Classification binding thrombin data set

+The test set consists of 634 data points, each of which represents
+a molecule that is either active (A) or inactive (I).  The test set
+has the same format as the training set, with the exception that the
+activity value (A or I) for each data point is missing, that is, has
+been replaced by a question mark (?).  Please submit one prediction,
+A or I, for each data point.  Your submission should be in the form
+of a file that starts with your contact information, followed by a
+line with 5 asterisks, followed immediately by your predictions, with
+one line per data point.  The predictions should be in the same order
+as the test set data points.  So your prediction for the first example
+should appear on the first line after the asterisks, your prediction
+for the second example should appear on the second line after the
+asterisks, etc.  Hence, after your contact information, the prediction
+file will consist of 635 lines and have the form:
+
+*****
+I
+I
+A
+I
+A
+I
+
+etc.
+
+You may submit your prediction by email to page@biostat.wisc.edu
+or by anonymous ftp to ftp.biostat.wisc.edu, placing the file
+into the directory dropboxes/page/.  If using email, please use
+the subject line "KDDcup <name> thrombin" where <name> is your
+name.  If using ftp, please name the file KDDcup.<name>.thrombin
+where <name> is your name.  For example, my submission would be
+named KDDcup.DavidPage.thrombin
+
+Only one submission per person per task is permitted.  If you do not
+receive email confirmation of your submission within 24 hours, please
+email page@biostat.wisc.edu with subject "KDDcup no confirmation".
+
+For group entries, the contact information should include the names
+of everyone to be credited as a member of the group should your entry
+achieve the highest score.  But no person is to be listed on more than
+one entry per task.

+Prediction of Molecular Bioactivity for Drug Design -- Binding to Thrombin
+--------------------------------------------------------------------------
+
+Drugs are typically small organic molecules that achieve their desired
+activity by binding to a target site on a receptor. The first step in
+the discovery of a new drug is usually to identify and isolate the
+receptor to which it should bind, followed by testing many small
+molecules for their ability to bind to the target site. This leaves
+researchers with the task of determining what separates the active
+(binding) compounds from the inactive (non-binding) ones.  Such a
+determination can then be used in the design of new compounds that not
+only bind, but also have all the other properties required for a drug
+(solubility, oral absorption, lack of side effects, appropriate duration
+of action, toxicity, etc.). 
+
+The present training data set consists of 1909 compounds tested for
+their ability to bind to a target site on thrombin, a key receptor in
+blood clotting. The chemical structures of these compounds are not
+necessary for our analysis and are not included. Of these compounds, 42
+are active (bind well) and the others are inactive. Each compound is
+described by a single feature vector comprised of a class value (A for
+active, I for inactive) and 139,351 binary features, which describe
+three-dimensional properties of the molecule. The definitions of the
+individual bits are not included - we don't know what each individual
+bit means, only that they are generated in an internally consistent
+manner for all 1909 compounds. Biological activity in general, and
+receptor binding affinity in particular, correlate with various
+structural and physical properties of small organic molecules. The task
+is to determine which of these properties are critical in this case and
+to learn to accurately predict the class value.  To simulate the
+real-world drug design environment, the test set contains 636 additional
+compounds that were in fact generated based on the assay results
+recorded for the training set. In evaluating the accuracy, a
+differential cost model will be used, so that the sum of the costs of
+the actives will be equal to the sum of the costs of the inactives.
+
+We thank DuPont Pharmaceuticals for graciously providing this data set
+for the KDD Cup 2001 competition.  All publications referring to
+analysis of this data set should acknowledge DuPont Pharmaceuticals
+Research Laboratories and KDD Cup 2001.

+I
+A
+I
+I
+I
+A
+I
+I
+I
+A
+I
+I
+I
+A
+I
+A
+I
+I
+I
+I
+I
+I
+I
+I
+I
+I
+I
+A
+I
+A
+I
+I
+I
+I
+I
+A
+I
+I
+I
+A
+I
+I
+I
+I
+I
+I
+I
+I
+A
+A
+I
+I
+I
+I
+I
+A
+I
+A
+A
+I
+I
+I
+A
+I
+I
+I
+I
+A
+A
+I
+A
+I
+I
+A
+A
+I
+I
+I
+I
+I
+I
+I
+I
+I
+I
+I
+I
+I
+I
+I
+A
+I
+I
+A
+I
+A
+I
+I
+I
+A
+A
+I
+I
+I
+I
+I
+I
+A
+I
+I
+I
+I
+A
+A
+I
+I
+I
+I
+I
+I
+I
+I
+A
+A
+I
+A
+A
+I
+I
+I
+I
+I
+I
+I
+I
+I
+I
+A
+I
+I
+I
+I
+I
+I
+I
+I
+I
+A
+I
+I
+I
+I
+A
+I
+I
+I
+I
+I
+I
+I
+A
+I
+I
+A
+I
+A
+I
+I
+A
+I
+A
+I
+A
+I
+A
+I
+I
+I
+I
+I
+A
+I
+I
+A
+I
+I
+A
+I
+I
+I
+A
+I
+A
+I
+I
+A
+I
+I
+I
+I
+A
+I
+A
+I
+I
+I
+I
+I
+I
+I
+I
+I
+I
+I
+A
+I
+A
+I
+I
+I
+I
+I
+I
+A
+I
+I
+A
+A
+A
+I
+I
+A
+A
+I
+I
+I
+I
+A
+I
+I
+I
+I
+A
+I
+A
+I
+I
+I
+I
+I
+I
+I
+A
+A
+I
+I
+I
+I
+I
+I
+I
+I
+A
+A
+I
+I
+I
+I
+I
+I
+A
+A
+I
+I
+I
+I
+I
+I
+A
+I
+A
+I
+I
+I
+I
+I
+I
+I
+I
+I
+A
+I
+I
+A
+I
+I
+I
+I
+I
+I
+A
+A
+I
+I
+I
+I
+I
+A
+I
+I
+I
+I
+I
+A
+A
+A
+I
+A
+I
+I
+I
+I
+A
+A
+I
+A
+A
+I
+I
+I
+I
+I
+I
+I
+I
+I
+I
+I
+I
+A
+I
+I
+I
+I
+A
+A
+I
+I
+A
+I
+I
+I
+I
+I
+A
+A
+I
+A
+I
+I
+I
+I
+I
+I
+A
+A
+I
+I
+A
+I
+I
+I
+I
+I
+I
+I
+I
+I
+I
+I
+I
+A
+I
+I
+A
+I
+I
+A
+I
+I
+I
+I
+A
+A
+I
+A
+A
+I
+I
+A
+I
+I
+I
+I
+A
+I
+I
+I
+I
+I
+I
+I
+I
+I
+I
+A
+I
+I
+A
+A
+I
+I
+I
+A
+I
+I
+I
+I
+A
+I
+A
+I
+I
+I
+I
+I
+A
+I
+I
+I
+I
+I
+I
+I
+I
+I
+I
+I
+I
+I
+I
+I
+I
+I
+I
+A
+A
+A
+A
+I
+I
+I
+A
+A
+I
+I
+I
+I
+I
+A
+I
+A
+I
+I
+I
+I
+I
+A
+I
+I
+I
+I
+A
+A
+I
+I
+I
+I
+I
+I
+I
+I
+I
+I
+I
+I
+I
+I
+I
+I
+I
+A
+I
+A
+I
+I
+A
+I
+I
+I
+I
+A
+I
+I
+A
+A
+I
+I
+I
+A
+I
+A
+I
+I
+I
+I
+I
+I
+I
+A
+A
+I
+I
+I
+A
+I
+I
+I
+A
+I
+I
+I
+I
+I
+I
+A
+I
+I
+I
+I
+I
+A
+I
+I
+I
+I
+I
+A
+I
+I
+A
+I
+I
+I
+I
+I
+I
+A
+I
+I
+I
+I
+I
+I
+I
+I
+I
+I
+I
+I
+I
+I
+I
+I
+A
+I
+I
+A
+I
+I
+I
+I
+A
+I
+I
+I
+I
+I
+A
+I
+I
+I
+A
+I
+I
+I
+A
+I
+A
+A
+I
+A
+I
+I
+I
+I
+I
+I
+I
+A
+I
+I
+I
+A
+I
+A
+I
+I
+I
+I
+A
+I
+A
+I

+# -*- 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 scipy.sparse import csr_matrix
+
+__author__ = 'CMendezC'
+
+# Goal: training 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
+
+# Ouput:
+# 1) Classification model and evaluation report.
+
+# Execution:
+
+# python training-testing-binding-thrombin.py
+# --inputPath /home/binding-thrombin-dataset
+# --inputTrainingData thrombin.data
+# --inputTestingData Thrombin.testset
+# --inputTestingClasses Thrombin.testset.class
+# --outputModelPath /home/binding-thrombin-dataset/models
+# --outputModelFile SVM-model.mod
+# --outputReportPath /home/binding-thrombin-dataset/reports
+# --outputReportFile SVM.txt
+# --classifier SVM
+# --saveData
+
+# source activate python3
+# python training-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-model.mod --outputReportPath /home/compu2/bionlp/lcg-bioinfoI-bionlp/clasificacion-automatica/binding-thrombin-dataset/reports --outputReportFile SVM.txt --classifier SVM --saveData
+
+###########################################################
+#                       MAIN PROGRAM                      #
+###########################################################
+
+if __name__ == "__main__":
+    # Parameter definition
+    parser = argparse.ArgumentParser(description='Training and testing 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")
+
+    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))
+
+    # 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))
+
+    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))
+
+    if args.classifier == "BernoulliNB":
+        classifier = BernoulliNB()
+    elif args.classifier == "SVM":
+        classifier = SVC()
+    elif args.classifier == "kNN":
+        classifier = KNeighborsClassifier()
+    else:
+        print("Bad classifier")
+        exit()
+
+    print("Training...")
+    classifier.fit(X_train, y_train)
+    print("   Done!")
+
+    print("Testing (prediction in new data)...")
+    y_pred = classifier.predict(X_test)
+    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('Classifier: {}\n'.format(args.classifier))
+        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')
+    print("   Done!")
+
+    print("Training and testing done in: %fs" % (time() - t0))