我使用了Sklearn的Digits数据集,并尝试使用TSNE(t分布随机邻居嵌入)将尺寸从64减少到3:
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))
finalDf:
你错了:
TypeError: '(slice(None, None, None), 0)' is an invalid key
怎么了?有人可以帮我吗?
您正在尝试对无效的pandas数据帧进行numpy切片,因此请先将数据帧转换为numpy数组。
这是更新的代码:-
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))
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