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[MRG+2] Correct Deprecation of DPGMM and VBGMM. #7124

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Merged
merged 1 commit into from
Aug 6, 2016
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[MRG+2] Correct Deprecation of DPGMM and VBGMM. #7124

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37 changes: 24 additions & 13 deletions sklearn/mixture/dpgmm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -116,10 +116,7 @@ def _bound_state_log_lik(X, initial_bound, precs, means, covariance_type):
return bound


@deprecated("The DPGMM class is not working correctly and it's better "
"to not use it. DPGMM is deprecated in 0.18 and "
"will be removed in 0.20.")
class DPGMM(_GMMBase):
class _DPGMMBase(_GMMBase):
"""Variational Inference for the Infinite Gaussian Mixture Model.

DPGMM stands for Dirichlet Process Gaussian Mixture Model, and it
Expand Down Expand Up @@ -211,16 +208,16 @@ class DPGMM(_GMMBase):
algorithm, better for situations where there might be too little
data to get a good estimate of the covariance matrix.
"""

def __init__(self, n_components=1, covariance_type='diag', alpha=1.0,
random_state=None, tol=1e-3, verbose=0, min_covar=None,
n_iter=10, params='wmc', init_params='wmc'):
self.alpha = alpha
super(DPGMM, self).__init__(n_components, covariance_type,
random_state=random_state,
tol=tol, min_covar=min_covar,
n_iter=n_iter, params=params,
init_params=init_params, verbose=verbose)
super(_DPGMMBase, self).__init__(n_components, covariance_type,
random_state=random_state,
tol=tol, min_covar=min_covar,
n_iter=n_iter, params=params,
init_params=init_params,
verbose=verbose)

def _get_precisions(self):
"""Return precisions as a full matrix."""
Expand Down Expand Up @@ -619,10 +616,24 @@ def _fit(self, X, y=None):
return z


@deprecated("The VBGMM class is not working correctly and it's better"
" to not use it. VBGMM is deprecated in 0.18 and "
@deprecated("The DPGMM class is not working correctly and it's better "
"to not use it. DPGMM is deprecated in 0.18 and "
"will be removed in 0.20.")
class DPGMM(_DPGMMBase):
def __init__(self, n_components=1, covariance_type='diag', alpha=1.0,
random_state=None, tol=1e-3, verbose=0, min_covar=None,
n_iter=10, params='wmc', init_params='wmc'):
super(DPGMM, self).__init__(
n_components=n_components, covariance_type=covariance_type,
alpha=alpha, random_state=random_state, tol=tol, verbose=verbose,
min_covar=min_covar, n_iter=n_iter, params=params,
init_params=init_params)


@deprecated("The VBGMM class is not working correctly and it's better "
"to not use it. VBGMM is deprecated in 0.18 and "
"will be removed in 0.20.")
class VBGMM(DPGMM):
class VBGMM(_DPGMMBase):
"""Variational Inference for the Gaussian Mixture Model

Variational inference for a Gaussian mixture model probability
Expand Down
9 changes: 4 additions & 5 deletions sklearn/mixture/tests/test_dpgmm.py
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Expand Up @@ -182,12 +182,11 @@ class TestDPGMMWithFullCovars(unittest.TestCase, DPGMMTester):
setUp = GMMTester._setUp


@ignore_warnings(category=DeprecationWarning)
def test_VBGMM_deprecation():
assert_warns_message(DeprecationWarning, "The VBGMM class is"
" not working correctly and it's better"
" to not use it. VBGMM is deprecated in 0.18"
" and will be removed in 0.20.", VBGMM)
assert_warns_message(
DeprecationWarning,
"The VBGMM class is not working correctly and it's better to not use "
"it. VBGMM is deprecated in 0.18 and will be removed in 0.20.", VBGMM)


class VBGMMTester(GMMTester):
Expand Down
2 changes: 1 addition & 1 deletion sklearn/mixture/tests/test_gmm.py
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Expand Up @@ -309,7 +309,7 @@ def test_train_1d(self, params='wmc'):
with ignore_warnings(category=DeprecationWarning):
g.fit(X)
trainll = g.score(X)
if isinstance(g, mixture.DPGMM):
if isinstance(g, mixture.dpgmm._DPGMMBase):
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why that change?

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DPGMM and VBGMM are both deprecated and as VBGMM inherit from DPGMM you have two warnings messages when you call VBGMM.
I've corrected that to make sure the deprecation warning is meaningfull and moreover to be sure to pass the test once I will modify the deprecation message to refer to the new BayesianGaussianMixture class.

self.assertTrue(np.sum(np.abs(trainll / 100)) < 5)
else:
self.assertTrue(np.sum(np.abs(trainll / 100)) < 2)
Expand Down