blood_donation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Cell': {'cm_config': {'lineNumbers': False}},\n",
       " 'CodeCell': {'cm_config': {'autoCloseBrackets': False}},\n",
       " 'Notebook': {'Header': False, 'Toolbar': False}}"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from notebook.services.config import ConfigManager\n",
    "c = ConfigManager()\n",
    "c.update('notebook', {\"CodeCell\": {\"cm_config\": {\"autoCloseBrackets\": False}}})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# For rotatable 3D plot\n",
    "#%matplotlib notebook\n",
    "\n",
    "# For regular plot\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "\n",
    "from scipy.stats import boxcox\n",
    "\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler, PolynomialFeatures, RobustScaler\n",
    "from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression, SGDClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "from xgboost import XGBClassifier\n",
    "\n",
    "from sklearn.metrics import roc_curve, log_loss\n",
    "\n",
    "from keras.callbacks import EarlyStopping \n",
    "from keras.layers import Dense\n",
    "from keras.models import Sequential\n",
    "from keras.optimizers import Adam, SGD"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read raw data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {
    "scrolled": true
   },
   "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>MsL</th>\n",
       "      <th>NoD</th>\n",
       "      <th>MsF</th>\n",
       "      <th>Made</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>619</th>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>98</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>664</th>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>441</th>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>77</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>736</th>\n",
       "      <td>5</td>\n",
       "      <td>46</td>\n",
       "      <td>98</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>436</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>460</th>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>356</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>48</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>638</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>463</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372</th>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>539</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>734</th>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>573</th>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>482</th>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>330</th>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>606</th>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>577</th>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>21</td>\n",
       "      <td>6</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>532</th>\n",
       "      <td>23</td>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>23</td>\n",
       "      <td>6</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>715</th>\n",
       "      <td>16</td>\n",
       "      <td>5</td>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21</td>\n",
       "      <td>2</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>387</th>\n",
       "      <td>21</td>\n",
       "      <td>3</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>738</th>\n",
       "      <td>23</td>\n",
       "      <td>8</td>\n",
       "      <td>69</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>21</td>\n",
       "      <td>3</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>450</th>\n",
       "      <td>21</td>\n",
       "      <td>3</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440</th>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>25</td>\n",
       "      <td>6</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>674</th>\n",
       "      <td>21</td>\n",
       "      <td>2</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>79</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>361</th>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>698</th>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>433</th>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>86</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>360</th>\n",
       "      <td>21</td>\n",
       "      <td>2</td>\n",
       "      <td>52</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541</th>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>72</td>\n",
       "      <td>1</td>\n",
       "      <td>72</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>576 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     MsL  NoD  MsF  Made\n",
       "619    2   50   98     1\n",
       "664    0   13   28     1\n",
       "441    1   16   35     1\n",
       "160    2   20   45     1\n",
       "358    1   24   77     0\n",
       "335    4    4    4     0\n",
       "47     2    7   14     1\n",
       "164    1   12   35     0\n",
       "736    5   46   98     1\n",
       "436    0    3    4     0\n",
       "460    2   10   28     1\n",
       "285    1   13   47     0\n",
       "499    2    6   15     1\n",
       "356    2    5   11     1\n",
       "40     2   14   48     1\n",
       "191    2   15   49     1\n",
       "638    2    6   15     1\n",
       "345    2    3    4     1\n",
       "463    2    3    4     1\n",
       "372    4   11   28     0\n",
       "8      2    6   16     1\n",
       "539    2    6   16     1\n",
       "734    4   14   40     0\n",
       "573    4    6   14     0\n",
       "482    4    8   21     0\n",
       "330    1   14   58     0\n",
       "222    4   10   28     1\n",
       "175    4   10   28     1\n",
       "606    2   16   64     0\n",
       "340    2    8   28     1\n",
       "..   ...  ...  ...   ...\n",
       "577   11    4   74     0\n",
       "118   21    6   48     0\n",
       "532   23    2   23     0\n",
       "338   23    6   45     0\n",
       "365   14    2   35     1\n",
       "173   16    4   58     0\n",
       "715   16    5   71     0\n",
       "2     21    2   26     0\n",
       "387   21    3   35     0\n",
       "738   23    8   69     0\n",
       "610   21    3   38     0\n",
       "450   21    3   40     0\n",
       "232   21    1   21     0\n",
       "440   21    1   21     0\n",
       "183   25    6   50     0\n",
       "60    21    1   21     0\n",
       "58    21    1   21     0\n",
       "49    23    3   39     0\n",
       "674   21    2   33     0\n",
       "204   14    3   79     0\n",
       "361   23    1   23     1\n",
       "30    23    1   23     0\n",
       "337   23    1   23     0\n",
       "496   23    1   23     0\n",
       "169   23    1   23     0\n",
       "698   23    1   23     0\n",
       "433   16    3   86     0\n",
       "360   21    2   52     0\n",
       "541   39    1   39     0\n",
       "74    72    1   72     0\n",
       "\n",
       "[576 rows x 4 columns]"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_data = pd.read_csv(\"9db113a1-cdbe-4b1c-98c2-11590f124dd8.csv\", index_col=0)\n",
    "real_data = pd.read_csv(\"5c9fa979-5a84-45d6-93b9-543d1a0efc41.csv\", index_col=0)\n",
    "\n",
    "model_data = model_data.drop(columns='Total Volume Donated (c.c.)')  # This is just 250 times Number of Donations\n",
    "real_data = real_data.drop(columns='Total Volume Donated (c.c.)')\n",
    "new_colnames = dict(zip(model_data.columns, ['MsL', 'NoD', 'MsF', 'Made']))\n",
    "model_data = model_data.rename(new_colnames, axis=\"columns\")\n",
    "real_data = real_data.rename(new_colnames, axis=\"columns\")\n",
    "\n",
    "X = model_data.drop(columns='Made')\n",
    "y = model_data['Made']\n",
    "X_real = real_data\n",
    "model_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 576 entries, 619 to 74\n",
      "Data columns (total 4 columns):\n",
      "MsL     576 non-null int64\n",
      "NoD     576 non-null int64\n",
      "MsF     576 non-null int64\n",
      "Made    576 non-null int64\n",
      "dtypes: int64(4)\n",
      "memory usage: 22.5 KB\n"
     ]
    }
   ],
   "source": [
    "model_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## New features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Average time between donations (donation rate)\n",
    "# Commitment (Good: donates since many months, donates recently too)\n",
    "# Difference (Good: big), similar to commitment\n",
    "# NOTE: These did not help much...\n",
    "\n",
    "#X['Rate'] = (X['MsF'] - X['MsL']) / X['NoD'] + 0.001\n",
    "#X['Cmtd'] = np.maximum(X['MsL'] / X['MsF'], 1/1000)  # We do not want zeros\n",
    "#X['Diff'] = np.log(X['MsF'] - X['MsL'] + 1.001)\n",
    "\n",
    "#X_real['Rate'] = (X_real['MsF'] - X_real['MsL']) / X_real['NoD']\n",
    "#X_real['Cmtd'] = np.maximum(X_real['MsL'] / X_real['MsF'], 1/1000)  # We do not want zeros\n",
    "#X_real['Diff'] = np.log(X_real['MsF'] - X_real['MsL'] + 1.001)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Box-Cox transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = X + 0.00001\n",
    "X_real = X_real + 0.00001\n",
    "X = pd.concat([pd.Series(boxcox(X.iloc[:, x])[0], index=X.index, name=X.columns[x]) for x in range(X.shape[1])], axis=1) \n",
    "X_real = pd.concat([pd.Series(boxcox(X_real.iloc[:, x])[0], index=X_real.index, name=X_real.columns[x]) for x in range(X_real.shape[1])], axis=1) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interactions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PolynomialFeatures(degree=2, include_bias=True, interaction_only=True)"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Note: This did not help much\n",
    "\n",
    "# Interaction features\n",
    "poly = PolynomialFeatures(degree=2, interaction_only=True)\n",
    "poly.fit(X, y)\n",
    "#X = pd.DataFrame(poly.transform(X), index=X.index)\n",
    "poly.fit(X_real, y)\n",
    "#X_real = pd.DataFrame(poly.transform(X_real), index=X_real.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scaling\n",
    "scaler = RobustScaler()\n",
    "scaler.fit(X, y)\n",
    "X = pd.DataFrame(scaler.transform(X), index=X.index, columns=X.columns)\n",
    "scaler.fit(X_real)\n",
    "X_real = pd.DataFrame(scaler.transform(X_real), index=X_real.index, columns=X_real.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize scatter plots in matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x576 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "full_data = pd.concat([X, y], axis=1)\n",
    "ax = pd.plotting.scatter_matrix(full_data, figsize=(8, 8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "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>MsL</th>\n",
       "      <th>NoD</th>\n",
       "      <th>MsF</th>\n",
       "      <th>Made</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MsL</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.188706</td>\n",
       "      <td>0.252586</td>\n",
       "      <td>-0.277250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NoD</th>\n",
       "      <td>-0.188706</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.712147</td>\n",
       "      <td>0.240538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MsF</th>\n",
       "      <td>0.252586</td>\n",
       "      <td>0.712147</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.009530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Made</th>\n",
       "      <td>-0.277250</td>\n",
       "      <td>0.240538</td>\n",
       "      <td>-0.009530</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           MsL       NoD       MsF      Made\n",
       "MsL   1.000000 -0.188706  0.252586 -0.277250\n",
       "NoD  -0.188706  1.000000  0.712147  0.240538\n",
       "MsF   0.252586  0.712147  1.000000 -0.009530\n",
       "Made -0.277250  0.240538 -0.009530  1.000000"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_data.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Box plot of features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x21df7dc9b38>"
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X.boxplot(figsize=(7, 4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3D plot of original features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = Axes3D(fig)\n",
    "\n",
    "x1 = X['MsL']\n",
    "x2 = X['MsF']\n",
    "x3 = X['NoD']\n",
    "color = y\n",
    "ax.scatter(x1, x2, x3, c=color, cmap='bwr')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simple feed forward neural network model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 403 samples, validate on 173 samples\n",
      "Epoch 1/40\n",
      "403/403 [==============================] - 2s 5ms/step - loss: 0.5480 - acc: 0.7370 - val_loss: 0.4378 - val_acc: 0.8150\n",
      "Epoch 2/40\n",
      "403/403 [==============================] - 0s 352us/step - loss: 0.4983 - acc: 0.7395 - val_loss: 0.4211 - val_acc: 0.8092\n",
      "Epoch 3/40\n",
      "403/403 [==============================] - 0s 346us/step - loss: 0.4908 - acc: 0.7717 - val_loss: 0.4200 - val_acc: 0.8439\n",
      "Epoch 4/40\n",
      "403/403 [==============================] - 0s 254us/step - loss: 0.4876 - acc: 0.7692 - val_loss: 0.4193 - val_acc: 0.8555\n",
      "Epoch 5/40\n",
      "403/403 [==============================] - 0s 237us/step - loss: 0.4859 - acc: 0.7767 - val_loss: 0.4175 - val_acc: 0.8555\n",
      "Epoch 6/40\n",
      "403/403 [==============================] - 0s 252us/step - loss: 0.4837 - acc: 0.7792 - val_loss: 0.4188 - val_acc: 0.8497\n",
      "Epoch 7/40\n",
      "403/403 [==============================] - ETA: 0s - loss: 0.4649 - acc: 0.807 - 0s 242us/step - loss: 0.4830 - acc: 0.7841 - val_loss: 0.4177 - val_acc: 0.8497\n",
      "Epoch 8/40\n",
      "403/403 [==============================] - 0s 232us/step - loss: 0.4815 - acc: 0.7841 - val_loss: 0.4170 - val_acc: 0.8497\n",
      "Epoch 9/40\n",
      "403/403 [==============================] - 0s 206us/step - loss: 0.4806 - acc: 0.7866 - val_loss: 0.4170 - val_acc: 0.8497\n",
      "Epoch 10/40\n",
      "403/403 [==============================] - 0s 186us/step - loss: 0.4801 - acc: 0.7866 - val_loss: 0.4159 - val_acc: 0.8497\n",
      "Epoch 11/40\n",
      "403/403 [==============================] - 0s 226us/step - loss: 0.4793 - acc: 0.7866 - val_loss: 0.4168 - val_acc: 0.8497\n",
      "Epoch 12/40\n",
      "403/403 [==============================] - 0s 221us/step - loss: 0.4790 - acc: 0.7866 - val_loss: 0.4164 - val_acc: 0.8497\n",
      "Epoch 13/40\n",
      "403/403 [==============================] - 0s 219us/step - loss: 0.4786 - acc: 0.7891 - val_loss: 0.4162 - val_acc: 0.8497\n",
      "Epoch 14/40\n",
      "403/403 [==============================] - 0s 211us/step - loss: 0.4783 - acc: 0.7866 - val_loss: 0.4156 - val_acc: 0.8497\n",
      "Epoch 15/40\n",
      "403/403 [==============================] - 0s 218us/step - loss: 0.4785 - acc: 0.7866 - val_loss: 0.4155 - val_acc: 0.8497\n",
      "Epoch 16/40\n",
      "403/403 [==============================] - 0s 223us/step - loss: 0.4778 - acc: 0.7866 - val_loss: 0.4151 - val_acc: 0.8497\n",
      "Epoch 17/40\n",
      "403/403 [==============================] - 0s 223us/step - loss: 0.4774 - acc: 0.7866 - val_loss: 0.4157 - val_acc: 0.8497\n",
      "Epoch 18/40\n",
      "403/403 [==============================] - 0s 204us/step - loss: 0.4772 - acc: 0.7866 - val_loss: 0.4153 - val_acc: 0.8497\n",
      "Epoch 19/40\n",
      "403/403 [==============================] - 0s 225us/step - loss: 0.4770 - acc: 0.7866 - val_loss: 0.4156 - val_acc: 0.8497\n",
      "Epoch 20/40\n",
      "403/403 [==============================] - 0s 225us/step - loss: 0.4770 - acc: 0.7866 - val_loss: 0.4153 - val_acc: 0.8497\n",
      "Epoch 21/40\n",
      "403/403 [==============================] - 0s 223us/step - loss: 0.4767 - acc: 0.7866 - val_loss: 0.4151 - val_acc: 0.8497\n",
      "Epoch 22/40\n",
      "403/403 [==============================] - 0s 257us/step - loss: 0.4767 - acc: 0.7866 - val_loss: 0.4151 - val_acc: 0.8497\n",
      "Epoch 23/40\n",
      "403/403 [==============================] - 0s 235us/step - loss: 0.4766 - acc: 0.7866 - val_loss: 0.4153 - val_acc: 0.8497\n",
      "Epoch 24/40\n",
      "403/403 [==============================] - 0s 206us/step - loss: 0.4763 - acc: 0.7866 - val_loss: 0.4151 - val_acc: 0.8497\n",
      "Epoch 25/40\n",
      "403/403 [==============================] - 0s 211us/step - loss: 0.4761 - acc: 0.7866 - val_loss: 0.4149 - val_acc: 0.8497\n",
      "Epoch 26/40\n",
      "403/403 [==============================] - 0s 194us/step - loss: 0.4761 - acc: 0.7866 - val_loss: 0.4148 - val_acc: 0.8497\n",
      "Epoch 27/40\n",
      "403/403 [==============================] - 0s 232us/step - loss: 0.4758 - acc: 0.7866 - val_loss: 0.4150 - val_acc: 0.8497\n",
      "Epoch 28/40\n",
      "403/403 [==============================] - 0s 235us/step - loss: 0.4757 - acc: 0.7891 - val_loss: 0.4147 - val_acc: 0.8497\n",
      "Epoch 29/40\n",
      "403/403 [==============================] - 0s 206us/step - loss: 0.4756 - acc: 0.7866 - val_loss: 0.4146 - val_acc: 0.8497\n",
      "Epoch 30/40\n",
      "403/403 [==============================] - 0s 213us/step - loss: 0.4756 - acc: 0.7866 - val_loss: 0.4146 - val_acc: 0.8497\n",
      "Epoch 31/40\n",
      "403/403 [==============================] - 0s 203us/step - loss: 0.4753 - acc: 0.7866 - val_loss: 0.4145 - val_acc: 0.8497\n",
      "Epoch 32/40\n",
      "403/403 [==============================] - 0s 240us/step - loss: 0.4756 - acc: 0.7866 - val_loss: 0.4145 - val_acc: 0.8497\n",
      "Epoch 33/40\n",
      "403/403 [==============================] - 0s 213us/step - loss: 0.4753 - acc: 0.7866 - val_loss: 0.4145 - val_acc: 0.8497\n",
      "Epoch 34/40\n",
      "403/403 [==============================] - 0s 199us/step - loss: 0.4750 - acc: 0.7866 - val_loss: 0.4144 - val_acc: 0.8497\n",
      "Epoch 35/40\n",
      "403/403 [==============================] - 0s 224us/step - loss: 0.4749 - acc: 0.7841 - val_loss: 0.4143 - val_acc: 0.8497\n",
      "Epoch 36/40\n",
      "403/403 [==============================] - 0s 201us/step - loss: 0.4752 - acc: 0.7866 - val_loss: 0.4142 - val_acc: 0.8497\n",
      "Epoch 37/40\n",
      "403/403 [==============================] - 0s 199us/step - loss: 0.4750 - acc: 0.7866 - val_loss: 0.4141 - val_acc: 0.8497\n",
      "Epoch 38/40\n",
      "403/403 [==============================] - 0s 201us/step - loss: 0.4747 - acc: 0.7841 - val_loss: 0.4143 - val_acc: 0.8497\n",
      "Epoch 39/40\n",
      "403/403 [==============================] - 0s 225us/step - loss: 0.4747 - acc: 0.7866 - val_loss: 0.4142 - val_acc: 0.8497\n",
      "Epoch 40/40\n",
      "403/403 [==============================] - 0s 233us/step - loss: 0.4745 - acc: 0.7841 - val_loss: 0.4143 - val_acc: 0.8497\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x21df94c4438>"
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_num = X.shape[1]\n",
    "\n",
    "early = EarlyStopping(patience=2)\n",
    "optimizer = Adam(lr=0.01, decay=0.1, amsgrad=False)\n",
    "\n",
    "model_nn = Sequential()\n",
    "model_nn.add(Dense(60, activation='relu', input_shape=(input_num,)))\n",
    "model_nn.add(Dense(30, activation='relu'))\n",
    "model_nn.add(Dense(15, activation='relu'))\n",
    "model_nn.add(Dense(5, activation='relu'))\n",
    "model_nn.add(Dense(1, activation='sigmoid'))\n",
    "model_nn.compile(optimizer=optimizer, loss='binary_crossentropy', metrics=['accuracy'])\n",
    "model_nn.fit(X, y, epochs=40, validation_split=0.3, callbacks=[], batch_size=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict real data\n",
    "y_pred = model_nn.predict_proba(X_real)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prepare submission\n",
    "df_submit = pd.DataFrame(y_pred, index=X_real.index, columns=['Made Donation in March 2007'])\n",
    "df_submit.to_csv(\"result_nn.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classic models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5032361255912053\n"
     ]
    }
   ],
   "source": [
    "model1 = SVC(probability=True, kernel='rbf', random_state=42)\n",
    "model2 = SVC(probability=True, kernel='linear', random_state=42)\n",
    "model3 = LogisticRegression(penalty='l1', C=0.1)\n",
    "model4 = SGDClassifier(loss='log', max_iter=1000, alpha=0.05, penalty='l2')\n",
    "model5 = KNeighborsClassifier(n_neighbors=50)\n",
    "model6 = XGBClassifier(objective ='binary:logistic', colsample_bytree = 0.2, subsample = 0.3, learning_rate = 0.05,\n",
    "                max_depth = 3, alpha = 1, gamma = 7, n_estimators = 60)\n",
    "\n",
    "#param_grid = {'penalty': ['l1', 'l2'], 'alpha' : [0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1, 5]}\n",
    "#grid_search = GridSearchCV(model, param_grid, cv=10, scoring='neg_log_loss')\n",
    "#grid_search.fit(X, y)\n",
    "#print(grid_search.best_params_)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
    "\n",
    "model = model1\n",
    "steps = [\n",
    "#    ('scale', scaler), \n",
    "    ('model', model)\n",
    "]\n",
    "pl = Pipeline(steps)\n",
    "\n",
    "pl.fit(X_train, y_train)\n",
    "y_pred_train = pl.predict_proba(X_train)\n",
    "\n",
    "#score = -cross_val_score(pl, X, y, cv=5, scoring='neg_log_loss')\n",
    "#print(np.mean(score), np.std(score))\n",
    "\n",
    "print(log_loss(y_train, y_pred_train[:, 1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict real data\n",
    "pl.fit(X, y)\n",
    "y_pred = pl.predict_proba(X_real)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Submit data\n",
    "df_submit = pd.DataFrame(y_pred[:,1], index=X_real.index, columns=['Made Donation in March 2007'])\n",
    "df_submit.to_csv(\"result_classic.csv\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}