def test_load_100k(self):
# only run data download tests 20% of the time to speed up frequent testing
random.seed(time.time())
if random.random() > 0.8:
ml_100k = movielens.load_100k()
self.assertEqual(len(ml_100k), 100000)
def test_movielens_100k():
# only run data download tests 20% of the time to speed up frequent testing
random.seed(time.time())
if random.random() > 0.8:
ml_100k = movielens.load_100k()
assert len(ml_100k) == 100000
#
# 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 to run Probabilistic Matrix Factorization (PMF) model with Ratio Split evaluation strategy"""
import cornac
from cornac.datasets import movielens
from cornac.eval_methods import RatioSplit
from cornac.models import PMF
# Load the MovieLens 100K dataset
ml_100k = movielens.load_100k()
# Instantiate an evaluation method.
ratio_split = RatioSplit(data=ml_100k,
test_size=0.2,
rating_threshold=4.0,
exclude_unknowns=False)
# Instantiate a PMF recommender model.
pmf = PMF(k=10, max_iter=100, learning_rate=0.001, lamda=0.001)
# Instantiate evaluation metrics.
mae = cornac.metrics.MAE()
rmse = cornac.metrics.RMSE()
rec_20 = cornac.metrics.Recall(k=20)
pre_20 = cornac.metrics.Precision(k=20)