1.导入第三方包

  1. import os
  2. import gc
  3. import math
  5. import pandas as pd
  6. import numpy as np
  8. import lightgbm as lgb
  9. import xgboost as xgb
  10. from catboost import CatBoostRegressor
  11. from sklearn.linear_model import SGDRegressor, LinearRegression, Ridge
  12. from sklearn.preprocessing import MinMaxScaler
  15. from sklearn.model_selection import StratifiedKFold, KFold
  16. from sklearn.metrics import log_loss
  17. from sklearn.model_selection import train_test_split
  18. from sklearn.preprocessing import OneHotEncoder
  20. from tqdm import tqdm
  21. import matplotlib.pyplot as plt
  22. import time
  23. import warnings
  24. warnings.filterwarnings('ignore')

2.读取数据

  1. train = pd.read_csv('train.csv')
  2. test=pd.read_csv('testA.csv')
  3. 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 
  1. 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…

3.数据预处理

  1. def reduce_mem_usage(df):
  2.     start_mem = df.memory_usage().sum() / 1024**2 
  3.     print('Memory usage of dataframe is {:.2f} MB'.format(start_mem))
  5.     for col in df.columns:
  6.         col_type = df[col].dtype
  8.         if col_type != object:
  9.             c_min = df[col].min()
  10.             c_max = df[col].max()
  11.             if str(col_type)[:3] == 'int':
  12.                 if c_min > np.iinfo(np.int8).min and c_max < np.iinfo(np.int8).max:
  13.                     df[col] = df[col].astype(np.int8)
  14.                 elif c_min > np.iinfo(np.int16).min and c_max < np.iinfo(np.int16).max:
  15.                     df[col] = df[col].astype(np.int16)
  16.                 elif c_min > np.iinfo(np.int32).min and c_max < np.iinfo(np.int32).max:
  17.                     df[col] = df[col].astype(np.int32)
  18.                 elif c_min > np.iinfo(np.int64).min and c_max < np.iinfo(np.int64).max:
  19.                     df[col] = df[col].astype(np.int64)  
  20.             else:
  21.                 if c_min > np.finfo(np.float16).min and c_max < np.finfo(np.float16).max:
  22.                     df[col] = df[col].astype(np.float16)
  23.                 elif c_min > np.finfo(np.float32).min and c_max < np.finfo(np.float32).max:
  24.                     df[col] = df[col].astype(np.float32)
  25.                 else:
  26.                     df[col] = df[col].astype(np.float64)
  27.         else:
  28.             df[col] = df[col].astype('category')
  30.     end_mem = df.memory_usage().sum() / 1024**2 
  31.     print('Memory usage after optimization is: {:.2f} MB'.format(end_mem))
  32.     print('Decreased by {:.1f}%'.format(100 * (start_mem - end_mem) / start_mem))
  34.     return df
  1. # 简单预处理
  2. train_list = []
  4. for items in train.values:
  5.     train_list.append([items[0]] + [float(i) for i in items[1].split(',')] + [items[2]])
  7. train = pd.DataFrame(np.array(train_list))
  8. train.columns = ['id'] + ['s_'+str(i) for i in range(len(train_list[0])-2)] + ['label']
  9. train = reduce_mem_usage(train)
  11. test_list=[]
  12. for items in test.values:
  13.     test_list.append([items[0]] + [float(i) for i in items[1].split(',')])
  15. test = pd.DataFrame(np.array(test_list))
  16. test.columns = ['id'] + ['s_'+str(i) for i in range(len(test_list[0])-1)]
  17. 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%

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)

5.模型训练

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]]
[607.0736049372186]
************************************ 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

6.预测结果

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)