# 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 Social Bayesian Personalized Ranking with Epinions dataset"""
import cornac
from cornac.data import Reader, GraphModule
from cornac.datasets import epinions
from cornac.eval_methods import RatioSplit
ratio_split = RatioSplit(data=epinions.load_data(Reader(bin_threshold=4.0)),
test_size=0.1,
rating_threshold=0.5,
exclude_unknowns=True,
verbose=True,
user_graph=GraphModule(data=epinions.load_trust()))
sbpr = cornac.models.SBPR(k=10,
max_iter=50,
learning_rate=0.001,
lambda_u=0.015,
lambda_v=0.025,
lambda_b=0.01,
verbose=True)
rec_10 = cornac.metrics.Recall(k=10)
cornac.Experiment(eval_method=ratio_split, models=[sbpr],
metrics=[rec_10]).run()
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Example for Social Bayesian Personalized Ranking (SBPR) with Epinions dataset"""
import cornac
from cornac.data import Reader, GraphModality
from cornac.datasets import epinions
from cornac.eval_methods import RatioSplit
# SBPR integrates user social network into Bayesian Personalized Ranking.
# The necessary data can be loaded as follows
feedback = epinions.load_feedback(
Reader(bin_threshold=4.0
)) # feedback is binarised (turned into implicit) using Reader.
trust = epinions.load_trust()
# Instantiate a GraphModality, it make it convenient to work with graph (network) auxiliary information
# For more details, please refer to the tutorial on how to work with auxiliary data
user_graph_modality = GraphModality(data=trust)
# 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,
user_graph=user_graph_modality)
# Instantiate SBPR
sbpr = cornac.models.SBPR(k=10,
def test_load_trust(self):
# only run data download tests 20% of the time to speed up frequent testing
random.seed(time.time())
if random.random() > 0.8:
self.assertEqual(len(epinions.load_trust()), 487183)
# 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 Social Bayesian Personalized Ranking with Epinions dataset"""
import cornac
from cornac.data import Reader, GraphModality
from cornac.datasets import epinions
from cornac.eval_methods import RatioSplit
ratio_split = RatioSplit(data=epinions.load_data(Reader(bin_threshold=4.0)),
test_size=0.1,
rating_threshold=0.5,
exclude_unknowns=True,
verbose=True,
user_graph=GraphModality(data=epinions.load_trust()))
sbpr = cornac.models.SBPR(k=10,
max_iter=50,
learning_rate=0.001,
lambda_u=0.015,
lambda_v=0.025,
lambda_b=0.01,
verbose=True)
rec_10 = cornac.metrics.Recall(k=10)
cornac.Experiment(eval_method=ratio_split, models=[sbpr],
metrics=[rec_10]).run()
