在配方模型的配方中仅选择有序因子

问题描述

我需要使用tidymodels的食谱包创建食谱。在其中一个步骤中，我需要将有序因子转换为它们的序数得分。但是我似乎没有任何功能 使用以选择所有有序因子。

我知道有一个名为all_nominal()的函数，但该函数与作为因子的每一列匹配，可以有序或无序。我也尝试过has_type("ordered")，但这也不起作用。

当前，我必须手动输入列名称。有更简单的方法吗？

下面是我想做的一个例子：

library(mlbench)
data("BreastCancer")

rec <- recipe(Class ~ .,BreastCancer) %>%
    # Here,I want to select all ordered nominals instead of 
    #  listing them by name. Should there be a function 
    #  all_ordinal in recipes? Or is there another way
    #  to accomplish this?
    step_ordinalscore(Cl.thickness,Cell.size,Cell.shape,Marg.adhesion,Epith.c.size)

欢迎您的帮助，谢谢。

解决方法

没有用于顺序因子的特殊选择器功能，但是您可以自己找到它们，然后将名称向量用于import heartpy as hp import numpy as np import matplotlib.pyplot as plt import matplotlib.animation as animation from collections import deque from matplotlib.ticker import FuncFormatter #from . import config sample_rate = 40 def init(): # initialization function: plot the background of each frame line.set_ydata([np.nan]*len(x)) line1.set_ydata([np.nan]*len(x1)) # scatter2.set_xdata([np.nan]*len(x2)) # scatter3.set_ydata([np.nan]*len(x3)) # scatter4.set_ydata([np.nan]*len(x4)) # scatter5.set_ydata([np.nan]*len(y5)) scat.set_offsets([]*len(x)) return line,line1,scat,#scatter5,#scatter4,scatter5 def animate(i): read_fname = 'temp.1D' datax = np.loadtxt('temp.1D') wd,m = hp.process(datax,40 ) #run analysis hr = wd['hr'] rm = wd['rolling_mean'] yb = wd['ybeat'] pl = wd['peaklist'] rp = wd['removed_beats'] rp_y = wd['removed_beats_y'] #pl=ax.collections[pl].get_array() yhr = np.array(hr[:]) yrm = np.array(rm[:]) yyb = np.array(yb[:]) ypl = np.array(pl[:]) yrp = np.array(rp[:]) yrp_y = np.array(rp_y[:]) yhr = np.concatenate([yhr]) yrm = np.concatenate([yrm]) yyb = np.concatenate([yyb]) ypl = np.concatenate([ypl]) yrp = np.concatenate([rp]) yrp_y = np.concatenate([rp_y]) data.extend(yhr) data1.extend(yrm) data2.extend(ypl) # data3.extend(yrp) # data4.extend(yrp_y) data5.extend(yyb) #data25.extend(ypl,yyb) line.set_ydata(data) line1.set_ydata(data1) # scatter2.set_xdata(data2) # scatter3.set_ydata(data3) # scatter4.set_ydata(data4) # scatter5.set_ydata(data5) data25 =(np.hstack((ypl[:i,np.newaxis],yyb[:i,np.newaxis]))) scat.set_offsets(data25) print(yhr) print(yrm) print(ypl) return line,scatter5 max_x = 500 max_y = 1700 #max_x2 = 1000 #max_y5 = 1000 data = deque(np.zeros(max_x),maxlen=max_x) data1 = deque(np.zeros(max_x),maxlen=max_x) data2= deque(np.zeros(max_x),maxlen=max_x) #data3 = deque(np.zeros(max_x),maxlen=max_x) #data4 = deque(np.zeros(max_x),maxlen=max_x) #hold the last 10 values data5 = deque(np.zeros(max_y),maxlen=max_y) data25 = deque(np.zeros(max_x),maxlen=max_x) x = np.arange(0,max_x) x1 = np.arange(0,max_x) x2 = np.arange(0,max_x) #x3 = np.arange(0,max_x) #x4 = np.arange(0,max_x) y5 = np.arange(0,max_y) fig,ax = plt.subplots() #fig,ax1 = plt.subplots() ax.set_ylim(-500,max_y) ax.set_xlim(0,max_x-1) line,= ax.plot(x,np.linspace(0,1000,max_x,endpoint = False)) line1,= ax.plot(x1,endpoint = False)) scat = ax.scatter([],[],) #ax = plt.gca() #scatter2,= ax.plot(x2,marker="o",ls="",color = "red") #scatter3,= ax.scatter(x3,50,1000)) #scatter4 = ax.scatter(x4,1000)) #scatter5,= ax.plot(y5,color = "green") #ax.grid() #ax.relim() #ax.autoscale_view(True,True,True) ax.xaxis.set_major_formatter(FuncFormatter(lambda x,pos: '{:.0}s'.format(max_x -x -990))) plt.xlabel('Seconds ago') ani = animation.FuncAnimation(fig,animate,init_func=init,interval=25,blit=True,save_count = 10) plt.show() 。

step_ordinalscore()

^{由reprex package（v0.3.0.9001）于2020-10-19创建}