def test_refine_with_conditions(root, rows_csv, **unused):
with loom.preql.get_server(root, debug=True) as preql:
features = preql.feature_names
conditions = make_fully_observed_row(rows_csv)
preql.refine(
target_feature_sets=None,
query_feature_sets=None,
conditioning_row=None)
target_feature_sets = [
[features[0], features[1]],
[features[2]]]
query_feature_sets = [
[features[0], features[1]],
[features[2]],
[features[3]]]
assert_raises(
ValueError,
preql.refine,
target_feature_sets,
query_feature_sets,
conditions)
conditions[0] = None
assert_raises(
ValueError,
preql.refine,
target_feature_sets,
query_feature_sets,
conditions)
conditions[1] = None
conditions[2] = None
conditions[3] = None
preql.refine(
target_feature_sets,
query_feature_sets,
conditions)
def test_refine_shape(root, encoding, **unused):
with loom.preql.get_server(root, debug=True) as preql:
features = preql.feature_names
target_sets = [
features[2 * i:2 * (i + 1)] for i in xrange(len(features) / 2)
]
query_sets = [
features[2 * i:2 * (i + 1)] for i in xrange(len(features) / 2)
]
result = preql.refine(target_sets, query_sets, sample_count=10)
reader = csv.reader(StringIO(result))
header = reader.next()
header.pop(0)
assert_equal(header, map(min, query_sets))
for row, target_set in izip(reader, target_sets):
label = row.pop(0)
assert_equal(label, min(target_set))
assert_equal(len(row), len(query_sets))
def test_refine_shape(root, encoding, **unused):
with loom.preql.get_server(root, debug=True) as preql:
features = preql.feature_names
target_sets = [
features[2 * i: 2 * (i + 1)]
for i in xrange(len(features) / 2)
]
query_sets = [
features[2 * i: 2 * (i + 1)]
for i in xrange(len(features) / 2)
]
result = preql.refine(target_sets, query_sets, sample_count=10)
reader = csv.reader(StringIO(result))
header = reader.next()
header.pop(0)
assert_equal(header, map(min, query_sets))
for row, target_set in izip(reader, target_sets):
label = row.pop(0)
assert_equal(label, min(target_set))
assert_equal(len(row), len(query_sets))
