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[MRG] DOC included default values in AgglomerativeClustering class. #15764

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merged 4 commits into from
Dec 10, 2019
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[MRG] DOC included default values in AgglomerativeClustering class. #15764

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38f87a5
DOC included default values in AgglomerativeClustering class.
vachanda Dec 3, 2019
2491930
Adds explain for 'auto' keyword in 'compute_full_tree' parameter.
vachanda Dec 4, 2019
f2f304b
Moves the doc string to the correct class.
vachanda Dec 4, 2019
91788a5
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glemaitre Dec 10, 2019
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38 changes: 20 additions & 18 deletions sklearn/cluster/_hierarchical.py
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Original file line number Diff line number Diff line change
Expand Up @@ -683,41 +683,43 @@ class AgglomerativeClustering(ClusterMixin, BaseEstimator):

Parameters
----------
n_clusters : int or None, optional (default=2)
n_clusters : int or None, default=2
The number of clusters to find. It must be ``None`` if
``distance_threshold`` is not ``None``.

affinity : string or callable, default: "euclidean"
affinity : str or callable, default='euclidean'
Metric used to compute the linkage. Can be "euclidean", "l1", "l2",
"manhattan", "cosine", or "precomputed".
If linkage is "ward", only "euclidean" is accepted.
If "precomputed", a distance matrix (instead of a similarity matrix)
is needed as input for the fit method.

memory : None, str or object with the joblib.Memory interface, optional
memory : str or object with the joblib.Memory interface, default=None
Used to cache the output of the computation of the tree.
By default, no caching is done. If a string is given, it is the
path to the caching directory.

connectivity : array-like or callable, optional
connectivity : array-like or callable, default=None
Connectivity matrix. Defines for each sample the neighboring
samples following a given structure of the data.
This can be a connectivity matrix itself or a callable that transforms
the data into a connectivity matrix, such as derived from
kneighbors_graph. Default is None, i.e, the
hierarchical clustering algorithm is unstructured.

compute_full_tree : bool or 'auto' (optional)
Stop early the construction of the tree at n_clusters. This is
useful to decrease computation time if the number of clusters is
not small compared to the number of samples. This option is
useful only when specifying a connectivity matrix. Note also that
when varying the number of clusters and using caching, it may
be advantageous to compute the full tree. It must be ``True`` if
``distance_threshold`` is not ``None``.

linkage : {"ward", "complete", "average", "single"}, optional \
(default="ward")
compute_full_tree : 'auto' or bool, default='auto'
Stop early the construction of the tree at n_clusters. This is useful
to decrease computation time if the number of clusters is not small
compared to the number of samples. This option is useful only when
specifying a connectivity matrix. Note also that when varying the
number of clusters and using caching, it may be advantageous to compute
the full tree. It must be ``True`` if ``distance_threshold`` is not
``None``. By default `compute_full_tree` is "auto", which is equivalent
to `True` when `distance_threshold` is not `None` or that `n_clusters`
is inferior to 100 or `0.02 * n_samples`. Otherwise, "auto" is
equivalent to `False`.

linkage : {"ward", "complete", "average", "single"}, default="ward"
Which linkage criterion to use. The linkage criterion determines which
distance to use between sets of observation. The algorithm will merge
the pairs of cluster that minimize this criterion.
Expand All @@ -730,7 +732,7 @@ class AgglomerativeClustering(ClusterMixin, BaseEstimator):
- single uses the minimum of the distances between all observations
of the two sets.

distance_threshold : float, optional (default=None)
distance_threshold : float, default=None
The linkage distance threshold above which, clusters will not be
merged. If not ``None``, ``n_clusters`` must be ``None`` and
``compute_full_tree`` must be ``True``.
Expand All @@ -744,7 +746,7 @@ class AgglomerativeClustering(ClusterMixin, BaseEstimator):
``distance_threshold=None``, it will be equal to the given
``n_clusters``.

labels_ : array [n_samples]
labels_ : ndarray of shape (n_samples)
cluster labels for each point

n_leaves_ : int
Expand All @@ -753,7 +755,7 @@ class AgglomerativeClustering(ClusterMixin, BaseEstimator):
n_connected_components_ : int
The estimated number of connected components in the graph.

children_ : array-like, shape (n_samples-1, 2)
children_ : array-like of shape (n_samples-1, 2)
The children of each non-leaf node. Values less than `n_samples`
correspond to leaves of the tree which are the original samples.
A node `i` greater than or equal to `n_samples` is a non-leaf
Expand Down