result output artifact

bcipy/simulator/__main__.py

@@ -6,6 +6,7 @@
 import sys
 from pathlib import Path
 
+from bcipy.simulator.helpers.decision import MaxIterationsSim
 from bcipy.simulator.sim_factory import SimulationFactoryV2
 from bcipy.simulator.simulator_base import Simulator
 

bcipy/simulator/helpers/decision.py

@@ -38,6 +38,9 @@ def decide(self, series: List[InquiryResult]):
         """
         raise NotImplementedError()
 
+    def __str__(self):
+        return type(self).__name__
+
 
 class MinIterations(SimDecisionCriteria):
     """ Returns true if the minimum number of iterations have not yet

bcipy/simulator/helpers/state_manager.py

@@ -1,4 +1,5 @@
 import copy
+import dataclasses
 import logging
 import random
 from abc import ABC
@@ -46,6 +47,15 @@ def get_current_likelihood(self) -> Optional[np.ndarray]:
 
         return cur_likelihood
 
+    def to_json(self):
+        d = dataclasses.asdict(self)
+        d['series_results'] = [list(map(lambda ir: ir.to_json(), lis)) for lis in
+                               self.series_results]
+
+        d['decision_criterion'] = [str(dec) for dec in self.decision_criterion]
+
+        return d
+
 
 class StateManager(ABC):
 

bcipy/simulator/helpers/types.py

@@ -1,3 +1,4 @@
+import dataclasses
 from dataclasses import dataclass
 from typing import Optional, List, Dict
 
@@ -12,6 +13,14 @@ class SimEvidence:
     evidence: np.ndarray
     symbol_set: List[str]
 
+    def to_json(self) -> Dict:
+        d = dataclasses.asdict(self)
+        evidence_list = self.evidence.tolist()
+        d['evidence'] = evidence_list
+        d['evidence_type'] = self.evidence_type.name
+
+        return d
+
 
 @dataclass
 class InquiryResult:
@@ -21,3 +30,11 @@ class InquiryResult:
     evidences: Dict[str, SimEvidence]
     fused_likelihood: np.ndarray
     decision: Optional[str]
+
+    def to_json(self):
+        d = dataclasses.asdict(self)
+
+        d['fused_likelihood'] = self.fused_likelihood.tolist()
+        d['evidences'] = {k: v.to_json() for k, v in self.evidences.items()}
+
+        return d

bcipy/simulator/sim.py

@@ -1,7 +1,9 @@
+import dataclasses
+import json
 import logging
 import random
 from time import sleep
-from typing import Optional, Dict
+from typing import Optional, Dict, Any
 
 import numpy as np
 
@@ -103,6 +105,8 @@ def run(self):
 
         log.debug(f"Final State: {final_state}")
 
+        self.output_run()
+
     def __make_stimuli(self, state: SimState):
         # TODO abstract out
         subset_length = 10
@@ -140,5 +144,18 @@ def load_parameters(self, params: Optional[Parameters]):
         else:
             return self.data_engine.get_parameters()
 
+    def output_run(self):
+        """ Outputs the results of a run to json file """
+
+        final_state = self.state_manager.get_state()
+        final_state_json: Dict = final_state.to_json()
+
+        metric_dict = self.referee.score(self).__dict__
+        metric_dict.update(final_state_json)  # adding state data to metrics
+
+        output_path = "bcipy/simulator/generated"
+        with open(f"{output_path}/result.json", 'w') as output_file:
+            json.dump(metric_dict, output_file)
+
     def get_parameters(self):
         return self.parameters.copy()