Sentiment_Analysis_BERT.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Sentiment_Analysis_BERT.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "ghBBA2xfY-hE",
        "colab_type": "code",
        "outputId": "cc64bd84-061e-4f07-9e9a-d9ca3da96ee0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 326
        }
      },
      "source": [
        "!pip install kaggle\n",
        "!mkdir .kaggle\n",
        "\n",
        "import json\n",
        "token = {\"username\":\"amratye\",\"key\":\"8d63e175345272d74412f69d42d6c05a\"}\n",
        "with open('/content/.kaggle/kaggle.json', 'w') as file:\n",
        "    json.dump(token, file)\n",
        "    \n",
        "!cp /content/.kaggle/kaggle.json ~/.kaggle/kaggle.json\n",
        "!kaggle config set -n path -v{/content}\n",
        "!chmod 600 /root/.kaggle/kaggle.json\n",
        "!kaggle datasets download -d lakshmi25npathi/imdb-dataset-of-50k-movie-reviews    "
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: kaggle in /usr/local/lib/python3.6/dist-packages (1.5.6)\n",
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            "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.6/dist-packages (from python-slugify->kaggle) (1.3)\n",
            "mkdir: cannot create directory ‘.kaggle’: File exists\n",
            "Warning: Your Kaggle API key is readable by other users on this system! To fix this, you can run 'chmod 600 /root/.kaggle/kaggle.json'\n",
            "- path is now set to: {/content}\n",
            "Downloading imdb-dataset-of-50k-movie-reviews.zip to {/content}/datasets/lakshmi25npathi/imdb-dataset-of-50k-movie-reviews\n",
            " 62% 16.0M/25.7M [00:00<00:00, 18.6MB/s]\n",
            "100% 25.7M/25.7M [00:00<00:00, 47.7MB/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JeGLibVTZOTI",
        "colab_type": "code",
        "outputId": "e7c67892-0d17-4e4d-c306-bab48d3cbd42",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        }
      },
      "source": [
        "!unzip {/content}/datasets/lakshmi25npathi/imdb-dataset-of-50k-movie-reviews/imdb-dataset-of-50k-movie-reviews.zip"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  {/content}/datasets/lakshmi25npathi/imdb-dataset-of-50k-movie-reviews/imdb-dataset-of-50k-movie-reviews.zip\n",
            "  inflating: IMDB Dataset.csv        \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PSrO6knzG6sb",
        "colab_type": "code",
        "outputId": "f28378f0-19b5-40be-a5f6-b41c82808836",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "#Just initializing the bert's server and client \n",
        "!pip install tensorflow==1.15\n",
        "!pip install bert-serving-client\n",
        "!pip install -U bert-serving-server[http]"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting tensorflow==1.15\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/3f/98/5a99af92fb911d7a88a0005ad55005f35b4c1ba8d75fba02df726cd936e6/tensorflow-1.15.0-cp36-cp36m-manylinux2010_x86_64.whl (412.3MB)\n",
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            "Collecting gast==0.2.2\n",
            "  Downloading https://files.pythonhosted.org/packages/4e/35/11749bf99b2d4e3cceb4d55ca22590b0d7c2c62b9de38ac4a4a7f4687421/gast-0.2.2.tar.gz\n",
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            "Collecting tensorflow-estimator==1.15.1\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/de/62/2ee9cd74c9fa2fa450877847ba560b260f5d0fb70ee0595203082dafcc9d/tensorflow_estimator-1.15.1-py2.py3-none-any.whl (503kB)\n",
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            "Collecting tensorboard<1.16.0,>=1.15.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/1e/e9/d3d747a97f7188f48aa5eda486907f3b345cd409f0a0850468ba867db246/tensorboard-1.15.0-py3-none-any.whl (3.8MB)\n",
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            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.16.0,>=1.15.0->tensorflow==1.15) (3.2.1)\n",
            "Building wheels for collected packages: gast\n",
            "  Building wheel for gast (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for gast: filename=gast-0.2.2-cp36-none-any.whl size=7540 sha256=a59f2e61d5212219bab894730c4fdedf80aef62072c8fe9eb66c58add68c227d\n",
            "  Stored in directory: /root/.cache/pip/wheels/5c/2e/7e/a1d4d4fcebe6c381f378ce7743a3ced3699feb89bcfbdadadd\n",
            "Successfully built gast\n",
            "\u001b[31mERROR: tensorflow-probability 0.10.0rc0 has requirement gast>=0.3.2, but you'll have gast 0.2.2 which is incompatible.\u001b[0m\n",
            "Installing collected packages: gast, tensorflow-estimator, tensorboard, tensorflow\n",
            "  Found existing installation: gast 0.3.3\n",
            "    Uninstalling gast-0.3.3:\n",
            "      Successfully uninstalled gast-0.3.3\n",
            "  Found existing installation: tensorflow-estimator 2.2.0\n",
            "    Uninstalling tensorflow-estimator-2.2.0:\n",
            "      Successfully uninstalled tensorflow-estimator-2.2.0\n",
            "  Found existing installation: tensorboard 2.2.1\n",
            "    Uninstalling tensorboard-2.2.1:\n",
            "      Successfully uninstalled tensorboard-2.2.1\n",
            "  Found existing installation: tensorflow 2.2.0\n",
            "    Uninstalling tensorflow-2.2.0:\n",
            "      Successfully uninstalled tensorflow-2.2.0\n",
            "Successfully installed gast-0.2.2 tensorboard-1.15.0 tensorflow-1.15.0 tensorflow-estimator-1.15.1\n",
            "Collecting bert-serving-client\n",
            "  Downloading https://files.pythonhosted.org/packages/1f/09/aae1405378a848b2e87769ad89a43d6d71978c4e15534ca48e82e723a72f/bert_serving_client-1.10.0-py2.py3-none-any.whl\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from bert-serving-client) (1.18.4)\n",
            "Requirement already satisfied: pyzmq>=17.1.0 in /usr/local/lib/python3.6/dist-packages (from bert-serving-client) (19.0.1)\n",
            "Installing collected packages: bert-serving-client\n",
            "Successfully installed bert-serving-client-1.10.0\n",
            "Collecting bert-serving-server[http]\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/b0/bd/cab677bbd0c5fb08b72e468371d2bca6ed9507785739b4656b0b5470d90b/bert_serving_server-1.10.0-py3-none-any.whl (61kB)\n",
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            "\u001b[?25hRequirement already satisfied, skipping upgrade: six in /usr/local/lib/python3.6/dist-packages (from bert-serving-server[http]) (1.12.0)\n",
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            "Collecting GPUtil>=1.3.0\n",
            "  Downloading https://files.pythonhosted.org/packages/ed/0e/5c61eedde9f6c87713e89d794f01e378cfd9565847d4576fa627d758c554/GPUtil-1.4.0.tar.gz\n",
            "Requirement already satisfied, skipping upgrade: numpy in /usr/local/lib/python3.6/dist-packages (from bert-serving-server[http]) (1.18.4)\n",
            "Requirement already satisfied, skipping upgrade: termcolor>=1.1 in /usr/local/lib/python3.6/dist-packages (from bert-serving-server[http]) (1.1.0)\n",
            "Requirement already satisfied, skipping upgrade: bert-serving-client; extra == \"http\" in /usr/local/lib/python3.6/dist-packages (from bert-serving-server[http]) (1.10.0)\n",
            "Collecting flask-json; extra == \"http\"\n",
            "  Downloading https://files.pythonhosted.org/packages/6f/2d/4c21d98b11f3a206fabbdd965b53a2ca3ee9fab7646c93cf36c060e8f1a4/Flask_JSON-0.3.4-py3-none-any.whl\n",
            "Collecting flask-cors; extra == \"http\"\n",
            "  Downloading https://files.pythonhosted.org/packages/78/38/e68b11daa5d613e3a91e4bf3da76c94ac9ee0d9cd515af9c1ab80d36f709/Flask_Cors-3.0.8-py2.py3-none-any.whl\n",
            "Collecting flask-compress; extra == \"http\"\n",
            "  Downloading https://files.pythonhosted.org/packages/a0/96/cd684c1ffe97b513303b5bfd4bbfb4114c5f4a5ea8a737af6fd813273df8/Flask-Compress-1.5.0.tar.gz\n",
            "Requirement already satisfied, skipping upgrade: flask; extra == \"http\" in /usr/local/lib/python3.6/dist-packages (from bert-serving-server[http]) (1.1.2)\n",
            "Collecting brotli\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/f4/68/60a220454dc5083c6d59b41aa90bb1c96fad62a0abf3a33e0ef64b38638a/Brotli-1.0.7-cp36-cp36m-manylinux1_x86_64.whl (352kB)\n",
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            "\u001b[?25hRequirement already satisfied, skipping upgrade: Werkzeug>=0.15 in /usr/local/lib/python3.6/dist-packages (from flask; extra == \"http\"->bert-serving-server[http]) (1.0.1)\n",
            "Requirement already satisfied, skipping upgrade: click>=5.1 in /usr/local/lib/python3.6/dist-packages (from flask; extra == \"http\"->bert-serving-server[http]) (7.1.2)\n",
            "Requirement already satisfied, skipping upgrade: Jinja2>=2.10.1 in /usr/local/lib/python3.6/dist-packages (from flask; extra == \"http\"->bert-serving-server[http]) (2.11.2)\n",
            "Requirement already satisfied, skipping upgrade: itsdangerous>=0.24 in /usr/local/lib/python3.6/dist-packages (from flask; extra == \"http\"->bert-serving-server[http]) (1.1.0)\n",
            "Requirement already satisfied, skipping upgrade: MarkupSafe>=0.23 in /usr/local/lib/python3.6/dist-packages (from Jinja2>=2.10.1->flask; extra == \"http\"->bert-serving-server[http]) (1.1.1)\n",
            "Building wheels for collected packages: GPUtil, flask-compress\n",
            "  Building wheel for GPUtil (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for GPUtil: filename=GPUtil-1.4.0-cp36-none-any.whl size=7413 sha256=caad759f7a08af131227c9c846a1d656403ddffa6d0e25ad4174d8f3d4c62dfa\n",
            "  Stored in directory: /root/.cache/pip/wheels/3d/77/07/80562de4bb0786e5ea186911a2c831fdd0018bda69beab71fd\n",
            "  Building wheel for flask-compress (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for flask-compress: filename=Flask_Compress-1.5.0-cp36-none-any.whl size=5273 sha256=273ac756e8ccef3b5c1a51f7b8f786d97e5cb36685f2133064d08b9d11790e07\n",
            "  Stored in directory: /root/.cache/pip/wheels/f7/e9/e4/5afc286be7c87461375e33152558415dfeb0c8f5af3b50e742\n",
            "Successfully built GPUtil flask-compress\n",
            "Installing collected packages: GPUtil, flask-json, flask-cors, brotli, flask-compress, bert-serving-server\n",
            "Successfully installed GPUtil-1.4.0 bert-serving-server-1.10.0 brotli-1.0.7 flask-compress-1.5.0 flask-cors-3.0.8 flask-json-0.3.4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ArhipX6TCLdn",
        "colab_type": "code",
        "outputId": "79950930-9b76-4fb0-9a5e-65ee8952b653",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 344
        }
      },
      "source": [
        "#Downloading and unzipping the pre-trained bert model named \"uncased_L-12_H-768_A-12\"\n",
        "#if input is requested type \"A\"\n",
        "\n",
        "!wget https://storage.googleapis.com/bert_models/2018_10_18/uncased_L-12_H-768_A-12.zip\n",
        "!unzip uncased_L-12_H-768_A-12.zip\n",
        "!nohup bert-serving-start -model_dir=./uncased_L-12_H-768_A-12 > out.file 2>&1 &"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-05-18 06:13:28--  https://storage.googleapis.com/bert_models/2018_10_18/uncased_L-12_H-768_A-12.zip\n",
            "Resolving storage.googleapis.com (storage.googleapis.com)... 74.125.133.128, 2a00:1450:400c:c08::80\n",
            "Connecting to storage.googleapis.com (storage.googleapis.com)|74.125.133.128|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 407727028 (389M) [application/zip]\n",
            "Saving to: ‘uncased_L-12_H-768_A-12.zip’\n",
            "\n",
            "uncased_L-12_H-768_ 100%[===================>] 388.84M  86.8MB/s    in 4.7s    \n",
            "\n",
            "2020-05-18 06:13:33 (82.5 MB/s) - ‘uncased_L-12_H-768_A-12.zip’ saved [407727028/407727028]\n",
            "\n",
            "Archive:  uncased_L-12_H-768_A-12.zip\n",
            "   creating: uncased_L-12_H-768_A-12/\n",
            "  inflating: uncased_L-12_H-768_A-12/bert_model.ckpt.meta  \n",
            "  inflating: uncased_L-12_H-768_A-12/bert_model.ckpt.data-00000-of-00001  \n",
            "  inflating: uncased_L-12_H-768_A-12/vocab.txt  \n",
            "  inflating: uncased_L-12_H-768_A-12/bert_model.ckpt.index  \n",
            "  inflating: uncased_L-12_H-768_A-12/bert_config.json  \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aDcUonGUC7Y2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from bert_serving.client import BertClient\n",
        "bc = BertClient()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7nSS6A5TD1rK",
        "colab_type": "code",
        "outputId": "857aa273-0fe8-424f-cce0-cae8e90b9f73",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 217
        }
      },
      "source": [
        "#importing essential packages\n",
        "!pip install sklearn\n",
        "!pip install nltk\n",
        "import nltk\n",
        "nltk.download('punkt')\n",
        "nltk.download('stopwords')\n",
        "from sklearn import preprocessing\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from bs4 import BeautifulSoup as bf\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.tokenize import word_tokenize\n",
        "from nltk.stem.porter import PorterStemmer \n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "import tensorflow.keras.layers as k\n",
        "from sklearn.linear_model import LogisticRegression"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: sklearn in /usr/local/lib/python3.6/dist-packages (0.0)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.6/dist-packages (from sklearn) (0.22.2.post1)\n",
            "Requirement already satisfied: numpy>=1.11.0 in /usr/local/lib/python3.6/dist-packages (from scikit-learn->sklearn) (1.18.4)\n",
            "Requirement already satisfied: scipy>=0.17.0 in /usr/local/lib/python3.6/dist-packages (from scikit-learn->sklearn) (1.4.1)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from scikit-learn->sklearn) (0.14.1)\n",
            "Requirement already satisfied: nltk in /usr/local/lib/python3.6/dist-packages (3.2.5)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from nltk) (1.12.0)\n",
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n",
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S9TI2fGjDFsD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#loading the text\n",
        "data = pd.read_csv(\"IMDB Dataset.csv\")\n",
        "\n",
        "#cleaning the text\n",
        "label_encoder = preprocessing.LabelEncoder()\n",
        "porter = PorterStemmer()\n",
        "\n",
        "for i in range(50000):\n",
        "  data.loc[i][0] = bf(data.loc[i][0],'html.parser').get_text()\n",
        "\n",
        "stop = stopwords.words(\"english\")\n",
        "\n",
        "for i in range(50000):\n",
        "  data.loc[i][0] = [x for x in word_tokenize(data.loc[i][0]) if not x in stop]\n",
        "  data.loc[i][0] = [x for x in data.loc[i][0] if str(x).isalpha()]\n",
        "  data.loc[i][0] = [porter.stem(x) for x in data.loc[i][0]]\n",
        "  data.loc[i][0] = \" \".join(x for x in data.loc[i][0]) "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IDD6UbGvDu5o",
        "colab_type": "code",
        "outputId": "59a378df-6a41-4954-d924-99f29aa99d0d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        }
      },
      "source": [
        "#visualizing new data obtained\n",
        "\n",
        "data.head(10)\n"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>review</th>\n",
              "      <th>sentiment</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>one review mention watch Oz episod hook they r...</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>A wonder littl product the film techniqu fashi...</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>I thought wonder way spend time hot summer wee...</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>basic famili littl boy jake think zombi closet...</td>\n",
              "      <td>negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>petter mattei love time money visual stun film...</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>probabl favorit movi stori selfless sacrific d...</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>I sure would like see resurrect date seahunt s...</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>thi show amaz fresh innov idea first air the f...</td>\n",
              "      <td>negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>encourag posit comment film I look forward wat...</td>\n",
              "      <td>negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>If like origin gut wrench laughter like movi I...</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                              review sentiment\n",
              "0  one review mention watch Oz episod hook they r...  positive\n",
              "1  A wonder littl product the film techniqu fashi...  positive\n",
              "2  I thought wonder way spend time hot summer wee...  positive\n",
              "3  basic famili littl boy jake think zombi closet...  negative\n",
              "4  petter mattei love time money visual stun film...  positive\n",
              "5  probabl favorit movi stori selfless sacrific d...  positive\n",
              "6  I sure would like see resurrect date seahunt s...  positive\n",
              "7  thi show amaz fresh innov idea first air the f...  negative\n",
              "8  encourag posit comment film I look forward wat...  negative\n",
              "9  If like origin gut wrench laughter like movi I...  positive"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N1T7YK2tEn9S",
        "colab_type": "code",
        "outputId": "946df602-4179-4d41-a24e-792c338a6d95",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 128
        }
      },
      "source": [
        "#encoding the cleaned text into vectors\n",
        "from sklearn.model_selection import train_test_split\n",
        "train = bc.encode(list(data['review']))\n",
        "test = list(data['sentiment'])\n",
        "test = np.array([x == 'positive' for x in test]).astype(int)\n",
        "\n",
        "X_train,X_test,Y_train,Y_test = train_test_split(train,test,test_size = 0.2)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/bert_serving/client/__init__.py:299: UserWarning: some of your sentences have more tokens than \"max_seq_len=25\" set on the server, as consequence you may get less-accurate or truncated embeddings.\n",
            "here is what you can do:\n",
            "- disable the length-check by create a new \"BertClient(check_length=False)\" when you do not want to display this warning\n",
            "- or, start a new server with a larger \"max_seq_len\"\n",
            "  '- or, start a new server with a larger \"max_seq_len\"' % self.length_limit)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kGzn6Z12LqcW",
        "colab_type": "code",
        "outputId": "063e03e2-c0f0-415c-f360-04076aa6e030",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 181
        }
      },
      "source": [
        "#training the logistic regression model\n",
        "classifier = LogisticRegression()\n",
        "\n",
        "classifier.fit(X_train,Y_train)\n",
        "score = classifier.score(X_test,Y_test)\n",
        "print(\"The accuracy of the model is {} through Logistic Regression model\".format(score*100))"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The accuracy of the model is 68.28999999999999 through Logistic Regression model\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jUAZZnk2PGsh",
        "colab_type": "code",
        "outputId": "ed89ed93-fc51-44d5-ea2a-45998c770cfa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273
        }
      },
      "source": [
        "#building the model \n",
        "from tqdm import tqdm\n",
        "\n",
        "X_train = np.array(X_train)\n",
        "\n",
        "model = tf.keras.models.Sequential([\n",
        "           k.Dense(250,activation = 'relu',input_dim = np.array(X_train).shape[1]),\n",
        "           k.Dense(1,activation = 'sigmoid'),\n",
        "])\n",
        "\n",
        "model.compile(loss = 'binary_crossentropy',optimizer = 'adam',metrics=['accuracy'])\n",
        "\n",
        "for i in tqdm(range(2)):\n",
        "  model.fit(X_train,np.array(Y_train),validation_split = 0.2, verbose = 1)\n",
        "_,score = model.evaluate(X_test,Y_test)\n",
        "score = str(score*100)+str('%')\n",
        "print(\"The accuracy of the model is {}\".format(score))"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "If using Keras pass *_constraint arguments to layers.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/nn_impl.py:183: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r  0%|          | 0/2 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Train on 32000 samples, validate on 8000 samples\n",
            "32000/32000 [==============================] - 4s 117us/sample - loss: 0.6293 - acc: 0.6448 - val_loss: 0.6058 - val_acc: 0.6661\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 1/2 [00:04<00:04,  4.43s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Train on 32000 samples, validate on 8000 samples\n",
            "32000/32000 [==============================] - 3s 105us/sample - loss: 0.6042 - acc: 0.6662 - val_loss: 0.6025 - val_acc: 0.6690\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 2/2 [00:07<00:00,  3.91s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            " 2880/10000 [=======>......................] - ETA: 0s - loss: 0.5973 - acc: 0.6812"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "10000/10000 [==============================] - 1s 52us/sample - loss: 0.5985 - acc: 0.6796\n",
            "The accuracy of the model is 67.96000003814697%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ra-rQAkmGSs6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}