def test_vfdr_info_gain():
learner = VeryFastDecisionRulesClassifier(ordered_rules=True,
rule_prediction='first_hit',
nominal_attributes=[3, 4, 5],
expand_criterion='info_gain',
remove_poor_atts=True,
min_weight=100,
nb_prediction=False)
stream = AGRAWALGenerator(random_state=11)
cnt = 0
max_samples = 5000
predictions = array('i')
proba_predictions = []
wait_samples = 100
while cnt < max_samples:
X, y = stream.next_sample()
# Test every n samples
if (cnt % wait_samples == 0) and (cnt != 0):
predictions.append(learner.predict(X)[0])
proba_predictions.append(learner.predict_proba(X)[0])
learner.partial_fit(X, y)
cnt += 1
expected_predictions = array('i', [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0,
0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0,
0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0])
assert np.alltrue(predictions == expected_predictions)
expected_info = "VeryFastDecisionRulesClassifier(drift_detector=None, expand_confidence=1e-07, " \
"expand_criterion='info_gain', grace_period=200, max_rules=1000, min_weight=100, " \
"nb_prediction=False, nb_threshold=0, nominal_attributes=[3, 4, 5], ordered_rules=True, " \
"remove_poor_atts=True, rule_prediction='first_hit', tie_threshold=0.05)"
info = " ".join([line.strip() for line in learner.get_info().split()])
assert info == expected_info
expected_model_description_1 = 'Rule 0 :Att (2) <= 39.550| class :0 {0: 1365.7101742993455}\n' + \
'Rule 1 :Att (2) <= 58.180| class :1 {1: 1269.7307449971418}\n' + \
'Rule 2 :Att (2) <= 60.910| class :0 {0: 66.24158839706533, 1: 54.0}\n' + \
'Default Rule :| class :0 {0: 1316.7584116029348}'
expected_model_description_2 = 'Rule 0 :Att (2) <= 39.550| class :0 {0: 1365.7101742993455}\n' + \
'Rule 1 :Att (2) <= 58.180| class :1 {1: 1269.7307449971418}\n' + \
'Rule 2 :Att (2) <= 60.910| class :0 {0: 66.241588397065328, 1: 54.0}\n' + \
'Default Rule :| class :0 {0: 1316.7584116029348}'
assert (learner.get_model_description() == expected_model_description_1) or \
(learner.get_model_description() == expected_model_description_2)
# Following test only covers 'Number of rules' since 'model_size in bytes' is calculated using
# the 'calculate_object_size' utility function which is validated in its own test
expected_number_of_rules = 3
assert learner.get_model_measurements()['Number of rules: '] == expected_number_of_rules
def test_vfdr_hellinger():
learner = VeryFastDecisionRulesClassifier(ordered_rules=False,
rule_prediction='weighted_sum',
nominal_attributes=[3, 4, 5],
expand_criterion='hellinger',
remove_poor_atts=True,
min_weight=100,
nb_prediction=True)
stream = AGRAWALGenerator(random_state=11)
cnt = 0
max_samples = 5000
predictions = array('i')
proba_predictions = []
wait_samples = 100
while cnt < max_samples:
X, y = stream.next_sample()
# Test every n samples
if (cnt % wait_samples == 0) and (cnt != 0):
predictions.append(learner.predict(X)[0])
proba_predictions.append(learner.predict_proba(X)[0])
learner.partial_fit(X, y)
cnt += 1
expected_predictions = array('i', [
0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1,
0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0,
0
])
assert np.alltrue(predictions == expected_predictions)
expected_model_desc_1 = 'Rule 0 :Att (2) > 58.180 and Att (5) = 4.000| class :0 {0: 202.0, 1: 3.0}\n' + \
'Rule 1 :Att (2) <= 41.820| class :0 {0: 1387.1186637804824, 1: 151.83928023717402}\n' + \
'Default Rule :| class :1 {0: 512.8813362195176, 1: 1356.160719762826}'
expected_model_desc_2 = 'Rule 0 :Att (2) > 58.180 and Att (5) = 4.000| class :0 {0: 202.0, 1: 3.0}\n' + \
'Rule 1 :Att (2) <= 41.820| class :0 {0: 1387.1186637804824, 1: 151.83928023717402}\n' + \
'Default Rule :| class :1 {0: 512.8813362195176, 1: 1356.1607197628259}'
if sys.version_info.minor != 6:
assert (learner.get_model_description() == expected_model_desc_1) or \
(learner.get_model_description() == expected_model_desc_2)
def test_vfdr_foil():
learner = VeryFastDecisionRulesClassifier(ordered_rules=False,
rule_prediction='weighted_sum',
nominal_attributes=[3, 4, 5],
expand_criterion='foil_gain',
remove_poor_atts=True,
min_weight=100,
nb_prediction=True)
stream = AGRAWALGenerator(random_state=11)
cnt = 0
max_samples = 5000
predictions = array('i')
proba_predictions = []
wait_samples = 100
while cnt < max_samples:
X, y = stream.next_sample()
# Test every n samples
if (cnt % wait_samples == 0) and (cnt != 0):
predictions.append(learner.predict(X)[0])
proba_predictions.append(learner.predict_proba(X)[0])
learner.partial_fit(X, y)
cnt += 1
expected_predictions = array('i', [
0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,
0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
0
])
assert np.alltrue(predictions == expected_predictions)
expected_model_description = 'Rule 0 :Att (2) <= 25.450 | class: 1| class :0 {0: 464.44730579120136}\n' + \
'Rule 1 :Att (4) = 3.000 | class: 0| class :0 {0: 95.0, 1: 45.0}\n' + \
'Rule 2 :Att (2) <= 30.910 | class: 1| class :0 {0: 330.68821225514125}\n' + \
'Default Rule :| class :0 {0: 573.0, 1: 336.0}'
assert (learner.get_model_description() == expected_model_description)
stream = FileStream(path)
evaluator = flow_detection_classifier(classifier, stream)
stream = evaluator.stream.y
return stream
# Streams based on classifiers:
# KNNClassifier
make_stream(PATH, KNNClassifier(n_neighbors=5, max_window_size=1000, leaf_size=30))
make_stream(PATH, KNNClassifier(n_neighbors=8, max_window_size=2000, leaf_size=40))
#
# # HoeffdingTreeClassifier
make_stream(PATH, HoeffdingTreeClassifier(memory_estimate_period=1000000, grace_period=200, leaf_prediction='nba'))
make_stream(PATH, HoeffdingTreeClassifier(memory_estimate_period=2000000, grace_period=300, leaf_prediction='mc'))
#
# # AdditiveExpertEnsembleClassifier
make_stream(PATH, AdditiveExpertEnsembleClassifier(n_estimators=5, beta=0.8, gamma=0.1, pruning='weakest'))
make_stream(PATH, AdditiveExpertEnsembleClassifier(n_estimators=8, beta=0.9, gamma=0.3, pruning='oldest'))
#
# # VeryFastDecisionRulesClassifier
make_stream(PATH, VeryFastDecisionRulesClassifier(grace_period=200, tie_threshold=0.05, max_rules=20))
make_stream(PATH, VeryFastDecisionRulesClassifier(grace_period=300, tie_threshold=0.1, max_rules=30))
#
# # AdaptiveRandomForestClassifier
make_stream(PATH, AdaptiveRandomForestClassifier(n_estimators=10, lambda_value=6, performance_metric='acc'))
make_stream(PATH, AdaptiveRandomForestClassifier(n_estimators=20, lambda_value=8, performance_metric='kappa'))
# # AccuracyWeightedEnsembleClassifier
make_stream(PATH, AccuracyWeightedEnsembleClassifier(n_estimators=10, n_kept_estimators=30, window_size=200, n_splits=5))
make_stream(PATH, AccuracyWeightedEnsembleClassifier(n_estimators=15, n_kept_estimators=40, window_size=300, n_splits=8))