Datasets
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
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"<script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script><script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window._Plotly) {require(['plotly'],function(plotly) {window._Plotly=plotly;});}</script>"
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"<script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script><script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window._Plotly) {require(['plotly'],function(plotly) {window._Plotly=plotly;});}</script>"
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"metadata": {},
"output_type": "display_data"
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],
"source": [
"# Import libraries\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import getpass\n",
"import pdvega\n",
"import plotly.graph_objs as go\n",
"\n",
"from plotly.offline import iplot, init_notebook_mode\n",
"import plotly.io as pio\n",
"from plotly.graph_objs import *\n",
"\n",
"# for configuring connection \n",
"from configobj import ConfigObj\n",
"import os\n",
"\n",
"%matplotlib inline\n",
"\n",
"\n",
"import os\n",
"\n",
"\n",
"from sklearn import linear_model\n",
"from sklearn import metrics\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"#configure the notebook for use in offline mode\n",
"init_notebook_mode(connected=True)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Unnamed: 0</th>\n",
" <th>hospitalid</th>\n",
" <th>sodium</th>\n",
" <th>electivesurgery</th>\n",
" <th>vent</th>\n",
" <th>dialysis</th>\n",
" <th>gcs</th>\n",
" <th>urine</th>\n",
" <th>wbc</th>\n",
" <th>temperature</th>\n",
" <th>...</th>\n",
" <th>m11_True</th>\n",
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" <th>m19_True</th>\n",
" <th>m20_True</th>\n",
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" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <td>10.9</td>\n",
" <td>36.6</td>\n",
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" <td>1</td>\n",
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"text/plain": [
" Unnamed: 0 hospitalid sodium electivesurgery vent dialysis gcs \\\n",
"0 0 59.0 139.0 -1.0 0.0 0.0 15.0 \n",
"1 1 73.0 134.0 -1.0 0.0 0.0 13.0 \n",
"2 2 73.0 -1.0 1.0 1.0 0.0 15.0 \n",
"3 3 63.0 137.0 -1.0 0.0 0.0 15.0 \n",
"4 4 63.0 135.0 -1.0 0.0 0.0 15.0 \n",
"\n",
" urine wbc temperature ... m11_True m12_True m13_True m14_True \\\n",
"0 -1.0 14.7 36.1 ... 1 0 0 1 \n",
"1 -1.0 14.1 39.3 ... 1 0 0 1 \n",
"2 -1.0 8.0 34.8 ... 0 0 1 0 \n",
"3 -1.0 10.9 36.6 ... 1 0 1 1 \n",
"4 -1.0 5.9 35.0 ... 0 0 1 0 \n",
"\n",
" m15_True m16_True m17_True m18_True m19_True m20_True \n",
"0 1 0 0 0 1 0 \n",
"1 1 0 0 0 1 0 \n",
"2 0 1 0 1 0 0 \n",
"3 1 0 0 1 1 0 \n",
"4 0 0 0 1 0 0 \n",
"\n",
"[5 rows x 85 columns]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df= pd.read_csv(\"analysis.csv\")\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(95148, 85)"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.shape"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"del df['hospitalid']\n",
"\n",
"df = df.drop(df.columns[[0]], axis=1)\n",
"df = df.drop(df.columns[[63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82]], axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"sodium 18244\n",
"electivesurgery 74997\n",
"vent 0\n",
"dialysis 0\n",
"gcs 1728\n",
"urine 45829\n",
"wbc 22141\n",
"temperature 4139\n",
"respiratoryrate 582\n",
"heartrate 188\n",
"meanbp 263\n",
"creatinine 18332\n",
"ph 73474\n",
"hematocrit 20021\n",
"albumin 58143\n",
"pao2 73474\n",
"pco2 73474\n",
"bun 18774\n",
"glucose 10909\n",
"bilirubin 60797\n",
"fio2 73474\n",
"age 3356\n",
"thrombolytics 0\n",
"aids 0\n",
"hepaticfailure 0\n",
"lymphoma 0\n",
"metastaticcancer 0\n",
"leukemia 0\n",
"immunosuppression 0\n",
"cirrhosis 0\n",
" ... \n",
"admitsource_1.0 0\n",
"admitsource_2.0 0\n",
"admitsource_3.0 0\n",
"admitsource_4.0 0\n",
"admitsource_5.0 0\n",
"admitsource_6.0 0\n",
"admitsource_7.0 0\n",
"admitsource_8.0 0\n",
"diaggroup_ARF 0\n",
"diaggroup_Asthma-Emphys 0\n",
"diaggroup_CABG 0\n",
"diaggroup_CHF 0\n",
"diaggroup_CVA 0\n",
"diaggroup_CVOther 0\n",
"diaggroup_CardiacArrest 0\n",
"diaggroup_ChestPainUnknown 0\n",
"diaggroup_Coma 0\n",
"diaggroup_DKA 0\n",
"diaggroup_GIBleed 0\n",
"diaggroup_GIObstruction 0\n",
"diaggroup_Neuro 0\n",
"diaggroup_Other 0\n",
"diaggroup_Overdose 0\n",
"diaggroup_PNA 0\n",
"diaggroup_RespMedOther 0\n",
"diaggroup_Sepsis 0\n",
"diaggroup_Trauma 0\n",
"diaggroup_ValveDz 0\n",
"gender_Male 0\n",
"gender_Other 0\n",
"Length: 63, dtype: int64"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"missing_values_count = df.isnull().sum()\n",
"#df.replace('-1.0', np.nan)\n",
"df = df.replace({-1.0:np.nan, -1.0:np.nan})\n",
"df.head()\n",
"missing_values_count = df.isnull().sum()\n",
"missing_values_count"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**We moved all the pre-processing including splitting>imputation>Standardization to the CV iterations**"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"cols_to_norm=['gcs', 'urine', 'wbc', 'sodium',\n",
" 'temperature', 'respiratoryrate', 'heartrate', 'meanbp', 'creatinine',\n",
" 'ph', 'hematocrit', 'albumin', 'pao2', 'pco2', 'bun', 'glucose',\n",
" 'bilirubin', 'fio2', 'age', 'offset']\n",
"\n",
"X=df.drop('destcopy', 1)\n",
"y=df['destcopy']\n",
"df_cols = list(X) #fancy impute removes column names."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['sodium', 'electivesurgery', 'vent', 'dialysis', 'gcs', 'urine', 'wbc',\n",
" 'temperature', 'respiratoryrate', 'heartrate', 'meanbp', 'creatinine',\n",
" 'ph', 'hematocrit', 'albumin', 'pao2', 'pco2', 'bun', 'glucose',\n",
" 'bilirubin', 'fio2', 'age', 'thrombolytics', 'aids', 'hepaticfailure',\n",
" 'lymphoma', 'metastaticcancer', 'leukemia', 'immunosuppression',\n",
" 'cirrhosis', 'readmit', 'offset', 'destcopy', 'admitsource_1.0',\n",
" 'admitsource_2.0', 'admitsource_3.0', 'admitsource_4.0',\n",
" 'admitsource_5.0', 'admitsource_6.0', 'admitsource_7.0',\n",
" 'admitsource_8.0', 'diaggroup_ARF', 'diaggroup_Asthma-Emphys',\n",
" 'diaggroup_CABG', 'diaggroup_CHF', 'diaggroup_CVA', 'diaggroup_CVOther',\n",
" 'diaggroup_CardiacArrest', 'diaggroup_ChestPainUnknown',\n",
" 'diaggroup_Coma', 'diaggroup_DKA', 'diaggroup_GIBleed',\n",
" 'diaggroup_GIObstruction', 'diaggroup_Neuro', 'diaggroup_Other',\n",
" 'diaggroup_Overdose', 'diaggroup_PNA', 'diaggroup_RespMedOther',\n",
" 'diaggroup_Sepsis', 'diaggroup_Trauma', 'diaggroup_ValveDz',\n",
" 'gender_Male', 'gender_Other'],\n",
" dtype='object')"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.columns"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**XGB**"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"from collections import Counter"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\llois\\Anaconda\\lib\\site-packages\\sklearn\\externals\\six.py:31: DeprecationWarning:\n",
"\n",
"The module is deprecated in version 0.21 and will be removed in version 0.23 since we've dropped support for Python 2.7. Please rely on the official version of six (https://pypi.org/project/six/).\n",
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[(1, 59442), (2, 59442), (3, 59442), (4, 59442)]\n",
"For fold 1:\n",
"Accuracy: 0.7169732002101944\n",
"f-score: 0.7169732002101944\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.39 0.48 0.95 0.43 0.68 0.44 642\n",
" 2 0.79 0.90 0.41 0.84 0.61 0.39 6776\n",
" 3 0.46 0.20 0.95 0.28 0.44 0.18 1716\n",
" 4 0.15 0.10 0.98 0.12 0.31 0.09 381\n",
"\n",
"avg / total 0.68 0.72 0.57 0.69 0.57 0.34 9515\n",
"\n",
"[(1, 58698), (2, 58698), (3, 58698), (4, 58698)]\n",
"For fold 2:\n",
"Accuracy: 0.7043615344193379\n",
"f-score: 0.7043615344193379\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.47 0.51 0.93 0.49 0.69 0.46 974\n",
" 2 0.87 0.79 0.57 0.83 0.67 0.46 7520\n",
" 3 0.16 0.30 0.87 0.20 0.51 0.24 697\n",
" 4 0.10 0.10 0.97 0.10 0.30 0.08 324\n",
"\n",
"avg / total 0.75 0.70 0.64 0.73 0.65 0.43 9515\n",
"\n",
"[(1, 59633), (2, 59633), (3, 59633), (4, 59633)]\n",
"For fold 3:\n",
"Accuracy: 0.7086705202312139\n",
"f-score: 0.7086705202312139\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.57 0.59 0.93 0.58 0.74 0.53 1247\n",
" 2 0.81 0.86 0.54 0.83 0.68 0.48 6585\n",
" 3 0.33 0.23 0.91 0.27 0.46 0.20 1462\n",
" 4 0.10 0.10 0.98 0.10 0.31 0.08 221\n",
"\n",
"avg / total 0.69 0.71 0.66 0.70 0.65 0.43 9515\n",
"\n",
"[(1, 59870), (2, 59870), (3, 59870), (4, 59870)]\n",
"For fold 4:\n",
"Accuracy: 0.6671571203363111\n",
"f-score: 0.6671571203363111\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.48 0.58 0.92 0.52 0.73 0.51 1129\n",
" 2 0.78 0.84 0.51 0.81 0.66 0.45 6348\n",
" 3 0.30 0.19 0.93 0.23 0.42 0.16 1285\n",
" 4 0.22 0.13 0.96 0.17 0.36 0.12 753\n",
"\n",
"avg / total 0.63 0.67 0.65 0.65 0.61 0.39 9515\n",
"\n",
"[(1, 59781), (2, 59781), (3, 59781), (4, 59781)]\n",
"For fold 5:\n",
"Accuracy: 0.6906988964792433\n",
"f-score: 0.6906988964792433\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.53 0.55 0.94 0.54 0.72 0.49 1085\n",
" 2 0.78 0.87 0.50 0.82 0.66 0.45 6437\n",
" 3 0.40 0.19 0.94 0.26 0.43 0.17 1657\n",
" 4 0.12 0.16 0.96 0.14 0.39 0.14 336\n",
"\n",
"avg / total 0.66 0.69 0.64 0.67 0.61 0.39 9515\n",
"\n",
"[(1, 59994), (2, 59994), (3, 59994), (4, 59994)]\n",
"For fold 6:\n",
"Accuracy: 0.6855491329479769\n",
"f-score: 0.6855491329479769\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.42 0.55 0.93 0.47 0.71 0.49 785\n",
" 2 0.76 0.90 0.46 0.82 0.64 0.43 6224\n",
" 3 0.50 0.22 0.94 0.31 0.46 0.19 2026\n",
" 4 0.19 0.08 0.98 0.11 0.28 0.07 480\n",
"\n",
"avg / total 0.65 0.69 0.63 0.65 0.59 0.37 9515\n",
"\n",
"[(1, 59534), (2, 59534), (3, 59534), (4, 59534)]\n",
"For fold 7:\n",
"Accuracy: 0.7142406726221755\n",
"f-score: 0.7142406726221755\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.50 0.51 0.95 0.51 0.70 0.47 853\n",
" 2 0.80 0.90 0.46 0.84 0.64 0.43 6684\n",
" 3 0.35 0.26 0.93 0.29 0.49 0.22 1209\n",
" 4 0.26 0.08 0.98 0.12 0.28 0.07 769\n",
"\n",
"avg / total 0.67 0.71 0.61 0.69 0.60 0.38 9515\n",
"\n",
"[(1, 59573), (2, 59573), (3, 59573), (4, 59573)]\n",
"For fold 8:\n",
"Accuracy: 0.7141355754072517\n",
"f-score: 0.7141355754072517\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.55 0.46 0.96 0.50 0.66 0.42 933\n",
" 2 0.78 0.91 0.41 0.84 0.61 0.39 6645\n",
" 3 0.39 0.20 0.93 0.26 0.43 0.17 1675\n",
" 4 0.10 0.06 0.98 0.08 0.25 0.06 262\n",
"\n",
"avg / total 0.67 0.71 0.57 0.68 0.57 0.34 9515\n",
"\n",
"[(1, 59819), (2, 59819), (3, 59819), (4, 59819)]\n",
"For fold 9:\n",
"Accuracy: 0.6835190245953332\n",
"f-score: 0.6835190245953332\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.52 0.54 0.94 0.53 0.71 0.49 1031\n",
" 2 0.78 0.87 0.50 0.82 0.66 0.46 6399\n",
" 3 0.35 0.20 0.93 0.25 0.43 0.17 1495\n",
" 4 0.16 0.13 0.96 0.14 0.35 0.11 589\n",
"\n",
"avg / total 0.65 0.68 0.65 0.66 0.61 0.39 9514\n",
"\n",
"[(1, 59618), (2, 59618), (3, 59618), (4, 59618)]\n",
"For fold 10:\n",
"Accuracy: 0.6930838763926844\n",
"f-score: 0.6930838763926844\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.52 0.61 0.92 0.56 0.75 0.54 1157\n",
" 2 0.81 0.85 0.56 0.83 0.69 0.48 6600\n",
" 3 0.30 0.22 0.93 0.25 0.45 0.19 1156\n",
" 4 0.12 0.09 0.96 0.11 0.30 0.08 601\n",
"\n",
"avg / total 0.67 0.69 0.67 0.68 0.64 0.43 9514\n",
"\n",
"[(1, 59442), (2, 59442), (3, 59442), (4, 59442)]\n",
"For fold 1:\n",
"Accuracy: 0.7202312138728324\n",
"f-score: 0.7202312138728324\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.42 0.49 0.95 0.45 0.68 0.44 642\n",
" 2 0.79 0.91 0.41 0.84 0.61 0.39 6776\n",
" 3 0.45 0.21 0.94 0.29 0.44 0.18 1716\n",
" 4 0.18 0.11 0.98 0.14 0.33 0.10 381\n",
"\n",
"avg / total 0.68 0.72 0.57 0.69 0.57 0.34 9515\n",
"\n",
"[(1, 58698), (2, 58698), (3, 58698), (4, 58698)]\n",
"For fold 2:\n",
"Accuracy: 0.7106673673147662\n",
"f-score: 0.7106673673147662\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.48 0.54 0.93 0.50 0.71 0.48 974\n",
" 2 0.88 0.80 0.58 0.84 0.68 0.48 7520\n",
" 3 0.17 0.31 0.88 0.22 0.52 0.26 697\n",
" 4 0.09 0.08 0.97 0.09 0.28 0.07 324\n",
"\n",
"avg / total 0.76 0.71 0.66 0.73 0.66 0.45 9515\n",
"\n",
"[(1, 59633), (2, 59633), (3, 59633), (4, 59633)]\n",
"For fold 3:\n",
"Accuracy: 0.7089858118759853\n",
"f-score: 0.7089858118759853\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.57 0.58 0.93 0.57 0.73 0.52 1247\n",
" 2 0.81 0.86 0.55 0.83 0.68 0.48 6585\n",
" 3 0.34 0.25 0.91 0.29 0.48 0.21 1462\n",
" 4 0.06 0.06 0.98 0.06 0.24 0.05 221\n",
"\n",
"avg / total 0.69 0.71 0.66 0.70 0.65 0.44 9515\n",
"\n",
"[(1, 59870), (2, 59870), (3, 59870), (4, 59870)]\n",
"For fold 4:\n",
"Accuracy: 0.6689437729900157\n",
"f-score: 0.6689437729900157\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.47 0.57 0.91 0.52 0.72 0.51 1129\n",
" 2 0.78 0.84 0.52 0.81 0.67 0.46 6348\n",
" 3 0.31 0.20 0.93 0.24 0.43 0.17 1285\n",
" 4 0.23 0.13 0.96 0.16 0.35 0.11 753\n",
"\n",
"avg / total 0.64 0.67 0.66 0.65 0.62 0.40 9515\n",
"\n",
"[(1, 59781), (2, 59781), (3, 59781), (4, 59781)]\n",
"For fold 5:\n",
"Accuracy: 0.6909090909090909\n",
"f-score: 0.6909090909090909\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.54 0.56 0.94 0.55 0.73 0.51 1085\n",
" 2 0.79 0.87 0.51 0.83 0.66 0.46 6437\n",
" 3 0.41 0.20 0.94 0.27 0.43 0.17 1657\n",
" 4 0.08 0.11 0.95 0.09 0.32 0.09 336\n",
"\n",
"avg / total 0.67 0.69 0.65 0.67 0.62 0.40 9515\n",
"\n",
"[(1, 59994), (2, 59994), (3, 59994), (4, 59994)]\n",
"For fold 6:\n",
"Accuracy: 0.6831318970047294\n",
"f-score: 0.6831318970047294\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.42 0.53 0.93 0.47 0.70 0.47 785\n",
" 2 0.76 0.90 0.45 0.82 0.64 0.43 6224\n",
" 3 0.50 0.21 0.94 0.30 0.45 0.19 2026\n",
" 4 0.15 0.07 0.98 0.10 0.26 0.06 480\n",
"\n",
"avg / total 0.64 0.68 0.62 0.65 0.59 0.36 9515\n",
"\n",
"[(1, 59534), (2, 59534), (3, 59534), (4, 59534)]\n",
"For fold 7:\n",
"Accuracy: 0.7130846032580137\n",
"f-score: 0.7130846032580137\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.49 0.51 0.95 0.50 0.69 0.46 853\n",
" 2 0.80 0.89 0.47 0.84 0.65 0.43 6684\n",
" 3 0.36 0.28 0.93 0.31 0.51 0.24 1209\n",
" 4 0.29 0.08 0.98 0.13 0.29 0.07 769\n",
"\n",
"avg / total 0.67 0.71 0.61 0.69 0.60 0.38 9515\n",
"\n",
"[(1, 59573), (2, 59573), (3, 59573), (4, 59573)]\n",
"For fold 8:\n",
"Accuracy: 0.7142406726221755\n",
"f-score: 0.7142406726221755\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.54 0.47 0.96 0.50 0.67 0.43 933\n",
" 2 0.78 0.91 0.41 0.84 0.61 0.39 6645\n",
" 3 0.40 0.20 0.94 0.26 0.43 0.17 1675\n",
" 4 0.08 0.05 0.98 0.06 0.23 0.05 262\n",
"\n",
"avg / total 0.67 0.71 0.57 0.68 0.57 0.34 9515\n",
"\n",
"[(1, 59819), (2, 59819), (3, 59819), (4, 59819)]\n",
"For fold 9:\n",
"Accuracy: 0.6776329619508094\n",
"f-score: 0.6776329619508094\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.49 0.54 0.93 0.51 0.71 0.48 1031\n",
" 2 0.78 0.86 0.52 0.82 0.67 0.46 6399\n",
" 3 0.35 0.21 0.93 0.26 0.44 0.18 1495\n",
" 4 0.15 0.12 0.95 0.13 0.34 0.10 589\n",
"\n",
"avg / total 0.65 0.68 0.65 0.66 0.61 0.40 9514\n",
"\n",
"[(1, 59618), (2, 59618), (3, 59618), (4, 59618)]\n",
"For fold 10:\n",
"Accuracy: 0.6961320159764558\n",
"f-score: 0.6961320159764558\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.52 0.61 0.92 0.56 0.75 0.55 1157\n",
" 2 0.81 0.85 0.56 0.83 0.69 0.49 6600\n",
" 3 0.29 0.22 0.93 0.25 0.45 0.19 1156\n",
" 4 0.16 0.09 0.97 0.12 0.30 0.08 601\n",
"\n",
"avg / total 0.67 0.70 0.67 0.68 0.64 0.43 9514\n",
"\n",
"[(1, 59442), (2, 59442), (3, 59442), (4, 59442)]\n",
"For fold 1:\n",
"Accuracy: 0.7162375197057278\n",
"f-score: 0.7162375197057278\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.41 0.50 0.95 0.45 0.69 0.45 642\n",
" 2 0.79 0.91 0.40 0.84 0.60 0.38 6776\n",
" 3 0.45 0.18 0.95 0.26 0.42 0.16 1716\n",
" 4 0.16 0.11 0.98 0.13 0.32 0.10 381\n",
"\n",
"avg / total 0.68 0.72 0.56 0.68 0.56 0.34 9515\n",
"\n",
"[(1, 58698), (2, 58698), (3, 58698), (4, 58698)]\n",
"For fold 2:\n",
"Accuracy: 0.7158171308460326\n",
"f-score: 0.7158171308460325\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.48 0.55 0.93 0.51 0.71 0.49 974\n",
" 2 0.88 0.80 0.59 0.84 0.69 0.48 7520\n",
" 3 0.18 0.32 0.88 0.23 0.53 0.27 697\n",
" 4 0.07 0.07 0.97 0.07 0.26 0.06 324\n",
"\n",
"avg / total 0.76 0.72 0.66 0.74 0.66 0.45 9515\n",
"\n",
"[(1, 59633), (2, 59633), (3, 59633), (4, 59633)]\n",
"For fold 3:\n",
"Accuracy: 0.7233841303205465\n",
"f-score: 0.7233841303205465\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.59 0.58 0.94 0.58 0.74 0.53 1247\n",
" 2 0.81 0.88 0.53 0.84 0.68 0.48 6585\n",
" 3 0.36 0.23 0.92 0.28 0.46 0.20 1462\n",
" 4 0.11 0.06 0.99 0.08 0.25 0.06 221\n",
"\n",
"avg / total 0.69 0.72 0.65 0.70 0.65 0.43 9515\n",
"\n",
"[(1, 59870), (2, 59870), (3, 59870), (4, 59870)]\n",
"For fold 4:\n",
"Accuracy: 0.670940620073568\n",
"f-score: 0.670940620073568\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.50 0.60 0.92 0.54 0.74 0.54 1129\n",
" 2 0.78 0.84 0.52 0.81 0.66 0.45 6348\n",
" 3 0.31 0.20 0.93 0.24 0.43 0.17 1285\n",
" 4 0.21 0.12 0.96 0.16 0.34 0.11 753\n",
"\n",
"avg / total 0.64 0.67 0.66 0.65 0.61 0.40 9515\n",
"\n",
"[(1, 59781), (2, 59781), (3, 59781), (4, 59781)]\n",
"For fold 5:\n",
"Accuracy: 0.6832369942196532\n",
"f-score: 0.6832369942196532\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.51 0.56 0.93 0.53 0.72 0.50 1085\n",
" 2 0.79 0.86 0.51 0.82 0.66 0.46 6437\n",
" 3 0.37 0.19 0.93 0.25 0.42 0.16 1657\n",
" 4 0.10 0.13 0.96 0.11 0.35 0.11 336\n",
"\n",
"avg / total 0.66 0.68 0.65 0.66 0.62 0.40 9515\n",
"\n",
"[(1, 59994), (2, 59994), (3, 59994), (4, 59994)]\n",
"For fold 6:\n",
"Accuracy: 0.6956384655806621\n",
"f-score: 0.6956384655806621\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.44 0.54 0.94 0.49 0.71 0.49 785\n",
" 2 0.76 0.91 0.46 0.83 0.65 0.44 6224\n",
" 3 0.55 0.25 0.94 0.34 0.48 0.22 2026\n",
" 4 0.15 0.07 0.98 0.09 0.26 0.06 480\n",
"\n",
"avg / total 0.66 0.70 0.63 0.66 0.60 0.38 9515\n",
"\n",
"[(1, 59534), (2, 59534), (3, 59534), (4, 59534)]\n",
"For fold 7:\n",
"Accuracy: 0.7121387283236994\n",
"f-score: 0.7121387283236994\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.49 0.52 0.95 0.51 0.70 0.47 853\n",
" 2 0.79 0.89 0.45 0.84 0.64 0.42 6684\n",
" 3 0.33 0.24 0.93 0.28 0.47 0.21 1209\n",
" 4 0.32 0.09 0.98 0.14 0.30 0.08 769\n",
"\n",
"avg / total 0.67 0.71 0.60 0.68 0.59 0.37 9515\n",
"\n",
"[(1, 59573), (2, 59573), (3, 59573), (4, 59573)]\n",
"For fold 8:\n",
"Accuracy: 0.7149763531266421\n",
"f-score: 0.714976353126642\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.55 0.47 0.96 0.51 0.67 0.43 933\n",
" 2 0.78 0.91 0.40 0.84 0.60 0.38 6645\n",
" 3 0.41 0.19 0.94 0.26 0.43 0.17 1675\n",
" 4 0.06 0.04 0.98 0.05 0.20 0.04 262\n",
"\n",
"avg / total 0.67 0.71 0.57 0.68 0.57 0.34 9515\n",
"\n",
"[(1, 59819), (2, 59819), (3, 59819), (4, 59819)]\n",
"For fold 9:\n",
"Accuracy: 0.6824679419802396\n",
"f-score: 0.6824679419802396\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.52 0.55 0.94 0.53 0.72 0.49 1031\n",
" 2 0.78 0.87 0.51 0.82 0.66 0.46 6399\n",
" 3 0.33 0.21 0.92 0.26 0.44 0.18 1495\n",
" 4 0.18 0.13 0.96 0.15 0.35 0.11 589\n",
"\n",
"avg / total 0.65 0.68 0.65 0.66 0.62 0.40 9514\n",
"\n",
"[(1, 59618), (2, 59618), (3, 59618), (4, 59618)]\n",
"For fold 10:\n",
"Accuracy: 0.6994954803447551\n",
"f-score: 0.6994954803447551\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.51 0.61 0.92 0.55 0.75 0.54 1157\n",
" 2 0.81 0.85 0.55 0.83 0.69 0.49 6600\n",
" 3 0.31 0.22 0.93 0.26 0.45 0.19 1156\n",
" 4 0.15 0.09 0.97 0.11 0.29 0.08 601\n",
"\n",
"avg / total 0.67 0.70 0.67 0.68 0.64 0.43 9514\n",
"\n",
"[(1, 59442), (2, 59442), (3, 59442), (4, 59442)]\n",
"For fold 1:\n",
"Accuracy: 0.72044140830268\n",
"f-score: 0.72044140830268\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.42 0.49 0.95 0.45 0.68 0.44 642\n",
" 2 0.79 0.91 0.41 0.84 0.61 0.39 6776\n",
" 3 0.47 0.21 0.95 0.29 0.45 0.19 1716\n",
" 4 0.16 0.10 0.98 0.12 0.31 0.09 381\n",
"\n",
"avg / total 0.68 0.72 0.56 0.69 0.57 0.34 9515\n",
"\n",
"[(1, 58698), (2, 58698), (3, 58698), (4, 58698)]\n",
"For fold 2:\n",
"Accuracy: 0.7086705202312139\n",
"f-score: 0.7086705202312139\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.48 0.54 0.93 0.51 0.71 0.48 974\n",
" 2 0.88 0.80 0.58 0.84 0.68 0.48 7520\n",
" 3 0.16 0.29 0.88 0.20 0.50 0.24 697\n",
" 4 0.09 0.09 0.97 0.09 0.29 0.08 324\n",
"\n",
"avg / total 0.76 0.71 0.65 0.73 0.66 0.45 9515\n",
"\n",
"[(1, 59633), (2, 59633), (3, 59633), (4, 59633)]\n",
"For fold 3:\n",
"Accuracy: 0.7070940620073568\n",
"f-score: 0.7070940620073568\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.56 0.58 0.93 0.57 0.73 0.52 1247\n",
" 2 0.81 0.86 0.54 0.83 0.68 0.48 6585\n",
" 3 0.32 0.22 0.92 0.26 0.45 0.19 1462\n",
" 4 0.08 0.08 0.98 0.08 0.27 0.07 221\n",
"\n",
"avg / total 0.68 0.71 0.66 0.69 0.64 0.43 9515\n",
"\n",
"[(1, 59870), (2, 59870), (3, 59870), (4, 59870)]\n",
"For fold 4:\n",
"Accuracy: 0.6684182869153967\n",
"f-score: 0.6684182869153967\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.48 0.59 0.91 0.53 0.73 0.52 1129\n",
" 2 0.78 0.85 0.52 0.81 0.66 0.45 6348\n",
" 3 0.27 0.18 0.93 0.21 0.40 0.15 1285\n",
" 4 0.22 0.12 0.97 0.15 0.34 0.10 753\n",
"\n",
"avg / total 0.63 0.67 0.65 0.65 0.61 0.39 9515\n",
"\n",
"[(1, 59781), (2, 59781), (3, 59781), (4, 59781)]\n",
"For fold 5:\n",
"Accuracy: 0.6949027850761955\n",
"f-score: 0.6949027850761955\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.51 0.54 0.93 0.53 0.71 0.49 1085\n",
" 2 0.79 0.88 0.50 0.83 0.66 0.46 6437\n",
" 3 0.42 0.20 0.94 0.27 0.44 0.18 1657\n",
" 4 0.11 0.13 0.96 0.12 0.35 0.12 336\n",
"\n",
"avg / total 0.67 0.69 0.64 0.67 0.62 0.40 9515\n",
"\n",
"[(1, 59994), (2, 59994), (3, 59994), (4, 59994)]\n",
"For fold 6:\n",
"Accuracy: 0.6911192853389385\n",
"f-score: 0.6911192853389385\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.44 0.55 0.94 0.49 0.72 0.50 785\n",
" 2 0.76 0.91 0.47 0.83 0.65 0.44 6224\n",
" 3 0.51 0.23 0.94 0.32 0.47 0.20 2026\n",
" 4 0.16 0.07 0.98 0.10 0.27 0.07 480\n",
"\n",
"avg / total 0.65 0.69 0.63 0.66 0.60 0.38 9515\n",
"\n",
"[(1, 59534), (2, 59534), (3, 59534), (4, 59534)]\n",
"For fold 7:\n",
"Accuracy: 0.7078297425118234\n",
"f-score: 0.7078297425118234\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.48 0.50 0.95 0.49 0.69 0.45 853\n",
" 2 0.80 0.89 0.47 0.84 0.64 0.43 6684\n",
" 3 0.33 0.27 0.92 0.30 0.50 0.23 1209\n",
" 4 0.26 0.07 0.98 0.12 0.27 0.07 769\n",
"\n",
"avg / total 0.67 0.71 0.61 0.68 0.60 0.38 9515\n",
"\n",
"[(1, 59573), (2, 59573), (3, 59573), (4, 59573)]\n",
"For fold 8:\n",
"Accuracy: 0.7168681029952706\n",
"f-score: 0.7168681029952706\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.56 0.45 0.96 0.50 0.66 0.41 933\n",
" 2 0.78 0.91 0.41 0.84 0.61 0.39 6645\n",
" 3 0.40 0.21 0.93 0.28 0.44 0.18 1675\n",
" 4 0.11 0.07 0.98 0.09 0.27 0.06 262\n",
"\n",
"avg / total 0.68 0.72 0.57 0.69 0.58 0.35 9515\n",
"\n",
"[(1, 59819), (2, 59819), (3, 59819), (4, 59819)]\n",
"For fold 9:\n",
"Accuracy: 0.6847803237334454\n",
"f-score: 0.6847803237334454\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.50 0.54 0.93 0.52 0.71 0.49 1031\n",
" 2 0.79 0.87 0.52 0.83 0.67 0.47 6399\n",
" 3 0.35 0.23 0.92 0.27 0.46 0.19 1495\n",
" 4 0.19 0.12 0.97 0.15 0.34 0.11 589\n",
"\n",
"avg / total 0.65 0.68 0.66 0.66 0.62 0.40 9514\n",
"\n",
"[(1, 59618), (2, 59618), (3, 59618), (4, 59618)]\n",
"For fold 10:\n",
"Accuracy: 0.6996005886062645\n",
"f-score: 0.6996005886062645\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.51 0.61 0.92 0.56 0.75 0.54 1157\n",
" 2 0.82 0.85 0.56 0.83 0.69 0.49 6600\n",
" 3 0.30 0.22 0.93 0.26 0.46 0.19 1156\n",
" 4 0.14 0.08 0.97 0.10 0.28 0.07 601\n",
"\n",
"avg / total 0.67 0.70 0.68 0.68 0.64 0.44 9514\n",
"\n",
"[(1, 59442), (2, 59442), (3, 59442), (4, 59442)]\n",
"For fold 1:\n",
"Accuracy: 0.7178139779295849\n",
"f-score: 0.7178139779295849\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.42 0.51 0.95 0.46 0.69 0.46 642\n",
" 2 0.79 0.90 0.41 0.84 0.61 0.39 6776\n",
" 3 0.45 0.21 0.95 0.28 0.44 0.18 1716\n",
" 4 0.14 0.08 0.98 0.10 0.29 0.07 381\n",
"\n",
"avg / total 0.68 0.72 0.57 0.69 0.57 0.34 9515\n",
"\n",
"[(1, 58698), (2, 58698), (3, 58698), (4, 58698)]\n",
"For fold 2:\n",
"Accuracy: 0.7165528113504992\n",
"f-score: 0.7165528113504992\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.49 0.53 0.94 0.50 0.70 0.47 974\n",
" 2 0.88 0.80 0.58 0.84 0.68 0.48 7520\n",
" 3 0.18 0.32 0.88 0.23 0.53 0.27 697\n",
" 4 0.10 0.09 0.97 0.10 0.30 0.08 324\n",
"\n",
"avg / total 0.76 0.72 0.65 0.74 0.66 0.45 9515\n",
"\n",
"[(1, 59633), (2, 59633), (3, 59633), (4, 59633)]\n",
"For fold 3:\n",
"Accuracy: 0.7057277982133473\n",
"f-score: 0.7057277982133473\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.56 0.59 0.93 0.57 0.74 0.53 1247\n",
" 2 0.81 0.85 0.54 0.83 0.68 0.48 6585\n",
" 3 0.33 0.23 0.92 0.27 0.46 0.20 1462\n",
" 4 0.10 0.10 0.98 0.10 0.30 0.08 221\n",
"\n",
"avg / total 0.68 0.71 0.66 0.69 0.64 0.43 9515\n",
"\n",
"[(1, 59870), (2, 59870), (3, 59870), (4, 59870)]\n",
"For fold 4:\n",
"Accuracy: 0.6705202312138728\n",
"f-score: 0.6705202312138728\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.48 0.58 0.91 0.53 0.73 0.51 1129\n",
" 2 0.78 0.85 0.52 0.81 0.66 0.45 6348\n",
" 3 0.32 0.19 0.94 0.24 0.42 0.16 1285\n",
" 4 0.20 0.12 0.96 0.15 0.35 0.11 753\n",
"\n",
"avg / total 0.64 0.67 0.66 0.65 0.61 0.39 9515\n",
"\n",
"[(1, 59781), (2, 59781), (3, 59781), (4, 59781)]\n",
"For fold 5:\n",
"Accuracy: 0.6850236468733578\n",
"f-score: 0.6850236468733578\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.53 0.55 0.94 0.54 0.72 0.50 1085\n",
" 2 0.78 0.86 0.51 0.82 0.66 0.45 6437\n",
" 3 0.41 0.22 0.93 0.28 0.45 0.19 1657\n",
" 4 0.09 0.12 0.95 0.10 0.35 0.11 336\n",
"\n",
"avg / total 0.67 0.69 0.65 0.67 0.62 0.40 9515\n",
"\n",
"[(1, 59994), (2, 59994), (3, 59994), (4, 59994)]\n",
"For fold 6:\n",
"Accuracy: 0.6862848134524435\n",
"f-score: 0.6862848134524435\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.42 0.52 0.93 0.46 0.70 0.47 785\n",
" 2 0.76 0.90 0.46 0.82 0.64 0.43 6224\n",
" 3 0.52 0.23 0.94 0.32 0.46 0.20 2026\n",
" 4 0.18 0.09 0.98 0.12 0.30 0.08 480\n",
"\n",
"avg / total 0.65 0.69 0.63 0.65 0.59 0.37 9515\n",
"\n",
"[(1, 59534), (2, 59534), (3, 59534), (4, 59534)]\n",
"For fold 7:\n",
"Accuracy: 0.7088807146610615\n",
"f-score: 0.7088807146610615\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.49 0.50 0.95 0.49 0.69 0.45 853\n",
" 2 0.80 0.89 0.46 0.84 0.64 0.43 6684\n",
" 3 0.33 0.26 0.93 0.29 0.49 0.22 1209\n",
" 4 0.27 0.09 0.98 0.13 0.29 0.08 769\n",
"\n",
"avg / total 0.67 0.71 0.61 0.68 0.60 0.38 9515\n",
"\n",
"[(1, 59573), (2, 59573), (3, 59573), (4, 59573)]\n",
"For fold 8:\n",
"Accuracy: 0.7135049921177089\n",
"f-score: 0.713504992117709\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.57 0.45 0.96 0.50 0.66 0.41 933\n",
" 2 0.78 0.91 0.40 0.84 0.61 0.39 6645\n",
" 3 0.38 0.19 0.93 0.25 0.42 0.16 1675\n",
" 4 0.10 0.07 0.98 0.09 0.27 0.06 262\n",
"\n",
"avg / total 0.67 0.71 0.57 0.68 0.57 0.34 9515\n",
"\n",
"[(1, 59819), (2, 59819), (3, 59819), (4, 59819)]\n",
"For fold 9:\n",
"Accuracy: 0.6846752154719361\n",
"f-score: 0.6846752154719361\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.51 0.53 0.94 0.52 0.70 0.48 1031\n",
" 2 0.78 0.87 0.51 0.83 0.66 0.46 6399\n",
" 3 0.35 0.21 0.93 0.27 0.44 0.18 1495\n",
" 4 0.18 0.13 0.96 0.15 0.35 0.11 589\n",
"\n",
"avg / total 0.65 0.68 0.65 0.66 0.61 0.39 9514\n",
"\n",
"[(1, 59618), (2, 59618), (3, 59618), (4, 59618)]\n",
"For fold 10:\n",
"Accuracy: 0.6931889846541939\n",
"f-score: 0.6931889846541939\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.50 0.61 0.91 0.55 0.75 0.54 1157\n",
" 2 0.81 0.85 0.55 0.83 0.69 0.48 6600\n",
" 3 0.28 0.20 0.93 0.23 0.43 0.17 1156\n",
" 4 0.14 0.09 0.96 0.11 0.29 0.08 601\n",
"\n",
"avg / total 0.67 0.69 0.67 0.68 0.64 0.43 9514\n",
"\n",
"[(1, 59442), (2, 59442), (3, 59442), (4, 59442)]\n",
"For fold 1:\n",
"Accuracy: 0.7158171308460326\n",
"f-score: 0.7158171308460325\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.40 0.48 0.95 0.44 0.68 0.44 642\n",
" 2 0.79 0.90 0.40 0.84 0.60 0.38 6776\n",
" 3 0.46 0.21 0.95 0.29 0.44 0.18 1716\n",
" 4 0.12 0.07 0.98 0.09 0.27 0.07 381\n",
"\n",
"avg / total 0.68 0.72 0.56 0.68 0.57 0.34 9515\n",
"\n",
"[(1, 58698), (2, 58698), (3, 58698), (4, 58698)]\n",
"For fold 2:\n",
"Accuracy: 0.7128744088281661\n",
"f-score: 0.7128744088281661\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.47 0.54 0.93 0.50 0.71 0.48 974\n",
" 2 0.88 0.80 0.58 0.84 0.68 0.48 7520\n",
" 3 0.17 0.32 0.88 0.22 0.53 0.26 697\n",
" 4 0.08 0.07 0.97 0.07 0.26 0.06 324\n",
"\n",
"avg / total 0.76 0.71 0.65 0.73 0.66 0.45 9515\n",
"\n",
"[(1, 59633), (2, 59633), (3, 59633), (4, 59633)]\n",
"For fold 3:\n",
"Accuracy: 0.708039936941671\n",
"f-score: 0.708039936941671\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.57 0.58 0.93 0.58 0.74 0.52 1247\n",
" 2 0.81 0.86 0.54 0.83 0.68 0.48 6585\n",
" 3 0.33 0.23 0.91 0.27 0.46 0.19 1462\n",
" 4 0.08 0.09 0.98 0.08 0.29 0.08 221\n",
"\n",
"avg / total 0.69 0.71 0.66 0.70 0.65 0.43 9515\n",
"\n",
"[(1, 59870), (2, 59870), (3, 59870), (4, 59870)]\n",
"For fold 4:\n",
"Accuracy: 0.6690488702049395\n",
"f-score: 0.6690488702049395\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.48 0.57 0.92 0.52 0.72 0.51 1129\n",
" 2 0.78 0.84 0.52 0.81 0.66 0.45 6348\n",
" 3 0.32 0.21 0.93 0.25 0.44 0.18 1285\n",
" 4 0.21 0.12 0.96 0.15 0.34 0.11 753\n",
"\n",
"avg / total 0.64 0.67 0.66 0.65 0.61 0.39 9515\n",
"\n",
"[(1, 59781), (2, 59781), (3, 59781), (4, 59781)]\n",
"For fold 5:\n",
"Accuracy: 0.6901734104046243\n",
"f-score: 0.6901734104046243\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.51 0.55 0.93 0.53 0.72 0.50 1085\n",
" 2 0.78 0.87 0.50 0.83 0.66 0.45 6437\n",
" 3 0.41 0.19 0.94 0.26 0.42 0.16 1657\n",
" 4 0.11 0.15 0.96 0.13 0.37 0.13 336\n",
"\n",
"avg / total 0.66 0.69 0.64 0.67 0.61 0.40 9515\n",
"\n",
"[(1, 59994), (2, 59994), (3, 59994), (4, 59994)]\n",
"For fold 6:\n",
"Accuracy: 0.6906988964792433\n",
"f-score: 0.6906988964792433\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.44 0.55 0.94 0.49 0.72 0.50 785\n",
" 2 0.76 0.91 0.46 0.83 0.64 0.43 6224\n",
" 3 0.51 0.22 0.94 0.31 0.46 0.20 2026\n",
" 4 0.20 0.09 0.98 0.12 0.29 0.08 480\n",
"\n",
"avg / total 0.65 0.69 0.63 0.65 0.59 0.37 9515\n",
"\n",
"[(1, 59534), (2, 59534), (3, 59534), (4, 59534)]\n",
"For fold 7:\n",
"Accuracy: 0.7046768260641093\n",
"f-score: 0.7046768260641093\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.46 0.49 0.94 0.48 0.68 0.44 853\n",
" 2 0.80 0.88 0.48 0.84 0.65 0.44 6684\n",
" 3 0.33 0.26 0.92 0.29 0.49 0.23 1209\n",
" 4 0.29 0.10 0.98 0.15 0.32 0.09 769\n",
"\n",
"avg / total 0.67 0.70 0.62 0.68 0.61 0.38 9515\n",
"\n",
"[(1, 59573), (2, 59573), (3, 59573), (4, 59573)]\n",
"For fold 8:\n",
"Accuracy: 0.711823436678928\n",
"f-score: 0.711823436678928\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.55 0.46 0.96 0.50 0.67 0.42 933\n",
" 2 0.78 0.91 0.41 0.84 0.61 0.39 6645\n",
" 3 0.37 0.19 0.93 0.25 0.42 0.16 1675\n",
" 4 0.07 0.05 0.98 0.06 0.22 0.04 262\n",
"\n",
"avg / total 0.67 0.71 0.57 0.68 0.57 0.34 9515\n",
"\n",
"[(1, 59819), (2, 59819), (3, 59819), (4, 59819)]\n",
"For fold 9:\n",
"Accuracy: 0.6871978137481606\n",
"f-score: 0.6871978137481606\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.51 0.55 0.94 0.53 0.71 0.49 1031\n",
" 2 0.79 0.87 0.51 0.83 0.67 0.46 6399\n",
" 3 0.35 0.22 0.93 0.27 0.45 0.19 1495\n",
" 4 0.19 0.13 0.96 0.15 0.35 0.11 589\n",
"\n",
"avg / total 0.65 0.69 0.65 0.66 0.62 0.40 9514\n",
"\n",
"[(1, 59618), (2, 59618), (3, 59618), (4, 59618)]\n",
"For fold 10:\n",
"Accuracy: 0.6958166911919277\n",
"f-score: 0.6958166911919277\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.52 0.60 0.93 0.56 0.74 0.53 1157\n",
" 2 0.81 0.85 0.56 0.83 0.69 0.49 6600\n",
" 3 0.29 0.23 0.92 0.26 0.46 0.20 1156\n",
" 4 0.13 0.08 0.96 0.10 0.27 0.07 601\n",
"\n",
"avg / total 0.67 0.70 0.67 0.68 0.64 0.43 9514\n",
"\n",
"[(1, 59442), (2, 59442), (3, 59442), (4, 59442)]\n",
"For fold 1:\n",
"Accuracy: 0.7193904361534419\n",
"f-score: 0.7193904361534419\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.41 0.47 0.95 0.44 0.67 0.43 642\n",
" 2 0.79 0.91 0.40 0.85 0.60 0.38 6776\n",
" 3 0.45 0.20 0.95 0.28 0.44 0.18 1716\n",
" 4 0.16 0.09 0.98 0.12 0.30 0.08 381\n",
"\n",
"avg / total 0.68 0.72 0.56 0.69 0.57 0.34 9515\n",
"\n",
"[(1, 58698), (2, 58698), (3, 58698), (4, 58698)]\n",
"For fold 2:\n",
"Accuracy: 0.7104571728849185\n",
"f-score: 0.7104571728849185\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.48 0.54 0.93 0.51 0.71 0.49 974\n",
" 2 0.88 0.80 0.59 0.84 0.68 0.48 7520\n",
" 3 0.17 0.32 0.87 0.22 0.53 0.26 697\n",
" 4 0.08 0.07 0.97 0.07 0.26 0.06 324\n",
"\n",
"avg / total 0.76 0.71 0.66 0.73 0.66 0.45 9515\n",
"\n",
"[(1, 59633), (2, 59633), (3, 59633), (4, 59633)]\n",
"For fold 3:\n",
"Accuracy: 0.7130846032580137\n",
"f-score: 0.7130846032580137\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.57 0.60 0.93 0.59 0.75 0.54 1247\n",
" 2 0.81 0.87 0.54 0.84 0.68 0.48 6585\n",
" 3 0.33 0.22 0.92 0.27 0.45 0.19 1462\n",
" 4 0.10 0.09 0.98 0.10 0.30 0.08 221\n",
"\n",
"avg / total 0.69 0.71 0.66 0.70 0.65 0.43 9515\n",
"\n",
"[(1, 59870), (2, 59870), (3, 59870), (4, 59870)]\n",
"For fold 4:\n",
"Accuracy: 0.6694692590646348\n",
"f-score: 0.6694692590646348\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.49 0.60 0.92 0.54 0.74 0.53 1129\n",
" 2 0.78 0.84 0.52 0.81 0.66 0.45 6348\n",
" 3 0.30 0.20 0.93 0.24 0.43 0.17 1285\n",
" 4 0.20 0.10 0.96 0.14 0.32 0.09 753\n",
"\n",
"avg / total 0.63 0.67 0.66 0.65 0.61 0.40 9515\n",
"\n",
"[(1, 59781), (2, 59781), (3, 59781), (4, 59781)]\n",
"For fold 5:\n",
"Accuracy: 0.6890173410404624\n",
"f-score: 0.6890173410404624\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.51 0.57 0.93 0.54 0.73 0.51 1085\n",
" 2 0.79 0.87 0.50 0.82 0.66 0.45 6437\n",
" 3 0.41 0.19 0.94 0.26 0.42 0.16 1657\n",
" 4 0.10 0.14 0.96 0.12 0.36 0.12 336\n",
"\n",
"avg / total 0.66 0.69 0.64 0.67 0.62 0.40 9515\n",
"\n",
"[(1, 59994), (2, 59994), (3, 59994), (4, 59994)]\n",
"For fold 6:\n",
"Accuracy: 0.6895428271150814\n",
"f-score: 0.6895428271150814\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.42 0.55 0.93 0.48 0.72 0.50 785\n",
" 2 0.76 0.91 0.47 0.83 0.65 0.44 6224\n",
" 3 0.52 0.22 0.95 0.31 0.45 0.19 2026\n",
" 4 0.19 0.10 0.98 0.13 0.31 0.09 480\n",
"\n",
"avg / total 0.66 0.69 0.63 0.65 0.60 0.38 9515\n",
"\n",
"[(1, 59534), (2, 59534), (3, 59534), (4, 59534)]\n",
"For fold 7:\n",
"Accuracy: 0.6986862848134524\n",
"f-score: 0.6986862848134524\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.46 0.51 0.94 0.48 0.69 0.46 853\n",
" 2 0.80 0.87 0.48 0.83 0.65 0.44 6684\n",
" 3 0.32 0.26 0.92 0.29 0.49 0.23 1209\n",
" 4 0.23 0.09 0.97 0.13 0.30 0.08 769\n",
"\n",
"avg / total 0.66 0.70 0.62 0.68 0.60 0.38 9515\n",
"\n",
"[(1, 59573), (2, 59573), (3, 59573), (4, 59573)]\n",
"For fold 8:\n",
"Accuracy: 0.7104571728849185\n",
"f-score: 0.7104571728849185\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.55 0.47 0.96 0.51 0.67 0.43 933\n",
" 2 0.78 0.90 0.41 0.83 0.61 0.38 6645\n",
" 3 0.39 0.19 0.93 0.26 0.42 0.17 1675\n",
" 4 0.11 0.08 0.98 0.09 0.29 0.07 262\n",
"\n",
"avg / total 0.67 0.71 0.57 0.68 0.57 0.34 9515\n",
"\n",
"[(1, 59819), (2, 59819), (3, 59819), (4, 59819)]\n",
"For fold 9:\n",
"Accuracy: 0.6823628337187303\n",
"f-score: 0.6823628337187303\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.51 0.53 0.94 0.52 0.71 0.48 1031\n",
" 2 0.79 0.86 0.53 0.82 0.67 0.47 6399\n",
" 3 0.33 0.22 0.92 0.26 0.45 0.19 1495\n",
" 4 0.20 0.16 0.96 0.17 0.39 0.14 589\n",
"\n",
"avg / total 0.65 0.68 0.66 0.66 0.62 0.41 9514\n",
"\n",
"[(1, 59618), (2, 59618), (3, 59618), (4, 59618)]\n",
"For fold 10:\n",
"Accuracy: 0.6949758250998529\n",
"f-score: 0.6949758250998529\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.49 0.60 0.91 0.54 0.74 0.53 1157\n",
" 2 0.81 0.85 0.56 0.83 0.69 0.49 6600\n",
" 3 0.29 0.21 0.93 0.24 0.44 0.18 1156\n",
" 4 0.16 0.10 0.96 0.12 0.31 0.09 601\n",
"\n",
"avg / total 0.67 0.69 0.67 0.68 0.64 0.43 9514\n",
"\n",
"[(1, 59442), (2, 59442), (3, 59442), (4, 59442)]\n",
"For fold 1:\n",
"Accuracy: 0.7212821860220704\n",
"f-score: 0.7212821860220704\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.42 0.51 0.95 0.46 0.70 0.46 642\n",
" 2 0.79 0.91 0.41 0.85 0.61 0.39 6776\n",
" 3 0.47 0.19 0.95 0.27 0.43 0.17 1716\n",
" 4 0.17 0.10 0.98 0.13 0.32 0.09 381\n",
"\n",
"avg / total 0.68 0.72 0.56 0.69 0.57 0.34 9515\n",
"\n",
"[(1, 58698), (2, 58698), (3, 58698), (4, 58698)]\n",
"For fold 2:\n",
"Accuracy: 0.7012086179716237\n",
"f-score: 0.7012086179716237\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.47 0.53 0.93 0.50 0.70 0.47 974\n",
" 2 0.88 0.79 0.59 0.83 0.68 0.47 7520\n",
" 3 0.15 0.30 0.87 0.20 0.51 0.24 697\n",
" 4 0.06 0.06 0.97 0.06 0.24 0.05 324\n",
"\n",
"avg / total 0.76 0.70 0.66 0.72 0.66 0.44 9515\n",
"\n",
"[(1, 59633), (2, 59633), (3, 59633), (4, 59633)]\n",
"For fold 3:\n",
"Accuracy: 0.7096163951655281\n",
"f-score: 0.7096163951655281\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.58 0.58 0.94 0.58 0.74 0.53 1247\n",
" 2 0.81 0.86 0.54 0.83 0.68 0.48 6585\n",
" 3 0.32 0.23 0.91 0.27 0.46 0.20 1462\n",
" 4 0.09 0.07 0.98 0.08 0.27 0.06 221\n",
"\n",
"avg / total 0.69 0.71 0.66 0.70 0.65 0.44 9515\n",
"\n",
"[(1, 59870), (2, 59870), (3, 59870), (4, 59870)]\n",
"For fold 4:\n",
"Accuracy: 0.6687335785601681\n",
"f-score: 0.6687335785601681\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.48 0.59 0.91 0.53 0.73 0.52 1129\n",
" 2 0.78 0.85 0.52 0.81 0.66 0.45 6348\n",
" 3 0.30 0.18 0.93 0.23 0.41 0.16 1285\n",
" 4 0.20 0.12 0.96 0.15 0.35 0.11 753\n",
"\n",
"avg / total 0.63 0.67 0.66 0.65 0.61 0.39 9515\n",
"\n",
"[(1, 59781), (2, 59781), (3, 59781), (4, 59781)]\n",
"For fold 5:\n",
"Accuracy: 0.6735680504466631\n",
"f-score: 0.6735680504466631\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.49 0.55 0.93 0.52 0.71 0.49 1085\n",
" 2 0.79 0.84 0.53 0.81 0.67 0.46 6437\n",
" 3 0.38 0.22 0.92 0.28 0.45 0.19 1657\n",
" 4 0.10 0.15 0.95 0.12 0.38 0.13 336\n",
"\n",
"avg / total 0.66 0.67 0.66 0.66 0.62 0.40 9515\n",
"\n",
"[(1, 59994), (2, 59994), (3, 59994), (4, 59994)]\n",
"For fold 6:\n",
"Accuracy: 0.6888071466106148\n",
"f-score: 0.6888071466106148\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.42 0.53 0.93 0.47 0.71 0.48 785\n",
" 2 0.76 0.91 0.46 0.83 0.65 0.44 6224\n",
" 3 0.52 0.23 0.94 0.32 0.46 0.20 2026\n",
" 4 0.16 0.07 0.98 0.10 0.27 0.07 480\n",
"\n",
"avg / total 0.65 0.69 0.63 0.65 0.59 0.37 9515\n",
"\n",
"[(1, 59534), (2, 59534), (3, 59534), (4, 59534)]\n",
"For fold 7:\n",
"Accuracy: 0.7138202837624803\n",
"f-score: 0.7138202837624804\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.48 0.51 0.95 0.50 0.69 0.46 853\n",
" 2 0.80 0.90 0.46 0.84 0.64 0.43 6684\n",
" 3 0.35 0.26 0.93 0.29 0.49 0.22 1209\n",
" 4 0.29 0.08 0.98 0.13 0.29 0.08 769\n",
"\n",
"avg / total 0.67 0.71 0.61 0.69 0.60 0.38 9515\n",
"\n",
"[(1, 59573), (2, 59573), (3, 59573), (4, 59573)]\n",
"For fold 8:\n",
"Accuracy: 0.7114030478192328\n",
"f-score: 0.7114030478192328\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.55 0.45 0.96 0.50 0.66 0.41 933\n",
" 2 0.78 0.90 0.41 0.84 0.61 0.39 6645\n",
" 3 0.40 0.22 0.93 0.28 0.45 0.19 1675\n",
" 4 0.07 0.05 0.98 0.06 0.21 0.04 262\n",
"\n",
"avg / total 0.67 0.71 0.57 0.68 0.57 0.34 9515\n",
"\n",
"[(1, 59819), (2, 59819), (3, 59819), (4, 59819)]\n",
"For fold 9:\n",
"Accuracy: 0.684359890687408\n",
"f-score: 0.684359890687408\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.50 0.53 0.94 0.52 0.71 0.48 1031\n",
" 2 0.79 0.87 0.52 0.83 0.67 0.46 6399\n",
" 3 0.36 0.23 0.92 0.28 0.46 0.19 1495\n",
" 4 0.18 0.13 0.96 0.15 0.35 0.11 589\n",
"\n",
"avg / total 0.65 0.68 0.65 0.66 0.62 0.40 9514\n",
"\n",
"[(1, 59618), (2, 59618), (3, 59618), (4, 59618)]\n",
"For fold 10:\n",
"Accuracy: 0.6882488963632541\n",
"f-score: 0.6882488963632541\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.52 0.62 0.92 0.56 0.75 0.55 1157\n",
" 2 0.81 0.83 0.57 0.82 0.69 0.49 6600\n",
" 3 0.30 0.22 0.93 0.26 0.45 0.19 1156\n",
" 4 0.15 0.13 0.95 0.14 0.36 0.12 601\n",
"\n",
"avg / total 0.67 0.69 0.68 0.68 0.65 0.43 9514\n",
"\n",
"[(1, 59442), (2, 59442), (3, 59442), (4, 59442)]\n",
"For fold 1:\n",
"Accuracy: 0.7191802417235943\n",
"f-score: 0.7191802417235943\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.41 0.52 0.95 0.46 0.70 0.47 642\n",
" 2 0.79 0.90 0.41 0.84 0.61 0.39 6776\n",
" 3 0.47 0.20 0.95 0.28 0.43 0.17 1716\n",
" 4 0.17 0.11 0.98 0.13 0.32 0.10 381\n",
"\n",
"avg / total 0.68 0.72 0.57 0.69 0.57 0.34 9515\n",
"\n",
"[(1, 58698), (2, 58698), (3, 58698), (4, 58698)]\n",
"For fold 2:\n",
"Accuracy: 0.7133998949027851\n",
"f-score: 0.7133998949027851\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.48 0.54 0.93 0.51 0.71 0.48 974\n",
" 2 0.88 0.80 0.58 0.84 0.69 0.48 7520\n",
" 3 0.16 0.29 0.88 0.21 0.51 0.24 697\n",
" 4 0.09 0.08 0.97 0.09 0.28 0.07 324\n",
"\n",
"avg / total 0.76 0.71 0.66 0.73 0.66 0.45 9515\n",
"\n",
"[(1, 59633), (2, 59633), (3, 59633), (4, 59633)]\n",
"For fold 3:\n",
"Accuracy: 0.7060430898581188\n",
"f-score: 0.7060430898581188\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.57 0.59 0.93 0.58 0.74 0.53 1247\n",
" 2 0.81 0.85 0.55 0.83 0.68 0.48 6585\n",
" 3 0.33 0.24 0.91 0.28 0.47 0.20 1462\n",
" 4 0.08 0.08 0.98 0.08 0.27 0.07 221\n",
"\n",
"avg / total 0.69 0.71 0.66 0.69 0.65 0.44 9515\n",
"\n",
"[(1, 59870), (2, 59870), (3, 59870), (4, 59870)]\n",
"For fold 4:\n",
"Accuracy: 0.6672622175512349\n",
"f-score: 0.6672622175512349\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.47 0.57 0.91 0.52 0.72 0.50 1129\n",
" 2 0.77 0.85 0.51 0.81 0.66 0.44 6348\n",
" 3 0.29 0.18 0.93 0.22 0.41 0.15 1285\n",
" 4 0.20 0.11 0.96 0.14 0.32 0.10 753\n",
"\n",
"avg / total 0.63 0.67 0.65 0.64 0.60 0.38 9515\n",
"\n",
"[(1, 59781), (2, 59781), (3, 59781), (4, 59781)]\n",
"For fold 5:\n",
"Accuracy: 0.6905937992643195\n",
"f-score: 0.6905937992643195\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.52 0.54 0.94 0.53 0.71 0.49 1085\n",
" 2 0.79 0.87 0.50 0.83 0.66 0.45 6437\n",
" 3 0.43 0.21 0.94 0.28 0.44 0.18 1657\n",
" 4 0.11 0.15 0.95 0.12 0.37 0.13 336\n",
"\n",
"avg / total 0.67 0.69 0.65 0.67 0.62 0.40 9515\n",
"\n",
"[(1, 59994), (2, 59994), (3, 59994), (4, 59994)]\n",
"For fold 6:\n",
"Accuracy: 0.687651077246453\n",
"f-score: 0.687651077246453\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.42 0.53 0.93 0.47 0.71 0.48 785\n",
" 2 0.76 0.90 0.46 0.83 0.65 0.43 6224\n",
" 3 0.52 0.23 0.94 0.32 0.47 0.20 2026\n",
" 4 0.16 0.08 0.98 0.10 0.27 0.07 480\n",
"\n",
"avg / total 0.65 0.69 0.63 0.65 0.59 0.37 9515\n",
"\n",
"[(1, 59534), (2, 59534), (3, 59534), (4, 59534)]\n",
"For fold 7:\n",
"Accuracy: 0.7031003678402522\n",
"f-score: 0.7031003678402522\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.47 0.53 0.94 0.50 0.70 0.47 853\n",
" 2 0.80 0.87 0.49 0.84 0.66 0.45 6684\n",
" 3 0.33 0.27 0.92 0.29 0.49 0.23 1209\n",
" 4 0.28 0.11 0.97 0.16 0.33 0.10 769\n",
"\n",
"avg / total 0.67 0.70 0.63 0.68 0.61 0.39 9515\n",
"\n",
"[(1, 59573), (2, 59573), (3, 59573), (4, 59573)]\n",
"For fold 8:\n",
"Accuracy: 0.7104571728849185\n",
"f-score: 0.7104571728849185\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.53 0.45 0.96 0.49 0.66 0.41 933\n",
" 2 0.78 0.90 0.40 0.84 0.60 0.38 6645\n",
" 3 0.40 0.19 0.94 0.26 0.42 0.16 1675\n",
" 4 0.11 0.09 0.98 0.10 0.29 0.08 262\n",
"\n",
"avg / total 0.67 0.71 0.57 0.68 0.57 0.34 9515\n",
"\n",
"[(1, 59819), (2, 59819), (3, 59819), (4, 59819)]\n",
"For fold 9:\n",
"Accuracy: 0.6803657767500526\n",
"f-score: 0.6803657767500526\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.51 0.55 0.94 0.53 0.72 0.49 1031\n",
" 2 0.79 0.86 0.53 0.82 0.67 0.47 6399\n",
" 3 0.32 0.22 0.91 0.26 0.45 0.19 1495\n",
" 4 0.20 0.14 0.96 0.16 0.36 0.12 589\n",
"\n",
"avg / total 0.65 0.68 0.66 0.66 0.62 0.40 9514\n",
"\n",
"[(1, 59618), (2, 59618), (3, 59618), (4, 59618)]\n",
"For fold 10:\n",
"Accuracy: 0.6965524490224931\n",
"f-score: 0.6965524490224931\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.51 0.60 0.92 0.55 0.74 0.54 1157\n",
" 2 0.81 0.85 0.55 0.83 0.69 0.48 6600\n",
" 3 0.31 0.22 0.93 0.25 0.45 0.19 1156\n",
" 4 0.16 0.11 0.96 0.13 0.33 0.10 601\n",
"\n",
"avg / total 0.67 0.70 0.67 0.68 0.64 0.43 9514\n",
"\n",
"[(1, 59442), (2, 59442), (3, 59442), (4, 59442)]\n",
"For fold 1:\n",
"Accuracy: 0.7225433526011561\n",
"f-score: 0.7225433526011561\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.41 0.50 0.95 0.45 0.69 0.45 642\n",
" 2 0.79 0.91 0.41 0.85 0.61 0.39 6776\n",
" 3 0.47 0.19 0.95 0.27 0.43 0.17 1716\n",
" 4 0.19 0.12 0.98 0.14 0.34 0.10 381\n",
"\n",
"avg / total 0.68 0.72 0.56 0.69 0.57 0.34 9515\n",
"\n",
"[(1, 58698), (2, 58698), (3, 58698), (4, 58698)]\n",
"For fold 2:\n",
"Accuracy: 0.7017341040462428\n",
"f-score: 0.7017341040462428\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.46 0.52 0.93 0.49 0.70 0.47 974\n",
" 2 0.88 0.79 0.58 0.83 0.68 0.47 7520\n",
" 3 0.16 0.31 0.87 0.21 0.52 0.26 697\n",
" 4 0.06 0.06 0.97 0.06 0.23 0.05 324\n",
"\n",
"avg / total 0.75 0.70 0.65 0.72 0.65 0.44 9515\n",
"\n",
"[(1, 59633), (2, 59633), (3, 59633), (4, 59633)]\n",
"For fold 3:\n",
"Accuracy: 0.7066736731476616\n",
"f-score: 0.7066736731476616\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.58 0.58 0.94 0.58 0.73 0.52 1247\n",
" 2 0.81 0.86 0.54 0.83 0.68 0.48 6585\n",
" 3 0.33 0.24 0.91 0.28 0.47 0.20 1462\n",
" 4 0.10 0.10 0.98 0.10 0.31 0.09 221\n",
"\n",
"avg / total 0.69 0.71 0.66 0.70 0.65 0.43 9515\n",
"\n",
"[(1, 59870), (2, 59870), (3, 59870), (4, 59870)]\n",
"For fold 4:\n",
"Accuracy: 0.6660010509721492\n",
"f-score: 0.6660010509721492\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.48 0.59 0.91 0.53 0.74 0.53 1129\n",
" 2 0.78 0.84 0.52 0.81 0.66 0.45 6348\n",
" 3 0.29 0.18 0.93 0.22 0.41 0.15 1285\n",
" 4 0.18 0.11 0.96 0.14 0.32 0.10 753\n",
"\n",
"avg / total 0.63 0.67 0.66 0.64 0.61 0.39 9515\n",
"\n",
"[(1, 59781), (2, 59781), (3, 59781), (4, 59781)]\n",
"For fold 5:\n",
"Accuracy: 0.6910141881240147\n",
"f-score: 0.6910141881240147\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.52 0.53 0.94 0.53 0.71 0.48 1085\n",
" 2 0.78 0.88 0.49 0.83 0.66 0.45 6437\n",
" 3 0.40 0.19 0.94 0.26 0.42 0.16 1657\n",
" 4 0.09 0.11 0.96 0.10 0.32 0.10 336\n",
"\n",
"avg / total 0.66 0.69 0.64 0.67 0.61 0.39 9515\n",
"\n",
"[(1, 59994), (2, 59994), (3, 59994), (4, 59994)]\n",
"For fold 6:\n",
"Accuracy: 0.6917498686284813\n",
"f-score: 0.6917498686284813\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.44 0.56 0.94 0.49 0.72 0.50 785\n",
" 2 0.76 0.90 0.47 0.83 0.65 0.44 6224\n",
" 3 0.53 0.25 0.94 0.34 0.48 0.22 2026\n",
" 4 0.17 0.08 0.98 0.11 0.29 0.07 480\n",
"\n",
"avg / total 0.66 0.69 0.64 0.66 0.60 0.38 9515\n",
"\n",
"[(1, 59534), (2, 59534), (3, 59534), (4, 59534)]\n",
"For fold 7:\n",
"Accuracy: 0.7042564372044141\n",
"f-score: 0.7042564372044141\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.46 0.52 0.94 0.49 0.70 0.47 853\n",
" 2 0.80 0.87 0.50 0.84 0.66 0.45 6684\n",
" 3 0.34 0.29 0.92 0.31 0.51 0.25 1209\n",
" 4 0.30 0.11 0.98 0.16 0.33 0.10 769\n",
"\n",
"avg / total 0.67 0.70 0.63 0.68 0.62 0.40 9515\n",
"\n",
"[(1, 59573), (2, 59573), (3, 59573), (4, 59573)]\n",
"For fold 8:\n",
"Accuracy: 0.7128744088281661\n",
"f-score: 0.7128744088281661\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.57 0.46 0.96 0.51 0.67 0.42 933\n",
" 2 0.78 0.90 0.42 0.84 0.61 0.39 6645\n",
" 3 0.38 0.22 0.92 0.27 0.45 0.19 1675\n",
" 4 0.12 0.07 0.98 0.09 0.27 0.06 262\n",
"\n",
"avg / total 0.67 0.71 0.58 0.68 0.58 0.35 9515\n",
"\n",
"[(1, 59819), (2, 59819), (3, 59819), (4, 59819)]\n",
"For fold 9:\n",
"Accuracy: 0.6898255202858945\n",
"f-score: 0.6898255202858945\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.51 0.54 0.94 0.53 0.71 0.49 1031\n",
" 2 0.79 0.87 0.51 0.83 0.67 0.46 6399\n",
" 3 0.35 0.21 0.93 0.27 0.45 0.18 1495\n",
" 4 0.22 0.15 0.97 0.18 0.38 0.13 589\n",
"\n",
"avg / total 0.65 0.69 0.65 0.67 0.62 0.40 9514\n",
"\n",
"[(1, 59618), (2, 59618), (3, 59618), (4, 59618)]\n",
"For fold 10:\n",
"Accuracy: 0.6950809333613622\n",
"f-score: 0.6950809333613622\n",
" pre rec spe f1 geo iba sup\n",
"\n",
" 1 0.52 0.60 0.92 0.56 0.75 0.54 1157\n",
" 2 0.81 0.85 0.56 0.83 0.69 0.49 6600\n",
" 3 0.28 0.21 0.93 0.24 0.44 0.18 1156\n",
" 4 0.18 0.13 0.96 0.15 0.35 0.11 601\n",
"\n",
"avg / total 0.67 0.70 0.67 0.68 0.64 0.43 9514\n",
"\n"
]
},
{
"data": {
"text/plain": [
"<Figure size 576x396 with 0 Axes>"
]
},
"metadata": {},
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}
],
"source": [
"from sklearn.model_selection import KFold\n",
"from sklearn import preprocessing\n",
"from imblearn.over_sampling import SMOTENC\n",
"from sklearn.metrics import f1_score\n",
"from imblearn.metrics import classification_report_imbalanced\n",
"from yellowbrick.classifier import ROCAUC\n",
"# explicitly require this experimental feature\n",
"from sklearn.experimental import enable_iterative_imputer # noqa\n",
"# now you can import normally from sklearn.impute\n",
"from sklearn.impute import IterativeImputer\n",
"from sklearn.linear_model import LogisticRegression\n",
"from numpy import loadtxt\n",
"import os\n",
"os.environ['KMP_DUPLICATE_LIB_OK']='True'\n",
"from xgboost import XGBClassifier\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.metrics import accuracy_score\n",
"import io \n",
"\n",
"classes=['Death','Home','Nursing Home','Rehabilitation']\n",
"\n",
"\n",
"\n",
"kf = KFold(n_splits=10)\n",
"\n",
"\n",
"for i in range (1,11):\n",
"\n",
" for fold, (train_index, test_index) in enumerate(kf.split(X), 1):\n",
" X_train = X.iloc[train_index]\n",
" y_train = y.iloc[train_index] # Based on your code, you might need a ravel call here, but I would look into how you're generating your y\n",
" X_test = X.iloc[test_index]\n",
" y_test = y.iloc[test_index] # See comment on ravel and y_train\n",
" \n",
" \n",
" #------------------------------IMPUTE Training Set------------------------------------\n",
"\n",
" # Use MICE to fill in each row's missing features\n",
" X_train = pd.DataFrame(IterativeImputer(verbose=False, sample_posterior=True).fit_transform(X_train))\n",
" X_train.columns = df_cols\n",
"\n",
" #------------------------------IMPUTE Testing Set------------------------------------ \n",
"\n",
" # Use MICE to fill in each row's missing features\n",
" X_test = pd.DataFrame(IterativeImputer(verbose=False, sample_posterior=True).fit_transform(X_test))\n",
" X_test.columns = df_cols\n",
"\n",
"\n",
" #------------------------------Standardize Testing Set------------------------------------\n",
"\n",
" std_scale = preprocessing.StandardScaler().fit(X_train[cols_to_norm])\n",
" X_train[cols_to_norm] = std_scale.transform(X_train[cols_to_norm])\n",
" X_test[cols_to_norm] = std_scale.transform(X_test[cols_to_norm])\n",
" #------------------------------------------------------------------------------------------\n",
"\n",
" # Hyperparameters are optimized using hyperopt\n",
"\n",
" #sm = SMOTE()\n",
"\n",
" sm = SMOTENC(random_state=50, categorical_features=[1,2,3,22,23,24,25,26,27,28,29,30,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61]) \n",
" X_train_oversampled, y_train_oversampled = sm.fit_sample(X_train, y_train)\n",
" print(sorted(Counter(y_train_oversampled).items()))\n",
" model = XGBClassifier(max_depth=8, gamma=0.063, colsample_bytree=0.71) \n",
" model.fit(X_train_oversampled, y_train_oversampled) \n",
" y_pred = model.predict(X_test.values)\n",
" visualizer = ROCAUC(model, classes=classes)\n",
" visualizer.fit(X_train_oversampled, y_train_oversampled) # Fit the training data to the visualizer\n",
" visualizer.score(X_test.values, y_test) # Evaluate the model on the test data\n",
" visualizer.poof(\"XB_SMOTENC_{}_{}.pdf\".format(i, fold), clear_figure=True) \n",
" print(f'For fold {fold}:')\n",
" print(f'Accuracy: {model.score(X_test.values, y_test)}')\n",
" f1=f1_score(y_test, y_pred, average='micro')\n",
" print(f'f-score: {f1}')\n",
" print(classification_report_imbalanced(y_test, y_pred))\n",
" K= classification_report_imbalanced(y_test, y_pred)\n",
" df = pd.read_fwf(io.StringIO(K))\n",
" df.loc[\"1\":\"1\",\"pre\":\"sup\"].to_csv(\"XGB-SMOTENC-D.csv\" , sep=',', encoding='utf-8', doublequote=False, index=False, mode=\"a\", header=False)\n",
" df.loc[\"2\":\"2\",\"pre\":\"sup\"].to_csv(\"XGB-SMOTENC-H.csv\" , sep=',', encoding='utf-8', doublequote=False, index=False, mode=\"a\", header=False)\n",
" df.loc[\"3\":\"3\",\"pre\":\"sup\"].to_csv(\"XGB-SMOTENC-N.csv\" , sep=',', encoding='utf-8', doublequote=False, index=False, mode=\"a\", header=False)\n",
" df.loc[\"4\":\"4\",\"pre\":\"sup\"].to_csv(\"XGB-SMOTENC-R.csv\" , sep=',', encoding='utf-8', doublequote=False, index=False, mode=\"a\", header=False)\n",
" df.iloc[6:7,:].to_csv(\"XGB-SMOTENC-avg.csv\" , sep=',', encoding='utf-8', doublequote=False, index=False, mode=\"a\", header=False)\n",
"\n",
" #\n",
"\n",
"\n",
" "
]
},
{
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"source": []
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