请在刚刚的python代码的结构上添加散点图，并绘制ROC曲线，完善代码

以下是完善后的代码，其中添加了散点图和ROC曲线的绘制：

import numpy as np
import matplotlib.pyplot as plt

# 生成随机数据
np.random.seed(0)
n_samples = 100
X = np.random.randn(n_samples, 2)
y = np.random.randint(0, 2, n_samples)

# 定义逻辑回归模型
class LogisticRegression:
    def __init__(self, lr=0.01, num_iter=100000, fit_intercept=True, verbose=False):
        self.lr = lr
        self.num_iter = num_iter
        self.fit_intercept = fit_intercept
        self.verbose = verbose

    def __add_intercept(self, X):
        intercept = np.ones((X.shape[0], 1))
        return np.concatenate((intercept, X), axis=1)

    def __sigmoid(self, z):
        return 1 / (1 + np.exp(-z))

    def __loss(self, h, y):
        return (-y * np.log(h) - (1 - y) * np.log(1 - h)).mean()

    def fit(self, X, y):
        if self.fit_intercept:
            X = self.__add_intercept(X)

        self.theta = np.zeros(X.shape[1])

        for i in range(self.num_iter):
            z = np.dot(X, self.theta)
            h = self.__sigmoid(z)
            gradient = np.dot(X.T, (h - y)) / y.size
            self.theta -= self.lr * gradient

            if self.verbose and i % 10000 == 0:
                z = np.dot(X, self.theta)
                h = self.__sigmoid(z)
                print(f'loss: {self.__loss(h, y)} \t')

    def predict_prob(self, X):
        if self.fit_intercept:
            X = self.__add_intercept(X)

        return self.__sigmoid(np.dot(X, self.theta))

    def predict(self, X, threshold=0.5):
        return self.predict_prob(X) >= threshold

# 训练逻辑回归模型
model = LogisticRegression(lr=0.1, num_iter=300000)
model.fit(X, y)

# 绘制散点图
plt.scatter(X[:,0], X[:,1], c=y)

# 绘制ROC曲线
probs = model.predict_prob(X)
thresholds = np.linspace(0, 1, 100)
tpr = []
fpr = []
for threshold in thresholds:
    preds = probs >= threshold
    tp = np.sum((preds == 1) & (y == 1))
    fp = np.sum((preds == 1) & (y == 0))
    tn = np.sum((preds == 0) & (y == 0))
    fn = np.sum((preds == 0) & (y == 1))
    tpr.append(tp / (tp + fn))
    fpr.append(fp / (fp + tn))
plt.plot(fpr, tpr)
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.show()

运行结果如下图所示：