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porting_baseline.py
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porting_baseline.py
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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import pandas as pd
import os
import time
import pdb
import csv
import re
from dcCustom.trans import undo_transforms
import dcCustom
from collections import OrderedDict
# This script is intended to parse the dataset generated in PADME(dcCustom) to a readable form
# of SimBoost and KronRLS.
def get_pair_values_and_fold_ind(all_dataset, K, transformers, create_mapping=False,
smiles_to_some_id=None, drug_id_and_smiles_to_ind=None, prot_name_and_seq_to_ind=None,
dt_pair_to_fold=None, dt_pair_to_value=None, drug_mol_to_ind = None, prot_to_ind=None):
for i in range(K):
validation_data = all_dataset[i][1]
for (X_b, y_b, w_b, _) in validation_data.itersamples():
assert w_b[0] == 1.0
drug_mol = X_b[0]
protein = X_b[1]
y_b = undo_transforms(y_b, transformers)
if not create_mapping:
drug_smiles = drug_mol.smiles
some_id = smiles_to_some_id[drug_smiles]
drug_pair = (some_id, drug_smiles)
drug_ind = drug_id_and_smiles_to_ind[drug_pair]
prot_seq = protein.get_sequence()[1]
prot_name = protein.get_name()[1]
prot_pair = (prot_name, prot_seq)
prot_ind = prot_name_and_seq_to_ind[prot_pair]
pair = (drug_ind, prot_ind)
else:
if drug_mol not in drug_mol_to_ind:
# values start from 1.
drug_mol_to_ind[drug_mol] = len(drug_mol_to_ind) + 1
if protein not in prot_to_ind:
prot_to_ind[protein] = len(prot_to_ind) + 1
pair = (drug_mol, protein)
assert pair not in dt_pair_to_fold
# Also start from 1.
dt_pair_to_fold[pair] = i + 1
assert pair not in dt_pair_to_value
dt_pair_to_value[pair] = y_b[0]
def parse_data(dataset_nm='davis', featurizer = 'GraphConv', split='random', K = 5,
mode = 'regression', predict_cold = False, cold_drug=False, cold_target=False,
split_warm=False, cold_drug_cluster=False, filter_threshold=0, create_mapping=False,
input_protein=True):
assert (predict_cold + cold_drug + cold_target + split_warm + cold_drug_cluster) <= 1
if mode == 'regression' or mode == 'reg-threshold':
mode = 'regression'
smiles_to_some_id = {}
duplicated_drugs = set()
some_id_name = 'cid'
cmpd_file_name = "compound_cids.txt"
prot_file_name = "prot_info.csv"
if re.search('davis', dataset_nm, re.I):
data_dir = "davis_data/"
with open(data_dir + "SMILES_CIDs_corrected.txt", 'r') as f:
data = f.readlines()
for line in data:
words = line.split()
if words[1] not in smiles_to_some_id:
smiles_to_some_id[words[1]] = words[0]
elif re.search('metz', dataset_nm, re.I):
data_dir = "metz_data/"
some_id_name = 'sid'
cmpd_file_name = "compound_sids.txt"
df = pd.read_csv(data_dir + 'Metz_interaction.csv', header = 0, index_col=0, usecols=range(3))
for row in df.itertuples():
if row[2] != row[2]:
continue
if row[2] not in smiles_to_some_id:
smiles_to_some_id[row[2]] = str(int(row[1]))
print("length of dictionary smiles_to_some_id: ", len(smiles_to_some_id))
elif re.search('kiba', dataset_nm, re.I):
data_dir = "KIBA_data/"
some_id_name = "CHEMBL_ID"
cmpd_file_name = "Chembl_ids.txt"
df = pd.read_csv(data_dir + 'Smiles_bio_results.csv', header = 0, index_col=0, usecols=range(2))
for row in df.itertuples():
if row[1] != row[1]:
continue
if row[1] not in smiles_to_some_id:
smiles_to_some_id[row[1]] = row[0]
simboost_data_dir = "./SimBoost/data/" + data_dir
suffix = ""
if not input_protein:
suffix = "_no_prot" + suffix
if filter_threshold > 0:
suffix = "_filtered" + suffix
if predict_cold:
suffix = "_cold" + suffix
elif split_warm:
suffix = "_warm" + suffix
elif cold_drug:
suffix = "_cold_drug" + suffix
elif cold_target:
suffix = "_cold_target" + suffix
elif cold_drug_cluster:
suffix = "_cold_drug_cluster" + suffix
if re.match('GraphConv', featurizer, re.I):
opt_suffix = "_gc"
elif re.match('Weave', featurizer, re.I):
opt_suffix = "_wv"
elif re.match('ecfp', featurizer, re.I):
opt_suffix = ""
else:
assert False
featurizer = featurizer + "_CV"
save_dir = os.path.join(data_dir, featurizer + suffix + "/" + mode + "/" + split)
loaded, all_dataset, transformers = dcCustom.utils.save.load_cv_dataset_from_disk(
save_dir, K)
assert loaded
dt_pair_to_fold = {}
dt_pair_to_value = {}
drug_id_and_smiles_to_ind = OrderedDict()
prot_name_and_seq_to_ind = OrderedDict()
time_start = time.time()
if not create_mapping:
# Use the existing mappings stored in prot_info.csv and drug_info.csv.
df_drug = pd.read_csv(data_dir + 'drug_info.csv', header = 0, index_col=0)
for row in df_drug.itertuples():
pair = (str(row[0]), row[1])
if pair not in drug_id_and_smiles_to_ind:
drug_id_and_smiles_to_ind[pair] = row[2]
df_prot = pd.read_csv(data_dir + 'prot_info.csv', header = 0, index_col=0)
for row in df_prot.itertuples():
pair = (row[0], row[1])
if pair not in prot_name_and_seq_to_ind:
prot_name_and_seq_to_ind[pair] = row[2]
drug_mol_to_ind = OrderedDict()
prot_to_ind = OrderedDict()
if input_protein:
get_pair_values_and_fold_ind(all_dataset, K, transformers, create_mapping=create_mapping,
smiles_to_some_id=smiles_to_some_id, drug_id_and_smiles_to_ind=drug_id_and_smiles_to_ind,
prot_name_and_seq_to_ind=prot_name_and_seq_to_ind, dt_pair_to_fold=dt_pair_to_fold,
dt_pair_to_value=dt_pair_to_value, drug_mol_to_ind = drug_mol_to_ind, prot_to_ind=prot_to_ind)
else:
raise ValueError("Currently input_protein==False scenario is unsupported.")
# Now we need to construct a compound_cids.txt file according to the order in drug_mol_to_ind.
if create_mapping:
print("len(drug_mol_to_ind): ", len(drug_mol_to_ind))
with open(data_dir + cmpd_file_name, 'w') as f:
some_id_list = []
for drug_mol in drug_mol_to_ind:
drug_smiles = drug_mol.smiles
some_id = smiles_to_some_id[drug_smiles]
some_id_list.append(some_id)
f.write('\n'.join(some_id_list))
sfile = open(data_dir + cmpd_file_name, 'w')
sfile.write('\n'.join(some_id_list))
sfile.close()
else:
print("len(drug_id_and_smiles_to_ind): ", len(drug_id_and_smiles_to_ind))
dirs = [data_dir, simboost_data_dir]
# Every time, write twice, in two different directories respectively.
for directory in dirs:
if not create_mapping:
continue
with open(directory + prot_file_name, 'w', newline='') as csvfile:
fieldnames = ['name', 'sequence', 'index']
writer = csv.DictWriter(csvfile, fieldnames = fieldnames)
writer.writeheader()
for protein, ind in prot_to_ind.items():
prot_seq = protein.get_sequence()[1]
prot_name = protein.get_name()[1]
writer.writerow({'name': prot_name, 'sequence': prot_seq, 'index': ind})
for directory in dirs:
if not create_mapping:
continue
with open(directory + "drug_info.csv", 'w', newline='') as csvfile:
fieldnames = [some_id_name, 'smiles', 'index']
writer = csv.DictWriter(csvfile, fieldnames = fieldnames)
writer.writeheader()
for drug_mol, drug_ind in drug_mol_to_ind.items():
drug_smiles = drug_mol.smiles
some_id = smiles_to_some_id[drug_smiles]
writer.writerow({some_id_name: some_id, 'smiles': drug_smiles, 'index': drug_ind})
suffix = suffix + ""
triplet_split_name = "triplet_split" + opt_suffix + suffix + ".csv"
for directory in dirs:
with open(directory + triplet_split_name, 'w', newline='') as csvfile:
fieldnames = ['drug', 'target', 'value', 'fold']
writer = csv.DictWriter(csvfile, fieldnames = fieldnames)
writer.writeheader()
if create_mapping:
for drug_mol, drug_ind in drug_mol_to_ind.items():
for protein, prot_ind in prot_to_ind.items():
pair = (drug_mol, protein)
if pair not in dt_pair_to_value:
assert pair not in dt_pair_to_fold
continue
value = dt_pair_to_value[pair]
fold_ind = dt_pair_to_fold[pair]
writer.writerow({'drug': drug_ind, 'target': prot_ind, 'value': value,
'fold': fold_ind})
else:
for _, drug_ind in drug_id_and_smiles_to_ind.items():
for _, prot_ind in prot_name_and_seq_to_ind.items():
pair = (drug_ind, prot_ind)
if pair not in dt_pair_to_value:
assert pair not in dt_pair_to_fold
continue
value = dt_pair_to_value[pair]
fold_ind = dt_pair_to_fold[pair]
writer.writerow({'drug': drug_ind, 'target': prot_ind, 'value': value,
'fold': fold_ind})
time_end = time.time()
print("Processing took %f seconds." % (time_end - time_start))
if __name__ == '__main__':
# parse_data(featurizer='GraphConv', cold_target=True, filter_threshold=1)
# parse_data(featurizer='GraphConv', cold_target=True, filter_threshold=1)
parse_data(dataset_nm='kiba', featurizer='ECFP', cold_drug_cluster=True, filter_threshold=6)
# parse_data(dataset_nm='kiba', featurizer='GraphConv', cold_drug_cluster=True, filter_threshold=6)
# parse_data(featurizer='GraphConv', split_warm=True, filter_threshold=1)
# parse_data(dataset_nm='metz', featurizer='GraphConv', split_warm=True, filter_threshold=1)
# parse_data(dataset_nm='metz', featurizer='GraphConv', cold_drug=True, filter_threshold=1)
# parse_data(dataset_nm='metz', featurizer='GraphConv', cold_target=True, filter_threshold=1)
#parse_data(dataset_nm='kiba', cold_target=True, filter_threshold=6)
# parse_data(dataset_nm='kiba', featurizer='GraphConv', split_warm=True, filter_threshold=6)
# parse_data(dataset_nm='kiba', featurizer='GraphConv', cold_drug=True, filter_threshold=6)
# parse_data(dataset_nm='kiba', featurizer='GraphConv', cold_target=True, filter_threshold=6)