Manifold 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 sklearn.manifold import TSNE ## >>> tsne = TSNE( ## … perplexity=10.0, ## … early_exaggeration=12.0, ## … learning_rate=”auto”, ## … max_iter=1000, ## … n_iter_without_progress=300, ## … min_grad_norm=1e-07, ## … metric=”euclidean”, ## … metric_params=None, ## … init=”pca”, ## … verbose=0, ## … random_state=0, ## … method=”barnes_hut”, ## … angle=0.5, ## … n_jobs=None, ## … )

## >>> from techminer2.packages.factor_analysis.tfidf import manifold_terms_by_dimension_map ## >>> plot = ( ## … ManifoldTermsByDimensionMap() ## … # ## … # 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) ## … # ## … # MANIFOLD: ## … .using_manifold_estimator(tsne) ## … # ## … # TFIDF: ## … .using_binary_term_frequencies(False) ## … .using_row_normalization(None) ## … .using_idf_reweighting(False) ## … .using_idf_weights_smoothing(False) ## … .using_sublinear_tf_scaling(False) ## … # ## … # MAP: ## … .using_node_colors([“#7793a5”]) ## … .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/_generated/packages/factor_analysis/tfidf/manifold_terms_by_dimension_map.html”)