def update_fit_params( self, weights, ): self.updated_weights = weights filter_weights(self.updated_weights) self.preds_mapper = None
def update_fit_params( self, X_train, y_train, eval_set, weights ): self.output_dim = y_train.shape[1] self.updated_weights = weights filter_weights(self.updated_weights)
def update_fit_params(self, X_train, T_train, y_train, eval_set, weights): if len(y_train.shape) != 2: msg = "Targets should be 2D : (n_samples, n_regression) " + \ f"but y_train.shape={y_train.shape} given.\n" + \ "Use reshape(-1, 1) for single regression." raise ValueError(msg) self.output_dim = y_train.shape[1] self.preds_mapper = None self.updated_weights = weights filter_weights(self.updated_weights)
def update_fit_params(self, X_train, y_train, eval_set, weights): output_dim, train_labels = infer_multitask_output(y_train) self.output_dim = output_dim self.classes_ = train_labels self.target_mapper = [ {class_label: index for index, class_label in enumerate(classes)} for classes in self.classes_ ] self.preds_mapper = [ {index: class_label for index, class_label in enumerate(classes)} for classes in self.classes_ ] self.updated_weights = weights filter_weights(self.updated_weights)
def update_fit_params(self, X_train, y_train, eval_set, weights): output_dim, train_labels = infer_multitask_output(y_train) for _, y in eval_set: for task_idx in range(y.shape[1]): check_output_dim(train_labels[task_idx], y[:, task_idx]) self.output_dim = output_dim self.classes_ = train_labels self.target_mapper = [{ class_label: index for index, class_label in enumerate(classes) } for classes in self.classes_] self.preds_mapper = [{ index: class_label for index, class_label in enumerate(classes) } for classes in self.classes_] self.updated_weights = weights filter_weights(self.updated_weights)
def update_fit_params( self, weights, ): self.updated_weights = weights filter_weights(self.updated_weights)