Python 如何更改seaborn散点图上点的分层_Python_Scikit Learn_Seaborn_Pca

Python 如何更改seaborn散点图上点的分层

python scikit-learn

Python 如何更改seaborn散点图上点的分层,python,scikit-learn,seaborn,pca,Python,Scikit Learn,Seaborn,Pca,我正在对一个垃圾邮件数据集进行PCA，在我想要将主成分相互对比（pc1vspc2、pc1vspc3和pc2vspc3）之前，一切都很好。散点图运行正常，但我想在非散点数据上显示垃圾数据点我上下寻找了一种方法，但似乎找不到任何有效的方法 #Seperating Feautures X = df.iloc[:,:54] #Seperating Target, changing 0's to non-spam & 1's to spam Y = df['Spam_Indicator'].va

我正在对一个垃圾邮件数据集进行PCA，在我想要将主成分相互对比（pc1vspc2、pc1vspc3和pc2vspc3）之前，一切都很好。散点图运行正常，但我想在非散点数据上显示垃圾数据点

我上下寻找了一种方法，但似乎找不到任何有效的方法

#Seperating Feautures
X = df.iloc[:,:54]
#Seperating Target, changing 0's to non-spam & 1's to spam
Y = df['Spam_Indicator'].values.tolist()
for i in range(len(Y)):
    if Y[i] == 1:
        Y[i] = 'Spam'
    else:
        Y[i] = 'Non-spam'
Y = np.asarray(Y)

#no of principal components
N = 3
col_numbering = [str(x) for x in range(1,N + 1)]

#Applies PCA reducing from 54 to N dimensions
pca = PCA(n_components = N)
X_red = pca.fit_transform(X)
X_red = pd.DataFrame(data = X_red, columns = col_numbering)

#Prints the components, explained variance and explained variance ratio
#print('Components:',pca.components_)
print('Explained Variance:' ,pca.explained_variance_)
print('Explained Variance Ratio:' ,pca.explained_variance_ratio_)


plt.figure(figsize=(20,10))
plt.subplot(1,3,1)
sns.scatterplot(x = '1', y = '2', data = X_red, hue = Y,
                alpha = .75, hue_norm = (0.7))
plt.subplot(1,3,2)
sns.scatterplot(x = '1', y = '3', data = X_red, hue = Y,
                alpha = .75, hue_norm = (0.7))
plt.subplot(1,3,3)
sns.scatterplot(x = '2', y = '3', data = X_red, hue = Y,
                alpha = .75, hue_norm = (0.7))
plt.show()

这是我所拥有的图像，这样你就能更好地知道我在问什么

考虑一个直观的标签，我观察到在散点图中，组

放在组

之上