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POConverting numpy array to rpy2 matrix (forecast package, xreg parameter)
 

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  1. POConverting numpy array to rpy2 matrix (forecast package, xreg parameter)
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    <p>I don't seem to be able to get the line <code>fit = forecast.Arima(series, order=order, xreg=r_exog_train)</code> to work. It does work without the xreg parameter so I'm pretty sure it's the numpy array to rpy2 matrix conversion that makes a problem. </p> <p>Does anyone observe a mistake here? Thanks!</p> <p>This is the error I get (parts in German unfortunately):</p> <pre><code>Fehler in `colnames&lt;-`(`*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 &lt;module&gt; 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&lt;-`(`*tmp*`, value = if (ncol( xreg) == 1) nmxreg else paste(nmxreg, : Lõnge von 'dimnames' [2] ungleich der Arrayausdehnung </code></pre> <p>Here is the code example:</p> <pre><code># 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 </code></pre>
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    1. COAs a general comment, I would limit the mixing of R into Python as much as possible. Just create the `doforecast` function in R, source the function into the current workspace, and call it. This way you limit the interface of R and Python to just a few lines of R code in Python. This makes it less likely you'll run into rpy specific problems, and makes debugging the R code easier.
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      1. USPaul Hiemstra
    2. COThanks Paul, that makes sense. Still, I would have the same problem of getting the xreg matrix to work.
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    3. COTrue, but still as a general way of avoiding issues I would make the code as independent of each other as you can.
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      1. POConverting numpy array to rpy2 matrix (forecast package, xreg parameter)
      1. USPaul Hiemstra
 

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