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Currently, h = model.fit(learning_problem=lp).best_hypotheses() returns an OWLClassExpression.
It would be useful to add a new argument to the fit method, e.g., return_info or verbose. If the latter evaluates to True, then fit returns a tuple: h, info = model.fit(learning_problem=lp, .best_hypotheses()). The returned info can be a list or dictionary containing at least the quality of the computed solution(s).
The text was updated successfully, but these errors were encountered:
h = model.fit(learning_problem=lp).best_hypotheses()
returns an OWLClassExpression.fit
method, e.g.,return_info
orverbose
. If the latter evaluates to True, thenfit
returns a tuple:h, info = model.fit(learning_problem=lp, .best_hypotheses())
. The returnedinfo
can be alist
ordictionary
containing at least the quality of the computed solution(s).The text was updated successfully, but these errors were encountered: