>>> 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 DocumentsByThemeDataFrame
>>> (
... DocumentsByThemeDataFrame()
... #
... # 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("example/")
... .where_database_is("main")
... .where_record_years_range_is(None, None)
... .where_record_citations_range_is(None, None)
... .where_records_match(None)
... #
... .run()
... ).head()
cluster 0 ... 9
article ...
Alt R., 2018, ELECTRON MARK, V28, P235 0.033337 ... 0.033336
Anagnostopoulos I., 2018, J ECON BUS, V100, P7 0.949990 ... 0.005557
Anshari M., 2019, ENERGY PROCEDIA, V156, P234 0.871405 ... 0.014288
Arner D.W., 2017, NORTHWEST J INTL LAW BUS, V37... 0.007696 ... 0.007693
Brummer C., 2019, GEORGET LAW J, V107, P235 0.014291 ... 0.014288
[5 rows x 10 columns]