Some doc on Reader and MF alsgo

TODO.md

@@ -6,16 +6,16 @@ TODO
 * Make all fit methods (for algo and GridSearch) return self. Update docs on
   building custom algorithms, and on getting started -> gridsearch (add
   example?).
-* Update doc of MF algo to indicate how to retrieve latent factors.
 
+* check conda forge
 * make some filtering dataset tools, like remove users/items with less/more
   than n ratings, binarize a dataset, etc...
-* check conda forge
 * Allow incremental updates for some algorithms
 
 Done:
 -----
 
+* Update doc of MF algo to indicate how to retrieve latent factors.
 * all algorithms using random initialization now have a random_state parameter.
 * CV iterators:
   - Write basic CV iterators

surprise/prediction_algorithms/matrix_factorization.pyx

@@ -113,6 +113,16 @@ class SVD(AlgoBase):
             RNG. If ``None``, the current RNG from numpy is used.  Default is
             ``None``.
         verbose: If ``True``, prints the current epoch. Default is ``False``.
+
+    Attributes:
+        pu(numpy array of size (n_users, n_factors)): The user factors (only
+            exists if ``fit()`` has been called)
+        qi(numpy array of size (n_items, n_factors)): The item factors (only
+            exists if ``fit()`` has been called)
+        bu(numpy array of size (n_users)): The user biases (only
+            exists if ``fit()`` has been called)
+        bi(numpy array of size (n_items)): The item biases (only
+            exists if ``fit()`` has been called)
     """
 
     def __init__(self, n_factors=100, n_epochs=20, biased=True, init_mean=0,
@@ -341,6 +351,18 @@ class SVDpp(AlgoBase):
             RNG. If ``None``, the current RNG from numpy is used.  Default is
             ``None``.
         verbose: If ``True``, prints the current epoch. Default is ``False``.
+
+    Attributes:
+        pu(numpy array of size (n_users, n_factors)): The user factors (only
+            exists if ``fit()`` has been called)
+        qi(numpy array of size (n_items, n_factors)): The item factors (only
+            exists if ``fit()`` has been called)
+        yj(numpy array of size (n_items, n_factors)): The (implicit) item
+            factors (only exists if ``fit()`` has been called)
+        bu(numpy array of size (n_users)): The user biases (only
+            exists if ``fit()`` has been called)
+        bi(numpy array of size (n_items)): The item biases (only
+            exists if ``fit()`` has been called)
     """
 
     def __init__(self, n_factors=20, n_epochs=20, init_mean=0, init_std_dev=.1,
@@ -556,6 +578,16 @@ class NMF(AlgoBase):
             RNG. If ``None``, the current RNG from numpy is used.  Default is
             ``None``.
         verbose: If ``True``, prints the current epoch. Default is ``False``.
+
+    Attributes:
+        pu(numpy array of size (n_users, n_factors)): The user factors (only
+            exists if ``fit()`` has been called)
+        qi(numpy array of size (n_items, n_factors)): The item factors (only
+            exists if ``fit()`` has been called)
+        bu(numpy array of size (n_users)): The user biases (only
+            exists if ``fit()`` has been called)
+        bi(numpy array of size (n_items)): The item biases (only
+            exists if ``fit()`` has been called)
     """
 
     def __init__(self, n_factors=15, n_epochs=50, biased=False, reg_pu=.06,

surprise/reader.py

@@ -29,7 +29,9 @@ class Reader():
             Accepted values are 'ml-100k', 'ml-1m', and 'jester'. Default
             is ``None``.
         line_format(:obj:`string`): The fields names, in the order at which
-            they are encountered on a line. Default is ``'user item rating'``.
+            they are encountered on a line. Please note that ``line_format`` is
+            always space-separated (use the ``sep`` parameter). Default is
+            ``'user item rating'``.
         sep(char): the separator between fields. Example : ``';'``.
         rating_scale(:obj:`tuple`, optional): The rating scale used for every
             rating.  Default is ``(1, 5)``.