使用python编程实现逻辑回归模型，在给定数据集上，绘制损失函数曲线图。使用混淆矩阵、错误率、精度、查全率、查准率、F1指标评估逻辑回归模型性能表现，请写出代码

import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score, f1_score

# 生成样本数据
np.random.seed(0)
X_train = np.random.randn(100, 2) * 2 + 2
y_train = np.zeros(100)
X_test = np.random.randn(50, 2) * 2 + 2
y_test = np.zeros(50)
X_train = np.concatenate([X_train, np.random.randn(100, 2) * 2 - 2])
y_train = np.concatenate([y_train, np.ones(100)])
X_test = np.concatenate([X_test, np.random.randn(50, 2) * 2 - 2])
y_test = np.concatenate([y_test, np.ones(50)])

# 训练逻辑回归模型
clf = LogisticRegression()
clf.fit(X_train, y_train)

# 绘制损失函数曲线图
plt.plot(clf.loss_curve_)
plt.xlabel('Iterations')
plt.ylabel('Loss')
plt.show()

# 预测测试数据集
y_pred = clf.predict(X_test)

# 计算混淆矩阵
cm = confusion_matrix(y_test, y_pred)

# 计算错误率和精度
error_rate = 1 - accuracy_score(y_test, y_pred)
accuracy = accuracy_score(y_test, y_pred)

# 计算查全率、查准率和F1指标
precision = precision_score(y_test, y_pred)
recall = recall_score(y_test, y_pred)
f1 = f1_score(y_test, y_pred)

# 打印结果
print('混淆矩阵：\n', cm)
print('错误率：', error_rate)
print('精度：', accuracy)
print('查全率：', recall)
print('查准率：', precision)
print('F1指标：', f1)