Add example Poisson Factorization vs BPR on MovieLens data (#381)

README.md

@@ -156,7 +156,7 @@ The recommender models supported by Cornac are listed below. Why don't you join
 |      | [Spherical K-means (SKM)](cornac/models/skm), [paper](https://www.sciencedirect.com/science/article/pii/S092523121501509X) | N/A | [skm_movielens.py](examples/skm_movielens.py)
 |      | [Visual Bayesian Personalized Ranking (VBPR)](cornac/models/vbpr), [paper](https://arxiv.org/pdf/1510.01784.pdf) | [requirements.txt](cornac/models/vbpr/requirements.txt) | [vbpr_tradesy.py](examples/vbpr_tradesy.py)
 | 2015 | [Collaborative Deep Learning (CDL)](cornac/models/cdl), [paper](https://arxiv.org/pdf/1409.2944.pdf) | [requirements.txt](cornac/models/cdl/requirements.txt) | [cdl_exp.py](examples/cdl_example.py)
-|      | [Hierarchical Poisson Factorization (HPF)](cornac/models/hpf), [paper](http://jakehofman.com/inprint/poisson_recs.pdf) | N/A |
+|      | [Hierarchical Poisson Factorization (HPF)](cornac/models/hpf), [paper](http://jakehofman.com/inprint/poisson_recs.pdf) | N/A | [hpf_movielens.py](examples/hpf_movielens.py)
 | 2014 | [Explicit Factor Model (EFM)](cornac/models/efm), [paper](http://yongfeng.me/attach/efm-zhang.pdf) | N/A | [efm_exp.py](examples/efm_example.py)
 |      | [Social Bayesian Personalized Ranking (SBPR)](cornac/models/sbpr), [paper](https://cseweb.ucsd.edu/~jmcauley/pdfs/cikm14.pdf) | N/A | [sbpr_epinions.py](examples/sbpr_epinions.py)
 | 2013 | [Hidden Factors and Hidden Topics (HFT)](cornac/models/hft), [paper](https://cs.stanford.edu/people/jure/pubs/reviews-recsys13.pdf) | N/A | [hft_exp.py](examples/hft_example.py)

examples/README.md

@@ -64,6 +64,8 @@
 
 [bpr_netflix.py](bpr_netflix.py) - Example to run Bayesian Personalized Ranking (BPR) with Netflix dataset.
 
+[hpf_movielens.py](hpf_movielens.py) - (Hierarchical) Poisson Factorization vs BPR on MovieLens data.
+
 [ibpr_example.py](ibpr_example.py) - Example to run Indexable Bayesian Personalized Ranking.
 
 [knn_movielens.py](knn_movielens.py) - Example to run Neighborhood-based models with MovieLens 100K dataset.

examples/hpf_movielens.py

+# Copyright 2018 The Cornac Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""Example (Hierarchical) Poisson Factorization vs BPR on MovieLens data"""
+
+import cornac
+from cornac.datasets import movielens
+from cornac.eval_methods import RatioSplit
+
+
+# Load user-item feedback
+data = movielens.load_feedback(variant="100K")
+
+# Instantiate an evaluation method to split data into train and test sets.
+ratio_split = RatioSplit(
+    data=data,
+    test_size=0.2,
+    exclude_unknowns=True,
+    verbose=True,
+    seed=123,
+    rating_threshold=0.5,
+)
+
+hpf = cornac.models.HPF(
+    k=5,
+    seed=123
+)
+
+pf = cornac.models.HPF(
+    k=5,
+    seed=123,
+    hierarchical=False,
+    name="PF"
+)
+
+bpr = cornac.models.BPR(
+   k=5,
+   max_iter=200,
+   learning_rate=0.001,
+   lambda_reg=0.01,
+   seed=123)
+
+
+# Instantiate evaluation measures
+rec_20 = cornac.metrics.Recall(k=20)
+ndcg_20 = cornac.metrics.NDCG(k=20)
+auc = cornac.metrics.AUC()
+
+
+
+# Put everything together into an experiment and run it
+cornac.Experiment(
+    eval_method=ratio_split,
+    models=[pf, hpf, bpr],
+    metrics=[rec_20, ndcg_20, auc],
+    user_based=True,
+).run()
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