how_to_clstm_classification_malay_10fold.ipynb

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  "metadata": {
    "colab": {
      "name": "Copy of clstm_classification_malay_10fold.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "ny02hYzdvSpv",
        "outputId": "9251ee15-eca4-47e6-d31c-5260bb4179fc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "# !pip uninstall tensorflow\n",
        "# !pip install tensorflow==2.1.0\n",
        "# !pip install --upgrade gensim --user\n",
        "# # tf.compat.v1.disable_v2_behavior()\n",
        "# !pip install -q pyyaml h5py  # Required to save models in HDF5 format\n",
        "# !pip install np_utils\n",
        "# !pip install --upgrade --user keras\n",
        "# import nltk\n",
        "# nltk.download('punkt')\n",
        "import tensorflow as tf\n",
        "print(tf.__version__)\n",
        "tf.test.gpu_device_name()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.1.0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/device:GPU:0'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YMVyz3b-QEwr",
        "outputId": "2fd31310-eaab-4b92-a8e2-893ea13fbad1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount(\"/content/gdrive\")"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GHd8fAfjr3P5"
      },
      "source": [
        "import pandas as pd\n",
        "import json\n",
        "import os\n",
        "import glob\n",
        "import pickle\n",
        "#from os import path\n",
        "# import malaya"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xw6oEX0hRIOw",
        "outputId": "76d61629-ae25-4127-baba-126fb91db06a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        }
      },
      "source": [
        "df_allnews_unpickled = pd.read_pickle(\"./malaya_fake_news_preprocessed_dataframe.pkl\")        #Load FakeNews Dataset from your path\n",
        "df_allnews_unpickled"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>news</th>\n",
              "      <th>tokens</th>\n",
              "      <th>rejoined</th>\n",
              "      <th>length</th>\n",
              "      <th>label</th>\n",
              "      <th>real</th>\n",
              "      <th>fake</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>demi lovato telah jujur kejam tentang pertempu...</td>\n",
              "      <td>[demi, lovato, telah, jujur, kejam, tentang, p...</td>\n",
              "      <td>demi lovato telah jujur kejam tentang pertempu...</td>\n",
              "      <td>448</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>selamat pagi inilah yang perlu anda ketahui se...</td>\n",
              "      <td>[selamat, pagi, inilah, yang, perlu, anda, ket...</td>\n",
              "      <td>selamat pagi inilah yang perlu anda ketahui se...</td>\n",
              "      <td>682</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>tahniah untuk bintang glee lea michele pelakon...</td>\n",
              "      <td>[tahniah, untuk, bintang, glee, lea, michele, ...</td>\n",
              "      <td>tahniah untuk bintang glee lea michele pelakon...</td>\n",
              "      <td>247</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ia adalah satu kejutan yang mengejutkan untuk ...</td>\n",
              "      <td>[ia, adalah, satu, kejutan, yang, mengejutkan,...</td>\n",
              "      <td>ia adalah satu kejutan yang mengejutkan untuk ...</td>\n",
              "      <td>718</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>transkrip minggu ini dengan george stephanopou...</td>\n",
              "      <td>[transkrip, minggu, ini, dengan, george, steph...</td>\n",
              "      <td>transkrip minggu ini dengan george stephanopou...</td>\n",
              "      <td>668</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37587</th>\n",
              "      <td>katie holmes tidak mengandung anak jamie foxx ...</td>\n",
              "      <td>[katie, holmes, tidak, mengandung, anak, jamie...</td>\n",
              "      <td>katie holmes tidak mengandung anak jamie foxx ...</td>\n",
              "      <td>336</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37588</th>\n",
              "      <td>donald trump menegaskan bahawa serangan siber ...</td>\n",
              "      <td>[donald, trump, menegaskan, bahawa, serangan, ...</td>\n",
              "      <td>donald trump menegaskan bahawa serangan siber ...</td>\n",
              "      <td>248</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37589</th>\n",
              "      <td>ia bukan cerita pertama yang bermula dengan pe...</td>\n",
              "      <td>[ia, bukan, cerita, pertama, yang, bermula, de...</td>\n",
              "      <td>ia bukan cerita pertama yang bermula dengan pe...</td>\n",
              "      <td>663</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37590</th>\n",
              "      <td>presiden donald trump mengumumkan bahawa dia t...</td>\n",
              "      <td>[presiden, donald, trump, mengumumkan, bahawa,...</td>\n",
              "      <td>presiden donald trump mengumumkan bahawa dia t...</td>\n",
              "      <td>100</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37591</th>\n",
              "      <td>jika hillary clinton dipilih sebagai presiden ...</td>\n",
              "      <td>[jika, hillary, clinton, dipilih, sebagai, pre...</td>\n",
              "      <td>jika hillary clinton dipilih sebagai presiden ...</td>\n",
              "      <td>688</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "<p>37592 rows × 7 columns</p>\n",
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            "text/plain": [
              "                                                    news  ... fake\n",
              "0      demi lovato telah jujur kejam tentang pertempu...  ...    0\n",
              "1      selamat pagi inilah yang perlu anda ketahui se...  ...    0\n",
              "2      tahniah untuk bintang glee lea michele pelakon...  ...    0\n",
              "3      ia adalah satu kejutan yang mengejutkan untuk ...  ...    0\n",
              "4      transkrip minggu ini dengan george stephanopou...  ...    0\n",
              "...                                                  ...  ...  ...\n",
              "37587  katie holmes tidak mengandung anak jamie foxx ...  ...    1\n",
              "37588  donald trump menegaskan bahawa serangan siber ...  ...    0\n",
              "37589  ia bukan cerita pertama yang bermula dengan pe...  ...    0\n",
              "37590  presiden donald trump mengumumkan bahawa dia t...  ...    0\n",
              "37591  jika hillary clinton dipilih sebagai presiden ...  ...    1\n",
              "\n",
              "[37592 rows x 7 columns]"
            ]
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          "metadata": {
            "tags": []
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          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lx1TuO9GcMeH"
      },
      "source": [
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\", category=DeprecationWarning) \n",
        "\n",
        "import tensorflow as tf\n",
        "from __future__ import division, print_function\n",
        "import gensim\n",
        "from gensim import models\n",
        "from tensorflow.keras.callbacks import ModelCheckpoint\n",
        "from tensorflow.keras.layers import Dense, Dropout, Reshape, Flatten, concatenate, Input, Conv1D, GlobalMaxPooling1D, Embedding, Bidirectional, LSTM\n",
        "from tensorflow.keras.regularizers import l2\n",
        "from tensorflow.keras.regularizers import L1L2\n",
        "from tensorflow.keras.initializers import RandomNormal\n",
        "import tensorflow.keras.backend as K\n",
        "# from keras.layers.recurrent import LSTM\n",
        "# from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "from tensorflow.keras.models import Model\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.model_selection import KFold\n",
        "import numpy as np\n",
        "\n",
        "# demonstration of calculating metrics for a neural network model using sklearn\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import precision_score\n",
        "from sklearn.metrics import average_precision_score\n",
        "from sklearn.metrics import recall_score\n",
        "from sklearn.metrics import precision_recall_curve\n",
        "from sklearn.metrics import f1_score\n",
        "from sklearn.metrics import confusion_matrix"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vy2li9HNry5N"
      },
      "source": [
        "Build vocabulary for all the words inside the dataframe"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4aWDcBP3YJ_U",
        "outputId": "67ec4cca-87c4-459a-a153-ec5d8e98028b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        }
      },
      "source": [
        "all_words = [word for tokens in df_allnews_unpickled[\"tokens\"] for word in tokens]\n",
        "all_sentence_lengths = [len(tokens) for tokens in df_allnews_unpickled[\"tokens\"]]\n",
        "ave_sentence_lengths = sum(all_sentence_lengths)/len(all_sentence_lengths)\n",
        "ALL_VOCAB = sorted(list(set(all_words)))\n",
        "\n",
        "print(\"%s words total, with a vocabulary size of %s\" % (len(all_words), len(ALL_VOCAB)))\n",
        "print(\"Max sentence length is %s\" % max(all_sentence_lengths))\n",
        "print(\"Average sentence length is %s\" % ave_sentence_lengths)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "16195904 words total, with a vocabulary size of 186872\n",
            "Max sentence length is 991\n",
            "Average sentence length is 430.8337944243456\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AZ_2wHrDr6nV"
      },
      "source": [
        "Load Word2Vec Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k7orZb4jw9NF"
      },
      "source": [
        "from gensim.models.callbacks import CallbackAny2Vec\n",
        "\n",
        "loss_list = list()\n",
        "\n",
        "# # This function is only redefined because the Malay Word2Vec is trained with this function\n",
        "# # For some reason, gensim could not Word2Vec without this function\n",
        "# # Not used in this Notebook\n",
        "class LossLogger(CallbackAny2Vec):\n",
        "    '''Callback to print loss after each epoch.'''\n",
        "\n",
        "    def __init__(self):\n",
        "        self.epoch = 0\n",
        "        self.loss_to_be_subbed = 0\n",
        "\n",
        "    def on_epoch_end(self, model):\n",
        "        loss = model.get_latest_training_loss()\n",
        "        loss_now = loss - self.loss_to_be_subbed\n",
        "        loss_list.append(loss_now)\n",
        "        self.loss_to_be_subbed = loss\n",
        "        print('Loss after epoch {}: {}'.format(self.epoch, loss_now))\n",
        "        self.epoch += 1"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rZ32wAfYoZMd",
        "outputId": "7ac38c0e-3227-43d8-c897-1b1949b64815",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        }
      },
      "source": [
        "word2vec_path = './mswiki.model'\n",
        "# word2vec = models.KeyedVectors.load_word2vec_format(word2vec_path, binary=True)\n",
        "# word2vec = gensim.models.KeyedVectors.load(word2vec_path)\n",
        "word2vec = gensim.models.Word2Vec.load(word2vec_path)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/smart_open/smart_open_lib.py:252: UserWarning: This function is deprecated, use smart_open.open instead. See the migration notes for details: https://github.com/RaRe-Technologies/smart_open/blob/master/README.rst#migrating-to-the-new-open-function\n",
            "  'See the migration notes for details: %s' % _MIGRATION_NOTES_URL\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nRf5GwszQPlE"
      },
      "source": [
        "Convert list (of words) into sequences (of int) based on the vocabulary list.\n",
        "<br> Add reserved indices at the beginning of the converted sequence."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3TFcinToxnRC",
        "outputId": "63a2b88c-090d-4358-919e-b608cbc16272",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "tokenizer = Tokenizer(num_words=len(ALL_VOCAB), lower=True, char_level=False, oov_token='<UNK>')\n",
        "tokenizer.fit_on_texts(df_allnews_unpickled[\"tokens\"].tolist())\n",
        "sequences_all = tokenizer.texts_to_sequences(df_allnews_unpickled[\"tokens\"].tolist())\n",
        "\n",
        "for i in range(len(sequences_all)):\n",
        "  for j in range(len(sequences_all[i])):\n",
        "    sequences_all[i][j] += 2         # # The first 4 [0,1,2,3] indices are reserved\n",
        "sequences_all = [[1] + sub for sub in sequences_all]    #at <START> tag at the beginning of all subitems\n",
        "\n",
        "all_word_index = tokenizer.word_index\n",
        "print('Found %s unique tokens.' % len(all_word_index))\n",
        "\n",
        "\n",
        "# # The first 4 [0,1,2,3] indices are reserved\n",
        "# all_word_index = {k:(v+2) for k,v in all_word_index.items()}\n",
        "for key, value in all_word_index.items():\n",
        "  all_word_index[key] += 2\n",
        "all_word_index[\"<PAD>\"] = 0\n",
        "all_word_index[\"<START>\"] = 1\n",
        "all_word_index[\"<UNUSED>\"] = 2\n",
        "all_word_index[\"<UNK>\"] = 3  # unknown"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 186873 unique tokens.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nvN5eXW8Wk5Q",
        "outputId": "6a7f0aff-2236-4009-9bfe-05c000de3d7e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "all_word_index = tokenizer.word_index\n",
        "print('Found %s unique tokens.' % len(all_word_index))"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 186876 unique tokens.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WsP7P6iuQyHX"
      },
      "source": [
        "Define function(s) to decode sequences back to text"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DjIlIOaOQxGs"
      },
      "source": [
        "reverse_word_index = dict([(value, key) for (key, value) in all_word_index.items()])\n",
        "def decode_news(text):\n",
        "    return ' '.join([reverse_word_index.get(i, '?') for i in text])"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3VzRBgKqUsAM",
        "outputId": "9b76b10f-003e-422e-ae4f-4130b71fc6ff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "# MAX_SEQUENCE_LENGTH = 50\n",
        "# MAX_SEQUENCE_LENGTH = 128\n",
        "MAX_SEQUENCE_LENGTH = max(all_sentence_lengths)\n",
        "EMBEDDING_DIM = 300\n",
        "print (MAX_SEQUENCE_LENGTH)\n",
        "print (EMBEDDING_DIM)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "991\n",
            "300\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "volPAhlliQp1"
      },
      "source": [
        "Pad the sequence by MAX_SEQUENCE_LENGTH"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6UH6fn8FVb9h"
      },
      "source": [
        "# train_clstm_data = pad_sequences(training_sequences, maxlen=MAX_SEQUENCE_LENGTH)\n",
        "all_data = pad_sequences(sequences_all, value=all_word_index[\"<PAD>\"], padding='post', truncating='post', maxlen=MAX_SEQUENCE_LENGTH)"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZnLU6PSJjDq1"
      },
      "source": [
        "Initialize Embedding Layer weights based on Word2Vec values.\n",
        "<br> The embedding will be initialized randomly for words that does not exist in Word2Vec model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_lK-4yF_awkF",
        "outputId": "c177ede7-e5a3-4458-c835-0f5bcc77fcaf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "count_vpre = 0\n",
        "\n",
        "embedding_weights = np.zeros((len(all_word_index), EMBEDDING_DIM))\n",
        "for word,index in all_word_index.items():\n",
        "  # embedding_weights[index,:] = word2vec[word] if word in word2vec else np.random.rand(EMBEDDING_DIM)\n",
        "  if word in word2vec:\n",
        "    embedding_weights[index,:] = word2vec[word]\n",
        "    count_vpre += 1\n",
        "  else:\n",
        "    # embedding_weights[index,:] = np.random.RandomState(3435).rand(EMBEDDING_DIM)\n",
        "    embedding_weights[index,:] = (0.5*np.random.RandomState(3435).rand(EMBEDDING_DIM)) - 0.25     #Initialize unknown vector between [-0.25, 0.25]\n",
        "\n",
        "\n",
        "print(embedding_weights.shape)\n",
        "print(\"Number of words present in the set of pre-trained word vectors %s\" % count_vpre)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(186876, 300)\n",
            "Number of words present in the set of pre-trained word vectors 60562\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NAUyOMwakcae"
      },
      "source": [
        "idx = 8\n",
        "\n",
        "for key, value in all_word_index.items():\n",
        "  if value == idx:\n",
        "    print(key, value)\n",
        "\n",
        "print(embedding_weights[idx])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bsuDvKk6ooAx"
      },
      "source": [
        "label_names = ['real', 'fake']\n",
        "labels_list = df_allnews_unpickled[label_names].values\n",
        "# print (labels_list[42])\n",
        "# print (sequences_all[42])\n",
        "# # Zip back the news and labels into a temporary list\n",
        "all_newsnlabels = list(zip(all_data, labels_list))\n",
        "# print (all_newsnlabels[42][0])\n",
        "# print (all_newsnlabels[42][1])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yo3uTcIjKuCV"
      },
      "source": [
        "l2_rate = 1e-3\n",
        "dropout_rate = 0.5\n",
        "n_filter = 300\n",
        "filter_size = 3\n",
        "lr_rate = 5e-4\n",
        "# print (str(lr_rate))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CBUPN9CJT9-H"
      },
      "source": [
        "Define C-LSTM Architecture"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QSmaNykpSG6G"
      },
      "source": [
        "def CLSTM(embeddings, max_sequence_length, num_words, embedding_dim, labels_index):\n",
        "    \n",
        "    embedding_layer = Embedding(num_words,\n",
        "                            embedding_dim,\n",
        "                            weights=[embeddings],\n",
        "                            input_length=max_sequence_length,\n",
        "                            trainable=False)\n",
        "    \n",
        "    sequence_input = Input(shape=(max_sequence_length,), dtype='int32')\n",
        "    embedded_sequences = embedding_layer(sequence_input)\n",
        "    \n",
        "    # TODO start    \n",
        "    conv_output = Conv1D(filters=n_filter, kernel_size=filter_size, strides=1, activation=\"relu\")(embedded_sequences)\n",
        "\n",
        "    # x1 = Dense(100, activation='relu')(conv_output)\n",
        "\n",
        "    # l_clstm = Bidirectional(LSTM(embedding_dim))(embedded_sequences)\n",
        "    lstm_output = LSTM(embedding_dim, go_backwards=True, dropout=dropout_rate)(conv_output)\n",
        "\n",
        "    # x3 = Dropout(0.5)(lstm_output)  \n",
        "    # x2 = Dense(embedding_dim, activation='relu')(x1)    \n",
        "    # x3 = Dropout(0.2)(x2)\n",
        "    # preds = Dense(labels_index, activation='sigmoid')(x3)\n",
        "    # preds = Dense(labels_index, activation='softmax')(x3)\n",
        "    preds = Dense(labels_index, activity_regularizer=l2(l2_rate), activation=\"softmax\")(lstm_output)    #, activity_regularizer=l2(l2_rate)\n",
        "    # TODO end\n",
        "    \n",
        "\n",
        "    model = Model(sequence_input, preds)\n",
        "    \n",
        "    \n",
        "    model.compile(loss='binary_crossentropy',\n",
        "                  optimizer='adam',         \n",
        "                  metrics=['accuracy'])           #normal use:  optimizer=tf.keras.optimizers.RMSprop(lr=lr_rate), \n",
        "    model.summary()\n",
        "    return model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AokXV_O313UT",
        "outputId": "d84dbea5-4700-4cbb-8be6-38d48e3148db",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        }
      },
      "source": [
        "model = CLSTM(embedding_weights, MAX_SEQUENCE_LENGTH, len(all_word_index), \n",
        "                EMBEDDING_DIM, len(list(label_names)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         [(None, 994)]             0         \n",
            "_________________________________________________________________\n",
            "embedding (Embedding)        (None, 994, 300)          57945300  \n",
            "_________________________________________________________________\n",
            "conv1d (Conv1D)              (None, 992, 300)          270300    \n",
            "_________________________________________________________________\n",
            "lstm (LSTM)                  (None, 300)               721200    \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 2)                 602       \n",
            "=================================================================\n",
            "Total params: 58,937,402\n",
            "Trainable params: 992,102\n",
            "Non-trainable params: 57,945,300\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wc35nr9dxk4J"
      },
      "source": [
        "model.save_weights('./clstm/clstm_model_10fold.h5')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WyWNU5_Ou8ga"
      },
      "source": [
        "model.load_weights('./clstm/clstm_model_10fold.h5')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-kI0jPjMvkwz"
      },
      "source": [
        "weights = []\n",
        "for layer in model.layers:\n",
        "    weights.append(layer.get_weights()) # list of numpy arrays\n",
        "\n",
        "print ((weights[2][0]))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jFEdktxoSD23"
      },
      "source": [
        "K-fold Cross Validation stars here"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LD3nbtcKh6qb"
      },
      "source": [
        "num_folds = 10\n",
        "num_epochs = 3    \n",
        "batch_size = 50\n",
        "fold_no = 1\n",
        "kfold = KFold(n_splits=num_folds, shuffle=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lcPpQlkgykjn",
        "outputId": "7e3b8e62-ee70-486b-82f7-196ddbcc19e7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        }
      },
      "source": [
        "# data_train, data_test = train_test_split(all_newsnlabels, test_size=0.10, random_state=42)\n",
        "inputs = all_data\n",
        "targets = labels_list\n",
        "print (len(inputs))\n",
        "print (len(targets))\n",
        "\n",
        "arr_id = np.asarray(df_allnews_unpickled.index)\n",
        "print (len(arr_id))\n",
        "\n",
        "# x_train = np.asarray([row[0] for row in data_train])\n",
        "# y_train = np.asarray([row[1] for row in data_train])\n",
        "# x_test = np.asarray([row[0] for row in data_test])\n",
        "# y_test = np.asarray([row[1] for row in data_test])\n",
        "# print (x_train[42])\n",
        "# print (y_train[42])\n",
        "# print (inputs[39505])\n",
        "# print (targets[39505])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "39486\n",
            "39486\n",
            "39486\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oWPZf3T0Y1qT",
        "outputId": "87ce783f-bbda-4abd-f47b-f371d516695b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "print (arr_id[19525])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "19525\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qBCrlwUudN-S",
        "outputId": "2e4424d0-5bea-40c2-cff3-bb0bbfe6cacb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "fold_no = 1\n",
        "results_history = []\n",
        "\n",
        "acc_per_fold = []\n",
        "loss_per_fold = []\n",
        "\n",
        "# # Lists for metrics result\n",
        "all_accuracy = []\n",
        "all_precision = []\n",
        "all_recall = []\n",
        "all_f1 = []\n",
        "\n",
        "# # Lists for plotting Precision-Recall Curve\n",
        "all_arr_precision = []\n",
        "all_arr_recall = []\n",
        "all_thresholds = []\n",
        "all_ave_precision = []\n",
        "\n",
        "# # Lists for Confusion Matrices\n",
        "all_cf_matrices = []\n",
        "\n",
        "# # Lists for Noise Data\n",
        "all_wrong = []\n",
        "\n",
        "for train, test in kfold.split(inputs, targets):\n",
        "  model.load_weights('./clstm/clstm_model_10fold.h5')     #Load the same initial weights for all splits\n",
        "  x_train = np.asarray(inputs[train])\n",
        "  y_train = np.asarray(targets[train])\n",
        "  x_test = np.asarray(inputs[test])\n",
        "  y_test = np.asarray(targets[test])\n",
        "  \n",
        "  temp_arr_id = np.asarray(arr_id[test])\n",
        "\n",
        "  # # Generate a print\n",
        "  print('------------------------------------------------------------------------')\n",
        "  print(f'Training for fold {fold_no} ...')\n",
        "  \n",
        "  checkpoint_path = \"./clstm/clstm_training_10fold/cp_10fold_{}.ckpt\".format(fold_no)\n",
        "  # Create a callback that saves the model's weights\n",
        "  cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_path,\n",
        "                                                 save_weights_only=True,\n",
        "                                                 verbose=1)\n",
        "  \n",
        "  hist = model.fit(x_train, y_train, epochs=num_epochs, validation_data=(x_test,y_test),     #validation_split = 0.1,\n",
        "                 shuffle=True, batch_size=batch_size, callbacks=None)    #callbacks=[cp_callback]\n",
        "  \n",
        "  # results_history.append(hist)\n",
        "  results_history.append(hist.history)\n",
        "\n",
        "  #EVALUATE MODEL ON TEST SET\n",
        "  results_scores = model.evaluate(x_test, y_test)\n",
        "  print(f'Score for fold {fold_no}: {model.metrics_names[0]} of {results_scores[0]}; {model.metrics_names[1]} of {results_scores[1]*100}%')\n",
        "  acc_per_fold.append(results_scores[1] * 100)\n",
        "  loss_per_fold.append(results_scores[0])\n",
        "\n",
        "\n",
        "  # METRICS CALCULATION STARTS HERE\n",
        "  # predict probabilities for test set\n",
        "  yhat_probs = model.predict(x_test, verbose=0)\n",
        "  # y_probs = np.max(yhat_probs, axis=1)  #Predicted Probabilities\n",
        "  y_probs = yhat_probs[:, 0] #Predicted Probabilities     #Consider only positive-predicted class for Precision Recall Curve\n",
        "  \n",
        "  # predict crisp classes for test set\n",
        "  ## supposed use argmax but index of labels are arranged as [real, fake] = [0, 1]\n",
        "  ## argmin is used to invert and get [1, 0] instead\n",
        "  yhat_classes = yhat_probs.argmin(axis=-1)   #Predicted Classes\n",
        "  y_test_conv = y_test.argmin(axis=-1)        #Actual Classes\n",
        "\n",
        "  for m in range(len(yhat_classes)):\n",
        "    if (yhat_classes[m] != y_test_conv[m]):\n",
        "      all_wrong.append(temp_arr_id[m])\n",
        "    \n",
        "  ## Metrics calculation\n",
        "  # accuracy: (tp + tn) / (p + n)\n",
        "  accuracy = accuracy_score(y_test_conv, yhat_classes)\n",
        "  all_accuracy.append(accuracy)\n",
        "  # precision tp / (tp + fp)\n",
        "  precision = precision_score(y_test_conv, yhat_classes)\n",
        "  all_precision.append(precision)\n",
        "  # recall: tp / (tp + fn)\n",
        "  recall = recall_score(y_test_conv, yhat_classes)\n",
        "  all_recall.append(recall)\n",
        "  # f1: 2 tp / (2 tp + fp + fn)\n",
        "  f1 = f1_score(y_test_conv, yhat_classes)\n",
        "  all_f1.append(f1)\n",
        "\n",
        "  ## Precision-Curve calculation\n",
        "  arr_precision, arr_recall, thresholds = precision_recall_curve(y_test_conv, y_probs)\n",
        "  thresholds = np.concatenate([thresholds,[thresholds[thresholds.size-1]]])\n",
        "  all_arr_precision.append(arr_precision)\n",
        "  all_arr_recall.append(arr_recall)\n",
        "  all_thresholds.append(thresholds)\n",
        "\n",
        "  ave_precision = average_precision_score(y_test_conv, y_probs)\n",
        "  all_ave_precision.append(ave_precision)\n",
        "\n",
        "  ## Confusion Matrix calculation\n",
        "  cf_matrix = confusion_matrix(y_test_conv, yhat_classes)\n",
        "  all_cf_matrices.append(cf_matrix)\n",
        "\n",
        "  # Increase fold number\n",
        "  fold_no = fold_no + 1\n",
        "####################################\n",
        "\n",
        "# == Provide average scores ==\n",
        "print('------------------------------------------------------------------------')\n",
        "print('Score per fold')\n",
        "for i in range(0, len(acc_per_fold)):\n",
        "  print('------------------------------------------------------------------------')\n",
        "  print(f'> Fold {i+1} - Loss: {loss_per_fold[i]} - Accuracy: {acc_per_fold[i]}%')\n",
        "print('------------------------------------------------------------------------')\n",
        "print('Average scores for all folds:')\n",
        "print(f'> Accuracy: {np.mean(acc_per_fold)} (+- {np.std(acc_per_fold)})')\n",
        "print(f'> Loss: {np.mean(loss_per_fold)}')\n",
        "print('------------------------------------------------------------------------')\n",
        "\n",
        "\n",
        "\n",
        "# # Save history object as a file\n",
        "with open('./clstm/CLSTMTrainHistoryDict_10fold', 'wb') as file_pi:\n",
        "    pickle.dump(results_history, file_pi)\n",
        "    # pickle.dump(hist.history, file_pi)\n",
        "# hist = pickle.load(open('./clstm/CLSTMTrainHistoryDict_10fold', \"rb\"))\n",
        "\n",
        "# # Create Dataframe to save all Metrics Result\n",
        "df_allmetrics_10fold = pd.DataFrame(acc_per_fold)\n",
        "df_allmetrics_10fold.columns = ['acc_per_fold']\n",
        "df_allmetrics_10fold['loss_per_fold'] = loss_per_fold\n",
        "\n",
        "df_allmetrics_10fold['all_accuracy'] = all_accuracy\n",
        "df_allmetrics_10fold['all_precision'] = all_precision\n",
        "df_allmetrics_10fold['all_recall'] = all_recall\n",
        "df_allmetrics_10fold['all_f1'] = all_f1\n",
        "\n",
        "df_allmetrics_10fold['all_arr_precision'] = all_arr_precision\n",
        "df_allmetrics_10fold['all_arr_recall'] = all_arr_recall\n",
        "df_allmetrics_10fold['all_thresholds'] = all_thresholds\n",
        "df_allmetrics_10fold['all_ave_precision'] = all_ave_precision\n",
        "\n",
        "df_allmetrics_10fold['all_cf_matrices'] = all_cf_matrices\n",
        "\n",
        "df_allmetrics_10fold.to_pickle(\"./clstm/clstm_all_metrics_10fold.pkl\")\n",
        "# df_allmetrics_10fold\n",
        "\n",
        "# # Create Dataframe to save all wrongly predicted index\n",
        "df_indices = pd.DataFrame(all_wrong)\n",
        "df_indices.columns = ['indices']\n",
        "df_indices.to_pickle(\"./clstm/clstm_indices_wrong.pkl\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "------------------------------------------------------------------------\n",
            "Training for fold 1 ...\n",
            "Train on 35537 samples, validate on 3949 samples\n",
            "Epoch 1/3\n",
            "35537/35537 [==============================] - 115s 3ms/sample - loss: 0.4552 - accuracy: 0.7898 - val_loss: 0.3436 - val_accuracy: 0.8562\n",
            "Epoch 2/3\n",
            "35537/35537 [==============================] - 106s 3ms/sample - loss: 0.3318 - accuracy: 0.8618 - val_loss: 0.3246 - val_accuracy: 0.8668\n",
            "Epoch 3/3\n",
            "35537/35537 [==============================] - 106s 3ms/sample - loss: 0.2917 - accuracy: 0.8809 - val_loss: 0.2896 - val_accuracy: 0.8838\n",
            "3949/3949 [==============================] - 6s 1ms/sample - loss: 0.2896 - accuracy: 0.8838\n",
            "Score for fold 1: loss of 0.28962204106685996; accuracy of 88.37680220603943%\n",
            "------------------------------------------------------------------------\n",
            "Training for fold 2 ...\n",
            "Train on 35537 samples, validate on 3949 samples\n",
            "Epoch 1/3\n",
            "35537/35537 [==============================] - 107s 3ms/sample - loss: 0.4632 - accuracy: 0.7853 - val_loss: 0.3608 - val_accuracy: 0.8481\n",
            "Epoch 2/3\n",
            "35537/35537 [==============================] - 107s 3ms/sample - loss: 0.3398 - accuracy: 0.8562 - val_loss: 0.3087 - val_accuracy: 0.8752\n",
            "Epoch 3/3\n",
            "35537/35537 [==============================] - 107s 3ms/sample - loss: 0.2972 - accuracy: 0.8766 - val_loss: 0.3010 - val_accuracy: 0.8769\n",
            "3949/3949 [==============================] - 6s 1ms/sample - loss: 0.3010 - accuracy: 0.8769\n",
            "Score for fold 2: loss of 0.3009656130829833; accuracy of 87.69308924674988%\n",
            "------------------------------------------------------------------------\n",
            "Training for fold 3 ...\n",
            "Train on 35537 samples, validate on 3949 samples\n",
            "Epoch 1/3\n",
            "35537/35537 [==============================] - 105s 3ms/sample - loss: 0.4682 - accuracy: 0.7834 - val_loss: 0.4048 - val_accuracy: 0.8182\n",
            "Epoch 2/3\n",
            "35537/35537 [==============================] - 105s 3ms/sample - loss: 0.3366 - accuracy: 0.8599 - val_loss: 0.3428 - val_accuracy: 0.8554\n",
            "Epoch 3/3\n",
            "35537/35537 [==============================] - 105s 3ms/sample - loss: 0.2914 - accuracy: 0.8800 - val_loss: 0.3157 - val_accuracy: 0.8686\n",
            "3949/3949 [==============================] - 6s 1ms/sample - loss: 0.3157 - accuracy: 0.8686\n",
            "Score for fold 3: loss of 0.31568973189397653; accuracy of 86.8574321269989%\n",
            "------------------------------------------------------------------------\n",
            "Training for fold 4 ...\n",
            "Train on 35537 samples, validate on 3949 samples\n",
            "Epoch 1/3\n",
            "35537/35537 [==============================] - 107s 3ms/sample - loss: 0.4609 - accuracy: 0.7895 - val_loss: 0.3538 - val_accuracy: 0.8534\n",
            "Epoch 2/3\n",
            "35537/35537 [==============================] - 107s 3ms/sample - loss: 0.3386 - accuracy: 0.8587 - val_loss: 0.3042 - val_accuracy: 0.8782\n",
            "Epoch 3/3\n",
            "35537/35537 [==============================] - 107s 3ms/sample - loss: 0.2934 - accuracy: 0.8797 - val_loss: 0.2933 - val_accuracy: 0.8830\n",
            "3949/3949 [==============================] - 6s 2ms/sample - loss: 0.2933 - accuracy: 0.8830\n",
            "Score for fold 4: loss of 0.2933490758879211; accuracy of 88.3008360862732%\n",
            "------------------------------------------------------------------------\n",
            "Training for fold 5 ...\n",
            "Train on 35537 samples, validate on 3949 samples\n",
            "Epoch 1/3\n",
            "35537/35537 [==============================] - 105s 3ms/sample - loss: 0.4641 - accuracy: 0.7864 - val_loss: 0.3753 - val_accuracy: 0.8351\n",
            "Epoch 2/3\n",
            "35537/35537 [==============================] - 106s 3ms/sample - loss: 0.3350 - accuracy: 0.8602 - val_loss: 0.3183 - val_accuracy: 0.8615\n",
            "Epoch 3/3\n",
            "35537/35537 [==============================] - 106s 3ms/sample - loss: 0.2987 - accuracy: 0.8771 - val_loss: 0.3087 - val_accuracy: 0.8752\n",
            "3949/3949 [==============================] - 6s 1ms/sample - loss: 0.3087 - accuracy: 0.8752\n",
            "Score for fold 5: loss of 0.3087164387729507; accuracy of 87.51582503318787%\n",
            "------------------------------------------------------------------------\n",
            "Training for fold 6 ...\n",
            "Train on 35537 samples, validate on 3949 samples\n",
            "Epoch 1/3\n",
            "35537/35537 [==============================] - 107s 3ms/sample - loss: 0.4562 - accuracy: 0.7887 - val_loss: 0.3287 - val_accuracy: 0.8658\n",
            "Epoch 2/3\n",
            "35537/35537 [==============================] - 107s 3ms/sample - loss: 0.3340 - accuracy: 0.8618 - val_loss: 0.3073 - val_accuracy: 0.8797\n",
            "Epoch 3/3\n",
            "35537/35537 [==============================] - 107s 3ms/sample - loss: 0.2969 - accuracy: 0.8783 - val_loss: 0.2895 - val_accuracy: 0.8860\n",
            "3949/3949 [==============================] - 6s 2ms/sample - loss: 0.2895 - accuracy: 0.8860\n",
            "Score for fold 6: loss of 0.28952095932101984; accuracy of 88.60471248626709%\n",
            "------------------------------------------------------------------------\n",
            "Training for fold 7 ...\n",
            "Train on 35538 samples, validate on 3948 samples\n",
            "Epoch 1/3\n",
            "35538/35538 [==============================] - 105s 3ms/sample - loss: 0.4642 - accuracy: 0.7849 - val_loss: 0.3946 - val_accuracy: 0.8285\n",
            "Epoch 2/3\n",
            "35538/35538 [==============================] - 105s 3ms/sample - loss: 0.3396 - accuracy: 0.8571 - val_loss: 0.3201 - val_accuracy: 0.8706\n",
            "Epoch 3/3\n",
            "35538/35538 [==============================] - 106s 3ms/sample - loss: 0.2971 - accuracy: 0.8782 - val_loss: 0.3059 - val_accuracy: 0.8761\n",
            "3948/3948 [==============================] - 6s 2ms/sample - loss: 0.3059 - accuracy: 0.8761\n",
            "Score for fold 7: loss of 0.3058798502915536; accuracy of 87.61398196220398%\n",
            "------------------------------------------------------------------------\n",
            "Training for fold 8 ...\n",
            "Train on 35538 samples, validate on 3948 samples\n",
            "Epoch 1/3\n",
            "35538/35538 [==============================] - 107s 3ms/sample - loss: 0.4647 - accuracy: 0.7832 - val_loss: 0.3952 - val_accuracy: 0.8202\n",
            "Epoch 2/3\n",
            "35538/35538 [==============================] - 107s 3ms/sample - loss: 0.3374 - accuracy: 0.8584 - val_loss: 0.3084 - val_accuracy: 0.8673\n",
            "Epoch 3/3\n",
            "35538/35538 [==============================] - 107s 3ms/sample - loss: 0.2927 - accuracy: 0.8800 - val_loss: 0.2930 - val_accuracy: 0.8804\n",
            "3948/3948 [==============================] - 6s 2ms/sample - loss: 0.2930 - accuracy: 0.8804\n",
            "Score for fold 8: loss of 0.29297327207214563; accuracy of 88.04457783699036%\n",
            "------------------------------------------------------------------------\n",
            "Training for fold 9 ...\n",
            "Train on 35538 samples, validate on 3948 samples\n",
            "Epoch 1/3\n",
            "35538/35538 [==============================] - 105s 3ms/sample - loss: 0.4598 - accuracy: 0.7867 - val_loss: 0.3639 - val_accuracy: 0.8473\n",
            "Epoch 2/3\n",
            "35538/35538 [==============================] - 106s 3ms/sample - loss: 0.3336 - accuracy: 0.8606 - val_loss: 0.3101 - val_accuracy: 0.8663\n",
            "Epoch 3/3\n",
            "35538/35538 [==============================] - 106s 3ms/sample - loss: 0.2968 - accuracy: 0.8773 - val_loss: 0.3145 - val_accuracy: 0.8685\n",
            "3948/3948 [==============================] - 6s 1ms/sample - loss: 0.3145 - accuracy: 0.8685\n",
            "Score for fold 9: loss of 0.3145086666854078; accuracy of 86.85410618782043%\n",
            "------------------------------------------------------------------------\n",
            "Training for fold 10 ...\n",
            "Train on 35538 samples, validate on 3948 samples\n",
            "Epoch 1/3\n",
            "35538/35538 [==============================] - 107s 3ms/sample - loss: 0.4498 - accuracy: 0.7946 - val_loss: 0.3441 - val_accuracy: 0.8592\n",
            "Epoch 2/3\n",
            "35538/35538 [==============================] - 107s 3ms/sample - loss: 0.3339 - accuracy: 0.8618 - val_loss: 0.3116 - val_accuracy: 0.8713\n",
            "Epoch 3/3\n",
            "35538/35538 [==============================] - 107s 3ms/sample - loss: 0.2965 - accuracy: 0.8782 - val_loss: 0.3145 - val_accuracy: 0.8721\n",
            "3948/3948 [==============================] - 6s 2ms/sample - loss: 0.3145 - accuracy: 0.8721\n",
            "Score for fold 10: loss of 0.31451769694006554; accuracy of 87.20871210098267%\n",
            "------------------------------------------------------------------------\n",
            "Score per fold\n",
            "------------------------------------------------------------------------\n",
            "> Fold 1 - Loss: 0.28962204106685996 - Accuracy: 88.37680220603943%\n",
            "------------------------------------------------------------------------\n",
            "> Fold 2 - Loss: 0.3009656130829833 - Accuracy: 87.69308924674988%\n",
            "------------------------------------------------------------------------\n",
            "> Fold 3 - Loss: 0.31568973189397653 - Accuracy: 86.8574321269989%\n",
            "------------------------------------------------------------------------\n",
            "> Fold 4 - Loss: 0.2933490758879211 - Accuracy: 88.3008360862732%\n",
            "------------------------------------------------------------------------\n",
            "> Fold 5 - Loss: 0.3087164387729507 - Accuracy: 87.51582503318787%\n",
            "------------------------------------------------------------------------\n",
            "> Fold 6 - Loss: 0.28952095932101984 - Accuracy: 88.60471248626709%\n",
            "------------------------------------------------------------------------\n",
            "> Fold 7 - Loss: 0.3058798502915536 - Accuracy: 87.61398196220398%\n",
            "------------------------------------------------------------------------\n",
            "> Fold 8 - Loss: 0.29297327207214563 - Accuracy: 88.04457783699036%\n",
            "------------------------------------------------------------------------\n",
            "> Fold 9 - Loss: 0.3145086666854078 - Accuracy: 86.85410618782043%\n",
            "------------------------------------------------------------------------\n",
            "> Fold 10 - Loss: 0.31451769694006554 - Accuracy: 87.20871210098267%\n",
            "------------------------------------------------------------------------\n",
            "Average scores for all folds:\n",
            "> Accuracy: 87.70700752735138 (+- 0.5885576254182597)\n",
            "> Loss: 0.30257433460148836\n",
            "------------------------------------------------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0R2ySYs2DKqp"
      },
      "source": [
        "#TODO: Determine history for each fold\n",
        "\n",
        "# history_dict = hist.history\n",
        "# history_dict = results_history[0]\n",
        "# # history_dict = hist\n",
        "# history_dict.keys()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DvbznpxfRIRl"
      },
      "source": [
        "Plot Loss and Accuracy for each Fold between Training and Validation for each epoch.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tyOe8kQrRHq9",
        "outputId": "f0b24640-8c21-41dd-99e9-3e140e418eea",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "acc = []\n",
        "val_acc = []\n",
        "loss = []\n",
        "val_loss = []\n",
        "\n",
        "for i in range(len(results_history)):\n",
        "  # history_dict = results_history[i].history\n",
        "  history_dict = results_history[i]\n",
        "  \n",
        "  # print(history_dict.keys())\n",
        "\n",
        "  acc.append(history_dict['accuracy'])\n",
        "  val_acc.append(history_dict['val_accuracy'])\n",
        "  loss.append(history_dict['loss'])\n",
        "  val_loss.append(history_dict['val_loss'])\n",
        "  \n",
        "\n",
        "\n",
        "# # Loss Plot\n",
        "epochs = range(1, len(acc[0]) + 1)\n",
        "fig_row = -(-(len(acc)) // 2)\n",
        "fig_col = 2\n",
        "plt.rcParams['figure.figsize'] = [12, 18]\n",
        "for i in range(len(loss)):\n",
        "  plt.subplot(fig_row, fig_col, i+1)\n",
        "  plt.plot(epochs, loss[i], 'r', label='Training loss')\n",
        "  plt.plot(epochs, val_loss[i], 'r--', label='Validation loss')\n",
        "  plt.title('C-LSTM Training and Validation Loss, Fold {}'.format(i+1))\n",
        "  plt.xlabel('Epochs')\n",
        "  plt.ylabel('Loss')\n",
        "  plt.legend()\n",
        "  plt.grid(True)\n",
        "  # plt.grid(which='minor', linestyle=':', linewidth='0.5', color='black')\n",
        "  # plt.minorticks_on()\n",
        "plt.tight_layout(pad=3.0)\n",
        "plt.savefig(\"./clstm/clstm_loss_10fold.png\", format='png', dpi=150, bbox_inches='tight')\n",
        "plt.show()\n",
        "\n",
        "\n",
        "# # Accuracy Plot\n",
        "plt.clf()   # clear figure\n",
        "for i in range(len(acc)):\n",
        "  plt.subplot(fig_row, fig_col, i+1)\n",
        "  plt.plot(epochs, acc[i], 'b', label='Training acc')\n",
        "  plt.plot(epochs, val_acc[i], 'b--', label='Validation acc')\n",
        "  plt.title('C-LSTM Training and Validation Accuracy, Fold {}'.format(i+1))\n",
        "  plt.xlabel('Epochs')\n",
        "  plt.ylabel('Accuracy')\n",
        "  plt.legend()\n",
        "  plt.grid(True)\n",
        "plt.tight_layout(pad=3.0)\n",
        "plt.savefig(\"./clstm/clstm_accuracy_10fold.png\", format='png', dpi=150, bbox_inches='tight')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x1296 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x1296 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OxY3Er8-ackQ"
      },
      "source": [
        "Calculate Model Metrics\n",
        "<br> Precision, Recall, Accuracy, F1-Score"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hy2iGSSZxyiK",
        "outputId": "efe59ed3-0e41-4689-b785-7c87e8e3cb92",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "idx_best = 0\n",
        "best_acc = 0\n",
        "\n",
        "# == Provide average scores ==\n",
        "print('------------------------------------------------------------------------')\n",
        "print('Metrics Score per fold')\n",
        "for i in range(0, len(all_accuracy)):\n",
        "  print('------------------------------------------------------------------------')\n",
        "  print(f'> Fold {i+1}')\n",
        "  print('Accuracy: %f' % all_accuracy[i])\n",
        "  print('Precision: %f' % all_precision[i])\n",
        "  print('Recall: %f' % all_recall[i])\n",
        "  print('F1 score: %f' % all_f1[i])\n",
        "  if (all_accuracy[i] >= best_acc):\n",
        "    idx_best = i\n",
        "    best_acc = all_accuracy[i]\n",
        "print('------------------------------------------------------------------------')\n",
        "print('------------------------------------------------------------------------')\n",
        "print('Average scores for all folds:')\n",
        "print(f'> Average Accuracy: {np.mean(np.asarray(all_accuracy))}')\n",
        "print(f'> Average Precision: {np.mean(np.asarray(all_precision))}')\n",
        "print(f'> Average Recall: {np.mean(np.asarray(all_recall))}')\n",
        "print(f'> Average F1 Score: {np.mean(np.asarray(all_f1))}')\n",
        "print('------------------------------------------------------------------------')\n",
        "print('------------------------------------------------------------------------')\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "------------------------------------------------------------------------\n",
            "Metrics Score per fold\n",
            "------------------------------------------------------------------------\n",
            "> Fold 1\n",
            "Accuracy: 0.879555\n",
            "Precision: 0.865841\n",
            "Recall: 0.961856\n",
            "F1 score: 0.911326\n",
            "------------------------------------------------------------------------\n",
            "> Fold 2\n",
            "Accuracy: 0.876487\n",
            "Precision: 0.870159\n",
            "Recall: 0.945491\n",
            "F1 score: 0.906262\n",
            "------------------------------------------------------------------------\n",
            "> Fold 3\n",
            "Accuracy: 0.858770\n",
            "Precision: 0.841472\n",
            "Recall: 0.955788\n",
            "F1 score: 0.894994\n",
            "------------------------------------------------------------------------\n",
            "> Fold 4\n",
            "Accuracy: 0.875981\n",
            "Precision: 0.860994\n",
            "Recall: 0.963558\n",
            "F1 score: 0.909393\n",
            "------------------------------------------------------------------------\n",
            "> Fold 5\n",
            "Accuracy: 0.879524\n",
            "Precision: 0.877206\n",
            "Recall: 0.947491\n",
            "F1 score: 0.910995\n",
            "------------------------------------------------------------------------\n",
            "> Fold 6\n",
            "Accuracy: 0.878765\n",
            "Precision: 0.889519\n",
            "Recall: 0.926320\n",
            "F1 score: 0.907547\n",
            "------------------------------------------------------------------------\n",
            "> Fold 7\n",
            "Accuracy: 0.873450\n",
            "Precision: 0.852661\n",
            "Recall: 0.967213\n",
            "F1 score: 0.906332\n",
            "------------------------------------------------------------------------\n",
            "> Fold 8\n",
            "Accuracy: 0.874968\n",
            "Precision: 0.871683\n",
            "Recall: 0.944466\n",
            "F1 score: 0.906616\n",
            "------------------------------------------------------------------------\n",
            "> Fold 9\n",
            "Accuracy: 0.879524\n",
            "Precision: 0.889440\n",
            "Recall: 0.926161\n",
            "F1 score: 0.907429\n",
            "------------------------------------------------------------------------\n",
            "> Fold 10\n",
            "Accuracy: 0.872437\n",
            "Precision: 0.858807\n",
            "Recall: 0.959007\n",
            "F1 score: 0.906145\n",
            "------------------------------------------------------------------------\n",
            "------------------------------------------------------------------------\n",
            "Average scores for all folds:\n",
            "> Average Accuracy: 0.8749461008692515\n",
            "> Average Precision: 0.8677781644801609\n",
            "> Average Recall: 0.9497351334022104\n",
            "> Average F1 Score: 0.9067040283579241\n",
            "------------------------------------------------------------------------\n",
            "------------------------------------------------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "omMUUs8W9pPr"
      },
      "source": [
        "Plot Precision-Recall Curve"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pUQtT0NqtxXA"
      },
      "source": [
        "# import matplotlib.pyplot as pltdf_prerec_unpickled = pd.read_pickle(\"./clstm/clstm_all_metrics_10fold.pkl\")\n",
        "\n",
        "# df_prerec_unpickled\n",
        "# all_arr_precision = df_prerec_unpickled['all_arr_precision'].tolist()\n",
        "# all_arr_recall = df_prerec_unpickled['all_arr_recall'].tolist()\n",
        "# all_ave_precision = df_prerec_unpickled['all_ave_precision'].tolist()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rEoLXKNQ0a1N",
        "outputId": "99e658b8-c73b-4351-a477-8da2b85d0f36",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 730
        }
      },
      "source": [
        "import matplotlib.cm as cm\n",
        "\n",
        "colors = cm.tab10_r(np.linspace(0, 1, len(all_arr_recall)))\n",
        "\n",
        "\n",
        "plt.clf()   # clear figure\n",
        "plt.rcParams['figure.figsize'] = [20, 12]\n",
        "\n",
        "for i in range(len(all_arr_recall)):\n",
        "  plt.plot(all_arr_recall[i], all_arr_precision[i], label='C-LSTM Fold {}, AP={:0.2f}'.format(i+1, all_ave_precision[i]))\n",
        "  # plt.plot(np.linspace(0, 1, thresholds[i].size), thresholds[i], linestyle='--', label='Threshold')\n",
        "plt.title('C-LSTM Precision-Recall')\n",
        "plt.xlabel('Recall')\n",
        "plt.ylabel('Precision')\n",
        "plt.legend(loc=4)\n",
        "plt.grid(True)\n",
        "# plt.grid(which='minor', linestyle=':', linewidth='0.5', color='black')\n",
        "# plt.minorticks_on()\n",
        "plt.savefig(\"./clstm/clstm_10fold_precision_recall.png\", format='png', dpi=150, bbox_inches='tight')\n",
        "plt.show()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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QwttDCLtDCPtCCD9/gfdXhxC+GkJ4NITwRAjhnZezPZIkSZIkSXphLlt4FEJIAr8NvAO4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\n3tPL5+9+E5+/+03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f2UcikaDdbjE1U2JJbpBKu0apNsPq5asYPXOK+tzQv0ZMkw5dwssY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8Neh6aVcbk7cDoacTgbsdsiCMJbHyzSdR1dlxEEK+fO/SUVOYGqFpidfQJRtOF2d6PrKlaLH7e7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\nihUrCAwMJDIy8qynpQHYzE52f1BK4a6G7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rWrlSdzn6S5yzd1MTs8m6vTrmZB0gLC/MLO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\nRERERERERMQtFY9ERERERERERMSt/w+iLFVsfeJ7MAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1440x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cH7lzjNr97zb"
      },
      "source": [
        "Draw Confusion Matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UGRKcCCh9hma"
      },
      "source": [
        "# unique, counts = np.unique(y_test_conv, return_counts=True)\n",
        "# print(dict(zip(unique, counts)))\n",
        "# cf_matrix = confusion_matrix(y_test_conv, yhat_classes)\n",
        "# cf_matrix_best = all_cf_matrices[idx_best]\n",
        "# print(cf_matrix_best)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z3zGbGIn-Q3L",
        "outputId": "01c7e691-f291-4829-de91-00a6abb0ac47",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 794
        }
      },
      "source": [
        "import seaborn as sns\n",
        "\n",
        "plt.rcParams['figure.figsize'] = [12, 9]\n",
        "for j in range(len(all_cf_matrices)):\n",
        "  cf_matrix_best = all_cf_matrices[j]\n",
        "\n",
        "  stats_text = \"\\n\\nAccuracy={:0.3f}\\nPrecision={:0.3f}\\nRecall={:0.3f}\\nF1 Score={:0.3f}\".format(\n",
        "                  all_accuracy[j],all_precision[j],all_recall[j],all_f1[j])\n",
        "\n",
        "  group_names = ['True Neg','False Pos','False Neg','True Pos']\n",
        "\n",
        "  group_counts = [\"{0:0.0f}\".format(value) for value in\n",
        "                  cf_matrix_best.flatten()]\n",
        "\n",
        "  group_percentages = [\"{0:.2%}\".format(value) for value in\n",
        "                      cf_matrix_best.flatten()/np.sum(cf_matrix_best)]\n",
        "\n",
        "  labels = [f'{v1}\\n{v2}\\n{v3}' for v1, v2, v3 in\n",
        "            zip(group_names,group_counts,group_percentages)]\n",
        "            \n",
        "  labels = np.asarray(labels).reshape(2,2)\n",
        "  categories = ['Fake', 'Real']\n",
        "  # cmap = sns.dark_palette(\"red\")\n",
        "  # cmap = sns.cubehelix_palette(8, start=.5, rot=-.75, as_cmap=True)\n",
        "  # cmap = sns.color_palette(\"GnBu_d\")\n",
        "\n",
        "  plt.clf() \n",
        "  plt.figure()\n",
        "  svm = sns.heatmap(cf_matrix_best, annot=labels, fmt='', cmap='PuBu',xticklabels=categories,yticklabels=categories)\n",
        "\n",
        "  plt.ylabel('True Label')\n",
        "  plt.xlabel('Predicted Label' + stats_text)\n",
        "\n",
        "  # figure = svm.get_figure()    \n",
        "  # figure.savefig('./clstm/clstm_conf_matrix.png', dpi=150)\n",
        "\n",
        "  plt.savefig(\"./clstm/cfpr/clstm_best_10fold_conf_matrix_fold{}.png\".format(j+1), format='png', dpi=150, bbox_inches='tight')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x648 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x648 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x648 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x648 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x648 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x648 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x648 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x648 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x648 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x648 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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HAoesaOMQQp8QwvAQwvDXn3k4y13Tn0UIYZW/w309aig169Sj2XobJemTpPL1zrtv0aB+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\n/wOO8M9fGawJIAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 864x648 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b4fEIvKdX26j"
      },
      "source": [
        "for m in range(len(all_arr_precision)):\n",
        "  df_prerec = pd.DataFrame(all_arr_precision[m])\n",
        "  df_prerec.columns = ['precision']\n",
        "  df_prerec['recall'] = all_arr_recall[m]\n",
        "  df_prerec['threshold'] = all_thresholds[m]\n",
        "  df_prerec\n",
        "  df_prerec.to_pickle(\"./clstm/cfpr/clstm_best_10fold_precison_recall_curve_fold{}.pkl\".format(m+1))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NZtuwyjDF9Kz",
        "outputId": "f50f5ecf-95f7-491c-f6f5-f5e8b78a8869",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "!jupyter nbconvert --to html ./clstm/clstm_classification_malay_10fold.ipynb"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[NbConvertApp] Converting notebook ./clstm/clstm_classification_malay_10fold.ipynb to html\n",
            "[NbConvertApp] Writing 984975 bytes to ./clstm/clstm_classification_malay_10fold.html\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}