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address dana's comment

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yangdsh 2020-04-16 17:16:59 +00:00 committed by Dana Van Aken
parent 2f7396d275
commit ad96c03902
1 changed files with 5 additions and 5 deletions
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10
server/website/website/tasks/async_tasks.py
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@ -1049,6 +1049,9 @@ def map_workload(map_workload_input):
# Compute workload mapping data for each unique workload # Compute workload mapping data for each unique workload
for unique_workload in unique_workloads: for unique_workload in unique_workloads:
# do not include the workload of the current session
if newest_result.workload.pk == unique_workload:
continue
workload_obj = Workload.objects.get(pk=unique_workload) workload_obj = Workload.objects.get(pk=unique_workload)
wkld_results = Result.objects.filter(workload=workload_obj) wkld_results = Result.objects.filter(workload=workload_obj)
if wkld_results.exists() is False: if wkld_results.exists() is False:
@ -1091,11 +1094,11 @@ def map_workload(map_workload_input):
'rowlabels': rowlabels, 'rowlabels': rowlabels,
} }
if len(workload_data) < 2: if len(workload_data) == 0:
# The background task that aggregates the data has not finished running yet # The background task that aggregates the data has not finished running yet
target_data.update(mapped_workload=None, scores=None) target_data.update(mapped_workload=None, scores=None)
LOG.debug('%s: Result = %s\n', task_name, _task_result_tostring(target_data)) LOG.debug('%s: Result = %s\n', task_name, _task_result_tostring(target_data))
LOG.info('%s: Skipping workload mapping because less than 2 workloads are available.', LOG.info('%s: Skipping workload mapping because no different workload is available.',
task_name) task_name)
return target_data, algorithm return target_data, algorithm
@ -1125,9 +1128,6 @@ def map_workload(map_workload_input):
scores = {} scores = {}
for workload_id, workload_entry in list(workload_data.items()): for workload_id, workload_entry in list(workload_data.items()):
LOG.info('%s: %s', newest_result.workload.pk, workload_id)
if newest_result.workload.pk == workload_id:
continue
predictions = np.empty_like(y_target) predictions = np.empty_like(y_target)
X_workload = workload_entry['X_matrix'] X_workload = workload_entry['X_matrix']
X_scaled = X_scaler.transform(X_workload) X_scaled = X_scaler.transform(X_workload)