def testFitAndEvaluateMultiClassFullDontThrowException(self):
n_classes = 3
learner_config = learner_pb2.LearnerConfig()
learner_config.num_classes = n_classes
learner_config.constraints.max_tree_depth = 1
learner_config.multi_class_strategy = (
learner_pb2.LearnerConfig.FULL_HESSIAN)
head_fn = estimator.core_multiclass_head(n_classes=n_classes)
model_dir = tempfile.mkdtemp()
config = run_config.RunConfig()
classifier = estimator.CoreGradientBoostedDecisionTreeEstimator(
learner_config=learner_config,
head=head_fn,
num_trees=1,
center_bias=False,
examples_per_layer=7,
model_dir=model_dir,
config=config,
feature_columns=[core_feature_column.numeric_column("x")])
classifier.train(input_fn=_multiclass_train_input_fn, steps=100)
classifier.evaluate(input_fn=_multiclass_train_input_fn, steps=1)
classifier.predict(input_fn=_eval_input_fn)
def testFitAndEvaluateMultiClassFullDontThrowException(self):
n_classes = 3
learner_config = learner_pb2.LearnerConfig()
learner_config.num_classes = n_classes
learner_config.constraints.max_tree_depth = 1
learner_config.multi_class_strategy = (
learner_pb2.LearnerConfig.FULL_HESSIAN)
head_fn = estimator.core_multiclass_head(n_classes=n_classes)
model_dir = tempfile.mkdtemp()
config = run_config.RunConfig()
classifier = estimator.CoreGradientBoostedDecisionTreeEstimator(
learner_config=learner_config,
head=head_fn,
num_trees=1,
center_bias=False,
examples_per_layer=7,
model_dir=model_dir,
config=config,
feature_columns=[core_feature_column.numeric_column("x")])
classifier.train(input_fn=_multiclass_train_input_fn, steps=100)
classifier.evaluate(input_fn=_multiclass_train_input_fn, steps=1)
classifier.predict(input_fn=_eval_input_fn)
