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I 1410 negbinom log priors #1415

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I 1410 negbinom log priors #1415

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be441fa
Binomial log prior class
I-Bouros Nov 15, 2021
f214dfe
Correct test for BinomialLogPrior class
I-Bouros Nov 15, 2021
5394a79
Initial NegBinomialLogPrior class
I-Bouros Nov 16, 2021
413d403
Finish test class for NegBinomialLogPrior
I-Bouros Nov 16, 2021
74c1d44
Run all tests
I-Bouros Nov 16, 2021
3b051bf
Add tests to improve codecov coverage
I-Bouros Nov 9, 2022
97f66dc
Merge branch 'master' into i-1410-negbinom-log-priors
I-Bouros Nov 9, 2022
cd95c71
Undoing accidental change to init
MichaelClerx Nov 7, 2023
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6 changes: 6 additions & 0 deletions docs/source/log_priors.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -15,6 +15,7 @@ Example::
Overview:

- :class:`BetaLogPrior`
- :class:`BinomialLogPrior`
- :class:`CauchyLogPrior`
- :class:`ComposedLogPrior`
- :class:`ExponentialLogPrior`
Expand All @@ -24,6 +25,7 @@ Overview:
- :class:`InverseGammaLogPrior`
- :class:`LogNormalLogPrior`
- :class:`MultivariateGaussianLogPrior`
- :class:`NegBinomialLogPrior`
- :class:`NormalLogPrior`
- :class:`StudentTLogPrior`
- :class:`TruncatedGaussianLogPrior`
Expand All @@ -32,6 +34,8 @@ Overview:

.. autoclass:: BetaLogPrior

.. autoclass:: BinomialLogPrior

.. autoclass:: CauchyLogPrior

.. autoclass:: ComposedLogPrior
Expand All @@ -50,6 +54,8 @@ Overview:

.. autoclass:: MultivariateGaussianLogPrior

.. autoclass:: NegBinomialLogPrior

.. autoclass:: NormalLogPrior

.. autoclass:: StudentTLogPrior
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91 changes: 48 additions & 43 deletions pints/__init__.py
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Expand Up @@ -36,9 +36,11 @@ def _load_version_int():
except Exception as e: # pragma: no cover
raise RuntimeError('Unable to read version number (' + str(e) + ').')


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Did these changes get introduced when merging? Might be a good idea to remove them all again!

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Probably, I didn't use modify .init with anything like that

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For some reason I cannot pull the master into my own repo? Does that happen to you too?

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Hmmm, no! Do you have the right permissions to work on a branch in the PINTS repo directly? That might be easier

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I've sent you an invitation!

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Having said that, now that we've started this PR Maybe keep it on a fork :D
I think you might need to do something like:

  • switch to your local version of master git checkout master
  • make sure you have the pints-team/pints repo as a "remote". Check with git remote -v and if there's no sign of it you can add a remote with something like git remote add pints https://github.com/pints-team/pints.git (change "pints" to some name you like)
  • pull in master from the remote git pull pints master

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https://www.jquery-az.com/pull-git-remote-branch/

__version_int__ = _load_version_int()
__version__ = '.'.join([str(x) for x in __version_int__])


#
# Expose pints version
#
Expand All @@ -64,21 +66,21 @@ def version(formatted=False):
#
# Core classes
#
from ._core import ForwardModel, ForwardModelS1
from ._core import TunableMethod
from ._core import SingleOutputProblem, MultiOutputProblem
from ._core import ForwardModel, ForwardModelS1 # noqa
from ._core import TunableMethod # noqa
from ._core import SingleOutputProblem, MultiOutputProblem # noqa

#
# Utility classes and methods
#
from ._util import strfloat, vector, matrix2d
from ._util import Timer
from ._logger import Logger, Loggable
from ._util import strfloat, vector, matrix2d # noqa
from ._util import Timer # noqa
from ._logger import Logger, Loggable # noqa

#
# Logs of probability density functions (not necessarily normalised)
#
from ._log_pdfs import (
from ._log_pdfs import ( # noqa
LogPDF,
LogPrior,
LogPosterior,
Expand All @@ -90,8 +92,9 @@ def version(formatted=False):
#
# Log-priors
#
from ._log_priors import (
from ._log_priors import ( # noqa
BetaLogPrior,
BinomialLogPrior,
CauchyLogPrior,
ComposedLogPrior,
ExponentialLogPrior,
Expand All @@ -101,6 +104,7 @@ def version(formatted=False):
InverseGammaLogPrior,
LogNormalLogPrior,
MultivariateGaussianLogPrior,
NegBinomialLogPrior,
NormalLogPrior,
StudentTLogPrior,
TruncatedGaussianLogPrior,
Expand All @@ -110,7 +114,7 @@ def version(formatted=False):
#
# Log-likelihoods
#
from ._log_likelihoods import (
from ._log_likelihoods import ( # noqa
AR1LogLikelihood,
ARMA11LogLikelihood,
CauchyLogLikelihood,
Expand All @@ -129,7 +133,7 @@ def version(formatted=False):
#
# Boundaries
#
from ._boundaries import (
from ._boundaries import ( # noqa
Boundaries,
LogPDFBoundaries,
RectangularBoundaries,
Expand All @@ -138,7 +142,7 @@ def version(formatted=False):
#
# Error measures
#
from ._error_measures import (
from ._error_measures import ( # noqa
ErrorMeasure,
MeanSquaredError,
NormalisedRootMeanSquaredError,
Expand All @@ -152,7 +156,7 @@ def version(formatted=False):
#
# Parallel function evaluation
#
from ._evaluation import (
from ._evaluation import ( # noqa
evaluate,
Evaluator,
ParallelEvaluator,
Expand All @@ -164,7 +168,7 @@ def version(formatted=False):
#
# Optimisation
#
from ._optimisers import (
from ._optimisers import ( # noqa
curve_fit,
fmin,
Optimisation,
Expand All @@ -173,6 +177,7 @@ def version(formatted=False):
Optimiser,
PopulationBasedOptimiser,
)

from ._optimisers._adam import Adam
from ._optimisers._cmaes import CMAES
from ._optimisers._cmaes_bare import BareCMAES
Expand All @@ -187,7 +192,7 @@ def version(formatted=False):
#
# Diagnostics
#
from ._diagnostics import (
from ._diagnostics import ( # noqa
effective_sample_size,
rhat,
rhat_all_params,
Expand All @@ -197,7 +202,7 @@ def version(formatted=False):
#
# MCMC
#
from ._mcmc import (
from ._mcmc import ( # noqa
mcmc_sample,
MCMCController,
MCMCSampler,
Expand All @@ -206,38 +211,38 @@ def version(formatted=False):
SingleChainMCMC,
)
# base classes first
from ._mcmc._adaptive_covariance import AdaptiveCovarianceMC
from ._mcmc._adaptive_covariance import AdaptiveCovarianceMC # noqa

# methods
from ._mcmc._differential_evolution import DifferentialEvolutionMCMC
from ._mcmc._dram_ac import DramACMC
from ._mcmc._dream import DreamMCMC
from ._mcmc._dual_averaging import DualAveragingAdaption
from ._mcmc._emcee_hammer import EmceeHammerMCMC
from ._mcmc._haario_ac import HaarioACMC
from ._mcmc._haario_bardenet_ac import HaarioBardenetACMC
from ._mcmc._haario_bardenet_ac import AdaptiveCovarianceMCMC
from ._mcmc._hamiltonian import HamiltonianMCMC
from ._mcmc._mala import MALAMCMC
from ._mcmc._metropolis import MetropolisRandomWalkMCMC
from ._mcmc._monomial_gamma_hamiltonian import MonomialGammaHamiltonianMCMC
from ._mcmc._nuts import NoUTurnMCMC
from ._mcmc._population import PopulationMCMC
from ._mcmc._rao_blackwell_ac import RaoBlackwellACMC
from ._mcmc._relativistic import RelativisticMCMC
from ._mcmc._slice_doubling import SliceDoublingMCMC
from ._mcmc._slice_rank_shrinking import SliceRankShrinkingMCMC
from ._mcmc._slice_stepout import SliceStepoutMCMC
from ._mcmc._summary import MCMCSummary
from ._mcmc._differential_evolution import DifferentialEvolutionMCMC # noqa
from ._mcmc._dram_ac import DramACMC # noqa
from ._mcmc._dream import DreamMCMC # noqa
from ._mcmc._dual_averaging import DualAveragingAdaption # noqa
from ._mcmc._emcee_hammer import EmceeHammerMCMC # noqa
from ._mcmc._haario_ac import HaarioACMC # noqa
from ._mcmc._haario_bardenet_ac import HaarioBardenetACMC # noqa
from ._mcmc._haario_bardenet_ac import AdaptiveCovarianceMCMC # noqa
from ._mcmc._hamiltonian import HamiltonianMCMC # noqa
from ._mcmc._mala import MALAMCMC # noqa
from ._mcmc._metropolis import MetropolisRandomWalkMCMC # noqa
from ._mcmc._monomial_gamma_hamiltonian import MonomialGammaHamiltonianMCMC # noqa
from ._mcmc._nuts import NoUTurnMCMC # noqa
from ._mcmc._population import PopulationMCMC # noqa
from ._mcmc._rao_blackwell_ac import RaoBlackwellACMC # noqa
from ._mcmc._relativistic import RelativisticMCMC # noqa
from ._mcmc._slice_doubling import SliceDoublingMCMC # noqa
from ._mcmc._slice_rank_shrinking import SliceRankShrinkingMCMC # noqa
from ._mcmc._slice_stepout import SliceStepoutMCMC # noqa
from ._mcmc._summary import MCMCSummary # noqa


#
# Nested samplers
#
from ._nested import NestedSampler
from ._nested import NestedController
from ._nested._rejection import NestedRejectionSampler
from ._nested._ellipsoid import NestedEllipsoidSampler
from ._nested import NestedSampler # noqa
from ._nested import NestedController # noqa
from ._nested._rejection import NestedRejectionSampler # noqa
from ._nested._ellipsoid import NestedEllipsoidSampler # noqa


#
Expand All @@ -252,13 +257,13 @@ def version(formatted=False):
#
# Sampling initialising
#
from ._sample_initial_points import sample_initial_points
from ._sample_initial_points import sample_initial_points # noqa


#
# Transformations
#
from ._transformation import (
from ._transformation import ( # noqa
ComposedTransformation,
IdentityTransformation,
LogitTransformation,
Expand All @@ -278,7 +283,7 @@ def version(formatted=False):
#
# Noise generators (always import!)
#
from . import noise
from . import noise # noqa

#
# Remove any imported modules, so we don't expose them as part of pints
Expand Down
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