>>> from sklearn.decomposition import PCA
>>> from techminer2.factor_analysis.tfidf import terms_by_dimension_map
>>> plot = terms_by_dimension_map(
... #
... # PARAMS:
... field="author_keywords",
... #
... # TF PARAMS:
... is_binary=True,
... cooc_within=1,
... #
... # TF-IDF PARAMS:
... norm=None,
... use_idf=False,
... smooth_idf=False,
... sublinear_tf=False,
... #
... # TERM PARAMS:
... top_n=20,
... occ_range=(None, None),
... gc_range=(None, None),
... custom_terms=None,
... #
... # DESOMPOSITION PARAMS:
... decomposition_estimator = 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,
... ),
... #
... # MAP PARAMS:
... dim_x=0,
... dim_y=1,
... node_color="#465c6b",
... node_size=10,
... textfont_size=8,
... textfont_color="#465c6b",
... xaxes_range=None,
... yaxes_range=None,
... #
... # DATABASE PARAMS:
... root_dir="example/",
... database="main",
... year_filter=(None, None),
... cited_by_filter=(None, None),
... )
>>> # plot.write_html("sphinx/_static/factor_analysis/tfidf/terms_by_dimension_map.html")