Training, crossvalidation and testing structural domain dataset
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... | @@ -292,9 +292,12 @@ if __name__ == "__main__": | ... | @@ -292,9 +292,12 @@ if __name__ == "__main__": |
292 | oFile.write('Classifier: {}\n'.format(args.classifier)) | 292 | oFile.write('Classifier: {}\n'.format(args.classifier)) |
293 | oFile.write('Kernel: {}\n'.format(args.kernel)) | 293 | oFile.write('Kernel: {}\n'.format(args.kernel)) |
294 | oFile.write('Accuracy: {}\n'.format(accuracy_score(y_test, y_pred))) | 294 | oFile.write('Accuracy: {}\n'.format(accuracy_score(y_test, y_pred))) |
295 | - oFile.write('Precision: {}\n'.format(precision_score(y_test, y_pred, average='weighted'))) | 295 | + #oFile.write('Precision: {}\n'.format(precision_score(y_test, y_pred, average='weighted'))) |
296 | - oFile.write('Recall: {}\n'.format(recall_score(y_test, y_pred, average='weighted'))) | 296 | + #oFile.write('Recall: {}\n'.format(recall_score(y_test, y_pred, average='weighted'))) |
297 | - oFile.write('F-score: {}\n'.format(f1_score(y_test, y_pred, average='weighted'))) | 297 | + #oFile.write('F-score: {}\n'.format(f1_score(y_test, y_pred, average='weighted'))) |
298 | + oFile.write('Precision: {}\n'.format(precision_score(y_test, y_pred))) | ||
299 | + oFile.write('Recall: {}\n'.format(recall_score(y_test, y_pred))) | ||
300 | + oFile.write('F-score: {}\n'.format(f1_score(y_test, y_pred))) | ||
298 | oFile.write('Confusion matrix: \n') | 301 | oFile.write('Confusion matrix: \n') |
299 | oFile.write(str(confusion_matrix(y_test, y_pred)) + '\n') | 302 | oFile.write(str(confusion_matrix(y_test, y_pred)) + '\n') |
300 | oFile.write('Classification report: \n') | 303 | oFile.write('Classification report: \n') | ... | ... |
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