word_dict[word] = word_dict.get(word,0)+1 优化这段代码

可以简化为使用 defaultdict 类型的 word_dict，可以避免每次在获取字典值时都要嵌套调用 get() 方法。示例如下：

from collections import defaultdict

word_dict = defaultdict(int)
word_dict[word] += 1

这样就能方便地对 word_dict 进行更新和统计，而无需调用 get() 方法。

相关问题

把这段代码import math from collections import defaultdict corpus =["二价 二价 二价 四价 预约", "四价 四价 四价 九价 预约", "九价 九价 九价 九价 预约"] words = [] for sentence in corpus: words.append(sentence.strip().split()) # 进行词频统计 def Counter(words): word_count = [] for sentence in words: word_dict = defaultdict(int) for word in sentence: word_dict[word] += 1 word_count.append(word_dict) return word_count word_count = Counter(words) # 计算TF(word代表被计算的单词，word_dict是被计算单词所在句子分词统计词频后的字典) def tf(word, word_dict): return word_dict[word] / sum(word_dict.values()) # 统计含有该单词的句子数 def count_sentence(word, word_count): return sum([1 for i in word_count if i.get(word)]) # i[word] >= 1 # 计算IDF def idf(word, word_count): return math.log((len(word_count) / (count_sentence(word, word_count) + 1)),10) # 计算TF-IDF def tfidf(word, word_dict, word_count): return tf(word, word_dict) * idf(word, word_count) p = 1 for word_dict in word_count: print("part:{}".format(p)) p += 1 for word, cnt in word_dict.items(): print("word: {} ---- TF-IDF:{}".format(word, tfidf(word, word_dict, word_count))) print("word: {} ---- TF:{}".format(word, tf(word, word_dict))) print("word: {} ---- IDF:{}".format(word, idf(word, word_count))) print("word: {} ---- count_sentence:{}".format(word, count_sentence(word, word_count)))中计算idf的def idf(word, word_count)部分改成自定义输入权重[1，2，3]得到最终的改进后的TF-IDF值，请帮我改进一下代码

好的，根据您的要求，我修改了代码，将idf的计算方式修改为自定义输入权重[1,2,3]，代码如下：

import math
from collections import defaultdict

corpus =["二价 二价 二价 四价 预约", "四价 四价 四价 九价 预约", "九价 九价 九价 九价 预约"]
words = []
for sentence in corpus:
    words.append(sentence.strip().split())

# 进行词频统计
def Counter(words):
    word_count = []
    for sentence in words:
        word_dict = defaultdict(int)
        for word in sentence:
            word_dict[word] += 1
        word_count.append(word_dict)
    return word_count

word_count = Counter(words)

# 计算TF(word代表被计算的单词，word_dict是被计算单词所在句子分词统计词频后的字典)
def tf(word, word_dict):
    return word_dict[word] / sum(word_dict.values())

# 统计含有该单词的句子数
def count_sentence(word, word_count):
    return sum([1 for i in word_count if i.get(word)])  # i[word] >= 1

# 计算IDF
def idf(word, word_count, weights):
    return math.log((len(word_count) / (count_sentence(word, word_count) + 1)),10) * weights[word]

# 计算TF-IDF
def tfidf(word, word_dict, word_count, weights):
    return tf(word, word_dict) * idf(word, word_count, weights)

# 自定义权重
weights = {
    "二价": 1,
    "四价": 2,
    "九价": 3,
}

p = 1
for word_dict in word_count:
    print("part:{}".format(p))
    p += 1
    for word, cnt in word_dict.items():
        print("word: {} ---- TF-IDF:{}".format(word, tfidf(word, word_dict, word_count, weights)))
        print("word: {} ---- TF:{}".format(word, tf(word, word_dict)))
        print("word: {} ---- IDF:{}".format(word, idf(word, word_count, weights)))
        print("word: {} ---- count_sentence:{}".format(word, count_sentence(word, word_count)))

在这个代码中，我们增加了一个新的参数weights，用来存储自定义的权重，然后在idf计算时，乘以相应的权重。最后在计算tfidf时，将权重传递给idf函数即可。

在下面这段代码的基础上进行修改import math from collections import defaultdict corpus =["二价 二价 二价 四价 预约", "四价 四价 四价 九价 预约", "九价 九价 九价 九价 预约"] words = [] for sentence in corpus: words.append(sentence.strip().split()) # 进行词频统计 def Counter(words): word_count = [] for sentence in words: word_dict = defaultdict(int) for word in sentence: word_dict[word] += 1 word_count.append(word_dict) return word_count word_count = Counter(words) # 计算TF(word代表被计算的单词，word_dict是被计算单词所在句子分词统计词频后的字典) def tf(word, word_dict): return word_dict[word] / sum(word_dict.values()) # 统计含有该单词的句子数 def count_sentence(word, word_count): return sum([1 for i in word_count if i.get(word)]) # i[word] >= 1 # 计算IDF def idf(word, word_count): return math.log((len(word_count) / (count_sentence(word, word_count) + 1)),10) # 计算TF-IDF def tfidf(word, word_dict, word_count): return tf(word, word_dict) * idf(word, word_count) p = 1 for word_dict in word_count: print("part:{}".format(p)) p += 1 for word, cnt in word_dict.items(): print("word: {} ---- TF-IDF:{}".format(word, tfidf(word, word_dict, word_count))) print("word: {} ---- TF:{}".format(word, tf(word, word_dict))) print("word: {} ---- IDF:{}".format(word, idf(word, word_count))) print("word: {} ---- count_sentence:{}".format(word, count_sentence(word, word_count)))，将IDF进行改进，其中自定义热度权重文件weight.txt中我想存入的是每一个文档的热度权重，改进的idf值就是总文档热度权重总和除以包含某词所在的文档的热度权重之和然后再取对数，请写出改进后的python代码

以下是改进后的代码：

import math
from collections import defaultdict

corpus =["二价 二价 二价 四价 预约", "四价 四价 四价 九价 预约", "九价 九价 九价 九价 预约"]
words = []
for sentence in corpus:
    words.append(sentence.strip().split())

# 进行词频统计
def Counter(words):
    word_count = []
    for sentence in words:
        word_dict = defaultdict(int)
        for word in sentence:
            word_dict[word] += 1
        word_count.append(word_dict)
    return word_count

word_count = Counter(words)

# 读取热度权重文件
with open('weight.txt', 'r') as f:
    weight = [float(line.strip()) for line in f.readlines()]

# 计算IDF
def idf(word, word_count, weight):
    count = count_sentence(word, word_count)
    if count == 0:
        return 0
    else:
        return math.log(sum(weight) / (weighted_count_sentence(word, word_count, weight) + 1), 10)

# 统计包含该单词的文档数
def count_sentence(word, word_count):
    return sum([1 for i in word_count if i.get(word)])

# 统计包含该单词的文档的热度权重之和
def weighted_count_sentence(word, word_count, weight):
    count = 0
    for i in word_count:
        if i.get(word):
            count += weight[word_count.index(i)]
    return count

# 计算TF-IDF
def tfidf(word, word_dict, word_count, weight):
    return tf(word, word_dict) * idf(word, word_count, weight)

# 计算TF
def tf(word, word_dict):
    return word_dict[word] / sum(word_dict.values())

# 输出结果
p = 1
for word_dict in word_count:
    print("part:{}".format(p))
    p += 1
    for word, cnt in word_dict.items():
        print("word: {} ---- TF-IDF:{}".format(word, tfidf(word, word_dict, word_count, weight)))
        print("word: {} ---- TF:{}".format(word, tf(word, word_dict)))
        print("word: {} ---- IDF:{}".format(word, idf(word, word_count, weight)))
        print("word: {} ---- count_sentence:{}".format(word, count_sentence(word, word_count)))