def test_hat_mc(test_path):
stream = ConceptDriftStream(stream=SEAGenerator(random_state=1,
noise_percentage=0.05),
drift_stream=SEAGenerator(
random_state=2,
classification_function=2,
noise_percentage=0.05),
random_state=1,
position=250,
width=10)
stream.prepare_for_use()
learner = HAT(leaf_prediction='mc')
cnt = 0
max_samples = 1000
y_pred = array('i')
y_proba = []
wait_samples = 20
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_proba.append(learner.predict_proba(X)[0])
learner.partial_fit(X, y)
cnt += 1
expected_predictions = array('i', [
1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0,
1
])
assert np.alltrue(y_pred == expected_predictions)
test_file = os.path.join(test_path, 'test_hoeffding_adaptive_tree_mc.npy')
data = np.load(test_file)
assert np.allclose(y_proba, data)
expected_info = 'HAT: max_byte_size: 33554432 - memory_estimate_period: 1000000 - grace_period: 200' \
' - split_criterion: info_gain - 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: mc - nb_threshold: 0' \
' - nominal_attributes: [] - '
assert learner.get_info() == expected_info
expected_model_1 = 'Leaf = Class 1.0 | {0.0: 0.005295278636481529, 1.0: 1.9947047213635185}\n'
expected_model_2 = 'Leaf = Class 1.0 | {0.0: 0.0052952786364815294, 1.0: 1.9947047213635185}\n'
expected_model_3 = 'Leaf = Class 1.0 | {1.0: 1.9947047213635185, 0.0: 0.0052952786364815294}\n'
assert (learner.get_model_description() == expected_model_1) \
or (learner.get_model_description() == expected_model_2) \
or (learner.get_model_description() == expected_model_3)
stream.restart()
X, y = stream.next_sample(5000)
learner = HAT(max_byte_size=30, leaf_prediction='mc', grace_period=10)
learner.partial_fit(X, y)
def test_hat_mc(test_path):
stream = ConceptDriftStream(stream=SEAGenerator(random_state=1,
noise_percentage=0.05),
drift_stream=SEAGenerator(
random_state=2,
classification_function=2,
noise_percentage=0.05),
random_state=1,
position=250,
width=10)
stream.prepare_for_use()
learner = HAT(leaf_prediction='mc')
cnt = 0
max_samples = 1000
y_pred = array('i')
y_proba = []
wait_samples = 20
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_proba.append(learner.predict_proba(X)[0])
learner.partial_fit(X, y)
cnt += 1
expected_predictions = array('i', [
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1
])
assert np.alltrue(y_pred == expected_predictions)
test_file = os.path.join(test_path, 'test_hoeffding_adaptive_tree_mc.npy')
data = np.load(test_file)
assert np.allclose(y_proba, data)
expected_info = "HAT(binary_split=False, grace_period=200, leaf_prediction='mc',\n" \
" max_byte_size=33554432, memory_estimate_period=1000000, nb_threshold=0,\n" \
" no_preprune=False, nominal_attributes=None, remove_poor_atts=False,\n" \
" split_confidence=1e-07, split_criterion='info_gain',\n" \
" stop_mem_management=False, tie_threshold=0.05)"
assert learner.get_info() == expected_info
expected_model_1 = 'Leaf = Class 1.0 | {0.0: 398.0, 1.0: 1000.0}\n'
assert (learner.get_model_description() == expected_model_1)
assert type(learner.predict(X)) == np.ndarray
assert type(learner.predict_proba(X)) == np.ndarray
stream.restart()
X, y = stream.next_sample(5000)
learner = HAT(max_byte_size=30, leaf_prediction='mc', grace_period=10)
learner.partial_fit(X, y)
def test_concept_drift_stream(test_path):
stream = ConceptDriftStream(random_state=1, position=20, width=5)
stream.prepare_for_use()
assert stream.n_remaining_samples() == -1
expected_names = [
"salary", "commission", "age", "elevel", "car", "zipcode", "hvalue",
"hyears", "loan"
]
assert stream.feature_names == expected_names
expected_targets = [0, 1]
assert stream.target_values == expected_targets
assert stream.target_names == ['target']
assert stream.n_features == 9
assert stream.n_cat_features == 3
assert stream.n_num_features == 6
assert stream.n_targets == 1
assert stream.get_info() == 'ConceptDriftStream: ' \
'First Stream: AGRAWALGenerator - ' \
'Drift Stream: AGRAWALGenerator - ' \
'alpha: 0.0 - position: 20 - width: 5'
assert stream.has_more_samples() is True
assert stream.is_restartable() is True
# Load test data corresponding to first 10 instances
test_file = os.path.join(test_path, 'concept_drift_stream.npz')
data = np.load(test_file)
X_expected = data['X']
y_expected = data['y']
X, y = stream.next_sample()
assert np.alltrue(X[0] == X_expected[0])
assert np.alltrue(y[0] == y_expected[0])
X, y = stream.last_sample()
assert np.alltrue(X[0] == X_expected[0])
assert np.alltrue(y[0] == y_expected[0])
stream.restart()
X, y = stream.next_sample(30)
assert np.alltrue(X == X_expected)
assert np.alltrue(y == y_expected)
assert stream.n_targets == np.array(y).ndim
assert stream.n_features == X.shape[1]
assert 'stream' == stream.get_class_type()
def test_knn_adwin():
stream = ConceptDriftStream(stream=SEAGenerator(random_state=1),
drift_stream=SEAGenerator(
random_state=2, classification_function=2),
random_state=1,
position=250,
width=10)
learner = KNNADWINClassifier(n_neighbors=8,
leaf_size=40,
max_window_size=200)
cnt = 0
max_samples = 1000
predictions = array('i')
correct_predictions = 0
wait_samples = 20
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])
if y[0] == predictions[-1]:
correct_predictions += 1
learner.partial_fit(X, y)
cnt += 1
expected_predictions = array('i', [
1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0,
1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1,
1
])
assert np.alltrue(predictions == expected_predictions)
expected_correct_predictions = 46
assert correct_predictions == expected_correct_predictions
learner.reset()
assert learner.data_window.size == 0
expected_info = "KNNADWINClassifier(leaf_size=40, max_window_size=200, " \
"metric='euclidean', n_neighbors=8)"
info = " ".join([line.strip() for line in learner.get_info().split()])
assert info == expected_info
stream.restart()
X, y = stream.next_sample(max_samples)
learner.fit(X[:950], y[:950])
predictions = learner.predict(X[951:])
correct_predictions = sum(np.array(predictions) == y[951:])
expected_correct_predictions = 47
assert correct_predictions == expected_correct_predictions
assert type(learner.predict(X)) == np.ndarray
assert type(learner.predict_proba(X)) == np.ndarray
def test_knn_adwin():
stream = ConceptDriftStream(stream=SEAGenerator(random_state=1),
drift_stream=SEAGenerator(
random_state=2, classification_function=2),
random_state=1,
position=250,
width=10)
stream.prepare_for_use()
learner = KNNAdwin(n_neighbors=8, leaf_size=40, max_window_size=200)
cnt = 0
max_samples = 1000
predictions = array('i')
correct_predictions = 0
wait_samples = 20
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])
if y[0] == predictions[-1]:
correct_predictions += 1
learner.partial_fit(X, y)
cnt += 1
expected_predictions = array('i', [
1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0,
1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1,
1
])
assert np.alltrue(predictions == expected_predictions)
expected_correct_predictions = 46
assert correct_predictions == expected_correct_predictions
learner.reset()
assert learner.window.n_samples == 0
expected_info = 'KNNAdwin(leaf_size=40, max_window_size=200, n_neighbors=8,\n' \
' nominal_attributes=None)'
assert learner.get_info() == expected_info
stream.restart()
X, y = stream.next_sample(max_samples)
learner.fit(X[:950], y[:950])
predictions = learner.predict(X[951:])
correct_predictions = sum(np.array(predictions) == y[951:])
expected_correct_predictions = 47
assert correct_predictions == expected_correct_predictions
assert type(learner.predict(X)) == np.ndarray
assert type(learner.predict_proba(X)) == np.ndarray
def test_hat_nb(test_path):
stream = ConceptDriftStream(stream=SEAGenerator(random_state=1,
noise_percentage=0.05),
drift_stream=SEAGenerator(
random_state=2,
classification_function=2,
noise_percentage=0.05),
random_state=1,
position=250,
width=10)
stream.prepare_for_use()
learner = HAT(leaf_prediction='nb')
cnt = 0
max_samples = 1000
y_pred = array('i')
y_proba = []
wait_samples = 20
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_proba.append(learner.predict_proba(X)[0])
learner.partial_fit(X, y)
cnt += 1
expected_predictions = array('i', [
1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0,
1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1,
1
])
assert np.alltrue(y_pred == expected_predictions)
test_file = os.path.join(test_path, 'test_hoeffding_adaptive_tree_nb.npy')
data = np.load(test_file)
assert np.allclose(y_proba, data)
expected_info = 'HAT: max_byte_size: 33554432 - memory_estimate_period: 1000000 - grace_period: 200' \
' - split_criterion: info_gain - 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: nb - nb_threshold: 0' \
' - nominal_attributes: [] - '
assert learner.get_info() == expected_info
assert type(learner.predict(X)) == np.ndarray
assert type(learner.predict_proba(X)) == np.ndarray
def test_hoeffding_adaptive_tree_nb(test_path):
stream = ConceptDriftStream(stream=SEAGenerator(random_state=1,
noise_percentage=0.05),
drift_stream=SEAGenerator(
random_state=2,
classification_function=2,
noise_percentage=0.05),
random_state=1,
position=250,
width=10)
learner = HoeffdingAdaptiveTreeClassifier(leaf_prediction='nb',
random_state=1)
cnt = 0
max_samples = 1000
y_pred = array('i')
y_proba = []
wait_samples = 20
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_proba.append(learner.predict_proba(X)[0])
learner.partial_fit(X, y)
cnt += 1
expected_predictions = array('i', [
1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0,
1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1,
1
])
assert np.alltrue(y_pred == expected_predictions)
test_file = os.path.join(test_path, 'test_hoeffding_adaptive_tree_nb.npy')
data = np.load(test_file)
assert np.allclose(y_proba, data)
expected_info = "HoeffdingAdaptiveTreeClassifier(binary_split=False, bootstrap_sampling=True, grace_period=200, " \
"leaf_prediction='nb', 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, split_criterion='info_gain', 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
assert type(learner.predict_proba(X)) == np.ndarray
def test_concept_drift_stream(test_path):
stream = ConceptDriftStream(random_state=1, position=20, width=5)
assert stream.n_remaining_samples() == -1
expected_names = ["salary", "commission", "age", "elevel", "car", "zipcode", "hvalue", "hyears", "loan"]
assert stream.feature_names == expected_names
expected_targets = [0, 1]
assert stream.target_values == expected_targets
assert stream.target_names == ['target']
assert stream.n_features == 9
assert stream.n_cat_features == 3
assert stream.n_num_features == 6
assert stream.n_targets == 1
assert stream.has_more_samples() is True
assert stream.is_restartable() is True
# Load test data corresponding to first 10 instances
test_file = os.path.join(test_path, 'concept_drift_stream.npz')
data = np.load(test_file)
X_expected = data['X']
y_expected = data['y']
X, y = stream.next_sample()
assert np.alltrue(X[0] == X_expected[0])
assert np.alltrue(y[0] == y_expected[0])
X, y = stream.last_sample()
assert np.alltrue(X[0] == X_expected[0])
assert np.alltrue(y[0] == y_expected[0])
stream.restart()
X, y = stream.next_sample(30)
assert np.alltrue(X == X_expected)
assert np.alltrue(y == y_expected)
assert stream.n_targets == np.array(y).ndim
assert stream.n_features == X.shape[1]
assert 'stream' == stream._estimator_type
expected_info = "ConceptDriftStream(alpha=0.0,\n" \
" drift_stream=AGRAWALGenerator(balance_classes=False,\n" \
" classification_function=2,\n" \
" perturbation=0.0,\n" \
" random_state=112),\n" \
" position=20, random_state=1,\n" \
" stream=AGRAWALGenerator(balance_classes=False,\n" \
" classification_function=0,\n" \
" perturbation=0.0, random_state=112),\n" \
" width=5)"
assert stream.get_info() == expected_info
window_size=WINDOW_SIZE,
logging=False)
goowe_2.prepare_post_analysis_req(num_features, num_targets, num_classes,
target_values)
goowe_3 = GooweMS(goowe_1,
goowe_2,
n_max_components=N_MAX_CLASSIFIERS,
chunk_size=CHUNK_SIZE,
window_size=WINDOW_SIZE,
logging=False)
goowe_3.prepare_post_analysis_req(num_features, num_targets, num_classes,
target_values)
# For the first chunk, there is no prediction.
X_init, y_init = stream_1.next_sample(CHUNK_SIZE)
goowe_1.partial_fit(X_init, y_init)
X_init, y_init = stream_2.next_sample(CHUNK_SIZE)
goowe_2.partial_fit(X_init, y_init)
X_init, y_init = stream_3.next_sample(CHUNK_SIZE)
#a = goowe_3.predict(X_init)
#print('==============', a)
goowe_3.update(X_init, y_init, 1, 1)
# TODO: update_from(goowe_1, goowe_2) :: updates existing goowe by selecting N_MAX_CLASSIFIERS / 2 components from each of them.
accuracy_1 = 0.0
total_1 = 0.0
true_predictions_1 = 0.0
# Imports
from skmultiflow.data.sine_generator import SineGenerator
import matplotlib.pyplot as plt
from skmultiflow.data import ConceptDriftStream
import numpy as np
# Setting up the stream
# stream = SineGenerator(classification_function = 2, random_state = 112,
# balance_classes = False, has_noise = False)
stream = ConceptDriftStream(random_state=123456, position=25000)
# Retrieving one sample
# stream.generate_drift()
a = stream.next_sample(100)
b = []
for i in range(a[0].size):
b.append(a[0].item(i))
plt.plot(b)
# fig= plt.gcf()
# fig.set_size_inches(20, 5.5)
# plt.ylabel('value')
# plt.xlabel('Time')
# plt.show()
# b=[]
# for i in range(a[1].size):
# b.append(a[1].item(i))
#
# plt.plot(b,color='r', linestyle='--',linewidth=0.3)
fig = plt.gcf()
fig.set_size_inches(20, 5.5)
