def testContribsForOnlyABiasNode(self):
"""Tests case when, after training, only left with a bias node.
For example, this could happen if the final ensemble contains one tree that
got pruned up to the root.
"""
with self.test_session() as session:
tree_ensemble_config = boosted_trees_pb2.TreeEnsemble()
text_format.Merge(
"""
trees {
nodes {
leaf {
scalar: 1.72
}
}
}
tree_weights: 0.1
tree_metadata: {
num_layers_grown: 0
}
""", tree_ensemble_config)
tree_ensemble = boosted_trees_ops.TreeEnsemble(
'ensemble',
serialized_proto=tree_ensemble_config.SerializeToString())
tree_ensemble_handle = tree_ensemble.resource_handle
resources.initialize_resources(resources.shared_resources()).run()
# All features are unused.
feature_0_values = [36, 32]
feature_1_values = [13, -29]
feature_2_values = [11, 27]
# Expected logits are computed by traversing the logit path and
# subtracting child logits from parent logits.
bias = 1.72 * 0.1 # Root node of tree_0.
expected_feature_ids = ((), ())
expected_logits_paths = ((bias, ), (bias, ))
bucketized_features = [
feature_0_values, feature_1_values, feature_2_values
]
debug_op = boosted_trees_ops.example_debug_outputs(
tree_ensemble_handle,
bucketized_features=bucketized_features,
logits_dimension=1)
serialized_examples_debug_outputs = session.run(debug_op)
feature_ids = []
logits_paths = []
for example in serialized_examples_debug_outputs:
example_debug_outputs = boosted_trees_pb2.DebugOutput()
example_debug_outputs.ParseFromString(example)
feature_ids.append(example_debug_outputs.feature_ids)
logits_paths.append(example_debug_outputs.logits_path)
self.assertAllClose(feature_ids, expected_feature_ids)
self.assertAllClose(logits_paths, expected_logits_paths)
def testContribsForOnlyABiasNode(self):
"""Tests case when, after training, only left with a bias node.
For example, this could happen if the final ensemble contains one tree that
got pruned up to the root.
"""
with self.cached_session() as session:
tree_ensemble_config = boosted_trees_pb2.TreeEnsemble()
text_format.Merge(
"""
trees {
nodes {
leaf {
scalar: 1.72
}
}
}
tree_weights: 0.1
tree_metadata: {
num_layers_grown: 0
}
""", tree_ensemble_config)
tree_ensemble = boosted_trees_ops.TreeEnsemble(
'ensemble', serialized_proto=tree_ensemble_config.SerializeToString())
tree_ensemble_handle = tree_ensemble.resource_handle
resources.initialize_resources(resources.shared_resources()).run()
# All features are unused.
feature_0_values = [36, 32]
feature_1_values = [13, -29]
feature_2_values = [11, 27]
# Expected logits are computed by traversing the logit path and
# subtracting child logits from parent logits.
bias = 1.72 * 0.1 # Root node of tree_0.
expected_feature_ids = ((), ())
expected_logits_paths = ((bias,), (bias,))
bucketized_features = [
feature_0_values, feature_1_values, feature_2_values
]
debug_op = boosted_trees_ops.example_debug_outputs(
tree_ensemble_handle,
bucketized_features=bucketized_features,
logits_dimension=1)
serialized_examples_debug_outputs = session.run(debug_op)
feature_ids = []
logits_paths = []
for example in serialized_examples_debug_outputs:
example_debug_outputs = boosted_trees_pb2.DebugOutput()
example_debug_outputs.ParseFromString(example)
feature_ids.append(example_debug_outputs.feature_ids)
logits_paths.append(example_debug_outputs.logits_path)
self.assertAllClose(feature_ids, expected_feature_ids)
self.assertAllClose(logits_paths, expected_logits_paths)
def testContribsMultipleTree(self):
"""Tests that the contribs work when we have multiple trees."""
with self.cached_session() as session:
tree_ensemble_config = boosted_trees_pb2.TreeEnsemble()
text_format.Merge(
"""
trees {
nodes {
bucketized_split {
feature_id: 2
threshold: 28
left_id: 1
right_id: 2
}
metadata {
gain: 7.62
original_leaf: {scalar: 2.1}
}
}
nodes {
leaf {
scalar: 1.14
}
}
nodes {
leaf {
scalar: 8.79
}
}
}
trees {
nodes {
bucketized_split {
feature_id: 2
threshold: 26
left_id: 1
right_id: 2
}
}
nodes {
bucketized_split {
feature_id: 0
threshold: 50
left_id: 3
right_id: 4
}
metadata {
original_leaf: {scalar: 5.5}
}
}
nodes {
leaf {
scalar: 7.0
}
}
nodes {
leaf {
scalar: 5.0
}
}
nodes {
leaf {
scalar: 6.0
}
}
}
trees {
nodes {
bucketized_split {
feature_id: 0
threshold: 34
left_id: 1
right_id: 2
}
}
nodes {
leaf {
scalar: -7.0
}
}
nodes {
leaf {
scalar: 5.0
}
}
}
tree_weights: 0.1
tree_weights: 0.2
tree_weights: 1.0
tree_metadata: {
num_layers_grown: 1
}
tree_metadata: {
num_layers_grown: 2
}
tree_metadata: {
num_layers_grown: 1
}
""", tree_ensemble_config)
tree_ensemble = boosted_trees_ops.TreeEnsemble(
'ensemble', serialized_proto=tree_ensemble_config.SerializeToString())
tree_ensemble_handle = tree_ensemble.resource_handle
resources.initialize_resources(resources.shared_resources()).run()
feature_0_values = [36, 32]
feature_1_values = [13, -29] # Unused. Feature is not in above ensemble.
feature_2_values = [11, 27]
# Expected logits are computed by traversing the logit path and
# subtracting child logits from parent logits.
bias = 2.1 * 0.1 # Root node of tree_0.
expected_feature_ids = ((2, 2, 0, 0), (2, 2, 0))
# example_0 : (bias, 0.1 * 1.14, 0.2 * 5.5 + .114, 0.2 * 5. + .114,
# 1.0 * 5.0 + 0.2 * 5. + .114)
# example_1 : (bias, 0.1 * 1.14, 0.2 * 7 + .114,
# 1.0 * -7. + 0.2 * 7 + .114)
expected_logits_paths = ((bias, 0.114, 1.214, 1.114, 6.114),
(bias, 0.114, 1.514, -5.486))
bucketized_features = [
feature_0_values, feature_1_values, feature_2_values
]
debug_op = boosted_trees_ops.example_debug_outputs(
tree_ensemble_handle,
bucketized_features=bucketized_features,
logits_dimension=1)
serialized_examples_debug_outputs = session.run(debug_op)
feature_ids = []
logits_paths = []
for example in serialized_examples_debug_outputs:
example_debug_outputs = boosted_trees_pb2.DebugOutput()
example_debug_outputs.ParseFromString(example)
feature_ids.append(example_debug_outputs.feature_ids)
logits_paths.append(example_debug_outputs.logits_path)
self.assertAllClose(feature_ids, expected_feature_ids)
self.assertAllClose(logits_paths, expected_logits_paths)
def testContribsMultipleTreeWhenFirstTreeIsABiasNode(self):
"""Tests case when, after training, first tree contains only a bias node."""
with self.cached_session() as session:
tree_ensemble_config = boosted_trees_pb2.TreeEnsemble()
text_format.Merge(
"""
trees {
nodes {
leaf {
scalar: 1.72
}
}
}
trees {
nodes {
bucketized_split {
feature_id: 2
threshold: 26
left_id: 1
right_id: 2
}
}
nodes {
bucketized_split {
feature_id: 0
threshold: 50
left_id: 3
right_id: 4
}
metadata {
original_leaf: {scalar: 5.5}
}
}
nodes {
leaf {
scalar: 7.0
}
}
nodes {
leaf {
scalar: 5.0
}
}
nodes {
leaf {
scalar: 6.0
}
}
}
tree_weights: 1.
tree_weights: 0.1
tree_metadata: {
num_layers_grown: 0
}
tree_metadata: {
num_layers_grown: 1
}
""", tree_ensemble_config)
tree_ensemble = boosted_trees_ops.TreeEnsemble(
'ensemble', serialized_proto=tree_ensemble_config.SerializeToString())
tree_ensemble_handle = tree_ensemble.resource_handle
resources.initialize_resources(resources.shared_resources()).run()
feature_0_values = [36, 32]
feature_1_values = [13, -29] # Unused feature.
feature_2_values = [11, 27]
# Expected logits are computed by traversing the logit path and
# subtracting child logits from parent logits.
expected_feature_ids = ((2, 0), (2,))
# bias = 1.72 * 1. # Root node of tree_0.
# example_0 : (bias, 0.1 * 5.5 + bias, 0.1 * 5. + bias)
# example_1 : (bias, 0.1 * 7. + bias )
expected_logits_paths = ((1.72, 2.27, 2.22), (1.72, 2.42))
bucketized_features = [
feature_0_values, feature_1_values, feature_2_values
]
debug_op = boosted_trees_ops.example_debug_outputs(
tree_ensemble_handle,
bucketized_features=bucketized_features,
logits_dimension=1)
serialized_examples_debug_outputs = session.run(debug_op)
feature_ids = []
logits_paths = []
for example in serialized_examples_debug_outputs:
example_debug_outputs = boosted_trees_pb2.DebugOutput()
example_debug_outputs.ParseFromString(example)
feature_ids.append(example_debug_outputs.feature_ids)
logits_paths.append(example_debug_outputs.logits_path)
self.assertAllClose(feature_ids, expected_feature_ids)
self.assertAllClose(logits_paths, expected_logits_paths)
def testContribsMultipleTree(self):
"""Tests that the contribs work when we have multiple trees."""
with self.cached_session() as session:
tree_ensemble_config = boosted_trees_pb2.TreeEnsemble()
text_format.Merge(
"""
trees {
nodes {
bucketized_split {
feature_id: 2
threshold: 28
left_id: 1
right_id: 2
}
metadata {
gain: 7.62
original_leaf: {scalar: 2.1}
}
}
nodes {
leaf {
scalar: 1.14
}
}
nodes {
leaf {
scalar: 8.79
}
}
}
trees {
nodes {
bucketized_split {
feature_id: 2
threshold: 26
left_id: 1
right_id: 2
}
}
nodes {
bucketized_split {
feature_id: 0
threshold: 50
left_id: 3
right_id: 4
}
metadata {
original_leaf: {scalar: 5.5}
}
}
nodes {
leaf {
scalar: 7.0
}
}
nodes {
leaf {
scalar: 5.0
}
}
nodes {
leaf {
scalar: 6.0
}
}
}
trees {
nodes {
bucketized_split {
feature_id: 0
threshold: 34
left_id: 1
right_id: 2
}
}
nodes {
leaf {
scalar: -7.0
}
}
nodes {
leaf {
scalar: 5.0
}
}
}
tree_weights: 0.1
tree_weights: 0.2
tree_weights: 1.0
tree_metadata: {
num_layers_grown: 1}
tree_metadata: {
num_layers_grown: 2}
tree_metadata: {
num_layers_grown: 1}
""", tree_ensemble_config)
tree_ensemble = boosted_trees_ops.TreeEnsemble(
'ensemble',
serialized_proto=tree_ensemble_config.SerializeToString())
tree_ensemble_handle = tree_ensemble.resource_handle
resources.initialize_resources(resources.shared_resources()).run()
feature_0_values = [36, 32]
feature_1_values = [13, -29
] # Unused. Feature is not in above ensemble.
feature_2_values = [11, 27]
# Expected logits are computed by traversing the logit path and
# subtracting child logits from parent logits.
bias = 2.1 * 0.1 # Root node of tree_0.
expected_feature_ids = ((2, 2, 0, 0), (2, 2, 0))
# example_0 : (bias, 0.1 * 1.14, 0.2 * 5.5 + .114, 0.2 * 5. + .114,
# 1.0 * 5.0 + 0.2 * 5. + .114)
# example_1 : (bias, 0.1 * 1.14, 0.2 * 7 + .114,
# 1.0 * -7. + 0.2 * 7 + .114)
expected_logits_paths = ((bias, 0.114, 1.214, 1.114, 6.114),
(bias, 0.114, 1.514, -5.486))
bucketized_features = [
feature_0_values, feature_1_values, feature_2_values
]
debug_op = boosted_trees_ops.example_debug_outputs(
tree_ensemble_handle,
bucketized_features=bucketized_features,
logits_dimension=1)
serialized_examples_debug_outputs = session.run(debug_op)
feature_ids = []
logits_paths = []
for example in serialized_examples_debug_outputs:
example_debug_outputs = boosted_trees_pb2.DebugOutput()
example_debug_outputs.ParseFromString(example)
feature_ids.append(example_debug_outputs.feature_ids)
logits_paths.append(example_debug_outputs.logits_path)
self.assertAllClose(feature_ids, expected_feature_ids)
self.assertAllClose(logits_paths, expected_logits_paths)
def testContribsMultipleTreeWhenFirstTreeIsABiasNode(self):
"""Tests case when, after training, first tree contains only a bias node."""
with self.test_session() as session:
tree_ensemble_config = boosted_trees_pb2.TreeEnsemble()
text_format.Merge(
"""
trees {
nodes {
leaf {
scalar: 1.72
}
}
}
trees {
nodes {
bucketized_split {
feature_id: 2
threshold: 26
left_id: 1
right_id: 2
}
}
nodes {
bucketized_split {
feature_id: 0
threshold: 50
left_id: 3
right_id: 4
}
metadata {
original_leaf: {scalar: 5.5}
}
}
nodes {
leaf {
scalar: 7.0
}
}
nodes {
leaf {
scalar: 5.0
}
}
nodes {
leaf {
scalar: 6.0
}
}
}
tree_weights: 1.
tree_weights: 0.1
tree_metadata: {
num_layers_grown: 0
}
tree_metadata: {
num_layers_grown: 1
}
""", tree_ensemble_config)
tree_ensemble = boosted_trees_ops.TreeEnsemble(
'ensemble',
serialized_proto=tree_ensemble_config.SerializeToString())
tree_ensemble_handle = tree_ensemble.resource_handle
resources.initialize_resources(resources.shared_resources()).run()
feature_0_values = [36, 32]
feature_1_values = [13, -29] # Unused feature.
feature_2_values = [11, 27]
# Expected logits are computed by traversing the logit path and
# subtracting child logits from parent logits.
expected_feature_ids = ((2, 0), (2, ))
# bias = 1.72 * 1. # Root node of tree_0.
# example_0 : (bias, 0.1 * 5.5 + bias, 0.1 * 5. + bias)
# example_1 : (bias, 0.1 * 7. + bias )
expected_logits_paths = ((1.72, 2.27, 2.22), (1.72, 2.42))
bucketized_features = [
feature_0_values, feature_1_values, feature_2_values
]
debug_op = boosted_trees_ops.example_debug_outputs(
tree_ensemble_handle,
bucketized_features=bucketized_features,
logits_dimension=1)
serialized_examples_debug_outputs = session.run(debug_op)
feature_ids = []
logits_paths = []
for example in serialized_examples_debug_outputs:
example_debug_outputs = boosted_trees_pb2.DebugOutput()
example_debug_outputs.ParseFromString(example)
feature_ids.append(example_debug_outputs.feature_ids)
logits_paths.append(example_debug_outputs.logits_path)
self.assertAllClose(feature_ids, expected_feature_ids)
self.assertAllClose(logits_paths, expected_logits_paths)