S73.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v-AaVsDdPP2K"
      },
      "source": [
        "#BiLSTM with CNN"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#1. Install and Import Dependincies"
      ],
      "metadata": {
        "id": "rav8qO8cRE94"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "G0w3J6zp9DPJ"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf\n",
        "import keras\n",
        "from sklearn.model_selection import train_test_split"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "Dn1fBsXl9NNt",
        "colab": {
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          "height": 293
        },
        "outputId": "8823de79-a4e0-433f-d099-ad3f09ae3125"
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      "outputs": [
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              "   id                                            product  \\\n",
              "0   0  Redmi Note 11T 5G (Matte Black, 6GB RAM, 128GB...   \n",
              "1   1  Redmi Note 11T 5G (Matte Black, 6GB RAM, 128GB...   \n",
              "2   2  Redmi Note 11T 5G (Matte Black, 6GB RAM, 128GB...   \n",
              "3   3  Redmi Note 11T 5G (Matte Black, 6GB RAM, 128GB...   \n",
              "4   4  Redmi Note 11T 5G (Matte Black, 6GB RAM, 128GB...   \n",
              "\n",
              "                                               title  rating  \\\n",
              "0  4.0 out of 5 stars\\n\"Redmi 11T 5G: Affordable ...       4   \n",
              "1              4.0 out of 5 stars\\nFunction not good       4   \n",
              "2        4.0 out of 5 stars\\nCamera  call record ing       4   \n",
              "3  5.0 out of 5 stars\\nA Stellar Performer - Five...       5   \n",
              "4             4.0 out of 5 stars\\nNot wort the money       4   \n",
              "\n",
              "                                          reviewText  \n",
              "0  The Xiaomi Redmi Note 11 T 5G impresses with i...  \n",
              "1  Phone is good but function are not . Finger pr...  \n",
              "2  Call recording  is difficult  and  photos by c...  \n",
              "3  The Redmi Note 11T is an absolute gem of a sma...  \n",
              "4  Best mobiles are available at cheaper price fo...  "
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              "      <td>4.0 out of 5 stars\\nNot wort the money</td>\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
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          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "data=pd.read_csv('/content/Reviews.csv',encoding='cp1252')\n",
        "data.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Cleaning Text"
      ],
      "metadata": {
        "id": "OMW2AxVsdsiE"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NTzmmNh89XIN",
        "outputId": "f346c8e1-d2b5-4ad8-eb6c-598099858b98"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ]
        }
      ],
      "source": [
        "import nltk\n",
        "import string\n",
        "nltk.download('stopwords')\n",
        "from nltk.corpus import stopwords\n",
        "lines=[]\n",
        "\n",
        "r = str(data['reviewText'])\n",
        "\n",
        "for i,line in enumerate(r):\n",
        "  for ele in line:\n",
        "    if ele in string.punctuation:\n",
        "      line=line.replace(ele,'')\n",
        "  lines.append(line)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "BgaZRrDn9aTG"
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      "outputs": [],
      "source": [
        "def new_dataset(data,target):\n",
        "  lines_=pd.DataFrame(data=data)\n",
        "  new_data=[lines_,target]\n",
        "  concat=pd.concat(new_data,axis=1)\n",
        "  return concat"
      ]
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              "     0  rating\n",
              "0    0     4.0\n",
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              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-8cdd48e0-c3c4-4271-894e-fae72782e69d button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-8cdd48e0-c3c4-4271-894e-fae72782e69d');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-6dfd2980-0f24-4dda-9fb4-a5a5446ff149\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6dfd2980-0f24-4dda-9fb4-a5a5446ff149')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-6dfd2980-0f24-4dda-9fb4-a5a5446ff149 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "new_data=new_dataset(lines,data['rating'])\n",
        "new_data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "V7ccR0B_-iZq"
      },
      "outputs": [],
      "source": [
        "new_data[0]=new_data[0].astype('string')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UjnhpKU9-nmh"
      },
      "source": [
        "def get_metrics(y_test, y_pred_proba):\n",
        "    print('ACCURACY_SCORE: ', round(accuracy_score(y_test, y_pred_proba >= 0.5), 4))\n",
        "    print('F1_SCORE: ', round(f1_score(y_test, y_pred_proba >= 0.5, average = \"macro\"), 4))\n",
        "    print('ROC_AUC_SCORE: ', round(roc_auc_score(y_test, y_pred_proba), 4))\n",
        "    print('CONFUSION_MATRIX:\\n', confusion_matrix(y_test, y_pred_proba >= 0.5),'\\n')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "apKs8EheBkC6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "eadea592-3e92-4b03-8726-2d83405d5bf6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     0  rating\n",
              "0    0     4.0\n",
              "1          4.0\n",
              "2          4.0\n",
              "3          5.0\n",
              "4          4.0\n",
              "..  ..     ...\n",
              "655  b     NaN\n",
              "656  j     NaN\n",
              "657  e     NaN\n",
              "658  c     NaN\n",
              "659  t     NaN\n",
              "\n",
              "[660 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-aab56081-a28a-46a1-8fec-2f06aaed3d59\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>rating</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td></td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td></td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td></td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td></td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>655</th>\n",
              "      <td>b</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>656</th>\n",
              "      <td>j</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>657</th>\n",
              "      <td>e</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>658</th>\n",
              "      <td>c</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>659</th>\n",
              "      <td>t</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>660 rows × 2 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-aab56081-a28a-46a1-8fec-2f06aaed3d59')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-aab56081-a28a-46a1-8fec-2f06aaed3d59 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-aab56081-a28a-46a1-8fec-2f06aaed3d59');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-281716d1-3341-43ee-9d63-801b8b43ec34\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-281716d1-3341-43ee-9d63-801b8b43ec34')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-281716d1-3341-43ee-9d63-801b8b43ec34 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "new_data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "Auq2bFLDBk96",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2407b6a5-bc2a-41b4-f93a-4fa24ee1a5ac"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 660 entries, 0 to 659\n",
            "Data columns (total 2 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   0       660 non-null    string \n",
            " 1   rating  100 non-null    float64\n",
            "dtypes: float64(1), string(1)\n",
            "memory usage: 10.4 KB\n"
          ]
        }
      ],
      "source": [
        "new_data.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "KDeQzWZZCtbs"
      },
      "outputs": [],
      "source": [
        "import nltk\n",
        "new_lines=[]\n",
        "for line in new_data[0]:\n",
        "  lines=nltk.tokenize.sent_tokenize\n",
        "  new_lines.append(lines)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Tokenization and Padding the sequence input"
      ],
      "metadata": {
        "id": "j1CeF4Sod16f"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bHOCw_ZzC4TU",
        "outputId": "9d0af8dc-5c30-4c8d-901a-a8c5d7ef95ea"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ]
        }
      ],
      "source": [
        "from keras.preprocessing.text import Tokenizer\n",
        "from keras.utils import pad_sequences\n",
        "new_list=[]\n",
        "\n",
        "nltk.download('punkt')\n",
        "from keras.preprocessing.text import Tokenizer\n",
        "def sequence_data_dataset(data,n):\n",
        "  # for str_ in data[0]:\n",
        "\n",
        "  # tokens=nltk.word_tokenize(data)\n",
        "  # sents=nltk.sent_tokenize(data)\n",
        "  tokenize=Tokenizer(num_words=5000)\n",
        "  # if len(tokens)<n:\n",
        "  # for i in range(0,len(sents),1):\n",
        "  tokenize.fit_on_texts(data)\n",
        "  tokens_=tokenize.texts_to_sequences(data)\n",
        "  tokens_=pad_sequences(tokens_,padding='post',maxlen=n)\n",
        "\n",
        "  return tokens_,tokenize\n",
        "#   concat=[pd.DataFrame(data=new_list),data['sentiment']]\n",
        "#   return pd.concat(concat,axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "5GsaoUqWDEmx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "64f37399-cf5b-4ecf-eb7e-da321a9b6266"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(660, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "new_dataset,tokenize=sequence_data_dataset(new_data[0],7)\n",
        "new_dataset\n",
        "new_dataset.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "gtWmwR22DJ9n",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ceceae4c-9113-4452-817a-cc876c6b76f8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[23,  0,  0, ...,  0,  0,  0],\n",
              "       [ 0,  0,  0, ...,  0,  0,  0],\n",
              "       [ 0,  0,  0, ...,  0,  0,  0],\n",
              "       ...,\n",
              "       [ 1,  0,  0, ...,  0,  0,  0],\n",
              "       [10,  0,  0, ...,  0,  0,  0],\n",
              "       [ 3,  0,  0, ...,  0,  0,  0]], dtype=int32)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "new_dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "5gmmyXEnDM06"
      },
      "outputs": [],
      "source": [
        "X=new_dataset\n",
        "# Y=new_data['sentiment']\n",
        "Y = pd.get_dummies(new_data['rating'])"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#splitting the Dataset(New) into train and test"
      ],
      "metadata": {
        "id": "SwAfn9XEd_-0"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "hYx5rw63DS7a"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train,x_test,y_train,y_test=train_test_split(X,Y,test_size=0.2,random_state=0)\n",
        "x_train,x_val,y_train,y_val=train_test_split(x_train,y_train,test_size=0.1,random_state=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "nmxuKT1PDXcj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b6ef8a1e-f267-428b-b0fd-1fca4561b237"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(475, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "x_train.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "ylZy1v4WDZ_P",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "157b78a3-c407-4649-91a0-ba2e64c3600b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(475, 4)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "y_train.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Bidirectional-LSTM With CNN\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "lsc_8HMneGLb"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "yvtSjuu6DecM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b6b84cf8-f63f-4fe5-9693-cdf767573be9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " embedding (Embedding)       (None, 7, 15)             90000     \n",
            "                                                                 \n",
            " bidirectional (Bidirection  (None, 128)               40960     \n",
            " al)                                                             \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 128)               0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 4)                 516       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 131476 (513.58 KB)\n",
            "Trainable params: 131476 (513.58 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n",
            "Epoch 1/30\n",
            "3/3 [==============================] - 6s 779ms/step - loss: 0.1932 - accuracy: 0.3421 - val_loss: 0.2183 - val_accuracy: 0.0737\n",
            "Epoch 2/30\n",
            "3/3 [==============================] - 0s 75ms/step - loss: 0.1923 - accuracy: 0.0895 - val_loss: 0.2175 - val_accuracy: 0.0737\n",
            "Epoch 3/30\n",
            "3/3 [==============================] - 0s 71ms/step - loss: 0.1916 - accuracy: 0.0789 - val_loss: 0.2166 - val_accuracy: 0.0737\n",
            "Epoch 4/30\n",
            "3/3 [==============================] - 0s 80ms/step - loss: 0.1902 - accuracy: 0.0737 - val_loss: 0.2156 - val_accuracy: 0.0737\n",
            "Epoch 5/30\n",
            "3/3 [==============================] - 0s 76ms/step - loss: 0.1885 - accuracy: 0.0684 - val_loss: 0.2144 - val_accuracy: 0.0737\n",
            "Epoch 6/30\n",
            "3/3 [==============================] - 0s 65ms/step - loss: 0.1870 - accuracy: 0.0684 - val_loss: 0.2128 - val_accuracy: 0.0737\n",
            "Epoch 7/30\n",
            "3/3 [==============================] - 0s 77ms/step - loss: 0.1842 - accuracy: 0.0737 - val_loss: 0.2109 - val_accuracy: 0.0737\n",
            "Epoch 8/30\n",
            "3/3 [==============================] - 0s 70ms/step - loss: 0.1810 - accuracy: 0.0763 - val_loss: 0.2082 - val_accuracy: 0.0737\n",
            "Epoch 9/30\n",
            "3/3 [==============================] - 0s 60ms/step - loss: 0.1784 - accuracy: 0.0605 - val_loss: 0.2049 - val_accuracy: 0.0737\n",
            "Epoch 10/30\n",
            "3/3 [==============================] - 0s 93ms/step - loss: 0.1703 - accuracy: 0.0711 - val_loss: 0.2014 - val_accuracy: 0.0737\n",
            "Epoch 11/30\n",
            "3/3 [==============================] - 0s 34ms/step - loss: 0.1685 - accuracy: 0.0605 - val_loss: 0.1998 - val_accuracy: 0.0737\n",
            "Epoch 12/30\n",
            "3/3 [==============================] - 0s 45ms/step - loss: 0.1519 - accuracy: 0.0816 - val_loss: 0.2039 - val_accuracy: 0.0737\n",
            "Epoch 13/30\n",
            "3/3 [==============================] - 0s 42ms/step - loss: 0.1541 - accuracy: 0.0763 - val_loss: 0.2134 - val_accuracy: 0.0737\n",
            "Epoch 14/30\n",
            "3/3 [==============================] - 0s 38ms/step - loss: 0.1571 - accuracy: 0.0789 - val_loss: 0.2188 - val_accuracy: 0.0737\n",
            "Epoch 15/30\n",
            "3/3 [==============================] - 0s 35ms/step - loss: 0.1708 - accuracy: 0.0737 - val_loss: 0.2181 - val_accuracy: 0.0737\n",
            "Epoch 16/30\n",
            "3/3 [==============================] - 0s 92ms/step - loss: 0.1604 - accuracy: 0.0868 - val_loss: 0.2141 - val_accuracy: 0.0737\n",
            "Epoch 17/30\n",
            "3/3 [==============================] - 0s 37ms/step - loss: 0.1709 - accuracy: 0.0763 - val_loss: 0.2096 - val_accuracy: 0.0737\n",
            "Epoch 18/30\n",
            "3/3 [==============================] - 0s 44ms/step - loss: 0.1573 - accuracy: 0.1026 - val_loss: 0.2062 - val_accuracy: 0.0737\n",
            "Epoch 19/30\n",
            "3/3 [==============================] - 0s 39ms/step - loss: 0.1482 - accuracy: 0.1026 - val_loss: 0.2048 - val_accuracy: 0.0737\n",
            "Epoch 20/30\n",
            "3/3 [==============================] - 0s 45ms/step - loss: 0.1661 - accuracy: 0.0737 - val_loss: 0.2044 - val_accuracy: 0.0737\n",
            "Epoch 21/30\n",
            "3/3 [==============================] - 0s 42ms/step - loss: 0.1743 - accuracy: 0.0868 - val_loss: 0.2049 - val_accuracy: 0.0737\n",
            "Epoch 22/30\n",
            "3/3 [==============================] - 0s 37ms/step - loss: 0.1705 - accuracy: 0.0921 - val_loss: 0.2059 - val_accuracy: 0.0737\n",
            "Epoch 23/30\n",
            "3/3 [==============================] - 0s 38ms/step - loss: 0.1740 - accuracy: 0.1000 - val_loss: 0.2075 - val_accuracy: 0.0737\n",
            "Epoch 24/30\n",
            "3/3 [==============================] - 0s 43ms/step - loss: 0.1798 - accuracy: 0.0789 - val_loss: 0.2096 - val_accuracy: 0.0737\n",
            "Epoch 25/30\n",
            "3/3 [==============================] - 0s 53ms/step - loss: 0.1650 - accuracy: 0.0921 - val_loss: 0.2114 - val_accuracy: 0.0737\n",
            "Epoch 26/30\n",
            "3/3 [==============================] - 0s 43ms/step - loss: 0.1852 - accuracy: 0.0842 - val_loss: 0.2126 - val_accuracy: 0.0737\n",
            "Epoch 27/30\n",
            "3/3 [==============================] - 0s 44ms/step - loss: 0.1690 - accuracy: 0.0868 - val_loss: 0.2137 - val_accuracy: 0.0737\n",
            "Epoch 28/30\n",
            "3/3 [==============================] - 0s 37ms/step - loss: 0.1881 - accuracy: 0.0789 - val_loss: 0.2146 - val_accuracy: 0.0737\n",
            "Epoch 29/30\n",
            "3/3 [==============================] - 0s 37ms/step - loss: 0.1636 - accuracy: 0.0974 - val_loss: 0.2150 - val_accuracy: 0.0737\n",
            "Epoch 30/30\n",
            "3/3 [==============================] - 0s 36ms/step - loss: 0.1847 - accuracy: 0.0684 - val_loss: 0.2151 - val_accuracy: 0.0737\n"
          ]
        }
      ],
      "source": [
        "model1 = keras.Sequential([\n",
        "    keras.layers.Embedding(6000, 15, input_length=7),\n",
        "    keras.layers.Bidirectional(keras.layers.LSTM(64)),\n",
        "    keras.layers.Dropout(0.2),\n",
        "    keras.layers.Dense(4, activation='softmax') #change value accorind to y train value like 3 or 4 or 5\n",
        "])\n",
        "\n",
        "# compiles model\n",
        "model1.compile(loss='categorical_crossentropy',\n",
        "              optimizer='adam',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "# model summary\n",
        "model1.summary()\n",
        "\n",
        "num_epochs = 30\n",
        "history1 = model1.fit(x_train,y_train, epochs=num_epochs, batch_size = 128, verbose=1, validation_split=0.2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "wBQ3vguhDh7l",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "77a876be-5953-42d4-d59e-80aaab1e6747"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])\n"
          ]
        }
      ],
      "source": [
        "print(history1.history.keys())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 503
        },
        "id": "NPh-Oi5rDl5p",
        "outputId": "fbe5f36c-083e-429a-e437-3b04d5b6815c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f1900bfa500>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x1000 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "fig,ax=plt.subplots(1,2,sharey=True,figsize=(9,5))\n",
        "fig=plt.figure(figsize=(8,10))\n",
        "\n",
        "ax[0].plot(pd.DataFrame(history1.history['accuracy']))\n",
        "ax[0].plot(pd.DataFrame(history1.history['val_accuracy']))\n",
        "ax[0].set_title('Accuracy')\n",
        "ax[0].legend(['train','test'])\n",
        "\n",
        "ax[1].plot(pd.DataFrame(history1.history['loss']))\n",
        "ax[1].plot(pd.DataFrame(history1.history['val_loss']))\n",
        "ax[1].set_title('Loss')\n",
        "ax[1].legend(['train','test'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "Sm30IJIbDrj5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "241f106c-c1ce-483c-a8e2-3e015127126a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " embedding_1 (Embedding)     (None, 7, 32)             160000    \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 7, 32)             0         \n",
            "                                                                 \n",
            " conv1d (Conv1D)             (None, 7, 32)             3104      \n",
            "                                                                 \n",
            " max_pooling1d (MaxPooling1  (None, 3, 32)             0         \n",
            " D)                                                              \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 3, 32)             0         \n",
            "                                                                 \n",
            " bidirectional_1 (Bidirecti  (None, 64)                16640     \n",
            " onal)                                                           \n",
            "                                                                 \n",
            " dropout_3 (Dropout)         (None, 64)                0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 4)                 260       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 180004 (703.14 KB)\n",
            "Trainable params: 180004 (703.14 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n",
            "None\n",
            "Epoch 1/30\n",
            "8/8 [==============================] - 8s 21ms/step - loss: 0.1973 - accuracy: 0.0926 - precision: 0.0000e+00 - recall: 0.0000e+00\n",
            "Epoch 2/30\n",
            "8/8 [==============================] - 0s 25ms/step - loss: 0.1954 - accuracy: 0.0526 - precision: 0.0000e+00 - recall: 0.0000e+00\n",
            "Epoch 3/30\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.1929 - accuracy: 0.0632 - precision: 0.0000e+00 - recall: 0.0000e+00\n",
            "Epoch 4/30\n",
            "8/8 [==============================] - 0s 27ms/step - loss: 0.1882 - accuracy: 0.0653 - precision: 0.0000e+00 - recall: 0.0000e+00\n",
            "Epoch 5/30\n",
            "8/8 [==============================] - 0s 21ms/step - loss: 0.1902 - accuracy: 0.0421 - precision: 0.0000e+00 - recall: 0.0000e+00\n",
            "Epoch 6/30\n",
            "8/8 [==============================] - 0s 25ms/step - loss: 0.1807 - accuracy: 0.0611 - precision: 0.0357 - recall: 0.0294\n",
            "Epoch 7/30\n",
            "8/8 [==============================] - 0s 18ms/step - loss: 0.1847 - accuracy: 0.0674 - precision: 0.0584 - recall: 0.1176\n",
            "Epoch 8/30\n",
            "8/8 [==============================] - 0s 21ms/step - loss: 0.1893 - accuracy: 0.1011 - precision: 0.0669 - recall: 0.2353\n",
            "Epoch 9/30\n",
            "8/8 [==============================] - 0s 18ms/step - loss: 0.2084 - accuracy: 0.0968 - precision: 0.0489 - recall: 0.1618\n",
            "Epoch 10/30\n",
            "8/8 [==============================] - 0s 20ms/step - loss: 0.2292 - accuracy: 0.0968 - precision: 0.0309 - recall: 0.1176\n",
            "Epoch 11/30\n",
            "8/8 [==============================] - 0s 18ms/step - loss: 0.2334 - accuracy: 0.1137 - precision: 0.0447 - recall: 0.1912\n",
            "Epoch 12/30\n",
            "8/8 [==============================] - 0s 20ms/step - loss: 0.2132 - accuracy: 0.1263 - precision: 0.0661 - recall: 0.2500\n",
            "Epoch 13/30\n",
            "8/8 [==============================] - 0s 20ms/step - loss: 0.1877 - accuracy: 0.1200 - precision: 0.0730 - recall: 0.2941\n",
            "Epoch 14/30\n",
            "8/8 [==============================] - 0s 21ms/step - loss: 0.2228 - accuracy: 0.1158 - precision: 0.0730 - recall: 0.2941\n",
            "Epoch 15/30\n",
            "8/8 [==============================] - 0s 19ms/step - loss: 0.2261 - accuracy: 0.0863 - precision: 0.0512 - recall: 0.2206\n",
            "Epoch 16/30\n",
            "8/8 [==============================] - 0s 21ms/step - loss: 0.2098 - accuracy: 0.1074 - precision: 0.0639 - recall: 0.2500\n",
            "Epoch 17/30\n",
            "8/8 [==============================] - 0s 19ms/step - loss: 0.1961 - accuracy: 0.0968 - precision: 0.0657 - recall: 0.2647\n",
            "Epoch 18/30\n",
            "8/8 [==============================] - 0s 23ms/step - loss: 0.2209 - accuracy: 0.1053 - precision: 0.0489 - recall: 0.2206\n",
            "Epoch 19/30\n",
            "8/8 [==============================] - 0s 23ms/step - loss: 0.2249 - accuracy: 0.1032 - precision: 0.0459 - recall: 0.1912\n",
            "Epoch 20/30\n",
            "8/8 [==============================] - 0s 20ms/step - loss: 0.2000 - accuracy: 0.1221 - precision: 0.0476 - recall: 0.1912\n",
            "Epoch 21/30\n",
            "8/8 [==============================] - 0s 23ms/step - loss: 0.2042 - accuracy: 0.0863 - precision: 0.0500 - recall: 0.2059\n",
            "Epoch 22/30\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.2590 - accuracy: 0.0926 - precision: 0.0383 - recall: 0.1618\n",
            "Epoch 23/30\n",
            "8/8 [==============================] - 0s 17ms/step - loss: 0.2139 - accuracy: 0.1011 - precision: 0.0414 - recall: 0.1618\n",
            "Epoch 24/30\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.2306 - accuracy: 0.1116 - precision: 0.0469 - recall: 0.1912\n",
            "Epoch 25/30\n",
            "8/8 [==============================] - 0s 24ms/step - loss: 0.1883 - accuracy: 0.1158 - precision: 0.0799 - recall: 0.3676\n",
            "Epoch 26/30\n",
            "8/8 [==============================] - 0s 19ms/step - loss: 0.2163 - accuracy: 0.0926 - precision: 0.0582 - recall: 0.2500\n",
            "Epoch 27/30\n",
            "8/8 [==============================] - 0s 18ms/step - loss: 0.2073 - accuracy: 0.1158 - precision: 0.0714 - recall: 0.2941\n",
            "Epoch 28/30\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.2078 - accuracy: 0.1074 - precision: 0.0699 - recall: 0.2794\n",
            "Epoch 29/30\n",
            "8/8 [==============================] - 0s 25ms/step - loss: 0.2130 - accuracy: 0.1053 - precision: 0.0530 - recall: 0.2206\n",
            "Epoch 30/30\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.2253 - accuracy: 0.0926 - precision: 0.0481 - recall: 0.2059\n"
          ]
        }
      ],
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import Embedding, Conv1D, MaxPooling1D, Bidirectional, LSTM, Dense, Dropout\n",
        "from keras.metrics import Precision, Recall\n",
        "# from tf.keras.optimizers import SGD\n",
        "# from keras.optimizers import RMSprop\n",
        "from keras import datasets\n",
        "\n",
        "from keras.callbacks import LearningRateScheduler\n",
        "from keras.callbacks import History\n",
        "\n",
        "from keras import losses\n",
        "\n",
        "vocab_size = 5000\n",
        "embedding_size = 32\n",
        "epochs=30\n",
        "learning_rate = 0.1\n",
        "decay_rate = learning_rate / epochs\n",
        "momentum = 0.8\n",
        "\n",
        "# sgd = SGD(lr=learning_rate, momentum=momentum, decay=decay_rate, nesterov=False)\n",
        "# Build model\n",
        "model= Sequential()\n",
        "model.add(Embedding(vocab_size, embedding_size, input_length=7))\n",
        "model.add(Dropout(0.4))\n",
        "model.add(Conv1D(filters=32, kernel_size=3, padding='same', activation='relu'))\n",
        "model.add(MaxPooling1D(pool_size=2))\n",
        "model.add(Dropout(0.4))\n",
        "model.add(Bidirectional(LSTM(32)))\n",
        "model.add(Dropout(0.4))\n",
        "model.add(Dense(4, activation='softmax')) #change value accorind to y train value like 3 or 4 or 5\n",
        "import tensorflow as tf\n",
        "tf.keras.utils.plot_model(model, show_shapes=True)\n",
        "\n",
        "print(model.summary())\n",
        "\n",
        "# Compile model\n",
        "model.compile(loss='categorical_crossentropy', optimizer='adam',\n",
        "               metrics=['accuracy', Precision(), Recall()])\n",
        "\n",
        "# Train model\n",
        "\n",
        "batch_size = 64\n",
        "history = model.fit( x_train, y_train,batch_size=batch_size, epochs=epochs, verbose=1)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model1.compile(loss='categorical_crossentropy',\n",
        "              optimizer='adam',\n",
        "              metrics=['accuracy', tf.keras.metrics.Precision(), tf.keras.metrics.Recall()])\n"
      ],
      "metadata": {
        "id": "OU2-cKYGZW9-"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "x62q1FTFDvbT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "outputId": "5fd5efd3-0312-4057-b9ed-d7334078c04c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf\n",
        "\n",
        "def plot_training_hist(history):\n",
        "    '''Function to plot history for accuracy and loss'''\n",
        "\n",
        "    fig, ax = plt.subplots(1, 2, figsize=(10, 4))\n",
        "\n",
        "    # Plot accuracy and validation accuracy\n",
        "    ax[0].plot(history.history['accuracy'])\n",
        "    ax[0].plot(history.history['val_accuracy'])\n",
        "    ax[0].set_title('Model Accuracy')\n",
        "    ax[0].set_xlabel('epoch')\n",
        "    ax[0].set_ylabel('accuracy')\n",
        "    ax[0].legend(['train', 'validation'], loc='best')\n",
        "\n",
        "    # Plot loss and validation loss\n",
        "    ax[1].plot(history.history['loss'])\n",
        "    ax[1].plot(history.history['val_loss'])\n",
        "    ax[1].set_title('Model Loss')\n",
        "    ax[1].set_xlabel('epoch')\n",
        "    ax[1].set_ylabel('loss')\n",
        "    ax[1].legend(['train', 'validation'], loc='best')\n",
        "\n",
        "# Assuming history1 contains training history\n",
        "plot_training_hist(history1)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Prediction"
      ],
      "metadata": {
        "id": "4UEpsX2feSg6"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "i-CdolDBDzFl"
      },
      "outputs": [],
      "source": [
        "def predict_class(text):\n",
        "    '''Function to predict sentiment class of the passed text'''\n",
        "\n",
        "    sentiment_classes = ['Negative', 'Neutral', 'Positive']\n",
        "    max_len=7\n",
        "\n",
        "    # Transforms text to a sequence of integers using a tokenizer object\n",
        "    xt = tokenize.texts_to_sequences(text)\n",
        "    # Pad sequences to the same length\n",
        "    xt = pad_sequences(xt, padding='post', maxlen=max_len)\n",
        "    # Do the prediction using the loaded model\n",
        "    yt = model.predict(xt).argmax(axis=1)\n",
        "    # Print the predicted sentiment\n",
        "    print('The predicted sentiment is', sentiment_classes[yt[0]])"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Confusion Matrix"
      ],
      "metadata": {
        "id": "lBOQ4EuqeYtW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "import seaborn as sns\n",
        "def plot_confusion_matrix(x_test,y_test):\n",
        "  sentiment_classes = ['Negative', 'Neutral', 'Positive']\n",
        "  y_pred=model.predict(x_test)\n",
        "  cm=confusion_matrix(np.argmax(y_pred,axis=1),np.argmax(np.array(y_test),axis=1))\n",
        "  plt.figure(figsize=(8,6))\n",
        "  sns.heatmap(cm, cmap=plt.cm.Blues, annot=True, fmt='d',\n",
        "                xticklabels=sentiment_classes,\n",
        "                yticklabels=sentiment_classes)\n",
        "  plt.title('Confusion matrix', fontsize=16)\n",
        "  plt.xlabel('Actual label', fontsize=12)\n",
        "  plt.ylabel('Predicted label', fontsize=12)\n",
        "  # # print(np.argmax(y_pred,axis=1))\n",
        "  # print(sentiment[np.argmax(y_pred,axis=1)])\n",
        "plot_confusion_matrix(x_val,y_val)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 587
        },
        "id": "Wh1_grr9Z3O2",
        "outputId": "c58a4cf5-0ebb-42c3-9b4c-f22bec3ebdb5"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2/2 [==============================] - 0s 8ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}