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if save_best_model_per_estimator is False and retrain_final is True, unfit the model after evaluation in HPO. retrain if using ray. update ITER_HP in config after a trial is finished. change prophet logging level. example and notebook update. allow settings to be passed to AutoML constructor. Are you planning to add multi-output-regression capability to FLAML #192 Is multi-tasking allowed? #277 can pass the auotml setting to the constructor instead of requiring a derived class. remove model_history. checkpoint bug fix. * model_history meaning save_best_model_per_estimator * ITER_HP * example update * prophet logging level * comment update in forecast notebook * print format improvement * allow settings to be passed to AutoML constructor * checkpoint bug fix * time limit for autohf regression test * skip slow test on macos * cleanup before del
52 lines
1.2 KiB
Python
52 lines
1.2 KiB
Python
import os
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import pytest
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@pytest.mark.skipif(os.name == "posix", reason="do not run on mac os")
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def test_regression():
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from flaml import AutoML
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from datasets import load_dataset
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train_dataset = (
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load_dataset("glue", "stsb", split="train[:1%]").to_pandas().iloc[:20]
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)
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dev_dataset = (
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load_dataset("glue", "stsb", split="train[1%:2%]").to_pandas().iloc[:20]
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)
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custom_sent_keys = ["sentence1", "sentence2"]
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label_key = "label"
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X_train = train_dataset[custom_sent_keys]
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y_train = train_dataset[label_key]
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X_val = dev_dataset[custom_sent_keys]
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y_val = dev_dataset[label_key]
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automl = AutoML()
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automl_settings = {
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"gpu_per_trial": 0,
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"max_iter": 2,
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"time_budget": 5,
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"task": "seq-regression",
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"metric": "rmse",
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"starting_points": {"transformer": {"num_train_epochs": 1}},
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}
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automl_settings["custom_hpo_args"] = {
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"model_path": "google/electra-small-discriminator",
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"output_dir": "test/data/output/",
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"ckpt_per_epoch": 5,
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"fp16": False,
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}
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automl.fit(
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X_train=X_train, y_train=y_train, X_val=X_val, y_val=y_val, **automl_settings
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)
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if __name__ == "main":
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test_regression()
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