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='c')
output = porter.export(embed_data=True)

print(output)


    

    




#include <stdlib.h>
#include <stdio.h>
#include <math.h>

int predict(float features[4]) {

    int classes[3];
        
    if (features[3] <= 0.800000011920929) {
        classes[0] = 50; 
        classes[1] = 0; 
        classes[2] = 0; 
    } else {
        if (features[3] <= 1.75) {
            if (features[2] <= 4.950000047683716) {
                if (features[3] <= 1.6500000357627869) {
                    classes[0] = 0; 
                    classes[1] = 47; 
                    classes[2] = 0; 
                } else {
                    classes[0] = 0; 
                    classes[1] = 0; 
                    classes[2] = 1; 
                }
            } else {
                if (features[3] <= 1.550000011920929) {
                    classes[0] = 0; 
                    classes[1] = 0; 
                    classes[2] = 3; 
                } else {
                    if (features[0] <= 6.949999809265137) {
                        classes[0] = 0; 
                        classes[1] = 2; 
                        classes[2] = 0; 
                    } else {
                        classes[0] = 0; 
                        classes[1] = 0; 
                        classes[2] = 1; 
                    }
                }
            }
        } else {
            if (features[2] <= 4.8500001430511475) {
                if (features[1] <= 3.100000023841858) {
                    classes[0] = 0; 
                    classes[1] = 0; 
                    classes[2] = 2; 
                } else {
                    classes[0] = 0; 
                    classes[1] = 1; 
                    classes[2] = 0; 
                }
            } else {
                classes[0] = 0; 
                classes[1] = 0; 
                classes[2] = 43; 
            }
        }
    }

    int index = 0;
    for (int i = 0; i < 3; i++) {
        index = classes[i] > classes[index] ? i : index;
    }
    return index;
}

int main(int argc, const char * argv[]) {

    /* Features: */
    double features[argc-1];
    int i;
    for (i = 1; i < argc; i++) {
        features[i-1] = atof(argv[i]);
    }

    /* Prediction: */
    printf("%d", predict(features));
    return 0;

}

In [5]:

    

# Save model:
# with open('tree.c', 'w') as f:
#     f.write(output)

# Compile model:
# $ gcc tree.c -std=c99 -lm -o tree

# Run classification:
# $ ./tree 1 2 3 4