logger.error("Invalid number of parameters.")
exit(-1)
bert_user_source = sys.argv[1]
bert_item_source = sys.argv[2]
graph_source = sys.argv[3]
dest = sys.argv[4]
prediction_dest = sys.argv[5]
print(bert_user_source)
print(bert_item_source)
print(graph_source)
print(dest)
print(prediction_dest)
user, item, rating = read_ratings('datasets/movielens/train2id.tsv')
graph_embeddings = read_graph_embeddings(graph_source)
user_bert_embeddings = read_bert_embedding(bert_user_source)
item_bert_embeddings = read_bert_embedding(bert_item_source)
X_graph, X_bert, dim_graph, dim_bert, y = matching_Bert_Graph(
user, item, rating, graph_embeddings, user_bert_embeddings,
item_bert_embeddings)
model = run_model(X_graph,
X_bert,
dim_graph,
dim_bert,
y,
epochs=25,
import numpy as np
import json
import tensorflow as tf
from tensorflow import keras
from numpy import loadtxt
from keras.models import Sequential
from keras.layers import Dense
from utilities.utils import read_ratings, read_graph_embeddings, read_bert_embedding, top_scores, matching_Bert_Graph_conf
graph_embeddings = read_graph_embeddings("embeddings/TRANSDembedding_768.json")
user_bert_embeddings = read_bert_embedding(
"embeddings/UserProfiles_lastLayer.json")
item_bert_embeddings = read_bert_embedding(
"embeddings/ITEM_embeddingslastlayer.json")
user, item, rating = read_ratings('datasets/dbbook/test2id.tsv')
X, y, dim_embeddings = matching_Bert_Graph_conf(user, item, rating,
graph_embeddings,
user_bert_embeddings,
item_bert_embeddings)
model = tf.keras.models.load_model('results/model.h5')
score = model.predict([X[:, 0], X[:, 1], X[:, 2], X[:, 3]])
print("Computing predictions...")
score = score.reshape(1, -1)[0, :]
predictions = pd.DataFrame()
predictions['users'] = np.array(user) + 1
predictions['items'] = np.array(item) + 1
predictions['scores'] = score
