dataset pred for diann

diann_lib_processing.py

@@ -54,22 +54,22 @@ def predict(data_pred, model, output_path):
 
 
 if __name__ =='__main__':
-    df = load_lib('data/spectral_lib/first_lib.parquet')
-    df_2 = extract_sequence(df).reset_index(drop=True)
-    df_2.to_csv('data/spectral_lib/data_uniprot_base.csv', index=False)
-
-
-
-    # args = load_args()
-    #
-    # model = ModelTransformer(encoder_ff=args.encoder_ff, decoder_rt_ff=args.decoder_rt_ff,
-    #                          n_head=args.n_head, encoder_num_layer=args.encoder_num_layer,
-    #                          decoder_rt_num_layer=args.decoder_rt_num_layer, drop_rate=args.drop_rate,
-    #                          embedding_dim=args.embedding_dim, acti=args.activation, norm=args.norm_first)
-    #
-    # model.load_state_dict(torch.load(args.model_weigh, weights_only=True))
-    #
-    # data_test = load_data(data_source=args.dataset_test, batch_size=args.batch_size, length=30, mode=args.split_test,
-    #                       seq_col=args.seq_test)
-    #
-    # predict(data_test, model, args.output)
+    # df = load_lib('data/spectral_lib/first_lib.parquet')
+    # df_2 = extract_sequence(df).reset_index(drop=True)
+    # df_2.to_csv('data/spectral_lib/data_uniprot_base.csv', index=False)
+
+
+
+    args = load_args()
+
+    model = ModelTransformer(encoder_ff=args.encoder_ff, decoder_rt_ff=args.decoder_rt_ff,
+                             n_head=args.n_head, encoder_num_layer=args.encoder_num_layer,
+                             decoder_rt_num_layer=args.decoder_rt_num_layer, drop_rate=args.drop_rate,
+                             embedding_dim=args.embedding_dim, acti=args.activation, norm=args.norm_first)
+
+    model.load_state_dict(torch.load(args.model_weigh, weights_only=True))
+
+    data_test = load_data(data_source=args.dataset_test, batch_size=args.batch_size, length=30, mode=args.split_test,
+                          seq_col=args.seq_test)
+
+    predict(data_test, model, args.output)
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