def initialize(self):
for column_name, column_type in self.input_node.column_types.items():
if column_type == 'categorical':
self.categorical_col.append(
self.input_node.column_names.index(column_name))
elif column_type == 'numerical':
self.numerical_col.append(
self.input_node.column_names.index(column_name))
else:
raise ValueError('Unsupported column type: '
'{type}'.format(type=column_type))
for index, cat_col_index1 in enumerate(self.categorical_col):
self.label_encoders[cat_col_index1] = encoder.LabelEncoder()
self.value_counters[cat_col_index1] = collections.defaultdict(
return_zero)
self.count_frequency[cat_col_index1] = {}
for cat_col_index2 in self.categorical_col[index + 1:]:
self.categorical_categorical[(
cat_col_index1,
cat_col_index2)] = collections.defaultdict(return_zero)
for num_col_index in self.numerical_col:
self.numerical_categorical[(
num_col_index,
cat_col_index1)] = collections.defaultdict(return_zero)
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']:
self.label_encoder = encoder.LabelEncoder()
self.label_encoder.set_state(state['label_encoder'])
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 set_weights(self, weights):
for key, label_encoder_state in utils.to_type_key(weights['label_encoders'],
int).items():
self.label_encoders[key] = encoder.LabelEncoder()
self.label_encoders[key].set_state(label_encoder_state)
self.shape = weights['shape']
self.num_rows = weights['num_rows']
self.categorical_col = weights['categorical_col']
self.numerical_col = weights['numerical_col']
self.value_counters = utils.to_type_key(weights['value_counters'], int)
self.categorical_categorical = utils.to_type_key(
weights['categorical_categorical'], ast.literal_eval)
self.numerical_categorical = utils.to_type_key(
weights['numerical_categorical'], ast.literal_eval)
self.count_frequency = utils.to_type_key(weights['count_frequency'], int)
self.high_level1_col = weights['high_level1_col']
self.high_level2_col = weights['high_level2_col']
self.high_level_cat_cat = utils.to_type_key(
weights['high_level_cat_cat'], ast.literal_eval)
self.high_level_num_cat = utils.to_type_key(
weights['high_level_num_cat'], ast.literal_eval)
