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61 lines
2.1 KiB
Python
61 lines
2.1 KiB
Python
import sys
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import pytest
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@pytest.mark.skipif(sys.platform == "darwin", reason="do not run on mac os")
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def test_cv():
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from flaml import AutoML
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import pandas as pd
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import requests
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train_data = {
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"sentence1": [
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'Amrozi accused his brother , whom he called " the witness " , of deliberately distorting his evidence .',
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"Yucaipa owned Dominick 's before selling the chain to Safeway in 1998 for $ 2.5 billion .",
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"They had published an advertisement on the Internet on June 10 , offering the cargo for sale , he added .",
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"Around 0335 GMT , Tab shares were up 19 cents , or 4.4 % , at A $ 4.56 , having earlier set a record high of A $ 4.57 .",
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],
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"sentence2": [
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'Referring to him as only " the witness " , Amrozi accused his brother of deliberately distorting his evidence .',
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"Yucaipa bought Dominick 's in 1995 for $ 693 million and sold it to Safeway for $ 1.8 billion in 1998 .",
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"On June 10 , the ship 's owners had published an advertisement on the Internet , offering the explosives for sale .",
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"Tab shares jumped 20 cents , or 4.6 % , to set a record closing high at A $ 4.57 .",
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],
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"label": [1, 0, 1, 0],
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"idx": [0, 1, 2, 3],
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}
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train_dataset = pd.DataFrame(train_data)
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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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automl = AutoML()
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automl_settings = {
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"gpu_per_trial": 0,
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"max_iter": 3,
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"time_budget": 5,
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"task": "seq-classification",
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"metric": "accuracy",
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"n_splits": 3,
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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": 1,
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"fp16": False,
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}
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try:
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automl.fit(X_train=X_train, y_train=y_train, **automl_settings)
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except requests.exceptions.HTTPError:
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return
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if __name__ == "__main__":
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test_cv()
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