def main()

in fairmotion/tasks/clustering/clustering.py [0:0]

• 34 lines of code
• 11 McCabe index (conditional complexity)

def main(args):
    features = []
    names = []
    with open(args.features) as f:
        for line in f:
            line = line.strip()
            names.append(line.split(":")[0])
            features.append(
                [float(x) for x in line.split(":")[-1].split("\t")]
            )

    if 0.0 < args.clip_features < 100.0:
        np.percentile(
            features, args.clip_features, axis=0, overwrite_input=True
        )
    if args.normalize_features:
        features = normalize_features(features)

    if args.type == "kmeans":
        clusters = run_kmeans_clustering(features, names, args)
    elif args.type == "hierarchical":
        clusters = run_hierarchical_clustering(features, names, args)
    elif args.type == "optics":
        clusters = run_optics_clustering(features, names, args)
    elif args.type == "dbscan":
        clusters = run_dbscan_clustering(features, names, args)

    ranked_clusters = get_ranked_clusters(clusters)

    with open(args.output_file, "w") as f:
        for cluster in ranked_clusters:
            for (name, rank, score) in ranked_clusters[cluster]:
                f.write(
                    ",".join([str(cluster), str(rank), str(score)])
                    + ":"
                    + str(name)
                    + "\n"
                )