{"cells":[{"cell_type":"markdown","source":"Como no podía ser de otra manera, vamos a ver una implementación del algoritmo de Adaboost en nuestro famoso dataset de Iris :)","metadata":{"id":"LVDI1hvcEWE8","cell_id":"bfc708020ee849ada14ea79792f00ad7","deepnote_cell_type":"markdown"}},{"cell_type":"code","source":"from sklearn.ensemble import AdaBoostClassifier\nfrom sklearn import datasets\nfrom sklearn.model_selection import train_test_split\nfrom sklearn import metrics","metadata":{"id":"d_SUvBpGEWFJ","cell_id":"5ee21d994c5746898d3fd74f3183a49e","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":1311,"user_tz":240,"timestamp":1652653924649},"deepnote_cell_type":"code"},"outputs":[],"execution_count":1},{"cell_type":"code","source":"from sklearn.ensemble import AdaBoostRegressor","metadata":{"id":"t4jondJCIiG6","cell_id":"1891986b210543509130bc32e173d6dc","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":16,"user_tz":240,"timestamp":1652653924652},"deepnote_cell_type":"code"},"outputs":[],"execution_count":2},{"cell_type":"code","source":"#Cargamos la data!\niris = datasets.load_iris()\nX = iris.data\ny = iris.target","metadata":{"id":"P1MOgvRvEWFN","cell_id":"218d4cd090fd4b979286c5d418736dfc","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":16,"user_tz":240,"timestamp":1652653924653},"deepnote_cell_type":"code"},"outputs":[],"execution_count":3},{"cell_type":"code","source":"X","metadata":{"id":"SEB9EI_lEba3","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"0dbe895b2deb4fe28476911d06080a63","outputId":"61b16ed4-8301-4a50-cf3b-dd086fdcf91b","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":17,"user_tz":240,"timestamp":1652653924655},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"array([[5.1, 3.5, 1.4, 0.2],\n       [4.9, 3. , 1.4, 0.2],\n       [4.7, 3.2, 1.3, 0.2],\n       [4.6, 3.1, 1.5, 0.2],\n       [5. , 3.6, 1.4, 0.2],\n       [5.4, 3.9, 1.7, 0.4],\n       [4.6, 3.4, 1.4, 0.3],\n       [5. , 3.4, 1.5, 0.2],\n       [4.4, 2.9, 1.4, 0.2],\n       [4.9, 3.1, 1.5, 0.1],\n       [5.4, 3.7, 1.5, 0.2],\n       [4.8, 3.4, 1.6, 0.2],\n       [4.8, 3. , 1.4, 0.1],\n       [4.3, 3. , 1.1, 0.1],\n       [5.8, 4. , 1.2, 0.2],\n       [5.7, 4.4, 1.5, 0.4],\n       [5.4, 3.9, 1.3, 0.4],\n       [5.1, 3.5, 1.4, 0.3],\n       [5.7, 3.8, 1.7, 0.3],\n       [5.1, 3.8, 1.5, 0.3],\n       [5.4, 3.4, 1.7, 0.2],\n       [5.1, 3.7, 1.5, 0.4],\n       [4.6, 3.6, 1. , 0.2],\n       [5.1, 3.3, 1.7, 0.5],\n       [4.8, 3.4, 1.9, 0.2],\n       [5. , 3. , 1.6, 0.2],\n       [5. , 3.4, 1.6, 0.4],\n       [5.2, 3.5, 1.5, 0.2],\n       [5.2, 3.4, 1.4, 0.2],\n       [4.7, 3.2, 1.6, 0.2],\n       [4.8, 3.1, 1.6, 0.2],\n       [5.4, 3.4, 1.5, 0.4],\n       [5.2, 4.1, 1.5, 0.1],\n       [5.5, 4.2, 1.4, 0.2],\n       [4.9, 3.1, 1.5, 0.2],\n       [5. , 3.2, 1.2, 0.2],\n       [5.5, 3.5, 1.3, 0.2],\n       [4.9, 3.6, 1.4, 0.1],\n       [4.4, 3. , 1.3, 0.2],\n       [5.1, 3.4, 1.5, 0.2],\n       [5. , 3.5, 1.3, 0.3],\n       [4.5, 2.3, 1.3, 0.3],\n       [4.4, 3.2, 1.3, 0.2],\n       [5. , 3.5, 1.6, 0.6],\n       [5.1, 3.8, 1.9, 0.4],\n       [4.8, 3. , 1.4, 0.3],\n       [5.1, 3.8, 1.6, 0.2],\n       [4.6, 3.2, 1.4, 0.2],\n       [5.3, 3.7, 1.5, 0.2],\n       [5. , 3.3, 1.4, 0.2],\n       [7. , 3.2, 4.7, 1.4],\n       [6.4, 3.2, 4.5, 1.5],\n       [6.9, 3.1, 4.9, 1.5],\n       [5.5, 2.3, 4. , 1.3],\n       [6.5, 2.8, 4.6, 1.5],\n       [5.7, 2.8, 4.5, 1.3],\n       [6.3, 3.3, 4.7, 1.6],\n       [4.9, 2.4, 3.3, 1. ],\n       [6.6, 2.9, 4.6, 1.3],\n       [5.2, 2.7, 3.9, 1.4],\n       [5. , 2. , 3.5, 1. ],\n       [5.9, 3. , 4.2, 1.5],\n       [6. , 2.2, 4. , 1. ],\n       [6.1, 2.9, 4.7, 1.4],\n       [5.6, 2.9, 3.6, 1.3],\n       [6.7, 3.1, 4.4, 1.4],\n       [5.6, 3. , 4.5, 1.5],\n       [5.8, 2.7, 4.1, 1. ],\n       [6.2, 2.2, 4.5, 1.5],\n       [5.6, 2.5, 3.9, 1.1],\n       [5.9, 3.2, 4.8, 1.8],\n       [6.1, 2.8, 4. , 1.3],\n       [6.3, 2.5, 4.9, 1.5],\n       [6.1, 2.8, 4.7, 1.2],\n       [6.4, 2.9, 4.3, 1.3],\n       [6.6, 3. , 4.4, 1.4],\n       [6.8, 2.8, 4.8, 1.4],\n       [6.7, 3. , 5. , 1.7],\n       [6. , 2.9, 4.5, 1.5],\n       [5.7, 2.6, 3.5, 1. ],\n       [5.5, 2.4, 3.8, 1.1],\n       [5.5, 2.4, 3.7, 1. ],\n       [5.8, 2.7, 3.9, 1.2],\n       [6. , 2.7, 5.1, 1.6],\n       [5.4, 3. , 4.5, 1.5],\n       [6. , 3.4, 4.5, 1.6],\n       [6.7, 3.1, 4.7, 1.5],\n       [6.3, 2.3, 4.4, 1.3],\n       [5.6, 3. , 4.1, 1.3],\n       [5.5, 2.5, 4. , 1.3],\n       [5.5, 2.6, 4.4, 1.2],\n       [6.1, 3. , 4.6, 1.4],\n       [5.8, 2.6, 4. , 1.2],\n       [5. , 2.3, 3.3, 1. ],\n       [5.6, 2.7, 4.2, 1.3],\n       [5.7, 3. , 4.2, 1.2],\n       [5.7, 2.9, 4.2, 1.3],\n       [6.2, 2.9, 4.3, 1.3],\n       [5.1, 2.5, 3. , 1.1],\n       [5.7, 2.8, 4.1, 1.3],\n       [6.3, 3.3, 6. , 2.5],\n       [5.8, 2.7, 5.1, 1.9],\n       [7.1, 3. , 5.9, 2.1],\n       [6.3, 2.9, 5.6, 1.8],\n       [6.5, 3. , 5.8, 2.2],\n       [7.6, 3. , 6.6, 2.1],\n       [4.9, 2.5, 4.5, 1.7],\n       [7.3, 2.9, 6.3, 1.8],\n       [6.7, 2.5, 5.8, 1.8],\n       [7.2, 3.6, 6.1, 2.5],\n       [6.5, 3.2, 5.1, 2. ],\n       [6.4, 2.7, 5.3, 1.9],\n       [6.8, 3. , 5.5, 2.1],\n       [5.7, 2.5, 5. , 2. ],\n       [5.8, 2.8, 5.1, 2.4],\n       [6.4, 3.2, 5.3, 2.3],\n       [6.5, 3. , 5.5, 1.8],\n       [7.7, 3.8, 6.7, 2.2],\n       [7.7, 2.6, 6.9, 2.3],\n       [6. , 2.2, 5. , 1.5],\n       [6.9, 3.2, 5.7, 2.3],\n       [5.6, 2.8, 4.9, 2. ],\n       [7.7, 2.8, 6.7, 2. ],\n       [6.3, 2.7, 4.9, 1.8],\n       [6.7, 3.3, 5.7, 2.1],\n       [7.2, 3.2, 6. , 1.8],\n       [6.2, 2.8, 4.8, 1.8],\n       [6.1, 3. , 4.9, 1.8],\n       [6.4, 2.8, 5.6, 2.1],\n       [7.2, 3. , 5.8, 1.6],\n       [7.4, 2.8, 6.1, 1.9],\n       [7.9, 3.8, 6.4, 2. ],\n       [6.4, 2.8, 5.6, 2.2],\n       [6.3, 2.8, 5.1, 1.5],\n       [6.1, 2.6, 5.6, 1.4],\n       [7.7, 3. , 6.1, 2.3],\n       [6.3, 3.4, 5.6, 2.4],\n       [6.4, 3.1, 5.5, 1.8],\n       [6. , 3. , 4.8, 1.8],\n       [6.9, 3.1, 5.4, 2.1],\n       [6.7, 3.1, 5.6, 2.4],\n       [6.9, 3.1, 5.1, 2.3],\n       [5.8, 2.7, 5.1, 1.9],\n       [6.8, 3.2, 5.9, 2.3],\n       [6.7, 3.3, 5.7, 2.5],\n       [6.7, 3. , 5.2, 2.3],\n       [6.3, 2.5, 5. , 1.9],\n       [6.5, 3. , 5.2, 2. ],\n       [6.2, 3.4, 5.4, 2.3],\n       [5.9, 3. , 5.1, 1.8]])"},"metadata":{},"execution_count":4}],"execution_count":4},{"cell_type":"code","source":"y","metadata":{"id":"dGkO2ThlEcTa","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"39afea741c5f474a86abbb0241f2825e","outputId":"b724e1b3-2d2a-4418-9a19-52f6cafd5e8b","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":287,"user_tz":240,"timestamp":1652653929722},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"},"metadata":{},"execution_count":5}],"execution_count":5},{"cell_type":"code","source":"#Separación en Train y Test\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3) # 70% training and 30% test","metadata":{"id":"FeFpeDTtEWFP","cell_id":"52089237237f472b9cd40d55402e9663","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":6,"user_tz":240,"timestamp":1652653930916},"deepnote_cell_type":"code"},"outputs":[],"execution_count":6},{"cell_type":"code","source":"# Creamos un AdaBoostClassifier\nada = AdaBoostClassifier(n_estimators=50,learning_rate=0.1)","metadata":{"id":"fJDauD8aEWFR","cell_id":"8ebef14a9fd1420084f70a740183f49d","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":3,"user_tz":240,"timestamp":1652653931274},"deepnote_cell_type":"code"},"outputs":[],"execution_count":7},{"cell_type":"code","source":"#Fiteamos el modelo\nmodel = ada.fit(X_train, y_train)","metadata":{"id":"5VQYJc8REWFS","cell_id":"b648f9437a34486f88aa12f934ffc4b4","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":4,"user_tz":240,"timestamp":1652653931893},"deepnote_cell_type":"code"},"outputs":[],"execution_count":8},{"cell_type":"code","source":"#Predicción!\ny_pred = model.predict(X_test)","metadata":{"id":"UIO1tDtBEWFV","cell_id":"839344ce847542e6b8b7f7d61568f9d7","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":4,"user_tz":240,"timestamp":1652653932562},"deepnote_cell_type":"code"},"outputs":[],"execution_count":9},{"cell_type":"code","source":"y_pred","metadata":{"id":"3Vlmf1tPTkmL","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"8d0488cc95dd4c178c097baedc1a1cfc","outputId":"329b0c9f-6603-4103-faef-416012dd5c02","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":6,"user_tz":240,"timestamp":1652653932847},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"array([2, 0, 0, 1, 2, 0, 0, 2, 1, 1, 0, 1, 2, 2, 0, 0, 1, 2, 1, 0, 0, 0,\n       0, 1, 1, 1, 1, 1, 2, 1, 1, 0, 0, 1, 2, 0, 2, 1, 1, 0, 2, 2, 2, 2,\n       2])"},"metadata":{},"execution_count":10}],"execution_count":10},{"cell_type":"code","source":"#Accuracy!\nprint(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred))","metadata":{"id":"ssTrv_wnEWFX","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"72fb546d612b4f1e8428a7d36caef345","outputId":"9e8723d8-3bfe-48d5-a594-17b16cfe8777","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":5,"user_tz":240,"timestamp":1652653934249},"deepnote_cell_type":"code"},"outputs":[{"output_type":"stream","name":"stdout","text":"Accuracy: 0.9555555555555556\n"}],"execution_count":11},{"cell_type":"code","source":"from sklearn.metrics import classification_report\nprint(classification_report(y_test,y_pred))","metadata":{"id":"l2nom3vPIs5T","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"6f64ffa8b9c74b12b010c550c883be24","outputId":"51930863-4f29-4973-c424-7a04bd060885","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":5,"user_tz":240,"timestamp":1652653934820},"deepnote_cell_type":"code"},"outputs":[{"output_type":"stream","name":"stdout","text":"              precision    recall  f1-score   support\n\n           0       1.00      1.00      1.00        15\n           1       0.88      1.00      0.93        14\n           2       1.00      0.88      0.93        16\n\n    accuracy                           0.96        45\n   macro avg       0.96      0.96      0.96        45\nweighted avg       0.96      0.96      0.96        45\n\n"}],"execution_count":12},{"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":"Adaboost - CoderHouse (Ejemplo 1).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 ","delete_cmd_postfix":""}},"window_display":false,"types_to_exclude":["module","function","builtin_function_or_method","instance","_Feature"]},"language_info":{"name":"python","version":"3.8.5","mimetype":"text/x-python","file_extension":".py","pygments_lexer":"ipython3","codemirror_mode":{"name":"ipython","version":3},"nbconvert_exporter":"python"},"deepnote_notebook_id":"ad70077d77d24253a713d3725e98b7aa","deepnote_execution_queue":[]}}