4-ctm.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3fa22989",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install --upgrade pip\n",
    "!pip install tomotopy\n",
    "!pip install nltk\n",
    "!pip install pyvis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7983f368",
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "This example shows how to perform a Correlated Topic Model using tomotopy \n",
    "and visualize the correlation between topics.\n",
    "\n",
    "\n",
    "Required Packages:\n",
    "    nltk, sklearn, pyvis\n",
    "'''\n",
    "\n",
    "import pandas as pd\n",
    "import tomotopy as tp\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "import re\n",
    "from pyvis.network import Network\n",
    "\n",
    "nltk.download('stopwords')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a369607b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def listToString(s):\n",
    "    str1 = \"\"\n",
    "    for ele in s:\n",
    "        str1 += ele\n",
    " \n",
    "    return str1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ee5c3442",
   "metadata": {},
   "outputs": [],
   "source": [
    "#%pprint\n",
    "#stopwords.words('english')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e3ac96cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    corpus = tp.utils.Corpus.load('preprocessed_data_total_longest_10000.cps')\n",
    "except IOError:\n",
    "    porter_stemmer = nltk.PorterStemmer().stem\n",
    "    english_stops = set(porter_stemmer(w) for w in stopwords.words('english'))\n",
    "    pat = re.compile('^[a-z]{2,}$')\n",
    "    corpus = tp.utils.Corpus(\n",
    "        tokenizer=tp.utils.SimpleTokenizer(porter_stemmer), \n",
    "        stopwords=lambda x: x in english_stops or not pat.match(x)\n",
    "    )\n",
    "\n",
    "    data = pd.read_csv('data_total_longest_10000.csv')\n",
    "    data = data.dropna()\n",
    "    data = data.drop_duplicates()\n",
    "    data = data.values.tolist()\n",
    "    for d in range(len(data)):\n",
    "        data[d] = listToString(data[d])\n",
    "    \n",
    "    corpus.process(d.lower() for d in data)\n",
    "    corpus.save('preprocessed_data_total_longest_10000.cps')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c82f9782",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18467"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(corpus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "141f4cd4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num docs:18461, Num Vocabs:22651, Total Words:8456995\n",
      "Removed Top words:  peopl like get would use one studi make think time also thing year even say go know work gt need much could way http differ good see take realli mean still well effect actual lot mani want articl vaccin us\n"
     ]
    }
   ],
   "source": [
    "mdl = tp.CTModel(tw=tp.TermWeight.IDF, min_df=5, rm_top=40, k=39, corpus=corpus)\n",
    "mdl.train(0)\n",
    "\n",
    "# Since we have more than ten thousand of documents, \n",
    "# setting the `num_beta_sample` smaller value will not cause an inaccurate result.\n",
    "mdl.num_beta_sample = 5\n",
    "print('Num docs:{}, Num Vocabs:{}, Total Words:{}'.format(\n",
    "    len(mdl.docs), len(mdl.used_vocabs), mdl.num_words\n",
    "))\n",
    "print('Removed Top words: ', *mdl.removed_top_words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9155dc86",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's train the model\n",
    "for i in range(0, 1000, 20):\n",
    "    print('Iteration: {:04}, LL per word: {:.4}'.format(i, mdl.ll_per_word))\n",
    "    mdl.train(20)\n",
    "print('Iteration: {:04}, LL per word: {:.4}'.format(1000, mdl.ll_per_word))\n",
    "\n",
    "mdl.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dec61989",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Topic #0 scienc www com sourc org question\n",
      "Topic #1 infect immun viru protein genet diseas\n",
      "Topic #2 batteri data solar chang measur larg\n",
      "Topic #3 social school educ kid media american\n",
      "Topic #4 covid sexual person coffe issu case\n",
      "Topic #5 word languag look kind train hear\n",
      "Topic #6 probabl mayb read alreadi seem current\n",
      "Topic #7 muscl right look someth though abl\n",
      "Topic #8 diet fat weight pain vitamin exercis\n",
      "Topic #9 mask die life hous polic old\n",
      "Topic #10 societi trust experi religi religion physic\n",
      "Topic #11 matter come happen someth wikipedia en\n",
      "Topic #12 food product plastic oil carbon eat\n",
      "Topic #13 meat eat sleep day sugar drink\n",
      "Topic #14 cost money law trump pay price\n",
      "Topic #15 gun polit vote tax kid game\n",
      "Topic #16 vegan cat gov farm nih govern\n",
      "Topic #17 babi watch ask love ever attract\n",
      "Topic #18 smoke dog alcohol drug cannabi addict\n",
      "Topic #19 cultur face public partner decis state\n",
      "Topic #20 women men covid sex children depress\n",
      "Topic #21 water climat energi chang plant heat\n",
      "Topic #22 human name fast trait red high\n",
      "Topic #23 increas risk associ higher rate individu\n",
      "Topic #24 black light earth electr planet field\n",
      "Topic #25 cancer cell treatment global market patient\n",
      "Topic #26 brain human speci cognit memori function\n",
      "Topic #27 new research find show scientist may\n",
      "topic_network.html\n"
     ]
    }
   ],
   "source": [
    "# Let's visualize the result\n",
    "g = Network(width=800, height=800, font_color=\"#333\")\n",
    "correl = mdl.get_correlations().reshape([-1])\n",
    "correl.sort()\n",
    "top_tenth = mdl.k * (mdl.k - 1) // 10\n",
    "top_tenth = correl[-mdl.k - top_tenth]\n",
    "\n",
    "for k in range(mdl.k):\n",
    "    label = \"#{}\".format(k)\n",
    "    title= ' '.join(word for word, _ in mdl.get_topic_words(k, top_n=6))\n",
    "    print('Topic', label, title)\n",
    "    g.add_node(k, label=label, title=title, shape='ellipse')\n",
    "    for l, correlation in zip(range(k - 1), mdl.get_correlations(k)):\n",
    "        if correlation < top_tenth: continue\n",
    "        g.add_edge(k, l, value=float(correlation), title='{:.02}'.format(correlation))\n",
    "\n",
    "g.barnes_hut(gravity=-1000, spring_length=20)\n",
    "g.show_buttons()\n",
    "g.show(\"topic_network.html\", notebook=False)\n",
    "g.save_graph(\"graph_k_28.html\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5df80577",
   "metadata": {},
   "outputs": [],
   "source": [
    "mdl.save('data_total_longest_10000_k_39.bin')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a7835e0",
   "metadata": {},
   "source": [
    "# Coherence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b3547625",
   "metadata": {},
   "outputs": [],
   "source": [
    "import itertools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f52bff4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "mdl = tp.CTModel.load('../data_total_longest_10000_k_28.bin')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dc32ef0d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "\"\"\"\"for preset in ('u_mass', 'c_npmi', 'c_uci', 'c_v'):    \n",
    "    coh = tp.coherence.Coherence(mdl, coherence=preset)\n",
    "    average_coherence = coh.get_score()\n",
    "    coherence_per_topic = [coh.get_score(topic_id=k) for k in range(mdl.k)]\n",
    "    print('==== Coherence : {} ===='.format(preset))\n",
    "    print('Average:', average_coherence, '\\nPer Topic:', coherence_per_topic)\n",
    "    print()\"\"\"\n",
    "\n",
    "coh = tp.coherence.Coherence(mdl, coherence='c_v')\n",
    "average_coherence = coh.get_score()\n",
    "coherence_per_topic = [coh.get_score(topic_id=k) for k in range(mdl.k)]\n",
    "print('==== Coherence : {} ===='.format('c_v'))\n",
    "print('Average:', average_coherence, '\\nPer Topic:', coherence_per_topic)\n",
    "print()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c7e1cb5",
   "metadata": {},
   "source": [
    "# Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "88f484ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install pyLDAvis\n",
    "import pyLDAvis\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a4b93375",
   "metadata": {},
   "outputs": [],
   "source": [
    "topic_term_dists = np.stack([mdl.get_topic_word_dist(k) for k in range(mdl.k)])\n",
    "doc_topic_dists = np.stack([doc.get_topic_dist() for doc in mdl.docs])\n",
    "doc_topic_dists /= doc_topic_dists.sum(axis=1, keepdims=True)\n",
    "doc_lengths = np.array([len(doc.words) for doc in mdl.docs])\n",
    "vocab = list(mdl.used_vocabs)\n",
    "term_frequency = mdl.used_vocab_freq\n",
    "\n",
    "prepared_data = pyLDAvis.prepare(\n",
    "    topic_term_dists, \n",
    "    doc_topic_dists, \n",
    "    doc_lengths, \n",
    "    vocab, \n",
    "    term_frequency,\n",
    "    start_index=0,\n",
    "    sort_topics=False\n",
    ")\n",
    "pyLDAvis.save_html(prepared_data, 'ctmvis_k_28.html')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a3da6b4",
   "metadata": {},
   "source": [
    "# "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "411d29ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "330e4834",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = list(range(6,41))\n",
    "y = [0.3652, 0.3784, 0.4412, 0.4018, 0.433, 0.422, 0.3912, 0.4301, 0.4692, 0.5208, 0.4722, 0.5238, 0.5009, 0.5121, 0.4889, 0.4993, 0.5267, 0.5116, 0.3573, 0.5139, 0.5456, 0.5367, 0.5406, 0.4245, 0.3924, 0.4267, 0.5362, 0.5444, 0.5555, 0.524, 0.5414, 0.552, 0.5523, 0.5321, 0.54]\n",
    "\n",
    "plt.plot(x, y)\n",
    "plt.xlabel(\"Konu Sayısı\")\n",
    "plt.ylabel(\"Tutarlılık Değeri\")\n",
    "plt.show()\n",
    "#plt.savefig(\"coh.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "593ca6ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "mdl.get_correlations()   # 28"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0304c57f",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_corr = []\n",
    "for i in range(len(mdl.get_correlations())):\n",
    "    my_formatted_list = [ '%.2f' % elem for elem in mdl.get_correlations()[i] ]\n",
    "    new_corr.append(my_formatted_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2816b905",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c11ee9a1",
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "      <th>25</th>\n",
       "      <th>26</th>\n",
       "      <th>27</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.25</td>\n",
       "      <td>...</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.05</td>\n",
       "      <td>-0.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.13</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.34</td>\n",
       "      <td>...</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.21</td>\n",
       "      <td>0.42</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.42</td>\n",
       "      <td>...</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.24</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.46</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.46</td>\n",
       "      <td>...</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.27</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.52</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.25</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.54</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.51</td>\n",
       "      <td>...</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.27</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.53</td>\n",
       "      <td>...</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.27</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.57</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.54</td>\n",
       "      <td>...</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.22</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.49</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.51</td>\n",
       "      <td>...</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.25</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.51</td>\n",
       "      <td>1.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.27</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.57</td>\n",
       "      <td>...</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.28</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.55</td>\n",
       "      <td>...</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.21</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.46</td>\n",
       "      <td>...</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.23</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.51</td>\n",
       "      <td>...</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.27</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.51</td>\n",
       "      <td>...</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.24</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.28</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.53</td>\n",
       "      <td>...</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.25</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.52</td>\n",
       "      <td>...</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.21</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.49</td>\n",
       "      <td>...</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.21</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.46</td>\n",
       "      <td>...</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.16</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.40</td>\n",
       "      <td>...</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.17</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.40</td>\n",
       "      <td>...</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.29</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.23</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.49</td>\n",
       "      <td>...</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.45</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.12</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.40</td>\n",
       "      <td>...</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.45</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.15</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.35</td>\n",
       "      <td>...</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.02</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.26</td>\n",
       "      <td>...</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.05</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.30</td>\n",
       "      <td>...</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.23</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>-0.14</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.25</td>\n",
       "      <td>...</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.18</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>28 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       0     1     2     3     4     5     6     7     8     9   ...    18   \n",
       "0    1.00  0.13  0.21  0.24  0.27  0.25  0.27  0.27  0.22  0.25  ...  0.21  \\\n",
       "1    0.13  1.00  0.42  0.35  0.36  0.34  0.35  0.34  0.38  0.34  ...  0.29   \n",
       "2    0.21  0.42  1.00  0.46  0.44  0.45  0.45  0.43  0.40  0.42  ...  0.37   \n",
       "3    0.24  0.35  0.46  1.00  0.52  0.47  0.47  0.47  0.44  0.46  ...  0.44   \n",
       "4    0.27  0.36  0.44  0.52  1.00  0.54  0.53  0.50  0.46  0.50  ...  0.47   \n",
       "5    0.25  0.34  0.45  0.47  0.54  1.00  0.56  0.53  0.44  0.51  ...  0.46   \n",
       "6    0.27  0.35  0.45  0.47  0.53  0.56  1.00  0.57  0.47  0.53  ...  0.47   \n",
       "7    0.27  0.34  0.43  0.47  0.50  0.53  0.57  1.00  0.49  0.54  ...  0.49   \n",
       "8    0.22  0.38  0.40  0.44  0.46  0.44  0.47  0.49  1.00  0.51  ...  0.41   \n",
       "9    0.25  0.34  0.42  0.46  0.50  0.51  0.53  0.54  0.51  1.00  ...  0.49   \n",
       "10   0.27  0.36  0.45  0.48  0.51  0.51  0.53  0.52  0.50  0.57  ...  0.48   \n",
       "11   0.28  0.34  0.44  0.48  0.51  0.52  0.54  0.53  0.46  0.55  ...  0.50   \n",
       "12   0.21  0.31  0.39  0.40  0.43  0.43  0.45  0.47  0.40  0.46  ...  0.46   \n",
       "13   0.23  0.34  0.41  0.44  0.49  0.47  0.49  0.49  0.47  0.51  ...  0.48   \n",
       "14   0.27  0.32  0.42  0.47  0.49  0.49  0.51  0.50  0.43  0.51  ...  0.50   \n",
       "15   0.24  0.29  0.39  0.46  0.48  0.47  0.48  0.49  0.42  0.50  ...  0.48   \n",
       "16   0.28  0.32  0.41  0.46  0.51  0.51  0.52  0.51  0.44  0.53  ...  0.52   \n",
       "17   0.25  0.31  0.42  0.47  0.51  0.51  0.52  0.52  0.44  0.52  ...  0.50   \n",
       "18   0.21  0.29  0.37  0.44  0.47  0.46  0.47  0.49  0.41  0.49  ...  1.00   \n",
       "19   0.21  0.34  0.42  0.46  0.46  0.45  0.46  0.45  0.44  0.46  ...  0.43   \n",
       "20   0.16  0.34  0.31  0.41  0.44  0.38  0.39  0.37  0.44  0.40  ...  0.33   \n",
       "21   0.17  0.26  0.40  0.36  0.37  0.39  0.40  0.41  0.34  0.40  ...  0.35   \n",
       "22   0.23  0.37  0.45  0.44  0.47  0.50  0.52  0.49  0.44  0.49  ...  0.41   \n",
       "23   0.12  0.38  0.40  0.42  0.42  0.40  0.41  0.39  0.45  0.40  ...  0.33   \n",
       "24   0.15  0.26  0.37  0.31  0.33  0.37  0.37  0.36  0.32  0.35  ...  0.28   \n",
       "25   0.02  0.27  0.29  0.25  0.26  0.26  0.26  0.26  0.28  0.26  ...  0.20   \n",
       "26   0.05  0.29  0.33  0.28  0.30  0.31  0.32  0.31  0.30  0.30  ...  0.25   \n",
       "27  -0.14  0.28  0.33  0.27  0.26  0.29  0.29  0.28  0.26  0.25  ...  0.21   \n",
       "\n",
       "      19    20    21    22    23    24    25    26     27  \n",
       "0   0.21  0.16  0.17  0.23  0.12  0.15  0.02  0.05  -0.14  \n",
       "1   0.34  0.34  0.26  0.37  0.38  0.26  0.27  0.29   0.28  \n",
       "2   0.42  0.31  0.40  0.45  0.40  0.37  0.29  0.33   0.33  \n",
       "3   0.46  0.41  0.36  0.44  0.42  0.31  0.25  0.28   0.27  \n",
       "4   0.46  0.44  0.37  0.47  0.42  0.33  0.26  0.30   0.26  \n",
       "5   0.45  0.38  0.39  0.50  0.40  0.37  0.26  0.31   0.29  \n",
       "6   0.46  0.39  0.40  0.52  0.41  0.37  0.26  0.32   0.29  \n",
       "7   0.45  0.37  0.41  0.49  0.39  0.36  0.26  0.31   0.28  \n",
       "8   0.44  0.44  0.34  0.44  0.45  0.32  0.28  0.30   0.26  \n",
       "9   0.46  0.40  0.40  0.49  0.40  0.35  0.26  0.30   0.25  \n",
       "10  0.49  0.42  0.39  0.49  0.42  0.35  0.26  0.30   0.28  \n",
       "11  0.47  0.38  0.43  0.50  0.40  0.37  0.26  0.31   0.26  \n",
       "12  0.40  0.32  0.41  0.42  0.36  0.32  0.25  0.26   0.23  \n",
       "13  0.44  0.40  0.40  0.45  0.39  0.33  0.25  0.28   0.24  \n",
       "14  0.46  0.36  0.42  0.46  0.38  0.34  0.24  0.28   0.23  \n",
       "15  0.45  0.35  0.38  0.43  0.35  0.30  0.23  0.26   0.22  \n",
       "16  0.47  0.39  0.41  0.46  0.38  0.33  0.23  0.27   0.23  \n",
       "17  0.47  0.36  0.41  0.48  0.38  0.33  0.26  0.27   0.24  \n",
       "18  0.43  0.33  0.35  0.41  0.33  0.28  0.20  0.25   0.21  \n",
       "19  1.00  0.39  0.39  0.45  0.40  0.32  0.24  0.27   0.25  \n",
       "20  0.39  1.00  0.29  0.38  0.37  0.23  0.19  0.22   0.19  \n",
       "21  0.39  0.29  1.00  0.45  0.36  0.33  0.22  0.23   0.21  \n",
       "22  0.45  0.38  0.45  1.00  0.45  0.38  0.28  0.30   0.27  \n",
       "23  0.40  0.37  0.36  0.45  1.00  0.39  0.27  0.28   0.25  \n",
       "24  0.32  0.23  0.33  0.38  0.39  1.00  0.26  0.26   0.21  \n",
       "25  0.24  0.19  0.22  0.28  0.27  0.26  1.00  0.23   0.21  \n",
       "26  0.27  0.22  0.23  0.30  0.28  0.26  0.23  1.00   0.18  \n",
       "27  0.25  0.19  0.21  0.27  0.25  0.21  0.21  0.18   1.00  \n",
       "\n",
       "[28 rows x 28 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(new_corr)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "29c9e90d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_excel('corr_28.xlsx', index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4748bcee",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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