python - 如何修复 cm.spectral(模块 'matplotlib.cm' 没有属性 'spectral')？

Question

我尝试从网站 https://plot.ly/scikit-learn/plot-kmeans-silhouette-analysis/ 运行代码.当我运行时发现 matplotlib 模块已经没有光谱属性。我读了 informum 另一个属性被省略了。我该如何更换它？我试过 c.get_cmp、cm.nipy_sepctral、plt.cm.spectral。但仍然是一个错误。这是所有代码:

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？特别是线 colors = cm.spectral (cluster_labels.astype (float)/n_clusters)

Answer 1

光谱颜色图已从 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)