--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/feature_construction.py	Wed Jan 27 13:50:11 2021 +0000
@@ -0,0 +1,702 @@
+
+class FeatureConstruction:
+
+	def __init__(self):
+		"""
+		In development. Extract features from proteins.
+		"""
+		import pandas as pd
+		import json
+		import ast
+		from pathlib import Path
+		import os
+		from random import randint
+		data = pd.read_csv('files/NCBI_Phage_Bacteria_Data.csv', header=0, index_col=0)
+		with open('files/phagesProteins.json', encoding='utf-8') as F:
+			self.phagesProteins = json.loads(F.read())
+		self._filter_phage_domains()
+		# with open('files/bactProteins.json', encoding='utf-8') as F:
+		# 	self.bactProteins = json.loads(F.read())
+		# self._filter_bacteria()
+		all_phages = {}
+		ecoli = {}
+		kpneumoniae = {}
+		abaumannii = {}
+		my_file = Path("files/FeatureDataset")
+		if not my_file.is_file():
+			for phage in self.phageTails:
+				if phage in data.index and self.phageTails[phage]:
+					for bact in ast.literal_eval(data.loc[phage, 'Host_ID']):
+						bact = bact[:-2]
+						if bact + '.json' in os.listdir('files/bacteria'):
+							# if self.externalProts[bact]: # This verification is not necessary for carbohydrates
+							all_phages[phage + '--' + bact] = 'Yes'
+							name = data.loc[phage, 'Host']
+							if 'escherichia' in name.lower() or 'coli' in name.lower():
+								ecoli[bact] = 0
+							elif 'klebsiella' in name.lower() or 'pneumoniae' in name.lower():
+								kpneumoniae[bact] = 0
+							elif 'acinetobacter' in name.lower() or 'baumannii' in name.lower():
+								abaumannii[bact] = 0
+			for phage in self.phageTails:
+				if phage in data.index and self.phageTails[phage]:
+				# if self.phageTails[phage]:
+					name = data.loc[phage, 'Host']
+					if 'escherichia' in name.lower() or 'coli' in name.lower():
+						i = 0
+						while i < 12:
+							bact = list(kpneumoniae.keys())[randint(0, len(kpneumoniae.keys()) - 1)]
+							all_phages[phage + '--' + bact] = 'No'
+							i += 1
+					elif 'klebsiella' in name.lower() or 'pneumoniae' in name.lower():
+						i = 0
+						while i < 12:
+							bact = list(ecoli.keys())[randint(0, len(ecoli.keys()) - 1)]
+							all_phages[phage + '--' + bact] = 'No'
+							i += 1
+					elif 'acinetobacter' in name.lower() or 'baumannii' in name.lower():
+						i = 0
+						while i < 12:
+							bact = list(kpneumoniae.keys())[randint(0, len(kpneumoniae.keys()) - 1)]
+							all_phages[phage + '--' + bact] = 'No'
+							i += 1
+			self.features_data = pd.DataFrame({'ID': list(all_phages.keys()), 'Infects': list(all_phages.values())})
+			self.features_data = self.features_data.set_index('ID')
+		else:
+			self.import_feat_data()
+
+	def _filter_phage_domains(self):
+		import json
+		from pathlib import Path
+		'''
+		Filters out unwanted proteins. Domains that are unknown or are not associated with fibers, spikes, tails, enzymatic or binding are not considered.
+		Still in development.
+		:return: phageTails, a dictionary containing only
+		'''
+		my_file = Path("files/phageTails.json")
+		if not my_file.is_file():
+			self.phageTails = {}
+			for phage in self.phagesProteins:
+				self.phageTails[phage] = {}
+				for protein in self.phagesProteins[phage]:
+					if any(z in self.phagesProteins[phage][protein][0].lower() for z in ['fiber', 'fibre', 'spike', 'hydrolase', 'bind', 'depolymerase', 'peptidase', 'lyase', 'sialidase', 'dextranase', 'lipase', 'adhesin', 'baseplate', 'protein h', 'recognizing'
+																						 'protein j', 'protein g', 'gpe', 'duf4035', 'host specifity', 'cor protein', 'specificity', 'baseplate component', 'gp38', 'gp12 tail',  'receptor', 'recognition', 'tail']) \
+							and not any(z in self.phagesProteins[phage][protein][0].lower() for z in ['nucle', 'dna', 'rna', 'ligase', 'transferase', 'inhibitor', 'assembly', 'connect', 'nudix', 'atp', 'nad', 'transpos', 'ntp', 'molybdenum', 'hns',
+																								 'gtp', 'riib', 'inhibitor', 'replicat', 'codon', 'pyruvate', 'catalyst', 'hinge', 'sheath completion', 'head', 'capsid', 'tape', 'tip', 'strand', 'matur', 'portal'
+																									'terminase', 'nucl', 'promot', 'block', 'olfact', 'wedge', 'lysozyme', 'mur', 'sheat']):
+						self.phageTails[phage][protein] = self.phagesProteins[phage][protein]
+					'''else:
+						for i in self.phagesProteins[phage][protein]:
+							if type(i) == str:
+								if any(z in str(i).lower() for z in ['fiber', 'fibre', 'spike', 'hydrolase', 'bind', 'depolymerase', 'peptidase', 'lyase', 'sialidase', 'dextranase', 'lipase', 'adhesin', 'baseplate', 'protein h', 'recognizing'
+																	'protein j', 'protein g', 'gpe', 'duf4035', 'host specifity', 'cor protein', 'specificity', 'baseplate component', 'gp38', 'gp12 tail',  'receptor', 'recognition', 'tail']) \
+										and not any(z in str(i).lower() for z in ['nucle', 'dna', 'rna', 'ligase', 'transferase', 'inhibitor', 'assembly', 'connect', 'nudix', 'atp', 'nad', 'transpos', 'ntp', 'molybdenum', 'hns', 'gtp',
+																				  'riib', 'inhibitor', 'replicat', 'codon', 'pyruvate', 'catalyst', 'hinge', 'sheath completion', 'head', 'capsid', 'tape', 'tip', 'strand', 'matur', 'portal'
+																				'terminase', 'nucl']):
+									self.phageTails[phage][protein] = self.phagesProteins[phage][protein]
+							else:
+								for j in i:
+									if any(z in str(j).lower() for z in ['fiber', 'fibre', 'spike', 'hydrolase', 'bind', 'depolymerase', 'peptidase', 'lyase', 'sialidase', 'dextranase', 'lipase', 'adhesin', 'baseplate', 'protein h', 'recognizing'
+																		'protein j', 'protein g', 'gpe', 'duf4035', 'host specifity', 'cor protein', 'specificity', 'baseplate component', 'gp38', 'gp12 tail',  'receptor', 'recognition', 'tail']) \
+											and not any(z in str(j).lower() for z in ['nucle', 'dna', 'rna', 'ligase', 'transferase', 'inhibitor', 'assembly', 'connect', 'nudix', 'atp', 'nad', 'transpos', 'ntp', 'molybdenum', 'hns', 'gtp',
+																					  'riib', 'inhibitor', 'replicat', 'codon', 'pyruvate', 'catalyst', 'hinge', 'sheath completion', 'head', 'capsid', 'tape', 'tip', 'strand', 'matur', 'portal'
+																					'terminase', 'nucl']):
+										self.phageTails[phage][protein] = self.phagesProteins[phage][protein]'''
+			with open('files/phageTails.json', 'w') as f:
+				json.dump(self.phageTails, f)
+			self.__create_phage_fasta()
+		else:
+			with open('files/phageTails.json', encoding='utf-8') as F:
+				self.phageTails = json.loads(F.read())
+		return self.phageTails
+
+	def _filter_bacteria(self):
+		import json
+		from pathlib import Path
+		import pandas as pd
+		my_file = Path("files/externalProts.json")
+		if not my_file.is_file():
+			self.externalProts = {}
+			predictions = pd.read_csv('files/results_psort.txt', sep='\t', index_col=False)
+			predictions = predictions.set_index('SeqID')
+			predictions = predictions.drop_duplicates()
+			for bac in self.bactProteins:
+				self.externalProts[bac] = {}
+				for protein in self.bactProteins[bac]:
+					if protein + ' ' in predictions.index:
+						maxScore = 0.0
+						for loc in ['Cytoplasmic_Score', 'CytoplasmicMembrane_Score', 'Periplasmic_Score', 'OuterMembrane_Score', 'Extracellular_Score']:
+							if predictions.loc[protein + ' ', loc] > maxScore:
+								maxScore = predictions.loc[protein + ' ', loc]
+								location = loc
+						if location == 'CytoplasmicMembrane_Score' or location == 'OuterMembrane_Score' or location == 'Extracellular_Score':
+							self.externalProts[bac][protein] = self.bactProteins[bac][protein][1]
+			if self.externalProts != {}:
+				del self.bactProteins
+			with open('files/externalProts.json', 'w') as f:
+				json.dump(self.externalProts, f)
+		else:
+			with open('files/externalProts.json', encoding='utf-8') as F:
+				self.externalProts = json.loads(F.read())
+		return self.externalProts
+
+	def __create_phage_fasta(self):
+		"""
+		Creates a fasta file containing every protein sequence for every phage.
+		:return:
+		"""
+		with open('files/tails.fasta', 'w') as F:
+			for phage in self.phageTails:
+				for prot in self.phageTails[phage]:
+					F.write('>' + prot + '\n' + self.phageTails[phage][prot][1] + '\n')
+
+	def add_kmers(self):
+		from skbio import Sequence
+		import json
+		groups = '0123456'
+		freqs = {}
+		for i in groups:
+			for j in groups:
+				freqs[i+j] = 0.0
+		for i in freqs:
+			exec('phage_group_{0} = []'.format(i))
+			exec('bact_group_{0} = []'.format(i))
+		phage = ''
+		bact = ''
+		for ID in self.features_data.index:
+			done_phage = False
+			done_bact = False
+			if ID[:ID.find('--')] == phage:
+				for i in freqs.keys():
+					exec('phage_group_{0}.append(phage_group_{0}[-1])'.format(i))
+				done_phage = True
+			if ID[ID.find('--') + 2:] == bact:
+				for i in freqs.keys():
+					exec('bact_group_{0}.append(bact_group_{0}[-1])'.format(i))
+				done_bact = True
+			bact = ID[ID.find('--') + 2:]
+			phage = ID[:ID.find('--')]
+
+			if not done_phage:
+				totalKmers = freqs.copy()
+				count_prots = 0
+				for prot in self.list_prot[phage]:
+					max_freq = 0.0
+					min_freq = 1000000.0
+					count_prots += 1
+					seq = self.__get_conjoint_triad(self.list_prot[phage][prot][1].replace('X', 'D').replace('B', 'N').replace('Z', 'E').replace('U', 'C').replace('J', 'L'))
+					seq = Sequence(seq)
+					temp = seq.kmer_frequencies(2, overlap=True, relative=True)
+					for i in temp.keys():  # para normalizar
+						if temp[i] < min_freq:
+							min_freq = temp[i]
+						if temp[i] > max_freq:
+							max_freq = temp[i]
+					for i in temp.keys():
+						totalKmers[i] += temp[i] - (min_freq / max_freq)
+				if count_prots != 0:
+					for i in totalKmers.keys():
+						totalKmers[i] = totalKmers[i] / count_prots
+						temp_value = totalKmers[i]
+						exec('phage_group_{0}.append(temp_value)'.format(i))
+				else:
+					for i in totalKmers.keys():
+						exec('phage_group_{0}.append(0.0)'.format(i))
+
+			if not done_bact:
+				totalKmers = freqs.copy()
+				count_prots = 0
+				with open('files/bacteria/' + bact + '.json', encoding='utf-8') as F:
+					bact_prots = json.loads(F.read())
+				for prot in bact_prots:
+					max_freq = 0.0
+					min_freq = 1000000.0
+					count_prots += 1
+					seq = bact_prots[prot][1]
+					seq = seq[:seq.find('"')]
+					seq = self.__get_conjoint_triad(seq.replace('X', 'D').replace('B', 'N').replace('Z', 'E').replace('U', 'C').replace('J', 'L'))
+					seq = Sequence(seq)
+					temp = seq.kmer_frequencies(2, overlap=True, relative=True)
+					for i in temp.keys():  # para normalizar
+						if temp[i] < min_freq:
+							min_freq = temp[i]
+						if temp[i] > max_freq:
+							max_freq = temp[i]
+					for i in temp.keys():
+						totalKmers[i] += temp[i] - (min_freq / max_freq)
+				if count_prots != 0:
+					for i in totalKmers.keys():
+						totalKmers[i] = totalKmers[i] / count_prots
+						temp_value = totalKmers[i]
+						exec('bact_group_{0}.append(temp_value)'.format(i))
+				else:
+					for i in freqs.keys():
+						exec('bact_group_{0}.append(0.0)'.format(i))
+
+		for i in freqs.keys():
+			exec('self.features_data["phage_kmer_{0}"] = phage_group_{0}'.format(i))
+			exec('self.features_data["bact_kmer_{0}"] = bact_group_{0}'.format(i))
+
+	def get_kmers(self, phage, bacteria):
+		from skbio import Sequence
+		solution = []
+		groups = '0123456'
+		freqs = {}
+		for i in groups:
+			for j in groups:
+				freqs[i+j] = 0.0
+		for i in freqs:
+			exec('phage_group_{0} = 0.0'.format(i))
+			exec('bact_group_{0} = 0.0'.format(i))
+
+		totalKmers = freqs.copy()
+		count_prots = 0
+		for prot in phage:
+			max_freq = 0.0
+			min_freq = 1000000.0
+			count_prots += 1
+			seq = self.__get_conjoint_triad(phage[prot][1].replace('X', 'D').replace('B', 'N').replace('Z', 'E').replace('U', 'C'))
+			seq = Sequence(seq)
+			temp = seq.kmer_frequencies(2, overlap=True, relative=True)
+			for i in temp.keys(): #  para normalizar
+				if temp[i] < min_freq:
+					min_freq = temp[i]
+				if temp[i] > max_freq:
+					max_freq = temp[i]
+			for i in temp.keys():
+				totalKmers[i] += temp[i] - (min_freq / max_freq)
+		if count_prots != 0:
+			for i in totalKmers.keys():
+				totalKmers[i] = totalKmers[i] / count_prots
+				temp_value = totalKmers[i]
+				exec('phage_group_{0} += temp_value'.format(i))
+
+		totalKmers = freqs.copy()
+		count_prots = 0
+		for prot in bacteria:
+			max_freq = 0.0
+			min_freq = 1000000.0
+			count_prots += 1
+			seq = bacteria[prot][1]
+			seq = seq[:seq.find('"')]
+			seq = self.__get_conjoint_triad(seq.replace('X', 'D').replace('B', 'N').replace('Z', 'E').replace('U', 'C'))
+			seq = Sequence(seq)
+			temp = seq.kmer_frequencies(2, overlap=True, relative=True)
+			for i in temp.keys():  # para normalizar
+				if temp[i] < min_freq:
+					min_freq = temp[i]
+				if temp[i] > max_freq:
+					max_freq = temp[i]
+			for i in temp.keys():
+				totalKmers[i] += temp[i] - (min_freq / max_freq)
+		if count_prots != 0:
+			for i in totalKmers.keys():
+				totalKmers[i] = totalKmers[i] / count_prots
+				temp_value = totalKmers[i]
+				exec('bact_group_{0} += temp_value'.format(i))
+
+		for i in freqs.keys():
+			exec('solution.append(phage_group_{0})'.format(i))
+			exec('solution.append(bact_group_{0})'.format(i))
+		return solution
+
+	def add_composition(self):
+		from skbio import Sequence
+		import json
+		bact_comp = {}
+		phage_comp = {}
+		groups = '0123456'
+		for i in groups:
+			bact_comp['comp_' + i] = []
+			phage_comp['comp_' + i] = []
+		phage = ''
+		bact = ''
+		count = -1
+		for ID in self.features_data.index:
+			done_phage = False
+			done_bact = False
+			count += 1
+			if ID[:ID.find('--')] == phage:
+				for i in groups:
+					phage_comp['comp_' + i].append(phage_comp['comp_' + i][-1])
+				done_phage = True
+			if ID[ID.find('--') + 2:] == bact:
+				for i in groups:
+					bact_comp['comp_' + i].append(bact_comp['comp_' + i][-1])
+				done_bact = True
+			bact = ID[ID.find('--') + 2:]
+			phage = ID[:ID.find('--')]
+
+			if not done_phage:
+				count_prots = 0
+				for i in groups:
+					phage_comp['comp_' + i].append(0)
+				for prot in self.list_prot[phage]:
+					max_comp = 0.0
+					min_comp = 1000000.0
+					count_prots += 1
+					seq = self.__get_conjoint_triad(self.list_prot[phage][prot][1].replace('X', 'D').replace('B', 'N').replace('Z', 'E').replace('U', 'C'))
+					seq = Sequence(seq)
+					for i in groups: #  para normalizar
+						if seq.count(i) < min_comp:
+							min_comp = seq.count(i)
+						if seq.count(i) > max_comp:
+							max_comp = seq.count(i)
+					for i in groups:
+						phage_comp['comp_' + i][count] += seq.count(i) - (min_comp / max_comp)
+				total = 0
+				if count_prots != 0:
+					for i in groups:
+						phage_comp['comp_' + i][count] = phage_comp['comp_' + i][count] / count_prots
+						total += phage_comp['comp_' + i][count]
+					for i in groups:
+						phage_comp['comp_' + i][count] = phage_comp['comp_' + i][count] / total
+				else:
+					for i in groups:
+						phage_comp['comp_' + i][count] = 0.0
+
+			if not done_bact:
+				count_prots = 0
+				for i in groups:
+					bact_comp['comp_' + i].append(0)
+				with open('files/bacteria/' + bact + '.json', encoding='utf-8') as F:
+					bact_prots = json.loads(F.read())
+				for prot in bact_prots:
+					max_comp = 0.0
+					min_comp = 1000000.0
+					count_prots += 1
+					seq = bact_prots[prot][1]
+					seq = seq[:seq.find('"')]
+					seq = self.__get_conjoint_triad(seq.replace('X', 'D').replace('B', 'N').replace('Z', 'E').replace('U', 'C'))
+					seq = Sequence(seq)
+					for i in groups:
+						if seq.count(i) < min_comp:
+							min_comp = seq.count(i)
+						if seq.count(i) > max_comp:
+							max_comp = seq.count(i)
+					for i in groups:
+						bact_comp['comp_' + i][count] += seq.count(i) - (min_comp / max_comp)
+				total = 0
+				if count_prots != 0:
+					for i in groups:
+						bact_comp['comp_' + i][count] = bact_comp['comp_' + i][count] / count_prots
+						total += bact_comp['comp_' + i][count]
+				else:
+					for i in groups:
+						bact_comp['comp_' + i][count] = 0.0
+				if total != 0:
+					for i in groups:
+						bact_comp['comp_' + i][count] = bact_comp['comp_' + i][count] / total
+				else:
+					for i in groups:
+						bact_comp['comp_' + i][count] = 0.0
+
+		for i in groups:
+			self.features_data['bact_comp_' + i] = bact_comp['comp_' + i]
+			self.features_data['phage_comp_' + i] = phage_comp['comp_' + i]
+
+	def get_composition(self, phage, bacteria):
+		from skbio import Sequence
+		solution = []
+		bact_comp = {}
+		phage_comp = {}
+		phage_comp_carb = {}
+		groups = '0123456'
+		for i in groups:
+			bact_comp['comp_' + i] = 0
+			phage_comp['comp_' + i] = 0
+		count_prots = 0
+		for prot in phage:
+			max_comp = 0.0
+			min_comp = 1000000.0
+			count_prots += 1
+			seq = self.__get_conjoint_triad(phage[prot][1].replace('X', 'D').replace('B', 'N').replace('Z', 'E').replace('U', 'C'))
+			seq = Sequence(seq)
+			for i in groups: #  para normalizar
+				if seq.count(i) < min_comp:
+					min_comp = seq.count(i)
+				if seq.count(i) > max_comp:
+					max_comp = seq.count(i)
+			for i in groups:
+				phage_comp['comp_' + i] += seq.count(i) - (min_comp / max_comp)
+		total = 0
+		if count_prots != 0:
+			for i in groups:
+				phage_comp['comp_' + i] = phage_comp['comp_' + i] / count_prots
+				total += phage_comp['comp_' + i]
+			for i in groups:
+				phage_comp['comp_' + i] = phage_comp['comp_' + i] / total
+		else:
+			for i in groups:
+				phage_comp['comp_' + i] = 0.0
+
+		count_prots = 0
+		for prot in bacteria:
+			max_comp = 0.0
+			min_comp = 1000000.0
+			count_prots += 1
+			seq = bacteria[prot][1]
+			seq = seq[:seq.find('"')]
+			seq = self.__get_conjoint_triad(seq.replace('X', 'D').replace('B', 'N').replace('Z', 'E').replace('U', 'C'))
+			seq = Sequence(seq)
+			for i in groups:
+				if seq.count(i) < min_comp:
+					min_comp = seq.count(i)
+				if seq.count(i) > max_comp:
+					max_comp = seq.count(i)
+			for i in groups:
+				bact_comp['comp_' + i] += seq.count(i) - (min_comp / max_comp)
+		total = 0
+		if count_prots != 0:
+			for i in groups:
+				bact_comp['comp_' + i] = bact_comp['comp_' + i] / count_prots
+				total += bact_comp['comp_' + i]
+			for i in groups:
+				bact_comp['comp_' + i] = bact_comp['comp_' + i] / total
+		else:
+			for i in groups:
+				bact_comp['comp_' + i] = 0.0
+
+		for i in groups:
+			solution.append(bact_comp['comp_' + i])
+			solution.append(phage_comp['comp_' + i])
+		return solution
+
+	def add_grouping(self):
+		from skbio import Sequence
+		import json
+		bact_group = {}
+		phage_group = {}
+		groups = '0123456'
+		letters = 'ABCDEFGHIJ'
+		for i in groups:
+			for j in letters:
+				bact_group['group' + j + '_' + i] = []
+				phage_group['group' + j + '_' + i] = []
+		phage = ''
+		bact = ''
+		count = -1
+		for ID in self.features_data.index:
+			done_phage = False
+			done_bact = False
+			count += 1
+			if ID[:ID.find('--')] == phage:
+				for i in groups:
+					for j in letters:
+						phage_group['group' + j + '_' + i].append(phage_group['group' + j + '_' + i][-1])
+				done_phage = True
+			if ID[ID.find('--') + 2:] == bact:
+				for i in groups:
+					for j in letters:
+						bact_group['group' + j + '_' + i].append(bact_group['group' + j + '_' + i][-1])
+				done_bact = True
+			bact = ID[ID.find('--') + 2:]
+			phage = ID[:ID.find('--')]
+
+			if not done_phage:
+				count_prots = 0
+				for i in groups:
+					for j in letters:
+						phage_group['group' + j + '_' + i].append(0)
+				for prot in self.list_prot[phage]:
+					count_prots += 1
+					seq = self.__get_conjoint_triad(self.list_prot[phage][prot][1].replace('X', 'D').replace('B', 'N').replace('Z', 'E').replace('U', 'C'))
+					seq = Sequence(seq)
+					for j in letters:
+						group = self.__get_grouping(seq, j)
+						for i in groups:
+							phage_group['group' + j + '_' + i][count] += group[i]
+				if count_prots != 0:
+					for i in groups:
+						for j in letters:
+							phage_group['group' + j + '_' + i][count] = phage_group['group' + j + '_' + i][count] / count_prots
+				else:
+					for i in groups:
+						for j in letters:
+							phage_group['group' + j + '_' + i][count] = 0.0
+
+			if not done_bact:
+				count_prots = 0
+				for i in groups:
+					for j in letters:
+						bact_group['group' + j + '_' + i].append(0)
+				with open('files/bacteria/' + bact + '.json', encoding='utf-8') as F:
+					bact_prots = json.loads(F.read())
+				for prot in bact_prots:
+					count_prots += 1
+					seq = bact_prots[prot][1]
+					seq = seq[:seq.find('"')]
+					seq = self.__get_conjoint_triad(seq.replace('X', 'D').replace('B', 'N').replace('Z', 'E').replace('U', 'C'))
+					seq = Sequence(seq)
+					for j in letters:
+						group = self.__get_grouping(seq, j)
+						for i in groups:
+							bact_group['group' + j + '_' + i][count] += group[i]
+				if count_prots != 0:
+					for i in groups:
+						for j in letters:
+							bact_group['group' + j + '_' + i][count] = bact_group['group' + j + '_' + i][count] / count_prots
+				else:
+					for i in groups:
+						for j in letters:
+							bact_group['group' + j + '_' + i][count] = 0.0
+
+		for i in groups:
+			for j in letters:
+				self.features_data['bact_group' + j + '_' + i] = bact_group['group' + j + '_' + i]
+				self.features_data['phage_group' + j + '_' + i] = phage_group['group' + j + '_' + i]
+
+	def get_grouping(self, phage, bacteria):
+		from skbio import Sequence
+		bact_group = {}
+		phage_group = {}
+		groups = '0123456'
+		letters = 'ABCDEFGHIJ'
+		for i in groups:
+			for j in letters:
+				bact_group['group' + j + '_' + i] = 0
+				phage_group['group' + j + '_' + i] = 0
+		solution = []
+		count_prots = 0
+		for prot in phage:
+			count_prots += 1
+			seq = self.__get_conjoint_triad(phage[prot][1].replace('X', 'D').replace('B', 'N').replace('Z', 'E').replace('U', 'C'))
+			seq = Sequence(seq)
+			for j in letters:
+				group = self.__get_grouping(seq, j)
+				for i in groups:
+					phage_group['group' + j + '_' + i] += group[i]
+		if count_prots != 0:
+			for i in groups:
+				for j in letters:
+					phage_group['group' + j + '_' + i] = phage_group['group' + j + '_' + i] / count_prots
+		else:
+			for i in groups:
+				for j in letters:
+					phage_group['group' + j + '_' + i] = 0.0
+
+		count_prots = 0
+		for prot in bacteria:
+			count_prots += 1
+			seq = bacteria[prot][1]
+			seq = seq[:seq.find('"')]
+			seq = self.__get_conjoint_triad(seq.replace('X', 'D').replace('B', 'N').replace('Z', 'E').replace('U', 'C'))
+			seq = Sequence(seq)
+			for j in letters:
+				group = self.__get_grouping(seq, j)
+				for i in groups:
+					bact_group['group' + j + '_' + i] += group[i]
+		if count_prots != 0:
+			for i in groups:
+				for j in letters:
+					bact_group['group' + j + '_' + i] = bact_group['group' + j + '_' + i] / count_prots
+		else:
+			for i in groups:
+				for j in letters:
+					bact_group['group' + j + '_' + i] = 0.0
+
+		for i in groups:
+			for j in letters:
+				solution.append(bact_group['group' + j + '_' + i])
+				solution.append(phage_group['group' + j + '_' + i])
+		return solution
+
+	def __get_conjoint_triad(self, prot):
+		ctm = {'A':'0', 'G':'0', 'V':'0','C':'1', 'F':'2', 'I':'2', 'L':'2', 'P':'2', 'M':'3', 'S':'3', 'T':'3', 'Y':'3', 'H':'4', 'N':'4', 'Q':'4', 'W':'4', 'K':'5', 'R':'5', 'D':'6', 'E':'6'}
+		for i, j in ctm.items():
+			prot = prot.replace(i, j)
+		return prot
+
+	def __get_grouping(self, prot, let='A'):
+		from skbio import Sequence
+		groups = '0123456'
+		group = {}
+		for i in groups:
+			group[i] = 0.0
+		if let == 'A':
+			seq = Sequence(prot[:int(len(prot) * 0.25)])
+			for i in groups:
+				group[i] += seq.count(i) / len(seq)
+		elif let == 'B':
+			seq = Sequence(prot[int(len(prot) * 0.25):int(len(prot) * 0.5)])
+			for i in groups:
+				group[i] += seq.count(i) / len(seq)
+		elif let == 'C':
+			seq = Sequence(prot[int(len(prot) * 0.5):int(len(prot) * 0.75)])
+			for i in groups:
+				group[i] += seq.count(i) / len(seq)
+		elif let == 'D':
+			seq = Sequence(prot[int(len(prot) * 0.75):])
+			for i in groups:
+				group[i] += seq.count(i) / len(seq)
+		elif let == 'E':
+			seq = Sequence(prot[:int(len(prot) * 0.5)])
+			for i in groups:
+				group[i] += seq.count(i) / len(seq)
+		elif let == 'F':
+			seq = Sequence(prot[int(len(prot) * 0.5):])
+			for i in groups:
+				group[i] += seq.count(i) / len(seq)
+		elif let == 'G':
+			seq = Sequence(prot[int(len(prot) * 0.25):int(len(prot) * 0.75)])
+			for i in groups:
+				group[i] += seq.count(i) / len(seq)
+		elif let == 'H':
+			seq = Sequence(prot[:int(len(prot) * 0.75)])
+			for i in groups:
+				group[i] += seq.count(i) / len(seq)
+		elif let == 'I':
+			seq = Sequence(prot[int(len(prot) * 0.25):])
+			for i in groups:
+				group[i] += seq.count(i) / len(seq)
+		elif let == 'J':
+			seq = Sequence(prot[int(len(prot) * 0.125):int(len(prot) * 0.875)])
+			for i in groups:
+				group[i] += seq.count(i) / len(seq)
+		return group
+
+	def set_output(self):
+		import pandas as pd
+		output = []
+		data = pd.read_csv('files/NCBI_Phage_Bacteria_Data.csv', header=0, index_col=0, names=['Phage Name', 'Bacteria Name', 'Bacteria ID'])
+		for phage in self.features_data['ID']:
+			phage = phage[:phage.find('--')]
+			bact = data.loc[phage, 'Bacteria Name']
+			if 'escherichia' in bact.lower():
+				output.append('Escherichia coli')
+			elif 'klebsiella' in bact.lower():
+				output.append('Klebsiella pneumoniae')
+			elif 'acinetobacter' in bact.lower():
+				output.append('Acinetobacter baumannii')
+		self.features_data = self.features_data.set_index('ID')
+		self.features_data['Bacteria'] = output
+
+	def save_feat_data(self):
+		import pickle
+		with open('files/FeatureDataset', 'wb') as f:
+			pickle.dump(self.features_data, f)
+		return self.features_data
+
+	def import_feat_data(self):
+		import pickle
+		with open('files/FeatureDataset', 'rb') as f:
+			self.features_data = pickle.load(f)
+		return self.features_data
+
+
+if __name__ == '__main__':
+	test = FeatureConstruction()
+	# test.process_net_surf()
+	test.add_grouping()
+	test.add_composition()
+	test.add_kmers()
+	# test.set_output()
+	test.save_feat_data()
+	'''
+	test.process_net_surf()
+	test.add_aa_freq()
+	test.add_aromaticity()
+	test.add_flexibility()
+	test.add_molecular_weight()'''
+	# test.import_feat_data()
+	# test.netSurf()