def test_mnl_prediction(obs, alts):
"""
Confirm that fitted probabilities in the new codebase match urbansim.urbanchoice.
Only runs if the urbansim package has been installed.
"""
try:
from urbansim.urbanchoice.mnl import mnl_simulate
except:
print("Comparison of MNL simulation results skipped because urbansim is not installed")
return
# produce a fitted model
mct = MergedChoiceTable(obs, alts, 'choice', 5)
m = MultinomialLogit(mct, model_expression='obsval + altval - 1')
results = m.fit()
# get predicted probabilities using choicemodels
probs1 = results.probabilities(mct)
# compare to probabilities from urbansim.urbanchoice
dm = dmatrix(results.model_expression, data=mct.to_frame(), return_type='dataframe')
probs = mnl_simulate(data=dm, coeff=results.fitted_parameters,
numalts=mct.sample_size, returnprobs=True)
df = mct.to_frame()
df['prob'] = probs.flatten()
probs2 = df.prob
pd.testing.assert_series_equal(probs1, probs2)
def test_mnl_estimation(obs, alts):
"""
Confirm that estimated params from the new interface match urbansim.urbanchoice.
Only runs if the urbansim package has been installed.
"""
try:
from urbansim.urbanchoice.mnl import mnl_estimate
except:
print(
"Comparison of MNL estimation results skipped because urbansim is not installed"
)
return
model_expression = 'obsval + altval - 1'
mct = MergedChoiceTable(obs, alts, 'choice')
# new interface
m = MultinomialLogit(mct, model_expression)
r = m.fit().get_raw_results()
# old interface
dm = dmatrix(model_expression, mct.to_frame())
chosen = np.reshape(mct.to_frame()[mct.choice_col].values, (100, 5))
log_lik, fit = mnl_estimate(np.array(dm), chosen, numalts=5)
for k, v in log_lik.items():
assert (v == pytest.approx(r['log_likelihood'][k], 0.00001))
assert_frame_equal(
fit, r['fit_parameters'][['Coefficient', 'Std. Error', 'T-Score']])
def test_mnl_estimation(obs, alts):
"""
Confirm that estimated params from the new interface match urbansim.urbanchoice.
Only runs if the urbansim package has been installed.
"""
try:
from urbansim.urbanchoice.mnl import mnl_estimate
except:
print("Comparison of MNL estimation results skipped because urbansim is not installed")
return
model_expression = 'obsval + altval - 1'
mct = MergedChoiceTable(obs, alts, 'choice')
# new interface
m = MultinomialLogit(mct, model_expression)
r = m.fit().get_raw_results()
# old interface
dm = dmatrix(model_expression, mct.to_frame())
chosen = np.reshape(mct.to_frame()[mct.choice_col].values, (100, 5))
log_lik, fit = mnl_estimate(np.array(dm), chosen, numalts=5)
for k,v in log_lik.items():
assert(v == pytest.approx(r['log_likelihood'][k], 0.00001))
assert_frame_equal(fit, r['fit_parameters'][['Coefficient', 'Std. Error', 'T-Score']])
def test_mnl_estimation(obs, alts):
"""
Confirm that estimated params from the new interface match urbansim.urbanchoice.
"""
model_expression = 'obsval + altval - 1'
mct = MergedChoiceTable(obs, alts, 'choice')
# new interface
m = MultinomialLogit(mct, model_expression)
r = m.fit().get_raw_results()
# old interface
dm = dmatrix(model_expression, mct.to_frame())
chosen = np.reshape(mct.to_frame()[mct.choice_col].values, (100, 5))
log_lik, fit = mnl_estimate(np.array(dm), chosen, numalts=5)
for k,v in log_lik.items():
assert(v == pytest.approx(r['log_likelihood'][k], 0.00001))
assert_frame_equal(fit, r['fit_parameters'][['Coefficient', 'Std. Error', 'T-Score']])
def test_mnl_prediction(obs, alts):
"""
Confirm that fitted probabilities in the new codebase match urbansim.urbanchoice.
Only runs if the urbansim package has been installed.
"""
try:
from urbansim.urbanchoice.mnl import mnl_simulate
except:
print(
"Comparison of MNL simulation results skipped because urbansim is not installed"
)
return
# produce a fitted model
mct = MergedChoiceTable(obs, alts, 'choice', 5)
m = MultinomialLogit(mct, model_expression='obsval + altval - 1')
results = m.fit()
# get predicted probabilities using choicemodels
probs1 = results.probabilities(mct)
# compare to probabilities from urbansim.urbanchoice
dm = dmatrix(results.model_expression,
data=mct.to_frame(),
return_type='dataframe')
probs = mnl_simulate(data=dm,
coeff=results.fitted_parameters,
numalts=mct.sample_size,
returnprobs=True)
df = mct.to_frame()
df['prob'] = probs.flatten()
probs2 = df.prob
pd.testing.assert_series_equal(probs1, probs2)
def test_mnl_prediction(obs, alts):
"""
Confirm that fitted probabilities in the new codebase match urbansim.urbanchoice.
"""
# produce a fitted model
mct = MergedChoiceTable(obs, alts, 'choice', 5)
m = MultinomialLogit(mct, model_expression='obsval + altval - 1')
results = m.fit()
# get predicted probabilities using choicemodels
probs1 = results.probabilities(mct)
# compare to probabilities from urbansim.urbanchoice
dm = dmatrix(results.model_expression, data=mct.to_frame(), return_type='dataframe')
probs = mnl_simulate(data=dm, coeff=results.fitted_parameters,
numalts=mct.sample_size, returnprobs=True)
df = mct.to_frame()
df['prob'] = probs.flatten()
probs2 = df.prob
pd.testing.assert_series_equal(probs1, probs2)
def fit(self):
"""
Fit the model; save and report results. This uses the ChoiceModels estimation
engine (originally from UrbanSim MNL).
The `fit()` method can be run as many times as desired. Results will not be saved
with Orca or ModelManager until the `register()` method is run.
"""
# TO DO - update choicemodels to accept a column name for chosen alts
observations = self._get_df(tables=self.choosers,
filters=self.chooser_filters)
chosen = observations[self.choice_column]
alternatives = self._get_df(tables=self.alternatives,
filters=self.alt_filters)
data = MergedChoiceTable(observations=observations,
alternatives=alternatives,
chosen_alternatives=chosen,
sample_size=self._get_alt_sample_size())
model = MultinomialLogit(data=data.to_frame(),
observation_id_col=data.observation_id_col,
choice_col=data.choice_col,
model_expression=self.model_expression)
results = model.fit()
self.name = self._generate_name()
self.summary_table = str(results)
print(self.summary_table)
# For now, just save the summary table and fitted parameters
coefs = results.get_raw_results()['fit_parameters']['Coefficient']
self.fitted_parameters = coefs.tolist()
def run(self):
"""
Run the model step: calculate simulated choices and use them to update a column.
Predicted probabilities and simulated choices come from ChoiceModels. For now,
the choices are unconstrained (any number of choosers can select the same
alternative).
The predicted probabilities and simulated choices are saved to the class object
for interactive use (`probabilities` with type pd.DataFrame, and `choices` with
type pd.Series) but are not persisted in the dictionary representation of the
model step.
"""
observations = self._get_df(tables=self.out_choosers,
fallback_tables=self.choosers,
filters=self.out_chooser_filters)
alternatives = self._get_df(tables=self.out_alternatives,
fallback_tables=self.alternatives,
filters=self.out_alt_filters)
numalts = self._get_alt_sample_size()
mct = MergedChoiceTable(observations=observations,
alternatives=alternatives,
sample_size=numalts)
mct_df = mct.to_frame()
# Data columns need to align with the coefficients
dm = patsy.dmatrix(self.model_expression,
data=mct_df,
return_type='dataframe')
# Get probabilities and choices
probs = mnl.mnl_simulate(data=dm,
coeff=self.fitted_parameters,
numalts=numalts,
returnprobs=True)
# TO DO - this ends up recalculating the probabilities because there's not
# currently a code path to get both at once - fix this)
choice_positions = mnl.mnl_simulate(data=dm,
coeff=self.fitted_parameters,
numalts=numalts,
returnprobs=False)
ids = mct_df[mct.alternative_id_col].tolist()
choices = self._get_chosen_ids(ids, choice_positions)
# Save results to the class object (via df to include indexes)
mct_df['probability'] = np.reshape(probs, (probs.size, 1))
self.probabilities = mct_df[[
mct.observation_id_col, mct.alternative_id_col, 'probability'
]]
observations['choice'] = choices
self.choices = observations.choice
# Update Orca
if self.out_choosers is not None:
table = orca.get_table(self.out_choosers)
else:
table = orca.get_table(self.choosers)
if self.out_column is not None:
column = self.out_column
else:
column = self.choice_column
table.update_col_from_series(column, observations.choice, cast=True)
# Print a message about limited usage
print(
"Warning: choices are unconstrained; additional functionality in progress"
)
def fit(self, mct=None):
"""
Fit the model; save and report results. This uses the ChoiceModels estimation
engine (originally from UrbanSim MNL).
The `fit()` method can be run as many times as desired. Results will not be saved
with Orca or ModelManager until the `register()` method is run.
After sampling alternatives for each chooser, the merged choice table is saved to
the class object for diagnostic use (`mergedchoicetable` with type
choicemodels.tools.MergedChoiceTable).
Parameters
----------
mct : choicemodels.tools.MergedChoiceTable
This parameter is a temporary backdoor allowing us to pass in a more
complicated merged choice table than can be generated within the template, for
example including sampling weights or interaction terms. This will work for
model estimation, but is not yet hooked up to the prediction functionality.
Returns
-------
None
"""
if (mct is not None):
data = mct
else:
# TO DO - update choicemodels to accept a column name for chosen alts
observations = self._get_df(tables=self.choosers,
filters=self.chooser_filters)
if (self.chooser_sample_size is not None):
observations = observations.sample(self.chooser_sample_size)
chosen = observations[self.choice_column]
alternatives = self._get_df(tables=self.alternatives,
filters=self.alt_filters)
data = MergedChoiceTable(observations=observations,
alternatives=alternatives,
chosen_alternatives=chosen,
sample_size=self._get_alt_sample_size())
model = MultinomialLogit(data=data.to_frame(),
observation_id_col=data.observation_id_col,
choice_col=data.choice_col,
model_expression=self.model_expression)
results = model.fit()
self.name = self._generate_name()
self.summary_table = str(results)
print(self.summary_table)
# For now, just save the summary table and fitted parameters
coefs = results.get_raw_results()['fit_parameters']['Coefficient']
self.fitted_parameters = coefs.tolist()
# Save merged choice table to the class object for diagnostic use
self.mergedchoicetable = data
pd.set_option('display.float_format', lambda x: '%.3f' % x)
choosers = trips.loc[np.random.choice(trips.index, 500, replace=False)]
choosers = choosers.loc[choosers.trip_distance_miles.notnull()]
numalts = 10
merged = MergedChoiceTable(observations = choosers,
alternatives = tracts,
chosen_alternatives = choosers.full_tract_id,
sample_size = numalts)
model_expression = "home_density + work_density + school_density"
model = MultinomialLogit(merged.to_frame(),
merged.observation_id_col,
merged.choice_col,
model_expression)
results = model.fit()
results.report_fit()
"""
model_expression = OrderedDict([('home_density', 'all_same'),
('work_density', 'all_same'),
('school_density', 'all_same')])
model = MultinomialLogit(data = merged.to_frame(),
observation_id_col = merged.observation_id_col,