ns.values) if
i.startswith ( Corax )]]. values: _train.append (list (i)) i in work_test [[i for i in list (work_test.columns.values) if
i.startswith(laquo;Changeraquo;)]].values:_test.append(laquo;,raquo;.join(i.T.tolist()))_test =Np.array (X_test) i in work_test [[i for i in list (work_test.columns.values) if
i.startswith ( Corax )]]. values: _test.append (list (i))=preprocessing.MultiLabelBinarizer ()=lb.fit_transform (y_train) ( Getting results of % s % classifierDescriptions [classif])=Pipeline ([( vectorizer raquo ;, CountVectorizer ()), ( tfidf raquo ;,
TfidfTransformer ()), ( clf raquo ;, OneVsRestClassifier (selClassifiers [classif]))]). fit (X_train, Y)=classifier.predict (X_test) _labels=lb.inverse_transform (predicted )=DataFrame.from_items ([( Test raquo ;, X_test), ( RealAnswer raquo ;, y_test),
( Prediction , all_labels)])=0=0classifying, item, labels in zip (X_test, y_test, all_labels): res in labels: res in item: +=1 +=len (labels) ( Predicted correctly% s labels out of% s labels % (CorPred, Total)) ( Precision is% .2f %% % (100 * float (CorPred)/float (Total)) ) .Prediction=df.Prediction.map (replacer) .RealAnswer=df.RealAnswer.map (replacer) .to_csv (fileOut) main ():=datetime.now () ( Program started at% s % start) sys.argv [1] == test :( sys.argv [2], sys.argv [3], sys.argv [4]) sys.argv [1] == work :( sys.argv [2], sys.argv [3], sys.argv [4], sys.argv [5]): ( Unknown mode, only test or work modes are available )=datetime.now () ( Program finished at% s % end) ( It took% s seconds for program to complete % (end -) .total_seconds ()) __ name__ == __ main __ :
main ()
