DPT_predictions_based_on_pre_trained_model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score, recall_score, confusion_matrix, precision_score, f1_score\n",
    "import seaborn as sn\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn import svm\n",
    "from sklearn.model_selection import KFold"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing Expanded DPT Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel(\"Datasets/DrNimaDPT Expanded Dataset.xlsx\")\n",
    "df['ln(CSR7.5)'] = np.log(df['CSR7.5'])\n",
    "MSF = (df['Mw']/7.5)**(-2.56)\n",
    "df['CSR'] = df['CSR7.5'] * MSF\n",
    "df['ln(CSR)'] = np.log(df['CSR'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['No.', 'Location', 'Mw', 'SD(Mw)', 'MSF', 'PGA (g)', 'SD(PGA)',\n",
       "       'CSR7.5', 'SD(CSR7.5)', 'GC(%)', 'SD(GC)', 'N'120', 'SD(N'120)', 'N120',\n",
       "       'Phi', 'Vs (m/s)', 'Vs1 (Corrected)', 'SD(Vs)', 'Vs1', 'Vs1,sk',\n",
       "       'Vs1,sk corrected', 'σ'(KPa)', 'SD(σ')', 'σ(KPa)', 'SD(σ)', 'Ds (m)',\n",
       "       'Liq?', 'FC%', 'CRR(Vs)And', 'CRR(Vs)2017', 'CRR(Vs)2017Vs1,sc',\n",
       "       'CRR(corrected)And', 'CRR corrected2017', 'CRR(Vs1,sk)Anderus',\n",
       "       'CRR(Cao)', 'SD(CRR)', 'CRR Nima Logistic', 'CRR Nima', 'rd', 'SDrd',\n",
       "       'ln(CSR7.5)', 'CSR', 'ln(CSR)'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Number of Datapoints Dataset:  110\n"
     ]
    }
   ],
   "source": [
    "print(\"Total Number of Datapoints Dataset: \", len(df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    69\n",
       "0    41\n",
       "Name: Liq?, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Liq?'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.rename(columns = {\"Liq?\":\"Liq\"}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>N'120</th>\n",
       "      <th>CSR7.5</th>\n",
       "      <th>Liq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>110.000000</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>110.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>15.979923</td>\n",
       "      <td>0.301207</td>\n",
       "      <td>0.627273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.866782</td>\n",
       "      <td>0.126209</td>\n",
       "      <td>0.485743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.900000</td>\n",
       "      <td>0.120000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.221820</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>13.757305</td>\n",
       "      <td>0.277500</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>19.878021</td>\n",
       "      <td>0.341840</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>62.122996</td>\n",
       "      <td>0.770000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            N'120      CSR7.5         Liq\n",
       "count  110.000000  110.000000  110.000000\n",
       "mean    15.979923    0.301207    0.627273\n",
       "std      9.866782    0.126209    0.485743\n",
       "min      3.900000    0.120000    0.000000\n",
       "25%      9.000000    0.221820    0.000000\n",
       "50%     13.757305    0.277500    1.000000\n",
       "75%     19.878021    0.341840    1.000000\n",
       "max     62.122996    0.770000    1.000000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"N'120\",\"CSR7.5\",\"Liq\"]].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel=\"N'120\", ylabel='CSR7.5'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sn.scatterplot(x = df[\"N'120\"], y = df[\"CSR7.5\"], hue = df['Liq'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using Pre-Trained Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = df[[\"N'120\",\"CSR7.5\"]]\n",
    "target = df['Liq']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def MinMaxNormalization(data):\n",
    "    # MinMax Normalization\n",
    "    scaler = MinMaxScaler()\n",
    "    # fit scaler on data\n",
    "    scaler.fit(data)\n",
    "    # apply transform\n",
    "    minmax_normalized = scaler.transform(data)\n",
    "    return minmax_normalized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "normalized_features = MinMaxNormalization(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(normalized_features, target, test_size = 0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/500\n",
      "1/1 [==============================] - 0s 413ms/step - loss: 0.4347 - accuracy: 0.8182\n",
      "Epoch 2/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.4346 - accuracy: 0.8182\n",
      "Epoch 3/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.4343 - accuracy: 0.8182\n",
      "Epoch 4/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.4337 - accuracy: 0.8182\n",
      "Epoch 5/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.4330 - accuracy: 0.8182\n",
      "Epoch 6/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.4322 - accuracy: 0.8182\n",
      "Epoch 7/500\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.4312 - accuracy: 0.8182\n",
      "Epoch 8/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.4301 - accuracy: 0.8182\n",
      "Epoch 9/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.4290 - accuracy: 0.8182\n",
      "Epoch 10/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.4278 - accuracy: 0.8182\n",
      "Epoch 11/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.4266 - accuracy: 0.8182\n",
      "Epoch 12/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.4253 - accuracy: 0.8182\n",
      "Epoch 13/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.4241 - accuracy: 0.8182\n",
      "Epoch 14/500\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.4228 - accuracy: 0.8182\n",
      "Epoch 15/500\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.4216 - accuracy: 0.8182\n",
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      "Epoch 366/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3707 - accuracy: 0.8636\n",
      "Epoch 367/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3707 - accuracy: 0.8636\n",
      "Epoch 368/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.3707 - accuracy: 0.8636\n",
      "Epoch 369/500\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.3706 - accuracy: 0.8636\n",
      "Epoch 370/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3706 - accuracy: 0.8636\n",
      "Epoch 371/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3706 - accuracy: 0.8636\n",
      "Epoch 372/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3705 - accuracy: 0.8636\n",
      "Epoch 373/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.3705 - accuracy: 0.8636\n",
      "Epoch 374/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3705 - accuracy: 0.8636\n",
      "Epoch 375/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.3705 - accuracy: 0.8636\n",
      "Epoch 376/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3704 - accuracy: 0.8636\n",
      "Epoch 377/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.3704 - accuracy: 0.8636\n",
      "Epoch 378/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3704 - accuracy: 0.8636\n",
      "Epoch 379/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3704 - accuracy: 0.8636\n",
      "Epoch 380/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3703 - accuracy: 0.8636\n",
      "Epoch 381/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.3703 - accuracy: 0.8636\n",
      "Epoch 382/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3703 - accuracy: 0.8636\n",
      "Epoch 383/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3702 - accuracy: 0.8636\n",
      "Epoch 384/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3702 - accuracy: 0.8636\n",
      "Epoch 385/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.3702 - accuracy: 0.8636\n",
      "Epoch 386/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3702 - accuracy: 0.8636\n",
      "Epoch 387/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3701 - accuracy: 0.8636\n",
      "Epoch 388/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3701 - accuracy: 0.8636\n",
      "Epoch 389/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3701 - accuracy: 0.8636\n",
      "Epoch 390/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3701 - accuracy: 0.8636\n",
      "Epoch 391/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3700 - accuracy: 0.8636\n",
      "Epoch 392/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3700 - accuracy: 0.8636\n",
      "Epoch 393/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3700 - accuracy: 0.8636\n",
      "Epoch 394/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.3700 - accuracy: 0.8636\n",
      "Epoch 395/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.3699 - accuracy: 0.8636\n",
      "Epoch 396/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.3699 - accuracy: 0.8636\n",
      "Epoch 397/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.3699 - accuracy: 0.8636\n",
      "Epoch 398/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3699 - accuracy: 0.8636\n",
      "Epoch 399/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3698 - accuracy: 0.8636\n",
      "Epoch 400/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3698 - accuracy: 0.8636\n",
      "Epoch 401/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3698 - accuracy: 0.8636\n",
      "Epoch 402/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.3698 - accuracy: 0.8636\n",
      "Epoch 403/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3697 - accuracy: 0.8636\n",
      "Epoch 404/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3697 - accuracy: 0.8182\n",
      "Epoch 405/500\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.3697 - accuracy: 0.8182\n",
      "Epoch 406/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3697 - accuracy: 0.8182\n",
      "Epoch 407/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.3696 - accuracy: 0.8182\n",
      "Epoch 408/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3696 - accuracy: 0.8182\n",
      "Epoch 409/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3696 - accuracy: 0.8182\n",
      "Epoch 410/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3696 - accuracy: 0.8182\n",
      "Epoch 411/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3696 - accuracy: 0.8182\n",
      "Epoch 412/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3695 - accuracy: 0.8182\n",
      "Epoch 413/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3695 - accuracy: 0.8182\n",
      "Epoch 414/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3695 - accuracy: 0.8182\n",
      "Epoch 415/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.3695 - accuracy: 0.8182\n",
      "Epoch 416/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3694 - accuracy: 0.8182\n",
      "Epoch 417/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3694 - accuracy: 0.8182\n",
      "Epoch 418/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.3694 - accuracy: 0.8182\n",
      "Epoch 419/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3694 - accuracy: 0.8182\n",
      "Epoch 420/500\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.3693 - accuracy: 0.8182\n",
      "Epoch 421/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3693 - accuracy: 0.8182\n",
      "Epoch 422/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.3693 - accuracy: 0.8182\n",
      "Epoch 423/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.3693 - accuracy: 0.8182\n",
      "Epoch 424/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3693 - accuracy: 0.8182\n",
      "Epoch 425/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3692 - accuracy: 0.8182\n",
      "Epoch 426/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3692 - accuracy: 0.8182\n",
      "Epoch 427/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3692 - accuracy: 0.8182\n",
      "Epoch 428/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3692 - accuracy: 0.8182\n",
      "Epoch 429/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3692 - accuracy: 0.8182\n",
      "Epoch 430/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3691 - accuracy: 0.8182\n",
      "Epoch 431/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.3691 - accuracy: 0.8182\n",
      "Epoch 432/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.3691 - accuracy: 0.8182\n",
      "Epoch 433/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3691 - accuracy: 0.8182\n",
      "Epoch 434/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.3690 - accuracy: 0.8182\n",
      "Epoch 435/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3690 - accuracy: 0.8182\n",
      "Epoch 436/500\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.3690 - accuracy: 0.8182\n",
      "Epoch 437/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3690 - accuracy: 0.8182\n",
      "Epoch 438/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.3690 - accuracy: 0.8182\n",
      "Epoch 439/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3689 - accuracy: 0.8182\n",
      "Epoch 440/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3689 - accuracy: 0.8182\n",
      "Epoch 441/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3689 - accuracy: 0.8182\n",
      "Epoch 442/500\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.3689 - accuracy: 0.8182\n",
      "Epoch 443/500\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.3689 - accuracy: 0.8182\n",
      "Epoch 444/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3688 - accuracy: 0.8182\n",
      "Epoch 445/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3688 - accuracy: 0.8182\n",
      "Epoch 446/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3688 - accuracy: 0.8182\n",
      "Epoch 447/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.3688 - accuracy: 0.8182\n",
      "Epoch 448/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.3688 - accuracy: 0.8182\n",
      "Epoch 449/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3687 - accuracy: 0.8182\n",
      "Epoch 450/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3687 - accuracy: 0.8182\n",
      "Epoch 451/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3687 - accuracy: 0.8182\n",
      "Epoch 452/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3687 - accuracy: 0.8182\n",
      "Epoch 453/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3687 - accuracy: 0.8182\n",
      "Epoch 454/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3686 - accuracy: 0.8182\n",
      "Epoch 455/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3686 - accuracy: 0.8182\n",
      "Epoch 456/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3686 - accuracy: 0.8182\n",
      "Epoch 457/500\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.3686 - accuracy: 0.8182\n",
      "Epoch 458/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3686 - accuracy: 0.8182\n",
      "Epoch 459/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.3685 - accuracy: 0.8182\n",
      "Epoch 460/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3685 - accuracy: 0.8182\n",
      "Epoch 461/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.3685 - accuracy: 0.8182\n",
      "Epoch 462/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3685 - accuracy: 0.8182\n",
      "Epoch 463/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3685 - accuracy: 0.8182\n",
      "Epoch 464/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3685 - accuracy: 0.8182\n",
      "Epoch 465/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3684 - accuracy: 0.8182\n",
      "Epoch 466/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3684 - accuracy: 0.8182\n",
      "Epoch 467/500\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.3684 - accuracy: 0.8182\n",
      "Epoch 468/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.3684 - accuracy: 0.8182\n",
      "Epoch 469/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.3684 - accuracy: 0.8182\n",
      "Epoch 470/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3683 - accuracy: 0.8182\n",
      "Epoch 471/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3683 - accuracy: 0.8182\n",
      "Epoch 472/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3683 - accuracy: 0.8182\n",
      "Epoch 473/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.3683 - accuracy: 0.8182\n",
      "Epoch 474/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.3683 - accuracy: 0.8182\n",
      "Epoch 475/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.3683 - accuracy: 0.8182\n",
      "Epoch 476/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3682 - accuracy: 0.8182\n",
      "Epoch 477/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3682 - accuracy: 0.8182\n",
      "Epoch 478/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3682 - accuracy: 0.8182\n",
      "Epoch 479/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3682 - accuracy: 0.8182\n",
      "Epoch 480/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.3682 - accuracy: 0.8182\n",
      "Epoch 481/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3681 - accuracy: 0.8182\n",
      "Epoch 482/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.3681 - accuracy: 0.8182\n",
      "Epoch 483/500\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.3681 - accuracy: 0.8182\n",
      "Epoch 484/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3681 - accuracy: 0.8182\n",
      "Epoch 485/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3681 - accuracy: 0.8182\n",
      "Epoch 486/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3681 - accuracy: 0.8182\n",
      "Epoch 487/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3680 - accuracy: 0.8182\n",
      "Epoch 488/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.3680 - accuracy: 0.8182\n",
      "Epoch 489/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3680 - accuracy: 0.8182\n",
      "Epoch 490/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3680 - accuracy: 0.8182\n",
      "Epoch 491/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3680 - accuracy: 0.8182\n",
      "Epoch 492/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3680 - accuracy: 0.8182\n",
      "Epoch 493/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3679 - accuracy: 0.8182\n",
      "Epoch 494/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3679 - accuracy: 0.8182\n",
      "Epoch 495/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3679 - accuracy: 0.8182\n",
      "Epoch 496/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3679 - accuracy: 0.8182\n",
      "Epoch 497/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.3679 - accuracy: 0.8182\n",
      "Epoch 498/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3679 - accuracy: 0.8182\n",
      "Epoch 499/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3678 - accuracy: 0.8182\n",
      "Epoch 500/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3678 - accuracy: 0.8182\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x2a46530cd30>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pre_trained_model = tf.keras.models.load_model('main_model.h5')\n",
    "pre_trained_model.fit(X_train, y_train, epochs = 500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 2ms/step - loss: 0.4695 - accuracy: 0.7545\n"
     ]
    }
   ],
   "source": [
    "test_loss, test_accuracy = pre_trained_model.evaluate(normalized_features, target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predictions = pre_trained_model.predict(normalized_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(y_predictions)):\n",
    "    if y_predictions[i] < 0.5:\n",
    "        y_predictions[i] = 0\n",
    "    else:\n",
    "        y_predictions[i] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RECALL =  0.9130434782608695\n"
     ]
    }
   ],
   "source": [
    "print(\"RECALL = \",recall_score(target, y_predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ACCURACY =  0.7545454545454545\n"
     ]
    }
   ],
   "source": [
    "print(\"ACCURACY = \",accuracy_score(target, y_predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision =  0.75\n"
     ]
    }
   ],
   "source": [
    "print(\"Precision = \",precision_score(target, y_predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1Score =  0.8235294117647057\n"
     ]
    }
   ],
   "source": [
    "print(\"F1Score = \",f1_score(target, y_predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sn.heatmap(confusion_matrix(target, y_predictions), annot = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Without Fine-tune the pre-trained model\n",
    "# RECALL =  0.9420289855072463\n",
    "# ACCURACY =  0.7545454545454545"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/500\n",
      "1/1 [==============================] - 0s 288ms/step - loss: 0.3676 - accuracy: 0.8636\n",
      "Epoch 2/500\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.3676 - accuracy: 0.8636\n",
      "Epoch 3/500\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.3675 - accuracy: 0.8636\n",
      "Epoch 4/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.3673 - accuracy: 0.8636\n",
      "Epoch 5/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3671 - accuracy: 0.8636\n",
      "Epoch 6/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3668 - accuracy: 0.8636\n",
      "Epoch 7/500\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.3665 - accuracy: 0.8636\n",
      "Epoch 8/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3661 - accuracy: 0.8636\n",
      "Epoch 9/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3657 - accuracy: 0.8636\n",
      "Epoch 10/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3653 - accuracy: 0.8636\n",
      "Epoch 11/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3648 - accuracy: 0.8636\n",
      "Epoch 12/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3644 - accuracy: 0.8636\n",
      "Epoch 13/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3639 - accuracy: 0.8636\n",
      "Epoch 14/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3634 - accuracy: 0.8636\n",
      "Epoch 15/500\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.3629 - accuracy: 0.8636\n",
      "Epoch 16/500\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.3624 - accuracy: 0.8636\n",
      "Epoch 17/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3619 - accuracy: 0.8636\n",
      "Epoch 18/500\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.3614 - accuracy: 0.8636\n",
      "Epoch 19/500\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.3609 - accuracy: 0.8636\n",
      "Epoch 20/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3604 - accuracy: 0.8636\n",
      "Epoch 21/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3600 - accuracy: 0.8636\n",
      "Epoch 22/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3595 - accuracy: 0.8636\n",
      "Epoch 23/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3590 - accuracy: 0.8636\n",
      "Epoch 24/500\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.3586 - accuracy: 0.8636\n",
      "Epoch 25/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3581 - accuracy: 0.8636\n",
      "Epoch 26/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3577 - accuracy: 0.8636\n",
      "Epoch 27/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3573 - accuracy: 0.8636\n",
      "Epoch 28/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3569 - accuracy: 0.8636\n",
      "Epoch 29/500\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.3565 - accuracy: 0.8636\n",
      "Epoch 30/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3561 - accuracy: 0.8636\n",
      "Epoch 31/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.3557 - accuracy: 0.8636\n",
      "Epoch 32/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3554 - accuracy: 0.8636\n",
      "Epoch 33/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3550 - accuracy: 0.8636\n",
      "Epoch 34/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.3547 - accuracy: 0.8636\n",
      "Epoch 35/500\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.3544 - accuracy: 0.8636\n",
      "Epoch 36/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3540 - accuracy: 0.8636\n",
      "Epoch 37/500\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.3537 - accuracy: 0.8636\n",
      "Epoch 38/500\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.3534 - accuracy: 0.8636\n",
      "Epoch 39/500\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.3532 - accuracy: 0.8636\n",
      "Epoch 40/500\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.3529 - accuracy: 0.8636\n",
      "Epoch 41/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.3526 - accuracy: 0.8636\n",
      "Epoch 42/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.3524 - accuracy: 0.8636\n",
      "Epoch 43/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3521 - accuracy: 0.8636\n",
      "Epoch 44/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3519 - accuracy: 0.8636\n",
      "Epoch 45/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3516 - accuracy: 0.8636\n",
      "Epoch 46/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3514 - accuracy: 0.8636\n",
      "Epoch 47/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3512 - accuracy: 0.8636\n",
      "Epoch 48/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3510 - accuracy: 0.8636\n",
      "Epoch 49/500\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.3508 - accuracy: 0.8636\n",
      "Epoch 50/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.3506 - accuracy: 0.8636\n",
      "Epoch 51/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3504 - accuracy: 0.8636\n",
      "Epoch 52/500\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.3502 - accuracy: 0.8636\n",
      "Epoch 53/500\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.3500 - accuracy: 0.8636\n",
      "Epoch 54/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.3498 - accuracy: 0.8636\n",
      "Epoch 55/500\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.3497 - accuracy: 0.8636\n",
      "Epoch 56/500\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.3495 - accuracy: 0.8636\n",
      "Epoch 57/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3493 - accuracy: 0.8636\n",
      "Epoch 58/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3492 - accuracy: 0.8636\n",
      "Epoch 59/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.3490 - accuracy: 0.8636\n",
      "Epoch 60/500\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.3489 - accuracy: 0.9091\n",
      "Epoch 61/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3487 - accuracy: 0.9091\n",
      "Epoch 62/500\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.3485 - accuracy: 0.9091\n",
      "Epoch 63/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.3484 - accuracy: 0.9091\n",
      "Epoch 64/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3483 - accuracy: 0.9091\n",
      "Epoch 65/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.3481 - accuracy: 0.9091\n",
      "Epoch 66/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.3480 - accuracy: 0.9091\n",
      "Epoch 67/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.3478 - accuracy: 0.9091\n",
      "Epoch 68/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.3477 - accuracy: 0.9091\n",
      "Epoch 69/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.3476 - accuracy: 0.9091\n",
      "Epoch 70/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3474 - accuracy: 0.9091\n",
      "Epoch 71/500\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.3473 - accuracy: 0.9091\n",
      "Epoch 72/500\n",
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     ]
    },
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      "WARNING:tensorflow:5 out of the last 14 calls to <function Model.make_predict_function.<locals>.predict_function at 0x000002A46943DAF0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 2ms/step - loss: 0.3127 - accuracy: 0.8636\n",
      "Epoch 383/500\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 2ms/step - loss: 0.3098 - accuracy: 0.8636\n",
      "Epoch 465/500\n",
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      "Epoch 487/500\n",
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      "Epoch 488/500\n",
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      "Epoch 489/500\n",
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      "Epoch 490/500\n",
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      "Epoch 491/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3092 - accuracy: 0.8636\n",
      "Epoch 492/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.3092 - accuracy: 0.8636\n",
      "Epoch 493/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.3092 - accuracy: 0.8636\n",
      "Epoch 494/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.3092 - accuracy: 0.8636\n",
      "Epoch 495/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.3092 - accuracy: 0.8636\n",
      "Epoch 496/500\n",
      "1/1 [==============================] - 0s 998us/step - loss: 0.3091 - accuracy: 0.8636\n",
      "Epoch 497/500\n",
      "1/1 [==============================] - 0s 996us/step - loss: 0.3091 - accuracy: 0.8636\n",
      "Epoch 498/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.3091 - accuracy: 0.8636\n",
      "Epoch 499/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.3091 - accuracy: 0.8636\n",
      "Epoch 500/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.3091 - accuracy: 0.8636\n",
      "WARNING:tensorflow:5 out of the last 13 calls to <function Model.make_predict_function.<locals>.predict_function at 0x000002A4694D1A60> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "Accuracies: [0.75, 0.7613636363636364, 0.7954545454545454, 0.7613636363636364, 0.7386363636363636]\n",
      "Recalls: [0.9038461538461539, 0.9655172413793104, 0.9636363636363636, 0.9821428571428571, 1.0]\n",
      "f1scores: [0.810344827586207, 0.8421052631578948, 0.8548387096774193, 0.8396946564885496, 0.8270676691729323]\n",
      "precisions: [0.734375, 0.7466666666666667, 0.7681159420289855, 0.7333333333333333, 0.7051282051282052]\n"
     ]
    }
   ],
   "source": [
    "#Cross Validation Score\n",
    "\n",
    "\n",
    "kf = KFold(n_splits = 5, shuffle = True)\n",
    "accuracies = []\n",
    "recalls = []\n",
    "f1scores = []\n",
    "precisions = []\n",
    "for train, test in kf.split(df[[\"N'120\",'CSR7.5','Liq']]):\n",
    "\n",
    "    train_df = df.loc[train]\n",
    "    test_df = df.loc[test]\n",
    "    \n",
    "    pre_trained_model = tf.keras.models.load_model('main_model.h5')\n",
    "    pre_trained_model.fit(MinMaxNormalization(test_df[[\"N'120\",'CSR7.5']]),  test_df['Liq'].astype(np.float32), epochs = 500)\n",
    "\n",
    "\n",
    "    y_predictions = pre_trained_model.predict(MinMaxNormalization(train_df[[\"N'120\",'CSR7.5']]))\n",
    "    for i in range(len(y_predictions)):\n",
    "        if y_predictions[i] < 0.5:\n",
    "            y_predictions[i] = 0\n",
    "        else:\n",
    "            y_predictions[i] = 1\n",
    "\n",
    "    accuracies.append(accuracy_score(train_df['Liq'].astype(np.float32), y_predictions))\n",
    "    recalls.append(recall_score(train_df['Liq'].astype(np.float32), y_predictions))\n",
    "    f1scores.append(f1_score(train_df['Liq'].astype(np.float32), y_predictions))\n",
    "    precisions.append(precision_score(train_df['Liq'].astype(np.float32), y_predictions))\n",
    "\n",
    "print(\"Accuracies:\", accuracies)\n",
    "print(\"Recalls:\", recalls)\n",
    "print(\"f1scores:\", f1scores)\n",
    "print(\"precisions:\", precisions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dr Nima's Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "PL_Nima = pd.DataFrame(1 / (1 + np.exp(-5.721 + (0.208 * df[\"N'120\"]) - (1.772 * df[\"ln(CSR7.5)\"])  )), columns = ['PL'] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(PL_Nima)):\n",
    "    if PL_Nima.loc[i,'PL'] > 0.5:\n",
    "        PL_Nima.loc[i,'Liq'] = 1\n",
    "    else:\n",
    "        PL_Nima.loc[i,'Liq'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PL</th>\n",
       "      <th>Liq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.650091</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0.766231</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>0.252320</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0.659354</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>0.794495</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          PL  Liq\n",
       "7   0.650091  1.0\n",
       "37  0.766231  1.0\n",
       "46  0.252320  0.0\n",
       "33  0.659354  1.0\n",
       "80  0.794495  1.0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PL_Nima.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dr.Nima's accuracy:  0.8090909090909091\n",
      "Dr.Nima's recall:  0.8260869565217391\n",
      "Dr.Nima's precision:  0.8636363636363636\n",
      "Dr.Nima's f1-score:  0.8444444444444444\n"
     ]
    }
   ],
   "source": [
    "print(\"Dr.Nima's accuracy: \", accuracy_score(df['Liq'], PL_Nima['Liq']))\n",
    "print(\"Dr.Nima's recall: \", recall_score(df['Liq'], PL_Nima['Liq']))\n",
    "print(\"Dr.Nima's precision: \", precision_score(df['Liq'], PL_Nima['Liq']))\n",
    "print(\"Dr.Nima's f1-score: \", f1_score(df['Liq'], PL_Nima['Liq']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sn.heatmap(confusion_matrix(df['Liq'], PL_Nima['Liq']), annot = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cao 2013 Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "PL_Cao = pd.DataFrame(1 / (1 + np.exp(-8.40 + (0.35 * df[\"N'120\"]) - (2.12 * df[\"ln(CSR)\"])  )), columns = ['PL'] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(PL_Cao)):\n",
    "    if PL_Cao.loc[i,'PL'] > 0.5:\n",
    "        PL_Cao.loc[i,'Liq'] = 1\n",
    "    else:\n",
    "        PL_Cao.loc[i,'Liq'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PL</th>\n",
       "      <th>Liq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.424998</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>0.004822</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>0.738878</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>0.962011</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>0.053154</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          PL  Liq\n",
       "17  0.424998  0.0\n",
       "64  0.004822  0.0\n",
       "44  0.738878  1.0\n",
       "96  0.962011  1.0\n",
       "42  0.053154  0.0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PL_Cao.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cao 2013 accuracy:  0.7636363636363637\n",
      "Cao 2013 recall:  0.7971014492753623\n",
      "Cao 2013 precision:  0.8208955223880597\n",
      "Cao 2013 f1-score:  0.8088235294117647\n"
     ]
    }
   ],
   "source": [
    "print(\"Cao 2013 accuracy: \", accuracy_score(df['Liq'], PL_Cao['Liq']))\n",
    "print(\"Cao 2013 recall: \", recall_score(df['Liq'], PL_Cao['Liq']))\n",
    "print(\"Cao 2013 precision: \", precision_score(df['Liq'], PL_Cao['Liq']))\n",
    "print(\"Cao 2013 f1-score: \", f1_score(df['Liq'], PL_Cao['Liq']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sn.heatmap(confusion_matrix(df['Liq'], PL_Cao['Liq']), annot = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}