8000 DOC Fix documentation of default values in cluster module (#17455) · jayzed82/scikit-learn@7e0bebf · GitHub
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DOC Fix documentation of default values in cluster module (scikit-learn#17455)
* updated _affinity_propagation.py docstrings in line with scikit-learn#15761 * updated cluster module docstrings in line with scikit-learn#15761 * updated cluster module docstrings post review scikit-learn#15761 * updated cluster module docstrings post review scikit-learn#15761 Co-authored-by: Greg Knowles <gitgreg123@users.noreply.github.com>
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‎sklearn/cluster/_affinity_propagation.py

Lines changed: 19 additions & 19 deletions
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Original file line numberDiff line numberDiff line change
@@ -41,10 +41,10 @@ def affinity_propagation(S, *, preference=None, convergence_iter=15,
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Parameters
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----------
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44-
S : array-like, shape (n_samples, n_samples)
44+
S : array-like of shape (n_samples, n_samples)
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Matrix of similarities between points
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47-
preference : array-like, shape (n_samples,) or float, optional
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preference : array-like of shape (n_samples,) or float, default=None
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Preferences for each point - points with larger values of
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preferences are more likely to be chosen as exemplars. The number of
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exemplars, i.e. of clusters, is influenced by the input preferences
@@ -53,27 +53,27 @@ def affinity_propagation(S, *, preference=None, convergence_iter=15,
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number of clusters). For a smaller amount of clusters, this can be set
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to the minimum value of the similarities.
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56-
convergence_iter : int, optional, default: 15
56+
convergence_iter : int, default=15
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Number of iterations with no change in the number
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of estimated clusters that stops the convergence.
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60-
max_iter : int, optional, default: 200
60+
max_iter : int, default=200
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Maximum number of iterations
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63-
damping : float, optional, default: 0.5
63+
damping : float, default=0.5
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Damping factor between 0.5 and 1.
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66-
copy : boolean, optional, default: True
66+
copy : bool, default=True
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If copy is False, the affinity matrix is modified inplace by the
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algorithm, for memory efficiency
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verbose : boolean, optional, default: False
70+
verbose : bool, default=False
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The verbosity level
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73-
return_n_iter : bool, default False
73+
return_n_iter : bool, default=False
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Whether or not to return the number of iterations.
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76-
random_state : int or np.random.RandomStateInstance, default: 0
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random_state : int or RandomState instance, default=0
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Pseudo-random number generator to control the starting state.
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Use an int for reproducible results across function calls.
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See the :term:`Glossary <random_state>`.
@@ -84,10 +84,10 @@ def affinity_propagation(S, *, preference=None, convergence_iter=15,
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Returns
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-------
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87-
cluster_centers_indices : array, shape (n_clusters,)
87+
cluster_centers_indices : ndarray of shape (n_clusters,)
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index of clusters centers
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90-
labels : array, shape (n_samples,)
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labels : ndarray of shape (n_samples,)
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cluster labels for each point
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n_iter : int
@@ -292,7 +292,7 @@ class AffinityPropagation(ClusterMixin, BaseEstimator):
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verbose : bool, default=False
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Whether to be verbose.
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295-
random_state : int or np.random.RandomStateInstance, default: 0
295+
random_state : int or RandomState instance, default=0
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Pseudo-random number generator to control the starting state.
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Use an int for reproducible results across function calls.
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See the :term:`Glossary <random_state>`.
@@ -382,8 +382,8 @@ def fit(self, X, y=None):
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Parameters
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----------
385-
X : array-like or sparse matrix, shape (n_samples, n_features), or \
386-
array-like, shape (n_samples, n_samples)
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X : {array-like, sparse matrix} of shape (n_samples, n_features), or \
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array-like of shape (n_samples, n_samples)
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Training instances to cluster, or similarities / affinities between
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instances if ``affinity='precomputed'``. If a sparse feature matrix
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is provided, it will be converted into a sparse ``csr_matrix``.
@@ -428,13 +428,13 @@ def predict(self, X):
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Parameters
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----------
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X : array-like or sparse matrix, shape (n_samples, n_features)
431+
X : {array-like, sparse matrix} of shape (n_samples, n_features)
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New data to predict. If a sparse matrix is provided, it will be
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converted into a sparse ``csr_matrix``.
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Returns
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-------
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labels : ndarray, shape (n_samples,)
437+
labels : ndarray of shape (n_samples,)
438438
Cluster labels.
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"""
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check_is_fitted(self)
@@ -457,8 +457,8 @@ def fit_predict(self, X, y=None):
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Parameters
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----------
460-
X : array-like or sparse matrix, shape (n_samples, n_features), or \
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array-like, shape (n_samples, n_samples)
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X : {array-like, sparse matrix} of shape (n_samples, n_features), or \
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array-like of shape (n_samples, n_samples)
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Training instances to cluster, or similarities / affinities between
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instances if ``affinity='precomputed'``. If a sparse feature matrix
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is provided, it will be converted into a sparse ``csr_matrix``.
@@ -468,7 +468,7 @@ def fit_predict(self, X, y=None):
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Returns
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-------
471-
labels : ndarray, shape (n_samples,)
471+
labels : ndarray of shape (n_samples,)
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Cluster labels.
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"""
474474
return super().fit_predict(X, y)

‎sklearn/cluster/_agglomerative.py

Lines changed: 21 additions & 20 deletions
Original file line numberDiff line numberDiff line change
@@ -150,29 +150,29 @@ def ward_tree(X, *, connectivity=None, n_clusters=None, return_distance=False):
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Parameters
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----------
153-
X : array, shape (n_samples, n_features)
153+
X : array-like of shape (n_samples, n_features)
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feature matrix representing n_samples samples to be clustered
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156-
connectivity : sparse matrix (optional).
156+
connectivity : sparse matrix, default=None
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connectivity matrix. Defines for each sample the neighboring samples
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following a given structure of the data. The matrix is assumed to
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be symmetric and only the upper triangular half is used.
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Default is None, i.e, the Ward algorithm is unstructured.
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162-
n_clusters : int (optional)
162+
n_clusters : int, default=None
163163
Stop early the construction of the tree at n_clusters. This is
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useful to decrease computation time if the number of clusters is
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not small compared to the number of samples. In this case, the
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complete tree is not computed, thus the 'children' output is of
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limited use, and the 'parents' output should rather be used.
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This option is valid only when specifying a connectivity matrix.
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170-
return_distance : bool (optional)
170+
return_distance : bool, default=None
171171
If True, return the distance between the clusters.
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Returns
174174
-------
175-
children : 2D array, shape (n_nodes-1, 2)
175+
children : ndarray of shape (n_nodes-1, 2)
176176
The children of each non-leaf node. Values less than `n_samples`
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correspond to leaves of the tree which are the original samples.
178178
A node `i` greater than or equal to `n_samples` is a non-leaf
@@ -186,11 +186,11 @@ def ward_tree(X, *, connectivity=None, n_clusters=None, return_distance=False):
186186
n_leaves : int
187187
The number of leaves in the tree
188188
189-
parents : 1D array, shape (n_nodes, ) or None
189+
parents : ndarray of shape (n_nodes,) or None
190190
The parent of each node. Only returned when a connectivity matrix
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is specified, elsewhere 'None' is returned.
192192
193-
distances : 1D array, shape (n_nodes-1, )
193+
distances : ndarray of shape (n_nodes-1,)
194194
Only returned if return_distance is set to True (for compatibility).
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The distances between the centers of the nodes. `distances[i]`
196196
corresponds to a weighted euclidean distance between
@@ -356,24 +356,24 @@ def linkage_tree(X, connectivity=None, n_clusters=None, linkage='complete',
356356
357357
Parameters
358358
----------
359-
X : array, shape (n_samples, n_features)
359+
X : array-like of shape (n_samples, n_features)
360360
feature matrix representing n_samples samples to be clustered
361361
362-
connectivity : sparse matrix (optional).
362+
connectivity : sparse matrix, default=None
363363
connectivity matrix. Defines for each sample the neighboring samples
364364
following a given structure of the data. The matrix is assumed to
365365
be symmetric and only the upper triangular half is used.
366366
Default is None, i.e, the Ward algorithm is unstructured.
367367
368-
n_clusters : int (optional)
368+
n_clusters : int, default=None
369369
Stop early the construction of the tree at n_clusters. This is
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useful to decrease computation time if the number of clusters is
371371
not small compared to the number of samples. In this case, the
372372
complete tree is not computed, thus the 'children' output is of
373373
limited use, and the 'parents' output should rather be used.
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This option is valid only when specifying a connectivity matrix.
375375
376-
linkage : {"average", "complete", "single"}, optional, default: "complete"
376+
linkage : {"average", "complete", "single"}, default="complete"
377377
Which linkage criteria to use. The linkage criterion determines which
378378
distance to use between sets of observation.
379379
- average uses the average of the distances of each observation of
@@ -383,16 +383,16 @@ def linkage_tree(X, connectivity=None, n_clusters=None, linkage='complete',
383383
- single uses the minimum of the distances between all observations
384384
of the two sets.
385385
386-
affinity : string or callable, optional, default: "euclidean".
386+
affinity : str or callable, default="euclidean".
387387
which metric to use. Can be "euclidean", "manhattan", or any
388388
distance know to paired distance (see metric.pairwise)
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390-
return_distance : bool, default False
390+
return_distance : bool, default=False
391391
whether or not to return the distances between the clusters.
392392
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Returns
394394
-------
395-
children : 2D array, shape (n_nodes-1, 2)
395+
children : ndarray of shape (n_nodes-1, 2)
396396
The children of each non-leaf node. Values less than `n_samples`
397397
correspond to leaves of the tree which are the original samples.
398398
A node `i` greater than or equal to `n_samples` is a non-leaf
@@ -406,11 +406,11 @@ def linkage_tree(X, connectivity=None, n_clusters=None, linkage='complete',
406406
n_leaves : int
407407
The number of leaves in the tree.
408408
409-
parents : 1D array, shape (n_nodes, ) or None
409+
parents : ndarray of shape (n_nodes, ) or None
410410
The parent of each node. Only returned when a connectivity matrix
411411
is specified, elsewhere 'None' is returned.
412412
413-
distances : ndarray, shape (n_nodes-1,)
413+
distances : ndarray of shape (n_nodes-1,)
414414
Returned when return_distance is set to True.
415415
416416
distances[i] refers to the distance between children[i][0] and
@@ -636,7 +636,7 @@ def _hc_cut(n_clusters, children, n_leaves):
636636
n_clusters : int or ndarray
637637
The number of clusters to form.
638638
639-
children : 2D array, shape (n_nodes-1, 2)
639+
children : ndarray of shape (n_nodes-1, 2)
640640
The children of each non-leaf node. Values less than `n_samples`
641641
correspond to leaves of the tree which are the original samples.
642642
A node `i` greater than or equal to `n_samples` is a non-leaf
@@ -910,7 +910,8 @@ def fit_predict(self, X, y=None):
910910
911911
Parameters
912912
----------
913-
X : array-like, shape (n_samples, n_features) or (n_samples, n_samples)
913+
X : array-like of shape (n_samples, n_features) or \
914+
(n_samples, n_samples)
914915
Training instances to cluster, or distances between instances if
915916
``affinity='precomputed'``.
916917
@@ -919,7 +920,7 @@ def fit_predict(self, X, y=None):
919920
920921
Returns
921922
-------
922-
labels : ndarray, shape (n_samples,)
923+
labels : ndarray of shape (n_samples,)
923924
Cluster labels.
924925
"""
925926
return super().fit_predict(X, y)
@@ -957,7 +958,7 @@ class FeatureAgglomeration(AgglomerativeClustering, AgglomerationTransform):
957958
kneighbors_graph. Default is None, i.e, the
958959
hierarchical clustering algorithm is unstructured.
959960
960-
compute_full_tree : 'auto' or bool, optional, default='auto'
961+
compute_full_tree : 'auto' or bool, default='auto'
961962
Stop early the construction of the tree at n_clusters. This is useful
962963
to decrease computation time if the number of clusters is not small
963964
compared to the number of features. This option is useful only when

‎sklearn/cluster/_bicluster.py

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -111,7 +111,7 @@ def fit(self, X, y=None):
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Parameters
113113
----------
114-
X : array-like, shape (n_samples, n_features)
114+
X : array-like of shape (n_samples, n_features)
115115
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y : Ignored
117117

‎sklearn/cluster/_dbscan.py

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -46,7 +46,7 @@ def dbscan(X, eps=0.5, *, min_samples=5, metric='minkowski',
4646
The number of samples (or total weight) in a neighborhood for a point
4747
to be considered as a core point. This includes the point itself.
4848
49-
metric : string, or callable
49+
metric : str or callable, default='minkowski'
5050
The metric to use when calculating distance between instances in a
5151
feature array. If metric is a string or callable, it must be one of
5252
the options allowed by :func:`sklearn.metrics.pairwise_distances` for

‎sklearn/cluster/_feature_agglomeration.py

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -33,7 +33,7 @@ def transform(self, X):
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3434
Returns
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-------
36-
Y : array, shape = [n_samples, n_clusters] or [n_clusters]
36+
Y : ndarray of shape (n_samples, n_clusters) or (n_clusters,)
3737
The pooled values for each feature cluster.
3838
"""
3939
check_is_fitted(self)
@@ -67,7 +67,7 @@ def inverse_transform(self, Xred):
6767
6868
Returns
6969
-------
70-
X : array, shape=[n_samples, n_features] or [n_features]
70+
X : ndarray of shape (n_samples, n_features) or (n_features,)
7171
A vector of size n_samples with the values of Xred assigned to
7272
each of the cluster of samples.
7373
"""

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