本文主要是介绍心电图数据挖掘模型融合，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！
模型融合
在模型融合以后，评分达到了360.
1.导入相关所需库
import pandas as pd
import numpy as np
import warnings
import matplotlib
import matplotlib.pyplot as plt
import seaborn as snswarnings.filterwarnings('ignore')
%matplotlib inlineimport itertools
import matplotlib.gridspec as gridspec
from sklearn import datasets
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.ensemble import RandomForestClassifier,RandomForestRegressor
# from mlxtend.classifier import StackingClassifier
from sklearn.model_selection import cross_val_score, train_test_split
# from mlxtend.plotting import plot_learning_curves
# from mlxtend.plotting import plot_decision_regionsfrom sklearn.model_selection import StratifiedKFold
from sklearn.model_selection import train_test_split
from sklearn.model_selection import StratifiedKFold
from sklearn.model_selection import train_test_split
import lightgbm as lgb
from sklearn.neural_network import MLPClassifier,MLPRegressor
from sklearn.metrics import mean_squared_error, mean_absolute_error
2.引入一个降内存的函数，降低数据使用内存
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
3.导入数据，并进行数据的预处理
train = pd.read_csv('./data/train.csv')
test = pd.read_csv('./data/testA.csv')#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]])test_list = []
for items in test.values:test_list.append([items[0]] + [float(i) for i in items[1].split(',')])train = pd.DataFrame(np.array(train_list))
test = pd.DataFrame(np.array(test_list))# id列不算入特征
features = ['s_'+str(i) for i in range(len(train_list[0])-2)]
train.columns = ['id'] + features + ['label']
test.columns = ['id'] + featurestrain = reduce_mem_usage(train)
test = reduce_mem_usage(test)
4.划分训练集和测试集
# 根据8：2划分训练集和校验集
X_train = train.drop(['id','label'], axis=1)
y_train = train['label']# 测试集
X_test = test.drop(['id'], axis=1)# 第一次运行可以先用一个subdata，这样速度会快些
X_train = X_train.iloc[:50000,:20]
y_train = y_train.iloc[:50000]
X_test = X_test.iloc[:,:20]# 划分训练集和测试集
X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.2)
5.定义Random Forest，LGB，NN这三个单模函数
# 单模函数
def build_model_rf(X_train,y_train):model = RandomForestRegressor(n_estimators = 100)model.fit(X_train, y_train)return modeldef build_model_lgb(X_train,y_train):model = lgb.LGBMRegressor(num_leaves=63,learning_rate = 0.1,n_estimators = 100)model.fit(X_train, y_train)return modeldef build_model_nn(X_train,y_train):model = MLPRegressor(alpha=1e-05, hidden_layer_sizes=(5, 2), random_state=1,solver='lbfgs')model.fit(X_train, y_train)return model
6.对Random Forest，LGB，NN单模型进行训练
# 这里针对三个单模进行训练，其中subA_rf/lgb/nn都是可以提交的模型
# 单模没有进行调参，因此是弱分类器，效果可能不是很好。print('predict rf...')
model_rf = build_model_rf(X_train,y_train)
val_rf = model_rf.predict(X_val)
subA_rf = model_rf.predict(X_test)print('predict lgb...')
model_lgb = build_model_lgb(X_train,y_train)
val_lgb = model_lgb.predict(X_val)
subA_lgb = model_rf.predict(X_test)print('predict NN...')
model_nn = build_model_nn(X_train,y_train)
val_nn = model_nn.predict(X_val)
subA_nn = model_rf.predict(X_test)
7.加权融合
如果没有给权重矩阵，就是均值融合模型
权重矩阵可以进行自定义，这里我们是用三个单模进行融合。如果有更多需要更改矩阵size
# 加权融合模型，如果w没有变，就是均值融合
def Weighted_method(test_pre1,test_pre2,test_pre3,w=[1/3,1/3,1/3]):Weighted_result = w[0]*pd.Series(test_pre1)+w[1]*pd.Series(test_pre2)+w[2]*pd.Series(test_pre3)return Weighted_result# 初始权重，可以进行自定义，这里我们随便设置一个权重
w = [0.2, 0.3, 0.5]val_pre = Weighted_method(val_rf,val_lgb,val_nn,w)
MAE_Weighted = mean_absolute_error(y_val,val_pre)
print('MAE of Weighted of val:',MAE_Weighted)
val加权平均为0.25368…
8.将多个单模预测结果融合成融和模型结果
## 预测数据部分
subA = Weighted_method(subA_rf,subA_lgb,subA_nn,w)
9.Stacking融合
## Stacking## 第一层
train_rf_pred = model_rf.predict(X_train)
train_lgb_pred = model_lgb.predict(X_train)
train_nn_pred = model_nn.predict(X_train)stacking_X_train = pd.DataFrame()
stacking_X_train['Method_1'] = train_rf_pred
stacking_X_train['Method_2'] = train_lgb_pred
stacking_X_train['Method_3'] = train_nn_predstacking_X_val = pd.DataFrame()
stacking_X_val['Method_1'] = val_rf
stacking_X_val['Method_2'] = val_lgb
stacking_X_val['Method_3'] = val_nnstacking_X_test = pd.DataFrame()
stacking_X_test['Method_1'] = subA_rf
stacking_X_test['Method_2'] = subA_lgb
stacking_X_test['Method_3'] = subA_nn
stacking_X_test
# 第二层是用random forest
model_lr_stacking = build_model_rf(stacking_X_train,y_train)## 训练集
train_pre_Stacking = model_lr_stacking.predict(stacking_X_train)
print('MAE of stacking:',mean_absolute_error(y_train,train_pre_Stacking))## 验证集
val_pre_Stacking = model_lr_stacking.predict(stacking_X_val)
print('MAE of stacking:',mean_absolute_error(y_val,val_pre_Stacking))## 预测集
print('Predict stacking...')
subA_Stacking = model_lr_stacking.predict(stacking_X_test)
预测集要在天池平台上才能看到结果。
总结
-
结果层面的融合，这种是最常见的融合方法，其可行的融合方法也有很多，比如根据结果的得分进行加权融合，还可以做Log，exp处理等。在做结果融合的时候。有一个很重要的条件是模型结果的得分要比较近似但结果的差异要比较大，这样的结果融合往往有比较好的效果提升。如果不满足这个条件带来的效果很低，甚至是负效果。
-
特征层面的融合，这个层面叫融合融合并不准确，主要是队伍合并后大家可以相互学习特征工程。如果我们用同种模型训练，可以把特征进行切分给不同的模型，然后在后面进行模型或者结果融合有时也能产生比较好的效果。
-
模型层面的融合，模型层面的融合可能就涉及模型的堆叠和设计，比如加stacking，部分模型的结果作为特征输入等，这些就需要多实验和思考了，基于模型层面的融合最好不同模型类型要有一定的差异，用同种模型不同的参数的收益一般是比较小的。
这篇关于心电图数据挖掘模型融合的文章就介绍到这儿，希望我们推荐的文章对编程师们有所帮助！
