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'] 

Answer 2

Ok, this might be an old question but I use this kind of code:

First, we want to generate all the possible combinations of parameters:

def make_generator(parameters):     if not parameters:         yield dict()     else:         key_to_iterate = list(parameters.keys())[0]         next_round_parameters = {p : parameters[p]                     for p in parameters if p != key_to_iterate}         for val in parameters[key_to_iterate]:             for pars in make_generator(next_round_parameters):                 temp_res = pars                 temp_res[key_to_iterate] = val                 yield temp_res 

Then create a loop out of this:

# add fix parameters - here - it's just a random one fixed_params = {"max_iter":300 }   param_grid = {"n_clusters": range(2, 11)}  for params in make_generator(param_grid):     params.update(fixed_params)     ca = KMeans( **params )     ca.fit(_data)     labels = ca.labels_     # Estimate your clustering labels and      # make decision to save or discard it! 

Of course, it can be combined in a pretty function. So this solution is mostly an example.

Hope it helps someone!