WordEmbeddingRNN.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jVoK-4woh062"
      },
      "source": [
        "# Step1: Import Librarys"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mgZnscTEjZh8"
      },
      "source": [
        "## Step1.1: Install Librarys"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q0Y-79YDh6jO",
        "outputId": "95063300-4eb1-4ffb-e086-7eed2e2e6adf"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting datasets\n",
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            "Installing collected packages: xxhash, dill, multiprocess, huggingface-hub, datasets\n",
            "  Attempting uninstall: huggingface-hub\n",
            "    Found existing installation: huggingface-hub 0.20.3\n",
            "    Uninstalling huggingface-hub-0.20.3:\n",
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            "Successfully installed datasets-2.19.1 dill-0.3.8 huggingface-hub-0.23.0 multiprocess-0.70.16 xxhash-3.4.1\n",
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          ]
        }
      ],
      "source": [
        "!pip install datasets\n",
        "!pip install scikit-learn\n",
        "!pip install numpy gensim scikit-learn"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i_0hW1n1jft3"
      },
      "source": [
        "## Step1.2: Import Librarys"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "URLRFLIwjlpc"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras.datasets import imdb\n",
        "from datasets import load_dataset\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "from keras.preprocessing.text import Tokenizer\n",
        "from keras.preprocessing.sequence import pad_sequences\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Embedding, Flatten, Dense\n",
        "from sklearn.cluster import KMeans\n",
        "from keras.utils import to_categorical\n",
        "from sklearn.model_selection import train_test_split\n",
        "from gensim.models import Word2Vec\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from keras.layers import SimpleRNN\n",
        "from keras.layers import LSTM"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yVOMl7botSsz"
      },
      "source": [
        "# Step2: Set Global Variables"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "tcKZnGnTtYdi"
      },
      "outputs": [],
      "source": [
        "datasetLimit1 = 1000\n",
        "datasetLimit2 = 1000\n",
        "datasetLimit3 = 1000"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N99PD2A3hTSv"
      },
      "source": [
        "# Step3: Loading DataSets"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nkrHI73Zin-O"
      },
      "source": [
        "## Step3.1: Load IMDB Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "Pi-aVeHrSaj7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ad8575c8-0a31-4d40-b597-c74fd91b0a10"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/imdb.npz\n",
            "17464789/17464789 [==============================] - 0s 0us/step\n"
          ]
        }
      ],
      "source": [
        "Limited_train = 1000\n",
        "Limited_test = 200\n",
        "(train_data, train_label), (test_data, test_label) = imdb.load_data(num_words=25000)\n",
        "train_data1 = list(train_data[:Limited_train])\n",
        "train_label1 = list(train_label[:Limited_train])\n",
        "test_data1 = list(test_data[:Limited_test])\n",
        "test_label1 = list(test_label[:Limited_test])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pSSnkhAjpRfW",
        "outputId": "df025e25-a0f1-4905-a03d-7d7b01fa8566"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1000\n",
            "200\n",
            "1000\n",
            "200\n",
            "<class 'int'>\n",
            "<class 'list'>\n",
            "<class 'numpy.int64'>\n",
            "<class 'numpy.int64'>\n",
            "[1, 14, 22, 16, 43, 530, 973, 1622, 1385, 65, 458, 4468, 66, 3941, 4, 173, 36, 256, 5, 25, 100, 43, 838, 112, 50, 670, 22665, 9, 35, 480, 284, 5, 150, 4, 172, 112, 167, 21631, 336, 385, 39, 4, 172, 4536, 1111, 17, 546, 38, 13, 447, 4, 192, 50, 16, 6, 147, 2025, 19, 14, 22, 4, 1920, 4613, 469, 4, 22, 71, 87, 12, 16, 43, 530, 38, 76, 15, 13, 1247, 4, 22, 17, 515, 17, 12, 16, 626, 18, 19193, 5, 62, 386, 12, 8, 316, 8, 106, 5, 4, 2223, 5244, 16, 480, 66, 3785, 33, 4, 130, 12, 16, 38, 619, 5, 25, 124, 51, 36, 135, 48, 25, 1415, 33, 6, 22, 12, 215, 28, 77, 52, 5, 14, 407, 16, 82, 10311, 8, 4, 107, 117, 5952, 15, 256, 4, 2, 7, 3766, 5, 723, 36, 71, 43, 530, 476, 26, 400, 317, 46, 7, 4, 12118, 1029, 13, 104, 88, 4, 381, 15, 297, 98, 32, 2071, 56, 26, 141, 6, 194, 7486, 18, 4, 226, 22, 21, 134, 476, 26, 480, 5, 144, 30, 5535, 18, 51, 36, 28, 224, 92, 25, 104, 4, 226, 65, 16, 38, 1334, 88, 12, 16, 283, 5, 16, 4472, 113, 103, 32, 15, 16, 5345, 19, 178, 32]\n"
          ]
        }
      ],
      "source": [
        "print(len(train_data1))\n",
        "print(len(test_data1))\n",
        "print(len(train_label1))\n",
        "print(len(test_label1))\n",
        "print(type(train_data1[0][0]))\n",
        "print(type(test_data1[0]))\n",
        "print(type(train_label1[0]))\n",
        "print(type(test_label1[0]))\n",
        "print(train_data1[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kA0nSeKFQ1yH",
        "outputId": "ad5eb91e-2124-4842-fe4c-0211013cf99d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/imdb_word_index.json\n",
            "1641221/1641221 [==============================] - 0s 0us/step\n"
          ]
        }
      ],
      "source": [
        "word_index = imdb.get_word_index()\n",
        "reverse_word_index = dict(\n",
        "    [(value, key) for (key, value) in word_index.items()])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qb54dvsFQ5AX",
        "outputId": "fc3cc025-bb5a-4257-e4a6-53dc33f05444"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "[5]: the effort still been that usually makes for of finished sucking ended and an because before if just though something know novel female i i slowly lot of above and with connect in of script their that out end his deceptively i i\n",
            "[6]: the boiled full involving to impressive boring this as murdering naschy br villain council suggestion need has of costumes b message to may of props this echoed concentrates concept issue skeptical to god's he is and unfolds movie women like isn't surely i'm and to toward in here's for from did having because very quality it is and starship really book is both too worked carl of and br of reviewer closer figure really there will originals things is far this make mistakes and was couldn't of few br of you to don't female than place she to was between that nothing dose movies get are and br yes female just its because many br of overly to descent people time very bland\n",
            "[7]: the of installed local of their br of attention widow alexandre captures parties and and lands and excitement and page's garris and of hark english crushed like it years of disgruntled unintentional this hitchcock of summers learn everyone is quite and it arab such it bonus film of too seems he of enough for be blatantly editing fishburne and please great this of shoots thing 3 rooney it's mentioning and carmen given parties and back out interesting times no all average talking some of nor seems into best at of every cast i i inside keep inside large viewer who obscure and sopranos and movie of routines entirely you've see because you deals successful her anything it of dedicated bares hard assassin further that's takes as with by br of and in minute and they of westerns watch seemed impulses it's lee if oh this japan film around get an of sunny always life was between of installed with group rate code film's was although of arts had death time stark of anyway romantic their won in kevin only flying it's burgess only cut show if bauer is star stay movies both effectiveness stay damaged of music of tell missing they of here really me we value some silent music as had thought gullible realized she in sorry reasons is isaacs 10 this of installed shoots if average remembered in at is over worse film is denver it for had absolutely in naive want it for had absolutely in j want it for had back for it absolutely in one want shots has that movie of here write whatsoever it is macho set got worse of where wich once for of accent after saw she film of rest little esteem camera if best way elements know of installed also an were sense or in realistic actually satan he's score br any himself in another type english this is sooo was tom for dating get it's such from fantastic will pace new years of guy game in murders this us hard lives film reckless fact that out end is getting together br prostitution of seen in of jail for sees utterly it meet it's depth is had do you for was rather convince in why last very has i i throughout never keep viewer who of becoming switch routines entirely music even interest scene music is far br voice riveting is again something br decent contrasted she this is shoots not director have against people they line cinematography film is couples br posed knee is daniels of you it sees hero he's if can't is time husband silly hindi result music image sequences it's chase music is veteran include participate freeman not of installed it along are of hearing cutting music his get scene but of fact correct i i means this and this blockbuster as there for disappointed along wrong few has that if his weird way not girl display of love who so friendship in we down it director in situation line has was big why was your supposed last but especially i i of installed stream of pump internet br never give theme rest or really that best paltry release in for so multi random their even interest is judge once arts like have then own is ebay has have one is you for off his dutch we they an\n",
            "[8]: the out can't some to hit looking when is themselves novel as it theatre people have former some br is on dull this should rick to ragged thru characters one lost her show jews for of brosnan br realised back video excellent to plenty theatre involved and disparity off as in we no that how moments some to as on it early in we years was nothing does is 10 has as new part first that to when is possibly what have whatever of positive have 2 also of slow for as it by br though carelessly characters has so heart broad europe film show of try making to looks in one want key bodies br when is him their that it christmas not them his dancer want this and as it his for on of great stupid of their as end should is very together br twilight us writer br were machines go bad aka were young to might us father film any more it her get predictable of alleged for find dvd from through i'm her get up happily date of lionel with other is him pushed has columbo criticized to what's of thief this so superior too not as you it get up br is others their was out least that hilarious not was into through to why for as it by br of where suits was one your life\n",
            "[9]: the as on there plot she's iii film that for find that saw better just is along wrong silly awesome or play this you doing was one in own that successful are make raining old plot gets unfortunately of on was although except value omar that with her do they gets for that with timing really way that is played character i i what poor set but is along 100 studio on film is missing br received fact to is mercifully br fabulous longs them powers is tapes br enjoys indicate good women show to one good played i i was plain film because avoid for of totally it time do period it couple in college in viewers get br of my to of material it yet br out more\n"
          ]
        }
      ],
      "source": [
        "for doc_index in range(0,10):\n",
        "  decoded_review = \" \".join([reverse_word_index.get(i) for i in train_data[doc_index]])\n",
        "  print(f'[{doc_index}]: {decoded_review}')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NhX5m1E1i_Jm"
      },
      "source": [
        "## Step3.2: Load Persian Food Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "J-T-QhyxjPOw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444,
          "referenced_widgets": [
            "edb7cdade6e04dca9296e955cfe81918",
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            "daebd434307548eeb77ae1abb36ff1ec",
            "762037ad9cdb4a578201342a33bfec0a",
            "21493be19455464ea58b1688e942d1c6",
            "b5519ea5a8b44447ac179725c1d874fc",
            "dd1de6a80a6d40439bd7703799c2b7c3",
            "ec822ecead5c46c8b65d1054cfe4dd04",
            "54c164c921914d708d63172899fa860c",
            "3f6e4047c71c48669bd4392256f7c034",
            "85e3792dba00412d9db31fd6340ecdd0",
            "627a3021f4e74a47a040e159117cece7",
            "25494786ceac45abad4d5d975396ac84",
            "8d27304536a341bfabe645c7ed72a336",
            "e5073f00ee78412abd8ee6e5782b6e38",
            "3315373f35be4cbf8b8282bf309bc27b",
            "e5d9cee3c9414c3d8a22bf0f89884eaa",
            "de80b752083f49468ea5704774099f7d",
            "4a3816f564c7480eb4012b060f28377a",
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            "4f8db59a778c466a9a1130017c04dd95",
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            "62549c89382d4cf1990d6eaae6c88ff5",
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            "15601b6c5c484be9a515c1387f02825d",
            "a9a7b4002c1446ae9701ec1f2a209545",
            "9a46d456982f41ad9f8e7d42a3d6925f",
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            "93d0b9fb247a4effac97ef67a20b9927",
            "83f10b49a59d4c8fad2d91c3c2e9048d",
            "2c74dd09b14c4e108af58272174f3822",
            "25876cf1d6f244dd93310781bdd4a4a3",
            "bf14d7b0cfa74ea1ae6a1487072f7e08",
            "091ac39c77a741909c52e23bb9d21846",
            "16b8f6baeeb045ab8013d15f002d7840",
            "5de302a9a54740bf9469bf10696fbeb1",
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            "982e1257265444c993987184923d04f3",
            "5c7accf0118c4a61947db82ce6d2765b",
            "6c9fe343d2754ffd9f6747b31315e85f",
            "95b6bf194d8b432e86eba7d5101b478e",
            "dd119e1af1a04e13bacc7c522cc4e50e",
            "c864afb3438c4a6d997418eb3630c287",
            "babe83e5ab944f129916ef953b4db3e5",
            "43f5e0330b33437c8c5fd378e405edf3",
            "cea8c78acaeb4382b7f1547206987406",
            "8f3b7ebafb574654b6267155ec7315cb",
            "9f968aa024f54942a75af9016b545435",
            "0281e1c33782444dbabd109847ef1940",
            "de81019e5adb4a23a1671de1ef00c48c",
            "45db7b1b21e1413193000a50ec021869",
            "cd7ba0bbb941431a8f72e2af18c85931"
          ]
        },
        "outputId": "c6a879b1-ba6a-413f-a69c-9269ecff4df7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/524 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "edb7cdade6e04dca9296e955cfe81918"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/4.85M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "627a3021f4e74a47a040e159117cece7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/1.22M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3e639a45c8b042ca80145226214d4a66"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/55852 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "25876cf1d6f244dd93310781bdd4a4a3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split:   0%|          | 0/13964 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "dd119e1af1a04e13bacc7c522cc4e50e"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "persian_dataset = load_dataset(\"SeyedAli/Persian-Text-Sentiment\")\n",
        "train_label2_old = persian_dataset['train']['label'][:datasetLimit2]\n",
        "train_data2_old = persian_dataset['train']['text'][:datasetLimit2]\n",
        "test_label2_old = persian_dataset['test']['label'][:int((datasetLimit2)*0.2)]\n",
        "test_data2_old = persian_dataset['test']['text'][:int((datasetLimit2)*0.2)]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QSOrB2pRkqeG",
        "outputId": "8e8e59f4-1bce-44b3-a584-b165b21cbd1c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1\n",
            "این هندس فری تازه به دستم رسیده  و برای کسایی که انتظار شنیدن یک موسیقی با کیفیت دارن حتما پیشنهاد میکنم  بعد از شنیدن آهنگ با این هدست آدم به این فکر میکنه انگار تا حالا آهنگ نشنیده \n",
            "0\n",
            "مرغش خیلی پیر و ریش ریش بود واقعا بوی خیلی بد و مونده‌ای داشت با وجود رب انار واقعا متاسفم\n",
            "1000\n",
            "1000\n",
            "200\n",
            "200\n",
            "<class 'int'>\n",
            "<class 'str'>\n",
            "<class 'int'>\n",
            "<class 'str'>\n"
          ]
        }
      ],
      "source": [
        "print(train_label2_old[0])\n",
        "print(train_data2_old[0])\n",
        "print(test_label2_old[0])\n",
        "print(test_data2_old[0])\n",
        "print(len(train_label2_old))\n",
        "print(len(train_data2_old))\n",
        "print(len(test_label2_old))\n",
        "print(len(test_data2_old))\n",
        "print(type(persian_dataset['train'][0]['label']))\n",
        "print(type(persian_dataset['train'][0]['text']))\n",
        "print(type(persian_dataset['test'][0]['label']))\n",
        "print(type(persian_dataset['test'][0]['text']))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5mHi_2Y9rbaj"
      },
      "source": [
        "## Step3.3: Loading Persian News Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "dl2qfMHhrp7-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288,
          "referenced_widgets": [
            "19f31693df504622821bbed0bf68bdef",
            "dc5fd544a7ef4305a7f21bcbdaff955c",
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            "a209ee11a1694215aeb5d6f9df4c6eee",
            "0d320cc6ab29439bb95f7bfa857b3dd1",
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          ]
        },
        "outputId": "2a8fd677-8837-454b-f557-c7c4b49a3c32"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/datasets/load.py:1486: FutureWarning: The repository for saied/persian_news_dataset contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at https://hf.co/datasets/saied/persian_news_dataset\n",
            "You can avoid this message in future by passing the argument `trust_remote_code=True`.\n",
            "Passing `trust_remote_code=True` will be mandatory to load this dataset from the next major release of `datasets`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/1.84k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "19f31693df504622821bbed0bf68bdef"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading metadata:   0%|          | 0.00/1.25k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f1cf4b17968a45098ce53519eafd3a1e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/5.35k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a39e677ab3474ac2ab5a63309bfe1d12"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "dataset_news = load_dataset(\"saied/persian_news_dataset\", split=\"train\",streaming=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Llu3O7mnx76s"
      },
      "source": [
        "### S"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vuv_zRx4uRxr",
        "outputId": "d7c70e0d-d173-4108-c358-ec81bd8b400a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "10000 skipped\n",
            "20000 skipped\n",
            "30000 skipped\n",
            "40000 skipped\n",
            "50000 skipped\n",
            "60000 skipped\n",
            "70000 skipped\n",
            "80000 skipped\n",
            "90000 skipped\n",
            "100000 skipped\n",
            "110000 skipped\n",
            "120000 skipped\n",
            "130000 skipped\n",
            "140000 skipped\n",
            "150000 skipped\n",
            "160000 skipped\n",
            "170000 skipped\n",
            "180000 skipped\n",
            "190000 skipped\n",
            "200000 skipped\n",
            "210000 skipped\n",
            "1200\n"
          ]
        }
      ],
      "source": [
        "docs=[]\n",
        "document_count= (datasetLimit3 * 1.2)\n",
        "counter=0\n",
        "skip_counter=0\n",
        "for doc in dataset_news:\n",
        "    # print(f\"{doc['category']}\\t\\t{doc['title']}\\t\\t\\t\\t{doc['text']}\")\n",
        "    if len(doc['title'])==0 or len(doc['category'])==0:\n",
        "      skip_counter += 1\n",
        "      if skip_counter%10000 ==0:\n",
        "        print(f'{skip_counter} skipped')\n",
        "      continue\n",
        "    docs.append(doc)\n",
        "    counter += 1\n",
        "    if counter == document_count:\n",
        "      break\n",
        "print(len(docs))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2-7tNJ7QyAGl",
        "outputId": "11ac7b1a-ca93-44d5-94b4-9a4d73284752"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'text': 'الجزیره: نخست  وزیر رژیم صهیونیستی جنبش حماس را تهدید کرد\\n........................................................تهران، خبرگزاری جمهوری اسلامی            06/03/86\\nخارجی.سیاسی.غره.اسرائیل\\n    بخش انگلیسی زبان شبکه تلویزیونی الجزیره قطر روز یکشنبه گزارش داد:\\n\"ایهوداولمرت\" نخست وزیر رژیم صهیونیستی پس از اصابت موشک های  \"قسام\"\\nمتعلق به گروه مسلح \"حماس\" اعلام کرد که هیچ یک از اعضای حماس در امان\\nنخواهند بود.   الجزیره افزود: پس از آنکه رزمندگان فلسطینی روز یکشنبه دو فروند راکت\\nاز غزه به سمت شهر \"سدیروت\" در جنوب اسراییل پرتاب کردند، اولمرت تهدید\\nکرد که دامنه حملات نیروهای اسرائیلی را به غزه گسترش دهد.   وی همچنین در نشست کابینه رژیم صهیونیستی تاکید کرد که هیچ یک از\\nاعضای وابسته به حماس درامان نخواهند بود.   به گزارش الجزیره: گردانهای عزالدین قسام شاخه نظامی حماس مسوولیت\\nحمله موشکی امروز به جنوب فلسطین اشغالی را که به کشته شدن یک صهیونیست\\nمنجر شد بر عهده گرفت.   براساس بیانیه گردانهای عزالدین قسام که برای رسانه ها ارسال شده است\\nرزمندگان این گروه ساعت 15/9 دقیقه صبح امروز به وقت محلی و پنج دقیقه پس\\nازآن به شهرک های  سدیروت موشک شلیک کردند که به نوشته روزنامه های اسرائیل\\nیک نفر بر اثر ان به شدت زخمی شد.  هواپیماهای نظامی اسراییل نیمه شب شنبه سه حمله هوایی به مواضع جنبش\\nحماس در غزه انجام دادند که دراثر آن 5 فلسطینی به شهادت رسیدند.  جنگنده بمب افکنهای اسراییلی ظرف روزهای اخیر با بمباران مناطقی در غزه\\nدست کم 40 فلسطینی را به شهادت رساندند.  وزارت بهداشت فلسطین نیزاعلام کرد که طی یازده روز تهاجم نیروهای\\nاسراییلی چهل و هشت نفر در نوار غزه کشته شدند.   روز گذشته نیز در پی کشته شدن پنج تن از اعضای حماس در حملات هوایی\\nاسرائیل این گروه اعلام کرد که پانزده موشک و بیست گلوله خمپاره به\\nشهرک های  نزدیک نوار غزه شلیک کرده است. صویر/2351/1373\\nشماره 087   ساعت 43:16  تمام\\n                      انتهای پیام I06.14-43-16                   \\n\\n\\n            ',\n",
              " 'title': 'الجزیره: نخست  وزیر رژیم صهیونیستی جنبش حماس را تهدید کرد',\n",
              " 'category': 'جهان'}"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "docs[0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NsamZuejSVp1"
      },
      "source": [
        "# Step4: Preprocessing Dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ytUN-rB8zjw3"
      },
      "source": [
        "## Step4.2: Preprocessing persian food Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "qPyl43KRSeBD"
      },
      "outputs": [],
      "source": [
        "# Assuming you have a tokenizer set up for Persian text\n",
        "tokenizer = Tokenizer(num_words=10000)  # Adjust num_words as needed\n",
        "tokenizer.fit_on_texts(train_data2_old + test_data2_old)\n",
        "# Function to process text data\n",
        "def process_text(text):\n",
        "    # Tokenize the text\n",
        "    sequences = tokenizer.texts_to_sequences([text])\n",
        "\n",
        "    # Flatten the sequence to a single list of integers\n",
        "    indexed_tokens = [token for sequence in sequences for token in sequence]\n",
        "\n",
        "    return indexed_tokens\n",
        "\n",
        "# Process train and test data\n",
        "train_texts = train_data2_old\n",
        "test_texts = test_data2_old\n",
        "\n",
        "# Tokenize and map to integers\n",
        "train_tokenized = [process_text(text) for text in train_texts]\n",
        "test_tokenized = [process_text(text) for text in test_texts]\n",
        "\n",
        "# maxlen = max(len(seq) for seq in train_tokenized + test_tokenized)  # Find the maximum sequence length\n",
        "# train_padded = pad_sequences(train_tokenized, maxlen=maxlen, padding='post', truncating='post')\n",
        "# test_padded = pad_sequences(test_tokenized, maxlen=maxlen, padding='post', truncating='post')\n",
        "\n",
        "# Extract non-zero elements from padded sequences\n",
        "# train_non_zero = []\n",
        "# for seq in train_padded:\n",
        "#     non_zero_seq = seq[seq != 0]\n",
        "#     if len(non_zero_seq) == 0:\n",
        "#         non_zero_seq = [0]  # If all elements are removed, replace with a list containing one zero\n",
        "#     train_non_zero.append(non_zero_seq)\n",
        "\n",
        "# test_non_zero = []\n",
        "# for seq in test_padded:\n",
        "#     non_zero_seq = seq[seq != 0]\n",
        "#     if len(non_zero_seq) == 0:\n",
        "#         non_zero_seq = [0]  # If all elements are removed, replace with a list containing one zero\n",
        "#     test_non_zero.append(non_zero_seq)\n",
        "\n",
        "# Now, convert the padded sequences to NumPy arrays\n",
        "train_data2= train_tokenized\n",
        "test_data2 = test_tokenized\n",
        "\n",
        "# Convert labels to NumPy arrays\n",
        "train_label2 = train_label2_old\n",
        "test_label2 = test_label2_old"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L6GKGNFIynBQ"
      },
      "source": [
        "# Step5: Create Model for Vectorization (word Embeding)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Step5.1: Embedding Model"
      ],
      "metadata": {
        "id": "74blEiJwGcqP"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "tMT-tYbKbLpi"
      },
      "outputs": [],
      "source": [
        "def get_embedding_model(train_data, test_data, train_labels, test_labels, max_words=25000, embedding_dim=64, input_length=50, num_clusters=2):\n",
        "    train_padded = pad_sequences(train_data, maxlen=input_length)\n",
        "    test_padded = pad_sequences(test_data, maxlen=input_length)\n",
        "\n",
        "    model = Sequential()\n",
        "    model.add(Embedding(max_words, embedding_dim, input_length=input_length))\n",
        "    model.add(Flatten())\n",
        "    model.add(Dense(embedding_dim, activation='linear'))\n",
        "\n",
        "    model.compile(optimizer='adam', loss='mse')\n",
        "\n",
        "    train_embeddings = model.predict(train_padded)\n",
        "\n",
        "    test_embeddings = model.predict(test_padded)\n",
        "\n",
        "    kmeans = KMeans(n_clusters=num_clusters, random_state=42)\n",
        "    clusters = kmeans.fit_predict(test_embeddings)\n",
        "\n",
        "    binary_predictions = to_categorical(clusters)\n",
        "\n",
        "    return binary_predictions"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Step5.2: Create and Use Pretrain model Word2Vec"
      ],
      "metadata": {
        "id": "Sk4HyIyZGmmm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "word2vec_model = Word2Vec(sentences=train_data, vector_size=100, window=5, min_count=1, workers=4)\n",
        "\n",
        "word2vec_model.save(\"word2vec_model.bin\")"
      ],
      "metadata": {
        "id": "XHsI-nqQ2F_H"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def word_to_vec(train_data, test_data, train_labels):\n",
        "    word2vec_model = Word2Vec.load(\"/content/word2vec_model.bin\")\n",
        "\n",
        "    train_vectors = []\n",
        "    for sentence in train_data:\n",
        "        vectors = [word2vec_model.wv[word] if word in word2vec_model.wv else np.zeros(word2vec_model.vector_size) for word in sentence]\n",
        "        train_vectors.append(np.mean(vectors, axis=0))\n",
        "\n",
        "    test_vectors = []\n",
        "    for sentence in test_data:\n",
        "        vectors = [word2vec_model.wv[word] if word in word2vec_model.wv else np.zeros(word2vec_model.vector_size) for word in sentence]\n",
        "        test_vectors.append(np.mean(vectors, axis=0))\n",
        "\n",
        "    train_vectors = np.array(train_vectors)\n",
        "    test_vectors = np.array(test_vectors)\n",
        "    train_labels = np.array(train_labels)\n",
        "\n",
        "    knn_classifier = KNeighborsClassifier(n_neighbors=5)\n",
        "    knn_classifier.fit(train_vectors, train_labels)\n",
        "\n",
        "    binary_predictions = knn_classifier.predict(test_vectors)\n",
        "\n",
        "    return binary_predictions"
      ],
      "metadata": {
        "id": "trxVgPQ41hEj"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def word_to_vec2(train_data, test_data, train_labels, test_labels, max_words=25000, embedding_dim=300, input_length=50, num_clusters=2):\n",
        "    train_padded = pad_sequences(train_data, maxlen=input_length)\n",
        "    test_padded = pad_sequences(test_data, maxlen=input_length)\n",
        "\n",
        "    word2vec_model = Word2Vec.load(\"/content/word2vec_model.bin\")\n",
        "\n",
        "    embedding_matrix = np.zeros((max_words, embedding_dim))\n",
        "    for word, i in word_index.items():\n",
        "        if i < max_words and word in word2vec_model.wv:\n",
        "            embedding_matrix[i] = word2vec_model.wv[word]\n",
        "\n",
        "    model = Sequential()\n",
        "    model.add(Embedding(max_words, embedding_dim, input_length=input_length))\n",
        "    model.add(Flatten())\n",
        "    model.add(Dense(embedding_dim, activation='linear'))\n",
        "\n",
        "    model.layers[0].set_weights([embedding_matrix])\n",
        "    model.layers[0].trainable = False\n",
        "\n",
        "    model.compile(optimizer='adam', loss='mse')\n",
        "\n",
        "    train_embeddings = model.predict(train_padded)\n",
        "    test_embeddings = model.predict(test_padded)\n",
        "\n",
        "    kmeans = KMeans(n_clusters=num_clusters, random_state=42)\n",
        "    clusters = kmeans.fit_predict(test_embeddings)\n",
        "\n",
        "    binary_predictions = to_categorical(clusters)\n",
        "\n",
        "    return binary_predictions"
      ],
      "metadata": {
        "id": "tLfheK_v90bw"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Step5.3: Create SimpleRNN Model"
      ],
      "metadata": {
        "id": "HEyORjeDGwtf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def get_SimpleRNN_model(train_data, test_data, train_labels, test_labels, max_words=25000, embedding_dim=64, input_length=50, num_clusters=2):\n",
        "    train_padded = pad_sequences(train_data, maxlen=input_length)\n",
        "    test_padded = pad_sequences(test_data, maxlen=input_length)\n",
        "\n",
        "    model = Sequential()\n",
        "    model.add(Embedding(max_words, embedding_dim, input_length=input_length))\n",
        "    model.add(SimpleRNN(32, return_sequences=True))\n",
        "    model.add(SimpleRNN(16))\n",
        "    model.add(Flatten())\n",
        "    model.add(Dense(embedding_dim, activation='linear'))\n",
        "\n",
        "    model.compile(optimizer='adam', loss='mse')\n",
        "\n",
        "    train_embeddings = model.predict(train_padded)\n",
        "\n",
        "    test_embeddings = model.predict(test_padded)\n",
        "\n",
        "    kmeans = KMeans(n_clusters=num_clusters, random_state=42)\n",
        "    clusters = kmeans.fit_predict(test_embeddings)\n",
        "\n",
        "    binary_predictions = to_categorical(clusters)\n",
        "\n",
        "    return binary_predictions"
      ],
      "metadata": {
        "id": "4FSBYvFC3ijk"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Step5.4: Create LSTM Model"
      ],
      "metadata": {
        "id": "JNI94IpBG3fC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def LSTM2(train_data, test_data, train_labels, test_labels, max_words=25000, embedding_dim=300, input_length=50, num_clusters=2):\n",
        "    train_padded = pad_sequences(train_data, maxlen=input_length)\n",
        "    test_padded = pad_sequences(test_data, maxlen=input_length)\n",
        "\n",
        "    word2vec_model = Word2Vec.load(\"/content/word2vec_model.bin\")\n",
        "\n",
        "    embedding_matrix = np.zeros((max_words, embedding_dim))\n",
        "    for word, i in word_index.items():\n",
        "        if i < max_words and word in word2vec_model.wv:\n",
        "            embedding_matrix[i] = word2vec_model.wv[word]\n",
        "\n",
        "    model = Sequential()\n",
        "    model.add(Embedding(max_words, embedding_dim, input_length=input_length))\n",
        "    model.add(LSTM(32, return_sequences=True))\n",
        "    model.add(LSTM(16))\n",
        "\n",
        "    model.layers[0].set_weights([embedding_matrix])\n",
        "    model.layers[0].trainable = False\n",
        "\n",
        "    model.add(Dense(num_clusters, activation='softmax'))\n",
        "\n",
        "    model.compile(optimizer='adam', loss='categorical_crossentropy')\n",
        "\n",
        "    train_embeddings = model.predict(train_padded)\n",
        "    test_embeddings = model.predict(test_padded)\n",
        "\n",
        "    kmeans = KMeans(n_clusters=num_clusters, random_state=42)\n",
        "    clusters = kmeans.fit_predict(test_embeddings)\n",
        "\n",
        "    binary_predictions = to_categorical(clusters)\n",
        "\n",
        "    return binary_predictions"
      ],
      "metadata": {
        "id": "KuK-Sizs4B82"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Step5.5: Run 4 Function with 3 Dataset"
      ],
      "metadata": {
        "id": "lABTnuEHHGI9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "predictions11 = get_embedding_model(train_data1, test_data1, train_label1, test_label1)\n",
        "predictions12 = word_to_vec2(train_data1, test_data1, train_label1, test_label1)\n",
        "predictions13 = get_SimpleRNN_model(train_data1, test_data1, train_label1, test_label1)\n",
        "predictions14 = LSTM2(train_data1, test_data1, train_label1, test_label1)\n",
        "print(\"Test Predictions:\", predictions11)\n",
        "print(\"Test Predictions:\", predictions12)\n",
        "print(\"Test Predictions:\", predictions13)\n",
        "print(\"Test Predictions:\", predictions14)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dR8mSobfqnJ7",
        "outputId": "3e6ff1ef-92a3-4b5d-a9a2-52bec41c06f0"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "32/32 [==============================] - 0s 3ms/step\n",
            "7/7 [==============================] - 0s 4ms/step\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "32/32 [==============================] - 0s 12ms/step\n",
            "7/7 [==============================] - 0s 11ms/step\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:1033: ConvergenceWarning: Number of distinct clusters (1) found smaller than n_clusters (2). Possibly due to duplicate points in X.\n",
            "  return self.fit(X, sample_weight=sample_weight).labels_\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "32/32 [==============================] - 0s 8ms/step\n",
            "7/7 [==============================] - 0s 6ms/step\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "32/32 [==============================] - 1s 14ms/step\n",
            "7/7 [==============================] - 0s 14ms/step\n",
            "Test Predictions: [[1. 0.]\n",
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            "Test Predictions: [[1.]\n",
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            " [1.]]\n",
            "Test Predictions: [[0. 1.]\n",
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            "Test Predictions: [[1.]\n",
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            " [1.]]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:1033: ConvergenceWarning: Number of distinct clusters (1) found smaller than n_clusters (2). Possibly due to duplicate points in X.\n",
            "  return self.fit(X, sample_weight=sample_weight).labels_\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions21 = get_embedding_model(train_data2, test_data2, train_label2, test_label2)\n",
        "predictions22 = word_to_vec2(train_data2, test_data2, train_label2, test_label2)\n",
        "predictions23 = get_SimpleRNN_model(train_data2, test_data2, train_label2, test_label2)\n",
        "predictions24 = LSTM2(train_data2, test_data2, train_label2, test_label2)\n",
        "print(\"Test Predictions:\", predictions21)\n",
        "print(\"Test Predictions:\", predictions22)\n",
        "print(\"Test Predictions:\", predictions23)\n",
        "print(\"Test Predictions:\", predictions24)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "APYe0_plqncz",
        "outputId": "1678d2b5-a8be-4c6b-b566-d42deda01a5e"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "32/32 [==============================] - 0s 2ms/step\n",
            "7/7 [==============================] - 0s 3ms/step\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "32/32 [==============================] - 0s 12ms/step\n",
            "7/7 [==============================] - 0s 11ms/step\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:1033: ConvergenceWarning: Number of distinct clusters (1) found smaller than n_clusters (2). Possibly due to duplicate points in X.\n",
            "  return self.fit(X, sample_weight=sample_weight).labels_\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "32/32 [==============================] - 0s 7ms/step\n",
            "7/7 [==============================] - 0s 6ms/step\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "32/32 [==============================] - 2s 21ms/step\n",
            "7/7 [==============================] - 0s 22ms/step\n",
            "Test Predictions: [[1. 0.]\n",
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            "Test Predictions: [[1.]\n",
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            " [1.]]\n",
            "Test Predictions: [[0. 1.]\n",
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            "Test Predictions: [[1.]\n",
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        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:1033: ConvergenceWarning: Number of distinct clusters (1) found smaller than n_clusters (2). Possibly due to duplicate points in X.\n",
            "  return self.fit(X, sample_weight=sample_weight).labels_\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IYy3ftSjgohP"
      },
      "source": [
        "# Step6: Create Confusion Matrix"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ME4Pyjoigv8E"
      },
      "source": [
        "## Step6.1: Create initial Matrix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "hOOW95ICg1vt"
      },
      "outputs": [],
      "source": [
        "def create_confusion_matrix(test_data, test_label):\n",
        "    confusion_matrix = pd.DataFrame({\n",
        "        'Data': test_data,\n",
        "        'Label': test_label\n",
        "    })\n",
        "    return confusion_matrix\n",
        "\n",
        "confusion_matrix11 = create_confusion_matrix(test_data1, test_label1)\n",
        "confusion_matrix12 = create_confusion_matrix(test_data1, test_label1)\n",
        "confusion_matrix13 = create_confusion_matrix(test_data1, test_label1)\n",
        "confusion_matrix14 = create_confusion_matrix(test_data1, test_label1)\n",
        "\n",
        "confusion_matrix21 = create_confusion_matrix(test_data2, test_label2)\n",
        "confusion_matrix22 = create_confusion_matrix(test_data2, test_label2)\n",
        "confusion_matrix23 = create_confusion_matrix(test_data2, test_label2)\n",
        "confusion_matrix24 = create_confusion_matrix(test_data2, test_label2)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "confusion_matrix11"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "MK2GX17_ppSd",
        "outputId": "d6f8f098-795e-4446-c545-b23f80f9da4e"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                  Data  Label\n",
              "0    [1, 591, 202, 14, 31, 6, 717, 10, 10, 18142, 1...      0\n",
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              "3    [1, 13, 1228, 119, 14, 552, 7, 20, 190, 14, 58...      0\n",
              "4    [1, 40, 49, 85, 84, 1040, 146, 6, 783, 254, 43...      1\n",
              "..                                                 ...    ...\n",
              "195  [1, 10270, 2, 408, 178, 2000, 1137, 7, 841, 42...      1\n",
              "196  [1, 417, 85, 84, 37, 28, 165, 110, 14, 123, 12...      1\n",
              "197  [1, 132, 16, 14, 254, 8, 106, 12, 16, 24, 33, ...      0\n",
              "198  [1, 474, 119, 8, 870, 180, 5, 901, 35, 1089, 7...      0\n",
              "199  [1, 13, 296, 14, 20, 2855, 18, 4, 956, 12, 16,...      0\n",
              "\n",
              "[200 rows x 2 columns]"
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>[1, 111, 748, 4368, 1133, 2, 24563, 4, 87, 155...</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>[1, 13, 1228, 119, 14, 552, 7, 20, 190, 14, 58...</td>\n",
              "      <td>0</td>\n",
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              "      <th>4</th>\n",
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              "        const element = document.querySelector('#df-f5a29c0e-bab1-4557-b235-5308853cc680');\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "confusion_matrix11",
              "summary": "{\n  \"name\": \"confusion_matrix11\",\n  \"rows\": 200,\n  \"fields\": [\n    {\n      \"column\": \"Data\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UKJgxSeYhU6u"
      },
      "source": [
        "## Step6.2: Add Predicted Column"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "JhVgeKLzhUN9"
      },
      "outputs": [],
      "source": [
        "def add_predicted_labels(predictions, confusion_matrix):\n",
        "    predicted_labels = predictions.argmax(axis=1).tolist()\n",
        "    confusion_matrix['Predicted'] = predicted_labels\n",
        "    return confusion_matrix\n",
        "\n",
        "confusion_matrix11 = add_predicted_labels(predictions11, confusion_matrix11)\n",
        "confusion_matrix12 = add_predicted_labels(predictions12, confusion_matrix12)\n",
        "confusion_matrix13 = add_predicted_labels(predictions13, confusion_matrix13)\n",
        "confusion_matrix14 = add_predicted_labels(predictions14, confusion_matrix14)\n",
        "\n",
        "confusion_matrix21 = add_predicted_labels(predictions21, confusion_matrix21)\n",
        "confusion_matrix22 = add_predicted_labels(predictions22, confusion_matrix22)\n",
        "confusion_matrix23 = add_predicted_labels(predictions23, confusion_matrix23)\n",
        "confusion_matrix24 = add_predicted_labels(predictions24, confusion_matrix24)"
      ]
    },
    {
      "cell_type": "code",
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              "                                                  Data  Label  Predicted\n",
              "0    [1, 591, 202, 14, 31, 6, 717, 10, 10, 18142, 1...      0          0\n",
              "1    [1, 14, 22, 3443, 6, 176, 7, 5063, 88, 12, 267...      1          0\n",
              "2    [1, 111, 748, 4368, 1133, 2, 24563, 4, 87, 155...      1          1\n",
              "3    [1, 13, 1228, 119, 14, 552, 7, 20, 190, 14, 58...      0          0\n",
              "4    [1, 40, 49, 85, 84, 1040, 146, 6, 783, 254, 43...      1          1\n",
              "..                                                 ...    ...        ...\n",
              "195  [1, 10270, 2, 408, 178, 2000, 1137, 7, 841, 42...      1          1\n",
              "196  [1, 417, 85, 84, 37, 28, 165, 110, 14, 123, 12...      1          1\n",
              "197  [1, 132, 16, 14, 254, 8, 106, 12, 16, 24, 33, ...      0          1\n",
              "198  [1, 474, 119, 8, 870, 180, 5, 901, 35, 1089, 7...      0          0\n",
              "199  [1, 13, 296, 14, 20, 2855, 18, 4, 956, 12, 16,...      0          0\n",
              "\n",
              "[200 rows x 3 columns]"
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              "      <th>0</th>\n",
              "      <td>[1, 591, 202, 14, 31, 6, 717, 10, 10, 18142, 1...</td>\n",
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              "      <th>1</th>\n",
              "      <td>[1, 14, 22, 3443, 6, 176, 7, 5063, 88, 12, 267...</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>[1, 111, 748, 4368, 1133, 2, 24563, 4, 87, 155...</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>[1, 13, 1228, 119, 14, 552, 7, 20, 190, 14, 58...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>[1, 40, 49, 85, 84, 1040, 146, 6, 783, 254, 43...</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
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              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>195</th>\n",
              "      <td>[1, 10270, 2, 408, 178, 2000, 1137, 7, 841, 42...</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>[1, 417, 85, 84, 37, 28, 165, 110, 14, 123, 12...</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>[1, 132, 16, 14, 254, 8, 106, 12, 16, 24, 33, ...</td>\n",
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              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>[1, 474, 119, 8, 870, 180, 5, 901, 35, 1089, 7...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>199</th>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "<p>200 rows × 3 columns</p>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "confusion_matrix11",
              "summary": "{\n  \"name\": \"confusion_matrix11\",\n  \"rows\": 200,\n  \"fields\": [\n    {\n      \"column\": \"Data\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Predicted\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U77C-xi8kRYB"
      },
      "source": [
        "## Step6.3: Calculate the result of Confusion Matrix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "GfBvpJOBkQXi"
      },
      "outputs": [],
      "source": [
        "def compute_confusion_matrix_results(confusion_matrix):\n",
        "    conditions = [\n",
        "        (confusion_matrix['Label'] == 1) & (confusion_matrix['Predicted'] == 1),  # True Positive (TP)\n",
        "        (confusion_matrix['Label'] == 0) & (confusion_matrix['Predicted'] == 0),  # True Negative (TN)\n",
        "        (confusion_matrix['Label'] == 0) & (confusion_matrix['Predicted'] == 1),  # False Positive (FP)\n",
        "        (confusion_matrix['Label'] == 1) & (confusion_matrix['Predicted'] == 0)   # False Negative (FN)\n",
        "    ]\n",
        "\n",
        "    choices = ['TP', 'TN', 'FP', 'FN']\n",
        "\n",
        "    confusion_matrix['result'] = np.select(conditions, choices, default='')\n",
        "\n",
        "    return confusion_matrix"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "confusion_matrix11 = compute_confusion_matrix_results(confusion_matrix11)\n",
        "confusion_matrix12 = compute_confusion_matrix_results(confusion_matrix12)\n",
        "confusion_matrix13 = compute_confusion_matrix_results(confusion_matrix13)\n",
        "confusion_matrix14 = compute_confusion_matrix_results(confusion_matrix14)\n",
        "\n",
        "confusion_matrix21 = compute_confusion_matrix_results(confusion_matrix21)\n",
        "confusion_matrix22 = compute_confusion_matrix_results(confusion_matrix22)\n",
        "confusion_matrix23 = compute_confusion_matrix_results(confusion_matrix23)\n",
        "confusion_matrix24 = compute_confusion_matrix_results(confusion_matrix24)"
      ],
      "metadata": {
        "id": "6MmkNGsTrw9C"
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      "execution_count": 35,
      "outputs": []
    },
    {
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      "source": [
        "confusion_matrix11"
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        "id": "EY2zDUdZOGwF",
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      },
      "execution_count": 36,
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          "data": {
            "text/plain": [
              "                                                  Data  Label  Predicted  \\\n",
              "0    [1, 591, 202, 14, 31, 6, 717, 10, 10, 18142, 1...      0          0   \n",
              "1    [1, 14, 22, 3443, 6, 176, 7, 5063, 88, 12, 267...      1          0   \n",
              "2    [1, 111, 748, 4368, 1133, 2, 24563, 4, 87, 155...      1          1   \n",
              "3    [1, 13, 1228, 119, 14, 552, 7, 20, 190, 14, 58...      0          0   \n",
              "4    [1, 40, 49, 85, 84, 1040, 146, 6, 783, 254, 43...      1          1   \n",
              "..                                                 ...    ...        ...   \n",
              "195  [1, 10270, 2, 408, 178, 2000, 1137, 7, 841, 42...      1          1   \n",
              "196  [1, 417, 85, 84, 37, 28, 165, 110, 14, 123, 12...      1          1   \n",
              "197  [1, 132, 16, 14, 254, 8, 106, 12, 16, 24, 33, ...      0          1   \n",
              "198  [1, 474, 119, 8, 870, 180, 5, 901, 35, 1089, 7...      0          0   \n",
              "199  [1, 13, 296, 14, 20, 2855, 18, 4, 956, 12, 16,...      0          0   \n",
              "\n",
              "    result  \n",
              "0       TN  \n",
              "1       FN  \n",
              "2       TP  \n",
              "3       TN  \n",
              "4       TP  \n",
              "..     ...  \n",
              "195     TP  \n",
              "196     TP  \n",
              "197     FP  \n",
              "198     TN  \n",
              "199     TN  \n",
              "\n",
              "[200 rows x 4 columns]"
            ],
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              "      <th>1</th>\n",
              "      <td>[1, 14, 22, 3443, 6, 176, 7, 5063, 88, 12, 267...</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>TP</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>[1, 13, 1228, 119, 14, 552, 7, 20, 190, 14, 58...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>TN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>[1, 40, 49, 85, 84, 1040, 146, 6, 783, 254, 43...</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>TP</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>195</th>\n",
              "      <td>[1, 10270, 2, 408, 178, 2000, 1137, 7, 841, 42...</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>TP</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>[1, 417, 85, 84, 37, 28, 165, 110, 14, 123, 12...</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>TP</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>[1, 132, 16, 14, 254, 8, 106, 12, 16, 24, 33, ...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>FP</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>[1, 474, 119, 8, 870, 180, 5, 901, 35, 1089, 7...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>TN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>[1, 13, 296, 14, 20, 2855, 18, 4, 956, 12, 16,...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>TN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>200 rows × 4 columns</p>\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "type": "dataframe",
              "variable_name": "confusion_matrix11",
              "summary": "{\n  \"name\": \"confusion_matrix11\",\n  \"rows\": 200,\n  \"fields\": [\n    {\n      \"column\": \"Data\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Predicted\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"result\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"FN\",\n          \"FP\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fWtN-K91k9uz"
      },
      "source": [
        "# Step7: Calculate Measurments"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "_6ewNk1_lAGq"
      },
      "outputs": [],
      "source": [
        "def calculate_metrics(confusion_matrix):\n",
        "    TP = confusion_matrix[confusion_matrix['result'] == 'TP'].shape[0]\n",
        "    FP = confusion_matrix[confusion_matrix['result'] == 'FP'].shape[0]\n",
        "    TN = confusion_matrix[confusion_matrix['result'] == 'TN'].shape[0]\n",
        "    FN = confusion_matrix[confusion_matrix['result'] == 'FN'].shape[0]\n",
        "\n",
        "    precision = TP / (TP + FP) if TP + FP != 0 else 0\n",
        "    recall = TP / (TP + FN) if TP + FN != 0 else 0\n",
        "    accuracy = (TP + TN) / (TP + TN + FP + FN) if TP + TN + FP + FN != 0 else 0\n",
        "\n",
        "    return precision, recall, accuracy"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "precision11, recall11, accuracy11 = calculate_metrics(confusion_matrix11)\n",
        "precision12, recall12, accuracy12 = calculate_metrics(confusion_matrix12)\n",
        "precision13, recall13, accuracy13 = calculate_metrics(confusion_matrix13)\n",
        "precision14, recall14, accuracy14 = calculate_metrics(confusion_matrix14)\n",
        "\n",
        "precision21, recall21, accuracy21 = calculate_metrics(confusion_matrix21)\n",
        "precision22, recall22, accuracy22 = calculate_metrics(confusion_matrix22)\n",
        "precision23, recall23, accuracy23 = calculate_metrics(confusion_matrix23)\n",
        "precision24, recall24, accuracy24 = calculate_metrics(confusion_matrix24)"
      ],
      "metadata": {
        "id": "zE9VMOyqsQgS"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Precision: {precision11}\")\n",
        "print(f\"Recall: {recall11}\")\n",
        "print(f\"Accuracy: {accuracy11}\")\n",
        "print()\n",
        "print(f\"Precision: {precision12}\")\n",
        "print(f\"Recall: {recall12}\")\n",
        "print(f\"Accuracy: {accuracy12}\")\n",
        "print()\n",
        "print(f\"Precision: {precision13}\")\n",
        "print(f\"Recall: {recall13}\")\n",
        "print(f\"Accuracy: {accuracy13}\")\n",
        "print()\n",
        "print(f\"Precision: {precision14}\")\n",
        "print(f\"Recall: {recall14}\")\n",
        "print(f\"Accuracy: {accuracy14}\")\n",
        "print()\n",
        "\n",
        "print(f\"Precision: {precision21}\")\n",
        "print(f\"Recall: {recall21}\")\n",
        "print(f\"Accuracy: {accuracy21}\")\n",
        "print()\n",
        "print(f\"Precision: {precision22}\")\n",
        "print(f\"Recall: {recall22}\")\n",
        "print(f\"Accuracy: {accuracy22}\")\n",
        "print()\n",
        "print(f\"Precision: {precision23}\")\n",
        "print(f\"Recall: {recall23}\")\n",
        "print(f\"Accuracy: {accuracy23}\")\n",
        "print()\n",
        "print(f\"Precision: {precision24}\")\n",
        "print(f\"Recall: {recall24}\")\n",
        "print(f\"Accuracy: {accuracy24}\")\n",
        "print()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O0Xt_Vi3sQU_",
        "outputId": "99ddb40d-cfd1-4ef8-a4a2-20b6961e66f7"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precision: 0.47474747474747475\n",
            "Recall: 0.5222222222222223\n",
            "Accuracy: 0.525\n",
            "\n",
            "Precision: 0\n",
            "Recall: 0.0\n",
            "Accuracy: 0.55\n",
            "\n",
            "Precision: 0.4166666666666667\n",
            "Recall: 0.4444444444444444\n",
            "Accuracy: 0.47\n",
            "\n",
            "Precision: 0\n",
            "Recall: 0.0\n",
            "Accuracy: 0.55\n",
            "\n",
            "Precision: 0.6239316239316239\n",
            "Recall: 0.6403508771929824\n",
            "Accuracy: 0.575\n",
            "\n",
            "Precision: 0\n",
            "Recall: 0.0\n",
            "Accuracy: 0.43\n",
            "\n",
            "Precision: 0.6013986013986014\n",
            "Recall: 0.7543859649122807\n",
            "Accuracy: 0.575\n",
            "\n",
            "Precision: 0\n",
            "Recall: 0.0\n",
            "Accuracy: 0.43\n",
            "\n"
          ]
        }
      ]
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