docs/source/tutorial/quickstart.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Quick Start\n",
    "\n",
    "This tutorial aims to explain in a simple way, how to create decision matrices, \n",
    "how to analyze them and how to evaluate them with some multi-criteria analysis methods (MCDA).\n",
    "\n",
    "\n",
    "## Conceptual overview\n",
    "\n",
    "Multi-criteria data are complex. This is because at least two syntactically \n",
    "disconnected vectors are needed to describe a problem.\n",
    "\n",
    "1. `matrix`/$A$ choice set.\n",
    "2. And the vector of criteria optimality sense `objectives`/$C$. \n",
    "\n",
    "Additionally it can be accompanied by a vector `w`/$w_j$ with the weighting of the criteria. \n",
    "\n",
    "To summarize all these data (and some extra ones), *Scikit-Criteria* provides\n",
    "a `DecisionMatrix` object along with a `mkdm()` utility function to \n",
    "facilitate the creation and validation of the data.\n",
    "\n",
    "## Your first `DecisionMatrix` object\n",
    "\n",
    "First we need to import the the *Scikit-Criteria* module."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "nbsphinx": "hidden"
   },
   "outputs": [],
   "source": [
    "import skcriteria as skc  # we use skc as an abbreviation for scikit-criteria"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we need to create the `matrix` and `objectives` vectors. \n",
    "\n",
    "The `matrix` must be a **2D array-like** where every column is a criteria, \n",
    "and every row is an alternative."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[1, 2, 3], [4, 5, 6]]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2 alternatives by 3 criteria\n",
    "matrix = [\n",
    "    [1, 2, 3],  # alternative 1\n",
    "    [4, 5, 6],  # alternative 2\n",
    "]\n",
    "matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `objectives` vector must be a **1D array-like** with number of elements same \n",
    "as number of columns in the alternative matrix (`matrix`). Every component of the \n",
    "`objectives` vector represent the optimal sense of each criteria."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<function max>, <function max>, <function min>]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's says the first two alternatives are\n",
    "# for maximization and the last one for minimization\n",
    "objectives = [max, max, min]\n",
    "objectives"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "as you see the `max` and `min` are the built-in function for find max and mins in collections in python.\n",
    "\n",
    "As you can see the function usage makes the code more readable. Also you can use as aliases of minimization and maximization the numpy function `np.min`, `np.max`, `np.amin`, `np.amax`, `np.nanmin`, `np.nanmax`; the strings `\"min\"`, `\"minimize\"`, `\"max\"`, `\"maximize\"`, `\">\"`, `\"<\"`, `\"+\"`, `\"-\"`; and the values `-1` (minimize) and `1` (maximize).\n",
    "\n",
    "Now we can combine this two vectors in our *Scikit-Criteria* decision matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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",
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       "\n",
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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/>\n",
       "      <th>C0[▼ 1.0]</th>\n",
       "      <th>C1[▲ 1.0]</th>\n",
       "      <th>C2[▼ 1.0]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A1</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": [
       "   C0[▼ 1.0] C1[▲ 1.0] C2[▼ 1.0]\n",
       "A0         1         2         3\n",
       "A1         4         5         6\n",
       "[2 Alternatives x 3 Criteria]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we use the built-in function as aliases\n",
    "dm = skc.mkdm(matrix, [min, max, min])\n",
    "dm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see the output of the `DecisionMatrix` object is much more friendly than the plain python lists.\n",
    "\n",
    "To change the generic names of the alternatives (A0 and A1) and the criteria (C0, C1 and C2); let's assume that our data is about cars (*car 0* and *car 1*) and their characteristics of evaluation are *autonomy* ($maximize$), *comfort* ($maximize$) and *price* ($minimize$). \n",
    "\n",
    "To feed this information to our `DecisionMatrix` object we have the parameters: `alternatives` that accept the names of alternatives (must be the same number as the rows that `matrix` has), and `criteria` the criteria names (must have same number of elements with the columns that `matrix` has)"
   ]
  },
  {
   "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",
    "    objectives,\n",
    "    alternatives=[\"car 0\", \"car 1\"],\n",
    "    criteria=[\"autonomy\", \"comfort\", \"price\"],\n",
    ")\n",
    "dm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In our final step let's assume we know in our case, that the importance of the autonomy is the *50%*, the comfort only a *5%* and the price is *45%*. The param to feed this to the structure is called `weights` and must be a vector with the same elements as criterias on your alternative matrix (number of columns)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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[▲ 0.50]</th>\n",
       "      <th>comfort[▲ 0.05]</th>\n",
       "      <th>price[▼ 0.45]</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[▲ 0.50] comfort[▲ 0.05] price[▼ 0.45]\n",
       "car 0                1               2             3\n",
       "car 1                4               5             6\n",
       "[2 Alternatives x 3 Criteria]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dm = skc.mkdm(\n",
    "    matrix,\n",
    "    objectives,\n",
    "    weights=[0.5, 0.05, 0.45],\n",
    "    alternatives=[\"car 0\", \"car 1\"],\n",
    "    criteria=[\"autonomy\", \"comfort\", \"price\"],\n",
    ")\n",
    "dm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Manipulating the Data\n",
    "\n",
    "The data object are immutable, if you want to modify it you need create a new one. All the data are stored as [pandas dataframes](https://pandas.pydata.org/) and [numpy arrays](https://docs.scipy.org/doc/numpy/user/basics.creation.html)\n",
    "\n",
    "You can access to the different parts of your data, simply by typing `dm.<your-parameter-name>` for example:"
   ]
  },
  {
   "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>autonomy</th>\n",
       "      <th>comfort</th>\n",
       "      <th>price</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>"
      ],
      "text/plain": [
       "       autonomy  comfort  price\n",
       "car 0         1        2      3\n",
       "car 1         4        5      6"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dm.matrix  # note how this data ignores the objectives and the weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "autonomy    MAX\n",
       "comfort     MAX\n",
       "price       MIN\n",
       "Name: Objectives, dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dm.objectives"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "autonomy    0.50\n",
       "comfort     0.05\n",
       "price       0.45\n",
       "Name: Weights, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dm.weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(_ACArray(['car 0', 'car 1'], dtype=object),\n",
       " _ACArray(['autonomy', 'comfort', 'price'], dtype=object))"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dm.alternatives, dm.criteria"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you want (for example) change the names of the cars from `car 0` and `car 1`; to `VW` and `Ford` you must the copy method and provide the new names:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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[▲ 0.50]</th>\n",
       "      <th>comfort[▲ 0.05]</th>\n",
       "      <th>price[▼ 0.45]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>VW</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ford</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[▲ 0.50] comfort[▲ 0.05] price[▼ 0.45]\n",
       "VW                  1               2             3\n",
       "Ford                4               5             6\n",
       "[2 Alternatives x 3 Criteria]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dm = dm.copy(alternatives=[\"VW\", \"Ford\"])\n",
    "dm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\n",
    "**Note:** \n",
    "\n",
    "A more flexible data manipulation API will relased in future versions.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting\n",
    "\n",
    "The Data structure support some basic routines for plotting. \n",
    "\n",
    "The default scikit criteria uses the Heatmap plot to visualize all the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Criteria', ylabel='Alternatives'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dm.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the same fashion you can plot the weights of the criteria "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Criteria'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x57.6 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dm.plot.wheatmap()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can accessing the different kind of plot by passing the name of the plot as first parameter of the method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Density'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dm.plot(\"kde\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "or by using the name as method call inside the `plot` attribute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Density'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dm.plot.kde()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Every plot has their own set of parameters, defined by the subjacent function\n",
    "  \n",
    "Let's change the colors of the weight bar plot and show:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IA12SCsJAl6SCMNAlqSAMdEkqCANdkgrCQJekgigr0CNiWEQsiYhlETGuhf1fj4jnI2JBRMyJiL6VL1WStCmtBnpEdAQmAScBfYEvtRDY92Rm/8w8BPg34OZKFypJ2rRyRuiHA8sy86XMfA+4FzilaYfM/FuT5seArFyJkqRydCqjz57Ay03aq4AjmneKiG8AY4HOwJCWThQRFwIXAuy9995trVWStAkV+1A0Mydl5n7At4D/tZE+kzOzLjPrampqKnVpSRLlBforwF5N2r1L2zbmXuDUzahJktQO5QR6PbB/ROwTEZ2BkcD0ph0iYv8mzf8BvFi5EiVJ5Wh1Dj0z10XExcBjQEfg9sxcFBETgHmZOR24OCKOB94H3gBGbcmiJUkfVc6HomTmo8CjzbaNb/L+sgrXJUlqI58UlaSCMNAlqSAMdEkqCANdkgrCQJekgjDQJakgDHRJKggDXZIKwkCXpIIw0CWpIAx0SSoIA12SCsJAl6SCMNAlqSAMdEkqCANdkgrCQJekgjDQJakgDHRJKggDXZIKwkCXpIIw0CWpIAx0SSoIA12SCsJAl6SCMNAlqSAMdEkqCANdkgrCQJekgjDQJakgDHRJKggDXZIKwkCXpIIoK9AjYlhELImIZRExroX9YyPiTxHxXEQ8HhGfqnypkqRNaTXQI6IjMAk4CegLfCki+jbr9kegLjMHANOAf6t0oZKkTStnhH44sCwzX8rM94B7gVOadsjM32XmP0rNp4HelS1TktSacgJ9T+DlJu1VpW0bcz7wq5Z2RMSFETEvIua99tpr5VcpSWpVRT8UjYizgTrgxpb2Z+bkzKzLzLqamppKXlqSdnidyujzCrBXk3bv0rYNRMTxwFXAsZn5bmXKkySVq5wRej2wf0TsExGdgZHA9KYdImIg8FNgRGa+WvkyJUmtaTXQM3MdcDHwGLAYuD8zF0XEhIgYUep2I7Ar8EBELIiI6Rs5nSRpCylnyoXMfBR4tNm28U3eH1/huiRJbeSTopJUEAa6JBWEgS5JBWGgS1JBGOiSVBAGuiQVhIEuSQVhoEtSQRjoklQQBrokFYSBLkkFYaBLUkEY6JJUEAa6JBWEgS5JBWGgS1JBGOiSVBAGuiQVhIEuSQVhoEtSQRjoklQQBrokFYSBLkkFYaBLUkEY6JJUEAa6JBWEgS5JBWGgS1JBGOiSVBAGuiQVhIEuSQVhoEtSQZQV6BExLCKWRMSyiBjXwv5jImJ+RKyLiC9WvkxJUmtaDfSI6AhMAk4C+gJfioi+zbqtBEYD91S6QElSeTqV0edwYFlmvgQQEfcCpwB/Wt8hM1eU9n24BWqUJJWhnCmXPYGXm7RXlba1WURcGBHzImLea6+91p5TSJI2Yqt+KJqZkzOzLjPrampqtualJanwygn0V4C9mrR7l7ZJkrYh5QR6PbB/ROwTEZ2BkcD0LVuWJKmtWg30zFwHXAw8BiwG7s/MRRExISJGAETEYRGxCjgD+GlELNqSRUuSPqqcu1zIzEeBR5ttG9/kfT0NUzGSpCrxSVFJKggDXZIKwkCXpIIw0CWpIAx0SSoIA12SCsJAl6SCMNAlqSAMdEkqCANdkgrCQJekgjDQJakgDHRJKggDXZIKwkCXpIIoaz10qdr2nfjdapewxSz7n9WuQEXhCF2SCsJAl6SCMNAlqSAMdEkqCANdkgrCQJekgjDQJakgDHRJKggDXZIKwkCXpIIw0CWpIAx0SSoIA12SCsJAl6SCMNAlqSAMdEkqCANdkgqirECPiGERsSQilkXEuBb27xwR95X2/z4iaiteqSRpk1oN9IjoCEwCTgL6Al+KiL7Nup0PvJGZ/x34HnBDpQuVJG1aOSP0w4FlmflSZr4H3Auc0qzPKcCdpffTgOMiIipXpiSpNeV8SfSewMtN2quAIzbWJzPXRcRaoCfwetNOEXEhcGGp+feIWNKeorUDumza1rxaL5r9v7sldbxsa12pWhzbVdinNrajnECvmMycDEzemteU2ioi5mVmXbXrkNqqnCmXV4C9mrR7l7a12CciOgHdgdWVKFCSVJ5yAr0e2D8i9omIzsBIYHqzPtOBUaX3XwRmZmZWrkxJUmtanXIpzYlfDDwGdARuz8xFETEBmJeZ04HbgLsiYhmwhobQl7ZXTgtquxQOpCWpGHxSVJIKwkCXpIIw0CWpIAx0SSoIA10CIuKzEfGx0vuzI+LmiNjoE3nStshAlxr8GPhHRBwMXAEsB6ZWtySpbQx0qcG60sNwpwA/zMxJQLcq1yS1yVZdy0Xahr0VEf8CnA0cExEdgJ2qXJPUJo7QpQZnAu8C52fmX2lYs+jG6pYktY0jdKnB5Zn5rfWNzFwZEf2qWZDUVo7QpQZDW9h20lavQtoMjtC1Q4uIMcBFwL4R8VyTXd2Ap6pTldQ+Ls6lHVpEdAd2B/430PQL0N/KzDXVqUpqHwNdKil9IfoeNPmXa2aurF5FUts45SIBpTX/rwH+H/BhaXMCA6pVk9RWjtAloPTlLEdkpl+dqO2Wd7lIDV4G1la7CGlzOELXDi0ixpbe9gP6AP9BwwNGAGTmzdWoS2oP59C1o1u/XsvK0qtz6SVtdxyhS1JBOEKXgIj4dxruamlqLTAP+GlmvrP1q5Laxg9FpQYvAX8Hbi29/ga8BRxQakvbPKdcJCAi6jPzsJa2RcSizHShLm3zHKFLDXaNiL3XN0rvdy0136tOSVLbOIcuNbgCmBMRy4EA9gEuKn3P6J1VrUwqk1MuUklE7Ax8utRc4geh2t4Y6NqhRcSQzJwZEV9oaX9mPri1a5LayykX7eiOBWYCJ7ewLwEDXdsNR+iSVBDe5SIBEbFHRNwWEb8qtftGxPnVrktqCwNdajAFeAz4b6X2UuCfq1WM1B4GutSgV2beT+nLLTJzHfBBdUuS2sZAlxq8HRE9Ka3nEhGDcX10bWf8UFQ7tIj4Z+CpUvNm4CBgEVADnJGZz1apNKnNDHTt0CLiJuBIGh4oegF4BXgC+EVmvl7N2qS2MtAlICI6A3U0hPtnSq83M7NvVQuT2sAHi6QGuwC7Ad1Lr/8LPF/ViqQ2coSuHVpETKbh+0TfAn4PPA08nZlvVLUwqR28y0U7ur2BnYG/0jB/vgp4s5oFSe3lCF07vIgIGkbpR5ZeBwFrgLmZeXU1a5PawkCXSiKiN/BZGkJ9ONAzM/+pqkVJbWCga4cWEZfyXyPz92m4J3396/nM/LCK5Ult4l0u2tHVAg8Al2fmX6pci7RZHKFLUkF4l4skFYSBLkkFYaBLUkEY6JJUEP8fvTyrOe46/38AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dm.plot.wbar(cmap=\"viridis\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data transformation\n",
    "\n",
    "Data in its current form is difficult to understand and analyze. On one \n",
    "hand they are out of scale, and on the other they have both minimizing and \n",
    "maximizing criteria.\n",
    "\n",
    "<div class=\"alert alert-info\">\n",
    "Note: Scikit-Criteria objective preference\n",
    "\n",
    "For a design decision *Scikit-Criteria* always prefers **Maximize** objectives.\n",
    "There are some functionalities that trigger warnings against  **Minimize** \n",
    "criteria, and others that directly and others directly fail.\n",
    "</div>\n",
    "\n",
    "To solve these problems, we will use two processors:\n",
    "\n",
    "- First `InvertMinimize` which inverts the minimizing objectives.\n",
    "  by dividing out the inverse of each criterion value ($1/C_j$).\n",
    "- Second, `SumScaler` which will divide each criterion value by the total sum \n",
    "  of the criteria, taking all of them into the range $[0, 1]$.\n",
    "\n",
    "First we start by importing the two necessary modules."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from skcriteria.preprocessing import invert_objectives, scalers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data in its current form is difficult to understand and analyze. \n",
    "The first thing we must do now is to reverse the maximization criteria.\n",
    "\n",
    "This involves:\n",
    "\n",
    "1. Create the transformer and store it in the `inverter` variable.\n",
    "2. Apply the transformation by calling the `transform` method of the transformer and\n",
    "   passing it as parameter our decision matrix `dm`.\n",
    "3. Save the transformed decision matrix in a new variable `dmt`.\n",
    "\n",
    "In code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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[▲ 0.50]</th>\n",
       "      <th>comfort[▲ 0.05]</th>\n",
       "      <th>price[▲ 0.45]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>VW</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ford</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><em class=\"decisionmatrix-dim\">2 Alternatives x 3 Criteria</em>\n",
       "</div>"
      ],
      "text/plain": [
       "     autonomy[▲ 0.50] comfort[▲ 0.05] price[▲ 0.45]\n",
       "VW                  1               2      0.333333\n",
       "Ford                4               5      0.166667\n",
       "[2 Alternatives x 3 Criteria]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inverter = invert_objectives.InvertMinimize()\n",
    "dmt = inverter.transform(dm)\n",
    "dmt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next step is to scale the values between $[0, 1]$ using the `SumScaler`.\n",
    "\n",
    "For this step we need\n",
    "\n",
    "1. Create the transformer and store it in the `inverter` variable.\n",
    "   In this case the *scalers* support a parameter called `target` which can \n",
    "   have three different values:\n",
    "   \n",
    "   - `target=\"matrix\"` The matrix $A$ is normalized.\n",
    "   - `target=\"weights\"` normalizes the weights $w$.\n",
    "   - `target=\"both\"` normalizes matrix $A$ and weights $w$.\n",
    "\n",
    "    In our case we are going to ask the scaler to scale both components of the \n",
    "    decision matrix (`target=\"both\"`)\n",
    "2. Apply the transformation by calling the `transform` method of the transformer and\n",
    "   passing it as parameter our decision matrix `dmt`.\n",
    "3. Save the transformed decision by overwriting the variable `dmt`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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[▲ 0.50]</th>\n",
       "      <th>comfort[▲ 0.05]</th>\n",
       "      <th>price[▲ 0.45]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>VW</th>\n",
       "      <td>0.2</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ford</th>\n",
       "      <td>0.8</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><em class=\"decisionmatrix-dim\">2 Alternatives x 3 Criteria</em>\n",
       "</div>"
      ],
      "text/plain": [
       "     autonomy[▲ 0.50] comfort[▲ 0.05] price[▲ 0.45]\n",
       "VW                0.2        0.285714      0.666667\n",
       "Ford              0.8        0.714286      0.333333\n",
       "[2 Alternatives x 3 Criteria]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = scalers.SumScaler(target=\"both\")\n",
    "dmt = scaler.transform(dmt)\n",
    "dmt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can analyze if the matrix graphically by creating\n",
    "a graph for the matrix, and another for the weights.\n",
    "\n",
    "<div class=\"alert alert-info\">\n",
    "Note: Advanced plots with Matplotlib\n",
    "\n",
    "If you need more information on how to make graphs using \n",
    "*Matplotlib* please che this tutorial https://matplotlib.org/stable/tutorials/index\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# we are going to user matplotlib capabilities of creat multiple figures\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# we create 2 axis with the same y axis\n",
    "fig, axs = plt.subplots(1, 2, figsize=(12, 5), sharey=True)\n",
    "\n",
    "# in the first axis we plot the criteria KDE\n",
    "dmt.plot.kde(ax=axs[0])\n",
    "axs[0].set_title(\"Criteria\")\n",
    "\n",
    "\n",
    "# in the second axis we plot the weights as bars\n",
    "dmt.plot.wbar(ax=axs[1])\n",
    "axs[1].set_title(\"Weights\")\n",
    "\n",
    "# adjust the layout of the figute based on the content\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using this data to feed some MCDA methods\n",
    "\n",
    "### Weighted Sum Model\n",
    "\n",
    "Let's rank our dummy data by [Weighted Sum Model](https://en.wikipedia.org/wiki/Weighted_sum_model)\n",
    "\n",
    "First we need to import the required module"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from skcriteria.madm import simple"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To use the methods of MCDA structure we proceed in the same way as when using transformers:\n",
    "\n",
    "1. We create the decision maker and store it in some variable (`dec` in our case).\n",
    "2. Execute the `evaluate()` method inside the decision maker to create the result.\n",
    "3. We store the result in some variable (`rank` in our case).\n",
    "\n",
    "<div class=\"alert alert-info\">\n",
    "Note: Hyper-parameters\n",
    "\n",
    "Some multi-criteria methods support \"*hyper parameters*\", which are \n",
    "provided at the time of creation of the decision maker.\n",
    "\n",
    "We will see an example with the *ELECTRE-1* method later on.\n",
    "\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class='skcresult-rank skcresult'>\n",
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_55759\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_55759_level0_col0\" class=\"col_heading level0 col0\" >VW</th>\n",
       "      <th id=\"T_55759_level0_col1\" class=\"col_heading level0 col1\" >Ford</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_55759_level0_row0\" class=\"row_heading level0 row0\" >Rank</th>\n",
       "      <td id=\"T_55759_row0_col0\" class=\"data row0 col0\" >2</td>\n",
       "      <td id=\"T_55759_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": [
       "      VW  Ford\n",
       "Rank   2     1\n",
       "[Method: WeightedSumModel]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dec = simple.WeightedSumModel()\n",
    "rank = dec.evaluate(dmt)  # we use the tansformed version of the data\n",
    "rank"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that `WeightedSumModel` prefers the alternative *Ford* over the *VW*.\n",
    "\n",
    "\n",
    "We can access the intermediate calculators of the method through the `e_` \n",
    "attribute of the result object., which (in the case of `WeightedSumModel`) \n",
    "contains the resulting scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "extra({'score'})"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rank.e_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.41428571, 0.58571429])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rank.e_.score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Obviously you can acces all the parts of the ranking as attributes of \n",
    "result object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 1])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rank.rank_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['VW', 'Ford'], dtype=object)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rank.alternatives"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'WeightedSumModel'"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rank.method"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS)\n",
    "\n",
    "The following example will be approached with the \n",
    "[TOPSIS](https://en.wikipedia.org/wiki/TOPSIS). This method was chosen because \n",
    "of its popularity and because it uses another scaling technique \n",
    "(`VectorScaler`). \n",
    "\n",
    "So the first thing one would intuitively do is to invert the original matrix \n",
    "criteria (`dm`) and then apply the  normalization; but if we have several \n",
    "matrices or several methods this solution becomes cumbersome.\n",
    "\n",
    "The proposed solution of *Scikit-Criteria* is to offer `pipelines`. The\n",
    "pipelines combine one or several transformers and one decision-maker the\n",
    "facilitate the execution of the experiments.\n",
    "\n",
    "So, let's import the necessary modules for *TOPSIS* and the *pipelines*:\n",
    "\n",
    "<div class=\"alert alert-warning\">\n",
    "Distances and InvertMinimize\n",
    "\n",
    "Since `TOPSIS` uses distances as a comparison metric, it is not recommended to \n",
    "use the `InvertMinimize` transformer. Instead we use `NegateMinimize`.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from skcriteria.madm import similarity  # here lives TOPSIS\n",
    "from skcriteria.pipeline import mkpipe  # this function is for create pipelines"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The trick is that the weights still need to be scaled with `SumScaler` so be \n",
    "careful to assign the *targets* correctly in each transformer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SKCPipeline(steps=[('negateminimize', NegateMinimize()), ('vectorscaler', VectorScaler(target='matrix')), ('sumscaler', SumScaler(target='weights')), ('topsis', TOPSIS(metric='euclidean'))])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe = mkpipe(\n",
    "    invert_objectives.NegateMinimize(),\n",
    "    scalers.VectorScaler(target=\"matrix\"),  # this scaler transform the matrix\n",
    "    scalers.SumScaler(target=\"weights\"),  # and this transform the weights\n",
    "    similarity.TOPSIS(),\n",
    ")\n",
    "\n",
    "pipe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can directly call the pipeline `evaluate()` method with the original\n",
    "decision-matrix (`dm`).\n",
    "\n",
    "This method sequentially executes the three transformers and finally the evaluator to obtain a\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class='skcresult-rank skcresult'>\n",
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_63c68\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_63c68_level0_col0\" class=\"col_heading level0 col0\" >VW</th>\n",
       "      <th id=\"T_63c68_level0_col1\" class=\"col_heading level0 col1\" >Ford</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_63c68_level0_row0\" class=\"row_heading level0 row0\" >Rank</th>\n",
       "      <td id=\"T_63c68_row0_col0\" class=\"data row0 col0\" >2</td>\n",
       "      <td id=\"T_63c68_row0_col1\" class=\"data row0 col1\" >1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<em class='skcresult-method'>Method: TOPSIS</em>\n",
       "</div>"
      ],
      "text/plain": [
       "      VW  Ford\n",
       "Rank   2     1\n",
       "[Method: TOPSIS]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rank = pipe.evaluate(dm)\n",
    "rank"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "extra({'similarity', 'anti_ideal', 'ideal'})\n",
      "Ideal: [ 0.48507125  0.04642383 -0.20124612]\n",
      "Anti-Ideal: [ 0.12126781  0.01856953 -0.40249224]\n",
      "Similarity index: [0.35548671 0.64451329]\n"
     ]
    }
   ],
   "source": [
    "print(rank.e_)\n",
    "print(\"Ideal:\", rank.e_.ideal)\n",
    "print(\"Anti-Ideal:\", rank.e_.anti_ideal)\n",
    "print(\"Similarity index:\", rank.e_.similarity)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Where the `ideal` and `anti_ideal` are the normalizated sintetic better and \n",
    "worst altenatives created by TOPSIS, and the `similarity` is how far from the \n",
    "*anti-ideal* and how closer to the *ideal* are the real alternatives"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ÉLimination et Choix Traduisant la REalité (ELECTRE)\n",
    "\n",
    "For our final example, we are going to use the\n",
    "method [ELECTRE-I](https://en.wikipedia.org/wiki/%C3%89LECTRE) which has two\n",
    "particularities:\n",
    "\n",
    "1. It does not return a ranking but a kernel.\n",
    "2. It supports two hyper-parameters: a concordance threshold `p` and a discordance threshold `q`. \n",
    "\n",
    "Let's test the default threshold (`p=0.65, q=0.35`) but with two normalizations\n",
    "for different matrix: `VectorScaler` and `SumScaler`.\n",
    "\n",
    "For this we will make two pipelines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from skcriteria.madm import electre\n",
    "\n",
    "\n",
    "pipe_vector = mkpipe(\n",
    "    invert_objectives.InvertMinimize(),\n",
    "    scalers.VectorScaler(target=\"matrix\"),  # this scaler transform the matrix\n",
    "    scalers.SumScaler(target=\"weights\"),  # and this transform the weights\n",
    "    electre.ELECTRE1(p=0.65, q=0.35),\n",
    ")\n",
    "\n",
    "pipe_sum = mkpipe(\n",
    "    invert_objectives.InvertMinimize(),\n",
    "    scalers.SumScaler(target=\"weights\"),  # transform the matrix and weights\n",
    "    electre.ELECTRE1(p=0.65, q=0.35),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class='skcresult-kernel skcresult'>\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></th>\n",
       "      <th>VW</th>\n",
       "      <th>Ford</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Kernel</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><em class='skcresult-method'>Method: ELECTRE1</em>\n",
       "</div>"
      ],
      "text/plain": [
       "          VW  Ford\n",
       "Kernel  True  True\n",
       "[Method: ELECTRE1]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kernel_vector = pipe_vector.evaluate(dm)\n",
    "kernel_vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class='skcresult-kernel skcresult'>\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></th>\n",
       "      <th>VW</th>\n",
       "      <th>Ford</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Kernel</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><em class='skcresult-method'>Method: ELECTRE1</em>\n",
       "</div>"
      ],
      "text/plain": [
       "          VW  Ford\n",
       "Kernel  True  True\n",
       "[Method: ELECTRE1]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kernel_sum = pipe_sum.evaluate(dm)\n",
    "kernel_sum"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As can be seen for this case both scalings give the same results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scikit-Criteria version: 0.7dev0\n",
      "Running datetime: 2022-04-10 21:06:20.171375\n"
     ]
    }
   ],
   "source": [
    "import datetime as dt\n",
    "import skcriteria\n",
    "\n",
    "print(\"Scikit-Criteria version:\", skcriteria.VERSION)\n",
    "print(\"Running datetime:\", dt.datetime.now())"
   ]
  }
 ],
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