def test_word2vec(self):
document_schema_fields = [
'description', 'experienceRequirements', 'qualifications', 'skills'
]
job_postings_generator = JobPostingCollectionSample(num_records=50)
corpus_generator = Word2VecGensimCorpusCreator(
job_postings_generator,
document_schema_fields=document_schema_fields)
w2v = Word2VecModel(size=16, min_count=3, iter=4, window=6, workers=3)
trainer = EmbeddingTrainer(w2v)
trainer.train(corpus_generator)
v1 = w2v.infer_vector(["media"])
v2 = w2v.infer_vector(["media"])
assert_array_equal(v1, v2)
# test unseen vocab
assert w2v.infer_vector(["sports"]).shape[0] == 16
# test a list that has some words not in vocab
sentence_with_unseen_word = ["sports", "news", "and", "media"]
sentecne_without_unseen_word = ["news", "and", "media"]
assert_array_equal(w2v.infer_vector(sentence_with_unseen_word),
w2v.infer_vector(sentecne_without_unseen_word))
def test_doc2vec(self):
document_schema_fields = [
'description', 'experienceRequirements', 'qualifications', 'skills'
]
job_postings_generator = JobPostingCollectionSample(num_records=50)
corpus_generator = Doc2VecGensimCorpusCreator(
job_postings_generator,
document_schema_fields=document_schema_fields)
d2v = Doc2VecModel(size=16,
min_count=1,
dm=0,
alpha=0.025,
min_alpha=0.025)
trainer = EmbeddingTrainer(d2v)
trainer.train(corpus_generator)
# Since the inference of doc2vec is an non-deterministic algorithm, we need to reset the random seed for testing.
d2v.random.seed(0)
v1 = d2v.infer_vector(["media", "news"])
d2v.random.seed(0)
v2 = d2v.infer_vector(["media", "news"])
assert_array_equal(v1, v2)
# test unseen word
self.assertRaises(KeyError, lambda: d2v["sports"])
# test unseen sentence
v1 = d2v.infer_vector(["sports"])
v2 = d2v.infer_vector(["sports"])
assert_array_equal(v1, v2)
def test_combined_cls_local(self, mock_getcwd):
with tempfile.TemporaryDirectory() as td:
mock_getcwd.return_value = td
model_storage = ModelStorage(FSStore(td))
jobpostings = JobPostingCollectionSample()
corpus_generator = Word2VecGensimCorpusCreator(jobpostings,
raw=True)
w2v = Word2VecModel(size=10,
min_count=0,
alpha=0.025,
min_alpha=0.025)
trainer = EmbeddingTrainer(w2v, model_storage=model_storage)
trainer.train(corpus_generator, lookup=True)
matrix = DesignMatrix(jobpostings, self.major_group, self.pipe_x,
self.pipe_y)
matrix.build()
X = matrix.X
rf = ProxyObjectWithStorage(RandomForestClassifier(), None, None,
matrix.target_variable)
rf.fit(X, matrix.y)
proxy_rf = ProxyObjectWithStorage(rf, None, None,
matrix.target_variable)
# Remove the last step in the pipe_x
# the input of predict_soc should be tokenized words
new_pipe_x = self.pipe_x
new_pipe_x.generators.pop()
new_matrix = DesignMatrix(JobPostingCollectionSample(),
self.major_group, new_pipe_x)
new_matrix.build()
ccls = CombinedClassifier(w2v, rf)
assert len(ccls.predict_soc([new_matrix.X[0]])[0]) == 2
def test_embedding_feature(self):
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,
raw=True)
w2v = Word2VecModel(size=10, min_count=0, iter=4, window=6, workers=3)
trainer = EmbeddingTrainer(w2v)
trainer.train(corpus_generator)
job_postings = RawCorpusCreator(
JobPostingCollectionSample(num_records=50))
raw1, raw2 = tee(job_postings)
fc = SequenceFeatureCreator(raw1,
sentence_tokenizer=sentence_tokenize,
word_tokenizer=word_tokenize,
embedding_model=w2v,
features=["EmbeddingFeature"])
fc = iter(fc)
self.assertEqual(
next(fc).shape[0],
np.array(
next(iter(word_tokenizer_gen(
sentence_tokenizer_gen(raw2))))).shape[0])
self.assertEqual(next(fc)[0].shape[0], 10)
def test_fasttext(self):
document_schema_fields = [
'description', 'experienceRequirements', 'qualifications', 'skills'
]
job_postings_generator = JobPostingCollectionSample(num_records=50)
corpus_generator = Word2VecGensimCorpusCreator(
job_postings_generator,
document_schema_fields=document_schema_fields)
fasttext = FastTextModel(size=16, min_count=3, window=6, iter=4)
trainer = EmbeddingTrainer(fasttext)
trainer.train(corpus_generator)
v1 = fasttext.infer_vector(["media"])
v2 = fasttext.infer_vector(["media"])
assert_array_equal(v1, v2)
# FastText models support vector lookups for out-of-vocabulary words by summing up character ngrams belonging to the word
assert "sports" not in fasttext.wv.vocab
assert fasttext["sports"].shape == (16, )
# test unseen word and none of the character ngrams of the word are present in the training data
self.assertRaises(KeyError, lambda: fasttext["axe"])
# test a list that has some words not in vocab
v1 = fasttext.infer_vector(["media", "sport", "axe"])
v2 = fasttext.infer_vector(["media", "sport"])
assert_array_equal(v1, v2)
def train_embedding(self):
jobpostings = list(JobPostingCollectionSample())
corpus_generator = Word2VecGensimCorpusCreator(jobpostings, raw=True)
w2v = Word2VecModel(size=10, min_count=0, alpha=0.025, min_alpha=0.025)
trainer = EmbeddingTrainer(corpus_generator, w2v)
trainer.train(True)
self.embedding_model = w2v
self.jobpostings = jobpostings
def test_embedding_trainer_doc2vec_with_other(self):
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)
trainer = EmbeddingTrainer(Doc2VecModel(), Word2VecModel(),
FastTextModel())
self.assertRaises(TypeError, lambda: trainer.train(corpus_generator))
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_combined_cls_local(self, mock_getcwd):
with tempfile.TemporaryDirectory() as td:
mock_getcwd.return_value = td
jobpostings = list(JobPostingCollectionSample())
corpus_generator = Word2VecGensimCorpusCreator(jobpostings, raw=True)
w2v = Word2VecModel(storage=FSStore(td), size=10, min_count=0, alpha=0.025, min_alpha=0.025)
trainer = EmbeddingTrainer(corpus_generator, w2v)
trainer.train(True)
matrix = create_training_set(jobpostings, SOCMajorGroup())
X = EmbeddingTransformer(w2v).transform(matrix.X)
rf = RandomForestClassifier()
rf.fit(X, matrix.y)
ccls = CombinedClassifier(w2v, rf, matrix.target_variable)
assert len(ccls.predict_soc([matrix.X[0]])[0]) == 2
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
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_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_visualize_in_tensorboard(self):
document_schema_fields = [
'description', 'experienceRequirements', 'qualifications', 'skills'
]
job_postings_generator = JobPostingCollectionSample(num_records=50)
corpus_generator = Word2VecGensimCorpusCreator(
job_postings_generator,
document_schema_fields=document_schema_fields)
w2v = Word2VecModel(size=16, min_count=3, iter=4, window=6, workers=3)
trainer = EmbeddingTrainer(w2v)
trainer.train(corpus_generator)
with tempfile.TemporaryDirectory() as td:
with mock.patch('os.getcwd') as mock_getcwd:
mock_getcwd.return_value = td
visualize_in_tensorboard(w2v)
assert len(
set(
os.listdir(
os.path.join(os.getcwd(),
w2v.model_name.split('.')[0])))) == 7
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_skill_feature(self):
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,
raw=True)
w2v = Word2VecModel(size=10, min_count=0, iter=4, window=6, workers=3)
trainer = EmbeddingTrainer(w2v)
trainer.train(corpus_generator)
raw = RawCorpusCreator(JobPostingCollectionSample())
raw1, raw2 = tee(raw)
# default
fc = SequenceFeatureCreator(raw1, embedding_model=w2v)
self.assertEqual(
fc.selected_features,
["StructuralFeature", "ContextualFeature", "EmbeddingFeature"])
self.assertEqual(
fc.all_features,
["StructuralFeature", "ContextualFeature", "EmbeddingFeature"])
fc = iter(fc)
self.assertEqual(
next(fc).shape[0],
np.array(
next(iter(word_tokenizer_gen(
sentence_tokenizer_gen(raw2))))).shape[0])
self.assertEqual(next(fc)[0].shape[0], 29)
# Not Supported
fc = SequenceFeatureCreator(raw1, features=["FeatureNotSupported"])
fc = iter(fc)
self.assertRaises(TypeError, lambda: next(fc))
def test_tester(self):
document_schema_fields = ['description','experienceRequirements', 'qualifications', 'skills']
corpus_generator = Word2VecGensimCorpusCreator(JobPostingCollectionSample(num_records=30), document_schema_fields=document_schema_fields)
w2v = Word2VecModel(size=10, min_count=3, iter=4, window=6, workers=3)
trainer = EmbeddingTrainer(w2v)
trainer.train(corpus_generator)
jp = JobPostingCollectionSample()
train_gen = islice(jp, 30)
test_gen = islice(jp, 30, None)
train_matrix = DesignMatrix(train_gen, self.fullsoc, self.pipe_x, self.pipe_y)
train_matrix.build()
occ_trainer = OccupationClassifierTrainer(train_matrix, 2, grid_config=self.grid_config)
occ_trainer.train(save=False)
cc = CombinedClassifier(w2v, occ_trainer.best_estimators[0])
steps = self.pipe_x.generators[:-1]
test_gen = (t for t in test_gen if t['onet_soc_code'] is not '')
tester = OccupationClassifierTester(test_data_generator=test_gen, preprocessing=steps, classifier=cc)
result = list(tester)
assert len(tester) == len(result) == 18
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():
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']
from skills_ml.job_postings.common_schema import JobPostingGenerator
from skills_ml.job_postings.corpora import Doc2VecGensimCorpusCreator, Word2VecGensimCorpusCreator
from skills_ml.algorithms.embedding.train import EmbeddingTrainer
def get_time_range(start='2011-01-01', freq='Q', periods=24):
return list(
map(lambda x: datetime_to_quarter(x),
pd.date_range(start=start, freq=freq, periods=periods)))
if __name__ == '__main__':
time_range = get_time_range(start='2011-01-01', freq='Q', periods=1)
job_postings_generator = JobPostingGenerator(
s3_conn=s3_conn,
quarters=time_range,
s3_path='open-skills-private/job_postings_common',
source="all")
corpus_generator = Word2VecGensimCorpusCreator(job_postings_generator)
trainer = EmbeddingTrainer(
s3_conn=s3_conn,
corpus_generator=corpus_generator,
model_s3_path='open-skills-private/model_cache/embedding/',
batch_size=4000,
model_type='word2vec')
# The train method takes whatever arugments gensim.models.word2vec.Word2Vec or gensim.model.doc2vec.Doc2Vec has
trainer.train(size=100, iter=4, window=8, workers=cores)
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
job_postings_generator = JobPostingCollectionSample(num_records=30)
corpus_generator = Word2VecGensimCorpusCreator(
job_postings_generator,
document_schema_fields=document_schema_fields)
w2v = Word2VecModel(storage=FSStore(td),
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())
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 = Word2VecModel.load(FSStore(td), 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())
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
w2v.save('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_fasttext_local(self, mock_getcwd):
document_schema_fields = [
'description', 'experienceRequirements', 'qualifications', 'skills'
]
with tempfile.TemporaryDirectory() as td:
mock_getcwd.return_value = td
job_postings_generator = JobPostingCollectionSample(num_records=30)
corpus_generator = Word2VecGensimCorpusCreator(
job_postings_generator,
document_schema_fields=document_schema_fields)
fasttext = FastTextModel(storage=FSStore(td),
size=10,
min_count=3,
iter=4,
window=6,
workers=3)
trainer = EmbeddingTrainer(corpus_generator, fasttext)
trainer.train()
trainer.save_model()
vocab_size = len(fasttext.wv.vocab.keys())
assert fasttext.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)
fasttext_loaded = FastTextModel.load(FSStore(td),
fasttext.model_name)
new_trainer = EmbeddingTrainer(corpus_generator, fasttext_loaded)
new_trainer.train()
new_trainer.save_model()
new_vocab_size = len(fasttext_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
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])
sys.path.append('../')
import boto
s3_conn = boto.connect_s3()
import multiprocessing
cores = multiprocessing.cpu_count()
import pandas as pd
from skills_utils.time import datetime_to_quarter
from skills_ml.algorithms.embedding.train import EmbeddingTrainer
def get_time_range(start='2011-01-01', freq='Q', periods=24):
return list(map(lambda x: datetime_to_quarter(x), pd.date_range(start=start, freq=freq, periods=periods)))
if __name__ == '__main__':
time_range = get_time_range(start='2011-01-01', freq='Q', periods=24)
trainer = EmbeddingTrainer(s3_conn=s3_conn,
quarters=time_range,
source='nlx',
jp_s3_path='open-skills-private/job_postings_common',
model_s3_path='open-skills-private/model_cache/embedding/',
batch_size=4000,
model_type='word2vec')
# The train method takes whatever arugments gensim.models.word2vec.Word2Vec or gensim.model.doc2vec.Doc2Vec has
trainer.train(size=100, iter=4, window=8, workers=cores)