如果数据具有单个功能，则可以使用array.reshape（-1，1）重塑数据

Question

How can I use metrics.silouhette_score on a dataset which has 1300 images that I have their ResNet50 feature vectors (each of length 2048) and a discrete class label between 1 to 9?

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'))

I get this error:

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

For this other code:

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'))

I get this error:

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

If I run this other code:

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

I get this as an output: https://pastebin.com/raw/hk2axdWL

How can I fix this code so that I can print the single silhouette score?

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

I have pasted one line of my feature vector file (a .txt file) here: https://pastebin.com/raw/hk2axdWL (consists of 2048 numbers separated by space)

Answer 1

I was eventually able to figure this out. I needed to create the feature vector same exact format as sklearn required them:

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'))