心跳信号分类预测（一）赛题理解及Baseline

心跳信号分类预测（一）

比赛链接

比赛链接

赛题概况

比赛要求参赛选手根据给定的数据集，建立模型，预测不同的心跳信号。赛题以预测心电图心跳信号类别为任务，数据集报名后可见并可下载，该该数据来自某平台心电图数据记录，总数据量超过20万，主要为1列心跳信号序列数据，其中每个样本的信号序列采样频次一致，长度相等。为了保证比赛的公平性，将会从中抽取10万条作为训练集，2万条作为测试集A，2万条作为测试集B，同时会对心跳信号类别（label）信息进行脱敏。

数据概况

train.csv

id 为心跳信号分配的唯一标识
heartbeat_signals 心跳信号序列(数据之间采用“,”进行分隔)
label 心跳信号类别（0、1、2、3）
testA.csv

id 心跳信号分配的唯一标识
heartbeat_signals 心跳信号序列(数据之间采用“,”进行分隔)

评测标准

提交4种不同心跳信号预测的概率，选手提交结果与实际心跳类型结果进行对比，求预测的概率与真实值差值的绝对值（越小越好）。

具体计算公式如下：

Baseline

导入第三方包

import os
import gc
import math

import pandas as pd
import numpy as np

import lightgbm as lgb
import xgboost as xgb
from catboost import CatBoostRegressor
from sklearn.linear_model import SGDRegressor, LinearRegression, Ridge
from sklearn.preprocessing import MinMaxScaler


from sklearn.model_selection import StratifiedKFold, KFold
from sklearn.metrics import log_loss
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder

from tqdm import tqdm
import matplotlib.pyplot as plt
import time
import warnings
warnings.filterwarnings('ignore')

读数据`

train = pd.read_csv('train.csv')
test=pd.read_csv('testA.csv')
train.head()

数据预处理：通过替换数据类型、用category类代替object类的方法来减小内存占用

def reduce_mem_usage(df):
    start_mem = df.memory_usage().sum() / 1024**2 
    print('Memory usage of dataframe is {:.2f} MB'.format(start_mem))
    
    for col in df.columns:
        col_type = df[col].dtype
        
        if col_type != object:
            c_min = df[col].min()
            c_max = df[col].max()
            if str(col_type)[:3] == 'int':
                if c_min > np.iinfo(np.int8).min and c_max < np.iinfo(np.int8).max:
                    df[col] = df[col].astype(np.int8)
                elif c_min > np.iinfo(np.int16).min and c_max < np.iinfo(np.int16).max:
                    df[col] = df[col].astype(np.int16)
                elif c_min > np.iinfo(np.int32).min and c_max < np.iinfo(np.int32).max:
                    df[col] = df[col].astype(np.int32)
                elif c_min > np.iinfo(np.int64).min and c_max < np.iinfo(np.int64).max:
                    df[col] = df[col].astype(np.int64)  
            else:
                if c_min > np.finfo(np.float16).min and c_max < np.finfo(np.float16).max:
                    df[col] = df[col].astype(np.float16)
                elif c_min > np.finfo(np.float32).min and c_max < np.finfo(np.float32).max:
                    df[col] = df[col].astype(np.float32)
                else:
                    df[col] = df[col].astype(np.float64)
        else:
            df[col] = df[col].astype('category')

    end_mem = df.memory_usage().sum() / 1024**2 
    print('Memory usage after optimization is: {:.2f} MB'.format(end_mem))
    print('Decreased by {:.1f}%'.format(100 * (start_mem - end_mem) / start_mem))
    
    return df

将题目中所给的heartbeat-signal信息重新编码整理，并对内存进行优化

train_list = []

for items in train.values:
    train_list.append([items[0]] + [float(i) for i in items[1].split(',')] + [items[2]])

train = pd.DataFrame(np.array(train_list))
train.columns = ['id'] + ['s_'+str(i) for i in range(len(train_list[0])-2)] + ['label']
train = reduce_mem_usage(train)

test_list=[]
for items in test.values:
    test_list.append([items[0]] + [float(i) for i in items[1].split(',')])

test = pd.DataFrame(np.array(test_list))
test.columns = ['id'] + ['s_'+str(i) for i in range(len(test_list[0])-1)]
test = reduce_mem_usage(test)

结果如下：

选取四类心跳信号作图：

模型训练

x_train = train.drop(['id','label'], axis=1)
y_train = train['label']
x_test=test.drop(['id'], axis=1)
def abs_sum(y_pre,y_tru):
    y_pre=np.array(y_pre)
    y_tru=np.array(y_tru)
    loss=sum(sum(abs(y_pre-y_tru)))
    return loss
def cv_model(clf, train_x, train_y, test_x, clf_name):
    folds = 5
    seed = 2021
    kf = KFold(n_splits=folds, shuffle=True, random_state=seed)
    test = np.zeros((test_x.shape[0],4))

    cv_scores = []
    onehot_encoder = OneHotEncoder(sparse=False)
    for i, (train_index, valid_index) in enumerate(kf.split(train_x, train_y)):
        print('************************************ {} ************************************'.format(str(i+1)))
        trn_x, trn_y, val_x, val_y = train_x.iloc[train_index], train_y[train_index], train_x.iloc[valid_index], train_y[valid_index]
        
        if clf_name == "lgb":
            train_matrix = clf.Dataset(trn_x, label=trn_y)
            valid_matrix = clf.Dataset(val_x, label=val_y)

            params = {
                'boosting_type': 'gbdt',
                'objective': 'multiclass',
                'num_class': 4,
                'num_leaves': 2 ** 5,
                'feature_fraction': 0.8,
                'bagging_fraction': 0.8,
                'bagging_freq': 4,
                'learning_rate': 0.1,
                'seed': seed,
                'nthread': 28,
                'n_jobs':24,
                'verbose': -1,
            }

            model = clf.train(params, 
                      train_set=train_matrix, 
                      valid_sets=valid_matrix, 
                      num_boost_round=2000, 
                      verbose_eval=100, 
                      early_stopping_rounds=200)
            val_pred = model.predict(val_x, num_iteration=model.best_iteration)
            test_pred = model.predict(test_x, num_iteration=model.best_iteration) 
            
        val_y=np.array(val_y).reshape(-1, 1)
        val_y = onehot_encoder.fit_transform(val_y)
        print('预测的概率矩阵为：')
        print(test_pred)
        test += test_pred
        score=abs_sum(val_y, val_pred)
        cv_scores.append(score)
        print(cv_scores)
    print("%s_scotrainre_list:" % clf_name, cv_scores)
    print("%s_score_mean:" % clf_name, np.mean(cv_scores))
    print("%s_score_std:" % clf_name, np.std(cv_scores))
    test=test/kf.n_splits

    return test
def lgb_model(x_train, y_train, x_test):
    lgb_test = cv_model(lgb, x_train, y_train, x_test, "lgb")
    return lgb_test
lgb_test = lgb_model(x_train, y_train, x_test)

训练结果，本地CPU跑大概4分钟跑一遍

输出

temp=pd.DataFrame(lgb_test)
result=pd.read_csv('sample_submit.csv')
result['label_0']=temp[0]
result['label_1']=temp[1]
result['label_2']=temp[2]
result['label_3']=temp[3]
result.to_csv('submit.csv',index=False)