def setAnswerSet(self, answer_set_id):
r = self.session.get(lsrh.getAnswerSetURL(answer_set_id))
try:
lsrh.checkStatus(r, (200,), "Answer set doesn't exist")
except:
return
self.answer_set_id = answer_set_id
def addAnswerSet(self, trained_models = [], tags = []):
data = { "prompt": lsrh.getPromptURL(self.prompt_id),
"trained_models": [lsrh.getTrainedModelURL(t) for t in trained_models] if len(trained_models) > 0 else [],
"tags": tags }
r = self.session.post(lsrh.getAnswerSetURL(), data=json.dumps(data))
lsrh.checkStatus(r, (201,), "Failed to create answer set.")
self.answer_set_id = lsrh.getIdFromResponse(r)
def runPredictions(self):
''' Returns list of urls of prediction results. '''
data = { "answer_set": lsrh.getAnswerSetURL(self.answer_set_id) }
r = self.session.post(lsrh.getPredictionTaskURL(), data=json.dumps(data))
lsrh.checkStatus(r, (201,), "Failed to create prediction task")
predictionTaskData = r.json()
r2 = self.session.post(predictionTaskData["process"])
r3 = self.waitForTask(predictionTaskData["url"], "prediction task")
return r3.json()["prediction_results"]
def uploadAnswerSet(self, fname):
if self.answer_set_id == None:
self.addAnswerSet()
r = self.uploadToS3('answerset', fname)
soup = bsoup(r.content)
key = soup.find('key').get_text()
data2 = {'prompt': lsrh.getPromptURL(self.prompt_id), 'answer_set': lsrh.getAnswerSetURL(self.answer_set_id), 's3_key': key, 'content_type': 'text/csv'}
# create and start upload task
r2 = self.session.post(lsrh.getAnswerSetUploadTaskURL(), data=json.dumps(data2))
answerUploadTaskData = r2.json()
r3 = self.session.post(answerUploadTaskData["process"])
lsrh.checkStatus(r3, (200, 202), "Queueing of answerset upload task failed.")
r4 = self.waitForTask(answerUploadTaskData["url"], "answer set upload task")
return r4.json()["answer_set"]
