def confidence_for_intro_to_adv(simple_students, intro_grade, adv_grade):
intro_with_grade = lambda student: in_sets_test(student,
[intro_prog],
[intro_grade])
adv_with_grade = lambda student: in_sets_test(student,
[adv_prog],
[adv_grade])
return alg.confidence(simple_students, intro_with_grade, adv_with_grade)
def data_asked():
results = []
threshold_for_rule = 0.1
last_n = 30
the_courses = unique_courses_from_codes(adv_g4_stripped)
large_freq_itemsets = alg.apriori(threshold_for_rule,
the_courses,
adv_g4_stripped)[-last_n:]
premises = []
for itemsetsup in large_freq_itemsets:
premises.append(itemsetsup[0])
the_base_set = setify(strip_all_but_codes
(take_out_course_not_grade(adv_prog, [4])))
for fr_itemset in premises:
premise = lambda tr: fr_itemset <= tr
consequent = lambda tr: adv_prog in tr
print(the_base_set, "\n", fr_itemset, "\n", adv_prog, "\n")
conf = alg.confidence(the_base_set, premise, consequent)
results.append({'premise': fr_itemset, 'conf': conf})
return results
