def test_MAR_MaxSC_OneClass_4(self):
# From publication example 3.a
analyzer = GCA(self.db_rules,
1 / 7) # percentage indicated in publication
analyzer.clean_database()
analyzer.mine()
rule_miner = RAMCM(analyzer.lcg_into_list())
lcg_S = rule_miner.MFCS_FromLattice(rule_miner.lcg,
set(['c', 'e', 'a', 'g', 'i']),
2 / 7, 1 / 7, 5 / 7)
# Generate rules for S_star_S1 = set(['c', 'e', 'a', 'g', 'i'])
L_C1 = set(['c', 'e', 'g'])
match = analyzer.search_node_with_closure(L_C1, lcg_S)
gen_L_C1 = match.generators
R1 = set(['a', 'i'])
S_star_S1 = set(['a', 'c', 'i'])
match = analyzer.search_node_with_closure(S_star_S1, lcg_S)
gen_S_star_S1 = match.generators
S1 = set(['c', 'e', 'a', 'g', 'i'])
rules = rule_miner.MAR_MaxSC_OneClass(L_C1, gen_L_C1, R1, S_star_S1,
gen_S_star_S1, S_star_S1)
self.assertEqual(len(rules), 2)
expected_rules = []
def test_mine_CAR(self):
analyzer = GCA(self.db_RAR, 0.25)
analyzer.clean_database()
analyzer.mine()
L = set([3, 5, 7])
S = set([1, 3, 5, 7])
L_node = analyzer.search_node_with_closure(L)
S_node = analyzer.search_node_with_closure(S)
rule_miner = RAMMax(analyzer.lcg_into_list())
RAR = rule_miner.mine_RAR(L_node, S_node, 0.25, 1.0, 0.0, 1.0)
CAR2 = rule_miner.mine_CAR2(L_node, S_node, RAR, analyzer)
self.assertTrue(len(CAR2), 13)
rules = []
rules.append(Rule(set([5]), set([1, 7])))
rules.append(Rule(set([5]), set([1, 3])))
rules.append(Rule(set([5]), set([1])))
rules.append(Rule(set([7]), set([1, 5])))
rules.append(Rule(set([7]), set([1, 3])))
rules.append(Rule(set([7]), set([1])))
rules.append(Rule(set([3, 5]), set([1, 7])))
rules.append(Rule(set([5, 7]), set([1, 3])))
rules.append(Rule(set([3, 5, 7]), set([1])))
rules.append(Rule(set([5, 7]), set([1])))
rules.append(Rule(set([3, 5]), set([1])))
rules.append(Rule(set([3, 7]), set([1, 5])))
rules.append(Rule(set([3, 7]), set([1])))
for i in range(len(CAR2)):
self.assertEqual(frozenset(CAR2[i].left), frozenset(rules[i].left))
self.assertEqual(frozenset(CAR2[i].right),
frozenset(rules[i].right))
def test_mine_rules_1_integer(self):
analyzer = GCA(self.db_rules_integer,
1 / 7) # percentage indicated in publication
analyzer.clean_database()
analyzer.mine()
L1 = set({3, 5, 7})
R1 = set([1, 9])
rule_miner = RAMCM(analyzer.lcg_into_list())
rule_miner.mine(1 / 7, 5 / 7, 1 / 3, 0.9, L1, R1)
self.assertEqual(len(rule_miner.ars), 14)
def test_mine_rules_2_integer(self):
# From publication example 3.b
analyzer = GCA(self.db_rules_integer,
1 / 7) # percentage indicated in publication
analyzer.clean_database()
analyzer.mine()
L1 = set({1})
R1 = set([3, 6, 8, 9])
rule_miner = RAMCM(analyzer.lcg_into_list())
rule_miner.mine(1 / 7, 5 / 7, 1 / 3, 0.9, L1, R1)
self.assertEqual(len(rule_miner.ars), 12)
def test_mine_rules_1(self):
# From publication example 3.a
analyzer = GCA(self.db_rules,
1 / 7) # percentage indicated in publication
analyzer.clean_database()
analyzer.mine()
L1 = set({'c', 'e', 'g'})
R1 = set(['a', 'i'])
rule_miner = RAMCM(analyzer.lcg_into_list())
rule_miner.mine(1 / 7, 5 / 7, 1 / 3, 0.9, L1, R1)
self.assertEqual(len(rule_miner.ars), 14)
def test_mine_consequent_LS_2(self):
analyzer = GCA(self.db, 0.0)
analyzer.clean_database()
analyzer.mine()
L = set(['c', 'd'])
S = set(['a', 'c', 'd', 't', 'w'])
L_node = analyzer.search_node_with_closure(L)
S_node = analyzer.search_node_with_closure(S)
rule_miner = RAMM(analyzer.lcg_into_list())
C_LS = rule_miner.mine_cars_L_S(L_node, S_node, 0, 1, 0, 1, analyzer)
self.assertTrue(True)
def test_mine_RAR(self):
analyzer = GCA(self.db_RAR, 0.0)
analyzer.clean_database()
analyzer.mine()
L = set([3, 5, 7])
S = set([1, 3, 5, 7])
L_node = analyzer.search_node_with_closure(L)
S_node = analyzer.search_node_with_closure(S)
rule_miner = RAMMax(analyzer.lcg_into_list())
RAR = rule_miner.mine_RAR(L_node, S_node)
self.assertTrue(True)
def test_MFS_RestrictMaxSC_1(self):
# From publication example 3.a
analyzer = GCA(self.db_rules,
1 / 7) # percentage indicated in publication
analyzer.clean_database()
analyzer.mine()
rule_miner = RAMCM(analyzer.lcg_into_list())
lcg_S = rule_miner.MFCS_FromLattice(rule_miner.lcg,
set(['c', 'e', 'a', 'g', 'i']),
2 / 7, 1 / 7, 1)
#Enumerate left side
Y = set(['c', 'e', 'g'])
X = set([])
Z1 = set(['c', 'e', 'g'])
match = analyzer.search_node_with_closure(Y, lcg_S)
gen_X_Y = match.generators
fs_star_Y = rule_miner.MFS_RestrictMaxSC(Y, X, Z1, gen_X_Y)
self.assertEqual(len(fs_star_Y), 6)
expected_itemsets = []
expected_itemsets.append(set(['e']))
expected_itemsets.append(set(['e', 'c']))
expected_itemsets.append(set(['e', 'g']))
expected_itemsets.append(set(['e', 'c', 'g']))
expected_itemsets.append(set(['g']))
expected_itemsets.append(set(['g', 'c']))
for itemset in expected_itemsets:
self.assertIn(itemset, fs_star_Y)
#Enumerate right side in accordance with left hand side 'e'
Y = frozenset(['c', 'e', 'a', 'g', 'i']).difference(frozenset('e'))
X = set(['e'])
Z1 = set(['a', 'i'])
match = analyzer.search_node_with_closure(Y, lcg_S)
gen_X_Y = match.generators
fs_star_Y = rule_miner.MFS_RestrictMaxSC(Y, X, Z1, gen_X_Y)
self.assertEqual(len(fs_star_Y), 2)
expected_itemsets = []
expected_itemsets.append(set(['a']))
expected_itemsets.append(set(['a', 'i']))
for itemset in expected_itemsets:
self.assertIn(itemset, fs_star_Y)
def test_all_rules(self):
analyzer = GCA(self.db_RAR, 0.25)
analyzer.clean_database()
analyzer.mine()
lattice = analyzer.lcg_into_lattice()
rule_miner = RAMMax(analyzer.lcg_into_list())
nb_rules = 0
nb_basic_rules = 0
for node in lattice.values():
S = node.fci
print('S: ' + str(S.closure))
to_extract = deque()
to_extract.append(node)
to_extract.extend(node.children)
visited = deque()
while len(to_extract) > 0:
current = to_extract.popleft()
visited.append(current)
L = current.fci
RAR = rule_miner.mine_RAR(L, S, 0.95, 1.0, 0.95, 1.0)
nb_consequent = len(rule_miner.mine_CAR2(L, S, RAR, analyzer))
nb_basic_rules += len(RAR)
nb_rules += nb_consequent
print(' - L:' + str(L.closure) + ',gen: ' +
str(L.generators) + ', nb BR min/max: ' + str(len(RAR)) +
', nb CR: ' + str(nb_consequent) + ', TBR: ' +
str(nb_basic_rules) + ', TBC: ' + str(nb_rules))
for child in current.children:
for grandchild in child.children:
if grandchild not in to_extract and grandchild not in visited:
to_extract.append(grandchild)
else:
print('Child: ' + str(grandchild.fci.closure) +
', gen: ' + str(grandchild.fci.generators) +
' already waiting for extraction or visited')
print('nb rules: ' + str(nb_rules))
self.assertTrue(False)
def test_mine_basic_rules_LS_1(self):
analyzer = GCA(self.db, 0.0)
analyzer.clean_database()
analyzer.mine()
L = set(['a', 'c', 't', 'w'])
S = set(['a', 'c', 'd', 't', 'w'])
L_node = analyzer.search_node_with_closure(L)
S_node = analyzer.search_node_with_closure(S)
rule_miner = RAMM(analyzer.lcg_into_list())
B_LS = rule_miner.mine_LS(L_node, S_node, 0.0, 1.0, 0.0, 1.0)
rules = []
rules.append(Rule(set(['a', 't']), set(['d'])))
rules.append(Rule(set(['t', 'w']), set(['d'])))
self.assertEqual(frozenset(B_LS[0].left), frozenset(rules[0].left))
self.assertEqual(frozenset(B_LS[0].right), frozenset(rules[0].right))
self.assertEqual(frozenset(B_LS[1].left), frozenset(rules[1].left))
self.assertEqual(frozenset(B_LS[1].right), frozenset(rules[1].right))
def test_mine_db_rules(self):
analyzer = GCA(self.db_rules,
1 / 7) #percentage indicated in publication
analyzer.clean_database()
analyzer.mine()
self.assertEqual(len(analyzer.lcg_into_list()), 10)
expected_LGC = []
expected_LGC.append(
GCA.Node(2 / 7, set(['a', 'c', 'e', 'g', 'i']),
[['a', 'e'], ['a', 'g']], None))
expected_LGC.append(
GCA.Node(2 / 7, set(['b', 'c', 'e', 'g', 'i']), [['b']], None))
expected_LGC.append(
GCA.Node(2 / 7, set(['a', 'c', 'f', 'h', 'i']),
[['c', 'f'], ['c', 'h']], None))
expected_LGC.append(
GCA.Node(1 / 7, set(['a', 'd', 'f', 'h', 'i']), [['d']], None))
expected_LGC.append(
GCA.Node(4 / 7, set(['c', 'e', 'g', 'i']), [['e'], ['g']], None))
expected_LGC.append(
GCA.Node(4 / 7, set(['a', 'c', 'i']), [['a', 'c']], None))
expected_LGC.append(
GCA.Node(3 / 7, set(['a', 'f', 'h', 'i']), [['f'], ['h']], None))
expected_LGC.append(GCA.Node(6 / 7, set(['c', 'i']), [['c']], None))
expected_LGC.append(GCA.Node(5 / 7, set(['a', 'i']), [['a']], None))
expected_LGC.append(GCA.Node(7 / 7, set(['i']), [['i']], None))
for index, expected in enumerate(expected_LGC):
#check closure
match = analyzer.search_node_with_closure(expected.closure)
self.assertSequenceEqual(expected.closure, match.closure)
#check support
self.assertEqual(expected.support, match.support)
#check generators
for generator in expected.generators:
match = analyzer.search_node_with_generator(None, generator)
self.assertIsNotNone(match)
def test_MFCS_FromLattice(self):
analyzer = GCA(self.db_rules,
1 / 7) # percentage indicated in publication
analyzer.clean_database()
analyzer.mine()
rule_miner = RAMCM(analyzer.lcg_into_list())
lcg_S = rule_miner.MFCS_FromLattice(
rule_miner.lcg, set(['a', 'c', 'f', 'h', 'i']),
rule_miner._get_support(set(['a', 'c', 'f', 'h', 'i'])), 1 / 7, 1)
self.assertEqual(len(lcg_S), 6)
expected_LGC = []
expected_LGC.append(
GCA.Node(2 / 7, set(['a', 'c', 'f', 'h', 'i']),
[['c', 'f'], ['c', 'h']], None))
expected_LGC.append(
GCA.Node(4 / 7, set(['a', 'c', 'i']), [['a', 'c']], None))
expected_LGC.append(
GCA.Node(3 / 7, set(['a', 'f', 'h', 'i']), [['f'], ['h']], None))
expected_LGC.append(GCA.Node(6 / 7, set(['c', 'i']), [['c']], None))
expected_LGC.append(GCA.Node(5 / 7, set(['a', 'i']), [['a']], None))
expected_LGC.append(GCA.Node(7 / 7, set(['i']), [['i']], None))
for index, expected in enumerate(expected_LGC):
# check closure
match = analyzer.search_node_with_closure(expected.closure, lcg_S)
self.assertSequenceEqual(expected.closure, match.closure)
# check support
self.assertEqual(expected.support, match.support)
# check generators
for generator in expected.generators:
self.assertTrue(
is_in_generators(generator, match.generators, True))
def setUp(self):
"""
Validate the developments with the indications published here:
https://pdfs.semanticscholar.org/56a4/ec156b26225b5922182bacc4c5b26fd5a555.pdf
"""
self.db = []
self.db.append(['a', 'b', 'c', 'e', 'g', 'h'])
self.db.append(['a', 'c', 'd', 'f', 'h'])
self.db.append(['a', 'd', 'e', 'f', 'g', 'h'])
self.db.append(['b', 'c', 'e', 'f', 'g', 'h'])
self.db.append(['b', 'c', 'e'])
self.db.append(['b', 'c'])
self.db_rules = [] #database used for rules association mining
self.db_rules.append(['a', 'c', 'e', 'g', 'i'])
self.db_rules.append(['a', 'c', 'f', 'h', 'i'])
self.db_rules.append(['a', 'd', 'f', 'h', 'i'])
self.db_rules.append(['b', 'c', 'e', 'g', 'i'])
self.db_rules.append(['a', 'c', 'e', 'g', 'i'])
self.db_rules.append(['b', 'c', 'e', 'g', 'i'])
self.db_rules.append(['a', 'c', 'f', 'h', 'i'])
self.db_rules_integer = [
] # database used for rules association mining with integer
self.db_rules_integer.append([1, 3, 5, 7, 9])
self.db_rules_integer.append([1, 3, 6, 8, 9])
self.db_rules_integer.append([1, 4, 6, 8, 9])
self.db_rules_integer.append([2, 3, 5, 7, 9])
self.db_rules_integer.append([1, 3, 5, 7, 9])
self.db_rules_integer.append([2, 3, 5, 7, 9])
self.db_rules_integer.append([1, 3, 6, 8, 9])
self.analyzer = GCA([], 1)
root = None
a = GCA.Node(3, ('a'), ['a'], (1, 2, 3), root)
b = GCA.Node(4, ('b'), ['b'], (1, 4, 5, 6), root)
c = GCA.Node(5, ('c'), ['c'], (1, 2, 4, 5, 6), root)
d = GCA.Node(2, ('d'), ['d'], (2, 3), root)
e = GCA.Node(4, ('e'), ['e'], (1, 3, 4, 5), root)
f = GCA.Node(3, ('f'), ['f'], (2, 3, 4), root)
g = GCA.Node(3, ('g'), ['g'], (1, 3, 4), root)
h = GCA.Node(4, ('h'), ['h'], (1, 2, 3, 4), root)
self.L1 = [d, a, f, g, b, e, h, c]
dc = GCA.Node(1, ('a', 'd', 'f', 'h', 'c'), ['d', 'c'], set([2]), d)
de = GCA.Node(1, ('a', 'd', 'f', 'h', 'e'), ['d', 'e'], set([3]), d)
dg = GCA.Node(1, ('a', 'd', 'f', 'h', 'e', 'g'), ['d', 'g'], set([3]),
d)
af = GCA.Node(2, ('a', 'f', 'h'), ['a', 'f'], (2, 3), a)
ag = GCA.Node(2, ('a', 'h', 'e', 'g'), ['a', 'g'], (1, 3), a)
ab = GCA.Node(1, ('a', 'h', 'b', 'c'), ['a', 'b'], set([1]), a)
ac = GCA.Node(2, ('a', 'h', 'c'), ['a', 'c'], (1, 2), a)
ae = GCA.Node(2, ('a', 'e'), ['a', 'e'], (1, 3), a)
gb = GCA.Node(2, ('e', 'g', 'h', 'b', 'c'), ['g', 'b'], (1, 4), g)
gc = GCA.Node(2, ('g', 'c'), ['g', 'c'], (1, 4), g)
be = GCA.Node(3, ('b', 'c', 'e'), ['b', 'e'], (1, 4, 5), b)
bh = GCA.Node(2, ('b', 'c', 'h'), ['b', 'h'], (1, 4), b)
eh = GCA.Node(3, ('e', 'h'), ['e', 'h'], (1, 3, 4), e)
ec = GCA.Node(3, ('e', 'c'), ['e', 'c'], (1, 4, 5), e)
fg = GCA.Node(2, ('f', 'h', 'e', 'g'), ['f', 'g'], (3, 4), f)
fb = GCA.Node(1, ('f', 'h', 'b', 'c'), ['f', 'b'], set([4]), f)
fc = GCA.Node(2, ('f', 'h', 'c'), ['f', 'c'], (2, 4), f)
fe = GCA.Node(2, ('f', 'h', 'e'), ['f', 'e'], (3, 4), f)
hc = GCA.Node(3, ('h', 'c'), ['h', 'c'], (1, 2, 4), h)
#Order here is important, it depends of the order at L1
self.L2 = [
dg, de, dc, af, ag, ab, ae, ac, fg, fb, fe, fc, gb, gc, be, bh, eh,
ec, hc
]
def test_mine(self):
analyzer = GCA(
self.db,
0.16) #percentage to get a min_supp of 1 matching the publication
analyzer.clean_database()
analyzer.mine()
#closed_items = analyzer.lcg_into_list() for double hash
db_size = len(self.db)
expected_LGC = []
expected_LGC.append(
GCA.Node(2 / analyzer.db_length, set(['a', 'd', 'f', 'h']),
[['d'], ['a', 'f']], None))
expected_LGC.append(
GCA.Node(3 / analyzer.db_length, set(['a', 'h']), [['a']], None))
expected_LGC.append(
GCA.Node(3 / analyzer.db_length, set(['f', 'h']), [['f']], None))
expected_LGC.append(
GCA.Node(3 / analyzer.db_length, set(['e', 'g', 'h']),
[['g'], ['e', 'h']], None))
expected_LGC.append(
GCA.Node(4 / analyzer.db_length, set(['b', 'c']), [['b']], None))
expected_LGC.append(
GCA.Node(4 / analyzer.db_length, set(['e']), [['e']], None))
expected_LGC.append(
GCA.Node(4 / analyzer.db_length, set(['h']), [['h']], None))
expected_LGC.append(
GCA.Node(5 / analyzer.db_length, set(['c']), [['c']], None))
expected_LGC.append(
GCA.Node(1 / analyzer.db_length, set([
'a', 'd', 'f', 'h', 'e', 'g'
]), [['d', 'g'], ['d', 'e'], ['a', 'f', 'g'], ['a', 'f', 'e']],
None))
expected_LGC.append(
GCA.Node(1 / analyzer.db_length, set(['a', 'd', 'f', 'h', 'c']),
[['d', 'c'], ['a', 'f', 'c']], None))
#TODO: check with publication's authors since aheg appears in two transactions in the database.
#TODO: the example illustration shows an error with support of 1 but two transactions 1 and 3
#expected_LGC.append(GCA.Node(1/analyzer.db_length,set(['a','h','e','g']),[['a','g'],['a','e']],None))
expected_LGC.append(
GCA.Node(2 / analyzer.db_length, set(['a', 'h', 'e', 'g']),
[['a', 'g'], ['a', 'e']], None))
expected_LGC.append(
GCA.Node(1 / analyzer.db_length,
set(['a', 'h', 'b', 'c', 'e', 'g']),
[['a', 'b'], ['a', 'g', 'c'], ['a', 'e', 'c']], None))
expected_LGC.append(
GCA.Node(2 / analyzer.db_length, set(['a', 'h', 'c']),
[['a', 'c']], None))
expected_LGC.append(
GCA.Node(2 / analyzer.db_length, set(['f', 'h', 'e', 'g']),
[['f', 'g'], ['f', 'e']], None))
expected_LGC.append(
GCA.Node(1 / analyzer.db_length,
set(['f', 'h', 'b', 'c', 'e', 'g']),
[['f', 'b'], ['f', 'g', 'c'], ['f', 'e', 'c']], None))
expected_LGC.append(
GCA.Node(2 / analyzer.db_length, set(['f', 'h', 'c']),
[['f', 'c']], None))
expected_LGC.append(
GCA.Node(2 / analyzer.db_length, set(['e', 'g', 'h', 'b', 'c']),
[['g', 'b'], ['g', 'c'], ['b', 'h'], ['c', 'e', 'h']],
None))
expected_LGC.append(
GCA.Node(3 / analyzer.db_length, set(['b', 'c', 'e']),
[['b', 'e'], ['c', 'e']], None))
expected_LGC.append(
GCA.Node(3 / analyzer.db_length, set(['h', 'c']), [['h', 'c']],
None))
for index, expected in enumerate(expected_LGC):
#check closure
match = analyzer.search_node_with_closure(expected.closure)
self.assertSequenceEqual(expected.closure, match.closure)
#check support
self.assertEqual(expected.support, match.support)
#check generators
for generator in expected.generators:
#match = analyzer.search_node_with_generator(None, generator)
self.assertIsNotNone(match)
self.assertEqual(len(expected_LGC), len(analyzer.lcg_into_list()))
def use_reference(file):
db = []
with open('./../data/' + file + '.dat') as csvfile:
reader = csvfile.read()
rows = reader.split('\n')
for row in rows:
transaction = row.split(' ')
db.append(transaction)
min_support = 0.95
analyzer = GCA(db, min_support)
analyzer.mine()
frequent_items = analyzer.lcg_into_list()
lattice = analyzer.lcg_into_lattice()
nb_frequent_items = len(frequent_items)
print('Nb frequent items with min_support = ' + str(min_support) + ': ' +
str(nb_frequent_items))
rule_miner = RAMMax(analyzer.lcg_into_list())
#rule_miner = RAMin(analyzer.lcg_into_list())
nb_rules = 0
nb_basic_rules = 0
print('Extract rules from frequent items: ')
rules = deque()
for node in lattice.values():
S = node.fci
print('S: ' + str(S.closure))
to_extract = deque()
to_extract.append(node)
visited = deque()
while len(to_extract) > 0:
current = to_extract.popleft()
visited.append(current)
L = current.fci
#RAR = rule_miner.mine_basic(L, S)
RAR = rule_miner.mine_RAR(L, S, 0.95, 1.0, 0.95, 1.0)
ne_rules = rule_miner.mine_CAR2(L, S, RAR, analyzer)
is_new_rule = True
for new_rule in ne_rules:
for saved_rules in rules:
if new_rule.left == saved_rules.left and new_rule.right == saved_rules.right:
is_new_rule = False
break
if is_new_rule:
rules.append(new_rule)
nb_rules += 1
#nb_basic_rules += len(RAR)
print(' - L:' + str(L.closure) + ',gen: ' + str(L.generators) +
', nb BR min/max: ' + str(len(RAR)) + ', TBR: ' +
str(nb_basic_rules) + ', TBC: ' + str(nb_rules))
#print(' - L:' + str(L.closure) + ',gen: ' + str(L.generators) + ', nb BR min/max: ' + str(len(RAR)) + ', TBR: ' + str(nb_basic_rules))
for rule in RAR:
print(' - ' + rule.to_str())
for child in current.children:
for grandchild in child.children:
if grandchild not in to_extract and grandchild not in visited:
to_extract.append(grandchild)
print('nb rules: ' + str(nb_basic_rules))
def find_closed_items():
print('Load deck')
card_loader = MagicLoader()
card_loader.load('./../data/magic_cards/AllCards-x.json')
print('Clean deck')
deck_loader = DeckManager()
list_files = os.listdir("./../data/decks_mtgdeck_net")
for i in range(len(list_files)): # returns list
list_files[i] = './../data/decks_mtgdeck_net/' + list_files[i]
deck_loader.load_from_mtgdeck_csv(list_files, card_loader)
deck_loader.extract_lands(card_loader.lands, card_loader)
analyzer = GCA(deck_loader.decks, 0.05)
print('Start mining ' + str(len(deck_loader.decks)) + ' decks')
analyzer.mine()
print('nb closed items = ' + str(len(analyzer.lcg_into_list())))
#deck_loader.write_frequent_items_into_csv('genclose_results', analyzer.get_closed_items_closures(), card_loader)
frequent_items = analyzer.lcg_into_list()
lattice = analyzer.lcg_into_lattice()
generated_rules = []
'''
rule_miner = RAMCM(frequent_items)
for pair in combinations(list(range(nb_frequent_items)), 2):
L1 = frequent_items[pair[0]].closure
R1 = frequent_items[pair[1]].closure
rule_miner.mine(0.3, 1.0, 0.33, 1.0, L1, R1)
generated_rules.extend(rule_miner.ars)
'''
rule_miner = RAMMax(analyzer.lcg_into_list())
nb_rules = 0
nb_basic_rules = 0
print('Extract rules from frequent items: ')
for node in lattice.values():
S = node.fci
#print('S: ' + str(S.closure))
to_extract = deque()
to_extract.append(node)
to_extract.extend(node.children)
visited = deque()
while len(to_extract) > 0:
current = to_extract.popleft()
visited.append(current)
L = current.fci
RAR = rule_miner.mine_RAR(L, S, 0.05, 0.08, 0.7, 0.9)
'''
nb_consequent = len(rule_miner.mine_CAR2(L, S, RAR, analyzer))
nb_basic_rules += len(RAR)
nb_rules += nb_consequent
print(' - L:' + str(L.closure) + ',gen: ' + str(L.generators) + ', nb BR min/max: ' + str(
len(RAR)) + ', nb CR: ' + str(nb_consequent) + ', TBR: ' + str(nb_basic_rules) + ', TBC: ' + str(
nb_rules))
for child in current.children:
for grandchild in child.children:
if grandchild not in to_extract and grandchild not in visited:
to_extract.append(grandchild)
'''
for rule in RAR:
text = str(round(rule.support, 2)) + ' - ' + str(
round(rule.confidence, 2)) + ': '
for l in rule.left:
text += card_loader.hash_id_name[l] + ' + '
text += ' ----> '
for r in rule.right:
text += card_loader.hash_id_name[r] + ' + '
print(text)
print('nb rules: ' + str(nb_rules))
