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{
"cells": [
{
"cell_type": "markdown",
"id": "b1b37ca8-25a3-440c-9b68-7f72ce670ade",
"metadata": {},
"source": [
"# 2.4 基因大模型的生物序列特征提取"
]
},
{
"cell_type": "markdown",
"id": "d3d04215-2b6c-41fb-92a4-90c82d322ba4",
"metadata": {},
"source": [
"使用 GPT-2 模型获取文本的特征向量是一个常见的需求，尤其是在进行文本分类、相似度计算或其他下游任务时。Hugging Face 的 transformers 库提供了简单易用的接口来实现这一点。以下是详细的步骤和代码示例，帮助你从 GPT-2 模型中提取文本的特征向量。"
]
},
{
"cell_type": "markdown",
"id": "3ff5b7c6-e57c-4839-8510-f764154faa65",
"metadata": {},
"source": [
"使用 GPT-2 模型获取文本的特征向量是一个常见的需求，尤其是在进行文本分类、相似度计算或其他下游任务时。Hugging Face 的 `transformers` 库提供了简单易用的接口来实现这一点。以下是详细的步骤和代码示例，帮助你从 GPT-2 模型中提取文本的特征向量。\n",
"\n",
"### 方法 1: 使用隐藏状态（Hidden States）\n",
"\n",
"GPT-2 是一个基于 Transformer 的语言模型，它在每一层都有隐藏状态（hidden states），这些隐藏状态可以作为文本的特征表示。你可以选择最后一层的隐藏状态作为最终的特征向量，或者对多层的隐藏状态进行平均或拼接。\n",
"\n",
"\n",
"### 方法 2: 使用池化策略\n",
"\n",
"另一种方法是通过对所有 token 的隐藏状态进行池化操作来获得句子级别的特征向量。常见的池化方法包括：\n",
"\n",
"- **均值池化**（Mean Pooling）：对所有 token 的隐藏状态求平均。\n",
"- **最大池化**（Max Pooling）：对每个维度取最大值。"
]
},
{
"cell_type": "code",
"execution_count": 43,
"id": "e7fe053b-d6da-488a-9c62-24e4b40a992d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'input_ids': tensor([[ 1, 191, 29, 753, 1241, 2104, 12297, 357, 85, 4395,\n",
" 26392, 16]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])}\n",
"torch.Size([768])\n",
"torch.Size([768])\n",
"torch.Size([768])\n"
]
}
],
"source": [
"from transformers import AutoTokenizer, AutoModel\n",
"tokenizer = AutoTokenizer.from_pretrained('dna_bpe_dict')\n",
"tokenizer.tokenize(\"GAGCACATTCGCCTGCGTGCGCACTCACACACACGTTCAAAAAGAGTCCATTCGATTCTGGCAGTAG\")\n",
"#result: [G','AGCAC','ATTCGCC',....]\n",
"\n",
"model = AutoModel.from_pretrained('dna_gpt2_v0')\n",
"import torch\n",
"dna = \"ACGTAGCATCGGATCTATCTATCGACACTTGGTTATCGATCTACGAGCATCTCGTTAGC\"\n",
"inputs = tokenizer(dna, return_tensors = 'pt')\n",
"print(inputs)\n",
"\n",
"outputs = model(inputs[\"input_ids\"])\n",
"#outputs = model(**inputs)\n",
"\n",
"hidden_states = outputs.last_hidden_state # [1, sequence_length, 768] outputs.last_hidden_state or outputs[0]\n",
"\n",
"# embedding with mean pooling\n",
"embedding_mean = torch.mean(hidden_states[0], dim=0)\n",
"print(embedding_mean.shape) # expect to be 768\n",
"\n",
"# embedding with max pooling\n",
"embedding_max = torch.max(hidden_states[0], dim=0)[0]\n",
"print(embedding_max.shape) # expect to be 768\n",
"\n",
"# embedding with first token\n",
"embedding_first_token = hidden_states[0][0]\n",
"print(embedding_first_token.shape) # expect to be 768"
]
},
{
"cell_type": "code",
"execution_count": 44,
"id": "a1f2b545-283a-4613-a953-beb82f427826",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Some weights of BertModel were not initialized from the model checkpoint at dna_bert_v0 and are newly initialized: ['bert.pooler.dense.bias', 'bert.pooler.dense.weight']\n",
"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'input_ids': tensor([[ 6, 200, 16057, 10, 1256, 2123, 12294, 366, 13138, 7826,\n",
" 82, 25]]), 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])}\n",
"torch.Size([768])\n",
"torch.Size([768])\n",
"torch.Size([768])\n"
]
}
],
"source": [
"from transformers import AutoTokenizer, AutoModel\n",
"import torch\n",
"\n",
"tokenizer = AutoTokenizer.from_pretrained('dna_wordpiece_dict')\n",
"tokenizer.tokenize(\"GAGCACATTCGCCTGCGTGCGCACTCACACACACGTTCAAAAAGAGTCCATTCGATTCTGGCAGTAG\")\n",
"#result: [G','AGCAC','ATTCGCC',....]\n",
"\n",
"model = AutoModel.from_pretrained('dna_bert_v0')\n",
"dna = \"ACGTAGCATCGGATCTATCTATCGACACTTGGTTATCGATCTACGAGCATCTCGTTAGC\"\n",
"inputs = tokenizer(dna, return_tensors = 'pt')\n",
"print(inputs)\n",
"\n",
"outputs = model(inputs[\"input_ids\"])\n",
"#outputs = model(**inputs)\n",
"\n",
"hidden_states = outputs.last_hidden_state # [1, sequence_length, 768] outputs.last_hidden_state or outputs[0]\n",
"\n",
"# embedding with mean pooling\n",
"embedding_mean = torch.mean(hidden_states[0], dim=0)\n",
"print(embedding_mean.shape) # expect to be 768\n",
"\n",
"# embedding with max pooling\n",
"embedding_max = torch.max(hidden_states[0], dim=0)[0]\n",
"print(embedding_max.shape) # expect to be 768\n",
"\n",
"# embedding with first token\n",
"embedding_first_token = hidden_states[0][0]\n",
"print(embedding_first_token.shape) # expect to be 768"
]
},
{
"cell_type": "markdown",
"id": "56761874-9af7-4b90-aa8b-131e5b8c69b6",
"metadata": {},
"source": [
"## 特征提取并分类\n",
"\n",
"我们使用第一章中的\"dnagpt/dna_core_promoter\"数据集，演示下使用我们训练的DNA GPT2或者DNA bert模型，提取序列特征，然使用最基础的逻辑回归分类方法，对序列进行分类。"
]
},
{
"cell_type": "code",
"execution_count": 45,
"id": "f1ca177c-a80f-48a1-b2f9-16c13b3350db",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"\"\\nimport os\\n\\n# 设置环境变量, autodl专区 其他idc\\nos.environ['HF_ENDPOINT'] = 'https://hf-mirror.com'\\n\\n# 打印环境变量以确认设置成功\\nprint(os.environ.get('HF_ENDPOINT'))\\n\""
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import subprocess\n",
"import os\n",
"# 设置环境变量, autodl一般区域\n",
"result = subprocess.run('bash -c \"source /etc/network_turbo && env | grep proxy\"', shell=True, capture_output=True, text=True)\n",
"output = result.stdout\n",
"for line in output.splitlines():\n",
" if '=' in line:\n",
" var, value = line.split('=', 1)\n",
" os.environ[var] = value\n",
"\n",
"#或者\n",
"\"\"\"\n",
"import os\n",
"\n",
"# 设置环境变量, autodl专区 其他idc\n",
"os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com'\n",
"\n",
"# 打印环境变量以确认设置成功\n",
"print(os.environ.get('HF_ENDPOINT'))\n",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": 46,
"id": "2295739c-e80a-47be-9400-88bfab4b0bb6",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"DatasetDict({\n",
" train: Dataset({\n",
" features: ['sequence', 'label'],\n",
" num_rows: 59196\n",
" })\n",
"})"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from datasets import load_dataset\n",
"dna_data = load_dataset(\"dnagpt/dna_core_promoter\")\n",
"dna_data"
]
},
{
"cell_type": "markdown",
"id": "c804bced-f151-43a7-8a95-156db358da3e",
"metadata": {},
"source": [
"这里，我们不需要关注这个数据的具体生物学含义，只需知道sequence是具体的DNA序列，label是分类标签，有两个类别0和1即可"
]
},
{
"cell_type": "code",
"execution_count": 47,
"id": "9a47a1b1-21f2-4d71-801c-50f88e326ed3",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'sequence': 'CATGCGGGTCGATATCCTATCTGAATCTCTCAGCCCAAGAGGGAGTCCGCTCATCTATTCGGCAGTACTG',\n",
" 'label': 0}"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dna_data[\"train\"][0]"
]
},
{
"cell_type": "markdown",
"id": "cde7986d-a225-41ca-8f11-614d079fd2bf",
"metadata": {},
"source": [
"这里使用scikit-learn库来构建逻辑回归分类器。首先是特征提取："
]
},
{
"cell_type": "code",
"execution_count": 52,
"id": "4010d991-056a-43ce-8cca-30eeec8678f5",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.datasets import load_iris\n",
"from sklearn.metrics import accuracy_score\n",
"\n",
"\n",
"def get_gpt2_feature(sequence):\n",
" return \n",
"\n",
"# 加载数据集\n",
"data = load_iris()\n",
"X = data.data[data.target < 2] # 只选择前两个类别\n",
"y = data.target[data.target < 2]\n",
"\n",
"X = []\n",
"Y = []\n",
"\n",
"for item in dna_data[\"train\"]:\n",
" sequence = item[\"sequence\"]\n",
" label = item[\"label\"]\n",
" x_v = get_gpt2_feature(sequence)\n",
" y_v = label\n",
" X.append(x_v)\n",
" Y.append(y_v)"
]
},
{
"cell_type": "code",
"execution_count": 49,
"id": "8af0effa-b2b6-4e49-9256-cead146d848c",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[5.1, 3.5, 1.4, 0.2],\n",
" [4.9, 3. , 1.4, 0.2],\n",
" [4.7, 3.2, 1.3, 0.2],\n",
" [4.6, 3.1, 1.5, 0.2],\n",
" [5. , 3.6, 1.4, 0.2],\n",
" [5.4, 3.9, 1.7, 0.4],\n",
" [4.6, 3.4, 1.4, 0.3],\n",
" [5. , 3.4, 1.5, 0.2],\n",
" [4.4, 2.9, 1.4, 0.2],\n",
" [4.9, 3.1, 1.5, 0.1],\n",
" [5.4, 3.7, 1.5, 0.2],\n",
" [4.8, 3.4, 1.6, 0.2],\n",
" [4.8, 3. , 1.4, 0.1],\n",
" [4.3, 3. , 1.1, 0.1],\n",
" [5.8, 4. , 1.2, 0.2],\n",
" [5.7, 4.4, 1.5, 0.4],\n",
" [5.4, 3.9, 1.3, 0.4],\n",
" [5.1, 3.5, 1.4, 0.3],\n",
" [5.7, 3.8, 1.7, 0.3],\n",
" [5.1, 3.8, 1.5, 0.3],\n",
" [5.4, 3.4, 1.7, 0.2],\n",
" [5.1, 3.7, 1.5, 0.4],\n",
" [4.6, 3.6, 1. , 0.2],\n",
" [5.1, 3.3, 1.7, 0.5],\n",
" [4.8, 3.4, 1.9, 0.2],\n",
" [5. , 3. , 1.6, 0.2],\n",
" [5. , 3.4, 1.6, 0.4],\n",
" [5.2, 3.5, 1.5, 0.2],\n",
" [5.2, 3.4, 1.4, 0.2],\n",
" [4.7, 3.2, 1.6, 0.2],\n",
" [4.8, 3.1, 1.6, 0.2],\n",
" [5.4, 3.4, 1.5, 0.4],\n",
" [5.2, 4.1, 1.5, 0.1],\n",
" [5.5, 4.2, 1.4, 0.2],\n",
" [4.9, 3.1, 1.5, 0.2],\n",
" [5. , 3.2, 1.2, 0.2],\n",
" [5.5, 3.5, 1.3, 0.2],\n",
" [4.9, 3.6, 1.4, 0.1],\n",
" [4.4, 3. , 1.3, 0.2],\n",
" [5.1, 3.4, 1.5, 0.2],\n",
" [5. , 3.5, 1.3, 0.3],\n",
" [4.5, 2.3, 1.3, 0.3],\n",
" [4.4, 3.2, 1.3, 0.2],\n",
" [5. , 3.5, 1.6, 0.6],\n",
" [5.1, 3.8, 1.9, 0.4],\n",
" [4.8, 3. , 1.4, 0.3],\n",
" [5.1, 3.8, 1.6, 0.2],\n",
" [4.6, 3.2, 1.4, 0.2],\n",
" [5.3, 3.7, 1.5, 0.2],\n",
" [5. , 3.3, 1.4, 0.2],\n",
" [7. , 3.2, 4.7, 1.4],\n",
" [6.4, 3.2, 4.5, 1.5],\n",
" [6.9, 3.1, 4.9, 1.5],\n",
" [5.5, 2.3, 4. , 1.3],\n",
" [6.5, 2.8, 4.6, 1.5],\n",
" [5.7, 2.8, 4.5, 1.3],\n",
" [6.3, 3.3, 4.7, 1.6],\n",
" [4.9, 2.4, 3.3, 1. ],\n",
" [6.6, 2.9, 4.6, 1.3],\n",
" [5.2, 2.7, 3.9, 1.4],\n",
" [5. , 2. , 3.5, 1. ],\n",
" [5.9, 3. , 4.2, 1.5],\n",
" [6. , 2.2, 4. , 1. ],\n",
" [6.1, 2.9, 4.7, 1.4],\n",
" [5.6, 2.9, 3.6, 1.3],\n",
" [6.7, 3.1, 4.4, 1.4],\n",
" [5.6, 3. , 4.5, 1.5],\n",
" [5.8, 2.7, 4.1, 1. ],\n",
" [6.2, 2.2, 4.5, 1.5],\n",
" [5.6, 2.5, 3.9, 1.1],\n",
" [5.9, 3.2, 4.8, 1.8],\n",
" [6.1, 2.8, 4. , 1.3],\n",
" [6.3, 2.5, 4.9, 1.5],\n",
" [6.1, 2.8, 4.7, 1.2],\n",
" [6.4, 2.9, 4.3, 1.3],\n",
" [6.6, 3. , 4.4, 1.4],\n",
" [6.8, 2.8, 4.8, 1.4],\n",
" [6.7, 3. , 5. , 1.7],\n",
" [6. , 2.9, 4.5, 1.5],\n",
" [5.7, 2.6, 3.5, 1. ],\n",
" [5.5, 2.4, 3.8, 1.1],\n",
" [5.5, 2.4, 3.7, 1. ],\n",
" [5.8, 2.7, 3.9, 1.2],\n",
" [6. , 2.7, 5.1, 1.6],\n",
" [5.4, 3. , 4.5, 1.5],\n",
" [6. , 3.4, 4.5, 1.6],\n",
" [6.7, 3.1, 4.7, 1.5],\n",
" [6.3, 2.3, 4.4, 1.3],\n",
" [5.6, 3. , 4.1, 1.3],\n",
" [5.5, 2.5, 4. , 1.3],\n",
" [5.5, 2.6, 4.4, 1.2],\n",
" [6.1, 3. , 4.6, 1.4],\n",
" [5.8, 2.6, 4. , 1.2],\n",
" [5. , 2.3, 3.3, 1. ],\n",
" [5.6, 2.7, 4.2, 1.3],\n",
" [5.7, 3. , 4.2, 1.2],\n",
" [5.7, 2.9, 4.2, 1.3],\n",
" [6.2, 2.9, 4.3, 1.3],\n",
" [5.1, 2.5, 3. , 1.1],\n",
" [5.7, 2.8, 4.1, 1.3]])"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X"
]
},
{
"cell_type": "code",
"execution_count": 51,
"id": "868a3cab-e991-4990-9ec5-3e632a41a599",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5ab0c188-6476-43c4-b361-a2bfe0ec7a8a",
"metadata": {},
"outputs": [],
"source": [
"# 将数据分为训练集和测试集\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
"\n",
"# 创建逻辑回归模型\n",
"model = LogisticRegression()\n",
"\n",
"# 训练模型\n",
"model.fit(X_train, y_train)\n",
"\n",
"# 在测试集上进行预测\n",
"y_pred = model.predict(X_test)\n",
"\n",
"# 计算准确率\n",
"accuracy = accuracy_score(y_test, y_pred)\n",
"print(f\"Accuracy: {accuracy * 100:.2f}%\")\n",
"\n",
"# 输出部分预测结果与真实标签对比\n",
"for i in range(5):\n",
" print(f\"True: {y_test[i]}, Predicted: {y_pred[i]}\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.3"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|