sklearn-porter

In [1]:

    

import sys
sys.path.append('../../../../..')


    

In [2]:

    

from sklearn.datasets import load_iris

iris_data = load_iris()

X = iris_data.data
y = iris_data.target

print(X.shape, y.shape)


    

    




((150, 4), (150,))

In [3]:

    

from sklearn.tree import tree

clf = tree.DecisionTreeClassifier()
clf.fit(X, y)


    

    
Out[3]:





DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
            max_features=None, max_leaf_nodes=None,
            min_impurity_decrease=0.0, min_impurity_split=None,
            min_samples_leaf=1, min_samples_split=2,
            min_weight_fraction_leaf=0.0, presort=False, random_state=None,
            splitter='best')

In [4]:

    

from sklearn_porter import Porter

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

print(output)


    

    




import com.google.gson.Gson;
import java.io.File;
import java.io.FileNotFoundException;
import java.util.Scanner;


class DecisionTreeClassifier {

    private class Classifier {
        private int[] leftChilds;
        private int[] rightChilds;
        private double[] thresholds;
        private int[] indices;
        private int[][] classes;
    }
    private Classifier clf;

    public DecisionTreeClassifier(String file) throws FileNotFoundException {
        String jsonStr = new Scanner(new File(file)).useDelimiter("\\Z").next();
        this.clf = new Gson().fromJson(jsonStr, Classifier.class);
    }

    public int predict(double[] features, int node) {
        if (this.clf.thresholds[node] != -2) {
            if (features[this.clf.indices[node]] <= this.clf.thresholds[node]) {
                return predict(features, this.clf.leftChilds[node]);
            } else {
                return predict(features, this.clf.rightChilds[node]);
            }
        }
        return findMax(this.clf.classes[node]);
    }
    public int predict(double[] features) {
        return this.predict(features, 0);
    }

    private 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 void main(String[] args) throws FileNotFoundException {
        if (args.length > 0 && args[0].endsWith(".json")) {

            // Features:
            double[] features = new double[args.length-1];
            for (int i = 1, l = args.length; i < l; i++) {
                features[i - 1] = Double.parseDouble(args[i]);
            }

            // Parameters:
            String modelData = args[0];

            // Estimators:
            DecisionTreeClassifier clf = new DecisionTreeClassifier(modelData);

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

        }
    }
}

In [5]:

    

# Save classifier:
# with open('DecisionTreeClassifier.java', 'w') as f:
#     f.write(output)

# Check model data:
# $ cat data.json

# Download dependencies:
# $ wget -O gson.jar http://central.maven.org/maven2/com/google/code/gson/gson/2.8.5/gson-2.8.5.jar

# Compile model:
# $ javac -cp .:gson.jar DecisionTreeClassifier.java

# Run classification:
# $ java -cp .:gson.jar DecisionTreeClassifier data.json 1 2 3 4