prep.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Automatically created module for IPython interactive environment\n"
     ]
    }
   ],
   "source": [
    "print(__doc__)\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.datasets import load_breast_cancer\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def underline_key(phrase_u, phrase_nu):\n",
    "    sp0, sp1 = \"\\033[1;;4m\", \"\\033[1;;2m\" \n",
    "    text_u = sp0+phrase_u+sp1+\" \"+phrase_nu\n",
    "    print(text_u)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "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>id</th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>diagnosis_num</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>radius_worst</th>\n",
       "      <th>texture_worst</th>\n",
       "      <th>perimeter_worst</th>\n",
       "      <th>area_worst</th>\n",
       "      <th>smoothness_worst</th>\n",
       "      <th>compactness_worst</th>\n",
       "      <th>concavity_worst</th>\n",
       "      <th>concave_points_worst</th>\n",
       "      <th>symmetry_worst</th>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>842302</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>...</td>\n",
       "      <td>25.38</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>842517</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>...</td>\n",
       "      <td>24.99</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>84300903</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>...</td>\n",
       "      <td>23.57</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84348301</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>...</td>\n",
       "      <td>14.91</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84358402</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>...</td>\n",
       "      <td>22.54</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id diagnosis  diagnosis_num  radius_mean  texture_mean  \\\n",
       "0    842302         M              0        17.99         10.38   \n",
       "1    842517         M              0        20.57         17.77   \n",
       "2  84300903         M              0        19.69         21.25   \n",
       "3  84348301         M              0        11.42         20.38   \n",
       "4  84358402         M              0        20.29         14.34   \n",
       "\n",
       "   perimeter_mean  area_mean  smoothness_mean  compactness_mean  \\\n",
       "0          122.80     1001.0          0.11840           0.27760   \n",
       "1          132.90     1326.0          0.08474           0.07864   \n",
       "2          130.00     1203.0          0.10960           0.15990   \n",
       "3           77.58      386.1          0.14250           0.28390   \n",
       "4          135.10     1297.0          0.10030           0.13280   \n",
       "\n",
       "   concavity_mean  ...  radius_worst  texture_worst  perimeter_worst  \\\n",
       "0          0.3001  ...         25.38          17.33           184.60   \n",
       "1          0.0869  ...         24.99          23.41           158.80   \n",
       "2          0.1974  ...         23.57          25.53           152.50   \n",
       "3          0.2414  ...         14.91          26.50            98.87   \n",
       "4          0.1980  ...         22.54          16.67           152.20   \n",
       "\n",
       "   area_worst  smoothness_worst  compactness_worst  concavity_worst  \\\n",
       "0      2019.0            0.1622             0.6656           0.7119   \n",
       "1      1956.0            0.1238             0.1866           0.2416   \n",
       "2      1709.0            0.1444             0.4245           0.4504   \n",
       "3       567.7            0.2098             0.8663           0.6869   \n",
       "4      1575.0            0.1374             0.2050           0.4000   \n",
       "\n",
       "   concave_points_worst  symmetry_worst  fractal_dimension_worst  \n",
       "0                0.2654          0.4601                  0.11890  \n",
       "1                0.1860          0.2750                  0.08902  \n",
       "2                0.2430          0.3613                  0.08758  \n",
       "3                0.2575          0.6638                  0.17300  \n",
       "4                0.1625          0.2364                  0.07678  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_data = pd.read_csv(\"data/data-breast-cancer-wisconsin-QueryResult.csv\")\n",
    "df_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['id', 'diagnosis', 'diagnosis_num', 'radius_mean', 'texture_mean', 'perimeter_mean', 'area_mean', 'smoothness_mean', 'compactness_mean', 'concavity_mean', 'concave_points_mean', 'symmetry_mean', 'fractal_dimension_mean', 'radius_se', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se', 'compactness_se', 'concavity_se', 'concave_points_se', 'symmetry_se', 'fractal_dimension_se', 'radius_worst', 'texture_worst', 'perimeter_worst', 'area_worst', 'smoothness_worst', 'compactness_worst', 'concavity_worst', 'concave_points_worst', 'symmetry_worst', 'fractal_dimension_worst']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "id                           int64\n",
       "diagnosis                   object\n",
       "diagnosis_num                int64\n",
       "radius_mean                float64\n",
       "texture_mean               float64\n",
       "perimeter_mean             float64\n",
       "area_mean                  float64\n",
       "smoothness_mean            float64\n",
       "compactness_mean           float64\n",
       "concavity_mean             float64\n",
       "concave_points_mean        float64\n",
       "symmetry_mean              float64\n",
       "fractal_dimension_mean     float64\n",
       "radius_se                  float64\n",
       "texture_se                 float64\n",
       "perimeter_se               float64\n",
       "area_se                    float64\n",
       "smoothness_se              float64\n",
       "compactness_se             float64\n",
       "concavity_se               float64\n",
       "concave_points_se          float64\n",
       "symmetry_se                float64\n",
       "fractal_dimension_se       float64\n",
       "radius_worst               float64\n",
       "texture_worst              float64\n",
       "perimeter_worst            float64\n",
       "area_worst                 float64\n",
       "smoothness_worst           float64\n",
       "compactness_worst          float64\n",
       "concavity_worst            float64\n",
       "concave_points_worst       float64\n",
       "symmetry_worst             float64\n",
       "fractal_dimension_worst    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(list(df_data.columns.values))\n",
    "df_data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1;;4mX_train shape:\u001b[1;;2m (426, 30)\n",
      "\u001b[1;;4my_train shape:\u001b[1;;2m (426,)\n",
      "\u001b[1;;4mX_test shape:\u001b[1;;2m (143, 30)\n",
      "\u001b[1;;4my_test shape:\u001b[1;;2m (143,)\n"
     ]
    }
   ],
   "source": [
    "#MODIFY THE FOLLIWING TWO LINES FOR DIFFERENT DATA SET\n",
    "df_data_x = df_data.iloc[:,3:]\n",
    "df_data_y = df_data.iloc[:,2]\n",
    "# test/training x/y variables used for multiple parts of code\n",
    "X_train, X_test, y_train, y_test = train_test_split(df_data_x, df_data_y, random_state = 0)\n",
    "underline_key(\"X_train shape:\",str(X_train.shape))\n",
    "underline_key(\"y_train shape:\",str(y_train.shape))\n",
    "underline_key(\"X_test shape:\",str(X_test.shape))\n",
    "underline_key(\"y_test shape:\",str(y_test.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_names = list(df_data.columns.values[3:])\n",
    "class_names = list(df_data.diagnosis.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import pickle\n",
    "\n",
    "with open('saved_variables/df_data', 'wb') as open_pickle_file:\n",
    "    pickle.dump([df_data], open_pickle_file)\n",
    "    \n",
    "with open('saved_variables/X_y_train_test', 'wb') as open_pickle_file:\n",
    "    pickle.dump([X_train, X_test, y_train, y_test], open_pickle_file)\n",
    "    \n",
    "with open('saved_variables/df_X_y_data', 'wb') as open_pickle_file:\n",
    "    pickle.dump([df_data_x, df_data_y, df_data], open_pickle_file)\n",
    "    \n",
    "with open('saved_variables/feature_class_names', 'wb') as open_pickle_file:\n",
    "    pickle.dump([feature_names, class_names], open_pickle_file)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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 },
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}