Merge pull request #72 from jacquelinegarrahan/fix-keras-return

Fix dict formatting

lume_model/keras/__init__.py

@@ -78,13 +78,7 @@ def evaluate(self, input_variables: Dict[str, InputVariable]) -> Dict[str, Outpu
             Dict[str, OutputVariable]: List of output variables
 
         """
-        self.input_variables = {var.name: var for var in input_variables}
-
-        # convert list of input variables to dictionary
-        input_dictionary = {
-            input_variable.name: input_variable.value
-            for input_variable in input_variables
-        }
+        self.input_variables = input_variables
 
         # converts from input_dict -> formatted input
         formatted_input = self.format_input(input_dictionary)
@@ -97,37 +91,39 @@ def evaluate(self, input_variables: Dict[str, InputVariable]) -> Dict[str, Outpu
         # prepare outputs will format return variables (dict-> variables)
         return self._prepare_outputs(output)
 
-    def random_evaluate(self) -> List[OutputVariable]:
+    def random_evaluate(self) -> Dict[str, OutputVariable]:
         """Return a random evaluation of the model.
 
         Returns:
-            List[OutputVariable]: List of outputs associated with random input
+            Dict[str, OutputVariable]: Outputs of random evaluation
 
         """
         random_input = copy.deepcopy(self.input_variables)
-        for variable in self.input_variables:
-            if self.input_variables[variable].variable_type == "scalar":
+        for variable in self.input_variables.values():
+            if variable.variable_type == "scalar":
                 random_input[variable].value = np.random.uniform(
-                    self.input_variables[variable].value_range[0],
-                    self.input_variables[variable].value_range[1],
+                    variable.value_range[0],
+                    variable.value_range[1],
                 )
 
             else:
-                random_input[variable].value = self.input_variables[variable].default
+                variable.value = variable.default
 
-        return self.evaluate(list(random_input.values()))
+        return self.evaluate(random_input)
 
     def _prepare_outputs(
         self, predicted_output: Dict[str, Any]
-    ) -> List[OutputVariable]:
+    ) -> Dict[str, OutputVariable]:
         """Prepares the model outputs to be served so that no additional manipulation
         occurs in the BaseModel class.
 
         Args:
-            model_outputs (dict): Dictionary of output variables to np.ndarrays of outputs
+            model_outputs (dict): Dictionary of output variables to np.ndarrays of 
+                outputs
 
         Returns:
-            List[OutputVariable]: List of output variables.
+            Dict[str, OutputVariable]: Dictionary of output variable name to output 
+                variable.
         """
         for variable in self.output_variables.values():
             if variable.variable_type == "scalar":