r - 将numpy数组转换为rpy2矩阵(预测包，xreg参数)

Question

我似乎无法使 fit = Forecast.Arima(series, order=order, xreg=r_exog_train) 行正常工作。它确实可以在没有 xreg 参数的情况下工作，所以我很确定 numpy 数组到 rpy2 矩阵的转换会产生问题。

有人发现这里有错误吗？谢谢!

这是我收到的错误(不幸的是，部分内容是德语):

Fehler in `colnames<-`(`*tmp*`, value = if (ncol(xreg) == 1) nmxreg else paste(n
mxreg,  :
  Länge von 'dimnames' [2] ungleich der Arrayausdehnung
Traceback (most recent call last):
  File "r.py", line 58, in <module>
    res = do_forecast(series, horizon=horizon, exog=(exog_train, exog_test))
  File "r.py", line 39, in do_forecast
    fit = forecast.Arima(series, order=order, xreg=exog_train)
  File "C:\Python27\lib\site-packages\rpy2\robjects\functions.py", line 86, in _
_call__
    return super(SignatureTranslatedFunction, self).__call__(*args, **kwargs)
  File "C:\Python27\lib\site-packages\rpy2\robjects\functions.py", line 35, in _
_call__
    res = super(Function, self).__call__(*new_args, **new_kwargs)
rpy2.rinterface.RRuntimeError: Fehler in `colnames<-`(`*tmp*`, value = if (ncol(
xreg) == 1) nmxreg else paste(nmxreg,  :
  Lõnge von 'dimnames' [2] ungleich der Arrayausdehnung   

下面是代码示例:

# Python wrapper for R forecast stuff
import numpy as np

print 'Start importing R.'
from rpy2 import robjects 
from rpy2.robjects.packages import importr
from rpy2.robjects.numpy2ri import numpy2ri
robjects.conversion.py2ri = numpy2ri

base = importr('base')
forecast = importr('forecast')
stats = importr('stats')
ts = robjects.r['ts']
print 'Finished importing R.'


def nparray2rmatrix(x):
  nr, nc = x.shape
  xvec = robjects.FloatVector(x.transpose().reshape((x.size)))
  xr = robjects.r.matrix(xvec, nrow=nr, ncol=nc)
  return xr

def nparray2rmatrix_alternative(x):
  nr, nc = x.shape
  xvec = robjects.FloatVector(x.reshape((x.size)))
  xr = robjects.r.matrix(xvec, nrow=nr, ncol=nc, byrow=True)
  return xr

def do_forecast(series, frequency=None, horizon=30, summary=False, exog=None):
  if frequency:
    series = ts(series, frequency=frequency)
  else:
    series = ts(series)
  if exog:
    exog_train, exog_test = exog
    r_exog_train = nparray2rmatrix(exog_train)
    r_exog_test = nparray2rmatrix(exog_test)
    order = robjects.IntVector([1, 0, 2]) # c(1,0,2) # TODO find right model
    fit = forecast.Arima(series, order=order, xreg=r_exog_train)
    forecast_result = forecast.forecast(fit, h=horizon, xreg=r_exog_test)
  else:
    # fit = forecast.auto_arima(series)
    #robjects.r.plot(series)
    fit = stats.HoltWinters(series)
    forecast_result = forecast.forecast(fit, h=horizon)
  if summary:
    modsummary = base.summary(fit)
    print modsummary
  forecast_values = np.array(list(forecast_result.rx2('mean')))
  return forecast_values


# Example
series = np.arange(100)
exog_train = np.ones((100,2))
exog_test = np.ones((100,2))
horizon = 100
res = do_forecast(series, horizon=horizon, exog=(exog_train, exog_test))
print res

Answer 1

我通过明确声明“dimnames”来使其工作，如下所示:xr = robjects.r.matrix(xvec, nrow=nr, ncol=nc, dimnames=dimnames)。显然预测包需要这个。