12.TextMining_TopicExtraction_3z3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Warsztaty Python w Data Science\n",
    "\n",
    "---\n",
    "## Text Mining - część 3 z 3  \n",
    "\n",
    "### Preprocessing\n",
    "### Ekstrakcja tematów\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Czynniki wpływające na sukces ekstrakcji tematu\n",
    "\n",
    "1. Jakość wstępnej obróbki\n",
    "2. Róznorodnośc danych\n",
    "3. Długo, długo nic ...\n",
    "4. Dobór algorytmu\n",
    "5. Parametryzacja algorytmu"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://scikit-learn.org/stable/modules/generated/sklearn.datasets.fetch_20newsgroups.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`import nltk` \n",
    "\n",
    "`nltk.download('stopwords')`\n",
    "\n",
    "`nltk.download('wordnet')`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gzip\n",
    "import pandas as pd\n",
    "\n",
    "df = pd.read_json(\n",
    "         gzip.open('data/newsgroups.json.gz', 'rt', encoding='utf-8')\n",
    ")\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.target_names.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "example = df['content'][1]\n",
    "example\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_data = []\n",
    "\n",
    "for row in df.iterrows():\n",
    "    raw_data.append(row[1]['content'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_data[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.corpus import stopwords\n",
    "\n",
    "stop_words = stopwords.words('english')\n",
    "len(stop_words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(stop_words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "stop_words.extend(['from', 'subject', 're', 'edu', 'use'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nltk\n",
    "from nltk.corpus import wordnet as wn\n",
    "from nltk.stem.wordnet import WordNetLemmatizer\n",
    "\n",
    "wn_lemmatizer = WordNetLemmatizer()\n",
    "    \n",
    "def lemmatize(word):\n",
    "    return wn_lemmatizer.lemmatize(word)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lemmatize('automata')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import spacy\n",
    "spacy.load('en_core_web_sm')\n",
    "from spacy.lang.en import English\n",
    "parser = English()\n",
    "\n",
    "def tokenize(text):\n",
    "    result = []\n",
    "    tokens = parser(text)\n",
    "    for token in tokens:\n",
    "        if token.orth_.isspace():\n",
    "            continue\n",
    "        elif token.like_url:\n",
    "            result.append('URL')\n",
    "        elif token.orth_.startswith('@'):\n",
    "            result.append('SCREEN_NAME')\n",
    "        else:\n",
    "            result.append(token.lower_)\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(tokenize(example))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocessing(text):\n",
    "    tokens = tokenize(text)\n",
    "    tokens = [token for token in tokens if len(token) > 4]\n",
    "    tokens = [token for token in tokens if len(token) < 14]\n",
    "    tokens = [token for token in tokens if token not in stop_words]\n",
    "    tokens = [lemmatize(token) for token in tokens]\n",
    "    return tokens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(preprocessing(example))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "\n",
    "# Zmień z Markdown na Code\n",
    "\n",
    "text_data = [ preprocessing(text) for text in raw_data ]\n",
    "\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Pikluj co się da!\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "pickle.dump(text_data, open('data/text_data.pkl', 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "text_data = pickle.load(open('data/text_data.pkl', 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "from gensim import corpora\n",
    "\n",
    "dictionary = corpora.Dictionary(text_data)\n",
    "corpus = [dictionary.doc2bow(text) for text in text_data]\n",
    "\n",
    "pickle.dump(corpus, open('data\\corpus.pkl', 'wb'))\n",
    "dictionary.save('data\\dictionary.gensim')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<>:3: DeprecationWarning: invalid escape sequence \\c\n",
      "<>:4: DeprecationWarning: invalid escape sequence \\d\n",
      "<>:3: DeprecationWarning: invalid escape sequence \\c\n",
      "<>:4: DeprecationWarning: invalid escape sequence \\d\n",
      "<>:3: DeprecationWarning: invalid escape sequence \\c\n",
      "<>:4: DeprecationWarning: invalid escape sequence \\d\n",
      "<ipython-input-7-29ca36d5a6f8>:3: DeprecationWarning: invalid escape sequence \\c\n",
      "  corpus = pickle.load(open('data\\corpus.pkl', 'rb'))\n",
      "<ipython-input-7-29ca36d5a6f8>:4: DeprecationWarning: invalid escape sequence \\d\n",
      "  dictionary = pickle.load(open('data\\dictionary.gensim', 'rb'))\n"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "\n",
    "corpus = pickle.load(open('data\\corpus.pkl', 'rb'))\n",
    "dictionary = pickle.load(open('data\\dictionary.gensim', 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gensim\n",
    "NUM_TOPICS = 5\n",
    "\n",
    "# ldamodel = gensim.models.ldamodel.LdaModel(corpus, num_topics = NUM_TOPICS, id2word=dictionary, passes=15)\n",
    "ldamodel = gensim.models.ldamulticore.LdaModel(corpus, num_topics = NUM_TOPICS, id2word=dictionary, passes=15)\n",
    "ldamodel.save('data/model5.gensim')\n",
    "topics = ldamodel.print_topics(num_words=4)\n",
    "for topic in topics:\n",
    "    print(topic)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "\n",
    "# Zmień z Markdown na Code\n",
    "\n",
    "import gensim\n",
    "NUM_TOPICS = 20\n",
    "\n",
    "# ldamodel = gensim.models.ldamodel.LdaModel(corpus, num_topics = NUM_TOPICS, id2word=dictionary, passes=15)\n",
    "ldamodel = gensim.models.ldamulticore.LdaModel(corpus, num_topics = NUM_TOPICS, id2word=dictionary, passes=15)\n",
    "ldamodel.save('data/model20.gensim')\n",
    "topics = ldamodel.print_topics(num_words=4)\n",
    "for topic in topics:\n",
    "    print(topic)\n",
    "    \n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sesa443784\\AppData\\Local\\Continuum\\anaconda3\\envs\\py3k\\lib\\site-packages\\scipy\\sparse\\sparsetools.py:21: DeprecationWarning: `scipy.sparse.sparsetools` is deprecated!\n",
      "scipy.sparse.sparsetools is a private module for scipy.sparse, and should not be used.\n",
      "  _deprecated()\n"
     ]
    }
   ],
   "source": [
    "import gensim\n",
    "\n",
    "ldamodel = gensim.models.ldamodel.LdaModel.load('data/model20.gensim')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "topics = ldamodel.print_topics(num_words=8)\n",
    "for topic in topics:\n",
    "    print(topic)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_doc = 'Data science is a new technology that uses statistics and computer science'\n",
    "new_doc = preprocessing(new_doc)\n",
    "new_doc_bow = dictionary.doc2bow(new_doc)\n",
    "\n",
    "print(ldamodel.get_document_topics(new_doc_bow))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rez = ldamodel.get_document_topics(new_doc_bow)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rez.sort(key=lambda x: x[1], reverse=True)\n",
    "rez"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'preprocessing' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-19-3bca1ff4f74f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mexample_doc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpreprocessing\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mexample\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mexample_bow\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdictionary\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdoc2bow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnew_doc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mldamodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_document_topics\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mexample_bow\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'preprocessing' is not defined"
     ]
    }
   ],
   "source": [
    "example_doc = preprocessing(example)\n",
    "example_bow = dictionary.doc2bow(new_doc)\n",
    "print(ldamodel.get_document_topics(example_bow))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sesa443784\\AppData\\Local\\Continuum\\anaconda3\\envs\\py3k\\lib\\site-packages\\joblib\\numpy_pickle.py:103: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\n",
      "  pickler.file_handle.write(chunk.tostring('C'))\n",
      "C:\\Users\\sesa443784\\AppData\\Local\\Continuum\\anaconda3\\envs\\py3k\\lib\\site-packages\\joblib\\numpy_pickle.py:103: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\n",
      "  pickler.file_handle.write(chunk.tostring('C'))\n"
     ]
    },
    {
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\"without\", \"without\", \"without\", \"without\", \"without\", \"woman\", \"woman\", \"woman\", \"woman\", \"woman\", \"worcester\", \"workspace\", \"workstation\", \"workstation\", \"workstation\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"worship\", \"would\", \"would\", \"would\", \"would\", \"would\", \"would\", \"would\", \"would\", \"would\", \"would\", \"would\", \"would\", \"would\", \"would\", \"would\", \"would\", \"would\", \"wpi.edu\", \"wpi.edu\", \"wright\", \"wright\", \"wright\", \"writes\", \"writes\", \"writes\", \"writes\", \"writes\", \"writes\", \"writes\", \"writes\", \"writes\", \"writes\", \"writes\", \"writes\", \"writes\", \"writes\", \"writes\", \"wysiwyg\", \"x#define\", \"x11r4\", \"x11r4\", \"xfree86\", \"xfree86\", \"xterm\", \"xterm\", \"xterm\", \"xview\", \"xvoid\", \"yalcin\", \"yamaha\", \"yank\", \"yankee\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"yeast\", \"zionist\", \"zoology\", \"zx-11\", \"|_____|/\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [17, 19, 5, 11, 6, 13, 20, 8, 15, 18, 3, 16, 12, 1, 2, 14, 4, 9, 7, 10]};\n",
       "\n",
       "function LDAvis_load_lib(url, callback){\n",
       "  var s = document.createElement('script');\n",
       "  s.src = url;\n",
       "  s.async = true;\n",
       "  s.onreadystatechange = s.onload = callback;\n",
       "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
       "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "}\n",
       "\n",
       "if(typeof(LDAvis) !== \"undefined\"){\n",
       "   // already loaded: just create the visualization\n",
       "   !function(LDAvis){\n",
       "       new LDAvis(\"#\" + \"ldavis_el2504021581494001362828730103\", ldavis_el2504021581494001362828730103_data);\n",
       "   }(LDAvis);\n",
       "}else if(typeof define === \"function\" && define.amd){\n",
       "   // require.js is available: use it to load d3/LDAvis\n",
       "   require.config({paths: {d3: \"https://d3js.org/d3.v5\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      LDAvis_load_lib(\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.3.0/pyLDAvis/js/ldavis.v3.0.0.js\", function(){\n",
       "        new LDAvis(\"#\" + \"ldavis_el2504021581494001362828730103\", ldavis_el2504021581494001362828730103_data);\n",
       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & LDAvis\n",
       "    LDAvis_load_lib(\"https://d3js.org/d3.v5.js\", function(){\n",
       "         LDAvis_load_lib(\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.3.0/pyLDAvis/js/ldavis.v3.0.0.js\", function(){\n",
       "                 new LDAvis(\"#\" + \"ldavis_el2504021581494001362828730103\", ldavis_el2504021581494001362828730103_data);\n",
       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pyLDAvis\n",
    "from pyLDAvis import gensim as ldavisgensim\n",
    "\n",
    "v = ldavisgensim.prepare(ldamodel, corpus, dictionary)\n",
    "pyLDAvis.display(v)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`!pip install pyldavis==2.1.0`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "#Zadanie 1.\n",
    "\n",
    "## Zbudowac wyszukiwarkę \n",
    "    1. Zbudowac model korpusu\n",
    "    2. "
   ]
  }
 ],
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