Untitled1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set()\n",
    "sns.set_style('whitegrid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "recall=np.array([0.999184838,\n",
    "                0.999184838,\n",
    "                0.9991848379865498,\n",
    "                0.999184837986549])\n",
    "precision=np.array([0.990105008077544,\n",
    "                   0.984538152610442,\n",
    "                   0.9819747646705388,\n",
    "                   0.988508064516129])\n",
    "specificity=np.array([0.896842105263157,\n",
    "                     0.837894736842105,\n",
    "                     0.8105263157894737,\n",
    "                     0.88])\n",
    "accuracy=np.array([0.990152359717577,\n",
    "                  0.98494983277592,\n",
    "                  0.9743589743589743,\n",
    "                  0.988665923448532])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.DataFrame({'recall':recall,\n",
    "                'precision':precision,\n",
    "               'specificity':specificity,\n",
    "               'accuracy':accuracy})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.loc[:,['recall', 'precision', 'specificity', 'accuracy']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fedf1f41d68>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.boxplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "recall         0.999185\n",
       "precision      0.986281\n",
       "specificity    0.856316\n",
       "accuracy       0.984532\n",
       "dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "recall         7.765725e-12\n",
       "precision      3.704233e-03\n",
       "specificity    3.932487e-02\n",
       "accuracy       7.126074e-03\n",
       "dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}