efm_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
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# 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.
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# ============================================================================
"""Example for Explicit Factor Models"""

import cornac
from cornac.datasets import amazon_toy
from cornac.data import SentimentModality
from cornac.eval_methods import RatioSplit

# Load rating and sentiment information
rating = amazon_toy.load_feedback()
sentiment = amazon_toy.load_sentiment()

# Instantiate a SentimentModality, it make it convenient to work with sentiment information
md = SentimentModality(data=sentiment)

# Define an evaluation method to split feedback into train and test sets
split_data = RatioSplit(data=rating,
                        test_size=0.15,
                        exclude_unknowns=True, verbose=True,
                        sentiment=md, seed=123)

# Instantiate the EFM model
efm = cornac.models.EFM(num_explicit_factors=40, num_latent_factors=60, num_most_cared_aspects=15,
                        rating_scale=5.0, alpha=0.85,
                        lambda_x=1, lambda_y=1, lambda_u=0.01, lambda_h=0.01, lambda_v=0.01,
                        max_iter=100, num_threads=1,
                        trainable=True, verbose=True, seed=123)

# Instantiate evaluation metrics
rmse = cornac.metrics.RMSE()
ndcg_50 = cornac.metrics.NDCG(k=50)
auc = cornac.metrics.AUC()

# Put everything together into an experiment and run it
exp = cornac.Experiment(eval_method=split_data,
                        models=[efm],
                        metrics=[rmse, ndcg_50, auc])
exp.run()