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fuzzy-rules-generator/density_fuzzy.ipynb

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initial
2024-11-01 11:04:05 +04:00
{
"cells": [
{
"cell_type": "code",
"execution_count": 7,
"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",
"execution_count": 8,
"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": {
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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",
"# temp = ctrl.Antecedent(np.arange(20, 70, 1), \"temp\")\n",
"# al = ctrl.Antecedent(np.arange(0, 0.3, 0.001), \"al\")\n",
"# ti = ctrl.Antecedent(np.arange(0, 0.3, 0.001), \"ti\")\n",
"# density = ctrl.Consequent(np.arange(1, 1.3, 0.00001), \"density\")\n",
"\n",
"temp = ctrl.Antecedent(density_train[\"T\"].sort_values().unique(), \"temp\")\n",
"al = ctrl.Antecedent(density_train[\"Al2O3\"].sort_values().unique(), \"al\")\n",
"ti = ctrl.Antecedent(density_train[\"TiO2\"].sort_values().unique(), \"ti\")\n",
"density = ctrl.Consequent(np.arange(1.03, 1.22, 0.0001), \"density\")\n",
"\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",
"density.automf(3, variable_type=\"quant\")\n",
"# density[\"low\"] = fuzz.trimf(density.universe, [1.03, 1.06, 1.09])\n",
"# density[\"average\"] = fuzz.trimf(density.universe, [1.06, 1.14, 1.18])\n",
"# density[\"high\"] = fuzz.trimf(density.universe, [1.09, 1.18, 1.22])\n",
"density.view()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"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",
" density[\"low\"],\n",
")\n",
"rule13 = ctrl.Rule(\n",
" temp[\"high\"] & al[\"low\"] & ti[\"low\"],\n",
" density[\"low\"],\n",
")\n",
"\n",
"rule21 = ctrl.Rule(\n",
" temp[\"low\"] & al[\"average\"] & ti[\"low\"],\n",
" density[\"average\"],\n",
")\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",
" density[\"low\"],\n",
")\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",
" density[\"average\"],\n",
")\n",
"rule42 = ctrl.Rule(\n",
" temp[\"average\"] & al[\"low\"] & ti[\"average\"],\n",
" density[\"average\"],\n",
")\n",
"rule43 = ctrl.Rule(\n",
" temp[\"high\"] & al[\"low\"] & ti[\"average\"],\n",
" density[\"low\"],\n",
")\n",
"\n",
"rule51 = ctrl.Rule(\n",
" temp[\"low\"] & al[\"low\"] & ti[\"high\"],\n",
" density[\"high\"],\n",
")\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",
" density[\"average\"],\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 10,
"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",
" IF (temp[average] AND al[low]) AND ti[low] THEN density[low]\n",
" \tAND aggregation function : fmin\n",
" \tOR aggregation function : fmax,\n",
" IF (temp[high] AND al[low]) AND ti[low] THEN density[low]\n",
" \tAND aggregation function : fmin\n",
" \tOR aggregation function : fmax,\n",
" IF (temp[low] AND al[average]) AND ti[low] THEN density[average]\n",
" \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",
" IF (temp[high] AND al[average]) AND ti[low] THEN density[low]\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[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",
" IF (temp[low] AND al[low]) AND ti[average] THEN density[average]\n",
" \tAND aggregation function : fmin\n",
" \tOR aggregation function : fmax,\n",
" IF (temp[average] AND al[low]) AND ti[average] THEN density[average]\n",
" \tAND aggregation function : fmin\n",
" \tOR aggregation function : fmax,\n",
" IF (temp[high] AND al[low]) AND ti[average] THEN density[low]\n",
" \tAND aggregation function : fmin\n",
" \tOR aggregation function : fmax,\n",
" IF (temp[low] AND al[low]) AND ti[high] THEN density[high]\n",
" \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",
" IF (temp[high] AND al[low]) AND ti[high] THEN density[average]\n",
" \tAND aggregation function : fmin\n",
" \tOR aggregation function : fmax]"
]
},
"execution_count": 10,
"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",
"execution_count": 11,
"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",
"Antecedent: al = 0.3\n",
" - 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",
" IF (temp[average] 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[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",
" density[low] : 0.0\n",
"\n",
"RULE #2:\n",
" IF (temp[high] 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[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",
" density[low] : 0.0\n",
"\n",
"RULE #3:\n",
" IF (temp[low] AND al[average]) AND ti[low] THEN density[average]\n",
"\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",
" density[average] : 0.0\n",
"\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",
" IF (temp[high] 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[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",
" density[low] : 0.0\n",
"\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",
" IF (temp[low] AND al[low]) AND ti[average] THEN density[average]\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[average] : 0.0\n",
" (temp[low] AND al[low]) AND ti[average] = 0.0\n",
" Activation (THEN-clause):\n",
" density[average] : 0.0\n",
"\n",
"RULE #10:\n",
" IF (temp[average] AND al[low]) AND ti[average] THEN density[average]\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[average] : 0.0\n",
" (temp[average] AND al[low]) AND ti[average] = 0.0\n",
" Activation (THEN-clause):\n",
" density[average] : 0.0\n",
"\n",
"RULE #11:\n",
" IF (temp[high] AND al[low]) AND ti[average] THEN density[low]\n",
"\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",
" density[low] : 0.0\n",
"\n",
"RULE #12:\n",
" IF (temp[low] 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[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",
" density[high] : 0.0\n",
"\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",
" IF (temp[high] AND al[low]) AND ti[high] 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[low] : 0.0\n",
" - ti[high] : 0.0\n",
" (temp[high] AND al[low]) AND ti[high] = 0.0\n",
" Activation (THEN-clause):\n",
" density[average] : 0.0\n",
"\n",
"\n",
"==============================\n",
" Intermediaries and Conquests \n",
"==============================\n",
"Consequent: density = 1.1882499824468897\n",
" 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",
"\n"
]
},
{
"data": {
"text/plain": [
"np.float64(1.1882499824468897)"
]
},
"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",
"execution_count": 12,
"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",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"ename": "KeyError",
"evalue": "'density'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn[13], line 3\u001b[0m\n\u001b[0;32m 1\u001b[0m result_train \u001b[38;5;241m=\u001b[39m density_train\u001b[38;5;241m.\u001b[39mcopy()\n\u001b[1;32m----> 3\u001b[0m result_train[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDensityPred\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[43mresult_train\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfuzzy_pred\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m 5\u001b[0m result_train\u001b[38;5;241m.\u001b[39mhead(\u001b[38;5;241m15\u001b[39m)\n",
"File \u001b[1;32mc:\\Users\\user\\Projects\\python\\fuzzy\\.venv\\Lib\\site-packages\\pandas\\core\\frame.py:10374\u001b[0m, in \u001b[0;36mDataFrame.apply\u001b[1;34m(self, func, axis, raw, result_type, args, by_row, engine, engine_kwargs, **kwargs)\u001b[0m\n\u001b[0;32m 10360\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapply\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m frame_apply\n\u001b[0;32m 10362\u001b[0m op \u001b[38;5;241m=\u001b[39m frame_apply(\n\u001b[0;32m 10363\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m 10364\u001b[0m func\u001b[38;5;241m=\u001b[39mfunc,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 10372\u001b[0m kwargs\u001b[38;5;241m=\u001b[39mkwargs,\n\u001b[0;32m 10373\u001b[0m )\n\u001b[1;32m> 10374\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mop\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39m__finalize__(\u001b[38;5;28mself\u001b[39m, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mapply\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
"File \u001b[1;32mc:\\Users\\user\\Projects\\python\\fuzzy\\.venv\\Lib\\site-packages\\pandas\\core\\apply.py:916\u001b[0m, in \u001b[0;36mFrameApply.apply\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 913\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mraw:\n\u001b[0;32m 914\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapply_raw(engine\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mengine, engine_kwargs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mengine_kwargs)\n\u001b[1;32m--> 916\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply_standard\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[1;32mc:\\Users\\user\\Projects\\python\\fuzzy\\.venv\\Lib\\site-packages\\pandas\\core\\apply.py:1063\u001b[0m, in \u001b[0;36mFrameApply.apply_standard\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 1061\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mapply_standard\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m 1062\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mengine \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpython\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m-> 1063\u001b[0m results, res_index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply_series_generator\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1064\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 1065\u001b[0m results, res_index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapply_series_numba()\n",
"File \u001b[1;32mc:\\Users\\user\\Projects\\python\\fuzzy\\.venv\\Lib\\site-packages\\pandas\\core\\apply.py:1081\u001b[0m, in \u001b[0;36mFrameApply.apply_series_generator\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 1078\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m option_context(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmode.chained_assignment\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m 1079\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, v \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(series_gen):\n\u001b[0;32m 1080\u001b[0m \u001b[38;5;66;03m# ignore SettingWithCopy here in case the user mutates\u001b[39;00m\n\u001b[1;32m-> 1081\u001b[0m results[i] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mv\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1082\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(results[i], ABCSeries):\n\u001b[0;32m 1083\u001b[0m \u001b[38;5;66;03m# If we have a view on v, we need to make a copy because\u001b[39;00m\n\u001b[0;32m 1084\u001b[0m \u001b[38;5;66;03m# series_generator will swap out the underlying data\u001b[39;00m\n\u001b[0;32m 1085\u001b[0m results[i] \u001b[38;5;241m=\u001b[39m results[i]\u001b[38;5;241m.\u001b[39mcopy(deep\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n",
"Cell \u001b[1;32mIn[12], line 6\u001b[0m, in \u001b[0;36mfuzzy_pred\u001b[1;34m(row)\u001b[0m\n\u001b[0;32m 4\u001b[0m sim\u001b[38;5;241m.\u001b[39minput[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mti\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m row[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTiO2\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m 5\u001b[0m sim\u001b[38;5;241m.\u001b[39mcompute()\n\u001b[1;32m----> 6\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43msim\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moutput\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdensity\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\n",
"\u001b[1;31mKeyError\u001b[0m: 'density'"
]
}
],
"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",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
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" 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",
" <td>1.060000</td>\n",
" </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",
" <td>1.060000</td>\n",
" </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",
" <td>1.096363</td>\n",
" </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",
" <td>1.097343</td>\n",
" </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",
" <td>1.097343</td>\n",
" </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",
" <td>1.088627</td>\n",
" </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",
" <td>1.060000</td>\n",
" </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",
" <td>1.159583</td>\n",
" </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",
" <td>1.126222</td>\n",
" </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",
" <td>1.096363</td>\n",
" </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",
" <td>1.096363</td>\n",
" </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",
" <td>1.097343</td>\n",
" </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",
" <td>1.163333</td>\n",
" </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",
" <td>1.161428</td>\n",
" </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",
" <td>1.162778</td>\n",
" </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",
" <td>1.141253</td>\n",
" </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",
" <td>1.126667</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" T Al2O3 TiO2 Density DensityPred\n",
"0 30 0.00 0.00 1.05696 1.060000\n",
"1 55 0.00 0.00 1.04158 1.060000\n",
"2 25 0.05 0.00 1.08438 1.096363\n",
"3 30 0.05 0.00 1.08112 1.097343\n",
"4 35 0.05 0.00 1.07781 1.097343\n",
"5 40 0.05 0.00 1.07446 1.088627\n",
"6 60 0.05 0.00 1.06053 1.060000\n",
"7 35 0.30 0.00 1.17459 1.159583\n",
"8 65 0.30 0.00 1.14812 1.126222\n",
"9 45 0.00 0.05 1.07424 1.096363\n",
"10 50 0.00 0.05 1.07075 1.096363\n",
"11 55 0.00 0.05 1.06721 1.097343\n",
"12 20 0.00 0.30 1.22417 1.163333\n",
"13 30 0.00 0.30 1.21310 1.161428\n",
"14 40 0.00 0.30 1.20265 1.162778\n",
"15 60 0.00 0.30 1.18265 1.141253\n",
"16 70 0.00 0.30 1.17261 1.126667"
]
},
"execution_count": 19,
"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",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'RMSE_train': 0.024131296333542294,\n",
" 'RMSE_test': 0.03056470202709202,\n",
" 'RMAE_test': 0.16058558998263095,\n",
" 'R2_test': 0.7532040538162098}"
]
},
"execution_count": 10,
"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
}
Reference in New Issue Copy Permalink