def setUp(self):
self.input_data = [[-1., 0.], [-1., 2.], [1., 0.], [1., -2.]]
self.input_labels = [0., 1., 2., 3.]
self.tree = [[1, 0], [-1, 0], [-1, 0]]
self.tree_weights = [[1.0, 0.0], [1.0, 0.0], [1.0, 0.0]]
self.tree_thresholds = [0., 0., 0.]
self.ops = training_ops.Load()
def setUp(self):
self.input_data = [[-1., 0.], [-1., 2.], [1., 0.], [1., -2.]]
self.input_labels = [0., 1., 2., 3.]
self.tree = [[1, 0], [-1, 0], [-1, 0]]
self.tree_weights = [[1.0, 0.0], [1.0, 0.0], [1.0, 0.0]]
self.tree_thresholds = [0., 0., 0.]
self.ops = training_ops.Load()
self.params = tensor_forest.ForestHParams(num_features=2,
hybrid_tree_depth=2,
base_random_seed=10,
feature_bagging_fraction=1.0,
regularization_strength=0.01,
regularization="",
weight_init_mean=0.0,
weight_init_std=0.1)
self.params.num_nodes = 2**self.params.hybrid_tree_depth - 1
self.params.num_leaves = 2**(self.params.hybrid_tree_depth - 1)
self.params.num_features_per_node = (
self.params.feature_bagging_fraction * self.params.num_features)
self.params.regression = False
def __init__(self, params, layer_num, device_assigner, *args, **kwargs):
super(KFeatureDecisionsToDataLayer,
self).__init__(params, layer_num, device_assigner, *args,
**kwargs)
self.training_ops = training_ops.Load()