{"cells":[{"cell_type":"code","source":"from google.colab import drive\nimport os\ndrive.mount('/content/drive')\n# Establecer ruta de acceso en drive\nimport os\nprint(os.getcwd())\nos.chdir(\"/content/drive/My Drive\")\nprint(os.getcwd())","metadata":{"id":"N-aXaawsE735","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"3eebee2f5f934d6ea0591b979614234d","outputId":"62eb308d-02aa-42f9-a92f-db04e4cc36c2","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":2470,"user_tz":240,"timestamp":1652654218422},"deepnote_cell_type":"code"},"outputs":[{"output_type":"stream","name":"stdout","text":"Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n/content/drive/My Drive\n/content/drive/My Drive\n"}],"execution_count":10},{"cell_type":"code","source":"import numpy as np \nimport pandas as pd \nimport matplotlib.pyplot as plt \nimport seaborn as sns \n\ndf = pd.read_csv('Breast_cancer_data.csv')\ndf.head()","metadata":{"id":"NXgGXyKtE3AF","colab":{"height":206,"base_uri":"https://localhost:8080/"},"cell_id":"93d06bdddfdb4b93b9ba8e8a5482105c","outputId":"6ab28d83-46d3-4952-85ce-8e64ec853c6b","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":664,"user_tz":240,"timestamp":1652654220114},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"   mean_radius  mean_texture  mean_perimeter  mean_area  mean_smoothness  \\\n0        17.99         10.38          122.80     1001.0          0.11840   \n1        20.57         17.77          132.90     1326.0          0.08474   \n2        19.69         21.25          130.00     1203.0          0.10960   \n3        11.42         20.38           77.58      386.1          0.14250   \n4        20.29         14.34          135.10     1297.0          0.10030   \n\n   diagnosis  \n0          0  \n1          0  \n2          0  \n3          0  \n4          0  ","text/html":"\n  <div id=\"df-0bc9b2e2-6bdc-4e5c-af6b-f699e879f9ff\">\n    <div class=\"colab-df-container\">\n      <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>mean_radius</th>\n      <th>mean_texture</th>\n      <th>mean_perimeter</th>\n      <th>mean_area</th>\n      <th>mean_smoothness</th>\n      <th>diagnosis</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>17.99</td>\n      <td>10.38</td>\n      <td>122.80</td>\n      <td>1001.0</td>\n      <td>0.11840</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>20.57</td>\n      <td>17.77</td>\n      <td>132.90</td>\n      <td>1326.0</td>\n      <td>0.08474</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>19.69</td>\n      <td>21.25</td>\n      <td>130.00</td>\n      <td>1203.0</td>\n      <td>0.10960</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>11.42</td>\n      <td>20.38</td>\n      <td>77.58</td>\n      <td>386.1</td>\n      <td>0.14250</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>20.29</td>\n      <td>14.34</td>\n      <td>135.10</td>\n      <td>1297.0</td>\n      <td>0.10030</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0bc9b2e2-6bdc-4e5c-af6b-f699e879f9ff')\"\n              title=\"Convert this dataframe to an interactive table.\"\n              style=\"display:none;\">\n        \n  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n       width=\"24px\">\n    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n  </svg>\n      </button>\n      \n  <style>\n    .colab-df-container {\n      display:flex;\n      flex-wrap:wrap;\n      gap: 12px;\n    }\n\n    .colab-df-convert {\n      background-color: #E8F0FE;\n      border: none;\n      border-radius: 50%;\n      cursor: pointer;\n      display: none;\n      fill: #1967D2;\n      height: 32px;\n      padding: 0 0 0 0;\n      width: 32px;\n    }\n\n    .colab-df-convert:hover {\n      background-color: #E2EBFA;\n      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n      fill: #174EA6;\n    }\n\n    [theme=dark] .colab-df-convert {\n      background-color: #3B4455;\n      fill: #D2E3FC;\n    }\n\n    [theme=dark] .colab-df-convert:hover {\n      background-color: #434B5C;\n      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n      fill: #FFFFFF;\n    }\n  </style>\n\n      <script>\n        const buttonEl =\n          document.querySelector('#df-0bc9b2e2-6bdc-4e5c-af6b-f699e879f9ff button.colab-df-convert');\n        buttonEl.style.display =\n          google.colab.kernel.accessAllowed ? 'block' : 'none';\n\n        async function convertToInteractive(key) {\n          const element = document.querySelector('#df-0bc9b2e2-6bdc-4e5c-af6b-f699e879f9ff');\n          const dataTable =\n            await google.colab.kernel.invokeFunction('convertToInteractive',\n                                                     [key], {});\n          if (!dataTable) return;\n\n          const docLinkHtml = 'Like what you see? Visit the ' +\n            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n            + ' to learn more about interactive tables.';\n          element.innerHTML = '';\n          dataTable['output_type'] = 'display_data';\n          await google.colab.output.renderOutput(dataTable, element);\n          const docLink = document.createElement('div');\n          docLink.innerHTML = docLinkHtml;\n          element.appendChild(docLink);\n        }\n      </script>\n    </div>\n  </div>\n  "},"metadata":{},"execution_count":11}],"execution_count":11},{"cell_type":"code","source":"#Info\ndf.info()","metadata":{"id":"jZCsSqjXE3AL","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"99d2d9c0f6be40f1ad3cfe6ee071050a","outputId":"ddefcb89-ff37-49e1-a36d-23c5b3c2964d","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":268,"user_tz":240,"timestamp":1652654222572},"deepnote_cell_type":"code"},"outputs":[{"output_type":"stream","name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 569 entries, 0 to 568\nData columns (total 6 columns):\n #   Column           Non-Null Count  Dtype  \n---  ------           --------------  -----  \n 0   mean_radius      569 non-null    float64\n 1   mean_texture     569 non-null    float64\n 2   mean_perimeter   569 non-null    float64\n 3   mean_area        569 non-null    float64\n 4   mean_smoothness  569 non-null    float64\n 5   diagnosis        569 non-null    int64  \ndtypes: float64(5), int64(1)\nmemory usage: 26.8 KB\n"}],"execution_count":12},{"cell_type":"code","source":"#Analizamos la variable target y su frecuencia\ndf['diagnosis'].value_counts()","metadata":{"id":"24okdbeFE3AM","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"621f894fedd742a798ec4b6375f78a88","outputId":"611b6c89-9d4d-4700-c581-c602f5297cbc","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":7,"user_tz":240,"timestamp":1652654222817},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"1    357\n0    212\nName: diagnosis, dtype: int64"},"metadata":{},"execution_count":13}],"execution_count":13},{"cell_type":"code","source":"#Definimos X e y\nX = df[['mean_radius','mean_texture','mean_perimeter','mean_area','mean_smoothness']]\ny = df['diagnosis']","metadata":{"id":"zMgPm_xLE3AO","cell_id":"7e630a04d83f4ae5a68841ab2b071cca","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":6,"user_tz":240,"timestamp":1652654225092},"deepnote_cell_type":"code"},"outputs":[],"execution_count":14},{"cell_type":"code","source":"X","metadata":{"id":"3X49vw_4FZRb","colab":{"height":423,"base_uri":"https://localhost:8080/"},"cell_id":"28799dab13994fd28ba477a2769b028e","outputId":"ce83d672-ec09-481b-874e-bbc5df337acf","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":7,"user_tz":240,"timestamp":1652654226392},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"     mean_radius  mean_texture  mean_perimeter  mean_area  mean_smoothness\n0          17.99         10.38          122.80     1001.0          0.11840\n1          20.57         17.77          132.90     1326.0          0.08474\n2          19.69         21.25          130.00     1203.0          0.10960\n3          11.42         20.38           77.58      386.1          0.14250\n4          20.29         14.34          135.10     1297.0          0.10030\n..           ...           ...             ...        ...              ...\n564        21.56         22.39          142.00     1479.0          0.11100\n565        20.13         28.25          131.20     1261.0          0.09780\n566        16.60         28.08          108.30      858.1          0.08455\n567        20.60         29.33          140.10     1265.0          0.11780\n568         7.76         24.54           47.92      181.0          0.05263\n\n[569 rows x 5 columns]","text/html":"\n  <div id=\"df-38927be3-1b1c-466f-8a7c-ac9a4eead12a\">\n    <div class=\"colab-df-container\">\n      <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>mean_radius</th>\n      <th>mean_texture</th>\n      <th>mean_perimeter</th>\n      <th>mean_area</th>\n      <th>mean_smoothness</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>17.99</td>\n      <td>10.38</td>\n      <td>122.80</td>\n      <td>1001.0</td>\n      <td>0.11840</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>20.57</td>\n      <td>17.77</td>\n      <td>132.90</td>\n      <td>1326.0</td>\n      <td>0.08474</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>19.69</td>\n      <td>21.25</td>\n      <td>130.00</td>\n      <td>1203.0</td>\n      <td>0.10960</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>11.42</td>\n      <td>20.38</td>\n      <td>77.58</td>\n      <td>386.1</td>\n      <td>0.14250</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>20.29</td>\n      <td>14.34</td>\n      <td>135.10</td>\n      <td>1297.0</td>\n      <td>0.10030</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>564</th>\n      <td>21.56</td>\n      <td>22.39</td>\n      <td>142.00</td>\n      <td>1479.0</td>\n      <td>0.11100</td>\n    </tr>\n    <tr>\n      <th>565</th>\n      <td>20.13</td>\n      <td>28.25</td>\n      <td>131.20</td>\n      <td>1261.0</td>\n      <td>0.09780</td>\n    </tr>\n    <tr>\n      <th>566</th>\n      <td>16.60</td>\n      <td>28.08</td>\n      <td>108.30</td>\n      <td>858.1</td>\n      <td>0.08455</td>\n    </tr>\n    <tr>\n      <th>567</th>\n      <td>20.60</td>\n      <td>29.33</td>\n      <td>140.10</td>\n      <td>1265.0</td>\n      <td>0.11780</td>\n    </tr>\n    <tr>\n      <th>568</th>\n      <td>7.76</td>\n      <td>24.54</td>\n      <td>47.92</td>\n      <td>181.0</td>\n      <td>0.05263</td>\n    </tr>\n  </tbody>\n</table>\n<p>569 rows × 5 columns</p>\n</div>\n      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-38927be3-1b1c-466f-8a7c-ac9a4eead12a')\"\n              title=\"Convert this dataframe to an interactive table.\"\n              style=\"display:none;\">\n        \n  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n       width=\"24px\">\n    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n  </svg>\n      </button>\n      \n  <style>\n    .colab-df-container {\n      display:flex;\n      flex-wrap:wrap;\n      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google.colab.kernel.accessAllowed ? 'block' : 'none';\n\n        async function convertToInteractive(key) {\n          const element = document.querySelector('#df-38927be3-1b1c-466f-8a7c-ac9a4eead12a');\n          const dataTable =\n            await google.colab.kernel.invokeFunction('convertToInteractive',\n                                                     [key], {});\n          if (!dataTable) return;\n\n          const docLinkHtml = 'Like what you see? Visit the ' +\n            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n            + ' to learn more about interactive tables.';\n          element.innerHTML = '';\n          dataTable['output_type'] = 'display_data';\n          await google.colab.output.renderOutput(dataTable, element);\n          const docLink = document.createElement('div');\n          docLink.innerHTML = docLinkHtml;\n          element.appendChild(docLink);\n        }\n      </script>\n    </div>\n  </div>\n  "},"metadata":{},"execution_count":15}],"execution_count":15},{"cell_type":"code","source":"y","metadata":{"id":"cNWH1eX6FaKv","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"ccf49a8bff0545c4b108cd3e801cd801","outputId":"a06c743d-fd41-4f9c-b450-90d7afb571b2","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":5,"user_tz":240,"timestamp":1652654228745},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"0      0\n1      0\n2      0\n3      0\n4      0\n      ..\n564    0\n565    0\n566    0\n567    0\n568    1\nName: diagnosis, Length: 569, dtype: int64"},"metadata":{},"execution_count":16}],"execution_count":16},{"cell_type":"code","source":"#Separamos en Train y Test\nfrom sklearn.model_selection import train_test_split\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 42)","metadata":{"id":"Wqt3BFEqE3AP","cell_id":"497556e69aa9467d81c90d84911cd57e","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":305,"user_tz":240,"timestamp":1652654230613},"deepnote_cell_type":"code"},"outputs":[],"execution_count":17},{"cell_type":"code","source":"!pip install lightgbm","metadata":{"id":"BbGunKHmKB-9","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"c91a3c14b3454bffb928f00b836a12ac","outputId":"2b0edaa9-fe89-4331-ba0d-6a80c572d4c6","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":3779,"user_tz":240,"timestamp":1652654235087},"deepnote_cell_type":"code"},"outputs":[{"output_type":"stream","name":"stdout","text":"Requirement already satisfied: lightgbm in /usr/local/lib/python3.7/dist-packages (2.2.3)\nRequirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from lightgbm) (1.0.2)\nRequirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from lightgbm) (1.21.6)\nRequirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from lightgbm) (1.4.1)\nRequirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->lightgbm) (1.1.0)\nRequirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->lightgbm) (3.1.0)\n"}],"execution_count":18},{"cell_type":"code","source":"import lightgbm as lgb #pip install lightgbm\nclf = lgb.LGBMClassifier()\nclf.fit(X_train, y_train)\ny_pred=clf.predict(X_test)","metadata":{"id":"lM4KMo16E3AQ","cell_id":"4ad7f912dfa74668ad18e32a94f8ae13","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":355,"user_tz":240,"timestamp":1652654235433},"deepnote_cell_type":"code"},"outputs":[],"execution_count":19},{"cell_type":"code","source":"#Accuracy\nfrom sklearn.metrics import accuracy_score\naccuracy=accuracy_score(y_pred, y_test)\nprint('LightGBM Model accuracy score: {0:0.4f}'.format(accuracy_score(y_test, y_pred)))","metadata":{"id":"D9J_HLf8E3AQ","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"71f6c11502da476fb8120c0b456f48dd","outputId":"548c5fac-a623-4e38-de3c-4d64bcbe96d0","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":4,"user_tz":240,"timestamp":1652654239488},"deepnote_cell_type":"code"},"outputs":[{"output_type":"stream","name":"stdout","text":"LightGBM Model accuracy score: 0.9591\n"}],"execution_count":20},{"cell_type":"markdown","source":"<a style='text-decoration:none;line-height:16px;display:flex;color:#5B5B62;padding:10px;justify-content:end;' href='https://deepnote.com?utm_source=created-in-deepnote-cell&projectId=4f9037d8-f2e9-4683-9ce3-67576f784ec7' target=\"_blank\">\n<img alt='Created in deepnote.com' style='display:inline;max-height:16px;margin:0px;margin-right:7.5px;' src='data:image/svg+xml;base64,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' > </img>\nCreated in <span style='font-weight:600;margin-left:4px;'>Deepnote</span></a>","metadata":{"created_in_deepnote_cell":true,"deepnote_cell_type":"markdown"}}],"nbformat":4,"nbformat_minor":0,"metadata":{"toc":{"sideBar":true,"nav_menu":{},"toc_cell":false,"title_cell":"Table of Contents","toc_position":{},"skip_h1_title":false,"title_sidebar":"Contents","base_numbering":1,"number_sections":true,"toc_window_display":false,"toc_section_display":true},"colab":{"name":"LightGBM - CoderHouse (Ejemplo 3).ipynb","provenance":[],"collapsed_sections":[]},"deepnote":{},"kernelspec":{"name":"python3","language":"python","display_name":"Python 3"},"varInspector":{"cols":{"lenVar":40,"lenName":16,"lenType":16},"kernels_config":{"r":{"library":"var_list.r","varRefreshCmd":"cat(var_dic_list()) ","delete_cmd_prefix":"rm(","delete_cmd_postfix":") "},"python":{"library":"var_list.py","varRefreshCmd":"print(var_dic_list())","delete_cmd_prefix":"del 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