pmlearn/linear_model/logistic.py

Killed 30 out of 36 mutants

Survived

Survived mutation testing. These mutants show holes in your test suite.

Mutant 56

--- pmlearn/linear_model/logistic.py
+++ pmlearn/linear_model/logistic.py
@@ -96,7 +96,7 @@
 
     def __init__(self):
         super(HierarchicalLogisticRegression, self).__init__()
-        self.num_cats = None
+        self.num_cats = ""
 
     def create_model(self):
         """

Mutant 68

--- pmlearn/linear_model/logistic.py
+++ pmlearn/linear_model/logistic.py
@@ -128,7 +128,7 @@
         model = pm.Model()
 
         with model:
-            mu_alpha = pm.Normal('mu_alpha', mu=0, sd=100)
+            mu_alpha = pm.Normal('mu_alpha', mu=0, sd=101)
             sigma_alpha = pm.HalfNormal('sigma_alpha', sd=100)
 
             mu_beta = pm.Normal('mu_beta', mu=0, sd=100)

Mutant 70

--- pmlearn/linear_model/logistic.py
+++ pmlearn/linear_model/logistic.py
@@ -129,7 +129,7 @@
 
         with model:
             mu_alpha = pm.Normal('mu_alpha', mu=0, sd=100)
-            sigma_alpha = pm.HalfNormal('sigma_alpha', sd=100)
+            sigma_alpha = pm.HalfNormal('XXsigma_alphaXX', sd=100)
 
             mu_beta = pm.Normal('mu_beta', mu=0, sd=100)
             sigma_beta = pm.HalfNormal('sigma_beta', sd=100)

Mutant 73

--- pmlearn/linear_model/logistic.py
+++ pmlearn/linear_model/logistic.py
@@ -131,7 +131,7 @@
             mu_alpha = pm.Normal('mu_alpha', mu=0, sd=100)
             sigma_alpha = pm.HalfNormal('sigma_alpha', sd=100)
 
-            mu_beta = pm.Normal('mu_beta', mu=0, sd=100)
+            mu_beta = pm.Normal('XXmu_betaXX', mu=0, sd=100)
             sigma_beta = pm.HalfNormal('sigma_beta', sd=100)
 
             alpha = pm.Normal('alpha', mu=mu_alpha, sd=sigma_alpha,

Mutant 75

--- pmlearn/linear_model/logistic.py
+++ pmlearn/linear_model/logistic.py
@@ -131,7 +131,7 @@
             mu_alpha = pm.Normal('mu_alpha', mu=0, sd=100)
             sigma_alpha = pm.HalfNormal('sigma_alpha', sd=100)
 
-            mu_beta = pm.Normal('mu_beta', mu=0, sd=100)
+            mu_beta = pm.Normal('mu_beta', mu=0, sd=101)
             sigma_beta = pm.HalfNormal('sigma_beta', sd=100)
 
             alpha = pm.Normal('alpha', mu=mu_alpha, sd=sigma_alpha,

Mutant 86

--- pmlearn/linear_model/logistic.py
+++ pmlearn/linear_model/logistic.py
@@ -141,7 +141,7 @@
 
             c = model_cats
 
-            linear_function = alpha[c] + tt.sum(betas[c] * model_input, 1)
+            linear_function = alpha[c] + tt.sum(betas[c] / model_input, 1)
 
             p = pm.invlogit(linear_function)