from sklearn.metrics import matthews_corrcoef
import numpy as np
def compute_MCC(references_dataset, predictions_dataset, ref_col='ner_tags', pred_col='pred_ner_tags'):
    # computes the Matthews correlation coeff between two datasets
    
    # sort by id
    references_dataset = references_dataset.sort('unique_id')
    predictions_dataset = predictions_dataset.sort('unique_id')
    
    # check that tokens match
    assert(references_dataset['tokens']==predictions_dataset['tokens'])
    
    # the lists have to be flattened
    flat_ref_tags = np.concatenate(references_dataset[ref_col])
    flat_pred_tags = np.concatenate(predictions_dataset[pred_col])
    
    mcc_score = matthews_corrcoef(y_true=flat_ref_tags,
                                  y_pred=flat_pred_tags)
    
    return(mcc_score)