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='ruby')
output = porter.export()

print(output)


    

    




class LinearSVC

	def initialize (coefficients, intercepts)
		@coefficients = coefficients
		@intercepts = intercepts
	end

	def predict (features)
    	classVal = -1.0/0.0
    	classIdx = -1
    	for i in 0 ... @intercepts.length
    		prob = 0
    		for j in 0 ... @coefficients[i].length
    			prob += @coefficients[i][j] * features[j].to_f
    		end
    		if prob + @intercepts[i] > classVal
    			classVal = prob + @intercepts[i]
    			classIdx = i
    		end
    	end
    	return classIdx
    end

end

if ARGV.length == 4

	# Features:
	features = ARGV.collect { |i| i.to_f }

	# Parameters:
	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]]
	intercepts = [0.10956037385996253, 1.6860176665898299, -1.7096500573837932]

	# Prediction:
	clf = LinearSVC.new coefficients, intercepts
	estimation = clf.predict features
	puts estimation

end