refresh

eval.py

@@ -34,6 +34,6 @@ if __name__ == '__main__':
     #     pred_dir = f.readline()
     #     pred_dir = pred_dir.split()[5].split('/')[-1]
     # pred_result = './predict/{}/test.txt'.format(pred_dir)
-    pred_result = './predict/lr-1e-5-batch-32-e-10-layer9-0429-z-10-step-74133/test.txt'
+    pred_result = './predict/lr-1e-5-batch-32-e-10-layer3-0503-z-1-step-82370/test.txt'
     test_label_path = './data/test_ic50'
     eval(pred_result, test_label_path)

pre_test.sh

-CUDA_VISIBLE_DEVICES=1 python run_interaction.py --task=test --b=64 --output=./predict/lr-1e-5-batch-32-e-10-layer3-0503-z-1-step-65896 --config=./config/config_layer_3.json --init=./model/lr-1e-5-batch-32-e-10-layer3-0503-z-1/epoch-7-step-65896-loss-0.39236504916727866.pth --do_eval=False
+CUDA_VISIBLE_DEVICES=1 python run_interaction.py --task=test --b=64 --output=./predict/lr-1e-5-batch-32-e-10-layer3-0503-z-1-step-65896 --config=./config/config_layer_3.json --init=./model/lr-1e-5-batch-32-e-10-layer3-0503-z-1/epoch-7-step-65896-loss-0.39236504916727866.pth --do_eval='True'
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predict/lr-1e-5-batch-32-e-10-layer3-0503-z-1-step-65896/eval_results

+RMSE : 1.0513631270914532 ; Pearson Correlation Coefficient : 0.7008084995004041
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predict/lr-1e-5-batch-32-e-10-layer3-0503-z-1-step-82370/eval_results

+RMSE : 1.0322941283479312 ; Pearson Correlation Coefficient : 0.7143108905685205
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predict/lr-1e-5-batch-32-e-10-layer9-0429-z-10-step-74133/eval_results

-RMSE : 1.3901506777912391 ; Pearson Correlation Coefficient : 0.7332126683709386
+RMSE : 1.3901506777912391 ; Pearson Correlation Coefficient : 0.7332126683709385
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test.py

-from transformers import BertTokenizer, BertModel
+from transformers import BertTokenizer
+from modeling_bert import BertForMaskedLM
 import torch
 
-a = torch.Tensor([0])
-if a > -1:
-    print('------')
+tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
+model = BertForMaskedLM.from_pretrained('bert-base-uncased')
+inputs = tokenizer("The capital of France is [MASK].", return_tensors="pt")
\ No newline at end of file
+labels = tokenizer("The capital of France is Paris.", return_tensors="pt")["input_ids"]
+outputs = model(**inputs, labels=labels)
+loss = outputs.loss
+logits = outputs.logits
+
+print('----------------')
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utils/normalize_data.py

@@ -41,6 +41,6 @@ if __name__ == '__main__':
     # z_score(data, save, enlarge)
 
     # reform result
-    result = '../predict/lr-1e-5-batch-32-e-10-layer9-0429-z-10-step-74133/test_10.txt'
-    save = '../predict/lr-1e-5-batch-32-e-10-layer9-0429-z-10-step-74133/test.txt'
-    reform(result, save, average, std, 100)
+    result = '../predict/lr-1e-5-batch-32-e-10-layer3-0503-z-1-step-82370/test_1.txt'
+    save = '../predict/lr-1e-5-batch-32-e-10-layer3-0503-z-1-step-82370/test.txt'
+    reform(result, save, average, std, 1)
\ No newline at end of file