如何将轮廓分数附加到列表中
How to append silhouette score to the List
我想在循环中将轮廓分数附加到列表中。
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_score
ks = range(1, 11) # for 1 to 10 clusters
#sse = []
sil = []
for k in ks:
# Create a KMeans instance with k clusters: model
kmeans = KMeans(n_clusters = k)
# Fit model to samples
#kmeans.fit(X)
cluster_labels = kmeans.fit_predict(X) #X is dataset that preprocess already.
silhouette = silhouette_score(X, cluster_labels)
# Append the inertia to the list of inertias
#sse.append(kmeans.inertia_)
#Append silhouette to the list
sil.append(silhouette)
但是,当我使用 silhouette_score
设置轮廓时,在第 21 行出现以下错误
ValueError Traceback (most recent call last)
<ipython-input-12-2570ccf62502> in <module>()
18 #kmeans.fit(X)
19 cluster_labels = kmeans.fit_predict(X)
--->20 silhouette = silhouette_score(X, cluster_labels)
21
22
这是全部代码还是其中的一部分?如果在此之前没有代码,则很明显 X
在赋值之前未定义或使用。
因此,将行放在您分配 X
的位置,一切都会正常进行。
否则,请将完整的跟踪添加到错误中
from sklearn.datasets import make_blobs
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_samples, silhouette_score
X, y = make_blobs(n_samples=500,
n_features=2,
centers=4,
cluster_std=1,
center_box=(-10.0, 10.0),
shuffle=True,
random_state=1)
sil=[]
#start the cluster range from 2
range_n_clusters = range(2,10)
for n_clusters in range_n_clusters:
clusterer = KMeans(n_clusters=n_clusters, random_state=10)
cluster_labels = clusterer.fit_predict(X)
silhouette_avg = silhouette_score(X, cluster_labels)
print("For n_clusters =", n_clusters,
"The average silhouette_score is :", silhouette_avg)
sil.append(silhouette_avg)
这是一个将 Kmeans 聚类应用于随机样本并根据剪影分数找到最佳聚类的示例。我认为这会对您有所帮助,或者请提供更多信息
我想在循环中将轮廓分数附加到列表中。
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_score
ks = range(1, 11) # for 1 to 10 clusters
#sse = []
sil = []
for k in ks:
# Create a KMeans instance with k clusters: model
kmeans = KMeans(n_clusters = k)
# Fit model to samples
#kmeans.fit(X)
cluster_labels = kmeans.fit_predict(X) #X is dataset that preprocess already.
silhouette = silhouette_score(X, cluster_labels)
# Append the inertia to the list of inertias
#sse.append(kmeans.inertia_)
#Append silhouette to the list
sil.append(silhouette)
但是,当我使用 silhouette_score
设置轮廓时,在第 21 行出现以下错误 ValueError Traceback (most recent call last)
<ipython-input-12-2570ccf62502> in <module>()
18 #kmeans.fit(X)
19 cluster_labels = kmeans.fit_predict(X)
--->20 silhouette = silhouette_score(X, cluster_labels)
21
22
这是全部代码还是其中的一部分?如果在此之前没有代码,则很明显 X
在赋值之前未定义或使用。
因此,将行放在您分配 X
的位置,一切都会正常进行。
否则,请将完整的跟踪添加到错误中
from sklearn.datasets import make_blobs
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_samples, silhouette_score
X, y = make_blobs(n_samples=500,
n_features=2,
centers=4,
cluster_std=1,
center_box=(-10.0, 10.0),
shuffle=True,
random_state=1)
sil=[]
#start the cluster range from 2
range_n_clusters = range(2,10)
for n_clusters in range_n_clusters:
clusterer = KMeans(n_clusters=n_clusters, random_state=10)
cluster_labels = clusterer.fit_predict(X)
silhouette_avg = silhouette_score(X, cluster_labels)
print("For n_clusters =", n_clusters,
"The average silhouette_score is :", silhouette_avg)
sil.append(silhouette_avg)
这是一个将 Kmeans 聚类应用于随机样本并根据剪影分数找到最佳聚类的示例。我认为这会对您有所帮助,或者请提供更多信息