def test_bpr_train_stores_data():
bpr = BPR(1, 2, 3)
bpr.train([
(0, 1),
(0, 2),
(1, 0),
(1, 2),
], batch_size=4)
assert_equal(bpr._train_users, set([0, 1]))
assert_equal(bpr._train_items, set([0, 1, 2]))
assert_equal(bpr._train_dict, {
0: [ 1, 2 ],
1: [ 0, 2 ],
})
def test_bpr_predictions():
bpr = BPR(10, 100, 50)
train_data = zip(randint(100, size=1000), randint(50, size=1000))
bpr.train(train_data, epochs=1)
assert_equal(bpr.predictions(0).shape, (50,))
assert_equal(bpr.prediction(0,0), bpr.predictions(0)[0])
assert_equal(len(bpr.top_predictions(0, topn=20)), 20)
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)
session = sessions[s]
ssl = usl[s] + 1
for i in range(ssl):
a.append((user, session[i][1]))
return a
print("converting training data to array")
a = convert_data(train, train_sl)
training_array, uti, iti = load_data_from_array(a)
a = convert_data(test, test_sl)
testing_array, uti, iti = load_data_from_array(a, uti, iti)
print("creating BPR model")
bpr = BPR(embedding_size,
len(list(uti.keys())),
len(list(iti.keys())),
learning_rate=learning_rate)
print("training model")
split2 = int(len(training_array) / 2)
split1 = int(split2 / 2)
split3 = split1 + split2
users = train.keys()
session_batch = []
sl = []
for user in users:
session_batch.append(test[user][0])
sl.append(test_sl[user][0])
session_batch = [[event[1] for event in session] for session in session_batch]
for s in range(len(session_batch)):
# 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)
