import pandas as pd from openpyxl import Workbook # 获取主题下词语的概率分布 def get_topic_word_distribution(lda, tf_feature_names): arr = lda.transform(tf_vectorizer.transform([' '.join(tf_feature_names)])) return arr[0] # 打印主题下词语的概率分布 def print_topic_word_distribution(lda, tf_feature_names, n_top_words): dist = get_topic_word_distribution(lda, tf_feature_names) for i in range(lda.n_components): print("Topic {}: {}".format(i, ', '.join("{:.4f}".format(x) for x in dist[i]))) # 输出每个主题下词语的概率分布至Excel表格 def output_topic_word_distribution_to_excel(lda, tf_feature_names, n_top_words,filename): # 创建Excel工作簿和工作表 wb = Workbook() ws = wb.active ws.title = "Topic Word Distribution" # 添加表头 ws.cell(row=1, column=1).value = "Topic" for j in range(n_top_words): ws.cell(row=1, column=j+2).value = tf_feature_names[j] # 添加每个主题下词语的概率分布 dist = get_topic_word_distribution(lda, tf_feature_names) for i in range(lda.n_components): ws.cell(row=i+2, column=1).value = i for j in range(n_top_words): ws.cell(row=i+2, column=j+2).value = dist[i][j] # 保存Excel文件 wb.save(filename) n_top_words = 30 tf_feature_names = tf_vectorizer.get_feature_names() topic_word = print_topic_word_distribution(lda, tf_feature_names, n_top_words)报错Traceback (most recent call last): File "D:\python\lda3\data_1.py", line 157, in <module> topic_word = print_topic_word_distribution(lda, tf_feature_names, n_top_words) File "D:\python\lda3\data_1.py", line 130, in print_topic_word_distribution print("Topic {}: {}".format(i, ', '.join("{:.4f}".format(x) for x in dist[i]))) TypeError: 'numpy.float64' object is not iterable

这个错误提示是因为 `dist[i]` 是一个 `numpy.float64` 类型的对象，不是一个可迭代的对象，无法使用 `{:.4f}` 格式化它。你可以尝试修改 print_topic_word_distribution 函数中的代码，将 `dist[i]` 直接格式化为字符串即可，例如：

def print_topic_word_distribution(lda, tf_feature_names, n_top_words):
    dist = get_topic_word_distribution(lda, tf_feature_names)
    for i in range(lda.n_components):
        dist_str = ', '.join("{:.4f}".format(dist[i]))  # 将 dist[i] 直接格式化为字符串
        print("Topic {}: {}".format(i, dist_str))

另外，你在调用 `print_topic_word_distribution` 函数时，似乎没有给 `topic_word` 赋值，可以直接将调用语句改为 `print_topic_word_distribution(lda, tf_feature_names, n_top_words)` 即可。

相关问题

import pandas as pd from openpyxl import Workbook # 获取主题下词语的概率分布 def get_topic_word_distribution(lda, tf_feature_names): arr = lda.transform(tf_vectorizer.transform([' '.join(tf_feature_names)])) return arr[0] # 打印主题下词语的概率分布 def print_topic_word_distribution(lda, tf_feature_names, n_top_words,n_topics): dist = get_topic_word_distribution(lda, tf_feature_names,n_topics) for i in range(n_topics): print("Topic {}: {}".format(i, ', '.join("{:.4f}".format(x) for x in dist[i]))) # 输出每个主题下词语的概率分布至Excel表格 def output_topic_word_distribution_to_excel(lda, tf_feature_names, n_top_words, n_topics,filename): # 创建Excel工作簿和工作表 wb = Workbook() ws = wb.active ws.title = "Topic Word Distribution" # 添加表头 ws.cell(row=1, column=1).value = "Topic" for j in range(n_top_words): ws.cell(row=1, column=j+2).value = tf_feature_names[j] # 添加每个主题下词语的概率分布 dist = get_topic_word_distribution(lda, tf_feature_names, n_topics) for i in range(n_topics): ws.cell(row=i+2, column=1).value = i for j in range(n_top_words): ws.cell(row=i+2, column=j+2).value = dist[i][j] # 保存Excel文件 wb.save(filename) n_top_words = 30 tf_feature_names = tf_vectorizer.get_feature_names() topic_word = print_topic_word_distribution(lda, tf_feature_names, n_top_words, n_topics) #print_topic_word_distribution(lda, tf_feature_names, n_top_words) output_topic_word_distribution_to_excel(lda, tf_feature_names, n_top_words, n_topics, "topic_word_distribution.xlsx")报错Traceback (most recent call last): File "D:\python\lda3\data_1.py", line 157, in <module> topic_word = print_topic_word_distribution(lda, tf_feature_names, n_top_words, n_topics) File "D:\python\lda3\data_1.py", line 128, in print_topic_word_distribution dist = get_topic_word_distribution(lda, tf_feature_names,n_topics) TypeError: get_topic_word_distribution() takes 2 positional arguments but 3 were given

错误提示表明 get_topic_word_distribution() 函数只需要两个位置参数，但是在 print_topic_word_distribution() 函数中却传递了三个位置参数。检查一下代码，发现 print_topic_word_distribution() 函数中还传递了一个 n_topics 参数，它应该被移除。修改代码如下：

def print_topic_word_distribution(lda, tf_feature_names, n_top_words):
    dist = get_topic_word_distribution(lda, tf_feature_names)
    for i in range(lda.n_components):
        print("Topic {}: {}".format(i, ', '.join("{:.4f}".format(x) for x in dist[i])))
        
def output_topic_word_distribution_to_excel(lda, tf_feature_names, n_top_words, filename):
    # 创建Excel工作簿和工作表
    wb = Workbook()
    ws = wb.active
    ws.title = "Topic Word Distribution"
    
    # 添加表头
    ws.cell(row=1, column=1).value = "Topic"
    for j in range(n_top_words):
        ws.cell(row=1, column=j+2).value = tf_feature_names[j]
    
    # 添加每个主题下词语的概率分布
    dist = get_topic_word_distribution(lda, tf_feature_names)
    for i in range(lda.n_components):
        ws.cell(row=i+2, column=1).value = i
        for j in range(n_top_words):
            ws.cell(row=i+2, column=j+2).value = dist[i][j]
    
    # 保存Excel文件
    wb.save(filename)

注意，这里需要使用 lda.n_components 属性来获取主题数，因为 lda.transform() 返回的数组的第一维的长度就等于主题数。

import pandas as pd import os import shutil import time from openpyxl import load_workbook import logging

好的，这段代码是用Python编写的一些常用库的导入语句，它们分别是：

1. `pandas`：一个强大的数据处理库，常用于数据分析和操作大型表格（DataFrame）。
2. `os`：操作系统模块，提供文件和目录操作的功能，如读取、写入文件路径，以及处理文件和目录结构。
3. `shutil`：高级文件和文件夹操作模块，支持复制、移动、删除等文件系统操作。
4. `time`：时间处理模块，可以获取当前时间、睡眠等待等，对于定时任务很有帮助。
5. `openpyxl`：一个用于读写Excel .xlsx/xlsm文件的库，主要用于处理电子表格数据。
6. `logging`：日志记录模块，用于创建和管理程序运行过程中的详细信息记录，有助于调试和错误追踪。

这段代码通常会在需要读取Excel数据并进行一些文件操作，同时需要记录日志的场景下使用。例如，可能会加载一个Excel文件到pandas DataFrame，然后对数据进行处理，期间通过`logging`记录关键步骤的信息。