sepsis_lr.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EARLY DETECTION OF SEPSIS ON CLINICAL DATA USING MACHINE LEARNING ALGORITHMS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# IMPORT STATEMENTS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "matplotlib.pyplot is a collection of command style functions that make matplotlib work like MATLAB. Each pyplot function makes some change to a figure: e.g., creates a figure, creates a plotting area in a figure, plots some lines in a plotting area, decorates the plot with labels, etc.\n",
    "\n",
    "Seaborn is a library for making statistical graphics in Python.\n",
    "\n",
    "pandas is a software library written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series.\n",
    "\n",
    "The sklearn.preprocessing package provides several common utility functions and transformer classes to change raw feature vectors into a representation that is more suitable for the downstream estimators.\n",
    "\n",
    "train_test_split is a function in Sklearn model selection for splitting data arrays into two subsets: for training data and for testing data. With this function, you don't need to divide the dataset manually."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "from sklearn import preprocessing\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "read_csv is an important pandas function to read csv files and do operations on it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.read_csv(\"C:/Users/hcyen/SCA/sepsis.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The value_counts() method returns a Series containing the counts of unique values. This means, for any column in a dataframe, this method returns the count of unique entries in that column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    37945\n",
       "1      864\n",
       "Name: SepsisLabel, dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['SepsisLabel'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "method plt.pie() is used to create a plot.\n",
    "\n",
    "plt.show() is used to display the figure.\n",
    "\n",
    "The countplot plot can be thought of as a histogram across a categorical variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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": [
    "plt.pie(dataset['SepsisLabel'].value_counts(), labels=['0','1'], autopct='%1.1f%%', shadow=True)\n",
    "plt.show()\n",
    "sns.countplot(dataset['SepsisLabel'], label=\"Count\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Resample arrays or sparse matrices in a consistent way."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.utils import resample\n",
    "df_majority = dataset[dataset.SepsisLabel==0]\n",
    "df_minority = dataset[dataset.SepsisLabel==1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The resample() function is used to resample time-series data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_minority_upsampled = resample(df_minority, \n",
    "                                 replace=True,     # sample with replacement\n",
    "                                 n_samples=37945,    # to match majority class\n",
    "                                 random_state=123) # reproducible results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The concat() function does all of the heavy lifting of performing concatenation operations along an axis while performing optional set logic of the indexes on the other axes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_upsampled = pd.concat([df_majority, df_minority_upsampled])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    37945\n",
       "0    37945\n",
       "Name: SepsisLabel, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_upsampled.SepsisLabel.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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sA2zXtrgyewpPsZR62ngGg2OvIXtKZfFGR447hdcYPd4Eg+VsjKc6yFzKN+CFdenmnrSWlxsJT3BcvZR5rRtM5zD9Or63gUh3GmfFVvd9w1mEAMC21D2mM4D5cn6U/Wg9+XFqiVyQIExLu7odeMR0DjBczsZ4qoXMru2Qj7a7u9a36U9M5hHCcXmAea2eOMQyPXMCvAgUA7z8aXqJ4SyigGmtdVFA/cp0jl5eKOcaMg/+ijy1Kr1aznkKU3oc3mBe6xrTOXoZL2f2SfBNwCBXo9/d5Bi7RUcUtpDNL0xn2JvxcmYtJfNy0sBjK1PvOa42drGxKEwpR2+1lHrGdI69eaKcjfFUAngNGLqhTXes2uGuMJ1JFBYNvzB5B8r+eKKcWa8CQYB/W5Z6RWZPkSvdab0zZHtnI6iXl8q5nsx5z6FrW9y2ZVvcxaYDicKQSOofMa8156+V/yqeKWdjPKWBJ4AwoOYvTb6edHSP4Vgiz3Uk9YbBYetfTOfYH8+UE6AxnvoUeAcYviWhuxZtcIw9v0UUhu60rvfasWYvT5Uz6/dkbiWz5y9Nvt2ZktcFiv7R1qPjg37a/h+mcxyI58rZGE9tBl4Ghrf1kHqlOf2a6UwiPzku3zWd4WA8V86sZ7Mfg/ctSy1t69EtRtOIvLOrSy8e+JO2503nOBhPljN7QfwfgOFJB/ehFaln5I4V0VdSjk6lXX2t6RxfxZPlzPoj0A2En1ubbl6+1ZWL4kWf+GSX+/+G/KzdM9fQHohny5m9auhBYDigfv5Wzx/be7SRF8qI/LEl4a55elX6NtM5DoVny5m1GFgCjGjrIbVgRerpQlneRn/ZzuTfJDjptwlmzM9sWLd0aWYv6CB2d4LZCzrY1bX//xYPvJ8kdneC2N0JHng/CUBPWjPnoQ4m3ZPgniXJPV97wzNdLNvsyTMJfS7p6PTaFvfK77/Q7ZrOcig8Xc7shQkPkbkovuCWty/PDfP+jRHevSECQMMbPZw3LsCamyKcNy5Awxv7XqPR0qX54as9vPNXpSz+q1J++GoPu7o0Cz9JM73KZsW3S5m/NFPO5VscXA1Tq+ycfl+mrN7p/uqM+zqWmc5xqDxdToDGeGoXcD+yvOXpeJq5U4IAzJ0S5Pfx9D5fs3BtmtnHBKgsUQwsUcw+JsBza9MELehKQ3qvOeOOl3v4x3OLchXfqC0Jd03T6vTfmc5xODxfzqw/Wd4+uDz/l7dKwfkLOpk+P7FnptuacKkqy/zIqsostnXsuzrb2O4yuuKLH+uocouN7S6zjw2wJeFS868dfO/0IhrjKaZX2Ywo88v/Akcu6eiUn5azvQKmAxyKxnhK11UHHwLuBMILP0k3Tx5mvXzW2MC5prP1lzevL2VEtoCzF3Ry3OBDK9H+/s5SQMBSPHxpGICUo7ngoU4arw5zy8JuPm91uW5KkLrqYB9+B96gtWbJRmeen5azvXzz12Z2efvvZJa31s/fSr62Zqez0nCsftM7ow0ttfj6cQEWb3QYFrHY3J75y39zu8vQ0n1/fKPKLda3fjFBbGhz95kd71mSZO6UIG+vdwjZ8OhlJfzTa/l5j8Hijc7Cn7yZbDCd40j4ppxZS8ic/xwDMO+Vnqd3dLqbzUbqex1JTXuP3vP58584TBpqUzchwAPLUwA8sDzFxdX7LnwuGB/g+XVpdnVpdnVpnl+X5oLxX3zdri7Ns2vSXDclSGdKY6nMErp738NX31u901n9y0XJK7KPwvEd5bdjt7rqYBC4BTgW2DS+0ir/8ayiG0qCqtRwtD6zbpfL1x/tBDIbONdMCnL7WUXs7HS54okuPm/VjKlQPH55mMoSxbubHH77bpJ/rSsB4L5lSe58PTMT3n5mEd+cGtrz7/7b57q55LgAZ0cDdKc1dY90srFdc+P0EDfVhPYN41PbOtyWe5YkZ857pce37+HxXTkB6qqDFcAdZO5eaZk1zh590ymhubalCuOcgDiozpTufnB56tIbn+36g+ksR8Nvy1oAGuOpVuAuoAQIv/Sps/6Z1ekmw7GEBziudp9dnZ7n92KCT8sJ0BhPrQd+AwwDAvctSy1bstF523AsYdgL65yHH1qR+pnpHH3Bt+UEaIyn3iPzaJMxgPrRaz3Pr9jqvGs4ljBk4dr0wl8vSd7g1w2gL/N1ObOagDeBsYC646Wepo+2O/LGsgKzcG36zV8vSX6jMZ7yxHtO+oLvy5n9W/I+Mg+mHqOBv3+xpzG+w/nAbDKRKy+uS7/z6yXJKxrjqe2ms/Ql35cToDGeSgH/H1gBjHE1+vsv9DwlM2j+e25tetFd7yQvb4ynNpnO0tfyopwAjfFUkswG0UdkC3rbCz1Pr9gq717JV8/EU2/esyR5dXZzMO/48jznwdRVB4uBbwNTgM8AfcdZReefPNKeaTaZ6Cuu1vqpj9OvPbA8NbcxnvrMdJ7+knflBKirDoaAG4AZZAt6/dTg1IsmBGrlQgV/607r7nuXJp/74zrnpsZ4aoPpPP0pL8sJey7zux44HfgcSJ8btUffOCN0ZT5d6ldIdnS6u376ZvL3q3a4t2cfoZrX8racAHXVQQuoBS4DtgKd4yut8r8/M3TV4LBVZTadOByrdzrr73w9+W8tXfquxniqIG62z+ty9qqrDk4jcxzaBbREQgR+eE7RJbFB9gmGo4lD8PKn6Q9/9U7ynx3Nw9mNv4JQEOUEqKsOjgZuBsqBTQC3zgydedZYe5ZSymg2sX9pV6cXLE+99dSq9A+Bl7PPlCoYBVNO2HM3y/8CjidzHOpecGwget2U4MVlRWqA2XRib1sS7ra730m+9ME29x8b46mPTecxoaDKCXs2iq4Ezge2AJ0VRYRuPa3oz6YMs06WWdQsx9XO85+k35+/NPW8o7mrMZ7aajqTKQVXToC66qACTgb+J5m3aW8C9Jzxgeg3TpRZ1JQtCXfrXYuSi1Zud58EnmyMpzpNZzKpIMvZq646OBC4lkxRZRY1JDtbLpu/NPWGo5lfqMvYLyvocsKeWfQUYC5fmkWvPTF4UXmRqjQaMM9tbnc3/+qd5GKZLfdV8OXstb9ZNGRjXT81OP2caOCscFBFzCbML7u79c4nPkotb4ynlwP3ymy5LynnXvY6Fp0LFJMpaaq8iOC3poVOnTnaPj1kq8J4RHo/6Ujq9j+sSb/38AepzxzNC8hseUBSzv2oqw5GgNnAn5N5JvNmwBkeUSXfmhY6c2qVdXLAUr54ILdX9KR198vN6ff+fVnq0640y4An8vmi9b4g5TyIuupgJZnL/2YBSTIzqR5faZVfPzV4zsTB1olyIf3BJR3ds3ijs+Lepcm1u7pZBTwKrC60CwqOhJTzENRVB6uAS4AaMpcAbgUYVa5KrzghOHXGCHtGJKQqTGb0mpYud+vrnzkrHl2Z2pZIshF4BFiRL8/3yQUp52Goqw5GgcvJXGGUArYBaUuhLjkuEJs1LnDy6HI1vlBPwTiudta2uB8/szq96rXPnATQCjwGLG6Mp/LwmfL9S8p5mLKbRqOBs4GzyLwMaieQADh+iDXw0onBGZOHWVOLA6rEXNLcSSR165KNzrKHP0ht2Nqh08Bq4Dngw+wjZMQRkHIehbrqYCmZG7prgSFAN7AdcEM21uxjAtFTRtrVsUFWdb4te3d16e3xnU781WZn/VvrnW6decHxS8DrjfHURtP58oGUsw9k7xuNAeeRKStkZtLdgAtQM9IeduZYu3riYKt6cFiN8NvS19Xa3dyuP1+53Y3/8ZP05/GdrgXYZC7a+AOwTE6J9C0pZx/LXsxwPHBq9qMis9O7k8xxKmMrVGT2sYEJ4yut0cMj1ogBxQyxPNZWx9VOS5feuqldb1q1w/38v9amt7V06d5zvFuBt4APgWbZee0fUs5+VFcdDJOZUaeTmVGLycykrUBH9nMiIQLTq+zhE4dYVdEB1ohcF3bvIjbvdjet3O5ufm+z05Z0KMtm1mSOI98CVgHbpZD9T8qZI3XVwQAQBSaTeTLgKDKzqiJzvNbOlwo7cbBVOTxilQ0Oq7KBJSpSUaTKyotUWSREWTioIiVBIge7GEJrTdol1ZWmvSOpE4mkbm/roX13t27f1a0T2zp0+5aE2/7Rdrcz6VBK5sVQOptpF/Ax8B6Z85KJ/vpvI/ZPymlI9r7SYcAIMu8areaLwkLmmcIpMkviJNCT/bjPDyxgoYIWVsjG1qBTDm7KxU27e742SOZ1iUXZj70v4ty7iGvIzIqbgE1SRvOknB6yV2GHAWXAIDK7wIOBgcAAMmU61BP5ikzJE2QK2EJmN3k7maX1LqSIniXl9JHsrnCYTHGDZIrXu2uqAIdMcd3s5wkgIRcA+JOUUwiPypt3pQiRb6ScPqaUmqOUiiul1iql6k3nEX1LlrU+pZSyyZx7nA1sAJYAV2utPzIaTPQZmTn96xRgrdZ6ndY6CfwOuNhwJtGHpJz+NRLY+72UG7L/TOQJKad/7e/SPjlGySNSTv/aQOa+0l6jyL4DRuQHKad/LQFiSqlxSqkQcBXQaDiT6EPyBDmf0lqnlVLfARaSuULoPq31SsOxRB+SUylCeJQsa4XwKCmnEB4l5RTCo6ScQniUlFMIj5JyCuFRUk4hPErKKYRHSTmF8CgppxAeJeUUwqOknEJ4lJRTCI+ScgrhUVJOITzqvwE246vhxXqjvgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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": [
    "plt.pie(df_upsampled['SepsisLabel'].value_counts(), labels=['1','0'], autopct='%1.1f%%', shadow=True)\n",
    "plt.show()\n",
    "sns.countplot(df_upsampled['SepsisLabel'], label=\"Count\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we've read our dataset.\n",
    "\n",
    "Create dataset X by excluding label column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_upsampled[df_upsampled.columns[0:40]].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create dataset Y by only label column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = df_upsampled[df_upsampled.columns[40:]].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Printing dimensions of sepsis dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sepsis dimensions : (75890, 41)\n"
     ]
    }
   ],
   "source": [
    "print(\"sepsis dimensions : {}\".format(df_upsampled.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Printing dimensions of sepsis dataset without label column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sepsis dimensions without label : (75890, 40)\n"
     ]
    }
   ],
   "source": [
    "print(\"sepsis dimensions without label : {}\".format(X.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Printing dimensions of sepsis dataset only with label column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sepsis dimensions only label : (75890, 1)\n"
     ]
    }
   ],
   "source": [
    "print(\"sepsis dimensions only label : {}\".format(Y.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "LabelEncoder is used to normalize labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hcyen\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:235: 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"
     ]
    }
   ],
   "source": [
    "labelencoder_Y = preprocessing.LabelEncoder()\n",
    "Y = labelencoder_Y.fit_transform(Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For splitting data into train set and test set, train_test_split function is used"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data dimensions :(60712, 40)\n",
      "Testing data dimensions :(15178, 40)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.20, random_state=0)\n",
    "print(\"Training data dimensions :{}\".format(X_train.shape))\n",
    "print(\"Testing data dimensions :{}\".format(X_test.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comparing Machine Learning Algorithms Consistently"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The key to a fair comparison of machine learning algorithms is ensuring that each algorithm is evaluated in the same way on the same data.\n",
    "\n",
    "You can achieve this by forcing each algorithm to be evaluated on a consistent test harness."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the followin code, we have considered the following machine learning algorithms\n",
    "\n",
    "MLP Classifier,\n",
    "\n",
    "AdaBoost Classifier,\n",
    "\n",
    "Gradient Boosting Classifier,\n",
    "\n",
    "GaussianNB,\n",
    "\n",
    "Linear Discriminant Analysis,\n",
    "\n",
    "Quadratic Discriminant Analysis.\n",
    "\n",
    "And also print the accuracy for the algorithms mentioned above respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==============================\n",
      "MLPClassifier\n",
      "****Results****\n",
      "Accuracy: 94.0506%\n",
      "Log Loss: 0.17823628759351812\n",
      "==============================\n",
      "AdaBoostClassifier\n",
      "****Results****\n",
      "Accuracy: 79.8261%\n",
      "Log Loss: 0.673179268387368\n",
      "==============================\n",
      "GradientBoostingClassifier\n",
      "****Results****\n",
      "Accuracy: 91.3954%\n",
      "Log Loss: 0.31111735634487514\n",
      "==============================\n",
      "GaussianNB\n",
      "****Results****\n",
      "Accuracy: 57.7678%\n",
      "Log Loss: 2.120984212868764\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hcyen\\Anaconda3\\lib\\site-packages\\sklearn\\discriminant_analysis.py:388: UserWarning: Variables are collinear.\n",
      "  warnings.warn(\"Variables are collinear.\")\n",
      "C:\\Users\\hcyen\\Anaconda3\\lib\\site-packages\\sklearn\\discriminant_analysis.py:693: UserWarning: Variables are collinear\n",
      "  warnings.warn(\"Variables are collinear\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==============================\n",
      "LinearDiscriminantAnalysis\n",
      "****Results****\n",
      "Accuracy: 72.4074%\n",
      "Log Loss: 0.5490759157904654\n",
      "==============================\n",
      "QuadraticDiscriminantAnalysis\n",
      "****Results****\n",
      "Accuracy: 50.9224%\n",
      "Log Loss: 14.830397068643624\n",
      "==============================\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, log_loss\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC, LinearSVC, NuSVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "classifiers = [\n",
    "    MLPClassifier(\n",
    "    activation='tanh',\n",
    "    solver='lbfgs',\n",
    "    early_stopping=False,\n",
    "    hidden_layer_sizes=(40,10,10,10,10, 2),\n",
    "    random_state=1,\n",
    "    batch_size='auto',\n",
    "    max_iter=13000,\n",
    "    learning_rate_init=1e-5,\n",
    "    tol=1e-4,\n",
    "),\n",
    "    AdaBoostClassifier(),\n",
    "    GradientBoostingClassifier(),\n",
    "    GaussianNB(),\n",
    "    LinearDiscriminantAnalysis(),\n",
    "    QuadraticDiscriminantAnalysis()]\n",
    "\n",
    "# Logging for Visual Comparison\n",
    "log_cols=[\"Classifier\", \"Accuracy\", \"Log Loss\"]\n",
    "log = pd.DataFrame(columns=log_cols)\n",
    "\n",
    "for clf in classifiers:\n",
    "    clf.fit(X_train, Y_train)\n",
    "    name = clf.__class__.__name__\n",
    "    \n",
    "    print(\"=\"*30)\n",
    "    print(name)\n",
    "    \n",
    "    print('****Results****')\n",
    "    train_predictions = clf.predict(X_test)\n",
    "    acc = accuracy_score(Y_test, train_predictions)\n",
    "    print(\"Accuracy: {:.4%}\".format(acc))\n",
    "    \n",
    "    train_predictions = clf.predict_proba(X_test)\n",
    "    ll = log_loss(Y_test, train_predictions)\n",
    "    print(\"Log Loss: {}\".format(ll))\n",
    "    \n",
    "    log_entry = pd.DataFrame([[name, acc*100, ll]], columns=log_cols)\n",
    "    log = log.append(log_entry)\n",
    "    \n",
    "print(\"=\"*30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For comparision purpose, We have printed Bar Graph for Classifier Accuracy and Classifier Log Loss."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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"
    },
    {
     "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": [
    "sns.set_color_codes(\"muted\")\n",
    "sns.barplot(x='Accuracy', y='Classifier', data=log, color=\"b\")\n",
    "\n",
    "plt.xlabel('Accuracy %')\n",
    "plt.title('Classifier Accuracy')\n",
    "plt.show()\n",
    "\n",
    "sns.set_color_codes(\"muted\")\n",
    "sns.barplot(x='Log Loss', y='Classifier', data=log, color=\"g\")\n",
    "\n",
    "plt.xlabel('Log Loss')\n",
    "plt.title('Classifier Log Loss')\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}