def test_monte_carlo_choices():
"""
Test simulation of choices without capacity constraints. This test just verifies that
the code runs, using a fairly large synthetic dataset.
"""
data = build_data(1000, 100)
monte_carlo_choices(data)
def test_monte_carlo_choices():
"""
Test simulation of choices without capacity constraints. This test just verifies that
the code runs, using a fairly large synthetic dataset.
"""
data = build_data(1000, 100)
monte_carlo_choices(data)
def test_simulation_accuracy():
"""
This test checks that the simulation tool is generating choices that match the
provided probabilities.
"""
data = build_data(5, 3)
# Get values associated with an arbitrary row
r = np.random.randint(0, 15, 1)
row = pd.DataFrame(data).reset_index().iloc[r]
oid = int(row.oid)
aid = int(row.aid)
prob = float(
pd.DataFrame(data).query('oid==' + str(oid) + ' & aid==' +
str(aid)).sum())
n = 1000
count = 0
for i in range(n):
choices = monte_carlo_choices(data)
if (choices.loc[oid] == aid):
count += 1
assert (count / n > prob - 0.1)
assert (count / n < prob + 0.1)
def test_simulation_accuracy():
"""
This test checks that the simulation tool is generating choices that match the
provided probabilities.
"""
data = build_data(5,3)
# Get values associated with an arbitrary row
r = np.random.randint(0, 15, 1)
row = pd.DataFrame(data).reset_index().iloc[r]
oid = int(row.oid)
aid = int(row.aid)
prob = float(pd.DataFrame(data).query('oid=='+str(oid)+' & aid=='+str(aid)).sum())
n = 1000
count = 0
for i in range(n):
choices = monte_carlo_choices(data)
if (choices.loc[oid] == aid):
count += 1
assert(count/n > prob-0.1)
assert(count/n < prob+0.1)
def run(self, chooser_batch_size=None, interaction_terms=None):
"""
Run the model step: simulate choices and use them to update an Orca column.
The simulated choices are saved to the class object for diagnostics. If choices
are unconstrained, the choice table and the probabilities of sampled alternatives
are saved as well.
Parameters
----------
chooser_batch_size : int
This parameter gets passed to
choicemodels.tools.simulation.iterative_lottery_choices and is a temporary
workaround for dealing with memory issues that arise from generating massive
merged choice tables for simulations that involve large numbers of choosers,
large numbers of alternatives, and large numbers of predictors. It allows the
user to specify a batch size for simulating choices one chunk at a time.
interaction_terms : pandas.Series, pandas.DataFrame, or list of either, optional
Additional column(s) of interaction terms whose values depend on the
combination of observation and alternative, to be merged onto the final data
table. If passed as a Series or DataFrame, it should include a two-level
MultiIndex. One level's name and values should match an index or column from
the observations table, and the other should match an index or column from the
alternatives table.
Returns
-------
None
"""
check_choicemodels_version()
from choicemodels import MultinomialLogit
from choicemodels.tools import (MergedChoiceTable, monte_carlo_choices,
iterative_lottery_choices)
# Clear simulation attributes from the class object
self.mergedchoicetable = None
self.probabilities = None
self.choices = None
if interaction_terms is not None:
uniq_intx_idx_names = set([
idx for intx in interaction_terms for idx in intx.index.names
])
obs_extra_cols = to_list(self.chooser_size) + \
list(uniq_intx_idx_names)
alts_extra_cols = to_list(
self.alt_capacity) + list(uniq_intx_idx_names)
else:
obs_extra_cols = to_list(self.chooser_size)
alts_extra_cols = to_list(self.alt_capacity)
# get any necessary extra columns from the mct intx operations spec
if self.mct_intx_ops:
intx_extra_obs_cols = self.mct_intx_ops.get('extra_obs_cols', [])
intx_extra_obs_cols = to_list(intx_extra_obs_cols)
obs_extra_cols += intx_extra_obs_cols
intx_extra_alts_cols = self.mct_intx_ops.get('extra_alts_cols', [])
intx_extra_alts_cols = to_list(intx_extra_alts_cols)
alts_extra_cols += intx_extra_alts_cols
observations = get_data(tables=self.out_choosers,
fallback_tables=self.choosers,
filters=self.out_chooser_filters,
model_expression=self.model_expression,
extra_columns=obs_extra_cols)
if len(observations) == 0:
print("No valid choosers")
return
alternatives = get_data(tables=self.out_alternatives,
fallback_tables=self.alternatives,
filters=self.out_alt_filters,
model_expression=self.model_expression,
extra_columns=alts_extra_cols)
if len(alternatives) == 0:
print("No valid alternatives")
return
# Remove filter columns before merging, in case column names overlap
expr_cols = columns_in_formula(self.model_expression)
obs_cols = set(
observations.columns) & set(expr_cols + to_list(obs_extra_cols))
observations = observations[list(obs_cols)]
alt_cols = set(
alternatives.columns) & set(expr_cols + to_list(alts_extra_cols))
alternatives = alternatives[list(alt_cols)]
# Callables for iterative choices
def mct(obs, alts, intx_ops=None):
this_mct = MergedChoiceTable(obs,
alts,
sample_size=self.alt_sample_size,
interaction_terms=interaction_terms)
if intx_ops:
this_mct = self.perform_mct_intx_ops(this_mct)
this_mct.sample_size = self.alt_sample_size
return this_mct
def probs(mct):
return self.model.probabilities(mct)
if self.constrained_choices is True:
choices = iterative_lottery_choices(
observations,
alternatives,
mct_callable=mct,
probs_callable=probs,
alt_capacity=self.alt_capacity,
chooser_size=self.chooser_size,
max_iter=self.max_iter,
chooser_batch_size=chooser_batch_size,
mct_intx_ops=self.mct_intx_ops)
else:
choicetable = mct(observations,
alternatives,
intx_ops=self.mct_intx_ops)
probabilities = probs(choicetable)
choices = monte_carlo_choices(probabilities)
# Save data to class object if available
self.mergedchoicetable = choicetable
self.probabilities = probabilities
# Save choices to class object for diagnostics
self.choices = choices
# Update Orca
update_column(table=self.out_choosers,
fallback_table=self.choosers,
column=self.out_column,
fallback_column=self.choice_column,
data=choices)