>>> from sklearn.decomposition import PCA
>>> from techminer2.factor_analysis.co_occurrence import terms_by_dimension_frame
>>> terms_by_dimension_frame(
... #
... # PARAMS:
... field="author_keywords",
... association_index=None,
... #
... # ITEM 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,
... ),
... #
... # DATABASE PARAMS:
... root_dir="example/",
... database="main",
... year_filter=(None, None),
... cited_by_filter=(None, None),
... ).head()
dim 0 1 2 3 4
rows
FINTECH 31:5168 28.659528 -0.524730 -0.513789 -0.042977 0.238539
INNOVATION 07:0911 2.377465 5.757771 2.713115 -1.188306 -0.116040
FINANCIAL_SERVICES 04:0667 -0.090716 2.761290 0.416833 2.583089 -0.502611
FINANCIAL_INCLUSION 03:0590 -0.631683 -0.611095 -1.728676 -0.825425 -0.947171
FINANCIAL_TECHNOLOGY 03:0461 -1.487691 0.959672 -0.271058 0.837526 -0.690393