def test_amazon_toy(self):
random.seed(time.time())
if random.random() > 0.8:
ratings = toy.load_feedback()
sentiments = toy.load_sentiment()
self.assertEqual(len(ratings), 167597)
self.assertEqual(len(sentiments), 149877)
import os
import glob
import cornac
import numpy as np
from cornac.utils import cache
from cornac.models import ComparERObj
from cornac.metrics import AUC, Recall, NDCG
from cornac.datasets import amazon_toy
from cornac.experiment import Experiment
from cornac.data.reader import Reader
from cornac.eval_methods import StratifiedSplit
from cornac.data.sentiment import SentimentModality
rating = amazon_toy.load_feedback(fmt="UIRT", reader=Reader(min_user_freq=10))
sentiment_data = amazon_toy.load_sentiment()
md = SentimentModality(data=sentiment_data)
eval_method = StratifiedSplit(
rating,
group_by="user",
chrono=True,
sentiment=md,
test_size=0.2,
val_size=0.16,
exclude_unknowns=True,
verbose=True,
)
pretrained_mter_model_path = "dist/toy/result/EFM"
# distributed under the License is distributed on an "AS IS" BASIS,
# 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
# Load rating and sentiment information
data = amazon_toy.load_feedback()
sentiment = amazon_toy.load_sentiment()
# Instantiate a SentimentModality, it makes it convenient to work with sentiment information
md = SentimentModality(data=sentiment)
# Define an evaluation method to split feedback into train and test sets
eval_method = RatioSplit(
data,
test_size=0.2,
rating_threshold=1.0,
sentiment=md,
exclude_unknowns=True,
verbose=True,
seed=123,
)
#
# 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.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""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,
