AssoRules.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.chdir(\"C:\\\\Users\\gusta\\JupyterLab\\Laboratorio de Modelacion 2\\Data\")\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from itertools import combinations\n",
    "\n",
    "\n",
    "import csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "category = pd.read_csv(\"cat_nombres.csv\")\n",
    "department = pd.read_csv(\"data_assorules_hat.csv\")\n",
    "\n",
    "\n",
    "transactions = pd.read_csv(\"data_assorules.csv\")\n",
    "\n",
    "categorias = sorted(transactions['Category'].unique())\n",
    "\n",
    "supp=transactions.groupby(\"Category\").apply(lambda x: x.Visit_nbr.count())\n",
    "\n",
    "\n",
    "soportes = pd.read_csv(\"soporte_conjunto.csv\").drop('Unnamed: 0',axis=1).set_axis(categorias).rename(columns=int)/supp.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Implementamos a continuacion algunas de las reglas de asociacion propuestas para este proyecto. Detallaremos en cada una de ellas, como se obtienen a partir del soporte de cada item, asi como de los soportes por pares."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1) Confidence"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comenzamos definiendo del indicador $Confidence$ o \"Confianza\" en español. Este indica la proporción de transacciones en que encontramos un itemset $Y$ que también contienen el itemset $X$. Definimos Confidence como:\n",
    "\\begin{align}\n",
    "conf(X \\Rightarrow Y):= \\dfrac{supp(X \\Rightarrow Y)}{supp(X)} = \\dfrac{supp(X \\cup Y)}{Supp(X)} \\in [0,1].\n",
    "\\end{align}\n",
    "Que en términos de probabilidad queda escrito como:\n",
    "\\begin{equation}\n",
    "conf(X\\Rightarrow Y) = \\dfrac{\\mathbb{P}(X\\cap Y)}{\\mathbb{P}(X)} = \\mathbb{P}(X\\vert Y)\n",
    "\\end{equation}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "soportes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Desde la tabla de soportes no es dificil construir una nueva tabla de confianza entre pares de productos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "conf_table = np.zeros((272,272))\n",
    "conf_df= pd.DataFrame(conf_table, columns= categorias).set_axis(categorias).fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "for cat in categorias:\n",
    "    for cat2 in categorias:\n",
    "        if (soportes[cat][cat]!=0) and (soportes[cat2][cat2]!=0):\n",
    "            if (cat >= cat2):\n",
    "                conf_df[cat][cat2] = soportes[cat][cat2]/soportes[cat][cat]\n",
    "            elif (cat < cat2):\n",
    "                conf_df[cat][cat2] = soportes[cat2][cat]/soportes[cat2][cat2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "conf_df.to_csv('confidence.csv')                "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>496</th>\n",
       "      <th>497</th>\n",
       "      <th>500</th>\n",
       "      <th>504</th>\n",
       "      <th>505</th>\n",
       "      <th>506</th>\n",
       "      <th>507</th>\n",
       "      <th>508</th>\n",
       "      <th>509</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.156998</td>\n",
       "      <td>0.407065</td>\n",
       "      <td>0.175266</td>\n",
       "      <td>0.195531</td>\n",
       "      <td>0.132070</td>\n",
       "      <td>0.213245</td>\n",
       "      <td>0.191104</td>\n",
       "      <td>0.167847</td>\n",
       "      <td>0.115578</td>\n",
       "      <td>...</td>\n",
       "      <td>0.165138</td>\n",
       "      <td>0.037559</td>\n",
       "      <td>0.082833</td>\n",
       "      <td>0.042529</td>\n",
       "      <td>0.061224</td>\n",
       "      <td>0.098712</td>\n",
       "      <td>0.079439</td>\n",
       "      <td>0.058242</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.156998</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.033728</td>\n",
       "      <td>0.025519</td>\n",
       "      <td>0.034218</td>\n",
       "      <td>0.025375</td>\n",
       "      <td>0.031788</td>\n",
       "      <td>0.047776</td>\n",
       "      <td>0.029883</td>\n",
       "      <td>0.028894</td>\n",
       "      <td>...</td>\n",
       "      <td>0.018349</td>\n",
       "      <td>0.032864</td>\n",
       "      <td>0.020408</td>\n",
       "      <td>0.016092</td>\n",
       "      <td>0.040816</td>\n",
       "      <td>0.012876</td>\n",
       "      <td>0.014019</td>\n",
       "      <td>0.014286</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.407065</td>\n",
       "      <td>0.033728</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.268648</td>\n",
       "      <td>0.280028</td>\n",
       "      <td>0.220422</td>\n",
       "      <td>0.267550</td>\n",
       "      <td>0.253707</td>\n",
       "      <td>0.258330</td>\n",
       "      <td>0.175879</td>\n",
       "      <td>...</td>\n",
       "      <td>0.256881</td>\n",
       "      <td>0.098592</td>\n",
       "      <td>0.120048</td>\n",
       "      <td>0.079310</td>\n",
       "      <td>0.095238</td>\n",
       "      <td>0.128755</td>\n",
       "      <td>0.102804</td>\n",
       "      <td>0.071429</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.175266</td>\n",
       "      <td>0.025519</td>\n",
       "      <td>0.268648</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.171788</td>\n",
       "      <td>0.090186</td>\n",
       "      <td>0.115232</td>\n",
       "      <td>0.080725</td>\n",
       "      <td>0.077884</td>\n",
       "      <td>0.050251</td>\n",
       "      <td>...</td>\n",
       "      <td>0.146789</td>\n",
       "      <td>0.028169</td>\n",
       "      <td>0.022809</td>\n",
       "      <td>0.024138</td>\n",
       "      <td>0.020408</td>\n",
       "      <td>0.047210</td>\n",
       "      <td>0.028037</td>\n",
       "      <td>0.023077</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.195531</td>\n",
       "      <td>0.034218</td>\n",
       "      <td>0.280028</td>\n",
       "      <td>0.171788</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.046775</td>\n",
       "      <td>0.058278</td>\n",
       "      <td>0.041186</td>\n",
       "      <td>0.036964</td>\n",
       "      <td>0.037688</td>\n",
       "      <td>...</td>\n",
       "      <td>0.055046</td>\n",
       "      <td>0.014085</td>\n",
       "      <td>0.014406</td>\n",
       "      <td>0.011494</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004292</td>\n",
       "      <td>0.009346</td>\n",
       "      <td>0.007692</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>506</th>\n",
       "      <td>0.098712</td>\n",
       "      <td>0.012876</td>\n",
       "      <td>0.128755</td>\n",
       "      <td>0.047210</td>\n",
       "      <td>0.004292</td>\n",
       "      <td>0.025751</td>\n",
       "      <td>0.004292</td>\n",
       "      <td>0.008584</td>\n",
       "      <td>0.094421</td>\n",
       "      <td>0.004292</td>\n",
       "      <td>...</td>\n",
       "      <td>0.008584</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.064378</td>\n",
       "      <td>0.017167</td>\n",
       "      <td>0.085837</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.126168</td>\n",
       "      <td>0.001099</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>507</th>\n",
       "      <td>0.079439</td>\n",
       "      <td>0.014019</td>\n",
       "      <td>0.102804</td>\n",
       "      <td>0.028037</td>\n",
       "      <td>0.009346</td>\n",
       "      <td>0.014019</td>\n",
       "      <td>0.009346</td>\n",
       "      <td>0.004673</td>\n",
       "      <td>0.051402</td>\n",
       "      <td>0.004673</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004673</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.051402</td>\n",
       "      <td>0.004673</td>\n",
       "      <td>0.060748</td>\n",
       "      <td>0.126168</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.001099</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>508</th>\n",
       "      <td>0.058242</td>\n",
       "      <td>0.014286</td>\n",
       "      <td>0.071429</td>\n",
       "      <td>0.023077</td>\n",
       "      <td>0.007692</td>\n",
       "      <td>0.020879</td>\n",
       "      <td>0.006593</td>\n",
       "      <td>0.008791</td>\n",
       "      <td>0.047253</td>\n",
       "      <td>0.005495</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003297</td>\n",
       "      <td>0.001099</td>\n",
       "      <td>0.005495</td>\n",
       "      <td>0.001099</td>\n",
       "      <td>0.001099</td>\n",
       "      <td>0.001099</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>509</th>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>510</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>272 rows × 272 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3         4         5         6    \\\n",
       "0    1.000000  0.156998  0.407065  0.175266  0.195531  0.132070  0.213245   \n",
       "1    0.156998  1.000000  0.033728  0.025519  0.034218  0.025375  0.031788   \n",
       "2    0.407065  0.033728  1.000000  0.268648  0.280028  0.220422  0.267550   \n",
       "3    0.175266  0.025519  0.268648  1.000000  0.171788  0.090186  0.115232   \n",
       "4    0.195531  0.034218  0.280028  0.171788  1.000000  0.046775  0.058278   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "506  0.098712  0.012876  0.128755  0.047210  0.004292  0.025751  0.004292   \n",
       "507  0.079439  0.014019  0.102804  0.028037  0.009346  0.014019  0.009346   \n",
       "508  0.058242  0.014286  0.071429  0.023077  0.007692  0.020879  0.006593   \n",
       "509  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "510  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "\n",
       "          7         8         9    ...       496       497       500  \\\n",
       "0    0.191104  0.167847  0.115578  ...  0.165138  0.037559  0.082833   \n",
       "1    0.047776  0.029883  0.028894  ...  0.018349  0.032864  0.020408   \n",
       "2    0.253707  0.258330  0.175879  ...  0.256881  0.098592  0.120048   \n",
       "3    0.080725  0.077884  0.050251  ...  0.146789  0.028169  0.022809   \n",
       "4    0.041186  0.036964  0.037688  ...  0.055046  0.014085  0.014406   \n",
       "..        ...       ...       ...  ...       ...       ...       ...   \n",
       "506  0.008584  0.094421  0.004292  ...  0.008584  0.000000  0.064378   \n",
       "507  0.004673  0.051402  0.004673  ...  0.004673  0.000000  0.051402   \n",
       "508  0.008791  0.047253  0.005495  ...  0.000000  0.003297  0.001099   \n",
       "509  0.000000  0.000000  0.000000  ...  0.000000  0.000000  0.000000   \n",
       "510  0.000000  0.000000  0.000000  ...  0.000000  0.000000  0.000000   \n",
       "\n",
       "          504       505       506       507       508  509  510  \n",
       "0    0.042529  0.061224  0.098712  0.079439  0.058242  0.0  0.0  \n",
       "1    0.016092  0.040816  0.012876  0.014019  0.014286  0.0  0.0  \n",
       "2    0.079310  0.095238  0.128755  0.102804  0.071429  0.0  0.0  \n",
       "3    0.024138  0.020408  0.047210  0.028037  0.023077  0.0  0.0  \n",
       "4    0.011494  0.000000  0.004292  0.009346  0.007692  0.0  0.0  \n",
       "..        ...       ...       ...       ...       ...  ...  ...  \n",
       "506  0.017167  0.085837  1.000000  0.126168  0.001099  0.0  0.0  \n",
       "507  0.004673  0.060748  0.126168  1.000000  0.001099  0.0  0.0  \n",
       "508  0.005495  0.001099  0.001099  0.001099  1.000000  0.0  0.0  \n",
       "509  0.000000  0.000000  0.000000  0.000000  0.000000  0.0  0.0  \n",
       "510  0.000000  0.000000  0.000000  0.000000  0.000000  0.0  0.0  \n",
       "\n",
       "[272 rows x 272 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conf_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2) Lift"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Definamos ahora el indicador $Lift$. Este mide que tanto mas aparece el itemset $X \\cup Y$ respecto a lo que se espera que ocurra en caso de que $X$ e $Y$ fueran estadisticamente independientes. Definimos el $Lift$ como:\n",
    "\\begin{align}\n",
    "Lift(X \\Rightarrow Y):= \\dfrac{conf(X \\Rightarrow Y)}{supp(Y)}  \\in [0,\\infty].\n",
    "\\end{align}\n",
    "Que en términos de probabilidad queda escrito como:\n",
    "\\begin{equation}\n",
    "Lift(X\\Rightarrow Y) = \\dfrac{\\mathbb{P}(X\\cap Y)}{\\mathbb{P}(X)\\mathbb{P}(Y)}\n",
    "\\end{equation}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "lift_table = np.zeros((272,272))\n",
    "lift_df= pd.DataFrame(lift_table, columns= categorias).set_axis(categorias).fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "for cat in categorias:\n",
    "    for cat2 in categorias:\n",
    "        if (cat in conf_df.columns) and (cat2 in conf_df.index) and (soportes[cat][cat]!=0) and soportes[cat2][cat2]!=0:\n",
    "            if cat>= cat2:\n",
    "                lift_df[cat][cat2] = conf_df[cat][cat2]/soportes[cat2][cat2]\n",
    "            elif cat< cat2:\n",
    "                lift_df[cat][cat2] = conf_df[cat2][cat]/soportes[cat][cat] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
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       "      <td>14.887181</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>103.816470</td>\n",
       "      <td>35.148335</td>\n",
       "      <td>164.530566</td>\n",
       "      <td>44.200864</td>\n",
       "      <td>46.073154</td>\n",
       "      <td>36.266138</td>\n",
       "      <td>44.020099</td>\n",
       "      <td>41.742516</td>\n",
       "      <td>42.503159</td>\n",
       "      <td>28.937537</td>\n",
       "      <td>...</td>\n",
       "      <td>42.264733</td>\n",
       "      <td>16.221323</td>\n",
       "      <td>19.751569</td>\n",
       "      <td>13.048976</td>\n",
       "      <td>15.669578</td>\n",
       "      <td>21.184193</td>\n",
       "      <td>16.914357</td>\n",
       "      <td>11.752183</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>44.699292</td>\n",
       "      <td>26.593198</td>\n",
       "      <td>44.200864</td>\n",
       "      <td>634.729950</td>\n",
       "      <td>109.038804</td>\n",
       "      <td>57.244065</td>\n",
       "      <td>73.141067</td>\n",
       "      <td>51.238497</td>\n",
       "      <td>49.435444</td>\n",
       "      <td>31.895977</td>\n",
       "      <td>...</td>\n",
       "      <td>93.171369</td>\n",
       "      <td>17.879717</td>\n",
       "      <td>14.477634</td>\n",
       "      <td>15.321068</td>\n",
       "      <td>12.953672</td>\n",
       "      <td>29.965792</td>\n",
       "      <td>17.796167</td>\n",
       "      <td>14.647614</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>49.867429</td>\n",
       "      <td>35.658541</td>\n",
       "      <td>46.073154</td>\n",
       "      <td>109.038804</td>\n",
       "      <td>1580.619413</td>\n",
       "      <td>73.932978</td>\n",
       "      <td>92.115568</td>\n",
       "      <td>65.099646</td>\n",
       "      <td>58.425645</td>\n",
       "      <td>59.571083</td>\n",
       "      <td>...</td>\n",
       "      <td>87.006573</td>\n",
       "      <td>22.262245</td>\n",
       "      <td>22.770028</td>\n",
       "      <td>18.168039</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.783774</td>\n",
       "      <td>14.772144</td>\n",
       "      <td>12.158611</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>506</th>\n",
       "      <td>25.175255</td>\n",
       "      <td>13.417631</td>\n",
       "      <td>21.184193</td>\n",
       "      <td>29.965792</td>\n",
       "      <td>6.783774</td>\n",
       "      <td>17.819073</td>\n",
       "      <td>12.866708</td>\n",
       "      <td>32.007792</td>\n",
       "      <td>22.252814</td>\n",
       "      <td>12.203976</td>\n",
       "      <td>...</td>\n",
       "      <td>178.245226</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>174.928538</td>\n",
       "      <td>44.663746</td>\n",
       "      <td>1321.682287</td>\n",
       "      <td>9714.364807</td>\n",
       "      <td>1225.644158</td>\n",
       "      <td>10.675126</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>507</th>\n",
       "      <td>20.259892</td>\n",
       "      <td>14.608916</td>\n",
       "      <td>16.914357</td>\n",
       "      <td>17.796167</td>\n",
       "      <td>14.772144</td>\n",
       "      <td>9.700570</td>\n",
       "      <td>28.018159</td>\n",
       "      <td>17.424803</td>\n",
       "      <td>12.114266</td>\n",
       "      <td>13.287506</td>\n",
       "      <td>...</td>\n",
       "      <td>97.035368</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>139.670356</td>\n",
       "      <td>12.157305</td>\n",
       "      <td>935.368142</td>\n",
       "      <td>1225.644158</td>\n",
       "      <td>10576.855140</td>\n",
       "      <td>11.622918</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>508</th>\n",
       "      <td>14.853761</td>\n",
       "      <td>14.887181</td>\n",
       "      <td>11.752183</td>\n",
       "      <td>14.647614</td>\n",
       "      <td>12.158611</td>\n",
       "      <td>14.447809</td>\n",
       "      <td>19.766657</td>\n",
       "      <td>32.781607</td>\n",
       "      <td>11.136411</td>\n",
       "      <td>15.623771</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>35.032456</td>\n",
       "      <td>2.985960</td>\n",
       "      <td>14.294853</td>\n",
       "      <td>16.920438</td>\n",
       "      <td>10.675126</td>\n",
       "      <td>11.622918</td>\n",
       "      <td>2487.304396</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>509</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>510</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>272 rows × 272 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            0            1           2           3            4          5    \\\n",
       "0    255.036282    40.040227  103.816470   44.699292    49.867429  33.682566   \n",
       "1     40.040227  1042.102670   35.148335   26.593198    35.658541  26.442838   \n",
       "2    103.816470    35.148335  164.530566   44.200864    46.073154  36.266138   \n",
       "3     44.699292    26.593198   44.200864  634.729950   109.038804  57.244065   \n",
       "4     49.867429    35.658541   46.073154  109.038804  1580.619413  73.932978   \n",
       "..          ...          ...         ...         ...          ...        ...   \n",
       "506   25.175255    13.417631   21.184193   29.965792     6.783774  17.819073   \n",
       "507   20.259892    14.608916   16.914357   17.796167    14.772144   9.700570   \n",
       "508   14.853761    14.887181   11.752183   14.647614    12.158611  14.447809   \n",
       "509    0.000000     0.000000    0.000000    0.000000     0.000000   0.000000   \n",
       "510    0.000000     0.000000    0.000000    0.000000     0.000000   0.000000   \n",
       "\n",
       "           6          7          8          9    ...         496        497  \\\n",
       "0    54.385220  48.738400  42.807006  29.476555  ...   42.116083   9.578827   \n",
       "1    33.126443  49.787442  31.141552  30.111007  ...   19.121150  34.247506   \n",
       "2    44.020099  41.742516  42.503159  28.937537  ...   42.264733  16.221323   \n",
       "3    73.141067  51.238497  49.435444  31.895977  ...   93.171369  17.879717   \n",
       "4    92.115568  65.099646  58.425645  59.571083  ...   87.006573  22.262245   \n",
       "..         ...        ...        ...        ...  ...         ...        ...   \n",
       "506  12.866708  32.007792  22.252814  12.203976  ...  178.245226   0.000000   \n",
       "507  28.018159  17.424803  12.114266  13.287506  ...   97.035368   0.000000   \n",
       "508  19.766657  32.781607  11.136411  15.623771  ...    0.000000  35.032456   \n",
       "509   0.000000   0.000000   0.000000   0.000000  ...    0.000000   0.000000   \n",
       "510   0.000000   0.000000   0.000000   0.000000  ...    0.000000   0.000000   \n",
       "\n",
       "            500        504          505          506           507  \\\n",
       "0     21.125454  10.846371    15.614466    25.175255     20.259892   \n",
       "1     21.267401  16.769468    42.534803    13.417631     14.608916   \n",
       "2     19.751569  13.048976    15.669578    21.184193     16.914357   \n",
       "3     14.477634  15.321068    12.953672    29.965792     17.796167   \n",
       "4     22.770028  18.168039     0.000000     6.783774     14.772144   \n",
       "..          ...        ...          ...          ...           ...   \n",
       "506  174.928538  44.663746  1321.682287  9714.364807   1225.644158   \n",
       "507  139.670356  12.157305   935.368142  1225.644158  10576.855140   \n",
       "508    2.985960  14.294853    16.920438    10.675126     11.622918   \n",
       "509    0.000000   0.000000     0.000000     0.000000      0.000000   \n",
       "510    0.000000   0.000000     0.000000     0.000000      0.000000   \n",
       "\n",
       "             508  509  510  \n",
       "0      14.853761  0.0  0.0  \n",
       "1      14.887181  0.0  0.0  \n",
       "2      11.752183  0.0  0.0  \n",
       "3      14.647614  0.0  0.0  \n",
       "4      12.158611  0.0  0.0  \n",
       "..           ...  ...  ...  \n",
       "506    10.675126  0.0  0.0  \n",
       "507    11.622918  0.0  0.0  \n",
       "508  2487.304396  0.0  0.0  \n",
       "509     0.000000  0.0  0.0  \n",
       "510     0.000000  0.0  0.0  \n",
       "\n",
       "[272 rows x 272 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lift_df.to_csv('lift.csv')\n",
    "lift_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3) ADDED-VALUE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En tercer lugar, veremos el indicador de $Added$-$Value$ o \"valor agregado\" en español. Este mide que tanto aumenta la probabilidad de aparicion de un itemset $Y$ dado que el conjunto tambien contenga a $X$, es decir, medimos el aumento de $\\mathbb{P}(Y\\vert X)$ respecto a $\\mathbb{P}(Y)$. Definimos el indicador como\n",
    "\\begin{equation}\n",
    "AV(X\\Rightarrow Y) := conf(Y\\Rightarrow X) - supp(Y) = \\dfrac{supp(X\\cup Y)}{supp(X)} - supp(Y)\n",
    "\\end{equation}\n",
    "De este modo, en terminos de probabilidad tenemos que\n",
    "\\begin{equation}\n",
    "AV(X\\Rightarrow Y) = \\mathbb{P}(Y\\vert X) - \\mathbb{P}(Y)\n",
    "\\end{equation}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "added_value = np.zeros((272,272))\n",
    "AD_df= pd.DataFrame(added_value, columns= categorias).set_axis(categorias).fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "for cat in categorias:\n",
    "    for cat2 in categorias:\n",
    "        if (cat in conf_df.columns) and (cat2 in conf_df.index):\n",
    "            if cat>=cat2:\n",
    "                AD_df[cat][cat2] = conf_df[cat2][cat] - (soportes[cat2][cat2])\n",
    "            elif cat<cat2:\n",
    "                AD_df[cat][cat2] = conf_df[cat2][cat] - (soportes[cat][cat])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "AD_df.to_csv('added_value.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.250803</td>\n",
       "      <td>0.092514</td>\n",
       "      <td>0.113970</td>\n",
       "      <td>0.073232</td>\n",
       "      <td>0.089160</td>\n",
       "      <td>0.122677</td>\n",
       "      <td>0.096726</td>\n",
       "      <td>0.065351</td>\n",
       "      <td>-0.006078</td>\n",
       "      <td>-0.006078</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.191610</td>\n",
       "      <td>0.033258</td>\n",
       "      <td>0.273950</td>\n",
       "      <td>0.170212</td>\n",
       "      <td>0.999367</td>\n",
       "      <td>0.046142</td>\n",
       "      <td>0.057645</td>\n",
       "      <td>0.040553</td>\n",
       "      <td>0.036331</td>\n",
       "      <td>0.037056</td>\n",
       "      <td>...</td>\n",
       "      <td>0.054413</td>\n",
       "      <td>0.013452</td>\n",
       "      <td>0.013773</td>\n",
       "      <td>0.010862</td>\n",
       "      <td>-0.000633</td>\n",
       "      <td>0.003659</td>\n",
       "      <td>0.008713</td>\n",
       "      <td>0.007060</td>\n",
       "      <td>-0.000633</td>\n",
       "      <td>-0.000633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506</th>\n",
       "      <td>0.094791</td>\n",
       "      <td>0.011916</td>\n",
       "      <td>0.122677</td>\n",
       "      <td>0.045635</td>\n",
       "      <td>0.003659</td>\n",
       "      <td>0.024306</td>\n",
       "      <td>0.003958</td>\n",
       "      <td>0.008316</td>\n",
       "      <td>0.090178</td>\n",
       "      <td>0.003940</td>\n",
       "      <td>...</td>\n",
       "      <td>0.008536</td>\n",
       "      <td>-0.000094</td>\n",
       "      <td>0.064010</td>\n",
       "      <td>0.016783</td>\n",
       "      <td>0.085772</td>\n",
       "      <td>0.999897</td>\n",
       "      <td>0.126065</td>\n",
       "      <td>0.000996</td>\n",
       "      <td>-0.000103</td>\n",
       "      <td>-0.000103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>507</th>\n",
       "      <td>0.075518</td>\n",
       "      <td>0.013059</td>\n",
       "      <td>0.096726</td>\n",
       "      <td>0.026462</td>\n",
       "      <td>0.008713</td>\n",
       "      <td>0.012574</td>\n",
       "      <td>0.009012</td>\n",
       "      <td>0.004405</td>\n",
       "      <td>0.047159</td>\n",
       "      <td>0.004321</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004625</td>\n",
       "      <td>-0.000094</td>\n",
       "      <td>0.051034</td>\n",
       "      <td>0.004289</td>\n",
       "      <td>0.060683</td>\n",
       "      <td>0.126065</td>\n",
       "      <td>0.999905</td>\n",
       "      <td>0.001004</td>\n",
       "      <td>-0.000095</td>\n",
       "      <td>-0.000095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>508</th>\n",
       "      <td>0.054321</td>\n",
       "      <td>0.013326</td>\n",
       "      <td>0.065351</td>\n",
       "      <td>0.021501</td>\n",
       "      <td>0.007060</td>\n",
       "      <td>0.019434</td>\n",
       "      <td>0.006260</td>\n",
       "      <td>0.008523</td>\n",
       "      <td>0.043010</td>\n",
       "      <td>0.005143</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000048</td>\n",
       "      <td>0.003203</td>\n",
       "      <td>0.000731</td>\n",
       "      <td>0.005110</td>\n",
       "      <td>0.001034</td>\n",
       "      <td>0.000996</td>\n",
       "      <td>0.001004</td>\n",
       "      <td>0.999598</td>\n",
       "      <td>-0.000402</td>\n",
       "      <td>-0.000402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>509</th>\n",
       "      <td>-0.003921</td>\n",
       "      <td>-0.000960</td>\n",
       "      <td>-0.006078</td>\n",
       "      <td>-0.001575</td>\n",
       "      <td>-0.000633</td>\n",
       "      <td>-0.001445</td>\n",
       "      <td>-0.000334</td>\n",
       "      <td>-0.000268</td>\n",
       "      <td>-0.004243</td>\n",
       "      <td>-0.000352</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000048</td>\n",
       "      <td>-0.000094</td>\n",
       "      <td>-0.000368</td>\n",
       "      <td>-0.000384</td>\n",
       "      <td>-0.000065</td>\n",
       "      <td>-0.000103</td>\n",
       "      <td>-0.000095</td>\n",
       "      <td>-0.000402</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>510</th>\n",
       "      <td>-0.003921</td>\n",
       "      <td>-0.000960</td>\n",
       "      <td>-0.006078</td>\n",
       "      <td>-0.001575</td>\n",
       "      <td>-0.000633</td>\n",
       "      <td>-0.001445</td>\n",
       "      <td>-0.000334</td>\n",
       "      <td>-0.000268</td>\n",
       "      <td>-0.004243</td>\n",
       "      <td>-0.000352</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000048</td>\n",
       "      <td>-0.000094</td>\n",
       "      <td>-0.000368</td>\n",
       "      <td>-0.000384</td>\n",
       "      <td>-0.000065</td>\n",
       "      <td>-0.000103</td>\n",
       "      <td>-0.000095</td>\n",
       "      <td>-0.000402</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>272 rows × 272 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3         4         5         6    \\\n",
       "0    0.996079  0.153077  0.403144  0.171345  0.191610  0.128149  0.209324   \n",
       "1    0.153077  0.999040  0.032769  0.024559  0.033258  0.024415  0.030828   \n",
       "2    0.403144  0.032769  0.993922  0.262570  0.273950  0.214344  0.261472   \n",
       "3    0.171345  0.024559  0.262570  0.998425  0.170212  0.088611  0.113656   \n",
       "4    0.191610  0.033258  0.273950  0.170212  0.999367  0.046142  0.057645   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "506  0.094791  0.011916  0.122677  0.045635  0.003659  0.024306  0.003958   \n",
       "507  0.075518  0.013059  0.096726  0.026462  0.008713  0.012574  0.009012   \n",
       "508  0.054321  0.013326  0.065351  0.021501  0.007060  0.019434  0.006260   \n",
       "509 -0.003921 -0.000960 -0.006078 -0.001575 -0.000633 -0.001445 -0.000334   \n",
       "510 -0.003921 -0.000960 -0.006078 -0.001575 -0.000633 -0.001445 -0.000334   \n",
       "\n",
       "          7         8         9    ...       496       497       500  \\\n",
       "0    0.187183  0.163926  0.111657  ...  0.161217  0.033638  0.078912   \n",
       "1    0.046816  0.028924  0.027935  ...  0.017389  0.031904  0.019449   \n",
       "2    0.247629  0.252252  0.169801  ...  0.250803  0.092514  0.113970   \n",
       "3    0.079149  0.076309  0.048676  ...  0.145214  0.026594  0.021234   \n",
       "4    0.040553  0.036331  0.037056  ...  0.054413  0.013452  0.013773   \n",
       "..        ...       ...       ...  ...       ...       ...       ...   \n",
       "506  0.008316  0.090178  0.003940  ...  0.008536 -0.000094  0.064010   \n",
       "507  0.004405  0.047159  0.004321  ...  0.004625 -0.000094  0.051034   \n",
       "508  0.008523  0.043010  0.005143  ... -0.000048  0.003203  0.000731   \n",
       "509 -0.000268 -0.004243 -0.000352  ... -0.000048 -0.000094 -0.000368   \n",
       "510 -0.000268 -0.004243 -0.000352  ... -0.000048 -0.000094 -0.000368   \n",
       "\n",
       "          504       505       506       507       508       509       510  \n",
       "0    0.038608  0.057303  0.094791  0.075518  0.054321 -0.003921 -0.003921  \n",
       "1    0.015132  0.039857  0.011916  0.013059  0.013326 -0.000960 -0.000960  \n",
       "2    0.073232  0.089160  0.122677  0.096726  0.065351 -0.006078 -0.006078  \n",
       "3    0.022562  0.018833  0.045635  0.026462  0.021501 -0.001575 -0.001575  \n",
       "4    0.010862 -0.000633  0.003659  0.008713  0.007060 -0.000633 -0.000633  \n",
       "..        ...       ...       ...       ...       ...       ...       ...  \n",
       "506  0.016783  0.085772  0.999897  0.126065  0.000996 -0.000103 -0.000103  \n",
       "507  0.004289  0.060683  0.126065  0.999905  0.001004 -0.000095 -0.000095  \n",
       "508  0.005110  0.001034  0.000996  0.001004  0.999598 -0.000402 -0.000402  \n",
       "509 -0.000384 -0.000065 -0.000103 -0.000095 -0.000402  0.000000  0.000000  \n",
       "510 -0.000384 -0.000065 -0.000103 -0.000095 -0.000402  0.000000  0.000000  \n",
       "\n",
       "[272 rows x 272 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AD_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4) Conviction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Definimos el indicador $Conviction$ o \"Conviccion\" como la como la comparacion entre la probabilidad de que aparezca el itemset $X$ sin que lo haga $Y$ de existir una dependencia estadistica y la frecuencia real de apariciones de $X$ sin $Y$, definimos lo anterior como\n",
    "\\begin{equation}\n",
    "conviction(X\\Rightarrow Y) = \\dfrac{1-supp(Y)}{1-conf(X\\Rightarrow Y)}.\n",
    "\\end{equation}\n",
    "Lo cual queda escrito en términos de probabilidad como\n",
    "\\begin{equation}\n",
    "conviction(X\\Rightarrow Y) = \\dfrac{\\mathbb{P}(X)\\mathbb{P}(\\overline{Y})}{\\mathbb{P}(X\\cap \\overline{Y})} = \\dfrac{\\mathbb{P}(\\overline{Y})}{\\mathbb{P}(\\overline{Y}\\vert X)} \\in [0, \\infty]\n",
    "\\end{equation}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "conviction = np.zeros((272,272))\n",
    "conviction_df= pd.DataFrame(conviction, columns= categorias).set_axis(categorias).fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "for cat in categorias:\n",
    "    for cat2 in categorias:\n",
    "        if (cat in conf_df.columns) and (cat2 in conf_df.index) and (conf_df[cat][cat2]!=1): #La diagonal queda con elementos nulos, pero no es relevante pues es conviction(X->X)\n",
    "                conviction_df[cat][cat2] = (1 - soportes[cat2][cat2])/(1 - conf_df[cat][cat2])\n",
    "conviction_df.to_csv('conviction.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
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       "      <th>1</th>\n",
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       "      <td>1.679914</td>\n",
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       "      <td>1.238182</td>\n",
       "      <td>1.147649</td>\n",
       "      <td>1.266060</td>\n",
       "      <td>1.231405</td>\n",
       "      <td>1.196990</td>\n",
       "      <td>1.126248</td>\n",
       "      <td>...</td>\n",
       "      <td>1.193106</td>\n",
       "      <td>1.034950</td>\n",
       "      <td>1.086039</td>\n",
       "      <td>1.040323</td>\n",
       "      <td>1.061041</td>\n",
       "      <td>1.105173</td>\n",
       "      <td>1.082035</td>\n",
       "      <td>1.057680</td>\n",
       "      <td>0.996079</td>\n",
       "      <td>0.996079</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.185099</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.033912</td>\n",
       "      <td>1.025202</td>\n",
       "      <td>1.034437</td>\n",
       "      <td>1.025051</td>\n",
       "      <td>1.031841</td>\n",
       "      <td>1.049165</td>\n",
       "      <td>1.029815</td>\n",
       "      <td>1.028766</td>\n",
       "      <td>...</td>\n",
       "      <td>1.017714</td>\n",
       "      <td>1.032988</td>\n",
       "      <td>1.019854</td>\n",
       "      <td>1.015380</td>\n",
       "      <td>1.041553</td>\n",
       "      <td>1.012071</td>\n",
       "      <td>1.013245</td>\n",
       "      <td>1.013519</td>\n",
       "      <td>0.999040</td>\n",
       "      <td>0.999040</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.676276</td>\n",
       "      <td>1.028616</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.359021</td>\n",
       "      <td>1.380501</td>\n",
       "      <td>1.274949</td>\n",
       "      <td>1.356982</td>\n",
       "      <td>1.331812</td>\n",
       "      <td>1.340113</td>\n",
       "      <td>1.206040</td>\n",
       "      <td>...</td>\n",
       "      <td>1.337500</td>\n",
       "      <td>1.102632</td>\n",
       "      <td>1.129519</td>\n",
       "      <td>1.079541</td>\n",
       "      <td>1.098545</td>\n",
       "      <td>1.140807</td>\n",
       "      <td>1.107809</td>\n",
       "      <td>1.070378</td>\n",
       "      <td>0.993922</td>\n",
       "      <td>0.993922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.210602</td>\n",
       "      <td>1.024570</td>\n",
       "      <td>1.365177</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.205518</td>\n",
       "      <td>1.097395</td>\n",
       "      <td>1.128459</td>\n",
       "      <td>1.086100</td>\n",
       "      <td>1.082754</td>\n",
       "      <td>1.051251</td>\n",
       "      <td>...</td>\n",
       "      <td>1.170196</td>\n",
       "      <td>1.027364</td>\n",
       "      <td>1.021729</td>\n",
       "      <td>1.023121</td>\n",
       "      <td>1.019225</td>\n",
       "      <td>1.047896</td>\n",
       "      <td>1.027225</td>\n",
       "      <td>1.022009</td>\n",
       "      <td>0.998425</td>\n",
       "      <td>0.998425</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.242269</td>\n",
       "      <td>1.034775</td>\n",
       "      <td>1.388064</td>\n",
       "      <td>1.206656</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.048406</td>\n",
       "      <td>1.061213</td>\n",
       "      <td>1.042295</td>\n",
       "      <td>1.037726</td>\n",
       "      <td>1.038507</td>\n",
       "      <td>...</td>\n",
       "      <td>1.057583</td>\n",
       "      <td>1.013644</td>\n",
       "      <td>1.013974</td>\n",
       "      <td>1.010988</td>\n",
       "      <td>0.999367</td>\n",
       "      <td>1.003675</td>\n",
       "      <td>1.008795</td>\n",
       "      <td>1.007114</td>\n",
       "      <td>0.999367</td>\n",
       "      <td>0.999367</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>506</th>\n",
       "      <td>1.109410</td>\n",
       "      <td>1.012939</td>\n",
       "      <td>1.147665</td>\n",
       "      <td>1.049442</td>\n",
       "      <td>1.004207</td>\n",
       "      <td>1.026326</td>\n",
       "      <td>1.004207</td>\n",
       "      <td>1.008554</td>\n",
       "      <td>1.104152</td>\n",
       "      <td>1.004207</td>\n",
       "      <td>...</td>\n",
       "      <td>1.008554</td>\n",
       "      <td>0.999897</td>\n",
       "      <td>1.068697</td>\n",
       "      <td>1.017363</td>\n",
       "      <td>1.093784</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.144267</td>\n",
       "      <td>1.000997</td>\n",
       "      <td>0.999897</td>\n",
       "      <td>0.999897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>507</th>\n",
       "      <td>1.086192</td>\n",
       "      <td>1.014122</td>\n",
       "      <td>1.114478</td>\n",
       "      <td>1.028749</td>\n",
       "      <td>1.009339</td>\n",
       "      <td>1.014122</td>\n",
       "      <td>1.009339</td>\n",
       "      <td>1.004600</td>\n",
       "      <td>1.054088</td>\n",
       "      <td>1.004600</td>\n",
       "      <td>...</td>\n",
       "      <td>1.004600</td>\n",
       "      <td>0.999905</td>\n",
       "      <td>1.054088</td>\n",
       "      <td>1.004600</td>\n",
       "      <td>1.064576</td>\n",
       "      <td>1.144277</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.001005</td>\n",
       "      <td>0.999905</td>\n",
       "      <td>0.999905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>508</th>\n",
       "      <td>1.061417</td>\n",
       "      <td>1.014085</td>\n",
       "      <td>1.076490</td>\n",
       "      <td>1.023211</td>\n",
       "      <td>1.007347</td>\n",
       "      <td>1.020914</td>\n",
       "      <td>1.006232</td>\n",
       "      <td>1.008464</td>\n",
       "      <td>1.049174</td>\n",
       "      <td>1.005121</td>\n",
       "      <td>...</td>\n",
       "      <td>0.999598</td>\n",
       "      <td>1.002904</td>\n",
       "      <td>1.000698</td>\n",
       "      <td>1.005121</td>\n",
       "      <td>1.000698</td>\n",
       "      <td>1.000698</td>\n",
       "      <td>1.000698</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.999598</td>\n",
       "      <td>0.999598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>509</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>510</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>272 rows × 272 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3         4         5         6    \\\n",
       "0    0.000000  1.181586  1.679914  1.207758  1.238182  1.147649  1.266060   \n",
       "1    1.185099  0.000000  1.033912  1.025202  1.034437  1.025051  1.031841   \n",
       "2    1.676276  1.028616  0.000000  1.359021  1.380501  1.274949  1.356982   \n",
       "3    1.210602  1.024570  1.365177  0.000000  1.205518  1.097395  1.128459   \n",
       "4    1.242269  1.034775  1.388064  1.206656  0.000000  1.048406  1.061213   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "506  1.109410  1.012939  1.147665  1.049442  1.004207  1.026326  1.004207   \n",
       "507  1.086192  1.014122  1.114478  1.028749  1.009339  1.014122  1.009339   \n",
       "508  1.061417  1.014085  1.076490  1.023211  1.007347  1.020914  1.006232   \n",
       "509  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000   \n",
       "510  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000   \n",
       "\n",
       "          7         8         9    ...       496       497       500  \\\n",
       "0    1.231405  1.196990  1.126248  ...  1.193106  1.034950  1.086039   \n",
       "1    1.049165  1.029815  1.028766  ...  1.017714  1.032988  1.019854   \n",
       "2    1.331812  1.340113  1.206040  ...  1.337500  1.102632  1.129519   \n",
       "3    1.086100  1.082754  1.051251  ...  1.170196  1.027364  1.021729   \n",
       "4    1.042295  1.037726  1.038507  ...  1.057583  1.013644  1.013974   \n",
       "..        ...       ...       ...  ...       ...       ...       ...   \n",
       "506  1.008554  1.104152  1.004207  ...  1.008554  0.999897  1.068697   \n",
       "507  1.004600  1.054088  1.004600  ...  1.004600  0.999905  1.054088   \n",
       "508  1.008464  1.049174  1.005121  ...  0.999598  1.002904  1.000698   \n",
       "509  1.000000  1.000000  1.000000  ...  1.000000  1.000000  1.000000   \n",
       "510  1.000000  1.000000  1.000000  ...  1.000000  1.000000  1.000000   \n",
       "\n",
       "          504       505       506       507       508       509       510  \n",
       "0    1.040323  1.061041  1.105173  1.082035  1.057680  0.996079  0.996079  \n",
       "1    1.015380  1.041553  1.012071  1.013245  1.013519  0.999040  0.999040  \n",
       "2    1.079541  1.098545  1.140807  1.107809  1.070378  0.993922  0.993922  \n",
       "3    1.023121  1.019225  1.047896  1.027225  1.022009  0.998425  0.998425  \n",
       "4    1.010988  0.999367  1.003675  1.008795  1.007114  0.999367  0.999367  \n",
       "..        ...       ...       ...       ...       ...       ...       ...  \n",
       "506  1.017363  1.093784  0.000000  1.144267  1.000997  0.999897  0.999897  \n",
       "507  1.004600  1.064576  1.144277  0.000000  1.001005  0.999905  0.999905  \n",
       "508  1.005121  1.000698  1.000698  1.000698  0.000000  0.999598  0.999598  \n",
       "509  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000  \n",
       "510  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000  \n",
       "\n",
       "[272 rows x 272 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conviction_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Veamos en particular la esquina superior del DF obtenido para el indicador $conviction$. Notemos que hemos conseguido identificar una regla de asociacion asimétrica"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
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       "      <th>4</th>\n",
       "      <th>5</th>\n",
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       "      <td>1.679914</td>\n",
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       "      <td>1.238182</td>\n",
       "      <td>1.147649</td>\n",
       "      <td>1.266060</td>\n",
       "      <td>1.231405</td>\n",
       "      <td>1.196990</td>\n",
       "      <td>1.126248</td>\n",
       "      <td>...</td>\n",
       "      <td>1.193106</td>\n",
       "      <td>1.034950</td>\n",
       "      <td>1.086039</td>\n",
       "      <td>1.040323</td>\n",
       "      <td>1.061041</td>\n",
       "      <td>1.105173</td>\n",
       "      <td>1.082035</td>\n",
       "      <td>1.057680</td>\n",
       "      <td>0.996079</td>\n",
       "      <td>0.996079</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.185099</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.033912</td>\n",
       "      <td>1.025202</td>\n",
       "      <td>1.034437</td>\n",
       "      <td>1.025051</td>\n",
       "      <td>1.031841</td>\n",
       "      <td>1.049165</td>\n",
       "      <td>1.029815</td>\n",
       "      <td>1.028766</td>\n",
       "      <td>...</td>\n",
       "      <td>1.017714</td>\n",
       "      <td>1.032988</td>\n",
       "      <td>1.019854</td>\n",
       "      <td>1.015380</td>\n",
       "      <td>1.041553</td>\n",
       "      <td>1.012071</td>\n",
       "      <td>1.013245</td>\n",
       "      <td>1.013519</td>\n",
       "      <td>0.999040</td>\n",
       "      <td>0.999040</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.676276</td>\n",
       "      <td>1.028616</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.359021</td>\n",
       "      <td>1.380501</td>\n",
       "      <td>1.274949</td>\n",
       "      <td>1.356982</td>\n",
       "      <td>1.331812</td>\n",
       "      <td>1.340113</td>\n",
       "      <td>1.206040</td>\n",
       "      <td>...</td>\n",
       "      <td>1.337500</td>\n",
       "      <td>1.102632</td>\n",
       "      <td>1.129519</td>\n",
       "      <td>1.079541</td>\n",
       "      <td>1.098545</td>\n",
       "      <td>1.140807</td>\n",
       "      <td>1.107809</td>\n",
       "      <td>1.070378</td>\n",
       "      <td>0.993922</td>\n",
       "      <td>0.993922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.210602</td>\n",
       "      <td>1.024570</td>\n",
       "      <td>1.365177</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.205518</td>\n",
       "      <td>1.097395</td>\n",
       "      <td>1.128459</td>\n",
       "      <td>1.086100</td>\n",
       "      <td>1.082754</td>\n",
       "      <td>1.051251</td>\n",
       "      <td>...</td>\n",
       "      <td>1.170196</td>\n",
       "      <td>1.027364</td>\n",
       "      <td>1.021729</td>\n",
       "      <td>1.023121</td>\n",
       "      <td>1.019225</td>\n",
       "      <td>1.047896</td>\n",
       "      <td>1.027225</td>\n",
       "      <td>1.022009</td>\n",
       "      <td>0.998425</td>\n",
       "      <td>0.998425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.242269</td>\n",
       "      <td>1.034775</td>\n",
       "      <td>1.388064</td>\n",
       "      <td>1.206656</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.048406</td>\n",
       "      <td>1.061213</td>\n",
       "      <td>1.042295</td>\n",
       "      <td>1.037726</td>\n",
       "      <td>1.038507</td>\n",
       "      <td>...</td>\n",
       "      <td>1.057583</td>\n",
       "      <td>1.013644</td>\n",
       "      <td>1.013974</td>\n",
       "      <td>1.010988</td>\n",
       "      <td>0.999367</td>\n",
       "      <td>1.003675</td>\n",
       "      <td>1.008795</td>\n",
       "      <td>1.007114</td>\n",
       "      <td>0.999367</td>\n",
       "      <td>0.999367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.150501</td>\n",
       "      <td>1.024552</td>\n",
       "      <td>1.280891</td>\n",
       "      <td>1.097538</td>\n",
       "      <td>1.047554</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.081648</td>\n",
       "      <td>1.052297</td>\n",
       "      <td>1.084365</td>\n",
       "      <td>1.075575</td>\n",
       "      <td>...</td>\n",
       "      <td>1.122087</td>\n",
       "      <td>1.008020</td>\n",
       "      <td>1.019358</td>\n",
       "      <td>1.017263</td>\n",
       "      <td>1.026486</td>\n",
       "      <td>1.024948</td>\n",
       "      <td>1.012752</td>\n",
       "      <td>1.019848</td>\n",
       "      <td>0.998555</td>\n",
       "      <td>0.998555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.270620</td>\n",
       "      <td>1.032487</td>\n",
       "      <td>1.364825</td>\n",
       "      <td>1.129863</td>\n",
       "      <td>1.061530</td>\n",
       "      <td>1.082852</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.066428</td>\n",
       "      <td>1.018544</td>\n",
       "      <td>1.013675</td>\n",
       "      <td>...</td>\n",
       "      <td>1.047727</td>\n",
       "      <td>1.013947</td>\n",
       "      <td>1.004490</td>\n",
       "      <td>1.005445</td>\n",
       "      <td>1.006513</td>\n",
       "      <td>1.003975</td>\n",
       "      <td>1.009097</td>\n",
       "      <td>1.006301</td>\n",
       "      <td>0.999666</td>\n",
       "      <td>0.999666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.235921</td>\n",
       "      <td>1.049891</td>\n",
       "      <td>1.339597</td>\n",
       "      <td>1.087522</td>\n",
       "      <td>1.042676</td>\n",
       "      <td>1.053537</td>\n",
       "      <td>1.066498</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.010145</td>\n",
       "      <td>1.012451</td>\n",
       "      <td>...</td>\n",
       "      <td>1.018418</td>\n",
       "      <td>0.999732</td>\n",
       "      <td>1.006985</td>\n",
       "      <td>1.003191</td>\n",
       "      <td>0.999732</td>\n",
       "      <td>1.008388</td>\n",
       "      <td>1.004425</td>\n",
       "      <td>1.008599</td>\n",
       "      <td>0.999732</td>\n",
       "      <td>0.999732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.196603</td>\n",
       "      <td>1.026430</td>\n",
       "      <td>1.342587</td>\n",
       "      <td>1.079861</td>\n",
       "      <td>1.033977</td>\n",
       "      <td>1.081326</td>\n",
       "      <td>1.014561</td>\n",
       "      <td>1.006128</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.208266</td>\n",
       "      <td>...</td>\n",
       "      <td>1.233381</td>\n",
       "      <td>1.076631</td>\n",
       "      <td>1.073047</td>\n",
       "      <td>1.043745</td>\n",
       "      <td>1.084269</td>\n",
       "      <td>1.099580</td>\n",
       "      <td>1.049714</td>\n",
       "      <td>1.045143</td>\n",
       "      <td>0.995757</td>\n",
       "      <td>0.995757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.130284</td>\n",
       "      <td>1.029392</td>\n",
       "      <td>1.212988</td>\n",
       "      <td>1.052540</td>\n",
       "      <td>1.038799</td>\n",
       "      <td>1.076752</td>\n",
       "      <td>1.013656</td>\n",
       "      <td>1.012366</td>\n",
       "      <td>1.212988</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.008904</td>\n",
       "      <td>0.999648</td>\n",
       "      <td>1.006901</td>\n",
       "      <td>1.003107</td>\n",
       "      <td>1.006495</td>\n",
       "      <td>1.003957</td>\n",
       "      <td>1.004342</td>\n",
       "      <td>1.005171</td>\n",
       "      <td>0.999648</td>\n",
       "      <td>0.999648</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 272 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        0         1         2         3         4         5         6    \\\n",
       "0  0.000000  1.181586  1.679914  1.207758  1.238182  1.147649  1.266060   \n",
       "1  1.185099  0.000000  1.033912  1.025202  1.034437  1.025051  1.031841   \n",
       "2  1.676276  1.028616  0.000000  1.359021  1.380501  1.274949  1.356982   \n",
       "3  1.210602  1.024570  1.365177  0.000000  1.205518  1.097395  1.128459   \n",
       "4  1.242269  1.034775  1.388064  1.206656  0.000000  1.048406  1.061213   \n",
       "5  1.150501  1.024552  1.280891  1.097538  1.047554  0.000000  1.081648   \n",
       "6  1.270620  1.032487  1.364825  1.129863  1.061530  1.082852  0.000000   \n",
       "7  1.235921  1.049891  1.339597  1.087522  1.042676  1.053537  1.066498   \n",
       "8  1.196603  1.026430  1.342587  1.079861  1.033977  1.081326  1.014561   \n",
       "9  1.130284  1.029392  1.212988  1.052540  1.038799  1.076752  1.013656   \n",
       "\n",
       "        7         8         9    ...       496       497       500       504  \\\n",
       "0  1.231405  1.196990  1.126248  ...  1.193106  1.034950  1.086039  1.040323   \n",
       "1  1.049165  1.029815  1.028766  ...  1.017714  1.032988  1.019854  1.015380   \n",
       "2  1.331812  1.340113  1.206040  ...  1.337500  1.102632  1.129519  1.079541   \n",
       "3  1.086100  1.082754  1.051251  ...  1.170196  1.027364  1.021729  1.023121   \n",
       "4  1.042295  1.037726  1.038507  ...  1.057583  1.013644  1.013974  1.010988   \n",
       "5  1.052297  1.084365  1.075575  ...  1.122087  1.008020  1.019358  1.017263   \n",
       "6  1.066428  1.018544  1.013675  ...  1.047727  1.013947  1.004490  1.005445   \n",
       "7  0.000000  1.010145  1.012451  ...  1.018418  0.999732  1.006985  1.003191   \n",
       "8  1.006128  0.000000  1.208266  ...  1.233381  1.076631  1.073047  1.043745   \n",
       "9  1.012366  1.212988  0.000000  ...  1.008904  0.999648  1.006901  1.003107   \n",
       "\n",
       "        505       506       507       508       509       510  \n",
       "0  1.061041  1.105173  1.082035  1.057680  0.996079  0.996079  \n",
       "1  1.041553  1.012071  1.013245  1.013519  0.999040  0.999040  \n",
       "2  1.098545  1.140807  1.107809  1.070378  0.993922  0.993922  \n",
       "3  1.019225  1.047896  1.027225  1.022009  0.998425  0.998425  \n",
       "4  0.999367  1.003675  1.008795  1.007114  0.999367  0.999367  \n",
       "5  1.026486  1.024948  1.012752  1.019848  0.998555  0.998555  \n",
       "6  1.006513  1.003975  1.009097  1.006301  0.999666  0.999666  \n",
       "7  0.999732  1.008388  1.004425  1.008599  0.999732  0.999732  \n",
       "8  1.084269  1.099580  1.049714  1.045143  0.995757  0.995757  \n",
       "9  1.006495  1.003957  1.004342  1.005171  0.999648  0.999648  \n",
       "\n",
       "[10 rows x 272 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conviction_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4) Collective Strength"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"Collective Strength\" pondera la tasa $v(X)$ que corresponde a la fraccion de transacciones que contienen una parte del itemset requerido pero no su totalidad. La definimos como\n",
    "\\begin{equation}\n",
    "S(X) := \\dfrac{1-v(X)}{1-E[v(X)]} \\dfrac{E[v(X)]}{v(X)} = \\dfrac{\\mathbb{P}(X\\cap Y) + \\mathbb{P}(\\overline{Y} \\vert \\overline{X})}{\\mathbb{P}(X)\\mathbb{P}(Y) + \\mathbb{P}(\\overline{X})\\mathbb{P}(\\overline{Y})} \\in [0,\\infty].\n",
    "\\end{equation}\n",
    "Interpretamos este indicador como,\n",
    "\\begin{equation}\n",
    "    \\begin{cases}\n",
    "    S(X)= 0 \\, \\text{, los items son perfectamente negativamente correlacionados}, \\\\\n",
    "    S(X)\\approx 1 \\, \\text{, los items se comportan como estadisticamente independientes,} \\\\\n",
    "    S(X) \\approx \\infty \\, \\text{, los items estan perfectamente correlacionados.}\n",
    "    \\end{cases}\n",
    "\\end{equation}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "coll_str = np.zeros((272,272))\n",
    "collective_df= pd.DataFrame(coll_str, columns= categorias).set_axis(categorias).fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "for cat in categorias:\n",
    "    for cat2 in categorias:\n",
    "        if (cat in collective_df.columns) and (cat2 in collective_df.index):\n",
    "            if cat>=cat2:\n",
    "                collective_df[cat][cat2] = (soportes[cat][cat2] + (soportes[cat][cat2]/(1-soportes[cat][cat])))/(soportes[cat][cat]*soportes[cat2][cat2] + (1-soportes[cat][cat])*(1-soportes[cat2][cat2]))\n",
    "            elif cat<cat2:\n",
    "                collective_df[cat][cat2] = (soportes[cat2][cat] + (soportes[cat2][cat]/(1-soportes[cat2][cat2])))/(soportes[cat][cat]*soportes[cat2][cat2] + (1-soportes[cat][cat])*(1-soportes[cat2][cat2]))\n",
    "collective_df.to_csv('collective_strength.csv')            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
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       "              6             7         8             9    ...           496  \\\n",
       "0    1.428922e-04  1.029433e-04  0.001439  8.165490e-05  ...  1.596870e-05   \n",
       "1    2.123758e-05  2.565956e-05  0.000255  2.035324e-05  ...  1.769041e-06   \n",
       "2    1.796698e-04  1.369626e-04  0.002220  1.245270e-04  ...  2.489411e-05   \n",
       "3    7.703371e-05  4.338255e-05  0.000666  3.541876e-05  ...  1.416106e-05   \n",
       "4    3.892283e-05  2.211308e-05  0.000316  2.653902e-05  ...  5.305388e-06   \n",
       "..            ...           ...       ...           ...  ...           ...   \n",
       "506  8.840394e-07  1.767963e-06  0.000020  8.840554e-07  ...  1.767574e-06   \n",
       "507  1.768056e-06  8.839704e-07  0.000010  8.840443e-07  ...  8.837759e-07   \n",
       "508  5.306617e-06  7.075026e-06  0.000038  4.422261e-06  ...  0.000000e+00   \n",
       "509  0.000000e+00  8.838451e-07  0.000003  0.000000e+00  ...  0.000000e+00   \n",
       "510  1.856196e-05  2.121228e-05  0.000190  8.839189e-06  ...  8.836506e-06   \n",
       "\n",
       "          497           500           504           505           506  \\\n",
       "0    0.000007  6.124267e-05  3.284108e-05  7.984553e-06  2.040613e-05   \n",
       "1    0.000006  1.504406e-05  1.238953e-05  5.307257e-06  2.653780e-06   \n",
       "2    0.000019  8.895004e-05  6.137703e-05  1.244737e-05  2.667444e-05   \n",
       "3    0.000005  1.682432e-05  1.859576e-05  2.655265e-06  9.736528e-06   \n",
       "4    0.000003  1.061587e-05  8.846773e-06  0.000000e+00  8.843039e-07   \n",
       "..        ...           ...           ...           ...           ...   \n",
       "506  0.000000  1.326105e-05  3.536337e-06  1.767604e-05  2.059337e-04   \n",
       "507  0.000000  9.724646e-06  8.840732e-07  1.148928e-05  2.386326e-05   \n",
       "508  0.000003  8.844666e-07  4.422405e-06  8.841986e-07  8.842322e-07   \n",
       "509  0.000000  0.000000e+00  8.839478e-07  0.000000e+00  0.000000e+00   \n",
       "510  0.000002  7.955400e-06  1.237527e-05  1.767331e-06  4.418495e-06   \n",
       "\n",
       "              507           508       509       510  \n",
       "0    1.508260e-05  4.704387e-05  0.000002  0.000168  \n",
       "1    2.653746e-06  1.150487e-05  0.000000  0.000025  \n",
       "2    1.956101e-05  5.782046e-05  0.000003  0.000270  \n",
       "3    5.310766e-06  1.859625e-05  0.000000  0.000106  \n",
       "4    1.768586e-06  6.192905e-06  0.000000  0.000051  \n",
       "..            ...           ...       ...       ...  \n",
       "506  2.386326e-05  8.842322e-07  0.000000  0.000004  \n",
       "507  1.891368e-04  8.842248e-07  0.000000  0.000003  \n",
       "508  8.842248e-07  8.048920e-04  0.000000  0.000009  \n",
       "509  0.000000e+00  0.000000e+00  0.000000  0.000000  \n",
       "510  2.651075e-06  8.839634e-06  0.000000  0.000000  \n",
       "\n",
       "[272 rows x 272 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "collective_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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