pairwise_tukeyhsd() missing 1 required positional argument: 'groups'中文回答
时间: 2023-11-03 19:04:56 浏览: 53
这个错误提示通常是因为在调用 `pairwise_tukeyhsd()` 函数时没有传递正确的参数。具体来说,这个函数需要传递两个参数:`groups` 和 `endog`,分别表示待比较的组和对应的因变量。请检查一下你的代码,确认是否正确传递了这两个参数,并且它们的值符合你的预期。如果还有疑问,可以贴出相关的代码,方便更好地帮助你解决问题。
相关问题
pairwise_tukeyhsd() missing 1 required positional argument: 'groups'
`pairwise_tukeyhsd()` is a function from the `statsmodels.stats.multicomp` module that performs Tukey's honestly significant difference (HSD) test for multiple pairwise comparisons between group means. The error message you are seeing suggests that you did not provide the required argument `groups` when calling the function.
The `groups` argument should be a list or array-like object that specifies the group labels for each observation. For example, if you have three groups labeled "A", "B", and "C", and each group has 10 observations, you could create the `groups` array like this:
```python
import numpy as np
groups = np.array(["A"] * 10 + ["B"] * 10 + ["C"] * 10)
```
Then you could call `pairwise_tukeyhsd()` with your data and the `groups` argument:
```python
from statsmodels.stats.multicomp import pairwise_tukeyhsd
# data is a 1D array of numeric values
results = pairwise_tukeyhsd(data, groups)
```
Make sure that the length of your `groups` array matches the length of your data array, and that the group labels are in the correct order.
上述代码运行后报错了怎么修改,报错信息:Traceback (most recent call last): File "E:/PyCharm Community Edition 2020.2.2/Project/WordDict/newsim.py", line 279, in <module> sim = cosine_similarity(word_tensor, encoded_layers[0][1:-1])[0].max() TypeError: cosine_similarity() missing 1 required positional argument: 'topics'
根据报错信息,`cosine_similarity()` 函数缺少一个名为 `topics` 的参数,因此需要检查一下函数的参数是否正确。
`cosine_similarity()` 函数的正确用法应该是 `cosine_similarity(X, Y=None, dense_output=True)`,其中 `X` 为形状为 `(n_samples_X, n_features)` 的数组,`Y` 为形状为 `(n_samples_Y, n_features)` 的数组,`dense_output` 为布尔值,表示是否返回密集的输出。
因此,你需要修改 `cosine_similarity()` 函数的调用方式,确保传入了正确的参数。具体来说,你需要将 `encoded_layers[0][1:-1]` 作为第一个参数 `X`,`word_tensor` 作为第二个参数 `Y`,并将 `dense_output` 设置为 `False`,以便返回稀疏矩阵。
修改后的代码如下所示:
```
from sklearn.metrics.pairwise import cosine_similarity
...
for i in range(1, len(tokens)-1):
word = tokens[i]
if word in seed_words:
continue
word_tensor = encoded_layers[0][i].reshape(1, -1)
sim = cosine_similarity(encoded_layers[0][1:-1], word_tensor, dense_output=False)[0].max()
```
注意,新的调用方式中,`encoded_layers[0][1:-1]` 被作为第一个参数传入,而 `word_tensor` 被作为第二个参数传入。此外,`dense_output` 被设置为 `False`,以便返回稀疏矩阵。