def finalize(self):
self._output_shape = self.targets[0].shape
if self.num_classes > 2:
self._one_hot_encoder = encoder.OneHotEncoder()
self._one_hot_encoder.fit_with_one_hot_encoded(self.targets)
self.targets = self._one_hot_encoder.decode(self.targets)
super().finalize()
def _fit(self, y):
super()._fit(y)
if isinstance(y, tf.data.Dataset):
if not self.num_classes:
for y in tf.data.Dataset:
shape = y.shape[0]
break
if shape == 1:
self.num_classes = 2
else:
self.num_classes = shape
self.set_loss()
return
if isinstance(y, pd.DataFrame):
y = y.values
if isinstance(y, pd.Series):
y = y.values.reshape(-1, 1)
# Not label.
if len(y.flatten()) != len(y):
self.num_classes = y.shape[1]
self.set_loss()
return
labels = set(y.flatten())
if self.num_classes is None:
self.num_classes = len(labels)
if self.num_classes == 2:
self.label_encoder = encoder.LabelEncoder()
elif self.num_classes > 2:
self.label_encoder = encoder.OneHotEncoder()
self.set_loss()
self.label_encoder.fit_with_labels(y)
def set_state(self, state):
super().set_state(state)
self.num_classes = state['num_classes']
self.multi_label = state['multi_label']
self.dropout_rate = state['dropout_rate']
self.label_encoder = None
if state['label_encoder']:
if state['encoder_class'] == 'one_hot_encoder':
self.label_encoder = encoder.OneHotEncoder()
else:
self.label_encoder = encoder.LabelEncoder()
self.label_encoder.set_state(state['label_encoder'])
def clear_weights(self):
super().clear_weights()
self.lgbm = lgb.LGBMClassifier(random_state=self.seed)
self._one_hot_encoder = encoder.OneHotEncoder()
def set_weights(self, weights):
super().set_weights(weights)
self._one_hot_encoder = encoder.OneHotEncoder()
self._one_hot_encoder.set_state(weights['one_hot_encoder'])
self.num_classes = weights['num_classes']