How can GridSearchCV be used for clustering (MeanShift or DBSCAN)?

Question

I\'m trying to cluster some text documents using scikit-learn. I\'m trying out both DBSCAN and MeanShift and want to determine which hyperparameters (e.g.

Answer 1

The following function for DBSCAN might help. I've written it to iterate over the hyperparameters eps and min_samples and included optional arguments for min and max clusters. As DBSCAN is unsupervised, I have not included an evaluation parameter.

def dbscan_grid_search(X_data, lst, clst_count, eps_space = 0.5,
                       min_samples_space = 5, min_clust = 0, max_clust = 10):

    """
Performs a hyperparameter grid search for DBSCAN.

Parameters:
    * X_data            = data used to fit the DBSCAN instance
    * lst               = a list to store the results of the grid search
    * clst_count        = a list to store the number of non-whitespace clusters
    * eps_space         = the range values for the eps parameter
    * min_samples_space = the range values for the min_samples parameter
    * min_clust         = the minimum number of clusters required after each search iteration in order for a result to be appended to the lst
    * max_clust         = the maximum number of clusters required after each search iteration in order for a result to be appended to the lst


Example:

# Loading Libraries
from sklearn import datasets
from sklearn.preprocessing import StandardScaler
import pandas as pd

# Loading iris dataset
iris = datasets.load_iris()
X = iris.data[:, :] 
y = iris.target

# Scaling X data
dbscan_scaler = StandardScaler()

dbscan_scaler.fit(X)

dbscan_X_scaled = dbscan_scaler.transform(X)

# Setting empty lists in global environment
dbscan_clusters = []
cluster_count   = []


# Inputting function parameters
dbscan_grid_search(X_data = dbscan_X_scaled,
                   lst = dbscan_clusters,
                   clst_count = cluster_count
                   eps_space = pd.np.arange(0.1, 5, 0.1),
                   min_samples_space = pd.np.arange(1, 50, 1),
                   min_clust = 3,
                   max_clust = 6)

"""

    # Importing counter to count the amount of data in each cluster
    from collections import Counter


    # Starting a tally of total iterations
    n_iterations = 0


    # Looping over each combination of hyperparameters
    for eps_val in eps_space:
        for samples_val in min_samples_space:

            dbscan_grid = DBSCAN(eps = eps_val,
                                 min_samples = samples_val)


            # fit_transform
            clusters = dbscan_grid.fit_predict(X = X_data)


            # Counting the amount of data in each cluster
            cluster_count = Counter(clusters)


            # Saving the number of clusters
            n_clusters = sum(abs(pd.np.unique(clusters))) - 1


            # Increasing the iteration tally with each run of the loop
            n_iterations += 1


            # Appending the lst each time n_clusters criteria is reached
            if n_clusters >= min_clust and n_clusters <= max_clust:

                dbscan_clusters.append([eps_val,
                                        samples_val,
                                        n_clusters])


                clst_count.append(cluster_count)

    # Printing grid search summary information
    print(f"""Search Complete. \nYour list is now of length {len(lst)}. """)
    print(f"""Hyperparameter combinations checked: {n_iterations}. \n""")