02_Consummer-Complain-Classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4870212e-8584-458a-8b68-6b7567dffd9d",
   "metadata": {},
   "source": [
    "# Install requirements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "69b4a324",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Requirement already satisfied: mypy-extensions>=0.4.3 in /opt/app-root/lib/python3.8/site-packages (from black->ipython>=7.23.1->ipykernel->ipynbname->-r requirements.txt (line 15)) (0.4.3)\n",
      "Requirement already satisfied: executing in /opt/app-root/lib/python3.8/site-packages (from stack-data->ipython>=7.23.1->ipykernel->ipynbname->-r requirements.txt (line 15)) (0.8.2)\n",
      "Requirement already satisfied: asttokens in /opt/app-root/lib/python3.8/site-packages (from stack-data->ipython>=7.23.1->ipykernel->ipynbname->-r requirements.txt (line 15)) (2.0.5)\n",
      "Requirement already satisfied: pure-eval in /opt/app-root/lib/python3.8/site-packages (from stack-data->ipython>=7.23.1->ipykernel->ipynbname->-r requirements.txt (line 15)) (0.2.1)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install -r requirements.txt\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8e8d1d55-f228-462c-ac0f-dafc2e03bfb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/opt/app-root/src/NLP-Consumer-Complain-Classification'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "cwd = os.getcwd()\n",
    "cwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5fc4b5c7-a1fc-42e8-8c00-f2987f6803c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "# sys.path is a list of absolute path strings\n",
    "sys.path.append(cwd)\n",
    "from src.dataloading.read_dataset import ReadData\n",
    "from src.features.build_features import BuildFeatures\n",
    "from src.modules.build_model import BuildModel\n",
    "from src.modules.train_model import TrainModel\n",
    "from src.modules.predict_model import Predictor,Transformer\n",
    "\n",
    "# from src.modules.predict_model import BuildModel\n",
    "# from src.modules.train_model import BuildModel\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cea4e1d0-9de3-4fce-b43d-3a2463510d19",
   "metadata": {},
   "source": [
    "# Load Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9230902c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import sys\n",
    "# # sys.path is a list of absolute path strings\n",
    "# sys.path.append('/opt/app-root/src/anz_ml_project/')\n",
    "\n",
    "# from src.features.build_features import BuildFeatures\n",
    "\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "import tensorflow_addons as tfa\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "import sklearn.feature_extraction.text as text\n",
    "from sklearn import model_selection, preprocessing, linear_model, naive_bayes, metrics, svm\n",
    "# from sklearn.naive_bayes import MultinomialNB\n",
    "# from sklearn.linear_model import LogisticRegression\n",
    "# from sklearn.ensemble import RandomForestClassifier\n",
    "# from sklearn.svm import LinearSVC\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "from textblob import TextBlob\n",
    "from nltk.stem import PorterStemmer,SnowballStemmer\n",
    "from textblob import Word\n",
    "from sklearn.feature_extraction.text import CountVectorizer,TfidfVectorizer\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.tokenize import sent_tokenize, word_tokenize\n",
    "from nltk.tokenize.toktok import ToktokTokenizer\n",
    "# from wordcloud import WordCloudfrom wordcloud import WordCloud\n",
    "from io import StringIO\n",
    "import string\n",
    "import gensim\n",
    "from gensim.models import Word2Vec\n",
    "import itertools\n",
    "import scipy\n",
    "from scipy import spatial\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import re\n",
    "import nltk\n",
    "import joblib\n",
    "\n",
    "import warnings\n",
    "\n",
    "import subprocess\n",
    "import ipynbname\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import absl.logging\n",
    "absl.logging.set_verbosity(absl.logging.ERROR)\n",
    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
    "\n",
    "\n",
    "\n",
    "tokenizer = ToktokTokenizer()\n",
    "# stopword_list = nltk.download('stopwords')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1aabb275",
   "metadata": {},
   "source": [
    "# Readinng the data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1c8f24db-e2bb-4dff-ba49-aa89fda5098c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Bank account or service' 'Consumer Loan' 'Credit card'\n",
      " 'Credit reporting' 'Debt collection' 'Money transfers' 'Mortgage'\n",
      " 'Other financial service' 'Payday loan' 'Prepaid card' 'Student loan']\n",
      "(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10]), array([119,  90, 184, 242, 436,  22, 400,   2,  26,  10,  69]))\n",
      "(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10]), array([ 30,  22,  46,  61, 109,   5, 100,   1,   7,   2,  17]))\n"
     ]
    }
   ],
   "source": [
    "train_data, test_data, train_labels, test_labels,enc = ReadData(DATA_PATH=\"./dataset/csv/data.csv\").ReadDataFrameData()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "50f7d20f-dba4-4216-8eff-7b3a5cfc12ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data1 = test_data.copy()\n",
    "type(test_data1)\n",
    "test_data2 = test_data1.to_frame()\n",
    "test_labels1 = test_labels\n",
    "type(test_data2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7541b519-9301-4792-8994-5a6a7ef5bb61",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "consumer_complaint_narrative    failed check paperwork stated real money washe...\n",
       "Name: 737, dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "str1 = test_data2.iloc[1]\n",
    "str1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9b198734-8199-4497-b98a-e8c53f768eef",
   "metadata": {},
   "outputs": [],
   "source": [
    "label1= test_labels1[1]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d70fe7e6-69e8-4ba7-bfcf-16a5fcf2f0e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading model 4553\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-02-25 05:39:14.942661: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/nvidia/lib:/usr/local/nvidia/lib64\n",
      "2022-02-25 05:39:14.942693: W tensorflow/stream_executor/cuda/cuda_driver.cc:269] failed call to cuInit: UNKNOWN ERROR (303)\n",
      "2022-02-25 05:39:14.942715: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (jupyterhub-nb-amirgholipour): /proc/driver/nvidia/version does not exist\n",
      "2022-02-25 05:39:14.942930: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded model\n",
      "Models have just loaded!!!!\n",
      "Step1: Loading models\n"
     ]
    },
    {
     "ename": "KeyError",
     "evalue": "'data'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "File \u001b[0;32m/opt/app-root/lib64/python3.8/site-packages/pandas/core/indexes/base.py:3080\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3079\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 3080\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;48;5;17mself\u001b[39;49m\u001b[38;5;241;48;5;17m.\u001b[39;49m\u001b[48;5;17m_engine\u001b[49m\u001b[38;5;241;48;5;17m.\u001b[39;49m\u001b[48;5;17mget_loc\u001b[49m\u001b[48;5;17m(\u001b[49m\u001b[48;5;17mcasted_key\u001b[49m\u001b[48;5;17m)\u001b[49m\n\u001b[1;32m   3081\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
      "File \u001b[0;32mpandas/_libs/index.pyx:70\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mpandas/_libs/index.pyx:101\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:4554\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:4562\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'data'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Input \u001b[0;32mIn [9]\u001b[0m, in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mprediction\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m predict\n\u001b[0;32m----> 3\u001b[0m \u001b[48;5;17mpredict\u001b[49m\u001b[48;5;17m(\u001b[49m\u001b[48;5;17m \u001b[49m\u001b[48;5;17mstr1\u001b[49m\u001b[48;5;17m)\u001b[49m\n",
      "File \u001b[0;32m~/NLP-Consumer-Complain-Classification/prediction.py:35\u001b[0m, in \u001b[0;36mpredict\u001b[0;34m(X)\u001b[0m\n\u001b[1;32m     32\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mpredict\u001b[39m(X):\n\u001b[1;32m     34\u001b[0m     \u001b[38;5;28mprint\u001b[39m (\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mStep1: Loading models\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m---> 35\u001b[0m     \u001b[38;5;28mprint\u001b[39m (\u001b[48;5;17mX\u001b[49m\u001b[48;5;17m[\u001b[49m\u001b[38;5;124;48;5;17m'\u001b[39;49m\u001b[38;5;124;48;5;17mdata\u001b[39;49m\u001b[38;5;124;48;5;17m'\u001b[39;49m\u001b[48;5;17m]\u001b[49m)\n\u001b[1;32m     36\u001b[0m     \u001b[38;5;66;03m# model, labelencoder, tokenizer = load()\u001b[39;00m\n\u001b[1;32m     38\u001b[0m     \u001b[38;5;28mprint\u001b[39m (\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mStep1 finished!!!!\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
      "File \u001b[0;32m/opt/app-root/lib64/python3.8/site-packages/pandas/core/series.py:853\u001b[0m, in \u001b[0;36mSeries.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    850\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values[key]\n\u001b[1;32m    852\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m key_is_scalar:\n\u001b[0;32m--> 853\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;48;5;17mself\u001b[39;49m\u001b[38;5;241;48;5;17m.\u001b[39;49m\u001b[48;5;17m_get_value\u001b[49m\u001b[48;5;17m(\u001b[49m\u001b[48;5;17mkey\u001b[49m\u001b[48;5;17m)\u001b[49m\n\u001b[1;32m    855\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_hashable(key):\n\u001b[1;32m    856\u001b[0m     \u001b[38;5;66;03m# Otherwise index.get_value will raise InvalidIndexError\u001b[39;00m\n\u001b[1;32m    857\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    858\u001b[0m         \u001b[38;5;66;03m# For labels that don't resolve as scalars like tuples and frozensets\u001b[39;00m\n",
      "File \u001b[0;32m/opt/app-root/lib64/python3.8/site-packages/pandas/core/series.py:961\u001b[0m, in \u001b[0;36mSeries._get_value\u001b[0;34m(self, label, takeable)\u001b[0m\n\u001b[1;32m    958\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values[label]\n\u001b[1;32m    960\u001b[0m \u001b[38;5;66;03m# Similar to Index.get_value, but we do not fall back to positional\u001b[39;00m\n\u001b[0;32m--> 961\u001b[0m loc \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;48;5;17mself\u001b[39;49m\u001b[38;5;241;48;5;17m.\u001b[39;49m\u001b[48;5;17mindex\u001b[49m\u001b[38;5;241;48;5;17m.\u001b[39;49m\u001b[48;5;17mget_loc\u001b[49m\u001b[48;5;17m(\u001b[49m\u001b[48;5;17mlabel\u001b[49m\u001b[48;5;17m)\u001b[49m\n\u001b[1;32m    962\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mindex\u001b[38;5;241m.\u001b[39m_get_values_for_loc(\u001b[38;5;28mself\u001b[39m, loc, label)\n",
      "File \u001b[0;32m/opt/app-root/lib64/python3.8/site-packages/pandas/core/indexes/base.py:3082\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3080\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine\u001b[38;5;241m.\u001b[39mget_loc(casted_key)\n\u001b[1;32m   3081\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[0;32m-> 3082\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m   3084\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m tolerance \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m   3085\u001b[0m     tolerance \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_convert_tolerance(tolerance, np\u001b[38;5;241m.\u001b[39masarray(key))\n",
      "\u001b[0;31mKeyError\u001b[0m: 'data'"
     ]
    }
   ],
   "source": [
    "from prediction import predict\n",
    "\n",
    "predict( str1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2c06c482-66d6-4ee3-9741-461ce26560d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "joblib.dump(enc, './models/labelencoder.pkl')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "409a6120-e387-4d17-aa38-7768f7c279b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1600,)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# from src.features.build_features import BuildFeatures\n",
    "train_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "05cf964d-4da8-424e-ab28-d6fd39e6a3c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(400,)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_labels.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4f116d8-378f-42ba-ae65-f6bda55a4e63",
   "metadata": {},
   "source": [
    "# Prepare data for modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c80efa33-a23a-427e-8711-9b661198c995",
   "metadata": {},
   "outputs": [],
   "source": [
    "#### if you would like to use glove weights\n",
    "weight_flag = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e347a28f-63d6-4aa1-aa8e-7f24b2ce0650",
   "metadata": {},
   "outputs": [],
   "source": [
    "BFCLASS = BuildFeatures(TRAIN_DATA=train_data,TEST_DATA=test_data,TRAIN_LABELS=train_labels,TEST_LABELS=test_labels, GloveData=\"./dataset/glove.6B.50d.txt\",EMBEDDING_DIM=50, WEIGHT_FLAG = weight_flag)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9c1e5e30-daed-4acd-8da9-028c765b79ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum Sequence length is 348 .\n",
      "Found 7380 unique tokens.\n",
      "Shape of train data tensor: (1600, 348)\n",
      "Shape of train label tensor: (1600, 11)\n",
      "Shape of test label tensor: (400, 11)\n",
      "Found 400000 word vectors.\n"
     ]
    }
   ],
   "source": [
    "\n",
    "if weight_flag== True:\n",
    "    train_data,test_data,train_labels , test_labels,word_index,tokenizer,MAX_SEQUENCE_LENGTH,embedding_matrix = BFCLASS.PreProcessingTextData()\n",
    "\n",
    "\n",
    "else:\n",
    "    train_data,test_data,train_labels , test_labels,word_index,tokenizer,MAX_SEQUENCE_LENGTH = BFCLASS.PreProcessingTextData()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "22b3687a-45fe-48de-bf47-59e1a7f9e9c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['./models/tokenizer.pkl']"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(tokenizer, './models/tokenizer.pkl')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3ae61e24-45bc-4ac9-9413-6995ce125fb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7380"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(word_index)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f577c518",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Deep Learning define, train and test model\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "752ffe69-6213-4614-aa12-32b1cbdfd885",
   "metadata": {},
   "source": [
    "## Define the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b740adae-5c4f-4d0c-90ef-b8cfb9da8720",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " embedding (Embedding)       (None, 348, 50)           369050    \n",
      "                                                                 \n",
      " bidirectional (Bidirectiona  (None, 348, 200)         120800    \n",
      " l)                                                              \n",
      "                                                                 \n",
      " bidirectional_1 (Bidirectio  (None, 512)              935936    \n",
      " nal)                                                            \n",
      "                                                                 \n",
      " dense (Dense)               (None, 11)                5643      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1,431,429\n",
      "Trainable params: 1,431,429\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "if weight_flag== True:\n",
    "    model = BuildModel(WORD_INDEX=word_index, MAX_SEQUENCE_LENGTH=MAX_SEQUENCE_LENGTH, EMWEIGHTS=embedding_matrix).SetupModel()\n",
    "else:\n",
    "    model = BuildModel(WORD_INDEX=word_index, MAX_SEQUENCE_LENGTH=MAX_SEQUENCE_LENGTH, EMWEIGHTS=[]).SetupModel()    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c37d919-de54-4565-a4b6-74a58e4f1329",
   "metadata": {},
   "source": [
    "## Train the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "df9aca2b-be70-4ca2-b89d-c33df03f2d3c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "25/25 [==============================] - 41s 1s/step - loss: 1.8557 - acc: 0.3613 - val_loss: 1.6105 - val_acc: 0.4425\n",
      "Epoch 2/50\n",
      "25/25 [==============================] - 35s 1s/step - loss: 1.5211 - acc: 0.4913 - val_loss: 1.3709 - val_acc: 0.5275\n",
      "Epoch 3/50\n",
      "25/25 [==============================] - 35s 1s/step - loss: 1.2751 - acc: 0.5756 - val_loss: 1.1898 - val_acc: 0.6375\n",
      "Epoch 4/50\n",
      "25/25 [==============================] - 35s 1s/step - loss: 1.1198 - acc: 0.6263 - val_loss: 1.0584 - val_acc: 0.6725\n",
      "Epoch 5/50\n",
      "25/25 [==============================] - 35s 1s/step - loss: 1.0042 - acc: 0.6587 - val_loss: 1.1620 - val_acc: 0.6175\n",
      "Epoch 6/50\n",
      "25/25 [==============================] - 35s 1s/step - loss: 0.9027 - acc: 0.7094 - val_loss: 0.9159 - val_acc: 0.7400\n",
      "Epoch 7/50\n",
      "25/25 [==============================] - 35s 1s/step - loss: 0.8232 - acc: 0.7319 - val_loss: 0.8851 - val_acc: 0.7475\n",
      "Epoch 8/50\n",
      "25/25 [==============================] - 35s 1s/step - loss: 0.7280 - acc: 0.7588 - val_loss: 0.8502 - val_acc: 0.7625\n",
      "Epoch 9/50\n",
      "25/25 [==============================] - 35s 1s/step - loss: 0.7135 - acc: 0.7681 - val_loss: 0.8944 - val_acc: 0.7450\n",
      "Epoch 10/50\n",
      "25/25 [==============================] - 35s 1s/step - loss: 0.6041 - acc: 0.8181 - val_loss: 0.9227 - val_acc: 0.7575\n",
      "Epoch 11/50\n",
      "25/25 [==============================] - 35s 1s/step - loss: 0.5531 - acc: 0.8300 - val_loss: 0.8692 - val_acc: 0.7550\n",
      "Epoch 12/50\n",
      "25/25 [==============================] - 35s 1s/step - loss: 0.4923 - acc: 0.8338 - val_loss: 0.8487 - val_acc: 0.7700\n",
      "Epoch 13/50\n",
      "25/25 [==============================] - 35s 1s/step - loss: 0.4918 - acc: 0.8375 - val_loss: 0.8014 - val_acc: 0.7950\n",
      "Epoch 14/50\n",
      "25/25 [==============================] - 35s 1s/step - loss: 0.4495 - acc: 0.8562 - val_loss: 0.8956 - val_acc: 0.7825\n",
      "Epoch 15/50\n",
      "25/25 [==============================] - 35s 1s/step - loss: 0.3883 - acc: 0.8687 - val_loss: 0.9310 - val_acc: 0.7675\n",
      "Epoch 16/50\n",
      "25/25 [==============================] - 35s 1s/step - loss: 0.3708 - acc: 0.8756 - val_loss: 0.8948 - val_acc: 0.7775\n",
      "Epoch 17/50\n",
      "25/25 [==============================] - 35s 1s/step - loss: 0.3417 - acc: 0.8863 - val_loss: 0.9036 - val_acc: 0.7900\n",
      "Epoch 18/50\n",
      "25/25 [==============================] - 35s 1s/step - loss: 0.3382 - acc: 0.8869 - val_loss: 1.0248 - val_acc: 0.7550\n",
      "Epoch 19/50\n",
      "25/25 [==============================] - 35s 1s/step - loss: 0.3104 - acc: 0.9013 - val_loss: 0.9243 - val_acc: 0.7700\n",
      "Epoch 20/50\n",
      "25/25 [==============================] - 35s 1s/step - loss: 0.2550 - acc: 0.9181 - val_loss: 1.0070 - val_acc: 0.7825\n",
      "Epoch 21/50\n",
      "25/25 [==============================] - 35s 1s/step - loss: 0.2320 - acc: 0.9281 - val_loss: 0.9405 - val_acc: 0.7850\n",
      "Epoch 22/50\n",
      "25/25 [==============================] - 35s 1s/step - loss: 0.2283 - acc: 0.9300 - val_loss: 1.0307 - val_acc: 0.7625\n",
      "Epoch 23/50\n",
      "25/25 [==============================] - 35s 1s/step - loss: 0.1919 - acc: 0.9375 - val_loss: 0.9701 - val_acc: 0.8000\n",
      "Epoch 24/50\n",
      "25/25 [==============================] - 35s 1s/step - loss: 0.2017 - acc: 0.9356 - val_loss: 1.0476 - val_acc: 0.7625\n",
      "Epoch 25/50\n",
      "25/25 [==============================] - 35s 1s/step - loss: 0.2015 - acc: 0.9350 - val_loss: 1.0043 - val_acc: 0.7775\n",
      "Epoch 26/50\n",
      "25/25 [==============================] - 35s 1s/step - loss: 0.1704 - acc: 0.9438 - val_loss: 0.9956 - val_acc: 0.7775\n",
      "Epoch 27/50\n",
      "25/25 [==============================] - 35s 1s/step - loss: 0.1286 - acc: 0.9638 - val_loss: 0.9763 - val_acc: 0.8000\n",
      "Epoch 28/50\n",
      "25/25 [==============================] - 35s 1s/step - loss: 0.1204 - acc: 0.9575 - val_loss: 1.0639 - val_acc: 0.7800\n",
      "Epoch 29/50\n",
      "25/25 [==============================] - 35s 1s/step - loss: 0.1314 - acc: 0.9563 - val_loss: 1.0148 - val_acc: 0.7925\n",
      "Epoch 30/50\n",
      "25/25 [==============================] - 35s 1s/step - loss: 0.1162 - acc: 0.9669 - val_loss: 1.0849 - val_acc: 0.7875\n",
      "Epoch 31/50\n",
      "25/25 [==============================] - 35s 1s/step - loss: 0.1050 - acc: 0.9694 - val_loss: 1.0451 - val_acc: 0.7950\n",
      "Epoch 32/50\n",
      "25/25 [==============================] - 35s 1s/step - loss: 0.0954 - acc: 0.9694 - val_loss: 1.0191 - val_acc: 0.8000\n",
      "Epoch 33/50\n",
      "25/25 [==============================] - 35s 1s/step - loss: 0.0945 - acc: 0.9669 - val_loss: 1.2761 - val_acc: 0.7550\n",
      "Epoch 34/50\n",
      "25/25 [==============================] - 35s 1s/step - loss: 0.1284 - acc: 0.9556 - val_loss: 1.0711 - val_acc: 0.7925\n",
      "Epoch 35/50\n",
      "25/25 [==============================] - 35s 1s/step - loss: 0.0775 - acc: 0.9725 - val_loss: 1.0970 - val_acc: 0.7975\n",
      "Epoch 36/50\n",
      "25/25 [==============================] - 35s 1s/step - loss: 0.0728 - acc: 0.9731 - val_loss: 1.1659 - val_acc: 0.7900\n",
      "Epoch 37/50\n",
      "25/25 [==============================] - 35s 1s/step - loss: 0.0861 - acc: 0.9712 - val_loss: 1.1294 - val_acc: 0.7900\n",
      "Epoch 38/50\n",
      "25/25 [==============================] - 35s 1s/step - loss: 0.0761 - acc: 0.9769 - val_loss: 1.1638 - val_acc: 0.7875\n",
      "Epoch 39/50\n",
      "25/25 [==============================] - 35s 1s/step - loss: 0.0682 - acc: 0.9750 - val_loss: 1.0788 - val_acc: 0.8000\n",
      "Epoch 40/50\n",
      "25/25 [==============================] - 35s 1s/step - loss: 0.0632 - acc: 0.9806 - val_loss: 1.1116 - val_acc: 0.7875\n",
      "Epoch 41/50\n",
      "25/25 [==============================] - 35s 1s/step - loss: 0.0462 - acc: 0.9831 - val_loss: 1.1555 - val_acc: 0.7950\n",
      "Epoch 42/50\n",
      "25/25 [==============================] - 35s 1s/step - loss: 0.0381 - acc: 0.9862 - val_loss: 1.1940 - val_acc: 0.7975\n",
      "Epoch 43/50\n",
      "25/25 [==============================] - 35s 1s/step - loss: 0.0583 - acc: 0.9825 - val_loss: 1.3213 - val_acc: 0.7750\n",
      "Epoch 44/50\n",
      "25/25 [==============================] - 35s 1s/step - loss: 0.0550 - acc: 0.9819 - val_loss: 1.1679 - val_acc: 0.7950\n",
      "Epoch 45/50\n",
      "25/25 [==============================] - 35s 1s/step - loss: 0.0483 - acc: 0.9837 - val_loss: 1.0912 - val_acc: 0.7875\n",
      "Epoch 46/50\n",
      "25/25 [==============================] - 35s 1s/step - loss: 0.0463 - acc: 0.9869 - val_loss: 1.2142 - val_acc: 0.7950\n",
      "Epoch 47/50\n",
      "25/25 [==============================] - 35s 1s/step - loss: 0.0771 - acc: 0.9762 - val_loss: 1.1771 - val_acc: 0.7825\n",
      "Epoch 48/50\n",
      "25/25 [==============================] - 35s 1s/step - loss: 0.0784 - acc: 0.9756 - val_loss: 1.1490 - val_acc: 0.7825\n",
      "Epoch 49/50\n",
      "25/25 [==============================] - 35s 1s/step - loss: 0.0458 - acc: 0.9844 - val_loss: 1.1304 - val_acc: 0.7900\n",
      "Epoch 50/50\n",
      "25/25 [==============================] - 35s 1s/step - loss: 0.0458 - acc: 0.9844 - val_loss: 1.1995 - val_acc: 0.7875\n"
     ]
    }
   ],
   "source": [
    "model, history= TrainModel(model, tokenizer, enc,train_data, train_labels,test_data, test_labels, BATCH_SIZE=64,EPOCHS=50).ModelTraining()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08d0644c-f188-43c9-a17c-c327145a641e",
   "metadata": {},
   "source": [
    "### Plot the training and testing Loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d9428b35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig1 = plt.figure()\n",
    "plt.plot(history.history['loss'],'r',linewidth=3.0)\n",
    "plt.plot(history.history['val_loss'],'b',linewidth=3.0)\n",
    "plt.legend(['Training loss', 'Validation Loss'],fontsize=18)\n",
    "plt.xlabel('Epochs ',fontsize=16)\n",
    "plt.ylabel('Loss',fontsize=16)\n",
    "plt.title('Loss Curves :RNN - LSTM',fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f7712ce-2e36-476e-80fa-68b568cb52f5",
   "metadata": {},
   "source": [
    "#### Plot the training and testing Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1d876903",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig1 = plt.figure()\n",
    "plt.plot(history.history['acc'],'r',linewidth=3.0)\n",
    "plt.plot(history.history['val_acc'],'b',linewidth=3.0)\n",
    "plt.legend(['Training acc', 'Validation acc'],fontsize=18)\n",
    "plt.xlabel('Epochs ',fontsize=16)\n",
    "plt.ylabel('Accuracy',fontsize=16)\n",
    "plt.title('Accuracy Curves :RNN - LSTM',fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a92b61f",
   "metadata": {},
   "source": [
    "After hours of training we get good results with LSTM(type of recurrent neural network) compared to CNN. From the learning curves it is clear the model needs to be tuned for overfitting by selecting hyperparameters such as no of epochs via early stopping and dropout for regularization.\n",
    "\n",
    "We could further improve our final result by ensembling our xgboost and Neural network models by using Logistic Regression as our base model.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "80a43af4-e74f-43f9-a69d-c04475179984",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "10d220c55dad87bc296f91a276cffc08c658df4f112171a2982baf6251a25e4e"
  },
  "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}