1050.</td>\n",
       "      <td>Message-ID: &lt;20553983.1075854731575.JavaMail.e...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0                      file  \\\n",
       "0              0     allen-p/_sent_mail/1.   \n",
       "1              1    allen-p/_sent_mail/10.   \n",
       "2              2   allen-p/_sent_mail/100.   \n",
       "3              3  allen-p/_sent_mail/1000.   \n",
       "4              4  allen-p/_sent_mail/1001.   \n",
       "5              5  allen-p/_sent_mail/1002.   \n",
       "6              6  allen-p/_sent_mail/1003.   \n",
       "7              7  allen-p/_sent_mail/1004.   \n",
       "8              8   allen-p/_sent_mail/101.   \n",
       "9              9   allen-p/_sent_mail/102.   \n",
       "10            10   allen-p/_sent_mail/103.   \n",
       "11            11   allen-p/_sent_mail/104.   \n",
       "12            12   allen-p/_sent_mail/105.   \n",
       "13            13   allen-p/_sent_mail/106.   \n",
       "14            14   allen-p/_sent_mail/107.   \n",
       "15            15   allen-p/_sent_mail/108.   \n",
       "16            16   allen-p/_sent_mail/109.   \n",
       "17            17    allen-p/_sent_mail/11.   \n",
       "18            18   allen-p/_sent_mail/110.   \n",
       "19            19   allen-p/_sent_mail/111.   \n",
       "20            20   allen-p/_sent_mail/112.   \n",
       "21            21   allen-p/_sent_mail/113.   \n",
       "22            22   allen-p/_sent_mail/114.   \n",
       "23            23   allen-p/_sent_mail/115.   \n",
       "24            24   allen-p/_sent_mail/116.   \n",
       "25            25   allen-p/_sent_mail/117.   \n",
       "26            26   allen-p/_sent_mail/118.   \n",
       "27            27   allen-p/_sent_mail/119.   \n",
       "28            28    allen-p/_sent_mail/12.   \n",
       "29            29   allen-p/_sent_mail/120.   \n",
       "...          ...                       ...   \n",
       "9970        9970   bass-e/_sent_mail/1024.   \n",
       "9971        9971   bass-e/_sent_mail/1025.   \n",
       "9972        9972   bass-e/_sent_mail/1026.   \n",
       "9973        9973   bass-e/_sent_mail/1027.   \n",
       "9974        9974   bass-e/_sent_mail/1028.   \n",
       "9975        9975   bass-e/_sent_mail/1029.   \n",
       "9976        9976    bass-e/_sent_mail/103.   \n",
       "9977        9977   bass-e/_sent_mail/1030.   \n",
       "9978        9978   bass-e/_sent_mail/1031.   \n",
       "9979        9979   bass-e/_sent_mail/1032.   \n",
       "9980        9980   bass-e/_sent_mail/1033.   \n",
       "9981        9981   bass-e/_sent_mail/1034.   \n",
       "9982        9982   bass-e/_sent_mail/1035.   \n",
       "9983        9983   bass-e/_sent_mail/1036.   \n",
       "9984        9984   bass-e/_sent_mail/1037.   \n",
       "9985        9985   bass-e/_sent_mail/1038.   \n",
       "9986        9986   bass-e/_sent_mail/1039.   \n",
       "9987        9987    bass-e/_sent_mail/104.   \n",
       "9988        9988   bass-e/_sent_mail/1040.   \n",
       "9989        9989   bass-e/_sent_mail/1041.   \n",
       "9990        9990   bass-e/_sent_mail/1042.   \n",
       "9991        9991   bass-e/_sent_mail/1043.   \n",
       "9992        9992   bass-e/_sent_mail/1044.   \n",
       "9993        9993   bass-e/_sent_mail/1045.   \n",
       "9994        9994   bass-e/_sent_mail/1046.   \n",
       "9995        9995   bass-e/_sent_mail/1047.   \n",
       "9996        9996   bass-e/_sent_mail/1048.   \n",
       "9997        9997   bass-e/_sent_mail/1049.   \n",
       "9998        9998    bass-e/_sent_mail/105.   \n",
       "9999        9999   bass-e/_sent_mail/1050.   \n",
       "\n",
       "                                                message  \n",
       "0     Message-ID: <18782981.1075855378110.JavaMail.e...  \n",
       "1     Message-ID: <15464986.1075855378456.JavaMail.e...  \n",
       "2     Message-ID: <24216240.1075855687451.JavaMail.e...  \n",
       "3     Message-ID: <13505866.1075863688222.JavaMail.e...  \n",
       "4     Message-ID: <30922949.1075863688243.JavaMail.e...  \n",
       "5     Message-ID: <30965995.1075863688265.JavaMail.e...  \n",
       "6     Message-ID: <16254169.1075863688286.JavaMail.e...  \n",
       "7     Message-ID: <17189699.1075863688308.JavaMail.e...  \n",
       "8     Message-ID: <20641191.1075855687472.JavaMail.e...  \n",
       "9     Message-ID: <30795301.1075855687494.JavaMail.e...  \n",
       "10    Message-ID: <33076797.1075855687515.JavaMail.e...  \n",
       "11    Message-ID: <25459584.1075855687536.JavaMail.e...  \n",
       "12    Message-ID: <13116875.1075855687561.JavaMail.e...  \n",
       "13    Message-ID: <2707340.1075855687584.JavaMail.ev...  \n",
       "14    Message-ID: <2465689.1075855687605.JavaMail.ev...  \n",
       "15    Message-ID: <1115198.1075855687626.JavaMail.ev...  \n",
       "16    Message-ID: <19773657.1075855687649.JavaMail.e...  \n",
       "17    Message-ID: <7391389.1075855378477.JavaMail.ev...  \n",
       "18    Message-ID: <12759088.1075855687671.JavaMail.e...  \n",
       "19    Message-ID: <29177675.1075855687692.JavaMail.e...  \n",
       "20    Message-ID: <24729148.1075855687713.JavaMail.e...  \n",
       "21    Message-ID: <17610321.1075855687735.JavaMail.e...  \n",
       "22    Message-ID: <26575732.1075855687756.JavaMail.e...  \n",
       "23    Message-ID: <15294346.1075855687778.JavaMail.e...  \n",
       "24    Message-ID: <25140503.1075855687800.JavaMail.e...  \n",
       "25    Message-ID: <19034252.1075855687825.JavaMail.e...  \n",
       "26    Message-ID: <719350.1075855687850.JavaMail.eva...  \n",
       "27    Message-ID: <10523086.1075855687873.JavaMail.e...  \n",
       "28    Message-ID: <8572706.1075855378498.JavaMail.ev...  \n",
       "29    Message-ID: <29665600.1075855687895.JavaMail.e...  \n",
       "...                                                 ...  \n",
       "9970  Message-ID: <14436266.1075854730984.JavaMail.e...  \n",
       "9971  Message-ID: <31782828.1075854731007.JavaMail.e...  \n",
       "9972  Message-ID: <21002405.1075854731028.JavaMail.e...  \n",
       "9973  Message-ID: <28358669.1075854731050.JavaMail.e...  \n",
       "9974  Message-ID: <17008743.1075854731072.JavaMail.e...  \n",
       "9975  Message-ID: <15753020.1075854731094.JavaMail.e...  \n",
       "9976  Message-ID: <22494944.1075854709158.JavaMail.e...  \n",
       "9977  Message-ID: <16539833.1075854731116.JavaMail.e...  \n",
       "9978  Message-ID: <20070752.1075854731138.JavaMail.e...  \n",
       "9979  Message-ID: <6432003.1075854731161.JavaMail.ev...  \n",
       "9980  Message-ID: <23855039.1075854731182.JavaMail.e...  \n",
       "9981  Message-ID: <23292901.1075854731204.JavaMail.e...  \n",
       "9982  Message-ID: <4230720.1075854731226.JavaMail.ev...  \n",
       "9983  Message-ID: <3885684.1075854731249.JavaMail.ev...  \n",
       "9984  Message-ID: <9771073.1075854731272.JavaMail.ev...  \n",
       "9985  Message-ID: <7040082.1075854731297.JavaMail.ev...  \n",
       "9986  Message-ID: <9732572.1075854731328.JavaMail.ev...  \n",
       "9987  Message-ID: <14743104.1075854709181.JavaMail.e...  \n",
       "9988  Message-ID: <31048717.1075854731349.JavaMail.e...  \n",
       "9989  Message-ID: <30821657.1075854731371.JavaMail.e...  \n",
       "9990  Message-ID: <6282142.1075854731395.JavaMail.ev...  \n",
       "9991  Message-ID: <21726237.1075854731420.JavaMail.e...  \n",
       "9992  Message-ID: <13540869.1075854731445.JavaMail.e...  \n",
       "9993  Message-ID: <15234130.1075854731466.JavaMail.e...  \n",
       "9994  Message-ID: <19710283.1075854731488.JavaMail.e...  \n",
       "9995  Message-ID: <21376194.1075854731510.JavaMail.e...  \n",
       "9996  Message-ID: <2401643.1075854731531.JavaMail.ev...  \n",
       "9997  Message-ID: <32002412.1075854731553.JavaMail.e...  \n",
       "9998  Message-ID: <21429983.1075854709202.JavaMail.e...  \n",
       "9999  Message-ID: <20553983.1075854731575.JavaMail.e...  \n",
       "\n",
       "[10000 rows x 3 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emails"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# At this point we are going to tokenize the bodies and convert them\n",
    "# into a document-term matrix.\n",
    "\n",
    "# Some note on min_df and max_df\n",
    "# max_df=0.3 means \"ignore all terms that appear in more then 30% of the documents\"\n",
    "# min_df=2 means \"ignore all terms that appear in less then 2 documents\"\n",
    "#Removal of stop words – Stop words like “and”, “the”, “of”, etc are very common in all English sentences and are not very meaningful in deciding spam or legitimate status, so these words have been removed from the emails.\n",
    "stopwords = ENGLISH_STOP_WORDS.union(['ect', 'hou', 'com', 'recipient'])\n",
    "vect = TfidfVectorizer(analyzer='word', stop_words=stopwords, max_df=0.3, min_df=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    features     score\n",
      "0   meetings  0.383128\n",
      "1       trip  0.324351\n",
      "2        ski  0.280451\n",
      "3   business  0.276205\n",
      "4      takes  0.204126\n",
      "5        try  0.161225\n",
      "6  presenter  0.158455\n",
      "7  stimulate  0.155878\n",
      "8      quiet  0.148051\n",
      "9     speaks  0.148051\n"
     ]
    }
   ],
   "source": [
    "X = vect.fit_transform(email_df.body)\n",
    "features = vect.get_feature_names()\n",
    "\n",
    "# Let's print the top 10 terms in document 1\n",
    "print(top_feats_in_doc(X, features, 1, 10))\n",
    "\n",
    "# Now we print the top terms across all documents.\n",
    "#print(top_mean_feats(X, features, None, 0.1, 10)) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# As clustering algorithm KMeams is a perfect fit.\n",
    "n_clusters = 5\n",
    "clf = KMeans(n_clusters=n_clusters, \n",
    "            max_iter=100, \n",
    "            init='k-means++', \n",
    "            n_init=1)\n",
    "labels = clf.fit_predict(X)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_tfidf_classfeats_h(top_feats_per_cluster(X, labels, features, 0.1, 25))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# As clustering algorithm KMeams is a perfect fit.\n",
    "n_clusters = 3\n",
    "clf = KMeans(n_clusters=n_clusters, \n",
    "            max_iter=100, \n",
    "            init='k-means++', \n",
    "            n_init=1)\n",
    "labels = clf.fit_predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_tfidf_classfeats_h(top_feats_per_cluster(X, labels, features, 0.1, 25))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reading mails from directory "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_email_bodies():\n",
    "  emails = pd.read_csv('E://data//split_emails.csv')\n",
    "  email_df = pd.DataFrame(parse_into_emails(emails.message))\n",
    "  email_df.drop(email_df.query(\"body == '' | to == '' | from_ == ''\").index, inplace=True)\n",
    "  email_df.drop_duplicates(inplace=True)\n",
    "  return email_df['body']\n",
    "\n",
    "\n",
    "class EmailDataset: \n",
    "  def __init__(self):\n",
    "    stopwords = ENGLISH_STOP_WORDS.union(['ect', 'hou', 'com', 'recipient'])\n",
    "    self.vec = TfidfVectorizer(analyzer='word', stop_words=stopwords, max_df=0.3, min_df=2)\n",
    "    self.emails = read_email_bodies() \n",
    "\n",
    "    # train on the given email data.\n",
    "    self.train()\n",
    "  \n",
    "  def train(self):\n",
    "    self.vec_train = self.vec.fit_transform(self.emails)\n",
    "  \n",
    "  def query(self, keyword, limit):\n",
    "    vec_keyword = self.vec.transform([keyword])\n",
    "    cosine_sim = linear_kernel(vec_keyword, self.vec_train).flatten()\n",
    "    related_email_indices = cosine_sim.argsort()[:-limit:-1]\n",
    "    #print(related_email_indices)\n",
    "    return related_email_indices\n",
    "\n",
    "  def find_email_by_index(self, i):\n",
    "    return self.emails.as_matrix()[i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  (0, 16969)\t1.0\n",
      "[1. 0. 0. ... 0. 0. 0.]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer, ENGLISH_STOP_WORDS\n",
    "from sklearn.metrics.pairwise import linear_kernel\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "\n",
    "# Just like in part_1, read and preprocess emails\n",
    "emails = pd.read_csv('E://data//split_emails.csv') \n",
    "email_df = pd.DataFrame(parse_into_emails(emails.message))\n",
    "email_df.drop(email_df.query(\"body == '' | to == '' | from_ == ''\").index, inplace=True)\n",
    "\n",
    "stopwords = ENGLISH_STOP_WORDS.union(['ect', 'hou', 'com', 'recipient'])\n",
    "vec = TfidfVectorizer(analyzer='word', stop_words=stopwords, max_df=0.3, min_df=2)\n",
    "vec_train = vec.fit_transform(email_df.body)\n",
    "\n",
    "# print out the vector of the first email\n",
    "print(vec_train[0:1])\n",
    "\n",
    "# Find cosine similarity between the first email and all others.\n",
    "cosine_sim = linear_kernel(vec_train[0:1], vec_train).flatten()\n",
    "# print out the cosine similarities\n",
    "print(cosine_sim)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "john\n",
      "[2162 5900 5839 6133 5570 7483 4707 3604 5549 7526]\n",
      "1 John,Do you still want to get together this week?Phillip\n",
      "2 mike.grigsby@enron.com, john.lavorato@enron.com*~*~*~*~*~*~*~*~*~*John LavoratoJohn ArnoldScott NealTom MartinMike Grigsby\n",
      "3 Here are the positions that you requested from John.\n",
      "4 Looks fine to me.--John-----Original Message-----John/Kevin/John --Can you take a look at this and tell me if anything gives you heartburn.  Its a sidebar for an article on energy trading in Global Finance.  The reporter's deadline is Fri., June 8.  Thanks.Eric\n",
      "5 drinks at your place before?john\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\admin\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:17: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n"
     ]
    }
   ],
   "source": [
    "# Finding emails related to a query.\n",
    "query = input()\n",
    "\n",
    "# Transform the query into the original vector\n",
    "vec_query = vec.transform([query])\n",
    "\n",
    "cosine_sim = linear_kernel(vec_query, vec_train).flatten()\n",
    "\n",
    "# Find top 10 most related emails to the query.\n",
    "related_email_indices = cosine_sim.argsort()[:-11:-1]\n",
    "# print out the indices of the 10 most related emails.\n",
    "print(related_email_indices)\n",
    "\n",
    "# print out the first email \n",
    "#first_email_index = related_email_indices[0]\n",
    "for i in range(5):\n",
    "    print(i+1,email_df.body.as_matrix()[related_email_indices[i]])\n",
    "\n",
    "    \n",
    "#print(email_df.body.as_matrix()[first_email_index])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trueJeanie Slonetrue or false-John Griffith should be moved into your cost center with thesame salary, same title effective Feb. 01.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\admin\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:29: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# use the EmailDataset class to query for keywords.\n",
    "ds = EmailDataset()\n",
    "results = ds.query('salary', 10)\n",
    "\n",
    "# Print out the first result.\n",
    "print(ds.find_email_by_index(results[0]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter the number of query terms on the basis of which you want to categorize mails2\n",
      "john\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\admin\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:29: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "salary\n",
      "{'john': ['John,Do you still want to get together this week?Phillip', 'The EconomistEnergy Risk ManagementHavard Business ReviewThanks,John'], 'salary': ['trueJeanie Slonetrue or false-John Griffith should be moved into your cost center with thesame salary, same title effective Feb. 01.', 'Randy,Can you send me a schedule of the salary and level of everyone in thescheduling group.  Plus your thoughts on any changes that need to be made.(Patti S for example)Phillip']}\n"
     ]
    }
   ],
   "source": [
    "n=int(input('Enter the number of query terms on the basis of which you want to categorize mails'))\n",
    "dic={}\n",
    "for i in range(n):\n",
    "    li=[]\n",
    "    s=input()\n",
    "    results = ds.query(s, 11)\n",
    "    for j in range(2):\n",
    "        li.append(ds.find_email_by_index(results[j]))\n",
    "    dic[s]=li\n",
    "print(dic)\n",
    "    "
   ]
  }
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