OLL client is a client for using OLL on Python. OLL is a library supporting several for online-learning algorithms.

oll_client.py

from subprocess import Popen, PIPE
import time, sys, os

"""
oll_client 0.0.3 - Online Machine Learning Module

OLL client is a client for using OLL, which is a machine learning library have implemented several online-learning algorithms, on Python.

Currently, Oll supports following algorithms:

- Perceptron
- Averaged Perceptron
- Passive Agressive (PA, PA-I, PA-II)
- ALMA (modified slightly from original)
- Confidence Weighted Linear-Classification.

When using this module, please install OLL at following Web site: 
https://code.google.com/p/oll/

Author:
	Yukino Ikegami

Lisence:
	MIT License

Usage:
	import oll_client
	oll = oll_client.OLL('PA1', debug=True)
	oll.train(1, {0:1.0, 1:2.0, 2:-1.0}) # Add training example
	oll.train(-1, {0:-0.5, 1:1.0, 2:-0.5})
	oll.test(0, {0:1.0, 1:1.0}) # Test Example
	oll.save('model1') # Save current model to modelfile
	oll.load('model1') # Load a model from modelfile
	oll.test(0, {0:1.0, 1:1.0})
"""

class OLL:
	def __init__(self, algorythm='P', tempfile='tempfile.txt', oll_check=True, debug=False):
		if oll_check:
			test = Popen('which oll_line', shell=True, stdin=PIPE, stdout=PIPE)
			if len(test.communicate()[0]) < 1:
				print 'Please install oll https://code.google.com/p/oll/'
				sys.exit()
		
		self.algorythm = algorythm
		self.tempfile = tempfile
		initialize_file(tempfile)
		command = 'oll_line %s > %s' % (algorythm, tempfile)
		self.oll = Popen(command, shell=True, stdin=PIPE, stdout=PIPE)
		self.debug = debug
	
	def __del__(self):
		os.remove(self.tempfile)
	
	def train(self, label, data):
		query = 'A %d ' % (label)
		for feature, value in data.items():
			query += '%d:%s ' % (feature, value)
		query = query[:-1]+'\n'
		return self._send(query)
	
	def test(self, label, data):
		query = 'T %d ' % (label)
		for feature, value in data.items():
			query += '%d:%s ' % (feature, value)
		query = query[:-1]+'\n'
		return self._send(query)

	def save(self, model):
		query = 'S %s\n' % (model)
		return self._send(query)

	def load(self, model):
		query = 'L %s\n' % (model)
		return self._send(query)

	def _send(self, query):
		if self.debug:
			print query[:-1]
		self.oll.stdin.write(query)
		self.oll.stdin.flush()
		if query[0] == 'T':
			return self._get_result_from_file()
		return True

	def _get_result_from_file(self):
		while True:
			time.sleep(0.0001)
			with open(self.tempfile,'r') as out:
				result = out.read().decode('utf8').strip()
				if len(result) > 0:
					break
		initialize_file(self.tempfile)
		result = result.replace(u'\x00','')
		if self.debug:
			print float(result)
		return float(result)

def initialize_file(filename):
	open(filename,'w')

if __name__ == '__main__':
	o = OLL('PA1', debug=True)
	o.train(1, {0:1.0, 1:2.0, 2:-1.0})
	o.train(-1, {0:-0.5, 1:1.0, 2:-0.5})
	o.test(0, {0:1.0, 1:1.0})
	o.save('model1')
	o.load('model1')
	o.test(0, {0:1.0, 1:1.0})