Joint Tour Participation - Optimize Two Person Households

[danielsclint]

Disclaimer: This is feature enhancement request. If I understand the code correctly, this should save some time while not impacting the results of the model. If my understanding of the code is incorrect, disregard. This came out of investigating #288.

If a two person household has been chosen to participate in a joint tour, it doesn't seem necessary to send the two person household participants through the Monte Carlo simulation. If the two-person household has already been determined to participate in a joint tour, the Monte Carlo simulation is going to iterate until both participants have been chosen. It would seem easier to process two person household in a deterministic manner and only use the Monte Carlo simulation to determine participants in households with three or more people.

activitysim/activitysim/abm/models/joint_tour_participation.py

Lines 100 to 201 in 7b57c94

	def participants_chooser(probs, choosers, spec, trace_label):
	"""
	custom alternative to logit.make_choices for simulate.simple_simulate
	Choosing participants for mixed tours is trickier than adult or child tours becuase we
	need at least one adult and one child participant in a mixed tour. We call logit.make_choices
	and then check to see if the tour statisfies this requirement, and rechoose for any that
	fail until all are satisfied.
	In principal, this shold always occur eventually, but we fail after MAX_ITERATIONS,
	just in case there is some failure in program logic (haven't seen this occur.)
	Parameters
	----------
	probs : pandas.DataFrame
	Rows for choosers and columns for the alternatives from which they
	are choosing. Values are expected to be valid probabilities across
	each row, e.g. they should sum to 1.
	choosers : pandas.dataframe
	simple_simulate choosers df
	spec : pandas.DataFrame
	simple_simulate spec df
	We only need spec so we can know the column index of the 'participate' alternative
	indicating that the participant has been chosen to participate in the tour
	trace_label : str
	Returns - same as logit.make_choices
	-------
	choices, rands
	choices, rands as returned by logit.make_choices (in same order as probs)
	"""
	assert probs.index.equals(choosers.index)
	# choice is boolean (participate or not)
	model_settings = config.read_model_settings('joint_tour_participation.yaml')
	choice_col = model_settings.get('participation_choice', 'participate')
	assert choice_col in spec.columns, \
	"couldn't find participation choice column '%s' in spec"
	PARTICIPATE_CHOICE = spec.columns.get_loc(choice_col)
	MAX_ITERATIONS = model_settings.get('max_participation_choice_iterations', 5000)
	trace_label = tracing.extend_trace_label(trace_label, 'participants_chooser')
	candidates = choosers.copy()
	choices_list = []
	rands_list = []
	num_tours_remaining = len(candidates.tour_id.unique())
	logger.info('%s %s joint tours to satisfy.', trace_label, num_tours_remaining,)
	iter = 0
	while candidates.shape[0] > 0:
	iter += 1
	if iter > MAX_ITERATIONS:
	logger.warning('%s max iterations exceeded (%s).', trace_label, MAX_ITERATIONS)
	diagnostic_cols = ['tour_id', 'household_id', 'composition', 'adult']
	unsatisfied_candidates = candidates[diagnostic_cols].join(probs)
	tracing.write_csv(unsatisfied_candidates,
	file_name='%s.UNSATISFIED' % trace_label, transpose=False)
	print(unsatisfied_candidates.head(20))
	assert False
	choices, rands = logit.make_choices(probs, trace_label=trace_label, trace_choosers=choosers)
	participate = (choices == PARTICIPATE_CHOICE)
	# satisfaction indexed by tour_id
	tour_satisfaction = get_tour_satisfaction(candidates, participate)
	num_tours_satisfied_this_iter = tour_satisfaction.sum()
	if num_tours_satisfied_this_iter > 0:
	num_tours_remaining -= num_tours_satisfied_this_iter
	satisfied = reindex(tour_satisfaction, candidates.tour_id)
	choices_list.append(choices[satisfied])
	rands_list.append(rands[satisfied])
	# remove candidates of satisfied tours
	probs = probs[~satisfied]
	candidates = candidates[~satisfied]
	logger.info('%s iteration %s : %s joint tours satisfied %s remaining' %
	(trace_label, iter, num_tours_satisfied_this_iter, num_tours_remaining,))
	choices = pd.concat(choices_list)
	rands = pd.concat(rands_list).reindex(choosers.index)
	# reindex choices and rands to match probs and v index
	choices = choices.reindex(choosers.index)
	rands = rands.reindex(choosers.index)
	assert choices.index.equals(choosers.index)
	assert rands.index.equals(choosers.index)
	logger.info('%s %s iterations to satisfy all joint tours.', trace_label, iter,)
	return choices, rands

[dhensle]

#569