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)
