Carlos-Francisco Méndez-Cruz / lcg-bioinfoI-bionlp

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Carlos-Francisco Méndez-Cruz
36a4d6ab 1 parent 984c4884

Training and testing binding thrombin dataset

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Showing 1 changed file with 8 additions and 1 deletions
...@@ -111,8 +111,10 @@ if __name__ == "__main__": ...@@ -111,8 +111,10 @@ if __name__ == "__main__":
111 joblib.dump(X_train, os.path.join(args.outputModelPath, args.inputTrainingData + '.jlb')) 111 joblib.dump(X_train, os.path.join(args.outputModelPath, args.inputTrainingData + '.jlb'))
112 joblib.dump(y_train, os.path.join(args.outputModelPath, args.inputTrainingData + '.class.jlb')) 112 joblib.dump(y_train, os.path.join(args.outputModelPath, args.inputTrainingData + '.class.jlb'))
113 else: 113 else:
114 + print(" Saving matrix and classes...")
114 X_train = joblib.load(os.path.join(args.outputModelPath, args.inputTrainingData + '.jlb')) 115 X_train = joblib.load(os.path.join(args.outputModelPath, args.inputTrainingData + '.jlb'))
115 y_train = joblib.load(os.path.join(args.outputModelPath, args.inputTrainingData + '.class.jlb')) 116 y_train = joblib.load(os.path.join(args.outputModelPath, args.inputTrainingData + '.class.jlb'))
117 + print(" Done!")
116 118
117 print(" Number of training classes: {}".format(len(y_train))) 119 print(" Number of training classes: {}".format(len(y_train)))
118 print(" Number of training class A: {}".format(y_train.count('A'))) 120 print(" Number of training class A: {}".format(y_train.count('A')))
...@@ -139,20 +141,25 @@ if __name__ == "__main__": ...@@ -139,20 +141,25 @@ if __name__ == "__main__":
139 joblib.dump(X_test, os.path.join(args.outputModelPath, args.inputTestingData + '.jlb')) 141 joblib.dump(X_test, os.path.join(args.outputModelPath, args.inputTestingData + '.jlb'))
140 joblib.dump(y_test, os.path.join(args.outputModelPath, args.inputTestingClasses + '.class.jlb')) 142 joblib.dump(y_test, os.path.join(args.outputModelPath, args.inputTestingClasses + '.class.jlb'))
141 else: 143 else:
144 + print(" Saving matrix and classes...")
142 X_test = joblib.load(os.path.join(args.outputModelPath, args.inputTestingData + '.jlb')) 145 X_test = joblib.load(os.path.join(args.outputModelPath, args.inputTestingData + '.jlb'))
143 y_test = joblib.load(os.path.join(args.outputModelPath, args.inputTestingClasses + '.class.jlb')) 146 y_test = joblib.load(os.path.join(args.outputModelPath, args.inputTestingClasses + '.class.jlb'))
147 + print(" Done!")
144 148
145 print(" Number of testing classes: {}".format(len(y_test))) 149 print(" Number of testing classes: {}".format(len(y_test)))
146 print(" Number of testing class A: {}".format(y_test.count('A'))) 150 print(" Number of testing class A: {}".format(y_test.count('A')))
147 print(" Number of testing class I: {}".format(y_test.count('I'))) 151 print(" Number of testing class I: {}".format(y_test.count('I')))
148 print(" Shape of testing matrix: {}".format(X_test.shape)) 152 print(" Shape of testing matrix: {}".format(X_test.shape))
149 153
150 - if args.classifier == "MultinomialNB": 154 + if args.classifier == "BernoulliNB":
151 classifier = BernoulliNB() 155 classifier = BernoulliNB()
152 elif args.classifier == "SVM": 156 elif args.classifier == "SVM":
153 classifier = SVC() 157 classifier = SVC()
154 elif args.classifier == "NearestCentroid": 158 elif args.classifier == "NearestCentroid":
155 classifier = NearestCentroid() 159 classifier = NearestCentroid()
160 + else:
161 + print("Bad classifier")
162 + exit()
156 163
157 print("Training...") 164 print("Training...")
158 classifier.fit(X_train, y_train) 165 classifier.fit(X_train, y_train)
......