hpvis.finished_runs_over_time(all_runs)
# This one visualizes the spearman rank correlation coefficients of the losses
# between different budgets.
hpvis.correlation_across_budgets(result)
# For model based optimizers, one might wonder how much the model actually helped.
# The next plot compares the performance of configs picked by the model vs. random ones
hpvis.performance_histogram_model_vs_random(all_runs, id2conf)
plt.show()
d1 = res.get_pandas_dataframe()[0]
loss = res.get_pandas_dataframe()[1]
d1['loss'] = loss
if False:
result = res
# get all executed runs
all_runs = result.get_all_runs()
# get the 'dict' that translates config ids to the actual configurations
id2conf = result.get_id2config_mapping()
lcs = result.get_learning_curves()
hpvis.interactive_HBS_plot(lcs,
tool_tip_strings=hpvis.default_tool_tips(
result, lcs))
import hpbandster.core.result as hpres import hpbandster.visualization as hpvis result = search._res # + # get all executed runs all_runs = result.get_all_runs() # get the 'dict' that translates config ids to the actual configurations id2conf = result.get_id2config_mapping() lcs = result.get_learning_curves() hpvis.interactive_HBS_plot(lcs) # - result.get_all_runs()[0].info['test_score_mean'], result.get_all_runs()[0].loss # + # Here is how you get he incumbent (best configuration) inc_id = result.get_incumbent_id() # let's grab the run on the highest budget inc_runs = result.get_runs_by_id(inc_id) inc_run = inc_runs[-1] # We have access to all information: the config, the loss observed during #optimization, and all the additional information inc_loss = inc_run.loss