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：

首先，我们需要导入相关的库：

import numpy as np
import pandas as pd
from sklearn.datasets import load_breast_cancer
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split

然后，我们加载乳腺癌数据集：

data = load_breast_cancer()
X = data.data
y = data.target

接着，我们将数据集分成训练集和测试集：

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

我们使用逻辑回归模型进行训练：

model = LogisticRegression()
model.fit(X_train, y_train)

现在，我们可以用模型对测试集进行预测：

y_pred = model.predict(X_test)

接下来，我们可以根据预测结果计算性能评价指标：

混淆矩阵：

from sklearn.metrics import confusion_matrix

cm = confusion_matrix(y_test, y_pred)
print(cm)

准确率、精确率、召回率、F1值：

from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score

accuracy = accuracy_score(y_test, y_pred)
precision = precision_score(y_test, y_pred)
recall = recall_score(y_test, y_pred)
f1 = f1_score(y_test, y_pred)

print("Accuracy:", accuracy)
print("Precision:", precision)
print("Recall:", recall)
print("F1 score:", f1)

完整的代码如下：

import numpy as np
import pandas as pd
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

data = load_breast_cancer()
X = data.data
y = data.target

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

model = LogisticRegression()
model.fit(X_train, y_train)

y_pred = model.predict(X_test)

cm = confusion_matrix(y_test, y_pred)
accuracy = accuracy_score(y_test, y_pred)
precision = precision_score(y_test, y_pred)
recall = recall_score(y_test, y_pred)
f1 = f1_score(y_test, y_pred)

print("Confusion matrix:")
print(cm)
print("Accuracy:", accuracy)
print("Precision:", precision)
print("Recall:", recall)
print("F1 score:", f1)

希望对你有所帮助！