如何运行 auto.arima 使用 rpy2

Question

我想从 Python 调用 R 的 auto.arima 函数。我想我还没有完全理解这个界面。有人可以帮我吗 - 将时间序列对象发送到 R，调用预测相关函数并取回结果？

这是我到目前为止所做的：

from rpy2.robjects import r
from rpy2.robjects import pandas2ri

#create a python time series
count = range(1, 51)
df['count'] = count
df['date'] = pd.date_range('2016-01-01', '2016-02-19')
df.set_index('date', inlace = True)
df.sort_index(inplace = True)

pandas2ri.activate()
r_timeseries = pandas2ri.py2ri(df)
r('fit <- auto.arima(r_timeseries)')

我想我必须导入一些 R 包（比如 forecast）。不确定如何在 Python 中执行此操作，正确地将 python 时间序列对象传递给 R 等

In [63]: r_ts = pandas2ri.py2ri(df)

In [64]: r_ts
Out[64]:
<DataFrame - Python:0x1126a93f8 / R:0x7ff7bfa51bc8>
[IntVector]
  X0: <class 'rpy2.robjects.vectors.IntVector'>
  <IntVector - Python:0x1126a96c8 / R:0x7ff7be1af1c0>
[       1,        2,        3, ...,       48,       49,       50]

并且，当我尝试调用预测时

In [83]: x = r('forecast(r_ts)')
/Library/Python/2.7/site-packages/rpy2/robjects/functions.py:106: UserWarning: Error in forecast(r_ts) : object 'r_ts' not found

  res = super(Function, self).__call__(*new_args, **new_kwargs)
---------------------------------------------------------------------------
RRuntimeError                             Traceback (most recent call last)
<ipython-input-83-0765ffc30741> in <module>()
----> 1 x = r('forecast(r_ts)')

/Library/Python/2.7/site-packages/rpy2/robjects/__init__.pyc in __call__(self, string)
    319     def __call__(self, string):
    320         p = _rparse(text=StrSexpVector((string,)))
--> 321         res = self.eval(p)
    322         return conversion.ri2py(res)
    323

/Library/Python/2.7/site-packages/rpy2/robjects/functions.pyc in __call__(self, *args, **kwargs)
    176                 v = kwargs.pop(k)
    177                 kwargs[r_k] = v
--> 178         return super(SignatureTranslatedFunction, self).__call__(*args, **kwargs)
    179
    180 pattern_link = re.compile(r'\link\{(.+?)\}')

/Library/Python/2.7/site-packages/rpy2/robjects/functions.pyc in __call__(self, *args, **kwargs)
    104         for k, v in kwargs.items():
    105             new_kwargs[k] = conversion.py2ri(v)
--> 106         res = super(Function, self).__call__(*new_args, **new_kwargs)
    107         res = conversion.ri2ro(res)
    108         return res

RRuntimeError: Error in forecast(r_ts) : object 'r_ts' not found

我也尝试了以下方法：

In [99]: f = r('forecast.auto.arima(r_ts)')
---------------------------------------------------------------------------
RRuntimeError                             Traceback (most recent call last)
<ipython-input-99-1c4610d2740d> in <module>()
----> 1 f = r('forecast.auto.arima(r_ts)')

/Library/Python/2.7/site-packages/rpy2/robjects/__init__.pyc in __call__(self, string)
    319     def __call__(self, string):
    320         p = _rparse(text=StrSexpVector((string,)))
--> 321         res = self.eval(p)
    322         return conversion.ri2py(res)
    323

/Library/Python/2.7/site-packages/rpy2/robjects/functions.pyc in __call__(self, *args, **kwargs)
    176                 v = kwargs.pop(k)
    177                 kwargs[r_k] = v
--> 178         return super(SignatureTranslatedFunction, self).__call__(*args, **kwargs)
    179
    180 pattern_link = re.compile(r'\link\{(.+?)\}')

/Library/Python/2.7/site-packages/rpy2/robjects/functions.pyc in __call__(self, *args, **kwargs)
    104         for k, v in kwargs.items():
    105             new_kwargs[k] = conversion.py2ri(v)
--> 106         res = super(Function, self).__call__(*new_args, **new_kwargs)
    107         res = conversion.ri2ro(res)
    108         return res

RRuntimeError: Error in eval(expr, envir, enclos) :
  could not find function "forecast.auto.arima"

Answer 1

在第一次尝试中，您告诉 R 使用变量 r_ts，它现在不太关心（名称 r_ts 在您的 Python 命名空间中定义），并且在第二次尝试中，你被添加到这个函数名称中，R 对此一无所知。两条错误消息都准确地将此报告为问题。

您的第一次尝试可以重写为：

x = r('forecast')(r_ts)

Answer 2

你可以试试我做的

import rpy2.robjects as ro
from rpy2.robjects import pandas2ri
pandas2ri.activate()

ro.r('library(forecast)')

rdf = pandas2ri.py2ri(df)
ro.globalenv['r_timeseries'] = rdf
pred = ro.r('as.data.frame(forecast(auto.arima(r_timeseries),h=5))')

通过这种方式，您可以将 pred 处理为这样的数据框

    Point Forecast  Lo 80  Hi 80  Lo 95  Hi 95
51              51     51     51     51     51
52              52     52     52     52     52
53              53     53     53     53     53
54              54     54     54     54     54
55              55     55     55     55     55

如何 运行 auto.arima 使用 rpy2

how to run auto.arima using rpy2

rpy2

如何运行 auto.arima 使用 rpy2