def testFitAndEvaluateMultiClassFullDontThrowException(self):
learner_config = learner_pb2.LearnerConfig()
learner_config.num_classes = 3
learner_config.constraints.max_tree_depth = 1
learner_config.multi_class_strategy = (
learner_pb2.LearnerConfig.FULL_HESSIAN)
model_dir = tempfile.mkdtemp()
config = run_config.RunConfig()
classifier = estimator.GradientBoostedDecisionTreeClassifier(
learner_config=learner_config,
n_classes=learner_config.num_classes,
num_trees=1,
examples_per_layer=7,
model_dir=model_dir,
config=config,
center_bias=False,
feature_columns=[contrib_feature_column.real_valued_column("x")])
classifier.fit(input_fn=_multiclass_train_input_fn, steps=100)
classifier.evaluate(input_fn=_eval_input_fn, steps=1)
classifier.export(self._export_dir_base)
result_iter = classifier.predict(input_fn=_eval_input_fn)
for prediction_dict in result_iter:
self.assertTrue("classes" in prediction_dict)
def testFitAndEvaluateDontThrowException(self):
learner_config = learner_pb2.LearnerConfig()
learner_config.num_classes = 2
learner_config.constraints.max_tree_depth = 1
model_dir = tempfile.mkdtemp()
config = run_config.RunConfig()
classifier = estimator.GradientBoostedDecisionTreeClassifier(
learner_config=learner_config,
num_trees=1,
examples_per_layer=3,
model_dir=model_dir,
config=config,
feature_columns=[contrib_feature_column.real_valued_column("x")])
classifier.fit(input_fn=_train_input_fn, steps=15)
classifier.evaluate(input_fn=_eval_input_fn, steps=1)
classifier.export(self._export_dir_base)
def testOverridesGlobalSteps(self):
learner_config = learner_pb2.LearnerConfig()
learner_config.num_classes = 2
learner_config.constraints.max_tree_depth = 2
model_dir = tempfile.mkdtemp()
config = run_config.RunConfig()
classifier = estimator.GradientBoostedDecisionTreeClassifier(
learner_config=learner_config,
num_trees=1,
examples_per_layer=3,
model_dir=model_dir,
config=config,
feature_columns=[contrib_feature_column.real_valued_column("x")],
output_leaf_index=False,
override_global_step_value=10000000)
classifier.fit(input_fn=_train_input_fn, steps=15)
self._assert_checkpoint(classifier.model_dir, global_step=10000000)
def testThatLeafIndexIsInPredictions(self):
learner_config = learner_pb2.LearnerConfig()
learner_config.num_classes = 2
learner_config.constraints.max_tree_depth = 1
model_dir = tempfile.mkdtemp()
config = run_config.RunConfig()
classifier = estimator.GradientBoostedDecisionTreeClassifier(
learner_config=learner_config,
num_trees=1,
examples_per_layer=3,
model_dir=model_dir,
config=config,
feature_columns=[contrib_feature_column.real_valued_column("x")],
output_leaf_index=True)
classifier.fit(input_fn=_train_input_fn, steps=15)
result_iter = classifier.predict(input_fn=_eval_input_fn)
for prediction_dict in result_iter:
self.assertTrue("leaf_index" in prediction_dict)
self.assertTrue("logits" in prediction_dict)
def testForcedInitialSplits(self):
learner_config = learner_pb2.LearnerConfig()
learner_config.num_classes = 2
learner_config.constraints.max_tree_depth = 3
initial_subtree = """
nodes {
dense_float_binary_split {
feature_column: 0
threshold: -0.5
left_id: 1
right_id: 2
}
node_metadata {
gain: 0
}
}
nodes {
dense_float_binary_split {
feature_column: 1
threshold: 0.52
left_id: 3
right_id: 4
}
node_metadata {
gain: 0
}
}
nodes {
dense_float_binary_split {
feature_column: 1
threshold: 0.554
left_id: 5
right_id: 6
}
node_metadata {
gain: 0
}
}
nodes {
leaf {
vector {
value: 0.0
}
}
}
nodes {
leaf {
vector {
value: 0.0
}
}
}
nodes {
leaf {
vector {
value: 0.0
}
}
}
nodes {
leaf {
vector {
value: 0.0
}
}
}
"""
tree_proto = tree_config_pb2.DecisionTreeConfig()
text_format.Merge(initial_subtree, tree_proto)
# Set initial subtree info.
learner_config.each_tree_start.CopyFrom(tree_proto)
learner_config.each_tree_start_num_layers = 2
model_dir = tempfile.mkdtemp()
config = run_config.RunConfig()
classifier = estimator.GradientBoostedDecisionTreeClassifier(
learner_config=learner_config,
num_trees=2,
examples_per_layer=6,
model_dir=model_dir,
config=config,
center_bias=False,
feature_columns=[contrib_feature_column.real_valued_column("x")],
output_leaf_index=False)
classifier.fit(input_fn=_train_input_fn, steps=100)
# When no override of global steps, 5 steps were used.
ensemble = self._assert_checkpoint_and_return_model(
classifier.model_dir, global_step=6)
# TODO(nponomareva): find a better way to test this.
expected_ensemble = """
trees {
nodes {
dense_float_binary_split {
threshold: -0.5
left_id: 1
right_id: 2
}
node_metadata {
}
}
nodes {
dense_float_binary_split {
feature_column: 1
threshold: 0.52
left_id: 3
right_id: 4
}
node_metadata {
}
}
nodes {
dense_float_binary_split {
feature_column: 1
threshold: 0.554
left_id: 5
right_id: 6
}
node_metadata {
}
}
nodes {
leaf {
vector {
value: 0.0
}
}
}
nodes {
leaf {
vector {
value: 0.0
}
}
}
nodes {
dense_float_binary_split {
threshold: 1.0
left_id: 7
right_id: 8
}
node_metadata {
gain: 0.888888895512
}
}
nodes {
leaf {
vector {
value: 0.0
}
}
}
nodes {
leaf {
vector {
value: -2.0
}
}
}
nodes {
leaf {
vector {
value: 2.00000023842
}
}
}
}
trees {
nodes {
dense_float_binary_split {
threshold: -0.5
left_id: 1
right_id: 2
}
node_metadata {
}
}
nodes {
dense_float_binary_split {
feature_column: 1
threshold: 0.52
left_id: 3
right_id: 4
}
node_metadata {
}
}
nodes {
dense_float_binary_split {
feature_column: 1
threshold: 0.554
left_id: 5
right_id: 6
}
node_metadata {
}
}
nodes {
leaf {
vector {
value: 0.0
}
}
}
nodes {
leaf {
vector {
value: 0.0
}
}
}
nodes {
dense_float_binary_split {
threshold: 1.0
left_id: 7
right_id: 8
}
node_metadata {
gain: 0.727760672569
}
}
nodes {
leaf {
vector {
value: 0.0
}
}
}
nodes {
leaf {
vector {
value: -1.81873059273
}
}
}
nodes {
leaf {
vector {
value: 1.81873047352
}
}
}
}
trees {
nodes {
dense_float_binary_split {
threshold: -0.5
left_id: 1
right_id: 2
}
node_metadata {
}
}
nodes {
dense_float_binary_split {
feature_column: 1
threshold: 0.52
left_id: 3
right_id: 4
}
node_metadata {
}
}
nodes {
dense_float_binary_split {
feature_column: 1
threshold: 0.554
left_id: 5
right_id: 6
}
node_metadata {
}
}
nodes {
leaf {
vector {
value: 0.0
}
}
}
nodes {
leaf {
vector {
value: 0.0
}
}
}
nodes {
leaf {
vector {
value: 0.0
}
}
}
nodes {
leaf {
vector {
value: 0.0
}
}
}
}
tree_weights: 0.10000000149
tree_weights: 0.10000000149
tree_weights: 0.10000000149
tree_metadata {
num_tree_weight_updates: 1
num_layers_grown: 3
is_finalized: true
}
tree_metadata {
num_tree_weight_updates: 1
num_layers_grown: 3
is_finalized: true
}
tree_metadata {
num_tree_weight_updates: 1
num_layers_grown: 2
}
growing_metadata {
num_layers_attempted: 3
}
"""
self.assertProtoEquals(expected_ensemble, ensemble)
