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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>
43 lines
1.0 KiB
Python
43 lines
1.0 KiB
Python
import sys
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import pytest
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from utils import get_toy_data_seqregression, 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_regression():
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try:
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import ray
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if not ray.is_initialized():
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ray.init()
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except ImportError:
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return
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from flaml import AutoML
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X_train, y_train, X_val, y_val = get_toy_data_seqregression()
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automl = AutoML()
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automl_settings = get_automl_settings()
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automl_settings["task"] = "seq-regression"
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automl_settings["metric"] = "pearsonr"
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automl_settings["starting_points"] = {"transformer": {"num_train_epochs": 1}}
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automl_settings["use_ray"] = {"local_dir": "data/outut/"}
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ray.shutdown()
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ray.init()
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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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automl.predict(X_val)
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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_regression()
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