The following code snippet illustrates the issue:
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
import numpy as np
(nrows, ncolumns) = (1912392, 131)
X = np.random.random((nrows, ncolumns))
pca = PCA(n_components=28, random_state=0)
transformed_X1 = pca.fit_transform(X)
pca1 = pca.fit(X)
transformed_X2 = pca1.transform(X)
print((transformed_X1 != transformed_X2).sum()) # Gives output as 53546976
scalar = StandardScaler()
scaled_X1 = scalar.fit_transform(X)
scalar2 = scalar.fit(X)
scaled_X2 = scalar2.transform(X)
(scaled_X1 != scaled_X2).sum() # Gives output as 0
Can someone explain as to why the first output is not zero and the second output is?
Using this works:
pca = PCA(n_components=28, svd_solver = 'full')
transformed_X1 = pca.fit_transform(X)
pca1 = pca.fit(X)
transformed_X2 = pca1.transform(X)
print(np.allclose(transformed_X1, transformed_X2))
True
Apparently svd_solver = 'random'
(which is what 'auto'
defaults to) has enough process difference between .fit(X).transform(X)
and fit_transform(X)
to give different results even with the same seed. Also remember floating point errors make ==
and /=
unreliable judges of equality of different processes, so use np.allclose()
.
It seems like StandardScaler.fit_transform()
just directly uses .fit(X).transform(X)
under the hood, so there were no floating point errors there to trip you up.
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