log_path.split('/')[-2]
时间: 2024-05-29 18:11:49 浏览: 8
This code will take the string value of log_path and split it using the forward slash (/) as the delimiter. It will then return the second to last element of the resulting list.
For example, if log_path is "/var/log/nginx/access.log", the code will split it into a list ['var', 'log', 'nginx', 'access.log'] and return the second to last element 'nginx'.
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介绍一下以下代码的逻辑 # data file path train_raw_path='./data/tianchi_fresh_comp_train_user.csv' train_file_path = './data/preprocessed_train_user.csv' item_file_path='./data/tianchi_fresh_comp_train_item.csv' #offline_train_file_path = './data/ccf_data_revised/ccf_offline_stage1_train.csv' #offline_test_file_path = './data/ccf_data_revised/ccf_offline_stage1_test_revised.csv' # split data path #active_user_offline_data_path = './data/data_split/active_user_offline_record.csv' #active_user_online_data_path = './data/data_split/active_user_online_record.csv' #offline_user_data_path = './data/data_split/offline_user_record.csv' #online_user_data_path = './data/data_split/online_user_record.csv' train_path = './data/data_split/train_data/' train_feature_data_path = train_path + 'features/' train_raw_data_path = train_path + 'raw_data.csv' #train_cleanedraw_data_path=train_path+'cleanedraw_data.csv' train_subraw_data_path=train_path+'subraw_data.csv' train_dataset_path = train_path + 'dataset.csv' train_subdataset_path=train_path+'subdataset.csv' train_raw_online_data_path = train_path + 'raw_online_data.csv' validate_path = './data/data_split/validate_data/' validate_feature_data_path = validate_path + 'features/' validate_raw_data_path = validate_path + 'raw_data.csv' #validate_cleaneraw_data_path=validate_path+'cleanedraw_data.csv' validate_dataset_path = validate_path + 'dataset.csv' validate_raw_online_data_path = validate_path + 'raw_online_data.csv' predict_path = './data/data_split/predict_data/' predict_feature_data_path = predict_path + 'features/' predict_raw_data_path = predict_path + 'raw_data.csv' predict_dataset_path = predict_path + 'dataset.csv' predict_raw_online_data_path = predict_path + 'raw_online_data.csv' # model path model_path = './data/model/model' model_file = '/model' model_dump_file = '/model_dump.txt' model_fmap_file = '/model.fmap' model_feature_importance_file = '/feature_importance.png' model_feature_importance_csv = '/feature_importance.csv' model_train_log = '/train.log' model_params = '/param.json' val_diff_file = '/val_diff.csv' # submission path submission_path = './data/submission/submission' submission_hist_file = '/hist.png' submission_file = '/tianchi_mobile_recommendation_predict.csv' # raw field name user_label = 'user_id' item_label = 'item_id' action_label = 'behavior_type' user_geohash_label='user_geohash' category_label='item_category' action_time_label='time' probability_consumed_label = 'Probability' # global values consume_time_limit = 15 train_feature_start_time = '20141119' train_feature_end_time = '20141217' train_dataset_time = '20141218' #train_dataset_end_time = '20141218' validate_feature_start_time = '20141118' validate_feature_end_time = '20141216' validate_dataset_time = '20141217' #validate_dataset_end_time = '20160514' predict_feature_start_time = '20141120' predict_feature_end_time = '20141218' predict_dataset_time = '20141219' #predict_dataset_end_time = '20160731'
这段代码主要是定义了一些文件路径和全局变量,方便后续数据处理和模型训练使用。
首先,代码定义了一些数据文件的路径,包括训练数据文件、商品数据文件等。这些路径可以根据实际情况进行修改。
接下来,代码定义了一些数据划分的路径,包括训练集、验证集和预测集的文件夹路径。这些路径用于存放数据划分后的特征数据、原始数据和数据集文件。
然后,代码定义了一些模型相关的路径,包括模型文件夹路径、模型文件名、模型输出文件等。这些路径用于存放训练好的模型以及相关的输出文件。
接着,代码定义了一些字段的名称,包括用户ID、商品ID、行为类型、用户地理哈希、商品类别、行为时间等。这些名称用于在数据处理和模型训练中标识相应的字段。
最后,代码定义了一些全局变量,包括消费时间限制、特征提取的起止时间、数据集时间等。这些变量用于在数据处理和特征提取过程中进行控制。
总体来说,这段代码主要是为了方便管理文件路径和全局变量,并提供了一些标识字段和控制变量,使得后续的数据处理和模型训练更加方便和可控。
帮我优化一下这段python代码 import datetime class error: def __init__(self, log_path): pass self.log_path = log_path self.port = port self.end_time = end_time self.start_time = start_time def network_error(self, log_path: str, port: str, end_time: float, start_time: float = 0): with open(log_path, 'r') as f: for line in f.readlines(): log_time_str = line.split()[0] # 就看第一个是不是时间,如何是是时间戳的话看下要不要转 datetime_object = datetime.datetime.strptime( log_time_str, '%Y-%m-%d %H:%M:%S') log_time = datetime_object.timestamp() if start_time <= log_time <= end_time: if port in line: return True def no_finf_element(self, log_path: str, end_time: float, start_time: float = 0): with open(log_path, 'r') as f: for line in f.readlines(): log_time_str = line.split()[0] # 就看第一个是不是时间,如何是是时间戳的话看下要不要转 datetime_object = datetime.datetime.strptime( log_time_str, '%Y-%m-%d %H:%M:%S') log_time = datetime_object.timestamp() if start_time <= log_time <= end_time: if 'no such element' in line: return True def error_judge(self): if network_error(self.log_path, self.port, self.end_time, self.start_time) == True and no_finf_elemen(self.log_path, self.end_time, self.start_time) == True: pass # 不执行重跑 else: pass # 执行重跑
以下是对代码的优化建议:
1. class error 中的 __init__ 方法中有一个无用的 pass,可以删除。
2. class error 中的 network_error 和 no_finf_element 方法中有相同的代码,可以将重复代码提取出来,写成一个私有方法,然后在这两个方法中调用。
3. network_error 和 no_finf_element 方法中的循环读取文件的方式不够高效,可以使用 readline() 方法替换 readlines() 方法,每次只读取一行,这样可以减少内存占用。
4. network_error 和 no_finf_element 方法中可以使用 with open(log_path, 'r') as f: 的方式打开文件,这样可以自动关闭文件,避免忘记关闭导致的资源泄漏。
5. 在 error_judge 方法中,if network_error(...) == True 可以简写成 if network_error(...):
6. 在 error_judge 方法中,if no_finf_elemen(...) == True 应该是 if no_finf_element(...) == True:
下面是优化后的代码:
```python
import datetime
class error:
def __init__(self, log_path, port, end_time, start_time=0):
self.log_path = log_path
self.port = port
self.end_time = end_time
self.start_time = start_time
def _check_error(self, log_path, end_time, start_time, error_str):
with open(log_path, 'r') as f:
line = f.readline()
while line:
log_time_str = line.split()[0]
datetime_object = datetime.datetime.strptime(log_time_str, '%Y-%m-%d %H:%M:%S')
log_time = datetime_object.timestamp()
if start_time <= log_time <= end_time:
if error_str in line:
return True
line = f.readline()
return False
def network_error(self):
return self._check_error(self.log_path, self.end_time, self.start_time, self.port)
def no_finf_element(self):
return self._check_error(self.log_path, self.end_time, self.start_time, 'no such element')
def error_judge(self):
if self.network_error() and self.no_finf_element():
pass # 不执行重跑
else:
pass # 执行重跑
```