convlab2.policy.rule.crosswoz package¶
Submodules¶
convlab2.policy.rule.crosswoz.evaluate module¶
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convlab2.policy.rule.crosswoz.evaluate.
begin_active_tuple_num
(data)¶
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convlab2.policy.rule.crosswoz.evaluate.
begin_da_type
(data)¶
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convlab2.policy.rule.crosswoz.evaluate.
calculateF1
(predict_golden)¶
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convlab2.policy.rule.crosswoz.evaluate.
calculateJointState
(predict_golden)¶
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convlab2.policy.rule.crosswoz.evaluate.
calculateSlotState
(predict_golden)¶
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convlab2.policy.rule.crosswoz.evaluate.
end_usr_da_type
(data)¶
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convlab2.policy.rule.crosswoz.evaluate.
eval_begin_da_predict
(data)¶
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convlab2.policy.rule.crosswoz.evaluate.
eval_simulator_performance
(data, goal_type=None)¶
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convlab2.policy.rule.crosswoz.evaluate.
eval_state_predict
(data)¶
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convlab2.policy.rule.crosswoz.evaluate.
read_zipped_json
(filepath, filename)¶
convlab2.policy.rule.crosswoz.rule_simulator module¶
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class
convlab2.policy.rule.crosswoz.rule_simulator.
Simulator
¶ Bases:
convlab2.policy.policy.Policy
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begin_da
()¶
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get_goal
()¶
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get_reward
()¶
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infer_goal_type
(goal)¶
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init_session
(goal=None, state=None, turn_num=0, da_seq=[])¶ Init the class variables for a new session.
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is_terminated
()¶
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predict
(sys_act)¶ Predict the next agent action given dialog state.
- Args:
- state (dict or list of list):
when the policy takes dialogue state as input, the type is dict. else when the policy takes dialogue act as input, the type is list of list.
- Returns:
- action (list of list or str):
when the policy outputs dialogue act, the type is list of list. else when the policy outputs utterance directly, the type is str.
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state_predict
()¶
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state_update
(prev_user_da, prev_sys_da)¶
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