├── README.md └── DeepLearning.ipynb /README.md: -------------------------------------------------------------------------------- 1 | # Persian News Classification 2 | 3 | This repository contains Jupyter notebooks for analyzing and modeling a dataset of Persian news articles for classification purposes. 4 | 5 | ## Notebooks 6 | 7 | - **EDA.ipynb**: 8 | - Generates word clouds 9 | - Plots the distribution of labels 10 | - Displays dataframe information 11 | 12 | - **MachineLearning.ipynb**: 13 | - Preprocesses the data 14 | - Applies TF-IDF transformation 15 | - Trains multiple machine learning models using grid search for hyperparameter tuning 16 | - Models include ensemble methods, linear models, naive Bayes, nearest neighbors, decision trees, and gradient boosting 17 | 18 | - **DeepLearning.ipynb**: 19 | - Trains Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) 20 | 21 | For more details on the implementation and results, please refer to the individual notebooks. -------------------------------------------------------------------------------- /DeepLearning.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": { 6 | "id": "view-in-github", 7 | "colab_type": "text" 8 | }, 9 | "source": [ 10 | " $\"Open$ " 11 | ] 12 | }, 13 | { 14 | "cell_type": "markdown", 15 | "metadata": { 16 | "id": "bu-uvwDp3LYq" 17 | }, 18 | "source": [ 19 | "# import and install libs" 20 | ] 21 | }, 22 | { 23 | "cell_type": "code", 24 | "execution_count": 1, 25 | "metadata": { 26 | "id": "Pp_d6_8RFgJK" 27 | }, 28 | "outputs": [], 29 | "source": [ 30 | "from google.colab import output\n", 31 | "!pip install hazm\n", 32 | "!pip install mapply\n", 33 | "\n", 34 | "\n", 35 | "import warnings\n", 36 | "import hazm\n", 37 | "from hazm import *\n", 38 | "import re\n", 39 | "import string\n", 40 | "import glob\n", 41 | "from hazm import stopwords_list\n", 42 | "import pandas as pd\n", 43 | "import time\n", 44 | "import os\n", 45 | "import mapply\n", 46 | "\n", 47 | "\n", 48 | "\n", 49 | "import torch\n", 50 | "import numpy as np\n", 51 | "from sklearn.preprocessing import LabelEncoder\n", 52 | "from torch.utils.data import Dataset, DataLoader\n", 53 | "from sklearn.model_selection import train_test_split\n", 54 | "from gensim.models import Word2Vec\n", 55 | "import torch.nn.functional as F\n", 56 | "import torch.nn as nn\n", 57 | "import torch.optim as optim\n", 58 | "from sklearn.metrics import accuracy_score\n", 59 | "\n", 60 | "from google.colab import drive\n", 61 | "drive.mount('/content/drive')\n", 62 | "\n", 63 | "\n", 64 | "output.clear()\n", 65 | "\n" 66 | ] 67 | }, 68 | { 69 | "cell_type": "markdown", 70 | "metadata": { 71 | "id": "zZHbdMF33SYT" 72 | }, 73 | "source": [ 74 | "# Initialization\n", 75 | "\n", 76 | "1. **Parallel Processing Setup:** \n", 77 | " - `mapply.init(...)` initializes a parallel processing framework with multiple workers (`n_workers=-1` uses all available processors), processing data in chunks (`chunk_size=100` and `max_chunks_per_worker=8`), with a visible progress bar.\n", 78 | "\n", 79 | "2. **Text Cleaning Components:** \n", 80 | " - **Punctuations:** \n", 81 | " - Combines English (`string.punctuation`) and Persian punctuation symbols (`persian_punctuations`) into a single list for later removal.\n", 82 | " - **Diacritics:** \n", 83 | " - Compiles a regex (`arabic_diacritics`) to remove common Arabic diacritics (e.g., Tashdid, Fatha, etc.).\n", 84 | " - **Lemmatization and Normalization:** \n", 85 | " - Initializes `hazm.Lemmatizer()` for lemmatizing Persian words. \n", 86 | " - Initializes a `Normalizer()` to standardize text.\n", 87 | "\n", 88 | "\n", 89 | "3. **Stopwords Loading:** \n", 90 | " - Uses `glob` to find all text files with Persian stopwords in a specific folder.\n", 91 | " - Reads each file and compiles a master list of stopwords.\n", 92 | " - Removes newline characters from each stopword.\n", 93 | " - Extends the list with additional stopwords from `stopwords_list()` from hazm.\n", 94 | "\n", 95 | "\n", 96 | "\n", 97 | "This setup prepares your environment to clean, normalize, and process Persian text data efficiently in a parallelized manner." 98 | ] 99 | }, 100 | { 101 | "cell_type": "code", 102 | "execution_count": 2, 103 | "metadata": { 104 | "id": "oZcxRsdKGNyu" 105 | }, 106 | "outputs": [], 107 | "source": [ 108 | "mapply.init(\n", 109 | " n_workers=-1,\n", 110 | " chunk_size=100,\n", 111 | " max_chunks_per_worker=8,\n", 112 | " progressbar=True,\n", 113 | ")\n", 114 | "\n", 115 | "persian_punctuations = '''`÷×؛#<>_()*&^%][ـ،/:\"؟.,'{}~¦+|!”…“–ـ'''\n", 116 | "punctuations_list = string.punctuation + persian_punctuations\n", 117 | "arabic_diacritics = re.compile(\"\"\"\n", 118 | " ّ | # Tashdid\n", 119 | " َ | # Fatha\n", 120 | " ً | # Tanwin Fath\n", 121 | " ُ | # Damma\n", 122 | " ٌ | # Tanwin Damm\n", 123 | " ِ | # Kasra\n", 124 | " ٍ | # Tanwin Kasr\n", 125 | " ْ | # Sukun\n", 126 | " ـ # Tatwil/Kashida\n", 127 | " \"\"\", re.VERBOSE)\n", 128 | "lemmatizer = hazm.Lemmatizer()\n", 129 | "normalizer = Normalizer()\n", 130 | "\n", 131 | "\n", 132 | "file_list = glob.glob('/content/drive/MyDrive/NLP/persian_stopwords' + '/*.txt')\n", 133 | "\n", 134 | "stop_words = []\n", 135 | "\n", 136 | "for file_path in file_list:\n", 137 | " with open(file_path) as f:\n", 138 | " stop_words.extend(f.readlines())\n", 139 | "\n", 140 | "for i in range(len(stop_words)):\n", 141 | " stop_words[i]=stop_words[i].replace('\\n','')\n", 142 | "\n", 143 | "stop_words.extend(stopwords_list())\n", 144 | "output.clear()" 145 | ] 146 | }, 147 | { 148 | "cell_type": "markdown", 149 | "metadata": { 150 | "id": "NVhuH0nwYgfa" 151 | }, 152 | "source": [ 153 | "# read dataset" 154 | ] 155 | }, 156 | { 157 | "cell_type": "code", 158 | "execution_count": 3, 159 | "metadata": { 160 | "colab": { 161 | "base_uri": "https://localhost:8080/", 162 | "height": 206 163 | }, 164 | "id": "tyuNT5RSGOFA", 165 | "outputId": "4316685a-dd35-4902-f7cc-6c335cc3af2d" 166 | }, 167 | "outputs": [ 168 | { 169 | "output_type": "execute_result", 170 | "data": { 171 | "text/plain": [ 172 | " title \\\n", 173 | "0 قارایی: در تلاشم تا سیاه نمایی درباره ایران با... \n", 174 | "1 توجه ویژه به اقوام و چهره های مردمی در فصل جدی... \n", 175 | "2 جان فدا| ویژه برنامه هاى تلویزیون در سومین سال... \n", 176 | "3 محمد علی صائب رئیس خبرگزاری صدا وسیما شد \n", 177 | "4 «همراه با خاطره ها» تمدید شد \n", 178 | "\n", 179 | " text label \n", 180 | "0 خبرگزاری فارس - گروه هنر و رسانه - علی عبدالهی... culture-media \n", 181 | "1 به گزارش خبرگزاری فارس، پویان هدایتی، تهیه کن... culture-media \n", 182 | "2 به گزارش خبرنگار رادیو و تلویزیون خبرگزاری فار... culture-media \n", 183 | "3 به گزارش خبرگزاری فارس، علیرضا خدابخشی، معاون ... culture-media \n", 184 | "4 به گزارش خبرگزاری فارس به نقل از روابط عمومی و... culture-media " 185 | ], 186 | "text/html": [ 187 | "\n", 188 | "

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	title	text	label
0	قارایی: در تلاشم تا سیاه نمایی درباره ایران با...	خبرگزاری فارس - گروه هنر و رسانه - علی عبدالهی...	culture-media
1	توجه ویژه به اقوام و چهره های مردمی در فصل جدی...	به گزارش خبرگزاری فارس، پویان هدایتی، تهیه کن...	culture-media
2	جان فدا\| ویژه برنامه هاى تلویزیون در سومین سال...	به گزارش خبرنگار رادیو و تلویزیون خبرگزاری فار...	culture-media
3	محمد علی صائب رئیس خبرگزاری صدا وسیما شد	به گزارش خبرگزاری فارس، علیرضا خدابخشی، معاون ...	culture-media
4	«همراه با خاطره ها» تمدید شد	به گزارش خبرگزاری فارس به نقل از روابط عمومی و...	culture-media

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\\u0647\\u0627 \\u0647\\u0633\\u062a\\u0646\\u062f\\u060c \\u06af\\u0641\\u062a: \\u0628\\u0647 \\u0647\\u0645\\u06cc\\u0646 \\u062f\\u0644\\u06cc\\u0644 \\u0628\\u0631\\u06af\\u0632\\u0627\\u0631\\u06cc \\u0646\\u0645\\u0627\\u06cc\\u0634\\u06af\\u0627\\u0647 \\u062f\\u0631 \\u0645\\u062c\\u0645\\u0648\\u0639\\u0647 \\u0634\\u0647\\u0631 \\u0622\\u0641\\u062a\\u0627\\u0628 \\u0628\\u0647 \\u062a\\u0645\\u0631\\u06a9\\u0632 \\u0628\\u06cc\\u0634\\u062a\\u0631 \\u0648 \\u062f\\u06cc\\u062f\\u0647 \\u0634\\u062f\\u0646 \\u0634\\u0631\\u06a9\\u062a \\u0647\\u0627\\u06cc \\u0641\\u0639\\u0627\\u0644 \\u062f\\u0631 \\u0627\\u06cc\\u0646 \\u062d\\u0648\\u0632\\u0647 \\u06a9\\u0645\\u06a9 \\u0645\\u06cc \\u06a9\\u0646\\u062f. \\u067e\\u0627\\u06cc\\u0627\\u0646 \\u067e\\u06cc\\u0627\\u0645/\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"label\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 5,\n \"samples\": [\n \"economy\",\n \"sports\",\n \"politics\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" 460 | } 461 | }, 462 | "metadata": {}, 463 | "execution_count": 3 464 | } 465 | ], 466 | "source": [ 467 | "df = pd.read_csv(\"/content/drive/MyDrive/get news/farsnews/farsnews_fainal.csv\")\n", 468 | "\n", 469 | "df = df.drop(['Unnamed: 0', 'date'], axis=1)\n", 470 | "df = df.dropna()\n", 471 | "df.head(5)" 472 | ] 473 | }, 474 | { 475 | "cell_type": "code", 476 | "execution_count": 4, 477 | "metadata": { 478 | "id": "DDvlsvAWGWgV" 479 | }, 480 | "outputs": [], 481 | "source": [ 482 | "class preprocessing:\n", 483 | " def __init__(self):\n", 484 | " pass\n", 485 | "\n", 486 | " def _remove_diacritics(self, text):\n", 487 | " text = re.sub(arabic_diacritics, '', text)\n", 488 | " return text\n", 489 | "\n", 490 | "\n", 491 | " def _remove_crash_data(self, text):\n", 492 | " if isinstance(text, str):\n", 493 | " return text\n", 494 | " else:\n", 495 | " return None\n", 496 | "\n", 497 | " def _remove_punctuations(self, text):\n", 498 | " translator = str.maketrans('', '', punctuations_list)\n", 499 | " return text.translate(translator)\n", 500 | "\n", 501 | " def _remove_repeating_char(self, text):\n", 502 | " return re.sub(r'(.)\\1+', r'\\1', text)\n", 503 | "\n", 504 | "\n", 505 | " def _normalize_persian(self, text):\n", 506 | " text = re.sub(\"[إأآا]\", \"ا\", text)\n", 507 | " text = re.sub(\"ي\", \"ی\", text)\n", 508 | " text = re.sub(\"ؤ\", \"و\", text)\n", 509 | " text = re.sub(\"ئ\", \"ی\", text)\n", 510 | " text = re.sub(\"ة\", \"ه\", text)\n", 511 | " text = re.sub(\"ك\" ,\"ک\" , text)\n", 512 | " text = re.sub(\"[^ابپتثجچحخدذرزژسشصضطظعغفقکگلمنوهی]\", \" \", text)\n", 513 | " text = re.sub(\"[^\\S\\n\\t]+\", ' ', text)\n", 514 | " text = normalizer.normalize(text)\n", 515 | " return text\n", 516 | "\n", 517 | "\n", 518 | " def _tokenize(self, text):\n", 519 | " return word_tokenize(text)\n", 520 | "\n", 521 | " def _remove_stopwords(self, words):\n", 522 | " return [word for word in words if word not in stop_words and len(word) > 2]\n", 523 | "\n", 524 | " def _lemmatizer(self, words):\n", 525 | " result = list()\n", 526 | " for token in words:\n", 527 | " result.append(lemmatizer.lemmatize(token))\n", 528 | " return self._remove_stopwords(result)" 529 | ] 530 | }, 531 | { 532 | "cell_type": "code", 533 | "execution_count": 5, 534 | "metadata": { 535 | "colab": { 536 | "base_uri": "https://localhost:8080/", 537 | "height": 241, 538 | "referenced_widgets": [ 539 | "6baf6a0ea60a423dbf7ffac0f5761e5c", 540 | "de6a7c5ce5284507aa82b1d4c0170393", 541 | "da8a4395ea2440f5875788f328419627", 542 | "225e86d7282b4b509f6b62f7899a243a", 543 | "0d7a51735dab4d549937b5e7e53591ba", 544 | "f31a2928f25b4fe78eeb69530f6a63d5", 545 | "1cf5fc27417a47c98e49ec203f33a2f2", 546 | "ac0186ce68c448e6b6259e3a2ebd46dd", 547 | "ef21bc1c2dd540ab98ea755c235eb156", 548 | "39ad9fa51dd040e6a920d27de9cc6f14", 549 | "6fd3144a82c14c7c95341193cb23b919", 550 | "fd9d2b5c879b4bf3bd4a98ad1652e046", 551 | "a114e175966c4c53b4ca8fcb4495a215", 552 | "5637b79669534291b55d68de556a7e52", 553 | "115837e3aed4403798cd17b5b1ead02d", 554 | "c71687f02e3b4066920152385763efb7", 555 | "ddeb7dc1af44465e83e662f3278a4ff1", 556 | "ea21f28286484130a45c3c537cac8b80", 557 | "3255e852649a4ee889330c1f209f1fcf", 558 | "0b8a896d3321495388be9ef990537d66", 559 | "5ff9d1e9c0b943aa9fbd46e73d5d26c8", 560 | "581443f16d324e3d9604f786d495d27d", 561 | "ae2e51d3d5014fa4a1cb602d0adcfca3", 562 | "306577b615234f1fab6dd7e5c01ac654", 563 | "ac745dae99444b91befa52b2328f42d6", 564 | "fab1d434c00a4bc7a202b94265cefd10", 565 | "9c35b59e342440d7a2168f8af58474ae", 566 | "d04f3ceea2e14c0ca79957017a6d106d", 567 | "fa2cd250648b42448d2b87bf69786806", 568 | "7c03309dca71444fb21a257e647830b4", 569 | "031edc1cc43e40abb8b53aeb75d02fe4", 570 | "54778f911d1942b7870f2e15204c4086", 571 | "6d98087402f5451d801cdefc9166fef4", 572 | "c8a65b8bbd8246c5a6779d605a15acda", 573 | "3e90afc7b1bd44f79180145ca551dfc5", 574 | "ecfd7754da16443e83c6dac4f0bf02c3", 575 | "168127473db44622972c91bcb69180eb", 576 | "ebd6950b63644a1ea4625b662fb78ae0", 577 | "841b320404c14f28af512b7809793e0a", 578 | "8891e9873adf43aba1285634f9ee3e60", 579 | "19744e7597894884814efeac0508d6e8", 580 | "4d30c02dc2ed4d728e44b540d57dd1a2", 581 | "c47744d043594db1ba02e170e22d1965", 582 | "3de3f487470a4c8abe8016e5f256cdce", 583 | "ad7d2b4774cd4181b4bbd0a3588f7cb4", 584 | "6f4f74e433c34b32b4874e717151cdc5", 585 | "11d6a99882fe440bb2e437d990656bbd", 586 | "71c90889df844b66af254e7af3087cc0", 587 | "d098d2b9a5d54d7fbed72fb602c3c9bd", 588 | "353c5973a79b402e92f2140b436e6c1d", 589 | "32dc283e573543c78866f706a5f93821", 590 | "fa75dee6148d400eb308a1a074d347f8", 591 | "35c78d8f8a874b1f84c8ee9af7fd983a", 592 | "d139ecb729a8412186359f745633608d", 593 | "cba0438ee33240c487effa861e7ef120", 594 | "44c526d02a4e4ccc8a8ceba0f3048b62", 595 | "406deff2072444be8ff157c18a0a3db4", 596 | "79e5bab57f704e729df0e6fb154ca65e", 597 | "dbdc4d1ccbf347038c546871ce61c162", 598 | "1245d881567d4a76ac1bd009d75e485b", 599 | "bc4a9355999047aa8f74658eefc57ac5", 600 | "12cda2b1613a4acc808542ee5bbe61d2", 601 | "1a21df1ffde5404a9c4d6f26b5b48f80", 602 | "69dbb3ac985f4ec58d309475bb7f6197", 603 | "0babb170cc3346b1bed2598728795253", 604 | "8b2b275049f34f8f858229ac032badc7", 605 | "e7ca008ce8ca4d659d918adb6d801c3a", 606 | "0d9e005f6d9a43d9b6e0817a7da29fe1", 607 | "a8e26ade5f19471b8f7bada0d0fa0992", 608 | "954cdf24e128439e85cb713a714308e0", 609 | "26160ed6b8bc45fc8f3c24077c4b47e7", 610 | "2bee186efbbc489aae07101939c02953", 611 | "13d35f069c69446ba91e53df0858cdd3", 612 | "cb2a3803b4814a6ea7ce4e0b0f1e8bff", 613 | "aa82c8e2500740a5906e80304eedcca9", 614 | "0d7cb55684f146128c57da44db95ae5c", 615 | "085a2bb9d5024835a4f834a4f251c9d7" 616 | ] 617 | }, 618 | "id": "YTw9q4LhGYHM", 619 | "outputId": "9eae4e03-22fb-4004-fc6b-a6e6d2167e77" 620 | }, 621 | "outputs": [ 622 | { 623 | "output_type": "display_data", 624 | "data": { 625 | "text/plain": [ 626 | " 0%| | 0/16 [00:00:10: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at /pytorch/torch/csrc/utils/tensor_new.cpp:254.)\n", 768 | " return torch.tensor(vectors, dtype=torch.float)\n" 769 | ] 770 | }, 771 | { 772 | "output_type": "stream", 773 | "name": "stdout", 774 | "text": [ 775 | "torch.Size([64942, 24, 100])\n" 776 | ] 777 | } 778 | ], 779 | "source": [ 780 | "sentences = df['title - preproces'].tolist()\n", 781 | "w2v_model = Word2Vec(sentences=sentences, vector_size=100, window=5, min_count=1, workers=4)\n", 782 | "\n", 783 | "max_length = max(len(tokens) for tokens in sentences)\n", 784 | "\n", 785 | "def transform_text_to_tensor(tokens, model, max_length):\n", 786 | " vectors = [model.wv[token] for token in tokens if token in model.wv]\n", 787 | " while len(vectors) < max_length:\n", 788 | " vectors.append(np.zeros(model.vector_size))\n", 789 | " return torch.tensor(vectors, dtype=torch.float)\n", 790 | "\n", 791 | "tensor_list = [transform_text_to_tensor(tokens, w2v_model, max_length) for tokens in sentences]\n", 792 | "final_tensor = torch.stack(tensor_list)\n", 793 | "print(final_tensor.shape)" 794 | ] 795 | }, 796 | { 797 | "cell_type": "markdown", 798 | "metadata": { 799 | "id": "zsoXznbJXIeL" 800 | }, 801 | "source": [ 802 | "## Split the data into Train and Test\n" 803 | ] 804 | }, 805 | { 806 | "cell_type": "code", 807 | "execution_count": 7, 808 | "metadata": { 809 | "id": "B60el6r0XdeJ" 810 | }, 811 | "outputs": [], 812 | "source": [ 813 | "X_train, X_test, y_train, y_test = train_test_split(final_tensor, df[\"label\"].values, test_size=0.2, random_state=42)" 814 | ] 815 | }, 816 | { 817 | "cell_type": "markdown", 818 | "metadata": { 819 | "id": "wJZQOdlfXUfy" 820 | }, 821 | "source": [ 822 | "## Convert to torch.Tensor\n" 823 | ] 824 | }, 825 | { 826 | "cell_type": "code", 827 | "execution_count": 8, 828 | "metadata": { 829 | "id": "MmNYBMF_XbPd" 830 | }, 831 | "outputs": [], 832 | "source": [ 833 | "label_encoder = LabelEncoder()\n", 834 | "y_train = label_encoder.fit_transform(y_train)\n", 835 | "y_test = label_encoder.transform(y_test)\n", 836 | "y_train = torch.tensor(y_train, dtype=torch.long)\n", 837 | "y_test = torch.tensor(y_test, dtype=torch.long)" 838 | ] 839 | }, 840 | { 841 | "cell_type": "markdown", 842 | "metadata": { 843 | "id": "POqNLxPpXj8Q" 844 | }, 845 | "source": [ 846 | "## Dataset and DataLoader\n" 847 | ] 848 | }, 849 | { 850 | "cell_type": "code", 851 | "execution_count": 9, 852 | "metadata": { 853 | "id": "_C2eMTd7O97y" 854 | }, 855 | "outputs": [], 856 | "source": [ 857 | "class TextDataset(Dataset):\n", 858 | " def __init__(self, X, y):\n", 859 | " self.X = X.unsqueeze(1) # Adding the channel dimension: (batch_size, 1, seq_len, embedding_dim)\n", 860 | " self.y = y\n", 861 | "\n", 862 | " def __len__(self):\n", 863 | " return len(self.X)\n", 864 | "\n", 865 | " def __getitem__(self, idx):\n", 866 | " return self.X[idx], self.y[idx]\n", 867 | "\n", 868 | "train_dataset = TextDataset(X_train, y_train)\n", 869 | "test_dataset = TextDataset(X_test, y_test)\n", 870 | "train_loader = DataLoader(train_dataset, batch_size=100, shuffle=True)\n", 871 | "test_loader = DataLoader(test_dataset, batch_size=100, shuffle=False)" 872 | ] 873 | }, 874 | { 875 | "cell_type": "markdown", 876 | "metadata": { 877 | "id": "JiR0Z1iKXps6" 878 | }, 879 | "source": [ 880 | "# Define the CNN model\n", 881 | "\n", 882 | "\n", 883 | "\n", 884 | "## Model Architecture\n", 885 | "The `TextCNN` model consists of:\n", 886 | "- Convolutional layers with varying kernel sizes (3, 4, 5) to capture different n-gram features.\n", 887 | "- ReLU activation function.\n", 888 | "- Max pooling layers to retain the most important features from each filter.\n", 889 | "- A fully connected layer for classification.\n", 890 | "- Dropout for regularization.\n", 891 | "\n" 892 | ] 893 | }, 894 | { 895 | "cell_type": "code", 896 | "execution_count": 10, 897 | "metadata": { 898 | "id": "fuLigzNHH75E" 899 | }, 900 | "outputs": [], 901 | "source": [ 902 | "class TextCNN(nn.Module):\n", 903 | " def __init__(self, embedding_dim, num_filters, num_classes, dropout=0.5):\n", 904 | " super(TextCNN, self).__init__()\n", 905 | "\n", 906 | " self.conv1 = nn.Conv2d(in_channels=1, out_channels=num_filters, kernel_size=(3, embedding_dim))\n", 907 | " self.conv2 = nn.Conv2d(in_channels=1, out_channels=num_filters, kernel_size=(4, embedding_dim))\n", 908 | " self.conv3 = nn.Conv2d(in_channels=1, out_channels=num_filters, kernel_size=(5, embedding_dim))\n", 909 | "\n", 910 | " self.dropout = nn.Dropout(dropout)\n", 911 | " self.fc = nn.Linear(num_filters * 3, num_classes)\n", 912 | "\n", 913 | " def forward(self, x):\n", 914 | " x1 = F.relu(self.conv1(x)).squeeze(3)\n", 915 | " x2 = F.relu(self.conv2(x)).squeeze(3)\n", 916 | " x3 = F.relu(self.conv3(x)).squeeze(3)\n", 917 | "\n", 918 | " x1 = F.max_pool1d(x1, kernel_size=x1.size(2)).squeeze(2)\n", 919 | " x2 = F.max_pool1d(x2, kernel_size=x2.size(2)).squeeze(2)\n", 920 | " x3 = F.max_pool1d(x3, kernel_size=x3.size(2)).squeeze(2)\n", 921 | "\n", 922 | " x = torch.cat([x1, x2, x3], dim=1)\n", 923 | " x = self.dropout(x)\n", 924 | " logits = self.fc(x)\n", 925 | " return logits\n" 926 | ] 927 | }, 928 | { 929 | "cell_type": "markdown", 930 | "metadata": { 931 | "id": "lB6XMveeYDSC" 932 | }, 933 | "source": [ 934 | "## Train the model\n", 935 | "\n", 936 | "\n", 937 | "\n", 938 | "### Hyperparameters\n", 939 | "Several hyperparameters are used in training the TextCNN model:\n", 940 | "- `embedding_dim`: The size of the word embeddings (e.g., 100, 300). This determines the dimensionality of word representations.\n", 941 | "- `num_filters`: The number of filters in each convolutional layer. A higher number allows the model to capture more features.\n", 942 | "- `num_classes`: The number of output classes for classification.\n", 943 | "- `dropout`: The dropout rate used for regularization to prevent overfitting.\n", 944 | "- `learning_rate`: The step size for the Adam optimizer. Typically set to 0.001.\n", 945 | "- `num_epochs`: The number of training iterations over the dataset.\n", 946 | "- `batch_size`: The number of samples processed before updating the model weights.\n" 947 | ] 948 | }, 949 | { 950 | "cell_type": "code", 951 | "execution_count": 11, 952 | "metadata": { 953 | "colab": { 954 | "base_uri": "https://localhost:8080/" 955 | }, 956 | "id": "F6Ic5Gx7OP9C", 957 | "outputId": "d74f64db-7200-477c-d432-957d9b37796e" 958 | }, 959 | "outputs": [ 960 | { 961 | "output_type": "stream", 962 | "name": "stdout", 963 | "text": [ 964 | "Epoch 1/25, Loss: 0.8109\n", 965 | "Epoch 2/25, Loss: 0.7159\n", 966 | "Epoch 3/25, Loss: 0.6884\n", 967 | "Epoch 4/25, Loss: 0.6679\n", 968 | "Epoch 5/25, Loss: 0.6549\n", 969 | "Epoch 6/25, Loss: 0.6437\n", 970 | "Epoch 7/25, Loss: 0.6369\n", 971 | "Epoch 8/25, Loss: 0.6282\n", 972 | "Epoch 9/25, Loss: 0.6220\n", 973 | "Epoch 10/25, Loss: 0.6153\n", 974 | "Epoch 11/25, Loss: 0.6121\n", 975 | "Epoch 12/25, Loss: 0.6029\n", 976 | "Epoch 13/25, Loss: 0.5987\n", 977 | "Epoch 14/25, Loss: 0.5952\n", 978 | "Epoch 15/25, Loss: 0.5936\n", 979 | "Epoch 16/25, Loss: 0.5910\n", 980 | "Epoch 17/25, Loss: 0.5841\n", 981 | "Epoch 18/25, Loss: 0.5823\n", 982 | "Epoch 19/25, Loss: 0.5806\n", 983 | "Epoch 20/25, Loss: 0.5758\n", 984 | "Epoch 21/25, Loss: 0.5720\n", 985 | "Epoch 22/25, Loss: 0.5702\n", 986 | "Epoch 23/25, Loss: 0.5696\n", 987 | "Epoch 24/25, Loss: 0.5606\n", 988 | "Epoch 25/25, Loss: 0.5618\n" 989 | ] 990 | } 991 | ], 992 | "source": [ 993 | "# Model, loss function, and optimizer\n", 994 | "embedding_dim = 100\n", 995 | "num_filters = 100\n", 996 | "num_classes = len(set(df[\"label\"]))\n", 997 | "model = TextCNN(embedding_dim, num_filters, num_classes)\n", 998 | "\n", 999 | "criterion = nn.CrossEntropyLoss()\n", 1000 | "optimizer = optim.Adam(model.parameters(), lr=0.001)\n", 1001 | "\n", 1002 | "num_epochs = 25\n", 1003 | "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", 1004 | "model.to(device)\n", 1005 | "\n", 1006 | "for epoch in range(num_epochs):\n", 1007 | " model.train()\n", 1008 | " total_loss = 0\n", 1009 | " for X_batch, y_batch in train_loader:\n", 1010 | " X_batch, y_batch = X_batch.to(device), y_batch.to(device)\n", 1011 | " optimizer.zero_grad()\n", 1012 | " outputs = model(X_batch)\n", 1013 | " loss = criterion(outputs, y_batch)\n", 1014 | " loss.backward()\n", 1015 | " optimizer.step()\n", 1016 | " total_loss += loss.item()\n", 1017 | " print(f\"Epoch {epoch+1}/{num_epochs}, Loss: {total_loss/len(train_loader):.4f}\")" 1018 | ] 1019 | }, 1020 | { 1021 | "cell_type": "markdown", 1022 | "metadata": { 1023 | "id": "kfNlu2rCYXVI" 1024 | }, 1025 | "source": [ 1026 | "## Evaluate the model" 1027 | ] 1028 | }, 1029 | { 1030 | "cell_type": "code", 1031 | "execution_count": 12, 1032 | "metadata": { 1033 | "colab": { 1034 | "base_uri": "https://localhost:8080/" 1035 | }, 1036 | "id": "viNUEpTcQAg5", 1037 | "outputId": "49a01d49-d2e0-4c28-b23c-3a46fdae3545" 1038 | }, 1039 | "outputs": [ 1040 | { 1041 | "output_type": "stream", 1042 | "name": "stdout", 1043 | "text": [ 1044 | "Test Accuracy: 0.7991\n" 1045 | ] 1046 | } 1047 | ], 1048 | "source": [ 1049 | "model.eval()\n", 1050 | "all_preds = []\n", 1051 | "all_labels = []\n", 1052 | "with torch.no_grad():\n", 1053 | " for X_batch, y_batch in test_loader:\n", 1054 | " X_batch, y_batch = X_batch.to(device), y_batch.to(device)\n", 1055 | " outputs = model(X_batch)\n", 1056 | " preds = torch.argmax(outputs, dim=1)\n", 1057 | " all_preds.extend(preds.cpu().numpy())\n", 1058 | " all_labels.extend(y_batch.cpu().numpy())\n", 1059 | "\n", 1060 | "accuracy = accuracy_score(all_labels, all_preds)\n", 1061 | "print(f\"Test Accuracy: {accuracy:.4f}\")" 1062 | ] 1063 | }, 1064 | { 1065 | "cell_type": "markdown", 1066 | "metadata": { 1067 | "id": "B340vc08ZCS7" 1068 | }, 1069 | "source": [ 1070 | "# RNNModel\n", 1071 | "\n", 1072 | "\n", 1073 | "### **Architecture of `RNNModel`**\n", 1074 | "This model is designed to process **sequential input data** (such as time-series data, natural language, or any ordered input) and output a classification prediction.\n", 1075 | "\n", 1076 | "#### **1. Input Layer**\n", 1077 | "- The model takes input sequences of shape **(batch_size, sequence_length, input_size)**.\n", 1078 | "- `input_size`: The number of features in each time step.\n", 1079 | "- `batch_first=True` ensures that batch size is the first dimension.\n", 1080 | "\n", 1081 | "#### **2. LSTM Layer**\n", 1082 | "- `self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)`\n", 1083 | "- This layer consists of **LSTM units** that process sequential data step-by-step, maintaining long-term dependencies.\n", 1084 | "- `hidden_size`: Defines the number of neurons in the LSTM hidden layers.\n", 1085 | "- `num_layers`: Specifies how many stacked LSTM layers are used.\n", 1086 | "- Outputs:\n", 1087 | " - `output`: Contains the output of all time steps in the sequence.\n", 1088 | " - `(h_n, c_n)`: The final hidden and cell states of the LSTM.\n", 1089 | "\n", 1090 | "#### **3. Fully Connected (FC) Layer**\n", 1091 | "- `self.fc = nn.Linear(hidden_size, num_classes)`\n", 1092 | "- The last hidden state of the LSTM (corresponding to the final time step) is passed through a fully connected (linear) layer.\n", 1093 | "- `num_classes`: Determines the output size, typically the number of categories in classification problems.\n", 1094 | "\n" 1095 | ] 1096 | }, 1097 | { 1098 | "cell_type": "code", 1099 | "execution_count": 13, 1100 | "metadata": { 1101 | "id": "2EPT0Lfo1Ug7" 1102 | }, 1103 | "outputs": [], 1104 | "source": [ 1105 | "class RNNModel(nn.Module):\n", 1106 | " def __init__(self, input_size, hidden_size, num_layers, num_classes):\n", 1107 | " super(RNNModel, self).__init__()\n", 1108 | " self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)\n", 1109 | " self.fc = nn.Linear(hidden_size, num_classes)\n", 1110 | "\n", 1111 | " def forward(self, x):\n", 1112 | "\n", 1113 | " output, (h_n, c_n) = self.lstm(x)\n", 1114 | " last_output = output[:, -1, :]\n", 1115 | " out = self.fc(last_output)\n", 1116 | " return out" 1117 | ] 1118 | }, 1119 | { 1120 | "cell_type": "markdown", 1121 | "metadata": { 1122 | "id": "t4DvjdZ5ZV_A" 1123 | }, 1124 | "source": [ 1125 | "## Train the model\n", 1126 | "\n", 1127 | "\n", 1128 | "### **Hyperparameters in the RNN Model** \n", 1129 | "\n", 1130 | "Hyperparameters are key settings that define how the model learns. In the given `RNNModel`, several hyperparameters affect the performance, efficiency, and accuracy of the model. Let's break them down:\n", 1131 | "\n", 1132 | "---\n", 1133 | "\n", 1134 | "### **1. Model Architecture Hyperparameters**\n", 1135 | "These define the structure of the neural network.\n", 1136 | "\n", 1137 | "- **`input_size = 100`** \n", 1138 | " - Determines the number of features in each input time step. \n", 1139 | " - In this case, each input vector has 100 features. \n", 1140 | "\n", 1141 | "- **`hidden_size = 128`** \n", 1142 | " - Represents the number of neurons in the hidden layer of the LSTM. \n", 1143 | " - A larger hidden size allows the model to learn more complex patterns but increases computational cost. \n", 1144 | "\n", 1145 | "- **`num_layers = 1`** \n", 1146 | " - Defines the number of stacked LSTM layers. \n", 1147 | " - More layers allow the network to capture deeper sequential dependencies, but too many layers may lead to vanishing gradients or overfitting. \n", 1148 | "\n", 1149 | "- **`num_classes = len(label_encoder.classes_)`** \n", 1150 | " - Defines the output size, corresponding to the number of classes in a classification problem. \n", 1151 | " - The model outputs probabilities for each class using **CrossEntropyLoss**.\n", 1152 | "\n", 1153 | "---\n", 1154 | "\n", 1155 | "### **2. Training Hyperparameters**\n", 1156 | "These define how the model learns from data.\n", 1157 | "\n", 1158 | "- **`criterion = nn.CrossEntropyLoss()`** \n", 1159 | " - The loss function used to measure the difference between predicted outputs and true labels. \n", 1160 | " - Suitable for multi-class classification tasks. \n", 1161 | "\n", 1162 | "- **`optimizer = torch.optim.Adam(model.parameters(), lr=0.001)`** \n", 1163 | " - **Adam (Adaptive Moment Estimation)** is chosen as the optimizer, balancing speed and efficiency in training. \n", 1164 | " - The **learning rate (`lr=0.001`)** controls how much the model updates weights in response to loss gradients. \n", 1165 | " - A **higher learning rate** can make training faster but may cause the model to converge to a suboptimal solution. A **lower learning rate** improves stability but slows training.\n", 1166 | "\n", 1167 | "- **`num_epochs = 20`** \n", 1168 | " - Defines the number of times the entire dataset is passed through the model. \n", 1169 | " - More epochs allow better learning but can lead to overfitting if too high. \n", 1170 | "\n", 1171 | "- **Batch Processing (`train_loader`)** \n", 1172 | " - The training loop iterates over batches of data rather than the entire dataset. \n", 1173 | " - Helps in faster computation and better generalization. \n", 1174 | " - `batch_X.squeeze(1).to(device)` ensures correct input dimensions for LSTM processing. \n" 1175 | ] 1176 | }, 1177 | { 1178 | "cell_type": "code", 1179 | "execution_count": 14, 1180 | "metadata": { 1181 | "colab": { 1182 | "base_uri": "https://localhost:8080/" 1183 | }, 1184 | "id": "fAavF22W2i41", 1185 | "outputId": "2aed65ad-45c9-4e2c-881b-a183050b170c" 1186 | }, 1187 | "outputs": [ 1188 | { 1189 | "output_type": "stream", 1190 | "name": "stdout", 1191 | "text": [ 1192 | "Epoch [1/25], Avg Loss: 0.9610\n", 1193 | "Epoch [2/25], Avg Loss: 0.6857\n", 1194 | "Epoch [3/25], Avg Loss: 0.6236\n", 1195 | "Epoch [4/25], Avg Loss: 0.5804\n", 1196 | "Epoch [5/25], Avg Loss: 0.5535\n", 1197 | "Epoch [6/25], Avg Loss: 0.5326\n", 1198 | "Epoch [7/25], Avg Loss: 0.5120\n", 1199 | "Epoch [8/25], Avg Loss: 0.4948\n", 1200 | "Epoch [9/25], Avg Loss: 0.4770\n", 1201 | "Epoch [10/25], Avg Loss: 0.4670\n", 1202 | "Epoch [11/25], Avg Loss: 0.4544\n", 1203 | "Epoch [12/25], Avg Loss: 0.4398\n", 1204 | "Epoch [13/25], Avg Loss: 0.4297\n", 1205 | "Epoch [14/25], Avg Loss: 0.4167\n", 1206 | "Epoch [15/25], Avg Loss: 0.4042\n", 1207 | "Epoch [16/25], Avg Loss: 0.3953\n", 1208 | "Epoch [17/25], Avg Loss: 0.3866\n", 1209 | "Epoch [18/25], Avg Loss: 0.3731\n", 1210 | "Epoch [19/25], Avg Loss: 0.3620\n", 1211 | "Epoch [20/25], Avg Loss: 0.3543\n", 1212 | "Epoch [21/25], Avg Loss: 0.3471\n", 1213 | "Epoch [22/25], Avg Loss: 0.3354\n", 1214 | "Epoch [23/25], Avg Loss: 0.3247\n", 1215 | "Epoch [24/25], Avg Loss: 0.3126\n", 1216 | "Epoch [25/25], Avg Loss: 0.3041\n" 1217 | ] 1218 | } 1219 | ], 1220 | "source": [ 1221 | "input_size = 100\n", 1222 | "hidden_size = 128\n", 1223 | "num_layers = 1\n", 1224 | "num_classes = len(label_encoder.classes_)\n", 1225 | "model = RNNModel(input_size, hidden_size, num_layers, num_classes).to(device)\n", 1226 | "\n", 1227 | "criterion = nn.CrossEntropyLoss()\n", 1228 | "optimizer = torch.optim.Adam(model.parameters(), lr=0.001)\n", 1229 | "\n", 1230 | "num_epochs = 25\n", 1231 | "for epoch in range(num_epochs):\n", 1232 | " model.train()\n", 1233 | " total_loss = 0\n", 1234 | " for batch_X, batch_y in train_loader:\n", 1235 | " batch_X = batch_X.squeeze(1).to(device)\n", 1236 | " batch_y = batch_y.to(device)\n", 1237 | "\n", 1238 | " optimizer.zero_grad()\n", 1239 | " outputs = model(batch_X)\n", 1240 | " loss = criterion(outputs, batch_y)\n", 1241 | "\n", 1242 | " loss.backward()\n", 1243 | " optimizer.step()\n", 1244 | "\n", 1245 | " total_loss += loss.item()\n", 1246 | "\n", 1247 | " avg_loss = total_loss / len(train_loader)\n", 1248 | " print(f'Epoch [{epoch+1}/{num_epochs}], Avg Loss: {avg_loss:.4f}')" 1249 | ] 1250 | }, 1251 | { 1252 | "cell_type": "markdown", 1253 | "metadata": { 1254 | "id": "VUqsg8CcZj4m" 1255 | }, 1256 | "source": [ 1257 | "## Evaluate the model" 1258 | ] 1259 | }, 1260 | { 1261 | "cell_type": "code", 1262 | "execution_count": 15, 1263 | "metadata": { 1264 | "colab": { 1265 | "base_uri": "https://localhost:8080/" 1266 | }, 1267 | "id": "Lsud7tg32llQ", 1268 | "outputId": "68c223dc-848d-4849-a1f6-33d914629e9a" 1269 | }, 1270 | "outputs": [ 1271 | { 1272 | "output_type": "stream", 1273 | "name": "stdout", 1274 | "text": [ 1275 | "Test Accuracy: 83.36%\n" 1276 | ] 1277 | } 1278 | ], 1279 | "source": [ 1280 | "def evaluate(model, test_loader, device):\n", 1281 | " model.eval()\n", 1282 | " with torch.no_grad():\n", 1283 | " correct = 0\n", 1284 | " total = 0\n", 1285 | " for batch_X, batch_y in test_loader:\n", 1286 | " batch_X = batch_X.squeeze(1).to(device)\n", 1287 | " batch_y = batch_y.to(device)\n", 1288 | "\n", 1289 | " outputs = model(batch_X)\n", 1290 | " _, predicted = torch.max(outputs.data, 1)\n", 1291 | "\n", 1292 | " total += batch_y.size(0)\n", 1293 | " correct += (predicted == batch_y).sum().item()\n", 1294 | "\n", 1295 | " accuracy = 100 * correct / total\n", 1296 | " return accuracy\n", 1297 | "\n", 1298 | "accuracy = evaluate(model, test_loader, device)\n", 1299 | "print(f'Test Accuracy: {accuracy:.2f}%')" 1300 | ] 1301 | }, 1302 | { 1303 | "cell_type": "code", 1304 | "source": [], 1305 | "metadata": { 1306 | "id": "aOs4dBuPg_60" 1307 | }, 1308 | "execution_count": null, 1309 | "outputs": [] 1310 | } 1311 | ], 1312 | "metadata": { 1313 | "colab": { 1314 | "provenance": [], 1315 | "authorship_tag": "ABX9TyMALziu5FoPf3hlb+uNA7qS", 1316 | "include_colab_link": true 1317 | }, 1318 | "kernelspec": { 1319 | 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