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 import svm

clf = svm.LinearSVC(C=1., random_state=0)
clf.fit(X, y)


    

    




/opt/miniconda/envs/sklearn-porter/lib/python2.7/site-packages/sklearn/svm/base.py:922: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
  "the number of iterations.", ConvergenceWarning)






    
Out[3]:





LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,
     intercept_scaling=1, loss='squared_hinge', max_iter=1000,
     multi_class='ovr', penalty='l2', random_state=0, tol=0.0001,
     verbose=0)

In [4]:

    

from sklearn_porter import Porter

porter = Porter(clf, language='js')
output = porter.export()

print(output)


    

    




var LinearSVC = function(coefficients, intercepts) {

    this.coefficients = coefficients;
    this.intercepts = intercepts;

    this.predict = function(features) {
        var classIdx = 0,
            classVal = Number.NEGATIVE_INFINITY,
            prob = 0.;
        for (var i = 0, il = this.intercepts.length; i < il; i++) {
            prob = 0.;
            for (var j = 0, jl = this.coefficients[0].length; j < jl; j++) {
                prob += this.coefficients[i][j] * features[j];
            }
            if (prob + this.intercepts[i] > classVal) {
                classVal = prob + this.intercepts[i];
                classIdx = i;
            }
        }
        return classIdx;
    };

};

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

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

        // Parameters:
        var coefficients = [[0.1842387105153816, 0.45123226735416666, -0.8079442418237148, -0.4507120994138801], [0.05538075463135362, -0.9011091317807134, 0.40964725305179495, -0.9616708418438406], [-0.8507797840387784, -0.9866797559823655, 1.380978948733964, 1.865367212912733]];
        var intercepts = [0.10956037385996253, 1.6860176665898299, -1.7096500573837932];

        // Prediction:
        var clf = new LinearSVC(coefficients, intercepts);
        var prediction = clf.predict(features);
        console.log(prediction);

    }
}

In [5]:

    

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

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