绘制PCA的某些尺寸时会出现3D绘图和图例

Question

我有以下数据和标签，我通过主成分分析转换。 标签仅为0或1。你知道吗

from mpl_toolkits.mplot3d import Axes3D
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
import seaborn as sns
import numpy as np

fields = ["Occupancy", "Temperature", "Humidity", "Light", "CO2", "HumidityRatio", "NSM", "WeekStatus"]
df = pd.read_csv('datatraining-updated.csv', skipinitialspace=True, usecols=fields, sep=',') 
#Get the output from pandas as a numpy matrix
final_data=df.values
#Data
X = final_data[:,1:8]  
#Labels
y = final_data[:,0]
#Normalize features
X_scaled = StandardScaler().fit_transform(X)
#Apply PCA on them
pca = PCA(n_components=7).fit(X_scaled)
#Transform them with PCA
X_reduced = pca.transform(X_scaled)

然后，我只想在一个3D图中，展示3个方差最大的PCA特征，我可以找到它们如下

#Show variable importance
importance = pca.explained_variance_ratio_
print('Explained variation per principal component: 
{}'.format(importance))

之后，我只想绘制前3个方差最高的特征。所以，我之前在下面的代码中选择了它们

X_reduced=X_reduced[:, [0, 4, 5]]

好吧，这是我的问题：我可以在没有传说的情况下绘制它们。当我试图用下面的代码绘制它们时

# Create plot
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax = fig.gca(projection='3d')
colors = ("red", "gray")

for data, color, group in zip(X_reduced, colors, y):
    dim1,dim2,dim3=data
    ax.scatter(dim1, dim2, dim3, c=color, edgecolors='none',  
    label=group)

plt.title('Matplot 3d scatter plot')
plt.legend(y)
plt.show()

我得到以下错误：

plot_data-3d-pca.py:56: UserWarning: Requested projection is different from current axis projection, creating new axis with requested projection.
  ax = fig.gca(projection='3d')
plot_data-3d-pca.py:56: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.
  ax = fig.gca(projection='3d')
Traceback (most recent call last):
  File "/home/unica-server/.local/lib/python3.6/site-packages/matplotlib/backends/backend_gtk3.py", line 307, in idle_draw
    self.draw()
  File "/home/unica-server/.local/lib/python3.6/site-packages/matplotlib/backends/backend_gtk3agg.py", line 76, in draw
    self._render_figure(allocation.width, allocation.height)
  File "/home/unica-server/.local/lib/python3.6/site-packages/matplotlib/backends/backend_gtk3agg.py", line 20, in _render_figure
    backend_agg.FigureCanvasAgg.draw(self)
  File "/home/unica-server/.local/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py", line 388, in draw
    self.figure.draw(self.renderer)
  File "/home/unica-server/.local/lib/python3.6/site-packages/matplotlib/artist.py", line 38, in draw_wrapper
    return draw(artist, renderer, *args, **kwargs)
  File "/home/unica-server/.local/lib/python3.6/site-packages/matplotlib/figure.py", line 1709, in draw
    renderer, self, artists, self.suppressComposite)
  File "/home/unica-server/.local/lib/python3.6/site-packages/matplotlib/image.py", line 135, in _draw_list_compositing_images
    a.draw(renderer)
  File "/home/unica-server/.local/lib/python3.6/site-packages/matplotlib/artist.py", line 38, in draw_wrapper
    return draw(artist, renderer, *args, **kwargs)
  File "/home/unica-server/.local/lib/python3.6/site-packages/mpl_toolkits/mplot3d/axes3d.py", line 292, in draw
    reverse=True)):
  File "/home/unica-server/.local/lib/python3.6/site-packages/mpl_toolkits/mplot3d/axes3d.py", line 291, in <lambda>
    key=lambda col: col.do_3d_projection(renderer),
  File "/home/unica-server/.local/lib/python3.6/site-packages/mpl_toolkits/mplot3d/art3d.py", line 545, in do_3d_projection
    ecs = (_zalpha(self._edgecolor3d, vzs) if self._depthshade else
  File "/home/unica-server/.local/lib/python3.6/site-packages/mpl_toolkits/mplot3d/art3d.py", line 847, in _zalpha
    rgba = np.broadcast_to(mcolors.to_rgba_array(colors), (len(zs), 4))
  File "<__array_function__ internals>", line 6, in broadcast_to
  File "/home/unica-server/.local/lib/python3.6/site-packages/numpy/lib/stride_tricks.py", line 182, in broadcast_to
    return _broadcast_to(array, shape, subok=subok, readonly=True)
  File "/home/unica-server/.local/lib/python3.6/site-packages/numpy/lib/stride_tricks.py", line 127, in _broadcast_to
    op_flags=['readonly'], itershape=shape, order='C')
ValueError: operands could not be broadcast together with remapped shapes [original->remapped]: (0,4) and requested shape (1,4)

我的y的形状是（8143，）和X的形状是（8143，3）

我犯了什么错？你知道吗

编辑：我使用的数据可以找到here

Answer 1

我用另一种方法解决了这个错误。你知道吗

我不知道，对于每个标签，我必须做不同的散点图。多亏了this post我找到了答案。你知道吗

我的解决方案是首先从一个类中分离标签和数据，然后对另一个类执行相同的操作。最后，我用不同的散点图分别绘制它们。因此，首先我识别不同的标签（我只有两个标签，0或1）和它们的数据（它们对应的Xs）。你知道吗

#Get where are the 0s and 1s labels
index_class1 = np.where(np.isin(y, 0))
index_class2 = np.where(np.isin(y, 1))

#Get reduced PCA for each label
X_reducedclass1=X_reduced[index_class1][:]
X_reducedclass2=X_reduced[index_class2][:]

然后，我将在不同的散点图中绘制每个类的PCA缩减向量

colors = ['blue', 'red']

# To getter a better understanding of interaction of the dimensions
# plot the first three PCA dimensions
fig = plt.figure(1, figsize=(8, 6))
ax = Axes3D(fig, elev=-150, azim=110)
scatter1=ax.scatter(X_reducedclass1[:, 0], X_reducedclass1[:, 4], X_reducedclass1[:, 5], c=colors[0], cmap=plt.cm.Set1, edgecolor='k', s=40)
scatter2=ax.scatter(X_reducedclass2[:, 0], X_reducedclass2[:, 4], X_reducedclass2[:, 5], c=colors[1], cmap=plt.cm.Set1, edgecolor='k', s=40)

ax.set_title("Data Visualization with 3 highest variance dimensions with PCA")
ax.set_xlabel("1st eigenvector")
ax.w_xaxis.set_ticklabels([])
ax.set_ylabel("2nd eigenvector")
ax.w_yaxis.set_ticklabels([])
ax.set_zlabel("3rd eigenvector")
ax.w_zaxis.set_ticklabels([])

#ax.legend(np.unique(y))
ax.legend([scatter1, scatter2], ['Empty', 'Full'], loc="upper right")

plt.show()

这给了我一个美丽的画面

当然，这样的代码也可以用for循环来简化（尽管我不知道怎么做）。你知道吗