SHC_SVR.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn import preprocessing\n",
    "from sklearn.model_selection import train_test_split,KFold,cross_val_score\n",
    "from sklearn.svm import SVR\n",
    "import lightgbm as lgb\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from hyperopt import fmin,tpe,hp,Trials\n",
    "from hyperopt.pyll.stochastic import sample\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .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>Temperature</th>\n",
       "      <th>Nanparticle Heat Capacity</th>\n",
       "      <th>Volume fraction</th>\n",
       "      <th>CuO/water heat capacity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>298.31</td>\n",
       "      <td>0.536</td>\n",
       "      <td>0.4</td>\n",
       "      <td>4.082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>300.32</td>\n",
       "      <td>0.535</td>\n",
       "      <td>0.4</td>\n",
       "      <td>4.081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>302.35</td>\n",
       "      <td>0.533</td>\n",
       "      <td>0.4</td>\n",
       "      <td>4.079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>304.36</td>\n",
       "      <td>0.538</td>\n",
       "      <td>0.4</td>\n",
       "      <td>4.082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>306.37</td>\n",
       "      <td>0.548</td>\n",
       "      <td>0.4</td>\n",
       "      <td>4.085</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Temperature  Nanparticle Heat Capacity  Volume fraction  \\\n",
       "0       298.31                      0.536              0.4   \n",
       "1       300.32                      0.535              0.4   \n",
       "2       302.35                      0.533              0.4   \n",
       "3       304.36                      0.538              0.4   \n",
       "4       306.37                      0.548              0.4   \n",
       "\n",
       "   CuO/water heat capacity  \n",
       "0                    4.082  \n",
       "1                    4.081  \n",
       "2                    4.079  \n",
       "3                    4.082  \n",
       "4                    4.085  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cuo_water_data=pd.read_excel(\"Mech Project.xlsx\").astype(\"float64\")\n",
    "cuo_water_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "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>Temperature</th>\n",
       "      <th>Nanparticle Heat Capacity</th>\n",
       "      <th>Volume fraction</th>\n",
       "      <th>CuO/water heat capacity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Temperature</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.859940e-01</td>\n",
       "      <td>-2.395325e-04</td>\n",
       "      <td>0.036872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nanparticle Heat Capacity</th>\n",
       "      <td>0.985994</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.110806e-14</td>\n",
       "      <td>0.036781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Volume fraction</th>\n",
       "      <td>-0.000240</td>\n",
       "      <td>1.110806e-14</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>-0.999136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CuO/water heat capacity</th>\n",
       "      <td>0.036872</td>\n",
       "      <td>3.678085e-02</td>\n",
       "      <td>-9.991359e-01</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Temperature  Nanparticle Heat Capacity  \\\n",
       "Temperature                   1.000000               9.859940e-01   \n",
       "Nanparticle Heat Capacity     0.985994               1.000000e+00   \n",
       "Volume fraction              -0.000240               1.110806e-14   \n",
       "CuO/water heat capacity       0.036872               3.678085e-02   \n",
       "\n",
       "                           Volume fraction  CuO/water heat capacity  \n",
       "Temperature                  -2.395325e-04                 0.036872  \n",
       "Nanparticle Heat Capacity     1.110806e-14                 0.036781  \n",
       "Volume fraction               1.000000e+00                -0.999136  \n",
       "CuO/water heat capacity      -9.991359e-01                 1.000000  "
      ]
     },
     "execution_count": 374,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cuo_water_data.corr(method=\"pearson\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 403,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=cuo_water_data[cuo_water_data.columns[0:3]]\n",
    "y=cuo_water_data[cuo_water_data.columns[3]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=y.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc_X=preprocessing.MinMaxScaler()\n",
    "sc_y=preprocessing.MinMaxScaler()\n",
    "X=sc_X.fit_transform(X)\n",
    "y=sc_y.fit_transform(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(105, 3)"
      ]
     },
     "execution_count": 378,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 379,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(105, 1)"
      ]
     },
     "execution_count": 379,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_state=45\n",
    "num_folds=10\n",
    "\n",
    "kf=KFold(n_splits=num_folds,random_state=random_state,shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=random_state)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "metadata": {},
   "outputs": [],
   "source": [
    "def objective(params, cv=kf, X=X_train, y=y_train):\n",
    "\n",
    "    params = {'kernel': params['kernel'],\n",
    "              'C': params['C'],\n",
    "              'epsilon': params['epsilon']}\n",
    "\n",
    "    model = SVR(**params)\n",
    "\n",
    "    score = -cross_val_score(model, X, y, cv=cv,\n",
    "                             scoring='neg_mean_squared_error', n_jobs=-1).mean()\n",
    "\n",
    "    return score\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "metadata": {},
   "outputs": [],
   "source": [
    "space = {\n",
    "    'kernel': hp.choice('kernel', ['linear','rbf','sigmoid','poly']),\n",
    "    'C': hp.quniform('C', 0.005, 500, 0.01),\n",
    "    'epsilon': hp.uniform('epsilon', 0, 1),\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "metadata": {},
   "outputs": [],
   "source": [
    "kernel_dict={\n",
    "    0:'linear',\n",
    "    1:'rbf',\n",
    "    2:'sigmoid',\n",
    "    3:'poly'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "metadata": {},
   "outputs": [],
   "source": [
    "MAX_EVALS=50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'objective' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "File \u001b[1;32m<timed exec>:4\u001b[0m, in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'objective' is not defined"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "trials = Trials()\n",
    "\n",
    "best = fmin(fn=objective, space=space, algo=tpe.suggest,\n",
    "            max_evals=MAX_EVALS, rstate=np.random.default_rng(random_state),trials=trials)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 194.51, 'epsilon': 0.005217629802907759, 'kernel': 0}"
      ]
     },
     "execution_count": 387,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\aritr\\AppData\\Roaming\\Python\\Python310\\site-packages\\sklearn\\utils\\validation.py:1111: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "data": {
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      ],
      "text/plain": [
       "SVR(C=194.51, epsilon=0.005217629802907759, kernel='linear')"
      ]
     },
     "execution_count": 388,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = SVR(epsilon=best['epsilon'], C=best['C'], kernel=kernel_dict[best['kernel']])\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best MSE:  4.3419856652538686e-05\n"
     ]
    }
   ],
   "source": [
    "tpe_test_score = mean_squared_error(y_test, model.predict(X_test))\n",
    "\n",
    "print(\"Best MSE: \", trials.best_trial['result']['loss'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 390,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.363931648183716e-05"
      ]
     },
     "execution_count": 390,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tpe_test_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 391,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(21,)"
      ]
     },
     "execution_count": 391,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred=model.predict(X_test)\n",
    "y_pred.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 392,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=y_pred.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 393,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=sc_y.inverse_transform(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 394,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test=sc_y.inverse_transform(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 395,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_pred,y_test,s=50,c=\"red\")\n",
    "plt.plot(y_pred,y_test,c=\"blue\")\n",
    "plt.title(\"SVR for Testing data\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 396,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_pred=model.predict(X_train)\n",
    "y_train_pred=sc_y.inverse_transform(y_train_pred.reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_train_pred,y_train,s=50,c=\"red\")\n",
    "plt.plot(y_train_pred,y_train,c=\"blue\")\n",
    "plt.title(\"SVR for Training data\")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>ANN</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import levenberg_marquardt as lm\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "random_state=87"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=cuo_water_data[cuo_water_data.columns[0:3]]\n",
    "y=cuo_water_data[cuo_water_data.columns[3]].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Temperature</th>\n",
       "      <th>Nanparticle Heat Capacity</th>\n",
       "      <th>Volume fraction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>105 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Temperature  Nanparticle Heat Capacity  Volume fraction\n",
       "0            NaN                        NaN              NaN\n",
       "1            NaN                        NaN              NaN\n",
       "2            NaN                        NaN              NaN\n",
       "3            NaN                        NaN              NaN\n",
       "4            NaN                        NaN              NaN\n",
       "..           ...                        ...              ...\n",
       "100          NaN                        NaN              NaN\n",
       "101          NaN                        NaN              NaN\n",
       "102          NaN                        NaN              NaN\n",
       "103          NaN                        NaN              NaN\n",
       "104          NaN                        NaN              NaN\n",
       "\n",
       "[105 rows x 3 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[X.isnull() == True]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=y.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc_X=preprocessing.MinMaxScaler()\n",
    "sc_y=preprocessing.MinMaxScaler()\n",
    "X=sc_X.fit_transform(X)\n",
    "y=sc_y.fit_transform(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=random_state)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.optimizers import Adam,SGD,RMSprop,Adadelta,Adagrad,Adamax,Nadam,Ftrl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "LeakyReLU=tf.keras.layers.LeakyReLU(alpha=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def objective_ann(params,X_train=X_train,y_train=y_train):\n",
    "    learning_rate=params['learning_rate']\n",
    "    \n",
    "    optimizerL = ['SGD', 'Adam', 'RMSprop', 'Adadelta',\n",
    "                'Adagrad', 'Adamax', 'Nadam', 'Ftrl']\n",
    "    \n",
    "    optimizerD = {'Adam': Adam(lr=learning_rate), 'SGD': SGD(lr=learning_rate),\n",
    "                'RMSprop': RMSprop(lr=learning_rate), 'Adadelta': Adadelta(lr=learning_rate),\n",
    "                'Adagrad': Adagrad(lr=learning_rate), 'Adamax': Adamax(lr=learning_rate),\n",
    "                'Nadam': Nadam(lr=learning_rate), 'Ftrl': Ftrl(lr=learning_rate)}\n",
    "    \n",
    "    activationL = ['relu', 'sigmoid', 'softplus', 'softsign', 'tanh', 'selu',\n",
    "                'elu', 'exponential', LeakyReLU, 'relu']\n",
    "\n",
    "    nodes=(int)(params['nodes'])\n",
    "    opt=optimizerL[params['optimizer']]\n",
    "    opt=optimizerD[opt]\n",
    "    activation=activationL[params['activation']]\n",
    "    batch_size=(int)(params['batch_size'])\n",
    "    epochs=(int)(params['epochs'])\n",
    "    #num_hidden_layers=(int)(params['num_hidden_layers'])\n",
    "    num_hidden_layers=1\n",
    "    \n",
    "    def get_model():    \n",
    "        model=tf.keras.models.Sequential()\n",
    "         \n",
    "        model.add(tf.keras.layers.Dense(nodes,input_dim=X_train.shape[1],activation=activation))\n",
    "        model.add(tf.keras.layers.Dropout(rate=params['dropout_prob']))             \n",
    "            \n",
    "        for i in range(num_hidden_layers-1):\n",
    "            model.add(tf.keras.layers.Dense(nodes,activation=activation))\n",
    "            model.add(tf.keras.layers.Dropout(rate=params['dropout_prob']))       \n",
    "        \n",
    "        model.add(tf.keras.layers.Dense(1,activation='sigmoid'))\n",
    "        model.add(tf.keras.layers.Dropout(rate=params['dropout_prob']))\n",
    "        \n",
    "        model.compile(loss=tf.keras.losses.mean_squared_error,optimizer=opt,metrics=['mse'])\n",
    "        return model\n",
    "\n",
    "    es=tf.keras.callbacks.EarlyStopping(monitor='val_loss',mode='max',verbose=0,patience=20)\n",
    "    nn=tf.keras.wrappers.scikit_learn.KerasRegressor(build_fn=get_model,epochs=epochs,batch_size=batch_size,verbose=0)\n",
    "    \n",
    "    \n",
    "    kfold=KFold(n_splits=10,shuffle=True,random_state=random_state)\n",
    "    score=-cross_val_score(nn,X_train,y_train,error_score='raise',scoring='neg_mean_squared_error',cv=kfold,fit_params={'callbacks':[es]},n_jobs=-1).mean()\n",
    "    \n",
    "    return score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from hyperopt.pyll.base import scope"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "ann_params_space={\n",
    "    'activation':hp.choice('activation',[0,1,2,3,4,5,6,7,8,9]),\n",
    "    'optimizer':hp.choice('optimizer',[0,1,2,3,4,5,6,7]),\n",
    "    'num_hidden_layers':hp.quniform('num_hidden_layers',1,6,1),\n",
    "    'nodes':scope.int(hp.quniform('nodes',1,200,1)),\n",
    "    'dropout_prob':hp.uniform('dropout_prob',0,1),\n",
    "    'learning_rate': hp.loguniform('learning_rate',np.log(0.001),np.log(0.2)), \n",
    "    'batch_size':hp.quniform('batch_size',1, 100,1),\n",
    "    'epochs':hp.quniform('epochs',10, 500,10)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  1%|          | 6/500 [01:06<1:31:36, 11.13s/trial, best loss: 0.09873961390119983]\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32mc:\\Users\\aritr\\OneDrive\\Desktop\\Mech project\\project.ipynb Cell 39\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/aritr/OneDrive/Desktop/Mech%20project/project.ipynb#X53sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m ann_trials\u001b[39m=\u001b[39mTrials()\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/aritr/OneDrive/Desktop/Mech%20project/project.ipynb#X53sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m best\u001b[39m=\u001b[39mfmin(fn\u001b[39m=\u001b[39;49mobjective_ann,space\u001b[39m=\u001b[39;49mann_params_space,algo\u001b[39m=\u001b[39;49mtpe\u001b[39m.\u001b[39;49msuggest,max_evals\u001b[39m=\u001b[39;49m\u001b[39m500\u001b[39;49m,rstate\u001b[39m=\u001b[39;49mnp\u001b[39m.\u001b[39;49mrandom\u001b[39m.\u001b[39;49mdefault_rng(random_state),trials\u001b[39m=\u001b[39;49mann_trials)\n",
      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python310\\site-packages\\hyperopt\\fmin.py:540\u001b[0m, in \u001b[0;36mfmin\u001b[1;34m(fn, space, algo, max_evals, timeout, loss_threshold, trials, rstate, allow_trials_fmin, pass_expr_memo_ctrl, catch_eval_exceptions, verbose, return_argmin, points_to_evaluate, max_queue_len, show_progressbar, early_stop_fn, trials_save_file)\u001b[0m\n\u001b[0;32m    537\u001b[0m     fn \u001b[39m=\u001b[39m __objective_fmin_wrapper(fn)\n\u001b[0;32m    539\u001b[0m \u001b[39mif\u001b[39;00m allow_trials_fmin \u001b[39mand\u001b[39;00m \u001b[39mhasattr\u001b[39m(trials, \u001b[39m\"\u001b[39m\u001b[39mfmin\u001b[39m\u001b[39m\"\u001b[39m):\n\u001b[1;32m--> 540\u001b[0m     \u001b[39mreturn\u001b[39;00m trials\u001b[39m.\u001b[39;49mfmin(\n\u001b[0;32m    541\u001b[0m         fn,\n\u001b[0;32m    542\u001b[0m         space,\n\u001b[0;32m    543\u001b[0m         algo\u001b[39m=\u001b[39;49malgo,\n\u001b[0;32m    544\u001b[0m         max_evals\u001b[39m=\u001b[39;49mmax_evals,\n\u001b[0;32m    545\u001b[0m         timeout\u001b[39m=\u001b[39;49mtimeout,\n\u001b[0;32m    546\u001b[0m         loss_threshold\u001b[39m=\u001b[39;49mloss_threshold,\n\u001b[0;32m    547\u001b[0m         max_queue_len\u001b[39m=\u001b[39;49mmax_queue_len,\n\u001b[0;32m    548\u001b[0m         rstate\u001b[39m=\u001b[39;49mrstate,\n\u001b[0;32m    549\u001b[0m         pass_expr_memo_ctrl\u001b[39m=\u001b[39;49mpass_expr_memo_ctrl,\n\u001b[0;32m    550\u001b[0m         verbose\u001b[39m=\u001b[39;49mverbose,\n\u001b[0;32m    551\u001b[0m         catch_eval_exceptions\u001b[39m=\u001b[39;49mcatch_eval_exceptions,\n\u001b[0;32m    552\u001b[0m         return_argmin\u001b[39m=\u001b[39;49mreturn_argmin,\n\u001b[0;32m    553\u001b[0m         show_progressbar\u001b[39m=\u001b[39;49mshow_progressbar,\n\u001b[0;32m    554\u001b[0m         early_stop_fn\u001b[39m=\u001b[39;49mearly_stop_fn,\n\u001b[0;32m    555\u001b[0m         trials_save_file\u001b[39m=\u001b[39;49mtrials_save_file,\n\u001b[0;32m    556\u001b[0m     )\n\u001b[0;32m    558\u001b[0m \u001b[39mif\u001b[39;00m trials \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m    559\u001b[0m     \u001b[39mif\u001b[39;00m os\u001b[39m.\u001b[39mpath\u001b[39m.\u001b[39mexists(trials_save_file):\n",
      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python310\\site-packages\\hyperopt\\base.py:671\u001b[0m, in \u001b[0;36mTrials.fmin\u001b[1;34m(self, fn, space, algo, max_evals, timeout, loss_threshold, max_queue_len, rstate, verbose, pass_expr_memo_ctrl, catch_eval_exceptions, return_argmin, show_progressbar, early_stop_fn, trials_save_file)\u001b[0m\n\u001b[0;32m    666\u001b[0m \u001b[39m# -- Stop-gap implementation!\u001b[39;00m\n\u001b[0;32m    667\u001b[0m \u001b[39m#    fmin should have been a Trials method in the first place\u001b[39;00m\n\u001b[0;32m    668\u001b[0m \u001b[39m#    but for now it's still sitting in another file.\u001b[39;00m\n\u001b[0;32m    669\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39m.\u001b[39;00m\u001b[39mfmin\u001b[39;00m \u001b[39mimport\u001b[39;00m fmin\n\u001b[1;32m--> 671\u001b[0m \u001b[39mreturn\u001b[39;00m fmin(\n\u001b[0;32m    672\u001b[0m     fn,\n\u001b[0;32m    673\u001b[0m     space,\n\u001b[0;32m    674\u001b[0m     algo\u001b[39m=\u001b[39;49malgo,\n\u001b[0;32m    675\u001b[0m     max_evals\u001b[39m=\u001b[39;49mmax_evals,\n\u001b[0;32m    676\u001b[0m     timeout\u001b[39m=\u001b[39;49mtimeout,\n\u001b[0;32m    677\u001b[0m     loss_threshold\u001b[39m=\u001b[39;49mloss_threshold,\n\u001b[0;32m    678\u001b[0m     trials\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m,\n\u001b[0;32m    679\u001b[0m     rstate\u001b[39m=\u001b[39;49mrstate,\n\u001b[0;32m    680\u001b[0m     verbose\u001b[39m=\u001b[39;49mverbose,\n\u001b[0;32m    681\u001b[0m     max_queue_len\u001b[39m=\u001b[39;49mmax_queue_len,\n\u001b[0;32m    682\u001b[0m     allow_trials_fmin\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m,  \u001b[39m# -- prevent recursion\u001b[39;49;00m\n\u001b[0;32m    683\u001b[0m     pass_expr_memo_ctrl\u001b[39m=\u001b[39;49mpass_expr_memo_ctrl,\n\u001b[0;32m    684\u001b[0m     catch_eval_exceptions\u001b[39m=\u001b[39;49mcatch_eval_exceptions,\n\u001b[0;32m    685\u001b[0m     return_argmin\u001b[39m=\u001b[39;49mreturn_argmin,\n\u001b[0;32m    686\u001b[0m     show_progressbar\u001b[39m=\u001b[39;49mshow_progressbar,\n\u001b[0;32m    687\u001b[0m     early_stop_fn\u001b[39m=\u001b[39;49mearly_stop_fn,\n\u001b[0;32m    688\u001b[0m     trials_save_file\u001b[39m=\u001b[39;49mtrials_save_file,\n\u001b[0;32m    689\u001b[0m )\n",
      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python310\\site-packages\\hyperopt\\fmin.py:586\u001b[0m, in \u001b[0;36mfmin\u001b[1;34m(fn, space, algo, max_evals, timeout, loss_threshold, trials, rstate, allow_trials_fmin, pass_expr_memo_ctrl, catch_eval_exceptions, verbose, return_argmin, points_to_evaluate, max_queue_len, show_progressbar, early_stop_fn, trials_save_file)\u001b[0m\n\u001b[0;32m    583\u001b[0m rval\u001b[39m.\u001b[39mcatch_eval_exceptions \u001b[39m=\u001b[39m catch_eval_exceptions\n\u001b[0;32m    585\u001b[0m \u001b[39m# next line is where the fmin is actually executed\u001b[39;00m\n\u001b[1;32m--> 586\u001b[0m rval\u001b[39m.\u001b[39;49mexhaust()\n\u001b[0;32m    588\u001b[0m \u001b[39mif\u001b[39;00m return_argmin:\n\u001b[0;32m    589\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(trials\u001b[39m.\u001b[39mtrials) \u001b[39m==\u001b[39m \u001b[39m0\u001b[39m:\n",
      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python310\\site-packages\\hyperopt\\fmin.py:364\u001b[0m, in \u001b[0;36mFMinIter.exhaust\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    362\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mexhaust\u001b[39m(\u001b[39mself\u001b[39m):\n\u001b[0;32m    363\u001b[0m     n_done \u001b[39m=\u001b[39m \u001b[39mlen\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtrials)\n\u001b[1;32m--> 364\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mrun(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mmax_evals \u001b[39m-\u001b[39;49m n_done, block_until_done\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49masynchronous)\n\u001b[0;32m    365\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtrials\u001b[39m.\u001b[39mrefresh()\n\u001b[0;32m    366\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\n",
      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python310\\site-packages\\hyperopt\\fmin.py:300\u001b[0m, in \u001b[0;36mFMinIter.run\u001b[1;34m(self, N, block_until_done)\u001b[0m\n\u001b[0;32m    297\u001b[0m     time\u001b[39m.\u001b[39msleep(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpoll_interval_secs)\n\u001b[0;32m    298\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m    299\u001b[0m     \u001b[39m# -- loop over trials and do the jobs directly\u001b[39;00m\n\u001b[1;32m--> 300\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mserial_evaluate()\n\u001b[0;32m    302\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtrials\u001b[39m.\u001b[39mrefresh()\n\u001b[0;32m    303\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtrials_save_file \u001b[39m!=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39m\"\u001b[39m:\n",
      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python310\\site-packages\\hyperopt\\fmin.py:178\u001b[0m, in \u001b[0;36mFMinIter.serial_evaluate\u001b[1;34m(self, N)\u001b[0m\n\u001b[0;32m    176\u001b[0m ctrl \u001b[39m=\u001b[39m base\u001b[39m.\u001b[39mCtrl(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtrials, current_trial\u001b[39m=\u001b[39mtrial)\n\u001b[0;32m    177\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m--> 178\u001b[0m     result \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mdomain\u001b[39m.\u001b[39;49mevaluate(spec, ctrl)\n\u001b[0;32m    179\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m \u001b[39mas\u001b[39;00m e:\n\u001b[0;32m    180\u001b[0m     logger\u001b[39m.\u001b[39merror(\u001b[39m\"\u001b[39m\u001b[39mjob exception: \u001b[39m\u001b[39m%s\u001b[39;00m\u001b[39m\"\u001b[39m \u001b[39m%\u001b[39m \u001b[39mstr\u001b[39m(e))\n",
      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python310\\site-packages\\hyperopt\\base.py:892\u001b[0m, in \u001b[0;36mDomain.evaluate\u001b[1;34m(self, config, ctrl, attach_attachments)\u001b[0m\n\u001b[0;32m    883\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m    884\u001b[0m     \u001b[39m# -- the \"work\" of evaluating `config` can be written\u001b[39;00m\n\u001b[0;32m    885\u001b[0m     \u001b[39m#    either into the pyll part (self.expr)\u001b[39;00m\n\u001b[0;32m    886\u001b[0m     \u001b[39m#    or the normal Python part (self.fn)\u001b[39;00m\n\u001b[0;32m    887\u001b[0m     pyll_rval \u001b[39m=\u001b[39m pyll\u001b[39m.\u001b[39mrec_eval(\n\u001b[0;32m    888\u001b[0m         \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mexpr,\n\u001b[0;32m    889\u001b[0m         memo\u001b[39m=\u001b[39mmemo,\n\u001b[0;32m    890\u001b[0m         print_node_on_error\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mrec_eval_print_node_on_error,\n\u001b[0;32m    891\u001b[0m     )\n\u001b[1;32m--> 892\u001b[0m     rval \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mfn(pyll_rval)\n\u001b[0;32m    894\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(rval, (\u001b[39mfloat\u001b[39m, \u001b[39mint\u001b[39m, np\u001b[39m.\u001b[39mnumber)):\n\u001b[0;32m    895\u001b[0m     dict_rval \u001b[39m=\u001b[39m {\u001b[39m\"\u001b[39m\u001b[39mloss\u001b[39m\u001b[39m\"\u001b[39m: \u001b[39mfloat\u001b[39m(rval), \u001b[39m\"\u001b[39m\u001b[39mstatus\u001b[39m\u001b[39m\"\u001b[39m: STATUS_OK}\n",
      "\u001b[1;32mc:\\Users\\aritr\\OneDrive\\Desktop\\Mech project\\project.ipynb Cell 39\u001b[0m in \u001b[0;36mobjective_ann\u001b[1;34m(params, X_train, y_train)\u001b[0m\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/aritr/OneDrive/Desktop/Mech%20project/project.ipynb#X53sZmlsZQ%3D%3D?line=40'>41</a>\u001b[0m nn\u001b[39m=\u001b[39mtf\u001b[39m.\u001b[39mkeras\u001b[39m.\u001b[39mwrappers\u001b[39m.\u001b[39mscikit_learn\u001b[39m.\u001b[39mKerasRegressor(build_fn\u001b[39m=\u001b[39mget_model,epochs\u001b[39m=\u001b[39mepochs,batch_size\u001b[39m=\u001b[39mbatch_size,verbose\u001b[39m=\u001b[39m\u001b[39m0\u001b[39m)\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/aritr/OneDrive/Desktop/Mech%20project/project.ipynb#X53sZmlsZQ%3D%3D?line=43'>44</a>\u001b[0m kfold\u001b[39m=\u001b[39mKFold(n_splits\u001b[39m=\u001b[39m\u001b[39m10\u001b[39m,shuffle\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m,random_state\u001b[39m=\u001b[39mrandom_state)\n\u001b[1;32m---> <a href='vscode-notebook-cell:/c%3A/Users/aritr/OneDrive/Desktop/Mech%20project/project.ipynb#X53sZmlsZQ%3D%3D?line=44'>45</a>\u001b[0m score\u001b[39m=\u001b[39m\u001b[39m-\u001b[39mcross_val_score(nn,X_train,y_train,error_score\u001b[39m=\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39mraise\u001b[39;49m\u001b[39m'\u001b[39;49m,scoring\u001b[39m=\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39mneg_mean_squared_error\u001b[39;49m\u001b[39m'\u001b[39;49m,cv\u001b[39m=\u001b[39;49mkfold,fit_params\u001b[39m=\u001b[39;49m{\u001b[39m'\u001b[39;49m\u001b[39mcallbacks\u001b[39;49m\u001b[39m'\u001b[39;49m:[es]},n_jobs\u001b[39m=\u001b[39;49m\u001b[39m-\u001b[39;49m\u001b[39m1\u001b[39;49m)\u001b[39m.\u001b[39mmean()\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/aritr/OneDrive/Desktop/Mech%20project/project.ipynb#X53sZmlsZQ%3D%3D?line=46'>47</a>\u001b[0m \u001b[39mreturn\u001b[39;00m score\n",
      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python310\\site-packages\\sklearn\\model_selection\\_validation.py:515\u001b[0m, in \u001b[0;36mcross_val_score\u001b[1;34m(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch, error_score)\u001b[0m\n\u001b[0;32m    512\u001b[0m \u001b[39m# To ensure multimetric format is not supported\u001b[39;00m\n\u001b[0;32m    513\u001b[0m scorer \u001b[39m=\u001b[39m check_scoring(estimator, scoring\u001b[39m=\u001b[39mscoring)\n\u001b[1;32m--> 515\u001b[0m cv_results \u001b[39m=\u001b[39m cross_validate(\n\u001b[0;32m    516\u001b[0m     estimator\u001b[39m=\u001b[39;49mestimator,\n\u001b[0;32m    517\u001b[0m     X\u001b[39m=\u001b[39;49mX,\n\u001b[0;32m    518\u001b[0m     y\u001b[39m=\u001b[39;49my,\n\u001b[0;32m    519\u001b[0m     groups\u001b[39m=\u001b[39;49mgroups,\n\u001b[0;32m    520\u001b[0m     scoring\u001b[39m=\u001b[39;49m{\u001b[39m\"\u001b[39;49m\u001b[39mscore\u001b[39;49m\u001b[39m\"\u001b[39;49m: scorer},\n\u001b[0;32m    521\u001b[0m     cv\u001b[39m=\u001b[39;49mcv,\n\u001b[0;32m    522\u001b[0m     n_jobs\u001b[39m=\u001b[39;49mn_jobs,\n\u001b[0;32m    523\u001b[0m     verbose\u001b[39m=\u001b[39;49mverbose,\n\u001b[0;32m    524\u001b[0m     fit_params\u001b[39m=\u001b[39;49mfit_params,\n\u001b[0;32m    525\u001b[0m     pre_dispatch\u001b[39m=\u001b[39;49mpre_dispatch,\n\u001b[0;32m    526\u001b[0m     error_score\u001b[39m=\u001b[39;49merror_score,\n\u001b[0;32m    527\u001b[0m )\n\u001b[0;32m    528\u001b[0m \u001b[39mreturn\u001b[39;00m cv_results[\u001b[39m\"\u001b[39m\u001b[39mtest_score\u001b[39m\u001b[39m\"\u001b[39m]\n",
      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python310\\site-packages\\sklearn\\model_selection\\_validation.py:266\u001b[0m, in \u001b[0;36mcross_validate\u001b[1;34m(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch, return_train_score, return_estimator, error_score)\u001b[0m\n\u001b[0;32m    263\u001b[0m \u001b[39m# We clone the estimator to make sure that all the folds are\u001b[39;00m\n\u001b[0;32m    264\u001b[0m \u001b[39m# independent, and that it is pickle-able.\u001b[39;00m\n\u001b[0;32m    265\u001b[0m parallel \u001b[39m=\u001b[39m Parallel(n_jobs\u001b[39m=\u001b[39mn_jobs, verbose\u001b[39m=\u001b[39mverbose, pre_dispatch\u001b[39m=\u001b[39mpre_dispatch)\n\u001b[1;32m--> 266\u001b[0m results \u001b[39m=\u001b[39m parallel(\n\u001b[0;32m    267\u001b[0m     delayed(_fit_and_score)(\n\u001b[0;32m    268\u001b[0m         clone(estimator),\n\u001b[0;32m    269\u001b[0m         X,\n\u001b[0;32m    270\u001b[0m         y,\n\u001b[0;32m    271\u001b[0m         scorers,\n\u001b[0;32m    272\u001b[0m         train,\n\u001b[0;32m    273\u001b[0m         test,\n\u001b[0;32m    274\u001b[0m         verbose,\n\u001b[0;32m    275\u001b[0m         \u001b[39mNone\u001b[39;49;00m,\n\u001b[0;32m    276\u001b[0m         fit_params,\n\u001b[0;32m    277\u001b[0m         return_train_score\u001b[39m=\u001b[39;49mreturn_train_score,\n\u001b[0;32m    278\u001b[0m         return_times\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m,\n\u001b[0;32m    279\u001b[0m         return_estimator\u001b[39m=\u001b[39;49mreturn_estimator,\n\u001b[0;32m    280\u001b[0m         error_score\u001b[39m=\u001b[39;49merror_score,\n\u001b[0;32m    281\u001b[0m     )\n\u001b[0;32m    282\u001b[0m     \u001b[39mfor\u001b[39;49;00m train, test \u001b[39min\u001b[39;49;00m cv\u001b[39m.\u001b[39;49msplit(X, y, groups)\n\u001b[0;32m    283\u001b[0m )\n\u001b[0;32m    285\u001b[0m _warn_or_raise_about_fit_failures(results, error_score)\n\u001b[0;32m    287\u001b[0m \u001b[39m# For callabe scoring, the return type is only know after calling. If the\u001b[39;00m\n\u001b[0;32m    288\u001b[0m \u001b[39m# return type is a dictionary, the error scores can now be inserted with\u001b[39;00m\n\u001b[0;32m    289\u001b[0m \u001b[39m# the correct key.\u001b[39;00m\n",
      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python310\\site-packages\\joblib\\parallel.py:1056\u001b[0m, in \u001b[0;36mParallel.__call__\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m   1053\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_iterating \u001b[39m=\u001b[39m \u001b[39mFalse\u001b[39;00m\n\u001b[0;32m   1055\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backend\u001b[39m.\u001b[39mretrieval_context():\n\u001b[1;32m-> 1056\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mretrieve()\n\u001b[0;32m   1057\u001b[0m \u001b[39m# Make sure that we get a last message telling us we are done\u001b[39;00m\n\u001b[0;32m   1058\u001b[0m elapsed_time \u001b[39m=\u001b[39m time\u001b[39m.\u001b[39mtime() \u001b[39m-\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_start_time\n",
      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python310\\site-packages\\joblib\\parallel.py:935\u001b[0m, in \u001b[0;36mParallel.retrieve\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    933\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m    934\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mgetattr\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backend, \u001b[39m'\u001b[39m\u001b[39msupports_timeout\u001b[39m\u001b[39m'\u001b[39m, \u001b[39mFalse\u001b[39;00m):\n\u001b[1;32m--> 935\u001b[0m         \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_output\u001b[39m.\u001b[39mextend(job\u001b[39m.\u001b[39;49mget(timeout\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mtimeout))\n\u001b[0;32m    936\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[0;32m    937\u001b[0m         \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_output\u001b[39m.\u001b[39mextend(job\u001b[39m.\u001b[39mget())\n",
      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python310\\site-packages\\joblib\\_parallel_backends.py:542\u001b[0m, in \u001b[0;36mLokyBackend.wrap_future_result\u001b[1;34m(future, timeout)\u001b[0m\n\u001b[0;32m    539\u001b[0m \u001b[39m\"\"\"Wrapper for Future.result to implement the same behaviour as\u001b[39;00m\n\u001b[0;32m    540\u001b[0m \u001b[39mAsyncResults.get from multiprocessing.\"\"\"\u001b[39;00m\n\u001b[0;32m    541\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m--> 542\u001b[0m     \u001b[39mreturn\u001b[39;00m future\u001b[39m.\u001b[39;49mresult(timeout\u001b[39m=\u001b[39;49mtimeout)\n\u001b[0;32m    543\u001b[0m \u001b[39mexcept\u001b[39;00m CfTimeoutError \u001b[39mas\u001b[39;00m e:\n\u001b[0;32m    544\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mTimeoutError\u001b[39;00m \u001b[39mfrom\u001b[39;00m \u001b[39me\u001b[39;00m\n",
      "File \u001b[1;32md:\\Program Files\\Python310\\lib\\concurrent\\futures\\_base.py:441\u001b[0m, in \u001b[0;36mFuture.result\u001b[1;34m(self, timeout)\u001b[0m\n\u001b[0;32m    438\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_state \u001b[39m==\u001b[39m FINISHED:\n\u001b[0;32m    439\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m__get_result()\n\u001b[1;32m--> 441\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_condition\u001b[39m.\u001b[39;49mwait(timeout)\n\u001b[0;32m    443\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_state \u001b[39min\u001b[39;00m [CANCELLED, CANCELLED_AND_NOTIFIED]:\n\u001b[0;32m    444\u001b[0m     \u001b[39mraise\u001b[39;00m CancelledError()\n",
      "File \u001b[1;32md:\\Program Files\\Python310\\lib\\threading.py:320\u001b[0m, in \u001b[0;36mCondition.wait\u001b[1;34m(self, timeout)\u001b[0m\n\u001b[0;32m    318\u001b[0m \u001b[39mtry\u001b[39;00m:    \u001b[39m# restore state no matter what (e.g., KeyboardInterrupt)\u001b[39;00m\n\u001b[0;32m    319\u001b[0m     \u001b[39mif\u001b[39;00m timeout \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m--> 320\u001b[0m         waiter\u001b[39m.\u001b[39;49macquire()\n\u001b[0;32m    321\u001b[0m         gotit \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m\n\u001b[0;32m    322\u001b[0m     \u001b[39melse\u001b[39;00m:\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "ann_trials=Trials()\n",
    "\n",
    "best=fmin(fn=objective_ann,space=ann_params_space,algo=tpe.suggest,max_evals=500,rstate=np.random.default_rng(random_state),trials=ann_trials)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'state': 2,\n",
       "  'tid': 0,\n",
       "  'spec': None,\n",
       "  'result': {'loss': 0.26224127026788635, 'status': 'ok'},\n",
       "  'misc': {'tid': 0,\n",
       "   'cmd': ('domain_attachment', 'FMinIter_Domain'),\n",
       "   'workdir': None,\n",
       "   'idxs': {'activation': [0],\n",
       "    'batch_size': [0],\n",
       "    'dropout_prob': [0],\n",
       "    'epochs': [0],\n",
       "    'learning_rate': [0],\n",
       "    'nodes': [0],\n",
       "    'num_hidden_layers': [0],\n",
       "    'optimizer': [0]},\n",
       "   'vals': {'activation': [7],\n",
       "    'batch_size': [58.0],\n",
       "    'dropout_prob': [0.6807824657058615],\n",
       "    'epochs': [200.0],\n",
       "    'learning_rate': [0.004967944759844677],\n",
       "    'nodes': [159.0],\n",
       "    'num_hidden_layers': [2.0],\n",
       "    'optimizer': [6]}},\n",
       "  'exp_key': None,\n",
       "  'owner': None,\n",
       "  'version': 0,\n",
       "  'book_time': datetime.datetime(2023, 3, 7, 13, 7, 22, 63000),\n",
       "  'refresh_time': datetime.datetime(2023, 3, 7, 13, 7, 44, 431000)},\n",
       " {'state': 2,\n",
       "  'tid': 1,\n",
       "  'spec': None,\n",
       "  'result': {'loss': 0.11406292818860975, 'status': 'ok'},\n",
       "  'misc': {'tid': 1,\n",
       "   'cmd': ('domain_attachment', 'FMinIter_Domain'),\n",
       "   'workdir': None,\n",
       "   'idxs': {'activation': [1],\n",
       "    'batch_size': [1],\n",
       "    'dropout_prob': [1],\n",
       "    'epochs': [1],\n",
       "    'learning_rate': [1],\n",
       "    'nodes': [1],\n",
       "    'num_hidden_layers': [1],\n",
       "    'optimizer': [1]},\n",
       "   'vals': {'activation': [1],\n",
       "    'batch_size': [52.0],\n",
       "    'dropout_prob': [0.14031198335076267],\n",
       "    'epochs': [230.0],\n",
       "    'learning_rate': [0.020390959174775118],\n",
       "    'nodes': [115.0],\n",
       "    'num_hidden_layers': [5.0],\n",
       "    'optimizer': [0]}},\n",
       "  'exp_key': None,\n",
       "  'owner': None,\n",
       "  'version': 0,\n",
       "  'book_time': datetime.datetime(2023, 3, 7, 13, 7, 44, 442000),\n",
       "  'refresh_time': datetime.datetime(2023, 3, 7, 13, 7, 54, 572000)},\n",
       " {'state': 2,\n",
       "  'tid': 2,\n",
       "  'spec': None,\n",
       "  'result': {'loss': 0.1329432024366293, 'status': 'ok'},\n",
       "  'misc': {'tid': 2,\n",
       "   'cmd': ('domain_attachment', 'FMinIter_Domain'),\n",
       "   'workdir': None,\n",
       "   'idxs': {'activation': [2],\n",
       "    'batch_size': [2],\n",
       "    'dropout_prob': [2],\n",
       "    'epochs': [2],\n",
       "    'learning_rate': [2],\n",
       "    'nodes': [2],\n",
       "    'num_hidden_layers': [2],\n",
       "    'optimizer': [2]},\n",
       "   'vals': {'activation': [0],\n",
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       " {'state': 1,\n",
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       "  'result': {'status': 'new'},\n",
       "  'misc': {'tid': 100,\n",
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       "  'exp_key': None,\n",
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       "  'version': 0,\n",
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       "  'refresh_time': datetime.datetime(2023, 3, 7, 13, 18, 46, 654000)}]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(ann_trials.trials)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'best' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32mc:\\Users\\aritr\\OneDrive\\Desktop\\Mech project\\project.ipynb Cell 40\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/aritr/OneDrive/Desktop/Mech%20project/project.ipynb#X54sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m best\n",
      "\u001b[1;31mNameError\u001b[0m: name 'best' is not defined"
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   "source": [
    "best"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'learning_rate'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32mc:\\Users\\aritr\\OneDrive\\Desktop\\Mech project\\project.ipynb Cell 41\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/aritr/OneDrive/Desktop/Mech%20project/project.ipynb#X54sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m test_loss\u001b[39m=\u001b[39mobjective_ann(best,X_train\u001b[39m=\u001b[39;49mX_test,y_train\u001b[39m=\u001b[39;49my_test)\n",
      "\u001b[1;32mc:\\Users\\aritr\\OneDrive\\Desktop\\Mech project\\project.ipynb Cell 41\u001b[0m in \u001b[0;36mobjective_ann\u001b[1;34m(params, X_train, y_train)\u001b[0m\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/aritr/OneDrive/Desktop/Mech%20project/project.ipynb#X54sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mobjective_ann\u001b[39m(params,X_train\u001b[39m=\u001b[39mX_train,y_train\u001b[39m=\u001b[39my_train):\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/aritr/OneDrive/Desktop/Mech%20project/project.ipynb#X54sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m     learning_rate\u001b[39m=\u001b[39mparams[\u001b[39m'\u001b[39;49m\u001b[39mlearning_rate\u001b[39;49m\u001b[39m'\u001b[39;49m]\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/aritr/OneDrive/Desktop/Mech%20project/project.ipynb#X54sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m     optimizerL \u001b[39m=\u001b[39m [\u001b[39m'\u001b[39m\u001b[39mSGD\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39mAdam\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39mRMSprop\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39mAdadelta\u001b[39m\u001b[39m'\u001b[39m,\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/aritr/OneDrive/Desktop/Mech%20project/project.ipynb#X54sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m                 \u001b[39m'\u001b[39m\u001b[39mAdagrad\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39mAdamax\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39mNadam\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39mFtrl\u001b[39m\u001b[39m'\u001b[39m]\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/aritr/OneDrive/Desktop/Mech%20project/project.ipynb#X54sZmlsZQ%3D%3D?line=6'>7</a>\u001b[0m     optimizerD \u001b[39m=\u001b[39m {\u001b[39m'\u001b[39m\u001b[39mAdam\u001b[39m\u001b[39m'\u001b[39m: Adam(lr\u001b[39m=\u001b[39mlearning_rate), \u001b[39m'\u001b[39m\u001b[39mSGD\u001b[39m\u001b[39m'\u001b[39m: SGD(lr\u001b[39m=\u001b[39mlearning_rate),\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/aritr/OneDrive/Desktop/Mech%20project/project.ipynb#X54sZmlsZQ%3D%3D?line=7'>8</a>\u001b[0m                 \u001b[39m'\u001b[39m\u001b[39mRMSprop\u001b[39m\u001b[39m'\u001b[39m: RMSprop(lr\u001b[39m=\u001b[39mlearning_rate), \u001b[39m'\u001b[39m\u001b[39mAdadelta\u001b[39m\u001b[39m'\u001b[39m: Adadelta(lr\u001b[39m=\u001b[39mlearning_rate),\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/aritr/OneDrive/Desktop/Mech%20project/project.ipynb#X54sZmlsZQ%3D%3D?line=8'>9</a>\u001b[0m                 \u001b[39m'\u001b[39m\u001b[39mAdagrad\u001b[39m\u001b[39m'\u001b[39m: Adagrad(lr\u001b[39m=\u001b[39mlearning_rate), \u001b[39m'\u001b[39m\u001b[39mAdamax\u001b[39m\u001b[39m'\u001b[39m: Adamax(lr\u001b[39m=\u001b[39mlearning_rate),\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/aritr/OneDrive/Desktop/Mech%20project/project.ipynb#X54sZmlsZQ%3D%3D?line=9'>10</a>\u001b[0m                 \u001b[39m'\u001b[39m\u001b[39mNadam\u001b[39m\u001b[39m'\u001b[39m: Nadam(lr\u001b[39m=\u001b[39mlearning_rate), \u001b[39m'\u001b[39m\u001b[39mFtrl\u001b[39m\u001b[39m'\u001b[39m: Ftrl(lr\u001b[39m=\u001b[39mlearning_rate)}\n",
      "\u001b[1;31mKeyError\u001b[0m: 'learning_rate'"
     ]
    }
   ],
   "source": [
    "test_loss=objective_ann(best,X_train=X_test,y_train=y_test)"
   ]
  }
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