Song analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting package metadata (current_repodata.json): ...working... done\n",
      "Solving environment: ...working... done\n",
      "\n",
      "# All requested packages already installed.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!conda install -y nltk "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import collections\n",
    "import nltk\n",
    "import networkx as nx\n",
    "import xlsxwriter\n",
    "import matplotlib\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "showing info https://raw.githubusercontent.com/nltk/nltk_data/gh-pages/index.xml\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#nltk.download()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": [
     "outputPrepend"
    ]
   },
   "outputs": [],
   "source": [
    "    with open('data/dataset.txt') as file_:\n",
    "        songs = file_.readlines()\n",
    "    lines = []\n",
    "    for song in songs:\n",
    "        song = song.strip()\n",
    "        title, author, lyrics = song.split('\\t')\n",
    "        lines.append({\"title\":title, \"author\":author,\"lyrics\":lyrics.replace(\"\\\\\",\"\\n\")})\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['title', 'author', 'lyrics', 'tokens', 'counts'])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lines[0].keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(lines[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"Our life together is so precious together,\\nWe have grown - we have grown,\\nAlthough our love is still special,\\nLet's take our chance and fly away somewhere alone,\\nIt's been so long since we took the time,\\nNo-one's to blame,\\nI know time flies so quikly,\\nBut when I see you darling,\\nIt's like we both are falling in love again,\\nIt'll be just like starting over - starting over,\\nEveryday we used to make it love,\\nWhy can't we be making love nice and easy,\\nIt's time to spread our wing's and fly,\\nDon't let another day go by my love,\\nIt'll be just like starting over - starting over,\\nWhy don't we take off alone,\\nTake a trip far, far away,\\nWe'll be together on our own again,\\nLike we used to in the early days,\\nWell, well, well darling,\\nIt's been so long since we took the time,\\nNo-one's to blame,\\nI know time flies so quikly,\\nBut when I see you darling,\\nIt's like we both are falling in love again,\\nIt'll be just like starting over - starting over,\\nEveryday we used to make it love,\\nWhy can't we be making love nice and easy,\\nIt's time to spread our wing's and fly,\\nDon't let another day go by my love,\\nIt'll be just like starting over - starting over,\\nOur life together is so precious together,\\nWe have grown - we have grown,\\nAlthough our love is still special,\\nLet's take our chance and fly away somewhere alone.\""
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lines[0][\"lyrics\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'(Just Like) Starting Over'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lines[0][\"title\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# tokenize\n",
    "\n",
    "stop_words = set(nltk.corpus.stopwords.words('english'))\n",
    "\n",
    "for indx, line in enumerate(lines):\n",
    " \n",
    "    lines[indx][\"tokens\"] = nltk.word_tokenize(line[\"lyrics\"].lower())\n",
    "    # only alfanum\n",
    "    lines[indx][\"tokens\"] = [word for word in lines[indx][\"tokens\"] if word.isalnum()]\n",
    "\n",
    "    # remove stop wrord\n",
    "    lines[indx][\"tokens\"] = [word for word in lines[indx][\"tokens\"] if not word in stop_words] \n",
    "\n",
    "    #counts = dict()\n",
    "    #for size in 1, 2, 3: # count frequency, bigrams and trigrams\n",
    "    #    counts[size] = FreqDist(ngrams(lines[0][\"tokens\"], size))\n",
    "    counts = nltk.FreqDist(lines[indx][\"tokens\"])\n",
    "    \n",
    "    lines[indx][\"counts\"] = counts\n",
    "\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['life',\n",
       " 'together',\n",
       " 'precious',\n",
       " 'together',\n",
       " 'grown',\n",
       " 'grown',\n",
       " 'although',\n",
       " 'love',\n",
       " 'still',\n",
       " 'special',\n",
       " 'let',\n",
       " 'take',\n",
       " 'chance',\n",
       " 'fly',\n",
       " 'away',\n",
       " 'somewhere',\n",
       " 'alone',\n",
       " 'long',\n",
       " 'since',\n",
       " 'took',\n",
       " 'time',\n",
       " 'blame',\n",
       " 'know',\n",
       " 'time',\n",
       " 'flies',\n",
       " 'quikly',\n",
       " 'see',\n",
       " 'darling',\n",
       " 'like',\n",
       " 'falling',\n",
       " 'love',\n",
       " 'like',\n",
       " 'starting',\n",
       " 'starting',\n",
       " 'everyday',\n",
       " 'used',\n",
       " 'make',\n",
       " 'love',\n",
       " 'ca',\n",
       " 'making',\n",
       " 'love',\n",
       " 'nice',\n",
       " 'easy',\n",
       " 'time',\n",
       " 'spread',\n",
       " 'wing',\n",
       " 'fly',\n",
       " 'let',\n",
       " 'another',\n",
       " 'day',\n",
       " 'go',\n",
       " 'love',\n",
       " 'like',\n",
       " 'starting',\n",
       " 'starting',\n",
       " 'take',\n",
       " 'alone',\n",
       " 'take',\n",
       " 'trip',\n",
       " 'far',\n",
       " 'far',\n",
       " 'away',\n",
       " 'together',\n",
       " 'like',\n",
       " 'used',\n",
       " 'early',\n",
       " 'days',\n",
       " 'well',\n",
       " 'well',\n",
       " 'well',\n",
       " 'darling',\n",
       " 'long',\n",
       " 'since',\n",
       " 'took',\n",
       " 'time',\n",
       " 'blame',\n",
       " 'know',\n",
       " 'time',\n",
       " 'flies',\n",
       " 'quikly',\n",
       " 'see',\n",
       " 'darling',\n",
       " 'like',\n",
       " 'falling',\n",
       " 'love',\n",
       " 'like',\n",
       " 'starting',\n",
       " 'starting',\n",
       " 'everyday',\n",
       " 'used',\n",
       " 'make',\n",
       " 'love',\n",
       " 'ca',\n",
       " 'making',\n",
       " 'love',\n",
       " 'nice',\n",
       " 'easy',\n",
       " 'time',\n",
       " 'spread',\n",
       " 'wing',\n",
       " 'fly',\n",
       " 'let',\n",
       " 'another',\n",
       " 'day',\n",
       " 'go',\n",
       " 'love',\n",
       " 'like',\n",
       " 'starting',\n",
       " 'starting',\n",
       " 'life',\n",
       " 'together',\n",
       " 'precious',\n",
       " 'together',\n",
       " 'grown',\n",
       " 'grown',\n",
       " 'although',\n",
       " 'love',\n",
       " 'still',\n",
       " 'special',\n",
       " 'let',\n",
       " 'take',\n",
       " 'chance',\n",
       " 'fly',\n",
       " 'away',\n",
       " 'somewhere',\n",
       " 'alone']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lines[0][\"tokens\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FreqDist({'love': 10, 'starting': 8, 'like': 7, 'time': 6, 'together': 5, 'grown': 4, 'let': 4, 'take': 4, 'fly': 4, 'away': 3, ...})"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lines[0][\"counts\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('love', 10), ('starting', 8), ('like', 7), ('time', 6), ('together', 5)]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lines[0][\"counts\"].most_common(5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x14ba67df130>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lines[0][\"counts\"].plot(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('nothing', 12), ('gon', 12), ('na', 12), ('change', 12), ('world', 12)]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lines[5][\"counts\"].most_common(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "126"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lines[0][\"counts\"].N()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "tags": [
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend"
    ]
   },
   "outputs": [],
   "source": [
    "# create Lars magne table\n",
    "# word counter song\n",
    "\n",
    "lmtablesheet = {}\n",
    "lmtablesheet[\"sheet1\"] = []\n",
    "lmtablesheet[\"sheet2\"] = []\n",
    "\n",
    "# Sheet number 1\n",
    "lmtablesheet[\"sheet1\"].append([\"id\",\"Name\"])\n",
    "\n",
    "for indx, line in enumerate(lines):\n",
    "    lmtablesheet[\"sheet1\"].append([indx,line[\"title\"]])\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sheet number 2\n",
    "\n",
    "lmtablesheet[\"sheet2\"].append([\"Source\",\"Target\",\"Type\",\"Weigth\"])\n",
    "\n",
    "# adding a connect if both songs contains a word\n",
    "for indx1,song1 in  enumerate(lines):\n",
    "    for i in song1[\"counts\"]:\n",
    "        if song1[\"counts\"][i] > 4: # must be mentioned more than this\n",
    "            for indx2,song2 in  enumerate(lines):\n",
    "                for j in song2[\"counts\"]:\n",
    "                    if song2[\"counts\"][j] > 4: # must be mentioned more than this\n",
    "                        if (j == i) and (indx1 != indx2): # found both places\n",
    "                            lmtablesheet[\"sheet2\"].append([indx1,indx2,\"Undirected\",song1[\"counts\"][i]*song2[\"counts\"][j]])\n",
    "                            \n",
    "\n",
    "                           "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "tags": [
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend"
    ]
   },
   "outputs": [],
   "source": [
    "\n",
    "workbook = xlsxwriter.Workbook('data/songs.xlsx')\n",
    "worksheet1 = workbook.add_worksheet()\n",
    "worksheet2 = workbook.add_worksheet()\n",
    "\n",
    "# Start from the first cell. Rows and columns are zero indexed.\n",
    "row = 0\n",
    "col = 0\n",
    "for [id,name] in lmtablesheet[\"sheet1\"]:\n",
    "    worksheet1.write(row, col,     id)\n",
    "    worksheet1.write(row, col + 1, name)\n",
    "    row += 1\n",
    "    \n",
    "\n",
    "row = 0\n",
    "col = 0\n",
    "for [source,target,types,weigth] in lmtablesheet[\"sheet2\"]:\n",
    "    worksheet2.write(row, col,  source   )\n",
    "    worksheet2.write(row, col + 1, target)\n",
    "    worksheet2.write(row, col + 2, types)\n",
    "    worksheet2.write(row, col + 3, weigth)\n",
    "    row += 1 \n",
    "\n",
    "\n",
    "\n",
    "workbook.close()\n",
    "\n",
    "# Write each dataframe to a different worksheet.\n",
    "\n",
    "\n",
    "# Close the Pandas Excel writer and output the Excel file.\n",
    "#writer.save()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "# another type of table\n",
    "lmtable = []\n",
    "for song in lines:\n",
    "    for j in song[\"counts\"]:\n",
    "        \n",
    "        lmtable.append({\"word\":j, \"count\":song[\"counts\"][j], \"title\":song[\"title\"]})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'word': 'life', 'count': 2, 'title': '(Just Like) Starting Over'},\n",
       " {'word': 'together', 'count': 5, 'title': '(Just Like) Starting Over'},\n",
       " {'word': 'precious', 'count': 2, 'title': '(Just Like) Starting Over'},\n",
       " {'word': 'grown', 'count': 4, 'title': '(Just Like) Starting Over'},\n",
       " {'word': 'although', 'count': 2, 'title': '(Just Like) Starting Over'},\n",
       " {'word': 'love', 'count': 10, 'title': '(Just Like) Starting Over'},\n",
       " {'word': 'still', 'count': 2, 'title': '(Just Like) Starting Over'},\n",
       " {'word': 'special', 'count': 2, 'title': '(Just Like) Starting Over'},\n",
       " {'word': 'let', 'count': 4, 'title': '(Just Like) Starting Over'},\n",
       " {'word': 'take', 'count': 4, 'title': '(Just Like) Starting Over'}]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lmtable[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "tags": [
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend"
    ]
   },
   "outputs": [],
   "source": [
    "G = nx.Graph()\n",
    "# adding all songs as nodes\n",
    "for song in lines:\n",
    "    G.add_node(song[\"title\"])\n",
    "\n",
    "\n",
    "# adding a connect if both songs contains a word\n",
    "for song1 in lines:\n",
    "    for i in song1[\"counts\"]:\n",
    "        if song1[\"counts\"][i] > 4: # must be mentioned more than this\n",
    "            for song2 in lines:\n",
    "                for j in song2[\"counts\"]:\n",
    "                    if song2[\"counts\"][j] > 2: # must be mentioned more than this\n",
    "                        if j == i: # found both places\n",
    "                            G.add_edge(song1[\"title\"],song2[\"title\"])\n",
    "    \n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['figure.figsize'] = [20, 20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nx.draw(G, with_labels=True, font_weight='bold') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Being For The Benefit Of Mr. Kite!',\n",
       " 'Cleanup Time',\n",
       " 'Flying',\n",
       " 'Jealous guy',\n",
       " 'Lady Madonna',\n",
       " 'Maggie Mae',\n",
       " 'Norwegian Wood (This bird has flown)',\n",
       " 'Revolution 9',\n",
       " 'The End',\n",
       " 'Tomorrow Never Knows']"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# list non-connected nodes\n",
    "list(nx.isolates(G))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "file_extension": ".py",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2"
  },
  "mimetype": "text/x-python",
  "name": "python",
  "npconvert_exporter": "python",
  "pygments_lexer": "ipython3",
  "version": 3
 },
 "nbformat": 4,
 "nbformat_minor": 2
}