def similarity_metrics(corpus):
    model = NGramGraphModel(corpus)
    print "Bigram Graph Class"
    model.bigram_graph_class()
    
    print "Calculating Similarity Metrics"
    corpus = model.calc_sim_metrics()
    
    return corpus
def n_gram_graph_model(data):
    #data = pd.DataFrame()
    
    class_labels =  ["too localized", "open", "off topic", "not constructive", "not a real question"]
    
    graph_models = {}
    
    #Constroi o modelo em grafo para cada classe
    for class_label in class_labels:

        data_label = data[data["OpenStatus"] == class_label]
        corpus = format_data(data_label)

        model = NGramGraphModel(corpus)
        
        graph_model = model.bigram_graph_class()

        graph_models[class_label] = graph_model
  
    corpus = format_data(data)
    
    model = NGramGraphModel(corpus)
    
    all_data = model.calc_sim_metrics(graph_models, corpus)
    
    df_data = pd.DataFrame.from_dict(all_data, orient="index")
    
    return df_data