cjnolet/cuml_hdbscan_general_usage.md

## cuml_hdbscan_general_usage.md

      
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              cuml_hdbscan_general_usage.md
            
          
    Basic Usage

Example of training an HDBSCAN model using the hdbscan Python package in Scikit-learn contrib:
from sklearn import datasets
from hdbscan import HDBSCAN

X = datasets.make_moons(n_samples=50, noise=0.05)

model = HDBSCAN(min_samples=5)
y_hat = model.fit_predict(X)
And the same code using the GPU-Accelerated HDBSCAN in cuML (spoiler alert: the only difference is the import).
from sklearn import datasets
from cuml.cluster import HDBSCAN

X = datasets.make_moons(n_samples=50, noise=0.05)

model = HDBSCAN(min_samples=5)
y_hat = model.fit_predict(X)
Plotting

We can plot the minimum spanning tree the same way we would for the original HDBSCAN implementation:
model.minimum_spanning_tree_.plot()
The single linkage dendrogram and condensed tree can be plotted as well:
model.single_linkage_tree_.plot()
model.condensed_tree_.plot()