ModuleNotFoundError: No module named 'pos_neg_examples_generator'
时间: 2024-06-06 20:04:34 浏览: 29
这个错误提示意味着Python无法找到名为'pos_neg_examples_generator'的模块。可能是因为你没有正确安装该模块或者它不存在于当前的工作目录或系统路径中。你可以尝试使用以下方法解决该问题:
1. 确认你已经正确安装了该模块,你可以使用pip install pos_neg_examples_generator来安装该模块。
2. 确认该模块是否存在于当前工作目录或系统路径中,如果不存在,你需要将其添加到路径中。
3. 如果你是在虚拟环境中工作,请确保该模块已经安装到虚拟环境中,并且你已经激活了该虚拟环境。
相关问题
class_weight = {1: (pos_num+neg_num)/2/pos_num, 0: (pos_num+neg_num)/2/neg_num}
This formula calculates the class weights for a binary classification problem where the positive class has a higher number of samples than the negative class.
- `pos_num` is the number of samples in the positive class
- `neg_num` is the number of samples in the negative class
- `(pos_num neg_num)/2` calculates the average number of samples in each class
- `(pos_num neg_num)/2/pos_num` calculates the weight for the positive class as the inverse of the positive class frequency in the dataset
- `(pos_num neg_num)/2/neg_num` calculates the weight for the negative class as the inverse of the negative class frequency in the dataset
The resulting class weights will be used to balance the contribution of each class during training, so that the model can learn to classify both classes equally well.
优化import jieba def senti(text): pos_num = 0 neg_num = 0 words = jieba.lcut(text) for word in words: if word in poss: pos_num = pos_num + 1 if word in negs: neg_num = neg_num + 1 return {"pos": pos_num, "neg": neg_num} file = 'data/txts/联美控股2017.txt' text = open(file, encoding='gbk').read() senti(text)
这段代码使用了 jieba 库进行中文分词,并对文本进行情感分析。如果要对代码进行优化,可以考虑以下几点:
1. 将 jieba 库的导入语句放在函数外部,避免在每次调用函数时都重复导入该库。
2. 为了提高代码的效率,可以将情感词典 poss 和 negs 转换成 set 类型,这样可以更快地进行查找操作。
3. 可以使用 Python 的 Counter 类来统计每个情感词在文本中出现的次数,避免手动计数。
下面是优化后的代码:
```
import jieba
from collections import Counter
# 导入情感词典
with open('data/pos.txt', encoding='utf-8') as f:
poss = set(f.read().split())
with open('data/neg.txt', encoding='utf-8') as f:
negs = set(f.read().split())
# 定义情感分析函数
def senti(text):
pos_num = 0
neg_num = 0
words = jieba.lcut(text)
counter = Counter(words)
for word in counter:
if word in poss:
pos_num += counter[word]
if word in negs:
neg_num += counter[word]
return {"pos": pos_num, "neg": neg_num}
# 读取文本文件并进行情感分析
file = 'data/txts/联美控股2017.txt'
text = open(file, encoding='gbk').read()
senti(text)
```
这样一来,代码的效率会更高,而且更易于维护和扩展。
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