def train(self, loss, predictions_dict, labels):
"""Grows a new tree and adds it to the ensemble.
Args:
loss: A scalar tensor representing average loss of examples.
predictions_dict: Dictionary of Rank 2 `Tensor` representing information
about predictions per example.
labels: Rank 2 `Tensor` representing labels per example.
Returns:
An op that adds a new tree to the ensemble.
Raises:
ValueError: if inputs are not valid.
"""
# Get the worker device from input dependencies.
input_deps = (self._dense_floats + self._sparse_float_indices +
self._sparse_int_indices)
worker_device = input_deps[0].device
# Get tensors relevant for training and form the loss.
predictions = predictions_dict[PREDICTIONS]
partition_ids = predictions_dict[PARTITION_IDS]
ensemble_stamp = predictions_dict[ENSEMBLE_STAMP]
gradients = gradients_impl.gradients(loss,
predictions,
name="Gradients",
colocate_gradients_with_ops=False,
gate_gradients=0,
aggregation_method=None)[0]
strategy = self._learner_config.multi_class_strategy
class_id = -1
# Handle different multiclass strategies.
if strategy == learner_pb2.LearnerConfig.TREE_PER_CLASS:
# We build one vs rest trees.
gradient_shape = tensor_shape.scalar()
hessian_shape = tensor_shape.scalar()
if self._logits_dimension == 1:
# We have only 1 score, gradients is of shape [batch, 1].
hessians = gradients_impl.gradients(
gradients,
predictions,
name="Hessian",
colocate_gradients_with_ops=False,
gate_gradients=0,
aggregation_method=None)[0]
squeezed_gradients = array_ops.squeeze(gradients, axis=[1])
squeezed_hessians = array_ops.squeeze(hessians, axis=[1])
else:
hessian_list = self._diagonal_hessian(gradients, predictions)
# Assemble hessian list into a tensor.
hessians = array_ops.stack(hessian_list, axis=1)
# Choose the class for which the tree is built (one vs rest).
class_id = math_ops.to_int32(
predictions_dict[NUM_TREES_ATTEMPTED] %
self._logits_dimension)
# Use class id tensor to get the column with that index from gradients
# and hessians.
squeezed_gradients = array_ops.squeeze(
_get_column_by_index(gradients, class_id))
squeezed_hessians = array_ops.squeeze(
_get_column_by_index(hessians, class_id))
else:
# Other multiclass strategies.
gradient_shape = tensor_shape.TensorShape([self._logits_dimension])
if strategy == learner_pb2.LearnerConfig.FULL_HESSIAN:
hessian_shape = tensor_shape.TensorShape(
([self._logits_dimension, self._logits_dimension]))
hessian_list = self._full_hessian(gradients, predictions)
else:
# Diagonal hessian strategy.
hessian_shape = tensor_shape.TensorShape(
([self._logits_dimension]))
hessian_list = self._diagonal_hessian(gradients, predictions)
squeezed_gradients = gradients
hessians = array_ops.stack(hessian_list, axis=1)
squeezed_hessians = hessians
# Get the weights for each example for quantiles calculation,
weights = self._get_weights(hessian_shape, squeezed_hessians)
regularization_config = self._learner_config.regularization
min_node_weight = self._learner_config.constraints.min_node_weight
# Create all handlers ensuring resources are evenly allocated across PS.
fc_name_idx = 0
handlers = []
init_stamp_token = constant_op.constant(0, dtype=dtypes.int64)
with ops.device(self._get_replica_device_setter(worker_device)):
# Create handlers for dense float columns
for dense_float_column_idx in range(len(self._dense_floats)):
fc_name = self._fc_names[fc_name_idx]
handlers.append(
ordinal_split_handler.DenseSplitHandler(
l1_regularization=regularization_config.l1,
l2_regularization=regularization_config.l2,
tree_complexity_regularization=(
regularization_config.tree_complexity),
min_node_weight=min_node_weight,
feature_column_group_id=dense_float_column_idx,
epsilon=0.01,
num_quantiles=100,
dense_float_column=self.
_dense_floats[dense_float_column_idx],
name=fc_name,
gradient_shape=gradient_shape,
hessian_shape=hessian_shape,
multiclass_strategy=strategy,
init_stamp_token=init_stamp_token))
fc_name_idx += 1
# Create handlers for sparse float columns.
for sparse_float_column_idx in range(
len(self._sparse_float_indices)):
fc_name = self._fc_names[fc_name_idx]
handlers.append(
ordinal_split_handler.SparseSplitHandler(
l1_regularization=regularization_config.l1,
l2_regularization=regularization_config.l2,
tree_complexity_regularization=(
regularization_config.tree_complexity),
min_node_weight=min_node_weight,
feature_column_group_id=sparse_float_column_idx,
epsilon=0.01,
num_quantiles=100,
sparse_float_column=sparse_tensor.SparseTensor(
self.
_sparse_float_indices[sparse_float_column_idx],
self._sparse_float_values[sparse_float_column_idx],
self._sparse_float_shapes[sparse_float_column_idx]
),
name=fc_name,
gradient_shape=gradient_shape,
hessian_shape=hessian_shape,
multiclass_strategy=strategy,
init_stamp_token=init_stamp_token))
fc_name_idx += 1
# Create handlers for sparse int columns.
for sparse_int_column_idx in range(len(self._sparse_int_indices)):
fc_name = self._fc_names[fc_name_idx]
handlers.append(
categorical_split_handler.EqualitySplitHandler(
l1_regularization=regularization_config.l1,
l2_regularization=regularization_config.l2,
tree_complexity_regularization=(
regularization_config.tree_complexity),
min_node_weight=min_node_weight,
feature_column_group_id=sparse_int_column_idx,
sparse_int_column=sparse_tensor.SparseTensor(
self._sparse_int_indices[sparse_int_column_idx],
self._sparse_int_values[sparse_int_column_idx],
self._sparse_int_shapes[sparse_int_column_idx]),
name=fc_name,
gradient_shape=gradient_shape,
hessian_shape=hessian_shape,
multiclass_strategy=strategy,
init_stamp_token=init_stamp_token))
fc_name_idx += 1
# Create steps accumulator.
steps_accumulator = stats_accumulator_ops.StatsAccumulator(
stamp_token=0,
gradient_shape=tensor_shape.scalar(),
hessian_shape=tensor_shape.scalar(),
name="StepsAccumulator")
# Create bias stats accumulator.
bias_stats_accumulator = stats_accumulator_ops.StatsAccumulator(
stamp_token=0,
gradient_shape=gradient_shape,
hessian_shape=hessian_shape,
name="BiasAccumulator")
# Create ensemble stats variables.
num_layer_examples = variables.Variable(
initial_value=array_ops.zeros([], dtypes.int64),
name="num_layer_examples",
trainable=False)
num_layer_steps = variables.Variable(initial_value=array_ops.zeros(
[], dtypes.int64),
name="num_layer_steps",
trainable=False)
num_layers = variables.Variable(initial_value=array_ops.zeros(
[], dtypes.int64),
name="num_layers",
trainable=False)
active_tree = variables.Variable(initial_value=array_ops.zeros(
[], dtypes.int64),
name="active_tree",
trainable=False)
active_layer = variables.Variable(initial_value=array_ops.zeros(
[], dtypes.int64),
name="active_layer",
trainable=False)
# Create ensemble stats summaries.
summary.scalar("layer_stats/num_examples", num_layer_examples)
summary.scalar("layer_stats/num_steps", num_layer_steps)
summary.scalar("ensemble_stats/active_tree", active_tree)
summary.scalar("ensemble_stats/active_layer", active_layer)
# Update bias stats.
stats_update_ops = []
continue_centering = variables.Variable(
initial_value=self._center_bias,
name="continue_centering",
trainable=False)
stats_update_ops.append(
control_flow_ops.cond(
continue_centering,
self._make_update_bias_stats_fn(ensemble_stamp, predictions,
gradients,
bias_stats_accumulator),
control_flow_ops.no_op))
# Update handler stats.
handler_reads = {}
for handler in handlers:
handler_reads[handler] = handler.scheduled_reads()
handler_results = batch_ops_utils.run_handler_scheduled_ops(
handler_reads, ensemble_stamp, worker_device)
per_handler_updates = {}
# Two values per handler. First one is if the handler is active for the
# current layer. The second one is if the handler is going to be active
# for the next layer.
subsampling_type = self._learner_config.WhichOneof("feature_fraction")
if subsampling_type == "feature_fraction_per_level":
seed = predictions_dict[NUM_LAYERS_ATTEMPTED]
active_handlers_current_layer = stateless.stateless_random_uniform(
shape=[len(handlers)], seed=[seed, 1])
active_handlers_next_layer = stateless.stateless_random_uniform(
shape=[len(handlers)], seed=[seed + 1, 1])
active_handlers = array_ops.stack(
[active_handlers_current_layer, active_handlers_next_layer],
axis=1)
active_handlers = (active_handlers <
self._learner_config.feature_fraction_per_level)
elif subsampling_type == "feature_fraction_per_tree":
seed = predictions_dict[NUM_TREES_ATTEMPTED]
active_handlers_current_layer = stateless.stateless_random_uniform(
shape=[len(handlers)], seed=[seed, 2])
active_handlers_current_layer = (
active_handlers_current_layer <
self._learner_config.feature_fraction_per_tree)
active_handlers = array_ops.stack(
active_handlers_current_layer,
array_ops.ones([len(handlers)], dtype=dtypes.bool))
else:
active_handlers = array_ops.ones([len(handlers), 2],
dtype=dtypes.bool)
# Prepare empty gradients and hessians when handlers are not ready.
empty_hess_shape = [1] + hessian_shape.as_list()
empty_grad_shape = [1] + gradient_shape.as_list()
empty_gradients = constant_op.constant([],
dtype=dtypes.float32,
shape=empty_grad_shape)
empty_hessians = constant_op.constant([],
dtype=dtypes.float32,
shape=empty_hess_shape)
for handler_idx in range(len(handlers)):
handler = handlers[handler_idx]
is_active = active_handlers[handler_idx]
updates, scheduled_updates = handler.update_stats(
ensemble_stamp, partition_ids, squeezed_gradients,
squeezed_hessians, empty_gradients, empty_hessians, weights,
is_active, handler_results[handler])
stats_update_ops.append(updates)
per_handler_updates[handler] = scheduled_updates
update_results = batch_ops_utils.run_handler_scheduled_ops(
per_handler_updates, ensemble_stamp, worker_device)
for update in update_results.values():
stats_update_ops += update
# Accumulate a step after updating stats.
batch_size = math_ops.cast(array_ops.shape(labels)[0], dtypes.float32)
with ops.control_dependencies(stats_update_ops):
add_step_op = steps_accumulator.add(ensemble_stamp, [0], [[0, 0]],
[batch_size], [1.0])
# Determine learning rate.
learning_rate_tuner = self._learner_config.learning_rate_tuner.WhichOneof(
"tuner")
if learning_rate_tuner == "fixed" or learning_rate_tuner == "dropout":
tuner = getattr(self._learner_config.learning_rate_tuner,
learning_rate_tuner)
learning_rate = tuner.learning_rate
else:
# TODO (nponomareva, soroush) do the line search. id:498 gh:499
raise ValueError("Line search learning rate is not yet supported.")
# After adding the step, decide if further processing is needed.
ensemble_update_ops = [add_step_op]
with ops.control_dependencies([add_step_op]):
if self._is_chief:
dropout_seed = predictions_dict[NUM_TREES_ATTEMPTED]
# Get accumulated steps and examples for the current layer.
_, _, _, _, acc_examples, acc_steps = steps_accumulator.serialize(
)
acc_examples = math_ops.cast(acc_examples[0], dtypes.int64)
acc_steps = math_ops.cast(acc_steps[0], dtypes.int64)
ensemble_update_ops.append(
num_layer_examples.assign(acc_examples))
ensemble_update_ops.append(num_layer_steps.assign(acc_steps))
# Determine whether we need to update tree ensemble.
examples_per_layer = self._examples_per_layer
if callable(examples_per_layer):
examples_per_layer = examples_per_layer(active_layer)
ensemble_update_ops.append(
control_flow_ops.cond(
acc_examples >= examples_per_layer,
self._make_update_ensemble_fn(
ensemble_stamp, steps_accumulator,
bias_stats_accumulator, continue_centering,
learning_rate, handlers, num_layers, active_tree,
active_layer, dropout_seed, class_id),
control_flow_ops.no_op))
# Calculate the loss to be reported.
# Note, the loss is calculated from the prediction considering dropouts, so
# that the value might look staggering over steps when the dropout ratio is
# high. eval_loss might be referred instead in the aspect of convergence.
return control_flow_ops.group(*ensemble_update_ops)
def testGenerateFeatureSplitCandidatesWithMinNodeWeight(self):
with self.test_session() as sess:
# The data looks like the following:
# Example | Gradients | Partition | Dense Quantile |
# i0 | (0.2, 0.12) | 0 | 1 |
# i1 | (-0.5, 0.07) | 0 | 1 |
# i2 | (1.2, 0.2) | 0 | 0 |
# i3 | (4.0, 2.0) | 1 | 1 |
dense_column = array_ops.constant([0.52, 0.52, 0.3, 0.52])
gradients = array_ops.constant([0.2, -0.5, 1.2, 4.0])
hessians = array_ops.constant([0.12, 0.07, 0.2, 2])
partition_ids = array_ops.constant([0, 0, 0, 1], dtype=dtypes.int32)
gradient_shape = tensor_shape.scalar()
hessian_shape = tensor_shape.scalar()
class_id = -1
split_handler = ordinal_split_handler.DenseSplitHandler(
l1_regularization=0.1,
l2_regularization=1,
tree_complexity_regularization=0.5,
min_node_weight=1.5,
epsilon=0.001,
num_quantiles=10,
feature_column_group_id=0,
dense_float_column=dense_column,
init_stamp_token=0,
gradient_shape=gradient_shape,
hessian_shape=hessian_shape,
multiclass_strategy=learner_pb2.LearnerConfig.TREE_PER_CLASS)
resources.initialize_resources(resources.shared_resources()).run()
empty_gradients, empty_hessians = get_empty_tensors(
gradient_shape, hessian_shape)
example_weights = array_ops.ones([4, 1], dtypes.float32)
update_1 = split_handler.update_stats_sync(
0,
partition_ids,
gradients,
hessians,
empty_gradients,
empty_hessians,
example_weights,
is_active=array_ops.constant([True, True]))
with ops.control_dependencies([update_1]):
are_splits_ready = split_handler.make_splits(0, 1, class_id)[0]
with ops.control_dependencies([are_splits_ready]):
update_2 = split_handler.update_stats_sync(
1,
partition_ids,
gradients,
hessians,
empty_gradients,
empty_hessians,
example_weights,
is_active=array_ops.constant([True, True]))
with ops.control_dependencies([update_2]):
are_splits_ready2, partitions, gains, splits = (
split_handler.make_splits(1, 2, class_id))
are_splits_ready, are_splits_ready2, partitions, gains, splits = (
sess.run([
are_splits_ready, are_splits_ready2, partitions, gains, splits
]))
# During the first iteration, inequality split handlers are not going to
# have any splits. Make sure that we return not_ready in that case.
self.assertFalse(are_splits_ready)
self.assertTrue(are_splits_ready2)
self.assertAllEqual([0, 1], partitions)
# Check the gain on partition 0 to be -0.5.
split_info = split_info_pb2.SplitInfo()
split_info.ParseFromString(splits[0])
left_child = split_info.left_child.vector
right_child = split_info.right_child.vector
split_node = split_info.split_node.dense_float_binary_split
# Make sure the gain is subtracted by the tree complexity regularization.
self.assertAllClose(-0.5, gains[0], 0.00001)
self.assertEqual(0, split_node.feature_column)
# Check the split on partition 1.
# (-4 + 0.1) / (2 + 1)
expected_left_weight = -1.3
expected_right_weight = 0
# Verify candidate for partition 1, there's only one active bucket here
# so -0.5 gain is expected (because of tree complexity.
split_info = split_info_pb2.SplitInfo()
split_info.ParseFromString(splits[1])
left_child = split_info.left_child.vector
right_child = split_info.right_child.vector
split_node = split_info.split_node.dense_float_binary_split
self.assertAllClose(-0.5, gains[1], 0.00001)
self.assertAllClose([expected_left_weight], left_child.value, 0.00001)
self.assertAllClose([expected_right_weight], right_child.value, 0.00001)
self.assertEqual(0, split_node.feature_column)
self.assertAllClose(0.52, split_node.threshold, 0.00001)
def testGenerateFeatureSplitCandidatesInactive(self):
with self.test_session() as sess:
# The data looks like the following:
# Example | Gradients | Partition | Dense Quantile |
# i0 | (0.2, 0.12) | 0 | 1 |
# i1 | (-0.5, 0.07) | 0 | 1 |
# i2 | (1.2, 0.2) | 0 | 0 |
# i3 | (4.0, 0.13) | 1 | 1 |
dense_column = array_ops.constant([0.52, 0.52, 0.3, 0.52])
gradients = array_ops.constant([0.2, -0.5, 1.2, 4.0])
hessians = array_ops.constant([0.12, 0.07, 0.2, 0.13])
partition_ids = array_ops.constant([0, 0, 0, 1], dtype=dtypes.int32)
gradient_shape = tensor_shape.scalar()
hessian_shape = tensor_shape.scalar()
class_id = -1
split_handler = ordinal_split_handler.DenseSplitHandler(
l1_regularization=0.1,
l2_regularization=1,
tree_complexity_regularization=0,
min_node_weight=0,
epsilon=0.001,
num_quantiles=10,
feature_column_group_id=0,
dense_float_column=dense_column,
init_stamp_token=0,
gradient_shape=gradient_shape,
hessian_shape=hessian_shape,
multiclass_strategy=learner_pb2.LearnerConfig.TREE_PER_CLASS)
resources.initialize_resources(resources.shared_resources()).run()
empty_gradients, empty_hessians = get_empty_tensors(
gradient_shape, hessian_shape)
example_weights = array_ops.ones([4, 1], dtypes.float32)
update_1 = split_handler.update_stats_sync(
0,
partition_ids,
gradients,
hessians,
empty_gradients,
empty_hessians,
example_weights,
is_active=array_ops.constant([True, False]))
with ops.control_dependencies([update_1]):
are_splits_ready = split_handler.make_splits(0, 1, class_id)[0]
with ops.control_dependencies([are_splits_ready]):
update_2 = split_handler.update_stats_sync(
1,
partition_ids,
gradients,
hessians,
empty_gradients,
empty_hessians,
example_weights,
is_active=array_ops.constant([False, True]))
with ops.control_dependencies([update_2]):
are_splits_ready2, partitions, gains, splits = (
split_handler.make_splits(1, 2, class_id))
are_splits_ready, are_splits_ready2, partitions, gains, splits = (
sess.run([
are_splits_ready, are_splits_ready2, partitions, gains, splits
]))
# During the first iteration, inequality split handlers are not going to
# have any splits. Make sure that we return not_ready in that case.
self.assertFalse(are_splits_ready)
self.assertTrue(are_splits_ready2)
# The handler was inactive, so it shouldn't return any splits.
self.assertEqual(len(partitions), 0)
self.assertEqual(len(gains), 0)
self.assertEqual(len(splits), 0)
def testGenerateFeatureSplitCandidates(self):
with self.test_session() as sess:
# The data looks like the following:
# Example | Gradients | Partition | Dense Quantile |
# i0 | (0.2, 0.12) | 0 | 1 |
# i1 | (-0.5, 0.07) | 0 | 1 |
# i2 | (1.2, 0.2) | 0 | 0 |
# i3 | (4.0, 0.13) | 1 | 1 |
dense_column = array_ops.constant([0.52, 0.52, 0.3, 0.52])
gradients = array_ops.constant([0.2, -0.5, 1.2, 4.0])
hessians = array_ops.constant([0.12, 0.07, 0.2, 0.13])
partition_ids = array_ops.constant([0, 0, 0, 1], dtype=dtypes.int32)
class_id = -1
gradient_shape = tensor_shape.scalar()
hessian_shape = tensor_shape.scalar()
split_handler = ordinal_split_handler.DenseSplitHandler(
l1_regularization=0.1,
l2_regularization=1,
tree_complexity_regularization=0,
min_node_weight=0,
epsilon=0.001,
num_quantiles=10,
feature_column_group_id=0,
dense_float_column=dense_column,
init_stamp_token=0,
gradient_shape=gradient_shape,
hessian_shape=hessian_shape,
multiclass_strategy=learner_pb2.LearnerConfig.TREE_PER_CLASS)
resources.initialize_resources(resources.shared_resources()).run()
empty_gradients, empty_hessians = get_empty_tensors(
gradient_shape, hessian_shape)
example_weights = array_ops.ones([4, 1], dtypes.float32)
update_1 = split_handler.update_stats_sync(
0,
partition_ids,
gradients,
hessians,
empty_gradients,
empty_hessians,
example_weights,
is_active=array_ops.constant([True, True]))
with ops.control_dependencies([update_1]):
are_splits_ready = split_handler.make_splits(0, 1, class_id)[0]
with ops.control_dependencies([are_splits_ready]):
update_2 = split_handler.update_stats_sync(
1,
partition_ids,
gradients,
hessians,
empty_gradients,
empty_hessians,
example_weights,
is_active=array_ops.constant([True, True]))
with ops.control_dependencies([update_2]):
are_splits_ready2, partitions, gains, splits = (
split_handler.make_splits(1, 2, class_id))
are_splits_ready, are_splits_ready2, partitions, gains, splits = (
sess.run([
are_splits_ready, are_splits_ready2, partitions, gains, splits
]))
# During the first iteration, inequality split handlers are not going to
# have any splits. Make sure that we return not_ready in that case.
self.assertFalse(are_splits_ready)
self.assertTrue(are_splits_ready2)
self.assertAllEqual([0, 1], partitions)
# Check the split on partition 0.
# -(1.2 - 0.1) / (0.2 + 1)
expected_left_weight = -0.91666
# expected_left_weight * -(1.2 - 0.1)
expected_left_gain = 1.0083333333333331
# (-0.5 + 0.2 + 0.1) / (0.19 + 1)
expected_right_weight = 0.1680672
# expected_right_weight * -(-0.5 + 0.2 + 0.1))
expected_right_gain = 0.033613445378151252
# (0.2 + -0.5 + 1.2 - 0.1) ** 2 / (0.12 + 0.07 + 0.2 + 1)
expected_bias_gain = 0.46043165467625885
split_info = split_info_pb2.SplitInfo()
split_info.ParseFromString(splits[0])
left_child = split_info.left_child.vector
right_child = split_info.right_child.vector
split_node = split_info.split_node.dense_float_binary_split
self.assertAllClose(
expected_left_gain + expected_right_gain - expected_bias_gain, gains[0],
0.00001)
self.assertAllClose([expected_left_weight], left_child.value, 0.00001)
self.assertAllClose([expected_right_weight], right_child.value, 0.00001)
self.assertEqual(0, split_node.feature_column)
self.assertAllClose(0.3, split_node.threshold, 0.00001)
# Check the split on partition 1.
# (-4 + 0.1) / (0.13 + 1)
expected_left_weight = -3.4513274336283186
# (-4 + 0.1) ** 2 / (0.13 + 1)
expected_left_gain = 13.460176991150442
expected_right_weight = 0
expected_right_gain = 0
# (-4 + 0.1) ** 2 / (0.13 + 1)
expected_bias_gain = 13.460176991150442
# Verify candidate for partition 1, there's only one active bucket here
# so zero gain is expected.
split_info = split_info_pb2.SplitInfo()
split_info.ParseFromString(splits[1])
left_child = split_info.left_child.vector
right_child = split_info.right_child.vector
split_node = split_info.split_node.dense_float_binary_split
self.assertAllClose(0.0, gains[1], 0.00001)
self.assertAllClose([expected_left_weight], left_child.value, 0.00001)
self.assertAllClose([expected_right_weight], right_child.value, 0.00001)
self.assertEqual(0, split_node.feature_column)
self.assertAllClose(0.52, split_node.threshold, 0.00001)
def testGenerateFeatureSplitCandidatesMulticlassDiagonalHessian(self):
with self.test_session() as sess:
dense_column = array_ops.constant([0.52, 0.52, 0.3, 0.52])
# Batch size is 4, 2 gradients per each instance.
gradients = array_ops.constant(
[[0.2, 0.1], [-0.5, 0.2], [1.2, 3.4], [4.0, -3.5]], shape=[4, 2])
# Each hessian is a diagonal of a full hessian matrix.
hessian_0 = [0.12, 0.11]
hessian_1 = [0.07, 0.2]
hessian_2 = [0.2, 0.9]
hessian_3 = [0.13, 2.2]
hessians = array_ops.constant(
[hessian_0, hessian_1, hessian_2, hessian_3])
partition_ids = array_ops.constant([0, 0, 0, 1], dtype=dtypes.int32)
class_id = -1
gradient_shape = tensor_shape.TensorShape([2])
hessian_shape = tensor_shape.TensorShape([2])
split_handler = ordinal_split_handler.DenseSplitHandler(
l1_regularization=0,
l2_regularization=1,
tree_complexity_regularization=0,
min_node_weight=0,
epsilon=0.001,
num_quantiles=3,
feature_column_group_id=0,
dense_float_column=dense_column,
init_stamp_token=0,
gradient_shape=gradient_shape,
hessian_shape=hessian_shape,
multiclass_strategy=learner_pb2.LearnerConfig.DIAGONAL_HESSIAN)
resources.initialize_resources(resources.shared_resources()).run()
empty_gradients, empty_hessians = get_empty_tensors(
gradient_shape, hessian_shape)
example_weights = array_ops.ones([4, 1], dtypes.float32)
update_1 = split_handler.update_stats_sync(
0,
partition_ids,
gradients,
hessians,
empty_gradients,
empty_hessians,
example_weights,
is_active=array_ops.constant([True, True]))
with ops.control_dependencies([update_1]):
are_splits_ready = split_handler.make_splits(0, 1, class_id)[0]
with ops.control_dependencies([are_splits_ready]):
update_2 = split_handler.update_stats_sync(
1,
partition_ids,
gradients,
hessians,
empty_gradients,
empty_hessians,
example_weights,
is_active=array_ops.constant([True, True]))
with ops.control_dependencies([update_2]):
are_splits_ready2, partitions, gains, splits = (
split_handler.make_splits(1, 2, class_id))
are_splits_ready, are_splits_ready2, partitions, gains, splits = (
sess.run([
are_splits_ready, are_splits_ready2, partitions, gains, splits
]))
# During the first iteration, inequality split handlers are not going to
# have any splits. Make sure that we return not_ready in that case.
self.assertFalse(are_splits_ready)
self.assertTrue(are_splits_ready2)
split_info = split_info_pb2.SplitInfo()
split_info.ParseFromString(splits[0])
left_child = split_info.left_child.vector
right_child = split_info.right_child.vector
split_node = split_info.split_node.dense_float_binary_split
# Each leaf has 2 element vector.
self.assertEqual(2, len(left_child.value))
self.assertEqual(2, len(right_child.value))
self.assertEqual(0, split_node.feature_column)
self.assertAllClose(0.3, split_node.threshold, 1e-6)
