Training, crossvalidation and testing dataset

@@ -237,6 +237,7 @@ if __name__ == "__main__":
     if args.reduction is not None:
         X_test = reduc.transform(X_test)
     y_pred = myClassifier.predict(X_test)
+    best_parameters = myClassifier.best_estimator_.get_params()
     print("   Done!")
 
     print("Saving report...")
@@ -253,6 +254,8 @@ if __name__ == "__main__":
         oFile.write(str(confusion_matrix(y_test, y_pred)) + '\n')
         oFile.write('Classification report: \n')
         oFile.write(classification_report(y_test, y_pred) + '\n')
+        for param in sorted(best_parameters.keys()):
+            oFile.write("\t%s: %r\n" % (param, best_parameters[param]))
     print("   Done!")
 
     print("Training and testing done in: %fs" % (time() - t0))

@@ -54,14 +54,16 @@ __author__ = 'CMendezC'
 # --outputModelPath /home/compu2/bionlp/lcg-bioinfoI-bionlp/clasificacion-automatica/structural-domain-dataset/models
 # --outputModelFile SVM-lineal-model.mod
 # --outputReportPath /home/compu2/bionlp/lcg-bioinfoI-bionlp/clasificacion-automatica/structural-domain-dataset/reports
-# --outputReportFile SVM-lineal.txt
+# --outputReportFile SVM-linear.txt
 # --classifier SVM
 # --saveData
 # --kernel linear
 # --reduction SVD200
 # --removeStopWords
 # --vectorizer b
-# python training-crossvalidation-testing-dom.py --inputPath /home/compu2/bionlp/lcg-bioinfoI-bionlp/clasificacion-automatica/structural-domain-dataset --inputTrainingData trainData.txt --inputTrainingClasses trainClasses.txt --inputTestingData testData.txt --inputTestingClasses testClasses.txt --outputModelPath /home/compu2/bionlp/lcg-bioinfoI-bionlp/clasificacion-automatica/structural-domain-dataset/models --outputModelFile SVM-lineal-model.mod --outputReportPath /home/compu2/bionlp/lcg-bioinfoI-bionlp/clasificacion-automatica/structural-domain-dataset/reports --outputReportFile SVM-lineal.txt --classifier SVM --kernel linear --saveData --vectorizer b
+# --ngrinitial 2
+# --ngrfinal 2
+# python training-crossvalidation-testing-dom.py --inputPath /home/compu2/bionlp/lcg-bioinfoI-bionlp/clasificacion-automatica/structural-domain-dataset --inputTrainingData trainData.txt --inputTrainingClasses trainClasses.txt --inputTestingData testData.txt --inputTestingClasses testClasses.txt --outputModelPath /home/compu2/bionlp/lcg-bioinfoI-bionlp/clasificacion-automatica/structural-domain-dataset/models --outputModelFile SVM-lineal-model.mod --outputReportPath /home/compu2/bionlp/lcg-bioinfoI-bionlp/clasificacion-automatica/structural-domain-dataset/reports --outputReportFile SVM-linear.txt --classifier SVM --kernel linear --saveData --vectorizer b --ngrinitial 2 --ngrfinal 2 --removeStopWords
 # --reduction SVD200
 # --removeStopWords
 
@@ -283,6 +285,7 @@ if __name__ == "__main__":
     if args.reduction is not None:
         X_test = reduc.transform(X_test)
     y_pred = myClassifier.predict(X_test)
+    best_parameters = myClassifier.best_estimator_.get_params()
     print("   Done!")
 
     print("Saving report...")
@@ -299,6 +302,9 @@ if __name__ == "__main__":
         oFile.write(str(confusion_matrix(y_test, y_pred)) + '\n')
         oFile.write('Classification report: \n')
         oFile.write(classification_report(y_test, y_pred) + '\n')
+        for param in sorted(best_parameters.keys()):
+            oFile.write("\t%s: %r\n" % (param, best_parameters[param]))
+
     print("   Done!")
 
     print("Training and testing done in: %fs" % (time() - t0))