original_docstring: 
      Give emotion scores to input sentence.
      
docstring: Give emotion scores to input sentence.
docstring_tokens: Give emotion scores to input sentence .
code: def scoreInput(originalEmo,sentenceID, sentence:str,trainingData,emoLabels):
      '''
      Give emotion scores to input sentence.
      '''
      sentence=re.sub('[^A-Za-z0-9,;:\-\(\)\'\"\!\?\.]',' ', sentence)
      
      #Load Emotion Classifier   
      global EC    
      try:
          EC
      except NameError:
          print('{} : {} : STATUS : Loading Emotion Model from {}.'.format(script, datetime.datetime.now(), path_to_emotionClassifiers))
          EC=css.BiLSTM()
          EC.loadModel(trainingData,path_to_emotionClassifiers)
  
      # Scored input will be saved here
      nameFILE=path_to_Output+'scored_input.txt'
      with open(nameFILE,'a') as scoredINPUT:
          labelAndScores=css.makePrediction(sentence,EC,emoLabels) # (classified emo, [emo1score, emo2score...})
          scores=list(labelAndScores[1])
          input_emotion_score=np.max(scores)
          ORIGINALscores=[str(round(x,3)) for x in scores]
          mystr='\t'+'\t'.join(ORIGINALscores)
          scoredINPUT.write(str(sentenceID)+'\t'+originalEmo+'\t'+sentence+mystr+'\n')
  
      print('{} : {} : STATUS : Processed input sentence: {}.'.format(script, datetime.datetime.now(), str(sentenceID)))
      return(input_emotion_score,ORIGINALscores)
code_tokens: def scoreInput ( originalEmo , sentenceID , sentence : str , trainingData , emoLabels ) : sentence = re . sub ( '[^A-Za-z0-9,;:\-\(\)\'\"\!\?\.]' , ' ' , sentence ) global EC try : EC except NameError : print ( '{} : {} : STATUS : Loading Emotion Model from {}.' . format ( script , datetime . datetime . now ( ) , path_to_emotionClassifiers ) ) EC = css . BiLSTM ( ) EC . loadModel ( trainingData , path_to_emotionClassifiers ) nameFILE = path_to_Output + 'scored_input.txt' with open ( nameFILE , 'a' ) as scoredINPUT : labelAndScores = css . makePrediction ( sentence , EC , emoLabels ) scores = list ( labelAndScores [ 1 ] ) input_emotion_score = np . max ( scores ) ORIGINALscores = [ str ( round ( x , 3 ) ) for x in scores ] mystr = '\t' + '\t' . join ( ORIGINALscores ) scoredINPUT . write ( str ( sentenceID ) + '\t' + originalEmo + '\t' + sentence + mystr + '\n' ) print ( '{} : {} : STATUS : Processed input sentence: {}.' . format ( script , datetime . datetime . now ( ) , str ( sentenceID ) ) ) return ( input_emotion_score , ORIGINALscores )
short_docstring: Give emotion scores to input sentence.
short_docstring_tokens: Give emotion scores to input sentence .
comment: '''
      Give emotion scores to input sentence.
      '''$SEP$#Load Emotion Classifier   $SEP$# Scored input will be saved here$SEP$# (classified emo, [emo1score, emo2score...})