Soporte.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",
    "import csv\n",
    "\n",
    "transactions = pd.read_csv(\"data_assorules.csv\")\n",
    "category = pd.read_csv(\"cat_nombres.csv\")\n",
    "department = pd.read_csv(\"data_assorules_hat.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Empezaremos viendo la forma en que se nos presentan los datos, este corresponde a un DataFrame de la siguiente forma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Visit_date</th>\n",
       "      <th>Visit_nbr</th>\n",
       "      <th>Category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>2017-01-02</td>\n",
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       "      <td>74</td>\n",
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      ],
      "text/plain": [
       "    Visit_date  Visit_nbr  Category\n",
       "0   2017-01-02  700200000         3\n",
       "1   2017-01-02  700200001       126\n",
       "2   2017-01-02  700200001       130\n",
       "3   2017-01-02  700200002       127\n",
       "4   2017-01-02  700200002        38\n",
       "5   2017-01-02  700200002       130\n",
       "6   2017-01-02  700200002        15\n",
       "7   2017-01-02  700200002       125\n",
       "8   2017-01-02  700200003        37\n",
       "9   2017-01-02  700200004       119\n",
       "10  2017-01-02  700200004       165\n",
       "11  2017-01-02  700200004       117\n",
       "12  2017-01-02  700200005       101\n",
       "13  2017-01-02  700200006       353\n",
       "14  2017-01-02  700200007        74"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions.head(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1) Soporte de cada categoria\n",
    "##### Iniciamos agrupando en el DataFrame por categorias y contamos las apariciones de cada una, para obtener el soporte como la frecuencia de aparición de cada categoria "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_supp=transactions.groupby(\"Category\").apply(lambda x: x.Visit_nbr.count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2263447"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_supp.sum() #corresponde a la cantidad total de visitas durante el periodo a estudiar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Category\n",
       "0       8875\n",
       "1       2172\n",
       "2      13757\n",
       "3       3566\n",
       "4       1432\n",
       "       ...  \n",
       "506      233\n",
       "507      214\n",
       "508      910\n",
       "509        8\n",
       "510     2014\n",
       "Length: 272, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_supp #corresponde a la cantidad de apariciones de cada categoria en el periodo a estudiar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2) Soporte Conjunto"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def genera_pares(x):\n",
    "    res = list(combinations(x.Category.sort_values(), 2))\n",
    "    if res != []:\n",
    "        item1, item2 = zip(*res)\n",
    "        return pd.DataFrame({'item1':item1, 'item2':item2, 'count':np.ones(len(res))})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Construiremos un nuevo DataFrame a partir de una matriz cuadadrada de tamaño dado por el numero total de categorias $272$ en que la diagonal será el soporte de las categorias individualmente y donde el elemento $S[i,j]$ corresponderá al soporte del par de categorias {i,j}, es decir, el numero de tickets en que se encuentran ambas categorias.\n",
    "\n",
    "Usaremos el comando $pivot\\_table$ de $pandas$ para crear un DataFrame con forma de matriz superior con la informacion que buscamos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorias = sorted(transactions['Category'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "transactions.groupby('Visit_nbr').apply(genera_pares).reset_index().drop(['Visit_nbr','level_1'],axis=1).groupby(['item1','item2']).agg(sum).to_csv('ItemsPorPares.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>10.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>268 rows × 270 columns</p>\n",
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      ],
      "text/plain": [
       "item2    1       2      3      4      5      6      7       8      9    \\\n",
       "item1                                                                    \n",
       "0      341.0  5600.0  625.0  280.0  432.0  161.0  116.0  1612.0   92.0   \n",
       "1        0.0   464.0   91.0   49.0   83.0   24.0   29.0   287.0   23.0   \n",
       "2        0.0     0.0  958.0  401.0  721.0  202.0  154.0  2481.0  140.0   \n",
       "3        0.0     0.0    0.0  246.0  295.0   87.0   49.0   748.0   40.0   \n",
       "4        0.0     0.0    0.0    0.0  153.0   44.0   25.0   355.0   30.0   \n",
       "...      ...     ...    ...    ...    ...    ...    ...     ...    ...   \n",
       "504      0.0     0.0    0.0    0.0    0.0    0.0    0.0     0.0    0.0   \n",
       "505      0.0     0.0    0.0    0.0    0.0    0.0    0.0     0.0    0.0   \n",
       "506      0.0     0.0    0.0    0.0    0.0    0.0    0.0     0.0    0.0   \n",
       "507      0.0     0.0    0.0    0.0    0.0    0.0    0.0     0.0    0.0   \n",
       "508      0.0     0.0    0.0    0.0    0.0    0.0    0.0     0.0    0.0   \n",
       "\n",
       "item2     10   ...   496   497    500   504   505   506   507   508  509  \\\n",
       "item1          ...                                                         \n",
       "0      1492.0  ...  18.0   8.0   69.0  37.0   9.0  23.0  17.0  53.0  2.0   \n",
       "1       275.0  ...   2.0   7.0   17.0  14.0   6.0   3.0   3.0  13.0  0.0   \n",
       "2      1986.0  ...  28.0  21.0  100.0  69.0  14.0  30.0  22.0  65.0  3.0   \n",
       "3       557.0  ...  16.0   6.0   19.0  21.0   3.0  11.0   6.0  21.0  0.0   \n",
       "4       248.0  ...   6.0   3.0   12.0  10.0   0.0   1.0   2.0   7.0  0.0   \n",
       "...       ...  ...   ...   ...    ...   ...   ...   ...   ...   ...  ...   \n",
       "504       0.0  ...   0.0   0.0    0.0   0.0   1.0   4.0   1.0   5.0  1.0   \n",
       "505       0.0  ...   0.0   0.0    0.0   0.0   0.0  20.0  13.0   1.0  0.0   \n",
       "506       0.0  ...   0.0   0.0    0.0   0.0   0.0   0.0  27.0   1.0  0.0   \n",
       "507       0.0  ...   0.0   0.0    0.0   0.0   0.0   0.0   0.0   1.0  0.0   \n",
       "508       0.0  ...   0.0   0.0    0.0   0.0   0.0   0.0   0.0   0.0  0.0   \n",
       "\n",
       "item2    510  \n",
       "item1         \n",
       "0      189.0  \n",
       "1       28.0  \n",
       "2      304.0  \n",
       "3      120.0  \n",
       "4       58.0  \n",
       "...      ...  \n",
       "504     14.0  \n",
       "505      2.0  \n",
       "506      5.0  \n",
       "507      3.0  \n",
       "508     10.0  \n",
       "\n",
       "[268 rows x 270 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "por_pares = pd.read_csv(\"ItemsPorPares.csv\")\n",
    "supp_table = pd.pivot_table(por_pares, index='item1', columns= 'item2')['count'].fillna(0)\n",
    "supp_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Concluimos esta primera parte creando el DataFrame propuesto, es decir, $final\\_sop$. Que consiste en su diagonal del soporte de cada categoria y en los terminos sobre la diagonal el soporte de cada par de items que se haya comprado al menos una vez en el mismo ticket."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "S= np.zeros((272,272))\n",
    "final_sop = pd.DataFrame(S, columns= categorias).set_axis(categorias).fillna(0)\n",
    "for cat in categorias:\n",
    "    for cat2 in categorias:\n",
    "        if (cat in supp_table.columns) and (cat2 in supp_table.index):\n",
    "            if cat == cat2:\n",
    "                final_sop[cat][cat]= cat_supp[cat]\n",
    "            elif (cat >= cat2):\n",
    "                final_sop[cat][cat2]= supp_table[cat][cat2]\n",
    "        else:\n",
    "            final_sop[cat][cat2]=0\n",
    "final_sop[0][0]=cat_supp[0]              #Como 0 no esta en la indexacion de las columnas, lo agregamos manualmente\n",
    "final_sop.to_csv('soporte_conjunto.csv')          #Guardamos la tabla de soportes en archivo csv para analisis posteriores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</table>\n",
       "<p>272 rows × 272 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        0       1        2       3       4      5      6      7       8    \\\n",
       "0    8875.0   341.0   5600.0   625.0   280.0  432.0  161.0  116.0  1612.0   \n",
       "1       0.0  2172.0    464.0    91.0    49.0   83.0   24.0   29.0   287.0   \n",
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       "3       0.0     0.0      0.0  3566.0   246.0  295.0   87.0   49.0   748.0   \n",
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       "..      ...     ...      ...     ...     ...    ...    ...    ...     ...   \n",
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       "\n",
       "       9    ...   496   497    500   504   505    506    507    508  509  \\\n",
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       "\n",
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       "..     ...  \n",
       "506    5.0  \n",
       "507    3.0  \n",
       "508   10.0  \n",
       "509    0.0  \n",
       "510    0.0  \n",
       "\n",
       "[272 rows x 272 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_sop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax= plt.subplots(figsize=(15,15))\n",
    "sns.heatmap(final_sop, ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Hemos conseguido el soporte de conjunto para todos los pares de items que se pueden conformar y aparecen al menos una vez juntos en los tickets, y los hemos almacenado en un nuevo archivo .csv para analisis posteriores"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}