From 323f1738880476cac56d8442e2b5888e22071c8b Mon Sep 17 00:00:00 2001
From: Schneider Leo <leo.schneider@etu.ec-lyon.fr>
Date: Tue, 22 Oct 2024 17:02:58 +0200
Subject: [PATCH] datasets

---
 alignement.py                           |    82 +-
 database/data_ISA_augmented_1_30_01.csv | 79328 ++++++++++++++++++
 database/data_ISA_augmented_2_30_01.csv | 84466 +++++++++++++++++++
 database/data_ISA_augmented_3_30_01.csv | 93561 ++++++++++++++++++++++
 model_custom.py                         |     2 +-
 prosit_data_merge.py                    |   187 +-
 6 files changed, 257491 insertions(+), 135 deletions(-)
 create mode 100644 database/data_ISA_augmented_1_30_01.csv
 create mode 100644 database/data_ISA_augmented_2_30_01.csv
 create mode 100644 database/data_ISA_augmented_3_30_01.csv

diff --git a/alignement.py b/alignement.py
index 73ce439..b246561 100644
--- a/alignement.py
+++ b/alignement.py
@@ -210,46 +210,46 @@ def compare_include_df(df, sub_df, save = True, path = 'temp.png'):
 
 
 
-#create augmented dataset from ISA data + column invariant prosit peptides
-# df_base = pd.read_pickle('database/data_DIA_ISA_55_train.pkl')
-# df_base = df_base[['Sequence','Retention time']]
-#
-# df_1 = pd.read_pickle('database/data_prosit_threshold_1.pkl')
-# df_1['Sequence']= df_1['Sequence'].map(numerical_to_alphabetical_str)
-#
-# df_2 = pd.read_pickle('database/data_prosit_threshold_2.pkl')
-# df_2['Sequence']= df_2['Sequence'].map(numerical_to_alphabetical_str)
-#
-# df_3 = pd.read_pickle('database/data_prosit_threshold_3.pkl')
-# df_3['Sequence']= df_3['Sequence'].map(numerical_to_alphabetical_str)
-#
-# df_augmented_1 = pd.concat([df_1,df_base],axis=0).reset_index(drop=True)
-# df_augmented_1.columns=['sequence','irt']
-# df_augmented_1['state']='train'
-# df_augmented_1.to_csv('database/data_ISA_augmented_1.csv')
-#
-# df_augmented_2 = pd.concat([df_2,df_base],axis=0).reset_index(drop=True)
-# df_augmented_2.columns=['sequence','irt']
-# df_augmented_2['state']='train'
-# df_augmented_2.to_csv('database/data_ISA_augmented_2.csv')
-#
-# df_augmented_3 = pd.concat([df_3,df_base],axis=0).reset_index(drop=True)
-# df_augmented_3.columns=['sequence','irt']
-# df_augmented_3['state']='train'
-# df_augmented_3.to_csv('database/data_ISA_augmented_3.csv')
+# create augmented dataset from ISA data + column invariant prosit peptides
+df_base = pd.read_pickle('database/data_DIA_ISA_55_train_30_01.pkl')
+df_base = df_base[['Sequence','Retention time']]
 
+df_1 = pd.read_pickle('database/data_prosit_threshold_1.pkl')
+df_1['Sequence']= df_1['Sequence'].map(numerical_to_alphabetical_str)
 
-#testing intersection between test and augmented dataset
-# df_sup = pd.read_pickle('database/data_prosit_threshold_3.pkl')
-# df_test = pd.read_pickle('database/data_DIA_ISA_55_test.pkl')
-#
-# inter = []
-# n = 0
-# df_sup['Sequence']= df_sup['Sequence'].map(numerical_to_alphabetical_str)
-# groups_sup = df_sup.groupby('Sequence')
-#
-# groups_test = df_test.groupby('Sequence')
-# for seq, _ in groups_sup:
-#     for seq2, _ in groups_test:
-#         if seq2==seq :
-#             inter.append(seq)
\ No newline at end of file
+df_2 = pd.read_pickle('database/data_prosit_threshold_2.pkl')
+df_2['Sequence']= df_2['Sequence'].map(numerical_to_alphabetical_str)
+
+df_3 = pd.read_pickle('database/data_prosit_threshold_3.pkl')
+df_3['Sequence']= df_3['Sequence'].map(numerical_to_alphabetical_str)
+
+df_augmented_1 = pd.concat([df_1,df_base],axis=0).reset_index(drop=True)
+df_augmented_1.columns=['sequence','irt']
+df_augmented_1['state']='train'
+df_augmented_1.to_csv('database/data_ISA_augmented_1_30_01.csv')
+
+df_augmented_2 = pd.concat([df_2,df_base],axis=0).reset_index(drop=True)
+df_augmented_2.columns=['sequence','irt']
+df_augmented_2['state']='train'
+df_augmented_2.to_csv('database/data_ISA_augmented_2_30_01.csv')
+
+df_augmented_3 = pd.concat([df_3,df_base],axis=0).reset_index(drop=True)
+df_augmented_3.columns=['sequence','irt']
+df_augmented_3['state']='train'
+df_augmented_3.to_csv('database/data_ISA_augmented_3_30_01.csv')
+
+
+# testing intersection between test and augmented dataset
+df_sup = pd.read_pickle('database/data_prosit_threshold_3.pkl')
+df_test = pd.read_pickle('database/data_DIA_ISA_55_test.pkl')
+
+inter = []
+n = 0
+df_sup['Sequence']= df_sup['Sequence'].map(numerical_to_alphabetical_str)
+groups_sup = df_sup.groupby('Sequence')
+
+groups_test = df_test.groupby('Sequence')
+for seq, _ in groups_sup:
+    for seq2, _ in groups_test:
+        if seq2==seq :
+            inter.append(seq)
\ No newline at end of file
diff --git a/database/data_ISA_augmented_1_30_01.csv b/database/data_ISA_augmented_1_30_01.csv
new file mode 100644
index 0000000..c37be1d
--- /dev/null
+++ b/database/data_ISA_augmented_1_30_01.csv
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diff --git a/model_custom.py b/model_custom.py
index 42dbcb4..6617c4d 100644
--- a/model_custom.py
+++ b/model_custom.py
@@ -43,7 +43,7 @@ class Model_Common_Transformer(nn.Module):
                  n_head=1, seq_length=25,
                  charge_max=4, charge_frag_max=3, encoder_ff=512, encoder_num_layer=1, decoder_rt_num_layer=1,
                  decoder_int_num_layer=1, acti='relu', norm=False):
-        self.charge_max = charge_max #TODO filter charge in train to be in 1-4 0-5 atm
+        self.charge_max = charge_max
         self.seq_length = seq_length
         self.nb_aa = nb_aa
         self.charge_frag_max = charge_frag_max
diff --git a/prosit_data_merge.py b/prosit_data_merge.py
index 02b48fd..bd75994 100644
--- a/prosit_data_merge.py
+++ b/prosit_data_merge.py
@@ -66,97 +66,98 @@ def alphabetical_to_numerical(seq):
             dec += 4
     return np.array(num)
 
-
-# sources = ('data/intensity/sequence_train.npy',
-#              'data/intensity/intensity_train.npy',
-#              'data/intensity/precursor_charge_train.npy',
-#              'data/intensity/precursor_charge_train.npy')
-#
-#
-# data_rt = pd.read_csv('database/data_unique_ptms.csv')
-# data_rt['Sequence']=data_rt['mod_sequence']
-#
-# padding(data_rt, 'Sequence', 30)
-# data_rt['Sequence'] = data_rt['Sequence'].map(alphabetical_to_numerical)
-#
-# data_rt =data_rt.drop(columns='mod_sequence')
-#
-# data_int = load_intensity_df_from_files(sources[0], sources[1], sources[2], sources[3])
-#
-# seq_rt = data_rt.Sequence
-# seq_int = data_int.seq
-# seq_rt = seq_rt.tolist()
-# seq_int = seq_int.tolist()
-# seq_rt = [tuple(l) for l in seq_rt]
-# seq_int = [tuple(l) for l in seq_int]
-#
-# ind_dict_rt = dict((k, i) for i, k in enumerate(seq_rt))
-# inter = set(ind_dict_rt).intersection(seq_int)
-# ind_dict_rt = [ind_dict_rt[x] for x in inter]
-#
-#
-# data_int.irt = np.zeros(data_int.energy.shape)
-#
-# i=0
-# for ind in ind_dict_rt :
-#     print(i,'/',len(ind_dict_rt))
-#     i+=1
-#     ind_int = [k for k, x in enumerate(seq_int) if x == seq_rt[ind]]
-#     data_int.irt[ind_int] = data_rt.irt[ind]
-#
-# np.save('data/intensity/irt_train.npy',data_int.irt)
-
-
-# sources = ('data/intensity/sequence_holdout.npy',
-#              'data/intensity/intensity_holdout.npy',
-#              'data/intensity/precursor_charge_holdout.npy',
-#              'data/intensity/precursor_charge_holdout.npy')
-#
-#
-# data_rt = pd.read_csv('database/data_unique_ptms.csv')
-# data_rt['Sequence']=data_rt['mod_sequence']
-#
-# padding(data_rt, 'Sequence', 30)
-# data_rt['Sequence'] = data_rt['Sequence'].map(alphabetical_to_numerical)
-#
-# data_rt =data_rt.drop(columns='mod_sequence')
-#
-# data_int = load_intensity_df_from_files(sources[0], sources[1], sources[2], sources[3])
-#
-# seq_rt = data_rt.Sequence
-# seq_int = data_int.seq
-# seq_rt = seq_rt.tolist()
-# seq_int = seq_int.tolist()
-# seq_rt = [tuple(l) for l in seq_rt]
-# seq_int = [tuple(l) for l in seq_int]
-#
-# ind_dict_rt = dict((k, i) for i, k in enumerate(seq_rt))
-# inter = set(ind_dict_rt).intersection(seq_int)
-# ind_dict_rt = [ind_dict_rt[x] for x in inter]
-#
-#
-# data_int.irt = np.zeros(data_int.energy.shape)
-#
-# i=0
-# for ind in ind_dict_rt :
-#     print(i,'/',len(ind_dict_rt))
-#     i+=1
-#     ind_int = [k for k, x in enumerate(seq_int) if x == seq_rt[ind]]
-#     data_int.irt[ind_int] = data_rt.irt[ind]
-#
-# np.save('data/intensity/irt_holdout.npy',data_int.irt)
-
-df = pd.read_pickle('database/data_prosit_merged_holdout.pkl')
-print(len(df))
-possible_charges = [1,2,3,4]
-df = df[df['Charge'].isin(possible_charges)]
-print(len(df))
-df.to_pickle('database/data_prosit_merged_holdout.pkl')
-df = pd.read_pickle('database/data_prosit_merged_train.pkl')
-print(len(df))
-df=df.loc[df['Retention time']!=0]
-df = df[df['Charge'].isin(possible_charges)]
-print(len(df))
-df.to_pickle('database/data_prosit_merged_train.pkl')
-
+if __name__ == '__main__' :
+
+    # sources = ('data/intensity/sequence_train.npy',
+    #              'data/intensity/intensity_train.npy',
+    #              'data/intensity/precursor_charge_train.npy',
+    #              'data/intensity/precursor_charge_train.npy')
+    #
+    #
+    # data_rt = pd.read_csv('database/data_unique_ptms.csv')
+    # data_rt['Sequence']=data_rt['mod_sequence']
+    #
+    # padding(data_rt, 'Sequence', 30)
+    # data_rt['Sequence'] = data_rt['Sequence'].map(alphabetical_to_numerical)
+    #
+    # data_rt =data_rt.drop(columns='mod_sequence')
+    #
+    # data_int = load_intensity_df_from_files(sources[0], sources[1], sources[2], sources[3])
+    #
+    # seq_rt = data_rt.Sequence
+    # seq_int = data_int.seq
+    # seq_rt = seq_rt.tolist()
+    # seq_int = seq_int.tolist()
+    # seq_rt = [tuple(l) for l in seq_rt]
+    # seq_int = [tuple(l) for l in seq_int]
+    #
+    # ind_dict_rt = dict((k, i) for i, k in enumerate(seq_rt))
+    # inter = set(ind_dict_rt).intersection(seq_int)
+    # ind_dict_rt = [ind_dict_rt[x] for x in inter]
+    #
+    #
+    # data_int.irt = np.zeros(data_int.energy.shape)
+    #
+    # i=0
+    # for ind in ind_dict_rt :
+    #     print(i,'/',len(ind_dict_rt))
+    #     i+=1
+    #     ind_int = [k for k, x in enumerate(seq_int) if x == seq_rt[ind]]
+    #     data_int.irt[ind_int] = data_rt.irt[ind]
+    #
+    # np.save('data/intensity/irt_train.npy',data_int.irt)
+
+
+    # sources = ('data/intensity/sequence_holdout.npy',
+    #              'data/intensity/intensity_holdout.npy',
+    #              'data/intensity/precursor_charge_holdout.npy',
+    #              'data/intensity/precursor_charge_holdout.npy')
+    #
+    #
+    # data_rt = pd.read_csv('database/data_unique_ptms.csv')
+    # data_rt['Sequence']=data_rt['mod_sequence']
+    #
+    # padding(data_rt, 'Sequence', 30)
+    # data_rt['Sequence'] = data_rt['Sequence'].map(alphabetical_to_numerical)
+    #
+    # data_rt =data_rt.drop(columns='mod_sequence')
+    #
+    # data_int = load_intensity_df_from_files(sources[0], sources[1], sources[2], sources[3])
+    #
+    # seq_rt = data_rt.Sequence
+    # seq_int = data_int.seq
+    # seq_rt = seq_rt.tolist()
+    # seq_int = seq_int.tolist()
+    # seq_rt = [tuple(l) for l in seq_rt]
+    # seq_int = [tuple(l) for l in seq_int]
+    #
+    # ind_dict_rt = dict((k, i) for i, k in enumerate(seq_rt))
+    # inter = set(ind_dict_rt).intersection(seq_int)
+    # ind_dict_rt = [ind_dict_rt[x] for x in inter]
+    #
+    #
+    # data_int.irt = np.zeros(data_int.energy.shape)
+    #
+    # i=0
+    # for ind in ind_dict_rt :
+    #     print(i,'/',len(ind_dict_rt))
+    #     i+=1
+    #     ind_int = [k for k, x in enumerate(seq_int) if x == seq_rt[ind]]
+    #     data_int.irt[ind_int] = data_rt.irt[ind]
+    #
+    # np.save('data/intensity/irt_holdout.npy',data_int.irt)
+
+    # df = pd.read_pickle('database/data_prosit_merged_holdout.pkl')
+    # print(len(df))
+    # possible_charges = [1,2,3,4]
+    # df = df[df['Charge'].isin(possible_charges)]
+    # print(len(df))
+    # df.to_pickle('database/data_prosit_merged_holdout.pkl')
+    # df = pd.read_pickle('database/data_prosit_merged_train.pkl')
+    # print(len(df))
+    # df=df.loc[df['Retention time']!=0]
+    # df = df[df['Charge'].isin(possible_charges)]
+    # print(len(df))
+    # df.to_pickle('database/data_prosit_merged_train.pkl')
+    pass
 
-- 
GitLab