def get_intermediate_plot(study: Study,topx=10,num_trials_threshold=30,color_scale=None) -> "go.Figure":
layout = go.Layout(
title="Intermediate Values Plot",
xaxis={"title": "Step"},
yaxis={"title": "Intermediate Value"},
)
#this value will determine which trials we want
df=study.trials_dataframe()
v=df.sort_values(by=["value"],ascending=True).reset_index().iloc[topx]["value"]
target_state = [TrialState.PRUNED, TrialState.COMPLETE, TrialState.RUNNING]
trials = [trial for trial in study.trials if (trial.state in target_state)
and (trial.value and trial.value<v) and len(trial.intermediate_values)<num_trials_threshold]
if len(trials) == 0:
_logger.warning("Study instance does not contain trials.")
return go.Figure(data=[], layout=layout)
traces = []
for i,trial in enumerate(trials):
if trial.intermediate_values:
sorted_intermediate_values = sorted(trial.intermediate_values.items())
trace = go.Scatter(
x=tuple((x for x, _ in sorted_intermediate_values)),
y=tuple((y for _, y in sorted_intermediate_values)),
mode="lines+markers",
marker={"maxdisplayed": 10},
marker_symbol=i if i<4 else i*2,
marker_size=10,
marker_color=color_scale[i],
name="Trial{}".format(trial.number),
)
traces.append(trace)
if not traces:
_logger.warning(
"You need to set up the pruning feature to utilize `plot_intermediate_values()`"
)
return go.Figure(data=[], layout=layout)
figure = go.Figure(data=traces, layout=layout,)
return figure
def create_log_file(study: Study, log_file: str):
"""
Create Log File
===============
Allows to create a log file containing a pandas `DataFrame` of trials in the `Study`.
Parameters
----------
study : optuna.study.Study
Set of `Trial` objects deriving from an Ask-and-Tell interface.
log_file : str
Name of file reporting the study results.
Returns
-------
None
"""
df = study.trials_dataframe(attrs=('number', 'params', 'value', 'state'))
with open(log_file, 'w') as file:
print(df.to_string(), file=file)
