Classification transcription factor structural domain sentences

@@ -47,10 +47,10 @@ from scipy.sparse import csr_matrix
 # source activate python3
 # python training-crossvalidation-testing-dom-v1.py
 # --inputPath /home/text-dom-dataset
-# --inputTrainingData trainData.txt
-# --inputTrainingClasses trainClasses.txt
-# --inputTestingData testData.txt
-# --inputTestingClasses testClasses.txt
+# --inputTrainingData train-data.txt
+# --inputTrainingClasses train-classes.txt
+# --inputTestingData test-data.txt
+# --inputTestingClasses test-classes.txt
 # --outputModelPath /home/text-dom-dataset/models
 # --outputModelFile SVM-lineal-model.mod
 # --outputReportPath /home/text-dom-dataset/reports
@@ -59,8 +59,10 @@ from scipy.sparse import csr_matrix
 # --saveData
 # --kernel linear
 # --vectorizer b
-# --ngrinitial 2
-# --ngrfinal 2
+# --ngrinitial 1
+# --ngrfinal 1
+
+# python training-crossvalidation-testing-dom-v1.py --inputPath /home/laigen-supervised-learning/text-dom-dataset --inputTrainingData train-data.txt --inputTrainingClasses train-classes.txt --inputTestingData test-data.txt --inputTestingClasses test-classes.txt --outputModelPath /home/laigen-supervised-learning/text-dom-dataset/models --outputModelFile SVM-lineal-model.mod --outputReportPath /home/laigen-supervised-learning/text-dom-dataset/reports --outputReportFile SVM-linear.txt --classifier SVM --saveData --kernel linear --vectorizer b --ngrinitial 1 --ngrfinal 1
 
 ###########################################################
 #                       MAIN PROGRAM                      #
@@ -284,6 +286,8 @@ if __name__ == "__main__":
         X_test = reduc.transform(X_test)
     y_pred = myClassifier.predict(X_test)
     best_parameters = myClassifier.best_estimator_.get_params()
+    if args.classifier == "SVM":
+        confidence_scores = classifier.decision_function(X_test)
     print("   Done!")
 
     print("Saving report...")
@@ -304,6 +308,13 @@ if __name__ == "__main__":
         oFile.write('Best parameters: \n')
         for param in sorted(best_parameters.keys()):
             oFile.write("\t%s: %r\n" % (param, best_parameters[param]))
+        if args.classifier == "SVM":
+            oFile.write('\nWeights assigned to the features: \n')
+            oFile.write("{}\n".format(classifier.coef_))
+            oFile.write('Confidence scores: \n')
+            oFile.write("{}\n".format(confidence_scores))
+            oFile.write('Number of support vectors per class: \n{}\n'.format(classifier.n_support_))
+            oFile.write('Support vectors: \n{}\n'.format(classifier.support_vectors_))
 
     print("   Done!")