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leliel12/mergetworanks.ipynb

Created April 23, 2022 16:26
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mergetworanks.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## How to merge multiple ranks into a single dataframe"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import skcriteria as skc"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div class=\"decisionmatrix\">\n",
"<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/>\n",
" <th>autonomy[▲ 1.0]</th>\n",
" <th>comfort[▲ 1.0]</th>\n",
" <th>price[▲ 1.0]</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>car 0</th>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>car 1</th>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>6</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div><em class=\"decisionmatrix-dim\">2 Alternatives x 3 Criteria</em>\n",
"</div>"
],
"text/plain": [
" autonomy[▲ 1.0] comfort[▲ 1.0] price[▲ 1.0]\n",
"car 0 1 2 3\n",
"car 1 4 5 6\n",
"[2 Alternatives x 3 Criteria]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dm = skc.mkdm(\n",
" matrix=[\n",
" [1, 2, 3], # alternative 1\n",
" [4, 5, 6], # alternative 2\n",
" ],\n",
" objectives=[max, max, max],\n",
" alternatives=[\"car 0\", \"car 1\"],\n",
" criteria=[\"autonomy\", \"comfort\", \"price\"],\n",
")\n",
"dm\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create two ranks with two methods"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"from skcriteria.madm import simple"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div class='skcresult-rank skcresult'>\n",
"<style type=\"text/css\">\n",
"</style>\n",
"<table id=\"T_596ab\">\n",
" <thead>\n",
" <tr>\n",
" <th class=\"blank level0\" >&nbsp;</th>\n",
" <th id=\"T_596ab_level0_col0\" class=\"col_heading level0 col0\" >car 0</th>\n",
" <th id=\"T_596ab_level0_col1\" class=\"col_heading level0 col1\" >car 1</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th id=\"T_596ab_level0_row0\" class=\"row_heading level0 row0\" >Rank</th>\n",
" <td id=\"T_596ab_row0_col0\" class=\"data row0 col0\" >2</td>\n",
" <td id=\"T_596ab_row0_col1\" class=\"data row0 col1\" >1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<em class='skcresult-method'>Method: WeightedSumModel</em>\n",
"</div>"
],
"text/plain": [
" car 0 car 1\n",
"Rank 2 1\n",
"[Method: WeightedSumModel]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rank0 = simple.WeightedSumModel().evaluate(dm)\n",
"rank0"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div class='skcresult-rank skcresult'>\n",
"<style type=\"text/css\">\n",
"</style>\n",
"<table id=\"T_6e026\">\n",
" <thead>\n",
" <tr>\n",
" <th class=\"blank level0\" >&nbsp;</th>\n",
" <th id=\"T_6e026_level0_col0\" class=\"col_heading level0 col0\" >car 0</th>\n",
" <th id=\"T_6e026_level0_col1\" class=\"col_heading level0 col1\" >car 1</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th id=\"T_6e026_level0_row0\" class=\"row_heading level0 row0\" >Rank</th>\n",
" <td id=\"T_6e026_row0_col0\" class=\"data row0 col0\" >2</td>\n",
" <td id=\"T_6e026_row0_col1\" class=\"data row0 col1\" >1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<em class='skcresult-method'>Method: WeightedProductModel</em>\n",
"</div>"
],
"text/plain": [
" car 0 car 1\n",
"Rank 2 1\n",
"[Method: WeightedProductModel]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rank1 = simple.WeightedProductModel().evaluate(dm)\n",
"rank1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": []
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 13,
"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>WeightedSumModel</th>\n",
" <th>WeightedProductModel</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" WeightedSumModel WeightedProductModel\n",
"0 2 2\n",
"1 1 1"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.DataFrame({rank0.method: rank0.values, rank1.method: rank1.values})"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"interpreter": {
"hash": "bb4160009357201beaf583af3fb6532839cedbd2b920c61e4caa741a05856261"
},
"kernelspec": {
"display_name": "Python 3.9.7 ('skcriteria')",
"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": "3.9.7"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}
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