import sys, re, math, time
import numpy as np
import matplotlib.pyplot as plt
import json
import pickle
import collections
from collections import OrderedDict
from matplotlib.pyplot import cm
#from keras.preprocessing.sequence import pad_sequences
## ######################## ##
#
# Define CHARSET, CHARLEN
#
## ######################## ##
# CHARPROTSET = { 'A': 0, 'C': 1, 'D': 2, 'E': 3, 'F': 4, 'G': 5, 'H': 6, \
# 'I': 7, 'K': 8, 'L': 9, 'M': 10, 'N': 11, 'P': 12, 'Q': 13, \
# 'R': 14, 'S': 15, 'T': 16, 'V': 17, 'W': 18, 'Y': 19, 'X': 20, \
# 'O': 20, 'U': 20,
# 'B': (2, 11),
# 'Z': (3, 13),
# 'J': (7, 9) }
# CHARPROTLEN = 21
CHARPROTSET = { "A": 1, "C": 2, "B": 3, "E": 4, "D": 5, "G": 6,
"F": 7, "I": 8, "H": 9, "K": 10, "M": 11, "L": 12,
"O": 13, "N": 14, "Q": 15, "P": 16, "S": 17, "R": 18,
"U": 19, "T": 20, "W": 21,
"V": 22, "Y": 23, "X": 24,
"Z": 25 }
CHARPROTLEN = 25
CHARCANSMISET = { "#": 1, "%": 2, ")": 3, "(": 4, "+": 5, "-": 6,
".": 7, "1": 8, "0": 9, "3": 10, "2": 11, "5": 12,
"4": 13, "7": 14, "6": 15, "9": 16, "8": 17, "=": 18,
"A": 19, "C": 20, "B": 21, "E": 22, "D": 23, "G": 24,
"F": 25, "I": 26, "H": 27, "K": 28, "M": 29, "L": 30,
"O": 31, "N": 32, "P": 33, "S": 34, "R": 35, "U": 36,
"T": 37, "W": 38, "V": 39, "Y": 40, "[": 41, "Z": 42,
"]": 43, "_": 44, "a": 45, "c": 46, "b": 47, "e": 48,
"d": 49, "g": 50, "f": 51, "i": 52, "h": 53, "m": 54,
"l": 55, "o": 56, "n": 57, "s": 58, "r": 59, "u": 60,
"t": 61, "y": 62}
CHARCANSMILEN = 62
CHARISOSMISET = {"#": 29, "%": 30, ")": 31, "(": 1, "+": 32, "-": 33, "/": 34, ".": 2,
"1": 35, "0": 3, "3": 36, "2": 4, "5": 37, "4": 5, "7": 38, "6": 6,
"9": 39, "8": 7, "=": 40, "A": 41, "@": 8, "C": 42, "B": 9, "E": 43,
"D": 10, "G": 44, "F": 11, "I": 45, "H": 12, "K": 46, "M": 47, "L": 13,
"O": 48, "N": 14, "P": 15, "S": 49, "R": 16, "U": 50, "T": 17, "W": 51,
"V": 18, "Y": 52, "[": 53, "Z": 19, "]": 54, "\\": 20, "a": 55, "c": 56,
"b": 21, "e": 57, "d": 22, "g": 58, "f": 23, "i": 59, "h": 24, "m": 60,
"l": 25, "o": 61, "n": 26, "s": 62, "r": 27, "u": 63, "t": 28, "y": 64}
CHARISOSMILEN = 64
## ######################## ##
#
# Encoding Helpers
#
## ######################## ##
# Y = -(np.log10(Y/(math.pow(math.e,9))))
def one_hot_smiles(line, MAX_SMI_LEN, smi_ch_ind):
X = np.zeros((MAX_SMI_LEN, len(smi_ch_ind))) #+1
for i, ch in enumerate(line[:MAX_SMI_LEN]):
X[i, (smi_ch_ind[ch]-1)] = 1
return X #.tolist()
def one_hot_sequence(line, MAX_SEQ_LEN, smi_ch_ind):
X = np.zeros((MAX_SEQ_LEN, len(smi_ch_ind)))
for i, ch in enumerate(line[:MAX_SEQ_LEN]):
X[i, (smi_ch_ind[ch])-1] = 1
return X #.tolist()
def label_smiles(line, MAX_SMI_LEN, smi_ch_ind):
X = np.zeros(MAX_SMI_LEN)
for i, ch in enumerate(line[:MAX_SMI_LEN]): # x, smi_ch_ind, y
X[i] = smi_ch_ind[ch]
return X #.tolist()
def label_sequence(line, MAX_SEQ_LEN, smi_ch_ind):
X = np.zeros(MAX_SEQ_LEN)
for i, ch in enumerate(line[:MAX_SEQ_LEN]):
X[i] = smi_ch_ind[ch]
return X #.tolist()
## ######################## ##
#
# DATASET Class
#
## ######################## ##
# works for large dataset
class DataSet(object):
def __init__(self, fpath, fpath_test, setting_no, seqlen, smilen, need_shuffle = False):
### TODO : ADD SMILES TYPE CHOICE HERE
self.SEQLEN = seqlen
self.SMILEN = smilen
#self.NCLASSES = n_classes
self.charseqset = CHARPROTSET
self.charseqset_size = CHARPROTLEN
self.charsmiset = CHARISOSMISET ###HERE CAN BE EDITED
self.charsmiset_size = CHARISOSMILEN
self.PROBLEMSET = setting_no
# read raw file
#self._raw = self.read_sets( fpath, setting_no )
# iteration flags
# self._num_data = len(self._raw)
def read_sets(self, FLAGS): ### fpath should be the dataset folder /kiba/ or /davis/
print("Reading %s start" % FLAGS.train_path)
test_fold = json.load(open(FLAGS.test_path + "folds/test_fold.txt"))
train_folds = json.load(open(FLAGS.train_path + "folds/train_fold.txt"))
return test_fold, train_folds
def parse_train_test_data(self, FLAGS, with_label=True):
print("Read %s start" % FLAGS.train_path)
tr_ligands = json.load(open(FLAGS.train_path+"ligands_iso.txt"), object_pairs_hook=OrderedDict)
tr_proteins = json.load(open(FLAGS.train_path+"proteins.txt"), object_pairs_hook=OrderedDict)
tr_Y = pickle.load(open(FLAGS.train_path + "Y","rb"), encoding='latin1') ### TODO: read from raw
if FLAGS.isLog:
tr_Y = -(np.log10(tr_Y/(math.pow(math.e,9))))
print("Read %s start" % FLAGS.test_path)
te_ligands = json.load(open(FLAGS.test_path+"ligands.txt"), object_pairs_hook=OrderedDict)
te_proteins = json.load(open(FLAGS.test_path+"proteins.txt"), object_pairs_hook=OrderedDict)
te_Y = pickle.load(open(FLAGS.test_path + "Y","rb"), encoding='latin1') ### TODO: read from raw
tr_XD = []
tr_XT = []
te_XD = []
te_XT = []
if with_label:
for d in tr_ligands.keys():
tr_XD.append(label_smiles(tr_ligands[d], self.SMILEN, self.charsmiset))
for t in tr_proteins.keys():
tr_XT.append(label_sequence(tr_proteins[t], self.SEQLEN, self.charseqset))
for d in te_ligands.keys():
te_XD.append(label_smiles(te_ligands[d], self.SMILEN, self.charsmiset))
for t in te_proteins.keys():
te_XT.append(label_sequence(te_proteins[t], self.SEQLEN, self.charseqset))
else:
for d in tr_ligands.keys():
tr_XD.append(one_hot_smiles(tr_ligands[d], self.SMILEN, self.charsmiset))
for t in tr_proteins.keys():
tr_XT.append(one_hot_sequence(tr_proteins[t], self.SEQLEN, self.charseqset))
for d in te_ligands.keys():
te_XD.append(one_hot_smiles(te_ligands[d], self.SMILEN, self.charsmiset))
for t in te_proteins.keys():
te_XT.append(one_hot_sequence(te_proteins[t], self.SEQLEN, self.charseqset))
return tr_XD, tr_XT, tr_Y, te_XD, te_XT, te_Y
Datasets
Models
