def test_build(self):
ReviewModality(data=[]).build(uid_map=self.uid_map,
iid_map=self.iid_map,
dok_matrix=self.dok_matrix)
ReviewModality(data=self.review_data,
filter_by='user').build(uid_map=self.uid_map,
iid_map=self.iid_map,
dok_matrix=self.dok_matrix)
ReviewModality(data=self.review_data,
filter_by='item').build(uid_map=self.uid_map,
iid_map=self.iid_map,
dok_matrix=self.dok_matrix)
try:
ReviewModality().build()
except ValueError:
assert True
def test_with_modalities(self):
data = Reader().read("./tests/data.txt")
sentiment_data = Reader().read("./tests/sentiment_data.txt",
fmt="UITup",
sep=",",
tup_sep=":")
review_data = Reader().read("./tests/review.txt", fmt="UIReview")
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_text = ReviewModality(data=review_data, filter_by='item')
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
# limitations under the License.
# ============================================================================
import cornac
from cornac.data import Reader
from cornac.datasets import amazon_digital_music
from cornac.eval_methods import RatioSplit
from cornac.data import ReviewModality
from cornac.data.text import BaseTokenizer
feedback = amazon_digital_music.load_feedback()
reviews = amazon_digital_music.load_review()
review_modality = ReviewModality(
data=reviews,
tokenizer=BaseTokenizer(stop_words="english"),
max_vocab=4000,
max_doc_freq=0.5,
)
ratio_split = RatioSplit(
data=feedback,
test_size=0.1,
val_size=0.1,
exclude_unknowns=True,
review_text=review_modality,
verbose=True,
seed=123,
)
pretrained_word_embeddings = {} # You can load pretrained word embedding here
def test_init(self):
try:
ReviewModality(filter_by='something')
except ValueError:
assert True