def test_t_predict_is_length():
    """
    Tests that the prediction IS dataframe length is equal to the number of steps h
    """
    model = pf.GASLLEV(data=data, family=pf.t())
    x = model.fit()
    assert(model.predict_is(h=5).shape[0] == 5)

def test2_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 + x2", data=data, family=pf.t())
    x = model.fit()
    assert(model.predict_is(h=5).shape[0] == 5)

def test_t_bbvi_mini_batch_elbo():
    """
    Tests that the ELBO increases
    """
    model = pf.GASLLEV(data=data, family=pf.t())
    x = model.fit('BBVI',iterations=100, mini_batch=32, record_elbo=True, map_start=False)
    assert(x.elbo_records[-1]>x.elbo_records[0])

def test_t_bbvi_elbo():
    """
    Tests that the ELBO increases
    """
    model = pf.GAS(data=data, ar=1, sc=1, family=pf.t())
    x = model.fit('BBVI',iterations=100, record_elbo=True)
    assert(x.elbo_records[-1]>x.elbo_records[0])

def test_normal_bbvi_mini_batch_elbo():
    """
    Tests that the ELBO increases
    """
    model = pf.GASReg(formula="y ~ x1 + x2", data=data, family=pf.t())
    x = model.fit('BBVI',iterations=100, mini_batch=32, record_elbo=True)
    assert(x.elbo_records[-1]>x.elbo_records[0])

def test2_bbvi_mini_batch_elbo():
    """
    Tests that the ELBO increases
    """
    model = pf.GASX(formula="y ~ x1 + x2", data=data, ar=1, sc=1, family=pf.t())
    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 test2_ppc():
    """
    Tests PPC value
    """
    model = pf.GASReg(formula="y ~ x1  + x2", data=data, family=pf.t())
    x = model.fit('BBVI', iterations=100)
    p_value = model.ppc()
    assert(0.0 <= p_value <= 1.0)

def test2_normal_predict_is_nans():
    """
    Tests that the predictions in-sample are not NaNs
    """
    model = pf.GASReg(formula="y ~ x1 + x2", data=data, family=pf.t())
    x = model.fit()
    x.summary()
    assert(len(model.predict_is(h=5).values[np.isnan(model.predict_is(h=5).values)]) == 0)

def test_normal_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.t())
    x = model.fit()
    x.summary()
    assert(model.predict(h=5, oos_data=data_oos).shape[0] == 5)

def test_t_predict_length():
	"""
	Tests that the prediction dataframe length is equal to the number of steps h
	"""
	model = pf.GAS(data=data, ar=2, sc=2, family=pf.t())
	x = model.fit()
	x.summary()
	assert(model.predict(h=5).shape[0] == 5)

def test_t_ppc():
    """
    Tests PPC value
    """
    model = pf.GASLLEV(data=data, family=pf.t())
    x = model.fit('BBVI', iterations=100)
    p_value = model.ppc()
    assert(0.0 <= p_value <= 1.0)

def test_t_predict_nans():
	"""
	Tests that the predictions are not nans
	"""
	model = pf.GAS(data=data, ar=2, sc=2, family=pf.t())
	x = model.fit()
	x.summary()
	assert(len(model.predict(h=5).values[np.isnan(model.predict(h=5).values)]) == 0)

def test_t_predict_is_nans():
    """
    Tests that the in-sample predictions are not nans
    """
    model = pf.GASLLEV(data=data, family=pf.t())
    x = model.fit()
    x.summary()
    assert(len(model.predict_is(h=5).values[np.isnan(model.predict_is(h=5).values)]) == 0)

def test2_ppc():
    """
    Tests PPC value
    """
    model = pf.GASX(formula="y ~ x1 + x2", data=data, ar=1, sc=1, family=pf.t())
    x = model.fit("BBVI", iterations=100)
    p_value = model.ppc()
    assert 0.0 <= p_value <= 1.0

def test_t_sample_model():
    """
    Tests sampling function
    """
    model = pf.GASLLEV(data=data, family=pf.t())
    x = model.fit('BBVI', iterations=100)
    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.GASReg(formula="y ~ x1 + x2", data=data, family=pf.t())
    x = model.fit('BBVI', iterations=100)
    sample = model.sample(nsims=100)
    assert(sample.shape[0]==100)
    assert(sample.shape[1]==len(data))

def test2_sample_model():
    """
    Tests sampling function
    """
    model = pf.GASX(formula="y ~ x1 + x2", data=data, ar=1, sc=1, family=pf.t())
    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_predict_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", data=data, ar=1, sc=1, family=pf.t())
    x = model.fit()
    predictions = model.predict(h=10, oos_data=data_oos, intervals=False)
    assert not np.all(predictions.values == predictions.values[0])

def test_normal_predict_nans():
    """
    Tests that the predictions are not NaNs
    """
    model = pf.GASReg(formula="y ~ x1", data=data, family=pf.t())
    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_t_predict_is_intervals_mh():
    """
    Tests prediction intervals are ordered correctly
    """
    model = pf.GASLLEV(data=data, family=pf.t())
    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))