3. Keras Hyperparameter Tuner.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6d32dd69",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "from keras_tuner.tuners import RandomSearch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "73aa584f",
   "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>T</th>\n",
       "      <th>TM</th>\n",
       "      <th>Tm</th>\n",
       "      <th>SLP</th>\n",
       "      <th>H</th>\n",
       "      <th>VV</th>\n",
       "      <th>V</th>\n",
       "      <th>VM</th>\n",
       "      <th>PM 2.5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.4</td>\n",
       "      <td>9.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1017.6</td>\n",
       "      <td>93.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4.3</td>\n",
       "      <td>9.4</td>\n",
       "      <td>219.720833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.8</td>\n",
       "      <td>12.7</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1018.5</td>\n",
       "      <td>87.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>4.4</td>\n",
       "      <td>11.1</td>\n",
       "      <td>182.187500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.7</td>\n",
       "      <td>13.4</td>\n",
       "      <td>2.4</td>\n",
       "      <td>1019.4</td>\n",
       "      <td>82.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>4.8</td>\n",
       "      <td>11.1</td>\n",
       "      <td>154.037500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.6</td>\n",
       "      <td>15.5</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1018.7</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>8.1</td>\n",
       "      <td>20.6</td>\n",
       "      <td>223.208333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12.4</td>\n",
       "      <td>20.9</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1017.3</td>\n",
       "      <td>61.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>8.7</td>\n",
       "      <td>22.2</td>\n",
       "      <td>200.645833</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      T    TM   Tm     SLP     H   VV    V    VM      PM 2.5\n",
       "0   7.4   9.8  4.8  1017.6  93.0  0.5  4.3   9.4  219.720833\n",
       "1   7.8  12.7  4.4  1018.5  87.0  0.6  4.4  11.1  182.187500\n",
       "2   6.7  13.4  2.4  1019.4  82.0  0.6  4.8  11.1  154.037500\n",
       "3   8.6  15.5  3.3  1018.7  72.0  0.8  8.1  20.6  223.208333\n",
       "4  12.4  20.9  4.4  1017.3  61.0  1.3  8.7  22.2  200.645833"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('Real_Combine (1).csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "28188055",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "T         0\n",
       "TM        0\n",
       "Tm        0\n",
       "SLP       0\n",
       "H         0\n",
       "VV        0\n",
       "V         0\n",
       "VM        0\n",
       "PM 2.5    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=df.dropna()\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ea414681",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creation of feature set and target set\n",
    "x=df.iloc[:,:-1]\n",
    "y=df.iloc[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "739a5e4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def hyper_tune(param):\n",
    "    model = keras.Sequential()\n",
    "    for i in range(param.Int('num_layers' ,2,20)):\n",
    "        model.add(layers.Dense(units=param.Int('units_'+str(i),\n",
    "                                              min_value=32,\n",
    "                                              max_value=512,\n",
    "                                              step=32),\n",
    "                              activation='tanh'))\n",
    "    model.add(layers.Dense(1,activation='linear'))\n",
    "    model.compile(\n",
    "        optimizer=keras.optimizers.Adam(param.Choice('learning_rate' , [1e-2 , 1e-3, 1e-4])),\n",
    "        loss='mean_absolute_error',\n",
    "        metrics=['mean_absolute_error'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "dee0b834",
   "metadata": {},
   "outputs": [],
   "source": [
    "tuner = RandomSearch(\n",
    "    hyper_tune,\n",
    "    objective='val_mean_absolute_error',\n",
    "    max_trials=5,\n",
    "    executions_per_trial=3,\n",
    "    directory='project',\n",
    "    overwrite=True,\n",
    "    project_name = 'Air Quality Index AQI'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "590f74e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Search space summary\n",
      "Default search space size: 4\n",
      "num_layers (Int)\n",
      "{'default': None, 'conditions': [], 'min_value': 2, 'max_value': 20, 'step': 1, 'sampling': 'linear'}\n",
      "units_0 (Int)\n",
      "{'default': None, 'conditions': [], 'min_value': 32, 'max_value': 512, 'step': 32, 'sampling': 'linear'}\n",
      "units_1 (Int)\n",
      "{'default': None, 'conditions': [], 'min_value': 32, 'max_value': 512, 'step': 32, 'sampling': 'linear'}\n",
      "learning_rate (Choice)\n",
      "{'default': 0.01, 'conditions': [], 'values': [0.01, 0.001, 0.0001], 'ordered': True}\n"
     ]
    }
   ],
   "source": [
    "tuner.search_space_summary()   #skip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2e3c6250",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split as tts\n",
    "x_train,x_test,y_train,y_test = tts(x,y,test_size=0.3,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "70dae071",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trial 5 Complete [00h 00m 06s]\n",
      "val_mean_absolute_error: 75.34569040934245\n",
      "\n",
      "Best val_mean_absolute_error So Far: 64.87789662679036\n",
      "Total elapsed time: 00h 00m 25s\n",
      "INFO:tensorflow:Oracle triggered exit\n"
     ]
    }
   ],
   "source": [
    "tuner.search(x_train , y_train , epochs=5, validation_data=(x_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "154e6bfb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "24/24 [==============================] - 1s 7ms/step - loss: 81.6617 - mean_absolute_error: 81.6617 - val_loss: 64.8997 - val_mean_absolute_error: 64.8997\n",
      "Epoch 2/5\n",
      "24/24 [==============================] - 0s 4ms/step - loss: 66.5585 - mean_absolute_error: 66.5585 - val_loss: 66.2889 - val_mean_absolute_error: 66.2889\n",
      "Epoch 3/5\n",
      "24/24 [==============================] - 0s 4ms/step - loss: 66.2680 - mean_absolute_error: 66.2680 - val_loss: 65.7947 - val_mean_absolute_error: 65.7947\n",
      "Epoch 4/5\n",
      "24/24 [==============================] - 0s 4ms/step - loss: 66.2747 - mean_absolute_error: 66.2747 - val_loss: 65.7599 - val_mean_absolute_error: 65.7599\n",
      "Epoch 5/5\n",
      "24/24 [==============================] - 0s 4ms/step - loss: 66.3516 - mean_absolute_error: 66.3516 - val_loss: 65.4565 - val_mean_absolute_error: 65.4565\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#Get the best Hyperparameters found during the search\n",
    "best_hps = tuner.get_best_hyperparameters(1)[0]\n",
    "\n",
    "#Build the Model witht he best hyperparameters\n",
    "model=hyper_tune(best_hps)\n",
    "\n",
    "#Train the model with the best hyperparameters on the full training set\n",
    "history = model.fit(x_train,y_train , epochs=5 ,validation_data = (x_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "43e1be49",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot the Training and Validation Metrics for each Epoch\n",
    "plt.plot(history.history['mean_absolute_error'] , label='training')\n",
    "plt.plot(history.history['val_mean_absolute_error'] , label='validation')\n",
    "plt.title('Model Performance During Training')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Mean Absolute Error')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "929d54ce",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.17"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}