Merge pull request #59 from Princeton-CDH/revise-hawkdove-distributions

fine-tune variant distributions

simulatingrisk/hawkdovemulti/model.py

@@ -224,33 +224,41 @@ def get_risk_attitude_generator(self):
             while True:
                 yield self.random.randint(self.min_risk_level, self.max_risk_level)
         if self.risk_distribution == "normal":
-            # return values from a normal distribution centered around 4
+            # return values from a normal distribution centered around 4.5
             while True:
-                yield int(self.random.gauss(4, 1.5))
+                yield round(self.random.gauss(4.5, 1.5))
         elif self.risk_distribution == "skewed left":
             # return values from a triangler distribution centered around 0
             while True:
                 yield round(
                     self.random.triangular(self.min_risk_level, self.max_risk_level, 0)
                 )
         elif self.risk_distribution == "skewed right":
-            # return values from a triangler distribution centered around 8
+            # return values from a triangular distribution centered around 9
             while True:
                 yield round(
-                    self.random.triangular(self.min_risk_level, self.max_risk_level, 8)
+                    self.random.triangular(self.min_risk_level, self.max_risk_level, 9)
                 )
         elif self.risk_distribution == "bimodal":
             # to generate a bimodal distribution, alternately generate
             # values from two different normal distributions centered
             # around the beginning and end of our risk attitude range
             while True:
-                yield int(self.random.gauss(1, 0.75))
-                yield int(self.random.gauss(7, 0.75))
+                yield round(self.random.gauss(0, 1.5))
+                yield round(self.random.gauss(9, 1.5))
+                # NOTE: on smaller grids, using 0/9 makes it extremely
+                # unlikely to get mid-range risk values (4/5)
 
     def get_risk_attitude(self):
         """return the next value from risk attitude generator, based on
         configured distribution."""
         val = next(self.risk_attitude_generator)
+
+        # for bimodal distribution, clamp values to range
+        if self.risk_distribution == "bimodal":
+            return max(self.min_risk_level, min(self.max_risk_level, val))
+
+        # for all other distributions:
         # occasionally generators will return values that are out of range.
         # rather than capping to the min/max and messing up the distribution,
         # just get the next value

tests/test_hawkdovemulti.py

@@ -326,7 +326,7 @@ def test_get_risk_attitude_generator():
     model.risk_distribution = "normal"
     model.random.gauss.return_value = 3.3  # value to convert to int
     next(model.get_risk_attitude_generator())
-    model.random.gauss.assert_called_with(4, 1.5)
+    model.random.gauss.assert_called_with(4.5, 1.5)
 
     model.risk_distribution = "skewed left"
     model.random.triangular.return_value = 2.1  # value to round
@@ -339,7 +339,7 @@ def test_get_risk_attitude_generator():
     model.random.triangular.return_value = 7.6  # value to round
     next(model.get_risk_attitude_generator())
     model.random.triangular.assert_called_with(
-        model.min_risk_level, model.max_risk_level, 8
+        model.min_risk_level, model.max_risk_level, 9
     )
 
     # bimodal returns values from from two different distributions; call twice
@@ -348,8 +348,8 @@ def test_get_risk_attitude_generator():
     risk_gen = model.get_risk_attitude_generator()
     next(risk_gen)
     next(risk_gen)
-    model.random.gauss.assert_any_call(1, 0.75)
-    model.random.gauss.assert_any_call(7, 0.75)
+    model.random.gauss.assert_any_call(0, 1.5)
+    model.random.gauss.assert_any_call(9, 1.5)
 
 
 def test_get_risk_attitude():