def mx_mul(mlst, x_test, y_test,yk0=5,fgInt=False,fgDebug=False):
#1
print('\ny_pred,预测')
df9,xc,mxn9=x_test.copy(),0,len(mlst)
df9['y_test']=y_test
#2
for msgn in mlst:
xc+=1;tim0=arrow.now()
mx=xmodel[msgn]
y_pred = mx.predict(x_test.values)
#3
if xc==1:df9['y_sum']=y_pred
else:df9['y_sum']=df9['y_sum']+y_pred
#4
tn=zt.timNSec('',tim0)
df9['y_pred']=y_pred
#4.b
if fgInt:
df9['y_predsr']=df9['y_pred']
df9['y_pred']=round(df9['y_predsr']).astype(int)
#5
dacc=ai_acc_xed(df9,yk0,fgDebug)
xss='y_pred{0:02},kok:{1:.2f}%'.format(xc,dacc);print(xc,xss,msgn,tn,'s')
ysgn='y_pred'+str(xc);df9[ysgn]=y_pred
#6
df9['y_pred']=df9['y_sum']/mxn9
if fgInt:
df9['y_predsr']=df9['y_pred']
df9['y_pred']=round(df9['y_predsr']).astype(int)
#7
dacc=zat.ai_acc_xed(df9,yk0,fgDebug)
#8
if fgDebug:
df9.to_csv('tmp/df9_pred.csv');
#9
print('@mx:mx_sum,kok:{0:.2f}%'.format(dacc))
return dacc,df9
def mx_fun_call(df,xlst,ysgn,funSgn,yksiz=1,yk0=5,fgInt=False,fgDebug=False):
#1
df[ysgn]=df[ysgn].astype(float)
df[ysgn]=round(df[ysgn]*yksiz).astype(int)
#2
x,y= df[xlst],df[ysgn]
#3
x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=1)
num_train, num_feat = x_train.shape
num_test, num_feat = x_test.shape
print('\nn_tran:,',num_train,' ,ntst,' , num_test, ' ,dimension:,',num_feat,',kbin,',fgInt)
#4
print('\ny_pred,预测')
df9=x_test.copy()
mx_fun=mxfunSgn[funSgn]
mx =mx_fun(x_train.values,y_train.values)
#5
y_pred = mx.predict(x_test.values)
df9['y_test'],df9['y_pred']=y_test,y_pred
#6
if fgInt:
df9['y_predsr']=df9['y_pred']
df9['y_pred']=round(df9['y_predsr']).astype(int)
#7
dacc=zat.ai_acc_xed(df9,yk0,fgDebug)
#8
if fgDebug:
#print(df9.head())
print('@fun name:',mx_fun.__name__)
df.to_csv('tmp/df_sr.csv');
df9.to_csv('tmp/df9_pred.csv');
#------------
#9
print('@mx:mx_sum,kok:{0:.2f}%'.format(dacc))
return dacc,df9
def mx_fun8mx(mx,x_test,y_test,yk0=5,fgInt=False,fgDebug=False):
#1
df9=x_test.copy()
#mx=....
#2
y_pred = mx.predict(x_test.values)
df9['y_test'],df9['y_pred']=y_test,y_pred
#3
if fgInt:
df9['y_predsr']=df9['y_pred']
df9['y_pred']=round(df9['y_predsr']).astype(int)
#4
dacc=zat.ai_acc_xed(df9,yk0,fgDebug)
#5
if fgDebug:
#print(df9.head())
#print('@fun name:',mx_fun.__name__)
df9.to_csv('tmp/df9_pred.csv',index=False);
#
#6
#print('@mx:mx_sum,kok:{0:.2f}%'.format(dacc))
return dacc,df9
def mx_fun010(funSgn,x_train, x_test, y_train, y_test,yk0=5,fgInt=False,fgDebug=False):
#1
df9=x_test.copy()
mx_fun=mxfunSgn[funSgn]
mx =mx_fun(x_train.values,y_train.values)
#2
y_pred = mx.predict(x_test.values)
df9['y_test'],df9['y_pred']=y_test,y_pred
#3
if fgInt:
df9['y_predsr']=df9['y_pred']
df9['y_pred']=round(df9['y_predsr']).astype(int)
#4
dacc=zat.ai_acc_xed(df9,yk0,fgDebug)
#5
if fgDebug:
#print(df9.head())
print('@fun name:',mx_fun.__name__)
df9.to_csv('tmp/df9_pred.csv');
#
#6
print('@mx:mx_sum,kok:{0:.2f}%'.format(dacc))
return dacc,df9