Iris dataset for automatic clasification

@@ -6,7 +6,6 @@ from optparse import OptionParser
 from sklearn.naive_bayes import MultinomialNB
 from sklearn.tree import DecisionTreeClassifier
 from sklearn.svm import SVC
-from sklearn.neural_network import MLPClassifier
 from sklearn.linear_model import Perceptron
 from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix, \
     classification_report
@@ -24,7 +23,7 @@ __author__ = 'CMendezC'
 # 5) --inputEvaluationClasses File to read test true classes.
 # 6) --outputPath Path to place output files.
 # 7) --outputFile File to place evaluation report.
-# 8) --classifier Classifier: MultinomialNB, SVM, DecisionTree, Perceptron, MLPClassifier.
+# 8) --classifier Classifier: MultinomialNB, SVM, DecisionTree, Perceptron.
 
 # Ouput:
 # 1) Evaluation report.
@@ -38,10 +37,8 @@ __author__ = 'CMendezC'
 # --inputEvaluationClasses test_TrueClasses.txt
 # --outputPath /home/cmendezc/borrame/lcg-bioinfoI-bionlp/clasificacion-automatica/iris-dataset/reports
 # --outputFile report-Iris-MultinomialNB.txt
-# --classifier MLPClassifier
+# --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
-# 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-MLPClassifier.txt --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-Perceptron.txt --classifier Perceptron
 
 ###########################################################
@@ -138,8 +135,6 @@ if __name__ == "__main__":
         classifier = DecisionTreeClassifier()
     elif options.classifier == "Perceptron":
         classifier = Perceptron()
-    elif options.classifier == "MLPClassifier":
-        classifier = MLPClassifier(solver='lbfgs', hidden_layer_sizes=(3))
 
     print("   Training...")
     classifier.fit(dataTraining, trueTrainingClasses)
@@ -166,8 +161,8 @@ if __name__ == "__main__":
         oFile.write(str(confusion_matrix(trueEvaluationClasses, y_pred)) + '\n')
         oFile.write('Classification report: \n')
         oFile.write(classification_report(trueEvaluationClasses, y_pred) + '\n')
-        if options.classifier == "MLPClassifier":
-            oFile.write("Weight matrices\n")
+        if options.classifier == "Perceptron":
+            oFile.write("Perceptron\n")
             for coef in classifier.coefs_:
                 oFile.write("coef.shape: {}\n".format(coef.shape))
     print("     Saving test report done!")