diabetes prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "# One of the way to build keras model is by using sequential API. \n",
    "# Sequential API allows models to be built layer by layer\n",
    "# In this problem we have 8 input neurons-12 hidden neurons-8 hidden \n",
    "# neurons-1 output neurons, i.e 4 layers\n",
    "from keras.models import Sequential\n",
    "# Since these 4 layers are connected, thus we use Dense class\n",
    "from keras.layers import Dense\n",
    "from keras import optimizers\n",
    "from keras import metrics\n",
    "import numpy as np\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import pyplot\n",
    "from PIL import Image, ImageFilter\n",
    "import numpy as np\n",
    "import os\n",
    "from keras.optimizers import Adam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 768 observation and 9 variables\n",
    "features_and_targets = np.loadtxt('pima-indians-diabetes.csv',delimiter=',')\n",
    "np.random.shuffle(features_and_targets)\n",
    "\n",
    "# Features(first 8 variables) and Targets(last variables) are assigned \n",
    "# to X and Y.\n",
    "X = features_and_targets[:,0:8]\n",
    "Y = features_and_targets[:,8]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 150 Epochs----------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "768/768 [==============================] - 0s 68us/step\n",
      "\n",
      "acc: 72.79%\n",
      "\n",
      "loss: 59.93%\n",
      "\n",
      "================\n",
      "Confusion Matrix\n",
      "================\n",
      "True negatives:  418\n",
      "False negatives:  127\n",
      "False positives:  82\n",
      "True positives:  141\n"
     ]
    }
   ],
   "source": [
    "# np.random.seed(7) will produce the same set of network weights \n",
    "np.random.seed(7)\n",
    "\n",
    "# building keras model using sequential API\n",
    "model = Sequential()\n",
    "\n",
    "# adding 1st hidden layer which has 12 nuerons, input layer has 8 nuerons,\n",
    "# for 8 variables in 'X' dataset. The inputs are specifically mentioned \n",
    "# by using \"input_dim\" argument.\n",
    "# activation function \"relu\" is used.\n",
    "# The addition of product of inputs and the random weights of all the layers\n",
    "# below will passed through the respective activation function.\n",
    "model.add(Dense(12, input_dim=8, activation='relu'))\n",
    "\n",
    "# adding 2nd hidden layer with 8 nuerons and passing it thtough relu function.\n",
    "# The \"relu\" will predict the output from the 2nd hidden layer in 1 or 0. \n",
    "model.add(Dense(8, activation='relu'))\n",
    "\n",
    "# adding 3rd hidden/output layer which has 1 neurons, the activation\n",
    "# function used here is \"sigmoid\" which predicts the targets, ie. \"Y\"\n",
    "# by values between 1 and 0\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "opt = Adam(lr=0.0001)\n",
    "# Adam optimizer default learning rate is 0.001.\n",
    "\n",
    "# after adding all the layers to the model, compiling them together with\n",
    "# additional properties such as loss \"binary_crossentropy\" , \n",
    "# gradient descent optimizer called \"adam\". The predicted observations \n",
    "# from the sigmoid activation function will be used to compute \n",
    "# loss = -{(y\\log(p) + (1 - y)\\log(1 - p))}, where log is the natural log, \n",
    "# y is binary indicator (0 or 1) and p is predicted probability. Further \n",
    "# the weights will be reduced by optimizer till the loss is low or until\n",
    "# the specified epochs are completed. Metric allows to monitor the model while \n",
    "# training. accuracy value and loss is generated for each epoch in verbose output.\n",
    "# compiling the model involves Tensorflow at backend using which the weights are\n",
    "# evaluated and the predictions are made.\n",
    "model.compile(loss='binary_crossentropy', optimizer=opt, metrics=['accuracy'])\n",
    "\n",
    "# The model created above will be now trained with data using fit() function.\n",
    "# For every epoch the loss will be decreased to its minimum.\n",
    "# verbose = 0 turns off the output progress bars during training\n",
    "one_fifty_history = model.fit(X, Y, epochs=150, verbose=0)\n",
    "\n",
    "# evaluate() function is used to check how the model will perform\n",
    "# on the test data set, here we will evaluate with the same dataset passed in\n",
    "# while training the model.When the score are printed in the output\n",
    "# it shows the values of overall [loss, accuracy] of the model\n",
    "scores = model.evaluate(X, Y)\n",
    "\n",
    "# predict() function is used for predicting the targets of the new dataset if any.\n",
    "# here we will just predict for the features dataset we have and check its accuracy\n",
    "# in the output below\n",
    "Y_predict = model.predict(X)\n",
    "\n",
    "# model.metrics_names[0:2] has first element \"loss\" and 2nd \"accuracy\"\n",
    "# where as scores 2nd element is loss and we are printing the same.\n",
    "print(\"\\n%s: %.2f%%\" % (model.metrics_names[1], scores[1]*100))\n",
    "print(\"\\n%s: %.2f%%\" % (model.metrics_names[0], scores[0]*100))\n",
    "\n",
    "# round() function rounds off the number to the nearest integer. \n",
    "rounded = [round(i[0]) for i in Y_predict]\n",
    "y_pred = np.array(rounded,dtype='int64')\n",
    "\n",
    "#confusion matrix is used to evaluate the quality of the output of the model.\n",
    "#The diagonal elements represent the number of points for which the predicted \n",
    "#label is equal to the true label, while off-diagonal elements are those that \n",
    "#are mislabeled by the model.\n",
    "print('')\n",
    "print('================')\n",
    "print('Confusion Matrix')\n",
    "print('================')\n",
    "CM = confusion_matrix(Y, y_pred)\n",
    "print('True negatives: ',CM[0,0])\n",
    "print('False negatives: ',CM[1,0])\n",
    "print('False positives: ',CM[0,1])\n",
    "print('True positives: ',CM[1,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "768"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "418+127+82+141 \n",
    "# in 768 total predicted values, 418+141 were correctly \n",
    "# predicted however, 127+82 were incorrectly predicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGBA size=150x150 at 0x7F2A910EF7F0>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conf_matrix = Image.open('conf_matrix.png') \n",
    "cm = conf_matrix.resize((150,150),Image.ANTIALIAS)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.5993209903438886, 0.7278645833333334]\n"
     ]
    },
    {
     "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": [
    "print(scores)\n",
    "acc_onefifty = one_fifty_history .history['acc']\n",
    "loss_onefifty= one_fifty_history .history['loss']\n",
    "epoch = range(0,151)\n",
    "ax = plt.subplot(111)\n",
    "ax.plot(acc_onefifty, label=\"Accuracy\")\n",
    "plt.legend()\n",
    "ax.plot(loss_onefifty, label=\"Loss\")\n",
    "plt.legend()\n",
    "plt.xlabel(\"Number of epochs\")\n",
    "plt.ylabel(\"loss/accuracy values\")\n",
    "plt.title(\"Change in Loss and Accuracy with respect to number of epochs\")\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "plt.xticks(np.arange(min(epoch), max(epoch)+1,15))\n",
    "plt.show()\n",
    "# The plot shows the model has increase in accuracy from approximately \n",
    "# 0.5 to 0.72(i.e 50% to 72% in 150epoch)\n",
    "# and loss reduces from approximately 5.8 to 0.6 during \n",
    "# the training process for 150 epochs. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "ax = plt.subplot(1,1,1)\n",
    "plt.hist(y_pred,alpha=0.5,histtype='stepfilled',label=\"prediction\")\n",
    "ax.legend()\n",
    "plt.hist(Y,alpha=0.5,histtype='stepfilled', label=\"Y_observed\")\n",
    "ax.legend()\n",
    "plt.xlabel(\"values of observations\")\n",
    "plt.ylabel(\"number of obseravtions\")\n",
    "plt.title(\"Comparisons of observed and predicted \\n values for 150 epochs\")\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "plt.show()\n",
    "plt.rcParams['figure.figsize'] = [10,5]\n",
    "# The prediction which are closer to 1's and 0's are plotted over \n",
    "# Y_observed. 72% are predicted close/equal to actual values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5000 Epochs----------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "768/768 [==============================] - 0s 125us/step\n",
      "\n",
      "acc: 79.56%\n",
      "\n",
      "loss: 41.73%\n",
      "\n",
      "================\n",
      "Confusion Matrix\n",
      "================\n",
      "True negatives:  449\n",
      "False negatives:  106\n",
      "False positives:  51\n",
      "True positives:  162\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(7)\n",
    "model = Sequential()\n",
    "model.add(Dense(12, input_dim=8, activation='relu'))\n",
    "model.add(Dense(8, activation='relu'))\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "opt = Adam(lr=0.0001)\n",
    "model.compile(loss='binary_crossentropy', optimizer= opt, metrics=['accuracy'])\n",
    "five_thousand_history = model.fit(X, Y, epochs=5000, verbose=0)\n",
    "scores = model.evaluate(X, Y)\n",
    "Y_predict = model.predict(X)\n",
    "print(\"\\n%s: %.2f%%\" % (model.metrics_names[1], scores[1]*100))\n",
    "print(\"\\n%s: %.2f%%\" % (model.metrics_names[0], scores[0]*100))\n",
    "rounded = [round(i[0]) for i in Y_predict]\n",
    "y_pred = np.array(rounded,dtype='int64')\n",
    "print('')\n",
    "print('================')\n",
    "print('Confusion Matrix')\n",
    "print('================')\n",
    "CM = confusion_matrix(Y, y_pred)\n",
    "print('True negatives: ',CM[0,0])\n",
    "print('False negatives: ',CM[1,0])\n",
    "print('False positives: ',CM[0,1])\n",
    "print('True positives: ',CM[1,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.41730467478434247, 0.7955729166666666]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(scores)\n",
    "acc_five_thousand = five_thousand_history .history['acc']\n",
    "loss_five_thousand = five_thousand_history .history['loss']\n",
    "epoch = range(0,5001)\n",
    "ax = plt.subplot(111)\n",
    "ax.plot(acc_five_thousand, label=\"Accuracy\")\n",
    "plt.legend()\n",
    "ax.plot(loss_five_thousand, label=\"Loss\")\n",
    "plt.legend()\n",
    "plt.xlabel(\"Number of epochs\")\n",
    "plt.ylabel(\"loss/accuracy values\")\n",
    "plt.title(\"Change in Loss and Accuracy with respect to number of epochs\")\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "plt.xticks(np.arange(min(epoch), max(epoch)+1,1000))\n",
    "plt.show()\n",
    "# The plot shows the model has increasein accuracy from approximately \n",
    "# 0.5 to 0.80(i.e 50% to 80% in 5000 epoch)\n",
    "# and loss reduces from approximately 5.8 to 0.41 during \n",
    "# the training process for 5000 epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.subplot(1,1,1)\n",
    "plt.hist(y_pred,alpha=0.5,histtype='stepfilled',label=\"prediction\")\n",
    "ax.legend()\n",
    "plt.hist(Y,alpha=0.5,histtype='stepfilled', label=\"Y_observed\")\n",
    "ax.legend()\n",
    "plt.xlabel(\"values of observations\")\n",
    "plt.ylabel(\"number of obseravtions\")\n",
    "plt.title(\"Comparisons of observed and predicted \\n values of 1st variable for 5000 epochs\")\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "plt.show()\n",
    "plt.rcParams['figure.figsize'] = [10,5]\n",
    "# The prediction which are closer to 1's and 0's are plotted over \n",
    "# Y_observed. 85% are predicted close/equal to actual values\n",
    "# and 15% are not."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 20000 Epochs with default learning rate of 0.001 ----------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "768/768 [==============================] - 0s 91us/step\n",
      "\n",
      "acc: 84.77%\n",
      "\n",
      "loss: 34.30%\n",
      "\n",
      "================\n",
      "Confusion Matrix\n",
      "================\n",
      "True negatives:  453\n",
      "False negatives:  70\n",
      "False positives:  47\n",
      "True positives:  198\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(7)\n",
    "model = Sequential()\n",
    "model.add(Dense(12, input_dim=8, activation='relu'))\n",
    "model.add(Dense(8, activation='relu'))\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "twenty_history = model.fit(X, Y, epochs=20000, verbose=0)\n",
    "scores = model.evaluate(X, Y)\n",
    "Y_predict = model.predict(X)\n",
    "print(\"\\n%s: %.2f%%\" % (model.metrics_names[1], scores[1]*100))\n",
    "print(\"\\n%s: %.2f%%\" % (model.metrics_names[0], scores[0]*100))\n",
    "rounded = [round(i[0]) for i in Y_predict]\n",
    "y_pred = np.array(rounded,dtype='int64')\n",
    "print('')\n",
    "print('================')\n",
    "print('Confusion Matrix')\n",
    "print('================')\n",
    "CM = confusion_matrix(Y, y_pred)\n",
    "print('True negatives: ',CM[0,0])\n",
    "print('False negatives: ',CM[1,0])\n",
    "print('False positives: ',CM[0,1])\n",
    "print('True positives: ',CM[1,1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The accuracy and loss are unchanged for 5000 and 20000 epochs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.3429684154689312, 0.84765625]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(scores)\n",
    "acc_twenty = twenty_history .history['acc']\n",
    "loss_twenty = twenty_history .history['loss']\n",
    "epoch = range(0,20000)\n",
    "ax = plt.subplot(111)\n",
    "ax.plot(acc_twenty, label=\"Accuracy\")\n",
    "plt.legend()\n",
    "ax.plot(loss_twenty, label=\"Loss\")\n",
    "plt.legend()\n",
    "plt.xlabel(\"Number of epochs\")\n",
    "plt.ylabel(\"loss/accuracy values\")\n",
    "plt.title(\"Change in Loss and Accuracy with respect to number of epochs\",fontweight='bold')\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "plt.xticks(np.arange(min(epoch), max(epoch)+2, 5000))\n",
    "plt.show()\n",
    "# accuracy is changed from 80% of 5k epochs to 84.77% of 20k epochs\n",
    "# loss is changed from 41% of 5k epochs to 34% of 20k epochs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.subplot(1,1,1)\n",
    "plt.hist(y_pred,alpha=0.5,histtype='stepfilled',label=\"prediction\")\n",
    "ax.legend()\n",
    "plt.hist(Y,alpha=0.5,histtype='stepfilled', label=\"Y_observed\")\n",
    "ax.legend()\n",
    "plt.xlabel(\"value of obseravtions\")\n",
    "plt.ylabel(\"number of observations\")\n",
    "plt.title(\"Comparisons of observed and predicted \\n values for 20000 epochs\")\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "plt.show()\n",
    "plt.rcParams['figure.figsize'] = [10,5]\n",
    "# The prediction which are closer to 1's and 0's are plotted over \n",
    "# Y_observed. 85% are predicted close/equal to actual values\n",
    "# and 15% are not."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "refrence:\n",
    "\n",
    "https://ml-cheatsheet.readthedocs.io/en/latest/loss_functions.html \n",
    "\n",
    "https://keras.io/losses/"
   ]
  }
 ],
 "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}