import torch
from torch import Tensor, nn
from spflow.exceptions import InvalidParameterCombinationError
from spflow.meta.data import Scope
from spflow.modules.leaves.leaf import LeafModule
from spflow.utils.leaves import init_parameter, _handle_mle_edge_cases
from spflow.utils.projections import proj_bounded_to_real, proj_real_to_bounded
[docs]
class Binomial(LeafModule):
"""Binomial distribution leaf module for probabilistic circuits.
Implements univariate Binomial distributions as leaf nodes in probabilistic
circuits. Supports parameter learning through maximum likelihood estimation
and efficient inference through PyTorch's built-in distributions.
The Binomial distribution models the number of successes in a fixed number
of independent Bernoulli trials, with probability mass function::
P(X = k | n, p) = C(n, k) * p^k * (1-p)^(n-k)
where n is the number of trials (fixed), p is the success probability (learnable,
stored in logit-space for numerical stability), and k is the number of successes (0 ≤ k ≤ n).
Attributes:
p: Success probability parameter(s) in [0, 1] (BoundedParameter).
n: Number of trials parameter(s), non-negative integers (fixed buffer).
distribution: Underlying torch.distributions.Binomial.
"""
[docs]
def __init__(
self,
scope: Scope,
out_channels: int = None,
num_repetitions: int = 1,
total_count: Tensor | None = None,
probs: Tensor | None = None,
logits: Tensor | None = None,
parameter_fn: nn.Module = None,
validate_args: bool | None = True,
):
"""Initialize Binomial distribution leaf module.
Args:
scope: Scope object specifying the scope of the distribution.
out_channels: Number of output channels (inferred from params if None).
num_repetitions: Number of repetitions for the distribution.
total_count: Number of trials tensor (required).
probs: Success probability tensor (optional, randomly initialized if None).
logits: Log-odds tensor for success probability.
parameter_fn: Optional neural network for parameter generation.
validate_args: Whether to enable torch.distributions argument validation.
"""
if total_count is None:
raise InvalidParameterCombinationError("'n' parameter is required for Binomial distribution")
if probs is not None and logits is not None:
raise InvalidParameterCombinationError("Binomial accepts either probs or logits, not both.")
param_source = logits if logits is not None else probs
super().__init__(
scope=scope,
out_channels=out_channels,
num_repetitions=num_repetitions,
params=[param_source],
parameter_fn=parameter_fn,
validate_args=validate_args,
)
init_fn = torch.randn if logits is not None else torch.rand
init_value = init_parameter(param=param_source, event_shape=self.event_shape, init=init_fn)
# Register total_count as a fixed buffer
total_count = torch.broadcast_to(total_count, self.event_shape).clone()
self.register_buffer("_total_count", total_count)
logits_tensor = init_value if logits is not None else proj_bounded_to_real(init_value, lb=0.0, ub=1.0)
self._logits = nn.Parameter(logits_tensor)
@property
def total_count(self) -> Tensor:
"""Returns the number of trials."""
return self._total_count
@total_count.setter
def total_count(self, total_count: Tensor):
"""Sets the number of trials.
Args:
total_count: Floating point representing the number of trials.
"""
self._total_count = total_count
@property
def probs(self) -> Tensor:
"""Success probability in natural space (read via inverse projection of logit_p)."""
return proj_real_to_bounded(self._logits, lb=0.0, ub=1.0)
@probs.setter
def probs(self, value: Tensor) -> None:
"""Set success probability (stores as logit_p, no validation after init)."""
value_tensor = torch.as_tensor(value, dtype=self._logits.dtype, device=self._logits.device)
self._logits.data = proj_bounded_to_real(value_tensor, lb=0.0, ub=1.0)
@property
def logits(self) -> Tensor:
"""Logits for the success probability."""
return self._logits
@logits.setter
def logits(self, value: Tensor) -> None:
value_tensor = torch.as_tensor(value, dtype=self._logits.dtype, device=self._logits.device)
self._logits.data = value_tensor
@property
def _supported_value(self):
"""Fallback value for unsupported data."""
return 0.0
@property
def _torch_distribution_class(self) -> type[torch.distributions.Binomial]:
return torch.distributions.Binomial
[docs]
def params(self) -> dict[str, Tensor]:
"""Returns distribution parameters."""
return {"total_count": self.total_count, "logits": self.logits}
def _compute_parameter_estimates(
self, data: Tensor, weights: Tensor, bias_correction: bool
) -> dict[str, Tensor]:
"""Compute raw MLE estimates for binomial distribution (without broadcasting).
Args:
data: Input data tensor.
weights: Weight tensor for each data point.
bias_correction: Not used for Binomial (included for interface consistency).
Returns:
Dictionary with 'probs' estimate (shape: out_features).
"""
normalized_weights = weights / weights.sum(dim=0)
n_total = normalized_weights.sum(dim=0) * self.total_count
n_success = (normalized_weights * data).sum(0)
probs_est = n_success / n_total
# Handle edge cases (NaN, out of bounds) before broadcasting
probs_est = _handle_mle_edge_cases(probs_est, lb=0.0, ub=1.0)
return {"probs": probs_est}
def _set_mle_parameters(self, params_dict: dict[str, Tensor]) -> None:
"""Set MLE-estimated parameters for Binomial distribution.
Explicitly handles the parameter type:
- probs: Property with setter, calls property setter which updates _logits
Args:
params_dict: Dictionary with 'probs' parameter value.
"""
self.probs = params_dict["probs"] # Uses property setter