def main(datafile, min_sup, min_conf, name_file):
# Expects the sparse vector format data
reader = csv.reader(datafile)
data = []
max_val = 0
ignore = {0}
for row in reader:
r = []
for idx, val in enumerate(row):
if idx in ignore:
continue
i = int(val)
r.append(i)
if i > max_val:
max_val = i
data.append(frozenset(r))
freq_sets = apriori.find_frequent_itemsets(data, max_val + 1, min_sup)
rules = apriori.find_association_rules(data, freq_sets, min_conf)
if name_file:
reader = csv.reader(name_file)
lookup = {int(l[0]) : l[1] + l[2] for l in reader}
print("\n##Skyline frequent itemsets\n~~~")
for i, s in enumerate(freq_sets):
print("Itemset {}:\n\tContains: {}\n\tSupport: {}".format(i, ", ".join(lookup[p] for p in s), apriori.support(s, data)))
print("~~~\n\n##Skyline association rules\n~~~")
for i, r in enumerate(rules):
print("Rule {}:\n\tLHS: {}\n\tRHS: {}\n\tSupport: {}\n\tConfidence: {}".format(i, ", ".join(lookup[p] for p in r[0]), lookup[r[1]], r[2] * 100, r[3] * 100))
print("~~~")
else:
for i, r in enumerate(rules):
print("Rule {}: {} ---> {} [sup={} conf={}]".format(i, ", ".join(str(i) for i in r[0]), r[1], r[2] * 100, r[3] * 100))
def main(file_name, minimum_support_count, minimum_confidence):
with open(file_name) as f:
rows = [
tuple(filter(bool, row)) for row in csv.reader(f, delimiter=' ')
]
frequent_itemsets = apriori.find_association_rules(rows,
minimum_support_count,
minimum_confidence)
pprint.pprint(frequent_itemsets)
def main(file_name, minimum_support_count, minimum_confidence):
with open(file_name) as f:
rows = [tuple(filter(bool, row)) for row in csv.reader(f, delimiter=" ")]
frequent_itemsets = apriori.find_association_rules(rows, minimum_support_count, minimum_confidence)
pprint.pprint(frequent_itemsets)
