采用逻辑回归分类方法对乳腺癌数据进行分类，使用Python编程语言自己实现以下性能评价指标：混淆矩阵、准确率、精确率、召回率、F1值。

好的，下面是Python代码实现：

import numpy as np
from sklearn.datasets import load_breast_cancer
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score, f1_score

# 导入乳腺癌数据集
data = load_breast_cancer()
X = data.data
y = data.target

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 训练模型
lr = LogisticRegression()
lr.fit(X_train, y_train)

# 预测结果
y_pred = lr.predict(X_test)

# 计算混淆矩阵
cm = confusion_matrix(y_test, y_pred)
print('Confusion Matrix:\n', cm)

# 计算准确率
accuracy = accuracy_score(y_test, y_pred)
print('Accuracy:', accuracy)

# 计算精确率
precision = precision_score(y_test, y_pred)
print('Precision:', precision)

# 计算召回率
recall = recall_score(y_test, y_pred)
print('Recall:', recall)

# 计算F1值
f1 = f1_score(y_test, y_pred)
print('F1 Score:', f1)

其中，`load_breast_cancer`函数用于导入乳腺癌数据集，`train_test_split`函数用于划分训练集和测试集，`LogisticRegression`类用于训练逻辑回归模型，`confusion_matrix`、`accuracy_score`、`precision_score`、`recall_score`、`f1_score`函数用于计算各种性能评价指标。