Merge pull request #56 from PetrochukM/update

Release 0.3.7 - 5 fixed issues and a new label_encoder

Author: PetrochukM

.flake8

@@ -1,4 +1,4 @@
 [flake8]
-ignore = E402, E722, E731
+ignore = E402, E722, E731, W504
 max-line-length = 100
 exclude = examples/

examples/snli/train.py

@@ -77,8 +77,8 @@
 best_dev_acc = -1
 header = '  Time Epoch Iteration Progress    (%Epoch)   Loss   Dev/Loss     Accuracy  Dev/Accuracy'
 dev_log_template = ' '.join(
-    '{:>6.0f},{:>5.0f},{:>9.0f},{:>5.0f}/{:<5.0f} {:>7.0f}%,{:>8.6f},{:8.6f},{:12.4f},{:12.4f}'.
-    split(','))
+    '{:>6.0f},{:>5.0f},{:>9.0f},{:>5.0f}/{:<5.0f} {:>7.0f}%,{:>8.6f},{:8.6f},{:12.4f},{:12.4f}'
+    .split(','))
 log_template = ' '.join(
     '{:>6.0f},{:>5.0f},{:>9.0f},{:>5.0f}/{:<5.0f} {:>7.0f}%,{:>8.6f},{},{:12.4f},{}'.split(','))
 makedirs(args.save_path)
@@ -108,8 +108,7 @@
         answer = model(premise_batch, hypothesis_batch)
 
         # calculate accuracy of predictions in the current batch
-        n_correct += (torch.max(answer,
-                                1)[1].view(label_batch.size()) == label_batch).sum()
+        n_correct += (torch.max(answer, 1)[1].view(label_batch.size()) == label_batch).sum()
         n_total += premise_batch.size()[1]
         train_acc = 100. * n_correct / n_total
 
@@ -150,8 +149,8 @@
             for dev_batch_idx, (premise_batch, hypothesis_batch,
                                 label_batch) in enumerate(dev_iterator):
                 answer = model(premise_batch, hypothesis_batch)
-                n_dev_correct += (torch.max(answer, 1)[1].view(
-                    label_batch.size()) == label_batch).sum()
+                n_dev_correct += (torch.max(answer,
+                                            1)[1].view(label_batch.size()) == label_batch).sum()
                 dev_loss = criterion(answer, label_batch)
             dev_acc = 100. * n_dev_correct / len(dev)
 

examples/snli/util.py

@@ -57,10 +57,9 @@ def collate_fn(batch, train=True):
     """ list of tensors to a batch tensors """
     premise_batch, _ = pad_batch([row['premise'] for row in batch])
     hypothesis_batch, _ = pad_batch([row['hypothesis'] for row in batch])
-    label_batch = [row['label'] for row in batch]
+    label_batch = torch.stack([row['label'] for row in batch])
 
     # PyTorch RNN requires batches to be transposed for speed and integration with CUDA
-    transpose = (
-        lambda b: torch.stack(b).t_().squeeze(0).contiguous())
+    transpose = (lambda b: b.t_().squeeze(0).contiguous())
 
     return (transpose(premise_batch), transpose(hypothesis_batch), transpose(label_batch))

requirements.txt

@@ -19,12 +19,12 @@ flake8
 # Mocking
 mock
 
-# # Optional NLP Utilties
+# Optional NLP Utilties
 # nltk
 # spacy
 # sacremoses
 
-# # Optional CUDA Utilties
+# Optional CUDA Utilties
 # pynvrtc
 # cupy
 

setup.py

@@ -36,7 +36,7 @@ def find_version(*file_paths):
     long_description=long_description,
     long_description_content_type='text/markdown',
     license='BSD',
-    install_requires=['numpy', 'pandas', 'tqdm', 'ujson', 'requests'],
+    install_requires=['numpy', 'pandas', 'tqdm', 'requests'],
     classifiers=[
         'Development Status :: 4 - Beta',
         'Intended Audience :: Developers',

tests/datasets/test_simple_qa.py

@@ -1,16 +1,16 @@
 import os
 import shutil
-import pytest
 
 import mock
+import pytest
 
 from torchnlp.datasets import simple_qa_dataset
 from tests.datasets.utils import urlretrieve_side_effect
 
 directory = 'tests/_test_data/'
 
 
-@pytest.mark.skip(reason="Simple Questions dataset url returns 404.")
+@pytest.mark.skip(reason="Simple Questions dataset url sometimes returns 404.")
 @mock.patch("urllib.request.urlretrieve")
 def test_simple_qa_dataset_row(mock_urlretrieve):
     mock_urlretrieve.side_effect = urlretrieve_side_effect

tests/datasets/test_smt.py

@@ -35,7 +35,7 @@ def test_smt_dataset_row(mock_urlretrieve):
             " splash even greater than Arnold Schwarzenegger , Jean-Claud Van Damme or Steven" +
             " Segal .",
         'label':
-            'positive'
+            'very positive'
     }
 
     # Clean up

tests/nn/test_weight_drop.py

@@ -27,7 +27,7 @@ def test_weight_drop_lstm():
     run2 = [x.sum() for x in wd_lstm(input_)[0].data]
 
     # First time step, not influenced by hidden to hidden weights, should be equal
-    assert pytest.approx(run1[0]) == pytest.approx(run2[0])
+    assert pytest.approx(run1[0].item()) == pytest.approx(run2[0].item())
     # Second step should not
     assert run1[1] != run2[1]
 
@@ -40,7 +40,7 @@ def test_weight_drop_gru():
     run2 = [x.sum() for x in wd_lstm(input_)[0].data]
 
     # First time step, not influenced by hidden to hidden weights, should be equal
-    assert pytest.approx(run1[0]) == pytest.approx(run2[0])
+    assert pytest.approx(run1[0].item()) == pytest.approx(run2[0].item())
     # Second step should not
     assert run1[1] != run2[1]
 
@@ -53,7 +53,7 @@ def test_weight_drop():
     run2 = [x.sum() for x in wd_lstm(input_)[0].data]
 
     # First time step, not influenced by hidden to hidden weights, should be equal
-    assert pytest.approx(run1[0]) == pytest.approx(run2[0])
+    assert pytest.approx(run1[0].item()) == pytest.approx(run2[0].item())
     # Second step should not
     assert run1[1] != run2[1]
 
@@ -66,6 +66,6 @@ def test_weight_drop_zero():
     run2 = [x.sum() for x in wd_lstm(input_)[0].data]
 
     # First time step, not influenced by hidden to hidden weights, should be equal
-    assert pytest.approx(run1[0]) == pytest.approx(run2[0])
+    assert pytest.approx(run1[0].item()) == pytest.approx(run2[0].item())
     # Second step should not
-    assert pytest.approx(run1[1]) == pytest.approx(run2[1])
+    assert pytest.approx(run1[1].item()) == pytest.approx(run2[1].item())

tests/test_label_encoder.py

@@ -0,0 +1,44 @@
+import pickle
+
+import pytest
+
+from torchnlp.label_encoder import LabelEncoder
+from torchnlp.label_encoder import UNKNOWN_TOKEN
+
+
+@pytest.fixture
+def encoder():
+    sample = ['people/deceased_person/place_of_death', 'symbols/name_source/namesakes']
+    return LabelEncoder(sample)
+
+
+def test_label_encoder_vocab(encoder):
+    assert len(encoder.vocab) == 3
+    assert len(encoder.vocab) == encoder.vocab_size
+
+
+def test_label_encoder_scalar(encoder):
+    input_ = 'symbols/namesake/named_after'
+    output = encoder.encode(input_)[0]
+    assert encoder.decode(output) == UNKNOWN_TOKEN
+
+
+def test_label_encoder_unknown(encoder):
+    input_ = 'symbols/namesake/named_after'
+    output = encoder.encode(input_)
+    assert len(output) == 1
+    assert encoder.decode(output) == UNKNOWN_TOKEN
+
+
+def test_label_encoder_known():
+    input_ = 'symbols/namesake/named_after'
+    sample = ['people/deceased_person/place_of_death', 'symbols/name_source/namesakes']
+    sample.append(input_)
+    encoder = LabelEncoder(sample)
+    output = encoder.encode(input_)
+    assert len(output) == 1
+    assert encoder.decode(output) == input_
+
+
+def test_is_pickleable(encoder):
+    pickle.dumps(encoder)

tests/text_encoders/test_subword_tokenizer.py

@@ -89,8 +89,10 @@ def test_encode_decode(self):
 
         original = 'This is a coded sentence encoded by the SubwordTextTokenizer.'
 
-        encoder = SubwordTextTokenizer.build_to_target_size_from_corpus(
-            [corpus, original], target_size=100, min_val=2, max_val=10)
+        encoder = SubwordTextTokenizer.build_to_target_size_from_corpus([corpus, original],
+                                                                        target_size=100,
+                                                                        min_val=2,
+                                                                        max_val=10)
 
         # Encoding should be reversible.
         encoded = encoder.encode(original)