def make_records(self): # record all the results' details into files
print("in make record. we will calculate metrics after here.")
_, Decoder = self.sess.run(
[self.cost, self.Decoder],
feed_dict={
self.input_R_U: self.train_R,
self.input_R_I: self.train_R,
self.input_OH_I: self.I_OH_mat,
self.input_P_cor: [[0, 0]],
self.input_N_cor: [[0, 0]],
self.row_idx: np.reshape(xrange(self.num_rows),
(self.num_rows, 1)),
self.col_idx: np.reshape(xrange(self.num_cols),
(self.num_cols, 1))
})
if self.base == 'i':
[precision, recall, f_score,
NDCG] = utility.test_model_all(Decoder.T, self.vali_R.T,
self.train_R.T, self.price_R.T,
'save_intermediate_results')
else:
[precision, recall, f_score,
NDCG] = utility.test_model_all(Decoder, self.vali_R, self.train_R,
self.price_R,
'save_intermediate_results')
utility.metric_record(precision, recall, f_score, NDCG, self.args,
self.metric_path)
utility.test_model_factor(Decoder, self.vali_R, self.train_R)
return precision, recall, f_score, NDCG
def make_records(self): # record all the results' details into files
_, Decoder, l1, l2 = self.sess.run(
[self.cost, self.Decoder, self.pre_cost3, self.pre_cost4],
feed_dict={
self.input_R_U: self.train_R,
self.input_R_I: self.train_R,
self.input_OH_U: self.U_OH_mat,
self.input_OH_I: self.I_OH_mat,
self.input_P_cor: [[0, 0]],
self.input_N_cor: [[0, 0]],
self.row_idx: np.reshape(range(self.num_rows),
(self.num_rows, 1)),
self.col_idx: np.reshape(range(self.num_cols),
(self.num_cols, 1))
})
if self.base == 'i':
[precision, recall, f_score, NDCG,
r_f_table] = utility.test_model_all(Decoder.T, self.vali_R.T,
self.train_R.T)
else:
[precision, recall, f_score, NDCG,
r_f_table] = utility.test_model_all(Decoder, self.vali_R,
self.train_R)
utility.metric_record(precision, recall, f_score, NDCG, self.args,
self.metric_path)
utility.test_model_factor(Decoder, self.vali_R, self.train_R)
print("******** max_epoch ********")
print(self.max_epoch)
return precision, recall, f_score, NDCG
def make_records(self): # record all the results' details into files
_, Decoder = self.sess.run(
[self.cost, self.Decoder],
feed_dict={
self.input_R_U: self.train_R,
self.input_R_U_index: self.U_OH_mat,
self.is_training_ph: 0,
self.anneal_ph: 0.2,
self.row_idx: np.reshape(range(self.num_rows),
(self.num_rows, 1))
})
if self.base == 'i':
[precision, recall, f_score, NDCG,
r_f_table] = utility.test_model_all(Decoder.T, self.vali_R.T,
self.train_R.T)
else:
[precision, recall, f_score, NDCG,
r_f_table] = utility.test_model_all(Decoder, self.vali_R,
self.train_R)
utility.metric_record(precision, recall, f_score, NDCG, self.args,
self.metric_path)
utility.test_model_factor(Decoder, self.vali_R, self.train_R)
print("******** max_epoch ********")
print(self.max_epoch)
return precision, recall, f_score, NDCG