我尝试从网站https://plot.ly/scikit-learn/plot-kmeans-silhouette-analysis/运行代码。当我运行时，事实证明matplotlib模块已经不具有光谱属性。我读到inforum的另一个属性已被省略。我该如何更换？我已经尝试过c.get_cmp，cm.nipy_sepctral，plt.cm.spectral。但仍然是一个错误。这就是所有代码：
import plotly.plotly as py
import plotly.graph_objs as go
from plotly import tools
from __future__ import print_function
from sklearn.datasets import make_blobs
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_samples, silhouette_score
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import numpy as np
print(__doc__)
# This particular setting has one distinct cluster and 3 clusters placed close
# together.
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) # For reproducibility
range_n_clusters = [2, 3, 4, 5, 6]
figures = []
for n_clusters in range_n_clusters:
# Create a subplot with 1 row and 2 columns
fig = tools.make_subplots(rows=1, cols=2,
print_grid=False,
subplot_titles=('The silhouette plot for the various clusters.',
'The visualization of the clustered data.'))
# The 1st subplot is the silhouette plot
# The silhouette coefficient can range from -1, 1 but in this example all
# lie within [-0.1, 1]
fig['layout']['xaxis1'].update(title='The silhouette coefficient values',
range=[-0.1, 1])
# The (n_clusters+1)*10 is for inserting blank space between silhouette
# plots of individual clusters, to demarcate them clearly.
fig['layout']['yaxis1'].update(title='Cluster label',
showticklabels=False,
range=[0, len(X) + (n_clusters + 1) * 10])
# Initialize the clusterer with n_clusters value and a random generator
# seed of 10 for reproducibility.
clusterer = KMeans(n_clusters=n_clusters, random_state=10)
cluster_labels = clusterer.fit_predict(X)
# The silhouette_score gives the average value for all the samples.
# This gives a perspective into the density and separation of the formed
# clusters
silhouette_avg = silhouette_score(X, cluster_labels)
print("For n_clusters =", n_clusters,
"The average silhouette_score is :", silhouette_avg)
# Compute the silhouette scores for each sample
sample_silhouette_values = silhouette_samples(X, cluster_labels)
y_lower = 10
for i in range(n_clusters):
# Aggregate the silhouette scores for samples belonging to
# cluster i, and sort them
ith_cluster_silhouette_values = \
sample_silhouette_values[cluster_labels == i]
ith_cluster_silhouette_values.sort()
size_cluster_i = ith_cluster_silhouette_values.shape[0]
y_upper = y_lower + size_cluster_i
colors = cm.spectral(cluster_labels.astype(float) / n_clusters)
filled_area = go.Scatter(y=np.arange(y_lower, y_upper),
x=ith_cluster_silhouette_values,
mode='lines',
showlegend=False,
line=dict(width=0.5,
color=colors),
fill='tozerox')
fig.append_trace(filled_area, 1, 1)
# Compute the new y_lower for next plot
y_lower = y_upper + 10 # 10 for the 0 samples
# The vertical line for average silhouette score of all the values
axis_line = go.Scatter(x=[silhouette_avg],
y=[0, len(X) + (n_clusters + 1) * 10],
showlegend=False,
mode='lines',
line=dict(color="red", dash='dash',
width =1) )
fig.append_trace(axis_line, 1, 1)
# 2nd Plot showing the actual clusters formed
colors = matplotlib.colors.colorConverter.to_rgb(cm.spectral(float(i) / n_clusters))
colors = 'rgb'+str(colors)
clusters = go.Scatter(x=X[:, 0],
y=X[:, 1],
showlegend=False,
mode='markers',
marker=dict(color=colors,
size=4)
)
fig.append_trace(clusters, 1, 2)
# Labeling the clusters
centers_ = clusterer.cluster_centers_
# Draw white circles at cluster centers
centers = go.Scatter(x=centers_[:, 0],
y=centers_[:, 1],
showlegend=False,
mode='markers',
marker=dict(color='green', size=10,
line=dict(color='black',
width=1))
)
fig.append_trace(centers, 1, 2)
fig['layout']['xaxis2'].update(title='Feature space for the 1st feature',
zeroline=False)
fig['layout']['yaxis2'].update(title='Feature space for the 2nd feature',
zeroline=False)
fig['layout'].update(title="Silhouette analysis for KMeans clustering on sample data "
"with n_clusters = %d" % n_clusters)
figures.append(fig)
这是一个错误：
c:\python36\lib\site-packages\plotly\graph_objs\_deprecations.py:318: DeprecationWarning:
plotly.graph_objs.Font is deprecated.
Please replace it with one of the following more specific types
- plotly.graph_objs.layout.Font
- plotly.graph_objs.layout.hoverlabel.Font
- etc.
AttributeError Traceback (most recent call last)
<ipython-input-20-548945d711c4> in <module>()
47 y_upper = y_lower + size_cluster_i
48
---> 49 colors = cm.spectral(cluster_labels.astype(float) / n_clusters)
50
51 filled_area = go.Scatter(y=np.arange(y_lower, y_upper),
AttributeError: module 'matplotlib.cm' has no attribute 'spectral'
如何替换这些属性？尤其是颜色color = cm.spectral（cluster_labels.astype（float）/ n_clusters）
答案 0 :(得分：4)
光谱色图已从2.2版的matplotlib中删除，请使用"Spectral"或"nipy_spectral"或any other valid colormap。
最好通过名称字符串获取颜色图
cmap = cm.get_cmap("Spectral")
colors = cmap(a / b)