IF YOU WOULD LIKE TO GET AN ACCOUNT, please write an email to s dot adaszewski at gmail dot com. User accounts are meant only to report issues and/or generate pull requests. This is a purpose-specific Git hosting for ADARED projects. Thank you for your understanding!
瀏覽代碼

Add test_unigram_03().

master
Stanislaw Adaszewski 4 年之前
父節點
當前提交
7ab97e2fb6
共有 2 個文件被更改,包括 45 次插入16 次删除
  1. +1
    -3
      src/decagon_pytorch/sampling.py
  2. +44
    -13
      tests/decagon_pytorch/test_sampling.py

+ 1
- 3
src/decagon_pytorch/sampling.py 查看文件

@@ -7,15 +7,13 @@ from typing import List, \
def fixed_unigram_candidate_sampler(
true_classes: Union[np.array, torch.Tensor],
num_true: int,
num_samples: int,
range_max: int,
unigrams: List[Union[int, float]],
distortion: float = 1.):
if isinstance(true_classes, torch.Tensor):
true_classes = true_classes.detach().cpu().numpy()
if true_classes.shape != (num_samples, num_true):
if true_classes.shape[0] != num_samples:
raise ValueError('true_classes must be a 2D matrix with shape (num_samples, num_true)')
unigrams = np.array(unigrams)
if distortion != 1.:


tests/decagon_pytorch/test_unigram.py → tests/decagon_pytorch/test_sampling.py 查看文件

@@ -6,6 +6,7 @@ import torch.utils.data
from typing import List, \
Union
import decagon_pytorch.sampling
import scipy.stats
def test_unigram_01():
@@ -78,9 +79,7 @@ def test_unigram_02():
neg_samples = decagon_pytorch.sampling.fixed_unigram_candidate_sampler(
true_classes=true_classes,
num_true=num_true,
num_samples=batch_size,
range_max=range_max,
distortion=distortion,
unigrams=unigrams)
@@ -120,22 +119,54 @@ def test_unigram_02():
def test_unigram_03():
range_max = 7
distortion = 0.75
batch_size = 500
batch_size = 25
unigrams = [ 1, 3, 2, 1, 2, 1, 3]
num_true = 1
true_classes = np.zeros((batch_size, num_true), dtype=np.int64)
for i in range(batch_size):
true_classes[i, 0] = i % range_max
true_classes_tf = tf.convert_to_tensor(true_classes)
neg_samples, _, _ = tf.nn.fixed_unigram_candidate_sampler(
true_classes=true_classes_tf,
num_true=num_true,
num_sampled=batch_size,
unique=False,
range_max=range_max,
distortion=distortion,
unigrams=unigrams)
true_classes_tf = tf.convert_to_tensor(true_classes)
true_classes_torch = torch.tensor(true_classes)
counts_tf = defaultdict(list)
counts_torch = defaultdict(list)
for i in range(100):
neg_samples, _, _ = tf.nn.fixed_unigram_candidate_sampler(
true_classes=true_classes_tf,
num_true=num_true,
num_sampled=batch_size,
unique=False,
range_max=range_max,
distortion=distortion,
unigrams=unigrams)
counts = defaultdict(int)
with tf.Session() as sess:
neg_samples = neg_samples.eval()
for x in neg_samples:
counts[x] += 1
for k, v in counts.items():
counts_tf[k].append(v)
neg_samples = decagon_pytorch.sampling.fixed_unigram_candidate_sampler(
true_classes=true_classes,
num_samples=batch_size,
distortion=distortion,
unigrams=unigrams)
counts = defaultdict(int)
for x in neg_samples:
counts[x] += 1
for k, v in counts.items():
counts_torch[k].append(v)
for i in range(range_max):
print('counts_tf[%d]:' % i, counts_tf[i])
print('counts_torch[%d]:' % i, counts_torch[i])
for i in range(range_max):
statistic, pvalue = scipy.stats.ttest_ind(counts_tf[i], counts_torch[i])
assert pvalue * range_max > .05

Loading…
取消
儲存