"""
Clustering tools based on the Mapper algorithm.
"""
import tdamapper.core
from tdamapper._common import EstimatorMixin, ParamsMixin, clone, deprecated
from tdamapper.core import TrivialCover, mapper_connected_components
[docs]
class TrivialClustering(tdamapper.core.TrivialClustering):
"""
**DEPRECATED**: This class is deprecated and will be removed in a future
release. Use :class:`tdamapper.core.TrivialClustering`.
"""
@deprecated(
"This class is deprecated and will be removed in a future release. "
"Use tdamapper.core.TrivialClustering."
)
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
[docs]
class FailSafeClustering(tdamapper.core.FailSafeClustering):
"""
**DEPRECATED**: This class is deprecated and will be removed in a future
release. Use :class:`tdamapper.core.FailSafeClustering`.
"""
@deprecated(
"This class is deprecated and will be removed in a future release. "
"Use tdamapper.core.FailSafeClustering."
)
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
class _MapperClustering(EstimatorMixin, ParamsMixin):
def __init__(self, cover=None, clustering=None, n_jobs=1):
self.cover = cover
self.clustering = clustering
self.n_jobs = n_jobs
def fit(self, X, y=None):
X, y = self._validate_X_y(X, y)
cover = TrivialCover() if self.cover is None else self.cover
cover = clone(cover)
clustering = (
tdamapper.core.TrivialClustering()
if self.clustering is None
else self.clustering
)
clustering = clone(clustering)
n_jobs = self.n_jobs
y = X if y is None else y
itm_lbls = mapper_connected_components(
X,
y,
cover,
clustering,
n_jobs=n_jobs,
)
self.labels_ = [itm_lbls[i] for i, _ in enumerate(X)]
self._set_n_features_in(X)
return self
[docs]
class MapperClustering(_MapperClustering):
"""
**DEPRECATED**: This class is deprecated and will be removed in a future
release. Use :class:`tdamapper.learn.MapperClustering`.
"""
@deprecated(
"This class is deprecated and will be removed in a future release. "
"Use tdamapper.learn.MapperClustering."
)
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)