sentence_gru_lstm.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "sentence_gru_lstm.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "P_kCQZU6n29u",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import os\n",
        "import dill\n",
        "import pandas as pd\n",
        "import glob, csv\n",
        "import nltk\n",
        "from nltk.stem import WordNetLemmatizer \n",
        "nltk.download('wordnet')\n",
        "nltk.download('punkt')\n",
        "lemmatizer = WordNetLemmatizer()\n",
        "\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib.pyplot import figure\n",
        "import numpy as np\n",
        "import re\n",
        "import string\n",
        "import seaborn as sns\n",
        "from sklearn.preprocessing import StandardScaler"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_o9h-tZyn-NT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n2k4aGWan-J9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!unzip '/content/C50.zip'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3FjEsTVEoE5e",
        "colab_type": "text"
      },
      "source": [
        "Train preprocess"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ADMmGP-on-Hg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_file_df = pd.DataFrame()\n",
        "temp_df = pd.DataFrame()\n",
        "for i, filepath in enumerate(glob.iglob('/content/C50train/*/*.txt')):\n",
        "    print('Analysing', i , ' -> ', ' of Author ', str(filepath.split(\"/\")[3]))\n",
        "    temp_df = pd.read_csv(filepath, delimiter=\"\\t\", header = None, error_bad_lines=False, quoting = csv.QUOTE_NONE, encoding='utf-8')\n",
        "    temp_df['Author'] = filepath.split(\"/\")[3]\n",
        "    temp_frame = [train_file_df, temp_df]\n",
        "    train_file_df = pd.concat(temp_frame)\n",
        "    \n",
        "train_file_df = train_file_df.rename(columns={ 0 : 'text'})"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lzjm5W-zn9-1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_file_df.head()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oSnKJHg39f37",
        "colab_type": "text"
      },
      "source": [
        "Create new col with author number mappings for ML"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ezG_wSJpoPRg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# create new col with author number mappings for ML\n",
        "auth_sort = sorted(train_file_df['Author'].unique())\n",
        "dictOfAuthors = { i : auth_sort[i] for i in range(0, len(auth_sort) ) }\n",
        "swap_dict = {value:key for key, value in dictOfAuthors.items()}\n",
        "train_file_df['Author_num'] = train_file_df['Author'].map(swap_dict)\n",
        "train_file_df"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "98rpD2VuoRwA",
        "colab_type": "text"
      },
      "source": [
        "Test Preprocess"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cwoApETJoPOK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_file_df = pd.DataFrame()\n",
        "temp_df = pd.DataFrame()\n",
        "for i, filepath in enumerate(glob.iglob('/content/C50test/*/*.txt')):\n",
        "    print('Analysing', i , ' -> ', ' of Author ', str(filepath.split(\"/\")[3]))\n",
        "    temp_df = pd.read_csv(filepath, delimiter=\"\\t\", header = None, error_bad_lines=False, quoting = csv.QUOTE_NONE, encoding='utf-8')\n",
        "    temp_df['Author'] = filepath.split(\"/\")[3]\n",
        "    temp_frame = [test_file_df, temp_df]\n",
        "    test_file_df = pd.concat(temp_frame)\n",
        "    \n",
        "test_file_df = test_file_df.rename(columns={ 0 : 'text'})\n",
        "test_file_df.head(3)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IW97mS7ZoalI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_file_df['Author_num'] = test_file_df['Author'].map(swap_dict)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "veTN469kRB1p",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_file_df"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TQ90iFqdOGpu",
        "colab_type": "text"
      },
      "source": [
        "## Choosen"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4N2vp3FSGQUT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_file_df = test_file_df.drop(columns='Author')\n",
        "train_file_df = train_file_df.drop(columns='Author')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bCQzDoAannB0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_file_df.head()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lqaqTzGvJrM7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "list_to_choose_test = test_file_df.text.apply(lambda x : len(x)) > 0 \n",
        "test_file_df_choosen = test_file_df[list_to_choose_test]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s_96miMNI7u6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "list_to_choose_train = train_file_df.text.apply(lambda x : len(x)) > 0 \n",
        "train_file_df_choosen = train_file_df[list_to_choose_train]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RFm-MJLvJmfx",
        "colab_type": "text"
      },
      "source": [
        "csv save"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GeRJ3Y7ArIc4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_file_df_choosen.to_csv(r'/content/drive/My Drive/CIS 519/Project/train_choosen.csv', index = False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B22BFtYCr50R",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_file_df_choosen.to_csv(r'/content/drive/My Drive/CIS 519/Project/test_choosen.csv', index = False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z6ON2-QgqEZ_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch\n",
        "from torchtext import data\n",
        "TEXT = data.Field(sequential=True, tokenize=\"spacy\", lower=True, include_lengths=True)\n",
        "SCORE = data.Field(sequential=False, use_vocab=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u3XsWmBHyQoo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_file_df_choosen"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JV19MOqi9zOO",
        "colab_type": "text"
      },
      "source": [
        "**Datafields**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pF7Lnd4mo3PX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "datafields = [(\"text\", TEXT),\n",
        "              (\"Author_num\", SCORE)]\n",
        "\n",
        "train= data.TabularDataset(\n",
        "    path='/content/drive/My Drive/CIS 519/Project/train_choosen.csv', \n",
        "    format='csv',fields=datafields,skip_header = True)\n",
        "\n",
        "val = data.TabularDataset(\n",
        "    path='/content/drive/My Drive/CIS 519/Project/test_choosen.csv', \n",
        "    format='csv',fields=datafields,skip_header = True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L0AoTVSv6pQJ",
        "colab_type": "text"
      },
      "source": [
        "**GloVe Implementation**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WH7Iz--jsRkS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from torchtext import vocab\n",
        "from torchtext.vocab import GloVe\n",
        "TEXT.build_vocab(train, val, min_freq = 3, vectors=GloVe(name='6B', dim=100))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zmLn5GW66sDG",
        "colab_type": "text"
      },
      "source": [
        "##Iterators"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "03Jic_6d0Oj-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "device = torch.device('cuda:0')\n",
        "BATCH_SIZE = 64\n",
        "train_iterator = data.BucketIterator(\n",
        "    train, \n",
        "    batch_size = BATCH_SIZE,\n",
        "    sort_key = lambda x: len(x.text),\n",
        "    sort_within_batch = True,\n",
        "    repeat=False, \n",
        "    shuffle=True,\n",
        "    device = device)\n",
        "\n",
        "val_iterator = data.BucketIterator(\n",
        "    val, \n",
        "    batch_size = BATCH_SIZE,\n",
        "    sort=False,\n",
        "    sort_key = lambda x: len(x.text),\n",
        "    sort_within_batch = True,\n",
        "    repeat=False, \n",
        "    shuffle=False,\n",
        "    device = device)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lyiFXjb86kjq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import random\n",
        "import numpy as np\n",
        "import matplotlib\n",
        "import pandas as pd\n",
        "import dill\n",
        "import random\n",
        "import torch.optim as optim\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h0BMgXMZ7XFg",
        "colab_type": "text"
      },
      "source": [
        "**Network** Architecture"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HSYaBTAL7WPF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class AuthorClassifier(nn.Module):\n",
        "\n",
        "\n",
        "    def __init__(self, mode, output_size, hidden_size, vocab_size, embedding_length, word_embeddings):\n",
        "      super(AuthorClassifier, self).__init__()\n",
        "\n",
        "      if mode not in ['rnn', 'lstm', 'gru', 'bilstm']:\n",
        "        raise ValueError(\"Choose a mode from - rnn / lstm / gru / bilstm\")\n",
        "\n",
        "      self.mode = mode\n",
        "      self.output_size = output_size\n",
        "      self.hidden_size = hidden_size\n",
        "      self.vocab_size = vocab_size\n",
        "      self.embedding_length = embedding_length\n",
        "      self.embedding = nn.Embedding(self.vocab_size,self.embedding_length)\n",
        "      self.embedding.weight = nn.Parameter(word_embeddings,requires_grad = False)\n",
        "\n",
        " \n",
        "      if self.mode == 'rnn':\n",
        "        self.network = nn.RNN(self.embedding_length,self.hidden_size)\n",
        "      elif self.mode == 'lstm':\n",
        "        self.network = nn.LSTM(self.embedding_length,self.hidden_size)\n",
        "      elif self.mode == 'gru':\n",
        "        self.network = nn.GRU(self.embedding_length,self.hidden_size)\n",
        "      elif self.mode == 'bilstm':\n",
        "        self.network = nn.LSTM(self.embedding_length,self.hidden_size,bidirectional = True)\n",
        "      self.fclayer = nn.Linear(self.hidden_size,self.output_size)\n",
        "      \n",
        "\n",
        "    def forward(self, text, text_lengths):\n",
        "      text_embeddings = self.embedding(text)\n",
        "      pack_sequence = nn.utils.rnn.pack_padded_sequence(text_embeddings,text_lengths)\n",
        "\n",
        "      if self.mode in ('lstm','bilstm'):\n",
        "        a,(hidden,cell) = self.network(pack_sequence)\n",
        "        if self.mode == 'bilstm':\n",
        "          hidden = hidden[0,:,:]+ hidden[1,:,:]\n",
        "      else:\n",
        "        a,hidden = self.network(pack_sequence) \n",
        "      hidden = hidden.squeeze(0)\n",
        "      pred = self.fclayer(hidden)\n",
        "      return pred"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bm7vVUG6-MUU",
        "colab_type": "text"
      },
      "source": [
        "**Training and testing methods**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S-XyNBt67tDR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import sklearn\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import plot_confusion_matrix\n",
        "\n",
        "def train_classifier(model, dataset_iterator, loss_function, optimizer, num_epochs, log = \"runs\", verbose = False, recurrent = True):\n",
        "  writer = SummaryWriter(log_dir=log)\n",
        "  model.train()\n",
        "  step = 0\n",
        "  f1score_train = []\n",
        "  loss_train = []\n",
        "  accuracy_train = []\n",
        "\n",
        "\n",
        "  for epoch in range(num_epochs):\n",
        "    correct = 0\n",
        "    total = 0\n",
        "    total_loss = 0\n",
        "    f1 = 0\n",
        "    f1_step = 0\n",
        "    \n",
        "    \n",
        "    for batch in dataset_iterator:\n",
        "      comment, comment_lengths = batch.text\n",
        "      labels = batch.Author_num\n",
        "\n",
        "      batch_size = len(labels)\n",
        "      optimizer.zero_grad()\n",
        "      output = model(comment, comment_lengths).squeeze(0)\n",
        "\n",
        "      loss = loss_function(output, labels.long())\n",
        "      loss.backward() \n",
        "      nn.utils.clip_grad_norm(model.parameters(),0.5)\n",
        "      optimizer.step()\n",
        "\n",
        "      pred = torch.max(output.data,1).indices\n",
        "      f1 += sklearn.metrics.f1_score((labels.cpu()).numpy(), (pred.cpu()).numpy(),average= 'macro')\n",
        "      correct += (torch.sum(pred == labels)).item()\n",
        "      total += len(labels)\n",
        "      total_loss += loss.item()\n",
        "      loss_total.append(total_loss/total)\n",
        "      f1_step += 1\n",
        "\n",
        "      if ((step % 100) == 0):\n",
        "        writer.add_scalar(\"Loss/train\", total_loss/total, step)\n",
        "        writer.add_scalar(\"Acc/train\", correct/total, step)\n",
        "        writer.add_scalar(\"F1 Score/train\", f1/f1_step, step)\n",
        "        \n",
        "      step = step+1\n",
        "    f1score_train.append(f1/f1_step)\n",
        "    accuracy_train.append(correct/total)\n",
        "    loss_train.append(total_loss/total)\n",
        "\n",
        "    print('---Training---',\"Epoch: %s Acc: %s Loss: %s\"%(epoch+1, correct/total, total_loss/total),'F1 Score:',f1/f1_step,)\n",
        "\n",
        "  return loss_train,f1score_train,accuracy_train\n",
        "\n",
        "def evaluate_classifier(model, dataset_iterator, loss_function, recurrent = True):\n",
        "  model.eval()\n",
        "\n",
        "  correct = 0\n",
        "  total = 0\n",
        "  total_loss = 0\n",
        "  overall_pred = []\n",
        "  overall_label = []\n",
        "  accuracy_test = []\n",
        "  loss_test = []\n",
        "\n",
        "  f1_step = 0\n",
        "  f1 = 0\n",
        "\n",
        "  for batch in dataset_iterator:\n",
        "    comment, comment_lengths = batch.text\n",
        "    labels = batch.Author_num\n",
        "    output = model(comment, comment_lengths).squeeze(0)\n",
        "    loss = loss_function(output, labels.long())\n",
        "    pred = torch.max(output.data,1).indices \n",
        "    correct += (torch.sum(pred == labels)).item()\n",
        "    total += len(labels)\n",
        "    total_loss += loss.item()\n",
        "    ap = pred.cpu()\n",
        "    a = np.asarray(ap)\n",
        "    labels = labels.cpu()\n",
        "    b = np.asarray(labels)\n",
        "    f1_step += 1\n",
        "    overall_pred.append(a)\n",
        "    overall_label.append(b)\n",
        "\n",
        "  overall_p= [val for sublist in overall_pred for val in sublist]\n",
        "  overall_l = [val for sublist in overall_label for val in sublist]\n",
        "  f1ss = sklearn.metrics.f1_score(overall_l,overall_p,average= 'macro')\n",
        "  accuracy_test.append(correct/total)\n",
        "  loss_test.append(total_loss/total)\n",
        "  print(\"Validation statistics: Acc: %s Loss: %s\"%(correct/total, total_loss/total),'F1 Score:',f1ss)\n",
        "  return overall_pred,overall_label,accuracy_test,loss_test,f1ss"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jbU_TvFV-YXv",
        "colab_type": "text"
      },
      "source": [
        "**LSTM**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q4uw1SWX8OXB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from torch.utils.tensorboard import SummaryWriter\n",
        "\n",
        "output_size = 50\n",
        "hidden_size = 300\n",
        "vocab_size = len(TEXT.vocab)\n",
        "embedding_length = 100\n",
        "word_embeddings = TEXT.vocab.vectors\n",
        "num_epochs = 1\n",
        "mode = 'lstm'\n",
        "\n",
        "model = AuthorClassifier(mode, output_size, hidden_size, vocab_size, embedding_length, word_embeddings)\n",
        "model = model.to(device)\n",
        "\n",
        "\n",
        "loss_function = nn.CrossEntropyLoss()\n",
        "\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=1.25*1e-2)\n",
        "log_dir = 'runs/lstm1'\n",
        "final_acc_train_lstm = []\n",
        "final_loss_train_lstm = []\n",
        "final_loss_test_lstm = []\n",
        "final_acc_test_lstm = []\n",
        "final_f1score_train_lstm = []\n",
        "final_f1score_test_lstm = []\n",
        "\n",
        "\n",
        "for multi in range(20):\n",
        "  loss_train,f1score_train,accuracy_train = train_classifier(model, train_iterator, loss_function, optimizer, log = log_dir, num_epochs = num_epochs)\n",
        "  overall_pred,overall_label,accuracy_test,loss_test,f1ss = evaluate_classifier(model, val_iterator, loss_function)\n",
        "  final_acc_train_lstm.append(accuracy_train[0])\n",
        "  final_acc_test_lstm.append(accuracy_test[0])\n",
        "  final_f1score_train_lstm.append(f1score_train[0])\n",
        "  final_f1score_test_lstm.append(f1ss)\n",
        "  final_loss_train_lstm.append(loss_train[0])\n",
        "  final_loss_test_lstm.append(loss_test[0])\n",
        "\n",
        "\n",
        "cf = np.zeros((50,50))\n",
        "\n",
        "overall_pred = [val for sublist in overall_pred for val in sublist]\n",
        "\n",
        "overall_label = [val for sublist in overall_label for val in sublist]\n",
        "\n",
        "ziplist = list(zip(overall_label,overall_pred))\n",
        "for coordinate in ziplist:\n",
        "  cf[coordinate]+=1\n",
        "\n",
        "plt.figure(figsize = (20,20))\n",
        "ax = sns.heatmap(cf,annot=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y0Q3dPjn-hAA",
        "colab_type": "text"
      },
      "source": [
        "**GRU**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fJHQzjx49TOZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "from torch.utils.tensorboard import SummaryWriter\n",
        "\n",
        "\n",
        "output_size = 50\n",
        "hidden_size = 300\n",
        "vocab_size = len(TEXT.vocab)\n",
        "embedding_length = 100\n",
        "word_embeddings = TEXT.vocab.vectors\n",
        "num_epochs = 1\n",
        "mode = 'gru'\n",
        "\n",
        "model = AuthorClassifier(mode, output_size, hidden_size, vocab_size, embedding_length, word_embeddings)\n",
        "model = model.to(device)\n",
        "\n",
        "\n",
        "loss_function = nn.CrossEntropyLoss()\n",
        "\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=1.25*1e-2)\n",
        "log_dir = 'runs/gru'\n",
        "final_acc_train_gru  = []\n",
        "final_acc_test_gru = []\n",
        "final_f1score_train_gru = []\n",
        "final_f1score_test_gru = []\n",
        "final_loss_train_gru = []\n",
        "final_loss_test_gru = []\n",
        "\n",
        "for multi in range(20):\n",
        "  loss_train,f1score_train,accuracy_train = train_classifier(model, train_iterator, loss_function, optimizer, log = log_dir, num_epochs = num_epochs)\n",
        "  overall_pred,overall_label,accuracy_test,loss_test,f1ss = evaluate_classifier(model, val_iterator, loss_function)\n",
        "  final_acc_train_gru.append(accuracy_train[0])\n",
        "  final_loss_train_gru.append(loss_train[0])\n",
        "  final_acc_test_gru.append(accuracy_test[0])\n",
        "  final_f1score_train_gru.append(f1score_train[0])\n",
        "  final_f1score_test_gru.append(f1ss)\n",
        "  final_loss_test_gru.append(loss_test[0])\n",
        "\n",
        "cf = np.zeros((50,50))\n",
        "\n",
        "overall_pred = [val for sublist in overall_pred for val in sublist]\n",
        "\n",
        "overall_label = [val for sublist in overall_label for val in sublist]\n",
        "\n",
        "ziplist = list(zip(overall_label,overall_pred))\n",
        "for coordinate in ziplist:\n",
        "  cf[coordinate]+=1\n",
        "\n",
        "plt.figure(figsize = (20,20))\n",
        "ax = sns.heatmap(cf,annot=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BqLcOYwi-oOI",
        "colab_type": "text"
      },
      "source": [
        "**BiLSTM**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9uk6YkzqVvzx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "from torch.utils.tensorboard import SummaryWriter\n",
        "\n",
        "output_size = 50\n",
        "hidden_size = 300\n",
        "vocab_size = len(TEXT.vocab)\n",
        "embedding_length = 100\n",
        "word_embeddings = TEXT.vocab.vectors\n",
        "num_epochs = 1\n",
        "mode = 'bilstm'\n",
        "\n",
        "model = AuthorClassifier(mode, output_size, hidden_size, vocab_size, embedding_length, word_embeddings)\n",
        "model = model.to(device)\n",
        "\n",
        "\n",
        "loss_function = nn.CrossEntropyLoss()\n",
        "\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=1.25*1e-2)\n",
        "log_dir = 'runs/bilstm'\n",
        "final_acc_train_bilstm  = []\n",
        "final_acc_test_bilstm = []\n",
        "final_f1score_train_bilstm = []\n",
        "final_f1score_test_bilstm = []\n",
        "final_loss_train_bilstm = []\n",
        "final_loss_test_bilstm = []\n",
        "\n",
        "for multi in range(20):\n",
        "  loss_train,f1score_train,accuracy_train = train_classifier(model, train_iterator, loss_function, optimizer, log = log_dir, num_epochs = num_epochs)\n",
        "  overall_pred,overall_label,accuracy_test,loss_test,f1ss = evaluate_classifier(model, val_iterator, loss_function)\n",
        "  final_acc_train_bilstm.append(accuracy_train[0])\n",
        "  final_loss_train_bilstm.append(loss_train[0])\n",
        "  final_acc_test_bilstm.append(accuracy_test[0])\n",
        "  final_f1score_train_bilstm.append(f1score_train[0])\n",
        "  final_f1score_test_bilstm.append(f1ss)\n",
        "  final_loss_test_bilstm.append(loss_test[0])\n",
        "\n",
        "cf = np.zeros((50,50))\n",
        "\n",
        "overall_pred = [val for sublist in overall_pred for val in sublist]\n",
        "overall_label = [val for sublist in overall_label for val in sublist]\n",
        "\n",
        "ziplist = list(zip(overall_label,overall_pred))\n",
        "for coordinate in ziplist:\n",
        "  cf[coordinate]+=1\n",
        "\n",
        "plt.figure(figsize = (20,20))\n",
        "ax = sns.heatmap(cf,annot=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Erimf66a-vfx",
        "colab_type": "text"
      },
      "source": [
        "**Plot Accuracy**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RVJnK4LqwHx9",
        "colab_type": "code",
        "outputId": "10480dae-b8a7-46d4-9c34-4b7181acb077",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        }
      },
      "source": [
        "#Plot - accuracy\n",
        "import plotly.graph_objects as go\n",
        "fig_accuracy = go.Figure()\n",
        "\n",
        "fig_accuracy.add_trace(go.Scatter(\n",
        "    y=final_acc_train_lstm,\n",
        "    connectgaps=True, marker_color='rgba(128, 0, 0, 0.9)', name = 'Training accuracy lstm'))\n",
        "\n",
        "fig_accuracy.add_trace(go.Scatter(\n",
        "    y=final_acc_test_lstm,\n",
        "    connectgaps=True, marker_color='rgba(255, 0, 0, 0.9)', name = 'Testing accuracy lstm'))\n",
        "\n",
        "fig_accuracy.add_trace(go.Scatter(\n",
        "    y=final_acc_train_gru,\n",
        "    connectgaps=True, marker_color='rgba(0, 128, 0, 0.9)', name = 'Training accuracy gru'))\n",
        "fig_accuracy.add_trace(go.Scatter(\n",
        "    y=final_acc_test_gru,\n",
        "    connectgaps=True, marker_color = 'rgba(0, 255, 0, 0.9)', name = 'Testing accuracy gru'))\n",
        "\n",
        "fig_accuracy.add_trace(go.Scatter(\n",
        "    y=final_acc_train_bilstm,\n",
        "    connectgaps=True, marker_color='rgba(0, 0, 128, 0.9)', name = 'Training accuracy bilstm'))\n",
        "fig_accuracy.add_trace(go.Scatter(\n",
        "    y=final_acc_test_bilstm, connectgaps=True, marker_color='rgba(0, 0, 255, 0.9)',\n",
        "    name='Test accuracy bilstm'))\n",
        "\n",
        "fig_accuracy.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "                        {\"responsive\": true}\n",
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              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
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              "\n",
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              "</body>\n",
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          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xnJNtCEj-zz5",
        "colab_type": "text"
      },
      "source": [
        "**LOSS PLOTS**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "23WM5fJjHnZz",
        "colab_type": "code",
        "outputId": "2a796264-aaf5-489c-b5ec-9b2f35bb6ca4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        }
      },
      "source": [
        "#Loss Plots\n",
        "import plotly.graph_objects as go\n",
        "fig_loss = go.Figure()\n",
        "\n",
        "fig_loss.add_trace(go.Scatter(\n",
        "    y=final_loss_train_lstm,\n",
        "    connectgaps=True, marker_color='rgba(128, 0, 0, 0.9)', name = 'Training Loss lstm'))\n",
        "\n",
        "fig_loss.add_trace(go.Scatter(\n",
        "    y=final_loss_test_lstm,\n",
        "    connectgaps=True, marker_color='rgba(255, 0, 0, 0.9)', name = 'Testing Loss lstm'))\n",
        "\n",
        "fig_loss.add_trace(go.Scatter(\n",
        "    y=final_loss_train_gru,\n",
        "    connectgaps=True, marker_color='rgba(0, 128, 0, 0.9)', name = 'Training Loss gru'))\n",
        "\n",
        "fig_loss.add_trace(go.Scatter(\n",
        "    y=final_loss_test_gru,\n",
        "    connectgaps=True, marker_color='rgba(0, 255, 0, 0.9)', name = 'Testing Loss gru'))\n",
        "\n",
        "fig_loss.add_trace(go.Scatter(\n",
        "    y=final_loss_train_bilstm,\n",
        "    connectgaps=True, marker_color='rgba(0, 0, 128, 0.9)', name = 'Training Loss bilstm'))\n",
        "\n",
        "fig_loss.add_trace(go.Scatter(\n",
        "    y=final_loss_test_bilstm,\n",
        "    connectgaps=True, marker_color='rgba(0, 0, 255, 0.9)', name = 'Testing Loss bilstm'))\n",
        "\n",
        "fig_loss.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          },
          "metadata": {
            "tags": []
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y5QaxowP-28v",
        "colab_type": "text"
      },
      "source": [
        "**Plot - f1score**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cguxoZgIICrZ",
        "colab_type": "code",
        "outputId": "e4d10f1f-8e53-4532-d276-c6fad7db47c8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        }
      },
      "source": [
        "#Plot - f1score\n",
        "import plotly.graph_objects as go\n",
        "fig_f1score = go.Figure()\n",
        "\n",
        "fig_f1score.add_trace(go.Scatter(\n",
        "    y=final_f1score_train_lstm,\n",
        "    connectgaps=True, marker_color='rgba(128, 0, 0, 0.9)', name = 'Training f1score lstm'))\n",
        "\n",
        "fig_f1score.add_trace(go.Scatter(\n",
        "    y=final_f1score_test_lstm,\n",
        "    connectgaps=True, marker_color='rgba(255, 0, 0, 0.9)', name = 'Testing f1score lstm'))\n",
        "\n",
        "fig_f1score.add_trace(go.Scatter(\n",
        "    y=final_f1score_train_gru,\n",
        "    connectgaps=True, marker_color='rgba(0, 128, 0, 0.9)', name = 'Training f1score gru'))\n",
        "fig_f1score.add_trace(go.Scatter(\n",
        "    y=final_f1score_test_gru,\n",
        "    connectgaps=True, marker_color = 'rgba(0, 255, 0, 0.9)', name = 'Testing f1score gru'))\n",
        "\n",
        "fig_f1score.add_trace(go.Scatter(\n",
        "    y=final_f1score_train_bilstm,\n",
        "    connectgaps=True, marker_color='rgba(0, 0, 128, 0.9)', name = 'Training f1score bilstm'))\n",
        "fig_f1score.add_trace(go.Scatter(\n",
        "    y=final_f1score_test_bilstm, connectgaps=True, marker_color='rgba(0, 0, 255, 0.9)',\n",
        "    name='Test f1score bilstm'))\n",
        "\n",
        "fig_f1score.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
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              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
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              "\n",
              "                        })\n",
              "                };\n",
              "                \n",
              "            </script>\n",
              "        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}