def main():
data = orange.ExampleTable(join(util.module_path(), "f_assoc"))
data = orange.Preprocessor_discretize(data, method=orange.EquiNDiscretization(numberOfIntervals=4))
rules = orange.AssociationRulesInducer(data, support=0.2)
orngAssoc.sort(rules, ["confidence", "support"])
orngAssoc.printRules(rules, ["support", "confidence"])
def startMining(self, var, sup):
data = orange.ExampleTable("data/finalData.csv")
#was47
data = data.select(range(44))
minSupport = float(sup)
rules = orange.AssociationRulesInducer(data,
support=minSupport,
max_item_sets=30000000)
print "%i rules with support higher than or equal to %5.3f found." % (
len(rules), minSupport)
orngAssoc.printRules(rules[:10], ["support", "confidence"])
def startMining(self, var, sup):
data = orange.ExampleTable("data/finalData.csv")
#was47
data = data.select(range(44))
minSupport = float(sup)
rules = orange.AssociationRulesInducer(data, support = minSupport, max_item_sets = 30000000)
print "%i rules with support higher than or equal to %5.3f found." % (len(rules), minSupport)
orngAssoc.printRules(rules[:10], ["support", "confidence"])
def startMining(self, var):
data = orange.ExampleTable("data/finalData.csv")
#was47
data = data.select(range(44))
minSupport = 0.4
rules = orange.AssociationRulesInducer(data, support = minSupport, max_item_sets = 30000000)
orig_stdout = sys.stdout
f = open('results/{}_assocrules.txt'.format(var), 'w')
sys.stdout = f
print "%i rules with support higher than or equal to %5.3f found." % (len(rules), minSupport)
orngAssoc.printRules(rules[:10], ["support", "confidence"]) > f
sys.stdout = orig_stdout
f.close()
import Orange, orngAssoc
data = Orange.data.Table("TermPost.basket")
rules = Orange.associate.AssociationRulesSparseInducer(data, support=0.05)
orngAssoc.sort(rules, ["confidence","support"])
orngAssoc.printRules(rules[100:200], ["support", "confidence"])
#print "%4s %4s %s" % ("Supp", "Conf", "Rule")
#for r in rules[:500]:
# print "%4.1f %4.1f %s" % (r.support, r.confidence, r)
# Description: Association rule sorting and filtering
# Category: description
# Uses: imports-85
# Classes: orngAssoc.build, Preprocessor_discretize, EquiNDiscretization
# Referenced: assoc.htm
import orange, orngAssoc
data = orange.ExampleTable("imports-85")
data = orange.Preprocessor_discretize(data, \
method=orange.EquiNDiscretization(numberOfIntervals=3))
data = data.select(range(10))
rules = orange.AssociationRulesInducer(data, support=0.4)
n = 5
print "%i most confident rules:" % (n)
orngAssoc.sort(rules, ["confidence", "support"])
orngAssoc.printRules(rules[0:n], ['confidence', 'support', 'lift'])
conf = 0.8
lift = 1.1
print "\nRules with confidence>%5.3f and lift>%5.3f" % (conf, lift)
rulesC = rules.filter(lambda x: x.confidence > conf and x.lift > lift)
orngAssoc.sort(rulesC, ['confidence'])
orngAssoc.printRules(rulesC, ['confidence', 'support', 'lift'])
# Description: Association rule sorting and filtering
# Category: description
# Uses: imports-85
# Classes: orngAssoc.build, Preprocessor_discretize, EquiNDiscretization
# Referenced: assoc.htm
import orange, orngAssoc
data = orange.ExampleTable("imports-85")
data = orange.Preprocessor_discretize(data, \
method=orange.EquiNDiscretization(numberOfIntervals=3))
data = data.select(range(10))
rules = orange.AssociationRulesInducer(data, support = 0.4)
n = 5
print "%i most confident rules:" % (n)
orngAssoc.sort(rules, ["confidence"])
orngAssoc.printRules(rules[0:n], ['confidence','support','lift'])
conf = 0.8; lift = 1.1
print "\nRules with confidence>%5.3f and lift>%5.3f" % (conf, lift)
rulesC=rules.filter(lambda x: x.confidence>conf and x.lift>lift)
orngAssoc.sort(rulesC, ['confidence'])
orngAssoc.printRules(rulesC, ['confidence','support','lift'])
import Orange, orngAssoc
data = Orange.data.Table("TermPost.basket")
rules = Orange.associate.AssociationRulesSparseInducer(data, support=0.05)
orngAssoc.sort(rules, ["confidence", "support"])
orngAssoc.printRules(rules[100:200], ["support", "confidence"])
#print "%4s %4s %s" % ("Supp", "Conf", "Rule")
#for r in rules[:500]:
# print "%4.1f %4.1f %s" % (r.support, r.confidence, r)
# Description: Creates a list of association rules, selects five rules and prints them out
# Category: description
# Uses: imports-85
# Classes: orngAssoc.build, Preprocessor_discretize, EquiNDiscretization
# Referenced: assoc.htm
import orange, orngAssoc
data = orange.ExampleTable("imports-85")
data = orange.Preprocessor_discretize(data, \
method=orange.EquiNDiscretization(numberOfIntervals=3))
data = data.select(range(10))
rules = orange.AssociationRulesInducer(data, support=0.4)
print "%i rules with support higher than or equal to %5.3f found.\n" % (len(rules), 0.4)
orngAssoc.sort(rules, ["support", "confidence"])
orngAssoc.printRules(rules[:5], ["support", "confidence"])
print
del rules[:3]
orngAssoc.printRules(rules[:5], ["support", "confidence"])
print