Add Python 3 support via future package

.travis.yml

@@ -1,14 +1,15 @@
 language: python
 python:
     - "2.7"
+    - "3.5"
 cache:
     - apt
 before_install:
     - sudo apt-get update -qq
     - sudo apt-get install -y build-essential libblas-dev liblapack-dev gfortran make libedit-dev
     - sudo add-apt-repository -y ppa:ubuntu-toolchain-r/test && sudo apt-get update -q && sudo apt-get install -y gcc-4.7 g++-4.7 && sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-4.7 20 && sudo update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-4.7 20 && sudo update-alternatives --config gcc && sudo update-alternatives --config g++
     - sudo add-apt-repository -y ppa:boost-latest/ppa && sudo apt-get update -q && sudo apt-get install -y boost1.55
-    - sudo add-apt-repository -y ppa:kalakris/cmake && sudo apt-get update -q && sudo apt-get install -y cmake
+    - sudo add-apt-repository -y ppa:roblib/ppa && sudo apt-get update -q && sudo apt-get install -y cmake cmake-data
     - sudo add-apt-repository -y ppa:chris-lea/python-numpy && sudo apt-get update -q && sudo apt-get install -y python-numpy
     - wget http://ppa.launchpad.net/libreoffice/ppa/ubuntu/pool/main/d/doxygen/doxygen_1.8.7-2~precise1_amd64.deb -O doxygen.deb
     - sudo dpkg -i doxygen.deb

conda-recipe/moe/meta.yaml

@@ -29,6 +29,8 @@ requirements:
     - scipy
     - pyramid
     - pyramid_mako
+    - pyramid_debugtoolbar
+    - waitress
     - colander
     - libstdcplusplus
     - simplejson

development.ini

@@ -1,27 +1,39 @@
-[app:MOE]
+###
+# app configuration
+# http://docs.pylonsproject.org/projects/pyramid/en/latest/narr/environment.html
+###
+
+[app:main]
 use = egg:MOE
-reload_templates = true
-debug_authorization = false
-debug_notfound = false
-debug_routematch = false
-debug_templates = true
-default_locale_name = en
+pyramid.reload_templates = true
+pyramid.debug_authorization = false
+pyramid.debug_notfound = false
+pyramid.debug_routematch = false
+pyramid.default_locale_name = en
+pyramid.includes = 
+    pyramid_debugtoolbar
+
+# By default, the toolbar only appears for clients from IP addresses
+# '127.0.0.1' and '::1'.
+# debugtoolbar.hosts = 127.0.0.1 ::1
 use_mongo = false
 mongodb.url = mongodb://localhost
-mongodb.port = 27017
 mongodb.db_name = mydb
 
-[pipeline:main]
-pipeline =
-    egg:WebError#evalerror
-    MOE
+###
+# wsgi server configuration
+###
 
 [server:main]
-use = egg:Paste#http
+use = egg:waitress#main
 host = 0.0.0.0
 port = 6543
 
-# Begin logging configuration
+###
+# logging configuration
+# http://docs.pylonsproject.org/projects/pyramid/en/latest/narr/logging.html
+###
+
 
 [loggers]
 keys = root, moe
@@ -38,7 +50,6 @@ args = ('%(here)s/moe.log','a')
 level = DEBUG
 formatter = generic
 
-# used by all applications in the Pyramid process that ask for a logger
 [logger_root]
 level = INFO
 handlers = console, filelog

docs/cpp_rst_maker.py

@@ -80,13 +80,13 @@ def create_rst_file(file_base, files):
     )
     index_count = 1
     for file_type in CPP_FILE_TYPES:
-        if files.has_key(file_type):
+        if file_type in files:
             fout.write('    {0:d}. `{1:s}`_\n'.format(index_count, files[file_type]))
             index_count += 1
     fout.write('\n')
 
     for file_type in CPP_FILE_TYPES:
-        if files.has_key(file_type):
+        if file_type in files:
             fout.write("""
 {0:s}
 {1:s}
@@ -102,7 +102,7 @@ def create_rst_files_for_cpp():
     """Generate all rst files."""
     cpp_files = get_cpp_files()
     create_cpp_tree(cpp_files)
-    for file_base, files in cpp_files.iteritems():
+    for file_base, files in cpp_files.items():
         create_rst_file(file_base, files)
 
 

docs/install.rst

@@ -50,7 +50,7 @@ To ensure consistency, be sure to use full paths throughout the installation.
 
 Requires:
 
-1. ``python 2.6.7+`` - http://python.org/download/
+1. ``python 2.6.7+`` or ``python 3.3+`` - http://python.org/download/
 2. ``gcc 4.7.3+`` - http://gcc.gnu.org/install/
 3. ``cmake 2.8.9+`` - http://www.cmake.org/cmake/help/install.html
 4. ``boost 1.51+`` - http://www.boost.org/users/download/

moe/__init__.py

@@ -1,5 +1,6 @@
 # -*- coding: utf-8 -*-
 """Base pyramid app for MOE."""
+from __future__ import print_function
 from pyramid.config import Configurator
 
 from moe.resources import Root
@@ -44,7 +45,7 @@ def main(global_config, **settings):
     app = config.make_wsgi_app()
 
     # Message to the user
-    print """
+    print("""
     Congratulations! MOE is now running.
 
     You can access the web interface at: http://localhost:6543
@@ -54,6 +55,6 @@ def main(global_config, **settings):
 
     Note: If you installed MOE within a docker container you may need to specify the IP address of the VM instead of localhost.
     In OSX and Windows this is the startup information when you run boot2docker, or can be set in $DOCKER_HOST.
-    """
+    """)
 
     return app

moe/bandit/bandit_interface.py

@@ -1,11 +1,13 @@
 # -*- coding: utf-8 -*-
 r"""Interface for Bandit functions, supports allocate arms and choose arm."""
+from builtins import object
 import numpy
 
 from abc import ABCMeta, abstractmethod
+from future.utils import with_metaclass
 
 
-class BanditInterface(object):
+class BanditInterface(with_metaclass(ABCMeta, object)):
 
     r"""Interface for a bandit algorithm.
 
@@ -16,8 +18,6 @@ class BanditInterface(object):
 
     """
 
-    __metaclass__ = ABCMeta
-
     @abstractmethod
     def allocate_arms(self):
         r"""Compute the allocation to each arm given ``historical_info``, running bandit `subtype`` endpoint with hyperparameters in `hyperparameter_info``.
@@ -46,9 +46,9 @@ def choose_arm(arms_to_allocations):
         if not arms_to_allocations:
             raise ValueError('arms_to_allocations is empty!')
 
-        allocations = numpy.array(arms_to_allocations.values())
+        allocations = numpy.array(list(arms_to_allocations.values()))
         # The winning arm is chosen based on the distribution of arm allocations.
         winner = numpy.argmax(numpy.random.dirichlet(allocations))
         # While the internal order of a dict is unknowable a priori, the order presented by the various iterators
         # and list-ify methods is always the same as long as the dict is not modified between calls to these methods.
-        return arms_to_allocations.keys()[winner]
+        return list(arms_to_allocations.keys())[winner]

moe/bandit/bla/bla.py

@@ -44,7 +44,7 @@ def __init__(
         self._historical_info = copy.deepcopy(historical_info)
         self._subtype = subtype
         # Validate that every arm is a Bernoulli arm.
-        for arm in self._historical_info.arms_sampled.itervalues():
+        for arm in self._historical_info.arms_sampled.values():
             if not isinstance(arm, BernoulliArm):
                 raise ValueError('All arms have to be Bernoulli arms!')
 
@@ -122,5 +122,5 @@ def get_winning_arm_names(self, arms_sampled):
         if not arms_sampled:
             raise ValueError('arms_sampled is empty!')
 
-        bla_payoff_arm_name_list = [(self.get_bla_payoff(sampled_arm), arm_name) for arm_name, sampled_arm in arms_sampled.iteritems()]
+        bla_payoff_arm_name_list = [(self.get_bla_payoff(sampled_arm), arm_name) for arm_name, sampled_arm in arms_sampled.items()]
         return get_winning_arm_names_from_payoff_arm_name_list(bla_payoff_arm_name_list)

moe/bandit/data_containers.py

@@ -1,5 +1,6 @@
 # -*- coding: utf-8 -*-
 """Data containers convenient for/used to interact with bandit members."""
+from builtins import object
 import pprint
 
 import numpy
@@ -202,7 +203,7 @@ def __str__(self, pretty_print=True):
     def json_payload(self):
         """Construct a json serializeable and MOE REST recognizeable dictionary of the historical data."""
         json_arms_sampled = {}
-        for name, arm in self._arms_sampled.iteritems():
+        for name, arm in self._arms_sampled.items():
             json_arms_sampled[name] = arm.json_payload()
         return {'arms_sampled': json_arms_sampled}
 
@@ -217,7 +218,7 @@ def validate_sample_arms(sample_arms):
 
         """
         if sample_arms:
-            for arm in sample_arms.itervalues():
+            for arm in sample_arms.values():
                 arm.validate()
 
     def append_sample_arms(self, sample_arms, validate=True):
@@ -246,7 +247,7 @@ def _update_historical_data(self, sample_arms):
         :param sample_arms: the already-sampled arms: wins, losses, and totals
         :type sample_arms: dictionary of (arm name, SampleArm) key-value pairs
         """
-        for name, arm in sample_arms.iteritems():
+        for name, arm in sample_arms.items():
             if name in self._arms_sampled:
                 self._arms_sampled[name] += arm
             else:

moe/bandit/epsilon/epsilon_first.py

@@ -95,7 +95,7 @@ def allocate_arms(self):
         if not arms_sampled:
             raise ValueError('sample_arms is empty!')
 
-        num_sampled = sum([sampled_arm.total for sampled_arm in arms_sampled.itervalues()])
+        num_sampled = sum([sampled_arm.total for sampled_arm in arms_sampled.values()])
         # Exploration phase, trials 1,2,..., epsilon * T
         # Allocate equal probability to all arms
         if num_sampled < self._total_samples * self._epsilon:

moe/bandit/epsilon/epsilon_greedy.py

@@ -4,6 +4,7 @@
 See :class:`moe.bandit.epsilon.epsilon_interface.EpsilonInterface` for further details on this bandit.
 
 """
+from __future__ import division
 from moe.bandit.constant import DEFAULT_EPSILON, EPSILON_SUBTYPE_GREEDY
 from moe.bandit.epsilon.epsilon_interface import EpsilonInterface
 
@@ -75,7 +76,7 @@ def allocate_arms(self):
         arms_to_allocations = {}
 
         # With probability epsilon, choose a winning arm at random. Therefore, we split the allocation epsilon among all arms.
-        for arm_name in arms_sampled.iterkeys():
+        for arm_name in arms_sampled.keys():
             arms_to_allocations[arm_name] = epsilon_allocation
 
         # With probability 1-epsilon, split allocation among winning arms.

moe/bandit/epsilon/epsilon_interface.py

@@ -4,6 +4,7 @@
 See :mod:`moe.bandit.bandit_interface` for further details on bandit.
 
 """
+from __future__ import division
 import copy
 
 import numpy
@@ -62,7 +63,7 @@ def get_winning_arm_names(arms_sampled):
             raise ValueError('arms_sampled is empty!')
 
         avg_payoff_arm_name_list = []
-        for arm_name, sampled_arm in arms_sampled.iteritems():
+        for arm_name, sampled_arm in arms_sampled.items():
             avg_payoff = numpy.float64(sampled_arm.win - sampled_arm.loss) / sampled_arm.total if sampled_arm.total > 0 else 0
             avg_payoff_arm_name_list.append((avg_payoff, arm_name))
 

moe/bandit/ucb/ucb1.py

@@ -4,6 +4,7 @@
 See :class:`moe.bandit.ucb.ucb_interface.UCBInterface` for further details on this bandit.
 
 """
+from __future__ import division
 import math
 
 import numpy

moe/bandit/ucb/ucb1_tuned.py

@@ -4,6 +4,7 @@
 See :class:`moe.bandit.ucb.ucb_interface.UCBInterface` for further details on this bandit.
 
 """
+from __future__ import division
 import math
 
 import numpy

moe/bandit/ucb/ucb_interface.py

@@ -58,7 +58,7 @@ def get_unsampled_arm_names(arms_sampled):
         if not arms_sampled:
             raise ValueError('arms_sampled is empty!')
 
-        unsampled_arm_name_list = [name for name, sampled_arm in arms_sampled.iteritems() if sampled_arm.total == 0]
+        unsampled_arm_name_list = [name for name, sampled_arm in arms_sampled.items() if sampled_arm.total == 0]
         return frozenset(unsampled_arm_name_list)
 
     @abstractmethod
@@ -131,8 +131,8 @@ def get_winning_arm_names(self, arms_sampled):
         if unsampled_arm_names:
             return unsampled_arm_names
 
-        number_sampled = sum([sampled_arm.total for sampled_arm in arms_sampled.itervalues()])
+        number_sampled = sum([sampled_arm.total for sampled_arm in arms_sampled.values()])
 
-        ucb_payoff_arm_name_list = [(self.get_ucb_payoff(sampled_arm, number_sampled), arm_name) for arm_name, sampled_arm in arms_sampled.iteritems()]
+        ucb_payoff_arm_name_list = [(self.get_ucb_payoff(sampled_arm, number_sampled), arm_name) for arm_name, sampled_arm in arms_sampled.items()]
 
         return get_winning_arm_names_from_payoff_arm_name_list(ucb_payoff_arm_name_list)

moe/bandit/utils.py

@@ -1,5 +1,8 @@
 # -*- coding: utf-8 -*-
 """Utilities for bandit."""
+from __future__ import division
+from builtins import map
+from builtins import zip
 
 
 def get_winning_arm_names_from_payoff_arm_name_list(payoff_arm_name_list):
@@ -20,10 +23,10 @@ def get_winning_arm_names_from_payoff_arm_name_list(payoff_arm_name_list):
         best_payoff, _ = max(payoff_arm_name_list)
 
         # Filter out arms that have payoff less than the best payoff
-        winning_arm_payoff_name_list = filter(lambda payoff_arm_name: payoff_arm_name[0] == best_payoff, payoff_arm_name_list)
+        winning_arm_payoff_name_list = [payoff_arm_name for payoff_arm_name in payoff_arm_name_list if payoff_arm_name[0] == best_payoff]
         # Extract a list of winning arm names from a list of (payoff, arm name) tuples.
-        _, winning_arm_name_list = map(list, zip(*winning_arm_payoff_name_list))
-        winning_arm_names = frozenset(winning_arm_name_list)
+        _, winning_arm_name_iter  = map(list, zip(*winning_arm_payoff_name_list))
+        winning_arm_names = frozenset(winning_arm_name_iter)
         return winning_arm_names
 
 
@@ -46,14 +49,14 @@ def get_equal_arm_allocations(arms_sampled, winning_arm_names=None):
 
     # If no ``winning_arm_names`` given, split allocations among ``arms_sampled``.
     if winning_arm_names is None:
-        winning_arm_names = frozenset([arm_name for arm_name in arms_sampled.iterkeys()])
+        winning_arm_names = frozenset([arm_name for arm_name in arms_sampled.keys()])
 
     num_winning_arms = len(winning_arm_names)
     arms_to_allocations = {}
 
     winning_arm_allocation = 1.0 / num_winning_arms
     # Split allocation among winning arms, all other arms get allocation of 0.
-    for arm_name in arms_sampled.iterkeys():
+    for arm_name in arms_sampled.keys():
         arms_to_allocations[arm_name] = winning_arm_allocation if arm_name in winning_arm_names else 0.0
 
     return arms_to_allocations

moe/easy_interface/experiment.py

@@ -1,5 +1,6 @@
 # -*- coding: utf-8 -*-
 """Classes for MOE optimizable experiments."""
+from builtins import object
 import pprint
 
 from moe.optimal_learning.python.data_containers import HistoricalData

moe/easy_interface/simple_endpoint.py

@@ -1,7 +1,9 @@
 # -*- coding: utf-8 -*-
 """Simple functions for hitting the REST endpoints of a MOE service."""
+from future import standard_library
+standard_library.install_aliases()
 import contextlib
-import urllib2
+import urllib.request, urllib.error, urllib.parse
 
 import simplejson as json
 
@@ -20,8 +22,8 @@ def call_endpoint_with_payload(rest_host, rest_port, endpoint, json_payload, tes
     """Send a POST request to a ``url`` with a given ``json_payload``, return the response as a dict."""
     if testapp is None:
         url = "http://{0}:{1:d}{2}".format(rest_host, rest_port, endpoint)
-        request = urllib2.Request(url, json_payload, {'Content-Type': 'application/json'})
-        with contextlib.closing(urllib2.urlopen(request)) as f:
+        request = urllib.request.Request(url, json_payload, {'Content-Type': 'application/json'})
+        with contextlib.closing(urllib.request.urlopen(request)) as f:
             response = f.read()
     else:
         response = testapp.post(endpoint, json_payload).body
@@ -49,7 +51,8 @@ def gp_next_points(
     if 'domain_info' not in raw_payload:
         raw_payload['domain_info'] = experiment_payload.get('domain_info')
 
-    json_payload = json.dumps(raw_payload)
+    #payload must be a byte object
+    json_payload = json.dumps(raw_payload).encode('utf-8')
 
     json_response = call_endpoint_with_payload(rest_host, rest_port, endpoint, json_payload, testapp)