esda.adbscan.ADBSCAN¶
-
class esda.adbscan.ADBSCAN(eps, min_samples, algorithm=
'auto', n_jobs=1, pct_exact=0.1, reps=100, keep_solus=False, pct_thr=0.9)[source]¶ A-DBSCAN, as introduced in [Arribas-Bel et al., 2021].
A-DSBCAN is an extension of the original DBSCAN algorithm that creates an ensemble of solutions generated by running DBSCAN on a random subset and “extending” the solution to the rest of the sample through nearest-neighbor regression.
See the original reference ([Arribas-Bel et al., 2021]) for more details or the notebook guide for an illustration. …
- Parameters:¶
- eps : float¶
The maximum distance between two samples for them to be considered as in the same neighborhood.
- min_samples : int¶
The number of samples (or total weight) in a neighborhood for a point to be considered as a core point. This includes the point itself.
- algorithm : {'auto', 'ball_tree', 'kd_tree', 'brute'}, optional¶
The algorithm to be used by the NearestNeighbors module to compute pointwise distances and find nearest neighbors. See NearestNeighbors module documentation for details.
- n_jobs : int¶
[Optional. Default=1] The number of parallel jobs to run. If -1, then the number of jobs is set to the number of CPU cores.
- pct_exact : float¶
[Optional. Default=0.1] Proportion of the entire dataset used to calculate DBSCAN in each draw
- reps : int¶
[Optional. Default=100] Number of random samples to draw in order to build final solution
- keep_solus : bool¶
[Optional. Default=False] If True, the solus and solus_relabelled objects are kept, else it is deleted to save memory
- pct_thr : float¶
[Optional. Default=0.9] Minimum proportion of replications that a non-noise label need to be assigned to an observation for that observation to be labelled as such
- labels_[source]¶
[Only available after fit] Cluster labels for each point in the dataset given to fit(). Noisy (if the proportion of the most common label is < pct_thr) samples are given the label -1.
- Type:¶
array
- votes[source]¶
[Only available after fit] Table indexed on X.index with labels_ under the lbls column, and the frequency across draws of that label under pct
- Type:¶
DataFrame
- solus[source]¶
[Only available after fit] Each solution of labels for every draw
- Type:¶
DataFrame, shape = [n, reps]
- solus_relabelled[source]¶
[Only available after fit] Each solution of labels for every draw, relabelled to be consistent across solutions
- Type:¶
DataFrame, shape = [n, reps]
Examples
>>> import pandas >>> from esda.adbscan import ADBSCAN >>> import numpy as np >>> np.random.seed(10) >>> db = pandas.DataFrame( ... {'X': np.random.random(25), 'Y': np.random.random(25)} ... )ADBSCAN can be run following scikit-learn like API as:
>>> np.random.seed(10) >>> clusterer = ADBSCAN(0.03, 3, reps=10, keep_solus=True) >>> _ = clusterer.fit(db) >>> clusterer.labels_ <StringArray> ['-1', '-1', '-1', '0', '-1', '-1', '-1', '0', '-1', '-1', '-1', '-1', '-1', '-1', '0', '0', '0', '-1', '0', '-1', '0', '-1', '-1', '-1', '-1'] Length: 25, dtype: strWe can inspect the winning label for each observation, as well as the proportion of votes:
>>> print(clusterer.votes.head().to_string()) lbls pct 0 -1 0.7 1 -1 0.5 2 -1 0.7 3 0 1.0 4 -1 0.7If you have set the option to keep them, you can even inspect each solution that makes up the ensemble:
>>> print(clusterer.solus.head().to_string()) rep-00 rep-01 rep-02 rep-03 rep-04 rep-05 rep-06 rep-07 rep-08 rep-09 0 0 1 1 0 1 0 0 0 1 0 1 1 1 1 1 0 1 0 1 1 1 2 0 1 1 0 0 1 0 0 1 0 3 0 1 1 0 0 1 1 1 0 0 4 0 1 1 1 0 1 0 1 0 1If we select only one replication and the proportion of the entire dataset that is sampled to 100%, we obtain a traditional DBSCAN:
>>> clusterer = ADBSCAN(0.2, 5, reps=1, pct_exact=1) >>> np.random.seed(10) >>> _ = clusterer.fit(db) >>> clusterer.labels_ <StringArray> [ '0', '-1', '0', '0', '0', '-1', '-1', '0', '-1', '-1', '0', '-1', '-1', '-1', '0', '0', '0', '-1', '0', '0', '0', '-1', '-1', '0', '-1'] Length: 25, dtype: strMethods
fit(X[, y, sample_weight, xy])Perform ADBSCAN clustering from fetaures
fit_predict(X[, y])Perform clustering on X and returns cluster labels.
Get metadata routing of this object.
get_params([deep])Get parameters for this estimator.
set_fit_request(*[, sample_weight, xy])Configure whether metadata should be requested to be passed to the
fitmethod.set_params(**params)Set the parameters of this estimator.
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fit(X, y=
None, sample_weight=None, xy=['X', 'Y'])[source]¶ Perform ADBSCAN clustering from fetaures …
- Parameters:¶
- X : DataFrame¶
Features
- sample_weight : Series, shape (n_samples,)¶
[Optional. Default=None] Weight of each sample, such that a sample with a weight of at least
min_samplesis by itself a core sample; a sample with negative weight may inhibit its eps-neighbor from being core. Note that weights are absolute, and default to 1.- xy : list¶
[Default=`[‘X’, ‘Y’]`] Ordered pair of names for XY coordinates in xys
- y : Ignored¶
-
fit_predict(X, y=
None, **kwargs)[source]¶ Perform clustering on X and returns cluster labels.
- get_metadata_routing()[source]¶
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
- Returns:¶
routing – A
MetadataRequestencapsulating routing information.- Return type:¶
MetadataRequest
-
set_fit_request(*, sample_weight=
'$UNCHANGED$', xy='$UNCHANGED$')[source]¶ Configure whether metadata should be requested to be passed to the
fitmethod.Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with
enable_metadata_routing=True(seesklearn.set_config()). Please check the User Guide on how the routing mechanism works.The options for each parameter are:
True: metadata is requested, and passed tofitif provided. The request is ignored if metadata is not provided.False: metadata is not requested and the meta-estimator will not pass it tofit.None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.str: metadata should be passed to the meta-estimator with this given alias instead of the original name.
The default (
sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.Added in version 1.3.