Section_06_Text_Classification_using_RNNs.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/rrajasek95/nlp-243-notebooks/blob/main/Section_06_Text_Classification_using_RNNs.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a5bpndvTRp33"
      },
      "source": [
        "# Text Classification using PyTorch"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e97sph_qRw7j"
      },
      "source": [
        "import os\n",
        "import spacy # Text preprocessing\n",
        "\n",
        "# Pytrch\n",
        "import torch\n",
        "import torch.nn as nn # Neural Net Layers module\n",
        "import torch.optim as optim # Optimizers module\n",
        "import torch.nn.functional as F # Functions module - activations, utilities like padding\n",
        "import numpy as np\n",
        "\n",
        "from tqdm.auto import tqdm # Add progress bar\n",
        "\n",
        "from torch.utils.data import DataLoader, Dataset # Preparing data in batches for pytorch training\n",
        "\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "import math\n",
        "from itertools import chain\n",
        "\n",
        "# Gensim word embeddings\n",
        "import gensim\n",
        "\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_zBD-IUA1F_s"
      },
      "source": [
        "## BBC News Dataset\n",
        "\n",
        "The BBC News Dataset is a small collection of news articles from 5 different topics - business, entertainment, politics, tech, sports.\n",
        "\n",
        "Therfore, this makes for a pretty decent dataset for article classification."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ahQvld3R1Koi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "951d38bc-ee21-45b1-9b63-718a01a3bef6"
      },
      "source": [
        "!wget http://mlg.ucd.ie/files/datasets/bbc-fulltext.zip\n",
        "!unzip bbc-fulltext.zip\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-11-23 08:13:28--  http://mlg.ucd.ie/files/datasets/bbc-fulltext.zip\n",
            "Resolving mlg.ucd.ie (mlg.ucd.ie)... 137.43.93.132\n",
            "Connecting to mlg.ucd.ie (mlg.ucd.ie)|137.43.93.132|:80... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 2874078 (2.7M) [application/zip]\n",
            "Saving to: ‘bbc-fulltext.zip’\n",
            "\n",
            "bbc-fulltext.zip    100%[===================>]   2.74M  --.-KB/s    in 0.1s    \n",
            "\n",
            "2020-11-23 08:13:28 (20.3 MB/s) - ‘bbc-fulltext.zip’ saved [2874078/2874078]\n",
            "\n",
            "Archive:  bbc-fulltext.zip\n",
            "   creating: bbc/\n",
            "   creating: bbc/business/\n",
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            "   creating: bbc/politics/\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BAXQI6YO1eXs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a2b76df8-a0f9-4c3f-8257-b778b07ca5b5"
      },
      "source": [
        "!cat bbc/business/001.txt"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Ad sales boost Time Warner profit\n",
            "\n",
            "Quarterly profits at US media giant TimeWarner jumped 76% to $1.13bn (£600m) for the three months to December, from $639m year-earlier.\n",
            "\n",
            "The firm, which is now one of the biggest investors in Google, benefited from sales of high-speed internet connections and higher advert sales. TimeWarner said fourth quarter sales rose 2% to $11.1bn from $10.9bn. Its profits were buoyed by one-off gains which offset a profit dip at Warner Bros, and less users for AOL.\n",
            "\n",
            "Time Warner said on Friday that it now owns 8% of search-engine Google. But its own internet business, AOL, had has mixed fortunes. It lost 464,000 subscribers in the fourth quarter profits were lower than in the preceding three quarters. However, the company said AOL's underlying profit before exceptional items rose 8% on the back of stronger internet advertising revenues. It hopes to increase subscribers by offering the online service free to TimeWarner internet customers and will try to sign up AOL's existing customers for high-speed broadband. TimeWarner also has to restate 2000 and 2003 results following a probe by the US Securities Exchange Commission (SEC), which is close to concluding.\n",
            "\n",
            "Time Warner's fourth quarter profits were slightly better than analysts' expectations. But its film division saw profits slump 27% to $284m, helped by box-office flops Alexander and Catwoman, a sharp contrast to year-earlier, when the third and final film in the Lord of the Rings trilogy boosted results. For the full-year, TimeWarner posted a profit of $3.36bn, up 27% from its 2003 performance, while revenues grew 6.4% to $42.09bn. \"Our financial performance was strong, meeting or exceeding all of our full-year objectives and greatly enhancing our flexibility,\" chairman and chief executive Richard Parsons said. For 2005, TimeWarner is projecting operating earnings growth of around 5%, and also expects higher revenue and wider profit margins.\n",
            "\n",
            "TimeWarner is to restate its accounts as part of efforts to resolve an inquiry into AOL by US market regulators. It has already offered to pay $300m to settle charges, in a deal that is under review by the SEC. The company said it was unable to estimate the amount it needed to set aside for legal reserves, which it previously set at $500m. It intends to adjust the way it accounts for a deal with German music publisher Bertelsmann's purchase of a stake in AOL Europe, which it had reported as advertising revenue. It will now book the sale of its stake in AOL Europe as a loss on the value of that stake.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TMnbgxXONQw9"
      },
      "source": [
        "### Download Word2Vec Wikipedia embeddings"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s7oZ3W7JNPqK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c67eab53-e4b4-40b4-a8c8-06d9e424612f"
      },
      "source": [
        "# !wget http://vectors.nlpl.eu/repository/20/4.zip\n",
        "# The above Gigaword vectors require part-of-speech information. Think about how you'll use these\n",
        "# in your model - this would work better than context-free word embeddings\n",
        "\n",
        "!wget http://vectors.nlpl.eu/repository/20/6.zip"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-11-23 08:13:29--  http://vectors.nlpl.eu/repository/20/6.zip\n",
            "Resolving vectors.nlpl.eu (vectors.nlpl.eu)... 129.240.189.225\n",
            "Connecting to vectors.nlpl.eu (vectors.nlpl.eu)|129.240.189.225|:80... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 635351287 (606M) [application/zip]\n",
            "Saving to: ‘6.zip’\n",
            "\n",
            "6.zip               100%[===================>] 605.92M  97.2MB/s    in 7.0s    \n",
            "\n",
            "2020-11-23 08:13:36 (86.6 MB/s) - ‘6.zip’ saved [635351287/635351287]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_dLS1rFKNygP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "94d5c2a0-4c30-43ae-ec43-8505f75a5852"
      },
      "source": [
        "# !unzip 4.zip gigaword\n",
        "!unzip 6.zip -d wikipedia"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  6.zip\n",
            "  inflating: wikipedia/meta.json     \n",
            "  inflating: wikipedia/model.bin     \n",
            "  inflating: wikipedia/model.txt     \n",
            "  inflating: wikipedia/README        \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BhuniooCNq21"
      },
      "source": [
        "word2vec_weights = gensim.models.KeyedVectors.load_word2vec_format(\"wikipedia/model.txt\") # Load embeddings into Gensim class containing matrix"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4eJP3Zs-qWki"
      },
      "source": [
        "## Prepare dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nwglkqDn1l9t"
      },
      "source": [
        "def load_bbc_dataset(dataset_path):\n",
        "\n",
        "    data = []\n",
        "\n",
        "    for topic in ['business', 'entertainment', 'politics', 'sport', 'tech']:\n",
        "\n",
        "        topic_path = os.path.join(dataset_path, topic)\n",
        "        for filename in os.listdir(topic_path):\n",
        "            file_path = os.path.join(topic_path, filename)\n",
        "\n",
        "            try:\n",
        "                with open(file_path, 'r') as text_file:\n",
        "                    text = text_file.read()\n",
        "\n",
        "                data.append((text, topic)) # (X = article text, y = article topic)\n",
        "            except UnicodeDecodeError:\n",
        "                print(\"Failed to read:\", file_path,\"! Skipping\")\n",
        "    \n",
        "    return data"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GESykEdK2d72",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d8d6e0b4-ed9e-4b04-ca64-fd01a4e4633e"
      },
      "source": [
        "data = load_bbc_dataset('bbc')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Failed to read: bbc/sport/199.txt ! Skipping\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DRTftN_V3Ff0"
      },
      "source": [
        "texts, labels = zip(*data)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hMgh8Oin3bRt"
      },
      "source": [
        "train_texts, valid_texts, train_labels, valid_labels = train_test_split(texts, labels, stratify=labels)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IC4ZLi2RrmF3"
      },
      "source": [
        "## Feature Builder Classes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bx7eNx5em5CS"
      },
      "source": [
        "# We'll be using SkLearn's TfIdfVectorizer to construct our n-gram feature vectors\n",
        "\n",
        "# We'll create our custom sequencer class for converting a text into a sequence of integers corresponding to our tokens\n",
        "# Input : \"Hello, my name is Rishi\"\n",
        "# Output: [4, 3, 2 5, 6 , 7 ] # 4: Hello, 3: ',', 2: 'my', ... , 7: 'Rishi'\n",
        "# We want the indices to access the vectors for the given word\n",
        "# 4 - Index of vector corresponding to the word 'Hello'\n",
        "\n",
        "# Our word vectors:\n",
        "# 0 (say) [-0.016457 0.005666 0.021893 0.036763 -0.099810 0.020892 -0.023565 0.061527 -0.064225 -0.127303 -0.029338 0.054085 -0.028404 -0.021510 -0.099026 -0.030219 -0.113324 0.159025 -0.006395 -0.012752 -0.043114 0.055606 0.008410 -0.057141 0.150728 0.022703 0.059621 0.036312 0.081985 -0.036570 -0.057276 -0.105478 -0.004486 -0.064055 -0.125824 -0.076262 0.066562 -0.185299 -0.004718 -0.106012 0.078044 -0.049623 -0.019707 0.024215 0.070698 0.128529 -0.036913 -0.000408 -0.053999 -0.000760 0.011069 -0.037236 0.038052 0.047209 -0.000934 0.014226 -0.101589 -0.021408 0.037043 0.028972 -0.048394 -0.048568 0.023561 0.050198 0.002572 0.029351 -0.097906 0.068975 0.066806 -0.002132 0.041009 -0.013165 -0.001345 0.027810 -0.056385 -0.024247 -0.029757 0.031317 -0.020337 0.054462 0.089233 -0.126477 0.026837 -0.037890 0.143336 0.012796 -0.030611 0.113524 0.080372 -0.006593 -0.039996 -0.002388 0.020406 -0.005260 0.013018 -0.004855 0.000909 0.024569 0.032159 0.085467 0.002894 -0.002156 0.075847 -0.027901 0.162719 -0.017862 -0.020725 -0.013756 0.032742 -0.107903 -0.020145 0.104665 -0.048859 0.127686 0.065073 0.025002 0.036685 -0.015755 0.096920 0.061627 -0.018842 -0.055340 0.040091 0.063783 0.040945 0.132622 0.036789 -0.045669 0.042439 0.057564 0.002319 0.078522 -0.043101 0.024797 0.029955 -0.063213 -0.066685 -0.057995 0.044187 0.013773 0.049962 0.011659 -0.073489 0.085285 -0.002402 0.082264 0.110752 -0.091364 -0.082374 0.020575 -0.089784 -0.034675 -0.023478 -0.012981 -0.075382 -0.087736 0.101732 -0.049771 0.073409 0.105999 -0.008192 0.051214 0.005423 0.003315 0.051006 -0.072713 0.098589 -0.044787 0.030751 0.029813 -0.005148 0.005983 -0.043757 -0.030320 0.013599 0.009227 -0.028190 0.031872 -0.056918 0.047188 0.086135 0.053166 -0.081615 0.071527 0.085285 -0.034258 -0.095423 0.090446 0.049369 0.025583 0.022480 0.019886 0.095464 -0.025733 -0.054728 -0.028570 0.022430 -0.012296 0.039433 0.041846 -0.052139 -0.056371 0.079290 0.080066 0.027186 0.012930 -0.018256 -0.016459 0.060026 -0.042765 -0.020518 0.006542 -0.100084 -0.047055 0.098878 0.008997 0.043938 0.044421 0.013212 0.014310 0.008998 0.045343 -0.001792 0.043302 -0.082872 -0.063939 0.019913 0.047750 -0.058111 0.019319 -0.109353 -0.050987 -0.003592 -0.028676 -0.043118 0.039557 0.036327 -0.032287 0.021005 0.018498 -0.017329 0.009347 -0.004317 0.013634 -0.087900 -0.049935 0.007746 -0.038557 0.016165 -0.001408 -0.021590 -0.021483 0.041102 0.034813 0.024639 -0.014953 0.034395 0.042431 -0.034290 0.113096 0.028386 0.012610 0.113622 0.099868 -0.047553 -0.055141 0.081419 0.060897 0.033517 0.070338 0.040594 0.008233 0.002827 -0.048878 -0.006415 0.004285 -0.038513 0.050271 -0.026621 -0.035559 0.085345 -0.006597 0.023721 -0.046521 -0.052554 -0.081140 -0.101331 -0.006354 0.060702 0.041869 0.001872 -0.029082 -0.040995 -0.042205 -0.024152 -0.061239 0.037529 -0.052604 0.073380 0.021318]\n",
        "# 1 (year) [-0.020862 0.091081 0.030143 0.044928 -0.039632 -0.052762 0.086554 -0.033820 -0.039126 -0.049076 0.019706 -0.016391 0.039894 0.070685 -0.009264 0.013761 -0.014918 -0.005033 -0.026868 0.139029 -0.092375 0.072021 0.086156 0.060858 0.022265 0.070539 -0.000366 -0.077498 0.074328 0.018448 -0.133731 -0.070511 -0.007111 -0.090394 -0.002611 -0.077230 0.020074 0.065541 0.003582 -0.071871 0.076368 -0.007130 0.053619 0.000117 -0.002221 0.011619 -0.099297 -0.032122 -0.021738 -0.005857 0.035957 -0.015488 -0.054147 0.045498 -0.023322 -0.028272 -0.012185 -0.088205 0.069354 0.032746 0.047092 -0.083746 -0.030912 0.061607 0.102859 -0.011405 -0.007592 -0.013440 0.083539 0.028081 -0.004936 0.017838 0.070643 -0.039028 -0.017780 -0.090181 -0.076588 -0.001165 0.036132 -0.102343 0.106976 0.015121 0.054096 -0.043179 -0.042845 -0.013959 -0.044288 0.061936 -0.042800 0.049818 -0.005679 -0.040999 0.007476 0.041139 -0.020163 -0.072105 0.067300 -0.024149 0.047047 -0.012251 0.021856 -0.230840 -0.069708 -0.128597 0.046794 0.003120 -0.098918 0.038630 0.015836 0.041364 0.004654 0.030140 -0.026638 -0.040064 -0.004387 0.033325 -0.028964 0.063713 0.082938 0.047717 -0.028720 -0.000124 -0.058471 0.054698 -0.077716 0.014115 0.045998 -0.039880 0.037951 -0.022623 -0.073551 -0.029734 -0.006686 -0.046788 -0.047048 0.048809 0.065685 -0.094081 0.009960 0.040303 0.109173 -0.079072 -0.038558 0.043107 -0.046333 -0.051847 -0.055877 0.035927 -0.000557 -0.028910 -0.001796 0.009779 0.070724 -0.076734 -0.049698 -0.051177 -0.010030 -0.032417 0.112298 -0.048902 -0.110678 0.024402 -0.093728 0.013824 0.027871 -0.020682 -0.000717 0.087400 0.072489 -0.054522 -0.016094 -0.073715 -0.037509 0.001349 -0.107182 0.002886 -0.007559 -0.038211 0.006419 -0.116266 0.035283 -0.045140 0.036843 0.087636 -0.004077 0.071094 0.029225 0.058551 0.066372 0.092901 0.043376 -0.007894 0.005130 -0.070292 -0.052419 -0.028484 0.089438 -0.008769 -0.055218 0.019262 0.033993 0.008990 0.143733 -0.010378 0.078207 -0.031403 0.004595 -0.095942 -0.056159 0.050025 -0.027929 -0.024954 -0.114869 -0.073308 0.035274 -0.097693 -0.029095 -0.020764 -0.005055 0.009123 0.021029 -0.123420 0.029189 -0.003864 0.081216 0.044239 -0.149860 -0.049339 0.064162 0.108673 0.028682 -0.057684 0.019785 -0.004504 -0.115881 0.052063 0.119204 0.009445 -0.049341 -0.054128 0.065719 -0.006246 0.065020 0.005824 -0.094724 -0.091890 0.064122 -0.080926 0.031800 -0.029981 0.018315 -0.034446 0.021722 0.071480 -0.060150 -0.064157 0.015135 -0.054347 -0.010469 0.031822 0.011353 -0.066325 -0.103637 -0.053428 0.021063 -0.063239 -0.027001 0.004307 -0.055074 -0.036124 -0.058356 -0.004795 0.007763 -0.019251 0.068137 -0.048656 -0.087174 -0.046017 -0.067088 -0.023913 0.057596 0.070647 0.042291 0.029272 -0.065840 -0.045579 -0.084960 -0.014945 0.117556 -0.029472 0.023953 0.036586 0.027706 -0.081025 -0.004470 -0.034535 0.033065 -0.054836 -0.043534 -0.055042]\n",
        "# 314814 (wn) [-0.020862 0.091081 0.030143 0.044928 -0.039632 -0.052762 0.086554 -0.033820 -0.039126 -0.049076 0.019706 -0.016391 0.039894 0.070685 -0.009264 0.013761 -0.014918 -0.005033 -0.026868 0.139029 -0.092375 0.072021 0.086156 0.060858 0.022265 0.070539 -0.000366 -0.077498 0.074328 0.018448 -0.133731 -0.070511 -0.007111 -0.090394 -0.002611 -0.077230 0.020074 0.065541 0.003582 -0.071871 0.076368 -0.007130 0.053619 0.000117 -0.002221 0.011619 -0.099297 -0.032122 -0.021738 -0.005857 0.035957 -0.015488 -0.054147 0.045498 -0.023322 -0.028272 -0.012185 -0.088205 0.069354 0.032746 0.047092 -0.083746 -0.030912 0.061607 0.102859 -0.011405 -0.007592 -0.013440 0.083539 0.028081 -0.004936 0.017838 0.070643 -0.039028 -0.017780 -0.090181 -0.076588 -0.001165 0.036132 -0.102343 0.106976 0.015121 0.054096 -0.043179 -0.042845 -0.013959 -0.044288 0.061936 -0.042800 0.049818 -0.005679 -0.040999 0.007476 0.041139 -0.020163 -0.072105 0.067300 -0.024149 0.047047 -0.012251 0.021856 -0.230840 -0.069708 -0.128597 0.046794 0.003120 -0.098918 0.038630 0.015836 0.041364 0.004654 0.030140 -0.026638 -0.040064 -0.004387 0.033325 -0.028964 0.063713 0.082938 0.047717 -0.028720 -0.000124 -0.058471 0.054698 -0.077716 0.014115 0.045998 -0.039880 0.037951 -0.022623 -0.073551 -0.029734 -0.006686 -0.046788 -0.047048 0.048809 0.065685 -0.094081 0.009960 0.040303 0.109173 -0.079072 -0.038558 0.043107 -0.046333 -0.051847 -0.055877 0.035927 -0.000557 -0.028910 -0.001796 0.009779 0.070724 -0.076734 -0.049698 -0.051177 -0.010030 -0.032417 0.112298 -0.048902 -0.110678 0.024402 -0.093728 0.013824 0.027871 -0.020682 -0.000717 0.087400 0.072489 -0.054522 -0.016094 -0.073715 -0.037509 0.001349 -0.107182 0.002886 -0.007559 -0.038211 0.006419 -0.116266 0.035283 -0.045140 0.036843 0.087636 -0.004077 0.071094 0.029225 0.058551 0.066372 0.092901 0.043376 -0.007894 0.005130 -0.070292 -0.052419 -0.028484 0.089438 -0.008769 -0.055218 0.019262 0.033993 0.008990 0.143733 -0.010378 0.078207 -0.031403 0.004595 -0.095942 -0.056159 0.050025 -0.027929 -0.024954 -0.114869 -0.073308 0.035274 -0.097693 -0.029095 -0.020764 -0.005055 0.009123 0.021029 -0.123420 0.029189 -0.003864 0.081216 0.044239 -0.149860 -0.049339 0.064162 0.108673 0.028682 -0.057684 0.019785 -0.004504 -0.115881 0.052063 0.119204 0.009445 -0.049341 -0.054128 0.065719 -0.006246 0.065020 0.005824 -0.094724 -0.091890 0.064122 -0.080926 0.031800 -0.029981 0.018315 -0.034446 0.021722 0.071480 -0.060150 -0.064157 0.015135 -0.054347 -0.010469 0.031822 0.011353 -0.066325 -0.103637 -0.053428 0.021063 -0.063239 -0.027001 0.004307 -0.055074 -0.036124 -0.058356 -0.004795 0.007763 -0.019251 0.068137 -0.048656 -0.087174 -0.046017 -0.067088 -0.023913 0.057596 0.070647 0.042291 0.029272 -0.065840 -0.045579 -0.084960 -0.014945 0.117556 -0.029472 0.023953 0.036586 0.027706 -0.081025 -0.004470 -0.034535 0.033065 -0.054836 -0.043534 -0.055042]\n",
        "# VOCAB_SIZE x 300 Matrix\n",
        "\n",
        "class Sequencer(object):\n",
        "    def __init__(self, corpus, bos_token='<s>', eos_token='</s>', unk_token='<unk>', pad_token='<pad>'):\n",
        "        self.word2idx = {}\n",
        "        self.idx2word = {}\n",
        "\n",
        "        self.unk_index = self.add_token(unk_token) \n",
        "        self.pad_index = self.add_token(pad_token)\n",
        "        self.bos_index = self.add_token(bos_token)\n",
        "        self.eos_index = self.add_token(eos_token)\n",
        "        self.nlp = spacy.load('en')\n",
        "        self.tokenizer = lambda text: [t.text for t in self.nlp(text)]\n",
        "\n",
        "    def add_token(self, token):\n",
        "\n",
        "        self.word2idx[token] = new_index = len(self.word2idx)\n",
        "        self.idx2word[new_index] = token\n",
        "\n",
        "        return new_index\n",
        "\n",
        "    def encode(self, text):\n",
        "        # Input will look like:\n",
        "        # [<s>, w1, w2, ..., wn, </s>]\n",
        "        tokens = self.tokenizer(text)\n",
        "\n",
        "        sequence = [self.bos_index]\n",
        "        for token in tokens:\n",
        "\n",
        "            index = self.word2idx.get(token, self.unk_index)\n",
        "            sequence.append(index)\n",
        "        sequence.append(self.eos_index)\n",
        "\n",
        "        return sequence\n",
        "\n",
        "    def create_padded_tensor_with_lengths(self, sequences):\n",
        "        # Input: [[4, 2, 3], [5, 4, 2, 4, 6]]\n",
        "        # Output:\n",
        "        # Tensor\n",
        "        # 4 2 3 0 0\n",
        "        # 5 4 2 4 6\n",
        "\n",
        "        # Given a list of sequences, pad all to the same length\n",
        "        lengths = [len(sequence) for sequence in sequences]\n",
        "        max_seq_len = max(lengths)\n",
        "        tensor = torch.full((len(sequences), max_seq_len), self.pad_index, dtype=torch.long)\n",
        "\n",
        "        for i, sequence in enumerate(sequences):\n",
        "            for j, token in enumerate(sequence):\n",
        "                tensor[i][j] = token\n",
        "        \n",
        "        return tensor, lengths\n",
        "\n",
        "class W2VSequencer(object):\n",
        "    def __init__(self, gensim_w2v):\n",
        "        # Performs the same thing as the previous class\n",
        "        # Except it uses the vocabulary of words from the word embeddings we use\n",
        "\n",
        "        self.nlp = spacy.load('en')\n",
        "        self.w2v = gensim_w2v\n",
        "        self.w2v.add(['<unk>'], [np.random.uniform(low=-1, high=1.0, size=(300,))])\n",
        "\n",
        "        self.unk_index = self.w2v.vocab.get('<unk>')\n",
        "        self.tokenizer = lambda text: [t.text for t in self.nlp(text)]\n",
        "\n",
        "    def encode(self, text):\n",
        "        # Input will look like:\n",
        "        # [<s>, w1, w2, ..., wn, </s>]\n",
        "        sequence = []\n",
        "        tokens = self.tokenizer(text)\n",
        "        for token in tokens:\n",
        "\n",
        "            index = self.w2v.vocab.get(token, self.unk_index).index\n",
        "            sequence.append(index)\n",
        "\n",
        "        return sequence\n",
        "\n",
        "    def create_padded_tensor_with_lengths(self, sequences):\n",
        "        # Given a list of sequences, pad all to the same length\n",
        "\n",
        "        lengths = [len(sequence) for sequence in sequences]\n",
        "        max_seq_len = max(lengths)\n",
        "        tensor = torch.full((len(sequences), max_seq_len), 0, dtype=torch.long)\n",
        "\n",
        "        for i, sequence in enumerate(sequences):\n",
        "            for j, token in enumerate(sequence):\n",
        "                tensor[i][j] = token\n",
        "        \n",
        "        return tensor, lengths\n",
        "\n",
        "# For converting labels into indices 0 - L1, 1 - L2\n",
        "class LabelIndexer(object):\n",
        "    def __init__(self, labels):\n",
        "        self.label2idx = {label: i for i, label in enumerate(labels)}\n",
        "        self.idx2label = {i:label for label, i in self.label2idx.items()}\n",
        "        self.labels = labels\n",
        "\n",
        "    def encode(self, y):\n",
        "        return self.label2idx[y]\n",
        "\n",
        "    def encode_batch(self, ys):\n",
        "        return torch.LongTensor([self.encode(y) for y in ys])\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2C5iDbYurq3H"
      },
      "source": [
        "## Dataset class"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m9HfC-yw36E2"
      },
      "source": [
        "class BBCNewsDataset(Dataset):\n",
        "    # This class is an interface for our training/test dataset\n",
        "    # with pytorch\n",
        "\n",
        "    # It prepares our training/test examples into a form that pytorch expects\n",
        "    # Input: [\"Warner Bros gets $1m profit \", \"business\"]\n",
        "    # Output: [[TFIDF/Word Vector for text, 0]]\n",
        "    def __init__(self, texts, labels, input_transformer, output_transformer):\n",
        "        self.texts = texts\n",
        "        self.labels = labels\n",
        "        self.input_transformer = input_transformer\n",
        "        self.output_transformer = output_transformer\n",
        "\n",
        "    def __getitem__(self, index): # Return a single example\n",
        "    \n",
        "        text = self.texts[index]\n",
        "        label = self.labels[index]\n",
        "\n",
        "        x = self.input_transformer(text) # Produces TF-IDF/List of indices from text\n",
        "        y = self.output_transformer(label) # Produces index from the label text i.e. \"business\" to 0\n",
        "        \n",
        "        return x, y\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.texts)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_5I-d1gwr25y"
      },
      "source": [
        "## Model classes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dmG925DhRDtr"
      },
      "source": [
        "class SimpleLSTMClassifier(nn.Module):\n",
        "    \"\"\"\n",
        "    This model comprises a simple LSTM whose final hidden state is used\n",
        "    to output a prediction\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(self, output_size, n_layers=2, rnn_hidden_size=50, dropout_p=0.1, bidirectional=True, w2v_weights=None):\n",
        "        super(SimpleLSTMClassifier, self).__init__()\n",
        "\n",
        "        # Embedding: takes an integer index representing the word, and returns the corresponding vector for that word\n",
        "        self.embedding = nn.Embedding.from_pretrained(w2v_weights)\n",
        "        # self.embedding = nn.Embedding(vocab_size, embedding_dim) - nn.Embedding(314815, 300)\n",
        "        embed_dim = 300\n",
        "\n",
        "        n_directions = (2 if bidirectional else 1)\n",
        "\n",
        "        self.frozen_embedding = nn.Embedding.from_pretrained(w2v_weights)\n",
        "        self.frozen_embedding.requires_grad = False # Disable gradient updates, freezes the parameters from updates during training\n",
        "\n",
        "        # self.add_dropout = dropout\n",
        "        # self.dropout = nn.Dropout(dropout_p)\n",
        "\n",
        "        self.rnn = nn.LSTM(\n",
        "            input_size=2*embed_dim,\n",
        "            num_layers=n_layers,\n",
        "            bidirectional=bidirectional,\n",
        "            hidden_size=rnn_hidden_size, \n",
        "            bias=True, \n",
        "            dropout=dropout_p,\n",
        "            batch_first=True\n",
        "            )\n",
        "        \n",
        "        linear_input = n_layers * (2 if bidirectional else 1) * rnn_hidden_size\n",
        "\n",
        "        self.fc = nn.Linear(linear_input, output_size)\n",
        "\n",
        "\n",
        "\n",
        "    def forward(self, x, lengths):\n",
        "        # x size: [batch_size, seq_len]\n",
        "        # After embedding: [batch_size, seq_len, emb_dim = 300] # 16 * 100 * 300 \n",
        "        # After transposing it: [batch_size, emb_dim, seq_len]\n",
        "        batch_size = x.size(0)\n",
        "        embed = self.embedding(x)\n",
        "        embed_frozen = self.frozen_embedding(x)\n",
        "\n",
        "        # embed/embed_frozen shape: [batch_size, seq_len, embed_dim]\n",
        "\n",
        "        combined_embedding = torch.cat((embed, embed_frozen), dim=-1)\n",
        "        # combined_embedding shape: [batch_size, seq_len, 2*embed_dim]\n",
        "        packed_input = nn.utils.rnn.pack_padded_sequence(combined_embedding, lengths, batch_first=True, enforce_sorted=False)\n",
        "        # packed_input (PackedTensor)\n",
        "\n",
        "        output, (h_n, c_n) = self.rnn(packed_input)\n",
        "\n",
        "        # output: [batch_size, seq_len, hidden_size]\n",
        "        #  h_n: [batch_size, n_layers * n_directions, hidden_size]\n",
        "\n",
        "\n",
        "        # last_indices = torch.tensor(lengths, device=device) - 1\n",
        "\n",
        "        # h_squeezed = h_n.squeeze(0)\n",
        "        \n",
        "\n",
        "        # last_hiddens = output[torch.arange(batch_size), last_indices]\n",
        "        logits = self.fc(h_n.view(batch_size, -1))\n",
        "        # logits = self.fc(last_hiddens)\n",
        "\n",
        "        return logits"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EhS-a6qVr880"
      },
      "source": [
        "## Trainer Class"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YFGQwavETUM-"
      },
      "source": [
        "class MultiClassTrainer(object):\n",
        "    \"\"\"\n",
        "    Trainer for training a multi-class classification model\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(self, model, optimizer, loss_fn, device=\"cpu\", log_every_n=None):\n",
        "        self.model = model.to(device)\n",
        "        self.optimizer = optimizer\n",
        "        self.device = device\n",
        "        self.loss_fn = loss_fn\n",
        "        \n",
        "        self.log_every_n = log_every_n if log_every_n else 0\n",
        "\n",
        "\n",
        "    def _print_summary(self):\n",
        "        print(self.model)\n",
        "        print(self.optimizer)\n",
        "        print(self.loss_fn)\n",
        "\n",
        "    def train(self, loader):\n",
        "        \"\"\"\n",
        "        Run a single epoch of training\n",
        "        \"\"\"\n",
        "\n",
        "        self.model.train() # Run model in training mode\n",
        "\n",
        "        loss_history = []\n",
        "        running_loss = 0.\n",
        "        running_loss_history = []\n",
        "\n",
        "        for i, batch in tqdm(enumerate(loader)):\n",
        "            batch_size = batch[0].shape[0]\n",
        "            self.optimizer.zero_grad() # Always set gradient to 0 before computing it\n",
        "\n",
        "            logits = self.model(batch[0].to(self.device), batch[1]) # __call__ model() in this case: __call__ internally calls forward()\n",
        "            # [batch_size, num_classes]\n",
        "\n",
        "            loss = self.loss_fn(logits, batch[2].view(-1).to(self.device)) # Compute loss: Cross entropy loss\n",
        "\n",
        "            loss_history.append(loss.item())\n",
        "\n",
        "            \n",
        "\n",
        "            running_loss += (loss_history[-1] - running_loss) / (i + 1) # Compute rolling average\n",
        "\n",
        "            loss.backward() # Perform backprop, which will compute dL/dw\n",
        "\n",
        "            if self.log_every_n and i % self.log_every_n == 0:\n",
        "                print(\"Running loss: \", running_loss)\n",
        "\n",
        "                # print(\"Gradients:\")\n",
        "                # for p in list(filter(lambda p: p.grad is not None, self.model.parameters())):\n",
        "                #     print(p.grad.data.norm(2).item())\n",
        "\n",
        "            running_loss_history.append(running_loss)\n",
        "            \n",
        "            nn.utils.clip_grad_norm_(self.model.parameters(), 3.0) # We clip gradient's norm to 3\n",
        "\n",
        "            self.optimizer.step() # Update step: w = w - eta * dL / dW : eta = 1e-2 (0.01), gradient = 5e30; update value of 5e28\n",
        "\n",
        "        print(\"Epoch completed!\")\n",
        "        print(\"Epoch Loss: \", running_loss)\n",
        "        print(\"Epoch Perplexity: \", math.exp(running_loss))\n",
        "\n",
        "        # The history information can allow us to draw a loss plot\n",
        "        return loss_history, running_loss_history\n",
        "\n",
        "    def evaluate(self, loader, labels):\n",
        "        \"\"\"\n",
        "        Evaluate the model on a validation set\n",
        "        \"\"\"\n",
        "\n",
        "        self.model.eval() # Run model in eval mode (disables dropout layer)\n",
        "\n",
        "        batch_wise_true_labels = []\n",
        "        batch_wise_predictions = []\n",
        "\n",
        "        loss_history = []\n",
        "        running_loss = 0.\n",
        "        running_loss_history = []\n",
        "\n",
        "        with torch.no_grad(): # Disable gradient computation - required only during training\n",
        "            for i, batch in tqdm(enumerate(loader)):\n",
        "                # batch[0] shape: (batch_size, input_size)\n",
        "\n",
        "                logits = self.model(batch[0].to(self.device), batch[1]) # Run forward pass (except we don't store gradients)\n",
        "                # logits shape: (batch_size, num_classes)\n",
        "                \n",
        "                loss = self.loss_fn(logits, batch[2].view(-1).to(self.device)) # Compute loss\n",
        "                # No backprop is done during validation\n",
        "                \n",
        "                # Instead of using CrossEntropyLoss, you use BCEWithLogitsLoss\n",
        "                # BCEWithLogitsLoss - independently calculates loss for each class\n",
        "                \n",
        "\n",
        "                loss_history.append(loss.item())\n",
        "\n",
        "                running_loss += (loss_history[-1] - running_loss) / (i + 1) # Compute rolling average\n",
        "                \n",
        "                running_loss_history.append(running_loss)\n",
        "\n",
        "                # logits : [batch_size, num_classes] and each of the values in logits can be anything (-infinity, +infity)\n",
        "                # Converts the raw outputs into probabilities for each class using softmax\n",
        "                probs = F.softmax(logits, dim=-1) \n",
        "                # probs shape: (batch_size, num_classes)\n",
        "                # -1 dimension picks the last dimension in the shape of the tensor, in this case 'num_classes'\n",
        "                \n",
        "\n",
        "                # softmax vector: [[0.1, 0.2, 0.6, 0.1, 0.0], [0.9, 0.01, 0.01, 0.01, 0.07]]\n",
        "                # output tensor: [2, 0]\n",
        "                predictions = torch.argmax(probs, dim=-1) # Output predictions; Argmax picks the index with the highest probability among all the classes (choosing our most probable class)\n",
        "                # predictions shape: (batch_size)\n",
        "\n",
        "                batch_wise_true_labels.append(batch[2].tolist())\n",
        "                batch_wise_predictions.append(predictions.tolist())\n",
        "        \n",
        "        # flatten the list of predictions using itertools\n",
        "        all_true_labels = list(chain.from_iterable(batch_wise_true_labels))\n",
        "        all_predictions = list(chain.from_iterable(batch_wise_predictions))\n",
        "\n",
        "        # Now we can generate a classification report\n",
        "        print(\"Classification report after epoch:\")\n",
        "        print(classification_report(all_true_labels, all_predictions, target_names=labels))\n",
        "\n",
        "        return loss_history, running_loss_history\n",
        "\n",
        "    def get_model_dict(self):\n",
        "        return self.model.state_dict()\n",
        "\n",
        "    def run_training(self, train_loader, valid_loader, labels, n_epochs=10):\n",
        "        # Useful for us to review what experiment we're running\n",
        "        # Normally, you'd want to save this to a file\n",
        "        self._print_summary()\n",
        "\n",
        "        train_losses = []\n",
        "        train_running_losses = []\n",
        "\n",
        "        valid_losses = []\n",
        "        valid_running_losses = []\n",
        "\n",
        "        for i in range(n_epochs):\n",
        "            loss_history, running_loss_history = self.train(train_loader)\n",
        "            valid_loss_history, valid_running_loss_history = self.evaluate(valid_loader, labels)\n",
        "\n",
        "            train_losses.append(loss_history)\n",
        "            train_running_losses.append(running_loss_history)\n",
        "\n",
        "            valid_losses.append(valid_loss_history)\n",
        "            valid_running_losses.append(valid_running_loss_history)\n",
        "\n",
        "        # Training done, let's look at the loss curves\n",
        "        all_train_losses = list(chain.from_iterable(train_losses))\n",
        "        all_train_running_losses = list(chain.from_iterable(train_running_losses))\n",
        "\n",
        "        all_valid_losses = list(chain.from_iterable(valid_losses))\n",
        "        all_valid_running_losses = list(chain.from_iterable(valid_running_losses))\n",
        "\n",
        "        train_epoch_idx = range(len(all_train_losses))\n",
        "        valid_epoch_idx = range(len(all_valid_losses))\n",
        "        # sns.lineplot(epoch_idx, all_losses)\n",
        "        sns.lineplot(train_epoch_idx, all_train_running_losses)\n",
        "        sns.lineplot(valid_epoch_idx, all_valid_running_losses)\n",
        "        plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wfqkatI8subr"
      },
      "source": [
        "## Data Preparation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OlWWpjuLonQS"
      },
      "source": [
        "sequencer = W2VSequencer(word2vec_weights)\n",
        "sequence_input_transformer = lambda text: sequencer.encode(text)\n",
        "\n",
        "label_indexer = LabelIndexer(list(set(labels)))\n",
        "output_transformer = lambda label: torch.LongTensor([label_indexer.encode(label)])\n",
        "\n",
        "def prepare_batch(batch, sequencer):\n",
        "    # batch: [batch_len, (text, label)]\n",
        "    texts, labels = zip(*batch)\n",
        "    text_tensor, lengths = sequencer.create_padded_tensor_with_lengths(texts)\n",
        "    return (text_tensor, lengths, torch.stack(labels))\n",
        "\n",
        "train_sequence_dataset = BBCNewsDataset(train_texts, train_labels, sequence_input_transformer, output_transformer)\n",
        "valid_sequence_dataset = BBCNewsDataset(valid_texts, valid_labels, sequence_input_transformer, output_transformer)\n",
        "\n",
        "train_sequence_loader = torch.utils.data.DataLoader(train_sequence_dataset, batch_size=4, collate_fn=lambda batch: prepare_batch(batch, sequencer))\n",
        "valid_sequence_loader = torch.utils.data.DataLoader(valid_sequence_dataset, batch_size=4, collate_fn=lambda batch: prepare_batch(batch, sequencer))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hONukdH2sBtu"
      },
      "source": [
        "## Running Training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EiEAoKjriMPu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bf5c6ce4-db78-4438-e4f8-a67012120400"
      },
      "source": [
        "hidden_size = 64\n",
        "output_size = len(label_indexer.label2idx) #\n",
        "\n",
        "print(output_size)\n",
        "\n",
        "# Our experimental hyperparameters\n",
        "LEARNING_RATE = 1e-3\n",
        "\n",
        "loss_fn = nn.CrossEntropyLoss() # L = - sum (y_i log (y^i_hat))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eO6ct-1fuaB0"
      },
      "source": [
        "### LSTM Classifier"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J6DMaZlqubJ5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
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          ]
        },
        "outputId": "f37648af-d8ea-40f5-ccb6-b9a1626d2c3f"
      },
      "source": [
        "# Model 1: LSTM\n",
        "lstm_clf = SimpleLSTMClassifier(output_size, rnn_hidden_size=hidden_size, w2v_weights=torch.FloatTensor(word2vec_weights.vectors))\n",
        "optimizer = optim.Adam(lstm_clf.parameters(), lr=LEARNING_RATE, weight_decay=0.001)\n",
        "\n",
        "\n",
        "lstm_trainer = MultiClassTrainer(lstm_clf, optimizer, loss_fn, device=device, log_every_n=10)\n",
        "lstm_trainer.run_training(train_sequence_loader, valid_sequence_loader, label_indexer.labels, n_epochs=30)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "SimpleLSTMClassifier(\n",
            "  (embedding): Embedding(302867, 300)\n",
            "  (frozen_embedding): Embedding(302867, 300)\n",
            "  (rnn): LSTM(600, 64, num_layers=2, batch_first=True, dropout=0.1, bidirectional=True)\n",
            "  (fc): Linear(in_features=256, out_features=5, bias=True)\n",
            ")\n",
            "Adam (\n",
            "Parameter Group 0\n",
            "    amsgrad: False\n",
            "    betas: (0.9, 0.999)\n",
            "    eps: 1e-08\n",
            "    lr: 0.001\n",
            "    weight_decay: 0.001\n",
            ")\n",
            "CrossEntropyLoss()\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8977fc7321d84fd2b983175700922292",
              "version_minor": 0,
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            "Classification report after epoch:\n",
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            "\n",
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            "Classification report after epoch:\n",
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            "\n",
            "Epoch completed!\n",
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            "Classification report after epoch:\n",
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            "\n",
            "Epoch completed!\n",
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            "Epoch Perplexity:  3.006220648504221\n"
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            "\n",
            "Classification report after epoch:\n",
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            "\n",
            "entertainment       0.67      0.47      0.55        97\n",
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            "\n",
            "Epoch completed!\n",
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            "Classification report after epoch:\n",
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            "Epoch completed!\n",
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            "Classification report after epoch:\n",
            "               precision    recall  f1-score   support\n",
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            "Epoch completed!\n",
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            "Classification report after epoch:\n",
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            "\n",
            "Epoch completed!\n",
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            "Classification report after epoch:\n",
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            "\n",
            "Epoch completed!\n",
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            "Classification report after epoch:\n",
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            "\n",
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            "\n",
            "Epoch completed!\n",
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            "Classification report after epoch:\n",
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            "Classification report after epoch:\n",
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            "Classification report after epoch:\n",
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            "\n",
            "Epoch completed!\n",
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            "Classification report after epoch:\n",
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            "\n",
            "Epoch completed!\n",
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            "Classification report after epoch:\n",
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            "\n",
            "Epoch completed!\n",
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            "Classification report after epoch:\n",
            "               precision    recall  f1-score   support\n",
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            "Classification report after epoch:\n",
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            "Classification report after epoch:\n",
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            "Classification report after epoch:\n",
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            "\n",
            "Epoch completed!\n",
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            "Classification report after epoch:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
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            "\n",
            "Epoch completed!\n",
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            "\n",
            "Classification report after epoch:\n",
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            "\n",
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            "Classification report after epoch:\n",
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            "Epoch completed!\n",
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            "Epoch completed!\n",
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            "Classification report after epoch:\n",
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            "\n",
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            "Classification report after epoch:\n",
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            " weighted avg       0.86      0.86      0.86       556\n",
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            "\n",
            "Epoch completed!\n",
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            "Classification report after epoch:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "entertainment       0.92      0.91      0.91        97\n",
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            "\n",
            "Epoch completed!\n",
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            "\n",
            "Classification report after epoch:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "entertainment       0.72      0.90      0.80        97\n",
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            "\n",
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            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
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            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q1uEgyP4V6SL"
      },
      "source": [
        "## Saving the models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V6UHJnYkV55E"
      },
      "source": [
        "lstm_state_dict = lstm_trainer.get_model_dict()\n",
        "\n",
        "torch.save(lstm_state_dict, \"lstm_checkpoint.pt\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cLxFTvS9WXkl"
      },
      "source": [
        "## Restoring the models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NdkFPvpfWWzz"
      },
      "source": [
        "cnn_restored_state_dict = torch.load(\"cnn_checkpoint.pt\")\n",
        "\n",
        "# Configure the model the exact same way\n",
        "cnn_restored = TextConvolver(input_size, output_size, [3, 4, 5], channel_size=100, dropout_p=0.5, w2v_weights=torch.FloatTensor(word2vec_weights.vectors))\n",
        "\n",
        "# Restore state dict to load the same weights again\n",
        "cnn_restored.load_state_dict(cnn_restored_state_dict)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EKdUB3tm-5Rd"
      },
      "source": [
        "# Additional References\n",
        "\n",
        "* Chris Olah's great article on LSTMs: https://colah.github.io/posts/2015-08-Understanding-LSTMs/\n",
        "* Tips for Training Recurrent Neural Networks: https://danijar.com/tips-for-training-recurrent-neural-networks/\n",
        "* The Unreasonable Effectiveness of Recurrent Neural Networks: https://karpathy.github.io/2015/05/21/rnn-effectiveness/\n",
        "* Char-RNN (RNN Language Model on Characters): https://github.com/karpathy/char-rnn\n",
        "* Speed up your RNN using Sequence Bucketing: https://www.kaggle.com/bminixhofer/speed-up-your-rnn-with-sequence-bucketing\n",
        "\n",
        "## Additional(er) References (may be useful for you project)\n",
        "\n",
        "* Attention? Attention! by Lillian Weng: https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "138qSV9IAmej"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}