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{
"cells": [
{
"cell_type": "code",
2024-11-05 10:22:38 +04:00
"execution_count": 58,
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"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>T</th>\n",
" <th>Al2O3</th>\n",
" <th>TiO2</th>\n",
" <th>Density</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>20</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.06250</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>25</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.05979</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>35</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.05404</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" T Al2O3 TiO2 Density\n",
"0 20 0.0 0.0 1.06250\n",
"1 25 0.0 0.0 1.05979\n",
"2 35 0.0 0.0 1.05404"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"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>T</th>\n",
" <th>Al2O3</th>\n",
" <th>TiO2</th>\n",
" <th>Density</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>30</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>1.05696</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>55</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>1.04158</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>25</td>\n",
" <td>0.05</td>\n",
" <td>0.0</td>\n",
" <td>1.08438</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" T Al2O3 TiO2 Density\n",
"0 30 0.00 0.0 1.05696\n",
"1 55 0.00 0.0 1.04158\n",
"2 25 0.05 0.0 1.08438"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import pandas as pd\n",
"\n",
"density_train = pd.read_csv(\"data/density_train.csv\", sep=\";\", decimal=\",\")\n",
"density_test = pd.read_csv(\"data/density_test.csv\", sep=\";\", decimal=\",\")\n",
"\n",
"display(density_train.head(3))\n",
"display(density_test.head(3))"
]
},
{
"cell_type": "code",
2024-11-05 10:22:38 +04:00
"execution_count": 59,
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"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\Users\\user\\Projects\\python\\fuzzy\\.venv\\Lib\\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": {
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"image/png": "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
2024-11-01 11:04:05 +04:00
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"image/png": "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"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"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",
"temp = ctrl.Antecedent(density_train[\"T\"].sort_values().unique(), \"temp\")\n",
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"al = ctrl.Antecedent(np.arange(0, 0.3, 0.005), \"al\")\n",
"ti = ctrl.Antecedent(np.arange(0, 0.3, 0.005), \"ti\")\n",
"density = ctrl.Consequent(np.arange(1.03, 1.22, 0.00001), \"density\")\n",
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"\n",
"temp.automf(3, variable_type=\"quant\")\n",
"temp.view()\n",
"al.automf(3, variable_type=\"quant\")\n",
"al.view()\n",
"ti.automf(3, variable_type=\"quant\")\n",
"ti.view()\n",
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"density.automf(5, variable_type=\"quant\")\n",
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"density.view()"
]
},
{
"cell_type": "code",
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"execution_count": 60,
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"metadata": {},
"outputs": [],
"source": [
"rule11 = ctrl.Rule(\n",
" temp[\"low\"] & al[\"low\"] & ti[\"low\"],\n",
" density[\"low\"],\n",
")\n",
"rule12 = ctrl.Rule(\n",
" temp[\"average\"] & al[\"low\"] & ti[\"low\"],\n",
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" density[\"lower\"],\n",
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")\n",
"rule13 = ctrl.Rule(\n",
" temp[\"high\"] & al[\"low\"] & ti[\"low\"],\n",
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" density[\"lower\"],\n",
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")\n",
"\n",
"rule21 = ctrl.Rule(\n",
" temp[\"low\"] & al[\"average\"] & ti[\"low\"],\n",
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" density[\"low\"],\n",
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")\n",
"rule22 = ctrl.Rule(\n",
" temp[\"average\"] & al[\"average\"] & ti[\"low\"],\n",
" density[\"low\"],\n",
")\n",
"rule23 = ctrl.Rule(\n",
" temp[\"high\"] & al[\"average\"] & ti[\"low\"],\n",
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" density[\"lower\"],\n",
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")\n",
"\n",
"rule31 = ctrl.Rule(\n",
" temp[\"low\"] & al[\"high\"] & ti[\"low\"],\n",
" density[\"high\"],\n",
")\n",
"rule32 = ctrl.Rule(\n",
" temp[\"low\"] & al[\"high\"] & ti[\"low\"],\n",
" density[\"high\"],\n",
")\n",
"rule33 = ctrl.Rule(\n",
" temp[\"high\"] & al[\"high\"] & ti[\"low\"],\n",
" density[\"average\"],\n",
")\n",
"\n",
"rule41 = ctrl.Rule(\n",
" temp[\"low\"] & al[\"low\"] & ti[\"average\"],\n",
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" density[\"low\"],\n",
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")\n",
"rule42 = ctrl.Rule(\n",
" temp[\"average\"] & al[\"low\"] & ti[\"average\"],\n",
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" density[\"low\"],\n",
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")\n",
"rule43 = ctrl.Rule(\n",
" temp[\"high\"] & al[\"low\"] & ti[\"average\"],\n",
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" density[\"lower\"],\n",
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")\n",
"\n",
"rule51 = ctrl.Rule(\n",
" temp[\"low\"] & al[\"low\"] & ti[\"high\"],\n",
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" density[\"higher\"],\n",
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")\n",
"rule52 = ctrl.Rule(\n",
" temp[\"average\"] & al[\"low\"] & ti[\"high\"],\n",
" density[\"high\"],\n",
")\n",
"rule53 = ctrl.Rule(\n",
" temp[\"high\"] & al[\"low\"] & ti[\"high\"],\n",
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" density[\"high\"],\n",
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")"
]
},
{
"cell_type": "code",
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"execution_count": 61,
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"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[IF (temp[low] AND al[low]) AND ti[low] THEN density[low]\n",
" \tAND aggregation function : fmin\n",
" \tOR aggregation function : fmax,\n",
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" IF (temp[average] AND al[low]) AND ti[low] THEN density[lower]\n",
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" \tAND aggregation function : fmin\n",
" \tOR aggregation function : fmax,\n",
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" IF (temp[high] AND al[low]) AND ti[low] THEN density[lower]\n",
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" \tAND aggregation function : fmin\n",
" \tOR aggregation function : fmax,\n",
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" IF (temp[low] AND al[average]) AND ti[low] THEN density[low]\n",
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" \tAND aggregation function : fmin\n",
" \tOR aggregation function : fmax,\n",
" IF (temp[average] AND al[average]) AND ti[low] THEN density[low]\n",
" \tAND aggregation function : fmin\n",
" \tOR aggregation function : fmax,\n",
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" IF (temp[high] AND al[average]) AND ti[low] THEN density[lower]\n",
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" \tAND aggregation function : fmin\n",
" \tOR aggregation function : fmax,\n",
" IF (temp[low] AND al[high]) AND ti[low] THEN density[high]\n",
" \tAND aggregation function : fmin\n",
" \tOR aggregation function : fmax,\n",
" IF (temp[low] AND al[high]) AND ti[low] THEN density[high]\n",
" \tAND aggregation function : fmin\n",
" \tOR aggregation function : fmax,\n",
" IF (temp[high] AND al[high]) AND ti[low] THEN density[average]\n",
" \tAND aggregation function : fmin\n",
" \tOR aggregation function : fmax,\n",
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" IF (temp[low] AND al[low]) AND ti[average] THEN density[low]\n",
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" \tAND aggregation function : fmin\n",
" \tOR aggregation function : fmax,\n",
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" IF (temp[average] AND al[low]) AND ti[average] THEN density[low]\n",
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" \tAND aggregation function : fmin\n",
" \tOR aggregation function : fmax,\n",
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" IF (temp[high] AND al[low]) AND ti[average] THEN density[lower]\n",
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" \tAND aggregation function : fmin\n",
" \tOR aggregation function : fmax,\n",
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" IF (temp[low] AND al[low]) AND ti[high] THEN density[higher]\n",
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" \tAND aggregation function : fmin\n",
" \tOR aggregation function : fmax,\n",
" IF (temp[average] AND al[low]) AND ti[high] THEN density[high]\n",
" \tAND aggregation function : fmin\n",
" \tOR aggregation function : fmax,\n",
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" IF (temp[high] AND al[low]) AND ti[high] THEN density[high]\n",
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" \tAND aggregation function : fmin\n",
" \tOR aggregation function : fmax]"
]
},
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"execution_count": 61,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fuzzy_rules = [\n",
" rule11,\n",
" rule12,\n",
" rule13,\n",
" rule21,\n",
" rule22,\n",
" rule23,\n",
" rule31,\n",
" rule32,\n",
" rule33,\n",
" rule41,\n",
" rule42,\n",
" rule43,\n",
" rule51,\n",
" rule52,\n",
" rule53,\n",
"]\n",
"\n",
"density_cntrl = ctrl.ControlSystem(fuzzy_rules)\n",
"\n",
"sim = ctrl.ControlSystemSimulation(density_cntrl)\n",
"\n",
"fuzzy_rules"
]
},
{
"cell_type": "code",
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"execution_count": 62,
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"=============\n",
" Antecedents \n",
"=============\n",
"Antecedent: temp = 20\n",
" - low : 1.0\n",
" - average : 0.0\n",
" - high : 0.0\n",
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"Antecedent: al = 0.295\n",
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" - low : 0.0\n",
" - average : 0.0\n",
" - high : 1.0\n",
"Antecedent: ti = 0.0\n",
" - low : 1.0\n",
" - average : 0.0\n",
" - high : 0.0\n",
"\n",
"=======\n",
" Rules \n",
"=======\n",
"RULE #0:\n",
" IF (temp[low] AND al[low]) AND ti[low] THEN density[low]\n",
"\tAND aggregation function : fmin\n",
"\tOR aggregation function : fmax\n",
"\n",
" Aggregation (IF-clause):\n",
" - temp[low] : 1.0\n",
" - al[low] : 0.0\n",
" - ti[low] : 1.0\n",
" (temp[low] AND al[low]) AND ti[low] = 0.0\n",
" Activation (THEN-clause):\n",
" density[low] : 0.0\n",
"\n",
"RULE #1:\n",
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" IF (temp[average] AND al[low]) AND ti[low] THEN density[lower]\n",
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"\tAND aggregation function : fmin\n",
"\tOR aggregation function : fmax\n",
"\n",
" Aggregation (IF-clause):\n",
" - temp[average] : 0.0\n",
" - al[low] : 0.0\n",
" - ti[low] : 1.0\n",
" (temp[average] AND al[low]) AND ti[low] = 0.0\n",
" Activation (THEN-clause):\n",
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" density[lower] : 0.0\n",
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"\n",
"RULE #2:\n",
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" IF (temp[high] AND al[low]) AND ti[low] THEN density[lower]\n",
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"\tAND aggregation function : fmin\n",
"\tOR aggregation function : fmax\n",
"\n",
" Aggregation (IF-clause):\n",
" - temp[high] : 0.0\n",
" - al[low] : 0.0\n",
" - ti[low] : 1.0\n",
" (temp[high] AND al[low]) AND ti[low] = 0.0\n",
" Activation (THEN-clause):\n",
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" density[lower] : 0.0\n",
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"\n",
"RULE #3:\n",
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" IF (temp[low] AND al[average]) AND ti[low] THEN density[low]\n",
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"\tAND aggregation function : fmin\n",
"\tOR aggregation function : fmax\n",
"\n",
" Aggregation (IF-clause):\n",
" - temp[low] : 1.0\n",
" - al[average] : 0.0\n",
" - ti[low] : 1.0\n",
" (temp[low] AND al[average]) AND ti[low] = 0.0\n",
" Activation (THEN-clause):\n",
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" density[low] : 0.0\n",
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"\n",
"RULE #4:\n",
" IF (temp[average] AND al[average]) AND ti[low] THEN density[low]\n",
"\tAND aggregation function : fmin\n",
"\tOR aggregation function : fmax\n",
"\n",
" Aggregation (IF-clause):\n",
" - temp[average] : 0.0\n",
" - al[average] : 0.0\n",
" - ti[low] : 1.0\n",
" (temp[average] AND al[average]) AND ti[low] = 0.0\n",
" Activation (THEN-clause):\n",
" density[low] : 0.0\n",
"\n",
"RULE #5:\n",
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" IF (temp[high] AND al[average]) AND ti[low] THEN density[lower]\n",
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"\tAND aggregation function : fmin\n",
"\tOR aggregation function : fmax\n",
"\n",
" Aggregation (IF-clause):\n",
" - temp[high] : 0.0\n",
" - al[average] : 0.0\n",
" - ti[low] : 1.0\n",
" (temp[high] AND al[average]) AND ti[low] = 0.0\n",
" Activation (THEN-clause):\n",
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" density[lower] : 0.0\n",
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"\n",
"RULE #6:\n",
" IF (temp[low] AND al[high]) AND ti[low] THEN density[high]\n",
"\tAND aggregation function : fmin\n",
"\tOR aggregation function : fmax\n",
"\n",
" Aggregation (IF-clause):\n",
" - temp[low] : 1.0\n",
" - al[high] : 1.0\n",
" - ti[low] : 1.0\n",
" (temp[low] AND al[high]) AND ti[low] = 1.0\n",
" Activation (THEN-clause):\n",
" density[high] : 1.0\n",
"\n",
"RULE #7:\n",
" IF (temp[low] AND al[high]) AND ti[low] THEN density[high]\n",
"\tAND aggregation function : fmin\n",
"\tOR aggregation function : fmax\n",
"\n",
" Aggregation (IF-clause):\n",
" - temp[low] : 1.0\n",
" - al[high] : 1.0\n",
" - ti[low] : 1.0\n",
" (temp[low] AND al[high]) AND ti[low] = 1.0\n",
" Activation (THEN-clause):\n",
" density[high] : 1.0\n",
"\n",
"RULE #8:\n",
" IF (temp[high] AND al[high]) AND ti[low] THEN density[average]\n",
"\tAND aggregation function : fmin\n",
"\tOR aggregation function : fmax\n",
"\n",
" Aggregation (IF-clause):\n",
" - temp[high] : 0.0\n",
" - al[high] : 1.0\n",
" - ti[low] : 1.0\n",
" (temp[high] AND al[high]) AND ti[low] = 0.0\n",
" Activation (THEN-clause):\n",
" density[average] : 0.0\n",
"\n",
"RULE #9:\n",
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" IF (temp[low] AND al[low]) AND ti[average] THEN density[low]\n",
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"\tAND aggregation function : fmin\n",
"\tOR aggregation function : fmax\n",
"\n",
" Aggregation (IF-clause):\n",
" - temp[low] : 1.0\n",
" - al[low] : 0.0\n",
" - ti[average] : 0.0\n",
" (temp[low] AND al[low]) AND ti[average] = 0.0\n",
" Activation (THEN-clause):\n",
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" density[low] : 0.0\n",
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"\n",
"RULE #10:\n",
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" IF (temp[average] AND al[low]) AND ti[average] THEN density[low]\n",
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"\tAND aggregation function : fmin\n",
"\tOR aggregation function : fmax\n",
"\n",
" Aggregation (IF-clause):\n",
" - temp[average] : 0.0\n",
" - al[low] : 0.0\n",
" - ti[average] : 0.0\n",
" (temp[average] AND al[low]) AND ti[average] = 0.0\n",
" Activation (THEN-clause):\n",
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" density[low] : 0.0\n",
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"\n",
"RULE #11:\n",
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" IF (temp[high] AND al[low]) AND ti[average] THEN density[lower]\n",
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"\tAND aggregation function : fmin\n",
"\tOR aggregation function : fmax\n",
"\n",
" Aggregation (IF-clause):\n",
" - temp[high] : 0.0\n",
" - al[low] : 0.0\n",
" - ti[average] : 0.0\n",
" (temp[high] AND al[low]) AND ti[average] = 0.0\n",
" Activation (THEN-clause):\n",
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" density[lower] : 0.0\n",
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"\n",
"RULE #12:\n",
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" IF (temp[low] AND al[low]) AND ti[high] THEN density[higher]\n",
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"\tAND aggregation function : fmin\n",
"\tOR aggregation function : fmax\n",
"\n",
" Aggregation (IF-clause):\n",
" - temp[low] : 1.0\n",
" - al[low] : 0.0\n",
" - ti[high] : 0.0\n",
" (temp[low] AND al[low]) AND ti[high] = 0.0\n",
" Activation (THEN-clause):\n",
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" density[higher] : 0.0\n",
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"\n",
"RULE #13:\n",
" IF (temp[average] AND al[low]) AND ti[high] THEN density[high]\n",
"\tAND aggregation function : fmin\n",
"\tOR aggregation function : fmax\n",
"\n",
" Aggregation (IF-clause):\n",
" - temp[average] : 0.0\n",
" - al[low] : 0.0\n",
" - ti[high] : 0.0\n",
" (temp[average] AND al[low]) AND ti[high] = 0.0\n",
" Activation (THEN-clause):\n",
" density[high] : 0.0\n",
"\n",
"RULE #14:\n",
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" IF (temp[high] AND al[low]) AND ti[high] THEN density[high]\n",
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"\tAND aggregation function : fmin\n",
"\tOR aggregation function : fmax\n",
"\n",
" Aggregation (IF-clause):\n",
" - temp[high] : 0.0\n",
" - al[low] : 0.0\n",
" - ti[high] : 0.0\n",
" (temp[high] AND al[low]) AND ti[high] = 0.0\n",
" Activation (THEN-clause):\n",
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" density[high] : 0.0\n",
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"\n",
"\n",
"==============================\n",
" Intermediaries and Conquests \n",
"==============================\n",
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"Consequent: density = 1.1724924997377486\n",
" lower:\n",
" Accumulate using accumulation_max : 0.0\n",
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" low:\n",
" Accumulate using accumulation_max : 0.0\n",
" average:\n",
" Accumulate using accumulation_max : 0.0\n",
" high:\n",
" Accumulate using accumulation_max : 1.0\n",
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" higher:\n",
" Accumulate using accumulation_max : 0.0\n",
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"\n"
]
},
{
"data": {
"text/plain": [
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"np.float64(1.1724924997377486)"
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]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sim.input[\"temp\"] = 20\n",
"sim.input[\"al\"] = 0.3\n",
"sim.input[\"ti\"] = 0.0\n",
"sim.compute()\n",
"sim.print_state()\n",
"display(sim.output[\"density\"])"
]
},
{
"cell_type": "code",
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"execution_count": 63,
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"metadata": {},
"outputs": [],
"source": [
"def fuzzy_pred(row):\n",
" sim.input[\"temp\"] = row[\"T\"]\n",
" sim.input[\"al\"] = row[\"Al2O3\"]\n",
" sim.input[\"ti\"] = row[\"TiO2\"]\n",
" sim.compute()\n",
" return sim.output[\"density\"]"
]
},
{
"cell_type": "code",
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"execution_count": 65,
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"metadata": {},
"outputs": [
{
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"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>T</th>\n",
" <th>Al2O3</th>\n",
" <th>TiO2</th>\n",
" <th>Density</th>\n",
" <th>DensityPred</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>20</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>1.06250</td>\n",
" <td>1.077498</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>25</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>1.05979</td>\n",
" <td>1.076593</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>35</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>1.05404</td>\n",
" <td>1.069156</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>40</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>1.05103</td>\n",
" <td>1.061106</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>45</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>1.04794</td>\n",
" <td>1.045833</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>50</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>1.04477</td>\n",
" <td>1.046360</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>60</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>1.03826</td>\n",
" <td>1.047642</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>65</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>1.03484</td>\n",
" <td>1.046360</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>70</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>1.03182</td>\n",
" <td>1.045833</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>20</td>\n",
" <td>0.05</td>\n",
" <td>0.0</td>\n",
" <td>1.08755</td>\n",
" <td>1.077498</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>45</td>\n",
" <td>0.05</td>\n",
" <td>0.0</td>\n",
" <td>1.07105</td>\n",
" <td>1.067145</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>50</td>\n",
" <td>0.05</td>\n",
" <td>0.0</td>\n",
" <td>1.06760</td>\n",
" <td>1.067145</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>55</td>\n",
" <td>0.05</td>\n",
" <td>0.0</td>\n",
" <td>1.06409</td>\n",
" <td>1.067988</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>65</td>\n",
" <td>0.05</td>\n",
" <td>0.0</td>\n",
" <td>1.05691</td>\n",
" <td>1.062538</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>70</td>\n",
" <td>0.05</td>\n",
" <td>0.0</td>\n",
" <td>1.05291</td>\n",
" <td>1.047191</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" T Al2O3 TiO2 Density DensityPred\n",
"0 20 0.00 0.0 1.06250 1.077498\n",
"1 25 0.00 0.0 1.05979 1.076593\n",
"2 35 0.00 0.0 1.05404 1.069156\n",
"3 40 0.00 0.0 1.05103 1.061106\n",
"4 45 0.00 0.0 1.04794 1.045833\n",
"5 50 0.00 0.0 1.04477 1.046360\n",
"6 60 0.00 0.0 1.03826 1.047642\n",
"7 65 0.00 0.0 1.03484 1.046360\n",
"8 70 0.00 0.0 1.03182 1.045833\n",
"9 20 0.05 0.0 1.08755 1.077498\n",
"10 45 0.05 0.0 1.07105 1.067145\n",
"11 50 0.05 0.0 1.06760 1.067145\n",
"12 55 0.05 0.0 1.06409 1.067988\n",
"13 65 0.05 0.0 1.05691 1.062538\n",
"14 70 0.05 0.0 1.05291 1.047191"
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
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}
],
"source": [
"result_train = density_train.copy()\n",
"\n",
"result_train[\"DensityPred\"] = result_train.apply(fuzzy_pred, axis=1)\n",
"\n",
"result_train.head(15)"
]
},
{
"cell_type": "code",
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"execution_count": 66,
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"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>T</th>\n",
" <th>Al2O3</th>\n",
" <th>TiO2</th>\n",
" <th>Density</th>\n",
" <th>DensityPred</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>30</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>1.05696</td>\n",
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" <td>1.073918</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>55</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>1.04158</td>\n",
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" <td>1.047642</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>25</td>\n",
" <td>0.05</td>\n",
" <td>0.00</td>\n",
" <td>1.08438</td>\n",
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" <td>1.076518</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>30</td>\n",
" <td>0.05</td>\n",
" <td>0.00</td>\n",
" <td>1.08112</td>\n",
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" <td>1.073918</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>35</td>\n",
" <td>0.05</td>\n",
" <td>0.00</td>\n",
" <td>1.07781</td>\n",
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" <td>1.069156</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>40</td>\n",
" <td>0.05</td>\n",
" <td>0.00</td>\n",
" <td>1.07446</td>\n",
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" <td>1.067145</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>60</td>\n",
" <td>0.05</td>\n",
" <td>0.00</td>\n",
" <td>1.06053</td>\n",
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" <td>1.067988</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>35</td>\n",
" <td>0.30</td>\n",
" <td>0.00</td>\n",
" <td>1.17459</td>\n",
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" <td>1.172492</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>65</td>\n",
" <td>0.30</td>\n",
" <td>0.00</td>\n",
" <td>1.14812</td>\n",
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" <td>1.124995</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>45</td>\n",
" <td>0.00</td>\n",
" <td>0.05</td>\n",
" <td>1.07424</td>\n",
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" <td>1.067145</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>50</td>\n",
" <td>0.00</td>\n",
" <td>0.05</td>\n",
" <td>1.07075</td>\n",
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" <td>1.067145</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>55</td>\n",
" <td>0.00</td>\n",
" <td>0.05</td>\n",
" <td>1.06721</td>\n",
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" <td>1.067988</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>20</td>\n",
" <td>0.00</td>\n",
" <td>0.30</td>\n",
" <td>1.22417</td>\n",
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" <td>1.204157</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>30</td>\n",
" <td>0.00</td>\n",
" <td>0.30</td>\n",
" <td>1.21310</td>\n",
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" <td>1.180834</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>40</td>\n",
" <td>0.00</td>\n",
" <td>0.30</td>\n",
" <td>1.20265</td>\n",
2024-11-05 10:22:38 +04:00
" <td>1.173397</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>60</td>\n",
" <td>0.00</td>\n",
" <td>0.30</td>\n",
" <td>1.18265</td>\n",
2024-11-05 10:22:38 +04:00
" <td>1.172492</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>70</td>\n",
" <td>0.00</td>\n",
" <td>0.30</td>\n",
" <td>1.17261</td>\n",
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" <td>1.172492</td>\n",
2024-11-01 11:04:05 +04:00
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" T Al2O3 TiO2 Density DensityPred\n",
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"0 30 0.00 0.00 1.05696 1.073918\n",
"1 55 0.00 0.00 1.04158 1.047642\n",
"2 25 0.05 0.00 1.08438 1.076518\n",
"3 30 0.05 0.00 1.08112 1.073918\n",
"4 35 0.05 0.00 1.07781 1.069156\n",
"5 40 0.05 0.00 1.07446 1.067145\n",
"6 60 0.05 0.00 1.06053 1.067988\n",
"7 35 0.30 0.00 1.17459 1.172492\n",
"8 65 0.30 0.00 1.14812 1.124995\n",
"9 45 0.00 0.05 1.07424 1.067145\n",
"10 50 0.00 0.05 1.07075 1.067145\n",
"11 55 0.00 0.05 1.06721 1.067988\n",
"12 20 0.00 0.30 1.22417 1.204157\n",
"13 30 0.00 0.30 1.21310 1.180834\n",
"14 40 0.00 0.30 1.20265 1.173397\n",
"15 60 0.00 0.30 1.18265 1.172492\n",
"16 70 0.00 0.30 1.17261 1.172492"
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]
},
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"execution_count": 66,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"result_test = density_test.copy()\n",
"\n",
"result_test[\"DensityPred\"] = result_test.apply(fuzzy_pred, axis=1)\n",
"\n",
"result_test"
]
},
{
"cell_type": "code",
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"execution_count": 67,
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"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
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"{'RMSE_train': 0.0156766544822719,\n",
" 'RMSE_test': 0.014603864614526584,\n",
" 'RMAE_test': 0.1057231829134674,\n",
" 'R2_test': 0.9436578668511394}"
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]
},
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"execution_count": 67,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import math\n",
"from sklearn import metrics\n",
"\n",
"\n",
"rmetrics = {}\n",
"rmetrics[\"RMSE_train\"] = math.sqrt(\n",
" metrics.mean_squared_error(result_train[\"Density\"], result_train[\"DensityPred\"])\n",
")\n",
"rmetrics[\"RMSE_test\"] = math.sqrt(\n",
" metrics.mean_squared_error(result_test[\"Density\"], result_test[\"DensityPred\"])\n",
")\n",
"rmetrics[\"RMAE_test\"] = math.sqrt(\n",
" metrics.mean_absolute_error(result_test[\"Density\"], result_test[\"DensityPred\"])\n",
")\n",
"rmetrics[\"R2_test\"] = metrics.r2_score(\n",
" result_test[\"Density\"], result_test[\"DensityPred\"]\n",
")\n",
"\n",
"rmetrics"
]
}
],
"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": "3.12.7"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
