LSTM_LV1_mf2k+FINE_TUNED1.ipynb

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    {
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     "output_type": "stream",
     "text": [
      "/kaggle/input/dataset-tachygraphy/Tachygraphy_dataset_main.csv\n"
     ]
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   ],
   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9aca824",
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    },
    "tags": []
   },
   "source": [
    "**Text Preprocessing**"
   ]
  },
  {
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   "id": "254f618f",
   "metadata": {
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   "source": [
    "# To ignore warinings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
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   "execution_count": 3,
   "id": "034ee84b",
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    {
     "data": {
      "text/plain": [
       "(4958, 3)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df=pd.read_csv(\"/kaggle/input/dataset-tachygraphy/Tachygraphy_dataset_main.csv\")\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "85409c6c",
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    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Text</th>\n",
       "      <th>Meaning</th>\n",
       "      <th>Sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Last session of the day http://twitpic.com/67ezh</td>\n",
       "      <td>Last session of the day http://twitpic.com/67ezh</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Shanghai is also really exciting (precisely --...</td>\n",
       "      <td>Shanghai is also really exciting (precisely --...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>submit the report ASAP!</td>\n",
       "      <td>submit the report as soon as possilbe</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>happy bday!</td>\n",
       "      <td>Happy Birthday!</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>The OGs - I like it!!</td>\n",
       "      <td>The original gangsters - i like it!</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Text  \\\n",
       "0   Last session of the day http://twitpic.com/67ezh   \n",
       "1  Shanghai is also really exciting (precisely --...   \n",
       "2                            submit the report ASAP!   \n",
       "3                                        happy bday!   \n",
       "4                              The OGs - I like it!!   \n",
       "\n",
       "                                             Meaning Sentiment  \n",
       "0   Last session of the day http://twitpic.com/67ezh   neutral  \n",
       "1  Shanghai is also really exciting (precisely --...  positive  \n",
       "2              submit the report as soon as possilbe  negative  \n",
       "3                                    Happy Birthday!  positive  \n",
       "4                The original gangsters - i like it!  positive  "
      ]
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   "source": [
    "df.head()"
   ]
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "class\n",
       "neutral     1743\n",
       "positive    1643\n",
       "negative    1572\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=df.drop('Meaning',axis=1)\n",
    "df = df.rename(columns={'Text': 'tweet'})\n",
    "df = df.rename(columns={'Sentiment': 'class'})\n",
    "df['class'] = df['class'].str.lower()\n",
    "sentiment_counts = df['class'].value_counts()\n",
    "sentiment_counts"
   ]
  },
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   "cell_type": "code",
   "execution_count": 6,
   "id": "5cc10bcc",
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tweet    1\n",
       "class    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
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   },
   "outputs": [],
   "source": [
    "df=df.fillna('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b55159aa",
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    "tags": []
   },
   "outputs": [],
   "source": [
    "import re\n",
    "from bs4 import BeautifulSoup\n",
    "\n",
    "def text_cleaning(text):\n",
    "    text = str(text)\n",
    "    soup = BeautifulSoup(text, \"html.parser\")    ###removing html tages\n",
    "    text = re.sub(r'\\[[^]]*\\]', '', soup.get_text())      ##removing text within square brackets\n",
    "    pattern = r\"[^a-zA-Z0-9\\s,']\"                  # Removing unwanted characters\n",
    "    text = re.sub(pattern, '', text)\n",
    "    url_pattern = r'http\\S+|www\\S+'            ###removing urls\n",
    "    text = re.sub(url_pattern, '', text)\n",
    "    return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7e498a29",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-24T14:45:57.176686Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df['tweet']=df['tweet'].apply(text_cleaning).tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "299c7b89",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-08-24 14:46:00.171332: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "2024-08-24 14:46:00.171439: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "2024-08-24 14:46:00.292559: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Embedding, LSTM, SpatialDropout1D\n",
    "from sklearn.model_selection import train_test_split\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8cd47dd7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-24T14:46:09.420655Z",
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    },
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     "duration": 0.044691,
     "end_time": "2024-08-24T14:46:09.450437",
     "exception": false,
     "start_time": "2024-08-24T14:46:09.405746",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.preprocessing.text import Tokenizer\n",
    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
    "from tensorflow.keras.utils import to_categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "59bc589b",
   "metadata": {
    "execution": {
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     "end_time": "2024-08-24T14:46:09.483874",
     "exception": false,
     "start_time": "2024-08-24T14:46:09.463641",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.preprocessing.text import Tokenizer\n",
    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
    "from tensorflow.keras.utils import to_categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "433eca91",
   "metadata": {
    "execution": {
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     "exception": false,
     "start_time": "2024-08-24T14:46:09.496835",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['tweet'].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3ae3a064",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['class'].dtype"
   ]
  },
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   "cell_type": "code",
   "execution_count": 15,
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   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4948</th>\n",
       "      <td>time flies by when you're facetiming the bae</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4949</th>\n",
       "      <td>U dont know how I feel about this</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4950</th>\n",
       "      <td>come to my job I'll give you a baconator lol</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4951</th>\n",
       "      <td>Rihanna just made Maya Angelou her avi  you're...</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4952</th>\n",
       "      <td>im the lil bitch in the theaters tryna hold ba...</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4953</th>\n",
       "      <td>make a pet face  wtf wrong with me tonight haha</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4954</th>\n",
       "      <td>I dnt care anymore  Boyz ain't worth d drama</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4955</th>\n",
       "      <td>No relationship is perfect tho me bae goo from...</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4956</th>\n",
       "      <td>Over here tryna get my nail polishes and shit lol</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4957</th>\n",
       "      <td>No one was loved d way i luv U</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  tweet     class\n",
       "4948       time flies by when you're facetiming the bae  positive\n",
       "4949                  U dont know how I feel about this  negative\n",
       "4950       come to my job I'll give you a baconator lol  positive\n",
       "4951  Rihanna just made Maya Angelou her avi  you're...  negative\n",
       "4952  im the lil bitch in the theaters tryna hold ba...  negative\n",
       "4953    make a pet face  wtf wrong with me tonight haha  negative\n",
       "4954      I dnt care anymore  Boyz ain't worth d drama   negative\n",
       "4955  No relationship is perfect tho me bae goo from...  negative\n",
       "4956  Over here tryna get my nail polishes and shit lol  negative\n",
       "4957                     No one was loved d way i luv U  positive"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d8d49705",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-24T14:46:09.618744Z",
     "iopub.status.busy": "2024-08-24T14:46:09.618470Z",
     "iopub.status.idle": "2024-08-24T14:46:10.159151Z",
     "shell.execute_reply": "2024-08-24T14:46:10.158054Z"
    },
    "papermill": {
     "duration": 0.557205,
     "end_time": "2024-08-24T14:46:10.161359",
     "exception": false,
     "start_time": "2024-08-24T14:46:09.604154",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3286\n",
      "3144\n"
     ]
    }
   ],
   "source": [
    "df = df[df['class'] != \"neutral\"]  ###droping neutral columns\n",
    "df['tweet'] = df['tweet'].apply(lambda x: x.lower())\n",
    "\n",
    "print(df[ df['class'] == 'positive'].size)\n",
    "print(df[ df['class'] == 'negative'].size)\n",
    "\n",
    "for idx,row in df.iterrows():\n",
    "    row[0] = row[0].replace('rt',' ')\n",
    "    \n",
    "max_fatures = 2000\n",
    "tokenizer = Tokenizer(num_words=max_fatures, split=' ')\n",
    "tokenizer.fit_on_texts(df['tweet'].values)\n",
    "X = tokenizer.texts_to_sequences(df['tweet'].values)\n",
    "X = pad_sequences(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "68a140b2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-24T14:46:10.189810Z",
     "iopub.status.busy": "2024-08-24T14:46:10.189525Z",
     "iopub.status.idle": "2024-08-24T14:46:10.419619Z",
     "shell.execute_reply": "2024-08-24T14:46:10.417966Z"
    },
    "papermill": {
     "duration": 0.248806,
     "end_time": "2024-08-24T14:46:10.424009",
     "exception": false,
     "start_time": "2024-08-24T14:46:10.175203",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Embedding, SpatialDropout1D, LSTM, Dense, Dropout, Bidirectional\n",
    "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras.regularizers import l2\n",
    "from tensorflow.keras.layers import Embedding\n",
    "from tensorflow.keras.initializers import Constant\n",
    "from tensorflow.keras.optimizers import RMSprop\n",
    "from keras_tuner import RandomSearch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0ac1ca6a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-24T14:46:10.460294Z",
     "iopub.status.busy": "2024-08-24T14:46:10.459798Z",
     "iopub.status.idle": "2024-08-24T14:49:11.127572Z",
     "shell.execute_reply": "2024-08-24T14:49:11.126010Z"
    },
    "papermill": {
     "duration": 180.688663,
     "end_time": "2024-08-24T14:49:11.130396",
     "exception": false,
     "start_time": "2024-08-24T14:46:10.441733",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2024-08-24 14:46:11--  http://nlp.stanford.edu/data/glove.6B.zip\r\n",
      "Resolving nlp.stanford.edu (nlp.stanford.edu)... 171.64.67.140\r\n",
      "Connecting to nlp.stanford.edu (nlp.stanford.edu)|171.64.67.140|:80... connected.\r\n",
      "HTTP request sent, awaiting response... 302 Found\r\n",
      "Location: https://nlp.stanford.edu/data/glove.6B.zip [following]\r\n",
      "--2024-08-24 14:46:11--  https://nlp.stanford.edu/data/glove.6B.zip\r\n",
      "Connecting to nlp.stanford.edu (nlp.stanford.edu)|171.64.67.140|:443... connected.\r\n",
      "HTTP request sent, awaiting response... 301 Moved Permanently\r\n",
      "Location: https://downloads.cs.stanford.edu/nlp/data/glove.6B.zip [following]\r\n",
      "--2024-08-24 14:46:11--  https://downloads.cs.stanford.edu/nlp/data/glove.6B.zip\r\n",
      "Resolving downloads.cs.stanford.edu (downloads.cs.stanford.edu)... 171.64.64.22\r\n",
      "Connecting to downloads.cs.stanford.edu (downloads.cs.stanford.edu)|171.64.64.22|:443... connected.\r\n",
      "HTTP request sent, awaiting response... 200 OK\r\n",
      "Length: 862182613 (822M) [application/zip]\r\n",
      "Saving to: 'glove.6B.zip'\r\n",
      "\r\n",
      "glove.6B.zip        100%[===================>] 822.24M  5.01MB/s    in 2m 38s  \r\n",
      "\r\n",
      "2024-08-24 14:48:50 (5.19 MB/s) - 'glove.6B.zip' saved [862182613/862182613]\r\n",
      "\r\n",
      "Archive:  glove.6B.zip\r\n",
      "  inflating: glove.6B.50d.txt        \r\n",
      "  inflating: glove.6B.100d.txt       \r\n",
      "  inflating: glove.6B.200d.txt       \r\n",
      "  inflating: glove.6B.300d.txt       \r\n"
     ]
    }
   ],
   "source": [
    "!wget http://nlp.stanford.edu/data/glove.6B.zip\n",
    "!unzip glove.6B.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "caa6c9f9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-24T14:49:11.325028Z",
     "iopub.status.busy": "2024-08-24T14:49:11.324580Z",
     "iopub.status.idle": "2024-08-24T14:49:22.949150Z",
     "shell.execute_reply": "2024-08-24T14:49:22.948359Z"
    },
    "papermill": {
     "duration": 11.722361,
     "end_time": "2024-08-24T14:49:22.951345",
     "exception": false,
     "start_time": "2024-08-24T14:49:11.228984",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "embedding_index = {}\n",
    "with open('glove.6B.100d.txt', encoding='utf-8') as f:\n",
    "    for line in f:\n",
    "        values = line.split()\n",
    "        word = values[0]\n",
    "        coefs = np.asarray(values[1:], dtype='float32')\n",
    "        embedding_index[word] = coefs\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6a78d671",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-24T14:49:23.099845Z",
     "iopub.status.busy": "2024-08-24T14:49:23.099325Z",
     "iopub.status.idle": "2024-08-24T14:49:23.103359Z",
     "shell.execute_reply": "2024-08-24T14:49:23.102576Z"
    },
    "jupyter": {
     "source_hidden": true
    },
    "papermill": {
     "duration": 0.078923,
     "end_time": "2024-08-24T14:49:23.105180",
     "exception": false,
     "start_time": "2024-08-24T14:49:23.026257",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# max_features = 5000  \n",
    "# embed_dim = 128 \n",
    "# lstm_out = 64  \n",
    "# batch_size = 32\n",
    "# epochs = 20\n",
    "# learning_rate = 0.001"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7f41d7cf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-24T14:49:23.250296Z",
     "iopub.status.busy": "2024-08-24T14:49:23.250013Z",
     "iopub.status.idle": "2024-08-24T14:49:23.254208Z",
     "shell.execute_reply": "2024-08-24T14:49:23.253442Z"
    },
    "papermill": {
     "duration": 0.078943,
     "end_time": "2024-08-24T14:49:23.256107",
     "exception": false,
     "start_time": "2024-08-24T14:49:23.177164",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "max_features = 5000  \n",
    "embed_dim = 128 \n",
    "lstm_out = 64  \n",
    "batch_size = 64\n",
    "epochs = 20\n",
    "initial_learning_rate = 0.0005"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b3f83aea",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-24T14:49:23.403165Z",
     "iopub.status.busy": "2024-08-24T14:49:23.402470Z",
     "iopub.status.idle": "2024-08-24T14:49:23.419122Z",
     "shell.execute_reply": "2024-08-24T14:49:23.418489Z"
    },
    "papermill": {
     "duration": 0.091973,
     "end_time": "2024-08-24T14:49:23.421026",
     "exception": false,
     "start_time": "2024-08-24T14:49:23.329053",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "embedding_dim = 100\n",
    "embedding_matrix = np.zeros((max_features, embedding_dim))\n",
    "for word, i in tokenizer.word_index.items():\n",
    "    if i < max_features:\n",
    "        embedding_vector = embedding_index.get(word)\n",
    "        if embedding_vector is not None:\n",
    "            embedding_matrix[i] = embedding_vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "fcb26149",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-24T14:49:23.572766Z",
     "iopub.status.busy": "2024-08-24T14:49:23.572383Z",
     "iopub.status.idle": "2024-08-24T14:49:23.577149Z",
     "shell.execute_reply": "2024-08-24T14:49:23.576373Z"
    },
    "papermill": {
     "duration": 0.081161,
     "end_time": "2024-08-24T14:49:23.579001",
     "exception": false,
     "start_time": "2024-08-24T14:49:23.497840",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# # from tensorflow.keras.models import Sequential\n",
    "# # from tensorflow.keras.layers import Embedding, SpatialDropout1D, LSTM, Dense\n",
    "\n",
    "\n",
    "# # Model\n",
    "# model = Sequential()\n",
    "# model.add(Embedding(max_features, embedding_dim, embeddings_initializer=Constant(embedding_matrix),\n",
    "#                     input_length=X.shape[1], trainable=True))\n",
    "# model.add(SpatialDropout1D(0.3))\n",
    "# model.add(Bidirectional(LSTM(128, return_sequences=True)))\n",
    "# model.add(LSTM(64, dropout=0.4, recurrent_dropout=0.4))\n",
    "# model.add(Dense(64, activation='relu', kernel_regularizer=tf.keras.regularizers.l2(0.01)))\n",
    "# model.add(Dropout(0.4))\n",
    "# model.add(Dense(2, activation='softmax'))  # Assuming 2 classes for classification\n",
    "\n",
    "# # model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "# # print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e01c54b4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-24T14:49:23.725575Z",
     "iopub.status.busy": "2024-08-24T14:49:23.724911Z",
     "iopub.status.idle": "2024-08-24T14:49:23.729752Z",
     "shell.execute_reply": "2024-08-24T14:49:23.728941Z"
    },
    "papermill": {
     "duration": 0.079361,
     "end_time": "2024-08-24T14:49:23.731691",
     "exception": false,
     "start_time": "2024-08-24T14:49:23.652330",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "tf.random.set_seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "de9983aa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-24T14:49:23.877463Z",
     "iopub.status.busy": "2024-08-24T14:49:23.877211Z",
     "iopub.status.idle": "2024-08-24T14:49:23.880888Z",
     "shell.execute_reply": "2024-08-24T14:49:23.880058Z"
    },
    "papermill": {
     "duration": 0.078386,
     "end_time": "2024-08-24T14:49:23.882701",
     "exception": false,
     "start_time": "2024-08-24T14:49:23.804315",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# optimizer = Adam(learning_rate=initial_learning_rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "bcc482a1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-24T14:49:24.037725Z",
     "iopub.status.busy": "2024-08-24T14:49:24.036981Z",
     "iopub.status.idle": "2024-08-24T14:49:24.040810Z",
     "shell.execute_reply": "2024-08-24T14:49:24.039936Z"
    },
    "papermill": {
     "duration": 0.082847,
     "end_time": "2024-08-24T14:49:24.042596",
     "exception": false,
     "start_time": "2024-08-24T14:49:23.959749",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# model.compile(loss='categorical_crossentropy', optimizer=RMSprop(learning_rate=0.001), metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "228401d0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-24T14:49:24.190550Z",
     "iopub.status.busy": "2024-08-24T14:49:24.190008Z",
     "iopub.status.idle": "2024-08-24T14:49:24.193798Z",
     "shell.execute_reply": "2024-08-24T14:49:24.192903Z"
    },
    "papermill": {
     "duration": 0.07978,
     "end_time": "2024-08-24T14:49:24.195767",
     "exception": false,
     "start_time": "2024-08-24T14:49:24.115987",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "09b45004",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-24T14:49:24.341719Z",
     "iopub.status.busy": "2024-08-24T14:49:24.341370Z",
     "iopub.status.idle": "2024-08-24T14:49:24.344982Z",
     "shell.execute_reply": "2024-08-24T14:49:24.344146Z"
    },
    "papermill": {
     "duration": 0.078416,
     "end_time": "2024-08-24T14:49:24.346838",
     "exception": false,
     "start_time": "2024-08-24T14:49:24.268422",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# early_stopping = EarlyStopping(monitor='val_loss', patience=5, verbose=1, restore_best_weights=True)\n",
    "# reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=2, min_lr=0.00001, verbose=1)\n",
    "# model_checkpoint = ModelCheckpoint('best_model.keras', save_best_only=True, monitor='val_loss', mode='min')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "4d5019b4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-24T14:49:24.501228Z",
     "iopub.status.busy": "2024-08-24T14:49:24.500904Z",
     "iopub.status.idle": "2024-08-24T14:49:24.513901Z",
     "shell.execute_reply": "2024-08-24T14:49:24.512872Z"
    },
    "papermill": {
     "duration": 0.094223,
     "end_time": "2024-08-24T14:49:24.515948",
     "exception": false,
     "start_time": "2024-08-24T14:49:24.421725",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2572, 29) (2572, 2)\n",
      "(643, 29) (643, 2)\n"
     ]
    }
   ],
   "source": [
    "Y = pd.get_dummies(df['class']).values\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X,Y, test_size = 0.2, random_state = 42)\n",
    "print(X_train.shape,Y_train.shape)\n",
    "print(X_test.shape,Y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "52cd7a60",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-24T14:49:24.664307Z",
     "iopub.status.busy": "2024-08-24T14:49:24.664031Z",
     "iopub.status.idle": "2024-08-24T14:49:24.668627Z",
     "shell.execute_reply": "2024-08-24T14:49:24.667809Z"
    },
    "papermill": {
     "duration": 0.080485,
     "end_time": "2024-08-24T14:49:24.670498",
     "exception": false,
     "start_time": "2024-08-24T14:49:24.590013",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "validation_size = 500\n",
    "\n",
    "X_validate = X_test[-validation_size:]\n",
    "Y_validate = Y_test[-validation_size:]\n",
    "X_test = X_test[:-validation_size]\n",
    "Y_test = Y_test[:-validation_size]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "157fb58b",
   "metadata": {
    "papermill": {
     "duration": 0.072313,
     "end_time": "2024-08-24T14:49:24.814703",
     "exception": false,
     "start_time": "2024-08-24T14:49:24.742390",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# MODEL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "41724019",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-24T14:49:24.960321Z",
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     "shell.execute_reply": "2024-08-24T14:49:24.969641Z"
    },
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     "duration": 0.085666,
     "end_time": "2024-08-24T14:49:24.972288",
     "exception": false,
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     "status": "completed"
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    "tags": []
   },
   "outputs": [],
   "source": [
    "# Define the hyperparameter tuning function\n",
    "def build_model(hp):\n",
    "    model = Sequential()\n",
    "    \n",
    "    # Embedding layer with pre-trained GloVe embeddings\n",
    "    model.add(Embedding(max_features,\n",
    "                        embedding_dim,\n",
    "                        embeddings_initializer=Constant(embedding_matrix),\n",
    "                        input_length=X.shape[1],\n",
    "                        trainable=True))\n",
    "    \n",
    "    # Tuning SpatialDropout1D rate\n",
    "    model.add(SpatialDropout1D(hp.Float('spatial_dropout', min_value=0.1, max_value=0.5, step=0.1)))\n",
    "    \n",
    "    # Bidirectional LSTM layer with tunable units\n",
    "    model.add(Bidirectional(LSTM(units=hp.Int('lstm_units_1', min_value=64, max_value=128, step=32), \n",
    "                                 return_sequences=True)))\n",
    "    \n",
    "    # Second LSTM layer with dropout and recurrent dropout\n",
    "    model.add(LSTM(units=hp.Int('lstm_units_2', min_value=32, max_value=64, step=32),\n",
    "                   dropout=hp.Float('dropout', min_value=0.2, max_value=0.5, step=0.1),\n",
    "                   recurrent_dropout=hp.Float('recurrent_dropout', min_value=0.2, max_value=0.5, step=0.1)))\n",
    "    \n",
    "    # Dense layer with L2 regularization\n",
    "    model.add(Dense(units=hp.Int('dense_units', min_value=32, max_value=64, step=16),\n",
    "                    activation='relu',\n",
    "                    kernel_regularizer=tf.keras.regularizers.l2(0.01)))\n",
    "    \n",
    "    # Dropout layer\n",
    "    model.add(Dropout(hp.Float('dropout_dense', min_value=0.2, max_value=0.5, step=0.1)))\n",
    "    \n",
    "    # Output layer\n",
    "    model.add(Dense(2, activation='softmax'))\n",
    "    \n",
    "    # Compile the model\n",
    "#     model.compile(\n",
    "#         optimizer=hp.Choice('optimizer', values=['adam', 'rmsprop']),\n",
    "#         loss='categorical_crossentropy',\n",
    "#         metrics=['accuracy']\n",
    "#     )\n",
    "    \n",
    "    model.compile(optimizer=tf.keras.optimizers.Adam(hp.Float('learning_rate', min_value=1e-4, max_value=1e-2, sampling='LOG')),\n",
    "                  loss='categorical_crossentropy',\n",
    "                  metrics=['accuracy'])\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "485904a2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-24T14:49:25.119697Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Instantiate the RandomSearch tuner\n",
    "tuner = RandomSearch(\n",
    "    build_model,\n",
    "    objective='val_accuracy',\n",
    "    max_trials=20,  # Number of different models to try\n",
    "    executions_per_trial=4,  # Number of times to train each model\n",
    "    directory='my_dir',\n",
    "    project_name='lstm_hyperparameter_tuning'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "711311b5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-24T14:49:26.073430Z",
     "iopub.status.busy": "2024-08-24T14:49:26.073117Z",
     "iopub.status.idle": "2024-08-24T14:49:26.078418Z",
     "shell.execute_reply": "2024-08-24T14:49:26.077644Z"
    },
    "papermill": {
     "duration": 0.081806,
     "end_time": "2024-08-24T14:49:26.080388",
     "exception": false,
     "start_time": "2024-08-24T14:49:25.998582",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Search space summary\n",
      "Default search space size: 8\n",
      "spatial_dropout (Float)\n",
      "{'default': 0.1, 'conditions': [], 'min_value': 0.1, 'max_value': 0.5, 'step': 0.1, 'sampling': 'linear'}\n",
      "lstm_units_1 (Int)\n",
      "{'default': None, 'conditions': [], 'min_value': 64, 'max_value': 128, 'step': 32, 'sampling': 'linear'}\n",
      "lstm_units_2 (Int)\n",
      "{'default': None, 'conditions': [], 'min_value': 32, 'max_value': 64, 'step': 32, 'sampling': 'linear'}\n",
      "dropout (Float)\n",
      "{'default': 0.2, 'conditions': [], 'min_value': 0.2, 'max_value': 0.5, 'step': 0.1, 'sampling': 'linear'}\n",
      "recurrent_dropout (Float)\n",
      "{'default': 0.2, 'conditions': [], 'min_value': 0.2, 'max_value': 0.5, 'step': 0.1, 'sampling': 'linear'}\n",
      "dense_units (Int)\n",
      "{'default': None, 'conditions': [], 'min_value': 32, 'max_value': 64, 'step': 16, 'sampling': 'linear'}\n",
      "dropout_dense (Float)\n",
      "{'default': 0.2, 'conditions': [], 'min_value': 0.2, 'max_value': 0.5, 'step': 0.1, 'sampling': 'linear'}\n",
      "learning_rate (Float)\n",
      "{'default': 0.0001, 'conditions': [], 'min_value': 0.0001, 'max_value': 0.01, 'step': None, 'sampling': 'log'}\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Display search space summary\n",
    "tuner.search_space_summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "5da2d2f8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-24T14:49:26.226216Z",
     "iopub.status.busy": "2024-08-24T14:49:26.225966Z",
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     "shell.execute_reply": "2024-08-24T14:49:26.229776Z"
    },
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     "end_time": "2024-08-24T14:49:26.232677",
     "exception": false,
     "start_time": "2024-08-24T14:49:26.153057",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# batch_size = 32\n",
    "# model.fit(X_train, Y_train, epochs = 20, batch_size=batch_size, verbose = 2)\n",
    "\n",
    "# Training\n",
    "# history = model.fit(X_train, Y_train,\n",
    "#                     epochs=epochs,\n",
    "#                     batch_size=batch_size,\n",
    "#                     validation_data=(X_test, Y_test),\n",
    "#                     verbose=2,\n",
    "#                     callbacks=[early_stopping, reduce_lr, model_checkpoint])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "f96b4af1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-24T14:49:26.377332Z",
     "iopub.status.busy": "2024-08-24T14:49:26.377058Z",
     "iopub.status.idle": "2024-08-24T15:28:52.692322Z",
     "shell.execute_reply": "2024-08-24T15:28:52.691342Z"
    },
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     "duration": 2366.389883,
     "end_time": "2024-08-24T15:28:52.694245",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trial 20 Complete [00h 02m 55s]\n",
      "val_accuracy: 0.779720276594162\n",
      "\n",
      "Best val_accuracy So Far: 0.8111888021230698\n",
      "Total elapsed time: 00h 39m 26s\n"
     ]
    }
   ],
   "source": [
    "# Perform hyperparameter tuning\n",
    "tuner.search(X_train, Y_train, \n",
    "             epochs=20, \n",
    "             validation_data=(X_test, Y_test), \n",
    "             batch_size=64, \n",
    "             callbacks=[tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=3)])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eddee223",
   "metadata": {
    "papermill": {
     "duration": 0.071931,
     "end_time": "2024-08-24T15:28:52.839736",
     "exception": false,
     "start_time": "2024-08-24T15:28:52.767805",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## BEST HYPERPARAMS AS PERR TUNER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "b9ca446e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-24T15:28:52.985094Z",
     "iopub.status.busy": "2024-08-24T15:28:52.984752Z",
     "iopub.status.idle": "2024-08-24T15:28:52.991527Z",
     "shell.execute_reply": "2024-08-24T15:28:52.990592Z"
    },
    "papermill": {
     "duration": 0.082015,
     "end_time": "2024-08-24T15:28:52.993501",
     "exception": false,
     "start_time": "2024-08-24T15:28:52.911486",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The hyperparameter search is complete. \n",
      "Optimal SpatialDropout1D rate: 0.5\n",
      "Optimal LSTM units for first layer: 128\n",
      "Optimal LSTM units for second layer: 64\n",
      "Optimal dropout rate for second LSTM layer: 0.4\n",
      "Optimal recurrent dropout rate for second LSTM layer: 0.30000000000000004\n",
      "Optimal dense layer units: 48\n",
      "Optimal dropout rate for dense layer: 0.4\n",
      "Optimal learning rate: 0.0014505782975686376\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Display best hyperparameters\n",
    "best_hps = tuner.get_best_hyperparameters(num_trials=3)[0]\n",
    "print(f\"\"\"\n",
    "The hyperparameter search is complete. \n",
    "Optimal SpatialDropout1D rate: {best_hps.get('spatial_dropout')}\n",
    "Optimal LSTM units for first layer: {best_hps.get('lstm_units_1')}\n",
    "Optimal LSTM units for second layer: {best_hps.get('lstm_units_2')}\n",
    "Optimal dropout rate for second LSTM layer: {best_hps.get('dropout')}\n",
    "Optimal recurrent dropout rate for second LSTM layer: {best_hps.get('recurrent_dropout')}\n",
    "Optimal dense layer units: {best_hps.get('dense_units')}\n",
    "Optimal dropout rate for dense layer: {best_hps.get('dropout_dense')}\n",
    "Optimal learning rate: {best_hps.get('learning_rate')}\n",
    "\"\"\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4854fa94",
   "metadata": {
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     "duration": 0.072863,
     "end_time": "2024-08-24T15:28:53.138887",
     "exception": false,
     "start_time": "2024-08-24T15:28:53.066024",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## SECOND BEST HYPERPARAMS AS PER TUNER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "5dd2f088",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-24T15:28:53.284681Z",
     "iopub.status.busy": "2024-08-24T15:28:53.284348Z",
     "iopub.status.idle": "2024-08-24T15:28:53.290038Z",
     "shell.execute_reply": "2024-08-24T15:28:53.289148Z"
    },
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     "duration": 0.080962,
     "end_time": "2024-08-24T15:28:53.292175",
     "exception": false,
     "start_time": "2024-08-24T15:28:53.211213",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The hyperparameter search is complete. \n",
      "Optimal SpatialDropout1D rate: 0.4\n",
      "Optimal LSTM units for first layer: 64\n",
      "Optimal LSTM units for second layer: 32\n",
      "Optimal dropout rate for second LSTM layer: 0.2\n",
      "Optimal recurrent dropout rate for second LSTM layer: 0.2\n",
      "Optimal dense layer units: 48\n",
      "Optimal dropout rate for dense layer: 0.4\n",
      "Optimal learning rate: 0.0009181671251157912\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Display 2nd best hyperparameters\n",
    "best_hps2 = tuner.get_best_hyperparameters(num_trials=3)[1]\n",
    "print(f\"\"\"\n",
    "The hyperparameter search is complete. \n",
    "Optimal SpatialDropout1D rate: {best_hps2.get('spatial_dropout')}\n",
    "Optimal LSTM units for first layer: {best_hps2.get('lstm_units_1')}\n",
    "Optimal LSTM units for second layer: {best_hps2.get('lstm_units_2')}\n",
    "Optimal dropout rate for second LSTM layer: {best_hps2.get('dropout')}\n",
    "Optimal recurrent dropout rate for second LSTM layer: {best_hps2.get('recurrent_dropout')}\n",
    "Optimal dense layer units: {best_hps2.get('dense_units')}\n",
    "Optimal dropout rate for dense layer: {best_hps2.get('dropout_dense')}\n",
    "Optimal learning rate: {best_hps2.get('learning_rate')}\n",
    "\"\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "c9ffcc2e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-24T15:28:53.438379Z",
     "iopub.status.busy": "2024-08-24T15:28:53.438137Z",
     "iopub.status.idle": "2024-08-24T15:28:53.441884Z",
     "shell.execute_reply": "2024-08-24T15:28:53.441059Z"
    },
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     "duration": 0.079673,
     "end_time": "2024-08-24T15:28:53.444263",
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     "start_time": "2024-08-24T15:28:53.364590",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# # Display best hyperparameters\n",
    "# best_hps = tuner.get_best_hyperparameters(num_trials=1)[0]\n",
    "# print(f\"\"\"\n",
    "# The hyperparameter search is complete. \n",
    "# Optimal dropout rate: {best_hps.get('dropout_rate')}\n",
    "# Optimal LSTM units: {best_hps.get('lstm_units')}\n",
    "# Optimal dense layer units: {best_hps.get('dense_units')}\n",
    "# Optimal optimizer: {best_hps.get('optimizer')}\n",
    "# \"\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "7c8e0cf1",
   "metadata": {
    "execution": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 63ms/step - accuracy: 0.5151 - loss: 1.1846 - val_accuracy: 0.5664 - val_loss: 0.9636\n",
      "Epoch 2/20\n",
      "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 52ms/step - accuracy: 0.5801 - loss: 0.9237 - val_accuracy: 0.6993 - val_loss: 0.7619\n",
      "Epoch 3/20\n",
      "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 52ms/step - accuracy: 0.6612 - loss: 0.7386 - val_accuracy: 0.7413 - val_loss: 0.6338\n",
      "Epoch 4/20\n",
      "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 51ms/step - accuracy: 0.7384 - loss: 0.6214 - val_accuracy: 0.7273 - val_loss: 0.6174\n",
      "Epoch 5/20\n",
      "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 52ms/step - accuracy: 0.7433 - loss: 0.5694 - val_accuracy: 0.7413 - val_loss: 0.5646\n",
      "Epoch 6/20\n",
      "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 52ms/step - accuracy: 0.7774 - loss: 0.4951 - val_accuracy: 0.7762 - val_loss: 0.5396\n",
      "Epoch 7/20\n",
      "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 52ms/step - accuracy: 0.8020 - loss: 0.4532 - val_accuracy: 0.7692 - val_loss: 0.5249\n",
      "Epoch 8/20\n",
      "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 50ms/step - accuracy: 0.8327 - loss: 0.4171 - val_accuracy: 0.7832 - val_loss: 0.5461\n",
      "Epoch 9/20\n",
      "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 51ms/step - accuracy: 0.8444 - loss: 0.3863 - val_accuracy: 0.7762 - val_loss: 0.4969\n",
      "Epoch 10/20\n",
      "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 50ms/step - accuracy: 0.8668 - loss: 0.3538 - val_accuracy: 0.7972 - val_loss: 0.4864\n",
      "Epoch 11/20\n",
      "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 52ms/step - accuracy: 0.8617 - loss: 0.3310 - val_accuracy: 0.7972 - val_loss: 0.4891\n",
      "Epoch 12/20\n",
      "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 52ms/step - accuracy: 0.8674 - loss: 0.3120 - val_accuracy: 0.7972 - val_loss: 0.5241\n",
      "Epoch 13/20\n",
      "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 51ms/step - accuracy: 0.8877 - loss: 0.2998 - val_accuracy: 0.8182 - val_loss: 0.5358\n"
     ]
    }
   ],
   "source": [
    "# Build the model with the best hyperparameters\n",
    "model = tuner.hypermodel.build(best_hps)\n",
    "\n",
    "# Train the final model\n",
    "history = model.fit(X_train, Y_train, \n",
    "                    epochs=20, \n",
    "                    validation_data=(X_test, Y_test), \n",
    "                    batch_size=64, \n",
    "                    callbacks=[tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=3)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "f025b103",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-24T15:29:26.049591Z",
     "iopub.status.busy": "2024-08-24T15:29:26.048977Z",
     "iopub.status.idle": "2024-08-24T15:29:26.189260Z",
     "shell.execute_reply": "2024-08-24T15:29:26.188334Z"
    },
    "papermill": {
     "duration": 0.296917,
     "end_time": "2024-08-24T15:29:26.191156",
     "exception": false,
     "start_time": "2024-08-24T15:29:25.894239",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - accuracy: 0.8105 - loss: 0.5235\n",
      "Test accuracy: 0.8182\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model\n",
    "test_loss, test_acc = model.evaluate(X_test, Y_test)\n",
    "print(f\"Test accuracy: {test_acc:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "b5e66a58",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-24T15:29:26.405067Z",
     "iopub.status.busy": "2024-08-24T15:29:26.404783Z",
     "iopub.status.idle": "2024-08-24T15:29:55.885316Z",
     "shell.execute_reply": "2024-08-24T15:29:55.884533Z"
    },
    "papermill": {
     "duration": 29.588884,
     "end_time": "2024-08-24T15:29:55.887279",
     "exception": false,
     "start_time": "2024-08-24T15:29:26.298395",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 53ms/step - accuracy: 0.5003 - loss: 1.0412 - val_accuracy: 0.5385 - val_loss: 0.9439\n",
      "Epoch 2/20\n",
      "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 41ms/step - accuracy: 0.5723 - loss: 0.9133 - val_accuracy: 0.6224 - val_loss: 0.8284\n",
      "Epoch 3/20\n",
      "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 40ms/step - accuracy: 0.6443 - loss: 0.7945 - val_accuracy: 0.6224 - val_loss: 0.7413\n",
      "Epoch 4/20\n",
      "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 41ms/step - accuracy: 0.6929 - loss: 0.7019 - val_accuracy: 0.6573 - val_loss: 0.6695\n",
      "Epoch 5/20\n",
      "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 41ms/step - accuracy: 0.7544 - loss: 0.6018 - val_accuracy: 0.7133 - val_loss: 0.6145\n",
      "Epoch 6/20\n",
      "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 42ms/step - accuracy: 0.7593 - loss: 0.5455 - val_accuracy: 0.7343 - val_loss: 0.5865\n",
      "Epoch 7/20\n",
      "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 41ms/step - accuracy: 0.8107 - loss: 0.4805 - val_accuracy: 0.7273 - val_loss: 0.5839\n",
      "Epoch 8/20\n",
      "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 41ms/step - accuracy: 0.8360 - loss: 0.4226 - val_accuracy: 0.7343 - val_loss: 0.5493\n",
      "Epoch 9/20\n",
      "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 41ms/step - accuracy: 0.8349 - loss: 0.4171 - val_accuracy: 0.7902 - val_loss: 0.5096\n",
      "Epoch 10/20\n",
      "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 42ms/step - accuracy: 0.8514 - loss: 0.3945 - val_accuracy: 0.7762 - val_loss: 0.5325\n",
      "Epoch 11/20\n",
      "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 42ms/step - accuracy: 0.8536 - loss: 0.3498 - val_accuracy: 0.7902 - val_loss: 0.5092\n",
      "Epoch 12/20\n",
      "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 43ms/step - accuracy: 0.8654 - loss: 0.3321 - val_accuracy: 0.7692 - val_loss: 0.5463\n",
      "Epoch 13/20\n",
      "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 41ms/step - accuracy: 0.8719 - loss: 0.3161 - val_accuracy: 0.7692 - val_loss: 0.5244\n",
      "Epoch 14/20\n",
      "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 42ms/step - accuracy: 0.8857 - loss: 0.3062 - val_accuracy: 0.7832 - val_loss: 0.5698\n"
     ]
    }
   ],
   "source": [
    "# Build the model with the 2nd best hyperparameters\n",
    "model2 = tuner.hypermodel.build(best_hps2)\n",
    "\n",
    "# Train the final model\n",
    "history = model2.fit(X_train, Y_train, \n",
    "                    epochs=20, \n",
    "                    validation_data=(X_test, Y_test), \n",
    "                    batch_size=64, \n",
    "                    callbacks=[tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=3)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "5c4311d9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-24T15:29:56.152533Z",
     "iopub.status.busy": "2024-08-24T15:29:56.151977Z",
     "iopub.status.idle": "2024-08-24T15:29:56.275175Z",
     "shell.execute_reply": "2024-08-24T15:29:56.274151Z"
    },
    "papermill": {
     "duration": 0.257418,
     "end_time": "2024-08-24T15:29:56.277148",
     "exception": false,
     "start_time": "2024-08-24T15:29:56.019730",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - accuracy: 0.7706 - loss: 0.5608\n",
      "Test accuracy: 0.7832\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the 2nd model\n",
    "test_loss, test_acc = model2.evaluate(X_test, Y_test)\n",
    "print(f\"Test accuracy: {test_acc:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "c4a766c0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-24T15:29:56.545613Z",
     "iopub.status.busy": "2024-08-24T15:29:56.544819Z",
     "iopub.status.idle": "2024-08-24T15:29:56.653369Z",
     "shell.execute_reply": "2024-08-24T15:29:56.652480Z"
    },
    "papermill": {
     "duration": 0.24326,
     "end_time": "2024-08-24T15:29:56.655383",
     "exception": false,
     "start_time": "2024-08-24T15:29:56.412123",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3/3 - 0s - 20ms/step - accuracy: 0.8182 - loss: 0.5358\n",
      "loss_score: 0.54\n",
      "accuracy: 0.82\n"
     ]
    }
   ],
   "source": [
    "score,acc = model.evaluate(X_test, Y_test,verbose = 2, batch_size = batch_size)\n",
    "print(\"loss_score: %.2f\" % (score))\n",
    "print(\"accuracy: %.2f\" % (acc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "5e611ce7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-24T15:29:56.921526Z",
     "iopub.status.busy": "2024-08-24T15:29:56.920872Z",
     "iopub.status.idle": "2024-08-24T15:30:31.290385Z",
     "shell.execute_reply": "2024-08-24T15:30:31.289217Z"
    },
    "papermill": {
     "duration": 34.504468,
     "end_time": "2024-08-24T15:30:31.292565",
     "exception": false,
     "start_time": "2024-08-24T15:29:56.788097",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "pos_acc 80.59701492537313 %\n",
      "neg_acc 83.62068965517241 %\n"
     ]
    }
   ],
   "source": [
    "pos_cnt, neg_cnt, pos_correct, neg_correct = 0, 0, 0, 0\n",
    "for x in range(len(X_validate)):\n",
    "    \n",
    "    result = model.predict(X_validate[x].reshape(1,X_test.shape[1]),batch_size=1,verbose = 2)[0]\n",
    "   \n",
    "    if np.argmax(result) == np.argmax(Y_validate[x]):\n",
    "        if np.argmax(Y_validate[x]) == 0:\n",
    "            neg_correct += 1\n",
    "        else:\n",
    "            pos_correct += 1\n",
    "       \n",
    "    if np.argmax(Y_validate[x]) == 0:\n",
    "        neg_cnt += 1\n",
    "    else:\n",
    "        pos_cnt += 1\n",
    "\n",
    "\n",
    "\n",
    "print(\"pos_acc\", pos_correct/pos_cnt*100, \"%\")\n",
    "print(\"neg_acc\", neg_correct/neg_cnt*100, \"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "0b3e7a24",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-24T15:30:31.687643Z",
     "iopub.status.busy": "2024-08-24T15:30:31.686715Z",
     "iopub.status.idle": "2024-08-24T15:30:32.131897Z",
     "shell.execute_reply": "2024-08-24T15:30:32.130589Z"
    },
    "papermill": {
     "duration": 0.641694,
     "end_time": "2024-08-24T15:30:32.134650",
     "exception": false,
     "start_time": "2024-08-24T15:30:31.492956",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[   0    0    0    0    0    0    0    1   79  477    7   18  559 1000\n",
      "    39  275   55    7   10 1913    6    5  458  165  118   13    4 1562]]\n",
      "1/1 - 0s - 391ms/step\n",
      "negative\n"
     ]
    }
   ],
   "source": [
    "twt = ['I been told you that yo folks dont care about you . Im starving and my dad deadass didnt make me a plate']\n",
    "twt = tokenizer.texts_to_sequences(twt)\n",
    "twt = pad_sequences(twt, maxlen=28, dtype='int32', value=0)\n",
    "print(twt)\n",
    "sentiment = model.predict(twt,batch_size=1,verbose = 2)[0]\n",
    "if(np.argmax(sentiment) == 0):\n",
    "    print(\"negative\")\n",
    "elif (np.argmax(sentiment) == 1):\n",
    "    print(\"positive\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "2c95f379",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-24T15:30:32.535495Z",
     "iopub.status.busy": "2024-08-24T15:30:32.534572Z",
     "iopub.status.idle": "2024-08-24T15:30:34.248119Z",
     "shell.execute_reply": "2024-08-24T15:30:34.246948Z"
    },
    "papermill": {
     "duration": 1.920286,
     "end_time": "2024-08-24T15:30:34.251092",
     "exception": false,
     "start_time": "2024-08-24T15:30:32.330806",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "model.save('lstm_model.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e6a03c5b",
   "metadata": {
    "papermill": {
     "duration": 0.177782,
     "end_time": "2024-08-24T15:30:34.613860",
     "exception": false,
     "start_time": "2024-08-24T15:30:34.436078",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "\n"
   ]
  }
 ],
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     "sourceType": "datasetVersion"
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   "language": "python",
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