I have an R script in Azure Machine Learning that takes two inputs. I have since been working on a project that will take advantage of the webservice I created within Azure. When I use whole numbers as the values, everything works fine. In my C# code, these values are still doubles, and I use ToString to format them for the HTTP request. I can send the data, and get 100% accurate results back. However, when I send values that actually contain digits after the decimal, I get a bad request response. I think the issue is with how the R script reads in from Azure Machine Learning inputs. So far I have this:
#R Script in Azure ML:
1: objCoFrame <- maml.mapInputPort(2) # class: data.frame
2: objCoVector <- as.vector(objCoFrame[1,])
which was doing the trick with integers. I have also tried
2: objCoVector <- as.vector(as.numeric(objCoFrame[1,]))
but got the same result.
The Bad Request Response Content reads:
{
"error":
{
"code":"BadArgument",
"message":"Invalid argument provided.",
"details":
[{
"code":"InputParseError",
"target":"rhsValues",
"message":"Parsing of input vector failed. Verify the input vector has the correct number of columns and data types. Additional details: Input string was not in a correct format.."
}]
}
}
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