dalian-university/ipfcst-forecast-wind-LSTM-v2.0-dq+rp/data_features.py

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# time: 2023/4/12 17:42
# file: data_features.py
# author: David
# company: shenyang JY
import pandas as pd
from sklearn.model_selection import train_test_split
import numpy as np
from data_utils import *


class data_features(object):
    def __init__(self, opt, mean, std):
        self.opt = opt
        self.time_step = self.opt.Model["time_step"]
        self.mean = mean
        self.std = std
        self.columns = list()

    def get_train_data(self, dfs):
        train_x, valid_x, train_y, valid_y = [], [], [], []
        for df in dfs:
            datax, datay = self.get_data_features(df)
            trainx = np.array(datax)
            trainy = [y['C_REAL_VALUE'].values for y in datay]
            trainy = np.expand_dims(np.array(trainy), axis=-1)  # 在最后一维加一维度
            tx, vx, ty, vy = train_test_split(trainx, trainy, test_size=self.opt.valid_data_rate,
                                                                  random_state=self.opt.Model["random_seed"],
                                                                  shuffle=self.opt.shuffle_train_data)  # 划分训练和验证集
            train_x.append(tx)
            valid_x.append(vx)
            train_y.append(ty)
            valid_y.append(vy)

        train_x = np.concatenate(train_x, axis=0)
        valid_x = np.concatenate(valid_x, axis=0)
        train_y = np.concatenate(train_y, axis=0)
        valid_y = np.concatenate(valid_y, axis=0)

        train_x = self.norm_features(train_x)
        valid_x = self.norm_features(valid_x)
        train_y = self.norm_label(train_y)
        valid_y = self.norm_label(valid_y)

        return train_x, valid_x, train_y, valid_y

    def get_test_data(self, dfs):
        test_x, test_y, data_y = [], [], []
        for df in dfs:
            datax, datay = self.get_data_features(df)
            trainx = np.array(datax)
            trainy = [y['C_REAL_VALUE'].values for y in datay]
            trainy = np.expand_dims(np.array(trainy), axis=-1)  # 在最后一维加一维度
            test_x.append(trainx)
            test_y.append(trainy)
            data_y.append(datay)

        test_x = np.concatenate(test_x, axis=0)
        test_y = np.concatenate(test_y, axis=0)

        test_x = self.norm_features(test_x)
        test_y = self.norm_label(test_y)

        return test_x, test_y, data_y

    def get_data_features(self, df):
        norm_data = df.reset_index()
        feature_data = norm_data[:-self.opt.predict_points]
        label_data = norm_data[self.opt.predict_points:].reset_index(drop=True)
        time_step = self.opt.Model["time_step"]
        time_step_loc = time_step - 1
        train_num = int(len(feature_data))
        time_rp = [feature_data.loc[i:i + time_step_loc, ['C_TIME', 'C_REAL_VALUE']] for i in range(train_num - time_step)]
        dq = [label_data.loc[i:i + time_step_loc, 'C_FP_VALUE'] for i in range(train_num - time_step)]
        features_x, features_y = [], []
        for row in zip(time_rp, dq):
            row0 = row[0]
            row1 = row[1]
            row0['C_FP_VALUE'] = row1
            row0.set_index('C_TIME', inplace=True, drop=False)
            row0["C_TIME"] = row0["C_TIME"].apply(datetime_to_timestr)
            features_x.append(row0)
        self.columns = row0.columns.tolist()

        features_y = [label_data.loc[i:i + time_step_loc, ['C_TIME', 'C_REAL_VALUE', 'C_FP_VALUE']] for i in range(train_num - time_step)]
        return features_x, features_y

    def norm_features(self, data: np.ndarray):
        mean = np.array([self.mean[col] for col in self.columns])
        std = np.array([self.std[col] for col in self.columns])
        data = (data - mean) / std  # 归一化
        return data

    def norm_label(self, label_data: np.ndarray):
        return (label_data - self.mean['C_REAL_VALUE']) / self.std['C_REAL_VALUE']