EnsembleMethod.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "import pandas as pd\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Activation, LocallyConnected2D, Flatten, Conv2D, MaxPooling2D\n",
    "from keras.layers import Dropout\n",
    "from keras.utils import np_utils\n",
    "from keras import backend as K\n",
    "K.set_image_dim_ordering('th')\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn.preprocessing as pre\n",
    "from keras.initializers import *\n",
    "from keras.optimizers import *\n",
    "from sklearn.model_selection import train_test_split\n",
    "from keras import regularizers\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "import seaborn as sb\n",
    "from keras.models import load_model\n",
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the data and the models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "modelArray=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test=np.load('x_test_new.npy')\n",
    "y_test=np.load('y_test_new.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "model1=load_model('model1New.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "model2=load_model('model2New.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "model3=load_model('model3New.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "model4=load_model('model4New.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "model5=load_model('model5New.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "modelArray.append(model1)\n",
    "modelArray.append(model2)\n",
    "modelArray.append(model3)\n",
    "modelArray.append(model4)\n",
    "modelArray.append(model5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictArray=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "for model in modelArray:\n",
    "    predictArray.append(model.predict(x_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "voting=[0,0]\n",
    "for p1,p2,p3,p4,p5 in zip(predictArray[0],predictArray[1],predictArray[2],predictArray[3],predictArray[4]):\n",
    "    if(p1<0.5):\n",
    "        voting[0]+=1\n",
    "    else:\n",
    "        voting[1]+=1\n",
    "    if(p2<0.5):\n",
    "        voting[0]+=1\n",
    "    else:\n",
    "        voting[1]+=1\n",
    "    if(p3<0.5):\n",
    "        voting[0]+=1\n",
    "    else:\n",
    "        voting[1]+=1\n",
    "    if(p4<0.5):\n",
    "        voting[0]+=1\n",
    "    else:\n",
    "        voting[1]+=1\n",
    "    if(p5<0.5):\n",
    "        voting[0]+=1\n",
    "    else:\n",
    "        voting[1]+=1\n",
    "    y_pred.append(voting)\n",
    "    voting=[0,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "error=0\n",
    "for yp,yo in zip(y_pred,y_test):\n",
    "    if(np.argmax(yp)!=yo):\n",
    "        error+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the Model is: 81.11% \n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy of the Model is: %.2f%% \" % (100-((error/len(y_test))*100)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=model.predict_classes(x_test)\n",
    "y_test= [int(i) for i in y_test]\n",
    "y_pred=y_pred.reshape(180)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix\n"
     ]
    },
    {
     "data": {
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       "\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pred: yes</th>\n",
       "      <th>Pred: no</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>True:yes</th>\n",
       "      <td>96</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True: no</th>\n",
       "      <td>14</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "          Pred: yes  Pred: no\n",
       "True:yes         96        19\n",
       "True: no         14        51"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Confusion Matrix\")\n",
    "pd.DataFrame(confusion_matrix(y_test, y_pred),\n",
    "                   index=['True:yes', 'True: no'],\n",
    "                   columns=['Pred: yes', 'Pred: no'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#model1.save('model1.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#np.save('x_test',x_test)\n",
    "#np.save('y_test',y_test)"
   ]
  }
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