TSVD scipy와 scikit_learn

from sklearn.datasets import load_iris
import matplotlib.pyplot as plt
import numpy as np
from scipy.sparse.linalg import svds
from scipy.linalg import svd

iris = load_iris()
iris_ftrs = iris.data
# 2개의 주요 component로 TruncatedSVD 변환
U, Sigma, Vt = svd(iris_ftrs, full_matrices=False)
num_components = 2
U_tr, Sigma_tr, Vt_tr = svds(iris_ftrs, k=num_components)
print('\nTruncated SVD 분해 행렬 차원:',U_tr, Sigma_tr, Vt_tr.shape)
matrix_tr = np.dot(U_tr,np.diag(Sigma_tr))  # output of TruncatedSVD
plt.scatter(x=matrix_tr[:,0], y= matrix_tr[:,1])
plt.xlabel('TruncatedSVD Component 1')
plt.ylabel('TruncatedSVD Component 2')
print(matrix_tr)

사이킷럿

from sklearn.decomposition import TruncatedSVD, PCA
from sklearn.datasets import load_iris
import matplotlib.pyplot as plt

iris = load_iris()
iris_ftrs = iris.data
# 2개의 주요 component로 TruncatedSVD 변환
tsvd = TruncatedSVD(n_components=2)
tsvd.fit(iris_ftrs)
iris_tsvd = tsvd.transform(iris_ftrs)

# Scatter plot 2차원으로 TruncatedSVD 변환 된 데이터 표현. 품종은 색깔로 구분
plt.scatter(x=iris_tsvd[:,0], y= iris_tsvd[:,1], c= iris.target)
plt.xlabel('TruncatedSVD Component 1')
plt.ylabel('TruncatedSVD Component 2')
print(iris_tsvd)