Colab_IR_final.ipynb

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      "source": [
        "<div style=\"direction:rtl;line-height:300%;\">\n",
        "<font face=\"XB Zar\" size=5>\n",
        "<div align=center>\n",
        "<font face=\"B Titr\" size=5>\n",
        "<p></p><p></p>\n",
        "بسمه تعالی\n",
        "<p></p>\n",
        "</font>\n",
        "<p></p>\n",
        "<font>\n",
        "<br>\n",
        "درس بازیابی پیشرفته اطلاعات\n",
        "<br>\n",
        "مدرس: دکتر سلیمانی\n",
        "</font>\n",
        "<p></p>\n",
        "<br>\n",
        "<font>\n",
        "<b>فاز دوم پروژه</b>\n",
        "</font>\n",
        "<br>\n",
        "<br>\n",
        "موعد تحویل: ۵ خرداد ۱۴۰۲ <br>\n",
        "<br>\n",
        "<br>\n",
        "<br>\n",
        "<font>\n",
        "دانشگاه صنعتی شریف\n",
        "<br>\n",
        "دانشکده مهندسی کامپیوتر\n",
        "<br>\n",
        "<br>\n",
        "</font>\n",
        "</div>\n",
        "</font>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "11_5VtrN-MaS"
      },
      "source": [
        "<div dir=\"rtl\">\n",
        "<font face=\"XB Zar\" size=4 >\n",
        "سامانه‌های مبتنی بر یادگیری ماشین در بخش‌های مختلف از روش‌های یادگیری ماشین استفاده می‌کنند. دسته‌بندی و\n",
        "خوشه‌بندی\n",
        "دو کار مورد نیاز این سامانه‌ها هستند. در این تمرین به پیاده‌سازی این کارها با روش‌های مختلف و پیاده‌سازی قابلیت‌های جدید برای سیستم بازیابی روی مقالات علمی می‌پردازیم. دادگان این فاز را می‌توانید از\n",
        "<a href=\"https://www.kaggle.com/datasets/spsayakpaul/arxiv-paper-abstracts?resource=download\">این لینک</a>\n",
        "دانلود کنید.\n",
        "</font>\n",
        "</div>"
      ]
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    {
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      "metadata": {
        "id": "67ICoKZQ-MaT"
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      "source": [
        "<div dir=\"rtl\">\n",
        "<font face=\"XB Zar\" size=6>\n",
        "    <h1>\n",
        "    <b>دسته‌بندی توسط NaiveBayes (۱۸ نمره)</b>\n",
        "    </h1>\n",
        "    <p></p>\n",
        "    <p></p>\n",
        "</font>\n",
        "<font face=\"XB Zar\" size=3>\n",
        "در این قسمت شما به دسته‌بندی اسناد می‌پردازید. برای این منظور، ابتدا برای هر سند، بردار مربوط به تعداد کلمات کل را بسازید و سپس classifier NaiveBaise را خودتان از پایه پیاده‌سازی کرده و اسناد را دسته‌بندی کنید.\n",
        "در این تسک می‌توانید از کل دیتاست استفاده نکنید ولی باید دیتاست شما شامل هر سه کلاس موجود در دیتاست اصلی باشد.\n",
        "</font>\n",
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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "g11iqMSt-MaT"
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      "outputs": [],
      "source": [
        "%pip install torch\n",
        "%pip install scikit-learn\n",
        "%pip install transformers===4.28.0\n",
        "\n",
        "from IPython.display import clear_output\n",
        "clear_output()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FeoR_e_f-MaU"
      },
      "source": [
        "<div dir=\"rtl\">\n",
        "<font face=\"XB Zar\" size=5>\n",
        "    <h2>\n",
        "    <b>دانلود دیتاست و آشنایی با آن</b>\n",
        "    </h2>\n",
        "    <p></p>\n",
        "    <p></p>\n",
        "</font>\n",
        "<font face=\"XB Zar\" size=3>\n",
        "در این قسمت دیتاست زیر را در kaggle دریافت کرده و ویژگی‌های آن را مشاهده کنید.\n",
        "</font>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qChL30_S-MaU"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import nltk\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from tqdm import tqdm\n",
        "from nltk import word_tokenize\n",
        "import string\n",
        "from sklearn.model_selection import train_test_split\n",
        "import torch\n",
        "from transformers import (\n",
        "    BertTokenizer,\n",
        "    BertForSequenceClassification,\n",
        "    TrainingArguments,\n",
        "    Trainer,\n",
        ")\n",
        "from sklearn.metrics import precision_recall_fscore_support\n",
        "from sklearn.metrics import recall_score\n",
        "from sklearn.metrics import f1_score\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ioe_E-J9-MaV"
      },
      "outputs": [],
      "source": [
        "df = pd.read_csv(\"./arxiv_data.csv\")"
      ]
    },
    {
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        "colab": {
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        "id": "qkjKQpP6-MaV",
        "outputId": "172d82ed-a825-4b72-b2c5-02436db4b0d5"
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              "                                              titles  \\\n",
              "0  Survey on Semantic Stereo Matching / Semantic ...   \n",
              "1  FUTURE-AI: Guiding Principles and Consensus Re...   \n",
              "2  Enforcing Mutual Consistency of Hard Regions f...   \n",
              "3  Parameter Decoupling Strategy for Semi-supervi...   \n",
              "4  Background-Foreground Segmentation for Interio...   \n",
              "5  EdgeFlow: Achieving Practical Interactive Segm...   \n",
              "6  Efficient Hybrid Transformer: Learning Global-...   \n",
              "7  Towards to Robust and Generalized Medical Imag...   \n",
              "8  Semi-supervised Meta-learning with Disentangle...   \n",
              "9  Semi-supervised Contrastive Learning for Label...   \n",
              "\n",
              "                                           summaries  \\\n",
              "0  Stereo matching is one of the widely used tech...   \n",
              "1  The recent advancements in artificial intellig...   \n",
              "2  In this paper, we proposed a novel mutual cons...   \n",
              "3  Consistency training has proven to be an advan...   \n",
              "4  To ensure safety in automated driving, the cor...   \n",
              "5  High-quality training data play a key role in ...   \n",
              "6  Semantic segmentation of fine-resolution urban...   \n",
              "7  To mitigate the radiologist's workload, comput...   \n",
              "8  Generalising deep models to new data from new ...   \n",
              "9  The success of deep learning methods in medica...   \n",
              "\n",
              "                         terms  \n",
              "0           ['cs.CV', 'cs.LG']  \n",
              "1  ['cs.CV', 'cs.AI', 'cs.LG']  \n",
              "2           ['cs.CV', 'cs.AI']  \n",
              "3                    ['cs.CV']  \n",
              "4           ['cs.CV', 'cs.LG']  \n",
              "5           ['cs.CV', 'cs.HC']  \n",
              "6                    ['cs.CV']  \n",
              "7           ['cs.CV', 'cs.AI']  \n",
              "8                    ['cs.CV']  \n",
              "9                    ['cs.CV']  "
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              "      <td>The recent advancements in artificial intellig...</td>\n",
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              "      <td>To ensure safety in automated driving, the cor...</td>\n",
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              "      <td>Semantic segmentation of fine-resolution urban...</td>\n",
              "      <td>['cs.CV']</td>\n",
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              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Towards to Robust and Generalized Medical Imag...</td>\n",
              "      <td>To mitigate the radiologist's workload, comput...</td>\n",
              "      <td>['cs.CV', 'cs.AI']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Semi-supervised Meta-learning with Disentangle...</td>\n",
              "      <td>Generalising deep models to new data from new ...</td>\n",
              "      <td>['cs.CV']</td>\n",
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              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Semi-supervised Contrastive Learning for Label...</td>\n",
              "      <td>The success of deep learning methods in medica...</td>\n",
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      "source": [
        "# Show the first 10 rows of the dataset.\n",
        "df.head(10)"
      ]
    },
    {
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          "data": {
            "text/plain": [
              "Index(['titles', 'summaries', 'terms'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "# Show the headers of the dataset.\n",
        "df.columns"
      ]
    },
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            "text/plain": [
              "(51774, 3)"
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          "metadata": {},
          "execution_count": 8
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      "source": [
        "# Show the number of rows and columns of the dataset\n",
        "df.shape"
      ]
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      "source": [
        "<div dir=\"rtl\">\n",
        "<font face=\"XB Zar\" size=5>\n",
        "    <h2>\n",
        "    <b>کاهش سایز دیتاست</b>\n",
        "    </h2>\n",
        "    <p></p>\n",
        "    <p></p>\n",
        "</font>\n",
        "<font face=\"XB Zar\" size=3>\n",
        "    در این قسمت می‌توانید سایز دیتاست را به دلخواه کم کرده تا NaiveBayes عملکرد بهتری بتواند از خودش نشان دهد.\n",
        "    کاهش سایز دیتاست می‌تواند باعث افزایش سرعت و حافظه‌ی مورد نیاز شود.\n",
        "</font>\n",
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      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FifKPazG-MaY"
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      "outputs": [],
      "source": [
        "# reduce the size of dataset so naive bayes can run\n",
        "df = df.sample(frac=0.2, random_state=1)"
      ]
    },
    {
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              "                                                  titles  \\\n",
              "931    Self-Learning to Detect and Segment Cysts in L...   \n",
              "40820  Online trajectory recovery from offline handwr...   \n",
              "8930   SpotPatch: Parameter-Efficient Transfer Learni...   \n",
              "46601  Understanding the role of importance weighting...   \n",
              "13852                  Towards Differentiable Resampling   \n",
              "43241  Variational approximations using Fisher diverg...   \n",
              "15644  Learning to Simulate Dynamic Environments with...   \n",
              "14205  Communication-efficient distributed SGD with S...   \n",
              "23277                Multilayer Clustered Graph Learning   \n",
              "26855  Sparse + Low Rank Decomposition of Annihilatin...   \n",
              "\n",
              "                                               summaries  \\\n",
              "931    Image segmentation is a fundamental problem in...   \n",
              "40820  In general, it is straightforward to render an...   \n",
              "8930   Deep learning based object detectors are commo...   \n",
              "46601  The recent paper by Byrd & Lipton (2019), base...   \n",
              "13852  Resampling is a key component of sample-based ...   \n",
              "43241  Modern applications of Bayesian inference invo...   \n",
              "15644  Simulation is a crucial component of any robot...   \n",
              "14205  Large-scale distributed training of neural net...   \n",
              "23277  Multilayer graphs are appealing mathematical t...   \n",
              "26855  Recently, so called annihilating filer-based l...   \n",
              "\n",
              "                                            terms  \n",
              "931                                     ['cs.CV']  \n",
              "40820                          ['cs.CV', 'cs.LG']  \n",
              "8930                                    ['cs.CV']  \n",
              "46601                                   ['cs.LG']  \n",
              "13852               ['cs.LG', 'cs.RO', 'stat.ML']  \n",
              "43241  ['stat.ML', 'cs.LG', 'stat.CO', 'stat.ME']  \n",
              "15644                                   ['cs.CV']  \n",
              "14205    ['cs.LG', 'cs.DC', 'math.OC', 'stat.ML']  \n",
              "23277                                   ['cs.LG']  \n",
              "26855                                   ['cs.CV']  "
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              "      <th></th>\n",
              "      <th>titles</th>\n",
              "      <th>summaries</th>\n",
              "      <th>terms</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>931</th>\n",
              "      <td>Self-Learning to Detect and Segment Cysts in L...</td>\n",
              "      <td>Image segmentation is a fundamental problem in...</td>\n",
              "      <td>['cs.CV']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40820</th>\n",
              "      <td>Online trajectory recovery from offline handwr...</td>\n",
              "      <td>In general, it is straightforward to render an...</td>\n",
              "      <td>['cs.CV', 'cs.LG']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8930</th>\n",
              "      <td>SpotPatch: Parameter-Efficient Transfer Learni...</td>\n",
              "      <td>Deep learning based object detectors are commo...</td>\n",
              "      <td>['cs.CV']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46601</th>\n",
              "      <td>Understanding the role of importance weighting...</td>\n",
              "      <td>The recent paper by Byrd &amp; Lipton (2019), base...</td>\n",
              "      <td>['cs.LG']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13852</th>\n",
              "      <td>Towards Differentiable Resampling</td>\n",
              "      <td>Resampling is a key component of sample-based ...</td>\n",
              "      <td>['cs.LG', 'cs.RO', 'stat.ML']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43241</th>\n",
              "      <td>Variational approximations using Fisher diverg...</td>\n",
              "      <td>Modern applications of Bayesian inference invo...</td>\n",
              "      <td>['stat.ML', 'cs.LG', 'stat.CO', 'stat.ME']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15644</th>\n",
              "      <td>Learning to Simulate Dynamic Environments with...</td>\n",
              "      <td>Simulation is a crucial component of any robot...</td>\n",
              "      <td>['cs.CV']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14205</th>\n",
              "      <td>Communication-efficient distributed SGD with S...</td>\n",
              "      <td>Large-scale distributed training of neural net...</td>\n",
              "      <td>['cs.LG', 'cs.DC', 'math.OC', 'stat.ML']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23277</th>\n",
              "      <td>Multilayer Clustered Graph Learning</td>\n",
              "      <td>Multilayer graphs are appealing mathematical t...</td>\n",
              "      <td>['cs.LG']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26855</th>\n",
              "      <td>Sparse + Low Rank Decomposition of Annihilatin...</td>\n",
              "      <td>Recently, so called annihilating filer-based l...</td>\n",
              "      <td>['cs.CV']</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "df.head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CMsCD5lT-MaZ",
        "outputId": "2724226a-6d04-45be-d648-8d903de35852"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10355, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v7ndQ0Sr-MaZ"
      },
      "source": [
        "<div dir=\"rtl\">\n",
        "<font face=\"XB Zar\" size=5>\n",
        "    <h2>\n",
        "    <b>پیش‌پردازش دیتاست</b>\n",
        "    </h2>\n",
        "    <p></p>\n",
        "    <p></p>\n",
        "</font>\n",
        "<font face=\"XB Zar\" size=3>\n",
        "    در این قسمت می‌توانید با استفاده از تابع زیر یا توابعی که در فاز 1 تعریف کردید به پیش‌پردازش دیتاست بپردازید.\n",
        "    داده‌ای که با آن در این بخش کار می‌کنید چند برچسبی می‌باشند. اما برای این بخش ما تنها برچسب اول از هر مقاله را در نظر می‌گیریم و به صورت تک برچسبی به داده‌ها نگاه می‌کنیم.\n",
        "</font>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DpbkVr5i-MaZ",
        "outputId": "04b2fbb2-2186-4860-d988-72b96bc5c476"
      },
      "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]   Unzipping tokenizers/punkt.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "import nltk\n",
        "import ssl\n",
        "\n",
        "try:\n",
        "    _create_unverified_https_context = ssl._create_unverified_context\n",
        "except AttributeError:\n",
        "    pass\n",
        "else:\n",
        "    ssl._create_default_https_context = _create_unverified_https_context\n",
        "\n",
        "nltk.download(\"stopwords\")\n",
        "nltk.download(\"punkt\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "w0K9igDb-Maa"
      },
      "outputs": [],
      "source": [
        "def preprocess_text(\n",
        "    text,\n",
        "    minimum_length=1,\n",
        "    stopword_removal=True,\n",
        "    stopwords_domain=[],\n",
        "    lower_case=True,\n",
        "    punctuation_removal=True,\n",
        "):\n",
        "    normalized_tokens = word_tokenize(text)\n",
        "\n",
        "    if stopword_removal:\n",
        "        # Remove stopwords in English and also the given domain stopwords\n",
        "        stopwords = [x.lower() for x in nltk.corpus.stopwords.words(\"english\")]\n",
        "        domain_stopwords = [x.lower() for x in stopwords_domain]\n",
        "        normalized_tokens = [\n",
        "            word\n",
        "            for word in normalized_tokens\n",
        "            if word.lower() not in domain_stopwords + stopwords\n",
        "        ]\n",
        "\n",
        "    if punctuation_removal:\n",
        "        # Remove punctuations\n",
        "        normalized_tokens = [\n",
        "            word for word in normalized_tokens if word not in string.punctuation\n",
        "        ]\n",
        "\n",
        "    if lower_case:\n",
        "        # Convert everything to lowercase and filter based on a min length\n",
        "        normalized_tokens = [\n",
        "            word.lower() for word in normalized_tokens if len(word) > minimum_length\n",
        "        ]\n",
        "    else:\n",
        "        normalized_tokens = [\n",
        "            word for word in normalized_tokens if len(word) > minimum_length\n",
        "        ]\n",
        "\n",
        "    return normalized_tokens"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oosPMuK--Maa"
      },
      "outputs": [],
      "source": [
        "# pre_process the title and summaries of the dataframe\n",
        "\n",
        "df[\"titles\"] = df[\"titles\"].apply(preprocess_text)\n",
        "df[\"summaries\"] = df[\"summaries\"].apply(preprocess_text)\n",
        "# set the terms (they are in form of '[a.b, c.d]') to a list of terms\n",
        "df[\"terms\"] = df[\"terms\"].apply(lambda x: x[1:-1].split(\", \"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 505
        },
        "id": "wpFQm3tg-Maa",
        "outputId": "8852adf6-0d3a-4c01-d37e-95dbc5591f44"
      },
      "outputs": [
        {
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            "text/plain": [
              "                                                  titles  \\\n",
              "931    [self-learning, detect, segment, cysts, lung, ...   \n",
              "40820  [online, trajectory, recovery, offline, handwr...   \n",
              "8930   [spotpatch, parameter-efficient, transfer, lea...   \n",
              "46601  [understanding, role, importance, weighting, d...   \n",
              "13852              [towards, differentiable, resampling]   \n",
              "43241  [variational, approximations, using, fisher, d...   \n",
              "15644  [learning, simulate, dynamic, environments, ga...   \n",
              "14205  [communication-efficient, distributed, sgd, sk...   \n",
              "23277           [multilayer, clustered, graph, learning]   \n",
              "26855  [sparse, low, rank, decomposition, annihilatin...   \n",
              "\n",
              "                                               summaries  \\\n",
              "931    [image, segmentation, fundamental, problem, me...   \n",
              "40820  [general, straightforward, render, offline, ha...   \n",
              "8930   [deep, learning, based, object, detectors, com...   \n",
              "46601  [recent, paper, byrd, lipton, 2019, based, emp...   \n",
              "13852  [resampling, key, component, sample-based, rec...   \n",
              "43241  [modern, applications, bayesian, inference, in...   \n",
              "15644  [simulation, crucial, component, robotic, syst...   \n",
              "14205  [large-scale, distributed, training, neural, n...   \n",
              "23277  [multilayer, graphs, appealing, mathematical, ...   \n",
              "26855  [recently, called, annihilating, filer-based, ...   \n",
              "\n",
              "                                            terms  \n",
              "931                                     ['cs.CV']  \n",
              "40820                          ['cs.CV', 'cs.LG']  \n",
              "8930                                    ['cs.CV']  \n",
              "46601                                   ['cs.LG']  \n",
              "13852               ['cs.LG', 'cs.RO', 'stat.ML']  \n",
              "43241  ['stat.ML', 'cs.LG', 'stat.CO', 'stat.ME']  \n",
              "15644                                   ['cs.CV']  \n",
              "14205    ['cs.LG', 'cs.DC', 'math.OC', 'stat.ML']  \n",
              "23277                                   ['cs.LG']  \n",
              "26855                                   ['cs.CV']  "
            ],
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              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-65a31121-af63-41f5-b52a-3f4ea1fb9464 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-65a31121-af63-41f5-b52a-3f4ea1fb9464');\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",
              "          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": 18
        }
      ],
      "source": [
        "df.head(10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZBdWrNW--Maa"
      },
      "source": [
        "<div dir=\"rtl\">\n",
        "<font face=\"XB Zar\" size=5>\n",
        "    <h2>\n",
        "    <b>آماده‌سازی دیتای train و test</b>\n",
        "    </h2>\n",
        "    <p></p>\n",
        "    <p></p>\n",
        "</font>\n",
        "<font face=\"XB Zar\" size=3>\n",
        "    در این قسمت با ساخت ماتریس doc-word می‌توانید دیتای مورد نیاز برای NaiveBayes را آماده کنید. در این ماتریس هر سطر نشان‌دهنده آیدی داکیومنت و هر ستون نشان‌دهنده یک کلمه در کل vocabulary شما می‌باشد.\n",
        "   همچنین می‌توانید از CountVectorizer استفاده کنید. \n",
        "</font>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LYaBFpcR-Maa",
        "outputId": "f1b9c048-1536-4e7b-ec30-faad5b95ac65"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10355, 29807)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "# TODO\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "\n",
        "cv = CountVectorizer()\n",
        "joined = df[\"titles\"] + df[\"summaries\"]\n",
        "joined = joined.apply(lambda x: \" \".join(x))\n",
        "word_count_vector = cv.fit_transform(joined)\n",
        "\n",
        "word_count_vector.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tG7jk2XP-Maa"
      },
      "source": [
        "<div dir=\"rtl\">\n",
        "<font face=\"XB Zar\" size=5>\n",
        "    <h2>\n",
        "    <b>دسته‌بندی با استفاده از NaiveBayes</b>\n",
        "    </h2>\n",
        "    در این بخش کلاس زیر را تکمیل نمایید تا classifier NaiveBayes را <u><b>از پایه</b></u> پیاده‌سازی کنید.\n",
        "</font>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7NvqZ3fZ-Mab"
      },
      "outputs": [],
      "source": [
        "from tqdm import tqdm"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YSmrGUQF-Mab"
      },
      "outputs": [],
      "source": [
        "class NaiveBayes:\n",
        "    def __init__(self, alpha=1.0):\n",
        "        self.prior = None\n",
        "        self.word_counts = None\n",
        "        self.lk_word = None\n",
        "        self.alpha = alpha\n",
        "\n",
        "    def fit(self, x, y):\n",
        "        \"\"\"\n",
        "        Fit the features and the labels\n",
        "        Calculate prior, word_counts and lk_word\n",
        "\n",
        "        Parameters\n",
        "        ----------\n",
        "        x: np.ndarray\n",
        "            An m * n matrix - m is count\n",
        "            of docs and n is size of vocabulary\n",
        "\n",
        "        y: np.ndarray\n",
        "            The real class label for each doc\n",
        "\n",
        "        Returns\n",
        "        -------\n",
        "        self\n",
        "            Returns self as a classifier\n",
        "        \"\"\"\n",
        "        # TODO\n",
        "        self.prior = {}\n",
        "        self.word_counts = {}\n",
        "        self.lk_word = {}\n",
        "        y_modified = np.copy(y)\n",
        "\n",
        "        # If y label is a list, set the label to the first element\n",
        "        for i in range(len(y_modified)):\n",
        "            if type(y_modified[i]) == list:\n",
        "                y_modified[i] = y_modified[i][0]\n",
        "\n",
        "        # Get unique labels\n",
        "        self.labels = np.unique(y_modified)\n",
        "        print(self.labels)\n",
        "\n",
        "        # Calculate prior probabilities\n",
        "        for label in tqdm(self.labels):\n",
        "            self.prior[label] = np.sum(y_modified == label) / len(y_modified)\n",
        "\n",
        "        # Initialize word counts and likelihood probabilities dictionaries\n",
        "        for label in tqdm(self.labels):\n",
        "            self.word_counts[label] = np.zeros(x.shape[1])\n",
        "            self.lk_word[label] = np.zeros(x.shape[1])\n",
        "\n",
        "        # Update word counts for each class\n",
        "        for label in tqdm(self.labels):\n",
        "            for doc in x[y_modified == label]:\n",
        "                self.word_counts[label] += doc\n",
        "\n",
        "        # Calculate likelihood probabilities with Laplace smoothing\n",
        "        for label in tqdm(self.labels):\n",
        "            self.lk_word[label] = (self.word_counts[label] + self.alpha) / (\n",
        "                np.sum(self.word_counts[label]) + self.alpha * x.shape[1]\n",
        "            )\n",
        "\n",
        "        return self\n",
        "\n",
        "    def predict(self, x):\n",
        "        \"\"\"\n",
        "        Parameters\n",
        "        ----------\n",
        "        x: np.ndarray\n",
        "            An k * n matrix - k is count\n",
        "            of docs and n is vocabulary size\n",
        "\n",
        "        Returns\n",
        "        -------\n",
        "        np.ndarray\n",
        "            Return the predicted class for each doc\n",
        "            with the highest probability (argmax)\n",
        "        \"\"\"\n",
        "\n",
        "        # TODO (use tqdm to show progress bar)\n",
        "        # make it as fast as possible\n",
        "        # Hint: use np.log to avoid underflow\n",
        "        predictions = []\n",
        "        for doc in tqdm(x):\n",
        "            pred = []\n",
        "            for label in self.labels:\n",
        "                pred.append(\n",
        "                    np.log(self.prior[label])\n",
        "                    + np.sum(np.log(self.lk_word[label]) * doc)\n",
        "                )\n",
        "            predictions.append(self.labels[np.argmax(pred)])\n",
        "\n",
        "        self.predictions = predictions\n",
        "        return np.array(predictions)\n",
        "\n",
        "    def get_params(self, deep=True):\n",
        "        return {\"alpha\": self.alpha}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "W8xLXznx-Mab",
        "outputId": "1725128d-dccb-4788-801d-6e6beedc655c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[\"'cs.CV'\" \"'cs.LG'\" \"'stat.ML'\"]\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 3/3 [00:00<00:00, 2667.00it/s]\n",
            "100%|██████████| 3/3 [00:00<00:00, 5670.53it/s]\n",
            "100%|██████████| 3/3 [00:00<00:00,  5.24it/s]\n",
            "100%|██████████| 3/3 [00:00<00:00, 4580.60it/s]\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<__main__.NaiveBayes at 0x281492a40>"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# use your classifier to fit on the training data\n",
        "# then try to predict classes of test data\n",
        "\n",
        "# TODO\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    word_count_vector, df[\"terms\"], test_size=0.1, random_state=1\n",
        ")\n",
        "\n",
        "nb = NaiveBayes()\n",
        "nb.fit(X_train.toarray(), y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Vx_hoxPL-Mab",
        "outputId": "26ef17f9-7858-4ce1-868a-d0fbcf4791f7"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 1036/1036 [00:00<00:00, 2967.72it/s]\n"
          ]
        }
      ],
      "source": [
        "y_pred = nb.predict(X_test.toarray())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PTECong4-Mac",
        "outputId": "f0f31a84-6e6a-42a6-a75a-48d33c477c98"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Actual: [\"'cs.CV'\", \"'eess.SP'\"] , Predicted: 'cs.CV'\n",
            "Actual: [\"'cs.LG'\", \"'math.OC'\"] , Predicted: 'cs.LG'\n",
            "Actual: [\"'cs.CV'\"] , Predicted: 'cs.CV'\n",
            "Actual: [\"'cs.LG'\", \"'cs.NE'\", \"'stat.ML'\"] , Predicted: 'cs.LG'\n",
            "Actual: [\"'cs.LG'\"] , Predicted: 'cs.LG'\n",
            "Actual: [\"'cs.CV'\"] , Predicted: 'cs.CV'\n",
            "Actual: [\"'cs.LG'\", \"'cs.CV'\", \"'stat.ML'\"] , Predicted: 'cs.CV'\n",
            "Actual: [\"'cs.CV'\"] , Predicted: 'cs.CV'\n",
            "Actual: [\"'cs.LG'\", \"'cs.CR'\", \"'stat.ML'\"] , Predicted: 'cs.LG'\n",
            "Actual: [\"'cs.CV'\"] , Predicted: 'cs.CV'\n"
          ]
        }
      ],
      "source": [
        "# print some test labels vs predicted labels\n",
        "for i in range(10):\n",
        "    print(f\"Actual: {y_test.iloc[i]} , Predicted: {y_pred[i]}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E7fnG83s-Mac"
      },
      "source": [
        "<div dir=\"rtl\">\n",
        "<font face=\"XB Zar\" size=5>\n",
        "    <h2>\n",
        "    <b>ارزیابی مدل train شده</b>\n",
        "    </h2>\n",
        "    در این بخش precision، recall، F1 score را در حالت macro و micro\n",
        "    و accuracy مدل\n",
        "    را با استفاده از sklearn به دست آورید.\n",
        "</font>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "evcGP5vR-Mac",
        "outputId": "c64586a8-dee5-461a-931e-2e7e479caeba"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy is 0.8793436293436293\n",
            "Recall(macro) is 0.6816486465761828\n",
            "Recall(micro) is 0.8793436293436293\n",
            "Precision(macro) is 0.7540515927223773\n",
            "Precision(micro) is 0.8793436293436293\n",
            "F1(macro) is 0.6980808829585702\n",
            "F1(micro) is 0.8793436293436293\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import accuracy_score, recall_score, precision_score, f1_score\n",
        "\n",
        "# make y_test as like as y copy in the last cell (1st label)\n",
        "y_test_modified = np.copy(y_test)\n",
        "for i in range(len(y_test_modified)):\n",
        "    if type(y_test_modified[i]) == list:\n",
        "        y_test_modified[i] = y_test_modified[i][0]\n",
        "\n",
        "# TODO\n",
        "print(f\"Accuracy is {accuracy_score(y_test_modified, y_pred)}\")\n",
        "print(f\"Recall(macro) is {recall_score(y_test_modified, y_pred, average='macro')}\")\n",
        "print(f\"Recall(micro) is {recall_score(y_test_modified, y_pred, average='micro')}\")\n",
        "print(\n",
        "    f\"Precision(macro) is {precision_score(y_test_modified, y_pred, average='macro')}\"\n",
        ")\n",
        "print(\n",
        "    f\"Precision(micro) is {precision_score(y_test_modified, y_pred, average='micro')}\"\n",
        ")\n",
        "print(f\"F1(macro) is {f1_score(y_test_modified, y_pred, average='macro')}\")\n",
        "print(f\"F1(micro) is {f1_score(y_test_modified, y_pred, average='micro')}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WtGxBvRV-Mac"
      },
      "source": [
        "<div dir=\"rtl\">\n",
        "<font face=\"XB Zar\" size=4>\n",
        "در بخش پایین roc curve مربوط به این دسته‌بندی non-binary را به دست آورده و به صورت مختصر تحلیل کنید.\n",
        "</div>\n",
        "</font>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "351tdn6n-Mac",
        "outputId": "03ef4804-ec21-42df-8660-715165ac6e77"
      },
      "outputs": [
        {
          "ename": "TypeError",
          "evalue": "list indices must be integers or slices, not tuple",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "Cell \u001b[0;32mIn[20], line 17\u001b[0m\n\u001b[1;32m     15\u001b[0m roc_auc \u001b[39m=\u001b[39m {}\n\u001b[1;32m     16\u001b[0m \u001b[39mfor\u001b[39;00m i \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(\u001b[39mlen\u001b[39m(nb\u001b[39m.\u001b[39mlabels)):\n\u001b[0;32m---> 17\u001b[0m     fpr[i], tpr[i], _ \u001b[39m=\u001b[39m roc_curve(y_test_binarized[:, i], nb\u001b[39m.\u001b[39;49mpredictions[:, i])\n\u001b[1;32m     18\u001b[0m     roc_auc[i] \u001b[39m=\u001b[39m roc_auc_score(y_test_binarized[:, i], nb\u001b[39m.\u001b[39mpredictions[:, i])\n\u001b[1;32m     20\u001b[0m \u001b[39m# Plot all ROC curves\u001b[39;00m\n",
            "\u001b[0;31mTypeError\u001b[0m: list indices must be integers or slices, not tuple"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import roc_curve, roc_auc_score\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# binarize the labels (for one vs all roc curve)\n",
        "from sklearn.preprocessing import label_binarize\n",
        "\n",
        "y_test_binarized = label_binarize(y_test_modified, classes=nb.labels)\n",
        "\n",
        "# TODO\n",
        "# calculate the roc curve for each class\n",
        "# then plot the roc curve for each class\n",
        "# finally calculate the auc score\n",
        "fpr = {}\n",
        "tpr = {}\n",
        "roc_auc = {}\n",
        "for i in range(len(nb.labels)):\n",
        "    fpr[i], tpr[i], _ = roc_curve(y_test_binarized[:, i], nb.predictions[:, i])\n",
        "    roc_auc[i] = roc_auc_score(y_test_binarized[:, i], nb.predictions[:, i])\n",
        "\n",
        "# Plot all ROC curves\n",
        "plt.figure()\n",
        "for i in range(len(nb.labels)):\n",
        "    plt.plot(\n",
        "        fpr[i], tpr[i], label=f\"ROC curve of class {nb.labels[i]} (area = {roc_auc[i]})\"\n",
        "    )\n",
        "plt.plot([0, 1], [0, 1], \"k--\")\n",
        "plt.xlim([0.0, 1.05])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel(\"False Positive Rate\")\n",
        "plt.ylabel(\"True Positive Rate\")\n",
        "plt.title(\"ROC curve for each class\")\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hu5BbAyZ-Mac"
      },
      "source": [
        "_Explanation_:\n",
        "..."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HDt08gW6-Mad"
      },
      "source": [
        "<div dir=\"rtl\">\n",
        "<font face=\"XB Zar\" size=4>\n",
        "در این بخش confusion matrix را بدون استفاده از sklearn و به کمک matplotlib و seaborn بکشید.\n",
        "</font>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eMW0glFQ-Mad",
        "outputId": "360c5d1a-f7ec-4d25-a519-5450b115e02b"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# TODO confusion matrix without importing confusion_matrix from sklearn.metrics\n",
        "\n",
        "\n",
        "def create_confusion_matrix(y_test, y_pred, labels):\n",
        "    \"\"\"\n",
        "    Parameters\n",
        "    ----------\n",
        "    y_test: np.ndarray\n",
        "        The real class label for each doc\n",
        "\n",
        "    y_pred: np.ndarray\n",
        "        The predicted class label for each doc\n",
        "\n",
        "    labels: np.ndarray\n",
        "        The unique class labels\n",
        "\n",
        "    Returns\n",
        "    -------\n",
        "    np.ndarray\n",
        "        The confusion matrix\n",
        "    \"\"\"\n",
        "    confusion_matrix = np.zeros((len(labels), len(labels)))\n",
        "    for i in range(len(y_test)):\n",
        "        confusion_matrix[\n",
        "            np.where(labels == y_test[i]), np.where(labels == y_pred[i])\n",
        "        ] += 1\n",
        "    return confusion_matrix\n",
        "\n",
        "\n",
        "confusion_matrix = create_confusion_matrix(y_test_modified, y_pred, nb.labels)\n",
        "plt.figure(figsize=(10, 8))\n",
        "sns.heatmap(\n",
        "    confusion_matrix,\n",
        "    annot=True,\n",
        "    cmap=\"Reds\",\n",
        "    xticklabels=nb.labels,\n",
        "    yticklabels=nb.labels,\n",
        ")\n",
        "plt.xlabel(\"Predicted\")\n",
        "plt.ylabel(\"Actual\")\n",
        "plt.title(\"Confusion Matrix\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HeomKKcx-Mad"
      },
      "source": [
        "<div dir=\"rtl\">\n",
        "<font face=\"XB Zar\" size=5>\n",
        "    <h1>\n",
        "    <b>دسته بندی با شبکه های عصبی (۳۲ نمره)</b>\n",
        "    </h1>\n",
        "    <p></p>\n",
        "    <p></p>\n",
        "</font>\n",
        "<font face=\"XB Zar\" size=3>\n",
        "     <br>\n",
        "    شما یک مجموعه داده از مقالات علمی دارید، هرکدام با یک چکیده و موضوع مربوطه نشان داده شده است. هدف ساختن یک مدل شبکه عصبی است که بتواند بر اساس چکیده مقاله علمی و عنوان آن، موضوع آن را پیش بینی کند.\n",
        "</font>\n",
        "</div>\n",
        "   "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zeB2UmD9-Mad"
      },
      "source": [
        "<div dir=\"rtl\">\n",
        "<font face=\"XB Zar\" size=5>\n",
        "    <h2>\n",
        "    <b>داده ها</b>\n",
        "    </h2>\n",
        "    <p></p>\n",
        "    <p></p>\n",
        "</font>\n",
        "<font face=\"XB Zar\" size=3>\n",
        "     <br>\n",
        "    داده‌ای که با آن در این بخش کار می‌کنید چند برچسبی می‌باشند. اما برای این بخش ما تنها برچسب اول از هر مقاله را در نظر می‌گیریم و به صورت تک برچسبی به داده‌ها نگاه می‌کنیم.\n",
        "</font>\n",
        "</div>\n",
        "   "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CxYCvklh-Mad"
      },
      "source": [
        "## Requirements"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vSp1k5-S-Mad"
      },
      "outputs": [],
      "source": [
        "# You may not use all of these imports, but they are here to help you get started\n",
        "# you can add cells if you needed\n",
        "\n",
        "import nltk\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from tqdm import tqdm\n",
        "from nltk import word_tokenize\n",
        "import string\n",
        "from sklearn.model_selection import train_test_split\n",
        "import torch\n",
        "from sklearn.metrics import precision_recall_fscore_support\n",
        "from sklearn.metrics import f1_score\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import confusion_matrix\n",
        "import fasttext\n",
        "import os\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "\n",
        "import torch\n",
        "from torch import nn\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "from time import time\n",
        "from IPython.display import display"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BY7Qez2k-Mad"
      },
      "source": [
        "## Read and preprocess data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EEk7F-En-Mad"
      },
      "source": [
        "\n",
        "<div dir=\"rtl\">\n",
        "<font face=\"XB Zar\" size=5>\n",
        "</font>\n",
        "<font face=\"XB Zar\" size=3>\n",
        "     <br>\n",
        "در ابتدا دیتا را لود کنید. آن را تمیز کنید یا به عبارتی EDA مناسب در حدی که داده‌های null یا موارد این‌چنینی نداشته باشیم، بر روی آن انجام دهید.\n",
        " پیش پردازش‌های مورد نیاز که در فاز قبل با آن آشنا شدید را روی داده انجام دهید.\n",
        "    برای کتگوری هر مقاله برچسب اول آن را از دیتاست در نظر بگیرید.\n",
        "</font>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 74,
      "metadata": {
        "id": "JjMq2Rds-Mae"
      },
      "outputs": [],
      "source": [
        "# read data\n",
        "df = pd.read_csv(\"./arxiv_data.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 75,
      "metadata": {
        "id": "Z43v6JZM-Mae"
      },
      "outputs": [],
      "source": [
        "df[\"terms\"] = df[\"terms\"].apply(lambda x: x[1:-1].split(\", \"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 78,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8cKEyc6K-Mae",
        "outputId": "b0640cf0-63cf-4808-cd1d-d78e3feea6d6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['an', 'abstract', 'is', 'a', 'summari', 'of', 'the', 'main', 'articl']"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ],
      "source": [
        "def clean_data(text : str):\n",
        "    \"\"\"Preprocesses the text with tokenization, case folding, stemming and lemmatization, and punctuations\n",
        "\n",
        "    Parameters\n",
        "    ----------\n",
        "    text : str\n",
        "        The title or abstract of an article\n",
        "\n",
        "    Returns\n",
        "    -------\n",
        "    list\n",
        "        A list of tokens\n",
        "    \"\"\"\n",
        "\n",
        "    # TODO: tokenize, case_folding, stem, lemmatize, punctuations\n",
        "    tokenized = nltk.word_tokenize(text)\n",
        "    case_folded = [word.lower() for word in tokenized]\n",
        "    stemmer = nltk.stem.PorterStemmer()\n",
        "    stemmed = [stemmer.stem(word) for word in case_folded]\n",
        "    lemmatizer = nltk.stem.WordNetLemmatizer()\n",
        "    lemmatized = [lemmatizer.lemmatize(word) for word in stemmed]\n",
        "    punctuations = [word for word in lemmatized if word.isalpha()]\n",
        "    return punctuations\n",
        "\n",
        "clean_data(\"An abstract is a summary of the main article.\") # return [\"an\", \"abstract\", \"is\", \"a\", \"summary\", \"of\", \"the\", \"main\", \"article\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 79,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0t8_RzUT-Mae",
        "outputId": "1d62cbf4-b226-4aef-f725-218316e74469"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[('the', 3), ('is', 2), ('world', 2), ('an', 1), ('abstract', 1), ('a', 1), ('summary', 1), ('of', 1), ('main', 1), ('article', 1), ('hello', 1), ('most', 1), ('common', 1), ('phrase', 1), ('in', 1)]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('the', 3),\n",
              " ('is', 2),\n",
              " ('world', 2),\n",
              " ('an', 1),\n",
              " ('abstract', 1),\n",
              " ('a', 1),\n",
              " ('summary', 1),\n",
              " ('of', 1),\n",
              " ('main', 1),\n",
              " ('article', 1),\n",
              " ('hello', 1),\n",
              " ('most', 1),\n",
              " ('common', 1),\n",
              " ('phrase', 1),\n",
              " ('in', 1)]"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ],
      "source": [
        "## 2 points\n",
        "\n",
        "def find_stop_words(all_text : list[str], num_token=30):\n",
        "    \"\"\"Detects stop-words\n",
        "\n",
        "     Parameters\n",
        "    ----------\n",
        "    all_text : list of all tokens\n",
        "        (result of clean_data(text) for all the text)\n",
        "\n",
        "    Returns\n",
        "    -------\n",
        "    Return Value is optional but must print the stop words and number of their occurence\n",
        "    \"\"\"\n",
        "\n",
        "    # TODO: find num_token top most repetitive terms and report them\n",
        "    freq_dist = nltk.FreqDist()\n",
        "    for text in all_text:\n",
        "        for word in text:\n",
        "            freq_dist[word] += 1\n",
        "    \n",
        "    most_commons = freq_dist.most_common(num_token)\n",
        "    print(most_commons)\n",
        "\n",
        "    return most_commons\n",
        "    \n",
        "test = [\n",
        "    [\"an\", \"abstract\", \"is\", \"a\", \"summary\", \"of\", \"the\", \"main\", \"article\"],\n",
        "    [\"hello\", \"world\", \"is\", \"the\", \"most\", \"common\", \"phrase\", \"in\", \"the\", \"world\"],\n",
        "]\n",
        "\n",
        "find_stop_words(test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 80,
      "metadata": {
        "id": "79PadUO1-Mae"
      },
      "outputs": [],
      "source": [
        "# perform data cleaning and eda (you can add cells here)\n",
        "# todo\n",
        "\n",
        "df[\"titles\"] = df[\"titles\"].fillna(\"\")\n",
        "df[\"summaries\"] = df[\"summaries\"].fillna(\"\")\n",
        "\n",
        "original_df = df.copy()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 81,
      "metadata": {
        "id": "RM4wEqMN-Maf"
      },
      "outputs": [],
      "source": [
        "def preprocess_text(\n",
        "    text,\n",
        "    minimum_length=1,\n",
        "    stopword_removal=True,\n",
        "    stopwords_domain=[],\n",
        "    lower_case=True,\n",
        "    punctuation_removal=True,\n",
        "):\n",
        "    \"\"\"\n",
        "    preprocess text by removing stopwords, punctuations, and converting to lowercase, and also filter based on a min length\n",
        "    for stopwords use nltk.corpus.stopwords.words('english')\n",
        "    for punctuations use string.punctuation\n",
        "\n",
        "    Parameters\n",
        "    ----------\n",
        "    text: str\n",
        "        text to be preprocessed\n",
        "    minimum_length: int\n",
        "        minimum length of the token\n",
        "    stopword_removal: bool\n",
        "        whether to remove stopwords\n",
        "    stopwords_domain: list\n",
        "        list of stopwords to be removed base on domain\n",
        "    lower_case: bool\n",
        "        whether to convert to lowercase\n",
        "    punctuation_removal: bool\n",
        "        whether to remove punctuations\n",
        "    \"\"\"\n",
        "    # todo\n",
        "    tokenized = nltk.word_tokenize(text)\n",
        "    if lower_case:\n",
        "        tokenized = [word.lower() for word in tokenized]\n",
        "    if stopword_removal:\n",
        "        stop_words = nltk.corpus.stopwords.words(\"english\")\n",
        "        tokenized = [word for word in tokenized if word not in stop_words]\n",
        "    if punctuation_removal:\n",
        "        tokenized = [word for word in tokenized if word not in string.punctuation]\n",
        "    if minimum_length:\n",
        "        tokenized = [word for word in tokenized if len(word) >= minimum_length]\n",
        "    return tokenized"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d0k75ypu-Maf"
      },
      "source": [
        "\n",
        "<div dir=\"rtl\">\n",
        "<font face=\"XB Zar\" size=5>\n",
        "</font>\n",
        "<font face=\"XB Zar\" size=3>\n",
        "     <br>\n",
        "\n",
        "</font>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 82,
      "metadata": {
        "id": "XqrxOlLO-Maf"
      },
      "outputs": [],
      "source": [
        "# preprocess text\n",
        "df[\"preprocessed_abstract\"] = df[\"summaries\"].apply(lambda x: preprocess_text(x))\n",
        "df[\"preprocessed_title\"] = df[\"titles\"].apply(lambda x: preprocess_text(x))\n",
        "preprocessed_total = df[\"preprocessed_abstract\"] + df[\"preprocessed_title\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IPQbjwYx-Maf",
        "outputId": "6dbf6d63-d2a9-4960-c73b-879c3c64e6b8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['stereo', 'matching', 'one', 'widely', 'used']"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "preprocessed_total[0][:5]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Nz14fEyn-Maf"
      },
      "source": [
        "## Vectorize data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8dttgl28-Maf"
      },
      "source": [
        "\n",
        "<div dir=\"rtl\">\n",
        "<font face=\"XB Zar\" size=5>\n",
        "</font>\n",
        "<font face=\"XB Zar\" size=3>\n",
        "     <br>\n",
        "در این قسمت از fasttext کمک می‌گیریم تا به یک embedding اولیه برای هر مقاله برسیم.\n",
        "    با استفاده از داده‌هایی که داریم یک مدل fasttext آموزش دهید که برای هر توکن یک امبدینگ ۱۰۰تایی بدهد.\n",
        "    در مرحله‌ی بعد میانگین وزن دار امبدینگ های fasttext\n",
        "        توکن‌های ورودی (چکیده + عنوان)\n",
        "    را بر اساس tfidif آن‌ها محاسبه کنید و به امبدینگ نهایی متن برسید.\n",
        "    <br>\n",
        "    در واقع به عبارت ساده‌تر بر اساس میانگین وزن‌دار که وزن‌های ما tfidf توکن‌ها می‌باشد به امبدینگ نهایی متن بر اساس fasttext می‌رسیم.\n",
        "    </font>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MxrFi6B6-Maf"
      },
      "outputs": [],
      "source": [
        "class FastText:\n",
        "    def __init__(self, preprocessor=None, method=\"skipgram\"):\n",
        "        self.method = method\n",
        "        self.model = None\n",
        "        self.preprocessor = preprocessor\n",
        "\n",
        "    def train(self, texts):\n",
        "        \"\"\"\n",
        "        train the fasttext model and save it into self.model\n",
        "        Parameters\n",
        "        ----------\n",
        "        texts: list of list of str\n",
        "        \"\"\"\n",
        "        # todo\n",
        "        if self.preprocessor:\n",
        "            for text in texts:\n",
        "                text = [self.preprocessor(word) for word in text]\n",
        "            print(\"Preprocessing done!\")\n",
        "\n",
        "        print(\"Training model...\")\n",
        "        if self.method == \"skipgram\":\n",
        "            self.model = fasttext.train_unsupervised(texts, model=\"skipgram\")\n",
        "        elif self.method == \"cbow\":\n",
        "            self.model = fasttext.train_unsupervised(texts, model=\"cbow\")\n",
        "        else:\n",
        "            raise ValueError(\"Method must be either skipgram or cbow\")\n",
        "        print(\"Training done!\")\n",
        "\n",
        "    def get_word_embedding(self, word):\n",
        "        \"\"\"\n",
        "        get the embedding of a word\n",
        "        Parameters\n",
        "        ----------\n",
        "        word: str\n",
        "        Returns embedding of the word\n",
        "        \"\"\"\n",
        "        # todo\n",
        "        embedding = self.model.get_word_vector(word)\n",
        "        return embedding\n",
        "\n",
        "    def get_query_embedding(self, query, tf_idf_vectorizer):\n",
        "        \"\"\"\n",
        "        get the embedding of the query. You can use the tf_idf_vectorizer to get the weights of the words in the query. preprocess the query using self.preprocessor if it is not None\n",
        "        Parameters\n",
        "        ----------\n",
        "        query: str\n",
        "        tf_idf_vectorizer: TfidfVectorizer\n",
        "        Returns embedding of the query\n",
        "        \"\"\"\n",
        "        # todo\n",
        "        if self.preprocessor:\n",
        "            query = self.preprocessor(query)\n",
        "        query_embed = np.zeros((1, 100))\n",
        "        for word in query:\n",
        "            if word in tf_idf_vectorizer.vocabulary_:\n",
        "                word_embed = self.get_word_embedding(word)\n",
        "                word_weight = tf_idf_vectorizer.idf_[\n",
        "                    tf_idf_vectorizer.vocabulary_[word]\n",
        "                ]\n",
        "                query_embed += word_embed * word_weight\n",
        "        return query_embed\n",
        "\n",
        "    def save_FastText_model(self, path=\"FastText_model.bin\"):\n",
        "        self.model.save_model(path)\n",
        "\n",
        "    def load_FastText_model(self, path=\"FastText_model.bin\"):\n",
        "        self.model = fasttext.load_model(path)\n",
        "\n",
        "    def prepare(self, dataset, mode, save=False):\n",
        "        if mode == \"train\":\n",
        "            self.train(dataset)\n",
        "        if mode == \"load\":\n",
        "            self.load_FastText_model()\n",
        "        if save:\n",
        "            self.save_FastText_model()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YZChpBql-Maf"
      },
      "outputs": [],
      "source": [
        "FastText_model = FastText(preprocessor=preprocess_text)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "I7P26ZRp-Mag"
      },
      "outputs": [],
      "source": [
        "# Save the pandas series to a txt file\n",
        "text_list = preprocessed_total.tolist()\n",
        "\n",
        "with open(\"preprocessed_total.txt\", \"w\") as f:\n",
        "    for item in text_list:\n",
        "        f.write(\" \".join(item) + \"\\n\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LnAHJ01b-Mag",
        "outputId": "267e2d02-063e-4895-dad5-9159af0bee86"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Warning : `load_model` does not return WordVectorModel or SupervisedModel any more, but a `FastText` object which is very similar.\n"
          ]
        }
      ],
      "source": [
        "# FastText_model.prepare(\"./preprocessed_total.txt\", mode=\"train\", save=True)\n",
        "FastText_model.prepare(preprocessed_total, mode='load')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eaX2yVY1-Mag",
        "outputId": "aa451572-3278-47ef-cfd0-8537a355289e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[(0.8873776793479919, 'covid-19'),\n",
              " (0.8581413626670837, 'covid19'),\n",
              " (0.8046349287033081, 'dr-covid'),\n",
              " (0.7988560795783997, 'pandemic'),\n",
              " (0.7684428691864014, 'pandemics'),\n",
              " (0.747352659702301, 'outbreak'),\n",
              " (0.7317118048667908, 'endemic'),\n",
              " (0.7267407774925232, 'outbreaks'),\n",
              " (0.7000961303710938, 'hypoxemia'),\n",
              " (0.6973095536231995, 'covid-related')]"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "text = \"covid\"\n",
        "\n",
        "FastText_model.model.get_nearest_neighbors(text)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "x--aw5zG-Mag"
      },
      "outputs": [],
      "source": [
        "class TF_IDF:\n",
        "    def __init__(self):\n",
        "        self.vectorizer = TfidfVectorizer()\n",
        "\n",
        "    def fit_vectorizer(self, data):\n",
        "        \"\"\"\n",
        "        fit the vectorizer on the data\n",
        "        Parameters\n",
        "        ----------\n",
        "        data: list of list of str\n",
        "        \"\"\"\n",
        "        # todo\n",
        "        flattened_data = [\" \".join(text) for text in data]\n",
        "        self.vectorizer.fit(flattened_data)\n",
        "        return self"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ePBMZKA3-Mag",
        "outputId": "2d40cf87-d943-4737-a948-62ce66d00da7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<__main__.TF_IDF at 0x2b0cf7dc0>"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "TF_IDF_model = TF_IDF()\n",
        "TF_IDF_model.fit_vectorizer(preprocessed_total)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FQSA-7EH-Mag",
        "outputId": "855a1de4-184a-4475-b6f8-a2e2122678c4"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[ 1.61460665, -1.65360045,  1.26062871,  0.08732843, -0.18338009,\n",
              "        -1.50571329, -0.67883593,  1.72764868,  2.60821217,  0.61810635,\n",
              "        -3.3838394 ,  1.27166542,  0.57148305,  0.73514611,  2.1740703 ,\n",
              "         0.17366932,  1.7059285 ,  1.24747679,  0.90337786,  3.79671943,\n",
              "        -2.70416868,  0.98618379,  0.41615663,  5.40333533,  1.13975252,\n",
              "        -1.67530316, -1.94843012,  3.55183524,  1.31366718,  0.85241711,\n",
              "        -1.48689598,  3.29364064, -1.22943958, -2.93269956, -2.36275935,\n",
              "        -2.41381866, -0.4318797 ,  6.05777764, -0.56127417, -2.50565684,\n",
              "        -2.51067221,  1.78529689,  4.72935292,  1.66308981, -0.07644829,\n",
              "        -4.83382249,  5.48113728,  2.67239267, -1.93812156,  3.46176714,\n",
              "         0.38868949, -3.84364295, -3.76700035, -0.86432794, -2.4627789 ,\n",
              "        -0.53371342,  2.73459846,  3.11285448,  2.92089325, -1.50960928,\n",
              "        -0.16807449,  1.65387893,  1.92404107,  2.52339655,  3.09710598,\n",
              "        -1.13881099,  3.87948191, -4.5551585 , -4.27111888, -3.87794393,\n",
              "        -0.13532898,  2.2946561 , -0.42682254, -1.14648288,  0.42657214,\n",
              "         2.88879395,  0.78848559,  3.14491129, -1.8172773 , -4.39310122,\n",
              "        -1.27060413, -1.38489008,  1.18821827, -1.51400387, -0.53371948,\n",
              "         2.78351605, -2.19602495,  0.64531988, -3.09978878, -2.11126302,\n",
              "        -2.03925538, -2.47112121,  2.76702464, -1.56302825,  0.97600783,\n",
              "         1.91921866, -1.13846816,  3.22438759,  4.08980632,  0.22490239]])"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "text = \"backpropagation is good\"\n",
        "TF_IDF_model.vectorizer.transform([\" \".join(text)])\n",
        "FastText_model.get_query_embedding(text, TF_IDF_model.vectorizer)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WRXz_C_e-Mag"
      },
      "source": [
        "\n",
        "<div dir=\"rtl\">\n",
        "<font face=\"XB Zar\" size=5>\n",
        "</font>\n",
        "<font face=\"XB Zar\" size=3>\n",
        "     <br>\n",
        "حال به کمک مدلی که نوشته‌اید دیتای ورودی شبکه‌ی عصبی را بسازید.\n",
        "    به عبارتی به ازای هر مقاله embedding مربوطه را بدست آورید.\n",
        "    همچنین برچسب‌ها را نیز به عدد تبدیل کنید تا برای شبکه‌ی عصبی قابل فهم باشد.\n",
        "    </font>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wbRwLEQH-Mah"
      },
      "outputs": [],
      "source": [
        "# TODO\n",
        "\n",
        "from enum import Enum\n",
        "\n",
        "\n",
        "class LABELS(Enum):\n",
        "    \"\"\"\n",
        "    Enum class for labels\n",
        "    \"'cs.CV'\" \"'cs.LG'\" \"'stat.ML'\"\n",
        "    \"\"\"\n",
        "    CS_CV = 0\n",
        "    CS_LG = 1\n",
        "    STAT_ML = 2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qyglpfzI-Mah",
        "outputId": "f639954b-0072-44f3-a97f-0c517cb0d244"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Embeddings loaded!\n"
          ]
        }
      ],
      "source": [
        "loaded = False\n",
        "# if embeddings.npy exists, load it\n",
        "if os.path.exists(\"embeddings.npy\"):\n",
        "    embeddings = np.load(\"embeddings.npy\")\n",
        "    print(\"Embeddings loaded!\")\n",
        "    loaded = True"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Sx6ybgRs-Mah"
      },
      "outputs": [],
      "source": [
        "# Make the input data of NN ready using TF-IDF and FastText. It means foreach document, get respective embedding.\n",
        "\n",
        "# get the embedding of each document\n",
        "embeddings = []\n",
        "for text in tqdm(preprocessed_total):\n",
        "    text = \" \".join(text)\n",
        "    embedding = FastText_model.get_query_embedding(text, TF_IDF_model.vectorizer)\n",
        "    embeddings.append(embedding)\n",
        "\n",
        "# convert the list of embeddings to a numpy array\n",
        "embeddings = np.array(embeddings)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ye4RtfBh-Mah",
        "outputId": "960e5a96-6ba5-41f3-9bd8-fc0678789f4b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
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              "         -7.09200086e+01]],\n",
              "\n",
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              "\n",
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              "         -1.59554185e+02, -7.34920272e+01,  4.17917521e+01,\n",
              "         -2.78852309e+01]],\n",
              "\n",
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              "         -5.18431809e+01,  5.67932470e+00,  2.39225578e+02,\n",
              "          1.80006490e+01,  5.43559343e+00,  4.55674336e+01,\n",
              "         -8.31314334e+01,  3.95358345e+01, -1.28192369e+02,\n",
              "          9.56425569e+01,  2.01711495e+02, -2.71155824e+01,\n",
              "         -2.32184911e+01, -1.21393489e+02,  5.01707156e+01,\n",
              "         -1.00075231e+02, -9.66520367e+01, -3.54461991e+01,\n",
              "          6.45359373e+00, -3.34849315e+01, -8.23205431e+01,\n",
              "         -3.57728874e+01, -1.98534108e+01, -3.02697330e+01,\n",
              "          9.41697359e+01,  1.30327096e+02,  4.89375195e+01,\n",
              "          8.11527458e+01, -8.32228958e+01, -4.48648513e+01,\n",
              "         -7.15283705e+01, -4.84457982e+00, -7.54766263e+01,\n",
              "          9.52648355e+01,  2.75195797e+01, -1.29159836e+01,\n",
              "         -2.05770413e+01, -9.40925925e+01,  1.47167799e+02,\n",
              "         -1.96495027e+02,  3.03634003e+01, -3.48333769e+01,\n",
              "         -2.77558431e+02, -9.83914801e+01,  6.33513508e+01,\n",
              "         -2.37696537e+01]]])"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "embeddings[:5]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 87,
      "metadata": {
        "id": "bHu3AavD-Mah"
      },
      "outputs": [],
      "source": [
        "y = df[\"terms\"].values\n",
        "y_modified = np.copy(y)\n",
        "\n",
        "\n",
        "# If y label is a list, set the label to the first element\n",
        "for i in range(len(y_modified)):\n",
        "    if type(y_modified[i]) == list:\n",
        "        y_modified[i] = y_modified[i][0]\n",
        "\n",
        "# Convert the labels to numbers\n",
        "labels = []\n",
        "for label in y_modified:\n",
        "    if label == \"'cs.CV'\":\n",
        "        labels.append(LABELS.CS_CV.value)\n",
        "    elif label == \"'cs.LG'\":\n",
        "        labels.append(LABELS.CS_LG.value)\n",
        "    elif label == \"'stat.ML'\":\n",
        "        labels.append(LABELS.STAT_ML.value)\n",
        "    else:\n",
        "        raise ValueError(\"Label is not valid\")\n",
        "\n",
        "labels = torch.tensor(labels)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DigJN58M-Mah"
      },
      "outputs": [],
      "source": [
        "# save the embeddings to a file\n",
        "if not loaded:\n",
        "    np.save(\"embeddings.npy\", embeddings)\n",
        "    print(\"Embeddings saved!\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L1QSy1cg-Mah"
      },
      "source": [
        "## Prepare data for model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FMXMVCNw-Mai"
      },
      "source": [
        "\n",
        "<div dir=\"rtl\">\n",
        "<font face=\"XB Zar\" size=5>\n",
        "</font>\n",
        "<font face=\"XB Zar\" size=3>\n",
        "     <br>\n",
        "     در این قسمت dataset ورودی شبکه‌ی عصبی را بسازید.\n",
        "     همچنین دیتا‌ را به قسمت‌های train test validation تقسیم کنید.\n",
        "     80% داده برای آموزش\n",
        "     10% را برای  validation\n",
        "     و 10% را برای تست در نظر بگیرید.\n",
        "     در نهایت dataloaderهای مربوطه را بسازید.\n",
        "    </font>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 89,
      "metadata": {
        "id": "FtOr8GXC-Mai"
      },
      "outputs": [],
      "source": [
        "class PapersDataSet(Dataset):\n",
        "    def __init__(self, embeddings: list, labels: list):\n",
        "        # todo\n",
        "        self.embeddings = embeddings\n",
        "        self.labels = labels\n",
        "        self.length = len(self.embeddings)\n",
        "\n",
        "    def __len__(self):\n",
        "        # todo\n",
        "        return self.length\n",
        "\n",
        "    def __getitem__(self, i):\n",
        "        # todo\n",
        "        return self.embeddings[i], self.labels[i]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 90,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TAlgEFRt-Mai",
        "outputId": "fb90c6af-aca0-4640-a948-1578bc89e64f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, ..., 2, 0, 2])"
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ],
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "labels = LabelEncoder().fit_transform(labels)\n",
        "labels"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Aijor9Rf-Mai"
      },
      "outputs": [],
      "source": [
        "dataset = PapersDataSet(embeddings, labels)\n",
        "\n",
        "# split the dataset to train and test\n",
        "train_size = int(0.8 * len(dataset))\n",
        "validation_size = int(0.1 * len(dataset))\n",
        "test_size = len(dataset) - train_size - validation_size\n",
        "\n",
        "train_dataset, validation_dataset, test_dataset = torch.utils.data.random_split(\n",
        "    dataset, [train_size, validation_size, test_size]\n",
        ")\n",
        "\n",
        "# Calculate the labels. Respective label to each embedding in the dataset. use LABELS enum class\n",
        "train_labels = [label for _, label in train_dataset]\n",
        "validation_labels = [label for _, label in validation_dataset]\n",
        "test_labels = [label for _, label in test_dataset]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "msAS296t-Mai"
      },
      "outputs": [],
      "source": [
        "# todo split the data into train, val, test and create dataloaders\n",
        "train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)\n",
        "val_loader = DataLoader(validation_dataset, batch_size=64, shuffle=True)\n",
        "test_loader = DataLoader(test_dataset, batch_size=64, shuffle=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9iOJ07VG-Mai"
      },
      "source": [
        "## Build model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "36N2V3Sg-Mai"
      },
      "source": [
        "\n",
        "<div dir=\"rtl\">\n",
        "<font face=\"XB Zar\" size=5>\n",
        "</font>\n",
        "<font face=\"XB Zar\" size=3>\n",
        "     <br>\n",
        "حال دراین قسمت مدل شبکه‌ی عصبی خود را تعریف کنید.\n",
        "    </font>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Wez19pe3-Mai"
      },
      "outputs": [],
      "source": [
        "class ClassifierModel(nn.Module):\n",
        "    def __init__(self, in_features=100, num_classes=3):\n",
        "        # todo\n",
        "        super(ClassifierModel, self).__init__()\n",
        "        self.fc1 = nn.Linear(in_features, 64)\n",
        "        self.bn1 = nn.BatchNorm1d(64)\n",
        "        self.dropout1 = nn.Dropout(0.5)\n",
        "        self.relu = nn.ReLU()\n",
        "        self.fc2 = nn.Linear(64, 32)\n",
        "        self.bn2 = nn.BatchNorm1d(32)\n",
        "        self.dropout2 = nn.Dropout(0.5)\n",
        "        self.fc3 = nn.Linear(32, num_classes)\n",
        "        self.model = nn.Sequential(\n",
        "            self.fc1,\n",
        "            self.bn1,\n",
        "            self.dropout1,\n",
        "            self.relu,\n",
        "            self.fc2,\n",
        "            self.bn2,\n",
        "            self.dropout2,\n",
        "            self.relu,\n",
        "            self.fc3,\n",
        "        )\n",
        "\n",
        "    def forward(self, x):\n",
        "        # todo\n",
        "        return self.model(x)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JvxNgshZ-Mai"
      },
      "source": [
        "## Train model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kiRsi2ww-Maj"
      },
      "source": [
        "\n",
        "\n",
        "<div dir=\"rtl\">\n",
        "<font face=\"XB Zar\" size=5>\n",
        "</font>\n",
        "<font face=\"XB Zar\" size=3>\n",
        "     <br>\n",
        "در این قسمت به کمک داده‌ی آموزش و validation مدل خود را آموزش دهید.\n",
        "اطلاعات مورد نیاز نظیر lossهای train و validation را ذخیره کنید تا در ادامه به کمک نمودار یادگیری مدل خود را ارزیابی کنید.\n",
        "    </font>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NnJZbNgF-Maj",
        "outputId": "f1d4377d-d322-453c-8366-fcd0bec048c7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "device is mps\n",
            "Model Architecture:\n",
            "ClassifierModel(\n",
            "  (fc1): Linear(in_features=100, out_features=64, bias=True)\n",
            "  (bn1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "  (dropout1): Dropout(p=0.5, inplace=False)\n",
            "  (relu): ReLU()\n",
            "  (fc2): Linear(in_features=64, out_features=32, bias=True)\n",
            "  (bn2): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "  (dropout2): Dropout(p=0.5, inplace=False)\n",
            "  (fc3): Linear(in_features=32, out_features=3, bias=True)\n",
            "  (model): Sequential(\n",
            "    (0): Linear(in_features=100, out_features=64, bias=True)\n",
            "    (1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    (2): Dropout(p=0.5, inplace=False)\n",
            "    (3): ReLU()\n",
            "    (4): Linear(in_features=64, out_features=32, bias=True)\n",
            "    (5): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    (6): Dropout(p=0.5, inplace=False)\n",
            "    (7): ReLU()\n",
            "    (8): Linear(in_features=32, out_features=3, bias=True)\n",
            "  )\n",
            ")\n"
          ]
        }
      ],
      "source": [
        "device = torch.device(\n",
        "    \"mps\"\n",
        "    if torch.backends.mps.is_available()\n",
        "    else \"cuda:0\"\n",
        "    if torch.cuda.is_available()\n",
        "    else \"cpu\"\n",
        ")\n",
        "model = ClassifierModel().to(device)\n",
        "print(f\"device is {device}\")\n",
        "print(\"Model Architecture:\")\n",
        "print(model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "l2Gs7gv5-Maj"
      },
      "outputs": [],
      "source": [
        "# todo define the loss function and the optimizer. We suggest using Adam optimizer\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "learning_rate = 1e-3\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gFHCX_gS-Maj"
      },
      "outputs": [],
      "source": [
        "def eval_epoch(\n",
        "    model: nn.Module,\n",
        "    criterion: nn.Module,\n",
        "    dataloader: torch.utils.data.DataLoader,\n",
        "    test_mode=False,\n",
        "):\n",
        "    \"\"\"\n",
        "    Evaluate the model on the given dataloader. used for validation and test\n",
        "    Parameters\n",
        "    ----------\n",
        "    model: nn.Module\n",
        "    criterion: nn.Module\n",
        "    dataloader: torch.utils.data.DataLoader\n",
        "    test_mode: bool\n",
        "        If True, the function will print 'Test' instead of 'Validation'\n",
        "    Returns\n",
        "    -------\n",
        "    eval_loss: float\n",
        "        The loss on the given dataloader\n",
        "    predicted_labels: list\n",
        "        The predicted labels\n",
        "    true_labels: list\n",
        "        The true labels\n",
        "    f1_score_macro: float\n",
        "        The f1 score on the given dataloader\n",
        "    \"\"\"\n",
        "    # todo\n",
        "    if test_mode:\n",
        "        print(\"Test\")\n",
        "    else:\n",
        "        print(\"Validation\")\n",
        "\n",
        "    model.eval()\n",
        "    eval_loss = 0\n",
        "    predicted_labels = []\n",
        "    true_labels = []\n",
        "    with torch.no_grad():\n",
        "        for inputs, labels in tqdm(dataloader):\n",
        "            inputs = inputs.to(torch.float32).to(device).view(inputs.shape[0], -1)\n",
        "            labels = labels.to(device)\n",
        "            outputs = model(inputs)\n",
        "            eval_loss += criterion(outputs, labels).item()\n",
        "            _, predicted = torch.max(outputs.data, 1)\n",
        "            predicted_labels.extend(predicted.tolist())\n",
        "            true_labels.extend(labels.tolist())\n",
        "\n",
        "    eval_loss /= len(dataloader)\n",
        "\n",
        "    f1_score_macro = f1_score(true_labels, predicted_labels, average=\"macro\")\n",
        "\n",
        "    return eval_loss, predicted_labels, true_labels, f1_score_macro"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "743DSkdN-Maj",
        "outputId": "aed1409c-8206-497c-af2d-9bc6db10d68f"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 648/648 [00:05<00:00, 123.60it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Validation\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 81/81 [00:00<00:00, 412.48it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1 finished in 5.47s\n",
            "[Epoch 1]\tTrain Loss: 0.47789\tValidation Loss: 0.00007\t F1 score macro: 0.599362\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 648/648 [00:05<00:00, 125.21it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Validation\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 81/81 [00:00<00:00, 408.86it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 2 finished in 5.40s\n",
            "[Epoch 2]\tTrain Loss: 0.39897\tValidation Loss: 0.00007\t F1 score macro: 0.599362\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 648/648 [00:05<00:00, 123.53it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Validation\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 81/81 [00:00<00:00, 387.67it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 3 finished in 5.49s\n",
            "[Epoch 3]\tTrain Loss: 0.38983\tValidation Loss: 0.00007\t F1 score macro: 0.599362\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 648/648 [00:05<00:00, 126.42it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Validation\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 81/81 [00:00<00:00, 405.26it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 4 finished in 5.35s\n",
            "[Epoch 4]\tTrain Loss: 0.38405\tValidation Loss: 0.00007\t F1 score macro: 0.599362\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 648/648 [00:05<00:00, 127.72it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Validation\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 81/81 [00:00<00:00, 416.11it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 5 finished in 5.30s"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "[Epoch 5]\tTrain Loss: 0.37916\tValidation Loss: 0.00006\t F1 score macro: 0.599362\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 648/648 [00:05<00:00, 128.23it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Validation\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 81/81 [00:00<00:00, 414.24it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 6 finished in 5.28s\n",
            "[Epoch 6]\tTrain Loss: 0.37252\tValidation Loss: 0.00006\t F1 score macro: 0.599362\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 648/648 [00:05<00:00, 127.98it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Validation\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 81/81 [00:00<00:00, 416.01it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 7 finished in 5.29s"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "[Epoch 7]\tTrain Loss: 0.37109\tValidation Loss: 0.00006\t F1 score macro: 0.599362\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 648/648 [00:05<00:00, 122.42it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Validation\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 81/81 [00:00<00:00, 326.36it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 8 finished in 5.57s\n",
            "[Epoch 8]\tTrain Loss: 0.37181\tValidation Loss: 0.00006\t F1 score macro: 0.599362\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 648/648 [00:06<00:00, 107.40it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Validation\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 81/81 [00:00<00:00, 313.20it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 9 finished in 6.33s\n",
            "[Epoch 9]\tTrain Loss: 0.36494\tValidation Loss: 0.00006\t F1 score macro: 0.599362\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 648/648 [00:05<00:00, 127.61it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Validation\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 81/81 [00:00<00:00, 437.12it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 10 finished in 5.29s\n",
            "[Epoch 10]\tTrain Loss: 0.36617\tValidation Loss: 0.00006\t F1 score macro: 0.599362\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 648/648 [00:05<00:00, 128.60it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Validation\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 81/81 [00:00<00:00, 437.66it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 11 finished in 5.26s\n",
            "[Epoch 11]\tTrain Loss: 0.36553\tValidation Loss: 0.00006\t F1 score macro: 0.599362\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 648/648 [00:05<00:00, 110.16it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Validation\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 81/81 [00:00<00:00, 237.55it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 12 finished in 6.25s\n",
            "[Epoch 12]\tTrain Loss: 0.35937\tValidation Loss: 0.00006\t F1 score macro: 0.599362\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 648/648 [00:06<00:00, 103.15it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Validation\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 81/81 [00:00<00:00, 290.82it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 13 finished in 6.59s\n",
            "[Epoch 13]\tTrain Loss: 0.35969\tValidation Loss: 0.00006\t F1 score macro: 0.599362\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 648/648 [00:05<00:00, 111.47it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Validation\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 81/81 [00:00<00:00, 392.74it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 14 finished in 6.05s\n",
            "[Epoch 14]\tTrain Loss: 0.35929\tValidation Loss: 0.00006\t F1 score macro: 0.599362\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
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            "Epoch 39 finished in 5.28s\n",
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            "Epoch 40 finished in 5.28s\n",
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        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 81/81 [00:00<00:00, 441.37it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 43 finished in 5.31s\n",
            "[Epoch 43]\tTrain Loss: 0.34181\tValidation Loss: 0.00006\t F1 score macro: 0.599362\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 648/648 [00:05<00:00, 127.04it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Validation\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 81/81 [00:00<00:00, 385.82it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 44 finished in 5.35s\n",
            "[Epoch 44]\tTrain Loss: 0.34202\tValidation Loss: 0.00006\t F1 score macro: 0.599362\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 648/648 [00:05<00:00, 125.02it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Validation\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 81/81 [00:00<00:00, 444.56it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 45 finished in 5.40s\n",
            "[Epoch 45]\tTrain Loss: 0.33926\tValidation Loss: 0.00006\t F1 score macro: 0.599362\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 648/648 [00:05<00:00, 113.36it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Validation\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 81/81 [00:00<00:00, 374.43it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 46 finished in 5.96s\n",
            "[Epoch 46]\tTrain Loss: 0.34313\tValidation Loss: 0.00006\t F1 score macro: 0.599362\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 648/648 [00:06<00:00, 105.62it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Validation\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 81/81 [00:00<00:00, 246.42it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 47 finished in 6.49s\n",
            "[Epoch 47]\tTrain Loss: 0.34120\tValidation Loss: 0.00006\t F1 score macro: 0.599362\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 648/648 [00:05<00:00, 108.44it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Validation\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 81/81 [00:00<00:00, 253.69it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 48 finished in 6.32s\n",
            "[Epoch 48]\tTrain Loss: 0.34095\tValidation Loss: 0.00006\t F1 score macro: 0.599362\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 648/648 [00:06<00:00, 106.56it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Validation\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 81/81 [00:00<00:00, 292.31it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 49 finished in 6.40s\n",
            "[Epoch 49]\tTrain Loss: 0.33579\tValidation Loss: 0.00006\t F1 score macro: 0.599362\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 648/648 [00:05<00:00, 109.47it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Validation\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 81/81 [00:00<00:00, 444.93it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 50 finished in 6.13s\n",
            "[Epoch 50]\tTrain Loss: 0.33993\tValidation Loss: 0.00006\t F1 score macro: 0.599362\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "\"\"\"\n",
        "Train the model for num_epochs epochs\n",
        "epoch_true and epoch_all are used to calculate the accuracy.\n",
        "epoch_true is the number of correct predictions and epoch_all is the total number of predictions in the epoch\n",
        "\"\"\"\n",
        "num_epochs = 50\n",
        "\n",
        "train_loss_arr, val_loss_arr = [], []\n",
        "f1_macro_scores_train, f1_macro_scores_val = [], []\n",
        "best_val_loss = np.inf\n",
        "best_model = None\n",
        "\n",
        "for epoch in range(num_epochs):\n",
        "    start_time = time()\n",
        "\n",
        "    train_loss, val_loss = 0, 0\n",
        "    epoch_all = 0\n",
        "    epoch_true = 0\n",
        "\n",
        "    predicted_labels = []\n",
        "    true_labels = []\n",
        "\n",
        "    model.train()\n",
        "\n",
        "    with tqdm(enumerate(train_loader), total=len(train_loader)) as pbar:\n",
        "        for i, (x, label) in pbar:\n",
        "            optimizer.zero_grad()\n",
        "            x = x.to(torch.float32).to(device).view(x.shape[0], -1)\n",
        "            label = label.to(device)\n",
        "            y_pred = model(x)\n",
        "            loss = criterion(y_pred, label)\n",
        "            loss.backward()\n",
        "            _, preds = torch.max(y_pred, dim=1)\n",
        "            optimizer.step()\n",
        "            train_loss += loss.item() * x.size(0)\n",
        "\n",
        "            predicted_labels.extend(preds.tolist())\n",
        "            true_labels.extend(label.tolist())\n",
        "\n",
        "    model.eval()\n",
        "    f1_score_macro_train = f1_score(true_labels, predicted_labels, average=\"macro\")\n",
        "    val_loss, predicted_labels, true_labels, f1_score_macro_val = eval_epoch(\n",
        "        model, criterion, val_loader\n",
        "    )\n",
        "\n",
        "    train_loss /= len(train_loader.dataset)\n",
        "    val_loss /= len(val_loader.dataset)\n",
        "    train_loss_arr.append(train_loss)\n",
        "    val_loss_arr.append(val_loss)\n",
        "    f1_macro_scores_train.append(f1_score_macro_train)\n",
        "    f1_macro_scores_val.append(f1_score_macro_val)\n",
        "\n",
        "    if val_loss < best_val_loss:\n",
        "        best_val_loss = val_loss\n",
        "        best_model = model\n",
        "        torch.save(model.state_dict(), \"best_model.pt\")\n",
        "\n",
        "    end_time = time()\n",
        "\n",
        "    print(f\"Epoch {epoch + 1} finished in {end_time - start_time:.2f}s\")\n",
        "\n",
        "    print(\n",
        "        f\"[Epoch {epoch + 1}]\\t\"\n",
        "        f\"Train Loss: {train_loss:.5f}\\t\"\n",
        "        f\"Validation Loss: {val_loss:.5f}\\t F1 score macro: {f1_score_macro:.6f}\"\n",
        "    )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "905b1GLT-Maj"
      },
      "source": [
        "<div dir=\"rtl\">\n",
        "<font face=\"XB Zar\" size=5>\n",
        "</font>\n",
        "<font face=\"XB Zar\" size=3>\n",
        "     <br>\n",
        "نمودار loss بر حسب epoch را برای داده‌های train و validation رسم کنید.\n",
        "    </font>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bAKTiXnu-Maj",
        "outputId": "ab046f1a-8ad1-4f6f-d794-410bf06e70e1"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# todo: plot the train and validation loss by epoch\n",
        "# plot them seperately\n",
        "plt.plot(train_loss_arr, label=\"train loss\")\n",
        "plt.xlabel(\"epoch\")\n",
        "plt.ylabel(\"Training loss\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VgXA_UNi-Maj",
        "outputId": "8b6e9b45-e588-44bc-8c52-5286d3d50390"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.plot(val_loss_arr, label=\"val loss\", color=\"red\")\n",
        "plt.xlabel(\"epoch\")\n",
        "plt.ylabel(\"Validation loss\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AoqvWh2p-Mak"
      },
      "source": [
        "<div dir=\"rtl\">\n",
        "<font face=\"XB Zar\" size=5>\n",
        "</font>\n",
        "<font face=\"XB Zar\" size=3>\n",
        "     <br>\n",
        "نمودار f1 score بر حسب epoch را برای داده‌های train و validation رسم کنید.\n",
        "    </font>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "V6uQU4oN-Mak",
        "outputId": "b9131883-82f8-41da-becd-a7803cf62bde"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# todo: plot the f1 score by epoch\n",
        "plt.plot(f1_macro_scores_train, label=\"train f1 score\")\n",
        "plt.plot(f1_macro_scores_val, label=\"val f1 score\")\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-bB9D3ao-Mak"
      },
      "source": [
        "## Evaluate model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_WqW9LCQ-Mak"
      },
      "source": [
        "<div dir=\"rtl\">\n",
        "<font face=\"XB Zar\" size=5>\n",
        "</font>\n",
        "<font face=\"XB Zar\" size=3>\n",
        "     <br>\n",
        "حال داده‌ی test را به مدلتان دهید و بر اساس خروجی‌ها موارد زیر را به دست آورید.\n",
        "    <br>\n",
        "    <br>\n",
        "    <ul>\n",
        "        <li>accuracy</li>\n",
        "        <li>Loss</li>\n",
        "        <li>F1-Macro</li>\n",
        "        <li>F1-Micro</li>\n",
        "        <li>Confusion matrix</li>\n",
        "        <li>Macro-average precision</li>\n",
        "        <li>Macro-average recall</li>\n",
        "        <li>Micro-average precision</li>\n",
        "        <li>Micro-average recall</li>\n",
        "    </ul>\n",
        "    </font>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wSIEXM9H-Mak",
        "outputId": "4e7c46ba-b69a-47dd-a989-e09a58294eab"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Test\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 81/81 [00:00<00:00, 356.09it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Test Loss: 0.33003068264619806\t F1 score macro: 0.5957372179616404\n",
            "Accuracy: 0.8648126689841638\n",
            "Macro Average Precision: 0.90594809589223\n",
            "Macro Average Recall: 0.614612062179023\n",
            "Micro Average Precision: 0.8648126689841638\n",
            "Micro Average Recall: 0.8648126689841638\n",
            "F-1 score macro: 0.5957372179616404\n",
            "F-1 score micro: 0.8648126689841638\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from sklearn.metrics import (\n",
        "    accuracy_score,\n",
        "    precision_score,\n",
        "    recall_score,\n",
        "    f1_score,\n",
        "    confusion_matrix,\n",
        ")\n",
        "import seaborn as sns\n",
        "\n",
        "# todo: evaluate the model on the test set\n",
        "# print the required metrics\n",
        "model.eval()\n",
        "eval_loss, predicted_labels, true_labels, f1_score_macro = eval_epoch(\n",
        "    model, criterion, test_loader, test_mode=True\n",
        ")\n",
        "\n",
        "print(f\"Test Loss: {eval_loss}\\t F1 score macro: {f1_score_macro}\")\n",
        "print(f\"Accuracy: {accuracy_score(true_labels, predicted_labels)}\")\n",
        "print(\n",
        "    f\"Macro Average Precision: {precision_score(true_labels, predicted_labels, average='macro')}\"\n",
        ")\n",
        "print(\n",
        "    f\"Macro Average Recall: {recall_score(true_labels, predicted_labels, average='macro')}\"\n",
        ")\n",
        "print(\n",
        "    f\"Micro Average Precision: {precision_score(true_labels, predicted_labels, average='micro')}\"\n",
        ")\n",
        "print(\n",
        "    f\"Micro Average Recall: {recall_score(true_labels, predicted_labels, average='micro')}\"\n",
        ")\n",
        "print(f\"F-1 score macro: {f1_score(true_labels, predicted_labels, average='macro')}\")\n",
        "print(f\"F-1 score micro: {f1_score(true_labels, predicted_labels, average='micro')}\")\n",
        "\n",
        "# Draw confusion matrix\n",
        "cm = confusion_matrix(true_labels, predicted_labels)\n",
        "sns.heatmap(cm, annot=True, cmap=\"Reds\", fmt=\"d\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hgCd3cGc-Mak"
      },
      "source": [
        "<div dir=\"rtl\">\n",
        "<font face=\"XB Zar\" size=3>\n",
        "     <br>\n",
        "در نهایت در این قسمت تابعی بنویسید که یک متن را به عنوان ورودی بگیرد و بر اساس مدلتان برچسب‌ مناسب را به آن بدهد.\n",
        "    <br>\n",
        "    <br>\n",
        "    </font>\n",
        "</div>\n",
        "    "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ycHm_ott-Mak"
      },
      "outputs": [],
      "source": [
        "def predict(x):\n",
        "    \"\"\"\n",
        "    predict the class code of a given query\n",
        "    :param x: the query\n",
        "    :return: the class of the query in the form of real strings.\n",
        "    \"\"\"\n",
        "    # todo\n",
        "    x = FastText_model.get_query_embedding(x, TF_IDF_model.vectorizer)\n",
        "    x = torch.tensor(x, dtype=torch.float32)\n",
        "    x = x.to(device)\n",
        "    x = x.view(x.shape[0], -1)\n",
        "    predicted_label = model(x)\n",
        "    predicted_class = predicted_label.argmax(dim=1)\n",
        "    if predicted_class == 0:\n",
        "        predicted_class = \"cv.CV\"\n",
        "    elif predicted_class == 1:\n",
        "        predicted_class = \"cs.LG\"\n",
        "    elif predicted_class == 2:\n",
        "        predicted_class = \"stat.ML\"\n",
        "    else:\n",
        "        raise ValueError(\"Invalid class\")\n",
        "    return predicted_class"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Rk5z7Zp_-Mak",
        "outputId": "de92fba4-6c81-498e-c6a4-0fd7e3a41f8e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "'cs.LG'"
            ]
          },
          "execution_count": 76,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "predict(\"hardware and computerc architecture is good\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RUqo_4EO-Mak"
      },
      "outputs": [],
      "source": [
        "PATH = \"nn_fasttext_model.pt\"\n",
        "\n",
        "# Save\n",
        "torch.save(model, PATH)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_gcBJKJ2-Mal"
      },
      "source": [
        "<div dir=\"rtl\">\n",
        "<font face=\"XB Zar\" size=5>\n",
        "    <h1>\n",
        "    <b>دسته بندی با استفاده از مدل های زبانی (۴۰ نمره)</b>\n",
        "    </h1>\n",
        "    <p></p>\n",
        "    <p></p>\n",
        "</font>\n",
        "<font face=\"XB Zar\" size=3>\n",
        "     <br>\n",
        "در این قسمت قرار است تا به کمک language modelهای از پیش آموزش شده \n",
        "و fine-tune کردن آن‌ها طبقه‌بند خودمان را درست کنیم.\n",
        "</font>\n",
        "</div>\n",
        "   "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TqiZFslx-Mal"
      },
      "source": [
        "## Requirements"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 83,
      "metadata": {
        "id": "tBMm4kBH-Mal"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import nltk\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from tqdm import tqdm\n",
        "from nltk import word_tokenize\n",
        "import string\n",
        "from sklearn.model_selection import train_test_split\n",
        "import torch\n",
        "from transformers import (\n",
        "    BertTokenizer,\n",
        "    BertForSequenceClassification,\n",
        "    TrainingArguments,\n",
        "    Trainer,\n",
        ")\n",
        "from sklearn.metrics import precision_recall_fscore_support\n",
        "from sklearn.metrics import recall_score\n",
        "from sklearn.metrics import f1_score\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
        "import random"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hQo5ziUQ-Mal"
      },
      "source": [
        "## Read and preprocess data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IMFQsXEr-Mal"
      },
      "source": [
        "\n",
        "<div dir=\"rtl\">\n",
        "<font face=\"XB Zar\" size=5>\n",
        "</font>\n",
        "<font face=\"XB Zar\" size=3>\n",
        "     <br>\n",
        "در ابتدا دیتا را لود کنید. آن را تمیز کنید یا به عبارتی EDA مناسب در حدی که داده‌های null یا موارد این‌چنینی نداشته باشیم، بر روی آن انجام دهید.\n",
        " پیش پردازش‌های مورد نیاز که در فاز قبل با آن آشنا شدید را روی داده انجام دهید.\n",
        "    برای کتگوری هر مقاله برچسب اول آن را از دیتاست در نظر بگیرید.\n",
        "</font>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "w5UAi0Nf-Mal"
      },
      "outputs": [],
      "source": [
        "# Already Done! In the previous sections"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 84,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "xPlEyzBv-Mal",
        "outputId": "eb651fc6-81a5-4dd9-d82a-daa8a1089fd3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'stereo matching one widely used techniques inferring depth stereo images owing robustness speed beco'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 84
        }
      ],
      "source": [
        "texts = [\" \".join(it) for it in preprocessed_total]\n",
        "texts[0][:100]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 91,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZyQAvU_6-Mal",
        "outputId": "a74d0b08-2d99-4210-8cdd-98dcdcea4fd2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, ..., 2, 0, 2])"
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ],
      "source": [
        "labels"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MYkj-AI6-Mal"
      },
      "source": [
        "## Prepare data for model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6Omcv9QU-Mal"
      },
      "source": [
        "\n",
        "<div dir=\"rtl\">\n",
        "<font face=\"XB Zar\" size=5>\n",
        "</font>\n",
        "<font face=\"XB Zar\" size=3>\n",
        "     <br>\n",
        "     در این قسمت برچسب‌ها و متن‌ها را برای مدل آماده کنید.\n",
        "     برای متن هم عنوان و هم چکیده را در نظر بگیرید و این دو را به هم بچسبانید.\n",
        "     به صورت رندم، ۱۰۰۰۰ داده را برای تمرین انتخاب کنید.\n",
        "     همچنین جدا‌سازی داده‌ها را انجام دهید.\n",
        "     در این بخش کافی است تا داده‌ها را به دو دسته‌ی آموزش و تست تقسیم کنید. 80% داده‌ها برای آموزش و 20% را برای تست قرار دهید.\n",
        "    </font>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 92,
      "metadata": {
        "id": "M0uNpul7-Mal"
      },
      "outputs": [],
      "source": [
        "# todo\n",
        "# calculate labels\n",
        "# calulcate final texts\n",
        "# select 10000 data randomly\n",
        "# split data into train and test\n",
        "\n",
        "\n",
        "random_state = 1\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    texts, labels, test_size=0.2, random_state=random_state\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = X_train[:10000]\n",
        "y_train = y_train[:10000]\n",
        "X_test = X_test[:10000]\n",
        "y_test = y_test[:10000]"
      ],
      "metadata": {
        "id": "FfH_ObCxVXMn"
      },
      "execution_count": 93,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hlUYII0_-Mal"
      },
      "source": [
        "## Transformer based classification"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aYjlsVb4-Mam"
      },
      "source": [
        "\n",
        "<div dir=\"rtl\">\n",
        "<font face=\"XB Zar\" size=5>\n",
        "</font>\n",
        "<font face=\"XB Zar\" size=3>\n",
        "     <br>\n",
        "مدلی که در این قسمت استفاده می‌کنیم مدل معروف bert می‌باشد.\n",
        "    برای این کار از کتابخانه transformers استفاده می‌کنیم.\n",
        "    مدل و tokenizer مربوطه را لود کنید.\n",
        "    <br><b><u>\n",
        "    به کمک داده‌های قسمت قبل و به کمک \n",
        "    Trainer\n",
        "    مربوط به کتاب‌خانه‌ی transformers عملیات fine-tune را انجام دهید.\n",
        "    <br>\n",
        "    همچنین یکبار وزن‌های مدل برت را فریز کنید و فقط دسته‌بند مربوطه را آموزش دهید.\n",
        "    </u></b><br>\n",
        "    مطالعه‌ی بیشتر کتاب‌خانه‌ی transformers بسیار توصیه می‌شود.\n",
        "    </font>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 250,
          "referenced_widgets": [
            "bf03b967dd9e46739176080d1ebde044",
            "a6359bec2cae40e5a652945eb2db030e",
            "fa7a1a237cb94b928a2d607a0a73c8ae",
            "1feef5a0a55e41d38b62e347fd26768a",
            "1f03fa54e2c5408188ee69bf73cead9c",
            "a02ed874953c4fbb95d92502b334dea8",
            "1a0a2e12600e40c0b0d256c6f593360e",
            "457d489b532c435bb24f3fb2e9f09c4a",
            "8cd97cbeeb7b48749494d549fc50e07b",
            "5e5ab05b13794e17b642d4a62d59448d",
            "d4dddff49c9240f1baf285c9fec12ce7",
            "8036324d832a4159be8c814667990c98",
            "580fe55e48b84238ba6e21ed81300d27",
            "8307866b080841f4a5d47897771ddf17",
            "dc7e3719cc2d4fed982934e8b92c88ec",
            "5a8fa21657e9413ba557bd4aa0c74e10",
            "3f1b9884cb4d404fa5ebf55800c9062b",
            "4fab8d324c3445fe9b4ac602668701d8",
            "91e1fd61f1824debbffe978a0adab931",
            "90ebd9eaf36145bda64416ca1c35abae",
            "9b1cf8f399f247e6b6af8afcf0fd5d0e",
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            "3f8d89b26b2d4b5bac5b7e7a0ce58ca0",
            "330f8ee2ce67410f9a80301ee040866f",
            "6aeabca29f0c4ed7a8b09e194d8be57d",
            "dae71983537449e083fe847968d98dfe",
            "35e51fb0f2104886b2fb54f21560d961",
            "b4c96f7b5ff04e22a32f0089028c672e",
            "13402e5c6c144441b2b7400a7ce4cb68",
            "87dc2a73ce8944c9af71eb808fa20fb1",
            "06d189d5704c47dc830bd38f804a2fb3",
            "e548e38bed724ae2a5270bfe2e951619",
            "9685f3b4521d42d28c2e47a2d4866e87",
            "1076c4a5ba0d4cbba556f5d60b094876",
            "be132f7c255d49d6a3571e4803934c48",
            "6d77d07f8328489682d365d1ee76dc2f",
            "e243cc999c9f45b2af30f082763435d2",
            "6d1e85c017a445279dc0a2cd429ac3ec",
            "49e230f87d7d47439d4abfbc9a68db3d",
            "b200408275fc4d789dd140f72ffe8fea",
            "7f1fe4de92e54920b7ca6570ca73a9d8",
            "ae66fd753a324bf39c756430bcafce3d",
            "0abd39b81251448aabd1e0eefda68d78"
          ]
        },
        "id": "D9TgoObc-Mam",
        "outputId": "543460c6-307a-43be-800f-2718ff081097"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bf03b967dd9e46739176080d1ebde044"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/440M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8036324d832a4159be8c814667990c98"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForSequenceClassification: ['cls.predictions.transform.dense.weight', 'cls.seq_relationship.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.bias', 'cls.predictions.decoder.weight', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.bias']\n",
            "- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c9404b3ef14f4df197345c038fd567d8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9685f3b4521d42d28c2e47a2d4866e87"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "MODEL_NAME = \"bert-base-uncased\"\n",
        "device = torch.device(\n",
        "    \"mps\"\n",
        "    if torch.backends.mps.is_available()\n",
        "    else \"cuda\"\n",
        "    if torch.cuda.is_available()\n",
        "    else \"cpu\"\n",
        ")\n",
        "\n",
        "# todo\n",
        "# you can add more cells here if you need\n",
        "model = BertForSequenceClassification.from_pretrained(MODEL_NAME, num_labels=3)\n",
        "tokenizer = BertTokenizer.from_pretrained(MODEL_NAME)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 94,
      "metadata": {
        "id": "K-CD5ehn-Mam"
      },
      "outputs": [],
      "source": [
        "# todo\n",
        "train_encodings, test_encodings, label_train, label_test = train_test_split(\n",
        "    texts, labels, test_size=0.2, random_state=random_state\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_encodings = train_encodings[:10000]\n",
        "label_train = label_train[:10000]\n",
        "test_encodings = test_encodings[:10000]\n",
        "label_test = label_test[:10000]"
      ],
      "metadata": {
        "id": "zBOH0ho0Vrw7"
      },
      "execution_count": 95,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 96,
      "metadata": {
        "id": "7kgrkLkP-Mam"
      },
      "outputs": [],
      "source": [
        "# todo\n",
        "class CustomDataset(torch.utils.data.Dataset):\n",
        "    def __init__(self, encodings, labels):\n",
        "        self.encodings = encodings\n",
        "        self.labels = labels\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        txt = self.encodings[idx]\n",
        "        label = self.labels[idx]\n",
        "\n",
        "        encoding = tokenizer.encode_plus(\n",
        "            txt,\n",
        "            add_special_tokens=True,\n",
        "            return_tensors=\"pt\",\n",
        "            max_length=256,\n",
        "            truncation=True,\n",
        "            padding=\"max_length\",\n",
        "        )\n",
        "\n",
        "        return {\n",
        "            \"input_ids\": encoding[\"input_ids\"].squeeze(),\n",
        "            \"attention_mask\": encoding[\"attention_mask\"].squeeze(),\n",
        "            \"labels\": torch.tensor(label),\n",
        "        }\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.labels)\n",
        "\n",
        "\n",
        "train_dataset = CustomDataset(train_encodings, y_train)\n",
        "test_dataset = CustomDataset(test_encodings, y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 97,
      "metadata": {
        "id": "bFxdqKUb-Mam"
      },
      "outputs": [],
      "source": [
        "from torch.utils.data import DataLoader\n",
        "\n",
        "train_loader = DataLoader(train_dataset, batch_size=8, shuffle=True)\n",
        "val_loader = DataLoader(test_dataset, batch_size=8, shuffle=False)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def compute_metrics(pred):\n",
        "    labels = pred.label_ids\n",
        "    predictions = pred.predictions.argmax(-1)\n",
        "    \n",
        "    precision, recall, f1, _ = precision_recall_fscore_support(labels, predictions, average='macro')\n",
        "    \n",
        "    return {\n",
        "        'precision': precision,\n",
        "        'recall': recall,\n",
        "        'f1': f1\n",
        "    }"
      ],
      "metadata": {
        "id": "WxyrT_tLY61L"
      },
      "execution_count": 99,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 100,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 242
        },
        "id": "yaB17fFq-Mam",
        "outputId": "c113c070-af6c-4c19-f446-5c8fe78cfe74"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:391: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='2500' max='2500' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [2500/2500 22:11, Epoch 2/2]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Precision</th>\n",
              "      <th>Recall</th>\n",
              "      <th>F1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.575500</td>\n",
              "      <td>0.516775</td>\n",
              "      <td>0.764031</td>\n",
              "      <td>0.742355</td>\n",
              "      <td>0.751420</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.272200</td>\n",
              "      <td>0.539419</td>\n",
              "      <td>0.761746</td>\n",
              "      <td>0.739534</td>\n",
              "      <td>0.749380</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=2500, training_loss=0.24485196822583674, metrics={'train_runtime': 1332.0789, 'train_samples_per_second': 15.014, 'train_steps_per_second': 1.877, 'total_flos': 2631134177280000.0, 'train_loss': 0.24485196822583674, 'epoch': 2.0})"
            ]
          },
          "metadata": {},
          "execution_count": 100
        }
      ],
      "source": [
        "model = model.to(device)\n",
        "\n",
        "training_args = TrainingArguments(\n",
        "    output_dir=\"./results\",\n",
        "    num_train_epochs=2,\n",
        "    per_device_train_batch_size=8,\n",
        "    per_device_eval_batch_size=8,\n",
        "    warmup_steps=500,\n",
        "    weight_decay=0.001,\n",
        "    logging_dir=\"./logs\",\n",
        "    logging_steps=10,\n",
        "    load_best_model_at_end=True,\n",
        "    evaluation_strategy=\"epoch\",\n",
        "    save_strategy=\"epoch\",\n",
        "    metric_for_best_model=\"f1\",\n",
        "    greater_is_better=True,\n",
        ")\n",
        "\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=train_dataset,\n",
        "    eval_dataset=test_dataset,\n",
        "    compute_metrics=compute_metrics,\n",
        ")\n",
        "\n",
        "trainer.train()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 102,
      "metadata": {
        "id": "xVkZv4c0-Mam",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 395
        },
        "outputId": "2be3cf44-7c24-43ea-f4fa-a259dc795c3f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForSequenceClassification: ['cls.predictions.transform.dense.weight', 'cls.seq_relationship.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.bias', 'cls.predictions.decoder.weight', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.bias']\n",
            "- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:391: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='2500' max='2500' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [2500/2500 12:21, Epoch 2/2]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Precision</th>\n",
              "      <th>Recall</th>\n",
              "      <th>F1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.888200</td>\n",
              "      <td>0.813358</td>\n",
              "      <td>0.459971</td>\n",
              "      <td>0.374047</td>\n",
              "      <td>0.318162</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.865200</td>\n",
              "      <td>0.793122</td>\n",
              "      <td>0.470667</td>\n",
              "      <td>0.450168</td>\n",
              "      <td>0.433768</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=2500, training_loss=0.860349822807312, metrics={'train_runtime': 741.6442, 'train_samples_per_second': 26.967, 'train_steps_per_second': 3.371, 'total_flos': 2634099025920000.0, 'train_loss': 0.860349822807312, 'epoch': 2.0})"
            ]
          },
          "metadata": {},
          "execution_count": 102
        }
      ],
      "source": [
        "# todo\n",
        "# freeze bert weights and then train model_2\n",
        "import torch.nn as nn\n",
        "\n",
        "model_2 = BertForSequenceClassification.from_pretrained(MODEL_NAME, num_labels=3)\n",
        "\n",
        "for name, param in model_2.named_parameters():\n",
        "    if \"bert\" in name:\n",
        "        param.requires_grad = False\n",
        "\n",
        "model_2.classifier = torch.nn.Sequential(\n",
        "    nn.Linear(model.config.hidden_size, 128),\n",
        "    nn.ReLU(),\n",
        "    nn.Dropout(0.5),\n",
        "    nn.Linear(128, 3),\n",
        ")\n",
        "\n",
        "model_2 = model_2.to(device)\n",
        "\n",
        "training_args = TrainingArguments(\n",
        "    output_dir=\"./results\",\n",
        "    num_train_epochs=2,\n",
        "    per_device_train_batch_size=8,\n",
        "    per_device_eval_batch_size=8,\n",
        "    warmup_steps=500,\n",
        "    weight_decay=0.001,\n",
        "    logging_dir=\"./logs\",\n",
        "    logging_steps=10,\n",
        "    load_best_model_at_end=True,\n",
        "    evaluation_strategy=\"epoch\",\n",
        "    save_strategy=\"epoch\",\n",
        "    metric_for_best_model=\"f1\",\n",
        "    greater_is_better=True,\n",
        ")\n",
        "\n",
        "trainer = Trainer(\n",
        "    model=model_2,\n",
        "    args=training_args,\n",
        "    train_dataset=train_dataset,\n",
        "    eval_dataset=test_dataset,\n",
        "    compute_metrics=compute_metrics,\n",
        ")\n",
        "\n",
        "trainer.train()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sWOKoDba-Mam"
      },
      "source": [
        "## Evaluate model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UF1HTooJ-Mam"
      },
      "source": [
        "<div dir=\"rtl\">\n",
        "<font face=\"XB Zar\" size=3>\n",
        "     <br>\n",
        "حال داده‌ی test را به هر دو مدل بدهید و بر اساس خروجی‌ها موارد زیر را به دست آورید.\n",
        "    <br>\n",
        "    <br>\n",
        "    <ul>\n",
        "        <li>accuracy</li>\n",
        "        <li>F1-Macro</li>\n",
        "        <li>F1-Micro</li>\n",
        "        <li>Confusion matrix</li>\n",
        "        <li>Macro-average precision</li>\n",
        "        <li>Macro-average recall</li>\n",
        "        <li>Micro-average precision</li>\n",
        "        <li>Micro-average recall</li>\n",
        "    </ul>\n",
        "    </font>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 105,
      "metadata": {
        "id": "8U5eSS2I-Mam",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dfcf9dc4-8a9c-4cda-e5fe-316398e6ad43"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 1250/1250 [05:21<00:00,  3.89it/s]\n"
          ]
        }
      ],
      "source": [
        "import seaborn as sns\n",
        "from sklearn.metrics import precision_score\n",
        "\n",
        "# you can add more cells here if you need\n",
        "# todo\n",
        "y_pred_transformers = []\n",
        "y_pred_transformers_2 = []\n",
        "y_true = []\n",
        "\n",
        "for batch in tqdm(val_loader):\n",
        "    input_ids = batch[\"input_ids\"].to(device)\n",
        "    attention_mask = batch[\"attention_mask\"].to(device)\n",
        "    labels = batch[\"labels\"].to(device)\n",
        "\n",
        "    with torch.no_grad():\n",
        "        outputs = model(input_ids, attention_mask=attention_mask)\n",
        "\n",
        "    y_pred_transformers.extend(outputs.logits.argmax(-1).tolist())\n",
        "    y_true.extend(labels.tolist())\n",
        "\n",
        "    with torch.no_grad():\n",
        "        outputs = model_2(input_ids, attention_mask=attention_mask)\n",
        "\n",
        "    y_pred_transformers_2.extend(outputs.logits.argmax(-1).tolist())\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# todo\n",
        "print(\"Accuracy of BERT is:\", accuracy_score(y_true, y_pred_transformers))\n",
        "print(\"Accuracy of BERT_2 is:\", accuracy_score(y_true, y_pred_transformers_2))\n",
        "\n",
        "print(\n",
        "    \"F1 score(Macro) of BERT is:\",\n",
        "    f1_score(y_true, y_pred_transformers, average=\"macro\"),\n",
        ")\n",
        "print(\n",
        "    \"F1 score(Micro) of BERT is:\",\n",
        "    f1_score(y_true, y_pred_transformers, average=\"micro\"),\n",
        ")\n",
        "print(\n",
        "    \"F1 score(Macro) of BERT_2 is:\",\n",
        "    f1_score(y_true, y_pred_transformers_2, average=\"macro\"),\n",
        ")\n",
        "print(\n",
        "    \"F1 score(Micro) of BERT_2 is:\",\n",
        "    f1_score(y_true, y_pred_transformers_2, average=\"micro\"),\n",
        ")\n",
        "\n",
        "print(\n",
        "    \"Precision score(Macro) of BERT is:\",\n",
        "    precision_score(y_true, y_pred_transformers, average=\"macro\"),\n",
        ")\n",
        "print(\n",
        "    \"Precision score(Micro) of BERT is:\",\n",
        "    precision_score(y_true, y_pred_transformers, average=\"micro\"),\n",
        ")\n",
        "print(\n",
        "    \"Precision score(Macro) of BERT_2 is:\",\n",
        "    precision_score(y_true, y_pred_transformers_2, average=\"macro\"),\n",
        ")\n",
        "print(\n",
        "    \"Precision score(Micro) of BERT_2 is:\",\n",
        "    precision_score(y_true, y_pred_transformers_2, average=\"micro\"),\n",
        ")\n",
        "\n",
        "print(\n",
        "    \"Recall score(Macro) of BERT is:\",\n",
        "    recall_score(y_true, y_pred_transformers, average=\"macro\"),\n",
        ")\n",
        "print(\n",
        "    \"Recall score(Micro) of BERT is:\",\n",
        "    recall_score(y_true, y_pred_transformers, average=\"micro\"),\n",
        ")\n",
        "print(\n",
        "    \"Recall score(Macro) of BERT_2 is:\",\n",
        "    recall_score(y_true, y_pred_transformers_2, average=\"macro\"),\n",
        ")\n",
        "print(\n",
        "    \"Recall score(Micro) of BERT_2 is:\",\n",
        "    recall_score(y_true, y_pred_transformers_2, average=\"micro\"),\n",
        ")\n",
        "\n",
        "# Confusion Matrix\n",
        "print(\"Confusion Matrix of BERT is:\")\n",
        "\n",
        "sns.heatmap(\n",
        "    confusion_matrix(y_true, y_pred_transformers), annot=True, fmt=\"d\", cmap=\"Blues\"\n",
        ")\n",
        "plt.show()\n",
        "\n",
        "print(\"Confusion Matrix of BERT_2 is:\")\n",
        "sns.heatmap(\n",
        "    confusion_matrix(y_true, y_pred_transformers_2), annot=True, fmt=\"d\", cmap=\"Reds\"\n",
        ")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "mzxXKONhjpcK",
        "outputId": "35dc1227-5fa0-4035-b418-4ca6bae14d96"
      },
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy of BERT is: 0.8871\n",
            "Accuracy of BERT_2 is: 0.6766\n",
            "F1 score(Macro) of BERT is: 0.7514202705214661\n",
            "F1 score(Micro) of BERT is: 0.8871\n",
            "F1 score(Macro) of BERT_2 is: 0.433768081202285\n",
            "F1 score(Micro) of BERT_2 is: 0.6766\n",
            "Precision score(Macro) of BERT is: 0.7640305900280396\n",
            "Precision score(Micro) of BERT is: 0.8871\n",
            "Precision score(Macro) of BERT_2 is: 0.47066686052072254\n",
            "Precision score(Micro) of BERT_2 is: 0.6766\n",
            "Recall score(Macro) of BERT is: 0.7423548084977778\n",
            "Recall score(Micro) of BERT is: 0.8871\n",
            "Recall score(Macro) of BERT_2 is: 0.4501677601441938\n",
            "Recall score(Micro) of BERT_2 is: 0.6766\n",
            "Confusion Matrix of BERT is:\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix of BERT_2 is:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wGiE21Mc-Man"
      },
      "source": [
        "<div dir=\"rtl\">\n",
        "<font face=\"XB Zar\" size=3>\n",
        "     <br>\n",
        "     نتایج به دست آمده برای دو مدل آموزش داده شده را مقایسه کنید و سپس نتایج مدل بهتر را با خروجی‌های fasttext که در قسمت قبل استخراج کردید نیز مقایسه کنید و تحلیل‌های خود را بنویسید.\n",
        "    <br>\n",
        "    <br>\n",
        "    </font>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZEygAnAx-Man"
      },
      "source": [
        "<div dir=\"rtl\">\n",
        "<font face=\"XB Zar\" size=3>\n",
        "پاسخ خود را در این قسمت بنویسید\n",
        "</font>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4CZS8yzZ-Man"
      },
      "source": [
        "_Explanation_:\n",
        "\n",
        "..."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O1C341WM-Man"
      },
      "source": [
        "<div dir=\"rtl\">\n",
        "<font face=\"XB Zar\" size=3>\n",
        "     <br>\n",
        "در نهایت در این قسمت تابعی بنویسید که یک متن را به عنوان ورودی بگیرد و بر اساس مدلتان برچسب‌ مناسب را به آن بدهد.\n",
        "    <br>\n",
        "    <br>\n",
        "    </font>\n",
        "</div>\n",
        "    "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 113,
      "metadata": {
        "id": "DyKt-gpd-Man"
      },
      "outputs": [],
      "source": [
        "def predict(x, model):\n",
        "    \"\"\"\n",
        "    predict the class code of a given query\n",
        "    :param x: the query\n",
        "    :return: the class of the query in the form of real strings.\n",
        "    \"\"\"\n",
        "    # todo\n",
        "    embedd = tokenizer.encode_plus(\n",
        "        x,\n",
        "        add_special_tokens=True,\n",
        "        return_tensors=\"pt\",\n",
        "        padding=\"max_length\",\n",
        "        truncation=True,\n",
        "        max_length=256,\n",
        "    )\n",
        "    embedd = embedd.to(device)\n",
        "\n",
        "    predicted_class = model(**embedd).logits.argmax(-1).item()\n",
        "    if predicted_class == 0:\n",
        "        predicted_class = \"cv.CV\"\n",
        "    elif predicted_class == 1:\n",
        "        predicted_class = \"cv.ML\"\n",
        "    elif predicted_class == 2:\n",
        "        predicted_class = \"stat.ML\"\n",
        "    else:\n",
        "        raise ValueError(\"Invalid class code\")\n",
        "\n",
        "    return predicted_class"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 115,
      "metadata": {
        "id": "EhBiAYT8-Man",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "6ca8ff79-0e3e-4954-d1a6-4596b5c09a69"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'cv.ML'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 115
        }
      ],
      "source": [
        "predict(\"machine learning is kind of fun!\", model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 103,
      "metadata": {
        "id": "G1Gpmnsd-Man"
      },
      "outputs": [],
      "source": [
        "# It is highly recommended to save your final models\n",
        "# todo\n",
        "torch.save(model.state_dict(), \"bert_first_model.pt\")\n",
        "torch.save(model_2.state_dict(), \"bert_second_model.pt\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ysW7xp67-Man"
      },
      "source": [
        "<div dir=\"rtl\">\n",
        "<font face=\"XB Zar\" size=5>\n",
        "    <h2>\n",
        "    <b>جستجو و بازیابی اسناد در دسته‌های مختلف</b>\n",
        "    </h2>\n",
        "</font>\n",
        "<font face=\"XB Zar\" size=4>\n",
        "در این بخش می‌خواهیم به تابع search مربوط به فاز قبل قابلیتی اضافه کنیم که بر اساس آن بتوان جستجو و بازیابی پرسمان را به یک دسته خاص محدود کرد و یا مشخص کرد که جستجو بر روی تمام اسناد انجام شود. همانند فاز قبل:\n",
        "<br>\n",
        " پرسمانی که از کاربر می‌گیرید را در مجموعه اسناد نمایه شده جست و جو کنید. توجه داشته باشید که جست و جویی که انجام می‌دهید هم باید در عنوان مقاله و هم در چکیده آن انجام شود. در نهایت، اسناد باید به ترتیب امتیاز نهایی‌شان برگردانده شوند. امتیاز نهایی هر سند نیز از جمع وزن‌دار امتیاز جستجو در عنوان و جستجو در چکیده مقاله به دست می‌آید.\n",
        "<br>\n",
        "ورودی‌های تابع search در این فاز، همانند فاز قبل است؛ تنها ورودی category به آن اضافه شده است. در صورتی که ورودی category برابر all باشد، جستجو در تمامی اسناد انجام می‌شود. در صورتی که category برابر نام یک دسته باشد، جستجو تنها در دسته گفته شده انجام می‌شود.\n",
        "<br>\n",
        "(برای این قسمت از کدهایی که در فاز قبل زده‌اید استفاده کنید و قابلیت گفته شده را به آن اضافه کنید.)\n",
        "</font>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CFPqism1-Man"
      },
      "outputs": [],
      "source": [
        "def search(\n",
        "    title_query: str,\n",
        "    abstract_query: str,\n",
        "    max_result_count: int,\n",
        "    method: str = \"ltn-lnn\",\n",
        "    weight: float = 0.5,\n",
        "    print=False,\n",
        "    category=\"all\",\n",
        "):\n",
        "    \"\"\"\n",
        "    Finds relevant documents to query\n",
        "\n",
        "    Parameters\n",
        "    ---------------------------------------------------------------------------------------------------\n",
        "    max_result_count: Return top 'max_result_count' docs which have the highest scores.\n",
        "                      notice that if max_result_count = -1, then you have to return all docs\n",
        "\n",
        "    mode: 'detailed' for searching in title and text separately.\n",
        "          'overall' for all words, and weighted by where the word apears on.\n",
        "\n",
        "    method: 'ltn-lnn' or 'ltc-lnc' or 'okapi25'\n",
        "\n",
        "    category: 'all' for searching in all documents.\n",
        "              'category_name' for searching in a specific category with category_name label.\n",
        "\n",
        "    Returns\n",
        "    ----------------------------------------------------------------------------------------------------\n",
        "    list\n",
        "    Retreived documents with snippet\n",
        "    \"\"\"\n",
        "    # TODO: retun top 'max_result_count' documents for your searched query"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PJft5Uuh-Man"
      },
      "source": [
        "<div dir=\"rtl\">\n",
        "<font face=\"XB Zar\" size=6>\n",
        "    <h1>\n",
        "    <b>خوشه‌بندی (۴۰ نمره)</b>\n",
        "    </h1>\n",
        "    <p></p>\n",
        "    <p></p>\n",
        "</font>\n",
        "<font face=\"XB Zar\" size=3>\n",
        "در این قسمت شما به خوشه‌بندی اسناد و استفاده از آن خوشه‌ها برای اهداف مختلف میپردازید. برای این منظور، ابتدا برای هر سند، بردار جاسازی (Embedding) تولید کرده و با استفاده از دو الگوریتم kmeans و خوشه‌بندی سلسله‌مراتبی (Hierarchical Clustering)، خوشه‌ها را ایجاد می‌کنید.\n",
        "</font>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GsF8DqDw-Man"
      },
      "source": [
        "<div dir=\"rtl\">\n",
        "<font face=\"XB Zar\" size=5>\n",
        "    <h2>\n",
        "    <b>استخراج بردار جاسازی اسناد</b>\n",
        "    </h2>\n",
        "    <p></p>\n",
        "    <p></p>\n",
        "</font>\n",
        "<font face=\"XB Zar\" size=3>\n",
        "اگرچه امکان ایجاد بردار جاسازی برای هر سند با استفاده از روش ‌Bag of Words نیز وجود دارد، اما برای اینکه احتمالا بتوانید به نتابج بهتری در خوشه‌بندی برسید، در این قسمت از مدل‌های زبانی برمبنای مبدل‌ها (Transformers) برای استخراج این بردارها استفاده می‌کنید.\n",
        "برای این منظور می‌توانید از\n",
        "<a href=\"https://huggingface.co/\">HuggingFace</a>\n",
        "  و مدل‌های متعددی که در آن وجود دارد استفاده کنید.\n",
        "  <br>\n",
        "  در تابع extract_embedding پیاده‌سازی مورد نیاز را انجام دهید. این تابع لیستی از اسناد را به عنوان ورودی دریافت میکند و در خروجی لیستی از بردار‌های جاسازی متناظر با هر عنصر در ورودی را برمی‌گرداند.\n",
        "  <br>\n",
        "برای محاسبه بردار جاسازی هر سند، روش های مختلفی وجود دارد. استفاده از بردار جاسازی توکن CLS در مدل ‌BERT، میانگین‌گیری (وزن‌دار یا بدون وزن) از بردارهای جاسازی هر کلمه ورودی و ... از روش‌های موجود هستند که می‌توانید به دلخواه خود انتخاب کنید.\n",
        "</font>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6-B39GDl-Mao"
      },
      "outputs": [],
      "source": [
        "## ؟ points\n",
        "\n",
        "from typing import List\n",
        "\n",
        "\n",
        "def extract_embedding(doc_list: List):\n",
        "    \"\"\"Extracts embedding vector for each document in doc_list\n",
        "\n",
        "    Parameters\n",
        "    ----------\n",
        "    doc_list : List\n",
        "        A list of documents\n",
        "\n",
        "    Returns\n",
        "    -------\n",
        "    list\n",
        "        A list of embedding vectors\n",
        "    \"\"\"\n",
        "\n",
        "    # TODO: Extract a vector for each input document using a transformer-based language model\n",
        "    pass\n",
        "\n",
        "\n",
        "docs_embedding = extract_embedding(\n",
        "    [str, str, str, ...]\n",
        ")  # return [[NUM, NUM, NUM, ...], [NUM, NUM, NUM, ...], [NUM, NUM, NUM, ...]]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BEfJFfuL-Mao"
      },
      "source": [
        "<div dir=\"rtl\">\n",
        "<font face=\"XB Zar\" size=5>\n",
        "    <h2>\n",
        "    <b>کاهش بعد بردارها برای رسم نمودار دوبعدی</b>\n",
        "    </h2>\n",
        "    <p></p>\n",
        "    <p></p>\n",
        "</font>\n",
        "<font face=\"XB Zar\" size=3>\n",
        "برای اینکه بتوانید در مراحل بعدی، نتایج خوشه‌بندی را مشاهده کنید، در این قسمت به پیاده‌سازی تابع کاهش بعد بردارهای جاسازی با استفاده از روش T-SNE می‌پردازید.\n",
        "برای اینکار تابع convert_to_2d_tsne را پیاده‌سازی می‌کنید که لیستی از بردارهای جاسازی را به عنوان ورودی دریافت می‌کند و در خروجی، لیستی از بردارهای جاسازی کاهش بعد داده شده به دو بعد را تولید می‌کند. برای پیاده سازی این تابع می‌توانید از کتابخانه‌های آماده استفاده کنید.\n",
        "<br>\n",
        "توجه کنید که از بردارهای خروجی این قسمت <u>صرفا برای رسم نمودار</u> استفاده می‌کنید و تمامی مراحلی که در ادامه طی می‌کنید (به جز رسم نمودار)، باید با استفاده از بردارهای کاهش بعد داده <u>نشده</u> انجام شوند.\n",
        "</font>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6Yczubzr-Mao"
      },
      "outputs": [],
      "source": [
        "## ؟ points\n",
        "\n",
        "from typing import List\n",
        "\n",
        "\n",
        "def convert_to_2d_tsne(emb_vecs: List):\n",
        "    \"\"\"Converts each raw embedding vector to 2d vector\n",
        "\n",
        "    Parameters\n",
        "    ----------\n",
        "    emb_vecs : List\n",
        "        A list of vectors\n",
        "\n",
        "    Returns\n",
        "    -------\n",
        "    list\n",
        "        A list of 2d vectors\n",
        "    \"\"\"\n",
        "\n",
        "    # TODO: Convert each input vector to 2d vector\n",
        "    pass\n",
        "\n",
        "\n",
        "docs_embedding_2d = convert_to_2d_tsne(\n",
        "    [[NUM, NUM, NUM, ...], [NUM, NUM, NUM, ...], [NUM, NUM, NUM, ...]]\n",
        ")  # return [[NUM, NUM], [NUM, NUM], [NUM, NUM], ...]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OqQflkcj-Mao"
      },
      "source": [
        "<div dir=\"rtl\">\n",
        "<font face=\"XB Zar\" size=5>\n",
        "    <h2>\n",
        "    <b>رسم نمودار</b>\n",
        "    </h2>\n",
        "    <p></p>\n",
        "    <p></p>\n",
        "</font>\n",
        "<font face=\"XB Zar\" size=3>\n",
        "در این قسمت، تابع plot_docs را پیاده‌سازی می‌کنید که لیستی از بردارهای کاهش بعد داده شده و لیستی از شماره‌ خوشه‌‌های هربردار (برای رنگ‌آمیزی نقاط) را به عنوان ورودی دریافت کرده و نمودار دوبعدی ای را رسم می‌کند که در آن به هر خوشه یک رنگ مجزا اختصاص داده شده است و هر سند به عنوان یک نقطه نشان داده می‌شود که به رنگ خوشه‌ای است که به آن نسبت داده شده است. \n",
        "</font>\n",
        "</div>\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DtwUU8U0-Mao"
      },
      "outputs": [],
      "source": [
        "## ؟ points\n",
        "\n",
        "from typing import List\n",
        "\n",
        "\n",
        "def plot_docs(emb_vecs_2d: List, labels: List):\n",
        "    \"\"\"Draws a 2d plot of input vectors\n",
        "\n",
        "    Parameters\n",
        "    ----------\n",
        "    emb_2d_vecs : List\n",
        "        A list of 2d vectors\n",
        "\n",
        "    Returns\n",
        "    -------\n",
        "    \"\"\"\n",
        "\n",
        "    # TODO: Draw a 2d plot from input vectors and color each cluster with specific color.\n",
        "    pass\n",
        "\n",
        "\n",
        "plot_docs([[NUM, NUM], [NUM, NUM], [NUM, NUM], ...])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bKXwfIkd-Mao"
      },
      "source": [
        "<div dir=\"rtl\">\n",
        "<font face=\"XB Zar\" size=5>\n",
        "    <h2>\n",
        "    <b>خوشه‌بندی مستندها</b>\n",
        "    </h2>\n",
        "    <p></p>\n",
        "    <p></p>\n",
        "</font>\n",
        "<font face=\"XB Zar\" size=3>\n",
        "در این قسمت شما به خوشه‌بندی اسناد با استفاده از بردارهای جاسازی مستخرج از مدل زبانی با دو روش Kmeans و خوشه‌بندی سلسله‌مراتبی می‌پردازید.\n",
        "</font>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fIMOKqKP-Mao"
      },
      "source": [
        "<div dir=\"rtl\">\n",
        "<font face=\"XB Zar\" size=4>\n",
        "    <h3>\n",
        "    <b>روش K-means</b>\n",
        "    </h3>\n",
        "    <p></p>\n",
        "    <p></p>\n",
        "</font>\n",
        "<font face=\"XB Zar\" size=3>\n",
        "در این قسمت، شما ابتدا الگوریتم خوشه‌بندی K-means را \n",
        "<u><b>از پایه</b></u>\n",
        " پیاده‌سازی کرده و سپس با استفاده از آن، خوشه‌های اسناد را ایجاد می‌کنید. الگوریتم را با استفاده از چند مقدار مختلف تعداد خوشه‌ها (k) اجرا کنید. در هربار اجرا، با استفاده از تعدادی از اسناد موجود در هر خوشه، موضوع آن خوشه را تعیین کرده و خوشه‌بندی حاصله را با استفاده از بردار‌های دوبعدی قسمت قبل، رسم کنید. با اینکار، پیاده‌سازی خود و همچنین کارایی این الگوریتم در خوشه‌بندی اسناد و قرار دادن اسناد مشابه در خوشه‌های یکسان را بررسی کنید.\n",
        "<br>\n",
        " نمودار silhouette score برای مقدار‌های محتلف k را رسم کرده و silhouette analysis برای انتخاب k مناسب انجام دهید. \n",
        " همچنین با استفاده از داده‌های دارای برچسب، مقدار purity به ازای k را رسم کرده و مقدار purity برای k نهایی را گزارش کنید.\n",
        "<br>\n",
        "پیاده‌سازی خود را در تابع cluster_kmeans قرار دهید. این تابع یک لیست از بردارهای جاسازی دریافت کرده و در خروجی، مختصات مرکز هر خوشه را به همراه لیستی از شماره خوشه‌های متناظر با هر بردار جاسازی تولید می‌کند.\n",
        "توجه کنید که الگوریتم می‌بایست از پایه پیاده‌سازی شود و امکان استفاده از پیاده‌سازی‌های آماده آن وجود ندارد.\n",
        "</font>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bu25zgBT-Mao"
      },
      "outputs": [],
      "source": [
        "## ؟ points\n",
        "\n",
        "from typing import List\n",
        "\n",
        "\n",
        "def cluster_kmeans(emb_vecs: List, n_clusters: int):\n",
        "    \"\"\"Clusters input vectors using K-means method\n",
        "\n",
        "    Parameters\n",
        "    ----------\n",
        "    emb_vecs : List\n",
        "        A list of vectors\n",
        "\n",
        "    Returns\n",
        "    -------\n",
        "    list\n",
        "        Two lists: 1) A list containing cluster centers 2) A list containing cluster index for each input vector\n",
        "    \"\"\"\n",
        "\n",
        "    # TODO: Implement K-means method\n",
        "    pass\n",
        "\n",
        "\n",
        "cluster_centers, cluster_kmeans = cluster_kmeans(\n",
        "    [[NUM, NUM, NUM, ...], [NUM, NUM, NUM, ...], [NUM, NUM, NUM, ...]]\n",
        ")  # return [NUM, NUM, NUM, ...]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kqVeADOf-Mao"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import silhouette_samples, silhouette_score\n",
        "\n",
        "# TODO: silhouette analysis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7NlP9V4V-Map"
      },
      "outputs": [],
      "source": [
        "# TODO: plot purity for different value of k"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rYAn72TS-Map"
      },
      "source": [
        "<div dir=\"rtl\">\n",
        "<font face=\"XB Zar\" size=4>\n",
        "    <h3>\n",
        "    <b>روش خوشه‌بندی سلسله‌مراتبی (Hierarchical Clustering)</b>\n",
        "    </h3>\n",
        "    <p></p>\n",
        "    <p></p>\n",
        "</font>\n",
        "<font face=\"XB Zar\" size=3>\n",
        "تکنیک خوشه‌بندی سلسله مراتبی یکی از تکنیک‌های خوشه‌بندی در یادگیری ماشین است. در این قسمت شما می‌توانید از لایببری scipy یا هر لایبرری دیگری در پایتون استفاده کنید تا داده‌ها را به صورت سلسله‌مراتبی خوشه‌بندی کنید. سپس می‌توانید خوشه‌ها را با matplotlib مشاهده کنید.\n",
        "</font>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "a5c3IJJO-Map"
      },
      "outputs": [],
      "source": [
        "import scipy.cluster.hierarchy as shc\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# TODO"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_zI93ER6-Map"
      },
      "source": [
        "<div dir=\"rtl\">\n",
        "<font face=\"XB Zar\" size=5>\n",
        "    <h2>\n",
        "    <b>خوشه‌بندی خروجی‌های سیستم جستجو فاز ۱</b>\n",
        "    </h2>\n",
        "    <p></p>\n",
        "    <p></p>\n",
        "</font>\n",
        "<font face=\"XB Zar\" size=3>\n",
        "در این قسمت شما به افزودن قابلیت خوشه‌بندی در سیستم جستجویی که در فاز ۱ درس توسعه داده‌اید می‌پردازید. برای این منظور، توابعی که تابحال پیاده‌سازی کرده‌اید را به نحوی به سامانه جستجوی فاز ۱ خود اضافه می‌کنید که اسناد خروجی سامانه را خوشه‌بندی کرده و در نمایش خروجی،‌ اسنادی که در یک خوشه هستند را مشخص کند.\n",
        "می‌توانید با فراخوانی توابعی که در فاز ۱ پیاده‌سازی کرده‌اید و ایجاد تغییر در آن‌ها در سلول زیر، این‌کار را انجام دهید. نحوه پیاده‌سازی این بخش به عهده خودتان است.\n",
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        "<div dir=\"rtl\">\n",
        "<font face=\"XB Zar\" size=5>\n",
        "    <h2>\n",
        "    <b>جست‌و‌جوی اسناد مشابه با یک سند</b>\n",
        "    </h2>\n",
        "    <p></p>\n",
        "    <p></p>\n",
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        "<font face=\"XB Zar\" size=3>\n",
        "در این قسمت، شما به پیاده‌سازی تابع find_similar_docs می‌پردازید که در آن با دریافت اندیس یک سند در مجموعه دادگان، تعداد اسناد مشابه مورد نظر و لیست بردارهای جاسازی، اندیس اسناد مشابه با آن در مجموعه دادگان را در خروجی تولید می‌کند. برای این منظور، به تعداد num_of_similar_docs تا از نزدیک‌ترین بردارها را به بردار مدنظر پیدا کرده و اندیس آن‌ها را به عنوان خروجی برمیگردانید. اسناد مشابه باید به ترتیب شباهت (مقدار شباهت بردار جاسازی آن‌ها به بردار جاسازی سند ورودی) مرتب شده باشند. انتخاب معیار شباهت برعهده خودتان است.\n",
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        "## ؟ points\n",
        "\n",
        "from typing import List\n",
        "\n",
        "\n",
        "def find_similar_docs(input_doc_index: int, num_of_similar_docs: int, emb_vecs: List):\n",
        "    \"\"\"Finds similar documents to input in dataset\n",
        "\n",
        "    Parameters\n",
        "    ----------\n",
        "    input_doc_index: int\n",
        "        Index of input document vector in emb_vecs list to search for specific paper\n",
        "\n",
        "    num_of_similar_docs:\n",
        "        Number of similar documents to return\n",
        "\n",
        "    emb_vecs : List\n",
        "        A list of vectors corresponding to documents\n",
        "\n",
        "    Returns\n",
        "    -------\n",
        "    List\n",
        "        A list of similar document indexes to input document\n",
        "    \"\"\"\n",
        "\n",
        "    # TODO: Implement a function to find similar documents to input document.\n",
        "    pass\n",
        "\n",
        "\n",
        "find_similar_docs(\n",
        "    int, int, [[NUM, NUM, NUM, ...], [NUM, NUM, NUM, ...], [NUM, NUM, NUM, ...]]\n",
        ")"
      ]
    }
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