{ "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中,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": 1, "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(\"fig-res-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": 2, "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": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'Original Data')" ] }, "execution_count": 3, "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(\"fig-res-logistic_train_data.pdf\")\n", "plt.title(\"Original Data\")" ] }, { "cell_type": "code", "execution_count": 4, "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": 6, "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", " FIXME: change to same API to sklean\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": 7, "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": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "accuracy train = 0.866667\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('fig-res-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": 17, "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": 13, "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": 18, "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=dig_label)\n", "plt.colorbar()\n", "plt.savefig(\"fig-res-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": 19, "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(\"fig-res-isomap_visualize.pdf\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4.3 示例程序" ] }, { "cell_type": "code", "execution_count": 20, "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": 21, "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(\"fig-res-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 }