def __init__(self):
self.y_true = [0, 1, 200, 0, 1, 200, 0, 1, 200, 0, 1, 200]
self.y_pred = [0, 1, 200, 1, 200, 0, 1, 1, 1, 0, 1, 0]
self.target_variable = FullSOC()
def full_soc(self):
return FullSOC()
job_postings = list(job_samples)
random.shuffle(job_postings)
train_data = job_postings[:30]
test_data = job_postings[30:]
train_bytes = json.dumps(train_data).encode()
test_bytes = json.dumps(test_data).encode()
logging.info("Loading Embedding Model")
model_storage = ModelStorage(FSStore('/your/model/path'))
w2v = model_storage.load_model(model_name='your_model_name')
full_soc = FullSOC()
def basic_filter(doc):
"""
Return the document except for the document which soc is unknown or empty or not in the
soc code pool of current O*Net version
"""
if full_soc.filter_func(doc) and doc['onet_soc_code'] in full_soc.choices:
return doc
else:
return None
class JobGenerator(object):
def __init__(self, data):
self.data = data