cornac/examples/ngcf_example.py at master · PreferredAI/cornac · GitHub

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# 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 NGCF, using the CiteULike dataset

"""

import cornac

from cornac.datasets import citeulike

from cornac.eval_methods import RatioSplit

# Load user-item feedback

data = citeulike.load_feedback()

# Instantiate an evaluation method to split data into train and test sets.

ratio_split = RatioSplit(

data=data,

val_size=0.1,

test_size=0.1,

exclude_unknowns=True,

verbose=True,

seed=123,

rating_threshold=0.5,

)

# Instantiate the NGCF model

ngcf = cornac.models.NGCF(

seed=123,

num_epochs=1000,

emb_size=64,

layer_sizes=[64, 64, 64],

dropout_rates=[0.1, 0.1, 0.1],

early_stopping={"min_delta": 1e-4, "patience": 50},

batch_size=1024,

learning_rate=0.001,

lambda_reg=1e-5,

verbose=True,

)

# Instantiate evaluation measures

rec_20 = cornac.metrics.Recall(k=20)

ndcg_20 = cornac.metrics.NDCG(k=20)

# Put everything together into an experiment and run it

cornac.Experiment(

eval_method=ratio_split,

models=[ngcf],

metrics=[rec_20, ndcg_20],

user_based=True,

).run()

cornac/examples/ngcf_example.py at master · PreferredAI/cornac · GitHub

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