hft_example.py

# Copyright 2018 The Cornac Authors. All Rights Reserved.
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"""Example for Hidden Factors as Topics (HFT) with Movilen 1M dataset """

import cornac
from cornac.data import Reader
from cornac.datasets import movielens
from cornac.eval_methods import RatioSplit
from cornac.data import TextModality
from cornac.data.text import BaseTokenizer

# HFT jointly models the user-item preferences and item texts (e.g., product reviews) with shared item factors
# Below we fit HFT to the MovieLens 1M dataset. We need  both the ratings and movie plots information
plots, movie_ids = movielens.load_plot()
ml_1m = movielens.load_feedback(variant='1M', reader=Reader(item_set=movie_ids))

# Instantiate a TextModality, it make it convenient to work with text auxiliary information
# For more details, please refer to the tutorial on how to work with auxiliary data
item_text_modality = TextModality(corpus=plots, ids=movie_ids,
                                  tokenizer=BaseTokenizer(sep='\t', stop_words='english'),
                                  max_vocab=5000, max_doc_freq=0.5)

# Define an evaluation method to split feedback into train and test sets
ratio_split = RatioSplit(data=ml_1m, test_size=0.2, exclude_unknowns=True,
                         item_text=item_text_modality, verbose=True, seed=123)

# Instantiate HFT
hft = cornac.models.HFT(k=10, max_iter=40, grad_iter=5, l2_reg=0.001, lambda_text=0.01, vocab_size=5000, seed=123)

# Instantiate MSE for evaluation
mse = cornac.metrics.MSE()

# Put everything together into an experiment and run it
exp = cornac.Experiment(eval_method=ratio_split,
                        models=[hft],
                        metrics=[mse],
                        user_based=False)
exp.run()