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Add type hints to distribution parameters #6635

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Apr 7, 2023
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32 changes: 27 additions & 5 deletions pymc/distributions/continuous.py
Original file line number Diff line number Diff line change
Expand Up @@ -816,7 +816,13 @@ class HalfNormal(PositiveContinuous):
rv_op = halfnormal

@classmethod
def dist(cls, sigma=None, tau=None, *args, **kwargs):
def dist(
cls,
sigma: Optional[DIST_PARAMETER_TYPES] = None,
tau: Optional[DIST_PARAMETER_TYPES] = None,
*args,
**kwargs,
):
tau, sigma = get_tau_sigma(tau=tau, sigma=sigma)

return super().dist([0.0, sigma], **kwargs)
Expand Down Expand Up @@ -948,7 +954,14 @@ class Wald(PositiveContinuous):
rv_op = wald

@classmethod
def dist(cls, mu=None, lam=None, phi=None, alpha=0.0, **kwargs):
def dist(
cls,
mu: Optional[DIST_PARAMETER_TYPES] = None,
lam: Optional[DIST_PARAMETER_TYPES] = None,
phi: Optional[DIST_PARAMETER_TYPES] = None,
alpha: Optional[DIST_PARAMETER_TYPES] = 0.0,
**kwargs,
):
mu, lam, phi = cls.get_mu_lam_phi(mu, lam, phi)
alpha = pt.as_tensor_variable(floatX(alpha))
mu = pt.as_tensor_variable(floatX(mu))
Expand Down Expand Up @@ -1115,7 +1128,16 @@ class Beta(UnitContinuous):
rv_op = pytensor.tensor.random.beta

@classmethod
def dist(cls, alpha=None, beta=None, mu=None, sigma=None, nu=None, *args, **kwargs):
def dist(
cls,
alpha: Optional[DIST_PARAMETER_TYPES] = None,
beta: Optional[DIST_PARAMETER_TYPES] = None,
mu: Optional[DIST_PARAMETER_TYPES] = None,
sigma: Optional[DIST_PARAMETER_TYPES] = None,
nu: Optional[DIST_PARAMETER_TYPES] = None,
*args,
**kwargs,
):
alpha, beta = cls.get_alpha_beta(alpha, beta, mu, sigma, nu)
alpha = pt.as_tensor_variable(floatX(alpha))
beta = pt.as_tensor_variable(floatX(beta))
Expand Down Expand Up @@ -1243,7 +1265,7 @@ class Kumaraswamy(UnitContinuous):
rv_op = kumaraswamy

@classmethod
def dist(cls, a, b, *args, **kwargs):
def dist(cls, a: DIST_PARAMETER_TYPES, b: DIST_PARAMETER_TYPES, *args, **kwargs):
a = pt.as_tensor_variable(floatX(a))
b = pt.as_tensor_variable(floatX(b))

Expand Down Expand Up @@ -1329,7 +1351,7 @@ class Exponential(PositiveContinuous):
rv_op = exponential

@classmethod
def dist(cls, lam, *args, **kwargs):
def dist(cls, lam: DIST_PARAMETER_TYPES, *args, **kwargs):
lam = pt.as_tensor_variable(floatX(lam))

# PyTensor exponential op is parametrized in terms of mu (1/lam)
Expand Down