class AnalyzeCompleteLapPercentage(GraphAnalyzer):
def __init__(self, guru_parent_redraw,
matplotlib_canvas: FigureCanvasTkAgg,
control_frame: tk.Frame):
super().__init__(guru_parent_redraw, matplotlib_canvas, control_frame)
self._episode_control = EpisodeCheckButtonControl(
guru_parent_redraw, control_frame, True)
def build_control_frame(self, control_frame):
self._episode_control.add_to_control_frame()
def add_plots(self):
if not self.all_episodes:
return
axes: Axes = self.graph_figure.add_subplot()
# Plot data
if self._episode_control.show_all():
self._add_plot_for_episodes(axes, "All Episodes",
self.all_episodes, "C1")
if self.filtered_episodes and self._episode_control.show_filtered():
self._add_plot_for_episodes(axes, "Filtered Episodes",
self.filtered_episodes, "C2")
if self._episode_control.show_evaluations():
add_plot_for_evaluations(axes, "Evaluations",
self.evaluation_phases, "C3")
# Format the plot
axes.set_title("Complete Lap Percentage")
axes.set_xlabel("Training Iteration")
if axes.has_data():
axes.legend(frameon=True, framealpha=0.8, shadow=True)
def _add_plot_for_episodes(self, axes: Axes, label, episodes, colour):
(plot_x, plot_y) = get_plot_data_for_episodes(episodes)
axes.plot(plot_x, plot_y, colour, label=label)
self._plot_solo_items(axes, plot_x, plot_y, colour)
class AnalyzeTrainingProgress(GraphAnalyzer):
def __init__(self, guru_parent_redraw,
matplotlib_canvas: FigureCanvasTkAgg,
control_frame: tk.Frame):
super().__init__(guru_parent_redraw, matplotlib_canvas, control_frame)
self.episode_control = EpisodeCheckButtonControl(
guru_parent_redraw, control_frame, True)
self._stats_control = StatsControl(guru_parent_redraw, control_frame)
self._scale_control = GraphScaleControl(guru_parent_redraw,
control_frame)
self._line_fitting_control = GraphLineFittingControl(
guru_parent_redraw, control_frame)
self._evaluation_pairs_control = EvaluationPairsControl(
guru_parent_redraw, control_frame)
def build_control_frame(self, control_frame):
self.episode_control.add_to_control_frame()
self._stats_control.add_to_control_frame()
self._scale_control.add_to_control_frame()
self._line_fitting_control.add_to_control_frame()
self._evaluation_pairs_control.add_to_control_frame()
def add_plots(self):
if not self.all_episodes:
return
gs = GridSpec(1, 2)
axes_left: Axes = self.graph_figure.add_subplot(gs[0, 0])
axes_right: Axes = self.graph_figure.add_subplot(gs[0, 1])
self.create_plot_iteration_vs_total_reward(axes_left)
self.create_plot_iteration_vs_percent_complete(axes_right)
def create_plot_iteration_vs_total_reward(self, axes):
# Plot data
if self.all_episodes and self.episode_control.show_all():
if self._stats_control.show_median():
self.add_plot_iteration_vs_total_reward(
axes, "All - Median", self.all_episodes, np.median, "C5")
if self._stats_control.show_mean():
self.add_plot_iteration_vs_total_reward(
axes, "All - Mean", self.all_episodes, np.mean, "C6")
if self._stats_control.show_best():
self.add_plot_iteration_vs_total_reward(
axes, "All - Best", self.all_episodes, np.max, "C7")
if self._stats_control.show_worst():
self.add_plot_iteration_vs_total_reward(
axes, "All - Worst", self.all_episodes, np.min, "C8")
if self.filtered_episodes and self.episode_control.show_filtered():
if self._stats_control.show_median():
self.add_plot_iteration_vs_total_reward(
axes, "Filtered - Median", self.filtered_episodes,
np.median, "C1")
if self._stats_control.show_mean():
self.add_plot_iteration_vs_total_reward(
axes, "Filtered - Mean", self.filtered_episodes, np.mean,
"C2")
if self._stats_control.show_best():
self.add_plot_iteration_vs_total_reward(
axes, "Filtered - Best", self.filtered_episodes, np.max,
"C3")
if self._stats_control.show_worst():
self.add_plot_iteration_vs_total_reward(
axes, "Filtered - Worst", self.filtered_episodes, np.min,
"C4")
if self.episode_control.show_evaluations():
if self._stats_control.show_median():
self.add_plot_iteration_vs_evaluation_total_reward(
axes, "Evaluations - Median", self.evaluation_phases,
np.median, "C9")
if self._stats_control.show_mean():
self.add_plot_iteration_vs_evaluation_total_reward(
axes, "Evaluations - Mean", self.evaluation_phases,
np.mean, "C10")
if self._stats_control.show_best():
self.add_plot_iteration_vs_evaluation_total_reward(
axes, "Evaluations - Best", self.evaluation_phases, np.max,
"C11")
if self._stats_control.show_worst():
self.add_plot_iteration_vs_evaluation_total_reward(
axes, "Evaluations - Worst", self.evaluation_phases,
np.min, "C12")
# Format the plot
axes.set_title("Total Reward")
axes.set_xlabel("Training Iteration")
if self.log_meta and self.is_fixed_scale():
best = self.log_meta.episode_stats.best_reward
worst = self.log_meta.episode_stats.worst_reward
if best != worst:
border = 0.02 * (best - worst)
axes.set_ybound(worst - border, best + border)
if axes.has_data():
axes.legend(frameon=True, framealpha=0.8, shadow=True)
def create_plot_iteration_vs_percent_complete(self, axes):
# Plot data
if self.episode_control.show_all():
if self._stats_control.show_median():
self.add_plot_iteration_vs_percent_complete(
axes, "All - Median", self.all_episodes, np.median, "C5")
if self._stats_control.show_mean():
self.add_plot_iteration_vs_percent_complete(
axes, "All - Mean", self.all_episodes, np.mean, "C6")
if self._stats_control.show_best():
self.add_plot_iteration_vs_percent_complete(
axes, "All - Best", self.all_episodes, np.max, "C7")
if self._stats_control.show_worst():
self.add_plot_iteration_vs_percent_complete(
axes, "All - Worst", self.all_episodes, np.min, "C8")
if self.filtered_episodes and self.episode_control.show_filtered():
if self._stats_control.show_median():
self.add_plot_iteration_vs_percent_complete(
axes, "Filtered - Median", self.filtered_episodes,
np.median, "C1")
if self._stats_control.show_mean():
self.add_plot_iteration_vs_percent_complete(
axes, "Filtered - Mean", self.filtered_episodes, np.mean,
"C2")
if self._stats_control.show_best():
self.add_plot_iteration_vs_percent_complete(
axes, "Filtered - Best", self.filtered_episodes, np.max,
"C3")
if self._stats_control.show_worst():
self.add_plot_iteration_vs_percent_complete(
axes, "Filtered - Worst", self.filtered_episodes, np.min,
"C4")
if self.episode_control.show_evaluations():
if self._stats_control.show_median():
self.add_plot_iteration_vs_evaluation_percent_complete(
axes, "Evaluations - Median", self.evaluation_phases,
np.median, "C9")
if self._stats_control.show_mean():
self.add_plot_iteration_vs_evaluation_percent_complete(
axes, "Evaluations - Mean", self.evaluation_phases,
np.mean, "C10")
if self._stats_control.show_best():
self.add_plot_iteration_vs_evaluation_percent_complete(
axes, "Evaluations - Best", self.evaluation_phases, np.max,
"C11")
if self._stats_control.show_worst():
self.add_plot_iteration_vs_evaluation_percent_complete(
axes, "Evaluations - Worst", self.evaluation_phases,
np.min, "C12")
# Format the plot
axes.set_title("Track Completion")
axes.set_xlabel("Training Iteration")
axes.yaxis.set_major_formatter(PercentFormatter())
if self.is_fixed_scale():
axes.set_ybound(0, 105)
if axes.has_data():
axes.legend(frameon=True, framealpha=0.8, shadow=True)
def is_fixed_scale(self):
return self._scale_control.fixed_scale()
def add_plot_iteration_vs_total_reward(self, axes: Axes, label, episodes,
stat_method, colour):
(plot_x, plot_y) = get_plot_data_iteration_vs_total_reward(
episodes, stat_method)
self.plot_data_or_smooth_it(axes, label, plot_x, plot_y, colour)
def add_plot_iteration_vs_evaluation_total_reward(self, axes: Axes, label,
evaluation_phases,
stat_method, colour):
(plot_x,
plot_y) = self.get_plot_data_iteration_vs_evaluation_total_reward(
evaluation_phases, stat_method)
self.plot_data_or_smooth_it(axes, label, plot_x, plot_y, colour)
def add_plot_iteration_vs_percent_complete(self, axes: Axes, label,
episodes, stat_method, colour):
(plot_x, plot_y) = get_plot_data_iteration_vs_percent_complete(
episodes, stat_method)
self.plot_data_or_smooth_it(axes, label, plot_x, plot_y, colour)
def add_plot_iteration_vs_evaluation_percent_complete(
self, axes: Axes, label, evaluation_phases, stat_method, colour):
(plot_x,
plot_y) = self.get_plot_data_iteration_vs_evaluation_percent_complete(
evaluation_phases, stat_method)
self.plot_data_or_smooth_it(axes, label, plot_x, plot_y, colour)
def plot_data_or_smooth_it(self, axes: Axes, label: str,
plot_x: np.ndarray, plot_y: np.ndarray,
colour: str):
if self._line_fitting_control.linear_fitting():
(x_values, y_values, r) = get_linear_regression(plot_x, plot_y)
elif self._line_fitting_control.quadratic_fitting():
(x_values,
y_values) = get_polynomial_quadratic_regression(plot_x, plot_y)
else:
x_values = plot_x
y_values = plot_y
axes.plot(x_values, y_values, colour, label=label)
self._plot_solo_items(axes, x_values, y_values, colour)
def get_plot_data_iteration_vs_evaluation_total_reward(
self, evaluation_phases, stat_method):
plot_iteration = []
plot_data = []
previous_eval = None
for i, this_eval in enumerate(evaluation_phases):
if self._evaluation_pairs_control.show_combined() and i % 2 == 1:
plot_value = stat_method(
np.concatenate((this_eval.rewards, previous_eval.rewards)))
plot_data.append(plot_value)
plot_iteration.append(i - 0.5)
elif ((self._evaluation_pairs_control.show_odd() and i % 2 == 1) or
(self._evaluation_pairs_control.show_even() and i % 2 == 0)
or self._evaluation_pairs_control.show_separate()):
plot_data.append(stat_method(this_eval.rewards))
plot_iteration.append(i)
previous_eval = this_eval
return np.array(plot_iteration), np.array(plot_data)
def get_plot_data_iteration_vs_evaluation_percent_complete(
self, evaluation_phases, stat_method):
plot_iteration = []
plot_data = []
previous_eval = None
for i, this_eval in enumerate(evaluation_phases):
if self._evaluation_pairs_control.show_combined() and i % 2 == 1:
plot_value = stat_method(
np.concatenate(
(this_eval.progresses, previous_eval.progresses)))
plot_data.append(plot_value)
plot_iteration.append(i - 0.5)
elif ((self._evaluation_pairs_control.show_odd() and i % 2 == 1) or
(self._evaluation_pairs_control.show_even() and i % 2 == 0)
or self._evaluation_pairs_control.show_separate()):
plot_data.append(stat_method(this_eval.progresses))
plot_iteration.append(i)
previous_eval = this_eval
return np.array(plot_iteration), np.array(plot_data)