PJN1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 145,
     "referenced_widgets": [
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     ]
    },
    "id": "_JdffckRV-Kg",
    "outputId": "4df2b13a-6018-42c0-f94d-6018267e475c"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2b74265bb203417e81da322331418496",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading builder script:   0%|          | 0.00/1.67k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b87d70db335d42cba937b500ac8e9330",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading readme:   0%|          | 0.00/201 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "29e94e0cd33949b182580fbf17320f20",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading data:   0%|          | 0.00/19.5M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d3fa85c9849543a0b9b4473f16e7d51e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating corpus split: 0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from datasets import load_dataset\n",
    "\n",
    "dataset = load_dataset(\"clarin-knext/fiqa-pl\", 'corpus')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aQ7Tzn1Qyf7H"
   },
   "source": [
    "Zadanie 1 godziny"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "f7NOe8zriD22"
   },
   "outputs": [],
   "source": [
    "import re\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "big_text = ' '.join(dataset['corpus']['text'])\n",
    "\n",
    "hours = re.findall(\"[^[:](((([0-1][0-9]|[0-9]|2[0-4]):([0-5][0-9]))|(o godzinie ([0-1][0-9]|[0-9]|2[0-4])\\.([0-5][0-9]))))\", big_text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "t6YlIX6mzqBU"
   },
   "source": [
    "Zadanie 1 daty"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "7C1IzjAdE-Dp"
   },
   "outputs": [],
   "source": [
    "months_all = re.findall(\"((([0-2][0-9]|3[0-1]|[0-9]) (stycznia|lutego|marca|kwietnia|maja|Maj|czerwca|lipca|sierpnia|września|października|Październik|listopada|grudnia))|(([0-2][0-9]|3[0-1]|[0-9])[^.]\\.(1[0-2]|0[0-9])\\.(20[0-2][0-9]|[0-2][0-9])[^.])|((20[0-2][0-9]|[0-2][0-9])-(1[0-2]|0[0-9])-([0-2][0-9]|3[0-1]|[0-9])))\", big_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 873
    },
    "id": "euff_V5W-gpW",
    "outputId": "1f9c6a03-2c5d-417d-cf16-1782e596b341"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def proccess_hour(hour):\n",
    "  if hour[3] != '':\n",
    "    return int(hour[3])\n",
    "  else:\n",
    "    return int(hour[-2])\n",
    "\n",
    "proccessed_hours = 25*[0]\n",
    "labels = [f\"{i}\" for i in range(25)]\n",
    "for hour in hours:\n",
    "  proccessed_hours[proccess_hour(hour)] += 1\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(15,10))\n",
    "\n",
    "ax.bar(labels, proccessed_hours)\n",
    "\n",
    "ax.set_xlabel(\"Godzina\")\n",
    "labels2 = ax.bar(labels, proccessed_hours)\n",
    "ax.set_ylabel(\"Liczba znalezionych godzin\")\n",
    "ax.set_title(\"Rozkład godzin w tekście\")\n",
    "ax.bar_label(labels2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 873
    },
    "id": "6gQejTjjAJOl",
    "outputId": "ec0a6059-7cd3-414a-847e-c75fd11cb638"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dictionary = {\"stycznia\": 1, \"lutego\": 2, \"marca\": 3, \"kwietnia\": 4, \"maja\": 5, \"Maj\": 5, \"czerwca\": 6, \"lipca\": 7, \"sierpnia\": 8, \"września\": 9, \"października\": 10, \"Październik\": 10, \"listopada\": 11, \"grudnia\": 12}\n",
    "\n",
    "labels = [\"styczeń\", \"luty\", \"marzec\", \"kwiecień\", \"maj\", \"czerwiec\", \"lipiec\", \"sierpień\", \"wrzesień\", \"październik\", \"listopad\", \"grudzień\"]\n",
    "proccessed_months = 12*[0]\n",
    "\n",
    "def proccess_month(date):\n",
    "  if date[3] != '':\n",
    "    return dictionary[date[3]]\n",
    "  if date[-2] != '':\n",
    "    return int(date[-2])\n",
    "  return int(date[6])\n",
    "\n",
    "for date in months_all:\n",
    "  proccessed_months[proccess_month(date)-1] += 1\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(15,10))\n",
    "\n",
    "\n",
    "labels2 = ax.bar(labels, proccessed_months)\n",
    "ax.bar(labels, proccessed_months)\n",
    "\n",
    "ax.set_xlabel(\"Miesiąc\")\n",
    "ax.set_ylabel(\"Liczba znalezionych miesięcy\")\n",
    "ax.set_title(\"Rozkład miesięcy używanych w datach w tekście\")\n",
    "ax.bar_label(labels2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VH1vtkJwzy3f"
   },
   "source": [
    "Zadanie 4,5,6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "lwnWJNCWdTjX",
    "outputId": "44102833-f963-4b24-becb-3ea940ab7306"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "463\n",
      "215\n",
      "248\n"
     ]
    }
   ],
   "source": [
    "#zadanie 4\n",
    "january_all = re.findall(\"\\\\b((s|S)tycz((eń)|(ni((ami)|(ach)|a|(owi)|(em)|u|om|e|(ów)|))))\\\\b\", big_text)\n",
    "print(len(january_all))\n",
    "\n",
    "#zadanie 5\n",
    "january_number_before = re.findall(\"[0-9] (\\\\b(s|S)tycz((eń)|(ni((ami)|(ach)|a|(owi)|(em)|u|om|e|(ów)|))))\\\\b\", big_text)\n",
    "print(len(january_number_before))\n",
    "\n",
    "#zadanie 6\n",
    "january_not_number_before = re.findall(\"(?<![0-9] )(\\\\b(((s|S)tycz((eń)|(ni((ami)|(ach)|a|(owi)|(em)|u|om|e|(ów)|))))\\\\b))\", big_text)\n",
    "print(len(january_not_number_before))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ebzv-lghCsSF"
   },
   "source": [
    "**215(odmiana \"styczeń\", przed którą jest liczba i spacja) + 248(Odmiana słowa \"styczeń\" przed którą nie stoi liczba i spacja) = 463**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xKQQqq9FFpX-"
   },
   "source": [
    "Are regular expressions good at capturing times?\n",
    "\n",
    "Wyrażenia regularne, dość dobrze poradziły sobie z zadaniem znajdywania godzin w tekście. Godzina ma bardzo ustrukturyzowany format i dość łatwo ją znaleźć. Niestety w tekście znajdują się też godziny, których nie udało mi się uzyskać za pomocą stworzonego przeze mnie wyrażenia regularnego, gdyż ich struktura jest zbyt podobna do dat/minuty i sekundy filmu/kwocie pieniędzy.\n",
    "\n",
    "\n",
    "Are regular expressions good at capturing dates?\n",
    "\n",
    "Według mnie, wyrażenia regularne nie są dobre w wychwytywaniu dat w tekście. Data w przeciwieństwie do godziny może być zapisana i często jest zapisywana słownie, co bardzo utrudnia jej wyłapywanie. Mnogość dostęnych formatów i ich podobieństwo do innych oznaczeń, powoduje, że zagadnienie znajdowania dat za pomocą wyrażeń regularnych jest bardzo skomplikowane i czasochłonne.\n",
    "\n",
    "How one can be sure that the expression has matched all and only the correct expressions of a given type?\n",
    "\n",
    "Według mnie, nie ma sposobu, aby to zweryfikować. Tworząc wyrażenie regularne, trzeba wykazać się kretywnością, aby przewidzieć w jakiej formie mogą występować w tekście interesujące nas dane. Następnie należy dokonywać kolejnych iteracji, podczas których dokonujemy sprawdzenia nowego regex'a, a następnie weryfikujemy, czy znajdywane przez niego dane są poprawne, czy nie. Dobrym sposobem jest również przeglądnięcie tekstu, w poszukiwaniu form które w nim występują."
   ]
  }
 ],
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