def plot_learning(exp, cfg):
objective_means = np.array([[exp.trials[trial].objective_mean]
for trial in exp.trials])
cumulative = optimization_trace_single_method(
y=np.maximum.accumulate(objective_means.T, axis=1) * 1.01,
ylabel=cfg.metric.name,
trace_color=tuple((83, 78, 194)),
# optimum=-3.32237, # Known minimum objective for Hartmann6 function.
)
all = optimization_trace_single_method(
y=objective_means.T,
ylabel=cfg.metric.name,
model_transitions=[cfg.bo.random],
trace_color=tuple((114, 110, 180)),
# optimum=-3.32237, # Known minimum objective for Hartmann6 function.
)
layout_learn = cumulative[0]['layout']
layout_learn['paper_bgcolor'] = 'rgba(0,0,0,0)'
layout_learn['plot_bgcolor'] = 'rgba(0,0,0,0)'
layout_learn['showlegend'] = False
d1 = cumulative[0]['data']
d2 = all[0]['data']
for t in d1:
t['legendgroup'] = cfg.metric.name + ", cum. max"
if 'name' in t and t['name'] == 'Generator change':
t['name'] = 'End Random Iterations'
else:
t['name'] = cfg.metric.name + ", cum. max"
t['line']['color'] = 'rgba(200,20,20,0.5)'
t['line']['width'] = 4
for t in d2:
t['legendgroup'] = cfg.metric.name
if 'name' in t and t['name'] == 'Generator change':
t['name'] = 'End Random Iterations'
else:
t['name'] = cfg.metric.name
t['line']['color'] = 'rgba(20,20,200,0.5)'
t['line']['width'] = 4
fig = {
"data": d1 + d2, # data,
"layout": layout_learn,
}
return fig
def get_optimization_trace(self,
objective_optimum: Optional[float] = None
) -> AxPlotConfig:
"""Retrieves the plot configuration for optimization trace, which shows
the evolution of the objective mean over iterations.
Args:
objective_optimum: Optimal objective, if known, for display in the
visualization.
"""
if not self.experiment.trials:
raise ValueError("Cannot generate plot as there are no trials.")
objective_name = self.experiment.optimization_config.objective.metric.name
best_objectives = np.array([[
checked_cast(Trial, trial).objective_mean
for trial in self.experiment.trials.values()
]])
hover_labels = [
_format_dict(not_none(checked_cast(Trial, trial).arm).parameters)
for trial in self.experiment.trials.values()
]
return optimization_trace_single_method(
y=(np.minimum.accumulate(best_objectives, axis=1)
if self.experiment.optimization_config.objective.minimize else
np.maximum.accumulate(best_objectives, axis=1)),
optimum=objective_optimum,
title="Model performance vs. # of iterations",
ylabel=objective_name.capitalize(),
hover_labels=hover_labels,
)
def make_plots(benchmark_result: BenchmarkResult, problem_name: str,
include_individual: bool) -> List[AxPlotConfig]:
plots: List[AxPlotConfig] = []
# Plot objective at true best
plots.append(
optimization_trace_all_methods(
y_dict=benchmark_result.objective_at_true_best,
optimum=benchmark_result.optimum,
title=f"{problem_name}: cumulative best objective",
ylabel="Objective at best-feasible point observed so far",
))
if include_individual:
# Plot individual plots of a single method on a single problem.
for m, y in benchmark_result.objective_at_true_best.items():
plots.append(
optimization_trace_single_method(
y=y,
optimum=benchmark_result.optimum,
# model_transitions=benchmark_result.model_transitions[m],
title=f"{problem_name}, {m}: cumulative best objective",
ylabel="Objective at best-feasible point observed so far",
))
# Plot time
plots.append(
optimization_times(
fit_times=benchmark_result.fit_times,
gen_times=benchmark_result.gen_times,
title=f"{problem_name}: optimization times",
))
return plots
def performance_plot(experiment, best_vals):
best_objectives = np.array([[trial.objective_mean for trial in experiment.trials.values()]])
best_objective_plot = optimization_trace_single_method(
y=np.minimum.accumulate(best_objectives, axis=1),
optimum=best_vals[0]['loss'],
title="Model performance vs. # of iterations",
ylabel="loss")
render(best_objective_plot)
def plot_learning(exp, cfg):
objective_means = np.array([[exp.trials[trial].objective_mean]
for trial in exp.trials])
cumulative = optimization_trace_single_method(
y=np.maximum.accumulate(objective_means.T, axis=1),
ylabel=cfg.metric.name,
trace_color=(83, 78, 194),
# optimum=-3.32237, # Known minimum objective for Hartmann6 function.
)
all = optimization_trace_single_method(
y=objective_means.T,
ylabel=cfg.metric.name,
model_transitions=[cfg.bo.random],
trace_color=(114, 110, 180),
# optimum=-3.32237, # Known minimum objective for Hartmann6 function.
)
layout_learn = cumulative[0]['layout']
layout_learn['paper_bgcolor'] = 'rgba(0,0,0,0)'
layout_learn['plot_bgcolor'] = 'rgba(0,0,0,0)'
d1 = cumulative[0]['data']
d2 = all[0]['data']
for t in d1:
t['legendgroup'] = cfg.metric.name + ", cum. max"
if 'name' in t and t['name'] == 'Generator change':
t['name'] = 'End Random Iterations'
else:
t['name'] = cfg.metric.name + ", cum. max"
for t in d2:
t['legendgroup'] = cfg.metric.name
if 'name' in t and t['name'] == 'Generator change':
t['name'] = 'End Random Iterations'
else:
t['name'] = cfg.metric.name
fig = {
"data": d1 + d2, # data,
"layout": layout_learn,
}
import plotly.graph_objects as go
return go.Figure(fig)
def make_plots(
benchmark_result: BenchmarkResult, problem_name: str, include_individual: bool
) -> List[AxPlotConfig]:
plots: List[AxPlotConfig] = []
# Plot objective at true best
ylabel = (
"Feasible Hypervolume"
if benchmark_result.is_multi_objective
else "Objective at best-feasible point observed so far"
)
plots.append(
optimization_trace_all_methods(
y_dict=benchmark_result.true_performance,
optimum=benchmark_result.optimum,
title=f"{problem_name}: Optimization Performance",
ylabel=ylabel,
)
)
if include_individual:
# Plot individual plots of a single method on a single problem.
for m, y in benchmark_result.true_performance.items():
plots.append(
optimization_trace_single_method(
y=y,
optimum=benchmark_result.optimum,
# model_transitions=benchmark_result.model_transitions[m],
title=f"{problem_name}, {m}: cumulative best objective",
ylabel=ylabel,
)
)
# Plot time
plots.append(
optimization_times(
fit_times=benchmark_result.fit_times,
gen_times=benchmark_result.gen_times,
title=f"{problem_name}: cumulative optimization times",
)
)
if benchmark_result.pareto_frontiers is not None:
plots.append(
plot_multiple_pareto_frontiers(
frontiers=not_none(benchmark_result.pareto_frontiers),
CI_level=0.0,
)
)
return plots
def testTraces(self):
exp = get_branin_experiment(with_batch=True)
exp.trials[0].run()
model = Models.BOTORCH(
# Model bridge kwargs
experiment=exp,
data=exp.fetch_data(),
)
# Assert that each type of plot can be constructed successfully
plot = optimization_trace_single_method_plotly(
np.array([[1, 2, 3], [4, 5, 6]]),
list(model.metric_names)[0],
optimization_direction="minimize",
)
self.assertIsInstance(plot, go.Figure)
plot = optimization_trace_single_method(
np.array([[1, 2, 3], [4, 5, 6]]),
list(model.metric_names)[0],
optimization_direction="minimize",
)
self.assertIsInstance(plot, AxPlotConfig)
"bounds": [256, 2048],
"log_scale": False
},
{
"name": "batch_size",
"type": "choice",
"values": [32, 64, 128, 256, 512]
},
],
evaluation_function=train_evaluate,
objective_name='accuracy',
# generation_strategy=ax.models.random.sobol.SobolGenerator,
)
# import pdb; pdb.set_trace()
render(
plot_contour(model=model,
param_x='lr',
param_y='training_split',
metric_name='accuracy'))
print(best_parameters, values[0])
best_objectives = np.array(
[[trial.objective_mean * 100 for trial in experiment.trials.values()]])
best_objective_plot = optimization_trace_single_method(
y=np.maximum.accumulate(best_objectives, axis=1),
title="Model performance vs. # of iterations",
ylabel="Classification Accuracy, %",
)
render(best_objective_plot)