From ef47ce3036988ed814fc9bfc051ae583b34f7eae Mon Sep 17 00:00:00 2001 From: PBenavides Date: Tue, 4 Aug 2020 20:50:31 -0500 Subject: [PATCH] notebook corregido --- ..._Introduccion_KMeans_JUPYTERNOTEBOOK.ipynb | 1454 +++++++++++++++++ 1 file changed, 1454 insertions(+) create mode 100644 Sesion1_Introduccion_KMeans_JUPYTERNOTEBOOK.ipynb diff --git a/Sesion1_Introduccion_KMeans_JUPYTERNOTEBOOK.ipynb b/Sesion1_Introduccion_KMeans_JUPYTERNOTEBOOK.ipynb new file mode 100644 index 0000000..f2112b9 --- /dev/null +++ b/Sesion1_Introduccion_KMeans_JUPYTERNOTEBOOK.ipynb @@ -0,0 +1,1454 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "wWyTYshTiaPG" + }, + "source": [ + "# Clusterización de personas" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "ApIpHsYBiaPJ" + }, + "source": [ + "## Presentación del caso" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "PGTg8IfHiaPL" + }, + "source": [ + "Este ejercicio nos permite ejecutar un código para agrupar personas según las características de cada una. Para el presente ejemplo se ha considerado **100 personajes famosos a nivel mundial relacionados al cine, música, arte y deporte**.\n", + "\n", + "![Image of Yaktocat](https://www.lifeder.com/wp-content/uploads/2017/01/dinámicas-de-cohesión.jpg)\n", + "\n", + "El dataset se ha obtenido gracias a un **estudio** realizados sobre los razgos de la personalidad basado en los tweets publicados por el famoso. Cada rasgo ha sido **cuantificado en una columna**: Openess, Extraversion y Agreeablenes\n", + "\n", + "1.- *Openess*: Honestidad, franqueza, transparencia\n", + "\n", + "2.- *Extraversion*: Extraversión, ímpetu, dinamismo, entusiasmo.\n", + "\n", + "3.- *Agreeablenes*: Agradable, amable, buen tipo, cordial.\n", + "\n", + "El reto será comparar la agrupación obtenida por KMeans y la clasificación por sus trabajos. Tomar nota que el dataset tiene registrada en la columna **categoría** el trabajo:\n", + "\n", + "**Categoría 1:** Actores y actrices de Hollywood\n", + "\n", + "**Categoría 2:** Cantantes\n", + "\n", + "**Categoría 3:** TV Host (presentadores de televisión)\n", + "\n", + "**Categoría 4:** Deportistas\n", + "\n", + "**Categoría 5:** Políticos\n", + "\n", + "**Categoría 6:** Escritores" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "gs0pv_9eiaPM" + }, + "source": [ + "## Importación de librerías" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "57SEYIBHiaPN" + }, + "outputs": [], + "source": [ + "\"\"\" Importamos la librería pandas que nos sirve para manipular datos tabulares (filas y columnas) como archivos csv\n", + "Además renombramos la librería con el acrónimo pd para llamar la librería de forma más rápida a lo largo del código \"\"\"\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "q8jeBPOHiaPU" + }, + "outputs": [], + "source": [ + "\"\"\" Importamos la librería numpy que nos sirve para manipular datos numéricos así como funciones y variables matemáticas\n", + "Además renombramos la librería con el acrónimo np para llamar la librería de forma más rápida a lo largo del código \"\"\"\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "TQUE_KIFiaPa" + }, + "outputs": [], + "source": [ + "\"\"\" Importamos la librería pyplot de matplotlib para poder realizar gráficos básicos\n", + "Además renombramos la librería con el acrónimo plt para llamar la librería de forma más rápida a lo largo del código \"\"\"\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "DX3dkpQkiaPd" + }, + "outputs": [], + "source": [ + "#Configuramos el estilo ggplot predeterminado de las gráficas\n", + "plt.style.use('ggplot')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "l6LkBqn-iaPg" + }, + "outputs": [], + "source": [ + "\"\"\" Importamos la librería seaborn para poder realizar gráficos más elaborados\n", + "Además renombramos la librería con el acrónimo sb para llamar la librería de forma más rápida a lo largo del código \"\"\"\n", + "import seaborn as sb" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "6KPnX7g1iaPx" + }, + "source": [ + "## Lectura de los datos" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "uCUg48QCiaPz" + }, + "outputs": [], + "source": [ + "\"\"\" Para leer los datos utilizamos el método read_csv de la librería pandas\n", + "Además hemos guardado los datos en una nueva variable denominada data\"\"\"\n", + "data = pd.read_csv(\"https://raw.githubusercontent.com/javalpe/datasets/master/clustering_stars.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "GvJ6IqJ6iaP5", + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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usuarioopexagcategoria
0antoniobanderas41.32818239.17333321.0705051
1charliesheen36.27234840.06515228.7068941
2CourteneyCox53.11048039.00608017.2064001
3Diane_Keaton46.95585435.37617923.7475611
4EdwardNorton40.04643937.72219730.5682581
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" + ], + "text/plain": [ + " usuario op ex ag categoria\n", + "0 antoniobanderas 41.328182 39.173333 21.070505 1\n", + "1 charliesheen 36.272348 40.065152 28.706894 1\n", + "2 CourteneyCox 53.110480 39.006080 17.206400 1\n", + "3 Diane_Keaton 46.955854 35.376179 23.747561 1\n", + "4 EdwardNorton 40.046439 37.722197 30.568258 1" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\"\"\" Para verificar que hemos leído correctamente los datos ejecutamos el método head\n", + "Por defecto el método head arroja los primeros cinco (5) elementos, pero puedes variar el número\n", + "Otras variaciones son (-1)=todos los elementos o tail() que muestra los últimos cinco elementos\"\"\"\n", + "data.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "3RLUQluOiaP9" + }, + "source": [ + "## Preprocesamiento de los datos I" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "0AoxkaioiaP-" + }, + "source": [ + "Esta parte del proceso es muy importante porque se debe preparar los datos para ejecutar adecuadamente el modelo de machine learning. Para ello nos aseguramos de realizar los siguientes pasos previos a la aplicación del modelo:\n", + "\n", + "1.- Reemplazar y/o eliminar los vacíos y nulos\n", + "\n", + "2.- Reemplazar y/o eliminar los outliers\n", + "\n", + "3.- Transformar el tipo de dato de categórico a numérico de ser necesario\n", + "\n", + "4.- Normalizar los datos para evitar sesgo por escala" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "PcukMeZZiaP_" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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opexagcategoria
count100.000000100.000000100.000000100.000000
mean42.55100441.79031323.9484593.450000
std7.8455286.3683877.5992551.695955
min30.02046526.8555649.3059851.000000
25%36.58683237.88510917.4082862.000000
50%40.36146142.21037723.7791653.000000
75%47.36016445.29912229.4114965.000000
max66.66556459.82484440.0964586.000000
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" + ], + "text/plain": [ + " op ex ag categoria\n", + "count 100.000000 100.000000 100.000000 100.000000\n", + "mean 42.551004 41.790313 23.948459 3.450000\n", + "std 7.845528 6.368387 7.599255 1.695955\n", + "min 30.020465 26.855564 9.305985 1.000000\n", + "25% 36.586832 37.885109 17.408286 2.000000\n", + "50% 40.361461 42.210377 23.779165 3.000000\n", + "75% 47.360164 45.299122 29.411496 5.000000\n", + "max 66.665564 59.824844 40.096458 6.000000" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\"\"\"\" Para obtener una vista rápida de las principales características estadísticas sobre las variables cuantitativas de la data \n", + "utilizamos el método describe: conteo, promedio(mean), desviación estandar(std), minimo(min), percentiles (25,50,75), maximo\"\"\"\n", + "data.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "1ooK-nNjiaQC" + }, + "source": [ + "Como conclusión podemos comprobar que **no existen nulos ni vacíos en ninguna columna** porque de otro modo la variable *count* de la primera fila no resultaría 100 en todas las columnas." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "5Az04AuYiaQF" + }, + "source": [ + "También podemos obtener el **número de personas que actualmente se encuentran en cada grupo** Para ello utilizamos dos métodos:\n", + "\n", + "1.- groupby: que agrega o agrupa los datos en base a una característica (columna)\n", + "\n", + "2.- size: calcular el número de registros" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "JZ0igyHGiaQG", + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "categoria\n", + "1 17\n", + "2 17\n", + "3 17\n", + "4 17\n", + "5 17\n", + "6 15\n", + "dtype: int64\n" + ] + } + ], + "source": [ + "#Utilizamos print para mostrar el resultado y el método size fuera de groupby para contar los elementos de cada grupo\n", + "print(data.groupby('categoria').size())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "Nag2SsZkiaQP" + }, + "source": [ + "Como resultado podemos visualizar que cada categoría tiene la misma cantidad de personas (excepto la categoría 6, pero es muy similar al resto). Esto es importante porque nos permite prevenir un **sesgo** del modelo ante data **desbalanceada**." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "aMJLqTZYiaQQ" + }, + "source": [ + "## Preprocesamiento de los datos II" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "s_1lAuxPiaQS" + }, + "source": [ + "Una opción rápida para verificar las distribuciones de los datos es graficándolos. Para ello Python cuenta con varias gráficas o también puedes hacer uso de las librerías **pyplot o seaborn**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "ObkJuzJoiaQX" + }, + "source": [ + "*Histograma:* Permite graficar las distribuciones de cada variable a través de un conteo en intervalos. Para ello utilizamos el método **hist()**" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "LIEk0SX4iaQX" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[,\n", + " ],\n", + " [,\n", + " ]],\n", + " dtype=object)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Primero elinamos temporalmente categoría y luego aplicamos el histograma sobre las variables cuantitativas dependientes\n", + "data.drop(['categoria'],1).hist()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "Oiiv1zK9iaQd" + }, + "source": [ + "*Diagrama de dispersión:* Permite graficar las distribuciones de cada variable a través de un diagrama de puntos sobre un plano cartesiano. Además realiza las gráficas combinando todas las variables de 2 en 2. Para ello utilizamos el método **pairplot** de la librería **seaborn**" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "JkUYduO5iaQe" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\"\"\" El método pairplot dibuja todas las gráficas entre variables comparándolas en pares.\n", + "Además cuenta con un parámetro hue para añadir un filtro y el parámetro kind define el tipo de gráfica\"\"\"\n", + "sb.pairplot(data, hue='categoria',vars=[\"op\",\"ex\",\"ag\"],kind='scatter');\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "KFT_KPooiaQh" + }, + "source": [ + "*Boxplot:* Permite graficar la distribución de una variable para identificar **outliers**. De forma ideal se deben eliminar o reemplazar todos los outliers para representar el comportamiento real de los datos **sin valores atípicos**. Sin embargo, para no perder información se puede admitir un porcentaje mínimo de outliers: **5% -10%**" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "GlW7bkZaiaQi" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\"\"\" Dibujamos el boxplot de la variable openess a través del método boxplot de la librería seaborn (sb) \n", + "El eje X es la variable categoría y la variable Y es la variable openess. El parámetro width es el ancho de la caja\"\"\"\n", + "sb.boxplot( x=data[\"categoria\"], y=data[\"op\"], width=0.8);\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "cXBMIFBkiaQk" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\"\"\" Dibujamos el boxplot de la variable extraversion a través del método boxplot de la librería seaborn (sb) \n", + "El eje X es la variable categoría y la variable Y es la variable extraversion. El parámetro width es el ancho de la caja\"\"\"\n", + "sb.boxplot( x=data[\"categoria\"], y=data[\"ex\"], width=0.8);\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "zaO6iuJYiaQs" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\"\"\" Dibujamos el boxplot de la variable Agreeablenes a través del método boxplot de la librería seaborn (sb) \n", + "El eje X es la variable categoría y la variable Y es la variable Agreeablenes. El parámetro width es el ancho de la caja\"\"\"\n", + "sb.boxplot( x=data[\"categoria\"], y=data[\"ag\"], width=0.8);\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "rqc0cgOCiaQ2" + }, + "source": [ + "A modo de conclusión del Preprocesamiento no se ha encontrado valores vacíos o nulos. Además se ha admitido un porcentaje mínimo de outliers en los datos. Por otro lado las escalas en que se encuentran las variables son similares por lo tanto no es necesario una normalización de los datos." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "3O2ENHbTiaQ2" + }, + "source": [ + "## Modelamiento" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "1co9nNpgiaQ3" + }, + "source": [ + "Ahora que ya contamos con los datos limpios y listos para ser procesados por nuestro modelo, procedemos a ejecutar el algoritmo de KMeans para agrupar a las 140 personas." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "8udwupXCiaQ4" + }, + "source": [ + "El primer paso será definir el **número óptimo de cluster** y para ello utilizamos el método **score** que nos permite comparar cuaánto mejora la precisión del modelo a medida que aumenta el número de cluster." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "MHdX6IOziaPp" + }, + "outputs": [], + "source": [ + "#Importamos KMeans de sklearn.cluster para poder ejecutar el modelo de agrupamiento\n", + "from sklearn.cluster import KMeans" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "XOkOPOBZiaQ8" + }, + "outputs": [], + "source": [ + "#Cremos una lista con números del 1 al 10 para simular el número de cluster\n", + "numeros_cluster = range(1, 10)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "qAtsKfK5iaQ_" + }, + "outputs": [], + "source": [ + "#Creamos una variable kmeans que guarde el modelo KMeans con un número de cluster distinto por cada valor de la lista\n", + "kmeans = [KMeans(n_clusters=i) for i in numeros_cluster]" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "GqHikgNOxtmJ" + }, + "outputs": [], + "source": [ + "#Con ayuda del numpy convertimos en arreglos de números a las variables dependientes como X y la variable categoría como Y\n", + "X = np.array(data[[\"op\",\"ex\",\"ag\"]])\n", + "y = np.array(data['categoria'])" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "HK2cJVBqiaRC" + }, + "outputs": [], + "source": [ + "#Calculamos el score obtenido al entrenar los datos almacenados en X (arreglo de variables independientes) por cada kmeans\n", + "score = [kmeans[i].fit(X).score(X) for i in range(len(kmeans))]" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "NSwAB-JKiaRF" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Finalmente graficamos el número de cluster versus el score obtenido\n", + "plt.plot(numeros_cluster,score) #El eje X es el número de clusters y el eje Y es el score del KMeans con ese número de clusters\n", + "plt.xlabel('Número de cluster') #Titulamos el eje X\n", + "plt.ylabel('Puntaje acumulado') #Titulamos el eje Y\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "dW-Ni-TJiaRI" + }, + "source": [ + "Se evidencia que con solos **2 clusters** aumentamos el puntaje de -16,000 hasta -8,000 (50% mejor). Luego con **3 clusters** aumentamos el puntaje de -8,000 hasta -6,000 (25% mejor). Por ello solo nos quedaremos con 3 clusters, ya que a partir de 4 cluster la mejora será mínima." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "Ky5Jj4_uiaRJ" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[37.082881 44.85263626 30.41982791]\n", + " [43.31885448 40.30086934 18.74398511]\n", + " [55.03157579 36.64084605 16.63908463]]\n" + ] + } + ], + "source": [ + "\"\"\" Actualizamos la variable kmeans para definir el modelo KMeans con tres clusters fijos y entrenados con los datos de X \n", + "Además almacenamos los tres núcleos de cada cluster en una variable centroides con el método cluster_centers_\"\"\"\n", + "kmeans = KMeans(n_clusters=3).fit(X)\n", + "centroides = kmeans.cluster_centers_\n", + "print(centroides)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "xaUd66zHiaRO" + }, + "source": [ + "Una vez hemos obtenido el modelo KMeans con el **número de clúster óptimos** procedemos a generar las **predicciones** ubicando a las personas según su cluster asignado" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "vASCDpG5iaRP" + }, + "outputs": [], + "source": [ + "# Almacenamos las 140 predicciones en una variable target que indica a cuál de los 3 cluster pertenece cada persona\n", + "target = kmeans.predict(X)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "nC6JaOYIiaRS" + }, + "outputs": [], + "source": [ + "#Añadimos una columna adicional en nuestra data para mostrar el cluster correspondiente de la variable target\n", + "data['cluster']=target" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "qfRNR-p4iaRe" + }, + "source": [ + "Cómo se evidencia hemos formado **tres grupos claramente separados** según las características registradas en los datos: openess, extraversion y agreeablenes. Adicionalmente podemos comparar de dos en dos estas características para ver cómo se comporta cada cluster en cada gráfica:" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "Vd-YIfAgiaRf" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Solo vamos a graficar los valores de las columnas \"op\" (openess) y \"ex\" (extraversion) \n", + "f1 = data['op'].values\n", + "f2 = data['ex'].values\n", + "\n", + "labels = kmeans.predict(X)\n", + "# Getting the cluster centers\n", + "C = kmeans.cluster_centers_\n", + "colores=['red','green','blue']\n", + "asignar=[]\n", + "for row in labels:\n", + " asignar.append(colores[row])\n", + " \n", + "plt.scatter(f1, f2, c=asignar, s=50)\n", + "plt.scatter(centroides[:, 0], centroides[:, 1], marker='*', c=colores, s=100)\n", + "plt.xlabel('honestidad')\n", + "plt.ylabel('ímpetu')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "g1YCwVYGiaRh" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Solo vamos a graficar los valores de las columnas \"op\" (openess) y \"ag\" (agreeablenes) \n", + "f1 = data['op'].values\n", + "f2 = data['ag'].values\n", + " \n", + "plt.scatter(f1, f2, c=asignar, s=50)\n", + "plt.scatter(centroides[:, 0], centroides[:, 2], marker='*', c=colores, s=100)\n", + "plt.xlabel('honestidad')\n", + "plt.ylabel('amabilidad')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "Z9PCGJO8iaRk" + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Solo vamos a graficar los valores de las columnas \"ex\" (extraversion) y \"ag\" (agreeablenes) \n", + "f1 = data['ex'].values\n", + "f2 = data['ag'].values\n", + " \n", + "plt.scatter(f1, f2, c=asignar, s=50)\n", + "plt.xlabel('ímpetu')\n", + "plt.ylabel('amabilidad')\n", + "plt.scatter(centroides[:, 1], centroides[:, 2], marker='*', c=colores, s=100)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "F9XRf9LuiaRz" + }, + "source": [ + "# Análisis de resultados" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "InawCcwGtr_r" + }, + "source": [ + "Para visualizar el valor **promedio** de cada variable podemos mostrar una tabla similar a las tablas dinámicas que ejecutamos en Excel y así comprobar el comportamiento diferente entre cada cluster" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "nCH8s8l8tr_v" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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agexop
cluster
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" + ], + "text/plain": [ + " ag ex op\n", + "cluster \n", + "0 30.419828 44.852636 37.082881\n", + "1 18.743985 40.300869 43.318854\n", + "2 16.639085 36.640846 55.031576" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Para mostrar tablas dinámicas en Python usamos el método pivot_table y el parámetro aggfunc para definir qué mostrar\n", + "data.pivot_table(['op', 'ex', 'ag'], 'cluster', aggfunc=np.mean)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "Cede4fMOtr_8" + }, + "outputs": [], + "source": [ + "data['categoria'].replace({1: \"HollywoodStar\", 2: \"Cantantes\", 3:\"TV Host\", 4:\"Deportistas\", 5:\"Políticos\", 6:\"Escritores\"}, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "BospURZttsAH" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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agexop
clustercategoria
0Cantantes28.39733751.55140742.732829
Deportistas29.21660942.92867435.604041
Escritores29.33880744.74465937.712156
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Deportistas19.61348442.09476641.309330
Escritores21.11937741.57589343.935936
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Políticos17.27609637.96940540.396016
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2Cantantes16.67456936.60049955.227808
Deportistas19.97274838.62022952.895725
HollywoodStar17.05352135.32088455.513652
TV Host10.04952444.30466751.830571
\n", + "
" + ], + "text/plain": [ + " ag ex op\n", + "cluster categoria \n", + "0 Cantantes 28.397337 51.551407 42.732829\n", + " Deportistas 29.216609 42.928674 35.604041\n", + " Escritores 29.338807 44.744659 37.712156\n", + " HollywoodStar 32.900812 41.004334 37.525900\n", + " Políticos 28.036263 40.320000 35.790875\n", + " TV Host 33.539274 48.939392 37.301041\n", + "1 Cantantes 17.483562 42.009475 48.298906\n", + " Deportistas 19.613484 42.094766 41.309330\n", + " Escritores 21.119377 41.575893 43.935936\n", + " HollywoodStar 19.135183 40.207704 44.924606\n", + " Políticos 17.276096 37.969405 40.396016\n", + " TV Host 20.723138 39.629197 44.318033\n", + "2 Cantantes 16.674569 36.600499 55.227808\n", + " Deportistas 19.972748 38.620229 52.895725\n", + " HollywoodStar 17.053521 35.320884 55.513652\n", + " TV Host 10.049524 44.304667 51.830571" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.pivot_table(['op', 'ex', 'ag'], ['cluster','categoria'], aggfunc=np.mean)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "jGmrCFIgiaR4" + }, + "source": [ + "## Elementos representativos de cada cluster" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "YqfrYKYXiaR4" + }, + "source": [ + "Para obtener los elementos más representativos de cada cluster vamos a seleccionar aquellos elementos que **están más cerca a los centroides** de cada cluster" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "EIux6v5viaPt" + }, + "outputs": [], + "source": [ + "#Importamos pairwise_distances_argmin_min para medir la distancia entre los elementos y su centroide para cada cluster\n", + "from sklearn.metrics import pairwise_distances_argmin_min" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "FQjM54n-iaR5" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([46, 64, 5], dtype=int64)" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\"\"\" Creamos una variable mas_centrados para descubrir los registros que están más cerca a los centroides de cada cluster\n", + "Para ello utilizamos la función pairwise_distances_argmin_min de la librería sklearn.metrics \"\"\"\n", + "mas_centrados, _ = pairwise_distances_argmin_min(centroides, X)\n", + "mas_centrados" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "bHqWw5zCiaR9" + }, + "source": [ + "Para descubrir el nombre de los elementos seleccionados utilizamos los números como **índices** de la columna **usuario**" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "BoXrK-eYiaR_" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "maria_patino\n", + "paugasol\n", + "EmWatson\n" + ] + } + ], + "source": [ + "nombres = data['usuario']\n", + "for row in mas_centrados:\n", + " print(nombres[row])" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "Xqb_RqfKiaSG" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1]\n" + ] + } + ], + "source": [ + "\"\"\" Adicionalmente podemos crear una nueva variable predictora X_new considerando que tenga la misma cantidad de columnas\n", + "En nuestro ejemplo necesitamos tres números continuos para op,ex,ag \"\"\"\n", + "X_new = np.array([[45.92,57.74,15.66]])\n", + "\n", + "new_labels = kmeans.predict(X_new)\n", + "print(new_labels)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "colab": { + "name": "Clusterizacion_con_KMeans.ipynb", + "provenance": [] + }, + "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": "3.7.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +}