optimize_radius not using information of clusters

[marco-2023]

The function optimize_radius in the file utils.py is not using the cluster_idsparameters. The select_from_cluster method of the DirectedSphereExclusion is also not using the cluster_ids. Because of this, this selection algorithm fails when used with cluster labels.

[xychem]

The cluster_ids don't be used in utils.py, it seems work when you insert following code to the raw code.

    if cluster_ids is not None:
        # the feature matrix(n_samples,n_features) of different cluster
        X = X[cluster_ids]

if X.shape[0] < size:
        raise RuntimeError(
            f"Size of samples to be selected is greater than existing the number of samples; "
            f"{size} > {X.shape[0]}."
        )
    # set the limits on # of selected points according to the tolerance percentage

    if cluster_ids is not None:
        # the feature matrix(n_samples,n_features) of different cluster
        X = X[cluster_ids]

    error = size * obj.tol
    lower_size = round(size - error)
    upper_size = round(size + error)

An other question is the tol of class optisim in dissimilarity.py is wrong, I think it can be 0.05. The iteration in ultis.py will not execute due to the huge tolerance.

def __init__(self, r=None, k=10, tol=5.0, eps=0, p=2, start_id=0, random_seed=42, n_iter=10):

I get the following pictures after doing these.

optisim

DirectedSphereExclusion

It is strange that I get thirteen points in the second picture but it looks like the points are selected by thier cluster_labels.

[FarnazH]

Thanks @xychem, this 183e253 fixes one of the issues you reported in #154 (comment). I also encountered it when putting together the quick_start.ipynb notebook in PR #186. Don't know why this item was not addressed before closing the issue.

[FanwangM]

This issue was closed because #155 attempted to fix the problem. Accodring to @xychem's code, it seemed working. I will take a closer look at the quick_start.ipynb.