def score(self, expert_evaluation, expert_trajectory: Trajectory, streaming_enviroment, trace_list,
video_csv_list, add_data=False):
"""
Wrapper for the base scoring function
:param expert_evaluation:
:param expert_trajectory:
:param streaming_enviroment:
:param trace_list: Which traces did we evaluate
:param video_csv_list: Which videos did we evaluate
:param add_data:
:return:
"""
expert_trajectory.convert_list()
behavioural_cloning_trace_generator_testing = TrajectoryVideoStreaming(self, streaming_enviroment,
trace_list=trace_list,
video_csv_list=video_csv_list)
state_t = np.array([self.classifier.extract_features_observation(state_t) for state_t, _, _ in
tqdm(expert_trajectory.trajectory_list, desc='transforming')])
state_t = pd.DataFrame(state_t, columns=self.classifier.extract_features_names())
self.impute_NaN_inplace(state_t)
expert_action = expert_trajectory.trajectory_action_t_arr
approx_action = self.classifier.predict(state_t)
expert_action = expert_action.ravel()
behavioural_cloning_evaluation, behavioural_cloning_evaluation_trajectory = behavioural_cloning_trace_generator_testing.create_trajectories(
random_action_probability=0, cores_avail=1)
return self.score_comparison(expert_evaluation=expert_evaluation,
expert_trajectory=expert_trajectory,
expert_action=expert_action,
approx_evaluation=behavioural_cloning_evaluation,
approx_trajectory=behavioural_cloning_evaluation_trajectory,
approx_action=approx_action, add_data=add_data)
def score(self, expert_evaluation, expert_trajectory: Trajectory, streaming_enviroment, trace_list,
video_csv_list, add_data=False):
expert_trajectory.convert_list()
behavioural_cloning_trace_generator_testing = TrajectoryVideoStreaming(self, streaming_enviroment,
trace_list=trace_list,
video_csv_list=video_csv_list)
state_t_testing = expert_trajectory.trajectory_state_t_arr
state_t_future_testing = expert_trajectory.trajectory_state_t_future
expert_action = expert_trajectory.trajectory_action_t_arr
approx_action = self.policy_network.model.predict([state_t_testing, state_t_future_testing]).argmax(-1)
expert_action = expert_action.ravel()
behavioural_cloning_evaluation, behavioural_cloning_evaluation_trajectory = behavioural_cloning_trace_generator_testing.create_trajectories(
random_action_probability=0, cores_avail=1)
return self.score_comparison(expert_evaluation=expert_evaluation,
expert_trajectory=expert_trajectory,
expert_action=expert_action,
approx_evaluation=behavioural_cloning_evaluation,
approx_trajectory=behavioural_cloning_evaluation_trajectory,
approx_action=approx_action, add_data=add_data)
def score(self, expert_evaluation, expert_trajectory: Trajectory, streaming_enviroment, trace_list,
video_csv_list, add_data=False):
expert_trajectory.convert_list()
behavioural_cloning_trace_generator_testing = TrajectoryVideoStreaming(self, streaming_enviroment,
trace_list=trace_list,
video_csv_list=video_csv_list)
state_t = np.array([self.abr_policy_learner.extract_features_observation(state_t) for state_t, _, _ in
tqdm(expert_trajectory.trajectory_list, desc='transforming')])
state_t = pd.DataFrame(state_t, columns=self.abr_policy_learner.extract_features_names())
expert_action = expert_trajectory.trajectory_action_t_arr
approx_action = self.abr_policy_learner.predict(state_t)
behavioural_cloning_evaluation, behavioural_cloning_evaluation_trajectory = behavioural_cloning_trace_generator_testing.create_trajectories(
random_action_probability=0, cores_avail=1)
return self.score_comparison(expert_evaluation=expert_evaluation,
expert_trajectory=expert_trajectory,
expert_action=expert_action,
approx_evaluation=behavioural_cloning_evaluation,
approx_trajectory=behavioural_cloning_evaluation_trajectory,
approx_action=approx_action, add_data=add_data)