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evaluation.py
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evaluation.py
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import numpy as np
import pandas as pd
import torch
import pickle
import dgl
import argparse
from sklearn.neighbors import NearestNeighbors
def check_param_num(model):
'''
check num of model parameters
:model: pytorch model object
:return: int
'''
param_num = 0
for parameter in model.parameters():
param_num += parameter.shape[0]
return param
def node_to_item(nodes, id_dict, cateogry_dict):
'''
Transform node id to real item id
:items: node id list
:id_dict: {node id: item category id}
:category_dict: {item category id: real item id}
'''
ids = [id_dict[i] for i in nodes]
ids = [cateogry_dict[i] for i in ids]
return ids
def get_blocks(seeds, item_ntype, textset, sampler):
blocks = []
for seed in seeds:
block = sampler.get_block(seed, item_ntype, textset)
blocks.append(block)
return blocks
def get_all_emb(gnn, seed_array, textset, item_ntype, neighbor_sampler, batch_size, device='cuda'):
seeds = torch.arange(seed_array.shape[0]).split(batch_size)
testset = get_blocks(seeds, item_ntype, textset, neighbor_sampler)
gnn = gnn.to(device)
gnn.eval()
with torch.no_grad():
h_item_batches = []
for blocks in testset:
for i in range(len(blocks)):
blocks[i] = blocks[i].to(device)
h_item_batches.append(gnn.get_repr(blocks))
h_item = torch.cat(h_item_batches, 0)
return h_item
def item_by_user_batch(graph, user_ntype, item_ntype, user_to_item_etype, weight, args):
'''
:return: list of interacted node ids by every users
'''
rec_engine = LatestNNRecommender(
user_ntype, item_ntype, user_to_item_etype, weight, args.batch_size)
graph_slice = graph.edge_type_subgraph([rec_engine.user_to_item_etype])
n_users = graph.number_of_nodes(rec_engine.user_ntype) # 유저개수
latest_interactions = dgl.sampling.select_topk(graph_slice, args.k, rec_engine.timestamp, edge_dir='out')
user, latest_items = latest_interactions.all_edges(form='uv', order='srcdst')
# user, latest_items = (k * n_users)
items_df = pd.DataFrame({'user': user.numpy(), 'item': latest_items.numpy()}).groupby('user')
items_batch = [items_df.get_group(i)['item'].values for i in np.unique(user)]
return items_batch
def prec(recommendations, ground_truth):
n_users, n_items = ground_truth.shape
K = recommendations.shape[1]
user_idx = np.repeat(np.arange(n_users), K)
item_idx = recommendations.flatten()
relevance = ground_truth[user_idx, item_idx].reshape((n_users, K))
hit = relevance.any(axis=1).mean()
return hit
class LatestNNRecommender(object):
def __init__(self, user_ntype, item_ntype, user_to_item_etype, timestamp, batch_size):
self.user_ntype = user_ntype
self.item_ntype = item_ntype
self.user_to_item_etype = user_to_item_etype
self.batch_size = batch_size
self.timestamp = timestamp
def recommend(self, full_graph, K, h_user, h_item):
"""
Return a (n_user, K) matrix of recommended items for each user
"""
graph_slice = full_graph.edge_type_subgraph([self.user_to_item_etype])
n_users = full_graph.number_of_nodes(self.user_ntype)
latest_interactions = dgl.sampling.select_topk(graph_slice, K, self.timestamp, edge_dir='out')
user, latest_items = latest_interactions.all_edges(form='uv', order='srcdst')
# each user should have at least one "latest" interaction
assert torch.equal(user, torch.arange(n_users))
recommended_batches = []
user_batches = torch.arange(n_users).split(self.batch_size)
for user_batch in user_batches:
latest_item_batch = latest_items[user_batch]
dist = h_item[latest_item_batch] @ h_item.t()
# 기존 인터랙션 삭제
# 이 부분을 주석처리했음
# for i, u in enumerate(user_batch.tolist()):
# interacted_items = full_graph.successors(u, etype=self.user_to_item_etype)
# dist[i, interacted_items] = -np.inf
recommended_batches.append(dist.topk(K, 1)[1])
recommendations = torch.cat(recommended_batches, 0)
return recommendations
def evaluate_nn(dataset, h_item, k, batch_size):
g = dataset['train-graph']
val_matrix = dataset['val-matrix'].tocsr()
test_matrix = dataset['test-matrix'].tocsr()
item_texts = dataset['item-texts']
user_ntype = dataset['user-type']
item_ntype = dataset['item-type']
user_to_item_etype = dataset['user-to-item-type']
timestamp = dataset['timestamp-edge-column']
rec_engine = LatestNNRecommender(
user_ntype, item_ntype, user_to_item_etype, timestamp, batch_size)
recommendations = rec_engine.recommend(g, k, None, h_item).cpu().numpy()
return prec(recommendations, val_matrix)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('dataset_path', type=str)
parser.add_argument('item_embedding_path', type=str)
parser.add_argument('-k', type=int, default=10)
parser.add_argument('--batch-size', type=int, default=32)
args = parser.parse_args()
with open(args.dataset_path, 'rb') as f:
dataset = pickle.load(f)
with open(args.item_embedding_path, 'rb') as f:
emb = torch.FloatTensor(pickle.load(f))
print(evaluate_nn(dataset, emb, args.k, args.batch_size))