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[MNT] Clustering module 1.0 deprecations #2306

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[MNT] Clustering module 1.0 deprecations #2306

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78c67c8
clustering deprecations
chrisholder Nov 5, 2024
491117f
notebook
chrisholder Nov 5, 2024
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2 changes: 0 additions & 2 deletions aeon/clustering/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7,7 +7,6 @@
"TimeSeriesCLARANS",
"TimeSeriesKMeans",
"TimeSeriesKShape",
"TimeSeriesKShapes",
"TimeSeriesKernelKMeans",
"ElasticSOM",
"KSpectralCentroid",
Expand All @@ -21,7 +20,6 @@
from aeon.clustering._k_medoids import TimeSeriesKMedoids
from aeon.clustering._k_sc import KSpectralCentroid
from aeon.clustering._k_shape import TimeSeriesKShape
from aeon.clustering._k_shapes import TimeSeriesKShapes
from aeon.clustering._kernel_k_means import TimeSeriesKernelKMeans
from aeon.clustering.base import BaseClusterer
from aeon.clustering.dummy import DummyClusterer
12 changes: 0 additions & 12 deletions aeon/clustering/_k_means.py
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Expand Up @@ -169,20 +169,8 @@ def __init__(
averaging_method: Union[str, Callable[[np.ndarray], np.ndarray]] = "ba",
distance_params: Optional[dict] = None,
average_params: Optional[dict] = None,
init_algorithm: Optional[Union[str, np.ndarray]] = None,
):
self.init = init
self.init_algorithm = init_algorithm
if init_algorithm is not None:
import warnings

warnings.warn(
"The 'init_algorithm' parameter is deprecated and will be "
"removed in a future. Version Use 'init' instead.",
DeprecationWarning,
stacklevel=2,
)
self.init = self.init_algorithm
self.distance = distance
self.n_init = n_init
self.max_iter = max_iter
Expand Down
185 changes: 0 additions & 185 deletions aeon/clustering/_k_shapes.py
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3 changes: 0 additions & 3 deletions aeon/clustering/deep_learning/_ae_bgru.py
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Expand Up @@ -22,8 +22,6 @@ class AEBiGRUClusterer(BaseDeepClusterer):
----------
n_clusters : int, default=None
Number of clusters for the deep learnign model.
clustering_algorithm : str, default="deprecated"
Use 'estimator' parameter instead.
clustering_params : dict, default=None
Use 'estimator' parameter instead.
estimator : aeon clusterer, default=None
Expand Down Expand Up @@ -100,7 +98,6 @@ class AEBiGRUClusterer(BaseDeepClusterer):
def __init__(
self,
n_clusters=None,
clustering_algorithm="deprecated",
estimator=None,
clustering_params=None,
latent_space_dim=128,
Expand Down
4 changes: 0 additions & 4 deletions aeon/clustering/deep_learning/_ae_fcn.py
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Expand Up @@ -27,8 +27,6 @@ class AEFCNClusterer(BaseDeepClusterer):
An aeon estimator to be built using the transformed data.
Defaults to aeon TimeSeriesKMeans() with euclidean distance
and mean averaging method and n_clusters set to 2.
clustering_algorithm : str, default="deprecated"
Please use 'estimator' parameter.
clustering_params : dict, default=None
Please use 'estimator' parameter.
latent_space_dim : int, default=128
Expand Down Expand Up @@ -124,7 +122,6 @@ def __init__(
self,
n_clusters=None,
estimator=None,
clustering_algorithm="deprecated",
clustering_params=None,
latent_space_dim=128,
temporal_latent_space=False,
Expand Down Expand Up @@ -177,7 +174,6 @@ def __init__(
super().__init__(
estimator=estimator,
n_clusters=n_clusters,
clustering_algorithm=clustering_algorithm,
clustering_params=clustering_params,
batch_size=batch_size,
last_file_name=last_file_name,
Expand Down
4 changes: 0 additions & 4 deletions aeon/clustering/deep_learning/_ae_resnet.py
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Expand Up @@ -30,8 +30,6 @@ class AEResNetClusterer(BaseDeepClusterer):
An aeon estimator to be built using the transformed data.
Defaults to aeon TimeSeriesKMeans() with euclidean distance
and mean averaging method and n_clusters set to 2.
clustering_algorithm : str, default="deprecated"
Please use 'estimator' parameter.
clustering_params : dict, default=None
Please use 'estimator' parameter.
latent_space_dim : int, default=128
Expand Down Expand Up @@ -134,7 +132,6 @@ def __init__(
n_clusters=None,
estimator=None,
n_residual_blocks=3,
clustering_algorithm="deprecated",
clustering_params=None,
n_conv_per_residual_block=3,
n_filters=None,
Expand Down Expand Up @@ -188,7 +185,6 @@ def __init__(
super().__init__(
estimator=estimator,
n_clusters=n_clusters,
clustering_algorithm=clustering_algorithm,
clustering_params=clustering_params,
batch_size=batch_size,
last_file_name=last_file_name,
Expand Down
22 changes: 0 additions & 22 deletions aeon/clustering/deep_learning/base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -19,8 +19,6 @@ class BaseDeepClusterer(BaseClusterer):
An aeon estimator to be built using the transformed data.
Defaults to aeon TimeSeriesKMeans() with euclidean distance
and mean averaging method and n_clusters set to 2.
clustering_algorithm : str, default="deprecated"
Please use 'estimator' parameter.
clustering_params : dict, default=None
Please use 'estimator' parameter.
batch_size : int, default = 40
Expand All @@ -44,14 +42,12 @@ def __init__(
self,
n_clusters=None,
estimator=None,
clustering_algorithm="deprecated",
clustering_params=None,
batch_size=32,
last_file_name="last_file",
):
self.estimator = estimator
self.n_clusters = n_clusters
self.clustering_algorithm = clustering_algorithm
self.clustering_params = clustering_params
self.batch_size = batch_size
self.last_file_name = last_file_name
Expand Down Expand Up @@ -109,8 +105,6 @@ def _fit_clustering(self, X):
X : np.ndarray, shape=(n_cases, n_timepoints, n_channels)
The input time series.
"""
import warnings

self._estimator = (
TimeSeriesKMeans(
n_clusters=2, distance="euclidean", averaging_method="mean"
Expand All @@ -120,22 +114,6 @@ def _fit_clustering(self, X):
)

# to be removed in 1.0.0
if (
self.clustering_algorithm != "deprecated"
or self.clustering_params is not None
or self.n_clusters is not None
):
warnings.warn(
"The 'n_clusters' 'clustering_algorithm' and "
"'clustering_params' parameters "
"will be removed in v1.0.0. "
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n_clusters and clustering_params are also to be deprecated, all replaced by estimator now

"Their usage will not have an effect, "
"please use the new 'estimator' parameter to directly "
"give an aeon clusterer as input.",
FutureWarning,
stacklevel=2,
)

latent_space = self.model_.layers[1].predict(X)
self._estimator.fit(X=latent_space)

Expand Down
2 changes: 1 addition & 1 deletion examples/networks/deep_learning.ipynb
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Expand Up @@ -281,7 +281,7 @@
"aefcn = AEFCNClusterer(\n",
" n_clusters=2,\n",
" temporal_latent_space=False,\n",
" clustering_algorithm=\"kmeans\",\n",
" estimator=\"kmeans\",\n",
" n_epochs=10,\n",
")\n",
"\n",
Expand Down
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