relevanceai.operations_new.cooccurrence_network.transform#

Build a co-occurrence network based on the documents in your dataset

Module Contents#

relevanceai.operations_new.cooccurrence_network.transform.preprocess(data, stopwords_list=[])#
class relevanceai.operations_new.cooccurrence_network.transform.WordDictionary(docs)#
get_df_table(self)#
get_ids(self, doc_id)#
get_docs(self, word: str)#
get_word(self, id)#
get_id(self, word: str)#
update_df_table(self, word: str)#
class relevanceai.operations_new.cooccurrence_network.transform.CoOccurNetTransform(max_number_of_clusters=15, min_number_of_clusters=3, number_of_concepts=100, **kwargs)#

To write your own operation, you need to add: - name - transform

find_max_vertex(self, visited, weights)#
maximum_spanning_tree(self, graph)#
concurrence_matrix(self, top_ids, texts, word_dict)#
get_clusters_labels(self, mat)#
transform(self, documents, text_field='content', stopwords_list=[], center_word=None)#

abstractmethod for transform

property name(self)#

abstractproperty for name