GridSearchCVv2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Grid Search CV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import Dropout\n",
    "from keras.wrappers.scikit_learn import KerasClassifier\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X shape (10000, 10)\n",
      "[[619 'France' 'Female' ... 1 1 101348.88]\n",
      " [608 'Spain' 'Female' ... 0 1 112542.58]\n",
      " [502 'France' 'Female' ... 1 0 113931.57]\n",
      " ...\n",
      " [709 'France' 'Female' ... 0 1 42085.58]\n",
      " [772 'Germany' 'Male' ... 1 0 92888.52]\n",
      " [792 'France' 'Female' ... 1 0 38190.78]]\n",
      "y shape (10000,)\n",
      "[1 0 1 ... 1 1 0]\n"
     ]
    }
   ],
   "source": [
    "dataset = pd.read_csv('Churn_Modelling.csv')\n",
    "X = dataset.iloc[:, 3:13].values\n",
    "y = dataset.iloc[:, 13].values\n",
    "print('X shape', X.shape)\n",
    "print(X)\n",
    "print('y shape', y.shape)\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/agha/anaconda3/lib/python3.5/site-packages/sklearn/preprocessing/_encoders.py:363: FutureWarning: The handling of integer data will change in version 0.22. Currently, the categories are determined based on the range [0, max(values)], while in the future they will be determined based on the unique values.\n",
      "If you want the future behaviour and silence this warning, you can specify \"categories='auto'\".\n",
      "In case you used a LabelEncoder before this OneHotEncoder to convert the categories to integers, then you can now use the OneHotEncoder directly.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/home/agha/anaconda3/lib/python3.5/site-packages/sklearn/preprocessing/_encoders.py:385: DeprecationWarning: The 'categorical_features' keyword is deprecated in version 0.20 and will be removed in 0.22. You can use the ColumnTransformer instead.\n",
      "  \"use the ColumnTransformer instead.\", DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[0.0000000e+00, 0.0000000e+00, 6.1900000e+02, ..., 1.0000000e+00,\n",
       "        1.0000000e+00, 1.0134888e+05],\n",
       "       [0.0000000e+00, 1.0000000e+00, 6.0800000e+02, ..., 0.0000000e+00,\n",
       "        1.0000000e+00, 1.1254258e+05],\n",
       "       [0.0000000e+00, 0.0000000e+00, 5.0200000e+02, ..., 1.0000000e+00,\n",
       "        0.0000000e+00, 1.1393157e+05],\n",
       "       ...,\n",
       "       [0.0000000e+00, 0.0000000e+00, 7.0900000e+02, ..., 0.0000000e+00,\n",
       "        1.0000000e+00, 4.2085580e+04],\n",
       "       [1.0000000e+00, 0.0000000e+00, 7.7200000e+02, ..., 1.0000000e+00,\n",
       "        0.0000000e+00, 9.2888520e+04],\n",
       "       [0.0000000e+00, 0.0000000e+00, 7.9200000e+02, ..., 1.0000000e+00,\n",
       "        0.0000000e+00, 3.8190780e+04]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n",
    "labelencoder_X_1 = LabelEncoder()\n",
    "# label encoder column 1\n",
    "X[:, 1] = labelencoder_X_1.fit_transform(X[:, 1])\n",
    "labelencoder_X_2 = LabelEncoder()\n",
    "# label encoder column 2\n",
    "X[:, 2] = labelencoder_X_2.fit_transform(X[:, 2])\n",
    "onehotencoder = OneHotEncoder(categorical_features = [1])\n",
    "X = onehotencoder.fit_transform(X).toarray()\n",
    "X = X[:, 1:]\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)\n",
    "\n",
    "# Feature Scaling\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def brain_ann(hidden_layers, kernel_initializer, optimizer):\n",
    "    classifier = Sequential()\n",
    "    classifier.add(Dense(units = hidden_layers, kernel_initializer = kernel_initializer, activation = 'relu', input_dim = 11))\n",
    "    classifier.add(Dense(units = 1, kernel_initializer = kernel_initializer, activation = 'sigmoid'))\n",
    "    classifier.compile(optimizer = optimizer, loss = 'binary_crossentropy', metrics = ['accuracy'])\n",
    "    \n",
    "    return classifier    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    },
    {
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      "7200/7200 [==============================] - 15s 2ms/step - loss: 0.4488 - acc: 0.8000\n",
      "Epoch 1/1\n",
      "7200/7200 [==============================] - 15s 2ms/step - loss: 0.4414 - acc: 0.8100\n",
      "Epoch 1/1\n",
      "7200/7200 [==============================] - 15s 2ms/step - loss: 0.4430 - acc: 0.8119\n",
      "Epoch 1/1\n",
      "7200/7200 [==============================] - 16s 2ms/step - loss: 0.4513 - acc: 0.7975\n",
      "Epoch 1/1\n",
      "7200/7200 [==============================] - 17s 2ms/step - loss: 0.4467 - acc: 0.8094\n",
      "Epoch 1/1\n",
      "7200/7200 [==============================] - 15s 2ms/step - loss: 0.4420 - acc: 0.8046\n",
      "Epoch 1/1\n",
      "7200/7200 [==============================] - 17s 2ms/step - loss: 0.4549 - acc: 0.7976\n",
      "Epoch 1/1\n",
      "7200/7200 [==============================] - 17s 2ms/step - loss: 0.4526 - acc: 0.8028\n",
      "Epoch 1/1\n",
      "7200/7200 [==============================] - 17s 2ms/step - loss: 0.4359 - acc: 0.8139\n",
      "Epoch 1/1\n",
      "7200/7200 [==============================] - 15s 2ms/step - loss: 0.4558 - acc: 0.7947\n",
      "Epoch 1/1\n",
      "7200/7200 [==============================] - 18s 3ms/step - loss: 0.4494 - acc: 0.8029\n",
      "Epoch 1/1\n",
      "7200/7200 [==============================] - 15s 2ms/step - loss: 0.4387 - acc: 0.8093\n",
      "Epoch 1/1\n",
      "7200/7200 [==============================] - 16s 2ms/step - loss: 0.4478 - acc: 0.8067\n",
      "Epoch 1/1\n",
      "7200/7200 [==============================] - 16s 2ms/step - loss: 0.4487 - acc: 0.8058\n",
      "Epoch 1/1\n",
      "7200/7200 [==============================] - 15s 2ms/step - loss: 0.4421 - acc: 0.8103\n",
      "Epoch 1/1\n",
      "7200/7200 [==============================] - 17s 2ms/step - loss: 0.4433 - acc: 0.8079\n",
      "Epoch 1/1\n",
      "7200/7200 [==============================] - 16s 2ms/step - loss: 0.4399 - acc: 0.8056\n",
      "Epoch 1/1\n",
      "7200/7200 [==============================] - 16s 2ms/step - loss: 0.4456 - acc: 0.8092\n",
      "Epoch 1/1\n",
      "7200/7200 [==============================] - 17s 2ms/step - loss: 0.4574 - acc: 0.7897\n",
      "Epoch 1/1\n",
      "7200/7200 [==============================] - 18s 2ms/step - loss: 0.4462 - acc: 0.8014\n",
      "Epoch 1/1\n",
      "7200/7200 [==============================] - 16s 2ms/step - loss: 0.4518 - acc: 0.7958\n",
      "Epoch 1/1\n",
      "7200/7200 [==============================] - 16s 2ms/step - loss: 0.4415 - acc: 0.8024\n",
      "Epoch 1/1\n",
      "7200/7200 [==============================] - 16s 2ms/step - loss: 0.4559 - acc: 0.7911\n",
      "Epoch 1/1\n",
      "7200/7200 [==============================] - 16s 2ms/step - loss: 0.4578 - acc: 0.7969\n",
      "Epoch 1/1\n",
      "7200/7200 [==============================] - 16s 2ms/step - loss: 0.4441 - acc: 0.8082\n",
      "Epoch 1/1\n",
      "7200/7200 [==============================] - 16s 2ms/step - loss: 0.4463 - acc: 0.8046\n",
      "Epoch 1/1\n",
      "7200/7200 [==============================] - 16s 2ms/step - loss: 0.4375 - acc: 0.8097\n",
      "Epoch 1/1\n",
      "7200/7200 [==============================] - 16s 2ms/step - loss: 0.4414 - acc: 0.8051\n",
      "Epoch 1/1\n",
      "7200/7200 [==============================] - 17s 2ms/step - loss: 0.4310 - acc: 0.8149\n",
      "Epoch 1/1\n",
      "7200/7200 [==============================] - 16s 2ms/step - loss: 0.4428 - acc: 0.8093\n",
      "Epoch 1/1\n",
      "7200/7200 [==============================] - 17s 2ms/step - loss: 0.4420 - acc: 0.8094\n",
      "Epoch 1/1\n",
      "7200/7200 [==============================] - 16s 2ms/step - loss: 0.4554 - acc: 0.7974\n",
      "Epoch 1/1\n",
      "7200/7200 [==============================] - 16s 2ms/step - loss: 0.4480 - acc: 0.7985\n",
      "Epoch 1/1\n",
      "7200/7200 [==============================] - 16s 2ms/step - loss: 0.4318 - acc: 0.8185\n",
      "Epoch 1/1\n",
      "7200/7200 [==============================] - 16s 2ms/step - loss: 0.4427 - acc: 0.8086\n",
      "Epoch 1/1\n",
      "7200/7200 [==============================] - 17s 2ms/step - loss: 0.4434 - acc: 0.8072\n",
      "Epoch 1/1\n",
      "7200/7200 [==============================] - 16s 2ms/step - loss: 0.4474 - acc: 0.7979\n",
      "Epoch 1/1\n",
      "7200/7200 [==============================] - 17s 2ms/step - loss: 0.4510 - acc: 0.8040\n",
      "Epoch 1/1\n",
      "8000/8000 [==============================] - 18s 2ms/step - loss: 0.4454 - acc: 0.8131\n"
     ]
    }
   ],
   "source": [
    "classifier = KerasClassifier(build_fn= brain_ann)\n",
    "parameters = {'batch_size': [10],\n",
    "              'nb_epoch': [320, 420],\n",
    "              'hidden_layers': [56, 66],\n",
    "              'kernel_initializer': ['uniform', 'glorot_uniform'],\n",
    "              'optimizer': ['adam', 'rmsprop'] }\n",
    "grid_search = GridSearchCV(estimator=classifier,\n",
    "                           param_grid=parameters,\n",
    "                           scoring= 'accuracy',\n",
    "                           cv=10)\n",
    "\n",
    "grid_search = grid_search.fit(X_train, y_train)\n",
    "best_parameters = grid_search.best_params_\n",
    "best_score = grid_search.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'batch_size': 10,\n",
       " 'hidden_layers': 66,\n",
       " 'kernel_initializer': 'uniform',\n",
       " 'nb_epoch': 320,\n",
       " 'optimizer': 'adam'}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.83375"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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