def decision_function(self,X):
if self.is_fitted == False:
raise NotFittedError("This KOMD instance is not fitted yet. Call 'fit' with appropriate arguments before using this method.")
if self.multiclass_:
raise ValueError('Scores are not available for multiclass problems, use predict')
KL = process_list(X,self.generator) # X can be a samples matrix or Kernel List
return self.estimator.decision_function(self.how_to(KL,self.weights))
def decision_function(self,X):
if self.is_fitted == False:
raise NotFittedError("This KOMD instance is not fitted yet. Call 'fit' with appropriate arguments before using this method.")
if self.multiclass_:
raise ValueError('Scores are not available for multiclass problems, use predict')
KL = process_list(X,self.generator) # X can be a samples matrix or Kernel List
return self.estimator.decision_function(self.how_to(KL,self.weights))
def predict_proba(self, X):
if not self.is_fitted:
raise NotFittedError(
"This KOMD instance is not fitted yet. Call 'fit' with appropriate arguments before using this method."
)
KL = process_list(X, self.generator)
return self.clf.predict_proba(
KL) if self.multiclass_ else self.estimator.predict_proba(
self.how_to(KL, self.weights))
def _prepare(self,X,Y):
'''preprocess data before training'''
check_classification_targets(Y)
self.classes_ = np.unique(Y)
if len(self.classes_) < 2:
raise ValueError("The number of classes has to be almost 2; got ", len(self.classes_))
self.multiclass_ = len(self.classes_) > 2
KL = process_list(X,self.generator) # X can be a samples matrix or Kernels List
self.KL, self.Y = check_KL_Y(KL,Y)
self.n_kernels = len(self.KL)
return
def _prepare(self,X,Y):
'''preprocess data before training'''
check_classification_targets(Y)
self.classes_ = np.unique(Y)
if len(self.classes_) < 2:
raise ValueError("The number of classes has to be almost 2; got ", len(self.classes_))
self.multiclass_ = len(self.classes_) > 2
KL = process_list(X,self.generator) # X can be a samples matrix or Kernels List
self.KL, self.Y = check_KL_Y(KL,Y)
self.n_kernels = len(self.KL)
return
def predict(self,X):
if not self.is_fitted :
raise NotFittedError("This KOMD instance is not fitted yet. Call 'fit' with appropriate arguments before using this method.")
KL = process_list(X,self.generator)
return self.clf.predict(KL) if self.multiclass_ else self.estimator.predict(self.how_to(KL,self.weights))
