Python 使用数组重塑数据。如果数据只有一个特征，则重塑（-1，1）

我如何在一个数据集上使用metrics.silouhette_分数，该数据集有1300个图像，它们的ResNet50特征向量每个长度为2048，离散类标签介于1到9之间

import pandas as pd
import numpy as np
from sklearn.metrics import pairwise_distances
from sklearn import cluster, datasets, preprocessing, metrics
from sklearn.cluster import KMeans
df = pd.read_csv("master.csv")
labels = list(df['Q3 Theme1'])
labels_reshaped = np.ndarray(labels).reshape(-1,1)
X = open('entire_dataset__resnet50_feature_vectors.txt')
X_Data = X.read()
print('Silhouette Score:', metrics.silhouette_score(X_Data, labels_reshaped,
                                                    metric='cosine'))

我得到这个错误：

Traceback (most recent call last):
  File "/dataset/silouhette_score.py", line 8, in <module>
    labels_reshaped = np.ndarray(labels).reshape(-1,1)
ValueError: sequence too large; cannot be greater than 32

Process finished with exit code 1

Traceback (most recent call last):
  File "/dataset/silouhette_score.py", line 8, in <module>
    labels_reshaped = np.ndarray(labels).reshape(1,-1)
ValueError: sequence too large; cannot be greater than 32

Process finished with exit code 1

我得到这个错误：

Traceback (most recent call last):
  File "/dataset/silouhette_score.py", line 8, in <module>
    labels_reshaped = np.ndarray(labels).reshape(-1,1)
ValueError: sequence too large; cannot be greater than 32

Process finished with exit code 1

Traceback (most recent call last):
  File "/dataset/silouhette_score.py", line 8, in <module>
    labels_reshaped = np.ndarray(labels).reshape(1,-1)
ValueError: sequence too large; cannot be greater than 32

Process finished with exit code 1

我将此作为输出：

如何修复此代码，以便打印单个轮廓分数

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.

Process finished with exit code 1

---

我在这里粘贴了一行我的特征向量文件.txt文件：由2048个由空格分隔的数字组成

我最终能够弄明白这一点。我需要创建与sklearn所需格式相同的特征向量：

import pandas as pd
from sklearn import metrics


df = pd.read_csv("master.csv")
labels = list(df['Q3 Theme1'])
X = open('entire_dataset__resnet50_feature_vectors.txt')
#X_Data = X.read()

fv = []
for line in X:
    line = line.strip("\n")
    tmp_arr = line.split(' ')
    print(tmp_arr)
    fv.append(tmp_arr)

print(fv)
print('Silhouette Score:', metrics.silhouette_score(fv, labels,
                                                    metric='cosine'))