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# Copyright 2024 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 Comparative Aspects and Opinions Ranking for Recommendation Explanations""" | ||
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import cornac | ||
from cornac.datasets import amazon_toy | ||
from cornac.data import SentimentModality | ||
from cornac.eval_methods import StratifiedSplit | ||
from cornac.metrics import NDCG, RMSE, AUC | ||
from cornac import Experiment | ||
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rating = amazon_toy.load_feedback(fmt="UIRT") | ||
sentiment = amazon_toy.load_sentiment() | ||
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# Instantiate a SentimentModality, it makes it convenient to work with sentiment information | ||
md = SentimentModality(data=sentiment) | ||
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# Define an evaluation method to split feedback into train and test sets | ||
eval_method = StratifiedSplit( | ||
rating, | ||
group_by="user", | ||
chrono=True, | ||
sentiment=md, | ||
test_size=1, | ||
val_size=1, | ||
exclude_unknowns=True, | ||
verbose=True, | ||
seed=123, | ||
) | ||
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companion = cornac.models.Companion( | ||
n_top_aspects=0, | ||
max_iter=10000, | ||
verbose=True, | ||
seed=123, | ||
) | ||
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# Instantiate and run an experiment | ||
exp = Experiment( | ||
eval_method=eval_method, | ||
models=[companion], | ||
metrics=[RMSE(), NDCG(k=20), AUC()], | ||
) | ||
exp.run() |