{"cells":[{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\n# Creamos las categorias\nsexo=['M','F','Otro']\nestatura= ['<150cm','150-170cm','170-190cm','>190cm']\ncabello=['Corto','Mediano (hasta orejas)','Largo (supera hombros)']\ncolor=['Rojo','Rubio','Castaño','Negro','Otro']\ndelineador=['Si','No']","metadata":{"id":"otEDvDjUmvXP","cell_id":"8666752ffe9646b784babf8c4499ba61","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":676,"user_tz":180,"timestamp":1650200830363},"deepnote_cell_type":"code"},"outputs":[],"execution_count":1},{"cell_type":"code","source":"# Creacion de datos artificiales\nv_sexo=np.random.choice(sexo, 1000)\nv_estatura=np.random.choice(estatura, 1000)\nv_cabello=np.random.choice(cabello, 1000)\nv_color=np.random.choice(color, 1000)\n#v_delineador=np.random.choice(delineador, 100)","metadata":{"id":"7ydtJZItojkD","cell_id":"d656507654c44b7498b14c315a180df2","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":370,"user_tz":180,"timestamp":1650202610281},"deepnote_cell_type":"code"},"outputs":[],"execution_count":51},{"cell_type":"code","source":"# Creacion del dataframe\ndf=pd.DataFrame()\ndf['Sexo']=v_sexo \ndf['Estatura']=v_estatura\ndf['Cabello']=v_cabello\ndf['Color']=v_color\ndf['Delineador'] = np.where(((df['Sexo']=='F') & ((df['Estatura'] == '<150cm')|(df['Estatura'] == '150-170cm'))) | \n                            ((df['Sexo']=='M') & ((df['Estatura'] == '150-170cm')|(df['Estatura'] == '>190cm'))), \n                            'Si', 'No')\n\ndf.head()","metadata":{"id":"p6VEFNkdo9Qw","colab":{"height":206,"base_uri":"https://localhost:8080/"},"cell_id":"c05db57fe0c144808efbab8aee0b8dbd","outputId":"52365f3e-d01a-4be1-8673-164dfdb86cfc","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":317,"user_tz":180,"timestamp":1650203201139},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"   Sexo   Estatura                 Cabello  Color Delineador\n0  Otro  150-170cm                   Corto   Rojo         No\n1     M     >190cm  Largo (supera hombros)   Otro         Si\n2  Otro  170-190cm  Largo (supera hombros)  Negro         No\n3  Otro     >190cm  Largo (supera hombros)   Rojo         No\n4  Otro     >190cm                   Corto   Rojo         No","text/html":"\n  <div id=\"df-6125de20-97ab-4461-89c0-e9f1506c01f9\">\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>Sexo</th>\n      <th>Estatura</th>\n      <th>Cabello</th>\n      <th>Color</th>\n      <th>Delineador</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Otro</td>\n      <td>150-170cm</td>\n      <td>Corto</td>\n      <td>Rojo</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>M</td>\n      <td>&gt;190cm</td>\n      <td>Largo (supera hombros)</td>\n      <td>Otro</td>\n      <td>Si</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Otro</td>\n      <td>170-190cm</td>\n      <td>Largo (supera hombros)</td>\n      <td>Negro</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Otro</td>\n      <td>&gt;190cm</td>\n      <td>Largo (supera hombros)</td>\n      <td>Rojo</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Otro</td>\n      <td>&gt;190cm</td>\n      <td>Corto</td>\n      <td>Rojo</td>\n      <td>No</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6125de20-97ab-4461-89c0-e9f1506c01f9')\"\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 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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-6125de20-97ab-4461-89c0-e9f1506c01f9 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-6125de20-97ab-4461-89c0-e9f1506c01f9');\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":65}],"execution_count":65},{"cell_type":"code","source":"df.Delineador.value_counts()","metadata":{"id":"RXDNoybgvTFK","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"d23261efb00b4138896a2304993b1a7d","outputId":"5f256912-e634-4ad5-ec8e-cf8ee3ab5018","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":293,"user_tz":180,"timestamp":1650203203788},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"No    677\nSi    323\nName: Delineador, dtype: int64"},"metadata":{},"execution_count":66}],"execution_count":66},{"cell_type":"code","source":"# Split en train y test \nX= df.drop(columns='Delineador')\ny= df['Delineador']\n#from sklearn.preprocessing import OneHotEncoder\nX_new=pd.get_dummies(X)\nfrom sklearn.model_selection import train_test_split\nX_train, X_test, y_train, y_test = train_test_split(X_new, y, test_size=0.33, random_state=42)","metadata":{"id":"1ZXIdwnNpSQl","cell_id":"ab0eb90be9b84b3fb7cc433c3070e47c","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":253,"user_tz":180,"timestamp":1650203205846},"deepnote_cell_type":"code"},"outputs":[],"execution_count":67},{"cell_type":"code","source":"# Entrenar el arbol\nfrom sklearn import tree\nclf = tree.DecisionTreeClassifier()\nclf = clf.fit(X_train, y_train)","metadata":{"id":"WRX-jiX3p4Ac","cell_id":"39dc1ad8875d43789b5e6267288dda62","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":245,"user_tz":180,"timestamp":1650203207171},"deepnote_cell_type":"code"},"outputs":[],"execution_count":68},{"cell_type":"code","source":"y_train.unique()","metadata":{"id":"le6Pc08Mt66i","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"41920984329c4fd6b98ee0d11eae67a4","outputId":"fee2014b-cc37-4ed4-892e-dee50de7b719","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":424,"user_tz":180,"timestamp":1650203208663},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"array(['No', 'Si'], dtype=object)"},"metadata":{},"execution_count":69}],"execution_count":69},{"cell_type":"code","source":"# Graficando\nfrom matplotlib import pyplot as plt\nfig = plt.figure(figsize=(18,10))\n_ = tree.plot_tree(clf,feature_names=X_train.columns,  \n                   class_names=y_train.unique(),\n                   filled=True)","metadata":{"id":"ETaEXIoJsCl0","colab":{"height":575,"base_uri":"https://localhost:8080/"},"cell_id":"2858575b8a9849a8aab06e6e1a8852f5","outputId":"83eb645e-9971-4714-ef33-183d6be8b2a1","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":2163,"user_tz":180,"timestamp":1650203212250},"deepnote_cell_type":"code"},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1296x720 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}],"execution_count":70},{"cell_type":"code","source":"y_pred= clf.predict(X_test)","metadata":{"id":"s0ZsFW-dwS5E","cell_id":"8ce96f8523ae4e2e93042ce808b79748","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":266,"user_tz":180,"timestamp":1650203216439},"deepnote_cell_type":"code"},"outputs":[],"execution_count":71},{"cell_type":"code","source":"from sklearn.metrics import classification_report\nprint(classification_report(y_true=y_test,y_pred=y_pred))","metadata":{"id":"5zlgwJELvgQQ","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"4b53e2cf793e4b108523734bbb165c50","outputId":"5172813f-a307-482a-c71e-c845741509c2","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":254,"user_tz":180,"timestamp":1650203218078},"deepnote_cell_type":"code"},"outputs":[{"output_type":"stream","name":"stdout","text":"              precision    recall  f1-score   support\n\n          No       1.00      1.00      1.00       215\n          Si       1.00      1.00      1.00       115\n\n    accuracy                           1.00       330\n   macro avg       1.00      1.00      1.00       330\nweighted avg       1.00      1.00      1.00       330\n\n"}],"execution_count":72},{"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":{"colab":{"name":"Clase_1.ipynb","provenance":[],"authorship_tag":"ABX9TyO/4l4u10cO4wQ4TW9xkv49","collapsed_sections":[]},"deepnote":{},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"deepnote_notebook_id":"a4788b6f52d848ae946315184c115459","deepnote_execution_queue":[]}}