Training, crossvalidation and testing dataset

+# -*- encoding: utf-8 -*-
+
+import os
+from time import time
+import argparse
+from sklearn.naive_bayes import BernoulliNB
+from sklearn.svm import SVC
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix, \
+    classification_report
+from sklearn.externals import joblib
+from sklearn import model_selection
+from scipy.sparse import csr_matrix
+import scipy
+from imblearn.under_sampling import RandomUnderSampler
+from imblearn.over_sampling import RandomOverSampler
+
+__author__ = 'CMendezC'
+
+# Goal: training, crossvalidation and testing binding thrombin data set
+
+# Parameters:
+# 1) --inputPath Path to read input files.
+# 2) --inputTrainingData File to read training data.
+# 3) --inputTestingData File to read testing data.
+# 4) --inputTestingClasses File to read testing classes.
+# 5) --outputModelPath Path to place output model.
+# 6) --outputModelFile File to place output model.
+# 7) --outputReportPath Path to place evaluation report.
+# 8) --outputReportFile File to place evaluation report.
+# 9) --classifier Classifier: BernoulliNB, SVM, kNN.
+# 10) --saveData Save matrices
+# 11) --kernel Kernel
+# 12) --imbalanced Imbalanced method
+
+# Ouput:
+# 1) Classification model and evaluation report.
+
+# Execution:
+
+# python training-crossvalidation-testing-binding-thrombin.py
+# --inputPath /home/compu2/bionlp/lcg-bioinfoI-bionlp/clasificacion-automatica/binding-thrombin-dataset
+# --inputTrainingData thrombin.data
+# --inputTestingData Thrombin.testset
+# --inputTestingClasses Thrombin.testset.class
+# --outputModelPath /home/compu2/bionlp/lcg-bioinfoI-bionlp/clasificacion-automatica/binding-thrombin-dataset/models
+# --outputModelFile SVM-lineal-model.mod
+# --outputReportPath /home/compu2/bionlp/lcg-bioinfoI-bionlp/clasificacion-automatica/binding-thrombin-dataset/reports
+# --outputReportFile SVM-lineal.txt
+# --classifier SVM
+# --saveData
+# --kernel linear
+# --imbalanced RandomUS
+
+# source activate python3
+# python training-crossvalidation-testing-binding-thrombin.py --inputPath /home/compu2/bionlp/lcg-bioinfoI-bionlp/clasificacion-automatica/binding-thrombin-dataset --inputTrainingData thrombin.data --inputTestingData Thrombin.testset --inputTestingClasses Thrombin.testset.class --outputModelPath /home/compu2/bionlp/lcg-bioinfoI-bionlp/clasificacion-automatica/binding-thrombin-dataset/models --outputModelFile SVM-lineal-model.mod --outputReportPath /home/compu2/bionlp/lcg-bioinfoI-bionlp/clasificacion-automatica/binding-thrombin-dataset/reports --outputReportFile SVM-lineal.txt --classifier SVM --kernel linear --imbalanced RandomUS
+
+###########################################################
+#                       MAIN PROGRAM                      #
+###########################################################
+
+if __name__ == "__main__":
+    # Parameter definition
+    parser = argparse.ArgumentParser(description='Training validation Binding Thrombin Dataset.')
+    parser.add_argument("--inputPath", dest="inputPath",
+                      help="Path to read input files", metavar="PATH")
+    parser.add_argument("--inputTrainingData", dest="inputTrainingData",
+                      help="File to read training data", metavar="FILE")
+    parser.add_argument("--inputTestingData", dest="inputTestingData",
+                      help="File to read testing data", metavar="FILE")
+    parser.add_argument("--inputTestingClasses", dest="inputTestingClasses",
+                      help="File to read testing classes", metavar="FILE")
+    parser.add_argument("--outputModelPath", dest="outputModelPath",
+                      help="Path to place output model", metavar="PATH")
+    parser.add_argument("--outputModelFile", dest="outputModelFile",
+                      help="File to place output model", metavar="FILE")
+    parser.add_argument("--outputReportPath", dest="outputReportPath",
+                      help="Path to place evaluation report", metavar="PATH")
+    parser.add_argument("--outputReportFile", dest="outputReportFile",
+                      help="File to place evaluation report", metavar="FILE")
+    parser.add_argument("--classifier", dest="classifier",
+                      help="Classifier", metavar="NAME",
+                      choices=('BernoulliNB', 'SVM', 'kNN'), default='SVM')
+    parser.add_argument("--saveData", dest="saveData", action='store_true',
+                      help="Save matrices")
+    parser.add_argument("--kernel", dest="kernel",
+                      help="Kernel SVM", metavar="NAME",
+                      choices=('linear', 'rbf', 'poly'), default='linear')
+    parser.add_argument("--imbalanced", dest="imbalanced",
+                      choices=('RandomUS', 'RandomOS'), default=None,
+                      help="Undersampling: RandomUS. Oversampling: RandomOS", metavar="TEXT")
+
+    args = parser.parse_args()
+
+    # Printing parameter values
+    print('-------------------------------- PARAMETERS --------------------------------')
+    print("Path to read input files: " + str(args.inputPath))
+    print("File to read training data: " + str(args.inputTrainingData))
+    print("File to read testing data: " + str(args.inputTestingData))
+    print("File to read testing classes: " + str(args.inputTestingClasses))
+    print("Path to place output model: " + str(args.outputModelPath))
+    print("File to place output model: " + str(args.outputModelFile))
+    print("Path to place evaluation report: " + str(args.outputReportPath))
+    print("File to place evaluation report: " + str(args.outputReportFile))
+    print("Classifier: " + str(args.classifier))
+    print("Save matrices: " + str(args.saveData))
+    print("Kernel: " + str(args.kernel))
+    print("Imbalanced: " + str(args.imbalanced))
+
+    # Start time
+    t0 = time()
+
+    print("Reading training data and true classes...")
+    X_train = None
+    if args.saveData:
+        y_train = []
+        trainingData = []
+        with open(os.path.join(args.inputPath, args.inputTrainingData), encoding='utf8', mode='r') \
+                as iFile:
+            for line in iFile:
+                line = line.strip('\r\n')
+                listLine = line.split(',')
+                y_train.append(listLine[0])
+                trainingData.append(listLine[1:])
+        # X_train = np.matrix(trainingData)
+        X_train = csr_matrix(trainingData, dtype='double')
+        print("   Saving matrix and classes...")
+        joblib.dump(X_train, os.path.join(args.outputModelPath, args.inputTrainingData + '.jlb'))
+        joblib.dump(y_train, os.path.join(args.outputModelPath, args.inputTrainingData + '.class.jlb'))
+        print("      Done!")
+    else:
+        print("   Loading matrix and classes...")
+        X_train = joblib.load(os.path.join(args.outputModelPath, args.inputTrainingData + '.jlb'))
+        y_train = joblib.load(os.path.join(args.outputModelPath, args.inputTrainingData + '.class.jlb'))
+        print("      Done!")
+
+    print("   Number of training classes: {}".format(len(y_train)))
+    print("   Number of training class A: {}".format(y_train.count('A')))
+    print("   Number of training class I: {}".format(y_train.count('I')))
+    print("   Shape of training matrix: {}".format(X_train.shape))
+
+    if args.imbalanced != None:
+        t1 = time()
+        # Combination over and under sampling
+        jobs = 15
+        if args.imbalanced == "RandomOS":
+            sm = RandomOverSampler(random_state=42)
+        # Under sampling
+        elif args.imbalanced == "RandomUS":
+            sm = RandomUnderSampler(random_state=42)
+
+        # Apply transformation
+        X_train, y_train = sm.fit_sample(X_train, y_train)
+
+        print("  After transformtion with {}".format(args.imbalanced))
+        print("   Number of training classes: {}".format(len(y_train)))
+        print("   Number of training class A: {}".format(list(y_train).count('A')))
+        print("   Number of training class I: {}".format(list(y_train).count('I')))
+        print("   Shape of training matrix: {}".format(X_train.shape))
+        print("      Data transformation done in : %fs" % (time() - t1))
+
+    print("Reading testing data and true classes...")
+    X_test = None
+    if args.saveData:
+        y_test = []
+        testingData = []
+        with open(os.path.join(args.inputPath, args.inputTestingData), encoding='utf8', mode='r') \
+                as iFile:
+            for line in iFile:
+                line = line.strip('\r\n')
+                listLine = line.split(',')
+                testingData.append(listLine[1:])
+        X_test = csr_matrix(testingData, dtype='double')
+        with open(os.path.join(args.inputPath, args.inputTestingClasses), encoding='utf8', mode='r') \
+                as iFile:
+            for line in iFile:
+                line = line.strip('\r\n')
+                y_test.append(line)
+        print("   Saving matrix and classes...")
+        joblib.dump(X_test, os.path.join(args.outputModelPath, args.inputTestingData + '.jlb'))
+        joblib.dump(y_test, os.path.join(args.outputModelPath, args.inputTestingClasses + '.class.jlb'))
+        print("      Done!")
+    else:
+        print("   Loading matrix and classes...")
+        X_test = joblib.load(os.path.join(args.outputModelPath, args.inputTestingData + '.jlb'))
+        y_test = joblib.load(os.path.join(args.outputModelPath, args.inputTestingClasses + '.class.jlb'))
+        print("      Done!")
+
+    print("   Number of testing classes: {}".format(len(y_test)))
+    print("   Number of testing class A: {}".format(y_test.count('A')))
+    print("   Number of testing class I: {}".format(y_test.count('I')))
+    print("   Shape of testing matrix: {}".format(X_test.shape))
+
+    jobs = -1
+    paramGrid = []
+    nIter = 20
+    crossV = 10
+    print("Defining randomized grid search...")
+    if args.classifier == 'SVM':
+        # SVM
+        classifier = SVC(args.kernel)
+    elif args.classifier == 'BernoulliNB':
+        # BernoulliNB
+        classifier = BernoulliNB()
+    elif args.classifier == 'kNN':
+        # kNN
+        k_range = list(range(1, 7, 2))
+        classifier = KNeighborsClassifier()
+    else:
+        print("Bad classifier")
+        exit()
+    print("   Done!")
+
+    print("Training...")
+    classifier.fit(X_train, y_train)
+    print("   Done!")
+
+    y_pred = classifier.predict(X_test)
+    best_parameters = classifier.best_estimator_.get_params()
+    print("   Done!")
+
+    print("Saving report...")
+    with open(os.path.join(args.outputReportPath, args.outputReportFile), mode='w', encoding='utf8') as oFile:
+        oFile.write('**********        EVALUATION REPORT     **********\n')
+        oFile.write('Reduction: {}\n'.format(args.reduction))
+        oFile.write('Classifier: {}\n'.format(args.classifier))
+        oFile.write('Kernel: {}\n'.format(args.kernel))
+        oFile.write('Accuracy: {}\n'.format(accuracy_score(y_test, y_pred)))
+        oFile.write('Precision: {}\n'.format(precision_score(y_test, y_pred, average='weighted')))
+        oFile.write('Recall: {}\n'.format(recall_score(y_test, y_pred, average='weighted')))
+        oFile.write('F-score: {}\n'.format(f1_score(y_test, y_pred, average='weighted')))
+        oFile.write('Confusion matrix: \n')
+        oFile.write(str(confusion_matrix(y_test, y_pred)) + '\n')
+        oFile.write('Classification report: \n')
+        oFile.write(classification_report(y_test, y_pred) + '\n')
+        oFile.write('Best parameters: \n')
+        for param in sorted(best_parameters.keys()):
+            oFile.write("\t%s: %r\n" % (param, best_parameters[param]))
+    print("   Done!")
+
+    print("Training and testing done in: %fs" % (time() - t0))