Add example for VMF model with Amazon Clothing dataset (PreferredAI#287)

README.md

@@ -148,7 +148,7 @@ The recommender models supported by Cornac are listed below. Why don't you join
 |      | [Multi-Layer Perceptron (MLP)](cornac/models/ncf), [paper](https://arxiv.org/pdf/1708.05031.pdf) | [requirements.txt](cornac/models/ncf/requirements.txt) | [ncf_exp.py](examples/ncf_example.py)
 |      | [Neural Matrix Factorization (NeuMF) / Neural Collaborative Filtering (NCF)](cornac/models/ncf), [paper](https://arxiv.org/pdf/1708.05031.pdf) | [requirements.txt](cornac/models/ncf/requirements.txt) | [ncf_exp.py](examples/ncf_example.py)
 |      | [Online Indexable Bayesian Personalized Ranking (Online IBPR)](cornac/models/online_ibpr), [paper](http://www.hadylauw.com/publications/cikm17a.pdf) | [requirements.txt](cornac/models/online_ibpr/requirements.txt) |
-|      | [Visual Matrix Factorization (VMF)](cornac/models/vmf), [paper](http://papers.www2017.com.au.s3-website-ap-southeast-2.amazonaws.com/proceedings/p1113.pdf) | [requirements.txt](cornac/models/vmf/requirements.txt) |
+|      | [Visual Matrix Factorization (VMF)](cornac/models/vmf), [paper](http://papers.www2017.com.au.s3-website-ap-southeast-2.amazonaws.com/proceedings/p1113.pdf) | [requirements.txt](cornac/models/vmf/requirements.txt) | [vmf_clothing.py](examples/vmf_clothing.py)
 | 2016 | [Collaborative Deep Ranking (CDR)](cornac/models/cdr), [paper](http://inpluslab.com/chenliang/homepagefiles/paper/hao-pakdd2016.pdf) | [requirements.txt](cornac/models/cdr/requirements.txt) | [cdr_exp.py](examples/cdr_example.py)
 |      | [Collaborative Ordinal Embedding (COE)](cornac/models/coe), [paper](http://www.hadylauw.com/publications/sdm16.pdf) | [requirements.txt](cornac/models/coe/requirements.txt) |
 |      | [Convolutional Matrix Factorization (ConvMF)](cornac/models/conv_mf), [paper](http://uclab.khu.ac.kr/resources/publication/C_351.pdf) | [requirements.txt](cornac/models/conv_mf/requirements.txt) | [convmf_exp.py](examples/conv_mf_example.py)

examples/README.md

@@ -50,6 +50,8 @@
 
 [vbpr_tradesy.py](vbpr_tradesy.py) - Visual Bayesian Personalized Ranking (VBPR) with Tradesy dataset.
 
+[vmf_clothing.py](vmf_clothing.py) - Visual Matrix Factorization (VMF) with Amazon Clothing dataset.
+
 ## Unimodal Algorithms
 
 [biased_mf.py](biased_mf.py) - Matrix Factorization (MF) with biases.

examples/vmf_clothing.py

@@ -0,0 +1,61 @@
+# 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 for Visual Matrix Factorization (VMF)"""
+
+import cornac
+from cornac.datasets import amazon_clothing
+from cornac.data import ImageModality
+from cornac.eval_methods import RatioSplit
+
+
+# The necessary data can be loaded as follows
+feedback = amazon_clothing.load_feedback()
+features, item_ids = amazon_clothing.load_image()
+
+# Instantiate a ImageModality, it make it convenient to work with visual auxiliary information
+# For more details, please refer to the tutorial on how to work with auxiliary data
+item_image_modality = ImageModality(features=features, ids=item_ids, normalized=True)
+
+# Define an evaluation method to split feedback into train and test sets
+ratio_split = RatioSplit(
+    data=feedback,
+    test_size=0.1,
+    rating_threshold=0.5,
+    exclude_unknowns=True,
+    verbose=True,
+    item_image=item_image_modality,
+)
+
+# Instantiate VMF
+vmf = cornac.models.VMF(
+    k=10,
+    d=10,
+    n_epochs=100,
+    batch_size=100,
+    learning_rate=0.001,
+    gamma=0.9,
+    lambda_u=0.001,
+    lambda_v=0.001,
+    lambda_p=1.0,
+    lambda_e=10.0,
+    use_gpu=True,
+    verbose=True,
+)
+
+# Instantiate evaluation measures
+rec_100 = cornac.metrics.Recall(k=100)
+
+# Put everything together into an experiment and run it
+cornac.Experiment(eval_method=ratio_split, models=[vmf], metrics=[rec_100]).run()