move custom ordinalRegressionMetric to space

pipeline.py

@@ -6,46 +6,6 @@ import json
 import numpy as np
 from fastai.learner import load_learner
 
-class OrdinalRegressionMetric(Metric):
-    def __init__(self):
-        super().__init__()
-        self.total = 0
-        self.count = 0
-
-    def accumulate(self, learn):
-        # Get predictions and targets
-        preds, targs = learn.pred, learn.y
-
-        # Your custom logic to convert predictions and targets to numeric values
-        preds_numeric = torch.argmax(preds, dim=1)
-        targs_numeric = targs
-
-        #print("preds_numeric: ",preds_numeric)
-        #print("targs_numeric: ",targs_numeric)
-
-        # Calculate the metric (modify this based on your specific needs)
-        squared_diff = torch.sum(torch.sqrt((preds_numeric - targs_numeric)**2))
-
-        # Normalize by the maximum possible difference
-        max_diff = torch.sqrt((torch.max(targs_numeric) - torch.min(targs_numeric))**2)
-
-        #print("squared_diff: ",squared_diff)
-        #print("max_diff: ",max_diff)
-
-        # Update the metric value
-        self.total += squared_diff
-        #print("self.total: ",self.total)
-        self.count += max_diff
-        #print("self.count: ",self.count)
-    @property
-    def value(self):
-        if self.count == 0:
-            return 0.0  # or handle this case appropriately
-        #print("self.total / self.count: ", (self.total / self.count))
-        # Calculate the normalized metric value
-        metric_value = 1/(self.total / self.count)
-        return metric_value
-
 class PreTrainedPipeline():
     def __init__(self, path=""):
         # IMPLEMENT_THIS