def __init__(self, data: Data, **kwargs):
if not np.all(data.obs >= 0):
raise ValueError(
"Binomial model requires observations to be non-negative.")
if len(data.col_obs) != 2:
raise ValueError("Binomial model need 2 columns of observations, "
"one for number of events, one for sample size.")
if any(np.diff(data.get_cols(data.col_obs), axis=1) < 0):
raise ValueError(
"Binomial model requires number of events less or equal than sample size."
)
self.obs_1s = data.get_cols(data.col_obs[0])
self.obs_0s = np.diff(data.get_cols(data.col_obs), axis=1).ravel()
self.obs_sample_sizes = data.get_cols(data.col_obs[1])
super().__init__(data, **kwargs)
def get_mat(self, data: Data) -> np.ndarray:
"""Get design matrix.
Parameters
----------
data : Data
Data object that provides the covariates.
Returns
-------
np.ndarray
Design matrix.
"""
self.check_data(data)
cov = data.get_cols(self.name)
return self.spline.design_mat(cov, l_extra=True, r_extra=True)
def check_data(self, data: Data):
"""Check if the data contains the column name `name`. And create the
spline object, if only `spline_specs` is provided.
Parameters
----------
data : Data
Data object to be checked.
"""
super().check_data(data)
if self.spline is None:
cov = data.get_cols(self.name)
self.spline = self.spline_specs.create_spline(cov)
for prior in self.linear_upriors + self.linear_gpriors:
if isinstance(prior, SplinePrior):
prior.attach_spline(self.spline)
def test_post_init_fill_df(df_simple):
data = Data(COL_OBS, df=df_simple)
assert data.num_obs == NUM_OBS
assert all(data.weights == 1.0)
assert all(data.offset == 0.0)
assert all(data.get_cols('intercept') == 1.0)
