# -*- coding: utf-8 -*-
import subprocess as subp
from sklearn import svm
from sklearn.datasets import load_iris
from sklearn_porter import Porter
iris_data = load_iris()
X, y = iris_data.data, iris_data.target
clf = svm.LinearSVC(C=1., random_state=0)
clf.fit(X, y)
data = Porter(clf, language='c').export(details=True)
# Save model:
with open(data.get('filename'), 'w') as f:
f.write(data.get('model'))
# Compile model:
command = data.get('cmd').get('compilation')
subp.call(command, shell=True)
# Use the model:
features = ' '.join([repr(x) for x in X[0]])
command = '%s %s' % (data.get('cmd').get('execution'), features)
prediction = subp.check_output(command, shell=True)
print('Ported classifier: %s' % prediction) # class: 0
print('Original classifier: %s' % clf.predict([X[0]])[0]) # class: 0
import subprocess as subp
from sklearn import svm
from sklearn.datasets import load_iris
from sklearn_porter import Porter
X, y = load_iris(return_X_y=True)
clf = svm.LinearSVC(C=1., random_state=0)
clf.fit(X, y)
# Cheese!
data = Porter(language='c', with_details=True).port(clf)
# Save model:
with open(data.get('filename'), 'w') as file:
file.write(data.get('model'))
# Compile model:
command = data.get('compiling_cmd')
subp.call(command, shell=True)
# Use the model:
features = ' '.join([repr(x) for x in X[0]])
command = '%s %s' % (data.get('execution_cmd'), features)
prediction = subp.check_output(command, shell=True)
print('Ported classifier: %s' % prediction) # class: 0
print('Original classifier: %s' % clf.predict([X[0]])[0]) # class: 0
