clustering_por_dias_precio.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from procesadoDatosPrecioString import procesar_datos_string \n",
    "from sklearn.cluster import KMeans\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import normalize\n",
    "from sklearn.metrics import silhouette_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Archivo donde están los datos del precio (sin extensión): precio\n"
     ]
    }
   ],
   "source": [
    "datos = procesar_datos_string()\n",
    "datos\n",
    "labels = list(datos.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "datos = datos.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "datos = normalize(datos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(datos[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "score_list = []\n",
    "maximo = (0,0)\n",
    "for x in range(2,20):\n",
    "    clusterer = KMeans(n_clusters=x, random_state=1)\n",
    "    cluster_labels = clusterer.fit_predict(datos)\n",
    "    score = silhouette_score(datos, cluster_labels)\n",
    "    score_list.append(score)\n",
    "    if score > maximo[1]:\n",
    "        maximo = (x, score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "El maximo se obtiene en el nodo 2 con una puntuacion de 0.2792197086604209\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlabel(\"Number of clusters\")\n",
    "plt.ylabel(\"Silhouette Score\")\n",
    "plt.bar([x for x in range(2, 20, 1)], score_list)\n",
    "print(\"El maximo se obtiene en el nodo {} con una puntuacion de {}\".format(maximo[0], maximo[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}