Source code for gluonts.mx.distribution.inflated_beta

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# Licensed under the Apache License, Version 2.0 (the "License").
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#     http://www.apache.org/licenses/LICENSE-2.0
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from typing import Dict, Tuple

import numpy as np

from gluonts.core.component import validated
from gluonts.mx import Tensor

from .beta import Beta
from .deterministic import Deterministic
from .distribution import getF, softplus
from .distribution_output import DistributionOutput
from .mixture import MixtureDistribution


[docs]class ZeroAndOneInflatedBeta(MixtureDistribution): r""" Zero And One Inflated Beta distribution as in Raydonal Ospina, Silvia L.P. Ferrari: Inflated Beta Distributions Parameters ---------- alpha Tensor containing the alpha shape parameters, of shape `(*batch_shape, *event_shape)`. beta Tensor containing the beta shape parameters, of shape `(*batch_shape, *event_shape)`. zero_probability Tensor containing the probability of zeros, of shape `(*batch_shape, *event_shape)`. one_probability Tensor containing the probability of ones, of shape `(*batch_shape, *event_shape)`. F """ is_reparameterizable = False @validated() def __init__( self, alpha: Tensor, beta: Tensor, zero_probability: Tensor, one_probability: Tensor, ) -> None: F = getF(alpha) self.alpha = alpha self.beta = beta self.zero_probability = zero_probability self.one_probability = one_probability self.beta_probability = 1 - zero_probability - one_probability self.beta_distribution = Beta(alpha=alpha, beta=beta) mixture_probs = F.stack( zero_probability, one_probability, self.beta_probability, axis=-1 ) super().__init__( components=[ Deterministic(alpha.zeros_like()), Deterministic(alpha.ones_like()), self.beta_distribution, ], mixture_probs=mixture_probs, )
[docs] def log_prob(self, x: Tensor) -> Tensor: F = self.F # mask zeros for the Beta distribution input to prevent NaN gradients inputs = F.where( F.broadcast_logical_or(x == 0, x == 1), x.zeros_like() + 0.5, x ) # compute log density, case by case return F.where( x == 1, F.log(self.one_probability.broadcast_like(x)), F.where( x == 0, F.log(self.zero_probability.broadcast_like(x)), F.log(self.beta_probability) + self.beta_distribution.log_prob(inputs), ), )
[docs]class ZeroInflatedBeta(ZeroAndOneInflatedBeta): r""" Zero Inflated Beta distribution as in Raydonal Ospina, Silvia L.P. Ferrari: Inflated Beta Distributions Parameters ---------- alpha Tensor containing the alpha shape parameters, of shape `(*batch_shape, *event_shape)`. beta Tensor containing the beta shape parameters, of shape `(*batch_shape, *event_shape)`. zero_probability Tensor containing the probability of zeros, of shape `(*batch_shape, *event_shape)`. F """ is_reparameterizable = False @validated() def __init__( self, alpha: Tensor, beta: Tensor, zero_probability: Tensor ) -> None: super().__init__( alpha=alpha, beta=beta, zero_probability=zero_probability, one_probability=alpha.zeros_like(), )
[docs]class OneInflatedBeta(ZeroAndOneInflatedBeta): r""" One Inflated Beta distribution as in Raydonal Ospina, Silvia L.P. Ferrari: Inflated Beta Distributions Parameters ---------- alpha Tensor containing the alpha shape parameters, of shape `(*batch_shape, *event_shape)`. beta Tensor containing the beta shape parameters, of shape `(*batch_shape, *event_shape)`. one_probability Tensor containing the probability of ones, of shape `(*batch_shape, *event_shape)`. F """ is_reparameterizable = False @validated() def __init__( self, alpha: Tensor, beta: Tensor, one_probability: Tensor ) -> None: super().__init__( alpha=alpha, beta=beta, zero_probability=alpha.zeros_like(), one_probability=one_probability, )
[docs]class ZeroAndOneInflatedBetaOutput(DistributionOutput): args_dim: Dict[str, int] = { "alpha": 1, "beta": 1, "zero_probability": 1, "one_probability": 1, } distr_cls: type = ZeroAndOneInflatedBeta
[docs] @classmethod def domain_map(cls, F, alpha, beta, zero_probability, one_probability): r""" Maps raw tensors to valid arguments for constructing a ZeroAndOneInflatedBeta distribution. Parameters ---------- F: alpha: Tensor of shape `(*batch_shape, 1)` beta: Tensor of shape `(*batch_shape, 1)` zero_probability: Tensor of shape `(*batch_shape, 1)` Returns ------- Tuple[Tensor, Tensor, Tensor, Tensor]: Four squeezed tensors, of shape `(*batch_shape)`: First two have entries mapped to the positive orthant, zero_probability is mapped to (0, 1), one_probability is mapped to (0, 1-zero_probability) """ epsilon = np.finfo(cls._dtype).eps # machine epsilon alpha = softplus(F, alpha) + epsilon beta = softplus(F, beta) + epsilon zero_probability = F.sigmoid(zero_probability) one_probability = (1 - zero_probability) * F.sigmoid(one_probability) return ( alpha.squeeze(axis=-1), beta.squeeze(axis=-1), zero_probability.squeeze(axis=-1), one_probability.squeeze(axis=-1), )
@property def event_shape(self) -> Tuple: return () @property def value_in_support(self) -> float: return 0.5
[docs]class ZeroInflatedBetaOutput(ZeroAndOneInflatedBetaOutput): args_dim: Dict[str, int] = {"alpha": 1, "beta": 1, "zero_probability": 1} distr_cls: type = ZeroInflatedBeta
[docs] @classmethod def domain_map(cls, F, alpha, beta, zero_probability): r""" Maps raw tensors to valid arguments for constructing a ZeroInflatedBeta distribution. Parameters ---------- F: alpha: Tensor of shape `(*batch_shape, 1)` beta: Tensor of shape `(*batch_shape, 1)` zero_probability: Tensor of shape `(*batch_shape, 1)` Returns ------- Tuple[Tensor, Tensor, Tensor]: Three squeezed tensors, of shape `(*batch_shape)`: First two have entries mapped to the positive orthant, last is mapped to (0,1) """ epsilon = np.finfo(cls._dtype).eps # machine epsilon alpha = softplus(F, alpha) + epsilon beta = softplus(F, beta) + epsilon zero_probability = F.sigmoid(zero_probability) return ( alpha.squeeze(axis=-1), beta.squeeze(axis=-1), zero_probability.squeeze(axis=-1), )
[docs]class OneInflatedBetaOutput(ZeroInflatedBetaOutput): args_dim: Dict[str, int] = {"alpha": 1, "beta": 1, "one_probability": 1} distr_cls: type = OneInflatedBeta
[docs] @classmethod def domain_map(cls, F, alpha, beta, one_probability): return super().domain_map(F, alpha, beta, one_probability)