from magichour.api.local.modelgen import events
from magichour.api.local.util.log import get_logger, log_time
from magichour.api.local.util.modelgen import tfidf_filter_events
logger = get_logger(__name__)
@log_time
def paris_substep(gen_windows, *args, **kwargs):
logger.info("Running PARIS algorithm... (%s)", kwargs)
gen_events = events.paris(gen_windows, *args, **kwargs)
return gen_events
@log_time
def fp_growth_substep(gen_windows, *args, **kwargs):
logger.info("Running fp_growth algorithm... (%s)", kwargs)
gen_events = events.fp_growth(gen_windows, *args, **kwargs)
return gen_events
@log_time
def event_step(gen_windows, event_algorithm="fp_growth", *args, **kwargs):
CHOICES = {"fp_growth": fp_growth_substep, "paris": paris_substep}
event_fn = CHOICES.get(event_algorithm, None)
if not event_fn:
log_exc(logger, "event_algorithm must be one of: %s" % CHOICES)
gen_events = event_fn(gen_windows, *args, **kwargs)
logger.info("==========Custom post processing for sample data==========")
# Note that calling this will reassign random event IDs.
threshold = kwargs.pop("tfidf_threshold", 0)
from magichour.api.local.util.log import get_logger, log_time
from magichour.api.local.modeleval.apply import apply_events
logger = get_logger(__name__)
@log_time
def evalapply_step(gen_events, timed_templates, **kwargs):
logger.info("Applying events to timed templates...")
timed_events = apply_events(gen_events, timed_templates, **kwargs)
return timed_events
