Remove boosted trees tests from github.

PiperOrigin-RevId: 292635535

official/r1/boosted_trees/train_higgs_test.csv

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official/r1/boosted_trees/train_higgs_test.py

-# 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 unittest
-
-import numpy as np
-import pandas as pd
-import tensorflow as tf
-
-# pylint: disable=g-bad-import-order
-from official.r1.boosted_trees import train_higgs
-from official.utils.misc import keras_utils
-from official.utils.testing import integration
-
-TEST_CSV = os.path.join(os.path.dirname(__file__), "train_higgs_test.csv")
-
-tf.compat.v1.logging.set_verbosity(tf.compat.v1.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.io.gfile.copy(tmpfile.name, self.input_npz)
-
-  @unittest.skipIf(keras_utils.is_v2_0(), "TF 1.0 only test.")
-  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)
-
-  @unittest.skipIf(keras_utils.is_v2_0(), "TF 1.0 only test.")
-  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))
-
-  @unittest.skipIf(keras_utils.is_v2_0(), "TF 1.0 only test.")
-  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, train_epochs=None, epochs_between_evals=None)
-    self.assertTrue(tf.gfile.Exists(os.path.join(model_dir, "checkpoint")))
-
-  @unittest.skipIf(keras_utils.is_v2_0(), "TF 1.0 only test.")
-  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, train_epochs=None, epochs_between_evals=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()