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-#!/usr/bin/env python
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-# -*- coding: utf-8 -*-
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-# time: 2023/5/8 13:15
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-# file: loss.py.py
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-# author: David
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-# company: shenyang JY
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-import tensorflow as tf
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-tf.compat.v1.set_random_seed(1234)
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-
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-
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-def rmse(y_true, y_pred):
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- return tf.sqrt(tf.reduce_mean(tf.square(y_pred - y_true)))
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-
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-class SouthLoss(tf.keras.losses.Loss):
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- def __init__(self, opt, name='south_loss'):
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- """
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- 南网新规则损失函数
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- :param cap:装机容量
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- """
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- super(SouthLoss, self).__init__(name=name)
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- self.cap = opt.cap*0.2 # 没有归一化cap,必须要先进行归一化
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- self.opt = opt
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- # self.cap01 = opt.cap*0.1
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-
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- def call(self, y_true, y_predict):
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- """
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- 自动调用
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- :param y_true: 标签
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- :param y_predict: 预测
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- :return: 损失值
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- """
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- # 计算实际和预测的差值
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- # y_true = y_true * self.opt.std['C_REAL_VALUE'] + self.opt.mean['C_REAL_VALUE']
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- # y_predict = y_predict * self.opt.std['C_REAL_VALUE'] + self.opt.mean['C_REAL_VALUE']
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- y_true = y_true[:, 15]
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- y_predict = y_predict[:, 15]
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- diff = y_true - y_predict
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- logistic_values = tf.sigmoid(10000 * (y_true - self.cap))
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- base = logistic_values * y_true + (1-logistic_values)*self.cap
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- loss = K.square(diff/base)
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- # loss = K.mean(loss, axis=-1)
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- return loss
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-
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- def call2(self, y_true, y_predict):
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- y_true = y_true * self.opt.std['C_REAL_VALUE'] + self.opt.mean['C_REAL_VALUE']
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- y_predict = y_predict * self.opt.std['C_REAL_VALUE'] + self.opt.mean['C_REAL_VALUE']
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- y_true = y_true[:, 15]
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- y_predict = y_predict[:, 15]
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- diff = y_true - y_predict
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- logistic_values = tf.sigmoid(10000 * (y_true - self.cap))
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- base = logistic_values * y_true + (1 - logistic_values) * self.cap
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- loss = K.square(diff / base)
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-
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- mask_logical = tf.logical_and(tf.greater(y_true, self.cap01), tf.greater(y_predict, self.cap01))
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- count = tf.reduce_sum(tf.cast(mask_logical, tf.float32), axis=-1)
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- safe_count = tf.maximum(count, 1)
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- # reduce_sum_loss = tf.reduce_sum(loss, axis=-1)
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- mean_loss = loss / safe_count
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- return mean_loss
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-
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- def call1(self, y_true, y_predict):
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- y_true = y_true * self.opt.std['C_REAL_VALUE'] + self.opt.mean['C_REAL_VALUE']
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- y_predict = y_predict * self.opt.std['C_REAL_VALUE'] + self.opt.mean['C_REAL_VALUE']
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- base = tf.where(y_true > self.cap, y_true, tf.ones_like(y_true)*self.cap)
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- loss = (y_true - y_predict) / base
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- squared_loss = tf.square(loss)
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- mean_squared_loss = tf.reduce_mean(squared_loss, axis=[1])
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- return mean_squared_loss
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-
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-
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-class NorthEastLoss(tf.keras.losses.Loss):
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- def __init__(self, opt, name='northeast_loss'):
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- """
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- 东北新规则超短期损失函数
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- """
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- super(NorthEastLoss, self).__init__(name=name)
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- self.opt = opt
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- self.cap = round(opt.cap*0.1, 2)
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-
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- def call(self, y_true, y_predict):
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- # 这里我们添加了一个小的 epsilon 值来避免除以 0
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- # y_true = y_true * self.opt.std['C_REAL_VALUE'] + self.opt.mean['C_REAL_VALUE']
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- # y_predict = y_predict * self.opt.std['C_REAL_VALUE'] + self.opt.mean['C_REAL_VALUE']
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-
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- mask_logical = tf.logical_and(tf.greater(y_true, self.cap), tf.greater(y_predict, self.cap))
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- # mask = tf.cast(~mask_logical, tf.float32)
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- # y_true = y_true * (1 - mask) + 0 * mask
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- # y_predict = y_predict * (1 - mask) + 0 * mask
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-
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-
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- epsilon = tf.keras.backend.epsilon()
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- y_predict_safe = y_predict + epsilon
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-
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- # 计算 (y_true - y_predict) / y_predict_safe
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- difference_over_predict = tf.abs(y_predict - y_true) / tf.abs(y_predict_safe)
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-
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- # 将结果中大于等于 1 的部分置为 1,剩下的保留原值
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- masked_difference = tf.where(difference_over_predict >= 1, tf.ones_like(difference_over_predict)*1, difference_over_predict) #tf.where的操作是逐元素的,并且它不会改变张量中元素的数学性质(如可微性、可导性)。
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-
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- # 这里我们先沿着特征维度求和,但你也可以选择平均(使用 tf.reduce_mean 而不是 tf.reduce_sum)
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- count = tf.reduce_sum(tf.cast(mask_logical, tf.float32), axis=-1)
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- sum_diff = tf.reduce_sum(masked_difference, axis=-1)
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- # mean_loss = tf.reduce_mean(masked_difference, axis=[1])
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- safe_count = tf.maximum(count, 1)
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- mean = sum_diff / safe_count
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- mean1 = tf.reduce_sum(masked_difference, axis=-1)
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- return mean
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-
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-
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-class NorthWestLoss(tf.keras.losses.Loss):
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- def __init__(self, name='northwest_loss'):
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- """
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- 东北新规则超短期损失函数
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- """
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- super(NorthWestLoss, self).__init__(name=name)
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-
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- def call(self, y_true, y_pred):
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- # 保证预测值和真实值是浮点数
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- y_pred = tf.cast(y_pred, tf.float32)
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- y_true = tf.cast(y_true, tf.float32)
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-
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- # 避免除零错误
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- epsilon = 1e-8
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- y_pred_adjusted = y_pred + epsilon
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- y_true_adjusted = y_true + epsilon
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-
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- # 计算 |Pr - Pn|
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- abs_diff = tf.abs(y_pred - y_true)
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-
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- # 计算 |Pr - Pn| 的总和
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- sum_abs_diff = tf.reduce_sum(abs_diff)
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-
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- # 计算每个差值的权重 |Pr - Pn| / sum(|Pr - Pn|)
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- weights = abs_diff / (sum_abs_diff + epsilon) # 添加 epsilon 避免除零
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-
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- # 计算 |Pr/(Pr + Pn) - 0.5|
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- ratios = tf.abs((y_pred_adjusted / (y_pred_adjusted + y_true_adjusted)) - 0.5)
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-
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- # 计算最终的损失值
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- loss = 1.0 - 2.0 * tf.reduce_sum(ratios * weights)
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- return loss
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