Demo Notebook Feature Engineering.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Demo Notebook Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Initializing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## load packages and set constants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "%load_ext autotime\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%config IPCompleter.greedy=True\n",
    "from IPython.core.debugger import set_trace\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as ticker\n",
    "import seaborn as sns\n",
    "import time\n",
    "import importlib\n",
    "import re\n",
    "import math\n",
    "import itertools\n",
    "import operator\n",
    "from datetime import datetime\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.linear_model import ElasticNet\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.gaussian_process import GaussianProcessClassifier\n",
    "from sklearn.gaussian_process.kernels import DotProduct\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "#from sklearn.preprocessing import LabelEncoder\n",
    "#from sklearn.metrics import r2_score, mean_squared_error\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import chi2\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pd.set_option(\"display.max_rows\", 101)\n",
    "pd.set_option(\"display.max_columns\", 60)\n",
    "plt.rcParams[\"figure.figsize\"] = (30, 15)\n",
    "#plt.style.use('ggplot')\n",
    "sns.set_style('whitegrid')\n",
    "\n",
    "random_seed = 999"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 42.4 ms\n"
     ]
    }
   ],
   "source": [
    "dict_model = {}\n",
    "dict_model['LR'] = 'Logistic Regression'\n",
    "dict_model['RF'] = 'Random Forest Classifier'\n",
    "dict_model['AB'] = 'AdaBoost Classifier'\n",
    "dict_model['EN'] = 'ElasticNet'\n",
    "dict_model['SVC'] = 'C-Support Vector Classification'\n",
    "dict_model['SGD'] = 'Stochastic Gradient Descent'\n",
    "dict_model['kNN'] = 'Nearest Neighbors Classification'\n",
    "dict_model['GPC'] = 'Gaussian Process Classification'\n",
    "dict_model['DT'] = 'Decision Tree Classifier'\n",
    "dict_model['GB'] = 'Gradient Boosting Classifier'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 169 ms\n"
     ]
    }
   ],
   "source": [
    "def score_MCC(y_true, y_pred):\n",
    "    tn, fp, fn, tp = confusion_matrix(y_true, y_pred).ravel()\n",
    "    with np.errstate(divide='ignore',invalid='ignore'):\n",
    "        mcc = ((tp * tn) -(fp * fn)) / (math.sqrt((tp + fp) * (tp + fn) * (tn + fp) * (tn + fn)))\n",
    "    return tp, fp, tn, fn, mcc\n",
    "\n",
    "def load_madelon(local=True):\n",
    "    if local:\n",
    "        #URLS for dataset included in package\n",
    "        train_data_url='data/madelon/madelon_train.data'\n",
    "        train_label_url='data/madelon/madelon_train.labels'\n",
    "        test_data_url='data/madelon/madelon_valid.data'\n",
    "        test_label_url='data/madelon/madelon_valid.labels'\n",
    "    else:\n",
    "        # URLS for dataset via UCI\n",
    "        train_data_url='https://archive.ics.uci.edu/ml/machine-learning-databases/madelon/MADELON/madelon_train.data'\n",
    "        train_label_url='https://archive.ics.uci.edu/ml/machine-learning-databases/madelon/MADELON/madelon_train.labels'\n",
    "        test_data_url='https://archive.ics.uci.edu/ml/machine-learning-databases/madelon/MADELON/madelon_valid.data'\n",
    "        test_label_url='https://archive.ics.uci.edu/ml/machine-learning-databases/madelon/MADELON/madelon_valid.labels'\n",
    "    \n",
    "    X_data = pd.read_csv(train_data_url, sep=\" \", header=None)\n",
    "    y_data = pd.read_csv(train_label_url, sep=\" \", header=None)\n",
    "    data = X_data.iloc[:,0:500]\n",
    "    data['target'] = y_data[0]\n",
    "    X_test = pd.read_csv(test_data_url, sep=\" \", header=None)\n",
    "    y_test = pd.read_csv(test_label_url, sep=\" \", header=None)\n",
    "    test = X_test.iloc[:,0:500]\n",
    "    test['target'] = y_test[0]\n",
    "    return data, test\n",
    "\n",
    "def run_alg(dict_alg, X_train, X_test, y_train, y_test):\n",
    "    col_names = ['algorithm', 'TP', 'FP', 'TN', 'FN', 'MCC']\n",
    "    df = pd.DataFrame(columns=col_names)\n",
    "    for key in dict_alg.keys():\n",
    "        model = dict_alg[key]\n",
    "        model.fit(X_train, y_train)\n",
    "        y_pred = model.predict(X_test)\n",
    "        # handling the continuos results and forcing them to binary\n",
    "        if key in ['EN']:\n",
    "            y_pred = [1 if i >=0 else -1 for i in y_pred]\n",
    "        df = df.append(pd.DataFrame([dict(zip(col_names,[dict_model[key]] + list(score_MCC(y_test, y_pred))))]), ignore_index=True)\n",
    "    return df[col_names] # forcing right layout\n",
    "\n",
    "def run_tune_alg(dict_alg, dict_param, X_train, X_test, y_train, y_test):\n",
    "    col_names = ['algorithm', 'TP', 'FP', 'TN', 'FN', 'MCC']\n",
    "    df = pd.DataFrame(columns=col_names)\n",
    "    for key in dict_alg.keys():\n",
    "        print(key)\n",
    "        model = dict_alg[key]\n",
    "        param_grid = dict_param[key]\n",
    "        grid_search = GridSearchCV(model, param_grid=param_grid)\n",
    "        grid_search.fit(X_train, y_train)\n",
    "        y_pred = grid_search.predict(X_test)\n",
    "        if key in ['EN']:\n",
    "            y_pred = [1 if i >=0 else -1 for i in y_pred]\n",
    "        df = df.append(pd.DataFrame([dict(zip(col_names,[dict_model[key]] + list(score_MCC(y_test, y_pred))))]), ignore_index=True)\n",
    "    return df[col_names] # forcing right layout\n",
    "\n",
    "def prep_data(X, y, random_seed):\n",
    "    return train_test_split(X, y, test_size=0.33, random_state=random_seed)\n",
    "\n",
    "def prep_df(df_train, df_test):\n",
    "    X_train = df_train.iloc[:,:-1]\n",
    "    y_train = df_train.iloc[:,-1]\n",
    "    X_test = df_test.iloc[:,:-1]\n",
    "    y_test = df_test.iloc[:,-1]\n",
    "    return X_train, X_test, y_train, y_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Loading Data Sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 373 ms\n"
     ]
    }
   ],
   "source": [
    "df_train, df_test = load_madelon()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "hidden": true
   },
   "outputs": [
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       "      <td>432</td>\n",
       "      <td>494</td>\n",
       "      <td>493</td>\n",
       "      <td>434</td>\n",
       "      <td>505</td>\n",
       "      <td>497</td>\n",
       "      <td>486</td>\n",
       "      <td>471</td>\n",
       "      <td>467</td>\n",
       "      <td>476</td>\n",
       "      <td>455</td>\n",
       "      <td>517</td>\n",
       "      <td>483</td>\n",
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       "      <td>512</td>\n",
       "      <td>552</td>\n",
       "      <td>505</td>\n",
       "      <td>464</td>\n",
       "      <td>472</td>\n",
       "      <td>419</td>\n",
       "      <td>...</td>\n",
       "      <td>476</td>\n",
       "      <td>500</td>\n",
       "      <td>479</td>\n",
       "      <td>483</td>\n",
       "      <td>560</td>\n",
       "      <td>474</td>\n",
       "      <td>486</td>\n",
       "      <td>459</td>\n",
       "      <td>494</td>\n",
       "      <td>509</td>\n",
       "      <td>456</td>\n",
       "      <td>472</td>\n",
       "      <td>520</td>\n",
       "      <td>468</td>\n",
       "      <td>482</td>\n",
       "      <td>473</td>\n",
       "      <td>473</td>\n",
       "      <td>498</td>\n",
       "      <td>485</td>\n",
       "      <td>488</td>\n",
       "      <td>479</td>\n",
       "      <td>452</td>\n",
       "      <td>435</td>\n",
       "      <td>486</td>\n",
       "      <td>508</td>\n",
       "      <td>481</td>\n",
       "      <td>504</td>\n",
       "      <td>495</td>\n",
       "      <td>511</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 501 columns</p>\n",
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       "4  497  486  471  467  476  455  517  483  465  512  552  505  464  472  419   \n",
       "\n",
       "    ...    471  472  473  474  475  476  477  478  479  480  481  482  483  \\\n",
       "0   ...    470  515  476  467  401  485  499  495  490  508  463  487  531   \n",
       "1   ...    478  465  479  482  549  470  506  481  494  492  448  492  447   \n",
       "2   ...    481  485  478  479  454  503  501  500  484  479  470  466  529   \n",
       "3   ...    481  457  476  481  602  500  503  481  487  465  473  477  515   \n",
       "4   ...    476  500  479  483  560  474  486  459  494  509  456  472  520   \n",
       "\n",
       "   484  485  486  487  488  489  490  491  492  493  494  495  496  497  498  \\\n",
       "0  515  476  482  463  467  479  477  481  477  485  511  485  481  479  475   \n",
       "1  598  507  478  483  492  485  463  478  487  338  513  486  483  492  510   \n",
       "2  482  486  487  480  522  481  487  481  492  650  506  501  480  489  499   \n",
       "3  525  468  487  458  489  490  491  480  474  572  454  469  475  482  494   \n",
       "4  468  482  473  473  498  485  488  479  452  435  486  508  481  504  495   \n",
       "\n",
       "   499  target  \n",
       "0  496      -1  \n",
       "1  517      -1  \n",
       "2  498      -1  \n",
       "3  461       1  \n",
       "4  511       1  \n",
       "\n",
       "[5 rows x 501 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 108 ms\n"
     ]
    }
   ],
   "source": [
    "df_train.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2000, 501)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 134 ms\n"
     ]
    }
   ],
   "source": [
    "df_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "hidden": true
   },
   "outputs": [
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       "      <td>485</td>\n",
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       "      <td>540</td>\n",
       "      <td>477</td>\n",
       "      <td>485</td>\n",
       "      <td>386</td>\n",
       "      <td>477</td>\n",
       "      <td>469</td>\n",
       "      <td>474</td>\n",
       "      <td>484</td>\n",
       "      <td>485</td>\n",
       "      <td>506</td>\n",
       "      <td>493</td>\n",
       "      <td>465</td>\n",
       "      <td>562</td>\n",
       "      <td>478</td>\n",
       "      <td>474</td>\n",
       "      <td>498</td>\n",
       "      <td>482</td>\n",
       "      <td>481</td>\n",
       "      <td>521</td>\n",
       "      <td>475</td>\n",
       "      <td>470</td>\n",
       "      <td>463</td>\n",
       "      <td>509</td>\n",
       "      <td>525</td>\n",
       "      <td>479</td>\n",
       "      <td>467</td>\n",
       "      <td>552</td>\n",
       "      <td>517</td>\n",
       "      <td>1</td>\n",
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       "      <td>474</td>\n",
       "      <td>523</td>\n",
       "      <td>479</td>\n",
       "      <td>495</td>\n",
       "      <td>488</td>\n",
       "      <td>485</td>\n",
       "      <td>476</td>\n",
       "      <td>497</td>\n",
       "      <td>478</td>\n",
       "      <td>510</td>\n",
       "      <td>479</td>\n",
       "      <td>514</td>\n",
       "      <td>446</td>\n",
       "      <td>470</td>\n",
       "      <td>487</td>\n",
       "      <td>480</td>\n",
       "      <td>441</td>\n",
       "      <td>467</td>\n",
       "      <td>485</td>\n",
       "      <td>474</td>\n",
       "      <td>502</td>\n",
       "      <td>492</td>\n",
       "      <td>476</td>\n",
       "      <td>460</td>\n",
       "      <td>505</td>\n",
       "      <td>481</td>\n",
       "      <td>506</td>\n",
       "      <td>469</td>\n",
       "      <td>457</td>\n",
       "      <td>...</td>\n",
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       "      <td>487</td>\n",
       "      <td>478</td>\n",
       "      <td>471</td>\n",
       "      <td>527</td>\n",
       "      <td>519</td>\n",
       "      <td>478</td>\n",
       "      <td>520</td>\n",
       "      <td>478</td>\n",
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       "      <td>455</td>\n",
       "      <td>500</td>\n",
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       "      <td>510</td>\n",
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       "      <td>522</td>\n",
       "      <td>343</td>\n",
       "      <td>509</td>\n",
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       "      <td>475</td>\n",
       "      <td>493</td>\n",
       "      <td>506</td>\n",
       "      <td>491</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 501 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     0    1    2    3    4    5    6    7    8    9   10   11   12   13   14  \\\n",
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       "4  495  474  523  479  495  488  485  476  497  478  510  479  514  446  470   \n",
       "\n",
       "    15   16   17   18   19   20   21   22   23   24   25   26   27   28   29  \\\n",
       "0  500  494  493  472  477  497  503  474  472  483  538  454  468  490  510   \n",
       "1  481  471  466  476  476  485  470  490  488  504  490  483  478  491  490   \n",
       "2  477  458  516  482  467  466  499  479  476  512  547  457  461  479  474   \n",
       "3  492  476  481  443  486  508  492  484  468  559  465  491  503  472  485   \n",
       "4  487  480  441  467  485  474  502  492  476  460  505  481  506  469  457   \n",
       "\n",
       "    ...    471  472  473  474  475  476  477  478  479  480  481  482  483  \\\n",
       "0   ...    469  454  477  472  538  509  488  493  487  499  486  462  523   \n",
       "1   ...    476  439  478  481  429  481  493  501  492  480  465  474  528   \n",
       "2   ...    478  494  478  486  517  478  494  462  495  503  502  462  462   \n",
       "3   ...    481  540  477  485  386  477  469  474  484  485  506  493  465   \n",
       "4   ...    482  487  478  471  527  519  478  520  478  470  455  500  488   \n",
       "\n",
       "   484  485  486  487  488  489  490  491  492  493  494  495  496  497  498  \\\n",
       "0  432  484  474  458  485  464  455  480  543  259  413  520  485  498  523   \n",
       "1  472  483  472  492  469  468  486  480  535  534  514  452  484  495  548   \n",
       "2  507  480  472  446  508  477  491  476  498  495  508  528  486  465  508   \n",
       "3  562  478  474  498  482  481  521  475  470  463  509  525  479  467  552   \n",
       "4  480  495  481  498  495  464  510  471  522  343  509  520  475  493  506   \n",
       "\n",
       "   499  target  \n",
       "0  510      -1  \n",
       "1  477      -1  \n",
       "2  503      -1  \n",
       "3  517       1  \n",
       "4  491      -1  \n",
       "\n",
       "[5 rows x 501 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 104 ms\n"
     ]
    }
   ],
   "source": [
    "df_test.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(600, 501)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 34 ms\n"
     ]
    }
   ],
   "source": [
    "df_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Setting up stack of algorithms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "- Logistic Regression\n",
    "- Random Forest\n",
    "- Ada Boost\n",
    "- Elastic Net\n",
    "- SVC (rbf kernel)\n",
    "- Stochastic Gradient Descent\n",
    "- Nearest Neighbors Classification\n",
    "- Gaussian Process Classification (GPC)\n",
    "- Decision Tree\n",
    "- Gradient Boosting Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 45.2 ms\n"
     ]
    }
   ],
   "source": [
    "dict_alg = {}\n",
    "dict_alg['LR'] = LogisticRegression(random_state=random_seed)\n",
    "dict_alg['RF'] = RandomForestClassifier(random_state=random_seed)\n",
    "dict_alg['AB'] = AdaBoostClassifier(random_state=random_seed)\n",
    "dict_alg['EN'] = ElasticNet(random_state=random_seed)\n",
    "dict_alg['SVC'] = SVC(random_state=random_seed) # rbf kernel is default\n",
    "dict_alg['SGD'] = SGDClassifier(random_state=random_seed)\n",
    "dict_alg['kNN'] = KNeighborsClassifier()\n",
    "dict_alg['GPC'] = GaussianProcessClassifier(random_state=random_seed)\n",
    "dict_alg['DT'] = DecisionTreeClassifier(random_state=random_seed)\n",
    "dict_alg['GB'] = GradientBoostingClassifier(random_state=random_seed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "## scoring via Matthews Correlation Coefficient (MCC):"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "for more details refer https://en.wikipedia.org/wiki/Matthews_correlation_coefficient\n",
    "\n",
    "$\n",
    "MCC = \\frac{TP \\times TN – FP \\times FN}{\\sqrt{ (TP +FP) \\times (TP + FN) \\times (TN + FP) \\times (TN + FN)}}\n",
    "$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "## output from each algorithm to be captured:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "| algorithm | TP | FP | TN | FN | MCC |\n",
    "| --- | --- | --- | --- | --- | --- |\n",
    "| Logistic Regression | | | | | |\n",
    "| Random Forest | | | | | |\n",
    "| Ada Boost | | | | | |\n",
    "| Elastic Net | | | | | |\n",
    "| SVC (rbf kernel) | | | | | |\n",
    "| Stochastic Gradient Descent | | | | | |\n",
    "| Nearest Neighbors Classification | | | | | |\n",
    "| Gaussian Process Classification (GPC) | | | | | |\n",
    "| Decision Tree | | | | | | "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reducing DataSet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Running Boruta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "hidden": true,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "\n",
      "\n",
      "BorutaPy finished running.\n",
      "\n",
      "Iteration: \t100 / 100\n",
      "Confirmed: \t20\n",
      "Tentative: \t1\n",
      "Rejected: \t479\n",
      "Confirmed   1: 28\n",
      "Confirmed   1: 48\n",
      "Confirmed   1: 64\n",
      "Confirmed   1: 105\n",
      "Confirmed   1: 128\n",
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      "Confirmed   1: 433\n",
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      "Confirmed   1: 451\n",
      "Confirmed   1: 453\n",
      "Confirmed   1: 472\n",
      "Confirmed   1: 475\n",
      "Confirmed   1: 493\n",
      "Rejected    2: 10\n",
      "Rejected    3: 349\n",
      "Rejected    4: 431\n",
      "Rejected    5: 455\n",
      "Rejected    7: 278\n",
      "Rejected    7: 334\n",
      "Rejected    7: 348\n",
      "Rejected    9: 4\n",
      "Rejected   10: 282\n",
      "Rejected   10: 417\n",
      "Rejected   12: 296\n",
      "Rejected   13: 136\n",
      "Rejected   14: 411\n",
      "Rejected   15: 481\n",
      "Rejected   16: 424\n",
      "Rejected   17: 286\n",
      "Rejected   18: 199\n",
      "Rejected   18: 410\n",
      "Rejected   20: 298\n",
      "Rejected   21: 149\n",
      "Rejected   22: 38\n",
      "Rejected   23: 184\n",
      "Rejected   24: 13\n",
      "Rejected   25: 323\n",
      "Rejected   26: 55\n",
      "Rejected   27: 211\n",
      "Rejected   28: 303\n",
      "Rejected   29: 285\n",
      "Rejected   30: 430\n",
      "Rejected   31: 377\n",
      "Rejected   32: 244\n",
      "Rejected   33: 56\n",
      "Rejected   34: 343\n",
      "Rejected   35: 363\n",
      "Rejected   36: 457\n",
      "Rejected   37: 243\n",
      "Rejected   38: 116\n",
      "Rejected   39: 119\n",
      "Rejected   40: 329\n",
      "Rejected   41: 310\n",
      "Rejected   42: 467\n",
      "Rejected   43: 317\n",
      "Rejected   44: 347\n",
      "Rejected   45: 137\n",
      "Rejected   46: 238\n",
      "Rejected   47: 147\n",
      "Rejected   48: 32\n",
      "Rejected   49: 44\n",
      "Rejected   49: 384\n",
      "Rejected   51: 180\n",
      "Rejected   52: 43\n",
      "Rejected   54: 217\n",
      "Rejected   54: 389\n",
      "Rejected   54: 478\n",
      "Rejected   56: 129\n",
      "Rejected   56: 130\n",
      "Rejected   58: 73\n",
      "Rejected   59: 370\n",
      "Rejected   60: 156\n",
      "Rejected   60: 412\n",
      "Rejected   62: 106\n",
      "Rejected   63: 494\n",
      "Rejected   64: 294\n",
      "Rejected   65: 444\n",
      "Rejected   66: 284\n",
      "Rejected   67: 259\n",
      "Rejected   67: 419\n",
      "Rejected   69: 300\n",
      "Rejected   70: 425\n",
      "Rejected   71: 374\n",
      "Rejected   72: 60\n",
      "Rejected   72: 175\n",
      "Rejected   74: 490\n",
      "Rejected   75: 131\n",
      "Rejected   76: 152\n",
      "Rejected   77: 111\n",
      "Rejected   78: 358\n",
      "Rejected   78: 463\n",
      "Rejected   80: 413\n",
      "Rejected   81: 361\n",
      "Rejected   82: 436\n",
      "Rejected   83: 277\n",
      "Rejected   84: 367\n",
      "Rejected   85: 177\n",
      "Rejected   86: 354\n",
      "Rejected   87: 46\n",
      "Rejected   88: 81\n",
      "Rejected   88: 458\n",
      "Rejected   90: 301\n",
      "Rejected   91: 251\n",
      "Rejected   92: 6\n",
      "Rejected   93: 26\n",
      "Rejected   93: 140\n",
      "Rejected   95: 306\n",
      "Rejected   96: 226\n",
      "Rejected   97: 420\n",
      "Rejected   98: 12\n",
      "Rejected   99: 264\n",
      "Rejected  100: 321\n",
      "Rejected  101: 496\n",
      "Rejected  102: 224\n",
      "Rejected  103: 171\n",
      "Rejected  104: 59\n",
      "Rejected  105: 468\n",
      "Rejected  106: 202\n",
      "Rejected  107: 319\n",
      "Rejected  107: 359\n",
      "Rejected  111: 51\n",
      "Rejected  111: 109\n",
      "Rejected  111: 245\n",
      "Rejected  111: 326\n",
      "Rejected  111: 382\n",
      "Rejected  114: 79\n",
      "Rejected  115: 77\n",
      "Rejected  116: 74\n",
      "Rejected  117: 406\n",
      "Rejected  118: 99\n",
      "Rejected  119: 5\n",
      "Rejected  120: 54\n",
      "Rejected  120: 68\n",
      "Rejected  122: 340\n",
      "Rejected  122: 397\n",
      "Rejected  124: 191\n",
      "Rejected  126: 85\n",
      "Rejected  126: 235\n",
      "Rejected  126: 287\n",
      "Rejected  128: 250\n",
      "Rejected  129: 84\n",
      "Rejected  129: 127\n",
      "Rejected  131: 18\n",
      "Rejected  132: 295\n",
      "Rejected  133: 125\n",
      "Rejected  134: 188\n",
      "Rejected  135: 42\n",
      "Rejected  135: 49\n",
      "Rejected  137: 213\n",
      "Rejected  138: 362\n",
      "Rejected  139: 408\n",
      "Rejected  140: 379\n",
      "Rejected  141: 215\n",
      "Rejected  141: 432\n",
      "Rejected  143: 448\n",
      "Rejected  144: 33\n",
      "Rejected  144: 351\n",
      "Rejected  146: 29\n",
      "Rejected  146: 279\n",
      "Rejected  148: 139\n",
      "Rejected  149: 159\n",
      "Rejected  150: 150\n",
      "Rejected  151: 104\n",
      "Rejected  152: 372\n",
      "Rejected  152: 391\n",
      "Rejected  154: 337\n",
      "Rejected  155: 271\n",
      "Rejected  156: 393\n",
      "Rejected  157: 158\n",
      "Rejected  157: 403\n",
      "Rejected  159: 209\n",
      "Rejected  159: 484\n",
      "Rejected  161: 66\n",
      "Rejected  162: 262\n",
      "Rejected  163: 386\n",
      "Rejected  164: 269\n",
      "Rejected  165: 465\n",
      "Rejected  165: 495\n",
      "Rejected  167: 164\n",
      "Rejected  168: 192\n",
      "Rejected  169: 135\n",
      "Rejected  170: 121\n",
      "Rejected  171: 221\n",
      "Rejected  172: 330\n",
      "Rejected  173: 477\n",
      "Rejected  174: 273\n",
      "Rejected  175: 308\n",
      "Rejected  175: 314\n",
      "Rejected  177: 82\n",
      "Rejected  177: 234\n",
      "Rejected  179: 246\n",
      "Rejected  180: 488\n",
      "Rejected  181: 255\n",
      "Rejected  182: 89\n",
      "Rejected  182: 454\n",
      "Rejected  184: 441\n",
      "Rejected  185: 445\n",
      "Rejected  187: 193\n",
      "Rejected  187: 270\n",
      "Rejected  187: 456\n",
      "Rejected  189: 229\n",
      "Rejected  190: 331\n",
      "Rejected  191: 356\n",
      "Rejected  192: 100\n",
      "Rejected  193: 265\n",
      "Rejected  194: 434\n",
      "Rejected  195: 200\n",
      "Rejected  196: 290\n",
      "Rejected  197: 339\n",
      "Rejected  197: 414\n",
      "Rejected  199: 346\n",
      "Rejected  199: 487\n",
      "Rejected  202: 36\n",
      "Rejected  202: 223\n",
      "Rejected  202: 352\n",
      "Rejected  204: 98\n",
      "Rejected  204: 302\n",
      "Rejected  206: 427\n",
      "Rejected  207: 25\n",
      "Rejected  208: 95\n",
      "Rejected  209: 464\n",
      "Rejected  210: 24\n",
      "Rejected  211: 114\n",
      "Rejected  211: 305\n",
      "Rejected  213: 78\n",
      "Rejected  213: 220\n",
      "Rejected  215: 83\n",
      "Rejected  216: 483\n",
      "Rejected  217: 65\n",
      "Rejected  217: 375\n",
      "Rejected  220: 144\n",
      "Rejected  220: 155\n",
      "Rejected  220: 161\n",
      "Rejected  220: 466\n",
      "Rejected  223: 172\n",
      "Rejected  224: 292\n",
      "Rejected  225: 47\n",
      "Rejected  225: 240\n",
      "Rejected  227: 383\n",
      "Rejected  228: 15\n",
      "Rejected  229: 124\n",
      "Rejected  230: 497\n",
      "Rejected  231: 443\n",
      "Rejected  232: 91\n",
      "Rejected  233: 88\n",
      "Rejected  235: 289\n",
      "Rejected  235: 365\n",
      "Rejected  235: 366\n",
      "Rejected  237: 17\n",
      "Rejected  237: 459\n",
      "Rejected  239: 206\n",
      "Rejected  240: 471\n",
      "Rejected  241: 260\n",
      "Rejected  242: 72\n",
      "Rejected  243: 52\n",
      "Rejected  243: 174\n",
      "Rejected  245: 31\n",
      "Rejected  245: 304\n",
      "Rejected  247: 409\n",
      "Rejected  248: 480\n",
      "Rejected  249: 97\n",
      "Rejected  250: 388\n",
      "Rejected  251: 45\n",
      "Rejected  251: 267\n",
      "Rejected  253: 182\n",
      "Rejected  254: 233\n",
      "Rejected  255: 201\n",
      "Rejected  256: 181\n",
      "Rejected  256: 263\n",
      "Rejected  258: 16\n",
      "Rejected  259: 163\n",
      "Rejected  259: 256\n",
      "Rejected  261: 462\n",
      "Rejected  262: 416\n",
      "Rejected  263: 11\n",
      "Rejected  263: 41\n",
      "Rejected  265: 190\n",
      "Rejected  266: 107\n",
      "Rejected  267: 103\n",
      "Rejected  268: 1\n",
      "Rejected  269: 333\n",
      "Rejected  269: 470\n",
      "Rejected  271: 27\n",
      "Rejected  271: 422\n",
      "Rejected  273: 57\n",
      "Rejected  273: 160\n",
      "Rejected  275: 313\n",
      "Rejected  276: 148\n",
      "Rejected  277: 162\n",
      "Rejected  277: 341\n",
      "Rejected  279: 75\n",
      "Rejected  280: 87\n",
      "Rejected  281: 34\n",
      "Rejected  282: 371\n",
      "Rejected  283: 257\n",
      "Rejected  283: 268\n",
      "Rejected  285: 115\n",
      "Rejected  286: 415\n",
      "Rejected  287: 117\n",
      "Rejected  288: 437\n",
      "Rejected  289: 179\n",
      "Rejected  291: 118\n",
      "Rejected  291: 134\n",
      "Rejected  291: 486\n",
      "Rejected  293: 35\n",
      "Rejected  294: 50\n",
      "Rejected  294: 61\n",
      "Rejected  297: 165\n",
      "Rejected  297: 207\n",
      "Rejected  297: 450\n",
      "Rejected  299: 381\n",
      "Rejected  300: 342\n",
      "Rejected  301: 19\n",
      "Rejected  302: 492\n",
      "Rejected  303: 212\n",
      "Rejected  304: 62\n",
      "Rejected  305: 266\n",
      "Rejected  306: 170\n",
      "Rejected  307: 438\n",
      "Rejected  308: 307\n",
      "Rejected  309: 328\n",
      "Rejected  310: 418\n",
      "Rejected  310: 440\n",
      "Rejected  312: 58\n",
      "Rejected  312: 498\n",
      "Rejected  314: 146\n",
      "Rejected  314: 275\n",
      "Rejected  316: 315\n",
      "Rejected  317: 299\n",
      "Rejected  318: 222\n",
      "Rejected  319: 369\n",
      "Rejected  320: 67\n",
      "Rejected  321: 141\n",
      "Rejected  321: 312\n",
      "Rejected  323: 110\n",
      "Rejected  323: 218\n",
      "Rejected  326: 183\n",
      "Rejected  326: 187\n",
      "Rejected  326: 236\n",
      "Rejected  329: 53\n",
      "Rejected  329: 216\n",
      "Rejected  329: 469\n",
      "Rejected  331: 196\n",
      "Rejected  332: 208\n",
      "Rejected  333: 194\n",
      "Rejected  333: 291\n",
      "Rejected  335: 449\n",
      "Rejected  336: 373\n",
      "Rejected  337: 421\n",
      "Rejected  338: 185\n",
      "Rejected  339: 231\n",
      "Rejected  340: 157\n",
      "Rejected  341: 145\n",
      "Rejected  342: 167\n",
      "Rejected  342: 225\n",
      "Rejected  344: 380\n",
      "Rejected  346: 93\n",
      "Rejected  346: 335\n",
      "Rejected  346: 344\n",
      "Rejected  346: 395\n",
      "Rejected  349: 186\n",
      "Rejected  350: 70\n",
      "Rejected  350: 460\n",
      "Rejected  352: 197\n",
      "Rejected  353: 71\n",
      "Rejected  354: 398\n",
      "Rejected  355: 151\n",
      "Rejected  356: 368\n",
      "Rejected  357: 272\n",
      "Rejected  358: 499\n",
      "Rejected  359: 37\n",
      "Rejected  359: 253\n",
      "Rejected  361: 297\n",
      "Rejected  361: 309\n",
      "Rejected  363: 2\n",
      "Rejected  364: 320\n",
      "Rejected  364: 435\n",
      "Rejected  366: 446\n",
      "Rejected  367: 92\n",
      "Rejected  368: 126\n",
      "Rejected  368: 401\n",
      "Rejected  370: 94\n",
      "Rejected  370: 360\n",
      "Rejected  372: 258\n",
      "Rejected  373: 396\n",
      "Rejected  374: 489\n",
      "Rejected  375: 123\n",
      "Rejected  376: 210\n",
      "Rejected  376: 316\n",
      "Rejected  378: 21\n",
      "Rejected  379: 214\n",
      "Rejected  379: 428\n",
      "Rejected  381: 355\n",
      "Rejected  382: 176\n",
      "Rejected  383: 138\n",
      "Rejected  383: 232\n",
      "Rejected  385: 461\n",
      "Rejected  386: 23\n",
      "Rejected  386: 324\n",
      "Rejected  388: 447\n",
      "Rejected  389: 327\n",
      "Rejected  390: 195\n",
      "Rejected  390: 429\n",
      "Rejected  392: 426\n",
      "Rejected  393: 169\n",
      "Rejected  394: 242\n",
      "Rejected  395: 230\n",
      "Rejected  395: 400\n",
      "Rejected  397: 8\n",
      "Rejected  398: 102\n",
      "Rejected  399: 30\n",
      "Rejected  400: 80\n",
      "Rejected  401: 178\n",
      "Rejected  402: 203\n",
      "Rejected  403: 76\n",
      "Rejected  404: 353\n",
      "Rejected  405: 14\n",
      "Rejected  405: 108\n",
      "Rejected  407: 249\n",
      "Rejected  408: 293\n",
      "Rejected  408: 311\n",
      "Rejected  410: 452\n",
      "Rejected  411: 132\n",
      "Rejected  411: 248\n",
      "Rejected  413: 405\n",
      "Rejected  413: 479\n",
      "Rejected  415: 345\n",
      "Rejected  416: 474\n",
      "Rejected  417: 239\n",
      "Rejected  419: 261\n",
      "Rejected  419: 322\n",
      "Rejected  419: 350\n",
      "Rejected  419: 439\n",
      "Rejected  422: 482\n",
      "Rejected  423: 69\n",
      "Rejected  424: 385\n",
      "Rejected  425: 40\n",
      "Rejected  425: 476\n",
      "Rejected  427: 122\n",
      "Rejected  428: 86\n",
      "Rejected  429: 112\n",
      "Rejected  430: 189\n",
      "Rejected  431: 63\n",
      "Rejected  431: 254\n",
      "Rejected  433: 205\n",
      "Rejected  434: 274\n",
      "Rejected  435: 113\n",
      "Rejected  436: 96\n",
      "Rejected  437: 101\n",
      "Rejected  438: 0\n",
      "Rejected  438: 142\n",
      "Rejected  440: 485\n",
      "Rejected  441: 133\n",
      "Rejected  442: 20\n",
      "Rejected  443: 143\n",
      "Rejected  444: 22\n",
      "Rejected  444: 376\n",
      "Rejected  446: 9\n",
      "Rejected  447: 364\n",
      "Rejected  448: 392\n",
      "Rejected  449: 3\n",
      "Rejected  449: 491\n",
      "Rejected  451: 399\n",
      "Rejected  452: 394\n",
      "Rejected  453: 407\n",
      "Rejected  454: 219\n",
      "Rejected  455: 252\n",
      "Rejected  456: 120\n",
      "Rejected  456: 237\n",
      "Rejected  458: 390\n",
      "Rejected  459: 283\n",
      "Rejected  460: 173\n",
      "Rejected  461: 332\n",
      "Rejected  462: 198\n",
      "Rejected  463: 325\n",
      "Rejected  464: 227\n",
      "Rejected  465: 473\n",
      "Rejected  466: 247\n",
      "Rejected  467: 168\n",
      "Rejected  468: 154\n",
      "Rejected  469: 357\n",
      "Rejected  470: 7\n",
      "Rejected  470: 280\n",
      "Rejected  472: 166\n",
      "Rejected  473: 288\n",
      "Rejected  474: 39\n",
      "Rejected  475: 387\n",
      "Rejected  476: 402\n",
      "Rejected  477: 423\n",
      "Rejected  478: 276\n",
      "Rejected  479: 90\n",
      "Rejected  479: 404\n",
      "Rejected  481: 228\n",
      "time: 1min 38s\n"
     ]
    }
   ],
   "source": [
    "from boruta import BorutaPy\n",
    "\n",
    "def borutas_rank(li_dum_feat, ranking_):\n",
    "    dict_rank = dict(zip(li_dum_feat, ranking_))\n",
    "    ranked = sorted(dict_rank.items(), key=operator.itemgetter(1))\n",
    "    for i in range(len(ranked)):\n",
    "        if ranked[i][1] == 1:\n",
    "            print('Confirmed {:>3}: {}'.format(ranked[i][1], ranked[i][0]))\n",
    "        else:\n",
    "            print('Rejected {:>4}: {}'.format(ranked[i][1], ranked[i][0]))\n",
    "\n",
    "X_train = df_train.iloc[:,:-1].values\n",
    "y_train = df_train.iloc[:,-1].ravel()\n",
    "X_test = df_test.iloc[:,:-1].values\n",
    "y_test = df_test.iloc[:,-1].ravel()\n",
    "li_dum_feat = list(df_train.iloc[:,:-1].columns.values)\n",
    "rf = RandomForestClassifier(n_jobs=-1, class_weight='balanced', max_depth=5)\n",
    "feat_selector = BorutaPy(rf, n_estimators='auto', verbose=1, random_state=1)\n",
    "feat_selector.fit(X_train,y_train)\n",
    "X_train = feat_selector.transform(X_train)\n",
    "X_test = feat_selector.transform(X_test)\n",
    "borutas_rank(li_dum_feat, feat_selector.ranking_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Closer look at the selection\n",
    "Comment: I have evaluated the burota package used above in-depth. Although I agree with the principle idea in the paper (but I think it does not constitute a universally viable and usable solution), I take issue with the ACTUAL implementation: The packages presented, apart from having un-fixed bugs and poor maintenance do not follow the algorithm and logic laid out in the academic paper. The result is an approximation that does a more or less close guess of what the actual paper asks for.<br>\n",
    "**So let's have a closer look:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 39.2 ms\n"
     ]
    }
   ],
   "source": [
    "def borutas_confirmed(li_dum_feat, ranking_):\n",
    "    dict_rank = dict(zip(li_dum_feat, ranking_))\n",
    "    ranked = sorted(dict_rank.items(), key=operator.itemgetter(1))\n",
    "    result = []\n",
    "    for i in range(len(ranked)):\n",
    "        if ranked[i][1] == 1:\n",
    "            result += [ranked[i][0]]\n",
    "    return result\n",
    "li_keep = borutas_confirmed(li_dum_feat, feat_selector.ranking_)\n",
    "li_target = ['target']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 65.9 ms\n"
     ]
    }
   ],
   "source": [
    "df_explore = df_train[li_keep + li_target].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
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AYEl0YAAAsCIW8QIAAPgWOjAAAFiR3do9CgoYAAAsqLEPS/R1Rsuz6upqpaen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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe841f30828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 677 ms\n"
     ]
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(10, 8))\n",
    "corr = df_explore.corr()\n",
    "sns.heatmap(corr, mask=np.zeros_like(corr, dtype=np.bool), cmap=sns.diverging_palette(220, 10, as_cmap=True),\n",
    "            square=True, ax=ax);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Yes, the odd one that does not belong is feature 204. But knowing a bit about this dataset, I know that there are 20 features in it that carry signal. So let's run a baseline model fit for a start."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Setting Baseline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 5.8 s\n"
     ]
    }
   ],
   "source": [
    "df_base = run_alg(dict_alg, X_train, X_test, y_train, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>algorithm</th>\n",
       "      <th>TP</th>\n",
       "      <th>FP</th>\n",
       "      <th>TN</th>\n",
       "      <th>FN</th>\n",
       "      <th>MCC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>175</td>\n",
       "      <td>119</td>\n",
       "      <td>181</td>\n",
       "      <td>125</td>\n",
       "      <td>0.186704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Random Forest Classifier</td>\n",
       "      <td>264</td>\n",
       "      <td>36</td>\n",
       "      <td>264</td>\n",
       "      <td>36</td>\n",
       "      <td>0.760000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AdaBoost Classifier</td>\n",
       "      <td>198</td>\n",
       "      <td>123</td>\n",
       "      <td>177</td>\n",
       "      <td>102</td>\n",
       "      <td>0.250615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ElasticNet</td>\n",
       "      <td>179</td>\n",
       "      <td>118</td>\n",
       "      <td>182</td>\n",
       "      <td>121</td>\n",
       "      <td>0.203344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>C-Support Vector Classification</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>300</td>\n",
       "      <td>298</td>\n",
       "      <td>0.057831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Stochastic Gradient Descent</td>\n",
       "      <td>300</td>\n",
       "      <td>300</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Nearest Neighbors Classification</td>\n",
       "      <td>266</td>\n",
       "      <td>37</td>\n",
       "      <td>263</td>\n",
       "      <td>34</td>\n",
       "      <td>0.763372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Gaussian Process Classification</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>300</td>\n",
       "      <td>296</td>\n",
       "      <td>0.081923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Decision Tree Classifier</td>\n",
       "      <td>256</td>\n",
       "      <td>55</td>\n",
       "      <td>245</td>\n",
       "      <td>44</td>\n",
       "      <td>0.670451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Gradient Boosting Classifier</td>\n",
       "      <td>248</td>\n",
       "      <td>58</td>\n",
       "      <td>242</td>\n",
       "      <td>52</td>\n",
       "      <td>0.633460</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          algorithm   TP   FP   TN   FN       MCC\n",
       "0               Logistic Regression  175  119  181  125  0.186704\n",
       "1          Random Forest Classifier  264   36  264   36  0.760000\n",
       "2               AdaBoost Classifier  198  123  177  102  0.250615\n",
       "3                        ElasticNet  179  118  182  121  0.203344\n",
       "4   C-Support Vector Classification    2    0  300  298  0.057831\n",
       "5       Stochastic Gradient Descent  300  300    0    0       NaN\n",
       "6  Nearest Neighbors Classification  266   37  263   34  0.763372\n",
       "7   Gaussian Process Classification    4    0  300  296  0.081923\n",
       "8          Decision Tree Classifier  256   55  245   44  0.670451\n",
       "9      Gradient Boosting Classifier  248   58  242   52  0.633460"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 65.3 ms\n"
     ]
    }
   ],
   "source": [
    "df_base"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Diving deeper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 315 ms\n"
     ]
    }
   ],
   "source": [
    "X = df_train.iloc[:,:-1]\n",
    "y = df_train.iloc[:,-1]\n",
    "model = RandomForestClassifier(random_state=random_seed)\n",
    "model.fit(X, y)\n",
    "\n",
    "dict_rank = dict(zip(li_dum_feat, model.feature_importances_))\n",
    "ranked = sorted(dict_rank.items(), key=operator.itemgetter(1))\n",
    "\n",
    "dic_top = ranked[-30:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(326, 0.0043743811091274702),\n",
       " (342, 0.004392572238136239),\n",
       " (278, 0.0043946488729463499),\n",
       " (45, 0.004401840213279949),\n",
       " (389, 0.0044353502928818987),\n",
       " (344, 0.0045144348635325973),\n",
       " (108, 0.0046220156420849132),\n",
       " (140, 0.0046357174533606105),\n",
       " (427, 0.0046647413335699061),\n",
       " (481, 0.0048679308986957995),\n",
       " (431, 0.005090092243996096),\n",
       " (434, 0.005594778806843251),\n",
       " (472, 0.0057183232520326656),\n",
       " (281, 0.0057307057213648229),\n",
       " (493, 0.0058081235656055078),\n",
       " (453, 0.0058391723602477562),\n",
       " (64, 0.0067001194971565937),\n",
       " (451, 0.0069999614047553714),\n",
       " (318, 0.0072034489585373358),\n",
       " (455, 0.0073330518611686383),\n",
       " (336, 0.0081585056893605512),\n",
       " (48, 0.0086445393948557034),\n",
       " (442, 0.0092396534918066184),\n",
       " (475, 0.0093723482182482588),\n",
       " (378, 0.0094064154818299522),\n",
       " (28, 0.0098226505157841155),\n",
       " (128, 0.011334410374096575),\n",
       " (338, 0.020204571457461944),\n",
       " (105, 0.021293717052790533),\n",
       " (241, 0.022774417333280238)]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 36.8 ms\n"
     ]
    }
   ],
   "source": [
    "dic_top"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Most in here are \"old\" customers. After some back-and forth, it is clear that '455' is valuable, but was rejected by the boruta package (actually as 5th feature to be rejected).<br>\n",
    "So lets do the correlation matrix agian without 204 but with 455:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
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FmZ4SAACtjw/vGCotLdUzzzyjM2fO6I477tDEiRPPGbNz507NmTNHTqdTXbp00cqVK1v0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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe841f30ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 799 ms\n"
     ]
    }
   ],
   "source": [
    "li_keep.remove(204)\n",
    "li_keep += [455]\n",
    "df_explore = df_train[li_keep + li_target].copy()\n",
    "f, ax = plt.subplots(figsize=(10, 8))\n",
    "corr = df_explore.corr()\n",
    "sns.heatmap(corr, mask=np.zeros_like(corr, dtype=np.bool), cmap=sns.diverging_palette(220, 10, as_cmap=True),\n",
    "            square=True, ax=ax);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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9LyEhQWvWrNHy5cu1YMEC1dTUmJkSAABNk80LGFPbHbt27VJBQYGKiopUU1Oj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\n+2Lu3LkICwuTZGNrv3z8+DG2bt1qtV9WVVUhLS0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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe841bcaac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 875 ms\n"
     ]
    }
   ],
   "source": [
    "li_seq = [48, 378, 451, 318, 28, 475, 241, 128, 105, 493, 453, 433, 281, 153, 472, 442, 338, 455, 64, 336, 'target']\n",
    "df_train_sml = df_train[li_seq].copy()\n",
    "df_test_sml = df_test[li_seq].copy()\n",
    "f, ax = plt.subplots(figsize=(10, 8))\n",
    "corr = df_train_sml.corr()\n",
    "sns.heatmap(corr, mask=np.zeros_like(corr, dtype=np.bool), cmap=sns.diverging_palette(220, 10, as_cmap=True),\n",
    "            square=True, ax=ax);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 5.66 s\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = prep_df(df_train_sml, df_test_sml)\n",
    "df_deeper = run_alg(dict_alg, X_train, X_test, y_train, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>algorithm</th>\n",
       "      <th>TP</th>\n",
       "      <th>FP</th>\n",
       "      <th>TN</th>\n",
       "      <th>FN</th>\n",
       "      <th>MCC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>175</td>\n",
       "      <td>122</td>\n",
       "      <td>178</td>\n",
       "      <td>125</td>\n",
       "      <td>0.176676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Random Forest Classifier</td>\n",
       "      <td>260</td>\n",
       "      <td>36</td>\n",
       "      <td>264</td>\n",
       "      <td>40</td>\n",
       "      <td>0.746733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AdaBoost Classifier</td>\n",
       "      <td>200</td>\n",
       "      <td>103</td>\n",
       "      <td>197</td>\n",
       "      <td>100</td>\n",
       "      <td>0.323350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ElasticNet</td>\n",
       "      <td>180</td>\n",
       "      <td>119</td>\n",
       "      <td>181</td>\n",
       "      <td>120</td>\n",
       "      <td>0.203334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>C-Support Vector Classification</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>300</td>\n",
       "      <td>299</td>\n",
       "      <td>0.040859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Stochastic Gradient Descent</td>\n",
       "      <td>229</td>\n",
       "      <td>218</td>\n",
       "      <td>82</td>\n",
       "      <td>71</td>\n",
       "      <td>0.042062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Nearest Neighbors Classification</td>\n",
       "      <td>272</td>\n",
       "      <td>34</td>\n",
       "      <td>266</td>\n",
       "      <td>28</td>\n",
       "      <td>0.793492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Gaussian Process Classification</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>300</td>\n",
       "      <td>293</td>\n",
       "      <td>0.108648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Decision Tree Classifier</td>\n",
       "      <td>255</td>\n",
       "      <td>58</td>\n",
       "      <td>242</td>\n",
       "      <td>45</td>\n",
       "      <td>0.657284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Gradient Boosting Classifier</td>\n",
       "      <td>245</td>\n",
       "      <td>54</td>\n",
       "      <td>246</td>\n",
       "      <td>55</td>\n",
       "      <td>0.636670</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          algorithm   TP   FP   TN   FN       MCC\n",
       "0               Logistic Regression  175  122  178  125  0.176676\n",
       "1          Random Forest Classifier  260   36  264   40  0.746733\n",
       "2               AdaBoost Classifier  200  103  197  100  0.323350\n",
       "3                        ElasticNet  180  119  181  120  0.203334\n",
       "4   C-Support Vector Classification    1    0  300  299  0.040859\n",
       "5       Stochastic Gradient Descent  229  218   82   71  0.042062\n",
       "6  Nearest Neighbors Classification  272   34  266   28  0.793492\n",
       "7   Gaussian Process Classification    7    0  300  293  0.108648\n",
       "8          Decision Tree Classifier  255   58  242   45  0.657284\n",
       "9      Gradient Boosting Classifier  245   54  246   55  0.636670"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 69.5 ms\n"
     ]
    }
   ],
   "source": [
    "df_deeper"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "So let's have a closer look at these features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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aHrcxNXTaYHN6sDRfMeK1+Tnp+OByB7psruAbYTKxSO4zoVCIt99+G1KpFG63G4899hju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yIK3bVh0dHRkEqlkEgkWLhwIdUlQjzoxjatK9Sm8Yc6esTtueeeQ15eHvbv34+33noL9957L954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e3f0jePqhXORlJE040f9qcSa+aOuBskWDrY8uoqUQQeJrpsBbm7nMMuh0OqxatWpC\nn3tLXJPw6O634L0v2mG2OgAAF9X9+PHahXTuBYhMJpuwmXKgikGMV1FRga1bt2LHjh0QCASevb1W\nrFiB3NxctLe3o7CwcFK/qYoGRGJBgUiMCQhuXLMtCAFE5u8r2DFNVyCCEBIaNNCLczd1gzh9vQff\nyU3D/fNSJz0v5HPxsCIJp24P4Wq3CStyJrchkc3bLMNMRNJd30iJJRhxaLQmOFkWhy8PAKwTP1qV\nhjv9Nvz5zjCart7B0swEABNnEGL59xGsOPh8PrRaLdRqNWQyGZRKJfbt2zehTUlJCT744AOUl5ej\nubnZUwwiPT3dUwzi3r4qlQoLFiwA4JoxX7RoEQDAYDAgNTUVXC4XarUaKpXKM/tOCCGEBBMN9OLc\nr05ch4DHwYYV2VO2uV+agMt6K07f6MHyeSkhjC5+BKvk9HjjZxn8eT83oVAYMXeiI+WueDDiuGjq\nwNn2PuiH7fibNQosV6ShgGVxu78NX3WOYO39ueBxOBNmEGL59xHMOJ555pmAF4PYt28f2tvbwTAM\ncnJyUFNTAwA4d+4c3n77bfB4PHA4HNTU1CAtLS1IvwFCCCHkLhroxbFmdT/+dFWH7xdIkZQw9Z8C\nh2GwdkkmGi51o73Pv3wEMjPustF6vd5TNjqYswwlJSV44YUX8Oyzz0Kn00GlUmHlypUhPWYykZNl\ncfpGD3LTE7FqvmvmnMMw+H6BDL/96g7a9EO4T043WgJh9erVqKqqmvDYXItBvPPOO17bl5WVoays\nbA7REkIIIbNDA704Vv/ndoiFPKxbnOmz7XdyJfj0Wx2+uNkbgsjij3umoLq6Gvv27Qv6LEN+fj42\nbNiAjRs3gsvlorq6mipuhtk1jQn9Zhs2rsiekI+XL0uGkM/B5c4BGugREiZOJ4tWjQl60wgKslMg\nTRGGOyRCCPGJBnpxSj84gk+uaPA/H16ABL7vC3w+l4MHF6bj9PUedBqpAmcwFBcX44033sADDzzg\neSxYswwAsG3bNmzbtm0OEZNA+svtPqSK+CjInjiY43E4uD87FVe7B2B3OMMUHSHx7bVPruG3X90B\nADTd7MWP1y3EvDRRmKMihJDpUc3uOPUfX6thc7D4n9/L87vPgwvSAQCHvp5ceYwQMntqgxm3e4bx\n4MJ0cDmTq2sW5qRi1O5Em34oDNEREt+ULRrUNd3GgwvT8fz38yHgcfDvZ+/A4WTDHRohhEyLBnpx\niGVZfHShE2uXZGBhZpLf/dISBVgmF+M/z6lhtdPMAiGB8vvmbgBAkcJ7kY7FWUngcRjc6qGBHiGh\nZHc48eonrVg+LwUVK+dBmiLEE6vmwWi24XLXQLjDI4SQadFALw61dA6gw2BGZVHOjPs+tDADvUNW\n/Omq1ndjQohPLMvidxe7kJeRCEmiwGsbHpcDRXoiFUMiJMR+39wNtcGC5x9f6pltXyYXI0ucgDM3\ne8CyNKtHSDBZrA446TybNRroxaFjzd3gcxmU3S/33fge+bJkKNJF+GAsV4EQMjdXu01o0w9NOZvn\ntjAzCZr+EZhGbCGKjJD4xrIs9n9+C/fJxXi8QOp5nMMwWLckE5qBEVxU94cxQkJim6p3GA/u/Qyv\n/+k6zqkM4Q4nKvk10GtqakJZWRlKS0tx4MCBSc+zLIs9e/agtLQUFRUVuHr1qs++r732GtavX4+K\nigps374dJpMJANDZ2YmVK1eisrISlZWVqK6unusxknGcThbKyxoUL81CaiJ/xv05DIMfPpiHs+0G\n3NQNBiFCQuJLw6Uu8LkMCnNSp223MDMJLIALd4yhCYyQOPbh2Q78yyfXcVM/hPuzU3Doa/WE51fM\nSwWHAa1uISTAPjzbgQ/PduC3X97B078+C5vDCbGQh2PN3RikG50z5nOg53A4UFtbi/r6eiiVShw/\nfhxtbW0T2jQ1NUGlUuHEiRPYvXs3du3a5bPv2rVrcfz4cRw7dgwLFixAXV2d5/Vyc3PR0NCAhoYG\n1NbWBvBw44/7hHH/t+/TG9AMjGD9NBuk+/I3a+ZDwOXg389SURZC5sLhZPH75m4UL5UiUTB9EWSF\nJBEcBjh7m+5qEhIKFzqM4HMZrPByE0Yk4GJRVjJOXNXR8k1CgqBZ3Y9OowWbinLwN2sUcDhZ/OVW\nX7jDijo+B3otLS3Iy8uDQqGAQCBAeXk5GhsbJ7RpbGzEpk2bwDAMioqKYDKZoNfrp+27bt068Hiu\nC5uioiJotXRXLBSua12zcI8ty5r1a2QkJ2BDoRwfX+iE2WoPVGiExJ2zt/ugM41i0+p5PtsKeBzk\npIlwsYNm9AgJNpvDiZbOfiyflwrhFFsQ3Z+dgvbeYdykariEBFxLVz8kiXwU5qQiMzkBy3NS8dXt\nPioGOEM+B3o6nQ5y+d1cLplMBp1ON20buVwOnU7nV18A+Pjjj/Hoo496fu7s7ERlZSWefvppnD9/\nfmZHRKZ1QzeIVfNdJ81cPP1wHgZH7fj9pe4ARUZI/DlysQvJCTw8XiDzq/28NBGudpvgpLLuhARV\nq8aEEZsT38mVTNnm/rE9Lz9rnXxdQwiZPfOoHW36IRTmpIFhXEWQ1uRJMGp34g4VJZuRsG+Yvn//\nfnC5XDzxxBMAAKlUilOnTkEikeDKlSvYvn07lEolkpOTJ/VtbW0NaCwjIyMBf83ZClQsGq3J82+L\nzYkOgxmPKNImvPb4Nt7YbTZotBrPz62tw0hiWSyQCFB36jpWJg95TsRgi8XPiMQns9WOP17WoHxl\n9pQzBvfKSRPhbLsBdwzmGW2NQgiZmW86jEgV8bEoa+rzLEXEx1JZMr681Yd/fGxJCKMjJHhG7Q4c\nb9GgTT+EguwUbHlQEbJrPLer3SY4WWDl/LvLphdkJIHLMLjVM4x8mTik8UQznwM9mUw2YVmlTqeD\nTCabto1Wq4VMJoPdbp+275EjR/D555/j/fff9/wRCQQCCASuEuMrVqxAbm4u2tvbUVhYOCm2goIC\nf4/TL62trQF/zdkKVCwXTXfz6Jo7+wEY8NS65SgYV+FvfBtvNFoNsuV3c/oKCnIBANuGk/HPH1+G\ngS/FuvzMOcfqj1j8jMa7cOFCQF+PRK4/XdVi2OrA5u/M97vPvDQRAOD/PX0Lq+anQaM14aKpAz98\nKDdYYRISd7QDI7ipG8Jjy7LA8XGB+71FGfiv852w2p0Q8KiQOYl+p6/34MIdI3LTE/HV7T6894UK\nP163MKQxXNOakJ4kQHaq0POYgMeBIl2E2720VHomfP5fqbCwECqVCmq1GlarFUqlEiUlJRPalJSU\n4OjRo2BZFpcuXYJYLIZUKp22b1NTE+rr67F//36IRCLPaxkMBjgcDgCAWq2GSqWCQqEI5DHHrfae\nYSTwOLjSNTChQMtsVRblIDM5Af/fmdsBjJKQ+HDkmy7Ml4jw3QXpfveRpiSAy2HQ3W8JYmSExLff\nXewCC2D1NMs23b63OBMWm2PsRioh0c1otuLPbb0oUqRh66OLUCAX47VPrsE4bA1ZDE6WharPtWrl\n3pnERVnJ6DJaYLE6QhZPtPM5o8fj8VBdXY2qqio4HA5s3rwZ+fn5OHToEABgy5YtKC4uxunTp1Fa\nWgqRSIS9e/dO2xcAdu/eDavVimeffRYAsGrVKtTW1uLcuXN4++23wePxwOFwUFNTg7S06feXIv65\n3TuMhZlJPu9Q+kvI5+J/fS8P+z69gRu6QSylqXRC/KIZsODPbb34p5J8cDj+n488DgfyFCG6aKBH\nSFCwLIvDF9TIy0j0K5f94UXpYBjgy1t9M7ppQ0gk+upWH1gW+O/3y8BhGJTeL0fryZv46EIn/u7R\nRSGJoWdwFBabAwsyJi+bXpyVjJPX9GjvpTw9f/mVo1dcXIzi4uIJj23ZssXzb4Zh8NJLL/ndFwA+\n/fRTr+3LyspQVlbmT1hkBkwWG3qHRvHdBb7vUM7E0w/n4e2TN/HzI5c9S9BoGRkh0/vdxS6wLPCD\n1Tkz7jsvTYTLXf1U0p2QIPimw4jbPcN+n5tpiQIUyFPw1e0+7Ph+fpCjIyS4WrUmLMpKQlqiK4VK\nnirEmjwJ/v3sHfx43cIZ3ZicLdVYsZUFGYmTnpsvEYHDAF395qDHEStoQXmccN/9WJQ5uajNXEiS\nBPhOrgSX1P20kSUhfmBZFke+6cKaPAkWzKKgijxViBGbE6YR2tpktpqbm1FWVobS0lIcOHBg0vMs\ny2LPnj0oLS1FRUUFrl696nmuqanJa9+33noLFRUVqKysxHPPPTehwnRdXR1KS0tRVlaGM2fOBPfg\nyJwcPt+JRAEXhV72zpvKd/LS0NI5AAdVw/XLVOeQ22zOv3feeQePPPIIKisrUVlZidOnT4fkWGLJ\nrZ4h9A5Zcd9YNVm3Hz2cC1WfGefvhGZrnzt9ZogTeEhPEkx6js/lIDM5AZqBkZDEEgtooBcnbvcO\nQ8jnIDtN6LvxDK1dkgmnk8WXt2kjSxJfxue6+pvz+k1HP9r0Q9j8gP9FWMaTil3LyfSD9EU3Gw6H\nAwcPHkR9fT2USiWOHz+Otra2CW2ampqgUqlw4sQJ7N69G7t27fL0ra2t9dq3qqoKx44dQ0NDAx57\n7DG8++67AIC2tjYolUoolUrU19ejpqbGk4dOIovZasfxFg02FmYjwc9KuABQpJBgaNSOWz1UJMKX\n6c4ht9mcfwDwzDPPoKGhAQ0NDV5XkpHpNY5tE1Ign5iG8/0CGXgcBiev6UMSh6pvGHkZiVNW+sxO\nFdJAbwbCvr0CCY0OwzBy0xMDkp9378VsZnIC7stOwdnbBjy2VDrpeVrKSchd7/9FBbGQhydW+d4k\n3RtZiutmjd40Ci+7zhAfWlpaIJPJPEW+ysvL0djYiCVL7pbHb2xsxKZNm8AwDIqKimAymaDX69HV\n1YW8vDyvfcdvAWSxWDwXKY2NjSgvL4dAIIBCoUBeXh5aWlqwevXqEB418ccfL2sxNGrH36xRoG0G\nm6AXjVWxvtTRT7nqPrS0tEx5DrnN5vwjc3fymh7ZqULPsk23480aKNITcfRiF3LT7y6nDMa1nWHY\nin6zDd9blDFlm+xUEZo7B9Bvtk6KlUxGM3pxYMTmgN40CkX65PXOgfLIElflsW86QjO1T0g0cc/2\n7f/8FpQt3fgfaxRISpjdfbYkAReJAi70g6MBjjI+6HQ6ZGTcvYiQyWQTllm628jlcs/PcrkcOp1u\n0uP39n3zzTdRXFyMY8eO4Sc/+YnX1/L2fiQyHL6gxoKMxBnnsi/KTIJYyMNFNVXe9MWf82G2598H\nH3yAiooK/PznP8fAwEAQjyL22B1ONKsHptyfdZlMDK1pBAOW4KboXO12fW7urYS8cW+58K1m+j2g\niQvN6MUBtdEMFphwJybQ8jISMV8iwhdtvXhwYXrAKnsSEku+vNULlgX+9nsLZv0aDMNAKk6A3jQC\nZAfvnCYzt3PnTuzcuRN1dXX44IMPsGPHjhn1b21t9fr4yMjIlM+FSyTGBMw+rs4BK766bcDfrpbg\n2rVr0Gj9u4hsbXXlvy+R8HH2phatrd4vqyLx9xWJMc3Wli1b8I//+I9gGAb/+q//ildffRWvvPKK\n17bjjzmWfgf+mOp42w2uSpfJzCg0Ws2k59N5rpzwczc6sVzmGmi5//YD6dQV180SzsgANNpBr204\nVicA4POLNyGx9kz7evH2+XpDA7040GEwgwGgkATvopBhGKxbkon/OKfGNY0J98/zP5GdkHgwNGrH\nl7f7sEqRhlwv1cRmQpoixOXOAbDs1Hc9iXcymQx9fXfziXU6HWQy2aQ2Wq3W87NWq4VMJoPdbp/w\nuLe+AFBRUYGtW7dix44dk15rqj4AUFBQ4PXx1tbWKZ8Ll0iMCZh9XP9+9DIEXA7+aeMDyBIn4KLJ\nvz1mL46NB9NSknFJ04OzBhGeWbsgYHEFU7BjunDhwqTH/DkfZnP+ZWZmeh5/6qmn8A//8A9TxjX+\nmCPxcwmmqY63+esOAF0oXDgPGV62FZGzLFKuD6HPykW2PBsAUFAQ+KWb+ovfIC2Rj4WK6aveiq+Y\n0OdM9PnZxcvn6+1cc6Olm3FAbTBDmpIA4QySy2dj+bxUpCXyceZmb1DfJ1Y1NTXhhRdeCGglstde\new3r169HRUUFtm/fDpPJdVXS2dmJlStXeiqUVVdXB/8A41zTjR7YHSxKlknn/FpScQIsNgfMNqry\nN1OFhYXQarVQq9WwWq1QKpUoKSmZ0KakpARHjx4Fy7K4dOkSxGIxpFIpCgsLoVKpvPZVqVSe/o2N\njVi0aJHntZRKJaxWK9RqNVQqFVauXBmy4yW+9Zut+PhCFyqL5iFL7HvvPG8UkkSwAO1x6cN055Db\nbM4/vf5uoZDPPvvMs2cz8U9zZz9SRXyvlS4B1838vIwkqPqCu63Bt90mzEv1fQNTmpKANr33GT8y\nEc3oxTiWZaE2WLB8XorvxnPE5TBYuzgTyssaqA3moOYExhp3NbEXX3wRjz/+OJ588kmUlJRMSDIf\nX4msubkZu3btwuHDhz19Dx48CJlMNqHv2rVr8cILL4DH4+H1119HXV0dfvaznwEAcnNz0dDQEK5D\njis60wi+vNWH1bkSZM7yQnI8qdi1dMZgoS0WZorH4+GZZ55BVVUVHA4HNm/ejPz8fBw6dAiAawlY\ncXExTp8+jdLSUohEIuzdu9fTt7q6elJfANi3bx/a29vBMAxycnJQU1MDAMjPz8eGDRuwceNGcLlc\nVFdXg8sN7k03MjPv/0UFi82B59YtnPVrzB/7vlMbaH+v6Ux1Ds31/Hv99ddx7do1AEBOTg5qa2vD\nc4BR6pJ6AKsUaVNWugRcKTqXu4JXBGVwxIbbvcN4vMD3zdDM5AR8qzGBZdlpYyY00It5hmErLDYH\n5gdx2eZ4a/IkaLymw5m2XvzwQaq26S93JTKZTAaBQBCwSmTr1q3z9C8qKsInn3wS8mOLdw4ni4ZL\nXRDwOFi/Qu67gx9kKa7BosFMZfpnY/Xq1aiqqprw2JYtWzz/ZhgGL730kte+xcXFXku3v/POO1O+\n37Zt27Bt27ZZRkuCqd9sxa/PtOO/3y9DQfbsb4gmJ/AgSeRDbaSBni/ezqG5nn+vv/56YIOMIxar\nAzd0gyj1McBakOEq1KLqG0ZREAZ618dy8vw5hvlQAAAgAElEQVSZ0csSJ2Cw3Y7eIeusZ+HjBQ30\nYpx7GUmOJDS5PAl8Lh5ckI4zN3thHLZCMsUyADKRt2piLS0t07aZrhLZvX0B4OOPP8aGDRs8P3d2\ndqKyshJisRjPP/881qxZE8hDImP2/qEVqj4znvzOfCTPstLmvZITeBDxuTBYaKBHyFwcaLqNoVE7\n7pOn+LUP5nQU6Ym4E+SlbYQEwvi/9U6jGQ4ni94hK+TTDLLkqUIk8DhQ9ZlRpJhZZVp/XNe5Bnqy\nVN/7PWeO5RHe7hmigZ4PNNCLcV39FnA5jGcGIBQeWpSBppu9uKjuR8l9c89HInO3f/9+cLlcPPHE\nEwAAqVSKU6dOQSKR4MqVK9i+fTuUSuWEvcDcIqlqVaTE4o5Do3UtHRm2OsEwDBL5jKcSmdXhxHsX\nDGhoNWGVXIjshBFPNTNv1cr8rfLnliZk0Ddsg0arCUr1s5mItM+FEH/c7hlC/Zl2rFKkQe7HxaUv\nCkkiWjoHoDeNQJoy99cjJBT0JtdWPTIff7MchkFueiLu9AXn++ambghJAi7SRHyfbd0DvfbeYTw0\nzZ57hAZ6Ma+r3wJ5ihA8Tujq7kgSBViQkYRmdT/+27KskL1vNAtWJTIAOHLkCD7//HO8//77nrXs\nAoEAAoFrtnXFihXIzc1Fe3s7CgsLJ8UmFAojpmpVOCtojb8DqtGaIJelQ2ez4Y+XNRgcdeXKcRkG\nv79hgSwlAde0gxgcseOZv1qAJdLkCVuOXPQypsuWe9+/aCrzNQ5c7uyHXCZHQUHe7A4qQCKlspm/\ncUxXoYzEB6eTxf/TcAUJfA42BGhJtWJs5czbjTcnVZ5eHfw0eUJmRTc4Ai6HmbIQy3jzJYn4/Loe\nVrsz4HFc1w5iiUzsV85dWiIfAh4Ht3vDe5MzGlDVzRjGsiy6+y3ImWbjyWApUqShZ2gU3f0jIX/v\naOSuJqbX6wNaiaypqQn19fXYv38/RKK7fwcGgwEOh2vZn7sSoDvHj/jn1HU9/uu8GmmJfDyxah6e\nWDUP6/IzsTrXldC+YYUcH/7dQ9j1xPKg7CspFQsxYmcxbKXlm4TM1L+dasMXbX34+YYCiIW+ZxD8\nIU8VgQHQPUDfeyR66E2jyEpOAJfj+3tqvkQEFkB3EKrL3tQPYql08qoibzgMg4UZSbjdMxTwOGIN\nzejFsA6DGSM2Z1gGeoU5qTjW0o3mzv6Qv3c0clcTq66uxr59+wJWiWz37t2wWq149tlnAQCrVq1C\nbW0tzp07h7fffhs8Hg8cDgc1NTVIS0sLz8FHIfWAFY2tJhQp0rD5O/MnfEH+8KHQFCGSji3H1pno\nopIQwDXrrtGapt0D74cP5eI/z3Xgzc9u4Aerc7DlQQUOfa0OyPsLeBxkihOgoS0WSBTRD474XSXd\nfT0Z6G1EDMNW9A5ZsUwu9rvPwswk3KAtFnyigV4M+7bbtT4sOy30uQIiAReLs5Ko/O0MFBcX4403\n3sADDzzgeWyulcg+/fRTr+3LyspQVlY2x4jjk8PJ4uStIWQmJ2BTUY5fd0GDwb3Fgn5wNCzvT0g0\ncbIsegdH8c8fteA/z6vxSH4m9v6gMODfTfNShUHfa4yQQBm1O2A02/BAnn/XiSkiPlKEvIAP9G6M\nFWLJl4nRZfTvtRdlJeGzVh3sDid4XFqgOBUa6MWwVo0JDHwn2AZLQXYKGi5146Z+CEtl/t+lISSS\nXewwwjTqxN9+Rw4BL3xfLilCHgRcBnqa0SNkAqvdiS9u9eKaxoR+iw0OJwur3Qm7k4WAy0HVuoX4\n5w33gR+Ei8PsVBGaOwdgHrUjMUBVdgkJlp6xG4XSGVSuzJEkotPPwZi/bo4N9JbKkv0e6OWmJ8Lu\nZKEZ8H9GMh7R/4ViWKt2EJnJCUH5MvNHgTwFDejGiataGuiRmOBwsvj8Rg+ykrhYFua/aYZhIBFx\n0TNEM3qEuA2MOPCfn7dBPziK3PRELJOJweMy4HE4yEpOwFK5GKkiPg6f7wzK+7tX0GhMI1ic5V++\nESHh4q64KZ1BZfacNBFaNSaYRmxICVB+6w3dEMQJPMhnMDHhHtypjWYa6E2DBnox7JrWFJCS0bOV\nIuJDIRHh0291+D9L8sMWByGBcqtnCIZhK9Yv9a8yWLBJRFx009JNQgAANocTyusmmG3As2sXIF8a\n+psx2WP7kHX3W2igRyJe79AoOAyQkeT/QG/+WHXZK10D+KvFmQGJ44ZuEPmy5Bl9ryokrsFdp8EC\nLA5IGDGJFrXGqMERG9QGC7LDONADgKVyMVq6BmActoY1DkIC4ZsOI0R8LhZJfJehDgWJiAvTiB2D\nI7Zwh0JI2P3xigZ9Zgf+Zo0iLIM8AEhO4CFFyIOGKm+SKNA3bEVaomBGuebugiyXOwcCFsdsUnyy\n04TgMK4ZPTI1GujFqOta13rnmUyDB0N+VjJYFvjiVm9Y4yBkrkZsDnzbbcLK+alhK8Byr3QRFwBw\nu4f2EiLxraPPjK/bDSiUCWdUuS8YslNF0AxQ5U0S+fqGRpGZPLMbl0kJPEgS+WjpCsxAr3doFIZh\n64wHenwuB9mpIqgNNNCbDg30YlSre6AX5hm9HEkixEIe/nyTBnokul3tHoDdyeI7uZJwh+IhGRvo\n3aK9hEic+7dTN8FhGKyZH/rthO41L02InsFR2ByB31SakEBhWRZ9w1akz2DZpltOmggtAdo+64bW\nXYhl5jdoFOkiqANcGCbW+DXQa2pqQllZGUpLS3HgwIFJz7Msiz179qC0tBQVFRW4evWqz76vvfYa\n1q9fj4qKCmzfvh0mk8nzXF1dHUpLS1FWVoYzZ87M5fji1g3tIMQJPKSKApMoO1tcDoO/WpyBMzd7\nwbJsWGMhZC6+1QwiVcT35CdEgpQELjgMDfRIfNMPjuDIN1347sJ0JAu44Q4H2akiOFna45JEtqFR\nO0btzhnP6AHAfEki1AZLQNJyboyruDlTCkkizej54HOg53A4UFtbi/r6eiiVShw/fhxtbW0T2jQ1\nNUGlUuHEiRPYvXs3du3a5bPv2rVrcfz4cRw7dgwLFixAXV0dAKCtrQ1KpRJKpRL19fWoqamBw+EI\n8GHHvpv6QSyZYWJrsKzLz0JXvwV3aG8hEqVGbA606QdxnzwyirC4cTkMMpIScEtPSzdJ/Gq42A27\nk8VDC9PDHQoAeHLjKU+PRLK+IdcgbSaFWNxyxm54Xg7A8s0b+iGkivjImsEWD26K9EToB0cxYqNx\nwlR8DvRaWlqQl5cHhUIBgUCA8vJyNDY2TmjT2NiITZs2gWEYFBUVwWQyQa/XT9t33bp14PFcRT+L\nioqg1Wo9r1VeXg6BQACFQoG8vDy0tLQE+rhjXpt+GPnSyKj49fDYl+/XKkOYIyFkdv5yqxc2B4uC\n7JRwhzJJljiBZvRI3GJZFh9d6ESRIg1ScXhTFdwkSQIk8DjoDvCm0oQEUt/YbFzGLGb0PAVZAjDQ\nu6kbxNJZTkwo0l1xBHpfv1jic6Cn0+kgl8s9P8tkMuh0umnbyOVy6HQ6v/oCwMcff4xHH33U7/cj\n0zMOW9E7NIolETLQWyJNhiSRj3PtNNAj0enTb/UQ8DhYlJkU7lAmyRInQNU3DDvlA/mtubk5ZOkI\nnZ2dWLlyJSorK1FZWYnq6urgH2AcudptwnXdIDY/MD/coXhwGAbZqUKa0SMRrW9sawVJ4swHekI+\nFwsyEudceZNlWdzQDSF/lvvSurdYoMqbUwv7Pnr79+8Hl8vFE088MeO+ra2tAY1lZGQk4K85W3OJ\n5YrO9eWSMNoPjdE+51jsNhs0Ws2s+1+7Noz7Mvj44oYWra1zK0sfK58RiS5nbvZgcVYyeNzIq1+V\nlZwAm4OF2mjBwggciEYah8OBgwcP4sMPP4RMJsOTTz6JkpISLFmyxNNmfDpCc3Mzdu3ahcOHD3vS\nEQ4ePDip79q1a/HCCy+Ax+Ph9ddfR11dHX72s58BAHJzc9HQ0BCuQ45pn1zRgsMAf12YjT9e0YY7\nHI/sVBEu3DHCybLgRNByb0LcZrO1wngrclJxsWNuBVn0g6MYsNhw3ywr5bo3Su+kPL0p+RzoyWQy\nz7JKwDXjJpPJpm2j1Wohk8lgt9un7XvkyBF8/vnneP/99z1Ttv68n1tBQYGv8GektbU14K85W7ON\n5cOzHTindW2gPMhNRbZ87vt9abQaZMuzZ92/oCAX/61HgL1/uIaM+QvntLwmFj6j6Vy4cCGgr0fm\nrqPPjE6jBRUr08IdilfuvIY2/RAN9PzQ0tICmUwGhUIBAJ6UgvEDvanSEbq6ujzpCPf2Xbdunad/\nUVERPvnkk9AeWJz6rFWHNXnpkCRFxt6WbvIUIawOJ/rNNqRHWGyEAIDRbEXGHP42C3NScbxFA8Ow\nddZ/49fmUHETcN3oFPA4VHlzGj5vTxcWFkKlUkGtVsNqtUKpVKKkpGRCm5KSEhw9ehQsy+LSpUsQ\ni8WQSqXT9m1qakJ9fT32798PkUg04bWUSiWsVivUajVUKhVWrlwZ4MOObfrBEfC5DNISw1txc7zv\nLnDl6Z1rN4Y5EkJm5s9trq1BFmdFxlLoe2UmuwZ6lKfnH51Oh4yMDM/PwU5HAFzLNysrK/H000/j\n/PnzgTycuNZpNOOadhCP3y8NdyiTyFJc5yVV3iSRyjhsndN1YmFOKoC55enNZWsFAOBwGMyX0F56\n0/E5o8fj8VBdXY2qqio4HA5s3rwZ+fn5OHToEABgy5YtKC4uxunTp1FaWgqRSIS9e/dO2xcAdu/e\nDavVimeffRYAsGrVKtTW1iI/Px8bNmzAxo0bweVyUV1dDS43/OWSo4l+cBRZyQkRtVxkRU4qEngc\nXOwwonzl7GcHCQm1L271QpaS4HdFsA/PdgQ5oolEAq6rIIueBnqR4N50BKlUilOnTkEikeDKlSvY\nvn07lEolkpMn3ziYail4JC4Tj4SYjl1zXWAuFAyhtbUVGq1pzqkGgcLaXTmzbd09SGOGUSAQhP33\nda9wfYZNTU14+eWX4XQ68dRTT2Hr1q0TnmdZFi+//DJOnz4NoVCIV199FcuXL/er73vvvYfXXnsN\nX375JdLTI6MKaySy2p0YtjpmlZ/ntnxsoHelawDFS7Nm9RrXdYPIEifMakbQ/V3L4zBo7uz3/PzD\nh3JnFUus8itHr7i4GMXFxRMe27Jli+ffDMPgpZde8rsvAHz66adTvt+2bduwbds2f0IjXvQMjSJ3\nbN1ypOBzOViRk4pL6sBssElIMLm/MJwsi1PX9Fgmi6xtFe61OCuJZvT8JJPJ0NfX5/k52OkIAoEA\nAoHrImbFihXIzc1Fe3s7CgsLJ8U21VLwSFqy7hbumD4824GTd3qQniRADyNBjwnIlifNOdUgkNKu\nDMLs5CNbng2hcDjuPkNvqQjT5bm6zSZHFgA0Gg2++OILzJs3L2jHFCv6za6Km2lzGOilivjIm2NB\nlhu6QSyb5WyemyRRALWBlm5OJfIqC5A5sTmcGDDbkJU88/1Igm3V/DRc6R6AjaoDkijRMzgKs9WB\nRVmRnfu2RJqMWz3DYFk23KFEvMLCQmi12pClIxgMBs9esO50BHeOH5k9h5NFe+9wxFSX9kaWIoTO\nNBruMCJKsLbsAoBXXnkFP/vZzyL6plykMJptAADJHFN8VuSkznrp5gdf3UGrxgSWZfHh2Q7PfzMl\nSRTAYnPQXnpTCHvVTRJYhmErWAAZkTjQU6TivS+cuKEbxPJ5qeEOhxCf2ntdG5EvyIjsgd7irGQM\nWGzoG7Z6cvaIdzweD88880zI0hHOnTuHt99+GzweDxwOBzU1NUhLi8zCPtGkq9+CUbszYnNnAVee\nXlvPEBxOugHj5i3P9d69kmeSI+vu+9lnn0EqleK+++4L8hHEhn7L3Gf0AFeenrJFA+OwdcYFkYzD\nVtgcLGQpc9v/0v2+hmEr5qWJfLSOPzTQizG9Q667h5mz2AAz2FYrJACAZvUADfRIVFD1DUMs5EV8\n1Tz3xe4t/RAN9PywevVqVFVVTXgsWOkIZWVlKCsrm0O0xJvbY0uVI7nSrCxFCIeTRd/QKEBj+6Cx\nWCyoq6vDe++951f78XmJkZBrGkru41XrjeAwwHB/D8wDs5sBbW0dRorDtWRS+eVlPJAzs5Shax09\nAACubQga7exnvp0W1zZit7t0YEYS0No67Hku3j5fb2igF2P6hlx3aSLxYk+RLkJ6kgCX1EZKlvWi\nqakJ1dXV4PP5AUtQf+2113Dq1Cnw+Xzk5ubilVdeQUpKCgCgrq4OH330ETgcDn75y1/ikUceCe0B\nRziWZXGnz4wFGUkRvxRo8djytVs9w3hoUYaP1oREv1s9Q8hOFSI5IXIvY9wzFbrBUdDllkswtuzq\n6OjwVLZ1t//BD36Aw4cPIytrcpGQ8XmJ4c41DTX38do+60NaogPzsmefz1hQkIt5eTb84oQGA9xU\nFBQs8d1pnNGvhwEM4v6FOUjgzb7oYqrVDlzuBwRJyJZnoaDg7vVlvHy+023NRTl6MaZ3aBRJAi6E\n/MirVMowDApzUnG5yxTuUCKOO8n8xRdfhFKpxPHjx9HW1jahzfgE9d27d2PXrl0T+tbX10/qu3bt\nWhw/fhzHjh3DggULUFdXBwBoa2uDUqmEUqlEfX09ampqPHlExKXfbMOAxYYFETxj4JadIoSIz6WC\nLCQu2BxOdBjMET2bB7j2uGRAWyyMF4wtu5YtW4Yvv/wSJ0+exMmTJyGXy3HkyBGvgzzi0m+2zjk/\nDwBSE/nITU/ElVnk6elMI5Ak8uc0yAMAEZ+LBB4HhrG8QzIR3WKKMb1DkZejc29ybZt+EFa7EwIe\n3WdwcyeZy2SyCUnmwdrEubGxEeXl5RAIBFAoFMjLy0NLSwtWr14d2gOPYKo+d35eZFWw9YbDYbCI\nKm+SOHFdOwibg4246tL34nM5yEhOGBvoRW4uYSgFa8suMjP9ZhuWyedW7dJ9bZci4uOr23348GzH\njFZr6Uwjc87PA1yTCOlJAk8lUTIRDfRiTN/QKPLnWKo2mLJThbA5WLTph3D/vJRwhxMxgpWgPt7H\nH3+MDRs2eF5r1apVE/p42/g5nqmNFgh4nIB8EYXC4qxkXFQbwx0GIUF3scP1dx7pAz3AVZCFZvQm\nCsaWXeOdPHly7kHGsBGbA4Oj9jltlj7e/DQRrnQNYHjU7ncfq92J3qFRFGQH5jowLVEAwzBVuPWG\nBnoxZGjUjsFRe0QWYnGTp7oumr/VmGigF0L3buI8E5GUzByqWDRaE27rBiBN5EKn0056PlI2ZXbH\n0do6jBTGgk6DBZcuX0VCiGfLI+VvJFLiIMF1saMf4gQeUkWBuVANJlmKEN92mzBqp22FSGTo7ncV\nUJnLZunjzU93VbpUG81+92nvHYaTRcBupKYl8nG7Z4i2GPKCBnox5M7YUrNI3FrBLTM5AUI+B60a\nytMbLxgJ6m7eNnH25/3chEJhxCQzhyqx+mujCr3mXjySn4XscbOlbpGyKbM7joKCXDxs68YHl4xI\nyFQE7C6pvyIl4d3fOKZLXCeR76K6H4r0xIgvkgS4LmRZAOoBG4rCHQwhADqNroHeXLdWcJuflggG\nmNGm5dd1gwBcM96BIEkUYNTuhIX20puEkqRiiPskSw/QyRsMHIbBMnkKvu2mgd547iRzvV4fkk2c\nS0pKoFQqYbVaPZs4r1y5MqTHHMm6+y1wstGxNMzNs8UC5emRGGYctqK9dxiKKDk33Reyd/opf4hE\nBvdALxDFWABAwONAniqc0Yze1e4BcBkGWQGamHAfi5EKskxCM3oxRG1wnWSSpMheznJ/dgr+eEUD\nlmWj4o5sKLiTzKurq7Fv376gb+Kcn5+PDRs2YOPGjeByuaiurgaXG3mVWsOlY+xcmi+Jns1XF2Ym\ngcMAN3U00COx61JnPwDXdj3RICMpATwOA5WRBnokMnT1m8FhALEwcNeKCkkiWrr64XSy4HB8X9dd\n7hyAPFUIHjcw803uZajGYTrP7kUDvRiiNpoh5HMgisCtFca7P1uMQ193QDMwgnlp0fFlHQrFxcV4\n44038MADD3geC9YmzgCwbds2bNu2bQ4Rxy61wQxJIj+gX4TBJuRzkZeRhBtjS2IIiRXjKzd/1qoD\nAyAnSr47uBwGWeIEmtEjEaPTaEGqiA+uHwMyfynSE/G1yoDbvUNYIp2+IKDTyeJy1wAK5IFLMXAP\n9Kjy5mS0dDOGdBjMkCQKIn6WzL1sYP/ntyZtvUBIJFAbLVGzNGy8ZTKxJ/eBkFikNpghTxXOee+t\nUJKKE3Cnn5aUkcjQabQELD/PzT3DfuGO78rPdwxmDI7YA7piRsjnIIHHgdFC59m9aKAXQ9QGM9KT\nIjc/z02eIgQDQDNAJadJ5NEMWDBgsUEhib6B3lK5GKreYYxQQjqJQU6WhdpojrpzU5YihH7YjqEZ\nlJ8nJFi6jJaAVdx0y0pOQFICD2dvG3y2bRlbfp0TwIEewzCQJArQT0s3J6GBXoxwOlmog3DyBkMC\nn4v0JAE0A/5XaCIkVC51uL6EoqkQy4dnO/Dh2Q70DI7CyQJtesrTI7Gnd3AUIzZn1M22S8WuEvI3\nabadhJnVwUI3OBKwQixuDMNgYUYivrrd53OLg5bOASTwOJ7zIlDSEvlUjMULGujFiJ6hUVjtzqiY\n0QNcG6fTjB6JRBfV/eByGGSnRsdG6eO5K/xRnh6JReqxZf+KKCqSBNw9L6lQEgm33mE7WDZwWyuM\ntzArGd0DI570nKlcUvfj/nkpAc0RBFx5ekazlfbSuwcVY4ly7hw3Va9rD71omNEDAHmqCFe6TRil\nJWYkwlzq6Me8AFYDCyV3hb/rWhrokdijNrgKjmWKI3evWG8kSQIkcBnKnyVhpxt2LR8O9Iwe4Kr8\nDABf3u6bctbdbLWjpbMfP163KODvL0nkY9TuhMliR2oQji9aRd+VDPHKOFZpKFpm9OaNzZZoTTSr\nRyKHzeFES1d/VC3bHM9d4e8aDfSm1dzcjLKyMpSWluLAgQOTnmdZFnv27EFpaSkqKipw9epVz3NN\nTU1e+7722mtYv349KioqsH37dphMd/cKraurQ2lpKcrKynDmzJngHlwMc+fncSK84Ni9OAwDRSqf\nZtpJ2OmHXEsbgzEpIBUnID1JgK9u9U3Z5rzKCJuDxfcWZwT8/d2zlJ39/u/nFw9ooBcjDMNWMHCt\nUY4G8rGBHi3fJJHkunYwKnOAxpOnCNGqMfluGKccDgcOHjyI+vp6KJVKHD9+HG1tbRPaNDU1QaVS\n4cSJE9i9ezd27drl6VtbW+u179q1a3H8+HEcO3YMCxYsQF1dHQCgra0NSqUSSqUS9fX1qKmpgcNB\nKxlmatTugHZgJGrPzbw0AS3dJGGnG7KDwwAposBfK3IYBo/mZ+LUdT0cTu/LJ7+83Qceh8GaPEnA\n3989ePW1dDTe0EAvRhjNVoiFPPCjZLlZqogPAY+DnsHRcIdCiMfFDldp6Gir6jdedpoI+sFROrem\n0NLSAplMBoVCAYFAgPLycjQ2Nk5o09jYiE2bNoFhGBQVFcFkMkGv16OlpQV5eXle+65btw48nisb\noqioCFqt1vNa5eXlEAgEUCgUyMvLQ0tLS2gPOgZ0GS1gEb3nZp5EAK1pBANU/p2EkX7IjuxUUcDz\n49wev18Go9k25TYLX97qwypFGpISAp855l6OSgO9iaJjVEB8Mgxbo2bZJuCq0CQVJ0A3SDN6JHJc\n7OhHZnJC1MyMe+NeFv0tzep5pdPpkJFxd9mQTCaDTqeb1EYul3t+lsvl0Ol0kx731hcAPv74Yzz6\n6KNeX2uqPmR60VqIxS0vzfX9TJU3STjphuwB3dbgXsVLs8DnMvisdfL/4/rNVlzuGsD3FgV+2SYA\niARcCHgcdBpp6eZ4VIwlRhjNNiwaS4SNFjKxkHIWSES5qO7H6tw0MFGWAzRedqrrS/xq9wCKl2aF\nOZr4s3//fnC5XDzxxBMz7tva2ur18ZGRkSmfC5dQxqTRmnCz24Q0IQcDxh4MTNPWbrNBo9WEJK6Z\nWJHhuq9+uvkmkiwpYY7GJRL/rkhw6YZteCQneAM9sZCPhxdl4MRVLX6+4b4J36XHWzRwOFmsXyGf\n5hVmz7WXHh9dNKM3gV8DvaamJrz88stwOp146qmnsHXr1gnPsyyLl19+GadPn4ZQKMSrr76K5cuX\nT9v3j3/8I/7t3/4Nt27dwuHDh1FYWAgA6OzsxMaNG7Fw4UIAwKpVq1BbWxuwA45FdocTJostqmb0\nAECakoALHUb0m61BKfVLyEwYh61o7x3GU2vmhzuUOREJuFCki3C1m2b0vJHJZOjru1ssQKfTQSaT\nTWrjXnoJAFqtFjKZDHa7fcLj9/Y9cuQIPv/8c7z//vueC5x7X8vb+7kVFBR4fby1tXXK58IllDF9\nM3AHPd/0Y4k0Bdny7GnbarQan23CYb54CIkCLgaZ5Ij5LIP9GV64cCFor01mzuZwos/swPwgz4pX\nFuXg/zrcjL/c6sPaJZmex49e7EK+NBnL5wXvRockUUBLN+/hc+nmdMnnbrNJXF+6dCneeecdfPe7\n3530nrm5uWhoaEBDQwMN8vzQb7aBhauEczSRjpXIps2dSTi5Nxt/67MbAIC+IWuYI5q75dmp+JYG\nel4VFhZCq9VCrVbDarVCqVSipKRkQpuSkhIcPXoULMvi0qVLEIvFkEqlKCwshEql8tq3qakJ9fX1\n2L9/P0Qi0YTXUiqVsFqtUKvVUKlUWLlyZUiPOdr1W2wYHLVHbSEWwFWoIl8mplUsJGy0AyNwssD8\nIOe5/vXKbGQmC/Den9s9j3X0mXH+jhGbVucEdcVMWiKflm7ew+eM3vjkcwCe5PMlS5Z42kyVuN7V\n1TVl38WLFwfpkOKPwb21QpTNiknFrlyiG7ohrFmQHuZoSLzrMFjAAEG/2xkK989LwSdXtRgatSM5\nCEnv0YzH4+GZZ55BVVUVHA4HNm/ejKwsVQsAACAASURBVPz8fBw6dAgAsGXLFhQXF+P06dMoLS2F\nSCTC3r17PX2rq6sn9QWA3bt3w2q14tlnnwVwdzVKfn4+NmzYgI0bN4LL5aK6uhpcLjc8Bx+l1AbX\nhVu0FmJxWypNxqnrPeEOg8Qp9dgAKEciwp2+4A2GhHwufvRQHv618SZaOvtRmJOKvX9ohYDLwf+x\nOido7wu4ZvRMI3YMWGxIDUJl0Wjk8wrAWyL5vRXDZpK47k+1sc7OTlRWVkIsFuP555/HmjVrvLYL\n9NrySFqv7m8sGq0JKq1rmto2bITGHvi7+MHKeWBZFnwOcPbaHaxOGfarTzR+RiQ6dBrNkKUIkcCL\n/ovwwvmpAIDLnQNB2a8o2q1evRpVVVUTHtuyZYvn3wzD4KWXXvLat7i4GMXFxZMe//TTT6d8v23b\ntmHbtm2zjJaoDWbwOIxnW55otVQmxuELnTAOW6NuBQ6Jfu7ctflBHugBwP/6qwX46EInfvy/z2Pj\nCjk+uarFP6+/D/PSgnsj1Z0G1GW00EBvTMTd6pVKpTh16hQkEgmuXLmC7du3Q6lUIjk5eVLbQK8t\nj6Q8CH9juWjqwKUeDXgcMxbn5gRlI9lg5jxIU8ww2AV+/96j8TOaCcppCA8ny0JtNKMwJy3coQRE\n0XzXcVxS99NAj0Q9tdGCHEnwSsKHylK5GABwQzeIh4JUeZCQqXQaXatW3AW7gik9SYD3n/0u/seB\nr/Cbr+7g+/dJ8XePLAz6+7q3WOjqt+D+IOYCRhOfOXr+JJJPlbg+kyR0N4FAAInEtZHiihUrkJub\ni/b29mn7xDvDWDGTYAzygk0qTqBNZEnY9Q6OYsTmRG569C/bBFz5unkZiWhW94c7FELmZNTuQHe/\nBblRvmwTAJbKXDes4z1Pr6mpCWVlZSgtLcWBAwcmPc+yLPbs2YPS0lJUVFTg6tWrPvu+9dZbqKio\nQGVlJZ577jnawsSLTqMF6YmuLQhCIV8mxl/+7xK01q7Hr5/5Lngh2Of57qbplKfn5vO3Pl3yudts\nEtenYjAY4HA4AMCTuO7O8SPeGYetSE+KzilqWYqQNpElYaf2LGmJ/otJt1Xz03CJBnokyrVqBmF3\nspgfxYVY3OQpQogTeLgRxzc3g1Xgr6qqCseOHUNDQwMee+wxvPvuu6E+tIjX1W+GLDm0C/mEfC6E\n/NClQyQKuBDxuVR5cxyfn/hUyedzTVz/9NNPsXv3bhgMBvz93/89CgoK8Otf/xrnzp3D22+/DR6P\nBw6Hg5qaGqSlxcZyqmAxmK3IzYjOL8HxlTcfyJOEORoSr9QGMxJ4HGSN/T3GgiJFGn7f3A3twEjU\n5zaR+HWxwwgAyI2BgR7DMMiXJcf1jF6wCvyNT++xWCxRvRdqsHQaLciXROekgL8YhsF8iYhm9Mbx\na2jvLfl8ronrpaWlKC0tnfR4WVkZysrK/AmLALBYHRixOT3T1dFGmuK6AG3TD8b9QK+pqQnV1dXg\n8/m0X2WIqY1mKNITo3L581RWKe7m6a1PDc4GtYQE2yV1P1KEvJgprLBMLsYnV7RgWTYuByPBLPD3\n5ptv4ujRoxCLxfjNb34TxKOIPnaHE5qBEawdK9QVy1wDPZrRc4u4YixkZgzDY1srRGkFr7REPoR8\nTlwvZQHuLkl58cUX8fjjj+PJJ59ESUnJhLuc45ezNDc3Y9euXTh8+LCn78GDByGTySb0de9X6e1G\njHu/yng3andAOzCCx5ZlhTuUgFo+LwUCHgfnVAasX0EDPRKdLnb0R/X+eW5/uGHCRVMHTBY7jGYb\neoesMbWCIBLs3LkTO3fuRF1dHT744APs2LHDa7vx1bLjoXr2H26YMDjqgMPJQmsaxb6Gs0F7r30N\n3iu0b1w6uTCKRhucvV6TYEVH3xBaW1vj4vP1hQZ6Uc6zh16UDvQ4DIMl0mTcjPNN093LWWQyGQQC\nAe1XGUJd/RawQExcTI4n5HOxWpGGs+194Q6FkFnpHRpFh8GMDTF0o0I2torlpm4wLgd6cynwZ7fb\n/SrwV1FRga1bt0450BtfLTuSKnkHy0VTB0Z6hwEYkZbID1oV9ekUFOR6jSsYktNsOH79GuYvXILO\n9raY/3yB6Su2h6b0Dgka49iMXrQu3QSAfKkYbXGcswB4X85yb9WwmSxn8afimHu/yqeffhrnz58P\nwFFEJ7XBtcQj2jdj9uahRRn4ttsE0wgVOyLR51KHq5hQLBVJkqa4BnfX4/Q7L1gF/lQqlad/Y2Mj\nFi1aFMrDinjGsUkBcUJ49on98GzHpP+CJUfiqp7d1U/LNwGa0Yt6hmErEgWhrWoUaEukyfjdxS4M\njtggFsZGHkakm8l+lZG09CEYsdzsNiBVyIHJ2AN/F5LYbTZotN6XqITSVHG0tg4DALK5FjhZ4Hdn\nWvDg/OBdLEfK30ikxEEC45sOI3gcBjlB3mQ5lMQJPCQJuLimic+BXrAK/O3btw/t7e1gGAY5OTmo\nqakJ2zFGon7PQC/253fcN4Y6DRbkxF8a7CQ00ItyRrM1apdtui2VuTaRbdMPYXVufBZkCcVylvEE\nAgEEAtffzfj9Kt3FWsYTCoURs/Qh0MtsWJZFz390YLE0ZUbLWTRaTViWv/gbh3uZzILFDrzUqEO3\nLTGon2GkLH/yN47plrmQyHH+jtGTaxorGIZBdqoI32qCk58UDYJR4O+dd94JbJAxxmi2QSzkgceJ\n/ZHP/HEzejnxeUk5Qez83zNOGYatUb1sEwDypa5ZpP+fvTsPj6o8+wf+nSWTbSZ7ZrKQhCQkECAs\nIigCpgQGkBBCRdvSaitKQV9/tipiaxdkUUqrotX3rYWitFWhroWWiCwBEpA9hGwEsu+Zmex7ZjIz\n5/dHMmP2feacOXN/rosLMjknuc9w7jnnOc/z3I89z9MzDUnRaDS0XqUVVTZ2oFmrR5Anf3oMgO+G\nyfw7rQL+Hk5IzGS/95GQ0eg0GJFe1oB7eFiN2d/dCXfVzeg0GNkOhdiJ+jYdPHhSuXY43q4SODuI\nUFJLSywA1KNn0wxGBg1tnZgZaNvlcoO8XOAoFiLfjht6piEp27dvx1tvvUXrVVqJaY0uvhVi6SnU\nxxUpudVo0eohdaSPfGIbsiuboNUbcW+IFxrb+TXH1N/DCTq9EYXVrZjqJ2M7HGIHGto6zT1dfCcQ\nCBDi7YLSulYA/aej2Bu66tswVVMHDAwDLxvv0fv0ehm8XCVIvluNyd6u+PF9/asz2YOYmBjs27cP\n8+bNM79G61Va1q3SBoiFAl4vKB7q44rzd6txo7gO35sqZzscQkYktaTrIcy9kz2RlKNhOZqJ5e/e\ndcN9u6qRGnrE4owMg4Y2HaID3QEwbIdjFSHeLiiopoYeQEM3bVppd7e0p43P0QMAucwR6uYOtsMg\ndiatrAEBHs4QC/n7URji5QqhALhaVMd2KISMWGpJHQI9nM3LEfCJj9QRErEQOXZakIVYV2N7J4yM\n7S7DNRaTvV1RWtcGI2MfDduh8Pfuxg6U1XU19PiQvHI3JzS0dUKrN7AdCrETOr0RmRWNCObxsE0A\nkIiFmOTpgquFtJ4esQ0Mw+BGcT3uncy/+XkAIBIKMFUhQ3ZlI9uhEDtQx4NluEYrxNsVOr0RtW10\nT0kNPRtWVt8GoQBw58EEW0X3wrHVzVqWIyH2IquyETq9kdfz80xCfVyRUd6IFq2e7VA4IT09HStX\nroRSqcSBAwf6fZ9hGLz22mtQKpWIj49Hdna2+XspKSkD7nvixAnExcVh2rRpyMzMNL9eXl6OWbNm\nISEhAQkJCdi+fbtlD44HyuvboWnW4l4eFmIxmRnojszyRjDU40AszLTeMh86BUZqsnfXdb2yiV/z\ne8eCGno2rLSuDe7ODhDxoFyuXNY1PEfdRA09Yh3Xu4cymi4IfBYhl0JvZHApv4btUFhnMBhw6NAh\nHDx4EImJiTh+/Djy8/N7bZOSkoLi4mKcOnUKu3fvxo4dO8z77tq1a8B9IyMj8d5772H+/Pn9fmdw\ncDCOHTuGY8eOYdeuXRY/Rltnmp/Hx4qbJrMmuaOpQ0+VAYnF1bXqeNMpMFLBpoZeMzX0qKFnw0rr\n2ngxPw8AvKQSiIUCaJponh6xjuvFdQj1cYXMif8Xv2BvF7hKREjOrWY7FNZlZGRAoVAgKCgIEokE\ncXFxSEpK6rVNUlIS1q1bB4FAgDlz5qCpqQkajQYZGRkICQkZcN/w8HCEhYWxcUi8k1pSD6mjGNP8\n3NgOxWJmTeqqlp1e3sByJITv6tp08HCR8KJTYKT83Z0hEQmpoQdq6Nm0srp2m6+4aSIUCOBLBVmI\nlRiNDK4X12PBZC+2Q7EKsVCIYG9XfJ1ZhU+ulJjX2bNHarUa3t7e5q8VCgXUanW/bfz8/Mxf+/n5\nQa1W93t9oH0HUl5ejoSEBDz22GO4cePGBBwFv90oqcfcYA9e35hGKmRwFAuRWU7z9Ihl1bfqeHOv\nOFIioQBBXs6ooqGbtLyCrWrT6VHTosU9wfxZ+0zh5oSimla2wyB2IFfTjMb2TswP9YJObx+LFkcq\npMipakJNiw6+3XNiieXJ5XKcO3cOnp6eyMrKwrPPPovExERIpf3Lfufk5Az4Mzo6Ogb9HlssFVOr\nzoi7qibcM8vD/POrVE0j3l/f2YkqVdWExzVefePKz21FqKcDruRWISeHnQYtF88rMvHqWnWYHmDb\n6y2PxWRvVxSpqcecGno2qqyuHQA/llYwUbg54VZZA5o6OuFmB8PpCHuudc/Puy/UCxfy7GPeWqS8\na72uXHWzXTf0FAoFamu/q0CqVquhUCj6baNSqcxfq1QqKBQK6PX6Xq8PtG9fEokEEknX5/TMmTMR\nHByMoqIiREdH99s2KipqwJ+Rk5Mz6PfYYqmYknLUMDLFiFswFVHhPgCAtKaR9z5Xqarg7+c/4XGN\nV9+4oqKCcX+eEZ/dKEPk1Gms9F5a+rxKTU212M8mI9Oi1aNVZ7CrQiwmwd4u+Da/GgzDQCDg7+iA\n4dDQTRtlXlqBR93xpsqbeWpaW4hY1rWiOvi5OWGSpzPboViNp6sEvlJH5GnsO7+io6OhUqlQVlYG\nnU6HxMRExMbG9tomNjYWR48eBcMwuHXrFmQyGeRyOaKjo1FcXDzkvn3V1dXBYOgq8V1WVobi4mIE\nBQVZ7Phs3aFviyEWCpCnbuH9EOM5QR5o0xlwV2XfOUksh0/LcI3WZG9XdOgZVLfYd5E/6tGzUaYh\njt5S/iSvaWHcXHUL5oXYx9wpYn0Mw+B6cR3uC/W2u6d8kQoprhbVodNghIPIPp/zicViPPHEE9i0\naRMMBgPWr1+PiIgIHDlyBACwYcMGxMTEIDk5GUqlEs7OztizZ4953+3bt/fbFwBOnz6N3bt3o66u\nDlu2bEFUVBQ++OADXL9+He+++y7EYjGEQiF27twJDw/+DLmfaIXVLQjycrGL83Ned1XR1JI6TA/g\nb+EZwp7S7oaep4v9jZIK6a68WVLbZq7sbo+ooWejCmta4OUqgYuEP/+F7i4OkIiE9HSTWFRpXRvU\nTVrMD7W/hwkRChm+LahFUU0rIhUytsNhzdy5c7Fp06Zer23YsMH8b4FAgFdffXXAfWNiYhATE9Pv\ndaVSCaVS2e/1lStXYuXKleOM2D40tOlQ1diB2Cg526FYxSRPZ8hljrhRUo/HF05mOxzCQ/beowcA\nxTWtmG8nhdcGwv9HZjxVUN2KMB9XtsOYUEKBAHI3GlpGLKvn/Dx7E+rjCrFQgFwaHk046GpRHRgA\nYT79C9XwkUAgwL2TPXGjuJ7tUAhPlda1wclBCGcHEduhWF2gpzOEAtj9WpXU0LNRhdWtCPPlV0MP\nABQyJ9xVtbAdBuGx68V18HRxwBRf+7iZ7MlBJESYryty1ZRjhHsuF9TCQSRAkB3NnZ0X4oWKhnao\nGmlpITLxyura4OUisatpCqa5vZ/fKIdUIkRKnn2vHzuihl5KSgpWrlwJpVKJAwcO9Ps+wzB47bXX\noFQqER8fj+zs7GH3PXHiBOLi4jBt2jRkZmb2+nn79++HUqnEypUrceHChbEeG281dXSipkWLMB7e\nqCrcHFHTokVdq47tUAhPXSqoxYJQLwh5vEbXUCLkMsoxwklXCmsR4uUKsR3MzzMxzdO7XlzHciSE\nj0rr2nhVnX20PJxEqG2x72vdsJ+mBoMBu3btwsGDB5GYmIjjx48jPz+/1zYpKSkoLi7GqVOnsHv3\nbuzYsWPYfSMjI/Hee+9h/vz5vX5Wfn4+EhMTkZiYiIMHD2Lnzp3mimWkS2F1VyEWvg3dBAC5uSAL\nDS0jE6+0tg3l9e1YPMWH7VBYY5qbR0OkCZfUtmhxR9XMy5EqQ5kZ4AapoxhXCmuH35iQUTAaGZTV\nt9vl/DwTdycRalu1YBiG7VBYM2xDLyMjAyEhIQgKCoJEIkFcXBySkpJ6bZOUlIR169ZBIBBgzpw5\naGpqgkajGXLf8PBwhIWF9ft9SUlJiIuLg0QiQVBQEEJCQpCRkTFBh8sPhdVdw6742aNHDT1iORfz\nu9bMe8COG3o+Ugk8XRxo+CbhlKvdc2f5+ABzKGKREAtCvXCpgBp6ZGJpmrXQ6Y1239Dr6DSivq2T\n7VBYM2xDT61Ww8/Pz/y1QqGAWq0echs/Pz+o1eoR7TuW32fvCqtbIRIKEOzlwnYoE87NSQyZk5ga\nesQivs2vgb+7k93dTPYkEAgQoZChQNMCrZ5GSxBuuFxQCxeJCIGe/LuuDeeBcG8U1bSisqGd7VCs\nwhLTgf74xz9i1apViI+Px7PPPoumpiarHAuXfbe0gv029Dydu4rQFFTb74NNm67Nn5OTM6E/r6Oj\nY8J/5lgNFUtaoRp+UjEK8u6iSmX5DzN9ZyeqVFUW/z0mQW4i3CrUDHj8tvJ/RLjHaGRwqaAGy6IU\ndjUxfSBTFTJcK6rD9aJ6LI6w395Nwh0X82twX6gXRHY4d/aB8K4cvFxQi/XzJrEcjWWZpvQcOnQI\nCoUCjzzyCGJjYzFlyhTzNj2nA6Wnp2PHjh34/PPPh9x30aJF2Lp1K8RiMd544w3s378f27ZtY/FI\n2Vdqx0srmHh1N/Ty1C12u8TCsA09hUIBlUpl/lqtVkOhUAy5jUqlgkKhgF6vH3bfsfw+k6ioqOHC\nH5WcnJwJ/5ljNVQslYlqRAd5IyoqCmlNpRaPpUpVBX8/f4v/HpO5Rhf8J70S06ZN63dDbiv/R2OV\nmpo6oT+PfOd2VRPq2zqxaIo326GwLtxXCrFQgHN3NdTQI6wrq2tDUU0rfrowhO1QrObw1e+u3UaG\ngYtEhG/za3jf0Os5pQeAeUpPz4beYNOBKioqBt138eLF5v3nzJmDb775xroHxkGldW0QCgAPO1ws\n3UTmKISDSGDXc9KHHboZHR2N4uJilJWVQafTITExEbGxsb22iY2NxdGjR8EwDG7dugWZTAa5XD6i\nffuKjY1FYmIidDodysrKUFxcjFmzZo3vKHmkTadHcW0rpvnzd7Hj6QFuaO7Qo7zePoaxmKSkpGDr\n1q1U3dZCvu2en7conBo2ErEQoT6uOHdXw3YohOBCXlduLonwZTkSdggFAkTIpTifWw2Dkd9FI6wx\nHejLL7/Egw8+aIHobUthdQsmebpALLSfKrZ9CQQCyGVOyNfQ0M3BNxCLsX37dmzatAkGgwHr169H\nREQEjhw5AgDYsGEDYmJikJycDKVSCWdnZ+zZs2fIfQHg9OnT2L17N+rq6rBlyxZERUXhgw8+QERE\nBB566CGsXr0aIpEI27dvh0hkfws9DiZX3QKGAab5ubEdisXMCHAHAGRXNiKIh/MQB2IakvLyyy9j\n+fLlEzacxVTd9tVXX+31+3pWt1Wr1di4cSNOnjzJ61y7mF+DSIXUXNnV3k31k+F4RhVKalsR4m2/\ncxYJ+y7kVSPA3Qnhvq64VmSfywxM83dDenkjbpU1mJdcIKP3/vvvQyQSYe3atYNu03PKBZ+nYNwu\nr4XcWdRr6o21p+KwTd/ZCZmDEbcr6nn7/zycEc3Ri4mJQUxMTK/XNmzYYP63QCDodyM51L4AoFQq\noVQqB9znmWeewTPPPDOS0OzO3e45eVE87tGb5ieDSChAdmUTVs203pBRNpmGsygUil4Vasc7nCU8\nPHzA3zdYddu5c+da5XitTas34HpxHX40P7jXkCl7NlUhw3FU4dwdDZ5YFMp2OMRO6Q1GXMyvweqZ\n/nY9dzZS3nXdS8pR87qhZ8npQF999RXOnz+Pv//970OeSz2nXHBpOshEMhoZVB0uQUxUAPzl390v\nWnsqDtuqVFUIkTvjTrYKgaFT4ObEz2GsQ037sd/+XBuVU9UMF4kIQTyuTObkIEK4ryuyK+2nahZV\nt7WsmyUN6Og02vX6eX15Sx0R5uOKc3er2Q6F2LH08kY0d+ixJNK+c9NZIsL8yZ44e4ffw6ktNR0o\nJSUFBw8exPvvvw9nZ2c2Do1TVE0daO808HIZrtGSyxwBdBVksUc2XXXTHt1RNWGqnwxCnlcmmxHg\njksFNWyHQcCtoS2jjeXr3K6HBZdKWiEUANdzCiERj//5FleGv4w3jlm+IiTerUFaRjacHMb+vnDl\nHOFKHGTkLuRVQyCgubMAsDxKgdcSc1Be34ZJPH2Ya6npQLt374ZOp8PGjRsBALNnz8auXbvYOUgO\nKKxuBQCE+7iiuLaN5WjYZWro5Wuaed1bPhhq6NkQhmGQU9WM1dF+w29s42YEuOHfaRWobtbCtztJ\n+YzL1W2dnJw4M7RltMNsTFVpK2/nIcTbFSGTAickDq4MfxlvHPdNd8HRnKuoc/DBsqihz5mhcGX4\n00jjoOq23HD4aim+ulmBQA9nnMhSDb8Dzy3rbugl5Wjwswcmsx2OxVhiOtDp06cnNkgbV1jT1XsV\n5iu1+4aep6sEjmKh3fbo0dBNG1JS24bG9k7MmuTBdigWFx3YVZAls6KB5UiswzQkRaPRUHXbCdbc\n0Ymqxg5EyGkIS1/zQz3hIhFR9U3CinadAeX1bZSb3UJ9XBHm44okng/fJJZXWN0KV4kICjf+Pygf\njlAgwBS5FHl2WnmTGno2JK2sHgAwJ8gOGnqT3CESCpBWah8NPdOQlL1792L16tV46KGHzMNZTENa\nYmJiEBQUBKVSid///vfmJ549h7P03Bfoesr54IMPIi0tDVu2bMFTTz0FAL2q227atInX1W1NH+6R\nCv4WMBorR7EIi6f44NydajAMv8u695Senj7gciQmtJSJdRRUt8DIAFPklJtAVw9ngIczvs2vwaGL\nRVQ4ioxZQXULQn1d7brAUU8RcqndLrFAQzdtyK3SBrhIRHZxw+oiEWOan8xuGnpAV0Nu3759mDdv\nnvk1qm47fnnqZkgdxfBzp2UVBrJ0mhynbquRp2mxi88Wg8GAQ4cO4fDhw/2WIzGhpUysI1fdDCcH\nIYLtZBmdkZjmL8PF/BrkalrMI1sIGa3C6lbcO9n+5qMNJkIhw9FblWjR6iF1tK+mD/Xo2ZBbZQ2Y\n1d3TZQ/mBnvgVlkD7xeQJZZjZBjkaVoQIZdCSE82+zl8tRQNbZ0AgH2ncu2iByEjIwMKhQJBQUG9\nljLpabClTEzLoAy0b3h4OMLCwvr9vsGWMrF3DMMgV92MKb5Su7mmjUSIlytcJCJkVzayHQqxUU0d\nnahoaMdUP/4/uBupKd3DwwvssFePGno2oqPTgNtVTZgbbD9PaOYEeaJFq0dBtf0lJpkYlQ3taNMZ\nEGEHPVVj5e7sAH93J9xVN7MdilWo1Wp4e3ubv6alTNiRU9WMpg69XfQij4ZIKMB0fzfcVTVDbzCy\nHQ6xQXndn+VTKbfMTPOAc+3kOteTffVf2rDsykZ0Ghi7mJ9nMje461jTSuvpZoCMSa66BQJ89zSP\nDCxSIcOFvGp0dBrYDsWuDbY0BBeXjRhvTJ9ndg3Ldxe2oUqlnaiwOLP0SV+jicvf2YAbeiOu5ZZh\nvme7xWLi4nlFxu+uiual9xXs5QKJWGiX8/SooWcjLuXXQiAA5k/2YjsUqwnzcYWXqwRXi+rww/nB\nbIdDbFCeuhkBHs52NyZ/tKYqZEjOrbaLqmQKhQK1tbXmr7m0lMlgS0NwZfmKnsYbU3byZfi7OyEi\neNIERsWdpU/6Gk1cvnIjThfkoKpNZNH/d0ufV7SMCTty1c1wlYgQ6EELx5uIRUKE+bjaZY8eDd20\nERfzazAjwA1erhK2Q7EagUCA+8O8cLWwzq4qApKJ0djeibL6NkQoqDdvOEFeLnB2EOGuiv8Xwejo\naKhUqiGXI6GlTCyrsb0TqaX1NLRsEGKhENP83HC7qgmdNHyTjNJdVTMiFDIIae5rL1H+XTllb+gx\ntw1o0+lxs7QeTy4KZTsUq7s/zBtfZ6pQXt+OIKrMRkbhUn4NjAwQSaXbhyUSChChkCJX3QyjkeH1\nDYJYLMYTTzyBTZs2wWAwYP369ealTICuSrcxMTFITk6GUqmEs7Mz9uzZY97XtJRJz32BrqVMdu/e\njbq6OmzZsgVRUVH44IMPei1lIhKJeL2UyUh9m18Dg5GhoWVDmBnghltlDbhWVIdFU3zYDofYkFx1\nM5ZHDT3SwB7NCHDDv9MqUNOihY/UftYXpIaeDbheXI9OA4NFU3zsoipez2OsbdEBAC4X1FJDj4xK\ncm41nByEdN6M0FSFDBnljciubEL0JH6XdZ87dy42bdrU6zVaysR6zt3RwM1JTLk5hClyGRxEApzI\nqqKGHhmx6mYtalt1iKSKm/1M93cDANyubMKDkb4sR2M9NHTTBlzIrYZEJLSr+XkmcpkjXB3FuFxY\nO/zGhHRjGAYpudUIp9LtIxahkEEA4OwdDduhEB5jGAbnc6uxJNKXcnMIErEQUxUynMxWw0hLDJER\nMi3LYWrUkO9MD+hu6NnZ8E1q6HEcwzA4naPGwnBvOEvsb7iPQCBAuK8rLuTV0MWOjFi+pgWVjR00\nbHMUpI5iTPJ0xrm71NAjlpNdixFc7wAAIABJREFU2YTqZi2+Z0dP1MdqRqA7qpu1SC2tZzsUYiOy\nKroaejMCqaHXl4eLBIEezsiupIYe4ZC3z+ShpLYNni4Suxi2OZCpChlqWrTIrKAFZMnIJHX3SlEh\nltGJ9JMhvbwBtS0TV+6ekJ6Sc6sBADFTqaE3nKkKGSQiIb7JUg2/MSEAMisaMdnbBW5ODmyHwknT\nA9xwu9K+7iWpocdxtysbIQAQ5W+/PRORChkEAhpSRkbubI4G0/3d4OFiP1VqJ8I0hRsYBjh/t5rt\nUAgPHb5ais9ulCHAwwlnbtPn+XCcHERYEuGDb7JUVHmajEhWRRNmBvJ7jvVYHL5aisNXS2E0Miis\nbsWhb4vspvOEGnocd7uyCUFeLpDZ8dMZV0cx7gn2pIYeGZGGNh1ulNRhWZSc7VBsjr+HE+QyR5zJ\nUbMdCuGhdp0BpbVttKzCKKya6YeKhnYa0UKGVd+qQ0VDO6KpoTeoSZ7OYABUNnSwHYrVUEOPw/I1\nzahs7KCkBRA7TY7MikZUt+rZDoVw3Pm71TAywDIqLz1qQoEAyukKJOdWo6PTwHY4hGfuqpvAAJjq\nR/OHRko5XQGRUIATNHyTDMP0MIB69AYX6NlV6be8vo3lSKyHGnoc9tXNCggFwCyelzofCUN3IZb9\n12vsprudjE3SHQ18pBLMoovdmCinK9CmM+ByAVW6JRPrdmUTZN1Ff8jIeLhIsDDMm4ZvkmFllDcA\nAGYG0LVvMFJHMTxdHFBW3852KFZDDT2OMjIMjt2qxBS51K6HbZr4SB0R6OGMvBod26EQDus0GHH+\nrgZLp8p5vei3JS0M94bUUYxTt6kHgUycjk4DctUtiPJ3g1BAuTkaq2b6oaimFbnqFrZDIRyWWlKP\nKXIp3F3onnEokzxdqEevr5SUFKxcuRJKpRIHDhzo932GYfDaa69BqVQiPj4e2dnZw+7b0NCAjRs3\nYsWKFdi4cSMaG7u6nMvLyzFr1iwkJCQgISEB27dvH+8x2qT0qnZUNLRjTpAn26FwxqxJ7tC06lFD\nFQHJIG4U16O5Q0/DNsfBUSxCTKQvzuRoaEkTMmEuFdRAZzCa17IiI7dihgICAXAiq4rtUAhHGY0M\nbpY24N4QumccziRPZzS0daK5o5PtUKxi2IaewWDArl27cPDgQSQmJuL48ePIz8/vtU1KSgqKi4tx\n6tQp7N69Gzt27Bh23wMHDmDhwoU4deoUFi5c2KsRGBwcjGPHjuHYsWPYtWvXBB6u7Ui82wQvVwlm\n0EXRbNYkDwBAevfwBEL6OntHDYlIiCURPmyHYtNWzFCgulmLW5RrZIKcylbDUSxEmI8r26HYHLnM\nCfeGeNIyC2RQBdUtaGzvxD3U0BvWJPM8PfsYvjlsQy8jIwMhISEICgqCRCJBXFwckpKSem2TlJSE\ndevWQSAQYM6cOWhqaoJGoxlyX9M+ALBu3TqcOXPGAodnm6oa23G5rA0/uDcIDiIaXWvi7uyAAJkY\nGWWNNFeB9PPJlRJ8dbMCId4uOHarkuZyjsP3psohFgpw+jZV3yTjZzAyOJOjRqRCBjFd08Zk1Ux/\n3FE1o6imle1Qxs0So8ROnDiBuLg4TJs2DZmZmVY5Di5JLakHAOrRG4FAD2eIBAKU1NrH8M1hP3HV\najX8/PzMXysUCqjV6iG38fPzg1qtHnLf2tpayOVd5c99fX1RW/vdxP/y8nIkJCTgsccew40bN8Z4\naLbryNVSMAzwk/uC2Q6FcyJ9HFHdosXtqia2Q5lwKSkp2Lp1Kw2RHqOqxg7UtupoIvoEcHd2wH1h\nXjiVTT0IZPzSSutR06KjYZtjYFr/S9tdBXfviTssRzQ+lholFhkZiffeew/z58+39iFxwo2Seni5\nShBKPebDkoiFCPBwQkmt7T80GQkx2wEAgEAggKB7crZcLse5c+fg6emJrKwsPPvss0hMTIRUKu23\nX05OzoTG0dHRMeE/c7Q6DQw+ulyKe/wd0aIuQZWK/QaNvrMTVSpuzA2Y7CaEUAAcSsrEU/d6sxrL\nRJ4vBoMBv/vd7/DrX/8ay5cvxyOPPILY2FhMmTLFvE3Pi196ejp27NiBzz//3HzxO3ToEBQKRa99\nTUOkN2/ejAMHDuDAgQPYtm0bgO+GSPNFZkUjhALQzeQEWTnDD9uPZSNP3YwIWveMjMOp22o4iAS0\nft44eLhIMMnTGdmVtr2eXs+RXgDMI716XusGGyVWUVEx6L7h4eGsHA9XXCmshZ+bE45cK2M7FJsw\n2ccVlwpq0dFpgJODiO1wLGrYhp5CoYBK9d1TXbVaDYVCMeQ2KpUKCoUCer1+0H29vb2h0Wggl8uh\n0Wjg5eUFAJBIJJBIJACAmTNnIjg4GEVFRYiOju4XW1RU1GiOdVg5OTkT/jNH63hGJerbi7D2AV9E\nRUUhrYn94WdVqir4+/mzHUYXVRUiFUBKaQf2/ngqq8OAJvJ8SUtLQ0REBBQKRa9hzuO9+CUlJeGj\njz4C0DVE+vHHHzc39PiEYRhkVjQi3FcKV0dOPL+yeatm+uHV/2TjeEYVXlDSDToZG4ZhcCKrCgvD\nfXh/Q2VpM/zdcPK2GhUN7Qj0sM0lKgYa6ZWRkTHkNkONEuu7rz0qrW1DeX074md5sB2KzZjs7YoL\neTXIKG/EglAvtsOxqGHviKKjo1FcXIyysjIoFAokJibirbfe6rVNbGwsPv74Y8TFxSE9PR0ymQxy\nuRxeXl6D7hsbG4ujR49i8+bNOHr0KJYtWwYAqKurg7u7O0QiEcrKylBcXGy+gbUHb5y8C08XB2ha\nO2mO0SDuDfHEx1dLcf5uNZZP50d1RUtd/EYyRFomk+H555/Hvffea5Fjs4b08kbUterwvUhftkPh\nDbnMCfeFeiExswrPL48wj7ogZDRuljagrK4dv1wWCZ3eyHY4Nm1GoDtO3lbjZJYKTy4OZTscm9Vz\nJA4XRnKN14ncrpFfboL2YUdfcWmEljUMdryO3Z9FX1+/A1kHv+c1DtvQE4vF2L59OzZt2gSDwYD1\n69cjIiICR44cAQBs2LABMTExSE5OhlKphLOzM/bs2TPkvgCwefNmPP/88/jiiy8QEBCAd955BwBw\n/fp1vPvuuxCLxRAKhdi5cyc8POzjKUVqSR1KatsQF+0PocA+yr6OxVQ/N/hIHfHpjTLeNPSsYaxD\npLl0IRwslgOXqyEWAj7i4S90E4ErF0tLxPHWse9+nhOjRb6mFd9czsBkT8mg+3DlHBlpHOnp6fjN\nb34Do9GIRx99FJs3b+71fYZh8PrrryM5ORlOTk7Yu3cvZsyYAaBrCPXrr7/eb9+Ghga88MILqKio\nQGBgIN555x24u7ujvLwcq1evRmho14357Nmz7aqa9H9uVcBRLMTKGQr8N539nLFlPlJH+Lk54Rsb\nbuhZapTYaPQcicOFkVzj9X9pNyGXOWJ66KRhH8hxaoSWFQx1vPK7rShoFtn8/z8ApKamDvq9EY1x\niomJQUxMTK/XNmzYYP63QCDAq6++OuJ9AcDT0xP/+Mc/+r2+cuVKrFy5ciRh8c775wvh7CDCvZM9\nUVejYTsczhIJBVh/TyAOXiyCprkDcpkT2yGNG5eHSDs5OXHmg3Cgi3K7zoAL/ypFdKAHJk8KtEoc\nXLlYWjoOqUcnkovuIKfFCQ89MHXQ7bhyszSSOAwGAzZv3ozDhw/3m9NqQvNhJ0anwYjjGVVYHqWA\nzIkWcZ4I0wPccO6uxmavfZYaJWavjEYGlwtq8WCkL426GKUpcimuFdXxfp4e1TnmiFx1M87kqLEw\n3BuOYv6ecBPl0XuDYDAy+OpmBduhTAjTxU+j0UCn0yExMRGxsbG9tjENd2YYBrdu3TJf/HpeOPvu\na9oHQL8h0gZDVxU3Wx8ifSKrCs1aPeZRWekJJ3NyQKivK45nVvFmSZOMjAwoFApaMsgKzt7RoLZV\nh3VzrfMAxh7MDHAHw8Bmlz7pOdJr9erVeOihh8yjxEwjxWJiYhAUFASlUonf//735o6EwfYFgNOn\nT+PBBx9EWloatmzZgqeeeoq1Y7SmrMpG1LbqaO3YMZgil0KrN+Jm99IUfEVVCzhif3JXb97CMHYr\nSdqKa0V1CPFywcELhZA5ivGT+0PYDmlcTBew7du346233qIh0iPEMAw+/LYI4b6umExlpS1iVqAH\njt6qwB1VM6L8bb+iqVqthrf3d5+zNB/Wcv51rRRymSOWTqW5sxNF4eaIUB9XfJOlwk/us83rniVG\niSmVSiiVyokN1AacydFAKOha+/SbLFoOZzRCvV0hFgpwIb8GD0zhb0OZGnocUNHQjmO3KvD4whCq\nGDgK9072xJc3K1DMk0UvY2JisG/fPsybN8/8Gg2RHtrlwlpkVTRh78PRMPKjw4lzpge44b8Zlfhv\neiUvGnrWMNFLBnFlDmRPw8WkadEjObcaP5jpgbzcuwBgleWCuDJ/tq+JjGu+nwO+zK7BtVtZkDmO\nfQQQF88rMjpn76hxT7AnvFwHn0NNBuboIMLcYA9czKvBr1axHY3lUKuCA/73bB4EAmDTkjAk361m\nOxybER3ogcTMKlwprB1+Y8JL758vgI9UgnVzA3kzjJdrpI5iLJ7ig6NpFXhpxVQIhbY9D0ShUPTq\nbePSfNjB5hdyZQ5kT8PF9J9v7oAB8OxDcxHk5QIAVlkuiCvzZ/uayLiW3+OGz7K+RVGnG344J3jM\nP8fS59VQBSLI+KkaO5BV0YSXVw0+f5oMLSbSF2+dzrXZOa8jQXP0WJavacGn18vwk/tCbHZdHLZI\nxELMC/ZEdmUjNE0dbIdDrCwltxoX8mrwdEw4rydSc8Ej8yahsrEDlwps/6FKdHQ0VCrVgHNaTWg+\n7Pi0aPX45EoJVs3wMzfyyMSZNckdYb6u+PxGOduhEJYcvlqKPV939cZqO420HNcYLZ+uAMMASTn8\nLYBIPXose+PkHbhIxHgudsrwG5N+7gvzxrcFtThyrQy/XB7BdjjESvQGI/Z8nYMgL2c8vtA256nY\nEuV0BdycxPgitQyLbXzSv1gsxhNPPEFLBlnQp9fL0NShx+YHw9gOhZcEAgF+ND8Ie76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K24efMmzp49i9jYWLz44ou4cuUKXnrpJYSHh+PDDz/EV199hbi4OAQFBfXbNzw8HC4uLsjN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\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hmzRxnd82nyiAWCjAw+H3N6LlHlKE+rriWilteklIh5LaFriIBD1OQO8syFuG8gYd2oyW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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe84172cac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 6.05 s\n"
     ]
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (15, 20))\n",
    "for i, col in enumerate(X_train.columns):\n",
    "    plt.subplot(5, 4, 1+i)\n",
    "    sns.distplot(X_train[col])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "This figure shows the normalized histograms of the 20 relevant features. Most features show relatively normal or bimodal distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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kKz7iesCAAa0oca2oS0/NrmtPOumkhueKuuzB\njTQ7r9d1Dz7vvPNy+umnZ+nSpdlqq60ybty4VpfUbd2df5tttll++tOf5qijjkpbW1v23nvv7Lvv\nvq1tYi1pq6qqanURAAAAAHy2PA4GAAAAUAAhEAAAAEABhEAAAAAABRACAQAAABRACAQAAABQACEQ\nAAAAQAGEQAAAAAAFEAIBAAAAFOD/AeVKCLPH9/i9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe83f581b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 838 ms\n"
     ]
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (20, 15))\n",
    "plt.errorbar(range(20), X_train.mean(), yerr=X_train.std(), fmt='ko', ecolor='b')\n",
    "plt.xticks(range(20), X_train.columns)\n",
    "plt.title('Mean and STD for Important Features')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "This figure shows the means plotted with their standard deviations of the 20 features. While the means fall within a fairly small range, the standard deviations vary greatly between features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Feature Engineering 1st step"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "As above graphs show, the scale of the features varies greatly. Alo, the mean of all features is roughly between 480 and 510. The target is either 1 or -1.<br>\n",
    "This imbalance in values, amplitude and mean can be a factor deteriorating the results. So it is good practice to scale the values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "## Scaling the features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 33.9 ms\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = prep_df(df_train_sml, df_test_sml)\n",
    "scaler = StandardScaler(copy=True, with_mean=True, with_std=True)\n",
    "scaler.fit(X_train)\n",
    "X_train_scale = scaler.transform(X_train)\n",
    "X_test_scale = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 7.78 s\n"
     ]
    }
   ],
   "source": [
    "df_StdScaler = run_alg(dict_alg, X_train_scale, X_test_scale, y_train, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>algorithm</th>\n",
       "      <th>TP</th>\n",
       "      <th>FP</th>\n",
       "      <th>TN</th>\n",
       "      <th>FN</th>\n",
       "      <th>MCC</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>177</td>\n",
       "      <td>119</td>\n",
       "      <td>181</td>\n",
       "      <td>123</td>\n",
       "      <td>0.193351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Random Forest Classifier</td>\n",
       "      <td>260</td>\n",
       "      <td>38</td>\n",
       "      <td>262</td>\n",
       "      <td>40</td>\n",
       "      <td>0.740016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AdaBoost Classifier</td>\n",
       "      <td>200</td>\n",
       "      <td>103</td>\n",
       "      <td>197</td>\n",
       "      <td>100</td>\n",
       "      <td>0.323350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ElasticNet</td>\n",
       "      <td>300</td>\n",
       "      <td>300</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>C-Support Vector Classification</td>\n",
       "      <td>261</td>\n",
       "      <td>49</td>\n",
       "      <td>251</td>\n",
       "      <td>39</td>\n",
       "      <td>0.707060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Stochastic Gradient Descent</td>\n",
       "      <td>153</td>\n",
       "      <td>142</td>\n",
       "      <td>158</td>\n",
       "      <td>147</td>\n",
       "      <td>0.036672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Nearest Neighbors Classification</td>\n",
       "      <td>278</td>\n",
       "      <td>30</td>\n",
       "      <td>270</td>\n",
       "      <td>22</td>\n",
       "      <td>0.826961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Gaussian Process Classification</td>\n",
       "      <td>283</td>\n",
       "      <td>25</td>\n",
       "      <td>275</td>\n",
       "      <td>17</td>\n",
       "      <td>0.860306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Decision Tree Classifier</td>\n",
       "      <td>254</td>\n",
       "      <td>58</td>\n",
       "      <td>242</td>\n",
       "      <td>46</td>\n",
       "      <td>0.653857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Gradient Boosting Classifier</td>\n",
       "      <td>245</td>\n",
       "      <td>54</td>\n",
       "      <td>246</td>\n",
       "      <td>55</td>\n",
       "      <td>0.636670</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          algorithm   TP   FP   TN   FN       MCC\n",
       "0               Logistic Regression  177  119  181  123  0.193351\n",
       "1          Random Forest Classifier  260   38  262   40  0.740016\n",
       "2               AdaBoost Classifier  200  103  197  100  0.323350\n",
       "3                        ElasticNet  300  300    0    0       NaN\n",
       "4   C-Support Vector Classification  261   49  251   39  0.707060\n",
       "5       Stochastic Gradient Descent  153  142  158  147  0.036672\n",
       "6  Nearest Neighbors Classification  278   30  270   22  0.826961\n",
       "7   Gaussian Process Classification  283   25  275   17  0.860306\n",
       "8          Decision Tree Classifier  254   58  242   46  0.653857\n",
       "9      Gradient Boosting Classifier  245   54  246   55  0.636670"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 51.8 ms\n"
     ]
    }
   ],
   "source": [
    "df_StdScaler"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Particular of interest here is that the Gaussian Process (GPC) comes in really high. This is starting to look interesting, considering the shape of the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 34.6 ms\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = prep_df(df_train_sml, df_test_sml)\n",
    "scaler = MinMaxScaler(feature_range=(-1, 1), copy=True)\n",
    "scaler.fit(X_train)\n",
    "X_train_scale = scaler.transform(X_train)\n",
    "X_test_scale = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 7.76 s\n"
     ]
    }
   ],
   "source": [
    "df_MinMaxScaler = run_alg(dict_alg, X_train_scale, X_test_scale, y_train, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "        text-align: right;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>algorithm</th>\n",
       "      <th>TP</th>\n",
       "      <th>FP</th>\n",
       "      <th>TN</th>\n",
       "      <th>FN</th>\n",
       "      <th>MCC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>180</td>\n",
       "      <td>116</td>\n",
       "      <td>184</td>\n",
       "      <td>120</td>\n",
       "      <td>0.213352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Random Forest Classifier</td>\n",
       "      <td>261</td>\n",
       "      <td>36</td>\n",
       "      <td>264</td>\n",
       "      <td>39</td>\n",
       "      <td>0.750038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AdaBoost Classifier</td>\n",
       "      <td>200</td>\n",
       "      <td>103</td>\n",
       "      <td>197</td>\n",
       "      <td>100</td>\n",
       "      <td>0.323350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ElasticNet</td>\n",
       "      <td>300</td>\n",
       "      <td>300</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>C-Support Vector Classification</td>\n",
       "      <td>196</td>\n",
       "      <td>74</td>\n",
       "      <td>226</td>\n",
       "      <td>104</td>\n",
       "      <td>0.408715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Stochastic Gradient Descent</td>\n",
       "      <td>232</td>\n",
       "      <td>229</td>\n",
       "      <td>71</td>\n",
       "      <td>68</td>\n",
       "      <td>0.011851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Nearest Neighbors Classification</td>\n",
       "      <td>282</td>\n",
       "      <td>32</td>\n",
       "      <td>268</td>\n",
       "      <td>18</td>\n",
       "      <td>0.834242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Gaussian Process Classification</td>\n",
       "      <td>260</td>\n",
       "      <td>50</td>\n",
       "      <td>250</td>\n",
       "      <td>40</td>\n",
       "      <td>0.700389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Decision Tree Classifier</td>\n",
       "      <td>255</td>\n",
       "      <td>59</td>\n",
       "      <td>241</td>\n",
       "      <td>45</td>\n",
       "      <td>0.654046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Gradient Boosting Classifier</td>\n",
       "      <td>245</td>\n",
       "      <td>54</td>\n",
       "      <td>246</td>\n",
       "      <td>55</td>\n",
       "      <td>0.636670</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          algorithm   TP   FP   TN   FN       MCC\n",
       "0               Logistic Regression  180  116  184  120  0.213352\n",
       "1          Random Forest Classifier  261   36  264   39  0.750038\n",
       "2               AdaBoost Classifier  200  103  197  100  0.323350\n",
       "3                        ElasticNet  300  300    0    0       NaN\n",
       "4   C-Support Vector Classification  196   74  226  104  0.408715\n",
       "5       Stochastic Gradient Descent  232  229   71   68  0.011851\n",
       "6  Nearest Neighbors Classification  282   32  268   18  0.834242\n",
       "7   Gaussian Process Classification  260   50  250   40  0.700389\n",
       "8          Decision Tree Classifier  255   59  241   45  0.654046\n",
       "9      Gradient Boosting Classifier  245   54  246   55  0.636670"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 52.1 ms\n"
     ]
    }
   ],
   "source": [
    "df_MinMaxScaler"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Seems like both scalers have the positive sides, this means we might want to consider running both in comparission on the next steps."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Feature Engineering 2nd step:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "I know about this dataset that it consists of five original features that get transformed, noise added, then made redundant, out of these ten are randomly selected (with replacement) for duplication, followed by additional manipulation and noise overlay.<br>\n",
    "Bottom line is the dataset has five dimensions. So let's use PCA to reduce the 20 features to 5 dimensions:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "## PCA without scaler:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 76.3 ms\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = prep_df(df_train_sml, df_test_sml)\n",
    "pca = PCA(n_components=5)\n",
    "pca.fit(X_train)\n",
    "X_train = pca.transform(X_train)\n",
    "X_test = pca.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 5.12 s\n"
     ]
    }
   ],
   "source": [
    "df_pca_none = run_alg(dict_alg, X_train, X_test, y_train, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>algorithm</th>\n",
       "      <th>TP</th>\n",
       "      <th>FP</th>\n",
       "      <th>TN</th>\n",
       "      <th>FN</th>\n",
       "      <th>MCC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>178</td>\n",
       "      <td>122</td>\n",
       "      <td>178</td>\n",
       "      <td>122</td>\n",
       "      <td>0.186667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Random Forest Classifier</td>\n",
       "      <td>253</td>\n",
       "      <td>31</td>\n",
       "      <td>269</td>\n",
       "      <td>47</td>\n",
       "      <td>0.741055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AdaBoost Classifier</td>\n",
       "      <td>190</td>\n",
       "      <td>108</td>\n",
       "      <td>192</td>\n",
       "      <td>110</td>\n",
       "      <td>0.273339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ElasticNet</td>\n",
       "      <td>177</td>\n",
       "      <td>119</td>\n",
       "      <td>181</td>\n",
       "      <td>123</td>\n",
       "      <td>0.193351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>C-Support Vector Classification</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>300</td>\n",
       "      <td>300</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Stochastic Gradient Descent</td>\n",
       "      <td>137</td>\n",
       "      <td>158</td>\n",
       "      <td>142</td>\n",
       "      <td>163</td>\n",
       "      <td>-0.070010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Nearest Neighbors Classification</td>\n",
       "      <td>271</td>\n",
       "      <td>38</td>\n",
       "      <td>262</td>\n",
       "      <td>29</td>\n",
       "      <td>0.777016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Gaussian Process Classification</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>299</td>\n",
       "      <td>255</td>\n",
       "      <td>0.275625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Decision Tree Classifier</td>\n",
       "      <td>245</td>\n",
       "      <td>66</td>\n",
       "      <td>234</td>\n",
       "      <td>55</td>\n",
       "      <td>0.597068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Gradient Boosting Classifier</td>\n",
       "      <td>251</td>\n",
       "      <td>56</td>\n",
       "      <td>244</td>\n",
       "      <td>49</td>\n",
       "      <td>0.650177</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          algorithm   TP   FP   TN   FN       MCC\n",
       "0               Logistic Regression  178  122  178  122  0.186667\n",
       "1          Random Forest Classifier  253   31  269   47  0.741055\n",
       "2               AdaBoost Classifier  190  108  192  110  0.273339\n",
       "3                        ElasticNet  177  119  181  123  0.193351\n",
       "4   C-Support Vector Classification    0    0  300  300       NaN\n",
       "5       Stochastic Gradient Descent  137  158  142  163 -0.070010\n",
       "6  Nearest Neighbors Classification  271   38  262   29  0.777016\n",
       "7   Gaussian Process Classification   45    1  299  255  0.275625\n",
       "8          Decision Tree Classifier  245   66  234   55  0.597068\n",
       "9      Gradient Boosting Classifier  251   56  244   49  0.650177"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 49.6 ms\n"
     ]
    }
   ],
   "source": [
    "df_pca_none"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "## PCA with Standard Scaler:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 73 ms\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = prep_df(df_train_sml, df_test_sml)\n",
    "scaler = StandardScaler(copy=True, with_mean=True, with_std=True)\n",
    "scaler.fit(X_train)\n",
    "X_train = scaler.transform(X_train)\n",
    "X_test = scaler.transform(X_test)\n",
    "pca = PCA(n_components=5)\n",
    "pca.fit(X_train)\n",
    "X_train = pca.transform(X_train)\n",
    "X_test = pca.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 7.46 s\n"
     ]
    }
   ],
   "source": [
    "df_pca_std = run_alg(dict_alg, X_train, X_test, y_train, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>algorithm</th>\n",
       "      <th>TP</th>\n",
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       "      <th>0</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>178</td>\n",
       "      <td>123</td>\n",
       "      <td>177</td>\n",
       "      <td>122</td>\n",
       "      <td>0.183334</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Random Forest Classifier</td>\n",
       "      <td>259</td>\n",
       "      <td>36</td>\n",
       "      <td>264</td>\n",
       "      <td>41</td>\n",
       "      <td>0.743437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AdaBoost Classifier</td>\n",
       "      <td>197</td>\n",
       "      <td>107</td>\n",
       "      <td>193</td>\n",
       "      <td>103</td>\n",
       "      <td>0.300027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ElasticNet</td>\n",
       "      <td>300</td>\n",
       "      <td>300</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>C-Support Vector Classification</td>\n",
       "      <td>280</td>\n",
       "      <td>28</td>\n",
       "      <td>272</td>\n",
       "      <td>20</td>\n",
       "      <td>0.840299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Stochastic Gradient Descent</td>\n",
       "      <td>184</td>\n",
       "      <td>141</td>\n",
       "      <td>159</td>\n",
       "      <td>116</td>\n",
       "      <td>0.143834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Nearest Neighbors Classification</td>\n",
       "      <td>278</td>\n",
       "      <td>30</td>\n",
       "      <td>270</td>\n",
       "      <td>22</td>\n",
       "      <td>0.826961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Gaussian Process Classification</td>\n",
       "      <td>283</td>\n",
       "      <td>26</td>\n",
       "      <td>274</td>\n",
       "      <td>17</td>\n",
       "      <td>0.857052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Decision Tree Classifier</td>\n",
       "      <td>230</td>\n",
       "      <td>57</td>\n",
       "      <td>243</td>\n",
       "      <td>70</td>\n",
       "      <td>0.577209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Gradient Boosting Classifier</td>\n",
       "      <td>248</td>\n",
       "      <td>65</td>\n",
       "      <td>235</td>\n",
       "      <td>52</td>\n",
       "      <td>0.610574</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          algorithm   TP   FP   TN   FN       MCC\n",
       "0               Logistic Regression  178  123  177  122  0.183334\n",
       "1          Random Forest Classifier  259   36  264   41  0.743437\n",
       "2               AdaBoost Classifier  197  107  193  103  0.300027\n",
       "3                        ElasticNet  300  300    0    0       NaN\n",
       "4   C-Support Vector Classification  280   28  272   20  0.840299\n",
       "5       Stochastic Gradient Descent  184  141  159  116  0.143834\n",
       "6  Nearest Neighbors Classification  278   30  270   22  0.826961\n",
       "7   Gaussian Process Classification  283   26  274   17  0.857052\n",
       "8          Decision Tree Classifier  230   57  243   70  0.577209\n",
       "9      Gradient Boosting Classifier  248   65  235   52  0.610574"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 46.2 ms\n"
     ]
    }
   ],
   "source": [
    "df_pca_std"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "## PCA with MinMax Scaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 51.1 ms\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = prep_df(df_train_sml, df_test_sml)\n",
    "scaler = MinMaxScaler(feature_range=(-1, 1), copy=True)\n",
    "scaler.fit(X_train)\n",
    "X_train = scaler.transform(X_train)\n",
    "X_test = scaler.transform(X_test)\n",
    "pca = PCA(n_components=5)\n",
    "pca.fit(X_train)\n",
    "X_train = pca.transform(X_train)\n",
    "X_test = pca.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 7.17 s\n"
     ]
    }
   ],
   "source": [
    "df_pca_minmax = run_alg(dict_alg, X_train, X_test, y_train, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>algorithm</th>\n",
       "      <th>TP</th>\n",
       "      <th>FP</th>\n",
       "      <th>TN</th>\n",
       "      <th>FN</th>\n",
       "      <th>MCC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>178</td>\n",
       "      <td>123</td>\n",
       "      <td>177</td>\n",
       "      <td>122</td>\n",
       "      <td>0.183334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Random Forest Classifier</td>\n",
       "      <td>250</td>\n",
       "      <td>35</td>\n",
       "      <td>265</td>\n",
       "      <td>50</td>\n",
       "      <td>0.717564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AdaBoost Classifier</td>\n",
       "      <td>190</td>\n",
       "      <td>121</td>\n",
       "      <td>179</td>\n",
       "      <td>110</td>\n",
       "      <td>0.230155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ElasticNet</td>\n",
       "      <td>300</td>\n",
       "      <td>300</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>C-Support Vector Classification</td>\n",
       "      <td>230</td>\n",
       "      <td>53</td>\n",
       "      <td>247</td>\n",
       "      <td>70</td>\n",
       "      <td>0.590950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Stochastic Gradient Descent</td>\n",
       "      <td>255</td>\n",
       "      <td>261</td>\n",
       "      <td>39</td>\n",
       "      <td>45</td>\n",
       "      <td>-0.028820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Nearest Neighbors Classification</td>\n",
       "      <td>281</td>\n",
       "      <td>34</td>\n",
       "      <td>266</td>\n",
       "      <td>19</td>\n",
       "      <td>0.824364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Gaussian Process Classification</td>\n",
       "      <td>260</td>\n",
       "      <td>52</td>\n",
       "      <td>248</td>\n",
       "      <td>40</td>\n",
       "      <td>0.693889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Decision Tree Classifier</td>\n",
       "      <td>239</td>\n",
       "      <td>72</td>\n",
       "      <td>228</td>\n",
       "      <td>61</td>\n",
       "      <td>0.557041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Gradient Boosting Classifier</td>\n",
       "      <td>252</td>\n",
       "      <td>65</td>\n",
       "      <td>235</td>\n",
       "      <td>48</td>\n",
       "      <td>0.624337</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          algorithm   TP   FP   TN   FN       MCC\n",
       "0               Logistic Regression  178  123  177  122  0.183334\n",
       "1          Random Forest Classifier  250   35  265   50  0.717564\n",
       "2               AdaBoost Classifier  190  121  179  110  0.230155\n",
       "3                        ElasticNet  300  300    0    0       NaN\n",
       "4   C-Support Vector Classification  230   53  247   70  0.590950\n",
       "5       Stochastic Gradient Descent  255  261   39   45 -0.028820\n",
       "6  Nearest Neighbors Classification  281   34  266   19  0.824364\n",
       "7   Gaussian Process Classification  260   52  248   40  0.693889\n",
       "8          Decision Tree Classifier  239   72  228   61  0.557041\n",
       "9      Gradient Boosting Classifier  252   65  235   48  0.624337"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 65.2 ms\n"
     ]
    }
   ],
   "source": [
    "df_pca_minmax"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# with Standard Scaler, PCA, Standard Scaler and tuned algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 81.3 ms\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = prep_df(df_train_sml, df_test_sml)\n",
    "scaler = StandardScaler(copy=True, with_mean=True, with_std=True)\n",
    "scaler.fit(X_train)\n",
    "X_train = scaler.transform(X_train)\n",
    "X_test = scaler.transform(X_test)\n",
    "pca = PCA(n_components=5)\n",
    "pca.fit(X_train)\n",
    "X_train = pca.transform(X_train)\n",
    "X_test = pca.transform(X_test)\n",
    "scaler2 = StandardScaler(copy=True, with_mean=True, with_std=True)\n",
    "scaler2.fit(X_train)\n",
    "X_train = scaler2.transform(X_train)\n",
    "X_test = scaler2.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 40.7 ms\n"
     ]
    }
   ],
   "source": [
    "dict_alg = {}\n",
    "dict_alg['LR'] = LogisticRegression(random_state=random_seed)\n",
    "dict_alg['RF'] = RandomForestClassifier(n_estimators=500, criterion='entropy', max_depth=64, random_state=random_seed)\n",
    "dict_alg['AB'] = AdaBoostClassifier(random_state=random_seed)\n",
    "dict_alg['EN'] = ElasticNet(random_state=random_seed)\n",
    "dict_alg['SVC'] = SVC(decision_function_shape='ovo', C=1, gamma=.9, random_state=random_seed)\n",
    "dict_alg['SGD'] = SGDClassifier(random_state=random_seed)\n",
    "dict_alg['kNN'] = KNeighborsClassifier(algorithm='auto', leaf_size=2, n_neighbors=10, weights='distance')\n",
    "dict_alg['GPC'] = GaussianProcessClassifier(random_state=random_seed)\n",
    "dict_alg['DT'] = DecisionTreeClassifier(random_state=random_seed)\n",
    "dict_alg['GB'] = GradientBoostingClassifier(random_state=random_seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 12.1 s\n"
     ]
    }
   ],
   "source": [
    "df_pca_tuned = run_alg(dict_alg, X_train, X_test, y_train, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>algorithm</th>\n",
       "      <th>TP</th>\n",
       "      <th>FP</th>\n",
       "      <th>TN</th>\n",
       "      <th>FN</th>\n",
       "      <th>MCC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>178</td>\n",
       "      <td>123</td>\n",
       "      <td>177</td>\n",
       "      <td>122</td>\n",
       "      <td>0.183334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Random Forest Classifier</td>\n",
       "      <td>268</td>\n",
       "      <td>40</td>\n",
       "      <td>260</td>\n",
       "      <td>32</td>\n",
       "      <td>0.760270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AdaBoost Classifier</td>\n",
       "      <td>197</td>\n",
       "      <td>107</td>\n",
       "      <td>193</td>\n",
       "      <td>103</td>\n",
       "      <td>0.300027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ElasticNet</td>\n",
       "      <td>300</td>\n",
       "      <td>300</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>C-Support Vector Classification</td>\n",
       "      <td>282</td>\n",
       "      <td>23</td>\n",
       "      <td>277</td>\n",
       "      <td>18</td>\n",
       "      <td>0.863453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Stochastic Gradient Descent</td>\n",
       "      <td>191</td>\n",
       "      <td>196</td>\n",
       "      <td>104</td>\n",
       "      <td>109</td>\n",
       "      <td>-0.017415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Nearest Neighbors Classification</td>\n",
       "      <td>281</td>\n",
       "      <td>29</td>\n",
       "      <td>271</td>\n",
       "      <td>19</td>\n",
       "      <td>0.840467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Gaussian Process Classification</td>\n",
       "      <td>279</td>\n",
       "      <td>36</td>\n",
       "      <td>264</td>\n",
       "      <td>21</td>\n",
       "      <td>0.811014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Decision Tree Classifier</td>\n",
       "      <td>230</td>\n",
       "      <td>57</td>\n",
       "      <td>243</td>\n",
       "      <td>70</td>\n",
       "      <td>0.577209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Gradient Boosting Classifier</td>\n",
       "      <td>248</td>\n",
       "      <td>65</td>\n",
       "      <td>235</td>\n",
       "      <td>52</td>\n",
       "      <td>0.610574</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          algorithm   TP   FP   TN   FN       MCC\n",
       "0               Logistic Regression  178  123  177  122  0.183334\n",
       "1          Random Forest Classifier  268   40  260   32  0.760270\n",
       "2               AdaBoost Classifier  197  107  193  103  0.300027\n",
       "3                        ElasticNet  300  300    0    0       NaN\n",
       "4   C-Support Vector Classification  282   23  277   18  0.863453\n",
       "5       Stochastic Gradient Descent  191  196  104  109 -0.017415\n",
       "6  Nearest Neighbors Classification  281   29  271   19  0.840467\n",
       "7   Gaussian Process Classification  279   36  264   21  0.811014\n",
       "8          Decision Tree Classifier  230   57  243   70  0.577209\n",
       "9      Gradient Boosting Classifier  248   65  235   52  0.610574"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 48.9 ms\n"
     ]
    }
   ],
   "source": [
    "df_pca_tuned"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| approach | best | runner-up | time|\n",
    "| --- | --- | --- | --- | \n",
    "| baseline | 0.763 | 0.60 | < 10 sec. |\n",
    "| modified features | 0.793 | 0.47 | < 10 sec.(\\*) |\n",
    "| Standard Scaler | 0.860 | 0.827 | < 10 sec. |\n",
    "| MinMax Scaler | 0.834 | 0.750 | < 10 sec. |\n",
    "| PCA | 0.777 | 0.741 | < 10 sec. |\n",
    "| Std. Scaler, PCA | 0.857 | 0.840 | < 10 sec. |\n",
    "| MinMax Scaler, PCA | 0.824 | 0.718 | < 10 sec. |\n",
    "| Std. Scaler, PCA, Std. Scaler, tuned algo | 0.863 | 0.840 | < 15 sec.(\\*\\*) |\n",
    "\n",
    "(\\*) with manual preparation of a few minutes<br>\n",
    "(\\*\\*) with manual effort to tune algorithm to demonstrate effort not worth the gains over Standard Scaler alone or Std. Sclr + PCA solution (with default algo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
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    "builtin_function_or_method",
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 },
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}