remove distributions and populates

Author: christopher-wild

causal_testing/json_front/json_class.py

@@ -20,6 +20,7 @@
 
 logger = logging.getLogger(__name__)
 
+
 class JsonUtility(ABC):
     """
     The JsonUtility Class provides the functionality to use structured JSON to setup and run causal tests on the
@@ -73,10 +74,10 @@ def set_variables(self, inputs: dict, outputs: dict, metas: dict, distributions:
             :param distributions:
             :param populates:
         """
-        self.inputs = [Input(i['name'], i['type'], distributions[i['distribution']]) for i in
+        self.inputs = [Input(i['name'], i['type'], i['distribution']) for i in
                        inputs]
         self.outputs = [Output(i['name'], i['type']) for i in outputs]
-        self.metas = [Meta(i['name'], i['type'], populates[i['populate']]) for i in
+        self.metas = [Meta(i['name'], i['type'], [i['populate']]) for i in
                       metas] if metas else list()
 
     def setup(self):
@@ -131,8 +132,6 @@ def execute_tests(self, effects: dict, mutates: dict, estimators: dict, f_flag:
 
     def _json_parse(self):
         """Parse a JSON input file into inputs, outputs, metas and a test plan
-            :param distributions: dictionary of user defined scipy distributions
-            :param populates: dictionary of user defined populate functions
         """
         with open(self.json_path) as f:
             self.test_plan = json.load(f)

examples/poisson/run_causal_tests.py

@@ -92,10 +92,11 @@ def populate_num_shapes_unit(data):
     area = data['width'] * data['height']
     data['num_shapes_unit'] = data['num_shapes_abs'] / area
 
+
 inputs = [
-    {"name": "width", "type": float, "distribution": "uniform"},
-    {"name": "height", "type": float, "distribution": "uniform"},
-    {"name": "intensity", "type": float, "distribution": "uniform"}
+    {"name": "width", "type": float, "distribution": scipy.stats.uniform(0, 10)},
+    {"name": "height", "type": float, "distribution": scipy.stats.uniform(0, 10)},
+    {"name": "intensity", "type": float, "distribution": scipy.stats.uniform(0, 10)}
 ]
 
 outputs = [
@@ -104,23 +105,13 @@ def populate_num_shapes_unit(data):
 ]
 
 metas = [
-    {"name": "num_lines_unit", "type": float, "populate": "populate_num_lines_unit"},
-    {"name": "num_shapes_unit", "type": float, "populate": "populate_num_shapes_unit"},
-    {"name": "width_plus_height", "type": float, "populate": "populate_width_height"}
+    {"name": "num_lines_unit", "type": float, "populate": populate_num_lines_unit},
+    {"name": "num_shapes_unit", "type": float, "populate": populate_num_shapes_unit},
+    {"name": "width_plus_height", "type": float, "populate": populate_width_height}
 ]
 
 constraints = ["width > 0", "height > 0", "intensity > 0"]
 
-populates = {
-    "populate_width_height": populate_width_height,
-    "populate_num_lines_unit": populate_num_lines_unit,
-    "populate_num_shapes_unit": populate_num_shapes_unit
-}
-
-distributions = {
-    "uniform": scipy.stats.uniform(0, 10)
-}
-
 effects = {
     "PoissonWidthHeight": PoissonWidthHeight(),
     "Positive": Positive(),
@@ -136,9 +127,9 @@ def populate_num_shapes_unit(data):
 }
 
 # Create input structure required to create a modelling scenario
-modelling_inputs = [Input(i['name'], i['type'], distributions[i['distribution']]) for i in inputs] + \
+modelling_inputs = [Input(i['name'], i['type'], i['distribution']) for i in inputs] + \
                    [Output(i['name'], i['type']) for i in outputs] + \
-                   [Meta(i['name'], i['type'], populates[i['populate']]) for i in metas] if metas else list()
+                   [Meta(i['name'], i['type'], [i['populate']]) for i in metas] if metas else list()
 
 # Create modelling scenario to access z3 variable mirrors
 modelling_scenario = Scenario(modelling_inputs, None)