Scikit Learn GridSearchCV without cross validation (unsupervised learning)

Question

Is it possible to use GridSearchCV without cross validation? I am trying to optimize the number of clusters in KMeans clustering via grid search, and thus I don't need or want cross validation.

The documentation is also confusing me because under the fit() method, it has an option for unsupervised learning (says to use None for unsupervised learning). But if you want to do unsupervised learning, you need to do it without cross validation and there appears to be no option to get rid of cross validation.

Answer 1

After much searching, I was able to find this thread. It appears that you can get rid of cross validation in GridSearchCV if you use:

cv=[(slice(None), slice(None))]

I have tested this against my own coded version of grid search without cross validation and I get the same results from both methods. I am posting this answer to my own question in case others have the same issue.

Edit: to answer jjrr's question in the comments, here is an example use case:

from sklearn.metrics import silhouette_score as sc  def cv_silhouette_scorer(estimator, X):     estimator.fit(X)     cluster_labels = estimator.labels_     num_labels = len(set(cluster_labels))     num_samples = len(X.index)     if num_labels == 1 or num_labels == num_samples:         return -1     else:         return sc(X, cluster_labels)  cv = [(slice(None), slice(None))] gs = GridSearchCV(estimator=sklearn.cluster.MeanShift(), param_grid=param_dict,                    scoring=cv_silhouette_scorer, cv=cv, n_jobs=-1) gs.fit(df[cols_of_interest])