天池大赛-心跳信号分类预测：赛题理解与baseline解析
- 比赛地址：零基础入门数据挖掘-心跳信号分类预测
- 参考资料：由DataWhale开源的学习资料
1. 赛题简介
本次新人赛是Datawhale与天池联合发起的0基础入门系列赛事 —— 心跳信号分类预测。
赛题以心电图心跳信号数据为背景，要求选手根据心电图感应数据预测心跳信号所属类别，其中心跳信号对应正常病例以及受不同心律不齐和心肌梗塞影响的病例，这是一个多分类的问题。通过这道赛题来引导大家了解医疗大数据的应用，帮助竞赛新人进行自我练习、自我提高。
为了更好的引导大家入门，DataWhale还特别为本赛题定制了学习方案，其中包括数据科学库、通用流程和baseline方案学习三部分。通过对本方案的完整学习，可以帮助掌握数据竞赛基本技能。
2. 赛题数据
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。
字段表
| Field | Description |
|---|---|
| id | 为心跳信号分配的唯一标识 |
| heartbeat_signals | 心跳信号序列 |
| label | 心跳信号类别（0、1、2、3） |
3. 评测标准
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（越小越好）。
具体计算公式如下：
针对某一个信号，若真实值为 [ y 1 , y 2 , y 3 , y 4 ] [y_1,y_2,y_3,y_4] [y1,y2,y3,y4]，模型预测概率值为 [ a 1 , a 2 , a 3 , a 4 ] [a_1,a_2,a_3,a_4] [a1,a2,a3,a4]，那么该模型的平均指标 a b s − s u m abs-sum abs−sum为
a b s − s u m = ∑ j = 1 n ∑ i = 1 4 ∣ y i − a i ∣ {abs-sum={\mathop{ \sum }\limits_{{j=1}}^{{n}}{{\mathop{ \sum }\limits_{{i=1}}^{{4}}{{ \left| {y\mathop{{}}\nolimits_{{i}}-a\mathop{{}}\nolimits_{{i}}} \right| }}}}}} abs−sum=j=1∑ni=1∑4∣yi−ai∣
4. baseline解析
4.1 导入第三方包
import pandas as pd
import numpy as np
import lightgbm as lgb
from sklearn.model_selection import KFold
from sklearn.preprocessing import OneHotEncoder
import warnings
warnings.filterwarnings('ignore')
4.2 读取数据
train = pd.read_csv('train.csv')
test = pd.read_csv('testA.csv')
train.head()
| id | heartbeat_signals | label |
|---|---|---|
| 0 | 0.9912297987616655,0.9435330436439665,0.764677… | 0.0 |
| 1 | 0.9912297987616655,0.9435330436439665,0.764677… | 0.0 |
| 2 | 1.0,0.9591487564065292,0.7013782792997189,0.23… | 2.0 |
| 3 | 0.9757952826275774,0.9340884687738161,0.659636… | 0.0 |
| 4 | 0.0,0.055816398940721094,0.26129357194994196,0… | 2.0 |
test.head()
| id | hearbeat_signals |
|---|---|
| 100000 | 0.9915713654170097,1.0,0.6318163407681274,0.13… |
| 100001 | 0.6075533139615096,0.5417083883163654,0.340694… |
| 100002 | 0.9752726292239277,0.6710965234906665,0.686758… |
| 100003 | 0.9956348033996116,0.9170249621481004,0.521096… |
| 100004 | 1.0,0.8879490481178918,0.745564725322326,0.531… |
4.3 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
| 数据类型 | 描述 |
|---|---|
| int8 | 字节（-128 to 127） |
| int16 | 整数（-32768 to 32767） |
| int32 | 整数（-2147483648 to 2147483647） |
| int64 | 整数（-9223372036854775808 to 9223372036854775807） |
| float16 | 半精度浮点数，包括：1 个符号位，5 个指数位，10 个尾数位 |
| float32 | 单精度浮点数，包括：1 个符号位，8 个指数位，23 个尾数位 |
| float64 | 双精度浮点数，包括：1 个符号位，11 个指数位，52 个尾数位 |
从int8 -> int64占用内存逐渐增大，float16 -> float64同理。这段预处理的代码考察DataFrame中的每一列的最大值与最小值，然后为每一列分配最合适的数据类型，可以大大减少占用的内存。
# 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]])
# items[0]: id
# items[1]: 心跳信号，字符串类型，通过split将其分割
# items[2]: label
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)
# 将train转为Dataframe并用上述方法降低内存占用
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)
# 对test进行同样操作
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%
4.4 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)
4.5 模型训练
# 定义评分标准abs-sum
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) # k折交叉验证
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]]
[607.0736049372186]
************************************ 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]]
[607.0736049372186, 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]]
[607.0736049372186, 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]]
[607.0736049372186, 623.4313863731124, 508.02381607269535, 660.4867407547267]
************************************ 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]]
[607.0736049372186, 623.4313863731124, 508.02381607269535, 660.4867407547267, 539.2160054696063]
lgb_scotrainre_list: [607.0736049372186, 623.4313863731124, 508.02381607269535, 660.4867407547267, 539.2160054696063]
lgb_score_mean: 587.646310721472
lgb_score_std: 55.94453640571462
4.6 预测结果
# 将结果输出为csv
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)
5. 提交结果
