def loss_fn(labels, logits):
result = losses.per_example_maxent_loss(
labels=labels,
logits=logits,
weights=weight_column,
num_classes=n_classes)
return result[0]
def loss_fn(labels, logits, weights=None):
result = losses.per_example_maxent_loss(
labels=labels,
logits=logits,
weights=weights,
num_classes=n_classes)
return math_ops.reduce_mean(result[0])
def loss_fn(labels, logits, weights=None):
result = losses.per_example_maxent_loss(
labels=labels,
logits=logits,
weights=weights,
num_classes=n_classes)
return math_ops.reduce_mean(result[0])
def loss_fn(labels, logits):
result = losses.per_example_maxent_loss(
labels=labels,
logits=logits,
weights=weight_column,
num_classes=n_classes)
return result[0]
def loss_fn(labels, logits):
result = losses.per_example_maxent_loss(
# Don't pass the weights: head already multiplies by them.
labels=labels,
logits=logits,
weights=None,
num_classes=n_classes)
return result[0]
def loss_fn(labels, logits):
result = losses.per_example_maxent_loss(
# Don't pass the weights: head already multiplies by them.
labels=labels, logits=logits, weights=None, num_classes=n_classes)
return result[0]
def tree_loss_fn(labels, logits):
result = bt_losses.per_example_maxent_loss(
labels=labels, logits=logits, num_classes=n_classes, weights=None)
return result[0]
def testTrainFnMulticlassTreePerClass(self):
"""Tests the GBDT train for multiclass tree per class strategy."""
with self.test_session() as sess:
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 = 1
# Use full hessian multiclass strategy.
learner_config.multi_class_strategy = (
learner_pb2.LearnerConfig.TREE_PER_CLASS)
learner_config.num_classes = 5
learner_config.regularization.l1 = 0
# To make matrix inversible.
learner_config.regularization.l2 = 1e-5
learner_config.constraints.max_tree_depth = 1
learner_config.constraints.min_node_weight = 0
features = {
"dense_float": array_ops.constant(
[[1.0], [1.5], [2.0]], dtypes.float32),
}
gbdt_model = gbdt_batch.GradientBoostedDecisionTreeModel(
is_chief=True,
num_ps_replicas=0,
center_bias=False,
ensemble_handle=ensemble_handle,
examples_per_layer=1,
learner_config=learner_config,
features=features)
batch_size = 3
predictions = array_ops.constant(
[[0.0, -1.0, 0.5, 1.2, 3.1], [1.0, 0.0, 0.8, 0.3, 1.0],
[0.0, 0.0, 0.0, 2.0, 1.2]],
dtype=dtypes.float32)
labels = array_ops.constant([[2], [2], [3]], dtype=dtypes.float32)
weights = array_ops.ones([batch_size, 1], dtypes.float32)
partition_ids = array_ops.zeros([batch_size], 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,
# This should result in a tree built for a class 2.
"num_trees": 13,
}
# Create train op.
train_op = gbdt_model.train(
loss=math_ops.reduce_mean(
losses.per_example_maxent_loss(
labels,
weights,
predictions,
num_classes=learner_config.num_classes)[0]),
predictions_dict=predictions_dict,
labels=labels)
variables.global_variables_initializer().run()
resources.initialize_resources(resources.shared_resources()).run()
# On first run, expect no splits to be chosen because the quantile
# buckets will not be ready.
train_op.run()
stamp_token, serialized = model_ops.tree_ensemble_serialize(
ensemble_handle)
output = tree_config_pb2.DecisionTreeEnsembleConfig()
output.ParseFromString(serialized.eval())
self.assertEqual(len(output.trees), 0)
self.assertEqual(len(output.tree_weights), 0)
self.assertEqual(stamp_token.eval(), 1)
# Update the stamp to be able to run a second time.
sess.run([ensemble_stamp.assign_add(1)])
# On second run, expect a trivial split to be chosen to basically
# predict the average.
train_op.run()
output = tree_config_pb2.DecisionTreeEnsembleConfig()
output.ParseFromString(serialized.eval())
stamp_token, serialized = model_ops.tree_ensemble_serialize(
ensemble_handle)
output.ParseFromString(serialized.eval())
self.assertEqual(len(output.trees), 1)
self.assertAllClose(output.tree_weights, [1])
self.assertEqual(stamp_token.eval(), 2)
# One node for a split, two children nodes.
self.assertEqual(3, len(output.trees[0].nodes))
# Leafs will have a sparse vector for class 3.
self.assertEqual(1,
len(output.trees[0].nodes[1].leaf.sparse_vector.index))
self.assertEqual(3, output.trees[0].nodes[1].leaf.sparse_vector.index[0])
self.assertAlmostEqual(
-1.13134455681, output.trees[0].nodes[1].leaf.sparse_vector.value[0])
self.assertEqual(1,
len(output.trees[0].nodes[2].leaf.sparse_vector.index))
self.assertEqual(3, output.trees[0].nodes[2].leaf.sparse_vector.index[0])
self.assertAlmostEqual(
0.893284678459, output.trees[0].nodes[2].leaf.sparse_vector.value[0])
def testTrainFnMulticlassDiagonalHessian(self):
"""Tests the GBDT train for multiclass diagonal hessian."""
with self.test_session() as sess:
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 = 1
# Use full hessian multiclass strategy.
learner_config.multi_class_strategy = (
learner_pb2.LearnerConfig.DIAGONAL_HESSIAN)
learner_config.num_classes = 5
learner_config.regularization.l1 = 0
# To make matrix inversible.
learner_config.regularization.l2 = 1e-5
learner_config.constraints.max_tree_depth = 1
learner_config.constraints.min_node_weight = 0
batch_size = 3
features = {}
features["dense_float"] = array_ops.constant(
[0.3, 1.5, 1.1], dtype=dtypes.float32)
gbdt_model = gbdt_batch.GradientBoostedDecisionTreeModel(
is_chief=True,
num_ps_replicas=0,
center_bias=False,
ensemble_handle=ensemble_handle,
examples_per_layer=1,
learner_config=learner_config,
features=features)
predictions = array_ops.constant(
[[0.0, -1.0, 0.5, 1.2, 3.1], [1.0, 0.0, 0.8, 0.3, 1.0],
[0.0, 0.0, 0.0, 0.0, 1.2]],
dtype=dtypes.float32)
labels = array_ops.constant([[2], [2], [3]], dtype=dtypes.float32)
weights = array_ops.ones([batch_size, 1], dtypes.float32)
partition_ids = array_ops.zeros([batch_size], 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,
"num_trees": 0,
}
# Create train op.
train_op = gbdt_model.train(
loss=math_ops.reduce_mean(
losses.per_example_maxent_loss(
labels,
weights,
predictions,
num_classes=learner_config.num_classes)[0]),
predictions_dict=predictions_dict,
labels=labels)
variables.global_variables_initializer().run()
resources.initialize_resources(resources.shared_resources()).run()
# On first run, expect no splits to be chosen because the quantile
# buckets will not be ready.
train_op.run()
stamp_token, serialized = model_ops.tree_ensemble_serialize(
ensemble_handle)
output = tree_config_pb2.DecisionTreeEnsembleConfig()
output.ParseFromString(serialized.eval())
self.assertEqual(len(output.trees), 0)
self.assertEqual(len(output.tree_weights), 0)
self.assertEqual(stamp_token.eval(), 1)
# Update the stamp to be able to run a second time.
sess.run([ensemble_stamp.assign_add(1)])
# On second run, expect a trivial split to be chosen to basically
# predict the average.
train_op.run()
output = tree_config_pb2.DecisionTreeEnsembleConfig()
output.ParseFromString(serialized.eval())
stamp_token, serialized = model_ops.tree_ensemble_serialize(
ensemble_handle)
output.ParseFromString(serialized.eval())
self.assertEqual(len(output.trees), 1)
# We got 3 nodes: one parent and 2 leafs.
self.assertEqual(len(output.trees[0].nodes), 3)
self.assertAllClose(output.tree_weights, [1])
self.assertEqual(stamp_token.eval(), 2)
# Leafs should have a dense vector of size 5.
expected_leaf_1 = [-1.0354, -1.0107, 17.2976, -1.1313, -4.5023]
expected_leaf_2 = [-1.2924, -1.1376, 2.2042, 3.1052, -1.6269]
self.assertArrayNear(expected_leaf_1,
output.trees[0].nodes[1].leaf.vector.value, 1e-3)
self.assertArrayNear(expected_leaf_2,
output.trees[0].nodes[2].leaf.vector.value, 1e-3)
def testTrainFnMulticlassTreePerClass(self):
"""Tests the GBDT train for multiclass tree per class strategy."""
with self.test_session() as sess:
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 = 1
# Use full hessian multiclass strategy.
learner_config.multi_class_strategy = (
learner_pb2.LearnerConfig.TREE_PER_CLASS)
learner_config.num_classes = 5
learner_config.regularization.l1 = 0
# To make matrix inversible.
learner_config.regularization.l2 = 1e-5
learner_config.constraints.max_tree_depth = 1
learner_config.constraints.min_node_weight = 0
features = {
"dense_float":
array_ops.constant([[1.0], [1.5], [2.0]], dtypes.float32),
}
gbdt_model = gbdt_batch.GradientBoostedDecisionTreeModel(
is_chief=True,
num_ps_replicas=0,
center_bias=False,
ensemble_handle=ensemble_handle,
examples_per_layer=1,
learner_config=learner_config,
features=features)
batch_size = 3
predictions = array_ops.constant(
[[0.0, -1.0, 0.5, 1.2, 3.1], [1.0, 0.0, 0.8, 0.3, 1.0],
[0.0, 0.0, 0.0, 2.0, 1.2]],
dtype=dtypes.float32)
labels = array_ops.constant([[2], [2], [3]], dtype=dtypes.float32)
weights = array_ops.ones([batch_size, 1], dtypes.float32)
partition_ids = array_ops.zeros([batch_size], 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,
# This should result in a tree built for a class 2.
"num_trees": 13,
}
# Create train op.
train_op = gbdt_model.train(loss=math_ops.reduce_mean(
losses.per_example_maxent_loss(
labels,
weights,
predictions,
num_classes=learner_config.num_classes)[0]),
predictions_dict=predictions_dict,
labels=labels)
variables.global_variables_initializer().run()
resources.initialize_resources(resources.shared_resources()).run()
# On first run, expect no splits to be chosen because the quantile
# buckets will not be ready.
train_op.run()
stamp_token, serialized = model_ops.tree_ensemble_serialize(
ensemble_handle)
output = tree_config_pb2.DecisionTreeEnsembleConfig()
output.ParseFromString(serialized.eval())
self.assertEqual(len(output.trees), 0)
self.assertEqual(len(output.tree_weights), 0)
self.assertEqual(stamp_token.eval(), 1)
# Update the stamp to be able to run a second time.
sess.run([ensemble_stamp.assign_add(1)])
# On second run, expect a trivial split to be chosen to basically
# predict the average.
train_op.run()
output = tree_config_pb2.DecisionTreeEnsembleConfig()
output.ParseFromString(serialized.eval())
stamp_token, serialized = model_ops.tree_ensemble_serialize(
ensemble_handle)
output.ParseFromString(serialized.eval())
self.assertEqual(len(output.trees), 1)
self.assertAllClose(output.tree_weights, [1])
self.assertEqual(stamp_token.eval(), 2)
# One node for a split, two children nodes.
self.assertEqual(3, len(output.trees[0].nodes))
# Leafs will have a sparse vector for class 3.
self.assertEqual(
1, len(output.trees[0].nodes[1].leaf.sparse_vector.index))
self.assertEqual(
3, output.trees[0].nodes[1].leaf.sparse_vector.index[0])
self.assertAlmostEqual(
-1.13134455681,
output.trees[0].nodes[1].leaf.sparse_vector.value[0])
self.assertEqual(
1, len(output.trees[0].nodes[2].leaf.sparse_vector.index))
self.assertEqual(
3, output.trees[0].nodes[2].leaf.sparse_vector.index[0])
self.assertAlmostEqual(
0.893284678459,
output.trees[0].nodes[2].leaf.sparse_vector.value[0])
def testTrainFnMulticlassDiagonalHessian(self):
"""Tests the GBDT train for multiclass diagonal hessian."""
with self.test_session() as sess:
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 = 1
# Use full hessian multiclass strategy.
learner_config.multi_class_strategy = (
learner_pb2.LearnerConfig.DIAGONAL_HESSIAN)
learner_config.num_classes = 5
learner_config.regularization.l1 = 0
# To make matrix inversible.
learner_config.regularization.l2 = 1e-5
learner_config.constraints.max_tree_depth = 1
learner_config.constraints.min_node_weight = 0
batch_size = 3
features = {}
features["dense_float"] = array_ops.ones([batch_size, 1],
dtypes.float32)
gbdt_model = gbdt_batch.GradientBoostedDecisionTreeModel(
is_chief=True,
num_ps_replicas=0,
center_bias=False,
ensemble_handle=ensemble_handle,
examples_per_layer=1,
learner_config=learner_config,
features=features)
predictions = array_ops.constant(
[[0.0, -1.0, 0.5, 1.2, 3.1], [1.0, 0.0, 0.8, 0.3, 1.0],
[0.0, 0.0, 0.0, 0.0, 1.2]],
dtype=dtypes.float32)
labels = array_ops.constant([[2], [2], [3]], dtype=dtypes.float32)
weights = array_ops.ones([batch_size, 1], dtypes.float32)
partition_ids = array_ops.zeros([batch_size], 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,
"num_trees": 0,
}
# Create train op.
train_op = gbdt_model.train(loss=math_ops.reduce_mean(
losses.per_example_maxent_loss(
labels,
weights,
predictions,
num_classes=learner_config.num_classes)[0]),
predictions_dict=predictions_dict,
labels=labels)
variables.global_variables_initializer().run()
resources.initialize_resources(resources.shared_resources()).run()
# On first run, expect no splits to be chosen because the quantile
# buckets will not be ready.
train_op.run()
stamp_token, serialized = model_ops.tree_ensemble_serialize(
ensemble_handle)
output = tree_config_pb2.DecisionTreeEnsembleConfig()
output.ParseFromString(serialized.eval())
self.assertEqual(len(output.trees), 0)
self.assertEqual(len(output.tree_weights), 0)
self.assertEqual(stamp_token.eval(), 1)
# Update the stamp to be able to run a second time.
sess.run([ensemble_stamp.assign_add(1)])
# On second run, expect a trivial split to be chosen to basically
# predict the average.
train_op.run()
output = tree_config_pb2.DecisionTreeEnsembleConfig()
output.ParseFromString(serialized.eval())
stamp_token, serialized = model_ops.tree_ensemble_serialize(
ensemble_handle)
output.ParseFromString(serialized.eval())
self.assertEqual(len(output.trees), 1)
self.assertAllClose(output.tree_weights, [1])
self.assertEqual(stamp_token.eval(), 2)
# Leaf should have a dense vector of size 5.
expected = [
-1.26767396927, -1.13043296337, 4.58542203903, 1.81428349018,
-2.43038392067
]
for i in range(learner_config.num_classes):
self.assertAlmostEqual(
expected[i], output.trees[0].nodes[1].leaf.vector.value[i])
