数据挖掘-心跳信号分类预测-Task03

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import pandas as pd
import numpy as np
from sklearn.metrics import f1_score, make_scorer

import os
import seaborn as sns
import matplotlib.pyplot as plt

import warnings
warnings.filterwarnings("ignore")


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

# 读取数据
data = pd.read_csv('../data/train.csv')
# 简单预处理
data_list = []
for items in data.values:
    data_list.append([items[0]] + [float(i) for i in items[1].split(',')] + [items[2]])

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

data = reduce_mem_usage(data)

from sklearn.model_selection import KFold
# 分离数据集，方便进行交叉验证
X_train = data.drop(['id','label'], axis=1)
y_train = data['label']

# 5折交叉验证
folds = 5
seed = 2021
kf = KFold(n_splits=folds, shuffle=True, random_state=seed)


def f1_score_vali(preds, data_vali):
    labels = data_vali.get_label()
    preds = np.argmax(preds.reshape(4, -1), axis=0)
    score_vali = f1_score(y_true=labels, y_pred=preds, average='macro')
    return 'f1_score', score_vali, True

def abs_sum(y_pre,y_tru):
    y_pre=np.array(y_pre)
    y_tru=np.array(y_tru)
    loss=(int)(sum(sum(abs(y_pre-y_tru))))
    return 'f1_score',loss, True


"""对训练集数据进行划分，分成训练集和验证集，并进行相应的操作"""
from sklearn.model_selection import train_test_split
import lightgbm as lgb
# 数据集划分
X_train_split, X_val, y_train_split, y_val = train_test_split(X_train, y_train, test_size=0.2)
train_matrix = lgb.Dataset(X_train_split, label=y_train_split)
valid_matrix = lgb.Dataset(X_val, label=y_val)

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from sklearn.model_selection import cross_val_score

"""定义优化函数"""
def rf_cv_lgb(num_leaves, max_depth, bagging_fraction, feature_fraction, bagging_freq, min_data_in_leaf,
              min_child_weight, min_split_gain, reg_lambda, reg_alpha):
    # 建立模型
    model_lgb = lgb.LGBMClassifier(boosting_type='gbdt', objective='multiclass', num_class=4,
                                   learning_rate=0.1, n_estimators=5000,
                                   num_leaves=int(num_leaves), max_depth=int(max_depth),
                                   bagging_fraction=round(bagging_fraction, 2), feature_fraction=round(feature_fraction, 2),
                                   bagging_freq=int(bagging_freq), min_data_in_leaf=int(min_data_in_leaf),
                                   min_child_weight=min_child_weight, min_split_gain=min_split_gain,
                                   reg_lambda=reg_lambda, reg_alpha=reg_alpha,
                                   n_jobs= 8
                                  )
    f1 = make_scorer(f1_score, average='micro')
    val = cross_val_score(model_lgb, X_train_split, y_train_split, cv=5, scoring=f1).mean()

    return val

from bayes_opt import BayesianOptimization
"""定义优化参数"""
bayes_lgb = BayesianOptimization(
    rf_cv_lgb,
    {
        'num_leaves':(10, 200),
        'max_depth':(3, 20),
        'bagging_fraction':(0.5, 1.0),
        'feature_fraction':(0.5, 1.0),
        'bagging_freq':(0, 100),
        'min_data_in_leaf':(10,100),
        'min_child_weight':(0, 10),
        'min_split_gain':(0.0, 1.0),
        'reg_alpha':(0.0, 10),
        'reg_lambda':(0.0, 10),
    }
)

"""开始优化"""
bayes_lgb.maximize()

print(bayes_lgb.max)

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"""调整一个较小的学习率，并通过cv函数确定当前最优的迭代次数"""
base_params_lgb = {
                    'boosting_type': 'gbdt',
                    'objective': 'multiclass',
                    'num_class': 4,
                    'learning_rate': 0.01,
                    'num_leaves': 126,
                    'max_depth': 15,
                    'min_data_in_leaf': 10,
                    'min_child_weight':7.46,
                    'bagging_fraction': 1.0,
                    'feature_fraction': 0.5,
                    'bagging_freq': 43,
                    'reg_lambda': 7.81,
                    'reg_alpha': 0,
                    'min_split_gain': 0,
                    'nthread': 10,
                    'verbose': -1
            }



cv_result_lgb = lgb.cv(
    train_set=train_matrix,
    early_stopping_rounds=1000,
    num_boost_round=20000,
    nfold=5,
    stratified=True,
    shuffle=True,
    params=base_params_lgb,
    feval=f1_score_vali,
    seed=0
)
print('迭代次数{}'.format(len(cv_result_lgb['f1_score-mean'])))
print('最终模型的f1为{}'.format(max(cv_result_lgb['f1_score-mean'])))

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# 优化后参数
import lightgbm as lgb
"""使用lightgbm 5折交叉验证进行建模预测"""
cv_scores = []
for i, (train_index, valid_index) in enumerate(kf.split(X_train, y_train)):
    print('************************************ {} ************************************'.format(str(i+1)))
    X_train_split, y_train_split, X_val, y_val = X_train.iloc[train_index], y_train[train_index], X_train.iloc[valid_index], y_train[valid_index]

    train_matrix = lgb.Dataset(X_train_split, label=y_train_split)
    valid_matrix = lgb.Dataset(X_val, label=y_val)

    params = {
                'boosting_type': 'gbdt',
                'objective': 'multiclass',
                'num_class': 4,
                'learning_rate': 0.01,
                'num_leaves': 116,
                'max_depth': 18,
                'min_data_in_leaf': 87,
                'min_child_weight':3.04,
                'bagging_fraction': 0.77,
                'feature_fraction': 0.52,
                'bagging_freq': 31,
                'reg_lambda': 3.95,
                'reg_alpha': 0.14,
                'min_split_gain': 0.212,
                'nthread': 10,
                'verbose': -1,
            }

    model = lgb.train(params, train_set=train_matrix, num_boost_round=4006, valid_sets=valid_matrix,
                      verbose_eval=1000, early_stopping_rounds=200, feval=f1_score_vali)
    val_pred = model.predict(X_val, num_iteration=model.best_iteration)
    val_pred = np.argmax(val_pred, axis=1)
    cv_scores.append(f1_score(y_true=y_val, y_pred=val_pred, average='macro'))
    print(cv_scores)

print("lgb_scotrainre_list:{}".format(cv_scores))
print("lgb_score_mean:{}".format(np.mean(cv_scores)))
print("lgb_score_std:{}".format(np.std(cv_scores)))