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ncf_test.py
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ncf_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 NCF."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import unittest
import mock
import numpy as np
import tensorflow as tf
from official.recommendation import constants as rconst
from official.recommendation import data_pipeline
from official.recommendation import neumf_model
from official.recommendation import ncf_common
from official.recommendation import ncf_estimator_main
from official.recommendation import ncf_keras_main
from official.utils.misc import keras_utils
from official.utils.testing import integration
from tensorflow.python.eager import context # pylint: disable=ungrouped-imports
NUM_TRAIN_NEG = 4
class NcfTest(tf.test.TestCase):
@classmethod
def setUpClass(cls): # pylint: disable=invalid-name
super(NcfTest, cls).setUpClass()
ncf_common.define_ncf_flags()
def setUp(self):
self.top_k_old = rconst.TOP_K
self.num_eval_negatives_old = rconst.NUM_EVAL_NEGATIVES
rconst.NUM_EVAL_NEGATIVES = 2
def tearDown(self):
rconst.NUM_EVAL_NEGATIVES = self.num_eval_negatives_old
rconst.TOP_K = self.top_k_old
@unittest.skipIf(keras_utils.is_v2_0(), "TODO(b/136018594)")
def get_hit_rate_and_ndcg(self, predicted_scores_by_user, items_by_user,
top_k=rconst.TOP_K, match_mlperf=False):
rconst.TOP_K = top_k
rconst.NUM_EVAL_NEGATIVES = predicted_scores_by_user.shape[1] - 1
batch_size = items_by_user.shape[0]
users = np.repeat(np.arange(batch_size)[:, np.newaxis],
rconst.NUM_EVAL_NEGATIVES + 1, axis=1)
users, items, duplicate_mask = \
data_pipeline.BaseDataConstructor._assemble_eval_batch(
users, items_by_user[:, -1:], items_by_user[:, :-1], batch_size)
g = tf.Graph()
with g.as_default():
logits = tf.convert_to_tensor(
predicted_scores_by_user.reshape((-1, 1)), tf.float32)
softmax_logits = tf.concat([tf.zeros(logits.shape, dtype=logits.dtype),
logits], axis=1)
duplicate_mask = tf.convert_to_tensor(duplicate_mask, tf.float32)
metric_ops = neumf_model._get_estimator_spec_with_metrics(
logits=logits, softmax_logits=softmax_logits,
duplicate_mask=duplicate_mask, num_training_neg=NUM_TRAIN_NEG,
match_mlperf=match_mlperf).eval_metric_ops
hr = metric_ops[rconst.HR_KEY]
ndcg = metric_ops[rconst.NDCG_KEY]
init = [tf.compat.v1.global_variables_initializer(),
tf.compat.v1.local_variables_initializer()]
with self.session(graph=g) as sess:
sess.run(init)
return sess.run([hr[1], ndcg[1]])
def test_hit_rate_and_ndcg(self):
# Test with no duplicate items
predictions = np.array([
[2., 0., 1.], # In top 2
[1., 0., 2.], # In top 1
[2., 1., 0.], # In top 3
[3., 4., 2.] # In top 3
])
items = np.array([
[2, 3, 1],
[3, 1, 2],
[2, 1, 3],
[1, 3, 2],
])
hr, ndcg = self.get_hit_rate_and_ndcg(predictions, items, 1)
self.assertAlmostEqual(hr, 1 / 4)
self.assertAlmostEqual(ndcg, 1 / 4)
hr, ndcg = self.get_hit_rate_and_ndcg(predictions, items, 2)
self.assertAlmostEqual(hr, 2 / 4)
self.assertAlmostEqual(ndcg, (1 + math.log(2) / math.log(3)) / 4)
hr, ndcg = self.get_hit_rate_and_ndcg(predictions, items, 3)
self.assertAlmostEqual(hr, 4 / 4)
self.assertAlmostEqual(ndcg, (1 + math.log(2) / math.log(3) +
2 * math.log(2) / math.log(4)) / 4)
hr, ndcg = self.get_hit_rate_and_ndcg(predictions, items, 1,
match_mlperf=True)
self.assertAlmostEqual(hr, 1 / 4)
self.assertAlmostEqual(ndcg, 1 / 4)
hr, ndcg = self.get_hit_rate_and_ndcg(predictions, items, 2,
match_mlperf=True)
self.assertAlmostEqual(hr, 2 / 4)
self.assertAlmostEqual(ndcg, (1 + math.log(2) / math.log(3)) / 4)
hr, ndcg = self.get_hit_rate_and_ndcg(predictions, items, 3,
match_mlperf=True)
self.assertAlmostEqual(hr, 4 / 4)
self.assertAlmostEqual(ndcg, (1 + math.log(2) / math.log(3) +
2 * math.log(2) / math.log(4)) / 4)
# Test with duplicate items. In the MLPerf case, we treat the duplicates as
# a single item. Otherwise, we treat the duplicates as separate items.
predictions = np.array([
[2., 2., 3., 1.], # In top 4. MLPerf: In top 3
[1., 0., 2., 3.], # In top 1. MLPerf: In top 1
[2., 3., 2., 0.], # In top 4. MLPerf: In top 3
[2., 4., 2., 3.] # In top 2. MLPerf: In top 2
])
items = np.array([
[2, 2, 3, 1],
[2, 3, 4, 1],
[2, 3, 2, 1],
[3, 2, 1, 4],
])
hr, ndcg = self.get_hit_rate_and_ndcg(predictions, items, 1)
self.assertAlmostEqual(hr, 1 / 4)
self.assertAlmostEqual(ndcg, 1 / 4)
hr, ndcg = self.get_hit_rate_and_ndcg(predictions, items, 2)
self.assertAlmostEqual(hr, 2 / 4)
self.assertAlmostEqual(ndcg, (1 + math.log(2) / math.log(3)) / 4)
hr, ndcg = self.get_hit_rate_and_ndcg(predictions, items, 3)
self.assertAlmostEqual(hr, 2 / 4)
self.assertAlmostEqual(ndcg, (1 + math.log(2) / math.log(3)) / 4)
hr, ndcg = self.get_hit_rate_and_ndcg(predictions, items, 4)
self.assertAlmostEqual(hr, 4 / 4)
self.assertAlmostEqual(ndcg, (1 + math.log(2) / math.log(3) +
2 * math.log(2) / math.log(5)) / 4)
hr, ndcg = self.get_hit_rate_and_ndcg(predictions, items, 1,
match_mlperf=True)
self.assertAlmostEqual(hr, 1 / 4)
self.assertAlmostEqual(ndcg, 1 / 4)
hr, ndcg = self.get_hit_rate_and_ndcg(predictions, items, 2,
match_mlperf=True)
self.assertAlmostEqual(hr, 2 / 4)
self.assertAlmostEqual(ndcg, (1 + math.log(2) / math.log(3)) / 4)
hr, ndcg = self.get_hit_rate_and_ndcg(predictions, items, 3,
match_mlperf=True)
self.assertAlmostEqual(hr, 4 / 4)
self.assertAlmostEqual(ndcg, (1 + math.log(2) / math.log(3) +
2 * math.log(2) / math.log(4)) / 4)
hr, ndcg = self.get_hit_rate_and_ndcg(predictions, items, 4,
match_mlperf=True)
self.assertAlmostEqual(hr, 4 / 4)
self.assertAlmostEqual(ndcg, (1 + math.log(2) / math.log(3) +
2 * math.log(2) / math.log(4)) / 4)
_BASE_END_TO_END_FLAGS = ['-batch_size', '1044', '-train_epochs', '1']
@unittest.skipIf(keras_utils.is_v2_0(), "TODO(b/136018594)")
@mock.patch.object(rconst, "SYNTHETIC_BATCHES_PER_EPOCH", 100)
def test_end_to_end_estimator(self):
integration.run_synthetic(
ncf_estimator_main.main, tmp_root=self.get_temp_dir(),
extra_flags=self._BASE_END_TO_END_FLAGS)
@unittest.skipIf(keras_utils.is_v2_0(), "TODO(b/136018594)")
@mock.patch.object(rconst, "SYNTHETIC_BATCHES_PER_EPOCH", 100)
def test_end_to_end_estimator_mlperf(self):
integration.run_synthetic(
ncf_estimator_main.main, tmp_root=self.get_temp_dir(),
extra_flags=self._BASE_END_TO_END_FLAGS + ['-ml_perf', 'True'])
@mock.patch.object(rconst, "SYNTHETIC_BATCHES_PER_EPOCH", 100)
def test_end_to_end_keras_no_dist_strat(self):
integration.run_synthetic(
ncf_keras_main.main, tmp_root=self.get_temp_dir(),
extra_flags=self._BASE_END_TO_END_FLAGS +
['-distribution_strategy', 'off'])
@mock.patch.object(rconst, "SYNTHETIC_BATCHES_PER_EPOCH", 100)
@unittest.skipUnless(keras_utils.is_v2_0(), 'TF 2.0 only test.')
def test_end_to_end_keras_dist_strat(self):
integration.run_synthetic(
ncf_keras_main.main, tmp_root=self.get_temp_dir(),
extra_flags=self._BASE_END_TO_END_FLAGS + ['-num_gpus', '0'])
@mock.patch.object(rconst, "SYNTHETIC_BATCHES_PER_EPOCH", 100)
@unittest.skipUnless(keras_utils.is_v2_0(), 'TF 2.0 only test.')
def test_end_to_end_keras_dist_strat_ctl(self):
flags = (self._BASE_END_TO_END_FLAGS +
['-num_gpus', '0'] +
['-keras_use_ctl', 'True'])
integration.run_synthetic(
ncf_keras_main.main, tmp_root=self.get_temp_dir(),
extra_flags=flags)
@mock.patch.object(rconst, "SYNTHETIC_BATCHES_PER_EPOCH", 100)
@unittest.skipUnless(keras_utils.is_v2_0(), 'TF 2.0 only test.')
def test_end_to_end_keras_1_gpu_dist_strat_fp16(self):
if context.num_gpus() < 1:
self.skipTest(
"{} GPUs are not available for this test. {} GPUs are available".
format(1, context.num_gpus()))
integration.run_synthetic(
ncf_keras_main.main, tmp_root=self.get_temp_dir(),
extra_flags=self._BASE_END_TO_END_FLAGS + ['-num_gpus', '1',
'--dtype', 'fp16'])
@mock.patch.object(rconst, "SYNTHETIC_BATCHES_PER_EPOCH", 100)
@unittest.skipUnless(keras_utils.is_v2_0(), 'TF 2.0 only test.')
def test_end_to_end_keras_1_gpu_dist_strat_ctl_fp16(self):
if context.num_gpus() < 1:
self.skipTest(
'{} GPUs are not available for this test. {} GPUs are available'.
format(1, context.num_gpus()))
integration.run_synthetic(
ncf_keras_main.main, tmp_root=self.get_temp_dir(),
extra_flags=self._BASE_END_TO_END_FLAGS + ['-num_gpus', '1',
'--dtype', 'fp16',
'--keras_use_ctl'])
@mock.patch.object(rconst, 'SYNTHETIC_BATCHES_PER_EPOCH', 100)
@unittest.skipUnless(keras_utils.is_v2_0(), 'TF 2.0 only test.')
def test_end_to_end_keras_2_gpu_fp16(self):
if context.num_gpus() < 2:
self.skipTest(
"{} GPUs are not available for this test. {} GPUs are available".
format(2, context.num_gpus()))
integration.run_synthetic(
ncf_keras_main.main, tmp_root=self.get_temp_dir(),
extra_flags=self._BASE_END_TO_END_FLAGS + ['-num_gpus', '2',
'--dtype', 'fp16'])
if __name__ == "__main__":
tf.test.main()