clustering_por_horas_precio.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "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": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Archivo donde están los datos del precio (sin extensión): precio\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>00:00</th>\n",
       "      <th>01:00</th>\n",
       "      <th>02:00</th>\n",
       "      <th>03:00</th>\n",
       "      <th>04:00</th>\n",
       "      <th>05:00</th>\n",
       "      <th>06:00</th>\n",
       "      <th>07:00</th>\n",
       "      <th>08:00</th>\n",
       "      <th>09:00</th>\n",
       "      <th>...</th>\n",
       "      <th>14:00</th>\n",
       "      <th>15:00</th>\n",
       "      <th>16:00</th>\n",
       "      <th>17:00</th>\n",
       "      <th>18:00</th>\n",
       "      <th>19:00</th>\n",
       "      <th>20:00</th>\n",
       "      <th>21:00</th>\n",
       "      <th>22:00</th>\n",
       "      <th>23:00</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20170101</th>\n",
       "      <td>132.63</td>\n",
       "      <td>135.59</td>\n",
       "      <td>129.90</td>\n",
       "      <td>125.38</td>\n",
       "      <td>123.84</td>\n",
       "      <td>122.72</td>\n",
       "      <td>122.60</td>\n",
       "      <td>122.55</td>\n",
       "      <td>119.35</td>\n",
       "      <td>116.78</td>\n",
       "      <td>...</td>\n",
       "      <td>120.95</td>\n",
       "      <td>120.15</td>\n",
       "      <td>120.83</td>\n",
       "      <td>127.76</td>\n",
       "      <td>134.42</td>\n",
       "      <td>135.79</td>\n",
       "      <td>137.63</td>\n",
       "      <td>132.23</td>\n",
       "      <td>129.79</td>\n",
       "      <td>121.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20170102</th>\n",
       "      <td>128.92</td>\n",
       "      <td>123.87</td>\n",
       "      <td>120.91</td>\n",
       "      <td>119.43</td>\n",
       "      <td>118.55</td>\n",
       "      <td>120.34</td>\n",
       "      <td>124.24</td>\n",
       "      <td>130.16</td>\n",
       "      <td>133.22</td>\n",
       "      <td>131.35</td>\n",
       "      <td>...</td>\n",
       "      <td>128.04</td>\n",
       "      <td>127.02</td>\n",
       "      <td>127.31</td>\n",
       "      <td>136.53</td>\n",
       "      <td>140.28</td>\n",
       "      <td>141.97</td>\n",
       "      <td>138.66</td>\n",
       "      <td>134.19</td>\n",
       "      <td>130.78</td>\n",
       "      <td>129.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20170103</th>\n",
       "      <td>126.81</td>\n",
       "      <td>124.39</td>\n",
       "      <td>120.24</td>\n",
       "      <td>115.85</td>\n",
       "      <td>116.10</td>\n",
       "      <td>120.33</td>\n",
       "      <td>129.89</td>\n",
       "      <td>141.30</td>\n",
       "      <td>139.10</td>\n",
       "      <td>139.06</td>\n",
       "      <td>...</td>\n",
       "      <td>138.93</td>\n",
       "      <td>138.66</td>\n",
       "      <td>138.89</td>\n",
       "      <td>139.75</td>\n",
       "      <td>141.50</td>\n",
       "      <td>142.64</td>\n",
       "      <td>145.28</td>\n",
       "      <td>142.51</td>\n",
       "      <td>142.26</td>\n",
       "      <td>139.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20170104</th>\n",
       "      <td>136.96</td>\n",
       "      <td>129.05</td>\n",
       "      <td>126.49</td>\n",
       "      <td>124.19</td>\n",
       "      <td>122.97</td>\n",
       "      <td>124.63</td>\n",
       "      <td>135.63</td>\n",
       "      <td>145.85</td>\n",
       "      <td>143.32</td>\n",
       "      <td>144.58</td>\n",
       "      <td>...</td>\n",
       "      <td>137.84</td>\n",
       "      <td>135.87</td>\n",
       "      <td>136.71</td>\n",
       "      <td>141.10</td>\n",
       "      <td>144.29</td>\n",
       "      <td>145.16</td>\n",
       "      <td>145.13</td>\n",
       "      <td>140.49</td>\n",
       "      <td>137.25</td>\n",
       "      <td>134.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20170105</th>\n",
       "      <td>123.83</td>\n",
       "      <td>121.60</td>\n",
       "      <td>122.75</td>\n",
       "      <td>120.03</td>\n",
       "      <td>113.54</td>\n",
       "      <td>115.70</td>\n",
       "      <td>125.36</td>\n",
       "      <td>136.40</td>\n",
       "      <td>137.20</td>\n",
       "      <td>138.79</td>\n",
       "      <td>...</td>\n",
       "      <td>139.61</td>\n",
       "      <td>139.33</td>\n",
       "      <td>137.68</td>\n",
       "      <td>141.74</td>\n",
       "      <td>142.33</td>\n",
       "      <td>143.88</td>\n",
       "      <td>142.67</td>\n",
       "      <td>142.33</td>\n",
       "      <td>138.63</td>\n",
       "      <td>142.45</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>20191227</th>\n",
       "      <td>90.86</td>\n",
       "      <td>88.50</td>\n",
       "      <td>83.36</td>\n",
       "      <td>82.35</td>\n",
       "      <td>78.64</td>\n",
       "      <td>82.90</td>\n",
       "      <td>87.77</td>\n",
       "      <td>93.20</td>\n",
       "      <td>97.07</td>\n",
       "      <td>99.13</td>\n",
       "      <td>...</td>\n",
       "      <td>94.15</td>\n",
       "      <td>91.62</td>\n",
       "      <td>95.92</td>\n",
       "      <td>101.56</td>\n",
       "      <td>107.16</td>\n",
       "      <td>106.26</td>\n",
       "      <td>101.26</td>\n",
       "      <td>98.59</td>\n",
       "      <td>96.92</td>\n",
       "      <td>95.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20191228</th>\n",
       "      <td>93.63</td>\n",
       "      <td>93.48</td>\n",
       "      <td>88.48</td>\n",
       "      <td>85.65</td>\n",
       "      <td>83.10</td>\n",
       "      <td>84.19</td>\n",
       "      <td>84.21</td>\n",
       "      <td>86.01</td>\n",
       "      <td>88.76</td>\n",
       "      <td>97.19</td>\n",
       "      <td>...</td>\n",
       "      <td>90.65</td>\n",
       "      <td>86.48</td>\n",
       "      <td>92.92</td>\n",
       "      <td>100.95</td>\n",
       "      <td>105.82</td>\n",
       "      <td>105.30</td>\n",
       "      <td>101.23</td>\n",
       "      <td>96.63</td>\n",
       "      <td>97.22</td>\n",
       "      <td>91.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20191229</th>\n",
       "      <td>94.88</td>\n",
       "      <td>92.36</td>\n",
       "      <td>90.90</td>\n",
       "      <td>86.37</td>\n",
       "      <td>84.35</td>\n",
       "      <td>85.05</td>\n",
       "      <td>86.04</td>\n",
       "      <td>88.51</td>\n",
       "      <td>90.04</td>\n",
       "      <td>93.09</td>\n",
       "      <td>...</td>\n",
       "      <td>91.40</td>\n",
       "      <td>92.45</td>\n",
       "      <td>95.46</td>\n",
       "      <td>103.83</td>\n",
       "      <td>105.84</td>\n",
       "      <td>105.72</td>\n",
       "      <td>101.66</td>\n",
       "      <td>103.12</td>\n",
       "      <td>101.81</td>\n",
       "      <td>97.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20191230</th>\n",
       "      <td>96.99</td>\n",
       "      <td>88.24</td>\n",
       "      <td>85.59</td>\n",
       "      <td>85.14</td>\n",
       "      <td>84.54</td>\n",
       "      <td>85.05</td>\n",
       "      <td>94.41</td>\n",
       "      <td>104.09</td>\n",
       "      <td>105.27</td>\n",
       "      <td>105.25</td>\n",
       "      <td>...</td>\n",
       "      <td>94.65</td>\n",
       "      <td>94.60</td>\n",
       "      <td>97.94</td>\n",
       "      <td>106.06</td>\n",
       "      <td>109.18</td>\n",
       "      <td>110.88</td>\n",
       "      <td>110.83</td>\n",
       "      <td>109.95</td>\n",
       "      <td>105.32</td>\n",
       "      <td>103.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20191231</th>\n",
       "      <td>101.20</td>\n",
       "      <td>98.25</td>\n",
       "      <td>95.39</td>\n",
       "      <td>94.16</td>\n",
       "      <td>92.93</td>\n",
       "      <td>95.34</td>\n",
       "      <td>94.67</td>\n",
       "      <td>100.64</td>\n",
       "      <td>100.84</td>\n",
       "      <td>102.79</td>\n",
       "      <td>...</td>\n",
       "      <td>97.28</td>\n",
       "      <td>99.60</td>\n",
       "      <td>103.13</td>\n",
       "      <td>107.78</td>\n",
       "      <td>109.30</td>\n",
       "      <td>107.03</td>\n",
       "      <td>103.11</td>\n",
       "      <td>100.34</td>\n",
       "      <td>100.36</td>\n",
       "      <td>98.68</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1095 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           00:00   01:00   02:00   03:00   04:00   05:00   06:00   07:00  \\\n",
       "20170101  132.63  135.59  129.90  125.38  123.84  122.72  122.60  122.55   \n",
       "20170102  128.92  123.87  120.91  119.43  118.55  120.34  124.24  130.16   \n",
       "20170103  126.81  124.39  120.24  115.85  116.10  120.33  129.89  141.30   \n",
       "20170104  136.96  129.05  126.49  124.19  122.97  124.63  135.63  145.85   \n",
       "20170105  123.83  121.60  122.75  120.03  113.54  115.70  125.36  136.40   \n",
       "...          ...     ...     ...     ...     ...     ...     ...     ...   \n",
       "20191227   90.86   88.50   83.36   82.35   78.64   82.90   87.77   93.20   \n",
       "20191228   93.63   93.48   88.48   85.65   83.10   84.19   84.21   86.01   \n",
       "20191229   94.88   92.36   90.90   86.37   84.35   85.05   86.04   88.51   \n",
       "20191230   96.99   88.24   85.59   85.14   84.54   85.05   94.41  104.09   \n",
       "20191231  101.20   98.25   95.39   94.16   92.93   95.34   94.67  100.64   \n",
       "\n",
       "           08:00   09:00  ...   14:00   15:00   16:00   17:00   18:00   19:00  \\\n",
       "20170101  119.35  116.78  ...  120.95  120.15  120.83  127.76  134.42  135.79   \n",
       "20170102  133.22  131.35  ...  128.04  127.02  127.31  136.53  140.28  141.97   \n",
       "20170103  139.10  139.06  ...  138.93  138.66  138.89  139.75  141.50  142.64   \n",
       "20170104  143.32  144.58  ...  137.84  135.87  136.71  141.10  144.29  145.16   \n",
       "20170105  137.20  138.79  ...  139.61  139.33  137.68  141.74  142.33  143.88   \n",
       "...          ...     ...  ...     ...     ...     ...     ...     ...     ...   \n",
       "20191227   97.07   99.13  ...   94.15   91.62   95.92  101.56  107.16  106.26   \n",
       "20191228   88.76   97.19  ...   90.65   86.48   92.92  100.95  105.82  105.30   \n",
       "20191229   90.04   93.09  ...   91.40   92.45   95.46  103.83  105.84  105.72   \n",
       "20191230  105.27  105.25  ...   94.65   94.60   97.94  106.06  109.18  110.88   \n",
       "20191231  100.84  102.79  ...   97.28   99.60  103.13  107.78  109.30  107.03   \n",
       "\n",
       "           20:00   21:00   22:00   23:00  \n",
       "20170101  137.63  132.23  129.79  121.53  \n",
       "20170102  138.66  134.19  130.78  129.82  \n",
       "20170103  145.28  142.51  142.26  139.56  \n",
       "20170104  145.13  140.49  137.25  134.17  \n",
       "20170105  142.67  142.33  138.63  142.45  \n",
       "...          ...     ...     ...     ...  \n",
       "20191227  101.26   98.59   96.92   95.65  \n",
       "20191228  101.23   96.63   97.22   91.98  \n",
       "20191229  101.66  103.12  101.81   97.65  \n",
       "20191230  110.83  109.95  105.32  103.42  \n",
       "20191231  103.11  100.34  100.36   98.68  \n",
       "\n",
       "[1095 rows x 24 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datos = procesar_datos_string()\n",
    "datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "datos = datos.T\n",
    "labels = list(datos.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "datos = datos.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "datos = normalize(datos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1095"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(datos[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "score_list = []\n",
    "maximo = (0,0)\n",
    "for x in range(2,23):\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": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "El maximo se obtiene en el nodo 2 con una puntuacion de 0.42143692700070673\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([i for i in range(2,23)], score_list)\n",
    "print(\"El maximo se obtiene en el nodo {} con una puntuacion de {}\".format(maximo[0], maximo[1]))"
   ]
  }
 ],
 "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
}