rpy2 出现问题,rpart 将数据从 python 正确传递到 r
题
我正在尝试使用 Python 2.6.5 和 R 10.0 通过 RPY2 运行 rpart。
我在 python 中创建一个数据框并将其传递,但收到一条错误消息:
Error in function (x) : binary operation on non-conformable arrays
Traceback (most recent call last):
File "partitioningSANDBOX.py", line 86, in <module>
model=r.rpart(**rpart_params)
File "build/bdist.macosx-10.3-fat/egg/rpy2/robjects/functions.py", line 83, in __call__
File "build/bdist.macosx-10.3-fat/egg/rpy2/robjects/functions.py", line 35, in __call__
rpy2.rinterface.RRuntimeError: Error in function (x) : binary operation on non-conformable arrays
谁能帮助我确定我做错了什么而引发此错误?
我的代码的相关部分是这样的:
import numpy as np
import rpy2
import rpy2.robjects as rob
import rpy2.robjects.numpy2ri
#Fire up the interface to R
r = rob.r
r.library("rpart")
datadict = dict(zip(['responsev','predictorv'],[cLogEC,csplitData]))
Rdata = r['data.frame'](**datadict)
Rformula = r['as.formula']('responsev ~.')
#Generate an RPART model in R.
Rpcontrol = r['rpart.control'](minsplit=10, xval=10)
rpart_params = {'formula' : Rformula, \
'data' : Rdata,
'control' : Rpcontrol}
model=r.rpart(**rpart_params)
cLogEC 和 csplitData 这两个变量是 float 类型的 numpy 数组。
另外,我的数据框如下所示:
In [2]: print Rdata
------> print(Rdata)
responsev predictorv
1 0.6020600 312
2 0.3010300 300
3 0.4771213 303
4 0.4771213 249
5 0.9242793 239
6 1.1986571 297
7 0.7075702 287
8 1.8115750 270
9 0.6020600 296
10 1.3856063 248
11 0.6127839 295
12 0.3010300 283
13 1.1931246 345
14 0.3010300 270
15 0.3010300 251
16 0.3010300 246
17 0.3010300 273
18 0.7075702 252
19 0.4771213 252
20 0.9294189 223
21 0.6127839 252
22 0.7075702 267
23 0.9294189 252
24 0.3010300 378
25 0.3010300 282
公式如下:
In [3]: print Rformula
------> print(Rformula)
responsev ~ .
解决方案
该问题与 rpart 中的 R 特殊代码有关(准确地说,是以下块,特别是最后一行:
m <- match.call(expand.dots = FALSE)
m$model <- m$method <- m$control <- NULL
m$x <- m$y <- m$parms <- m$... <- NULL
m$cost <- NULL
m$na.action <- na.action
m[[1L]] <- as.name("model.frame")
m <- eval(m, parent.frame())
).
解决这个问题的一种方法是避免输入该代码块(见下文),或者可以从 Python 构造一个嵌套计算(以便 Parent.frame() 运行)。这并不像人们希望的那么简单,但也许我将来会找到时间让它变得更容易。
from rpy2.robjects import DataFrame, Formula
import rpy2.robjects.numpy2ri as npr
import numpy as np
from rpy2.robjects.packages import importr
rpart = importr('rpart')
stats = importr('stats')
cLogEC = np.random.uniform(size=10)
csplitData = np.array(range(10), 'i')
dataf = DataFrame({'responsev': cLogEC,
'predictorv': csplitData})
formula = Formula('responsev ~.')
rpart.rpart(formula=formula, data=dataf,
control=rpart.rpart_control(minsplit = 10, xval = 10),
model = stats.model_frame(formula, data=dataf))
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