Python代码,满足以下要求1、构造集成分类模型 2、构造一般分类模型 3、输出模型的混淆矩阵图 4、输出模型的一些分类评价指标值 5、基于一些评价指标如F1-score，Accuracy等，对集成模型与一般分类模型进行比较。

非常感谢您的提问。基于您的要求，我建议您使用scikit-learn库来构造分类模型，并使用matplotlib库来绘制混淆矩阵图。下面是一个示例代码：

from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import confusion_matrix, classification_report
import matplotlib.pyplot as plt
import numpy as np

# 加载数据集
iris = datasets.load_iris()
X = iris.data
y = iris.target

# 划分数据集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1)

# 构造集成分类器
rfc = RandomForestClassifier(n_estimators=10, random_state=1)
rfc.fit(X_train, y_train)

# 构造一般分类器
from sklearn.linear_model import LogisticRegression
lr = LogisticRegression(random_state=1)
lr.fit(X_train, y_train)

# 预测结果
y_pred_rfc = rfc.predict(X_test)
y_pred_lr = lr.predict(X_test)

# 输出混淆矩阵图
def plot_confusion_matrix(cm):
    plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)
    plt.colorbar()
    plt.xticks(np.arange(3), iris.target_names)
    plt.yticks(np.arange(3), iris.target_names)
    plt.xlabel('Predicted label')
    plt.ylabel('True Label')
    plt.show()

cm_rfc = confusion_matrix(y_test, y_pred_rfc)
cm_lr = confusion_matrix(y_test, y_pred_lr)
plot_confusion_matrix(cm_rfc)
plot_confusion_matrix(cm_lr)

# 输出分类评价指标值
report_rfc = classification_report(y_test, y_pred_rfc)
report_lr = classification_report(y_test, y_pred_lr)
print('Random Forest Classifier:\n', report_rfc)
print('Logistic Regression:\n', report_lr)

# 输出F1-score和Accuracy
from sklearn.metrics import f1_score, accuracy_score
f1_rfc = f1_score(y_test, y_pred_rfc, average='weighted')
f1_lr = f1_score(y_test, y_pred_lr, average='weighted')
accuracy_rfc = accuracy_score(y_test, y_pred_rfc)
accuracy_lr = accuracy_score(y_test, y_pred_lr)
print('F1-score (Random Forest Classifier):', f1_rfc)
print('F1-score (Logistic Regression):', f1_lr)
print('Accuracy (Random Forest Classifier):', accuracy_rfc)
print('Accuracy (Logistic Regression):', accuracy_lr)

# 比较集成模型与一般分类模型
if f1_rfc > f1_lr:
    print('The Random Forest Classifier performs better than the Logistic Regression.')
elif f1_rfc < f1_lr:
    print('The Logistic Regression performs better than the Random Forest Classifier.')
else:
    print('The Random Forest Classifier and the Logistic Regression have the same performance.')

该程序构造了一个随机森林集成分类器（Random Forest Classifier）和一个逻辑回归分类器（Logistic Regression）来对鸢尾花数据集（Iris dataset）进行分类。程序输出了混淆矩阵图、分类报告、F1-score和Accuracy等评价指标，并比较了两种分类模型的性能。

希望能够帮助您！如有需要，请随时告诉我。