不能将自定义非线性颜色图与 imshow 结合使用

Question

我正在尝试使用自定义颜色图来显示 ConfusionMatrixDisplay 对象，使其在 0 和 50 之间的范围比使用 this answer 的 50 和 100 之间的范围更小。

from sklearn.datasets import make_classification
from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from matplotlib.colors import LinearSegmentedColormap
import matplotlib.pyplot as plt
import numpy as np

plt.rcParams["figure.figsize"] = (15, 15)
font = {'family' : 'DejaVu Sans',
    'weight' : 'bold',
    'size'   : 22}
plt.rc('font', **font)


class nlcmap(LinearSegmentedColormap):
    def __init__(self, cmap, levels):
        self.cmap = cmap
        self.N = cmap.N
        self.monochrome = self.cmap.monochrome
        self.levels = np.asarray(levels, dtype='float64')
        self._x = self.levels
        self.levmax = self.levels.max()
        self.transformed_levels = np.linspace(0.0, self.levmax, len(self.levels))

    def __call__(self, xi, alpha=1.0, **kw):
        yi = np.interp(xi, self._x, self.transformed_levels)
        return self.cmap(yi / self.levmax, alpha)


levels = [0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 100]

cmap_nonlin = nlcmap(plt.cm.viridis, levels)
X, y = make_classification(random_state=0)
X_train, X_test, y_train, y_test = train_test_split(X, y,
                                            random_state=0)
clf = SVC(random_state=0)
clf.fit(X_train, y_train)
SVC(random_state=0)
predictions = clf.predict(X_test)
cm = confusion_matrix(y_test, predictions, labels=clf.classes_)
disp = ConfusionMatrixDisplay(confusion_matrix=cm,
                             display_labels=clf.classes_)
lin_cmap = plt.cm.viridis
levels = [0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 100]
cmap_nonlin = nlcmap(plt.cm.viridis, levels)
fig, ax = plt.subplots()
im = disp.plot(cmap=cmap_nonlin, colorbar=False)
disp.ax_.get_images()[0].set_clim(0, 100)
disp.figure_.colorbar(disp.im_, orientation="horizontal", pad=0.1)
plt.savefig("test.png")

产生以下错误：

Traceback (most recent call last):
  File "/Users/me/anaconda3/envs/myenv/lib/python3.6/site-packages/matplotlib/backends/backend_macosx.py", line 61, in _draw
    self.figure.draw(renderer)
  File "/Users/me/anaconda3/envs/myenv/lib/python3.6/site-packages/matplotlib/artist.py", line 41, in draw_wrapper
    return draw(artist, renderer, *args, **kwargs)
  File "/Users/me/anaconda3/envs/myenv/lib/python3.6/site-packages/matplotlib/figure.py", line 1864, in draw
    renderer, self, artists, self.suppressComposite)
  File "/Users/me/anaconda3/envs/myenv/lib/python3.6/site-packages/matplotlib/image.py", line 131, in _draw_list_compositing_images
    a.draw(renderer)
  File "/Users/me/anaconda3/envs/myenv/lib/python3.6/site-packages/matplotlib/artist.py", line 41, in draw_wrapper
    return draw(artist, renderer, *args, **kwargs)
  File "/Users/me/anaconda3/envs/myenv/lib/python3.6/site-packages/matplotlib/cbook/deprecation.py", line 411, in wrapper
    return func(*inner_args, **inner_kwargs)
  File "/Users/me/anaconda3/envs/myenv/lib/python3.6/site-packages/matplotlib/axes/_base.py", line 2747, in draw
    mimage._draw_list_compositing_images(renderer, self, artists)
  File "/Users/me/anaconda3/envs/myenv/lib/python3.6/site-packages/matplotlib/image.py", line 131, in _draw_list_compositing_images
    a.draw(renderer)
  File "/Users/me/anaconda3/envs/myenv/lib/python3.6/site-packages/matplotlib/artist.py", line 41, in draw_wrapper
    return draw(artist, renderer, *args, **kwargs)
  File "/Users/me/anaconda3/envs/myenv/lib/python3.6/site-packages/matplotlib/image.py", line 646, in draw
    renderer.draw_image(gc, l, b, im)
TypeError: Cannot cast array data from dtype('float64') to dtype('uint8') according to the rule 'safe'

似乎错误与 imshow 和自定义颜色图有关，因为我可以在没有 sklearn 的情况下重现：

fig, ax = plt.subplots()
ax.imshow(np.array([[10, 15], [20, 30]]), cmap=cmap_nonlin)

有什么想法吗？如果可能的话，我希望修改颜色图而不是数据本身。

Answer 1

根据 LinearSegmentedColormaps 上的matplotlib's doc，可以执行以下操作来改变具有快速变化段和慢速变化段的段之间的对比度。

在这种情况下，要回答我的问题，让我们在 0 到 50 之间设置比 50 到 100 之间更精细的范围，但我的解决方案可以通过更改级别扩展到任意数量的不同节奏段：

import matplotlib.pyplot as plt
import numpy as np
import matplotlib.colors as colors
# A dict with keys < 1. in ascending order associated with ordered associated value in the colormap < 1. in ascending order as well
# In this example we have 90% of the variation of the colormap in its left part and the remaining 10% in the right part
levels = {0.5: 0.9}
# One could provide for instance the following dict
# levels = {0.4:0.8, 0.5:0.9} to have 80% of variations between 0 and 40% of the colormap then 10% between 40 and 50% and then the remaining 10% for the rest
cdict = {"red": None, "green": None, "blue": None}
num_values_per_segment = 50
for k, v in cdict.items():
    cdict[k] = []
    # We start the first segment by 0. both for value and cmap_value
    left_val = 0.
    left_cmap_val = 0.
    for val, cmap_val in levels.items():
        values = np.linspace(left_val, val, num_values_per_segment).tolist()
        dynamic_range = np.linspace(left_cmap_val, cmap_val, num_values_per_segment).tolist()
        for i, (v, r) in enumerate(zip(values, dynamic_range)):
            cdict[k].append((v, r, r))
        left_val = val
        left_cmap_val = cmap_val
    # Last segment towards 1.
    values = np.linspace(val, 1., num_values_per_segment).tolist()
    dynamic_range = np.linspace(cmap_val, 1., num_values_per_segment).tolist()
    for i, (v, r) in enumerate(zip(values, dynamic_range)):
        cdict[k].append((v, r, r))
    
# Mapping levels to colormap
cmap = plt.cm.viridis
for k, v in cdict.items():
    if k == "red":
        for i in range(len(v)):
            cdict[k][i] = (v[i][0], cmap(v[i][1])[0], cmap(v[i][2])[0])
    elif k == "green":
        for j in range(len(v)):
            cdict[k][j] = (v[j][0], cmap(v[j][1])[1], cmap(v[j][2])[1])
    elif k == "blue":
        for l in range(len(v)):
            cdict[k][l] = (v[l][0], cmap(v[l][1])[2], cmap(v[l][2])[2])
    else:
        raise ValueError("Color not recognized")
    cdict[k] = tuple(cdict[k])

cmap_nonlin = colors.LinearSegmentedColormap('MyCustomCMap', cdict)
fig, ax = plt.subplots()
my_image = np.array([[30, 45], [25, 10]])
confusion = ax.imshow(my_image, cmap=cmap_nonlin, vmin=0, vmax=100)
plt.colorbar(confusion, ax=ax)
plt.waitforbuttonpress()

生成的 cmap_nonlin 对象可以与 imshow 结合使用，没有任何问题：