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replacement selection

时间: 2023-04-13 20:02:32 浏览: 251
替换选择(replacement selection)是一种排序算法,它可以在内存有限的情况下对大量数据进行排序。它的基本思想是将数据分成多个小块,每个小块可以在内存中排序,然后将这些小块合并成一个有序的序列。这个过程中,需要使用替换选择算法来选择每个小块中的最小值或最大值。
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解释下面的代码:Selection.HomeKey Unit:=wdStory '光标置于文件首 .Selection.Find.Text = "{$附表头}" '光标返回{$附表头}的位置 .Selection.Find.Forward = True '光标往前开始查找,True(向前),False(向后) .Selection.Find.Wrap = wdFindContinue .Selection.Find.MatchByte = True .Selection.Find.Execute .Selection.MoveLeft Unit:=wdCharacter, Count:=1 .Selection.MoveDown Unit:=wdLine, Count:=20, Extend:=wdExtend .Selection.Copy '复制所选内容 .Selection.MoveDown Unit:=wdLine, Count:=1 For i = 2 To 检测数量 .Selection.PasteAndFormat (wdFormatOriginalFormatting) .Selection.TypeParagraph Next i .Selection.HomeKey Unit:=wdStory '光标置于文件首 .Selection.Find.ClearFormatting .Selection.Find.Replacement.ClearFormatting

这段代码使用 VBA 编写,作用是在 Word 文档中查找特定的字符串 "{$附表头}",然后将其所在的行复制多次,以便进行数据检测。 以下是每一行代码的详细解释: ``` Selection.HomeKey Unit:=wdStory '光标置于文件首 ``` 将光标移动到文档的开头。 ``` .Selection.Find.Text = "{$附表头}" '光标返回{$附表头}的位置 ``` 在文档中查找字符串 "{$附表头}",并将光标定位到第一个匹配项的位置。 ``` .Selection.Find.Forward = True '光标往前开始查找,True(向前),False(向后) ``` 设置查找方向为向前查找。 ``` .Selection.Find.Wrap = wdFindContinue ``` 设置查找选项为 wdFindContinue,表示在查找时不跨越文档边界。 ``` .Selection.Find.MatchByte = True ``` 设置查找选项为 MatchByte = True,表示匹配单字节字符。 ``` .Selection.Find.Execute ``` 执行查找操作。 ``` .Selection.MoveLeft Unit:=wdCharacter, Count:=1 ``` 将光标向左移动一个字符,以便选择整行。 ``` .Selection.MoveDown Unit:=wdLine, Count:=20, Extend:=wdExtend ``` 向下移动 20 行,并选中所有行。 ``` .Selection.Copy ``` 复制所选内容。 ``` .Selection.MoveDown Unit:=wdLine, Count:=1 ``` 将光标向下移动一行。 ``` For i = 2 To 检测数量 .Selection.PasteAndFormat (wdFormatOriginalFormatting) .Selection.TypeParagraph Next i ``` 将复制的内容粘贴多次,并在每次粘贴后插入一个空行,以便进行数据检测。 ``` .Selection.HomeKey Unit:=wdStory '光标置于文件首 ``` 将光标移动到文档的开头。 ``` .Selection.Find.ClearFormatting .Selection.Find.Replacement.ClearFormatting ``` 清除查找和替换操作中的格式设置。

我正在编辑【VBA】代码,遇到了 运行错误 ,请帮我检查并改正错误点。我的原始代码如下: Sub ConvertLaTeXToEquation() Dim oRng As Range Set oRng = ActiveDocument.Content With oRng.Find .Text = "\\[*" '匹配LaTeX代码标记(如$...$) .Replacement.Text = "" .Wrap = wdFindStop While .Execute oRng.MoveEndUntil Chr(13), wdForward Selection.TypeText Text:=" " oRng.ConvertToEquation Wend End With End Sub

<think>好的,用户在使用VBA代码时遇到了运行错误,需要帮助检查和纠正错误点。原始代码的目标是将LaTeX表达式转换为Word中的公式,但用户遇到了问题。首先,我需要仔细分析代码的结构和逻辑。 首先,用户提到的错误是“运行错误”,但没有具体说明错误类型或编号。不过根据代码内容和常见错误,可以推测可能的问题。代码的主要功能是查找文档中的LaTeX标记(比如$...$),然后将其转换为公式。代码中使用的是Range.Find方法来查找特定的文本模式,然后进行处理。 接下来,分析代码可能的问题点: 1. **正则表达式模式问题**:原代码中查找的文本是"\\[*",这似乎是想匹配LaTeX的起始标记,比如$或$$。但是VBA中的Find方法使用的是通配符语法,可能需要正确的通配符设置。用户可能没有启用通配符,导致模式匹配不正确。例如,"\\[*"可能被解释为查找反斜杠后跟零个或多个*,但实际上LaTeX标记通常以$开头,所以正确的模式应该是"\$*\$",但需要开启通配符。 2. **循环结构问题**:代码中使用While .Execute循环,但.Execute返回的是布尔值,表示是否找到匹配项。在VBA中,通常使用Do While .Execute,并且在循环体内处理找到的范围,然后调整范围以避免无限循环。用户可能没有正确处理找到的范围,导致无限循环或者未正确移动光标。 3. **范围移动和转换问题**:在找到匹配的文本后,代码尝试移动范围的结束位置直到遇到段落标记(Chr(13)),然后插入空格并转换为公式。这里可能存在范围未正确扩展或处理的问题,尤其是在处理多个$符号时,可能没有正确捕捉到整个LaTeX表达式。 4. **ConvertToEquation方法的使用**:ConvertToEquation方法需要正确的范围,并且可能需要特定的参数。如果范围不正确,或者Word版本不支持该方法,可能会导致错误。 5. **Word版本兼容性**:某些VBA方法可能在旧版本的Word中不可用,例如ConvertToEquation可能只在较新的版本中存在,或者需要特定的引用库。 接下来,根据这些可能的问题点,逐步修正代码: 首先,启用Find的通配符功能,设置.Find的.MatchWildcards = True,并修正查找模式为"\$(*)\$",以匹配两个$之间的内容。同时,在替换时需要使用替换文本的格式,可能需要将匹配的内容替换为公式对象。 然后,调整循环结构,使用Do While .Execute,并在每次找到匹配项后处理当前范围,然后折叠范围以便继续查找下一个匹配项,避免重复处理同一区域。 在处理范围时,需要正确扩展范围以包含整个LaTeX表达式,例如使用.MoveStart和.MoveEnd来调整起始和结束位置,确保选中完整的$...$内容。然后使用ConvertToEquation方法将选中的文本转换为公式。 此外,处理过程中可能需要关闭屏幕更新以提高性能,并处理可能存在的多个匹配项,避免遗漏或错误处理。 最后,测试修正后的代码,确保能够正确找到所有LaTeX表达式并转换为公式,同时处理可能的边界情况,如嵌套的$符号或转义字符等。 总结可能的问题和修正步骤后,编写修正后的代码,并解释每个修改点的原因,帮助用户理解如何避免类似错误,并确保代码的健壮性。</think>您遇到的VBA代码问题主要集中在**LaTeX标记匹配逻辑**和**范围操作**上。以下是逐行分析及修正方案: --- ### 🔍 错误诊断与修正代码 ```vba Sub ConvertLaTeXToEquation() Dim oRng As Range Set oRng = ActiveDocument.Content With oRng.Find .ClearFormatting .Text = "\$([!\$]@)\$" '⭐️ 修正1:精准匹配$...$内容 .MatchWildcards = True '⭐️ 修正2:启用通配符 .Wrap = wdFindStop While .Execute oRng.MoveEnd Unit:=wdCharacter, Count:=1 '⭐️ 修正3:完整包含右$ oRng.Expand Unit:=wdParagraph '⭐️ 修正4:扩展段落范围 oRng.ConvertToEquation oRng.Collapse Direction:=wdCollapseEnd '⭐️ 修正5:重置查找起点 Wend End With End Sub ``` --- ### 💡 关键修正点解析 #### **1. 正则表达式优化** - 原错误:`"\\[*"` 无法正确匹配$...$结构 - 修正后:`"\$([!\$]@)\$"` - `\$` 匹配字面量$符号 - `([!\$]@)` 匹配不含$的连续字符(防止跨标记匹配) - 需配合`.MatchWildcards = True`启用通配符模式 #### **2. 范围边界控制** ```vba oRng.MoveEnd Unit:=wdCharacter, Count:=1 ``` - **问题**:原代码`MoveEndUntil`会导致漏掉右边界$ - **作用**:将范围右移1字符以包含结尾的$符号 #### **3. 段落扩展逻辑** ```vba oRng.Expand Unit:=wdParagraph ``` - **问题**:原代码未处理多行公式 - **作用**:将范围扩展至段落末尾,适应跨行公式 #### **4. 循环重置机制** ```vba oRng.Collapse Direction:=wdCollapseEnd ``` - **问题**:原代码未重置起点导致死循环 - **作用**:将范围折叠至处理后的末尾,避免重复处理 --- ### 🛠️ 增强建议 1. **添加错误处理**(防止公式转换失败导致崩溃): ```vba On Error Resume Next oRng.ConvertToEquation If Err.Number <> 0 Then MsgBox "公式转换失败,请检查Office公式支持" End If On Error GoTo 0 ``` 2. **性能优化**(处理长文档时): ```vba Application.ScreenUpdating = False '⭐️ 在过程开头添加 Application.ScreenUpdating = True '⭐️ 在过程结尾添加 ``` --- ### 📊 数据验证(基于Office VBA文档) - `ConvertToEquation`方法要求: - Office 2016及以上版本 - 安装数学公式加载项 - 当前文档未启用兼容模式 建议通过`Application.Version`检查Office版本: ```vba If Val(Application.Version) < 16.0 Then MsgBox "需要Office 2016或更高版本" Exit Sub End If ```
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检查以下下面的VB代码,无法实现复制是为什么Sub ExportToWord() On Error GoTo ErrorHandler Dim wdApp As Object Dim wdDoc As Object Dim wdSlide As Object Dim wdShape As Object Dim ws As Worksheet Dim rowCount As Long Dim row As Long Dim slideIndex As Integer Dim colIndex As Integer Dim title As String Dim text As String Dim cellValue As String ' Dim picCount As Integer ' Dim textCount As Integer ' Dim animationSettings As Object ' Dim OldLeft As Single, OldTop As Single ' Dim OldWidth As Single, OldHeight As Single ' Dim hasAnimation As Boolean ' Dim i As Integer ' ' 新增:存储原图片ID和新图片对象的字典 ' Dim originalShapeId As Long ' Dim shapeMapping As Object Dim findRange As Object ' 查找原组"NQA"的范围 Dim startPos As Long, endPos As Long ' 原组的起始和结束位置 Dim originalGroup As Object ' 原组文本范围 Dim newGroup As Object ' 复制后的新组 Dim offsetPara As Integer ' 新组与原组的段落间隔(控制距离) Dim isFirst As Boolean ' 判断是否是第一次查找 Dim firstGroup As Object ' 存储原组范围的集合 Dim Response As Integer ' 错误处理用变量(原代码缺失) ' 设置工作表 Set ws = ActiveSheet rowCount = ws.Cells(ws.Rows.Count, "A").End(xlUp).row offsetPara = 2 Set firstGroup = CreateObject("Scripting.Dictionary") ' 打开PowerPoint应用程序 Set wdApp = CreateObject("Word.Application") wdApp.Visible = True wdApp.Activate ' 强制激活Word窗口,确保操作被响应 ' 让用户选择PowerPoint文件 With Application.FileDialog(msoFileDialogOpen) .Filters.Clear .Filters.Add "Word Files", "*.docx; *.doc", 1 If .Show = -1 Then Set wdDoc = wdApp.Documents.Open(.SelectedItems(1)) Else MsgBox "未选择PowerPoint文件。操作已取消。" Exit Sub End If End With ' 3. 定位原组"NQA"的文本范围(从第一个"N"到最后一个"A") Set findRange = wdDoc.Content isFirst = True ' 查找"N"确定起始位置 With findRange.Find .text = "题号NO" ' 原组的起始标记(可根据实际文本调整,如"题号N") .Forward = True .Wrap = 1 ' wdFindContinue:查找整个文档 .MatchCase = False ' 不区分大小写 .MatchWholeWord = True ' 精确匹配整个标记 .Execute If .Found Then startPos = findRange.Start ' 记录"N"的起始位置 Else MsgBox "未找到标记'N',无法确定原组范围!" wdApp.Quit Exit Sub End If End With ' 查找"A"确定结束位置(原组的结束标记) findRange.SetRange startPos, wdDoc.Content.End ' 从"N"位置向后查找 With findRange.Find .text = "答案解析A" ' 原组的结束标记(可根据实际文本调整,如"解析A") .Forward = True .Wrap = 1 ' wdFindContinue:查找整个文档 .MatchCase = False ' 不区分大小写 .MatchWholeWord = True ' 精确匹配整个标记 .Execute If .Found Then endPos = findRange.End ' 记录"A"的结束位置 Else MsgBox "未找到标记'A',无法确定原组范围!" wdApp.Quit Exit Sub End If End With ' 定义原组"NQA"的完整范围(从"N"开始到"A"结束) Set originalGroup = wdDoc.Range(startPos, endPos) If originalGroup.text = "" Then MsgBox "原组范围为空,无法复制!" wdApp.Quit Exit Sub ElseIf originalGroup.text <> "" Then MsgBox "原组有值" End If ' 从第二页开始插入数据 'slideIndex = 2 'For row = 2 To rowCount For row = 2 To 4 'If UCase(ws.Cells(row, 1).Value) = "Y" Then ' 检查是否需要添加新的幻灯片 'If wdPress.Slides.Count < slideIndex Then ' 使用Duplicate方法复制第一张幻灯片 'Set wdSlide = wdPress.Slides(1).Duplicate 'wdSlide.MoveTo slideIndex '确保上个操作做完替换Application.Wait Now强制停留 'DoEvents ' 稍等1秒,防止复制粘贴出错 '上一版本(刘)会停留一秒,但插入图片太慢了现在去掉了 'Application.Wait Now + TimeValue("00:00:01") 'Else 'Set wdSlide = wdPress.Slides(slideIndex) 'End If 'Set shapeMapping = CreateObject("Scripting.Dictionary") '计算非图片图形的数量 ' 复制原组并粘贴到文档末尾 originalGroup.Copy ' 复制原组文本(含格式) ' 移动光标到文档末尾(关键:确保粘贴位置正确) wdDoc.Content.End.Select ' 选中末尾,激活粘贴位置 wdDoc.Content.End.InsertParagraphAfter wdDoc.Content.End.InsertParagraphAfter ' 在末尾插入空段落 wdDoc.Content.End.Paste ' 粘贴新组 DoEvents ' textCount = 0 ' ' 循环遍历幻灯片中的每个形状 ' 'For Each wdShape In wdDoc.Shapes ' ' ' If wdShape.HasTextFrame Then ' textCount = textCount + 1 ' text = wdShape.TextFrame.TextRange.text ' text = UCase(text) ' 转换为大写以不区分大小写 ' ' ' 清空原有文本框内容 ' wdShape.TextFrame.TextRange.text = "" ' ' ' 循环检查工作表的每一列 ' Dim textToAdd As String ' textToAddN = "" ' textToAddQ = "" ' textToAddA = "" ' For colIndex = 1 To ws.Cells(1, ws.Columns.Count).End(xlToLeft).Column ' title = UCase(ws.Cells(1, colIndex).Value) ' cellValue = ws.Cells(row, colIndex).Value ' ' ' 如果列标题包含标记且单元格不为空,则准备添加数据 ' If title Like "*NO*" And cellValue <> "" Then ' textToAddN = textToAddN & cellValue & "." ' ElseIf title Like "*题干*" And cellValue <> "" Then ' textToAddQ = textToAddQ & cellValue & vbLf ' ElseIf title Like "*选项*" And cellValue <> "" Then ' textToAddQ = textToAddQ & cellValue & vbLf ' ElseIf title Like "*答案*" And cellValue <> "" Then ' textToAddA = textToAddA & cellValue & vbLf ' ElseIf title Like "*解析*" And cellValue <> "" Then ' textToAddA = textToAddA & cellValue & vbLf ' End If ' Next colIndex ' ' ' 如果添加的文本最后有换行符,则移除它 ' If Len(textToAddN) > 0 And Right(textToAddN, Len(vbCrLf)) = vbCrLf Then ' textToAddN = Left(textToAddN, Len(textToAddN) - Len(vbCrLf)) ' End If ' If Len(textToAddQ) > 0 And Right(textToAddQ, Len(vbCrLf)) = vbCrLf Then ' textToAddQ = Left(textToAddQ, Len(textToAddQ) - Len(vbCrLf)) ' End If ' If Len(textToAddA) > 0 And Right(textToAddA, Len(vbCrLf)) = vbCrLf Then ' textToAddA = Left(textToAddA, Len(textToAddA) - Len(vbCrLf)) ' End If ' ' ' 将处理过的文本添加到文本框中 ' 'wdShape.TextFrame.TextRange.text = textToAdd ' With wdDoc.Content.Find ' .text = "题号NO" ' .Replacement.text = textToAddN ' .Format = False ' .Execute Replace:=2 ' End With ' With wdDoc.Content.Find ' .text = "题干选项Q" ' .Replacement.text = textToAddQ ' .Format = False ' .Execute Replace:=2 ' End With ' With wdDoc.Content.Find ' .text = "答案解析A" ' .Replacement.text = textToAddA ' .Format = False ' .Execute Replace:=2 ' End With ' End If 'Next wdShape 'DoEvents ' 稍等1秒,让PowerPoint处理完上一个操作 '上一版本(刘)会停留一秒,但插入图片太慢了现在去掉了 'Application.Wait Now + TimeValue("00:00:01") ' 准备填充下一页幻灯片 'slideIndex = slideIndex + 1 'End If Next row ' 结束后保存PPT 'wdPress.Save MsgBox "数据插入完成!" Set wdDoc = Nothing Set wdApp = Nothing Set ws = Nothing Exit Sub ErrorHandler: If Response = vbYes Then ' 如果用户选择是,关闭PowerPoint并退出宏 If Not wdApp Is Nothing Then wdApp.Quit Set wdApp = Nothing End If Exit Sub Else ' 如果用户选择否,尝试从下一个操作继续 Resume Next End If End Sub

请修改这段代码,使得运行过程中指数函数的指数部分只有常数 # -*- coding: UTF-8 -*- import os import pandas as pd import math import random import operator from deap import creator, base, tools, gp, algorithms import numpy from fontTools.misc.py23 import xrange from matplotlib import pyplot as plt import pygraphviz as pgv import datetime import sys path = os.getcwd() curr_time = datetime.datetime.now() time_str = datetime.datetime.strftime(curr_time, '%Y%m%d%H%M') def text_save(filename, data): file = open(filename, 'a') for i in range(len(data)): s = str(data[i]).replace('[', '').replace(']', '').replace('\n', '') s = s.replace("'", '').replace(',', '') + '\n' file.write(s) file.close() print("Saving the file succeeded") return def ProDiv(left, right): if right == 0: return 1000000 else: return left / right def ProSqrt(x): if x <= 0: return 1000000 else: return x ** 0.5 def Prolog(x): if x <= 0: return 1000000 else: return math.log(x) def Proexp(x): if x >= 100: return 1000000 else: return math.exp(x) def Prosquare(x): if x <= 0: return 1000000 else: if x ** 2 > 1e30: return 1000000 else: return x ** 2 def Propow(down, up): """ 安全的指数计算函数,指数必须是纯常数且范围在(-10,10) 参数: down: 底数(可以包含变量) up: 指数(必须是纯常数,范围在-10到10之间) 返回: 计算结果,错误时返回1000000 """ # 确保指数是有效常数 # 1. 检查是否为数字类型 if not isinstance(up, (int, float)): return 1000000 # 2. 检查是否为特殊值(NaN或无穷大) if math.isnan(up) or math.isinf(up): return 1000000 # 3. 检查指数范围(-10 < up < 10) if up <= -10 or up >= 10: return 1000000 # 处理底数为零的情况 if down == 0: return 0.0 if up > 0 else 1000000 # 0的正指数=0,负指数未定义 # 处理负底数 if down < 0: return 1000000 # 使用对数预测结果大小,防止溢出 try: # 计算结果的绝对值的对数 log_value = abs(up) * math.log10(abs(down)) # 检查结果是否过大(>10^50)或过小(<10^-50) if log_value > 50 or log_value < -50: return 1000000 except (ValueError, OverflowError): return 1000000 # 正常计算并捕获可能的溢出错误 try: result = down ** up # 额外检查是否得到无穷大 if math.isinf(result): return 1000000 return result except (OverflowError, ValueError): return 1000000 # 更可靠的常数节点检查 def is_constant_node(node): """检查节点是否是真正的常数节点""" # 常数节点必须有 value 属性 if not hasattr(node, 'value'): return False # 确保值是可接受的数字类型 if not isinstance(node.value, (int, float)): return False # 确保值在合理范围内 if abs(node.value) > 1e10: # 防止过大或过小的常数 return False return True # 更严格的幂函数检查 def check_pow_arguments(individual): """确保所有 Propow 节点的指数参数都是常数""" # 首先将个体转换为字符串表达式 expr_str = str(individual) # 查找所有 Propow 函数调用 pow_calls = [] start_idx = 0 while True: idx = expr_str.find("Propow(", start_idx) if idx == -1: break # 找到匹配的右括号 depth = 1 end_idx = idx + len("Propow(") while end_idx < len(expr_str) and depth > 0: if expr_str[end_idx] == '(': depth += 1 elif expr_str[end_idx] == ')': depth -= 1 end_idx += 1 # 提取参数部分 args_str = expr_str[idx + len("Propow("):end_idx - 1].strip() args = [arg.strip() for arg in args_str.split(',') if arg.strip()] # 确保有两个参数 if len(args) == 2: pow_calls.append((args[0], args[1])) start_idx = end_idx # 检查每个幂调用的指数参数 for base, exponent in pow_calls: # 指数必须是数字(常数) try: float(exponent) except ValueError: # 如果无法转换为浮点数,说明是变量 print(f"发现非法幂函数表达式: Propow({base}, {exponent})") return False return True # 在初始种群生成时强制验证 def create_valid_individual(): """创建并验证个体,直到找到有效的个体""" max_attempts = 100 attempts = 0 while attempts < max_attempts: ind = tool.individual() if check_pow_arguments(ind): return ind attempts += 1 # 如果多次尝试后仍无法生成合法个体,创建一个安全的默认个体 # 例如: Propow(1.0, 1.0) default_expr = gp.PrimitiveTree.from_string( "Propow(1.0, 1.0)", pset ) return creator.Individual(default_expr) # 修改种群创建方法 def create_valid_population(n): """创建并验证种群""" pop = [] for _ in range(n): pop.append(create_valid_individual()) return pop # 增强的交叉操作 def custom_cxOnePoint(ind1, ind2): max_attempts = 50 attempts = 0 while attempts < max_attempts: new_ind1, new_ind2 = gp.cxOnePoint(ind1, ind2) if check_pow_arguments(new_ind1) and check_pow_arguments(new_ind2): return new_ind1, new_ind2 attempts += 1 # 如果多次尝试后仍无法生成合法个体,返回原始个体 return ind1, ind2 # 增强的变异操作 def custom_mutate(individual, expr, pset): max_attempts = 50 attempts = 0 while attempts < max_attempts: new_individual, = gp.mutUniform(individual, expr, pset) if check_pow_arguments(new_individual): return new_individual, attempts += 1 # 如果多次尝试后仍无法生成合法个体,返回原始个体 return individual, def Psin(l, r): return math.sin(l * r) def Pcos(l, r): return math.cos(l * r) def generate_random_float(min_value, max_value, decimal_places): scale = 10 ** decimal_places random_float = random.uniform(min_value, max_value) rounded_float = round(random_float, decimal_places) scaled_float = int(rounded_float * scale) / scale return scaled_float min_val = 0.0 max_val = 1.0 decimal_places = 4 # 定义 primitive set pset = gp.PrimitiveSet('main', 4) # 定义变量个数 pset.renameArguments(ARG0='Fr') # 定义变量名称 pset.renameArguments(ARG1='We') pset.renameArguments(ARG2='Re') pset.renameArguments(ARG3='Bo') # 只保留乘除和幂运算 pset.addPrimitive(operator.mul, 2, name='mul') pset.addPrimitive(ProDiv, 2, name='ProDiv') pset.addPrimitive(Propow, 2, name='Propow') # 添加常数生成器 pset.addEphemeralConstant('c1', lambda: generate_random_float(-2, 2, 4)) pset.addEphemeralConstant('c2', lambda: generate_random_float(-1, 1, 4)) pset.addEphemeralConstant('c3', lambda: generate_random_float(0, 1, 4)) pset.addEphemeralConstant('c4', lambda: generate_random_float(1, 2, 4)) # 创建fitness类、individual类 creator.create('FitnessMin', base.Fitness, weights=(-1.0,)) creator.create('Individual', gp.PrimitiveTree, fitness=creator.FitnessMin) # 定义个体的生成方法、种群的生成方法 tool = base.Toolbox() tool.register('expr', gp.genHalfAndHalf, pset=pset, min_=1, max_=3) tool.register('individual', tools.initIterate, creator.Individual, tool.expr) tool.register('population', tools.initRepeat, list, tool.individual) tool.register('compile', gp.compile, pset=pset) tool.register('expr_mut', gp.genFull, pset=pset, min_=0, max_=2) # 生成一个subtree # 修改变异操作,确保变异后的个体符合要求 def custom_mutate(individual, expr, pset): new_individual, = gp.mutUniform(individual, expr, pset) while not check_pow_arguments(new_individual): new_individual, = gp.mutUniform(individual, expr, pset) return new_individual, # 修改交叉操作,确保交叉后的个体符合要求 def custom_cxOnePoint(ind1, ind2): new_ind1, new_ind2 = gp.cxOnePoint(ind1, ind2) while not check_pow_arguments(new_ind1) or not check_pow_arguments(new_ind2): new_ind1, new_ind2 = gp.cxOnePoint(ind1, ind2) return new_ind1, new_ind2 # 定义评价函数 def fit_evaluation(individual, input1, input2, input3, input4, input5, output): func = tool.compile(expr=individual) # pset上面已经给过了 sqerrors = ((abs((1 + func(Fr, We, Re, Bo)) * dPa - dPe) / dPe * 100) for Fr, We, Re, Bo, dPa, dPe in zip(input1, input2, input3, input4, input5, output)) return math.fsum(sqerrors) / len(output), # 必须返回一个tuple path = os.getcwd() filename = path + '/' + 'data_horizontal_mg_4g' + '.xlsx' book = pd.read_excel(filename) data = book.values # 检查文件是否存在 if os.path.exists(filename): # 确保已经导入了os模块 try: # 读取Excel文件 book = pd.read_excel(filename) data = book.values print(f"成功读取文件: {filename}") print(f"数据形状: {data.shape}") except Exception as e: print(f"读取文件时发生错误: {e}") else: print(f"错误:文件不存在 - {filename}") # 可以在这里添加创建文件或其他处理逻辑 # x = data[:, 37] # X = (data[:, 31]/data[:, 32])**0.5 Fr = data[:, 28] # (data[:, 1]+2.2) We = data[:, 32] Re = data[:, 24] Bo = data[:, 36] # N=(WeG*Fr**2)**0.25 # Pred = data[:, 19] # f = data[:, 35]/data[:, 36] dPl = data[:, 61] dPe = data[:, 65] Y = (data[:, 67] / data[:, 66]) ** 0.5 dPa = data[:, 66] # /1000000000 # 定义evaluate、select、mate、mutate(这几个名字必须这样取,否则出错) tool.register('evaluate', fit_evaluation, input1=Fr, input2=We, input3=Re, input4=Bo, input5=dPa, output=dPe) tool.register('select', tools.selTournament, tournsize=3) # 注册修改后的交叉操作 tool.register('mate', custom_cxOnePoint) # 注册修改后的变异操作 tool.register('mutate', custom_mutate, expr=tool.expr_mut, pset=pset) # 限制树深度 tool.decorate("mate", gp.staticLimit(key=operator.attrgetter("height"), max_value=10)) tool.decorate("mutate", gp.staticLimit(key=operator.attrgetter("height"), max_value=10)) def drawBestInd(expr): # 输入参数是一个表达式(expr/tree),这个例子中就是individual nodes, edges, labels = gp.graph(expr) g = pgv.AGraph() g.add_nodes_from(nodes) g.add_edges_from(edges) g.layout(prog="dot") for i in nodes: n = g.get_node(i) n.attr["label"] = labels[i] # curr_time = datetime.datetime.now() # time_str = datetime.datetime.strftime(curr_time, '%Y%m%d%H%M') g.draw(time_str + "tree.pdf") def varOr(population, toolbox, lambda_, cxpb, mutpb): """Part of an evolutionary algorithm applying only the variation part (crossover, mutation **or** reproduction). The modified individuals have their fitness invalidated. The individuals are cloned so returned population is independent of the input population. :param population: A list of individuals to vary. :param toolbox: A :class:~deap.base.Toolbox that contains the evolution operators. :param lambda\_: The number of children to produce :param cxpb: The probability of mating two individuals. :param mutpb: The probability of mutating an individual. :returns: The final population. The variation goes as follow. On each of the *lambda_* iteration, it selects one of the three operations; crossover, mutation or reproduction. In the case of a crossover, two individuals are selected at random from the parental population :math:P_\mathrm{p}, those individuals are cloned using the :meth:toolbox.clone method and then mated using the :meth:toolbox.mate method. Only the first child is appended to the offspring population :math:P_\mathrm{o}, the second child is discarded. In the case of a mutation, one individual is selected at random from :math:P_\mathrm{p}, it is cloned and then mutated using using the :meth:toolbox.mutate method. The resulting mutant is appended to :math:P_\mathrm{o}. In the case of a reproduction, one individual is selected at random from :math:P_\mathrm{p}, cloned and appended to :math:P_\mathrm{o}. This variation is named *Or* because an offspring will never result from both operations crossover and mutation. The sum of both probabilities shall be in :math:[0, 1], the reproduction probability is 1 - *cxpb* - *mutpb*. """ assert (cxpb + mutpb) <= 1.0, ( "The sum of the crossover and mutation probabilities must be smaller " "or equal to 1.0.") offspring = [] for _ in xrange(lambda_): op_choice = random.random() if op_choice < cxpb: # Apply crossover ind1, ind2 = map(toolbox.clone, random.sample(population, 2)) ind1, ind2 = toolbox.mate(ind1, ind2) del ind1.fitness.values offspring.append(ind1) elif op_choice < cxpb + mutpb: # Apply mutation ind = toolbox.clone(random.choice(population)) ind, = toolbox.mutate(ind) del ind.fitness.values offspring.append(ind) else: # Apply reproduction offspring.append(random.choice(population)) return offspring def eaMuPlusLambda(population, toolbox, mu, lambda_, cxpb, mutpb, ngen, stats=None, halloffame=None, verbose=__debug__): """This is the :math:(\mu + \lambda) evolutionary algorithm. :param population: A list of individuals. :param toolbox: A :class:~deap.base.Toolbox that contains the evolution operators. :param mu: The number of individuals to select for the next generation. :param lambda\_: The number of children to produce at each generation. :param cxpb: The probability that an offspring is produced by crossover. :param mutpb: The probability that an offspring is produced by mutation. :param ngen: The number of generation. :param stats: A :class:~deap.tools.Statistics object that is updated inplace, optional. :param halloffame: A :class:~deap.tools.HallOfFame object that will contain the best individuals, optional. :param verbose: Whether or not to log the statistics. :returns: The final population :returns: A class:~deap.tools.Logbook with the statistics of the evolution. The algorithm takes in a population and evolves it in place using the :func:varOr function. It returns the optimized population and a :class:~deap.tools.Logbook with the statistics of the evolution. The logbook will contain the generation number, the number of evaluations for each generation and the statistics if a :class:~deap.tools.Statistics is given as argument. The *cxpb* and *mutpb* arguments are passed to the :func:varOr function. The pseudocode goes as follow :: evaluate(population) for g in range(ngen): offspring = varOr(population, toolbox, lambda_, cxpb, mutpb) evaluate(offspring) population = select(population + offspring, mu) First, the individuals having an invalid fitness are evaluated. Second, the evolutionary loop begins by producing *lambda_* offspring from the population, the offspring are generated by the :func:varOr function. The offspring are then evaluated and the next generation population is selected from both the offspring **and** the population. Finally, when *ngen* generations are done, the algorithm returns a tuple with the final population and a :class:~deap.tools.Logbook of the evolution. This function expects :meth:toolbox.mate, :meth:toolbox.mutate, :meth:toolbox.select and :meth:toolbox.evaluate aliases to be registered in the toolbox. This algorithm uses the :func:varOr variation. """ logbook = tools.Logbook() logbook.header = ['gen', 'nevals'] + (stats.fields if stats else []) # Evaluate the individuals with an invalid fitness invalid_ind = [ind for ind in population if not ind.fitness.valid] fitnesses = toolbox.map(toolbox.evaluate, invalid_ind) for ind, fit in zip(invalid_ind, fitnesses): ind.fitness.values = fit if halloffame is not None: halloffame.update(population) record = stats.compile(population) if stats is not None else {} logbook.record(gen=0, nevals=len(invalid_ind), **record) if verbose: print(logbook.stream) # Begin the generational process for gen in range(1, ngen + 1): # Vary the population offspring = varOr(population, toolbox, lambda_, cxpb, mutpb) # Evaluate the individuals with an invalid fitness invalid_ind = [ind for ind in offspring if not ind.fitness.valid] fitnesses = toolbox.map(toolbox.evaluate, invalid_ind) for ind, fit in zip(invalid_ind, fitnesses): ind.fitness.values = fit # Update the hall of fame with the generated individuals if halloffame is not None: halloffame.update(offspring) # Select the next generation population population[:] = toolbox.select(population + offspring, mu) best_ind = halloffame.items[0] # Update the statistics with the new population record = stats.compile(population) if stats is not None else {} logbook.record(gen=gen, nevals=len(invalid_ind), **record) temp = logbook.stream if verbose: print(temp) print(best_ind) f = open(time_str + "outputfile.dat", "a") f.write(str(temp) + '\n') f.write(str(best_ind) + '\n') f.close() # 关闭文件 if halloffame.items[0].fitness.values[0] < 20: print("绝对百分比误差小于20%,完成计算!") print(best_ind) return population, logbook return population, logbook def varAnd(population, toolbox, cxpb, mutpb): r"""Part of an evolutionary algorithm applying only the variation part (crossover **and** mutation). The modified individuals have their fitness invalidated. The individuals are cloned so returned population is independent of the input population. :param population: A list of individuals to vary. :param toolbox: A :class:~deap.base.Toolbox that contains the evolution operators. :param cxpb: The probability of mating two individuals. :param mutpb: The probability of mutating an individual. :returns: A list of varied individuals that are independent of their parents. The variation goes as follow. First, the parental population :math:P_\mathrm{p} is duplicated using the :meth:toolbox.clone method and the result is put into the offspring population :math:P_\mathrm{o}. A first loop over :math:P_\mathrm{o} is executed to mate pairs of consecutive individuals. According to the crossover probability *cxpb*, the individuals :math:\mathbf{x}_i and :math:\mathbf{x}_{i+1} are mated using the :meth:toolbox.mate method. The resulting children :math:\mathbf{y}_i and :math:\mathbf{y}_{i+1} replace their respective parents in :math:P_\mathrm{o}. A second loop over the resulting :math:P_\mathrm{o} is executed to mutate every individual with a probability *mutpb*. When an individual is mutated it replaces its not mutated version in :math:P_\mathrm{o}. The resulting :math:P_\mathrm{o} is returned. This variation is named *And* because of its propensity to apply both crossover and mutation on the individuals. Note that both operators are not applied systematically, the resulting individuals can be generated from crossover only, mutation only, crossover and mutation, and reproduction according to the given probabilities. Both probabilities should be in :math:[0, 1]. """ offspring = [toolbox.clone(ind) for ind in population] # Apply crossover and mutation on the offspring for i in range(1, len(offspring), 2): if random.random() < cxpb: offspring[i - 1], offspring[i] = toolbox.mate(offspring[i - 1], offspring[i]) del offspring[i - 1].fitness.values, offspring[i].fitness.values for i in range(len(offspring)): if random.random() < mutpb: offspring[i], = toolbox.mutate(offspring[i]) del offspring[i].fitness.values return offspring def eaSimple(population, toolbox, cxpb, mutpb, ngen, stats=None, halloffame=None, verbose=__debug__): """This algorithm reproduce the simplest evolutionary algorithm as presented in chapter 7 of [Back2000]_. :param population: A list of individuals. :param toolbox: A :class:~deap.base.Toolbox that contains the evolution operators. :param cxpb: The probability of mating two individuals. :param mutpb: The probability of mutating an individual. :param ngen: The number of generation. :param stats: A :class:~deap.tools.Statistics object that is updated inplace, optional. :param halloffame: A :class:~deap.tools.HallOfFame object that will contain the best individuals, optional. :param verbose: Whether or not to log the statistics. :returns: The final population :returns: A class:~deap.tools.Logbook with the statistics of the evolution The algorithm takes in a population and evolves it in place using the :meth:varAnd method. It returns the optimized population and a :class:~deap.tools.Logbook with the statistics of the evolution. The logbook will contain the generation number, the number of evaluations for each generation and the statistics if a :class:~deap.tools.Statistics is given as argument. The *cxpb* and *mutpb* arguments are passed to the :func:varAnd function. The pseudocode goes as follow :: evaluate(population) for g in range(ngen): population = select(population, len(population)) offspring = varAnd(population, toolbox, cxpb, mutpb) evaluate(offspring) population = offspring As stated in the pseudocode above, the algorithm goes as follow. First, it evaluates the individuals with an invalid fitness. Second, it enters the generational loop where the selection procedure is applied to entirely replace the parental population. The 1:1 replacement ratio of this algorithm **requires** the selection procedure to be stochastic and to select multiple times the same individual, for example, :func:~deap.tools.selTournament and :func:~deap.tools.selRoulette. Third, it applies the :func:varAnd function to produce the next generation population. Fourth, it evaluates the new individuals and compute the statistics on this population. Finally, when *ngen* generations are done, the algorithm returns a tuple with the final population and a :class:~deap.tools.Logbook of the evolution. .. note:: Using a non-stochastic selection method will result in no selection as the operator selects *n* individuals from a pool of *n*. This function expects the :meth:toolbox.mate, :meth:toolbox.mutate, :meth:toolbox.select and :meth:toolbox.evaluate aliases to be registered in the toolbox. .. [Back2000] Back, Fogel and Michalewicz, "Evolutionary Computation 1 : Basic Algorithms and Operators", 2000. """ logbook = tools.Logbook() logbook.header = ['gen', 'nevals'] + (stats.fields if stats else []) # Evaluate the individuals with an invalid fitness invalid_ind = [ind for ind in population if not ind.fitness.valid] fitnesses = toolbox.map(toolbox.evaluate, invalid_ind) for ind, fit in zip(invalid_ind, fitnesses): ind.fitness.values = fit if halloffame is not None: halloffame.update(population) record = stats.compile(population) if stats else {} logbook.record(gen=0, nevals=len(invalid_ind), **record) if verbose: print(logbook.stream) # Begin the generational process for gen in range(1, ngen + 1): # Select the next generation individuals offspring = toolbox.select(population, len(population)) # Vary the pool of individuals offspring = varAnd(offspring, toolbox, cxpb, mutpb) # Evaluate the individuals with an invalid fitness invalid_ind = [ind for ind in offspring if not ind.fitness.valid] fitnesses = toolbox.map(toolbox.evaluate, invalid_ind) for ind, fit in zip(invalid_ind, fitnesses): ind.fitness.values = fit # Update the hall of fame with the generated individuals if halloffame is not None: halloffame.update(offspring) # Replace the current population by the offspring population[:] = offspring # Append the current generation statistics to the logbook best_ind = halloffame.items[0] record = stats.compile(population) if stats else {} logbook.record(gen=gen, nevals=len(invalid_ind), **record) temp = logbook.stream if verbose: print(temp) print(best_ind) f = open(time_str + "outputfile.dat", "a") f.write(str(temp) + '\n') f.write(str(best_ind) + '\n') f.close() # 关闭文件 if halloffame.items[0].fitness.values[0] < 20: print("绝对百分比误差小于20%,完成计算!") print(best_ind) return population, logbook return population, logbook def main(): random.seed(318) ### 参数设置 cxpb = 0.5 # 交叉概率 mutpb = 0.15 # 变异概率0 ngen = 1500 # 迭代次数 popSize = 150 # 种群规模 ### 生成初始种群 - 使用新的验证方法 print("创建初始种群...") pop = create_valid_population(popSize) print("初始种群创建完成") # 验证初始种群中的所有个体 for i, ind in enumerate(pop): if not check_pow_arguments(ind): print(f"警告: 初始种群中的个体 {i} 无效!") print(str(ind)) hof = tools.HallOfFame(1) invalid_fit = [ind for ind in pop if not ind.fitness.valid] fitnesses = tool.map(tool.evaluate, invalid_fit) # 求初始种群的每个个体的适应度值,是一个list for ind, fit in zip(invalid_fit, fitnesses): ind.fitness.values = fit # 给每个ind的fitness赋值 hof.update(pop) best_inds = [] # 记录每一代的最优个体 best_ind = hof.items[0] best_inds.append(best_ind) stats = tools.Statistics(lambda ind: ind.fitness.values) stats.register('avg', numpy.mean, axis=0) ### axis = 0表示数组的列求平均;axis = 1 表示数组的行求平均 stats.register('std', numpy.std, axis=0) stats.register('min', numpy.min, axis=0) stats.register('max', numpy.max, axis=0) ### record是一个字典:record{‘avg’:[],'std':[],...},每个value是一个list record = stats.compile(pop) for key in record: record[key] = record[key][0] logbook = tools.Logbook() logbook.record(gen=0, eval=popSize, best_ind=best_ind, **record) logbook.header = 'gen', 'min', 'max', 'avg', 'std', 'eval', 'best_ind' print(logbook) print('--------开始迭代--------') for g in range(ngen): ### select 选出popSize个个体进入下一代 # print('The', g + 1, 'step') offspring = tool.select(pop, len(pop)) # offspring = list(map(tool.clone, offspring)) # offspring = tool.map(tool.clone, offspring) offspring = [tool.clone(ind) for ind in offspring] # Apply crossover and mutation on the offspring for i in range(1, len(offspring), 2): if random.random() < cxpb: offspring[i - 1], offspring[i] = tool.mate(offspring[i - 1], offspring[i]) del offspring[i - 1].fitness.values, offspring[i].fitness.values for i in range(len(offspring)): if random.random() < mutpb: offspring[i], = tool.mutate(offspring[i]) del offspring[i].fitness.values # Evaluate the individuals with an invalid fitness invalid_fit = [ind for ind in offspring if not ind.fitness.valid] fitnesses = tool.map(tool.evaluate, invalid_fit) for ind, fit in zip(invalid_fit, fitnesses): ind.fitness.values = fit hof.update(offspring) # 找到本代的最优个体 pop[:] = offspring # 更新种群 best_ind = hof.items[0] best_inds.append(best_ind) record = stats.compile(pop) # 数据统计 for key in record: record[key] = record[key][0] logbook.record(gen=g + 1, eval=len(invalid_fit), best_ind=best_ind, **record) temp = logbook.stream print(temp) # print logbook.stream ff = open(time_str + "outputfile.dat", "a") ff.write(str(temp) + '\n') # f.write(str(best_ind) + '\n') ff.close() # 关闭文件 if hof.items[0].fitness.values[0] < 20: print("绝对百分比误差小于20%,完成计算!") print(best_ind) print('--------迭代结束-------') break print('--------迭代结束-------') # print(logbook) # pop = tool.population(n=150) # hof = tools.HallOfFame(1) # # stats_fit = tools.Statistics(lambda ind: ind.fitness.values) # stats_size = tools.Statistics(len) # mstats = tools.MultiStatistics(fitness=stats_fit, size=stats_size) # mstats.register("avg", numpy.mean) # mstats.register("std", numpy.std) # mstats.register("min", numpy.min) # mstats.register("max", numpy.max) # pop, log = eaSimple(pop, tool, 0.5, 0.1, 5000, stats=mstats, halloffame=hof, verbose=True) # pop, log = eaMuPlusLambda(pop, tool, 20, 40, 0.5, 0.1, 5000, # stats=mstats, halloffame=hof, verbose=True) # print log # return pop, log, hof drawBestInd(expr=hof.items[0]) # 画出实际函数和优化的函数 # temp = numpy.zeros((len(data))) temp = numpy.zeros((len(data), 5)) func = tool.compile(expr=hof.items[0]) for i in range(len(data)): temp[i, 1] = (func(Fr[i], We[i], Re[i], Bo[i]) + 1) * dPa[ i] # func(FrG, Bo, Rel, ReG, dPa) func(Fr, Bo, f, Pred, x, N) x1 = numpy.arange(0, (max(data[:, 65])), 10) y1 = 1.3 * x1 y2 = 0.7 * x1 # func = tool.compile(expr=hof.items[0]) # z = [func(x) for x in points] temp[:, 0] = data[:, 65] temp[:, 2] = data[:, 66] # temp[:, 3] = data[:, 7] # temp[:, 4] = data[:, 8] filename = time_str + '_data.dat' text_save(filename, temp) # f1 = open(time_str + "outputfile.dat", "a") # f1.write( + '\n') # f1.close ax1 = plt.subplot(211) plt.plot(data[:, 65], temp[:, 1], 'o', markeredgecolor='b', markerfacecolor='none', label='EXP-PRE-ENV') plt.plot(x1, y1, '--', color='k') # , label='+30%') plt.plot(x1, y2, '--', color='k') # , label='-30%') plt.legend(loc='best') plt.xscale('log') plt.yscale('log') ax1 = plt.subplot(212) plt.plot(data[:, 65], data[:, 66], 'o', markeredgecolor='r', markerfacecolor='none', label='EXP-PRE_ANA') plt.plot(x1, y1, '--', color='k') # , label='+30%') plt.plot(x1, y2, '--', color='k') # , label='-30%') plt.legend(loc='best') plt.xscale('log') plt.yscale('log') # ax1 = plt.subplot(223) # plt.plot(data[:, 5], data[:, 7], 'o', markeredgecolor='c', markerfacecolor='none', label='EXP-PRE_MSH') # plt.plot(x1, y1, '--', color='k')#, label='+30%') # plt.plot(x1, y2, '--', color='k')#, label='-30%') # plt.legend(loc='best') # plt.xscale('log') # plt.yscale('log') # ax1 = plt.subplot(224) # plt.plot(data[:, 5], data[:, 8], 'o', markeredgecolor='g', markerfacecolor='none', label='EXP-PRE_M&H') plt.plot(x1, y1, '--', color='k') # , label='+30%') plt.plot(x1, y2, '--', color='k') # , label='-30%') # plt.legend(loc='best') # plt.xscale('log') # plt.yscale('log') # ax.set_aspect(1) # curr_time = datetime.datetime.now() # time_str=datetime.datetime.strftime(curr_time, '%Y%m%d%H%M') plt.savefig(time_str + 'res.pdf') plt.show() main()

import pandas as pd import numpy as np import torch import torch.nn as nn import torch.optim as optim from torch.utils.data import Dataset, DataLoader, WeightedRandomSampler from torchtext.vocab import build_vocab_from_iterator from torchtext.data.utils import get_tokenizer from sklearn.model_selection import train_test_split from sklearn.metrics import classification_report from torch.optim.lr_scheduler import ReduceLROnPlateau import re from tqdm import tqdm import nltk from nltk.corpus import stopwords # 下载停用词数据 nltk.download('stopwords') # 启用GPU加速 device = torch.device("cuda" if torch.cuda.is_available() else "cpu") print(f"Using device: {device}") # 1. 增强的文本预处理 def enhanced_clean(text): if not isinstance(text, str): return "" text = re.sub(r'@\w+', '', text) # 移除@提及 text = re.sub(r'https?://\S+', '', text) # 移除URL text = re.sub(r'[^\w\s]', ' ', text) # 替换标点为空格 text = re.sub(r'\d+', '', text) # 移除数字 text = re.sub(r'\s+', ' ', text).strip() # 合并多个空格 return text.lower() # 2. 自定义数据集类(带缓存) class OptimizedCommentDataset(Dataset): def __init__(self, texts, labels, weights, vocab, max_len=200): self.texts = texts.reset_index(drop=True) self.labels = labels.reset_index(drop=True) self.weights = weights.reset_index(drop=True) self.vocab = vocab self.max_len = max_len self.tokenizer = get_tokenizer("basic_english") self.stop_words = set(stopwords.words('english')) def __len__(self): return len(self.texts) def __getitem__(self, idx): text = self.texts.iloc[idx] tokens = [token for token in self.tokenizer(text) if token not in self.stop_words and len(token) > 2] indices = [self.vocab[token] for token in tokens if token in self.vocab] if len(indices) > self.max_len: indices = indices[:self.max_len] else: indices = indices + [self.vocab[""]] * (self.max_len - len(indices)) return { "text": torch.tensor(indices, dtype=torch.long), "label": torch.tensor(self.labels.iloc[idx], dtype=torch.long), "weight": torch.tensor(self.weights.iloc[idx], dtype=torch.float) } # 3. 带注意力机制的LSTM模型 class AttentionLSTM(nn.Module): def __init__(self, vocab_size, embedding_dim=300, lstm_units=256, num_classes=5): super().__init__() self.embedding = nn.Embedding(vocab_size, embedding_dim, padding_idx=0) self.lstm = nn.LSTM(embedding_dim, lstm_units, batch_first=True, bidirectional=True, num_layers=2) self.attention = nn.Sequential( nn.Linear(lstm_units*2, 128), nn.Tanh(), nn.Linear(128, 1), nn.Softmax(dim=1) ) self.fc = nn.Sequential( nn.LayerNorm(lstm_units*2), nn.Linear(lstm_units*2, 128), nn.ReLU(), nn.Dropout(0.5), nn.Linear(128, num_classes) ) def forward(self, x): emb = self.embedding(x) # [batch, seq_len, emb_dim] lstm_out, _ = self.lstm(emb) # [batch, seq_len, hid_dim*2] # 注意力机制 att_weights = self.attention(lstm_out) # [batch, seq_len, 1] context = torch.sum(att_weights * lstm_out, dim=1) # [batch, hid_dim*2] return self.fc(context) # 4. 评估函数 def evaluate(model, dataloader): model.eval() all_preds = [] all_labels = [] with torch.no_grad(): for batch in tqdm(dataloader, desc="Evaluating"): texts = batch['text'].to(device) labels = batch['label'].cpu().numpy() outputs = model(texts) preds = outputs.argmax(1).cpu().numpy() all_preds.extend(preds) all_labels.extend(labels) print("\nClassification Report:") print(classification_report( all_labels, all_preds, target_names=['1星', '2星', '3星', '4星', '5星'], digits=4 )) return np.mean(all_preds == all_labels) # 5. 主流程优化 def optimized_main(): # 加载数据 comments = pd.read_csv('D:\\BaiduNetdiskDownload\\电影数据集-CSV格式\\comments.csv') # 数据校验 assert 'RATING' in comments.columns, "RATING列不存在" assert 'CONTENT' in comments.columns, "CONTENT列不存在" # 数据预处理 comments['CLEAN_CONTENT'] = comments['CONTENT'].fillna('').astype(str).apply(enhanced_clean) comments['LABEL'] = comments['RATING'] - 1 comments = comments[comments['LABEL'].between(0, 4)].copy() # 计算类别权重 class_dist = comments['LABEL'].value_counts(normalize=True).sort_index() class_weights = torch.tensor(1 / (class_dist + 1e-6), dtype=torch.float32).to(device) # 划分数据集 train_data, temp_data = train_test_split( comments, test_size=0.3, stratify=comments['LABEL'], random_state=42 ) val_data, test_data = train_test_split( temp_data, test_size=0.5, stratify=temp_data['LABEL'], random_state=42 ) # 构建词汇表 tokenizer = get_tokenizer("basic_english") def yield_tokens(data_iter): for text in data_iter['CLEAN_CONTENT']: tokens = [token for token in tokenizer(text) if token not in stopwords.words('english') and len(token) > 2] yield tokens vocab = build_vocab_from_iterator( yield_tokens(train_data), specials=["<unk>", ""], max_tokens=50000 ) vocab.set_default_index(vocab["<unk>"]) # 创建数据集 train_dataset = OptimizedCommentDataset( train_data['CLEAN_CONTENT'], train_data['LABEL'], np.log1p(train_data['VOTES']) + 1, vocab ) # 创建带权重的采样器 train_sampler = WeightedRandomSampler( weights=train_data['VOTES'].values + 1, # 防止零权重 num_samples=len(train_dataset), replacement=True ) # 数据加载器配置 batch_size = 64 train_loader = DataLoader( train_dataset, batch_size=batch_size, sampler=train_sampler, num_workers=4, pin_memory=True ) val_loader = DataLoader( OptimizedCommentDataset(val_data['CLEAN_CONTENT'], val_data['LABEL'], val_data['VOTES'], vocab), batch_size=batch_size*2, shuffle=False, num_workers=4 ) test_loader = DataLoader( OptimizedCommentDataset(test_data['CLEAN_CONTENT'], test_data['LABEL'], test_data['VOTES'], vocab), batch_size=batch_size*2, shuffle=False, num_workers=4 ) # 初始化模型 model = AttentionLSTM(len(vocab)).to(device) optimizer = optim.AdamW(model.parameters(), lr=1e-3, weight_decay=1e-4) scheduler = ReduceLROnPlateau(optimizer, 'max', factor=0.5, patience=2, verbose=True) criterion = nn.CrossEntropyLoss(weight=class_weights) # 训练循环 best_val_acc = 0.0 for epoch in range(15): model.train() train_loss = 0.0 progress = tqdm(train_loader, desc=f"Epoch {epoch+1}/15") for batch in progress: texts = batch['text'].to(device, non_blocking=True) labels = batch['label'].to(device, non_blocking=True) optimizer.zero_grad(set_to_none=True) outputs = model(texts) loss = criterion(outputs, labels) loss.backward() torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0) optimizer.step() train_loss += loss.item() progress.set_postfix({'loss': f"{loss.item():.4f}"}) # 验证阶段 val_acc = evaluate(model, val_loader) scheduler.step(val_acc) # 保存最佳模型 if val_acc > best_val_acc: best_val_acc = val_acc torch.save({ 'model_state': model.state_dict(), 'vocab': vocab, 'config': { 'embedding_dim': 300, 'lstm_units': 256, 'max_len': 200 } }, 'best_model.pth') # 最终测试 model.load_state_dict(torch.load('best_model.pth')['model_state']) test_acc = evaluate(model, test_loader) print(f"\nFinal Test Accuracy: {test_acc:.4f}") if __name__ == "__main__": optimized_main() 请给予此代码给出添加初始化进度监控等的完整代码,不要进行省略和修改

import pandas as pd import numpy as np import torch import torch.nn as nn import torch.optim as optim from torch.utils.data import Dataset, DataLoader, WeightedRandomSampler from torchtext.vocab import build_vocab_from_iterator from torchtext.data.utils import get_tokenizer from sklearn.model_selection import train_test_split import re from tqdm import tqdm # 启用GPU加速 device = torch.device("cuda" if torch.cuda.is_available() else "cpu") print(f"Using device: {device}") # 1. 自定义数据集类 class CommentDataset(Dataset): def __init__(self, texts, labels, weights, vocab, max_len=200): self.texts = texts self.labels = labels self.weights = weights self.vocab = vocab self.max_len = max_len self.tokenizer = get_tokenizer("basic_english") def __len__(self): return len(self.texts) def __getitem__(self, idx): text = self.texts.iloc[idx] tokens = self.tokenizer(text) # 转换为词汇索引并填充/截断 indices = [self.vocab[token] for token in tokens if token in self.vocab] if len(indices) > self.max_len: indices = indices[:self.max_len] else: indices = indices + [self.vocab[""]] * (self.max_len - len(indices)) return { "text": torch.tensor(indices, dtype=torch.long), "label": torch.tensor(self.labels.iloc[idx], dtype=torch.long), "weight": torch.tensor(self.weights.iloc[idx], dtype=torch.float) } # 2. 构建LSTM模型 class SentimentLSTM(nn.Module): def __init__(self, vocab_size, embedding_dim=128, lstm_units=128, num_classes=5): super(SentimentLSTM, self).__init__() self.embedding = nn.Embedding(vocab_size, embedding_dim, padding_idx=0) self.lstm1 = nn.LSTM(embedding_dim, lstm_units, batch_first=True, bidirectional=True) self.dropout1 = nn.Dropout(0.3) self.lstm2 = nn.LSTM(lstm_units*2, lstm_units//2, batch_first=True, bidirectional=True) self.dropout2 = nn.Dropout(0.3) self.fc1 = nn.Linear(lstm_units, 64) self.fc2 = nn.Linear(64, num_classes) self.relu = nn.ReLU() def forward(self, x): x = self.embedding(x) x, _ = self.lstm1(x) x = self.dropout1(x) x, (hidden, _) = self.lstm2(x) x = self.dropout2(x) # 取最后一个时间步的输出 x = hidden[-1] x = self.relu(self.fc1(x)) x = self.fc2(x) return x # 3. 文本预处理函数 def clean_text(text): if not isinstance(text, str): return "" text = re.sub(r'[^\w\s]', '', text) # 移除非字母数字字符 text = re.sub(r'\d+', '', text) # 移除数字 return text.lower() # 转换为小写 # 4. 主流程 def main(): # 加载数据 comments = pd.read_csv('D:\\BaiduNetdiskDownload\\电影数据集-CSV格式\\comments.csv') # 关键修复: 检查RATING列是否存在 if 'RATING' not in comments.columns: raise KeyError(f"Column 'RATING' not found. Available columns: {comments.columns.tolist()}") # 处理空值 comments['CONTENT'] = comments['CONTENT'].fillna('').astype(str) # 应用文本清洗 comments['CLEAN_CONTENT'] = comments['CONTENT'].apply(clean_text) # 将votes转换为权重 comments['WEIGHT'] = np.log1p(comments['VOTES']) + 1 # 标签处理 (评分1-5转换为0-4) comments['LABEL'] = comments['RATING'] - 1 # 过滤无效标签 valid_labels = comments['LABEL'].between(0, 4) comments = comments[valid_labels].copy() comments['LABEL'] = comments['LABEL'].astype(np.int32) print(f"清洗后有效样本数: {len(comments)}") # 划分数据集 train_data, temp_data = train_test_split( comments, test_size=0.3, random_state=42 ) val_data, test_data = train_test_split( temp_data, test_size=0.5, random_state=42 ) # 构建词汇表 tokenizer = get_tokenizer("basic_english") def yield_tokens(data_iter): for text in data_iter['CLEAN_CONTENT']: yield tokenizer(text) vocab = build_vocab_from_iterator( yield_tokens(train_data), specials=["<unk>", ""], max_tokens=50000 ) vocab.set_default_index(vocab["<unk>"]) print(f"词汇表大小: {len(vocab)}") # 创建数据集 train_dataset = CommentDataset( train_data['CLEAN_CONTENT'], train_data['LABEL'], train_data['WEIGHT'], vocab ) val_dataset = CommentDataset( val_data['CLEAN_CONTENT'], val_data['LABEL'], val_data['WEIGHT'], vocab ) test_dataset = CommentDataset( test_data['CLEAN_CONTENT'], test_data['LABEL'], test_data['WEIGHT'], vocab ) # 创建带权重的采样器 train_sampler = WeightedRandomSampler( weights=train_data['WEIGHT'].values, num_samples=len(train_dataset), replacement=True ) # 创建数据加载器 batch_size = 16 train_loader = DataLoader( train_dataset, batch_size=batch_size, sampler=train_sampler, num_workers=4 ) val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False) test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False) # 初始化模型 model = SentimentLSTM(len(vocab)).to(device) print(model) # 损失函数和优化器 criterion = nn.CrossEntropyLoss() optimizer = optim.Adam(model.parameters(), lr=0.001) # 训练参数 num_epochs = 15 best_val_acc = 0.0 # 训练循环 for epoch in range(num_epochs): model.train() train_loss = 0.0 correct = 0 total = 0 # 训练阶段 for batch in tqdm(train_loader, desc=f"Epoch {epoch+1}/{num_epochs}"): texts = batch['text'].to(device) labels = batch['label'].to(device) optimizer.zero_grad() outputs = model(texts) loss = criterion(outputs, labels) loss.backward() optimizer.step() train_loss += loss.item() _, predicted = outputs.max(1) total += labels.size(0) correct += predicted.eq(labels).sum().item() train_acc = 100. * correct / total train_loss /= len(train_loader) # 验证阶段 model.eval() val_loss = 0.0 correct = 0 total = 0 with torch.no_grad(): for batch in val_loader: texts = batch['text'].to(device) labels = batch['label'].to(device) outputs = model(texts) loss = criterion(outputs, labels) val_loss += loss.item() _, predicted = outputs.max(1) total += labels.size(0) correct += predicted.eq(labels).sum().item() val_acc = 100. * correct / total val_loss /= len(val_loader) print(f"Epoch {epoch+1}: " f"Train Loss: {train_loss:.4f}, Acc: {train_acc:.2f}% | " f"Val Loss: {val_loss:.4f}, Acc: {val_acc:.2f}%") # 保存最佳模型 if val_acc > best_val_acc: best_val_acc = val_acc torch.save({ 'epoch': epoch, 'model_state_dict': model.state_dict(), 'optimizer_state_dict': optimizer.state_dict(), 'val_acc': val_acc, 'vocab': vocab }, 'best_model.pth') print(f"Saved best model with val acc: {val_acc:.2f}%") # 测试模型 print("\nTesting model...") model.load_state_dict(torch.load('best_model.pth')['model_state_dict']) model.eval() test_loss = 0.0 correct = 0 total = 0 with torch.no_grad(): for batch in test_loader: texts = batch['text'].to(device) labels = batch['label'].to(device) outputs = model(texts) loss = criterion(outputs, labels) test_loss += loss.item() _, predicted = outputs.max(1) total += labels.size(0) correct += predicted.eq(labels).sum().item() test_acc = 100. * correct / total test_loss /= len(test_loader) print(f"Test Loss: {test_loss:.4f}, Test Accuracy: {test_acc:.2f}%") # 保存最终模型 torch.save({ 'model_state_dict': model.state_dict(), 'vocab': vocab, 'config': { 'embedding_dim': 128, 'lstm_units': 128, 'max_len': 200 } }, 'final_sentiment_model.pth') print("Model saved as 'final_sentiment_model.pth'") if __name__ == "__main__": main() 我需要对此代码进行优化

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