bioresponse_1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction\n",
    "Aim of the project is to predict wheter a molecule elicit a biological response or not. The project first appeared as Kaggle [Bioresponse Competition](https://www.kaggle.com/c/bioresponse).\n",
    "It is a Binary Classification problem; the target variable is `Activity`: an experimentally measured biological response to molecules in the dataset. `Activity` is `1` if there is a measured biological response, otherwise it is `0`.\n",
    "Available features are 1776 Molecular Descriptors (columns `D1` - `D1776`). \n",
    "\n",
    "The \"physical meaning\" of the available Molecular Descriptors, as well as the specific Biological Response which was measured, are not made known to competition partecipants. No \"domain knowledge\" is applicable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Library Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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 sklearn.decomposition import PCA\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.gaussian_process import GaussianProcessClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Activity</th>\n",
       "      <th>D1</th>\n",
       "      <th>D2</th>\n",
       "      <th>D3</th>\n",
       "      <th>D4</th>\n",
       "      <th>D5</th>\n",
       "      <th>D6</th>\n",
       "      <th>D7</th>\n",
       "      <th>D8</th>\n",
       "      <th>D9</th>\n",
       "      <th>...</th>\n",
       "      <th>D1767</th>\n",
       "      <th>D1768</th>\n",
       "      <th>D1769</th>\n",
       "      <th>D1770</th>\n",
       "      <th>D1771</th>\n",
       "      <th>D1772</th>\n",
       "      <th>D1773</th>\n",
       "      <th>D1774</th>\n",
       "      <th>D1775</th>\n",
       "      <th>D1776</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.497009</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.132956</td>\n",
       "      <td>0.678031</td>\n",
       "      <td>0.273166</td>\n",
       "      <td>0.585445</td>\n",
       "      <td>0.743663</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>0.606291</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.111209</td>\n",
       "      <td>0.803455</td>\n",
       "      <td>0.106105</td>\n",
       "      <td>0.411754</td>\n",
       "      <td>0.836582</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0.033300</td>\n",
       "      <td>0.480124</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.209791</td>\n",
       "      <td>0.610350</td>\n",
       "      <td>0.356453</td>\n",
       "      <td>0.517720</td>\n",
       "      <td>0.679051</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.538825</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.196344</td>\n",
       "      <td>0.724230</td>\n",
       "      <td>0.235606</td>\n",
       "      <td>0.288764</td>\n",
       "      <td>0.805110</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.517794</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.494734</td>\n",
       "      <td>0.781422</td>\n",
       "      <td>0.154361</td>\n",
       "      <td>0.303809</td>\n",
       "      <td>0.812646</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 1777 columns</p>\n",
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      ],
      "text/plain": [
       "   Activity        D1        D2    D3   D4        D5        D6        D7  \\\n",
       "0         1  0.000000  0.497009  0.10  0.0  0.132956  0.678031  0.273166   \n",
       "1         1  0.366667  0.606291  0.05  0.0  0.111209  0.803455  0.106105   \n",
       "2         1  0.033300  0.480124  0.00  0.0  0.209791  0.610350  0.356453   \n",
       "3         1  0.000000  0.538825  0.00  0.5  0.196344  0.724230  0.235606   \n",
       "4         0  0.100000  0.517794  0.00  0.0  0.494734  0.781422  0.154361   \n",
       "\n",
       "         D8        D9  ...  D1767  D1768  D1769  D1770  D1771  D1772  D1773  \\\n",
       "0  0.585445  0.743663  ...      0      0      0      0      0      0      0   \n",
       "1  0.411754  0.836582  ...      1      1      1      1      0      1      0   \n",
       "2  0.517720  0.679051  ...      0      0      0      0      0      0      0   \n",
       "3  0.288764  0.805110  ...      0      0      0      0      0      0      0   \n",
       "4  0.303809  0.812646  ...      0      0      0      0      0      0      0   \n",
       "\n",
       "   D1774  D1775  D1776  \n",
       "0      0      0      0  \n",
       "1      0      1      0  \n",
       "2      0      0      0  \n",
       "3      0      0      0  \n",
       "4      0      0      0  \n",
       "\n",
       "[5 rows x 1777 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"input/bioresponse/train.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3751, 1777)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No missing values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().any().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Activity</th>\n",
       "      <th>D1</th>\n",
       "      <th>D2</th>\n",
       "      <th>D3</th>\n",
       "      <th>D4</th>\n",
       "      <th>D5</th>\n",
       "      <th>D6</th>\n",
       "      <th>D7</th>\n",
       "      <th>D8</th>\n",
       "      <th>D9</th>\n",
       "      <th>...</th>\n",
       "      <th>D1767</th>\n",
       "      <th>D1768</th>\n",
       "      <th>D1769</th>\n",
       "      <th>D1770</th>\n",
       "      <th>D1771</th>\n",
       "      <th>D1772</th>\n",
       "      <th>D1773</th>\n",
       "      <th>D1774</th>\n",
       "      <th>D1775</th>\n",
       "      <th>D1776</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3751.000000</td>\n",
       "      <td>3751.000000</td>\n",
       "      <td>3751.000000</td>\n",
       "      <td>3751.000000</td>\n",
       "      <td>3751.000000</td>\n",
       "      <td>3751.000000</td>\n",
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       "      <td>3751.000000</td>\n",
       "      <td>3751.000000</td>\n",
       "      <td>3751.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>3751.000000</td>\n",
       "      <td>3751.000000</td>\n",
       "      <td>3751.000000</td>\n",
       "      <td>3751.000000</td>\n",
       "      <td>3751.000000</td>\n",
       "      <td>3751.000000</td>\n",
       "      <td>3751.000000</td>\n",
       "      <td>3751.000000</td>\n",
       "      <td>3751.000000</td>\n",
       "      <td>3751.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.542255</td>\n",
       "      <td>0.076948</td>\n",
       "      <td>0.592436</td>\n",
       "      <td>0.068142</td>\n",
       "      <td>0.038990</td>\n",
       "      <td>0.212112</td>\n",
       "      <td>0.686653</td>\n",
       "      <td>0.274713</td>\n",
       "      <td>0.455133</td>\n",
       "      <td>0.749517</td>\n",
       "      <td>...</td>\n",
       "      <td>0.026926</td>\n",
       "      <td>0.014663</td>\n",
       "      <td>0.013863</td>\n",
       "      <td>0.021861</td>\n",
       "      <td>0.015196</td>\n",
       "      <td>0.016796</td>\n",
       "      <td>0.012263</td>\n",
       "      <td>0.011730</td>\n",
       "      <td>0.020261</td>\n",
       "      <td>0.011197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.498278</td>\n",
       "      <td>0.079989</td>\n",
       "      <td>0.105860</td>\n",
       "      <td>0.078414</td>\n",
       "      <td>0.115885</td>\n",
       "      <td>0.102592</td>\n",
       "      <td>0.078702</td>\n",
       "      <td>0.090017</td>\n",
       "      <td>0.162731</td>\n",
       "      <td>0.071702</td>\n",
       "      <td>...</td>\n",
       "      <td>0.161889</td>\n",
       "      <td>0.120215</td>\n",
       "      <td>0.116938</td>\n",
       "      <td>0.146249</td>\n",
       "      <td>0.122348</td>\n",
       "      <td>0.128522</td>\n",
       "      <td>0.110074</td>\n",
       "      <td>0.107683</td>\n",
       "      <td>0.140911</td>\n",
       "      <td>0.105236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.282128</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002630</td>\n",
       "      <td>0.137873</td>\n",
       "      <td>0.006130</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.275590</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.033300</td>\n",
       "      <td>0.517811</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.138118</td>\n",
       "      <td>0.625627</td>\n",
       "      <td>0.207374</td>\n",
       "      <td>0.378062</td>\n",
       "      <td>0.707339</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.066700</td>\n",
       "      <td>0.585989</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.190926</td>\n",
       "      <td>0.674037</td>\n",
       "      <td>0.277845</td>\n",
       "      <td>0.499942</td>\n",
       "      <td>0.738961</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.668395</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.261726</td>\n",
       "      <td>0.740663</td>\n",
       "      <td>0.335816</td>\n",
       "      <td>0.569962</td>\n",
       "      <td>0.788177</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.964381</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.994735</td>\n",
       "      <td>0.790831</td>\n",
       "      <td>0.989870</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 1777 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Activity           D1           D2           D3           D4  \\\n",
       "count  3751.000000  3751.000000  3751.000000  3751.000000  3751.000000   \n",
       "mean      0.542255     0.076948     0.592436     0.068142     0.038990   \n",
       "std       0.498278     0.079989     0.105860     0.078414     0.115885   \n",
       "min       0.000000     0.000000     0.282128     0.000000     0.000000   \n",
       "25%       0.000000     0.033300     0.517811     0.000000     0.000000   \n",
       "50%       1.000000     0.066700     0.585989     0.050000     0.000000   \n",
       "75%       1.000000     0.100000     0.668395     0.100000     0.000000   \n",
       "max       1.000000     1.000000     0.964381     0.950000     1.000000   \n",
       "\n",
       "                D5           D6           D7           D8           D9  ...  \\\n",
       "count  3751.000000  3751.000000  3751.000000  3751.000000  3751.000000  ...   \n",
       "mean      0.212112     0.686653     0.274713     0.455133     0.749517  ...   \n",
       "std       0.102592     0.078702     0.090017     0.162731     0.071702  ...   \n",
       "min       0.002630     0.137873     0.006130     0.000000     0.275590  ...   \n",
       "25%       0.138118     0.625627     0.207374     0.378062     0.707339  ...   \n",
       "50%       0.190926     0.674037     0.277845     0.499942     0.738961  ...   \n",
       "75%       0.261726     0.740663     0.335816     0.569962     0.788177  ...   \n",
       "max       1.000000     0.994735     0.790831     0.989870     1.000000  ...   \n",
       "\n",
       "             D1767        D1768        D1769        D1770        D1771  \\\n",
       "count  3751.000000  3751.000000  3751.000000  3751.000000  3751.000000   \n",
       "mean      0.026926     0.014663     0.013863     0.021861     0.015196   \n",
       "std       0.161889     0.120215     0.116938     0.146249     0.122348   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "50%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "75%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "             D1772        D1773        D1774        D1775        D1776  \n",
       "count  3751.000000  3751.000000  3751.000000  3751.000000  3751.000000  \n",
       "mean      0.016796     0.012263     0.011730     0.020261     0.011197  \n",
       "std       0.128522     0.110074     0.107683     0.140911     0.105236  \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000  \n",
       "25%       0.000000     0.000000     0.000000     0.000000     0.000000  \n",
       "50%       0.000000     0.000000     0.000000     0.000000     0.000000  \n",
       "75%       0.000000     0.000000     0.000000     0.000000     0.000000  \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000  \n",
       "\n",
       "[8 rows x 1777 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_name = \"Activity\"\n",
    "y = df.loc[:, target_name]\n",
    "X = df.drop(target_name, axis=\"columns\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dataset is balanced:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    2034\n",
       "0    1717\n",
       "Name: Activity, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5422553985603839"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are many input features, so it is pointless to visualize each one.\n",
    "We are informed by [Data Description](https://www.kaggle.com/c/bioresponse/data) that input features are already normalized. Can we visually confirm that information, by looking at Violin Plots of Maximum, Minimum and Mean of Features?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "max_X = X.max()\n",
    "\n",
    "sns.violinplot(max_X);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Distribution of Maximum is clearly peaked around 1, but some features have a much lower Maximum."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "min_X = X.min()\n",
    "\n",
    "sns.violinplot(min_X);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Distribution of Minimum is clearly peaked around 0, but some features have a much higher Minimum."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean_X = X.mean()\n",
    "\n",
    "sns.violinplot(mean_X);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Distribution  of Mean is peaked around 0, but the peak is larger than Distribution of Minimum. Some Features have much larger Mean.\n",
    "\n",
    "\n",
    "Our visualization confirmed that all features were subjected to Normalization, i.e. \"Min-Max Scaling\", but they can still benefit from some sort of Scaling.\n",
    "\n",
    "\n",
    "Are Minimum, Maximum and Mean equally distributed on both classes?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_active = df.query(f\"{target_name} == 1\").drop(target_name, axis=\"columns\")\n",
    "X_inactive = df.query(f\"{target_name} == 0\").drop(target_name, axis=\"columns\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Amax\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Amin\n"
     ]
    },
    {
     "data": {
      "image/png": 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OLAU+OqIVRU+/+9DMEsDfAHeWq6CI8vO7mCJ/mvJj5M8kvGhmC5xzp0a4tqjwsw8/BzzunPuBmS0H/re3D3MjX170BHkE9x5wSY+fp3Dh4fa5dcwsRf6Q/GRZqosGP/sQM7sBuB9Y6ZzrLFNtUTHQPhwNLACeN7P9wFXAWg00uYDfv+dfOue6nHP7gJ3kA0/y/OzDu4CnAJxzG4BR5G/CLEUEGXCvArPMbJqZpckPIlnba521wB3e9G3Ac87rXRXAxz70Tq/9L/Lhpj6PC/W7D51zp51z9c65RudcI/l+zJXOuU3BlBtafv6ef0F+0BNmVk/+lOXeslYZbn724bvA9QBmNo98wB0va5UREljAeX1qXwfWAW8DTznn3jKz75jZSm+1x4DxZrYbuBfocwj3B5HPffjfgBpgjZm9Zma9/2A+0HzuQxmAz/24Dmgys+3AeuCbzrmmYCoOH5/78C+Ar5jZ68BPgTv1n/6+6VZdIiISS7qTiYiIxJICTkREYkkBJyIisaSAExGRWFLAiYhILCngREQklhRwIiISSwo4ERGJJQWciIjEkgJORERiSQEnEiJm1mpm04OuQyQOFHAifTCz/d5XDY3U8z9vZl/uOc85V+Oc0x32RYaBAk5ERGJJAScyADO708xeMrPvm1mzme0zs5t6LP+Smb1tZi1mttfMvtpr+1u8ryo6Y2Z7zOwTZvZd4Frgh95pyR966zozm2lmV5nZETNL9nieW81smzedMLP7vOdrMrOnzGxcefaISDQo4ET8WUb+G6jrge8Bj5mZecuOAZ8CaoEvAX9jZksAzOwjwN8D3wTGAtcB+51z9wMvAl/3Tkt+veeLOec2Am3Ax3vM/jzwhDf9DeDTwEeBSUAz8NBwNlgk6hRwIv4ccM79yDnXDfwE+BDQAOCc+7/OuT0u7wXgGfJHZwB3AX/nnPuNcy7nnDvknNvh8zV/CnwOwMxGA5/05gF8FbjfOfeec64T+CvgNjNLDb2pIvGggBPx50hhwjnX7k3WAJjZTWa20cxOmtkp8kFU761zCbBnkK/5BLDKzCqBVcAW59wBb9lU4Odmdsp7zbeBbrzQFREFnMiQeOHzj8D3gQbn3Fjgn4DC6cuDwIw+Nnf9PbdzbjtwALiJ809PFp73Jufc2B6PUc65Q4NvjUi8KOBEhiYNVALHgaw3+OSPeix/DPiSmV3vDQyZbGZzvWVHgYGueXuCfH/bdcCaHvMfAb5rZlMBzGyCmd0y9OaIxIcCTmQInHMt5APoKfIDPT4PrO2x/BW8gSfAaeAF8qcXAR4k32/WbGb/o4+X+CnwMeA559yJHvMf9F7nGTNrATaSHwgjIh5zrt+zJCIiIpGkIzgREYklBZyIiMSSAk5ERGJJASciIrFU0l0P6uvrXWNj4wiVIiIiUprNmzefcM5NKLaspIBrbGxk06ZNw1OViIjIEJnZgb6W6RSliIjEkgJORERiSQEnIiKxpIATEZFYUsCJiEgsKeBERCSWFHAiIhJLCjgREYmlUAbc5s2b+fGPfxx0GSIiEmEl3cmkXB577DGeffZZ5s+fz7Jl+g5HEREpXSiP4F7b+hoAD/znB8jlcgFXIyIiURS6gDt69ChNJ5uoy9Tx9o63Wbt2bdAliYhIBIUu4N544w0A5rbOZWz3WH7w/R/Q0dERcFUiIhI1oQu4bdu2kSBBbbaWOWfmcOz4MR5//PGgyxIRkYgJZcCN7h5NkiTju8bT0NnAI//zEU6cOBF0aSIiEiGhCrhcLse217dRm6k9N29O6xw6Ozp56KGHAqxMRESiJlQBd+DAAdra2xjbNfbcvJruGho6Gvjtb34bYGUiIhI1oQq4wgCTMV1jzptfm63l2PFjnDp1KoiyREQkgkIVcNu2bSNlKWq6a86bX5vNn7LcuXNnEGWJiEgEhS7gajO1JHqVpYATEZFShSbgurq6eHv72xecngRI59KMslHs2LEjgMpERCSKQhNwu3btItOVKRpwhlHTWcPb298OoDIREYmi0ATcuQEm2QsDDmB0djS7d+8mm82WsywREYmoUAVcpVVS1V1VdPno7GgyXRkOHDhQ5spERCSKQhNwr299ndrOWgwrulwDTUREpBShCLizZ8+ye+/uov1vBdXZahIkNNBERER8CUXAbd++nVwu12/AJUlSk6tRwImIiC+hCLjCAJOx2bH9rleT0UhKERHxJxQBt3XrVqqppjJX2e96umWXiIj4FXjAOefY9OomxnT0fXqyYHR2NIBOU4qIyIACD7jDhw9zounEed8g0BeNpBQREb8CD7itW7cCUNdVN+C6lblK3bJLRER8CUXApSx17vTjQGo6a9jxtgJORET6F3jAbXp1E2M6x1zwDQJ9GZ0dza7du3TLLhER6VegAdfW1sY777zj6/RkQW22lq6uLvbv3z9yhYmISOQFGnDbtm0j53IlBVzhVKYGmoiISH8CDbjCABM/IygLarI1umWXiIgMKNCA27JlC7W5Wipche9tEiSo7a7l5Y0vj2BlIiISdYEFXC6XY8vmLYzpHPgC794azjbw+rbXOXjw4AhUJiIicRBYwO3Zs4e29raS+t8KJnVMAuBXv/rVcJclIiIxEVjAlXKBd28X5S5ifNd4fvnzX+KcG+7SREQkBgILuC1btvT7Dd4DmXR2Evvf3c+bb745zJWJiEgcBBZwmzdtZkzHmD6/wXsgF3deTIIEa9euHebKREQkDgIJuJMnT/LuwXcHdXqyoMJVMLFjIr/+1a91VxMREblAIAG3bds2YHD9bz1N6pjEyeaTbNiwYTjKEhGRGAkk4Nra2gBI59JDep4JmQmkLa3TlCIicoHAb7Y8FEmSNLQ38My6Z86FpoiICEQ84AAmd0ymo7ODNWvW6JIBERE5J7QB15xqZnfVbppTzf2uV9dVR113HQ888AB/fPsfn+vfExGRD7ZU0AUU05xq5s3Jb7Jq9SqeXvM0Cw4toC5bfECKYSxrWsbBiw6ybdM2Vq9ezY033siCBQuKr2/5yxJ6H+0V5vdWWM/McM71uX1/+nruUvh5vWKvE+RR7XC0u6BnOwb7vP3ti/6e0892fa1jZuc9t3Ou33UHer1SDed7UE4juQ9K/V0a7r8hv3+nYX3vBrM/Cp+fBZdddhlXX331iLdxwIAzs7uBuwEuvfTSES2moCndxKrVq/j2/d8GYMPDG/oMOMjfgHnq2alc3HExz9U/x7p161i3bl1ZahURkdKtW7eOxsbGEX2NAQPOOfco8CjA0qVLy3I4MD4znqfXPA2QP4LLFD8aK3A43hv1Hrtrd+NwXHP1NcydN/e8dXr+D6JwJNbX/5h7Hqld8FpFlvX+H3rvn/vbvtSfe75e7yPKUrYdai1+1h1uhfdwKHX3PhIvVutg93mxo3w//zMf6HczTO9BOQ2l3f39XRSeu/c+7+v5hquWws899fX701dtQepdy1Deg8WLFzN16tQRrzmUpyjrsnUsOLSADQ9vYEGm79OTkA+3V8a9QlOqiYULFvKt+77FlVdeWcZqRUQkjEIZcJAPuf6CreBU6hRNqSbuvfde7r777tD8b0dERIIV2lGUfh0adYjKdCVf+MIXFG4iInJOpAMuR46jVUe5/obrqampCbocEREJkUACrqoq/xU5mURmSM9zPH2cTjq55ZZbhqMsERGJkUACbuHChUC+/2woDo86zNjasVxzzTXDUZaIiMRIIAFXX1/PlMlTaE73f5eS/nRZF8cuOsanVn6KioqKYaxORETiILA+uA8v/TCnK0/jGNy1Okcrj9Ltulm5cuUwVyYiInEQWMAtXryYDjpoT7YPavvDow4zZfIUFi1aNMyViYhIHAQWcEuWLAHgVEXp/XAdiQ5OpE/w6Vs/rUsDRESkqMACbubMmVRXVQ/4bQHFHK48DMDNN9883GWJiEhMBBZwyWSSKxZfwelRp0ve9kjVERYuWDjiN+oUEZHoCvRC7yVLlnA6cZou6/K9TY4cZ5JnWHbVshGsTEREoi7wgIPS+uFak63kyDF37tyBVxYRkQ+sQANu0aJFJCxBc4X/friWVAuAAk5ERPoVaMDV1NQwa+asko7gWlItVKQq1P8mIiL9Cvxmy0uWLinpgu8zqTPMmDFDdy8REZF+BR5wixcvpst10ZJs8bV+W2Ub8+bPG+GqREQk6gIPuMJAEz/3pey0Ts5yVv1vIiIyoMADbsqUKYwfN97XQJPCAJM5c+aMdFkiIhJxgQecmbHkw0t8XfB9JnUGUMCJiMjAAg84yPfDtdFGp3X2u15LqoX68fWMGzeuTJWJiEhUhSLgCt8IcLqi/6O41spWDTARERFfQhFw8+fPJ2GJfq+Hy5GjJdGiASYiIuJLKAKuurqa6dOnczrV9xGcbtElIiKlCEXAASy6fBFnRp3p84JvjaAUEZFShCfgFi2i03VyNnG26PIzqTNUpCqYNm1amSsTEZEoCk3ALVy4EOh7oElLqoUZM2aQSqXKWZaIiERUaAJu9uzZVKQqOJUqPtCkdVQr8y+bX+aqREQkqkITcOl0mnnz5nE6feERXKd10uE61P8mIiK+hSbgwBtokr5woInuYCIiIqUKVcAtXLiQrMvSmmw9b75GUIqISKlCF3DABRd8n644rVt0iYhISUIVcNOmTaO6qvq8C77bE+0cHXWUFR9fEWBlIiISNaEKuEQiwYKFCzhd+X7A7ajZQboyzT333BNgZSIiEjWhCjjIX/Ddkmyhm26aU80cGXWEL3/lyzQ0NARdmoiIREjoAm7hwoX5GyunWthRu4P68fXcddddQZclIiIRE7qAK3x1zs6anTSnmvnze/+cqqqqgKsSEZGoCV3AXXzxxYyrG0dTuonZs2Zz6623Bl2SiIhEUOgCzsxYdHn+KO6+v7yPZDIZcEUiIhJFobxz8Z133snll1/ONddcE3QpIiISUaEMuOXLl7N8+fKgyxARkQgL3SlKERGR4aCAExGRWFLAiYhILCngREQklhRwIiISSwo4ERGJJQWciIjEkgJORERiyZxz/lc2Ow4cGLlyLlAPnCjj642kOLUF1J6wU3vCK05tgeDbM9U5N6HYgpICrtzMbJNzbmnQdQyHOLUF1J6wU3vCK05tgXC3R6coRUQklhRwIiISS2EPuEeDLmAYxaktoPaEndoTXnFqC4S4PaHugxMRERmssB/BiYiIDEogAWdmnzCznWa228zuK7K80sx+5i1/2cwaeyz7S2/+TjO7sZx192Ww7TGzRjM7a2aveY9Hyl17MT7ac52ZbTGzrJnd1mvZHWa2y3vcUb6qixtiW7p7vDdry1d133y0514z225m28zsWTOb2mNZqN4bGHJ7ovj+/Fsze8Or+SUzm99jWRQ/24q2JzSfbc65sj6AJLAHmA6kgdeB+b3W+VPgEW/6s8DPvOn53vqVwDTveZLlbsMwtqcReDPI+gfZnkZgEfD3wG095o8D9nr/1nnTdVFsi7esNej3YxDtWQFUedNf6/G7Fqr3ZqjtifD7U9tjeiXw/7zpqH629dWeUHy2BXEE9xFgt3Nur3MuAzwJ3NJrnVuAn3jT/wBcb2bmzX/SOdfpnNsH7PaeL0hDaU8YDdge59x+59w2INdr2xuB3zjnTjrnmoHfAJ8oR9F9GEpbwshPe9Y759q9HzcCU7zpsL03MLT2hJGf9pzp8WM1UBgEEcnPtn7aEwpBBNxk4GCPn9/z5hVdxzmXBU4D431uW25DaQ/ANDPbamYvmNm1I12sD0PZx2F7f4Zazygz22RmG83s08Nb2qCU2p67gH8e5LblMJT2QETfHzP7MzPbA3wP+EYp25bZUNoDIfhsSwXwmsWOXHqnfl/r+Nm23IbSnj8Alzrnmszsw8AvzOyyXv8rKreh7OOwvT9DredS59xhM5sOPGdmbzjn9gxTbYPhuz1mdjuwFPhoqduW0VDaAxF9f5xzDwEPmdnngf8E3OF32zIbSntC8dkWxBHce8AlPX6eAhzuax0zSwFjgJM+ty23QbfHOx3RBOCc20z+fPfsEa+4f0PZx2F7f4ZUj3PusPfvXuB5YPFwFjcIvtpjZjcA9wMrnXOdpWxbZkNpT2Tfnx6eBApHnpF9f3o4157QfLYF0HGZIt/BPXbnj0AAAAErSURBVI33Oy4v67XOn3H+oIynvOnLOL8jdi/Bd8QOpT0TCvWT78g9BIwLe3t6rPs4Fw4y2Ud+EEOdNx1Ye4bYljqg0puuB3bRq4M9jO0h/yG/B5jVa36o3pthaE9U359ZPaZvBjZ501H9bOurPaH4bAtqx30SeMf7xb3fm/cd8v9DAxgFrCHf0foKML3Htvd72+0EbgryF2Co7QH+NfCW94uzBbg56Lb4bM+V5P931wY0AW/12PZPvHbuBr4U1bYAVwNveO/NG8BdQbfFZ3t+CxwFXvMea8P63gylPRF+fx70/uZfA9bTIzAi+tlWtD1h+WzTnUxERCSWdCcTERGJJQWciIjEkgJORERiSQEnIiKxpIATEZFYUsCJiEgsKeBERCSWFHAiIhJL/x+yP77/eeyI1wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Mean\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "for statistic in [np.max, np.min, np.mean]:\n",
    "    statistic_active = statistic(X_active)\n",
    "    statistic_inactive = statistic(X_inactive)\n",
    "    \n",
    "    print(statistic.__name__.title())\n",
    "    \n",
    "    fig, (ax1, ax2) = plt.subplots(2, 1, constrained_layout=True)\n",
    "    #fig.suptitle(statistic.__name__.title())\n",
    "    \n",
    "    ax1.set_title(\"Active\")\n",
    "    sns.violinplot(statistic_active, color=\"green\", ax=ax1)\n",
    "    \n",
    "    ax2.set_title(\"Inactive\")\n",
    "    sns.violinplot(statistic_inactive, color=\"purple\", ax=ax2)\n",
    "    \n",
    "    plt.show()\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Minimum, Maximum and Mean of Descriptors are equally distributed between `Activity` classes."
   ]
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}