ncf_example.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.
# ============================================================================


import cornac
from cornac.eval_methods import RatioSplit
from cornac.datasets import amazon_clothing
from cornac.data import Reader

# Load the Amazon Clothing  dataset, and binarise ratings using cornac.data.Reader
feedback = amazon_clothing.load_feedback(reader=Reader(bin_threshold=1.0))

# Define an evaluation method to split feedback into train and test sets
ratio_split = RatioSplit(data=feedback,
                         test_size=0.2, rating_threshold=1.0, seed=123,
                         exclude_unknowns=True, verbose=True)

# Instantiate the recommender models to be compared
gmf = cornac.models.GMF(num_factors=8, num_epochs=10, learner='adam',
                        batch_size=256, lr=0.001, num_neg=50, seed=123)
mlp = cornac.models.MLP(layers=[64, 32, 16, 8], act_fn='tanh', learner='adam',
                        num_epochs=10, batch_size=256, lr=0.001, num_neg=50, seed=123)
neumf1 = cornac.models.NeuMF(num_factors=8, layers=[64, 32, 16, 8], act_fn='tanh', learner='adam',
                             num_epochs=10, batch_size=256, lr=0.001, num_neg=50, seed=123)
neumf2 = cornac.models.NeuMF(name='NeuMF_pretrained', learner='adam',
                             num_epochs=10, batch_size=256, lr=0.001, num_neg=50, seed=123,
                             num_factors=gmf.num_factors, layers=mlp.layers, act_fn=mlp.act_fn).pretrain(gmf, mlp)

# Instantiate evaluation metrics
ndcg_50 = cornac.metrics.NDCG(k=50)
rec_50 = cornac.metrics.Recall(k=50)

# Put everything together into an experiment and run it
cornac.Experiment(eval_method=ratio_split,
                  models=[gmf, mlp, neumf1, neumf2],
                  metrics=[ndcg_50, rec_50]).run()