使用Python使用散點圖繪制包括原始數據在內的PCA結果

Question

我作為練習對虹膜數據進行了PCA。 這是我的代碼：

#!/usr/bin/env python
# -*- coding: utf-8 -*-
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import style
style.use("ggplot")
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA # as sklearnPCA
import pandas as pd
#=================
df = pd.read_csv('iris.csv');
# Split the 1st 4 columns comprising values
# and the last column that has species
X = df.ix[:,0:4].values
y = df.ix[:,4].values

X_std = StandardScaler().fit_transform(X);  # standardization of data

# Fit the model with X_std and apply the dimensionality reduction on X_std.
pca = PCA(n_components=2) # 2 PCA components;
Y_pca = pca.fit_transform(X_std)

# How to plot my results???? I am struck here! 

請提供有關如何繪制原始虹膜數據和使用散點圖得出的PCA的建議。

Answer 1

我認為這是可視化的方式。 我將PC1放在X軸上，將PC2放在Y軸上，並根據其類別為每個點着色。 這是代碼：

#first we need to map colors on labels
dfcolor = pd.DataFrame([['setosa','red'],['versicolor','blue'],['virginica','yellow']],columns=['Species','Color'])
mergeddf = pd.merge(df,dfcolor)

#Then we do the graph
plt.scatter(Y_pca[:,0],Y_pca[:,1],color=mergeddf['Color'])
plt.show()