Datawhale AI春训营 AI4S蛋白质赛道学习笔记

Datawhale AI春训营

AI4S蛋白质赛道学习笔记

流程

报名赛事在http://competition.sais.com.cn/competitionDetail/532313/format?spm=CHANNEL-0001

进入之后注册，然后填写个人手机号，通过支付宝进行实名认证等即可报名参与赛道

要报名赛道之后才可以下载相关的数据集和baseline代码等官方数据

1. 报名赛事
2. 下载docker、安装docker，打开docker
3. 使用免费云算力训练模型、运行模型训练的baseline
   git lfs install
   git clone https://www.modelscope.cn/datasets/Datawhale/sais_third_synthetic_baseline.git
4. 开通阿里云镜像服务，创建镜像仓库 ，命名为 sais_synthetic
5. 下载训练模型等五个文件
   model.pkl,ml_baseline.py,Dockerfile,requirements.txt,run.sh
6. 在本地进行docker打包并推送
   docker login --username=xx xxxx
   大约3分钟
   docker build -t sais_synthetic:v1 .
   大约耗时5分钟
   docker tag sais_synthetic:v1 xxxxxx/sais_medicine:v1
   docker tag sais_synthetic:v1 crpi-yimn1cg16ys23bar.cn-hangzhou.personal.cr.aliyuncs.com/sais_synthetic_wumao/sais_synthetic:v1
   docker push xxxxx/sais_synthetic:v1
   docker push crpi-yimn1cg16ys23bar.cn-hangzhou.personal.cr.aliyuncs.com/sais_synthetic_wumao/sais_synthetic:v1
7. 然后提交镜像、获得分数

model.pkl生成

model.pkl是在jupter notebook中的，打开克隆的datawhale的baseline仓库

里面有ml_baseline.ipynb,运行这个notebook就可以生成model.pkl了

!pip install gensim
import pickle
import gensim
import gensim.modelsimport os
import sys
import random
import numpy as np
import pandas as pd
from joblib import load, dumpfrom sklearn.linear_model import LogisticRegression, SGDClassifier
from sklearn.naive_bayes import GaussianNB 
from sklearn.model_selection import cross_val_predict
from sklearn.metrics import classification_reportdatas = pickle.load(open("WSAA_data_public.pkl", "rb"))random_seed = random.randint(0, 10000)
model_w2v = gensim.models.Word2Vec(sentences=[' '.join(x["sequence"]) for x in datas],vector_size=random.choice([10, 20, 40, 50, 100]),min_count=1,seed=random_seed
)data_x = []
data_y = []
for data in datas:sequence = list(data["sequence"])for idx, (_, y) in enumerate(zip(sequence, data['label'])):data_x.append(model_w2v.wv[sequence[max(0, idx-2): min(len(sequence), idx+2)]].mean(0))data_y.append(y)
model = GaussianNB()
pred = cross_val_predict(model, data_x, data_y
)
print(classification_report(data_y, pred))model = GaussianNB()
model.fit(data_x, data_y)
dump((model, model_w2v), "model.pkl")

然后生成的这个model.pkg和配合的Dockerfile，脚本等按照要求推送到ali云的镜像平台，然后就可以提交了

安装docker后构建：

docker build -t sais_synthetic:v2 .
docker images

推送：

docker tag sais_synthetic:v2 xxxxxx/sais_medicine:v1 docker push xxxxx/sais_synthetic:v1# 例如
docker tag sais_synthetic:v1 crpi-yimn1cg16ys23bar.cn-hangzhou.personal.cr.aliyuncs.com/sais_synthetic_wumao/sais_synthetic:v1
docker push crpi-yimn1cg16ys23bar.cn-hangzhou.personal.cr.aliyuncs.com/sais_synthetic_wumao/sais_synthetic:v1

上传之后，就可以到官网提交了，记得复制外网地址哦