Python 朴素贝叶斯训练问题 - cannoy perform reduce with flexible type

如何解决Python 朴素贝叶斯训练问题 - cannoy perform reduce with flexible type

我的目标是训练贝叶斯模型来审查正面和负面句子。我有 6000 个词来训练模型，想用“我感觉很开心”这样的句子来测试它。

我目前正在将 numpy 与 sklearn 一起使用，但遇到类型错误的问题：“无法使用灵活类型执行 reduce”。

我有一个像 words = ["good","happy","bad","sad"] 这样的单词列表和一个对应的列表 wordTypes = ["positive","positive","negative","negative"]。我试图简化它以得到一些工作，所以结果是下面的代码。我还在 X 上使用了“str”而不是正负，以及方括号而不是方括号。

from sklearn.naive_bayes import GaussianNB
import numpy as np
nv = GaussianNB()

X = [["happy","positive"],["good",["bad","negative"],["poor","negative"]]
y = ["positive","negative"]
     
nv.fit(X,y)

我也尝试过如下，但得到以下结果：ValueError: Expected 2D array,got 1D array instead: array=['happy' 'good' 'bad' 'poor']. Reshape your data either using array.reshape(-1,1) if your data has a single feature or array.reshape(1,-1) if it contains a single sample.

X = ["happy","good","poor"]
y = ["positive","negative"]         

我是否误解了它们的结构？