def retrieve():
print("request received!")
retrieve_request = request.get_json()
userId = retrieve_request.get("userId")
candidateIds = retrieve_request.get("candidateIds")
excludeIds = retrieve_request.get("excludeIds")
# offset = retrieve_request.get('offset')
limit = retrieve_request.get("limit")
# retrievalCriteria = retrieve_request.get('retrievalCriteria')
# load data and model, get recommendations
data = Dataset.load_builtin('ml-100k')
trainset = data.build_full_trainset()
_, loaded_algo = dump.load(os.path.expanduser('./SVD_model_couchDB'))
print("file loaded")
predictions_loaded_algo = loaded_algo.test(trainset.build_testset())
recs = get_all_recs(predictions_loaded_algo)[int(userId)]
# recs = get_top_n(predictions_loaded_algo,int(limit))[int(userId)]
response_list = {'source':{"id":"SVD_model"},'movieIds':[]}
i = 0
# Format recommendations
for i in range(0, len(recs)):
if (len(response_list['movieIds']) >= int(limit)):
break;
if (recs[i][0] in candidateIds) and (recs[i][0] not in excludeIds):
response_list['movieIds'].append(recs[i][0])
i = i+1
return jsonify(response_list)
def score():
print("request received!")
score_request = request.form
#score_json = score_request.get_json()
userId = score_request.get("userId")
candidateIds = score_request.get("candidateIds")
# print(candidateIds)
excludeIds = []
limit = score_request.get("limit")
# retrievalCriteria = score_request.get('retrievalCriteria')
# Parse a string representation of a list to a list
candidateIds = ast.literal_eval(candidateIds)
for i in candidateIds[1:-1]:
i = int(i)
# load data and model
data = Dataset.load_builtin('ml-100k')
trainset = data.build_full_trainset()
# Make predictions on the testset
_, loaded_algo = dump.load(os.path.expanduser('./SVD_model'))
print("file loaded")
predictions_loaded_algo = algo.test(trainset.build_testset())
# Get recommendations for a given user
recs = get_all_recs(predictions_loaded_algo)[int(userId)]
response_list = {'source':{"id":"SVD_model"},'movieIds':[],'predictedRatings':[]}
i = 0
# Format recommendations
for i in range(0, len(recs)):
print("i="+str(i))
if (len(response_list['movieIds']) >= int(limit)):
break;
if (recs[i][0] in candidateIds) and (recs[i][0] not in excludeIds):
response = {
'movieId':recs[i][0],
'predicted rating':recs[i][1]
}
response_list['predictedRatings'].append(response)
response_list['movieIds'].append(recs[i][0])
print(response_list)
return jsonify(response_list)