Iris dataset for automatic clasification

-from sklearn.naive_bayes import MultinomialNB, BernoulliNB
-
-def scores(list1, list2):
-    errores = 0
-    aciertos = 0
-    if len(list1) != len(list2):
-        print("ERROR. LENGTH MISMATCH")
-    for i in range(len(list1)):
-        if list1[i] == list2[i]:
-            aciertos += 1
-        else:
-            errores += 1
-    cocienteErrores = errores / len(list1)
-    return [aciertos, errores, cocienteErrores]
-
-data = []
-lista = []
-with open("C:\Users\cmendezc\Dropbox (UNAM-CCG)\Actividades_CCG\LICENCIATURA_LCG\BioInfo-I\lcg-bioinfoI-bionlp\clasificacion-automatica\iris-datasetdata.txt", encoding='utf8') \
-        as dataFile:
-    for line in dataFile:
-        listaFloat = []
-        line = line.strip('\n')
-        lista = line.split('\t')
-        for elem in lista:
-            listaFloat.append(float(elem))
-        data.append(listaFloat)
-
-print(data)
-
-target = []
-with open("C:\\Users\\cmendezc\\Documents\\GENOMICAS\\LICENCIATURA_LCGPDCB\\dataSet_Iris\\true_Classes.txt", encoding='utf8') \
-        as classFile:
-    for line in classFile:
-        line = line.strip('\n')
-        target.append(line)
-
-myMultinomialNB = MultinomialNB()
-myBernoulliNB = BernoulliNB()
-
-y_pred = myMultinomialNB.fit(data, target).predict(data)
-
-'''
-for i in range(len(iris.target)):
-    print(str(iris.target[i]) + "\t" + str(y_pred[i]) + "\t" + str(iris.data[i]))
-'''
-
-myRandomForest = RandomForestClassifier()
-y_pred = myRandomForest.fit(data, target).predict(data)
-results = scores(target, y_pred)
-print("Errores: {}".format(results[1]))
-print("Aciertos: {}".format(results[0]))
-print("Cociente error: {}".format(results[2]))
\ No newline at end of file

-from sklearn import datasets
-from sklearn.naive_bayes import MultinomialNB, BernoulliNB
-from sklearn.ensemble import RandomForestClassifier
-
-def scores(list1, list2):
-    errores = 0
-    aciertos = 0
-    if len(list1) != len(list2):
-        print("ERROR. LENGTH MISMATCH")
-    for i in range(len(list1)):
-        if list1[i] == list2[i]:
-            aciertos += 1
-        else:
-            errores += 1
-    cocienteErrores = errores / len(list1)
-    return [aciertos, errores, cocienteErrores]
-
-iris = datasets.load_iris()
-
-myMultinomialNB = MultinomialNB()
-myBernoulliNB = BernoulliNB()
-
-y_pred = myMultinomialNB.fit(iris.data, iris.target).predict(iris.data)
-
-'''
-for i in range(len(iris.target)):
-    print(str(iris.target[i]) + "\t" + str(y_pred[i]) + "\t" + str(iris.data[i]))
-'''
-
-myRandomForest = RandomForestClassifier()
-y_pred = myRandomForest.fit(iris.data, iris.target).predict(iris.data)
-results = scores(iris.target, y_pred)
-print("Errores: {}".format(results[1]))
-print("Aciertos: {}".format(results[0]))
-print("Cociente error: {}".format(results[2]))
\ No newline at end of file

-# -*- encoding: utf-8 -*-
-
-import os
-from time import time
-from optparse import OptionParser
-from sklearn.naive_bayes import MultinomialNB
-from sklearn.ensemble import RandomForestClassifier
-from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix, \
-    classification_report
-import sys
-
-__author__ = 'CMendezC'
-
-# Goal: training and test Iris dataset
-
-# Parameters:
-# 1) --inputPath Path to read input files.
-# 2) --inputFileData File to read data.
-# 3) --inputFileTrueClasses File to read text true classes.
-# 4) --outputPath Path to place output files.
-# 5) --outputFile File to place evaluation report.
-# 6) --classifier Classifier: MultinomialNB, SVM, RandomForest.
-
-# Ouput:
-# 1) Evaluation report.
-
-# Execution:
-# C:\Anaconda3\python trainingTest_Iris.py
-# --inputPath C:\Users\cmendezc\Documents\GENOMICAS\LICENCIATURA_LCGPDCB\dataSet_Iris
-# --inputFileData data.txt
-# --inputFileTrueClasses true_Classes.txt
-# --outputPath C:\Users\cmendezc\Documents\GENOMICAS\LICENCIATURA_LCGPDCB\dataSet_Iris
-# --outputFile report_MultinomialNB.txt
-# --classifier MultinomialNB
-
-# C:\Anaconda3\python trainingTest_Iris.py --inputPath C:\Users\cmendezc\Documents\GENOMICAS\LICENCIATURA_LCGPDCB\dataSet_Iris --inputFileData data.txt --inputFileTrueClasses true_Classes.txt --outputPath C:\Users\cmendezc\Documents\GENOMICAS\LICENCIATURA_LCGPDCB\dataSet_Iris --outputFile report_MultinomialNB.txt --classifier MultinomialNB
-
-###########################################################
-#                       MAIN PROGRAM                      #
-###########################################################
-
-if __name__ == "__main__":
-    # Parameter definition
-    parser = OptionParser()
-    parser.add_option("--inputPath", dest="inputPath",
-                      help="Path to read input files", metavar="PATH")
-    parser.add_option("--inputFileData", dest="inputFileData",
-                      help="File to read data", metavar="FILE")
-    parser.add_option("--inputFileTrueClasses", dest="inputFileTrueClasses",
-                      help="File to read true classes", metavar="FILE")
-    parser.add_option("--outputPath", dest="outputPath",
-                      help="Path to place output files", metavar="PATH")
-    parser.add_option("--outputFile", dest="outputFile",
-                      help="File to write evaluation report", metavar="FILE")
-    parser.add_option("--classifier", dest="classifier",
-                      help="Classifier", metavar="CLASSIFIER")
-
-    (options, args) = parser.parse_args()
-    if len(args) > 0:
-        parser.error("None parameters indicated.")
-        sys.exit(1)
-
-    # Printing parameter values
-    print('-------------------------------- PARAMETERS --------------------------------')
-    print("Path to read input files: " + str(options.inputPath))
-    print("File to read data: " + str(options.inputFileData))
-    print("File to read true classes: " + str(options.inputFileTrueClasses))
-    print("Path to place output files: " + str(options.outputPath))
-    print("File to write evaluation report: " + str(options.outputFile))
-    print("Classifier: " + str(options.outputFile))
-
-    # Start time
-    t0 = time()
-
-    print("   Reading data and true classes...")
-    trueClasses = []
-    with open(os.path.join(options.inputPath, options.inputFileTrueClasses), encoding='utf8', mode='r') \
-            as classFile:
-        for line in classFile:
-            line = line.strip('\r\n')
-            trueClasses.append(line)
-    print(trueClasses)
-
-    data = []
-    with open(os.path.join(options.inputPath, options.inputFileData), encoding='utf8', mode='r') \
-            as dataFile:
-        for line in dataFile:
-            listTemp = []
-            listFloat = []
-            line = line.strip('\r\n')
-            listTemp = line.split('\t')
-            for elem in listTemp:
-                listFloat.append(float(elem))
-            data.append(listFloat)
-    print(data)
-    print("     Reading data and true classes done!")
-
-    if options.classifier == "MultinomialNB":
-        classifier = MultinomialNB()
-    elif options.classifier == "SVM":
-        pass
-    elif options.classifier == "RandomForest":
-        classifier = RandomForestClassifier()
-
-    print("   Training...")
-    y_pred = classifier.fit(data, trueClasses).predict(data)
-    print("     Training done!")
-
-    # for i in range(len(trueClasses)):
-    #     print(str(trueClasses[i]) + "\t" + str(y_pred[i]))
-
-    print("   Saving test report...")
-    with open(os.path.join(options.outputPath, options.outputFile), mode='w', encoding='utf8') as oFile:
-        oFile.write('**********        EVALUATION REPORT     **********\n')
-        oFile.write('Classifier: {}\n'.format(options.classifier))
-        oFile.write('Accuracy: {}\n'.format(accuracy_score(trueClasses, y_pred)))
-        oFile.write('Precision: {}\n'.format(precision_score(trueClasses, y_pred, average='weighted')))
-        oFile.write('Recall: {}\n'.format(recall_score(trueClasses, y_pred, average='weighted')))
-        oFile.write('F-score: {}\n'.format(f1_score(trueClasses, y_pred, average='weighted')))
-        # oFile.write('{}\t{}\t{}\t{}\n'.format(accuracy_score(trueClasses, y_pred),
-        #                                       precision_score(trueClasses, y_pred, average='weighted'),
-        #                                       recall_score(trueClasses, y_pred, average='weighted'),
-        #                                       f1_score(trueClasses, y_pred, average='weighted')))
-        oFile.write('Confusion matrix: \n')
-        oFile.write(str(confusion_matrix(trueClasses, y_pred)) + '\n')
-        oFile.write('Classification report: \n')
-        oFile.write(classification_report(trueClasses, y_pred) + '\n')
-    print("     Saving test report done!")
-
-    print("Training and test done in: %fs" % (time() - t0))