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[MRG] Fixed NMF IndexError #11667

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Merged
merged 17 commits into from
Feb 12, 2019
Merged

[MRG] Fixed NMF IndexError #11667

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5d933b5
Fixed initialization procedure condition
zjpoh Jul 24, 2018
9caee62
Reverted pydoc changes
zjpoh Jul 24, 2018
14b1ea6
Added unit test
zjpoh Jul 25, 2018
0d7c1bd
Update per TomDLT comments
zjpoh Jul 26, 2018
c759c4b
Modify per jnothman comment
zjpoh Sep 10, 2018
4fa03ab
Merge branch 'master' into nmf_index_error
zjpoh Sep 10, 2018
5370f4a
Revert changes to default init
zjpoh Sep 13, 2018
a1832a0
Fix per jnothman comments
zjpoh Sep 14, 2018
bc48fbb
Merge branch 'master' into nmf_index_error
zjpoh Sep 14, 2018
91b4bb6
Merge branch 'master' into nmf_index_error
zjpoh Jan 16, 2019
020742a
Move whats new to v0.21
zjpoh Jan 16, 2019
6d56bcd
Fix docstring
zjpoh Jan 16, 2019
c007b38
Fix unit test
Jan 16, 2019
0a7426f
Fix spacing
Jan 16, 2019
47be426
flake8 fix
zjpoh Jan 23, 2019
8fcbf39
Merge branch 'master' into nmf_index_error
zjpoh Feb 11, 2019
1b998aa
Update v0.21.rst
zjpoh Feb 12, 2019
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10 changes: 10 additions & 0 deletions doc/whats_new/v0.21.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -76,6 +76,16 @@ Support for Python 3.4 and below has been officially dropped.
the default value is used.
:issue:`12988` by :user:`Zijie (ZJ) Poh <zjpoh>`.

:mod:`sklearn.decomposition`
............................

- |Fix| Fixed a bug in :class:`decomposition.NMF` where `init = 'nndsvd'`,
`init = 'nndsvda'`, and `init = 'nndsvdar'` are allowed when
`n_components < n_features` instead of
`n_components <= min(n_samples, n_features)`.
:issue:`11650` by :user:`Hossein Pourbozorg <hossein-pourbozorg>` and
:user:`Zijie (ZJ) Poh <zjpoh>`.

:mod:`sklearn.discriminant_analysis`
....................................

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14 changes: 11 additions & 3 deletions sklearn/decomposition/nmf.py
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Expand Up @@ -261,7 +261,8 @@ def _initialize_nmf(X, n_components, init=None, eps=1e-6,
Default: None.
Valid options:

- None: 'nndsvd' if n_components < n_features, otherwise 'random'.
- None: 'nndsvd' if n_components <= min(n_samples, n_features),
otherwise 'random'.

- 'random': non-negative random matrices, scaled with:
sqrt(X.mean() / n_components)
Expand Down Expand Up @@ -304,8 +305,14 @@ def _initialize_nmf(X, n_components, init=None, eps=1e-6,
check_non_negative(X, "NMF initialization")
n_samples, n_features = X.shape

if (init is not None and init != 'random'
and n_components > min(n_samples, n_features)):
raise ValueError("init = '{}' can only be used when "
"n_components <= min(n_samples, n_features)"
.format(init))

if init is None:
if n_components < n_features:
if n_components <= min(n_samples, n_features):
init = 'nndsvd'
else:
init = 'random'
Expand Down Expand Up @@ -1104,7 +1111,8 @@ class NMF(BaseEstimator, TransformerMixin):
Default: None.
Valid options:

- None: 'nndsvd' if n_components < n_features, otherwise random.
- None: 'nndsvd' if n_components <= min(n_samples, n_features),
otherwise random.

- 'random': non-negative random matrices, scaled with:
sqrt(X.mean() / n_components)
Expand Down
34 changes: 23 additions & 11 deletions sklearn/decomposition/tests/test_nmf.py
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Expand Up @@ -53,6 +53,14 @@ def test_parameter_checking():
clf = NMF(2, tol=0.1).fit(A)
assert_raise_message(ValueError, msg, clf.transform, -A)

for init in ['nndsvd', 'nndsvda', 'nndsvdar']:
msg = ("init = '{}' can only be used when "
"n_components <= min(n_samples, n_features)"
.format(init))
assert_raise_message(ValueError, msg, NMF(3, init).fit, A)
assert_raise_message(ValueError, msg, nmf._initialize_nmf, A,
3, init)


def test_initialize_close():
# Test NNDSVD error
Expand Down Expand Up @@ -197,17 +205,21 @@ def test_non_negative_factorization_consistency():
A = np.abs(rng.randn(10, 10))
A[:, 2 * np.arange(5)] = 0

for solver in ('cd', 'mu'):
W_nmf, H, _ = non_negative_factorization(
A, solver=solver, random_state=1, tol=1e-2)
W_nmf_2, _, _ = non_negative_factorization(
A, H=H, update_H=False, solver=solver, random_state=1, tol=1e-2)

model_class = NMF(solver=solver, random_state=1, tol=1e-2)
W_cls = model_class.fit_transform(A)
W_cls_2 = model_class.transform(A)
assert_array_almost_equal(W_nmf, W_cls, decimal=10)
assert_array_almost_equal(W_nmf_2, W_cls_2, decimal=10)
for init in ['random', 'nndsvd']:
for solver in ('cd', 'mu'):
W_nmf, H, _ = non_negative_factorization(
A, init=init, solver=solver, random_state=1, tol=1e-2)
W_nmf_2, _, _ = non_negative_factorization(
A, H=H, update_H=False, init=init, solver=solver,
random_state=1, tol=1e-2)

model_class = NMF(init=init, solver=solver, random_state=1,
tol=1e-2)
W_cls = model_class.fit_transform(A)
W_cls_2 = model_class.transform(A)

assert_array_almost_equal(W_nmf, W_cls, decimal=10)
assert_array_almost_equal(W_nmf_2, W_cls_2, decimal=10)


def test_non_negative_factorization_checking():
Expand Down