diff --git a/finetune_cross_100/build_paws_data.ipynb b/finetune_cross_100/build_paws_data.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..9dbce338bcad59c72b9ac2c6db4cd4c96aea5003 --- /dev/null +++ b/finetune_cross_100/build_paws_data.ipynb @@ -0,0 +1,360 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "a460ddd6-72b4-42b9-a923-8416a75ef87e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "https://hf-mirror.com\n" + ] + } + ], + "source": [ + "import os\n", + "\n", + "# 设置环境变量\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'))" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "18b5c7c9-6f3b-498e-8671-a4cd065d0bd4", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/maris/miniconda3/envs/dnagpt/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], + "source": [ + "from datasets import load_dataset\n", + "from transformers import AutoTokenizer, DataCollatorWithPadding\n", + "from transformers import Trainer\n", + "import evaluate\n", + "import numpy as np\n", + "from transformers import TrainingArguments\n", + "from transformers import AutoModelForSequenceClassification\n", + "\n", + "\n", + "# 假设你的JSON文件名为 'data.json' 并且每行是一个独立的JSON对象\n", + "raw_datasets = load_dataset('paws-x', 'en') # 或者指定特定语言如 'zh' 表示中文,https://huggingface.co/datasets/google-research-datasets/paws-x" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "ba9dc8c5-1c9e-47c5-9c77-b06573e05432", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DatasetDict({\n", + " train: Dataset({\n", + " features: ['id', 'sentence1', 'sentence2', 'label'],\n", + " num_rows: 49401\n", + " })\n", + " test: Dataset({\n", + " features: ['id', 'sentence1', 'sentence2', 'label'],\n", + " num_rows: 2000\n", + " })\n", + " validation: Dataset({\n", + " features: ['id', 'sentence1', 'sentence2', 'label'],\n", + " num_rows: 2000\n", + " })\n", + "})" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw_datasets" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "fbe3f78a-da0f-4f9a-87fc-4d484b4a3b48", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'id': 1,\n", + " 'sentence1': 'In Paris , in October 1560 , he secretly met the English ambassador , Nicolas Throckmorton , asking him for a passport to return to England through Scotland .',\n", + " 'sentence2': 'In October 1560 , he secretly met with the English ambassador , Nicolas Throckmorton , in Paris , and asked him for a passport to return to Scotland through England .',\n", + " 'label': 0}" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw_datasets[\"train\"][0]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "7f048fc9-e8ca-4f27-bfb6-46ef8c12bc52", + "metadata": {}, + "outputs": [], + "source": [ + "raw_datasets_zh = load_dataset('paws-x', 'zh') # 或者指定特定语言如 'zh' 表示中文,https://huggingface.co/datasets/google-research-datasets/paws-x" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "d6128b62-6789-4934-a3dd-2c8fd6e7cb2d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DatasetDict({\n", + " train: Dataset({\n", + " features: ['id', 'sentence1', 'sentence2', 'label'],\n", + " num_rows: 49401\n", + " })\n", + " test: Dataset({\n", + " features: ['id', 'sentence1', 'sentence2', 'label'],\n", + " num_rows: 2000\n", + " })\n", + " validation: Dataset({\n", + " features: ['id', 'sentence1', 'sentence2', 'label'],\n", + " num_rows: 2000\n", + " })\n", + "})" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw_datasets_zh" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "9e936f7d-a0b3-4698-97a9-bd07379cd199", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'id': 1,\n", + " 'sentence1': '1560年10月，他在巴黎秘密会见了英国大使Nicolas Throckmorton，要求他通过苏格兰返回英国。',\n", + " 'sentence2': '1560年10月，他在巴黎秘密会见了英国大使尼古拉斯·斯罗克莫顿，并要求他通过英格兰返回苏格兰的护照。',\n", + " 'label': 0}" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw_datasets_zh[\"train\"][0]" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "6f3ab940-cfb0-4753-83c1-a8ccc31cce26", + "metadata": {}, + "outputs": [], + "source": [ + "raw_datasets_fr = load_dataset('paws-x', 'fr') # 或者指定特定语言如 'zh' 表示中文,https://huggingface.co/datasets/google-research-datasets/paws-x" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "a9d707a7-a46f-4b15-b2e6-1c61df79be9f", + "metadata": {}, + "outputs": [], + "source": [ + "raw_datasets_de = load_dataset('paws-x', 'de') # 或者指定特定语言如 'zh' 表示中文,https://huggingface.co/datasets/google-research-datasets/paws-x" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "f9092ac6-6467-4666-bd27-3000c158add5", + "metadata": {}, + "outputs": [], + "source": [ + "raw_datasets_es = load_dataset('paws-x', 'es') # 或者指定特定语言如 'zh' 表示中文,https://huggingface.co/datasets/google-research-datasets/paws-x" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "8876048c-90c4-4964-9804-115de7128b78", + "metadata": {}, + "outputs": [], + "source": [ + "raw_datasets_ja = load_dataset('paws-x', 'ja') # 或者指定特定语言如 'zh' 表示中文,https://huggingface.co/datasets/google-research-datasets/paws-x" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "a14dcbc9-73a3-4102-88e5-b94d2cfadb36", + "metadata": {}, + "outputs": [], + "source": [ + "raw_datasets_ko = load_dataset('paws-x', 'ko') # 或者指定特定语言如 'zh' 表示中文,https://huggingface.co/datasets/google-research-datasets/paws-x" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "ee6b5d7c-c993-45f9-a008-53e2653818f2", + "metadata": {}, + "outputs": [], + "source": [ + "data_list = []\n", + "for index in range(0, 49401):\n", + " data_en = raw_datasets[\"train\"][index]\n", + " data_fr = raw_datasets_fr[\"train\"][index]\n", + " data_de = raw_datasets_de[\"train\"][index]\n", + " data_zh = raw_datasets_zh[\"train\"][index]\n", + " data_es = raw_datasets_es[\"train\"][index]\n", + " data_ja = raw_datasets_ja[\"train\"][index]\n", + " data_ko = raw_datasets_ko[\"train\"][index]\n", + "\n", + " if data_en[\"id\"]==index+1 and data_fr[\"id\"]==index+1 and data_de[\"id\"]==index+1 and data_zh[\"id\"]==index+1 and data_es[\"id\"]==index+1 and data_ja[\"id\"]==index+1 and data_ko[\"id\"]==index+1:\n", + " data = {\n", + " \"data_id\":data_en[\"id\"],\n", + " \"sentence1\":data_en[\"sentence1\"],\n", + " \"sentence1_fr\":data_fr[\"sentence1\"],\n", + " \"sentence1_de\":data_de[\"sentence1\"],\n", + " \"sentence1_zh\":data_zh[\"sentence1\"],\n", + " \"sentence1_es\":data_es[\"sentence1\"],\n", + " \"sentence1_ja\":data_ja[\"sentence1\"],\n", + " \"sentence1_ko\":data_ko[\"sentence1\"],\n", + " \"label\":data_en[\"label\"],\n", + " \"sentence2\":data_en[\"sentence2\"],\n", + " \"sentence2_fr\":data_fr[\"sentence2\"],\n", + " \"sentence2_de\":data_de[\"sentence2\"],\n", + " \"sentence2_zh\":data_zh[\"sentence2\"],\n", + " \"sentence2_es\":data_es[\"sentence2\"],\n", + " \"sentence2_ja\":data_ja[\"sentence2\"],\n", + " \"sentence2_ko\":data_ko[\"sentence2\"],\n", + " }\n", + " data_list.append(data)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "cc4f5c16-ed09-4866-ab49-736e8940f45c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "49401" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(data_list)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "afc22ff2-06fe-4999-b72c-208e97dc62cb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import sql_appbk\n", + "sql_appbk.insert_data_list(data_list, \"pawx\")" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "819d9d2c-2ae8-449f-9582-8ff608bd349f", + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "ins_file = open(\"paws-x-multi-pair.jsonl\", \"w\")\n", + "\n", + "for item in data_list:\n", + " ins_file.write(json.dumps(item, ensure_ascii=False)+\"\\n\")\n", + "\n", + "ins_file.close()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "baa248d1-090f-4be7-878d-746f10bbb444", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "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": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/finetune_cross_100/gpt2_gene_multiv2_ft_de_en.jsonl b/finetune_cross_100/gpt2_gene_multiv2_ft_de_en.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..f5e6608a20ff268c417fe9da70e8e2f0e6919eb8 --- /dev/null +++ b/finetune_cross_100/gpt2_gene_multiv2_ft_de_en.jsonl @@ -0,0 +1,100 @@ +{"seed": 0, "dna_protein_pair_full": {"accuracy": 0.4445, "f1": 0.11191047162270183}, "dna_protein_pair_rand_full": {"accuracy": 0.5025, "f1": 0.039806996381182146}} +{"seed": 1, "dna_protein_pair_full": {"accuracy": 0.328, "f1": 0.45232273838630804}, "dna_protein_pair_rand_full": {"accuracy": 0.488125, "f1": 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a/finetune_cross_100/gpt2_gene_multiv2_ft_nl_nl_test_others.py b/finetune_cross_100/gpt2_gene_multiv2_ft_nl_nl_test_others.py new file mode 100644 index 0000000000000000000000000000000000000000..55e31d053a52ce78bbec3329a45b4c54debe2156 --- /dev/null +++ b/finetune_cross_100/gpt2_gene_multiv2_ft_nl_nl_test_others.py @@ -0,0 +1,252 @@ +# import os + +# # 设置环境变量 +# os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com' + +# # 打印环境变量以确认设置成功 +# print(os.environ.get('HF_ENDPOINT')) + +# import subprocess +# import os + +# result = subprocess.run('bash -c "source /etc/network_turbo && env | grep proxy"', shell=True, capture_output=True, text=True) +# output = result.stdout +# for line in output.splitlines(): +# if '=' in line: +# var, value = line.split('=', 1) +# os.environ[var] = value + + +from datasets import load_dataset +from transformers import AutoTokenizer, DataCollatorWithPadding +from transformers import Trainer +import evaluate +import numpy as np +from transformers import TrainingArguments +from transformers import AutoModelForSequenceClassification +import json +from transformers import set_seed +import random +import numpy as np +import torch +from tqdm import tqdm +import sys + + +# seed = 42 +# random.seed(seed) +# np.random.seed(seed) +# torch.manual_seed(seed) +# torch.cuda.manual_seed_all(seed) + + +# 动态生成随机种子 + +seed = int(sys.argv[1]) +lang1 = sys.argv[2] #语言1 +lang2 = sys.argv[3] #语言2 + + +#seed = random.randint(0, 10000) +#print(f"Generated seed: {seed}") +set_seed(seed) +result = {} +result["seed"] = seed + + +# 假设你的JSON文件名为 'data.json' 并且每行是一个独立的JSON对象 +raw_datasets = load_dataset('dnagpt/paws-x-multi-pair')['train'].train_test_split(test_size=0.1) #默认已经shuffle + +g_lang1_s = "sentence1_"+lang1 +g_lang2_s = "sentence2_"+lang2 + +# 定义翻转标签的函数 +def flip_data(example): + # 截取 sentence1 和 sentence2 的前 50 个字符,如果dna序列过长，bert分词会产生错误，只生成unk一个token + example["sentence1"] = example[g_lang1_s] + example["sentence2"] = example[g_lang2_s] + return example + +raw_datasets = raw_datasets.map(flip_data, batched=False) + + + +#分词器 +tokenizer = AutoTokenizer.from_pretrained("dnagpt/gpt2_gene_multi_v2") +tokenizer.pad_token = tokenizer.eos_token + + +#分词函数 +def tokenize_function(example): + return tokenizer(example["sentence1"], example["sentence2"], truncation=True,max_length=256, padding="max_length") + +#构建分词后的数据集 +tokenized_datasets = raw_datasets.map(tokenize_function, batched=True) + +#训练数据构建 +data_collator = DataCollatorWithPadding(tokenizer=tokenizer) + + +#指标函数定义 +def compute_metrics(eval_pred): + predictions, labels = eval_pred + predictions = np.argmax(predictions, axis=1) + return {'accuracy': (predictions==labels).sum() / len(labels)} + + + +training_args = TrainingArguments( + output_dir="ds_job_dna_2222", + learning_rate=1e-5, + lr_scheduler_type="constant_with_warmup", + warmup_ratio=0.1, + optim='adamw_torch', + weight_decay=0.0, + seed=seed, # 使用动态生成的随机种子 + per_device_train_batch_size=64, + per_device_eval_batch_size=64, + num_train_epochs=4, #训练多少轮 + evaluation_strategy="epoch", + save_strategy="epoch", + logging_strategy="epoch", + load_best_model_at_end=True +) + +#模型定义，文本分类模型 +model = AutoModelForSequenceClassification.from_pretrained("dnagpt/gpt2_gene_multi_v2", num_labels=2) +model.config.pad_token_id = model.config.eos_token_id + +trainer = Trainer( + model, + training_args, + train_dataset=tokenized_datasets["train"], + eval_dataset=tokenized_datasets["test"], + data_collator=data_collator, + tokenizer=tokenizer, + compute_metrics=compute_metrics, +) + +trainer.train() #模型训练 + + + + +#模型测试 蛋白质数据集 +raw_datasets_dna_protein = load_dataset('dnagpt/gene_lan_transfer', 'dna_protein_pair')['train'].train_test_split(test_size=0.5) #默认已经shuffle + +# 定义翻转标签的函数 +def flip_labels(example): + # 截取 sentence1 和 sentence2 的前 50 个字符,如果dna序列过长，bert分词会产生错误，只生成unk一个token + example["sentence1"] = example["sentence1"] + example["sentence2"] = example["sentence2"] + example['label'] = 1 - example['label'] + return example + +# 应用翻转标签函数 +flipped_datasets_dna_protein = raw_datasets_dna_protein.map(flip_labels, batched=False) +tokenized_datasets_dna_protein = flipped_datasets_dna_protein.map(tokenize_function, batched=True,num_proc=4) + + + +# 确保模型在 GPU 上 +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") +model.to(device) +model.eval() # 进入推理模式，加速推理 + +# 取出测试集数据 +test_dataset = tokenized_datasets_dna_protein["test"] + +# 预存预测结果 +preds = [] +labels = [] + +# 批量大小（建议 64、128、256 视显存大小调整） +batch_size = 64 + +# 直接遍历数据集进行推理 +for i in tqdm(range(0, len(test_dataset), batch_size), desc="Predicting"): + batch = test_dataset[i : i + batch_size] + + # 转换为 Tensor 并移动到 GPU + inputs = { + "input_ids": torch.tensor(batch["input_ids"]).to(device), + "attention_mask": torch.tensor(batch["attention_mask"]).to(device), + } + batch_labels = batch["label"] # 原始标签 + + with torch.no_grad(): # 关闭梯度计算，减少内存占用 + outputs = model(**inputs) + batch_preds = torch.argmax(outputs.logits, axis=-1).cpu().numpy() # 取最大概率的类别 + + preds.extend(batch_preds) + labels.extend(batch_labels) + +metric = evaluate.load("glue", "mrpc") +ret = metric.compute(predictions=preds, references=labels) + + +result["dna_protein_pair_full"] = ret + + + + +############################################################# +#模型测试 蛋白质数据集，随机版本 +raw_datasets_dna_protein = load_dataset('dnagpt/gene_lan_transfer', 'dna_protein_pair_rand')['train'].train_test_split(test_size=0.2) #默认已经shuffle + +# 定义翻转标签的函数 +def flip_labels(example): + # 截取 sentence1 和 sentence2 的前 50 个字符,如果dna序列过长，bert分词会产生错误，只生成unk一个token + example["sentence1"] = example["sentence1"] + example["sentence2"] = example["sentence2"] + example['label'] = 1 - example['label'] + return example + +# 应用翻转标签函数 +flipped_datasets_dna_protein = raw_datasets_dna_protein.map(flip_labels, batched=False) +tokenized_datasets_dna_protein = flipped_datasets_dna_protein.map(tokenize_function, batched=True,num_proc=4) + + +# 确保模型在 GPU 上 +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") +model.to(device) +model.eval() # 进入推理模式，加速推理 + +# 取出测试集数据 +test_dataset = tokenized_datasets_dna_protein["test"] + +# 预存预测结果 +preds = [] +labels = [] + +# 批量大小（建议 64、128、256 视显存大小调整） +batch_size = 64 + +# 直接遍历数据集进行推理 +for i in tqdm(range(0, len(test_dataset), batch_size), desc="Predicting"): + batch = test_dataset[i : i + batch_size] + + # 转换为 Tensor 并移动到 GPU + inputs = { + "input_ids": torch.tensor(batch["input_ids"]).to(device), + "attention_mask": torch.tensor(batch["attention_mask"]).to(device), + } + batch_labels = batch["label"] # 原始标签 + + with torch.no_grad(): # 关闭梯度计算，减少内存占用 + outputs = model(**inputs) + batch_preds = torch.argmax(outputs.logits, axis=-1).cpu().numpy() # 取最大概率的类别 + + preds.extend(batch_preds) + labels.extend(batch_labels) +metric = evaluate.load("glue", "mrpc") +ret = metric.compute(predictions=preds, references=labels) + + + +result["dna_protein_pair_rand_full"] = ret + + + +print(json.dumps(result)) + diff --git a/finetune_cross_100/gpt2_gene_multiv2_ft_zh_de.jsonl b/finetune_cross_100/gpt2_gene_multiv2_ft_zh_de.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..021c7d304a1cd1b0488a44279729bf913b60a5e0 --- /dev/null +++ b/finetune_cross_100/gpt2_gene_multiv2_ft_zh_de.jsonl @@ -0,0 +1,100 @@ +{"seed": 0, "dna_protein_pair_full": {"accuracy": 0.4755, "f1": 0.04200913242009133}, "dna_protein_pair_rand_full": {"accuracy": 0.5096875, "f1": 0.03564843269821758}} +{"seed": 1, "dna_protein_pair_full": {"accuracy": 0.3395, "f1": 0.43329043329043326}, "dna_protein_pair_rand_full": {"accuracy": 0.51, "f1": 0.571818678317859}} +{"seed": 2, "dna_protein_pair_full": {"accuracy": 0.381, "f1": 0.3883399209486166}, "dna_protein_pair_rand_full": {"accuracy": 0.634375, "f1": 0.6323067253299811}} +{"seed": 3, "dna_protein_pair_full": {"accuracy": 0.498, "f1": 0.009861932938856016}, "dna_protein_pair_rand_full": {"accuracy": 0.50875, "f1": 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"dna_protein_pair_full": {"accuracy": 0.4765, "f1": 0.05929919137466307}, "dna_protein_pair_rand_full": {"accuracy": 0.4975, "f1": 0.053003533568904596}} +{"seed": 98, "dna_protein_pair_full": {"accuracy": 0.4185, "f1": 0.19292158223455932}, "dna_protein_pair_rand_full": {"accuracy": 0.465625, "f1": 0.052106430155210645}} +{"seed": 99, "dna_protein_pair_full": {"accuracy": 0.579, "f1": 0.37257824143070045}, "dna_protein_pair_rand_full": {"accuracy": 0.586875, "f1": 0.41813380281690143}} diff --git a/finetune_cross_100/run_ft_all.sh b/finetune_cross_100/run_ft_all.sh new file mode 100644 index 0000000000000000000000000000000000000000..311a92bde6e7c50d499aba3706f23c865b7de88b --- /dev/null +++ b/finetune_cross_100/run_ft_all.sh @@ -0,0 +1,9 @@ +#!/bin/bash +export HF_ENDPOINT=https://hf-mirror.com +lang1=$1 +lang2=$2 +for ((i=0;i<100;i++)) +do + echo "----------------------------------------------"$i + python gpt2_gene_multiv2_ft_nl_nl_test_others.py $i $lang1 $lang2 >> gpt2_gene_multiv2_ft_${lang1}_${lang2}.json +done diff --git a/finetune_pt_100/gpt2_gene_eng_zh_de_es_ft_en.jsonl b/finetune_pt_100/gpt2_gene_eng_zh_de_es_ft_en.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..faa86b9de69d30b90b0a5dd6b5d419ee6f34fbbe --- /dev/null +++ b/finetune_pt_100/gpt2_gene_eng_zh_de_es_ft_en.jsonl @@ -0,0 +1,98 @@ +{"seed": 2, "dna_protein_pair_full": {"accuracy": 0.495, "f1": 0.04716981132075472}, "dna_protein_pair_rand_full": {"accuracy": 0.502, "f1": 0.035351089588377724}} +{"seed": 3, "dna_protein_pair_full": {"accuracy": 0.41, "f1": 0.2976190476190476}, "dna_protein_pair_rand_full": {"accuracy": 0.544375, "f1": 0.47771887089840953}} +{"seed": 4, "dna_protein_pair_full": {"accuracy": 0.5325, "f1": 0.2889733840304182}, "dna_protein_pair_rand_full": {"accuracy": 0.50825, "f1": 0.3363697705802969}} +{"seed": 5, "dna_protein_pair_full": {"accuracy": 0.4825, "f1": 0.6173752310536045}, "dna_protein_pair_rand_full": {"accuracy": 0.5185, 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'https://hf-mirror.com' + +# # 打印环境变量以确认设置成功 +# print(os.environ.get('HF_ENDPOINT')) + +# import subprocess +# import os + +# result = subprocess.run('bash -c "source /etc/network_turbo && env | grep proxy"', shell=True, capture_output=True, text=True) +# output = result.stdout +# for line in output.splitlines(): +# if '=' in line: +# var, value = line.split('=', 1) +# os.environ[var] = value + + +from datasets import load_dataset +from transformers import AutoTokenizer, DataCollatorWithPadding +from transformers import Trainer +import evaluate +import numpy as np +from transformers import TrainingArguments +from transformers import AutoModelForSequenceClassification +import json +from transformers import set_seed +import random +import numpy as np +import torch +from tqdm import tqdm +import sys + + +# seed = 42 +# random.seed(seed) +# np.random.seed(seed) +# torch.manual_seed(seed) +# torch.cuda.manual_seed_all(seed) + + +# 动态生成随机种子 + +seed = int(sys.argv[1]) +lang = sys.argv[2] #语言 + +#seed = random.randint(0, 10000) +#print(f"Generated seed: {seed}") +set_seed(seed) +result = {} +result["seed"] = seed + + + +# 假设你的JSON文件名为 'data.json' 并且每行是一个独立的JSON对象 +raw_datasets = load_dataset('paws-x', lang) # 或者指定特定语言如 'zh' 表示中文,https://huggingface.co/datasets/google-research-datasets/paws-x + +#分词器 +tokenizer = AutoTokenizer.from_pretrained("dnagpt/gene_eng_zh_de_es") +tokenizer.pad_token = tokenizer.eos_token + + +#分词函数 +def tokenize_function(example): + return tokenizer(example["sentence1"], example["sentence2"], truncation=True,max_length=256, padding="max_length") + +#构建分词后的数据集 +tokenized_datasets = raw_datasets.map(tokenize_function, batched=True) + +#训练数据构建 +data_collator = DataCollatorWithPadding(tokenizer=tokenizer) + + +#指标函数定义 +def compute_metrics(eval_pred): + predictions, labels = eval_pred + predictions = np.argmax(predictions, axis=1) + return {'accuracy': (predictions==labels).sum() / len(labels)} + + + +training_args = TrainingArguments( + output_dir="ds_job_dna_2222", + learning_rate=1e-5, + lr_scheduler_type="constant_with_warmup", + warmup_ratio=0.1, + optim='adamw_torch', + weight_decay=0.0, + seed=seed, # 使用动态生成的随机种子 + per_device_train_batch_size=64, + per_device_eval_batch_size=64, + num_train_epochs=4, #训练多少轮 + evaluation_strategy="epoch", + save_strategy="epoch", + logging_strategy="epoch", + load_best_model_at_end=True +) + +#模型定义，文本分类模型 +model = AutoModelForSequenceClassification.from_pretrained("dnagpt/gene_eng_zh_de_es", num_labels=2) +model.config.pad_token_id = model.config.eos_token_id + +trainer = Trainer( + model, + training_args, + train_dataset=tokenized_datasets["train"], + eval_dataset=tokenized_datasets["validation"], + data_collator=data_collator, + tokenizer=tokenizer, + compute_metrics=compute_metrics, +) + +trainer.train() #模型训练 + + + + +#模型测试 蛋白质数据集 +raw_datasets_dna_protein = load_dataset('dnagpt/gene_lan_transfer', 'dna_protein_pair')['train'].train_test_split(test_size=0.1) #默认已经shuffle + +# 定义翻转标签的函数 +def flip_labels(example): + # 截取 sentence1 和 sentence2 的前 50 个字符,如果dna序列过长，bert分词会产生错误，只生成unk一个token + example["sentence1"] = example["sentence1"] + example["sentence2"] = example["sentence2"] + example['label'] = 1 - example['label'] + return example + +# 应用翻转标签函数 +flipped_datasets_dna_protein = raw_datasets_dna_protein.map(flip_labels, batched=False) +tokenized_datasets_dna_protein = flipped_datasets_dna_protein.map(tokenize_function, batched=True,num_proc=4) + + + +# 确保模型在 GPU 上 +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") +model.to(device) +model.eval() # 进入推理模式，加速推理 + +# 取出测试集数据 +test_dataset = tokenized_datasets_dna_protein["test"] + +# 预存预测结果 +preds = [] +labels = [] + +# 批量大小（建议 64、128、256 视显存大小调整） +batch_size = 64 + +# 直接遍历数据集进行推理 +for i in tqdm(range(0, len(test_dataset), batch_size), desc="Predicting"): + batch = test_dataset[i : i + batch_size] + + # 转换为 Tensor 并移动到 GPU + inputs = { + "input_ids": torch.tensor(batch["input_ids"]).to(device), + "attention_mask": torch.tensor(batch["attention_mask"]).to(device), + } + batch_labels = batch["label"] # 原始标签 + + with torch.no_grad(): # 关闭梯度计算，减少内存占用 + outputs = model(**inputs) + batch_preds = torch.argmax(outputs.logits, axis=-1).cpu().numpy() # 取最大概率的类别 + + preds.extend(batch_preds) + labels.extend(batch_labels) + +metric = evaluate.load("glue", "mrpc") +ret = metric.compute(predictions=preds, references=labels) + + +result["dna_protein_pair_full"] = ret + + + + +############################################################# +#模型测试 蛋白质数据集，随机版本 +raw_datasets_dna_protein = load_dataset('dnagpt/gene_lan_transfer', 'dna_protein_pair_rand')['train'].train_test_split(test_size=0.5) #默认已经shuffle + +# 定义翻转标签的函数 +def flip_labels(example): + # 截取 sentence1 和 sentence2 的前 50 个字符,如果dna序列过长，bert分词会产生错误，只生成unk一个token + example["sentence1"] = example["sentence1"] + example["sentence2"] = example["sentence2"] + example['label'] = 1 - example['label'] + return example + +# 应用翻转标签函数 +flipped_datasets_dna_protein = raw_datasets_dna_protein.map(flip_labels, batched=False) +tokenized_datasets_dna_protein = flipped_datasets_dna_protein.map(tokenize_function, batched=True,num_proc=4) + + +# 确保模型在 GPU 上 +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") +model.to(device) +model.eval() # 进入推理模式，加速推理 + +# 取出测试集数据 +test_dataset = tokenized_datasets_dna_protein["test"] + +# 预存预测结果 +preds = [] +labels = [] + +# 批量大小（建议 64、128、256 视显存大小调整） +batch_size = 64 + +# 直接遍历数据集进行推理 +for i in tqdm(range(0, len(test_dataset), batch_size), desc="Predicting"): + batch = test_dataset[i : i + batch_size] + + # 转换为 Tensor 并移动到 GPU + inputs = { + "input_ids": torch.tensor(batch["input_ids"]).to(device), + "attention_mask": torch.tensor(batch["attention_mask"]).to(device), + } + batch_labels = batch["label"] # 原始标签 + + with torch.no_grad(): # 关闭梯度计算，减少内存占用 + outputs = model(**inputs) + batch_preds = torch.argmax(outputs.logits, axis=-1).cpu().numpy() # 取最大概率的类别 + + preds.extend(batch_preds) + labels.extend(batch_labels) +metric = evaluate.load("glue", "mrpc") +ret = metric.compute(predictions=preds, references=labels) + + + +result["dna_protein_pair_rand_full"] = ret + + + +print(json.dumps(result)) + diff --git a/finetune_pt_100/gpt2_gene_zh_ft_en.jsonl b/finetune_pt_100/gpt2_gene_zh_ft_en.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..1642303b45aba893d786269d1e970b3c15498d57 --- /dev/null +++ b/finetune_pt_100/gpt2_gene_zh_ft_en.jsonl @@ -0,0 +1,100 @@ +{"seed": 0, "dna_protein_pair_full": {"accuracy": 0.6725, "f1": 0.6449864498644986}, "dna_protein_pair_rand_full": {"accuracy": 0.537625, "f1": 0.5682269172405743}} +{"seed": 1, "dna_protein_pair_full": {"accuracy": 0.5775, "f1": 0.5212464589235127}, "dna_protein_pair_rand_full": {"accuracy": 0.52425, "f1": 0.4945551128818061}} +{"seed": 2, "dna_protein_pair_full": {"accuracy": 0.5975, "f1": 0.5413105413105413}, 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"dna_protein_pair_full": {"accuracy": 0.4725, "f1": 0.6011342155009451}, "dna_protein_pair_rand_full": {"accuracy": 0.4735, "f1": 0.5990862364363221}} +{"seed": 76, "dna_protein_pair_full": {"accuracy": 0.5725, "f1": 0.5714285714285714}, "dna_protein_pair_rand_full": {"accuracy": 0.566375, "f1": 0.5439726567635073}} +{"seed": 77, "dna_protein_pair_full": {"accuracy": 0.4625, "f1": 0.16342412451361868}, "dna_protein_pair_rand_full": {"accuracy": 0.53875, "f1": 0.3151447661469933}} +{"seed": 78, "dna_protein_pair_full": {"accuracy": 0.505, "f1": 0.029411764705882353}, "dna_protein_pair_rand_full": {"accuracy": 0.504875, "f1": 0.02221673660824488}} +{"seed": 79, "dna_protein_pair_full": {"accuracy": 0.41, "f1": 0.44075829383886256}, "dna_protein_pair_rand_full": {"accuracy": 0.54075, "f1": 0.5027070925825663}} +{"seed": 80, "dna_protein_pair_full": {"accuracy": 0.5, "f1": 0.6655518394648829}, "dna_protein_pair_rand_full": {"accuracy": 0.5, "f1": 0.660671869697998}} +{"seed": 81, "dna_protein_pair_full": {"accuracy": 0.5475, "f1": 0.35125448028673834}, "dna_protein_pair_rand_full": {"accuracy": 0.50875, "f1": 0.33749157113958195}} +{"seed": 82, "dna_protein_pair_full": {"accuracy": 0.4075, "f1": 0.5683060109289617}, "dna_protein_pair_rand_full": {"accuracy": 0.534625, "f1": 0.6613915416098226}} +{"seed": 83, "dna_protein_pair_full": {"accuracy": 0.395, "f1": 0.550185873605948}, "dna_protein_pair_rand_full": {"accuracy": 0.52825, "f1": 0.6432217810550198}} +{"seed": 84, "dna_protein_pair_full": {"accuracy": 0.575, "f1": 0.4178082191780822}, "dna_protein_pair_rand_full": {"accuracy": 0.517625, "f1": 0.410929629064265}} +{"seed": 85, "dna_protein_pair_full": {"accuracy": 0.4625, "f1": 0.6126126126126126}, "dna_protein_pair_rand_full": {"accuracy": 0.499625, "f1": 0.6507285577174766}} +{"seed": 86, "dna_protein_pair_full": {"accuracy": 0.495, "f1": 0.10619469026548672}, "dna_protein_pair_rand_full": {"accuracy": 0.491625, "f1": 0.05965317919075144}} +{"seed": 87, "dna_protein_pair_full": {"accuracy": 0.5125, "f1": 0.0}, "dna_protein_pair_rand_full": {"accuracy": 0.505625, "f1": 0.025622074402562207}} +{"seed": 88, "dna_protein_pair_full": {"accuracy": 0.5675, "f1": 0.2171945701357466}, "dna_protein_pair_rand_full": {"accuracy": 0.482, "f1": 0.1518624641833811}} +{"seed": 89, "dna_protein_pair_full": {"accuracy": 0.6175, "f1": 0.6450116009280742}, "dna_protein_pair_rand_full": {"accuracy": 0.51925, "f1": 0.5156171284634761}} +{"seed": 90, "dna_protein_pair_full": {"accuracy": 0.5375, "f1": 0.11483253588516747}, "dna_protein_pair_rand_full": {"accuracy": 0.5065, "f1": 0.09491059147180192}} +{"seed": 91, "dna_protein_pair_full": {"accuracy": 0.4775, "f1": 0.6193078324225865}, "dna_protein_pair_rand_full": {"accuracy": 0.49925, "f1": 0.6276951672862453}} +{"seed": 92, "dna_protein_pair_full": {"accuracy": 0.5475, "f1": 0.11707317073170732}, "dna_protein_pair_rand_full": {"accuracy": 0.513625, "f1": 0.16555865322753593}} +{"seed": 93, "dna_protein_pair_full": {"accuracy": 0.515, "f1": 0.039603960396039604}, "dna_protein_pair_rand_full": {"accuracy": 0.50925, "f1": 0.07623529411764705}} +{"seed": 94, "dna_protein_pair_full": {"accuracy": 0.615, "f1": 0.3888888888888889}, "dna_protein_pair_rand_full": {"accuracy": 0.548625, "f1": 0.38262950931783213}} +{"seed": 95, "dna_protein_pair_full": {"accuracy": 0.575, "f1": 0.38848920863309355}, "dna_protein_pair_rand_full": {"accuracy": 0.488375, "f1": 0.2013658536585366}} +{"seed": 96, "dna_protein_pair_full": {"accuracy": 0.6325, "f1": 0.5558912386706949}, "dna_protein_pair_rand_full": {"accuracy": 0.4755, "f1": 0.38583138173302106}} +{"seed": 97, "dna_protein_pair_full": {"accuracy": 0.5275, "f1": 0.25882352941176473}, "dna_protein_pair_rand_full": {"accuracy": 0.5235, "f1": 0.2427493047278506}} +{"seed": 98, "dna_protein_pair_full": {"accuracy": 0.4575, "f1": 0.026905829596412557}, "dna_protein_pair_rand_full": {"accuracy": 0.509875, "f1": 0.08065650644783118}} +{"seed": 99, "dna_protein_pair_full": {"accuracy": 0.61, "f1": 0.5214723926380368}, "dna_protein_pair_rand_full": {"accuracy": 0.51675, "f1": 0.4329715459078909}} diff --git a/finetune_pt_100/gpt2_gene_zh_ft_test_others.py b/finetune_pt_100/gpt2_gene_zh_ft_test_others.py new file mode 100644 index 0000000000000000000000000000000000000000..8a8b139ec49f51bca51fad9cc37ea90f6485cd76 --- /dev/null +++ b/finetune_pt_100/gpt2_gene_zh_ft_test_others.py @@ -0,0 +1,237 @@ +# import os + +# # 设置环境变量 +# os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com' + +# # 打印环境变量以确认设置成功 +# print(os.environ.get('HF_ENDPOINT')) + +# import subprocess +# import os + +# result = subprocess.run('bash -c "source /etc/network_turbo && env | grep proxy"', shell=True, capture_output=True, text=True) +# output = result.stdout +# for line in output.splitlines(): +# if '=' in line: +# var, value = line.split('=', 1) +# os.environ[var] = value + + +from datasets import load_dataset +from transformers import AutoTokenizer, DataCollatorWithPadding +from transformers import Trainer +import evaluate +import numpy as np +from transformers import TrainingArguments +from transformers import AutoModelForSequenceClassification +import json +from transformers import set_seed +import random +import numpy as np +import torch +from tqdm import tqdm +import sys + + +# seed = 42 +# random.seed(seed) +# np.random.seed(seed) +# torch.manual_seed(seed) +# torch.cuda.manual_seed_all(seed) + + +# 动态生成随机种子 + +seed = int(sys.argv[1]) +lang = sys.argv[2] #语言 + +#seed = random.randint(0, 10000) +#print(f"Generated seed: {seed}") +set_seed(seed) +result = {} +result["seed"] = seed + + + +# 假设你的JSON文件名为 'data.json' 并且每行是一个独立的JSON对象 +raw_datasets = load_dataset('paws-x', lang) # 或者指定特定语言如 'zh' 表示中文,https://huggingface.co/datasets/google-research-datasets/paws-x + +#分词器 +tokenizer = AutoTokenizer.from_pretrained("dnagpt/gene_eng_zh") +tokenizer.pad_token = tokenizer.eos_token + + +#分词函数 +def tokenize_function(example): + return tokenizer(example["sentence1"], example["sentence2"], truncation=True,max_length=256, padding="max_length") + +#构建分词后的数据集 +tokenized_datasets = raw_datasets.map(tokenize_function, batched=True) + +#训练数据构建 +data_collator = DataCollatorWithPadding(tokenizer=tokenizer) + + +#指标函数定义 +def compute_metrics(eval_pred): + predictions, labels = eval_pred + predictions = np.argmax(predictions, axis=1) + return {'accuracy': (predictions==labels).sum() / len(labels)} + + + +training_args = TrainingArguments( + output_dir="ds_job_dna_2222", + learning_rate=1e-5, + lr_scheduler_type="constant_with_warmup", + warmup_ratio=0.1, + optim='adamw_torch', + weight_decay=0.0, + seed=seed, # 使用动态生成的随机种子 + per_device_train_batch_size=64, + per_device_eval_batch_size=64, + num_train_epochs=4, #训练多少轮 + evaluation_strategy="epoch", + save_strategy="epoch", + logging_strategy="epoch", + load_best_model_at_end=True +) + +#模型定义，文本分类模型 +model = AutoModelForSequenceClassification.from_pretrained("dnagpt/gene_eng_zh", num_labels=2) +model.config.pad_token_id = model.config.eos_token_id + +trainer = Trainer( + model, + training_args, + train_dataset=tokenized_datasets["train"], + eval_dataset=tokenized_datasets["validation"], + data_collator=data_collator, + tokenizer=tokenizer, + compute_metrics=compute_metrics, +) + +trainer.train() #模型训练 + + + + +#模型测试 蛋白质数据集 +raw_datasets_dna_protein = load_dataset('dnagpt/gene_lan_transfer', 'dna_protein_pair')['train'].train_test_split(test_size=0.1) #默认已经shuffle + +# 定义翻转标签的函数 +def flip_labels(example): + # 截取 sentence1 和 sentence2 的前 50 个字符,如果dna序列过长，bert分词会产生错误，只生成unk一个token + example["sentence1"] = example["sentence1"] + example["sentence2"] = example["sentence2"] + example['label'] = 1 - example['label'] + return example + +# 应用翻转标签函数 +flipped_datasets_dna_protein = raw_datasets_dna_protein.map(flip_labels, batched=False) +tokenized_datasets_dna_protein = flipped_datasets_dna_protein.map(tokenize_function, batched=True,num_proc=4) + + + +# 确保模型在 GPU 上 +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") +model.to(device) +model.eval() # 进入推理模式，加速推理 + +# 取出测试集数据 +test_dataset = tokenized_datasets_dna_protein["test"] + +# 预存预测结果 +preds = [] +labels = [] + +# 批量大小（建议 64、128、256 视显存大小调整） +batch_size = 64 + +# 直接遍历数据集进行推理 +for i in tqdm(range(0, len(test_dataset), batch_size), desc="Predicting"): + batch = test_dataset[i : i + batch_size] + + # 转换为 Tensor 并移动到 GPU + inputs = { + "input_ids": torch.tensor(batch["input_ids"]).to(device), + "attention_mask": torch.tensor(batch["attention_mask"]).to(device), + } + batch_labels = batch["label"] # 原始标签 + + with torch.no_grad(): # 关闭梯度计算，减少内存占用 + outputs = model(**inputs) + batch_preds = torch.argmax(outputs.logits, axis=-1).cpu().numpy() # 取最大概率的类别 + + preds.extend(batch_preds) + labels.extend(batch_labels) + +metric = evaluate.load("glue", "mrpc") +ret = metric.compute(predictions=preds, references=labels) + + +result["dna_protein_pair_full"] = ret + + + + +############################################################# +#模型测试 蛋白质数据集，随机版本 +raw_datasets_dna_protein = load_dataset('dnagpt/gene_lan_transfer', 'dna_protein_pair_rand')['train'].train_test_split(test_size=0.5) #默认已经shuffle + +# 定义翻转标签的函数 +def flip_labels(example): + # 截取 sentence1 和 sentence2 的前 50 个字符,如果dna序列过长，bert分词会产生错误，只生成unk一个token + example["sentence1"] = example["sentence1"] + example["sentence2"] = example["sentence2"] + example['label'] = 1 - example['label'] + return example + +# 应用翻转标签函数 +flipped_datasets_dna_protein = raw_datasets_dna_protein.map(flip_labels, batched=False) +tokenized_datasets_dna_protein = flipped_datasets_dna_protein.map(tokenize_function, batched=True,num_proc=4) + + +# 确保模型在 GPU 上 +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") +model.to(device) +model.eval() # 进入推理模式，加速推理 + +# 取出测试集数据 +test_dataset = tokenized_datasets_dna_protein["test"] + +# 预存预测结果 +preds = [] +labels = [] + +# 批量大小（建议 64、128、256 视显存大小调整） +batch_size = 64 + +# 直接遍历数据集进行推理 +for i in tqdm(range(0, len(test_dataset), batch_size), desc="Predicting"): + batch = test_dataset[i : i + batch_size] + + # 转换为 Tensor 并移动到 GPU + inputs = { + "input_ids": torch.tensor(batch["input_ids"]).to(device), + "attention_mask": torch.tensor(batch["attention_mask"]).to(device), + } + batch_labels = batch["label"] # 原始标签 + + with torch.no_grad(): # 关闭梯度计算，减少内存占用 + outputs = model(**inputs) + batch_preds = torch.argmax(outputs.logits, axis=-1).cpu().numpy() # 取最大概率的类别 + + preds.extend(batch_preds) + labels.extend(batch_labels) +metric = evaluate.load("glue", "mrpc") +ret = metric.compute(predictions=preds, references=labels) + + + +result["dna_protein_pair_rand_full"] = ret + + + +print(json.dumps(result)) + diff --git a/finetune_pt_100/run_ft_all.sh b/finetune_pt_100/run_ft_all.sh new file mode 100644 index 0000000000000000000000000000000000000000..ab21e745d9df11fa0c953d12c3e0978a886fa03c --- /dev/null +++ b/finetune_pt_100/run_ft_all.sh @@ -0,0 +1,8 @@ +#!/bin/bash +export HF_ENDPOINT=https://hf-mirror.com +lang=$1 +for ((i=0;i<100;i++)) +do + echo "----------------------------------------------"$i + python gpt2_gene_zh_ft_test_others.py $i $lang >> gpt2_gene_zh_ft_${lang}.json +done diff --git a/finetune_same_100/get_acc_stat_ft100.ipynb b/finetune_same_100/get_acc_stat_ft100.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..4824ec3f6a6a5e74d93f98af8ac4ff73fcf18508 --- /dev/null +++ b/finetune_same_100/get_acc_stat_ft100.ipynb @@ -0,0 +1,312 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "8159f2eb-88ce-4c45-b1ae-584ce3a1976f", + "metadata": {}, + "outputs": [], + "source": [ + "import json" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "7ec806af-bdd5-41de-9c06-72db421c7f7b", + "metadata": {}, + "outputs": [], + "source": [ + "!grep 'seed' gpt2_gene_multiv2_ft_ko.json > gpt2_gene_multiv2_ft_ko.jsonl" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "179a6741-6649-4bea-be83-7fc9fd6c13c6", + "metadata": {}, + "outputs": [], + "source": [ + "filename = \"gpt2_gene_multiv2_ft_ko.jsonl\"\n", + "data_list = []\n", + "for line in open(filename):\n", + " data = json.loads(line)\n", + " data_list.append(data)\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "c8cc78e9-fbdf-4c95-847f-44ea953a38ec", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dna_protein_pair_full: 0.6775\n", + "dna_protein_pair_rand_full: 0.63375\n" + ] + } + ], + "source": [ + "# 假设您的数据存储在一个名为data_list的列表中\n", + "# 初始化一个字典来保存每个键的最大accuracy值\n", + "max_accuracies = {}\n", + "dna_protein_pair_full_list = []\n", + "dna_protein_pair_rand_full_list = []\n", + "\n", + "# 遍历列表中的每个字典\n", + "for data in data_list:\n", + " for key, metrics in data.items():\n", + " if key not in ['seed']: # 忽略非目标键，例如'seed'\n", + " if isinstance(metrics, dict) and 'accuracy' in metrics:\n", + " accuracy = metrics['accuracy']\n", + " if accuracy<0.5:\n", + " accuracy = 1 - accuracy\n", + " \n", + " if \"dna_protein_pair_full\"==key:\n", + " dna_protein_pair_full_list.append(accuracy)\n", + "\n", + " if \"dna_protein_pair_rand_full\"==key:\n", + " dna_protein_pair_rand_full_list.append(accuracy)\n", + "\n", + " \n", + " if key not in max_accuracies or accuracy > max_accuracies[key]:\n", + " max_accuracies[key] = accuracy\n", + "\n", + "# 打印每个键的最大accuracy值\n", + "for key, max_accuracy in max_accuracies.items():\n", + " print(f\"{key}: {max_accuracy}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "0d2f40f8-a817-4b6b-ae17-310478f6f8d8", + "metadata": {}, + "outputs": [], + "source": [ + "#!pip install matplotlib seaborn" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "367b765c-ec8d-4cbb-a76c-d1e891816e14", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Frequency')" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "# 假设这是你的列表\n", + "#dna_protein_pair_rand_full_list = [0.1, 0.2, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2] # 示例数据\n", + "\n", + "# 使用Freedman-Diaconis规则自动确定bin的数量\n", + "bins = np.histogram_bin_edges(dna_protein_pair_full_list, bins='fd')\n", + "\n", + "# 设置图形大小\n", + "plt.figure(figsize=(10, 6))\n", + "\n", + "# 使用seaborn绘制直方图\n", + "sns.histplot(data=dna_protein_pair_full_list, bins=bins, kde=True)\n", + "\n", + "# 添加标题和轴标签\n", + "plt.title('Distribution of Accuracy Value (Test dna_protein_pair)')\n", + "plt.xlabel('Values')\n", + "plt.ylabel('Frequency')\n", + "\n", + "# 显示图形" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "07bfc560-96b2-4ccb-909d-e8d14f8472c0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Frequency')" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "# 假设这是你的列表\n", + "#dna_protein_pair_rand_full_list = [0.1, 0.2, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2] # 示例数据\n", + "\n", + "# 使用Freedman-Diaconis规则自动确定bin的数量\n", + "bins = np.histogram_bin_edges(dna_protein_pair_rand_full_list, bins='fd')\n", + "\n", + "# 设置图形大小\n", + "plt.figure(figsize=(10, 6))\n", + "\n", + "# 使用seaborn绘制直方图\n", + "sns.histplot(data=dna_protein_pair_rand_full_list, bins=bins, kde=True)\n", + "\n", + "# 添加标题和轴标签\n", + "plt.title('Distribution of Accuracy Value (Test dna_protein_pair_rand)')\n", + "plt.xlabel('Values')\n", + "plt.ylabel('Frequency')\n", + "\n", + "# 显示图形" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "094948c7-3797-46ef-96cb-8a784ec937b5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "平均值: 0.542962962962963\n", + "方差: 0.0017891375171467762\n", + "标准差: 0.04229819756380614\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "\n", + "# 示例数据（Python list，包含 float 类型的数值）\n", + "data = dna_protein_pair_full_list\n", + "\n", + "# 计算平均值\n", + "mean_value = np.mean(data)\n", + "\n", + "# 计算方差（注意：np.var默认计算总体方差，如果需要样本方差，请设置ddof=1）\n", + "variance_value = np.var(data)\n", + "\n", + "# 计算标准差\n", + "std_value = np.std(data)\n", + "\n", + "print(\"平均值:\", mean_value)\n", + "print(\"方差:\", variance_value)\n", + "print(\"标准差:\", std_value)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "8d9c667f-2620-4039-85b5-fa8b24a8e14f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "大于0.7的元素个数： 1\n" + ] + } + ], + "source": [ + "count = sum(1 for x in dna_protein_pair_full_list if x >= 0.7)\n", + "print(\"大于0.7的元素个数：\", count)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "276f8fe0-7227-4c12-999b-8c025cb963cb", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "b550e6b5-a89f-48e7-95ca-7b6097867d0b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "108" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(dna_protein_pair_full_list)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "35e0c7c6-bb4b-4333-8dae-1536f545f421", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "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 +} diff --git a/finetune_same_100/gpt2_gene_multiv2_ft_de.jsonl b/finetune_same_100/gpt2_gene_multiv2_ft_de.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..a76bedcd5371c05b85cecf977b02d4d9611a232c --- /dev/null +++ b/finetune_same_100/gpt2_gene_multiv2_ft_de.jsonl @@ -0,0 +1,100 @@ +{"seed": 0, "dna_protein_pair_full": {"accuracy": 0.4775, "f1": 0.0}, "dna_protein_pair_rand_full": {"accuracy": 0.515, "f1": 0.015228426395939087}} +{"seed": 1, "dna_protein_pair_full": {"accuracy": 0.42, "f1": 0.5671641791044776}, "dna_protein_pair_rand_full": {"accuracy": 0.52275, "f1": 0.6233971197474847}} +{"seed": 2, "dna_protein_pair_full": {"accuracy": 0.3525, "f1": 0.4788732394366197}, "dna_protein_pair_rand_full": {"accuracy": 0.599, "f1": 0.6507729153059003}} +{"seed": 3, "dna_protein_pair_full": {"accuracy": 0.395, "f1": 0.3163841807909605}, "dna_protein_pair_rand_full": {"accuracy": 0.569125, "f1": 0.3514581373471308}} +{"seed": 4, "dna_protein_pair_full": {"accuracy": 0.3175, "f1": 0.3389830508474576}, "dna_protein_pair_rand_full": {"accuracy": 0.526875, "f1": 0.3106902203605901}} +{"seed": 5, "dna_protein_pair_full": {"accuracy": 0.325, "f1": 0.44214876033057854}, "dna_protein_pair_rand_full": {"accuracy": 0.51175, "f1": 0.5479166666666667}} +{"seed": 6, "dna_protein_pair_full": {"accuracy": 0.395, "f1": 0.5363984674329502}, "dna_protein_pair_rand_full": {"accuracy": 0.5485, "f1": 0.6443481685703033}} +{"seed": 7, "dna_protein_pair_full": {"accuracy": 0.43, "f1": 0.2348993288590604}, "dna_protein_pair_rand_full": {"accuracy": 0.624, "f1": 0.48633879781420764}} +{"seed": 8, "dna_protein_pair_full": {"accuracy": 0.4725, "f1": 0.47117794486215536}, "dna_protein_pair_rand_full": {"accuracy": 0.686375, "f1": 0.7222406730875678}} +{"seed": 9, "dna_protein_pair_full": {"accuracy": 0.335, "f1": 0.4166666666666667}, "dna_protein_pair_rand_full": {"accuracy": 0.597625, "f1": 0.5659970338411757}} +{"seed": 10, 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"dna_protein_pair_rand_full": {"accuracy": 0.504875, "f1": 0.6688403979600368}} +{"seed": 96, "dna_protein_pair_full": {"accuracy": 0.36, "f1": 0.4900398406374502}, "dna_protein_pair_rand_full": {"accuracy": 0.576875, "f1": 0.6348829683960738}} +{"seed": 97, "dna_protein_pair_full": {"accuracy": 0.33, "f1": 0.30569948186528495}, "dna_protein_pair_rand_full": {"accuracy": 0.488125, "f1": 0.42494031737115573}} +{"seed": 98, "dna_protein_pair_full": {"accuracy": 0.3375, "f1": 0.35523114355231145}, "dna_protein_pair_rand_full": {"accuracy": 0.422625, "f1": 0.09732264999022865}} +{"seed": 99, "dna_protein_pair_full": {"accuracy": 0.4875, "f1": 0.6519524617996605}, "dna_protein_pair_rand_full": {"accuracy": 0.511125, "f1": 0.6667802675300333}} diff --git a/finetune_same_100/gpt2_gene_multiv2_ft_nl_test_others.py b/finetune_same_100/gpt2_gene_multiv2_ft_nl_test_others.py new file mode 100644 index 0000000000000000000000000000000000000000..823584f0190210515de6085e96c5890a475f7f44 --- /dev/null +++ b/finetune_same_100/gpt2_gene_multiv2_ft_nl_test_others.py @@ -0,0 +1,237 @@ +# import os + +# # 设置环境变量 +# os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com' + +# # 打印环境变量以确认设置成功 +# print(os.environ.get('HF_ENDPOINT')) + +# import subprocess +# import os + +# result = subprocess.run('bash -c "source /etc/network_turbo && env | grep proxy"', shell=True, capture_output=True, text=True) +# output = result.stdout +# for line in output.splitlines(): +# if '=' in line: +# var, value = line.split('=', 1) +# os.environ[var] = value + + +from datasets import load_dataset +from transformers import AutoTokenizer, DataCollatorWithPadding +from transformers import Trainer +import evaluate +import numpy as np +from transformers import TrainingArguments +from transformers import AutoModelForSequenceClassification +import json +from transformers import set_seed +import random +import numpy as np +import torch +from tqdm import tqdm +import sys + + +# seed = 42 +# random.seed(seed) +# np.random.seed(seed) +# torch.manual_seed(seed) +# torch.cuda.manual_seed_all(seed) + + +# 动态生成随机种子 + +seed = int(sys.argv[1]) +lang = sys.argv[2] #语言 + +#seed = random.randint(0, 10000) +#print(f"Generated seed: {seed}") +set_seed(seed) +result = {} +result["seed"] = seed + + + +# 假设你的JSON文件名为 'data.json' 并且每行是一个独立的JSON对象 +raw_datasets = load_dataset('paws-x', lang) # 或者指定特定语言如 'zh' 表示中文,https://huggingface.co/datasets/google-research-datasets/paws-x + +#分词器 +tokenizer = AutoTokenizer.from_pretrained("dnagpt/gpt2_gene_multi_v2") +tokenizer.pad_token = tokenizer.eos_token + + +#分词函数 +def tokenize_function(example): + return tokenizer(example["sentence1"], example["sentence2"], truncation=True,max_length=256, padding="max_length") + +#构建分词后的数据集 +tokenized_datasets = raw_datasets.map(tokenize_function, batched=True) + +#训练数据构建 +data_collator = DataCollatorWithPadding(tokenizer=tokenizer) + + +#指标函数定义 +def compute_metrics(eval_pred): + predictions, labels = eval_pred + predictions = np.argmax(predictions, axis=1) + return {'accuracy': (predictions==labels).sum() / len(labels)} + + + +training_args = TrainingArguments( + output_dir="ds_job_dna_2222", + learning_rate=1e-5, + lr_scheduler_type="constant_with_warmup", + warmup_ratio=0.1, + optim='adamw_torch', + weight_decay=0.0, + seed=seed, # 使用动态生成的随机种子 + per_device_train_batch_size=64, + per_device_eval_batch_size=64, + num_train_epochs=4, #训练多少轮 + evaluation_strategy="epoch", + save_strategy="epoch", + logging_strategy="epoch", + load_best_model_at_end=True +) + +#模型定义，文本分类模型 +model = AutoModelForSequenceClassification.from_pretrained("dnagpt/gpt2_gene_multi_v2", num_labels=2) +model.config.pad_token_id = model.config.eos_token_id + +trainer = Trainer( + model, + training_args, + train_dataset=tokenized_datasets["train"], + eval_dataset=tokenized_datasets["validation"], + data_collator=data_collator, + tokenizer=tokenizer, + compute_metrics=compute_metrics, +) + +trainer.train() #模型训练 + + + + +#模型测试 蛋白质数据集 +raw_datasets_dna_protein = load_dataset('dnagpt/gene_lan_transfer', 'dna_protein_pair')['train'].train_test_split(test_size=0.1) #默认已经shuffle + +# 定义翻转标签的函数 +def flip_labels(example): + # 截取 sentence1 和 sentence2 的前 50 个字符,如果dna序列过长，bert分词会产生错误，只生成unk一个token + example["sentence1"] = example["sentence1"] + example["sentence2"] = example["sentence2"] + example['label'] = 1 - example['label'] + return example + +# 应用翻转标签函数 +flipped_datasets_dna_protein = raw_datasets_dna_protein.map(flip_labels, batched=False) +tokenized_datasets_dna_protein = flipped_datasets_dna_protein.map(tokenize_function, batched=True,num_proc=4) + + + +# 确保模型在 GPU 上 +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") +model.to(device) +model.eval() # 进入推理模式，加速推理 + +# 取出测试集数据 +test_dataset = tokenized_datasets_dna_protein["test"] + +# 预存预测结果 +preds = [] +labels = [] + +# 批量大小（建议 64、128、256 视显存大小调整） +batch_size = 64 + +# 直接遍历数据集进行推理 +for i in tqdm(range(0, len(test_dataset), batch_size), desc="Predicting"): + batch = test_dataset[i : i + batch_size] + + # 转换为 Tensor 并移动到 GPU + inputs = { + "input_ids": torch.tensor(batch["input_ids"]).to(device), + "attention_mask": torch.tensor(batch["attention_mask"]).to(device), + } + batch_labels = batch["label"] # 原始标签 + + with torch.no_grad(): # 关闭梯度计算，减少内存占用 + outputs = model(**inputs) + batch_preds = torch.argmax(outputs.logits, axis=-1).cpu().numpy() # 取最大概率的类别 + + preds.extend(batch_preds) + labels.extend(batch_labels) + +metric = evaluate.load("glue", "mrpc") +ret = metric.compute(predictions=preds, references=labels) + + +result["dna_protein_pair_full"] = ret + + + + +############################################################# +#模型测试 蛋白质数据集，随机版本 +raw_datasets_dna_protein = load_dataset('dnagpt/gene_lan_transfer', 'dna_protein_pair_rand')['train'].train_test_split(test_size=0.5) #默认已经shuffle + +# 定义翻转标签的函数 +def flip_labels(example): + # 截取 sentence1 和 sentence2 的前 50 个字符,如果dna序列过长，bert分词会产生错误，只生成unk一个token + example["sentence1"] = example["sentence1"] + example["sentence2"] = example["sentence2"] + example['label'] = 1 - example['label'] + return example + +# 应用翻转标签函数 +flipped_datasets_dna_protein = raw_datasets_dna_protein.map(flip_labels, batched=False) +tokenized_datasets_dna_protein = flipped_datasets_dna_protein.map(tokenize_function, batched=True,num_proc=4) + + +# 确保模型在 GPU 上 +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") +model.to(device) +model.eval() # 进入推理模式，加速推理 + +# 取出测试集数据 +test_dataset = tokenized_datasets_dna_protein["test"] + +# 预存预测结果 +preds = [] +labels = [] + +# 批量大小（建议 64、128、256 视显存大小调整） +batch_size = 64 + +# 直接遍历数据集进行推理 +for i in tqdm(range(0, len(test_dataset), batch_size), desc="Predicting"): + batch = test_dataset[i : i + batch_size] + + # 转换为 Tensor 并移动到 GPU + inputs = { + "input_ids": torch.tensor(batch["input_ids"]).to(device), + "attention_mask": torch.tensor(batch["attention_mask"]).to(device), + } + batch_labels = batch["label"] # 原始标签 + + with torch.no_grad(): # 关闭梯度计算，减少内存占用 + outputs = model(**inputs) + batch_preds = torch.argmax(outputs.logits, axis=-1).cpu().numpy() # 取最大概率的类别 + + preds.extend(batch_preds) + labels.extend(batch_labels) +metric = evaluate.load("glue", "mrpc") +ret = metric.compute(predictions=preds, references=labels) + + + +result["dna_protein_pair_rand_full"] = ret + + + +print(json.dumps(result)) + diff --git a/finetune_same_100/gpt2_gene_multiv2_ft_zh.jsonl b/finetune_same_100/gpt2_gene_multiv2_ft_zh.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..80547638f1af91ce8620b2be5a13cd10d7c60e5c --- /dev/null +++ b/finetune_same_100/gpt2_gene_multiv2_ft_zh.jsonl @@ -0,0 +1,100 @@ +{"seed": 0, "dna_protein_pair_full": {"accuracy": 0.3725, "f1": 0.21316614420062696}, "dna_protein_pair_rand_full": {"accuracy": 0.46475, "f1": 0.04971149578339991}} +{"seed": 1, "dna_protein_pair_full": {"accuracy": 0.305, "f1": 0.4034334763948498}, "dna_protein_pair_rand_full": {"accuracy": 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"dna_protein_pair_full": {"accuracy": 0.36, "f1": 0.4900398406374502}, "dna_protein_pair_rand_full": {"accuracy": 0.340625, "f1": 0.24524252396623264}} +{"seed": 99, "dna_protein_pair_full": {"accuracy": 0.4525, "f1": 0.6137566137566137}, "dna_protein_pair_rand_full": {"accuracy": 0.534, "f1": 0.6745810055865922}} diff --git a/finetune_same_100/run_ft_all.sh b/finetune_same_100/run_ft_all.sh new file mode 100644 index 0000000000000000000000000000000000000000..381293b694365139d72276ba7c4a135873dfa060 --- /dev/null +++ b/finetune_same_100/run_ft_all.sh @@ -0,0 +1,8 @@ +#!/bin/bash +export HF_ENDPOINT=https://hf-mirror.com +lang=$1 +for ((i=0;i<100;i++)) +do + echo "----------------------------------------------"$i + python gpt2_gene_multiv2_ft_nl_test_others.py $i $lang >> gpt2_gene_multiv2_ft_${lang}.json +done