def test_predict():
model = pf.GASRank(data=data,
team_1="HomeTeam",
team_2="AwayTeam",
score_diff="PointsDiff",
family=pf.Normal())
model.fit()
prediction = model.predict("Denver Broncos",
"Carolina Panthers",
neutral=True)
assert (len(prediction[np.isnan(prediction)]) == 0)
def test_mle():
"""
Tests latent variable list length is correct, and that the estimated
latent variables are not nan
"""
model = pf.GASRank(data=data,
team_1="HomeTeam",
team_2="AwayTeam",
score_diff="PointsDiff",
family=pf.Normal())
x = model.fit()
assert (len(model.latent_variables.z_list) == 3)
lvs = np.array([i.value for i in model.latent_variables.z_list])
assert (len(lvs[np.isnan(lvs)]) == 0)
def test_t_mle_two_components():
"""
Tests latent variable list length is correct, and that the estimated
latent variables are not nan
"""
model = pf.GASRank(data=data.iloc[0:300, :],
team_1="HomeTeam",
team_2="AwayTeam",
score_diff="PointsDiff",
family=pf.t())
model.add_second_component("HQB", "AQB")
x = model.fit('BBVI', iterations=50, map_start=False)
assert (len(model.latent_variables.z_list) == 5)
lvs = np.array([i.value for i in model.latent_variables.z_list])
assert (len(lvs[np.isnan(lvs)]) == 0)
prediction = model.predict("Denver Broncos",
"Carolina Panthers",
"Peyton Manning",
"Cam Newton",
neutral=True)
assert (len(prediction[np.isnan(prediction)]) == 0)