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comparison phage_host_prediction/machine_learning.py @ 2:3e1e8be4e65c draft default tip

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author pedro_araujo
date Fri, 02 Apr 2021 10:11:13 +0000
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1:d9cda08472ea 2:3e1e8be4e65c
1
2 class PredictInteraction:
3
4 def __init__(self, data = 'FeatureDataset'):
5 import pickle
6 from sklearn.preprocessing import LabelEncoder
7 with open('files/' + data, 'rb') as f:
8 self.dataset = pickle.load(f)
9 self.dataset = self.dataset.dropna()
10 le = LabelEncoder()
11 le.fit(['Yes', 'No'])
12 self.output = le.transform(self.dataset['Infects'].values)
13 self.dataset = self.dataset.drop('Infects', 1)
14 self.__standardize()
15 self.__split_train_test()
16
17 def __standardize(self):
18 from sklearn.preprocessing import StandardScaler
19 self.scaler = StandardScaler()
20 self.scaler.fit(self.dataset)
21 self.data_z = self.scaler.transform(self.dataset)
22
23 def __cross_validation(self, method):
24 from sklearn.model_selection import cross_val_score
25 from sklearn.model_selection import StratifiedKFold
26 # from sklearn.model_selection import ShuffleSplit
27 # cv = ShuffleSplit(n_splits=4, test_size=0.3) # cross validation normal
28 skf = StratifiedKFold(5, shuffle=True)
29 scores = cross_val_score(method, self.data_z, self.output, cv=skf, scoring='f1_weighted')
30 print(scores)
31 return skf
32
33 def __split_train_test(self):
34 from sklearn.model_selection import train_test_split
35 self.data_z, self.X_val, self.output, self.y_val = train_test_split(self.data_z, self.output, test_size=0.2)
36 self.X_train, self.X_test, self.y_train, self.y_test = train_test_split(self.data_z, self.output, test_size=0.3)
37
38 def run_knn(self, n=2):
39 from sklearn.neighbors import KNeighborsClassifier
40 from sklearn.metrics import confusion_matrix
41 neigh = KNeighborsClassifier(n_neighbors=2, weights='distance', p=1, algorithm='auto', leaf_size=5)
42 neigh.fit(self.X_train, self.y_train)
43 # print(neigh.score(self.X_test, self.y_test))
44 self.__score_metrics(neigh)
45 print(confusion_matrix(self.y_test, neigh.predict(self.X_test)))
46 # import time
47 # start_time = time.time()
48 # cv = self.__cross_validation(neigh)
49 # print("--- %s seconds ---" % (time.time() - start_time))
50 # self.__plot_roc_cv(neigh, cv)
51 # self.__auc_curve(neigh)
52 # self.__permutation_importance(self._hyperparameters_knn(neigh))
53
54 def _hyperparameters_knn(self, method):
55 from sklearn.model_selection import GridSearchCV
56 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]}
57 clf = GridSearchCV(method, parameters)
58 clf.fit(self.X_val, self.y_val)
59 self.__score_metrics(clf)
60 print(clf.best_params_)
61 return clf
62
63 def run_random_forest(self):
64 from sklearn.ensemble import RandomForestClassifier
65 from sklearn.metrics import confusion_matrix
66 clf = RandomForestClassifier(n_estimators=200, bootstrap=False, criterion='gini', min_samples_leaf=2, min_samples_split=4, oob_score=False)
67 clf = clf.fit(self.X_train, self.y_train)
68 # print(clf.score(self.X_test, self.y_test))
69 self.__score_metrics(clf)
70 print(confusion_matrix(self.y_test, clf.predict(self.X_test)))
71 # self.recursive_feature_elimination(clf)
72 # import time
73 # start_time = time.time()
74 # cv = self.__cross_validation(clf)
75 # print("--- %s seconds ---" % (time.time() - start_time))
76 # self.__plot_roc_cv(clf, cv)
77 # self.__auc_curve(clf)
78 # self.__permutation_importance(clf)
79
80 def _hyperparameters_rf(self, method):
81 from sklearn.model_selection import GridSearchCV
82 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]}
83 clf = GridSearchCV(method, parameters)
84 clf.fit(self.X_val, self.y_val)
85 self.__score_metrics(clf)
86 print(clf.best_params_)
87 return clf
88
89 def run_svm(self, c=0.1):
90 from sklearn.svm import SVC
91 from sklearn.metrics import confusion_matrix
92 svm = SVC(C=10, degree=2, gamma='auto', kernel='rbf')
93 svm = svm.fit(self.X_train, self.y_train)
94 # print(svm.score(self.X_test, self.y_test))
95 self.__score_metrics(svm)
96 print(confusion_matrix(self.y_test, svm.predict(self.X_test)))
97 # import time
98 # start_time = time.time()
99 # cv = self.__cross_validation(svm)
100 # print("--- %s seconds ---" % (time.time() - start_time))
101 # self.recursive_feature_elimination(svm)
102 # self.__plot_roc_cv(svm, cv)
103 # self.__auc_curve(svm)
104 # self.__permutation_importance(svm)
105
106 def _hyperparameters_svm(self, method):
107 from sklearn.model_selection import GridSearchCV
108 parameters = {'C': [0.01, 0.1, 1, 10], 'kernel': ['linear','rbf','poly','sigmoid', 'precomputed'], 'degree': [2, 3, 4], 'gamma': ['scale', 'auto']}
109 clf = GridSearchCV(method, parameters)
110 clf.fit(self.X_val, self.y_val)
111 self.__score_metrics(clf)
112 print(clf.best_params_)
113 return clf
114
115 def run_neural_networks(self, alpha=1):
116 from sklearn.neural_network import MLPClassifier
117 from sklearn.metrics import confusion_matrix
118 clf = MLPClassifier(alpha=0.0001, activation='tanh', hidden_layer_sizes=200, learning_rate='adaptive', solver='adam')
119 clf.fit(self.X_train, self.y_train)
120 self.__score_metrics(clf)
121 print(confusion_matrix(self.y_test, clf.predict(self.X_test)))
122 # import time
123 # start_time = time.time()
124 # cv = self.__cross_validation(clf)
125 # print("--- %s seconds ---" % (time.time() - start_time))
126 # self.__plot_roc_cv(clf, cv)
127 # self.__auc_curve(clf)
128 # self.__permutation_importance(clf)
129
130 def _hyperparameters_ann(self, method):
131 from sklearn.model_selection import GridSearchCV
132 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']}
133 clf = GridSearchCV(method, parameters)
134 clf.fit(self.X_val, self.y_val)
135 self.__score_metrics(clf)
136 print(clf.best_params_)
137 return clf
138
139 def run_logistic_reg(self, c=1):
140 from sklearn.linear_model import LogisticRegression
141 from sklearn.metrics import confusion_matrix
142 clf = LogisticRegression(C=10, penalty='l2', solver='liblinear')
143 clf.fit(self.X_train, self.y_train)
144 self.__score_metrics(clf)
145 print(confusion_matrix(self.y_test, clf.predict(self.X_test)))
146 # import time
147 # start_time = time.time()
148 # cv = self.__cross_validation(clf)
149 # print("--- %s seconds ---" % (time.time() - start_time))
150 # self.__plot_roc_cv(clf, cv)
151 # self.__auc_curve(clf)
152 # self.__permutation_importance(clf)
153
154 def _hyperparameters_lr(self, method):
155 from sklearn.model_selection import GridSearchCV
156 parameters = {'penalty': ['l1', 'l2', 'elasticnet', 'none'], 'C': [0.01, 0.1, 1, 10], 'solver': ['lbfgs', 'liblinear', 'newton-cg', 'sag', 'saga']}
157 clf = GridSearchCV(method, parameters)
158 clf.fit(self.X_val, self.y_val)
159 self.__score_metrics(clf)
160 print(clf.best_params_)
161 return clf
162
163 def hyperparameter_tuning(self): # Best Params: {'bootstrap': False, 'ccp_alpha': 0.0, 'criterion': 'gini', 'max_features': 'sqrt', 'n_estimators': 250}
164 from sklearn.model_selection import GridSearchCV
165 from sklearn.ensemble import RandomForestClassifier
166 clf = RandomForestClassifier()
167 n_estimators = [50, 100, 150, 200, 250]
168 criterion = ['gini', 'entropy']
169 max_features = ['auto', 'sqrt', 'log2']
170 bootstrap = [True, False]
171 ccp_alpha = [0.0, 0.01, 0.02]
172 param_grid = dict(n_estimators=n_estimators, criterion=criterion, max_features=max_features, bootstrap=bootstrap, ccp_alpha=ccp_alpha)
173 grid = GridSearchCV(clf, param_grid, n_jobs=-1)
174 grid_result = grid.fit(self.X_val, self.y_val)
175 print('Best Score: ', grid_result.best_score_)
176 print('Best Params: ', grid_result.best_params_)
177
178 def __score_metrics(self, method):
179 from sklearn.metrics import matthews_corrcoef, f1_score, precision_score, recall_score
180 print(matthews_corrcoef(self.y_test, method.predict(self.X_test)))
181 print(f1_score(self.y_test, method.predict(self.X_test), average='binary'))
182 print(precision_score(self.y_test, method.predict(self.X_test)))
183 print(recall_score(self.y_test, method.predict(self.X_test)))
184
185 def __auc_curve(self, method):
186 import matplotlib.pyplot as plt
187 from sklearn import metrics
188 metrics.plot_roc_curve(method, self.X_test, self.y_test)
189 plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')
190 plt.xlim(0, 0.2)
191 plt.show()
192
193 def __plot_roc_cv(self, method, cv):
194 import numpy as np
195 import matplotlib.pyplot as plt
196 from sklearn import datasets
197 from sklearn.metrics import auc
198 from sklearn.metrics import plot_roc_curve
199 tprs = []
200 aucs = []
201 mean_fpr = np.linspace(0, 1, 100)
202 fig, ax = plt.subplots()
203 for i, (train, test) in enumerate(cv.split(self.data_z, self.output)):
204 method.fit(self.data_z[train], self.output[train])
205 viz = plot_roc_curve(method, self.data_z[test], self.output[test], name='ROC fold {}'.format(i), alpha=0.3, lw=1, ax=ax)
206 interp_tpr = np.interp(mean_fpr, viz.fpr, viz.tpr)
207 interp_tpr[0] = 0.0
208 tprs.append(interp_tpr)
209 aucs.append(viz.roc_auc)
210 ax.plot([0, 1], [0, 1], linestyle='--', lw=2, color='r', label='Chance', alpha=.8)
211 mean_tpr = np.mean(tprs, axis=0)
212 mean_tpr[-1] = 1.0
213 mean_auc = auc(mean_fpr, mean_tpr)
214 std_auc = np.std(aucs)
215 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)
216 std_tpr = np.std(tprs, axis=0)
217 tprs_upper = np.minimum(mean_tpr + std_tpr, 1)
218 tprs_lower = np.maximum(mean_tpr - std_tpr, 0)
219 ax.fill_between(mean_fpr, tprs_lower, tprs_upper, color='grey', alpha=.2, label=r'$\pm$ 1 std. dev.')
220 ax.set(xlim=[-0.05, 1.05], ylim=[-0.05, 1.05], title="Receiver operating characteristic example")
221 ax.legend(loc="lower right")
222 plt.show()
223
224 def __permutation_importance(self, method):
225 from sklearn.inspection import permutation_importance
226 r = permutation_importance(method, self.X_test, self.y_test, n_repeats=5)
227 for i in r.importances_mean.argsort()[::-1]:
228 if r.importances_mean[i] - 2 * r.importances_std[i] > 0.001:
229 print(f"{self.dataset.columns[i]:<8}"
230 f" {r.importances_mean[i]:.3f}"
231 f" +/- {r.importances_std[i]:.3f}")
232
233 def recursive_feature_elimination(self, method):
234 from sklearn.feature_selection import RFECV
235 selector = RFECV(method, cv=5)#, min_features_to_select=200)
236 selector.fit(self.data_z, self.output)
237 print(selector.ranking_)
238 self.data_reduced = selector.transform(self.data_z)
239
240 def predict_interaction(self, phage, bacteria):
241 from sklearn.svm import LinearSVC
242 from sklearn.ensemble import RandomForestClassifier
243 import numpy as np
244 from feature_construction import FeatureConstruction
245
246 phageProts = self.__find_phage_proteins(phage) # dictionary
247 bactProts = self.__find_bact_proteins(bacteria)
248 if not phageProts or not bactProts:
249 print('oops')
250 return None
251 list_carb = {}
252 list_prot = {}
253 for prot in phageProts.keys():
254 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']):
255 list_carb[prot] = phageProts[prot]
256 else:
257 list_prot[prot] = phageProts[prot]
258 inter = np.array([])
259 fc = FeatureConstruction()
260 grouping = fc.get_grouping(phage=list_prot, phage_carb=list_carb, bacteria=bactProts) # takes a list of protein sequences
261 inter = np.append(inter, grouping)
262 composition = fc.get_composition(phage=list_prot, phage_carb=list_carb, bacteria=bactProts)
263 inter = np.append(inter, composition)
264 kmers = fc.get_kmers(phage=list_prot, phage_carb=list_carb, bacteria=bactProts)
265 inter = np.append(inter, kmers)
266 inter = inter.reshape(1, -1)
267 inter = self.scaler.transform(inter)
268 # svm = LinearSVC(C=0.01, tol=0.010, dual=False)
269 clf = RandomForestClassifier(n_estimators=250, bootstrap=False, ccp_alpha=0.0, max_features='sqrt')
270 clf = clf.fit(self.data_z, self.output)
271 pred = clf.predict(inter)[0]
272 print(pred)
273 return pred
274
275 def __find_phage_proteins(self, phage):
276 import json
277 with open('files/phageTails.json', encoding='utf-8') as F:
278 phageTails = json.loads(F.read())
279 phageProts = {}
280 if phage in phageTails.keys():
281 for prot in phageTails[phage]:
282 phageProts[prot] = [phageTails[phage][prot][0], phageTails[phage][prot][1]]
283 else:
284 from domain_search import DomainSearch
285 phageProts = self.__find_proteins(phage)
286 ds = DomainSearch()
287 phageProts = ds.find_domains_interpro(phageProts)
288 phageProts = ds.find_domains_blast(phageProts)
289 phageProts = ds.find_domains_uniprot(phageProts)
290 return phageProts
291
292 def __find_bact_proteins(self, bacteria):
293 import os
294 import json
295 if bacteria + '.json' in os.listdir('files/bacteria'):
296 with open('files/bacteria/' + bacteria + '.json', encoding='utf-8') as F:
297 bactProts = json.loads(F.read())
298 else:
299 pass
300 # bactProts = self.__find_proteins(bacteria)
301 # Implementar previsão de localização celular
302 return bactProts
303
304 def __find_proteins(self, id):
305 from Bio import Entrez
306 from Bio import SeqIO
307 Entrez.email = 'pedro_araujo97@hotmail.com'
308 prots = {}
309 with Entrez.efetch(db="nucleotide", rettype="gb", retmode="text", id=id) as handle:
310 genomeBac = SeqIO.read(handle, "gb")
311 for feat in genomeBac.features:
312 if feat.type == 'CDS':
313 try: prots[feat.qualifiers['protein_id'][0]] = [feat.qualifiers['product'][0], feat.qualifiers['translation'][0]]
314 except: pass
315 if len(genomeBac.features) <= 5:
316 with Entrez.efetch(db="nucleotide", rettype="gbwithparts", retmode="text", id=id) as handle:
317 genomeBac = handle.readlines()
318 for i in range(len(genomeBac)):
319 if ' CDS ' in genomeBac[i]:
320 j = i
321 protDone = False
322 while j < len(genomeBac):
323 if protDone:
324 break
325 if '/product=' in genomeBac[j]:
326 product = genomeBac[j].strip()[10:]
327 j += 1
328 elif '_id=' in genomeBac[j]:
329 protKey = genomeBac[j].strip()[13:-1]
330 j += 1
331 elif '/translation=' in genomeBac[j]:
332 protSeq = genomeBac[j].strip()[14:]
333 j += 1
334 for k in range(j, len(genomeBac)):
335 if genomeBac[k].islower():
336 j = k
337 protDone = True
338 break
339 else:
340 protSeq += genomeBac[k].strip()
341 else:
342 j += 1
343 prots[protKey] = [product, protSeq[:protSeq.find('"')]]
344 return prots
345
346
347 if __name__ == '__main__':
348 ml = PredictInteraction('dataset_reduced') # feature_dataset
349 # ml.predict_interaction('NC_050143', 'NC_020524.1') # NC_010468.1 NC_013941.1 NZ_CP029060.1 NZ_CP027394.1 NZ_CP025089.1
350 ml.predict_interaction('KM607000', 'NC_020524') # NC_010468.1 NC_013941.1 NZ_CP029060.1 NZ_CP027394.1 NZ_CP025089.1
351 ml.run_knn(2)
352 ml.run_random_forest()
353 ml.run_svm(0.001)
354 ml.run_neural_networks(0.0001)
355 ml.run_logistic_reg(0.01)
356
357 import pandas as pd
358 import ast
359 data = pd.read_csv('files/NCBI_Phage_Bacteria_Data.csv', header=0, index_col=0)
360 abaumannii = {}
361 for phage in data.index:
362 name = data.loc[phage, 'Host']
363 if 'acinetobacter' in name.lower():
364 for bact in ast.literal_eval(data.loc[phage, 'Host_ID']):
365 abaumannii[bact] = 0
366 list_yes = {}
367 list_yes['KT588074'] = []
368 for bact in abaumannii.keys():
369 predict = ml.predict_interaction('KT588074', bact)
370 if predict == 'Yes':
371 list_yes['KT588074'].append(bact)