diff --git a/diann_lib_processing.py b/diann_lib_processing.py
index 483c773060ccfe9ed7e426485a2e779fc273e331..d5edc205c9fb00cadb288ec52b36d2cc4b6a9c96 100644
--- a/diann_lib_processing.py
+++ b/diann_lib_processing.py
@@ -5,7 +5,7 @@ import pyarrow.parquet as pq
import pyarrow as pa
import torch
import matplotlib.pyplot as plt
-# from loess.loess_1d import loess_1d
+from loess.loess_1d import loess_1d
from model.model import ModelTransformer
from config import load_args
@@ -96,98 +96,94 @@ def predict(data_pred, model, output_path):
if __name__ =='__main__':
- # df = load_lib('spectral_lib/1-240711_ident_resistance_idbioriv_fluoroquinolones_conta_human_sang.parquet')
- # df = extract_sequence(df)
- # df.to_csv('spectral_lib/1-240711_ident_resistance_idbioriv_fluoroquinolones_conta_human_sang.csv')
- # plt.hist(df['RT'])
- # plt.savefig('test.png')
- #
- # df_2 = pd.read_csv('data_prosit/data.csv')
- #
- # plt.clf()
- # plt.hist(df_2['irt'])
- # plt.savefig('test2.png')
- #
- # df_2 = extract_sequence(df).reset_index(drop=True)
- #
- # pred = pd.read_csv('../output/out_lib_CITBASE_try_contaminant.csv')
- #
- # pred['seq']=pred['seq'].map(numerical_to_alphabetical_str)
- #
- # pred['Modified.Sequence']=pred['seq']
- #
- # result = pd.merge(df,pred[['Modified.Sequence','rt pred']],on='Modified.Sequence',how='left')
- #
- #
- #
- # #alignement
- #
- # ref = pd.read_csv('data_prosit/data_noc.csv')
- # df_ISA = pd.read_csv('data_ISA/data_aligned_isa_noc.csv')
- #
- # dataset, reference, column_dataset, column_ref, seq_data, seq_ref = df_ISA, ref, 'irt_scaled', 'irt', 'sequence','sequence',
- #
- # dataset_ref=dataset[dataset['state']=='train']
- # dataset_unique = dataset_ref[[seq_data,column_dataset]].groupby(seq_data).mean()
- # print('unique',len(dataset_unique))
- # reference_unique = reference[[seq_ref,column_ref]].groupby(seq_ref).mean()
- # seq_ref = reference_unique.index
- # seq_common = dataset_unique.index
- # seq_ref = seq_ref.tolist()
- # seq_common = seq_common.tolist()
- #
- # seq_ref = [tuple(l) for l in seq_ref]
- # seq_common = [tuple(l) for l in seq_common]
- #
- # ind_dict_ref = dict((k, i) for i, k in enumerate(seq_ref))
- # inter = set(ind_dict_ref).intersection(seq_common)
- # print(len(inter))
- #
- # ind_dict_ref = [ind_dict_ref[x] for x in inter]
- #
- # indices_common = dict((k, i) for i, k in enumerate(seq_common))
- # indices_common = [indices_common[x] for x in inter]
- #
- #
- # rt_ref = reference_unique[column_ref][ind_dict_ref].reset_index()
- # rt_data = dataset_unique[column_dataset][indices_common].reset_index()
- #
- # plt.scatter(rt_data[column_dataset].tolist(),rt_ref[column_ref].tolist(),s=0.1)
- # plt.savefig('test.png')
- #
- # #présence de NAN qui casse le réalignement (solution temporaire : remplacer par 0.
- # result['rt pred']=result['rt pred'].fillna(value=0)
- # xout, yout, wout = loess_1d(np.array(rt_data[column_dataset].tolist()), np.array(rt_ref[column_ref].tolist()),
- # xnew=result['rt pred'],
- # degree=1,
- # npoints=None, rotate=False, sigy=None)
- #
- #
- # #writing results
- #
- # result['RT'] = yout
- #
- # result = result.drop('rt pred', axis=1)
- #
- # table = pa.Table.from_pandas(result)
- #
- # pq.write_table(table, 'spectral_lib/first_lib_contaminant_prosit_aligned.parquet')
- #
+ df = load_lib('spectral_lib/1-240711_ident_resistance_idbioriv_fluoroquinolones_conta_human_sang.parquet')
+ df_2 = pd.read_csv('data_prosit/data.csv')
- args = load_args()
+ plt.clf()
+ plt.hist(df_2['irt'])
+ plt.savefig('test2.png')
- 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)
+ df_2 = extract_sequence(df).reset_index(drop=True)
- if torch.cuda.is_available():
- model = model.cuda()
+ pred = pd.read_csv('../output/out_transfer_prosit_isa_1-240711_ident_resistance_idbioriv_fluoroquinolones_conta_human_sang.csv')
- model.load_state_dict(torch.load(args.model_weigh, weights_only=True))
+ pred['seq']=pred['seq'].map(numerical_to_alphabetical_str)
- 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)
+ pred['Modified.Sequence']=pred['seq']
- predict(data_test, model, args.output)
+ result = pd.merge(df,pred[['Modified.Sequence','rt pred']],on='Modified.Sequence',how='left')
+
+
+
+ #alignement
+
+ ref = pd.read_csv('data_prosit/data_noc.csv')
+ df_ISA = pd.read_csv('data_ISA/data_aligned_isa_noc.csv')
+
+ dataset, reference, column_dataset, column_ref, seq_data, seq_ref = df_ISA, ref, 'irt_scaled', 'irt', 'sequence','sequence',
+
+ dataset_ref=dataset[dataset['state']=='train']
+ dataset_unique = dataset_ref[[seq_data,column_dataset]].groupby(seq_data).mean()
+ print('unique',len(dataset_unique))
+ reference_unique = reference[[seq_ref,column_ref]].groupby(seq_ref).mean()
+ seq_ref = reference_unique.index
+ seq_common = dataset_unique.index
+ seq_ref = seq_ref.tolist()
+ seq_common = seq_common.tolist()
+
+ seq_ref = [tuple(l) for l in seq_ref]
+ seq_common = [tuple(l) for l in seq_common]
+
+ ind_dict_ref = dict((k, i) for i, k in enumerate(seq_ref))
+ inter = set(ind_dict_ref).intersection(seq_common)
+ print(len(inter))
+
+ ind_dict_ref = [ind_dict_ref[x] for x in inter]
+
+ indices_common = dict((k, i) for i, k in enumerate(seq_common))
+ indices_common = [indices_common[x] for x in inter]
+
+
+ rt_ref = reference_unique[column_ref][ind_dict_ref].reset_index()
+ rt_data = dataset_unique[column_dataset][indices_common].reset_index()
+
+ plt.scatter(rt_data[column_dataset].tolist(),rt_ref[column_ref].tolist(),s=0.1)
+ plt.savefig('test.png')
+
+ #présence de NAN qui casse le réalignement (solution temporaire : remplacer par 0.
+ result['rt pred']=result['rt pred'].fillna(value=0)
+ xout, yout, wout = loess_1d(np.array(rt_data[column_dataset].tolist()), np.array(rt_ref[column_ref].tolist()),
+ xnew=result['rt pred'],
+ degree=1,
+ npoints=None, rotate=False, sigy=None)
+
+
+ #writing results
+
+ result['RT'] = yout
+
+ result = result.drop('rt pred', axis=1)
+
+ table = pa.Table.from_pandas(result)
+
+ pq.write_table(table, 'spectral_lib/1-240711_ident_resistance_idbioriv_fluoroquinolones_conta_human_sang_finetune_aligned.parquet')
+
+
+
+ # 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))
+ #
+ # 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)
diff --git a/identification/result_extraction.py b/identification/result_extraction.py
index e144a0ff7a3fbb38a5b4e46fc8abbc0ce4ed9fe2..3ef2a9a778d79843ffdd7078032ba094ea6337ac 100644
--- a/identification/result_extraction.py
+++ b/identification/result_extraction.py
@@ -41,7 +41,19 @@ def compare_error(path_1,path_2):
plt.savefig('error2.png')
return error_1,error_2
+def compare_with_db(path):
+ df = pd.read_csv(path, sep='\t', encoding='latin-1')
+ df_ref = pd.read_excel('250205_All_Peptides_panel_ID_+_RES.xlsx',names=['peptide','fonction'])
+ df2=df[df['Stripped.Sequence'].isin(df_ref['peptide'].to_list())]
+ corespondance = pd.merge(df2,df_ref,left_on='Stripped.Sequence',right_on='peptide',how="left")
+
+ return corespondance
+
if __name__ == '__main__':
- # compare_id('CITCRO_ANA_3/report_custom.tsv', 'CITCRO_ANA_3/report_first_lib.tsv', 'CITCRO_ANA_3/report_finetune.tsv','CITCRO_ANA_3')
- e1,e2 = compare_error('CITCRO_ANA_3/report_custom.tsv', 'CITCRO_ANA_3/report_first_lib.tsv')
\ No newline at end of file
+ # compare_id('CITAMA_ANA_5/julie_custom_nolib.tsv', 'CITAMA_ANA_5/julie_base_nolib.tsv', 'CITAMA_ANA_5/julie_finetune_nolib.tsv','CITAMA_ANA_5_julie_no_lib')
+ # e1,e2 = compare_error('CITAMA_ANA_5/report_custom.tsv', 'CITCRO_ANA_3/report_first_lib.tsv')
+
+ cor_base = compare_with_db('CITAMA_ANA_5/julie_base_nolib.tsv')
+ cor_custom = compare_with_db('CITAMA_ANA_5/julie_custom_nolib.tsv')
+ cor_finetune = compare_with_db('CITAMA_ANA_5/julie_finetune_nolib.tsv')
\ No newline at end of file