Unify quotation marks 🔨

examples/benchmark.py

@@ -1,6 +1,6 @@
-'''This module runs a 5-Fold CV for all the algorithms (default parameters) on
+"""This module runs a 5-Fold CV for all the algorithms (default parameters) on
 the movielens datasets, and reports average RMSE, MAE, and total computation
-time.  It is used for making tables in the README.md file'''
+time.  It is used for making tables in the README.md file"""
 
 from __future__ import (absolute_import, division, print_function,
                         unicode_literals)

examples/top_n_recommendations.py

@@ -14,7 +14,7 @@
 
 
 def get_top_n(predictions, n=10):
-    '''Return the top-N recommendation for each user from a set of predictions.
+    """Return the top-N recommendation for each user from a set of predictions.
 
     Args:
         predictions(list of Prediction objects): The list of predictions, as
@@ -25,7 +25,7 @@ def get_top_n(predictions, n=10):
     Returns:
     A dict where keys are user (raw) ids and values are lists of tuples:
         [(raw item id, rating estimation), ...] of size n.
-    '''
+    """
 
     # First map the predictions to each user.
     top_n = defaultdict(list)

surprise/__main__.py

@@ -33,8 +33,8 @@
 def main():
 
     class MyParser(argparse.ArgumentParser):
-        '''A parser which prints the help message when an error occurs. Taken from
-        http://stackoverflow.com/questions/4042452/display-help-message-with-python-argparse-when-script-is-called-without-any-argu.'''  # noqa
+        """A parser which prints the help message when an error occurs. Taken from
+        http://stackoverflow.com/questions/4042452/display-help-message-with-python-argparse-when-script-is-called-without-any-argu."""  # noqa
 
         def error(self, message):
             sys.stderr.write('error: %s\n' % message)

surprise/builtin_datasets.py

@@ -1,5 +1,5 @@
-'''This module contains built-in datasets that can be automatically
-downloaded.'''
+"""This module contains built-in datasets that can be automatically
+downloaded."""
 
 from __future__ import (absolute_import, division, print_function,
                         unicode_literals)
@@ -12,10 +12,10 @@
 
 
 def get_dataset_dir():
-    '''Return folder where downloaded datasets and other data are stored.
+    """Return folder where downloaded datasets and other data are stored.
     Default folder is ~/.surprise_data/, but it can also be set by the
     environment variable ``SURPRISE_DATA_FOLDER``.
-    '''
+    """
 
     folder = os.environ.get('SURPRISE_DATA_FOLDER', os.path.expanduser('~') +
                             '/.surprise_data/')

surprise/model_selection/split.py

@@ -1,4 +1,4 @@
-'''
+"""
 The :mod:`model_selection.split<surprise.model_selection.split>` module
 contains various cross-validation iterators. Design and tools are inspired from
 the mighty scikit learn.
@@ -22,7 +22,7 @@
 
     train_test_split
 
-'''
+"""
 
 from __future__ import (absolute_import, division, print_function,
                         unicode_literals)
@@ -40,7 +40,7 @@
 
 
 def get_cv(cv):
-    '''Return a 'validated' CV iterator.'''
+    """Return a 'validated' CV iterator."""
 
     if cv is None:
         return KFold(n_splits=5)
@@ -54,7 +54,7 @@ def get_cv(cv):
 
 
 class KFold():
-    '''A basic cross-validation iterator.
+    """A basic cross-validation iterator.
 
     Each fold is used once as a testset while the k - 1 remaining folds are
     used for training.
@@ -73,7 +73,7 @@ class KFold():
         shuffle(bool): Whether to shuffle the ratings in the ``data`` parameter
             of the ``split()`` method. Shuffling is not done in-place. Default
             is ``True``.
-    '''
+    """
 
     def __init__(self, n_splits=5, random_state=None, shuffle=True):
 
@@ -82,15 +82,15 @@ def __init__(self, n_splits=5, random_state=None, shuffle=True):
         self.random_state = random_state
 
     def split(self, data):
-        '''Generator function to iterate over trainsets and testsets.
+        """Generator function to iterate over trainsets and testsets.
 
         Args:
             data(:obj:`Dataset<surprise.dataset.Dataset>`): The data containing
                 ratings that will be devided into trainsets and testsets.
 
         Yields:
             tuple of (trainset, testset)
-        '''
+        """
 
         if self.n_splits > len(data.raw_ratings) or self.n_splits < 2:
             raise ValueError('Incorrect value for n_splits={0}. '
@@ -125,7 +125,7 @@ def get_n_folds(self):
 
 
 class RepeatedKFold():
-    '''
+    """
     Repeated :class:`KFold` cross validator.
 
     Repeats :class:`KFold` n times with different randomization in each
@@ -146,7 +146,7 @@ class RepeatedKFold():
         shuffle(bool): Whether to shuffle the ratings in the ``data`` parameter
             of the ``split()`` method. Shuffling is not done in-place. Default
             is ``True``.
-    '''
+    """
 
     def __init__(self, n_splits=5, n_repeats=10, random_state=None):
 
@@ -155,15 +155,15 @@ def __init__(self, n_splits=5, n_repeats=10, random_state=None):
         self.n_splits = n_splits
 
     def split(self, data):
-        '''Generator function to iterate over trainsets and testsets.
+        """Generator function to iterate over trainsets and testsets.
 
         Args:
             data(:obj:`Dataset<surprise.dataset.Dataset>`): The data containing
                 ratings that will be devided into trainsets and testsets.
 
         Yields:
             tuple of (trainset, testset)
-        '''
+        """
 
         rng = get_rng(self.random_state)
 
@@ -178,7 +178,7 @@ def get_n_folds(self):
 
 
 class ShuffleSplit():
-    '''A basic cross-validation iterator with random trainsets and testsets.
+    """A basic cross-validation iterator with random trainsets and testsets.
 
     Contrary to other cross-validation strategies, random splits do not
     guarantee that all folds will be different, although this is still very
@@ -210,7 +210,7 @@ class ShuffleSplit():
             this to `False` defeats the purpose of this iterator, but it's
             useful for the implementation of :func:`train_test_split`. Default
             is ``True``.
-    '''
+    """
 
     def __init__(self, n_splits=5, test_size=.2, train_size=None,
                  random_state=None, shuffle=True):
@@ -262,15 +262,15 @@ def validate_train_test_sizes(self, test_size, train_size, n_ratings):
         return int(train_size), int(test_size)
 
     def split(self, data):
-        '''Generator function to iterate over trainsets and testsets.
+        """Generator function to iterate over trainsets and testsets.
 
         Args:
             data(:obj:`Dataset<surprise.dataset.Dataset>`): The data containing
                 ratings that will be devided into trainsets and testsets.
 
         Yields:
             tuple of (trainset, testset)
-        '''
+        """
 
         test_size, train_size = self.validate_train_test_sizes(
             self.test_size, self.train_size, len(data.raw_ratings))
@@ -300,7 +300,7 @@ def get_n_folds(self):
 
 def train_test_split(data, test_size=.2, train_size=None, random_state=None,
                      shuffle=True):
-    '''Split a dataset into trainset and testset.
+    """Split a dataset into trainset and testset.
 
     See an example in the :ref:`User Guide <train_test_split_example>`.
 
@@ -328,14 +328,14 @@ def train_test_split(data, test_size=.2, train_size=None, random_state=None,
             only used if ``shuffle`` is ``True``.  Default is ``None``.
         shuffle(bool): Whether to shuffle the ratings in the ``data``
             parameter. Shuffling is not done in-place. Default is ``True``.
-    '''
+    """
     ss = ShuffleSplit(n_splits=1, test_size=test_size, train_size=train_size,
                       random_state=random_state, shuffle=shuffle)
     return next(ss.split(data))
 
 
 class LeaveOneOut():
-    '''Cross-validation iterator where each user has exactly one rating in the
+    """Cross-validation iterator where each user has exactly one rating in the
     testset.
 
     Contrary to other cross-validation strategies, ``LeaveOneOut`` does not
@@ -359,7 +359,7 @@ class LeaveOneOut():
             testset). Other users are discarded. Default is ``0``, so some
             users (having only one rating) may be in the testset and not in the
             trainset.
-    '''
+    """
 
     def __init__(self, n_splits=5, random_state=None, min_n_ratings=0):
 
@@ -368,15 +368,15 @@ def __init__(self, n_splits=5, random_state=None, min_n_ratings=0):
         self.min_n_ratings = min_n_ratings
 
     def split(self, data):
-        '''Generator function to iterate over trainsets and testsets.
+        """Generator function to iterate over trainsets and testsets.
 
         Args:
             data(:obj:`Dataset<surprise.dataset.Dataset>`): The data containing
                 ratings that will be devided into trainsets and testsets.
 
         Yields:
             tuple of (trainset, testset)
-        '''
+        """
 
         # map ratings to the users ids
         user_ratings = defaultdict(list)
@@ -410,23 +410,23 @@ def get_n_folds(self):
 
 
 class PredefinedKFold():
-    '''A cross-validation iterator to when a dataset has been loaded with the
+    """A cross-validation iterator to when a dataset has been loaded with the
     :meth:`load_from_folds <surprise.dataset.Dataset.load_from_folds>`
     method.
 
     See an example in the :ref:`User Guide <load_from_folds_example>`.
-    '''
+    """
 
     def split(self, data):
-        '''Generator function to iterate over trainsets and testsets.
+        """Generator function to iterate over trainsets and testsets.
 
         Args:
             data(:obj:`Dataset<surprise.dataset.Dataset>`): The data containing
                 ratings that will be devided into trainsets and testsets.
 
         Yields:
             tuple of (trainset, testset)
-        '''
+        """
 
         self.n_splits = len(data.folds_files)
         for train_file, test_file in data.folds_files:

surprise/model_selection/validation.py

@@ -1,5 +1,7 @@
-'''The validation module contains the cross_validate function, inspired from
-the mighty scikit learn.'''
+"""
+The validation module contains the cross_validate function, inspired from 
+the mighty scikit learn.
+"""
 
 from __future__ import (absolute_import, division, print_function,
                         unicode_literals)
@@ -17,7 +19,7 @@
 def cross_validate(algo, data, measures=['rmse', 'mae'], cv=None,
                    return_train_measures=False, n_jobs=1,
                    pre_dispatch='2*n_jobs', verbose=False):
-    '''
+    """
     Run a cross validation procedure for a given algorithm, reporting accuracy
     measures and computation times.
 
@@ -89,7 +91,7 @@ def cross_validate(algo, data, measures=['rmse', 'mae'], cv=None,
             - ``'test_time'``: numpy array with the testing time in seconds for
               each split.
 
-    '''
+    """
 
     measures = [m.lower() for m in measures]
 
@@ -130,7 +132,7 @@ def cross_validate(algo, data, measures=['rmse', 'mae'], cv=None,
 
 def fit_and_score(algo, trainset, testset, measures,
                   return_train_measures=False):
-    '''Helper method that trains an algorithm and compute accuracy measures on
+    """Helper method that trains an algorithm and compute accuracy measures on
     a testset. Also report train and test times.
 
     Args:
@@ -158,7 +160,7 @@ def fit_and_score(algo, trainset, testset, measures,
             - The fit time in seconds.
 
             - The testing time in seconds.
-    '''
+    """
 
     start_fit = time.time()
     algo.fit(trainset)
@@ -183,7 +185,7 @@ def fit_and_score(algo, trainset, testset, measures,
 
 def print_summary(algo, measures, test_measures, train_measures, fit_times,
                   test_times, n_splits):
-    '''Helper for printing the result of cross_validate.'''
+    """Helper for printing the result of cross_validate."""
 
     print('Evaluating {0} of algorithm {1} on {2} split(s).'.format(
           ', '.join((m.upper() for m in measures)),

surprise/prediction_algorithms/algo_base.py

@@ -129,15 +129,15 @@ def predict(self, uid, iid, r_ui=None, clip=True, verbose=False):
         return pred
 
     def default_prediction(self):
-        '''Used when the ``PredictionImpossible`` exception is raised during a
+        """Used when the ``PredictionImpossible`` exception is raised during a
         call to :meth:`predict()
         <surprise.prediction_algorithms.algo_base.AlgoBase.predict>`. By
         default, return the global mean of all ratings (can be overridden in
         child classes).
 
         Returns:
             (float): The mean of all ratings in the trainset.
-        '''
+        """
 
         return self.trainset.global_mean
 

surprise/prediction_algorithms/baseline_only.py

@@ -1,4 +1,5 @@
 """
+This class implements the baseline estimation.
 """
 
 from __future__ import (absolute_import, division, print_function,

surprise/reader.py

@@ -1,4 +1,4 @@
-'''This module contains the Reader class.'''
+"""This module contains the Reader class."""
 
 from __future__ import (absolute_import, division, print_function,
                         unicode_literals)
@@ -74,7 +74,7 @@ def __init__(self, name=None, line_format='user item rating', sep=None,
                             entities]
 
     def parse_line(self, line):
-        '''Parse a line.
+        """Parse a line.
 
         Ratings are translated so that they are all strictly positive.
 
@@ -84,7 +84,7 @@ def parse_line(self, line):
         Returns:
             tuple: User id, item id, rating and timestamp. The timestamp is set
             to ``None`` if it does no exist.
-            '''
+            """
 
         line = line.split(self.sep)
         try:

surprise/trainset.py

@@ -1,4 +1,4 @@
-'''This module contains the Trainset class.'''
+"""This module contains the Trainset class."""
 
 
 from __future__ import (absolute_import, division, print_function,