Eksploratorna analiza podataka.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Eksploratorna analiza podataka"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Izvlačenje potrebnih podataka iz datoteke CTG.xls "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from matplotlib import cm as cm\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#imena stupaca u CTG.xls koje ćemo koristiti\n",
    "column_names = ['LB', 'AC', 'FM', 'UC', 'DL', 'DS', 'DP', 'ASTV', 'MSTV', 'ALTV', 'MLTV', 'Width', 'Min', 'Max',\n",
    "                   'Nmax', 'Nzeros', 'Mode', 'Mean', 'Median', 'Variance', 'Tendency', 'NSP']\n",
    "\n",
    "#stupci koji sadrže int vrijednosti, samo za ljepši ispis\n",
    "int_columns = ['LB' ,'ASTV' ,'ALTV' ,'Width' ,'Min' ,'Max' ,'Nmax' ,'Nzeros' ,'Mode' ,'Mean' ,'Median' ,'Variance',\n",
    "               'Tendency', 'NSP']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_excel('CTG.xls', sheet_name = 'Data', skiprows = 1, \n",
    "                     usecols = [10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 45]\n",
    "                     , names = column_names)\n",
    "\n",
    "#izbacujemo sve retke u kojima je barem jedna varijabla nepoznata (NaN)\n",
    "data = data.dropna()\n",
    "\n",
    "for col in int_columns:\n",
    "    data[col] = (data[col]).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Median</th>\n",
       "      <th>Variance</th>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>131</td>\n",
       "      <td>0.009077</td>\n",
       "      <td>0.222390</td>\n",
       "      <td>0.006051</td>\n",
       "      <td>0.001513</td>\n",
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       "      <td>0</td>\n",
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       "      <td>71</td>\n",
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       "      <td>2</td>\n",
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       "      <td>141</td>\n",
       "      <td>137</td>\n",
       "      <td>141</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>130</td>\n",
       "      <td>0.005838</td>\n",
       "      <td>0.407840</td>\n",
       "      <td>0.004170</td>\n",
       "      <td>0.005004</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000834</td>\n",
       "      <td>21</td>\n",
       "      <td>2.3</td>\n",
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       "      <td>...</td>\n",
       "      <td>67</td>\n",
       "      <td>174</td>\n",
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       "      <td>125</td>\n",
       "      <td>135</td>\n",
       "      <td>76</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>15 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     LB        AC        FM        UC        DL   DS        DP  ASTV  MSTV  \\\n",
       "0   120  0.000000  0.000000  0.000000  0.000000  0.0  0.000000    73   0.5   \n",
       "1   132  0.006380  0.000000  0.006380  0.003190  0.0  0.000000    17   2.1   \n",
       "2   133  0.003322  0.000000  0.008306  0.003322  0.0  0.000000    16   2.1   \n",
       "3   134  0.002561  0.000000  0.007682  0.002561  0.0  0.000000    16   2.4   \n",
       "4   132  0.006515  0.000000  0.008143  0.000000  0.0  0.000000    16   2.4   \n",
       "5   134  0.001049  0.000000  0.010493  0.009444  0.0  0.002099    26   5.9   \n",
       "6   134  0.001403  0.000000  0.012623  0.008415  0.0  0.002805    29   6.3   \n",
       "7   122  0.000000  0.000000  0.000000  0.000000  0.0  0.000000    83   0.5   \n",
       "8   122  0.000000  0.000000  0.001517  0.000000  0.0  0.000000    84   0.5   \n",
       "9   122  0.000000  0.000000  0.002967  0.000000  0.0  0.000000    86   0.3   \n",
       "10  151  0.000000  0.000000  0.000834  0.000834  0.0  0.000000    64   1.9   \n",
       "11  150  0.000000  0.000000  0.000983  0.000983  0.0  0.000000    64   2.0   \n",
       "12  131  0.005076  0.072335  0.007614  0.002538  0.0  0.000000    28   1.4   \n",
       "13  131  0.009077  0.222390  0.006051  0.001513  0.0  0.000000    28   1.5   \n",
       "14  130  0.005838  0.407840  0.004170  0.005004  0.0  0.000834    21   2.3   \n",
       "\n",
       "    ALTV ...   Min  Max  Nmax  Nzeros  Mode  Mean  Median  Variance  Tendency  \\\n",
       "0     43 ...    62  126     2       0   120   137     121        73         1   \n",
       "1      0 ...    68  198     6       1   141   136     140        12         0   \n",
       "2      0 ...    68  198     5       1   141   135     138        13         0   \n",
       "3      0 ...    53  170    11       0   137   134     137        13         1   \n",
       "4      0 ...    53  170     9       0   137   136     138        11         1   \n",
       "5      0 ...    50  200     5       3    76   107     107       170         0   \n",
       "6      0 ...    50  200     6       3    71   107     106       215         0   \n",
       "7      6 ...    62  130     0       0   122   122     123         3         1   \n",
       "8      5 ...    62  130     0       0   122   122     123         3         1   \n",
       "9      6 ...    62  130     1       0   122   122     123         1         1   \n",
       "10     9 ...    56  186     2       0   150   148     151         9         1   \n",
       "11     8 ...    56  186     5       0   150   148     151        10         1   \n",
       "12     0 ...    88  154     5       0   135   134     137         7         1   \n",
       "13     0 ...    71  158     2       0   141   137     141        10         1   \n",
       "14     0 ...    67  174     7       0   143   125     135        76         0   \n",
       "\n",
       "    NSP  \n",
       "0     2  \n",
       "1     1  \n",
       "2     1  \n",
       "3     1  \n",
       "4     1  \n",
       "5     3  \n",
       "6     3  \n",
       "7     3  \n",
       "8     3  \n",
       "9     3  \n",
       "10    2  \n",
       "11    2  \n",
       "12    1  \n",
       "13    1  \n",
       "14    1  \n",
       "\n",
       "[15 rows x 22 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(15)"
   ]
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   "metadata": {},
   "source": [
    "Prvih 21 stupaca označava vrijednosti obilježja CTG snimki. <br>\n",
    "Zadnji stupac označava u koju od 3 klase snimka pripada. <br>\n",
    "Oznake klasa su 1=Normal, 2=Suspect, 3=Pathologic.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Iz datoteke čitamo i opis svakog obilježja."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "    #T_277956c6_61e2_11e9_8910_3c528207a1f5 td {\n",
       "          text-align: center;\n",
       "    }    #T_277956c6_61e2_11e9_8910_3c528207a1f5 th {\n",
       "          text-align: center;\n",
       "    }</style>  \n",
       "<table id=\"T_277956c6_61e2_11e9_8910_3c528207a1f5\" > \n",
       "<thead>    <tr> \n",
       "        <th class=\"blank level0\" ></th> \n",
       "        <th class=\"col_heading level0 col0\" >Data</th> \n",
       "        <th class=\"col_heading level0 col1\" >Description</th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <th id=\"T_277956c6_61e2_11e9_8910_3c528207a1f5level0_row0\" class=\"row_heading level0 row0\" >0</th> \n",
       "        <td id=\"T_277956c6_61e2_11e9_8910_3c528207a1f5row0_col0\" class=\"data row0 col0\" >LB</td> \n",
       "        <td id=\"T_277956c6_61e2_11e9_8910_3c528207a1f5row0_col1\" class=\"data row0 col1\" >baseline value (SisPorto)</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_277956c6_61e2_11e9_8910_3c528207a1f5level0_row1\" class=\"row_heading level0 row1\" >1</th> \n",
       "        <td id=\"T_277956c6_61e2_11e9_8910_3c528207a1f5row1_col0\" class=\"data row1 col0\" >AC</td> \n",
       "        <td id=\"T_277956c6_61e2_11e9_8910_3c528207a1f5row1_col1\" class=\"data row1 col1\" >accelerations (SisPorto)</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_277956c6_61e2_11e9_8910_3c528207a1f5level0_row2\" class=\"row_heading level0 row2\" >2</th> \n",
       "        <td id=\"T_277956c6_61e2_11e9_8910_3c528207a1f5row2_col0\" class=\"data row2 col0\" >FM</td> \n",
       "        <td id=\"T_277956c6_61e2_11e9_8910_3c528207a1f5row2_col1\" class=\"data row2 col1\" >foetal movement (SisPorto)</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_277956c6_61e2_11e9_8910_3c528207a1f5level0_row3\" class=\"row_heading level0 row3\" >3</th> \n",
       "        <td id=\"T_277956c6_61e2_11e9_8910_3c528207a1f5row3_col0\" class=\"data row3 col0\" >UC</td> \n",
       "        <td id=\"T_277956c6_61e2_11e9_8910_3c528207a1f5row3_col1\" class=\"data row3 col1\" >uterine contractions (SisPorto)</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_277956c6_61e2_11e9_8910_3c528207a1f5level0_row4\" class=\"row_heading level0 row4\" >4</th> \n",
       "        <td id=\"T_277956c6_61e2_11e9_8910_3c528207a1f5row4_col0\" class=\"data row4 col0\" >ASTV</td> \n",
       "        <td id=\"T_277956c6_61e2_11e9_8910_3c528207a1f5row4_col1\" class=\"data row4 col1\" >percentage of time with abnormal short term variability  (SisPorto)</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_277956c6_61e2_11e9_8910_3c528207a1f5level0_row5\" class=\"row_heading level0 row5\" >5</th> \n",
       "        <td id=\"T_277956c6_61e2_11e9_8910_3c528207a1f5row5_col0\" class=\"data row5 col0\" >mSTV</td> \n",
       "        <td id=\"T_277956c6_61e2_11e9_8910_3c528207a1f5row5_col1\" class=\"data row5 col1\" >mean value of short term variability  (SisPorto)</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_277956c6_61e2_11e9_8910_3c528207a1f5level0_row6\" class=\"row_heading level0 row6\" >6</th> \n",
       "        <td id=\"T_277956c6_61e2_11e9_8910_3c528207a1f5row6_col0\" class=\"data row6 col0\" >ALTV</td> \n",
       "        <td id=\"T_277956c6_61e2_11e9_8910_3c528207a1f5row6_col1\" class=\"data row6 col1\" >percentage of time with abnormal long term variability  (SisPorto)</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_277956c6_61e2_11e9_8910_3c528207a1f5level0_row7\" class=\"row_heading level0 row7\" >7</th> \n",
       "        <td id=\"T_277956c6_61e2_11e9_8910_3c528207a1f5row7_col0\" class=\"data row7 col0\" >mLTV</td> \n",
       "        <td id=\"T_277956c6_61e2_11e9_8910_3c528207a1f5row7_col1\" class=\"data row7 col1\" >mean value of long term variability  (SisPorto)</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_277956c6_61e2_11e9_8910_3c528207a1f5level0_row8\" class=\"row_heading level0 row8\" >8</th> \n",
       "        <td id=\"T_277956c6_61e2_11e9_8910_3c528207a1f5row8_col0\" class=\"data row8 col0\" >DL</td> \n",
       "        <td id=\"T_277956c6_61e2_11e9_8910_3c528207a1f5row8_col1\" class=\"data row8 col1\" >light decelerations</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_277956c6_61e2_11e9_8910_3c528207a1f5level0_row9\" class=\"row_heading level0 row9\" >9</th> \n",
       "        <td id=\"T_277956c6_61e2_11e9_8910_3c528207a1f5row9_col0\" class=\"data row9 col0\" >DS</td> \n",
       "        <td id=\"T_277956c6_61e2_11e9_8910_3c528207a1f5row9_col1\" class=\"data row9 col1\" >severe decelerations</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_277956c6_61e2_11e9_8910_3c528207a1f5level0_row10\" class=\"row_heading level0 row10\" >10</th> \n",
       "        <td id=\"T_277956c6_61e2_11e9_8910_3c528207a1f5row10_col0\" class=\"data row10 col0\" >DP</td> \n",
       "        <td id=\"T_277956c6_61e2_11e9_8910_3c528207a1f5row10_col1\" class=\"data row10 col1\" >prolongued decelerations</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_277956c6_61e2_11e9_8910_3c528207a1f5level0_row11\" class=\"row_heading level0 row11\" >11</th> \n",
       "        <td id=\"T_277956c6_61e2_11e9_8910_3c528207a1f5row11_col0\" class=\"data row11 col0\" >DR</td> \n",
       "        <td id=\"T_277956c6_61e2_11e9_8910_3c528207a1f5row11_col1\" class=\"data row11 col1\" >repetitive decelerations</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_277956c6_61e2_11e9_8910_3c528207a1f5level0_row12\" class=\"row_heading level0 row12\" >12</th> \n",
       "        <td id=\"T_277956c6_61e2_11e9_8910_3c528207a1f5row12_col0\" class=\"data row12 col0\" >Width</td> \n",
       "        <td id=\"T_277956c6_61e2_11e9_8910_3c528207a1f5row12_col1\" class=\"data row12 col1\" >histogram width</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_277956c6_61e2_11e9_8910_3c528207a1f5level0_row13\" class=\"row_heading level0 row13\" >13</th> \n",
       "        <td id=\"T_277956c6_61e2_11e9_8910_3c528207a1f5row13_col0\" class=\"data row13 col0\" >Min</td> \n",
       "        <td id=\"T_277956c6_61e2_11e9_8910_3c528207a1f5row13_col1\" class=\"data row13 col1\" >low freq. of the histogram</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_277956c6_61e2_11e9_8910_3c528207a1f5level0_row14\" class=\"row_heading level0 row14\" >14</th> \n",
       "        <td id=\"T_277956c6_61e2_11e9_8910_3c528207a1f5row14_col0\" class=\"data row14 col0\" >Max</td> \n",
       "        <td id=\"T_277956c6_61e2_11e9_8910_3c528207a1f5row14_col1\" class=\"data row14 col1\" >high freq. of the histogram</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_277956c6_61e2_11e9_8910_3c528207a1f5level0_row15\" class=\"row_heading level0 row15\" >15</th> \n",
       "        <td id=\"T_277956c6_61e2_11e9_8910_3c528207a1f5row15_col0\" class=\"data row15 col0\" >Nmax</td> \n",
       "        <td id=\"T_277956c6_61e2_11e9_8910_3c528207a1f5row15_col1\" class=\"data row15 col1\" >number of histogram peaks</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_277956c6_61e2_11e9_8910_3c528207a1f5level0_row16\" class=\"row_heading level0 row16\" >16</th> \n",
       "        <td id=\"T_277956c6_61e2_11e9_8910_3c528207a1f5row16_col0\" class=\"data row16 col0\" >Nzeros</td> \n",
       "        <td id=\"T_277956c6_61e2_11e9_8910_3c528207a1f5row16_col1\" class=\"data row16 col1\" >number of histogram zeros</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_277956c6_61e2_11e9_8910_3c528207a1f5level0_row17\" class=\"row_heading level0 row17\" >17</th> \n",
       "        <td id=\"T_277956c6_61e2_11e9_8910_3c528207a1f5row17_col0\" class=\"data row17 col0\" >Mode</td> \n",
       "        <td id=\"T_277956c6_61e2_11e9_8910_3c528207a1f5row17_col1\" class=\"data row17 col1\" >histogram mode</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_277956c6_61e2_11e9_8910_3c528207a1f5level0_row18\" class=\"row_heading level0 row18\" >18</th> \n",
       "        <td id=\"T_277956c6_61e2_11e9_8910_3c528207a1f5row18_col0\" class=\"data row18 col0\" >Mean</td> \n",
       "        <td id=\"T_277956c6_61e2_11e9_8910_3c528207a1f5row18_col1\" class=\"data row18 col1\" >histogram mean</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_277956c6_61e2_11e9_8910_3c528207a1f5level0_row19\" class=\"row_heading level0 row19\" >19</th> \n",
       "        <td id=\"T_277956c6_61e2_11e9_8910_3c528207a1f5row19_col0\" class=\"data row19 col0\" >Median</td> \n",
       "        <td id=\"T_277956c6_61e2_11e9_8910_3c528207a1f5row19_col1\" class=\"data row19 col1\" >histogram median</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_277956c6_61e2_11e9_8910_3c528207a1f5level0_row20\" class=\"row_heading level0 row20\" >20</th> \n",
       "        <td id=\"T_277956c6_61e2_11e9_8910_3c528207a1f5row20_col0\" class=\"data row20 col0\" >Variance</td> \n",
       "        <td id=\"T_277956c6_61e2_11e9_8910_3c528207a1f5row20_col1\" class=\"data row20 col1\" >histogram variance</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_277956c6_61e2_11e9_8910_3c528207a1f5level0_row21\" class=\"row_heading level0 row21\" >21</th> \n",
       "        <td id=\"T_277956c6_61e2_11e9_8910_3c528207a1f5row21_col0\" class=\"data row21 col0\" >Tendency</td> \n",
       "        <td id=\"T_277956c6_61e2_11e9_8910_3c528207a1f5row21_col1\" class=\"data row21 col1\" >histogram tendency: -1=left assymetric; 0=symmetric; 1=right assymetric</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x17585f22eb8>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_description = pd.read_excel('CTG.xls', sheet_name = 'Description', skiprows = 11, nrows=22, header=None, \n",
    "                                 usecols=[2,3], names=['Data','Description'])\n",
    "pd.options.display.max_colwidth=100\n",
    "styles = [\n",
    "    dict(selector=\"td\", props=[(\"text-align\", \"center\")]), dict(selector=\"th\", props=[(\"text-align\", \"center\")])]\n",
    "\n",
    "data_description.style.set_table_styles(styles)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Analiza dobivenih podataka"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.1. Distribucija klasa NSP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": false
   },
   "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": [
    "counts = data['NSP'].value_counts()\n",
    "plt.figure(figsize=(10,5))\n",
    "sns.barplot(counts.index, counts.values, color = 'lightcoral')\n",
    "\n",
    "plt.xticks((0,1,2),('Normal', 'Suspect', 'Pathogenic'))\n",
    "plt.ylabel('Number of cardiotocograms')\n",
    "plt.title('NSP distribution', fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vidimo da je većina primjeraka u klasi Normal, a najmanje ih je u klasi Pathogenic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Udio normalnih kardiotokograma: 0.7784571966133584\n",
      "Udio sumnjivih kardiotokograma: 0.138758231420508\n",
      "Udio patoloških kardiotokograma: 0.08278457196613359\n"
     ]
    }
   ],
   "source": [
    "norm = cnt_srs[1]\n",
    "susp = cnt_srs[2]\n",
    "path = cnt_srs[3]\n",
    "print(\"Udio normalnih kardiotokograma:\",norm/data.shape[0])\n",
    "print(\"Udio sumnjivih kardiotokograma:\",susp/data.shape[0])\n",
    "print(\"Udio patoloških kardiotokograma:\",path/data.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Zaključujemo da ćemo rješavati nebalansirani klasifikacijski problem."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.2. Analiza pojedinih obilježja"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Koje vrijednosti mogu poprimati dana obilježja?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LB</th>\n",
       "      <td>106.0</td>\n",
       "      <td>160.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AC</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.019284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FM</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.480634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UC</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.014925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DL</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.015385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DS</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DP</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.005348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASTV</th>\n",
       "      <td>12.0</td>\n",
       "      <td>87.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSTV</th>\n",
       "      <td>0.2</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ALTV</th>\n",
       "      <td>0.0</td>\n",
       "      <td>91.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MLTV</th>\n",
       "      <td>0.0</td>\n",
       "      <td>50.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Width</th>\n",
       "      <td>3.0</td>\n",
       "      <td>180.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Min</th>\n",
       "      <td>50.0</td>\n",
       "      <td>159.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Max</th>\n",
       "      <td>122.0</td>\n",
       "      <td>238.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nmax</th>\n",
       "      <td>0.0</td>\n",
       "      <td>18.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nzeros</th>\n",
       "      <td>0.0</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mode</th>\n",
       "      <td>60.0</td>\n",
       "      <td>187.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean</th>\n",
       "      <td>73.0</td>\n",
       "      <td>182.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Median</th>\n",
       "      <td>77.0</td>\n",
       "      <td>186.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Variance</th>\n",
       "      <td>0.0</td>\n",
       "      <td>269.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tendency</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NSP</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            min         max\n",
       "LB        106.0  160.000000\n",
       "AC          0.0    0.019284\n",
       "FM          0.0    0.480634\n",
       "UC          0.0    0.014925\n",
       "DL          0.0    0.015385\n",
       "DS          0.0    0.001353\n",
       "DP          0.0    0.005348\n",
       "ASTV       12.0   87.000000\n",
       "MSTV        0.2    7.000000\n",
       "ALTV        0.0   91.000000\n",
       "MLTV        0.0   50.700000\n",
       "Width       3.0  180.000000\n",
       "Min        50.0  159.000000\n",
       "Max       122.0  238.000000\n",
       "Nmax        0.0   18.000000\n",
       "Nzeros      0.0   10.000000\n",
       "Mode       60.0  187.000000\n",
       "Mean       73.0  182.000000\n",
       "Median     77.0  186.000000\n",
       "Variance    0.0  269.000000\n",
       "Tendency   -1.0    1.000000\n",
       "NSP         1.0    3.000000"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "values = dict()\n",
    "for col in column_names:\n",
    "    values[col] = dict()\n",
    "    values[col]['max'] = data[col].max()\n",
    "    values[col]['min'] = data[col].min()\n",
    "\n",
    "df = pd.DataFrame(values).T\n",
    "df = df[['min','max']]\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Koliko različitih vrijednosti poprima pojedino obilježje?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'LB': 48,\n",
       " 'AC': 1015,\n",
       " 'FM': 696,\n",
       " 'UC': 1315,\n",
       " 'DL': 751,\n",
       " 'DS': 8,\n",
       " 'DP': 166,\n",
       " 'ASTV': 75,\n",
       " 'MSTV': 57,\n",
       " 'ALTV': 87,\n",
       " 'MLTV': 249,\n",
       " 'Width': 154,\n",
       " 'Min': 109,\n",
       " 'Max': 86,\n",
       " 'Nmax': 18,\n",
       " 'Nzeros': 9,\n",
       " 'Mode': 88,\n",
       " 'Mean': 103,\n",
       " 'Median': 95,\n",
       " 'Variance': 133,\n",
       " 'Tendency': 3,\n",
       " 'NSP': 3}"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_values = dict()\n",
    "for col in column_names:\n",
    "    unique_values[col] = (data[col].unique()).shape[0]\n",
    "unique_values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Crtamo neke od histogram obilježja, za svaku NSP klasu."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x2160 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "normal = data.loc[data['NSP'] == 1, : ]\n",
    "suspect = data.loc[data['NSP'] == 2, : ]\n",
    "pathologic = data.loc[data['NSP'] == 3, : ]\n",
    "\n",
    "fig, axes = plt.subplots(4, 2, figsize=(20, 30))\n",
    "ax = axes.ravel()\n",
    "\n",
    "indices = list()\n",
    "indices.append(column_names.index('LB'))\n",
    "indices.append(column_names.index('UC'))\n",
    "indices.append(column_names.index('ASTV'))\n",
    "indices.append(column_names.index('MLTV'))\n",
    "indices.append(column_names.index('Width'))\n",
    "indices.append(column_names.index('Min'))\n",
    "indices.append(column_names.index('Max'))\n",
    "indices.append(column_names.index('Median'))\n",
    "\n",
    "j = 0\n",
    "\n",
    "for i in indices:\n",
    "    _, bins = np.histogram(data.iloc[:,i], bins = 50)\n",
    "    ax[j].hist(normal.iloc[:,i], bins = bins, color = 'g', alpha = .5, label = 'normal')\n",
    "    ax[j].hist(suspect.iloc[:,i], bins = bins, color = 'b', alpha = .5, label = 'suspect')\n",
    "    ax[j].hist(pathologic.iloc[:,i], bins = bins, color = 'r', alpha = .5, label = 'pathologic')\n",
    "    ax[j].set_title(data.columns.values[i], fontdict = {'fontsize': 16})\n",
    "    ax[j].set_yticks(()) \n",
    "    ax[j].legend(loc = 'upper right', fontsize = 14)\n",
    "    j += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Na temelju dobivenih histograma ne možemo direktno zaključiti koja obilježja će biti značajnija od ostalih."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Na kraju grafički prikazujemo korelacije između obilježja pomoću matrice korelacije."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_corr = data.drop('NSP', axis = 1).corr()\n",
    "cmap = cm.get_cmap('RdYlGn', 30)\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize = (8, 8))\n",
    "iax = ax.imshow(X_corr, interpolation = \"nearest\", cmap=cmap)\n",
    "ax.grid(False)\n",
    "ax.set(title = 'Correlation map')\n",
    "ax.set_xticks(range(X_corr.shape[1]))\n",
    "ax.set_xticklabels(data.drop('NSP',axis = 1).columns.values, rotation = 90)\n",
    "ax.set_yticks(range(X_corr.shape[1]))\n",
    "ax.set_yticklabels(data.drop('NSP',axis = 1).columns.values)\n",
    "divider = make_axes_locatable(ax)\n",
    "cax = divider.append_axes(\"right\", size = \"5%\", pad = 0.05)\n",
    "cbar = plt.colorbar(iax,cax)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vidimo da mnoga obilježja međusobno nisu korelirana, ali postoje i obilježja između kojih postoji gotovo potpuna korelacija (Mean-Mode, Median-Mode, Median-Mean). Također, pojavljuje se i negativna korelacija (Min-Width, Nmax-Min)."
   ]
  }
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