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Use TruncatedSVD instead of PCA #22

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Merged
cyrillustan merged 1 commit into from
Nov 25, 2021
Merged

Use TruncatedSVD instead of PCA #22

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9 changes: 6 additions & 3 deletions tensorpack/decomposition.py
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Original file line number Diff line number Diff line change
@@ -1,7 +1,7 @@
import numpy as np
import pandas as pd
from .cmtf import perform_CP, calcR2X
from statsmodels.multivariate.pca import PCA
from sklearn.decomposition import TruncatedSVD

class Decomposition():
def __init__(self, data, max_rr=6):
Expand All @@ -18,8 +18,11 @@ def perform_tfac(self):
def perform_PCA(self, flattenon=0):
dataShape = self.data.shape
flatData = np.reshape(np.moveaxis(self.data, flattenon, 0), (dataShape[flattenon], -1))
self.PCA = PCA(flatData, ncomp=max(self.rrs), missing='fill-em', standardize=False, demean=False, normalize=False)
recon = [self.PCA.scores[:, :rr] @ self.PCA.loadings.T[:rr, :] for rr in self.rrs]

tsvd = TruncatedSVD(n_components=max(self.rrs))
scores = tsvd.fit_transform(flatData)
loadings = tsvd.components_
recon = [scores[:, :rr] @ loadings[:rr, :] for rr in self.rrs]
self.PCAR2X = [calcR2X(c, mIn = flatData) for c in recon]
self.sizePCA = [sum(flatData.shape) * rr for rr in self.rrs]

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