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()