mirror of
https://github.com/microsoft/autogen.git
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ca35fa969f
* refactoring TransformersEstimator to support default and custom_hp * handling starting_points not in search space * addressing starting point more than max_iter * fixing upper < lower bug
59 lines
1.3 KiB
Python
59 lines
1.3 KiB
Python
import sys
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import pytest
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import requests
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from utils import get_toy_data_seqclassification, get_automl_settings
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@pytest.mark.skipif(sys.platform == "darwin", reason="do not run on mac os")
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def test_hf_data():
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from flaml import AutoML
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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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try:
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automl.fit(
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X_train=X_train,
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y_train=y_train,
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X_val=X_val,
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y_val=y_val,
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**automl_settings
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)
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automl.score(X_val, y_val, **{"metric": "accuracy"})
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automl.pickle("automl.pkl")
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except requests.exceptions.HTTPError:
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return
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automl = AutoML()
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automl_settings.pop("max_iter", None)
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automl_settings.pop("use_ray", None)
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automl_settings.pop("estimator_list", None)
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automl.retrain_from_log(
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X_train=X_train,
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y_train=y_train,
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train_full=True,
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record_id=0,
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**automl_settings
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)
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automl.predict(X_test)
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automl.predict(["test test", "test test"])
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automl.predict(
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[
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["test test", "test test"],
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["test test", "test test"],
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["test test", "test test"],
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]
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)
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automl.predict_proba(X_test)
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print(automl.classes_)
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if __name__ == "__main__":
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test_hf_data()
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