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* rm classification head in nlp * rm classification head in nlp * rm classification head in nlp * adding test cases for switch classification head * adding test cases for switch classification head * Update test/nlp/test_autohf_classificationhead.py Co-authored-by: Chi Wang <wang.chi@microsoft.com> * adding test cases for switch classification head * run each test separately * skip classification head test on windows * disabling wandb reporting * fix test nlp custom metric * fix test nlp custom metric * fix test nlp custom metric * fix test nlp custom metric * fix test nlp custom metric * fix test nlp custom metric * fix test nlp custom metric * fix test nlp custom metric * fix test nlp custom metric * fix test nlp custom metric * fix test nlp custom metric * Update website/docs/Examples/AutoML-NLP.md Co-authored-by: Chi Wang <wang.chi@microsoft.com> * Update website/docs/Examples/AutoML-NLP.md Co-authored-by: Chi Wang <wang.chi@microsoft.com> * fix test nlp custom metric Co-authored-by: Chi Wang <wang.chi@microsoft.com>
30 lines
714 B
Python
30 lines
714 B
Python
import sys
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import pytest
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from utils import get_toy_data_seqclassification, get_automl_settings
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import os
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import shutil
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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 requests
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X_train, y_train, X_val, y_val, X_test = get_toy_data_seqclassification()
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automl = AutoML()
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automl_settings = get_automl_settings()
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automl_settings["n_splits"] = 3
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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 os.path.exists("test/data/output/"):
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shutil.rmtree("test/data/output/")
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if __name__ == "__main__":
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test_cv()
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