def test_alternative_specific_coeffs(num_alts):
template = np.array(
[[0, 0, 0],
[1, 0, 0],
[0, 1, 0],
[0, 0, 1]])
fish = get_df({'data': 'fish.csv'})
fish_choosers = get_choosers({'choosers': 'fish_choosers.csv'})
fish_chosen = get_chosen(fish, num_alts, {'column': 'mode'})
# construct design matrix with columns repeated for 3 / 4 of alts
num_choosers = len(fish['chid'].unique())
intercept_df = pd.DataFrame(
np.tile(template, (num_choosers, 1)),
columns=[
'boat:(intercept)', 'charter:(intercept)', 'pier:(intercept)'])
income_df = pd.DataFrame(
np.tile(template, (num_choosers, 1)),
columns=[
'boat:income', 'charter:income', 'pier:income'])
for idx, row in fish.iterrows():
income_df.loc[idx] = income_df.loc[idx] * row['income']
dm = pd.concat([intercept_df, income_df], axis=1)
# construct choosers design matrix
num_choosers = len(fish_choosers['chid'].unique())
intercept_df = pd.DataFrame(
np.tile(template, (num_choosers, 1)),
columns=[
'boat:(intercept)', 'charter:(intercept)', 'pier:(intercept)'])
income_df = pd.DataFrame(
np.tile(template, (num_choosers, 1)),
columns=[
'boat:income', 'charter:income', 'pier:income'])
for idx, row in fish_choosers.iterrows():
income_df.loc[idx] = income_df.loc[idx] * row['income']
choosers_dm = pd.concat([intercept_df, income_df], axis=1)
# test estimation
expected = pd.Series([
7.389208e-01, 1.341291e+00, 8.141503e-01, 9.190636e-05,
-3.163988e-05, -1.434029e-04],
index=[
'boat:(intercept)', 'charter:(intercept)', 'pier:(intercept)',
'boat:income', 'charter:income', 'pier:income'])
log_like, fit = mnl.mnl_estimate(dm.values, fish_chosen, num_alts)
result = pd.Series(fit.Coefficient.values, index=dm.columns)
result, expected = result.align(expected)
npt.assert_allclose(result.values, expected.values, rtol=1e-4)
def test_alternative_specific_coeffs(num_alts):
template = np.array(
[[0, 0, 0],
[1, 0, 0],
[0, 1, 0],
[0, 0, 1]])
fish = get_df({'data': 'fish.csv'})
fish_choosers = get_choosers({'choosers': 'fish_choosers.csv'})
fish_chosen = get_chosen(fish, num_alts, {'column': 'mode'})
# construct design matrix with columns repeated for 3 / 4 of alts
num_choosers = len(fish['chid'].unique())
intercept_df = pd.DataFrame(
np.tile(template, (num_choosers, 1)),
columns=[
'boat:(intercept)', 'charter:(intercept)', 'pier:(intercept)'])
income_df = pd.DataFrame(
np.tile(template, (num_choosers, 1)),
columns=[
'boat:income', 'charter:income', 'pier:income'])
for idx, row in fish.iterrows():
income_df.loc[idx] = income_df.loc[idx] * row['income']
dm = pd.concat([intercept_df, income_df], axis=1)
# construct choosers design matrix
num_choosers = len(fish_choosers['chid'].unique())
intercept_df = pd.DataFrame(
np.tile(template, (num_choosers, 1)),
columns=[
'boat:(intercept)', 'charter:(intercept)', 'pier:(intercept)'])
income_df = pd.DataFrame(
np.tile(template, (num_choosers, 1)),
columns=[
'boat:income', 'charter:income', 'pier:income'])
for idx, row in fish_choosers.iterrows():
income_df.loc[idx] = income_df.loc[idx] * row['income']
choosers_dm = pd.concat([intercept_df, income_df], axis=1)
# test estimation
expected = pd.Series([
7.389208e-01, 1.341291e+00, 8.141503e-01, 9.190636e-05,
-3.163988e-05, -1.434029e-04],
index=[
'boat:(intercept)', 'charter:(intercept)', 'pier:(intercept)',
'boat:income', 'charter:income', 'pier:income'])
log_like, fit = mnl.mnl_estimate(dm.values, fish_chosen, num_alts)
result = pd.Series(fit.Coefficient.values, index=dm.columns)
result, expected = result.align(expected)
npt.assert_allclose(result.values, expected.values, rtol=1e-4)
def test_mnl_estimate(dm, chosen, num_alts, test_data):
log_like, fit = mnl.mnl_estimate(dm.values, chosen, num_alts)
result = pd.Series(fit.Coefficient.values, index=dm.columns)
result, expected = result.align(test_data['est_expected'])
npt.assert_allclose(result.values, expected.values, rtol=1e-4)
def fit_coeffs(dm, chosen, num_alts):
log_like, fit = mnl.mnl_estimate(dm.values, chosen, num_alts)
return fit.Coefficient.values
def test_mnl_estimate(dm, chosen, num_alts, test_data):
log_like, fit = mnl.mnl_estimate(dm.values, chosen, num_alts)
result = pd.Series(fit.Coefficient.values, index=dm.columns)
result, expected = result.align(test_data['est_expected'])
npt.assert_allclose(result.values, expected.values, rtol=1e-4)
def fit_coeffs(dm, chosen, num_alts):
log_like, fit = mnl.mnl_estimate(dm.values, chosen, num_alts)
return fit.Coefficient.values
