def testBiasEnsembleMultiClass(self):
with self.test_session():
tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
tree = tree_ensemble_config.trees.add()
tree_ensemble_config.tree_metadata.add().is_finalized = True
leaf = tree.nodes.add().leaf
_append_to_leaf(leaf, 0, -0.4)
_append_to_leaf(leaf, 1, 0.9)
tree_ensemble_config.tree_weights.append(1.0)
tree_ensemble_handle = model_ops.tree_ensemble_variable(
stamp_token=0,
tree_ensemble_config=tree_ensemble_config.SerializeToString(),
name="multiclass")
resources.initialize_resources(resources.shared_resources()).run()
# Prepare learner config.
learner_config = learner_pb2.LearnerConfig()
learner_config.num_classes = 3
result, dropout_info = self._get_predictions(
tree_ensemble_handle,
learner_config=learner_config.SerializeToString(),
reduce_dim=True)
self.assertAllClose([[-0.4, 0.9], [-0.4, 0.9]], result.eval())
# Empty dropout.
self.assertAllEqual([[], []], dropout_info.eval())
def testTreeFinalized(self):
with self.test_session():
tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
# Depth 3 tree.
tree1 = tree_ensemble_config.trees.add()
_set_float_split(tree1.nodes.add().dense_float_binary_split, 0, 9.0, 1, 2)
_set_float_split(tree1.nodes.add()
.sparse_float_binary_split_default_left.split, 0, -20.0,
3, 4)
_append_to_leaf(tree1.nodes.add().leaf, 0, 0.2)
_append_to_leaf(tree1.nodes.add().leaf, 0, 0.3)
_set_categorical_id_split(tree1.nodes.add().categorical_id_binary_split,
0, 9, 5, 6)
_append_to_leaf(tree1.nodes.add().leaf, 0, 0.5)
_append_to_leaf(tree1.nodes.add().leaf, 0, 0.6)
tree_ensemble_config.tree_weights.append(1.0)
tree_ensemble_config.tree_metadata.add().is_finalized = True
tree_ensemble_handle = model_ops.tree_ensemble_variable(
stamp_token=0,
tree_ensemble_config=tree_ensemble_config.SerializeToString(),
name="full_ensemble")
resources.initialize_resources(resources.shared_resources()).run()
result = prediction_ops.gradient_trees_partition_examples(
tree_ensemble_handle, [self._dense_float_tensor], [
self._sparse_float_indices1, self._sparse_float_indices2
], [self._sparse_float_values1, self._sparse_float_values2],
[self._sparse_float_shape1,
self._sparse_float_shape2], [self._sparse_int_indices1],
[self._sparse_int_values1], [self._sparse_int_shape1])
self.assertAllEqual([0, 0], result.eval())
def __init__(self, num_quantiles, epsilon, serialized_tf_config=None):
self._num_quantiles = num_quantiles
self._epsilon = epsilon
self._serialized_tf_config = serialized_tf_config
# _stamp_token is used to commit the state of the qaccumulator. In
# this case, the qaccumulator state is completely returned and stored
# as part of quantile_state/summary in the combiner fn (i.e the summary is
# extracted and stored outside the qaccumulator). So we don't use
# the timestamp mechanism to signify progress in the qaccumulator state.
self._stamp_token = 0
# Represents an empty summary. This could be changed to a tf.constant
# implemented by the quantile ops library.
self._empty_summary = None
# Create a new session with a new graph for quantile ops.
self._session = tf.Session(
graph=tf.Graph(),
config=_maybe_deserialize_tf_config(serialized_tf_config))
with self._session.graph.as_default():
with self._session.as_default():
self._qaccumulator = quantile_ops.QuantileAccumulator(
init_stamp_token=self._stamp_token,
num_quantiles=self._num_quantiles,
epsilon=self._epsilon,
name='qaccumulator')
resources.initialize_resources(
resources.shared_resources()).run()
def testAverageMoreThanNumTreesExist(self):
with self.test_session():
tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
adjusted_tree_ensemble_config = (
tree_config_pb2.DecisionTreeEnsembleConfig())
# When we say to average over more trees than possible, it is averaging
# across all trees.
total_num = 100
for i in range(0, total_num):
tree = tree_ensemble_config.trees.add()
_append_to_leaf(tree.nodes.add().leaf, 0, -0.4)
tree_ensemble_config.tree_metadata.add().is_finalized = True
tree_ensemble_config.tree_weights.append(1.0)
# This is how the weight will look after averaging
copy_tree = adjusted_tree_ensemble_config.trees.add()
_append_to_leaf(copy_tree.nodes.add().leaf, 0, -0.4)
adjusted_tree_ensemble_config.tree_metadata.add().is_finalized = True
adjusted_tree_ensemble_config.tree_weights.append(
1.0 * (total_num - i) / total_num)
# Prepare learner config WITH AVERAGING.
learner_config = learner_pb2.LearnerConfig()
learner_config.num_classes = 2
# We have only 100 trees but we ask to average over 250.
learner_config.averaging_config.average_last_n_trees = 250
# No averaging config.
learner_config_no_averaging = learner_pb2.LearnerConfig()
learner_config_no_averaging.num_classes = 2
tree_ensemble_handle = model_ops.tree_ensemble_variable(
stamp_token=0,
tree_ensemble_config=tree_ensemble_config.SerializeToString(),
name="existing")
# This is how our ensemble will "look" during averaging
adjusted_tree_ensemble_handle = model_ops.tree_ensemble_variable(
stamp_token=0,
tree_ensemble_config=adjusted_tree_ensemble_config.SerializeToString(
),
name="adjusted")
resources.initialize_resources(resources.shared_resources()).run()
result, dropout_info = self._get_predictions(
tree_ensemble_handle,
learner_config.SerializeToString(),
apply_averaging=True,
reduce_dim=True)
pattern_result, pattern_dropout_info = self._get_predictions(
adjusted_tree_ensemble_handle,
learner_config_no_averaging.SerializeToString(),
apply_averaging=False,
reduce_dim=True)
self.assertAllEqual(result.eval(), pattern_result.eval())
self.assertAllEqual(dropout_info.eval(), pattern_dropout_info.eval())
def testCachedPredictionOnEmptyEnsemble(self):
"""Tests that prediction on a dummy ensemble does not fail."""
with self.cached_session() as session:
# Create a dummy ensemble.
tree_ensemble = boosted_trees_ops.TreeEnsemble(
'ensemble', serialized_proto='')
tree_ensemble_handle = tree_ensemble.resource_handle
resources.initialize_resources(resources.shared_resources()).run()
# No previous cached values.
cached_tree_ids = [0, 0]
cached_node_ids = [0, 0]
# We have two features: 0 and 1. Values don't matter here on a dummy
# ensemble.
feature_0_values = [67, 5]
feature_1_values = [9, 17]
# Grow tree ensemble.
predict_op = boosted_trees_ops.training_predict(
tree_ensemble_handle,
cached_tree_ids=cached_tree_ids,
cached_node_ids=cached_node_ids,
bucketized_features=[feature_0_values, feature_1_values],
logits_dimension=1)
logits_updates, new_tree_ids, new_node_ids = session.run(predict_op)
# Nothing changed.
self.assertAllClose(cached_tree_ids, new_tree_ids)
self.assertAllClose(cached_node_ids, new_node_ids)
self.assertAllClose([[0], [0]], logits_updates)
def testBasicQuantileBucketsMultipleResources(self):
with self.test_session() as sess:
quantile_accumulator_handle_0 = self.create_resource("float_0", self.eps,
self.max_elements)
quantile_accumulator_handle_1 = self.create_resource("float_1", self.eps,
self.max_elements)
resources.initialize_resources(resources.shared_resources()).run()
summaries = boosted_trees_ops.make_quantile_summaries(
[self._feature_0, self._feature_1], self._example_weights,
epsilon=self.eps)
summary_op_0 = boosted_trees_ops.quantile_add_summaries(
quantile_accumulator_handle_0,
[summaries[0]])
summary_op_1 = boosted_trees_ops.quantile_add_summaries(
quantile_accumulator_handle_1,
[summaries[1]])
flush_op_0 = boosted_trees_ops.quantile_flush(
quantile_accumulator_handle_0, self.num_quantiles)
flush_op_1 = boosted_trees_ops.quantile_flush(
quantile_accumulator_handle_1, self.num_quantiles)
bucket_0 = boosted_trees_ops.get_bucket_boundaries(
quantile_accumulator_handle_0, num_features=1)
bucket_1 = boosted_trees_ops.get_bucket_boundaries(
quantile_accumulator_handle_1, num_features=1)
quantiles = boosted_trees_ops.boosted_trees_bucketize(
[self._feature_0, self._feature_1], bucket_0 + bucket_1)
sess.run([summary_op_0, summary_op_1])
sess.run([flush_op_0, flush_op_1])
self.assertAllClose(self._feature_0_boundaries, bucket_0[0].eval())
self.assertAllClose(self._feature_1_boundaries, bucket_1[0].eval())
self.assertAllClose(self._feature_0_quantiles, quantiles[0].eval())
self.assertAllClose(self._feature_1_quantiles, quantiles[1].eval())
def testBasicQuantileBucketsMultipleResources(self):
with self.cached_session() as sess:
quantile_accumulator_handle_0 = self.create_resource("float_0", self.eps,
self.max_elements)
quantile_accumulator_handle_1 = self.create_resource("float_1", self.eps,
self.max_elements)
resources.initialize_resources(resources.shared_resources()).run()
summaries = boosted_trees_ops.make_quantile_summaries(
[self._feature_0, self._feature_1], self._example_weights,
epsilon=self.eps)
summary_op_0 = boosted_trees_ops.quantile_add_summaries(
quantile_accumulator_handle_0,
[summaries[0]])
summary_op_1 = boosted_trees_ops.quantile_add_summaries(
quantile_accumulator_handle_1,
[summaries[1]])
flush_op_0 = boosted_trees_ops.quantile_flush(
quantile_accumulator_handle_0, self.num_quantiles)
flush_op_1 = boosted_trees_ops.quantile_flush(
quantile_accumulator_handle_1, self.num_quantiles)
bucket_0 = boosted_trees_ops.get_bucket_boundaries(
quantile_accumulator_handle_0, num_features=1)
bucket_1 = boosted_trees_ops.get_bucket_boundaries(
quantile_accumulator_handle_1, num_features=1)
quantiles = boosted_trees_ops.boosted_trees_bucketize(
[self._feature_0, self._feature_1], bucket_0 + bucket_1)
sess.run([summary_op_0, summary_op_1])
sess.run([flush_op_0, flush_op_1])
self.assertAllClose(self._feature_0_boundaries, bucket_0[0].eval())
self.assertAllClose(self._feature_1_boundaries, bucket_1[0].eval())
self.assertAllClose(self._feature_0_quantiles, quantiles[0].eval())
self.assertAllClose(self._feature_1_quantiles, quantiles[1].eval())
def testCreate(self):
with self.cached_session():
tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
tree = tree_ensemble_config.trees.add()
_append_to_leaf(tree.nodes.add().leaf, 0, -0.4)
tree_ensemble_config.tree_weights.append(1.0)
# Prepare learner config.
learner_config = learner_pb2.LearnerConfig()
learner_config.num_classes = 2
tree_ensemble_handle = model_ops.tree_ensemble_variable(
stamp_token=3,
tree_ensemble_config=tree_ensemble_config.SerializeToString(),
name="create_tree")
resources.initialize_resources(resources.shared_resources()).run()
result, _ = prediction_ops.gradient_trees_prediction(
tree_ensemble_handle,
self._seed, [self._dense_float_tensor], [
self._sparse_float_indices1, self._sparse_float_indices2
], [self._sparse_float_values1, self._sparse_float_values2],
[self._sparse_float_shape1,
self._sparse_float_shape2], [self._sparse_int_indices1],
[self._sparse_int_values1], [self._sparse_int_shape1],
learner_config=learner_config.SerializeToString(),
apply_dropout=False,
apply_averaging=False,
center_bias=False,
reduce_dim=True)
self.assertAllClose(result.eval(), [[-0.4], [-0.4]])
stamp_token = model_ops.tree_ensemble_stamp_token(tree_ensemble_handle)
self.assertEqual(stamp_token.eval(), 3)
def testBasicCallableParams(self):
for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
with self.cached_session():
var0 = resource_variable_ops.ResourceVariable([1.0, 2.0], dtype=dtype)
var1 = resource_variable_ops.ResourceVariable([3.0, 4.0], dtype=dtype)
grads0 = constant_op.constant([0.1, 0.1], dtype=dtype)
grads1 = constant_op.constant([0.01, 0.01], dtype=dtype)
lr = lambda: 3.0
sgd_op = gradient_descent.GradientDescentOptimizer(lr).apply_gradients(
zip([grads0, grads1], [var0, var1]))
# TODO(apassos) calling initialize_resources on all resources here
# doesn't work because the sessions and graph are reused across unit
# tests and this would mean trying to reinitialize variables. Figure out
# a long-term solution for this.
resources.initialize_resources([var0, var1]).run()
# Fetch params to validate initial values
self.assertAllCloseAccordingToType([1.0, 2.0], var0.eval())
self.assertAllCloseAccordingToType([3.0, 4.0], var1.eval())
# Run 1 step of sgd
sgd_op.run()
# Validate updated params
self.assertAllCloseAccordingToType([1.0 - 3.0 * 0.1, 2.0 - 3.0 * 0.1],
var0.eval())
self.assertAllCloseAccordingToType([3.0 - 3.0 * 0.01, 4.0 - 3.0 * 0.01],
var1.eval())
def testBasicCallableParams(self):
for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
with self.cached_session():
var0 = resource_variable_ops.ResourceVariable([1.0, 2.0],
dtype=dtype)
var1 = resource_variable_ops.ResourceVariable([3.0, 4.0],
dtype=dtype)
grads0 = constant_op.constant([0.1, 0.1], dtype=dtype)
grads1 = constant_op.constant([0.01, 0.01], dtype=dtype)
lr = lambda: 3.0
sgd_op = gradient_descent.SGD(lr).apply_gradients(
zip([grads0, grads1], [var0, var1]))
# TODO(apassos) calling initialize_resources on all resources here
# doesn't work because the sessions and graph are reused across unit
# tests and this would mean trying to reinitialize variables. Figure out
# a long-term solution for this.
resources.initialize_resources([var0, var1]).run()
# Fetch params to validate initial values
self.assertAllCloseAccordingToType([1.0, 2.0], var0.eval())
self.assertAllCloseAccordingToType([3.0, 4.0], var1.eval())
# Run 1 step of sgd
sgd_op.run()
# Validate updated params
self.assertAllCloseAccordingToType(
[1.0 - 3.0 * 0.1, 2.0 - 3.0 * 0.1], var0.eval())
self.assertAllCloseAccordingToType(
[3.0 - 3.0 * 0.01, 4.0 - 3.0 * 0.01], var1.eval())
def _testStreamingQuantileBucketsHelper(
self, inputs, num_quantiles=3, expected_buckets=None):
"""Helper to test quantile buckets on different inputs."""
# set generate_quantiles to True since the test will generate fewer
# boundaries otherwise.
with self.test_session() as sess:
accumulator = quantile_ops.QuantileAccumulator(
init_stamp_token=0, num_quantiles=num_quantiles,
epsilon=0.001, name="q1", generate_quantiles=True)
resources.initialize_resources(resources.shared_resources()).run()
input_column = array_ops.placeholder(dtypes.float32)
weights = array_ops.placeholder(dtypes.float32)
update = accumulator.add_summary(
stamp_token=0,
column=input_column,
example_weights=weights)
with self.test_session() as sess:
sess.run(update,
{input_column: inputs,
weights: [1] * len(inputs)})
with self.test_session() as sess:
sess.run(accumulator.flush(stamp_token=0, next_stamp_token=1))
are_ready_flush, buckets = (accumulator.get_buckets(stamp_token=1))
buckets, are_ready_flush = (sess.run(
[buckets, are_ready_flush]))
self.assertEqual(True, are_ready_flush)
# By default, use 3 quantiles, 4 boundaries for simplicity.
self.assertEqual(num_quantiles + 1, len(buckets))
if expected_buckets:
self.assertAllEqual(buckets, expected_buckets)
def _get_train_op_and_ensemble(self, head, config, is_classification,
train_in_memory):
"""Calls bt_model_fn() and returns the train_op and ensemble_serialzed."""
features, labels = _make_train_input_fn(is_classification)()
estimator_spec = boosted_trees._bt_model_fn( # pylint:disable=protected-access
features=features,
labels=labels,
mode=model_fn.ModeKeys.TRAIN,
head=head,
feature_columns=self._feature_columns,
tree_hparams=self._tree_hparams,
example_id_column_name=EXAMPLE_ID_COLUMN,
n_batches_per_layer=1,
config=config,
train_in_memory=train_in_memory)
resources.initialize_resources(resources.shared_resources()).run()
variables.global_variables_initializer().run()
variables.local_variables_initializer().run()
# Gets the train_op and serialized proto of the ensemble.
shared_resources = resources.shared_resources()
self.assertEqual(1, len(shared_resources))
train_op = estimator_spec.train_op
with ops.control_dependencies([train_op]):
_, ensemble_serialized = (
gen_boosted_trees_ops.boosted_trees_serialize_ensemble(
shared_resources[0].handle))
return train_op, ensemble_serialized
def testStreamingQuantileBuckets(self):
"""Sets up the quantile summary op test as follows.
100 batches of data is added to the accumulator. The batches are in form:
[0 1 .. 99]
[100 101 .. 200]
...
[9900 9901 .. 9999]
All the batches have 1 for all the example weights.
"""
with self.test_session() as sess:
accumulator = quantile_ops.QuantileAccumulator(
init_stamp_token=0, num_quantiles=3, epsilon=0.01, name="q1")
resources.initialize_resources(resources.shared_resources()).run()
weight_placeholder = array_ops.placeholder(dtypes.float32)
dense_placeholder = array_ops.placeholder(dtypes.float32)
update = accumulator.add_summary(
stamp_token=0,
column=dense_placeholder,
example_weights=weight_placeholder)
with self.test_session() as sess:
for i in range(100):
dense_float = np.linspace(
i * 100, (i + 1) * 100 - 1, num=100).reshape(-1, 1)
sess.run(update, {
dense_placeholder: dense_float,
weight_placeholder: np.ones(shape=(100, 1), dtype=np.float32)
})
with self.test_session() as sess:
sess.run(accumulator.flush(stamp_token=0, next_stamp_token=1))
are_ready_flush, buckets = (accumulator.get_buckets(stamp_token=1))
buckets, are_ready_flush = (sess.run([buckets, are_ready_flush]))
self.assertEqual(True, are_ready_flush)
self.assertAllEqual([0, 3335., 6671., 9999.], buckets)
def initialize_local_state(self, tf_config=None):
"""Called by the CombineFnWrapper's __init__ method.
This can be used to set non-pickleable local state. It is used in
conjunction with overriding __reduce__ so this state is not pickled. This
method must be called prior to any other method.
Args:
tf_config: (optional) A tf.ConfigProto
"""
# _stamp_token is used to commit the state of the qaccumulator. In
# this case, the qaccumulator state is completely returned and stored
# as part of quantile_state/summary in the combiner fn (i.e the summary is
# extracted and stored outside the qaccumulator). So we don't use
# the timestamp mechanism to signify progress in the qaccumulator state.
self._stamp_token = 0
# Represents an empty summary. This could be changed to a tf.constant
# implemented by the quantile ops library.
self._empty_summary = None
# Create a new session with a new graph for quantile ops.
self._session = tf.Session(graph=tf.Graph(), config=tf_config)
with self._session.graph.as_default():
with self._session.as_default():
self._qaccumulator = quantile_ops.QuantileAccumulator(
init_stamp_token=self._stamp_token,
num_quantiles=self._num_quantiles,
epsilon=self._epsilon,
name='qaccumulator')
resources.initialize_resources(
resources.shared_resources()).run()
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 testMinimizeResourceVariable(self):
for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
with self.cached_session():
var0 = resource_variable_ops.ResourceVariable([[1.0, 2.0]],
dtype=dtype)
var1 = resource_variable_ops.ResourceVariable([3.0],
dtype=dtype)
x = constant_op.constant([[4.0], [5.0]], dtype=dtype)
pred = math_ops.matmul(var0, x) + var1
loss = pred * pred
sgd_op = gradient_descent.SGD(1.0).minimize(loss)
# TODO(apassos) calling initialize_resources on all resources here
# doesn't work because the sessions and graph are reused across unit
# tests and this would mean trying to reinitialize variables. Figure out
# a long-term solution for this.
resources.initialize_resources([var0, var1]).run()
# Fetch params to validate initial values
self.assertAllCloseAccordingToType([[1.0, 2.0]], var0.eval())
self.assertAllCloseAccordingToType([3.0], var1.eval())
# Run 1 step of sgd
sgd_op.run()
# Validate updated params
np_pred = 1.0 * 4.0 + 2.0 * 5.0 + 3.0
np_grad = 2 * np_pred
self.assertAllCloseAccordingToType(
[[1.0 - np_grad * 4.0, 2.0 - np_grad * 5.0]], var0.eval())
self.assertAllCloseAccordingToType([3.0 - np_grad],
var1.eval())
def testWithExistingEnsembleAndShrinkage(self):
with self.test_session():
# Add shrinkage config.
learning_rate = 0.0001
tree_ensemble = tree_config_pb2.DecisionTreeEnsembleConfig()
# Add 10 trees with some weights.
for i in range(0, 5):
tree = tree_ensemble.trees.add()
_append_to_leaf(tree.nodes.add().leaf, 0, -0.4)
tree_ensemble.tree_weights.append(i + 1)
meta = tree_ensemble.tree_metadata.add()
meta.num_tree_weight_updates = 1
tree_ensemble_handle = model_ops.tree_ensemble_variable(
stamp_token=0,
tree_ensemble_config=tree_ensemble.SerializeToString(),
name="existing")
# Create non-zero feature importance.
feature_usage_counts = variables.Variable(
initial_value=np.array([4, 7], np.int64),
name="feature_usage_counts",
trainable=False)
feature_gains = variables.Variable(initial_value=np.array(
[0.2, 0.8], np.float32),
name="feature_gains",
trainable=False)
resources.initialize_resources(resources.shared_resources()).run()
variables.initialize_all_variables().run()
output_ensemble = tree_config_pb2.DecisionTreeEnsembleConfig()
with ops.control_dependencies([
ensemble_optimizer_ops.add_trees_to_ensemble(
tree_ensemble_handle,
self._ensemble_to_add.SerializeToString(),
feature_usage_counts, [1, 2],
feature_gains, [0.5, 0.3], [[], []],
learning_rate=learning_rate)
]):
output_ensemble.ParseFromString(
model_ops.tree_ensemble_serialize(tree_ensemble_handle)
[1].eval())
# The weights of previous trees stayed the same, new tree (LAST) is added
# with shrinkage weight.
self.assertAllClose([1.0, 2.0, 3.0, 4.0, 5.0, learning_rate],
output_ensemble.tree_weights)
# Check that all number of updates are equal to 1 (e,g, no old tree weight
# got adjusted.
for i in range(0, 6):
self.assertEqual(
1,
output_ensemble.tree_metadata[i].num_tree_weight_updates)
# Ensure feature importance was aggregated correctly.
self.assertAllEqual([5, 9], feature_usage_counts.eval())
self.assertArrayNear(
[0.2 + 0.5 * learning_rate, 0.8 + 0.3 * learning_rate],
feature_gains.eval(), 1e-6)
def testTrainFnNonChiefWithCentering(self):
"""Tests the train function running on worker with bias centering."""
with self.test_session():
ensemble_handle = model_ops.tree_ensemble_variable(
stamp_token=0, tree_ensemble_config="", name="tree_ensemble")
learner_config = learner_pb2.LearnerConfig()
learner_config.learning_rate_tuner.fixed.learning_rate = 0.1
learner_config.num_classes = 2
learner_config.regularization.l1 = 0
learner_config.regularization.l2 = 0
learner_config.constraints.max_tree_depth = 1
learner_config.constraints.min_node_weight = 0
features = {}
features["dense_float"] = array_ops.ones([4, 1], dtypes.float32)
gbdt_model = gbdt_batch.GradientBoostedDecisionTreeModel(
is_chief=False,
num_ps_replicas=0,
center_bias=True,
ensemble_handle=ensemble_handle,
examples_per_layer=1,
learner_config=learner_config,
features=features)
predictions = array_ops.constant([[0.0], [1.0], [0.0], [2.0]],
dtype=dtypes.float32)
partition_ids = array_ops.zeros([4], dtypes.int32)
ensemble_stamp = variables.Variable(initial_value=0,
name="ensemble_stamp",
trainable=False,
dtype=dtypes.int64)
predictions_dict = {
"predictions": predictions,
"predictions_no_dropout": predictions,
"partition_ids": partition_ids,
"ensemble_stamp": ensemble_stamp
}
labels = array_ops.ones([4, 1], dtypes.float32)
weights = array_ops.ones([4, 1], dtypes.float32)
# Create train op.
train_op = gbdt_model.train(loss=math_ops.reduce_mean(
_squared_loss(labels, weights, predictions)),
predictions_dict=predictions_dict,
labels=labels)
variables.global_variables_initializer().run()
resources.initialize_resources(resources.shared_resources()).run()
# Regardless of how many times the train op is run, a non-chief worker
# can only accumulate stats so the tree ensemble never changes.
for _ in range(5):
train_op.run()
stamp_token, serialized = model_ops.tree_ensemble_serialize(
ensemble_handle)
output = tree_config_pb2.DecisionTreeEnsembleConfig()
output.ParseFromString(serialized.eval())
self.assertEquals(len(output.trees), 0)
self.assertEquals(len(output.tree_weights), 0)
self.assertEquals(stamp_token.eval(), 0)
def _get_train_op_and_ensemble(self, head, config, is_classification,
train_in_memory):
"""Calls bt_model_fn() and returns the train_op and ensemble_serialzed."""
features, labels = _make_train_input_fn(is_classification)()
estimator_spec = boosted_trees._bt_model_fn( # pylint:disable=protected-access
features=features,
labels=labels,
mode=model_fn.ModeKeys.TRAIN,
head=head,
feature_columns=self._feature_columns,
tree_hparams=self._tree_hparams,
example_id_column_name=EXAMPLE_ID_COLUMN,
n_batches_per_layer=1,
config=config,
train_in_memory=train_in_memory)
resources.initialize_resources(resources.shared_resources()).run()
variables.global_variables_initializer().run()
variables.local_variables_initializer().run()
# Gets the train_op and serialized proto of the ensemble.
shared_resources = resources.shared_resources()
self.assertEqual(1, len(shared_resources))
train_op = estimator_spec.train_op
with ops.control_dependencies([train_op]):
_, ensemble_serialized = (
gen_boosted_trees_ops.boosted_trees_serialize_ensemble(
shared_resources[0].handle))
return train_op, ensemble_serialized
def testBasicQuantileBucketsSingleResourcesAddFlushed(self):
with self.cached_session():
quantile_accumulator_handle = self.create_resource(
"floats_0", self.eps, self.max_elements, 2)
resources.initialize_resources(resources.shared_resources()).run()
summaries = boosted_trees_ops.make_quantile_summaries(
[self._feature_0, self._feature_1],
self._example_weights,
epsilon=self.eps)
summary_op = boosted_trees_ops.quantile_add_summaries(
quantile_accumulator_handle, summaries)
flushed_summaries = flush_quantile_summaries(
quantile_accumulator_handle, num_features=2)
# We are testing whether the flushed summaries output at the previous step
# will give the same expected results by inputing it to add_summaries
summary_op_2 = boosted_trees_ops.quantile_add_summaries(
quantile_accumulator_handle, flushed_summaries)
flush_op = boosted_trees_ops.quantile_flush(
quantile_accumulator_handle, self.num_quantiles)
buckets = boosted_trees_ops.get_bucket_boundaries(
quantile_accumulator_handle, num_features=2)
quantiles = boosted_trees_ops.boosted_trees_bucketize(
[self._feature_0, self._feature_1], buckets)
self.evaluate(summary_op)
self.evaluate(summary_op_2)
self.evaluate(flush_op)
self.assertAllClose(self._feature_0_boundaries, buckets[0].eval())
self.assertAllClose(self._feature_1_boundaries, buckets[1].eval())
self.assertAllClose(self._feature_0_quantiles, quantiles[0].eval())
self.assertAllClose(self._feature_1_quantiles, quantiles[1].eval())
def testSaveRestoreBeforeFlush(self):
save_dir = os.path.join(self.get_temp_dir(), "save_restore")
save_path = os.path.join(tempfile.mkdtemp(prefix=save_dir), "hash")
with self.cached_session() as sess:
accumulator = boosted_trees_ops.QuantileAccumulator(
num_streams=2, num_quantiles=3, epsilon=self.eps, name="q0")
save = saver.Saver()
resources.initialize_resources(resources.shared_resources()).run()
summaries = accumulator.add_summaries([self._feature_0, self._feature_1],
self._example_weights)
self.evaluate(summaries)
buckets = accumulator.get_bucket_boundaries()
self.assertAllClose([], buckets[0].eval())
self.assertAllClose([], buckets[1].eval())
save.save(sess, save_path)
self.evaluate(accumulator.flush())
self.assertAllClose(self._feature_0_boundaries, buckets[0].eval())
self.assertAllClose(self._feature_1_boundaries, buckets[1].eval())
with self.session(graph=ops.Graph()) as sess:
accumulator = boosted_trees_ops.QuantileAccumulator(
num_streams=2, num_quantiles=3, epsilon=self.eps, name="q0")
save = saver.Saver()
save.restore(sess, save_path)
buckets = accumulator.get_bucket_boundaries()
self.assertAllClose([], buckets[0].eval())
self.assertAllClose([], buckets[1].eval())
def testSaveRestoreBeforeFlush(self):
save_dir = os.path.join(self.get_temp_dir(), "save_restore")
save_path = os.path.join(tempfile.mkdtemp(prefix=save_dir), "hash")
with self.cached_session() as sess:
accumulator = boosted_trees_ops.QuantileAccumulator(
num_streams=2, num_quantiles=3, epsilon=self.eps, name="q0")
save = saver.Saver()
resources.initialize_resources(resources.shared_resources()).run()
summaries = accumulator.add_summaries([self._feature_0, self._feature_1],
self._example_weights)
self.evaluate(summaries)
buckets = accumulator.get_bucket_boundaries()
self.assertAllClose([], buckets[0].eval())
self.assertAllClose([], buckets[1].eval())
save.save(sess, save_path)
self.evaluate(accumulator.flush())
self.assertAllClose(self._feature_0_boundaries, buckets[0].eval())
self.assertAllClose(self._feature_1_boundaries, buckets[1].eval())
with self.session(graph=ops.Graph()) as sess:
accumulator = boosted_trees_ops.QuantileAccumulator(
num_streams=2, num_quantiles=3, epsilon=self.eps, name="q0")
save = saver.Saver()
save.restore(sess, save_path)
buckets = accumulator.get_bucket_boundaries()
self.assertAllClose([], buckets[0].eval())
self.assertAllClose([], buckets[1].eval())
def testBasicResourceVariable(self):
for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
# train.GradientDescentOptimizer is V1 only API.
with ops.Graph().as_default(), self.cached_session():
var0 = resource_variable_ops.ResourceVariable([1.0, 2.0],
dtype=dtype)
var1 = resource_variable_ops.ResourceVariable([3.0, 4.0],
dtype=dtype)
grads0 = constant_op.constant([0.1, 0.1], dtype=dtype)
grads1 = constant_op.constant([0.01, 0.01], dtype=dtype)
sgd_op = gradient_descent.GradientDescentOptimizer(
3.0).apply_gradients(zip([grads0, grads1], [var0, var1]))
# TODO(apassos) calling initialize_resources on all resources here
# doesn't work because the sessions and graph are reused across unit
# tests and this would mean trying to reinitialize variables. Figure out
# a long-term solution for this.
resources.initialize_resources([var0, var1]).run()
# Fetch params to validate initial values
self.assertAllCloseAccordingToType([1.0, 2.0],
self.evaluate(var0))
self.assertAllCloseAccordingToType([3.0, 4.0],
self.evaluate(var1))
# Run 1 step of sgd
sgd_op.run()
# Validate updated params
self.assertAllCloseAccordingToType(
[1.0 - 3.0 * 0.1, 2.0 - 3.0 * 0.1], self.evaluate(var0))
self.assertAllCloseAccordingToType(
[3.0 - 3.0 * 0.01, 4.0 - 3.0 * 0.01], self.evaluate(var1))
def testCachedPredictionOnEmptyEnsemble(self):
"""Tests that prediction on a dummy ensemble does not fail."""
with self.test_session() as session:
# Create a dummy ensemble.
tree_ensemble = boosted_trees_ops.TreeEnsemble('ensemble',
serialized_proto='')
tree_ensemble_handle = tree_ensemble.resource_handle
resources.initialize_resources(resources.shared_resources()).run()
# No previous cached values.
cached_tree_ids = [0, 0]
cached_node_ids = [0, 0]
# We have two features: 0 and 1. Values don't matter here on a dummy
# ensemble.
feature_0_values = [67, 5]
feature_1_values = [9, 17]
# Grow tree ensemble.
predict_op = boosted_trees_ops.training_predict(
tree_ensemble_handle,
cached_tree_ids=cached_tree_ids,
cached_node_ids=cached_node_ids,
bucketized_features=[feature_0_values, feature_1_values],
logits_dimension=1)
logits_updates, new_tree_ids, new_node_ids = session.run(
predict_op)
# Nothing changed.
self.assertAllClose(cached_tree_ids, new_tree_ids)
self.assertAllClose(cached_node_ids, new_node_ids)
self.assertAllClose([[0], [0]], logits_updates)
def testCreate(self):
with self.test_session():
tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
tree = tree_ensemble_config.trees.add()
_append_to_leaf(tree.nodes.add().leaf, 0, -0.4)
tree_ensemble_config.tree_weights.append(1.0)
# Prepare learner config.
learner_config = learner_pb2.LearnerConfig()
learner_config.num_classes = 2
tree_ensemble_handle = model_ops.tree_ensemble_variable(
stamp_token=3,
tree_ensemble_config=tree_ensemble_config.SerializeToString(),
name="create_tree")
resources.initialize_resources(resources.shared_resources()).run()
result, _, _ = prediction_ops.gradient_trees_prediction(
tree_ensemble_handle,
self._seed, [self._dense_float_tensor],
[self._sparse_float_indices1, self._sparse_float_indices2],
[self._sparse_float_values1, self._sparse_float_values2],
[self._sparse_float_shape1, self._sparse_float_shape2],
[self._sparse_int_indices1], [self._sparse_int_values1],
[self._sparse_int_shape1],
learner_config=learner_config.SerializeToString(),
apply_dropout=False,
apply_averaging=False,
center_bias=False,
reduce_dim=True)
self.assertAllClose(result.eval(), [[-0.4], [-0.4]])
stamp_token = model_ops.tree_ensemble_stamp_token(
tree_ensemble_handle)
self.assertEqual(stamp_token.eval(), 3)
def _eval(self, var, accum, linear, grad, lr, l1, l2, l2_shrinkage=0,
lr_power=1, multiply_linear_by_lr=False):
dtype = np.float32
var = np.array(var, dtype=dtype)
accum = np.array(accum, dtype=dtype)
linear = np.array(linear, dtype=dtype)
grad = np.array(grad, dtype=dtype)
use_v2 = bool(l2_shrinkage)
with self.session() as session:
lr = constant_op.constant(lr, dtype=dtype)
l1 = constant_op.constant(l1, dtype=dtype)
l2 = constant_op.constant(l2, dtype=dtype)
l2_shrinkage = constant_op.constant(l2_shrinkage, dtype=dtype)
lr_power = constant_op.constant(lr_power, dtype=dtype)
v_var = resource_variable_ops.ResourceVariable(var, dtype=dtype)
v_accum = resource_variable_ops.ResourceVariable(accum, dtype=dtype)
v_linear = resource_variable_ops.ResourceVariable(linear, dtype=dtype)
resources.initialize_resources([v_var, v_accum, v_linear]).run()
assert not (use_v2 and multiply_linear_by_lr)
if use_v2:
session.run(training_ops.resource_apply_ftrl_v2(
v_var.handle, v_accum.handle, v_linear.handle,
grad, lr, l1, l2, l2_shrinkage, lr_power,
multiply_linear_by_lr=multiply_linear_by_lr))
else:
session.run(training_ops.resource_apply_ftrl(
v_var.handle, v_accum.handle, v_linear.handle,
grad, lr, l1, l2, lr_power,
multiply_linear_by_lr=multiply_linear_by_lr))
return (v_var.read_value().eval().reshape(var.shape),
v_accum.read_value().eval().reshape(accum.shape),
v_linear.read_value().eval().reshape(linear.shape))
def testMinimizeResourceVariable(self):
for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
with self.cached_session():
var0 = resource_variable_ops.ResourceVariable([[1.0, 2.0]], dtype=dtype)
var1 = resource_variable_ops.ResourceVariable([3.0], dtype=dtype)
x = constant_op.constant([[4.0], [5.0]], dtype=dtype)
pred = math_ops.matmul(var0, x) + var1
loss = pred * pred
sgd_op = gradient_descent.GradientDescentOptimizer(1.0).minimize(loss)
# TODO(apassos) calling initialize_resources on all resources here
# doesn't work because the sessions and graph are reused across unit
# tests and this would mean trying to reinitialize variables. Figure out
# a long-term solution for this.
resources.initialize_resources([var0, var1]).run()
# Fetch params to validate initial values
self.assertAllCloseAccordingToType([[1.0, 2.0]], var0.eval())
self.assertAllCloseAccordingToType([3.0], var1.eval())
# Run 1 step of sgd
sgd_op.run()
# Validate updated params
np_pred = 1.0 * 4.0 + 2.0 * 5.0 + 3.0
np_grad = 2 * np_pred
self.assertAllCloseAccordingToType(
[[1.0 - np_grad * 4.0, 2.0 - np_grad * 5.0]], var0.eval())
self.assertAllCloseAccordingToType([3.0 - np_grad], var1.eval())
def testDropout(self):
with self.test_session():
# Empty tree ensenble.
tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
# Add 1000 trees with some weights.
for i in range(0, 999):
tree = tree_ensemble_config.trees.add()
tree_ensemble_config.tree_metadata.add().is_finalized = True
_append_to_leaf(tree.nodes.add().leaf, 0, -0.4)
tree_ensemble_config.tree_weights.append(i + 1)
# Prepare learner/dropout config.
learner_config = learner_pb2.LearnerConfig()
learner_config.learning_rate_tuner.dropout.dropout_probability = 0.5
learner_config.learning_rate_tuner.dropout.learning_rate = 1.0
learner_config.num_classes = 2
# Apply dropout.
tree_ensemble_handle = model_ops.tree_ensemble_variable(
stamp_token=0,
tree_ensemble_config=tree_ensemble_config.SerializeToString(),
name="existing")
resources.initialize_resources(resources.shared_resources()).run()
result, dropout_info = self._get_predictions(
tree_ensemble_handle,
learner_config=learner_config.SerializeToString(),
apply_dropout=True,
apply_averaging=False,
center_bias=False,
reduce_dim=True)
# We expect approx 500 trees were dropped.
dropout_info = dropout_info.eval()
self.assertIn(dropout_info[0].size, range(400, 601))
self.assertEqual(dropout_info[0].size, dropout_info[1].size)
for i in range(dropout_info[0].size):
dropped_index = dropout_info[0][i]
dropped_weight = dropout_info[1][i]
# We constructed the trees so tree number + 1 is the tree weight, so
# we can check here the weights for dropped trees.
self.assertEqual(dropped_index + 1, dropped_weight)
# Don't apply dropout.
result_no_dropout, no_dropout_info = self._get_predictions(
tree_ensemble_handle,
learner_config=learner_config.SerializeToString(),
apply_dropout=False,
apply_averaging=False,
center_bias=False,
reduce_dim=True)
self.assertEqual(result.eval().size, result_no_dropout.eval().size)
for i in range(result.eval().size):
self.assertNotEqual(result.eval()[i], result_no_dropout.eval()[i])
# We expect none of the trees were dropped.
self.assertAllEqual([[], []], no_dropout_info.eval())
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 test_simple(self): with self.session(): TADDR_VALID = 'zrpull://127.0.0.1:5555' output = zmq_conn_handle(TADDR_VALID, ZMQ_HWM, 0) resources.initialize_resources(resources.local_resources()).run() # assertDTypeEqual not working for resource type. it trans tf.dtype to np.dtype and resource is incompatible with numpy #self.assertDtypeEqual(output, dtypes.resource.as_numpy_type) self.assertEqual(type(output.dtype), type(dtypes.resource))
def testSaveRestoreBeforeFlush(self):
save_dir = os.path.join(self.get_temp_dir(), "save_restore")
save_path = os.path.join(tempfile.mkdtemp(prefix=save_dir), "hash")
with self.test_session(graph=ops.Graph()) as sess:
accumulator = quantile_ops.QuantileAccumulator(init_stamp_token=0,
num_quantiles=3,
epsilon=0.33,
name="q0")
save = saver.Saver()
resources.initialize_resources(resources.shared_resources()).run()
sparse_indices_0 = constant_op.constant(
[[1, 0], [2, 1], [3, 0], [4, 2], [5, 0]], dtype=dtypes.int64)
sparse_values_0 = constant_op.constant([2.0, 3.0, 4.0, 5.0, 6.0],
dtype=dtypes.float32)
sparse_shape_0 = constant_op.constant([6, 3], dtype=dtypes.int64)
example_weights = constant_op.constant([10, 1, 1, 1, 1, 1],
dtype=dtypes.float32,
shape=[6, 1])
update = accumulator.add_summary(stamp_token=0,
column=sparse_tensor.SparseTensor(
sparse_indices_0,
sparse_values_0,
sparse_shape_0),
example_weights=example_weights)
update.run()
save.save(sess, save_path)
reset = accumulator.flush(stamp_token=0, next_stamp_token=1)
with ops.control_dependencies([reset]):
are_ready_flush, buckets = (accumulator.get_buckets(
stamp_token=1))
buckets, are_ready_flush = (sess.run([buckets, are_ready_flush]))
self.assertEqual(True, are_ready_flush)
self.assertAllEqual([2, 4, 6.], buckets)
with self.test_session(graph=ops.Graph()) as sess:
accumulator = quantile_ops.QuantileAccumulator(init_stamp_token=0,
num_quantiles=3,
epsilon=0.33,
name="q0")
save = saver.Saver()
# Restore the saved values in the parameter nodes.
save.restore(sess, save_path)
are_ready_noflush = accumulator.get_buckets(stamp_token=0)[0]
with ops.control_dependencies([are_ready_noflush]):
reset = accumulator.flush(stamp_token=0, next_stamp_token=1)
with ops.control_dependencies([reset]):
are_ready_flush, buckets = accumulator.get_buckets(
stamp_token=1)
buckets, are_ready_flush, are_ready_noflush = (sess.run(
[buckets, are_ready_flush, are_ready_noflush]))
self.assertFalse(are_ready_noflush)
self.assertTrue(are_ready_flush)
self.assertAllEqual([2, 4, 6.], buckets)
def testWithExistingEnsembleAndShrinkage(self):
with self.test_session():
# Add shrinkage config.
learning_rate = 0.0001
tree_ensemble = tree_config_pb2.DecisionTreeEnsembleConfig()
# Add 10 trees with some weights.
for i in range(0, 5):
tree = tree_ensemble.trees.add()
_append_to_leaf(tree.nodes.add().leaf, 0, -0.4)
tree_ensemble.tree_weights.append(i + 1)
meta = tree_ensemble.tree_metadata.add()
meta.num_tree_weight_updates = 1
tree_ensemble_handle = model_ops.tree_ensemble_variable(
stamp_token=0,
tree_ensemble_config=tree_ensemble.SerializeToString(),
name="existing")
# Create non-zero feature importance.
feature_usage_counts = variables.Variable(
initial_value=np.array([4, 7], np.int64),
name="feature_usage_counts",
trainable=False)
feature_gains = variables.Variable(
initial_value=np.array([0.2, 0.8], np.float32),
name="feature_gains",
trainable=False)
resources.initialize_resources(resources.shared_resources()).run()
variables.initialize_all_variables().run()
output_ensemble = tree_config_pb2.DecisionTreeEnsembleConfig()
with ops.control_dependencies([
ensemble_optimizer_ops.add_trees_to_ensemble(
tree_ensemble_handle,
self._ensemble_to_add.SerializeToString(),
feature_usage_counts, [1, 2],
feature_gains, [0.5, 0.3], [[], []],
learning_rate=learning_rate)
]):
output_ensemble.ParseFromString(
model_ops.tree_ensemble_serialize(tree_ensemble_handle)[1].eval())
# The weights of previous trees stayed the same, new tree (LAST) is added
# with shrinkage weight.
self.assertAllClose([1.0, 2.0, 3.0, 4.0, 5.0, learning_rate],
output_ensemble.tree_weights)
# Check that all number of updates are equal to 1 (e,g, no old tree weight
# got adjusted.
for i in range(0, 6):
self.assertEqual(
1, output_ensemble.tree_metadata[i].num_tree_weight_updates)
# Ensure feature importance was aggregated correctly.
self.assertAllEqual([5, 9], feature_usage_counts.eval())
self.assertArrayNear(
[0.2 + 0.5 * learning_rate, 0.8 + 0.3 * learning_rate],
feature_gains.eval(), 1e-6)
def testCreate(self):
with self.test_session():
ensemble = boosted_trees_ops.TreeEnsemble('ensemble')
resources.initialize_resources(resources.shared_resources()).run()
stamp_token = ensemble.get_stamp_token()
self.assertEqual(0, stamp_token.eval())
(_, num_trees, num_finalized_trees,
num_attempted_layers) = ensemble.get_states()
self.assertEqual(0, num_trees.eval())
self.assertEqual(0, num_finalized_trees.eval())
self.assertEqual(0, num_attempted_layers.eval())
def testCreate(self):
with self.cached_session():
ensemble = boosted_trees_ops.TreeEnsemble('ensemble')
resources.initialize_resources(resources.shared_resources()).run()
stamp_token = ensemble.get_stamp_token()
self.assertEqual(0, self.evaluate(stamp_token))
(_, num_trees, num_finalized_trees, num_attempted_layers,
nodes_range) = ensemble.get_states()
self.assertEqual(0, self.evaluate(num_trees))
self.assertEqual(0, self.evaluate(num_finalized_trees))
self.assertEqual(0, self.evaluate(num_attempted_layers))
self.assertAllEqual([0, 1], self.evaluate(nodes_range))
def testPredictFn(self):
"""Tests the predict function."""
with self.test_session() as sess:
# Create ensemble with one bias node.
ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
text_format.Merge(
"""
trees {
nodes {
leaf {
vector {
value: 0.25
}
}
}
}
tree_weights: 1.0
tree_metadata {
num_tree_weight_updates: 1
num_layers_grown: 1
is_finalized: true
}""", ensemble_config)
ensemble_handle = model_ops.tree_ensemble_variable(
stamp_token=3,
tree_ensemble_config=ensemble_config.SerializeToString(),
name="tree_ensemble")
resources.initialize_resources(resources.shared_resources()).run()
learner_config = learner_pb2.LearnerConfig()
learner_config.learning_rate_tuner.fixed.learning_rate = 0.1
learner_config.num_classes = 2
learner_config.regularization.l1 = 0
learner_config.regularization.l2 = 0
learner_config.constraints.max_tree_depth = 1
learner_config.constraints.min_node_weight = 0
features = {}
features["dense_float"] = array_ops.ones([4, 1], dtypes.float32)
gbdt_model = gbdt_batch.GradientBoostedDecisionTreeModel(
is_chief=False,
num_ps_replicas=0,
center_bias=True,
ensemble_handle=ensemble_handle,
examples_per_layer=1,
learner_config=learner_config,
features=features)
# Create predict op.
mode = model_fn.ModeKeys.EVAL
predictions_dict = sess.run(gbdt_model.predict(mode))
self.assertEquals(predictions_dict["ensemble_stamp"], 3)
self.assertAllClose(predictions_dict["predictions"],
[[0.25], [0.25], [0.25], [0.25]])
self.assertAllClose(predictions_dict["partition_ids"],
[0, 0, 0, 0])
def testCreate(self):
with self.cached_session():
ensemble = boosted_trees_ops.TreeEnsemble('ensemble')
resources.initialize_resources(resources.shared_resources()).run()
stamp_token = ensemble.get_stamp_token()
self.assertEqual(0, self.evaluate(stamp_token))
(_, num_trees, num_finalized_trees, num_attempted_layers,
nodes_range) = ensemble.get_states()
self.assertEqual(0, self.evaluate(num_trees))
self.assertEqual(0, self.evaluate(num_finalized_trees))
self.assertEqual(0, self.evaluate(num_attempted_layers))
self.assertAllEqual([0, 1], self.evaluate(nodes_range))
def testPredictFn(self):
"""Tests the predict function."""
with self.test_session() as sess:
# Create ensemble with one bias node.
ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
text_format.Merge("""
trees {
nodes {
leaf {
vector {
value: 0.25
}
}
}
}
tree_weights: 1.0
tree_metadata {
num_tree_weight_updates: 1
num_layers_grown: 1
is_finalized: true
}""", ensemble_config)
ensemble_handle = model_ops.tree_ensemble_variable(
stamp_token=3,
tree_ensemble_config=ensemble_config.SerializeToString(),
name="tree_ensemble")
resources.initialize_resources(resources.shared_resources()).run()
learner_config = learner_pb2.LearnerConfig()
learner_config.learning_rate_tuner.fixed.learning_rate = 0.1
learner_config.num_classes = 2
learner_config.regularization.l1 = 0
learner_config.regularization.l2 = 0
learner_config.constraints.max_tree_depth = 1
learner_config.constraints.min_node_weight = 0
features = {}
features["dense_float"] = array_ops.ones([4, 1], dtypes.float32)
gbdt_model = gbdt_batch.GradientBoostedDecisionTreeModel(
is_chief=False,
num_ps_replicas=0,
center_bias=True,
ensemble_handle=ensemble_handle,
examples_per_layer=1,
learner_config=learner_config,
features=features)
# Create predict op.
mode = model_fn.ModeKeys.EVAL
predictions_dict = sess.run(gbdt_model.predict(mode))
self.assertEquals(predictions_dict["ensemble_stamp"], 3)
self.assertAllClose(predictions_dict["predictions"], [[0.25], [0.25],
[0.25], [0.25]])
self.assertAllClose(predictions_dict["partition_ids"], [0, 0, 0, 0])
def testSaveRestoreBeforeFlush(self):
save_dir = os.path.join(self.get_temp_dir(), "save_restore")
save_path = os.path.join(tempfile.mkdtemp(prefix=save_dir), "hash")
with self.test_session(graph=ops.Graph()) as sess:
accumulator = quantile_ops.QuantileAccumulator(
init_stamp_token=0, num_quantiles=3, epsilon=0.33, name="q0")
save = saver.Saver()
resources.initialize_resources(resources.shared_resources()).run()
sparse_indices_0 = constant_op.constant(
[[1, 0], [2, 1], [3, 0], [4, 2], [5, 0]], dtype=dtypes.int64)
sparse_values_0 = constant_op.constant(
[2.0, 3.0, 4.0, 5.0, 6.0], dtype=dtypes.float32)
sparse_shape_0 = constant_op.constant([6, 3], dtype=dtypes.int64)
example_weights = constant_op.constant(
[10, 1, 1, 1, 1, 1], dtype=dtypes.float32, shape=[6, 1])
update = accumulator.add_summary(
stamp_token=0,
column=sparse_tensor.SparseTensor(sparse_indices_0, sparse_values_0,
sparse_shape_0),
example_weights=example_weights)
update.run()
save.save(sess, save_path)
reset = accumulator.flush(stamp_token=0, next_stamp_token=1)
with ops.control_dependencies([reset]):
are_ready_flush, buckets = (accumulator.get_buckets(stamp_token=1))
buckets, are_ready_flush = (sess.run([buckets, are_ready_flush]))
self.assertEqual(True, are_ready_flush)
self.assertAllEqual([2, 4, 6.], buckets)
with self.test_session(graph=ops.Graph()) as sess:
accumulator = quantile_ops.QuantileAccumulator(
init_stamp_token=0, num_quantiles=3, epsilon=0.33, name="q0")
save = saver.Saver()
# Restore the saved values in the parameter nodes.
save.restore(sess, save_path)
are_ready_noflush = accumulator.get_buckets(stamp_token=0)[0]
with ops.control_dependencies([are_ready_noflush]):
reset = accumulator.flush(stamp_token=0, next_stamp_token=1)
with ops.control_dependencies([reset]):
are_ready_flush, buckets = accumulator.get_buckets(stamp_token=1)
buckets, are_ready_flush, are_ready_noflush = (sess.run(
[buckets, are_ready_flush, are_ready_noflush]))
self.assertFalse(are_ready_noflush)
self.assertTrue(are_ready_flush)
self.assertAllEqual([2, 4, 6.], buckets)
def testEmpty(self):
with self.cached_session() as sess:
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 = [0, 0, 0, 1]
indices = constant_op.constant_v1([],
dtype=dtypes.int64,
shape=[0, 2])
values = constant_op.constant_v1([], dtype=dtypes.int64)
gradient_shape = tensor_shape.TensorShape([])
hessian_shape = tensor_shape.TensorShape([])
class_id = -1
split_handler = categorical_split_handler.EqualitySplitHandler(
l1_regularization=0.1,
l2_regularization=1,
tree_complexity_regularization=0,
min_node_weight=0,
sparse_int_column=sparse_tensor.SparseTensor(
indices, values, [4, 1]),
feature_column_group_id=0,
gradient_shape=gradient_shape,
hessian_shape=hessian_shape,
multiclass_strategy=learner_pb2.LearnerConfig.TREE_PER_CLASS,
init_stamp_token=0)
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, partitions, gains, splits = (
split_handler.make_splits(0, 1, class_id))
are_splits_ready, partitions, gains, splits = (sess.run(
[are_splits_ready, partitions, gains, splits]))
self.assertTrue(are_splits_ready)
self.assertEqual(len(partitions), 0)
self.assertEqual(len(gains), 0)
self.assertEqual(len(splits), 0)
def testMetadataMissing(self):
# Sometimes we want to do prediction on trees that are not added to ensemble
# (for example in
with self.test_session():
tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
# Bias tree.
tree1 = tree_ensemble_config.trees.add()
_append_to_leaf(tree1.nodes.add().leaf, 0, -0.4)
# Depth 3 tree.
tree2 = tree_ensemble_config.trees.add()
# We are not setting the tree_ensemble_config.tree_metadata in this test.
_set_float_split(tree2.nodes.add().dense_float_binary_split, 0, 9.0, 1, 2)
_set_float_split(tree2.nodes.add()
.sparse_float_binary_split_default_left.split, 0, -20.0,
3, 4)
_append_to_leaf(tree2.nodes.add().leaf, 0, 0.5)
_append_to_leaf(tree2.nodes.add().leaf, 0, 1.2)
_set_categorical_id_split(tree2.nodes.add().categorical_id_binary_split,
0, 9, 5, 6)
_append_to_leaf(tree2.nodes.add().leaf, 0, -0.9)
_append_to_leaf(tree2.nodes.add().leaf, 0, 0.7)
tree_ensemble_config.tree_weights.append(1.0)
tree_ensemble_config.tree_weights.append(1.0)
tree_ensemble_handle = model_ops.tree_ensemble_variable(
stamp_token=0,
tree_ensemble_config=tree_ensemble_config.SerializeToString(),
name="full_ensemble")
resources.initialize_resources(resources.shared_resources()).run()
# Prepare learner config.
learner_config = learner_pb2.LearnerConfig()
learner_config.num_classes = 2
result, dropout_info = self._get_predictions(
tree_ensemble_handle,
learner_config=learner_config.SerializeToString(),
reduce_dim=True)
# The first example will get bias -0.4 from first tree and
# leaf 4 payload of -0.9 hence -1.3, the second example will
# get the same bias -0.4 and leaf 3 payload (sparse feature missing)
# of 1.2 hence 0.8.
self.assertAllClose([[-1.3], [0.8]], result.eval())
# Empty dropout.
self.assertAllEqual([[], []], dropout_info.eval())
def testUsedHandlers(self):
with self.cached_session():
tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
tree_ensemble_config.growing_metadata.used_handler_ids.append(1)
tree_ensemble_config.growing_metadata.used_handler_ids.append(5)
stamp_token = 3
tree_ensemble_handle = model_ops.tree_ensemble_variable(
stamp_token=stamp_token,
tree_ensemble_config=tree_ensemble_config.SerializeToString(),
name="create_tree")
resources.initialize_resources(resources.shared_resources()).run()
result = model_ops.tree_ensemble_used_handlers(
tree_ensemble_handle, stamp_token, num_all_handlers=6)
self.assertAllEqual([0, 1, 0, 0, 0, 1], result.used_handlers_mask.eval())
self.assertEqual(2, result.num_used_handlers.eval())
def testStreamingQuantileBucketsLowPrecisionInput(self):
"""Tests inputs that simulate low precision float16 values."""
num_quantiles = 3
# set generate_quantiles to True since the test will generate fewer
# boundaries otherwise.
with self.test_session() as sess:
accumulator = quantile_ops.QuantileAccumulator(
init_stamp_token=0,
num_quantiles=num_quantiles,
epsilon=0.001,
name="q1",
generate_quantiles=True)
resources.initialize_resources(resources.shared_resources()).run()
input_column = array_ops.placeholder(dtypes.float32)
weights = array_ops.placeholder(dtypes.float32)
update = accumulator.add_summary(stamp_token=0,
column=input_column,
example_weights=weights)
with self.test_session() as sess:
# This input is generated by integer in the range [2030, 2060]
# but represented by with float16 precision. Integers <= 2048 are
# exactly represented, whereas numbers > 2048 are rounded; and hence
# numbers > 2048 are repeated. For precision loss / rounding, see:
# https://en.wikipedia.org/wiki/Half-precision_floating-point_format.
#
# The intent of the test is not handling of float16 values, but to
# validate the number of buckets is returned, in cases where the input
# may contain repeated values.
inputs = [
2030.0, 2031.0, 2032.0, 2033.0, 2034.0, 2035.0, 2036.0, 2037.0,
2038.0, 2039.0, 2040.0, 2041.0, 2042.0, 2043.0, 2044.0, 2045.0,
2046.0, 2047.0, 2048.0, 2048.0, 2050.0, 2052.0, 2052.0, 2052.0,
2054.0, 2056.0, 2056.0, 2056.0, 2058.0, 2060.0
]
sess.run(update, {
input_column: inputs,
weights: [1] * len(inputs)
})
with self.test_session() as sess:
sess.run(accumulator.flush(stamp_token=0, next_stamp_token=1))
are_ready_flush, buckets = (accumulator.get_buckets(stamp_token=1))
buckets, are_ready_flush = (sess.run([buckets, are_ready_flush]))
self.assertEqual(True, are_ready_flush)
self.assertEqual(num_quantiles + 1, len(buckets))
self.assertAllEqual([2030, 2040, 2050, 2060], buckets)
def tree_models(X, y, num_feat, num_class):
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=1)
num_features = num_feat
num_steps = 400
num_classes = num_class
num_trees = 10
max_nodes = 1000
X = tf.placeholder(tf.float32, shape=[None, num_features])
Y = tf.placeholder(tf.int64, shape=[None])
hparams = tensor_forest.ForestHParams(num_classes=num_classes, num_features=num_features, num_trees=num_trees, max_nodes=max_nodes).fill()
forest_graph = tensor_forest.RandomForestGraphs(hparams)
train_op = forest_graph.training_graph(X, Y)
loss_op = forest_graph.training_loss(X, Y)
infer_op, _, _, = forest_graph.inference_graph(X)
correct_prediction = tf.equal(tf.argmax(infer_op, 1), tf.cast(Y, tf.int64))
accuracy_op = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
init_vars = tf.group(tf.global_variables_initializer(), resources.initialize_resources(resources.shared_resources()))
rf_sess = tf.Session()
rf_sess.run(init_vars)
for i in range(1, num_steps + 1):
_, l = rf_sess.run([train_op, loss_op], feed_dict={X: X_train, Y: y_train})
if i % 50 == 0 or i == 1:
acc = rf_sess.run(accuracy_op, feed_dict={X: X_train, Y: y_train})
print("Step %i, Loss: %f, Acc: %f" % (i, l, acc))
print("Test Accuracy:", rf_sess.run(accuracy_op, feed_dict={X: X_test, Y: y_test}))
def save_model(sess, model):
"""
存储TF模型
"""
inputs = {"inputs": model.input} # 输入String图像
outputs = {"prob": model.predictions} # 输出
prediction_signature = tf.saved_model.signature_def_utils.predict_signature_def(inputs, outputs)
signature_map = {signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY: prediction_signature}
legacy_op = control_flow_ops.group(
tf.local_variables_initializer(),
resources.initialize_resources(resources.shared_resources()),
tf.tables_initializer())
res_dir = "data/model-tf"
print('[Info] 模型存储路径: {}'.format(res_dir))
builder = saved_model_builder.SavedModelBuilder(res_dir)
builder.add_meta_graph_and_variables(
sess, [tag_constants.SERVING],
signature_def_map=signature_map,
legacy_init_op=legacy_op)
builder.save()
def random_forest(num_classes=2,
num_features=46,
num_trees=100,
max_nodes=10000):
X = tf.placeholder(tf.float32, shape=[None, num_features])
# For random forest, labels must be integers (the class id)
Y = tf.placeholder(tf.int32, shape=[None])
# Random Forest Parameters
hparams = tensor_forest.ForestHParams(
num_classes=num_classes,
num_features=num_features,
num_trees=num_trees,
max_nodes=max_nodes,
).fill()
forest_graph = tensor_forest.RandomForestGraphs(hparams)
train_op = forest_graph.training_graph(X, Y)
loss_op = forest_graph.training_loss(X, Y)
# Measure the accuracy
infer_op, _, _ = forest_graph.inference_graph(X)
correct_prediction = tf.equal(tf.argmax(infer_op, 1), tf.cast(Y, tf.int64))
accuracy_op = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
init_vars = tf.group(
tf.global_variables_initializer(),
resources.initialize_resources(resources.shared_resources()),
)
# sess = tf.Session()
# sess.run(init_vars)
return infer_op, accuracy_op, train_op, loss_op, X, Y
def __init__(self, num_features, num_classes, num_trees, max_nodes):
tf.reset_default_graph()
self.X = tf.placeholder(tf.float32, shape=[None, num_features])
self.Y = tf.placeholder(tf.int32, shape=[None])
self.hparams = tensor_forest.ForestHParams(num_classes=num_classes,
num_features=num_features,
num_trees=num_trees,
max_nodes=max_nodes).fill()
print("test 1")
# build graph
self.forest_graph = tensor_forest.RandomForestGraphs(self.hparams)
self.train_op = self.forest_graph.training_graph(self.X, self.Y)
self.loss_op = self.forest_graph.training_loss(self.X, self.Y)
infer_op, _, _ = self.forest_graph.inference_graph(self.X)
self.infer_op = infer_op
print("test 2")
self.correct_pred = tf.equal(tf.argmax(self.infer_op, 1),
tf.cast(self.Y, tf.int64))
self.accuracy_op = tf.reduce_mean(
tf.cast(self.correct_pred, tf.float32))
self.init = tf.group(
tf.global_variables_initializer(),
resources.initialize_resources(resources.shared_resources()))
self.sess = None
def train_and_test_model(train_features, train_labels, train_targets,
test_features, test_targets, test_labels):
"""Trains and tests random forest model.
Args:
train_features: training features
train_labels: training labels, integer values
train_targets: one-hot row vector of labels
test_features: testing features
test_targets: one-hot row vector of test labels
test_labels: labels for testing
Returns:
The loss for the model
"""
# initialize model and build it
config = Config()
forest_model = RandomForest(config)
forest_model.build()
# initialize tensorflow variables
init_vars = tf.group(
tf.global_variables_initializer(),
resources.initialize_resources(resources.shared_resources()))
with tf.Session() as session:
session.run(init_vars)
# train
loss = forest_model.do_train(session, train_features, train_labels,
train_targets)
print "Final train loss: {}".format(loss)
# test
forest_model.do_test(session, test_features, test_targets, test_labels)
return loss
def testInactive(self):
with self.test_session() as sess:
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 = [0, 0, 0, 1]
indices = [[0, 0], [0, 1], [2, 0], [3, 0]]
values = array_ops.constant([1, 2, 2, 1], dtype=dtypes.int64)
gradient_shape = tensor_shape.scalar()
hessian_shape = tensor_shape.scalar()
class_id = -1
split_handler = categorical_split_handler.EqualitySplitHandler(
l1_regularization=0.1,
l2_regularization=1,
tree_complexity_regularization=0,
min_node_weight=0,
sparse_int_column=sparse_tensor.SparseTensor(indices, values, [4, 1]),
feature_column_group_id=0,
gradient_shape=gradient_shape,
hessian_shape=hessian_shape,
multiclass_strategy=learner_pb2.LearnerConfig.TREE_PER_CLASS,
init_stamp_token=0)
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([False, False]))
with ops.control_dependencies([update_1]):
are_splits_ready, partitions, gains, splits = (
split_handler.make_splits(0, 1, class_id))
are_splits_ready, partitions, gains, splits = (sess.run(
[are_splits_ready, partitions, gains, splits]))
self.assertTrue(are_splits_ready)
self.assertEqual(len(partitions), 0)
self.assertEqual(len(gains), 0)
self.assertEqual(len(splits), 0)
def testExcludeNonFinalTree(self):
with self.test_session():
tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
# Bias tree.
tree1 = tree_ensemble_config.trees.add()
tree_ensemble_config.tree_metadata.add().is_finalized = True
_append_to_leaf(tree1.nodes.add().leaf, 0, -0.4)
# Depth 3 tree.
tree2 = tree_ensemble_config.trees.add()
tree_ensemble_config.tree_metadata.add().is_finalized = False
_set_float_split(tree2.nodes.add().dense_float_binary_split, 0, 9.0, 1, 2)
_set_float_split(tree2.nodes.add()
.sparse_float_binary_split_default_left.split, 0, -20.0,
3, 4)
_append_to_leaf(tree2.nodes.add().leaf, 0, 0.5)
_append_to_leaf(tree2.nodes.add().leaf, 0, 1.2)
_set_categorical_id_split(tree2.nodes.add().categorical_id_binary_split,
0, 9, 5, 6)
_append_to_leaf(tree2.nodes.add().leaf, 0, -0.9)
_append_to_leaf(tree2.nodes.add().leaf, 0, 0.7)
tree_ensemble_config.tree_weights.append(1.0)
tree_ensemble_config.tree_weights.append(1.0)
tree_ensemble_handle = model_ops.tree_ensemble_variable(
stamp_token=0,
tree_ensemble_config=tree_ensemble_config.SerializeToString(),
name="full_ensemble")
resources.initialize_resources(resources.shared_resources()).run()
# Prepare learner config.
learner_config = learner_pb2.LearnerConfig()
learner_config.num_classes = 2
learner_config.growing_mode = learner_pb2.LearnerConfig.WHOLE_TREE
result, dropout_info = self._get_predictions(
tree_ensemble_handle,
learner_config=learner_config.SerializeToString(),
reduce_dim=True)
# All the examples should get only the bias since the second tree is
# non-finalized
self.assertAllClose([[-0.4], [-0.4]], result.eval())
# Empty dropout.
self.assertAllEqual([[], []], dropout_info.eval())
def testFullEnsembleMultiNotClassTreePerClassStrategyDenseVector(self):
with self.test_session():
tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
# Bias tree only for second class.
tree1 = tree_ensemble_config.trees.add()
tree_ensemble_config.tree_metadata.add().is_finalized = True
_append_multi_values_to_dense_leaf(tree1.nodes.add().leaf, [0, -0.2, -2])
# Depth 2 tree.
tree2 = tree_ensemble_config.trees.add()
tree_ensemble_config.tree_metadata.add().is_finalized = True
_set_float_split(tree2.nodes.add()
.sparse_float_binary_split_default_right.split, 1, 4.0,
1, 2)
_set_float_split(tree2.nodes.add().dense_float_binary_split, 0, 9.0, 3, 4)
_append_multi_values_to_dense_leaf(tree2.nodes.add().leaf, [0.5, 0, 0])
_append_multi_values_to_dense_leaf(tree2.nodes.add().leaf, [0, 1.2, -0.7])
_append_multi_values_to_dense_leaf(tree2.nodes.add().leaf, [-0.9, 0, 0])
tree_ensemble_config.tree_weights.append(1.0)
tree_ensemble_config.tree_weights.append(1.0)
tree_ensemble_handle = model_ops.tree_ensemble_variable(
stamp_token=0,
tree_ensemble_config=tree_ensemble_config.SerializeToString(),
name="ensemble_multi_class")
resources.initialize_resources(resources.shared_resources()).run()
# Prepare learner config.
learner_config = learner_pb2.LearnerConfig()
learner_config.num_classes = 3
learner_config.multi_class_strategy = (
learner_pb2.LearnerConfig.FULL_HESSIAN)
result, dropout_info = self._get_predictions(
tree_ensemble_handle,
learner_config=learner_config.SerializeToString(),
reduce_dim=False)
# The first example will get bias class 1 -0.2 and -2 for class 2 from
# first tree and leaf 2 payload (sparse feature missing) of 0.5 hence
# 0.5, -0.2], the second example will get the same bias and leaf 3 payload
# of class 1 1.2 and class 2-0.7 hence [0.0, 1.0, -2.7].
self.assertAllClose([[0.5, -0.2, -2.0], [0, 1.0, -2.7]], result.eval())
# Empty dropout.
self.assertAllEqual([[], []], dropout_info.eval())
def testStreamingQuantileBucketsLowPrecisionInput(self):
"""Tests inputs that simulate low precision float16 values."""
num_quantiles = 3
# set generate_quantiles to True since the test will generate fewer
# boundaries otherwise.
with self.test_session() as sess:
accumulator = quantile_ops.QuantileAccumulator(
init_stamp_token=0, num_quantiles=num_quantiles,
epsilon=0.001, name="q1", generate_quantiles=True)
resources.initialize_resources(resources.shared_resources()).run()
input_column = array_ops.placeholder(dtypes.float32)
weights = array_ops.placeholder(dtypes.float32)
update = accumulator.add_summary(
stamp_token=0,
column=input_column,
example_weights=weights)
with self.test_session() as sess:
# This input is generated by integer in the range [2030, 2060]
# but represented by with float16 precision. Integers <= 2048 are
# exactly represented, whereas numbers > 2048 are rounded; and hence
# numbers > 2048 are repeated. For precision loss / rounding, see:
# https://en.wikipedia.org/wiki/Half-precision_floating-point_format.
#
# The intent of the test is not handling of float16 values, but to
# validate the number of buckets is returned, in cases where the input
# may contain repeated values.
inputs = [
2030.0, 2031.0, 2032.0, 2033.0, 2034.0, 2035.0, 2036.0, 2037.0,
2038.0, 2039.0, 2040.0, 2041.0, 2042.0, 2043.0, 2044.0, 2045.0,
2046.0, 2047.0, 2048.0, 2048.0, 2050.0, 2052.0, 2052.0, 2052.0,
2054.0, 2056.0, 2056.0, 2056.0, 2058.0, 2060.0
]
sess.run(update,
{input_column: inputs,
weights: [1] * len(inputs)})
with self.test_session() as sess:
sess.run(accumulator.flush(stamp_token=0, next_stamp_token=1))
are_ready_flush, buckets = (accumulator.get_buckets(stamp_token=1))
buckets, are_ready_flush = (sess.run(
[buckets, are_ready_flush]))
self.assertEqual(True, are_ready_flush)
self.assertEqual(num_quantiles + 1, len(buckets))
self.assertAllEqual([2030, 2040, 2050, 2060], buckets)
def testWithExistingEnsemble(self):
with self.test_session():
# Create existing tree ensemble.
tree_ensemble_handle = model_ops.tree_ensemble_variable(
stamp_token=0,
tree_ensemble_config=self._tree_ensemble.SerializeToString(),
name="existing")
# Create non-zero feature importance.
feature_usage_counts = variables.Variable(
initial_value=np.array([0, 4, 1], np.int64),
name="feature_usage_counts",
trainable=False)
feature_gains = variables.Variable(
initial_value=np.array([0.0, 0.3, 0.05], np.float32),
name="feature_gains",
trainable=False)
resources.initialize_resources(resources.shared_resources()).run()
variables.initialize_all_variables().run()
output_ensemble = tree_config_pb2.DecisionTreeEnsembleConfig()
with ops.control_dependencies([
ensemble_optimizer_ops.add_trees_to_ensemble(
tree_ensemble_handle,
self._ensemble_to_add.SerializeToString(),
feature_usage_counts, [1, 2, 0],
feature_gains, [0.02, 0.1, 0.0], [[], []],
learning_rate=1)
]):
output_ensemble.ParseFromString(
model_ops.tree_ensemble_serialize(tree_ensemble_handle)[1].eval())
# Output.
self.assertEqual(3, len(output_ensemble.trees))
self.assertProtoEquals(self._tree_to_add, output_ensemble.trees[2])
self.assertAllEqual([1.0, 1.0, 1.0], output_ensemble.tree_weights)
self.assertEqual(2,
output_ensemble.tree_metadata[0].num_tree_weight_updates)
self.assertEqual(3,
output_ensemble.tree_metadata[1].num_tree_weight_updates)
self.assertEqual(1,
output_ensemble.tree_metadata[2].num_tree_weight_updates)
self.assertAllEqual([1, 6, 1], feature_usage_counts.eval())
self.assertArrayNear([0.02, 0.4, 0.05], feature_gains.eval(), 1e-6)
def testStreamingQuantileBucketsWithVaryingBatch(self):
"""Sets up the quantile summary op test as follows.
Creates batches examples with different number of inputs in each batch.
The input values are dense in the range [1 ... N]
The data looks like this:
| Batch | Start | InputList
| 1 | 1 | [1]
| 2 | 2 | [2, 3]
| 3 | 4 | [4, 5, 6]
| 4 | 7 | [7, 8, 9, 10]
| 5 | 11 | [11, 12, 13, 14, 15]
| 6 | 16 | [16, 17, 18, 19, 20, 21]
"""
num_quantiles = 3
with self.test_session() as sess:
accumulator = quantile_ops.QuantileAccumulator(
init_stamp_token=0, num_quantiles=num_quantiles,
epsilon=0.001, name="q1")
resources.initialize_resources(resources.shared_resources()).run()
input_column = array_ops.placeholder(dtypes.float32)
weights = array_ops.placeholder(dtypes.float32)
update = accumulator.add_summary(
stamp_token=0,
column=input_column,
example_weights=weights)
with self.test_session() as sess:
for i in range(1, 23):
# start = 1, 2, 4, 7, 11, 16 ... (see comment above)
start = int((i * (i-1) / 2) + 1)
sess.run(update,
{input_column: range(start, start+i),
weights: [1] * i})
with self.test_session() as sess:
sess.run(accumulator.flush(stamp_token=0, next_stamp_token=1))
are_ready_flush, buckets = (accumulator.get_buckets(stamp_token=1))
buckets, are_ready_flush = (sess.run(
[buckets, are_ready_flush]))
self.assertEqual(True, are_ready_flush)
self.assertEqual(num_quantiles + 1, len(buckets))
self.assertAllEqual([1, 86., 170., 253.], buckets)
def testEnsembleEmpty(self):
with self.test_session():
tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
tree_ensemble_handle = model_ops.tree_ensemble_variable(
stamp_token=0,
tree_ensemble_config=tree_ensemble_config.SerializeToString(),
name="full_ensemble")
resources.initialize_resources(resources.shared_resources()).run()
result = prediction_ops.gradient_trees_partition_examples(
tree_ensemble_handle, [self._dense_float_tensor], [
self._sparse_float_indices1, self._sparse_float_indices2
], [self._sparse_float_values1, self._sparse_float_values2],
[self._sparse_float_shape1,
self._sparse_float_shape2], [self._sparse_int_indices1],
[self._sparse_int_values1], [self._sparse_int_shape1])
self.assertAllEqual([0, 0], result.eval())
def testWithEmptyEnsembleAndShrinkage(self):
with self.test_session():
# Add shrinkage config.
learning_rate = 0.0001
tree_ensemble = tree_config_pb2.DecisionTreeEnsembleConfig()
tree_ensemble_handle = model_ops.tree_ensemble_variable(
stamp_token=0,
tree_ensemble_config=tree_ensemble.SerializeToString(),
name="existing")
# Create zero feature importance.
feature_usage_counts = variables.Variable(
initial_value=np.array([0, 0], np.int64),
name="feature_usage_counts",
trainable=False)
feature_gains = variables.Variable(
initial_value=np.array([0.0, 0.0], np.float32),
name="feature_gains",
trainable=False)
resources.initialize_resources(resources.shared_resources()).run()
variables.initialize_all_variables().run()
output_ensemble = tree_config_pb2.DecisionTreeEnsembleConfig()
with ops.control_dependencies([
ensemble_optimizer_ops.add_trees_to_ensemble(
tree_ensemble_handle,
self._ensemble_to_add.SerializeToString(),
feature_usage_counts, [1, 2],
feature_gains, [0.5, 0.3], [[], []],
learning_rate=learning_rate)
]):
output_ensemble.ParseFromString(
model_ops.tree_ensemble_serialize(tree_ensemble_handle)[1].eval())
# New tree is added with shrinkage weight.
self.assertAllClose([learning_rate], output_ensemble.tree_weights)
self.assertEqual(1,
output_ensemble.tree_metadata[0].num_tree_weight_updates)
self.assertAllEqual([1, 2], feature_usage_counts.eval())
self.assertArrayNear([0.5 * learning_rate, 0.3 * learning_rate],
feature_gains.eval(), 1e-6)
def testWithEmptyEnsemble(self):
with self.test_session():
# Create an empty ensemble.
tree_ensemble_handle = model_ops.tree_ensemble_variable(
stamp_token=0, tree_ensemble_config="", name="empty")
# Create zero feature importance.
feature_usage_counts = variables.Variable(
initial_value=array_ops.zeros([1], dtypes.int64),
name="feature_usage_counts",
trainable=False)
feature_gains = variables.Variable(
initial_value=array_ops.zeros([1], dtypes.float32),
name="feature_gains",
trainable=False)
resources.initialize_resources(resources.shared_resources()).run()
variables.initialize_all_variables().run()
with ops.control_dependencies([
ensemble_optimizer_ops.add_trees_to_ensemble(
tree_ensemble_handle,
self._ensemble_to_add.SerializeToString(),
feature_usage_counts, [2],
feature_gains, [0.4], [[]],
learning_rate=1.0)
]):
result = model_ops.tree_ensemble_serialize(tree_ensemble_handle)[1]
# Output.
output_ensemble = tree_config_pb2.DecisionTreeEnsembleConfig()
output_ensemble.ParseFromString(result.eval())
self.assertProtoEquals(self._tree_to_add, output_ensemble.trees[0])
self.assertEqual(1, len(output_ensemble.trees))
self.assertAllEqual([1.0], output_ensemble.tree_weights)
self.assertEqual(1,
output_ensemble.tree_metadata[0].num_tree_weight_updates)
self.assertAllEqual([2], feature_usage_counts.eval())
self.assertArrayNear([0.4], feature_gains.eval(), 1e-6)