hit_time_series-1DConv-Copy1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "705d3abb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import nqDataLoader as nq #data loading library\n",
    "from keras.preprocessing import sequence\n",
    "# np.random.seed(0)\n",
    "# cnn model\n",
    "import tensorflow as tf\n",
    "from numpy import mean\n",
    "from numpy import std\n",
    "from numpy import dstack\n",
    "from matplotlib import pyplot\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import Flatten\n",
    "from keras.layers import Dropout\n",
    "from keras.layers.convolutional import Conv1D\n",
    "from keras.layers.convolutional import MaxPooling1D\n",
    "# from keras.utils import to_categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1e360edf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<module 'tensorflow._api.v2.config' from 'C:\\\\Users\\\\micoa\\\\anaconda3\\\\envs\\\\classifiertest\\\\lib\\\\site-packages\\\\tensorflow\\\\_api\\\\v2\\\\config\\\\__init__.py'>\n"
     ]
    }
   ],
   "source": [
    "print(tf.config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6a7affcb",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pID</th>\n",
       "      <th>gt</th>\n",
       "      <th>updrs108</th>\n",
       "      <th>afTap</th>\n",
       "      <th>sTap</th>\n",
       "      <th>nqScore</th>\n",
       "      <th>typingSpeed</th>\n",
       "      <th>file_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>79.0</td>\n",
       "      <td>184.5</td>\n",
       "      <td>0.107179</td>\n",
       "      <td>56.866667</td>\n",
       "      <td>1424946827.1000_001_014.csv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1001</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>96.5</td>\n",
       "      <td>189.0</td>\n",
       "      <td>0.056286</td>\n",
       "      <td>118.000000</td>\n",
       "      <td>1427279751.1001_001_014.csv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1002</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>140.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>0.039519</td>\n",
       "      <td>119.037037</td>\n",
       "      <td>1426676689.1002_001_014.csv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1004</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>83.5</td>\n",
       "      <td>191.5</td>\n",
       "      <td>0.034853</td>\n",
       "      <td>74.266667</td>\n",
       "      <td>1429866367.1004_001_014.csv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1005</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>68.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>0.048307</td>\n",
       "      <td>74.969697</td>\n",
       "      <td>1430134526.1005_001_014.csv</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    pID  gt  updrs108  afTap   sTap   nqScore  typingSpeed  \\\n",
       "0  1000   1        27   79.0  184.5  0.107179    56.866667   \n",
       "1  1001   1        16   96.5  189.0  0.056286   118.000000   \n",
       "2  1002   0         5  140.0  158.0  0.039519   119.037037   \n",
       "3  1004   1        22   83.5  191.5  0.034853    74.266667   \n",
       "4  1005   1        17   68.0  150.0  0.048307    74.969697   \n",
       "\n",
       "                        file_1  \n",
       "0  1424946827.1000_001_014.csv  \n",
       "1  1427279751.1001_001_014.csv  \n",
       "2  1426676689.1002_001_014.csv  \n",
       "3  1429866367.1004_001_014.csv  \n",
       "4  1430134526.1005_001_014.csv  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## importing the early stage dataset \n",
    "early_stage = pd.read_csv('GT_DataPD_MIT-CS2PD.csv')\n",
    "# X = dataset.iloc[:, :-1].values\n",
    "# y = dataset.iloc[:, -1].values\n",
    "early_stage[\"gt\"] = early_stage[\"gt\"].astype(int)\n",
    "early_stage.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1ea5e941",
   "metadata": {},
   "outputs": [],
   "source": [
    "hit_time_series = []\n",
    "for index, row in early_stage.iterrows():\n",
    "    fileloc = row.file_1\n",
    "    keyPressed, htArr, pressArr, releaseArr =  nq.getDataFiltHelper( \"data_MIT-CS2PD/\" + early_stage.loc[index]['file_1'])\n",
    "    htArr =np.array(htArr)\n",
    "    hit_time_series.append(htArr)\n",
    "\n",
    "X1 = hit_time_series "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6f32abeb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "31\n"
     ]
    },
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pID</th>\n",
       "      <th>gt</th>\n",
       "      <th>updrs108</th>\n",
       "      <th>afTap</th>\n",
       "      <th>sTap</th>\n",
       "      <th>nqScore</th>\n",
       "      <th>typingSpeed</th>\n",
       "      <th>file_1</th>\n",
       "      <th>file_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>14.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>162.25</td>\n",
       "      <td>0.117543</td>\n",
       "      <td>189.372549</td>\n",
       "      <td>1402930351.011_001_014.csv</td>\n",
       "      <td>1403706430.011_003_014.csv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>162.25</td>\n",
       "      <td>0.070350</td>\n",
       "      <td>60.533333</td>\n",
       "      <td>1402932300.060_001_014.csv</td>\n",
       "      <td>1403708258.060_003_014.csv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>25.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>133.75</td>\n",
       "      <td>0.223411</td>\n",
       "      <td>54.333333</td>\n",
       "      <td>1401117235.067_001_014.csv</td>\n",
       "      <td>1401978395.067_003_014.csv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>68</td>\n",
       "      <td>0</td>\n",
       "      <td>6.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>159.00</td>\n",
       "      <td>0.074973</td>\n",
       "      <td>71.800000</td>\n",
       "      <td>1401114972.068_001_014.csv</td>\n",
       "      <td>1401980765.068_003_014.csv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>26.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>113.50</td>\n",
       "      <td>0.175751</td>\n",
       "      <td>39.614035</td>\n",
       "      <td>1404311419.070_001_014.csv</td>\n",
       "      <td>1404743687.070_003_014.csv</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pID  gt  updrs108  afTap    sTap   nqScore  typingSpeed  \\\n",
       "0   11   1     14.25    NaN  162.25  0.117543   189.372549   \n",
       "1   60   0      2.00    NaN  162.25  0.070350    60.533333   \n",
       "2   67   1     25.25    NaN  133.75  0.223411    54.333333   \n",
       "3   68   0      6.00    NaN  159.00  0.074973    71.800000   \n",
       "4   70   1     26.25    NaN  113.50  0.175751    39.614035   \n",
       "\n",
       "                       file_1                      file_2  \n",
       "0  1402930351.011_001_014.csv  1403706430.011_003_014.csv  \n",
       "1  1402932300.060_001_014.csv  1403708258.060_003_014.csv  \n",
       "2  1401117235.067_001_014.csv  1401978395.067_003_014.csv  \n",
       "3  1401114972.068_001_014.csv  1401980765.068_003_014.csv  \n",
       "4  1404311419.070_001_014.csv  1404743687.070_003_014.csv  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## importing the de-novo dataset \n",
    "de_novo = pd.read_csv('GT_DataPD_MIT-CS1PD.csv')\n",
    "# X = dataset.iloc[:, :-1].values\n",
    "# y = dataset.iloc[:, -1].values\n",
    "print(len(de_novo))\n",
    "de_novo[\"gt\"] = de_novo[\"gt\"].astype(int)\n",
    "de_novo.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5259add3",
   "metadata": {},
   "outputs": [],
   "source": [
    "##using both files \n",
    "hit_time_series = []\n",
    "for index, row in de_novo.iterrows():\n",
    "    fileloc1 = row.file_1\n",
    "    keyPressed, htArr1, pressArr, releaseArr =  nq.getDataFiltHelper( 'data_MIT-CS1PD/' + de_novo.loc[index]['file_1'])\n",
    "    htArr1 = np.array(htArr1)\n",
    "    keyPressed, htArr2, pressArr, releaseArr =  nq.getDataFiltHelper( 'data_MIT-CS1PD/' + de_novo.loc[index]['file_2'])\n",
    "    htArr2 = np.array(htArr2)\n",
    "    htArr =np.concatenate((htArr1,htArr2),axis =0)\n",
    "    htArr=np.array(htArr)\n",
    "    hit_time_series.append(htArr)\n",
    "X2 = hit_time_series "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "83b99f0c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(85,)\n",
      "[1 1 0 1 1 1 1 1 0 0 0 0 1 0 0 1 1 1 0 0 1 1 1 1 0 0 0 0 0 1 0 1 0 1 0 0 0\n",
      " 1 0 0 0 1 0 0 0 0 1 0 0 0 1 1 1 0 1 0 1 0 1 1 1 1 1 1 0 0 1 0 1 0 1 0 1 0\n",
      " 1 0 1 0 1 1 0 0 1 1 0]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<__array_function__ internals>:180: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n"
     ]
    }
   ],
   "source": [
    "X = np.concatenate((X1,X2),axis=0)\n",
    "y=  np.concatenate((early_stage['gt'],de_novo[\"gt\"]),axis=0)\n",
    "print(X.shape)\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "91950d0b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.11633377817478088\n"
     ]
    }
   ],
   "source": [
    "import itertools\n",
    "from sklearn.model_selection import train_test_split\n",
    "flat_list = list(itertools.chain(*X))\n",
    "value = sum(flat_list) / len(flat_list)\n",
    "print(value)\n",
    "X_padded =sequence.pad_sequences(X,dtype='float32',padding='post',maxlen=6000,value =0)\n",
    "X_train,X_test,y_train,y_test = train_test_split(X_padded,y,test_size=0.17,random_state=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "4fcea601",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def evaluate_model(trainX, trainy, testX, testy,n_filters):\n",
    "    verbose, epochs, batch_size = 0, 60, 10\n",
    "    n_timesteps, n_features = trainX.shape[1], trainX.shape[2]\n",
    "    model = Sequential()\n",
    "    model.add(Conv1D(filters=n_filters, kernel_size=3, activation='relu', input_shape=(n_timesteps,n_features)))\n",
    "    model.add(Conv1D(filters=n_filters, kernel_size=3, activation='relu'))\n",
    "    model.add(Dropout(0.5))\n",
    "    model.add(MaxPooling1D(pool_size=2))\n",
    "    model.add(Flatten())\n",
    "    model.add(Dense(100, activation='relu'))\n",
    "    model.add(Dense(1, activation='sigmoid'))\n",
    "    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "    # fit network\n",
    "    model.fit(trainX, trainy, epochs=epochs, batch_size=batch_size, verbose=verbose)\n",
    "    # evaluate model\n",
    "    _, accuracy = model.evaluate(testX, testy, batch_size=batch_size, verbose=0)\n",
    "    return accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ee4216c6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "622a0aa1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">p=8 #1: 58.824\n",
      ">p=8 #2: 70.588\n",
      ">p=8 #3: 70.588\n",
      ">p=8 #4: 52.941\n",
      ">p=8 #5: 64.706\n",
      ">p=8 #6: 64.706\n",
      ">p=8 #7: 64.706\n",
      ">p=8 #8: 58.824\n",
      ">p=8 #9: 64.706\n",
      ">p=8 #10: 64.706\n",
      ">p=16 #1: 58.824\n",
      ">p=16 #2: 64.706\n",
      ">p=16 #3: 64.706\n",
      ">p=16 #4: 64.706\n",
      ">p=16 #5: 58.824\n",
      ">p=16 #6: 64.706\n",
      ">p=16 #7: 58.824\n",
      ">p=16 #8: 64.706\n",
      ">p=16 #9: 58.824\n",
      ">p=16 #10: 58.824\n",
      ">p=32 #1: 64.706\n",
      ">p=32 #2: 70.588\n",
      ">p=32 #3: 64.706\n",
      ">p=32 #4: 58.824\n",
      ">p=32 #5: 58.824\n",
      ">p=32 #6: 64.706\n",
      ">p=32 #7: 76.471\n",
      ">p=32 #8: 64.706\n",
      ">p=32 #9: 64.706\n",
      ">p=32 #10: 52.941\n",
      ">p=64 #1: 64.706\n",
      ">p=64 #2: 52.941\n",
      ">p=64 #3: 64.706\n",
      ">p=64 #4: 52.941\n",
      ">p=64 #5: 64.706\n",
      ">p=64 #6: 52.941\n",
      ">p=64 #7: 64.706\n",
      ">p=64 #8: 58.824\n",
      ">p=64 #9: 64.706\n",
      ">p=64 #10: 64.706\n",
      ">p=128 #1: 58.824\n",
      ">p=128 #2: 70.588\n",
      ">p=128 #3: 70.588\n",
      ">p=128 #4: 58.824\n",
      ">p=128 #5: 58.824\n",
      ">p=128 #6: 64.706\n",
      ">p=128 #7: 58.824\n",
      ">p=128 #8: 64.706\n",
      ">p=128 #9: 64.706\n",
      ">p=128 #10: 64.706\n",
      ">p=256 #1: 64.706\n",
      ">p=256 #2: 58.824\n",
      ">p=256 #3: 52.941\n",
      ">p=256 #4: 64.706\n",
      ">p=256 #5: 58.824\n",
      ">p=256 #6: 70.588\n",
      ">p=256 #7: 70.588\n",
      ">p=256 #8: 64.706\n",
      ">p=256 #9: 64.706\n",
      ">p=256 #10: 64.706\n",
      "[[58.82353186607361, 70.58823704719543, 70.58823704719543, 52.941179275512695, 64.70588445663452, 64.70588445663452, 64.70588445663452, 58.82353186607361, 64.70588445663452, 64.70588445663452], [58.82353186607361, 64.70588445663452, 64.70588445663452, 64.70588445663452, 58.82353186607361, 64.70588445663452, 58.82353186607361, 64.70588445663452, 58.82353186607361, 58.82353186607361], [64.70588445663452, 70.58823704719543, 64.70588445663452, 58.82353186607361, 58.82353186607361, 64.70588445663452, 76.47058963775635, 64.70588445663452, 64.70588445663452, 52.941179275512695], [64.70588445663452, 52.941179275512695, 64.70588445663452, 52.941179275512695, 64.70588445663452, 52.941179275512695, 64.70588445663452, 58.82353186607361, 64.70588445663452, 64.70588445663452], [58.82353186607361, 70.58823704719543, 70.58823704719543, 58.82353186607361, 58.82353186607361, 64.70588445663452, 58.82353186607361, 64.70588445663452, 64.70588445663452, 64.70588445663452], [64.70588445663452, 58.82353186607361, 52.941179275512695, 64.70588445663452, 58.82353186607361, 70.58823704719543, 70.58823704719543, 64.70588445663452, 64.70588445663452, 64.70588445663452]] [8, 16, 32, 64, 128, 256]\n",
      "Param=8: 63.529% (+/-5.128)\n",
      "Param=16: 61.765% (+/-2.941)\n",
      "Param=32: 64.118% (+/-6.141)\n",
      "Param=64: 60.588% (+/-5.294)\n",
      "Param=128: 63.529% (+/-4.402)\n",
      "Param=256: 63.529% (+/-5.128)\n"
     ]
    },
    {
     "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": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "# run an experiment\n",
    "def run_experiment(params, repeats=10):\n",
    "    # load data\n",
    "    trainX,testX,trainy,testy = train_test_split(X_padded,y,test_size = 0.2,stratify=y)\n",
    "    trainX = np.array(trainX)\n",
    "    testX = np.array(testX)\n",
    "    sc = StandardScaler()\n",
    "    sc1 = MinMaxScaler(feature_range=(0,1))\n",
    "    trainX = sc.fit_transform(trainX)\n",
    "    trainX = sc1.fit_transform(trainX)\n",
    "    trainX = np.reshape(trainX,(trainX.shape[0],trainX.shape[1],1))\n",
    "    testX = sc.transform(testX)\n",
    "    testX = sc1.transform(testX)\n",
    "    testX = np.reshape(testX,(testX.shape[0],testX.shape[1],1))\n",
    "    # test each parameter\n",
    "    all_scores = list()\n",
    "    for p in params:\n",
    "        # repeat experiment\n",
    "        scores = list()\n",
    "        for r in range(repeats):\n",
    "            score = evaluate_model(trainX, trainy, testX, testy, p)\n",
    "            score = score * 100.0\n",
    "            print('>p=%d #%d: %.3f' % (p, r+1, score))\n",
    "            scores.append(score)\n",
    "        all_scores.append(scores)\n",
    "    # summarize results\n",
    "    summarize_results(all_scores, params)\n",
    "# summarize scores\n",
    "def summarize_results(scores, params):\n",
    "    print(scores, params)\n",
    "    # summarize mean and standard deviation\n",
    "    for i in range(len(scores)):\n",
    "        m, s = mean(scores[i]), std(scores[i])\n",
    "        print('Param=%d: %.3f%% (+/-%.3f)' % (params[i], m, s))\n",
    "    # boxplot of scores\n",
    "    pyplot.boxplot(scores, labels=params)\n",
    "# run the experiment\n",
    "n_params = [8, 16, 32, 64, 128, 256]\n",
    "run_experiment(n_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "60c51390",
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot a histogram of each variable in the dataset\n",
    "def plot_variable_distributions(trainX):\n",
    "    pyplot.figure()\n",
    "    xaxis = None\n",
    "    for i in range(trainX.shape[1]):\n",
    "        ax = pyplot.subplot(trainX.shape[1], 1, i+1, sharex=xaxis)\n",
    "        ax.set_xlim(-1, 1)\n",
    "        if i == 0:\n",
    "            xaxis = ax\n",
    "        pyplot.hist(trainX[:, i], bins=100)\n",
    "    pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "41afdac0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot_variable_distributions(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4cd7fb5d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e9cbdf6d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "bc816cc0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# from keras.models import Sequential\n",
    "# from keras.layers import Dense\n",
    "# from keras.layers import LSTM\n",
    "# from keras.layers import Dropout\n",
    "# from keras.callbacks import EarlyStopping\n",
    "# from matplotlib import pyplot\n",
    "# from keras import optimizers\n",
    "# import tensorflow as tf\n",
    "# clf = Sequential()\n",
    "# clf.add(LSTM(units = 30,return_sequences = True, input_shape = (X_train.shape[1], X_train.shape[2])))\n",
    "# clf.add(Dropout(0.2))\n",
    "# clf.add(LSTM(units = 30,return_sequences = True))\n",
    "# clf.add(Dropout(0.2))\n",
    "# clf.add(LSTM(units = 30,return_sequences = True))\n",
    "# clf.add(Dropout(0.2))\n",
    "# clf.add(LSTM(units =30 ))\n",
    "# clf.add(Dropout(0.2))\n",
    "# clf.add(Dense(units = 1,activation='sigmoid'))\n",
    "# es = EarlyStopping(monitor='loss', mode='auto', patience =50 ,verbose=1,restore_best_weights=True)\n",
    "# adm = tf.keras.optimizers.Adam(learning_rate=0.0001)\n",
    "# clf.compile(loss='binary_crossentropy', optimizer=adm, metrics=['accuracy'])\n",
    "# print(clf.summary())\n",
    "# history=clf.fit(X_train, y_train, epochs=200, batch_size=35)\n",
    "# scores = clf.evaluate(X_test, y_test, verbose=0)\n",
    "# print(\"Accuracy: %.2f%%\" % (scores[1]*100))\n",
    "# train_acc = clf.evaluate(X_train, y_train, verbose=0)\n",
    "# test_acc = clf.evaluate(X_test, y_test, verbose=0)\n",
    "# print('Train: %.3f, Test: %.3f' % (train_acc[1], test_acc[1]))\n",
    "# # plot training history\n",
    "# pyplot.plot(history.history['loss'], label='train loss')\n",
    "# pyplot.plot(history.history['accuracy'], label='train accuracy')\n",
    "# # pyplot.plot(history.history['val_loss'], label='test')\n",
    "# pyplot.legend()\n",
    "# pyplot.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
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