如何修改以下代码以添加图例？

# Code source: Gae"l Varoquaux

# License: BSD 3 clause

import numpy as np

import matplotlib.pyplot as plt

from mpl_toolkits.mplot3d import Axes3D

from sklearn import decomposition

from sklearn import datasets

np.random.seed(5)

centers = [[1, 1], [-1, -1], [1, -1]]

iris = datasets.load_iris()

X = iris.data#the floating point values

y = iris.target#unsigned integers specifying group

fig = plt.figure(1, figsize=(4, 3))

plt.clf()

ax = Axes3D(fig, rect=[0, 0, .95, 1], elev=48, azim=134)

plt.cla()

pca = decomposition.PCA(n_components=3)

pca.fit(X)

X = pca.transform(X)

for name, label in [('Setosa', 0), ('Versicolour', 1), ('Virginica', 2)]:

ax.text3D(X[y == label, 0].mean(),

X[y == label, 1].mean() + 1.5,

X[y == label, 2].mean(), name,

horizontalalignment='center',

bbox=dict(alpha=.5, edgecolor='w', facecolor='w'))

# Reorder the labels to have colors matching the cluster results

y = np.choose(y, [1, 2, 0]).astype(np.float)

ax.scatter(X[:, 0], X[:, 1], X[:, 2], c=y, cmap=plt.cm.spectral,

edgecolor='k')

ax.w_xaxis.set_ticklabels([])

ax.w_yaxis.set_ticklabels([])

ax.w_zaxis.set_ticklabels([])

plt.show()

解决方法:

另一个答案中存在一些问题,OP和回答者似乎都不清楚;因此,这不是一个完整的答案,而是现有答案的附录.

> 2.2版中的matplotlib已删除光谱色图,

使用Spectral或nipy_spectral或任何other valid colormap.

> matplotlib中的任何色彩映射范围都是0到1.如果使用该范围之外的任何值调用它,

它会给你最出色的颜色.要从色图中获取颜色,您需要对值进行标准化.

这是通过Normalize实例完成的.在这种情况下,这是分散的内部.

因此,使用sc = ax.scatter(…)然后使用sc.cmap(sc.norm(value))根据散点图中使用的相同映射获取值.

因此代码应该使用

[sc.cmap(sc.norm(i)) for i in [1, 2, 0]]

>传说不在图中.这个数字是4 x 3英寸(figsize =(4,3)).

轴占据宽度的95％(rect = [0,0,.95,1]).

对图例的调用将图例的右中心点置于轴宽度的1.7倍= 4 * 0.95 * 1.7 = 6.46英寸. (bbox_to_anchor =(1.7,0.5)).

来自我方的替代建议：使图形更大(figsize =(5.5,3)),使图例适合,使轴只占图形宽度的70％,这样你就有30％的图形.将图例的左侧放置在靠近轴边界的位置(bbox_to_anchor =(1.0,.5)).

你仍然可以看到包括jupyter笔记本中的图例在内的完整图形的原因是jupyter会将所有内容保存在画布中,即使它重叠并因此放大了图形.

总的来说,代码可能看起来像

import matplotlib.pyplot as plt

from mpl_toolkits.mplot3d import Axes3D

import numpy as np; np.random.seed(5)

from sklearn import decomposition, datasets

centers = [[1, 1], [-1, -1], [1, -1]]

iris = datasets.load_iris()

X = iris.data #the floating point values

y = iris.target #unsigned integers specifying group

fig = plt.figure(figsize=(5.5, 3))

ax = Axes3D(fig, rect=[0, 0, .7, 1], elev=48, azim=134)

pca = decomposition.PCA(n_components=3)

pca.fit(X)

X = pca.transform(X)

labelTups = [('Setosa', 0), ('Versicolour', 1), ('Virginica', 2)]

for name, label in labelTups:

ax.text3D(X[y == label, 0].mean(),

X[y == label, 1].mean() + 1.5,

X[y == label, 2].mean(), name,

horizontalalignment='center',

bbox=dict(alpha=.5, edgecolor='w', facecolor='w'))

# Reorder the labels to have colors matching the cluster results

y = np.choose(y, [1, 2, 0]).astype(np.float)

sc = ax.scatter(X[:, 0], X[:, 1], X[:, 2], c=y, cmap="Spectral", edgecolor='k')

ax.w_xaxis.set_ticklabels([])

ax.w_yaxis.set_ticklabels([])

ax.w_zaxis.set_ticklabels([])

colors = [sc.cmap(sc.norm(i)) for i in [1, 2, 0]]

custom_lines = [plt.Line2D([],[], ls="", marker='.',

mec='k', mfc=c, mew=.1, ms=20) for c in colors]

ax.legend(custom_lines, [lt[0] for lt in labelTups],

loc='center left', bbox_to_anchor=(1.0, .5))

plt.show()

并生产

标签：python,scikit-learn,matplotlib

来源： https://codeday.me/bug/20190828/1747897.html