def test2_skewt_predict_is_length(): """ Tests that the length of the predict IS dataframe is equal to no of steps h """ model = pf.GASX(formula="y ~ x1 + x2", data=data, ar=1, sc=1, family=pf.GASSkewt()) x = model.fit() assert(model.predict_is(h=5).shape[0] == 5)
def test_skewt_predict_is_length(): """ Tests that the prediction IS dataframe length is equal to the number of steps h """ model = pf.GASLLT(data=data, family=pf.GASSkewt()) x = model.fit() assert(model.predict_is(h=5).shape[0] == 5)
def test_skewt_predict_length():
"""
Tests that the length of the predict dataframe is equal to no of steps h
"""
model = pf.GASReg(formula="y ~ x1", data=data, family=pf.GASSkewt())
x = model.fit()
x.summary()
assert (model.predict(h=5, oos_data=data_oos).shape[0] == 5)
def test_skewt_predict_is_nans(): """ Tests that the in-sample predictions are not nans """ model = pf.GAS(data=data, ar=1, sc=1, family=pf.GASSkewt()) x = model.fit() x.summary() assert(len(model.predict_is(h=5).values[np.isnan(model.predict_is(h=5).values)]) == 0)
def test_skewt_predict_length(): """ Tests that the prediction dataframe length is equal to the number of steps h """ model = pf.GAS(data=data, ar=1, sc=1, family=pf.GASSkewt()) x = model.fit() x.summary() assert(model.predict(h=5).shape[0] == 5)
def test_skewt_predict_nans(): """ Tests that the predictions are not nans model = pf.GASLLT(data=data, family=pf.GASSkewt()) """ model = pf.GASLLT(data=data, family=pf.GASSkewt()) x = model.fit() x.summary() assert(len(model.predict(h=5).values[np.isnan(model.predict(h=5).values)]) == 0)
def test_skewt_pml():
"""
Tests a PML model estimated with Laplace approximation and that the length of the
latent variable list is correct, and that the estimated latent variables are not nan
"""
model = pf.GASLLT(data=data, family=pf.GASSkewt())
x = model.fit('PML')
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)
def test_skewt_mh():
"""
Tests an GAS model estimated with Metropolis-Hastings and that the length of the
latent variable list is correct, and that the estimated latent variables are not nan
"""
model = pf.GASLLT(data=data, family=pf.GASSkewt())
x = model.fit('M-H',nsims=300)
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)
def test_skewt_bbvi():
"""
Tests an GAS model estimated with BBVI and that the length of the latent variable
list is correct, and that the estimated latent variables are not nan
"""
model = pf.GASLLT(data=data, family=pf.GASSkewt())
x = model.fit('BBVI',iterations=100)
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)
def test_skewt_couple_terms_integ(): """ Tests latent variable list length is correct, and that the estimated latent variables are not nan """ model = pf.GASLLT(data=data, integ=1, family=pf.GASSkewt()) x = model.fit() 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)
def test_skewt_pml():
"""
Tests an GASX model estimated with PML, and tests that the latent variable
vector length is correct, and that value are not nan
"""
model = pf.GASX(formula="y ~ x1", data=data, ar=1, sc=1, family=pf.GASSkewt())
x = model.fit('PML')
assert(len(model.latent_variables.z_list) == 7)
lvs = np.array([i.value for i in model.latent_variables.z_list])
assert(len(lvs[np.isnan(lvs)]) == 0)
def test_skewt_no_terms(): """ Tests the length of the latent variable vector for an GASX model with no AR or MA terms, and tests that the values are not nan """ model = pf.GASX(formula="y ~ x1", data=data, ar=0, sc=0, family=pf.GASSkewt()) x = model.fit() 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)
def test2_skewt_mh():
"""
Tests an GASX model estimated with MEtropolis-Hastings, with multiple predictors, and
tests that the latent variable vector length is correct, and that value are not nan
"""
model = pf.GASX(formula="y ~ x1 + x2", data=data, ar=1, sc=1, family=pf.GASSkewt())
x = model.fit('M-H',nsims=300)
assert(len(model.latent_variables.z_list) == 8)
lvs = np.array([i.value for i in model.latent_variables.z_list])
assert(len(lvs[np.isnan(lvs)]) == 0)
def test2_skewt_bbvi():
"""
Tests an GASX model estimated with BBVI, with multiple predictors, and
tests that the latent variable vector length is correct, and that value are not nan
"""
model = pf.GASX(formula="y ~ x1 + x2", data=data, ar=1, sc=1, family=pf.GASSkewt())
x = model.fit('BBVI',iterations=100)
assert(len(model.latent_variables.z_list) == 8)
lvs = np.array([i.value for i in model.latent_variables.z_list])
assert(len(lvs[np.isnan(lvs)]) == 0)
def test_skewt_couple_terms_integ(): """ Tests an GAS model with 1 AR and 1 MA term, integrated once, and that the latent variable list length is correct, and that the estimated latent variables are not nan """ model = pf.GAS(data=data, ar=1, sc=1, integ=1, family=pf.GASSkewt()) x = model.fit() assert(len(model.latent_variables.z_list) == 6) lvs = np.array([i.value for i in model.latent_variables.z_list]) assert(len(lvs[np.isnan(lvs)]) == 0)
def test_skewt_couple_terms_integ(): """ Tests the length of the latent variable vector for an GASX model with 1 AR and 1 MA term and integrated once, and tests that the values are not nan """ model = pf.GASX(formula="y ~ x1", data=data, ar=1, sc=1, integ=1, family=pf.GASSkewt()) x = model.fit() assert(len(model.latent_variables.z_list) == 7) lvs = np.array([i.value for i in model.latent_variables.z_list]) assert(len(lvs[np.isnan(lvs)]) == 0)
def test_skewt_no_terms(): """ Tests an GAS model with no AR or MA terms, and that the latent variable list length is correct, and that the estimated latent variables are not nan """ model = pf.GAS(data=data, ar=0, sc=0, family=pf.GASSkewt()) x = model.fit() assert(len(model.latent_variables.z_list) == 4) lvs = np.array([i.value for i in model.latent_variables.z_list]) assert(len(lvs[np.isnan(lvs)]) == 0)
def test2_skewt_couple_terms(): """ Tests the length of the latent variable vector for an GASX model with 1 AR and 1 SC term, and two predictors, and tests that the values are not nan """ model = pf.GASX(formula="y ~ x1 + x2", data=data, ar=1, sc=1, family=pf.GASSkewt()) x = model.fit() assert(len(model.latent_variables.z_list) == 8) lvs = np.array([i.value for i in model.latent_variables.z_list]) assert(len(lvs[np.isnan(lvs)]) == 0)
def test_skew_t_model_fit(): model = pf.GASX(formula="y ~ x1", data=data, ar=1, sc=1, family=pf.GASSkewt()) x = model.fit()
x2 = np.random.normal(0,1,400) for i in range(1,len(y)): y[i] = 0.9*y[i-1] + noise[i] + 0.1*x1[i] - 0.3*x2[i] data = pd.DataFrame([y,x1,x2]).T data.columns = ['y', 'x1', 'x2'] y_oos = np.random.normal(0,1,30) x1_oos = np.random.normal(0,1,30) x2_oos = np.random.normal(0,1,30) data_oos = pd.DataFrame([y_oos,x1_oos,x2_oos]).T data_oos.columns = ['y', 'x1', 'x2'] example_model = pf.GASX(formula="y ~ x1", data=data, ar=1, sc=1, family=pf.GASSkewt()) x = example_model.fit() def test_skewt_no_terms(): """ Tests the length of the latent variable vector for an GASX model with no AR or MA terms, and tests that the values are not nan """ model = pf.GASX(formula="y ~ x1", data=data, ar=0, sc=0, family=pf.GASSkewt()) x = model.fit() 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) def test_skew_t_model_fit():