準確率並沒提升太多。 import matplotlib.pyplot as plt import numpy as np import pandas as pd import seaborn as sns ep=1e-5 func_dict={'add':lambda x,y:x+y,'mins':lambda x,y:x-y,'div':lambda x,y:x/(y+ep),'mul':lambda x,y:x*y}
df = pd.read_csv('train.csv') df=df.drop(['ID'],axis=1) prfe=df.iloc[:,:-1] me=np.reshape(prfe.var(axis=1).to_numpy(),(-1,1)) me1=np.reshape(prfe.std(axis=1).to_numpy(),(-1,1)) df=df.to_numpy() feature=np.abs(np.fft.fft(df[:,:-1]))




df1 = pd.read_csv('test.csv') df1=df1.drop(['ID'],axis=1) df1=df1.to_numpy() yunt_feature=np.abs(np.fft.fft(df1[:,:]))


feature=pd.DataFrame(feature) yunt_feature=pd.DataFrame(yunt_feature) def auto_fea(fe,dunc_dict,col_list):     for col_i in col_list:         for col_j in col_list:             if col_i!=col_j:                 for func_name,func in dunc_dict.items():                     func_feature=func(fe[col_i],fe[col_j])                     col_name='-'.join([str(col_i),func_name,str(col_j)])                     fe[col_name]=func_feature

feature=np.concatenate((feature,np.reshape(df[:,-1],(-1,1))),axis=1) train=pd.DataFrame(feature) heat=train.corr() fe=heat.index[abs(heat[feature.shape[1]-1])>0.3] train=train.to_numpy() feature=train[:,fe] feature=feature[:,:-1] feature=pd.DataFrame(feature)
yunt_feature=yunt_feature.to_numpy() yunt_feature=yunt_feature[:,fe[:-1]] yunt_feature=pd.DataFrame(yunt_feature) auto_fea(feature,func_dict,feature.columns) auto_fea(yunt_feature,func_dict,yunt_feature.columns) yunt_feature=yunt_feature.to_numpy() feature=feature.to_numpy()
feature=np.concatenate((feature,np.reshape(df[:,-1],(-1,1))),axis=1) train=pd.DataFrame(feature) heat=train.corr() fe1=heat.index[abs(heat[feature.shape[1]-1])>0.42] print(len(fe1)) train=train.to_numpy() train=train[:,fe1] yunt_feature=yunt_feature[:,fe1[:-1]] from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score from sklearn import tree from sklearn.model_selection import cross_val_score from sklearn.model_selection import KFold kf=KFold(n_splits=5,shuffle=False) for k in range(10):     sum=0     sum1=0     i=0     for train_index,test_index in kf.split(train):         i=i+1         tfeature=train[train_index,:-1]         label=train[train_index,-1]         clf=tree.DecisionTreeClassifier(criterion='gini',random_state=0,max_depth=k+1)             clf.fit(tfeature,label)         l=clf.predict(tfeature)         ttest=train[test_index,:-1]         testlabel=train[test_index,-1]         l1=clf.predict(ttest)         pr=accuracy_score(label, l)         pr1=accuracy_score(testlabel, l1)         sum=sum+pr         sum1=sum1+pr1     clf1=tree.DecisionTreeClassifier(criterion='gini',random_state=0,max_depth=k+1)     scores = cross_val_score(clf1, train[:,:-1], train[:,-1], cv=5)     print(k,sum/i,sum1/i,scores.mean())     clf1=tree.DecisionTreeClassifier(criterion='gini',random_state=0,max_depth=4+1)     clf1.fit(train[:,:-1],train[:,-1]) out=clf1.predict(yunt_feature) out=pd.DataFrame(out) out.columns = ['CLASS'] w=[] for k in range(out.shape[0]):     w.append(k+210) out['ID']=np.reshape(w,(-1,1)) out[['ID','CLASS']].to_csv('out.csv',index=False)