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)