def test_load_data_small(self):
# only run data download tests 20% of the time to speed up frequent testing
random.seed(time.time())
if random.random() > 0.8:
data = netflix.load_data_small()
self.assertEqual(len(data), 607803)
#
# 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 Bayesian Personalized Ranking with Netflix dataset"""
import cornac
from cornac.data import Reader
from cornac.datasets import netflix
from cornac.eval_methods import RatioSplit
ratio_split = RatioSplit(
data=netflix.load_data_small(reader=Reader(bin_threshold=1.0)),
test_size=0.1,
rating_threshold=1.0,
exclude_unknowns=True,
verbose=True)
most_pop = cornac.models.MostPop()
bpr = cornac.models.BPR(k=10,
max_iter=100,
learning_rate=0.001,
lambda_reg=0.01,
verbose=True)
auc = cornac.metrics.AUC()
rec_20 = cornac.metrics.Recall(k=20)
# -*- coding: utf-8 -*-
"""
Example for Bayesian Personalized Ranking with Netflix dataset (subset)
@author: Quoc-Tuan Truong <*****@*****.**>
"""
import cornac
from cornac.datasets import netflix
from cornac.eval_methods import RatioSplit
ratio_split = RatioSplit(data=netflix.load_data_small(),
test_size=0.1,
rating_threshold=1.0,
exclude_unknowns=True,
verbose=True)
bpr = cornac.models.BPR(k=10,
max_iter=100,
learning_rate=0.01,
lambda_reg=0.01)
auc = cornac.metrics.AUC()
rec_20 = cornac.metrics.Recall(k=20)
exp = cornac.Experiment(eval_method=ratio_split,
models=[bpr],
metrics=[auc, rec_20],
user_based=True)
exp.run()