algorithm_platform/data_processing/data_operation/data_handler.py

#!/usr/bin/env python
# -*- coding:utf-8 -*-
# @FileName  :data_handler.py
# @Time      :2025/1/8 14:56
# @Author    :David
# @Company: shenyang JY
import argparse, numbers, joblib
import pandas as pd
from io import BytesIO
from bson.decimal128 import Decimal128
from sklearn.preprocessing import MinMaxScaler
from common.processing_data_common import missing_features, str_to_list
from common.data_cleaning import *

class DataHandler(object):
    def __init__(self, logger, args):
        self.logger = logger
        self.opt = argparse.Namespace(**args)

    def get_train_data(self, dfs, col_time, features, target):
        train_x, valid_x, train_y, valid_y = [], [], [], []
        for i, df in enumerate(dfs, start=1):
            if len(df) < self.opt.Model["time_step"]:
                self.logger.info("特征处理-训练数据-不满足time_step")
            datax, datay = self.get_timestep_features(df, col_time, features, target, is_train=True)
            if len(datax) < 10:
                self.logger.info("特征处理-训练数据-无法进行最小分割")
                continue
            tx, vx, ty, vy = self.train_valid_split(datax, datay, valid_rate=self.opt.Model["valid_data_rate"], shuffle=self.opt.Model['shuffle_train_data'])
            train_x.extend(tx)
            valid_x.extend(vx)
            train_y.extend(ty)
            valid_y.extend(vy)

        train_y = np.concatenate([[y.iloc[:, 1].values for y in train_y]], axis=0)
        valid_y = np.concatenate([[y.iloc[:, 1].values for y in valid_y]], axis=0)

        train_x = np.array([x.values for x in train_x])
        valid_x = np.array([x.values for x in valid_x])

        return train_x, valid_x, train_y, valid_y

    def get_predict_data(self, dfs, features):
        test_x = []
        for i, df in enumerate(dfs, start=1):
            if len(df) < self.opt.Model["time_step"]:
                self.logger.info("特征处理-预测数据-不满足time_step")
                continue
            datax = self.get_predict_features(df, features)
            test_x.extend(datax)
        test_x = np.array(test_x)
        return test_x

    def get_predict_features(self, norm_data, features):
        """
        均分数据，获取预测数据集
        """
        time_step = self.opt.Model["time_step"]
        feature_data = norm_data.reset_index(drop=True)
        time_step_loc = time_step - 1
        iters = int(len(feature_data)) // self.opt.Model['time_step']
        features_x = np.array([feature_data.loc[i*time_step:i*time_step + time_step_loc, features].reset_index(drop=True) for i in range(iters)])
        return features_x

    def get_timestep_features(self, norm_data, col_time, features, target, is_train):
        """
        步长分割数据，获取时序训练集
        """
        time_step = self.opt.Model["time_step"]
        feature_data = norm_data.reset_index(drop=True)
        time_step_loc = time_step - 1
        train_num = int(len(feature_data))
        label_features = [col_time, target] if is_train is True else [col_time, target]
        nwp_cs = features
        nwp = [feature_data.loc[i:i + time_step_loc, nwp_cs].reset_index(drop=True) for i in range(train_num - time_step + 1)]  # 数据库字段 'C_T': 'C_WS170'
        labels = [feature_data.loc[i:i + time_step_loc, label_features].reset_index(drop=True) for i in range(train_num - time_step + 1)]
        features_x, features_y = [], []
        for i, row in enumerate(zip(nwp, labels)):
            features_x.append(row[0])
            features_y.append(row[1])
        return features_x, features_y

    def fill_train_data(self, unite, col_time):
        """
        补值
        """
        unite[col_time] = pd.to_datetime(unite[col_time])
        unite['time_diff'] = unite[col_time].diff()
        dt_short = pd.Timedelta(minutes=15)
        dt_long = pd.Timedelta(minutes=15 * self.opt.Model['how_long_fill'])
        data_train = self.missing_time_splite(unite, dt_short, dt_long, col_time)
        miss_points = unite[(unite['time_diff'] > dt_short) & (unite['time_diff'] < dt_long)]
        miss_number = miss_points['time_diff'].dt.total_seconds().sum(axis=0) / (15 * 60) - len(miss_points)
        self.logger.info("再次测算，需要插值的总点数为：{}".format(miss_number))
        if miss_number > 0 and self.opt.Model["train_data_fill"]:
            data_train = self.data_fill(data_train, col_time)
        return data_train

    def missing_time_splite(self, df, dt_short, dt_long, col_time):
        df.reset_index(drop=True, inplace=True)
        n_long, n_short, n_points = 0, 0, 0
        start_index = 0
        dfs = []
        for i in range(1, len(df)):
            if df['time_diff'][i] >= dt_long:
                df_long = df.iloc[start_index:i, :-1]
                dfs.append(df_long)
                start_index = i
                n_long += 1
            if df['time_diff'][i] > dt_short:
                self.logger.info(f"{df[col_time][i-1]} ~ {df[col_time][i]}")
                points = df['time_diff'].dt.total_seconds()[i]/(60*15)-1
                self.logger.info("缺失点数：{}".format(points))
                if df['time_diff'][i] < dt_long:
                    n_short += 1
                    n_points += points
                    self.logger.info("需要补值的点数：{}".format(points))
        dfs.append(df.iloc[start_index:, :-1])
        self.logger.info(f"数据总数：{len(df)}, 时序缺失的间隔：{n_short}, 其中，较长的时间间隔：{n_long}")
        self.logger.info("需要补值的总点数：{}".format(n_points))
        return dfs

    def data_fill(self, dfs, col_time, test=False):
        dfs_fill, inserts = [], 0
        for i, df in enumerate(dfs):
            df = rm_duplicated(df, self.logger)
            df1 = df.set_index(col_time, inplace=False)
            dff = df1.resample('15T').interpolate(method='linear')  # 采用线性补值，其他补值方法需要进一步对比
            dff.reset_index(inplace=True)
            points = len(dff) - len(df1)
            dfs_fill.append(dff)
            self.logger.info("{} ~ {} 有 {} 个点, 填补 {} 个点.".format(dff.iloc[0, 0], dff.iloc[-1, 0], len(dff), points))
            inserts += points
        name = "预测数据" if test is True else "训练集"
        self.logger.info("{}分成了{}段，实际一共补值{}点".format(name, len(dfs_fill), inserts))
        return dfs_fill

    def train_valid_split(self, datax, datay, valid_rate, shuffle):
        shuffle_index = np.random.permutation(len(datax))
        indexs = shuffle_index.tolist() if shuffle else np.arange(0, len(datax)).tolist()
        valid_size = int(len(datax) * valid_rate)
        valid_index = indexs[-valid_size:]
        train_index = indexs[:-valid_size]
        tx, vx, ty, vy = [], [], [], []
        for i, data in enumerate(zip(datax, datay)):
            if i in train_index:
                tx.append(data[0])
                ty.append(data[1])
            elif i in valid_index:
                vx.append(data[0])
                vy.append(data[1])
        return tx, vx, ty, vy

    def train_data_handler(self, data, opt):
        """
        训练数据预处理：
        清洗+补值+归一化
        Args:
            data: 从mongo中加载的数据
            opt：参数命名空间
        return:
            x_train
            x_valid
            y_train
            y_valid
        """
        col_time, features, target = opt.col_time, opt.features, opt.target
        # 清洗处理好的限电记录
        if 'is_limit' in data.columns:
            data = data[data['is_limit'] == False]
        # 筛选特征，数值化
        train_data = data[[col_time] + features + [target]]
        # 清洗特征平均缺失率大于20%的天
        # train_data = missing_features(train_data, features, col_time)
        train_data = train_data.sort_values(by=col_time)
        # train_data = train_data.sort_values(by=col_time).fillna(method='ffill').fillna(method='bfill')
        # 对清洗完限电的数据进行特征预处理：1.空值异常值清洗 2.缺值补值
        train_data_cleaned = key_field_row_cleaning(train_data, features + [target], self.logger)
        train_data_cleaned = train_data_cleaned.applymap(
            lambda x: float(x.to_decimal()) if isinstance(x, Decimal128) else float(x) if isinstance(x, numbers.Number) else x)
        # 创建特征和目标的标准化器
        train_scaler = MinMaxScaler(feature_range=(0, 1))
        target_scaler = MinMaxScaler(feature_range=(0, 1))
        # 标准化特征和目标
        scaled_train_data = train_scaler.fit_transform(train_data_cleaned[features])
        scaled_target = target_scaler.fit_transform(train_data_cleaned[[target]])
        train_data_cleaned[features] = scaled_train_data
        train_data_cleaned[[target]] = scaled_target
        train_datas = self.fill_train_data(train_data_cleaned, col_time)
        # 保存两个scaler
        scaled_train_bytes = BytesIO()
        scaled_target_bytes = BytesIO()

        joblib.dump(train_scaler, scaled_train_bytes)
        joblib.dump(target_scaler, scaled_target_bytes)
        scaled_train_bytes.seek(0)  # Reset pointer to the beginning of the byte stream
        scaled_target_bytes.seek(0)

        train_x, valid_x, train_y, valid_y = self.get_train_data(train_datas, col_time, features, target)
        return train_x, valid_x, train_y, valid_y, scaled_train_bytes, scaled_target_bytes

    def pre_data_handler(self, data, feature_scaler, opt):
        """
        预测数据简单处理
        Args:
            data: 从mongo中加载的数据
            opt：参数命名空间
        return:
            scaled_features: 反归一化的特征
        """
        if 'is_limit' in data.columns:
            data = data[data['is_limit'] == False]
        # features, time_steps, col_time, model_name, col_reserve = str_to_list(args['features']), int(
        #     args['time_steps']), args['col_time'], args['model_name'], str_to_list(args['col_reserve'])
        col_time, features = opt.col_time, opt.features
        pre_data = data.sort_values(by=col_time)[features]
        scaled_features = feature_scaler.transform(pre_data[features])
        pre_data[features] = scaled_features
        pre_x = self.get_predict_data([pre_data], features)
        return pre_x