def test_embedding_trainer_multicore_local(self, mock_getcwd):
document_schema_fields = [
'description', 'experienceRequirements', 'qualifications', 'skills'
]
with tempfile.TemporaryDirectory() as td:
mock_getcwd.return_value = td
model_storage = ModelStorage(FSStore(td))
job_postings_generator = JobPostingCollectionSample()
corpus_generator = Word2VecGensimCorpusCreator(
job_postings_generator,
document_schema_fields=document_schema_fields)
trainer = EmbeddingTrainer(FastTextModel(size=10,
min_count=3,
iter=4,
window=6,
workers=3),
FastTextModel(size=10,
min_count=3,
iter=4,
window=10,
workers=3),
Word2VecModel(size=10,
workers=3,
window=6),
Word2VecModel(size=10,
min_count=10,
window=10,
workers=3),
model_storage=model_storage)
trainer.train(corpus_generator, n_processes=4)
trainer.save_model()
assert set(os.listdir(os.getcwd())) == set(
[model.model_name for model in trainer._models])
def test_embedding_trainer_doc2vec_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/embedding"
s3_storage = S3Store(path=s3_path)
document_schema_fields = [
'description', 'experienceRequirements', 'qualifications', 'skills'
]
job_postings_generator = JobPostingCollectionSample(num_records=30)
corpus_generator = Doc2VecGensimCorpusCreator(
job_postings_generator,
document_schema_fields=document_schema_fields)
d2v = Doc2VecModel(storage=s3_storage,
size=10,
min_count=3,
iter=4,
window=6,
workers=3)
trainer = EmbeddingTrainer(corpus_generator, d2v)
trainer.train(lookup=True)
trainer.save_model()
vocab_size = len(d2v.wv.vocab.keys())
s3 = s3fs.S3FileSystem()
files = [f.split('/')[-1] for f in s3.ls(s3_path)]
assert d2v.model_name == trainer.model_name
assert set(files) == set([trainer.model_name])
self.assertDictEqual(trainer.lookup_dict, d2v.lookup_dict)
# Save as different name
d2v.save('other_name.model')
s3 = s3fs.S3FileSystem()
files = [f.split('/')[-1] for f in s3.ls(s3_path)]
assert set(files) == set([trainer.model_name, 'other_name.model'])
# Load
d2v_loaded = Doc2VecModel.load(s3_storage, trainer.model_name)
assert d2v_loaded.metadata['embedding_model']['hyperparameters'][
'vector_size'] == trainer.metadata['embedding_model'][
'hyperparameters']['vector_size']
# Change the store directory
new_s3_path = "s3://fake-open-skills/model_cache/embedding/other_directory"
trainer.save_model(S3Store(new_s3_path))
s3 = s3fs.S3FileSystem()
files = [f.split('/')[-1] for f in s3.ls(new_s3_path)]
assert set(files) == set([trainer.model_name])
def test_embedding_trainer_fasttext_local(self, mock_getcwd):
document_schema_fields = [
'description', 'experienceRequirements', 'qualifications', 'skills'
]
with tempfile.TemporaryDirectory() as td:
mock_getcwd.return_value = td
model_storage = ModelStorage(FSStore(td))
job_postings_generator = JobPostingCollectionSample(num_records=30)
corpus_generator = Word2VecGensimCorpusCreator(
job_postings_generator,
document_schema_fields=document_schema_fields)
fasttext = FastTextModel(size=10,
min_count=3,
iter=4,
window=6,
workers=3)
trainer = EmbeddingTrainer(fasttext, model_storage=model_storage)
trainer.train(corpus_generator)
trainer.save_model()
vocab_size = len(fasttext.wv.vocab.keys())
assert fasttext.model_name == trainer._models[0].model_name
assert set(os.listdir(os.getcwd())) == set(
[trainer._models[0].model_name])
# Test Online Training
job_postings_generator = JobPostingCollectionSample(num_records=50)
corpus_generator = Word2VecGensimCorpusCreator(
job_postings_generator,
document_schema_fields=document_schema_fields)
fasttext_loaded = model_storage.load_model(fasttext.model_name)
new_trainer = EmbeddingTrainer(fasttext_loaded,
model_storage=model_storage)
new_trainer.train(corpus_generator)
new_trainer.save_model()
new_vocab_size = len(fasttext_loaded.wv.vocab.keys())
assert set(os.listdir(os.getcwd())) == set([
trainer._models[0].model_name,
new_trainer._models[0].model_name
])
assert new_trainer.metadata['embedding_trainer'][
'models'] != trainer.metadata['embedding_trainer']['models']
assert vocab_size <= new_vocab_size
def test_embedding_trainer_doc2vec_local(self, mock_getcwd):
document_schema_fields = [
'description', 'experienceRequirements', 'qualifications', 'skills'
]
with tempfile.TemporaryDirectory() as td:
mock_getcwd.return_value = td
model_storage = ModelStorage(FSStore(td))
job_postings_generator = JobPostingCollectionSample(num_records=30)
corpus_generator = Doc2VecGensimCorpusCreator(
job_postings_generator,
document_schema_fields=document_schema_fields)
d2v = Doc2VecModel(size=10,
min_count=3,
iter=4,
window=6,
workers=3)
trainer = EmbeddingTrainer(d2v, model_storage=model_storage)
trainer.train(corpus_generator, lookup=True)
trainer.save_model()
vocab_size = len(d2v.wv.vocab.keys())
assert d2v.model_name == trainer._models[0].model_name
assert set(os.listdir(os.getcwd())) == set(
[trainer._models[0].model_name])
self.assertDictEqual(trainer.lookup_dict, d2v.lookup_dict)
# Save as different name
model_storage.save_model(d2v, 'other_name.model')
assert set(os.listdir(os.getcwd())) == set(
[trainer._models[0].model_name, 'other_name.model'])
# Load
d2v_loaded = model_storage.load_model(
trainer._models[0].model_name)
assert d2v_loaded.metadata["embedding_model"][
"model_type"] == list(
trainer.metadata["embedding_trainer"]
['models'].values())[0]['embedding_model']['model_type']
# Change the store directory
new_path = os.path.join(td, 'other_directory')
trainer.save_model(FSStore(new_path))
assert set(os.listdir(new_path)) == set(
[trainer._models[0].model_name])
def test_embedding_trainer():
s3_conn = boto.connect_s3()
bucket_name = 'fake-jb-bucket'
bucket = s3_conn.create_bucket(bucket_name)
job_posting_name = 'FAKE_jobposting'
s3_prefix_jb = 'fake-jb-bucket/job_postings'
s3_prefix_model = 'fake-jb-bucket/model_cache/embedding/'
quarters = '2011Q1'
with tempfile.TemporaryDirectory() as td:
with open(os.path.join(td, job_posting_name), 'w') as handle:
json.dump(sample_document, handle)
upload(s3_conn, os.path.join(td, job_posting_name), os.path.join(s3_prefix_jb, quarters))
# Doc2Vec
trainer = EmbeddingTrainer(s3_conn=s3_conn, quarters=['2011Q1'], jp_s3_path=s3_prefix_jb, model_s3_path=s3_prefix_model, model_type='doc2vec')
trainer.train()
files = list_files(s3_conn, os.path.join(s3_prefix_model, 'doc2vec_gensim_' + trainer.training_time))
assert len(files) == 3
assert files == ['doc2vec_gensim_' + trainer.training_time + '.model',
'lookup_doc2vec_gensim_' + trainer.training_time + '.json',
'metadata_doc2vec_gensim_' + trainer.training_time + '.json']
with tempfile.TemporaryDirectory() as td:
trainer.save_model(td)
assert set(os.listdir(td)) == set(['doc2vec_gensim_' + trainer.training_time + '.model',
'lookup_doc2vec_gensim_' + trainer.training_time + '.json',
'metadata_doc2vec_gensim_' + trainer.training_time + '.json'])
# Word2Vec
trainer = EmbeddingTrainer(s3_conn=s3_conn, quarters=['2011Q1'], jp_s3_path=s3_prefix_jb, model_s3_path=s3_prefix_model, model_type='word2vec')
trainer.train()
files = list_files(s3_conn, os.path.join(s3_prefix_model, 'word2vec_gensim_' + trainer.training_time))
assert len(files) == 2
assert files == ['metadata_word2vec_gensim_' + trainer.training_time + '.json',
'word2vec_gensim_' + trainer.training_time + '.model']
new_trainer = EmbeddingTrainer(s3_conn=s3_conn, quarters=['2011Q1'], jp_s3_path=s3_prefix_jb, model_s3_path=s3_prefix_model, model_type='word2vec')
new_trainer.load(trainer.modelname, s3_prefix_model)
assert new_trainer.metadata['metadata']['hyperparameters'] == trainer.metadata['metadata']['hyperparameters']
def test_embedding_trainer_word2vec_local(self, mock_getcwd):
document_schema_fields = [
'description', 'experienceRequirements', 'qualifications', 'skills'
]
with tempfile.TemporaryDirectory() as td:
mock_getcwd.return_value = td
model_storage = ModelStorage(FSStore(td))
job_postings_generator = JobPostingCollectionSample(num_records=30)
corpus_generator = Word2VecGensimCorpusCreator(
job_postings_generator,
document_schema_fields=document_schema_fields)
w2v = Word2VecModel(size=10,
min_count=3,
iter=4,
window=6,
workers=3)
trainer = EmbeddingTrainer(corpus_generator, w2v, model_storage)
trainer.train()
trainer.save_model()
vocab_size = len(w2v.wv.vocab.keys())
assert w2v.model_name == trainer.model_name
assert set(os.listdir(os.getcwd())) == set([trainer.model_name])
# Test Online Training
job_postings_generator = JobPostingCollectionSample(num_records=50)
corpus_generator = Word2VecGensimCorpusCreator(
job_postings_generator,
document_schema_fields=document_schema_fields)
w2v_loaded = model_storage.load_model(w2v.model_name)
new_trainer = EmbeddingTrainer(corpus_generator, w2v_loaded,
model_storage)
new_trainer.train()
new_trainer.save_model()
new_vocab_size = len(w2v_loaded.wv.vocab.keys())
assert set(os.listdir(os.getcwd())) == set(
[trainer.model_name, new_trainer.model_name])
assert new_trainer.metadata['embedding_trainer'][
'model_name'] != trainer.metadata['embedding_trainer'][
'model_name']
assert vocab_size <= new_vocab_size
# Save as different name
model_storage.save_model(w2v, 'other_name.model')
assert set(os.listdir(os.getcwd())) == set([
trainer.model_name, new_trainer.model_name, 'other_name.model'
])
# Change the store directory
new_path = os.path.join(td, 'other_directory')
new_trainer.save_model(FSStore(new_path))
assert set(os.listdir(new_path)) == set([new_trainer.model_name])
def test_embedding_trainer_multicore_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/embedding"
s3_storage = S3Store(path=s3_path)
model_storage = ModelStorage(s3_storage)
document_schema_fields = [
'description', 'experienceRequirements', 'qualifications', 'skills'
]
job_postings_generator = JobPostingCollectionSample()
corpus_generator = Word2VecGensimCorpusCreator(
job_postings_generator,
document_schema_fields=document_schema_fields)
trainer = EmbeddingTrainer(FastTextModel(size=10,
min_count=3,
iter=4,
window=6,
workers=3),
FastTextModel(size=10,
min_count=3,
iter=4,
window=10,
workers=3),
Word2VecModel(size=10, workers=3, window=6),
Word2VecModel(size=10,
min_count=10,
window=10,
workers=3),
model_storage=model_storage)
trainer.train(corpus_generator)
trainer.save_model()
s3 = s3fs.S3FileSystem()
files = [f.split('/')[-1] for f in s3.ls(s3_path)]
assert set(files) == set(
[model.model_name for model in trainer._models])
def test_embedding_trainer_word2vec_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/embedding"
s3_storage = S3Store(path=s3_path)
document_schema_fields = [
'description', 'experienceRequirements', 'qualifications', 'skills'
]
job_postings_generator = JobPostingCollectionSample(num_records=30)
corpus_generator = Word2VecGensimCorpusCreator(
job_postings_generator,
document_schema_fields=document_schema_fields)
w2v = Word2VecModel(storage=s3_storage,
size=10,
min_count=3,
iter=4,
window=6,
workers=3)
trainer = EmbeddingTrainer(corpus_generator, w2v)
trainer.train()
trainer.save_model()
vocab_size = len(w2v.wv.vocab.keys())
s3 = s3fs.S3FileSystem()
files = [f.split('/')[-1] for f in s3.ls(s3_path)]
assert w2v.model_name == trainer.model_name
assert set(files) == set([trainer.model_name])
# Test online training
job_postings_generator = JobPostingCollectionSample(num_records=50)
corpus_generator = Word2VecGensimCorpusCreator(
job_postings_generator,
document_schema_fields=document_schema_fields)
w2v_loaded = Word2VecModel.load(s3_storage, w2v.model_name)
new_trainer = EmbeddingTrainer(corpus_generator, w2v_loaded)
new_trainer.train()
new_trainer.save_model()
new_vocab_size = len(w2v_loaded.wv.vocab.keys())
s3 = s3fs.S3FileSystem()
files = [f.split('/')[-1] for f in s3.ls(s3_path)]
assert set(files) == set([new_trainer.model_name, trainer.model_name])
assert new_trainer.metadata['embedding_trainer'][
'model_name'] != trainer.metadata['embedding_trainer']['model_name']
assert vocab_size <= new_vocab_size
# Save as different name
w2v.save('other_name.model')
s3 = s3fs.S3FileSystem()
files = [f.split('/')[-1] for f in s3.ls(s3_path)]
assert set(files) == set(
[trainer.model_name, new_trainer.model_name, 'other_name.model'])
# Change the store directory
new_s3_path = "s3://fake-open-skills/model_cache/embedding/other_directory"
new_trainer.save_model(S3Store(new_s3_path))
s3 = s3fs.S3FileSystem()
files = [f.split('/')[-1] for f in s3.ls(new_s3_path)]
assert set(files) == set([new_trainer.model_name])
