{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyNmHgoV+wimoDpo9xMpFxxn"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","execution_count":4,"metadata":{"id":"BKOKO-XTh5pQ","executionInfo":{"status":"ok","timestamp":1710613913382,"user_tz":-420,"elapsed":312,"user":{"displayName":"Nhật Quang Đoàn","userId":"10175964550021301622"}}},"outputs":[],"source":["import numpy as np\n","from sklearn import datasets\n","\n","from sklearn.ensemble import RandomForestRegressor\n","from sklearn.model_selection import train_test_split\n","from sklearn.feature_selection import RFECV\n","import matplotlib.pyplot as plt\n","X,y = datasets.load_iris(return_X_y=True)"]},{"cell_type":"code","source":["X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.33, random_state=42)\n","rf = RandomForestRegressor(random_state=0)\n","\n","rf.fit(X_train,y_train)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":74},"id":"rznJTheliIWz","executionInfo":{"status":"ok","timestamp":1710613937025,"user_tz":-420,"elapsed":767,"user":{"displayName":"Nhật Quang Đoàn","userId":"10175964550021301622"}},"outputId":"0ac70b72-7115-4a8d-820c-5e9d680a75d4"},"execution_count":6,"outputs":[{"output_type":"execute_result","data":{"text/plain":["RandomForestRegressor(random_state=0)"],"text/html":["<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(random_state=0)</pre></div></div></div></div></div>"]},"metadata":{},"execution_count":6}]},{"cell_type":"code","source":["features = datasets.load_iris()['feature_names']\n","f_i = list(zip(features,rf.feature_importances_))\n","f_i.sort(key = lambda x : x[1])\n","plt.barh([x[0] for x in f_i],[x[1] for x in f_i])\n","\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":430},"id":"LObPxQ3iiTEH","executionInfo":{"status":"ok","timestamp":1710613975011,"user_tz":-420,"elapsed":671,"user":{"displayName":"Nhật Quang Đoàn","userId":"10175964550021301622"}},"outputId":"9f2c8bbd-f987-40e2-f5fc-443fa3ed0262"},"execution_count":9,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["rfe = RFECV(rf,cv=5,scoring=\"neg_mean_squared_error\")\n","\n","rfe.fit(X_train,y_train)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":117},"id":"jX0hshgZihdE","executionInfo":{"status":"ok","timestamp":1710614007387,"user_tz":-420,"elapsed":10834,"user":{"displayName":"Nhật Quang Đoàn","userId":"10175964550021301622"}},"outputId":"0fadff12-6668-4a49-e48f-0fbcfb667bc1"},"execution_count":10,"outputs":[{"output_type":"execute_result","data":{"text/plain":["RFECV(cv=5, estimator=RandomForestRegressor(random_state=0),\n","      scoring='neg_mean_squared_error')"],"text/html":["<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RFECV(cv=5, estimator=RandomForestRegressor(random_state=0),\n","      scoring=&#x27;neg_mean_squared_error&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RFECV</label><div class=\"sk-toggleable__content\"><pre>RFECV(cv=5, estimator=RandomForestRegressor(random_state=0),\n","      scoring=&#x27;neg_mean_squared_error&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(random_state=0)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(random_state=0)</pre></div></div></div></div></div></div></div></div></div></div>"]},"metadata":{},"execution_count":10}]},{"cell_type":"code","source":["selected_features = np.array(features)[rfe.get_support()]\n","print(selected_features)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"1NKgSoPMilpD","executionInfo":{"status":"ok","timestamp":1710614025232,"user_tz":-420,"elapsed":274,"user":{"displayName":"Nhật Quang Đoàn","userId":"10175964550021301622"}},"outputId":"5b451947-55b1-4d50-dffd-6ec1437f7c7c"},"execution_count":12,"outputs":[{"output_type":"stream","name":"stdout","text":["['sepal width (cm)' 'petal length (cm)' 'petal width (cm)']\n"]}]}]}