first weight setter

Author: jcapp4

extras/weight_loader.py

@@ -0,0 +1,91 @@
+import csv
+import torch
+
+
+#inint weights in a csv file
+def init_weights(path="/home/programmer/Bachelorarbeit/weights/first_it.csv",path_to_split="/home/programmer/Bachelorarbeit/split/splits.csv"):
+    with open(path_to_split, 'r') as csvfile:
+        with open(path, 'w') as to_file:
+            fieldnames = ['idents','label','weights']
+            writer = csv.writer(to_file)
+            writer.writerow(fieldnames)
+            reader = csv.reader(csvfile)
+            weight = 1 / get_size(path_to_split)
+            for row in reader:
+                if row[1] == "train" or row[1] == "validation":
+                    #print(type(row[0]))
+                    writer.writerow([int(row[0]),row[1],weight])
+
+def mock_init_weights(path="/home/programmer/Bachelorarbeit/weights/first_it.csv",path_to_split="/home/programmer/Bachelorarbeit/split/splits.csv"):
+    with open(path_to_split, 'r') as csvfile:
+        with open(path, 'w') as to_file:
+            fieldnames = ['idents','label','weights']
+            writer = csv.writer(to_file)
+            writer.writerow(fieldnames)
+            reader = csv.reader(csvfile)
+            weight = 1
+            for row in reader:
+                if row[1] == "train" or row[1] == "validation":
+                    writer.writerow([int(row[0]),row[1],weight])
+                    weight = weight + 1
+
+#check the size of a csv file given a filter for the second object
+# assumes csv file has a header      
+def get_size(path="/home/programmer/Bachelorarbeit/split/splits.csv",filter=["train","validation"]) -> int:
+    with open(path,'r') as file:
+        reader = csv.reader(file)
+        size = -1
+        for row in reader:
+            if row[1] in filter:
+                size = size + 1
+        return size
+#get a dictory with the ids and weights of the data points  
+def get_weights(idents:tuple[int,...],path="/home/programmer/Bachelorarbeit/weights/first_it.csv")-> dict[str,float]:
+    value = dict()
+    for i in idents:
+        weight = find_weight(path,i)
+        value.update({str(i):weight})
+    return value
+
+
+
+#finds the weight for a specific datapoint  
+def find_weight(path:str,ident:int)-> float:
+    with open(path,'r') as file:
+        reader = csv.reader(file)
+        for row in reader:
+            if row[0] == str(ident):
+                return float(row[2])
+            
+                
+    print(f"{ident} is not in file ")
+
+
+def create_data_weights(batchsize:int,dim:int,weights:dict[str,float],idents:tuple[int,...])-> torch.tensor:
+    weight = torch.empty(batchsize,dim)
+    index = 0
+    for i in idents:
+        w = weights[str(i)]
+        for j in range(0,dim):
+            weight[index][j] = w
+        index = index + 1
+    return weight
+
+
+
+def testing():
+    print("hello world")
+
+
+
+
+
+
+
+
+def create_class_weights()-> torch.tensor:
+    pass
+
+# mock_init_weights()
+# print(get_weights((233713,51990)))
+