{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 逻辑回归\n", "\n", "逻辑回归(Logistic Regression, LR)模型其实仅在线性回归的基础上,套用了一个逻辑函数,但也就由于这个逻辑函数,使得逻辑回归模型能够输出类别的概率。逻辑回归的本质是:假设数据服从这个分布,然后使用极大似然估计做参数的估计。\n", "\n", "![theory](images/linear_logistic_regression.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. 什么是回归\n", "\n", "一说回归最先想到的是终结者那句:I'll be back\n", "\n", "regress,re表示back,gress等于go,数值go back to mean value,也就是I'll be back 的意思\n", "\n", "在数理统计中,回归是确定多种变量相互依赖的定量关系的方法\n", "\n", "> 通俗理解:越来越接近期望值的过程,***回归*** 于事物的本质\n", "\n", "最简单的回归是线性回归(Linear Regression),也就是通过最小二乘等方法得到模型的参数。线性回归假设输出变量是若干输出变量的线性组合,并根据这一关系求解线性组合中的最优系数。\n", "\n", "通俗理解:输出一个线性函数,例如$y=f(x; \\theta)$,通过寻找最优的参数$\\theta$使得观测数据与模型数据相吻合。\n", "\n", "![linear regression](images/linear_regression.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "## 2. 逻辑回归模型\n", "回归是一种比较容易理解的模型,就相当于$y=f(x)$,表明自变量$x$与因变量$y$的关系。\n", "\n", "以常见的看医举例,医生治病时的望、闻、问、切,之后判定病人是否生病或生了什么病,其中的望闻问切就是获取自变量$x$,即特征数据,判断是否生病就相当于获取因变量$y$,即预测分类。$X$为数据点——肿瘤的大小,$Y$为观测值——是否是恶性肿瘤。通过构建线性回归模型,如$h_\\theta(x)$所示,构建线性回归模型后,即可以根据肿瘤大小,预测是否为恶性肿瘤$h_\\theta(x)) \\ge 0.5$为恶性,$h_\\theta(x) \\lt 0.5$为良性。\n", "\n", "![LinearRegression](images/fig1.gif)\n", "\n", "然而线性回归的鲁棒性很差,例如在上图的数据集上建立回归,因最右边噪点的存在,使回归模型在训练集上表现都很差。这主要是由于线性回归在整个实数域内敏感度一致,而分类范围,需要在$[0,1]$。\n", "\n", "逻辑回归就是一种减小预测范围,将预测值限定为$[0,1]$间的一种回归模型,其回归方程与回归曲线如下图所示。逻辑曲线在$z=0$时,十分敏感,在$z>>0$或$z<<0$处,都不敏感,将预测值限定为$(0,1)$。\n", "\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "plt.figure()\n", "plt.axis([-10,10,0,1])\n", "plt.grid(True)\n", "X=np.arange(-10,10,0.1)\n", "y=1/(1+np.e**(-X))\n", "plt.plot(X,y,'b-')\n", "plt.title(\"Logistic function\")\n", "plt.savefig(\"logstic_fuction.pdf\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.1 逻辑回归表达式\n", "\n", "这个函数称为Logistic函数(Logistic Function),也称为Sigmoid函数(Sigmoid Function)。函数公式如下:\n", "\n", "$$\n", "g(z) = \\frac{1}{1+e^{-z}}\n", "$$\n", "\n", "Logistic函数:\n", "* 当$z$趋近于无穷大时,$g(z)$趋近于1;\n", "* 当$z$趋近于无穷小时,$g(z)$趋近于0。\n", "\n", "Logistic函数的图形如上图所示。Logistic函数求导时有一个特性,这个特性将在下面的推导中用到,这个特性为:\n", "$$\n", "g'(z) = \\frac{d}{dz} \\frac{1}{1+e^{-z}} \\\\\n", " = \\frac{1}{(1+e^{-z})^2}(e^{-z}) \\\\\n", " = \\frac{1}{(1+e^{-z})} (1 - \\frac{1}{(1+e^{-z})}) \\\\\n", " = g(z)(1-g(z))\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "逻辑回归本质上是线性回归,只是在特征到结果的映射中加入了一层函数映射,即先把特征线性求和,然后使用函数$g(z)$将做为假设函数来预测。$g(z)$可以将连续值映射到0到1之间。线性回归模型的表达式带入$g(z)$,就得到逻辑回归的表达式:\n", "\n", "$$\n", "h_\\theta(x) = g(\\theta^T x) = \\frac{1}{1+e^{-\\theta^T x}}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.2 逻辑回归的软分类\n", "\n", "现在我们将y的取值$h_\\theta(x)$通过Logistic函数归一化到(0,1)间,$y$的取值有特殊的含义,它表示结果取1的概率,因此对于输入$x$分类结果为类别1和类别0的概率分别为:\n", "\n", "$$\n", "P(y=1|x,\\theta) = h_\\theta(x) \\\\\n", "P(y=0|x,\\theta) = 1 - h_\\theta(x)\n", "$$\n", "\n", "对上面的表达式合并一下就是:\n", "\n", "$$\n", "p(y|x,\\theta) = (h_\\theta(x))^y (1 - h_\\theta(x))^{1-y}\n", "$$\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.3 梯度上升\n", "\n", "得到了逻辑回归的表达式,下一步跟线性回归类似,构建似然函数,然后最大似然估计,最终推导出$\\theta$的迭代更新表达式。只不过这里用的不是梯度下降,而是梯度上升,因为这里是最大化似然函数。\n", "\n", "假设训练样本相互独立,那么似然函数表达式为:\n", "![Loss](images/eq_loss.png)\n", "\n", "同样对似然函数取log,转换为:\n", "![LogLoss](images/eq_logloss.png)\n", "\n", "转换后的似然函数对$\\theta$求偏导,在这里我们以只有一个训练样本的情况为例:\n", "![LogLossDiff](images/eq_logloss_diff.png)\n", "\n", "这个求偏导过程中:\n", "* 第一步是对$\\theta$偏导的转化,依据偏导公式:$y=lnx$, $y'=1/x$。\n", "* 第二步是根据$g(z)$求导的特性$g'(z) = g(z)(1 - g(z))$ 。\n", "* 第三步就是普通的变换。\n", "\n", "这样我们就得到了梯度上升每次迭代的更新方向,那么$\\theta$的迭代表达式为:\n", "$$\n", "\\theta = \\theta + \\eta (y^i - h_\\theta(x^i)) x_j^i\n", "$$\n", "\n", "其中$\\eta$是学习速率。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1.4 示例程序" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "#from __future__ import division\n", "import numpy as np\n", "import sklearn.datasets\n", "import matplotlib.pyplot as plt\n", "\n", "np.random.seed(0)\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'Original Data')" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# load sample data\n", "data, label = sklearn.datasets.make_moons(200, noise=0.30)\n", "\n", "plt.scatter(data[:,0], data[:,1], c=label)\n", "plt.savefig(\"logistic_train_data.pdf\")\n", "plt.title(\"Original Data\")" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "def plot_decision_boundary(predict_func, data, label, figName=None):\n", " \"\"\"画出结果图\n", " Args:\n", " pred_func (callable): 预测函数\n", " data (numpy.ndarray): 训练数据集合\n", " label (numpy.ndarray): 训练数据标签\n", " \"\"\"\n", " x_min, x_max = data[:, 0].min() - .5, data[:, 0].max() + .5\n", " y_min, y_max = data[:, 1].min() - .5, data[:, 1].max() + .5\n", " h = 0.01\n", "\n", " xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n", "\n", " Z = predict_func(np.c_[xx.ravel(), yy.ravel()])\n", " Z = Z.reshape(xx.shape)\n", "\n", " plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral) #画出登高线并填充\n", " plt.scatter(data[:, 0], data[:, 1], c=label, cmap=plt.cm.Spectral)\n", " if figName != None: plt.savefig(figName)\n", " plt.show()\n", "\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "\n", "def sigmoid(x):\n", " return 1.0 / (1 + np.exp(-x))\n", "\n", "class Logistic(object):\n", " \"\"\"logistic回归模型\"\"\"\n", " def __init__(self, data, label):\n", " self.data = data\n", " self.label = label\n", "\n", " # parameters\n", " self.data_num, n = np.shape(data)\n", " self.weights = np.ones(n)\n", " self.b = 1\n", "\n", " def train(self, num_iteration=150):\n", " \"\"\"随机梯度上升算法\n", " Args:\n", " num_iteration (int): 迭代次数\n", " \"\"\"\n", " # 学习速率\n", " alpha = 0.01\n", " \n", " for j in range(num_iteration):\n", " data_index = list(range(self.data_num))\n", " for i in range(self.data_num):\n", " rand_index = int(np.random.uniform(0, len(data_index)))\n", " error = self.label[rand_index] - \\\n", " sigmoid(sum(self.data[rand_index] * self.weights + self.b))\n", " self.weights += alpha * error * self.data[rand_index]\n", " self.b += alpha * error\n", " del(data_index[rand_index])\n", "\n", " def predict(self, predict_data):\n", " \"\"\"预测函数\"\"\"\n", " result = list(map(lambda x: 1 if sum(self.weights * x + self.b) > 0 else 0,\n", " predict_data))\n", " return np.array(result)\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "logistic = Logistic(data, label)\n", "logistic.train(200)\n", "plot_decision_boundary(lambda x: logistic.predict(x), data, label, \"logistic_pred_res.pdf\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. 如何用sklearn解决逻辑回归问题?" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "accuracy train = 0.816667\n", "accuracy test = 0.850000\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "\n", "import sklearn.datasets\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.metrics import confusion_matrix\n", "from sklearn.metrics import accuracy_score\n", "import matplotlib.pyplot as plt\n", "\n", "# 生成模拟数据\n", "data, label = sklearn.datasets.make_moons(200, noise=0.30)\n", "\n", "# 计算得到训练、测试数据个数\n", "N = len(data)\n", "N_train = int(N*0.6)\n", "N_test = N - N_train\n", "\n", "# 分割成训练、测试数据\n", "x_train = data[:N_train, :]\n", "y_train = label[:N_train]\n", "x_test = data[N_train:, :]\n", "y_test = label[N_train:]\n", "\n", "# 进行逻辑回归\n", "lr = LogisticRegression()\n", "lr.fit(x_train,y_train)\n", "\n", "# 预测\n", "pred_train = lr.predict(x_train)\n", "pred_test = lr.predict(x_test)\n", "\n", "# 计算训练/测试精度\n", "acc_train = accuracy_score(y_train, pred_train)\n", "acc_test = accuracy_score(y_test, pred_test)\n", "print(\"accuracy train = %f\" % acc_train)\n", "print(\"accuracy test = %f\" % acc_test)\n", "\n", "# 绘制混淆矩阵\n", "cm = confusion_matrix(y_test,pred_test)\n", "\n", "plt.matshow(cm)\n", "plt.title('Confusion Matrix')\n", "plt.colorbar()\n", "plt.ylabel('Groundtruth')\n", "plt.xlabel(u'Predict')\n", "plt.savefig('logistic_confusion_matrix.pdf')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4. 多类识别问题" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4.1 加载显示数据" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt \n", "from sklearn.datasets import load_digits\n", "\n", "# load data\n", "digits = load_digits()\n", "\n", "# copied from notebook 02_sklearn_data.ipynb\n", "fig = plt.figure(figsize=(6, 6)) # figure size in inches\n", "fig.subplots_adjust(left=0, right=1, bottom=0, top=1, hspace=0.05, wspace=0.05)\n", "\n", "# plot the digits: each image is 8x8 pixels\n", "for i in range(64):\n", " ax = fig.add_subplot(8, 8, i + 1, xticks=[], yticks=[])\n", " ax.imshow(digits.images[i], cmap=plt.cm.binary)\n", " \n", " # label the image with the target value\n", " ax.text(0, 7, str(digits.target[i]))" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(1797, 64)\n", "accuracy train = 1.000000, accuracy_test = 0.905556\n", "score_train = 1.000000, score_test = 0.905556\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/bushuhui/anaconda3/envs/dl/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", "Please also refer to the documentation for alternative solver options:\n", " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" ] } ], "source": [ "from sklearn.datasets import load_digits\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.metrics import accuracy_score\n", "from sklearn.manifold import Isomap\n", "\n", "import matplotlib.pyplot as plt \n", "\n", "# 加载示例数据\n", "digits, dig_label = load_digits(return_X_y=True)\n", "print(digits.shape)\n", "\n", "# 计算训练/测试数据个数\n", "N = len(digits)\n", "N_train = int(N*0.8)\n", "N_test = N - N_train\n", "\n", "# 分割训练/测试数据集\n", "x_train = digits[:N_train, :]\n", "y_train = dig_label[:N_train]\n", "x_test = digits[N_train:, :]\n", "y_test = dig_label[N_train:]\n", "\n", "# 进行逻辑回归分类\n", "lr = LogisticRegression()\n", "lr.fit(x_train, y_train)\n", "\n", "pred_train = lr.predict(x_train)\n", "pred_test = lr.predict(x_test)\n", "\n", "# 计算测试、训练精度\n", "acc_train = accuracy_score(y_train, pred_train)\n", "acc_test = accuracy_score(y_test, pred_test)\n", "print(\"accuracy train = %f, accuracy_test = %f\" % (acc_train, acc_test))\n", "\n", "score_train = lr.score(x_train, y_train)\n", "score_test = lr.score(x_test, y_test)\n", "print(\"score_train = %f, score_test = %f\" % (score_train, score_test))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4.2 可视化特征\n", "\n", "针对机器学习的问题,一个比较好的方法是通过降维的方法将原始的高维特征降到2-3维并可视化处理,通过这样的方法可以对所要处理的数据有一个初步的认识。这里介绍最简单的降维方法主成分分析(Principal Component Analysis, PCA)。PCA寻求具有最大方差的特征的正交线性组合,因此可以更好地了解数据的结构。在这里,我们将使用Randomized PCA,因为当数据个数$N$比较大时,计算的效率更好。\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from sklearn.decomposition import PCA\n", "pca = PCA(n_components=2, svd_solver=\"randomized\")\n", "proj = pca.fit_transform(digits.data)\n", "\n", "plt.scatter(proj[:, 0], proj[:, 1], c=digits.target)\n", "plt.colorbar()\n", "plt.savefig(\"pca_visualize.pdf\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "PCA的一个缺点是它可能会丢失数据中一些有趣的相互关系。如果想看到非线性的降维与映射\n", "我们可以使用几种流形模块中的方法。在这里,我们将使用[Isomap](https://blog.csdn.net/VictoriaW/article/details/78497316)(串联\n", "等距映射)是一种基于图论的流形降维方法。" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from sklearn.manifold import Isomap\n", "iso = Isomap(n_neighbors=5, n_components=2)\n", "proj = iso.fit_transform(digits.data)\n", "\n", "plt.scatter(proj[:, 0], proj[:, 1], c=digits.target)\n", "plt.colorbar()\n", "plt.savefig(\"isomap_visualize.pdf\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4.3 示例程序" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(1797, 64)\n", "accuracy train = 0.995825, accuracy_test = 0.961111\n", "score_train = 0.995825, score_test = 0.961111\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/bushuhui/anaconda3/envs/dl/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", "Please also refer to the documentation for alternative solver options:\n", " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" ] } ], "source": [ "from sklearn.datasets import load_digits\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.metrics import accuracy_score\n", "from sklearn.manifold import Isomap\n", "\n", "import matplotlib.pyplot as plt \n", "\n", "# 加载示例数据\n", "digits, dig_label = load_digits(return_X_y=True)\n", "print(digits.shape)\n", "\n", "# 进行特征降维\n", "feature_trans = True\n", "if feature_trans:\n", " iso = Isomap(n_neighbors=5, n_components=8)\n", " digits = iso.fit_transform(digits)\n", "\n", "# 计算训练/测试数据个数\n", "N = len(digits)\n", "N_train = int(N*0.8)\n", "N_test = N - N_train\n", "\n", "# 分割训练/测试数据集\n", "x_train = digits[:N_train, :]\n", "y_train = dig_label[:N_train]\n", "x_test = digits[N_train:, :]\n", "y_test = dig_label[N_train:]\n", "\n", "# 进行逻辑回归分类\n", "lr = LogisticRegression()\n", "lr.fit(x_train,y_train)\n", "\n", "pred_train = lr.predict(x_train)\n", "pred_test = lr.predict(x_test)\n", "\n", "# 计算测试、训练精度\n", "acc_train = accuracy_score(y_train, pred_train)\n", "acc_test = accuracy_score(y_test, pred_test)\n", "print(\"accuracy train = %f, accuracy_test = %f\" % (acc_train, acc_test))\n", "\n", "score_train = lr.score(x_train, y_train)\n", "score_test = lr.score(x_test, y_test)\n", "print(\"score_train = %f, score_test = %f\" % (score_train, score_test))\n", "\n" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from sklearn.metrics import confusion_matrix\n", "\n", "# plot confusion matrix\n", "cm = confusion_matrix(y_test,pred_test)\n", "\n", "plt.matshow(cm)\n", "plt.title(u'Confusion Matrix')\n", "plt.colorbar()\n", "plt.ylabel(u'Groundtruth')\n", "plt.xlabel(u'Predict')\n", "plt.savefig(\"sklean_isomap_confusion_matrix.pdf\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 5. 深入思考\n", "\n", "1. 如何得到错误分类数据的下标?\n", "2. 如何根据下标,将这些错误的数据可视化出来?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## References\n", "\n", "* [逻辑回归模型(Logistic Regression, LR)基础](https://www.cnblogs.com/sparkwen/p/3441197.html)\n", "* [逻辑回归(Logistic Regression)](http://www.cnblogs.com/BYRans/p/4713624.html)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.9" } }, "nbformat": 4, "nbformat_minor": 2 }