Stock_Prediction_Model_Random_Forrest.py 文件源码

def best_window(self, X_train, y_train, w_min, w_max, t_min,t_max,f_min,f_max):
        w_opt = 0
        t_opt = 0
        f_opt = 0
        accur_opt = 0.

        x_w = []
        y_accu= []

        # range of window : w_min --> w_max     
        for w in range(w_min,w_max+1):
            #X,y = preprocess_data(w)
            #X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33)
            t, f, accur = self.best_forrest(X_train,y_train,10,t_min,t_max,f_min,f_max)
            print('Window = '+str(w)+' days --> Best Forrest : number of trees : ' + str(t) + ', maximum of features : ' + str(f) + ', with accuracy :' + str(accur))

            if (accur > accur_opt) : w_opt, t_opt, f_opt, accur_opt = w, t, f, accur
            x_w.append(w), y_accu.append(accur)

        print('Best window : w = '+str(w_opt)+'. Best Forrest : number of trees : ' + str(t_opt) + ', maximum of features : ' + str(f_opt) + ', with accuracy :' + str(accur_opt))
        return w_opt, t_opt, f_opt