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evaluate_embeddings_NN.py
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evaluate_embeddings_NN.py
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import os
import numpy as np
import pandas as pd
from scipy import sparse
from tqdm import tqdm
import torch
import torch.optim as optim
from datasets import *
from utils import *
from model.EdgeReg import *
from model.EdgeReg_v2 import *
import argparse
##################################################################################################
parser = argparse.ArgumentParser()
parser.add_argument("-g", "--gpunum", help="GPU number to train the model.")
parser.add_argument("-d", "--dataset", help="Name of the dataset.")
parser.add_argument("-b", "--nbits", help="Number of bits of the embedded vector.", type=int)
parser.add_argument("-T", "--num_samples", default=1, type=int, help="number of samples from Q(z|x).")
parser.add_argument("--hash", action='store_true', help="enable this flag forces the model to hash the embedding before evaluation.")
args = parser.parse_args()
if not args.gpunum:
parser.error("Need to provide the GPU number.")
if not args.dataset:
parser.error("Need to provide the dataset.")
if not args.nbits:
parser.error("Need to provide the number of bits.")
if args.hash:
print("Evaluation on hash code.")
else:
print("Evaluation on embedding vectors.")
##################################################################################################
os.environ["CUDA_VISIBLE_DEVICES"]=args.gpunum
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
#########################################################################################################
class TextAndNearestNeighborsDataset(Dataset):
def __init__(self, dataset_name, data_dir, subset='train'):
"""
Args:
data_dir (string): Directory for loading and saving train, test, and cv dataframes.
subset (string): Specify subset of the datasets. The choices are: train, test, cv.
"""
self.dataset_name = dataset_name
self.data_dir = os.path.join(data_dir, dataset_name)
self.subset = subset
fn = '{}.{}.pkl'.format(dataset_name, subset)
self.df = self.load_df(self.data_dir, fn)
self.docid2index = {docid: index for index, docid in enumerate(list(self.df.index))}
if dataset_name in ['reuters', 'rcv1', 'tmc']:
self.single_label = False
else:
self.single_label = True
def load_df(self, data_dir, df_file):
df_file = os.path.join(data_dir, df_file)
return pd.read_pickle(df_file)
def __len__(self):
return len(self.df)
def __getitem__(self, idx):
doc_id = self.df.iloc[idx].name
doc_bow = self.df.iloc[idx].bow
doc_bow = torch.from_numpy(doc_bow.toarray().squeeze().astype(np.float32))
label = self.df.iloc[idx].label
label = torch.from_numpy(label.toarray().squeeze().astype(np.float32))
neighbors = torch.LongTensor(self.df.iloc[idx].neighbors)
return (doc_id, doc_bow, label, neighbors)
def num_classes(self):
return self.df.iloc[0].label.shape[1]
def num_features(self):
return self.df.iloc[0].bow.shape[1]
#########################################################################################################
dataset_name = args.dataset
data_dir = os.path.join('dataset/clean', dataset_name)
train_batch_size=100
test_batch_size=100
train_set = TextAndNearestNeighborsDataset(dataset_name, 'dataset/clean', 'train')
train_loader = torch.utils.data.DataLoader(dataset=train_set, batch_size=100, shuffle=False)
test_set = TextAndNearestNeighborsDataset(dataset_name, 'dataset/clean', 'test')
test_loader = torch.utils.data.DataLoader(dataset=test_set, batch_size=100, shuffle=False)
#########################################################################################################
y_dim = train_set.num_classes()
num_bits = args.nbits
num_features = train_set.num_features()
num_nodes = len(train_set)
edge_weight = 1.0
dropout_prob = 0.1
num_samples = args.num_samples
print("Train node2hash model ...")
print("dataset: {}".format(args.dataset))
print("numbits: {}".format(args.nbits))
print("T: {}".format(args.num_samples))
print("gpu id: {}".format(args.gpunum))
print("num train: {} num test: {}".format(len(train_set), len(test_set)))
#########################################################################################################
if num_samples == 1:
model = EdgeReg(dataset_name, num_features, num_nodes, num_bits, dropoutProb=0.1, device=device)
else:
print("number of samples (T) = {}".format(num_samples))
model = EdgeReg_v2(dataset_name, num_features, num_nodes, num_bits, dropoutProb=0.1, device=device, T=num_samples)
#########################################################################################################
if num_samples == 1:
saved_model_file = 'saved_models/node2hash.{}.T{}.bit{}.pth'.format(dataset_name, num_samples, num_bits)
else:
saved_model_file = 'saved_models/node2hash_v2.{}.T{}.bit{}.pth'.format(dataset_name, num_samples, num_bits)
print('load model {} ...'.format(saved_model_file))
model.load_state_dict(torch.load(saved_model_file))
model.to(device)
model.eval()
#########################################################################################################
import torch.nn.functional as F
# get non-binary code
if not args.hash:
with torch.no_grad():
train_zy = [(model.encode(xb.to(model.device))[0], yb) for _, xb, yb, _ in train_loader]
train_z, train_y = zip(*train_zy)
train_z = torch.cat(train_z, dim=0)
train_y = torch.cat(train_y, dim=0)
train_z_batch = train_z.unsqueeze(-1).transpose(2,0)
prec_at_100 = []
for _, xb, yb, _ in tqdm(test_loader, ncols=80):
test_z = model.encode(xb.to(model.device))[0]
test_y = yb
test_z_batch = test_z.unsqueeze(-1)
# compute cosine similarity
dist = F.cosine_similarity(test_z_batch, train_z_batch, dim=1)
ranklist = torch.argsort(dist, dim=1, descending=True)
top100 = ranklist[:, :100]
for eval_index in range(0, test_y.size(0)):
top100_labels = torch.index_select(train_y.to(device), 0, top100[eval_index]).type(torch.cuda.ByteTensor)
groundtruth_label = test_y[eval_index].type(torch.cuda.ByteTensor)
matches = (groundtruth_label.unsqueeze(0) & top100_labels).sum(dim=1) > 0
num_corrects = matches.sum().type(torch.cuda.FloatTensor)
prec_at_100.append((num_corrects/100.).item())
avg_prec_at_100 = np.mean(prec_at_100)
print('Nonhash: average prec at 100 = {:.4f}'.format(avg_prec_at_100))
with open('nonbinary_logs/Nonbinary.Experiment.{}.txt'.format(args.dataset), 'a') as handle:
handle.write('{}\t{}\t{}\t{}\n'.format(args.dataset, args.nbits, args.num_samples, avg_prec_at_100))
else:
with torch.no_grad():
train_b, test_b, train_y, test_y = model.get_binary_code(train_loader, test_loader)
retrieved_indices = retrieve_topk(test_b.to(device), train_b.to(device), topK=100)
avg_prec_at_100 = compute_precision_at_k(retrieved_indices, test_y.to(device), train_y.to(device), topK=100)
print('Hash: average prec at 100 = {:.4f}'.format(avg_prec_at_100))
with open('binary_logs/binary.Experiment.{}.txt'.format(args.dataset), 'a') as handle:
handle.write('{}\t{}\t{}\t{}\n'.format(args.dataset, args.nbits, args.num_samples, avg_prec_at_100))