def pet_activity_simple_simulate(set_random_seed, pets_merged, pet_spec):
# choosers: the choice model will be applied to each row of the choosers table (a pandas.DataFrame)
choosers = pets_merged.to_frame()
# spec: table of variable specifications and coefficient values of alternatives (a pandas.DataFrame table
spec = pet_spec
# locals whose values will be accessible to the execution context when the expressions in spec are applied to choosers
locals_d=None
# eval_variables evaluates each of the expressions in spec in the context of each row in of the choosers dataframe
model_design = asim.eval_variables(spec.index, choosers, locals_d)
print "\n### model_design - results of the expressions for each row in choosers"
print model_design
utilities = model_design.dot(spec)
print "\n### utilities - the net utility of each alternative for each row in choosers"
print utilities
probs = asim.utils_to_probs(utilities)
print "\n### probs - utilities normalized as relative choice probablities (summing to 1)"
print probs
# Make choices for each chooser from among the set of alternatives based on probability
choices = asim.make_choices(probs)
print "\n### choices - choices expressed as zero-based index into set of alternatives"
print choices
def test_eval_variables(spec, data):
result = asim.eval_variables(spec.index, data)
pdt.assert_frame_equal(
result,
pd.DataFrame([
[True, False, 4, 1],
[False, True, 4, 1],
[False, True, 5, 1]],
index=data.index, columns=spec.index),
check_names=False)
