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Don't assume order is the same in observed and predicted features #3243

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Don't assume order is the same in observed and predicted features #3243

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38 changes: 20 additions & 18 deletions ax/analysis/plotly/cross_validation.py
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Original file line number Diff line number Diff line change
Expand Up @@ -161,24 +161,26 @@ def _prepare_data(
f"trial {trial_index}, but has observations from trial "
f"{observed.features.trial_index}."
)
for i in range(len(observed.data.metric_names)):
# Find the index of the metric we want to plot
if not (
observed.data.metric_names[i] == metric_name
and predicted.metric_names[i] == metric_name
):
continue

record = {
"arm_name": observed.arm_name,
"observed": observed.data.means[i],
"predicted": predicted.means[i],
# Take the square root of the the SEM to get the standard deviation
"observed_sem": observed.data.covariance[i][i] ** 0.5,
"predicted_sem": predicted.covariance[i][i] ** 0.5,
}
records.append(record)
break
observed_i = next(
i
for i in range(len(observed.data.metric_names))
if observed.data.metric_names[i] == metric_name
)
predicted_i = next(
i
for i in range(len(observed.data.metric_names))
if predicted.metric_names[i] == metric_name
)

record = {
"arm_name": observed.arm_name,
"observed": observed.data.means[observed_i],
"predicted": predicted.means[predicted_i],
# Take the square root of the variance to get the standard deviation
"observed_sem": observed.data.covariance[observed_i][observed_i] ** 0.5,
"predicted_sem": predicted.covariance[predicted_i][predicted_i] ** 0.5,
}
records.append(record)

return pd.DataFrame.from_records(records)

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