def fit(self, x, y):
if self.method == 'poly':
FS = Filter_Selection('pearson', TopN=self.TopN)
FS.fit(x, y)
new_x = FS.transform(x)
Dchange = Data_Preprocess.Data_Change('poly')
Dchange.fit(new_x)
DChange_new_x = Dchange.transform(new_x)
standard = Data_Preprocess.Data_Change('avgstd')
standard.fit(DChange_new_x)
#赋值
self.Filter_Selection = FS
self.DChange = Dchange
self.Standard = standard
def get_vip(self,isplot=True):
#计算关键因子重要性
col_name = 'variable importance'
if self.method in ['knn','dt','svm','bp']:
res = None
else:
if self.method in ['logistic'] :
mean_coef = pd.DataFrame(abs(self.cls_model.coef_)).T.mean(axis=1)
var_importance = pd.DataFrame(mean_coef.values,index = self.factor_name , columns = [col_name])
# var_importance = pd.DataFrame(abs(self.cls_model.coef_),index = [col_name] ,columns = self.factor_name)
elif self.method in ['rf','adaBoost','gbm','xgb']:
coef = self.cls_model.best_estimator_.feature_importances_.reshape(-1,1)
var_importance = pd.DataFrame(abs(coef),columns = [col_name] ,index = self.factor_name)
res = var_importance.sort_values(col_name)
#对因子重要性进行归一化。
Dchange = Data_Preprocess.Data_Change('minmax')
Dchange.fit(res)
res = Dchange.transform(res)
#画条形图
if isplot:
plt = Data_plot.plot_bar_analysis(res)
plt.title('variable importance')
plt.show()
return res
def get_vip(self,isplot = True):
#计算关键因子，
if self.method in ['svr','knn','dt','bp']:
#上述算法没有办法衡量重要因子
return None
else:
col_name = 'variable importance'
if self.method in ['linear'] :
var_importance = pd.DataFrame(abs(self.reg_model.coef_),columns = [col_name] , index= self.factor_name)
elif self.method in ['ridge','lasso','ElasticNet','pls']:
coef = self.reg_model.best_estimator_.coef_.reshape(-1,1)
var_importance = pd.DataFrame(abs(coef),columns = [col_name] ,index = self.factor_name)
elif self.method in ['rf','adaBoost','gbm','xgb']:
# var_importance = None
coef = self.reg_model.best_estimator_.feature_importances_.reshape(-1,1)
var_importance = pd.DataFrame(abs(coef),columns = [col_name] ,index = self.factor_name)
res = var_importance.sort_values(col_name,ascending = False)
#对因子重要性进行归一化。
Dchange = Data_Preprocess.Data_Change('minmax')
Dchange.fit(res)
res = Dchange.transform(res)
#画条形图
if isplot:
plt = Data_plot.plot_bar_analysis(res,Top=15)
plt.title('variable importance')
plt.show()
return res
def data_change(self, method='minmax'):
Dchange = Data_Preprocess.Data_Change(method=method)
Dchange.fit(self.x)
self.x = Dchange.transform(self.x)
self.data.loc[:, self.x.columns] = self.x
#新增工序list
self.Pipeline_list.append(('data_change', Dchange))
self.data_change_model = Dchange
def predict(self,x):
'''
预测：
'''
res = []
for model_name in self.listModelName:
sub_model_res = []
for sub_model in self.train_model[model_name]:
sub_model_res.append(pd.DataFrame(sub_model.predict(x)))
#子模型结果融合
sub_model_res = pd.concat(sub_model_res,axis = 1).mean(axis = 1)
res.append(sub_model_res)
#不同模型结果融合
if self.stack_method == 'avg':
res = pd.concat(res,axis = 1).mean(axis = 1)
elif self.stack_method == 'weight':
res = pd.concat(res,axis = 1).values
#对mse进行归一化
mse = pd.DataFrame(self.mse_list)
Dchange = Data_Preprocess.Data_Change('minmax')
mse = Dchange.fit_transform(mse)
weight = np.array(mse).reshape(len(res),1)
res = np.dot(res,weight)
return res.values