-
Notifications
You must be signed in to change notification settings - Fork 0
/
evaluate_NFM.py
142 lines (117 loc) · 5.14 KB
/
evaluate_NFM.py
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
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
import numpy as np
import torch
import time
import torch.nn as nn
import math
import random
def getLabel(test_data, pred_data):
r = []
for i in range(len(test_data)):
groundTrue = test_data[i]
predictTopK = pred_data[i]
pred = list(map(lambda x: x in groundTrue, predictTopK))
pred = np.array(pred).astype("float")
r.append(pred)
return np.array(r).astype('float')
def Recall_ATk(test_data, r, k):
right_pred = r[:, :k].sum(1)
recall_n = np.array([len(test_data[i]) for i in range(len(test_data))])
recall = np.sum(right_pred / recall_n)
return {'recall': recall}
def NDCGatK_r(test_data, r, k):
assert len(r) == len(test_data)
pred_data = r[:, :k]
test_matrix = np.zeros((len(pred_data), k))
for i, items in enumerate(test_data):
length = k if k <= len(items) else len(items)
test_matrix[i, :length] = 1
max_r = test_matrix
idcg = np.sum(max_r * 1. / np.log2(np.arange(2, k + 2)), axis=1)
dcg = pred_data * (1. / np.log2(np.arange(2, k + 2)))
dcg = np.sum(dcg, axis=1)
idcg[idcg == 0.] = 1.
ndcg = dcg / idcg
ndcg[np.isnan(ndcg)] = 0.
return np.sum(ndcg)
def test_one_batch(X, topks):
sorted_items = X[0].numpy()
groundTrue = X[1]
r = getLabel(groundTrue, sorted_items)
pre, recall, ndcg = [], [], []
for k in topks:
ret = Recall_ATk(groundTrue, r, k)
recall.append(ret['recall'])
ndcg.append(NDCGatK_r(groundTrue, r, k))
return {'recall': np.array(recall),
'precision': np.array(pre),
'ndcg': np.array(ndcg)}
def get_metrics(model, item_tags, video_features, itemid_videohiddenid_map, tag_num, topks, split_index, batch_size=10240):
items = list(item_tags.keys())
result_matrix = np.zeros(shape=(len(items), tag_num))
steps = int(len(items) // batch_size + 1)
batchs_item_feature = []
for i in range(steps):
start_pos = i * batch_size
end_pos = int(min((i + 1) * batch_size, len(items)))
if start_pos < len(items):
batch_items = items[start_pos: end_pos]
batch_items_feature = []
for item in batch_items:
batch_items_feature.append(video_features[itemid_videohiddenid_map[item]])
batch_items_feature = torch.FloatTensor(batch_items_feature).cuda()
batchs_item_feature.append(batch_items_feature)
for i in range(tag_num):
one_tag_predictions = []
for j in range(len(batchs_item_feature)):
batch_items_feature = batchs_item_feature[j]
this_batch_size = len(batch_items_feature)
tag_id = torch.LongTensor([i]).repeat(this_batch_size).view(this_batch_size, 1).cuda()
category_id = torch.LongTensor([0, 1]).repeat(this_batch_size).view(this_batch_size, 2).cuda()
prediction = model.inference(tag_id, category_id, batch_items_feature)
prediction = prediction.detach().cpu().numpy().tolist()
one_tag_predictions += prediction
one_tag_predictions = np.array(one_tag_predictions)
result_matrix[:, i] = one_tag_predictions
# metrics
results = {'recall': np.zeros(len(topks)),
'ndcg': np.zeros(len(topks))}
max_k = max(topks)
rating = torch.FloatTensor(result_matrix).cuda()
# all the videos in test set do not exist in training set
# no need to drop tags
_, rating_K = torch.topk(rating, k=max_k)
rating = rating.cpu().numpy()
del rating
ground_true = []
for i in range(len(items)):
ground_true.append(item_tags[items[i]])
rating_list = [rating_K.cpu()]
ground_true_list = [ground_true]
X = zip(rating_list, ground_true_list)
pre_results = []
for x in X:
pre_results.append(test_one_batch(x, topks))
for result in pre_results:
results['recall'] += result['recall']
results['ndcg'] += result['ndcg']
results['recall'] /= float(len(items))
results['ndcg'] /= float(len(items))
return results
def get_pair_tags(pair_list):
item_tags = {}
for item, tag in pair_list:
if item not in item_tags:
item_tags[item] = [tag]
else:
item_tags[item].append(tag)
return item_tags
def Ranking(model, video_features_path, itemid_videohiddenid_map, valid_pair_list, train_pair_list, tag_num, topks, split_index):
valid_item_tags = get_pair_tags(valid_pair_list)
video_features = np.load(video_features_path, allow_pickle=True)[()]
valid_result = get_metrics(model, valid_item_tags, video_features, itemid_videohiddenid_map, tag_num, topks, split_index)
return valid_result
def test_Ranking(model, video_features_path, itemid_videohiddenid_map, test_pair_list, train_pair_list, tag_num, topks, split_index):
test_item_tags = get_pair_tags(test_pair_list)
video_features = np.load(video_features_path, allow_pickle=True)[()]
test_result = get_metrics(model, test_item_tags, video_features, itemid_videohiddenid_map, tag_num, topks, split_index)
return test_result