Iris dataset for automatic clasification

Carlos-Francisco Méndez-Cruz
Commit caac90815d7683b31b1c452242a6bf45264e68cc caac9081 1 parent 3742c095
Showing 1 changed file with 29 additions and 39 deletions
clasificacion-automatica/iris-dataset/trainingEvaluation_Iris_v1.py
--- a/clasificacion-automatica/iris-dataset/trainingEvaluation_Iris_v1.py
View file @caac908
+++ b/clasificacion-automatica/iris-dataset/trainingEvaluation_Iris_v1.py
View file @caac908
@@ -4,40 +4,30 @@ import os
 from time import time
 from optparse import OptionParser
 from sklearn.naive_bayes import MultinomialNB
-from sklearn.ensemble import RandomForestClassifier
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.svm import SVC
 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
+# Goal: training and evaluation Iris dataset
 # Parameters:
 # 1) --inputPath Path to read input files.
 # 2) --inputTrainingData File to read training data.
 # 3) --inputTrainingClasses File to read training true classes.
-# 4) --inputTestData File to read test data.
+# 4) --inputEvaluationData File to read test data.
-# 5) --inputTestClasses File to read test true classes.
+# 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, RandomForest.
+# 8) --classifier Classifier: MultinomialNB, SVM, DecisionTree.
 # Ouput:
 # 1) Evaluation report.
 # Execution:
-# C:\Anaconda3\python trainingTest_Iris_v2.py
-# --inputPath C:\Users\cmendezc\Documents\GENOMICAS\LICENCIATURA_LCGPDCB\dataSet_Iris
-# --inputTrainingData training_Data.txt
-# --inputTrainingClasses training_TrueClasses.txt
-# --inputTestData test_Data.txt
-# --inputTestClasses test_TrueClasses.txt
-# --outputPath C:\Users\cmendezc\Documents\GENOMICAS\LICENCIATURA_LCGPDCB\dataSet_Iris
-# --outputFile report_MultinomialNB.txt
-# --classifier MultinomialNB
-
-# C:\Anaconda3\python trainingTest_Iris_v2.py --inputPath C:\Users\cmendezc\Documents\GENOMICAS\LICENCIATURA_LCGPDCB\dataSet_Iris --inputTrainingData training_Data.txt --inputTrainingClasses training_TrueClasses.txt --inputTestData test_Data.txt --inputTestClasses test_TrueClasses.txt --outputPath C:\Users\cmendezc\Documents\GENOMICAS\LICENCIATURA_LCGPDCB\dataSet_Iris --outputFile report_MultinomialNB.txt --classifier MultinomialNB
 ###########################################################
 #                       MAIN PROGRAM                      #
@@ -52,9 +42,9 @@ if __name__ == "__main__":
                       help="File to read training data", metavar="FILE")
     parser.add_option("--inputTrainingClasses", dest="inputTrainingClasses",
                       help="File to read training true classes", metavar="FILE")
-    parser.add_option("--inputTestData", dest="inputTestData",
+    parser.add_option("--inputEvaluationData", dest="inputEvaluationData",
                       help="File to read test data", metavar="FILE")
-    parser.add_option("--inputTestClasses", dest="inputTestClasses",
+    parser.add_option("--inputEvaluationClasses", dest="inputEvaluationClasses",
                       help="File to read test true classes", metavar="FILE")
     parser.add_option("--outputPath", dest="outputPath",
                           help="Path to place output files", metavar="PATH")
@@ -73,8 +63,8 @@ if __name__ == "__main__":
     print("Path to read input files: " + str(options.inputPath))
     print("File to read training data: " + str(options.inputTrainingData))
     print("File to read training true classes: " + str(options.inputTrainingClasses))
-    print("File to read test data: " + str(options.inputTestData))
+    print("File to read evaluation data: " + str(options.inputEvaluationData))
-    print("File to read test true classes: " + str(options.inputTestClasses))
+    print("File to read evaluation true classes: " + str(options.inputEvaluationClasses))
     print("Path to place output files: " + str(options.outputPath))
     print("File to write evaluation report: " + str(options.outputFile))
     print("Classifier: " + str(options.outputFile))
@@ -82,24 +72,24 @@ if __name__ == "__main__":
     # Start time
     t0 = time()
-    print("   Reading training and test data and true classes...")
+    print("   Reading training and evaluation data and true classes...")
     trueTrainingClasses = []
-    trueTestClasses = []
+    trueEvaluationClasses = []
     with open(os.path.join(options.inputPath, options.inputTrainingClasses), encoding='utf8', mode='r') \
             as classFile:
         for line in classFile:
             line = line.strip('\r\n')
             trueTrainingClasses.append(line)
-    with open(os.path.join(options.inputPath, options.inputTestClasses), encoding='utf8', mode='r') \
+    with open(os.path.join(options.inputPath, options.inputEvaluationClasses), encoding='utf8', mode='r') \
             as classFile:
         for line in classFile:
             line = line.strip('\r\n')
-            trueTestClasses.append(line)
+            trueEvaluationClasses.append(line)
-    # print(trueTestClasses)
+    # print(trueEvaluationClasses)
     dataTraining = []
-    dataTest = []
+    dataEvaluation = []
     with open(os.path.join(options.inputPath, options.inputTrainingData), encoding='utf8', mode='r') \
             as dataFile:
         for line in dataFile:
@@ -112,7 +102,7 @@ if __name__ == "__main__":
             dataTraining.append(listFloat)
     print(dataTraining)
-    with open(os.path.join(options.inputPath, options.inputTestData), encoding='utf8', mode='r') \
+    with open(os.path.join(options.inputPath, options.inputEvaluationData), encoding='utf8', mode='r') \
             as dataFile:
         for line in dataFile:
             listTemp = []
@@ -121,21 +111,21 @@ if __name__ == "__main__":
             listTemp = line.split('\t')
             for elem in listTemp:
                 listFloat.append(float(elem))
-            dataTest.append(listFloat)
+            dataEvaluation.append(listFloat)
-    print(dataTest)
+    print(dataEvaluation)
     print("     Reading data and true classes done!")
     if options.classifier == "MultinomialNB":
         classifier = MultinomialNB()
     elif options.classifier == "SVM":
-        pass
+        classifier = SVC()
-    elif options.classifier == "RandomForest":
+    elif options.classifier == "DecisionTree":
-        classifier = RandomForestClassifier()
+        classifier = DecisionTreeClassifier()
     print("   Training...")
     classifier.fit(dataTraining, trueTrainingClasses)
     print("   Prediction...")
-    y_pred = classifier.predict(dataTest)
+    y_pred = classifier.predict(dataEvaluation)
     print("     Training and predition done!")
     # for i in range(len(trueClasses)):
@@ -145,18 +135,18 @@ if __name__ == "__main__":
     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(trueTestClasses, y_pred)))
+        oFile.write('Accuracy: {}\n'.format(accuracy_score(trueEvaluationClasses, y_pred)))
-        oFile.write('Precision: {}\n'.format(precision_score(trueTestClasses, y_pred, average='weighted')))
+        oFile.write('Precision: {}\n'.format(precision_score(trueEvaluationClasses, y_pred, average='weighted')))
-        oFile.write('Recall: {}\n'.format(recall_score(trueTestClasses, y_pred, average='weighted')))
+        oFile.write('Recall: {}\n'.format(recall_score(trueEvaluationClasses, y_pred, average='weighted')))
-        oFile.write('F-score: {}\n'.format(f1_score(trueTestClasses, y_pred, average='weighted')))
+        oFile.write('F-score: {}\n'.format(f1_score(trueEvaluationClasses, 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(trueTestClasses, y_pred)) + '\n')
+        oFile.write(str(confusion_matrix(trueEvaluationClasses, y_pred)) + '\n')
         oFile.write('Classification report: \n')
-        oFile.write(classification_report(trueTestClasses, y_pred) + '\n')
+        oFile.write(classification_report(trueEvaluationClasses, y_pred) + '\n')
     print("     Saving test report done!")
     print("Training and test done in: %fs" % (time() - t0))