Final_Best_LSTM_Run_seed42_08283val.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f21c86ac",
        "outputId": "856111d1-f204-4a35-bf3f-a1a66cf24d1f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n",
            "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
            "[nltk_data]     /root/nltk_data...\n",
            "[nltk_data]   Unzipping taggers/averaged_perceptron_tagger.zip.\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data] Downloading package omw-1.4 to /root/nltk_data...\n",
            "[nltk_data] Downloading collection 'all'\n",
            "[nltk_data]    | \n",
            "[nltk_data]    | Downloading package abc to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/abc.zip.\n",
            "[nltk_data]    | Downloading package alpino to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/alpino.zip.\n",
            "[nltk_data]    | Downloading package averaged_perceptron_tagger to\n",
            "[nltk_data]    |     /root/nltk_data...\n",
            "[nltk_data]    |   Package averaged_perceptron_tagger is already up-\n",
            "[nltk_data]    |       to-date!\n",
            "[nltk_data]    | Downloading package averaged_perceptron_tagger_ru to\n",
            "[nltk_data]    |     /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping\n",
            "[nltk_data]    |       taggers/averaged_perceptron_tagger_ru.zip.\n",
            "[nltk_data]    | Downloading package basque_grammars to\n",
            "[nltk_data]    |     /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping grammars/basque_grammars.zip.\n",
            "[nltk_data]    | Downloading package bcp47 to /root/nltk_data...\n",
            "[nltk_data]    | Downloading package biocreative_ppi to\n",
            "[nltk_data]    |     /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/biocreative_ppi.zip.\n",
            "[nltk_data]    | Downloading package bllip_wsj_no_aux to\n",
            "[nltk_data]    |     /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping models/bllip_wsj_no_aux.zip.\n",
            "[nltk_data]    | Downloading package book_grammars to\n",
            "[nltk_data]    |     /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping grammars/book_grammars.zip.\n",
            "[nltk_data]    | Downloading package brown to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/brown.zip.\n",
            "[nltk_data]    | Downloading package brown_tei to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/brown_tei.zip.\n",
            "[nltk_data]    | Downloading package cess_cat to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/cess_cat.zip.\n",
            "[nltk_data]    | Downloading package cess_esp to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/cess_esp.zip.\n",
            "[nltk_data]    | Downloading package chat80 to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/chat80.zip.\n",
            "[nltk_data]    | Downloading package city_database to\n",
            "[nltk_data]    |     /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/city_database.zip.\n",
            "[nltk_data]    | Downloading package cmudict to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/cmudict.zip.\n",
            "[nltk_data]    | Downloading package comparative_sentences to\n",
            "[nltk_data]    |     /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/comparative_sentences.zip.\n",
            "[nltk_data]    | Downloading package comtrans to /root/nltk_data...\n",
            "[nltk_data]    | Downloading package conll2000 to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/conll2000.zip.\n",
            "[nltk_data]    | Downloading package conll2002 to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/conll2002.zip.\n",
            "[nltk_data]    | Downloading package conll2007 to /root/nltk_data...\n",
            "[nltk_data]    | Downloading package crubadan to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/crubadan.zip.\n",
            "[nltk_data]    | Downloading package dependency_treebank to\n",
            "[nltk_data]    |     /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/dependency_treebank.zip.\n",
            "[nltk_data]    | Downloading package dolch to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/dolch.zip.\n",
            "[nltk_data]    | Downloading package europarl_raw to\n",
            "[nltk_data]    |     /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/europarl_raw.zip.\n",
            "[nltk_data]    | Downloading package extended_omw to\n",
            "[nltk_data]    |     /root/nltk_data...\n",
            "[nltk_data]    | Downloading package floresta to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/floresta.zip.\n",
            "[nltk_data]    | Downloading package framenet_v15 to\n",
            "[nltk_data]    |     /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/framenet_v15.zip.\n",
            "[nltk_data]    | Downloading package framenet_v17 to\n",
            "[nltk_data]    |     /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/framenet_v17.zip.\n",
            "[nltk_data]    | Downloading package gazetteers to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/gazetteers.zip.\n",
            "[nltk_data]    | Downloading package genesis to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/genesis.zip.\n",
            "[nltk_data]    | Downloading package gutenberg to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/gutenberg.zip.\n",
            "[nltk_data]    | Downloading package ieer to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/ieer.zip.\n",
            "[nltk_data]    | Downloading package inaugural to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/inaugural.zip.\n",
            "[nltk_data]    | Downloading package indian to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/indian.zip.\n",
            "[nltk_data]    | Downloading package jeita to /root/nltk_data...\n",
            "[nltk_data]    | Downloading package kimmo to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/kimmo.zip.\n",
            "[nltk_data]    | Downloading package knbc to /root/nltk_data...\n",
            "[nltk_data]    | Downloading package large_grammars to\n",
            "[nltk_data]    |     /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping grammars/large_grammars.zip.\n",
            "[nltk_data]    | Downloading package lin_thesaurus to\n",
            "[nltk_data]    |     /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/lin_thesaurus.zip.\n",
            "[nltk_data]    | Downloading package mac_morpho to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/mac_morpho.zip.\n",
            "[nltk_data]    | Downloading package machado to /root/nltk_data...\n",
            "[nltk_data]    | Downloading package masc_tagged to /root/nltk_data...\n",
            "[nltk_data]    | Downloading package maxent_ne_chunker to\n",
            "[nltk_data]    |     /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping chunkers/maxent_ne_chunker.zip.\n",
            "[nltk_data]    | Downloading package maxent_treebank_pos_tagger to\n",
            "[nltk_data]    |     /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping taggers/maxent_treebank_pos_tagger.zip.\n",
            "[nltk_data]    | Downloading package moses_sample to\n",
            "[nltk_data]    |     /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping models/moses_sample.zip.\n",
            "[nltk_data]    | Downloading package movie_reviews to\n",
            "[nltk_data]    |     /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/movie_reviews.zip.\n",
            "[nltk_data]    | Downloading package mte_teip5 to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/mte_teip5.zip.\n",
            "[nltk_data]    | Downloading package mwa_ppdb to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping misc/mwa_ppdb.zip.\n",
            "[nltk_data]    | Downloading package names to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/names.zip.\n",
            "[nltk_data]    | Downloading package nombank.1.0 to /root/nltk_data...\n",
            "[nltk_data]    | Downloading package nonbreaking_prefixes to\n",
            "[nltk_data]    |     /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/nonbreaking_prefixes.zip.\n",
            "[nltk_data]    | Downloading package nps_chat to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/nps_chat.zip.\n",
            "[nltk_data]    | Downloading package omw to /root/nltk_data...\n",
            "[nltk_data]    | Downloading package omw-1.4 to /root/nltk_data...\n",
            "[nltk_data]    |   Package omw-1.4 is already up-to-date!\n",
            "[nltk_data]    | Downloading package opinion_lexicon to\n",
            "[nltk_data]    |     /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/opinion_lexicon.zip.\n",
            "[nltk_data]    | Downloading package panlex_swadesh to\n",
            "[nltk_data]    |     /root/nltk_data...\n",
            "[nltk_data]    | Downloading package paradigms to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/paradigms.zip.\n",
            "[nltk_data]    | Downloading package pe08 to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/pe08.zip.\n",
            "[nltk_data]    | Downloading package perluniprops to\n",
            "[nltk_data]    |     /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping misc/perluniprops.zip.\n",
            "[nltk_data]    | Downloading package pil to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/pil.zip.\n",
            "[nltk_data]    | Downloading package pl196x to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/pl196x.zip.\n",
            "[nltk_data]    | Downloading package porter_test to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping stemmers/porter_test.zip.\n",
            "[nltk_data]    | Downloading package ppattach to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/ppattach.zip.\n",
            "[nltk_data]    | Downloading package problem_reports to\n",
            "[nltk_data]    |     /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/problem_reports.zip.\n",
            "[nltk_data]    | Downloading package product_reviews_1 to\n",
            "[nltk_data]    |     /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/product_reviews_1.zip.\n",
            "[nltk_data]    | Downloading package product_reviews_2 to\n",
            "[nltk_data]    |     /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/product_reviews_2.zip.\n",
            "[nltk_data]    | Downloading package propbank to /root/nltk_data...\n",
            "[nltk_data]    | Downloading package pros_cons to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/pros_cons.zip.\n",
            "[nltk_data]    | Downloading package ptb to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/ptb.zip.\n",
            "[nltk_data]    | Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]    |   Package punkt is already up-to-date!\n",
            "[nltk_data]    | Downloading package qc to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/qc.zip.\n",
            "[nltk_data]    | Downloading package reuters to /root/nltk_data...\n",
            "[nltk_data]    | Downloading package rslp to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping stemmers/rslp.zip.\n",
            "[nltk_data]    | Downloading package rte to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/rte.zip.\n",
            "[nltk_data]    | Downloading package sample_grammars to\n",
            "[nltk_data]    |     /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping grammars/sample_grammars.zip.\n",
            "[nltk_data]    | Downloading package semcor to /root/nltk_data...\n",
            "[nltk_data]    | Downloading package senseval to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/senseval.zip.\n",
            "[nltk_data]    | Downloading package sentence_polarity to\n",
            "[nltk_data]    |     /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/sentence_polarity.zip.\n",
            "[nltk_data]    | Downloading package sentiwordnet to\n",
            "[nltk_data]    |     /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/sentiwordnet.zip.\n",
            "[nltk_data]    | Downloading package shakespeare to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/shakespeare.zip.\n",
            "[nltk_data]    | Downloading package sinica_treebank to\n",
            "[nltk_data]    |     /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/sinica_treebank.zip.\n",
            "[nltk_data]    | Downloading package smultron to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/smultron.zip.\n",
            "[nltk_data]    | Downloading package snowball_data to\n",
            "[nltk_data]    |     /root/nltk_data...\n",
            "[nltk_data]    | Downloading package spanish_grammars to\n",
            "[nltk_data]    |     /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping grammars/spanish_grammars.zip.\n",
            "[nltk_data]    | Downloading package state_union to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/state_union.zip.\n",
            "[nltk_data]    | Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/stopwords.zip.\n",
            "[nltk_data]    | Downloading package subjectivity to\n",
            "[nltk_data]    |     /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/subjectivity.zip.\n",
            "[nltk_data]    | Downloading package swadesh to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/swadesh.zip.\n",
            "[nltk_data]    | Downloading package switchboard to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/switchboard.zip.\n",
            "[nltk_data]    | Downloading package tagsets to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping help/tagsets.zip.\n",
            "[nltk_data]    | Downloading package timit to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/timit.zip.\n",
            "[nltk_data]    | Downloading package toolbox to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/toolbox.zip.\n",
            "[nltk_data]    | Downloading package treebank to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/treebank.zip.\n",
            "[nltk_data]    | Downloading package twitter_samples to\n",
            "[nltk_data]    |     /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/twitter_samples.zip.\n",
            "[nltk_data]    | Downloading package udhr to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/udhr.zip.\n",
            "[nltk_data]    | Downloading package udhr2 to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/udhr2.zip.\n",
            "[nltk_data]    | Downloading package unicode_samples to\n",
            "[nltk_data]    |     /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/unicode_samples.zip.\n",
            "[nltk_data]    | Downloading package universal_tagset to\n",
            "[nltk_data]    |     /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping taggers/universal_tagset.zip.\n",
            "[nltk_data]    | Downloading package universal_treebanks_v20 to\n",
            "[nltk_data]    |     /root/nltk_data...\n",
            "[nltk_data]    | Downloading package vader_lexicon to\n",
            "[nltk_data]    |     /root/nltk_data...\n",
            "[nltk_data]    | Downloading package verbnet to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/verbnet.zip.\n",
            "[nltk_data]    | Downloading package verbnet3 to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/verbnet3.zip.\n",
            "[nltk_data]    | Downloading package webtext to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/webtext.zip.\n",
            "[nltk_data]    | Downloading package wmt15_eval to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping models/wmt15_eval.zip.\n",
            "[nltk_data]    | Downloading package word2vec_sample to\n",
            "[nltk_data]    |     /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping models/word2vec_sample.zip.\n",
            "[nltk_data]    | Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]    |   Package wordnet is already up-to-date!\n",
            "[nltk_data]    | Downloading package wordnet2021 to /root/nltk_data...\n",
            "[nltk_data]    | Downloading package wordnet31 to /root/nltk_data...\n",
            "[nltk_data]    | Downloading package wordnet_ic to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/wordnet_ic.zip.\n",
            "[nltk_data]    | Downloading package words to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/words.zip.\n",
            "[nltk_data]    | Downloading package ycoe to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/ycoe.zip.\n",
            "[nltk_data]    | \n",
            "[nltk_data]  Done downloading collection all\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 1
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "#for text pre-processing\n",
        "import re, string\n",
        "import nltk\n",
        "from nltk.tokenize import word_tokenize\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.tokenize import word_tokenize\n",
        "from nltk.stem import SnowballStemmer\n",
        "from nltk.corpus import wordnet\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "nltk.download('punkt')\n",
        "nltk.download('averaged_perceptron_tagger')\n",
        "nltk.download('wordnet')\n",
        "nltk.download('omw-1.4')\n",
        "\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.metrics import classification_report, f1_score, accuracy_score, confusion_matrix\n",
        "from sklearn.metrics import roc_curve, auc, roc_auc_score\n",
        "from sklearn.calibration import calibration_curve, CalibrationDisplay\n",
        "from sklearn import metrics\n",
        "from sklearn import feature_selection, model_selection, naive_bayes, pipeline, manifold, preprocessing\n",
        "\n",
        "## for plotting\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "\n",
        "import gensim\n",
        "## for deep learning\n",
        "from tensorflow.keras import models, layers, preprocessing as kprocessing\n",
        "from tensorflow.keras import backend as K\n",
        "# Modeling\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import LSTM, GRU, Dense, Embedding, Dropout, GlobalAveragePooling1D, Flatten, SpatialDropout1D, Bidirectional\n",
        "## for bert language model\n",
        "#import transformers\n",
        "import nltk\n",
        "\n",
        "nltk.download('all')"
      ],
      "id": "f21c86ac"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ziy3uYg0-OWq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "da9c90be-9434-4887-c216-6331c756422d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive \n",
        "drive.mount('/content/gdrive')"
      ],
      "id": "Ziy3uYg0-OWq"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "R3KI_uoa_4Vw"
      },
      "outputs": [],
      "source": [
        "#df = pd.read_csv('/content/gdrive/MyDrive/Colab Notebooks/Kaggle/train_data.csv')\n",
        "train_X_Path = \"/content/gdrive/MyDrive/Colab Notebooks/Kaggle/train_data.csv\"\n",
        "train_Y_Path = \"/content/gdrive/MyDrive/Colab Notebooks/Kaggle/train_results.csv\"\n",
        "test_X_Path = \"/content/gdrive/MyDrive/Colab Notebooks/Kaggle/test_data.csv\""
      ],
      "id": "R3KI_uoa_4Vw"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "D6A-Nrd5-VWf"
      },
      "outputs": [],
      "source": [
        "# Training data\n",
        "df = pd.read_csv(train_X_Path)\n",
        "# Dropping id column\n",
        "df.drop(['id'],axis=1,inplace=True)\n",
        "# Training labels\n",
        "df_train_Y = pd.read_csv(train_Y_Path)\n",
        "df_train_Y.head()\n",
        "# Dropping id column\n",
        "df_train_Y = df_train_Y.drop(columns=['id'],axis=1)\n",
        "# Converting categorical labels to numeric\n",
        "df_train_Y.target[df_train_Y.target == 'positive'] = 2\n",
        "df_train_Y.target[df_train_Y.target == 'negative'] = 0\n",
        "df_train_Y.target[df_train_Y.target == 'neutral'] = 1\n",
        "# combining text data and labels to one single dataframe\n",
        "df = pd.concat([df,df_train_Y],axis=1)"
      ],
      "id": "D6A-Nrd5-VWf"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "a25d2018",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "15a8dbc4-ef63-434b-fb68-40fcf64ea9ff"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "13156702\n"
          ]
        }
      ],
      "source": [
        "# Text Clean-Up\n",
        "\n",
        "#convert to lowercase, strip and remove punctuations\n",
        "def preprocess(text):\n",
        "    text = text.lower() \n",
        "    text=  text.strip()  \n",
        "    text=  re.compile('<.*?>').sub('', text) \n",
        "    text = re.compile('[%s]' % re.escape(string.punctuation)).sub(' ', text)  \n",
        "    text = re.sub('\\s+', ' ', text)  \n",
        "    text = re.sub(r'\\[[0-9]*\\]',' ',text) \n",
        "    text= re.sub(r'[^\\w\\s]', '', str(text).lower().strip())\n",
        "    text = re.sub(r'\\d',' ',text) \n",
        "    text = re.sub(r'\\s+',' ',text) \n",
        "    text = re.sub(r'https?://\\S+|www\\.\\S+', '', text) # Removes url\n",
        "    text = re.compile(r'<[^>]+>').sub('', text) #Removes HTML tags:\n",
        "    text = re.sub(r\"\\s+[a-zA-Z]\\s+\", ' ', text) # Single character removal\n",
        "    text = re.sub(r'\\s+', ' ', text) # Remove multiple spaces\n",
        "    return text\n",
        "\n",
        "# STOPWORD REMOVAL\n",
        "from nltk.corpus import stopwords\n",
        "stop = set(stopwords.words('english'))\n",
        "def remove_stopwords(str):\n",
        "    return \" \".join([word for word in str.split() if word not in stop])\n",
        "\n",
        "# Clean text without stop words removal\n",
        "df['clean_text'] = df['text'].map(lambda x: preprocess(x))\n",
        "#df['clean_text'] = df['clean_text'].map(lambda x: remove_stopwords(x))\n",
        "\n",
        "# Checking the status of clean text with stop words removed\n",
        "df['clean_text_len'] = df['clean_text'].apply(lambda x:len(str(x).split()))\n",
        "print(df['clean_text_len'].sum())\n",
        "df['clean_text_len'].describe()\n",
        "# removing samples with zero words\n",
        "no_text = df[df['clean_text_len']==0]\n",
        "df.drop(no_text.index,inplace=True)\n"
      ],
      "id": "a25d2018"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7689a2f4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "0c5b6abe-37e3-4b15-b7fa-1a99e6addb44"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Word  Frequency\n",
              "0                 to     368371\n",
              "1                the     341615\n",
              "2                 my     206635\n",
              "3                 it     198104\n",
              "4                and     197391\n",
              "...              ...        ...\n",
              "450421        michey          1\n",
              "450422        tudumo          1\n",
              "450423  faitaccompli          1\n",
              "450424          wnpn          1\n",
              "450425      jproudfo          1\n",
              "\n",
              "[450426 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-31097aae-1406-479b-be39-a85228f9a108\">\n",
              "    <div 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>Word</th>\n",
              "      <th>Frequency</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>to</td>\n",
              "      <td>368371</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>the</td>\n",
              "      <td>341615</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>my</td>\n",
              "      <td>206635</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>it</td>\n",
              "      <td>198104</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>and</td>\n",
              "      <td>197391</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>450421</th>\n",
              "      <td>michey</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>450422</th>\n",
              "      <td>tudumo</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>450423</th>\n",
              "      <td>faitaccompli</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>450424</th>\n",
              "      <td>wnpn</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>450425</th>\n",
              "      <td>jproudfo</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>450426 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-31097aae-1406-479b-be39-a85228f9a108')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-31097aae-1406-479b-be39-a85228f9a108 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-31097aae-1406-479b-be39-a85228f9a108');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "new_df = df.clean_text.str.split(expand=True).stack().value_counts().reset_index()\n",
        " \n",
        "new_df.columns = ['Word', 'Frequency']\n",
        "\n",
        "new_df\n"
      ],
      "id": "7689a2f4"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ad1F7oJm9agM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "9dc1bcdb-0383-4612-b6e5-5a3601ef1178"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                text target  \\\n",
              "0                  Anyway Im getting of for a while       2   \n",
              "1  My red, Apache isn't feelin too well this morn...      0   \n",
              "2  @danyelljoy you should be  its great. friday w...      2   \n",
              "3  its 11:30pm and i dont wanna sleep; so i debat...      2   \n",
              "4          Why does twitter eat my DM's?  Not happy       0   \n",
              "\n",
              "                                          clean_text  clean_text_len  \\\n",
              "0                     anyway im getting of for while               6   \n",
              "1  my red apache isn feelin too well this morning...              12   \n",
              "2  danyelljoy you should be its great friday will...              11   \n",
              "3  its pm and dont wanna sleep so debated with my...              22   \n",
              "4               why does twitter eat my dm not happy               8   \n",
              "\n",
              "                                              tokens  \n",
              "0              [anyway, im, getting, of, for, while]  \n",
              "1  [my, red, apache, isn, feelin, too, well, this...  \n",
              "2  [danyelljoy, you, should, be, its, great, frid...  \n",
              "3  [its, pm, and, dont, wanna, sleep, so, debated...  \n",
              "4      [why, does, twitter, eat, my, dm, not, happy]  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fa5b3587-524d-4593-ba6c-32e92e1f05c1\">\n",
              "    <div 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>text</th>\n",
              "      <th>target</th>\n",
              "      <th>clean_text</th>\n",
              "      <th>clean_text_len</th>\n",
              "      <th>tokens</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Anyway Im getting of for a while</td>\n",
              "      <td>2</td>\n",
              "      <td>anyway im getting of for while</td>\n",
              "      <td>6</td>\n",
              "      <td>[anyway, im, getting, of, for, while]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>My red, Apache isn't feelin too well this morn...</td>\n",
              "      <td>0</td>\n",
              "      <td>my red apache isn feelin too well this morning...</td>\n",
              "      <td>12</td>\n",
              "      <td>[my, red, apache, isn, feelin, too, well, this...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>@danyelljoy you should be  its great. friday w...</td>\n",
              "      <td>2</td>\n",
              "      <td>danyelljoy you should be its great friday will...</td>\n",
              "      <td>11</td>\n",
              "      <td>[danyelljoy, you, should, be, its, great, frid...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>its 11:30pm and i dont wanna sleep; so i debat...</td>\n",
              "      <td>2</td>\n",
              "      <td>its pm and dont wanna sleep so debated with my...</td>\n",
              "      <td>22</td>\n",
              "      <td>[its, pm, and, dont, wanna, sleep, so, debated...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Why does twitter eat my DM's?  Not happy</td>\n",
              "      <td>0</td>\n",
              "      <td>why does twitter eat my dm not happy</td>\n",
              "      <td>8</td>\n",
              "      <td>[why, does, twitter, eat, my, dm, not, happy]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fa5b3587-524d-4593-ba6c-32e92e1f05c1')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-fa5b3587-524d-4593-ba6c-32e92e1f05c1 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-fa5b3587-524d-4593-ba6c-32e92e1f05c1');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "tokenizer=nltk.tokenize.RegexpTokenizer(r'\\w+')\n",
        "df['tokens'] = df['clean_text'].apply(lambda x:tokenizer.tokenize(x))\n",
        "df.head()"
      ],
      "id": "ad1F7oJm9agM"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ORbwv0Vx9tNT"
      },
      "outputs": [],
      "source": [
        "import gensim"
      ],
      "id": "ORbwv0Vx9tNT"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2071ced1"
      },
      "outputs": [],
      "source": [
        "# Label Encoding\n",
        "from sklearn import preprocessing\n",
        "\n",
        "label_encoder = preprocessing.LabelEncoder()\n",
        "  \n",
        "# Encode labels in column 'target'.\n",
        "df['target']= label_encoder.fit_transform(df['target'])\n",
        "y = df['target']\n",
        "\n",
        "X = df['clean_text']"
      ],
      "id": "2071ced1"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "b0c19f81"
      },
      "outputs": [],
      "source": [
        "X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size = 0.20, random_state = 42)\n"
      ],
      "id": "b0c19f81"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fba645e7"
      },
      "outputs": [],
      "source": [
        "import tensorflow\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "\n",
        "max_len = df['clean_text_len'].max()\n",
        "#max_features = int(new_df.shape[0]/2)\n",
        "max_features = 50000"
      ],
      "id": "fba645e7"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1a28d148"
      },
      "outputs": [],
      "source": [
        "def Token_padding(data_train, data_test):\n",
        "\n",
        "  Tokens = Tokenizer(num_words = max_features, oov_token ='OOV')\n",
        "  Tokens.fit_on_texts(data_train)\n",
        "  #vocab_size = len(Tokens.word_index) + 1\n",
        "  encd_rev_train = Tokens.texts_to_sequences(data_train)\n",
        "  pad_sent_train = pad_sequences(encd_rev_train, maxlen=max_len, padding='post')\n",
        "\n",
        "  encd_rev_test = Tokens.texts_to_sequences(data_test)\n",
        "  pad_sent_test = pad_sequences(encd_rev_test, maxlen=max_len, padding='post')\n",
        "\n",
        "  return pad_sent_train, pad_sent_test"
      ],
      "id": "1a28d148"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "e165619d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ca174776-7d16-4399-e4c9-9013439e651b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(832258, 116) (208065, 116) (832258, 3) (208065, 3)\n"
          ]
        }
      ],
      "source": [
        "X_train_vector,  X_valid_vector = Token_padding(X_train, X_valid)\n",
        "\n",
        "Y_train = pd.get_dummies(y_train).values\n",
        "Y_valid = pd.get_dummies(y_valid).values\n",
        "\n",
        "print(X_train_vector.shape, X_valid_vector.shape, Y_train.shape,Y_valid.shape)"
      ],
      "id": "e165619d"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eaca144f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "753c77fc-b069-4ecd-e292-2d30687ebecf"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " embedding (Embedding)       (None, 116, 300)          15000000  \n",
            "                                                                 \n",
            " bidirectional (Bidirectiona  (None, 128)              186880    \n",
            " l)                                                              \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 128)               0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 64)                8256      \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 64)                0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 3)                 195       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 15,195,331\n",
            "Trainable params: 15,195,331\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/100\n",
            "6503/6503 [==============================] - 166s 24ms/step - loss: 0.4408 - accuracy: 0.7992 - val_loss: 0.4146 - val_accuracy: 0.8107\n",
            "Epoch 2/100\n",
            "6503/6503 [==============================] - 154s 24ms/step - loss: 0.3981 - accuracy: 0.8267 - val_loss: 0.4098 - val_accuracy: 0.8248\n",
            "Epoch 3/100\n",
            "6503/6503 [==============================] - 150s 23ms/step - loss: 0.3835 - accuracy: 0.8369 - val_loss: 0.4086 - val_accuracy: 0.8177\n",
            "Epoch 4/100\n",
            "6503/6503 [==============================] - 153s 24ms/step - loss: 0.3734 - accuracy: 0.8440 - val_loss: 0.3986 - val_accuracy: 0.8270\n",
            "Epoch 5/100\n",
            "6503/6503 [==============================] - 151s 23ms/step - loss: 0.3637 - accuracy: 0.8498 - val_loss: 0.4016 - val_accuracy: 0.8283\n",
            "Epoch 6/100\n",
            "6503/6503 [==============================] - 154s 24ms/step - loss: 0.3558 - accuracy: 0.8553 - val_loss: 0.4164 - val_accuracy: 0.8242\n",
            "Epoch 7/100\n",
            "6503/6503 [==============================] - 153s 24ms/step - loss: 0.3491 - accuracy: 0.8604 - val_loss: 0.4388 - val_accuracy: 0.8258\n",
            "Epoch 8/100\n",
            "6503/6503 [==============================] - 153s 24ms/step - loss: 0.3413 - accuracy: 0.8650 - val_loss: 0.4245 - val_accuracy: 0.8242\n",
            "Epoch 9/100\n",
            "6503/6503 [==============================] - 156s 24ms/step - loss: 0.3334 - accuracy: 0.8698 - val_loss: 0.4506 - val_accuracy: 0.8232\n",
            "Epoch 10/100\n",
            "6503/6503 [==============================] - ETA: 0s - loss: 0.3257 - accuracy: 0.8744Restoring model weights from the end of the best epoch: 5.\n",
            "6503/6503 [==============================] - 152s 23ms/step - loss: 0.3257 - accuracy: 0.8744 - val_loss: 0.4488 - val_accuracy: 0.8219\n",
            "Epoch 10: early stopping\n"
          ]
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "embed_dim = 300\n",
        "\n",
        "model_LSTM = tf.keras.Sequential([\n",
        "    tf.keras.layers.Embedding(max_features, embed_dim, input_length=max_len),\n",
        "    tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64)),\n",
        "    #tf.keras.layers.LSTM(32),\n",
        "    tf.keras.layers.Dropout(0.5),\n",
        "    #tf.keras.layers.Dense(512, activation='relu'),\n",
        "    tf.keras.layers.Dense(64, activation='relu'),\n",
        "    tf.keras.layers.Dropout(0.5),\n",
        "    tf.keras.layers.Dense(3, activation='softmax')\n",
        "])\n",
        "model_LSTM.summary()\n",
        "\n",
        "opt = keras.optimizers.RMSprop(learning_rate=0.001)\n",
        "model_LSTM.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy'])\n",
        "\n",
        "epochs = 100\n",
        "batch_size = 128\n",
        "\n",
        "es = keras.callbacks.EarlyStopping(\n",
        "        monitor=\"val_accuracy\", # metrics to monitor\n",
        "        patience=5, # how many epochs before stop\n",
        "        verbose=1,\n",
        "        mode=\"max\", # we need the maximum accuracy.\n",
        "        restore_best_weights=True)\n",
        "history = model_LSTM.fit(X_train_vector, Y_train, \n",
        "                    epochs = epochs, \n",
        "                    batch_size=batch_size,\n",
        "                    validation_data = (X_valid_vector, Y_valid),\n",
        "                    shuffle = True,\n",
        "                    callbacks=[es])\n"
      ],
      "id": "eaca144f"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "f41823c8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        },
        "outputId": "8d9c7a57-2976-49c9-cfdb-b6a3407b932a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1296x720 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Plot the loss and accuracy curves for training and validation \n",
        "fig, ax = plt.subplots(2,1, figsize=(18, 10))\n",
        "ax[0].plot(history.history['loss'], color='b', label=\"Training loss\")\n",
        "ax[0].plot(history.history['val_loss'], color='r', label=\"validation loss\",axes =ax[0])\n",
        "legend = ax[0].legend(loc='best', shadow=True)\n",
        "\n",
        "ax[1].plot(history.history['accuracy'], color='b', label=\"Training accuracy\")\n",
        "ax[1].plot(history.history['val_accuracy'], color='r',label=\"Validation accuracy\")\n",
        "legend = ax[1].legend(loc='best', shadow=True)\n",
        "\n",
        "fig = plt.figure(figsize=(10, 10)) # Set Figure"
      ],
      "id": "f41823c8"
    },
    {
      "cell_type": "code",
      "source": [
        "df_test_X = pd.read_csv(test_X_Path)\n",
        "df_test_X.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "f6xuaXSsddMs",
        "outputId": "2e797988-37e9-4e3b-e836-82e6682b8a46"
      },
      "id": "f6xuaXSsddMs",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   id                                               text\n",
              "0   0  so cut... missed out on all the TCE EXTREME ME...\n",
              "1   1     Thank you very much Wasin  this is awesome lol\n",
              "2   2  Squeezing in some internet time before Dustin ...\n",
              "3   3  @fanoou; Oh i do have moral but i'm gonna be b...\n",
              "4   4  @NSuissa   I just bought Sammy some joint/hip ..."
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c65be3fd-b371-45d5-86d0-55afd037f034\">\n",
              "    <div 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>id</th>\n",
              "      <th>text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>so cut... missed out on all the TCE EXTREME ME...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>Thank you very much Wasin  this is awesome lol</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>Squeezing in some internet time before Dustin ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>@fanoou; Oh i do have moral but i'm gonna be b...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>@NSuissa   I just bought Sammy some joint/hip ...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c65be3fd-b371-45d5-86d0-55afd037f034')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-c65be3fd-b371-45d5-86d0-55afd037f034 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-c65be3fd-b371-45d5-86d0-55afd037f034');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "df_test_X.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hulDd-R2dhgU",
        "outputId": "21767db2-72b4-4748-cfd7-3498d39a7113"
      },
      "id": "hulDd-R2dhgU",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(560175, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "df_test_X = df_test_X.drop(columns=['id'],axis=1)\n",
        "df_test_X.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "DkYxu-q2djfL",
        "outputId": "8b829d8d-533b-47ff-a0f6-aac7cbdb3300"
      },
      "id": "DkYxu-q2djfL",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                text\n",
              "0  so cut... missed out on all the TCE EXTREME ME...\n",
              "1     Thank you very much Wasin  this is awesome lol\n",
              "2  Squeezing in some internet time before Dustin ...\n",
              "3  @fanoou; Oh i do have moral but i'm gonna be b...\n",
              "4  @NSuissa   I just bought Sammy some joint/hip ..."
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a76485be-836c-418d-ae62-a7a86d131303\">\n",
              "    <div 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>text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>so cut... missed out on all the TCE EXTREME ME...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Thank you very much Wasin  this is awesome lol</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Squeezing in some internet time before Dustin ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>@fanoou; Oh i do have moral but i'm gonna be b...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>@NSuissa   I just bought Sammy some joint/hip ...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a76485be-836c-418d-ae62-a7a86d131303')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-a76485be-836c-418d-ae62-a7a86d131303 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-a76485be-836c-418d-ae62-a7a86d131303');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Clean text without stop words removal\n",
        "df_test_X['clean_text'] = df_test_X['text'].map(lambda x: preprocess(x))\n",
        "df_test_X.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "2o2jplsedl_j",
        "outputId": "3daf6a7f-0e80-4ac0-aeeb-d5d5bd7cf032"
      },
      "id": "2o2jplsedl_j",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                text  \\\n",
              "0  so cut... missed out on all the TCE EXTREME ME...   \n",
              "1     Thank you very much Wasin  this is awesome lol   \n",
              "2  Squeezing in some internet time before Dustin ...   \n",
              "3  @fanoou; Oh i do have moral but i'm gonna be b...   \n",
              "4  @NSuissa   I just bought Sammy some joint/hip ...   \n",
              "\n",
              "                                          clean_text  \n",
              "0  so cut missed out on all the tce extreme merch...  \n",
              "1      thank you very much wasin this is awesome lol  \n",
              "2  squeezing in some internet time before dustin ...  \n",
              "3  fanoou oh do have moral but m gonna be bigger ...  \n",
              "4  nsuissa just bought sammy some joint hip suppl...  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ffe4186e-0874-46c7-9591-0d4d1e39df12\">\n",
              "    <div 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>text</th>\n",
              "      <th>clean_text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>so cut... missed out on all the TCE EXTREME ME...</td>\n",
              "      <td>so cut missed out on all the tce extreme merch...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Thank you very much Wasin  this is awesome lol</td>\n",
              "      <td>thank you very much wasin this is awesome lol</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Squeezing in some internet time before Dustin ...</td>\n",
              "      <td>squeezing in some internet time before dustin ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>@fanoou; Oh i do have moral but i'm gonna be b...</td>\n",
              "      <td>fanoou oh do have moral but m gonna be bigger ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>@NSuissa   I just bought Sammy some joint/hip ...</td>\n",
              "      <td>nsuissa just bought sammy some joint hip suppl...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ffe4186e-0874-46c7-9591-0d4d1e39df12')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-ffe4186e-0874-46c7-9591-0d4d1e39df12 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-ffe4186e-0874-46c7-9591-0d4d1e39df12');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "42b39dd8"
      },
      "outputs": [],
      "source": [
        "\n",
        "# tokenizer=nltk.tokenize.RegexpTokenizer(r'\\w+')\n",
        "# df_test_X['tokens'] = df_test_X['clean_text'].apply(lambda x:tokenizer.tokenize(x))\n",
        "X_test = df_test_X['clean_text']\n",
        "X_train_vector,  X_test_vector = Token_padding(X_train, X_test)\n",
        "\n"
      ],
      "id": "42b39dd8"
    },
    {
      "cell_type": "code",
      "source": [
        "# df_test_X_padded = pad_sequences(encd_rev_test, maxlen=max_len)\n",
        "pred = model_LSTM.predict(X_test_vector)\n",
        "\n",
        "pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fLyq6OKpdo9V",
        "outputId": "e4c54ecc-2b3b-49e9-99b4-011b86c464f6"
      },
      "id": "fLyq6OKpdo9V",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "17506/17506 [==============================] - 114s 6ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[9.0958220e-01, 0.0000000e+00, 9.0417847e-02],\n",
              "       [1.3954627e-02, 0.0000000e+00, 9.8604530e-01],\n",
              "       [5.4267842e-01, 5.1230012e-17, 4.5732161e-01],\n",
              "       ...,\n",
              "       [9.9625742e-01, 0.0000000e+00, 3.7426015e-03],\n",
              "       [6.7774022e-01, 0.0000000e+00, 3.2225978e-01],\n",
              "       [1.7317753e-01, 0.0000000e+00, 8.2682252e-01]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#"
      ],
      "metadata": {
        "id": "JgPZzZhjdbns"
      },
      "id": "JgPZzZhjdbns",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6LKJFb-CBeF7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "197ce209-a885-427c-92b7-a3de306f234d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            id  target\n",
              "0            0       0\n",
              "1            1       2\n",
              "2            2       0\n",
              "3            3       2\n",
              "4            4       0\n",
              "...        ...     ...\n",
              "560170  560170       0\n",
              "560171  560171       0\n",
              "560172  560172       0\n",
              "560173  560173       0\n",
              "560174  560174       2\n",
              "\n",
              "[560175 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-81cf9aa9-0144-4782-9af4-262966a81bda\">\n",
              "    <div 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>id</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>560170</th>\n",
              "      <td>560170</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>560171</th>\n",
              "      <td>560171</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>560172</th>\n",
              "      <td>560172</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>560173</th>\n",
              "      <td>560173</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>560174</th>\n",
              "      <td>560174</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>560175 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-81cf9aa9-0144-4782-9af4-262966a81bda')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-81cf9aa9-0144-4782-9af4-262966a81bda 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-81cf9aa9-0144-4782-9af4-262966a81bda');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "list_index = list(df_test_X.index.values)\n",
        "\n",
        "y_pred_list = []\n",
        "for i in range(df_test_X.shape[0]):\n",
        "  y_pred_list.append(np.argmax(pred[i]))\n",
        "y_pred_list\n",
        "\n",
        "out = pd.DataFrame({'id': list_index,'target':y_pred_list})\n",
        "filename = 'RNN_LSTM_Tokenization_corrected_V2.csv'\n",
        "out\n",
        "\n"
      ],
      "id": "6LKJFb-CBeF7"
    },
    {
      "cell_type": "code",
      "source": [
        "out.to_csv(filename, index=False)\n",
        "print('Saved file: ' + filename)"
      ],
      "metadata": {
        "id": "IJ86BKiaRVXE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b6394d34-ee66-4693-ac38-ce29f3df471d"
      },
      "id": "IJ86BKiaRVXE",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saved file: RNN_LSTM_Tokenization_corrected_V2.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "87975895"
      },
      "outputs": [],
      "source": [
        "out.to_csv(\"/content/gdrive/MyDrive/Colab Notebooks/Kaggle/rnn_lstm_best_model_seed42.csv\", index=False)"
      ],
      "id": "87975895"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "33adcc08"
      },
      "outputs": [],
      "source": [],
      "id": "33adcc08"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6110ad98"
      },
      "outputs": [],
      "source": [],
      "id": "6110ad98"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dbf55004"
      },
      "outputs": [],
      "source": [],
      "id": "dbf55004"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "b4dc6647"
      },
      "outputs": [],
      "source": [],
      "id": "b4dc6647"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "d4a7fafd"
      },
      "outputs": [],
      "source": [],
      "id": "d4a7fafd"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0d84720e"
      },
      "outputs": [],
      "source": [],
      "id": "0d84720e"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "c82eccd8"
      },
      "outputs": [],
      "source": [],
      "id": "c82eccd8"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ed429e75"
      },
      "outputs": [],
      "source": [],
      "id": "ed429e75"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1b39f10e"
      },
      "outputs": [],
      "source": [],
      "id": "1b39f10e"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eb4f2b91"
      },
      "outputs": [],
      "source": [],
      "id": "eb4f2b91"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cc56dcd2"
      },
      "outputs": [],
      "source": [],
      "id": "cc56dcd2"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5697396c"
      },
      "outputs": [],
      "source": [],
      "id": "5697396c"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "44b639bf"
      },
      "outputs": [],
      "source": [],
      "id": "44b639bf"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1514bbb4"
      },
      "outputs": [],
      "source": [],
      "id": "1514bbb4"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "c6cd2e25"
      },
      "outputs": [],
      "source": [],
      "id": "c6cd2e25"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6b3f8a2f"
      },
      "outputs": [],
      "source": [
        "\n"
      ],
      "id": "6b3f8a2f"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ef47d783"
      },
      "outputs": [],
      "source": [],
      "id": "ef47d783"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "b26535ff"
      },
      "outputs": [],
      "source": [],
      "id": "b26535ff"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "a92dba8f"
      },
      "outputs": [],
      "source": [],
      "id": "a92dba8f"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "60ed42af"
      },
      "outputs": [],
      "source": [],
      "id": "60ed42af"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "facf9601"
      },
      "outputs": [],
      "source": [],
      "id": "facf9601"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4a4d386d"
      },
      "outputs": [],
      "source": [],
      "id": "4a4d386d"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6e45bc89"
      },
      "outputs": [],
      "source": [],
      "id": "6e45bc89"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "885c42fe"
      },
      "outputs": [],
      "source": [],
      "id": "885c42fe"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "afedd52e"
      },
      "outputs": [],
      "source": [],
      "id": "afedd52e"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2372c450"
      },
      "outputs": [],
      "source": [],
      "id": "2372c450"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "191284d4"
      },
      "outputs": [],
      "source": [],
      "id": "191284d4"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cf382667"
      },
      "outputs": [],
      "source": [],
      "id": "cf382667"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "99cd2687"
      },
      "outputs": [],
      "source": [],
      "id": "99cd2687"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fb44b6be"
      },
      "outputs": [],
      "source": [],
      "id": "fb44b6be"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "881b3b1a"
      },
      "outputs": [],
      "source": [],
      "id": "881b3b1a"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "c81296a5"
      },
      "outputs": [],
      "source": [],
      "id": "c81296a5"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "74931bd8"
      },
      "outputs": [],
      "source": [],
      "id": "74931bd8"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fd2df2c7"
      },
      "outputs": [],
      "source": [],
      "id": "fd2df2c7"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "99a2622b"
      },
      "outputs": [],
      "source": [],
      "id": "99a2622b"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7ae0ff4a"
      },
      "outputs": [],
      "source": [],
      "id": "7ae0ff4a"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ee110114"
      },
      "outputs": [],
      "source": [],
      "id": "ee110114"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2086fde7"
      },
      "outputs": [],
      "source": [],
      "id": "2086fde7"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "a98d61d4"
      },
      "outputs": [],
      "source": [],
      "id": "a98d61d4"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6dc7aeae"
      },
      "outputs": [],
      "source": [],
      "id": "6dc7aeae"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "04c0d8b0"
      },
      "outputs": [],
      "source": [
        "\n"
      ],
      "id": "04c0d8b0"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2c80e57e"
      },
      "outputs": [],
      "source": [],
      "id": "2c80e57e"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "861e78ca"
      },
      "outputs": [],
      "source": [],
      "id": "861e78ca"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "62e03596"
      },
      "outputs": [],
      "source": [],
      "id": "62e03596"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "486d993a"
      },
      "outputs": [],
      "source": [],
      "id": "486d993a"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "433607ea"
      },
      "outputs": [],
      "source": [
        "#"
      ],
      "id": "433607ea"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4a0500e2"
      },
      "outputs": [],
      "source": [
        "#"
      ],
      "id": "4a0500e2"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7b4c5306"
      },
      "outputs": [],
      "source": [],
      "id": "7b4c5306"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "f481cd13"
      },
      "outputs": [],
      "source": [],
      "id": "f481cd13"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "a5be39e4"
      },
      "outputs": [],
      "source": [],
      "id": "a5be39e4"
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": []
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.12"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}