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train_higgs_test.py
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train_higgs_test.py
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# Copyright 2018 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.
# ==============================================================================
"""Tests for boosted_tree."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import tempfile
import numpy as np
import pandas as pd
import tensorflow as tf
# pylint: disable=g-bad-import-order
from official.boosted_trees import train_higgs
from official.utils.testing import integration
TEST_CSV = os.path.join(os.path.dirname(__file__), "train_higgs_test.csv")
tf.logging.set_verbosity(tf.logging.ERROR)
class BaseTest(tf.test.TestCase):
"""Tests for Wide Deep model."""
@classmethod
def setUpClass(cls): # pylint: disable=invalid-name
super(BaseTest, cls).setUpClass()
train_higgs.define_train_higgs_flags()
def setUp(self):
# Create temporary CSV file
self.data_dir = self.get_temp_dir()
data = pd.read_csv(
TEST_CSV, dtype=np.float32, names=["c%02d" % i for i in range(29)]
).as_matrix()
self.input_npz = os.path.join(self.data_dir, train_higgs.NPZ_FILE)
# numpy.savez doesn't take gfile.Gfile, so need to write down and copy.
tmpfile = tempfile.NamedTemporaryFile()
np.savez_compressed(tmpfile, data=data)
tf.gfile.Copy(tmpfile.name, self.input_npz)
def test_read_higgs_data(self):
"""Tests read_higgs_data() function."""
# Error when a wrong data_dir is given.
with self.assertRaisesRegexp(RuntimeError, "Error loading data.*"):
train_data, eval_data = train_higgs.read_higgs_data(
self.data_dir + "non-existing-path",
train_start=0, train_count=15, eval_start=15, eval_count=5)
# Loading fine with the correct data_dir.
train_data, eval_data = train_higgs.read_higgs_data(
self.data_dir,
train_start=0, train_count=15, eval_start=15, eval_count=5)
self.assertEqual((15, 29), train_data.shape)
self.assertEqual((5, 29), eval_data.shape)
def test_make_inputs_from_np_arrays(self):
"""Tests make_inputs_from_np_arrays() function."""
train_data, _ = train_higgs.read_higgs_data(
self.data_dir,
train_start=0, train_count=15, eval_start=15, eval_count=5)
(input_fn, feature_names,
feature_columns) = train_higgs.make_inputs_from_np_arrays(
features_np=train_data[:, 1:], label_np=train_data[:, 0:1])
# Check feature_names.
self.assertAllEqual(feature_names,
["feature_%02d" % (i+1) for i in range(28)])
# Check feature columns.
self.assertEqual(28, len(feature_columns))
bucketized_column_type = type(
tf.feature_column.bucketized_column(
tf.feature_column.numeric_column("feature_01"),
boundaries=[0, 1, 2])) # dummy boundaries.
for feature_column in feature_columns:
self.assertIsInstance(feature_column, bucketized_column_type)
# At least 2 boundaries.
self.assertGreaterEqual(len(feature_column.boundaries), 2)
# Tests that the source column names of the bucketized columns match.
self.assertAllEqual(feature_names,
[col.source_column.name for col in feature_columns])
# Check features.
features, labels = input_fn().make_one_shot_iterator().get_next()
with tf.Session() as sess:
features, labels = sess.run((features, labels))
self.assertIsInstance(features, dict)
self.assertAllEqual(feature_names, sorted(features.keys()))
self.assertAllEqual([[15, 1]] * 28,
[features[name].shape for name in feature_names])
# Validate actual values of some features.
self.assertAllClose(
[0.869293, 0.907542, 0.798834, 1.344384, 1.105009, 1.595839,
0.409391, 0.933895, 1.405143, 1.176565, 0.945974, 0.739356,
1.384097, 1.383548, 1.343652],
np.squeeze(features[feature_names[0]], 1))
self.assertAllClose(
[-0.653674, -0.213641, 1.540659, -0.676015, 1.020974, 0.643109,
-1.038338, -2.653732, 0.567342, 0.534315, 0.720819, -0.481741,
1.409523, -0.307865, 1.474605],
np.squeeze(features[feature_names[10]], 1))
def test_end_to_end(self):
"""Tests end-to-end running."""
model_dir = os.path.join(self.get_temp_dir(), "model")
integration.run_synthetic(
main=train_higgs.main, tmp_root=self.get_temp_dir(), extra_flags=[
"--data_dir", self.data_dir,
"--model_dir", model_dir,
"--n_trees", "5",
"--train_start", "0",
"--train_count", "12",
"--eval_start", "12",
"--eval_count", "8",
],
synth=False, max_train=None)
self.assertTrue(tf.gfile.Exists(os.path.join(model_dir, "checkpoint")))
def test_end_to_end_with_export(self):
"""Tests end-to-end running."""
model_dir = os.path.join(self.get_temp_dir(), "model")
export_dir = os.path.join(self.get_temp_dir(), "export")
integration.run_synthetic(
main=train_higgs.main, tmp_root=self.get_temp_dir(), extra_flags=[
"--data_dir", self.data_dir,
"--model_dir", model_dir,
"--export_dir", export_dir,
"--n_trees", "5",
"--train_start", "0",
"--train_count", "12",
"--eval_start", "12",
"--eval_count", "8",
],
synth=False, max_train=None)
self.assertTrue(tf.gfile.Exists(os.path.join(model_dir, "checkpoint")))
self.assertTrue(tf.gfile.Exists(os.path.join(export_dir)))
if __name__ == "__main__":
tf.test.main()