Unity 机器学习代理工具包 (ML-Agents) 是一个开源项目,它使游戏和模拟能够作为训练智能代理的环境。
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from unittest import mock
import os
import pytest
import tempfile
import unittest
import time
from mlagents.trainers.stats import (
StatsReporter,
TensorboardWriter,
StatsSummary,
GaugeWriter,
ConsoleWriter,
StatsPropertyType,
StatsAggregationMethod,
)
def test_stat_reporter_add_summary_write():
# Test add_writer
StatsReporter.writers.clear()
mock_writer1 = mock.Mock()
mock_writer2 = mock.Mock()
StatsReporter.add_writer(mock_writer1)
StatsReporter.add_writer(mock_writer2)
assert len(StatsReporter.writers) == 2
# Test add_stats and summaries
statsreporter1 = StatsReporter("category1")
statsreporter2 = StatsReporter("category2")
for i in range(10):
statsreporter1.add_stat("key1", float(i))
statsreporter2.add_stat("key2", float(i))
statssummary1 = statsreporter1.get_stats_summaries("key1")
statssummary2 = statsreporter2.get_stats_summaries("key2")
assert statssummary1.num == 10
assert statssummary2.num == 10
assert statssummary1.mean == 4.5
assert statssummary2.mean == 4.5
assert statssummary1.std == pytest.approx(2.9, abs=0.1)
assert statssummary2.std == pytest.approx(2.9, abs=0.1)
# Test write_stats
step = 10
statsreporter1.write_stats(step)
mock_writer1.write_stats.assert_called_once_with(
"category1", {"key1": statssummary1}, step
)
mock_writer2.write_stats.assert_called_once_with(
"category1", {"key1": statssummary1}, step
)
def test_stat_reporter_property():
# Test add_writer
mock_writer = mock.Mock()
StatsReporter.writers.clear()
StatsReporter.add_writer(mock_writer)
assert len(StatsReporter.writers) == 1
statsreporter1 = StatsReporter("category1")
# Test add_property
statsreporter1.add_property("key", "this is a text")
mock_writer.add_property.assert_called_once_with(
"category1", "key", "this is a text"
)
@mock.patch("mlagents.trainers.stats.SummaryWriter")
def test_tensorboard_writer(mock_summary):
# Test write_stats
category = "category1"
with tempfile.TemporaryDirectory(prefix="unittest-") as base_dir:
tb_writer = TensorboardWriter(base_dir, clear_past_data=False)
statssummary1 = StatsSummary(
full_dist=[1.0], aggregation_method=StatsAggregationMethod.AVERAGE
)
tb_writer.write_stats("category1", {"key1": statssummary1}, 10)
# Test that the filewriter has been created and the directory has been created.
filewriter_dir = "{basedir}/{category}".format(
basedir=base_dir, category=category
)
assert os.path.exists(filewriter_dir)
mock_summary.assert_called_once_with(filewriter_dir)
# Test that the filewriter was written to and the summary was added.
mock_summary.return_value.add_scalar.assert_called_once_with("key1", 1.0, 10)
mock_summary.return_value.flush.assert_called_once()
# Test hyperparameter writing - no good way to parse the TB string though.
tb_writer.add_property(
"category1", StatsPropertyType.HYPERPARAMETERS, {"example": 1.0}
)
assert mock_summary.return_value.add_text.call_count >= 1
def test_tensorboard_writer_clear(tmp_path):
tb_writer = TensorboardWriter(tmp_path, clear_past_data=False)
statssummary1 = StatsSummary(
full_dist=[1.0], aggregation_method=StatsAggregationMethod.AVERAGE
)
tb_writer.write_stats("category1", {"key1": statssummary1}, 10)
# TB has some sort of timeout before making a new file
time.sleep(1.0)
assert len(os.listdir(os.path.join(tmp_path, "category1"))) > 0
# See if creating a new one doesn't delete it
tb_writer = TensorboardWriter(tmp_path, clear_past_data=False)
tb_writer.write_stats("category1", {"key1": statssummary1}, 10)
assert len(os.listdir(os.path.join(tmp_path, "category1"))) > 1
time.sleep(1.0)
# See if creating a new one deletes old ones
tb_writer = TensorboardWriter(tmp_path, clear_past_data=True)
tb_writer.write_stats("category1", {"key1": statssummary1}, 10)
assert len(os.listdir(os.path.join(tmp_path, "category1"))) == 1
def test_gauge_stat_writer_sanitize():
assert GaugeWriter.sanitize_string("Policy/Learning Rate") == "Policy.LearningRate"
assert (
GaugeWriter.sanitize_string("Very/Very/Very Nested Stat")
== "Very.Very.VeryNestedStat"
)
class ConsoleWriterTest(unittest.TestCase):
def test_console_writer(self):
# Test write_stats
with self.assertLogs("mlagents.trainers", level="INFO") as cm:
category = "category1"
console_writer = ConsoleWriter()
statssummary1 = StatsSummary(
full_dist=[1.0], aggregation_method=StatsAggregationMethod.AVERAGE
)
console_writer.write_stats(
category,
{
"Environment/Cumulative Reward": statssummary1,
"Is Training": statssummary1,
},
10,
)
statssummary2 = StatsSummary(
full_dist=[0.0], aggregation_method=StatsAggregationMethod.AVERAGE
)
console_writer.write_stats(
category,
{
"Environment/Cumulative Reward": statssummary2,
"Is Training": statssummary2,
},
10,
)
# Test hyperparameter writing
console_writer.add_property(
"category1", StatsPropertyType.HYPERPARAMETERS, {"example": 1.0}
)
self.assertIn(
"Mean Reward: 1.000. Std of Reward: 0.000. Training.", cm.output[0]
)
self.assertIn("Not Training.", cm.output[1])
self.assertIn("Hyperparameters for behavior name", cm.output[2])
self.assertIn("example:\t1.0", cm.output[2])
def test_selfplay_console_writer(self):
with self.assertLogs("mlagents.trainers", level="INFO") as cm:
category = "category1"
console_writer = ConsoleWriter()
console_writer.add_property(category, StatsPropertyType.SELF_PLAY, True)
statssummary1 = StatsSummary(
full_dist=[1.0], aggregation_method=StatsAggregationMethod.AVERAGE
)
console_writer.write_stats(
category,
{
"Environment/Cumulative Reward": statssummary1,
"Is Training": statssummary1,
"Self-play/ELO": statssummary1,
},
10,
)
self.assertIn(
"Mean Reward: 1.000. Std of Reward: 0.000. Training.", cm.output[0]
)