print("Test Set Classification Report:")什么意思

这段代码是在Python中使用print函数输出一行文本，文本内容为"Test Set Classification Report:"。这句话的意思是打印出"测试集分类报告"这个文本用于对代码的输出进行注释或记录。

相关问题

import pandas as pd from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import accuracy_score, confusion_matrix,classification_report import seaborn as sns import matplotlib.pyplot as plt # 读取数据 data = pd.read_excel('E:/桌面/预测脆弱性/20230523/预测样本/预测样本.xlsx') # 分割训练集和验证集 train_data = data.sample(frac=0.8, random_state=1) test_data = data.drop(train_data.index) # 定义特征变量和目标变量 features = ['高程', '起伏度', '桥梁长', '道路长', '平均坡度', '平均地温', 'T小于0', '相态'] target = '交通风险' # 训练随机森林模型 rf = RandomForestClassifier(n_estimators=100, random_state=1) rf.fit(train_data[features], train_data[target]) # 在验证集上进行预测并计算精度、召回率和F1值等指标 pred = rf.predict(test_data[features]) accuracy = accuracy_score(test_data[target], pred) confusion_mat = confusion_matrix(test_data[target], pred) classification_rep = classification_report(test_data[target], pred) print('Accuracy:', accuracy) print('Confusion matrix:') print(confusion_mat) print('Classification report:') print(classification_rep) # 输出混淆矩阵图片 sns.heatmap(confusion_mat, annot=True, cmap="Blues") plt.show() # 读取新数据文件并预测结果 new_data = pd.read_excel('E:/桌面/预测脆弱性/20230523/预测样本/预测结果/交通风险预测096.xlsx') new_pred = rf.predict(new_data[features]) new_data['交通风险预测结果'] = new_pred new_data.to_excel('E:/桌面/预测脆弱性/20230523/预测样本/预测结果/交通风险预测096结果.xlsx', index=False)修改代码使得显示决策树模型以及多分类的roc曲线和auc值

首先需要说明的一点是，随机森林模型是一个集成学习方法，其中包含了多个决策树模型。因此，如果要显示决策树模型，需要先在随机森林模型中选择一个决策树进行可视化。下面是修改后的代码，其中包括显示决策树模型和多分类的ROC曲线和AUC值：

import pandas as pd 
from sklearn.ensemble import RandomForestClassifier 
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, roc_curve, auc 
import seaborn as sns 
import matplotlib.pyplot as plt 

# 读取数据 
data = pd.read_excel('E:/桌面/预测脆弱性/20230523/预测样本/预测样本.xlsx') 

# 分割训练集和验证集 
train_data = data.sample(frac=0.8, random_state=1) 
test_data = data.drop(train_data.index) 

# 定义特征变量和目标变量 
features = ['高程', '起伏度', '桥梁长', '道路长', '平均坡度', '平均地温', 'T小于0', '相态'] 
target = '交通风险' 

# 训练随机森林模型 
rf = RandomForestClassifier(n_estimators=100, random_state=1) 
rf.fit(train_data[features], train_data[target]) 

# 显示决策树模型 
estimator = rf.estimators_[0] 
plt.figure(figsize=(20,20)) 
tree.plot_tree(estimator, feature_names=features, filled=True) 
plt.show() 

# 在验证集上进行预测并计算精度、召回率和F1值等指标 
pred = rf.predict(test_data[features]) 
accuracy = accuracy_score(test_data[target], pred) 
confusion_mat = confusion_matrix(test_data[target], pred) 
classification_rep = classification_report(test_data[target], pred) 

print('Accuracy:', accuracy) 
print('Confusion matrix:') 
print(confusion_mat) 
print('Classification report:') 
print(classification_rep) 

# 输出混淆矩阵图片 
sns.heatmap(confusion_mat, annot=True, cmap="Blues") 
plt.show() 

# 计算多分类的ROC曲线和AUC值 
n_classes = len(set(data[target])) 
y_test = pd.get_dummies(test_data[target]).values 
y_score = rf.predict_proba(test_data[features]) 
fpr = dict() 
tpr = dict() 
roc_auc = dict() 
for i in range(n_classes): 
    fpr[i], tpr[i], _ = roc_curve(y_test[:, i], y_score[:, i]) 
    roc_auc[i] = auc(fpr[i], tpr[i]) 

# 绘制ROC曲线 
plt.figure() 
colors = ['red', 'green', 'blue', 'orange'] 
for i, color in zip(range(n_classes), colors): 
    plt.plot(fpr[i], tpr[i], color=color, label='ROC curve of class {0} (AUC = {1:0.2f})'.format(i, roc_auc[i])) 
plt.plot([0, 1], [0, 1], 'k--') 
plt.xlim([0.0, 1.0]) 
plt.ylim([0.0, 1.05]) 
plt.xlabel('False Positive Rate') 
plt.ylabel('True Positive Rate') 
plt.title('Multi-class ROC curves') 
plt.legend(loc="lower right") 
plt.show() 

# 读取新数据文件并预测结果 
new_data = pd.read_excel('E:/桌面/预测脆弱性/20230523/预测样本/预测结果/交通风险预测096.xlsx') 
new_pred = rf.predict(new_data[features]) 
new_data['交通风险预测结果'] = new_pred 
new_data.to_excel('E:/桌面/预测脆弱性/20230523/预测样本/预测结果/交通风险预测096结果.xlsx', index=False)

修改后的代码中，首先使用`estimators_`属性选择了随机森林模型中的一个决策树，然后使用`plot_tree`函数对其进行可视化。接着，计算多分类的ROC曲线和AUC值，并使用`roc_curve`和`auc`函数分别计算每个类别的ROC曲线和AUC值。最后，使用`plt.plot`函数绘制多分类的ROC曲线，并在图例中显示每个类别的AUC值。

Task: - Download the UCM dataset from the official source or any reputable repository. - Preprocess the dataset by splitting it into training, validation, and test sets. Please use an 80-10-10 split, respectively. - Choose a suitable deep-learning algorithm for scene classification. You can use popular classifiers such as Convolutional Neural Networks (CNNs). - Implement the chosen algorithm in Python, using appropriate libraries. - Train the classifier using the training set and tune the hyperparameters using the validation set. - Evaluate the trained classifier on the test set using the following metrics: Accuracy: The overall classification accuracy. Precision: The ability to correctly classify positive samples. Recall: The ability to correctly identify positive samples. F1-score: The harmonic mean of precision and recall. Confusion Matrix: A table showing the number of correct and incorrect predictions for each class. Report: Prepare a one-page report summarizing the results of your scene classification experiment and the architecture of your network. Submit your Python code along with the one-page report summarizing the results. Make sure to organize your code and report in a clear and readable manner.

Sure, here's an outline of the steps you'll need to take to complete this task:

1. Download the UCM dataset from the official source or any reputable repository.
2. Preprocess the dataset by splitting it into training, validation, and test sets. Use an 80-10-10 split, respectively. You can use a library like scikit-learn to do this.
3. Choose a suitable deep-learning algorithm for scene classification. Convolutional Neural Networks (CNNs) are a popular choice for image classification tasks like this.
4. Implement the chosen algorithm in Python, using appropriate libraries such as TensorFlow or PyTorch.
5. Train the classifier using the training set and tune the hyperparameters using the validation set.
6. Evaluate the trained classifier on the test set using the following metrics: accuracy, precision, recall, F1-score, and confusion matrix. You can use libraries like scikit-learn to compute these metrics.
7. Prepare a one-page report summarizing the results of your scene classification experiment and the architecture of your network. Include any relevant information such as which hyperparameters you tuned and which ones you used for the final model.

Here's some sample code to get you started:

# Step 1: Download UCM dataset
# TODO: Download dataset and extract files

# Step 2: Preprocess dataset
from sklearn.model_selection import train_test_split

# TODO: Load dataset into memory
X_train_val, X_test, y_train_val, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
X_train, X_val, y_train, y_val = train_test_split(X_train_val, y_train_val, test_size=0.1, random_state=42)

# Step 3: Choose deep-learning algorithm
import tensorflow as tf
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense

model = tf.keras.Sequential([
    Conv2D(32, (3, 3), activation='relu', input_shape=(256, 256, 3)),
    MaxPooling2D((2, 2)),
    Conv2D(64, (3, 3), activation='relu'),
    MaxPooling2D((2, 2)),
    Conv2D(128, (3, 3), activation='relu'),
    MaxPooling2D((2, 2)),
    Conv2D(256, (3, 3), activation='relu'),
    MaxPooling2D((2, 2)),
    Flatten(),
    Dense(256, activation='relu'),
    Dense(21, activation='softmax')
])

# Step 4: Implement algorithm in Python
model.compile(optimizer='adam',
              loss='categorical_crossentropy',
              metrics=['accuracy'])

# Step 5: Train classifier
history = model.fit(X_train, y_train, epochs=10, validation_data=(X_val, y_val))

# Step 6: Evaluate trained classifier
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix

y_pred = model.predict(X_test)
y_pred_classes = np.argmax(y_pred, axis=1)
y_test_classes = np.argmax(y_test, axis=1)

accuracy = accuracy_score(y_test_classes, y_pred_classes)
precision = precision_score(y_test_classes, y_pred_classes, average='macro')
recall = recall_score(y_test_classes, y_pred_classes, average='macro')
f1 = f1_score(y_test_classes, y_pred_classes, average='macro')
confusion_mat = confusion_matrix(y_test_classes, y_pred_classes)

print("Accuracy:", accuracy)
print("Precision:", precision)
print("Recall:", recall)
print("F1-score:", f1)
print("Confusion matrix:\n", confusion_mat)

# Step 7: Prepare report
# TODO: Write report summarizing results and network architecture