def open_log(log_paths):
for L in log_paths:
try:
ax_client = AxClient.load_from_json_file(filepath=L)
break
except IOError:
ax_client = None
return ax_client
def initialize(filepath='ax_client_snapshot.json'):
ax_client = AxClient(verbose_logging=False)
try:
ax_client = ax_client.load_from_json_file(filepath=filepath)
except:
logging.warning("COULD NOT LOAD CURRENT EXPERIMENT. STARTING NEW..")
ax_client.create_experiment(
name="hypertune_simulation",
parameters=parameters,
objective_name="valid/hitrate",
outcome_constraints=["test/loglik <= 10000"])
return ax_client
# for i in range(25):
# parameters, trial_index = ax_client.get_next_trial()
# # Local evaluation here can be replaced with deployment to external system.
# ax_client.complete_trial(trial_index=trial_index, raw_data=evaluate(parameters))
# # _, trial_index = ax_client.get_next_trial()
# ax_client.log_trial_failure(trial_index=trial_index)
#
# ax_client.get_trials_data_frame().sort_values('trial_index')
# best_parameters, values = ax_client.get_best_parameters()
from ax.utils.notebook.plotting import render, init_notebook_plotting
from ax.plot.contour import plot_contour
plot = plot_contour(
model=gpei,
param_x=opt_list[0],
param_y=opt_list[1],
metric_name="base",
)
render(plot)
ax_client.generation_strategy.model = gpei
init_notebook_plotting(offline=True)
# render(ax_client.get_contour_plot())
render(ax_client.get_contour_plot(param_x=opt_list[0],
param_y=opt_list[0])) #, metric_name=base))
# render(ax_client.get_optimization_trace(objective_optimum=hartmann6.fmin)) # Objective_optimum is optional.
ax_client.save_to_json_file() # For custom filepath, pass `filepath` argument.
restored_ax_client = AxClient.load_from_json_file(
) # For custom filepath, pass `filepath` argument.
