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ENH Adding callable function option to bicluster method #29196

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ENH Adding callable function option to bicluster method #29196

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10 changes: 7 additions & 3 deletions 10 sklearn/cluster/_bicluster.py
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Original file line number Diff line number Diff line change
Expand Up @@ -381,12 +381,13 @@ class SpectralBiclustering(BaseSpectral):
The number of row and column clusters in the checkerboard
structure.

method : {'bistochastic', 'scale', 'log'}, default='bistochastic'
method : {'bistochastic', 'scale', 'log'} or callable, default='bistochastic'
Method of normalizing and converting singular vectors into
biclusters. May be one of 'scale', 'bistochastic', or 'log'.
The authors recommend using 'log'. If the data is sparse,
however, log normalization will not work, which is why the
default is 'bistochastic'.
Callable must take a 2D array and return a 2D array.

.. warning::
if `method='log'`, the data must not be sparse.
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Suggested change
if `method='log'`, the data must not be sparse.
if `method='log'`, the data must not be sparse.
.. versionadded:: 1.6
The support for passing a callable was added.

Expand Down Expand Up @@ -491,7 +492,7 @@ class SpectralBiclustering(BaseSpectral):
_parameter_constraints: dict = {
**BaseSpectral._parameter_constraints,
"n_clusters": [Interval(Integral, 1, None, closed="left"), tuple],
"method": [StrOptions({"bistochastic", "scale", "log"})],
"method": [StrOptions({"bistochastic", "scale", "log"}), callable],
"n_components": [Interval(Integral, 1, None, closed="left")],
"n_best": [Interval(Integral, 1, None, closed="left")],
}
Expand Down Expand Up @@ -558,7 +559,10 @@ def _check_parameters(self, n_samples):

def _fit(self, X):
n_sv = self.n_components
if self.method == "bistochastic":
if callable(self.method):
normalized_data = self.method(X)
n_sv += 1
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If someone add a log based normalization then this would be incorrect, isn't it?

elif self.method == "bistochastic":
normalized_data = _bistochastic_normalize(X)
n_sv += 1
elif self.method == "scale":
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2 changes: 1 addition & 1 deletion 2 sklearn/cluster/tests/test_bicluster.py
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Expand Up @@ -98,7 +98,7 @@ def test_spectral_biclustering(global_random_seed, csr_container):
)

non_default_params = {
"method": ["scale", "log"],
"method": ["scale", "log", lambda x: x / x.sum(axis=0)],
"svd_method": ["arpack"],
"n_svd_vecs": [20],
"mini_batch": [True],
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