def predict(index, s):
items = [i for i in s]
feature = VectorUDT().deserialize(pickle.loads(items[0]))
print(pickle.loads(items[1])[0])
model = pickle.load(open(pickle.loads(items[1])[0] + "/model.pkl", "rb"))
y = model.predict([feature.toArray()])
return [VectorUDT().serialize(Vectors.dense(y))]
def predict(index, s):
items = [i for i in s]
modelPath = pickle.loads(items[1])[0] + "/model.pkl"
if not hasattr(os, "mlsql_models"):
setattr(os, "mlsql_models", {})
if modelPath not in os.mlsql_models:
print("Load sklearn model %s" % modelPath)
os.mlsql_models[modelPath] = pickle.load(open(modelPath, "rb"))
model = os.mlsql_models[modelPath]
rawVector = pickle.loads(items[0])
feature = VectorUDT().deserialize(rawVector)
y = model.predict([feature.toArray()])
return [VectorUDT().serialize(Vectors.dense(y))]
def predict(index, s):
items = [i for i in s]
feature = VectorUDT().deserialize(pickle.loads(items[0]))
model = pickle.loads(pickle.loads(items[1])[0])
y = model.predict([feature.toArray()])
return [VectorUDT().serialize(Vectors.dense(y))]
