feature/graphical result

autofit/graphical/declarative/collection.py

@@ -1,20 +1,21 @@
-from typing import Union
+from typing import Union, Optional
 
 from autofit.graphical.declarative.factor.hierarchical import HierarchicalFactor
 from autofit.mapper.model import ModelInstance
 from autofit.tools.namer import namer
 from .abstract import AbstractDeclarativeFactor
+from autofit.non_linear.paths.abstract import AbstractPaths
+from autofit.non_linear.samples.pdf import SamplesPDF
+from autofit.non_linear.samples.summary import SamplesSummary
+from autofit.non_linear.analysis.combined import CombinedResult
 
 
 class FactorGraphModel(AbstractDeclarativeFactor):
     def __init__(
-            self,
-            *model_factors: Union[
-                AbstractDeclarativeFactor,
-                HierarchicalFactor
-            ],
-            name=None,
-            include_prior_factors=True,
+        self,
+        *model_factors: Union[AbstractDeclarativeFactor, HierarchicalFactor],
+        name=None,
+        include_prior_factors=True,
     ):
         """
         A collection of factors that describe models, which can be
@@ -40,11 +41,10 @@ def prior_model(self):
         in each model factor
         """
         from autofit.mapper.prior_model.collection import Collection
-        return Collection({
-            factor.name: factor.prior_model
-            for factor
-            in self.model_factors
-        })
+
+        return Collection(
+            {factor.name: factor.prior_model for factor in self.model_factors}
+        )
 
     @property
     def optimiser(self):
@@ -61,21 +61,13 @@ def info(self) -> str:
     def name(self):
         return self._name
 
-    def add(
-            self,
-            model_factor: AbstractDeclarativeFactor
-    ):
+    def add(self, model_factor: AbstractDeclarativeFactor):
         """
         Add another factor to this collection.
         """
-        self._model_factors.append(
-            model_factor
-        )
+        self._model_factors.append(model_factor)
 
-    def log_likelihood_function(
-            self,
-            instance: ModelInstance
-    ) -> float:
+    def log_likelihood_function(self, instance: ModelInstance) -> float:
         """
         Compute the combined likelihood of each factor from a collection of instances
         with the same ordering as the factors.
@@ -90,29 +82,69 @@ def log_likelihood_function(
         The combined likelihood of all factors
         """
         log_likelihood = 0
-        for model_factor, instance_ in zip(
-                self.model_factors,
-                instance
-        ):
-            log_likelihood += model_factor.log_likelihood_function(
-                instance_
-            )
+        for model_factor, instance_ in zip(self.model_factors, instance):
+            log_likelihood += model_factor.log_likelihood_function(instance_)
 
         return log_likelihood
 
     @property
     def model_factors(self):
         model_factors = list()
         for model_factor in self._model_factors:
-            if isinstance(
-                    model_factor,
-                    HierarchicalFactor
-            ):
-                model_factors.extend(
-                    model_factor.factors
-                )
+            if isinstance(model_factor, HierarchicalFactor):
+                model_factors.extend(model_factor.factors)
             else:
-                model_factors.append(
-                    model_factor
-                )
+                model_factors.append(model_factor)
         return model_factors
+
+    def make_result(
+        self,
+        samples_summary: SamplesSummary,
+        paths: AbstractPaths,
+        samples: Optional[SamplesPDF] = None,
+        search_internal: Optional[object] = None,
+        analysis: Optional[object] = None,
+    ) -> CombinedResult:
+        """
+        Make a result from the samples summary and paths.
+
+        The top level result accounts for the combined model.
+        There is one child result for each model factor.
+
+        Parameters
+        ----------
+        samples_summary
+            A summary of the samples
+        paths
+            Handles saving and loading data
+        samples
+            The full list of samples
+        search_internal
+        analysis
+
+        Returns
+        -------
+        A result with child results for each model factor
+        """
+        child_results = [
+            model_factor.analysis.make_result(
+                samples_summary=samples_summary.subsamples(
+                    model_factor.prior_model,
+                ),
+                paths=paths,
+                samples=samples.subsamples(model_factor.prior_model)
+                if samples
+                else None,
+                search_internal=search_internal,
+                analysis=model_factor,
+            )
+            for model_factor in self.model_factors
+        ]
+        return CombinedResult(
+            child_results,
+            samples_summary=samples_summary,
+            paths=paths,
+            samples=samples,
+            search_internal=search_internal,
+            analysis=analysis,
+        )

autofit/non_linear/analysis/combined.py

@@ -15,12 +15,15 @@
 logger = logging.getLogger(__name__)
 
 
-class CombinedResult:
+class CombinedResult(Result):
     def __init__(
         self,
         results: List[Result],
         samples: Optional[SamplesPDF] = None,
         samples_summary: Optional[SamplesSummary] = None,
+        paths: Optional[AbstractPaths] = None,
+        search_internal: Optional[object] = None,
+        analysis: Optional[Analysis] = None,
     ):
         """
         A `Result` object that is composed of multiple `Result` objects. This is used to combine the results of
@@ -32,9 +35,14 @@ def __init__(
         results
             The list of `Result` objects that are combined into this `CombinedResult` object.
         """
+        super().__init__(
+            samples_summary=samples_summary,
+            samples=samples,
+            paths=paths,
+            search_internal=search_internal,
+            analysis=analysis,
+        )
         self.child_results = results
-        self.samples = samples
-        self.samples_summary = samples_summary
 
     def __getattr__(self, item: str):
         """
@@ -415,4 +423,4 @@ def with_free_parameters(
         return FreeParameterAnalysis(*self.analyses, free_parameters=free_parameters)
 
     def compute_latent_samples(self, samples):
-        return self.analyses[0].compute_latent_samples(samples=samples)
+        return self.analyses[0].compute_latent_samples(samples=samples)

test_autofit/graphical/test_combined_analysis.py

@@ -0,0 +1,33 @@
+import autofit as af
+from autofit.non_linear.paths.null import NullPaths
+
+
+def test_make_result():
+    model = af.Model(af.Gaussian)
+    factor_graph_model = af.FactorGraphModel(
+        af.AnalysisFactor(
+            model,
+            af.Analysis(),
+        )
+    )
+    result = factor_graph_model.make_result(
+        samples_summary=af.SamplesSummary(
+            max_log_likelihood_sample=af.Sample(
+                0,
+                0,
+                0,
+                kwargs={
+                    ("0", "centre"): 1.0,
+                    ("0", "normalization"): 1.0,
+                    ("0", "sigma"): 1.0,
+                },
+            ),
+            model=af.Collection(model),
+        ),
+        paths=NullPaths(),
+    )
+    assert len(result.child_results) == 1
+    assert isinstance(result.model, af.Collection)
+
+    (child_result,) = result.child_results
+    assert child_result.model == model