def run_pipeline()

in src/util/grouping_pipeline.py [0:0]

• 32 lines of code
• 6 McCabe index (conditional complexity)

def run_pipeline(config, df, saved_set_name=None, model_provider = None):
    """
    Runs a tab grouping pipeline, grouping tabs into clusters and labeling each cluster
    Args:
        config: Configuration options
        df: Dataset
        saved_set_name: Name to save embeddings as a tsv for other use
        model_provider: Language model class
    Returns:
        dataset, rand score (based on labels), adjusted rand score
    """
    df = df.sample(frac=1)
    dbscan = config["clustering_method"] == "dbscan"
    pipeline = generate_pipeline(config, model_provider=model_provider)
    df = add_text_for_embedding(df, config["text_for_embedding"])
    model = pipeline.fit(df)
    pipeline_result = model.transform(df).toarray()
    if saved_set_name is not None:
        np.savetxt(f"output/{saved_set_name}.tsv", pipeline_result, delimiter="\t")
        df[["title", "smart_group_label"]].to_csv(f"output/{saved_set_name}_labels.tsv", sep="\t")
    embeddings_as_list = [pipeline_result.tolist() for _row in pipeline_result]
    if config["remap"] > 0:
        umap_model = umap.UMAP(
            n_neighbors=config["remap"],
            n_components=5,
            min_dist=0.0,
            metric="cosine"
        )
        pipeline_result = umap_model.fit_transform(pipeline_result)
    #       embeddings_as_list = [pipeline_result.tolist() for _row in pipeline_result]

    max_clusters = min(math.floor(math.log(len(embeddings_as_list)) * 2.0 + 1), len(embeddings_as_list))
    best_cluster = generate_best_cluster_model(pipeline_result, range(2, max_clusters),
                                               verbose=False, use_dbscan=dbscan, eps=config["dbscan_eps"],
                                               num_cluster_method=config["num_cluster_method"])
    if dbscan:
        clusters = best_cluster.fit_predict(pipeline_result)
    else:
        clusters = best_cluster.predict(pipeline_result)
    df["predicted_cluster"] = clusters
    df["embeddings"] = embeddings_as_list
    rscore = rand_score(df["smart_group_label"], df["predicted_cluster"])
    adj_rscore = adjusted_rand_score(df["smart_group_label"], df["predicted_cluster"])
    return df, rscore, adj_rscore