def test2_predict_is_nonconstant():
"""
We should not really have predictions that are constant (should be some difference)...
This captures bugs with the predict function not iterating forward
"""
model = pf.GASX(formula="y ~ x1 + x2", data=data, ar=1, sc=1, family=pf.Poisson())
assert(not np.all(predictions.values==predictions.values[0]))
def test_normal_bbvi_elbo():
"""
Tests that the ELBO increases
"""
model = pf.GASReg(formula="y ~ x1 + x2", data=data, family=pf.Poisson())
x = model.fit('BBVI',iterations=200, record_elbo=True, map_start=False)
assert(x.elbo_records[-1]>x.elbo_records[0])
def test_poisson_predict_is_length():
"""
Tests that the prediction IS dataframe length is equal to the number of steps h
"""
model = pf.GAS(data=countdata, ar=2, sc=2, family=pf.Poisson())
x = model.fit()
assert (model.predict_is(h=5).shape[0] == 5)
def test_bbvi_mini_batch_elbo():
"""
Tests that the ELBO increases
"""
model = pf.GASX(formula="y ~ x1", data=data, ar=1, sc=1, family=pf.Poisson())
x = model.fit('BBVI',iterations=500, mini_batch=32, record_elbo=True, map_start=False)
assert(x.elbo_records[-1]>x.elbo_records[0])
def test_normal_predict_is_length():
"""
Tests that the length of the predict IS dataframe is equal to no of steps h
"""
model = pf.GASReg(formula="y ~ x1", data=data, family=pf.Poisson())
x = model.fit()
assert(model.predict_is(h=5).shape[0] == 5)
def test_bbvi_mini_batch_elbo():
"""
Tests that the ELBO increases
"""
model = pf.ARIMA(data=data, ar=1, ma=1, family=pf.Poisson())
x = model.fit('BBVI', iterations=100, mini_batch=32, record_elbo=True)
assert (x.elbo_records[-1] > x.elbo_records[0])
def test_predict_length():
"""
Tests that the prediction dataframe length is equal to the number of steps h
"""
model = pf.ARIMA(data=data, ar=2, ma=2, family=pf.Poisson())
x = model.fit()
assert (model.predict(h=5).shape[0] == 5)
def test2_poisson_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.Poisson())
x = model.fit()
assert(model.predict_is(h=5).shape[0] == 5)
def test_poisson_bbvi_elbo():
"""
Tests that the ELBO increases
"""
model = pf.GAS(data=countdata, ar=1, sc=1, family=pf.Poisson())
x = model.fit('BBVI', iterations=200, record_elbo=True, map_start=False)
assert (x.elbo_records[-1] > x.elbo_records[0])
def gasy(self, topic_index=0):
model = pf.GAS(ar=2,
sc=2,
data=self.topic_counts[topic_index],
family=pf.Poisson())
x = model.fit()
x.summary()
model.plot_fit()
def test_ppc():
"""
Tests PPC value
"""
model = pf.ARIMA(data=data, ar=2, ma=2, family=pf.Poisson())
x = model.fit('BBVI', iterations=100)
p_value = model.ppc(nsims=100)
assert (0.0 <= p_value <= 1.0)
def test_predict_nans():
"""
Tests that the predictions are not nans
"""
model = pf.ARIMA(data=data, ar=2, ma=2, family=pf.Poisson())
x = model.fit()
assert (len(
model.predict(h=5).values[np.isnan(model.predict(h=5).values)]) == 0)
def test_poisson_predict_length():
"""
Tests that the prediction dataframe length is equal to the number of steps h
"""
model = pf.GASLLEV(data=countdata, family=pf.Poisson())
x = model.fit()
x.summary()
assert (model.predict(h=5).shape[0] == 5)
def test2_ppc():
"""
Tests PPC value
"""
model = pf.GASX(formula="y ~ x1 + x2", data=data, ar=1, sc=1, family=pf.Poisson())
x = model.fit('BBVI', iterations=100)
p_value = model.ppc()
assert(0.0 <= p_value <= 1.0)
def test_poisson_ppc():
"""
Tests PPC value
"""
model = pf.GAS(data=countdata, ar=1, sc=1, family=pf.Poisson())
x = model.fit('BBVI', iterations=200)
p_value = model.ppc()
assert (0.0 <= p_value <= 1.0)
def test_poisson_predict_length():
"""
Tests that the length of the predict dataframe is equal to no of steps h
"""
model = pf.GASX(formula="y ~ x1", data=data, ar=1, sc=1, family=pf.Poisson())
x = model.fit()
x.summary()
assert(model.predict(h=5, oos_data=data_oos).shape[0] == 5)
def test_poisson_ppc():
"""
Tests PPC value
"""
model = pf.GASLLEV(data=data, family=pf.Poisson())
x = model.fit('BBVI', iterations=100)
p_value = model.ppc()
assert (0.0 <= p_value <= 1.0)
def test2_poisson_predict_is_nans():
"""
Tests that the predictions in-sample are not NaNs
"""
model = pf.GASX(formula="y ~ x1 + x2", data=data, ar=1, sc=1, family=pf.Poisson())
x = model.fit()
x.summary()
assert(len(model.predict_is(h=5).values[np.isnan(model.predict_is(h=5).values)]) == 0)
def test_poisson_predict_nans():
"""
Tests that the predictions are not NaNs
"""
model = pf.GASX(formula="y ~ x1", data=data, ar=1, sc=1, family=pf.Poisson())
x = model.fit()
x.summary()
assert(len(model.predict(h=5, oos_data=data_oos).values[np.isnan(model.predict(h=5,
oos_data=data_oos).values)]) == 0)
def test_poisson_predict_is_nonconstant():
"""
We should not really have predictions that are constant (should be some difference)...
This captures bugs with the predict function not iterating forward
"""
model = pf.GAS(data=countdata, ar=1, sc=1, family=pf.Poisson())
x = model.fit()
predictions = model.predict_is(h=10, intervals=False)
assert (not np.all(predictions.values == predictions.values[0]))
def test_poisson_sample_model():
"""
Tests sampling function
"""
model = pf.GAS(data=countdata, ar=1, sc=1, family=pf.Poisson())
x = model.fit('BBVI', iterations=200)
sample = model.sample(nsims=100)
assert (sample.shape[0] == 100)
assert (sample.shape[1] == len(data) - 1)
def test2_sample_model():
"""
Tests sampling function
"""
model = pf.GASX(formula="y ~ x1 + x2", data=data, ar=1, sc=1, family=pf.Poisson())
x = model.fit('BBVI', iterations=100)
sample = model.sample(nsims=100)
assert(sample.shape[0]==100)
assert(sample.shape[1]==len(data)-1)
def test_sample_model():
"""
Tests sampling function
"""
model = pf.ARIMA(data=data, ar=2, ma=2, family=pf.Poisson())
x = model.fit('BBVI', iterations=100)
sample = model.sample(nsims=100)
assert (sample.shape[0] == 100)
assert (sample.shape[1] == len(data) - 2)
def test_poisson_predict_nans():
"""
Tests that the predictions are not nans
"""
model = pf.GAS(data=countdata, ar=2, sc=2, family=pf.Poisson())
x = model.fit()
x.summary()
assert (len(
model.predict(h=5).values[np.isnan(model.predict(h=5).values)]) == 0)
def test_poisson_laplace():
"""
Tests an GASX model estimated with Laplace approximation, 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.Poisson())
x = model.fit('Laplace')
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 test_poisson_mh():
"""
Tests an GASX model estimated with Metropolis-Hastings, 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.Poisson())
x = model.fit('M-H',nsims=300)
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 test_bbvi_mini_batch():
"""
Tests an GASX 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.GASX(formula="y ~ x1", data=data, ar=1, sc=1, family=pf.Poisson())
x = model.fit('BBVI',iterations=500, mini_batch=32)
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 test_poisson_couple_terms():
"""
Tests the length of the latent variable vector for an GASX model
with 1 AR and 1 SC term, and tests that the values are not nan
"""
model = pf.GASX(formula="y ~ x1", data=data, ar=1, sc=1, family=pf.Poisson())
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_predict_is_intervals_mh():
"""
Tests prediction intervals are ordered correctly
"""
model = pf.GASX(formula="y ~ x1 + x2", data=data, ar=1, sc=1, family=pf.Poisson())
x = model.fit('M-H', nsims=400)
predictions = model.predict_is(h=10, intervals=True)
assert(np.all(predictions['99% Prediction Interval'].values > predictions['95% Prediction Interval'].values))
assert(np.all(predictions['95% Prediction Interval'].values > predictions['5% Prediction Interval'].values))
assert(np.all(predictions['5% Prediction Interval'].values > predictions['1% Prediction Interval'].values))
def test2_poisson_pml():
"""
Tests an GASX model estimated with PML, 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.Poisson())
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)