def test_dlt_aggregated_with_regression(synthetic_data, estimator_type,
regressor_signs):
train_df, test_df, coef = synthetic_data
dlt = DLTAggregated(response_col='response',
date_col='week',
regressor_col=train_df.columns.tolist()[2:],
regressor_sign=regressor_signs,
seasonality=52,
num_warmup=50,
verbose=False,
estimator_type=estimator_type)
dlt.fit(train_df)
predict_df = dlt.predict(test_df)
regression_out = dlt.get_regression_coefs()
num_regressors = regression_out.shape[0]
expected_columns = ['week', 'prediction_5', 'prediction', 'prediction_95']
expected_shape = (51, len(expected_columns))
expected_regression_shape = (6, 3)
assert predict_df.shape == expected_shape
assert predict_df.columns.tolist() == expected_columns
assert regression_out.shape == expected_regression_shape
assert num_regressors == len(train_df.columns.tolist()[2:])
predict_df = dlt.predict(test_df, decompose=True)
assert any(predict_df['regression'].values)
def test_dlt_aggregated_univariate(synthetic_data, estimator_type):
train_df, test_df, coef = synthetic_data
dlt = DLTAggregated(response_col='response',
date_col='week',
seasonality=52,
num_warmup=50,
verbose=False,
estimator_type=estimator_type)
dlt.fit(train_df)
predict_df = dlt.predict(test_df)
expected_columns = ['week', 'prediction']
expected_shape = (51, len(expected_columns))
expected_num_parameters = 13
assert predict_df.shape == expected_shape
assert predict_df.columns.tolist() == expected_columns
assert len(dlt._posterior_samples) == expected_num_parameters
def test_dlt_aggregated_univariate(synthetic_data, estimator_type):
train_df, test_df, coef = synthetic_data
dlt = DLTAggregated(response_col='response',
date_col='week',
seasonality=52,
num_warmup=50,
verbose=False,
estimator_type=estimator_type)
dlt.fit(train_df)
init_call = dlt.get_init_values()
assert isinstance(init_call, DLTInitializer)
assert init_call.s == 52
init_values = init_call()
assert init_values['init_sea'].shape == (51, )
predict_df = dlt.predict(test_df)
expected_columns = ['week', 'prediction_5', 'prediction', 'prediction_95']
expected_shape = (51, len(expected_columns))
expected_num_parameters = 12
assert predict_df.shape == expected_shape
assert predict_df.columns.tolist() == expected_columns
assert len(dlt._posterior_samples) == expected_num_parameters