优化代码 if self.length == 0: logger.logger.warning("After cluster data clean, no effective data!") sys.exit()
时间: 2023-12-19 22:03:54 浏览: 32
可以优化为以下代码:
```
if self.length == 0:
logger.logger.warning("After cluster data clean, no effective data!")
sys.exit()
```
主要是将 `if` 语句和代码块的缩进保持一致,并且在每个关键字和符号之间加上空格,以提高代码的可读性。此外,可以考虑将 `sys.exit()` 改为抛出异常,以便在需要处理异常的情况下更加灵活。例如:
```
if self.length == 0:
logger.logger.warning("After cluster data clean, no effective data!")
raise ValueError("No effective data after cluster data clean.")
```
相关问题
优化代码 def cluster_format(self, start_time, end_time, save_on=True, data_clean=False, data_name=None): """ local format function is to format data from beihang. :param start_time: :param end_time: :return: """ # 户用簇级数据清洗 if data_clean: unused_index_col = [i for i in self.df.columns if 'Unnamed' in i] self.df.drop(columns=unused_index_col, inplace=True) self.df.drop_duplicates(inplace=True, ignore_index=True) self.df.reset_index(drop=True, inplace=True) dupli_header_lines = np.where(self.df['sendtime'] == 'sendtime')[0] self.df.drop(index=dupli_header_lines, inplace=True) self.df = self.df.apply(pd.to_numeric, errors='ignore') self.df['sendtime'] = pd.to_datetime(self.df['sendtime']) self.df.sort_values(by='sendtime', inplace=True, ignore_index=True) self.df.to_csv(data_name, index=False) # 调用基本格式化处理 self.df = super().format(start_time, end_time) module_number_register = np.unique(self.df['bat_module_num']) # if registered m_num is 0 and not changed, there is no module data if not np.any(module_number_register): logger.logger.warning("No module data!") sys.exit() if 'bat_module_voltage_00' in self.df.columns: volt_ref = 'bat_module_voltage_00' elif 'bat_module_voltage_01' in self.df.columns: volt_ref = 'bat_module_voltage_01' elif 'bat_module_voltage_02' in self.df.columns: volt_ref = 'bat_module_voltage_02' else: logger.logger.warning("No module data!") sys.exit() self.df.dropna(axis=0, subset=[volt_ref], inplace=True) self.df.reset_index(drop=True, inplace=True) self.headers = list(self.df.columns) # time duration of a cluster self.length = len(self.df) if self.length == 0: logger.logger.warning("After cluster data clean, no effective data!") raise ValueError("No effective data after cluster data clean.") self.cluster_stats(save_on) for m in range(self.mod_num): print(self.clusterid, self.mod_num) self.module_list.append(np.unique(self.df[f'bat_module_sn_{str(m).zfill(2)}'].dropna())[0])
Here are some possible optimizations for the given code:
1. Instead of using a list comprehension to find columns with 'Unnamed' in their names, you can use the `filter()` function along with a lambda function to achieve the same result in a more concise way:
```
unused_index_col = list(filter(lambda x: 'Unnamed' in x, self.df.columns))
```
2. Instead of dropping duplicates and resetting the index separately, you can use the `drop_duplicates()` function with the `ignore_index` parameter set to `True` to achieve both in one step:
```
self.df.drop_duplicates(inplace=True, ignore_index=True)
```
3. Instead of using `sys.exit()` to terminate the program when there is no module data, you can raise a `ValueError` with an appropriate error message:
```
raise ValueError("No module data!")
```
4. Instead of using a series of `if` statements to find the voltage reference column, you can use the `loc` accessor with a boolean mask to select the first column that starts with 'bat_module_voltage':
```
volt_ref_col = self.df.columns[self.df.columns.str.startswith('bat_module_voltage')][0]
```
5. Instead of using a loop to append a single item to a list, you can use the `append()` method directly:
```
self.module_list.append(np.unique(self.df[f'bat_module_sn_{str(m).zfill(2)}'].dropna())[0])
```
By applying these optimizations, the code can become more concise and efficient.
解释代码 def __init__(self, type='train', model='BILSTM'): self.config = Config() self.saver = None self.util = Util() self.loader = Loader() self.model_type = model self.logger = self.util.get_logger(self.config.log_file) self.model = cnn_model(self.config) if self.model_type == 'IDCNN' else BiLSTM_model(self.config) self.ckpt_path = self.config.cnn_ckpt_path if self.model_type == 'IDCNN' else self.config.lstm_ckpt_path if type == 'train': self.train()
这段代码定义了一个名为`__init__`的初始化函数,该函数接受两个参数:`type`和`model`,并且返回一个对象。
在函数内部,代码首先调用了一个名为`Config`的类,该类用于设置模型的各种配置参数。然后创建了一个`Saver`对象,该对象用于保存训练过程中的模型参数。接着创建了一个名为`Util`的工具类对象,该对象用于提供一些常用的工具函数。
接下来,代码定义了一个名为`Loader`的类,该类用于加载训练数据。然后,根据`model`参数的值,选择使用`cnn_model`函数或者`BiLSTM_model`函数来创建模型。这两个函数分别创建了一个基于CNN的模型和一个基于双向LSTM的模型。
接着,代码根据`model_type`参数的值,选择使用`cnn_ckpt_path`或者`lstm_ckpt_path`作为模型参数的保存路径。
最后,如果`type`参数的值为`train`,则调用`train()`函数进行模型的训练。这个函数没有在代码中给出,但可以猜测它会使用之前定义的模型和数据加载器来训练模型,并且在训练过程中使用`Saver`对象保存模型参数。