调用sklearn库实现对鸢尾花数据的分类，采用逻辑回归分类方法，并计算以下多分类性能评价指标（两种方式）：混淆矩阵、准确率、精确率、召回率、F1值、分类报告，并绘制PR曲线与ROC曲线

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首先，我们需要加载鸢尾花数据集，代码如下：

from sklearn.datasets import load_iris

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

接下来，我们将数据集分为训练集和测试集，并使用逻辑回归分类器进行训练和预测，代码如下：

from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
clf = LogisticRegression(max_iter=1000)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)

现在，我们可以使用以下两种方式计算分类性能评价指标：

### 方法一：使用sklearn.metrics库

from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score, f1_score, classification_report, plot_precision_recall_curve, plot_roc_curve

# 混淆矩阵
cm = confusion_matrix(y_test, y_pred)
print("Confusion Matrix:")
print(cm)

# 准确率
acc = accuracy_score(y_test, y_pred)
print("Accuracy:", acc)

# 精确率
prec = precision_score(y_test, y_pred, average='weighted')
print("Precision:", prec)

# 召回率
rec = recall_score(y_test, y_pred, average='weighted')
print("Recall:", rec)

# F1值
f1 = f1_score(y_test, y_pred, average='weighted')
print("F1 Score:", f1)

# 分类报告
report = classification_report(y_test, y_pred)
print("Classification Report:")
print(report)

# 绘制PR曲线
plot_precision_recall_curve(clf, X_test, y_test)

# 绘制ROC曲线
plot_roc_curve(clf, X_test, y_test)

### 方法二：手动计算

import numpy as np

# 混淆矩阵
n_classes = len(np.unique(y))
cm = np.zeros((n_classes, n_classes))
for i in range(len(y_test)):
    cm[y_test[i], y_pred[i]] += 1
print("Confusion Matrix:")
print(cm)

# 准确率
correct = 0
for i in range(len(y_test)):
    if y_test[i] == y_pred[i]:
        correct += 1
acc = correct / len(y_test)
print("Accuracy:", acc)

# 精确率、召回率、F1值
precisions = []
recalls = []
f1s = []
for i in range(n_classes):
    tp = cm[i, i]
    fp = sum(cm[:, i]) - tp
    fn = sum(cm[i, :]) - tp
    precision = tp / (tp + fp)
    recall = tp / (tp + fn)
    f1 = 2 * precision * recall / (precision + recall)
    precisions.append(precision)
    recalls.append(recall)
    f1s.append(f1)
prec = np.mean(precisions)
rec = np.mean(recalls)
f1 = np.mean(f1s)
print("Precision:", prec)
print("Recall:", rec)
print("F1 Score:", f1)

# 分类报告
report = "              precision    recall  f1-score   support\n\n"
for i in range(n_classes):
    report += f"       {i}       {precisions[i]:.4f}      {recalls[i]:.4f}      {f1s[i]:.4f}        {np.sum(y_test==i)}\n"
report += f"\n    micro avg       {prec:.4f}      {rec:.4f}      {f1:.4f}        {len(y_test)}\n"
report += f"    macro avg       {prec:.4f}      {rec:.4f}      {f1:.4f}        {len(y_test)}\n"
report += f" weighted avg       {prec:.4f}      {rec:.4f}      {f1:.4f}        {len(y_test)}\n"
print("Classification Report:")
print(report)

以上就是使用sklearn库实现对鸢尾花数据的分类，采用逻辑回归分类方法，并计算多分类性能评价指标的方法。