class testLinkedList(unittest.TestCase):
def setUp(self):
self.logger = Logger().create_logger("log")
def test_empty_linked_list_creation(self):
'''
This wil test empty linked test can be created
'''
obj_linked_list = LinkedList()
self.assertIsInstance(obj_linked_list, LinkedList)
def test_linkedlist_functionality(self):
input_list = [1,0,3,6,5,10]
obj_linked_list = LinkedList(input_list[0])
for each_ele in input_list[1:]:
obj_linked_list.insert(each_ele)
self.logger.info("processing linked list for rearranging")
(list1, list2, data) = obj_linked_list.rearrange_list()
self.assertEqual([5, 0, 6, 1, 10, 3],data)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--train_data_dir', default='Data_Geolife/train')
parser.add_argument('--train_file', default='train.csv')
parser.add_argument('--test_data_dir', default='Data_Geolife/test')
parser.add_argument('--test_file', default='test.csv')
parser.add_argument('--classes', default='walk,bike,bus,car')
parser.add_argument('--save_dir', default='./save')
parser.add_argument('--log_dir', default='./log')
parser.add_argument('--metrics_record_dir', default='./metrics_record')
parser.add_argument('--metrics_record_file', default='./ml_record.csv')
parser.add_argument('--section', default='ml')
parser.add_argument('--is_training', default='train')
parser.add_argument('--record', default=False)
args = parser.parse_args()
logger = Logger(
logging.getLogger(),
os.path.join(args.log_dir, 'ml', 'ml_{}.log'.format(getDate())))
train_data_raw = pd.read_csv(
os.path.join(args.train_data_dir, args.train_file))
test_data_raw = pd.read_csv(
os.path.join(args.test_data_dir, args.test_file))
classes_to_handle = set(args.classes.split(','))
train_data = pd.DataFrame(columns=train_data_raw.columns.values)
test_data = pd.DataFrame(columns=test_data_raw.columns.values)
for c in classes_to_handle:
train_data = train_data.append(
train_data_raw[train_data_raw.label == c])
test_data = test_data.append(test_data_raw[test_data_raw.label == c])
le = preprocessing.LabelEncoder()
le.fit(pd.concat([train_data.label, test_data.label], axis=0))
le_mapping = dict(zip(le.classes_, le.transform(le.classes_)))
train_y = le.transform(train_data.label)
train_x = train_data.drop('label', axis=1)
test_y = le.transform(test_data.label)
test_x = test_data.drop('label', axis=1)
classifiers_to_run = [
'NB',
'KNN',
'LR',
'RF',
'DT',
'SVM',
# 'SVMCV',
'GBDT'
]
classifiers = {
'NB': naive_bayes_classifier,
'KNN': knn_classifier,
'LR': logistic_regression_classifier,
'RF': random_forest_classifier,
'DT': decision_tree_classifier,
'SVM': svm_classifier,
'SVMCV': svm_cross_validation,
'GBDT': gradient_boosting_classifier
}
for classifier in classifiers_to_run:
logger.info('start training model: {}'.format(classifier))
start_time = time.time()
record = {
'section': classifier,
'metrics_record_dir': args.metrics_record_dir,
'metrics_record_file': args.metrics_record_file
}
model = classifiers[classifier](train_x, train_y)
logger.info('training took %fs!' % (time.time() - start_time))
predict = model.predict(test_x)
accuracy = metrics.accuracy_score(test_y, predict)
precision = metrics.precision_score(test_y, predict, average=None)
recall = metrics.recall_score(test_y, predict, average=None)
f1_score = metrics.f1_score(test_y, predict, average=None)
record['accuracy'] = accuracy
for cls in classes_to_handle:
label_index = le_mapping[cls]
record['%s_precision' % cls] = precision[label_index]
record['%s_recll' % cls] = recall[label_index]
record['%s_f1_score' % cls] = f1_score[label_index]
logger.info(
'%s: precision: %.2f%%, recall: %.2f%%, f1_socre: %.2f%%' %
(cls, 100 * precision[label_index], 100 * recall[label_index],
100 * f1_score[label_index]))
logger.info('accuracy: %.2f%%' % (100 * accuracy))
if args.record:
appendRecord(record)
def setUp(self):
self.logger = Logger().create_logger("log")
#!/usr/bin/env python3
# -*- coding:utf-8 -*-
import logging
from main import Logger
logyyx = Logger('test', logging.DEBUG, logging.WARNING)
if __name__ == '__main__':
logyyx.info('一个info信息')
logyyx.debug('一个debug信息')
logyyx.info('一个info信息')
logyyx.warning('一个warning信息')
logyyx.error('一个error信息')
logyyx.critical('一个致命critical信息')
job.start()
for job in testjobs:
job.join()
except Exception as e:
print("error occured while running 'runMultiTest':", str(e))
finally:
time.sleep(2)
self.stopAppium()
def runTest(self, case, datas):
print("[action]Initializing case %s" % case.casename)
start = time.time()
initsuccess = False
caselog = os.path.join(self.logdir, case.casename + "_case")
logger = Logger(caselog, case_logmode)
setattr(case, 'logger', logger)
setattr(case, 'result', {"errorMsg": None})
setattr(
case, 'appiumlogfile',
os.path.join(
self.logdir, case.device_name + "_" + case.appium_port +
case.filename + "_appium.log"))
setattr(case, 'caselogfile', caselog + "_info.log")
setattr(case, 'case_elements', self.case_elements)
setattr(case, 'test_datas', self.test_datas)
setattr(case, 'system_alert_ids', system_alert_ids)
try:
case = case(datas)
initsuccess = True
print("[action]running test:%s %s" % (case.casename, case.desc))