def test_vfdr():
learner = VFDR(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)
stream.prepare_for_use()
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 = 'VFDR: ordered_rules: True - grace_period: 200 - split_confidence: 1e-07 ' + \
'- tie_threshold: 0.05 - remove_poor_atts: True - rule_prediction: first_hit ' + \
'- nb_threshold: 0 - nominal_attributes: [3, 4, 5] - drift_detector: NoneType ' + \
'- Predict using Naive Bayes: False'
assert learner.get_info() == expected_info
expected_model_description = '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_ = '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) or \
(learner.get_model_description() == expected_model_description_)
expected_model_measurements = {'Number of rules: ': 3, 'model_size in bytes': 62295}
expected_model_measurements_ = {'Number of rules: ': 3, 'model_size in bytes': 73167}
if sys.version_info.minor != 6:
assert (learner.get_model_measurements() == expected_model_measurements) or\
(learner.get_model_measurements() == expected_model_measurements_)
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_hoeffding_adaptive_tree_alternate_tree():
stream = AGRAWALGenerator(random_state=7)
learner = HoeffdingAdaptiveTreeClassifier(random_state=1)
cnt = 0
change_point1 = 1500
change_point2 = 2500
change_point3 = 4000
max_samples = 5000
while cnt < max_samples:
X, y = stream.next_sample()
learner.partial_fit(X, y)
cnt += 1
if cnt > change_point1:
stream.generate_drift()
change_point1 = float('Inf')
expected_description = "if Attribute 2 <= 63.63636363636363:\n" \
" if Attribute 2 <= 39.54545454545455:\n" \
" Leaf = Class 0 | {0: 397.5023676194098}\n" \
" if Attribute 2 > 39.54545454545455:\n" \
" if Attribute 2 <= 58.81818181818181:\n" \
" Leaf = Class 1 | {1: 299.8923824199619}\n" \
" if Attribute 2 > 58.81818181818181:\n" \
" Leaf = Class 0 | {0: 54.0, 1: 20.107617580038095}\n" \
"if Attribute 2 > 63.63636363636363:\n" \
" Leaf = Class 0 | {0: 512.5755895049351}\n"
assert expected_description == learner.get_model_description()
if cnt > change_point2:
stream.generate_drift()
change_point2 = float('Inf')
expected_description = "if Attribute 8 <= 268547.7178694747:\n" \
" Leaf = Class 0 | {0: 446.18690518790413, 1: 80.6180778406834}\n" \
"if Attribute 8 > 268547.7178694747:\n" \
" Leaf = Class 1 | {0: 36.8130948120959, 1: 356.38192215931656}\n"
assert expected_description == learner.get_model_description()
if cnt > change_point3:
stream.generate_drift()
change_point3 = float('Inf')
expected_description = "Leaf = Class 0 | {0: 1083.0, 1: 2.0}\n"
assert expected_description == learner.get_model_description()
def test_vfdr_hellinger():
learner = VFDR(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)
stream.prepare_for_use()
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_description = '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_description_ = '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_description) or \
(learner.get_model_description() == expected_model_description_)
def test_vfdr_foil():
learner = VFDR(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)
stream.prepare_for_use()
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)
