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。

###########每个主题对应词语 n_top_words = 30 tf_feature_names = tf_vectorizer.get_feature_names() topic_word = print_top_words(lda, tf_feature_names, n_top_words)添加输出每个主题下词语的概率分布（归一化处理）至新的Excel表代码

这是一个比较复杂的任务，需要使用Python的pandas库和openpyxl库来实现。下面是一个示例代码，假设已经有了主题模型lda和tf_vectorizer。

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_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, 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_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)

# 示例代码
n_top_words = 30
tf_feature_names = tf_vectorizer.get_feature_names()
print_topic_word_distribution(lda, tf_feature_names, n_top_words)
output_topic_word_distribution_to_excel(lda, tf_feature_names, n_top_words, "topic_word_distribution.xlsx")

在示例代码中，我们首先定义了一个函数get_topic_word_distribution来获取主题下词语的概率分布。该函数使用lda.transform方法将词向量矩阵转化为主题分布矩阵，然后返回第一行（也就是我们需要的概率分布数组）。接着定义了一个函数print_topic_word_distribution来打印所有主题下词语的概率分布。最后定义了一个函数output_topic_word_distribution_to_excel来输出主题下词语的概率分布至Excel表格。

在output_topic_word_distribution_to_excel函数中，我们首先创建了一个Excel工作簿和工作表，并添加了表头。然后使用get_topic_word_distribution函数获取主题下词语的概率分布，并循环添加到Excel表格中。最后使用wb.save方法保存Excel文件。

请注意，由于Excel文件的写入速度比较慢，如果处理的数据量比较大，可能需要等待一段时间才能完成操作。