def test_lrtest():
"""
Ensures a correct result of the lrtest. The comparison values were
obtained from comparison with lrtest in R's lmtest package
"""
general = MixedLogit()
general.loglikelihood = 1312
restricted = MixedLogit()
restricted.loglikelihood = -1305
general.loglikelihood = -1312
general.coeff_ = np.zeros(4)
restricted.coeff_ = np.zeros(2)
obtained = lrtest(general, restricted)
expected = {'pval': 0.0009118819655545164, 'chisq': 14, 'degfree': 2}
def test_predict():
"""
Ensures that returned choice probabilities are consistent.
"""
# There is no need to initialize a random seed as the halton draws produce
# reproducible results
P = 1 # Without panel data
betas = np.array([.1, .1, .1, .1])
X_ = X.reshape(N, P, J, K)
model = MixedLogit()
model._rvidx, model._rvdist = np.array([True, True]), np.array(['n', 'n'])
model.alternatives = np.array([1, 2])
model.coeff_ = betas
model.randvars = randvars
model._isvars, model._asvars, model._varnames = [], varnames, varnames
model._fit_intercept = False
model.coeff_names = np.array(["a", "b", "sd.a", "sd.b"])
#model.fit(X, y, varnames, alts, ids, randvars, verbose=0, halton=True)
y_pred, proba, freq = model.predict(X,
varnames,
alts,
ids,
n_draws=R,
return_proba=True,
return_freq=True)
# Compute choice probabilities by hand
draws = model._get_halton_draws(N, R, K) # (N,Kr,R)
Br = betas[None, [0, 1], None] + draws * betas[None, [2, 3], None]
V = np.einsum('npjk,nkr -> npjr', X_, Br)
V[V > 700] = 700
eV = np.exp(V)
e_proba = eV / np.sum(eV, axis=2, keepdims=True)
expec_proba = e_proba.prod(axis=1).mean(axis=-1)
expec_ypred = model.alternatives[np.argmax(expec_proba, axis=1)]
alt_list, counts = np.unique(expec_ypred, return_counts=True)
expec_freq = dict(
zip(list(alt_list), list(np.round(counts / np.sum(counts), 3))))
assert np.array_equal(expec_ypred, y_pred)
assert expec_freq == freq