test_model2_conf2_strategy.py

import pandas as pd
import csv
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

graph_embeddings = read_graph_embeddings("embeddings/HolEembedding_768.json")
user_bert_embeddings = read_bert_embedding("embeddings/elmo_user_embeddings_nostopw_1024.json")
item_bert_embeddings = read_bert_embedding("embeddings/elmo_embeddings_nostopw_1024.json")

user, item, rating = read_ratings('datasets/movielens/test2id.tsv')
X_graph,X_bert,dim_graph,dim_bert,y = matching_Bert_Graph(user,item,rating,graph_embeddings,user_bert_embeddings,item_bert_embeddings)

model = tf.keras.models.load_model('results/model.h5')
score = model.predict([X_graph[:,0],X_graph[:,1],X_bert[:,0],X_bert[:,1]])

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

predictions = predictions.sort_values(by=['users','scores'],ascending=[True,False])

top_5_scores = top_scores(predictions,5)
top_5_scores.to_csv('predictions/top_5/predictions_1.tsv',sep='\t',header=False,index=False)
print("Writing top 5 scores succeeed")

top_10_scores = top_scores(predictions,10)
top_10_scores.to_csv('predictions/top_10/predictions_1.tsv',sep='\t',header=False,index=False)
print("Writing top 10 scores succeeed")


'''
# evaluate loaded model on test data
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy',tf.keras.metrics.Precision(),tf.keras.metrics.Recall()])
score = model.evaluate([X[:,0],X[:,1]], y, verbose=0)
print("%s: %.2f%%" % ('accuracy', score[1]*100))
print("%s: %.2f%%" % ('precision', score[2]*100))
print("%s: %.2f%%" % ('recall', score[3]*100))
f1_val = 2*(score[2]*score[3])/(score[2]+score[3])
print("%s: %.2f%%" % ('f1_score', f1_val*100))
'''