# pylint: disable=g-bad-file-header
# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# 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.
# ==============================================================================

"""Save and restore tests."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os
import random

import tensorflow as tf
from tensorflow.contrib.learn.python import learn
from tensorflow.contrib.learn.python.learn import datasets


class SaverTest(tf.test.TestCase):
  """Save and restore tests."""

  def testIris(self):
    path = tf.test.get_temp_dir() + '/tmp.saver'
    random.seed(42)
    iris = datasets.load_iris()
    classifier = learn.TensorFlowLinearClassifier(n_classes=3)
    classifier.fit(iris.data, iris.target)
    classifier.save(path)
    # TODO(ipolosukhin): Remove or restore.
    # new_classifier = learn.TensorFlowEstimator.restore(path)
    # self.assertEqual(type(new_classifier), type(classifier))
    # score = accuracy_score(iris.target, new_classifier.predict(iris.data))
    # self.assertGreater(score, 0.5, 'Failed with score = {0}'.format(score))

  def testCustomModel(self):
    path = tf.test.get_temp_dir() + '/tmp.saver2'
    random.seed(42)
    iris = datasets.load_iris()

    def _custom_model(X, y):
      # pylint: disable=invalid-name
      return learn.models.logistic_regression(X, y)

    classifier = learn.TensorFlowEstimator(model_fn=_custom_model, n_classes=3)
    classifier.fit(iris.data, iris.target)
    classifier.save(path)
    # TODO(ipolosukhin): Remove or restore.
    # new_classifier = learn.TensorFlowEstimator.restore(path)
    # self.assertEqual(type(new_classifier), type(classifier))
    # score = accuracy_score(iris.target, new_classifier.predict(iris.data))
    # self.assertGreater(score, 0.5, 'Failed with score = {0}'.format(score))

  def testDNN(self):
    path = tf.test.get_temp_dir() + '/tmp_saver3'
    random.seed(42)
    iris = datasets.load_iris()
    classifier = learn.TensorFlowDNNClassifier(hidden_units=[10, 20, 10],
                                               n_classes=3)
    classifier.fit(iris.data, iris.target)
    classifier.save(path)
    # TODO(ipolosukhin): Remove or restore.
    # new_classifier = learn.TensorFlowEstimator.restore(path)
    # self.assertEqual(type(new_classifier), type(classifier))
    # score = accuracy_score(iris.target, new_classifier.predict(iris.data))
    # self.assertGreater(score, 0.5, 'Failed with score = {0}'.format(score))

  def testNoFolder(self):
    with self.assertRaises(ValueError):
      learn.TensorFlowEstimator.restore('no_model_path')

  def testNoCheckpoints(self):
    path = tf.test.get_temp_dir() + '/tmp/tmp.saver4'
    random.seed(42)
    iris = datasets.load_iris()
    classifier = learn.TensorFlowDNNClassifier(hidden_units=[10, 20, 10],
                                               n_classes=3)
    classifier.fit(iris.data, iris.target)
    classifier.save(path)
    # TODO(ipolosukhin): Remove or restore.
#     os.remove(os.path.join(path, 'checkpoint'))
#     with self.assertRaises(NotImplementedError):
#       learn.TensorFlowEstimator.restore(path)

if __name__ == '__main__':
  tf.test.main()
