Bda

indica/bayesblackbox.py

@@ -0,0 +1,143 @@
+from copy import deepcopy
+import warnings
+from flatdict import FlatDict
+import numpy as np
+from scipy.stats import uniform
+np.seterr(all="ignore")
+warnings.simplefilter("ignore", category=FutureWarning)
+
+def gaussian(x, mean, sigma):
+    return 1 / (sigma * np.sqrt(2 * np.pi)) * np.exp(-1 / 2 * ((x - mean) / sigma) ** 2)
+
+
+def get_uniform(lower, upper):
+    # Less confusing parameterisation of scipy.stats uniform
+    return uniform(loc=lower, scale=upper - lower)
+
+
+def ln_prior(priors: dict, parameters: dict):
+    ln_prior = 0
+    for prior_name, prior_func in priors.items():
+        param_names_in_prior = [x for x in parameters.keys() if x in prior_name]
+        if param_names_in_prior.__len__() == 0:
+            # if prior assigned but no parameter then skip
+            continue
+        param_values = [parameters[x] for x in param_names_in_prior]
+        if hasattr(prior_func, "pdf"):
+            # for scipy.stats objects use pdf / for lambda functions just call
+            ln_prior += np.log(prior_func.pdf(*param_values))
+        else:
+            # if lambda prior with 2+ args is defined when only 1 of
+            # its parameters is given ignore it
+            if prior_func.__code__.co_argcount != param_values.__len__():
+                continue
+            else:
+                # Sorting to make sure args are given in the same order
+                # as the prior_name string
+                name_index = [
+                    prior_name.find(param_name_in_prior)
+                    for param_name_in_prior in param_names_in_prior
+                ]
+                sorted_name_index, sorted_param_values = (
+                    list(x) for x in zip(*sorted(zip(name_index, param_values)))
+                )
+                ln_prior += np.log(prior_func(*sorted_param_values))
+    return ln_prior
+
+class BayesBlackBox:
+    """
+    Bayesian black box model that creates _ln_posterior function from plasma and diagnostic model objects
+
+    Parameters
+    ----------
+    data
+        processed diagnostic data of format [diagnostic].[quantity]
+    plasma_context
+        plasma context has methods for using plasma object
+    model_handler
+        model_handler object to be called by ln_posterior
+    quant_to_optimise
+        quantity from data which will be optimised with bckc from diagnostic_models
+    priors
+        prior functions to apply to parameters e.g. scipy.stats.rv_continuous objects
+
+    """
+
+    def __init__(
+        self,
+        data: dict,
+        quant_to_optimise: list,
+        priors: dict,
+
+        plasma_context = None,
+        model_context = None,
+
+        percent_error: float = 0.10,
+    ):
+        self.data = data
+        self.quant_to_optimise = quant_to_optimise
+        self.priors = priors
+
+        self.plasma_context = plasma_context
+        self.model_context = model_context
+
+        self.percent_error = percent_error
+
+        missing_data = list(set(quant_to_optimise).difference(data.keys()))
+        if missing_data:  # gives list of keys in quant_to_optimise but not data
+            raise ValueError(f"{missing_data} not found in data given")
+
+    def ln_likelihood(self):
+        ln_likelihood = 0
+        for key in self.quant_to_optimise:
+            time_coord = self.plasma_context.plasma.time_to_calculate
+            model_data = self.bckc[key]
+            exp_data = self.data[key].sel(t=time_coord)
+            exp_error = exp_data * self.percent_error
+
+            if hasattr(self.data[key], "error"):
+                if (self.data[key].error != 0).any():
+                    # TODO: Some models have an error of 0 given
+                    exp_error = self.data[key].error.sel(t=time_coord)
+
+            _ln_likelihood = np.log(gaussian(model_data, exp_data, exp_error))
+            # treat channel as key dim which isn't averaged like other dims
+            if "channel" in _ln_likelihood.dims:
+                _ln_likelihood = _ln_likelihood.sum(dim="channel", skipna=True)
+            ln_likelihood += _ln_likelihood.mean(skipna=True).values
+        return ln_likelihood
+
+
+    def ln_posterior(self, parameters: dict, **kwargs):
+        """
+        Posterior probability given to optimisers
+
+        Parameters
+        ----------
+        parameters
+            inputs to optimise
+        kwargs
+            kwargs for models
+
+        Returns
+        -------
+        ln_posterior
+            log of posterior probability
+        blob
+            model outputs from bckc and kinetic profiles
+        """
+
+        _ln_prior = ln_prior(self.priors, parameters)
+        if _ln_prior == -np.inf:  # Don't call models if outside priors
+            return -np.inf, {}
+
+        self.plasma_context.update_profiles(parameters)
+        plasma_attributes = self.plasma_context.return_plasma_attrs()
+
+        self.bckc = FlatDict(self.model_context._build_bckc(parameters, **kwargs), ".")  # model calls
+
+        _ln_likelihood = self.ln_likelihood()  # compare results to data
+        ln_posterior = _ln_likelihood + _ln_prior
+
+        blob = deepcopy({**self.bckc, **plasma_attributes})
+        return ln_posterior, blob