파이썬 가장 짧은 PCA 시각화 코드

Jay

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3 min read

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Mar 15, 2019

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유방암 예제

1. 샘플데이터 가져오기

from sklearn.datasets import load_breast_cancercancer=load_breast_cancer()
X = cancer.data

2. 데이터 표준화

from sklearn.preprocessing import StandardScalerX_ = StandardScaler().fit_transform(X)

3. PCA(2D)

from sklearn.decomposition import PCApca = PCA(n_components=2)
pc = pca.fit_transform(X_)

4. Plot

import matplotlib.pyplot as pltplt.scatter(pc[:,0],pc[:,1])

양성 음성 PCA 시각화

유방암 예제

1. 샘플데이터 가져오기

from sklearn.datasets import load_breast_cancercancer=load_breast_cancer()X = cancer.datay = cancer.target

2. 데이터 표준화

from sklearn.preprocessing import StandardScalerX_ = StandardScaler().fit_transform(X)

3. PCA(2D)

from sklearn.decomposition import PCApca = PCA(n_components=2)
pc = pca.fit_transform(X_)

4. Categorize

import pandas as pdimport numpy as nppc_y = np.c_[pc,y]
df = pd.DataFrame(pc_y,columns=['PC1','PC2','diagnosis'])

5–1. Plot (matplotlib)

import matplotlib.pyplot as pltplt.scatter(x=df['PC1'],y=df['PC2'],c=df['diagnosis'])

5–2. Plot2 (seaborn)

import seaborn as snssns.scatterplot(data=df,x='PC1',y='PC2',hue='diagnosis')