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{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"|--- ap_hi <= 129.50\n",
"| |--- age <= 54.65\n",
"| | |--- cholesterol <= 2.50\n",
"| | | |--- age <= 43.79\n",
"| | | | |--- cholesterol <= 1.50\n",
"| | | | | |--- ap_hi <= 114.50\n",
"| | | | | | |--- class: 0\n",
"| | | | | |--- ap_hi > 114.50\n",
"| | | | | | |--- class: 0\n",
"| | | | |--- cholesterol > 1.50\n",
"| | | | | |--- bmi <= 28.87\n",
"| | | | | | |--- class: 0\n",
"| | | | | |--- bmi > 28.87\n",
"| | | | | | |--- class: 0\n",
"| | | |--- age > 43.79\n",
"| | | | |--- ap_hi <= 119.50\n",
"| | | | | |--- bmi <= 22.05\n",
"| | | | | | |--- class: 0\n",
"| | | | | |--- bmi > 22.05\n",
"| | | | | | |--- class: 0\n",
"| | | | |--- ap_hi > 119.50\n",
"| | | | | |--- bmi <= 27.71\n",
"| | | | | | |--- class: 0\n",
"| | | | | |--- bmi > 27.71\n",
"| | | | | | |--- class: 0\n",
"| | |--- cholesterol > 2.50\n",
"| | | |--- bmi <= 29.04\n",
"| | | | |--- age <= 41.60\n",
"| | | | | |--- ap_hi <= 115.00\n",
"| | | | | | |--- class: 0\n",
"| | | | | |--- ap_hi > 115.00\n",
"| | | | | | |--- class: 0\n",
"| | | | |--- age > 41.60\n",
"| | | | | |--- age <= 54.17\n",
"| | | | | | |--- class: 1\n",
"| | | | | |--- age > 54.17\n",
"| | | | | | |--- class: 0\n",
"| | | |--- bmi > 29.04\n",
"| | | | |--- age <= 54.01\n",
"| | | | | |--- age <= 39.75\n",
"| | | | | | |--- class: 0\n",
"| | | | | |--- age > 39.75\n",
"| | | | | | |--- class: 1\n",
"| | | | |--- age > 54.01\n",
"| | | | | |--- bmi <= 35.02\n",
"| | | | | | |--- class: 0\n",
"| | | | | |--- bmi > 35.02\n",
"| | | | | | |--- class: 1\n",
"| |--- age > 54.65\n",
"| | |--- cholesterol <= 2.50\n",
"| | | |--- age <= 60.71\n",
"| | | | |--- ap_hi <= 118.50\n",
"| | | | | |--- bmi <= 23.33\n",
"| | | | | | |--- class: 0\n",
"| | | | | |--- bmi > 23.33\n",
"| | | | | | |--- class: 0\n",
"| | | | |--- ap_hi > 118.50\n",
"| | | | | |--- bmi <= 32.89\n",
"| | | | | | |--- class: 0\n",
"| | | | | |--- bmi > 32.89\n",
"| | | | | | |--- class: 1\n",
"| | | |--- age > 60.71\n",
"| | | | |--- bmi <= 20.51\n",
"| | | | | |--- age <= 64.31\n",
"| | | | | | |--- class: 0\n",
"| | | | | |--- age > 64.31\n",
"| | | | | | |--- class: 1\n",
"| | | | |--- bmi > 20.51\n",
"| | | | | |--- ap_hi <= 115.50\n",
"| | | | | | |--- class: 0\n",
"| | | | | |--- ap_hi > 115.50\n",
"| | | | | | |--- class: 1\n",
"| | |--- cholesterol > 2.50\n",
"| | | |--- bmi <= 26.03\n",
"| | | | |--- age <= 60.89\n",
"| | | | | |--- age <= 60.48\n",
"| | | | | | |--- class: 1\n",
"| | | | | |--- age > 60.48\n",
"| | | | | | |--- class: 0\n",
"| | | | |--- age > 60.89\n",
"| | | | | |--- bmi <= 25.91\n",
"| | | | | | |--- class: 1\n",
"| | | | | |--- bmi > 25.91\n",
"| | | | | | |--- class: 0\n",
"| | | |--- bmi > 26.03\n",
"| | | | |--- age <= 59.39\n",
"| | | | | |--- bmi <= 35.93\n",
"| | | | | | |--- class: 1\n",
"| | | | | |--- bmi > 35.93\n",
"| | | | | | |--- class: 1\n",
"| | | | |--- age > 59.39\n",
"| | | | | |--- bmi <= 35.12\n",
"| | | | | | |--- class: 1\n",
"| | | | | |--- bmi > 35.12\n",
"| | | | | | |--- class: 1\n",
"|--- ap_hi > 129.50\n",
"| |--- ap_hi <= 138.50\n",
"| | |--- cholesterol <= 2.50\n",
"| | | |--- age <= 59.54\n",
"| | | | |--- bmi <= 21.64\n",
"| | | | | |--- bmi <= 17.30\n",
"| | | | | | |--- class: 1\n",
"| | | | | |--- bmi > 17.30\n",
"| | | | | | |--- class: 0\n",
"| | | | |--- bmi > 21.64\n",
"| | | | | |--- age <= 39.99\n",
"| | | | | | |--- class: 0\n",
"| | | | | |--- age > 39.99\n",
"| | | | | | |--- class: 1\n",
"| | | |--- age > 59.54\n",
"| | | | |--- age <= 62.46\n",
"| | | | | |--- bmi <= 20.61\n",
"| | | | | | |--- class: 0\n",
"| | | | | |--- bmi > 20.61\n",
"| | | | | | |--- class: 1\n",
"| | | | |--- age > 62.46\n",
"| | | | | |--- age <= 64.00\n",
"| | | | | | |--- class: 1\n",
"| | | | | |--- age > 64.00\n",
"| | | | | | |--- class: 1\n",
"| | |--- cholesterol > 2.50\n",
"| | | |--- bmi <= 30.74\n",
"| | | | |--- bmi <= 30.06\n",
"| | | | | |--- bmi <= 23.93\n",
"| | | | | | |--- class: 1\n",
"| | | | | |--- bmi > 23.93\n",
"| | | | | | |--- class: 1\n",
"| | | | |--- bmi > 30.06\n",
"| | | | | |--- bmi <= 30.69\n",
"| | | | | | |--- class: 1\n",
"| | | | | |--- bmi > 30.69\n",
"| | | | | | |--- class: 0\n",
"| | | |--- bmi > 30.74\n",
"| | | | |--- bmi <= 32.05\n",
"| | | | | |--- age <= 43.63\n",
"| | | | | | |--- class: 0\n",
"| | | | | |--- age > 43.63\n",
"| | | | | | |--- class: 1\n",
"| | | | |--- bmi > 32.05\n",
"| | | | | |--- bmi <= 32.34\n",
"| | | | | | |--- class: 1\n",
"| | | | | |--- bmi > 32.34\n",
"| | | | | | |--- class: 1\n",
"| |--- ap_hi > 138.50\n",
"| | |--- ap_hi <= 149.50\n",
"| | | |--- age <= 39.56\n",
"| | | | |--- bmi <= 38.19\n",
"| | | | | |--- age <= 39.54\n",
"| | | | | | |--- class: 1\n",
"| | | | | |--- age > 39.54\n",
"| | | | | | |--- class: 0\n",
"| | | | |--- bmi > 38.19\n",
"| | | | | |--- bmi <= 50.55\n",
"| | | | | | |--- class: 0\n",
"| | | | | |--- bmi > 50.55\n",
"| | | | | | |--- class: 1\n",
"| | | |--- age > 39.56\n",
"| | | | |--- age <= 47.57\n",
"| | | | | |--- bmi <= 19.23\n",
"| | | | | | |--- class: 0\n",
"| | | | | |--- bmi > 19.23\n",
"| | | | | | |--- class: 1\n",
"| | | | |--- age > 47.57\n",
"| | | | | |--- age <= 61.57\n",
"| | | | | | |--- class: 1\n",
"| | | | | |--- age > 61.57\n",
"| | | | | | |--- class: 1\n",
"| | |--- ap_hi > 149.50\n",
"| | | |--- bmi <= 20.48\n",
"| | | | |--- age <= 64.27\n",
"| | | | | |--- age <= 55.82\n",
"| | | | | | |--- class: 1\n",
"| | | | | |--- age > 55.82\n",
"| | | | | | |--- class: 1\n",
"| | | | |--- age > 64.27\n",
"| | | | | |--- class: 0\n",
"| | | |--- bmi > 20.48\n",
"| | | | |--- age <= 64.35\n",
"| | | | | |--- age <= 49.82\n",
"| | | | | | |--- class: 1\n",
"| | | | | |--- age > 49.82\n",
"| | | | | | |--- class: 1\n",
"| | | | |--- age > 64.35\n",
"| | | | | |--- bmi <= 36.80\n",
"| | | | | | |--- class: 1\n",
"| | | | | |--- bmi > 36.80\n",
"| | | | | | |--- class: 0\n",
"\n"
]
}
],
"source": [
"import pickle\n",
"import pandas as pd\n",
"from sklearn import tree\n",
"\n",
"df = pd.read_csv(\"data-cardio/cardio_clear.csv\", index_col=\"id\")\n",
"model = pickle.load(open(\"data-cardio//cardio.model.sav\", \"rb\"))\n",
"features = (\n",
" df\n",
" .drop([\"cardio\"], axis=1)\n",
" .columns.values.tolist()\n",
")\n",
"\n",
"rules = tree.export_text(model, feature_names=features)\n",
"print(rules)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"63"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"[if (ap_hi <= 129.5) and (age <= 54.65) and (cholesterol <= 2.5) and (age <= 43.792) and (cholesterol <= 1.5) and (ap_hi <= 114.5) -> 0,\n",
" if (ap_hi <= 129.5) and (age <= 54.65) and (cholesterol <= 2.5) and (age <= 43.792) and (cholesterol <= 1.5) and (ap_hi > 114.5) -> 0,\n",
" if (ap_hi <= 129.5) and (age <= 54.65) and (cholesterol <= 2.5) and (age <= 43.792) and (cholesterol > 1.5) and (bmi <= 28.874) -> 0,\n",
" if (ap_hi <= 129.5) and (age <= 54.65) and (cholesterol <= 2.5) and (age <= 43.792) and (cholesterol > 1.5) and (bmi > 28.874) -> 0,\n",
" if (ap_hi <= 129.5) and (age <= 54.65) and (cholesterol <= 2.5) and (age > 43.792) and (ap_hi <= 119.5) and (bmi <= 22.045) -> 0,\n",
" if (ap_hi <= 129.5) and (age <= 54.65) and (cholesterol <= 2.5) and (age > 43.792) and (ap_hi <= 119.5) and (bmi > 22.045) -> 0,\n",
" if (ap_hi <= 129.5) and (age <= 54.65) and (cholesterol <= 2.5) and (age > 43.792) and (ap_hi > 119.5) and (bmi <= 27.71) -> 0,\n",
" if (ap_hi <= 129.5) and (age <= 54.65) and (cholesterol <= 2.5) and (age > 43.792) and (ap_hi > 119.5) and (bmi > 27.71) -> 0,\n",
" if (ap_hi <= 129.5) and (age <= 54.65) and (cholesterol > 2.5) and (bmi <= 29.043) and (age <= 41.599) and (ap_hi <= 115.0) -> 0,\n",
" if (ap_hi <= 129.5) and (age <= 54.65) and (cholesterol > 2.5) and (bmi <= 29.043) and (age <= 41.599) and (ap_hi > 115.0) -> 0,\n",
" if (ap_hi <= 129.5) and (age <= 54.65) and (cholesterol > 2.5) and (bmi <= 29.043) and (age > 41.599) and (age <= 54.167) -> 1,\n",
" if (ap_hi <= 129.5) and (age <= 54.65) and (cholesterol > 2.5) and (bmi <= 29.043) and (age > 41.599) and (age > 54.167) -> 0,\n",
" if (ap_hi <= 129.5) and (age <= 54.65) and (cholesterol > 2.5) and (bmi > 29.043) and (age <= 54.008) and (age <= 39.751) -> 0,\n",
" if (ap_hi <= 129.5) and (age <= 54.65) and (cholesterol > 2.5) and (bmi > 29.043) and (age <= 54.008) and (age > 39.751) -> 1,\n",
" if (ap_hi <= 129.5) and (age <= 54.65) and (cholesterol > 2.5) and (bmi > 29.043) and (age > 54.008) and (bmi <= 35.021) -> 0,\n",
" if (ap_hi <= 129.5) and (age <= 54.65) and (cholesterol > 2.5) and (bmi > 29.043) and (age > 54.008) and (bmi > 35.021) -> 1,\n",
" if (ap_hi <= 129.5) and (age > 54.65) and (cholesterol <= 2.5) and (age <= 60.707) and (ap_hi <= 118.5) and (bmi <= 23.329) -> 0,\n",
" if (ap_hi <= 129.5) and (age > 54.65) and (cholesterol <= 2.5) and (age <= 60.707) and (ap_hi <= 118.5) and (bmi > 23.329) -> 0,\n",
" if (ap_hi <= 129.5) and (age > 54.65) and (cholesterol <= 2.5) and (age <= 60.707) and (ap_hi > 118.5) and (bmi <= 32.886) -> 0,\n",
" if (ap_hi <= 129.5) and (age > 54.65) and (cholesterol <= 2.5) and (age <= 60.707) and (ap_hi > 118.5) and (bmi > 32.886) -> 1,\n",
" if (ap_hi <= 129.5) and (age > 54.65) and (cholesterol <= 2.5) and (age > 60.707) and (bmi <= 20.512) and (age <= 64.308) -> 0,\n",
" if (ap_hi <= 129.5) and (age > 54.65) and (cholesterol <= 2.5) and (age > 60.707) and (bmi <= 20.512) and (age > 64.308) -> 1,\n",
" if (ap_hi <= 129.5) and (age > 54.65) and (cholesterol <= 2.5) and (age > 60.707) and (bmi > 20.512) and (ap_hi <= 115.5) -> 0,\n",
" if (ap_hi <= 129.5) and (age > 54.65) and (cholesterol <= 2.5) and (age > 60.707) and (bmi > 20.512) and (ap_hi > 115.5) -> 1,\n",
" if (ap_hi <= 129.5) and (age > 54.65) and (cholesterol > 2.5) and (bmi <= 26.032) and (age <= 60.891) and (age <= 60.479) -> 1,\n",
" if (ap_hi <= 129.5) and (age > 54.65) and (cholesterol > 2.5) and (bmi <= 26.032) and (age <= 60.891) and (age > 60.479) -> 0,\n",
" if (ap_hi <= 129.5) and (age > 54.65) and (cholesterol > 2.5) and (bmi <= 26.032) and (age > 60.891) and (bmi <= 25.912) -> 1,\n",
" if (ap_hi <= 129.5) and (age > 54.65) and (cholesterol > 2.5) and (bmi <= 26.032) and (age > 60.891) and (bmi > 25.912) -> 0,\n",
" if (ap_hi <= 129.5) and (age > 54.65) and (cholesterol > 2.5) and (bmi > 26.032) and (age <= 59.39) and (bmi <= 35.932) -> 1,\n",
" if (ap_hi <= 129.5) and (age > 54.65) and (cholesterol > 2.5) and (bmi > 26.032) and (age <= 59.39) and (bmi > 35.932) -> 1,\n",
" if (ap_hi <= 129.5) and (age > 54.65) and (cholesterol > 2.5) and (bmi > 26.032) and (age > 59.39) and (bmi <= 35.121) -> 1,\n",
" if (ap_hi <= 129.5) and (age > 54.65) and (cholesterol > 2.5) and (bmi > 26.032) and (age > 59.39) and (bmi > 35.121) -> 1,\n",
" if (ap_hi > 129.5) and (ap_hi <= 138.5) and (cholesterol <= 2.5) and (age <= 59.536) and (bmi <= 21.637) and (bmi <= 17.3) -> 1,\n",
" if (ap_hi > 129.5) and (ap_hi <= 138.5) and (cholesterol <= 2.5) and (age <= 59.536) and (bmi <= 21.637) and (bmi > 17.3) -> 0,\n",
" if (ap_hi > 129.5) and (ap_hi <= 138.5) and (cholesterol <= 2.5) and (age <= 59.536) and (bmi > 21.637) and (age <= 39.989) -> 0,\n",
" if (ap_hi > 129.5) and (ap_hi <= 138.5) and (cholesterol <= 2.5) and (age <= 59.536) and (bmi > 21.637) and (age > 39.989) -> 1,\n",
" if (ap_hi > 129.5) and (ap_hi <= 138.5) and (cholesterol <= 2.5) and (age > 59.536) and (age <= 62.463) and (bmi <= 20.614) -> 0,\n",
" if (ap_hi > 129.5) and (ap_hi <= 138.5) and (cholesterol <= 2.5) and (age > 59.536) and (age <= 62.463) and (bmi > 20.614) -> 1,\n",
" if (ap_hi > 129.5) and (ap_hi <= 138.5) and (cholesterol <= 2.5) and (age > 59.536) and (age > 62.463) and (age <= 63.998) -> 1,\n",
" if (ap_hi > 129.5) and (ap_hi <= 138.5) and (cholesterol <= 2.5) and (age > 59.536) and (age > 62.463) and (age > 63.998) -> 1,\n",
" if (ap_hi > 129.5) and (ap_hi <= 138.5) and (cholesterol > 2.5) and (bmi <= 30.744) and (bmi <= 30.056) and (bmi <= 23.927) -> 1,\n",
" if (ap_hi > 129.5) and (ap_hi <= 138.5) and (cholesterol > 2.5) and (bmi <= 30.744) and (bmi <= 30.056) and (bmi > 23.927) -> 1,\n",
" if (ap_hi > 129.5) and (ap_hi <= 138.5) and (cholesterol > 2.5) and (bmi <= 30.744) and (bmi > 30.056) and (bmi <= 30.69) -> 1,\n",
" if (ap_hi > 129.5) and (ap_hi <= 138.5) and (cholesterol > 2.5) and (bmi <= 30.744) and (bmi > 30.056) and (bmi > 30.69) -> 0,\n",
" if (ap_hi > 129.5) and (ap_hi <= 138.5) and (cholesterol > 2.5) and (bmi > 30.744) and (bmi <= 32.049) and (age <= 43.632) -> 0,\n",
" if (ap_hi > 129.5) and (ap_hi <= 138.5) and (cholesterol > 2.5) and (bmi > 30.744) and (bmi <= 32.049) and (age > 43.632) -> 1,\n",
" if (ap_hi > 129.5) and (ap_hi <= 138.5) and (cholesterol > 2.5) and (bmi > 30.744) and (bmi > 32.049) and (bmi <= 32.337) -> 1,\n",
" if (ap_hi > 129.5) and (ap_hi <= 138.5) and (cholesterol > 2.5) and (bmi > 30.744) and (bmi > 32.049) and (bmi > 32.337) -> 1,\n",
" if (ap_hi > 129.5) and (ap_hi > 138.5) and (ap_hi <= 149.5) and (age <= 39.558) and (bmi <= 38.186) and (age <= 39.538) -> 1,\n",
" if (ap_hi > 129.5) and (ap_hi > 138.5) and (ap_hi <= 149.5) and (age <= 39.558) and (bmi <= 38.186) and (age > 39.538) -> 0,\n",
" if (ap_hi > 129.5) and (ap_hi > 138.5) and (ap_hi <= 149.5) and (age <= 39.558) and (bmi > 38.186) and (bmi <= 50.547) -> 0,\n",
" if (ap_hi > 129.5) and (ap_hi > 138.5) and (ap_hi <= 149.5) and (age <= 39.558) and (bmi > 38.186) and (bmi > 50.547) -> 1,\n",
" if (ap_hi > 129.5) and (ap_hi > 138.5) and (ap_hi <= 149.5) and (age > 39.558) and (age <= 47.569) and (bmi <= 19.231) -> 0,\n",
" if (ap_hi > 129.5) and (ap_hi > 138.5) and (ap_hi <= 149.5) and (age > 39.558) and (age <= 47.569) and (bmi > 19.231) -> 1,\n",
" if (ap_hi > 129.5) and (ap_hi > 138.5) and (ap_hi <= 149.5) and (age > 39.558) and (age > 47.569) and (age <= 61.572) -> 1,\n",
" if (ap_hi > 129.5) and (ap_hi > 138.5) and (ap_hi <= 149.5) and (age > 39.558) and (age > 47.569) and (age > 61.572) -> 1,\n",
" if (ap_hi > 129.5) and (ap_hi > 138.5) and (ap_hi > 149.5) and (bmi <= 20.482) and (age <= 64.269) and (age <= 55.817) -> 1,\n",
" if (ap_hi > 129.5) and (ap_hi > 138.5) and (ap_hi > 149.5) and (bmi <= 20.482) and (age <= 64.269) and (age > 55.817) -> 1,\n",
" if (ap_hi > 129.5) and (ap_hi > 138.5) and (ap_hi > 149.5) and (bmi <= 20.482) and (age > 64.269) -> 0,\n",
" if (ap_hi > 129.5) and (ap_hi > 138.5) and (ap_hi > 149.5) and (bmi > 20.482) and (age <= 64.351) and (age <= 49.818) -> 1,\n",
" if (ap_hi > 129.5) and (ap_hi > 138.5) and (ap_hi > 149.5) and (bmi > 20.482) and (age <= 64.351) and (age > 49.818) -> 1,\n",
" if (ap_hi > 129.5) and (ap_hi > 138.5) and (ap_hi > 149.5) and (bmi > 20.482) and (age > 64.351) and (bmi <= 36.796) -> 1,\n",
" if (ap_hi > 129.5) and (ap_hi > 138.5) and (ap_hi > 149.5) and (bmi > 20.482) and (age > 64.351) and (bmi > 36.796) -> 0]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from src.rules import get_rules\n",
"\n",
"\n",
"rules = get_rules(model, features, [0, 1])\n",
"display(len(rules))\n",
"rules"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"63"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"[if (ap_hi <= 129.5) and (age <= 54.65) and (cholesterol <= 2.5) -> 0,\n",
" if (ap_hi <= 129.5) and (ap_hi > 114.5) and (age <= 54.65) and (cholesterol <= 2.5) -> 0,\n",
" if (ap_hi <= 129.5) and (age <= 54.65) and (cholesterol <= 2.5) and (cholesterol > 1.5) and (bmi <= 28.874) -> 0,\n",
" if (ap_hi <= 129.5) and (age <= 54.65) and (cholesterol <= 2.5) and (cholesterol > 1.5) and (bmi > 28.874) -> 0,\n",
" if (ap_hi <= 129.5) and (age <= 54.65) and (age > 43.792) and (cholesterol <= 2.5) and (bmi <= 22.045) -> 0,\n",
" if (ap_hi <= 129.5) and (age <= 54.65) and (age > 43.792) and (cholesterol <= 2.5) and (bmi > 22.045) -> 0,\n",
" if (ap_hi <= 129.5) and (ap_hi > 119.5) and (age <= 54.65) and (age > 43.792) and (cholesterol <= 2.5) and (bmi <= 27.71) -> 0,\n",
" if (ap_hi <= 129.5) and (ap_hi > 119.5) and (age <= 54.65) and (age > 43.792) and (cholesterol <= 2.5) and (bmi > 27.71) -> 0,\n",
" if (ap_hi <= 129.5) and (age <= 54.65) and (cholesterol > 2.5) and (bmi <= 29.043) -> 0,\n",
" if (ap_hi <= 129.5) and (ap_hi > 115.0) and (age <= 54.65) and (cholesterol > 2.5) and (bmi <= 29.043) -> 0,\n",
" if (ap_hi <= 129.5) and (age <= 54.65) and (age > 41.599) and (cholesterol > 2.5) and (bmi <= 29.043) -> 1,\n",
" if (ap_hi <= 129.5) and (age <= 54.65) and (age > 41.599) and (cholesterol > 2.5) and (bmi <= 29.043) -> 0,\n",
" if (ap_hi <= 129.5) and (age <= 54.65) and (cholesterol > 2.5) and (bmi > 29.043) -> 0,\n",
" if (ap_hi <= 129.5) and (age <= 54.65) and (age > 39.751) and (cholesterol > 2.5) and (bmi > 29.043) -> 1,\n",
" if (ap_hi <= 129.5) and (age <= 54.65) and (age > 54.008) and (cholesterol > 2.5) and (bmi > 29.043) and (bmi <= 35.021) -> 0,\n",
" if (ap_hi <= 129.5) and (age <= 54.65) and (age > 54.008) and (cholesterol > 2.5) and (bmi > 29.043) -> 1,\n",
" if (ap_hi <= 129.5) and (age > 54.65) and (age <= 60.707) and (cholesterol <= 2.5) and (bmi <= 23.329) -> 0,\n",
" if (ap_hi <= 129.5) and (age > 54.65) and (age <= 60.707) and (cholesterol <= 2.5) and (bmi > 23.329) -> 0,\n",
" if (ap_hi <= 129.5) and (ap_hi > 118.5) and (age > 54.65) and (age <= 60.707) and (cholesterol <= 2.5) and (bmi <= 32.886) -> 0,\n",
" if (ap_hi <= 129.5) and (ap_hi > 118.5) and (age > 54.65) and (age <= 60.707) and (cholesterol <= 2.5) and (bmi > 32.886) -> 1,\n",
" if (ap_hi <= 129.5) and (age > 54.65) and (age <= 64.308) and (cholesterol <= 2.5) and (bmi <= 20.512) -> 0,\n",
" if (ap_hi <= 129.5) and (age > 54.65) and (cholesterol <= 2.5) and (bmi <= 20.512) -> 1,\n",
" if (ap_hi <= 129.5) and (age > 54.65) and (cholesterol <= 2.5) and (bmi > 20.512) -> 0,\n",
" if (ap_hi <= 129.5) and (ap_hi > 115.5) and (age > 54.65) and (cholesterol <= 2.5) and (bmi > 20.512) -> 1,\n",
" if (ap_hi <= 129.5) and (age > 54.65) and (age <= 60.891) and (cholesterol > 2.5) and (bmi <= 26.032) -> 1,\n",
" if (ap_hi <= 129.5) and (age > 54.65) and (age <= 60.891) and (cholesterol > 2.5) and (bmi <= 26.032) -> 0,\n",
" if (ap_hi <= 129.5) and (age > 54.65) and (cholesterol > 2.5) and (bmi <= 26.032) -> 1,\n",
" if (ap_hi <= 129.5) and (age > 54.65) and (cholesterol > 2.5) and (bmi <= 26.032) and (bmi > 25.912) -> 0,\n",
" if (ap_hi <= 129.5) and (age > 54.65) and (age <= 59.39) and (cholesterol > 2.5) and (bmi > 26.032) and (bmi <= 35.932) -> 1,\n",
" if (ap_hi <= 129.5) and (age > 54.65) and (age <= 59.39) and (cholesterol > 2.5) and (bmi > 26.032) -> 1,\n",
" if (ap_hi <= 129.5) and (age > 54.65) and (cholesterol > 2.5) and (bmi > 26.032) and (bmi <= 35.121) -> 1,\n",
" if (ap_hi <= 129.5) and (age > 54.65) and (cholesterol > 2.5) and (bmi > 26.032) -> 1,\n",
" if (ap_hi > 129.5) and (ap_hi <= 138.5) and (cholesterol <= 2.5) and (age <= 59.536) and (bmi <= 21.637) -> 1,\n",
" if (ap_hi > 129.5) and (ap_hi <= 138.5) and (cholesterol <= 2.5) and (age <= 59.536) and (bmi <= 21.637) and (bmi > 17.3) -> 0,\n",
" if (ap_hi > 129.5) and (ap_hi <= 138.5) and (cholesterol <= 2.5) and (age <= 59.536) and (bmi > 21.637) -> 0,\n",
" if (ap_hi > 129.5) and (ap_hi <= 138.5) and (cholesterol <= 2.5) and (age <= 59.536) and (age > 39.989) and (bmi > 21.637) -> 1,\n",
" if (ap_hi > 129.5) and (ap_hi <= 138.5) and (cholesterol <= 2.5) and (age > 59.536) and (age <= 62.463) and (bmi <= 20.614) -> 0,\n",
" if (ap_hi > 129.5) and (ap_hi <= 138.5) and (cholesterol <= 2.5) and (age > 59.536) and (age <= 62.463) and (bmi > 20.614) -> 1,\n",
" if (ap_hi > 129.5) and (ap_hi <= 138.5) and (cholesterol <= 2.5) and (age > 59.536) and (age <= 63.998) -> 1,\n",
" if (ap_hi > 129.5) and (ap_hi <= 138.5) and (cholesterol <= 2.5) and (age > 59.536) -> 1,\n",
" if (ap_hi > 129.5) and (ap_hi <= 138.5) and (cholesterol > 2.5) and (bmi <= 30.744) -> 1,\n",
" if (ap_hi > 129.5) and (ap_hi <= 138.5) and (cholesterol > 2.5) and (bmi <= 30.744) and (bmi > 23.927) -> 1,\n",
" if (ap_hi > 129.5) and (ap_hi <= 138.5) and (cholesterol > 2.5) and (bmi <= 30.744) and (bmi > 30.056) -> 1,\n",
" if (ap_hi > 129.5) and (ap_hi <= 138.5) and (cholesterol > 2.5) and (bmi <= 30.744) and (bmi > 30.056) -> 0,\n",
" if (ap_hi > 129.5) and (ap_hi <= 138.5) and (cholesterol > 2.5) and (bmi > 30.744) and (bmi <= 32.049) and (age <= 43.632) -> 0,\n",
" if (ap_hi > 129.5) and (ap_hi <= 138.5) and (cholesterol > 2.5) and (bmi > 30.744) and (bmi <= 32.049) and (age > 43.632) -> 1,\n",
" if (ap_hi > 129.5) and (ap_hi <= 138.5) and (cholesterol > 2.5) and (bmi > 30.744) and (bmi <= 32.337) -> 1,\n",
" if (ap_hi > 129.5) and (ap_hi <= 138.5) and (cholesterol > 2.5) and (bmi > 30.744) -> 1,\n",
" if (ap_hi > 129.5) and (ap_hi <= 149.5) and (age <= 39.558) and (bmi <= 38.186) -> 1,\n",
" if (ap_hi > 129.5) and (ap_hi <= 149.5) and (age <= 39.558) and (age > 39.538) and (bmi <= 38.186) -> 0,\n",
" if (ap_hi > 129.5) and (ap_hi <= 149.5) and (age <= 39.558) and (bmi > 38.186) and (bmi <= 50.547) -> 0,\n",
" if (ap_hi > 129.5) and (ap_hi <= 149.5) and (age <= 39.558) and (bmi > 38.186) -> 1,\n",
" if (ap_hi > 129.5) and (ap_hi <= 149.5) and (age > 39.558) and (age <= 47.569) and (bmi <= 19.231) -> 0,\n",
" if (ap_hi > 129.5) and (ap_hi <= 149.5) and (age > 39.558) and (age <= 47.569) and (bmi > 19.231) -> 1,\n",
" if (ap_hi > 129.5) and (ap_hi <= 149.5) and (age > 39.558) and (age <= 61.572) -> 1,\n",
" if (ap_hi > 129.5) and (ap_hi <= 149.5) and (age > 39.558) -> 1,\n",
" if (ap_hi > 129.5) and (bmi <= 20.482) and (age <= 64.269) -> 1,\n",
" if (ap_hi > 129.5) and (bmi <= 20.482) and (age <= 64.269) and (age > 55.817) -> 1,\n",
" if (ap_hi > 129.5) and (bmi <= 20.482) and (age > 64.269) -> 0,\n",
" if (ap_hi > 129.5) and (bmi > 20.482) and (age <= 64.351) -> 1,\n",
" if (ap_hi > 129.5) and (bmi > 20.482) and (age <= 64.351) and (age > 49.818) -> 1,\n",
" if (ap_hi > 129.5) and (bmi > 20.482) and (bmi <= 36.796) and (age > 64.351) -> 1,\n",
" if (ap_hi > 129.5) and (bmi > 20.482) and (age > 64.351) -> 0]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from src.rules import normalise_rules\n",
"\n",
"\n",
"rules = normalise_rules(rules)\n",
"display(len(rules))\n",
"rules"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"60"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"[if (ap_hi <= 129.5) and (age <= 54.65) and (cholesterol <= 2.5) -> 0,\n",
" if (ap_hi <= 129.5) and (ap_hi > 114.5) and (age <= 54.65) and (cholesterol <= 2.5) -> 0,\n",
" if (ap_hi <= 129.5) and (age <= 54.65) and (cholesterol <= 2.5) and (cholesterol > 1.5) and (bmi <= 28.874) -> 0,\n",
" if (ap_hi <= 129.5) and (age <= 54.65) and (cholesterol <= 2.5) and (cholesterol > 1.5) and (bmi > 28.874) -> 0,\n",
" if (ap_hi <= 129.5) and (age <= 54.65) and (age > 43.792) and (cholesterol <= 2.5) and (bmi <= 22.045) -> 0,\n",
" if (ap_hi <= 129.5) and (age <= 54.65) and (age > 43.792) and (cholesterol <= 2.5) and (bmi > 22.045) -> 0,\n",
" if (ap_hi <= 129.5) and (ap_hi > 119.5) and (age <= 54.65) and (age > 43.792) and (cholesterol <= 2.5) and (bmi <= 27.71) -> 0,\n",
" if (ap_hi <= 129.5) and (ap_hi > 119.5) and (age <= 54.65) and (age > 43.792) and (cholesterol <= 2.5) and (bmi > 27.71) -> 0,\n",
" if (ap_hi <= 129.5) and (age <= 54.65) and (cholesterol > 2.5) and (bmi <= 29.043) -> 0,\n",
" if (ap_hi <= 129.5) and (ap_hi > 115.0) and (age <= 54.65) and (cholesterol > 2.5) and (bmi <= 29.043) -> 0,\n",
" if (ap_hi <= 129.5) and (age <= 54.65) and (age > 41.599) and (cholesterol > 2.5) and (bmi <= 29.043) -> 0.5,\n",
" if (ap_hi <= 129.5) and (age <= 54.65) and (cholesterol > 2.5) and (bmi > 29.043) -> 0,\n",
" if (ap_hi <= 129.5) and (age <= 54.65) and (age > 39.751) and (cholesterol > 2.5) and (bmi > 29.043) -> 1,\n",
" if (ap_hi <= 129.5) and (age <= 54.65) and (age > 54.008) and (cholesterol > 2.5) and (bmi > 29.043) and (bmi <= 35.021) -> 0,\n",
" if (ap_hi <= 129.5) and (age <= 54.65) and (age > 54.008) and (cholesterol > 2.5) and (bmi > 29.043) -> 1,\n",
" if (ap_hi <= 129.5) and (age > 54.65) and (age <= 60.707) and (cholesterol <= 2.5) and (bmi <= 23.329) -> 0,\n",
" if (ap_hi <= 129.5) and (age > 54.65) and (age <= 60.707) and (cholesterol <= 2.5) and (bmi > 23.329) -> 0,\n",
" if (ap_hi <= 129.5) and (ap_hi > 118.5) and (age > 54.65) and (age <= 60.707) and (cholesterol <= 2.5) and (bmi <= 32.886) -> 0,\n",
" if (ap_hi <= 129.5) and (ap_hi > 118.5) and (age > 54.65) and (age <= 60.707) and (cholesterol <= 2.5) and (bmi > 32.886) -> 1,\n",
" if (ap_hi <= 129.5) and (age > 54.65) and (age <= 64.308) and (cholesterol <= 2.5) and (bmi <= 20.512) -> 0,\n",
" if (ap_hi <= 129.5) and (age > 54.65) and (cholesterol <= 2.5) and (bmi <= 20.512) -> 1,\n",
" if (ap_hi <= 129.5) and (age > 54.65) and (cholesterol <= 2.5) and (bmi > 20.512) -> 0,\n",
" if (ap_hi <= 129.5) and (ap_hi > 115.5) and (age > 54.65) and (cholesterol <= 2.5) and (bmi > 20.512) -> 1,\n",
" if (ap_hi <= 129.5) and (age > 54.65) and (age <= 60.891) and (cholesterol > 2.5) and (bmi <= 26.032) -> 0.5,\n",
" if (ap_hi <= 129.5) and (age > 54.65) and (cholesterol > 2.5) and (bmi <= 26.032) -> 1,\n",
" if (ap_hi <= 129.5) and (age > 54.65) and (cholesterol > 2.5) and (bmi <= 26.032) and (bmi > 25.912) -> 0,\n",
" if (ap_hi <= 129.5) and (age > 54.65) and (age <= 59.39) and (cholesterol > 2.5) and (bmi > 26.032) and (bmi <= 35.932) -> 1,\n",
" if (ap_hi <= 129.5) and (age > 54.65) and (age <= 59.39) and (cholesterol > 2.5) and (bmi > 26.032) -> 1,\n",
" if (ap_hi <= 129.5) and (age > 54.65) and (cholesterol > 2.5) and (bmi > 26.032) and (bmi <= 35.121) -> 1,\n",
" if (ap_hi <= 129.5) and (age > 54.65) and (cholesterol > 2.5) and (bmi > 26.032) -> 1,\n",
" if (ap_hi > 129.5) and (ap_hi <= 138.5) and (cholesterol <= 2.5) and (age <= 59.536) and (bmi <= 21.637) -> 1,\n",
" if (ap_hi > 129.5) and (ap_hi <= 138.5) and (cholesterol <= 2.5) and (age <= 59.536) and (bmi <= 21.637) and (bmi > 17.3) -> 0,\n",
" if (ap_hi > 129.5) and (ap_hi <= 138.5) and (cholesterol <= 2.5) and (age <= 59.536) and (bmi > 21.637) -> 0,\n",
" if (ap_hi > 129.5) and (ap_hi <= 138.5) and (cholesterol <= 2.5) and (age <= 59.536) and (age > 39.989) and (bmi > 21.637) -> 1,\n",
" if (ap_hi > 129.5) and (ap_hi <= 138.5) and (cholesterol <= 2.5) and (age > 59.536) and (age <= 62.463) and (bmi <= 20.614) -> 0,\n",
" if (ap_hi > 129.5) and (ap_hi <= 138.5) and (cholesterol <= 2.5) and (age > 59.536) and (age <= 62.463) and (bmi > 20.614) -> 1,\n",
" if (ap_hi > 129.5) and (ap_hi <= 138.5) and (cholesterol <= 2.5) and (age > 59.536) and (age <= 63.998) -> 1,\n",
" if (ap_hi > 129.5) and (ap_hi <= 138.5) and (cholesterol <= 2.5) and (age > 59.536) -> 1,\n",
" if (ap_hi > 129.5) and (ap_hi <= 138.5) and (cholesterol > 2.5) and (bmi <= 30.744) -> 1,\n",
" if (ap_hi > 129.5) and (ap_hi <= 138.5) and (cholesterol > 2.5) and (bmi <= 30.744) and (bmi > 23.927) -> 1,\n",
" if (ap_hi > 129.5) and (ap_hi <= 138.5) and (cholesterol > 2.5) and (bmi <= 30.744) and (bmi > 30.056) -> 0.5,\n",
" if (ap_hi > 129.5) and (ap_hi <= 138.5) and (cholesterol > 2.5) and (bmi > 30.744) and (bmi <= 32.049) and (age <= 43.632) -> 0,\n",
" if (ap_hi > 129.5) and (ap_hi <= 138.5) and (cholesterol > 2.5) and (bmi > 30.744) and (bmi <= 32.049) and (age > 43.632) -> 1,\n",
" if (ap_hi > 129.5) and (ap_hi <= 138.5) and (cholesterol > 2.5) and (bmi > 30.744) and (bmi <= 32.337) -> 1,\n",
" if (ap_hi > 129.5) and (ap_hi <= 138.5) and (cholesterol > 2.5) and (bmi > 30.744) -> 1,\n",
" if (ap_hi > 129.5) and (ap_hi <= 149.5) and (age <= 39.558) and (bmi <= 38.186) -> 1,\n",
" if (ap_hi > 129.5) and (ap_hi <= 149.5) and (age <= 39.558) and (age > 39.538) and (bmi <= 38.186) -> 0,\n",
" if (ap_hi > 129.5) and (ap_hi <= 149.5) and (age <= 39.558) and (bmi > 38.186) and (bmi <= 50.547) -> 0,\n",
" if (ap_hi > 129.5) and (ap_hi <= 149.5) and (age <= 39.558) and (bmi > 38.186) -> 1,\n",
" if (ap_hi > 129.5) and (ap_hi <= 149.5) and (age > 39.558) and (age <= 47.569) and (bmi <= 19.231) -> 0,\n",
" if (ap_hi > 129.5) and (ap_hi <= 149.5) and (age > 39.558) and (age <= 47.569) and (bmi > 19.231) -> 1,\n",
" if (ap_hi > 129.5) and (ap_hi <= 149.5) and (age > 39.558) and (age <= 61.572) -> 1,\n",
" if (ap_hi > 129.5) and (ap_hi <= 149.5) and (age > 39.558) -> 1,\n",
" if (ap_hi > 129.5) and (bmi <= 20.482) and (age <= 64.269) -> 1,\n",
" if (ap_hi > 129.5) and (bmi <= 20.482) and (age <= 64.269) and (age > 55.817) -> 1,\n",
" if (ap_hi > 129.5) and (bmi <= 20.482) and (age > 64.269) -> 0,\n",
" if (ap_hi > 129.5) and (bmi > 20.482) and (age <= 64.351) -> 1,\n",
" if (ap_hi > 129.5) and (bmi > 20.482) and (age <= 64.351) and (age > 49.818) -> 1,\n",
" if (ap_hi > 129.5) and (bmi > 20.482) and (bmi <= 36.796) and (age > 64.351) -> 1,\n",
" if (ap_hi > 129.5) and (bmi > 20.482) and (age > 64.351) -> 0]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from src.rules import delete_same_rules\n",
"\n",
"\n",
"rules = delete_same_rules(rules)\n",
"display(len(rules))\n",
"rules"
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"57"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"[if (ap_hi <= 129.5) and (age <= 54.65) and (cholesterol <= 2.5) -> 0,\n",
" if (ap_hi <= 129.5) and (ap_hi > 114.5) and (age <= 54.65) and (cholesterol <= 2.5) -> 0,\n",
" if (ap_hi <= 129.5) and (age <= 54.65) and (cholesterol <= 2.5) and (cholesterol > 1.5) and (bmi <= 28.874) -> 0,\n",
" if (ap_hi <= 129.5) and (age <= 54.65) and (cholesterol <= 2.5) and (cholesterol > 1.5) and (bmi > 28.874) -> 0,\n",
" if (ap_hi <= 129.5) and (age <= 54.65) and (age > 43.792) and (cholesterol <= 2.5) and (bmi <= 22.045) -> 0,\n",
" if (ap_hi <= 129.5) and (age <= 54.65) and (age > 43.792) and (cholesterol <= 2.5) and (bmi > 22.045) -> 0,\n",
" if (ap_hi <= 129.5) and (ap_hi > 119.5) and (age <= 54.65) and (age > 43.792) and (cholesterol <= 2.5) and (bmi <= 27.71) -> 0,\n",
" if (ap_hi <= 129.5) and (ap_hi > 119.5) and (age <= 54.65) and (age > 43.792) and (cholesterol <= 2.5) and (bmi > 27.71) -> 0,\n",
" if (ap_hi <= 129.5) and (age <= 54.65) and (cholesterol > 2.5) and (bmi <= 29.043) -> 0,\n",
" if (ap_hi <= 129.5) and (ap_hi > 115.0) and (age <= 54.65) and (cholesterol > 2.5) and (bmi <= 29.043) -> 0,\n",
" if (ap_hi <= 129.5) and (age <= 54.65) and (cholesterol > 2.5) and (bmi > 29.043) -> 0,\n",
" if (ap_hi <= 129.5) and (age <= 54.65) and (age > 39.751) and (cholesterol > 2.5) and (bmi > 29.043) -> 1,\n",
" if (ap_hi <= 129.5) and (age <= 54.65) and (age > 54.008) and (cholesterol > 2.5) and (bmi > 29.043) and (bmi <= 35.021) -> 0,\n",
" if (ap_hi <= 129.5) and (age <= 54.65) and (age > 54.008) and (cholesterol > 2.5) and (bmi > 29.043) -> 1,\n",
" if (ap_hi <= 129.5) and (age > 54.65) and (age <= 60.707) and (cholesterol <= 2.5) and (bmi <= 23.329) -> 0,\n",
" if (ap_hi <= 129.5) and (age > 54.65) and (age <= 60.707) and (cholesterol <= 2.5) and (bmi > 23.329) -> 0,\n",
" if (ap_hi <= 129.5) and (ap_hi > 118.5) and (age > 54.65) and (age <= 60.707) and (cholesterol <= 2.5) and (bmi <= 32.886) -> 0,\n",
" if (ap_hi <= 129.5) and (ap_hi > 118.5) and (age > 54.65) and (age <= 60.707) and (cholesterol <= 2.5) and (bmi > 32.886) -> 1,\n",
" if (ap_hi <= 129.5) and (age > 54.65) and (age <= 64.308) and (cholesterol <= 2.5) and (bmi <= 20.512) -> 0,\n",
" if (ap_hi <= 129.5) and (age > 54.65) and (cholesterol <= 2.5) and (bmi <= 20.512) -> 1,\n",
" if (ap_hi <= 129.5) and (age > 54.65) and (cholesterol <= 2.5) and (bmi > 20.512) -> 0,\n",
" if (ap_hi <= 129.5) and (ap_hi > 115.5) and (age > 54.65) and (cholesterol <= 2.5) and (bmi > 20.512) -> 1,\n",
" if (ap_hi <= 129.5) and (age > 54.65) and (cholesterol > 2.5) and (bmi <= 26.032) -> 1,\n",
" if (ap_hi <= 129.5) and (age > 54.65) and (cholesterol > 2.5) and (bmi <= 26.032) and (bmi > 25.912) -> 0,\n",
" if (ap_hi <= 129.5) and (age > 54.65) and (age <= 59.39) and (cholesterol > 2.5) and (bmi > 26.032) and (bmi <= 35.932) -> 1,\n",
" if (ap_hi <= 129.5) and (age > 54.65) and (age <= 59.39) and (cholesterol > 2.5) and (bmi > 26.032) -> 1,\n",
" if (ap_hi <= 129.5) and (age > 54.65) and (cholesterol > 2.5) and (bmi > 26.032) and (bmi <= 35.121) -> 1,\n",
" if (ap_hi <= 129.5) and (age > 54.65) and (cholesterol > 2.5) and (bmi > 26.032) -> 1,\n",
" if (ap_hi > 129.5) and (ap_hi <= 138.5) and (cholesterol <= 2.5) and (age <= 59.536) and (bmi <= 21.637) -> 1,\n",
" if (ap_hi > 129.5) and (ap_hi <= 138.5) and (cholesterol <= 2.5) and (age <= 59.536) and (bmi <= 21.637) and (bmi > 17.3) -> 0,\n",
" if (ap_hi > 129.5) and (ap_hi <= 138.5) and (cholesterol <= 2.5) and (age <= 59.536) and (bmi > 21.637) -> 0,\n",
" if (ap_hi > 129.5) and (ap_hi <= 138.5) and (cholesterol <= 2.5) and (age <= 59.536) and (age > 39.989) and (bmi > 21.637) -> 1,\n",
" if (ap_hi > 129.5) and (ap_hi <= 138.5) and (cholesterol <= 2.5) and (age > 59.536) and (age <= 62.463) and (bmi <= 20.614) -> 0,\n",
" if (ap_hi > 129.5) and (ap_hi <= 138.5) and (cholesterol <= 2.5) and (age > 59.536) and (age <= 62.463) and (bmi > 20.614) -> 1,\n",
" if (ap_hi > 129.5) and (ap_hi <= 138.5) and (cholesterol <= 2.5) and (age > 59.536) and (age <= 63.998) -> 1,\n",
" if (ap_hi > 129.5) and (ap_hi <= 138.5) and (cholesterol <= 2.5) and (age > 59.536) -> 1,\n",
" if (ap_hi > 129.5) and (ap_hi <= 138.5) and (cholesterol > 2.5) and (bmi <= 30.744) -> 1,\n",
" if (ap_hi > 129.5) and (ap_hi <= 138.5) and (cholesterol > 2.5) and (bmi <= 30.744) and (bmi > 23.927) -> 1,\n",
" if (ap_hi > 129.5) and (ap_hi <= 138.5) and (cholesterol > 2.5) and (bmi > 30.744) and (bmi <= 32.049) and (age <= 43.632) -> 0,\n",
" if (ap_hi > 129.5) and (ap_hi <= 138.5) and (cholesterol > 2.5) and (bmi > 30.744) and (bmi <= 32.049) and (age > 43.632) -> 1,\n",
" if (ap_hi > 129.5) and (ap_hi <= 138.5) and (cholesterol > 2.5) and (bmi > 30.744) and (bmi <= 32.337) -> 1,\n",
" if (ap_hi > 129.5) and (ap_hi <= 138.5) and (cholesterol > 2.5) and (bmi > 30.744) -> 1,\n",
" if (ap_hi > 129.5) and (ap_hi <= 149.5) and (age <= 39.558) and (bmi <= 38.186) -> 1,\n",
" if (ap_hi > 129.5) and (ap_hi <= 149.5) and (age <= 39.558) and (age > 39.538) and (bmi <= 38.186) -> 0,\n",
" if (ap_hi > 129.5) and (ap_hi <= 149.5) and (age <= 39.558) and (bmi > 38.186) and (bmi <= 50.547) -> 0,\n",
" if (ap_hi > 129.5) and (ap_hi <= 149.5) and (age <= 39.558) and (bmi > 38.186) -> 1,\n",
" if (ap_hi > 129.5) and (ap_hi <= 149.5) and (age > 39.558) and (age <= 47.569) and (bmi <= 19.231) -> 0,\n",
" if (ap_hi > 129.5) and (ap_hi <= 149.5) and (age > 39.558) and (age <= 47.569) and (bmi > 19.231) -> 1,\n",
" if (ap_hi > 129.5) and (ap_hi <= 149.5) and (age > 39.558) and (age <= 61.572) -> 1,\n",
" if (ap_hi > 129.5) and (ap_hi <= 149.5) and (age > 39.558) -> 1,\n",
" if (ap_hi > 129.5) and (bmi <= 20.482) and (age <= 64.269) -> 1,\n",
" if (ap_hi > 129.5) and (bmi <= 20.482) and (age <= 64.269) and (age > 55.817) -> 1,\n",
" if (ap_hi > 129.5) and (bmi <= 20.482) and (age > 64.269) -> 0,\n",
" if (ap_hi > 129.5) and (bmi > 20.482) and (age <= 64.351) -> 1,\n",
" if (ap_hi > 129.5) and (bmi > 20.482) and (age <= 64.351) and (age > 49.818) -> 1,\n",
" if (ap_hi > 129.5) and (bmi > 20.482) and (bmi <= 36.796) and (age > 64.351) -> 1,\n",
" if (ap_hi > 129.5) and (bmi > 20.482) and (age > 64.351) -> 0]"
]
},
"execution_count": 102,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rules = [\n",
" rule for rule in rules if rule.get_consequent() == 0 or rule.get_consequent() == 1\n",
"]\n",
"display(len(rules))\n",
"rules"
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['(age <= 39.558)', '(age <= 43.632)', '(age <= 47.569)', '(age <= 54.65)', '(age <= 59.39)', '(age <= 59.536)', '(age <= 60.707)', '(age <= 61.572)', '(age <= 62.463)', '(age <= 63.998)', '(age <= 64.269)', '(age <= 64.308)', '(age <= 64.351)', '(age > 39.538)', '(age > 39.558)', '(age > 39.751)', '(age > 39.989)', '(age > 43.632)', '(age > 43.792)', '(age > 49.818)', '(age > 54.008)', '(age > 54.65)', '(age > 55.817)', '(age > 59.536)', '(age > 64.269)', '(age > 64.351)', '(ap_hi <= 129.5)', '(ap_hi <= 138.5)', '(ap_hi <= 149.5)', '(ap_hi > 114.5)', '(ap_hi > 115.0)', '(ap_hi > 115.5)', '(ap_hi > 118.5)', '(ap_hi > 119.5)', '(ap_hi > 129.5)', '(bmi <= 19.231)', '(bmi <= 20.482)', '(bmi <= 20.512)', '(bmi <= 20.614)', '(bmi <= 21.637)', '(bmi <= 22.045)', '(bmi <= 23.329)', '(bmi <= 26.032)', '(bmi <= 27.71)', '(bmi <= 28.874)', '(bmi <= 29.043)', '(bmi <= 30.744)', '(bmi <= 32.049)', '(bmi <= 32.337)', '(bmi <= 32.886)', '(bmi <= 35.021)', '(bmi <= 35.121)', '(bmi <= 35.932)', '(bmi <= 36.796)', '(bmi <= 38.186)', '(bmi <= 50.547)', '(bmi > 17.3)', '(bmi > 19.231)', '(bmi > 20.482)', '(bmi > 20.512)', '(bmi > 20.614)', '(bmi > 21.637)', '(bmi > 22.045)', '(bmi > 23.329)', '(bmi > 23.927)', '(bmi > 25.912)', '(bmi > 26.032)', '(bmi > 27.71)', '(bmi > 28.874)', '(bmi > 29.043)', '(bmi > 30.744)', '(bmi > 32.886)', '(bmi > 38.186)', '(cholesterol <= 2.5)', '(cholesterol > 1.5)', '(cholesterol > 2.5)']\n"
]
},
{
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" .dataframe thead th {\n",
" text-align: right;\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>(age &lt;= 39.558)</th>\n",
" <th>(age &lt;= 43.632)</th>\n",
" <th>(age &lt;= 47.569)</th>\n",
" <th>(age &lt;= 54.65)</th>\n",
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" <th>(bmi &gt; 32.886)</th>\n",
" <th>(bmi &gt; 38.186)</th>\n",
" <th>(cholesterol &lt;= 2.5)</th>\n",
" <th>(cholesterol &gt; 1.5)</th>\n",
" <th>(cholesterol &gt; 2.5)</th>\n",
" <th>consequent</th>\n",
" </tr>\n",
" <tr>\n",
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],
"text/plain": [
" (age <= 39.558) \\\n",
"rule \n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (cho... 0 \n",
"if (ap_hi <= 129.5) and (ap_hi > 114.5) and (ag... 0 \n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (cho... 0 \n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (cho... 0 \n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (age... 0 \n",
"\n",
" (age <= 43.632) \\\n",
"rule \n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (cho... 0 \n",
"if (ap_hi <= 129.5) and (ap_hi > 114.5) and (ag... 0 \n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (cho... 0 \n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (cho... 0 \n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (age... 0 \n",
"\n",
" (age <= 47.569) \\\n",
"rule \n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (cho... 0 \n",
"if (ap_hi <= 129.5) and (ap_hi > 114.5) and (ag... 0 \n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (cho... 0 \n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (cho... 0 \n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (age... 0 \n",
"\n",
" (age <= 54.65) \\\n",
"rule \n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (cho... 1 \n",
"if (ap_hi <= 129.5) and (ap_hi > 114.5) and (ag... 1 \n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (cho... 1 \n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (cho... 1 \n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (age... 1 \n",
"\n",
" (age <= 59.39) \\\n",
"rule \n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (cho... 0 \n",
"if (ap_hi <= 129.5) and (ap_hi > 114.5) and (ag... 0 \n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (cho... 0 \n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (cho... 0 \n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (age... 0 \n",
"\n",
" (age <= 59.536) \\\n",
"rule \n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (cho... 0 \n",
"if (ap_hi <= 129.5) and (ap_hi > 114.5) and (ag... 0 \n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (cho... 0 \n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (cho... 0 \n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (age... 0 \n",
"\n",
" (age <= 60.707) \\\n",
"rule \n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (cho... 0 \n",
"if (ap_hi <= 129.5) and (ap_hi > 114.5) and (ag... 0 \n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (cho... 0 \n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (cho... 0 \n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (age... 0 \n",
"\n",
" (age <= 61.572) \\\n",
"rule \n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (cho... 0 \n",
"if (ap_hi <= 129.5) and (ap_hi > 114.5) and (ag... 0 \n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (cho... 0 \n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (cho... 0 \n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (age... 0 \n",
"\n",
" (age <= 62.463) \\\n",
"rule \n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (cho... 0 \n",
"if (ap_hi <= 129.5) and (ap_hi > 114.5) and (ag... 0 \n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (cho... 0 \n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (cho... 0 \n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (age... 0 \n",
"\n",
" (age <= 63.998) ... \\\n",
"rule ... \n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (cho... 0 ... \n",
"if (ap_hi <= 129.5) and (ap_hi > 114.5) and (ag... 0 ... \n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (cho... 0 ... \n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (cho... 0 ... \n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (age... 0 ... \n",
"\n",
" (bmi > 27.71) \\\n",
"rule \n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (cho... 0 \n",
"if (ap_hi <= 129.5) and (ap_hi > 114.5) and (ag... 0 \n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (cho... 0 \n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (cho... 0 \n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (age... 0 \n",
"\n",
" (bmi > 28.874) \\\n",
"rule \n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (cho... 0 \n",
"if (ap_hi <= 129.5) and (ap_hi > 114.5) and (ag... 0 \n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (cho... 0 \n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (cho... 1 \n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (age... 0 \n",
"\n",
" (bmi > 29.043) \\\n",
"rule \n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (cho... 0 \n",
"if (ap_hi <= 129.5) and (ap_hi > 114.5) and (ag... 0 \n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (cho... 0 \n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (cho... 0 \n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (age... 0 \n",
"\n",
" (bmi > 30.744) \\\n",
"rule \n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (cho... 0 \n",
"if (ap_hi <= 129.5) and (ap_hi > 114.5) and (ag... 0 \n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (cho... 0 \n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (cho... 0 \n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (age... 0 \n",
"\n",
" (bmi > 32.886) \\\n",
"rule \n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (cho... 0 \n",
"if (ap_hi <= 129.5) and (ap_hi > 114.5) and (ag... 0 \n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (cho... 0 \n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (cho... 0 \n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (age... 0 \n",
"\n",
" (bmi > 38.186) \\\n",
"rule \n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (cho... 0 \n",
"if (ap_hi <= 129.5) and (ap_hi > 114.5) and (ag... 0 \n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (cho... 0 \n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (cho... 0 \n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (age... 0 \n",
"\n",
" (cholesterol <= 2.5) \\\n",
"rule \n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (cho... 1 \n",
"if (ap_hi <= 129.5) and (ap_hi > 114.5) and (ag... 1 \n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (cho... 1 \n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (cho... 1 \n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (age... 1 \n",
"\n",
" (cholesterol > 1.5) \\\n",
"rule \n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (cho... 0 \n",
"if (ap_hi <= 129.5) and (ap_hi > 114.5) and (ag... 0 \n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (cho... 1 \n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (cho... 1 \n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (age... 0 \n",
"\n",
" (cholesterol > 2.5) \\\n",
"rule \n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (cho... 0 \n",
"if (ap_hi <= 129.5) and (ap_hi > 114.5) and (ag... 0 \n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (cho... 0 \n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (cho... 0 \n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (age... 0 \n",
"\n",
" consequent \n",
"rule \n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (cho... 0 \n",
"if (ap_hi <= 129.5) and (ap_hi > 114.5) and (ag... 0 \n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (cho... 0 \n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (cho... 0 \n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (age... 0 \n",
"\n",
"[5 rows x 77 columns]"
]
},
"execution_count": 103,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from src.rules import get_features, vectorize_rules\n",
"\n",
"features = get_features(rules, [])\n",
"print(features)\n",
"\n",
"df_rules = vectorize_rules(rules, features)\n",
"df_rules.head(5)"
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{2: 0.2028684211063448,\n",
" 3: 0.16350739364416753,\n",
" 4: 0.17115418740422497,\n",
" 5: 0.18051062435509244,\n",
" 6: 0.17312188913678084,\n",
" 7: 0.20265014439953413,\n",
" 8: 0.2470239144182239,\n",
" 9: 0.26319032892830624}"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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cYvvNmzejX79+2LRpE7Zt24aQkBB06tQJFy5c0Lc5deoUHn74YYSFhWHz5s04ePAg3n33XXh6elrrbRERlduSJcDbb8vtzz4D+vSR22o10KED0K+f/GuJ8hIAoBKipGkLlhMdHY3WrVtjxowZAACtVouQkBC8/vrrGDdu3H2fr9Fo4O/vjxkzZmDgwIEAgL59+8LNzQ3ff/99uWLKzc2Fr68vcnJyuFQlEVnVxo3AE08AhYVyotjnn5vnuKZ8r1n1jKCgoAB79uxBXFzc3QBcXBAXF4dt27YZdYy8vDwUFhaiatWqAGQi+eWXX9CoUSPEx8cjICAA0dHR+Omnn0o9Rn5+PnJzcw1uRETWdvAg0KOHTALPPAN8+qkycVg1EVy9ehUajQaBgYEG+wMDA5GZmWnUMcaOHYvg4GB9Mrl8+TJu3bqFxMREPPHEE/jtt9/Qo0cP9OzZE7///nuJx0hISICvr6/+FhISUrE3RkRkoowM4Mkngdxc4JFHgO++A1wUunzHrqqPJiYmYunSpdi8ebO+/1+r1QIAunXrhjfeeAMAEBkZia1bt2L27NmIjY0tdpzx48dj9OjR+vu5ublMBkRkNdnZQOfOsohc06bATz8BSg5pWjURVK9eHWq1GllZWQb7s7KyULNmzTKfO3XqVCQmJmLDhg0IDw83OKarqyuaNm1q0L5JkybYsmVLicfy8PCAh4dHOd8FEVH55ecD3bsDR44AwcHA2rWAv7+yMVn1RMTd3R1RUVFITU3V79NqtUhNTUXbtm1Lfd7HH3+MKVOmICUlBa1atSp2zNatW+P48eMG+0+cOIG6deua9w0QEVWAVgsMHAj8/jvg7Q38+itQp47SUQEQVrZ06VLh4eEhFixYII4ePSqGDh0q/Pz8RGZmphBCiAEDBohx48bp2ycmJgp3d3exfPlycenSJf3t5s2b+jYrVqwQbm5uYu7cuSI9PV1Mnz5dqNVqkZaWZlRMOTk5AoDIyckx75slIrrH6NFCAEK4uQmxYYNlX8uU7zWrJwIhhJg+fbqoU6eOcHd3F23atBHbt2/XPxYbGysGDRqkv1+3bl0BoNht4sSJBsf85ptvRMOGDYWnp6eIiIgQP/30k9HxMBEQkaV9/rlMAoAQixZZ/vVM+V6z+jwCW8R5BERkScuWAc8+K9NAYiIwdqzlX9Nm5xEQETmbP/4Ann9eJoHhw+/OILYlTARERBZy5AjQrZusJtqjh1x4/t8VRW0BEwERkQVcuCDnCmRnA+3aAYsXW65WUEUxERARmVlOjpw1nJEBNG4MrFoFeHkpHVXpmAiIiMyooADo1UvWEQoMlBPGqlVTOqqyMREQEZmJEMALLwCpqUCVKnLCWL16Skd1f0wERERmMmGCHAtwdZUriT34oNIRGYeJgIjIDL76Ss4RAIB584D4eGXjMQUTARFRBf30E/Cf/8jtKVOAwYOVjMZ0TARERBWwdatcSlIIYOhQ4J13lI7IdEwERETldPw48NRTwJ078u/MmbY5Yex+mAiIiMohM1OuNXz9OtCmDfDDD3KQ2B4xERARmejmTaBLF+DsWaBBA2D1aqByZaWjKj8mAiIiE+gWmt+7F6hRA0hJAQIClI6qYpgIiIiMpBsQXrcOqFQJWLMGaNhQ6agqjomAiMhIEycCCxYALi5AcrIcG3AETAREREaYO1fOEQCA2bOBrl2VjcecmAiIiO5jzRrg1Vfl9rvvAi+/rGw85sZEQERUhp075TKTWi0wZAgwebLSEZkfEwERUSlOnpSXieblyTkDc+bY54Sx+2EiICIqweXL8sv/6lVZRXTZMsDNTemoLIOJgIjoX27floPBp04BoaHAL7/I9QUcFRMBEdE9iorkmMCuXXJlsZQUoGZNpaOyLCYCIqL/JwTw2mvyDMDTU5aOaNxY6agsz05LJBERVZxGA6SlAZcuAUFBwB9/yEVlXFxkEbm2bZWO0DqYCIjIKa1YAYwcCfz9d/HHpk8Hune3ekiKYSIgIqezYgXQu7fsCiqJo48J/BvHCIjIqWg08kygtCSgUgGjRsl2zoKJgIicSlpayd1BOkIAGRmynbNgIiAip3LpknnbOQImAiJyKkFB5m3nCJgIiMipxMQAtWuXXjNIpQJCQmQ7Z8FEQERORa0Gvvii5MFiXXKYNk22cxZMBETkdHr2lIXk/q12bWD5cvm4M+E8AiJyOrm5wOHDcnv+fMDDQ44JxMQ415mADhMBETmdNWuAggIgLAwYPFjpaJTHriEicjr/+5/826uXsnHYCiYCInIqeXnA2rVy29nGAkrDREBETiUlBfjnH7ngTMuWSkdjG5gIiMip3Nst5IjrD5cHEwEROY38fDlQDLBb6F5MBETkNFJT5aWjwcHAQw8pHY3tYCIgIqeh6xbq0UOuQkYSPwoicgpFRcDPP8ttXjZqiImAiJzCH38A164B1ao5V0E5YyiSCGbOnInQ0FB4enoiOjoaO3fuLLXtvHnzEBMTA39/f/j7+yMuLq7M9sOGDYNKpcK0adMsEDkR2Stdt1D37oArayoYsHoiSE5OxujRozFx4kTs3bsXERERiI+Px+XLl0tsv3nzZvTr1w+bNm3Ctm3bEBISgk6dOuHChQvF2q5cuRLbt29HcHCwpd8GEdkRrRZYuVJus1uoBMLK2rRpI4YPH66/r9FoRHBwsEhISDDq+UVFRcLb21ssXLjQYP/ff/8tatWqJQ4fPizq1q0rPv/8c6NjysnJEQBETk6O0c8hIvvx559CAEL4+Ahx547S0ViHKd9rVj0jKCgowJ49exAXF6ff5+Ligri4OGzbts2oY+Tl5aGwsBBVq1bV79NqtRgwYADeeustNGvW7L7HyM/PR25ursGNiByXrlvoqadkpVEyZNVEcPXqVWg0GgQGBhrsDwwMRGZmplHHGDt2LIKDgw2SSVJSElxdXTFixAijjpGQkABfX1/9LSQkxPg3QUR2RQgWmbsfu7pqKDExEUuXLsXKlSvh6ekJANizZw+++OILLFiwACoj54uPHz8eOTk5+ltGRoYlwyYiBe3bB5w7B1SqBMTHKx2NbbJqIqhevTrUajWysrIM9mdlZaFmzZplPnfq1KlITEzEb7/9hvDwcP3+tLQ0XL58GXXq1IGrqytcXV1x7tw5jBkzBqGhoSUey8PDAz4+PgY3InJMurOBzp1lMqDirJoI3N3dERUVhdTUVP0+rVaL1NRUtG3bttTnffzxx5gyZQpSUlLQqlUrg8cGDBiAgwcPYv/+/fpbcHAw3nrrLaxbt85i74WIbB+7hYxj9atpR48ejUGDBqFVq1Zo06YNpk2bhtu3b2PIkCEAgIEDB6JWrVpISEgAIPv/33vvPSxZsgShoaH6sYQqVaqgSpUqqFatGqpVq2bwGm5ubqhZsyYaN25s3TdHRDbl2DHg+HHA3R3o0kXpaGyX1RPBs88+iytXruC9995DZmYmIiMjkZKSoh9APn/+PFzuKQIya9YsFBQUoHfv3gbHmThxIiZNmmTN0InIzujOBjp2BNgDXDqVEEIoHYTScnNz4evri5ycHI4XEDmQyEjgwAHg22+B/+90cBqmfK/Z1VVDRETGOnVKJgG1Gnj6aaWjsW1MBETkkFaskH87dJCF5qh0TARE5JB4tZDxmAiIyOH8/TewY4dck7h7d6WjsX1MBETkcHSVRtu1A4KClI3FHjAREJHDYbeQaZgIiMihXL4MpKXJ7Z49lY3FXjAREJFD+flnuRBNVBRQt67S0dgHJgIicijsFjIdEwEROYwbNwBdTUsmAuMxERCRw1izBigqApo1Axo1Ujoa+8FEQEQOg91C5cNEQEQO4dYtQLcECROBaZgIiMghrF0L3LkDNGgAtGihdDT2hYmAiBzCvd1CRi5fTv+PiYCI7N6dO8Avv8htdguZjomAiOze+vVyjKB2beBfy5qTEZgIiMju6bqFevYEXPitZjJ+ZERk1woLgVWr5Da7hcqHiYCI7NqmTXJGcUAA0L690tHYJyYCIrJruiUpu3eX6xOT6ZgIiMhuaTR3F6Fht1D5MREQkd3680+5/oCfH/Doo0pHY7+YCIjIbum6hZ5+GnBzUzYWe2ZSIigqKsKnn36KV199FQBw6tQpbNy40SKBERGVRYi7iYDdQhXjakrj//znP9BoNNiyZQsAoFq1anj22Wexe/duiwRHRFSaXbuAjAygcmWgUyelo7FvJiWC7du3Y//+/WjZsiUAwM/PD4WFhRYJjIioLLqzgS5dAE9PZWOxdyZ1DXn+69PWaDTQarVmDYiI6H6E4NoD5mRSIggPD8eiRYug1Wpx8uRJDBs2DB06dLBQaEREJTt0CDh5EvDwAJ58Uulo7J9JieCzzz5DWloaMjMz0b59e7i4uCApKclSsRERlUjXLRQfD1SpomwsjsDoMQKNRoMpU6Zgzpw5mDNnjiVjIiIqE7uFzMvoMwK1Wo1NmzZZMhYiovs6cQI4fBhwdQWeekrpaByDSV1DTz75JD788ENcvHgRubm5+hsRkbXozgYeewzw91c2FkehEkIIYxu7lFDoW6VSQaPRmDUoa8vNzYWvry9ycnLg4+OjdDhEVIbWrYHdu4E5c4ChQ5WOxnaZ8r1m0jwCXipKREo6d04mAZVKVhsl8zApEQBARkYG0tLSAACxsbGoVauW2YMiIiqJ7mqhmBi5/gCZh0ljBD///DNatmyJH3/8EcuWLUPLli2xevVqS8VGRGSAtYUsw6QzgsmTJ2P79u1o2LAhAODkyZPo06cPnuLQPRFZWGamLDsNyLWJyXxMOiPQaDT6JAAADRs25LgBEVnFypWytER0NFC7ttLROBaTEkFAQAC+/vpraLVaaLVafPPNN6hRo4alYiMi0tN1C/FswPxMSgSzZ8/G119/DS8vL3h5eeHrr7/G7NmzLRUbEREA4No1uUg9wPEBSzBpjKBBgwbYvn07bt26BQCowiIfRGQFq1bJ9YkjIoAGDZSOxvGYdEYwd+5cXL9+HVWqVEGVKlVw7do1zJs3z1KxEREBYLeQpZmUCL766itUrVpVf79atWr46quvzB4UEZFObi7w229ym91ClmFSIiipGoW9l5cgItv2yy9AQQHQuDHQtKnS0TgmkxJBUFAQfvzxR/395ORkBAUFmfyiM2fORGhoKDw9PREdHY2dO3eW2nbevHmIiYmBv78//P39ERcXZ9C+sLAQY8eORYsWLVC5cmUEBwdj4MCBuHjxoslxEZHt0RWZ69lTlpYg8zOp6NyxY8fQvXt35OfnAwAqVaqEn3/+GQ888IDRL5icnIyBAwdi9uzZiI6OxrRp07Bs2TIcP34cASXMGe/fvz/at2+Pdu3awdPTE0lJSVi5ciWOHDmCWrVqIScnB71798bLL7+MiIgI3LhxAyNHjoRGo8Hu3buNiolF54hsU14eUKOG/Lt7NxAVpXRE9sOU7zWTEgEgu4KOHz8OAGjcuDHUarVJwUVHR6N169aYMWMGAFnILiQkBK+//jrGjRtn1Ov7+/tjxowZGDhwYIltdu3ahTZt2uDcuXOoU6fOfY/JREBkm1aulGcCdesCZ87wjMAUpnyvmdQ1tGvXLuTn56Np06Y4fPgwxo4da1IXTEFBAfbs2YO4uLi7Abi4IC4uDtu2bTPqGHl5eSgsLDQYtP63nJwcqFQq+Pn5lfh4fn6+wXoKXFOByDaxW8g6TEoEL730Ejw8PJCeno533nkHbm5uGDJkiNHPv3r1KjQaDQIDAw32BwYGIjMz06hjjB07FsHBwQbJ5F537tzB2LFj0a9fv1KzYEJCAnx9ffW3kJAQo98DEVlHQQGgq2nJq4Usy6REoFaroVarsXbtWrz66qtISEjA5cuXLRVbMYmJiVi6dClWrlwJT0/PYo8XFhaiT58+EEJg1qxZpR5n/PjxyMnJ0d8yMjIsGTYRlUNqqrx0tGZNoG1bpaNxbCYlgvz8fGRlZWH16tXo0KEDANMuH61evTrUajWysrIM9mdlZaFmzZplPnfq1KlITEzEb7/9hvDw8GKP65LAuXPnsH79+jL7xDw8PODj42NwIyLbousW6tEDKGFxRDIjkz7eN954A40bN4avry8efPBBnDp1Cv4mLBrq7u6OqKgopKam6vdptVqkpqaibRkp/+OPP8aUKVOQkpKCVq1aFXtclwTS09OxYcMGVKtWzZS3RUQ2pqgI+Plnuc1uISsQFVBUVCTy8/NNes7SpUuFh4eHWLBggTh69KgYOnSo8PPzE5mZmUIIIQYMGCDGjRunb5+YmCjc3d3F8uXLxaVLl/S3mzdvCiGEKCgoEE8//bSoXbu22L9/v0EbY2PLyckRAEROTo5J74WILGPjRiEAIapVE6KwUOlo7JMp32smL1V5L92YgSmeffZZXLlyBe+99x4yMzMRGRmJlJQU/QDy+fPn4XLPeeCsWbNQUFCA3r17Gxxn4sSJmDRpEi5cuIBVq1YBACIjIw3abNq0Sd+FRUT2Q9ct1K0b4FqhbykyhsnzCBwR5xEQ2Q6tFggJAS5eBNasAbp0UToi+2SxeQRERJa2Y4dMAj4+QClXiZOZmZwILl26hM2bNwMAioqKUFBQYO6YiMiJ6bqFunYFPDyUjcVZmJQIli9fjoceegiDBw8GABw5cgTdu3e3QFhE5IyEMJxNTNZhUiJISEjA3r179ZeMRkRE4Ny5cxYJjIicz/79wNmzgJcX8MQTSkfjPEyeWfzva/Td3d3NGhAROS/d2UDnzkDlysrG4kxMSgTe3t7IysqC6v+rP6WmppZZ/I2IyBTsFlKGSVfoJiYmonPnzjh9+jQefvhhnDlzBr/88oulYiMiJ3LsGPDXX4CbmxwoJusxKRG0bt0amzZtwtatWyGEQLt27Uot9UxEZArd2UDHjoCvr7KxOBuTuoZee+01+Pr6onPnznjyySfh5+eH1157zVKxEZETYbeQckxKBNu3by+2b+vWrWYLhoic0+nT8oohtVqWlSDrMqprKDk5GUuXLsWZM2fQ8550nZOTg8oc2ieiClqxQv6NjQWqV1c2FmdkVCIICwtDt27dsHfvXnS7J137+Pjg8ccft1hwROQc2C2kLKMSQUREBCIiIqBWqzFgwACDx1JSUvAEZ34QUTlduADoep179FA2Fmdl0hjB559/XmzfhAkTzBYMETmflSvl33btgOBgZWNxVkadEZw4cQJ//fUXcnJy9LX/ATlGkJeXZ7HgiMjx6bqFuBKZcoxKBNu2bcOCBQtw+fJlg7MCHx8ffPrppxYLjogc25UrwB9/yG12CynHqEQwaNAgDBo0CN988w1efPFFS8dERE7i55/lQjQPPgjUq6d0NM7LpDGCgQMH4tNPP9VPIjt16hQ2btxokcCIyPGxW8g2mFRi4vXXX4dGo8GWLVsAANWqVcOzzz6L3bt3WyQ4InJc2dlAaqrc5mWjyjIpEWzfvh379+9Hy5YtAQB+fn4oLCy0SGBE5NjWrAEKC4GmTYGwMKWjcW4mdQ15enoa3NdoNNBqtWYNiIicA7uFbIdJiSA8PByLFi2CVqvFyZMnMWzYMHTo0MFCoRGRo7p1C0hJkdvsFlKeSYngs88+Q1paGjIzM9G+fXu4uLggMTHRUrERkYNKSQHu3AHq1wciIpSOhkwaI6hSpQrmzJmDOXPmWCoeInIC93YL/f+Ch6QgkxLBd999V+L+gQMHmiUYInJ8d+7IgWKA3UK2wqREsHr1av32nTt3sGXLFjz00ENMBERktA0b5BhBrVpAmzZKR0OAiYlg2bJlBvfPnDmDd955x6wBEZFju7fktItJo5RkKRX6Z6hXrx6OHDlirliIyMEVFsqyEgC7hWyJSWcE91Ye1Wg02LFjBzw8PMweFBE5ps2bgRs3gBo1gJgYpaMhHZMSwb2VR11dXdGwYUMkJyebPSgicky6JSm7d5frE5NtMCkRbNq0yVJxEJGD02juLkLD2cS2xaREAMgB4/Xr1wMA4uPj0Yv/okRkhK1bgawswNcXePRRpaOhe5k0WPz+++8jISEBTZs2RbNmzZCQkIAPPvjAUrERkQPRdQs9/TTg7q5sLGRIJYQQxjYODw/H9u3bUalSJQDA7du30bZtWxw8eNBiAVpDbm4ufH19kZOTAx8fH6XDIXI4QgChocD588BPPwHduikdkeMz5XvNpDMCIYQ+CQBA5cqVYUIeISIntXu3TAKVKwOdOikdDf2bSWMEbdq0wYABA/Dyyy8DAL755hu04dRAIroPXbfQk08CXl7KxkLFmXRG8OWXXyI4OBijR4/G6NGjERQUhC+//NJSsdk0jUZeE/3DD/KvRqN0RES2SQiuPWDrTDojqFy5MpKSkiwVi91YsQIYORL4+++7+2rXBr74grMlie6l0QALFwLp6YCbGxAfr3REVBKTEkFRURH+97//4dSpUygqKtLvf++998wemK1asQLo3Vv+yrnXhQty//LlTAZEQPEfTIWFQIsW/MFki0zqGurbty+mT5+Oq1ev4ubNm/qbs9Bo5H/YJY2P6/aNGsVuIiLdD6Z7z5qBuz+YdGMGZBtMuny0cePG+Ouvv6BysJUkjL3MavNm4ybCbNoEcAVPclYajbxU9N9JQEelkl2pZ86wzIQlWezy0ZCQEBQUFFQoOHt26ZJ52xE5orS00pMAIM+eMzJkO7INRo0R6K4MatiwITp06IAePXrA09NT//iIESMsE52NCQoybzsiR8QfTPbHqESwb98+/XZYWBiOHTumv+9o3URliYmRp7QXLpQ8TqA75WV5XXJm/MFkf0waI3BUpvSl6QbBgOLJQKXiVUNEGg1Qt678wVQSjhFYh9nHCFatWlXmzVQzZ85EaGgoPD09ER0djZ07d5badt68eYiJiYG/vz/8/f0RFxdXrL0QAu+99x6CgoLg5eWFuLg4pKenmxyXMXr2lF/2tWoZ7ndxAZYuZRIgUquBJ54o+TFdB8K0aUwCtsSorqF7F6T5N5VKhaefftroF0xOTsbo0aMxe/ZsREdHY9q0aYiPj8fx48cREBBQrP3mzZvRr18/tGvXDp6enkhKSkKnTp1w5MgR1Pr/b+OPP/4YX375JRYuXIh69erh3XffRXx8PI4ePWowlmEuPXvKolm6QbERI+SqS/eUYSJyWvn5wLp1ctvPD8jOvvtY7doyCfAHk40RVtamTRsxfPhw/X2NRiOCg4NFQkKCUc8vKioS3t7eYuHChUIIIbRarahZs6b45JNP9G2ys7OFh4eH+OGHH4w6Zk5OjgAgcnJyTHgnd40aJQQgRJ8+5Xo6kUOZM0f+/xAcLMStW0Js2iTEkiXyb1GR0tE5D1O+14w6I0hPT8cDDzxQarnp8PBwo5JOQUEB9uzZg/Hjx+v3ubi4IC4uDtu2bTPqGHl5eSgsLETVqlUBAGfOnEFmZibi4uL0bXx9fREdHY1t27ahb9++xY6Rn5+P/Px8/f3c3FyjXrs0AwbIXzk//yx//fj5VehwRHarsBBISJDbb70lq41yTo3tMyoRvPHGG1izZg26lVBEXKVS4fTp00a92NWrV6HRaBAYGGiwPzAwEH/99ZdRxxg7diyCg4P1X/yZmZn6Y/z7mLrH/i0hIQGTJ0826vWM0bIl0LQpcPSoHD946SWzHZrIrvzwA3D2rFycfuhQpaMhYxk1WLxmzRoA8tf3v2/GJgFzSExMxNKlS7Fy5coK9f2PHz8eOTk5+ltGRkaF4lKpgIED5fb331foUER2S6MBPvxQbo8ZwzEze2LSzGKd06dPY9q0afoEYazq1atDrVYjKyvLYH9WVhZq1qxZ5nOnTp2KxMRE/PbbbwZdUbrnmXJMDw8P+Pj4GNwqqn9/mRD++EP+IiJyNsuXAydOAP7+wGuvKR0NmcKoRBAXF4f9+/cDAC5evIhWrVph3bp1ePPNN00qS+3u7o6oqCikpqbq92m1WqSmpqJt27alPu/jjz/GlClTkJKSglatWhk8Vq9ePdSsWdPgmLm5udixY0eZxzS32rXv1iFavNhqL0tkE7RaQLd8+ahRgLe3ouGQqYwZfQ4LC9Nvf/LJJ6J79+5CCCGuX78uWrRoYdJI9tKlS4WHh4dYsGCBOHr0qBg6dKjw8/MTmZmZQgghBgwYIMaNG6dvn5iYKNzd3cXy5cvFpUuX9LebN28atPHz8xM///yzOHjwoOjWrZuoV6+e+Oeff4yKqaJXDenMny+vlmjUSAittkKHIrIrP/0k/9v39hbi+nWloyEhLHDVkNc9a8tt3boVTz75JADA398frq4mLWmAZ599FleuXMF7772HzMxMREZGIiUlRT/Ye/78ebi43D1RmTVrFgoKCtBbN533/02cOBGTJk0CALz99tu4ffs2hg4diuzsbDz88MNISUmxyByCsvTqJU+JT5wAdu0CuIonOQMhgClT5PZ//iO7hsi+GFViolWrVvjpp5/g5+eHunXrYtu2bWjUqBEAWXvI2Ct+bJUpU7Hvp39/YMkS+T/E9OlmCpDIhqWkAJ07y8Fh3RVDpDyzl5iYMGECWrZsiQceeACPPvqoPgls3boVoaGhFQ7YkQwYIP8uXQo4ccVuchL3ng0MG8YkYK+MLjqXmZmJrKwshIeH6yuOXrx4EUVFRahTp45Fg7Q0c54RFBXJgeOsLDnBzITqG0R2R7dYk4cHcPo0EBysdESkY5GFaWrWrImIiAiDstPBwcF2nwTMzdUVeO45uc05BeTodGcDL77IJGDPyjWPgMqmm1y2erVhwS0iR7J1K7Bxo/zx8/bbSkdDFcFEYAEREUDz5rIK47JlSkdDZBm6WcSDBsn1B8h+MRFYgEp1d9CY3UPkiPbuBX79Va7DMW6c0tFQRTERWMhzz8mEkJYmV2IiciS6WcT9+gENGyobC1UcE4GF1K4NPPaY3F60SNlYiMzp8GFg5Ur5Q2fCBKWjIXNgIrCgeyuScmVochQffST/9uoly6+T/WMisKCePeVsy/R0oIxlmYnsxokTQHKy3H7nHWVjIfNhIrCgKlWAHj3kNgeNyREkJMhKo127ApGRSkdD5sJEYGG6q4d++IElJ8i+nT179wfNf/+raChkZkwEFvb440DNmsD168DatUpHQ1R+SUlyFbKOHYHoaKWjIXNiIrAwV1dZkRRg9xDZrwsXgG+/lds8G3A8TARWoOseWr0auHFD2ViIyuOTT2TXZkwM8MgjSkdD5sZEYAUREUCLFvJ/JJacIHuTlQXMnSu3331X2VjIMpgIrER3VvDdd8rGQWSqzz8H/vlHrrgXF6d0NGQJTARW0r+/rMvy55+ybjuRPbh+HZg5U27/979yNjE5HiYCKwkOllcQASw5Qfbjiy+AW7dk92bXrkpHQ5bCRGBF91YkZckJsnU5OcCXX8ptng04NiYCK+rRQ5acOHkS2L5d6WiIyvbVV3JhpSZNZLkUclxMBFZUpcrd/6E4p4Bs2e3bwGefye0JE+T4Fjku/vNama4iaXIyS06Q7ZozB7h6FWjQAOjbV+loyNKYCKzsscfkwPH163KFJyJbc+eOnEAGAOPHy9nx5NiYCKxMrZarlwHsHiLb9O23QGYmEBJy9wIHcmxMBAq4t+TE9evKxkJ0r4ICIDFRbo8dC7i7KxsPWQcTgQLCw+WtsBD48UeloyG66/vvgYwMWTH3xReVjoashYlAIfcuY0lkC4qK5MIzAPDWW4Cnp7LxkPUwESjkuefkJXlbtwKnTikdDZG8ku3UKaBaNeCVV5SOhqyJiUAhQUF3C3ix5AQpTasFPvxQbo8eDVSurGw8ZF1MBApiyQmyFStWAMeOAX5+wH/+o3Q0ZG1MBArq0UP+8jp1Cti2TeloyFkJAXzwgdweMQLw8VE2HrI+JgIFVa4M9OoltzloTEr55RfgwAFZAmXECKWjISUwEShM1z2UnAzk5ysbCzkfIYApU+T2a6/JgWJyPkwECnv0UVly4sYN+cuMyJo2bAB27gS8vIAxY5SOhpTCRKAwtVquXgawe4isTzc2MHQoEBCgbCykHCYCG6CbXPbLL8C1a8rGQs7jjz/kzd1dTiAj58VEYAOaNwciI1lygqxLdzbwwgtArVrKxkLKYiKwEffOKSCytB07gPXrZdfk2LFKR0NKYyKwEf36yZIT27YB6elKR0OOTjeLeMAAIDRU0VDIBjAR2IigIKBjR7nNkhNkSfv3yxLoLi5y4RkiJgIbohs0XrSIJSfIcnRnA88+CzRqpGwsZBuYCGxI9+5ydufp07IqKZG5HT0K/O9/cnvCBGVjIdvBRGBDKlViyQlbotEAmzcDP/wg/2o0SkdUcQkJ8myzRw95tRoRwERgc+4tOXHnjrKxOLMVK+Qg6qOPyrUjHn1U3l+xQunIyu/UKWDJErn9zjvKxkK2hYnAxnToANSuDWRns+SEUlasAHr3Bv7+23D/hQtyv70mg4QEue7Ak08CUVFKR0O2xOqJYObMmQgNDYWnpyeio6Oxc+fOUtseOXIEvXr1QmhoKFQqFaZNm1asjUajwbvvvot69erBy8sLDRo0wJQpUyDsdLSVJSeUpdEAI0eWPFiv2zdqlP11E50/DyxcKLf/+19lYyHbY9VEkJycjNGjR2PixInYu3cvIiIiEB8fj8uXL5fYPi8vD/Xr10diYiJq1qxZYpukpCTMmjULM2bMwLFjx5CUlISPP/4Y06dPt+RbsShd99CvvwJXryobi7NJSyt+JnAvIeTi7mlp1ovJHD7+WK5J/NhjQNu2SkdDtsaqieCzzz7Dyy+/jCFDhqBp06aYPXs2KlWqhG+//bbE9q1bt8Ynn3yCvn37wsPDo8Q2W7duRbdu3dClSxeEhoaid+/e6NSpU5lnGvn5+cjNzTW42ZJmzYCWLWXJieRkpaNxLpcumbedLbh0Cfj6a7n97rvKxkK2yWqJoKCgAHv27EGcbqFeAC4uLoiLi8O2CizP1a5dO6SmpuLEiRMAgAMHDmDLli3o3Llzqc9JSEiAr6+v/hYSElLu17cUlpywPiGAv/4yru3evTJR24OpU+VaF+3bA7GxSkdDtshqieDq1avQaDQIDAw02B8YGIjMzMxyH3fcuHHo27cvwsLC4ObmhpYtW2LUqFHor+toL8H48eORk5Ojv2VkZJT79S2lXz85XrBjB/D/OY4s6NIloGdP4P33jWs/dSrQtCmweLFtjxdcuQLMni23//tfQKVSNh6yTXZ/1dCPP/6IxYsXY8mSJdi7dy8WLlyIqVOnYqFuZKwEHh4e8PHxMbjZmpo1gU6d5DZLTliOEMCCBfJL/aefAFdX4Jln5Bfmv780dfuGDAFq1ABOngSefx4ID5eTtLRaJd5B2aZNA/LygFatgPh4paMhW2W1RFC9enWo1WpkZWUZ7M/Kyip1INgYb731lv6soEWLFhgwYADeeOMNJCQkVDRkxd3bPWSLXzL27tw5oHNn+cWenS0vqdyzR5YCX768eGnm2rXl/m+/lbO/ExIAf385W7d3b/n8NWtspzzIjRuA7poJng1QWayWCNzd3REVFYXU1FT9Pq1Wi9TUVLStwGUMeXl5cHExfBtqtRpaB/jm7NYN8PYGzp4F/vxT6Wgch1YLzJwpB+XXrQM8PICkJGD7dvnrHpDdRGfPAps2yUlYmzYBZ87I/YAsBTJunNw3caL8d9q/H3jqKXlVzoYNyieE6dOBmzeBFi1kXESlEla0dOlS4eHhIRYsWCCOHj0qhg4dKvz8/ERmZqYQQogBAwaIcePG6dvn5+eLffv2iX379omgoCDx5ptvin379on09HR9m0GDBolatWqJNWvWiDNnzogVK1aI6tWri7ffftvouHJycgQAkZOTY743ayaDBwsBCPHyy0pH4hiOHxciJkZ+poAQDz8s91XU1atCjB0rhJfX3WPHxgqRllbxY5dHbq4Q/v4yjqVLlYmBlGXK95pVE4EQQkyfPl3UqVNHuLu7izZt2ojt27frH4uNjRWDBg3S3z9z5owAUOwWGxurb5ObmytGjhwp6tSpIzw9PUX9+vXFO++8I/Lz842OyZYTwcaN8n9mX18h/vlH6WjsV2GhEElJQnh6ys+zcmUhpk8XQqMx7+tcuiTEyJFCuLvfTQjx8ULs3Gne17mfpCT52o0bC1FUZN3XJttg04nAFtlyItBohAgJkf9TL1umdDT26eBBIVq1uvvF3LGjEGfOWPY1z58X4pVXhHB1vfu63boJceCAZV9XCCFu3xYiIEC+5sKFln89sk2mfK/Z/VVDjs7FhSUnyqugQPbfP/ggsHs34OcnB3rXrbP8qlwhIfKyzePHgUGD5L/jzz8DERFA377Gz1coj3nzgMuXgXr15GXIRPfDRGAH7i05ceWKsrHYi507ZQJ4/31ZWqF7d3l1z5Ah1r16pn59eXnqkSNyIRhAzhZv1gwYPFhefWRO+fmynAQgB7Pd3Mx7fHJMTAR2oGlTeWliURFLTtxPXh7w1lvyyp0jR+T1/snJsmJoUJBycYWFAUuXAgcOyKvBtFpZBK5xY2DYsLLrG5liwQLg4kV5qeugQeY5Jjk+JgI7wZIT9/fHH7LrZepU+UXbv788C+jTx3auoQ8PlxPXduyQE7yKioA5c4CGDWVV0wpMskdhIZCYKLffflteFktkDCYCO6ErObFzp+x3prtu3gRee03W0Tl5Uk4EW71azsiuXl3p6ErWpg2QkiKT1yOPyC6dL74AGjSQXTrXrpl+zMWL5dyHgADgpZfMHjI5MCYCOxEQcLdEAEtO3JWSIvvbZ82S919+WXYJde2qbFzGiomRy2CuXw9ER8uuraQkOdA7aRKQk2PccTQa4KOP5PabbwJeXpaKmBwRE4EdYcmJu65fl33gnTvL9QHq1QNSU4G5cwFfX6WjM41KBcTFAdu2yTOZyEh5ljN5snxfiYnA7dtlH2PZMiA9HahaFXj1VauETQ6EicCOdOsG+PjIGjlbtigdjXL+9z85gP7dd/JLdNQo4NAhueiKPVOp5JnMnj3yi71JE1kvaPx4efXRtGmG61hrNPJsYvFi2QYA3nhDlr8gMgUTgR3x8pLFzQDnHDTOypKVQXv3lttNmsgaTJ9/DlSurHR05uPiIt/joUPy37lBAzkv4I035KDyrFmyMF5oKPDoo7IC6tmzMpHUrat09GSPVEIoXRpLebm5ufD19UVOTo5NlqS+1+bN8n9+X19ZQ98Z+oKFkOMio0bJLiG1Wg6o/ve/gKen0tFZXmGhvNT0/fdlN1hZVCpZIVVXHI+clynfazwjsDOPPALUqSMHEVevVjoay8vIALp0AQYOlEkgMhLYtQv44APnSAKAnBT20ktyDOCLL+QZQ1lGjbLtxXLI9jAR2BlnKTmh1coSDc2aAWvXAu7uwIcfystnW7ZUOjpleHjIeQhlXSgghEyeaWnWi4vsHxOBHdJdPZSSYt8lJ3SDnT/8IP/qfsWePCkHfl99VV4907atrPU/YQJLJly6ZN52RAATgV1q0kQuPVhUJMsW2KMVK+4Odj73nPwbGirr74SHA7//DlSqJK+USUuT75mML5OhZDkNsj9MBHbKnktOrFghr4r5d32dv/+Wg6L//CPPCA4dAkaOlIPDJMXEyDpCpZXMUKlk5dOYGOvGRfaNicBO9e0rvyB37bJsSWNz02jkl3tZ16r5+8tS0fXrWy8ue6FWywFjoHgy0N2fNo3Jk0zDRGCnAgLkrFrAvs4K0tLuX2nzxg3nnjB3Pz17yktEa9Uy3F+7Ni8dpfJhIrBjuu6hRYvsp+QEBzvNo2dPOYls0yZgyRL598wZJgEqH1elA6Dye+opWXLi/Hn5Szs2VumIynb8uLwk1Bgc7Lw/tRro0EHpKMgR8IzAjnl5yZILgG13D124AAwdKucE/PFH2W052ElkfUwEdk7XPbRsmbzaxpZcvw6MHSvr48ybJweKn3pK1gZSqTjYSWQrmAjsXEyMLDSWmwusWqV0NFJeniyd3KCBXD/3zh2gfXvZfbVqlSyBwMFOItvBRGDnXFxk9UlA+e6hwsK7yy6OHw9kZwPNm8uaSGlpwMMP323LwU4i28Hqo7Cv6qMlOX5cLo6uVsuFywMCrPv6Wq38Jf/f/8rCaIA8S5kyRc4aZjcPkfWx+qiTadwYaN1a9sH/8IN1X3vDBrn+7rPPyiRQo4ac8HT8uBy/YBIgsn1MBA7C2iUndu+Wyyt27ChX1KpSRa6xe+oUMGKErJRJRPaBicBB9O0LuLrKL+Vjxyz3OidOAH36yDOQ1FRZDXTkSJkAJk4EvL0t99pEZBlMBA6iRg3Llpy4cAF45RW5VvCyZfJSz4EDZWKYNs364xJEZD5MBA7EEiUnbtyQy0I2bAjMnSvHIbp2BQ4ckJVCQ0PN8zpEpBwmAgfy1FNyLeOMDFnPvyLy8oCkJFkBNCnJcC7A6tVAixbmiZmIlMdE4EA8PWX/PVD+7qGiIvnL/4EH5JmAbi7AqlXF5wIQkWNgInAwuu6h5cvlr3pjCSGf06yZHAu4eFHOBVi4UC4T+dRTpS+GQkT2jYnAwbRvL/vtb940vuREaqqcC/DMM3Lwt3p1OQB8/LgcEOZcACLHxkTgYEwpObFnj5wHEBcn5wVUqSIvAT19Wl4SyrkARM6BicAB6bqHUlLk+sA//ABs3iyv+AHuzgVo1UrODHZzk5PATp2Sk8I4F4DIuXBhGgfUqJEc7E1PB3r1urs/KEgO/G7cKJOCSiXPHt5/n5eBEjkzJgIHtGLF3eJv97p06e4SkF27Ah9+CISHWzc2IrI9TAQORqOR/ftlCQgAfvqJg8BEJHGMwMGkpQF//112m8uXZTsiIoCJwOHoun7M1Y6IHB8TgYMJCjJvOyJyfEwEDiYmRq79W9osYJUKCAmR7YiIACYCh6NWyxXCgOLJQHd/2jQOFBPRXUwEDqhnT1k3qFYtw/21a8v9XCCeiO7Fy0cdVM+eQLdu8uqgS5fkmEBMDM8EiKg4q58RzJw5E6GhofD09ER0dDR27txZatsjR46gV69eCA0NhUqlwrRp00psd+HCBTz//POoVq0avLy80KJFC+zevdtC78B+qNVAhw5Av37yL5MAEZXEqokgOTkZo0ePxsSJE7F3715EREQgPj4ely9fLrF9Xl4e6tevj8TERNSsWbPENjdu3ED79u3h5uaGtWvX4ujRo/j000/h7+9vybdCROQwVEIIYa0Xi46ORuvWrTFjxgwAgFarRUhICF5//XWMGzeuzOeGhoZi1KhRGDVqlMH+cePG4c8//0RaBWZI5ebmwtfXFzk5OfDx8Sn3cYiIbIUp32tWOyMoKCjAnj17EBcXd/fFXVwQFxeHbdu2lfu4q1atQqtWrfDMM88gICAALVu2xLx588p8Tn5+PnJzcw1uRETOymqJ4OrVq9BoNAgMDDTYHxgYiMzMzHIf9/Tp05g1axYeeOABrFu3Dq+++ipGjBiBhQsXlvqchIQE+Pr66m8hISHlfn0iIntn95eParVaPPjgg/joo4/QsmVLDB06FC+//DJmz55d6nPGjx+PnJwc/S0jI8OKERMR2RarJYLq1atDrVYjKyvLYH9WVlapA8HGCAoKQtOmTQ32NWnSBOfPny/1OR4eHvDx8TG4ERE5K6vNI3B3d0dUVBRSU1PRvXt3APLXfGpqKv7zn/+U+7jt27fH8ePHDfadOHECdevWNfoYuvFyjhUQkaPQfZ8ZdT2QsKKlS5cKDw8PsWDBAnH06FExdOhQ4efnJzIzM4UQQgwYMECMGzdO3z4/P1/s27dP7Nu3TwQFBYk333xT7Nu3T6Snp+vb7Ny5U7i6uooPP/xQpKeni8WLF4tKlSqJRYsWGR1XRkaGAMAbb7zx5nC3jIyM+34HWvXyUQCYMWMGPvnkE2RmZiIyMhJffvkloqOjAQAdOnRAaGgoFixYAAA4e/Ys6tWrV+wYsbGx2Lx5s/7+mjVrMH78eKSnp6NevXoYPXo0Xn75ZaNj0mq1uHjxIry9vaEqrVpbCXJzcxESEoKMjAx2L5UTP8OK4edXcY76GQohcPPmTQQHB8PFpexRAKsnAkfC+QcVx8+wYvj5VRw/Qwe4aoiIiCqGiYCIyMkxEVSAh4cHJk6cCA8PD6VDsVv8DCuGn1/F8TPkGAERkdPjGQERkZNjIiAicnJMBERETo6JgIjIyTERlENCQgJat24Nb29vBAQEoHv37sXqHZHxEhMToVKpii06RGXjEq3lp9Fo8O6776JevXrw8vJCgwYNMGXKFOPq8jggLl5fDr///juGDx+O1q1bo6ioCBMmTECnTp1w9OhRVK5cWenw7MquXbswZ84chIeHKx2KXdEt0froo49i7dq1qFGjBtLT07lEq5GSkpIwa9YsLFy4EM2aNcPu3bsxZMgQ+Pr6YsSIEUqHZ3W8fNQMrly5goCAAPz+++945JFHlA7Hbty6dQsPPvggvvrqK3zwwQeIjIzEtGnTlA7LLphjiVZn1rVrVwQGBuKbb77R7+vVqxe8vLywaNEiBSNTBruGzCAnJwcAULVqVYUjsS/Dhw9Hly5dDJYvJeOUZ4lWuqtdu3ZITU3FiRMnAAAHDhzAli1b0LlzZ4UjUwa7hipIq9Vi1KhRaN++PZo3b650OHZj6dKl2Lt3L3bt2qV0KHZJt0Tr6NGjMWHCBOzatQsjRoyAu7s7Bg0apHR4Nm/cuHHIzc1FWFgY1Go1NBoNPvzwQ/Tv31/p0BTBRFBBw4cPx+HDh7FlyxalQ7EbGRkZGDlyJNavXw9PT0+lw7FLWq0WrVq1wkcffQQAaNmyJQ4fPozZs2czERjhxx9/xOLFi7FkyRI0a9YM+/fvx6hRoxAcHOycn5/Rq7dQMcOHDxe1a9cWp0+fVjoUu7Jy5UoBQKjVav0NgFCpVEKtVouioiKlQ7R5derUES+++KLBvq+++koEBwcrFJF9qV27tpgxY4bBvilTpojGjRsrFJGyeEZQDkIIvP7661i5ciU2b95c4uI5VLrHH38chw4dMtg3ZMgQhIWFYezYsVCr1QpFZj/MsUSrM8vLyyu2WItarYZWq1UoImUxEZTD8OHDsWTJEvz888/w9vZGZmYmAMDX1xdeXl4KR2f7vL29i42nVK5cGdWqVeM4i5HeeOMNtGvXDh999BH69OmDnTt3Yu7cuZg7d67SodmFp556Ch9++CHq1KmDZs2aYd++ffjss8/wwgsvKB2aMpQ+JbFHKGVt0Pnz5ysdmt2KjY0VI0eOVDoMu7J69WrRvHlz4eHhIcLCwsTcuXOVDslu5ObmipEjR4o6deoIT09PUb9+ffHOO++I/Px8pUNTBOcREBE5Oc4jICJyckwEREROjomAiMjJMREQETk5JgIiIifHREBE5OSYCIiInBwTARGRk2MiIIdUVFSEyZMnIywsDM2bN0dkZCSGDh2K7OxsbN68GZGRkeU+9tmzZzF79mzzBWsDJk2ahDt37igdBimEiYAc0osvvojdu3dj27ZtOHz4MPbt24eOHTvi+vXrFT52RRJBUVFRhV/fEiZPnsxE4MSYCMjhnDx5EsuWLcP8+fP1a/iqVCo888wzqF+/vkHbs2fPws/PT3//1q1bUKlUAIB//vkHzz77LJo2bYqIiAh06tQJADBs2DAcP34ckZGRePrppwEA6enp6NKlC1q3bo3w8HDMmDFDf0yVSoWJEyeidevWGD9+PLZv346oqChERkaiefPmmDVrVonv45dffkHr1q0RERGByMhI7NixAwCwbt06PPjggwgPD0dsbCyOHj0KAMXOdA4fPozQ0FCD9zlx4kRERUWhYcOG+PXXX/XvBwBiYmIQGRmJy5cvm/yZk51TutgRkbklJyeL8PDwUh/ftGmTiIiIEEIIcebMGeHr66t/7ObNm0L3v8WKFStEp06d9I9du3at2POFEKKoqEhERUWJY8eOCSGEuH37tmjRooXYuXOnEEIWKZw8ebK+/dNPPy2WLFmiv3/9+vViMR4/flzUqFFDf8yCggKRnZ0tsrKyRNWqVcXBgweFEEIsWrRINGnSRGi12mJxHTp0SNStW1f/PgGI5cuXCyGEWLt2rWjUqJG+LQBx48aNUj8zcmw8IyAqRUREBI4dO4bXXnsNycnJcHNzK7Hd8ePHceTIEfTt2xeRkZFo164dbt68qf+lDsCgvPGjjz6KKVOm4P3338eWLVv0Zy33Wr9+PZ544gmEhYUBANzc3ODr64sdO3agRYsWaNGiBQCgf//+uHjxIi5cuHDf9+Pp6YmePXsCANq2bYtTp04Z/2GQQ+N6BORwHnzwQaSnp+PatWuoVq1amW1dXV2h0Wj09+/tJ69fvz6OHj2KjRs3YsOGDXj77bexf//+YscQQqBq1aolPqZTpUoV/faoUaPQrVs3bNiwARMmTEDz5s3x1VdfGf8Gy/FeAMDDw0Pf7aVbp5cI4BgBOaCGDRuiV69eePHFF5GdnQ1Afln/73//w+nTpw3a1qxZE0II/a/37777Tv/Y33//DZVKhaeffhpTp06FEAIZGRnw8fFBTk6Ovl3jxo3h4+OD+fPn6/edPHmy1IHp48ePo169enj55ZcxYcIEbN++vVib+Ph4rFu3Dn/99RcAoLCwEDk5OXjooYdw6NAhHD58GACwdOlS1KpVC7Vq1UL9+vVx7tw5XLlyBQDw/fffG/2ZeXt7G7wnci48IyCH9O233+KDDz5AdHQ0XF1dodVq8cgjj+Dxxx/H+fPn9e1cXV0xffp0dO3aFdWqVUPv3r31jx06dAjjx4+HEAJFRUUYMGAAwsPDUVRUhGbNmqF58+aoX78+Vq1ahTVr1mDUqFH4/PPPodFoUL16dSxZsqTE2GbMmIGNGzfC3d0darUan376abE2DRs2xPz58/H888+jsLAQarUas2fPRps2bbB48WIMHDgQRUVF8Pf3x7Jly6BSqRAcHIy3334bbdq0QWBgIDp37mz05zVmzBh07NgRlSpVwm+//YaAgAATPm2yd1yYhojIybFriIjIyTEREBE5OSYCIiInx0RAROTkmAiIiJwcEwERkZNjIiAicnJMBERETo6JgIjIyTEREBE5uf8DM/sAdMOEqYwAAAAASUVORK5CYII=",
"text/plain": [
"<Figure size 400x400 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"'The best clusters count is 9'"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from src.cluster_helper import draw_best_clusters_plot, get_best_clusters_num\n",
"\n",
"random_state = 9\n",
"\n",
"X = df_rules.copy()\n",
"X = X.drop([\"consequent\"], axis=1)\n",
"\n",
"clusters_score = get_best_clusters_num(X, random_state)\n",
"display(clusters_score)\n",
"\n",
"draw_best_clusters_plot(clusters_score)\n",
"\n",
"clusters_num = sorted(clusters_score.items(), key=lambda x: x[1], reverse=True)[0][0]\n",
"display(f\"The best clusters count is {clusters_num}\")"
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Кластер 1 (16):\n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (cholesterol <= 2.5) -> 0;\n",
"if (ap_hi <= 129.5) and (ap_hi > 114.5) and (age <= 54.65) and (cholesterol <= 2.5) -> 0;\n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (cholesterol <= 2.5) and (cholesterol > 1.5) and (bmi <= 28.874) -> 0;\n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (cholesterol <= 2.5) and (cholesterol > 1.5) and (bmi > 28.874) -> 0;\n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (age > 43.792) and (cholesterol <= 2.5) and (bmi <= 22.045) -> 0;\n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (age > 43.792) and (cholesterol <= 2.5) and (bmi > 22.045) -> 0;\n",
"if (ap_hi <= 129.5) and (ap_hi > 119.5) and (age <= 54.65) and (age > 43.792) and (cholesterol <= 2.5) and (bmi <= 27.71) -> 0;\n",
"if (ap_hi <= 129.5) and (ap_hi > 119.5) and (age <= 54.65) and (age > 43.792) and (cholesterol <= 2.5) and (bmi > 27.71) -> 0;\n",
"if (ap_hi <= 129.5) and (age > 54.65) and (age <= 60.707) and (cholesterol <= 2.5) and (bmi <= 23.329) -> 0;\n",
"if (ap_hi <= 129.5) and (age > 54.65) and (age <= 60.707) and (cholesterol <= 2.5) and (bmi > 23.329) -> 0;\n",
"if (ap_hi <= 129.5) and (ap_hi > 118.5) and (age > 54.65) and (age <= 60.707) and (cholesterol <= 2.5) and (bmi <= 32.886) -> 0;\n",
"if (ap_hi <= 129.5) and (ap_hi > 118.5) and (age > 54.65) and (age <= 60.707) and (cholesterol <= 2.5) and (bmi > 32.886) -> 1;\n",
"if (ap_hi <= 129.5) and (age > 54.65) and (age <= 64.308) and (cholesterol <= 2.5) and (bmi <= 20.512) -> 0;\n",
"if (ap_hi <= 129.5) and (age > 54.65) and (cholesterol <= 2.5) and (bmi <= 20.512) -> 1;\n",
"if (ap_hi <= 129.5) and (age > 54.65) and (cholesterol <= 2.5) and (bmi > 20.512) -> 0;\n",
"if (ap_hi <= 129.5) and (ap_hi > 115.5) and (age > 54.65) and (cholesterol <= 2.5) and (bmi > 20.512) -> 1\n",
"--------\n",
"Кластер 2 (8):\n",
"if (ap_hi > 129.5) and (ap_hi <= 138.5) and (cholesterol <= 2.5) and (age <= 59.536) and (bmi <= 21.637) -> 1;\n",
"if (ap_hi > 129.5) and (ap_hi <= 138.5) and (cholesterol <= 2.5) and (age <= 59.536) and (bmi <= 21.637) and (bmi > 17.3) -> 0;\n",
"if (ap_hi > 129.5) and (ap_hi <= 138.5) and (cholesterol <= 2.5) and (age <= 59.536) and (bmi > 21.637) -> 0;\n",
"if (ap_hi > 129.5) and (ap_hi <= 138.5) and (cholesterol <= 2.5) and (age <= 59.536) and (age > 39.989) and (bmi > 21.637) -> 1;\n",
"if (ap_hi > 129.5) and (ap_hi <= 138.5) and (cholesterol <= 2.5) and (age > 59.536) and (age <= 62.463) and (bmi <= 20.614) -> 0;\n",
"if (ap_hi > 129.5) and (ap_hi <= 138.5) and (cholesterol <= 2.5) and (age > 59.536) and (age <= 62.463) and (bmi > 20.614) -> 1;\n",
"if (ap_hi > 129.5) and (ap_hi <= 138.5) and (cholesterol <= 2.5) and (age > 59.536) and (age <= 63.998) -> 1;\n",
"if (ap_hi > 129.5) and (ap_hi <= 138.5) and (cholesterol <= 2.5) and (age > 59.536) -> 1\n",
"--------\n",
"Кластер 3 (6):\n",
"if (ap_hi <= 129.5) and (age > 54.65) and (cholesterol > 2.5) and (bmi <= 26.032) -> 1;\n",
"if (ap_hi <= 129.5) and (age > 54.65) and (cholesterol > 2.5) and (bmi <= 26.032) and (bmi > 25.912) -> 0;\n",
"if (ap_hi <= 129.5) and (age > 54.65) and (age <= 59.39) and (cholesterol > 2.5) and (bmi > 26.032) and (bmi <= 35.932) -> 1;\n",
"if (ap_hi <= 129.5) and (age > 54.65) and (age <= 59.39) and (cholesterol > 2.5) and (bmi > 26.032) -> 1;\n",
"if (ap_hi <= 129.5) and (age > 54.65) and (cholesterol > 2.5) and (bmi > 26.032) and (bmi <= 35.121) -> 1;\n",
"if (ap_hi <= 129.5) and (age > 54.65) and (cholesterol > 2.5) and (bmi > 26.032) -> 1\n",
"--------\n",
"Кластер 4 (3):\n",
"if (ap_hi > 129.5) and (bmi <= 20.482) and (age <= 64.269) -> 1;\n",
"if (ap_hi > 129.5) and (bmi <= 20.482) and (age <= 64.269) and (age > 55.817) -> 1;\n",
"if (ap_hi > 129.5) and (bmi <= 20.482) and (age > 64.269) -> 0\n",
"--------\n",
"Кластер 5 (4):\n",
"if (ap_hi > 129.5) and (ap_hi <= 149.5) and (age <= 39.558) and (bmi <= 38.186) -> 1;\n",
"if (ap_hi > 129.5) and (ap_hi <= 149.5) and (age <= 39.558) and (age > 39.538) and (bmi <= 38.186) -> 0;\n",
"if (ap_hi > 129.5) and (ap_hi <= 149.5) and (age <= 39.558) and (bmi > 38.186) and (bmi <= 50.547) -> 0;\n",
"if (ap_hi > 129.5) and (ap_hi <= 149.5) and (age <= 39.558) and (bmi > 38.186) -> 1\n",
"--------\n",
"Кластер 6 (6):\n",
"if (ap_hi > 129.5) and (ap_hi <= 138.5) and (cholesterol > 2.5) and (bmi <= 30.744) -> 1;\n",
"if (ap_hi > 129.5) and (ap_hi <= 138.5) and (cholesterol > 2.5) and (bmi <= 30.744) and (bmi > 23.927) -> 1;\n",
"if (ap_hi > 129.5) and (ap_hi <= 138.5) and (cholesterol > 2.5) and (bmi > 30.744) and (bmi <= 32.049) and (age <= 43.632) -> 0;\n",
"if (ap_hi > 129.5) and (ap_hi <= 138.5) and (cholesterol > 2.5) and (bmi > 30.744) and (bmi <= 32.049) and (age > 43.632) -> 1;\n",
"if (ap_hi > 129.5) and (ap_hi <= 138.5) and (cholesterol > 2.5) and (bmi > 30.744) and (bmi <= 32.337) -> 1;\n",
"if (ap_hi > 129.5) and (ap_hi <= 138.5) and (cholesterol > 2.5) and (bmi > 30.744) -> 1\n",
"--------\n",
"Кластер 7 (4):\n",
"if (ap_hi > 129.5) and (ap_hi <= 149.5) and (age > 39.558) and (age <= 47.569) and (bmi <= 19.231) -> 0;\n",
"if (ap_hi > 129.5) and (ap_hi <= 149.5) and (age > 39.558) and (age <= 47.569) and (bmi > 19.231) -> 1;\n",
"if (ap_hi > 129.5) and (ap_hi <= 149.5) and (age > 39.558) and (age <= 61.572) -> 1;\n",
"if (ap_hi > 129.5) and (ap_hi <= 149.5) and (age > 39.558) -> 1\n",
"--------\n",
"Кластер 8 (6):\n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (cholesterol > 2.5) and (bmi <= 29.043) -> 0;\n",
"if (ap_hi <= 129.5) and (ap_hi > 115.0) and (age <= 54.65) and (cholesterol > 2.5) and (bmi <= 29.043) -> 0;\n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (cholesterol > 2.5) and (bmi > 29.043) -> 0;\n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (age > 39.751) and (cholesterol > 2.5) and (bmi > 29.043) -> 1;\n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (age > 54.008) and (cholesterol > 2.5) and (bmi > 29.043) and (bmi <= 35.021) -> 0;\n",
"if (ap_hi <= 129.5) and (age <= 54.65) and (age > 54.008) and (cholesterol > 2.5) and (bmi > 29.043) -> 1\n",
"--------\n",
"Кластер 9 (4):\n",
"if (ap_hi > 129.5) and (bmi > 20.482) and (age <= 64.351) -> 1;\n",
"if (ap_hi > 129.5) and (bmi > 20.482) and (age <= 64.351) and (age > 49.818) -> 1;\n",
"if (ap_hi > 129.5) and (bmi > 20.482) and (bmi <= 36.796) and (age > 64.351) -> 1;\n",
"if (ap_hi > 129.5) and (bmi > 20.482) and (age > 64.351) -> 0\n",
"--------\n"
]
}
],
"source": [
"from sklearn import cluster\n",
"\n",
"from src.cluster_helper import print_cluster_result\n",
"\n",
"kmeans = cluster.KMeans(n_clusters=clusters_num, random_state=random_state)\n",
"kmeans.fit(X)\n",
"\n",
"print_cluster_result(X, clusters_num, kmeans.labels_)"
]
},
{
"cell_type": "code",
"execution_count": 107,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[[if (ap_hi = 7) and (age = 29.564) and (cholesterol = 1) -> 0,\n",
" if (ap_hi = 122.0) and (age = 29.564) and (cholesterol = 1) -> 0,\n",
" if (ap_hi = 7) and (age = 29.564) and (cholesterol = 2.0) and (bmi = 3.472) -> 0,\n",
" if (ap_hi = 7) and (age = 29.564) and (cholesterol = 2.0) and (bmi = 298.667) -> 0,\n",
" if (ap_hi = 7) and (age = 49.221) and (cholesterol = 1) and (bmi = 3.472) -> 0,\n",
" if (ap_hi = 7) and (age = 49.221) and (cholesterol = 1) and (bmi = 298.667) -> 0,\n",
" if (ap_hi = 124.5) and (age = 49.221) and (cholesterol = 1) and (bmi = 3.472) -> 0,\n",
" if (ap_hi = 124.5) and (age = 49.221) and (cholesterol = 1) and (bmi = 298.667) -> 0,\n",
" if (ap_hi = 7) and (age = 57.679) and (cholesterol = 1) and (bmi = 3.472) -> 0,\n",
" if (ap_hi = 7) and (age = 57.679) and (cholesterol = 1) and (bmi = 298.667) -> 0,\n",
" if (ap_hi = 124.0) and (age = 57.679) and (cholesterol = 1) and (bmi = 3.472) -> 0,\n",
" if (ap_hi = 124.0) and (age = 57.679) and (cholesterol = 1) and (bmi = 298.667) -> 1,\n",
" if (ap_hi = 7) and (age = 59.479) and (cholesterol = 1) and (bmi = 3.472) -> 0,\n",
" if (ap_hi = 7) and (age = 64.924) and (cholesterol = 1) and (bmi = 3.472) -> 1,\n",
" if (ap_hi = 7) and (age = 64.924) and (cholesterol = 1) and (bmi = 298.667) -> 0,\n",
" if (ap_hi = 122.5) and (age = 64.924) and (cholesterol = 1) and (bmi = 298.667) -> 1],\n",
" [if (ap_hi = 134.0) and (cholesterol = 1) and (age = 29.564) and (bmi = 3.472) -> 1,\n",
" if (ap_hi = 134.0) and (cholesterol = 1) and (age = 29.564) and (bmi = 19.469) -> 0,\n",
" if (ap_hi = 134.0) and (cholesterol = 1) and (age = 29.564) and (bmi = 298.667) -> 0,\n",
" if (ap_hi = 134.0) and (cholesterol = 1) and (age = 49.762) and (bmi = 298.667) -> 1,\n",
" if (ap_hi = 134.0) and (cholesterol = 1) and (age = 61.0) and (bmi = 3.472) -> 0,\n",
" if (ap_hi = 134.0) and (cholesterol = 1) and (age = 61.0) and (bmi = 298.667) -> 1,\n",
" if (ap_hi = 134.0) and (cholesterol = 1) and (age = 61.767) -> 1,\n",
" if (ap_hi = 134.0) and (cholesterol = 1) and (age = 64.924) -> 1],\n",
" [if (ap_hi = 7) and (age = 64.924) and (cholesterol = 3) and (bmi = 3.472) -> 1,\n",
" if (ap_hi = 7) and (age = 64.924) and (cholesterol = 3) and (bmi = 25.972) -> 0,\n",
" if (ap_hi = 7) and (age = 57.02) and (cholesterol = 3) and (bmi = 30.982) -> 1,\n",
" if (ap_hi = 7) and (age = 57.02) and (cholesterol = 3) and (bmi = 298.667) -> 1,\n",
" if (ap_hi = 7) and (age = 64.924) and (cholesterol = 3) and (bmi = 30.577) -> 1,\n",
" if (ap_hi = 7) and (age = 64.924) and (cholesterol = 3) and (bmi = 298.667) -> 1],\n",
" [if (ap_hi = 240) and (bmi = 3.472) and (age = 29.564) -> 1,\n",
" if (ap_hi = 240) and (bmi = 3.472) and (age = 60.043) -> 1,\n",
" if (ap_hi = 240) and (bmi = 3.472) and (age = 64.924) -> 0],\n",
" [if (ap_hi = 139.5) and (age = 29.564) and (bmi = 3.472) -> 1,\n",
" if (ap_hi = 139.5) and (age = 39.548) and (bmi = 3.472) -> 0,\n",
" if (ap_hi = 139.5) and (age = 29.564) and (bmi = 44.367) -> 0,\n",
" if (ap_hi = 139.5) and (age = 29.564) and (bmi = 298.667) -> 1],\n",
" [if (ap_hi = 134.0) and (cholesterol = 3) and (bmi = 3.472) -> 1,\n",
" if (ap_hi = 134.0) and (cholesterol = 3) and (bmi = 27.336) -> 1,\n",
" if (ap_hi = 134.0) and (cholesterol = 3) and (bmi = 31.396) and (age = 29.564) -> 0,\n",
" if (ap_hi = 134.0) and (cholesterol = 3) and (bmi = 31.396) and (age = 64.924) -> 1,\n",
" if (ap_hi = 134.0) and (cholesterol = 3) and (bmi = 31.54) -> 1,\n",
" if (ap_hi = 134.0) and (cholesterol = 3) and (bmi = 298.667) -> 1],\n",
" [if (ap_hi = 139.5) and (age = 43.563) and (bmi = 3.472) -> 0,\n",
" if (ap_hi = 139.5) and (age = 43.563) and (bmi = 298.667) -> 1,\n",
" if (ap_hi = 139.5) and (age = 50.565) -> 1,\n",
" if (ap_hi = 139.5) and (age = 64.924) -> 1],\n",
" [if (ap_hi = 7) and (age = 29.564) and (cholesterol = 3) and (bmi = 3.472) -> 0,\n",
" if (ap_hi = 122.25) and (age = 29.564) and (cholesterol = 3) and (bmi = 3.472) -> 0,\n",
" if (ap_hi = 7) and (age = 29.564) and (cholesterol = 3) and (bmi = 298.667) -> 0,\n",
" if (ap_hi = 7) and (age = 47.2) and (cholesterol = 3) and (bmi = 298.667) -> 1,\n",
" if (ap_hi = 7) and (age = 54.329) and (cholesterol = 3) and (bmi = 32.032) -> 0,\n",
" if (ap_hi = 7) and (age = 54.329) and (cholesterol = 3) and (bmi = 298.667) -> 1],\n",
" [if (ap_hi = 240) and (bmi = 298.667) and (age = 29.564) -> 1,\n",
" if (ap_hi = 240) and (bmi = 298.667) and (age = 57.084) -> 1,\n",
" if (ap_hi = 240) and (bmi = 28.639) and (age = 64.924) -> 1,\n",
" if (ap_hi = 240) and (bmi = 298.667) and (age = 64.924) -> 0]]"
]
},
"execution_count": 107,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from src.rules import simplify_and_group_rules\n",
"\n",
"clustered_rules = simplify_and_group_rules(\n",
" df, rules, clusters_num, kmeans.labels_\n",
")\n",
"clustered_rules"
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>age</th>\n",
" <td>68985.0</td>\n",
" <td>53.290421</td>\n",
" <td>6.757633</td>\n",
" <td>29.564122</td>\n",
" <td>48.340817</td>\n",
" <td>53.939875</td>\n",
" <td>58.380791</td>\n",
" <td>64.924433</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ap_hi</th>\n",
" <td>68985.0</td>\n",
" <td>126.325027</td>\n",
" <td>17.698621</td>\n",
" <td>7.000000</td>\n",
" <td>120.000000</td>\n",
" <td>120.000000</td>\n",
" <td>140.000000</td>\n",
" <td>240.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>cholesterol</th>\n",
" <td>68985.0</td>\n",
" <td>1.364384</td>\n",
" <td>0.678691</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>3.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>cardio</th>\n",
" <td>68985.0</td>\n",
" <td>0.494905</td>\n",
" <td>0.499978</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>bmi</th>\n",
" <td>68985.0</td>\n",
" <td>27.524761</td>\n",
" <td>6.081130</td>\n",
" <td>3.471784</td>\n",
" <td>23.875115</td>\n",
" <td>26.346494</td>\n",
" <td>30.119376</td>\n",
" <td>298.666667</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" count mean std min 25% \\\n",
"age 68985.0 53.290421 6.757633 29.564122 48.340817 \n",
"ap_hi 68985.0 126.325027 17.698621 7.000000 120.000000 \n",
"cholesterol 68985.0 1.364384 0.678691 1.000000 1.000000 \n",
"cardio 68985.0 0.494905 0.499978 0.000000 0.000000 \n",
"bmi 68985.0 27.524761 6.081130 3.471784 23.875115 \n",
"\n",
" 50% 75% max \n",
"age 53.939875 58.380791 64.924433 \n",
"ap_hi 120.000000 140.000000 240.000000 \n",
"cholesterol 1.000000 1.000000 3.000000 \n",
"cardio 0.000000 1.000000 1.000000 \n",
"bmi 26.346494 30.119376 298.666667 "
]
},
"execution_count": 108,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.describe().transpose()"
]
},
{
"cell_type": "code",
"execution_count": 109,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/user/Projects/python/fuzzy-rules-generator/.venv/lib/python3.12/site-packages/skfuzzy/control/fuzzyvariable.py:125: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
" fig.show()\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"from skfuzzy import control as ctrl\n",
"import skfuzzy as fuzz\n",
"\n",
"age = ctrl.Antecedent(np.arange(29, 65, 0.5), \"age\")\n",
"ap_hi = ctrl.Antecedent(np.arange(7, 240, 0.5), \"ap_hi\")\n",
"cholesterol = ctrl.Antecedent([1, 2, 3], \"cholesterol\")\n",
"bmi = ctrl.Antecedent(np.arange(3, 299, 0.05), \"bmi\")\n",
"cardio = ctrl.Consequent([0, 1], \"cardio\")\n",
"\n",
"age.automf(3, variable_type=\"quant\")\n",
"age.view()\n",
"ap_hi.automf(3, variable_type=\"quant\")\n",
"ap_hi.view()\n",
"cholesterol.automf(3, variable_type=\"quant\")\n",
"cholesterol.view()\n",
"bmi.automf(3, variable_type=\"quant\")\n",
"bmi.view()\n",
"cardio.automf(2, variable_type=\"quant\", names=[\"No\", \"Yes\"])\n",
"cardio.view()"
]
},
{
"cell_type": "code",
"execution_count": 110,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"41"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"[IF (ap_hi[low] AND age[low]) AND cholesterol[low] THEN cardio[No]\n",
" \tAND aggregation function : fmin\n",
" \tOR aggregation function : fmax,\n",
" IF (ap_hi[average] AND age[low]) AND cholesterol[low] THEN cardio[No]\n",
" \tAND aggregation function : fmin\n",
" \tOR aggregation function : fmax,\n",
" IF ((ap_hi[low] AND age[low]) AND cholesterol[average]) AND bmi[low] THEN cardio[No]\n",
" \tAND aggregation function : fmin\n",
" \tOR aggregation function : fmax,\n",
" IF ((ap_hi[low] AND age[low]) AND cholesterol[average]) AND bmi[high] THEN cardio[No]\n",
" \tAND aggregation function : fmin\n",
" \tOR aggregation function : fmax,\n",
" IF ((ap_hi[low] AND age[average]) AND cholesterol[low]) AND bmi[low] THEN cardio[No]\n",
" \tAND aggregation function : fmin\n",
" \tOR aggregation function : fmax,\n",
" IF ((ap_hi[low] AND age[average]) AND cholesterol[low]) AND bmi[high] THEN cardio[No]\n",
" \tAND aggregation function : fmin\n",
" \tOR aggregation function : fmax,\n",
" IF ((ap_hi[average] AND age[average]) AND cholesterol[low]) AND bmi[low] THEN cardio[No]\n",
" \tAND aggregation function : fmin\n",
" \tOR aggregation function : fmax,\n",
" IF ((ap_hi[average] AND age[average]) AND cholesterol[low]) AND bmi[high] THEN cardio[No]\n",
" \tAND aggregation function : fmin\n",
" \tOR aggregation function : fmax,\n",
" IF ((ap_hi[average] AND age[high]) AND cholesterol[low]) AND bmi[low] THEN cardio[No]\n",
" \tAND aggregation function : fmin\n",
" \tOR aggregation function : fmax,\n",
" IF ((ap_hi[low] AND age[high]) AND cholesterol[low]) AND bmi[low] THEN cardio[No]\n",
" \tAND aggregation function : fmin\n",
" \tOR aggregation function : fmax,\n",
" IF ((ap_hi[low] AND age[high]) AND cholesterol[low]) AND bmi[high] THEN cardio[No]\n",
" \tAND aggregation function : fmin\n",
" \tOR aggregation function : fmax,\n",
" IF ((ap_hi[average] AND age[high]) AND cholesterol[low]) AND bmi[high] THEN cardio[Yes]\n",
" \tAND aggregation function : fmin\n",
" \tOR aggregation function : fmax,\n",
" IF ((ap_hi[average] AND cholesterol[low]) AND age[low]) AND bmi[low] THEN cardio[No]\n",
" \tAND aggregation function : fmin\n",
" \tOR aggregation function : fmax,\n",
" IF ((ap_hi[average] AND cholesterol[low]) AND age[low]) AND bmi[high] THEN cardio[No]\n",
" \tAND aggregation function : fmin\n",
" \tOR aggregation function : fmax,\n",
" IF ((ap_hi[average] AND cholesterol[low]) AND age[average]) AND bmi[high] THEN cardio[Yes]\n",
" \tAND aggregation function : fmin\n",
" \tOR aggregation function : fmax,\n",
" IF ((ap_hi[average] AND cholesterol[low]) AND age[high]) AND bmi[low] THEN cardio[No]\n",
" \tAND aggregation function : fmin\n",
" \tOR aggregation function : fmax,\n",
" IF ((ap_hi[average] AND cholesterol[low]) AND age[high]) AND bmi[high] THEN cardio[Yes]\n",
" \tAND aggregation function : fmin\n",
" \tOR aggregation function : fmax,\n",
" IF (ap_hi[average] AND cholesterol[low]) AND age[high] THEN cardio[Yes]\n",
" \tAND aggregation function : fmin\n",
" \tOR aggregation function : fmax,\n",
" IF ((ap_hi[low] AND age[high]) AND cholesterol[high]) AND bmi[low] THEN cardio[Yes]\n",
" \tAND aggregation function : fmin\n",
" \tOR aggregation function : fmax,\n",
" IF ((ap_hi[low] AND age[high]) AND cholesterol[high]) AND bmi[high] THEN cardio[Yes]\n",
" \tAND aggregation function : fmin\n",
" \tOR aggregation function : fmax,\n",
" IF (ap_hi[high] AND bmi[low]) AND age[low] THEN cardio[Yes]\n",
" \tAND aggregation function : fmin\n",
" \tOR aggregation function : fmax,\n",
" IF (ap_hi[high] AND bmi[low]) AND age[high] THEN cardio[Yes]\n",
" \tAND aggregation function : fmin\n",
" \tOR aggregation function : fmax,\n",
" IF (ap_hi[average] AND age[average]) AND bmi[low] THEN cardio[No]\n",
" \tAND aggregation function : fmin\n",
" \tOR aggregation function : fmax,\n",
" IF (ap_hi[average] AND age[low]) AND bmi[low] THEN cardio[No]\n",
" \tAND aggregation function : fmin\n",
" \tOR aggregation function : fmax,\n",
" IF (ap_hi[average] AND age[low]) AND bmi[high] THEN cardio[Yes]\n",
" \tAND aggregation function : fmin\n",
" \tOR aggregation function : fmax,\n",
" IF ((ap_hi[average] AND cholesterol[high]) AND bmi[low]) AND age[low] THEN cardio[No]\n",
" \tAND aggregation function : fmin\n",
" \tOR aggregation function : fmax,\n",
" IF ((ap_hi[average] AND cholesterol[high]) AND bmi[low]) AND age[high] THEN cardio[Yes]\n",
" \tAND aggregation function : fmin\n",
" \tOR aggregation function : fmax,\n",
" IF (ap_hi[average] AND cholesterol[high]) AND bmi[low] THEN cardio[Yes]\n",
" \tAND aggregation function : fmin\n",
" \tOR aggregation function : fmax,\n",
" IF (ap_hi[average] AND cholesterol[high]) AND bmi[high] THEN cardio[Yes]\n",
" \tAND aggregation function : fmin\n",
" \tOR aggregation function : fmax,\n",
" IF (ap_hi[average] AND age[average]) AND bmi[low] THEN cardio[No]\n",
" \tAND aggregation function : fmin\n",
" \tOR aggregation function : fmax,\n",
" IF (ap_hi[average] AND age[average]) AND bmi[high] THEN cardio[Yes]\n",
" \tAND aggregation function : fmin\n",
" \tOR aggregation function : fmax,\n",
" IF ap_hi[average] AND age[average] THEN cardio[Yes]\n",
" \tAND aggregation function : fmin\n",
" \tOR aggregation function : fmax,\n",
" IF ap_hi[average] AND age[high] THEN cardio[Yes]\n",
" \tAND aggregation function : fmin\n",
" \tOR aggregation function : fmax,\n",
" IF ((ap_hi[low] AND age[low]) AND cholesterol[high]) AND bmi[low] THEN cardio[No]\n",
" \tAND aggregation function : fmin\n",
" \tOR aggregation function : fmax,\n",
" IF ((ap_hi[average] AND age[low]) AND cholesterol[high]) AND bmi[low] THEN cardio[No]\n",
" \tAND aggregation function : fmin\n",
" \tOR aggregation function : fmax,\n",
" IF ((ap_hi[low] AND age[low]) AND cholesterol[high]) AND bmi[high] THEN cardio[No]\n",
" \tAND aggregation function : fmin\n",
" \tOR aggregation function : fmax,\n",
" IF ((ap_hi[low] AND age[average]) AND cholesterol[high]) AND bmi[low] THEN cardio[No]\n",
" \tAND aggregation function : fmin\n",
" \tOR aggregation function : fmax,\n",
" IF ((ap_hi[low] AND age[average]) AND cholesterol[high]) AND bmi[high] THEN cardio[Yes]\n",
" \tAND aggregation function : fmin\n",
" \tOR aggregation function : fmax,\n",
" IF (ap_hi[high] AND bmi[high]) AND age[low] THEN cardio[Yes]\n",
" \tAND aggregation function : fmin\n",
" \tOR aggregation function : fmax,\n",
" IF (ap_hi[high] AND bmi[high]) AND age[high] THEN cardio[Yes]\n",
" \tAND aggregation function : fmin\n",
" \tOR aggregation function : fmax,\n",
" IF (ap_hi[high] AND bmi[low]) AND age[high] THEN cardio[Yes]\n",
" \tAND aggregation function : fmin\n",
" \tOR aggregation function : fmax]"
]
},
"execution_count": 110,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from src.rules import get_fuzzy_rules\n",
"\n",
"fuzzy_variables = {\n",
" \"age\": age,\n",
" \"ap_hi\": ap_hi,\n",
" \"cholesterol\": cholesterol,\n",
" \"bmi\": bmi,\n",
" \"consequent\": cardio,\n",
"}\n",
"fuzzy_rules = get_fuzzy_rules(clustered_rules, fuzzy_variables)\n",
"\n",
"fuzzy_cntrl = ctrl.ControlSystem(fuzzy_rules)\n",
"\n",
"sim = ctrl.ControlSystemSimulation(fuzzy_cntrl, lenient=False)\n",
"\n",
"display(len(fuzzy_rules))\n",
"fuzzy_rules"
]
},
{
"cell_type": "code",
"execution_count": 111,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"=============\n",
" Antecedents \n",
"=============\n",
"Antecedent: ap_hi = 110\n",
" - low : 0.11397849462365592\n",
" - average : 0.886021505376344\n",
" - high : 0.0\n",
"Antecedent: age = 50.358668\n",
" - low : 0.0\n",
" - average : 0.7966947605633802\n",
" - high : 0.2033052394366198\n",
"Antecedent: cholesterol = 1\n",
" - low : 1.0\n",
" - average : 0.0\n",
" - high : 0.0\n",
"Antecedent: bmi = 21.96712\n",
" - low : 0.8718221321169112\n",
" - average : 0.12817786788308883\n",
" - high : 0.0\n",
"\n",
"=======\n",
" Rules \n",
"=======\n",
"RULE #0:\n",
" IF (ap_hi[low] AND age[low]) AND cholesterol[low] THEN cardio[No]\n",
"\tAND aggregation function : fmin\n",
"\tOR aggregation function : fmax\n",
"\n",
" Aggregation (IF-clause):\n",
" - ap_hi[low] : 0.11397849462365592\n",
" - age[low] : 0.0\n",
" - cholesterol[low] : 1.0\n",
" (ap_hi[low] AND age[low]) AND cholesterol[low] = 0.0\n",
" Activation (THEN-clause):\n",
" cardio[No] : 0.0\n",
"\n",
"RULE #1:\n",
" IF (ap_hi[average] AND age[low]) AND cholesterol[low] THEN cardio[No]\n",
"\tAND aggregation function : fmin\n",
"\tOR aggregation function : fmax\n",
"\n",
" Aggregation (IF-clause):\n",
" - ap_hi[average] : 0.886021505376344\n",
" - age[low] : 0.0\n",
" - cholesterol[low] : 1.0\n",
" (ap_hi[average] AND age[low]) AND cholesterol[low] = 0.0\n",
" Activation (THEN-clause):\n",
" cardio[No] : 0.0\n",
"\n",
"RULE #2:\n",
" IF ((ap_hi[low] AND age[low]) AND cholesterol[average]) AND bmi[low] THEN cardio[No]\n",
"\tAND aggregation function : fmin\n",
"\tOR aggregation function : fmax\n",
"\n",
" Aggregation (IF-clause):\n",
" - ap_hi[low] : 0.11397849462365592\n",
" - age[low] : 0.0\n",
" - cholesterol[average] : 0.0\n",
" - bmi[low] : 0.8718221321169112\n",
" ((ap_hi[low] AND age[low]) AND cholesterol[average]) AND bmi[low] = 0.0\n",
" Activation (THEN-clause):\n",
" cardio[No] : 0.0\n",
"\n",
"RULE #3:\n",
" IF ((ap_hi[low] AND age[low]) AND cholesterol[average]) AND bmi[high] THEN cardio[No]\n",
"\tAND aggregation function : fmin\n",
"\tOR aggregation function : fmax\n",
"\n",
" Aggregation (IF-clause):\n",
" - ap_hi[low] : 0.11397849462365592\n",
" - age[low] : 0.0\n",
" - cholesterol[average] : 0.0\n",
" - bmi[high] : 0.0\n",
" ((ap_hi[low] AND age[low]) AND cholesterol[average]) AND bmi[high] = 0.0\n",
" Activation (THEN-clause):\n",
" cardio[No] : 0.0\n",
"\n",
"RULE #4:\n",
" IF ((ap_hi[low] AND age[average]) AND cholesterol[low]) AND bmi[low] THEN cardio[No]\n",
"\tAND aggregation function : fmin\n",
"\tOR aggregation function : fmax\n",
"\n",
" Aggregation (IF-clause):\n",
" - ap_hi[low] : 0.11397849462365592\n",
" - age[average] : 0.7966947605633802\n",
" - cholesterol[low] : 1.0\n",
" - bmi[low] : 0.8718221321169112\n",
" ((ap_hi[low] AND age[average]) AND cholesterol[low]) AND bmi[low] = 0.11397849462365592\n",
" Activation (THEN-clause):\n",
" cardio[No] : 0.11397849462365592\n",
"\n",
"RULE #5:\n",
" IF ((ap_hi[low] AND age[average]) AND cholesterol[low]) AND bmi[high] THEN cardio[No]\n",
"\tAND aggregation function : fmin\n",
"\tOR aggregation function : fmax\n",
"\n",
" Aggregation (IF-clause):\n",
" - ap_hi[low] : 0.11397849462365592\n",
" - age[average] : 0.7966947605633802\n",
" - cholesterol[low] : 1.0\n",
" - bmi[high] : 0.0\n",
" ((ap_hi[low] AND age[average]) AND cholesterol[low]) AND bmi[high] = 0.0\n",
" Activation (THEN-clause):\n",
" cardio[No] : 0.0\n",
"\n",
"RULE #6:\n",
" IF ((ap_hi[average] AND age[average]) AND cholesterol[low]) AND bmi[low] THEN cardio[No]\n",
"\tAND aggregation function : fmin\n",
"\tOR aggregation function : fmax\n",
"\n",
" Aggregation (IF-clause):\n",
" - ap_hi[average] : 0.886021505376344\n",
" - age[average] : 0.7966947605633802\n",
" - cholesterol[low] : 1.0\n",
" - bmi[low] : 0.8718221321169112\n",
" ((ap_hi[average] AND age[average]) AND cholesterol[low]) AND bmi[low] = 0.7966947605633802\n",
" Activation (THEN-clause):\n",
" cardio[No] : 0.7966947605633802\n",
"\n",
"RULE #7:\n",
" IF ((ap_hi[average] AND age[average]) AND cholesterol[low]) AND bmi[high] THEN cardio[No]\n",
"\tAND aggregation function : fmin\n",
"\tOR aggregation function : fmax\n",
"\n",
" Aggregation (IF-clause):\n",
" - ap_hi[average] : 0.886021505376344\n",
" - age[average] : 0.7966947605633802\n",
" - cholesterol[low] : 1.0\n",
" - bmi[high] : 0.0\n",
" ((ap_hi[average] AND age[average]) AND cholesterol[low]) AND bmi[high] = 0.0\n",
" Activation (THEN-clause):\n",
" cardio[No] : 0.0\n",
"\n",
"RULE #8:\n",
" IF ((ap_hi[average] AND age[high]) AND cholesterol[low]) AND bmi[low] THEN cardio[No]\n",
"\tAND aggregation function : fmin\n",
"\tOR aggregation function : fmax\n",
"\n",
" Aggregation (IF-clause):\n",
" - ap_hi[average] : 0.886021505376344\n",
" - age[high] : 0.2033052394366198\n",
" - cholesterol[low] : 1.0\n",
" - bmi[low] : 0.8718221321169112\n",
" ((ap_hi[average] AND age[high]) AND cholesterol[low]) AND bmi[low] = 0.2033052394366198\n",
" Activation (THEN-clause):\n",
" cardio[No] : 0.2033052394366198\n",
"\n",
"RULE #9:\n",
" IF ((ap_hi[low] AND age[high]) AND cholesterol[low]) AND bmi[low] THEN cardio[No]\n",
"\tAND aggregation function : fmin\n",
"\tOR aggregation function : fmax\n",
"\n",
" Aggregation (IF-clause):\n",
" - ap_hi[low] : 0.11397849462365592\n",
" - age[high] : 0.2033052394366198\n",
" - cholesterol[low] : 1.0\n",
" - bmi[low] : 0.8718221321169112\n",
" ((ap_hi[low] AND age[high]) AND cholesterol[low]) AND bmi[low] = 0.11397849462365592\n",
" Activation (THEN-clause):\n",
" cardio[No] : 0.11397849462365592\n",
"\n",
"RULE #10:\n",
" IF ((ap_hi[low] AND age[high]) AND cholesterol[low]) AND bmi[high] THEN cardio[No]\n",
"\tAND aggregation function : fmin\n",
"\tOR aggregation function : fmax\n",
"\n",
" Aggregation (IF-clause):\n",
" - ap_hi[low] : 0.11397849462365592\n",
" - age[high] : 0.2033052394366198\n",
" - cholesterol[low] : 1.0\n",
" - bmi[high] : 0.0\n",
" ((ap_hi[low] AND age[high]) AND cholesterol[low]) AND bmi[high] = 0.0\n",
" Activation (THEN-clause):\n",
" cardio[No] : 0.0\n",
"\n",
"RULE #11:\n",
" IF ((ap_hi[average] AND age[high]) AND cholesterol[low]) AND bmi[high] THEN cardio[Yes]\n",
"\tAND aggregation function : fmin\n",
"\tOR aggregation function : fmax\n",
"\n",
" Aggregation (IF-clause):\n",
" - ap_hi[average] : 0.886021505376344\n",
" - age[high] : 0.2033052394366198\n",
" - cholesterol[low] : 1.0\n",
" - bmi[high] : 0.0\n",
" ((ap_hi[average] AND age[high]) AND cholesterol[low]) AND bmi[high] = 0.0\n",
" Activation (THEN-clause):\n",
" cardio[Yes] : 0.0\n",
"\n",
"RULE #12:\n",
" IF ((ap_hi[average] AND cholesterol[low]) AND age[low]) AND bmi[low] THEN cardio[No]\n",
"\tAND aggregation function : fmin\n",
"\tOR aggregation function : fmax\n",
"\n",
" Aggregation (IF-clause):\n",
" - ap_hi[average] : 0.886021505376344\n",
" - cholesterol[low] : 1.0\n",
" - age[low] : 0.0\n",
" - bmi[low] : 0.8718221321169112\n",
" ((ap_hi[average] AND cholesterol[low]) AND age[low]) AND bmi[low] = 0.0\n",
" Activation (THEN-clause):\n",
" cardio[No] : 0.0\n",
"\n",
"RULE #13:\n",
" IF ((ap_hi[average] AND cholesterol[low]) AND age[low]) AND bmi[high] THEN cardio[No]\n",
"\tAND aggregation function : fmin\n",
"\tOR aggregation function : fmax\n",
"\n",
" Aggregation (IF-clause):\n",
" - ap_hi[average] : 0.886021505376344\n",
" - cholesterol[low] : 1.0\n",
" - age[low] : 0.0\n",
" - bmi[high] : 0.0\n",
" ((ap_hi[average] AND cholesterol[low]) AND age[low]) AND bmi[high] = 0.0\n",
" Activation (THEN-clause):\n",
" cardio[No] : 0.0\n",
"\n",
"RULE #14:\n",
" IF ((ap_hi[average] AND cholesterol[low]) AND age[average]) AND bmi[high] THEN cardio[Yes]\n",
"\tAND aggregation function : fmin\n",
"\tOR aggregation function : fmax\n",
"\n",
" Aggregation (IF-clause):\n",
" - ap_hi[average] : 0.886021505376344\n",
" - cholesterol[low] : 1.0\n",
" - age[average] : 0.7966947605633802\n",
" - bmi[high] : 0.0\n",
" ((ap_hi[average] AND cholesterol[low]) AND age[average]) AND bmi[high] = 0.0\n",
" Activation (THEN-clause):\n",
" cardio[Yes] : 0.0\n",
"\n",
"RULE #15:\n",
" IF ((ap_hi[average] AND cholesterol[low]) AND age[high]) AND bmi[low] THEN cardio[No]\n",
"\tAND aggregation function : fmin\n",
"\tOR aggregation function : fmax\n",
"\n",
" Aggregation (IF-clause):\n",
" - ap_hi[average] : 0.886021505376344\n",
" - cholesterol[low] : 1.0\n",
" - age[high] : 0.2033052394366198\n",
" - bmi[low] : 0.8718221321169112\n",
" ((ap_hi[average] AND cholesterol[low]) AND age[high]) AND bmi[low] = 0.2033052394366198\n",
" Activation (THEN-clause):\n",
" cardio[No] : 0.2033052394366198\n",
"\n",
"RULE #16:\n",
" IF ((ap_hi[average] AND cholesterol[low]) AND age[high]) AND bmi[high] THEN cardio[Yes]\n",
"\tAND aggregation function : fmin\n",
"\tOR aggregation function : fmax\n",
"\n",
" Aggregation (IF-clause):\n",
" - ap_hi[average] : 0.886021505376344\n",
" - cholesterol[low] : 1.0\n",
" - age[high] : 0.2033052394366198\n",
" - bmi[high] : 0.0\n",
" ((ap_hi[average] AND cholesterol[low]) AND age[high]) AND bmi[high] = 0.0\n",
" Activation (THEN-clause):\n",
" cardio[Yes] : 0.0\n",
"\n",
"RULE #17:\n",
" IF (ap_hi[average] AND cholesterol[low]) AND age[high] THEN cardio[Yes]\n",
"\tAND aggregation function : fmin\n",
"\tOR aggregation function : fmax\n",
"\n",
" Aggregation (IF-clause):\n",
" - ap_hi[average] : 0.886021505376344\n",
" - cholesterol[low] : 1.0\n",
" - age[high] : 0.2033052394366198\n",
" (ap_hi[average] AND cholesterol[low]) AND age[high] = 0.2033052394366198\n",
" Activation (THEN-clause):\n",
" cardio[Yes] : 0.2033052394366198\n",
"\n",
"RULE #18:\n",
" IF ((ap_hi[low] AND age[high]) AND cholesterol[high]) AND bmi[low] THEN cardio[Yes]\n",
"\tAND aggregation function : fmin\n",
"\tOR aggregation function : fmax\n",
"\n",
" Aggregation (IF-clause):\n",
" - ap_hi[low] : 0.11397849462365592\n",
" - age[high] : 0.2033052394366198\n",
" - cholesterol[high] : 0.0\n",
" - bmi[low] : 0.8718221321169112\n",
" ((ap_hi[low] AND age[high]) AND cholesterol[high]) AND bmi[low] = 0.0\n",
" Activation (THEN-clause):\n",
" cardio[Yes] : 0.0\n",
"\n",
"RULE #19:\n",
" IF ((ap_hi[low] AND age[high]) AND cholesterol[high]) AND bmi[high] THEN cardio[Yes]\n",
"\tAND aggregation function : fmin\n",
"\tOR aggregation function : fmax\n",
"\n",
" Aggregation (IF-clause):\n",
" - ap_hi[low] : 0.11397849462365592\n",
" - age[high] : 0.2033052394366198\n",
" - cholesterol[high] : 0.0\n",
" - bmi[high] : 0.0\n",
" ((ap_hi[low] AND age[high]) AND cholesterol[high]) AND bmi[high] = 0.0\n",
" Activation (THEN-clause):\n",
" cardio[Yes] : 0.0\n",
"\n",
"RULE #20:\n",
" IF (ap_hi[high] AND bmi[low]) AND age[low] THEN cardio[Yes]\n",
"\tAND aggregation function : fmin\n",
"\tOR aggregation function : fmax\n",
"\n",
" Aggregation (IF-clause):\n",
" - ap_hi[high] : 0.0\n",
" - bmi[low] : 0.8718221321169112\n",
" - age[low] : 0.0\n",
" (ap_hi[high] AND bmi[low]) AND age[low] = 0.0\n",
" Activation (THEN-clause):\n",
" cardio[Yes] : 0.0\n",
"\n",
"RULE #21:\n",
" IF (ap_hi[high] AND bmi[low]) AND age[high] THEN cardio[Yes]\n",
"\tAND aggregation function : fmin\n",
"\tOR aggregation function : fmax\n",
"\n",
" Aggregation (IF-clause):\n",
" - ap_hi[high] : 0.0\n",
" - bmi[low] : 0.8718221321169112\n",
" - age[high] : 0.2033052394366198\n",
" (ap_hi[high] AND bmi[low]) AND age[high] = 0.0\n",
" Activation (THEN-clause):\n",
" cardio[Yes] : 0.0\n",
"\n",
"RULE #22:\n",
" IF (ap_hi[average] AND age[average]) AND bmi[low] THEN cardio[No]\n",
"\tAND aggregation function : fmin\n",
"\tOR aggregation function : fmax\n",
"\n",
" Aggregation (IF-clause):\n",
" - ap_hi[average] : 0.886021505376344\n",
" - age[average] : 0.7966947605633802\n",
" - bmi[low] : 0.8718221321169112\n",
" (ap_hi[average] AND age[average]) AND bmi[low] = 0.7966947605633802\n",
" Activation (THEN-clause):\n",
" cardio[No] : 0.7966947605633802\n",
"\n",
"RULE #23:\n",
" IF (ap_hi[average] AND age[low]) AND bmi[low] THEN cardio[No]\n",
"\tAND aggregation function : fmin\n",
"\tOR aggregation function : fmax\n",
"\n",
" Aggregation (IF-clause):\n",
" - ap_hi[average] : 0.886021505376344\n",
" - age[low] : 0.0\n",
" - bmi[low] : 0.8718221321169112\n",
" (ap_hi[average] AND age[low]) AND bmi[low] = 0.0\n",
" Activation (THEN-clause):\n",
" cardio[No] : 0.0\n",
"\n",
"RULE #24:\n",
" IF (ap_hi[average] AND age[low]) AND bmi[high] THEN cardio[Yes]\n",
"\tAND aggregation function : fmin\n",
"\tOR aggregation function : fmax\n",
"\n",
" Aggregation (IF-clause):\n",
" - ap_hi[average] : 0.886021505376344\n",
" - age[low] : 0.0\n",
" - bmi[high] : 0.0\n",
" (ap_hi[average] AND age[low]) AND bmi[high] = 0.0\n",
" Activation (THEN-clause):\n",
" cardio[Yes] : 0.0\n",
"\n",
"RULE #25:\n",
" IF ((ap_hi[average] AND cholesterol[high]) AND bmi[low]) AND age[low] THEN cardio[No]\n",
"\tAND aggregation function : fmin\n",
"\tOR aggregation function : fmax\n",
"\n",
" Aggregation (IF-clause):\n",
" - ap_hi[average] : 0.886021505376344\n",
" - cholesterol[high] : 0.0\n",
" - bmi[low] : 0.8718221321169112\n",
" - age[low] : 0.0\n",
" ((ap_hi[average] AND cholesterol[high]) AND bmi[low]) AND age[low] = 0.0\n",
" Activation (THEN-clause):\n",
" cardio[No] : 0.0\n",
"\n",
"RULE #26:\n",
" IF ((ap_hi[average] AND cholesterol[high]) AND bmi[low]) AND age[high] THEN cardio[Yes]\n",
"\tAND aggregation function : fmin\n",
"\tOR aggregation function : fmax\n",
"\n",
" Aggregation (IF-clause):\n",
" - ap_hi[average] : 0.886021505376344\n",
" - cholesterol[high] : 0.0\n",
" - bmi[low] : 0.8718221321169112\n",
" - age[high] : 0.2033052394366198\n",
" ((ap_hi[average] AND cholesterol[high]) AND bmi[low]) AND age[high] = 0.0\n",
" Activation (THEN-clause):\n",
" cardio[Yes] : 0.0\n",
"\n",
"RULE #27:\n",
" IF (ap_hi[average] AND cholesterol[high]) AND bmi[low] THEN cardio[Yes]\n",
"\tAND aggregation function : fmin\n",
"\tOR aggregation function : fmax\n",
"\n",
" Aggregation (IF-clause):\n",
" - ap_hi[average] : 0.886021505376344\n",
" - cholesterol[high] : 0.0\n",
" - bmi[low] : 0.8718221321169112\n",
" (ap_hi[average] AND cholesterol[high]) AND bmi[low] = 0.0\n",
" Activation (THEN-clause):\n",
" cardio[Yes] : 0.0\n",
"\n",
"RULE #28:\n",
" IF (ap_hi[average] AND cholesterol[high]) AND bmi[high] THEN cardio[Yes]\n",
"\tAND aggregation function : fmin\n",
"\tOR aggregation function : fmax\n",
"\n",
" Aggregation (IF-clause):\n",
" - ap_hi[average] : 0.886021505376344\n",
" - cholesterol[high] : 0.0\n",
" - bmi[high] : 0.0\n",
" (ap_hi[average] AND cholesterol[high]) AND bmi[high] = 0.0\n",
" Activation (THEN-clause):\n",
" cardio[Yes] : 0.0\n",
"\n",
"RULE #29:\n",
" IF (ap_hi[average] AND age[average]) AND bmi[low] THEN cardio[No]\n",
"\tAND aggregation function : fmin\n",
"\tOR aggregation function : fmax\n",
"\n",
" Aggregation (IF-clause):\n",
" - ap_hi[average] : 0.886021505376344\n",
" - age[average] : 0.7966947605633802\n",
" - bmi[low] : 0.8718221321169112\n",
" (ap_hi[average] AND age[average]) AND bmi[low] = 0.7966947605633802\n",
" Activation (THEN-clause):\n",
" cardio[No] : 0.7966947605633802\n",
"\n",
"RULE #30:\n",
" IF (ap_hi[average] AND age[average]) AND bmi[high] THEN cardio[Yes]\n",
"\tAND aggregation function : fmin\n",
"\tOR aggregation function : fmax\n",
"\n",
" Aggregation (IF-clause):\n",
" - ap_hi[average] : 0.886021505376344\n",
" - age[average] : 0.7966947605633802\n",
" - bmi[high] : 0.0\n",
" (ap_hi[average] AND age[average]) AND bmi[high] = 0.0\n",
" Activation (THEN-clause):\n",
" cardio[Yes] : 0.0\n",
"\n",
"RULE #31:\n",
" IF ap_hi[average] AND age[average] THEN cardio[Yes]\n",
"\tAND aggregation function : fmin\n",
"\tOR aggregation function : fmax\n",
"\n",
" Aggregation (IF-clause):\n",
" - ap_hi[average] : 0.886021505376344\n",
" - age[average] : 0.7966947605633802\n",
" ap_hi[average] AND age[average] = 0.7966947605633802\n",
" Activation (THEN-clause):\n",
" cardio[Yes] : 0.7966947605633802\n",
"\n",
"RULE #32:\n",
" IF ap_hi[average] AND age[high] THEN cardio[Yes]\n",
"\tAND aggregation function : fmin\n",
"\tOR aggregation function : fmax\n",
"\n",
" Aggregation (IF-clause):\n",
" - ap_hi[average] : 0.886021505376344\n",
" - age[high] : 0.2033052394366198\n",
" ap_hi[average] AND age[high] = 0.2033052394366198\n",
" Activation (THEN-clause):\n",
" cardio[Yes] : 0.2033052394366198\n",
"\n",
"RULE #33:\n",
" IF ((ap_hi[low] AND age[low]) AND cholesterol[high]) AND bmi[low] THEN cardio[No]\n",
"\tAND aggregation function : fmin\n",
"\tOR aggregation function : fmax\n",
"\n",
" Aggregation (IF-clause):\n",
" - ap_hi[low] : 0.11397849462365592\n",
" - age[low] : 0.0\n",
" - cholesterol[high] : 0.0\n",
" - bmi[low] : 0.8718221321169112\n",
" ((ap_hi[low] AND age[low]) AND cholesterol[high]) AND bmi[low] = 0.0\n",
" Activation (THEN-clause):\n",
" cardio[No] : 0.0\n",
"\n",
"RULE #34:\n",
" IF ((ap_hi[average] AND age[low]) AND cholesterol[high]) AND bmi[low] THEN cardio[No]\n",
"\tAND aggregation function : fmin\n",
"\tOR aggregation function : fmax\n",
"\n",
" Aggregation (IF-clause):\n",
" - ap_hi[average] : 0.886021505376344\n",
" - age[low] : 0.0\n",
" - cholesterol[high] : 0.0\n",
" - bmi[low] : 0.8718221321169112\n",
" ((ap_hi[average] AND age[low]) AND cholesterol[high]) AND bmi[low] = 0.0\n",
" Activation (THEN-clause):\n",
" cardio[No] : 0.0\n",
"\n",
"RULE #35:\n",
" IF ((ap_hi[low] AND age[low]) AND cholesterol[high]) AND bmi[high] THEN cardio[No]\n",
"\tAND aggregation function : fmin\n",
"\tOR aggregation function : fmax\n",
"\n",
" Aggregation (IF-clause):\n",
" - ap_hi[low] : 0.11397849462365592\n",
" - age[low] : 0.0\n",
" - cholesterol[high] : 0.0\n",
" - bmi[high] : 0.0\n",
" ((ap_hi[low] AND age[low]) AND cholesterol[high]) AND bmi[high] = 0.0\n",
" Activation (THEN-clause):\n",
" cardio[No] : 0.0\n",
"\n",
"RULE #36:\n",
" IF ((ap_hi[low] AND age[average]) AND cholesterol[high]) AND bmi[low] THEN cardio[No]\n",
"\tAND aggregation function : fmin\n",
"\tOR aggregation function : fmax\n",
"\n",
" Aggregation (IF-clause):\n",
" - ap_hi[low] : 0.11397849462365592\n",
" - age[average] : 0.7966947605633802\n",
" - cholesterol[high] : 0.0\n",
" - bmi[low] : 0.8718221321169112\n",
" ((ap_hi[low] AND age[average]) AND cholesterol[high]) AND bmi[low] = 0.0\n",
" Activation (THEN-clause):\n",
" cardio[No] : 0.0\n",
"\n",
"RULE #37:\n",
" IF ((ap_hi[low] AND age[average]) AND cholesterol[high]) AND bmi[high] THEN cardio[Yes]\n",
"\tAND aggregation function : fmin\n",
"\tOR aggregation function : fmax\n",
"\n",
" Aggregation (IF-clause):\n",
" - ap_hi[low] : 0.11397849462365592\n",
" - age[average] : 0.7966947605633802\n",
" - cholesterol[high] : 0.0\n",
" - bmi[high] : 0.0\n",
" ((ap_hi[low] AND age[average]) AND cholesterol[high]) AND bmi[high] = 0.0\n",
" Activation (THEN-clause):\n",
" cardio[Yes] : 0.0\n",
"\n",
"RULE #38:\n",
" IF (ap_hi[high] AND bmi[high]) AND age[low] THEN cardio[Yes]\n",
"\tAND aggregation function : fmin\n",
"\tOR aggregation function : fmax\n",
"\n",
" Aggregation (IF-clause):\n",
" - ap_hi[high] : 0.0\n",
" - bmi[high] : 0.0\n",
" - age[low] : 0.0\n",
" (ap_hi[high] AND bmi[high]) AND age[low] = 0.0\n",
" Activation (THEN-clause):\n",
" cardio[Yes] : 0.0\n",
"\n",
"RULE #39:\n",
" IF (ap_hi[high] AND bmi[high]) AND age[high] THEN cardio[Yes]\n",
"\tAND aggregation function : fmin\n",
"\tOR aggregation function : fmax\n",
"\n",
" Aggregation (IF-clause):\n",
" - ap_hi[high] : 0.0\n",
" - bmi[high] : 0.0\n",
" - age[high] : 0.2033052394366198\n",
" (ap_hi[high] AND bmi[high]) AND age[high] = 0.0\n",
" Activation (THEN-clause):\n",
" cardio[Yes] : 0.0\n",
"\n",
"RULE #40:\n",
" IF (ap_hi[high] AND bmi[low]) AND age[high] THEN cardio[Yes]\n",
"\tAND aggregation function : fmin\n",
"\tOR aggregation function : fmax\n",
"\n",
" Aggregation (IF-clause):\n",
" - ap_hi[high] : 0.0\n",
" - bmi[low] : 0.8718221321169112\n",
" - age[high] : 0.2033052394366198\n",
" (ap_hi[high] AND bmi[low]) AND age[high] = 0.0\n",
" Activation (THEN-clause):\n",
" cardio[Yes] : 0.0\n",
"\n",
"\n",
"==============================\n",
" Intermediaries and Conquests \n",
"==============================\n",
"Consequent: cardio = 0.5000000000000001\n",
" No:\n",
" Accumulate using accumulation_max : 0.7966947605633802\n",
" Yes:\n",
" Accumulate using accumulation_max : 0.7966947605633802\n",
"\n"
]
},
{
"data": {
"text/plain": [
"np.float64(0.5000000000000001)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"1"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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e6hKFgzrZi4g9O3+s6N5Ap3bCzdOh3Vhw0EoUW6VCSK5bfqGVN1ce5N3fjtCxdiBv9G9GkLer2bFERMrf3m/h23Hg7lt0b6AarcxOJH9DhZCUmvWHzjDhi52AhTcHNKVTnSCzI4mIlI/8HPj5GfhjETToW3SXaHc/s1PJNVAhJKXqdHoOE7/YxYYjKTzUJYqJt9TFSZ3sRaQyO3MQloyEs4ehx0xoOVId4ysQFUJS6qxWg3d/O8IbKw/SLMyPtwc1J9TP3exYIiKlb+dn8OPj4FsD7vkIghuanUiKSYWQlJk/4s/x6Oc7yMwr5NV+TejeMMTsSCIipSM3HX58Av5cDM3uhV6vgoun2amkBFQISZm6kJXHk0v+ZOXeZEa0j2Byr/q4OqmTvYhUYKf+hK9GQEYy3P4mNOlvdiK5DqYv3pg3bx4RERG4ubnRpk0btm7d+pf7z549m3r16uHu7k5YWBgTJkwgJyennNJKcfl5uPD+0JY817sBn21J4K53NnL0TIbZsUREis8wYMv7sPDmotGfB9apCKoETC2EvvjiCyZOnMj06dPZvn07TZs2pXv37pw+ffqK+3/22WdMmjSJ6dOns2/fPhYtWsQXX3zBM888U87JpTgsFgsjOtRi6cPtycwtoPecGJbtOG52LBGRa5d9Hr64F356ElqNgtGroEqU2amkFJg6NdamTRtuuOEG5s6dC4DVaiUsLIxx48YxadKky/YfO3Ys+/btY/Xq1Re3Pf7442zZsoWYmJhrek5NjZkrI7eAZ5ft5pudJ+nXsgYv3NEQDxd1shcRG5awBb6+r2hdUN93oP5tZieSUmTaiFBeXh7btm2jW7du/wnj4EC3bt3YtGnTFY9p374927Ztuzh9dvToUZYvX06vXr2u+jy5ubmkpaVd8iXm8XJ14s0BzXi1XxN+/PMUvefEsO+UzomI2CCrFda/AR/2BJ9QeDBGRVAlZFohlJKSQmFhIcHBwZdsDw4OJikp6YrHDB48mBdeeIGOHTvi7OxMVFQUN954419Ojc2cORNfX9+LX2FhYaX6OqT4LBYL/VuF8f24Djg7OnDHvA38a/MxdbIXEduRcRr+dResfgE6PgYjfgQ/fX5URhVqTmLt2rXMmDGDd955hzZt2nD48GHGjx/Piy++yNSpU694zOTJk5k4ceLFP6elpREWFsYve5Lw8Mosr+hyFQ92ieLr7cd59ptYNhxOYdbdTfB1dzY7lojYsyNrYOnoosapnZ8sujfQ/h/MTiUADfuW+kOaVggFBgbi6OhIcnLyJduTk5MJCbny/WamTp3K0KFDGT16NACNGzcmMzOT+++/nylTpuBwhaZ2rq6uuLpe3vfqQnY+eQ75pfBK5Hr1bFSNGv4eLNtxnF5vrWfu4OY0r+lvdiwRsTeFBbB2RtF0WFA9aDYEXL2LFkpLpWXa1JiLiwstW7a8ZOGz1Wpl9erVtGvX7orHZGVlXVbsODoW3ZNG0yoVW+NQX8Z2rYODBfrN38R7vx3BatU5FZFyciGxaC1QzJtF64Ba319UBEmlZ+rU2MSJExk+fDitWrWidevWzJ49m8zMTEaOHAnAsGHDCA0NZebMmQD07t2bN954g+bNm1+cGps6dSq9e/e+WBBJxRXg6cKYzpGs3JPMzJ/2s/HIWd7o35QqXupkLyJlaP+P8M1D4OAI7cZBQC2zE0k5MrUQGjBgAGfOnGHatGkkJSXRrFkzVqxYcXEBdUJCwiUjQM8++ywWi4Vnn32WEydOEBQURO/evXn55ZfNeglSypwcHOjZuBqRQV58vf04PWav561BzWgfFWh2NBGpbApy4ZepsPU9CGkCTQaoTYYdstsWGwtWx+LhqWFPW5aWnc+X2xKJO5PJuJvr8OhNtdXJXkRKx9kj8OVwOLMPou+AiI7qGF8RtBpZ6g+pTxWxWT7uzozqUIuboqsy59dDDFqwmVOp2WbHEpGK7s8vYX4nyEyGDo9BrU4qguyYCiGxaQ4WCzfXD+a+jrU4mJxBz9nrWb0v+e8PFBH5X3mZ8M3DsHQMBDeAjhPBt4bZqcRkKoSkQogM9GJs19qE+Lpx38d/8OIPe8krsJodS0QqiuQ98F4X2L0Emg4uujTeyc3sVGIDVAhJheHp6sTQtuH0ahTCRxvjuevdDRw7q5tiishfMAz44wN4vyvkZ0GniRDW2uxUYkNUCEmFYrFY6FgniAc6R3LqQg693lrP97tOmh1LRGxR9gX4agT8MAFqtIIO48Er+O+OEjujQkgqpBr+HjzStTZRVb0Y9/kOJi/9k+y8QrNjiYitOL4N5neEQ79Ay5HQ+B5wdDE7ldggFUJSYbk5OzKgVRh3Ngtlybbj9Jkbw8HkdLNjiYiZrFbY8DZ8cGvRDRI7PQ7VmpqdSmyYCiGp0CwWCzfUCuChG2uTlpNP7zkxLN6aoJYrIvYoMwU+uwdWToVanYvuEu1RxexUYuNUCEmlEOLjxkNdatM41JdJS3fz6Oc7SM9RU10RuxG3Ht5tD4lboPUDEN2naERI5G+oEJJKw8XJgbta1GBAqzBW7kum19vr2X081exYIlKWrIWwZgZ83BvcfKHTE1A12uxUUoGoEJJKp2mYH4/cWBvDgDvf2cCimDhNlYlURmkn4aPbYN1rULcHtHmwqBgSKQYVQlIpVfFy5f5OkbSpFcCLP+xl9Md/cD4zz+xYIlJaDv5cNBV2Zj+0fQTqdgeLPtKk+PRdI5WWk6MDtzWpztC24Ww+epYeb61ja9w5s2OJyPUoyIOfp8Bn/cEntGgqrEqU2amkAlMhJJVedDUfHulaG3dnRwa+v4k5qw9RaNVUmUiFcy6u6LL4LfOhQV9odR+4eJqdSio4FUJiF/w8XLivYyRd6gbxxsqD3LtwC6fTcsyOJSLXKvbrohskph6H9o9C5I3qGC+lQoWQ2A1HBwu3NAhhVMda7DmZSvfZ6/jt4BmzY4nIX8nLgu8ehSWjILAudHwc/GqanUoqERVCYneigrwYe1MdgrxdGf7BVmb+tI/8QnWyF7E5p/fDghth1+fQZAA0HwrO6hgvpUuFkNglL1cnhrWLoEfDEBaui+Oe+ZtIPJdldiwRgaKO8ds/gfe7QE46dJwANdtpKkzKhAohsVsOFgud6wYxpnMkCWcz6fX2elbEnjI7loh9y0mDr++D78ZB9ebQ8THwrmZ2KqnEVAiJ3asZ4MEjXetQM8CDB/+1nanfxJKTr072IuXu5A6Y3wn2L4cWw4qmw9QxXsqYCiERwN3FkcGta9KnaXU+/z2BvvM2cORMhtmxROyDYcCmd2BhN8Ao6hhfvYXZqcROqBAS+X8Wi4W2kVV4qEsU5zLzuP3tGJZsO252LJHKLescfD4Qfp4M4R2g/TjwDDQ7ldgRFUIi/6OarzsP3RhFdDVvnvhqFxO+2ElmboHZsUQqn2Mbi9pkxMfADWOg4Z3g4GR2KrEz+o4TuQJXJ0f6tQwjKsiL73adZEfCeeYNaUHD6mroKHLdrIWw/g1YOwMCIqH1A+DuZ3YqsVMaERL5C81r+vPwjbXJK7By57yNfLIpXp3sRa5HehJ8cgeseRlq3wJtHlIRJKZSISTyN4K8XXmgSxQtwv2Z9u0eHvjnNlKz8s2OJVLxHF5VNBWWtBvaPgz1eoKDo9mpxM6pEBK5Bs6ODvRpWp0hbWoScziFHm+tY9ux82bHEqkYCvNh5TT4193gVbXoqrDAOmanEgFUCIkUS8PqvjzStTYujg70n7+Jd9YexqpO9iJXd/4YfNADNs6F6D5Fi6Jdvc1OJXKRCiGRYvL3cGF0p0g61gnk1RUHGPbBVs6k55odS8T27P2uqGP8+fiiy+KjbgKLPnbEtug7UqQEHB0sdG8Ywsj2Eew6foGeb61jw+EUs2OJ2Ib8HPjxcfhyaNFVYZ0mgn+E2alErkiFkMh1qBPszdiutfFzd+HehVv4x88HKFAne7FnKYdgwU2w7WNofA+0GA7OHmanErkqFUIi18nbzZkRHSK4pUEw76w9zID3N3PyQrbZsUTK387P4L3OkHW2qGN8eAd1jBebp0JIpBQ4WCzcWK8qoztGcvRMBj1mr2Pl3mSzY4mUj9wMWHo/fPMQhDQqKoJ8qpudSuSaqBASKUURgZ480rU2of7ujPnkD577bg+5BepkL5XYqT/hvU6w91todi80HQxOrmanErlmKoRESpmHixP3tgnn9ibV+NfmY9z1zkbiUjLNjiVSugwDti6AhTcX3Seo40So0crsVCLFpkJIpAxYLBbaRwXyQJcoktNyuO3t9Xy784TZsURKR/Z5+OJeWP4EhLWB9uOLbpQoUgGpEBIpQ6F+7jxyY23qBHsxfvFOnlqyi6w8dbKXCixxK7zbAY78Cq1GQaO7wVH9u6XiUiEkUsZcnR3p3zKMu1uE8s3Ok/SeE8P+pDSzY4kUj9Va1DH+gx5Fa4A6PQEhTcxOJXLdVAiJlAOLxULL8AAe7hJFZl4hfeZu4NMtx9TJXiqGjNPwr7tg9QsQ1RXaPgIeAWanEikVKoREylFVHzce6hJFsxp+TFkWyyOfbictR53sxYYdXVvUMf7ENmjzINS/XR3jpVJRISRSzpwdHejbPJRBrWvy64HT9HprPTsTL5gdS+RShQWw+kX4pC+4BxRNhQXVMzuVSKlTISRiksahvoztWgcLcPe7G1mw7qg62YttSD0OH/WCmDegXi9o8wC4+ZidSqRMqBASMVGApwtjOkfSPrIKLy/fx6iPf+dshjrZi4n2Ly+aCks5BO3GQp1b1DFeKjV9d4uYzMnBgZ6NqzG8XTh/xJ+n51vr2XTkrNmxxN4U5MJPT8PiQeBbEzo9XtQ5XqSSUyEkYiPqhfjwSNfaeLk5MWThZt5ceZBCTZVJeTh7BBZ2g98XQsO7iu4P5OJpdiqRcqFCSMSG+Lo7M6pDLbrWq8rbvx5i0ILNJKXmmB1LKrM/v4L5nSA9CTqMh1qd1TFe7IoKIREb42CxcHN0MPd1rMWBpHR6vLWONftPmx1LKpu8TPjmEVg6GqrWh04TwTfM7FQi5U6FkIiNigz04pGutQnxcWPkR7/z8o97ySuwmh1LKoPkPfBeF9j9FTQdVNQ13snN7FQiplAhJGLDvFyduLdtOL0ahfDBhnjufncjCWezzI4lFZVhwB8fwPtdIT8LOk4oapqqqTCxYyqERGycg8VCxzpBPNA5kpMXsun59jp++POk2bGkoslJha9GwA8ToEbLovVA3iFmpxIxnQohkQqihr8Hj3StTVSgF2M/28HkpbvJyS80O5ZUBMe3wfyOcOhnaDECGvcHRxezU4nYBBVCIhWIm7MjA24Io2+zUJZsS6T3nBgOJaebHUtsldUKG+fAB7cCDkVtMqo3MzuViE1RISRSwVgsFlrXCuChLrVJy8nn9jkxfPl7ojrZy6UyU+Cz/vDLsxDRCdqPBY8qZqcSsTkqhEQqqBBfNx7qUpvGob489fWfPLZ4J+nqZC8AceuL2mQkbobW90ODO8DByexUIjZJPxkiFZiLkwN3tahBVJAX3+w8wY7EC8wb3ILGNXzNjiZmsBbCb6/CulchIAraPgxu+l4Q+SsaERKpBJqG+TG2a20KrQZ3vrOBD2LiNFVmb9JOwse3FxVBdW6Ftg+pCBK5BiqERCqJKl6uPNA5kta1Anjhh72M+eQPzmfmmR1LysPBX+DdDpC8t2gUqG4PdYwXuUb6SRGpRJwcHbi9SXWGtg1n05Gz9HhrHb/HnzM7lpSVgjz4eQp8dg/4VIPOT0CV2manEqlQVAiJVELR1Yo62bs7OzLgvU3M/fWQOtlXNufi4IPusGV+0WLoVqPBxcvsVCIVjgohkUrKz8OF+zpG0qVuEK//cpChi7ZwOk2d7CuFPcuKbpCYmgjtH4XIrmqTIVJCKoREKjFHBwu3NAhhZIda7D6RSo+31vPbwTNmx5KSys+G7x8rapURWBc6TgS/mmanEqnQVAiJ2IHaVb0Y27U2gZ4uDP9gK7N+2k9+oTrZVyhnDsCCrrDzX9B4ADQfCs7uZqcSqfBML4TmzZtHREQEbm5utGnThq1bt/7l/hcuXOCRRx6hWrVquLq6UrduXZYvX15OaUUqLm83Z4a1j6B7wxDeX3eE/vM3cfy8OtnbPMOA7f+E97pA9gXoMBHC22kqTKSUlLgQWr16NbfffjtRUVFERUVx++23s2rVqmI9xhdffMHEiROZPn0627dvp2nTpnTv3p3Tp09fcf+8vDxuueUW4uPjWbJkCQcOHGDBggWEhoaW9GWI2BUHi4UudYO4v1Mk8Wcz6fnWelbEnjI7llxNbjosHQPfjYXqTaHjhKKrw0Sk1FiMEtx17Z133mH8+PH069ePdu3aAbB582aWLFnCm2++ySOPPHJNj9OmTRtuuOEG5s6dC4DVaiUsLIxx48YxadKky/afP38+r732Gvv378fZ2bm4sQFIS0vD19eXBatj8fD0LtFjiFQG2XmFLN1xnD0n0xjWLpxnekXj5uxodiz5t5M74avhkJ4MjftBaEuzE4mYr9XIUn/IEhVCNWrUYNKkSYwdO/aS7fPmzWPGjBmcOHHibx8jLy8PDw8PlixZQt++fS9uHz58OBcuXODbb7+97JhevXoREBCAh4cH3377LUFBQQwePJinn34aR8crv4Hn5uaSm5t78c9paWmEhYWpEBIBDMNgc9w5ftp9itpVvZg3pAVRQboE21SGAVveg5XPgne1orVAnkFmpxKxDWVQCJVoauzChQv06NHjsu233norqamp1/QYKSkpFBYWEhwcfMn24OBgkpKSrnjM0aNHWbJkCYWFhSxfvpypU6fy+uuv89JLL131eWbOnImvr+/Fr7CwsGvKJ2IPLBYL7SKr8GCXKM5m5nH72zF8ve242bHsV9Y5WDwYVjwNNdtDu0dVBImUsRIVQn369GHZsmWXbf/222+5/fbbrzvU1VitVqpWrcr7779Py5YtGTBgAFOmTGH+/PlXPWby5MmkpqZe/EpMTCyzfCIVVXU/dx6+MYr61bx5/KtdTPxiJ5m5BWbHsi/HNsH8DhC3Dm4YDQ3vBEf1xRYpayX6KWvQoAEvv/wya9euvWSN0IYNG3j88cd5++23L+776KOPXvExAgMDcXR0JDk5+ZLtycnJhISEXPGYatWq4ezsfMk0WHR0NElJSeTl5eHi4nLZMa6urri6uhb7NYrYG1cnR+5pGUZUkBff7zp5sZN9g+o+Zker3KyFEPMGrJkJ/hHQaQy4+5udSsRulKgQWrRoEf7+/uzdu5e9e/de3O7n58eiRYsu/tlisVy1EHJxcaFly5asXr364hohq9XK6tWrL1t79G8dOnTgs88+w2q14uBQNJh18OBBqlWrdsUiSESKr0VNf8L8PVj8ewJ9521g6u3R3Ns2HIsu1y596cmwdDTErYc6t0Cd7uCgBesi5alEhVBcXFypPPnEiRMZPnw4rVq1onXr1syePZvMzExGjixaDDVs2DBCQ0OZOXMmAA899BBz585l/PjxjBs3jkOHDjFjxoyrFlsiUjJB3q482CWK5btPMfXbPcQcTuHVu5vi61GyqzXlCg6vLro03loAbR8qulO0iJQ7UyegBwwYwJkzZ5g2bRpJSUk0a9aMFStWXFxAnZCQcHHkByAsLIyff/6ZCRMm0KRJE0JDQxk/fjxPP/20WS9BpNJydnTgjmahRAV5sWzHCXq+vY65g1vQoqamba5LYT6seRli3oSgaGg2GFx1BauIWa758vmJEyfy4osv4unpycSJE/9y3zfeeKNUwpUF3UdIpPjOZ+bxxR+JnLiQzZPd63F/p0gcHDRVVmwXEmDJKDixHer1gqiuYDH9Bv8iFUcZXD5/zSNCO3bsID8//+L/X43WEYhUPv6eLozpFMnKvcnM+mk/Gw6n8OaAZgR66UKEa7bve/jmYXB0hvbjihZGi4jpSnRDxYpMI0Ii1+dgcjpLth3HzdmBtwc2p33tQLMj2bb8HPjlWfh9AYQ0hSYDwMXD7FQiFZOt3FBRROxX3WBvxt5UGz93F4Ys3MLrvxygQJ3sryzlMCy8GbZ9BI36QcsRKoJEbEyJFktnZmYya9YsVq9ezenTp7FaL30TPHr0aKmEExHb5OPmzIgOEaw9cIa5aw6z6ehZ5gxqTjVfd7Oj2Y5di+GHCeDqAx0fAx81hxaxRSUqhEaPHs1vv/3G0KFDqVatmtYFidghB4uFm+pXpVagJ19tS6TH7PW8fk9TujUI/vuDK7PcDFj+BOz6HGq0hkZ3g5PWUsnlrFgoRPeN+m9OFFDeFUWJ1gj5+fnx448/0qFDh7LIVKa0Rkik9GXlFvD19uPsS0pnVIdaPN2zHq5OdvgGn7QbvhwOaceLpsJq3GB2IrFBBpCKN1lOAf9/A00NJhQxsBRkE2Q9jROFV97FzKvG/pu/vz8BAQGlnUVEKigPVyfubRvOxiNn+XhTPFvizjJvcAsiAj3NjlY+DAN+Xwg/PwOeVaHjRPCy85ExuapUvMlyC8HHywMXR5VB/2YA5zPduZCZSxXjXLn9u5RoROhf//oX3377LR9//DEeHhVr4Z9GhETK1onz2XzxRwLZeYXMuKsxdzSr5Gtjsi/Ad+Ng33cQ0Qmi+xRdIi9yBVYsJDnVxMfPDy8XlUD/Kzvf4PyFVILzE3DkChdhmDki1Lx580vWAh0+fJjg4GAiIiJwdr70h3779u2ll1BEKpRQf3cevrE23+48wfjFO9lwOIXn+zTC3aUSTpUl/g5LRkDWOWg5Cqo1MTuR2LhCHMHBkcr441AaHB0AiwNWHK5cCJWBay6E/t0YVUTk77g5O9K/VVEn+292nGTbsQu8M6QF9UIqySis1Qob34ZfXwTfMOj0BHhouYBcK4umw67C8l//LS/XXAhNnz69LHOISCVjsVhoFRFAWIAHX/yeSO+5MTzfpyEDbwir2FeaZpyBZffDkV8h6uaiVhnqGC9SYZXohoqJiYkcP3784p+3bt3KY489xvvvv19qwUSkcgj2cePBLlE0reHH5KW7GffZDtJy8s2OVTJHf4N328HxP6DNgxDdW0WQSAVXokJo8ODBrFmzBoCkpCS6devG1q1bmTJlCi+88EKpBhSRis/FyYE7m4cy8IYwVu1P5ra31rMr8YLZsa5dYQH8+hJ8cge4BxRNhQXVNzuVSLka8dh0LKEtmDX3w0u2f7NiDZbQFialun4lKoRiY2Np3bo1AF9++SWNGzdm48aNfPrpp3z00UelmU9EKpEmNfwY27UOBnD3uxtZuP4oNt/uMPUEfHwbrH+9aBqszQPg5mN2KhFTuLm58so7H3H+QprZUUpNiQqh/Px8XF2L7pS6atUq+vTpA0D9+vU5depU6aUTkUonwNOF+ztH0jayCi/9uI9RH/3Oucw8s2Nd2YGf4N32cOYgtBsLdW4Bi1o0iv3q1rE1IUFVmDn3g6vu8/WPq2nYtR+utdoQ0eY2Xp//z3JMWHwl+olu2LAh8+fPZ/369axcuZIePXoAcPLkSapUqVKqAUWk8nFycKBX42oMaxfO1vhz9Ji9ji1Hz5od6z8KcmHFZPh8IPjWgE6PQ0Ck2alETOfo6MiMSWOZ8+EXHD+ZfNnfb/tzL/0ffJqBfbqze9WXPDfxAaa+9i4fffGdCWmvTYnuLP3KK69w55138tprrzF8+HCaNm0KwHfffXdxykxE5O/UD/FhbNc6fPlHIoMWbGb8zXUZe1NtHB1MvKrs7BFYMhKS90DDOyGiM1Tkq9ykQsjOt3LkfEG5P2+UvxPuzsUbE7mz5000a1CX6a/PZ9Hrl15R/sb7n3Jzx9ZMnTAGgLpR4ew9dJTX5n/CiAF9Si13aSp2IWQYBpGRkSQkJFBQUIC/v//Fv7v//vsr3J2mRcRcvu7O3NexFr/uP83sVQfZeCSFtwc1J9jHrfzD7F4C3z8Kzp7QYXzRPYJEysGR8wXcvjil3J/3h4GBNKrqUuzjXpnyKDf1f5AnHhx6yfZ9h+K4o3uXS7Z1uKEZsxd+RmFhIY6OtneVZYkKodq1a7Nnzx7q1Klzyd9FRESUVi4RsSMOFgvdooOJDPTky22J9Ji9jjcGNKNrvarlEyAvC356Cnb8E0JbQuN7wMmEQkzsVpS/Ez8MDDTleUuic9uWdO/Sjskz5zKif+9STlW+iv0v4ODgQJ06dTh79uxlhZCIyPWIDPJibNc6fL0tkZEf/s79nSN54tZ6uDiV4QLl5L3w1XA4fwyaDoIarTUVJuXO3dmhRCMzZpr1zDia3TqIelHhF7dF16nFht93XbLfht93Ujcy3CZHg6CEi6VnzZrFk08+SWxsbGnnERE75+XqxNB2EfRsFMKi9XH0m7+RxHNZpf9EhgHbPoL3b4S8DOg4AcLaqAgSuUaNo+sw5M6evP3B4ovbHn/gXlbHbOXFNxdw8MgxPv7ye+Z++CVPPDD0Lx7JXCUqhIYNG8bWrVtp2rQp7u7uBAQEXPIlInI9HCwWOtUJ4v7OkRw/n03Pt9azfHcp3pojJ7VoQfT344umwjo8Bt4hpff4InbihScfwmr9T3PUFo2j+XL+Kyz+7mca3XwP0/7xLi88+aDNLpQGsBgluJvZxx9//Jd/P3z48BIHKmtpaWn4+vqyYHUsHp6VpAGkSCWWnVfINztPsPtEKkPa1GTq7Q1wc76OIfYT2+CrkZB5GhoPgOrNSi2ryN/Jx4kzLuEE+Xvh7KjRx/+VX2hw5nwGQXnHcOYKV9G1Glnqz1miVVK2XOiISOXi7uLIwBvCiAzy5Ms/Evk9/hzvDGlB7arF/EXGMGDTPFg1HXxCoePj4Fn+i1NFxLaUeAXikSNHePbZZxk0aBCnT58G4KeffmLPnj2lFk5EBIo62bepVYWHutTmQlY+t8+J4cs/Eq+9PUfmWfisP/wyBSI6QftxKoJEBChhIfTbb7/RuHFjtmzZwtKlS8nIyABg165dTJ8+/W+OFhEpmRBfNx6+sTYNq/vy1JI/eeyLnWTk/s1N6OI3wPz2kLAJbrgfGtwBDiW7ZFhEKp8SFUKTJk3ipZdeYuXKlbi4/Odyv5tuuonNmzeXWjgRkf/l4uTA3S1q0L9VDVbEJnHb2+uJPZF6+Y7WQlj7Cnx8O7h4F02FBTco/8AiYtNK9GvR7t27+eyzzy7bXrVqVVJSyv/OmCJif5qF+VPD34Mvfk+k77z1PNo+hIGta2KxWCA9mSprnsLh+CaofQvU7a5mqSJyRSV6Z/Dz87til/kdO3YQGhp63aFERK5FoJcrD3SOpHGgI+N730BwcDBVq1alalRjzsb9CW0egno9VQSJyFWV6N1h4MCBPP300yQlJWGxWLBarWzYsIEnnniCYcOGlXZGEZGrcnJ0oHuDK9wDqO3DEKi734vIXytRITRjxgzq169PWFgYGRkZNGjQgM6dO9O+fXueffbZ0s4oIvKXPLJPXrZt0bEgrMW+S5qI2JsSrRFycXFhwYIFTJ06ldjYWDIyMmjevLl6j4lIuQs79QuRW6dctv2dAx7sznXnzdbpVHWzXuFIEZESFkL/VrNmTcLCwoCi+3yIiJQXx8IcWux7jTqJX3LAvQ5w+pK/fyzyBJ+crUfPX/yZ3SaNTsH55gQVEZtW4hWEixYtolGjRri5ueHm5kajRo1YuHBhaWYTEbkin4yjdN84iMjjyzha7Tbiqt9+2T4NvLN5JTqOULcchq3349XdnuRrYEikRAzDoNuAB+k++OHL/u6dj77EL7ozx08mm5Ds+pWoEJo2bRrjx4+nd+/efPXVV3z11Vf07t2bCRMmMG3atNLOKCJSxDCIPL6MHhsG4JKfyp7IUZwJaHnVjvF+zoVMrp3IwNAzzD/gwYC1fhzP1BVkIsVlsVj48I3n2LIjlvf+ueTi9riEEzz18lvMefEpalQPNjFhyZXoHeHdd99lwYIFzJw5kz59+tCnTx9mzpzJ+++/zzvvvFPaGUVEcCrIpN2uybTdPY1zPvXZE3kf2W5//8brYIG+Ied4rl4CiRkWeq0K4OcTLn97nIhcKiw0hLeef5InXpxNXMIJDMPgvsef59YubWneqD497x2LV50OBDftxtBxz5Jy7vzFY5f8sIrGN/fHPaodVRp2pduAB8nMyjbx1fxHiQqh/Px8WrVqddn2li1bUlDwN7e7FxEpJv/UffTccA9hyas4HHoncaF9sDoUr5ip55XNrOg46nlm8sAmP6bv8CKnsIwCi1RSw/v35uaOrRk18XnmfvgFsQeO8N4rz3JT/wdo3rAef/z0L1Z8OpfklHP0f+BpAE4ln2HQI88wakAf9q39mrVL3ueunjdde6/AMlaixdJDhw7l3Xff5Y033rhk+/vvv8+QIUNKJZiICIZB3WOf0Xz/62S7BREbOZpc1yolfjgvJyuPR57g5zN+/OtoMFtTnJnXNo1Ib1VEYrKCHLiQUP7P61cTnNyKdcj7rz5Lw679WLdlO18veI33/vk1zRvVY8bkcRf3+eD16YTd0JODR46RkZVFQUEBd/W6ifAa1QFoHG07V5lfcyE0ceLEi/9vsVhYuHAhv/zyC23btgVgy5YtJCQk6IaKIlIqXPJSabN7KmGn15AU0JqE4G4YpdAs1WKBHlUvUM8rm7fiQrltlT8zWqRzZ3huKaQWKaELCbD0/vJ/3rveh8C6xTqkamAAD9x7N9/8vJa+Pbry6dKfWLPxD7zqdLhs3yPHjnNrl7bc3LE1jW8eQPcu7bi1S1v63dYNfz+f0noV1+Wa31V27NhxyZ9btmwJwJEjRwAIDAwkMDCQPXv2lGI8EbFHged30GHHk7gUpHMgbAAXfOqV+nPU8shlZv14PkgMZsLvvsSczuaF5hl4OtnGcL3YGb+aRUWJGc9bAk5Ojjg5OgKQkZVF71s688ozj162X7XgIBwdHVm5+F02/rGLX37bxJwPFzPllXls+eETatU0vy3XNRdCa9asKcscIiJgWGlwdBFNDs4lw6MGseEDyXP2LbOnc3e08kjEKRp5Z7IoIYTtZ515p20q0X6aKpNy5uRW7JEZW9GiUX2+Xv4rEWHVcXK6cllhsVjocEMzOtzQjGkT7ie89W0s+2kNEx+4t5zTXk7XkYqITXDLTeGm3++n6cE5nApsz76IYWVaBP23LlXSmBkdj7WwgDt+DeCfR9yxkXWcIjbvkREDOHchlUEPP8PvO/dwJD6Rn9duZOSE6RQWFrJl+25mvL2IP3btJeHEKZYu/5Uz584TXaeW2dGBEi6WzsnJYc6cOaxZs4bTp09jtV56l7Lt27eXSjgRsQ8hKRtpv2sSDtYC9ocPIc0rstwzhLrl8VL9Y3xyvCpTd/iz4bQzr7RMx9dFFZHIX6keEsSGbz7k6Rlvcevgh8nNzSe8Rgg9bmyPg4MDPt6erNuyndkLPyMtI5Pw0Gq8Pm0CPW+6fE2RGSxGCa5fGzJkCL/88gv9+vUjODj4svYa06dPL7WApS0tLQ1fX18WrI7Fw9Pb7Dgids1izafJoXdocHQRqV6RHAm9gwInr2I/zvm0DHqOfu6SbT8tfA5/n+I/FsCW8968dywEP1eDuW3SaF5FtwWR0pGPE2dcwgny98LZUa2p/ld+ocGZ8xkE5R3DmSv83LUaWerPWaIRoR9++IHly5fToYNtVHMiUvF4ZJ+kw86nqHJhN4lVb+JUYPur3iG6vLXxT6eWRw5vx1XnnrX+PNkokzF1s3CwjXgiUopKtEYoNDQUb2+NpohIydRIXk2vmLvxzjzGvlrDORXUwWaKoH+r6prPc/WO0avqOWbu9mJkjC8pObaVUUSuX4kKoddff52nn36aY8eOlXYeEanEHApzabl3Bp23P1Z0VVjUGDI8wsyOdVVOFhhS4wyTayey86wjPVcGsPG0s9mxRKQUlWhqrFWrVuTk5BAZGYmHhwfOzpe+MZw7d65UwolI5eGdGU/HHY/jm3GU+JCeJAe0srlRoKtp5pvJrOh45sVXY8g6P8ZFZ/FodCZOuu5WpMIrUSE0aNAgTpw4wYwZM664WFpE5L9FnPie1nteIN/Riz21RpHlHmJ2pGILcClgSp1EliVVYe6+QDadcebt1mlU87D+/cEilzDQtYhXZvzXf8tLiQqhjRs3smnTJpo2bVraeUSkEnEqyKLVnpeJPPkdZ/yaEB/SC6tjxe387mCBu6udpYFXFnPiq9NzVQCvt0rj5up5ZkeTCsKRQrAWklcILo5mp7E9hVbAsOJA+f2CUaJCqH79+mRnZ5d2FhGpRPzSDtBxx+N45pziSOgdpPhVnl+cor2zeSU6jnfjq3PfRj9G1c5iUpMMXDRVJn/DAQOPgnOkZbiAlwcujqA5lSIGkJZdgEtBmu0XQrNmzeLxxx/n5ZdfpnHjxpetEfLxsY1GaiJiAsOgdsKXtNz3CjmuVYiNHE2Oa6DZqUqdt5OVJ6OOs/y0P58cqXqxk324l9pzyF/zJR1yIK0gABwcUSn0bwaWgmyqGKnl+i9SokKoR48eANx8882XbDcMA4vFQmGh3ghE7JFzfhptdk+nZvIqkv1bcSzk1lLpGG+rLBa4Lfg89b2yeTuuOr1W+TOzZTp9wtTJXq7OAviRjk9BBoVofuy/OVFQ7mVhid6h1IBVRP5XlQt/0mHHE7jln+dg2D2c94k2O1K5ifLMYWZ0PAsTQnh0iy8bT2czvWk67pW3BpRS4ICBw5XunizlqkQ/pl26dCntHCJSURlWouM+punBt8h0q87uyPvJc/EzO1W583C0Mi7iJA29M/n4WDDbUpyY1zaNur4aIRexZSVe2rd+/Xruvfde2rdvz4kTJwD45z//SUxMTKmFExHb5pp7lhv/eJjmB94gqUob9tUaZpdF0L9ZLHBzYCov148np8BKn9X+fH7UTZ3sRWxYiQqhr7/+mu7du+Pu7s727dvJzS2aD09NTWXGjBmlGlBEbFPw2S30irmbwAu72B8+mMTgbhgWrXcACHPP4+X68XQISGPydh/GbfEhPV8LYkVsUYkKoZdeeon58+ezYMGCS64Y69ChA9u3by+1cCJieyzWAhofnMtNW8eQ5+xDbOQYUr1qmx3L5rg6GNwfnsT4WidYfcqFXiv9+fOcFg2J2JoSFUIHDhygc+fOl2339fXlwoUL15tJRGyUe3YSN28dRaMj73O8ahf2hw8h31kNmP9K+4B0ZkXH4WLJ5641/iw86K6pMhEbUqJCKCQkhMOHD1+2PSYmhsjIyOsOJSK2p/rp3+i1oR++GUfZFzGMk0GdwaI7CF6LENd8Xqh7jO5B53npT2/u2+DLuVxNlYnYghK9i40ZM4bx48ezZcsWLBYLJ0+e5NNPP+WJJ57goYceKu2MImIiB2s+Lfa9wo3bxpLpFkJs5BjSPcPNjlXhODnAsLDTPBWVyB8pTvRcGcCWM+pkL2K2Ek1YT5o0CavVys0330xWVhadO3fG1dWVJ554gnHjxpV2RhExiVdmIh13Po5f+kHiQ7qTHNC6wnSMt1Ut/TKZ5RHHnLjqDPrNj/ENMhkbnYWj/llFTFGiQshisTBlyhSefPJJDh8+TEZGBg0aNMDLy6u084mIScJP/kTr2OcocHRnT62RZLlXNztSpVHFpYCpdRP4+lQgs/dWYdMZF95unUZVd3WyFylvxSqERo0adU37ffDBB8UKMW/ePF577TWSkpJo2rQpc+bMoXXr1n973OLFixk0aBB33HEH33zzTbGeU0SuzLEwm5Z7Z1H7+FJSfBsRX+02Ch1dzY5V6ThaoH/1FBp4ZTE3vjo9VvrzRut0bgxRJ3uR8lSsNUIfffQRa9as4cKFC5w/f/6qX8XxxRdfMHHiRKZPn8727dtp2rQp3bt35/Tp0395XHx8PE888QSdOnUq1vOJyNX5ph+mx4aB1DrxPUer9+ZI6J0qgspYI58sXomOI8wtmxExfsz805N8DQyJlJtijQg99NBDfP7558TFxTFy5EjuvfdeAgICrivAG2+8wZgxYxg5ciQA8+fP58cff+SDDz5g0qRJVzymsLCQIUOG8Pzzz7N+/fq/vGQ/Nzf34g0fAdLS0q4rr0ilZBhEHf+alntnkefsy57I+8h2q2p2Krvh61zIpNrH+SE5gIWHgth8xpm5bdMI81RFJFLWijUiNG/ePE6dOsVTTz3F999/T1hYGP379+fnn3/GKMGNMfLy8ti2bRvdunX7TyAHB7p168amTZuuetwLL7xA1apVue+++/72OWbOnImvr+/Fr7CwsGLnFKnMnPIz6LDzSdrEPs9Z34YqgkziYIE+Ied4ru4xTmVZ6LUygJ+OazROpKwV+/J5V1dXBg0axMqVK9m7dy8NGzbk4YcfJiIigoyMjGI9VkpKCoWFhQQHB1+yPTg4mKSkpCseExMTw6JFi1iwYME1PcfkyZNJTU29+JWYmFisjCKVWUDqHnpt6Efo6d84VONu4qvfjtVBl3Sbqa5XDrPqx9HAO4OHNvvy7HYvctS3VaTMXNf93h0cHLBYLBiGQWFh2f+kpqenM3ToUBYsWEBgYOA1HePq6oqrq36rErmEYVAv/p80P/AmWW7BxEaNIdfF3+xU8v88naxMqHWSld5ZfBJXlT9SiqbKavuoIhIpbcUuhHJzc1m6dCkffPABMTEx3H777cydO5cePXrg4FC8AabAwEAcHR1JTk6+ZHtycjIhISGX7X/kyBHi4+Pp3bv3xW1Wa9EcupOTEwcOHCAqKqq4L0nErrjkXaDdn1MIPbOOU1Xaklj1ZgwHNUu1NRYL3Bp0gbqe2bwdV53bV/vzUvMM+kXkmB1NpFIpViH08MMPs3jxYsLCwhg1ahSff/75NY/MXImLiwstW7Zk9erV9O3bFygqbFavXs3YsWMv279+/frs3r37km3PPvss6enpvPXWW1r/I/I3gs5to8POJ3EuyOJAzYFc8K5rdiT5GxEeucyoH8+HicE88YcfG04782LzDLyc1bBMpDQUqxCaP38+NWvWJDIykt9++43ffvvtivstXbr0mh9z4sSJDB8+nFatWtG6dWtmz55NZmbmxavIhg0bRmhoKDNnzsTNzY1GjRpdcryfnx/AZdtF5D8sRiENjyyg8aF3SfcM40D4EPKcfcyOJdfIzdHgoYgkGnlnsTAhhB1nnZnXLo2GfgVmRxOp8IpVCA0bNgxLKd9ef8CAAZw5c4Zp06aRlJREs2bNWLFixcUF1AkJCcWechOR/3DLOUP7XZMIPvc7J4I6cULNUiusTlXSiPLM5u24UPr+6s+zTTIYFpWtrici18FilOS69wosLS0NX19fFqyOxcPT2+w4ImWq2pkY2u2ajMWwcqRGX9I9I8yOVCbOp2XQc/Rzl2z7aeFz+PtUzrY/eVYL/zpelZ/P+HNr9Rxea5WOr4tdvZWLvWo1stQfUr8WilRCFms+zfa/Qdc/HiLbNYjYqDGVtgiyRy4OBqNqJvN45HE2JDvTc6U/21Ku6yJgEbulQkikkvHMOsEtm4dRP/5jjgV342DNgRQ4eZodS8pAa/8MXomOw9sxn/6/+fPOfg+sGhgSKRYVQiKVSFjSSnpuuBuv7BPsjRhBUmB7tICkcgtyLWBa3WP0Dj7La7GeDFvvy5kcnXORa6VCSKQScCjMpdWel+i0YyLpHuHERo4h06OG2bGknDhZYFBoCs/USST2vCM9VgYQk6w7hItcCxVCIhWcT8ZRum8cRFTiUuKq3cbhGndT6OhmdiwxQZP/72Qf6prD0PV+vBbrSYH6tor8JRVCIhVYrePf0mPDAFzzL7AnchSnA1pqKszO+TkXMrl2IgOqp/Dufg8G/ObHiSy91YtcjX46RCogp4JM2u2aTLvdz3LOp/7/d4wP/vsDxS44WODOameZXi+BY+kWeq4M4JeTLmbHErFJKoREKhi/tP303HAPYUkrORx6J3GhfbA66ENOLlffK5tXouOo55nJ/Rv9eG6nF7nq2ypyCRVCIhWFYVD32Gd03zgYi7WA2MjRnPVrbHYqsXFeTlYejzzBiLBkPj3izp2/+hOXria7Iv+mQkikAnDOT6XTjsdotXcmZ/ybs7fWSHJdq5gdSyoIiwV6Vj3PC/XjOZ9jcNsqf75JcDU7lohNUCEkYuMCz++kV0w/QlI2czBsAMeq9cBw0F2EpfgiPXKZGR1PC990Htvqy5N/eJOlvq1i5/RuKmKrDCsNjn5Ak4NzyPCoQWzkGPJcfM1OJRWcu6OVsRGnaOSdxYcJwWw/68y8tqnU99XiIbFPGhESsUFuuSl0/f0Bmh58m1OB7dkXMUxFkJQaiwW6BqYyo348BQWF3LE6gE+PumFfLbhFiqgQErExwSmb6BVzN1VS97A/fAjHg28Ci35UpfTVcM/jpfrxdA64wJTtPjyy2YfUPN2HSuyL3l1FbITFWkCTg29z0+8PkOPsz+6oMaR5RZodSyo5FweD0eHJPBZ5grVJzvRa5c/Oc1o1IfZDhZCIDfDIPkW3LSNpcGQRx6t25UD4YAqcvMyOJXaknX86r0TH427Jp98af94/4K5O9mIXVAiJmCw0+Vd6xdyNT2Y8+2oN42RQR7XJEFNUdc3n+brH6Fn1HDN2ezMyxpezufpelMpNhZCISRwK82i5dyZdto8nwz2U3ZFjyPCoaXYssXNODnBvjTNMqp3IjrOO9FwZwKbT6mQvlZcKIRETeGce49ZNg6mT8CXxIT04FHYPhU7uZscSuai5byavRMcT5JzL4HV+vLHHk0JNlUklpEJIpJxFnPiBHhvuwT03hT21RpJcpbWmwsQmBbgU8GydBPpVS2HuPg8G/uZHUrY+NqRy0Xe0SDlxLMiizZ9Taf/nZFK96hAbOZos92pmxxL5Sw4W6Ff9LFPrJnAk1YEeKwP49ZSa/ErloUJIpBz4ph+kx8aBRJxazpHqfTgSegdWR/V6koqjgXc2rzSII9I9i1Eb/Hhplxd5VrNTiVw/FUIiZckwqJ3wJT02DsKpMIvYyPtI8W+mqTCpkHycCnkq6jhDayTz0WF3+q3xIyFDHyNSsek7WKSMOOen0WHnE7Te8yIpvk3YU2sUOa5BZscSuS4WC9wefJ7n6x0jOQt6rQrg+0SNbkrFpUJIpAxUubCbnjH9CD2zjkM1+hFfvReGgy5BlsqjtmcOs6LjaeKdwbgtvkze5k22OtlLBaRCSKQ0GVbqH/2IWzYPxbA4Ehs5hnO+DcxOJVImPBytPFrrJPfXPMXXx1zps9qfQ2mOZscSKRYVQiKlxDX3HDf+8TAtDrxOUkBr9tUaTq6Lv9mxRMqUxQI3B6Xycv14cgqs9F7lzxdx6mQvFYcKIZFSUPXs7/TacDdB53eyv+YgEkNuwbDoN2OxHzXd83i5fjztA9J4epsP47f6kJ6viwLE9qnFsMh1sBiFNDo8n0aH3yPNM4J94UPJd/Y2O5aIKVwdDB4IT6KRdyYLEqqx85w/89qm0dhfi4fEdmlESKSE3HOSuXnLKBodfo/jQV3YHz5ERZAI0CEgnVn143A2CrjzV38WHXLXVJnYLBVCIiVQ/fQ6esXcjW/GEfZFDONk1c5g0Y+TyL+FuOXzfL1j3Bp0nhd3eTN6oy/n1clebJDeuUWKwcGaT/N9r3HjtkfIcgsmNnIM6Z7hZscSsUnODgbDw07zVNRxtp5xoueqALae0W0kxLaoEBK5Rp5ZidyyaSj1jn3KseBbORg2gAInD7Njidi8ln4ZzIqOw88xl4G/+TFnn4c62YvNUCEkcg1qnlpBr5h78Mg5xd5aI0kKbKs2GSLFEOhSwLS6CfQNOcsbezy5d50vp9XJXmyAvgtF/oJjYTatY5+j484nSfOKYE/kaDLdq5sdS6RCcrTAgNAUnq2TyP4LjvRY6c9vSepkL+ZSISRyFT7pR+ixYSC1jn/H0eq3czj0Lgod3cyOJVLhNfLJ4pXoOGq65TA8xo+Zf3qSr072YhIVQiL/yzCITFxKj40DcC7IYE/kfZzxb6GpMJFS5OtcyNO1ExkcepqFhzy4Z60fiZn6SJLyp+86kf/ilJ9B+11P0zZ2Oud8G7An8j6y3aqaHUukUnKwwB0h53iu7jFOZFi4bVUAK06ok72ULxVCIv/PP3UPPTfcQ43kXzlU4y7iqvfGqo7xImWurlcOs6LjiPbM5MFNvkzd4UVOodmpxF6oEBIxDOrF/ZPum+7FgvX/O8Y3MjuViF3xcrIyIfIE99VMYvFRN/r+6s+RdPXrk7KnQkjsmkveBTpvG0fL/a+SHNCSvREjyXUNMDuWiF2yWODWoAu8VP8Y6bkGt6/yZ0m8LlCQsqVCSOxW0Llt9Iq5m+Bzv3Og5kASQrpjOOg3UBGzRXjkMqN+HK390nniDx8mbPUms0AXK0jZUPd5sTsWo5AGRxbS5NA7pHuEcSB8MHnOPmbHEpH/4uZo8HDEKRp5Z7IoIYSd55yZ2zaNhn7qZC+lSyNCYlfccs7Qdev9NDk0jxNBHdgXMVRFkIgN61wljZnR8RjWok72nxxWJ3spXSqExG5UO7OBXjF3EZC2j/3h93Kiald1jBepAKq75fFivWN0rXKBaTu9eWCTD6l5miqT0qFPAan0LNZ8mu1/g65/PEiOayC7o+4nzauW2bFEpBhcHAxG1Uzm8cjjbEh2pufKALad1eoOuX4qhKRS88w6wS2bh1M//mMSgrtxoOYgCpw8zY4lIiXU2j+DV6Lj8HLIo/9af97Z74FVU2VyHVQISaVVI2kVPTf0wyv7OPsiRnAqsL3aZIhUAkGuBUyvd4zbg8/xaqwXw9f7ciZHP9tSMiqEpNJxKMyl1Z6X6LxjAhkeYcRGjiHDo4bZsUSkFDlZYHDoGZ6pk8Du8470XBnAhmTdCV6KT4WQVCreGXF03zSYqMSviavWi0M1+qljvEgl1vT/O9lXc83h3vV+/CPWkwJ1spdiUCEklUat49/Sc2N/XPPOsTdyFKcDWmkqTMQO+DkX8kztRAZUT+Gd/R4M+M2Pk1n6eJNro+8UqfCcCrJot2sy7XY/y3nv+uyJHE2WW4jZsUSkHDlY4M5qZ5leL4Fj6RZ6rgxg5UkXs2NJBaBCSCo0v7T99NhwDzWTfuFwaF+OhvbB6qA3PxF7Vd8rm1ei46jjmcmYjX48v9OLXHWyl7+gQkgqJsOgzrHP6b5xMI7WfGIjx3DWr4nZqUTEBng5WXki8gQjwpL51xF37lrjT3yG+gjKlakQkgrHOT+VTjse44a9Mzjj34w9tUaS41rF7FgiYkMsFuhZ9Twv1I/nXI7Bbav8+TbB1exYYoNUCEmFEnh+J71i+hGSsomDYf05Vq0nhoPuLisiVxbpkcvM+vE090ln/FZfnvrDmyz1bZX/ok8QqRgMKw2OfkCTg3PI8KhBbOQY8lz8zE4lIhWAu6OVsRGnaOSdxYcJwWw768w7bVOp56vFQ6IRIakA3HJT6Pr7gzQ9+DanAtuxP2KoiiARKRaLBboGpjKjfjz5BYX0WR3AZ0fd1MleVAiJbQtO2UzPmH5USd3N/vDBHA++GcOiRY8iUjI13PN4uX48nQIu8Mx2H8Zu8SEtX/cbs2cqhMQmWawFNDn4Njf9fj+5zr7ERt5PmleU2bFEpBJwcTAYE57MY5EnWHPKmdtW+rPrnFaK2CsVQmJzPLKT6LZlJA2OLOJ41Rs5ED6EfGcvs2OJSCXTzj+dWdHxuFryuXuNPwsOuquTvR1SISQ2JTR5Db1i7sInM559tYZxMqiT2mSISJkJds3n+brH6Fn1HC//6c2oDb6cy9V7jj2xiUJo3rx5RERE4ObmRps2bdi6detV912wYAGdOnXC398ff39/unXr9pf7S8XgUJhHy72z6LL9UTLcq7M7cgwZHjXNjiUidsDJAe6tcYZJtRPZnuJIj5UBbD6jTvb2wvRC6IsvvmDixIlMnz6d7du307RpU7p3787p06evuP/atWsZNGgQa9asYdOmTYSFhXHrrbdy4sSJck4upcU78xi3bhpCnYQviA/pwaGw/hQ6uZsdS0TsTHPfTF6JjifQOZfBv/kxe68HhZoqq/QshmHuxYNt2rThhhtuYO7cuQBYrVbCwsIYN24ckyZN+tvjCwsL8ff3Z+7cuQwbNuxv909LS8PX15cFq2Px8PS+7vxyfcJP/kjr2OcpcPTgcI27yHKvZnYkqYDOp2XQc/Rzl2z7aeFz+PtobZkUn9WAr08FsvRUFW4Iyuft1mkEu1vNjiUArUaW+kOaOiKUl5fHtm3b6Nat28VtDg4OdOvWjU2bNl3TY2RlZZGfn09AQMAV/z43N5e0tLRLvsR8jgVZtPlzGh12TSLVqzaxkaNVBImITXCwwD3VU3i2biKHLjjQY6U/a06pmXNlZWohlJKSQmFhIcHBwZdsDw4OJikp6Zoe4+mnn6Z69eqXFFP/bebMmfj6+l78CgsLu+7ccn180w/SY+NAIk79yJHqfTgS2hero3oAiYhtaeidxSvRcUS4ZzNygx8v7/IiTwNDlY7pa4Sux6xZs1i8eDHLli3Dzc3tivtMnjyZ1NTUi1+JiYnlnFIuMgxqJ3xJj42DcCrMJjbyPlL8m+mqMBGxWT7OhTwVdZx7a5zmg8Pu3LPGn4SMCv3RKf/D1DtIBQYG4ujoSHJy8iXbk5OTCQkJ+ctj//GPfzBr1ixWrVpFkyZNrrqfq6srrq4abTCbc346rWOfIzzpF5L9W3Is5FYMB12VISK2z8ECvYPPEe2Vxdtx1em1KoBXWqVzW41cs6NJKTC1rHVxcaFly5asXr364jar1crq1atp167dVY979dVXefHFF1mxYgWtWrUqj6hyHapc2E3PDf0IPb2OQzX6EV/9NhVBIlLh1PbMYVZ0PI29M3hksy/PbPcmR31bKzzT7yk+ceJEhg8fTqtWrWjdujWzZ88mMzOTkSOLVoYPGzaM0NBQZs6cCcArr7zCtGnT+Oyzz4iIiLi4lsjLywsvL10hYlMMK/Xj/0mzA2+S6VaN2Kgx5Lr4m51KRKTEPBytjK91kkbemXwcH8y2FCfmtU2jto8qoorK9EJowIABnDlzhmnTppGUlESzZs1YsWLFxQXUCQkJODj8Z+Dq3XffJS8vj379+l3yONOnT+e5554rz+jyF1xzz9F29xRCz8Rwsko7jle9CcNBzVJFpOKzWKBbUCp1vXJ462h1bl/tzwvNMrgnIkdLHisg0+8jVN50H6GyV/Xs73TY9RROhdkcqd6HVO86ZkeSSk73ERKz5BRa+Oh4MGtS/LgjLIeXW6Tj5WxXH6vlqwzuI2T6iJBUHhajkEaH36PR4fdI86zJvvCh5Dur2BSRysvN0eDB8CQaeWex8FgIO8/5M69tGo38C8yOJtdI1wBKqXDPSeamLffR6PB8TgR1Yn/4vSqCRMRudAxIY2Z0HI5GAXeu8efDQ+7Y13xLxaVCSK5b9dPr6BVzN34Zh9gXMZQTVbuARd9aImJfqrnl80K9Y3QLPM/zu7y5f6MvF/K0aMjW6dNKSszBmk/z/f/gxm2PkOUWTGzk/aR7RpgdS0TENM4OBiPCTvNk1HE2nXGi58oAfk/R7UJsmQohKRHPrERu2TSUevH/4ljwLRwMG0CBk4fZsUREbEIrvwxeiY7D1zGXgb/5MXefOtnbKhVCUmw1T62gV8w9eOacYm+tESQFtlObDBGR/xHoUsC0ugn0CT7L63s8GbrOl9M5+ti1NTojcs0cC3O4IfYFOu58kjTPCGIjR5PpHmp2LBERm+VogYGhKTxTJ5G9Fxzp+Ys/65LUyd6WqBCSa+KTcZTuGwcRefwbjla7jcM17qLQ8cqNbkVE5FJNfIo62ddwy2FYjB+v7PYkX53sbYIKIflrhkFk4jJ6bBiAS34qeyJHcSagpabCRESKyc+5kEm1Exkcepr3DnjQf60fxzP1MWw2nQG5KqeCTNrvmkTb2Gmc841mT+R9ZLsFmx1LRKTCcrDAHSHneL7eMY5nWOi1KoAVJzRVZiYVQnJF/ql76RnTjxrJqzlc4y7iqvfG6qAfVhGR0lDXK4dZ0XHU98zkwU1+TNvhpU72JlEhJJcyDOrGf8qtm+7FYliJjRzDWd9GZqcSEal0vJysTIw8waiwJD4/6s6dv/pzNF3NqcubCiG5yCUvlU7bx9Nq3yxO+7dgb60R5LoGmB1LRKTSslige9ULvFQ/ntRcg9tW+bP0mC5EKU8qhASAoHPb6RlzNyFnt3AgbAAJ1bpjOKgnr4hIeYjwyGVm/Thu8Etn4u8+PP67N5kFuiilPOiTzs5ZjEIaHFlE40PzyPCoQWz4QPKcfc2OJSJid9wcDR6JOEUj70w+SAhh+1ln5rVNo4GfOtmXJY0I2TG33BS6br2fJofmciqwA/sihqkIEhExWZcqacyMjsdaWEDfX/355xF1si9LKoTsVMiZjfRafxcBafvYHz6E48Fd1TFeRMRGVHfL46X6x7ixygWm7vDmoc0+pKqTfZnQJ5+dsVjzaXpgNjf98QA5rgHsjhpDmlek2bFEROR/uDgY3FczmYmRJ1if5EzPlQFsP6sVLaVNhZAd8cg+yS1bRhB99EMSqt7MgZqDKXDyMjuWiIj8hTb+6cyKjsfLIY971voz/4AHVk2VlRoVQnaiRtJqesXcjVdmIvtqDedUUAe1yRARqSCquuYzvd4xbq96jlm7vRgR40tKjt7DS4MKoUrOoTCXVntepvOOx8jwCCM2ajQZHmFmxxIRkWJyssDgGmeYXDuRXWcd6bEygI2nnc2OVeGpEKrEvDPj6b5pMFGJS4ir1pNDNfpR6OhudiwREbkOzXwzeaVBHCEuOQxZ58frsZ4UqJN9iakQqqQiTnxPzw334JZ7jr21RnE64AZNhYmIVBL+zoVMqZPIPdVTmLffg0Hr/DiVpY/0ktC/WiXjVJBF211TaP/nM5z3rkds5Giy3EPMjiUiIqXMwQJ3VzvLtLoJHE2z0GNlAKtOqjl2cakQqkT80g7QY0N/wpNWcCT0Do6G3oHVUT8UIiKVWbR3Nq9Ex1HbI5PRG/14YacXuepkf81UCFUGhkGdY4vpvnEQjtZcYiNHk+LX1OxUIiJSTrydrDwZdYLhNZL55Ig7d6/xJz5DneyvhQqhCs45P42OOyZyw96XOePfjD21RpHjGmh2LBERKWcWC/QKPs+L9eJJyS7qZP9tgqvZsWyeCqEKrMr5XfSKuZtqKRs4GHYPx6r1VMd4ERE7F+mZy8zoeJr5pDN+qy9P/eFNtvq2XpU+NSsiw0p03Ec0Pfg2mW7ViY0cQ56Ln9mpRETERng4WhkXcYpG3ll8lBDM9rNOzGubRj1fLR76XxoRqmBcc8/S9Y+HaH7gTZKqtGFfrWEqgkRE5DIWC9wUmMrL9ePJLbDSZ3UAnx91Uyf7/6FCqAIJPruFXjF3U+XCn+wPH0xicDcMixbDiYjI1YW55/Fy/Xg6Blxg8nYfxm3xIS1f95X7NxVCFYDFWkCTg3O4aesY8px9iY0cQ6pXbbNjiYhIBeHqYHB/eDLja53g11PO3LbSn13ntDoGVAjZPPfsJG7eOoqGRxZwvOqN7A8fTL6zt9mxRESkAmofUNTJ3sWSz91r/Fl40N3up8pUCNmw0OS19Iq5G9+Mo+yNGM7JoE5g0SkTEZGSC3bN54W6x+gedJ6X/vRm1AZfzuXa71SZPlVtkENhHi32vUKX7ePIcq9GbOQYMjxrmh1LREQqCScHGBZ2mqdrJ7ItxYmeKwPYcsY+O9mrELIxXpkJ3Lr5Xuoe+5z4kO4cDOtPgZOH2bFERKQSauGbyazoOKo45zLoNz/e2utBoZ1NlakQsiHhJ5fTc8M9uOecZk+tkSRXaaOO8SIiUqaquBQwtU4Cd1U7y+y9ngxe50dytv2UB/bzSm2YY2E2rXdPo8Oup0n1imJP5Giy3KubHUtEROyEgwXuqZ7C1LqJHLrgQI+V/qw5ZR9Nu1UImcw3/RA9Ngyg1okfOFq9N0dC+1LoqN4wIiJS/hp6Z/FKdBwR7tmM3ODHjD89ybOanapsqRAyi2EQlbiEHhsH4lSYxZ7I0Zzxb66pMBERMZWPcyFPRR3n3tDTLDrkwT1r/EnMrLzlQuV9ZTbMOT+dDjufpE3s86T4NmJPrVFkuwWZHUtERAQomirrHXKO5+sd41QW9FoZwPLjlXO2QoVQOQu4EEvPDfcQevo3DtW4m/jqt2M42OcliyIiYtvqeOYwq34cjbwzeHizL1O2e5FTyfq26v7a5cUwqB//Cc0OzCbLLZjYqDHkuvibnUpEROQveTpZGV/rJA29s/gkvirbUpyZ2zaN2j6VoyLSiFA5cM07T5dtj9Bi/z9IDmjF3ogRKoJERKTCsFjglqALvFz/GOl5Vm5f7c9X8ZWjk70KoTIWdO4PesbcTdVz2zlQcyAJIbdiOKhjvIiIVDw13XOZUT+etn5pPPmHDxN+9yGjgney19RYGbEYhTQ8/D6ND88n3bMm+8OHkO/sY3YsERGR6+LmaPBQRBKNvLNYlBDCzrP+zG2bRiP/ArOjlYhGhMqAe85pbtoymsaH3+VEUEf2hd+rIkhERCqVTlXSmBkdh8Uo4M41/nx0uGJ2slchVMqqnVlPr5i78cs4yL6IoZyoeqM6xouISKVUzS2fF+sdo1vgeZ7b6c0Dm3y5kFexpsr0CV1KHKz5NNv/Ol3/eJgs1yBiI8eQ7hlhdiwREZEy5exgMCLsNE9EHWfjaSd6rfRnW0rFWXmjQqgUeGYd55bNw6gf/wnHgm/hYM2BFDh5mh1LRESk3Nzgl8Er0XH4OObR/zd/5u33wFoBpspUCF2nsFO/0HNDPzyzT7I3YiRJge3UJkNEROxSoEsB0+om0Cf4LP+I9WTYel/O5Nj2Z6IKoRJyLMzhhtgX6bTzcdI9womNHE2mR6jZsUREREzlaIGBoSk8UyeR2POO9FgZwPpk2+2goEKoBHwyjtJ94yAijy/jaLXbOFzjbgod3cyOJSIiYjOa+BR1sg91zWHYej9e3e1JgQ12slchVByGQeTxZfTYMACX/FT2RI7iTEBLTYWJiIhcgZ9zIZNrJzIw9AzzD3jQf60fJ7Jsq/SwrTQ2zKkgk3a7JtN29zTO+dRnT+R9ZLsFmx1LRETEpjlYoG/IOZ6rl0BihoWeKwP4+YSL2bEuUiF0DfxT99Fzwz2EJa/icOidxIX2wepgOydRRETE1tXzymZWdBz1PDN5YJMfz+30ItcG+raqEPorhkHd+E+5ddMQLEYhsZGjOevX2OxUIiIiFZKXk5XHI08wMiyJT4+4c+ev/hxNN7f/pgqhq3DJS6XT9vG02jeLM/7NizrGu1YxO5aIiEiFZrFAj6oXeLF+POdzDW5f5c+yY66m5VEhdAWB53fQM+ZuQs5u4UDYAI5V64HhUHHukikiImLrannkMrN+PK380pnwuy9P/O5Nlgl9W/Xp/t8MKw2OLqLJwblkeNQgNnwgec6+ZqcSERGplNwdrTwScYpG3pksSghh+1ln5rVNJdqv/BYPaUTo/7nlpnDT7/fT9OAcTgW2Z1/EMBVBIiIi5aBLlTRmRsdTWFjAHb8G8K8jbuXWyV6FEBCSspFeMXcRkLqX/eFDOB58kzrGi4iIlKNQtzxeqn+MLlUu8OwOHx7e7ENqOXSyt4lP+3nz5hEREYGbmxtt2rRh69atf7n/V199Rf369XFzc6Nx48YsX768RM9rsebT9MBbdP39QXJcAtgdNYY0r8gSPZaIiIhcHxcHg9E1k5kYeYJ1Sc70WuXPjrNlu4rH9ELoiy++YOLEiUyfPp3t27fTtGlTunfvzunTp6+4/8aNGxk0aBD33XcfO3bsoG/fvvTt25fY2NhiPa9HdhLdtowk+ugHJFa9iQM1B1Pg5FUaL0lERESuQxv/dGZFx+NhyaffWn/eO1B2newthlFes3BX1qZNG2644Qbmzp0LgNVqJSwsjHHjxjFp0qTL9h8wYACZmZn88MMPF7e1bduWZs2aMX/+/L99vrS0NHx9fTkzJQQvdxeO1LiTDI+w0ntBIlLuzqdl0HP0c5ds+2nhc/j76JcbkYqswIAvTgTxXXIVugTn8vGEu0r9OUy9aiwvL49t27YxefLki9scHBzo1q0bmzZtuuIxmzZtYuLEiZds6969O998880V98/NzSU3N/fin1NTUwFIdo3gcPRQCp09rvNViIjZCnIvv9N7gWsABW7eJqQRkdI0oLaV+v7neHefK2lpaXh7e2MpxR6fphZCKSkpFBYWEhx8ac+u4OBg9u/ff8VjkpKSrrh/UlLSFfefOXMmzz///GXbG03bDGwuWXARsXm9hz5qdgQRKWW+r8Lp06cJCgoqtces9PcRmjx58iUjSBcuXCA8PJyEhAR8fXV5vJnS0tIICwsjMTERHx8fs+PYPZ0P26FzYTt0LmzHv8+Fi0vp9vo0tRAKDAzE0dGR5OTkS7YnJycTEhJyxWNCQkKKtb+rqyuurpffutvX11ff1DbCx8dH58KG6HzYDp0L26FzYTtKc1oMTL5qzMXFhZYtW7J69eqL26xWK6tXr6Zdu3ZXPKZdu3aX7A+wcuXKq+4vIiIicjWmT41NnDiR4cOH06pVK1q3bs3s2bPJzMxk5MiRAAwbNozQ0FBmzpwJwPjx4+nSpQuvv/46t912G4sXL+aPP/7g/fffN/NliIiISAVkeiE0YMAAzpw5w7Rp00hKSqJZs2asWLHi4oLohIQEHBz+M3DVvn17PvvsM5599lmeeeYZ6tSpwzfffEOjRo2u6flcXV2ZPn36FafLpHzpXNgWnQ/boXNhO3QubEdZnQvT7yMkIiIiYhbT7ywtIiIiYhYVQiIiImK3VAiJiIiI3VIhJCIiInarUhZC8+bNIyIiAjc3N9q0acPWrVv/cv+vvvqK+vXr4+bmRuPGjVm+fHk5Ja38inMuFixYQKdOnfD398ff359u3br97bmT4inuz8a/LV68GIvFQt++fcs2oB0p7rm4cOECjzzyCNWqVcPV1ZW6devqvaqUFPdczJ49m3r16uHu7k5YWBgTJkwgJyennNJWXuvWraN3795Ur14di8Vy1R6i/23t2rW0aNECV1dXateuzUcffVT8JzYqmcWLFxsuLi7GBx98YOzZs8cYM2aM4efnZyQnJ19x/w0bNhiOjo7Gq6++auzdu9d49tlnDWdnZ2P37t3lnLzyKe65GDx4sDFv3jxjx44dxr59+4wRI0YYvr6+xvHjx8s5eeVU3PPxb3FxcUZoaKjRqVMn44477iifsJVccc9Fbm6u0apVK6NXr15GTEyMERcXZ6xdu9bYuXNnOSevfIp7Lj799FPD1dXV+PTTT424uDjj559/NqpVq2ZMmDChnJNXPsuXLzemTJliLF261ACMZcuW/eX+R48eNTw8PIyJEycae/fuNebMmWM4OjoaK1asKNbzVrpCqHXr1sYjjzxy8c+FhYVG9erVjZkzZ15x//79+xu33XbbJdvatGljPPDAA2Wa0x4U91z8r4KCAsPb29v4+OOPyyqiXSnJ+SgoKDDat29vLFy40Bg+fLgKoVJS3HPx7rvvGpGRkUZeXl55RbQbxT0XjzzyiHHTTTddsm3ixIlGhw4dyjSnvbmWQuipp54yGjZseMm2AQMGGN27dy/Wc1WqqbG8vDy2bdtGt27dLm5zcHCgW7dubNq06YrHbNq06ZL9Abp3737V/eXalORc/K+srCzy8/MJCAgoq5h2o6Tn44UXXqBq1arcd9995RHTLpTkXHz33Xe0a9eORx55hODgYBo1asSMGTMoLCwsr9iVUknORfv27dm2bdvF6bOjR4+yfPlyevXqVS6Z5T9K6/Pb9DtLl6aUlBQKCwsv3pX634KDg9m/f/8Vj0lKSrri/klJSWWW0x6U5Fz8r6effprq1atf9o0uxVeS8xETE8OiRYvYuXNnOSS0HyU5F0ePHuXXX39lyJAhLF++nMOHD/Pwww+Tn5/P9OnTyyN2pVSSczF48GBSUlLo2LEjhmFQUFDAgw8+yDPPPFMekeW/XO3zOy0tjezsbNzd3a/pcSrViJBUHrNmzWLx4sUsW7YMNzc3s+PYnfT0dIYOHcqCBQsIDAw0O47ds1qtVK1alffff5+WLVsyYMAApkyZwvz5882OZnfWrl3LjBkzeOedd9i+fTtLly7lxx9/5MUXXzQ7mpRQpRoRCgwMxNHRkeTk5Eu2JycnExIScsVjQkJCirW/XJuSnIt/+8c//sGsWbNYtWoVTZo0KcuYdqO45+PIkSPEx8fTu3fvi9usVisATk5OHDhwgKioqLINXUmV5GejWrVqODs74+joeHFbdHQ0SUlJ5OXl4eLiUqaZK6uSnIupU6cydOhQRo8eDUDjxo3JzMzk/vvvZ8qUKZf0xpSydbXPbx8fn2seDYJKNiLk4uJCy5YtWb169cVtVquV1atX065duyse065du0v2B1i5cuVV95drU5JzAfDqq6/y4osvsmLFClq1alUeUe1Ccc9H/fr12b17Nzt37rz41adPH7p27crOnTsJCwsrz/iVSkl+Njp06MDhw4cvFqMABw8epFq1aiqCrkNJzkVWVtZlxc6/C1RDrTvLVal9fhdvHbftW7x4seHq6mp89NFHxt69e43777/f8PPzM5KSkgzDMIyhQ4cakyZNurj/hg0bDCcnJ+Mf//iHsW/fPmP69Om6fL6UFPdczJo1y3BxcTGWLFlinDp16uJXenq6WS+hUinu+fhfumqs9BT3XCQkJBje3t7G2LFjjQMHDhg//PCDUbVqVeOll14y6yVUGsU9F9OnTze8vb2Nzz//3Dh69Kjxyy+/GFFRUUb//v3NegmVRnp6urFjxw5jx44dBmC88cYbxo4dO4xjx44ZhmEYkyZNMoYOHXpx/39fPv/kk08a+/btM+bNm6fL5/9tzpw5Rs2aNQ0XFxejdevWxubNmy/+XZcuXYzhw4dfsv+XX35p1K1b13BxcTEaNmxo/Pjjj+WcuPIqzrkIDw83gMu+pk+fXv7BK6ni/mz8NxVCpau452Ljxo1GmzZtDFdXVyMyMtJ4+eWXjYKCgnJOXTkV51zk5+cbzz33nBEVFWW4ubkZYWFhxsMPP2ycP3++/INXMmvWrLniZ8C///2HDx9udOnS5bJjmjVrZri4uBiRkZHGhx9+WOzntRiGxvJERETEPlWqNUIiIiIixaFCSEREROyWCiERERGxWyqERERExG6pEBIRERG7pUJIRERE7JYKIREREbFbKoRERETEbqkQEhG7YbFY+OabbwCIj4/HYrGwc+dOUzOJiLkqVfd5EZFrFRYWxqlTpwgMDDQ7ioiYSCNCIlKp5OXlXdN+jo6OhISE4OSk3wdF7JkKIRExndVq5dVXX6V27dq4urpSs2ZNXn75ZQCefvpp6tati4eHB5GRkUydOpX8/PyLxz733HM0a9aMhQsXUqtWLdzc3AA4dOgQnTt3xs3NjQYNGrBy5cpLnvNKU2O//fYbrVu3xtXVlWrVqjFp0iQKCgrK/h9AREyjX4VExHSTJ09mwYIFvPnmm3Ts2JFTp06xf/9+ALy9vfnoo4+oXr06u3fvZsyYMXh7e/PUU09dPP7w4cN8/fXXLF26FEdHR6xWK3fddRfBwcFs2bKF1NRUHnvssb/McOLECXr16sWIESP45JNP2L9/P2PGjMHNzY3nnnuuDF+9iJiq2P3qRURKUVpamuHq6mosWLDgmvZ/7bXXjJYtW1788/Tp0w1nZ2fj9OnTF7f9/PPPhpOTk3HixImL23766ScDMJYtW2YYhmHExcUZgLFjxw7DMAzjmWeeMerVq2dYrdaLx8ybN8/w8vIyCgsLr+MViogt04iQiJhq37595ObmcvPNN1/x77/44gvefvttjhw5QkZGBgUFBfj4+FyyT3h4OEFBQZc8ZlhYGNWrV7+4rV27dn+bo127dlgslovbOnToQEZGBsePH6dmzZoleXkiYuO0RkhETOXu7n7Vv9u0aRNDhgyhV69e/PDDD+zYsYMpU6ZctiDa09OzrGOKSCWlQkhETFWnTh3c3d1ZvXr1ZX+3ceNGwsPDmTJlCq1ataJOnTocO3bsbx8zOjqaxMRETp06dXHb5s2b//aYTZs2YRjGxW0bNmzA29ubGjVqFOMViUhFoqkxETGVm5sbTz/9NE899RQuLi506NCBM2fOsGfPHurUqUNCQgKLFy/mhhtu4Mcff2TZsmV/+5jdunWjbt26DB8+nNdee420tDSmTJnyl8c8/PDDzJ49m3HjxjF27FgOHDjA9OnTmThxIg4O+p1RpLLST7eImG7q1Kk8/vjjTJs2jejoaAYMGMDp06fp06cPEyZMYOzYsTRr1oyNGzcyderUv308BwcHli1bRnZ2Nq1bt2b06NEXL8e/mtDQUJYvX87WrVtp2rQpDz74IPfddx/PPvtsab1MEbFBFuO/x4FFRERE7IhGhERERMRuqRASERERu6VCSEREROyWCiERERGxWyqERERExG6pEBIRERG7pUJIRERE7JYKIREREbFbKoRERETEbqkQEhEREbulQkhERETs1v8BdyYDHdSYd1AAAAAASUVORK5CYII=",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sim.input[\"age\"] = 50.358668\n",
"sim.input[\"ap_hi\"] = 110\n",
"sim.input[\"cholesterol\"] = 1\n",
"sim.input[\"bmi\"] = 21.967120\n",
"sim.compute()\n",
"sim.print_state()\n",
"display(sim.output[\"cardio\"], 1 if sim.output[\"cardio\"] > 0.5 else 0)\n",
"cardio.view(sim=sim)"
]
},
{
"cell_type": "code",
"execution_count": 112,
"metadata": {},
"outputs": [
{
"data": {
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" vertical-align: top;\n",
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"\n",
" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>age</th>\n",
" <th>ap_hi</th>\n",
" <th>cholesterol</th>\n",
" <th>bmi</th>\n",
" </tr>\n",
" <tr>\n",
" <th>id</th>\n",
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" </tr>\n",
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" <td>57.745592</td>\n",
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" <td>28.393726</td>\n",
" </tr>\n",
" <tr>\n",
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" <td>59.670354</td>\n",
" <td>120</td>\n",
" <td>3</td>\n",
" <td>23.875115</td>\n",
" </tr>\n",
" <tr>\n",
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" </tr>\n",
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" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>61975</th>\n",
" <td>62.558865</td>\n",
" <td>120</td>\n",
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" <td>28.196921</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32741</th>\n",
" <td>57.882488</td>\n",
" <td>120</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>94833</th>\n",
" <td>51.371701</td>\n",
" <td>120</td>\n",
" <td>1</td>\n",
" <td>29.242109</td>\n",
" </tr>\n",
" <tr>\n",
" <th>95660</th>\n",
" <td>45.767167</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>81002</th>\n",
" <td>55.544300</td>\n",
" <td>150</td>\n",
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" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>55188 rows × 4 columns</p>\n",
"</div>"
],
"text/plain": [
" age ap_hi cholesterol bmi\n",
"id \n",
"94960 62.014018 120 1 26.892323\n",
"30807 57.745592 120 1 28.393726\n",
"26485 59.670354 120 3 23.875115\n",
"3868 49.715256 110 1 20.820940\n",
"45890 59.785347 160 1 23.529412\n",
"... ... ... ... ...\n",
"61975 62.558865 120 1 28.196921\n",
"32741 57.882488 120 1 29.043709\n",
"94833 51.371701 120 1 29.242109\n",
"95660 45.767167 120 1 24.977043\n",
"81002 55.544300 150 1 27.053803\n",
"\n",
"[55188 rows x 4 columns]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"id\n",
"94960 0\n",
"30807 0\n",
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"3868 1\n",
"45890 1\n",
" ..\n",
"61975 1\n",
"32741 0\n",
"94833 0\n",
"95660 0\n",
"81002 1\n",
"Name: cardio, Length: 55188, dtype: int64"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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" <td>2</td>\n",
" <td>27.764650</td>\n",
" </tr>\n",
" <tr>\n",
" <th>62336</th>\n",
" <td>51.692038</td>\n",
" <td>110</td>\n",
" <td>1</td>\n",
" <td>22.230987</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30330</th>\n",
" <td>47.697404</td>\n",
" <td>100</td>\n",
" <td>1</td>\n",
" <td>22.724403</td>\n",
" </tr>\n",
" <tr>\n",
" <th>62907</th>\n",
" <td>58.597087</td>\n",
" <td>120</td>\n",
" <td>1</td>\n",
" <td>23.828125</td>\n",
" </tr>\n",
" <tr>\n",
" <th>98612</th>\n",
" <td>51.404556</td>\n",
" <td>110</td>\n",
" <td>1</td>\n",
" <td>22.589551</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5767</th>\n",
" <td>62.033184</td>\n",
" <td>120</td>\n",
" <td>1</td>\n",
" <td>23.875115</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14769</th>\n",
" <td>41.506954</td>\n",
" <td>120</td>\n",
" <td>2</td>\n",
" <td>22.948116</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>13797 rows × 4 columns</p>\n",
"</div>"
],
"text/plain": [
" age ap_hi cholesterol bmi\n",
"id \n",
"42270 60.078305 140 1 45.918367\n",
"10780 55.360859 120 2 24.998904\n",
"42436 48.198445 100 3 21.926126\n",
"88647 41.517906 130 2 27.764650\n",
"62336 51.692038 110 1 22.230987\n",
"... ... ... ... ...\n",
"30330 47.697404 100 1 22.724403\n",
"62907 58.597087 120 1 23.828125\n",
"98612 51.404556 110 1 22.589551\n",
"5767 62.033184 120 1 23.875115\n",
"14769 41.506954 120 2 22.948116\n",
"\n",
"[13797 rows x 4 columns]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"id\n",
"42270 1\n",
"10780 0\n",
"42436 1\n",
"88647 1\n",
"62336 0\n",
" ..\n",
"30330 1\n",
"62907 0\n",
"98612 0\n",
"5767 0\n",
"14769 1\n",
"Name: cardio, Length: 13797, dtype: int64"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from sklearn.model_selection import train_test_split\n",
"\n",
"random_state = 9\n",
"\n",
"def fuzzy_pred(row):\n",
" sim.input[\"age\"] = row[\"age\"]\n",
" sim.input[\"ap_hi\"] = row[\"ap_hi\"]\n",
" sim.input[\"cholesterol\"] = row[\"cholesterol\"]\n",
" sim.input[\"bmi\"] = row[\"bmi\"]\n",
" sim.compute()\n",
" return 1 if sim.output[\"cardio\"] > 0.5 else 0\n",
"\n",
"y = df[\"cardio\"]\n",
"X = df.drop([\"cardio\"], axis=1).copy()\n",
"X_train, X_test, y_train, y_test = train_test_split(\n",
" X, y, test_size=0.2, random_state=random_state\n",
")\n",
"display(X_train, y_train, X_test, y_test)"
]
},
{
"cell_type": "code",
"execution_count": 113,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>age</th>\n",
" <th>ap_hi</th>\n",
" <th>cholesterol</th>\n",
" <th>bmi</th>\n",
" <th>Real</th>\n",
" <th>Inferred</th>\n",
" </tr>\n",
" <tr>\n",
" <th>id</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>42270</th>\n",
" <td>60.078305</td>\n",
" <td>140</td>\n",
" <td>1</td>\n",
" <td>45.918367</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10780</th>\n",
" <td>55.360859</td>\n",
" <td>120</td>\n",
" <td>2</td>\n",
" <td>24.998904</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42436</th>\n",
" <td>48.198445</td>\n",
" <td>100</td>\n",
" <td>3</td>\n",
" <td>21.926126</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>88647</th>\n",
" <td>41.517906</td>\n",
" <td>130</td>\n",
" <td>2</td>\n",
" <td>27.764650</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>62336</th>\n",
" <td>51.692038</td>\n",
" <td>110</td>\n",
" <td>1</td>\n",
" <td>22.230987</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23904</th>\n",
" <td>53.942613</td>\n",
" <td>120</td>\n",
" <td>1</td>\n",
" <td>35.491690</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>63516</th>\n",
" <td>40.305005</td>\n",
" <td>120</td>\n",
" <td>1</td>\n",
" <td>21.829952</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>84904</th>\n",
" <td>42.561056</td>\n",
" <td>140</td>\n",
" <td>1</td>\n",
" <td>32.882414</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20959</th>\n",
" <td>45.545395</td>\n",
" <td>160</td>\n",
" <td>1</td>\n",
" <td>43.827160</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>77652</th>\n",
" <td>54.115102</td>\n",
" <td>140</td>\n",
" <td>1</td>\n",
" <td>37.105751</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1000 rows × 6 columns</p>\n",
"</div>"
],
"text/plain": [
" age ap_hi cholesterol bmi Real Inferred\n",
"id \n",
"42270 60.078305 140 1 45.918367 1 1\n",
"10780 55.360859 120 2 24.998904 0 0\n",
"42436 48.198445 100 3 21.926126 1 0\n",
"88647 41.517906 130 2 27.764650 1 0\n",
"62336 51.692038 110 1 22.230987 0 0\n",
"... ... ... ... ... ... ...\n",
"23904 53.942613 120 1 35.491690 1 0\n",
"63516 40.305005 120 1 21.829952 0 0\n",
"84904 42.561056 140 1 32.882414 1 0\n",
"20959 45.545395 160 1 43.827160 1 0\n",
"77652 54.115102 140 1 37.105751 0 0\n",
"\n",
"[1000 rows x 6 columns]"
]
},
"execution_count": 113,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"result_test = X_test.copy()\n",
"result_test[\"Real\"] = y_test\n",
"result_test = result_test.head(1000)\n",
"result_test[\"Inferred\"] = result_test.apply(fuzzy_pred, axis=1)\n",
"result_test"
]
},
{
"cell_type": "code",
"execution_count": 114,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Precision_test'"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"np.float64(0.5469483568075117)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"'Recall_test'"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"np.float64(0.4707070707070707)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"'Accuracy_test'"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"0.545"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"'F1_test'"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"np.float64(0.505971769815418)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"'Confusion_matrix'"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"array([[312, 193],\n",
" [262, 233]])"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from sklearn import metrics\n",
"\n",
"display(\n",
" \"Precision_test\",\n",
" metrics.precision_score(result_test[\"Real\"], result_test[\"Inferred\"]),\n",
")\n",
"display(\n",
" \"Recall_test\", metrics.recall_score(result_test[\"Real\"], result_test[\"Inferred\"])\n",
")\n",
"display(\n",
" \"Accuracy_test\",\n",
" metrics.accuracy_score(result_test[\"Real\"], result_test[\"Inferred\"]),\n",
")\n",
"display(\n",
" \"F1_test\", \n",
" metrics.f1_score(result_test[\"Real\"], result_test[\"Inferred\"]),\n",
")\n",
"display(\n",
" \"Confusion_matrix\",\n",
" metrics.confusion_matrix(result_test[\"Real\"], result_test[\"Inferred\"]),\n",
")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.9"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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