add grid search

main.py

@@ -650,10 +650,30 @@ if __name__ == "__main__":
         )
 
         paramsGrid = {
-            'optimizer__lr': [0.001, 0.01, 0.1, 0.2, 0.3],
+            'optimizer__lr': [0.0001, 0.0003, 0.001],
+            'optimizer__weight_decay': [1e-4, 1e-5, 1e-6],
+            'module__hid_drop': [0.2, 0.5, 0.7],
+            'module__embed_dim': [300, 400, 500],
         }
 
-        grid = GridSearchCV(estimator=estimator, param_grid= paramsGrid, n_jobs=-1, cv=3)
+        grid = GridSearchCV(estimator=estimator, param_grid=paramsGrid, n_jobs=-1, cv=1)
+        data = np.array(model.triples['train'])
+        data = np.random.sample(0.2)
+        dataloader = iter(DataLoader(
+                TrainDataset(data, model.p),
+                batch_size=len(data),
+                shuffle=True,
+                num_workers=max(0, model.p.num_workers),
+                collate_fn=TrainDataset.collate_fn
+            ))
+        for step, batch in dataloader:
+            sub, rel, obj, label, neg_ent, sub_samp = model.read_batch(
+                batch, 'train')
+
+            dataset = np.stack([sub, rel, neg_ent, np.repeat(model.p.train_strategy)], axis = 1)
+            search = grid.fit(dataset, label)
+            print("BEST SCORE: ", search.best_score_)
+            print("BEST PARAMS: ", search.best_params_)
     if (args.test_only):
         save_path = os.path.join('./torch_saved', args.name)
         model.load_model(save_path)

models.py

@@ -437,9 +437,16 @@ class TuckER(torch.nn.Module):
 
 
 class FouriER(torch.nn.Module):
-    def __init__(self, params):
+    def __init__(self, params, hid_drop = None, embed_dim = None):
         super(FouriER, self).__init__()
 
+        if hid_drop is not None:
+            self.p.hid_drop = hid_drop
+        if embed_dim is not None:
+            self.p.ent_vec_dim = embed_dim
+            self.p.rel_vec_dim = embed_dim
+            self.p.embed_dim = embed_dim
+
         self.p = params
 
         image_h, image_w = self.p.image_h, self.p.image_w