pyGPGO/surrogates/RandomForest.py

Killed 17 out of 33 mutants

Timeouts

Mutants that made the test suite take a lot longer so the tests were killed.

Mutant 140

--- pyGPGO/surrogates/RandomForest.py
+++ pyGPGO/surrogates/RandomForest.py
@@ -66,7 +66,7 @@
                 var_tree = tree.tree_.impurity[tree.apply(Xstar)]
                 var_tree = np.clip(var_tree, eps, np.inf)
                 mean_tree = tree.predict(Xstar)
-                std += var_tree + mean_tree ** 2
+                std += var_tree + mean_tree * 2
 
             std /= len(trees)
             std -= ymean ** 2

Survived

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

Mutant 125

--- pyGPGO/surrogates/RandomForest.py
+++ pyGPGO/surrogates/RandomForest.py
@@ -30,7 +30,7 @@
         """
         self.X = X
         self.y = y
-        self.n = self.X.shape[0]
+        self.n = self.X.shape[1]
         self.model = RandomForestRegressor(**self.params)
         self.model.fit(X, y)

Mutant 126

--- pyGPGO/surrogates/RandomForest.py
+++ pyGPGO/surrogates/RandomForest.py
@@ -30,7 +30,7 @@
         """
         self.X = X
         self.y = y
-        self.n = self.X.shape[0]
+        self.n = None
         self.model = RandomForestRegressor(**self.params)
         self.model.fit(X, y)

Mutant 128

--- pyGPGO/surrogates/RandomForest.py
+++ pyGPGO/surrogates/RandomForest.py
@@ -34,7 +34,7 @@
         self.model = RandomForestRegressor(**self.params)
         self.model.fit(X, y)
 
-    def predict(self, Xstar, return_std = True, eps = 1e-6):
+    def predict(self, Xstar, return_std = False, eps = 1e-6):
         """
         Predicts 'posterior' mean and variance for the RF model.

Mutant 129

--- pyGPGO/surrogates/RandomForest.py
+++ pyGPGO/surrogates/RandomForest.py
@@ -34,7 +34,7 @@
         self.model = RandomForestRegressor(**self.params)
         self.model.fit(X, y)
 
-    def predict(self, Xstar, return_std = True, eps = 1e-6):
+    def predict(self, Xstar, return_std = True, eps = 1.000001):
         """
         Predicts 'posterior' mean and variance for the RF model.

Mutant 137

--- pyGPGO/surrogates/RandomForest.py
+++ pyGPGO/surrogates/RandomForest.py
@@ -66,7 +66,7 @@
                 var_tree = tree.tree_.impurity[tree.apply(Xstar)]
                 var_tree = np.clip(var_tree, eps, np.inf)
                 mean_tree = tree.predict(Xstar)
-                std += var_tree + mean_tree ** 2
+                std = var_tree + mean_tree ** 2
 
             std /= len(trees)
             std -= ymean ** 2

Mutant 138

--- pyGPGO/surrogates/RandomForest.py
+++ pyGPGO/surrogates/RandomForest.py
@@ -66,7 +66,7 @@
                 var_tree = tree.tree_.impurity[tree.apply(Xstar)]
                 var_tree = np.clip(var_tree, eps, np.inf)
                 mean_tree = tree.predict(Xstar)
-                std += var_tree + mean_tree ** 2
+                std -= var_tree + mean_tree ** 2
 
             std /= len(trees)
             std -= ymean ** 2

Mutant 139

--- pyGPGO/surrogates/RandomForest.py
+++ pyGPGO/surrogates/RandomForest.py
@@ -66,7 +66,7 @@
                 var_tree = tree.tree_.impurity[tree.apply(Xstar)]
                 var_tree = np.clip(var_tree, eps, np.inf)
                 mean_tree = tree.predict(Xstar)
-                std += var_tree + mean_tree ** 2
+                std += var_tree - mean_tree ** 2
 
             std /= len(trees)
             std -= ymean ** 2

Mutant 141

--- pyGPGO/surrogates/RandomForest.py
+++ pyGPGO/surrogates/RandomForest.py
@@ -66,7 +66,7 @@
                 var_tree = tree.tree_.impurity[tree.apply(Xstar)]
                 var_tree = np.clip(var_tree, eps, np.inf)
                 mean_tree = tree.predict(Xstar)
-                std += var_tree + mean_tree ** 2
+                std += var_tree + mean_tree ** 3
 
             std /= len(trees)
             std -= ymean ** 2

Mutant 142

--- pyGPGO/surrogates/RandomForest.py
+++ pyGPGO/surrogates/RandomForest.py
@@ -68,7 +68,7 @@
                 mean_tree = tree.predict(Xstar)
                 std += var_tree + mean_tree ** 2
 
-            std /= len(trees)
+            std = len(trees)
             std -= ymean ** 2
             std = np.sqrt(np.clip(std, eps, np.inf))
             return ymean, std

Mutant 144

--- pyGPGO/surrogates/RandomForest.py
+++ pyGPGO/surrogates/RandomForest.py
@@ -69,7 +69,7 @@
                 std += var_tree + mean_tree ** 2
 
             std /= len(trees)
-            std -= ymean ** 2
+            std = ymean ** 2
             std = np.sqrt(np.clip(std, eps, np.inf))
             return ymean, std
         return ymean

Mutant 145

--- pyGPGO/surrogates/RandomForest.py
+++ pyGPGO/surrogates/RandomForest.py
@@ -69,7 +69,7 @@
                 std += var_tree + mean_tree ** 2
 
             std /= len(trees)
-            std -= ymean ** 2
+            std += ymean ** 2
             std = np.sqrt(np.clip(std, eps, np.inf))
             return ymean, std
         return ymean

Mutant 146

--- pyGPGO/surrogates/RandomForest.py
+++ pyGPGO/surrogates/RandomForest.py
@@ -69,7 +69,7 @@
                 std += var_tree + mean_tree ** 2
 
             std /= len(trees)
-            std -= ymean ** 2
+            std -= ymean * 2
             std = np.sqrt(np.clip(std, eps, np.inf))
             return ymean, std
         return ymean

Mutant 147

--- pyGPGO/surrogates/RandomForest.py
+++ pyGPGO/surrogates/RandomForest.py
@@ -69,7 +69,7 @@
                 std += var_tree + mean_tree ** 2
 
             std /= len(trees)
-            std -= ymean ** 2
+            std -= ymean ** 3
             std = np.sqrt(np.clip(std, eps, np.inf))
             return ymean, std
         return ymean

Mutant 153

--- pyGPGO/surrogates/RandomForest.py
+++ pyGPGO/surrogates/RandomForest.py
@@ -123,7 +123,7 @@
         self.model = ExtraTreesRegressor(**self.params)
         self.model.fit(X, y)
 
-    def predict(self, Xstar, return_std = True, eps = 1e-6):
+    def predict(self, Xstar, return_std = False, eps = 1e-6):
         """
         Predicts 'posterior' mean and variance for the RF model.

Mutant 154

--- pyGPGO/surrogates/RandomForest.py
+++ pyGPGO/surrogates/RandomForest.py
@@ -123,7 +123,7 @@
         self.model = ExtraTreesRegressor(**self.params)
         self.model.fit(X, y)
 
-    def predict(self, Xstar, return_std = True, eps = 1e-6):
+    def predict(self, Xstar, return_std = True, eps = 1.000001):
         """
         Predicts 'posterior' mean and variance for the RF model.