“c”参数看起来像是一个单一的数值型RGB或RGBA序列。

Question

我在我的Jupyter笔记本中遇到了以下错误。我已将mathplotlib更新到最新版本，但仍然收到错误信息：

'c'参数看起来像一个单一的数值RGB或RGBA序列，应该避免这种情况，因为如果它的长度与'x'和'y'匹配，那么值映射会优先处理。 如果您确实想为所有点指定相同的RGB或RGBA值，请使用一个具有单行的2-D数组。

X=lab3_data
range_n_clusters = [2, 3, 4, 5, 6,7,8]

for n_clusters in range_n_clusters:
# Create a subplot with 1 row and 2 columns
fig, (ax1, ax2) = plt.subplots(1, 2)
fig.set_size_inches(18, 7)

# 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]
ax1.set_xlim([0, 1])
# The (n_clusters+1)*10 is for inserting blank space between silhouette
# plots of individual clusters, to demarcate them clearly.
ax1.set_ylim([0, len(X) + (n_clusters + 1) * 10])

# Initialize the clusterer with n_clusters value and a random generator
# seed of 10 for reproducibility.
clusterer = cluster.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

    color = cm.nipy_spectral(float(i) / n_clusters)
    ax1.fill_betweenx(np.arange(y_lower, y_upper),
                      0, ith_cluster_silhouette_values,
                      facecolor=color, edgecolor=color, alpha=0.7)

    # Label the silhouette plots with their cluster numbers at the middle
    ax1.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))

    # Compute the new y_lower for next plot
    y_lower = y_upper + 10  # 10 for the 0 samples

ax1.set_title("The silhouette plot for the various clusters.")
ax1.set_xlabel("The silhouette coefficient values")
ax1.set_ylabel("Cluster label")

# The vertical line for average silhouette score of all the values
ax1.axvline(x=silhouette_avg, color="red", linestyle="--")

ax1.set_yticks([])  # Clear the yaxis labels / ticks
ax1.set_xticks([0, 0.2, 0.4, 0.6, 0.8, 1])

# 2nd Plot showing the actual clusters formed
# append the cluster centers to the dataset
lab3_data_and_centers = np.r_[lab3_data,clusterer.cluster_centers_]
# project both th data and the k-Means cluster centers to a 2D space
XYcoordinates = manifold.MDS(n_components=2).fit_transform(lab3_data_and_centers)
# plot the transformed examples and the centers
# use the cluster assignment to colour the examples
# plot the transformed examples and the centers
# use the cluster assignment to colour the examples

clustering_scatterplot(points=XYcoordinates[:-n_clusters,:], 
                       labels=cluster_labels,
                       centers=XYcoordinates[-n_clusters:,:], 
                       title='MDS')

plt.suptitle(("Silhouette analysis for KMeans clustering on sample data "
              "with n_clusters = %d" % n_clusters),
             fontsize=14, fontweight='bold')


plt.show()

- niallo27

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请 1）包括您的导入 2）发布完整的错误跟踪（不清楚错误发生的确切位置）3）修复您的标签（听起来更像是 matplotlib 问题，而不是 machine-learning 或 jupyter-notebook 的问题） - desertnaut

我们只能猜测clustering_scatterplot的作用。 - Michel de Ruiter

有没有一种方法可以获取此消息的源代码行？ - isabella

Answer 1

你也可以使用以下方法将c参数变为二维数组：

    c=color.reshape(1,-1)

或者

    c=np.array([color])

或者只需将原始颜色数组改成2D：

    color = cm.nipy_spectral(float(i) / n_clusters).reshape(1,-1)

p.s.: 因为我需要 50 声望才能评论，所以我只是打开了一个新答案，虽然这应该只是在 D Adams 的解决方案下使用内置的 numpy.atleast_2D() 函数的评论。

- questionto42

Answer 2

作为解决方法，请放置：

 from matplotlib.axes._axes import _log as matplotlib_axes_logger
 matplotlib_axes_logger.setLevel('ERROR')

- Max Kleiner

Answer 3

首先生成数据并定义颜色：

import numpy
import matplotlib
import matplotlib.pyplot

#Make the color you actually want:
Color = numpy.array([.5, .6, .7])

#Make some data:
Vals = numpy.random.uniform( size = (10, 3) )
PointCount = Vals.shape[0]
Xvals = Vals[:, 0]
Yvals = Vals[:, 1]
Zvals = Vals[:, 2]

步骤二：重现问题：

#2D: Produce the warning
fig = matplotlib.pyplot.figure() 
subplot = fig.add_subplot(111)
matplotlib.pyplot.scatter( Xvals, Yvals, c= Color,   )

'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.

首先的解决方案是创建一个包含所需颜色副本的数组：

#Illustrate how to repeat a numpy array:
ValsCount = Vals.shape[0]
ColorsRepeated = numpy.repeat(numpy.atleast_2d(Color), ValsCount, axis = 0)
print ('ColorsRepeated')
print (ColorsRepeated)

#2D: Make scatter plot without color warning using repeat
fig = matplotlib.pyplot.figure() 
subplot = fig.add_subplot(111)
matplotlib.pyplot.scatter( Xvals, Yvals, c= ColorsRepeated, )

#3D: Make scatter plot without color warning using repeat
fig = matplotlib.pyplot.figure() 
subplot = fig.add_subplot(111, projection='3d')
matplotlib.pyplot.scatter( Xvals,  Yvals, Zvals, c=ColorsRepeated,  )

ColorsRepeated
[[0.5 0.6 0.7]
 [0.5 0.6 0.7]
 [0.5 0.6 0.7]
 [0.5 0.6 0.7]
 [0.5 0.6 0.7]
 [0.5 0.6 0.7]
 [0.5 0.6 0.7]
 [0.5 0.6 0.7]
 [0.5 0.6 0.7]
 [0.5 0.6 0.7]]

另一种解决方案是使用 matplotlib.pyplot.plot，它不会像 matplotlib.pyplot.scatter 那样提示警告，并且您可以通过常规绘图命令避免颜色重复问题，只需不连接点即可。

#2D: Make regular plot without using repeat
fig = matplotlib.pyplot.figure() 
subplot = fig.add_subplot(111)
matplotlib.pyplot.plot( Xvals, Yvals, c= Color, marker = '.', linestyle = '', )

另一个解决方案是将颜色作为一个只有一行的二维数组，这样可以解决 matplotlib.pyplot.scatter 的问题，但会导致 matplotlib.pyplot.plot 报错：

#2D: Use single row in 2D array to avoid warning
fig = matplotlib.pyplot.figure() 
subplot = fig.add_subplot(111)
matplotlib.pyplot.scatter( Xvals,  Yvals, c= numpy.atleast_2d(Color), )

使用普通的matplotlib.pyplot.plot命令绘制一行 2D 颜色图时会抛出错误：

#2D: Try and fail to use a single row in 2D array in a regular plot
fig = matplotlib.pyplot.figure() 
subplot = fig.add_subplot(111)
matplotlib.pyplot.plot( Xvals, Yvals,  c= numpy.atleast_2d(Color),  marker = '.', linestyle = '', )

ValueError: Invalid RGBA argument: array([[0.5, 0.6, 0.7]])

结论: 在颜色方面，matplotlib.pyplot.plot和matplotlib.pyplot.scatter的行为有所不同。matplotlib.pyplot.plot需要一个一维数组，而matplotlib.pyplot.scatter则需要一个二维数组。这个二维数组可以是单行，也可以是重复的，或者每个数据点使用不同的颜色。如果Matplotlib社区能够添加一个if语句来自动进行重复操作并删除警告，那将会很好。

- D Adams

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谢谢，对于散点图，最后一个解决方案可以正常工作。 - Mathador

Answer 4

在最新版本的matplotlib（3.0.3）中，参数“c”应该是一个二维数组。如果“c”的长度与“x”和“y”的长度相匹配，则每个点的颜色对应于“c”的元素。如果您想让所有点显示相同的颜色，“c”应该是一个带有单行的二维数组，例如c=np.array([0.5, 0.5, 0.5])。 祝一切顺利！ 原始回答: "最初的回答"

- zzd

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c=np.array([0.5, 0.5, 0.5]) 是一个一维数组。 - D A

Answer 5

 c=np.array([0.5, 0.5, 0.5]).reshape(1,-1)

- elyte5star

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如果您解释一下您提供的代码如何回答这个问题，那么这将是一个更好的答案。 - pppery