Python3 compatibility

examples/simple/make_plots.py

@@ -8,14 +8,15 @@
 
 
 from spearmint.utils.database.mongodb import MongoDB
+from spearmint.utils.fixes import items
 
 from spearmint.main import get_options, parse_resources_from_config, load_jobs, remove_broken_jobs, \
     load_task_group, load_hypers
 
 def print_dict(d, level=1):
     if isinstance(d, dict):
         if level > 1: print ""
-        for k, v in d.iteritems():
+        for k, v in items(d):
             print "  " * level, k,
             print_dict(v, level=level+1)
     else:

spearmint/choosers/acquisition_functions.py

@@ -183,6 +183,7 @@
 # its Institution.
 
 
+import sys
 import os
 import tempfile
 import copy
@@ -191,10 +192,14 @@
 import scipy.linalg   as spla
 import scipy.stats    as sps
 import scipy.optimize as spo
-import cPickle
 import multiprocessing
 import ast
 
+if sys.version < '3':
+    import cPickle
+else:
+    import pickle as cPickle
+
 def compute_ei(model, pred, ei_target=None, compute_grad=True):
     # TODO: use ei_target
     if pred.ndim == 1:

spearmint/choosers/default_chooser.py

@@ -193,6 +193,7 @@
 
 from .acquisition_functions  import compute_ei
 from ..utils.grad_check      import check_grad
+from ..utils.fixes           import items, xrange
 from ..grids                 import sobol_grid
 from ..models.abstract_model import function_over_hypers
 from ..                      import models
@@ -260,7 +261,7 @@ def fit(self, task_group, hypers=None, options=None):
                                         grid_seed=self.grid_seed)
 
         # A useful hack: add previously visited points to the grid
-        for task_name, task in task_group.tasks.iteritems():
+        for task_name, task in items(task_group.tasks):
             if task.has_valid_inputs():
                 self.grid = np.append(self.grid, task.valid_normalized_data_dict['inputs'], axis=0)
             if task.has_pending():
@@ -274,7 +275,7 @@ def fit(self, task_group, hypers=None, options=None):
 
         # print 'Fittings tasks: %s' % str(task_group.tasks.keys())
 
-        for task_name, task in task_group.tasks.iteritems():
+        for task_name, task in items(task_group.tasks):
             if task.type.lower() == 'objective':
                 data_dict = self.objective # confusing: this is how self.objective gets populated
             elif task.type.lower() == 'constraint':
@@ -351,7 +352,7 @@ def suggest(self):
         best_grid_ei  = grid_ei[best_grid_ind]
 
         if VERBOSE:
-            print 'Best EI before optimization: %f' % best_grid_ei
+            print('Best EI before optimization: %f' % best_grid_ei)
 
         if self.check_grad:
             check_grad(lambda x: self.acq_optimize_wrapper(x, current_best, True), 
@@ -387,8 +388,8 @@ def suggest(self):
         # Optimization should always be better unless the optimization
         # breaks in some way.
         if VERBOSE:
-            print 'Best EI after  optimization: %f' % best_opt_ei
-            print 'Suggested input %s' % cand[best_opt_ind]
+            print('Best EI after  optimization: %f' % best_opt_ei)
+            print('Suggested input %s' % cand[best_opt_ind])
 
         if best_opt_ei >= best_grid_ei:
             suggestion = cand[best_opt_ind]

spearmint/grids/sobol.py

@@ -182,8 +182,15 @@
 # to enter into this License and Terms of Use on behalf of itself and
 # its Institution.
 
+import sys
 import numpy as np
-import cPickle as pickle
+
+from spearmint.utils.fixes import xrange
+
+if sys.version < '3':
+    import cPickle as pickle
+else:
+    import pickle
 
 # Numba autojit might be nice.  Currently asplodes.
 def sobol(num_points, num_dims):
@@ -236,7 +243,8 @@ def sobol(num_points, num_dims):
     return Z
 
 def to_binary(X, bits):
-    return 1 & (X[:,np.newaxis]/2**np.arange(bits-1,-1,-1, dtype=np.uint32))
+    temp = X[:,np.newaxis]/2**np.arange(bits-1,-1,-1, dtype=np.uint32)
+    return np.ones_like(temp) & temp
 
 # These are the parameters for the Sobol sequence.
 # This is hilarious.

spearmint/kernels/__init__.py

@@ -1,8 +1,8 @@
-from matern           import Matern52
-from sum_kernel       import SumKernel
-from product_kernel   import ProductKernel
-from noise            import Noise
-from scale            import Scale
-from transform_kernel import TransformKernel
+from spearmint.kernels.matern import Matern52
+from spearmint.kernels.sum_kernel import SumKernel
+from spearmint.kernels.product_kernel import ProductKernel
+from spearmint.kernels.noise import Noise
+from spearmint.kernels.scale import Scale
+from spearmint.kernels.transform_kernel import TransformKernel
 
 __all__ = ["Matern52", "SumKernel", "ProductKernel", "Noise", "Scale", "TransformKernel"]

spearmint/kernels/kernel_utils.py

@@ -184,7 +184,13 @@
 
 
 import numpy as np
-import scipy.weave
+
+try:
+    import scipy.weave
+except ImportError:
+    pass
+
+from spearmint.utils.fixes import xrange
 from scipy.spatial.distance import cdist
 
 def dist2(ls, x1, x2=None):

spearmint/kernels/matern.py

@@ -184,7 +184,7 @@
 
 
 import numpy as np
-import kernel_utils
+from spearmint.kernels import kernel_utils
 
 from .abstract_kernel import AbstractKernel
 from ..utils          import priors

spearmint/kernels/product.py

@@ -196,6 +196,8 @@
 import logging
 #warnings.filterwarnings("ignore", category=RuntimeWarning)
 
+from spearmint.utils.fixes import xrange
+
 class productCov:
     def __init__(self, num_dimensions, **kwargs):
         # The sub-covariances of which this is the elementwise product

spearmint/launcher.py

@@ -190,6 +190,7 @@
 import numpy as np
 
 from spearmint.utils.database.mongodb import MongoDB
+from spearmint.utils.fixes import items
 
 def main():
     parser = optparse.OptionParser(usage="usage: %prog [options]")
@@ -271,7 +272,7 @@ def launch(db_address, experiment_name, job_id):
         import traceback
         traceback.print_exc()
         sys.stderr.write("Problem executing the function\n")
-        print sys.exc_info()
+        print(sys.exc_info())
 
     end_time = time.time()
 
@@ -305,7 +306,7 @@ def python_launcher(job):
 
     # Convert the JSON object into useful parameters.
     params = {}
-    for name, param in job['params'].iteritems():
+    for name, param in items(job['params']):
         vals = param['values']
 
         if param['type'].lower() == 'float':
@@ -351,7 +352,7 @@ def matlab_launcher(job):
     session.run("cd('%s')" % os.path.realpath(job['expt_dir']))
 
     session.run('params = struct()')
-    for name, param in job['params'].iteritems():
+    for name, param in items(job['params']):
         vals = param['values']
 
         # sys.stderr.write('%s = %s\n' % (param['name'], str(vals)))

spearmint/main.py

@@ -196,13 +196,15 @@
 from collections import OrderedDict
 
 from spearmint.utils.database.mongodb import MongoDB
+from spearmint.utils.fixes            import items
 from spearmint.tasks.task_group       import TaskGroup
 
 from spearmint.resources.resource import parse_resources_from_config
 from spearmint.resources.resource import print_resources_status
 
 from spearmint.utils.parsing import parse_db_address
 
+
 def get_options():
     parser = optparse.OptionParser(usage="usage: %prog [options] directory")
 
@@ -262,7 +264,7 @@ def main():
 
     while True:
 
-        for resource_name, resource in resources.iteritems():
+        for resource_name, resource in items(resources):
 
             jobs = load_jobs(db, experiment_name)
             # resource.printStatus(jobs)
@@ -313,7 +315,7 @@ def tired(db, experiment_name, resources):
     return True if no resources are accepting jobs
     """
     jobs = load_jobs(db, experiment_name)
-    for resource_name, resource in resources.iteritems():
+    for resource_name, resource in items(resources):
         if resource.acceptingJobs(jobs):
             return False
     return True

spearmint/models/__init__.py

@@ -1,4 +1,4 @@
-from gp            import GP
-from gp_classifier import GPClassifier
+from spearmint.models.gp import GP
+from spearmint.models.gp_classifier import GPClassifier
 
 __all__ = ["GP", "GPClassifier"]

spearmint/models/abstract_model.py

@@ -186,6 +186,8 @@
 
 from abc import ABCMeta, abstractmethod
 
+from spearmint.utils.fixes import xrange
+
 class AbstractModel(object):
     __metaclass__ = ABCMeta
 

spearmint/models/gp.py

@@ -190,18 +190,20 @@
 import scipy.stats  as sps
 
 from .abstract_model          import AbstractModel
+from ..utils.fixes            import items
 from ..utils.param            import Param as Hyperparameter
 from ..kernels                import Matern52, Noise, Scale, SumKernel, TransformKernel
 from ..sampling.slice_sampler import SliceSampler
 from ..utils                  import priors
+from ..utils.fixes            import items, xrange
 from ..transformations        import BetaWarp, Transformer
 
 try:
     module = sys.modules['__main__'].__file__
     log    = logging.getLogger(module)
 except:
     log    = logging.getLogger()
-    print 'Not running from main.'
+    print('Not running from main.')
 
 DEFAULT_MCMC_ITERS = 10
 DEFAULT_BURNIN     = 100
@@ -274,7 +276,7 @@ def _set_likelihood(self, options):
             self.noiseless = False
 
     def _set_params_from_dict(self, hypers_dict):
-        for name, hyper in self.params.iteritems():
+        for name, hyper in items(self.params):
             self.params[name].value = hypers_dict[name]
 
     def _reset_params(self):
@@ -463,7 +465,7 @@ def set_state(self, state):
     def to_dict(self):
         """return a dictionary that saves the values of the hypers and the chain length"""
         gp_dict = {'hypers' : {}}
-        for name, hyper in self.params.iteritems():
+        for name, hyper in items(self.params):
             gp_dict['hypers'][name] = hyper.value
 
         gp_dict['chain length'] = self.chain_length

spearmint/models/gp_classifier.py

@@ -191,11 +191,11 @@
 import scipy.optimize    as spo
 import scipy.io          as sio
 import scipy.stats       as sps
-import scipy.weave
 
 
 from .gp                                     import GP
 from ..utils.param                           import Param as Hyperparameter
+from ..utils.fixes                           import items, xrange
 from ..kernels                               import Matern52, Noise, Scale, SumKernel, TransformKernel
 from ..sampling.slice_sampler                import SliceSampler
 from ..sampling.whitened_prior_slice_sampler import WhitenedPriorSliceSampler
@@ -208,7 +208,7 @@
     log    = logging.getLogger(module)
 except:
     log = logging.getLogger()
-    print 'Not running from main.'
+    print('Not running from main.')
 
 class GPClassifier(GP):
     def __init__(self, num_dims, **options):
@@ -476,7 +476,7 @@ def to_dict(self):
         gp_dict = {}
 
         gp_dict['hypers'] = {}
-        for name, hyper in self.params.iteritems():
+        for name, hyper in items(self.params):
             gp_dict['hypers'][name] = hyper.value
 
         # Save the latent values as a dict with keys as hashes of the data

spearmint/resources/resource.py

@@ -190,6 +190,8 @@
 import numpy as np
 import sys
 
+from spearmint.utils.fixes import items
+
 def parse_resources_from_config(config):
     """Parse the config dict and return a dictionary of resource objects keyed by resource name"""
 
@@ -202,7 +204,7 @@ def parse_resources_from_config(config):
     # If resources are specified
     else:
         resources = dict()
-        for resource_name, resource_opts in config["resources"].iteritems():
+        for resource_name, resource_opts in items(config["resources"]):
             task_names = parse_tasks_in_resource_from_config(config, resource_name)
             resources[resource_name] = resource_factory(resource_name, task_names, resource_opts)
         return resources
@@ -217,7 +219,7 @@ def parse_tasks_in_resource_from_config(config, resource_name):
         return ['main']
     else:
         tasks = list()
-        for task_name, task_config in config["tasks"].iteritems():
+        for task_name, task_config in items(config["tasks"]):
             # If the user specified tasks but not specific resources for those tasks,
             # We have to assume the tasks run on all resources...
             if "resources" not in task_config:

spearmint/sampling/__init__.py

@@ -1,6 +1,6 @@
-from abstract_sampler             import AbstractSampler
-from slice_sampler                import SliceSampler
-from whitened_prior_slice_sampler import WhitenedPriorSliceSampler
-from elliptical_slice_sampler     import EllipticalSliceSampler
+from spearmint.sampling.abstract_sampler             import AbstractSampler
+from spearmint.sampling.slice_sampler                import SliceSampler
+from spearmint.sampling.whitened_prior_slice_sampler import WhitenedPriorSliceSampler
+from spearmint.sampling.elliptical_slice_sampler     import EllipticalSliceSampler
 
 __all__ = ["AbstractSampler", "SliceSampler", "WhitenedPriorSliceSampler", "EllipticalSliceSampler"]