Update model descriptions (#239)

cornac/models/cdl/recom_cdl.py

@@ -80,6 +80,7 @@ class CDL(Recommender):
 
     init_params: dictionary, optional, default: None
         List of initial parameters, e.g., init_params = {'U':U, 'V':V}
+
         U: ndarray, shape (n_users,k)
             The user latent factors, optional initialization via init_params.
         V: ndarray, shape (n_items,k)

cornac/models/cdr/recom_cdr.py

@@ -71,6 +71,7 @@ class CDR(Recommender):
 
     init_params: dictionary, optional, default: None
         List of initial parameters, e.g., init_params = {'U':U, 'V':V}
+
         U: ndarray, shape (n_users,k)
             The user latent factors, optional initialization via init_params.
         V: ndarray, shape (n_items,k)
@@ -79,7 +80,9 @@ class CDR(Recommender):
     seed: int, optional, default: None
         Random seed for weight initialization.
 
-    Reference: Collaborative Deep Ranking: A Hybrid Pair-Wise Recommendation Algorithm with Implicit Feedback
+    References
+    ----------
+    Collaborative Deep Ranking: A Hybrid Pair-Wise Recommendation Algorithm with Implicit Feedback
     Ying H., Chen L., Xiong Y., Wu J. (2016)
 
     """

cornac/models/ctr/recom_ctr.py

@@ -20,7 +20,7 @@
 
 
 class CTR(Recommender):
-    """Collaborative Topic Regression
+    """Collaborative Topic Regression.
 
     Parameters
     ----------
@@ -54,6 +54,7 @@ class CTR(Recommender):
 
     init_params: dictionary, optional, default: None
         List of initial parameters, e.g., init_params = {'U':U, 'V':V}
+
         U: ndarray, shape (n_users,k)
             The user latent factors, optional initialization via init_params.
         V: ndarray, shape (n_items,k)

cornac/models/efm/recom_efm.pyx

@@ -75,7 +75,8 @@ class EFM(Recommender):
 
     use_item_aspect_popularity: boolean, optional, default: True
         When False, item aspect frequency is omitted from item aspect quality computation formular.\
-        Specifically, :math:`Y_{ij} = 1 + \frac{N - 1}{1 + e^{-s_{ij}}}` if `p_i` is reviewed on feature `F_j`
+        Specifically, :math:`Y_{ij} = 1 + \\frac{N - 1}{1 + e^{-s_{ij}}}`
+        if :math:`p_i` is reviewed on feature :math:`F_j`
 
     max_iter: int, optional, default: 100
         Maximum number of iterations or the number of epochs.
@@ -95,7 +96,8 @@ class EFM(Recommender):
         When True, running logs are displayed.
 
     init_params: dictionary, optional, default: {}
-        List of initial parameters, e.g., init_params = {'U1':U1, 'U2':U2, 'V':V', H1':H1, 'H2':H2}
+        List of initial parameters, e.g., init_params = {'U1':U1, 'U2':U2, 'V':V, 'H1':H1, 'H2':H2}
+
         U1: ndarray, shape (n_users, n_explicit_factors)
             The user explicit factors, optional initialization via init_params.
         U2: ndarray, shape (n_ratings, n_explicit_factors)
@@ -116,6 +118,7 @@ class EFM(Recommender):
     Explicit factor models for explainable recommendation based on phrase-level sentiment analysis.
     In Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval (SIGIR '14).
     ACM, New York, NY, USA, 83-92. DOI: https://doi.org/10.1145/2600428.2609579
+
     """
 
     def __init__(self,  name="EFM",

cornac/models/mcf/recom_mcf.py

@@ -49,16 +49,17 @@ class MCF(Recommender):
         pre-trained (U and V are not None).
 
     item-affinity network: See "cornac/examples/mcf_office.py" for an example of how to use \
-        cornac's graph modality to load and provide the ``item-affinity network'' for MCF.
+        cornac's graph modality to load and provide the "item-affinity network" for MCF.
 
     verbose: boolean, optional, default: False
         When True, some running logs are displayed.
 
     init_params: dictionary, optional, default: {}
-        List of initial parameters, e.g., init_params = {'U':U, 'V':V}. \
-        U: a csc_matrix of shape (n_users,k), containing the user latent factors. \
-        V: a csc_matrix of shape (n_items,k), containing the item latent factors. \
-        Z: a csc_matrix of shape (n_items,k), containing the ``Also-Viewed'' item latent factors.
+        List of initial parameters, e.g., init_params = {'U':U, 'V':V}.
+
+        U: a csc_matrix of shape (n_users,k), containing the user latent factors.
+        V: a csc_matrix of shape (n_items,k), containing the item latent factors.
+        Z: a csc_matrix of shape (n_items,k), containing the "Also-Viewed" item latent factors.
 
     seed: int, optional, default: None
         Random seed for parameters initialization.
@@ -67,6 +68,7 @@ class MCF(Recommender):
     ----------
     * Park, Chanyoung, Donghyun Kim, Jinoh Oh, and Hwanjo Yu. "Do Also-Viewed Products Help User Rating Prediction?."\
      In Proceedings of WWW, pp. 1113-1122. 2017.
+
     """
 
     def __init__(self, k=5, max_iter=100, learning_rate=0.001, gamma=0.9, lamda=0.001, name="MCF",

cornac/models/sorec/recom_sorec.py

@@ -49,22 +49,27 @@ class SoRec(Recommender):
         The name of the recommender model.
 
     trainable: boolean, optional, default: True
-        When False, the model is not trained and Cornac assumes that the model already \
+        When False, the model is not trained and Cornac assumes that the model already
         pre-trained (U, V and Z are not None).
 
     verbose: boolean, optional, default: False
         When True, some running logs are displayed.
 
-    init_params: dictionary, optional, default: {'U':None,'V':None}
-        List of initial parameters, e.g., init_params = {'U':U, 'V':V,'Z':Z}. \
-        U: a ndarray of shape (n_users,k), containing the user latent factors. \
-        V: a ndarray of shape (n_items,k), containing the item latent factors. \
-        Z: a ndarray of shape (n_users,k), containing the social network latent factors. \
+    init_params: dictionary, optional, default: {'U':None, 'V':None}
+        List of initial parameters, e.g., init_params = {'U':U, 'V':V, 'Z':Z}.
+
+        U: a ndarray of shape (n_users, k)
+            Containing the user latent factors.
+        V: a ndarray of shape (n_items, k)
+            Containing the item latent factors.
+        Z: a ndarray of shape (n_users, k)
+            Containing the social network latent factors.
 
     References
     ----------
     * H. Ma, H. Yang, M. R. Lyu, and I. King. SoRec:Social recommendation using probabilistic matrix factorization. \
      CIKM ’08, pages 931–940, Napa Valley, USA, 2008.
+
     """
 
     def __init__(self, name="SoRec", k=5, max_iter=100, learning_rate=0.001, lamda_c=10, lamda=0.001, gamma=0.9,

cornac/models/vmf/recom_vmf.py

@@ -63,15 +63,16 @@ class VMF(Recommender):
         When False, the model is not trained and Cornac assumes that the model is already \
         pre-trained (The parameters of the model U, V, P, E are not None).
         
-    visual_features : See "cornac/examples/vmf_example.py" for an example of how to use \
-        cornac's visual modality to load and provide the ``item visual features'' for VMF.
+    visual_features: See "cornac/examples/vmf_example.py" for an example of how to use \
+        cornac's visual modality to load and provide the "item visual features" for VMF.
 
     verbose: boolean, optional, default: False
         When True, some running logs are displayed.
 
     init_params: dictionary, optional, default: {}
-        List of initial parameters, e.g., init_params = {'U':U, 'V':V, 'P': P, 'E': E}. \
-        U: numpy array of shape (n_users,k), user latent factors. \
+        List of initial parameters, e.g., init_params = {'U':U, 'V':V, 'P': P, 'E': E}.
+
+        U: numpy array of shape (n_users,k), user latent factors.
         V: numpy array of shape (n_items,k), item latent factors.
         P: numpy array of shape (n_users,d), user visual latent factors.
         E: numpy array of shape (d,c), embedding kernel matrix.
@@ -83,6 +84,7 @@ class VMF(Recommender):
     ----------
     * Park, Chanyoung, Donghyun Kim, Jinoh Oh, and Hwanjo Yu. "Do Also-Viewed Products Help User Rating Prediction?."\
      In Proceedings of WWW, pp. 1113-1122. 2017.
+
     """
 
     def __init__(self, name="VMF", k=10, d=10, n_epochs=100, batch_size=100, learning_rate=0.001, gamma=0.9,

cornac/models/wmf/recom_wmf.py

@@ -52,11 +52,12 @@ class WMF(Recommender):
         The batch size for SGD.
 
     trainable: boolean, optional, default: True
-        When False, the model is not trained and Cornac assumes that the model already 
+        When False, the model is not trained and Cornac assumes that the model already
         pre-trained (U and V are not None).
 
     init_params: dictionary, optional, default: None
         List of initial parameters, e.g., init_params = {'U':U, 'V':V}
+
         U: ndarray, shape (n_users,k)
             The user latent factors, optional initialization via init_params.
         V: ndarray, shape (n_items,k)
@@ -67,10 +68,10 @@ class WMF(Recommender):
 
     References
     ----------
-    * Hu, Y., Koren, Y., & Volinsky, C. (2008, December). Collaborative filtering for implicit feedback datasets.
+    * Hu, Y., Koren, Y., & Volinsky, C. (2008, December). Collaborative filtering for implicit feedback datasets. \
     In 2008 Eighth IEEE International Conference on Data Mining (pp. 263-272).
 
-    * Pan, R., Zhou, Y., Cao, B., Liu, N. N., Lukose, R., Scholz, M., & Yang, Q. (2008, December).
+    * Pan, R., Zhou, Y., Cao, B., Liu, N. N., Lukose, R., Scholz, M., & Yang, Q. (2008, December). \
     One-class collaborative filtering. In 2008 Eighth IEEE International Conference on Data Mining (pp. 502-511).
 
     """

docs/source/models.rst

@@ -21,7 +21,7 @@ VAE for Collaborative Filtering (VAECF)
    :members:
 
 Collaborative Variational Autoencoder (CVAE)
----------------------------------------
+----------------------------------------------
 .. automodule:: cornac.models.cvae.recom_cvae
    :members:
 
@@ -46,12 +46,12 @@ Multi-Layer Perceptron (MLP)
    :members:
 
 Neural Matrix Factorization (NeuMF)
------------------------------------------
+----------------------------------------------
 .. automodule:: cornac.models.ncf.recom_neumf
    :members:
 
 Online Indexable Bayesian Personalized Ranking (OIBPR)
--------------------------------------------------------
+-----------------------------------------------------------
 .. automodule:: cornac.models.online_ibpr.recom_online_ibpr
    :members:
 
@@ -66,7 +66,7 @@ Collaborative Deep Ranking (CDR)
    :members:
 
 Collaborative Ordinal Embedding (COE)
--------------------------------------------------------
+--------------------------------------------
 .. automodule:: cornac.models.coe.recom_coe
    :members:
 
@@ -86,17 +86,17 @@ Visual Bayesian Personalized Ranking (VBPR)
    :members:
 
 Collaborative Deep Learning (CDL)
----------------------------------------
+---------------------------------------------
 .. automodule:: cornac.models.cdl.recom_cdl
    :members:
 
 Hierarchical Poisson Factorization (HPF)
-----------------------------------------
+--------------------------------------------
 .. automodule:: cornac.models.hpf.recom_hpf
    :members:
 
 Explicit Factor Model (EFM)
-----------------------------------------
+--------------------------------------------
 .. automodule:: cornac.models.efm.recom_efm
    :members:
 
@@ -106,47 +106,47 @@ Social Bayesian Personalized Ranking (SBPR)
    :members:
 
 Hidden Factors and Hidden Topics (HFT)
------------------------------------------
+---------------------------------------------
 .. automodule:: cornac.models.hft.recom_hft
    :members:
 
 Collaborative Topic Modeling (CTR)
----------------------------------------
+-------------------------------------------
 .. automodule:: cornac.models.ctr.recom_ctr
    :members:
 
 Baseline Only
-----------------------------------------
+---------------------------------------------------
 .. autoclass:: cornac.models.baseline_only.recom_bo
    :members:
 
 Bayesian Personalized Ranking (BPR)
-----------------------------------------
+----------------------------------------------
 .. autoclass:: cornac.models.bpr.recom_bpr.BPR
    :members:
 
 Matrix Factorization (MF)
-----------------------------------------
+------------------------------------------
 .. automodule:: cornac.models.mf.recom_mf
    :members:
 
 Non-negative Matrix Factorization (NMF)
------------------------------------------
+--------------------------------------------
 .. automodule:: cornac.models.nmf.recom_nmf
    :members:
 
 Probabilitic Matrix Factorization (PMF)
------------------------------------------
+--------------------------------------------
 .. automodule:: cornac.models.pmf.recom_pmf
    :members:
 
 Singular Value Decomposition (SVD)
------------------------------------------
+-------------------------------------------
 .. automodule:: cornac.models.svd.recom_svd
    :members:
 
 Social Recommendation using PMF (SoRec)
------------------------------------------
+------------------------------------------------
 .. automodule:: cornac.models.sorec.recom_sorec
    :members: