From 012afbf15249cea9660bdff64ab9008d125e2793 Mon Sep 17 00:00:00 2001
From: Schneider Leo <leo.schneider@etu.ec-lyon.fr>
Date: Wed, 12 Feb 2025 16:27:56 +0100
Subject: [PATCH] df oktoberfest
---
diann_lib_processing.py | 55 +++++++++++++++++++----------------------
1 file changed, 26 insertions(+), 29 deletions(-)
diff --git a/diann_lib_processing.py b/diann_lib_processing.py
index bcc05ca..90ef002 100644
--- a/diann_lib_processing.py
+++ b/diann_lib_processing.py
@@ -93,16 +93,16 @@ def predict(data_pred, model, output_path):
if __name__ =='__main__':
- df = load_lib('data/spectral_lib/first_lib.parquet')
-
- plt.hist(df['RT'])
- plt.savefig('test.png')
-
- df_2 = pd.read_csv('data/data_prosit/data.csv')
-
- plt.clf()
- plt.hist(df_2['irt'])
- plt.savefig('test2.png')
+ # df = load_lib('data/spectral_lib/first_lib.parquet')
+ #
+ # plt.hist(df['RT'])
+ # plt.savefig('test.png')
+ #
+ # df_2 = pd.read_csv('data/data_prosit/data.csv')
+ #
+ # plt.clf()
+ # plt.hist(df_2['irt'])
+ # plt.savefig('test2.png')
# df_2 = extract_sequence(df).reset_index(drop=True)
#
@@ -124,23 +124,20 @@ if __name__ =='__main__':
- # 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, seq_length=30)
- #
- # if torch.cuda.is_available():
- # model = model.cuda()
- #
- # model.load_state_dict(torch.load(args.model_weigh, weights_only=True))
- #
- # print(args.dataset_test)
- # data_test = load_data(data_source='data/spectral_lib/data_uniprot_base.csv', batch_size=args.batch_size, length=30, mode=args.split_test,
- # seq_col=args.seq_test)
- #
- # predict(data_test, model, args.output)
+ 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, seq_length=30)
+
+ if torch.cuda.is_available():
+ model = model.cuda()
+
+ model.load_state_dict(torch.load(args.model_weigh, weights_only=True))
+
+ print(args.dataset_test)
+ data_test = load_data(data_source='data/spectral_lib/data_uniprot_base.csv', batch_size=args.batch_size, length=30, mode=args.split_test,
+ seq_col=args.seq_test)
- plt.hist(df['RT'])
- plt.savefig('test.png')
\ No newline at end of file
+ predict(data_test, model, args.output)
--
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