--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/machine_learning.py	Wed Jan 27 13:50:11 2021 +0000
@@ -0,0 +1,369 @@
+
+class PredictInteraction:
+
+	def __init__(self, data = 'FeatureDataset'):
+		import pickle
+		from sklearn.preprocessing import LabelEncoder
+		with open('files/' + data, 'rb') as f:
+			self.dataset = pickle.load(f)
+		self.dataset = self.dataset.dropna()
+		le = LabelEncoder()
+		le.fit(['Yes', 'No'])
+		self.output = le.transform(self.dataset['Infects'].values)
+		self.dataset = self.dataset.drop('Infects', 1)
+		self.__standardize()
+		self.__split_train_test()
+
+	def __standardize(self):
+		from sklearn.preprocessing import StandardScaler
+		self.scaler = StandardScaler()
+		self.scaler.fit(self.dataset)
+		self.data_z = self.scaler.transform(self.dataset)
+
+	def __cross_validation(self, method):
+		from sklearn.model_selection import cross_val_score
+		from sklearn.model_selection import StratifiedKFold
+		# from sklearn.model_selection import ShuffleSplit
+		# cv = ShuffleSplit(n_splits=4, test_size=0.3) # cross validation normal
+		skf = StratifiedKFold(5, shuffle=True)
+		scores = cross_val_score(method, self.data_z, self.output, cv=skf, scoring='f1_weighted')
+		print(scores)
+		return skf
+
+	def __split_train_test(self):
+		from sklearn.model_selection import train_test_split
+		self.data_z, self.X_val, self.output, self.y_val = train_test_split(self.data_z, self.output, test_size=0.2)
+		self.X_train, self.X_test, self.y_train, self.y_test = train_test_split(self.data_z, self.output, test_size=0.3)
+
+	def run_knn(self, n=2):
+		from sklearn.neighbors import KNeighborsClassifier
+		from sklearn.metrics import confusion_matrix
+		neigh = KNeighborsClassifier(n_neighbors=n, weights='distance', p=1, algorithm='brute')
+		neigh.fit(self.X_train, self.y_train)
+		# print(neigh.score(self.X_test, self.y_test))
+		self.__score_metrics(neigh)
+		# print(confusion_matrix(self.y_test, neigh.predict(self.X_test)))
+		import time
+		start_time = time.time()
+		cv = self.__cross_validation(neigh)
+		print("--- %s seconds ---" % (time.time() - start_time))
+		# self.__plot_roc_cv(neigh, cv)
+		# self.__auc_curve(neigh)
+		self.__permutation_importance(self._hyperparameters_knn(neigh))
+
+	def _hyperparameters_knn(self, method):
+		from sklearn.model_selection import GridSearchCV
+		parameters = {'leaf_size': [5, 15, 30, 50], 'n_neighbors': [2, 3, 5, 7], 'weights': ['uniform', 'distance'], 'algorithm': ['auto', 'ball_tree', 'kd_tree', 'brute'], 'p': [1, 2]}
+		clf = GridSearchCV(method, parameters)
+		clf.fit(self.X_val, self.y_val)
+		self.__score_metrics(clf)
+		print(clf.best_params_)
+		return clf
+
+	def run_random_forest(self):
+		from sklearn.ensemble import RandomForestClassifier
+		from sklearn.metrics import confusion_matrix
+		clf = RandomForestClassifier(n_estimators=250, bootstrap=False, ccp_alpha=0.0, max_features='sqrt')
+		clf = clf.fit(self.X_train, self.y_train)
+		# print(clf.score(self.X_test, self.y_test))
+		self.__score_metrics(clf)
+		# print(confusion_matrix(self.y_test, clf.predict(self.X_test)))
+		# self.recursive_feature_elimination(clf)
+		import time
+		start_time = time.time()
+		cv = self.__cross_validation(clf)
+		print("--- %s seconds ---" % (time.time() - start_time))
+		# self.__plot_roc_cv(clf, cv)
+		# self.__auc_curve(clf)
+		# self.__permutation_importance(clf)
+
+	def _hyperparameters_rf(self, method):
+		from sklearn.model_selection import GridSearchCV
+		parameters = {'n_estimators': [50, 100, 150, 200, 250], 'criterion': ['gini', 'entropy'], 'min_samples_split': [2, 4, 6], 'min_samples_leaf': [2, 4, 6], 'bootstrap': [True, False], 'oob_score': [True, False]}
+		clf = GridSearchCV(method, parameters)
+		clf.fit(self.X_val, self.y_val)
+		self.__score_metrics(clf)
+		print(clf.best_params_)
+		return clf
+
+	def run_svm(self, c=0.1):
+		from sklearn.svm import SVC
+		from sklearn.metrics import confusion_matrix
+		svm = SVC(C=c)
+		svm = svm.fit(self.X_train, self.y_train)
+		# print(svm.score(self.X_test, self.y_test))
+		self.__score_metrics(svm)
+		# print(confusion_matrix(self.y_test, svm.predict(self.X_test)))
+		import time
+		start_time = time.time()
+		cv = self.__cross_validation(svm)
+		print("--- %s seconds ---" % (time.time() - start_time))
+		# self.recursive_feature_elimination(svm)
+		# self.__plot_roc_cv(svm, cv)
+		# self.__auc_curve(svm)
+		# self.__permutation_importance(svm)
+
+	def _hyperparameters_svm(self, method):
+		from sklearn.model_selection import GridSearchCV
+		parameters = {'C': [0.01, 0.1, 1, 10], 'kernel': ['linear','rbf','poly','sigmoid', 'precomputed'], 'degree': [2, 3, 4], 'gamma': ['scale', 'auto']}
+		clf = GridSearchCV(method, parameters)
+		clf.fit(self.X_val, self.y_val)
+		self.__score_metrics(clf)
+		print(clf.best_params_)
+		return clf
+
+	def run_neural_networks(self, alpha=1):
+		from sklearn.neural_network import MLPClassifier
+		from sklearn.metrics import confusion_matrix
+		clf = MLPClassifier(alpha=alpha)
+		clf.fit(self.X_train, self.y_train)
+		self.__score_metrics(clf)
+		# print(confusion_matrix(self.y_test, clf.predict(self.X_test)))
+		import time
+		start_time = time.time()
+		cv = self.__cross_validation(clf)
+		print("--- %s seconds ---" % (time.time() - start_time))
+		# self.__plot_roc_cv(clf, cv)
+		# self.__auc_curve(clf)
+		# self.__permutation_importance(clf)
+
+	def _hyperparameters_ann(self, method):
+		from sklearn.model_selection import GridSearchCV
+		parameters = {'hidden_layer_sizes': [50, 100, 200], 'activation': ['identity', 'logistic', 'tanh', 'relu'], 'solver': ['lbfgs', 'sgd', 'adam'], 'alpha': [0.0001, 0.05], 'learning_rate': ['constant', 'invscaling', 'adaptive']}
+		clf = GridSearchCV(method, parameters)
+		clf.fit(self.X_val, self.y_val)
+		self.__score_metrics(clf)
+		print(clf.best_params_)
+		return clf
+
+	def run_logistic_reg(self, c=1):
+		from sklearn.linear_model import LogisticRegression
+		from sklearn.metrics import confusion_matrix
+		clf = LogisticRegression(C=c)
+		clf.fit(self.X_train, self.y_train)
+		self.__score_metrics(clf)
+		# print(confusion_matrix(self.y_test, clf.predict(self.X_test)))
+		import time
+		start_time = time.time()
+		cv = self.__cross_validation(clf)
+		print("--- %s seconds ---" % (time.time() - start_time))
+		# self.__plot_roc_cv(clf, cv)
+		# self.__auc_curve(clf)
+		# self.__permutation_importance(clf)
+
+	def _hyperparameters_lr(self, method):
+		from sklearn.model_selection import GridSearchCV
+		parameters = {'penalty': ['l1', 'l2', 'elasticnet', 'none'], 'C': [0.01, 0.1, 1, 10], 'solver': ['lbfgs', 'liblinear', 'newton-cg', 'sag', 'saga']}
+		clf = GridSearchCV(method, parameters)
+		clf.fit(self.X_val, self.y_val)
+		self.__score_metrics(clf)
+		print(clf.best_params_)
+		return clf
+
+	def hyperparameter_tuning(self):  # Best Params:  {'bootstrap': False, 'ccp_alpha': 0.0, 'criterion': 'gini', 'max_features': 'sqrt', 'n_estimators': 250}
+		from sklearn.model_selection import GridSearchCV
+		from sklearn.ensemble import RandomForestClassifier
+		clf = RandomForestClassifier()
+		n_estimators = [50, 100, 150, 200, 250]
+		criterion = ['gini', 'entropy']
+		max_features = ['auto', 'sqrt', 'log2']
+		bootstrap = [True, False]
+		ccp_alpha = [0.0, 0.01, 0.02]
+		param_grid = dict(n_estimators=n_estimators, criterion=criterion, max_features=max_features, bootstrap=bootstrap, ccp_alpha=ccp_alpha)
+		grid = GridSearchCV(clf, param_grid, n_jobs=-1)
+		grid_result = grid.fit(self.X_val, self.y_val)
+		print('Best Score: ', grid_result.best_score_)
+		print('Best Params: ', grid_result.best_params_)
+
+	def __score_metrics(self, method):
+		from sklearn.metrics import matthews_corrcoef, f1_score, precision_score, recall_score
+		print(matthews_corrcoef(self.y_test, method.predict(self.X_test)))
+		print(f1_score(self.y_test, method.predict(self.X_test), average=None))
+
+	def __auc_curve(self, method):
+		import matplotlib.pyplot as plt
+		from sklearn import metrics
+		metrics.plot_roc_curve(method, self.X_test, self.y_test)
+		plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')
+		plt.xlim(0, 0.2)
+		plt.show()
+
+	def __plot_roc_cv(self, method, cv):
+		import numpy as np
+		import matplotlib.pyplot as plt
+		from sklearn import datasets
+		from sklearn.metrics import auc
+		from sklearn.metrics import plot_roc_curve
+		tprs = []
+		aucs = []
+		mean_fpr = np.linspace(0, 1, 100)
+		fig, ax = plt.subplots()
+		for i, (train, test) in enumerate(cv.split(self.data_z, self.output)):
+			method.fit(self.data_z[train], self.output[train])
+			viz = plot_roc_curve(method, self.data_z[test], self.output[test], name='ROC fold {}'.format(i), alpha=0.3, lw=1, ax=ax)
+			interp_tpr = np.interp(mean_fpr, viz.fpr, viz.tpr)
+			interp_tpr[0] = 0.0
+			tprs.append(interp_tpr)
+			aucs.append(viz.roc_auc)
+		ax.plot([0, 1], [0, 1], linestyle='--', lw=2, color='r', label='Chance', alpha=.8)
+		mean_tpr = np.mean(tprs, axis=0)
+		mean_tpr[-1] = 1.0
+		mean_auc = auc(mean_fpr, mean_tpr)
+		std_auc = np.std(aucs)
+		ax.plot(mean_fpr, mean_tpr, color='b', label=r'Mean ROC (AUC = %0.2f $\pm$ %0.2f)' % (mean_auc, std_auc), lw=2, alpha=.8)
+		std_tpr = np.std(tprs, axis=0)
+		tprs_upper = np.minimum(mean_tpr + std_tpr, 1)
+		tprs_lower = np.maximum(mean_tpr - std_tpr, 0)
+		ax.fill_between(mean_fpr, tprs_lower, tprs_upper, color='grey', alpha=.2, label=r'$\pm$ 1 std. dev.')
+		ax.set(xlim=[-0.05, 1.05], ylim=[-0.05, 1.05], title="Receiver operating characteristic example")
+		ax.legend(loc="lower right")
+		plt.show()
+
+	def __permutation_importance(self, method):
+		from sklearn.inspection import permutation_importance
+		r = permutation_importance(method, self.X_test, self.y_test, n_repeats=5)
+		for i in r.importances_mean.argsort()[::-1]:
+			if r.importances_mean[i] - 2 * r.importances_std[i] > 0.001:
+				print(f"{self.dataset.columns[i]:<8}"
+					  f" {r.importances_mean[i]:.3f}"
+					  f" +/- {r.importances_std[i]:.3f}")
+
+	def recursive_feature_elimination(self, method):
+		from sklearn.feature_selection import RFECV
+		selector = RFECV(method, cv=5)#, min_features_to_select=200)
+		selector.fit(self.data_z, self.output)
+		print(selector.ranking_)
+		self.data_reduced = selector.transform(self.data_z)
+
+	def predict_interaction(self, phage, bacteria):
+		from sklearn.svm import LinearSVC
+		from sklearn.ensemble import RandomForestClassifier
+		import numpy as np
+		from feature_construction import FeatureConstruction
+
+		phageProts = self.__find_phage_proteins(phage)  # dictionary
+		bactProts = self.__find_bact_proteins(bacteria)
+		if not phageProts or not bactProts:
+			print('oops')
+			return None
+		list_carb = {}
+		list_prot = {}
+		for prot in phageProts.keys():
+			if any(z in phageProts[prot][0].lower() for z in ['lysin', 'collagen', 'glyco', 'galac', 'chitin', 'wall', 'pectin', 'glycan', 'sialidase', 'neuramin', 'amid', 'lysozyme', 'murami', 'pectate', 'sgnh']):
+				list_carb[prot] = phageProts[prot]
+			else:
+				list_prot[prot] = phageProts[prot]
+		inter = np.array([])
+		fc = FeatureConstruction()
+		grouping = fc.get_grouping(phage=list_prot, phage_carb=list_carb, bacteria=bactProts)  # takes a list of protein sequences
+		inter = np.append(inter, grouping)
+		composition = fc.get_composition(phage=list_prot, phage_carb=list_carb, bacteria=bactProts)
+		inter = np.append(inter, composition)
+		kmers = fc.get_kmers(phage=list_prot, phage_carb=list_carb, bacteria=bactProts)
+		inter = np.append(inter, kmers)
+		inter = inter.reshape(1, -1)
+		inter = self.scaler.transform(inter)
+		# svm = LinearSVC(C=0.01, tol=0.010, dual=False)
+		clf = RandomForestClassifier(n_estimators=250, bootstrap=False, ccp_alpha=0.0, max_features='sqrt')
+		clf = clf.fit(self.data_z, self.output)
+		pred = clf.predict(inter)[0]
+		print(pred)
+		return pred
+
+	def __find_phage_proteins(self, phage):
+		import json
+		with open('files/phageTails.json', encoding='utf-8') as F:
+			phageTails = json.loads(F.read())
+		phageProts = {}
+		if phage in phageTails.keys():
+			for prot in phageTails[phage]:
+				phageProts[prot] = [phageTails[phage][prot][0], phageTails[phage][prot][1]]
+		else:
+			from domain_search import DomainSearch
+			phageProts = self.__find_proteins(phage)
+			ds = DomainSearch()
+			phageProts = ds.find_domains_interpro(phageProts)
+			phageProts = ds.find_domains_blast(phageProts)
+			phageProts = ds.find_domains_uniprot(phageProts)
+		return phageProts
+
+	def __find_bact_proteins(self, bacteria):
+		import os
+		import json
+		if bacteria + '.json' in os.listdir('files/bacteria'):
+			with open('files/bacteria/' + bacteria + '.json', encoding='utf-8') as F:
+				bactProts = json.loads(F.read())
+		else:
+			pass
+			# bactProts = self.__find_proteins(bacteria)
+			# Implementar previsão de localização celular
+		return bactProts
+
+	def __find_proteins(self, id):
+		from Bio import Entrez
+		from Bio import SeqIO
+		Entrez.email = 'pedro_araujo97@hotmail.com'
+		prots = {}
+		with Entrez.efetch(db="nucleotide", rettype="gb", retmode="text", id=id) as handle:
+			genomeBac = SeqIO.read(handle, "gb")
+		for feat in genomeBac.features:
+			if feat.type == 'CDS':
+				try: prots[feat.qualifiers['protein_id'][0]] = [feat.qualifiers['product'][0], feat.qualifiers['translation'][0]]
+				except: pass
+		if len(genomeBac.features) <= 5:
+			with Entrez.efetch(db="nucleotide", rettype="gbwithparts", retmode="text", id=id) as handle:
+				genomeBac = handle.readlines()
+			for i in range(len(genomeBac)):
+				if ' CDS ' in genomeBac[i]:
+					j = i
+					protDone = False
+					while j < len(genomeBac):
+						if protDone:
+							break
+						if '/product=' in genomeBac[j]:
+							product = genomeBac[j].strip()[10:]
+							j += 1
+						elif '_id=' in genomeBac[j]:
+							protKey = genomeBac[j].strip()[13:-1]
+							j += 1
+						elif '/translation=' in genomeBac[j]:
+							protSeq = genomeBac[j].strip()[14:]
+							j += 1
+							for k in range(j, len(genomeBac)):
+								if genomeBac[k].islower():
+									j = k
+									protDone = True
+									break
+								else:
+									protSeq += genomeBac[k].strip()
+						else:
+							j += 1
+					prots[protKey] = [product, protSeq[:protSeq.find('"')]]
+		return prots
+
+
+if __name__ == '__main__':
+	ml = PredictInteraction('dataset_reduced')  # FeatureDataset
+	# ml.predict_interaction('NC_050143', 'NC_020524.1')  # NC_010468.1 NC_013941.1 NZ_CP029060.1 NZ_CP027394.1 NZ_CP025089.1
+	ml.predict_interaction('KM607000', 'NC_020524')  # NC_010468.1 NC_013941.1 NZ_CP029060.1 NZ_CP027394.1 NZ_CP025089.1
+	ml.run_knn(2)
+	ml.run_random_forest()
+	ml.run_svm(0.001)
+	ml.run_neural_networks(0.0001)
+	ml.run_logistic_reg(0.01)
+
+	import pandas as pd
+	import ast
+	data = pd.read_csv('files/NCBI_Phage_Bacteria_Data.csv', header=0, index_col=0)
+	abaumannii = {}
+	for phage in data.index:
+		name = data.loc[phage, 'Host']
+		if 'acinetobacter' in name.lower():
+			for bact in ast.literal_eval(data.loc[phage, 'Host_ID']):
+				abaumannii[bact] = 0
+	list_yes = {}
+	list_yes['KT588074'] = []
+	for bact in abaumannii.keys():
+		predict = ml.predict_interaction('KT588074', bact)
+		if predict == 'Yes':
+			list_yes['KT588074'].append(bact)