from sklearn.preprocessing import LabelEncoder import numpy as np class LabelProcessor: def __init__(self): self.encoder = LabelEncoder() self.char_set = self.load_characters() def load_characters(self): # 从标签文件生成字符集合 # 需根据实际情况实现 return sorted({'京', '沪', '0', '1',..., 'Z'}) def text_to_seq(self, text): return self.encoder.transform(list(text)) def seq_to_text(self, seq): return ''.join(self.encoder.inverse_transform(np.argmax(seq, axis=1)))报错 > & D:/anaconda3/envs/hjw/python.exe d:/anaconda3/envs/hjw/graduation_project/main.py Traceback (most recent call last): File "d:/anaconda3/envs/hjw/graduation_project/main.py", line 6, in <module> label_processor = LabelProcessor() File "d:\anaconda3\envs\hjw\graduation_project\utils.py", line 7, in __init__ self.char_set = self.load_characters() File "d:\anaconda3\envs\hjw\graduation_project\utils.py", line 12, in load_characters return sorted({'京', '沪', '0', '1',..., 'Z'}) TypeError: '<' not supported between instances of 'ellipsis' and 'str'

检查我的代码，数据库大小为408张，因为8类别最多到8-3-8，其余到x-6-8,代码如下：import os import pandas as pd import numpy as np import torch import torch.nn as nn from torch.utils.data import Dataset, DataLoader from torchvision import transforms from PIL import Image from sklearn.preprocessing import LabelEncoder from sklearn.model_selection import train_test_split # ==================== # 数据预处理模块（无需CSV） # ==================== class FingerprintDataset(Dataset): def init(self, data_root, transform=None): self.data_root = data_root self.transform = transform or transforms.Compose([ transforms.Resize((64, 64)), # 统一缩放尺寸 transforms.Grayscale(), transforms.ToTensor() ]) # 自动扫描文件并解析标签 self.file_list = [] self.labels = [] self.label_encoder = LabelEncoder() # 遍历文件夹获取所有图片 for file_name in os.listdir(data_root): if file_name.lower().endswith(('.bmp', '.png', '.jpg')): # 从文件名解析标签（假设文件名为 1-1-1.bmp 格式） label_str = os.path.splitext(file_name)[0] self.file_list.append(os.path.join(data_root, file_name)) self.labels.append(label_str) # 生成标签编码 self.encoded_labels = self.label_encoder.fit_transform(self.labels) self.num_classes = len(self.label_encoder.classes_) def len(self): return len(self.file_list) def getitem(self, idx): img_path = self.file_list[idx] image = Image.open(img_path) if self.transform: image = self.transform(image) label = self.encoded_labels[idx] return image.view(-1), torch.tensor(label, dtype=torch.long) # ==================== # 忆阻器权重加载模块 # ==================== def load_memristor_weights(excel_path): df = pd.read_excel(excel_path) ltp = torch.tensor(df['LTP'].values, dtype=torch.float32) ltd = torch.tensor(df['LTD'].values, dtype=torch.float32) return ltp, ltd # ==============

from sklearn.preprocessing import LabelEncoder import torch from torch.utils.data import Dataset class FingerprintDataset(Dataset): def __init__(self, annotations, transform=None): self.labels = [f...

# test2.py import pandas as pd import numpy as np from sklearn.compose import ColumnTransformer from sklearn.pipeline import Pipeline from sklearn.ensemble import RandomForestClassifier from sklearn.preprocessing import StandardScaler, OneHotEncoder from sklearn.impute import SimpleImputer from sklearn.model_selection import train_test_split import joblib import os class AutoFeatureProcessor: """自动特征类型检测与预处理""" def init(self): self.numeric_features = None self.categorical_features = None self.preprocessor = None def _auto_detect_features(self, X): """自动识别数值型和分类型特征""" categorical = [] numeric = [] # 正确获取特征数量（列数） num_features = X.shape # 修复点1：使用shape获取列数 for i in range(num_features): col = X[:, i] try: # 尝试转换为数值型 col_float = col.astype(np.float64) # 判断是否为离散型数值特征 if np.all(col_float == col_float.astype(int)) and len(np.unique(col_float)) <= 10: categorical.append(i) else: numeric.append(i) except: # 转换失败则视为分类型特征 categorical.append(i) return numeric, categorical def build_preprocessor(self, X): """构建预处理管道""" self.numeric_features, self.categorical_features = self._auto_detect_features(X) # 数值型特征处理流 numeric_transformer = Pipeline(steps=[ ('imputer', SimpleImputer(strategy='median')), ('scaler', StandardScaler())]) # 分类型特征处理流 categorical_transformer = Pipeline(steps=[ ('imputer', SimpleImputer(strategy='constant', fill_value='missing')), ('onehot', OneHotEncoder(handle_unknown='ignore'))]) # 组合处理器 self.preprocessor = ColumnTransformer( transformers=[ ('num', numeric_transformer, self.numeri z

from sklearn.preprocessing import StandardScaler, OneHotEncoder class AutoFeatureProcessor: def __init__(self): self.numeric_features = [] self.categorical_features = [] def _auto_detect_...

import sys import numpy as np import librosa import soundfile as sf import torch import torch.nn as nn from sklearn.cluster import KMeans from sklearn.preprocessing import StandardScaler from PyQt5.QtWidgets import (QApplication, QMainWindow, QPushButton, QVBoxLayout, QFileDialog, QTextEdit, QHBoxLayout, QLabel, QProgressBar) from PyQt5.QtCore import Qt, pyqtSignal, QThread # 配置参数 class Config: sr = 16000 # 采样率 n_fft = 512 # FFT窗口大小 hop_length = 256 # 帧移 duration = 10 # 处理时长(秒) n_mels = 128 # 梅尔频谱维度 batch_size = 32 train_epochs = 50 device = 'cuda' if torch.cuda.is_available() else 'cpu' # 去噪自编码器模型 class DAE(nn.Module): def init(self): super(DAE, self).init() self.encoder = nn.Sequential( nn.Conv2d(1, 16, 3, padding=1), nn.ReLU(), nn.MaxPool2d(2, 2)), self.decoder = nn.Sequential( nn.ConvTranspose2d(16, 1, 3, stride=2, padding=1, output_padding=1), nn.ReLU(), nn.ConvTranspose2d(1, 1, 3, stride=2, padding=1, output_padding=1), nn.Sigmoid() ) def forward(self, x): encoded = self.encoder(x) decoded = self.decoder(encoded) return decoded, encoded # 处理线程 class ProcessingThread(QThread): progress_updated = pyqtSignal(int, str) finished = pyqtSignal(bool, str) def init(self, audio_path): super().init() self.audio_path = audio_path def run(self): try: # 1. 加载音频 self.progress_updated.emit(0, "正在加载音频文件...") y, sr = librosa.load(self.audio_path, sr=Config.sr, duration=Config.duration) # 2. 提取特征 self.progress_updated.emit(20, "正在提取特征...") S = librosa.stft(y, n_fft=Config.n_fft, hop_length=Config.hop_length) mag = np.abs(S) mel_spec = librosa.feature.melspectrogram(S=mag, sr=sr, n_

input_tensor = torch.from_numpy(self.audio).float() with torch.no_grad(): output = self.model(input_tensor) self.finished.emit(output.numpy()) ### 四、系统整合流程 1. 音频输入 → 2. STFT特征...

class FeatureEngineer: def init(self, config): self.config = config self.tokenizer = BertTokenizer.from_pretrained('bert-base-chinese') self.bert = BertModel.from_pretrained('bert-base-chinese') # 初始化编码器 self.major_encoder = LabelEncoder() self.company_encoder = LabelEncoder() self.cert_encoder = MultiLabelBinarizer() def process(self, students, majors, companies, graduates): """执行特征处理流程""" # 编码分类特征 self._fit_encoders(students, graduates, majors, companies) # 处理各实体特征 student_feats = self._process_students(students) major_feats = self._process_majors(majors) company_feats = self._process_companies(companies) # 构建关系边 edges = self._build_edges(students, graduates, majors, companies) return student_feats, major_feats, company_feats, edges def _fit_encoders(self, students, graduates, majors, companies): """统一训练所有编码器""" # 专业编码 all_majors = pd.concat([students['当前专业'], graduates['专业'], majors['专业名称']]).unique() self.major_encoder.fit(all_majors) # 企业编码 all_companies = pd.concat([graduates['就业企业'], companies['企业名称']]).unique() self.company_encoder.fit(all_companies) # 证书编码 all_certs = set() for certs in students['技能证书']: all_certs.update(certs) self.cert_encoder.fit([[cert] for cert in all_certs]) def _process_students(self, students): """处理在校生特征""" # BERT编码兴趣描述 interest_emb = [] for text in students['兴趣描述']: inputs = self.tokenizer(text, return_tensors='pt', padding='max_length', truncation=True, max_length=64) with torch.no_grad(): outputs = self.bert(**inputs) interest_emb.append(outputs.last_hidden_state[:, 0, :].cpu().numpy()) interest_emb = np.concatenate(interest_emb, axis=0)

from sklearn.preprocessing import LabelEncoder def _fit_encoders(data): self.major_encoder = LabelEncoder() self.major_encoder.fit(data['majors']) # 专业名称编码 self.school_encoder = OneHot...

import numpy as np import pandas as pd import torch import torch.nn as nn from sklearn.preprocessing import StandardScaler, OneHotEncoder from sklearn.ensemble import RandomForestRegressor from sklearn.model_selection import train_test_split #======================== 数据加载与预处理 ======================== def load_data(): # 注意：请确认Excel中的目标列名称（示例中使用"燃烧效率"） df = pd.read_excel(r"C:\Users\Administrator\Desktop\掺氨比、燃尽风位置、主燃区温度\原数据1.xlsx") # 特征合并（数值型特征 + 编码后分类特征） X = df[["掺氨比", "主燃区温度", "燃尽风位置"]].values # 数值型特征 y = df["NO排放浓度"].values # 修改为实际目标列名 return X, y #======================== 自编码器模型 ======================== class CombustionVAE(nn.Module): def init(self, input_dim): super().init() # 编码器（输入维度需匹配特征总数） self.encoder = nn.Sequential( nn.Linear(input_dim, 16), nn.LeakyReLU(0.2), nn.Linear(16, 8), nn.Linear(8, 2) # 潜在空间维度设为2 ) # 解码器 self.decoder = nn.Sequential( nn.Linear(2, 8), nn.LeakyReLU(0.2), nn.Linear(8, 16), nn.Linear(16, input_dim) ) def forward(self, x): latent = self.encoder(x) return self.decoder(latent) #======================== 物理约束函数 ======================== def apply_physical_constraints(data, encoder): """ 应用燃烧过程物理限制： 1. 掺氨比 ∈ [0, 1] 2. 主燃区温度 ∈ [800, 1600] """ # 数值型特征约束 data[:, 0] = np.clip(data[:, 0], 0, 100) # 掺氨比 data[:, 1] = np.clip(data[:, 1], 800, 1600) # 温度 data[:, 2] = np.clip(data[:, 2], 0, 1) #燃尽风位置 return data #======================== 主流程 ======================== if name == "main": # 加载并预处理数据 X, y = load_data() scaler = StandardScaler() X_scaled = scaler.fit_transform(X) # 划分数据集 X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42) # 训练自编码器 device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model = CombustionVAE(input_dim=X_train.shape[1]).to(device) optimizer = torch.optim.Adam(model.parameters(), lr=0.001) print("开始训练自编码器...") for epoch in range(1000): inputs = torch.FloatTensor(X_train).to(device) reconstructed = model(inputs) # 带物理约束的损失函数 mse_loss = nn.MSELoss()(reconstructed, inputs) phys_loss = torch.mean(reconstructed[:,0] * reconstructed[:,1]) # 示例约束：掺氨比与温度负相关 loss = mse_loss + 0.05 * phys_loss optimizer.zero_grad() loss.backward() optimizer.step() if epoch % 100 == 0: print(f"Epoch {epoch}, Loss: {loss.item():.4f}") # 生成合成数据 with torch.no_grad(): model.eval() z = torch.randn(500, 2).to(device) # 生成5000个样本 synthetic = model.decoder(z).cpu().numpy() # 反标准化并应用约束 synthetic_data = scaler.inverse_transform(synthetic) synthetic_constrained = apply_physical_constraints(synthetic, model.encoder) synthetic_data = apply_physical_constraints(synthetic_data) 提示TypeError: apply_physical_constraints() missing 1 required positional argument: 'encoder'

嗯，用户遇到了一个TypeError，提示apply_physical_constraints()缺少一个必需的位置参数'encoder'。我需要仔细看看代码哪里出问题了。首先，用户定义了一个函数apply_physical_constraints，参数是data和encoder...

import numpy as np import pandas as pd import torch import torch.nn as nn from sklearn.preprocessing import StandardScaler, OneHotEncoder from sklearn.ensemble import RandomForestRegressor from sklearn.model_selection import train_test_split #======================== 数据加载与预处理 ======================== def load_data(): # 注意：请确认Excel中的目标列名称（示例中使用"燃烧效率"） df = pd.read_excel(r"C:\Users\Administrator\Desktop\掺氨比、燃尽风位置、主燃区温度\原数据1.xlsx") # 特征合并（数值型特征 + 编码后分类特征） X = df[["掺氨比", "主燃区温度", "燃尽风位置"]].values # 数值型特征 y = df["NO排放浓度"].values # 修改为实际目标列名 return X, y #======================== 自编码器模型 ======================== class CombustionVAE(nn.Module): def init(self, input_dim): super().init() # 编码器（输入维度需匹配特征总数） self.encoder = nn.Sequential( nn.Linear(input_dim, 16), nn.LeakyReLU(0.2), nn.Linear(16, 8), nn.Linear(8, 2) # 潜在空间维度设为2 ) # 解码器 self.decoder = nn.Sequential( nn.Linear(2, 8), nn.LeakyReLU(0.2), nn.Linear(8, 16), nn.Linear(16, input_dim) ) def forward(self, x): latent = self.encoder(x) return self.decoder(latent) #======================== 物理约束函数 ======================== def apply_physical_constraints(data, encoder): """ 应用燃烧过程物理限制： 1. 掺氨比 ∈ [0, 1] 2. 主燃区温度 ∈ [800, 1600] 3. 燃尽风位置编码需为有效独热向量 """ # 数值型特征约束 data[:, 0] = np.clip(data[:, 0], 0, 100) # 掺氨比 data[:, 1] = np.clip(data[:, 1], 800, 1600) # 温度 data[:, 2] = np.clip(data[:, 2], 0, 1) #燃尽风位置 # 分类特征约束（确保独热编码有效性） position_cols = data[:, 2:2+encoder.categories_[0].shape[0]] position_cols = np.where(position_cols > 0.5, 1.0, 0.0) # 阈值处理 data[:, 2:2+encoder.categories_[0].shape[0]] = position_cols return data #======================== 主流程 ======================== if name == "main": # 加载并预处理数据 X, y = load_data() scaler = StandardScaler() X_scaled = scaler.fit_transform(X) # 划分数据集 X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42) # 训练自编码器 device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model = CombustionVAE(input_dim=X_train.shape[1]).to(device) optimizer = torch.optim.Adam(model.parameters(), lr=0.001) print("开始训练自编码器...") for epoch in range(1000): inputs = torch.FloatTensor(X_train).to(device) reconstructed = model(inputs) # 带物理约束的损失函数 mse_loss = nn.MSELoss()(reconstructed, inputs) phys_loss = torch.mean(reconstructed[:,0] * reconstructed[:,1]) # 示例约束：掺氨比与温度负相关 loss = mse_loss + 0.05 * phys_loss optimizer.zero_grad() loss.backward() optimizer.step() if epoch % 100 == 0: print(f"Epoch {epoch}, Loss: {loss.item():.4f}") # 生成合成数据 with torch.no_grad(): model.eval() z = torch.randn(500, 2).to(device) # 生成5000个样本 synthetic = model.decoder(z).cpu().numpy() # 反标准化并应用约束 synthetic_data = scaler.inverse_transform(synthetic) # 生成标签（使用代理模型） print("\n训练代理模型生成标签...") proxy_model = RandomForestRegressor(n_estimators=100) proxy_model.fit(X_train, y_train) synthetic_labels = proxy_model.predict(scaler.transform(synthetic_data)) # 构建最终数据集 final_X = np.vstack([X, synthetic_data]) final_y = np.concatenate([y, synthetic_labels]) # 保存数据集（包含特征名称） feature_names = ["掺氨比", "主燃区温度", "燃尽风位置"] expanded_df = pd.DataFrame(final_X, columns=feature_names) expanded_df["NO排放浓度"] = final_y expanded_df.to_excel("扩展燃烧数据集.xlsx", index=False) print("\n数据扩展完成！生成样本量:", len(expanded_df)) 如何保存数据

但是在apply_physical_constraints函数中，却引用了encoder.categories_，这可能意味着用户预期对某个特征进行独热编码，但实际上没有执行这一步骤，导致encoder变量不存在，从而在运行时会出错。这是一个严重的...

from collections import Counter import numpy as np import pandas as pd import torch import matplotlib.pyplot as plt from sklearn.metrics import accuracy_score, classification_report from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.svm import SVC from torch.utils.data import DataLoader, Dataset from tqdm import tqdm from transformers import AutoTokenizer, BertModel import joblib from sklearn.metrics import confusion_matrix import seaborn as sns # 1. ====================== 配置参数 ====================== MODEL_PATH = r'D:\pythonProject5\bert-base-chinese' BATCH_SIZE = 64 MAX_LENGTH = 128 SAVE_DIR = r'D:\pythonProject5\BSVMC_model' DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu") # 2. ====================== 数据加载与划分 ====================== def load_data(file_path): """加载并预处理数据""" df = pd.read_csv(file_path).dropna(subset=['text', 'label']) texts = df['text'].astype(str).str.strip().tolist() labels = df['label'].astype(int).tolist() return texts, labels # 加载原始数据 texts, labels = load_data("train3.csv") # 第一次拆分：分出测试集（20%） train_val_texts, test_texts, train_val_labels, test_labels = train_test_split( texts, labels, test_size=0.2, stratify=labels, random_state=42 ) # 第二次拆分：分出训练集（70%）和验证集（30% of 80% = 24%） train_texts, val_texts, train_labels, val_labels = train_test_split( train_val_texts, train_val_labels, test_size=0.3, # 0.3 * 0.8 = 24% of original stratify=train_val_labels, random_state=42 ) # 3. ====================== 文本编码 ====================== tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH) def encode_texts(texts): return tokenizer( texts, truncation=True, padding="max_length", max_length=MAX_LENGTH, return_tensors="pt" ) # 编码所有数据集 train_encodings = encode_texts(train_texts) val_encodings = encode_texts(val_texts) test_encodings = encode_texts(test_texts) # 4. ====================== 数据集类 ====================== class TextDataset(Dataset): def init(self, encodings, labels): self.encodings = encodings self.labels = labels def getitem(self, idx): return { 'input_ids': self.encodings['input_ids'][idx], 'attention_mask': self.encodings['attention_mask'][idx], 'labels': torch.tensor(self.labels[idx]) } def len(self): return len(self.labels) # 创建所有数据集加载器 train_dataset = TextDataset(train_encodings, train_labels) val_dataset = TextDataset(val_encodings, val_labels) test_dataset = TextDataset(test_encodings, test_labels) train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True) val_loader = DataLoader(val_dataset, batch_size=BATCH_SIZE, shuffle=False) test_loader = DataLoader(test_dataset, batch_size=BATCH_SIZE, shuffle=False) # 5. ====================== 特征提取 ====================== def extract_features(bert_model, dataloader): """使用BERT提取CLS特征""" bert_model.eval() all_features = [] all_labels = [] with torch.no_grad(): for batch in tqdm(dataloader, desc="提取特征"): inputs = {k: v.to(DEVICE) for k, v in batch.items() if k != 'labels'} outputs = bert_model(**inputs) features = outputs.last_hidden_state[:, 0, :].cpu().numpy() all_features.append(features) all_labels.append(batch['labels'].numpy()) return np.vstack(all_features), np.concatenate(all_labels) # 加载并冻结BERT模型 bert_model = BertModel.from_pretrained(MODEL_PATH).to(DEVICE) for param in bert_model.parameters(): param.requires_grad = False # 提取所有特征 print("\n" + "=" * 30 + " 特征提取阶段 " + "=" * 30) train_features, train_labels = extract_features(bert_model, train_loader) val_features, val_labels = extract_features(bert_model, val_loader) test_features, test_labels = extract_features(bert_model, test_loader) # 6. ====================== 特征预处理 ====================== scaler = StandardScaler() train_features = scaler.fit_transform(train_features) # 只在训练集上fit val_features = scaler.transform(val_features) test_features = scaler.transform(test_features) # 7. ====================== 训练SVM ====================== print("\n" + "=" * 30 + " 训练SVM模型 " + "=" * 30) svm_model = SVC( kernel='rbf', C=1.0, gamma='scale', probability=True, random_state=42 ) svm_model.fit(train_features, train_labels) # 8. ====================== 评估模型 ====================== def evaluate(features, labels, model, dataset_name): preds = model.predict(features) acc = accuracy_score(labels, preds) print(f"\n[{dataset_name}] 评估结果：") print(f"准确率：{acc:.4f}") print(classification_report(labels, preds, digits=4)) return preds print("\n训练集评估：") _ = evaluate(train_features, train_labels, svm_model, "训练集") print("\n验证集评估：") val_preds = evaluate(val_features, val_labels, svm_model, "验证集") print("\n测试集评估：") test_preds = evaluate(test_features, test_labels, svm_model, "测试集") # 9. ====================== 保存模型 ====================== def save_pipeline(): """保存完整模型管道""" # 创建保存目录 import os os.makedirs(SAVE_DIR, exist_ok=True) # 保存BERT相关 bert_model.save_pretrained(SAVE_DIR) tokenizer.save_pretrained(SAVE_DIR) # 保存SVM和预处理 joblib.dump(svm_model, f"{SAVE_DIR}/svm_model.pkl") joblib.dump(scaler, f"{SAVE_DIR}/scaler.pkl") # 保存标签映射（假设标签为0: "中性", 1: "正面", 2: "负面"） label_map = {0: "中性", 1: "正面", 2: "负面"} joblib.dump(label_map, f"{SAVE_DIR}/label_map.pkl") print(f"\n模型已保存至 {SAVE_DIR} 目录") save_pipeline() # 10. ===================== 可视化 ====================== plt.figure(figsize=(15, 5)) # 决策值分布 plt.subplot(1, 2, 1) plt.plot(svm_model.decision_function(train_features[:100]), 'o', alpha=0.5) plt.title("训练集前100样本决策值分布") plt.xlabel("样本索引") plt.ylabel("决策值") # 生成混淆矩阵 cm = confusion_matrix(y_true=test_labels, y_pred=test_preds) # 可视化 plt.figure(figsize=(10, 7)) sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=['中性', '正面', '负面'], yticklabels=['中性', '正面', '负面']) plt.xlabel('Predicted label') plt.ylabel('True label') plt.title('confusion matrix') plt.show() # 准确率对比 plt.subplot(1, 2, 2) accuracies = [ accuracy_score(train_labels, svm_model.predict(train_features)), accuracy_score(val_labels, val_preds), accuracy_score(test_labels, test_preds) ] labels = ['train', 'Validation', 'test'] plt.bar(labels, accuracies, color=['blue', 'orange', 'green']) plt.ylim(0, 1) plt.title("Comparison of accuracy rates for each dataset") plt.ylabel("Accuracy rate") plt.tight_layout() plt.show()关于这个模型，直接画一下我的模型架构图

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