How to use the cornac.datasets.movielens.load_feedback function in cornac

def test_load_feedback(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_feedback()
            self.assertEqual(len(ml_100k), 100000)

        if random.random() > 0.8:
            ml_1m = movielens.load_feedback(variant='1M')
            self.assertEqual(len(ml_1m), 1000209)

def test_load_feedback(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_feedback()
            self.assertEqual(len(ml_100k), 100000)

        if random.random() > 0.8:
            ml_1m = movielens.load_feedback(variant='1M')
            self.assertEqual(len(ml_1m), 1000209)

#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# 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 Matrix Factorization with biases"""

import cornac
from cornac.datasets import movielens
from cornac.eval_methods import RatioSplit

ratio_split = RatioSplit(data=movielens.load_feedback(variant='1M'),
                         test_size=0.2,
                         exclude_unknowns=False,
                         verbose=True)

global_avg = cornac.models.GlobalAvg()
mf = cornac.models.MF(k=10, max_iter=25, learning_rate=0.01, lambda_reg=0.02,
                      use_bias=True, early_stop=True, verbose=True)

mae = cornac.metrics.MAE()
rmse = cornac.metrics.RMSE()

exp = cornac.Experiment(eval_method=ratio_split,
                        models=[global_avg, mf],
                        metrics=[mae, rmse],
                        user_based=True)
exp.run()

#     http://www.apache.org/licenses/LICENSE-2.0
#
# 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 Non-negative Matrix Factorization (NMF) model with Ratio Split evaluation strategy"""

import cornac
from cornac.datasets import movielens
from cornac.eval_methods import RatioSplit

# Load the MovieLens 100K dataset
ml_100k = movielens.load_feedback()

# Instantiate an evaluation method.
eval_method = RatioSplit(data=ml_100k, test_size=0.2, rating_threshold=4.0,
                         exclude_unknowns=True, verbose=True, seed=123)

# Instantiate a NMF recommender model.
nmf = cornac.models.NMF(k=15, max_iter=50, learning_rate=.005,
                        lambda_u=.06, lambda_v=.06, lambda_bu=.02, lambda_bi=.02,
                        use_bias=False, verbose=True, seed=123)

# 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)

#
# 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_feedback()

# 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)

# Instantiate and then run an experiment.
exp = cornac.Experiment(eval_method=ratio_split,
                        models=[pmf],

# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# 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.
# ============================================================================
"""Your very first example with Cornac"""

import cornac as cn

# Load MovieLens 100K dataset
ml_100k = cn.datasets.movielens.load_feedback()

# Split data based on ratio
ratio_split = cn.eval_methods.RatioSplit(data=ml_100k, test_size=0.2, rating_threshold=4.0, seed=123)

# Here we are comparing biased MF, PMF, and BPR
mf = cn.models.MF(k=10, max_iter=25, learning_rate=0.01, lambda_reg=0.02, use_bias=True, seed=123)
pmf = cn.models.PMF(k=10, max_iter=100, learning_rate=0.001, lamda=0.001, seed=123)
bpr = cn.models.BPR(k=10, max_iter=200, learning_rate=0.001, lambda_reg=0.01, seed=123)

# Define metrics used to evaluate the models
mae = cn.metrics.MAE()
rmse = cn.metrics.RMSE()
rec_20 = cn.metrics.Recall(k=20)
ndcg_20 = cn.metrics.NDCG(k=20)
auc = cn.metrics.AUC()

# 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 Convolutional Matrix Factorization"""

import cornac
from cornac.data import Reader
from cornac.datasets import movielens
from cornac.eval_methods import RatioSplit
from cornac.data import TextModality
from cornac.data.text import BaseTokenizer

plots, movie_ids = movielens.load_plot()
ml_1m = movielens.load_feedback(variant='1M', reader=Reader(item_set=movie_ids))

# build text modality
item_text_modality = TextModality(corpus=plots, ids=movie_ids,
                                  tokenizer=BaseTokenizer(sep='\t', stop_words='english'),
                                  max_vocab=8000, max_doc_freq=0.5)

ratio_split = RatioSplit(data=ml_1m, test_size=0.2, exclude_unknowns=True,
                         item_text=item_text_modality, verbose=True, seed=123)

convmf = cornac.models.ConvMF(n_epochs=5, verbose=True, seed=123)

rmse = cornac.metrics.RMSE()

exp = cornac.Experiment(eval_method=ratio_split,
                        models=[convmf],
                        metrics=[rmse],

#     http://www.apache.org/licenses/LICENSE-2.0
#
# 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.
# ============================================================================

import cornac
from cornac.datasets import movielens
from cornac.eval_methods import RatioSplit
from cornac.models import IBPR

# Load the MovieLens 1M dataset
ml_1m = movielens.load_feedback(variant='1M')

# Instantiate an evaluation method.
ratio_split = RatioSplit(data=ml_1m, test_size=0.2, rating_threshold=1.0,
                         exclude_unknowns=True, verbose=True)

# Instantiate a IBPR recommender model.
ibpr = IBPR(k=10, init_params={'U': None, 'V': None}, verbose=True)

# Instantiate evaluation metrics.
rec_20 = cornac.metrics.Recall(k=20)
pre_20 = cornac.metrics.Precision(k=20)

# Instantiate and then run an experiment.
exp = cornac.Experiment(eval_method=ratio_split,
                        models=[ibpr],
                        metrics=[rec_20, pre_20],