add parameter extraction and averaging

src/asses_results.py

@@ -19,6 +19,11 @@ def get_all_data_from_folder(foldername, datatype="txt"):
     return answers
 
 def process_survey_data(headers, answers):
+    """
+    Looks at the tags/questions and adds to every question every response of every participant.
+    In the end you get a dict with question names as keys and the values are the answers of every participant.
+    So the first participant will have the index 0 in every value.
+    """
     survey_group_answers = {}
     for responses in answers:
         question_num = 0
@@ -137,7 +142,7 @@ class Proccess_Data(object):
             return above_avg_indicies, below_avg_indicies
                 
     def compare_ai(self, correct_percentage):
-        gpt2, opt, gpt4o , gpt2_count, opt_count, gpt4o_count = 0, 0, 0, 0 ,0, 0
+        gpt2, opt, gpt4o , gpt2_count, opt_count, gpt4o_count = 0, 0, 0, 0, 0, 0
         for ai, correctness in zip(self.models, correct_percentage):
             if ai == "gpt2":
                 gpt2 += correctness
@@ -150,9 +155,30 @@ class Proccess_Data(object):
                 gpt4o_count += 1
         return {"gpt2": round(gpt2 / gpt2_count, 2), "opt": round(opt / opt_count, 2), "gpt4o": round(gpt4o / gpt4o_count, 2)}
     
-    def average_parameter(self):
-        """Looks at the parameters like coherence, conciseness, creawtivity and clarity of concept and calculates the average."""
-        pass
+    def average_parameter(self, parameter, model_name):
+        """Looks at the parameters like coherence, conciseness, creativity and clarity of concept and calculates the average."""
+        count, rate = 0, 0
+        if model_name != "human":
+            for question_num, correct_label in enumerate(self.correct_labels):
+                for keys in self.survey_data.keys():
+                    if parameter in keys and correct_label in keys:
+                        model = self.models[question_num]
+                        if model == model_name: 
+                            for rating in self.survey_data[keys]:
+                                if rating:
+                                    rate += int(rating)
+                                    count += 1
+        if model_name == "human":
+            for question_num, correct_label in enumerate(self.correct_labels):
+                for keys in self.survey_data.keys():
+                    if parameter in keys and correct_label not in keys:
+                        model = self.models[question_num]
+                        for rating in self.survey_data[keys]:
+                            if rating:
+                                rate += int(rating)
+                                count += 1
+
+        return round(rate / count, 2)
         
 if __name__ == "__main__":
     answers = get_all_data_from_folder("results", "csv")
@@ -182,8 +208,14 @@ if __name__ == "__main__":
     #correct_percentage = [i[2] for i in total_correct.values()] # extracts average percentage of correct answers
     #model_results = evaluator.compare_ai(correct_percentage)
 
-    test = evaluator.average_parameter()
-    print(test)
+    parameters = ["Coherence", "Conciseness", "Creativity", "Clarity of Concept"]
+    models = ["gpt2", "gpt4o", "opt", "human"]
+    
+    avg_scores = {
+    param: {model: evaluator.average_parameter(param, model) for model in models}
+    for param in parameters
+    }
+