Terms by Cluster FrameΒΆ
Example
>>> from sklearn.decomposition import LatentDirichletAllocation
>>> lda = LatentDirichletAllocation(
... n_components=10,
... learning_decay=0.7,
... learning_offset=50.0,
... max_iter=10,
... batch_size=128,
... evaluate_every=-1,
... perp_tol=0.1,
... mean_change_tol=0.001,
... max_doc_update_iter=100,
... random_state=0,
... )
>>> from techminer2.packages.topic_modeling.user import TermsByClusterDataFrame
>>> df = (
... TermsByClusterDataFrame()
... #
... # FIELD:
... .with_field("raw_descriptors")
... .having_terms_in_top(50)
... .having_terms_ordered_by("OCC")
... .having_term_occurrences_between(None, None)
... .having_term_citations_between(None, None)
... .having_terms_in(None)
... #
... # DECOMPOSITION:
... .using_decomposition_algorithm(lda)
... .using_top_terms_by_theme(5)
... #
... # TFIDF:
... .using_binary_term_frequencies(False)
... .using_row_normalization(None)
... .using_idf_reweighting(False)
... .using_idf_weights_smoothing(False)
... .using_sublinear_tf_scaling(False)
... #
... # DATABASE:
... .where_root_directory_is("examples/fintech/")
... .where_database_is("main")
... .where_record_years_range_is(None, None)
... .where_record_citations_range_is(None, None)
... .where_records_match(None)
... #
... .run()
... )
>>> df.head()
cluster 0 ... 9
term ...
0 FINTECH 38:6131 ... FINTECH 38:6131
1 FINANCIAL_TECHNOLOGY 11:1519 ... THE_FINANCIAL_INDUSTRY 09:2006
2 TECHNOLOGY 10:1220 ... A_SURVEY 03:0484
3 BANKS 08:1049 ... PRACTITIONERS 05:0992
4 REGULATORS 08:0974 ... THE_FIELD 05:0834
[5 rows x 10 columns]