def run_trial(stream_path, target_dir, drift_points, pairs):
# for example, stream_path='./benchmark_data/circles/circles_0.arff'
# 1. Creating a project
stream_name = os.path.splitext(os.path.basename(stream_path))[0]
project = Project(target_dir, stream_name)
# 2. Loading an arff file
labels, attributes, stream_records = ARFFReader.read(stream_path)
attributes_scheme = AttributeScheme.get_scheme(attributes)
# 4. Creating a color set for plotting results
# colors = [Color.Indigo[1], Color.Blue[1], Color.Green[1], Color.Lime[1], Color.Yellow[1],
# Color.Amber[1], Color.Orange[1], Color.Red[1], Color.Purple[1], Color.Pink[1],
# Color.Indigo[2], Color.Blue[2], Color.Green[2], Color.Lime[2], Color.Yellow[2],
# Color.Amber[2], Color.Orange[2], Color.Red[2], Color.Purple[2], Color.Pink[2],
# Color.Indigo[3], Color.Blue[3], Color.Green[3], Color.Lime[3], Color.Yellow[3],
# Color.Amber[3], Color.Orange[3], Color.Red[3], Color.Purple[3], Color.Pink[3]][:len(pairs)]
# 5. Defining actual locations of drifts, acceptance delay interval, and vector of weights
actual_drift_points = drift_points
drift_acceptance_interval = 250
w_vec = [1, 1, 1, 1, 1, 1]
# 6. Creating a Prequential Evaluation Process
pairs = [[pair[0](labels, attributes_scheme[pair[1]]),
copy(pair[2])] for pair in pairs]
prequential = PrequentialMultiPairs(
pairs,
attributes,
attributes_scheme,
actual_drift_points,
drift_acceptance_interval,
w_vec,
project,
legend_param=False) # color_set=colors,
prequential.run(stream_records, 1)
# Initializing a learner
learner = Logistic(labels, attributes_scheme['numeric'])
learner_copy = cp.deepcopy(learner)
# Initializing a Classifier-Detector Pairs
pairs = [[
OnlineAccuracyUpdatedEnsemble(labels,
attributes_scheme['numeric'],
learner_copy,
windowSize=__window_size,
classifierLimit=10),
DDM()
], [learner, DDM()]]
# Creating a save content
project = Project('projects/multi/{}'.format(dataset), sub_dataset)
# Creating a color set for plotting results
colors = ['#FF0000', '#3E8ABF', '#1891FF', '#4A0083', '#00FFFF']
# Initializing a evaluator
evaluator = EvaluateWithWindowSizeMulti(pairs, project, colors,
__window_size)
# train & test
for x, y, attribute in data.data(batch_size=1):
if attribute is not None:
attributes_scheme = AttributeScheme.get_scheme(attributes)
__numeric_attribute_scheme = attributes_scheme['nominal']
__numeric_attribute_scheme = attributes_scheme['numeric']
continue
The Tornado Framework
By Ali Pesaranghader
University of Ottawa, Ontario, Canada
E-mail: apesaran -at- uottawa -dot- ca / alipsgh -at- gmail -dot- com
"""
from data_structures.attribute_scheme import AttributeScheme
from classifier.__init__ import *
from drift_detection.__init__ import *
from filters.project_creator import Project
from streams.readers.arff_reader import ARFFReader
from tasks.__init__ import *
# 1. Creating a project
project = Project("projects/single", "sine1")
# 2. Loading an arff file
# labels, attributes, stream_records = ARFFReader.read("data_streams/sine1_w_50_n_0.1/sine1_w_50_n_0.1_101.arff")
labels, attributes, stream_records = ARFFReader.read("data_streams/catsdogs/catsdogs.arff")
attributes_scheme = AttributeScheme.get_scheme(attributes)
print(labels, attributes)
print(attributes_scheme)
# 3. Initializing a Learner
# learner = NaiveBayes(labels, attributes_scheme['nominal'])
learner = Catsdogs(labels, attributes_scheme['nominal'])
# 4. Initializing a drift detector
detector = FHDDM(n=50)
The Tornado Framework
By Ali Pesaranghader
University of Ottawa, Ontario, Canada
E-mail: apesaran -at- uottawa -dot- ca / alipsgh -at- gmail -dot- com
"""
from data_structures.attribute_scheme import AttributeScheme
from classifier.__init__ import *
from drift_detection.__init__ import *
from filters.project_creator import Project
from graphic.hex_colors import Color
from streams.readers.arff_reader import ARFFReader
from tasks.prequential_learner_detector_pairs import PrequentialMultiPairs
# 1. Creating a project
project = Project("projects/multi", "sine1")
# 2. Loading an arff file
labels, attributes, stream_records = ARFFReader.read(
"data_streams/sine1_w_50_n_0.1/sine1_w_50_n_0.1_101.arff")
attributes_scheme = AttributeScheme.get_scheme(attributes)
# 3. Initializing a Classifier-Detector Pairs
pairs = [
[NaiveBayes(labels, attributes_scheme['nominal']),
FHDDM()], [NaiveBayes(labels, attributes_scheme['nominal']),
FHDDMS()],
[NaiveBayes(labels, attributes_scheme['nominal']),
CUSUM()], [NaiveBayes(labels, attributes_scheme['nominal']),
PH()],
[NaiveBayes(labels, attributes_scheme['nominal']),
def sub_thread(self, dataset, sub_dataset, spilt_inform, folder_create,
length):
global global_count
global naive_bayes_batch_count
self.nb_set[sub_dataset] = NaiveBayes([0, 1], self.attributes)
self.last_wl_status[sub_dataset] = dict(r_l=0, hit=[])
self.instance_set[sub_dataset] = []
self.nb_classifier_accuracy[sub_dataset] = dict(all_count=0,
right_count=0,
accuracy=[])
self.nb_batch_count[sub_dataset] = 0
self.nb_drift_prob[sub_dataset] = dict(prob=[], ground_truth=[])
self.plot_risk_level[sub_dataset] = 0
self.data_statistics[sub_dataset] = dict(
name=sub_dataset,
delay=dict(time=[],
accuracy=[],
nb_prob=[],
bingo=[],
hit=[],
warning=[],
drift=[],
warningLevel=[],
attributes=self.construct_correlation(dataset),
batch_delay=2),
online=dict(weight=[], time=[], dataNum=[]))
self.configure[sub_dataset] = {}
self.warning_level_max[sub_dataset] = 0
# Set variables
date_time_flag = False
__batch_size = 24 * 3600
__instance_count = 0
__window_size = Dataset.get_online_learning_batch_interval(
dataset, sub_dataset)
__step = 1000
__start_point = 0
__count = 0
__last_unix_time = 0
__last_warning_status = False
__last_drift_status = False
__data_length = Dataset.get_length(dataset, sub_dataset)
__detect_interval = Dataset.get_detect_batch_interval(
dataset, sub_dataset)
lc = []
bc = []
current_warning_status = {}
warning_level_set = []
current_drift_status = {}
predict_corr = []
last_batch_attributions = None
detection = True
drift_status = False
# classifier flag
prsa_flag = False
# Creating a data stream
data = Data(dataset, sub_dataset)
labels, attributes = data.get_attributes()
attributes_scheme = AttributeScheme.get_scheme(attributes)
__numeric_attribute_scheme = attributes_scheme['numeric']
# Creating a save content
project = Project('./projects/single/{}'.format(dataset), sub_dataset)
self.sub_file_path[sub_dataset] = folder_create.sub_folder(sub_dataset)
# Initializing a learner
learner = Logistic(labels, attributes_scheme['numeric'])
learner = OnlineAccuracyUpdatedEnsemble(labels,
attributes_scheme['numeric'],
learner,
windowSize=__window_size,
classifierLimit=10)
# Initializing a drift detector
drift_detector = DDM(min_instance=__detect_interval)
# Initializing a evaluator
evaluator = EvaluateWithWindowSize(learner, drift_detector, project,
__window_size)
# train & test
for x, y, attribute in data.data(batch_size=1):
if attribute is not None:
attributes_scheme = AttributeScheme.get_scheme(attributes)
__numeric_attribute_scheme = attributes_scheme['numeric']
continue
instance = x.tolist()[0] + [int(y.tolist()[0][0])]
# print(instance)
# 每条数据的unix时间戳
# prsa data
if dataset == 'prsa_data':
date_time_flag = True
date_time = list(map(int, instance[:4]))
d = datetime.date(date_time[0], date_time[1], date_time[2])
tt = datetime.time(date_time[3])
datetime_str = str(d) + ' ' + str(tt)
unix_time = int(
time.mktime(
time.strptime(datetime_str, '%Y-%m-%d %H:%M:%S')))
if unix_time >= 1363881600:
prsa_flag = True
elif dataset == 'movie_data':
# movie data
if instance[-2] > 62091:
date_time_flag = True
prsa_flag = True
date_time = DistributedOnlineLearning.get_date(
instance[-2])
datetime_str = str(date_time) + ' ' + '00:00:00'
unix_time = int(
time.mktime(
time.strptime(datetime_str, '%Y-%m-%d %H:%M:%S')))
instance.pop(-2) # 去掉时间
elif dataset == 'netease_data':
date_time_flag = True
prsa_flag = True
unix_time = int(instance[0])
instance.pop(0) # 去掉时间
instance[0:len(instance) - 1] = Normalizer.normalize(
instance[0:len(instance) - 1], __numeric_attribute_scheme)
# print(instance)
__instance_count += 1
if __instance_count % 10000 == 0 or __instance_count == __data_length:
percentage = (__instance_count / __data_length) * 100
print(
sub_dataset, "%0.2f" % percentage +
"% of instances are prequentially processed!")
if __instance_count > __window_size and date_time_flag and prsa_flag:
if dataset == 'prsa_data':
if unix_time == 1363881600:
print(__instance_count)
__start_point = unix_time
self.data_statistics[sub_dataset]['delay'][
'time'].append(__start_point)
elif dataset == 'movie_data':
if __instance_count == __window_size + 1:
print(__instance_count)
__start_point = unix_time
self.data_statistics[sub_dataset]['delay'][
'time'].append(__start_point)
elif dataset == 'netease_data':
if __instance_count == __window_size + 1:
print(__instance_count)
__start_point = unix_time
self.data_statistics[sub_dataset]['delay'][
'time'].append(__start_point)
difference_value = unix_time - __start_point
if difference_value >= __batch_size and __instance_count != __data_length:
batch_interval = int(difference_value / __batch_size)
for cc in range(batch_interval):
if cc == 0:
r_f = False
else:
r_f = True
self.con.acquire() # 获得锁
__start_point += __batch_size
# for every batch
# 权重
self.data_statistics[sub_dataset]['online'][
'weight'].append(
learner.currentClassifierWeights.tolist())
# 属性记录
self.data_statistics[sub_dataset]['delay'][
'attributes'], last_batch_attributions, lc, bc = self.calculate_correlation(
predict_corr,
last_batch_attributions,
self.data_statistics[sub_dataset]['delay']
['attributes'],
spilt_inform,
lc,
bc,
repeat_flag=r_f)
# 准确度（可能会变, 目前是后端计算drift的准确率）
self.data_statistics[sub_dataset]['delay'][
'accuracy'].append(drift_detector.accuracy)
# batch开始时间
self.data_statistics[sub_dataset]['delay'][
'time'].append(__start_point)
# batch 数量
self.data_statistics[sub_dataset]['online'][
'dataNum'].append(__count)
# warning level
self.data_statistics[sub_dataset]['delay'][
'warningLevel'], warning_level_set, self.data_statistics[
sub_dataset]['delay'][
'hit'], self.warning_level_max[
sub_dataset] = self.calculate_warning_level(
warning_level_set,
self.data_statistics[sub_dataset]
['delay']['warningLevel'],
self.data_statistics[sub_dataset]
['delay']['hit'],
self.
warning_level_max[sub_dataset],
repeated_flag=r_f)
__count = 0
# self.last_wl_status[sub_dataset]['r_l'] = self.wl_transformer(self.data_statistics[sub_dataset]['delay']['warningLevel'][-1]['max'])
if len(self.last_wl_status[sub_dataset]['hit']) > 2:
self.last_wl_status[sub_dataset]['hit'].pop(0)
self.last_wl_status[sub_dataset]['hit'].append(
self.data_statistics[sub_dataset]['delay']
['hit'][-1])
else:
self.last_wl_status[sub_dataset]['hit'].append(
self.data_statistics[sub_dataset]['delay']
['hit'][-1])
global_count += 1
# self.plot_risk_level[sub_dataset] = self.data_statistics[sub_dataset]['delay']['warningLevel'][-1]['max']
if global_count == length:
global_count = 0
# 训练和测试每个模型的贝叶斯
d_s_n = self.nb_set.keys()
# 训练贝叶斯
if len(self.data_statistics[sub_dataset]['delay']
['warningLevel']) > 2:
for data_set_name in d_s_n:
self.instance_set[data_set_name] = [self.wl_transformer(self.data_statistics[value]['delay']['warningLevel'][-2]['max'])
for value in d_s_n if value != data_set_name] \
+ [max(self.last_wl_status[data_set_name]['hit'])]
if len(self.data_statistics[sub_dataset]
['delay']['warningLevel']) > 3:
# testing
for temple_name in d_s_n:
self.nb_set[temple_name].set_ready()
predict = self.nb_set[
temple_name].do_testing(
self.instance_set[temple_name])
self.data_statistics[temple_name][
'delay']['nb_prob'].append(
self.nb_set[temple_name].
drift_prob)
self.nb_drift_prob[temple_name][
'prob'].append(
self.nb_set[temple_name].
drift_prob)
self.nb_drift_prob[temple_name][
'ground_truth'].append(
self.data_statistics[
temple_name]['delay']
['warningLevel'][-2]['max'])
# 测试使用3个batch中最大的drift_level
# self.nb_drift_prob[temple_name]['ground_truth'].append(
# max(
# self.data_statistics[temple_name]['delay']['warningLevel'][-3]['max'],
# self.data_statistics[temple_name]['delay']['warningLevel'][-2]['max'],
# self.data_statistics[temple_name]['delay']['warningLevel'][-1]['max']
# )
# )
if predict == self.instance_set[
temple_name][-1]:
self.nb_classifier_accuracy[
temple_name][
'right_count'] += 1
self.nb_classifier_accuracy[
temple_name]['all_count'] += 1
self.nb_classifier_accuracy[
temple_name]['accuracy'].append(
round(
self.
nb_classifier_accuracy[
temple_name]
['right_count'] / self.
nb_classifier_accuracy[
temple_name]
['all_count'], 4))
# training
for temple_name in d_s_n:
self.nb_set[temple_name].do_training(
self.instance_set[temple_name],
drift_status)
else:
for temple_name in d_s_n:
self.nb_set[temple_name].do_training(
self.instance_set[temple_name],
drift_status)
self.con.notifyAll()
else:
self.con.wait()
predict_corr = [instance]
__count = 1
else:
__count += 1
predict_corr.append(instance)
predicted_value = learner.do_testing(instance) # 每一个实例训练一次
prediction_status = evaluator.calculate_accuracy(
predicted_value,
instance[-1],
output_size=__step,
output_flag=False)
if detection is True:
warning_status, drift_status = drift_detector.detect(
prediction_status)
if warning_status is not __last_warning_status:
if warning_status:
current_warning_status['start'] = unix_time
current_warning_status['max_accuracy'] = [
drift_detector.o_s_d_min
]
current_warning_status['max_accuracy_time'] = [
unix_time
]
current_warning_status['backend_accuracy'] = [
drift_detector.accuracy
]
else:
current_warning_status['end'] = __last_unix_time
self.data_statistics[sub_dataset]['delay'][
'warning'].append(current_warning_status)
current_warning_status = {}
else:
if warning_status:
current_warning_status['max_accuracy'].append(
drift_detector.o_s_d_min)
current_warning_status['max_accuracy_time'].append(
unix_time)
current_warning_status['backend_accuracy'].append(
drift_detector.accuracy)
if drift_status is not __last_drift_status:
if drift_status:
current_drift_status['start'] = unix_time
current_drift_status['max_accuracy'] = [
drift_detector.o_s_d_min
]
current_drift_status['max_accuracy_time'] = [
unix_time
]
current_drift_status['backend_accuracy'] = [
drift_detector.accuracy
]
else:
current_drift_status['end'] = __last_unix_time
self.data_statistics[sub_dataset]['delay'][
'drift'].append(current_drift_status)
current_drift_status = {}
else:
if drift_status:
current_drift_status['max_accuracy'].append(
drift_detector.o_s_d_min)
current_drift_status['max_accuracy_time'].append(
unix_time)
current_drift_status['backend_accuracy'].append(
drift_detector.accuracy)
__last_warning_status = warning_status
__last_drift_status = drift_status
__last_unix_time = unix_time
else:
# warning_status = False
drift_status = False
warning_level_set.append(
drift_detector.risk) # 记录ddm检测后的warning_level
# if 1365912000-12*3600 <= unix_time <= 1365998400-12*3600:
# if unix_time == 1365998400 - 13*3600:
# print(sub_dataset, len(warning_level_set), warning_level_set)
# if unix_time == 1365998400-12*3600:
# print(sub_dataset, self.data_statistics[sub_dataset]['delay']['warningLevel'][-1])
# if drift_status:
# print('数据集{}真实drift点->'.format(sub_dataset), unix_time, drift_detector.risk)
# if 1366401600 <= unix_time <= 1366488000:
# # print(sub_dataset, len(warning_level_set), warning_level_set)
# if unix_time == 1366488000:
# print(sub_dataset, self.data_statistics[sub_dataset]['delay']['warningLevel'][-1])
# if drift_status:
# print('数据集{}真实drift点->'.format(sub_dataset), unix_time, drift_detector.risk)
if __instance_count == __data_length: # 最后一个batch可能只有少部分数据，要考虑
print(sub_dataset, global_count)
self.con.acquire() # 获得锁
# 权重
self.data_statistics[sub_dataset]['online'][
'weight'].append(
learner.currentClassifierWeights.tolist())
# 属性记录
self.data_statistics[sub_dataset]['delay'][
'attributes'], last_batch_attributions, lc, bc = self.calculate_correlation(
predict_corr,
last_batch_attributions,
self.data_statistics[sub_dataset]['delay']
['attributes'],
spilt_inform,
lc,
bc,
repeat_flag=False)
# 准确度（可能会变, 目前是后端计算drift的准确率）
self.data_statistics[sub_dataset]['delay'][
'accuracy'].append(drift_detector.accuracy)
# batch 数量
self.data_statistics[sub_dataset]['online'][
'dataNum'].append(__count)
# warning level
self.data_statistics[sub_dataset]['delay'][
'warningLevel'], warning_level_set, self.data_statistics[
sub_dataset]['delay'][
'hit'], self.warning_level_max[
sub_dataset] = self.calculate_warning_level(
warning_level_set,
self.data_statistics[sub_dataset]
['delay']['warningLevel'],
self.data_statistics[sub_dataset]
['delay']['hit'],
self.warning_level_max[sub_dataset],
repeated_flag=False)
self.last_wl_status[sub_dataset][
'r_l'] = self.wl_transformer(
self.data_statistics[sub_dataset]['delay']
['warningLevel'][-1]['max'])
if len(self.last_wl_status[sub_dataset]['hit']) > 2:
self.last_wl_status[sub_dataset]['hit'].pop(0)
self.last_wl_status[sub_dataset]['hit'].append(
self.data_statistics[sub_dataset]['delay']['hit']
[-1])
else:
self.last_wl_status[sub_dataset]['hit'].append(
self.data_statistics[sub_dataset]['delay']['hit']
[-1])
global_count += 1
# 画 drift probability
# Zip.plot_multi_1(self.nb_drift_prob[sub_dataset], sub_dataset)
if global_count == length:
global_count = 0
# # 训练和测试每个模型的贝叶斯
d_s_n = self.nb_set.keys()
for data_set_name in d_s_n:
self.instance_set[data_set_name] = [self.wl_transformer(self.data_statistics[value]['delay']['warningLevel'][-2]['max'])
for value in d_s_n if value != data_set_name] \
+ [max(self.last_wl_status[data_set_name]['hit'])]
# testing
for temple_name in d_s_n:
self.nb_set[temple_name].set_ready()
predict = self.nb_set[temple_name].do_testing(
self.instance_set[temple_name])
self.data_statistics[temple_name]['delay'][
'nb_prob'].append(
self.nb_set[temple_name].drift_prob)
self.nb_drift_prob[temple_name]['prob'].append(
self.nb_set[temple_name].drift_prob)
self.nb_drift_prob[temple_name][
'ground_truth'].append(
self.data_statistics[temple_name]['delay']
['warningLevel'][-2]['max'])
# 测试使用3个batch中最大的drift_level
# self.nb_drift_prob[temple_name]['ground_truth'].append(
# max(
# self.data_statistics[temple_name]['delay']['warningLevel'][-3]['max'],
# self.data_statistics[temple_name]['delay']['warningLevel'][-2]['max'],
# self.data_statistics[temple_name]['delay']['warningLevel'][-1]['max']
# )
# )
if predict == self.instance_set[temple_name][-1]:
self.nb_classifier_accuracy[temple_name][
'right_count'] += 1
self.nb_classifier_accuracy[temple_name][
'all_count'] += 1
self.nb_classifier_accuracy[temple_name][
'accuracy'].append(
round(
self.nb_classifier_accuracy[
temple_name]['right_count'] /
self.nb_classifier_accuracy[
temple_name]['all_count'], 4))
# training
for temple_name in d_s_n:
self.nb_set[temple_name].do_training(
self.instance_set[temple_name], drift_status)
# 保存每个数据源的状态
# ① 每个数据源概念漂移检测+贝叶斯drift概率 + configure
for key_name in self.data_statistics.keys():
self.configure[key_name][
'timeStart'] = self.data_statistics[key_name][
'delay']['time'][0]
self.configure[key_name][
'timeEnd'] = self.data_statistics[key_name][
'delay']['time'][-1]
self.configure[key_name]['timeUnit'] = __batch_size
self.configure[key_name]['dataNumMax'] = max(
self.data_statistics[key_name]['online']
['dataNum'])
self.configure[key_name][
'warningLevelMax'] = self.warning_level_max[
key_name]
self.configure[key_name]['warningLevel'] = [[
0, 2
], [2, 3], [3, 10000]]
self.data_statistics[key_name]['delay'][
'hit'] = self.data_statistics[key_name][
'delay']['hit'][:-1]
self.save_file_1(self.configure,
self.sub_file_path,
type_name='configure')
self.save_file_1(self.data_statistics,
self.sub_file_path,
type_name=None)
self.save_file(self.nb_drift_prob,
folder_create.get_path(),
type_name='experiment_with_the_figure')
Zip.plot_multi(self.nb_classifier_accuracy)
Zip(folder_create.get_path())
# 提示所有数据训练完成，可结束主进程
print(
'All data has been trained. Please finish the main process manually!'
)
self.con.notifyAll()
else:
self.con.wait()
# training
learner.do_training(instance, drift_status)
else:
# training
learner.do_training(instance, drift_status)
def run(self, data_set, sub_data_set):
if data_set == 'prsa_data':
self.__batch_size = 24 * 3600 # 3600 represent 1 hour
elif data_set == 'movie_data':
self.__batch_size = 24 * 7 * 3600 # 3600 represent 1 hour
self.__data_length = Dataset.get_length(data_set, sub_data_set)
self.attributes_set = SaveDifferentData.construct_correlation(data_set)
self.spilt_inform = Dataset.get_spilt_inform(data_set)
data = Data(data_set, sub_data_set)
labels, attributes = data.get_attributes()
attributes_scheme = AttributeScheme.get_scheme(attributes)
self.__numeric_attribute_scheme = attributes_scheme['nominal']
self.__numeric_attribute_scheme = attributes_scheme['numeric']
# Initializing a learner
learner = Logistic(labels, attributes_scheme['numeric'])
learner = OnlineAccuracyUpdatedEnsemble(
labels,
attributes_scheme['numeric'],
learner,
windowSize=self.__window_size,
classifierLimit=self.__classifier_limit)
# Initializing a drift detector
drift_detector = DDM()
# Creating a save content
project = Project('./projects/distributed/{}'.format(data_set),
sub_data_set)
# Initializing a evaluator
evaluator = EvaluateWithWindowSize(learner, drift_detector, project,
self.__window_size)
# train & test
for x, y, attribute in data.data(batch_size=1):
if attribute is not None:
attributes_scheme = AttributeScheme.get_scheme(attributes)
self.__numeric_attribute_scheme = attributes_scheme['nominal']
self.__numeric_attribute_scheme = attributes_scheme['numeric']
continue
instance = x.tolist()[0] + [int(y.tolist()[0][0])]
# 每条数据的unix时间戳
# prsa data
if data_set == 'prsa_data':
self.date_time_flag = True
date_time = list(map(int, instance[:4]))
d = datetime.date(date_time[0], date_time[1], date_time[2])
t = datetime.time(date_time[3])
datetime_str = str(d) + ' ' + str(t)
self.unix_time = int(
time.mktime(
time.strptime(datetime_str, '%Y-%m-%d %H:%M:%S')))
if self.unix_time >= 1363881600:
self.prsa_flag = True
elif data_set == 'movie_data':
# movie data
if instance[-2] > 62091:
self.date_time_flag = True
self.prsa_flag = True
date_time = self._get_date(instance[-2])
datetime_str = str(date_time) + ' ' + '00:00:00'
self.unix_time = int(
time.mktime(
time.strptime(datetime_str, '%Y-%m-%d %H:%M:%S')))
instance[0:len(instance) - 1] = Normalizer.normalize(
instance[0:len(instance) - 1], self.__numeric_attribute_scheme)
self.__instance_count += 1
if self.__instance_count > self.__window_size and self.date_time_flag and self.prsa_flag:
if self.unix_time == 1363881600:
self.__start_point = self.unix_time
self.batch_start_time.append(self.__start_point)
if self.unix_time - self.__start_point >= self.__batch_size:
self.__start_point = self.unix_time
# for every batch
# 权重
self.weights.append(
learner.currentClassifierWeights.tolist())
# 属性记录
self.calculate_correlation()
# 准确度（可能会变, 目前是后端计算drift的准确率）
self.accuracy.append(drift_detector.accuracy)
# batch开始时间
self.batch_start_time.append(self.__start_point)
# batch 数量
self.batch_count.append(self.__count)
# 目前是 batch 中预测正确的个数， 后面改为concept drift 预测正确个数（后面为drift预测正确的个数）
self.right_count.append(self.__bingo)
# warning level
self.calculate_warning_level()
# print(batch_start_time, batch_count)
self.predict_corr = [instance]
self.__count = 1
self.__bingo = 0
else:
self.__count += 1
self.predict_corr.append(instance)
self.warning_level_set.append(drift_detector.risk)
predicted_value = learner.do_testing(instance)
prediction_status = evaluator.calculate_accuracy(
predicted_value,
instance[-1],
output_size=self.__step,
output_flag=False)
if prediction_status:
self.__bingo += 1
if self.detection is True:
self.warning_status, self.drift_status = drift_detector.detect(
prediction_status)
if self.warning_status is not self.__last_warning_status:
if self.warning_status:
self.current_warning_status[
'start'] = self.unix_time
self.current_warning_status['max_accuracy'] = [
drift_detector.o_s_d_min
]
self.current_warning_status[
'max_accuracy_time'] = [self.unix_time]
else:
self.current_warning_status[
'end'] = self.__last_unix_time
self.warning.append(self.current_warning_status)
self.current_warning_status = {}
else:
if self.warning_status:
self.current_warning_status['max_accuracy'].append(
drift_detector.o_s_d_min)
self.current_warning_status[
'max_accuracy_time'].append(self.unix_time)
if self.drift_status is not self.__last_drift_status:
if self.drift_status:
self.current_drift_status['start'] = self.unix_time
self.current_drift_status['max_accuracy'] = [
drift_detector.o_s_d_min
]
self.current_drift_status['max_accuracy_time'] = [
self.unix_time
]
else:
self.current_drift_status[
'end'] = self.__last_unix_time
self.drift.append(self.current_drift_status)
self.current_drift_status = {}
else:
if self.drift_status:
self.current_drift_status['max_accuracy'].append(
drift_detector.o_s_d_min)
self.current_drift_status[
'max_accuracy_time'].append(self.unix_time)
self.__last_warning_status = self.warning_status
self.__last_drift_status = self.drift_status
self.__last_unix_time = self.unix_time
else:
self.warning_status = False
self.drift_status = False
if self.__instance_count == self.__data_length: # 最后一个batch可能只有少部分数据，要考虑
# 权重
self.weights.append(
learner.currentClassifierWeights.tolist())
# 属性记录
self.calculate_correlation()
# 准确度（可能会变, 目前是后端计算drift的准确率）
self.accuracy.append(drift_detector.accuracy)
# batch 数量
self.batch_count.append(self.__count)
# 目前是 batch 中预测正确的个数， 后面改为concept drift 预测正确个数
self.right_count.append(self.__bingo)
# warning level
self.calculate_warning_level()
# training
learner.do_training(instance, self.drift_status)
else:
# training
learner.do_training(instance, self.drift_status)
self.save_file()
# classifier flag
flag = False
prsa_flag = False
attributes_set = construct_correlation(dataset)
# Creating a data stream
data = Data(dataset, sub_dataset)
labels, attributes = data.get_attributes()
attributes_scheme = AttributeScheme.get_scheme(attributes)
__numeric_attribute_scheme = attributes_scheme['nominal']
__numeric_attribute_scheme = attributes_scheme['numeric']
# Creating a save content
project = Project('projects/single/{}'.format(dataset),
sub_dataset)
sub_folder_path = folder_create.sub_folder(sub_dataset)
# Initializing a learner
learner = Logistic(labels, attributes_scheme['numeric'])
learner = OnlineAccuracyUpdatedEnsemble(
labels,
attributes_scheme['numeric'],
learner,
windowSize=__window_size,
classifierLimit=10)
# Initializing naive bayes
nb_attributes = construct_attribute()
nb_learner = NaiveBayes([0, 1], nb_attributes)
def sub_thread(self, dataset, d_id, sub_dataset, spilt_inform,
folder_create, length):
global global_count
# Set variables
date_time_flag = False
data_set_id = d_id
if dataset == 'prsa_data':
__batch_size = 24 * 3600 # 3600 represent 1 hour
elif dataset == 'movie_data':
__batch_size = 24 * 7 * 3600 # 3600 represent 1 hour
else:
__batch_size = 0
__instance_count = 0
__window_size = 500
__step = 1000
__start_point = 0
__count = 0
__bingo = 0
__last_unix_time = 0
__last_warning_status = False
__last_drift_status = False
__data_length = Dataset.get_length(dataset, sub_dataset)
configure = {}
data_statistics = dict()
delay = {}
online = {}
weights = []
accuracy = []
batch_start_time = []
batch_count = []
right_count = []
warning = []
current_warning_status = {}
warning_level = []
warning_level_set = []
drift = []
current_drift_status = {}
predict_corr = []
hit = []
last_batch_attributions = None
detection = True
drift_status = False
# classifier flag
prsa_flag = False
attributes_set = DistributedOnlineLearning.construct_correlation(
dataset)
# Creating a data stream
data = Data(dataset, sub_dataset)
labels, attributes = data.get_attributes()
attributes_scheme = AttributeScheme.get_scheme(attributes)
__numeric_attribute_scheme = attributes_scheme['numeric']
# Creating a save content
project = Project('projects/single/{}'.format(dataset), sub_dataset)
sub_folder_path = folder_create.sub_folder(sub_dataset)
# Initializing a learner
learner = Logistic(labels, attributes_scheme['numeric'])
learner = OnlineAccuracyUpdatedEnsemble(labels,
attributes_scheme['numeric'],
learner,
windowSize=__window_size,
classifierLimit=10)
# Initializing a drift detector
drift_detector = DDM()
# Initializing a evaluator
evaluator = EvaluateWithWindowSize(learner, drift_detector, project,
__window_size)
# train & test
for x, y, attribute in data.data(batch_size=1):
if attribute is not None:
attributes_scheme = AttributeScheme.get_scheme(attributes)
__numeric_attribute_scheme = attributes_scheme['numeric']
continue
instance = x.tolist()[0] + [int(y.tolist()[0][0])]
# 每条数据的unix时间戳
# prsa data
if dataset == 'prsa_data':
date_time_flag = True
date_time = list(map(int, instance[:4]))
d = datetime.date(date_time[0], date_time[1], date_time[2])
t = datetime.time(date_time[3])
datetime_str = str(d) + ' ' + str(t)
unix_time = int(
time.mktime(
time.strptime(datetime_str, '%Y-%m-%d %H:%M:%S')))
if unix_time >= 1363881600:
prsa_flag = True
elif dataset == 'movie_data':
# movie data
if instance[-2] > 62091:
date_time_flag = True
prsa_flag = True
date_time = DistributedOnlineLearning.get_date(
instance[-2])
datetime_str = str(date_time) + ' ' + '00:00:00'
unix_time = int(
time.mktime(
time.strptime(datetime_str, '%Y-%m-%d %H:%M:%S')))
instance[0:len(instance) - 1] = Normalizer.normalize(
instance[0:len(instance) - 1], __numeric_attribute_scheme)
__instance_count += 1
if __instance_count > __window_size and date_time_flag and prsa_flag:
if dataset == 'prsa_data':
if unix_time == 1363881600:
__start_point = unix_time
batch_start_time.append(__start_point)
elif dataset == 'movie_data':
if __instance_count == __window_size + 1:
__start_point = unix_time
batch_start_time.append(__start_point)
if unix_time - __start_point >= __batch_size:
self.con.acquire() # 获得锁
__start_point = unix_time
# for every batch
# 权重
weights.append(learner.currentClassifierWeights.tolist())
# 属性记录
attributes_set, last_batch_attributions = DistributedOnlineLearning.calculate_correlation(
predict_corr, last_batch_attributions, attributes_set,
spilt_inform)
# 准确度（可能会变, 目前是后端计算drift的准确率）
accuracy.append(drift_detector.accuracy)
# batch开始时间
batch_start_time.append(__start_point)
# batch 数量
batch_count.append(__count)
# 目前是 batch 中预测正确的个数， 后面改为concept drift 预测正确个数
right_count.append(__bingo)
# warning level
warning_level, warning_level_set, hit = DistributedOnlineLearning.calculate_warning_level(
warning_level_set, warning_level, hit)
predict_corr = [instance]
__count = 1
__bingo = 0
# 新增bayes
if len(warning_level) > 1:
inst = [
self.wl_transformer(warning_level[-2]['max']),
hit[-1]
]
if len(warning_level) > 2:
self.nb.set_ready()
predicted = self.nb.do_testing(inst)
print(data_set_id, predicted)
self.nb.do_training(inst)
else:
self.nb.do_training(inst)
global_count += 1
if global_count == length:
global_count = 0
self.con.notifyAll()
else:
self.con.wait()
else:
__count += 1
predict_corr.append(instance)
warning_level_set.append(drift_detector.risk)
predicted_value = learner.do_testing(instance)
prediction_status = evaluator.calculate_accuracy(
predicted_value,
instance[-1],
output_size=__step,
output_flag=False)
if prediction_status:
__bingo += 1
if detection is True:
warning_status, drift_status = drift_detector.detect(
prediction_status)
if warning_status is not __last_warning_status:
if warning_status:
current_warning_status['start'] = unix_time
current_warning_status['max_accuracy'] = [
drift_detector.o_s_d_min
]
current_warning_status['max_accuracy_time'] = [
unix_time
]
current_warning_status['backend_accuracy'] = [
drift_detector.accuracy
]
else:
current_warning_status['end'] = __last_unix_time
warning.append(current_warning_status)
current_warning_status = {}
else:
if warning_status:
current_warning_status['max_accuracy'].append(
drift_detector.o_s_d_min)
current_warning_status['max_accuracy_time'].append(
unix_time)
current_warning_status['backend_accuracy'].append(
drift_detector.accuracy)
if drift_status is not __last_drift_status:
if drift_status:
current_drift_status['start'] = unix_time
current_drift_status['max_accuracy'] = [
drift_detector.o_s_d_min
]
current_drift_status['max_accuracy_time'] = [
unix_time
]
current_drift_status['backend_accuracy'] = [
drift_detector.accuracy
]
else:
current_drift_status['end'] = __last_unix_time
drift.append(current_drift_status)
current_drift_status = {}
else:
if drift_status:
current_drift_status['max_accuracy'].append(
drift_detector.o_s_d_min)
current_drift_status['max_accuracy_time'].append(
unix_time)
current_drift_status['backend_accuracy'].append(
drift_detector.accuracy)
__last_warning_status = warning_status
__last_drift_status = drift_status
__last_unix_time = unix_time
else:
warning_status = False
drift_status = False
# if 1393401600 - 12*3600 <= unix_time <= 1393401600 + 12*3600:
# print("准确率为, S, P", evaluator.accuracy, drift_detector.S, drift_detector.P)
if __instance_count == __data_length: # 最后一个batch可能只有少部分数据，要考虑
self.con.acquire() # 获得锁
# 权重
weights.append(learner.currentClassifierWeights.tolist())
# 属性记录
attributes_set, last_batch_attributions = DistributedOnlineLearning.calculate_correlation(
predict_corr, last_batch_attributions, attributes_set,
spilt_inform)
# 准确度（可能会变, 目前是后端计算drift的准确率）
accuracy.append(drift_detector.accuracy)
# batch 数量
batch_count.append(__count)
# 目前是 batch 中预测正确的个数， 后面改为concept drift 预测正确个数
right_count.append(__bingo)
# warning level
warning_level, warning_level_set, hit = DistributedOnlineLearning.calculate_warning_level(
warning_level_set, warning_level, hit)
# 新增bayes
if len(warning_level) > 1:
inst = [
self.wl_transformer(warning_level[-2]['max']),
hit[-1]
]
if len(warning_level) > 2:
self.nb.do_testing(inst)
self.nb.do_training(inst)
else:
self.nb.do_training(inst)
global_count += 1
if global_count == length:
global_count = 0
if __instance_count == __data_length:
# 保存各种数据
pass
self.con.notifyAll()
else:
if __instance_count == __data_length:
# 保存各种数据
pass
self.con.wait()
# training
learner.do_training(instance, drift_status)
else:
# training
learner.do_training(instance, drift_status)
configure['timeStart'] = batch_start_time[0]
configure['timeEnd'] = batch_start_time[-1]
configure['timeUnit'] = __batch_size
configure['dataNumMax'] = max(batch_count)
data_statistics['name'] = sub_dataset
delay['time'] = batch_start_time
delay['accuracy'] = accuracy
delay['bingo'] = []
delay['hit'] = hit
delay['warning'] = warning
delay['drift'] = drift
delay['warningLevel'] = warning_level
delay['attributes'] = attributes_set
online['weight'] = weights
online['time'] = batch_start_time
online['dataNum'] = batch_count
data_statistics['delay'] = delay
data_statistics['online'] = online
def sub_thread(self, dataset, d_id, sub_dataset, spilt_inform,
folder_create, length):
global global_count
self.nb_set[sub_dataset] = NaiveBayes([0, 1], self.attributes)
self.last_wl_status[sub_dataset] = []
self.instance_set[sub_dataset] = []
self.nb_classifier_accuracy[sub_dataset] = dict(all_count=0,
right_count=0,
accuracy=[])
# Set variables
date_time_flag = False
data_set_id = d_id
__batch_size = 0
__instance_count = 0
__window_size = 500
__step = 1000
__start_point = 0
__count = 0
# __bingo = 0
__last_unix_time = 0
__last_warning_status = False
__last_drift_status = False
__warning_level_max = 0
__data_length = Dataset.get_length(dataset, sub_dataset)
lc = []
bc = []
configure = {}
data_statistics = dict()
delay = {}
online = {}
weights = []
accuracy = []
batch_start_time = []
batch_count = []
# right_count = []
warning = []
current_warning_status = {}
warning_level = []
warning_level_set = []
drift = []
current_drift_status = {}
predict_corr = []
hit = []
last_batch_attributions = None
detection = True
drift_status = False
# classifier flag
prsa_flag = False
attributes_set = DistributedOnlineLearning.construct_correlation(
dataset)
# Creating a data stream
data = Data(dataset, sub_dataset)
labels, attributes = data.get_attributes()
attributes_scheme = AttributeScheme.get_scheme(attributes)
__numeric_attribute_scheme = attributes_scheme['numeric']
# Creating a save content
project = Project('projects/single/{}'.format(dataset), sub_dataset)
sub_folder_path = folder_create.sub_folder(sub_dataset)
# Initializing a learner
learner = Logistic(labels, attributes_scheme['numeric'])
learner = OnlineAccuracyUpdatedEnsemble(labels,
attributes_scheme['numeric'],
learner,
windowSize=__window_size,
classifierLimit=10)
# Initializing a drift detector
drift_detector = DDM()
# Initializing a evaluator
evaluator = EvaluateWithWindowSize(learner, drift_detector, project,
__window_size)
# train & test
for x, y, attribute in data.data(batch_size=1):
if attribute is not None:
attributes_scheme = AttributeScheme.get_scheme(attributes)
__numeric_attribute_scheme = attributes_scheme['numeric']
continue
instance = x.tolist()[0] + [int(y.tolist()[0][0])]
# 每条数据的unix时间戳
# prsa data
if dataset == 'prsa_data':
date_time_flag = True
date_time = list(map(int, instance[:4]))
d = datetime.date(date_time[0], date_time[1], date_time[2])
tt = datetime.time(date_time[3])
datetime_str = str(d) + ' ' + str(tt)
unix_time = int(
time.mktime(
time.strptime(datetime_str, '%Y-%m-%d %H:%M:%S')))
if unix_time >= 1363881600:
prsa_flag = True
elif dataset == 'movie_data':
# movie data
if instance[-2] > 62091:
date_time_flag = True
prsa_flag = True
date_time = DistributedOnlineLearning.get_date(
instance[-2])
datetime_str = str(date_time) + ' ' + '00:00:00'
unix_time = int(
time.mktime(
time.strptime(datetime_str, '%Y-%m-%d %H:%M:%S')))
instance[0:len(instance) - 1] = Normalizer.normalize(
instance[0:len(instance) - 1], __numeric_attribute_scheme)
__instance_count += 1
if __instance_count > __window_size and date_time_flag and prsa_flag:
if dataset == 'prsa_data':
if unix_time == 1363881600:
__start_point = unix_time
batch_start_time.append(__start_point)
elif dataset == 'movie_data':
if __instance_count == __window_size + 1:
__start_point = unix_time
batch_start_time.append(__start_point)
difference_value = unix_time - __start_point
if difference_value >= __batch_size:
self.con.acquire() # 获得锁
batch_interval = int(difference_value / __batch_size)
for cc in range(batch_interval):
if cc == 0:
r_f = False
else:
r_f = True
__start_point += __batch_size
# for every batch
# 权重
weights.append(
learner.currentClassifierWeights.tolist())
# 属性记录
attributes_set, last_batch_attributions, lc, bc = self.calculate_correlation(
predict_corr,
last_batch_attributions,
attributes_set,
spilt_inform,
lc,
bc,
repeat_flag=r_f)
# 准确度（可能会变, 目前是后端计算drift的准确率）
accuracy.append(drift_detector.accuracy)
# batch开始时间
batch_start_time.append(__start_point)
# batch 数量
batch_count.append(__count)
# warning level
warning_level, warning_level_set, hit, __warning_level_max = self.calculate_warning_level(
warning_level_set,
warning_level,
hit,
__warning_level_max,
repeated_flag=r_f)
# 保存每个数据源的当前状态
self.last_wl_status[sub_dataset] = [
self.wl_transformer(warning_level[-1]['max']),
hit[-1]
]
__count = 0
global_count += 1
if global_count == length:
global_count = 0
# 训练和测试每个模型的贝叶斯
if len(warning_level) > 1:
d_s_n = self.nb_set.keys()
for data_set_name in d_s_n:
self.instance_set[data_set_name] = [self.last_wl_status[value][0]
for value in d_s_n if value != data_set_name]\
+ [self.last_wl_status[data_set_name][-1]]
if len(warning_level) > 2:
# testing
for temple_name in d_s_n:
self.nb_set[temple_name].set_ready()
predict = self.nb_set[
temple_name].do_testing(
self.instance_set[temple_name])
if predict == self.instance_set[
temple_name][-1]:
self.nb_classifier_accuracy[
temple_name][
'right_count'] += 1
self.nb_classifier_accuracy[
temple_name]['all_count'] += 1
self.nb_classifier_accuracy[
temple_name]['accuracy'].append(
round(
self.
nb_classifier_accuracy[
temple_name]
['right_count'] / self.
nb_classifier_accuracy[
temple_name]
['all_count'], 4))
# training
for temple_name in d_s_n:
self.nb_set[temple_name].do_training(
self.instance_set[temple_name],
drift_status)
else:
for temple_name in d_s_n:
self.nb_set[temple_name].do_training(
self.instance_set[temple_name],
drift_status)
# print(self.nb_classifier_accuracy)
self.con.notifyAll()
else:
self.con.wait()
predict_corr = [instance]
__count = 1
else:
__count += 1
predict_corr.append(instance)
warning_level_set.append(drift_detector.risk)
predicted_value = learner.do_testing(instance)
prediction_status = evaluator.calculate_accuracy(
predicted_value,
instance[-1],
output_size=__step,
output_flag=False)
# if prediction_status:
# __bingo += 1
if detection is True:
warning_status, drift_status = drift_detector.detect(
prediction_status)
if warning_status is not __last_warning_status:
if warning_status:
current_warning_status['start'] = unix_time
current_warning_status['max_accuracy'] = [
drift_detector.o_s_d_min
]
current_warning_status['max_accuracy_time'] = [
unix_time
]
current_warning_status['backend_accuracy'] = [
drift_detector.accuracy
]
else:
current_warning_status['end'] = __last_unix_time
warning.append(current_warning_status)
current_warning_status = {}
else:
if warning_status:
current_warning_status['max_accuracy'].append(
drift_detector.o_s_d_min)
current_warning_status['max_accuracy_time'].append(
unix_time)
current_warning_status['backend_accuracy'].append(
drift_detector.accuracy)
if drift_status is not __last_drift_status:
if drift_status:
current_drift_status['start'] = unix_time
current_drift_status['max_accuracy'] = [
drift_detector.o_s_d_min
]
current_drift_status['max_accuracy_time'] = [
unix_time
]
current_drift_status['backend_accuracy'] = [
drift_detector.accuracy
]
else:
current_drift_status['end'] = __last_unix_time
drift.append(current_drift_status)
current_drift_status = {}
else:
if drift_status:
current_drift_status['max_accuracy'].append(
drift_detector.o_s_d_min)
current_drift_status['max_accuracy_time'].append(
unix_time)
current_drift_status['backend_accuracy'].append(
drift_detector.accuracy)
__last_warning_status = warning_status
__last_drift_status = drift_status
__last_unix_time = unix_time
else:
# warning_status = False
drift_status = False
if __instance_count == __data_length: # 最后一个batch可能只有少部分数据，要考虑
self.con.acquire() # 获得锁
# 权重
weights.append(learner.currentClassifierWeights.tolist())
# 属性记录
attributes_set, last_batch_attributions, lc, bc = self.calculate_correlation(
predict_corr,
last_batch_attributions,
attributes_set,
spilt_inform,
lc,
bc,
repeat_flag=False)
# 准确度（可能会变, 目前是后端计算drift的准确率）
accuracy.append(drift_detector.accuracy)
# batch 数量
batch_count.append(__count)
# # 目前是 batch 中预测正确的个数， 后面改为concept drift 预测正确个数
# right_count.append(__bingo)
# warning level
warning_level, warning_level_set, hit, __warning_level_max = self.calculate_warning_level(
warning_level_set,
warning_level,
hit,
__warning_level_max,
repeated_flag=False)
global_count += 1
if global_count == length:
global_count = 0
# 训练和测试每个模型的贝叶斯
if len(warning_level) > 1:
d_s_n = self.nb_set.keys()
for data_set_name in d_s_n:
self.instance_set[data_set_name] = [self.last_wl_status[value][0]
for value in d_s_n if value != data_set_name] \
+ [self.last_wl_status[data_set_name][-1]]
if len(warning_level) > 2:
# testing
for temple_name in d_s_n:
predict = self.nb_set[
temple_name].do_testing(
self.instance_set[temple_name])
if predict == self.instance_set[
temple_name][-1]:
self.nb_classifier_accuracy[
temple_name]['right_count'] += 1
self.nb_classifier_accuracy[temple_name][
'all_count'] += 1
self.nb_classifier_accuracy[temple_name][
'accuracy'].append(
round(
self.nb_classifier_accuracy[
temple_name]['right_count']
/ self.nb_classifier_accuracy[
temple_name]['all_count'],
4))
# training
for temple_name in d_s_n:
self.nb_set[temple_name].do_training(
self.instance_set[temple_name],
drift_status)
else:
for temple_name in d_s_n:
self.nb_set[temple_name].do_training(
self.instance_set[temple_name],
drift_status)
# 保存最后一个数据源的状态
configure['timeStart'] = batch_start_time[0]
configure['timeEnd'] = batch_start_time[-1]
configure['timeUnit'] = __batch_size
configure['dataNumMax'] = max(batch_count)
configure['warningLevelMax'] = __warning_level_max
data_statistics['name'] = sub_dataset
delay['time'] = batch_start_time
delay['accuracy'] = accuracy
delay['bingo'] = []
delay['hit'] = hit[1:]
delay['warning'] = warning
delay['drift'] = drift
delay['warningLevel'] = warning_level
delay['attributes'] = attributes_set
online['weight'] = weights
online['time'] = batch_start_time
online['dataNum'] = batch_count
data_statistics['delay'] = delay
data_statistics['online'] = online
save_path = sub_folder_path + '/'
self.save_file(configure,
save_path,
type_name='configure')
self.save_file(data_statistics,
save_path,
type_name=None)
# 提示所有数据训练完成，可结束主进程
print(
'All data has been trained. Please finish the main process manually!'
)
Zip.plot_multi(self.nb_classifier_accuracy)
Zip(folder_create.get_path())
self.con.notifyAll()
else:
configure['timeStart'] = batch_start_time[0]
configure['timeEnd'] = batch_start_time[-1]
configure['timeUnit'] = __batch_size
configure['dataNumMax'] = max(batch_count)
data_statistics['name'] = sub_dataset
delay['time'] = batch_start_time
delay['accuracy'] = accuracy
delay['bingo'] = []
delay['hit'] = hit[1:]
delay['warning'] = warning
delay['drift'] = drift
delay['warningLevel'] = warning_level
delay['attributes'] = attributes_set
online['weight'] = weights
online['time'] = batch_start_time
online['dataNum'] = batch_count
data_statistics['delay'] = delay
data_statistics['online'] = online
save_path = sub_folder_path + '/'
self.save_file(configure,
save_path,
type_name='configure')
self.save_file(data_statistics,
save_path,
type_name=None)
self.con.wait()
# training
learner.do_training(instance, drift_status)
else:
# training
learner.do_training(instance, drift_status)
from data_structures.attribute_scheme import AttributeScheme
from classifier.__init__ import *
from drift_detection.__init__ import *
from filters.project_creator import Project
from streams.readers.arff_reader import ARFFReader
from tasks.__init__ import *
project_name = "airlines"
project = Project("projects", f"{project_name}")
labels, attributes, stream_records = ARFFReader.read(
f"data_streams/{project_name}.arff")
attributes_scheme = AttributeScheme.get_scheme(attributes)
actual_drift_points = [20000, 40000, 60000, 80000]
drift_acceptance_interval = 250
# 3. Creating classifier and drifter pairs
pairs = [
(HoeffdingTree(labels, attributes_scheme['nominal']), DDM(), "ddm"),
(NaiveBayes(labels, attributes_scheme['nominal']), DDM(), "ddm"),
(HoeffdingTree(labels,
attributes_scheme['nominal']), HDDM_A_test(), "hddm_a"),
(HoeffdingTree(labels,
attributes_scheme['nominal']), HDDM_W_test(), "hddm_w"),
(HoeffdingTree(labels, attributes_scheme['nominal']), EDDM(), "eddm"),
(HoeffdingTree(labels, attributes_scheme['nominal']),
ADWINChangeDetector(), "ADWIN")
]
from classifier.__init__ import *
from drift_detection.__init__ import *
from filters.project_creator import Project
from streams.readers.arff_reader import ARFFReader
from tasks.__init__ import *
from regression.__init__ import *
# 1. Creating a project
project = Project("projects/single", "expo")
# 2. Loading an arff file
labels, attributes, stream_records = ARFFReader.read(
"data_streams/_synthetic/EXPO/EXPO.arff")
# attributes_scheme = AttributeScheme.get_scheme(attributes)
# 3. Initializing a Learner
# learner = NaiveBayes(labels, attributes_scheme['nominal'])
learner = Expo()
# 4. Initializing a drift detector
detector = FHDDMS(n=100)
# actual_drift_points = [20000, 40000, 60000, 80000]
actual_drift_points = [500, 1000, 1500, 2000]
drift_acceptance_interval = 100
# 5. Creating a Prequential Evaluation Process
prequential = PrequentialRegressionDriftEvaluator(learner, detector,
actual_drift_points,
drift_acceptance_interval,
project)