def test_cumulative_cost_plotter_label():
plotter1 = ph.CumulativeCostPlotter(num_simulation_steps=10,
time_interval=1.0,
plot_frequency=2,
discounted=True)
assert plotter1.y_label == 'Cumulative Disc Cost'
assert [*plotter1.line_labels] == ['Total Cost']
plotter2 = ph.CumulativeCostPlotter(num_simulation_steps=10,
time_interval=1.0,
plot_frequency=2,
discounted=False)
assert plotter2.y_label == 'Cumulative Cost'
assert [*plotter2.line_labels] == ['Total Cost']
def test_cumulative_cost_plotter_discounted_small_plot_frequency():
plotter = ph.CumulativeCostPlotter(num_simulation_steps=10,
time_interval=1.0,
plot_frequency=2,
discounted=True)
cost = [np.array([1]), np.array([10]), np.array([20])]
data_dict = {'cost': cost}
reporter_cache = {'discount_factor': [np.array([[0.5]])]}
step = 2
for s in range(step):
plotter.handle(data_dict=data_dict,
reporter_cache=reporter_cache,
step=s)
cumul = plotter.get_new_line_data(data_dict=data_dict,
reporter_cache=reporter_cache,
step=step)
assert np.all(cumul == np.array([1, 6, 11]))
def get_handlers(server_mode: bool,
num_sim_steps: int,
plot_freq: int,
time_interval: int,
is_hedgehog: bool,
is_routing: bool,
reproduce_mode: bool = False) -> List[Handler]:
"""
Get the handlers used to validate the examples.
:param server_mode: when experiment runs locally, this flag controls whether to show live plots
and wait for input before closing them at the end of the simulation.
:param num_sim_steps: the number of steps the simulation runs.
:param plot_freq: number of timesteps after which the plots are updated.
:param time_interval: the time interval of the environment.
:param is_hedgehog: whether the use hedgehog specific handlers (eg for workload).
:param is_routing: whether the environment is a routing model.
:param reproduce_mode: flag indicating whether we are live plotting or reproducing from already
stored data.
:return: handlers: List of reporting handlers.
"""
if server_mode:
return []
handlers = [] # type: List[Handler]
compact_panel = False
if compact_panel:
rows = 3 if is_hedgehog else 2
plots = 6 if is_hedgehog else 4
fig = plt.figure(figsize=(12, 4 * rows))
axs = [
fig.add_subplot('{}2{}'.format(rows, i + 1)) for i in range(plots)
]
handlers.extend([
hand.StateCostPlotter(num_sim_steps,
time_interval,
plot_freq,
ax=axs[0],
do_plot_cost=False,
reproduce_mode=reproduce_mode),
hand.StateCostPlotter(num_sim_steps,
time_interval,
plot_freq,
ax=axs[1],
do_plot_state=False,
reproduce_mode=reproduce_mode),
hand.CumulativeCostPlotter(num_sim_steps,
time_interval,
plot_freq,
ax=axs[3],
discounted=False,
reproduce_mode=reproduce_mode),
])
if is_hedgehog:
handlers.extend([
hand.HedgingThresholdPlotter(num_sim_steps,
time_interval,
plot_freq,
ax=axs[2],
reproduce_mode=reproduce_mode),
hand.EffectiveCostErrorPlotter(num_sim_steps,
time_interval,
plot_freq,
ax=axs[5],
eff_cost_err_method='absolute',
reproduce_mode=reproduce_mode),
hand.IdlingPlotter(num_sim_steps,
time_interval,
plot_freq,
ax=axs[4],
reproduce_mode=reproduce_mode),
])
else:
rows = 3 if is_hedgehog else 2
plots = 9 if is_hedgehog else 4
plot_per_row = 3 if is_hedgehog else 2
width = 17 if is_hedgehog else 12
fig = plt.figure(figsize=(width, 3 * rows))
axs = [
fig.add_subplot(f'{rows}{plot_per_row}{i + 1}')
for i in range(plots)
]
handlers.extend([
hand.StateCostPlotter(num_sim_steps,
time_interval,
plot_freq,
ax=axs[0],
do_plot_cost=False,
reproduce_mode=reproduce_mode),
hand.StateCostPlotter(num_sim_steps,
time_interval,
plot_freq,
ax=axs[1],
do_plot_state=False,
reproduce_mode=reproduce_mode),
hand.CumulativeCostPlotter(num_sim_steps,
time_interval,
plot_freq,
ax=axs[2],
discounted=False,
reproduce_mode=reproduce_mode),
hand.ArrivalsPlotter(num_sim_steps,
time_interval,
plot_freq,
ax=axs[3],
reproduce_mode=reproduce_mode)
])
if is_hedgehog:
handlers.extend([
hand.WorkloadPlotter(num_sim_steps,
time_interval,
plot_freq,
ax=axs[4],
plot_fluid_model=False,
plot_hedging=False,
reproduce_mode=reproduce_mode),
hand.HedgingThresholdPlotter(num_sim_steps,
time_interval,
plot_freq,
ax=axs[5],
reproduce_mode=reproduce_mode),
hand.EffectiveCostErrorPlotter(num_sim_steps,
time_interval,
plot_freq,
ax=axs[6],
eff_cost_err_method='absolute',
reproduce_mode=reproduce_mode),
hand.IdlingPlotter(num_sim_steps,
time_interval,
plot_freq,
ax=axs[7],
reproduce_mode=reproduce_mode),
hand.ActionsToFluidPlotter(num_sim_steps,
time_interval,
plot_freq,
ax=axs[8])
])
if not reproduce_mode:
handlers.append(PrintStateHedging(plot_freq))
if not is_routing:
handlers.append(PrintInverseLoadings(plot_freq))
handlers.append(PrintActualtoFluidRatio(plot_freq))
return handlers