def test_knn_doc2vec_cls_s3(self):
client = boto3.client('s3')
client.create_bucket(Bucket='fake-open-skills',
ACL='public-read-write')
s3_path = f"s3://fake-open-skills/model_cache/soc_classifiers"
s3_storage = S3Store(path=s3_path)
model_storage = ModelStorage(s3_storage)
corpus_generator = FakeCorpusGenerator()
# Embedding has no lookup_dict
d2v = Doc2VecModel(size=10,
min_count=1,
dm=0,
alpha=0.025,
min_alpha=0.025)
trainer = EmbeddingTrainer(d2v, model_storage=model_storage)
trainer.train(corpus_generator, lookup=False)
self.assertRaises(ValueError,
lambda: KNNDoc2VecClassifier(embedding_model=d2v))
d2v = Doc2VecModel(size=10,
min_count=1,
dm=0,
alpha=0.025,
min_alpha=0.025)
trainer = EmbeddingTrainer(d2v, model_storage=model_storage)
trainer.train(corpus_generator, lookup=True)
# KNNDoc2VecClassifier only supports doc2vec now
self.assertRaises(NotImplementedError,
lambda: KNNDoc2VecClassifier(Word2VecModel()))
doc = docs.split(',')[0].split()
knn = KNNDoc2VecClassifier(embedding_model=d2v, k=0)
self.assertRaises(ValueError, lambda: knn.predict_soc([doc]))
knn = KNNDoc2VecClassifier(embedding_model=d2v, k=10)
soc_cls = SocClassifier(knn)
assert knn.predict_soc([doc])[0][0] == soc_cls.predict_soc([doc])[0][0]
# Build Annoy index
knn.build_ann_indexer(num_trees=5)
assert isinstance(knn.indexer, AnnoyIndexer)
# Save
s3 = s3fs.S3FileSystem()
model_storage.save_model(knn, knn.model_name)
files = [f.split('/')[-1] for f in s3.ls(s3_path)]
assert set(files) == set([knn.model_name])
# Load
new_knn = model_storage.load_model(knn.model_name)
assert new_knn.model_name == knn.model_name
assert new_knn.predict_soc([doc])[0][0] == '29-2061.00'
# Have to re-build the index whenever ones load the knn model to the memory
assert new_knn.indexer == None
def test_knn_doc2vec_cls_local(self, mock_getcwd):
with tempfile.TemporaryDirectory() as td:
mock_getcwd.return_value = td
model_storage = ModelStorage(FSStore(td))
corpus_generator = FakeCorpusGenerator()
d2v = Doc2VecModel(size=10,
min_count=1,
dm=0,
alpha=0.025,
min_alpha=0.025)
trainer = EmbeddingTrainer(d2v, model_storage=model_storage)
trainer.train(corpus_generator, lookup=True)
# KNNDoc2VecClassifier only supports doc2vec now
self.assertRaises(NotImplementedError,
lambda: KNNDoc2VecClassifier(Word2VecModel()))
doc = docs.split(',')[0].split()
knn = KNNDoc2VecClassifier(embedding_model=d2v, k=0)
self.assertRaises(ValueError, lambda: knn.predict_soc([doc]))
knn = KNNDoc2VecClassifier(embedding_model=d2v, k=1)
soc_cls = SocClassifier(knn)
assert knn.predict_soc([doc
])[0][0] == soc_cls.predict_soc([doc
])[0][0]
# Build Annoy index
knn.build_ann_indexer(num_trees=5)
assert isinstance(knn.indexer, AnnoyIndexer)
# Save
model_storage.save_model(knn, knn.model_name)
assert set(os.listdir(os.getcwd())) == set([knn.model_name])
assert isinstance(knn.indexer, AnnoyIndexer)
# Load
new_knn = model_storage.load_model(knn.model_name)
assert new_knn.model_name == knn.model_name
assert new_knn.predict_soc([doc])[0][0] == '29-2061.00'
# Have to re-build the index whenever ones load the knn model to the memory
assert new_knn.indexer == None