def test_python_command_execution(self):
"""Test command line execution."""
Shell.call('rm -rf tmp')
Shell.call('mkdir tmp')
filename = '{}.java'.format(self.tmp_fn)
cp_src = os.path.join('tmp', filename)
with open(cp_src, 'w') as f:
porter = Porter(self.estimator)
out = porter.export(method_name='predict', class_name=self.tmp_fn)
f.write(out)
# $ javac tmp/Tmp.java
cmd = ' '.join(['javac', cp_src])
Shell.call(cmd)
# Rename estimator for comparison:
filename = '{}_2.java'.format(self.tmp_fn)
cp_dest = os.path.join('tmp', filename)
# $ mv tmp/Brain.java tmp/Brain_2.java
cmd = ' '.join(['mv', cp_src, cp_dest])
Shell.call(cmd)
# Dump estimator:
filename = '{}.pkl'.format(self.tmp_fn)
pkl_path = os.path.join('tmp', filename)
joblib.dump(self.estimator, pkl_path)
# Port estimator:
cmd = 'python -m sklearn_porter.cli.__main__ -i {}' \
' --class_name Brain'.format(pkl_path)
Shell.call(cmd)
# Compare file contents:
equal = filecmp.cmp(cp_src, cp_dest)
self.assertEqual(equal, True)
def test_python_command_execution(self):
"""Test command line execution."""
Shell.call('rm -rf tmp')
Shell.call('mkdir tmp')
filename = '{}.java'.format(self.tmp_fn)
cp_src = os.path.join('tmp', filename)
with open(cp_src, 'w') as f:
porter = Porter(self.estimator)
out = porter.export(method_name='predict', class_name=self.tmp_fn)
f.write(out)
# $ javac tmp/Tmp.java
cmd = ' '.join(['javac', cp_src])
Shell.call(cmd)
# Rename estimator for comparison:
filename = '{}_2.java'.format(self.tmp_fn)
cp_dest = os.path.join('tmp', filename)
# $ mv tmp/Brain.java tmp/Brain_2.java
cmd = ' '.join(['mv', cp_src, cp_dest])
Shell.call(cmd)
# Dump estimator:
filename = '{}.pkl'.format(self.tmp_fn)
pkl_path = os.path.join('tmp', filename)
joblib.dump(self.estimator, pkl_path)
# Port estimator:
cmd = 'python -m sklearn_porter.cli.__main__ {}' \
' --class_name Brain'.format(pkl_path)
Shell.call(cmd)
# Compare file contents:
equal = filecmp.cmp(cp_src, cp_dest)
self.assertEqual(equal, True)
def main():
args = parse_args(sys.argv[1:])
# Check input data:
input_path = str(args.get('input'))
if not (input_path.endswith('.pkl') or input_path.endswith('.mdl')) or not os.path.isfile(input_path):
error = 'No valid estimator in pickle format was found.'
sys.exit('Error: {}'.format(error))
# Load data:
from sklearn.externals import joblib
estimator = joblib.load(input_path)
# Determine the target programming language:
language = str(args.get('language')) # with default language
languages = ['c', 'java', 'js', 'go', 'php', 'ruby']
for key in languages:
if args.get(key): # found explicit assignment
language = key
break
# Define destination path:
dest_dir = str(args.get('output'))
if dest_dir == '' or not os.path.isdir(dest_dir):
dest_dir = input_path.split(os.sep)
del dest_dir[-1]
dest_dir = os.sep.join(dest_dir)
# Port estimator:
try:
class_name = args.get('class_name')
method_name = args.get('method_name')
with_export = bool(args.get('export'))
with_checksum = bool(args.get('checksum'))
porter = Porter(estimator, language=language)
output = porter.export(class_name=class_name,
method_name=method_name,
export_dir=dest_dir,
export_data=with_export,
export_append_checksum=with_checksum,
num_format = float_formatter,
details=True)
except Exception as exception:
# Catch any exception and exit the process:
sys.exit('Error: {}'.format(str(exception)))
else:
# Print transpiled estimator to the console:
if bool(args.get('pipe', False)):
print(output.get('estimator'))
sys.exit(0)
only_data = bool(args.get('data'))
if not only_data:
filename = output.get('filename')
dest_path = dest_dir + os.sep + filename
# Save transpiled estimator:
with open(dest_path, 'w') as file_:
file_.write(output.get('estimator'))