training.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!python --version"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YPz6yR_316NG",
        "outputId": "f49a2d39-d63d-47f7-c656-f0cfe8fbc6dc"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Python 3.10.12\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install tensorflow_addons"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bXetbf0w3c8I",
        "outputId": "a8e6024d-4c2a-4ec7-e808-bbe1fb478c10"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting tensorflow_addons\n",
            "  Downloading tensorflow_addons-0.21.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (612 kB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/612.1 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m604.2/612.1 kB\u001b[0m \u001b[31m21.8 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m612.1/612.1 kB\u001b[0m \u001b[31m16.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from tensorflow_addons) (23.1)\n",
            "Collecting typeguard<3.0.0,>=2.7 (from tensorflow_addons)\n",
            "  Downloading typeguard-2.13.3-py3-none-any.whl (17 kB)\n",
            "Installing collected packages: typeguard, tensorflow_addons\n",
            "Successfully installed tensorflow_addons-0.21.0 typeguard-2.13.3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **LSTM Training**"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "id": "4sUnP4bU0MhN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#!pip install fasttext"
      ],
      "metadata": {
        "id": "ZcT1Ia_xtLgY"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import platform\n",
        "import os\n",
        "#import fasttext\n",
        "import sys\n",
        "import tensorflow as tf\n",
        "import configparser\n",
        "from tensorflow import keras\n",
        "from gensim.models.fasttext import FastText\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from sklearn.manifold import TSNE\n",
        "from tensorflow.keras.utils import pad_sequences, to_categorical\n",
        "import time\n",
        "import glob\n",
        "from tensorflow.keras import layers, Model\n",
        "from tensorflow.keras.initializers import Constant\n",
        "from tensorflow.keras.layers import Embedding, Bidirectional, LSTM, GlobalMaxPooling1D, Dense\n",
        "from tensorflow.keras.callbacks import ModelCheckpoint, ReduceLROnPlateau\n",
        "from tensorflow.keras.optimizers import Nadam\n",
        "import tensorflow_addons as tfa\n",
        "import pickle\n",
        "from keras import layers\n",
        "import sklearn\n",
        "from sklearn.metrics import f1_score\n",
        "from sklearn.model_selection import train_test_split\n",
        "from keras import backend\n",
        "from gensim.models.word2vec import Word2Vec\n",
        "from gensim.models.fasttext import FastText\n",
        "from sklearn.metrics import accuracy_score, f1_score, roc_auc_score\n",
        "import matplotlib.pyplot as plt"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/tensorflow_addons/utils/tfa_eol_msg.py:23: UserWarning: \n",
            "\n",
            "TensorFlow Addons (TFA) has ended development and introduction of new features.\n",
            "TFA has entered a minimal maintenance and release mode until a planned end of life in May 2024.\n",
            "Please modify downstream libraries to take dependencies from other repositories in our TensorFlow community (e.g. Keras, Keras-CV, and Keras-NLP). \n",
            "\n",
            "For more information see: https://github.com/tensorflow/addons/issues/2807 \n",
            "\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "execution_count": 4,
      "metadata": {
        "gather": {
          "logged": 1688987749253
        },
        "id": "6MUisHkO0MhU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0ca3e05d-0dfb-43da-d6aa-e4f09428b94b"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "outputs": [],
      "execution_count": 5,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "id": "PoyQIS4A0MhW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "colab_active = 'google.colab' in sys.modules\n",
        "print(\"Colab Active: \", colab_active)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LT1P0TGyd9ih",
        "outputId": "196a1ef8-6b10-466e-8984-9a9d2ade4ef6"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Colab Active:  True\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Downloads"
      ],
      "metadata": {
        "id": "7Cz7ZTCW2ziN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "if colab_active is True:\n",
        "  OS = platform.system()      # Operating System\n",
        "\n",
        "  def data_download(file_to_download, gdrive_code, OS, uncompress = True):\n",
        "    if not os.path.exists(file_to_download):\n",
        "      os.system('gdown --id \"'+gdrive_code+'\" --output '+file_to_download)\n",
        "      if OS == \"Linux\" and uncompress:\n",
        "          os.system('unzip -o -n \"./'+file_to_download+'\" -d '+os.path.dirname(file_to_download))\n",
        "      return True\n",
        "    else:\n",
        "      return None"
      ],
      "metadata": {
        "id": "NvzgJxJT3I2b"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "if colab_active is True:\n",
        "  out = data_download(\"./Sentiment_Social_Mood_Datasets.zip\", \"15eQXhShXHebzoxXqtAbkVsXeK3TN1C-1\", OS)"
      ],
      "metadata": {
        "id": "1lCOF7j422wf"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "if colab_active is True:\n",
        "  out = data_download(\"./corpus_2_not_utf.bin\", \"1AL6CHtciRLTfJsfs6Oldcyg4vlCQmjpb\", OS, uncompress = False)"
      ],
      "metadata": {
        "id": "_ApU3PLWrryG"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Configs"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "id": "WwRYaFBS0MhX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "max_tokens = 20000\n",
        "embedding_size = 300\n",
        "val_size = 0.1\n",
        "test_size = 0.1\n",
        "random_seed = 42\n",
        "shuffle = True\n",
        "\n",
        "iterations = 30\n",
        "batch_size = 32\n",
        "epochs = 20\n",
        "tpu = False"
      ],
      "outputs": [],
      "execution_count": 10,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1688987372741
        },
        "id": "xundqo-D0MhY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Set the TPU Environment"
      ],
      "metadata": {
        "id": "ur1cfBWPOMzi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "if tpu is True:\n",
        "  import tensorflow as tf\n",
        "  print(\"Tensorflow version \" + tf.__version__)\n",
        "\n",
        "  try:\n",
        "    tpu = tf.distribute.cluster_resolver.TPUClusterResolver()  # TPU detection\n",
        "    print('Running on TPU ', tpu.cluster_spec().as_dict()['worker'])\n",
        "  except ValueError:\n",
        "    raise BaseException('ERROR: Not connected to a TPU runtime; please see the previous cell in this notebook for instructions!')\n",
        "\n",
        "  tf.config.experimental_connect_to_cluster(tpu)\n",
        "  tf.tpu.experimental.initialize_tpu_system(tpu)\n",
        "  tpu_strategy = tf.distribute.experimental.TPUStrategy(tpu)\n",
        "\n",
        "  AUTO = tf.data.experimental.AUTOTUNE\n",
        "  batch_size = batch_size * tpu_strategy.num_replicas_in_sync"
      ],
      "metadata": {
        "id": "2lLPwy0SOIr8"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "id": "NaX07mH70MhZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "training_df = pd.read_csv(f\"./Sentiment_Social_Mood_Datasets/training_set.csv\", sep = ';', encoding='utf-8')\n",
        "training_df = training_df.loc[training_df['pol'] != 0]\n",
        "training_df['pol'].replace(2, 0, inplace=True)"
      ],
      "outputs": [],
      "execution_count": 12,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1688988001558
        },
        "id": "PeWVE5jR0Mhb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "texts = training_df['text']\n",
        "labels = training_df['pol']"
      ],
      "outputs": [],
      "execution_count": 13,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1688988003260
        },
        "id": "e1nKWv0U0Mhc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = Tokenizer(num_words = max_tokens)\n",
        "tokenizer.fit_on_texts(texts)\n",
        "indexed_texts = tokenizer.texts_to_sequences(texts)"
      ],
      "outputs": [],
      "execution_count": 14,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1688988005419
        },
        "id": "-n3dMnvO0Mhe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "word_index = tokenizer.word_index\n",
        "print(f'Found {len(word_index)} unique tokens.' )"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 20826 unique tokens.\n"
          ]
        }
      ],
      "execution_count": 15,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1688988007278
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wWpmy5jr0Mhf",
        "outputId": "149c380b-243c-4960-cb08-142299287744"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_x = pad_sequences(indexed_texts, padding='pre')"
      ],
      "outputs": [],
      "execution_count": 16,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1688988009250
        },
        "id": "dsPgsq4B0Mhg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#labels = to_categorical(np.asarray(labels))"
      ],
      "outputs": [],
      "execution_count": 17,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1688986213229
        },
        "id": "2mGnTSOa0Mhh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x_train, x_val, y_train, y_val = train_test_split(data_x, labels, test_size=val_size, random_state=random_seed, shuffle=shuffle)\n",
        "x_train, x_test, y_train, y_test = train_test_split(x_train, y_train, test_size=test_size, random_state=random_seed, shuffle=shuffle)"
      ],
      "outputs": [],
      "execution_count": 18,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1688988011015
        },
        "id": "Y7W4mj0v0Mhi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(x_train.shape, y_train.shape)\n",
        "print(x_val.shape, y_val.shape)\n",
        "print(x_test.shape, y_test.shape)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(6876, 46) (6876,)\n",
            "(850, 46) (850,)\n",
            "(765, 46) (765,)\n"
          ]
        }
      ],
      "execution_count": 19,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1688988012606
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-KQbot4a0Mhi",
        "outputId": "f2817ce5-7762-4145-8ab4-72e3d2726231"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "with open('preprocessed_datasets.pkl', 'wb') as file:\n",
        "    pickle.dump((x_test, y_test, tokenizer), file)\n",
        "    file.close()"
      ],
      "metadata": {
        "id": "atQQn8jLiOib"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Embedding Matrix"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "id": "a3yU4xN90Mhj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Import the word embedding model"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "id": "HCfWm53Z0Mhk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#embedding_model = Word2Vec.load('./Sentiment_Social_Mood_Datasets/corpus_2_not_utf_w2v.bin')\n",
        "#embedding_model = FastText.load('./corpus_2_not_utf.bin')\n",
        "embedding_model = FastText.load_fasttext_format('./corpus_2_not_utf.bin')"
      ],
      "outputs": [],
      "execution_count": 21,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1688988016509
        },
        "id": "cms0RyKj0Mhk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#**Embeddings Visual Sanity Check**"
      ],
      "metadata": {
        "id": "tgCpwjEIQt2c"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def embedding_space_visualization(root_word, model, plot_3d = True, plot_2d = False, topn = 20):\n",
        "\n",
        "  fig = plt.figure(figsize=(20, 17), dpi=80)\n",
        "  tot_words = [root_word] + [tupla[0] for tupla in model.wv.most_similar(root_word, topn=topn)]\n",
        "  X = model.wv[tot_words]\n",
        "\n",
        "  if plot_3d:\n",
        "    pca = TSNE(perplexity=topn, n_components=3, init='pca', random_state = 42)\n",
        "\n",
        "    X = pca.fit_transform(X)\n",
        "    pca_data = pd.DataFrame(X, index=tot_words, columns=[\"x\",\"y\",\"z\"])\n",
        "    pca_data[\"input\"] = 0\n",
        "    pca_data[\"input\"].iloc[0:1] = 1\n",
        "\n",
        "    ax = fig.add_subplot(111, projection='3d')\n",
        "    ax.scatter(pca_data[pca_data[\"input\"]==0]['x'], pca_data[pca_data[\"input\"]==0]['y'], pca_data[pca_data[\"input\"]==0]['z'], c=\"black\")\n",
        "    ax.scatter(pca_data[pca_data[\"input\"]==1]['x'], pca_data[pca_data[\"input\"]==1]['y'], pca_data[pca_data[\"input\"]==1]['z'], c=\"red\")\n",
        "    ax.set(xlabel=None, ylabel=None, zlabel=None, xticklabels=[], yticklabels=[], zticklabels=[])\n",
        "\n",
        "    for label, row in pca_data[[\"x\",\"y\",\"z\"]].iterrows():\n",
        "        x, y, z = row\n",
        "        ax.text(x, y, z, s=label)"
      ],
      "metadata": {
        "id": "Kl_JsGSsQoWw"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "embedding_model.wv.most_similar(\"covid\", topn=10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x-Qt-J69RwmX",
        "outputId": "411678e6-5d13-4717-d5b7-541e148bbfcd"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('covidcovid', 0.9673317074775696),\n",
              " ('covid[]', 0.9355220794677734),\n",
              " ('covidè', 0.9296701550483704),\n",
              " ('ècovid', 0.9140629768371582),\n",
              " ('xcovid', 0.9068671464920044),\n",
              " ('àcovid', 0.9053578972816467),\n",
              " ('ncovid', 0.9006012082099915),\n",
              " ('covidcoronavirus', 0.8957855105400085),\n",
              " ('coronaviruscovid', 0.895122230052948),\n",
              " ('dcovid', 0.8895282745361328)]"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "embedding_space_visualization(\"covid\", embedding_model, topn = 30)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "StLFnsHZQy1q",
        "outputId": "49fbc6ac-1254-434d-ada3-f96a12bc5514"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x1360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Build the embedding matrix"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "id": "Ltp9M7ta0Mhk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "embedding_matrix = np.zeros((len(word_index)+1, embedding_size))\n",
        "\n",
        "for word, idx in word_index.items():\n",
        "    try:\n",
        "        embedding_matrix[idx] = embedding_model[word]\n",
        "    except:\n",
        "        pass"
      ],
      "outputs": [],
      "execution_count": 25,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1688988018675
        },
        "id": "0NplUZwg0Mhl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## LSTM Model"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "id": "QRZRxgwL0Mhl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Layers"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "id": "DG_c83_P0Mhl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class BiLSTM():\n",
        "\n",
        "    @staticmethod\n",
        "    def build(embedding_matrix):\n",
        "      # Embedding layer\n",
        "      embedding_layer = Embedding(input_dim=embedding_matrix.shape[0],\n",
        "                                output_dim=embedding_matrix.shape[1],\n",
        "                                weights=[embedding_matrix],\n",
        "                                input_length=x_train.shape[1],\n",
        "                                trainable = True)\n",
        "      inputs = layers.Input(shape=(None,), dtype='int64')\n",
        "      embedded_sequences = embedding_layer(inputs)\n",
        "      # Hidden layers\n",
        "      x = Bidirectional(LSTM(128, return_sequences=True, dropout=0.5))(embedded_sequences)\n",
        "      x = Bidirectional(LSTM(32, return_sequences=True, dropout=0.5))(x)\n",
        "      x = GlobalMaxPooling1D()(x)\n",
        "      # Output layer\n",
        "      outputs = Dense(1, activation=\"sigmoid\")(x)\n",
        "      neural_model = Model(inputs, outputs)\n",
        "      neural_model.summary()\n",
        "\n",
        "      return neural_model"
      ],
      "metadata": {
        "id": "WrFHg1wDOjWi"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "if tpu is True:\n",
        "  with strategy.scope():\n",
        "    neural_model = BiLSTM.build(embedding_matrix)\n",
        "else:\n",
        "  neural_model = BiLSTM.build(embedding_matrix)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_1 (InputLayer)        [(None, None)]            0         \n",
            "                                                                 \n",
            " embedding (Embedding)       (None, None, 300)         6248100   \n",
            "                                                                 \n",
            " bidirectional (Bidirectiona  (None, None, 256)        439296    \n",
            " l)                                                              \n",
            "                                                                 \n",
            " bidirectional_1 (Bidirectio  (None, None, 64)         73984     \n",
            " nal)                                                            \n",
            "                                                                 \n",
            " global_max_pooling1d (Globa  (None, 64)               0         \n",
            " lMaxPooling1D)                                                  \n",
            "                                                                 \n",
            " dense (Dense)               (None, 1)                 65        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 6,761,445\n",
            "Trainable params: 6,761,445\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "execution_count": 27,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1688988415361
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2cm4c5k50Mhn",
        "outputId": "20e69f77-8b9d-4483-ae69-b34f0b7cbac7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Compilation & Callbacks"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "id": "TpyUepFE0Mhr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "default_callbacks = []"
      ],
      "outputs": [],
      "execution_count": 28,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1688988424438
        },
        "id": "b2OJEpEA0Mhr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "checkpoint = ModelCheckpoint('sentiment_bilstm_best.hdf5',\n",
        "                             save_weights_only=True,\n",
        "                             monitor='val_loss',\n",
        "                             verbose=1,\n",
        "                             save_best_only=True,\n",
        "                             mode='min')\n",
        "\n",
        "default_callbacks = default_callbacks + [checkpoint]\n",
        "\n",
        "reduce_lr = ReduceLROnPlateau(monitor='val_loss',\n",
        "                              factor=0.2,\n",
        "                              patience=3,\n",
        "                              min_delta = 0.01,\n",
        "                              min_lr=0.001)\n",
        "default_callbacks = default_callbacks+[reduce_lr]"
      ],
      "outputs": [],
      "execution_count": 29,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1688988427785
        },
        "id": "-I-6uR2Q0Mhr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "neural_model.compile(Nadam(1e-3), loss='binary_crossentropy', metrics=['accuracy'])"
      ],
      "outputs": [],
      "execution_count": 30,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1688988044251
        },
        "id": "LqXwN1Kr0Mht"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#neural_model.compile('rmsprop', loss='binary_crossentropy', metrics=['accuracy'])"
      ],
      "outputs": [],
      "execution_count": 31,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1688988431223
        },
        "id": "KyPAZyUW0Mhu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Training"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "id": "Hwcriq5Q0Mhv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "neural_model.fit(x_train, y_train,\n",
        "                 validation_data=(x_val, y_val),\n",
        "                 epochs=epochs,\n",
        "                 batch_size=batch_size,\n",
        "                 callbacks=default_callbacks,\n",
        "                 verbose=2)\n",
        "\n",
        "neural_model.save_weights('sentiment_bilstm_last.hdf5')"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "\n",
            "Epoch 1: val_loss improved from inf to 0.47162, saving model to sentiment_bilstm_best.hdf5\n",
            "215/215 - 45s - loss: 0.5510 - accuracy: 0.7267 - val_loss: 0.4716 - val_accuracy: 0.7894 - lr: 0.0010 - 45s/epoch - 207ms/step\n",
            "Epoch 2/20\n",
            "\n",
            "Epoch 2: val_loss did not improve from 0.47162\n",
            "215/215 - 7s - loss: 0.2626 - accuracy: 0.8941 - val_loss: 0.4853 - val_accuracy: 0.7871 - lr: 0.0010 - 7s/epoch - 34ms/step\n",
            "Epoch 3/20\n",
            "\n",
            "Epoch 3: val_loss did not improve from 0.47162\n",
            "215/215 - 6s - loss: 0.0995 - accuracy: 0.9668 - val_loss: 0.6057 - val_accuracy: 0.7812 - lr: 0.0010 - 6s/epoch - 27ms/step\n",
            "Epoch 4/20\n",
            "\n",
            "Epoch 4: val_loss did not improve from 0.47162\n",
            "215/215 - 6s - loss: 0.0395 - accuracy: 0.9875 - val_loss: 0.8153 - val_accuracy: 0.7753 - lr: 0.0010 - 6s/epoch - 26ms/step\n",
            "Epoch 5/20\n",
            "\n",
            "Epoch 5: val_loss did not improve from 0.47162\n",
            "215/215 - 4s - loss: 0.0263 - accuracy: 0.9920 - val_loss: 0.8475 - val_accuracy: 0.7729 - lr: 0.0010 - 4s/epoch - 20ms/step\n",
            "Epoch 6/20\n",
            "\n",
            "Epoch 6: val_loss did not improve from 0.47162\n",
            "215/215 - 4s - loss: 0.0193 - accuracy: 0.9940 - val_loss: 0.9459 - val_accuracy: 0.7729 - lr: 0.0010 - 4s/epoch - 20ms/step\n",
            "Epoch 7/20\n",
            "\n",
            "Epoch 7: val_loss did not improve from 0.47162\n",
            "215/215 - 5s - loss: 0.0129 - accuracy: 0.9962 - val_loss: 1.0324 - val_accuracy: 0.7718 - lr: 0.0010 - 5s/epoch - 22ms/step\n",
            "Epoch 8/20\n",
            "\n",
            "Epoch 8: val_loss did not improve from 0.47162\n",
            "215/215 - 4s - loss: 0.0085 - accuracy: 0.9980 - val_loss: 1.1790 - val_accuracy: 0.7659 - lr: 0.0010 - 4s/epoch - 17ms/step\n",
            "Epoch 9/20\n",
            "\n",
            "Epoch 9: val_loss did not improve from 0.47162\n",
            "215/215 - 5s - loss: 0.0078 - accuracy: 0.9978 - val_loss: 1.0352 - val_accuracy: 0.7671 - lr: 0.0010 - 5s/epoch - 22ms/step\n",
            "Epoch 10/20\n",
            "\n",
            "Epoch 10: val_loss did not improve from 0.47162\n",
            "215/215 - 4s - loss: 0.0108 - accuracy: 0.9959 - val_loss: 1.1560 - val_accuracy: 0.7576 - lr: 0.0010 - 4s/epoch - 18ms/step\n",
            "Epoch 11/20\n",
            "\n",
            "Epoch 11: val_loss did not improve from 0.47162\n",
            "215/215 - 4s - loss: 0.0072 - accuracy: 0.9983 - val_loss: 1.3757 - val_accuracy: 0.7459 - lr: 0.0010 - 4s/epoch - 17ms/step\n",
            "Epoch 12/20\n",
            "\n",
            "Epoch 12: val_loss did not improve from 0.47162\n",
            "215/215 - 4s - loss: 0.0032 - accuracy: 0.9991 - val_loss: 1.3847 - val_accuracy: 0.7541 - lr: 0.0010 - 4s/epoch - 19ms/step\n",
            "Epoch 13/20\n",
            "\n",
            "Epoch 13: val_loss did not improve from 0.47162\n",
            "215/215 - 4s - loss: 0.0115 - accuracy: 0.9967 - val_loss: 1.2487 - val_accuracy: 0.7224 - lr: 0.0010 - 4s/epoch - 18ms/step\n",
            "Epoch 14/20\n",
            "\n",
            "Epoch 14: val_loss did not improve from 0.47162\n",
            "215/215 - 4s - loss: 0.0108 - accuracy: 0.9967 - val_loss: 1.0913 - val_accuracy: 0.7541 - lr: 0.0010 - 4s/epoch - 17ms/step\n",
            "Epoch 15/20\n",
            "\n",
            "Epoch 15: val_loss did not improve from 0.47162\n",
            "215/215 - 4s - loss: 0.0080 - accuracy: 0.9974 - val_loss: 1.0935 - val_accuracy: 0.7318 - lr: 0.0010 - 4s/epoch - 17ms/step\n",
            "Epoch 16/20\n",
            "\n",
            "Epoch 16: val_loss did not improve from 0.47162\n",
            "215/215 - 5s - loss: 0.0064 - accuracy: 0.9983 - val_loss: 1.3977 - val_accuracy: 0.7494 - lr: 0.0010 - 5s/epoch - 21ms/step\n",
            "Epoch 17/20\n",
            "\n",
            "Epoch 17: val_loss did not improve from 0.47162\n",
            "215/215 - 4s - loss: 0.0031 - accuracy: 0.9993 - val_loss: 1.4875 - val_accuracy: 0.7494 - lr: 0.0010 - 4s/epoch - 17ms/step\n",
            "Epoch 18/20\n",
            "\n",
            "Epoch 18: val_loss did not improve from 0.47162\n",
            "215/215 - 4s - loss: 0.0025 - accuracy: 0.9991 - val_loss: 1.4016 - val_accuracy: 0.7565 - lr: 0.0010 - 4s/epoch - 18ms/step\n",
            "Epoch 19/20\n",
            "\n",
            "Epoch 19: val_loss did not improve from 0.47162\n",
            "215/215 - 4s - loss: 0.0017 - accuracy: 0.9994 - val_loss: 1.5489 - val_accuracy: 0.7565 - lr: 0.0010 - 4s/epoch - 19ms/step\n",
            "Epoch 20/20\n",
            "\n",
            "Epoch 20: val_loss did not improve from 0.47162\n",
            "215/215 - 3s - loss: 0.0026 - accuracy: 0.9990 - val_loss: 1.4224 - val_accuracy: 0.7494 - lr: 0.0010 - 3s/epoch - 16ms/step\n"
          ]
        }
      ],
      "execution_count": 32,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1688988512598
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J9Sz7hid0Mhv",
        "outputId": "aadf195e-4dcb-4a9c-dcbd-cf22ee6eb051"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_json = neural_model.to_json()\n",
        "with open(\"./sentiment_bilstm_topology.json\", \"w\") as json_file:\n",
        "  json_file.write(model_json)"
      ],
      "metadata": {
        "id": "dzHr2-0OPatA"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "neural_model.load_weights(\"sentiment_bilstm_last.hdf5\")"
      ],
      "metadata": {
        "id": "ZtesKxxN7Xqw"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Prediction of Last Model\n",
        "pred_test = neural_model.predict(x_test)\n",
        "pred_test = np.where(pred_test > 0.5, 1, 0)\n",
        "# Performance of Last Model\n",
        "accuracy_test = accuracy_score(y_test.values, pred_test)\n",
        "auc_roc_score = roc_auc_score(y_test.values, pred_test)\n",
        "f1_score_test = f1_score(y_test.values, pred_test)\n",
        "print('\\nAccuracy of Last Neural Model on Test Set : ', accuracy_test)\n",
        "print('ROC AUC Score of Last Neural Model on Test Set', auc_roc_score)\n",
        "print('F1_score of Last Neural Model on Test Set : ', f1_score_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tQxNny9w5cds",
        "outputId": "6a16cd97-c13d-4f8b-f0e6-2f0805dc37db"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "24/24 [==============================] - 3s 8ms/step\n",
            "\n",
            "Accuracy of Last Neural Model on Test Set :  0.74640522875817\n",
            "ROC AUC Score of Last Neural Model on Test Set 0.7403444482435666\n",
            "F1_score of Last Neural Model on Test Set :  0.6700680272108843\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<hr>"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "id": "uaj004Ey0Mhw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "neural_model.load_weights(\"sentiment_bilstm_best.hdf5\")"
      ],
      "metadata": {
        "id": "QrU04MRD7rIH"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Prediction of Best Model\n",
        "pred_test = neural_model.predict(x_test)\n",
        "pred_test = np.where(pred_test > 0.5, 1, 0)\n",
        "# Performance of Best Model\n",
        "accuracy_test = accuracy_score(y_test.values, pred_test)\n",
        "auc_roc_score = roc_auc_score(y_test.values, pred_test)\n",
        "f1_score_test = f1_score(y_test.values, pred_test)\n",
        "print('\\nAccuracy of Best Neural Model on Test Set : ', accuracy_test)\n",
        "print('ROC AUC Score of Best Neural Model on Test Set', auc_roc_score)\n",
        "print('F1_score of Best Neural Model on Test Set : ', f1_score_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2ptVxb6X7vPI",
        "outputId": "1af1e141-f4bb-4eca-ba7d-5b3a65d3608a"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "24/24 [==============================] - 0s 8ms/step\n",
            "\n",
            "Accuracy of Best Neural Model on Test Set :  0.8104575163398693\n",
            "ROC AUC Score of Best Neural Model on Test Set 0.7797582767181531\n",
            "F1_score of Best Neural Model on Test Set :  0.7173489278752437\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "name": "python310-sdkv2",
      "language": "python",
      "display_name": "Python 3.10 - SDK v2"
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      "name": "python",
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        "ms_spell_check_language": "en"
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        "AzureML": {
          "notebookHasBeenCompleted": true
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    "nteract": {
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    "kernel_info": {
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    "accelerator": "GPU"
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