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# 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.
# ============================================================================
"""Fit to and evaluate C2PF on the Office Amazon dataset"""
from cornac.data import GraphModality
from cornac.eval_methods import RatioSplit
from cornac.experiment import Experiment
from cornac import metrics
from cornac.models import C2PF
from cornac.datasets import amazon_office as office
# Load office ratings and item contexts, see C2PF paper for details
ratings = office.load_rating()
contexts = office.load_context()
item_graph_modality = GraphModality(data=contexts)
ratio_split = RatioSplit(data=ratings,
test_size=0.2, rating_threshold=3.5,
shuffle=True, exclude_unknowns=True,
verbose=True, item_graph=item_graph_modality)
c2pf = C2PF(k=100, max_iter=80, variant='c2pf')
# Evaluation metrics
nDgc = metrics.NDCG(k=-1)
mrr = metrics.MRR()
rec = metrics.Recall(k=20)
pre = metrics.Precision(k=20)
# Instantiate and run your experiment
exp = Experiment(eval_method=ratio_split,
models=[c2pf],
metrics=[nDgc, mrr, rec, pre])
exp.run()
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