File size: 2,615 Bytes
f60c555 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 |
import torch
import numpy as np
from utils.metrics import metrics_dict
def evaluate_model(self, epoch, eval_data, t_or_v):
self.model.eval()
with torch.no_grad():
batch_matrix_list = []
for batch_idx, batched_data in enumerate(eval_data):
scores = self.model.full_sort_predict(batched_data)
masked_items = batched_data[1]
scores[masked_items[0], masked_items[1] - self.model.n_users] = -1e10 # mask out pos items，restore ori_id
_, top_k_index = torch.topk(scores, max(self.topk), dim=-1) # nusers x topk
batch_matrix_list.append(top_k_index)
pos_items = eval_data.eval_items_per_u
pos_len_list = eval_data.eval_len_list
top_k_index = torch.cat(batch_matrix_list, dim=0).cpu().numpy()
assert len(pos_len_list) == len(top_k_index)
bool_rec_matrix = []
for m, n in zip(pos_items, top_k_index):
bool_rec_matrix.append([True if i in m else False for i in n])
bool_rec_matrix = np.asarray(bool_rec_matrix)
# get metrics
metric_dict = {}
result_list = cal_metrics(self.metrics, pos_len_list, bool_rec_matrix)
list_key = []
for metric, value in zip(self.metrics, result_list):
for k in self.topk:
key = '{}@{}'.format(metric, k)
list_key.append(key) if k == self.topk[-1] else None
metric_dict[key] = round(value[k - 1], 4) # Round to 4 decimal points
valid_score = metric_dict[self.valid_metric] if self.valid_metric else metric_dict['NDCG@20']
if self.writer is not None:
for idx in list_key:
self.writer.add_scalar(t_or_v + "_" + idx, metric_dict[idx], epoch) # Precision@20,Recall@20,NDCG@20
self.writer.add_histogram(t_or_v + '_user_visual_distribution', self.model.user_v_prefer, epoch)
self.writer.add_histogram(t_or_v + '_user_textual_distribution', self.model.user_t_prefer, epoch)
self.writer.add_embedding(self.model.user_id_embedding.weight, global_step=epoch,
tag=t_or_v + "user_id_embedding")
self.writer.add_embedding(self.model.item_id_embedding.weight, global_step=epoch,
tag=t_or_v + "item_id_embedding")
return valid_score, metric_dict
def cal_metrics(topk_metrics, pos_len_list, topk_index):
result_list = []
for metric in topk_metrics:
metric_fuc = metrics_dict[metric]
result = metric_fuc(topk_index, pos_len_list)
result_list.append(result)
return np.stack(result_list, axis=0)
|