2020-03-16DW数据挖掘心电图baseline_心电图使用index函数-优快云博客

本次竞赛聚焦于利用机器学习算法预测心电图心跳信号类别。数据集包含20万条心电信号记录，每条记录长度一致。选手需使用如XGBoost、LightGBM等算法训练模型，并通过绝对误差评估预测精度。

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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，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。
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)

应用方法

xgb、lgb、catboost

评分体系

各个预测结果与真实值的绝对值进行求个,分值越低代表预测越准确
选手需提交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。

具体计算公式如下：

总共有n个病例，针对某一个信号，若真实值为[y1,y2,y3,y4],模型预测概率值为[a1,a2,a3,a4],那么该模型的评价指标abs-sum为
${abs-sum={\mathop{ \sum }\limits_{{j=1}}^{{n}}{{\mathop{ \sum }\limits_{{i=1}}^{{4}}{{ \left| {y\mathop{{}}\nolimits_{{i}}-a\mathop{{}}\nolimits_{{i}}} \right| }}}}}}$
例如，某心跳信号类别为1，通过编码转成[0,1,0,0]，预测不同心跳信号概率为[0.1,0.7,0.1,0.1]，那么这个信号预测结果的abs-sum为
$\left| {0.1-0} \right| }+{ \left| {0.7-1} \right| }+{ \left| {0.1-0} \right| }+{ \left| {0.1-0} \right| }=0.6}$

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')

读取数据

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

	id	heartbeat_signals	label
0	0	0.9912297987616655,0.9435330436439665,0.764677...	0.0
1	1	0.9714822034884503,0.9289687459588268,0.572932...	0.0
2	2	1.0,0.9591487564065292,0.7013782792997189,0.23...	2.0
3	3	0.9757952826275774,0.9340884687738161,0.659636...	0.0
4	4	0.0,0.055816398940721094,0.26129357194994196,0...	2.0

test.head()

	id	heartbeat_signals
0	100000	0.9915713654170097,1.0,0.6318163407681274,0.13...
1	100001	0.6075533139615096,0.5417083883163654,0.340694...
2	100002	0.9752726292239277,0.6710965234906665,0.686758...
3	100003	0.9956348033996116,0.9170249621481004,0.521096...
4	100004	1.0,0.8879490481178918,0.745564725322326,0.531...

数据预处理

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

train.columns

Index(['id', 's_0', 's_1', 's_2', 's_3', 's_4', 's_5', 's_6', 's_7', 's_8',
       ...
       's_196', 's_197', 's_198', 's_199', 's_200', 's_201', 's_202', 's_203',
       's_204', 'label'],
      dtype='object', length=207)

# 简单预处理
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)

Memory usage of dataframe is 157.93 MB
Memory usage after optimization is: 39.67 MB
Decreased by 74.9%
Memory usage of dataframe is 31.43 MB
Memory usage after optimization is: 7.90 MB
Decreased by 74.9%

train.head()

	id	s_0	s_1	s_2	s_3	s_4	s_5	s_6	s_7	s_8	...	label
0	0.0	0.991211	0.943359	0.764648	0.618652	0.379639	0.190796	0.040222	0.026001	0.031708	...	0.0
1	1.0	0.971680	0.929199	0.572754	0.178467	0.122986	0.132324	0.094421	0.089600	0.030487	...	0.0
2	2.0	1.000000	0.958984	0.701172	0.231812	0.000000	0.080688	0.128418	0.187500	0.280762	...	2.0
3	3.0	0.975586	0.934082	0.659668	0.249878	0.237061	0.281494	0.249878	0.249878	0.241455	...	0.0
4	4.0	0.000000	0.055817	0.261230	0.359863	0.433105	0.453613	0.499023	0.542969	0.616699	...	2.0

5 rows × 207 columns

test.head()

	id	s_0	s_1	s_2	s_3	s_4	s_5	s_6	s_7	s_8	...	s_195	s_196	s_197	s_198	s_199	s_200	s_201	s_202	s_203	s_204
0	100000.0	0.991699	1.000000	0.631836	0.136230	0.041412	0.102722	0.120850	0.123413	0.107910	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000
1	100001.0	0.607422	0.541504	0.340576	0.000000	0.090698	0.164917	0.195068	0.168823	0.198853	...	0.389893	0.386963	0.367188	0.364014	0.360596	0.357178	0.350586	0.350586	0.350586	0.36377
2	100002.0	0.975098	0.670898	0.686523	0.708496	0.718750	0.716797	0.720703	0.701660	0.596680	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000
3	100003.0	0.995605	0.916992	0.520996	0.000000	0.221802	0.404053	0.490479	0.527344	0.518066	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000
4	100004.0	1.000000	0.888184	0.745605	0.531738	0.380371	0.224609	0.091125	0.057648	0.003914	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000

5 rows × 206 columns

训练数据/测试数据准备

x_train = train.drop(['id','label'], axis=1)
y_train = train['label']
x_test=test.drop(['id'], axis=1)

x_train

	s_0	s_1	s_2	s_3	s_4	s_5	s_6	s_7	s_8	s_9	...	s_195	s_196	s_197	s_198	s_199	s_200	s_201	s_202	s_203	s_204
0	0.991211	0.943359	0.764648	0.618652	0.379639	0.190796	0.040222	0.026001	0.031708	0.065552	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
1	0.971680	0.929199	0.572754	0.178467	0.122986	0.132324	0.094421	0.089600	0.030487	0.040497	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
2	1.000000	0.958984	0.701172	0.231812	0.000000	0.080688	0.128418	0.187500	0.280762	0.328369	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
3	0.975586	0.934082	0.659668	0.249878	0.237061	0.281494	0.249878	0.249878	0.241455	0.230713	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
4	0.000000	0.055817	0.261230	0.359863	0.433105	0.453613	0.499023	0.542969	0.616699	0.676758	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
99995	1.000000	0.677734	0.222412	0.257080	0.204712	0.054657	0.026154	0.118164	0.244873	0.328857	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
99996	0.926758	0.906250	0.637207	0.415039	0.374756	0.382568	0.358887	0.341309	0.336426	0.317139	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
99997	0.925781	0.587402	0.633301	0.632324	0.639160	0.614258	0.599121	0.517578	0.403809	0.253174	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
99998	1.000000	0.994629	0.829590	0.458252	0.264160	0.240234	0.213745	0.189331	0.203857	0.210815	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
99999	0.925781	0.916504	0.404297	0.000000	0.262939	0.385498	0.361084	0.332764	0.339844	0.350586	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0

100000 rows × 205 columns

y_train

0        0.0
1        0.0
2        2.0
3        0.0
4        2.0
        ... 
99995    0.0
99996    2.0
99997    3.0
99998    2.0
99999    0.0
Name: label, Length: 100000, dtype: float16

模型训练

# 输出结果的样式
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)

************************************ 1 ************************************
[LightGBM] [Warning] num_threads is set with nthread=28, will be overridden by n_jobs=24. Current value: num_threads=24
Training until validation scores don't improve for 200 rounds
[100]	valid_0's multi_logloss: 0.0525735
[200]	valid_0's multi_logloss: 0.0422444
[300]	valid_0's multi_logloss: 0.0407076
[400]	valid_0's multi_logloss: 0.0420398
Early stopping, best iteration is:
[289]	valid_0's multi_logloss: 0.0405457
预测的概率矩阵为：
[[9.99969791e-01 2.85197261e-05 1.00341946e-06 6.85357631e-07]
 [7.93287264e-05 7.69060914e-04 9.99151590e-01 2.00810971e-08]
 [5.75356884e-07 5.04051497e-08 3.15322414e-07 9.99999059e-01]
 ...
 [6.79267940e-02 4.30206297e-04 9.31640185e-01 2.81516302e-06]
 [9.99960477e-01 3.94098074e-05 8.34030725e-08 2.94638661e-08]
 [9.88705846e-01 2.14081630e-03 6.67418381e-03 2.47915423e-03]]
[607.0736049372185]
************************************ 2 ************************************
[LightGBM] [Warning] num_threads is set with nthread=28, will be overridden by n_jobs=24. Current value: num_threads=24
Training until validation scores don't improve for 200 rounds
[100]	valid_0's multi_logloss: 0.0566626
[200]	valid_0's multi_logloss: 0.0450852
[300]	valid_0's multi_logloss: 0.044078
[400]	valid_0's multi_logloss: 0.0455546
Early stopping, best iteration is:
[275]	valid_0's multi_logloss: 0.0437793
预测的概率矩阵为：
[[9.99991401e-01 7.69109547e-06 6.65504756e-07 2.42084688e-07]
 [5.72380482e-05 1.32812809e-03 9.98614607e-01 2.66534396e-08]
 [2.82123411e-06 4.13195205e-07 1.34026965e-06 9.99995425e-01]
 ...
 [6.96398024e-02 6.52459907e-04 9.29685742e-01 2.19960932e-05]
 [9.99972366e-01 2.75069005e-05 7.68142933e-08 5.07415018e-08]
 [9.67263676e-01 7.26154408e-03 2.41533542e-02 1.32142531e-03]]
[607.0736049372185, 623.4313863731124]
************************************ 3 ************************************
[LightGBM] [Warning] num_threads is set with nthread=28, will be overridden by n_jobs=24. Current value: num_threads=24
Training until validation scores don't improve for 200 rounds
[100]	valid_0's multi_logloss: 0.0498722
[200]	valid_0's multi_logloss: 0.038028
[300]	valid_0's multi_logloss: 0.0358066
[400]	valid_0's multi_logloss: 0.0361478
[500]	valid_0's multi_logloss: 0.0379597
Early stopping, best iteration is:
[340]	valid_0's multi_logloss: 0.0354344
预测的概率矩阵为：
[[9.99972032e-01 2.62406774e-05 1.17282152e-06 5.54230651e-07]
 [1.05242811e-05 6.50215805e-05 9.99924453e-01 6.93812546e-10]
 [1.93240868e-06 1.10384984e-07 3.76773426e-07 9.99997580e-01]
 ...
 [1.34894410e-02 3.84569683e-05 9.86471555e-01 5.46564350e-07]
 [9.99987431e-01 1.25532882e-05 1.03902298e-08 5.46727770e-09]
 [9.78722948e-01 1.06329839e-02 6.94192038e-03 3.70214810e-03]]
[607.0736049372185, 623.4313863731124, 508.02381607269535]
************************************ 4 ************************************
[LightGBM] [Warning] num_threads is set with nthread=28, will be overridden by n_jobs=24. Current value: num_threads=24
Training until validation scores don't improve for 200 rounds
[100]	valid_0's multi_logloss: 0.0564768
[200]	valid_0's multi_logloss: 0.0448698
[300]	valid_0's multi_logloss: 0.0446719
[400]	valid_0's multi_logloss: 0.0470399
Early stopping, best iteration is:
[250]	valid_0's multi_logloss: 0.0438853
预测的概率矩阵为：
[[9.99979692e-01 1.70821979e-05 1.27048476e-06 1.95571841e-06]
 [5.66207785e-05 4.02275314e-04 9.99541086e-01 1.82828519e-08]
 [2.62267451e-06 3.58613522e-07 4.78645006e-06 9.99992232e-01]
 ...
 [4.56636552e-02 5.69497433e-04 9.53758468e-01 8.37980573e-06]
 [9.99896785e-01 1.02796802e-04 2.46636563e-07 1.72061021e-07]
 [8.70911669e-01 1.73790185e-02 1.04478175e-01 7.23113697e-03]]
[607.0736049372185, 623.4313863731124, 508.02381607269535, 660.4867407547266]
************************************ 5 ************************************
[LightGBM] [Warning] num_threads is set with nthread=28, will be overridden by n_jobs=24. Current value: num_threads=24
Training until validation scores don't improve for 200 rounds
[100]	valid_0's multi_logloss: 0.0506398
[200]	valid_0's multi_logloss: 0.0396422
[300]	valid_0's multi_logloss: 0.0381065
[400]	valid_0's multi_logloss: 0.0390162
[500]	valid_0's multi_logloss: 0.0414986
Early stopping, best iteration is:
[324]	valid_0's multi_logloss: 0.0379497
预测的概率矩阵为：
[[9.99993352e-01 6.02902202e-06 1.13002685e-07 5.06277302e-07]
 [1.03959552e-05 5.03778956e-04 9.99485820e-01 5.07638601e-09]
 [1.92568065e-07 5.07155306e-08 4.94690856e-08 9.99999707e-01]
 ...
 [8.83103121e-03 2.51969353e-05 9.91142776e-01 9.96143937e-07]
 [9.99984791e-01 1.51997858e-05 5.62426491e-09 3.80450197e-09]
 [9.86084001e-01 8.75968498e-04 1.09742304e-02 2.06580027e-03]]
[607.0736049372185, 623.4313863731124, 508.02381607269535, 660.4867407547266, 539.2160054696064]
lgb_scotrainre_list: [607.0736049372185, 623.4313863731124, 508.02381607269535, 660.4867407547266, 539.2160054696064]
lgb_score_mean: 587.6463107214719
lgb_score_std: 55.944536405714565

lgb_test

array([[9.99981254e-01, 1.71125438e-05, 8.45046636e-07, 7.88733736e-07],
       [4.28215579e-05, 6.13652971e-04, 9.99343511e-01, 1.41575174e-08],
       [1.62884845e-06, 1.96662878e-07, 1.37365693e-06, 9.99996801e-01],
       ...,
       [4.11101448e-02, 3.43163508e-04, 9.58539745e-01, 6.94675406e-06],
       [9.99960370e-01, 3.94933168e-05, 8.45736848e-08, 5.23076338e-08],
       [9.58337628e-01, 7.65806626e-03, 3.06443728e-02, 3.35993298e-03]])

temp=pd.DataFrame(lgb_test)
temp

	0	1	2	3
0	0.999981	1.711254e-05	8.450466e-07	7.887337e-07
1	0.000043	6.136530e-04	9.993435e-01	1.415752e-08
2	0.000002	1.966629e-07	1.373657e-06	9.999968e-01
3	0.999970	1.909713e-05	1.097002e-05	3.576703e-08
4	0.999983	1.769712e-06	1.482817e-05	1.966254e-07
...	...	...	...	...
19995	0.998096	3.060176e-04	1.085313e-04	1.489757e-03
19996	0.999846	1.436305e-04	1.074898e-05	8.837766e-08
19997	0.041110	3.431635e-04	9.585397e-01	6.946754e-06
19998	0.999960	3.949332e-05	8.457368e-08	5.230763e-08
19999	0.958338	7.658066e-03	3.064437e-02	3.359933e-03

20000 rows × 4 columns

result=pd.read_csv('sample_submit.csv',encoding='gbk')
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)

submit_data = pd.read_csv('submit.csv')

submit_data

	id	label_0	label_1	label_2	label_3
0	NaN	0.999981	1.711254e-05	8.450466e-07	7.887337e-07
1	NaN	0.000043	6.136530e-04	9.993435e-01	1.415752e-08
2	NaN	0.000002	1.966629e-07	1.373657e-06	9.999968e-01
3	NaN	0.999970	1.909713e-05	1.097002e-05	3.576703e-08
4	NaN	0.999983	1.769712e-06	1.482817e-05	1.966254e-07
...	...	...	...	...	...
19995	NaN	0.998096	3.060176e-04	1.085313e-04	1.489757e-03
19996	NaN	0.999846	1.436305e-04	1.074898e-05	8.837766e-08
19997	NaN	0.041110	3.431635e-04	9.585397e-01	6.946754e-06
19998	NaN	0.999960	3.949332e-05	8.457368e-08	5.230763e-08
19999	NaN	0.958338	7.658066e-03	3.064437e-02	3.359933e-03

20000 rows × 5 columns