def modeleval_window(timed_templates, window_size=60, remove_junk_drawer=False):
windows = window(timed_templates, window_size, remove_junk_drawer, template_ids_only=False)
modeleval_windows = []
for window_id, timed_templates in windows.iteritems():
# It doesn't matter which TimedTemplate we take since all in the same window will resolve to the same start/end times.
m = timed_templates[0].ts % window_size
start_time = timed_templates[0].ts - m
end_time = start_time + window_size
modeleval_windows.append(ModelEvalWindow(start_time=start_time, end_time=end_time, timed_templates=timed_templates))
return modeleval_windows
def modelgen_window(timed_templates, window_size=60, remove_junk_drawer=False):
"""
This function was written to take in the output of the apply_template function.
It groups template occurrences into "windows" (aka transactions) that will be passed on to a
market basket analysis algorithm in events/events.py.
By default the window size is 60 seconds.
Args:
timed_templates: iterable of timed_templates
Kwargs:
window_size: # of seconds to allow for each window size (default: 60)
Returns:
windows: list of sets containing TimedTemplate named tuples
"""
windows = window(timed_templates, window_size, remove_junk_drawer, template_ids_only=True)
modelgen_windows = [ModelGenWindow(template_ids=template_ids) for window_id, template_ids in windows.iteritems()]
return modelgen_windows