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)
