filmOzetindenBenzerFilmlerinBulunmasi.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "filmOzetindenBenzerFilmlerinBulunmasi",
      "provenance": [],
      "collapsed_sections": [],
      "machine_shape": "hm"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "gpuClass": "standard",
    "accelerator": "TPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "Veri işleme ve analizi için pandas kütüphanesi kullanılacaktır. Pandas kütüphanesi pd rumuzu ile kısaltılmıştır."
      ],
      "metadata": {
        "id": "-IES9ZGkHVNH"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "D4DSrSk9PiW8"
      },
      "outputs": [],
      "source": [
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Verilerimiz Google Drive (bulut) üzerinde olduğu için Google Colab ile Google Drive arasında bağlantı kurulacaktır."
      ],
      "metadata": {
        "id": "-duDlDDyIIYp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive/')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g8ZpPY4-Pmv1",
        "outputId": "44d570a4-cddf-4246-9913-1f687b6362a7"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive/\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Google Drive içinde Colab Notebooks klasörünün içinde TDDI2022 isimli klasörde verilerimizi sakladığımız için bu klasöre erişilecektir.\n",
        "\n",
        "***NOT:*** Bu klasörü daha önce oluşturmadıysanız %md TDDI2022 komutu veya Google Drive Ara Yüzü ile klasörü oluşturunuz!"
      ],
      "metadata": {
        "id": "gmXrHZE_IShv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%cd drive/\n",
        "%cd MyDrive/\n",
        "%cd Colab Notebooks/\n",
        "# Google Drive içinde Colab Notebooks klasörünün içinde TDDI2022 diye bir klasör oluşturduktan sonra o klasöre erişiyoruz.\n",
        "# Bu klasörü daha önce oluşturmadıysanız %md TDDI2022 komutu veya Google Drive Ara Yüzü ile klasörü oluşturunuz!\n",
        "%cd TDDI2022/\n",
        "\n",
        "# Klasörün içeriğinin görülmesi için\n",
        "%ls"
      ],
      "metadata": {
        "id": "h47_PvbxT9QD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Google Colab Pro** Hizmetlerinden yararlanarak hem daha iyi Grafik İşlem Birimine (GPU) hem de daha yüksek bellek birimine (RAM) erişerek büyük veriyi işleyeceğiz. Google Colab Pro hesabını verimli kullanmak için sadece gerektiğinde **'Çalışma zamanı'** menüsünden **'Çalışma zamanı türünü değiştir'** diyerek GPU/TPU ve Yüksek RAM seçeneklerini kullanacağız."
      ],
      "metadata": {
        "id": "fxLvlwyHzbvt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "device_name = tf.test.gpu_device_name()\n",
        "if device_name != '/device:GPU:0':\n",
        "  print('GPU aracı bulunamadı!') # Gereksiz kullanım olmaması ve Google Colab PRO hesabımızı verimli kullanmak için şimdilik sadece yüksek RAM'de çalışıyoruz.\n",
        "else:\n",
        "  print('GPU bulundu: {}'.format(device_name))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9a0r4dkM4-59",
        "outputId": "0f011608-45d9-488a-ac34-ec5a02acb6ff"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "GPU aracı bulunamadı!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gpu_info = !nvidia-smi\n",
        "gpu_info = '\\n'.join(gpu_info)\n",
        "if gpu_info.find('failed') >= 0:\n",
        "  print('Bir GPU\\'ya bağlanılamadı!')\n",
        "else:\n",
        "  print(gpu_info)\n",
        "\n",
        "from psutil import virtual_memory\n",
        "ram_gb = virtual_memory().total / 1e9\n",
        "print('Çalışma zamanınız {:.1f} gigabytes\\'lık RAM\\'a sahip\\n'.format(ram_gb))\n",
        "\n",
        "if ram_gb < 20:\n",
        "  print('Yüksek bir RAM Çalışma Zamanı KULLANMIYORSUNUZ!')\n",
        "else:\n",
        "  print('Yüksek bir RAM Çalışma Zamanı Kullanıyorsunuz!')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DmlTqrQ6za2t",
        "outputId": "ab0be3fd-1958-4df5-eb13-4e401b6d7e75"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Bir GPU'ya bağlanılamadı!\n",
            "Çalışma zamanınız 37.8 gigabytes'lık RAM'a sahip\n",
            "\n",
            "Yüksek bir RAM Çalışma Zamanı Kullanıyorsunuz!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "TDDI2022 klasöründe bulunan ve proje kapsamında oluşturulan **filmOzetleriVeriKumesi_TURKCE.csv** dosyasının içindeki veriler **veri İskeleti** oluşturmak için pandas ile okunacaktır.\n",
        "\n",
        "Veri iskeleti oluşturulduktan sonra veri iskeleti ile ilgili bilgi ekrana yazdırılacaktır. Tam özetin yazdırılıp yazdırılmayacağı durumunu *verbose=True* (yazdırılsın) parametresi ile kontrol edilecektir. "
      ],
      "metadata": {
        "id": "_2QYxVBFIaZg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('filmOzetleriVeriKumesi_TURKCE.csv')\n",
        "df.info(verbose=True)"
      ],
      "metadata": {
        "id": "ZMriHlEGPqbV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6f73b5cb-c3d7-4313-c649-20cd1e3242c7"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 34886 entries, 0 to 34885\n",
            "Data columns (total 9 columns):\n",
            " #   Column          Non-Null Count  Dtype \n",
            "---  ------          --------------  ----- \n",
            " 0   Unnamed: 0      34886 non-null  int64 \n",
            " 1   Çıkış Yılı      34886 non-null  int64 \n",
            " 2   Orijinal İsmi   34886 non-null  object\n",
            " 3   Yapıldığı Ülke  34886 non-null  object\n",
            " 4   Yönetmeni       34886 non-null  object\n",
            " 5   Oyuncu Kadrosu  33464 non-null  object\n",
            " 6   Türü            34858 non-null  object\n",
            " 7   Veri Kaynağı    34886 non-null  object\n",
            " 8   Olay Dizisi     34886 non-null  object\n",
            "dtypes: int64(2), object(7)\n",
            "memory usage: 2.4+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Unnamed: 0 isimli sütun / alan proje kapsamında kullanılması zorunlu olan veri içermediği için silinecektir."
      ],
      "metadata": {
        "id": "g0h1Dkzo7nE-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.drop(columns='Unnamed: 0')\n",
        "df.info(verbose=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RmGgRutS76XU",
        "outputId": "b722a3e6-c55f-4cbd-9ce5-98c16fbdd98b"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 34886 entries, 0 to 34885\n",
            "Data columns (total 8 columns):\n",
            " #   Column          Non-Null Count  Dtype \n",
            "---  ------          --------------  ----- \n",
            " 0   Çıkış Yılı      34886 non-null  int64 \n",
            " 1   Orijinal İsmi   34886 non-null  object\n",
            " 2   Yapıldığı Ülke  34886 non-null  object\n",
            " 3   Yönetmeni       34886 non-null  object\n",
            " 4   Oyuncu Kadrosu  33464 non-null  object\n",
            " 5   Türü            34858 non-null  object\n",
            " 6   Veri Kaynağı    34886 non-null  object\n",
            " 7   Olay Dizisi     34886 non-null  object\n",
            "dtypes: int64(1), object(7)\n",
            "memory usage: 2.1+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Bellek ve işlem gücünden tasarruf etmek için df['Olay Dizisi'] alanında veri ön işlemleri yapmamız gerekiyor.\n",
        "\n",
        "**Kaynaklar:**\n",
        "* https://practicaldatascience.co.uk/machine-learning/how-to-preprocess-text-for-nlp-in-four-easy-steps"
      ],
      "metadata": {
        "id": "whkSb47-l036"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Film ararken işe yaramayacak \\n \\r \\\\ veya benzer karakterler veri iskeletinden temizlenecektir. "
      ],
      "metadata": {
        "id": "drTU7NWSIjin"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.replace('  ','')\n",
        "df = df.replace(' ','')\n",
        "df = df.replace('.','')\n",
        "df = df.replace(':','')\n",
        "df = df.replace('\\\\','')\n",
        "df = df.replace('\\n','')\n",
        "df = df.replace('\\r','')\n",
        "df = df.replace('i̇','i')\n",
        "df = df.replace('ý','ı')"
      ],
      "metadata": {
        "id": "ttq0BAf2KQ3_"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Özellikle olay dizisi orijinal kaynağı wiki olduğu için atıflardan kaynaklı [1] ve [2] gibi girdilere sahiptir. Bunları da temizlememiz gerekmektedir."
      ],
      "metadata": {
        "id": "-wY76O4oxy4p"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['Olay Dizisi'] = df['Olay Dizisi'].replace('1','')\n",
        "df['Olay Dizisi'] = df['Olay Dizisi'].replace('2','')\n",
        "df['Olay Dizisi'] = df['Olay Dizisi'].replace('3','')\n",
        "df['Olay Dizisi'] = df['Olay Dizisi'].replace('4','')\n",
        "df['Olay Dizisi'] = df['Olay Dizisi'].replace('5','')\n",
        "df['Olay Dizisi'] = df['Olay Dizisi'].replace('6','')\n",
        "df['Olay Dizisi'] = df['Olay Dizisi'].replace('7','')\n",
        "df['Olay Dizisi'] = df['Olay Dizisi'].replace('8','')\n",
        "df['Olay Dizisi'] = df['Olay Dizisi'].replace('9','')\n",
        "df['Olay Dizisi'] = df['Olay Dizisi'].replace('[','')\n",
        "df['Olay Dizisi'] = df['Olay Dizisi'].replace(']','')"
      ],
      "metadata": {
        "id": "rUZU0umBxyCD"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Özetten arama yapmak için istediğimiz özelliklerin olduğu bir film girdisi yapılacaktır.\n",
        "\n",
        "Örnek girdi \"uzayda kovboylar ile kızılderililerin savaşı\" olsun ve bu girdiye göre elimizdeki film listesinden film önerisi yapmaya çalışalım. Ayrıca büyük küçük harf ile ilgili farklılıklar yaşamamak için her zaman küçük harf olarak çalışalım."
      ],
      "metadata": {
        "id": "GSnisE3S_Czz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "arananFilmOzeti = 'son aşk'.lower()"
      ],
      "metadata": {
        "id": "LiGJu3jZAP17"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Aramayı yapmadan önce arananFilmOzeti içinde gereksiz kelime bırakmayalım. Simgeleştirme (tokenize) için de Türkçe'ye uyumlu bir kelime simgeleştirici (word tokenizer) kullanalım.\n",
        "\n",
        "**Kaynaklar:** \n",
        "\n",
        "* https://www.veribilimiokulu.com/natural-language-toolkitnltk/\n",
        "* https://medium.com/kodcular/do%C4%9Fal-dil-i%CC%87%C5%9Fleme-nedir-uygulamal%C4%B1-%C3%B6rnek-b8eb15af7fd5\n",
        "* https://melikebektas95.medium.com/zemberek-k%C3%BCt%C3%BCphanesi-ile-t%C3%BCrk%C3%A7e-metinlerde-kelime-k%C3%B6klerinin-bulunmas%C4%B1-6ddd3a875d5f"
      ],
      "metadata": {
        "id": "Xx00ndM8AV3j"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Türkçe öz bulma için Zemberek'i kurup deneme yapıyoruz.\n",
        "\n",
        "**Kaynaklar:**\n",
        "* https://github.com/otuncelli/turkish-stemmer-python\n",
        "* https://snowballstem.org/algorithms/turkish/stemmer.html\n",
        "* https://www.nltk.org/howto/stem.html"
      ],
      "metadata": {
        "id": "WmWtwss0tZQV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install zemberek-python\n",
        "\n",
        "from zemberek import TurkishMorphology\n",
        "\n",
        "ozBulucu = TurkishMorphology.create_with_defaults()\n",
        "\n",
        "kelimeler = ['kalem','ilişkilendiremediklerimiz','gözlük',' gözlem']\n",
        "\n",
        "for kelime in kelimeler:\n",
        "  kelime = str(ozBulucu.analyze(kelime))\n",
        "  print(kelime)\n",
        "  print(kelime[kelime.find('analysisResults=[')+17:kelime.find(':')])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sunwTqY5qGFO",
        "outputId": "fe7f2358-4653-483e-d8a5-7df4f9a0a2b6"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting zemberek-python\n",
            "  Downloading zemberek_python-0.1.3-py3-none-any.whl (93.6 MB)\n",
            "\u001b[K     |████████████████████████████████| 93.6 MB 36 kB/s \n",
            "\u001b[?25hCollecting antlr4-python3-runtime==4.8\n",
            "  Downloading antlr4-python3-runtime-4.8.tar.gz (112 kB)\n",
            "\u001b[K     |████████████████████████████████| 112 kB 73.5 MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.19.0 in /usr/local/lib/python3.7/dist-packages (from zemberek-python) (1.21.6)\n",
            "Building wheels for collected packages: antlr4-python3-runtime\n",
            "  Building wheel for antlr4-python3-runtime (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for antlr4-python3-runtime: filename=antlr4_python3_runtime-4.8-py3-none-any.whl size=141230 sha256=c1449ec999ab8bcc9c0cf6c0ba1bda8161447119a9eb466a6f3222d5f85979af\n",
            "  Stored in directory: /root/.cache/pip/wheels/ca/33/b7/336836125fc9bb4ceaa4376d8abca10ca8bc84ddc824baea6c\n",
            "Successfully built antlr4-python3-runtime\n",
            "Installing collected packages: antlr4-python3-runtime, zemberek-python\n",
            "Successfully installed antlr4-python3-runtime-4.8 zemberek-python-0.1.3\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:zemberek.morphology.turkish_morphology:TurkishMorphology instance initialized in 9.708778858184814\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2022-08-18 02:18:07,804 - zemberek.morphology.turkish_morphology - INFO\n",
            "Msg: TurkishMorphology instance initialized in 9.708778858184814\n",
            "\n",
            "WordAnalysis{input='kalem', normalizedInput='kalem', analysisResults=[Kale:Noun, Prop] kale:Noun+A3sg+m:P1sg [kale:Noun] kale:Noun+A3sg+m:P1sg [kalem:Noun] kalem:Noun+A3sg}\n",
            "Kale\n",
            "WordAnalysis{input='ilişkilendiremediklerimiz', normalizedInput='ilişkilendiremediklerimiz', analysisResults=[ilişki:Noun] ilişki:Noun+A3sg|len:Acquire→Verb|dir:Caus→Verb+eme:Unable|dik:PastPart→Noun+ler:A3pl+imiz:P1pl}\n",
            "ilişki\n",
            "WordAnalysis{input='gözlük', normalizedInput='gözlük', analysisResults=[gözlük:Noun] gözlük:Noun+A3sg [göz:Noun] göz:Noun+A3sg|lük:Ness→Noun+A3sg}\n",
            "gözlük\n",
            "WordAnalysis{input='gözlem', normalizedInput='gözlem', analysisResults=[gözlem:Noun] gözlem:Noun+A3sg [Gözlem:Noun, Prop] gözlem:Noun+A3sg}\n",
            "gözlem\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Zemberek'in sonuçları bizim için çok iyi ama maliyet etkinliği zaman açısından çok pahalı geldi. Türkçe'ye uyumlu diğer öz / kök bulucu araçları deniyoruz.\n",
        "\n",
        "**Kaynaklar:**\n",
        "* https://nlp.stanford.edu/IR-book/html/htmledition/stemming-and-lemmatization-1.html\n",
        "* https://www.tutorialspoint.com/natural_language_toolkit/natural_language_toolkit_stemming_lemmatization.htm"
      ],
      "metadata": {
        "id": "H0vje07VaRSp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "testEdilecekKelimeler = ['kalem','ilişkilendiremediklerimiz','gözlük','gözlem']\n",
        "print('Test edilecek kelimeler:',testEdilecekKelimeler)\n",
        "print('Kök Bulma YÖNTEM 1 - snowballstemmer','\\n',50*'-',sep='')\n",
        "!pip install snowballstemmer\n",
        "from snowballstemmer import TurkishStemmer\n",
        "turkStem = TurkishStemmer()\n",
        "testSonucu = []\n",
        "for kelime in testEdilecekKelimeler:\n",
        "  testSonucu.append(turkStem.stemWord(kelime))\n",
        "print(testSonucu,'\\n\\n\\n')\n",
        "\n",
        "print('Kök Bulma YÖNTEM 2 - nltk','\\n',50*'-',sep='')\n",
        "!pip install nltk\n",
        "from nltk.stem import PorterStemmer\n",
        "ps=PorterStemmer()\n",
        "testSonucu = []\n",
        "for kelime in testEdilecekKelimeler:\n",
        "  testSonucu.append(ps.stem(kelime))\n",
        "print(testSonucu,'\\n\\n\\n')\n",
        "\n",
        "print('Kök Bulma YÖNTEM 3 - TurkishStemmer','\\n',50*'-',sep='')\n",
        "!pip install TurkishStemmer\n",
        "from TurkishStemmer import TurkishStemmer\n",
        "stemmer = TurkishStemmer()\n",
        "testSonucu = []\n",
        "for kelime in testEdilecekKelimeler:\n",
        "  testSonucu.append(stemmer.stem(kelime))\n",
        "print(testSonucu,'\\n\\n\\n')\n",
        "\n",
        "print('Kök Bulma YÖNTEM 4 - WordNetLemmatizer','\\n',50*'-',sep='')\n",
        "import nltk\n",
        "nltk.download('wordnet')\n",
        "nltk.download('omw-1.4')\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "lemmatizer = WordNetLemmatizer()\n",
        "testSonucu = []\n",
        "for kelime in testEdilecekKelimeler:\n",
        "  testSonucu.append(lemmatizer.lemmatize(kelime))\n",
        "print(testSonucu,'\\n\\n\\n')\n",
        "\n",
        "print('Kök Bulma YÖNTEM 5 - LancasterStemmer','\\n',50*'-',sep='')\n",
        "import nltk\n",
        "from nltk.stem import LancasterStemmer\n",
        "stemmer = LancasterStemmer()\n",
        "testSonucu = []\n",
        "for kelime in testEdilecekKelimeler:\n",
        "  testSonucu.append(stemmer.stem(kelime))\n",
        "print(testSonucu,'\\n\\n\\n')\n",
        "\n",
        "print('Kök Bulma YÖNTEM 6 - RegexpStemmer','\\n',50*'-',sep='')\n",
        "import nltk\n",
        "from nltk.stem import RegexpStemmer\n",
        "stemmer = RegexpStemmer('tr')\n",
        "testSonucu = []\n",
        "for kelime in testEdilecekKelimeler:\n",
        "  testSonucu.append(stemmer.stem(kelime))\n",
        "print(testSonucu,'\\n\\n\\n')\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nKnCn-qNdDk4",
        "outputId": "465cdcec-bd90-4beb-e282-9be3ac0bd31f"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test edilecek kelimeler: ['kalem', 'ilişkilendiremediklerimiz', 'gözlük', 'gözlem']\n",
            "Kök Bulma YÖNTEM 1 - snowballstemmer\n",
            "--------------------------------------------------\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: snowballstemmer in /usr/local/lib/python3.7/dist-packages (2.2.0)\n",
            "['kale', 'ilişkilendiremedik', 'gözlük', 'gözle'] \n",
            "\n",
            "\n",
            "\n",
            "Kök Bulma YÖNTEM 2 - nltk\n",
            "--------------------------------------------------\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: nltk in /usr/local/lib/python3.7/dist-packages (3.7)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from nltk) (7.1.2)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from nltk) (4.64.0)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from nltk) (1.1.0)\n",
            "Requirement already satisfied: regex>=2021.8.3 in /usr/local/lib/python3.7/dist-packages (from nltk) (2022.6.2)\n",
            "['kalem', 'ilişkilendiremediklerimiz', 'gözlük', 'gözlem'] \n",
            "\n",
            "\n",
            "\n",
            "Kök Bulma YÖNTEM 3 - TurkishStemmer\n",
            "--------------------------------------------------\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting TurkishStemmer\n",
            "  Downloading TurkishStemmer-1.3-py3-none-any.whl (20 kB)\n",
            "Installing collected packages: TurkishStemmer\n",
            "Successfully installed TurkishStemmer-1.3\n",
            "['kalem', 'ilişkilendiremedik', 'gözlük', 'gözle'] \n",
            "\n",
            "\n",
            "\n",
            "Kök Bulma YÖNTEM 4 - WordNetLemmatizer\n",
            "--------------------------------------------------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data] Downloading package omw-1.4 to /root/nltk_data...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['kalem', 'ilişkilendiremediklerimiz', 'gözlük', 'gözlem'] \n",
            "\n",
            "\n",
            "\n",
            "Kök Bulma YÖNTEM 5 - LancasterStemmer\n",
            "--------------------------------------------------\n",
            "['kalem', 'ilişkilendiremediklerim', 'gözlük', 'gözlem'] \n",
            "\n",
            "\n",
            "\n",
            "Kök Bulma YÖNTEM 6 - RegexpStemmer\n",
            "--------------------------------------------------\n",
            "['kalem', 'ilişkilendiremediklerimiz', 'gözlük', 'gözlem'] \n",
            "\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "KÖK Bulma 3. Yöntemi seçtik.\n",
        "Gereksiz kelimeleri çıkartıyoruz. (Tokenizer olarak Türkçe'ye daha uyumlu olan TrTokenizer kullanıyoruz)\n",
        "\n",
        "**Kaynaklar:**\n",
        "* https://github.com/apdullahyayik/TrTokenizer\n",
        "* https://pythonprogramming.net/stop-words-nltk-tutorial/"
      ],
      "metadata": {
        "id": "4BDNqT9AtiAe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install trtokenizer\n",
        "from trtokenizer.tr_tokenizer import SentenceTokenizer, WordTokenizer\n",
        "\n",
        "cumleSimgelestirmeNesnesi = SentenceTokenizer()\n",
        "kelimeSimgelestirmeNesnesi = WordTokenizer()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m0CYjfd-ImgA",
        "outputId": "bbb6982d-5acb-482b-86db-4468c50c67cf"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting trtokenizer\n",
            "  Downloading trtokenizer-0.0.3-py3-none-any.whl (7.7 kB)\n",
            "Requirement already satisfied: regex in /usr/local/lib/python3.7/dist-packages (from trtokenizer) (2022.6.2)\n",
            "Installing collected packages: trtokenizer\n",
            "Successfully installed trtokenizer-0.0.3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Gereksiz kelimeleri çıkartıyoruz\n",
        "import nltk\n",
        "nltk.download('stopwords') # Gereksiz Kelimeler\n",
        "nltk.download('punkt')     # Noktalama İşaretleri\n",
        "from nltk.corpus import stopwords\n",
        "\n",
        "gereksizler = set(stopwords.words('turkish'))\n",
        "cumle = ' '\n",
        "kelimeler = kelimeSimgelestirmeNesnesi.tokenize(cumle.join(cumleSimgelestirmeNesnesi.tokenize(arananFilmOzeti)))\n",
        "gereksizKelimelerCikarilmisArananFilmOzeti = []\n",
        " \n",
        "for k in kelimeler:\n",
        "    if k not in gereksizler:\n",
        "        gereksizKelimelerCikarilmisArananFilmOzeti.append(k)\n",
        " \n",
        "print(gereksizKelimelerCikarilmisArananFilmOzeti)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yn8Bq1Q8CNjr",
        "outputId": "ce82a539-362d-4b02-bf3a-b4cb886dada8"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['son', 'aşk']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def simgelestir(sutun):\n",
        "  simgelesitirilmisSutundakiler = kelimeSimgelestirmeNesnesi.tokenize(sutun)\n",
        "  return list(set([w.lower() for w in simgelesitirilmisSutundakiler if w.isalpha()]))"
      ],
      "metadata": {
        "id": "ACpsAEdwmDMO"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['simgelestirilmis'] = df.apply(lambda x: simgelestir(x['Olay Dizisi']), axis=1)\n",
        "df[['Orijinal İsmi', 'simgelestirilmis']].head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "SnEvPpoWsSFH",
        "outputId": "90b5c407-f2f4-4955-de48-8759d00fb0a0"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                      Orijinal İsmi  \\\n",
              "0            Kansas Saloon Smashers   \n",
              "1     Love by the Light of the Moon   \n",
              "2           The Martyred Presidents   \n",
              "3  Terrible Teddy, the Grizzly King   \n",
              "4            Jack and the Beanstalk   \n",
              "\n",
              "                                    simgelestirilmis  \n",
              "0  [polis, barı, kafasına, ardından, herkesin, yü...  \n",
              "1  [neden, gökyüzünü, öğrenir, şapkasıyla, geçen,...  \n",
              "2  [film, adalet, yere, mezarın, suikast, her, ot...  \n",
              "3  [ardından, bıçaklamaya, kez, gibi, taşıyan, at...  \n",
              "4  [film, kata, ettiğini, annesinin, bahçeye, tır...  "
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def remove_stopwords(simgelestirilmisSutun):\n",
        "    stops = set(stopwords.words(\"turkish\"))\n",
        "    return [word for word in simgelestirilmisSutun if not word in stops]"
      ],
      "metadata": {
        "id": "fBpIz8vZ1egD"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['sadelestirilmis'] = df.apply(lambda x: remove_stopwords(x['simgelestirilmis']), axis=1)\n",
        "df[['Orijinal İsmi', 'sadelestirilmis']].head()"
      ],
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 206
        },
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              "                      Orijinal İsmi  \\\n",
              "0            Kansas Saloon Smashers   \n",
              "1     Love by the Light of the Moon   \n",
              "2           The Martyred Presidents   \n",
              "3  Terrible Teddy, the Grizzly King   \n",
              "4            Jack and the Beanstalk   \n",
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              "                                     sadelestirilmis  \n",
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              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['sadelestirilmis'] = df.apply(lambda x: remove_stopwords(x['sadelestirilmis']), axis=1)\n",
        "df[['Orijinal İsmi', 'sadelestirilmis']].head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "nVxZOGw55h-b",
        "outputId": "3436af46-e658-4274-b9a9-8333d1019a3a"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                      Orijinal İsmi  \\\n",
              "0            Kansas Saloon Smashers   \n",
              "1     Love by the Light of the Moon   \n",
              "2           The Martyred Presidents   \n",
              "3  Terrible Teddy, the Grizzly King   \n",
              "4            Jack and the Beanstalk   \n",
              "\n",
              "                                     sadelestirilmis  \n",
              "0  [polis, barı, kafasına, ardından, herkesin, yü...  \n",
              "1  [gökyüzünü, öğrenir, şapkasıyla, geçen, tünedi...  \n",
              "2  [film, adalet, yere, mezarın, suikast, oturur,...  \n",
              "3  [ardından, bıçaklamaya, taşıyan, atının, bir, ...  \n",
              "4  [film, kata, ettiğini, annesinin, bahçeye, tır...  "
            ],
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              "      <th>Orijinal İsmi</th>\n",
              "      <th>sadelestirilmis</th>\n",
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              "    <tr>\n",
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              "    </tr>\n",
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              "    </tr>\n",
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              "      <th>4</th>\n",
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              "                                                     [key], {});\n",
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            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install TurkishStemmer\n",
        "from TurkishStemmer import TurkishStemmer\n",
        "ozBulucu = TurkishStemmer()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8oinmumuoGPM",
        "outputId": "f3ee9503-2cac-4ec9-a7a5-95029d1e1d71"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: TurkishStemmer in /usr/local/lib/python3.7/dist-packages (1.3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Yöntem 2 ile devam ederek Yöntem 4'ün sonuçlarını karşılaştırmak istedik"
      ],
      "metadata": {
        "id": "xp_jltz5uzrd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('Kök Bulma YÖNTEM 2 - nltk','\\n',50*'-',sep='')\n",
        "!pip install nltk\n",
        "from nltk.stem import PorterStemmer\n",
        "ozBulucu=PorterStemmer()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AmUVgUE1u7Jj",
        "outputId": "48ea9f5f-b3db-4413-a2fc-7fb6d6d15a90"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Kök Bulma YÖNTEM 2 - nltk\n",
            "--------------------------------------------------\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: nltk in /usr/local/lib/python3.7/dist-packages (3.7)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from nltk) (4.64.0)\n",
            "Requirement already satisfied: regex>=2021.8.3 in /usr/local/lib/python3.7/dist-packages (from nltk) (2022.6.2)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from nltk) (7.1.2)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from nltk) (1.1.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def ozunuBuldur(simgelestirilmisSutun):\n",
        "  sonucListesi = []\n",
        "  for kelime in simgelestirilmisSutun:\n",
        "    kelime = str(ozBulucu.stem(kelime)).lower()\n",
        "    sonucListesi.append(kelime)\n",
        "  return sonucListesi"
      ],
      "metadata": {
        "id": "Igj1ub0y19qD"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(ozunuBuldur(gereksizKelimelerCikarilmisArananFilmOzeti))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PS76AXXB66-8",
        "outputId": "2b3624a9-3024-4b68-9758-c13f219d29ca"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['son', 'aşk']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Türkçe özünü bulma (stemmig) işlemi Google Colab Standart hesabı ile yaklaşık 6 dakika sürüyor. Google Colab Pro hesabı ile yüksek RAM (35.24 GB) ve TPU kullanarak süre 5 dakikaya düşüyor.\n",
        "\n",
        "**ÖNEMLİ BULGU:**\n",
        "\n",
        "NLTK Stemming Tool ile TurkishStemmer arasında 100000 civarında kelime oynuyor."
      ],
      "metadata": {
        "id": "wYbISgpRv4O1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['ozunuBulmus'] = df.apply(lambda x: ozunuBuldur(x['sadelestirilmis']), axis=1)\n",
        "df[['Orijinal İsmi', 'ozunuBulmus']].head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "vCsMsOhO2INh",
        "outputId": "52715a8c-facb-4ff8-d22c-d1d6b0b4fa3c"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                      Orijinal İsmi  \\\n",
              "0            Kansas Saloon Smashers   \n",
              "1     Love by the Light of the Moon   \n",
              "2           The Martyred Presidents   \n",
              "3  Terrible Teddy, the Grizzly King   \n",
              "4            Jack and the Beanstalk   \n",
              "\n",
              "                                         ozunuBulmus  \n",
              "0  [poli, barı, kafasına, ardından, herkesin, yüz...  \n",
              "1  [gökyüzünü, öğrenir, şapkasıyla, geçen, tünedi...  \n",
              "2  [film, adalet, yere, mezarın, suikast, oturur,...  \n",
              "3  [ardından, bıçaklamaya, taşıyan, atının, bir, ...  \n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def kelimeleriBirlestir(simgelestirilmisSutun):\n",
        "  simgelestirilmisSutun = list(set(simgelestirilmisSutun))\n",
        "  return (' '.join(simgelestirilmisSutun))"
      ],
      "metadata": {
        "id": "yUtxKG4k2lwi"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Ozet'] = df.apply(lambda x: kelimeleriBirlestir(x['ozunuBulmus']), axis=1)\n",
        "df[['Orijinal İsmi', 'Ozet']].head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "U9yIZMhS2vH4",
        "outputId": "4203cfc1-fce1-41c8-994f-60a459ee77af"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                      Orijinal İsmi  \\\n",
              "0            Kansas Saloon Smashers   \n",
              "1     Love by the Light of the Moon   \n",
              "2           The Martyred Presidents   \n",
              "3  Terrible Teddy, the Grizzly King   \n",
              "4            Jack and the Beanstalk   \n",
              "\n",
              "                                                Ozet  \n",
              "0  barı herkesin kafasına ardından yüzüne i̇rland...  \n",
              "1  gökyüzünü öğrenir şapkasıyla geçen tünediği iy...  \n",
              "2  film adalet yere mezarın suikast i̇lkind oturu...  \n",
              "3  ardından bıçaklamaya theodor taşıyan atının bi...  \n",
              "4  film kata ettiğini annesinin tırmandığında büy...  "
            ],
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              "  <thead>\n",
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              "      <td>barı herkesin kafasına ardından yüzüne i̇rland...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
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              "      <td>gökyüzünü öğrenir şapkasıyla geçen tünediği iy...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>The Martyred Presidents</td>\n",
              "      <td>film adalet yere mezarın suikast i̇lkind oturu...</td>\n",
              "    </tr>\n",
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              "      <th>3</th>\n",
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              "      <td>ardından bıçaklamaya theodor taşıyan atının bi...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Jack and the Beanstalk</td>\n",
              "      <td>film kata ettiğini annesinin tırmandığında büy...</td>\n",
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              "          const element = document.querySelector('#df-75833066-8cdf-4958-90b6-cdc120fa46a1');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info(verbose=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N85RCbd-3wuh",
        "outputId": "65e854bc-fd42-4f84-8d73-838372e81b55"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 34886 entries, 0 to 34885\n",
            "Data columns (total 12 columns):\n",
            " #   Column            Non-Null Count  Dtype \n",
            "---  ------            --------------  ----- \n",
            " 0   Çıkış Yılı        34886 non-null  int64 \n",
            " 1   Orijinal İsmi     34886 non-null  object\n",
            " 2   Yapıldığı Ülke    34886 non-null  object\n",
            " 3   Yönetmeni         34886 non-null  object\n",
            " 4   Oyuncu Kadrosu    33464 non-null  object\n",
            " 5   Türü              34858 non-null  object\n",
            " 6   Veri Kaynağı      34886 non-null  object\n",
            " 7   Olay Dizisi       34886 non-null  object\n",
            " 8   simgelestirilmis  34886 non-null  object\n",
            " 9   sadelestirilmis   34886 non-null  object\n",
            " 10  ozunuBulmus       34886 non-null  object\n",
            " 11  Ozet              34886 non-null  object\n",
            "dtypes: int64(1), object(11)\n",
            "memory usage: 3.2+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.drop(columns=['simgelestirilmis','sadelestirilmis','ozunuBulmus','Olay Dizisi'])\n",
        "df.info(verbose=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CmjQtzgU32wI",
        "outputId": "67baf697-0519-4dee-bf81-c787b336d4de"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 34886 entries, 0 to 34885\n",
            "Data columns (total 8 columns):\n",
            " #   Column          Non-Null Count  Dtype \n",
            "---  ------          --------------  ----- \n",
            " 0   Çıkış Yılı      34886 non-null  int64 \n",
            " 1   Orijinal İsmi   34886 non-null  object\n",
            " 2   Yapıldığı Ülke  34886 non-null  object\n",
            " 3   Yönetmeni       34886 non-null  object\n",
            " 4   Oyuncu Kadrosu  33464 non-null  object\n",
            " 5   Türü            34858 non-null  object\n",
            " 6   Veri Kaynağı    34886 non-null  object\n",
            " 7   Ozet            34886 non-null  object\n",
            "dtypes: int64(1), object(7)\n",
            "memory usage: 2.1+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Arama yapmadan önce gerekli TF / IDF matrisini (dizey) oluşturalım.\n",
        "\n",
        "**Kaynaklar:**\n",
        "\n",
        "* https://monkeylearn.com/blog/what-is-tf-idf/\n",
        "* https://medium.com/@cmukesh8688/tf-idf-vectorizer-scikit-learn-dbc0244a911a\n"
      ],
      "metadata": {
        "id": "cfbjZqz7GsqJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "import nltk\n",
        "nltk.download(\"stopwords\")\n",
        "nltk.download('punkt')\n",
        "from nltk.corpus import stopwords\n",
        "aranilacak_tfidfNesnesi = TfidfVectorizer(stop_words=stopwords.words(\"turkish\"))\n",
        "aranilacak_tfidfDizeyi = aranilacak_tfidfNesnesi.fit_transform(df['Ozet'])\n",
        "aranilacak_tfidfDizeyi.shape"
      ],
      "metadata": {
        "id": "8HU10SO5JYwg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6b9fb15f-492a-4cad-b2ef-6ff85d7897a6"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n",
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(34886, 215685)"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "ozBulucu TurkishStemmer olunca aranilacak_tfidfDizeyi.shape (34886, 113148)\n",
        "ozBulucu nltk.stem olunca aranilacak_tfidfDizeyi.shape (34886, 215685)"
      ],
      "metadata": {
        "id": "WS2buHqJrVmd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "aranilacak_tfidfNesnesi.get_feature_names()[5000:5010]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IW8YQdnVj4fD",
        "outputId": "af5f5eb2-06d9-48b9-9b62-4cea46410512"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['alışkanlıkları',\n",
              " 'alışkanlıkların',\n",
              " 'alışkanlıklarına',\n",
              " 'alışkanlıklarında',\n",
              " 'alışkanlıklarından',\n",
              " 'alışkanlıklarını',\n",
              " 'alışkanlıklarının',\n",
              " 'alışkanlıklarıyla',\n",
              " 'alışkanlıktan',\n",
              " 'alışkanlıktır']"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics.pairwise import linear_kernel\n",
        "cosine_sim = linear_kernel(aranilacak_tfidfDizeyi, aranilacak_tfidfDizeyi)\n",
        "cosine_sim.shape"
      ],
      "metadata": {
        "id": "Blf3oHCMkGVT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "76e271c7-c28f-43e1-aec0-7acd7d3e028e"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(34886, 34886)"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Yukarıdaki hesabı yapması için yaklaşık 25 GB'lık RAM yeterli oldu."
      ],
      "metadata": {
        "id": "nyP0B5OP0xQ8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "indices = pd.Series(df.index, index=df['Ozet']).drop_duplicates()\n",
        "print(indices[:10])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_FhIfU5dkPXZ",
        "outputId": "c798350b-56bb-4336-8213-632c58789f9d"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Ozet\n",
            "barı herkesin kafasına ardından yüzüne i̇rlandalı gitmesini mahvetmey birayla adamın salonda adama çektil birayı bir servisi basmakalıp poli doldurduktan nation takipçileri içeri barmen çalışıyor müşteriler kovasını kasayı önce maden ortaya şapkasını içki suyu saldırdılar gözlerin demirbaşları aynaları kırmaya sonra carri püskürtür grup yazar çıkmadan                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 0\n",
            "gökyüzünü öğrenir şapkasıyla geçen tünediği iyi sarılırlar üzerin parkın yanında görüşü görebilmek ay adam engellenir çift güler korkulukta bir omzunun genç havalandırır çitin terk asılı üzerind ağacın yüzle banka sahned ettiği geceleri kaşlarını şeyi yanından kadını çatmasına son yukarı gülümsemesi sonra kadının boyanmış ayın                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          1\n",
            "film adalet yere mezarın suikast i̇lkind oturur portrelerini yüzü lincoln bir william kameradan kurbanı portalı sekiz biraz uzun garfield abd görüntüleme kız sunağın seriyor ortasında üç olan atışta dakikadan ayaklarını iki saniyeden suikastçı başkanının ikinci çekimden jame süren dibind                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  2\n",
            "ardından bıçaklamaya theodor taşıyan atının bir ağaca soluna tüfeğini i̇kinci saniy yakından tutar aşağı çekimden coşkuyla görev havada notlar fotoğrafçısı tutuyor takip işaretlerini eden ormanda bilinciyl kamerasını atış mevsimind hala çekim düzeltir ön patikada düşürüyor kendini tepeden süren yere plandaki temsilcisi temsil farklı biraz üzerind dönemin yerind kış fotoğrafçı ödülünü şeyi sırasıyla sadec pankartlar yardımcısı sıradan kameraya oluşan ahşabın ediyor kadar düşer mensubu kedisi diğer yazılı basın teddi çekimin görünen doğru iki aktör ilk ev ancak doğrultuyor onu ilerliyor adam başkan                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       3\n",
            "film kata ettiğini annesinin tırmandığında büyüdüğünü kadar girdiği pren uyurken klasik kurtulan kovalar düşer uyandığında payı eski kıl evin ziyaret eden bir güvenli ipuçları neler taka bekleyeceğin bilinen zorla versiyonda kralın dair açıklar hale bırakmaya fasulyel fasuly peri bahçey uyarlaması fark kutlarken önce zorladığını sırığının indirilmiş olarak bunu olan sapına masalın gelmeden tahttan ön jack devin eve dönebileceğini yukarı ancak tepey sonra onu eder ineğini dev                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   4\n",
            "rüya minik alic davet kraliç şeyin bir alayına aniden sihirli küçük yönetilen dönüşür verdiğind yi vermey izin i̇çind düşesin büzülür sonra domuza aşağı girer olduğu başlayınca bahç çalışmasına elinden takip bebeği geçecek hayran gücendirdiğind etiketli kapı beni kendisin iskambil şişer katılmaya evi geldiğind olarak yürüyen çocuğu bebek şeyler kadını girecek uyanır normal beyaz i̇ç bulduğunda kartları yelpaz kulakları olduğunu ikna kullanması çılgın mutfağa ı üzerind kasıtsız cellat tarafından yazan partisi kapıdan bulmaya kaybolur anlar oyun oynaması büyük oluşan kadar dışarı diğerlerinden çay kraliyet görünür onun köpek süre küçülmesin çıkabilmek kedisi tutan kutular şişe boyutuna deliğinden ye onunla kıvranarak fırlatmaya aşçı doğru tavşanı tavşan cheshir yer ancak onu eder kraliçeyi kadının                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            5\n",
            "tren silah anlatmak dinamitl biner diğerleri acel kutu zorlandığı adamlar ıslatarak bayıltır taşıyan bir salonunda aniden haydutları doğulu deposundaki zorla treni emrini yakalayan operatörü çatışmada çalınan su ederken lokomotifin çalışır tekrar komik lokomotifl elçiyi bırakıldığı sonra i̇ki girer şimdi ofisin grup haydut durdurmaya alan ayaklarına postayı eşyayı ayırmaya değerli demiryolu doldurma istasyonun oluştururlar kapı öldürür ihalesini yolcuları soygunu onlara itfaiyeciyi kızı mühendis uyanır iletmey bağlı haydutlar durduğunda film kaçarlar etmey getirir arada arabaya bırakır ganimetlerini ekspr dan trenden zoruyla yabancının ateş haydutun yerlil haydutlara atlarının kaçmaya açılır suyla son zorlamasıyla lokomotifi yemeğini rahatlama açar operatör öldüren indirir tüfekl ofisind serbest vadid hepsini ona anında geri eşyaları girerek telgraf iki yolcu ancak mühendisi onu bilincin                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              6\n",
            "film açar çömlek umuduyla onun çanak yaşam başlar taşınan bir gitmey i̇şler aileyi karısı sakin konu şiddet fırlatmaya banliyöler tutuklanmasına yol ter başvurur                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 7\n",
            "portreleri gözlerindeki ormana tren ardından tipik gözleri sığ buldu emniyet gösteren diğerleri setten şekild yemin arasından sersemlemiş keser çete noktasına kraliç yolun ipl grubunu resimlerl kıyafetler bir bakarken bırakıp trafiğ gazetel elini treni kaçan trenin soyguncuları ayrılan ininin soygunun üyey yaya sonunda olmalarına kaldırmaya çalıp duvarlar hattına deniz kurtarır sarar takib takipçilerinden yaklaşır gizlic midillileri belli ni tekrar bağlar kovalamaca gönüllüleri motorunu atlar görüyor mücadelenin etrafını sonra başarılı i̇ki aşağı şimdi değerlileri kalanının haydut suçluların soyguncuların durdurmaya kraliçesi yakınlardaki içini görünüme önde güvenilir bıçak ciddi noktası giderl açılış yeni üyeyi çapraz ahıra dik demiryolu odanın görülürler heyecanlı kaldırır dolu ipi ayırarak eden ikisi kısmı teknel gelen arkasından sahnesi vurur cesurca çetenin ortalıkta odaya girdiğini bırakırlar anlamsız devam geçtiği sürede rayları doruk eder dekor haydutların yolcuları mühendi başında olarak sağ toplarlar yağma yürüyen şanslarını çeteden cephaneleri üzerinden bebeklerden aldıktan yapar liğin kendini çalılıklarda ederek sahn bağlı ulaşamıyorlar çıkıp liderlik düzine motor yere birbirin ilerisind desperado karşı yanıt eğilirken gölete çökmüş arada sırasında sonraki arama zorlanıyor ayrıntılarını sahip midillilerini ekip denemek genç yakalanmamak eng terk bağlanmış resimli kıvrımındaki dağ uzun yürekten aceleyl lideri götürülen onları baltayla fazla raydaki şeker hemen arkalarından şeyi tarafından yabani çetesini olan üyenin arabayı kısa son geçmeden kararlaştırılan inlerin bölünürken ulaşan ideal tepey zamanında yerin bandaj yolu bankaya kabinden ormandan rağmen zorunda kafasına oluşan korkmuş yolculardan tam kadar tane karıştırma otların indirirken esa öğrenen nöbetçi batılı ağızlıklarına iş tebrik demiryolunun ünlü dolanırken götürülür binerken tutmak verir polislerden ortasına çıkarıldığında köprünün alarm poli yatağına setin kurtulduklarına olay roman kahramanlıklarını dere midillilerin soyguncular tereddüt önlemek motoru geri yı işbirlikçi doğru alınır tabanca spot uygun beklediği inanarak yakın vahşi ulaştıklarında saklanıp ancak yedi onu fişeklerl yerleştirildi canlı bölmey    8\n",
            "uyuyamazlar bacadan işini evinden çocuk şehirli baba çıkarırlar şiirin geyiği gösteriliyor sahnel çocukları evin noel bir gerçek merdivenlerden iner ren savaşına çocuklar üzerind birlikt harri kullanılarak kızak tadını hediyelerinin asıp dizeleri atölyed çoraplarını ailenin yatarlar canlandırdığı sırada yastık beslerken hediyeleri ancak girer aşağı                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    9\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def tavsiyeVer(filmOzeti, cosine_sim=cosine_sim):\n",
        "    idx = indices[filmOzeti]\n",
        "    sim_scores = list(enumerate(cosine_sim[idx]))\n",
        "    sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)\n",
        "    sim_scores = sim_scores[1:11]\n",
        "    movie_indices = [i[0] for i in sim_scores]\n",
        "    return df['Orijinal İsmi'].iloc[movie_indices]"
      ],
      "metadata": {
        "id": "Pxj6Oar6k_s7"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(tavsiyeVer(gereksizKelimelerCikarilmisArananFilmOzeti))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        },
        "id": "KB2FEfhO1i9M",
        "outputId": "ccb2a6a2-2ace-4661-aa89-020d7e921e66"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-34-15e5448e4d45>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtavsiyeVer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgereksizKelimelerCikarilmisArananFilmOzeti\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[0;32m<ipython-input-33-a12de544dad1>\u001b[0m in \u001b[0;36mtavsiyeVer\u001b[0;34m(filmOzeti, cosine_sim)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mtavsiyeVer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilmOzeti\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcosine_sim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcosine_sim\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;32m----> 2\u001b[0;31m     \u001b[0midx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindices\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfilmOzeti\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      3\u001b[0m     \u001b[0msim_scores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcosine_sim\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\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[1;32m      4\u001b[0m     \u001b[0msim_scores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msorted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msim_scores\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreverse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0msim_scores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msim_scores\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m11\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    964\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    965\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 966\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_with\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\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    967\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    968\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_with\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\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;32m/usr/local/lib/python3.7/dist-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m_get_with\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1004\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1005\u001b[0m         \u001b[0;31m# handle the dup indexing case GH#4246\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1006\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\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   1007\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1008\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_values_tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\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;32m/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    929\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    930\u001b[0m             \u001b[0mmaybe_callable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_if_callable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 931\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmaybe_callable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\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    932\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    933\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_is_scalar_access\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtuple\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;32m/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_axis\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1151\u001b[0m                     \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Cannot index with multidimensional key\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1152\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1153\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_iterable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\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   1154\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1155\u001b[0m             \u001b[0;31m# nested tuple slicing\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_iterable\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1091\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1092\u001b[0m         \u001b[0;31m# A collection of keys\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1093\u001b[0;31m         \u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_listlike_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\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   1094\u001b[0m         return self.obj._reindex_with_indexers(\n\u001b[1;32m   1095\u001b[0m             \u001b[0;34m{\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallow_dups\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_get_listlike_indexer\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1312\u001b[0m             \u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_indexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reindex_non_unique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeyarr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1313\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1314\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_read_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\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   1315\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1316\u001b[0m         if needs_i8_conversion(ax.dtype) or isinstance(\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_validate_read_indexer\u001b[0;34m(self, key, indexer, axis)\u001b[0m\n\u001b[1;32m   1372\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0muse_interval_msg\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1373\u001b[0m                     \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1374\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"None of [{key}] are in the [{axis_name}]\"\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   1375\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1376\u001b[0m             \u001b[0mnot_found\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mensure_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmissing_mask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnonzero\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\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;31mKeyError\u001b[0m: \"None of [Index(['son', 'aşk'], dtype='object', name='Ozet')] are in the [index]\""
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Olay Dizilerimiz ile arananFilmOzetinin benzerliği üzerinde çalışmaya başlayabiliriz. Benzerlik araştırması için çeşitli algoritmalar vardır.\n",
        "\n",
        "![image.png](data:image/png;base64,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)\n",
        "\n",
        "**Kaynaklar:**\n",
        "\n",
        "* https://goodboychan.github.io/python/datacamp/natural_language_processing/2020/07/17/04-TF-IDF-and-similarity-scores.html\n",
        "* https://leimao.github.io/blog/Cosine-Similarity-VS-Pearson-Correlation-Coefficient/\n",
        "* https://towardsdatascience.com/17-types-of-similarity-and-dissimilarity-measures-used-in-data-science-3eb914d2681\n",
        "* https://towardsdatascience.com/9-distance-measures-in-data-science-918109d069fa\n",
        "* https://scikit-learn.org/stable/modules/metrics.html\n",
        "* https://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise_distances.html\n",
        "* https://docs.scipy.org/doc/scipy/reference/spatial.distance.html\n",
        "\n",
        "**UYARI:** Seçilen yöntemin *seyrek dizeyleri* (sparse matrices) destekleyip desteklemediğini dokümantasyonundan kontrol ediniz."
      ],
      "metadata": {
        "id": "4NSeJz0cQCOI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Benzerlik listesinden ilk olarak Euclidean benzerliği deneyelim\n",
        "# https://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.euclidean_distances.html\n",
        "\n",
        "from sklearn.metrics.pairwise import linear_kernel\n",
        "benzerlik = linear_kernel(aranilacak_tfidfDizeyi, aranilacak_tfidfDizeyi)\n",
        "\n",
        "filmBasliklari = pd.Series(df.index, index=df['Orijinal İsmi']).drop_duplicates()\n",
        "\n",
        "idx = filmBasliklari[gereksizKelimelerCikarilmisArananFilmOzeti]\n",
        "sim_scores = list(enumerate(benzerlik[idx]))\n",
        "sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)\n",
        "sim_scores = sim_scores[1:11]\n",
        "movie_indices = [i[0] for i in sim_scores]\n",
        "print(df['Orijinal İsim'].iloc[movie_indices])"
      ],
      "metadata": {
        "id": "pqhUw1n-RcEX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 380
        },
        "outputId": "52904418-1ac7-422a-bc3e-30cdc00ccd64"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-35-e4a8d826c69c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mfilmBasliklari\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Orijinal İsmi'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop_duplicates\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      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0midx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfilmBasliklari\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mgereksizKelimelerCikarilmisArananFilmOzeti\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     10\u001b[0m \u001b[0msim_scores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbenzerlik\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\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[1;32m     11\u001b[0m \u001b[0msim_scores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msorted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msim_scores\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreverse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    964\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    965\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 966\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_with\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\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    967\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    968\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_with\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\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;32m/usr/local/lib/python3.7/dist-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m_get_with\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1004\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1005\u001b[0m         \u001b[0;31m# handle the dup indexing case GH#4246\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1006\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\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   1007\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1008\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_values_tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\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;32m/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    929\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    930\u001b[0m             \u001b[0mmaybe_callable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_if_callable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 931\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmaybe_callable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\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    932\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    933\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_is_scalar_access\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtuple\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;32m/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_axis\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1151\u001b[0m                     \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Cannot index with multidimensional key\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1152\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1153\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_iterable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\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   1154\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1155\u001b[0m             \u001b[0;31m# nested tuple slicing\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_iterable\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1091\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1092\u001b[0m         \u001b[0;31m# A collection of keys\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1093\u001b[0;31m         \u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_listlike_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\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   1094\u001b[0m         return self.obj._reindex_with_indexers(\n\u001b[1;32m   1095\u001b[0m             \u001b[0;34m{\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallow_dups\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_get_listlike_indexer\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1312\u001b[0m             \u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_indexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reindex_non_unique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeyarr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1313\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1314\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_read_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\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   1315\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1316\u001b[0m         if needs_i8_conversion(ax.dtype) or isinstance(\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_validate_read_indexer\u001b[0;34m(self, key, indexer, axis)\u001b[0m\n\u001b[1;32m   1372\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0muse_interval_msg\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1373\u001b[0m                     \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1374\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"None of [{key}] are in the [{axis_name}]\"\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   1375\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1376\u001b[0m             \u001b[0mnot_found\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mensure_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmissing_mask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnonzero\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\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;31mKeyError\u001b[0m: \"None of [Index(['aşk', 'savaş'], dtype='object', name='Orijinal İsmi')] are in the [index]\""
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Çıkarım:**\n",
        "\n",
        "- Mevcut tüm RAM kullanıldıktan sonra oturumunuz çöktü mesajı aldık. 12 GB...\n",
        "\n",
        "**Kaynak:** https://www.datacamp.com/tutorial/recommender-systems-python"
      ],
      "metadata": {
        "id": "n2KExEvxJaco"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_extraction.text import TfidfVectorizer\n"
      ],
      "metadata": {
        "id": "nZq_73bVjc4L"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}