python用tsne降维图像,使用TSNE进行降维。为什么3D图形不起作用？

I have used the Digits dataset from Sklearn and I have tried to reduce the dimension from 64 to 3 using TSNE( t-Distributed Stochastic Neighbor Embedding):

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

import seaborn as sns

#%matplotib inline

from sklearn.manifold import TSNE

from sklearn.datasets import load_digits

from mpl_toolkits.mplot3d import Axes3D

digits = load_digits()

digits_df = pd.DataFrame(digits.data,)

digits_df["target"] = pd.Series(digits.target)

tsne = TSNE(n_components=3)

digits_tsne = tsne.fit_transform(digits_df.iloc[:,:64])

digits_df_tsne = pd.DataFrame(digits_tsne,

columns =["Component1","Component2","Component3"])

finalDf = pd.concat([digits_df_tsne, digits_df["target"]], axis = 1)

#Visualizing 3D

figure = plt.figure(figsize=(9,9))

axes = figure.add_subplot(111,projection = "3d")

dots = axes.scatter(xs = finalDf[:,0],ys = finalDf[:,1],zs = finalDf[:,2],

c = digits.target, cmap = plt.cm.get_cmap("nipy_spectral_r",10))

The finalDf:

Te error:

TypeError: '(slice(None, None, None), 0)' is an invalid key

What is wrong? Could someone help me?

解决方案

You're trying numpy slicing on pandas dataframe which is not valid, so first convert the dataframes to numpy arrays.

Here's the updated code: -

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

import seaborn as sns

#%matplotib inline

from sklearn.manifold import TSNE

from sklearn.datasets import load_digits

from mpl_toolkits.mplot3d import Axes3D

digits = load_digits()

digits_df = pd.DataFrame(digits.data,)

digits_df["target"] = pd.Series(digits.target)

tsne = TSNE(n_components=3)

digits_tsne = tsne.fit_transform(digits_df.iloc[:,:64])

digits_df_tsne = pd.DataFrame(digits_tsne,

columns =["Component1","Component2","Component3"])

finalDf = pd.concat([digits_df_tsne, digits_df["target"]], axis = 1)

#Visualizing 3D

figure = plt.figure(figsize=(9,9))

axes = figure.add_subplot(111,projection = "3d")

dots = axes.scatter(xs = finalDf.to_numpy()[:,0],ys = finalDf.to_numpy()[:,1],zs = finalDf.to_numpy()[:,2],

c = digits.target, cmap = plt.cm.get_cmap("nipy_spectral_r",10))