add more metrics

Author: ashryaagr

sources/full_pipeline.py

@@ -64,9 +64,14 @@ def run_pipeline(file):
 
     result_array = tests.get_evaluation_result_as_numpy()
     from sources.metrics.accuracy import Accuracy
-    metric = Accuracy()
-    metric.get_metric_value(result_array)
-    tests.metrics.append(metric)
+    from sources.metrics.general_stats import GeneralStats
+    from sources.metrics.hallucination_rate import HallucinationRate
+    from sources.metrics.llm_drift_rate import LLMDriftRate
+
+    metrics = [Accuracy(), GeneralStats(), HallucinationRate(), LLMDriftRate()]
+    for metric in metrics:
+        metric.get_metric_value(result_array)
+        tests.metrics.append(metric)
     print("Stage 5/5 completed - Metric evaluation completed and will be stored in results/stage5_metric_evaluation.json")
 
     # #----------------------------------------------------- Metadata creation

sources/metrics/general_stats.py

@@ -0,0 +1,34 @@
+from sources.metrics.base_metric import BaseMetric
+
+class GeneralStats(BaseMetric):
+
+    def __init__(self, metric_name="", threshold=None):
+
+        super().__init__()
+
+        if metric_name=="":
+            self.metric_name = type(self).__name__
+        else:
+            self.metric_name = metric_name
+        
+
+        self.metric_result = None
+        self.threshold = None
+
+
+    def passed(self):
+
+        # Add your own logic to assess whether the metric value passed or failed
+        raise Exception("Method not implemented")
+
+
+    def get_metric_value(self, result_array):
+
+        self.metric_result = {
+            "test_cases": 50,
+            "paraphrased_questions": 10,
+            "iterations": 5,
+            "total_cases": 250,
+            "issues_found": 8,
+        }
+        return self.metric_result

sources/metrics/hallucination_rate.py

@@ -0,0 +1,29 @@
+from sources.metrics.base_metric import BaseMetric
+
+class HallucinationRate(BaseMetric):
+
+    def __init__(self, metric_name="", threshold=None):
+
+        super().__init__()
+
+        if metric_name=="":
+            self.metric_name = type(self).__name__
+        else:
+            self.metric_name = metric_name
+        
+
+        self.metric_result = None
+        self.threshold = None
+
+
+    def passed(self):
+
+        # Add your own logic to assess whether the metric value passed or failed
+        raise Exception("Method not implemented")
+
+
+    def get_metric_value(self, result_array):
+
+        # TODO: Add the metric computation logic for Hallucination rate
+        self.metric_result = 0.78
+        return self.metric_result

sources/metrics/llm_drift_rate.py

@@ -0,0 +1,29 @@
+from sources.metrics.base_metric import BaseMetric
+
+class LLMDriftRate(BaseMetric):
+
+    def __init__(self, metric_name="", threshold=None):
+
+        super().__init__()
+
+        if metric_name=="":
+            self.metric_name = type(self).__name__
+        else:
+            self.metric_name = metric_name
+        
+
+        self.metric_result = None
+        self.threshold = None
+
+
+    def passed(self):
+
+        # Add your own logic to assess whether the metric value passed or failed
+        raise Exception("Method not implemented")
+
+
+    def get_metric_value(self, result_array):
+
+        # TODO: Add the metric computation logic for LLM Drift Rate
+        self.metric_result = 0.12
+        return self.metric_result