Merge branch 'hotfix-BEST-wrong-desitions'

Author: AlexRodis

bayesian_models/models.py

@@ -1405,10 +1405,11 @@ def predict(self, var_names:typing.Sequence[str]=['Δμ'],
                 - | multilevel_on:str='[' := A separator defining the
                     multilevel index. :code:`pymc` by default
                     concatinates the label according to the form:
-                    {var_name}[{feature_label}]. The argument will
-                    reindex them in a multilevel fashion of the form
-                    (var_name, feature_label) in the resulting
-                    dataframe. Set to None to disable this behavior.
+                    :code:`{var_name}[{feature_label}]`. The argument
+                    will reindex them in a multilevel fashion of the
+                    form :code:`(var_name, feature_label)` in the
+                    resulting dataframe. Set to None to disable this
+                    behavior.
 
                 - | extend_summary:bool=True := If True the new
                     Significance column extends the summary dataframe.
@@ -1454,14 +1455,14 @@ def predict(self, var_names:typing.Sequence[str]=['Δμ'],
             warn(("Length of variables, ropes and hdis not equal. The"
                   " shortest value will be considered"))
         results=dict()
-        null_interval = interval(0,0)
+        null_interval = interval()
         for var_name, rope,hdi in zip(var_names,ropes, hdis):
             raw_summary = az.summary(self.idata, var_names=[var_name],
             filter_vars='like', hdi_prob=hdi)
-            rope=interval(*rope)
+            rope=interval(rope)
             out=[]
             for idx,row in raw_summary.iterrows():
-                ci=interval(row[2], row[3])
+                ci=interval([row[2], row[3]])
                 if ci in rope:
                     out.append("Not Significant")
                 elif ci & rope != null_interval:

pyproject.toml

@@ -2,7 +2,7 @@
 
 name = "bayesian-models"
 
-version = "0.1.2"
+version = "0.1.3"
 authors = [
   { name="Alexander Rodis", email="alexanderrhodis@gmail.com" },
 ]

tests/BEST_test.py

@@ -251,7 +251,7 @@ def test_ground_truth(self):
         sig = results["Δσ"].loc[:,"Significance"]
         self.assertTrue( Δμ.iloc[0]-ref_val_mu <= ε)
 
-    def test_desition_rule(self):
+    def test_decision_rule(self):
         obj = BEST()(self.df, "group")
         obj.fit(tune=1000, draws=2000, chains=2,
                 progressbar=False)
@@ -402,4 +402,5 @@ def test_68(self):
         obj = BEST(
             std_difference=True, effect_magnitude=True
             )(df, "target")
-        self.assertTrue(True)
+        self.assertTrue(True)
+        

tests/core_modelbuilder_test.py

@@ -29,9 +29,7 @@
 from bayesian_models.core import ModelAdaptorComponent
 from bayesian_models.core import ResponseFunctionComponent
 from bayesian_models.core import ResponseFunctions
-from bayesian_models.core import NeuralNetCoreComponent
 from bayesian_models.data import Data
-from bayesian_models.models import Layer
 from bayesian_models.utilities import powerset, dict_powerset
 from bayesian_models.data import Data