Grid search for hyperparameter evaluation of clustering in scikit-learn

Question

I'm clustering a sample of about 100 records (unlabelled) and trying to use grid_search to evaluate the clustering algorithm with various hyperparameters. I'm scoring using silhouette_score which works fine.

My problem here is that I don't need to use the cross-validation aspect of the GridSearchCV/RandomizedSearchCV, but I can't find a simple GridSearch/RandomizedSearch. I can write my own but the ParameterSampler and ParameterGrid objects are very useful.

My next step will be to subclass BaseSearchCV and implement my own _fit() method, but thought it was worth asking is there a simpler way to do this, for example by passing something to the cv parameter?

def silhouette_score(estimator, X):
    clusters = estimator.fit_predict(X)
    score = metrics.silhouette_score(distance_matrix, clusters, metric='precomputed')
    return score

ca = KMeans()
param_grid = {"n_clusters": range(2, 11)}

# run randomized search
search = GridSearchCV(
    ca,
    param_distributions=param_dist,
    n_iter=n_iter_search,
    scoring=silhouette_score,
    cv= # can I pass something here to only use a single fold?
    )
search.fit(distance_matrix)

Answer 1

The clusteval library will help you to evaluate the data and find the optimal number of clusters. This library contains five methods that can be used to evaluate clusterings: silhouette, dbindex, derivative, dbscan and hdbscan.

pip install clusteval

Depending on your data, the evaluation method can be chosen.

# Import library
from clusteval import clusteval

# Set parameters, as an example dbscan
ce = clusteval(method='dbscan')

# Fit to find optimal number of clusters using dbscan
results= ce.fit(X)

# Make plot of the cluster evaluation
ce.plot()

# Make scatter plot. Note that the first two coordinates are used for plotting.
ce.scatter(X)

# results is a dict with various output statistics. One of them are the labels.
cluster_labels = results['labx']