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[MRG+1] OPTICS: improve docstring and add default values. #11987

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Sep 3, 2018
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[MRG+1] OPTICS: improve docstring and add default values. #11987

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ffbe9af
improve docstring
adrinjalali Sep 3, 2018
398a2e8
fix default metric
adrinjalali Sep 3, 2018
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54 changes: 28 additions & 26 deletions sklearn/cluster/optics_.py
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Original file line number Diff line number Diff line change
Expand Up @@ -41,20 +41,19 @@ def optics(X, min_samples=5, max_eps=np.inf, metric='euclidean',
X : array, shape (n_samples, n_features)
The data.

min_samples : int
min_samples : int (default=5)
The number of samples in a neighborhood for a point to be considered
as a core point.

max_eps : float, optional
max_eps : float, optional (default=np.inf)
The maximum distance between two samples for them to be considered
as in the same neighborhood. This is also the largest object size
expected within the dataset. Default value of "np.inf" will identify
as in the same neighborhood. Default value of "np.inf" will identify
clusters across all scales; reducing `max_eps` will result in
shorter run times.

metric : string or callable, optional
metric : string or callable, optional (default='euclidean')
The distance metric to use for neighborhood lookups. Default is
"minkowski". Other options include "euclidean", "manhattan",
"euclidean". Other options include "minkowski", "manhattan",
"chebyshev", "haversine", "seuclidean", "hamming", "canberra",
and "braycurtis". The "wminkowski" and "mahalanobis" metrics are
also valid with an additional argument.
Expand All @@ -68,20 +67,20 @@ def optics(X, min_samples=5, max_eps=np.inf, metric='euclidean',
metric_params : dict, optional (default=None)
Additional keyword arguments for the metric function.

maxima_ratio : float, optional
maxima_ratio : float, optional (default=.75)
The maximum ratio we allow of average height of clusters on the
right and left to the local maxima in question. The higher the
ratio, the more generous the algorithm is to preserving local
minima, and the more cuts the resulting tree will have.

rejection_ratio : float, optional
rejection_ratio : float, optional (default=.7)
Adjusts the fitness of the clustering. When the maxima_ratio is
exceeded, determine which of the clusters to the left and right to
reject based on rejection_ratio. Higher values will result in points
being more readily classified as noise; conversely, lower values will
result in more points being clustered.

similarity_threshold : float, optional
similarity_threshold : float, optional (default=.4)
Used to check if nodes can be moved up one level, that is, if the
new cluster created is too "similar" to its parent, given the
similarity threshold. Similarity can be determined by 1) the size
Expand All @@ -91,19 +90,21 @@ def optics(X, min_samples=5, max_eps=np.inf, metric='euclidean',
node. A lower value for the similarity threshold means less levels
in the tree.

significant_min : float, optional
significant_min : float, optional (default=.003)
Sets a lower threshold on how small a significant maxima can be.

min_cluster_size_ratio : float, optional
min_cluster_size_ratio : float, optional (default=.005)
Minimum percentage of dataset expected for cluster membership.

min_maxima_ratio : float, optional
min_maxima_ratio : float, optional (default=.001)
Used to determine neighborhood size for minimum cluster membership.
Each local maxima should be a largest value in a neighborhood
of the `size min_maxima_ratio * len(X)` from left and right.

algorithm : {'auto', 'ball_tree', 'kd_tree', 'brute'}, optional
Algorithm used to compute the nearest neighbors:

- 'ball_tree' will use :class:`BallTree`
- 'ball_tree' will use :class:`BallTree` (default)
- 'kd_tree' will use :class:`KDTree`
- 'brute' will use a brute-force search.
- 'auto' will attempt to decide the most appropriate algorithm
Expand Down Expand Up @@ -168,20 +169,19 @@ class OPTICS(BaseEstimator, ClusterMixin):

Parameters
----------
min_samples : int
min_samples : int (default=5)
The number of samples in a neighborhood for a point to be considered
as a core point.

max_eps : float, optional
max_eps : float, optional (default=np.inf)
The maximum distance between two samples for them to be considered
as in the same neighborhood. This is also the largest object size
expected within the dataset. Default value of "np.inf" will identify
as in the same neighborhood. Default value of "np.inf" will identify
clusters across all scales; reducing `max_eps` will result in
shorter run times.

metric : string or callable, optional
metric : string or callable, optional (default='euclidean')
The distance metric to use for neighborhood lookups. Default is
"minkowski". Other options include "euclidean", "manhattan",
"euclidean". Other options include "minkowski", "manhattan",
"chebyshev", "haversine", "seuclidean", "hamming", "canberra",
and "braycurtis". The "wminkowski" and "mahalanobis" metrics are
also valid with an additional argument.
Expand All @@ -195,20 +195,20 @@ class OPTICS(BaseEstimator, ClusterMixin):
metric_params : dict, optional (default=None)
Additional keyword arguments for the metric function.

maxima_ratio : float, optional
maxima_ratio : float, optional (default=.75)
The maximum ratio we allow of average height of clusters on the
right and left to the local maxima in question. The higher the
ratio, the more generous the algorithm is to preserving local
minima, and the more cuts the resulting tree will have.

rejection_ratio : float, optional
rejection_ratio : float, optional (default=.7)
Adjusts the fitness of the clustering. When the maxima_ratio is
exceeded, determine which of the clusters to the left and right to
reject based on rejection_ratio. Higher values will result in points
being more readily classified as noise; conversely, lower values will
result in more points being clustered.

similarity_threshold : float, optional
similarity_threshold : float, optional (default=.4)
Used to check if nodes can be moved up one level, that is, if the
new cluster created is too "similar" to its parent, given the
similarity threshold. Similarity can be determined by 1) the size
Expand All @@ -218,19 +218,21 @@ class OPTICS(BaseEstimator, ClusterMixin):
node. A lower value for the similarity threshold means less levels
in the tree.

significant_min : float, optional
significant_min : float, optional (default=.003)
Sets a lower threshold on how small a significant maxima can be.

min_cluster_size_ratio : float, optional
min_cluster_size_ratio : float, optional (default=.005)
Minimum percentage of dataset expected for cluster membership.

min_maxima_ratio : float, optional
min_maxima_ratio : float, optional (default=.001)
Used to determine neighborhood size for minimum cluster membership.
Each local maxima should be a largest value in a neighborhood
of the `size min_maxima_ratio * len(X)` from left and right.

algorithm : {'auto', 'ball_tree', 'kd_tree', 'brute'}, optional
Algorithm used to compute the nearest neighbors:

- 'ball_tree' will use :class:`BallTree`
- 'ball_tree' will use :class:`BallTree` (default)
- 'kd_tree' will use :class:`KDTree`
- 'brute' will use a brute-force search.
- 'auto' will attempt to decide the most appropriate algorithm
Expand Down