mirror of
https://github.com/vale981/ray
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50 lines
1.3 KiB
Python
50 lines
1.3 KiB
Python
import unittest
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import ray
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import ray.rllib.agents.slateq as slateq
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from ray.rllib.examples.env.recsim_recommender_system_envs import (
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LongTermSatisfactionRecSimEnv,
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)
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from ray.rllib.utils.test_utils import (
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check_compute_single_action,
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check_train_results,
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framework_iterator,
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)
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class TestSlateQ(unittest.TestCase):
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"""Sanity tests for Slateq algorithm."""
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def setUp(self):
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ray.init(num_cpus=4)
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def tearDown(self):
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ray.shutdown()
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def test_slateq_compilation(self):
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"""Test whether an A2CTrainer can be built with both frameworks."""
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config = {
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"env": LongTermSatisfactionRecSimEnv,
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}
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num_iterations = 1
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# Test only against torch (no other frameworks supported so far).
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for _ in framework_iterator(config, frameworks="torch"):
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trainer = slateq.SlateQTrainer(config=config)
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for i in range(num_iterations):
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results = trainer.train()
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check_train_results(results)
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print(results)
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check_compute_single_action(trainer)
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trainer.stop()
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def test_slateq_loss_function(self):
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pass
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if __name__ == "__main__":
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import pytest
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import sys
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sys.exit(pytest.main(["-v", __file__]))
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