forked from bernoullivae-hashing/Bernoulli-VAE-Hashing
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utils.py
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utils.py
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import tensorflow as tf
import numpy as np
import os
import scipy.io
from dotmap import DotMap
from tqdm import tqdm
from rank_metrics import *
################################################################################################################
def get_session(gpu_num="1", gpu_fraction=0.1):
os.environ["CUDA_VISIBLE_DEVICES"]=gpu_num
num_threads = os.environ.get('OMP_NUM_THREADS')
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=gpu_fraction)
if num_threads:
return tf.Session(config=tf.ConfigProto(
gpu_options=gpu_options, intra_op_parallelism_threads=num_threads))
else:
return tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))
################################################################################################################
def xavier_init(fan_in, fan_out, constant=1):
""" Xavier initialization of network weights"""
# https://stackoverflow.com/questions/33640581/how-to-do-xavier-initialization-on-tensorflow
low = -constant*np.sqrt(6.0/(fan_in + fan_out))
high = constant*np.sqrt(6.0/(fan_in + fan_out))
return tf.random_uniform((fan_in, fan_out),
minval=low, maxval=high,
dtype=tf.float32)
################################################################################################################
class Dense(object):
def __init__(self, output_dim, activation, bias=True):
self.output_dim = output_dim
self.activation = activation
self.has_build = False
self.bias = bias
def build(self, input_shapes):
input_dim = input_shapes[1]
self.W = tf.Variable(xavier_init(input_dim, self.output_dim))
self.b = tf.Variable(tf.zeros(self.output_dim))
def __call__(self, x):
if not self.has_build:
shape = x.get_shape()
shape = tuple([i.__int__() for i in shape])
# Handle when the input is 1D
if len(shape) == 1:
self.build([0, shape[0]])
else:
self.build(shape)
self.has_build = True
if self.activation == 'softplus':
transfer_fct = tf.nn.softplus
elif self.activation == 'sigmoid':
transfer_fct = tf.sigmoid
elif self.activation == 'tanh':
transfer_fct = tf.tanh
elif self.activation == 'relu':
transfer_fct = tf.nn.relu
elif self.activation == 'relu6':
transfer_fct = tf.nn.relu6
elif self.activation == 'elu':
transfer_fct = tf.nn.elu
elif self.activation == 'linear':
transfer_fct = None
else:
assert('Unknown activation function.')
transfer_fct = None
if self.bias == True:
if transfer_fct is None:
return tf.add(tf.matmul(x, self.W), self.b)
else:
return transfer_fct(tf.add(tf.matmul(x, self.W), self.b))
else:
if transfer_fct is None:
return tf.matmul(x, self.W)
else:
return transfer_fct(tf.matmul(x, self.W))
################################################################################################################
class MedianHashing(object):
def __init__(self):
self.threshold = None
self.latent_dim = None
def fit(self, X):
self.threshold = np.median(X, axis=0)
self.latent_dim = X.shape[1]
def transform(self, X):
assert(X.shape[1] == self.latent_dim)
binary_code = np.zeros(X.shape)
for i in range(self.latent_dim):
binary_code[np.nonzero(X[:,i] < self.threshold[i]),i] = 0
binary_code[np.nonzero(X[:,i] >= self.threshold[i]),i] = 1
return binary_code.astype(int)
def fit_transform(self, X):
self.fit(X)
return self.transform(X)
################################################################################################################
class HashNumber(object):
''' Represent a long integer value '''
def __init__(self, bin_array):
self.n_bits = len(bin_array)
if self.n_bits > 32:
assert(len(bin_array) % 32 == 0)
self.bin_code = []
for i in range(0, len(bin_array), 32):
self.bin_code.append(self._bitarray_to_bytes(bin_array[i:i+32]))
def distance(self, other):
assert(len(self.bin_code) == len(other.bin_code))
d = 0
for i in range(len(self.bin_code)):
d += self._hamming_distance(self.bin_code[i], other.bin_code[i])
return d
def _bitarray_to_bytes(self, s):
intstr = ''.join([str(i) for i in s])
v = int(intstr, 2)
return v
def _hamming_distance(self, b1, b2):
return bin(b1^b2).count("1")
################################################################################################################
def Load_Dataset(filename):
dataset = scipy.io.loadmat(filename)
x_train = dataset['train']
x_test = dataset['test']
x_cv = dataset['cv']
y_train = dataset['gnd_train']
y_test = dataset['gnd_test']
y_cv = dataset['gnd_cv']
data = DotMap()
data.n_trains = y_train.shape[0]
data.n_tests = y_test.shape[0]
data.n_cv = y_cv.shape[0]
data.n_tags = y_train.shape[1]
data.n_feas = x_train.shape[1]
## Convert sparse to dense matricesimport numpy as np
train = x_train.toarray()
nz_indices = np.where(np.sum(train, axis=1) > 0)[0]
train = train[nz_indices, :]
train_len = np.sum(train > 0, axis=1)
test = x_test.toarray()
test_len = np.sum(test > 0, axis=1)
cv = x_cv.toarray()
cv_len = np.sum(cv > 0, axis=1)
gnd_train = y_train[nz_indices, :]
gnd_test = y_test
gnd_cv = y_cv
data.train = train
data.test = test
data.cv = cv
data.train_len = train_len
data.test_len = test_len
data.cv_len = cv_len
data.gnd_train = gnd_train
data.gnd_test = gnd_test
data.gnd_cv = gnd_cv
return data
################################################################################################################
def run_topK_retrieval_experiment(codeTrain, codeTest, gnd_train, gnd_test, TopK=100):
y_train = gnd_train.astype(int)
y_test = gnd_test.astype(int)
assert(codeTrain.shape[1] == codeTest.shape[1])
assert(y_train.shape[1] == y_test.shape[1])
assert(codeTrain.shape[0] == y_train.shape[0])
assert(codeTest.shape[0] == y_test.shape[0])
cbTrain = [HashNumber(bitarray) for bitarray in codeTrain]
cbTest = [HashNumber(bitarray) for bitarray in codeTest]
p_at_k = []
avg_p = []
ndcg_score = []
bin_ndcg_score = []
avg_r = []
with tqdm(total=len(cbTest)) as pbar:
for idx, test_bin_code in enumerate(cbTest):
Dist = np.array([test_bin_code.distance(bincode) for bincode in cbTrain])
TopDocIdx = np.argsort(Dist)[:TopK]
# count number of matching labels
num_matches = np.sum(y_test[idx] & y_train[TopDocIdx], axis=1).astype(int)
num_relevant_items = np.sum(np.sum(y_test[idx] & y_train, axis=1) > 0)
relevance = (num_matches > 0).astype(int)
# This measurement is Recall at K
if num_relevant_items > 0:
avg_r.append(np.sum(num_matches) / float(num_relevant_items))
else:
avg_r.append(0.)
p_at_k.append(precision_at_k(relevance, TopK))
avg_p.append(average_precision(relevance))
bin_ndcg_score.append(ndcg_at_k(relevance, TopK))
ndcg_score.append(ndcg_at_k(num_matches, TopK))
pbar.update(1)
avg_prec_at_k = np.mean(p_at_k)
avg_recall_at_k = np.mean(avg_r)
avg_ndcg = np.mean(ndcg_score)
print('\nPrec@K = {:.4f}, Recall@K = {:.4f}, NDCG@K = {:.4f}'.format(avg_prec_at_k,
avg_recall_at_k,
avg_ndcg))
return avg_prec_at_k, avg_recall_at_k, avg_ndcg