Terms to Cluster Mapping

>>> #
>>> # TEST PREPARATION
>>> #
>>> from techminer2.thesaurus.descriptors import ApplyThesaurus, CreateThesaurus
>>> CreateThesaurus(root_directory="example/", quiet=True).run()
>>> ApplyThesaurus(root_directory="example/", quiet=True).run()

>>> #
>>> # CODE TESTED
>>> #
>>> from techminer2.packages.networks.co_occurrence.author_keywords import TermsToClustersMapping
>>> mapping = (
...     TermsToClustersMapping()
...     #
...     # FIELD:
...     .having_terms_in_top(20)
...     .having_terms_ordered_by("OCC")
...     .having_term_occurrences_between(None, None)
...     .having_term_citations_between(None, None)
...     .having_terms_in(None)
...     #
...     # COUNTERS:
...     .using_term_counters(True)
...     #
...     # NETWORK:
...     .using_clustering_algorithm_or_dict("louvain")
...     .using_association_index("association")
...     #
...     # DATABASE:
...     .where_root_directory_is("example/")
...     .where_database_is("main")
...     .where_record_years_range_is(None, None)
...     .where_record_citations_range_is(None, None)
...     .where_records_match(None)
...     #
...     .run()
... )
>>> from pprint import pprint
>>> pprint(mapping)
{'ARTIFICIAL_INTELLIGENCE 02:0327': 3,
 'BANKING 02:0291': 1,
 'BLOCKCHAIN 03:0369': 0,
 'BUSINESS_MODEL 03:0896': 0,
 'CASE_STUDIES 02:0340': 0,
 'CROWDFUNDING 03:0335': 0,
 'CYBER_SECURITY 02:0342': 0,
 'FINANCE 02:0309': 3,
 'FINANCIAL_INCLUSION 03:0590': 0,
 'FINANCIAL_INSTITUTION 02:0484': 1,
 'FINANCIAL_SERVICE 04:0667': 1,
 'FINANCIAL_TECHNOLOGIES 03:0461': 0,
 'FINTECH 31:5168': 0,
 'INNOVATION 07:0911': 1,
 'LENDINGCLUB 02:0253': 2,
 'MARKETPLACE_LENDING 03:0317': 2,
 'PEER_TO_PEER_LENDING 02:0253': 2,
 'REGTECH 02:0266': 0,
 'ROBOTS 02:0289': 3,
 'TECHNOLOGIES 02:0310': 1}

>>> #
>>> # CODE TESTED
>>> #
>>> from techminer2.packages.networks.co_occurrence.author_keywords import TermsToClustersMapping
>>> mapping = (
...     TermsToClustersMapping()
...     #
...     # FIELD:
...     .having_terms_in_top(20)
...     .having_terms_ordered_by("OCC")
...     .having_term_occurrences_between(None, None)
...     .having_term_citations_between(None, None)
...     .having_terms_in(None)
...     #
...     # COUNTERS:
...     .using_term_counters(False)
...     #
...     # NETWORK:
...     .using_clustering_algorithm_or_dict("louvain")
...     .using_association_index("association")
...     #
...     # DATABASE:
...     .where_root_directory_is("example/")
...     .where_database_is("main")
...     .where_record_years_range_is(None, None)
...     .where_record_citations_range_is(None, None)
...     .where_records_match(None)
...     #
...     .run()
... )
>>> from pprint import pprint
>>> pprint(mapping)
{'ARTIFICIAL_INTELLIGENCE': 3,
 'BANKING': 1,
 'BLOCKCHAIN': 0,
 'BUSINESS_MODEL': 0,
 'CASE_STUDIES': 0,
 'CROWDFUNDING': 0,
 'CYBER_SECURITY': 0,
 'FINANCE': 3,
 'FINANCIAL_INCLUSION': 0,
 'FINANCIAL_INSTITUTION': 1,
 'FINANCIAL_SERVICE': 1,
 'FINANCIAL_TECHNOLOGIES': 0,
 'FINTECH': 0,
 'INNOVATION': 1,
 'LENDINGCLUB': 2,
 'MARKETPLACE_LENDING': 2,
 'PEER_TO_PEER_LENDING': 2,
 'REGTECH': 0,
 'ROBOTS': 3,
 'TECHNOLOGIES': 1}