from sklearn.cluster import AgglomerativeClustering
import numpy as np

def cluster_aspect_terms(nlp, aspects):

  aspect_terms = sorted(list(set(aspects['aspect'].values)))

  aspect_terms_sizes = aspects.groupby('aspect').size().sort_index().values

  aspect_terms_vectors = [doc.vector for doc in nlp.pipe(aspect_terms)]

  clusterer = AgglomerativeClustering(n_clusters=None,
                                      affinity='cosine',
                                      linkage='average',
                                      distance_threshold=0.2)

  clusterer.fit(aspect_terms_vectors)

  term_replacements = {}

  for cluster in range(clusterer.n_clusters_):

    idxs = np.nonzero(clusterer.labels_ == cluster)[0]

    terms = [t for i, t in enumerate(aspect_terms) if i in idxs]

    sizes = aspect_terms_sizes[idxs]
    
    main_term = terms[np.argmax(sizes)]

    for term in terms:
      term_replacements[term] = main_term

  return term_replacements