main.py 文件源码

def scale_feature(self, col=None, scaling=None, scaling_parms=None):
        '''
        Scales a given set  of numerical columns. This only works for columns 
        with numerical values. 

        Parameters
        ----------
        col : a string of a column name, or a list of many columns names or
                None (default). If col is None, all numerical columns will 
                be used.
        scaling  : {'zscore', 'minmax_scale' (default), 'scale', 'maxabs_scale', 
                    'robust_scale'}
            User-defined scaling functions can also be used through self.transform_feature
        scaling_parms : dictionary
            any additional parameters to be used for sklearn's scaling functions.

        '''            
        self._validate_params(params_list   = {'col':col,'scaling':scaling},
                              expected_types= {'col':[str,list,type(None)], 'scaling':[str,type(None)]})        

        if scaling is None: scaling = 'minmax_scale'

        if scaling == 'zscore':
            scaling = 'lambda x: (x - x.mean()) / x.std()'
        elif scaling ==  'minmax_scale' and scaling_parms is None:
            scaling_parms = {'feature_range':(0, 1),'axis':0}
        elif scaling ==  'scale' and scaling_parms is None:
            scaling_parms = {'with_mean':True, 'with_std':True,'axis':0}
        elif scaling ==  'maxabs_scale' and scaling_parms is None:
            scaling_parms = {'axis':0}
        elif scaling ==  'robust_scale' and scaling_parms is None:
            scaling_parms = {'with_centering':True, 'with_scaling':True, 'axis':0} # 'quantile_range':(25.0, 75.0), 
        else:
            raise TypeError('UNSUPPORTED scaling TYPE')

        self.transform_feature(col=col, func_str=scaling, addtional_params=scaling_parms)