111 lines
3.8 KiB
Python
111 lines
3.8 KiB
Python
from typing import Any, Dict
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import ast
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import torch
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from torch.distributions import Binomial
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import scipy.stats as scst
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class BinomialEx(Binomial):
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"""
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A class to extend Binomial to add missing functionality.
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pragma: skip_doc
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"""
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def cdf(self, x: torch.Tensor):
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"""
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Computes the cumulative distribution function (CDF) of the Binomial distribution.
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Args:
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x (torch.Tensor): Input tensor containing values where the CDF is evaluated.
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Returns:
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torch.Tensor: Tensor containing the cumulative probabilities.
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"""
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a = x.detach().cpu().numpy()
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n = self.total_count.detach().cpu().numpy()
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probs = self.probs.detach().cpu().numpy()
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outputs = scst.binom.cdf(a, n, probs)
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return torch.tensor(outputs, dtype=torch.float32).to(x.device)
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def icdf(self, q: torch.Tensor):
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"""
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Computes the inverse of cumulative distribution function (CDF) of the Binomial distribution.
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Args:
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q (torch.Tensor): Input tensor containing values where the ICDF is evaluated.
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Returns:
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torch.Tensor: Tensor containing the inverse of cumulative probabilities.
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"""
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a = q.detach().cpu().numpy()
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n = self.total_count.detach().cpu().numpy()
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probs = self.probs.detach().cpu().numpy()
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outputs = scst.binom.ppf(a, n, probs)
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return torch.tensor(outputs, dtype=torch.float32).to(q.device)
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class PtdBinomial:
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"""
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Instantiates a Binomial distribution object.
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Args:
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total_count (str): Total count, or n. You can specify a scalar or a tuple of int.
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probs (str): Probabilities of the distsribution. You can specify a scalar or a tuple of float.
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logits (str): Logits of the distribution. You can specify a scalar or a tuple of float.
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category: PyTorch wrapper - Distribution
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"""
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@classmethod
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def INPUT_TYPES(cls) -> Dict[str, Any]:
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"""
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Defines the input types for the function.
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Returns:
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Dict[str, Any]: A dictionary specifying the required input types.
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"""
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return {
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"required": {
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"total_count": ("STRING", {"default": ""}),
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"probs": ("STRING", {"default": ""}),
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"logits": ("STRING", {"default": ""})
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}
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}
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RETURN_TYPES: tuple = ("PTDISTRIBUTION",)
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FUNCTION: str = "f"
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CATEGORY: str = "Distribution"
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def f(self, total_count: str, probs: str, logits: str):
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"""
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Instantiates a Binomial distribution object.
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Args:
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total_count (str): Total count, or n. You can specify a scalar or a tuple of int.
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probs (str): Probabilities of the distsribution. You can specify a scalar or a tuple of float.
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logits (str): Logits of the distribution. You can specify a scalar or a tuple of float.
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Returns:
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tuple: (torch.distributions.binomial.Binomial)
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"""
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n = ast.literal_eval(total_count)
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p = ast.literal_eval(probs) if probs else None
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l = ast.literal_eval(logits) if logits else None
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if p is not None and l is not None: # You cannot use if p because p or l can be "0".
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raise ValueError("You can specify either probabilities or logits, but not both.")
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if p is None and l is None:
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raise ValueError("You have to specify either probabilities or logits")
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if p is not None:
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dist = BinomialEx(
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total_count=torch.tensor(n, dtype=torch.int64),
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probs=torch.tensor(p, dtype=torch.float32))
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elif l is not None:
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dist = BinomialEx(
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total_count=torch.tensor(n, dtype=torch.int64),
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logits=torch.tensor(l, dtype=torch.float32))
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return(dist,)
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