def test_with_modalities(self):
data = Reader().read("./tests/data.txt")
sentiment_data = Reader().read("./tests/sentiment_data.txt",
fmt="UITup",
sep=",",
tup_sep=":")
bm = BaseMethod.from_splits(train_data=data[:-1], test_data=data[-1:])
self.assertIsNone(bm.user_text)
self.assertIsNone(bm.item_text)
self.assertIsNone(bm.user_image)
self.assertIsNone(bm.item_image)
self.assertIsNone(bm.user_graph)
self.assertIsNone(bm.item_graph)
self.assertIsNone(bm.sentiment)
bm.user_text = TextModality()
bm.item_image = ImageModality()
bm.sentiment = SentimentModality(data=sentiment_data)
bm._build_modalities()
try:
bm.user_text = ImageModality()
except ValueError:
assert True
try:
bm.item_text = ImageModality()
except ValueError:
assert True
try:
bm.user_image = TextModality()
except ValueError:
assert True
try:
bm.item_image = TextModality()
except ValueError:
assert True
try:
bm.user_graph = TextModality()
except ValueError:
assert True
try:
bm.item_graph = ImageModality()
except ValueError:
assert True
try:
bm.sentiment = TextModality()
except ValueError:
assert True
try:
bm.sentiment = ImageModality()
except ValueError:
assert True
def batch_image(self):
md = ImageModality()
md.build(id_map=None)
try:
md.batch_image(batch_ids=None)
except:
raise NotImplementedError
def test_with_modalities(self):
bm = BaseMethod()
self.assertIsNone(bm.user_text)
self.assertIsNone(bm.item_text)
self.assertIsNone(bm.user_image)
self.assertIsNone(bm.item_image)
self.assertIsNone(bm.user_graph)
self.assertIsNone(bm.item_graph)
bm.user_text = TextModality()
bm.item_image = ImageModality()
bm._build_modalities()
try:
bm.user_text = ImageModality()
except ValueError:
assert True
try:
bm.item_text = ImageModality()
except ValueError:
assert True
try:
bm.user_image = TextModality()
except ValueError:
assert True
try:
bm.item_image = TextModality()
except ValueError:
assert True
try:
bm.user_graph = TextModality()
except ValueError:
assert True
try:
bm.item_graph = ImageModality()
except ValueError:
assert True
"""
import cornac
from cornac.datasets import tradesy
from cornac.data import ImageModality
from cornac.eval_methods import RatioSplit
# VBPR extends Bayesian Personalized Randing to leverage item visual features (extracted from product images using CNN)
# The necessary data can be loaded as follows
feedback = tradesy.load_feedback()
features, item_ids = tradesy.load_feature() # BIG file
# Instantiate a ImageModality, it makes it convenient to work with visual auxiliary information
# For more details, please refer to the tutorial on how to work with auxiliary data
item_image_modality = ImageModality(features=features,
ids=item_ids,
normalized=True)
# Define an evaluation method to split feedback into train and test sets
ratio_split = RatioSplit(
data=feedback,
test_size=0.1,
rating_threshold=0.5,
exclude_unknowns=True,
verbose=True,
item_image=item_image_modality,
)
# Instantiate CVAE
vbpr = cornac.models.VBPR(
k=10,
def test_init(self):
md = ImageModality()
md.build(id_map=None)
self.assertIsNone(md.images)
self.assertIsNone(md.paths)