本文主要是介绍【天池】心跳信号分类预测 baseline Part1，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
1 导包
import os
import gc
import mathimport pandas as pd
import numpy as npimport lightgbm as lgb
import xgboost as xgb
from catboost import CatBoostRegressor
from sklearn.linear_model import SGDRegressor, LinearRegression, Ridge
from sklearn.preprocessing import MinMaxScalerfrom sklearn.model_selection import StratifiedKFold,KFold
from sklearn.metrics import log_loss
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoderfrom tqdm import tqdm
import matplotlib.pyplot as plt
import time
import warnings
warnings.filterwarnings('ignore')
2 导入数据
train = pd.read_csv('./train.csv')
test = pd.read_csv('./testA.csv')
3 数据探索
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 |
train['label'].value_counts()
0.0 64327
3.0 17912
2.0 14199
1.0 3562
Name: label, dtype: int64
4 数据预处理
# 通过转换数据格式减小数据内存消耗
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].dtypeif 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
# 将数据简单处理：数据切割以及打标签
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 | ... | s_196 | s_197 | s_198 | s_199 | s_200 | s_201 | s_202 | s_203 | s_204 | label | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.0 | 0.991211 | 0.943359 | 0.764648 | 0.618652 | 0.379639 | 0.190796 | 0.040222 | 0.026001 | 0.031708 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 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 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 2 | 2.0 | 1.000000 | 0.958984 | 0.701172 | 0.231812 | 0.000000 | 0.080688 | 0.128418 | 0.187500 | 0.280762 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 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 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 4 | 4.0 | 0.000000 | 0.055817 | 0.261230 | 0.359863 | 0.433105 | 0.453613 | 0.499023 | 0.542969 | 0.616699 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 |
5 rows × 207 columns
5 模型训练
x_train = train.drop(['id','label'],axis=1)
y_train = train ['label']
x_test = test.drop(['id'],axis=1)
# 定义结果评价函数
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
# 训练模型
def cv_model(clf, train_x, train_y, test_x, clf_name):folds = 5seed = 2021kf = KFold(n_splits=folds, shuffle=True, random_state=seed) # 5折交叉验证test = np.zeros((test_x.shape[0],4))cv_scores = []onehot_encoder = OneHotEncoder(sparse=False) # 数据转换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_predscore=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_splitsreturn test
# 采用基于GBDT算法的LightGBM框架建模，速度更快
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
5 模型预测
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)
6 提交结果
7 总结
这只是一个baseline，基本上没有对数据有其它的处理
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