def test_build(self):
data = Reader().read('./tests/sentiment_data.txt', fmt='UITup', sep=',', tup_sep=':')
md = SentimentModality(data=data)
uid_map = OrderedDict()
iid_map = OrderedDict()
for raw_uid, raw_iid, _ in data:
uid_map.setdefault(raw_uid, len(uid_map))
iid_map.setdefault(raw_iid, len(iid_map))
matrix = dok_matrix((len(uid_map), len(iid_map)), dtype=np.float32)
for raw_uid, raw_iid, _ in data:
user_idx = uid_map.get(raw_uid)
item_idx = iid_map.get(raw_iid)
matrix[user_idx, item_idx] = 1
md.build(uid_map=uid_map, iid_map=iid_map, dok_matrix=matrix)
self.assertEqual(md.num_aspects, 3)
self.assertEqual(md.num_opinions, 2)
self.assertEqual(len(md.sentiment), 4)
self.assertEqual(len(md.user_sentiment), 3)
self.assertEqual(len(md.item_sentiment), 3)
self.assertEqual(len(md.aspect_id_map), 3)
self.assertEqual(len(md.opinion_id_map), 2)
try:
SentimentModality().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=":")
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
# 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 Multi-Task Explainable Recommendation"""
from cornac.datasets import amazon_toy
from cornac.data import SentimentModality
from cornac.eval_methods import RatioSplit
from cornac.metrics import NDCG, RMSE
from cornac.models import MTER
from cornac import Experiment
data = amazon_toy.load_rating()
sentiment = amazon_toy.load_sentiment()
md = SentimentModality(data=sentiment)
eval_method = RatioSplit(data,
test_size=0.2,
rating_threshold=1.0,
sentiment=md,
exclude_unknowns=True,
verbose=True,
seed=123)
mter = MTER(n_user_factors=15,
n_item_factors=15,
n_aspect_factors=12,
n_opinion_factors=12,
n_bpr_samples=1000,
n_element_samples=200,
lambda_reg=0.1,
def test_init(self):
data = Reader().read('./tests/sentiment_data.txt', fmt='UITup', sep=',', tup_sep=':')
md = SentimentModality(data=data)
self.assertEqual(len(md.raw_data), 4)