Simple_Classification&Clustering_for_Breast_Cancer.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Wisconsin Breast Cancer Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load data\n",
    "ds = pd.read_csv('C:/Users/Bangga Rakana Adian/Desktop/Kuliah/python/breast-cancer.csv')\n",
    "y = ds['diagnosis'].replace(('M', 'B'), (0,1))\n",
    "x = ds.drop(['id', 'diagnosis', 'Unnamed: 32'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x:      radius_mean  texture_mean  perimeter_mean  area_mean  smoothness_mean  \\\n",
      "0         17.990         10.38          122.80     1001.0          0.11840   \n",
      "1         20.570         17.77          132.90     1326.0          0.08474   \n",
      "2         19.690         21.25          130.00     1203.0          0.10960   \n",
      "3         11.420         20.38           77.58      386.1          0.14250   \n",
      "4         20.290         14.34          135.10     1297.0          0.10030   \n",
      "5         12.450         15.70           82.57      477.1          0.12780   \n",
      "6         18.250         19.98          119.60     1040.0          0.09463   \n",
      "7         13.710         20.83           90.20      577.9          0.11890   \n",
      "8         13.000         21.82           87.50      519.8          0.12730   \n",
      "9         12.460         24.04           83.97      475.9          0.11860   \n",
      "10        16.020         23.24          102.70      797.8          0.08206   \n",
      "11        15.780         17.89          103.60      781.0          0.09710   \n",
      "12        19.170         24.80          132.40     1123.0          0.09740   \n",
      "13        15.850         23.95          103.70      782.7          0.08401   \n",
      "14        13.730         22.61           93.60      578.3          0.11310   \n",
      "15        14.540         27.54           96.73      658.8          0.11390   \n",
      "16        14.680         20.13           94.74      684.5          0.09867   \n",
      "17        16.130         20.68          108.10      798.8          0.11700   \n",
      "18        19.810         22.15          130.00     1260.0          0.09831   \n",
      "19        13.540         14.36           87.46      566.3          0.09779   \n",
      "20        13.080         15.71           85.63      520.0          0.10750   \n",
      "21         9.504         12.44           60.34      273.9          0.10240   \n",
      "22        15.340         14.26          102.50      704.4          0.10730   \n",
      "23        21.160         23.04          137.20     1404.0          0.09428   \n",
      "24        16.650         21.38          110.00      904.6          0.11210   \n",
      "25        17.140         16.40          116.00      912.7          0.11860   \n",
      "26        14.580         21.53           97.41      644.8          0.10540   \n",
      "27        18.610         20.25          122.10     1094.0          0.09440   \n",
      "28        15.300         25.27          102.40      732.4          0.10820   \n",
      "29        17.570         15.05          115.00      955.1          0.09847   \n",
      "..           ...           ...             ...        ...              ...   \n",
      "539        7.691         25.44           48.34      170.4          0.08668   \n",
      "540       11.540         14.44           74.65      402.9          0.09984   \n",
      "541       14.470         24.99           95.81      656.4          0.08837   \n",
      "542       14.740         25.42           94.70      668.6          0.08275   \n",
      "543       13.210         28.06           84.88      538.4          0.08671   \n",
      "544       13.870         20.70           89.77      584.8          0.09578   \n",
      "545       13.620         23.23           87.19      573.2          0.09246   \n",
      "546       10.320         16.35           65.31      324.9          0.09434   \n",
      "547       10.260         16.58           65.85      320.8          0.08877   \n",
      "548        9.683         19.34           61.05      285.7          0.08491   \n",
      "549       10.820         24.21           68.89      361.6          0.08192   \n",
      "550       10.860         21.48           68.51      360.5          0.07431   \n",
      "551       11.130         22.44           71.49      378.4          0.09566   \n",
      "552       12.770         29.43           81.35      507.9          0.08276   \n",
      "553        9.333         21.94           59.01      264.0          0.09240   \n",
      "554       12.880         28.92           82.50      514.3          0.08123   \n",
      "555       10.290         27.61           65.67      321.4          0.09030   \n",
      "556       10.160         19.59           64.73      311.7          0.10030   \n",
      "557        9.423         27.88           59.26      271.3          0.08123   \n",
      "558       14.590         22.68           96.39      657.1          0.08473   \n",
      "559       11.510         23.93           74.52      403.5          0.09261   \n",
      "560       14.050         27.15           91.38      600.4          0.09929   \n",
      "561       11.200         29.37           70.67      386.0          0.07449   \n",
      "562       15.220         30.62          103.40      716.9          0.10480   \n",
      "563       20.920         25.09          143.00     1347.0          0.10990   \n",
      "564       21.560         22.39          142.00     1479.0          0.11100   \n",
      "565       20.130         28.25          131.20     1261.0          0.09780   \n",
      "566       16.600         28.08          108.30      858.1          0.08455   \n",
      "567       20.600         29.33          140.10     1265.0          0.11780   \n",
      "568        7.760         24.54           47.92      181.0          0.05263   \n",
      "\n",
      "     compactness_mean  concavity_mean  concave points_mean  symmetry_mean  \\\n",
      "0             0.27760        0.300100             0.147100         0.2419   \n",
      "1             0.07864        0.086900             0.070170         0.1812   \n",
      "2             0.15990        0.197400             0.127900         0.2069   \n",
      "3             0.28390        0.241400             0.105200         0.2597   \n",
      "4             0.13280        0.198000             0.104300         0.1809   \n",
      "5             0.17000        0.157800             0.080890         0.2087   \n",
      "6             0.10900        0.112700             0.074000         0.1794   \n",
      "7             0.16450        0.093660             0.059850         0.2196   \n",
      "8             0.19320        0.185900             0.093530         0.2350   \n",
      "9             0.23960        0.227300             0.085430         0.2030   \n",
      "10            0.06669        0.032990             0.033230         0.1528   \n",
      "11            0.12920        0.099540             0.066060         0.1842   \n",
      "12            0.24580        0.206500             0.111800         0.2397   \n",
      "13            0.10020        0.099380             0.053640         0.1847   \n",
      "14            0.22930        0.212800             0.080250         0.2069   \n",
      "15            0.15950        0.163900             0.073640         0.2303   \n",
      "16            0.07200        0.073950             0.052590         0.1586   \n",
      "17            0.20220        0.172200             0.102800         0.2164   \n",
      "18            0.10270        0.147900             0.094980         0.1582   \n",
      "19            0.08129        0.066640             0.047810         0.1885   \n",
      "20            0.12700        0.045680             0.031100         0.1967   \n",
      "21            0.06492        0.029560             0.020760         0.1815   \n",
      "22            0.21350        0.207700             0.097560         0.2521   \n",
      "23            0.10220        0.109700             0.086320         0.1769   \n",
      "24            0.14570        0.152500             0.091700         0.1995   \n",
      "25            0.22760        0.222900             0.140100         0.3040   \n",
      "26            0.18680        0.142500             0.087830         0.2252   \n",
      "27            0.10660        0.149000             0.077310         0.1697   \n",
      "28            0.16970        0.168300             0.087510         0.1926   \n",
      "29            0.11570        0.098750             0.079530         0.1739   \n",
      "..                ...             ...                  ...            ...   \n",
      "539           0.11990        0.092520             0.013640         0.2037   \n",
      "540           0.11200        0.067370             0.025940         0.1818   \n",
      "541           0.12300        0.100900             0.038900         0.1872   \n",
      "542           0.07214        0.041050             0.030270         0.1840   \n",
      "543           0.06877        0.029870             0.032750         0.1628   \n",
      "544           0.10180        0.036880             0.023690         0.1620   \n",
      "545           0.06747        0.029740             0.024430         0.1664   \n",
      "546           0.04994        0.010120             0.005495         0.1885   \n",
      "547           0.08066        0.043580             0.024380         0.1669   \n",
      "548           0.05030        0.023370             0.009615         0.1580   \n",
      "549           0.06602        0.015480             0.008160         0.1976   \n",
      "550           0.04227        0.000000             0.000000         0.1661   \n",
      "551           0.08194        0.048240             0.022570         0.2030   \n",
      "552           0.04234        0.019970             0.014990         0.1539   \n",
      "553           0.05605        0.039960             0.012820         0.1692   \n",
      "554           0.05824        0.061950             0.023430         0.1566   \n",
      "555           0.07658        0.059990             0.027380         0.1593   \n",
      "556           0.07504        0.005025             0.011160         0.1791   \n",
      "557           0.04971        0.000000             0.000000         0.1742   \n",
      "558           0.13300        0.102900             0.037360         0.1454   \n",
      "559           0.10210        0.111200             0.041050         0.1388   \n",
      "560           0.11260        0.044620             0.043040         0.1537   \n",
      "561           0.03558        0.000000             0.000000         0.1060   \n",
      "562           0.20870        0.255000             0.094290         0.2128   \n",
      "563           0.22360        0.317400             0.147400         0.2149   \n",
      "564           0.11590        0.243900             0.138900         0.1726   \n",
      "565           0.10340        0.144000             0.097910         0.1752   \n",
      "566           0.10230        0.092510             0.053020         0.1590   \n",
      "567           0.27700        0.351400             0.152000         0.2397   \n",
      "568           0.04362        0.000000             0.000000         0.1587   \n",
      "\n",
      "     fractal_dimension_mean           ...             radius_worst  \\\n",
      "0                   0.07871           ...                   25.380   \n",
      "1                   0.05667           ...                   24.990   \n",
      "2                   0.05999           ...                   23.570   \n",
      "3                   0.09744           ...                   14.910   \n",
      "4                   0.05883           ...                   22.540   \n",
      "5                   0.07613           ...                   15.470   \n",
      "6                   0.05742           ...                   22.880   \n",
      "7                   0.07451           ...                   17.060   \n",
      "8                   0.07389           ...                   15.490   \n",
      "9                   0.08243           ...                   15.090   \n",
      "10                  0.05697           ...                   19.190   \n",
      "11                  0.06082           ...                   20.420   \n",
      "12                  0.07800           ...                   20.960   \n",
      "13                  0.05338           ...                   16.840   \n",
      "14                  0.07682           ...                   15.030   \n",
      "15                  0.07077           ...                   17.460   \n",
      "16                  0.05922           ...                   19.070   \n",
      "17                  0.07356           ...                   20.960   \n",
      "18                  0.05395           ...                   27.320   \n",
      "19                  0.05766           ...                   15.110   \n",
      "20                  0.06811           ...                   14.500   \n",
      "21                  0.06905           ...                   10.230   \n",
      "22                  0.07032           ...                   18.070   \n",
      "23                  0.05278           ...                   29.170   \n",
      "24                  0.06330           ...                   26.460   \n",
      "25                  0.07413           ...                   22.250   \n",
      "26                  0.06924           ...                   17.620   \n",
      "27                  0.05699           ...                   21.310   \n",
      "28                  0.06540           ...                   20.270   \n",
      "29                  0.06149           ...                   20.010   \n",
      "..                      ...           ...                      ...   \n",
      "539                 0.07751           ...                    8.678   \n",
      "540                 0.06782           ...                   12.260   \n",
      "541                 0.06341           ...                   16.220   \n",
      "542                 0.05680           ...                   16.510   \n",
      "543                 0.05781           ...                   14.370   \n",
      "544                 0.06688           ...                   15.050   \n",
      "545                 0.05801           ...                   15.350   \n",
      "546                 0.06201           ...                   11.250   \n",
      "547                 0.06714           ...                   10.830   \n",
      "548                 0.06235           ...                   10.930   \n",
      "549                 0.06328           ...                   13.030   \n",
      "550                 0.05948           ...                   11.660   \n",
      "551                 0.06552           ...                   12.020   \n",
      "552                 0.05637           ...                   13.870   \n",
      "553                 0.06576           ...                    9.845   \n",
      "554                 0.05708           ...                   13.890   \n",
      "555                 0.06127           ...                   10.840   \n",
      "556                 0.06331           ...                   10.650   \n",
      "557                 0.06059           ...                   10.490   \n",
      "558                 0.06147           ...                   15.480   \n",
      "559                 0.06570           ...                   12.480   \n",
      "560                 0.06171           ...                   15.300   \n",
      "561                 0.05502           ...                   11.920   \n",
      "562                 0.07152           ...                   17.520   \n",
      "563                 0.06879           ...                   24.290   \n",
      "564                 0.05623           ...                   25.450   \n",
      "565                 0.05533           ...                   23.690   \n",
      "566                 0.05648           ...                   18.980   \n",
      "567                 0.07016           ...                   25.740   \n",
      "568                 0.05884           ...                    9.456   \n",
      "\n",
      "     texture_worst  perimeter_worst  area_worst  smoothness_worst  \\\n",
      "0            17.33           184.60      2019.0           0.16220   \n",
      "1            23.41           158.80      1956.0           0.12380   \n",
      "2            25.53           152.50      1709.0           0.14440   \n",
      "3            26.50            98.87       567.7           0.20980   \n",
      "4            16.67           152.20      1575.0           0.13740   \n",
      "5            23.75           103.40       741.6           0.17910   \n",
      "6            27.66           153.20      1606.0           0.14420   \n",
      "7            28.14           110.60       897.0           0.16540   \n",
      "8            30.73           106.20       739.3           0.17030   \n",
      "9            40.68            97.65       711.4           0.18530   \n",
      "10           33.88           123.80      1150.0           0.11810   \n",
      "11           27.28           136.50      1299.0           0.13960   \n",
      "12           29.94           151.70      1332.0           0.10370   \n",
      "13           27.66           112.00       876.5           0.11310   \n",
      "14           32.01           108.80       697.7           0.16510   \n",
      "15           37.13           124.10       943.2           0.16780   \n",
      "16           30.88           123.40      1138.0           0.14640   \n",
      "17           31.48           136.80      1315.0           0.17890   \n",
      "18           30.88           186.80      2398.0           0.15120   \n",
      "19           19.26            99.70       711.2           0.14400   \n",
      "20           20.49            96.09       630.5           0.13120   \n",
      "21           15.66            65.13       314.9           0.13240   \n",
      "22           19.08           125.10       980.9           0.13900   \n",
      "23           35.59           188.00      2615.0           0.14010   \n",
      "24           31.56           177.00      2215.0           0.18050   \n",
      "25           21.40           152.40      1461.0           0.15450   \n",
      "26           33.21           122.40       896.9           0.15250   \n",
      "27           27.26           139.90      1403.0           0.13380   \n",
      "28           36.71           149.30      1269.0           0.16410   \n",
      "29           19.52           134.90      1227.0           0.12550   \n",
      "..             ...              ...         ...               ...   \n",
      "539          31.89            54.49       223.6           0.15960   \n",
      "540          19.68            78.78       457.8           0.13450   \n",
      "541          31.73           113.50       808.9           0.13400   \n",
      "542          32.29           107.40       826.4           0.10600   \n",
      "543          37.17            92.48       629.6           0.10720   \n",
      "544          24.75            99.17       688.6           0.12640   \n",
      "545          29.09            97.58       729.8           0.12160   \n",
      "546          21.77            71.12       384.9           0.12850   \n",
      "547          22.04            71.08       357.4           0.14610   \n",
      "548          25.59            69.10       364.2           0.11990   \n",
      "549          31.45            83.90       505.6           0.12040   \n",
      "550          24.77            74.08       412.3           0.10010   \n",
      "551          28.26            77.80       436.6           0.10870   \n",
      "552          36.00            88.10       594.7           0.12340   \n",
      "553          25.05            62.86       295.8           0.11030   \n",
      "554          35.74            88.84       595.7           0.12270   \n",
      "555          34.91            69.57       357.6           0.13840   \n",
      "556          22.88            67.88       347.3           0.12650   \n",
      "557          34.24            66.50       330.6           0.10730   \n",
      "558          27.27           105.90       733.5           0.10260   \n",
      "559          37.16            82.28       474.2           0.12980   \n",
      "560          33.17           100.20       706.7           0.12410   \n",
      "561          38.30            75.19       439.6           0.09267   \n",
      "562          42.79           128.70       915.0           0.14170   \n",
      "563          29.41           179.10      1819.0           0.14070   \n",
      "564          26.40           166.10      2027.0           0.14100   \n",
      "565          38.25           155.00      1731.0           0.11660   \n",
      "566          34.12           126.70      1124.0           0.11390   \n",
      "567          39.42           184.60      1821.0           0.16500   \n",
      "568          30.37            59.16       268.6           0.08996   \n",
      "\n",
      "     compactness_worst  concavity_worst  concave points_worst  symmetry_worst  \\\n",
      "0              0.66560          0.71190               0.26540          0.4601   \n",
      "1              0.18660          0.24160               0.18600          0.2750   \n",
      "2              0.42450          0.45040               0.24300          0.3613   \n",
      "3              0.86630          0.68690               0.25750          0.6638   \n",
      "4              0.20500          0.40000               0.16250          0.2364   \n",
      "5              0.52490          0.53550               0.17410          0.3985   \n",
      "6              0.25760          0.37840               0.19320          0.3063   \n",
      "7              0.36820          0.26780               0.15560          0.3196   \n",
      "8              0.54010          0.53900               0.20600          0.4378   \n",
      "9              1.05800          1.10500               0.22100          0.4366   \n",
      "10             0.15510          0.14590               0.09975          0.2948   \n",
      "11             0.56090          0.39650               0.18100          0.3792   \n",
      "12             0.39030          0.36390               0.17670          0.3176   \n",
      "13             0.19240          0.23220               0.11190          0.2809   \n",
      "14             0.77250          0.69430               0.22080          0.3596   \n",
      "15             0.65770          0.70260               0.17120          0.4218   \n",
      "16             0.18710          0.29140               0.16090          0.3029   \n",
      "17             0.42330          0.47840               0.20730          0.3706   \n",
      "18             0.31500          0.53720               0.23880          0.2768   \n",
      "19             0.17730          0.23900               0.12880          0.2977   \n",
      "20             0.27760          0.18900               0.07283          0.3184   \n",
      "21             0.11480          0.08867               0.06227          0.2450   \n",
      "22             0.59540          0.63050               0.23930          0.4667   \n",
      "23             0.26000          0.31550               0.20090          0.2822   \n",
      "24             0.35780          0.46950               0.20950          0.3613   \n",
      "25             0.39490          0.38530               0.25500          0.4066   \n",
      "26             0.66430          0.55390               0.27010          0.4264   \n",
      "27             0.21170          0.34460               0.14900          0.2341   \n",
      "28             0.61100          0.63350               0.20240          0.4027   \n",
      "29             0.28120          0.24890               0.14560          0.2756   \n",
      "..                 ...              ...                   ...             ...   \n",
      "539            0.30640          0.33930               0.05000          0.2790   \n",
      "540            0.21180          0.17970               0.06918          0.2329   \n",
      "541            0.42020          0.40400               0.12050          0.3187   \n",
      "542            0.13760          0.16110               0.10950          0.2722   \n",
      "543            0.13810          0.10620               0.07958          0.2473   \n",
      "544            0.20370          0.13770               0.06845          0.2249   \n",
      "545            0.15170          0.10490               0.07174          0.2642   \n",
      "546            0.08842          0.04384               0.02381          0.2681   \n",
      "547            0.22460          0.17830               0.08333          0.2691   \n",
      "548            0.09546          0.09350               0.03846          0.2552   \n",
      "549            0.16330          0.06194               0.03264          0.3059   \n",
      "550            0.07348          0.00000               0.00000          0.2458   \n",
      "551            0.17820          0.15640               0.06413          0.3169   \n",
      "552            0.10640          0.08653               0.06498          0.2407   \n",
      "553            0.08298          0.07993               0.02564          0.2435   \n",
      "554            0.16200          0.24390               0.06493          0.2372   \n",
      "555            0.17100          0.20000               0.09127          0.2226   \n",
      "556            0.12000          0.01005               0.02232          0.2262   \n",
      "557            0.07158          0.00000               0.00000          0.2475   \n",
      "558            0.31710          0.36620               0.11050          0.2258   \n",
      "559            0.25170          0.36300               0.09653          0.2112   \n",
      "560            0.22640          0.13260               0.10480          0.2250   \n",
      "561            0.05494          0.00000               0.00000          0.1566   \n",
      "562            0.79170          1.17000               0.23560          0.4089   \n",
      "563            0.41860          0.65990               0.25420          0.2929   \n",
      "564            0.21130          0.41070               0.22160          0.2060   \n",
      "565            0.19220          0.32150               0.16280          0.2572   \n",
      "566            0.30940          0.34030               0.14180          0.2218   \n",
      "567            0.86810          0.93870               0.26500          0.4087   \n",
      "568            0.06444          0.00000               0.00000          0.2871   \n",
      "\n",
      "     fractal_dimension_worst  \n",
      "0                    0.11890  \n",
      "1                    0.08902  \n",
      "2                    0.08758  \n",
      "3                    0.17300  \n",
      "4                    0.07678  \n",
      "5                    0.12440  \n",
      "6                    0.08368  \n",
      "7                    0.11510  \n",
      "8                    0.10720  \n",
      "9                    0.20750  \n",
      "10                   0.08452  \n",
      "11                   0.10480  \n",
      "12                   0.10230  \n",
      "13                   0.06287  \n",
      "14                   0.14310  \n",
      "15                   0.13410  \n",
      "16                   0.08216  \n",
      "17                   0.11420  \n",
      "18                   0.07615  \n",
      "19                   0.07259  \n",
      "20                   0.08183  \n",
      "21                   0.07773  \n",
      "22                   0.09946  \n",
      "23                   0.07526  \n",
      "24                   0.09564  \n",
      "25                   0.10590  \n",
      "26                   0.12750  \n",
      "27                   0.07421  \n",
      "28                   0.09876  \n",
      "29                   0.07919  \n",
      "..                       ...  \n",
      "539                  0.10660  \n",
      "540                  0.08134  \n",
      "541                  0.10230  \n",
      "542                  0.06956  \n",
      "543                  0.06443  \n",
      "544                  0.08492  \n",
      "545                  0.06953  \n",
      "546                  0.07399  \n",
      "547                  0.09479  \n",
      "548                  0.07920  \n",
      "549                  0.07626  \n",
      "550                  0.06592  \n",
      "551                  0.08032  \n",
      "552                  0.06484  \n",
      "553                  0.07393  \n",
      "554                  0.07242  \n",
      "555                  0.08283  \n",
      "556                  0.06742  \n",
      "557                  0.06969  \n",
      "558                  0.08004  \n",
      "559                  0.08732  \n",
      "560                  0.08321  \n",
      "561                  0.05905  \n",
      "562                  0.14090  \n",
      "563                  0.09873  \n",
      "564                  0.07115  \n",
      "565                  0.06637  \n",
      "566                  0.07820  \n",
      "567                  0.12400  \n",
      "568                  0.07039  \n",
      "\n",
      "[569 rows x 30 columns] \n",
      "\n",
      " y: 0      0\n",
      "1      0\n",
      "2      0\n",
      "3      0\n",
      "4      0\n",
      "5      0\n",
      "6      0\n",
      "7      0\n",
      "8      0\n",
      "9      0\n",
      "10     0\n",
      "11     0\n",
      "12     0\n",
      "13     0\n",
      "14     0\n",
      "15     0\n",
      "16     0\n",
      "17     0\n",
      "18     0\n",
      "19     1\n",
      "20     1\n",
      "21     1\n",
      "22     0\n",
      "23     0\n",
      "24     0\n",
      "25     0\n",
      "26     0\n",
      "27     0\n",
      "28     0\n",
      "29     0\n",
      "      ..\n",
      "539    1\n",
      "540    1\n",
      "541    1\n",
      "542    1\n",
      "543    1\n",
      "544    1\n",
      "545    1\n",
      "546    1\n",
      "547    1\n",
      "548    1\n",
      "549    1\n",
      "550    1\n",
      "551    1\n",
      "552    1\n",
      "553    1\n",
      "554    1\n",
      "555    1\n",
      "556    1\n",
      "557    1\n",
      "558    1\n",
      "559    1\n",
      "560    1\n",
      "561    1\n",
      "562    0\n",
      "563    0\n",
      "564    0\n",
      "565    0\n",
      "566    0\n",
      "567    0\n",
      "568    1\n",
      "Name: diagnosis, Length: 569, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print('x: {} \\n\\n y: {}'.format(x,y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Partition based Clustering Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 1 0]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "\n",
    "km = KMeans(n_clusters=2, random_state=10)\n",
    "km.fit(x)\n",
    "\n",
    "labels = km.labels_\n",
    "centers = km.cluster_centers_\n",
    "print(labels[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca = PCA(n_components=2)\n",
    "bc_pca = pca.fit_transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2949efa8b38>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 6))\n",
    "fig.suptitle('Visualizations of breast cancer clusters')\n",
    "fig.subplots_adjust(top=0.85, wspace=0.5)\n",
    "ax1.set_title('Actual Labels')\n",
    "ax2.set_title('Clustered Labels')\n",
    "\n",
    "for i in range(len(y)):\n",
    "    if y[i] == 0:\n",
    "        c1 = ax1.scatter(bc_pca[i,0], bc_pca[i,1],c='g', marker='.')\n",
    "    if y[i] == 1:\n",
    "        c2 = ax1.scatter(bc_pca[i,0], bc_pca[i,1],c='r', marker='.')\n",
    "        \n",
    "    if labels[i] == 0:\n",
    "        c3 = ax2.scatter(bc_pca[i,0], bc_pca[i,1],c='g', marker='.')\n",
    "    if labels[i] == 1:\n",
    "        c4 = ax2.scatter(bc_pca[i,0], bc_pca[i,1],c='r', marker='.')\n",
    "\n",
    "l1 = ax1.legend([c1, c2], ['0', '1'])\n",
    "l2 = ax2.legend([c3, c4], ['0', '1'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hierarchical Clustering Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  287.           336.             3.81596727     2.        ]\n",
      " [  106.           420.             4.11664267     2.        ]\n",
      " [   55.           251.             4.93361024     2.        ]\n",
      " ...\n",
      " [ 1130.          1132.          6196.07482529    86.        ]\n",
      " [ 1131.          1133.          8368.99225244   483.        ]\n",
      " [ 1134.          1135.         18371.10293626   569.        ]]\n"
     ]
    }
   ],
   "source": [
    "from scipy.cluster.hierarchy import dendrogram, linkage\n",
    "import numpy as np\n",
    "np.set_printoptions(suppress=True)\n",
    "\n",
    "Z = linkage(x, 'ward')\n",
    "print(Z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2949cec51d0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 3))\n",
    "plt.title('Hierarchical Clustering Dendrogram')\n",
    "plt.xlabel('Data point')\n",
    "plt.ylabel('Distance')\n",
    "dendrogram(Z)\n",
    "plt.axhline(y=10000, c='k', ls='--', lw=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.cluster.hierarchy import fcluster\n",
    "\n",
    "max_dist = 10000\n",
    "hc_labels = fcluster(Z, max_dist, criterion='distance')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2949cf657f0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4))\n",
    "fig.suptitle('Visualizing breast cancer clusters')\n",
    "fig.subplots_adjust(top=0.85, wspace=0.5)\n",
    "ax1.set_title('Actual Labels')\n",
    "ax2.set_title('Hierarchical Clustered Labels')\n",
    "\n",
    "for i in range(len(y)):\n",
    "    if y[i] == 0:\n",
    "        c1 = ax1.scatter(bc_pca[i,0], bc_pca[i,1],c='g', marker='.')\n",
    "    if y[i] == 1:\n",
    "        c2 = ax1.scatter(bc_pca[i,0], bc_pca[i,1],c='r', marker='.')\n",
    "        \n",
    "    if hc_labels[i] == 1:\n",
    "        c3 = ax2.scatter(bc_pca[i,0], bc_pca[i,1],c='g', marker='.')\n",
    "    if hc_labels[i] == 2:\n",
    "        c4 = ax2.scatter(bc_pca[i,0], bc_pca[i,1],c='r', marker='.')\n",
    "\n",
    "l1 = ax1.legend([c1, c2], ['0', '1'])\n",
    "l2 = ax2.legend([c3, c4], ['1', '2'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classification Model Evaluation Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(398, 30) (171,)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=42)\n",
    "print(X_train.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Bangga Rakana Adian\\Anaconda3\\envs\\mentoring_rm_3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
       "          n_jobs=None, penalty='l2', random_state=None, solver='warn',\n",
       "          tol=0.0001, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import linear_model\n",
    "\n",
    "logistic = linear_model.LogisticRegression()\n",
    "logistic.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 59,   4],\n",
       "       [  2, 106]], dtype=int64)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import model_evaluation_utils as meu\n",
    "import sklearn.metrics as metrics\n",
    "y_pred = logistic.predict(X_test)\n",
    "metrics.confusion_matrix(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## True Positive, False Positive, True Negative and False Negative"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "positive_class = 1\n",
    "TP = 106\n",
    "FP = 4\n",
    "TN = 59\n",
    "FN = 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Framework Accuracy: 0.96491\n"
     ]
    }
   ],
   "source": [
    "fw_acc = round(metrics.accuracy_score(y_test,y_pred),5)\n",
    "print('Framework Accuracy:', fw_acc)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Framework Precision: 0.96364\n"
     ]
    }
   ],
   "source": [
    "fw_prec = round(metrics.precision_score(y_test, y_pred), 5)\n",
    "print('Framework Precision:', fw_prec)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Framework Recall: 0.98148\n"
     ]
    }
   ],
   "source": [
    "fw_rec = round(metrics.recall_score(y_test, y_pred), 5)\n",
    "print('Framework Recall:', fw_rec)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## F1-Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Framework F1-Score: 0.97248\n"
     ]
    }
   ],
   "source": [
    "fw_f1 = round(metrics.f1_score(y_test,y_pred), 5)\n",
    "print('Framework F1-Score:', fw_f1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ROC Curve and AUC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2949d0b9f60>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "meu.plot_model_roc_curve(clf=logistic, features=X_test, true_labels=y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Clustering Model Evaluation Metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build two clustering models on the breast cancer dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "km2 = KMeans(n_clusters=2, random_state=42).fit(x)\n",
    "km2_labels = km2.labels_\n",
    "\n",
    "km5 = KMeans(n_clusters=5, random_state=42).fit(x)\n",
    "km5_labels = km5.labels_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Silhouette Coefficient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Silhouette Coefficient for num clusters=2:  0.6972646156059465\n",
      "Silhouette Coefficient for num clusters=5:  0.510229299790784\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "\n",
    "km2_silc = metrics.silhouette_score(x, km2_labels, metric='euclidean')\n",
    "km5_silc = metrics.silhouette_score(x, km5_labels, metric='euclidean')\n",
    "\n",
    "print('Silhouette Coefficient for num clusters=2: ', km2_silc)\n",
    "print('Silhouette Coefficient for num clusters=5: ', km5_silc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model tuning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build and Evaluate Default Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Default Model Stats:\n",
      "Model Performance metrics:\n",
      "------------------------------\n",
      "Accuracy: 0.6316\n",
      "Precision: 0.3989\n",
      "Recall: 0.6316\n",
      "F1 Score: 0.489\n",
      "\n",
      "Model Classification report:\n",
      "------------------------------\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.00      0.00      0.00        63\n",
      "           1       0.63      1.00      0.77       108\n",
      "\n",
      "   micro avg       0.63      0.63      0.63       171\n",
      "   macro avg       0.32      0.50      0.39       171\n",
      "weighted avg       0.40      0.63      0.49       171\n",
      "\n",
      "\n",
      "Prediction Confusion Matrix:\n",
      "------------------------------\n",
      "          Predicted:     \n",
      "                   0    1\n",
      "Actual: 0          0   63\n",
      "        1          0  108\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Bangga Rakana Adian\\Anaconda3\\envs\\mentoring_rm_3\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "C:\\Users\\Bangga Rakana Adian\\Anaconda3\\envs\\mentoring_rm_3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\Bangga Rakana Adian\\Anaconda3\\envs\\mentoring_rm_3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\Bangga Rakana Adian\\Anaconda3\\envs\\mentoring_rm_3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "# prepare datasets\n",
    "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=42)\n",
    "\n",
    "# build default SVM model\n",
    "def_svc = SVC(random_state=42)\n",
    "def_svc.fit(X_train, y_train)\n",
    "\n",
    "# predict and evaluate performance\n",
    "def_y_pred = def_svc.predict(X_test)\n",
    "print('Default Model Stats:')\n",
    "meu.display_model_performance_metrics(true_labels=y_test, predicted_labels=def_y_pred, classes=[0,1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tune Model with Grid Search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# Tuning hyper-parameters for accuracy\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Bangga Rakana Adian\\Anaconda3\\envs\\mentoring_rm_3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grid scores for all the models based on CV:\n",
      "\n",
      "0.95226 (+/-0.06310) for {'C': 1, 'kernel': 'linear', 'gamma': 0.001}\n",
      "0.91206 (+/-0.04540) for {'C': 1, 'kernel': 'rbf', 'gamma': 0.001}\n",
      "0.95226 (+/-0.06310) for {'C': 1, 'kernel': 'linear', 'gamma': 0.0001}\n",
      "0.92462 (+/-0.02338) for {'C': 1, 'kernel': 'rbf', 'gamma': 0.0001}\n",
      "0.96231 (+/-0.04297) for {'C': 10, 'kernel': 'linear', 'gamma': 0.001}\n",
      "0.90201 (+/-0.04734) for {'C': 10, 'kernel': 'rbf', 'gamma': 0.001}\n",
      "0.96231 (+/-0.04297) for {'C': 10, 'kernel': 'linear', 'gamma': 0.0001}\n",
      "0.92965 (+/-0.03425) for {'C': 10, 'kernel': 'rbf', 'gamma': 0.0001}\n",
      "0.95729 (+/-0.05989) for {'C': 50, 'kernel': 'linear', 'gamma': 0.001}\n",
      "0.90201 (+/-0.04734) for {'C': 50, 'kernel': 'rbf', 'gamma': 0.001}\n",
      "0.95729 (+/-0.05989) for {'C': 50, 'kernel': 'linear', 'gamma': 0.0001}\n",
      "0.93467 (+/-0.02975) for {'C': 50, 'kernel': 'rbf', 'gamma': 0.0001}\n",
      "0.95477 (+/-0.05772) for {'C': 100, 'kernel': 'linear', 'gamma': 0.001}\n",
      "0.90201 (+/-0.04734) for {'C': 100, 'kernel': 'rbf', 'gamma': 0.001}\n",
      "0.95477 (+/-0.05772) for {'C': 100, 'kernel': 'linear', 'gamma': 0.0001}\n",
      "0.93216 (+/-0.04674) for {'C': 100, 'kernel': 'rbf', 'gamma': 0.0001}\n",
      "\n",
      "Best parameters set found on development set: {'C': 10, 'kernel': 'linear', 'gamma': 0.001}\n",
      "Best model validation accuracy: 0.9623115577889447\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# setting the parameter grid\n",
    "grid_parameters = {'kernel': ['linear', 'rbf'], \n",
    "                   'gamma': [1e-3, 1e-4],\n",
    "                   'C': [1, 10, 50, 100]}\n",
    "\n",
    "# perform hyperparameter tuning\n",
    "print(\"# Tuning hyper-parameters for accuracy\\n\")\n",
    "clf = GridSearchCV(SVC(random_state=42), grid_parameters, cv=5, scoring='accuracy')\n",
    "clf.fit(X_train, y_train)\n",
    "# view accuracy scores for all the models\n",
    "print(\"Grid scores for all the models based on CV:\\n\")\n",
    "means = clf.cv_results_['mean_test_score']\n",
    "stds = clf.cv_results_['std_test_score']\n",
    "for mean, std, params in zip(means, stds, clf.cv_results_['params']):\n",
    "    print(\"%0.5f (+/-%0.05f) for %r\" % (mean, std * 2, params))\n",
    "# check out best model performance\n",
    "print(\"\\nBest parameters set found on development set:\", clf.best_params_)\n",
    "print(\"Best model validation accuracy:\", clf.best_score_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate Grid Search Tuned Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Tuned Model Stats:\n",
      "Model Performance metrics:\n",
      "------------------------------\n",
      "Accuracy: 0.9708\n",
      "Precision: 0.9709\n",
      "Recall: 0.9708\n",
      "F1 Score: 0.9708\n",
      "\n",
      "Model Classification report:\n",
      "------------------------------\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.97      0.96        63\n",
      "           1       0.98      0.97      0.98       108\n",
      "\n",
      "   micro avg       0.97      0.97      0.97       171\n",
      "   macro avg       0.97      0.97      0.97       171\n",
      "weighted avg       0.97      0.97      0.97       171\n",
      "\n",
      "\n",
      "Prediction Confusion Matrix:\n",
      "------------------------------\n",
      "          Predicted:     \n",
      "                   0    1\n",
      "Actual: 0         61    2\n",
      "        1          3  105\n"
     ]
    }
   ],
   "source": [
    "gs_best = clf.best_estimator_\n",
    "tuned_y_pred = gs_best.predict(X_test)\n",
    "\n",
    "print('\\n\\nTuned Model Stats:')\n",
    "meu.display_model_performance_metrics(true_labels=y_test, predicted_labels=tuned_y_pred, classes=[0,1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tune Model with Randomized Search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Bangga Rakana Adian\\Anaconda3\\envs\\mentoring_rm_3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters set found on development set:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'C': 16.383075364125, 'gamma': 0.035715835304634416, 'kernel': 'linear'}"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import scipy\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "param_grid = {'C': scipy.stats.expon(scale=10), \n",
    "              'gamma': scipy.stats.expon(scale=.1),\n",
    "              'kernel': ['rbf', 'linear']}\n",
    "#C: penalty parameter of error term\n",
    "#gamma:\n",
    "random_search = RandomizedSearchCV(SVC(random_state=42), param_distributions=param_grid,\n",
    "                                   n_iter=50, cv=5)\n",
    "random_search.fit(X_train, y_train)\n",
    "\n",
    "print(\"Best parameters set found on development set:\")\n",
    "random_search.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Deployment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Persist model to disk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['lr_model.pkl']"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.externals import joblib\n",
    "joblib.dump(logistic, 'lr_model.pkl') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load model from disk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
       "          n_jobs=None, penalty='l2', random_state=None, solver='warn',\n",
       "          tol=0.0001, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = joblib.load('lr_model.pkl') \n",
    "lr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predict with loaded model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 0 0 1 1 0 0 0 1 1 1]\n"
     ]
    }
   ],
   "source": [
    "print(lr.predict(X_test[0:11]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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