from packages  import *

class metrics:
    def __init__(self, c:Optional[float] = None, cv:Optional[List] = None, t:Optional[List] = None, method = 'regression')-> DataFrame:
        phase = [c, cv, t]
        index = np.array(["train", "cv", "test"])
        notnone = [i for i in range(3) if phase[i] != None]
        met_index = index[notnone]
        methods = ['regression', 'classification']
        perf = {}
        for i in notnone:
            if method == 'regression':
                perf[index[i]] = metrics.reg_(phase[i][0], phase[i][1])

            elif method == 'classification':
                perf[index[i]] = metrics.class_(phase[i][0], phase[i][1])
        
            if notnone == 1:
                self.ret = perf.T
            else:
                self.ret = DataFrame(perf).T
             
    @staticmethod
    def reg_(meas, pred):
           meas = np.array(meas)
           pred = np.array(pred)
           xbar = np.mean(meas) # the average of measured values
           e = np.subtract(meas , pred)
           e2 = e**2# the squared error

          # Sum of squared:
           # TOTAL
           sst = np.sum((meas - xbar)**2)
           # RESIDUAL
           ssr = np.sum(e2)
           # REGRESSION OR MODEL
           ssm = np.sum(pred - xbar)


          # Compute statistical metrics
           metr = {}
           metr['r'] = np.corrcoef(meas, pred)[0, 1]
           metr['r2'] = 1-ssr/sst
           metr['rmse'] = np.sqrt(np.mean(e2))
           metr['mae'] = np.mean(np.abs(e2))
           metr['rpd'] = np.std(meas)/np.sqrt(np.mean(e2))
           metr['rpiq'] = (np.quantile(meas, .75) - np.quantile(meas, .25))/np.sqrt(np.mean(e2))
           return metr

    @staticmethod
    def class_(meas, pred):
        pass

    @property
    def scores_(self):
        return self.ret