Migrating Python ML Models to other languages
I’ve mentioned in a previous blog post about experiencing some performance issues with using Python ML in production. We needed something quicker and the possible languages we considered were C, C++, Java and Go Lang.
But the data science team used R and Python, with just a few more people using Python than R on the team.
One option was to rewrite everything into the language used in production. As you can imagine no-one wanted to do that and there was no way of ensure a bug free solution and one that gave similar results to the R and Python models. The other option was to look for some code to convert the models from one language to another.
The R users was well versed in using PMML. Predictive Model Markup Language (PMML) has been around a long time and well known and used by certain groups of data scientists who have been around a while. It is also widely supported by many analytics vendors, and provides an inter-change format to allow predictive models to be described and exchanged. For newer people, they hadn’t heard of it. PMML is an XML based interchange specification.
But with PMML there are some limitation. Not with the specification but how it is implemented by the various vendors that support it. PMML supports the exchange of the model pipeline including the data transformations as well as the model specification. Most vendors only support some elements of this and maybe just a couple of models. And there-in lies the problem. How can a ML pipeline be migrated from, as Python, to some other language and/or tool. There are limitations.
If you do want to explore PMML with Python check out the sklearn2pmml package and is also available on PyPl. This package allows you to export the ML pipeline and the model specification. As with most other implementations of PMML there are some parts of the PMML specification not implement, but it is better than post of the other implementation out there.
An alternative is to look at code translations options. With these we want something that will take our ML pipeline and convert it to another programming language like C++, JAVA, Go, etc. There aren’t too many solutions available to do this. One such solution we’ve explored over the past couple of weeks is called m2cgen.
m2cgen (Model 2 Code Generator) is a lightweight library which provides an easy way to transpile trained statistical models into a native code (Python, C, Java, Go). You can supply M2cgen with a range of models (linear, SVM, tree, random forest, or boosting, etc) and the tool will output code in the chosen language that will represent the trained model. The code generated will generated into native code without dependencies. Other packages or libraries are not dependent or required in the translated language. For example here is an example Decision Tree translated into a number of different languages.
C
#include <string.h>
void score(double * input, double * output) {
double var0[3];
if ((input[2]) <= (2.6)) {
memcpy(var0, (double[]){1.0, 0.0, 0.0}, 3 * sizeof(double));
} else {
if ((input[2]) <= (4.8500004)) {
if ((input[3]) <= (1.6500001)) {
memcpy(var0, (double[]){0.0, 1.0, 0.0}, 3 * sizeof(double));
} else {
memcpy(var0, (double[]){0.0, 0.3333333333333333, 0.6666666666666666}, 3 * sizeof(double));
}
} else {
if ((input[3]) <= (1.75)) {
memcpy(var0, (double[]){0.0, 0.42857142857142855, 0.5714285714285714}, 3 * sizeof(double));
} else {
memcpy(var0, (double[]){0.0, 0.0, 1.0}, 3 * sizeof(double));
}
}
}
memcpy(output, var0, 3 * sizeof(double));
}
Java
public class Model {
public static double[] score(double[] input) {
double[] var0;
if ((input[2]) <= (2.6)) {
var0 = new double[] {1.0, 0.0, 0.0};
} else {
if ((input[2]) <= (4.8500004)) {
if ((input[3]) <= (1.6500001)) {
var0 = new double[] {0.0, 1.0, 0.0};
} else {
var0 = new double[] {0.0, 0.3333333333333333, 0.6666666666666666};
}
} else {
if ((input[3]) <= (1.75)) {
var0 = new double[] {0.0, 0.42857142857142855, 0.5714285714285714};
} else {
var0 = new double[] {0.0, 0.0, 1.0};
}
}
}
return var0;
}
}
Go Lang
func score(input []float64) []float64 {
var var0 []float64
if (input[2]) <= (2.6) {
var0 = []float64{1.0, 0.0, 0.0}
} else {
if (input[2]) <= (4.8500004) {
if (input[3]) <= (1.6500001) {
var0 = []float64{0.0, 1.0, 0.0}
} else {
var0 = []float64{0.0, 0.3333333333333333, 0.6666666666666666}
}
} else {
if (input[3]) <= (1.75) {
var0 = []float64{0.0, 0.42857142857142855, 0.5714285714285714}
} else {
var0 = []float64{0.0, 0.0, 1.0}
}
}
}
return var0
}
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