Iris dataset for automatic clasification

@@ -6,6 +6,7 @@ from optparse import OptionParser
 from sklearn.naive_bayes import MultinomialNB
 from sklearn.naive_bayes import MultinomialNB
 from sklearn.tree import DecisionTreeClassifier
 from sklearn.tree import DecisionTreeClassifier
 from sklearn.svm import SVC
 from sklearn.svm import SVC
+from sklearn.neural_network import MLPClassifier
 from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix, \
 from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix, \
     classification_report
     classification_report
 import sys
 import sys
@@ -22,23 +23,23 @@ __author__ = 'CMendezC'
 # 5) --inputEvaluationClasses File to read test true classes.
 # 5) --inputEvaluationClasses File to read test true classes.
 # 6) --outputPath Path to place output files.
 # 6) --outputPath Path to place output files.
 # 7) --outputFile File to place evaluation report.
 # 7) --outputFile File to place evaluation report.
-# 8) --classifier Classifier: MultinomialNB, SVM, DecisionTree.
+# 8) --classifier Classifier: MultinomialNB, SVM, DecisionTree, MLPClassifier.
 
 
 # Ouput:
 # Ouput:
 # 1) Evaluation report.
 # 1) Evaluation report.
 
 
 # Execution:
 # Execution:
 # python trainingEvaluation_Iris_v1.py
 # python trainingEvaluation_Iris_v1.py
-# --inputPath /home/cmendezc/gitlab_repositories/lcg-bioinfoI-bionlp/clasificacion-automatica/iris-dataset
+# --inputPath /home/cmendezc/borrame/lcg-bioinfoI-bionlp/clasificacion-automatica/iris-dataset
 # --inputTrainingData training_Data.txt
 # --inputTrainingData training_Data.txt
 # --inputTrainingClasses training_TrueClasses.txt
 # --inputTrainingClasses training_TrueClasses.txt
 # --inputEvaluationData test_Data.txt
 # --inputEvaluationData test_Data.txt
 # --inputEvaluationClasses test_TrueClasses.txt
 # --inputEvaluationClasses test_TrueClasses.txt
-# --outputPath /home/cmendezc/gitlab_repositories/lcg-bioinfoI-bionlp/clasificacion-automatica/iris-dataset/reports
+# --outputPath /home/cmendezc/borrame/lcg-bioinfoI-bionlp/clasificacion-automatica/iris-dataset/reports
 # --outputFile report-Iris-MultinomialNB.txt
 # --outputFile report-Iris-MultinomialNB.txt
-# --classifier MultinomialNB
+# --classifier MLPClassifier
 
 
-# python3 trainingEvaluation_Iris_v1.py --inputPath /home/cmendezc/gitlab_repositories/lcg-bioinfoI-bionlp/clasificacion-automatica/iris-dataset --inputTrainingData training_Data.txt --inputTrainingClasses training_TrueClasses.txt --inputEvaluationData test_Data.txt --inputEvaluationClasses test_TrueClasses.txt --outputPath /home/cmendezc/gitlab_repositories/lcg-bioinfoI-bionlp/clasificacion-automatica/iris-dataset/reports --outputFile report-Iris-MultinomialNB.txt --classifier MultinomialNB
+# python trainingEvaluation_Iris_v1.py --inputPath /home/cmendezc/borrame/lcg-bioinfoI-bionlp/clasificacion-automatica/iris-dataset --inputTrainingData training_Data.txt --inputTrainingClasses training_TrueClasses.txt --inputEvaluationData test_Data.txt --inputEvaluationClasses test_TrueClasses.txt --outputPath /home/cmendezc/borrame/lcg-bioinfoI-bionlp/clasificacion-automatica/iris-dataset/reports --outputFile report-Iris-MLPClassifier.txt --classifier MLPClassifier
 
 
 ###########################################################
 ###########################################################
 #                       MAIN PROGRAM                      #
 #                       MAIN PROGRAM                      #
@@ -132,6 +133,9 @@ if __name__ == "__main__":
         classifier = SVC()
         classifier = SVC()
     elif options.classifier == "DecisionTree":
     elif options.classifier == "DecisionTree":
         classifier = DecisionTreeClassifier()
         classifier = DecisionTreeClassifier()
+    elif options.classifier == "MLPClassifier":
+        classifier = MLPClassifier(solver='lbfgs', hidden_layer_sizes=(3))
+
 
 
     print("   Training...")
     print("   Training...")
     classifier.fit(dataTraining, trueTrainingClasses)
     classifier.fit(dataTraining, trueTrainingClasses)