Remove six, Remove __future__, update cython compiler directives, set…

… python_requires=">=3.7" (#417)

examples/baselines_conf.py

@@ -3,9 +3,6 @@
 computation.
 """
 
-from __future__ import (absolute_import, division, print_function,
-                        unicode_literals)
-
 from surprise import BaselineOnly
 from surprise import KNNBasic
 from surprise import Dataset

examples/benchmark.py

@@ -9,7 +9,6 @@
 import random
 
 import numpy as np
-import six
 from tabulate import tabulate
 
 from surprise import Dataset

examples/generate_grid_search_cv_results_example.py

@@ -7,7 +7,6 @@
                         unicode_literals)
 
 from tabulate import tabulate
-from six import iteritems
 
 from surprise import SVD
 from surprise import Dataset
@@ -32,7 +31,7 @@
 
 print()
 
-for key, val in iteritems(gs.cv_results):
+for key, val in gs.cv_results.items():
     print('{:<20}'.format("'" + key + "':"), end='')
     if isinstance(val[0], float):
         print([float('{:.2f}'.format(f)) for f in val])

requirements.txt

@@ -1,4 +1,3 @@
 joblib>=0.11
 numpy>=1.11.2
 scipy>=1.0.0
-six>=1.10.0

requirements_dev.txt

@@ -6,7 +6,6 @@ numpy>=1.11.2
 pandas
 pytest>=3.0.3
 scipy>=1.0.0
-six>=1.10.0
 sphinx>=1.4.9
 sphinx_rtd_theme
 sphinxcontrib-bibtex

requirements_travis.txt

@@ -6,7 +6,6 @@ numpy>=1.11.2
 pandas
 pytest>=3.0.3
 scipy>=1.0.0
-six>=1.10.0
 sphinx>=1.4.9
 sphinx_rtd_theme
 sphinxcontrib-bibtex

setup.py

@@ -100,7 +100,16 @@
 ]
 
 if USE_CYTHON:
-    ext_modules = cythonize(extensions)
+    ext_modules = cythonize(
+        extensions,
+        compiler_directives={
+            "language_level": 3,
+            "boundscheck": False,
+            "wraparound": False,
+            "initializedcheck": False,
+            "nonecheck": False,
+        },
+    )
     cmdclass.update({'build_ext': build_ext})
 else:
     ext_modules = extensions
@@ -125,12 +134,14 @@
         'Intended Audience :: Science/Research',
         'Topic :: Scientific/Engineering',
         'License :: OSI Approved :: BSD License',
-        'Programming Language :: Python :: 3',
-        'Programming Language :: Python :: 2.7',
+        'Programming Language :: Python :: 3.7',
+        'Programming Language :: Python :: 3.9',
+        'Programming Language :: Python :: 3.10',
     ],
     keywords='recommender recommendation system',
 
     packages=find_packages(exclude=['tests*']),
+    python_requires=">=3.7",
     include_package_data=True,
     ext_modules=ext_modules,
     cmdclass=cmdclass,

surprise/__main__.py

@@ -1,7 +1,5 @@
 #!/usr/bin/env python
 
-from __future__ import (absolute_import, division, print_function,
-                        unicode_literals)
 import random as rd
 import sys
 import shutil

surprise/accuracy.py

@@ -13,11 +13,8 @@
     fcp
 """
 
-from __future__ import (absolute_import, division, print_function,
-                        unicode_literals)
 from collections import defaultdict
 import numpy as np
-from six import iteritems
 
 
 def rmse(predictions, verbose=True):
@@ -155,7 +152,7 @@ def fcp(predictions, verbose=True):
     for u0, _, r0, est, _ in predictions:
         predictions_u[u0].append((r0, est))
 
-    for u0, preds in iteritems(predictions_u):
+    for u0, preds in predictions_u.items():
         for r0i, esti in preds:
             for r0j, estj in preds:
                 if esti > estj and r0i > r0j:

surprise/builtin_datasets.py

@@ -1,16 +1,13 @@
 """This module contains built-in datasets that can be automatically
 downloaded."""
 
-from __future__ import (absolute_import, division, print_function,
-                        unicode_literals)
-
 import zipfile
 import os
 import errno
 
 from os.path import join
 from collections import namedtuple
-from six.moves.urllib.request import urlretrieve
+from urllib.request import urlretrieve
 
 
 def get_dataset_dir():

surprise/dataset.py

@@ -23,15 +23,11 @@
 """
 
 
-from __future__ import (absolute_import, division, print_function,
-                        unicode_literals)
 from collections import defaultdict
 import sys
 import os
 import itertools
 
-from six.moves import input
-
 from .reader import Reader
 from .builtin_datasets import download_builtin_dataset
 from .builtin_datasets import BUILTIN_DATASETS

surprise/model_selection/search.py

@@ -1,20 +1,17 @@
-from __future__ import (absolute_import, division, print_function,
-                        unicode_literals)
 
-from abc import ABCMeta, abstractmethod
+from abc import ABC, abstractmethod
 from itertools import product
 import numpy as np
 from joblib import Parallel
 from joblib import delayed
-from six import moves, string_types, with_metaclass
 
 from .split import get_cv
 from .validation import fit_and_score
 from ..dataset import DatasetUserFolds
 from ..utils import get_rng
 
 
-class BaseSearchCV(with_metaclass(ABCMeta)):
+class BaseSearchCV(ABC):
     """Base class for hyper parameter search with cross-validation."""
 
     @abstractmethod
@@ -26,7 +23,7 @@ def __init__(self, algo_class, measures=['rmse', 'mae'], cv=None,
         self.measures = [measure.lower() for measure in measures]
         self.cv = cv
 
-        if isinstance(refit, string_types):
+        if isinstance(refit, str):
             if refit.lower() not in self.measures:
                 raise ValueError('It looks like the measure you want to use '
                                  'with refit ({}) is not in the measures '
@@ -482,7 +479,7 @@ def _sample_parameters(param_distributions, n_iter, random_state=None):
 
         else:
             combos = []
-            for _ in moves.range(n_iter):
+            for _ in range(n_iter):
                 params = dict()
                 for k, v in items:
                     if hasattr(v, 'rvs'):

surprise/model_selection/split.py

@@ -24,16 +24,11 @@
 
 """
 
-from __future__ import (absolute_import, division, print_function,
-                        unicode_literals)
 from itertools import chain
 from math import ceil, floor
 import numbers
 from collections import defaultdict
 
-from six import iteritems
-from six import string_types
-
 import numpy as np
 
 from ..utils import get_rng
@@ -46,7 +41,7 @@ def get_cv(cv):
         return KFold(n_splits=5)
     if isinstance(cv, numbers.Integral):
         return KFold(n_splits=cv)
-    if hasattr(cv, 'split') and not isinstance(cv, string_types):
+    if hasattr(cv, 'split') and not isinstance(cv, str):
         return cv  # str have split
 
     raise ValueError('Wrong CV object. Expecting None, an int or CV iterator, '
@@ -389,7 +384,7 @@ def split(self, data):
             # for each user, randomly choose a rating and put it in the
             # testset.
             raw_trainset, raw_testset = [], []
-            for uid, ratings in iteritems(user_ratings):
+            for uid, ratings in user_ratings.items():
                 if len(ratings) > self.min_n_ratings:
                     i = rng.randint(0, len(ratings))
                     raw_testset.append(ratings[i])

surprise/model_selection/validation.py

@@ -3,14 +3,11 @@
 the mighty scikit learn.
 """
 
-from __future__ import (absolute_import, division, print_function,
-                        unicode_literals)
 import time
 
 import numpy as np
 from joblib import Parallel
 from joblib import delayed
-from six import iteritems
 
 from .split import get_cv
 from .. import accuracy
@@ -203,15 +200,15 @@ def print_summary(algo, measures, test_measures, train_measures, fit_times,
         *['{:1.4f}'.format(v) for v in vals] +
         ['{:1.4f}'.format(np.mean(vals))] +
         ['{:1.4f}'.format(np.std(vals))])
-        for (key, vals) in iteritems(test_measures))
+        for (key, vals) in test_measures.items())
     if train_measures:
         s += '\n'
         s += '\n'.join(row_format.format(
             key.upper() + ' (trainset)',
             *['{:1.4f}'.format(v) for v in vals] +
             ['{:1.4f}'.format(np.mean(vals))] +
             ['{:1.4f}'.format(np.std(vals))])
-            for (key, vals) in iteritems(train_measures))
+            for (key, vals) in train_measures.items())
     s += '\n'
     s += row_format.format('Fit time',
                            *['{:.2f}'.format(t) for t in fit_times] +

surprise/prediction_algorithms/algo_base.py

@@ -4,8 +4,8 @@ class :class:`AlgoBase` from which every single prediction algorithm has to
 inherit.
 """
 
-from __future__ import (absolute_import, division, print_function,
-                        unicode_literals)
+
+
 
 from .. import similarities as sims
 from .predictions import PredictionImpossible

surprise/prediction_algorithms/baseline_only.py

@@ -2,9 +2,6 @@
 This class implements the baseline estimation.
 """
 
-from __future__ import (absolute_import, division, print_function,
-                        unicode_literals)
-
 from .algo_base import AlgoBase
 
 

surprise/prediction_algorithms/co_clustering.pyx

@@ -2,8 +2,8 @@
 the :mod:`co_clustering` module includes the :class:`CoClustering` algorithm.
 """
 
-from __future__ import (absolute_import, division, print_function,
-                        unicode_literals)
+
+
 
 cimport numpy as np  # noqa
 import numpy as np

surprise/prediction_algorithms/knns.py

@@ -2,10 +2,7 @@
 the :mod:`knns` module includes some k-NN inspired algorithms.
 """
 
-from __future__ import (absolute_import, division, print_function,
-                        unicode_literals)
 import numpy as np
-from six import iteritems
 import heapq
 
 from .predictions import PredictionImpossible
@@ -178,7 +175,7 @@ def fit(self, trainset):
         self.sim = self.compute_similarities()
 
         self.means = np.zeros(self.n_x)
-        for x, ratings in iteritems(self.xr):
+        for x, ratings in self.xr.items():
             self.means[x] = np.mean([r for (_, r) in ratings])
 
         return self
@@ -375,7 +372,7 @@ def fit(self, trainset):
         self.overall_sigma = np.std([r for (_, _, r)
                                      in self.trainset.all_ratings()])
 
-        for x, ratings in iteritems(self.xr):
+        for x, ratings in self.xr.items():
             self.means[x] = np.mean([r for (_, r) in ratings])
             sigma = np.std([r for (_, r) in ratings])
             self.sigmas[x] = self.overall_sigma if sigma == 0.0 else sigma

surprise/prediction_algorithms/matrix_factorization.pyx

@@ -3,12 +3,11 @@ the :mod:`matrix_factorization` module includes some algorithms using matrix
 factorization.
 """
 
-from __future__ import (absolute_import, division, print_function,
-                        unicode_literals)
+
+
 
 cimport numpy as np  # noqa
 import numpy as np
-from six.moves import range
 
 from .algo_base import AlgoBase
 from .predictions import PredictionImpossible

surprise/prediction_algorithms/optimize_baselines.pyx

@@ -3,12 +3,11 @@ This module includes the two methods for baseline computation: stochastic
 gradient descent and alternating least squares.
 """
 
-from __future__ import (absolute_import, division, print_function,
-                        unicode_literals)
+
+
 
 cimport numpy as np  # noqa
 import numpy as np
-from six.moves import range
 
 
 def baseline_als(self):

surprise/prediction_algorithms/predictions.py

@@ -4,8 +4,8 @@
 exception.
 """
 
-from __future__ import (absolute_import, division, print_function,
-                        unicode_literals)
+
+
 
 from collections import namedtuple
 

surprise/prediction_algorithms/random_pred.py

@@ -1,8 +1,8 @@
 """ Algorithm predicting a random rating.
 """
 
-from __future__ import (absolute_import, division, print_function,
-                        unicode_literals)
+
+
 
 import numpy as np