def test_bpr_train_and_test():
bpr = BPR(10, 200, 50)
train_data = zip(randint(100, size=1000), randint(50, size=1000))
bpr.train(train_data, batch_size=50)
assert(bpr.test(train_data) > 0.8)
test_data = zip(randint(100, size=1000), randint(50, size=1000))
assert(bpr.test(test_data) > 0.4 and bpr.test(test_data) < 0.6)
def test_bpr_train_no_epochs():
bpr = BPR(10, 100, 50)
train_data = zip(randint(100, size=1000), randint(50, size=1000))
bpr.train(train_data, epochs=0)
assert(bpr.test(train_data) > 0.4 and bpr.test(train_data) < 0.6)
# 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.
from theano_bpr.utils import load_data_from_csv
from theano_bpr import BPR
import sys
if len(sys.argv) != 3:
print "Usage: ./example.py training_data.csv testing_data.csv"
sys.exit(1)
# Loading train data
train_data, users_to_index, items_to_index = load_data_from_csv(sys.argv[1])
# Loading test data
test_data, users_to_index, items_to_index = load_data_from_csv(
sys.argv[2], users_to_index, items_to_index)
# Initialising BPR model, 10 latent factors
bpr = BPR(10, len(users_to_index.keys()), len(items_to_index.keys()))
# Training model, 30 epochs
bpr.train(train_data, epochs=30)
# Testing model
print bpr.test(test_data)
# you may not use this file except in compliance with the License.
# 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.
from theano_bpr.utils import load_data_from_csv
from theano_bpr import BPR
import sys
if len(sys.argv) != 3:
print "Usage: ./example.py training_data.csv testing_data.csv"
sys.exit(1)
# Loading train data
train_data, users_to_index, items_to_index = load_data_from_csv(sys.argv[1])
# Loading test data
test_data, users_to_index, items_to_index = load_data_from_csv(sys.argv[2], users_to_index, items_to_index)
# Initialising BPR model, 10 latent factors
bpr = BPR(10, len(users_to_index.keys()), len(items_to_index.keys()))
# Training model, 30 epochs
bpr.train(train_data, epochs=30)
# Testing model
print bpr.test(test_data)
