def delete(self, request, *args, **kwargs):
file_id = request.data.get('file_id')
where = DataSource.objects.get(id=file_id).where
if where == 'hdfs':
file = DataSource.objects.get(id=file_id)
hdfs_name = DataSource.objects.get(id=file_id).format_filename
client = Client(HDFS_HOST)
client.delete('/datahoop/' + hdfs_name, recursive=True)
file.delete()
else:
client = pymongo.MongoClient(settings.MONGO_DB_HOST, settings.MONGO_DB_PORT)
db = client.datahoop.data
file_id = DataSource.objects.filter(id=id).first()
obj_id = file_id.obj_id
file_id.delete()
db.remove({"_id": ObjectId(obj_id)})
client.close()
return HttpResponse(content_type='application/json')
def get(self, request): # delete mydata
file_id = request.GET.get('file_id')
try:
where = DataSource.objects.get(id=file_id).where
print(DataSource.objects.get(id=file_id))
print(where)
format_filename = DataSource.objects.get(
id=file_id).format_filename
format_name_count = DataSource.objects.filter(
format_filename=format_filename).count()
if where == 'hdfs' and format_name_count == 1:
file = DataSource.objects.get(id=file_id)
hdfs_name = DataSource.objects.get(id=file_id).format_filename
client = Client(HDFS_HOST)
client.delete('/datahoop/' + hdfs_name, recursive=True)
file.delete()
item = Collect.objects.filter(file_id=file_id)
if item:
item.delete()
elif where == 'hdfs' and format_name_count > 1:
file = DataSource.objects.get(id=file_id)
file.delete()
item = Collect.objects.filter(file_id=file_id)
if item:
item.delete()
else:
client = pymongo.MongoClient(settings.MONGO_DB_URI)
db = client.datahoop.data
data_obj = DataSource.objects.filter(id=file_id).first()
obj_id = data_obj.obj_id
data_obj.delete()
db.remove({"_id": ObjectId(obj_id)})
client.close()
item = Collect.objects.filter(file_id=file_id)
if item:
item.delete()
return JsonResponse({'status': True})
except:
return JsonResponse({'status': False})
def train(train_path,
test_path,
output_path,
target,
train_split_ratio=0.33,
penalty='l2',
dual=False,
tol=1e-4,
C=1.0,
random_state=None,
multi_class='ovr'):
# 设置起始时间
time.localtime()
time_trains_start = time.strftime('%Y{y}%m{m}%d{d} %H{h}%M{f}%S{s}'.format(
y='/', m='/', d='', h=':', f=':', s=''))
start_time = time.time()
# 设置输入文件路径
train_FILENAME = train_path + "/data/Data.csv" # hdfs文件路径
test_FILENAME = test_path + "/data/Data.csv" # hdfs文件路径
client = Client(HDFS_HOSTS1)
# 训练数据读取
with client.read(train_FILENAME) as tr_s:
tr_content = tr_s.read()
tr_s = str(tr_content, 'utf-8')
# 确保文件写入完毕
tr_file = open("trainData.csv", "w")
tr_file.flush()
os.fsync(tr_file)
tr_file.write(tr_s)
tr_file.close()
df_train = pd.read_csv("trainData.csv", header=0)
print(df_train)
# 测试数据读取
with client.read(test_FILENAME) as te_fs:
te_content = te_fs.read()
te_s = str(te_content, 'utf-8')
# 确保文件写入完毕
te_file = open("testData.csv", "w")
te_file.flush()
os.fsync(te_file)
te_file.write(te_s)
te_file.close()
df_test = pd.read_csv("testData.csv", header=0)
print(df_test)
min_max_scaler = preprocessing.MinMaxScaler(feature_range=(-1, 1))
test_data_num = df_train.shape[0]
train_data_num = df_train.shape[0]
# 处理预测集
df_test = min_max_scaler.fit_transform(df_test)
df_test = np.array(df_test)
# 数据处理和清洗
cols = [tmp_i for tmp_i in df_train.columns if tmp_i not in [target]]
X = df_train[cols]
X = np.array(X)
X = min_max_scaler.fit_transform(X)
Y = df_train[target]
Y = np.array(Y)
# 训练集数据分割
X_train, X_test, Y_train, Y_test = train_test_split(
X, Y, test_size=train_split_ratio)
# 使用 scikit learn 中的LR模型进行训练
clf = LogisticRegression(penalty,
dual,
tol,
C,
random_state,
multi_class,
solver='liblinear')
clf.fit(X_train, Y_train)
# 准确率train_acc
train_acc = clf.score(X_test, Y_test)
print('score Scikit learn: ', train_acc)
# 精确率train_precision_score
train_precision_score = precision_score(Y_test, clf.predict(X_test))
# 召回率train_recall_score
train_recall_score = recall_score(Y_test, clf.predict(X_test))
# F1_Score
train_f1_score = f1_score(Y_test, clf.predict(X_test))
# roc_auc_score
train_roc_auc_score1 = roc_auc_score(Y_test, clf.predict(X_test))
# 使用 scikit learn 中的LR模型进行预测
result = clf.predict(df_test)
# print(result)
# 设置终止时间，并计算总时间
train_end = time.time()
train_seconds = train_end - start_time
m, s = divmod(train_seconds, 60)
h, m = divmod(m, 60)
time_trains_all = "%02d:%02d:%02d" % (h, m, s)
# ++++++++++++++++++++++++++++++++++++++++训练结果保存+++++++++++++++++++++++++++++++++++++++#
## 保存摘要模型报告文件
# abstract_path = HDFS_HOSTS1 + output_path + '/abstract/data/'
abstract_path = output_path + '/abstract/data/'
f = open('abstract.csv', mode='w', newline='')
fileheader = [
'FrameWork', 'Version', 'model', 'accuracy', 'time_trains_start',
'time_trains_all', 'test_data_num', 'train_data_num'
]
w = csv.DictWriter(f, fileheader)
w.writeheader()
csv_dict = edict()
csv_dict.FrameWork = 'Scikit-learn'
csv_dict.Version = sklearn.__version__
csv_dict.model = '%s' % LogisticRegression
csv_dict.accuracy = str(train_acc)
csv_dict.time_trains_start = time_trains_start
csv_dict.time_trains_all = time_trains_all
csv_dict.test_data_num = str(test_data_num)
csv_dict.train_data_num = str(train_data_num)
w.writerow(csv_dict)
f.close()
client.delete(abstract_path + 'abstract.csv')
client.upload(abstract_path + 'abstract.csv', 'abstract.csv')
# if len(client.list(abstract_path)):
# client.delete(abstract_path + 'abstract.csv')
# client.upload(abstract_path + 'abstract.csv', 'abstract.csv')
# else:
# client.upload(abstract_path + 'abstract.csv', 'abstract.csv')
##保存模型版本信息csv文件
version_path = output_path + '/msg/data/'
f = open('msg.csv', mode='w', newline='')
fileheader = [
'accuracy', 'time_trains_start', 'time_trains_all', 'test_data_num',
'train_data_num'
]
w = csv.DictWriter(f, fileheader)
w.writeheader()
csv_dict = edict()
csv_dict.accuracy = str(train_acc)
csv_dict.time_trains_start = time_trains_start
csv_dict.time_trains_all = time_trains_all
csv_dict.test_data_num = str(test_data_num)
csv_dict.train_data_num = str(train_data_num)
w.writerow(csv_dict)
f.close()
client.delete(version_path + 'msg.csv')
client.upload(version_path + 'msg.csv', 'msg.csv')
## 保存训练评价指标模型报告文件
file_csv_path = output_path + '/evaluation/data/'
f = open('evaluation.csv', mode='w', newline='')
fileheader = [
'accuracy', 'train_precision_score', 'train_recall_score',
'train_f1_score', 'train_roc_auc_score1'
]
w = csv.DictWriter(f, fileheader)
w.writeheader()
csv_dict = edict()
csv_dict.accuracy = str(train_acc)
csv_dict.train_precision_score = train_precision_score
csv_dict.train_recall_score = train_recall_score
csv_dict.train_f1_score = train_f1_score
csv_dict.train_roc_auc_score1 = train_roc_auc_score1
w.writerow(csv_dict)
f.close()
client.delete(file_csv_path + 'evaluation.csv')
client.upload(file_csv_path + 'evaluation.csv', 'evaluation.csv')
# 保存测试集预测结果文件
file_csv_path = output_path + '/result/data/'
# 字典中的key值即为csv中列名
dataframe = pd.DataFrame({target: result})
# 将DataFrame存储为csv,index表示是否显示行名，default=True
dataframe.to_csv("result.csv", index=False, sep=',')
client.delete(file_csv_path + 'result.csv')
client.upload(file_csv_path + 'result.csv', 'result.csv')
class HDFSUtil:
def __init__(self, url):
self._client = Client(url)
def make_dir(self, hdfs_path):
"""
支持递归创建多级目录
:param hdfs_path:
:return:
"""
self._client.makedirs(hdfs_path)
def delete_hdfs_file(self, hdfs_path):
"""
删除HDFS文件
如果是目录, 必须为空
:param hdfs_path:
:return:
"""
self._client.delete(hdfs_path)
def delete_hdfs_dir(self, hdfs_dir):
"""
删除HDFS文件/目录
如果目录不为空, 递归删除
:param hdfs_dir:
:return:
"""
dir_list = self.hdfs_dir_list(hdfs_dir)
if dir_list is None or len(dir_list) == 0:
print('Delete File: {0}'.format(hdfs_dir))
self._client.delete(hdfs_dir)
else:
for file_name in dir_list:
self.delete_hdfs_dir(hdfs_dir + '/' + file_name)
print('Delete Dir: {0}'.format(hdfs_dir))
self._client.delete(hdfs_dir)
def upload_to_hdfs(self, local_path, hdfs_path):
"""
将本地文件/目录上传到HDFS上
如果目录不存在, 会自动创建
:param local_path:
:param hdfs_path:
:return:
"""
self._client.upload(hdfs_path, local_path, cleanup=True)
def download_from_hdfs(self, hdfs_path, local_path):
"""
将HDFS上的文件/目录下载到本地
:param hdfs_path:
:param local_path:
:return:
"""
self._client.download(hdfs_path, local_path, overwrite=True)
def write_to_hdfs(self, hdfs_path, data, overwrite=False, append=True):
"""
追加: overwrite=false, append=true => Default
复写: overwrite=true, append=false
overwrite和append逻辑必须互斥
:param hdfs_path:
:param data:
:param overwrite: Boolean 是否复写
:param append: Boolean 是否追加
:return:
"""
if not self._client.content(hdfs_path, strict=False):
print('File Not exist in HDFS')
self._client.write(hdfs_path, data, overwrite=overwrite, append=append)
def move_or_rename(self, hdfs_src_path, hdfs_dst_path):
"""
文件移动/重命名
:param hdfs_src_path:
:param hdfs_dst_path:
:return:
"""
self._client.rename(hdfs_src_path, hdfs_dst_path)
def hdfs_dir_list(self, hdfs_path):
"""
获取指定目录下的文件
当hdfs_path不是目录, 捕获异常并返回None
:param hdfs_path:
:return: List[filename] or None
"""
try:
return self._client.list(hdfs_path, status=False)
except HdfsError:
return None
if __name__ == '__main__':
hdfs_ip = "192.168.146.133"
hdfs_version = 3
hdfs_root = "~/test"
filepath = r"C:\Users\daqige\PycharmProjects\newLeetCode\convert.py"
hdfs_addr = "http://" + hdfs_ip + ":" + str(9870 if
(hdfs_version == 3) else 90070)
client = Client(hdfs_addr)
# print("创建文件夹")
# client.makedirs(hdfs_root)
# print(client.list("/"))
#
# print("上传文件")
# client.upload(hdfs_root, filepath)
# print(client.list(hdfs_root))
#
# print("修改文件名")
# client.rename(hdfs_root + "/convert.py", hdfs_root + "/ubuntu.py")
# print(client.list(hdfs_root))
print("下载文件")
client.download(hdfs_root + "/ubuntu.py", ".")
print(os.listdir("."))
print("删除文件")
client.delete(hdfs_root + "/ubuntu.py")
print(client.list(hdfs_root))
def interface(train_path,
test_path,
output_path,
target,
chaid_ratio,
train_split_ratio=0.3,
n_estimators=100,
max_depth=5,
min_samples_split=3,
min_samples_leaf=2,
min_split_gain=0.0,
colsample_bytree="log2",
subsample=0.8,
random_state=100):
# 设置起始时间
time.localtime()
time_trains_start = time.strftime('%Y{y}%m{m}%d{d} %H{h}%M{f}%S{s}'.format(
y='/', m='/', d='', h=':', f=':', s=''))
start_time = time.time()
# 设置输入文件路径
train_FILENAME = train_path + "/data/Data.csv" # hdfs文件路径
test_FILENAME = test_path + "/data/Data.csv" # hdfs文件路径
client = Client(HDFS_HOSTS1)
# 训练数据读取
with client.read(train_FILENAME) as tr_s:
tr_content = tr_s.read()
tr_s = str(tr_content, 'utf-8')
# 确保文件写入完毕
tr_file = open("trainData.csv", "w")
tr_file.flush()
os.fsync(tr_file)
tr_file.write(tr_s)
tr_file.close()
df_train = pd.read_csv("trainData.csv", header=0)
print(df_train)
# 测试数据读取
with client.read(test_FILENAME) as te_fs:
te_content = te_fs.read()
te_s = str(te_content, 'utf-8')
# 确保文件写入完毕
te_file = open("testData.csv", "w")
te_file.flush()
os.fsync(te_file)
te_file.write(te_s)
te_file.close()
df_test = pd.read_csv("testData.csv", header=0)
print(df_test)
test_data_num = df_train.shape[0]
train_data_num = df_train.shape[0]
# 卡方检测选出和label列最相关的前chaid_ratio（默认值为前80%）的列
ch2 = SelectKBest(chi2, k=int(df_train.shape[1] * chaid_ratio))
chi_df_train = pd.DataFrame(ch2.fit_transform(df_train, df_train[target]))
label_df = df_train[target]
# wine数据集 和 sonar 数据集
clf = RandomForestClassifier(n_estimators, max_depth, min_samples_split,
min_samples_leaf, min_split_gain,
colsample_bytree, subsample, random_state)
# 数据集分割与训练
train_count = int(train_split_ratio * len(chi_df_train))
clf.fit(chi_df_train.ix[:train_count], label_df.ix[:train_count])
train_acc = metrics.accuracy_score(
label_df.ix[:train_count], clf.predict(chi_df_train.ix[:train_count]))
print("模型的准确率：", train_acc)
# 精确率
train_precision_score = metrics.precision_score(
label_df.ix[:train_count], clf.predict(chi_df_train.ix[:train_count]))
# 召回率
train_recall_score = metrics.recall_score(
label_df.ix[:train_count], clf.predict(chi_df_train.ix[:train_count]))
# F1_Score
train_f1_score = metrics.f1_score(
label_df.ix[:train_count], clf.predict(chi_df_train.ix[:train_count]))
# roc_auc_score
train_roc_auc_score1 = metrics.roc_auc_score(
label_df.ix[:train_count], clf.predict(chi_df_train.ix[:train_count]))
# 对测试集进行处理，保证其和训练集卡方检测后的列数一致
ch2_list = list(ch2.get_support())
ch2_list.pop()
df_test_head = list(df_test.columns)
for x, y in zip(ch2_list, df_test_head):
if x == False:
df_test_head.remove(y)
df_test = df_test[df_test_head]
# 预测
result = clf.predict(df_test)
# print(result)
# 设置终止时间，并计算总时间
train_end = time.time()
train_seconds = train_end - start_time
m, s = divmod(train_seconds, 60)
h, m = divmod(m, 60)
time_trains_all = "%02d:%02d:%02d" % (h, m, s)
# print(time_trains_start,time_trains_all)
# ++++++++++++++++++++++++++++++++++++++++训练结果保存+++++++++++++++++++++++++++++++++++++++#
## 保存摘要模型报告文件
abstract_path = output_path + '/abstract/data/'
f = open('abstract.csv', mode='w', newline='')
fileheader = [
'FrameWork', 'Version', 'model', 'accuracy', 'time_trains_start',
'time_trains_all', 'test_data_num', 'train_data_num'
]
w = csv.DictWriter(f, fileheader)
w.writeheader()
csv_dict = edict()
csv_dict.FrameWork = 'Scikit-learn'
csv_dict.Version = sklearn.__version__
csv_dict.model = '%s' % RandomForestClassifier
csv_dict.accuracy = str(train_acc)
csv_dict.time_trains_start = time_trains_start
csv_dict.time_trains_all = time_trains_all
csv_dict.test_data_num = str(test_data_num)
csv_dict.train_data_num = str(train_data_num)
w.writerow(csv_dict)
f.close()
client.delete(abstract_path + 'abstract.csv')
client.upload(abstract_path + 'abstract.csv', 'abstract.csv')
##保存模型版本信息csv文件
version_path = output_path + '/msg/data/'
f = open('msg.csv', mode='w', newline='')
fileheader = [
'accuracy', 'time_trains_start', 'time_trains_all', 'test_data_num',
'train_data_num'
]
w = csv.DictWriter(f, fileheader)
w.writeheader()
csv_dict = edict()
csv_dict.accuracy = str(train_acc)
csv_dict.time_trains_start = time_trains_start
csv_dict.time_trains_all = time_trains_all
csv_dict.test_data_num = str(test_data_num)
csv_dict.train_data_num = str(train_data_num)
w.writerow(csv_dict)
f.close()
client.delete(version_path + 'msg.csv')
client.upload(version_path + 'msg.csv', 'msg.csv')
## 保存训练评价指标模型报告文件
file_csv_path = output_path + '/evaluation/data/'
f = open('evaluation.csv', mode='w', newline='')
fileheader = [
'accuracy', 'train_precision_score', 'train_recall_score',
'train_f1_score', 'train_roc_auc_score1'
]
w = csv.DictWriter(f, fileheader)
w.writeheader()
csv_dict = edict()
csv_dict.accuracy = str(train_acc)
csv_dict.train_precision_score = train_precision_score
csv_dict.train_recall_score = train_recall_score
csv_dict.train_f1_score = train_f1_score
csv_dict.train_roc_auc_score1 = train_roc_auc_score1
w.writerow(csv_dict)
f.close()
client.delete(file_csv_path + 'evaluation.csv')
client.upload(file_csv_path + 'evaluation.csv', 'evaluation.csv')
# 保存测试集预测结果文件
file_csv_path = output_path + '/result/data/'
dataframe = pd.DataFrame({target: result})
dataframe.to_csv("result.csv", index=False, sep=',')
client.delete(file_csv_path + 'result.csv')
client.upload(file_csv_path + 'result.csv', 'result.csv')
# 写入文件(覆盖)
client.write(file_name, data="hello hdfs !", overwrite=True)
# 写入文件(追加)
client.write(file_name, data="hello way !", overwrite=False, append=True)
# 读取文件内容
with client.read(file_name, encoding='utf-8') as f:
print(f.read())
# 文件下载
client.download(file_name, loacl_file_name, overwrite=True)
# 文件上传
client.upload(file_name + '111', loacl_file_name, cleanup=True)
# 删除文件
client.delete(file_name2)
# 文件重命名
client.rename(file_name, file_name2)
# 文件夹底下文件
files = client.list(file_dir, status=False)
for file in files:
print(file)
# 删除文件夹(递归删除、谨慎)
# client.delete(file_dir, recursive=True)
class RF_HDFS(object):
def __init__(self):
self.client = None
self.directory = None
def connect_and_login(self, **kwargs):
import requests
host = None
port = None
user = None
password = None
root = None
timeout = None
proxy = None
if 'host' in kwargs:
host = kwargs['host']
if 'port' in kwargs:
port = kwargs['port']
if 'kdc' in kwargs:
kdc = kwargs['kdc']
if 'user' in kwargs:
user = kwargs['user']
if 'password' in kwargs:
password = kwargs['password']
if 'root' in kwargs:
root = kwargs['root']
if 'proxy' in kwargs:
proxy = kwargs['proxy']
if 'timeout' in kwargs:
timeout = kwargs['timeout']
self.session = requests.Session()
adapter = requests.adapters.HTTPAdapter(pool_maxsize=0)
self.session.mount('http://', adapter)
self.session.mount('https://', adapter)
self.session.headers.update({'Connection':'Keep-Alive'})
self.connectionStatus = False
try:
timeout = int(timeout)
url = "http://" + host + ":" + str(port)
hdfsLogin = WebHDFS(url, kdc)
cookieStr = hdfsLogin.authenticate(user, password)
if cookieStr != None:
cookieList = cookieStr.split('=', 1)
cookieDict = {cookieList[0]: cookieList[1]}
requests.utils.add_dict_to_cookiejar(self.session.cookies, cookieDict)
self.client = Client(url, root=root, proxy=proxy, timeout=timeout, session=self.session)
except HdfsError as hdfsError:
raise HdfsLibraryError(str(hdfsError))
except Exception as exception:
raise HdfsLibraryError(str(exception))
self.connectionStatus = True
return self.client
def checkConnectionStatus(self):
return self.connectionStatus
def list_dir(self, directory):
output = []
try:
if directory != None:
output = self.client.list(directory, status=True)
else:
output = self.client.list(self.client.root, status=True)
except HdfsError as hdfsError:
raise HdfsLibraryError(str(hdfsError))
except Exception as exception:
raise HdfsLibraryError(str(exception))
return output
def list_names(self, directory):
output = []
try:
if directory != None:
output = self.client.list(directory, status=False)
else:
output = self.client.list(self.client.root, status=False)
except HdfsError as hdfsError:
raise HdfsLibraryError(str(hdfsError))
except Exception as exception:
raise HdfsLibraryError(str(exception))
return output
def upload(self, remote_path, local_path, overwrite=False, permission=None):
output = None
try:
output = self.client.upload(remote_path, local_path, overwrite, permission=permission)
except HdfsError as hdfsError:
# For some reason this exception includes the entire stack trace after
# the error message, so split on '\n' and only return the first line.
error = str(hdfsError).splitlines()[0]
raise HdfsLibraryError(error)
except Exception as exception:
raise HdfsLibraryError(str(exception))
return output
def download(self, remote_path, local_path, overwrite=False):
output = None
try:
output = self.client.download(remote_path, local_path, overwrite)
except HdfsError as hdfsError:
raise HdfsLibraryError(str(hdfsError))
except Exception as exception:
raise HdfsLibraryError(str(exception))
return output
def mkdir(self, directory, permission):
try:
# no return value
self.client.makedirs(directory, permission=permission)
except HdfsError as hdfsError:
raise HdfsLibraryError(str(hdfsError))
except Exception as exception:
raise HdfsLibraryError(str(exception))
def rmdir(self, directory):
try:
# no return value
if self.client.delete(directory, recursive=True) == False:
raise HdfsLibraryError("Directory does not exist: %r", directory)
except HdfsError as hdfsError:
raise HdfsLibraryError(str(hdfsError))
except Exception as exception:
raise HdfsLibraryError(str(exception))
def rename(self, src_file, dst_file):
try:
# no return value
self.client.rename(src_file, dst_file)
except HdfsError as hdfsError:
raise HdfsLibraryError(str(hdfsError))
except Exception as exception:
raise HdfsLibraryError(str(exception))
def delete(self, file):
try:
# no return value
if self.client.delete(file) == False:
raise HdfsLibraryError("File does not exist: %r", file)
except HdfsError as hdfsError:
raise HdfsLibraryError(str(hdfsError))
except Exception as exception:
raise HdfsLibraryError(str(exception))
def set_time(self, file, mod_time):
try:
# no return value
self.client.set_times(file, -1, mod_time)
except HdfsError as hdfsError:
raise HdfsLibraryError(str(hdfsError))
except Exception as exception:
raise HdfsLibraryError(str(exception))
def set_owner(self, file, owner, group):
try:
# no return value
self.client.set_owner(file, owner=owner, group=group)
except HdfsError as hdfsError:
raise HdfsLibraryError(str(hdfsError))
except Exception as exception:
raise HdfsLibraryError(str(exception))
def set_permission(self, file, permission):
try:
# no return value
self.client.set_permission(file, permission=permission)
except HdfsError as hdfsError:
raise HdfsLibraryError(str(hdfsError))
except Exception as exception:
raise HdfsLibraryError(str(exception))
def set_acl(self, file, aclspec):
try:
# no return value
self.client.set_acl(file, aclspec=aclspec)
except HdfsError as hdfsError:
raise HdfsLibraryError(str(hdfsError))
except Exception as exception:
raise HdfsLibraryError(str(exception))
def status(self, path):
output = ''
try:
output = self.client.status(path)
except HdfsError as hdfsError:
raise HdfsLibraryError(str(hdfsError))
except Exception as exception:
raise HdfsLibraryError(str(exception))
return output
def checksum(self, path):
output = ''
try:
output = self.client.checksum(path)
except HdfsError as hdfsError:
raise HdfsLibraryError(str(hdfsError))
except Exception as exception:
raise HdfsLibraryError(str(exception))
return output
def close(self):
self.session.close()
