{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Logo 1](https://git.wmi.amu.edu.pl/AITech/Szablon/raw/branch/master/Logotyp_AITech1.jpg)\n",
    "<div class=\"alert alert-block alert-info\">\n",
    "<h1> Ekstrakcja informacji </h1>\n",
    "<h2> 0. <i>Jezyk</i>  [ćwiczenia]</h2> \n",
    "<h3> Jakub Pokrywka (2022)</h3>\n",
    "</div>\n",
    "\n",
    "![Logo 2](https://git.wmi.amu.edu.pl/AITech/Szablon/raw/branch/master/Logotyp_AITech2.jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [],
   "source": [
    "NR_INDEKSU = 375985"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "import plotly.express as px\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import nltk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "program  :  program\n",
      "programs  :  program\n",
      "programmer  :  programm\n",
      "programming  :  program\n",
      "programmers  :  programm\n"
     ]
    }
   ],
   "source": [
    "ps = nltk.stem.PorterStemmer()\n",
    "\n",
    "for w in [\"program\", \"programs\", \"programmer\", \"programming\", \"programmers\"]:\n",
    "    print(w, \" : \", ps.stem(w))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to /home/kuba/nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n",
      "[nltk_data] Downloading package stopwords to /home/kuba/nltk_data...\n",
      "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.download('punkt')\n",
    "nltk.download('stopwords')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Python',\n",
       " 'is',\n",
       " 'dynamically-typed',\n",
       " 'and',\n",
       " 'garbage-collected',\n",
       " '.',\n",
       " 'It',\n",
       " 'supports',\n",
       " 'multiple',\n",
       " 'programming',\n",
       " 'paradigms',\n",
       " ',',\n",
       " 'including',\n",
       " 'structured',\n",
       " '(',\n",
       " 'particularly',\n",
       " ',',\n",
       " 'procedural',\n",
       " ')',\n",
       " ',',\n",
       " 'object-oriented',\n",
       " 'and',\n",
       " 'functional',\n",
       " 'programming',\n",
       " '.',\n",
       " 'It',\n",
       " 'is',\n",
       " 'often',\n",
       " 'described',\n",
       " 'as',\n",
       " 'a',\n",
       " '``',\n",
       " 'batteries',\n",
       " 'included',\n",
       " \"''\",\n",
       " 'language',\n",
       " 'due',\n",
       " 'to',\n",
       " 'its',\n",
       " 'comprehensive',\n",
       " 'standard',\n",
       " 'library',\n",
       " '.']"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text = \"\"\"Python is dynamically-typed and garbage-collected. It supports multiple programming paradigms, including structured (particularly, procedural), object-oriented and functional programming. It is often described as a \"batteries included\" language due to its comprehensive standard library.\"\"\"\n",
    "nltk.tokenize.word_tokenize(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Python is dynamically-typed and garbage-collected.',\n",
       " 'It supports multiple programming paradigms, including structured (particularly, procedural), object-oriented and functional programming.',\n",
       " 'It is often described as a \"batteries included\" language due to its comprehensive standard library.']"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.tokenize.sent_tokenize(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['aber',\n",
       " 'alle',\n",
       " 'allem',\n",
       " 'allen',\n",
       " 'aller',\n",
       " 'alles',\n",
       " 'als',\n",
       " 'also',\n",
       " 'am',\n",
       " 'an',\n",
       " 'ander',\n",
       " 'andere',\n",
       " 'anderem',\n",
       " 'anderen',\n",
       " 'anderer',\n",
       " 'anderes',\n",
       " 'anderm',\n",
       " 'andern',\n",
       " 'anderr',\n",
       " 'anders',\n",
       " 'auch',\n",
       " 'auf',\n",
       " 'aus',\n",
       " 'bei',\n",
       " 'bin',\n",
       " 'bis',\n",
       " 'bist',\n",
       " 'da',\n",
       " 'damit',\n",
       " 'dann',\n",
       " 'der',\n",
       " 'den',\n",
       " 'des',\n",
       " 'dem',\n",
       " 'die',\n",
       " 'das',\n",
       " 'dass',\n",
       " 'daß',\n",
       " 'derselbe',\n",
       " 'derselben',\n",
       " 'denselben',\n",
       " 'desselben',\n",
       " 'demselben',\n",
       " 'dieselbe',\n",
       " 'dieselben',\n",
       " 'dasselbe',\n",
       " 'dazu',\n",
       " 'dein',\n",
       " 'deine',\n",
       " 'deinem',\n",
       " 'deinen',\n",
       " 'deiner',\n",
       " 'deines',\n",
       " 'denn',\n",
       " 'derer',\n",
       " 'dessen',\n",
       " 'dich',\n",
       " 'dir',\n",
       " 'du',\n",
       " 'dies',\n",
       " 'diese',\n",
       " 'diesem',\n",
       " 'diesen',\n",
       " 'dieser',\n",
       " 'dieses',\n",
       " 'doch',\n",
       " 'dort',\n",
       " 'durch',\n",
       " 'ein',\n",
       " 'eine',\n",
       " 'einem',\n",
       " 'einen',\n",
       " 'einer',\n",
       " 'eines',\n",
       " 'einig',\n",
       " 'einige',\n",
       " 'einigem',\n",
       " 'einigen',\n",
       " 'einiger',\n",
       " 'einiges',\n",
       " 'einmal',\n",
       " 'er',\n",
       " 'ihn',\n",
       " 'ihm',\n",
       " 'es',\n",
       " 'etwas',\n",
       " 'euer',\n",
       " 'eure',\n",
       " 'eurem',\n",
       " 'euren',\n",
       " 'eurer',\n",
       " 'eures',\n",
       " 'für',\n",
       " 'gegen',\n",
       " 'gewesen',\n",
       " 'hab',\n",
       " 'habe',\n",
       " 'haben',\n",
       " 'hat',\n",
       " 'hatte',\n",
       " 'hatten',\n",
       " 'hier',\n",
       " 'hin',\n",
       " 'hinter',\n",
       " 'ich',\n",
       " 'mich',\n",
       " 'mir',\n",
       " 'ihr',\n",
       " 'ihre',\n",
       " 'ihrem',\n",
       " 'ihren',\n",
       " 'ihrer',\n",
       " 'ihres',\n",
       " 'euch',\n",
       " 'im',\n",
       " 'in',\n",
       " 'indem',\n",
       " 'ins',\n",
       " 'ist',\n",
       " 'jede',\n",
       " 'jedem',\n",
       " 'jeden',\n",
       " 'jeder',\n",
       " 'jedes',\n",
       " 'jene',\n",
       " 'jenem',\n",
       " 'jenen',\n",
       " 'jener',\n",
       " 'jenes',\n",
       " 'jetzt',\n",
       " 'kann',\n",
       " 'kein',\n",
       " 'keine',\n",
       " 'keinem',\n",
       " 'keinen',\n",
       " 'keiner',\n",
       " 'keines',\n",
       " 'können',\n",
       " 'könnte',\n",
       " 'machen',\n",
       " 'man',\n",
       " 'manche',\n",
       " 'manchem',\n",
       " 'manchen',\n",
       " 'mancher',\n",
       " 'manches',\n",
       " 'mein',\n",
       " 'meine',\n",
       " 'meinem',\n",
       " 'meinen',\n",
       " 'meiner',\n",
       " 'meines',\n",
       " 'mit',\n",
       " 'muss',\n",
       " 'musste',\n",
       " 'nach',\n",
       " 'nicht',\n",
       " 'nichts',\n",
       " 'noch',\n",
       " 'nun',\n",
       " 'nur',\n",
       " 'ob',\n",
       " 'oder',\n",
       " 'ohne',\n",
       " 'sehr',\n",
       " 'sein',\n",
       " 'seine',\n",
       " 'seinem',\n",
       " 'seinen',\n",
       " 'seiner',\n",
       " 'seines',\n",
       " 'selbst',\n",
       " 'sich',\n",
       " 'sie',\n",
       " 'ihnen',\n",
       " 'sind',\n",
       " 'so',\n",
       " 'solche',\n",
       " 'solchem',\n",
       " 'solchen',\n",
       " 'solcher',\n",
       " 'solches',\n",
       " 'soll',\n",
       " 'sollte',\n",
       " 'sondern',\n",
       " 'sonst',\n",
       " 'über',\n",
       " 'um',\n",
       " 'und',\n",
       " 'uns',\n",
       " 'unsere',\n",
       " 'unserem',\n",
       " 'unseren',\n",
       " 'unser',\n",
       " 'unseres',\n",
       " 'unter',\n",
       " 'viel',\n",
       " 'vom',\n",
       " 'von',\n",
       " 'vor',\n",
       " 'während',\n",
       " 'war',\n",
       " 'waren',\n",
       " 'warst',\n",
       " 'was',\n",
       " 'weg',\n",
       " 'weil',\n",
       " 'weiter',\n",
       " 'welche',\n",
       " 'welchem',\n",
       " 'welchen',\n",
       " 'welcher',\n",
       " 'welches',\n",
       " 'wenn',\n",
       " 'werde',\n",
       " 'werden',\n",
       " 'wie',\n",
       " 'wieder',\n",
       " 'will',\n",
       " 'wir',\n",
       " 'wird',\n",
       " 'wirst',\n",
       " 'wo',\n",
       " 'wollen',\n",
       " 'wollte',\n",
       " 'würde',\n",
       " 'würden',\n",
       " 'zu',\n",
       " 'zum',\n",
       " 'zur',\n",
       " 'zwar',\n",
       " 'zwischen']"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.corpus.stopwords.words('german')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('Python', 'is'), ('is', 'dynamically-typed'), ('dynamically-typed', 'and'), ('and', 'garbage-collected'), ('garbage-collected', '.'), ('.', 'It'), ('It', 'supports'), ('supports', 'multiple'), ('multiple', 'programming'), ('programming', 'paradigms'), ('paradigms', ','), (',', 'including'), ('including', 'structured'), ('structured', '('), ('(', 'particularly'), ('particularly', ','), (',', 'procedural'), ('procedural', ')'), (')', ','), (',', 'object-oriented'), ('object-oriented', 'and'), ('and', 'functional'), ('functional', 'programming'), ('programming', '.'), ('.', 'It'), ('It', 'is'), ('is', 'often'), ('often', 'described'), ('described', 'as'), ('as', 'a'), ('a', '``'), ('``', 'batteries'), ('batteries', 'included'), ('included', \"''\"), (\"''\", 'language'), ('language', 'due'), ('due', 'to'), ('to', 'its'), ('its', 'comprehensive'), ('comprehensive', 'standard'), ('standard', 'library'), ('library', '.')]\n"
     ]
    }
   ],
   "source": [
    "nltk_tokens = nltk.word_tokenize(text)\n",
    "print(list(nltk.bigrams(nltk_tokens)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.DataFrame([[random.choice(['ang','polski','hiszp']), np.random.geometric(0.2)]  for i in range(5000) ], columns=['jezyk', 'dlugosc'])\n",
    "fig = px.histogram(df, x=\"dlugosc\",facet_row='jezyk',nbins=50, hover_data=df.columns)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "?px.histogram"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ZADANIE 1 \n",
    "\n",
    "ZNAJDŹ PRZYKŁAD TEKSTÓW  Z TEJ SAMEJ DOMENY 1_000_000 słów (20 punktów):\n",
    "- język angielski \n",
    "- język polski\n",
    "- język z rodziny romańskich\n",
    "\n",
    "Narzędzia:\n",
    "- nltk, plotly express\n",
    "\n",
    "\n",
    "Dla każdego z języków:\n",
    "- policz ilosć unikalnych lowercase słów (ze stemmingiem i bez)\n",
    "- policz ilosć znaków\n",
    "- policz ilosć unikalnych znaków\n",
    "- policz ilosć zdań zdań\n",
    "- policz ilosć unikalnych zdań\n",
    "- podaj min, max, średnią oraz medianę ilości znaków w słowie \n",
    "- podaj min, max, średnią oraz medianę ilości słów w zdaniu\n",
    "- wygeneruj word cloud (normalnie i po usunięciu stopwordów)\n",
    "- wypisz 20 najbardziej popularnych słów (normalnie i po usunięciu stopwordów) (lowercase)\n",
    "- wypisz 20 najbardziej popularnych bigramów (normalnie i po usunięciu stopwordów)\n",
    "- narysuj wykres częstotliwości słów (histogram) w taki sposób żeby był maksymalnie czytelny, wypróbuj skali logarytmicznej x, y, usuwanie słów poniżej limitu wystąpień itp.  \n",
    "- dla próbki 10000 zdań sprawdź jak często langdetect https://pypi.org/project/langdetect/ się myli i jakie języki odgaduje \n",
    "- zilustruj prawo zipfa ( px.line z zaznaczonymi punktami)\n",
    "- napisz wnioski (20-50 zdań)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### START ZADANIA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### KONIEC ZADANIA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ZADANIE\n",
    "\n",
    "Weź teksty w języku polskim (mają składać sie po 5 osobnych dokumentów każdy:\n",
    "- tekst prawny\n",
    "- tekst naukowy\n",
    "- tekst z polskiego z powieści (np. wolne lektury)\n",
    "- tekst z polskiego internetu (reddit, wykop, komentarze)\n",
    "- transkrypcja tekstu mówionego\n",
    "\n",
    "\n",
    "- zilustruj gunning_fog INDEX z https://pypi.org/project/textstat/ (oś y) i średnią długość zdania (oś x) na jednym wykresie narysuj dla wszystkich tekstów na jednym wykresie , domeny oznacz kolorami (px.scatter)\n",
    "- zilustruj prawo Heap'a dla wszystkich tekstów na jednym wykresie, domeny oznacz kolorami (px.scatter)\n",
    "- napisz wnioski (20-50 zdań)\n",
    "\n",
    "NAPISZ WNIOSKI\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### START ZADANIA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### KONIEC ZADANIA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## WYKONANIE ZADAŃ\n",
    "Zgodnie z instrukcją 01_Kodowanie_tekstu.ipynb"
   ]
  }
 ],
 "metadata": {
  "author": "Jakub Pokrywka",
  "email": "kubapok@wmi.amu.edu.pl",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "lang": "pl",
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  },
  "subtitle": "0.Informacje na temat przedmiotu[ćwiczenia]",
  "title": "Ekstrakcja informacji",
  "year": "2021"
 },
 "nbformat": 4,
 "nbformat_minor": 4
}