{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### 评价指标\n", "- 参考百度百科,对于概念意义的理解有帮助 https://baike.baidu.com/item/ROC%E6%9B%B2%E7%BA%BF/775606?fr=aladdin\n", "- 机器学习之分类器性能指标之ROC曲线、AUC值 https://www.cnblogs.com/dlml/p/4403482.html\n", "- 4.4.2分类模型评判指标(一) - 混淆矩阵(Confusion Matrix) https://blog.csdn.net/Orange_Spotty_Cat/article/details/80520839\n", "- 准确率(accuracy)、召回率(recall)的意义和区别 https://blog.csdn.net/liu123641191/article/details/80364334" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "下面是个人的一个理解,方便记忆:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![evaluation](https://raw.githubusercontent.com/LittleSix1/img_hub/master/evaluation/%E8%AF%84%E4%BB%B7%E6%8C%87%E6%A0%87.png)" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [], "source": [ "from sklearn.datasets import load_iris\n", "import numpy as np\n", "import pandas as pd\n", "\n", "from sklearn.model_selection import train_test_split\n", "import matplotlib.pyplot as plt \n", "\n", "\n", "#model\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.svm import SVC\n", "from sklearn.tree import DecisionTreeClassifier\n", "from sklearn.ensemble import RandomForestClassifier\n", "from xgboost import XGBClassifier\n", "\n", "# evulatuon\n", "from sklearn.metrics import precision_score\n", "from sklearn.metrics import roc_curve\n", "from sklearn.metrics import recall_score\n", "from sklearn.metrics import f1_score\n", "from sklearn.metrics import roc_auc_score" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [], "source": [ "iris = load_iris()\n", "x = iris.data[:,:2] \n", "y = iris.target\n", "#为了构造一个二分类,将1 2类划分成非0类 \n", "y[np.where(y>0)] = 1\n", "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size= 0.3)" ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "D:\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n", " \"avoid this warning.\", FutureWarning)\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#用单个模型 做ROC曲线\n", "\n", "model =SVC(probability=True)\n", "model.fit(x_train,y_train)\n", "test_pred = model.predict_proba(x_test)[:,1]\n", "\n", "train_auc = roc_auc_score(y_test,test_pred)\n", "fpr,tpr,ther = roc_curve(y_test,test_pred)\n", "\n", "plt.figure()\n", "lw = 2\n", "\n", "plt.plot(fpr, tpr, color='darkorange',\n", " lw=lw, label='ROC curve (area = %0.2f)' % test_auc)\n", "\n", "plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n", "plt.xlim([0.0, 1.0])\n", "plt.ylim([0.0, 1.05])\n", "plt.xlabel('False Positive Rate')\n", "plt.ylabel('True Positive Rate')\n", "plt.title('Receiver operating characteristic example')\n", "plt.legend(loc=\"lower right\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [], "source": [ "models = [LogisticRegression(),SVC(probability=True),DecisionTreeClassifier(),RandomForestClassifier(),XGBClassifier()]\n", "names = [\"LR\",\"SVC\", 'DT', \"RF\",\"Xgb\"]" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "LR 训练集: accuracy:0.99,precision:0.985, recall:1.0, f1:0.993, auc:0.999\n", "LR 测试集: accuracy:1.0,precision:1.0, recall:1.0, f1:1.0, auc:1.0\n", "\n", "\n", "SVC 训练集: accuracy:1.0,precision:1.0, recall:1.0, f1:1.0, auc:1.0\n", "SVC 测试集: accuracy:1.0,precision:1.0, recall:1.0, f1:1.0, auc:1.0\n", "\n", "\n", "DT 训练集: accuracy:1.0,precision:1.0, recall:1.0, f1:1.0, auc:1.0\n", "DT 测试集: accuracy:0.956,precision:0.97, recall:0.97, f1:0.97, auc:0.943\n", "\n", "\n", "RF 训练集: accuracy:0.99,precision:1.0, recall:0.985, f1:0.992, auc:1.0\n", "RF 测试集: accuracy:0.911,precision:1.0, recall:0.879, f1:0.935, auc:1.0\n", "\n", "\n", "Xgb 训练集: accuracy:0.99,precision:0.985, recall:1.0, f1:0.993, auc:1.0\n", "Xgb 测试集: accuracy:0.956,precision:0.97, recall:0.97, f1:0.97, auc:0.995\n", "\n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "D:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n", " FutureWarning)\n", "D:\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n", " \"avoid this warning.\", FutureWarning)\n" ] } ], "source": [ "for name,model in zip(names,models):\n", " model.fit(x_train,y_train)\n", " \n", " y_train_pred = model.predict(x_train)\n", " y_test_pred = model.predict(x_test)\n", " \n", " #accuracyff\n", " train_accuracy = model.score(x_train,y_train)\n", " test_accuracy = model.score(x_test,y_test)\n", " #precision \n", " train_precision = precision_score(y_train,y_train_pred)\n", " test_precision = precision_score(y_test,y_test_pred) \n", " #recall\n", " train_recall = recall_score(y_train,y_train_pred)\n", " test_recall = recall_score(y_test,y_test_pred) \n", " #f1\n", " train_f1 = f1_score(y_train,y_train_pred)\n", " test_f1 = f1_score(y_test,y_test_pred) \n", " #auc 计算时,计算的应该是不同的概率画出来的曲线下的面积,而不是预测值对应的曲线下的面积\n", " #预测值 分类模型,应该全是0 或者 1 ,但是概率是类似于得分一样的值\n", " #根据资料貌似两种都行,都可以作为阈值来进行ROC曲线的绘制\n", " y_train_pred = model.predict_proba(x_train)[:,1]\n", " y_test_pred = model.predict_proba(x_test)[:,1]\n", " \n", " train_auc = roc_auc_score(y_train,y_train_pred)\n", " test_auc = roc_auc_score(y_test,y_test_pred)\n", " print('{} 训练集: accuracy:{:.3},precision:{:.3}, recall:{:.3}, f1:{:.3}, auc:{:.3}'.format(name,train_accuracy,train_precision,train_recall,train_f1,train_auc))\n", " print('{} 测试集: accuracy:{:.3},precision:{:.3}, recall:{:.3}, f1:{:.3}, auc:{:.3}'.format(name,test_accuracy,test_precision,test_recall,test_f1,test_auc))\n", " print('\\n')" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [], "source": [ "def draw_roc_curve(train_pre_proba,test_pre_proba,train_auc,test_auc,name):\n", " fpr,tpr,roc_auc = train_pre_proba\n", " test_fpr,test_tpr,test_roc_auc = test_pre_proba\n", " \n", " plt.figure()\n", " lw = 2\n", " plt.plot(fpr, tpr, color='darkorange',\n", " lw=lw, label='ROC curve (area = %0.2f)' % train_auc)\n", " plt.plot(test_fpr, test_tpr, color='red',\n", " lw=lw, label='ROC curve (area = %0.2f)' %test_auc)\n", " plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n", " plt.xlim([0.0, 1.0])\n", " plt.ylim([0.0, 1.05])\n", " plt.xlabel('False Positive Rate')\n", " plt.ylabel('True Positive Rate')\n", " plt.title('ROC example '+name)\n", " plt.legend(loc=\"lower right\")\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "D:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n", " FutureWarning)\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "D:\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n", " \"avoid this warning.\", FutureWarning)\n" ] }, { "data": { "image/png": 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\n", 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\n", 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\n", 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z5ncuXIilSJE87N17kpCQwt4OLVOzRGGMyRGWLPmLfv2+Y/fu4wA8+GAtxo1rRZEiNjRBajz6ZLaItBWRHSKyW0SGJvF5WRFZJiLrRGSjiLTzZDzGmJxHVXnwwfm0bj2b3buPU7VqMZYv7837799hScJNHruiEBEfYCrQCggHVovIAlXdmmCxYcDnqjpNRKoC3wHBnorJGJPziAjBwYXIk8eXF19syuDBDbN1AT9P8GTTU31gt6ruARCROcAdQMJEocDFm5QLAv96MB5jTA6xfv1/HDx4mttuc25xffbZRvTsGWp9EVfJk01PpYD9CabDXfMSGgncJyLhOFcTTyS1IRF5VETCRCTME4EaY7KH06ejGDx4MXXqzOD++7/m+PHzAPj7+1qSuAaeTBRJ3W+W+F7c7sBMVS0NtAP+T0SuiElVZ6hq3au9tcsYk72pKvPmbaNq1beZOHElAPfeW4PcubNmgezMxpNNT+FAmQTTpbmyaekhoC2Aqq4QkQCgKHDYg3EZY7KRf/45Sf/+i1i4cCcAdetezzvvdOCmm0p6ObLsw5PpdjVQUURCRMQP6AYsSLTMPqAlgIhUAQKAIx6MyRiTjagqXbp8zsKFOylQwJ8pU25j5cqHLEmkM49dUahqjIj0BxYDPsAHqrpFREYBYaq6AHgKeFdEBuE0S/XWrPaouDEmw8XFKblyCSLC+PGtmT49jIkT21CyZH5vh5YtWQkPY0yWcezYOYYOXQrAu+929HI0Wcu1lPCwnh5jTKanqnz00XoqV57Ke++tY9asjYSHn/J2WDmGlfAwxmRq27YdoW/fb/nll38AaNYsmGnT2lO6tI0TkVEsURhjMiVV5cUXlzF27O9ER8dRtGggb7zRmp49Q3NEtdfMxBKFMSZTEhEOHDhNdHQcjzxyE2PG3EpQUB5vh5UjWWe2MSbT+Pff0xw9eo7Q0BIAHD16jh07jtKoUVkvR5b1WWe2MSZLi42NY8qUVVSpMpVu3eZy4UIsAEWLBlqSyASs6ckY41Vr1x7ksccWEhbmFG5o0uQGTp2KomhRKwGeWbiVKFxPVpdV1d0ejscYk0OcOhXF8OE/MWXKauLilNKlCzB5cls6dapsndWZTKqJQkTaAxMAPyBERGoBI1T1Tk8HZ4zJnlSVJk0+ZMOGQ/j4CIMHN2DkyGbkz+/v7dBMEtzpoxgF3AycBFDV9UAFTwZljMneRIRBgxpQv34pwsIe5Y032liSyMTcaXqKVtWTiS4Fs9atUsYYr7pwIZYJE1bg4yMMGdIIgF69anLffaH4+Ng9NZmdO4lim4jcA+QSkRDgSWClZ8MyxmQXv/76D336fMvWrUfw9/ehV6+alCiRDxHBx8f6IrICd1J5f6AOEAd8BUTiJAtjjEnW0aPnePDB+TRpMpOtW49QsWIQCxfeS4kS+bwdmkkjd64o2qjqs8CzF2eISGecpGGMMZdRVWbOXM+QIUs4duw8fn4+PPdcY4YObUxAgN2RnxW5c0UxLIl5L6R3IMaY7GP27E0cO3aeFi1C2LixDyNHNrMkkYUl+39ORNrgDFNaSkQmJPioAE4zlDHGAHDuXDQREZGULJkfEeHtt9uxevW/9OhRw56JyAZSSvGHgc04fRJbEsw/DQz1ZFDGmKxj0aJdPP74d5QrV5glS3oiIlSqVJRKlYp6OzSTTpJNFKq6DlgnIh+ramQGxmSMyQIOHDjFwIGLmTt3KwD58/tz7Nh5K72RDbnTaFhKRF4BqgIBF2eq6o0ei8oYk2nFxsYxdepqhg37idOnL5A3b25GjWrOgAE34+trz0RkR+4kipnAaGA8cBvwANZHYUyOFBenNG06k99/3w9Ap06VmTSpLWXLFvRyZMaT3En/gaq6GEBV/1LVYUBzz4ZljMmMcuUSWrcuT5kyBZg/vxvz5nW1JJEDuHNFESXObQt/iUgf4ABQ3LNhGWMyA1Xl88+34Oubiy5dqgLw7LONGDy4Ifny+Xk5OpNR3EkUg4B8wADgFaAg8KAngzLGeN9ffx2nX7/v+OGHvyhWLJAWLUIoXDgP/v6++Fv9vhwl1UShqn+63p4GegKISGlPBmWM8Z6oqBhef/0PXnnlVyIjYyhcOIBXXmlBwYIBqa9ssqUUE4WI1ANKAb+p6lERqYZTyqMFYMnCmGzm55/30rfvt2zffhSAnj1DGT++NcWL5/VyZMabku3MFpHXgI+BHsD3IvICsAzYANitscZkM7GxcfTr5ySJSpWK8NNPvZg1605LEibFK4o7gJqqel5EgoB/XdM7MiY0Y4ynxcUpkZExBAbmxscnF9OmtWf58n945plG+PtbbSbjSOkvIVJVzwOo6nER2W5JwpjsY9OmQ/Tp8y2VKxfh/ffvAKBp02CaNg32bmAm00kpUZQTkYulxAUITjCNqnb2aGTGGI84e/YCo0b9woQJK4mJiePvv09w4sR5ChfO4+3QTCaVUqLokmh6iicDMcZ43jff7KB//0Xs2xeBCPTrV5dXXmlJoUJ2R5NJXkpFAX/MyECMMZ4TExNH165z+eqrbQDUqnUd77zTgfr1S3k5MpMVWG+VMTmAr28uChb0J18+P15+uTn9+9e3An7GbaKqntu4SFtgEuADvKeqY5JY5h5gJKDABlW9N6Vt1hXRsKNHoUgRD0RsTPbx55/hANx8s/PI07Fj5zh/PobSpQt4MyzjJSKyRlXrXs26bl9RiIi/qkalYXkfYCrQCggHVovIAlXdmmCZisBzQCNVPSEiVkPKmGt08mQkzz23lHfeWUPlykVZv74Pfn4+FCli40SYq5PqtaeI1BeRTcAu13RNEXnLjW3XB3ar6h5VvQDMwXk2I6FHgKmqegJAVQ+nKXpjTDxV5ZNPNlG58hSmT1+Dj08uOnasRGysjQpgro07VxSTgQ7A1wCqukFE3CkzXgrYn2A6HLg50TI3AojI7zjNUyNV9Xs3tm2MSWDXrmP06/cdS5fuAaBRozJMn96B6tXtIt1cO3cSRS5V/SfRAOmxbqyX1IjqiTtEfIGKQDOc2lG/ikh1VT152YZEHgUeBajjxo6NyUmio2Np0WIW4eGnCArKw7hxt/LAA7XJlSupf4LGpJ07iWK/iNQH1NXv8ASw0431woEyCaZL45QBSbzMSlWNBv4WkR04iWN1woVUdQYwA5zObDf2bUy2p6qICLlz+/DKKy1Ytmwv48bdSrFiVpvJpC937o/rCwwGygKHgAauealZDVQUkRAR8QO6AQsSLfM1rtHyRKQoTlPUHvdCNyZnOnToDD17zmP06OXx83r1qsmHH95hScJ4hDtXFDGq2i2tG1bVGBHpDyzG6X/4QFW3iMgoIExVF7g+ay0iW3Gas4ao6rG07suYnCAuTnn33TUMHfojJ09GUqhQAAMHNiB/fhtFyHhWqs9RiMhfwA7gM+ArVT2dEYElx56jMDnRhg3/0afPt6xc6Twb0bZtBaZObUe5coW9HJnJKjz6HIWqlheRW3Cajl4SkfXAHFWdczU7NMa4Lzo6luee+5E331xJbKxSsmQ+Jk1qy113VSXRDSbGeIxbz/Cr6h+qOgC4CTiFM6CRMcbDfH1zsW7df8TFKU88UZ9t2x7n7rurWZIwGSrVKwoRyYfzoFw3oAowH7jFw3EZk2Pt2xdBbGwcISGFERGmT29PREQUdete7+3QTA7lTmf2ZuAbYJyq/urheIzJsaKjY5k06U9GjPiZhg1Ls2RJT0SEihWtP854lzuJopyqWg0AYzxoxYr99OnzLRs3HgIgKCgP585Fkzevn5cjMyaFRCEib6jqU8CXksRDbjbCnTHX7sSJ8wwdupQZM9YCEBJSiKlT23HbbRW9HJkxl6R0RfGZ6782sp0xHhAVFUOtWu+wb18EuXPnYsiQW3jhhSYEBub2dmjGXCalEe5Wud5WUdXLkoXrQTobAc+Ya+Dv78tDD9Xmxx//Ztq09lStWszbIRmTJHceuFurqjclmrdOVWt7NLJk2AN3JquKjIzhtdd+pVKlotx7bw3AGaLUx0fsdlfjcR554E5EuuLcEhsiIl8l+Cg/cDLptYwxSVmy5C/69fuO3buPU7x4Xu68szJ58uS24UhNlpBSH8Uq4BhO1depCeafBtZ5Mihjsov//jvD4MGL+fTTzQBUq1aM6dM7kCeP9UOYrCOlPoq/gb+BpRkXjjHZQ2xsHO+8s4bnn/+RiIgo8uTxZcSIpgwa1BA/Px9vh2dMmqTU9PSLqjYVkRNcPuCQAKqqQR6PzpgsKjZWeeutVURERNGuXUWmTLmNkBAr4GeyppSani4Od1o0IwIxJqs7fTqK2FilUKEA/Px8ePfd2zl06AydO1exzmqTpSXbk5bgaewygI+qxgINgccAGx3FGBdV5auvtlGlylSeempx/PzGjcvSpYtVeTVZnzu3XHyNMwxqeWAWTmHATzwalTFZxN69J+nYcQ5dunzOgQOn2bz5CJGRMd4Oy5h05U6iiHONad0ZeFNVnwBKeTYsYzK36OhYxo79japVp7Jw4U4KFPBnypTb+OOPBwkIcKeEmjFZh1tDoYrI3UBPoJNrnt3bZ3Ksc+eiadDgPTZtOgxAt27VmTChNSVL5vdyZMZ4hjuJ4kGgH06Z8T0iEgJ86tmwjMm8AgNzU7fu9Zw7F83bb7endevy3g7JGI9KtYQHgIj4AhVck7tV1WuNsFbCw2Q0VWXWrA2ULx9E48ZlAYiIiMTPz8cenDNZhkfHzBaR/wH/BxzAeYbiOhHpqaq/X80OjclKtm07Qt++3/LLL/9QpUpR1q/vg5+fDwULBng7NGMyjDtNTxOBdqq6FUBEquAkjqvKTMZkBefPR/PKK78ybtzvREfHUaxYIM8915jcua02k8l53EkUfheTBICqbhMRG3bLZFvff7+bxx//jj17TgDwyCM3MWbMrQQF5fFyZMZ4hzuJYq2IvINzFQHQAysKaLKpM2cu0LPnPI4ePUf16sWZPr09jRqV9XZYxniVO4miDzAAeAanj2I58JYngzImI8XGxhEXp+TO7UO+fH5MmtSW8PBTDBrUgNy5rYCfMSne9SQiNYDywBZV3ZVhUaXA7noy6WnNmn957LGF3HFHJYYPb+rtcIzxmGu56ynZnjkReR6nfEcPYImIPHiV8RmT6Zw6FcWTTy6ifv33WLPmIP/3fxuJjo71dljGZEopNT31AEJV9ayIFAO+Az7ImLCM8QxVZe7crTz55PccPHgGHx9h8OAGvPRSc2tmMiYZKSWKKFU9C6CqR0TE7gs0Wdrp01F07TqXRYt2A3DzzaWYPr0DtWpd5+XIjMncUkoU5RKMlS1A+YRjZ6tqZ49GZkw6y5fPj6ioWAoW9GfMmFt59NE65MplJcCNSU1KiaJLoukpngzEGE9YvvwfSpbMR8WKRRARPvigIwEBvpQokc/boRmTZaQ0ZvaPGRmIMenp6NFzPPPMEj78cD0tW4awZElPRIQbbijk7dCMyXKscL7JVuLilJkz1zNkyBKOHz+Pn58P//tfWWJjFV9fa2Yy5mp4tINaRNqKyA4R2S0iQ1NY7i4RURGx+lHmqm3ZcphmzWby0EMLOH78PC1bhrBpU19GjGiGr6/di2HM1XL7ikJE/FU1Kg3L+wBTgVZAOLBaRBYkrBvlWi4/zpPff7q7bWMSi4iIpEGD9zlz5gLFi+dlwoTW3HtvDRuv2ph0kOrPLBGpLyKbgF2u6Zoi4k4Jj/o4Y1fsUdULwBzgjiSWexkYB0S6H7YxjouVBQoWDODZZxvRp08dtm9/nB49Qi1JGJNO3Lkenwx0AI4BqOoGoLkb65UC9ieYDifRWNsiUhsoo6oLU9qQiDwqImEiEubGfk0OcODAKe6663Nmz94YP++FF/7HtGkdKFzYqrwak57cSRS5VPWfRPPcqXWQ1M+5+MJSrgf4JgJPpbYhVZ2hqnWvtk6JyT5iYuKYNGkllStP5csvtzFixM/ExsYB2BWEMR7iTh/FfhGpD6ir3+EJYKcb64UDZRJMlwb+TTCdH6gO/Oz6B34dsEBEOqqqXTmYK6xefYA+fb5l7dqDAHTqVJnJk9vi42Md1cZ4kjuJoi9O81NZ4BCw1DUvNauBiiISgjOMajfg3osfqmoEUPTitIj8DDwTPQwfAAAe2UlEQVRtScIkdvbsBZ59dilvv70aVShbtiBvvXUbHTtW8nZoxuQIqSYKVT2Mc5JPE1WNEZH+wGLAB/hAVbeIyCggTFUXpDlakyP5+uZi6dI95MolDB7ckBEjmpI3rw2yaExGSXE8CgAReZcEfQsXqeqjngoqJTYeRc7w11/HKVQogCJFAgGn2SkgwJcaNUp4OTJjsiaPjEeRwFLgR9frd6A44PbzFMakRVRUDKNHL6d69Wk8++zS+Pn16pWyJGGMl7jT9PRZwmkR+T9gicciMjnWzz/vpW/fb9m+/Sjg3OEUGxtnndXGeNnV1HoKAW5I70BMznX48FmGDFnCrFkbAKhUqQjTprWnefMQL0dmjAE3EoWInOBSH0Uu4DiQbN0mY9Li6NFzVKkylePHz+Pv78MLL/yPZ55phL+/1as0JrNI8V+jOA841MS5vRUgTlPr/TYmDYoWDeSOOyoRHn6Kt99uT4UKQd4OyRiTiDt3Pa1R1ToZFE+q7K6nrO3s2QuMGvUL7dvfSJMmTgtmZGQM/v4+9mS1MR7k6bueVonITVezcWMS+uabHVSt+jbjxv1Bv37fEhfn/EgJCPC1JGFMJpZs05OI+KpqDNAYeERE/gLO4tRwUlW15GHcsn9/BE8++T3z5m0HoHbt63jnnQ42XrUxWURKfRSrgJuAThkUi8lmYmLimDz5T158cRlnz0aTL58fo0c35/HH69tAQsZkISklCgFQ1b8yKBaTzZw6FcVrr/3G2bPRdOlShTffbEvp0gW8HZYxJo1SShTFRGRwch+q6gQPxGOyuJMnI8mTxxd/f1+CgvLwzjsd8Pf3oX37G70dmjHmKqV0/e8D5MMpB57Uy5h4qsonn2yiUqUpjBv3e/z8zp2rWJIwJotL6YrioKqOyrBITJa1c+cx+vX7lh9//BuA5cv3oap2J5Mx2USqfRTGJCcyMoaxY3/j1Vd/48KFWIKC8vD6663o3buWJQljspGUEkXLDIvCZDn//XeGJk0+ZNeu4wD07l2L119vRdGigV6OzBiT3pJNFKp6PCMDMVlLiRJ5KVOmIL6+uZg2rT1NmwZ7OyRjjIdY5TXjlrg45d1319C8eQg33lgEEeGTTzpTuHAe/Px8vB2eMcaD7Kknk6oNG/6jUaMP6NPnW/r1+5aL9cFKlMhnScKYHMCuKEyyzpy5wMiRP/PmmyuJjVWuvz4/ffpcVU0xY0wWZonCJOnrr7fzxBOLCA8/Ra5cwhNP1Gf06BYUKODv7dCMMRnMEoW5woEDp+jWbS5RUbHUqVOS6dM7ULfu9d4OyxjjJZYoDADR0bH4+uZCRChVqgCvvNICPz8f+vWrZ2NWG5PD2RnA8Mcf+6lTZwazZ2+Mn/fUU7fwxBM3W5IwxliiyMmOHz/PY499Q6NGH7Bp02HefjsMG+nWGJOYNT3lQKrK7NkbeeqpHzhy5By5c+fimWca8cIL/7PSG8aYK1iiyGEOHTpD9+5fsmzZXgCaNr2BadPaU6VKMe8GZozJtCxR5DCFCgVw8OAZihYNZPz4VvTqVdOuIowxKbJEkQMsWfIXN91UkiJFAvH39+WLL+6mZMl8FCliBfyMMamzzuxs7ODB03Tv/iWtW8/m2WeXxs+vXr24JQljjNvsiiIbio2N45131vDccz9y6lQUefL4UqlSERtMyBhzVSxRZDNr1x6kT5+FrF79LwDt21dkypR2BAcX8nJkxpisyhJFNrJ370nq13+X2FilVKn8TJ58G3feWdmuIowx18SjiUJE2gKTAB/gPVUdk+jzwcDDQAxwBHhQVf/xZEzZWXBwIR54oBb58/vz0kvNyJ/fCvgZY66dxzqzRcQHmArcBlQFuotI1USLrQPqqmooMBcY56l4sqO9e09y++2f8ssve+PnzZhxOxMmtLEkYYxJN568oqgP7FbVPQAiMge4A9h6cQFVXZZg+ZXAfR6MJ9uIjo5lwoQVvPTSL5w/H8PRo+dYseIhAGtmMsakO08milLA/gTT4cDNKSz/ELAoqQ9E5FHgUYA66RVdFvXbb/vo02chW7YcAaBbt+pMmNDay1EZY7IzTyaKpH7aJllxTkTuA+oCTZP6XFVnADMA6orkyKp1J06cZ8iQJbz//joAypcvzNtvt6d16/JejswYk915MlGEA2USTJcG/k28kIjcCrwANFXVKA/Gk6XFxSnz5+8gd+5cDB3amOeea0yePLm9HZYxJgfwZKJYDVQUkRDgANANuDfhAiJSG3gHaKuqhz0YS5a0fftRQkIK4e/vS5EigXz8cWfKli1I5cpFvR2aMSYH8ViiUNUYEekPLMa5PfYDVd0iIqOAMFVdALwO5AO+cHXC7lPVjp6KKas4dy6aV15Zzuuv/8Hw4U0YPtxpkbNmpqwlOjqa8PBwIiMjvR2KyUECAgIoXbo0uXOnX4uDR5+jUNXvgO8SzXsxwftbPbn/rOj773fTr9+3/P33SQCOHj3n5YjM1QoPDyd//vwEBwfb3WgmQ6gqx44dIzw8nJCQkHTbrj2ZnUn8++9pBg78ni++cO4erlGjONOnd+CWW8qksqbJrCIjIy1JmAwlIhQpUoQjR46k63YtUWQCO3ceo27dGZw+fYHAwNyMHNmUgQMbkDu3j7dDM9fIkoTJaJ74m7NEkQlUrBhEvXqlyJs3N2+9dRs33GAF/IwxmYeNR+EFp05FMXDg9+zceQxwfgEsWNCNBQu6W5Iw6crHx4datWpRvXp1br/9dk6ePBn/2ZYtW2jRogU33ngjFStW5OWXX0b10mNKixYtom7dulSpUoXKlSvz9NNPe+MrpGjdunU8/PDD3g4jWceOHaN58+bky5eP/v37J7vc8ePHadWqFRUrVqRVq1acOHECcPocBgwYQIUKFQgNDWXt2rUAHDlyhLZt22bIdwBLFBlKVfniiy1UrjyFSZP+ZMCASw+i583r58XITHaVJ08e1q9fz+bNmwkKCmLq1KkAnD9/no4dOzJ06FB27tzJhg0b+OOPP3j77bcB2Lx5M/3792f27Nls27aNzZs3U65cuXSNLSYm5pq38eqrr/LEE09k6D7TIiAggJdffpnx48enuNyYMWNo2bIlu3btomXLlowZ49RPXbRoEbt27WLXrl3MmDGDvn37AlCsWDFKlizJ77//7vHvANb0lGH27DlB//7fsWjRbgAaNCjN2LF201eO8YaH+iqecr9QQcOGDdm4cSMAn3zyCY0aNaJ1a6f8S2BgIFOmTKFZs2Y8/vjjjBs3jhdeeIHKlSsD4OvrS79+/a7Y5pkzZ3jiiScICwtDRBgxYgRdunQhX758nDlzBoC5c+eycOFCZs6cSe/evQkKCmLdunXUqlWLefPmsX79egoVcq6kK1SowO+//06uXLno06cP+/btA+DNN9+kUaNGl+379OnTbNy4kZo1awKwatUqBg4cyPnz58mTJw8ffvghlSpVYubMmXz77bdERkZy9uxZfvrpJ15//XU+//xzoqKiuPPOO3nppZcA6NSpE/v37ycyMpInn3ySRx991O3jm5S8efPSuHFjdu/eneJy8+fP5+effwbg/vvvp1mzZowdO5b58+fTq1cvRIQGDRpw8uRJDh48SMmSJenUqRMff/zxFcfFEyxReNiFC7GMH/8HL7+8nMjIGAoVCmDMmJY88kgdcuWyjk6TMWJjY/nxxx956CGneOSWLVuoU+fyymnly5fnzJkznDp1is2bN/PUU0+lut2XX36ZggULsmnTJoD4JpOU7Ny5k6VLl+Lj40NcXBzz5s3jgQce4M8//yQ4OJgSJUpw7733MmjQIBo3bsy+ffto06YN27Ztu2w7YWFhVK9ePX66cuXKLF++HF9fX5YuXcrzzz/Pl19+CcCKFSvYuHEjQUFB/PDDD+zatYtVq1ahqnTs2JHly5fTpEkTPvjgA4KCgjh//jz16tWjS5cuFClS5LL9Dho0iGXLlpFYt27dGDp0aKrfPymHDh2iZMmSAJQsWZLDh53njw8cOECZMpfufCxdujQHDhygZMmS1K1bl2HDhl3V/tLKEoWH7d8fwahRvxAVFUuPHjV4443WlCiRz9thmYyWhl/+6en8+fPUqlWLvXv3UqdOHVq1agWQ4rC4ablrZunSpcyZMyd+unDhwqmuc/fdd+Pj49zR17VrV0aNGsUDDzzAnDlz6Nq1a/x2t26NLzTNqVOnOH36NPnz54+fd/DgQYoVKxY/HRERwf3338+uXbsQEaKjo+M/a9WqFUFBQQD88MMP/PDDD9SuXRtwrop27dpFkyZNmDx5MvPmzQNg//797Nq164pEMXHiRPcOTjpI2Gd00cX/P8WLF+fff6+oiuQRlig84MSJ8xQqFICIUL58EJMmtaVChSBatkzfNl5jUnOxjyIiIoIOHTowdepUBgwYQLVq1Vi+fPlly+7Zs4d8+fKRP39+qlWrxpo1a+KbdZKTXMJJOC/xk+l58+aNf9+wYUN2797NkSNH+Prrr+N/IcfFxbFixQry5MmT4ndLuO3hw4fTvHlz5s2bx969e2nWrFmS+1RVnnvuOR577LHLtvfzzz+zdOlSVqxYQWBgIM2aNUvyqXpPXFGUKFEivknp4MGDFC9eHHCuIPbvv1SEOzw8nOuvvx5wjmtKxyc9WWd2OoqLUz74YB0VKrzF7Nkb4+c/9lhdSxLGqwoWLMjkyZMZP3480dHR9OjRg99++42lS5cCzpXHgAEDeOaZZwAYMmQIr776Kjt37gScE/eECROu2G7r1q2ZMmVK/PTFpqcSJUqwbdu2+Kal5IgId955J4MHD6ZKlSrxv94Tb3f9+vVXrFulSpXL2v4jIiIoVaoUADNnzkx2n23atOGDDz6I70M5cOAAhw8fJiIigsKFCxMYGMj27dtZuXJlkutPnDiR9evXX/G62iQB0LFjRz766CMAPvroI+644474+bNmzUJVWblyJQULFoxvotq5c+dlTW+eZIkinWzZcphmzWby0EMLOH78fHyntTGZRe3atalZsyZz5swhT548zJ8/n9GjR1OpUiVq1KhBvXr14m/hDA0N5c0336R79+5UqVKF6tWrc/DgwSu2OWzYME6cOEH16tWpWbNm/C/tMWPG0KFDB1q0aBF/YktO165dmT17dnyzE8DkyZMJCwsjNDSUqlWrMn369CvWq1y5MhEREZw+fRqAZ555hueee45GjRoRGxub7P5at27NvffeS8OGDalRowZ33XUXp0+fpm3btsTExBAaGsrw4cNp0KBB6gfVDcHBwQwePJiZM2dSunTp+Ca1hx9+mLCwMACGDh3KkiVLqFixIkuWLIlPOu3ataNcuXJUqFCBRx55JP6uNIBly5bRvn37dIkxNZJUG1hmVldEw44ehUTtht5y7lw0L7/8C+PHryAmJo7ixfMycWIbunevbk/l5nDbtm2jSpUq3g4jW5s4cSL58+fP1M9SeEqTJk2YP39+kv1CSf3ticgaVa17NfuyPoprsHPnMdq0mc3evScRgT596vDqqy0pXDhj2g2Nyen69u3LF1984e0wMtyRI0cYPHiwWzcPpAdLFNfghhsKEhDgS82aJZg+vQMNGpT2dkjG5CgBAQH07NnT22FkuGLFitGpU6cM258lijSIiYlj+vQwunevTpEigfj7+/L99z0oVaoAvr7W3WOMyZ4sUbhp1aoD9OmzkHXr/mP9+v947z1nfCWrzWSMye4sUaQiIiKSF174ibffXo0qlC1bkDvuqOTtsIwxJsNYokiGqvLZZ1sYNGgx//13Bl/fXAwe3IAXX2xqBfyMMTmKNawnY8OGQ3Tv/iX//XeGW24pw9q1jzJ2bCtLEiZLsTLj3vfaa69RoUIFKlWqxOLFi5Nc5qeffuKmm26ievXq3H///fFVbk+cOMGdd95JaGgo9evXZ/PmzQBcuHCBJk2aZFw1XFXNUq86oHr0qHpCTEzsZdODBn2v7767RmNj4zyyP5O9bd261dshaN68eePf9+rVS0ePHq2qqufOndNy5crp4sWLVVX17Nmz2rZtW50yZYqqqm7atEnLlSun27ZtU1XV6OhonTp1arrGFh0dfc3buOuuu3T9+vUZus+02LJli4aGhmpkZKTu2bNHy5UrpzExMZctExsbq6VLl9YdO3aoqurw4cP1vffeU1XVp59+WkeOHKmqqtu2bdMWLVrErzdy5EidPXt2kvtN6m8PCNOrPO/aFYXLsmV/U736NJYv/yd+3oQJbXj44Zusyqu5diKeeaVBw4YNOXDgAJB8mfGL4yCkpcz4Aw88QI0aNQgNDY2v1pov36XCl3PnzqV3794A9O7dm8GDB9O8eXOGDBlCcHDwZVc5FSpU4NChQxw5coQuXbpQr1496tWrl+S4C0mVGb/llluoXbs2t9xyCzt27ACcch533303t99+e/z3ff3116lXrx6hoaGMGDEifpudOnWiTp06VKtWjRkzZqTp+CZl/vz5dOvWDX9/f0JCQqhQoQKrVq26bJljx47h7+/PjTfeCDgFDC8ex61bt9KyZUvAeRJ97969HDp0KD7Wjz/++JpjdEeO76M4fPgsQ4YsYdasDQBMmLCCJk1u8HJUxqQvKzPunTLjBw4cuKwUyMUy4QkVLVqU6OhowsLCqFu3LnPnzo0vBFizZk2++uorGjduzKpVq/jnn38IDw+nRIkSVK9endWrV6d6vNNDjk0UcXHK+++v5dlnl3LiRCT+/j4MG9aEIUNu8XZoJjvyUqkcKzPu8FaZcU2hTHjC6Tlz5jBo0CCioqJo3bo1vr7OqXno0KE8+eST1KpVixo1alC7du34z3x8fPDz87viuHhCjkwUf/99gvvum8cffzhZu3Xr8kyd2o4KFYK8HJkx6cvKjF+5T83AMuMplQlPqGHDhvz666+Ak8guVu0tUKAAH374YXzcISEhhISExK8XFRVFQEDAlQcnneXIPooCBfzZufMY112XjzlzuvD99z0sSZhszcqMX5KRZcY7duzInDlziIqK4u+//2bXrl3Ur1//iuUujmgXFRXF2LFj6dOnDwAnT57kwoULALz33ns0adKEAgUKAE7fRrFixcidO3ey3zW95JhEsXjxbqKinFvJihQJZMGCbmzf/jhdu1qVV5MzWJlxR0aWGa9WrRr33HMPVatWpW3btkydOjW+2a1du3bxI9S9/vrrVKlShdDQUG6//XZatGgBOFVgq1WrRuXKlVm0aBGTJk2K3/ayZcto167dNcfojmxfZnz//ggGDPier7/ezssvN2fYsCYejtAYh5UZ97ycXGa8c+fOvPbaa1SqdGWliPQuM55tryhiYuKYMGEFVapM5euvt5Mvnx9BQVb+25jspG/fvvj7+3s7jAx34cIFOnXqlGSS8IRs2Zm9cmU4ffosZMMG537jLl2qMGlSW0qVKuDlyIwx6Smnlhn38/OjV69eGba/bJco/vwznFtueR9VCA4uxJQpt9G+/Y3eDsvkUCndhmqMJ3iiOyHbJYr69UvRpk0Fate+jmHDmhAY6Pk7AoxJSkBAAMeOHaNIkSKWLEyGUFWOHTuW7rfMZvnO7F27jjFo0GImTGjDjTc68+Li1MpuGK+Ljo4mPDw8yXvxjfGUgIAASpcufcVtszlyzOyoqBjGjPmN1177jaioWAICfJk79x4ASxImU8idO/dlD0cZk1V59K4nEWkrIjtEZLeIXPE0ioj4i8hnrs//FJFgd7b74/L9hIZOZ+TIX4iKiuWBB2oxfXqH9A7fGGMMHryiEBEfYCrQCggHVovIAlXdmmCxh4ATqlpBRLoBY4GuV27tkr8pxK2d5wNQpUpRpk/vYEX8jDHGgzx5RVEf2K2qe1T1AjAHuCPRMncAH7nezwVaSiq9fifIQ0CAD6++2oL16/tYkjDGGA/zWGe2iNwFtFXVh13TPYGbVbV/gmU2u5YJd03/5VrmaKJtPQo86pqsDmz2SNBZT1HgaKpL5Qx2LC6xY3GJHYtLKqnqVZWZ9WRndlJXBomzkjvLoKozgBkAIhJ2tT332Y0di0vsWFxix+ISOxaXiEjY1a7ryaancKBMgunSwL/JLSMivkBB4LgHYzLGGJNGnkwUq4GKIhIiIn5AN2BBomUWAPe73t8F/KRZ7cEOY4zJ5jzW9KSqMSLSH1gM+AAfqOoWERmFM8j3AuB94P9EZDfOlUQ3NzZ97QPZZh92LC6xY3GJHYtL7FhcctXHIss9mW2MMSZjZdsy48YYY9KHJQpjjDEpyrSJwlPlP7IiN47FYBHZKiIbReRHEcm2TyGmdiwSLHeXiKiIZNtbI905FiJyj+tvY4uIfJLRMWYUN/6NlBWRZSKyzvXvJGPGEM1gIvKBiBx2PaOW1OciIpNdx2mjiNzk1oZVNdO9cDq//wLKAX7ABqBqomX6AdNd77sBn3k7bi8ei+ZAoOt935x8LFzL5QeWAyuBut6O24t/FxWBdUBh13Rxb8ftxWMxA+jrel8V2OvtuD10LJoANwGbk/m8HbAI5xm2BsCf7mw3s15ReKT8RxaV6rFQ1WWqes41uRLnmZXsyJ2/C4CXgXFAdq7v7c6xeASYqqonAFT1cAbHmFHcORYKXBzisiBXPtOVLajqclJ+Fu0OYJY6VgKFRKRkatvNrImiFLA/wXS4a16Sy6hqDBABFMmQ6DKWO8cioYdwfjFkR6keCxGpDZRR1YUZGZgXuPN3cSNwo4j8LiIrRaRthkWXsdw5FiOB+0QkHPgOeCJjQst00no+ATLveBTpVv4jG3D7e4rIfUBdoKlHI/KeFI+FiOQCJgK9MyogL3Ln78IXp/mpGc5V5q8iUl1VT3o4tozmzrHoDsxU1TdEpCHO81vVVTXO8+FlKld13sysVxRW/uMSd44FInIr8ALQUVWjMii2jJbasciPUzTyZxHZi9MGuyCbdmi7+29kvqpGq+rfwA6cxJHduHMsHgI+B1DVFUAATsHAnMat80limTVRWPmPS1I9Fq7mlndwkkR2bYeGVI6FqkaoalFVDVbVYJz+mo6qetXF0DIxd/6NfI1zowMiUhSnKWpPhkaZMdw5FvuAlgAiUgUnURzJ0CgzhwVAL9fdTw2ACFU9mNpKmbLpST1X/iPLcfNYvA7kA75w9efvU9WOXgvaQ9w8FjmCm8diMdBaRLYCscAQVT3mvag9w81j8RTwrogMwmlq6Z0df1iKyKc4TY1FXf0xI4DcAKo6Had/ph2wGzgHPODWdrPhsTLGGJOOMmvTkzHGmEzCEoUxxpgUWaIwxhiTIksUxhhjUmSJwhhjTIosUZhMR0RiRWR9gldwCssGJ1cpM437/NlVfXSDq+RFpavYRh8R6eV631tErk/w2XsiUjWd41wtIrXcWGegiARe675NzmWJwmRG51W1VoLX3gzabw9VrYlTbPL1tK6sqtNVdZZrsjdwfYLPHlbVrekS5aU438a9OAcClijMVbNEYbIE15XDryKy1vW6JYllqonIKtdVyEYRqeiaf1+C+e+IiE8qu1sOVHCt29I1hsEmV61/f9f8MXJpDJDxrnkjReRpEbkLp+bWx6595nFdCdQVkb4iMi5BzL1F5K2rjHMFCQq6icg0EQkTZ+yJl1zzBuAkrGUissw1r7WIrHAdxy9EJF8q+zE5nCUKkxnlSdDsNM817zDQSlVvAroCk5NYrw8wSVVr4Zyow13lGroCjVzzY4Eeqez/dmCTiAQAM4GuqloDp5JBXxEJAu4EqqlqKDA64cqqOhcIw/nlX0tVzyf4eC7QOcF0V+Czq4yzLU6ZjoteUNW6QCjQVERCVXUyTi2f5qra3FXKYxhwq+tYhgGDU9mPyeEyZQkPk+Odd50sE8oNTHG1ycfi1C1KbAXwgoiUBr5S1V0i0hKoA6x2lTfJg5N0kvKxiJwH9uKUoa4E/K2qO12ffwQ8DkzBGeviPRH5FnC7pLmqHhGRPa46O7tc+/jdtd20xJkXp1xFwhHK7hGRR3H+XZfEGaBnY6J1G7jm/+7ajx/OcTMmWZYoTFYxCDgE1MS5Er5iUCJV/URE/gTaA4tF5GGcssofqepzbuyjR8ICgiKS5PgmrtpC9XGKzHUD+gMt0vBdPgPuAbYD81RVxTlrux0nzihuY4CpQGcRCQGeBuqp6gkRmYlT+C4xAZaoavc0xGtyOGt6MllFQeCga/yAnji/pi8jIuWAPa7mlgU4TTA/AneJSHHXMkHi/pji24FgEangmu4J/OJq0y+oqt/hdBQndefRaZyy50n5CuiEM0bCZ655aYpTVaNxmpAauJqtCgBngQgRKQHclkwsK4FGF7+TiASKSFJXZ8bEs0Rhsoq3gftFZCVOs9PZJJbpCmwWkfVAZZwhH7finFB/EJGNwBKcZplUqWokTnXNL0RkExAHTMc56S50be8XnKudxGYC0y92Zifa7glgK3CDqq5yzUtznK6+jzeAp1V1A8742FuAD3Casy6aASwSkWWqegTnjqxPXftZiXOsjEmWVY81xhiTIruiMMYYkyJLFMYYY1JkicIYY0yKLFEYY4xJkSUKY4wxKbJEYYwxJkWWKIwxxqTo/wGVPMR04zIuYwAAAABJRU5ErkJggg==\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "for name,model in zip(names,models):\n", "\n", " model.fit(x_train,y_train) \n", " y_train_pred = model.predict_proba(x_train)[:,1]\n", " y_test_pred = model.predict_proba(x_test)[:,1]\n", "\n", "\n", " train_roc = roc_curve(y_train,y_train_pred)\n", " test_roc = roc_curve(y_test,y_test_pred)\n", "\n", " train_auc = roc_auc_score(y_train,y_train_pred)\n", " test_auc = roc_auc_score(y_test,y_test_pred)\n", " \n", "\n", "\n", " draw_roc_curve(train_roc,test_roc,train_auc,test_auc,name)\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:Anaconda3]", "language": "python", "name": "conda-env-Anaconda3-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.0" } }, "nbformat": 4, "nbformat_minor": 2 }