Add trainprep.

src/decagon_pytorch/data/trainprep.py

@@ -0,0 +1,2 @@
+def trainprep(data):
+    pass

src/decagon_pytorch/splits.py

@@ -1,7 +1,40 @@
 import torch
+from .data import Data, \
+    AdjListData
 
 
-def train_val_test_split_adj_mat(adj_mat, train_ratio, val_ratio, test_ratio):
+def train_val_test_split_adj_mat(adj_mat, train_ratio, val_ratio, test_ratio,
+    return_edges=False):
+
+    if train_ratio + val_ratio + test_ratio != 1.0:
+        raise ValueError('Train, validation and test ratios must add up to 1')
+
+    edges = torch.nonzero(adj_mat)
+    order = torch.randperm(len(edges))
+    edges = edges[order, :]
+    n = round(len(edges) * train_ratio)
+    edges_train = edges[:n]
+    n_1 = round(len(edges) * (train_ratio + val_ratio))
+    edges_val = edges[n:n_1]
+    edges_test = edges[n_1:]
+
+    adj_mat_train = torch.zeros_like(adj_mat)
+    adj_mat_train[edges_train[:, 0], edges_train[:, 1]] = 1
+
+    adj_mat_val = torch.zeros_like(adj_mat)
+    adj_mat_val[edges_val[:, 0], edges_val[:, 1]] = 1
+
+    adj_mat_test = torch.zeros_like(adj_mat)
+    adj_mat_test[edges_test[:, 0], edges_test[:, 1]] = 1
+
+    res = (adj_mat_train, adj_mat_val, adj_mat_test)
+    if return_edges:
+        res += (edges_train, edges_val, edges_test)
+
+    return res
+
+
+def train_val_test_split_edges(adj_mat, train_ratio, val_ratio, test_ratio):
     if train_ratio + val_ratio + test_ratio != 1.0:
         raise ValueError('Train, validation and test ratios must add up to 1')
 
@@ -23,4 +56,5 @@ def train_val_test_split_adj_mat(adj_mat, train_ratio, val_ratio, test_ratio):
     adj_mat_test = torch.zeros_like(adj_mat)
     adj_mat_test[edges_test[:, 0], edges_test[:, 1]] = 1
 
-    return adj_mat_train, adj_mat_val, adj_mat_test
+    return adj_mat_train, adj_mat_val, adj_mat_test, \
+        edges_train, edges_val, edges_test