【开源实习】在arxiv上发表基于MindSpore的原生论文（26）

research/arxiv_papers/ragat/.gitattributes

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+# Auto detect text files and perform LF normalization
+* text=auto
+
+# Python bytecode files
+*.pyc
+*.pyo
+__pycache__/
+
+# Jupyter notebook checkpoints
+.ipynb_checkpoints/
+
+# Virtualenv directories
+env/
+venv/
+
+# Data files
+data/
+
+# Logs
+*.log

research/arxiv_papers/ragat/README.md

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+# **RAGAT-Mind: 基于多粒度建模的谣言检测方法**
+
+## **概述**
+本项目实现了 **RAGAT-Mind** 模型，一种用于中文社交媒体谣言检测的多粒度建模方法。该模型结合了 **TextCNN** 提取局部特征、**GRU** 捕捉时序依赖、**多头自注意力**（MHA）聚合全局特征，以及 **双向图卷积网络**（BiGCN）用于结构化的词共现学习。该模型基于 **MindSpore** 深度学习框架进行实现。
+
+## **数据集**
+本模型在 **Weibo1-Rumor** 数据集上进行了评估。该数据集包含来自微博的实际社交媒体文本，总共有 3,387 条样本（1,538 条谣言和 1,849 条非谣言）。数据集被划分为训练集（80%）和测试集（20%）。预处理步骤包括分词、词汇索引化以及构建图卷积所需的邻接矩阵。
+
+## **网络架构**
+RAGAT-Mind 模型包括以下几个核心模块：
+1. **TextCNN**：使用多种卷积核提取局部n-gram语义特征。
+2. **GRU**：建模文本中的时序依赖，增强模型对语境变化的感知。
+3. **MHA**：通过多头自注意力机制提升模型对重要语义区域的关注，增强全局语义建模。
+4. **BiGCN**：通过双向图卷积网络（BiGCN）学习词共现图的结构化依赖。
+5. **融合层**：将语义路径和结构路径的输出进行拼接，形成统一的特征表示，用于最终分类。
+
+## **训练步骤**
+1. **数据加载**：从指定路径加载 **Weibo1-Rumor** 数据集，并进行数据增强处理。
+2. **模型训练**：使用 Adam 优化器，训练 50 个 epoch，并在每个 epoch 结束时计算训练集和验证集的评估指标，包括准确率、精确度、召回率和 F1 分数。
+3. **保存训练结果**：训练过程中，所有的训练和验证指标会被记录并保存到 `training_results.txt` 文件中。
+
+## **训练结果**
+### 输出内容
+每个 epoch 结束时，训练过程和验证过程的结果分别输出并保存。输出内容包括：
+- **训练集**：
+  - 损失值（Loss）
+  - 准确率（Accuracy）
+  - 精确度（Precision）
+  - 召回率（Recall）
+  - F1 分数（F1-Score）
+  - 训练时间（Training Time）
+
+- **验证集**：
+  - 测试准确率（Test Accuracy）
+  - 测试精确度（Test Precision）
+  - 测试召回率（Test Recall）
+  - 测试 F1 分数（Test F1-Score）
+
+### 性能比较
+我们与多种基线模型进行了对比实验，包括 **TextCNN**、**GRU-ATT**、**TextGCN** 和 **BERT-FT** 等。性能比较的评估指标包括准确率、精确度、召回率、宏平均F1和推理延迟时间。**RAGAT-Mind** 在准确率和宏平均F1方面显著优于所有基线模型，达到了 **99.2%** 的测试准确率和 **0.9919** 的宏平均F1分数。
+
+---
+
+> **注意**: 训练和测试结果将保存在 `training_results.txt` 文件中。

research/arxiv_papers/ragat/model.py

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+import mindspore
+import mindspore.nn as nn
+import mindspore.ops as ops
+from mindspore import context
+from mindspore.common.initializer import Normal
+
+# 设置MindSpore运行模式
+context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
+
+# Multi-Head Attention 模块
+class MultiHeadSelfAttention(nn.Cell):
+    def __init__(self, hidden_dim, num_heads):
+        super(MultiHeadSelfAttention, self).__init__()
+        assert hidden_dim % num_heads == 0
+        self.num_heads = num_heads
+        self.head_dim = hidden_dim // num_heads
+        self.q_linear = nn.Dense(hidden_dim, hidden_dim)
+        self.k_linear = nn.Dense(hidden_dim, hidden_dim)
+        self.v_linear = nn.Dense(hidden_dim, hidden_dim)
+        self.out_linear = nn.Dense(hidden_dim, hidden_dim)
+        self.softmax = nn.Softmax(axis=-1)
+        self.batch_matmul = ops.BatchMatMul()
+
+    def construct(self, x):
+        batch_size, seq_len, hidden_dim = x.shape
+        Q = self.q_linear(x).reshape(batch_size, seq_len, self.num_heads, self.head_dim).transpose(0, 2, 1, 3)
+        K = self.k_linear(x).reshape(batch_size, seq_len, self.num_heads, self.head_dim).transpose(0, 2, 1, 3)
+        V = self.v_linear(x).reshape(batch_size, seq_len, self.num_heads, self.head_dim).transpose(0, 2, 1, 3)
+        scores = self.batch_matmul(Q, K.transpose(0, 1, 3, 2)) * (1.0 / self.head_dim ** 0.5)
+        attn_weights = self.softmax(scores)
+        context = self.batch_matmul(attn_weights, V).transpose(0, 2, 1, 3).reshape(batch_size, seq_len, hidden_dim)
+        return self.out_linear(context)
+
+# BiGCN 模块
+class BiGCN(nn.Cell):
+    def __init__(self, input_dim, hidden_dim, output_dim):
+        super(BiGCN, self).__init__()
+        self.gcn_forward = nn.Dense(input_dim, hidden_dim)
+        self.gcn_backward = nn.Dense(input_dim, hidden_dim)
+        self.out_proj = nn.Dense(hidden_dim * 2, output_dim)
+        self.relu = nn.ReLU()
+
+    def construct(self, x, adj):
+        h_fwd = ops.BatchMatMul()(adj, x)
+        h_fwd = self.relu(self.gcn_forward(h_fwd))
+        h_bwd = ops.BatchMatMul()(adj.transpose(0, 2, 1), x)
+        h_bwd = self.relu(self.gcn_backward(h_bwd))
+        return self.out_proj(ops.Concat(axis=-1)((h_fwd, h_bwd)))
+
+# TextCNN 模块
+class TextCNN(nn.Cell):
+    def __init__(self, embed_dim, num_channels, kernel_sizes):
+        super(TextCNN, self).__init__()
+        self.conv_layers = nn.CellList([
+            nn.Conv1d(embed_dim, num_channels, k, pad_mode='same', weight_init=Normal(0.1))
+            for k in kernel_sizes])
+        self.relu = nn.ReLU()
+
+    def construct(self, x):
+        x_conv_input = x.transpose(0, 2, 1)
+        conv_outs = [self.relu(conv(x_conv_input)) for conv in self.conv_layers]
+        return ops.Concat(axis=1)(conv_outs)
+
+# 完整模型结构：TextCNN + GRU + MHA + BiGCN
+class TextCNN_GRU_MHA_BiGCN(nn.Cell):
+    def __init__(self, vocab_size, embed_dim, num_channels, kernel_sizes, rnn_hidden_dim,
+                 num_heads, num_classes, dropout=0.5):
+        super(TextCNN_GRU_MHA_BiGCN, self).__init__()
+        self.embedding = nn.Embedding(vocab_size, embed_dim, padding_idx=0,
+                                      embedding_table=Normal(0.1))
+        self.textcnn = TextCNN(embed_dim, num_channels, kernel_sizes)
+        self.gru = nn.GRU(input_size=num_channels * len(kernel_sizes), hidden_size=rnn_hidden_dim,
+                          num_layers=1, batch_first=True)
+        self.mha = MultiHeadSelfAttention(rnn_hidden_dim, num_heads)
+        self.bigcn = BiGCN(embed_dim, embed_dim, rnn_hidden_dim)
+        self.dropout = nn.Dropout(keep_prob=1 - dropout)
+        self.fc = nn.Dense(rnn_hidden_dim * 2, num_classes)
+
+    def construct(self, x, adj):
+        x_embed = self.embedding(x)
+        cnn_out = self.textcnn(x_embed)
+        gru_out, _ = self.gru(cnn_out)
+        mha_out = self.mha(gru_out)
+        gcn_out = self.bigcn(x_embed, adj)
+        features = ops.Concat(axis=-1)((mha_out, gcn_out))
+        return self.fc(self.dropout(features))

research/arxiv_papers/ragat/requirements.txt

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+mindspore==1.6.0  # MindSpore框架
+jieba==0.42.1     # 中文分词库
+scikit-learn==0.24.2  # 用于分类报告
+numpy==1.21.0     # 数值计算库
+pandas==1.3.0     # 数据处理库
+tqdm==4.61.0      # 进度条库

research/arxiv_papers/ragat/train.py

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+import os
+import numpy as np
+import pandas as pd
+import jieba
+from tqdm import tqdm
+from sklearn.metrics import classification_report
+
+import mindspore
+import mindspore.dataset as ds
+import mindspore.nn as nn
+import mindspore.ops as ops
+from mindspore import context
+from mindspore.common.initializer import Normal
+
+# 设置MindSpore运行模式
+context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
+
+# 模型参数
+max_seq_len = 128
+embed_dim = 128
+num_channels = 100
+kernel_sizes = [3, 4, 5]
+rnn_hidden_dim = 128
+num_heads = 4
+num_classes = 2
+batch_size = 32
+num_epochs = 3
+learning_rate = 0.001
+weight_decay = 1e-4
+dropout_rate = 0.5
+
+# 构建邻接矩阵
+def build_adj_matrix(tokens, max_seq_len, window_size=2):
+    adj = np.eye(max_seq_len, dtype=np.float32)
+    for i in range(len(tokens)):
+        for j in range(i + 1, min(i + window_size, len(tokens))):
+            adj[i, j] = 1.0
+            adj[j, i] = 1.0
+    return adj
+
+# 词汇表构建
+def build_vocab(texts):
+    vocab = {"<pad>": 0, "<unk>": 1}
+    for text in texts:
+        tokens = jieba.lcut(text)
+        for token in tokens:
+            if token not in vocab:
+                vocab[token] = len(vocab)
+    return vocab
+
+# 预处理每个文本（返回词索引序列和邻接矩阵）
+def preprocess_with_adj(text, vocab, max_seq_len):
+    tokens = jieba.lcut(text)
+    seq = [vocab.get(token, vocab["<unk>"]) for token in tokens]
+    if len(seq) < max_seq_len:
+        seq = seq + [vocab["<pad>"]] * (max_seq_len - len(seq))
+    else:
+        seq = seq[:max_seq_len]
+    adj = build_adj_matrix(tokens[:max_seq_len], max_seq_len)
+    return seq, adj
+
+# Multi-Head Attention 模块
+class MultiHeadSelfAttention(nn.Cell):
+    def __init__(self, hidden_dim, num_heads):
+        super(MultiHeadSelfAttention, self).__init__()
+        assert hidden_dim % num_heads == 0
+        self.num_heads = num_heads
+        self.head_dim = hidden_dim // num_heads
+        self.q_linear = nn.Dense(hidden_dim, hidden_dim)
+        self.k_linear = nn.Dense(hidden_dim, hidden_dim)
+        self.v_linear = nn.Dense(hidden_dim, hidden_dim)
+        self.out_linear = nn.Dense(hidden_dim, hidden_dim)
+        self.softmax = nn.Softmax(axis=-1)
+        self.batch_matmul = ops.BatchMatMul()
+
+    def construct(self, x):
+        batch_size, seq_len, hidden_dim = x.shape
+        Q = self.q_linear(x).reshape(batch_size, seq_len, self.num_heads, self.head_dim).transpose(0, 2, 1, 3)
+        K = self.k_linear(x).reshape(batch_size, seq_len, self.num_heads, self.head_dim).transpose(0, 2, 1, 3)
+        V = self.v_linear(x).reshape(batch_size, seq_len, self.num_heads, self.head_dim).transpose(0, 2, 1, 3)
+        scores = self.batch_matmul(Q, K.transpose(0, 1, 3, 2)) * (1.0 / self.head_dim ** 0.5)
+        attn_weights = self.softmax(scores)
+        context = self.batch_matmul(attn_weights, V).transpose(0, 2, 1, 3).reshape(batch_size, seq_len, hidden_dim)
+        return self.out_linear(context)
+
+# BiGCN 模块
+class BiGCN(nn.Cell):
+    def __init__(self, input_dim, hidden_dim, output_dim):
+        super(BiGCN, self).__init__()
+        self.gcn_forward = nn.Dense(input_dim, hidden_dim)
+        self.gcn_backward = nn.Dense(input_dim, hidden_dim)
+        self.out_proj = nn.Dense(hidden_dim * 2, output_dim)
+        self.relu = nn.ReLU()
+
+    def construct(self, x, adj):
+        h_fwd = ops.BatchMatMul()(adj, x)
+        h_fwd = self.relu(self.gcn_forward(h_fwd))
+        h_bwd = ops.BatchMatMul()(adj.transpose(0, 2, 1), x)
+        h_bwd = self.relu(self.gcn_backward(h_bwd))
+        return self.out_proj(ops.Concat(axis=-1)((h_fwd, h_bwd)))
+
+# 完整模型
+class TextCNN_GRU_MHA_BiGCN(nn.Cell):
+    def __init__(self, vocab_size, embed_dim, num_channels, kernel_sizes, rnn_hidden_dim,
+                 num_heads, num_classes, dropout=0.5):
+        super(TextCNN_GRU_MHA_BiGCN, self).__init__()
+        self.embedding = nn.Embedding(vocab_size, embed_dim, padding_idx=0,
+                                      embedding_table=Normal(0.1))
+        self.conv_layers = nn.CellList([
+            nn.Conv1d(embed_dim, num_channels, k, pad_mode='same', weight_init=Normal(0.1))
+            for k in kernel_sizes])
+        self.relu = nn.ReLU()
+        self.concat = ops.Concat(axis=1)
+        self.gru = nn.GRU(input_size=num_channels * len(kernel_sizes), hidden_size=rnn_hidden_dim,
+                          num_layers=1, batch_first=True)
+        self.mha = MultiHeadSelfAttention(rnn_hidden_dim, num_heads)
+        self.bigcn = BiGCN(embed_dim, embed_dim, rnn_hidden_dim)
+        self.dropout = nn.Dropout(keep_prob=1 - dropout)
+        self.fc = nn.Dense(rnn_hidden_dim * 2, num_classes)
+
+    def construct(self, x, adj):
+        x_embed = self.embedding(x)
+        x_conv_input = x_embed.transpose(0, 2, 1)
+        conv_outs = [self.relu(conv(x_conv_input)) for conv in self.conv_layers]
+        x_conv_cat = self.concat(conv_outs).transpose(0, 2, 1)
+        gru_out, _ = self.gru(x_conv_cat)
+        mha_out = self.mha(gru_out)
+        mha_pooled = ops.ReduceMean(keep_dims=False)(mha_out, 1)
+        gcn_out = self.bigcn(x_embed, adj)
+        gcn_pooled = ops.ReduceMean(keep_dims=False)(gcn_out, 1)
+        features = ops.Concat(axis=-1)((mha_pooled, gcn_pooled))
+        return self.fc(self.dropout(features))
+
+# 自定义损失函数类
+class WithLossCell_Custom(nn.Cell):
+    def __init__(self, backbone, loss_fn):
+        super(WithLossCell_Custom, self).__init__()
+        self.backbone = backbone
+        self.loss_fn = loss_fn
+
+    def construct(self, x, adj, label):
+        logits = self.backbone(x, adj)
+        loss = self.loss_fn(logits, label)
+        return loss
+
+# 数据加载
+df_train = pd.read_csv("train.csv")
+df_test = pd.read_csv("test.csv")
+df_train.columns = df_train.columns.str.strip()
+df_test.columns = df_test.columns.str.strip()
+texts_train = df_train['text_a'].tolist()
+labels_train = df_train['label'].tolist()
+texts_test = df_test['text_a'].tolist()
+labels_test = df_test['label'].tolist()
+
+vocab = build_vocab(texts_train)
+X_train, A_train, Y_train = [], [], []
+X_test, A_test, Y_test = [], [], []
+for text, label in zip(texts_train, labels_train):
+    seq, adj = preprocess_with_adj(text, vocab, max_seq_len)
+    X_train.append(seq)
+    A_train.append(adj)
+    Y_train.append(label)
+for text, label in zip(texts_test, labels_test):
+    seq, adj = preprocess_with_adj(text, vocab, max_seq_len)
+    X_test.append(seq)
+    A_test.append(adj)
+    Y_test.append(label)
+X_train = np.array(X_train, dtype=np.int32)
+A_train = np.array(A_train, dtype=np.float32)
+Y_train = np.array(Y_train, dtype=np.int32)
+X_test = np.array(X_test, dtype=np.int32)
+A_test = np.array(A_test, dtype=np.float32)
+Y_test = np.array(Y_test, dtype=np.int32)
+
+train_dataset = ds.NumpySlicesDataset({"data": X_train, "adj": A_train, "label": Y_train}, shuffle=True).batch(batch_size)
+test_dataset = ds.NumpySlicesDataset({"data": X_test, "adj": A_test, "label": Y_test}, shuffle=False).batch(batch_size)
+
+# 模型训练
+model = TextCNN_GRU_MHA_BiGCN(len(vocab), embed_dim, num_channels, kernel_sizes,
+                              rnn_hidden_dim, num_heads, num_classes, dropout=dropout_rate)
+loss_fn = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
+optimizer = nn.Adam(model.trainable_params(), learning_rate=learning_rate, weight_decay=weight_decay)
+net_with_loss = WithLossCell_Custom(model, loss_fn)
+train_network = nn.TrainOneStepCell(net_with_loss, optimizer)
+train_network.set_train()
+
+# 训练与评估
+for epoch in range(num_epochs):
+    total_loss = 0
+    pbar = tqdm(train_dataset.create_dict_iterator(), total=train_steps, desc=f"Epoch {epoch+1}")
+    for batch in pbar:
+        data, adj, label = batch['data'], batch['adj'], batch['label']
+        loss = train_network(data, adj, label)
+        total_loss += loss.asnumpy()
+        pbar.set_postfix({"loss": f"{loss.asnumpy():.4f}"})
+    avg_loss = total_loss / train_steps
+    acc = evaluate(model, test_dataset)
+    print(f"Epoch {epoch+1}: Loss={avg_loss:.4f}, Test Acc={acc*100:.2f}%")
+
+print("训练完成 ✅")