How can I call scikit-learn classifiers from Java?

There is JPMML project for this purpose.

First, you can serialize scikit-learn model to PMML (which is XML internally) using sklearn2pmml library directly from python or dump it in python first and convert using jpmml-sklearn in java or from a command line provided by this library. Next, you can load pmml file, deserialize and execute the loaded model using jpmml-evaluator in your Java code.

This way works with not all scikit-learn models, but with many of them.

As some commenters correctly pointed out, it's important to note that JPMML project is licensed under GNU AGPL. AGPL is a strong copyleft license, which may limit your ability to use the project. One of the examples may be if you develop a publically accessible service and want to keep the sources closed.

You can either use a porter, I have tested the sklearn-porter (https://github.com/nok/sklearn-porter), and it works well for Java.

My code is the following:

import pandas as pd
from sklearn import tree
from sklearn_porter import Porter

train_dataset = pd.read_csv('./result2.csv').as_matrix()

X_train = train_dataset[:90, :8]
Y_train = train_dataset[:90, 8:]

X_test = train_dataset[90:, :8]
Y_test = train_dataset[90:, 8:]

print X_train.shape
print Y_train.shape


clf = tree.DecisionTreeClassifier()
clf = clf.fit(X_train, Y_train)

porter = Porter(clf, language='java')
output = porter.export(embed_data=True)
print(output)

In my case, I'm using a DecisionTreeClassifier, and the output of

print(output)

is the following code as text in the console:

class DecisionTreeClassifier {

  private static int findMax(int[] nums) {
    int index = 0;
    for (int i = 0; i < nums.length; i++) {
        index = nums[i] > nums[index] ? i : index;
    }
    return index;
  }


  public static int predict(double[] features) {
    int[] classes = new int[2];

    if (features[5] <= 51.5) {
        if (features[6] <= 21.0) {

            // HUGE amount of ifs..........

        }
    }

    return findMax(classes);
  }

  public static void main(String[] args) {
    if (args.length == 8) {

        // Features:
        double[] features = new double[args.length];
        for (int i = 0, l = args.length; i < l; i++) {
            features[i] = Double.parseDouble(args[i]);
        }

        // Prediction:
        int prediction = DecisionTreeClassifier.predict(features);
        System.out.println(prediction);

    }
  }
}

Here is some code for the JPMML solution:

--PYTHON PART--

# helper function to determine the string columns which have to be one-hot-encoded in order to apply an estimator.
def determine_categorical_columns(df):
    categorical_columns = []
    x = 0
    for col in df.dtypes:
        if col == 'object':
            val = df[df.columns[x]].iloc[0]
            if not isinstance(val,Decimal):
                categorical_columns.append(df.columns[x])
        x += 1
    return categorical_columns

categorical_columns = determine_categorical_columns(df)
other_columns = list(set(df.columns).difference(categorical_columns))


#construction of transformators for our example
labelBinarizers = [(d, LabelBinarizer()) for d in categorical_columns]
nones = [(d, None) for d in other_columns]
transformators = labelBinarizers+nones

mapper = DataFrameMapper(transformators,df_out=True)
gbc = GradientBoostingClassifier()

#construction of the pipeline
lm = PMMLPipeline([
    ("mapper", mapper),
    ("estimator", gbc)
])

--JAVA PART --

//Initialisation.
String pmmlFile = "ScikitLearnNew.pmml";
PMML pmml = org.jpmml.model.PMMLUtil.unmarshal(new FileInputStream(pmmlFile));
ModelEvaluatorFactory modelEvaluatorFactory = ModelEvaluatorFactory.newInstance();
MiningModelEvaluator evaluator = (MiningModelEvaluator) modelEvaluatorFactory.newModelEvaluator(pmml);

//Determine which features are required as input
HashMap<String, Field>() inputFieldMap = new HashMap<String, Field>();
for (int i = 0; i < evaluator.getInputFields().size();i++) {
  InputField curInputField = evaluator.getInputFields().get(i);
  String fieldName = curInputField.getName().getValue();
  inputFieldMap.put(fieldName.toLowerCase(),curInputField.getField());
}


//prediction

HashMap<String,String> argsMap = new HashMap<String,String>();
//... fill argsMap with input

Map<FieldName, ?> res;
// here we keep only features that are required by the model
Map<FieldName,String> args = new HashMap<FieldName, String>();
Iterator<String> iter = argsMap.keySet().iterator();
while (iter.hasNext()) {
  String key = iter.next();
  Field f = inputFieldMap.get(key);
  if (f != null) {
    FieldName name =f.getName();
    String value = argsMap.get(key);
    args.put(name, value);
  }
}
//the model is applied to input, a probability distribution is obtained
res = evaluator.evaluate(args);
SegmentResult segmentResult = (SegmentResult) res;
Object targetValue = segmentResult.getTargetValue();
ProbabilityDistribution probabilityDistribution = (ProbabilityDistribution) targetValue;

I found myself in a similar situation. I'll recommend carving out a classifier microservice. You could have a classifier microservice which runs in python and then expose calls to that service over some RESTFul API yielding JSON/XML data-interchange format. I think this is a cleaner approach.

Alternatively you can just generate a Python code from a trained model. Here is a tool that can help you with that https://github.com/BayesWitnesses/m2cgen

You can also use OnnxRuntime, in fact it might be the best option among others. On a high level this is what you can do:

1. Generate model in scikit-learn.
2. Convert the model to onnx format
3. Save to disk
4. Load the model in Java via Onnx Runtime
5. Execute inference

Links :

• https://github.com/microsoft/onnxruntime
• https://onnxruntime.ai/docs/get-started/with-java.html