NMF code in 0.13 and before behaves differently compared to 0.14.
An example:
import numpy
import pickle
X=pickle.load(open("data.pkl"))
import sklearn
from sklearn import decomposition
print sklearn.__version__
nmf=decomposition.NMF(n_components=20)
nmf.fit(X)
This runs fine with sklearn <=0.13 with a sparse matrix (~= 40Mo) and
consume little memory (*I check with memory_profiler)*
But with 0.14 (using a different code), this produce:
python test_NMF_fit.py
0.14.1
Traceback (most recent call last):
File "test_NMF_fit.py", line 11, in <module>
nmf.fit(X)
File
"/home/sclincha/anaconda/lib/python2.7/site-packages/sklearn/decomposition/nmf.py",
line 556, in fit
self.fit_transform(X, **params)
File
"/home/sclincha/anaconda/lib/python2.7/site-packages/sklearn/decomposition/nmf.py",
line 531, in fit_transform
normWHT = np.trace(np.dot(np.dot(H.T, np.dot(W.T, W)), H))
MemoryError
Do you have any explanations/motivations for changing the implementations
of NMF ?
Is there some special cases where one should use 0.14 vs 0.13 ?
Cheers,
Stéphane
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