def testPredictionOnEmptyEnsemble(self):
"""Tests that prediction on a empty ensemble does not fail."""
with self.cached_session() as session:
# Create an empty ensemble.
tree_ensemble = boosted_trees_ops.TreeEnsemble('ensemble',
serialized_proto='')
tree_ensemble_handle = tree_ensemble.resource_handle
resources.initialize_resources(resources.shared_resources()).run()
feature_0_values = [36, 32]
feature_1_values = [11, 27]
expected_logits = [[0.0], [0.0]]
# Prediction should work fine.
predict_op = boosted_trees_ops.predict(
tree_ensemble_handle,
bucketized_features=[feature_0_values, feature_1_values],
logits_dimension=1)
logits = session.run(predict_op)
self.assertAllClose(expected_logits, logits)
def testPredictionOnEmptyEnsemble(self):
"""Tests that prediction on a empty ensemble does not fail."""
with self.cached_session() as session:
# Create an empty ensemble.
tree_ensemble = boosted_trees_ops.TreeEnsemble(
'ensemble', serialized_proto='')
tree_ensemble_handle = tree_ensemble.resource_handle
resources.initialize_resources(resources.shared_resources()).run()
feature_0_values = [36, 32]
feature_1_values = [11, 27]
expected_logits = [[0.0], [0.0]]
# Prediction should work fine.
predict_op = boosted_trees_ops.predict(
tree_ensemble_handle,
bucketized_features=[feature_0_values, feature_1_values],
logits_dimension=1)
logits = session.run(predict_op)
self.assertAllClose(expected_logits, logits)
def testPredictionMultipleTree(self):
"""Tests the predictions 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: 1
threshold: 28
left_id: 1
right_id: 2
}
metadata {
gain: 7.62
}
}
nodes {
leaf {
scalar: 1.14
}
}
nodes {
leaf {
scalar: 8.79
}
}
}
trees {
nodes {
bucketized_split {
feature_id: 1
threshold: 26
left_id: 1
right_id: 2
}
}
nodes {
bucketized_split {
feature_id: 0
threshold: 50
left_id: 3
right_id: 4
}
}
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_ensemble_config)
# Create existing ensemble with one root split
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 = [11, 27]
# Example 1: tree 0: 1.14, tree 1: 5.0, tree 2: 5.0 = >
# logit = 0.1*1.14+0.2*5.0+1*5
# Example 2: tree 0: 1.14, tree 1: 7.0, tree 2: -7 = >
# logit= 0.1*1.14+0.2*7.0-1*7.0
expected_logits = [[6.114], [-5.486]]
# Prediction should work fine.
predict_op = boosted_trees_ops.predict(
tree_ensemble_handle,
bucketized_features=[feature_0_values, feature_1_values],
logits_dimension=1)
logits = session.run(predict_op)
self.assertAllClose(expected_logits, logits)
def testCategoricalSplits(self):
"""Tests the predictions work for categorical splits."""
with self.cached_session() as session:
tree_ensemble_config = boosted_trees_pb2.TreeEnsemble()
text_format.Merge(
"""
trees {
nodes {
categorical_split {
feature_id: 1
value: 2
left_id: 1
right_id: 2
}
}
nodes {
categorical_split {
feature_id: 0
value: 13
left_id: 3
right_id: 4
}
}
nodes {
leaf {
scalar: 7.0
}
}
nodes {
leaf {
scalar: 5.0
}
}
nodes {
leaf {
scalar: 6.0
}
}
}
tree_weights: 1.0
""", tree_ensemble_config)
# Create existing ensemble with one root split
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 = [13, 1, 3]
feature_1_values = [2, 2, 1]
expected_logits = [[5.], [6.], [7.]]
# Prediction should work fine.
predict_op = boosted_trees_ops.predict(
tree_ensemble_handle,
bucketized_features=[feature_0_values, feature_1_values],
logits_dimension=1)
logits = session.run(predict_op)
self.assertAllClose(expected_logits, logits)
def testPredictionMultipleTreeMultiClass(self):
"""Tests the predictions 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: 1
threshold: 28
left_id: 1
right_id: 2
}
metadata {
gain: 7.62
}
}
nodes {
leaf {
vector: {
value: 0.51
}
vector: {
value: 1.14
}
}
}
nodes {
leaf {
vector: {
value: 1.29
}
vector: {
value: 8.79
}
}
}
}
trees {
nodes {
bucketized_split {
feature_id: 1
threshold: 26
left_id: 1
right_id: 2
}
}
nodes {
bucketized_split {
feature_id: 0
threshold: 50
left_id: 3
right_id: 4
}
}
nodes {
leaf {
vector: {
value: -4.33
}
vector: {
value: 7.0
}
}
}
nodes {
leaf {
vector: {
value: 0.2
}
vector: {
value: 5.0
}
}
}
nodes {
leaf {
vector: {
value: -4.1
}
vector: {
value: 6.0
}
}
}
}
trees {
nodes {
bucketized_split {
feature_id: 0
threshold: 34
left_id: 1
right_id: 2
}
}
nodes {
leaf {
vector: {
value: 2.0
}
vector: {
value: -7.0
}
}
}
nodes {
leaf {
vector: {
value: 6.3
}
vector: {
value: 5.0
}
}
}
}
tree_weights: 0.1
tree_weights: 0.2
tree_weights: 1.0
""", tree_ensemble_config)
# Create existing ensemble with one root split
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 = [11, 27]
# Example 1: tree 0: (0.51, 1.14), tree 1: (0.2, 5.0), tree 2: (6.3, 5.0)
#
# logits = (0.1*0.51+0.2*0.2+1*6.3,
# 0.1*1.14+0.2*5.0+1*5)
# Example 2: tree 0: (0.51, 1.14), tree 1: (-4.33, 7.0), tree 2: (2.0, -7)
#
# logits = (0.1*0.51+0.2*-4.33+1*2.0,
# 0.1*1.14+0.2*7.0+1*-7)
logits_dimension = 2
expected_logits = [[6.391, 6.114], [1.185, -5.486]]
# Prediction should work fine.
predict_op = boosted_trees_ops.predict(
tree_ensemble_handle,
bucketized_features=[feature_0_values, feature_1_values],
logits_dimension=logits_dimension)
logits = session.run(predict_op)
self.assertAllClose(expected_logits, logits)
def _bt_model_fn(
features,
labels,
mode,
head,
feature_columns,
tree_hparams,
n_batches_per_layer,
config,
closed_form_grad_and_hess_fn=None,
example_id_column_name=None,
# TODO(youngheek): replace this later using other options.
train_in_memory=False,
name='boosted_trees'):
"""Gradient Boosted Trees model_fn.
Args:
features: dict of `Tensor`.
labels: `Tensor` of shape [batch_size, 1] or [batch_size] labels of
dtype `int32` or `int64` in the range `[0, n_classes)`.
mode: Defines whether this is training, evaluation or prediction.
See `ModeKeys`.
head: A `head_lib._Head` instance.
feature_columns: Iterable of `feature_column._FeatureColumn` model inputs.
tree_hparams: TODO. collections.namedtuple for hyper parameters.
n_batches_per_layer: A `Tensor` of `int64`. Each layer is built after at
least n_batches_per_layer accumulations.
config: `RunConfig` object to configure the runtime settings.
closed_form_grad_and_hess_fn: a function that accepts logits and labels
and returns gradients and hessians. By default, they are created by
tf.gradients() from the loss.
example_id_column_name: Name of the feature for a unique ID per example.
Currently experimental -- not exposed to public API.
train_in_memory: `bool`, when true, it assumes the dataset is in memory,
i.e., input_fn should return the entire dataset as a single batch, and
also n_batches_per_layer should be set as 1.
name: Name to use for the model.
Returns:
An `EstimatorSpec` instance.
Raises:
ValueError: mode or params are invalid, or features has the wrong type.
"""
is_single_machine = (config.num_worker_replicas <= 1)
sorted_feature_columns = sorted(feature_columns, key=lambda tc: tc.name)
if train_in_memory:
assert n_batches_per_layer == 1, (
'When train_in_memory is enabled, input_fn should return the entire '
'dataset as a single batch, and n_batches_per_layer should be set as '
'1.')
if (not config.is_chief or config.num_worker_replicas > 1 or
config.num_ps_replicas > 0):
raise ValueError('train_in_memory is supported only for '
'non-distributed training.')
worker_device = control_flow_ops.no_op().device
# maximum number of splits possible in the whole tree =2^(D-1)-1
# TODO(youngheek): perhaps storage could be optimized by storing stats with
# the dimension max_splits_per_layer, instead of max_splits (for the entire
# tree).
max_splits = (1 << tree_hparams.max_depth) - 1
train_op = []
with ops.name_scope(name) as name:
# Prepare.
global_step = training_util.get_or_create_global_step()
bucket_size_list, feature_ids_list = _group_features_by_num_buckets(
sorted_feature_columns)
# Extract input features and set up cache for training.
training_state_cache = None
if mode == model_fn.ModeKeys.TRAIN and train_in_memory:
# cache transformed features as well for in-memory training.
batch_size = array_ops.shape(labels)[0]
input_feature_list, input_cache_op = (
_cache_transformed_features(features, sorted_feature_columns,
batch_size))
train_op.append(input_cache_op)
training_state_cache = _CacheTrainingStatesUsingVariables(
batch_size, head.logits_dimension)
else:
input_feature_list = _get_transformed_features(features,
sorted_feature_columns)
if mode == model_fn.ModeKeys.TRAIN and example_id_column_name:
example_ids = features[example_id_column_name]
training_state_cache = _CacheTrainingStatesUsingHashTable(
example_ids, head.logits_dimension)
# Create Ensemble resources.
tree_ensemble = boosted_trees_ops.TreeEnsemble(name=name)
# Create logits.
if mode != model_fn.ModeKeys.TRAIN:
logits = boosted_trees_ops.predict(
# For non-TRAIN mode, ensemble doesn't change after initialization,
# so no local copy is needed; using tree_ensemble directly.
tree_ensemble_handle=tree_ensemble.resource_handle,
bucketized_features=input_feature_list,
logits_dimension=head.logits_dimension)
else:
if is_single_machine:
local_tree_ensemble = tree_ensemble
ensemble_reload = control_flow_ops.no_op()
else:
# Have a local copy of ensemble for the distributed setting.
with ops.device(worker_device):
local_tree_ensemble = boosted_trees_ops.TreeEnsemble(
name=name + '_local', is_local=True)
# TODO(soroush): Do partial updates if this becomes a bottleneck.
ensemble_reload = local_tree_ensemble.deserialize(
*tree_ensemble.serialize())
if training_state_cache:
cached_tree_ids, cached_node_ids, cached_logits = (
training_state_cache.lookup())
else:
# Always start from the beginning when no cache is set up.
batch_size = array_ops.shape(labels)[0]
cached_tree_ids, cached_node_ids, cached_logits = (
array_ops.zeros([batch_size], dtype=dtypes.int32),
array_ops.zeros([batch_size], dtype=dtypes.int32),
array_ops.zeros(
[batch_size, head.logits_dimension], dtype=dtypes.float32))
with ops.control_dependencies([ensemble_reload]):
(stamp_token, num_trees, num_finalized_trees, num_attempted_layers,
last_layer_nodes_range) = local_tree_ensemble.get_states()
summary.scalar('ensemble/num_trees', num_trees)
summary.scalar('ensemble/num_finalized_trees', num_finalized_trees)
summary.scalar('ensemble/num_attempted_layers', num_attempted_layers)
partial_logits, tree_ids, node_ids = boosted_trees_ops.training_predict(
tree_ensemble_handle=local_tree_ensemble.resource_handle,
cached_tree_ids=cached_tree_ids,
cached_node_ids=cached_node_ids,
bucketized_features=input_feature_list,
logits_dimension=head.logits_dimension)
logits = cached_logits + partial_logits
# Create training graph.
def _train_op_fn(loss):
"""Run one training iteration."""
if training_state_cache:
train_op.append(training_state_cache.insert(tree_ids, node_ids, logits))
if closed_form_grad_and_hess_fn:
gradients, hessians = closed_form_grad_and_hess_fn(logits, labels)
else:
gradients = gradients_impl.gradients(loss, logits, name='Gradients')[0]
hessians = gradients_impl.gradients(
gradients, logits, name='Hessians')[0]
stats_summaries_list = []
for i, feature_ids in enumerate(feature_ids_list):
num_buckets = bucket_size_list[i]
summaries = [
array_ops.squeeze(
boosted_trees_ops.make_stats_summary(
node_ids=node_ids,
gradients=gradients,
hessians=hessians,
bucketized_features_list=[input_feature_list[f]],
max_splits=max_splits,
num_buckets=num_buckets),
axis=0) for f in feature_ids
]
stats_summaries_list.append(summaries)
accumulators = []
def grow_tree_from_stats_summaries(stats_summaries_list,
feature_ids_list):
"""Updates ensemble based on the best gains from stats summaries."""
node_ids_per_feature = []
gains_list = []
thresholds_list = []
left_node_contribs_list = []
right_node_contribs_list = []
all_feature_ids = []
assert len(stats_summaries_list) == len(feature_ids_list)
for i, feature_ids in enumerate(feature_ids_list):
(numeric_node_ids_per_feature, numeric_gains_list,
numeric_thresholds_list, numeric_left_node_contribs_list,
numeric_right_node_contribs_list) = (
boosted_trees_ops.calculate_best_gains_per_feature(
node_id_range=last_layer_nodes_range,
stats_summary_list=stats_summaries_list[i],
l1=tree_hparams.l1,
l2=tree_hparams.l2,
tree_complexity=tree_hparams.tree_complexity,
min_node_weight=tree_hparams.min_node_weight,
max_splits=max_splits))
all_feature_ids += feature_ids
node_ids_per_feature += numeric_node_ids_per_feature
gains_list += numeric_gains_list
thresholds_list += numeric_thresholds_list
left_node_contribs_list += numeric_left_node_contribs_list
right_node_contribs_list += numeric_right_node_contribs_list
grow_op = boosted_trees_ops.update_ensemble(
# Confirm if local_tree_ensemble or tree_ensemble should be used.
tree_ensemble.resource_handle,
feature_ids=all_feature_ids,
node_ids=node_ids_per_feature,
gains=gains_list,
thresholds=thresholds_list,
left_node_contribs=left_node_contribs_list,
right_node_contribs=right_node_contribs_list,
learning_rate=tree_hparams.learning_rate,
max_depth=tree_hparams.max_depth,
pruning_mode=boosted_trees_ops.PruningMode.NO_PRUNING)
return grow_op
if train_in_memory and is_single_machine:
train_op.append(distribute_lib.increment_var(global_step))
train_op.append(
grow_tree_from_stats_summaries(stats_summaries_list,
feature_ids_list))
else:
dependencies = []
for i, feature_ids in enumerate(feature_ids_list):
stats_summaries = stats_summaries_list[i]
accumulator = data_flow_ops.ConditionalAccumulator(
dtype=dtypes.float32,
# The stats consist of grads and hessians (the last dimension).
shape=[len(feature_ids), max_splits, bucket_size_list[i], 2],
shared_name='numeric_stats_summary_accumulator_' + str(i))
accumulators.append(accumulator)
apply_grad = accumulator.apply_grad(
array_ops.stack(stats_summaries, axis=0), stamp_token)
dependencies.append(apply_grad)
def grow_tree_from_accumulated_summaries_fn():
"""Updates the tree with the best layer from accumulated summaries."""
# Take out the accumulated summaries from the accumulator and grow.
stats_summaries_list = []
stats_summaries_list = [
array_ops.unstack(accumulator.take_grad(1), axis=0)
for accumulator in accumulators
]
grow_op = grow_tree_from_stats_summaries(stats_summaries_list,
feature_ids_list)
return grow_op
with ops.control_dependencies(dependencies):
train_op.append(distribute_lib.increment_var(global_step))
if config.is_chief:
min_accumulated = math_ops.reduce_min(
array_ops.stack(
[acc.num_accumulated() for acc in accumulators]))
train_op.append(
control_flow_ops.cond(
math_ops.greater_equal(min_accumulated,
n_batches_per_layer),
grow_tree_from_accumulated_summaries_fn,
control_flow_ops.no_op,
name='wait_until_n_batches_accumulated'))
return control_flow_ops.group(train_op, name='train_op')
estimator_spec = head.create_estimator_spec(
features=features,
mode=mode,
labels=labels,
train_op_fn=_train_op_fn,
logits=logits)
if mode == model_fn.ModeKeys.TRAIN:
# Add an early stop hook.
estimator_spec = estimator_spec._replace(
training_hooks=estimator_spec.training_hooks +
(_StopAtAttemptsHook(num_finalized_trees, num_attempted_layers,
tree_hparams.n_trees, tree_hparams.max_depth),))
return estimator_spec
def testPredictionMultipleTree(self):
"""Tests the predictions work when we have multiple trees."""
with self.test_session() as session:
tree_ensemble_config = boosted_trees_pb2.TreeEnsemble()
text_format.Merge(
"""
trees {
nodes {
bucketized_split {
feature_id: 1
threshold: 28
left_id: 1
right_id: 2
}
metadata {
gain: 7.62
}
}
nodes {
leaf {
scalar: 1.14
}
}
nodes {
leaf {
scalar: 8.79
}
}
}
trees {
nodes {
bucketized_split {
feature_id: 1
threshold: 26
left_id: 1
right_id: 2
}
}
nodes {
bucketized_split {
feature_id: 0
threshold: 50
left_id: 3
right_id: 4
}
}
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_ensemble_config)
# Create existing ensemble with one root split
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 = [11, 27]
# Example 1: tree 0: 1.14, tree 1: 5.0, tree 2: 5.0 = >
# logit = 0.1*5.0+0.2*5.0+1*5
# Example 2: tree 0: 1.14, tree 1: 7.0, tree 2: -7 = >
# logit= 0.1*1.14+0.2*7.0-1*7.0
expected_logits = [[6.114], [-5.486]]
# Do with parallelization, e.g. EVAL
predict_op = boosted_trees_ops.predict(
tree_ensemble_handle,
bucketized_features=[feature_0_values, feature_1_values],
logits_dimension=1)
logits = session.run(predict_op)
self.assertAllClose(expected_logits, logits)
# Do without parallelization, e.g. INFER - the result is the same
predict_op = boosted_trees_ops.predict(
tree_ensemble_handle,
bucketized_features=[feature_0_values, feature_1_values],
logits_dimension=1)
logits = session.run(predict_op)
self.assertAllClose(expected_logits, logits)
def _bt_model_fn(
features,
labels,
mode,
head,
feature_columns,
tree_hparams,
n_batches_per_layer,
config,
closed_form_grad_and_hess_fn=None,
example_id_column_name=None,
# TODO(youngheek): replace this later using other options.
train_in_memory=False,
name='boosted_trees'):
"""Gradient Boosted Trees model_fn.
Args:
features: dict of `Tensor`.
labels: `Tensor` of shape [batch_size, 1] or [batch_size] labels of
dtype `int32` or `int64` in the range `[0, n_classes)`.
mode: Defines whether this is training, evaluation or prediction.
See `ModeKeys`.
head: A `head_lib._Head` instance.
feature_columns: Iterable of `feature_column._FeatureColumn` model inputs.
tree_hparams: TODO. collections.namedtuple for hyper parameters.
n_batches_per_layer: A `Tensor` of `int64`. Each layer is built after at
least n_batches_per_layer accumulations.
config: `RunConfig` object to configure the runtime settings.
closed_form_grad_and_hess_fn: a function that accepts logits and labels
and returns gradients and hessians. By default, they are created by
tf.gradients() from the loss.
example_id_column_name: Name of the feature for a unique ID per example.
Currently experimental -- not exposed to public API.
train_in_memory: `bool`, when true, it assumes the dataset is in memory,
i.e., input_fn should return the entire dataset as a single batch, and
also n_batches_per_layer should be set as 1.
name: Name to use for the model.
Returns:
An `EstimatorSpec` instance.
Raises:
ValueError: mode or params are invalid, or features has the wrong type.
"""
is_single_machine = (config.num_worker_replicas <= 1)
sorted_feature_columns = sorted(feature_columns, key=lambda tc: tc.name)
if train_in_memory:
assert n_batches_per_layer == 1, (
'When train_in_memory is enabled, input_fn should return the entire '
'dataset as a single batch, and n_batches_per_layer should be set as '
'1.')
if (not config.is_chief or config.num_worker_replicas > 1 or
config.num_ps_replicas > 0):
raise ValueError('train_in_memory is supported only for '
'non-distributed training.')
worker_device = control_flow_ops.no_op().device
# maximum number of splits possible in the whole tree =2^(D-1)-1
# TODO(youngheek): perhaps storage could be optimized by storing stats with
# the dimension max_splits_per_layer, instead of max_splits (for the entire
# tree).
max_splits = (1 << tree_hparams.max_depth) - 1
train_op = []
with ops.name_scope(name) as name:
# Prepare.
global_step = training_util.get_or_create_global_step()
bucket_size_list, feature_ids_list = _group_features_by_num_buckets(
sorted_feature_columns)
# Extract input features and set up cache for training.
training_state_cache = None
if mode == model_fn.ModeKeys.TRAIN and train_in_memory:
# cache transformed features as well for in-memory training.
batch_size = array_ops.shape(labels)[0]
input_feature_list, input_cache_op = (
_cache_transformed_features(features, sorted_feature_columns,
batch_size))
train_op.append(input_cache_op)
training_state_cache = _CacheTrainingStatesUsingVariables(
batch_size, head.logits_dimension)
else:
input_feature_list = _get_transformed_features(features,
sorted_feature_columns)
if mode == model_fn.ModeKeys.TRAIN and example_id_column_name:
example_ids = features[example_id_column_name]
training_state_cache = _CacheTrainingStatesUsingHashTable(
example_ids, head.logits_dimension)
# Create Ensemble resources.
tree_ensemble = boosted_trees_ops.TreeEnsemble(name=name)
# Create logits.
if mode != model_fn.ModeKeys.TRAIN:
logits = boosted_trees_ops.predict(
# For non-TRAIN mode, ensemble doesn't change after initialization,
# so no local copy is needed; using tree_ensemble directly.
tree_ensemble_handle=tree_ensemble.resource_handle,
bucketized_features=input_feature_list,
logits_dimension=head.logits_dimension)
else:
if is_single_machine:
local_tree_ensemble = tree_ensemble
ensemble_reload = control_flow_ops.no_op()
else:
# Have a local copy of ensemble for the distributed setting.
with ops.device(worker_device):
local_tree_ensemble = boosted_trees_ops.TreeEnsemble(
name=name + '_local', is_local=True)
# TODO(soroush): Do partial updates if this becomes a bottleneck.
ensemble_reload = local_tree_ensemble.deserialize(
*tree_ensemble.serialize())
if training_state_cache:
cached_tree_ids, cached_node_ids, cached_logits = (
training_state_cache.lookup())
else:
# Always start from the beginning when no cache is set up.
batch_size = array_ops.shape(labels)[0]
cached_tree_ids, cached_node_ids, cached_logits = (
array_ops.zeros([batch_size], dtype=dtypes.int32),
array_ops.zeros([batch_size], dtype=dtypes.int32),
array_ops.zeros(
[batch_size, head.logits_dimension], dtype=dtypes.float32))
with ops.control_dependencies([ensemble_reload]):
(stamp_token, num_trees, num_finalized_trees, num_attempted_layers,
last_layer_nodes_range) = local_tree_ensemble.get_states()
summary.scalar('ensemble/num_trees', num_trees)
summary.scalar('ensemble/num_finalized_trees', num_finalized_trees)
summary.scalar('ensemble/num_attempted_layers', num_attempted_layers)
partial_logits, tree_ids, node_ids = boosted_trees_ops.training_predict(
tree_ensemble_handle=local_tree_ensemble.resource_handle,
cached_tree_ids=cached_tree_ids,
cached_node_ids=cached_node_ids,
bucketized_features=input_feature_list,
logits_dimension=head.logits_dimension)
logits = cached_logits + partial_logits
# Create training graph.
def _train_op_fn(loss):
"""Run one training iteration."""
if training_state_cache:
train_op.append(training_state_cache.insert(tree_ids, node_ids, logits))
if closed_form_grad_and_hess_fn:
gradients, hessians = closed_form_grad_and_hess_fn(logits, labels)
else:
gradients = gradients_impl.gradients(loss, logits, name='Gradients')[0]
hessians = gradients_impl.gradients(
gradients, logits, name='Hessians')[0]
stats_summaries_list = []
for i, feature_ids in enumerate(feature_ids_list):
num_buckets = bucket_size_list[i]
summaries = [
array_ops.squeeze(
boosted_trees_ops.make_stats_summary(
node_ids=node_ids,
gradients=gradients,
hessians=hessians,
bucketized_features_list=[input_feature_list[f]],
max_splits=max_splits,
num_buckets=num_buckets),
axis=0) for f in feature_ids
]
stats_summaries_list.append(summaries)
accumulators = []
def grow_tree_from_stats_summaries(stats_summaries_list,
feature_ids_list):
"""Updates ensemble based on the best gains from stats summaries."""
node_ids_per_feature = []
gains_list = []
thresholds_list = []
left_node_contribs_list = []
right_node_contribs_list = []
all_feature_ids = []
assert len(stats_summaries_list) == len(feature_ids_list)
for i, feature_ids in enumerate(feature_ids_list):
(numeric_node_ids_per_feature, numeric_gains_list,
numeric_thresholds_list, numeric_left_node_contribs_list,
numeric_right_node_contribs_list) = (
boosted_trees_ops.calculate_best_gains_per_feature(
node_id_range=last_layer_nodes_range,
stats_summary_list=stats_summaries_list[i],
l1=tree_hparams.l1,
l2=tree_hparams.l2,
tree_complexity=tree_hparams.tree_complexity,
min_node_weight=tree_hparams.min_node_weight,
max_splits=max_splits))
all_feature_ids += feature_ids
node_ids_per_feature += numeric_node_ids_per_feature
gains_list += numeric_gains_list
thresholds_list += numeric_thresholds_list
left_node_contribs_list += numeric_left_node_contribs_list
right_node_contribs_list += numeric_right_node_contribs_list
grow_op = boosted_trees_ops.update_ensemble(
# Confirm if local_tree_ensemble or tree_ensemble should be used.
tree_ensemble.resource_handle,
feature_ids=all_feature_ids,
node_ids=node_ids_per_feature,
gains=gains_list,
thresholds=thresholds_list,
left_node_contribs=left_node_contribs_list,
right_node_contribs=right_node_contribs_list,
learning_rate=tree_hparams.learning_rate,
max_depth=tree_hparams.max_depth,
pruning_mode=boosted_trees_ops.PruningMode.NO_PRUNING)
return grow_op
if train_in_memory and is_single_machine:
train_op.append(distribute_lib.increment_var(global_step))
train_op.append(
grow_tree_from_stats_summaries(stats_summaries_list,
feature_ids_list))
else:
dependencies = []
for i, feature_ids in enumerate(feature_ids_list):
stats_summaries = stats_summaries_list[i]
accumulator = data_flow_ops.ConditionalAccumulator(
dtype=dtypes.float32,
# The stats consist of grads and hessians (the last dimension).
shape=[len(feature_ids), max_splits, bucket_size_list[i], 2],
shared_name='numeric_stats_summary_accumulator_' + str(i))
accumulators.append(accumulator)
apply_grad = accumulator.apply_grad(
array_ops.stack(stats_summaries, axis=0), stamp_token)
dependencies.append(apply_grad)
def grow_tree_from_accumulated_summaries_fn():
"""Updates the tree with the best layer from accumulated summaries."""
# Take out the accumulated summaries from the accumulator and grow.
stats_summaries_list = []
stats_summaries_list = [
array_ops.unstack(accumulator.take_grad(1), axis=0)
for accumulator in accumulators
]
grow_op = grow_tree_from_stats_summaries(stats_summaries_list,
feature_ids_list)
return grow_op
with ops.control_dependencies(dependencies):
train_op.append(distribute_lib.increment_var(global_step))
if config.is_chief:
min_accumulated = math_ops.reduce_min(
array_ops.stack(
[acc.num_accumulated() for acc in accumulators]))
train_op.append(
control_flow_ops.cond(
math_ops.greater_equal(min_accumulated,
n_batches_per_layer),
grow_tree_from_accumulated_summaries_fn,
control_flow_ops.no_op,
name='wait_until_n_batches_accumulated'))
return control_flow_ops.group(train_op, name='train_op')
estimator_spec = head.create_estimator_spec(
features=features,
mode=mode,
labels=labels,
train_op_fn=_train_op_fn,
logits=logits)
if mode == model_fn.ModeKeys.TRAIN:
# Add an early stop hook.
estimator_spec = estimator_spec._replace(
training_hooks=estimator_spec.training_hooks +
(_StopAtAttemptsHook(num_finalized_trees, num_attempted_layers,
tree_hparams.n_trees, tree_hparams.max_depth),))
return estimator_spec
def testCategoricalSplits(self):
"""Tests the predictions work for categorical splits."""
with self.cached_session() as session:
tree_ensemble_config = boosted_trees_pb2.TreeEnsemble()
text_format.Merge(
"""
trees {
nodes {
categorical_split {
feature_id: 1
value: 2
left_id: 1
right_id: 2
}
}
nodes {
categorical_split {
feature_id: 0
value: 13
left_id: 3
right_id: 4
}
}
nodes {
leaf {
scalar: 7.0
}
}
nodes {
leaf {
scalar: 5.0
}
}
nodes {
leaf {
scalar: 6.0
}
}
}
tree_weights: 1.0
""", tree_ensemble_config)
# Create existing ensemble with one root split
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 = [13, 1, 3]
feature_1_values = [2, 2, 1]
expected_logits = [[5.], [6.], [7.]]
# Prediction should work fine.
predict_op = boosted_trees_ops.predict(
tree_ensemble_handle,
bucketized_features=[feature_0_values, feature_1_values],
logits_dimension=1)
logits = session.run(predict_op)
self.assertAllClose(expected_logits, logits)
def testPredictionMultipleTreeMultiClass(self):
"""Tests the predictions 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: 1
threshold: 28
left_id: 1
right_id: 2
}
metadata {
gain: 7.62
}
}
nodes {
leaf {
vector: {
value: 0.51
}
vector: {
value: 1.14
}
}
}
nodes {
leaf {
vector: {
value: 1.29
}
vector: {
value: 8.79
}
}
}
}
trees {
nodes {
bucketized_split {
feature_id: 1
threshold: 26
left_id: 1
right_id: 2
}
}
nodes {
bucketized_split {
feature_id: 0
threshold: 50
left_id: 3
right_id: 4
}
}
nodes {
leaf {
vector: {
value: -4.33
}
vector: {
value: 7.0
}
}
}
nodes {
leaf {
vector: {
value: 0.2
}
vector: {
value: 5.0
}
}
}
nodes {
leaf {
vector: {
value: -4.1
}
vector: {
value: 6.0
}
}
}
}
trees {
nodes {
bucketized_split {
feature_id: 0
threshold: 34
left_id: 1
right_id: 2
}
}
nodes {
leaf {
vector: {
value: 2.0
}
vector: {
value: -7.0
}
}
}
nodes {
leaf {
vector: {
value: 6.3
}
vector: {
value: 5.0
}
}
}
}
tree_weights: 0.1
tree_weights: 0.2
tree_weights: 1.0
""", tree_ensemble_config)
# Create existing ensemble with one root split
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 = [11, 27]
# Example 1: tree 0: (0.51, 1.14), tree 1: (0.2, 5.0), tree 2: (6.3, 5.0)
#
# logits = (0.1*0.51+0.2*0.2+1*6.3,
# 0.1*1.14+0.2*5.0+1*5)
# Example 2: tree 0: (0.51, 1.14), tree 1: (-4.33, 7.0), tree 2: (2.0, -7)
#
# logits = (0.1*0.51+0.2*-4.33+1*2.0,
# 0.1*1.14+0.2*7.0+1*-7)
logits_dimension = 2
expected_logits = [[6.391, 6.114], [1.185, -5.486]]
# Prediction should work fine.
predict_op = boosted_trees_ops.predict(
tree_ensemble_handle,
bucketized_features=[feature_0_values, feature_1_values],
logits_dimension=logits_dimension)
logits = session.run(predict_op)
self.assertAllClose(expected_logits, logits)
def _bt_model_fn(
features,
labels,
mode,
head,
feature_columns,
tree_hparams,
n_batches_per_layer,
config,
closed_form_grad_and_hess_fn=None,
example_id_column_name=None,
# TODO(youngheek): replace this later using other options.
train_in_memory=False,
name='boosted_trees'):
"""Gradient Boosted Trees model_fn.
Args:
features: dict of `Tensor`.
labels: `Tensor` of shape [batch_size, 1] or [batch_size] labels of
dtype `int32` or `int64` in the range `[0, n_classes)`.
mode: Defines whether this is training, evaluation or prediction.
See `ModeKeys`.
head: A `head_lib._Head` instance.
feature_columns: Iterable of `feature_column._FeatureColumn` model inputs.
tree_hparams: TODO. collections.namedtuple for hyper parameters.
n_batches_per_layer: A `Tensor` of `int64`. Each layer is built after at
least n_batches_per_layer accumulations.
config: `RunConfig` object to configure the runtime settings.
closed_form_grad_and_hess_fn: a function that accepts logits and labels
and returns gradients and hessians. By default, they are created by
tf.gradients() from the loss.
example_id_column_name: Name of the feature for a unique ID per example.
Currently experimental -- not exposed to public API.
train_in_memory: `bool`, when true, it assumes the dataset is in memory,
i.e., input_fn should return the entire dataset as a single batch, and
also n_batches_per_layer should be set as 1.
name: Name to use for the model.
Returns:
An `EstimatorSpec` instance.
Raises:
ValueError: mode or params are invalid, or features has the wrong type.
"""
is_single_machine = (config.num_worker_replicas <= 1)
sorted_feature_columns = sorted(feature_columns, key=lambda tc: tc.name)
center_bias = tree_hparams.center_bias
if train_in_memory:
assert n_batches_per_layer == 1, (
'When train_in_memory is enabled, input_fn should return the entire '
'dataset as a single batch, and n_batches_per_layer should be set as '
'1.')
if (not config.is_chief or config.num_worker_replicas > 1 or
config.num_ps_replicas > 0):
raise ValueError('train_in_memory is supported only for '
'non-distributed training.')
worker_device = control_flow_ops.no_op().device
train_op = []
with ops.name_scope(name) as name:
# Prepare.
global_step = training_util.get_or_create_global_step()
bucket_size_list, feature_ids_list = _group_features_by_num_buckets(
sorted_feature_columns)
# Create Ensemble resources.
tree_ensemble = boosted_trees_ops.TreeEnsemble(name=name)
# Create logits.
if mode != model_fn.ModeKeys.TRAIN:
input_feature_list = _get_transformed_features(features,
sorted_feature_columns)
logits = boosted_trees_ops.predict(
# For non-TRAIN mode, ensemble doesn't change after initialization,
# so no local copy is needed; using tree_ensemble directly.
tree_ensemble_handle=tree_ensemble.resource_handle,
bucketized_features=input_feature_list,
logits_dimension=head.logits_dimension)
return head.create_estimator_spec(
features=features,
mode=mode,
labels=labels,
train_op_fn=control_flow_ops.no_op,
logits=logits)
# ============== Training graph ==============
# Extract input features and set up cache for training.
training_state_cache = None
if train_in_memory:
# cache transformed features as well for in-memory training.
batch_size = array_ops.shape(labels)[0]
input_feature_list, input_cache_op = (
_cache_transformed_features(features, sorted_feature_columns,
batch_size))
train_op.append(input_cache_op)
training_state_cache = _CacheTrainingStatesUsingVariables(
batch_size, head.logits_dimension)
else:
input_feature_list = _get_transformed_features(features,
sorted_feature_columns)
if example_id_column_name:
example_ids = features[example_id_column_name]
training_state_cache = _CacheTrainingStatesUsingHashTable(
example_ids, head.logits_dimension)
# Variable that determines whether bias centering is needed.
center_bias_var = variable_scope.variable(
initial_value=center_bias, name='center_bias_needed', trainable=False)
if is_single_machine:
local_tree_ensemble = tree_ensemble
ensemble_reload = control_flow_ops.no_op()
else:
# Have a local copy of ensemble for the distributed setting.
with ops.device(worker_device):
local_tree_ensemble = boosted_trees_ops.TreeEnsemble(
name=name + '_local', is_local=True)
# TODO(soroush): Do partial updates if this becomes a bottleneck.
ensemble_reload = local_tree_ensemble.deserialize(
*tree_ensemble.serialize())
if training_state_cache:
cached_tree_ids, cached_node_ids, cached_logits = (
training_state_cache.lookup())
else:
# Always start from the beginning when no cache is set up.
batch_size = array_ops.shape(labels)[0]
cached_tree_ids, cached_node_ids, cached_logits = (
array_ops.zeros([batch_size], dtype=dtypes.int32),
_DUMMY_NODE_ID * array_ops.ones([batch_size], dtype=dtypes.int32),
array_ops.zeros(
[batch_size, head.logits_dimension], dtype=dtypes.float32))
with ops.control_dependencies([ensemble_reload]):
(stamp_token, num_trees, num_finalized_trees, num_attempted_layers,
last_layer_nodes_range) = local_tree_ensemble.get_states()
summary.scalar('ensemble/num_trees', num_trees)
summary.scalar('ensemble/num_finalized_trees', num_finalized_trees)
summary.scalar('ensemble/num_attempted_layers', num_attempted_layers)
partial_logits, tree_ids, node_ids = boosted_trees_ops.training_predict(
tree_ensemble_handle=local_tree_ensemble.resource_handle,
cached_tree_ids=cached_tree_ids,
cached_node_ids=cached_node_ids,
bucketized_features=input_feature_list,
logits_dimension=head.logits_dimension)
logits = cached_logits + partial_logits
# Create training graph.
def _train_op_fn(loss):
"""Run one training iteration."""
if training_state_cache:
# Cache logits only after center_bias is complete, if it's in progress.
train_op.append(
control_flow_ops.cond(
center_bias_var, control_flow_ops.no_op,
lambda: training_state_cache.insert(tree_ids, node_ids, logits))
)
if closed_form_grad_and_hess_fn:
gradients, hessians = closed_form_grad_and_hess_fn(logits, labels)
else:
gradients = gradients_impl.gradients(loss, logits, name='Gradients')[0]
hessians = gradients_impl.gradients(
gradients, logits, name='Hessians')[0]
# TODO(youngheek): perhaps storage could be optimized by storing stats
# with the dimension max_splits_per_layer, instead of max_splits (for the
# entire tree).
max_splits = _get_max_splits(tree_hparams)
stats_summaries_list = []
for i, feature_ids in enumerate(feature_ids_list):
num_buckets = bucket_size_list[i]
summaries = [
array_ops.squeeze(
boosted_trees_ops.make_stats_summary(
node_ids=node_ids,
gradients=gradients,
hessians=hessians,
bucketized_features_list=[input_feature_list[f]],
max_splits=max_splits,
num_buckets=num_buckets),
axis=0) for f in feature_ids
]
stats_summaries_list.append(summaries)
if train_in_memory and is_single_machine:
grower = _InMemoryEnsembleGrower(tree_ensemble, tree_hparams)
else:
grower = _AccumulatorEnsembleGrower(tree_ensemble, tree_hparams,
stamp_token, n_batches_per_layer,
bucket_size_list, config.is_chief)
update_model = control_flow_ops.cond(
center_bias_var,
functools.partial(
grower.center_bias,
center_bias_var,
gradients,
hessians,
),
functools.partial(grower.grow_tree, stats_summaries_list,
feature_ids_list, last_layer_nodes_range))
train_op.append(update_model)
with ops.control_dependencies([update_model]):
increment_global = distribute_lib.increment_var(global_step)
train_op.append(increment_global)
return control_flow_ops.group(train_op, name='train_op')
estimator_spec = head.create_estimator_spec(
features=features,
mode=mode,
labels=labels,
train_op_fn=_train_op_fn,
logits=logits)
# Add an early stop hook.
estimator_spec = estimator_spec._replace(
training_hooks=estimator_spec.training_hooks +
(_StopAtAttemptsHook(num_finalized_trees, num_attempted_layers,
tree_hparams.n_trees, tree_hparams.max_depth),))
return estimator_spec
def _bt_model_fn(
features,
labels,
mode,
head,
feature_columns,
tree_hparams,
n_batches_per_layer,
config,
closed_form_grad_and_hess_fn=None,
example_id_column_name=None,
# TODO(youngheek): replace this later using other options.
train_in_memory=False,
name='TreeEnsembleModel'):
"""Gradient Boosted Decision Tree model_fn.
Args:
features: dict of `Tensor`.
labels: `Tensor` of shape [batch_size, 1] or [batch_size] labels of
dtype `int32` or `int64` in the range `[0, n_classes)`.
mode: Defines whether this is training, evaluation or prediction.
See `ModeKeys`.
head: A `head_lib._Head` instance.
feature_columns: Iterable of `feature_column._FeatureColumn` model inputs.
tree_hparams: TODO. collections.namedtuple for hyper parameters.
n_batches_per_layer: A `Tensor` of `int64`. Each layer is built after at
least n_batches_per_layer accumulations.
config: `RunConfig` object to configure the runtime settings.
closed_form_grad_and_hess_fn: a function that accepts logits and labels
and returns gradients and hessians. By default, they are created by
tf.gradients() from the loss.
example_id_column_name: Name of the feature for a unique ID per example.
Currently experimental -- not exposed to public API.
train_in_memory: `bool`, when true, it assumes the dataset is in memory,
i.e., input_fn should return the entire dataset as a single batch, and
also n_batches_per_layer should be set as 1.
name: Name to use for the model.
Returns:
An `EstimatorSpec` instance.
Raises:
ValueError: mode or params are invalid, or features has the wrong type.
"""
is_single_machine = (config.num_worker_replicas == 1)
if train_in_memory:
assert n_batches_per_layer == 1, (
'When train_in_memory is enabled, input_fn should return the entire '
'dataset as a single batch, and n_batches_per_layer should be set as '
'1.')
worker_device = control_flow_ops.no_op().device
# maximum number of splits possible in the whole tree =2^(D-1)-1
# TODO(youngheek): perhaps storage could be optimized by storing stats with
# the dimension max_splits_per_layer, instead of max_splits (for the entire
# tree).
max_splits = (1 << tree_hparams.max_depth) - 1
with ops.name_scope(name) as name:
# Prepare.
global_step = training_util.get_or_create_global_step()
input_feature_list, num_buckets = _get_transformed_features(
features, feature_columns)
if train_in_memory and mode == model_fn.ModeKeys.TRAIN:
input_feature_list = [
_keep_as_local_variable(feature)
for feature in input_feature_list
]
num_features = len(input_feature_list)
cache = None
if mode == model_fn.ModeKeys.TRAIN:
if train_in_memory and is_single_machine: # maybe just train_in_memory?
batch_size = array_ops.shape(input_feature_list[0])[0]
cache = _CacheTrainingStatesUsingVariables(
batch_size, head.logits_dimension)
elif example_id_column_name:
example_ids = features[example_id_column_name]
cache = _CacheTrainingStatesUsingHashTable(
example_ids, head.logits_dimension)
# Create Ensemble resources.
if is_single_machine:
tree_ensemble = boosted_trees_ops.TreeEnsemble(name=name)
local_tree_ensemble = tree_ensemble
ensemble_reload = control_flow_ops.no_op()
else:
tree_ensemble = boosted_trees_ops.TreeEnsemble(name=name)
with ops.device(worker_device):
local_tree_ensemble = boosted_trees_ops.TreeEnsemble(
name=name + '_local', is_local=True)
# TODO(soroush): Do partial updates if this becomes a bottleneck.
ensemble_reload = local_tree_ensemble.deserialize(
*tree_ensemble.serialize())
# Create logits.
if mode != model_fn.ModeKeys.TRAIN:
logits = boosted_trees_ops.predict(
tree_ensemble_handle=local_tree_ensemble.resource_handle,
bucketized_features=input_feature_list,
logits_dimension=head.logits_dimension,
max_depth=tree_hparams.max_depth)
else:
if cache:
cached_tree_ids, cached_node_ids, cached_logits = cache.lookup(
)
else:
# Always start from the beginning when no cache is set up.
batch_size = array_ops.shape(input_feature_list[0])[0]
cached_tree_ids, cached_node_ids, cached_logits = (
array_ops.zeros([batch_size], dtype=dtypes.int32),
array_ops.zeros([batch_size], dtype=dtypes.int32),
array_ops.zeros([batch_size, head.logits_dimension],
dtype=dtypes.float32))
with ops.control_dependencies([ensemble_reload]):
(stamp_token, num_trees, num_finalized_trees,
num_attempted_layers) = local_tree_ensemble.get_states()
summary.scalar('ensemble/num_trees', num_trees)
summary.scalar('ensemble/num_finalized_trees',
num_finalized_trees)
summary.scalar('ensemble/num_attempted_layers',
num_attempted_layers)
partial_logits, tree_ids, node_ids = boosted_trees_ops.training_predict(
tree_ensemble_handle=local_tree_ensemble.resource_handle,
cached_tree_ids=cached_tree_ids,
cached_node_ids=cached_node_ids,
bucketized_features=input_feature_list,
logits_dimension=head.logits_dimension,
max_depth=tree_hparams.max_depth)
logits = cached_logits + partial_logits
# Create training graph.
def _train_op_fn(loss):
"""Run one training iteration."""
train_op = []
if cache:
train_op.append(cache.insert(tree_ids, node_ids, logits))
if closed_form_grad_and_hess_fn:
gradients, hessians = closed_form_grad_and_hess_fn(
logits, labels)
else:
gradients = gradients_impl.gradients(loss,
logits,
name='Gradients')[0]
hessians = gradients_impl.gradients(gradients,
logits,
name='Hessians')[0]
stats_summary_list = [
array_ops.squeeze(boosted_trees_ops.make_stats_summary(
node_ids=node_ids,
gradients=gradients,
hessians=hessians,
bucketized_features_list=[input_feature_list[f]],
max_splits=max_splits,
num_buckets=num_buckets),
axis=0) for f in range(num_features)
]
def grow_tree_from_stats_summaries(stats_summary_list):
"""Updates ensemble based on the best gains from stats summaries."""
(node_ids_per_feature, gains_list, thresholds_list,
left_node_contribs_list, right_node_contribs_list) = (
boosted_trees_ops.calculate_best_gains_per_feature(
node_id_range=array_ops.stack([
math_ops.reduce_min(node_ids),
math_ops.reduce_max(node_ids)
]),
stats_summary_list=stats_summary_list,
l1=tree_hparams.l1,
l2=tree_hparams.l2,
tree_complexity=tree_hparams.tree_complexity,
max_splits=max_splits))
grow_op = boosted_trees_ops.update_ensemble(
# Confirm if local_tree_ensemble or tree_ensemble should be used.
tree_ensemble.resource_handle,
feature_ids=math_ops.range(0,
num_features,
dtype=dtypes.int32),
node_ids=node_ids_per_feature,
gains=gains_list,
thresholds=thresholds_list,
left_node_contribs=left_node_contribs_list,
right_node_contribs=right_node_contribs_list,
learning_rate=tree_hparams.learning_rate,
max_depth=tree_hparams.max_depth,
pruning_mode=boosted_trees_ops.PruningMode.NO_PRUNING)
return grow_op
if train_in_memory and is_single_machine:
train_op.append(state_ops.assign_add(global_step, 1))
train_op.append(
grow_tree_from_stats_summaries(stats_summary_list))
else:
summary_accumulator = data_flow_ops.ConditionalAccumulator(
dtype=dtypes.float32,
# The stats consist of gradients and hessians (the last dimension).
shape=[num_features, max_splits, num_buckets, 2],
shared_name='stats_summary_accumulator')
apply_grad = summary_accumulator.apply_grad(
array_ops.stack(stats_summary_list, axis=0), stamp_token)
def grow_tree_from_accumulated_summaries_fn():
"""Updates the tree with the best layer from accumulated summaries."""
# Take out the accumulated summaries from the accumulator and grow.
stats_summary_list = array_ops.unstack(
summary_accumulator.take_grad(1), axis=0)
grow_op = grow_tree_from_stats_summaries(
stats_summary_list)
return grow_op
with ops.control_dependencies([apply_grad]):
train_op.append(state_ops.assign_add(global_step, 1))
if config.is_chief:
train_op.append(
control_flow_ops.cond(
math_ops.greater_equal(
summary_accumulator.num_accumulated(),
n_batches_per_layer),
grow_tree_from_accumulated_summaries_fn,
control_flow_ops.no_op,
name='wait_until_n_batches_accumulated'))
return control_flow_ops.group(train_op, name='train_op')
estimator_spec = head.create_estimator_spec(features=features,
mode=mode,
labels=labels,
train_op_fn=_train_op_fn,
logits=logits)
if mode == model_fn.ModeKeys.TRAIN:
# Add an early stop hook.
estimator_spec = estimator_spec._replace(
training_hooks=estimator_spec.training_hooks +
(StopAtNumTreesHook(num_trees, tree_hparams.n_trees), ))
return estimator_spec
