def test_hoeffding_tree_regressor_perceptron():
stream = RegressionGenerator(n_samples=500,
n_features=20,
n_informative=15,
random_state=1)
stream.prepare_for_use()
learner = HoeffdingTreeRegressor(leaf_prediction='perceptron',
random_state=1)
cnt = 0
max_samples = 500
y_pred = array('d')
y_true = array('d')
wait_samples = 10
while cnt < max_samples:
X, y = stream.next_sample()
# Test every n samples
if (cnt % wait_samples == 0) and (cnt != 0):
y_pred.append(learner.predict(X)[0])
y_true.append(y[0])
learner.partial_fit(X, y)
cnt += 1
expected_predictions = array('d', [
1198.4326121743168, 456.36607750881586, 927.9912160545144,
1160.4797981899128, 506.50541829176535, -687.8187227095925,
-677.8120094065415, 231.14888704761225, -284.46324039942937,
-255.69195985557175, 47.58787439365423, -135.22494016284043,
-10.351457437330152, 164.95903200643997, 360.72854984472383,
193.30633911830088, -64.23638301570358, 587.9771578214296,
649.8395655757931, 481.01214222804026, 305.4402728117724,
266.2096493865043, -445.11447171009775, -567.5748694154349,
-68.70070048021438, -446.79910655850153, -115.892348067663,
-98.26862866231015, 71.04707905920286, -10.239274802165584,
18.748731569441812, 4.971217265129857, 172.2223575990573,
-655.2864976783711, -129.69921313686626, -114.01187375876822,
-405.66166686550963, -215.1264381928009, -345.91020370426247,
-80.49330468453074, 108.78958382083302, 134.95267043280126,
-398.5273538477553, -157.1784910649728, 219.72541225645654,
-100.91598162899217, 80.9768574308987, -296.8856956382453,
251.9332271253148
])
assert np.allclose(y_pred, expected_predictions)
error = mean_absolute_error(y_true, y_pred)
expected_error = 362.98595964244623
assert np.isclose(error, expected_error)
expected_info = "HoeffdingTreeRegressor(binary_split=False, grace_period=200, leaf_prediction='perceptron', " \
"learning_ratio_const=True, learning_ratio_decay=0.001, learning_ratio_perceptron=0.02, " \
"max_byte_size=33554432, memory_estimate_period=1000000, nb_threshold=0, no_preprune=False, " \
"nominal_attributes=None, random_state=1, remove_poor_atts=False, split_confidence=1e-07, " \
"stop_mem_management=False, tie_threshold=0.05)"
info = " ".join([line.strip() for line in learner.get_info().split()])
assert info == expected_info
assert isinstance(learner.get_model_description(), type(''))
assert type(learner.predict(X)) == np.ndarray
def test_evaluate_delayed_multi_target_regression_coverage(tmpdir):
from skmultiflow.data import RegressionGenerator
from skmultiflow.trees import iSOUPTreeRegressor
max_samples = 1000
# Stream
data = RegressionGenerator(n_samples=max_samples,
n_features=20,
n_informative=15,
random_state=1,
n_targets=7)
# Get X and y
X, y = data.next_sample(max_samples)
time = generate_random_dates(seed=1, samples=max_samples)
# Setup temporal stream
stream = TemporalDataStream(X, y, time, ordered=False)
# Learner
mtrht = iSOUPTreeRegressor(leaf_prediction='adaptive')
output_file = os.path.join(str(tmpdir), "prequential_delayed_summary.csv")
metrics = [
'average_mean_square_error', 'average_mean_absolute_error',
'average_root_mean_square_error'
]
evaluator = EvaluatePrequentialDelayed(max_samples=max_samples,
metrics=metrics,
output_file=output_file)
evaluator.evaluate(stream=stream, model=mtrht, model_names=['MTRHT'])
def test_isoup_tree_model_description():
stream = RegressionGenerator(n_samples=700,
n_features=20,
n_informative=15,
random_state=1,
n_targets=3)
learner = iSOUPTreeRegressor(leaf_prediction='mean')
max_samples = 700
X, y = stream.next_sample(max_samples)
# Trying to predict without fitting
learner.predict(X[0])
learner.partial_fit(X, y)
expected_description = "if Attribute 11 <= 0.36737233297880056:\n" \
" Leaf = Statistics {0: 450.0000, 1: [-23322.8079, -30257.1616, -18740.9462], " \
"2: [22242706.1751, 29895648.2424, 18855571.7943]}\n" \
"if Attribute 11 > 0.36737233297880056:\n" \
" Leaf = Statistics {0: 250.0000, 1: [33354.8675, 32390.6094, 22886.4176], " \
"2: [15429435.6709, 17908472.4289, 10709746.1079]}\n" \
assert SequenceMatcher(None, expected_description,
learner.get_model_description()).ratio() > 0.9
def test_evaluate_delayed_regression_coverage(tmpdir):
# A simple coverage test. Tests for metrics are placed in the corresponding test module.
from skmultiflow.data import RegressionGenerator
from skmultiflow.trees import HoeffdingTreeRegressor
max_samples = 1000
# Generate data
data = RegressionGenerator(n_samples=max_samples)
# Get X and y
X, y = data.next_sample(max_samples)
time = generate_random_dates(seed=1, samples=max_samples)
# Setup temporal stream
stream = TemporalDataStream(X, y, time, ordered=False)
# Learner
htr = HoeffdingTreeRegressor()
output_file = os.path.join(str(tmpdir), "prequential_delayed_summary.csv")
metrics = ['mean_square_error', 'mean_absolute_error']
evaluator = EvaluatePrequentialDelayed(max_samples=max_samples,
metrics=metrics,
output_file=output_file)
evaluator.evaluate(stream=stream, model=htr, model_names=['HTR'])
def test_hoeffding_tree_regressor_coverage():
max_samples = 1000
max_size_mb = 2
stream = RegressionGenerator(
n_samples=max_samples, n_features=10, n_informative=7, n_targets=1,
random_state=42
)
X, y = stream.next_sample(max_samples)
# Cover memory management
tree = HoeffdingTreeRegressor(
leaf_prediction='mean', grace_period=100,
memory_estimate_period=100, max_byte_size=max_size_mb*2**20
)
tree.partial_fit(X, y)
# A tree without memory management enabled reaches over 3 MB in size
assert calculate_object_size(tree, 'MB') <= max_size_mb
# Typo in leaf prediction
tree = HoeffdingTreeRegressor(
leaf_prediction='percptron', grace_period=100,
memory_estimate_period=100, max_byte_size=max_size_mb*2**20
)
# Invalid split_criterion
tree.split_criterion = 'VR'
tree.partial_fit(X, y)
assert calculate_object_size(tree, 'MB') <= max_size_mb
tree.reset()
assert tree._estimator_type == 'regressor'
def test_hoeffding_tree():
stream = RegressionGenerator(n_samples=500,
n_features=20,
n_informative=15,
random_state=1)
stream.prepare_for_use()
learner = HoeffdingAdaptiveTreeRegressor(leaf_prediction='mean',
random_state=1)
cnt = 0
max_samples = 500
y_pred = array('d')
y_true = array('d')
wait_samples = 10
while cnt < max_samples:
X, y = stream.next_sample()
# Test every n samples
if (cnt % wait_samples == 0) and (cnt != 0):
y_pred.append(learner.predict(X)[0])
y_true.append(y[0])
learner.partial_fit(X, y)
cnt += 1
expected_predictions = array('d', [
102.38946041769101, 55.6584574987656, 5.746076599168373,
17.11797209372667, 2.566888222752787, 9.188247802192826,
17.87894804676911, 15.940629626883966, 8.981172175448485,
13.152624115190092, 11.106058099429399, 6.473195313058236,
4.723621479590173, 13.825568609556493, 8.698873073880696,
1.6452441811010252, 5.123496188584294, 6.34387187194982,
5.9977733790395105, 6.874251577667707, 4.605348088338317,
8.20112636572672, 9.032631648758098, 4.428189978974459,
4.249801041367518, 9.983272668044492, 12.859518508979734,
11.741395774380285, 11.230028410261868, 9.126921979081521,
9.132146661688296, 7.750655625124709, 6.445145118245414,
5.760928671876355, 4.041291302080659, 3.591837600560529,
0.7640424010500604, 0.1738639840537784, 2.2068337802212286,
-81.05302946841077, 96.17757415335177, -77.35894903819677,
95.85568683733698, 99.1981674250886, 99.89327888035015,
101.66673013734784, -79.1904234513751, -80.42952143783687,
100.63954789983896
])
assert np.allclose(y_pred, expected_predictions)
error = mean_absolute_error(y_true, y_pred)
expected_error = 143.11351404083086
assert np.isclose(error, expected_error)
expected_info = "HoeffdingAdaptiveTreeRegressor(binary_split=False, grace_period=200, leaf_prediction='mean', " \
"learning_ratio_const=True, learning_ratio_decay=0.001, learning_ratio_perceptron=0.02, " \
"max_byte_size=33554432, memory_estimate_period=1000000, nb_threshold=0, no_preprune=False, " \
"nominal_attributes=None, random_state=1, remove_poor_atts=False, split_confidence=1e-07, " \
"stop_mem_management=False, tie_threshold=0.05)"
info = " ".join([line.strip() for line in learner.get_info().split()])
assert info == expected_info
assert isinstance(learner.get_model_description(), type(''))
assert type(learner.predict(X)) == np.ndarray
def test_hoeffding_tree_coverage():
max_samples = 1000
max_size_mb = 2
stream = RegressionGenerator(
n_samples=max_samples, n_features=10, n_informative=7, n_targets=3,
random_state=42
)
X, y = stream.next_sample(max_samples)
# Will generate a warning concerning the invalid leaf prediction option
tree = StackedSingleTargetHoeffdingTreeRegressor(
leaf_prediction='mean', grace_period=200,
memory_estimate_period=100, max_byte_size=max_size_mb*2**20
)
# Trying to predict without fitting
tree.predict(X[0])
tree.partial_fit(X, y)
# A tree without memory management enabled reaches over 3 MB in size
assert calculate_object_size(tree, 'MB') <= max_size_mb
tree = StackedSingleTargetHoeffdingTreeRegressor(
leaf_prediction='adaptive', grace_period=200,
memory_estimate_period=100, max_byte_size=max_size_mb*2**20,
learning_ratio_const=False
)
tree.partial_fit(X, y)
assert calculate_object_size(tree, 'MB') <= max_size_mb
def test_hoeffding_tree_regressor_perceptron():
stream = RegressionGenerator(n_samples=500,
n_features=20,
n_informative=15,
random_state=1)
learner = HoeffdingTreeRegressor(leaf_prediction='perceptron',
random_state=1)
cnt = 0
max_samples = 500
y_pred = array('d')
y_true = array('d')
wait_samples = 10
while cnt < max_samples:
X, y = stream.next_sample()
# Test every n samples
if (cnt % wait_samples == 0) and (cnt != 0):
y_pred.append(learner.predict(X)[0])
y_true.append(y[0])
learner.partial_fit(X, y)
cnt += 1
expected_predictions = array('d', [
525.7553636732247, 352.8160300365902, 224.80744320456478,
193.72837054292074, 132.6059603765031, 117.06974933197759,
114.53342429855932, 89.37195405567235, 57.85335051891305,
60.00883955911155, 47.263185779784266, 25.17616431074491,
17.43259526890146, 47.33468996498019, 22.83975208548138,
-7.659282840823236, 8.564101665071064, 14.61585289361161,
11.560941733770441, 13.70120291865976, 1.1938438210799651,
19.01970713481836, 21.23459424444584, -5.667473522309328,
-5.203149619381393, 28.726275200889173, 41.03406433337882,
27.950322712127267, 21.267116786963925, 5.53344652490152,
6.753264259267268, -2.3288137435962213, -10.492766334689875,
-11.19641058176631, -20.134685945295644, -19.36581990084085,
-38.26894947177957, -34.90246284430353, -11.019543212232008,
-22.016714766708127, -18.710456277443544, -20.5568019328217,
-2.636583876625667, 24.787714491718187, 29.325261678088406,
45.31267371823666, -48.271054430207776, -59.7649172085901,
48.22724814037523
])
# assert np.allclose(y_pred, expected_predictions)
error = mean_absolute_error(y_true, y_pred)
expected_error = 152.12931270533377
assert np.isclose(error, expected_error)
expected_info = "HoeffdingTreeRegressor(binary_split=False, grace_period=200, leaf_prediction='perceptron', " \
"learning_ratio_const=True, learning_ratio_decay=0.001, learning_ratio_perceptron=0.02, " \
"max_byte_size=33554432, memory_estimate_period=1000000, nb_threshold=0, no_preprune=False, " \
"nominal_attributes=None, random_state=1, remove_poor_atts=False, split_confidence=1e-07, " \
"stop_mem_management=False, tie_threshold=0.05)"
info = " ".join([line.strip() for line in learner.get_info().split()])
assert info == expected_info
assert isinstance(learner.get_model_description(), type(''))
assert type(learner.predict(X)) == np.ndarray
def test_multi_output_learner_regressor():
stream = RegressionGenerator(n_samples=5500,
n_features=10,
n_informative=20,
n_targets=2,
random_state=1)
stream.prepare_for_use()
estimator = SGDRegressor(random_state=112,
tol=1e-3,
max_iter=10,
loss='squared_loss')
learner = MultiOutputLearner(base_estimator=estimator)
X, y = stream.next_sample(150)
learner.partial_fit(X, y)
cnt = 0
max_samples = 5000
predictions = []
true_targets = []
wait_samples = 100
correct_predictions = 0
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])
true_targets.append(y[0])
if np.array_equal(y[0], predictions[-1]):
correct_predictions += 1
learner.partial_fit(X, y)
cnt += 1
expected_performance = 2.444365309339395
performance = mean_absolute_error(true_targets, predictions)
assert np.isclose(performance, expected_performance)
assert learner._estimator_type == "regressor"
assert type(learner.predict(X)) == np.ndarray
with pytest.raises(AttributeError):
learner.predict_proba(X)
def test_hoeffding_adaptive_tree_regressor_perceptron():
stream = RegressionGenerator(n_samples=500, n_features=20, n_informative=15, random_state=1)
learner = HoeffdingAdaptiveTreeRegressor(leaf_prediction='perceptron', random_state=1)
cnt = 0
max_samples = 500
y_pred = array('d')
y_true = array('d')
wait_samples = 10
while cnt < max_samples:
X, y = stream.next_sample()
# Test every n samples
if (cnt % wait_samples == 0) and (cnt != 0):
y_pred.append(learner.predict(X)[0])
y_true.append(y[0])
learner.partial_fit(X, y)
cnt += 1
expected_predictions = array('d', [207.20901655684412, 106.30316877540555, 101.46950096324191,
114.38162776688861, 48.40271620592212, -79.94375846313639,
-76.69182794940929, 88.38425569670662, -13.92372162581644,
3.0549887923350507, 55.36276732455883, 32.0512081208464,
17.54953203218902, -1.7305966738232161, 43.54548690756897,
8.502241407478213, -61.14739038895263, 50.528736810827745,
9.679668917948607, 89.93098085572623, 85.1994809437223,
1.8721866382932664, -7.1972581323107825, -45.86230662663542,
3.111671172363243, 57.921908276916646, 61.43400576850072,
-16.61695641848216, -6.0769944259948065, 19.929266442289546,
-60.972801351912224, -0.3342549973033524,
-50.53334350658139, -14.885488543743078,
-13.255920225124637, 28.909916365484275,
-103.03499425386107, -36.44921969674884, -15.40018796932204,
-84.98471039676006, 38.270205984888065, -62.97228157481581,
-48.095864628804044, 95.5028130171316, 73.62390886812497,
152.7135140597221, -120.4662342226783, -77.68182541723442,
66.82059046110074])
assert np.allclose(y_pred, expected_predictions)
error = mean_absolute_error(y_true, y_pred)
expected_error = 126.11208652969131
assert np.isclose(error, expected_error)
expected_info = "HoeffdingAdaptiveTreeRegressor(binary_split=False, grace_period=200, " \
"leaf_prediction='perceptron', learning_ratio_const=True, learning_ratio_decay=0.001, " \
"learning_ratio_perceptron=0.02, max_byte_size=33554432, memory_estimate_period=1000000, " \
"no_preprune=False, nominal_attributes=None, random_state=1, " \
"remove_poor_atts=False, split_confidence=1e-07, stop_mem_management=False, tie_threshold=0.05)"
info = " ".join([line.strip() for line in learner.get_info().split()])
assert info == expected_info
assert isinstance(learner.get_model_description(), type(''))
assert type(learner.predict(X)) == np.ndarray
assert learner._estimator_type == 'regressor'
def test_stacked_single_target_hoeffding_tree_regressor_adaptive(test_path):
stream = RegressionGenerator(n_samples=2000, n_features=20,
n_informative=15, random_state=1,
n_targets=3)
learner = StackedSingleTargetHoeffdingTreeRegressor(
leaf_prediction='adaptive',
random_state=1
)
cnt = 0
max_samples = 2000
wait_samples = 200
y_pred = np.zeros((int(max_samples / wait_samples), 3))
y_true = np.zeros((int(max_samples / wait_samples), 3))
while cnt < max_samples:
X, y = stream.next_sample()
# Test every n samples
if (cnt % wait_samples == 0) and (cnt != 0):
y_pred[int(cnt / wait_samples), :] = learner.predict(X)
y_true[int(cnt / wait_samples), :] = y
learner.partial_fit(X, y)
cnt += 1
test_file = os.path.join(
test_path,
'expected_preds_stacked_single_target_hoeffding_tree_adaptive.npy'
)
expected_predictions = np.load(test_file)
assert np.allclose(y_pred, expected_predictions)
error = mean_absolute_error(y_true, y_pred)
expected_error = 152.8716829154756
assert np.isclose(error, expected_error)
expected_info = "StackedSingleTargetHoeffdingTreeRegressor(binary_split=False, grace_period=200,\n" \
" leaf_prediction='adaptive',\n" \
" learning_ratio_const=True,\n" \
" learning_ratio_decay=0.001,\n" \
" learning_ratio_perceptron=0.02,\n" \
" max_byte_size=33554432,\n" \
" memory_estimate_period=1000000,\n" \
" nb_threshold=0, no_preprune=False,\n" \
" nominal_attributes=None,\n" \
" random_state=1,\n" \
" remove_poor_atts=False,\n" \
" split_confidence=1e-07,\n" \
" stop_mem_management=False,\n" \
" tie_threshold=0.05)"
assert learner.get_info() == expected_info
assert isinstance(learner.get_model_description(), type(''))
def test_hoeffding_tree_regressor_perceptron():
stream = RegressionGenerator(n_samples=500, n_features=20, n_informative=15, random_state=1)
learner = HoeffdingTreeRegressor(leaf_prediction='perceptron', random_state=1)
cnt = 0
max_samples = 500
y_pred = array('d')
y_true = array('d')
wait_samples = 10
while cnt < max_samples:
X, y = stream.next_sample()
# Test every n samples
if (cnt % wait_samples == 0) and (cnt != 0):
y_pred.append(learner.predict(X)[0])
y_true.append(y[0])
learner.partial_fit(X, y)
cnt += 1
expected_predictions = array('d', [-106.84237763060068, -10.965517384802226,
-180.90711470797237, -218.20896751607663, -96.4271589961865,
110.51551963099622, 108.34616947202511, 30.1720109214627,
57.92205878998479, 77.82418885914053, 49.972060923364765,
68.56117081695875, 15.996949915551697, -34.22744443808294,
-19.762696110319702, -28.447329394752995,
-50.62864370485592, -47.37357781048561, -99.82613515424342,
13.985531117918336, 41.41709671929987, -34.679807275938174,
62.75626094547859, 30.925078688018893, 12.130320819235365,
119.3648998377624, 82.96422756064737, -6.920397563039609,
-12.701774870569059, 24.883730398016034, -74.22855883237567,
-0.8012436194087567, -83.03683748750394, 46.737839617687854,
0.537404558240671, 48.53591837633138, -86.2259777783834,
-24.985514024179967, 6.396035456152859, -90.19454995571908,
32.05821807667601, -83.08553684151566, -28.32223999320023,
113.28916673506842, 68.10498750807977, 173.9146410394573,
-150.2067507947196, -74.10346402222962, 54.39153137687993])
assert np.allclose(y_pred, expected_predictions)
error = mean_absolute_error(y_true, y_pred)
expected_error = 115.78916175164417
assert np.isclose(error, expected_error)
expected_info = "HoeffdingTreeRegressor(binary_split=False, grace_period=200, leaf_prediction='perceptron', " \
"learning_ratio_const=True, learning_ratio_decay=0.001, learning_ratio_perceptron=0.02, " \
"max_byte_size=33554432, memory_estimate_period=1000000, nb_threshold=0, no_preprune=False, " \
"nominal_attributes=None, random_state=1, remove_poor_atts=False, split_confidence=1e-07, " \
"stop_mem_management=False, tie_threshold=0.05)"
info = " ".join([line.strip() for line in learner.get_info().split()])
assert info == expected_info
assert isinstance(learner.get_model_description(), type(''))
assert type(learner.predict(X)) == np.ndarray
def test_multi_target_regression_hoeffding_tree_mean(test_path):
stream = RegressionGenerator(n_samples=500,
n_features=20,
n_informative=15,
random_state=1,
n_targets=3)
stream.prepare_for_use()
learner = MultiTargetRegressionHoeffdingTree(leaf_prediction='mean')
cnt = 0
max_samples = 500
wait_samples = 10
y_pred = np.zeros((int(max_samples / wait_samples), 3))
y_true = np.zeros((int(max_samples / wait_samples), 3))
while cnt < max_samples:
X, y = stream.next_sample()
# Test every n samples
if (cnt % wait_samples == 0) and (cnt != 0):
y_pred[int(cnt / wait_samples), :] = learner.predict(X)
y_true[int(cnt / wait_samples), :] = y
learner.partial_fit(X, y)
cnt += 1
test_file = os.path.join(
test_path, 'expected_preds_multi_target_regression_mean.npy')
expected_predictions = np.load(test_file)
# print(expected_predictions.shape)
assert np.allclose(y_pred, expected_predictions)
error = mean_absolute_error(y_true, y_pred)
expected_error = 167.40626294018753
assert np.isclose(error, expected_error)
expected_info = \
'MultiTargetRegressionHoeffdingTree: max_byte_size: 33554432 - ' \
'memory_estimate_period: 1000000 - grace_period: 200 - ' \
'split_criterion: intra cluster variance reduction - ' \
'split_confidence: 1e-07 - tie_threshold: 0.05 - binary_split: False' \
' - stop_mem_management: False - remove_poor_atts: False ' \
'- no_pre_prune: False - leaf_prediction: mean - nb_threshold: 0 - ' \
'nominal_attributes: [] - '
assert learner.get_info() == expected_info
assert isinstance(learner.get_model_description(), type(''))
def test_isoup_tree_mean(test_path):
stream = RegressionGenerator(n_samples=2000,
n_features=20,
n_informative=15,
random_state=1,
n_targets=3)
stream.prepare_for_use()
learner = iSOUPTreeRegressor(leaf_prediction='mean')
cnt = 0
max_samples = 2000
wait_samples = 200
y_pred = np.zeros((int(max_samples / wait_samples), 3))
y_true = np.zeros((int(max_samples / wait_samples), 3))
while cnt < max_samples:
X, y = stream.next_sample()
# Test every n samples
if (cnt % wait_samples == 0) and (cnt != 0):
y_pred[int(cnt / wait_samples), :] = learner.predict(X)
y_true[int(cnt / wait_samples), :] = y
learner.partial_fit(X, y)
cnt += 1
test_file = os.path.join(
test_path, 'expected_preds_multi_target_regression_mean.npy')
expected_predictions = np.load(test_file)
assert np.allclose(y_pred, expected_predictions)
error = mean_absolute_error(y_true, y_pred)
expected_error = 191.2823924547882
assert np.isclose(error, expected_error)
expected_info = "iSOUPTreeRegressor(binary_split=False, grace_period=200, leaf_prediction='mean', " \
"learning_ratio_const=True, learning_ratio_decay=0.001, learning_ratio_perceptron=0.02, " \
"max_byte_size=33554432, memory_estimate_period=1000000, nb_threshold=0, no_preprune=False, " \
"nominal_attributes=None, random_state=None, remove_poor_atts=False, split_confidence=1e-07, " \
"stop_mem_management=False, tie_threshold=0.05)"
info = " ".join([line.strip() for line in learner.get_info().split()])
assert info == expected_info
assert type(learner.predict(X)) == np.ndarray
def test_hoeffding_tree_regressor_model_description():
stream = RegressionGenerator(
n_samples=500, n_features=20, n_informative=15, random_state=1
)
learner = HoeffdingTreeRegressor(leaf_prediction='mean')
max_samples = 500
X, y = stream.next_sample(max_samples)
learner.partial_fit(X, y)
expected_description = "if Attribute 6 <= 0.1394515530995348:\n" \
" Leaf = Statistics {0: 276.0000, 1: -21537.4157, 2: 11399392.2187}\n" \
"if Attribute 6 > 0.1394515530995348:\n" \
" Leaf = Statistics {0: 224.0000, 1: 22964.8868, 2: 10433581.2534}\n"
assert SequenceMatcher(
None, expected_description, learner.get_model_description()
).ratio() > 0.9
def test_isoup_tree_coverage():
max_samples = 1000
max_size_mb = 2
stream = RegressionGenerator(n_samples=max_samples,
n_features=10,
n_informative=7,
n_targets=3,
random_state=42)
# Cover memory management
tree = iSOUPTreeRegressor(leaf_prediction='mean',
grace_period=200,
memory_estimate_period=100,
max_byte_size=max_size_mb * 2**20)
# Invalid split_criterion
tree.split_criterion = 'ICVR'
X, y = stream.next_sample(max_samples)
tree.partial_fit(X, y)
# A tree without memory management enabled reaches over 3 MB in size
assert calculate_object_size(tree, 'MB') <= max_size_mb
# Memory management in a tree with perceptron leaves (purposeful typo in leaf_prediction)
tree = iSOUPTreeRegressor(leaf_prediction='PERCEPTRON',
grace_period=200,
memory_estimate_period=100,
max_byte_size=max_size_mb * 2**20)
tree.partial_fit(X, y)
assert calculate_object_size(tree, 'MB') <= max_size_mb
# Memory management in a tree with adaptive leaves
tree = iSOUPTreeRegressor(leaf_prediction='adaptive',
grace_period=200,
memory_estimate_period=100,
max_byte_size=max_size_mb * 2**20)
tree.partial_fit(X, y)
assert calculate_object_size(tree, 'MB') <= max_size_mb
def test_adaptive_random_forest_regressor_perceptron():
stream = RegressionGenerator(n_samples=500,
n_features=20,
n_informative=15,
random_state=1)
learner1 = AdaptiveRandomForestRegressor(n_estimators=3,
max_features='log2',
leaf_prediction='perceptron',
aggregation_method='mean',
weighted_vote_strategy=None,
max_byte_size=float('Inf'),
random_state=1)
learner2 = AdaptiveRandomForestRegressor(n_estimators=3,
max_features='auto',
leaf_prediction='perceptron',
aggregation_method='median',
weighted_vote_strategy=None,
max_byte_size=float('Inf'),
random_state=1)
learner3 = AdaptiveRandomForestRegressor(n_estimators=3,
max_features=4,
leaf_prediction='perceptron',
aggregation_method='mean',
weighted_vote_strategy=None,
learning_ratio_const=False,
max_byte_size=float('Inf'),
random_state=1)
cnt = 0
max_samples = 500
y_pred1 = array('d')
y_pred2 = array('d')
y_pred3 = array('d')
y_true = array('d')
wait_samples = 10
while cnt < max_samples:
X, y = stream.next_sample()
# Test every n samples
if (cnt % wait_samples == 0) and (cnt != 0):
y_pred1.append(learner1.predict(X)[0])
y_pred2.append(learner2.predict(X)[0])
y_pred3.append(learner3.predict(X)[0])
y_true.append(y[0])
learner1.partial_fit(X, y)
learner2.partial_fit(X, y)
learner3.partial_fit(X, y)
cnt += 1
error1 = mean_absolute_error(y_true, y_pred1)
error2 = mean_absolute_error(y_true, y_pred2)
error3 = mean_absolute_error(y_true, y_pred3)
expected_error1 = 118.69
expected_error2 = 121.56
expected_error3 = 117.96
assert np.isclose(round(error1, 2), expected_error1)
assert np.isclose(round(error2, 2), expected_error2)
assert np.isclose(round(error3, 2), expected_error3)
learner1.reset()
expected_info = "AdaptiveRandomForestRegressor(aggregation_method='median', " \
"binary_split=False, drift_detection_criteria='mse', " \
"drift_detection_method=ADWIN(delta=0.001), grace_period=50, " \
"lambda_value=6, leaf_prediction='perceptron', learning_ratio_const=True, " \
"learning_ratio_decay=0.001, learning_ratio_perceptron=0.1, " \
"max_byte_size=inf, max_features=4, memory_estimate_period=2000000, " \
"n_estimators=3, no_preprune=False, nominal_attributes=None, " \
"random_state=1, remove_poor_atts=False, split_confidence=0.01, " \
"stop_mem_management=False, tie_threshold=0.05, " \
"warning_detection_method=ADWIN(delta=0.01), weighted_vote_strategy=None)"
info = " ".join([line.strip() for line in learner2.get_info().split()])
assert info == expected_info