00_ideas_and_issues.ipynb

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  "cells": [
    {
      "cell_type": "code",
      "execution_count": 3,
      "source": [
        "import pandas as pd\r\n",
        "df = pd.read_hdf('outputs/budzety_clean_topics_bert.h5', key='df')"
      ],
      "outputs": [],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Pomysły co robić dalej, problemy, wyzwania, itp..."
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "***"
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Wybór odpowiedniej metody reprezentacji tematów"
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "1. BERTopic  \r\n",
        "2. LDA  \r\n",
        "3. Top2Vec  \r\n",
        "4. inna..."
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "***"
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Różna ilość tematów"
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "Modyfikujemy zmienną `N_TOPICS` i patrzymy jakie tematy otrzymujemy.  \r\n",
        "Proponuję wybierać większą liczbę tematów niż proponowane 20."
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "***"
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Grupowanie tematów"
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "Utworzyć cechę `topics_groups` i poprzypisywać do niej poszczególne tematy. Tzn. grupa zielona to tematy 12, 5, 4 i 6, grupa czerwona to tematy 17, 8, 13, 1, 14 itp.  \r\n",
        "Grupy mogą mieć jakieś nazwy (podejście subiektywne) np. zieleń, transport, sport...\r\n",
        "Grupowanie tematów nie musi być robione w odniesieniu do kolorów. Można wejść na wyższy poziom klastrowania hierachicznego i np. podzielić je względem konkrentego poziomu  \r\n",
        "Np. pierwszy podział klastrowania to podział na grupy, do którtych należą tematy 15 12 5 4 6 17 8 13 1 14 (pierwsza grupa) i pozostałe tematy (jako druga grupa). Więc tworzymy tylko 2 grupy."
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "source": [
        "df[['nazwa', 'topics']]"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                   nazwa  topics\n",
              "0                                         Projektistatus      -1\n",
              "1      Doposażenie parku pomiędzy Estakadą Kwiatkowsk...       3\n",
              "2      Cykliczne warsztaty dla seniorów z dzielnicy O...       1\n",
              "3       Ogród Sąsiedzki przy Dzikim Sadzie, ul. Źródlana       8\n",
              "4      Montaż słupów oświetleniowych na chodniku przy...      -1\n",
              "...                                                  ...     ...\n",
              "23025                          Zielony skwer na Zabtociu      13\n",
              "23026            Znani Biatopradniczanie dla Mieszkanców       2\n",
              "23027  ZRÓBMY SKWER PRZY FABRYCZNEJ! Edukacja i kultu...      11\n",
              "23028                                  Zróbmy sobie Park      15\n",
              "23029                                   ZUMBA DLA DZIECI       3\n",
              "\n",
              "[23030 rows x 2 columns]"
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              "    <tr>\n",
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              "      <td>Doposażenie parku pomiędzy Estakadą Kwiatkowsk...</td>\n",
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              "      <th>3</th>\n",
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              "      <td>8</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Montaż słupów oświetleniowych na chodniku przy...</td>\n",
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              "    <tr>\n",
              "      <th>23027</th>\n",
              "      <td>ZRÓBMY SKWER PRZY FABRYCZNEJ! Edukacja i kultu...</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23028</th>\n",
              "      <td>Zróbmy sobie Park</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23029</th>\n",
              "      <td>ZUMBA DLA DZIECI</td>\n",
              "      <td>3</td>\n",
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              "  </tbody>\n",
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              "<p>23030 rows × 2 columns</p>\n",
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      ],
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    {
      "cell_type": "markdown",
      "source": [
        "![title](images/topics.png)"
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "***"
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Rozbicie algorytmu Bertopic"
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "Rozbijamy Bertopic na UMAP i HDBSCAN i próbujemy różnych parametrów tych dwóch algorytmów."
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "source": [
        "import hdbscan\r\n",
        "import umap\r\n",
        "from bertopic import BERTopic"
      ],
      "outputs": [],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "source": [
        "docs = list(df.loc[:, 'names'].values)\r\n",
        "docs = [str(elem) for elem in docs] "
      ],
      "outputs": [],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "source": [
        "# UMAP - zmniejszenie ilości wymiarów\r\n",
        "umap_model = umap.UMAP(n_neighbors=15,\r\n",
        "                       n_components=10,\r\n",
        "                       min_dist=0.0,\r\n",
        "                       metric='cosine',\r\n",
        "                       low_memory=False)\r\n",
        "\r\n",
        "# HDBSAN - klasteryzacja dokumentów\r\n",
        "hdbscan_model = hdbscan.HDBSCAN(min_cluster_size=10,\r\n",
        "                                min_samples=1,\r\n",
        "                                metric='euclidean',\r\n",
        "                                cluster_selection_method='eom',\r\n",
        "                                prediction_data=True)"
      ],
      "outputs": [],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "source": [
        "# Bertopic\r\n",
        "model = BERTopic(top_n_words=20,\r\n",
        "                       n_gram_range=(1,2), \r\n",
        "                       calculate_probabilities=True,\r\n",
        "                       umap_model= umap_model,\r\n",
        "                       hdbscan_model=hdbscan_model,\r\n",
        "                       verbose=True,\r\n",
        "                       language='polish')\r\n",
        "\r\n",
        "topics, probs = model.fit_transform(docs)\r\n",
        "# lub \r\n",
        "# topics, probs = model.fit_transform(docs, embeddings)\r\n",
        "# jeżeli bierzemy pod uwagę embeddingi (patrz punkt poniżej)"
      ],
      "outputs": [],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "***"
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Użycie różnych embeddingów"
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "Tokeny mogą być różnie reprezentowane (embedding)"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "source": [
        "from sklearn.feature_extraction.text import TfidfVectorizer\r\n",
        "\r\n",
        "vectorizer = TfidfVectorizer(min_df=5)\r\n",
        "embeddings = vectorizer.fit_transform(docs)\r\n",
        "topics, probs = model.fit_transform(docs, embeddings)\r\n",
        "\r\n",
        "# lub\r\n",
        "\r\n",
        "from sentence_transformers import SentenceTransformer\r\n",
        "\r\n",
        "sentence_model = SentenceTransformer(\"distiluse-base-multilingual-cased\")\r\n",
        "embeddings = sentence_model.encode(docs, show_progress_bar=False)\r\n",
        "topics, probs = model.fit_transform(docs, embeddings)"
      ],
      "outputs": [],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "Istnieje całe mnóstwo reprezentacji wektorowych, każda z nich może mieć dodatkowo swoje własne parametry"
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "***"
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Badanie trendów zmian"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "source": [
        "def most_common_topics(city: str, year: str, top: int = 3) -> pd.Series:\r\n",
        "  result = df[(df['miasto'] == city) & (df['rok'] == year)]['topics'].value_counts()[1:top+1]\r\n",
        "  return result"
      ],
      "outputs": [],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "Wybieramy miasto i patrzymy jakie były najpopularniejsze tematy na przestrzen lat.   \r\n",
        "(Domyślnie 3 tematy z pominięciem szumu)"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "source": [
        "most_common_topics('GDYNIA', '2018')"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     9\n",
              "14    8\n",
              "8     5\n",
              "Name: topics, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "source": [
        "most_common_topics('GDYNIA', '2018/19')"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Series([], Name: topics, dtype: int64)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "source": [
        "most_common_topics('GDYNIA', '2019')"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    44\n",
              "3    17\n",
              "1    11\n",
              "Name: topics, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "source": [
        "most_common_topics('GDYNIA', '2020')"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    39\n",
              "1    24\n",
              "3    18\n",
              "Name: topics, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "Widzimy, że w Gdyni najpopularniejsze były tematy `0 8 14`, potem `0 1 3` i rok później również `0 1 3`. To już jest podstawą do utworzenia pewnej historii."
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "Możemy nie patrzeć na zmiany tematów na przestrzeni lat, ale na zmiany w grupach tematów na przestrzeni lat."
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "Jeżeli mamy już informację o zmianie tematów na przestrzeni lat dla danego miasta to możemy sprawdzić z czego ona wynika.\r\n",
        "Czy na tą zmianę miało wpływ to, że:\r\n",
        "* miasta o podobnej liczbie ludności do wybranego wykazywały podobne trendy zmian na przestrzeni lat \r\n",
        "* miasta o podobnej powierzchni do wybranego wyjazywały podobne trendy zmian na przestrzeni lat\r\n",
        "* miasta sąsiadujące z wybranym miastem i ich najpopularniejsze tematy wpłynęły na zmianę w tematach wybranego miasta\r\n",
        "na przestrzeni lat\r\n",
        "* czy miasta o podobnym budżecie przeliczonym na 100 tys. mieszkańców jak nasze wybrane miasto \"interesowały\" się podobnym tematami"
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "***"
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Problemy do rozpracowania ::: Sprawdzenie poprawności klasyfikacji tematów"
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "Nie wszystkie tematy zostały dobrze sklasyfikowane"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "source": [
        "pd.set_option('max_colwidth', 400)\r\n",
        "pd.DataFrame(df[['rok', 'miasto', 'topics', 'topics_desc', 'nazwa', 'names']].iloc[18589]).T"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        rok    miasto topics  \\\n",
              "18589  2018  WARSZAWA     12   \n",
              "\n",
              "                                                                                 topics_desc  \\\n",
              "18589  [pies, psi, ursynów, pszczoła, komar, ursynowie, owad, nietoperz, zwierzę, wiewiórka]   \n",
              "\n",
              "                       nazwa               names  \n",
              "18589  Scrabble na Ursynowie  scrabble ursynowie  "
            ],
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              "      <th>miasto</th>\n",
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              "      <th>18589</th>\n",
              "      <td>2018</td>\n",
              "      <td>WARSZAWA</td>\n",
              "      <td>12</td>\n",
              "      <td>[pies, psi, ursynów, pszczoła, komar, ursynowie, owad, nietoperz, zwierzę, wiewiórka]</td>\n",
              "      <td>Scrabble na Ursynowie</td>\n",
              "      <td>scrabble ursynowie</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "Np. zauważyłem, że dla Ursynowa bardzo dużo wpada do kategorii `zwierzęcej` (temat nr 12)"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "source": [
        "df[df['nazwa'].str.contains('Ursyn')][['topics', 'topics_desc', 'nazwa']]"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       topics  \\\n",
              "17953      12   \n",
              "17967       7   \n",
              "17977      -1   \n",
              "18022      12   \n",
              "18069      14   \n",
              "...       ...   \n",
              "22108      12   \n",
              "22112      12   \n",
              "22114      12   \n",
              "22115      -1   \n",
              "22119      -1   \n",
              "\n",
              "                                                                                              topics_desc  \\\n",
              "17953               [pies, psi, ursynów, pszczoła, komar, ursynowie, owad, nietoperz, zwierzę, wiewiórka]   \n",
              "17967     [chodnik, warszawa, warszawski, warszawo, park, polski, budynek, miejsce, sąsiedzki, poznański]   \n",
              "17977       [chodnik, park, ulica, budowa, warsztat, koncert, miejsce, dzielnica, modernizacja, muzyczny]   \n",
              "18022               [pies, psi, ursynów, pszczoła, komar, ursynowie, owad, nietoperz, zwierzę, wiewiórka]   \n",
              "18069  [cmentarz, kościół, pomnik, rewitalizacja, rzeźba, wieża, chodnik, ulica, obserwatorium, widokowy]   \n",
              "...                                                                                                   ...   \n",
              "22108               [pies, psi, ursynów, pszczoła, komar, ursynowie, owad, nietoperz, zwierzę, wiewiórka]   \n",
              "22112               [pies, psi, ursynów, pszczoła, komar, ursynowie, owad, nietoperz, zwierzę, wiewiórka]   \n",
              "22114               [pies, psi, ursynów, pszczoła, komar, ursynowie, owad, nietoperz, zwierzę, wiewiórka]   \n",
              "22115       [chodnik, park, ulica, budowa, warsztat, koncert, miejsce, dzielnica, modernizacja, muzyczny]   \n",
              "22119       [chodnik, park, ulica, budowa, warsztat, koncert, miejsce, dzielnica, modernizacja, muzyczny]   \n",
              "\n",
              "                                                                                 nazwa  \n",
              "17953                       Doświetlenie przejść dla pieszych na Ursynowie Południowym  \n",
              "17967                                               Pij, Warszawo (Ursynów Południowy)  \n",
              "17977                                                               Ursynowskie Murale  \n",
              "18022                         Doświetlenie przejść dla pieszych na Ursynowie Północnym  \n",
              "18069                                 Ursynowskie rzeźby po 40-tu latach do renowacji   \n",
              "...                                                                                ...  \n",
              "22108                            Neon na budynku Dzielnicowego Ośrodka Kultury Ursynów  \n",
              "22112                                   Doświetlenie przejść dla pieszych na Ursynowie  \n",
              "22114  Bezpłatne zajęcia dla dzieci i młodzieży w Dzielnicowym Ośrodku Kultury Ursynów  \n",
              "22115                                                     Letnie koncerty na Ursynowie  \n",
              "22119            Piękne rośliny na ulicach Ursynowa, chroniące przed smogiem i hałasem  \n",
              "\n",
              "[111 rows x 3 columns]"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>topics</th>\n",
              "      <th>topics_desc</th>\n",
              "      <th>nazwa</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>17953</th>\n",
              "      <td>12</td>\n",
              "      <td>[pies, psi, ursynów, pszczoła, komar, ursynowie, owad, nietoperz, zwierzę, wiewiórka]</td>\n",
              "      <td>Doświetlenie przejść dla pieszych na Ursynowie Południowym</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17967</th>\n",
              "      <td>7</td>\n",
              "      <td>[chodnik, warszawa, warszawski, warszawo, park, polski, budynek, miejsce, sąsiedzki, poznański]</td>\n",
              "      <td>Pij, Warszawo (Ursynów Południowy)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17977</th>\n",
              "      <td>-1</td>\n",
              "      <td>[chodnik, park, ulica, budowa, warsztat, koncert, miejsce, dzielnica, modernizacja, muzyczny]</td>\n",
              "      <td>Ursynowskie Murale</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18022</th>\n",
              "      <td>12</td>\n",
              "      <td>[pies, psi, ursynów, pszczoła, komar, ursynowie, owad, nietoperz, zwierzę, wiewiórka]</td>\n",
              "      <td>Doświetlenie przejść dla pieszych na Ursynowie Północnym</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18069</th>\n",
              "      <td>14</td>\n",
              "      <td>[cmentarz, kościół, pomnik, rewitalizacja, rzeźba, wieża, chodnik, ulica, obserwatorium, widokowy]</td>\n",
              "      <td>Ursynowskie rzeźby po 40-tu latach do renowacji</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22108</th>\n",
              "      <td>12</td>\n",
              "      <td>[pies, psi, ursynów, pszczoła, komar, ursynowie, owad, nietoperz, zwierzę, wiewiórka]</td>\n",
              "      <td>Neon na budynku Dzielnicowego Ośrodka Kultury Ursynów</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22112</th>\n",
              "      <td>12</td>\n",
              "      <td>[pies, psi, ursynów, pszczoła, komar, ursynowie, owad, nietoperz, zwierzę, wiewiórka]</td>\n",
              "      <td>Doświetlenie przejść dla pieszych na Ursynowie</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22114</th>\n",
              "      <td>12</td>\n",
              "      <td>[pies, psi, ursynów, pszczoła, komar, ursynowie, owad, nietoperz, zwierzę, wiewiórka]</td>\n",
              "      <td>Bezpłatne zajęcia dla dzieci i młodzieży w Dzielnicowym Ośrodku Kultury Ursynów</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22115</th>\n",
              "      <td>-1</td>\n",
              "      <td>[chodnik, park, ulica, budowa, warsztat, koncert, miejsce, dzielnica, modernizacja, muzyczny]</td>\n",
              "      <td>Letnie koncerty na Ursynowie</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22119</th>\n",
              "      <td>-1</td>\n",
              "      <td>[chodnik, park, ulica, budowa, warsztat, koncert, miejsce, dzielnica, modernizacja, muzyczny]</td>\n",
              "      <td>Piękne rośliny na ulicach Ursynowa, chroniące przed smogiem i hałasem</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>111 rows × 3 columns</p>\n",
              "</div>"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "Chyba przyporządkowuje `Ursynów` jako `urson`"
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "![title](images/urson.png)  \r\n",
        "źródło: https://pl.wikipedia.org/wiki/Urson_ameryka%C5%84ski"
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "Podobnie wiele tematów z miasta Łódź jest błędnie przypisywanych do kategorii `morsko-rzecznej`"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "source": [
        "df[df['nazwa'].str.contains('Łodzi')][['topics', 'topics_desc', 'nazwa']]"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       topics  \\\n",
              "11504      15   \n",
              "11961      -1   \n",
              "12011      15   \n",
              "12057      15   \n",
              "12167      -1   \n",
              "...       ...   \n",
              "14974      15   \n",
              "14981      -1   \n",
              "14984      15   \n",
              "14985      15   \n",
              "14999       6   \n",
              "\n",
              "                                                                                         topics_desc  \\\n",
              "11504            [łódź, plaża, plażowy, łódzki, morski, kajakowy, łódka, kajak, łodzianin, pomorski]   \n",
              "11961  [chodnik, park, ulica, budowa, warsztat, koncert, miejsce, dzielnica, modernizacja, muzyczny]   \n",
              "12011            [łódź, plaża, plażowy, łódzki, morski, kajakowy, łódka, kajak, łodzianin, pomorski]   \n",
              "12057            [łódź, plaża, plażowy, łódzki, morski, kajakowy, łódka, kajak, łodzianin, pomorski]   \n",
              "12167  [chodnik, park, ulica, budowa, warsztat, koncert, miejsce, dzielnica, modernizacja, muzyczny]   \n",
              "...                                                                                              ...   \n",
              "14974            [łódź, plaża, plażowy, łódzki, morski, kajakowy, łódka, kajak, łodzianin, pomorski]   \n",
              "14981  [chodnik, park, ulica, budowa, warsztat, koncert, miejsce, dzielnica, modernizacja, muzyczny]   \n",
              "14984            [łódź, plaża, plażowy, łódzki, morski, kajakowy, łódka, kajak, łodzianin, pomorski]   \n",
              "14985            [łódź, plaża, plażowy, łódzki, morski, kajakowy, łódka, kajak, łodzianin, pomorski]   \n",
              "14999   [senior, zdrowie, kobieta, zdrowy, seniora, kuchnia, kulinarny, szpital, medyczny, catering]   \n",
              "\n",
              "                                                                                                                    nazwa  \n",
              "11504                                                                      Mistrzostwa Miasta Gorzowa w Smoczych Łodziach  \n",
              "11961                                                     PLAŻA W ARTURÓWKU- miejscem wypoczynku i rekreacji dla Łodzian.  \n",
              "12011  Kino plenerowe - letnie w Arturówku - miejscem wypoczynku i rekreacji dla Łodzian. Bliżej kultury na łonie natury.  \n",
              "12057                 Drzwi do przyszłości - kolorowe i bezpieczne wejście do sal zabaw i nauki dla najmłodszych Łodzian.  \n",
              "12167               Budowa boiska wielofunkcyjnego sportowego o sztucznej nawierzchni przy SP 113 w Łodzi, ul. Unicka 6.   \n",
              "...                                                                                                                   ...  \n",
              "14974                           Bezpieczna 4-stka monitoring wizyjny wokół szkoły podstawowej nr 4 w Łodzi, ul. Milionowa  \n",
              "14981                                                       Przywróćmy blask infrastrukturze sportowej w XXIX LO w Łodzi.  \n",
              "14984                                       Remont szatni w budynku Szkoły Podstawowej nr 64 w Łodzi, ul. Wróblewskiego 6  \n",
              "14985                                                         Sala językowa dla uczniów Szkoły Podstawowej nr 64 w Łodzi.  \n",
              "14999                                                     Centralnym Szpitalu Klinicznym Uniwersytetu Medycznego w Łodzi.  \n",
              "\n",
              "[187 rows x 3 columns]"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>topics</th>\n",
              "      <th>topics_desc</th>\n",
              "      <th>nazwa</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>11504</th>\n",
              "      <td>15</td>\n",
              "      <td>[łódź, plaża, plażowy, łódzki, morski, kajakowy, łódka, kajak, łodzianin, pomorski]</td>\n",
              "      <td>Mistrzostwa Miasta Gorzowa w Smoczych Łodziach</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11961</th>\n",
              "      <td>-1</td>\n",
              "      <td>[chodnik, park, ulica, budowa, warsztat, koncert, miejsce, dzielnica, modernizacja, muzyczny]</td>\n",
              "      <td>PLAŻA W ARTURÓWKU- miejscem wypoczynku i rekreacji dla Łodzian.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12011</th>\n",
              "      <td>15</td>\n",
              "      <td>[łódź, plaża, plażowy, łódzki, morski, kajakowy, łódka, kajak, łodzianin, pomorski]</td>\n",
              "      <td>Kino plenerowe - letnie w Arturówku - miejscem wypoczynku i rekreacji dla Łodzian. Bliżej kultury na łonie natury.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12057</th>\n",
              "      <td>15</td>\n",
              "      <td>[łódź, plaża, plażowy, łódzki, morski, kajakowy, łódka, kajak, łodzianin, pomorski]</td>\n",
              "      <td>Drzwi do przyszłości - kolorowe i bezpieczne wejście do sal zabaw i nauki dla najmłodszych Łodzian.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12167</th>\n",
              "      <td>-1</td>\n",
              "      <td>[chodnik, park, ulica, budowa, warsztat, koncert, miejsce, dzielnica, modernizacja, muzyczny]</td>\n",
              "      <td>Budowa boiska wielofunkcyjnego sportowego o sztucznej nawierzchni przy SP 113 w Łodzi, ul. Unicka 6.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14974</th>\n",
              "      <td>15</td>\n",
              "      <td>[łódź, plaża, plażowy, łódzki, morski, kajakowy, łódka, kajak, łodzianin, pomorski]</td>\n",
              "      <td>Bezpieczna 4-stka monitoring wizyjny wokół szkoły podstawowej nr 4 w Łodzi, ul. Milionowa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14981</th>\n",
              "      <td>-1</td>\n",
              "      <td>[chodnik, park, ulica, budowa, warsztat, koncert, miejsce, dzielnica, modernizacja, muzyczny]</td>\n",
              "      <td>Przywróćmy blask infrastrukturze sportowej w XXIX LO w Łodzi.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14984</th>\n",
              "      <td>15</td>\n",
              "      <td>[łódź, plaża, plażowy, łódzki, morski, kajakowy, łódka, kajak, łodzianin, pomorski]</td>\n",
              "      <td>Remont szatni w budynku Szkoły Podstawowej nr 64 w Łodzi, ul. Wróblewskiego 6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14985</th>\n",
              "      <td>15</td>\n",
              "      <td>[łódź, plaża, plażowy, łódzki, morski, kajakowy, łódka, kajak, łodzianin, pomorski]</td>\n",
              "      <td>Sala językowa dla uczniów Szkoły Podstawowej nr 64 w Łodzi.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14999</th>\n",
              "      <td>6</td>\n",
              "      <td>[senior, zdrowie, kobieta, zdrowy, seniora, kuchnia, kulinarny, szpital, medyczny, catering]</td>\n",
              "      <td>Centralnym Szpitalu Klinicznym Uniwersytetu Medycznego w Łodzi.</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>187 rows × 3 columns</p>\n",
              "</div>"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "***"
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Problemy do rozpracowania ::: Użycie właściwej metryki do walidacji"
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dwa podejścia do wyboru:  \r\n",
        "a) Traktujemy problem jako przypadek nienadzorowany i wybieramy metrykę (Może być to dość problematyczne, którą wybrać i raczej będą to metryki skupiające się na poprawności klasteryzacji, nie na poprawności przypisania tematu).  \r\n",
        "b) Traktujemy problem jako przypadek nadzorowany: poświęcamy wiele czasu na ręczne przypisanie wszystkich rekordów  do wybranego przez nas klastra, mając na uwadze to co się nam wydaje (dzielimy zbiór danych między osoby). Mamy w ten sposób zwykły problem klasyfikacji."
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "source": [],
      "outputs": [],
      "metadata": {}
    }
  ]
}