ludwig-ai--ludwig
wehub-resource-sync
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93 行
3.2 KiB
Python
93 行
3.2 KiB
Python
# Copyright (c) 2023 Predibase, Inc., 2019 Uber Technologies, Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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import pytest
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try:
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from ray import tune
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from ludwig.hyperopt.execution import get_build_hyperopt_executor
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except ImportError:
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RAY_AVAILABLE = False
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else:
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RAY_AVAILABLE = True
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# from ludwig.hyperopt.sampling import RayTuneSampler TDOO: remove
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from ludwig.constants import RAY, TYPE
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HYPEROPT_PARAMS = {
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"test_1": {
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"parameters": {
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"trainer.learning_rate": {"space": "uniform", "lower": 0.001, "upper": 0.1},
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"combiner.num_fc_layers": {"space": "qrandint", "lower": 3, "upper": 6, "q": 3},
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"utterance.cell_type": {"space": "grid_search", "values": ["rnn", "gru", "lstm"]},
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},
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},
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"test_2": {
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"parameters": {
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"trainer.learning_rate": {
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"space": "loguniform",
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"lower": 0.001,
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"upper": 0.1,
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"base": 10,
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},
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"combiner.num_fc_layers": {"space": "randint", "lower": 2, "upper": 6},
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"utterance.cell_type": {"space": "choice", "categories": ["rnn", "gru", "lstm"]},
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},
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},
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}
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if RAY_AVAILABLE:
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EXPECTED_SEARCH_SPACE = {
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"test_1": {
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"trainer.learning_rate": tune.uniform(0.001, 0.1),
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"combiner.num_fc_layers": tune.qrandint(3, 6, 3),
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"utterance.cell_type": tune.grid_search(["rnn", "gru", "lstm"]),
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},
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"test_2": {
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"trainer.learning_rate": tune.loguniform(0.001, 0.1),
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"combiner.num_fc_layers": tune.randint(2, 6),
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"utterance.cell_type": tune.choice(["rnn", "gru", "lstm"]),
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},
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}
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@pytest.mark.skipif(not RAY_AVAILABLE, reason="Ray is not installed for testing")
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@pytest.mark.parametrize("key", ["test_1", "test_2"])
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def test_grid_strategy(key):
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hyperopt_test_params = HYPEROPT_PARAMS[key]
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expected_search_space = EXPECTED_SEARCH_SPACE[key]
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tune_sampler_params = hyperopt_test_params["parameters"]
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hyperopt_executor = get_build_hyperopt_executor(RAY)(
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tune_sampler_params,
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"output_feature",
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"mse",
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"minimize",
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"validation",
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search_alg={TYPE: "variant_generator"},
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**{"type": "ray", "num_samples": 2, "scheduler": {"type": "fifo"}},
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)
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search_space = hyperopt_executor.search_space
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actual_params_keys = search_space.keys()
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expected_params_keys = expected_search_space.keys()
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for param in search_space:
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assert isinstance(search_space[param], type(expected_search_space[param]))
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assert actual_params_keys == expected_params_keys
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