def test_normal():
"""
Test for normal data.
"""
test_data = RelationRecord(frozenset(['A', 'B']), 0.5, [
OrderedStatistic(frozenset(), frozenset(['B']), 0.8, 1.2),
OrderedStatistic(frozenset(['A']), frozenset(), 0.8, 1.2),
OrderedStatistic(frozenset(['A']), frozenset(['B']), 0.8, 1.2),
])
output_file = StringIO()
dump_as_two_item_tsv(test_data, output_file)
output_file.seek(0)
result = output_file.read()
eq_(result, 'A\tB\t0.50000000\t0.80000000\t1.20000000' + linesep)
def test_normal():
"""
Test for normal data.
"""
transaction_manager = Mock(spec=TransactionManager)
min_support = 0.1
min_confidence = 0.1
min_lift = 0.5
max_length = 2
support_record = SupportRecord(frozenset(['A', 'B']), 0.5)
ordered_statistic1 = OrderedStatistic(frozenset(['A']), frozenset(['B']),
0.1, 0.7)
ordered_statistic2 = OrderedStatistic(frozenset(['A']), frozenset(['B']),
0.3, 0.5)
def gen_support_records(*args, **kwargs):
""" Mock for apyori.gen_support_records. """
eq_(args[1], min_support)
eq_(kwargs['max_length'], max_length)
yield support_record
def gen_ordered_statistics(*_):
""" Mock for apyori.gen_ordered_statistics. """
yield ordered_statistic1
yield ordered_statistic2
def filter_ordered_statistics(*args, **kwargs):
""" Mock for apyori.gen_ordered_statistics. """
eq_(kwargs['min_confidence'], min_confidence)
eq_(kwargs['min_lift'], min_lift)
eq_(len(list(args[0])), 2)
yield ordered_statistic1
result = list(
apriori(
transaction_manager,
min_support=min_support,
min_confidence=min_confidence,
min_lift=min_lift,
max_length=max_length,
_gen_support_records=gen_support_records,
_gen_ordered_statistics=gen_ordered_statistics,
_filter_ordered_statistics=filter_ordered_statistics,
))
eq_(result, [
RelationRecord(support_record.items, support_record.support,
[ordered_statistic1])
])
def test_filtered():
"""
Test for filtered data.
"""
transaction_manager = Mock(spec=TransactionManager)
dummy_return = OrderedStatistic(frozenset(['A']), frozenset(['B']), 0.1,
0.7)
def gen_support_records(*args, **kwargs): # pylint: disable=unused-argument
""" Mock for apyori.gen_support_records. """
yield dummy_return
def gen_ordered_statistics(*_):
""" Mock for apyori.gen_ordered_statistics. """
yield dummy_return
def filter_ordered_statistics(*args, **kwargs): # pylint: disable=unused-argument
""" Mock for apyori.gen_ordered_statistics. """
return iter([])
result = list(
apriori(
transaction_manager,
_gen_support_records=gen_support_records,
_gen_ordered_statistics=gen_ordered_statistics,
_filter_ordered_statistics=filter_ordered_statistics,
))
eq_(result, [])
def test_bad():
"""
Test for bad data.
"""
test_data = RelationRecord(
set(['A']), 0.5,
[OrderedStatistic(frozenset([]), frozenset(['A']), 0.8, 1.2)])
output_file = StringIO()
dump_as_json(test_data, output_file)
def test_normal():
"""
Test for normal data.
"""
transaction_manager = Mock(spec=TransactionManager)
transaction_manager.calc_support.side_effect = lambda key: {
frozenset(['A']): 0.8,
frozenset(['B']): 0.4,
frozenset(['C']): 0.2,
frozenset(['A', 'B']): 0.2,
frozenset(['A', 'C']): 0.1,
frozenset(['B', 'C']): 0.01,
}.get(key, 0.0)
test_data = SupportRecord(frozenset(['A', 'B', 'C']), 0.001)
results = list(gen_ordered_statistics(transaction_manager, test_data))
eq_(results, [
OrderedStatistic(
frozenset(['A', 'B']),
frozenset(['C']),
0.001 / 0.2,
0.001 / 0.2 / 0.2,
),
OrderedStatistic(
frozenset(['A', 'C']),
frozenset(['B']),
0.001 / 0.1,
0.001 / 0.1 / 0.4,
),
OrderedStatistic(
frozenset(['B', 'C']),
frozenset(['A']),
0.001 / 0.01,
0.001 / 0.01 / 0.8,
),
])
def test_normal():
"""
Test for normal data.
"""
test_data = RelationRecord(
frozenset(['A']), 0.5,
[OrderedStatistic(frozenset([]), frozenset(['A']), 0.8, 1.2)])
output_file = StringIO()
dump_as_json(test_data, output_file)
output_file.seek(0)
result = json.loads(output_file.read())
eq_(
result, {
'items': ['A'],
'support':
0.5,
'ordered_statistics': [{
'items_base': [],
'items_add': ["A"],
'confidence': 0.8,
'lift': 1.2
}]
})
"""
Tests for apyori.filter_ordered_statistics.
"""
from nose.tools import eq_
from apyori import OrderedStatistic
from apyori import filter_ordered_statistics
TEST_DATA = [
OrderedStatistic(frozenset(['A']), frozenset(['B']), 0.1, 0.7),
OrderedStatistic(frozenset(['A']), frozenset(['B']), 0.3, 0.5),
]
def test_normal():
"""
Test for normal data.
"""
result = list(filter_ordered_statistics(
TEST_DATA, min_confidence=0.1, min_lift=0.5))
eq_(result, TEST_DATA)
def test_min_confidence():
"""
Filter by minimum confidence.
"""
result = list(filter_ordered_statistics(
TEST_DATA, min_confidence=0.2, min_lift=0.1))