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

print(output)


    

    




var DecisionTreeClassifier = function() {

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

    this.predict = function(features) {
        var classes = new Array(3);
            
        if (features[2] <= 2.449999988079071) {
            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[2] <= 5.450000047683716) {
                            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; 
                }
            }
        }
    
        return findMax(classes);
    };

};

if (typeof process !== 'undefined' && typeof process.argv !== 'undefined') {
    if (process.argv.length - 2 === 4) {

        // Features:
        var features = process.argv.slice(2);

        // Prediction:
        var clf = new DecisionTreeClassifier();
        var prediction = clf.predict(features);
        console.log(prediction);

    }
}

In [5]:

    

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

# Run classification:
# if hash node 2/dev/null; then
#     node DecisionTreeClassifier.js 1 2 3 4
# fi