def update_costs_plot(self, experiment, version, checkpoint, episode):
speeds = DataReader.get_episode_speeds(
experiment, version, checkpoint, episode
)
self.costs_plot_figure.title = f"Costs and speed: episode {episode}"
if speeds is not None:
self.costs_plot_lines_speed.x = range(len(speeds))
self.costs_plot_lines_speed.y = speeds
self.costs_plot_lines_speed.labels = ["speed"]
costs = DataReader.get_episode_costs(
experiment, version, checkpoint, episode
)
if costs is not None:
x = costs.index
self.x_sc.min = x[0]
self.x_sc.max = x[-1]
self.costs_plot_lines_costs.x = x
self.costs_plot_lines_costs.y = [
costs["lane_cost"],
costs["pixel_proximity_cost"],
costs["collisions_per_frame"],
]
self.costs_plot_lines_costs.labels = [
"lane cost",
"pixel proximity cost",
"collisions per frame",
]
def get_episode_features(experiment, seed, checkpoint, episode):
""" Get features for one episode
This is used for dimensionality reduction, which is later used for
scatter plotting.
"""
history_size = 10
features = []
features.append(
DataReader.get_episode_speeds(experiment, seed, checkpoint,
episode)[-history_size:])
costs = DataReader.get_episode_costs(experiment, seed, checkpoint,
episode)
columns_to_save = [
'proximity_cost', 'lane_cost', 'pixel_proximity_cost'
]
for column in columns_to_save:
features.append(costs[column].to_numpy()[-history_size:])
features = np.stack(features)
features = features.flatten()
return features
def update_costs_plot(self, experiment, seed, checkpoint, episode):
speeds = DataReader.get_episode_speeds(experiment, seed, checkpoint,
episode)
self.costs_plot_figure.title = f'Costs and speed: episode {episode}'
if speeds is not None:
self.costs_plot_lines_speed.x = range(len(speeds))
self.costs_plot_lines_speed.y = speeds
self.costs_plot_lines_speed.labels = ['speed']
costs = DataReader.get_episode_costs(experiment, seed, checkpoint,
episode)
if costs is not None:
x = costs.index
self.x_sc.min = x[0]
self.x_sc.max = x[-1]
self.costs_plot_lines_costs.x = x
self.costs_plot_lines_costs.y = [
costs['lane_cost'], costs['pixel_proximity_cost'],
costs['collisions_per_frame']
]
self.costs_plot_lines_costs.labels = [
'lane cost', 'pixel proximity cost', 'collisions per frame'
]
