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nam_train_test.py
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nam_train_test.py
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# coding=utf-8
# Copyright 2020 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Lint as: python3
"""Tests functionality of training NAM models."""
import os
from absl import flags
from absl.testing import absltest
from absl.testing import flagsaver
from absl.testing import parameterized
import tensorflow.compat.v1 as tf
from neural_additive_models import nam_train
FLAGS = flags.FLAGS
class NAMTrainingTest(parameterized.TestCase):
"""Tests whether NAMs can be run without error."""
@parameterized.named_parameters(
('classification', 'BreastCancer', False),
('regression', 'Housing', True),
)
@flagsaver.flagsaver
def test_nam(self, dataset_name, regression):
"""Test whether the NAM training pipeline runs successfully or not."""
FLAGS.training_epochs = 4
FLAGS.save_checkpoint_every_n_epochs = 2
FLAGS.early_stopping_epochs = 2
FLAGS.dataset_name = dataset_name
FLAGS.regression = regression
FLAGS.num_basis_functions = 16
logdir = os.path.join(self.create_tempdir().full_path, dataset_name)
tf.gfile.MakeDirs(logdir)
data_gen, _ = nam_train.create_test_train_fold(fold_num=1)
nam_train.single_split_training(data_gen, logdir)
if __name__ == '__main__':
absltest.main()