def valscore(self, Xn, yn, scoring): if scoring == 'weighted': return (RegressorMixin.score(self, Xn, yn, sample_weight=self.caseweights_)) elif scoring == 'normal': return (RegressorMixin.score(self, Xn, yn)) else: ValueError('Scoring flag must be set to "weighted" or "normal".')
def valscore(self,Xn,yn,scoring): n,p,Xn = _predict_check_input(Xn) (n,p) = Xn.shape if p!= self.X.shape[1]: raise(ValueError('New data must have seame number of columns as the ones the model has been trained with')) if scoring=='weighted': return(RegressorMixin.score(self,Xn,yn,sample_weight=self.caseweights_)) elif scoring=='normal': return(RegressorMixin.score(self,Xn,yn)) else: raise(ValueError('Scoring flag must be set to "weighted" or "normal".'))
def valscore(self,Xn,yn,scoring): if type(Xn) == ps.core.frame.DataFrame: Xn = Xn.to_numpy() if type(yn) in [ps.core.frame.DataFrame,ps.core.series.Series]: yn = yn.to_numpy().T.astype('float64') (n,p) = Xn.shape if p!= self.X.shape[1]: raise(ValueError('New data must have seame number of columns as the ones the model has been trained with')) if scoring=='weighted': return(RegressorMixin.score(self,Xn,yn,sample_weight=self.caseweights_)) elif scoring=='normal': return(RegressorMixin.score(self,Xn,yn)) else: raise(ValueError('Scoring flag must be set to "weighted" or "normal".'))
def score(self, X, y, sample_weight=None): if len(X) == 1: output = self.predict(X) return output.shape[0] * mean_squared_error(y, output) return RegressorMixin.score(self, X, y, sample_weight=sample_weight)
def score(self, X, y, sample_weight=None): return RegressorMixin.score(self, X, y, sample_weight=sample_weight)
def score( self, X: np.ndarray, y: np.ndarray, sample_weight: Optional[np.ndarray] = None ) -> float: return RegressorMixin.score(self, X, y, sample_weight)