Datasets:
File size: 5,408 Bytes
24fff69 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 | import pandas as pd
from rdkit import Chem
from rdkit.Chem import PandasTools, Draw
from rdkit import DataStructs
from rdkit.ML.Cluster import Butina
from rdkit.Chem import rdMolDescriptors as rdmd
from rdkit.Chem import Descriptors, rdChemReactions
import seaborn as sns
from tqdm import tqdm
import os
from sklearn.model_selection import GroupKFold, train_test_split
def assign_seq_clusters(df, id_cutoff):
seqcol = 'sequence'
import tempfile
import uuid
# Create a temporary directory and get a unique filename within that directory
# with tempfile.TemporaryDirectory() as unique_filedir:
unique_filedir = str(uuid.uuid4())
# Using UUID for generating a unique filename
unique_filename = unique_filedir + '_temp_seqs.fasta'
print('Clustering seqs..')
seqs_written = set()
seqind_to_seq = {}
f = open(unique_filename,'w')
for ind,seq in enumerate(df[seqcol]):
if pd.isna(seq): continue
if not seq in seqs_written:
f.write(f'>{ind}\n{seq}\n')
seqs_written.add(seq)
seqind_to_seq[ind] = seq
else: continue
f.close()
cmd = f'mmseqs easy-cluster {unique_filename} {unique_filedir}_clusterRes /tmp/ --min-seq-id {id_cutoff} -v 0'
status = os.system(cmd)
print(status, cmd)
cluster_df = pd.read_csv(f'{unique_filedir}_clusterRes_cluster.tsv',sep='\t',names=['query_ind','target_ind'])
os.system(f'rm -r {unique_filedir}*')
q_to_tinds = {}
for qind, tind in zip(cluster_df.query_ind,cluster_df.target_ind):
if not qind in q_to_tinds:
q_to_tinds[qind] = [tind]
else:
q_to_tinds[qind].append(tind)
c = 0
ind_to_cluster = {}
for q, ts in q_to_tinds.items():
ind_to_cluster[q] = c
for t in ts: ind_to_cluster[t] = c
c+=1
seq_to_cluster = {seqind_to_seq[ind]:c for ind, c in ind_to_cluster.items()}
cluster_col = []
for seq in df[seqcol]:
if seq in seq_to_cluster:
cluster_col.append(seq_to_cluster[seq])
else:
# print('no cluster found for ', seq)
cluster_col.append(-1)
df[f'{seqcol}_{int(100*id_cutoff)}cluster'] = cluster_col
return df
def make_splits(df, frac = 0.1):
# ipdb.set_trace()
df.reset_index(drop=True, inplace=True)
train_df, test_df = train_test_split(df, test_size = frac, random_state=0)
train_df.reset_index(drop=True, inplace=True)
train_df, val_df = train_test_split(train_df, test_size = frac, random_state=0)
train_df.reset_index(drop=True, inplace=True)
val_df.reset_index(drop=True, inplace=True)
test_df.reset_index(drop=True, inplace=True)
print(f'Train: {len(train_df)*100/len(df)}')
print(f'Val: {len(val_df)*100/len(df)}')
print(f'Test: {len(test_df)*100/len(df)}')
return train_df, val_df, test_df
def print_stats(train, test, parameter, dire, sim_list = [0.4, 0.6, 0.8, 0.99]):
sequence_column = 'sequence'
smiles_column = 'smiles'
if parameter=='kcat': smiles_column='reactant_'+smiles_column
else: smiles_column='substrate_'+smiles_column
sim_list.reverse()
print('-'*20)
print('**Sequence stats**')
print('-'*20)
for sim in sim_list:
simperc = int(sim*100)
colname = sequence_column+f'_{simperc}cluster'
print('At sim-cutoff:', sim,)
now = test[~test[colname].isin(train[colname])]
now.to_csv(f'{dire}/{parameter}-seq_test_{colname}.csv')
perc = len(now)/len(test)
print('Test perc.:', perc)
print('Test number:', int(perc*len(test)))
print('\n')
print('-'*20)
def assign_all_clusters(df, is_kcat, sim_list = [0.4, 0.6, 0.8, 0.99]):
for sim in sim_list:
print(f'Assigning clusters at sim-cutoff = {sim}')
df = assign_seq_clusters(df, id_cutoff=sim)
return df
def make_and_savesplits(df, param, dire):
if param=='kcat': is_kcat = True
else: is_kcat = False
frac = 0.1
train, val, test = make_splits(df, frac)
seqpre = 'random'
if not os.path.exists(dire):
os.mkdir(dire)
train.to_csv(dire+param+f'-{seqpre}_train.csv')
val.to_csv(dire+param+f'-{seqpre}_val.csv')
test.to_csv(dire+param+f'-{seqpre}_test.csv')
trainval = pd.concat([train,val]).reset_index(drop=True)
trainval.to_csv(dire+param+f'-{seqpre}_trainval.csv')
trainvaltest = pd.concat([trainval,test]).reset_index(drop=True)
trainvaltest.to_csv(dire+param+f'-{seqpre}_trainvaltest.csv')
print_stats(trainval, test, param, dire)
return train, val, test
def main(args):
datafile, parameter, outdir = args.input_file, args.param, args.save_dir
df = pd.read_csv(datafile)
df = assign_all_clusters(df, is_kcat=(parameter=='kcat'))#,
df_train, df_val, df_test = make_and_savesplits(df, parameter, dire=outdir)
import argparse
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("--param", help="parameter name",
type=str, required=True)
parser.add_argument("--input_file", help="input_file",
type=str, required=True)
parser.add_argument("--save_dir", help="dir to save splits",
type=str, required=True)
args, unparsed = parser.parse_known_args()
return args
args = parse_args()
main(args)
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