'negative_item_input': negative_item_features
})['triplet_loss']
return (loss > 0).mean()
if __name__ == '__main__':
num_epochs = 5
# Read data
train, test = data.get_movielens_data()
num_users, num_items = train.shape
# Prepare the test triplets
test_uid, test_pid, test_nid = data.get_triplets(test)
test_user_features, test_positive_item_features, test_negative_item_features = data.get_dense_triplets(
test_uid, test_pid, test_nid, num_users, num_items)
# Sample triplets from the training data
uid, pid, nid = data.get_triplets(train)
user_features, positive_item_features, negative_item_features = data.get_dense_triplets(
uid, pid, nid, num_users, num_items)
model = get_graph(num_users, num_items, 256)
# Print the model structure
print(model.summary())
# Sanity check, should be around 0.5
print('AUC before training %s' % metrics.full_auc(model, test))
model.compile(loss=identity_loss, optimizer=Adam())
return model
if __name__ == '__main__':
latent_dim = 100
num_epochs = 10
# Read data
train, test = data.get_movielens_data()
num_users, num_items = train.shape
# Prepare the test triplets
test_uid, test_pid, test_nid = data.get_triplets(test)
model = build_model(num_users, num_items, latent_dim)
# Print the model structure
print(model.summary())
# Sanity check, should be around 0.5
print('AUC before training %s' % metrics.full_auc(model, test))
for epoch in range(num_epochs):
print('Epoch %s' % epoch)
# Sample triplets from the training data
uid, pid, nid = data.get_triplets(train)
from lightfm import LightFM
import data
import sys
import numpy as np
train, test = data.get_movielens_positive_data('/data/sidana/recnet_draft/' +
sys.argv[1] + '/lightfm')
uid_train, pid_train, nid_train = data.get_triplets(train)
uid_test, pid_test, nid_test = data.get_triplets(test)
model = LightFM(no_components=14,
loss='logistic',
learning_rate=0.01,
item_alpha=0.0001,
user_alpha=0.0001)
model.fit(train, epochs=10, num_threads=1)
train_all, test_all = data.get_movielens_data('/data/sidana/recnet_draft/' +
sys.argv[1] + '/lightfm')
uid_all_train, pid_all_train, nid_all_train = data.get_triplets(train_all)
uid_all_test, pid_all_test, nid_all_test = data.get_triplets(test_all)
export_basename = '/data/sidana/recnet_draft/' + sys.argv[
1] + '/lightfm_all' + '/vectors/'
export_pred = open(export_basename + 'pr', 'w')
#export_pred = open(export_basename + 'pr', 'w')
export_true = open(export_basename + 'gt', 'w')
#export_true = open(export_basename + 'gt', 'w')
for u in uid_test:
#num_items_all_test_u = test_all.getrow(u).indices.shape[0]