plotly를 이용한 pca

https://plotly.com/python/pca-visualization/

PCA Visualization

여기에 잘 나와있다. matplotlib를 이용하는것보다 훨씬 코드가 간단해진다.

 

2D

import pandas as pd
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA

df = pd.read_csv("")
pca_df = StandardScaler().fit_transform(df)

pca = PCA(n_components=2)
principalComponents = pca.fit_transform(pca_df)
fig = px.scatter(principalComponents, x=0, y=1, color=df['label'])
fig.show()

3D

pca = PCA(n_components=3)
components = pca.fit_transform(df)

total_var = pca.explained_variance_ratio_.sum() * 100

fig = px.scatter_3d(
    components, x=0, y=1, z=2, color=df['label'],
    title=f'Total Explained Variance: {total_var:.2f}%',
    labels={'0': 'PC 1', '1': 'PC 2', '2': 'PC 3'}
)
fig.show()