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remove sub-seconds from stats #147

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merged 5 commits into from
Oct 19, 2023
Merged

remove sub-seconds from stats #147

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12b493b
remove microseconds from stats
zkoppert Oct 17, 2023
f781c0a
use numpy round instead of relying on builtin
zkoppert Oct 17, 2023
8904436
Convert to total_seconds for math operations then return to timedelta…
zkoppert Oct 17, 2023
a3d915e
remove total_seconds change for unused code
zkoppert Oct 19, 2023
acfec8e
revert last commit
zkoppert Oct 19, 2023
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24 changes: 16 additions & 8 deletions labels.py
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Original file line number Diff line number Diff line change
Expand Up @@ -101,17 +101,25 @@ def get_stats_time_in_labels(
if issue.label_metrics[label] is None:
continue
if label not in time_in_labels:
time_in_labels[label] = [issue.label_metrics[label]]
time_in_labels[label] = [issue.label_metrics[label].total_seconds()]
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The rest of this PR is add numpy.round() and some cosmetic changes.

Just curious, why did you have to add the .total_seconds here?

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This was definitely a mistake. In fact if the label is not in time_in_labels then I don't think we should be doing anything! I'll fix the total_seconds part here and remove the whole line in a separate PR with some testing.

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Well, I was wrong. 😄 This is needed. This part of the code stores the timedelta information for a given label and instead of storing it as a timedelta object, i'm choosing to store it as a number representing total_seconds so that round and averaging is easier. It is later converted back into a timedelta object after the math operations are complete.

else:
time_in_labels[label].append(issue.label_metrics[label])
time_in_labels[label].append(
issue.label_metrics[label].total_seconds()
)

average_time_in_labels = {}
med_time_in_labels = {}
ninety_percentile_in_labels = {}
for label, time_list in time_in_labels.items():
average_time_in_labels[label] = numpy.average(time_list)
med_time_in_labels[label] = numpy.median(time_list)
ninety_percentile_in_labels[label] = numpy.percentile(time_list, 90, axis=0)
average_time_in_labels[label] = timedelta(
seconds=numpy.round(numpy.average(time_list))
)
med_time_in_labels[label] = timedelta(
seconds=numpy.round(numpy.median(time_list))
)
ninety_percentile_in_labels[label] = timedelta(
seconds=numpy.round(numpy.percentile(time_list, 90, axis=0))
)

for label in labels:
if label not in average_time_in_labels:
Expand All @@ -120,8 +128,8 @@ def get_stats_time_in_labels(
ninety_percentile_in_labels[label] = None

stats = {
'avg': average_time_in_labels,
'med': med_time_in_labels,
'90p': ninety_percentile_in_labels
"avg": average_time_in_labels,
"med": med_time_in_labels,
"90p": ninety_percentile_in_labels,
}
return stats
15 changes: 9 additions & 6 deletions time_to_answer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -43,16 +43,19 @@ def get_stats_time_to_answer(
# Calculate stats describing time to answer
num_issues_with_time_to_answer = len(issues_with_time_to_answer)
if num_issues_with_time_to_answer > 0:
average_time_to_answer = numpy.average(answer_times)
med_time_to_answer = numpy.median(answer_times)
ninety_percentile_time_to_answer = numpy.percentile(answer_times, 90, axis=0)
average_time_to_answer = numpy.round(numpy.average(answer_times))
med_time_to_answer = numpy.round(numpy.median(answer_times))
ninety_percentile_time_to_answer = numpy.round(
numpy.percentile(answer_times, 90, axis=0)
)
else:
return None

stats = {
'avg': timedelta(seconds=average_time_to_answer),
'med': timedelta(seconds=med_time_to_answer),
'90p': timedelta(seconds=ninety_percentile_time_to_answer)}
"avg": timedelta(seconds=average_time_to_answer),
"med": timedelta(seconds=med_time_to_answer),
"90p": timedelta(seconds=ninety_percentile_time_to_answer),
}

# Print the average time to answer converting seconds to a readable time format
print(f"Average time to answer: {timedelta(seconds=average_time_to_answer)}")
Expand Down
15 changes: 9 additions & 6 deletions time_to_close.py
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Original file line number Diff line number Diff line change
Expand Up @@ -84,16 +84,19 @@ def get_stats_time_to_close(
# Calculate stats describing time to close
num_issues_with_time_to_close = len(issues_with_time_to_close)
if num_issues_with_time_to_close > 0 and total_time_to_close is not None:
average_time_to_close = numpy.average(close_times)
med_time_to_close = numpy.median(close_times)
ninety_percentile_time_to_close = numpy.percentile(close_times, 90, axis=0)
average_time_to_close = numpy.round(numpy.average(close_times))
med_time_to_close = numpy.round(numpy.median(close_times))
ninety_percentile_time_to_close = numpy.round(
numpy.percentile(close_times, 90, axis=0)
)
else:
return None

stats = {
'avg': timedelta(seconds=average_time_to_close),
'med': timedelta(seconds=med_time_to_close),
'90p': timedelta(seconds=ninety_percentile_time_to_close)}
"avg": timedelta(seconds=average_time_to_close),
"med": timedelta(seconds=med_time_to_close),
"90p": timedelta(seconds=ninety_percentile_time_to_close),
}

# Print the average time to close converting seconds to a readable time format
print(f"Time to close: {timedelta(seconds=average_time_to_close)}")
Expand Down
35 changes: 25 additions & 10 deletions time_to_first_response.py
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Original file line number Diff line number Diff line change
Expand Up @@ -58,7 +58,13 @@ def measure_time_to_first_response(
number=20, sort="created", direction="asc"
) # type: ignore
for comment in comments:
if ignore_comment(issue.issue.user, comment.user, ignore_users, comment.created_at, ready_for_review_at):
if ignore_comment(
issue.issue.user,
comment.user,
ignore_users,
comment.created_at,
ready_for_review_at,
):
continue
first_comment_time = comment.created_at
break
Expand All @@ -68,8 +74,13 @@ def measure_time_to_first_response(
if pull_request:
review_comments = pull_request.reviews(number=50) # type: ignore
for review_comment in review_comments:
if ignore_comment(issue.issue.user, review_comment.user, ignore_users,
review_comment.submitted_at, ready_for_review_at):
if ignore_comment(
issue.issue.user,
review_comment.user,
ignore_users,
review_comment.submitted_at,
ready_for_review_at,
):
continue
first_review_comment_time = review_comment.submitted_at
break
Expand Down Expand Up @@ -119,7 +130,8 @@ def ignore_comment(
# ignore comments by the issue creator
or comment_user.login == issue_user.login
# ignore comments created before the issue was ready for review
or (ready_for_review_at and comment_created_at < ready_for_review_at))
or (ready_for_review_at and comment_created_at < ready_for_review_at)
)


def get_stats_time_to_first_response(
Expand All @@ -145,14 +157,17 @@ def get_stats_time_to_first_response(
if len(issues) - none_count <= 0:
return None

average_seconds_to_first_response = numpy.average(response_times)
med_seconds_to_first_response = numpy.median(response_times)
ninety_percentile_seconds_to_first_response = numpy.percentile(response_times, 90, axis=0)
average_seconds_to_first_response = numpy.round(numpy.average(response_times))
med_seconds_to_first_response = numpy.round(numpy.median(response_times))
ninety_percentile_seconds_to_first_response = numpy.round(
numpy.percentile(response_times, 90, axis=0)
)

stats = {
'avg': timedelta(seconds=average_seconds_to_first_response),
'med': timedelta(seconds=med_seconds_to_first_response),
'90p': timedelta(seconds=ninety_percentile_seconds_to_first_response)}
"avg": timedelta(seconds=average_seconds_to_first_response),
"med": timedelta(seconds=med_seconds_to_first_response),
"90p": timedelta(seconds=ninety_percentile_seconds_to_first_response),
}

# Print the average time to first response converting seconds to a readable time format
print(
Expand Down