def test_pattern_rec(self):
to_find1 = PNode([
'06b7bde2b5eb49efb00dc260edfd1bae = 1ec1b7ccd60a444d99e16cf4ce5021be',
'a71fb85d222b4d30819d8b442a6ab945 = 3b5ed2de66fc4892a9508e150c118941'
])
to_find1.add_child(
PNode([
'06b7bde2b5eb49efb00dc260edfd1bae = 92d3476104de4b06bd1a38573f07a5d7'
]))
to_find2 = PNode([
'a71fb85d222b4d30819d8b442a6ab945 = b2c04646744240568e66f9c13e434476'
])
to_find2.add_child(
PNode([
'06b7bde2b5eb49efb00dc260edfd1bae = 781831ce63d046da9b217b606aff5f45',
'a71fb85d222b4d30819d8b442a6ab945 = fc3c2abfc9304942bccf089977cacc16'
]))
to_find2.children[0].add_child(
PNode([
'a71fb85d222b4d30819d8b442a6ab945 = f9bfbd58d8764011b9266d06c00b84f5'
]))
thr = 5
top = -1
manager = Manager("output.csv", thr)
manager.compute_mining(True)
manager.filter.remove_empty()
f_patterns = manager.filter.get_first_n_frequents(top)
if top != -1:
print("LISTING TOP " + str(top) + " PATTERNS threshold = " +
str(thr))
else:
print("LISTING ALL FREQUENT PATTERNS threshold = " + str(thr))
print()
for pattern in f_patterns:
print("Number of appearences: " + str(pattern[1]))
print()
pattern[0].print_tree()
print()
if to_find1 in manager.filter.counters:
print("Pattern")
print()
to_find1.print_tree()
print()
print("Appeared " + str(manager.filter.counters[to_find1]) +
" times")
if to_find2 in manager.filter.counters:
print("Pattern")
print()
to_find2.print_tree()
print()
print("Appeared " + str(manager.filter.counters[to_find2]) +
" times")
def test_birth_errors(self):
try:
instance = "866110f3-4b1b-4a56-834b-fa3d39eea4ec"
asset = "f4646a37-c66e-3993-9f68-3a9e0e9a0793"
year = 2017
detections = Manager.compute_week_profiling(instance, asset, year, 'birth')
except Exception:
self.fail("Something went wrong")
def test_unexisting_lifeevent(self):
try:
instance = "866110f3-4b1b-4a56-834b-fa3d39eea4ec"
asset = "f4646a37-c66e-3993-9f68-3a9e0e9a0793"
year = 2017
detections = Manager.compute_week_profiling(instance, asset, year, 'fuffa')
self.fail("Something should be wrong, but it isn't")
except ValueError:
self.assertTrue(True)
def test_unexisting_entity(self):
try:
instance = "bla bla bla"
asset = "bla bla"
year = 2017
detections = Manager.compute_week_profiling(instance, asset, year, 'birth')
self.fail("Something should be wrong, but it isn't")
except TapoiNotFoundApiException:
self.assertTrue(True)
def post(self):
posted_data = request.get_json()
try:
instance = posted_data['instance']
asset = posted_data['asset']
year = posted_data['year']
life_event = posted_data['lifeEvent']
detections = Manager.compute_week_profiling(instance, asset, year, life_event)
return {
"detections": [d.to_repr() for d in detections],
"instance": instance,
"asset": asset,
"year": year,
"lifeEvent": life_event
}, 200
except KeyError:
return {"message": "Error, some keys are missing"}, 400
except TapoiNotFoundApiException:
return {"message": "The requested instance or asset don't exist"}, 404
except ValueError:
return {"message": "The requested life event is not supported"}, 422
argument_parser.add_argument("-in",
dest="input",
type=str,
help="Input file name (csv format)",
action="store",
default="input.csv")
argument_parser.add_argument(
"-thr",
dest="threshold",
type=int,
help="Minimum number of times a pattern has to appear",
action="store",
default=4)
args = argument_parser.parse_args()
if args.threshold < 1:
raise ValueError("The threshold must be at least 1. Given %d" %
args.threshold)
manager = Manager(args.input, args.threshold)
manager.compute_mining()
manager.filter.remove_empty()
patterns = manager.filter.get_by_importance()
with open(args.output, "w") as f_out:
for pattern, freq in patterns:
l = len(pattern.get_subtree())
f_out.write("Pattern: frequency: %d, length: %d, importance: %d\n" %
(freq, l, l * freq))
f_out.write(pattern.get_string())
f_out.write("\n\n")
def test_unexisting_year(self):
instance = "866110f3-4b1b-4a56-834b-fa3d39eea4ec"
asset = "f4646a37-c66e-3993-9f68-3a9e0e9a0793"
year = -1
detections = Manager.compute_week_profiling(instance, asset, year, 'wedding')
self.assertEqual(detections, [])
from __future__ import absolute_import
import time
from logic.manager import Manager, BaselineManager
simple_mining = False
print("Our implementation")
s1 = time.time()
manager = Manager("output1.csv", 2)
manager.compute_mining(simple_mining)
manager.filter.remove_empty()
fp = set([x for x, _ in manager.filter.get_by_importance()])
f1 = time.time()
print("Baseline")
s2 = time.time()
manager = BaselineManager("output1.csv", 2)
manager.compute_mining(simple_mining)
manager.filter.remove_empty()
fp1 = set([x for x, _ in manager.filter.get_by_importance()])
f2 = time.time()
print("time1: %f" % (f1 - s1))
print("time2: %f" % (f2 - s2))
print(len(fp))
print(len(fp1))
print(len(frozenset(fp).intersection(fp1)))