Terms by Dimensions Map

## >>> from sklearn.decomposition import PCA ## >>> pca = PCA( ## … n_components=5, ## … whiten=False, ## … svd_solver=”auto”, ## … tol=0.0, ## … iterated_power=”auto”, ## … n_oversamples=10, ## … power_iteration_normalizer=”auto”, ## … random_state=0, ## … ) ## >>> from techminer2.packages.factor_analysis.co_occurrence import terms_by_dimension_map ## >>> plot = ( ## … TermsByDimensionMap() ## … # ## … # FIELD: ## … .with_field(“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_estimator(pca) ## … # ## … # ASSOCIATION INDEX: ## … .using_association_index(None) ## … # ## … # MAP: ## … .using_plot_dimensions(0, 1) ## … .using_node_colors([“#465c6b”]) ## … .using_node_size(10) ## … .using_textfont_size(8) ## … .using_textfont_color(“#465c6b”) ## … # ## … .using_xaxes_range(None, None) ## … .using_yaxes_range(None, None) ## … .using_axes_visible(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() ## … ) >>> # plot.write_html(“docs_src/_static/factor_analysis/co_occurrence/terms_by_dimension_map.html”)