python_pca降维

python_pca降维

'''pca'''
'''from sklearn.decomposition import PCA
pca=PCA(n_components=2, copy=True, whiten=False, svd_solver='auto', tol=0.0, iterated_power='auto', random_state=123)

col_for_pca=[
'l_rec_m03_cnt'
,'l_rec_m06_cnt'
,'l_rec_m12_cnt'
,'l_rec_m24_cnt'
,'c_rec_m03_cnt'
,'c_rec_m06_cnt'
,'c_rec_m12_cnt'
,'c_rec_m24_cnt']
for_pca=df.loc[(df.APPDEC_DAY>=20170601) & (df.APPDEC_DAY<=20170731),col_for_pca]

pca.fit(for_pca)
joblib.dump(pca,'pca.model')'''