Clustering/K_Means_usando_Iris.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "K_Means_usando_Iris.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyOWbcU1VQUBiT7KzRMdrKmm"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B3IGWihqpZcZ"
      },
      "source": [
        "# K-Means utilizando el dataset Iris"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TsHDbe8FMoJK"
      },
      "source": [
        "from sklearn.cluster import KMeans\n",
        "from sklearn import datasets\n",
        "import pandas as pd \n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hURSzjE3Mmj4"
      },
      "source": [
        "### Cargando el data set Iris"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ucWq9u-HMvvK",
        "outputId": "fdcf0b43-9b12-412b-9189-cd31540a6b58"
      },
      "source": [
        "iris = datasets.load_iris()\n",
        "iris.keys()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['data', 'target', 'target_names', 'DESCR', 'feature_names', 'filename'])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B47tAOkrMxXV"
      },
      "source": [
        "### Creando las variables"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eBRBFEGEM3Eb"
      },
      "source": [
        "X_iris = iris.data\n",
        "Y_iris = iris.target"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "ckWvs_lVM3f9",
        "outputId": "02fbb109-7d37-47cd-97d6-a0b8695a19f2"
      },
      "source": [
        "# Creamos un data Frame\n",
        "x = pd.DataFrame(iris.data, columns = iris.feature_names)\n",
        "x"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sepal length (cm)</th>\n",
              "      <th>sepal width (cm)</th>\n",
              "      <th>petal length (cm)</th>\n",
              "      <th>petal width (cm)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>6.7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>6.3</td>\n",
              "      <td>2.5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>6.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>6.2</td>\n",
              "      <td>3.4</td>\n",
              "      <td>5.4</td>\n",
              "      <td>2.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>5.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1.8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>150 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
              "0                  5.1               3.5                1.4               0.2\n",
              "1                  4.9               3.0                1.4               0.2\n",
              "2                  4.7               3.2                1.3               0.2\n",
              "3                  4.6               3.1                1.5               0.2\n",
              "4                  5.0               3.6                1.4               0.2\n",
              "..                 ...               ...                ...               ...\n",
              "145                6.7               3.0                5.2               2.3\n",
              "146                6.3               2.5                5.0               1.9\n",
              "147                6.5               3.0                5.2               2.0\n",
              "148                6.2               3.4                5.4               2.3\n",
              "149                5.9               3.0                5.1               1.8\n",
              "\n",
              "[150 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "eC9Zt2jqM73P",
        "outputId": "d717654e-ff13-44ce-e76b-da526585ec73"
      },
      "source": [
        "y = pd.DataFrame(iris.target, columns = ['Target'])\n",
        "y"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>150 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Target\n",
              "0         0\n",
              "1         0\n",
              "2         0\n",
              "3         0\n",
              "4         0\n",
              "..      ...\n",
              "145       2\n",
              "146       2\n",
              "147       2\n",
              "148       2\n",
              "149       2\n",
              "\n",
              "[150 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v2GhblfPNDBx"
      },
      "source": [
        "### Graficando los datos"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "J-G44CZ2NGNw",
        "outputId": "c8173c42-1f97-49fb-a51f-f2e371601ff0"
      },
      "source": [
        "plt.scatter(x['petal length (cm)'], x['petal width (cm)'], c = 'blue')\n",
        "plt.xlabel('Petal Length', fontsize = 10)\n",
        "plt.ylabel('Petal Width', fontsize = 10)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Petal Width')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "r2soDDoDNIRr",
        "outputId": "96688d7f-9eee-47d6-c7fc-8bd97b22ade2"
      },
      "source": [
        "plt.scatter(x['sepal length (cm)'], x['sepal width (cm)'], c = 'blue')\n",
        "plt.xlabel('Sepal Length', fontsize = 10)\n",
        "plt.ylabel('Sepal Width', fontsize = 10)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Sepal Width')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FIc5-1ezOWJ4"
      },
      "source": [
        "### Estimación de un buen K usando el método del codo"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "Ide7GgSZOlmf",
        "outputId": "4d45a1b3-bfca-4783-c365-93610fef4204"
      },
      "source": [
        "wcss = [] # (within-cluster sums of squares)\n",
        "\n",
        "\n",
        "for i in range (1, 11): # Número de K's a evaluar\n",
        "    kmeans = KMeans(n_clusters = i # Llamamos a evaluar al KMeans en el rango elegido\n",
        "                    , max_iter = 1000 \n",
        "                    , random_state = 0) \n",
        "    kmeans.fit(x) \n",
        "    # .intertia_ --> Suma de distancias cuadradas de muestras a su centro de conglomerado más cercano.\n",
        "    # las agregamos a la lista wcss\n",
        "    wcss.append(kmeans.inertia_)\n",
        "\n",
        "plt.plot(range(1,11), wcss) #Dibuja X entre 1 y 10. En Y los valores de las sumas\n",
        "plt.title('Elbow Method')\n",
        "plt.grid()\n",
        "plt.xlabel('# of clusters')\n",
        "plt.ylabel('WCSS')"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'WCSS')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KE74JWAjOl6Z"
      },
      "source": [
        "### Creando el modelo"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pI24RgNcOpqT",
        "outputId": "e3dc84ab-7475-4ec6-c4c4-7026dc7f2f5d"
      },
      "source": [
        "from sklearn import metrics\n",
        "model = KMeans(n_clusters=3, max_iter = 1000) # k = 3\n",
        "model.fit(x) # Entrenamos con los valores\n",
        "y_labels = model.labels_ # Obtenemos las etiquetas basado en los datos que encontró\n",
        "y_kmeans = model.predict(x)\n",
        "accuracy = metrics.adjusted_rand_score(Y_iris, y_kmeans) # Le pasamos la info previa y la predicha\n",
        "print(accuracy)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.7302382722834697\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qLdR2p3uOp6M"
      },
      "source": [
        "### Graficando clasificación"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "mwhC-KZrO2ro",
        "outputId": "912378a2-5ac8-4517-f309-856451ea705d"
      },
      "source": [
        "# Aquí hacemos c = y_kmeans para diferenciar los puntos en diferentes colores de acuerdo a su cluster\n",
        "plt.scatter(x['petal length (cm)'], x['petal width (cm)'], c = y_kmeans, s= 30)\n",
        "plt.title('K-Means Iris - Petal')\n",
        "plt.xlabel('Petal Length', fontsize = 10)\n",
        "plt.ylabel('Petal Width', fontsize = 10)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Petal Width')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "zilrnom7O272",
        "outputId": "df7225df-04c4-4a5c-cb6c-4bd15c54b2eb"
      },
      "source": [
        "# Aquí hacemos c = y_kmeans para diferenciar los puntos en diferentes colores de acuerdo a su cluster\n",
        "plt.scatter(x['sepal length (cm)'], x['sepal width (cm)'], c = y_kmeans, s= 30)\n",
        "plt.title('K-Means Iris - Sepal')\n",
        "plt.xlabel('Sepal Length', fontsize = 10)\n",
        "plt.ylabel('Sepal Width', fontsize = 10)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Sepal Width')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n1Y19JipPBWz"
      },
      "source": [
        "---"
      ]
    }
  ]
}