Individual-CatBoostingFeatures.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "catboost version 1.0.6\n"
     ]
    }
   ],
   "source": [
    "# https://catboost.ai/en/docs/concepts/python-quickstart\n",
    "import catboost as cb\n",
    "from catboost import CatBoostClassifier\n",
    "\n",
    "\n",
    "print ('catboost version', cb.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# K-Fold CV for Boosting Methods Using SKLearn "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing required libraries\n",
    "from sklearn.datasets import load_breast_cancer\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.model_selection import KFold \n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import LeaveOneOut\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from scipy.stats import randint\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "from tqdm import tqdm\n",
    "import wfdb\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import LeaveOneOut\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from numpy import mean\n",
    "from numpy import absolute\n",
    "from numpy import sqrt\n",
    "import pandas as pd\n",
    "import random\n",
    "import statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost as xgb\n",
    "from sklearn import metrics\n",
    "from lightgbm import LGBMClassifier\n",
    "\n",
    "import lightgbm\n",
    "import lightgbm as lgb\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelBinarizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.dpi'] = 70 #display 70 dpi in Jupyter Notebook, may consider100 dpi \n",
    "plt.rcParams['savefig.dpi'] = 300 #define 300 dpi for saving figures\n",
    "\n",
    "import seaborn as sns\n",
    "## here are some settings \n",
    "sns.set_style('whitegrid')\n",
    "sns.set(rc={\"figure.dpi\":70, 'savefig.dpi':300}) #defining dpi setting\n",
    "sns.set_context('notebook')\n",
    "sns.set_style(\"ticks\")\n",
    "from IPython.display import set_matplotlib_formats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### To load sklearn dataset\n",
    "sklearn.datasets.load_breast_cancer(*, return_X_y=False, as_frame=False)[source]¶\n",
    "\n",
    "Load and return the breast cancer wisconsin dataset (classification)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  0%|          | 0/25 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sampto must be greater than sampfrom\n",
      "sampto must be greater than sampfrom\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 25/25 [00:22<00:00,  1.12it/s]\n"
     ]
    }
   ],
   "source": [
    "#data = load_breast_cancer(as_frame = True); \n",
    "#df = data.frame; \n",
    "#print(df.shape)\n",
    "# In this block use the code we looked at on 6/30 as a reference to save our rr-intervals as a .\n",
    "rlist = []\n",
    "records = 'mit-bih-raw\\RECORDS' # Replace the _______ with the name of the records file in your MIT-BIH data folder\n",
    "with open(records) as rfile: #Then we open the file \n",
    "                             #The 'with' command only opens the file while we are in it. Automatically closes the file when we're not\n",
    "    for record in rfile:  # Then we iterate through the lines in the file\n",
    "        record = record[0:len(record)-1] # Remove any erronious new line characters at the end ('\\n')\n",
    "        rlist.append(record) # Then build an array with it\n",
    "        \n",
    "\n",
    "###### Step 1: Initialize all Arrays\n",
    "             # Below, replace all of the ___ with the command that declares an array/list\n",
    "             # hint: https://stackoverflow.com/questions/1514553/how-to-declare-an-array-in-python\n",
    "samples = [] # will house the samples of all subjects\n",
    "good_list = [] # will list the names of the subjects we successfully extracted\n",
    "bad_list = [] # will house the names of the subjects we failed to extract\n",
    "qrs = [] # will house the indices of R-Peaks for all subjects\n",
    "atr_label = [] # will house the labels for each rhythm annotation for all subjects\n",
    "atr_locs = [] # will house the locations corresponding to the rhythm annotation labels\n",
    "\n",
    "\n",
    "###### Step 2: Extract Information\n",
    "for x in tqdm(rlist): #this will iterate through te records that we found above\n",
    "  \n",
    "    try: # A try statement will run the except statement if for some reason the try commands fail\n",
    "         # In this case I use the try statement because one of the subjects has no signal data causing failure\n",
    "         # I then use bad_list and good_list so that all of the indices in rlist match with the arrays we initialized in Step 1, above\n",
    "        ######################################################\n",
    "            # Below find the wfdb function that will return the information that is described below \n",
    "        # Then replace _____ with the correct function call\n",
    "        samp = wfdb.rdsamp('mit-bih-raw/'+x) # wfdb._____(file_location) will read the signal & header data and return a 2 value array\n",
    "            # samp[0] - the signal data is the raw reading from the ecg. Each value is a sample taken.\n",
    "            # samp[1] - the header data includes things about the signal data such as:\n",
    "              # samples per section, denoted 'fs'\n",
    "              # number of signals, denoted 'n_sig'\n",
    "            \n",
    "        ######################################################\n",
    "        samples.append(samp) #add it to our array for all subject\n",
    "            #What is our file extension that has the annotation we want? Find it here and replace _____ with it \n",
    "            #hint: READ THE VARIABLE NAMES!!!!\n",
    "        qrs_tmp = wfdb.rdann('mit-bih-raw/'+x, extension='qrs') #extract the QRS Info\n",
    "        qrs_locs = np.array(qrs_tmp.sample, dtype='int') #Get just the loccation of R-Peaks from the QRS Info\n",
    "        qrs.append(qrs_locs) # Add to our array for all subjects\n",
    "        \n",
    "            #Do the same thing here\n",
    "        atr = wfdb.rdann('mit-bih-raw/'+x,extension='atr') #extract the atr info which stores the rhythm type(s) over the whole signal\n",
    "        atr_label.append(atr.aux_note) # aux_note stores the type of rhythm - main two are '(N' for normal and '(AFIB' for AFIB\n",
    "        atr_locs.append(np.append(atr.sample, len(samp[0]))) #I add the length of the whole sample to the end for better visualization later\n",
    "        \n",
    "        good_list.append(x) # when all extraction is successful append the record name to good_list\n",
    "    except Exception as exep:\n",
    "        print(exep) # Alert the user of an exception\n",
    "        bad_list.append(x) # add to the bad list\n",
    "\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_df_list = []\n",
    "for subject in good_list:\n",
    "    data = pd.read_csv(f'subject-features-dataframes/subject{subject}.csv')\n",
    "    data = data.assign(Subject = subject)\n",
    "    #print(data)\n",
    "    features_df_list.append(data)\n",
    "#features_maindf = pd.concat(features_df_list, ignore_index = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Unnamed: 0', 'AppEn', 'SampEn', 'CosEn', 'RangeEn', 'FuzzyEn',\n",
      "       'EntropyAF', 'PermEn', 'BubbleEn', 'MultiscaleEn', 'Volatility',\n",
      "       'Rhythm', 'Subject'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "features_maindf = pd.concat(features_df_list, ignore_index = True)\n",
    "print(features_maindf.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CatBoosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def confusion_calcs(confusion_matrix):\n",
    "    temp_arr = confusion_matrix\n",
    "    tn = temp_arr[0]\n",
    "    fp = temp_arr[1]\n",
    "    fn = temp_arr[2]\n",
    "    tp = temp_arr[3]\n",
    "    sensitivity = tp / (tp + fn)\n",
    "    recall = sensitivity\n",
    "    specificity = tn / (tn + fp)\n",
    "    precision = tp / (tp + fp)\n",
    "    accuracy = (tp+tn) / (tp+tn+fn+fp)\n",
    "    f1 = 2*precision*recall/(precision+recall)\n",
    "    return [accuracy, sensitivity, specificity, precision, f1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_curve, auc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
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     "name": "stderr",
     "output_type": "stream",
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      "\r",
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "1it [00:09,  9.05s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "94:\tlearn: 0.2332622\ttotal: 276ms\tremaining: 14.5ms\n",
      "95:\tlearn: 0.2332495\ttotal: 279ms\tremaining: 11.6ms\n",
      "96:\tlearn: 0.2332386\ttotal: 282ms\tremaining: 8.71ms\n",
      "97:\tlearn: 0.2332290\ttotal: 284ms\tremaining: 5.81ms\n",
      "98:\tlearn: 0.2332070\ttotal: 287ms\tremaining: 2.9ms\n",
      "99:\tlearn: 0.2331595\ttotal: 290ms\tremaining: 0us\n",
      "---Run time is 8.949177500000005 seconds ---\n",
      "\n",
      "Accuracy of each fold: \n",
      " [0.8850114416475973, 0.939553752535497, 0.9937027707808564, 0.6660671462829736, 0.9822361546499477, 0.9225152129817444, 0.8879028491359178, 0.9904697072838666, 0.928714859437751, 0.88257993384785, 0.9800362976406534, 0.9698492462311558, 0.7301587301587301, 1.0, 0.2980849292256453, 0.8334071776694727, 0.976010966415353, 0.9976918638199653, 0.6348267117497887, 0.9439252336448598, 0.9662104362703166, 0.935969868173258, 0.9416490486257928]\n",
      "\n",
      "Avg accuracy : \n",
      "0.8820249712264778\n",
      "\n",
      "Std of accuracy : \n",
      "0.15967882716215004\n",
      "\n",
      "[[21673  3070]\n",
      " [ 2748 17477]]\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.88      0.88     24743\n",
      "           1       0.85      0.86      0.86     20225\n",
      "\n",
      "    accuracy                           0.87     44968\n",
      "   macro avg       0.87      0.87      0.87     44968\n",
      "weighted avg       0.87      0.87      0.87     44968\n",
      "\n",
      "\n",
      "[0.8850114416475973, 0.939553752535497, 0.9937027707808564, 0.6660671462829736, 0.9822361546499477, 0.9225152129817444, 0.8879028491359178, 0.9904697072838666, 0.928714859437751, 0.88257993384785, 0.9800362976406534, 0.9698492462311558, 0.7301587301587301, 1.0, 0.2980849292256453, 0.8334071776694727, 0.976010966415353, 0.9976918638199653, 0.6348267117497887, 0.9439252336448598, 0.9662104362703166, 0.935969868173258, 0.9416490486257928]\n",
      "\n",
      "8.949177500000005\n",
      "['SampEn']\n",
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    {
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     "output_type": "stream",
     "text": [
      "\r",
      "2it [00:18,  9.24s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "75:\tlearn: 0.2348381\ttotal: 225ms\tremaining: 71ms\n",
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      "93:\tlearn: 0.2343438\ttotal: 278ms\tremaining: 17.8ms\n",
      "94:\tlearn: 0.2343407\ttotal: 281ms\tremaining: 14.8ms\n",
      "95:\tlearn: 0.2343243\ttotal: 284ms\tremaining: 11.8ms\n",
      "96:\tlearn: 0.2343209\ttotal: 287ms\tremaining: 8.87ms\n",
      "97:\tlearn: 0.2343045\ttotal: 290ms\tremaining: 5.91ms\n",
      "98:\tlearn: 0.2343010\ttotal: 292ms\tremaining: 2.95ms\n",
      "99:\tlearn: 0.2342755\ttotal: 296ms\tremaining: 0us\n",
      "---Run time is 18.318821 seconds ---\n",
      "\n",
      "Accuracy of each fold: \n",
      " [0.8832951945080092, 0.9460446247464503, 0.9924433249370277, 0.6241007194244604, 0.9822361546499477, 0.9245436105476673, 0.9056515646893974, 0.988427501701838, 0.9292168674698795, 0.7938257993384785, 0.9818511796733213, 0.9547738693467337, 0.6970521541950113, 1.0, 0.35428809325562033, 0.9060700044306602, 0.9677861549006168, 0.9982688978649741, 0.5841081994928149, 0.9516217702034084, 0.9798973481608212, 0.9133709981167608, 0.9674418604651163]\n",
      "\n",
      "Avg accuracy : \n",
      "0.8794050387877834\n",
      "\n",
      "Std of accuracy : \n",
      "0.16032492249679162\n",
      "\n",
      "[[21097  3646]\n",
      " [ 2202 18023]]\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.85      0.88     24743\n",
      "           1       0.83      0.89      0.86     20225\n",
      "\n",
      "    accuracy                           0.87     44968\n",
      "   macro avg       0.87      0.87      0.87     44968\n",
      "weighted avg       0.87      0.87      0.87     44968\n",
      "\n",
      "\n",
      "[0.8832951945080092, 0.9460446247464503, 0.9924433249370277, 0.6241007194244604, 0.9822361546499477, 0.9245436105476673, 0.9056515646893974, 0.988427501701838, 0.9292168674698795, 0.7938257993384785, 0.9818511796733213, 0.9547738693467337, 0.6970521541950113, 1.0, 0.35428809325562033, 0.9060700044306602, 0.9677861549006168, 0.9982688978649741, 0.5841081994928149, 0.9516217702034084, 0.9798973481608212, 0.9133709981167608, 0.9674418604651163]\n",
      "\n",
      "18.318821\n",
      "['CosEn']\n",
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "3it [00:27,  9.11s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "92:\tlearn: 0.2343547\ttotal: 266ms\tremaining: 20ms\n",
      "93:\tlearn: 0.2343438\ttotal: 269ms\tremaining: 17.2ms\n",
      "94:\tlearn: 0.2343407\ttotal: 272ms\tremaining: 14.3ms\n",
      "95:\tlearn: 0.2343243\ttotal: 275ms\tremaining: 11.4ms\n",
      "96:\tlearn: 0.2343209\ttotal: 277ms\tremaining: 8.58ms\n",
      "97:\tlearn: 0.2343045\ttotal: 280ms\tremaining: 5.72ms\n",
      "98:\tlearn: 0.2343010\ttotal: 283ms\tremaining: 2.86ms\n",
      "99:\tlearn: 0.2342755\ttotal: 286ms\tremaining: 0us\n",
      "---Run time is 27.281922800000004 seconds ---\n",
      "\n",
      "Accuracy of each fold: \n",
      " [0.8832951945080092, 0.9460446247464503, 0.9924433249370277, 0.6241007194244604, 0.9822361546499477, 0.9245436105476673, 0.9056515646893974, 0.988427501701838, 0.9292168674698795, 0.7938257993384785, 0.9818511796733213, 0.9547738693467337, 0.6970521541950113, 1.0, 0.35428809325562033, 0.9060700044306602, 0.9677861549006168, 0.9982688978649741, 0.5841081994928149, 0.9516217702034084, 0.9798973481608212, 0.9133709981167608, 0.9674418604651163]\n",
      "\n",
      "Avg accuracy : \n",
      "0.8794050387877834\n",
      "\n",
      "Std of accuracy : \n",
      "0.16032492249679162\n",
      "\n",
      "[[21097  3646]\n",
      " [ 2202 18023]]\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.85      0.88     24743\n",
      "           1       0.83      0.89      0.86     20225\n",
      "\n",
      "    accuracy                           0.87     44968\n",
      "   macro avg       0.87      0.87      0.87     44968\n",
      "weighted avg       0.87      0.87      0.87     44968\n",
      "\n",
      "\n",
      "[0.8832951945080092, 0.9460446247464503, 0.9924433249370277, 0.6241007194244604, 0.9822361546499477, 0.9245436105476673, 0.9056515646893974, 0.988427501701838, 0.9292168674698795, 0.7938257993384785, 0.9818511796733213, 0.9547738693467337, 0.6970521541950113, 1.0, 0.35428809325562033, 0.9060700044306602, 0.9677861549006168, 0.9982688978649741, 0.5841081994928149, 0.9516217702034084, 0.9798973481608212, 0.9133709981167608, 0.9674418604651163]\n",
      "\n",
      "27.281922800000004\n",
      "['RangeEn']\n",
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      "86:\tlearn: 0.2971404\ttotal: 234ms\tremaining: 35ms\n",
      "87:\tlearn: 0.2971362\ttotal: 237ms\tremaining: 32.3ms\n",
      "88:\tlearn: 0.2971351\ttotal: 239ms\tremaining: 29.6ms\n",
      "89:\tlearn: 0.2971324\ttotal: 242ms\tremaining: 26.9ms\n",
      "90:\tlearn: 0.2971285\ttotal: 245ms\tremaining: 24.2ms\n",
      "91:\tlearn: 0.2971269\ttotal: 247ms\tremaining: 21.5ms\n",
      "92:\tlearn: 0.2971255\ttotal: 250ms\tremaining: 18.8ms\n",
      "93:\tlearn: 0.2971230\ttotal: 253ms\tremaining: 16.1ms\n",
      "94:\tlearn: 0.2971202\ttotal: 255ms\tremaining: 13.4ms\n",
      "95:\tlearn: 0.2971182\ttotal: 257ms\tremaining: 10.7ms\n",
      "96:\tlearn: 0.2971153\ttotal: 259ms\tremaining: 8.02ms\n",
      "97:\tlearn: 0.2971137\ttotal: 262ms\tremaining: 5.35ms\n",
      "98:\tlearn: 0.2971118\ttotal: 264ms\tremaining: 2.67ms\n",
      "99:\tlearn: 0.2971013\ttotal: 267ms\tremaining: 0us\n",
      "---Run time is 35.584886 seconds ---\n",
      "\n",
      "Accuracy of each fold: \n",
      " [0.9113272311212814, 0.929817444219067, 0.9445843828715366, 0.5755395683453237, 0.8244514106583072, 0.9598377281947261, 0.7169546940681925, 0.8257317903335603, 0.8498995983935743, 0.8919514884233738, 0.9251361161524501, 0.9504666188083274, 0.7070294784580499, 0.9687898089171975, 0.9075770191507078, 0.8480283562250776, 0.9821795750514051, 0.9757645701096365, 0.8436179205409975, 0.9444749862561848, 0.9469632164242943, 0.8700564971751412, 0.9471458773784355]\n",
      "\n",
      "Avg accuracy : \n",
      "0.8803184946642106\n",
      "\n",
      "Std of accuracy : \n",
      "0.09805436902301286\n",
      "\n",
      "[[21284  3459]\n",
      " [ 1946 18279]]\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.86      0.89     24743\n",
      "           1       0.84      0.90      0.87     20225\n",
      "\n",
      "    accuracy                           0.88     44968\n",
      "   macro avg       0.88      0.88      0.88     44968\n",
      "weighted avg       0.88      0.88      0.88     44968\n",
      "\n",
      "\n",
      "[0.9113272311212814, 0.929817444219067, 0.9445843828715366, 0.5755395683453237, 0.8244514106583072, 0.9598377281947261, 0.7169546940681925, 0.8257317903335603, 0.8498995983935743, 0.8919514884233738, 0.9251361161524501, 0.9504666188083274, 0.7070294784580499, 0.9687898089171975, 0.9075770191507078, 0.8480283562250776, 0.9821795750514051, 0.9757645701096365, 0.8436179205409975, 0.9444749862561848, 0.9469632164242943, 0.8700564971751412, 0.9471458773784355]\n",
      "\n",
      "35.584886\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "4it [00:35,  8.79s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "90:\tlearn: 0.4912973\ttotal: 269ms\tremaining: 26.6ms\n",
      "91:\tlearn: 0.4912955\ttotal: 272ms\tremaining: 23.7ms\n",
      "92:\tlearn: 0.4912836\ttotal: 275ms\tremaining: 20.7ms\n",
      "93:\tlearn: 0.4912800\ttotal: 279ms\tremaining: 17.8ms\n",
      "94:\tlearn: 0.4912636\ttotal: 282ms\tremaining: 14.8ms\n",
      "95:\tlearn: 0.4912508\ttotal: 285ms\tremaining: 11.9ms\n",
      "96:\tlearn: 0.4912394\ttotal: 288ms\tremaining: 8.9ms\n",
      "97:\tlearn: 0.4912272\ttotal: 291ms\tremaining: 5.95ms\n",
      "98:\tlearn: 0.4912144\ttotal: 295ms\tremaining: 2.97ms\n",
      "99:\tlearn: 0.4912079\ttotal: 298ms\tremaining: 0us\n",
      "---Run time is 44.3963438 seconds ---\n",
      "\n",
      "Accuracy of each fold: \n",
      " [0.7705949656750573, 0.911156186612576, 0.8633501259445844, 0.5497601918465228, 0.8025078369905956, 0.8787018255578093, 0.726296123306866, 0.8461538461538461, 0.7530120481927711, 0.5953693495038589, 0.8303085299455535, 0.7027997128499641, 0.6068027210884354, 0.8076433121019109, 0.7273105745212323, 0.8617634027470094, 0.772446881425634, 0.8066935949221004, 0.44167371090448015, 0.8785046728971962, 0.8721129170230967, 0.86691776522285, 0.8820295983086681]\n",
      "\n",
      "Avg accuracy : \n",
      "0.7719091258148965\n",
      "\n",
      "Std of accuracy : \n",
      "0.11944964068796382\n",
      "\n",
      "[[18026  6717]\n",
      " [ 3509 16716]]\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.84      0.73      0.78     24743\n",
      "           1       0.71      0.83      0.77     20225\n",
      "\n",
      "    accuracy                           0.77     44968\n",
      "   macro avg       0.78      0.78      0.77     44968\n",
      "weighted avg       0.78      0.77      0.77     44968\n",
      "\n",
      "\n",
      "[0.7705949656750573, 0.911156186612576, 0.8633501259445844, 0.5497601918465228, 0.8025078369905956, 0.8787018255578093, 0.726296123306866, 0.8461538461538461, 0.7530120481927711, 0.5953693495038589, 0.8303085299455535, 0.7027997128499641, 0.6068027210884354, 0.8076433121019109, 0.7273105745212323, 0.8617634027470094, 0.772446881425634, 0.8066935949221004, 0.44167371090448015, 0.8785046728971962, 0.8721129170230967, 0.86691776522285, 0.8820295983086681]\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "5it [00:44,  8.80s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "44.3963438\n",
      "['EntropyAF']\n",
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    }
   ],
   "source": [
    "#Ignore the Rest of the STuff. Main One\n",
    "\n",
    "from time import time\n",
    "import timeit #imports timeit module\n",
    "start_time = timeit.default_timer() #defines start time so computational time can be calculated\n",
    "\n",
    "model = CatBoostClassifier(learning_rate=0.1, iterations=100, depth=5, loss_function='Logloss', random_seed = 0)\n",
    "\n",
    "#acc_score = [];\n",
    "#Truth = [];\n",
    "#Output = [];\n",
    "iterations_list = []\n",
    "#max_depth_list = []\n",
    "#x_values = ['StoS']\n",
    "# 'StoS', 'StoR', 'StoL', 'RtoS', 'RtoR', 'RtoL', 'LtoS',\n",
    "#        'LtoR', 'LtoL', 'STD', 'CoefVar', 'Range', 'Volatility', 'ShEn',\n",
    "#        'AppEn', 'SampEn', 'IQR', 'Var', 'Rmean_Var', 'Mean_Absolute_Deviation'\n",
    "\n",
    "y_values = ['Rhythm']\n",
    "#for df in features_df_list:\n",
    "df = features_maindf\n",
    "\n",
    "\n",
    "\n",
    "all_features = ['AppEn', 'SampEn', 'CosEn', 'RangeEn', 'FuzzyEn',\n",
    "       'EntropyAF', 'PermEn', 'BubbleEn', 'MultiscaleEn', 'Volatility']\n",
    "dataScores = [[],[],[],[],[],[],[],[],[],[]]\n",
    "        \n",
    "for score_idx,temp in tqdm(enumerate(all_features)):    \n",
    "    \n",
    "    x_values = [temp]\n",
    "    print(x_values)\n",
    "\n",
    "    acc_score = [];\n",
    "    Truth = [];\n",
    "    Output = [];\n",
    "\n",
    "\n",
    "\n",
    "    for idx, subject in enumerate(good_list):\n",
    "\n",
    "        train_index = ~df['Subject'].str.contains(subject)\n",
    "        test_index = df['Subject'].str.contains(subject)\n",
    "        X_train , X_test = df.loc[train_index,x_values], df.loc[test_index,x_values]\n",
    "        y_train , y_test = df.loc[train_index,y_values].values.ravel(), df.loc[test_index,y_values].values.ravel()\n",
    "\n",
    "        model.fit(X_train, y_train)\n",
    "\n",
    "        # make the prediction using the resulting model\n",
    "        pred_values = model.predict(X_test)\n",
    "        #preds_proba = model.predict_proba(test_data)\n",
    "        #print(\"class = \", preds_class)\n",
    "        #print(\"proba = \", preds_proba)\n",
    "\n",
    "        acc = accuracy_score(y_test, pred_values)\n",
    "        acc_score.append(acc)\n",
    "\n",
    "        Truth.extend(y_test.reshape(y_test.shape[0])); ## it is a list\n",
    "        Output.extend(pred_values); ## it is a list  \n",
    "\n",
    "        #print(model.feature_importances_)\n",
    "    #     Importance = pd.DataFrame({'Importance':(model.feature_importances_*100)}, \n",
    "    #                           index = (X_train.columns))\n",
    "    #     Importance.sort_values(by = 'Importance', \n",
    "    #                        axis = 0, \n",
    "    #                        ascending = True).plot(kind = 'barh', \n",
    "    #                                               color = 'r')\n",
    "    #     plt.xlabel('Variable Importance')\n",
    "    #     plt.gca().legend_ = None\n",
    "        #plt.savefig('plot1.png')\n",
    "\n",
    "\n",
    "    elapsed = timeit.default_timer() - start_time #gives total computation time\n",
    "    print(\"---Run time is %s seconds ---\" % elapsed) #prints computation time\n",
    "    print()\n",
    "\n",
    "    print('Accuracy of each fold: \\n {}'.format(acc_score))\n",
    "    print()\n",
    "    try: \n",
    "        print('Avg accuracy : \\n{}'.format(np.mean(acc_score))); \n",
    "    except: \n",
    "        print('hi')\n",
    "    print()\n",
    "    print('Std of accuracy : \\n{}'.format(np.std(acc_score)))\n",
    "    print()\n",
    "    print(confusion_matrix(Truth, Output))\n",
    "    print()\n",
    "    print(classification_report(Truth, Output))\n",
    "    print()\n",
    "    print(acc_score)\n",
    "    print()\n",
    "    print(elapsed)\n",
    "    \n",
    "    temp_array = confusion_matrix(Truth, Output)\n",
    "    joint = temp_array[0].tolist() + temp_array[1].tolist()\n",
    "    temp_scores = confusion_calcs(joint)\n",
    "    \n",
    "    #dataScores[score_idx].append(all_features[score_idx])\n",
    "    #dataScores[score_idx].append(acc_score)\n",
    "    dataScores[score_idx].append(np.mean(acc_score))\n",
    "    dataScores[score_idx].append(np.std(acc_score))\n",
    "    \n",
    "    for confusionScore in temp_scores:\n",
    "        dataScores[score_idx].append(confusionScore)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "    \n",
    "export_df = pd.DataFrame(dataScores,columns=['Mean Acc.', 'SD of Acc.', 'Total Acc.', 'Sensitivity', \n",
    "                                             'Specificity','Precision','f1'])\n",
    "\n",
    "export_df = export_df.set_index(pd.Index(['AppEn', 'SampEn', 'CosEn', 'RangeEn', 'FuzzyEn',\n",
    "       'EntropyAF', 'PermEn', 'BubbleEn', 'MultiscaleEn', 'Volatility']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "export_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "#T_8bf20_row0_col0,#T_8bf20_row0_col4,#T_8bf20_row0_col5,#T_8bf20_row3_col2,#T_8bf20_row3_col6,#T_8bf20_row9_col1,#T_8bf20_row9_col3{\n",
       "            background-color:  lightgreen;\n",
       "        }</style><table id=\"T_8bf20_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Mean Acc.</th>        <th class=\"col_heading level0 col1\" >SD of Acc.</th>        <th class=\"col_heading level0 col2\" >Total Acc.</th>        <th class=\"col_heading level0 col3\" >Sensitivity</th>        <th class=\"col_heading level0 col4\" >Specificity</th>        <th class=\"col_heading level0 col5\" >Precision</th>        <th class=\"col_heading level0 col6\" >f1</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_8bf20_level0_row0\" class=\"row_heading level0 row0\" >AppEn</th>\n",
       "                        <td id=\"T_8bf20_row0_col0\" class=\"data row0 col0\" >0.882025</td>\n",
       "                        <td id=\"T_8bf20_row0_col1\" class=\"data row0 col1\" >0.159679</td>\n",
       "                        <td id=\"T_8bf20_row0_col2\" class=\"data row0 col2\" >0.870619</td>\n",
       "                        <td id=\"T_8bf20_row0_col3\" class=\"data row0 col3\" >0.864129</td>\n",
       "                        <td id=\"T_8bf20_row0_col4\" class=\"data row0 col4\" >0.875925</td>\n",
       "                        <td id=\"T_8bf20_row0_col5\" class=\"data row0 col5\" >0.850586</td>\n",
       "                        <td id=\"T_8bf20_row0_col6\" class=\"data row0 col6\" >0.857304</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_8bf20_level0_row1\" class=\"row_heading level0 row1\" >SampEn</th>\n",
       "                        <td id=\"T_8bf20_row1_col0\" class=\"data row1 col0\" >0.879405</td>\n",
       "                        <td id=\"T_8bf20_row1_col1\" class=\"data row1 col1\" >0.160325</td>\n",
       "                        <td id=\"T_8bf20_row1_col2\" class=\"data row1 col2\" >0.869952</td>\n",
       "                        <td id=\"T_8bf20_row1_col3\" class=\"data row1 col3\" >0.891125</td>\n",
       "                        <td id=\"T_8bf20_row1_col4\" class=\"data row1 col4\" >0.852645</td>\n",
       "                        <td id=\"T_8bf20_row1_col5\" class=\"data row1 col5\" >0.831741</td>\n",
       "                        <td id=\"T_8bf20_row1_col6\" class=\"data row1 col6\" >0.860410</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_8bf20_level0_row2\" class=\"row_heading level0 row2\" >CosEn</th>\n",
       "                        <td id=\"T_8bf20_row2_col0\" class=\"data row2 col0\" >0.879405</td>\n",
       "                        <td id=\"T_8bf20_row2_col1\" class=\"data row2 col1\" >0.160325</td>\n",
       "                        <td id=\"T_8bf20_row2_col2\" class=\"data row2 col2\" >0.869952</td>\n",
       "                        <td id=\"T_8bf20_row2_col3\" class=\"data row2 col3\" >0.891125</td>\n",
       "                        <td id=\"T_8bf20_row2_col4\" class=\"data row2 col4\" >0.852645</td>\n",
       "                        <td id=\"T_8bf20_row2_col5\" class=\"data row2 col5\" >0.831741</td>\n",
       "                        <td id=\"T_8bf20_row2_col6\" class=\"data row2 col6\" >0.860410</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_8bf20_level0_row3\" class=\"row_heading level0 row3\" >RangeEn</th>\n",
       "                        <td id=\"T_8bf20_row3_col0\" class=\"data row3 col0\" >0.880318</td>\n",
       "                        <td id=\"T_8bf20_row3_col1\" class=\"data row3 col1\" >0.098054</td>\n",
       "                        <td id=\"T_8bf20_row3_col2\" class=\"data row3 col2\" >0.879803</td>\n",
       "                        <td id=\"T_8bf20_row3_col3\" class=\"data row3 col3\" >0.903782</td>\n",
       "                        <td id=\"T_8bf20_row3_col4\" class=\"data row3 col4\" >0.860203</td>\n",
       "                        <td id=\"T_8bf20_row3_col5\" class=\"data row3 col5\" >0.840878</td>\n",
       "                        <td id=\"T_8bf20_row3_col6\" class=\"data row3 col6\" >0.871196</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_8bf20_level0_row4\" class=\"row_heading level0 row4\" >FuzzyEn</th>\n",
       "                        <td id=\"T_8bf20_row4_col0\" class=\"data row4 col0\" >0.771909</td>\n",
       "                        <td id=\"T_8bf20_row4_col1\" class=\"data row4 col1\" >0.119450</td>\n",
       "                        <td id=\"T_8bf20_row4_col2\" class=\"data row4 col2\" >0.772594</td>\n",
       "                        <td id=\"T_8bf20_row4_col3\" class=\"data row4 col3\" >0.826502</td>\n",
       "                        <td id=\"T_8bf20_row4_col4\" class=\"data row4 col4\" >0.728529</td>\n",
       "                        <td id=\"T_8bf20_row4_col5\" class=\"data row4 col5\" >0.713353</td>\n",
       "                        <td id=\"T_8bf20_row4_col6\" class=\"data row4 col6\" >0.765770</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_8bf20_level0_row5\" class=\"row_heading level0 row5\" >EntropyAF</th>\n",
       "                        <td id=\"T_8bf20_row5_col0\" class=\"data row5 col0\" >0.863567</td>\n",
       "                        <td id=\"T_8bf20_row5_col1\" class=\"data row5 col1\" >0.141360</td>\n",
       "                        <td id=\"T_8bf20_row5_col2\" class=\"data row5 col2\" >0.864793</td>\n",
       "                        <td id=\"T_8bf20_row5_col3\" class=\"data row5 col3\" >0.913572</td>\n",
       "                        <td id=\"T_8bf20_row5_col4\" class=\"data row5 col4\" >0.824920</td>\n",
       "                        <td id=\"T_8bf20_row5_col5\" class=\"data row5 col5\" >0.810075</td>\n",
       "                        <td id=\"T_8bf20_row5_col6\" class=\"data row5 col6\" >0.858716</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_8bf20_level0_row6\" class=\"row_heading level0 row6\" >PermEn</th>\n",
       "                        <td id=\"T_8bf20_row6_col0\" class=\"data row6 col0\" >0.631511</td>\n",
       "                        <td id=\"T_8bf20_row6_col1\" class=\"data row6 col1\" >0.113435</td>\n",
       "                        <td id=\"T_8bf20_row6_col2\" class=\"data row6 col2\" >0.637765</td>\n",
       "                        <td id=\"T_8bf20_row6_col3\" class=\"data row6 col3\" >0.606329</td>\n",
       "                        <td id=\"T_8bf20_row6_col4\" class=\"data row6 col4\" >0.663460</td>\n",
       "                        <td id=\"T_8bf20_row6_col5\" class=\"data row6 col5\" >0.595580</td>\n",
       "                        <td id=\"T_8bf20_row6_col6\" class=\"data row6 col6\" >0.600907</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_8bf20_level0_row7\" class=\"row_heading level0 row7\" >BubbleEn</th>\n",
       "                        <td id=\"T_8bf20_row7_col0\" class=\"data row7 col0\" >0.616064</td>\n",
       "                        <td id=\"T_8bf20_row7_col1\" class=\"data row7 col1\" >0.105295</td>\n",
       "                        <td id=\"T_8bf20_row7_col2\" class=\"data row7 col2\" >0.618484</td>\n",
       "                        <td id=\"T_8bf20_row7_col3\" class=\"data row7 col3\" >0.550260</td>\n",
       "                        <td id=\"T_8bf20_row7_col4\" class=\"data row7 col4\" >0.674251</td>\n",
       "                        <td id=\"T_8bf20_row7_col5\" class=\"data row7 col5\" >0.579968</td>\n",
       "                        <td id=\"T_8bf20_row7_col6\" class=\"data row7 col6\" >0.564723</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_8bf20_level0_row8\" class=\"row_heading level0 row8\" >MultiscaleEn</th>\n",
       "                        <td id=\"T_8bf20_row8_col0\" class=\"data row8 col0\" >0.855645</td>\n",
       "                        <td id=\"T_8bf20_row8_col1\" class=\"data row8 col1\" >0.162571</td>\n",
       "                        <td id=\"T_8bf20_row8_col2\" class=\"data row8 col2\" >0.847536</td>\n",
       "                        <td id=\"T_8bf20_row8_col3\" class=\"data row8 col3\" >0.856564</td>\n",
       "                        <td id=\"T_8bf20_row8_col4\" class=\"data row8 col4\" >0.840157</td>\n",
       "                        <td id=\"T_8bf20_row8_col5\" class=\"data row8 col5\" >0.814136</td>\n",
       "                        <td id=\"T_8bf20_row8_col6\" class=\"data row8 col6\" >0.834811</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_8bf20_level0_row9\" class=\"row_heading level0 row9\" >Volatility</th>\n",
       "                        <td id=\"T_8bf20_row9_col0\" class=\"data row9 col0\" >0.780561</td>\n",
       "                        <td id=\"T_8bf20_row9_col1\" class=\"data row9 col1\" >0.203759</td>\n",
       "                        <td id=\"T_8bf20_row9_col2\" class=\"data row9 col2\" >0.779732</td>\n",
       "                        <td id=\"T_8bf20_row9_col3\" class=\"data row9 col3\" >0.915797</td>\n",
       "                        <td id=\"T_8bf20_row9_col4\" class=\"data row9 col4\" >0.668512</td>\n",
       "                        <td id=\"T_8bf20_row9_col5\" class=\"data row9 col5\" >0.693085</td>\n",
       "                        <td id=\"T_8bf20_row9_col6\" class=\"data row9 col6\" >0.789026</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x274c5ff9040>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "export_df_highlight = export_df.copy()\n",
    "\n",
    "export_df_highlight.style.highlight_max(color = 'lightgreen', axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "#T_38b3d_row3_col1,#T_38b3d_row6_col4,#T_38b3d_row7_col0,#T_38b3d_row7_col2,#T_38b3d_row7_col3,#T_38b3d_row7_col5,#T_38b3d_row7_col6{\n",
       "            background-color:  lightcoral;\n",
       "        }</style><table id=\"T_38b3d_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Mean Acc.</th>        <th class=\"col_heading level0 col1\" >SD of Acc.</th>        <th class=\"col_heading level0 col2\" >Total Acc.</th>        <th class=\"col_heading level0 col3\" >Sensitivity</th>        <th class=\"col_heading level0 col4\" >Specificity</th>        <th class=\"col_heading level0 col5\" >Precision</th>        <th class=\"col_heading level0 col6\" >f1</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_38b3d_level0_row0\" class=\"row_heading level0 row0\" >AppEn</th>\n",
       "                        <td id=\"T_38b3d_row0_col0\" class=\"data row0 col0\" >0.882025</td>\n",
       "                        <td id=\"T_38b3d_row0_col1\" class=\"data row0 col1\" >0.159679</td>\n",
       "                        <td id=\"T_38b3d_row0_col2\" class=\"data row0 col2\" >0.870619</td>\n",
       "                        <td id=\"T_38b3d_row0_col3\" class=\"data row0 col3\" >0.864129</td>\n",
       "                        <td id=\"T_38b3d_row0_col4\" class=\"data row0 col4\" >0.875925</td>\n",
       "                        <td id=\"T_38b3d_row0_col5\" class=\"data row0 col5\" >0.850586</td>\n",
       "                        <td id=\"T_38b3d_row0_col6\" class=\"data row0 col6\" >0.857304</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_38b3d_level0_row1\" class=\"row_heading level0 row1\" >SampEn</th>\n",
       "                        <td id=\"T_38b3d_row1_col0\" class=\"data row1 col0\" >0.879405</td>\n",
       "                        <td id=\"T_38b3d_row1_col1\" class=\"data row1 col1\" >0.160325</td>\n",
       "                        <td id=\"T_38b3d_row1_col2\" class=\"data row1 col2\" >0.869952</td>\n",
       "                        <td id=\"T_38b3d_row1_col3\" class=\"data row1 col3\" >0.891125</td>\n",
       "                        <td id=\"T_38b3d_row1_col4\" class=\"data row1 col4\" >0.852645</td>\n",
       "                        <td id=\"T_38b3d_row1_col5\" class=\"data row1 col5\" >0.831741</td>\n",
       "                        <td id=\"T_38b3d_row1_col6\" class=\"data row1 col6\" >0.860410</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_38b3d_level0_row2\" class=\"row_heading level0 row2\" >CosEn</th>\n",
       "                        <td id=\"T_38b3d_row2_col0\" class=\"data row2 col0\" >0.879405</td>\n",
       "                        <td id=\"T_38b3d_row2_col1\" class=\"data row2 col1\" >0.160325</td>\n",
       "                        <td id=\"T_38b3d_row2_col2\" class=\"data row2 col2\" >0.869952</td>\n",
       "                        <td id=\"T_38b3d_row2_col3\" class=\"data row2 col3\" >0.891125</td>\n",
       "                        <td id=\"T_38b3d_row2_col4\" class=\"data row2 col4\" >0.852645</td>\n",
       "                        <td id=\"T_38b3d_row2_col5\" class=\"data row2 col5\" >0.831741</td>\n",
       "                        <td id=\"T_38b3d_row2_col6\" class=\"data row2 col6\" >0.860410</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_38b3d_level0_row3\" class=\"row_heading level0 row3\" >RangeEn</th>\n",
       "                        <td id=\"T_38b3d_row3_col0\" class=\"data row3 col0\" >0.880318</td>\n",
       "                        <td id=\"T_38b3d_row3_col1\" class=\"data row3 col1\" >0.098054</td>\n",
       "                        <td id=\"T_38b3d_row3_col2\" class=\"data row3 col2\" >0.879803</td>\n",
       "                        <td id=\"T_38b3d_row3_col3\" class=\"data row3 col3\" >0.903782</td>\n",
       "                        <td id=\"T_38b3d_row3_col4\" class=\"data row3 col4\" >0.860203</td>\n",
       "                        <td id=\"T_38b3d_row3_col5\" class=\"data row3 col5\" >0.840878</td>\n",
       "                        <td id=\"T_38b3d_row3_col6\" class=\"data row3 col6\" >0.871196</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_38b3d_level0_row4\" class=\"row_heading level0 row4\" >FuzzyEn</th>\n",
       "                        <td id=\"T_38b3d_row4_col0\" class=\"data row4 col0\" >0.771909</td>\n",
       "                        <td id=\"T_38b3d_row4_col1\" class=\"data row4 col1\" >0.119450</td>\n",
       "                        <td id=\"T_38b3d_row4_col2\" class=\"data row4 col2\" >0.772594</td>\n",
       "                        <td id=\"T_38b3d_row4_col3\" class=\"data row4 col3\" >0.826502</td>\n",
       "                        <td id=\"T_38b3d_row4_col4\" class=\"data row4 col4\" >0.728529</td>\n",
       "                        <td id=\"T_38b3d_row4_col5\" class=\"data row4 col5\" >0.713353</td>\n",
       "                        <td id=\"T_38b3d_row4_col6\" class=\"data row4 col6\" >0.765770</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_38b3d_level0_row5\" class=\"row_heading level0 row5\" >EntropyAF</th>\n",
       "                        <td id=\"T_38b3d_row5_col0\" class=\"data row5 col0\" >0.863567</td>\n",
       "                        <td id=\"T_38b3d_row5_col1\" class=\"data row5 col1\" >0.141360</td>\n",
       "                        <td id=\"T_38b3d_row5_col2\" class=\"data row5 col2\" >0.864793</td>\n",
       "                        <td id=\"T_38b3d_row5_col3\" class=\"data row5 col3\" >0.913572</td>\n",
       "                        <td id=\"T_38b3d_row5_col4\" class=\"data row5 col4\" >0.824920</td>\n",
       "                        <td id=\"T_38b3d_row5_col5\" class=\"data row5 col5\" >0.810075</td>\n",
       "                        <td id=\"T_38b3d_row5_col6\" class=\"data row5 col6\" >0.858716</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_38b3d_level0_row6\" class=\"row_heading level0 row6\" >PermEn</th>\n",
       "                        <td id=\"T_38b3d_row6_col0\" class=\"data row6 col0\" >0.631511</td>\n",
       "                        <td id=\"T_38b3d_row6_col1\" class=\"data row6 col1\" >0.113435</td>\n",
       "                        <td id=\"T_38b3d_row6_col2\" class=\"data row6 col2\" >0.637765</td>\n",
       "                        <td id=\"T_38b3d_row6_col3\" class=\"data row6 col3\" >0.606329</td>\n",
       "                        <td id=\"T_38b3d_row6_col4\" class=\"data row6 col4\" >0.663460</td>\n",
       "                        <td id=\"T_38b3d_row6_col5\" class=\"data row6 col5\" >0.595580</td>\n",
       "                        <td id=\"T_38b3d_row6_col6\" class=\"data row6 col6\" >0.600907</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_38b3d_level0_row7\" class=\"row_heading level0 row7\" >BubbleEn</th>\n",
       "                        <td id=\"T_38b3d_row7_col0\" class=\"data row7 col0\" >0.616064</td>\n",
       "                        <td id=\"T_38b3d_row7_col1\" class=\"data row7 col1\" >0.105295</td>\n",
       "                        <td id=\"T_38b3d_row7_col2\" class=\"data row7 col2\" >0.618484</td>\n",
       "                        <td id=\"T_38b3d_row7_col3\" class=\"data row7 col3\" >0.550260</td>\n",
       "                        <td id=\"T_38b3d_row7_col4\" class=\"data row7 col4\" >0.674251</td>\n",
       "                        <td id=\"T_38b3d_row7_col5\" class=\"data row7 col5\" >0.579968</td>\n",
       "                        <td id=\"T_38b3d_row7_col6\" class=\"data row7 col6\" >0.564723</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_38b3d_level0_row8\" class=\"row_heading level0 row8\" >MultiscaleEn</th>\n",
       "                        <td id=\"T_38b3d_row8_col0\" class=\"data row8 col0\" >0.855645</td>\n",
       "                        <td id=\"T_38b3d_row8_col1\" class=\"data row8 col1\" >0.162571</td>\n",
       "                        <td id=\"T_38b3d_row8_col2\" class=\"data row8 col2\" >0.847536</td>\n",
       "                        <td id=\"T_38b3d_row8_col3\" class=\"data row8 col3\" >0.856564</td>\n",
       "                        <td id=\"T_38b3d_row8_col4\" class=\"data row8 col4\" >0.840157</td>\n",
       "                        <td id=\"T_38b3d_row8_col5\" class=\"data row8 col5\" >0.814136</td>\n",
       "                        <td id=\"T_38b3d_row8_col6\" class=\"data row8 col6\" >0.834811</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_38b3d_level0_row9\" class=\"row_heading level0 row9\" >Volatility</th>\n",
       "                        <td id=\"T_38b3d_row9_col0\" class=\"data row9 col0\" >0.780561</td>\n",
       "                        <td id=\"T_38b3d_row9_col1\" class=\"data row9 col1\" >0.203759</td>\n",
       "                        <td id=\"T_38b3d_row9_col2\" class=\"data row9 col2\" >0.779732</td>\n",
       "                        <td id=\"T_38b3d_row9_col3\" class=\"data row9 col3\" >0.915797</td>\n",
       "                        <td id=\"T_38b3d_row9_col4\" class=\"data row9 col4\" >0.668512</td>\n",
       "                        <td id=\"T_38b3d_row9_col5\" class=\"data row9 col5\" >0.693085</td>\n",
       "                        <td id=\"T_38b3d_row9_col6\" class=\"data row9 col6\" >0.789026</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x27482ccc160>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "export_df_highlight.style.highlight_min(color = 'lightcoral', axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "export_df.to_csv(f'new-individual-features/CatBoost.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Ignore Stuff Below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Ignore Stuff Below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Ignore Stuff Below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Ignore Stuff Below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Ignore Stuff Below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Ignore Stuff Below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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