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[MRG] NMF and non_negative_factorization have inconsistent default init #12989

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Merged
merged 11 commits into from
Jan 30, 2019
Merged

[MRG] NMF and non_negative_factorization have inconsistent default init #12989

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287c2bf
Change default value of init
zjpoh Jan 16, 2019
bd12dc2
Fix default change
zjpoh Jan 16, 2019
7397101
Update per Joel's comments
Jan 16, 2019
3a30533
Change warn condition.
zjpoh Jan 18, 2019
f232c71
Fix test
Jan 22, 2019
e3a9f2f
Add change log
Jan 22, 2019
eda0f03
Fix unit test
Jan 22, 2019
dca91e1
Specify init for tests
zjpoh Jan 23, 2019
f7f66b8
Modify per qinhanmin2014 comments
zjpoh Jan 28, 2019
b640a86
Clarify deprecation message
zjpoh Jan 29, 2019
8e11622
Fix unit tests
zjpoh Jan 29, 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 @@ -58,6 +58,16 @@ Support for Python 3.4 and below has been officially dropped.
:class:`datasets.svmlight_format` :issue:`10727` by
:user:`Bryan K Woods <bryan-woods>`,

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

- |API| The default value of the :code:`init` argument in
:func:`decomposition.non_negative_factorization` will change from
:code:`random` to :code:`None` in version 0.23 to make it consistent with
:class:`decomposition.NMF`. A FutureWarning is raised when
the default value is used.
:issue:`12988` by :user:`Zijie (ZJ) Poh <zjpoh>`.

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

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27 changes: 22 additions & 5 deletions sklearn/decomposition/nmf.py
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Expand Up @@ -261,9 +261,11 @@ def _initialize_nmf(X, n_components, init=None, eps=1e-6,

init : None | 'random' | 'nndsvd' | 'nndsvda' | 'nndsvdar'
Method used to initialize the procedure.
Default: 'nndsvd' if n_components < n_features, otherwise 'random'.
Default: None.
Valid options:

- None: 'nndsvd' if n_components < n_features, otherwise 'random'.

- 'random': non-negative random matrices, scaled with:
sqrt(X.mean() / n_components)

Expand Down Expand Up @@ -831,7 +833,7 @@ def _fit_multiplicative_update(X, W, H, beta_loss='frobenius',


def non_negative_factorization(X, W=None, H=None, n_components=None,
init='random', update_H=True, solver='cd',
init='warn', update_H=True, solver='cd',
beta_loss='frobenius', tol=1e-4,
max_iter=200, alpha=0., l1_ratio=0.,
regularization=None, random_state=None,
Expand Down Expand Up @@ -878,11 +880,17 @@ def non_negative_factorization(X, W=None, H=None, n_components=None,
Number of components, if n_components is not set all features
are kept.

init : None | 'random' | 'nndsvd' | 'nndsvda' | 'nndsvdar' | 'custom'
init : None | 'random' | 'nndsvd' | 'nndsvda' | 'nndsvdar' | 'custom'
Method used to initialize the procedure.
Default: 'random'.

The default value will change from 'random' to None in version 0.23
to make it consistent with decomposition.NMF.

Valid options:

- None: 'nndsvd' if n_components < n_features, otherwise 'random'.

- 'random': non-negative random matrices, scaled with:
sqrt(X.mean() / n_components)

Expand Down Expand Up @@ -1009,6 +1017,13 @@ def non_negative_factorization(X, W=None, H=None, n_components=None,
raise ValueError("Tolerance for stopping criteria must be "
"positive; got (tol=%r)" % tol)

if init == "warn":
if n_components < n_features:
warnings.warn("The default value of init will change from "
"random to None in 0.23 to make it consistent "
"with decomposition.NMF.", FutureWarning)
init = "random"

# check W and H, or initialize them
if init == 'custom' and update_H:
_check_init(H, (n_components, n_features), "NMF (input H)")
Expand Down Expand Up @@ -1087,11 +1102,13 @@ class NMF(BaseEstimator, TransformerMixin):
Number of components, if n_components is not set all features
are kept.

init : 'random' | 'nndsvd' | 'nndsvda' | 'nndsvdar' | 'custom'
init : None | 'random' | 'nndsvd' | 'nndsvda' | 'nndsvdar' | 'custom'
Method used to initialize the procedure.
Default: 'nndsvd' if n_components < n_features, otherwise random.
Default: None.
Valid options:

- None: 'nndsvd' if n_components < n_features, otherwise random.

- 'random': non-negative random matrices, scaled with:
sqrt(X.mean() / n_components)

Expand Down
14 changes: 9 additions & 5 deletions sklearn/decomposition/tests/test_nmf.py
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Expand Up @@ -10,6 +10,7 @@
import pytest

from sklearn.utils.testing import assert_raise_message, assert_no_warnings
from sklearn.utils.testing import assert_warns_message
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_almost_equal
Expand Down Expand Up @@ -213,13 +214,16 @@ def test_non_negative_factorization_checking():
A = np.ones((2, 2))
# Test parameters checking is public function
nnmf = non_negative_factorization
assert_no_warnings(nnmf, A, A, A, np.int64(1))
msg = ("The default value of init will change from "
"random to None in 0.23 to make it consistent "
"with decomposition.NMF.")
assert_warns_message(FutureWarning, msg, nnmf, A, A, A, np.int64(1))
msg = ("Number of components must be a positive integer; "
"got (n_components=1.5)")
assert_raise_message(ValueError, msg, nnmf, A, A, A, 1.5)
assert_raise_message(ValueError, msg, nnmf, A, A, A, 1.5, 'random')
msg = ("Number of components must be a positive integer; "
"got (n_components='2')")
assert_raise_message(ValueError, msg, nnmf, A, A, A, '2')
assert_raise_message(ValueError, msg, nnmf, A, A, A, '2', 'random')
msg = "Negative values in data passed to NMF (input H)"
assert_raise_message(ValueError, msg, nnmf, A, A, -A, 2, 'custom')
msg = "Negative values in data passed to NMF (input W)"
Expand Down Expand Up @@ -380,8 +384,8 @@ def test_nmf_negative_beta_loss():

def _assert_nmf_no_nan(X, beta_loss):
W, H, _ = non_negative_factorization(
X, n_components=n_components, solver='mu', beta_loss=beta_loss,
random_state=0, max_iter=1000)
X, init='random', n_components=n_components, solver='mu',
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we should probably make sure that init is explicitly set elsewhere in the tests.

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Sure. Added explicit init to all tests other than the consistency check test between NMF and non_negative_factorization.

beta_loss=beta_loss, random_state=0, max_iter=1000)
assert not np.any(np.isnan(W))
assert not np.any(np.isnan(H))

Expand Down