{
 "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> 3. <i>tfidf (1)</i>  [ćwiczenia]</h2> \n",
    "<h3> Jakub Pokrywka (2021)</h3>\n",
    "</div>\n",
    "\n",
    "![Logo 2](https://git.wmi.amu.edu.pl/AITech/Szablon/raw/branch/master/Logotyp_AITech2.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Zajęcia 3\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import re"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## zbiór dokumentów"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "documents = ['Ala lubi zwierzęta i ma kota oraz psa!',\n",
    "             'Ola lubi zwierzęta oraz ma kota a także chomika!',\n",
    "             'I Jan jeździ na rowerze.',\n",
    "             '2 wojna światowa była wielkim konfliktem zbrojnym',\n",
    "             'Tomek lubi psy, ma psa  i jeździ na motorze i rowerze.',\n",
    "            ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CZEGO CHCEMY?\n",
    "- chcemy zamienić teksty na zbiór słów\n",
    "\n",
    "\n",
    "### PYTANIE\n",
    "- czy możemy ztokenizować tekst np. documents.split(' ') jakie wystąpią wtedy problemy?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ODPOWIEDŹ\n",
    "- lepiej użyć preprocessingu i dopiero później tokenizacji"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_str_cleaned(str_dirty):\n",
    "    punctuation = '!\"#$%&\\'()*+,-./:;<=>?@[\\\\]^_`{|}~'\n",
    "    new_str = str_dirty.lower()\n",
    "    new_str = re.sub(' +', ' ', new_str)\n",
    "    for char in punctuation:\n",
    "        new_str = new_str.replace(char,'')\n",
    "    return new_str\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_document = get_str_cleaned(documents[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ala lubi zwierzęta i ma kota oraz psa'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_document"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## tokenizacja"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tokenize_str(document):\n",
    "    return document.split(' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ala', 'lubi', 'zwierzęta', 'i', 'ma', 'kota', 'oraz', 'psa']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenize_str(sample_document)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "documents_cleaned = [get_str_cleaned(d) for d in documents]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ala lubi zwierzęta i ma kota oraz psa',\n",
       " 'ola lubi zwierzęta oraz ma kota a także chomika',\n",
       " 'i jan jeździ na rowerze',\n",
       " '2 wojna światowa była wielkim konfliktem zbrojnym',\n",
       " 'tomek lubi psy ma psa i jeździ na motorze i rowerze']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "documents_cleaned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "documents_tokenized = [tokenize_str(d) for d in documents_cleaned]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['ala', 'lubi', 'zwierzęta', 'i', 'ma', 'kota', 'oraz', 'psa'],\n",
       " ['ola', 'lubi', 'zwierzęta', 'oraz', 'ma', 'kota', 'a', 'także', 'chomika'],\n",
       " ['i', 'jan', 'jeździ', 'na', 'rowerze'],\n",
       " ['2', 'wojna', 'światowa', 'była', 'wielkim', 'konfliktem', 'zbrojnym'],\n",
       " ['tomek',\n",
       "  'lubi',\n",
       "  'psy',\n",
       "  'ma',\n",
       "  'psa',\n",
       "  'i',\n",
       "  'jeździ',\n",
       "  'na',\n",
       "  'motorze',\n",
       "  'i',\n",
       "  'rowerze']]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "documents_tokenized"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PYTANIA\n",
    "- jaki jest następny krok w celu stworzenia wektórów TF lub TF-IDF\n",
    "- jakie wielkości będzie wektor TF lub TF-IDF?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "vocabulary = []\n",
    "for document in documents_tokenized:\n",
    "    for word in document:\n",
    "        vocabulary.append(word)\n",
    "vocabulary = sorted(set(vocabulary))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['2',\n",
       " 'a',\n",
       " 'ala',\n",
       " 'była',\n",
       " 'chomika',\n",
       " 'i',\n",
       " 'jan',\n",
       " 'jeździ',\n",
       " 'konfliktem',\n",
       " 'kota',\n",
       " 'lubi',\n",
       " 'ma',\n",
       " 'motorze',\n",
       " 'na',\n",
       " 'ola',\n",
       " 'oraz',\n",
       " 'psa',\n",
       " 'psy',\n",
       " 'rowerze',\n",
       " 'także',\n",
       " 'tomek',\n",
       " 'wielkim',\n",
       " 'wojna',\n",
       " 'zbrojnym',\n",
       " 'zwierzęta',\n",
       " 'światowa']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vocabulary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ZADANIE 1 stworzyć funkcję word_to_index(word:str), funkcja ma zwarać one-hot vector w postaciu numpy array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def word_to_index(word):\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0.])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_to_index('psa')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ZADANIE 2 NAPISAC FUNKCJĘ, która bierze listę słów i zamienia na wetktor TF\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tf(document):\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 1., 0., 0., 1., 0., 0., 0., 1., 1., 1., 0., 0., 0., 1., 1.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 1., 0.])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf(documents_tokenized[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "documents_vectorized = list()\n",
    "for document in documents_tokenized:\n",
    "    document_vector = tf(document)\n",
    "    documents_vectorized.append(document_vector)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([0., 0., 1., 0., 0., 1., 0., 0., 0., 1., 1., 1., 0., 0., 0., 1., 1.,\n",
       "        0., 0., 0., 0., 0., 0., 0., 1., 0.]),\n",
       " array([0., 1., 0., 0., 1., 0., 0., 0., 0., 1., 1., 1., 0., 0., 1., 1., 0.,\n",
       "        0., 0., 1., 0., 0., 0., 0., 1., 0.]),\n",
       " array([0., 0., 0., 0., 0., 1., 1., 1., 0., 0., 0., 0., 0., 1., 0., 0., 0.,\n",
       "        0., 1., 0., 0., 0., 0., 0., 0., 0.]),\n",
       " array([1., 0., 0., 1., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "        0., 0., 0., 0., 1., 1., 1., 0., 1.]),\n",
       " array([0., 0., 0., 0., 0., 2., 0., 1., 0., 0., 1., 1., 1., 1., 0., 0., 1.,\n",
       "        1., 1., 0., 1., 0., 0., 0., 0., 0.])]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "documents_vectorized"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### IDF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wersja bez żadnej normalizacji\n",
    "\n",
    "\n",
    "$idf_i = \\Large\\frac{|D|}{|\\{d : t_i \\in d \\}|}$\n",
    "\n",
    "\n",
    "$|D|$ - ilość dokumentów w korpusie\n",
    "$|\\{d : t_i \\in d \\}|$ - ilość dokumentów w korpusie, gdzie dany term występuje chociaż jeden raz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5.        , 5.        , 5.        , 5.        , 5.        ,\n",
       "       1.66666667, 5.        , 2.5       , 5.        , 2.5       ,\n",
       "       1.66666667, 1.66666667, 5.        , 2.5       , 5.        ,\n",
       "       2.5       , 2.5       , 5.        , 2.5       , 5.        ,\n",
       "       5.        , 5.        , 5.        , 5.        , 2.5       ,\n",
       "       5.        ])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "idf = np.zeros(len(vocabulary))\n",
    "idf = len(documents_vectorized) / np.sum(np.array(documents_vectorized) != 0,axis=0)\n",
    "display(idf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(documents_vectorized)):\n",
    "    documents_vectorized[i] = documents_vectorized[i] * idf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ZADANIE 3 Napisać funkcję similarity, która zwraca podobieństwo kosinusowe między dwoma dokumentami w postaci zwektoryzowanej"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def similarity(query, document):\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Ala lubi zwierzęta i ma kota oraz psa!'"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "documents[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 1., 0., 0., 1., 0., 0., 0., 1., 1., 1., 0., 0., 0., 1., 1.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 1., 0.])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "documents_vectorized[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Ola lubi zwierzęta oraz ma kota a także chomika!'"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "documents[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 1., 0., 0., 1., 0., 0., 0., 0., 1., 1., 1., 0., 0., 1., 1., 0.,\n",
       "       0., 0., 1., 0., 0., 0., 0., 1., 0.])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "documents_vectorized[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5892556509887895"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "similarity(documents_vectorized[0],documents_vectorized[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def transform_query(query):\n",
    "    query_vector = tf(tokenize_str(get_str_cleaned(query)))\n",
    "    return query_vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0.])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transform_query('psa')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4999999999999999"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "similarity(transform_query('psa kota'), documents_vectorized[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Ala lubi zwierzęta i ma kota oraz psa!'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.4999999999999999"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Ola lubi zwierzęta oraz ma kota a także chomika!'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.2357022603955158"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'I Jan jeździ na rowerze.'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'2 wojna światowa była wielkim konfliktem zbrojnym'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Tomek lubi psy, ma psa  i jeździ na motorze i rowerze.'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.19611613513818402"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# tak są obsługiwane 2 słowa\n",
    "query = 'psa kota'\n",
    "for i in range(len(documents)):\n",
    "    display(documents[i])\n",
    "    display(similarity(transform_query(query), documents_vectorized[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Ala lubi zwierzęta i ma kota oraz psa!'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Ola lubi zwierzęta oraz ma kota a także chomika!'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'I Jan jeździ na rowerze.'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.4472135954999579"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'2 wojna światowa była wielkim konfliktem zbrojnym'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Tomek lubi psy, ma psa  i jeździ na motorze i rowerze.'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.2773500981126146"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# dlatego potrzebujemy mianownik w cosine similarity\n",
    "# dłuższe dokumenty, w który raz wystąpie słowo rower są gorzej punktowane od\n",
    "# krótszych. Jeżeli słowo rower wystąpiło w bardzo krótki dokumencie, to znaczy\n",
    "# że jest większe prawdopodobieństwo że dokument jest o rowerze\n",
    "query = 'rowerze'\n",
    "for i in range(len(documents)):\n",
    "    display(documents[i])\n",
    "    display(similarity(transform_query(query), documents_vectorized[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Ala lubi zwierzęta i ma kota oraz psa!'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.35355339059327373"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Ola lubi zwierzęta oraz ma kota a także chomika!'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'I Jan jeździ na rowerze.'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.4472135954999579"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'2 wojna światowa była wielkim konfliktem zbrojnym'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Tomek lubi psy, ma psa  i jeździ na motorze i rowerze.'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.5547001962252291"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# dlatego potrzebujemy term frequency → wiecej wystąpień słowa w dokumencie\n",
    "# znaczy bardziej dopasowany dokument\n",
    "query = 'i'\n",
    "for i in range(len(documents)):\n",
    "    display(documents[i])\n",
    "    display(similarity(transform_query(query), documents_vectorized[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'documents' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-3-ca637083c8f1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;31m# słowo chomik ma większą wagę od i, ponieważ występuje w mniejszej ilości dokumentów\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mquery\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'i chomika'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdocuments\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m     \u001b[0mdisplay\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdocuments\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0mdisplay\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msimilarity\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtransform_query\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdocuments_vectorized\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'documents' is not defined"
     ]
    }
   ],
   "source": [
    "# dlatego IDF - żeby ważniejsze słowa miał większą wagę\n",
    "# słowo chomik ma większą wagę od i, ponieważ występuje w mniejszej ilości dokumentów\n",
    "query = 'i chomika'\n",
    "for i in range(len(documents)):\n",
    "    display(documents[i])\n",
    "    display(similarity(transform_query(query), documents_vectorized[i]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Uwaga\n",
    "Powyższe przykłady pokazują score dokuemntu. Aby zrobić wyszukiwarkę, powinniśmy posortować te dokumenty po score (od największego) i zaprezentwoać w tej kolejności."
   ]
  }
 ],
 "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": "3.tfidf (1)[ćwiczenia]",
  "title": "Ekstrakcja informacji",
  "year": "2021"
 },
 "nbformat": 4,
 "nbformat_minor": 4
}