8000 [MRG+1] Deprecate pooling_func unused parameter in AgglomerativeClust… · scikit-learn/scikit-learn@b4561f0 · GitHub
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[MRG+1] Deprecate pooling_func unused parameter in AgglomerativeClustering (#9875)
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‎doc/whats_new/v0.20.rst

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@@ -142,3 +142,9 @@ Metrics
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for :func:`metrics.roc_auc_score`. Moreover using ``reorder=True`` can hide bugs
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due to floating point error in the input.
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:issue:`9851` by :user:`Hanmin Qin <qinhanmin2014>`.
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Cluster
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- Deprecate ``pooling_func`` unused parameter in
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:class:`cluster.AgglomerativeClustering`. :issue:`9875` by :user:`Kumar Ashutosh
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<thechargedneutron>`.

‎sklearn/cluster/hierarchical.py

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@@ -641,10 +641,12 @@ class AgglomerativeClustering(BaseEstimator, ClusterMixin):
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- complete or maximum linkage uses the maximum distances between
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all observations of the two sets.
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pooling_func : callable, default=np.mean
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This combines the values of agglomerated features into a single
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value, and should accept an array of shape [M, N] and the keyword
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argument ``axis=1``, and reduce it to an array of size [M].
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pooling_func : callable, default='deprecated'
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Ignored.
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.. deprecated:: 0.20
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``pooling_func`` has been deprecated in 0.20 and will be removed
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in 0.22.
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Attributes
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----------
@@ -670,7 +672,7 @@ class AgglomerativeClustering(BaseEstimator, ClusterMixin):
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def __init__(self, n_clusters=2, affinity="euclidean",
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memory=None,
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connectivity=None, compute_full_tree='auto',
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linkage='ward', pooling_func=np.mean):
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linkage='ward', pooling_func='deprecated'):
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self.n_clusters = n_clusters
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self.memory = memory
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self.connectivity = connectivity
@@ -694,6 +696,10 @@ def fit(self, X, y=None):
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-------
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self
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"""
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if self.pooling_func != 'deprecated':
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warnings.warn('Agglomerative "pooling_func" parameter is not used.'
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' It has been deprecated in version 0.20 and will be'
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'removed in 0.22', DeprecationWarning)
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X = check_array(X, ensure_min_samples=2, estimator=self)
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memory = check_memory(self.memory)
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@@ -829,6 +835,16 @@ class FeatureAgglomeration(AgglomerativeClustering, AgglomerationTransform):
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are merged to form node `n_features + i`
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"""
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def __init__(self, n_clusters=2, affinity="euclidean",
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memory=None,
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connectivity=None, compute_full_tree='auto',
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linkage='ward', pooling_func=np.mean):
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super(FeatureAgglomeration, self).__init__(
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n_clusters=n_clusters, memory=memory, connectivity=connectivity,
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compute_full_tree=compute_full_tree, linkage=linkage,
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affinity=affinity)
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self.pooling_func = pooling_func
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def fit(self, X, y=None, **params):
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"""Fit the hierarchical clustering on the data
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