def test1_rule_based_user():
"""
Method for testing the rule-based user on the movie booking data set
"""
simulation_mode = const.SEMANTIC_FRAME_SIMULATION_MODE
# the path to the user goals and load it
# TODO: change it with a relative path
goal_set_file_path = '/Users/vladimirilievski/Desktop/Vladimir/Master_Thesis_Swisscom/GitHub Repo/GO-Chatbots/resources/data/user_goals_first_turn_template.part.movie.v1.p'
goal_set = util.load_goal_set(goal_set_file_path)
# the path to the act set and load it
# TODO: change it with a relative path
act_set_file_path = '/Users/vladimirilievski/Desktop/Vladimir/Master_Thesis_Swisscom/GitHub Repo/GO-Chatbots/resources/data/dia_acts.txt'
act_set = util.text_to_dict(act_set_file_path)
# the path to the slot set and load it
# TODO: change it with a relative path
slot_set_file_path = '/Users/vladimirilievski/Desktop/Vladimir/Master_Thesis_Swisscom/GitHub Repo/GO-Chatbots/resources/data/slot_set.txt'
slot_set = util.text_to_dict(slot_set_file_path)
# the list of initial inform slots
init_inform_slots = ['moviename']
# the ultimate slot set
ultimate_request_slot = 'ticket'
# create the rule-based user
user = GORuleBasedUser(simulation_mode, goal_set, act_set, init_inform_slots, ultimate_request_slot)
# reset the user
user.reset()
def test1_rule_based_state_tracker():
"""
Method for testing the rule-based state tracker on the movie booking data set
"""
# the path to the act set and load it
# TODO: change it with a relative path
act_set_file_path = '/Users/vladimirilievski/Desktop/Vladimir/Master_Thesis_Swisscom/GitHub Repo/GO-Chatbots/resources/data/dia_acts.txt'
act_set = util.text_to_dict(act_set_file_path)
# the path to the slot set and load it
# TODO: change it with a relative path
slot_set_file_path = '/Users/vladimirilievski/Desktop/Vladimir/Master_Thesis_Swisscom/GitHub Repo/GO-Chatbots/resources/data/slot_set.txt'
slot_set = util.text_to_dict(slot_set_file_path)
# maximum number of turns
max_nb_turns = 30
# the dimensionality of the state
act_cardinality = len(act_set)
slot_cardinality = len(slot_set)
state_dim = 2 * act_cardinality + 7 * slot_cardinality + 3 + max_nb_turns
# Create the KB Helper class
ultimate_request_slot = 'ticket'
special_slots = ['numberofpeople']
filter_slots = ['ticket', 'numberofpeople', 'taskcomplete', 'closing']
knowledge_dict_path = '/Users/vladimirilievski/Desktop/Vladimir/Master_Thesis_Swisscom/GitHub Repo/GO-Chatbots/resources/data/movie_kb.1k.p'
knowledge_dict = pickle.load(open(knowledge_dict_path, 'rb'))
kb_helper = GOKBHelper(ultimate_request_slot, special_slots, filter_slots,
knowledge_dict)
# create the state tracker
state_tracker = GORuleBasedStateTracker(act_set=act_set,
slot_set=slot_set,
max_nb_turns=max_nb_turns,
kb_helper=kb_helper)
# reset the state tracker
state_tracker.reset()
# get three mock actions
init_usr_action, agt_action, usr_action = test1_actions()
state_tracker.update(init_usr_action, const.USR_SPEAKER_VAL)
# produce state for the agent
init_state = state_tracker.produce_state()
state_tracker.update(agt_action, const.AGT_SPEAKER_VAL)
state_tracker.update(usr_action, const.USR_SPEAKER_VAL)
state = state_tracker.produce_state()
def prepare_dialogue_system():
# the path to the act set and load it
# TODO: change it with a relative path
act_set_file_path = '/Users/vladimirilievski/Desktop/Vladimir/Master_Thesis_Swisscom/GitHub Repo/GO-Chatbots/resources/data/dia_acts.txt'
act_set = util.text_to_dict(act_set_file_path)
# the path to the slot set and load it
# TODO: change it with a relative path
slot_set_file_path = '/Users/vladimirilievski/Desktop/Vladimir/Master_Thesis_Swisscom/GitHub Repo/GO-Chatbots/resources/data/slot_set.txt'
slot_set = util.text_to_dict(slot_set_file_path)
# the path to the user goals and load it
# TODO: change it with a relative path
goal_set_file_path = '/Users/vladimirilievski/Desktop/Vladimir/Master_Thesis_Swisscom/GitHub Repo/GO-Chatbots/resources/data/user_goals_first_turn_template.part.movie.v1.p'
goal_set = util.load_goal_set(goal_set_file_path)
# the list of initial inform slots
init_inform_slots = ['moviename']
# the ultimate slot set
ultimate_request_slot = 'ticket'
kb_special_slots = ['numberofpeople']
kb_filter_slots = ['ticket', 'numberofpeople', 'taskcomplete', 'closing']
# feasible actions
agt_feasible_actions = feasible_actions()
# the agent memory
agt_memory = GOMemory(warmup_size=1000)
# the agent policy
agt_policy = EpsGreedyQPolicy(eps=0.1)
warmup_policy_request_set = ['moviename', 'starttime', 'city', 'date', 'theater', 'numberofpeople']
agt_warmup_policy = GORuleBasedPolicy(feasible_actions=agt_feasible_actions, request_set=warmup_policy_request_set)
# testing policy
agt_eval_policy = EpsGreedyQPolicy(eps=0.1)
# all system params
params = {}
params[const.MAX_NB_TURNS] = 40
params[
const.KB_PATH_KEY] = '/Users/vladimirilievski/Desktop/Vladimir/Master_Thesis_Swisscom/GitHub Repo/GO-Chatbots/resources/data/movie_kb.1k.p'
# Environment params
params[const.SIMULATION_MODE_KEY] = const.SEMANTIC_FRAME_SIMULATION_MODE
params[const.IS_TRAINING_KEY] = True
params[const.USER_TYPE_KEY] = const.RULE_BASED_USER
params[const.STATE_TRACKER_TYPE_KEY] = const.RULE_BASED_STATE_TRACKER
params[const.SUCCESS_REWARD_KEY] = 2 * params[const.MAX_NB_TURNS]
params[const.FAILURE_REWARD_KEY] = - params[const.MAX_NB_TURNS]
params[const.PER_TURN_REWARD_KEY] = -1
# TODO: change it with a relative path
params[
const.NLU_PATH_KEY] = "/Users/vladimirilievski/Desktop/Vladimir/Master_Thesis_Swisscom/GitHub Repo/GO-Chatbots/resources/models/nlu/lstm_[1468447442.91]_39_80_0.921.p"
params[
const.DIAACT_NL_PAIRS_PATH_KEY] = '/Users/vladimirilievski/Desktop/Vladimir/Master_Thesis_Swisscom/GitHub Repo/GO-Chatbots/resources/data/dia_act_nl_pairs.v6.json'
params[
const.NLG_PATH_KEY] = "/Users/vladimirilievski/Desktop/Vladimir/Master_Thesis_Swisscom/GitHub Repo/GO-Chatbots/resources/models/nlg/lstm_tanh_relu_[1468202263.38]_2_0.610.p"
# Agent params
params[const.AGENT_TYPE_KEY] = const.AGENT_TYPE_DQN
params[const.GAMMA_KEY] = .99
params[const.BATCH_SIZE_KEY] = 32
params[const.NB_STEPS_WARMUP_KEY] = 10000
params[const.TRAIN_INTERVAL_KEY] = 20
params[const.MEMORY_INTERVAL_KEY] = 50
params[const.TARGET_MODEL_UPDATE_KEY] = 100
params[const.ENABLE_DOUBLE_DQN_KEY] = False
params[const.ENABLE_DUELING_NETWORK_KEY] = False
params[const.DUELING_TYPE_KEY] = 'avg'
params[const.HIDDEN_SIZE_KEY] = 80
params[const.ACTIVATION_FUNCTION_KEY] = const.RELU
# create the dialogue system
dialogue_sys = GODialogSys(act_set=act_set, slot_set=slot_set, goal_set=goal_set,
init_inform_slots=init_inform_slots, ultimate_request_slot=ultimate_request_slot,
kb_special_slots=kb_special_slots, kb_filter_slots=kb_filter_slots,
agt_feasible_actions=agt_feasible_actions, agt_memory=agt_memory, agt_policy=agt_policy,
agt_warmup_policy=agt_warmup_policy,
agt_eval_policy=agt_eval_policy, params=params)
return dialogue_sys
def test1_environment():
"""
Method for testing the Environment class for the movie booking data set
"""
# the path to the act set and load it
# TODO: change it with a relative path
act_set_file_path = '/Users/vladimirilievski/Desktop/Vladimir/Master_Thesis_Swisscom/GitHub Repo/GO-Chatbots/resources/data/dia_acts.txt'
act_set = util.text_to_dict(act_set_file_path)
# the path to the slot set and load it
# TODO: change it with a relative path
slot_set_file_path = '/Users/vladimirilievski/Desktop/Vladimir/Master_Thesis_Swisscom/GitHub Repo/GO-Chatbots/resources/data/slot_set.txt'
slot_set = util.text_to_dict(slot_set_file_path)
# the path to the user goals and load it
# TODO: change it with a relative path
goal_set_file_path = '/Users/vladimirilievski/Desktop/Vladimir/Master_Thesis_Swisscom/GitHub Repo/GO-Chatbots/resources/data/user_goals_first_turn_template.part.movie.v1.p'
goal_set = util.load_goal_set(goal_set_file_path)
# the list of initial inform slots
init_inform_slots = ['moviename']
# the ultimate slot set
ultimate_request_slot = 'ticket'
# feasible actions
test_feasible_actions = test1_feasible_actions()
# define the arguments for the KB
ultimate_request_slot = 'ticket'
special_slots = ['numberofpeople']
filter_slots = ['ticket', 'numberofpeople', 'taskcomplete', 'closing']
knowledge_dict_path = '/Users/vladimirilievski/Desktop/Vladimir/Master_Thesis_Swisscom/GitHub Repo/GO-Chatbots/resources/data/movie_kb.1k.p'
knowledge_dict = pickle.load(open(knowledge_dict_path, 'rb'))
# Create the KB Helper class
kb_helper = GOKBHelper(ultimate_request_slot, special_slots, filter_slots,
knowledge_dict)
# all params
params = {}
params[const.SIMULATION_MODE_KEY] = const.SEMANTIC_FRAME_SIMULATION_MODE
params[const.IS_TRAINING_KEY] = True
params[const.USER_TYPE_KEY] = const.RULE_BASED_USER
params[const.STATE_TRACKER_TYPE_KEY] = const.RULE_BASED_STATE_TRACKER
params[const.MAX_NB_TURNS] = 30
params[const.SUCCESS_REWARD_KEY] = 2 * params[const.MAX_NB_TURNS]
params[const.FAILURE_REWARD_KEY] = -params[const.MAX_NB_TURNS]
params[const.PER_TURN_REWARD_KEY] = -1
params[
const.
NLU_PATH_KEY] = "/Users/vladimirilievski/Desktop/Vladimir/Master_Thesis_Swisscom/GitHub Repo/GO-Chatbots/resources/models/nlu/lstm_[1468447442.91]_39_80_0.921.p"
params[
const.
DIAACT_NL_PAIRS_PATH_KEY] = '/Users/vladimirilievski/Desktop/Vladimir/Master_Thesis_Swisscom/GitHub Repo/GO-Chatbots/resources/data/dia_act_nl_pairs.v6.json'
params[
const.
NLG_PATH_KEY] = "/Users/vladimirilievski/Desktop/Vladimir/Master_Thesis_Swisscom/GitHub Repo/GO-Chatbots/resources/models/nlg/lstm_tanh_relu_[1468202263.38]_2_0.610.p"
env = GOEnv(act_set=act_set,
slot_set=slot_set,
goal_set=goal_set,
init_inform_slots=init_inform_slots,
ultimate_request_slot=ultimate_request_slot,
feasible_actions=test_feasible_actions,
kb_helper=kb_helper,
params=params)
env.reset()
agt_action = {}
agt_action[const.DIA_ACT_KEY] = 'request'
agt_action[const.INFORM_SLOTS_KEY] = {}
agt_action[const.INFORM_SLOTS_KEY]['theater'] = 'manville 12 plex'
agt_action[const.REQUEST_SLOTS_KEY] = {}
agt_action[const.REQUEST_SLOTS_KEY]['starttime'] = 'UKN'
env.step(agt_action)