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[MRG+1] NMF speed-up for beta_loss = 0 #9277

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Merged
jnothman merged 6 commits into from
Jul 5, 2017
Merged

[MRG+1] NMF speed-up for beta_loss = 0 #9277

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f14c016
NMF speed-up for beta_loss = 0
Jul 4, 2017
e5396dd
newest update for NMF speed-up for beta_loss = 0
Jul 4, 2017
1931683
updated NMF speed-up for beta_loss = 0
Jul 4, 2017
f854d0d
updated NMF speed-up for beta_loss = 0
Jul 4, 2017
89fbed1
comment added
Jul 5, 2017
d147594
comment added
Jul 5, 2017
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15 changes: 15 additions & 0 deletions sklearn/decomposition/nmf.py
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Original file line number Diff line number Diff line change
Expand Up @@ -545,6 +545,13 @@ def _multiplicative_update_w(X, W, H, beta_loss, l1_reg_W, l2_reg_W, gamma,

if beta_loss == 1:
np.divide(X_data, WH_safe_X_data, out=WH_safe_X_data)
elif beta_loss == 0:
# speeds up computation time
# refer to /numpy/numpy/issues/9363
WH_safe_X_data **= -1
WH_safe_X_data **= 2
# element-wise multiplication
WH_safe_X_data *= X_data
else:
WH_safe_X_data **= beta_loss - 2
# element-wise multiplication
Expand Down Expand Up @@ -619,6 +626,13 @@ def _multiplicative_update_h(X, W, H, beta_loss, l1_reg_H, l2_reg_H, gamma):

if beta_loss == 1:
np.divide(X_data, WH_safe_X_data, out=WH_safe_X_data)
elif beta_loss == 0:
# speeds up computation time
# refer to /numpy/numpy/issues/9363
WH_safe_X_data **= -1
WH_safe_X_data **= 2
# element-wise multiplication
WH_safe_X_data *= X_data
else:
WH_safe_X_data **= beta_loss - 2
# element-wise multiplication
Expand Down Expand Up @@ -1167,6 +1181,7 @@ class NMF(BaseEstimator, TransformerMixin):
Fevotte, C., & Idier, J. (2011). Algorithms for nonnegative matrix
factorization with the beta-divergence. Neural Computation, 23(9).
"""

def __init__(self, n_components=None, init=None, solver='cd',
beta_loss='frobenius', tol=1e-4, max_iter=200,
random_state=None, alpha=0., l1_ratio=0., verbose=0,
Expand Down