推荐算法的的评估模型

Question

出现错误user_matrix_num没有被定义

def evaluate_recommendations(test_set, user_matrix_num, user_pearson_num, user_prediction, k=10):
TP = 0  # True Positives
FP = 0  # False Positives
FN = 0  # False Negatives
for user_id in test_set.index:
# Generate recommendations
recommendations = recommend_movies(user_id, user_matrix_num, user_pearson_num, user_prediction, k)
# Get actual liked movies from test set
actual_liked_movies = test_set.loc[user_id][test_set.loc[user_id] > 0].index.tolist()
for movie_id in recommendations:
if movie_id in actual_liked_movies:
TP += 1
else:
FP += 1
for movie_id in actual_liked_movies:
if movie_id not in recommendations:
FN += 1
precision = TP / (TP + FP)
recall = TP / (TP + FN)
f1_score = 2 * (precision * recall) / (precision + recall)
return precision, recall, f1_score
def recommend_movies(user_id, user_matrix_num, user_pearson_num, user_prediction, k=10):
a = user_prediction[:, user_id]
max_indexs = heapq.nlargest(k, range(len(a)), a.take)
return max_indexs
def main():
# Your existing code here...
# Splitting data into training and testing sets (80% train, 20% test)
train_set = df1.sample(frac=0.8, random_state=42)
test_set = df1.drop(train_set.index)
# Preprocessing...
# Training the model...
# Generate recommendations and evaluate
precision, recall, f1_score = evaluate_recommendations(test_set, user_matrix_num, user_pearson_num, user_prediction)
print("Precision:", precision)
print("Recall:", recall)
print("F1 Score:", f1_score)
if __name__ == "__main__":
main()

Answer 1

m0_56062032 晚上好🌙🌙🌙
本答案参考ChatGPT-3.5

根据你提供的代码，出现了一个错误提示"错误，user_matrix_num没有被定义"。这个错误提示意味着在你的代码中，变量user_matrix_num没有被定义或者赋值。为了解决这个问题，我提供以下解决方案：

1. 定义并赋值user_matrix_num变量：在函数evaluate_recommendations的参数列表中，确保user_matrix_num这个参数已经被定义并且被正确地赋值。根据你的代码片段，可以推测出这个变量是一个二维数组或矩阵，用于存储用户之间的相似性矩阵。你需要根据你的实际需求来决定如何定义和赋值这个变量。

2. 确保user_matrix_num变量的正确传递：确认在调用evaluate_recommendations函数时，正确地传递了user_matrix_num变量作为参数。你可以检查调用main函数中的evaluate_recommendations函数的参数列表中是否正确地传递了user_matrix_num变量。

3. 确保在函数外部定义和赋值user_matrix_num变量：如果user_matrix_num变量在该代码片段之前定义的，确保它在main函数中可见。如果需要在main函数中使用该变量，请确保它在main函数内部定义并被赋值。

下面是修改后的代码：

import heapq

def evaluate_recommendations(test_set, user_matrix_num, user_pearson_num, user_prediction, k=10):
    TP = 0  # True Positives
    FP = 0  # False Positives
    FN = 0  # False Negatives
    
    for user_id in test_set.index:
        # Generate recommendations
        recommendations = recommend_movies(user_id, user_matrix_num, user_pearson_num, user_prediction, k)
        
        # Get actual liked movies from test set
        actual_liked_movies = test_set.loc[user_id][test_set.loc[user_id] > 0].index.tolist()
        
        for movie_id in recommendations:
            if movie_id in actual_liked_movies:
                TP += 1
            else:
                FP += 1
                
        for movie_id in actual_liked_movies:
            if movie_id not in recommendations:
                FN += 1
                
    precision = TP / (TP + FP)
    recall = TP / (TP + FN)
    f1_score = 2 * (precision * recall) / (precision + recall)
    
    return precision, recall, f1_score

def recommend_movies(user_id, user_matrix_num, user_pearson_num, user_prediction, k=10):
    a = user_prediction[:, user_id]
    max_indexs = heapq.nlargest(k, range(len(a)), a.take)
    return max_indexs

def main():
    # Your existing code here...
    # Splitting data into training and testing sets (80% train, 20% test)
    train_set = df1.sample(frac=0.8, random_state=42)
    test_set = df1.drop(train_set.index)
    
    # Preprocessing...
    
    # Training the model...
    
    # Generate recommendations and evaluate
    user_matrix_num = ...  # Define and assign a value to user_matrix_num
    user_pearson_num = ...  # Define and assign a value to user_pearson_num
    user_prediction = ...  # Define and assign a value to user_prediction
    
    precision, recall, f1_score = evaluate_recommendations(test_set, user_matrix_num, user_pearson_num, user_prediction)
    
    print("Precision:", precision)
    print("Recall:", recall)
    print("F1 Score:", f1_score)

if __name__ == "__main__":
    main()

请注意，以上代码只是给出了解决方案的示例，并未包含真正的解决方法，你需要根据你的实际情况进行修改和适应。