self.columns = [[] for _ in range(8)]

这是一行Python代码，它创建了一个包含8个空列表的列表，并将其赋值给对象的`columns`属性。具体来说，这个代码会执行以下操作：

1. 创建一个包含8个元素的列表，每个元素都是一个空列表。
2. 使用列表推导式将这8个元素组成的列表赋值给对象的`columns`属性。

这样做的目的可能是为了在后续的代码中使用这个`columns`属性来存储某些数据，每个空列表都代表一个列。例如，可以将数据按列存储，而不是按行存储，以便在需要时更方便地操作列数据。

相关问题

class KnnRegressorCV: def __init__(self, ks=list(range(1, 21)), cv=LFold(5)): self.ks = ks self.cv = cv # YOUR CODE HERE def fit(self, x, y): df = pd.DataFrame(columns=["KValue", "LFoldID", "MSE_train", "MSE_test"]) count = 0 for k in self.ks: for index, each in enumerate(self.cv.split(x, y)): mse_train, mse_test = runknn(k, each[0], each[1], each[2].reshape(-1), each[3].reshape(-1)) df.at[count, "KValue"] = k df.at[count, "LFoldID"] = index df.at[count, "MSE_train"] = mse_train df.at[count, "MSE_test"] = mse_test count += 1 self.bestK = find_best_k(df, self.ks) self.y_train_ = y self.x_train_kdtree_ = KDTree(x) print("bestK internal:", self.bestK) return self # YOUR CODE HERE def predict(self, x): _, neighbours = self.x_train_kdtree_.query(x, k=self.bestK) neighbours = neighbours.reshape(len(x), self.k) neighbour_labels = self.y_train_[neighbours] pred = np.mean(neighbour_labels, axis=1) return pred给这段代码加注释

# 导入需要的库
import pandas as pd
from sklearn.neighbors import KDTree

# 定义 KNN 回归交叉验证类
class KnnRegressorCV:     
    def __init__(self, ks=list(range(1, 21)), cv=LFold(5)): # 初始化方法，参数包括 k 值列表和交叉验证方法
        self.ks = ks         # k 值列表
        self.cv = cv         # 交叉验证方法
         
    def fit(self, x, y):    # 模型训练方法，参数包括自变量和因变量
        # 定义空的数据框用于存储模型评估结果
        df = pd.DataFrame(columns=["KValue", "LFoldID", "MSE_train", "MSE_test"]) 
        count = 0  # 定义计数器
        for k in self.ks:  # 遍历 k 值列表
            for index, each in enumerate(self.cv.split(x, y)): # 遍历交叉验证的训练集和测试集
                # 调用 runknn 函数进行模型评估，返回训练集和测试集的 MSE
                mse_train, mse_test = runknn(k, each[0], each[1], each[2].reshape(-1), each[3].reshape(-1)) 
                # 将评估结果存入数据框
                df.at[count, "KValue"] = k 
                df.at[count, "LFoldID"] = index 
                df.at[count, "MSE_train"] = mse_train 
                df.at[count, "MSE_test"] = mse_test 
                count += 1     # 计数器加 1
        # 调用 find_best_k 函数找到 MSE 最小的 k 值
        self.bestK = find_best_k(df, self.ks) 
        self.y_train_ = y    # 存储因变量
        self.x_train_kdtree_ = KDTree(x)  # 存储自变量的 KDTree
        print("bestK internal:", self.bestK) # 打印最优的 k 值
        return self      # 返回模型自身
     
    def predict(self, x):   # 预测方法，参数为自变量
        _, neighbours = self.x_train_kdtree_.query(x, k=self.bestK) # 找到最近的 k 个邻居
        neighbours = neighbours.reshape(len(x), self.k)  # 将邻居的索引转为二维数组
        neighbour_labels = self.y_train_[neighbours]  # 找到邻居的因变量值
        pred = np.mean(neighbour_labels, axis=1)  # 对邻居因变量值求平均，作为预测值
        return pred      # 返回预测值

优化代码 def module_split(self, save_on=True): """ split module data :param save_on: :return: """ for ms in range(self.mod_num): m_sn = self.module_list[ms] module_path = os.path.join(self.result_path_down, m_sn) cols_obj = ChuNengPackMustCols(ms, self.mod_cell_num, self.mod_cell_num) # 传入当前的module序号（如0,1,2,3,4），电芯电压个数，温度NTC个数。 aim_cols = [i for i in cols_obj.total_cols if i in self.df.columns] print(m_sn, aim_cols) self.modules[m_sn] = rename_cols_normal(self.df.loc[:, aim_cols], ms, self.mod_cell_num) print("after change cols name:", ms, m_sn, self.modules[m_sn].columns.tolist()) self.modules[m_sn].dropna(axis=0, how='any', subset=['soc'], inplace=True) volt_col = [f'volt{i}' for i in range(self.mod_cell_num)] temp_col = [f'temp{i}' for i in range(self.mod_cell_num)] self.modules[m_sn].dropna(axis=0, how='any', subset=volt_col, inplace=True) self.modules[m_sn] = stat(self.modules[m_sn], volt_col, temp_col) self.modules[m_sn].reset_index(drop=True, inplace=True) print(self.modules[m_sn]['discharge_ah'].iloc[-1]) self.module_cap[m_sn] = [self.modules[m_sn]['discharge_ah'].iloc[-1], self.modules[m_sn]['charge_ah'].iloc[-1], self.modules[m_sn]['soh'].iloc[-1]] self.module_peaks[m_sn] = list(quick_report(self.modules[m_sn], module_path, f'quick_report_{m_sn[:8]}')) # check soc status mod_soc = self.modules[m_sn]['soc'] self.module_soc_sig[m_sn] = [np.nanmedian(mod_soc), np.max(mod_soc), np.min(mod_soc)] if save_on: single_variables_plot(mod_soc, module_path, f'{m_sn[:8]}_soc_distribution_box.png', 'box', 'SOC') single_variables_plot(mod_soc, module_path, f'{m_sn[:8]}_soc_distribution_violin.png', 'violin', 'SOC')

以下是代码优化后的版本：

def module_split(self, save_on=True):
    """
    Split module data
    :param save_on:
    :return:
    """
    for ms in range(self.mod_num):
        m_sn = self.module_list[ms]
        module_path = os.path.join(self.result_path_down, m_sn)
        cols_obj = ChuNengPackMustCols(ms, self.mod_cell_num, self.mod_cell_num)
        aim_cols = [i for i in cols_obj.total_cols if i in self.df.columns]
        print(m_sn, aim_cols)
        self.modules[m_sn] = rename_cols_normal(self.df.loc[:, aim_cols], ms, self.mod_cell_num)
        print("after change cols name:", ms, m_sn, self.modules[m_sn].columns.tolist())
        
        # Drop rows with NaN values in 'soc' and voltage columns
        volt_col = [f'volt{i}' for i in range(self.mod_cell_num)]
        temp_col = [f'temp{i}' for i in range(self.mod_cell_num)]
        self.modules[m_sn].dropna(axis=0, how='any', subset=['soc'] + volt_col, inplace=True)
        
        # Compute statistics on remaining data
        self.modules[m_sn] = stat(self.modules[m_sn], volt_col, temp_col)
        self.modules[m_sn].reset_index(drop=True, inplace=True)
        
        print(self.modules[m_sn]['discharge_ah'].iloc[-1])
        self.module_cap[m_sn] = [
            self.modules[m_sn]['discharge_ah'].iloc[-1],
            self.modules[m_sn]['charge_ah'].iloc[-1],
            self.modules[m_sn]['soh'].iloc[-1]
        ]
        self.module_peaks[m_sn] = list(quick_report(self.modules[m_sn], module_path, f'quick_report_{m_sn[:8]}'))
        
        # Compute SOC statistics and save plots
        mod_soc = self.modules[m_sn]['soc']
        self.module_soc_sig[m_sn] = [
            np.nanmedian(mod_soc),
            np.max(mod_soc),
            np.min(mod_soc)
        ]
        if save_on:
            for plot_type in ['box', 'violin']:
                single_variables_plot(mod_soc, module_path, f'{m_sn[:8]}_soc_distribution_{plot_type}.png', plot_type, 'SOC')

优化后的代码主要做了以下几个方面的改进：

1. 将所有行删除操作合并为一步，同时删除了温度列中的 NaN 值；
2. 统计数据前，只保留了保留了 'soc' 和电压列的非 NaN 行；
3. 在保存 SOC 分布图像时，避免了重复的代码。